Amplitude Models for Discrimination and Yield Estimation
Energy Technology Data Exchange (ETDEWEB)
Phillips, William Scott [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
2016-09-01
This seminar presentation describes amplitude models and yield estimations that look at the data in order to inform legislation. The following points were brought forth in the summary: global models that will predict three-component amplitudes (R-T-Z) were produced; Q models match regional geology; corrected source spectra can be used for discrimination and yield estimation; three-component data increase coverage and reduce scatter in source spectral estimates; three-component efforts must include distance-dependent effects; a community effort on instrument calibration is needed.
The Impact of Statistical Leakage Models on Design Yield Estimation
Directory of Open Access Journals (Sweden)
Rouwaida Kanj
2011-01-01
Full Text Available Device mismatch and process variation models play a key role in determining the functionality and yield of sub-100 nm design. Average characteristics are often of interest, such as the average leakage current or the average read delay. However, detecting rare functional fails is critical for memory design and designers often seek techniques that enable accurately modeling such events. Extremely leaky devices can inflict functionality fails. The plurality of leaky devices on a bitline increase the dimensionality of the yield estimation problem. Simplified models are possible by adopting approximations to the underlying sum of lognormals. The implications of such approximations on tail probabilities may in turn bias the yield estimate. We review different closed form approximations and compare against the CDF matching method, which is shown to be most effective method for accurate statistical leakage modeling.
Estimating Corporate Yield Curves
Antionio Diaz; Frank Skinner
2001-01-01
This paper represents the first study of retail deposit spreads of UK financial institutions using stochastic interest rate modelling and the market comparable approach. By replicating quoted fixed deposit rates using the Black Derman and Toy (1990) stochastic interest rate model, we find that the spread between fixed and variable rates of interest can be modeled (and priced) using an interest rate swap analogy. We also find that we can estimate an individual bank deposit yield curve as a spr...
Lawes, R.
2016-12-01
The Australian grain growing region is vast and occupies where some 25 million tonnes of wheat is produced from latitudes -27 to -42, where soils, crops and climates vary considerably. Predicting the area of individual crops is time consuming and currently conducted by survey, while yield estimates are derived from these areas and from information about grain receivables with little pre-harvest information available to industry. The existing approach fails to provide reliable, timely, small scale information about production. Similarly, previous attempts to predict yield using satellite derived information rely on information collected using the existing systems to calibrate models. We have developed a crop productivity and yield model - called C-Store Crop - that uses remotely sensed vegetation indices, along with site based rainfall, radiation and temperature information. Model calibration using 3000 points derived from farmer supplied yield maps for wheat, barley, canola and chickpea showed strong relationships (>70%) between modelled plant mass and observed crop yield at the paddock scale. C-Store Crop is being applied at 250m and 25m grid resolution. Farmer supplied yield data was also used to train a combination of Radar and Landsat images collected whilst the crop is growing to discriminate between crop types. Landsat information alone was unable to discriminate legume and cereal crops. Problems such as cloud prevented accessing appropriate scenes. Inclusion of Radar information reduced errors of commission and omission. By combining the C-Store Crop model with remote estimates of crop type, we anticipate predicting crop type and crop yield with uncertainty estimates across the Australian continent.
Comparison of statistical models to estimate daily milk yield in single milking testing schemes
Directory of Open Access Journals (Sweden)
Marija Klopcˇic
2010-01-01
Full Text Available Different statistical models were compared to estimate daily milk yield from morning or evening milking test results. The experiment was conducted on 14 family farms with 325 recorded cows. The amount of explained variance was higher for models including the effects of partial milk yield, the interval between successive milking, the interaction between partial milk yield and the milking interval and the farm (R2 = 0.976 for AM, R2 = 0.956 for PM than for models including partial milk yield effect only (R2 = 0.957 for AM, R2 = 0.937 for PM. Estimates of daily milk yield from linear models were more accurate than those obtained by doubling single milking weights. The results show that more complex model gives the best fit to the data. Differences between models according to determination and correlation coefficient were minor. Further investigations on larger sets of data are needed to draw more general conclusion.
Rice Yield Estimation by Integrating Remote Sensing with Rice Growth Simulation Model
Institute of Scientific and Technical Information of China (English)
O. ABOU-ISMAIL; HUANG Jing-Feng; WANG Ren-Chao
2004-01-01
Since remote sensing can provide information on the actual status of an agricultural crop, the integration between remote sensing data and crop growth simulation models has become an important trend for yield estimation and prediction.The main objective of this research was to combine a rice growth simulation model with remote sensing data to estimate rice grain yield for different growing seasons leading to an assessment of rice yield at regional levels. Integration between NOAA (National Oceanic and Atmospheric Administration) AVHRR (Advanced Very High Resolution Radiometer) data and the rice growth simulation model ORYZA1 to develop a new software, which was named as Rice-SRS Model, resulted in accurate estimates for rice yield in Shaoxing, China, with an estimation error reduced to 1.03% and 0.79% over-estimation and 0.79% under-estimation for early, single and late season rice, respectively. Selecting suitable dates for remote sensing images was an important factor which could influence estimation accuracy. Thus, given the different growing periods for each rice season, four images were needed for early and late rice, while five images were preferable for single season rice.Estimating rice yield using two or three images was possible, however, if images were obtained during the panicle initiation and heading stages.
Comparison of empirical models to estimate soil erosion and sediment yield in micro catchments
Directory of Open Access Journals (Sweden)
Lida Eisazadeh
2015-05-01
Full Text Available Assessment of sediment yield in soil conservation and watershed Project and implementation plan for water and soil resources management is so important. Regarding to somewhere that doesn’t have enough information and statistical data such as upper river branches, Empirical models should be used to estimate erosion and sediment yield. However the efficiency and usage of these models before calibration isn’t clear. In this research, the measurement of erosion and sediment yield of 10 basins upstream of reservoirshas been estimated by RUSLE and MPSIAC empirical models.In order to compare means between measured and estimated datat-test method was applied.Theresults indicated no significant differences between means of measured and estimated sediment yield in MPSAIC model in 5% level. In contrast, T-test showed contrary results in RUSLE model. Then the applicability and priority of two models were examined by statistical methodssuch as MAE and MBE methods. By regarding to accuracy and precision, MPSIAC model placed in first priorityto estimate soil erosion and sediment yield and has minimum value of MAE=0.79 and MBE = -0.59.
RICE YIELD ESTIMATION THROUGH ASSIMILATING SATELLITE DATA INTO A CROP SIMUMLATION MODEL
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N. T. Son
2016-06-01
Full Text Available Rice is globally the most important food crop, feeding approximately half of the world’s population, especially in Asia where around half of the world’s poorest people live. Thus, advanced spatiotemporal information of rice crop yield during crop growing season is critically important for crop management and national food policy making. The main objective of this study was to develop an approach to integrate remotely sensed data into a crop simulation model (DSSAT for rice yield estimation in Taiwan. The data assimilation was processed to integrate biophysical parameters into DSSAT model for rice yield estimation using the particle swarm optimization (PSO algorithm. The cost function was constructed based on the differences between the simulated leaf area index (LAI and MODIS LAI, and the optimization process starts from an initial parameterization and accordingly adjusts parameters (e.g., planting date, planting population, and fertilizer amount in the crop simulation model. The fitness value obtained from the cost function determined whether the optimization algorithm had reached the optimum input parameters using a user-defined tolerance. The results of yield estimation compared with the government’s yield statistics indicated the root mean square error (RMSE of 11.7% and mean absolute error of 9.7%, respectively. This study demonstrated the applicability of satellite data assimilation into a crop simulation model for rice yield estimation, and the approach was thus proposed for crop yield monitoring purposes in the study region.
Rice Yield Estimation Through Assimilating Satellite Data Into a Crop Simumlation Model
Son, N. T.; Chen, C. F.; Chen, C. R.; Chang, L. Y.; Chiang, S. H.
2016-06-01
Rice is globally the most important food crop, feeding approximately half of the world's population, especially in Asia where around half of the world's poorest people live. Thus, advanced spatiotemporal information of rice crop yield during crop growing season is critically important for crop management and national food policy making. The main objective of this study was to develop an approach to integrate remotely sensed data into a crop simulation model (DSSAT) for rice yield estimation in Taiwan. The data assimilation was processed to integrate biophysical parameters into DSSAT model for rice yield estimation using the particle swarm optimization (PSO) algorithm. The cost function was constructed based on the differences between the simulated leaf area index (LAI) and MODIS LAI, and the optimization process starts from an initial parameterization and accordingly adjusts parameters (e.g., planting date, planting population, and fertilizer amount) in the crop simulation model. The fitness value obtained from the cost function determined whether the optimization algorithm had reached the optimum input parameters using a user-defined tolerance. The results of yield estimation compared with the government's yield statistics indicated the root mean square error (RMSE) of 11.7% and mean absolute error of 9.7%, respectively. This study demonstrated the applicability of satellite data assimilation into a crop simulation model for rice yield estimation, and the approach was thus proposed for crop yield monitoring purposes in the study region.
A spectral-spatial-dynamic hierarchical Bayesian (SSD-HB) model for estimating soybean yield
Kazama, Yoriko; Kujirai, Toshihiro
2014-10-01
A method called a "spectral-spatial-dynamic hierarchical-Bayesian (SSD-HB) model," which can deal with many parameters (such as spectral and weather information all together) by reducing the occurrence of multicollinearity, is proposed. Experiments conducted on soybean yields in Brazil fields with a RapidEye satellite image indicate that the proposed SSD-HB model can predict soybean yield with a higher degree of accuracy than other estimation methods commonly used in remote-sensing applications. In the case of the SSD-HB model, the mean absolute error between estimated yield of the target area and actual yield is 0.28 t/ha, compared to 0.34 t/ha when conventional PLS regression was applied, showing the potential effectiveness of the proposed model.
Yield loss prediction models based on early estimation of weed pressure
DEFF Research Database (Denmark)
Asif, Ali; Streibig, Jens Carl; Andreasen, Christian
2013-01-01
Weed control thresholds have been used to reduce costs and avoid unacceptable yield loss. Estimation of weed infestation has often been based on counts of weed plants per unit area or measurement of their relative leaf area index. Various linear, hyperbolic, and sigmoidal regression models have...... been proposed to predict yield loss, relative to yield in weed free environment from early measurements of weed infestation. The models are integrated in some weed management advisory systems. Generally, the recommendations from the advisory systems are applied to the whole field, but weed control...... time of weeds relative to crop. The aim of the review is to analyze various approaches to estimate infestation of weeds and the literature about yield loss prediction for multispecies. We discuss limitations of regression models and possible modifications to include the influential factors related...
Estimating crop yield using a satellite-based light use efficiency model
DEFF Research Database (Denmark)
Yuan, Wenping; Chen, Yang; Xia, Jiangzhou
2016-01-01
for simulating crops’ GPP. At both irrigated and rainfed sites, the EC-LUE model exhibits a similar level of performance. However, large errors are found when simulating yield based on crop harvest index. This analysis highlights the need to improve the representation of the harvest index and carbon allocation...... primary production (GPP) and yield of crops. The EC-LUE model can explain on average approximately 90% of the variability in GPP for 36 FLUXNET sites globally. The results indicate that a universal set of parameters, independent of crop species (except for C4 crops), can be adopted in the EC-LUE model...... for improving crop yield estimations from satellite-based methods....
Muslim, Mohammad; Romshoo, Shakil Ahmad; Rather, A Q
2015-06-01
The Kashmir Himalayan region of India is expected to be highly prone to the change in agricultural land use because of its geo-ecological fragility, strategic location vis-à-vis the Himalayan landscape, its trans-boundary river basins, and inherent socio-economic instabilities. Food security and sustainability of the region are thus greatly challenged by these impacts. The effect of future climate change, increased competition for land and water, labor from non-agricultural sectors, and increasing population adds to this complex problem. In current study, paddy rice yield at regional level was estimated using GIS-based environment policy integrated climate (GEPIC) model. The general approach of current study involved combining regional level crop database, regional soil data base, farm management data, and climatic data outputs with GEPIC model. The simulated yield showed that estimated production to be 4305.55 kg/ha (43.05 q h(-1)). The crop varieties like Jhelum, K-39, Chenab, China 1039, China-1007, and Shalimar rice-1 grown in plains recorded average yield of 4783.3 kg/ha (47.83 q ha(-1)). Meanwhile, high altitude areas with varieties like Kohsaar, K-78 (Barkat), and K-332 recorded yield of 4102.2 kg/ha (41.02 q ha(-1)). The observed and simulated yield showed a good match with R (2) = 0.95, RMSE = 132.24 kg/ha, respectively.
Kolotii, Andrii; Kussul, Nataliia; Skakun, Sergii; Shelestov, Andrii; Ostapenko, Vadim; Oliinyk, Tamara
2015-04-01
Efficient and timely crop monitoring and yield forecasting are important tasks for ensuring of stability and sustainable economic development [1]. As winter crops pay prominent role in agriculture of Ukraine - the main focus of this study is concentrated on winter wheat. In our previous research [2, 3] it was shown that usage of biophysical parameters of crops such as FAPAR (derived from Geoland-2 portal as for SPOT Vegetation data) is far more efficient for crop yield forecasting to NDVI derived from MODIS data - for available data. In our current work efficiency of usage such biophysical parameters as LAI, FAPAR, FCOVER (derived from SPOT Vegetation and PROBA-V data at resolution of 1 km and simulated within WOFOST model) and NDVI product (derived from MODIS) for winter wheat monitoring and yield forecasting is estimated. As the part of crop monitoring workflow (vegetation anomaly detection, vegetation indexes and products analysis) and yield forecasting SPIRITS tool developed by JRC is used. Statistics extraction is done for landcover maps created in SRI within FP-7 SIGMA project. Efficiency of usage satellite based and modelled with WOFOST model biophysical products is estimated. [1] N. Kussul, S. Skakun, A. Shelestov, O. Kussul, "Sensor Web approach to Flood Monitoring and Risk Assessment", in: IGARSS 2013, 21-26 July 2013, Melbourne, Australia, pp. 815-818. [2] F. Kogan, N. Kussul, T. Adamenko, S. Skakun, O. Kravchenko, O. Kryvobok, A. Shelestov, A. Kolotii, O. Kussul, and A. Lavrenyuk, "Winter wheat yield forecasting in Ukraine based on Earth observation, meteorological data and biophysical models," International Journal of Applied Earth Observation and Geoinformation, vol. 23, pp. 192-203, 2013. [3] Kussul O., Kussul N., Skakun S., Kravchenko O., Shelestov A., Kolotii A, "Assessment of relative efficiency of using MODIS data to winter wheat yield forecasting in Ukraine", in: IGARSS 2013, 21-26 July 2013, Melbourne, Australia, pp. 3235 - 3238.
Regressions by leaps and bounds and biased estimation techniques in yield modeling
Marquina, N. E. (Principal Investigator)
1979-01-01
The author has identified the following significant results. It was observed that OLS was not adequate as an estimation procedure when the independent or regressor variables were involved in multicollinearities. This was shown to cause the presence of small eigenvalues of the extended correlation matrix A'A. It was demonstrated that the biased estimation techniques and the all-possible subset regression could help in finding a suitable model for predicting yield. Latent root regression was an excellent tool that found how many predictive and nonpredictive multicollinearities there were.
Refinement and evaluation of the Massachusetts firm-yield estimator model version 2.0
Levin, Sara B.; Archfield, Stacey A.; Massey, Andrew J.
2011-01-01
The firm yield is the maximum average daily withdrawal that can be extracted from a reservoir without risk of failure during an extended drought period. Previously developed procedures for determining the firm yield of a reservoir were refined and applied to 38 reservoir systems in Massachusetts, including 25 single- and multiple-reservoir systems that were examined during previous studies and 13 additional reservoir systems. Changes to the firm-yield model include refinements to the simulation methods and input data, as well as the addition of several scenario-testing capabilities. The simulation procedure was adapted to run at a daily time step over a 44-year simulation period, and daily streamflow and meteorological data were compiled for all the reservoirs for input to the model. Another change to the model-simulation methods is the adjustment of the scaling factor used in estimating groundwater contributions to the reservoir. The scaling factor is used to convert the daily groundwater-flow rate into a volume by multiplying the rate by the length of reservoir shoreline that is hydrologically connected to the aquifer. Previous firm-yield analyses used a constant scaling factor that was estimated from the reservoir surface area at full pool. The use of a constant scaling factor caused groundwater flows during periods when the reservoir stage was very low to be overestimated. The constant groundwater scaling factor used in previous analyses was replaced with a variable scaling factor that is based on daily reservoir stage. This change reduced instability in the groundwater-flow algorithms and produced more realistic groundwater-flow contributions during periods of low storage. Uncertainty in the firm-yield model arises from many sources, including errors in input data. The sensitivity of the model to uncertainty in streamflow input data and uncertainty in the stage-storage relation was examined. A series of Monte Carlo simulations were performed on 22 reservoirs
Arekhi, Saleh
2008-01-15
Use of an event scale MUSLE model for obtaining accurate long-term annual sediment yield estimates from micro-watersheds was evaluated. Such estimates are extremely important for designing appropriate soil/water conserving measures. For easy extraction and inputting of model input parameters, the proposed model was interfaced to an Arc-View/Spatial Analyst geographic information system. Application of this GIS interfaced MUSLE model on two gauged (pine and oak forest) hilly micro-watersheds viz., Salla Rautella (0.47 km2) and Naula (0.42 km2), in Almora district of Uttaranchal, India showed that it could estimate annual sediment yields with absolute mean relative errors ranging between 12-14%. Even long-term average sediment yields for Salla Rautella (observed: 9.58 tons and estimated: 10.92 tons) and Naula: (Observed: 23.89 tons and estimated: 26.61 tons) micro-watersheds could be quite realistically simulated by the proposed model.
Yamazaki, T; Hagiya, K; Takeda, H; Osawa, T; Yamaguchi, S; Nagamine, Y
2016-08-01
Pregnancy and calving are elements indispensable for dairy production, but the daily milk yield of cows decline as pregnancy progresses, especially during the late stages. Therefore, the effect of stage of pregnancy on daily milk yield must be clarified to accurately estimate the breeding values and lifetime productivity of cows. To improve the genetic evaluation model for daily milk yield and determine the effect of the timing of pregnancy on productivity, we used a test-day model to assess the effects of stage of pregnancy on variance component estimates, daily milk yields and 305-day milk yield during the first three lactations of Holstein cows. Data were 10 646 333 test-day records for the first lactation; 8 222 661 records for the second; and 5 513 039 records for the third. The data were analyzed within each lactation by using three single-trait random regression animal models: one model that did not account for the stage of pregnancy effect and two models that did. The effect of stage of pregnancy on test-day milk yield was included in the model by applying a regression on days pregnant or fitting a separate lactation curve for each days open (days from calving to pregnancy) class (eight levels). Stage of pregnancy did not affect the heritability estimates of daily milk yield, although the additive genetic and permanent environmental variances in late lactation were decreased by accounting for the stage of pregnancy effect. The effects of days pregnant on daily milk yield during late lactation were larger in the second and third lactations than in the first lactation. The rates of reduction of the 305-day milk yield of cows that conceived fewer than 90 days after the second or third calving were significantly (Ppregnancy in the first, compared with later, lactations should be included when determining the optimal number of days open to maximize lifetime productivity in dairy cows.
Estimating Rice Yield under Changing Weather Conditions in Kenya Using CERES Rice Model
W. O. Nyang’au; Mati, B. M.; Kalamwa, K.; Wanjogu, R. K.; L. K. Kiplagat
2014-01-01
Effects of change in weather conditions on the yields of Basmati 370 and IR 2793-80-1 cultivated under System of Rice Intensification (SRI) in Mwea and Western Kenya irrigation schemes were assessed through sensitivity analysis using the Ceres rice model v 4.5 of the DSSAT modeling system. Genetic coefficients were determined using 2010 experimental data. The model was validated using rice growth and development data during the 2011 cropping season. Two SRI farmers were selected randomly from...
Estimating Rice Yield under Changing Weather Conditions in Kenya Using CERES Rice Model
Directory of Open Access Journals (Sweden)
W. O. Nyang’au
2014-01-01
Full Text Available Effects of change in weather conditions on the yields of Basmati 370 and IR 2793-80-1 cultivated under System of Rice Intensification (SRI in Mwea and Western Kenya irrigation schemes were assessed through sensitivity analysis using the Ceres rice model v 4.5 of the DSSAT modeling system. Genetic coefficients were determined using 2010 experimental data. The model was validated using rice growth and development data during the 2011 cropping season. Two SRI farmers were selected randomly from each irrigation scheme and their farms were used as research fields. Daily maximum and minimum temperatures and precipitation were collected from the weather station in each of the irrigation schemes while daily solar radiation was generated using weatherman in the DSSAT shell. The study revealed that increase in both maximum and minimum temperatures affects Basmati 370 and IR 2793-80-1 grain yield under SRI. Increase in atmospheric CO2 concentration led to an increase in grain yield for both Basmati and IR 2793-80-1 under SRI and increase in solar radiation also had an increasing impact on both Basmati 370 and IR 2793-80-1 grain yield. The results of the study therefore show that weather conditions in Kenya affect rice yield under SRI and should be taken into consideration to improve food security.
Integrating remote sensing, geographic information system and modeling for estimating crop yield
Salazar, Luis Alonso
This thesis explores various aspects of the use of remote sensing, geographic information system and digital signal processing technologies for broad-scale estimation of crop yield in Kansas. Recent dry and drought years in the Great Plains have emphasized the need for new sources of timely, objective and quantitative information on crop conditions. Crop growth monitoring and yield estimation can provide important information for government agencies, commodity traders and producers in planning harvest, storage, transportation and marketing activities. The sooner this information is available the lower the economic risk translating into greater efficiency and increased return on investments. Weather data is normally used when crop yield is forecasted. Such information, to provide adequate detail for effective predictions, is typically feasible only on small research sites due to expensive and time-consuming collections. In order for crop assessment systems to be economical, more efficient methods for data collection and analysis are necessary. The purpose of this research is to use satellite data which provides 50 times more spatial information about the environment than the weather station network in a short amount of time at a relatively low cost. Specifically, we are going to use Advanced Very High Resolution Radiometer (AVHRR) based vegetation health (VH) indices as proxies for characterization of weather conditions.
The Rangeland Hydrology and Erosion Model (RHEM) is an event-based model that estimates runoff, erosion, and sediment delivery rates and volumes at the spatial scale of the hillslope and the temporal scale of a single rainfall event. It represents erosion processes on normal rangeland, as well as, r...
The scale mismatch between remotely sensed observations and crop growth models simulated state variables decreases the reliability of crop yield estimates. To overcome this problem, we used a two-step data assimilation phases: first we generated a complete leaf area index (LAI) time series by combin...
Directory of Open Access Journals (Sweden)
Xiuliang Jin
2016-11-01
Full Text Available Knowledge of spatial and temporal variations in crop growth is important for crop management and stable crop production for the food security of a country. A combination of crop growth models and remote sensing data is a useful method for monitoring crop growth status and estimating crop yield. The objective of this study was to use spectral-based biomass values generated from spectral indices to calibrate the AquaCrop model using the particle swarm optimization (PSO algorithm to improve biomass and yield estimations. Spectral reflectance and concurrent biomass and yield were measured at the Xiaotangshan experimental site in Beijing, China, during four winter wheat-growing seasons. The results showed that all of the measured spectral indices were correlated with biomass to varying degrees. The normalized difference matter index (NDMI was the best spectral index for estimating biomass, with the coefficient of determination (R2, root mean square error (RMSE, and relative RMSE (RRMSE values of 0.77, 1.80 ton/ha, and 25.75%, respectively. The data assimilation method (R2 = 0.83, RMSE = 1.65 ton/ha, and RRMSE = 23.60% achieved the most accurate biomass estimations compared with the spectral index method. The estimated yield was in good agreement with the measured yield (R2 = 0.82, RMSE = 0.55 ton/ha, and RRMSE = 8.77%. This study offers a new method for agricultural resource management through consistent assessments of winter wheat biomass and yield based on the AquaCrop model and remote sensing data.
DEVELOPMENT OF A NOVEL EMPIRICAL MODEL TO ESTIMATE THE KRAFT PULP YIELD OF FAST-GROWING EUCALYPTUS
Directory of Open Access Journals (Sweden)
Jing Liu,
2012-01-01
Full Text Available In this study, several kraft pulps were produced by kraft pulping of fast-growing Eucalyptus with a wide range of cooking conditions. The dependences between pulp yields and some pulp properties, namely, kappa number, HexA contents, and cellulose viscosities, were well investigated. It was found that kraft pulp yields linearly decreased with the reduction of HexA-free kappa number in two different stages, respectively, in which a transition point of measured pulp yield of 48.7% was observed. A similar relationship between pulp yield and HexA was also found, in which the resulting transition point of HexA content was 67 μmol/g. Moreover, the logarithm of pulp viscosity was linearly proportional to the reduction of lignin-free pulp yields. Then, a novel empirical model was successfully developed based on these findings. The parameters in this empirical model were calculated by least-squares estimation using the experimental data from active alkali values of 13.2, 14.7 and 17.8. Another data set was used to verify the effectiveness of this model in predicting the pulp yields. Finally, a good agreement (a linear regression coefficient of 90.59% between experimental and fitting data was obtained, which indicated that the kraft pulp yield of fast-growing Eucalyptus could be accurately predicted by this novel empirical model.
Yeom, J. M.; Kim, H. O.
2014-12-01
In this study, we estimated the rice paddy yield with moderate geostationary satellite based vegetation products and GRAMI model over South Korea. Rice is the most popular staple food for Asian people. In addition, the effects of climate change are getting stronger especially in Asian region, where the most of rice are cultivated. Therefore, accurate and timely prediction of rice yield is one of the most important to accomplish food security and to prepare natural disasters such as crop defoliation, drought, and pest infestation. In the present study, GOCI, which is world first Geostationary Ocean Color Image, was used for estimating temporal vegetation indices of the rice paddy by adopting atmospheric correction BRDF modeling. For the atmospheric correction with LUT method based on Second Simulation of the Satellite Signal in the Solar Spectrum (6S), MODIS atmospheric products such as MOD04, MOD05, MOD07 from NASA's Earth Observing System Data and Information System (EOSDIS) were used. In order to correct the surface anisotropy effect, Ross-Thick Li-Sparse Reciprocal (RTLSR) BRDF model was performed at daily basis with 16day composite period. The estimated multi-temporal vegetation images was used for crop classification by using high resolution satellite images such as Rapideye, KOMPSAT-2 and KOMPSAT-3 to extract the proportional rice paddy area in corresponding a pixel of GOCI. In the case of GRAMI crop model, initial conditions are determined by performing every 2 weeks field works at Chonnam National University, Gwangju, Korea. The corrected GOCI vegetation products were incorporated with GRAMI model to predict rice yield estimation. The predicted rice yield was compared with field measurement of rice yield.
Hoffman, A.; Forest, C. E.; Kemanian, A.
2016-12-01
A significant number of food-insecure nations exist in regions of the world where dust plays a large role in the climate system. While the impacts of common climate variables (e.g. temperature, precipitation, ozone, and carbon dioxide) on crop yields are relatively well understood, the impact of mineral aerosols on yields have not yet been thoroughly investigated. This research aims to develop the data and tools to progress our understanding of mineral aerosol impacts on crop yields. Suspended dust affects crop yields by altering the amount and type of radiation reaching the plant, modifying local temperature and precipitation. While dust events (i.e. dust storms) affect crop yields by depleting the soil of nutrients or by defoliation via particle abrasion. The impact of dust on yields is modeled statistically because we are uncertain which impacts will dominate the response on national and regional scales considered in this study. Multiple linear regression is used in a number of large-scale statistical crop modeling studies to estimate yield responses to various climate variables. In alignment with previous work, we develop linear crop models, but build upon this simple method of regression with machine-learning techniques (e.g. random forests) to identify important statistical predictors and isolate how dust affects yields on the scales of interest. To perform this analysis, we develop a crop-climate dataset for maize, soybean, groundnut, sorghum, rice, and wheat for the regions of West Africa, East Africa, South Africa, and the Sahel. Random forest regression models consistently model historic crop yields better than the linear models. In several instances, the random forest models accurately capture the temperature and precipitation threshold behavior in crops. Additionally, improving agricultural technology has caused a well-documented positive trend that dominates time series of global and regional yields. This trend is often removed before regression with
Principal component regression for crop yield estimation
Suryanarayana, T M V
2016-01-01
This book highlights the estimation of crop yield in Central Gujarat, especially with regard to the development of Multiple Regression Models and Principal Component Regression (PCR) models using climatological parameters as independent variables and crop yield as a dependent variable. It subsequently compares the multiple linear regression (MLR) and PCR results, and discusses the significance of PCR for crop yield estimation. In this context, the book also covers Principal Component Analysis (PCA), a statistical procedure used to reduce a number of correlated variables into a smaller number of uncorrelated variables called principal components (PC). This book will be helpful to the students and researchers, starting their works on climate and agriculture, mainly focussing on estimation models. The flow of chapters takes the readers in a smooth path, in understanding climate and weather and impact of climate change, and gradually proceeds towards downscaling techniques and then finally towards development of ...
Marshall, M.; Tu, K. P.
2015-12-01
Large-area crop yield models (LACMs) are commonly employed to address climate-driven changes in crop yield and inform policy makers concerned with climate change adaptation. Production efficiency models (PEMs), a class of LACMs that rely on the conservative response of carbon assimilation to incoming solar radiation absorbed by a crop contingent on environmental conditions, have increasingly been used over large areas with remote sensing spectral information to improve the spatial resolution of crop yield estimates and address important data gaps. Here, we present a new PEM that combines model principles from the remote sensing-based crop yield and evapotranspiration (ET) model literature. One of the major limitations of PEMs is that they are evaluated using data restricted in both space and time. To overcome this obstacle, we first validated the model using 2009-2014 eddy covariance flux tower Gross Primary Production data in a rice field in the Central Valley of California- a critical agro-ecosystem of the United States. This evaluation yielded a Willmot's D and mean absolute error of 0.81 and 5.24 g CO2/d, respectively, using CO2, leaf area, temperature, and moisture constraints from the MOD16 ET model, Priestley-Taylor ET model, and the Global Production Efficiency Model (GLOPEM). A Monte Carlo simulation revealed that the model was most sensitive to the Enhanced Vegetation Index (EVI) input, followed by Photosynthetically Active Radiation, vapor pressure deficit, and air temperature. The model will now be evaluated using 30 x 30m (Landsat resolution) biomass transects developed in 2011 and 2012 from spectroradiometric and other non-destructive in situ metrics for several cotton, maize, and rice fields across the Central Valley. Finally, the model will be driven by Daymet and MODIS data over the entire State of California and compared with county-level crop yield statistics. It is anticipated that the new model will facilitate agro-climatic decision-making in
Institute of Scientific and Technical Information of China (English)
无
2002-01-01
In the present study, a modified Hall-Petch correlation on the basis of dislocation pile-up model was used to estimate the yield strength of SiCp/Al composites. The experimental results show that the modified Hall-Petch correlation expressed as σcy=244+371λ-1/2 fits very well with the experimental data, which indicated that the strength increase of SiCp/Al composites might be due to the direct blocking of dislocation motion by the particulate-matrix interface,namely, the dislocation pile-up is the most possible strengthening mechanism for SiCp/Al composites.
Chahbi, Aicha; Zribi, Mehrez; Lili-Chabaane, Zohra
2016-04-01
In arid and semi-arid areas, population growth, urbanization, food security and climate change have an impact on agriculture in general and particular on the cereal production. Therefore to improve food security in arid countries, crop canopy monitoring and yield forecasting cereals are needed. Many models, based on the use of remote sensing or agro-meteorological models, have been developed to estimate the biomass and grain yield of cereals. Through the use of a rich database, acquired over a period of two years for more than 80 test fields, and from optical satellite SPOT/HRV images, the aim of the present study is to evaluate the feasibility of two yield prediction approaches. The first approach is based on the application of the semi-empirical growth model SAFY, developed to simulate the dynamics of the LAI and the grain yield, at the field scale. The model is able to reproduce the time evolution of the leaf area index of all fields with acceptable error. However, an inter-comparison between ground yield measurements and SAFY model simulations reveals that the yields are under-estimated by this model. We can explain the limits of the semi-empirical model SAFY by its simplicity and also by various factors that were not considered (fertilization, irrigation,...). To improve the yield estimation, a new approach is proposed: the grain yield is estimated in function of the LAI in the growth period between 25 March and 5 April. The LAI of this period is estimated by SAFY model. A linear relationship is developed between the measured grain yield and the LAI area of the maximum growth period.This approach is robust, the measured and estimated grain yields are well correlated. Following the validation of this approach, yield estimations are proposed for the entire studied site using the SPOT/HRV images.
Models for Broad Area Event Identification and Yield Estimation: Multiple Coda Types
2011-09-01
microearthquakes accompanying hydraulic fracturing in granitic rock, Bull. Seism . Soc. Am., 81, 553-575, 1991. Fisk, M. and S. R. Taylor, (2007...146882, pp. 13. Yang, X., T. Lay, X.-B. Xie, and M. S. Thorne (2007). Geometric spreading of Pn and Sn in a spherical Earth model, Bull. Seism . Soc
Xiong, Wei; Skalský, Rastislav; Porter, Cheryl H.; Balkovič, Juraj; Jones, James W.; Yang, Di
2016-09-01
Understanding the interactions between agricultural production and climate is necessary for sound decision-making in climate policy. Gridded and high-resolution crop simulation has emerged as a useful tool for building this understanding. Large uncertainty exists in this utilization, obstructing its capacity as a tool to devise adaptation strategies. Increasing focus has been given to sources of uncertainties for climate scenarios, input-data, and model, but uncertainties due to model parameter or calibration are still unknown. Here, we use publicly available geographical data sets as input to the Environmental Policy Integrated Climate model (EPIC) for simulating global-gridded maize yield. Impacts of climate change are assessed up to the year 2099 under a climate scenario generated by HadEM2-ES under RCP 8.5. We apply five strategies by shifting one specific parameter in each simulation to calibrate the model and understand the effects of calibration. Regionalizing crop phenology or harvest index appears effective to calibrate the model for the globe, but using various values of phenology generates pronounced difference in estimated climate impact. However, projected impacts of climate change on global maize production are consistently negative regardless of the parameter being adjusted. Different values of model parameter result in a modest uncertainty at global level, with difference of the global yield change less than 30% by the 2080s. The uncertainty subjects to decrease if applying model calibration or input data quality control. Calibration has a larger effect at local scales, implying the possible types and locations for adaptation.
Modeling of Yield Estimation for The Main Crops in Iran Based on Mechanization Index (hp ha-1
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K Abbasi
2014-09-01
Full Text Available Agricultural mechanization is a method for transiting from traditional agriculture towards industrial and sustainable one. Due to the limitation of natural resources and increasing population we need to have economical production of agricultural crops. For reaching this destination; agricultural mechanization has a remarkable role. So it is necessary to have an extensive view for mechanization, because with the help of mechanization the agricultural inputs such as seeds, fertilizer and even water and soil can effectively be managed for an economical and sustainable production. This study has been carried out in many provinces of Iran. The data of agricultural tractors and cereal combine harvesters were firstly gathered by means of questionnaire. The tractors were categorized in four power levels of less than 45, 45 to 80, 80 to 110, and more than 110 hp. In addition, it was also carried out for cereal combine harvesters; it was in three power levels, i.e. between 100 to 110, 110 to 155 and 155 to 210 horse-power in 3 ages, i.e. less than 13, between 13 to 20, and more than 20 years. Information regarding to cultivation areas, production volume, and yield of main crops gathered from statistics of Ministry of Jihad-e-Agriculture. Then agriculture mechanization level index (hp ha-1 in each province was calculated. Four main crops including irrigated and rain-fed wheat and irrigated and rain-fed barley, which met the required criteria to be used in the model, were statistically analyzed. Correlation analysis was carried out in order to get an effective model between yield of the four main crops in Iran and agriculture mechanization level index. Pearson correlation index showed that there is a direct and significant correlation between these variables. Subsequently, outliers were identified in order to get a model with necessary efficiency to predict the yield through mechanization level index, by scatter diagram and estimating regression lines in 1
Singh, Ajay; Singh, Avtar; Singh, Manvendra; Prakash, Ved; Ambhore, G. S.; Sahoo, S. K.; Dash, Soumya
2016-01-01
A single trait linear mixed random regression test-day model was applied for the first time for analyzing the first lactation monthly test-day milk yield records in Karan Fries cattle. The test-day milk yield data was modeled using a random regression model (RRM) considering different order of Legendre polynomial for the additive genetic effect (4th order) and the permanent environmental effect (5th order). Data pertaining to 1,583 lactation records spread over a period of 30 years were recorded and analyzed in the study. The variance component, heritability and genetic correlations among test-day milk yields were estimated using RRM. RRM heritability estimates of test-day milk yield varied from 0.11 to 0.22 in different test-day records. The estimates of genetic correlations between different test-day milk yields ranged 0.01 (test-day 1 [TD-1] and TD-11) to 0.99 (TD-4 and TD-5). The magnitudes of genetic correlations between test-day milk yields decreased as the interval between test-days increased and adjacent test-day had higher correlations. Additive genetic and permanent environment variances were higher for test-day milk yields at both ends of lactation. The residual variance was observed to be lower than the permanent environment variance for all the test-day milk yields. PMID:26954137
Yield estimation of metallic layers in integrated circuits
Institute of Scientific and Technical Information of China (English)
Wang Jun-Ping; Hao Yue; Zhang Jun-Ming
2007-01-01
In the existing models of estimating the yield and critical area, the defect outline is usually assumed to be circular, but the observed real defect outlines are irregular in shape. In this paper, estimation of the yield and critical area is made using the Monte Carlo technique and the relationship between the errors of yield estimated by circular defect and the rectangle degree of the defect is analysed. The rectangular model of a real defect is presented, and the yield model is provided correspondingly. The models take into account an outline similar to that of an original defect, the characteristics of two-dimensional distribution of defects, the feature of a layout routing, and the character of yield estimation. In order to make the models practicable, the critical area computations related to rectangular defect and regular (vertical or horizontal) routing are discussed. The critical areas associated with rectangular defect and non-regular routing are developed also, based on the mathematical morphology. The experimental results show that the new yield model may predict the yield caused by real defects more accurately than the circular model. It is significant that the yield is accurately estimated using the proposed model for 1C metals.
Estimating potential yield of wheat production in China based on cross-scale data-model fusion
Institute of Scientific and Technical Information of China (English)
Zhan TIAN; Honglin ZHONG; Runhe SHI; Laixiang SUN; Günther FISCHER; Zhuoran LIANG
2012-01-01
The response of the agro-ecological system to the environment includes the response of individual crop's physiologic process and the adaption of the crop community to the environment.Observation and simulation at the single scale level cannot fully explain the above process.It is necessary to develop cross-scale agro-ecological models and study the interaction of agro-ecological processes across different scales.In this research,two typical agroecological models,the Decision Support System for Agrotechnology Transfer (DSSAT) model and the Agroecological Zone (AEZ) model,are employed,and a framework for effective cross-scale data-model fusion is proposed and illustrated.The national observed data from 36 different agricultural observation stations and historical weather stations (1962-1999) are employed to estimate average crop productivity.Comparison of the two models' estimations are consistent,which would indicate the possibility ofcross-scale crop model fusion.
Silvestro, Paolo Cosmo; Casa, Raffaele; Pignatti, Stefano; Castaldi, Fabio; Yang, Hao; Yang, Guijun
2014-11-01
In the context of the Dragon-3 Farmland Drought project, our research deals with the development of methods for the assimilation of biophysical variables, estimated from multi-source remote sensing, into the AquaCrop model, in order to estimate the yield losses due to drought both at the farm and at the regional scale. The first part of this project was employed to refine a methodology to obtain maps of leaf area index (LAI), canopy cover (CC), fraction of adsorbed photosynthetically active radiation (FAPAR) and chlorophyll (Cab) from satellite optical data, using algorithms based on the training of artificial neural networks (ANN) on PROSAIL model simulations. In the second part, retrieved values of CC were assimilated into the AquaCrop model using the assimilation method of the Ensemble Kalman Filter to estimate grain wheat yield at the field scale.
Jain, M.; Singh, B.; Srivastava, A.; Lobell, D. B.
2015-12-01
Food security will be challenged over the upcoming decades due to increased food demand, natural resource degradation, and climate change. In order to identify potential solutions to increase food security in the face of these changes, tools that can rapidly and accurately assess farm productivity are needed. With this aim, we have developed generalizable methods to map crop yields at the field scale using a combination of satellite imagery and crop models, and implement this approach within Google Earth Engine. We use these methods to examine wheat yield trends in Northern India, which provides over 15% of the global wheat supply and where over 80% of farmers rely on wheat as a staple food source. In addition, we identify the extent to which farmers are shifting sow date in response to heat stress, and how well shifting sow date reduces the negative impacts of heat stress on yield. To identify local-level decision-making, we map wheat sow date and yield at a high spatial resolution (30 m) using Landsat satellite imagery from 1980 to the present. This unique dataset allows us to examine sow date decisions at the field scale over 30 years, and by relating these decisions to weather experienced over the same time period, we can identify how farmers learn and adapt cropping decisions based on weather through time.
Jin, Xiuliang; Li, Zhenhai; Yang, Guijun; Yang, Hao; Feng, Haikuan; Xu, Xingang; Wang, Jihua; Li, Xinchuan; Luo, Juhua
2017-04-01
Timely and accurate estimation of winter wheat yield at a regional scale is crucial for national food policy and security assessments. Near-infrared reflectance is not sensitive to the leaf area index (LAI) and biomass of winter wheat at medium to high canopy cover (CC), and most of the vegetation indices displayed saturation phenomenon. However, LAI and biomass at medium to high CC can be efficiently estimated using imaging data from radar with stronger penetration, such as RADARSAT-2. This study had the following three objectives: (i) to combine vegetation indices based on our previous studies for estimating CC and biomass for winter wheat using HJ-1A/B and RADARSAT-2 imaging data; (ii) to combine HJ-1A/B and RADARSAT-2 imaging data with the AquaCrop model using the particle swarm optimization (PSO) algorithm to estimate winter wheat yield; and (iii) to compare the results from the assimilation of HJ-1A/B + RADARSAT-2 imaging data, HJ-1A/B imaging data, and RADARSAT-2 imaging data into the AquaCrop model using the PSO algorithm. Remote sensing data and concurrent LAI, biomass, and yield of sample fields were acquired in Yangling District, Shaanxi, China, during the 2014 winter wheat growing season. The PSO optimization algorithm was used to integrate the AquaCrop model and remote sensing data for yield estimation. The modified triangular vegetation index 2 (MTVI2) × radar vegetation index (RVI) and the enhanced vegetation index (EVI) × RVI had good relationships with CC and biomass, respectively. The results indicated that the predicted and measured yield (R2 = 0.31 and RMSE = 0.94 ton/ha) had agreement when the estimated CC from the HJ-1A/B and RADARSAT-2 data was used as the dynamic input variable for the AquaCrop model. When the estimated biomass from the HJ-1A/B and RADARSAT-2 data was used as the dynamic input variable for the AquaCrop model, the predicted yield showed agreement with the measured yield (R2 = 0.42 and RMSE = 0.81 ton/ha). These results show
Rice yield estimation with multi-temporal Radarsat-2 data
Chen, Chi-Farn; Son, Nguyen-Thanh; Chen, Cheng-Ru
2015-04-01
Rice is the most important food crop in Taiwan. Monitoring rice crop yield is thus crucial for agronomic planners to formulate successful strategies to address national food security and rice grain export issues. However, there is a real challenge for this monitoring purpose because the size of rice fields in Taiwan was generally small and fragmented, and the cropping calendar was also different from region to region. Thus, satellite-based estimation of rice crop yield requires the data that have sufficient spatial and temporal resolutions. This study aimed to develop models to estimate rice crop yield from multi-temporal Radarsat-2 data (5 m resolution). Data processing were carried out for the first rice cropping season from February to July in 2014 in the western part of Taiwan, consisting of four main steps: (1) constructing time-series backscattering coefficient data, (2) spatiotemporal noise filtering of the time-series data, (3) establishment of crop yield models using the time-series backscattering coefficients and in-situ measured yield data, and (4) model validation using field data and government's yield statistics. The results indicated that backscattering behavior varied from region to region due to changes in cultural practices and cropping calendars. The highest correlation coefficient (R2 > 0.8) was obtained at the ripening period. The robustness of the established models was evaluated by comparisons between the estimated yields and in-situ measured yield data showed satisfactory results, with the root mean squared error (RMSE) smaller than 10%. Such results were reaffirmed by the correlation analysis between the estimated yields and government's rice yield statistics (R2 > 0.8). This study demonstrates advantages of using multi-temporal Radarsat-2 backscattering data for estimating rice crop yields in Taiwan prior to the harvesting period, and thus the methods were proposed for rice yield monitoring in other regions.
Zhang, Yuan; Yang, Bin; Liu, Xiaohui; Wang, Cuizhen
2017-05-01
Fast and accurate estimation of rice yield plays a role in forecasting rice productivity for ensuring regional or national food security. Microwave synthetic aperture radar (SAR) data has been proved to have a great potential for rice monitoring and parameters retrieval. In this study, a rice canopy scattering model (RCSM) was revised and then was applied to simulate the backscatter of rice canopy. The combination of RCSM and genetic algorithm (GA) was proposed for retrieving two important rice parameters relating to grain yield, ear length and ear number density, from a C-band, dual-polarization (HH and HV) Radarsat-2 SAR data. The stability of retrieved results of GA inversion was also evaluated by changing various parameter configurations. Results show that RCSM can effectively simulate backscattering coefficients of rice canopy at HH and HV mode with an error of operational yield estimation.
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Stričević Ružica J.
2014-01-01
Full Text Available Climate change impact on field production may play a great role in strategic planning on soil and water resources management. Therefore, the aim of this work was to find out the impact of climate change on sugar beet yield, irrigation depth variation and water saving practices. AquaCrop model v.4.0 was used for yield and irrigation requirement estimation. Input data for future climatic parameters were obtained from EBU-POM for four scenarios A1B, A2, A1B* and A* (*>CO2, and periods of observation were first (2010-2039; second (2040-2069, and third (2070-2099. Undoubtedly, yield will not be reduced in the first period by any scenario, on the contrary, it might be increased. In the second period, yield reduction was observed in A1B and A2 scenarios, hence without the increment of CO2 in the Vojvodina region, whereas in Central Serbia, yield reduction might be expected even in scenarios of A1B and A2*. Irrigation could ensure yield increment in both regions, provided that an increase is more considerable in Central Serbia, due to lower soil water capacity. Application of optimal irrigation depth yield could be increased by up to 57-97% in Vojvodina and 77-285% in Central Serbia. Lower values are obtained in the first period and the highest in the third period. Applying deficit irrigation, water saving would reduce yield in scenario A2, otherwise to obtain high yield, irrigation depth of 300-500 mm should be ensured in Central Serbia. In the same scenario, 300-420 mm of water for irrigation is needed in Vojvodina, but its water savings could be 80-120 mm, or 20%. In scenario A1B, to obtain high yield, 80 mm could be saved in both regions. [Projekat Ministarstva nauke Republike Srbije, br. TR 37005
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Javier Hernandez
2015-02-01
Full Text Available Plant breeding based on grain yield (GY is an expensive and time-consuming method, so new indirect estimation techniques to evaluate the performance of crops represent an alternative method to improve grain yield. The present study evaluated the ability of canopy reflectance spectroscopy at the range from 350 to 2500 nm to predict GY in a large panel (368 genotypes of wheat (Triticum aestivum L. through multivariate ridge regression models. Plants were treated under three water regimes in the Mediterranean conditions of central Chile: severe water stress (SWS, rain fed, mild water stress (MWS; one irrigation event around booting and full irrigation (FI with mean GYs of 1655, 4739, and 7967 kg∙ha−1, respectively. Models developed from reflectance data during anthesis and grain filling under all water regimes explained between 77% and 91% of the GY variability, with the highest values in SWS condition. When individual models were used to predict yield in the rest of the trials assessed, models fitted during anthesis under MWS performed best. Combined models using data from different water regimes and each phenological stage were used to predict grain yield, and the coefficients of determination (R2 increased to 89.9% and 92.0% for anthesis and grain filling, respectively. The model generated during anthesis in MWS was the best at predicting yields when it was applied to other conditions. Comparisons against conventional reflectance indices were made, showing lower predictive abilities. It was concluded that a Ridge Regression Model using a data set based on spectral reflectance at anthesis or grain filling represents an effective method to predict grain yield in genotypes under different water regimes.
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Luciana Miura Sugawara Berka
2003-01-01
Full Text Available Agrometeorological models interfaced with the Geographic Information System - GIS are an alternative to simulate and quantify the effect of weather spatial and temporal variability on crop yield. The objective of this work was to adapt and interface an agrometeorological model with a GIS to estimate soybean [Glycine max (L. Merr.] yield. Yield estimates were generated for 144 municipalities in the State of Paraná, Brazil, responsible for 90% of the soybean production in the State, from 1996/1997 to 2000/2001. The model uses agronomical parameters and meteorological data to calculate maximum yield which will be penalized under drought stress. Comparative analyses between the yield estimated by the model and that reported by the Paraná State Department of Agriculture (SEAB were performed using the "t" test for paired observations. For the 1996/1997 year the model overestimated yield by 10.8%, which may be attributed to the occurrence of fungal diseases not considered by the model. For 1997/1998, 1998/1999 and 1999/2000 no differences (P > 0.05 were found between the yield estimated by the model and SEAB's data. For 2000/2001 the model underestimated yield by 10.5% and the cause for this difference needs further investigation. The model interfaced with a GIS is an useful tool to monitor soybean crop during growing season to estimate crop yield.Os modelos agrometeorológicos integrados em Sistemas de Informação Geográfica - SIG são uma alternativa para simular e quantificar o efeito da variabilidade espacial e temporal do clima sobre a produtividade agrícola. O objetivo deste trabalho foi adaptar e integrar um modelo agrometeorológico num SIG para estimar a produtividade da soja [Glycine max (L. Merr.]. Foram geradas estimativas de produtividade para 144 municípios do Estado do Paraná, responsáveis por 90% da produção de soja no Estado, em cinco anos-safra no período de 1996/1997 a 2000/2001. O modelo utiliza parâmetros agronômicos e
Guo, Z; Lund, M S; Madsen, P; Korsgaard, I; Jensen, J
2002-06-01
The objectives of this study were to test for heterogeneity of genetic and environmental variance among completed and extended records from different lactations or different days in milk (DIM) and to build a model that accounts for this heterogeneity. A total of 147,457 305-d milk yield records from Danish Jersey cows calving between 1984 and early 1999 from two regions of Denmark were used in this study. Results showed that DIM and parity influenced parameters estimated from an animal model with repeated records. Therefore, the data were analyzed using random-regression models that allow the covariance between measurements to change gradually with DIM and parity. Random regressions were fitted for additive genetic effects and permanent environmental effects using second- or third-order normalized Legendre polynomials for DIM and parity. Variances of random-regression coefficients associated with all orders of the polynomials were significant. Based on these parameter estimates, a covariance function (CF) was defined. The CF showed that the heritability decreases over parities, but within each parity heritability increases with DIM, whereas variance of permanent environmental effects increases over parities and decreases with DIM. Generally, genetic correlations were higher between records with similar DIM and parity. The results indicate that there are problems with the extension procedure used to predict 305-d milk yields. Using the covariance functions estimated in this study, breeding values could be predicted that take into account the covariance structure between records from different parities and different DIM.
作物单产估算模型研究进展与展望%Research advances and perspectives on crop yield estimation models
Institute of Scientific and Technical Information of China (English)
程志强; 蒙继华
2015-01-01
Although crop yield estimation is a necessary requirement of modern agriculture, it is one of the most difficult things to monitor in agriculture. Timely and accurate simulation of crop yield is important for national agricultural decision-making, agricultural production management, grain storage safety, etc. Model simulations of crop growth and yield formation are currently the most commonly method of crop yield estimation. Crop growth and yield formation models were divided into four categories after comparison on theoretical basis, which were empirical linear models, crop growth models, light use efficiency (LUE) models and coupled models. As so many different crop growth models existed, further classification of the models was necessary. The empirical linear models was further divided into four sub-groups according to their estimation methods, while the crop growth models were further divided into four sub-groups on the basis of the main or special driving factors. Then the paper analyzed the merits and demerits of each group of models. Although empirical linear models were simple and needed less data, they had poor generalization in space and time. Crop growth models were more comprehensive and reasonable as they were capable of simulating almost all plant physiological processes and even human disturbances. The shortages of these models were also obvious. The models required more parameters, most of which were not easily accessible. The models also had high software, hardware and professional (knowledge) requirements to accomplish operations. LUE models were capable of comprehensive simulation of light use and easily fitted for remote sensing data to improve simulation precision. The most obvious demerit of LUE models was their inability to simulate human disturbances, a non-ignorable factor, as farm environment in modern agriculture was highly subjected to human activity. Although the coupled models combined the merits of both crop and LUE models, they also
Critical yield-point model to estimate damage caused by brown spot and powdery mildew in barley
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Lenita Agostinetto
2014-06-01
Full Text Available Barley (Hordeum vulgaris L. is the second most important winter crop in Southern Brazil. The excessive rainfall in this region during the crop-growing season increases the frequency and intensity of foliar fungal diseases. The research aimed to determine the damage function equations (DFE for the multiple pathosystem of barley brown spot and powdery mildew based on the relationship between grain yield and diseases intensity at different 'BRS Cauê' cultivar growth stages (GS during 2009 and 2010 growing seasons in Southern Brazil. The experiments were arranged in a randomized complete block design with nine treatments and four replicates. The disease gradients were generated by strobilurins and triazols fungicides rates and number of applications on barley cv. Cauê. The fungicide applications and disease incidence and severity assessments were performed at the 22, 31, 39, 45 and 56 plant GS. The DFE were obtained by variance analysis and linear regression between grain yield and diseases intensity. Significant and negative DFE were obtained and the damage coefficients (DC varied from 29.48 to 100.08 (2009 and from 36.08 to 113.57kg ha-1 (2010 for incidence, and from 219.5 to 6,276.6 (2009 and 102.3 to 5,292.5kg ha-1 (2010 for severity. The largest damage coefficients were obtained when diseases assessments were made on GS 22 and 31 on both growing seasons evaluated. DFE were used to calculate the economic damage threshold (EDT as a criterion to indicate the fungicide application moment to control the diseases in cultivars similar to 'BRS Cauê' in Southern Brazil.
Loos, S.; Perk, M. van der; Beek, L.P.H. van; Middelkoop, H.
2012-01-01
The RiNux model has been developed to simulate and predict monthly nutrient fluxes from land to coastal waters under various scenarios of global change. The RiNux model consists of different modules for simulating the hydrology, sediment transport, and nutrient transport within river basins at a 3 k
minimum variance estimation of yield parameters of rubber tree with ...
African Journals Online (AJOL)
2013-03-01
Mar 1, 2013 ... STAMP, an OxMetric modular software system for time series analysis, was used to estimate the yield ... derlying regression techniques. .... Kalman Filter Minimum Variance Estimation of Rubber Tree Yield Parameters. 83.
Saikia, C. K.; Roman-nieves, J. I.; Woods, M. T.
2013-12-01
Source parameters of nuclear and chemical explosions are often estimated by matching either the corner frequency and spectral level of a single event or the spectral ratio when spectra from two events are available with known source parameters for one. In this study, we propose an alternative method in which waveforms from two or more events can be simultaneously equalized by setting the differential of the processed seismograms at one station from any two individual events to zero. The method involves convolving the equivalent Mueller-Murphy displacement source time function (MMDSTF) of one event with the seismogram of the second event and vice-versa, and then computing their difference seismogram. MMDSTF is computed at the elastic radius including both near and far-field terms. For this method to yield accurate source parameters, an inherent assumption is that green's functions for the any paired events from the source to a receiver are same. In the frequency limit of the seismic data, this is a reasonable assumption and is concluded based on the comparison of green's functions computed for flat-earth models at various source depths ranging from 100m to 1Km. Frequency domain analysis of the initial P wave is, however, sensitive to the depth phase interaction, and if tracked meticulously can help estimating the event depth. We applied this method to the local waveforms recorded from the three SPE shots and precisely determined their yields. These high-frequency seismograms exhibit significant lateral path effects in spectrogram analysis and 3D numerical computations, but the source equalization technique is independent of any variation as long as their instrument characteristics are well preserved. We are currently estimating the uncertainty in the derived source parameters assuming the yields of the SPE shots as unknown. We also collected regional waveforms from 95 NTS explosions at regional stations ALQ, ANMO, CMB, COR, JAS LON, PAS, PFO and RSSD. We are
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Denise Cybis Fontana
2001-03-01
Full Text Available Este trabalho teve como objetivo parametrizar e validar o modelo multiplicativo de Jensen modificado para a estimativa do rendimento da cultura da soja no Estado do Rio Grande do Sul, em condições de lavoura. O ajuste foi feito usando dados meteorológicos de seis estações localizadas na região de produção significativa dessa cultura e dados de rendimento médio de todo o Estado, oriundos de estatísticas oficiais do IBGE, no período 1974/75 a 1994/95. O modelo apresentou bom ajuste, com coeficientes de determinação de 0,86 para o modelo completo (novembro a abril e 0,75 para o modelo reduzido (janeiro a março. A validação do modelo, feita com dados das safras 1995/96, 1996/97, 1997/98 e 1998/99, mostrou um bom desempenho, indicando que a água é o fator isolado que maior influência exerce na definição do rendimento da soja no Rio Grande do Sul e, portanto, pode ser incorporado a programas de previsão de safras.The objective of this study was to fit and validate a modified Jensen multiplicative model to estimate soybean grain yield in the State of Rio Grande do Sul, Brazil, under field conditions. The fitness was done using meteorological data from six weather stations located in the region of major production of this crop and data from averaged soybean grain yield over the whole state. The grain yield was obtained from official government statistics of IBGE (Instituto Brasileiro de Geografia e Estatística, from 1974/75 to 1994/95. The model showed a good fit, with determination coefficients varying from 0.86 for a complete model (November to April to 0.75 for a reduced one (January to March. The model validation, done with independent data of 1995/96, 1996/97, 1997/98 e 1998/99, had a good performance, showing that water is the isolated factor that has the major influence on soybean grain yield definition in Rio Grande do Sul, and, therefore, could be incorporated into programs for predicting the crop harvest.
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Paulo Augusto Manfron
2012-10-01
Full Text Available Despite the great importance of soybeans in Brazil, there have been few applications of soybean crop modeling on Brazilian conditions. Thus, the objective of this study was to use modified crop models to estimate the depleted and potential soybean crop yield in Brazil. The climatic variable data used in the modified simulation of the soybean crop models were temperature, insolation and rainfall. The data set was taken from 33 counties (28 Sao Paulo state counties, and 5 counties from other states that neighbor São Paulo. Among the models, modifications in the estimation of the leaf area of the soybean crop, which includes corrections for the temperature, shading, senescence, CO2, and biomass partition were proposed; also, the methods of input for the model’s simulation of the climatic variables were reconsidered. The depleted yields were estimated through a water balance, from which the depletion coefficient was estimated. It can be concluded that the adaptation soybean growth crop model might be used to predict the results of the depleted and potential yield of soybeans, and it can also be used to indicate better locations and periods of tillage.Aplicações de modelos de previsão de produtividade na cultura da soja são muito raros. Assim, o objetivo desta pesquisa foi realizar a estimação da produtividade deplecionada e potencial da cultura de soja, usando modelos de previsão modificados. Os dados climáticos utilizados nos modelos de simulação foram a temperatura, precipitação e insolação. Os dados foram proveniente de 33 municípios (28 do estado de São Paulo, e cinco municípios de estados vizinhos. Dentre os modelos propostos modificados está a estimação da área foliar da soja, com correções para temperatura, sombreamento, senescência, CO2, partição de biomassa, bem como os métodos de simulação das variávies climáticas do “input” para o modelo. As produções deplecionadas foram estimadas através do balan
Rasmussen, Teresa J.; Lee, Casey J.; Ziegler, Andrew C.
2008-01-01
Johnson County is one of the most rapidly developing counties in Kansas. Population growth and expanding urban land use affect the quality of county streams, which are important for human and environmental health, water supply, recreation, and aesthetic value. This report describes estimates of streamflow and constituent concentrations, loads, and yields in relation to watershed characteristics in five Johnson County streams using continuous in-stream sensor measurements. Specific conductance, pH, water temperature, turbidity, and dissolved oxygen were monitored in five watersheds from October 2002 through December 2006. These continuous data were used in conjunction with discrete water samples to develop regression models for continuously estimating concentrations of other constituents. Continuous regression-based concentrations were estimated for suspended sediment, total suspended solids, dissolved solids and selected major ions, nutrients (nitrogen and phosphorus species), and fecal-indicator bacteria. Continuous daily, monthly, seasonal, and annual loads were calculated from concentration estimates and streamflow. The data are used to describe differences in concentrations, loads, and yields and to explain these differences relative to watershed characteristics. Water quality at the five monitoring sites varied according to hydrologic conditions; contributing drainage area; land use (including degree of urbanization); relative contributions from point and nonpoint constituent sources; and human activity within each watershed. Dissolved oxygen (DO) concentrations were less than the Kansas aquatic-life-support criterion of 5.0 mg/L less than 10 percent of the time at all sites except Indian Creek, which had DO concentrations less than the criterion about 15 percent of the time. Concentrations of suspended sediment, chloride (winter only), indicator bacteria, and pesticides were substantially larger during periods of increased streamflow. Suspended
Exoplanet Yield Estimation for Decadal Study Concepts using EXOSIMS
Morgan, Rhonda; Lowrance, Patrick; Savransky, Dmitry; Garrett, Daniel
2016-01-01
The anticipated upcoming large mission study concepts for the direct imaging of exo-earths present an exciting opportunity for exoplanet discovery and characterization. While these telescope concepts would also be capable of conducting a broad range of astrophysical investigations, the most difficult technology challenges are driven by the requirements for imaging exo-earths. The exoplanet science yield for these mission concepts will drive design trades and mission concept comparisons.To assist in these trade studies, the Exoplanet Exploration Program Office (ExEP) is developing a yield estimation tool that emphasizes transparency and consistent comparison of various design concepts. The tool will provide a parametric estimate of science yield of various mission concepts using contrast curves from physics-based model codes and Monte Carlo simulations of design reference missions using realistic constraints, such as solar avoidance angles, the observatory orbit, propulsion limitations of star shades, the accessibility of candidate targets, local and background zodiacal light levels, and background confusion by stars and galaxies. The python tool utilizes Dmitry Savransky's EXOSIMS (Exoplanet Open-Source Imaging Mission Simulator) design reference mission simulator that is being developed for the WFIRST Preliminary Science program. ExEP is extending and validating the tool for future mission concepts under consideration for the upcoming 2020 decadal review. We present a validation plan and preliminary yield results for a point design.
Annual Corn Yield Estimation through Multi-temporal MODIS Data
Shao, Y.; Zheng, B.; Campbell, J. B.
2013-12-01
This research employed 13 years of the Moderate Resolution Imaging Spectroradiometer (MODIS) to estimate annual corn yield for the Midwest of the United States. The overall objective of this study was to examine if annual corn yield could be accurately predicted using MODIS time-series NDVI (Normalized Difference Vegetation Index) and ancillary data such monthly precipitation and temperature. MODIS-NDVI 16-Day composite images were acquired from the USGS EROS Data Center for calendar years 2000 to 2012. For the same time-period, county level corn yield statistics were obtained from the National Agricultural Statistics Service (NASS). The monthly precipitation and temperature measures were derived from Precipitation-Elevation Regressions on Independent Slopes Model (PRISM) climate data. A cropland mask was derived using 2006 National Land Cover Database. For each county and within the cropland mask, the MODIS-NDVI time-series data and PRISM climate data were spatially averaged, at their respective time steps. We developed a random forest predictive model with the MODIS-NDVI and climate data as predictors and corn yield as response. To assess the model accuracy, we used twelve years of data as training and the remaining year as hold-out testing set. The training and testing procedures were repeated 13 times. The R2 ranged from 0.72 to 0.83 for testing years. It was also found that the inclusion of climate data did not improve the model predictive performance. MODIS-NDVI time-series data alone might provide sufficient information for county level corn yield prediction.
CSIR Research Space (South Africa)
Malherbe, J
2014-07-01
Full Text Available Forecasts of a Global Coupled Model for austral summer with a 1 month lead are downscaled to end-of-season maize yields and accumulated streamflow over the Limpopo Province and adjacent districts in northeastern South Africa through application...
Spin-dependent np {yields}pn amplitude estimated from dp{yields}ppn
Energy Technology Data Exchange (ETDEWEB)
Glagolev, V.V.; Khvastunov, M.S.; Ladygina, N.B.; Piskunov, N.M. [Joint Institute for Nuclear Research, 141980 Dubna (Russian Federation); Hlavacova, J. [Technical University, Park Komenskeho 2, SK-04200, Kosice (Slovakia); Martinska, G.; Urban, J. [University of P.J. Safarik, Jesenna 5, SK-04154 Kosice (Slovakia); Musinsky, J. [Joint Institute for Nuclear Research, 141980 Dubna (Russian Federation); University of P.J. Safarik, Jesenna 5, SK-04154 Kosice (Slovakia); Pastircak, B. [Institute of Experimental Physics SAS, Watsonova 47, SK-04353, Kosice (Slovakia); Siemiarczuk, T. [Institute of Nuclear Studies,ul. Hoza 69, Warsaw, PL-00 681 (Poland)
2002-12-01
An estimation of the spin-dependent part of the np{yields}pn charge exchange amplitude was made on the basis of dp{yields}(pp)n data, taken at 1.67 GeV/c per nucleon in a full solid-angle arrangement. The np{yields}pn amplitude turned out to be entirely spin-dependent. This result shows new possibilities for experiments using polarized deuteron beams and polarized proton targets. (orig.)
Performance of a procedure for yield estimation in fruit orchards
DEFF Research Database (Denmark)
Aravena Zamora, Felipe; Potin, Camila; Wulfsohn, Dvora-Laio
for fruit yield estimation. In the Spring of 2009 we estimated the total number of fruit in several rows in each of 14 commercial fruit orchards growing apple, kiwi, and table grapes in central Chile. Survey times were 10-100 minutes for apples, 85 minutes for table grapes, and up to 150 minutes for kiwis....... At harvest in the Fall, the fruit were counted to obtain the true yield. Yields ranged from lows of several thousand (grape bunches), to highs of more than 40 thousand fruit (apples, kiwis). In 11 orchards, true errors less than 10% were obtained. In two highly variable orchards we obtained absolute true...
Directory of Open Access Journals (Sweden)
Howard Viator
2012-06-01
Full Text Available Estimating crop yield using remote sensing techniques has proven to be successful. However, sugarcane possesses unique characteristics; such as, a multi-year cropping cycle and plant height-limiting for midseason fertilizer application timing. Our study objective was to determine if sugarcane yield potential could be estimated using an in-season estimation of normalized difference vegetative index (NDVI. Sensor readings were taken using the GreenSeeker® handheld sensor from 2008 to 2011 in St. Gabriel and Jeanerette, LA, USA. In-season estimates of yield (INSEY values were calculated by dividing NDVI by thermal variables. Optimum timing for estimating sugarcane yield was between 601–750 GDD. In-season estimated yield values improved the yield potential (YP model compared to using NDVI. Generally, INSEY value showed a positive exponential relationship with yield (r^{2} values 0.48 and 0.42 for cane tonnage and sugar yield, respectively. When models were separated based on canopy structure there was an increase the strength of the relationship for the erectophile varieties (r^{2} 0.53 and 0.47 for cane tonnage and sugar yield, respectively; however, the model for planophile varieties weakened slightly. Results of this study indicate using an INSEY value for predicting sugarcane yield shows potential of being a valuable management tool for sugarcane producers in Louisiana.
Lofton, Josh; Tubana, Brenda S; Kanke, Yumiko; Teboh, Jasper; Viator, Howard; Dalen, Marilyn
2012-01-01
Estimating crop yield using remote sensing techniques has proven to be successful. However, sugarcane possesses unique characteristics; such as, a multi-year cropping cycle and plant height-limiting for midseason fertilizer application timing. Our study objective was to determine if sugarcane yield potential could be estimated using an in-season estimation of normalized difference vegetative index (NDVI). Sensor readings were taken using the GreenSeeker® handheld sensor from 2008 to 2011 in St. Gabriel and Jeanerette, LA, USA. In-season estimates of yield (INSEY) values were calculated by dividing NDVI by thermal variables. Optimum timing for estimating sugarcane yield was between 601-750 GDD. In-season estimated yield values improved the yield potential (YP) model compared to using NDVI. Generally, INSEY value showed a positive exponential relationship with yield (r(2) values 0.48 and 0.42 for cane tonnage and sugar yield, respectively). When models were separated based on canopy structure there was an increase the strength of the relationship for the erectophile varieties (r(2) 0.53 and 0.47 for cane tonnage and sugar yield, respectively); however, the model for planophile varieties weakened slightly. Results of this study indicate using an INSEY value for predicting sugarcane yield shows potential of being a valuable management tool for sugarcane producers in Louisiana.
Directory of Open Access Journals (Sweden)
Graciele R. Spezia
2012-04-01
Full Text Available Yield mapping represents the spatial variability concerning the features of a productive area and allows intervening on the next year production, for example, on a site-specific input application. The trial aimed at verifying the influence of a sampling density and the type of interpolator on yield mapping precision to be produced by a manual sampling of grains. This solution is usually adopted when a combine with yield monitor can not be used. An yield map was developed using data obtained from a combine equipped with yield monitor during corn harvesting. From this map, 84 sample grids were established and through three interpolators: inverse of square distance, inverse of distance and ordinary kriging, 252 yield maps were created. Then they were compared with the original one using the coefficient of relative deviation (CRD and the kappa index. The loss regarding yield mapping information increased as the sampling density decreased. Besides, it was also dependent on the interpolation method used. A multiple regression model was adjusted to the variable CRD, according to the following variables: spatial variability index and sampling density. This model aimed at aiding the farmer to define the sampling density, thus, allowing to obtain the manual yield mapping, during eventual problems in the yield monitor.O mapa de produtividade representa a variabilidade espacial das características de uma área cultivada e permite intervir na produção dos anos posteriores, na aplicação diferenciada de insumos. Este trabalho teve por objetivo verificar a influência da densidade amostral e do tipo de interpolação na exatidão dos mapas de produtividade, gerados a partir da amostragem manual de grãos, solução que pode ser adotada quando um monitor não pode ser utilizado. Um mapa de produtividade foi obtido com monitor de colheita comercial em lavoura de milho, a partir do qual foram estabelecidas 84 grades e, por meio de três interpoladores, o
Statistical modelling and deconvolution of yield meter data
DEFF Research Database (Denmark)
Tøgersen, Frede Aakmann; Waagepetersen, Rasmus Plenge
previously harvested along the swath. The unobserved yield is assumed to be a Gaussian random field and the yield monitoring system data is modelled as a convolution of the yield and an impulse response function. This results in an unusual spatial covariance structure (depending on the driving pattern......Data for yield maps can be obtained from modern combine harvesters equipped with a differential global positioning system and a yield monitoring system. Due to delay and smoothing effects in the combine harvester the recorded yield data for a location represents a shifted weighted average of yield...... of the combine harvester) for the yield monitoring system data. Parameters of the impulse response function and the spatial covariance function of the yield are estimated using maximum likelihood. The fitted model is assessed using certain empirical directional covariograms and the yield is finally predicted...
Incorporating phenology into yield models
Gray, J. M.; Friedl, M. A.
2015-12-01
Because the yields of many crops are sensitive to meteorological forcing during specific growth stages, phenological information has potential utility in yield mapping and forecasting exercises. However, most attempts to explain the spatiotemporal variability in crop yields with weather data have relied on growth stage definitions that do not change from year-to-year, even though planting, maturity, and harvesting dates show significant interannual variability. We tested the hypothesis that quantifying temperature exposures over dynamically determined growth stages would better explain observed spatiotemporal variability in crop yields than statically defined time periods. Specifically, we used National Agricultural and Statistics Service (NASS) crop progress data to identify the timing of the start of the maize reproductive growth stage ("silking"), and examined the correlation between county-scale yield anomalies and temperature exposures during either the annual or long-term average silking period. Consistent with our hypothesis and physical understanding, yield anomalies were more correlated with temperature exposures during the actual, rather than the long-term average, silking period. Nevertheless, temperature exposures alone explained a relatively low proportion of the yield variability, indicating that other factors and/or time periods are also important. We next investigated the potential of using remotely sensed land surface phenology instead of NASS progress data to retrieve crop growth stages, but encountered challenges related to crop type mapping and subpixel crop heterogeneity. Here, we discuss the potential of overcoming these challenges and the general utility of remotely sensed land surface phenology in crop yield mapping.
Soniat, Thomas M.; Klinck, John M.; Powell, Eric N.; Cooper, Nathan; Abdelguerfi, Mahdi; Hofmann, Eileen E.; Dahal, Janak; Tu, Shengru; Finigan, John; Eberline, Benjamin S.; La Peyre, Jerome F.; LaPeyre, Megan K.; Qaddoura, Fareed
2012-01-01
A numerical model is presented that defines a sustainability criterion as no net loss of shell, and calculates a sustainable harvest of seed (trend toward decreasing abundance of sack and seed oysters. Retrospective simulations provide estimates of annual sustainable harvests. Comparisons of simulated sustainable harvests with actual harvests show a trend toward unsustainable harvests toward the end of the time series. Stock assessments combined with shell-neutral models can be used to estimate sustainable harvest and manage cultch through shell planting when actual harvest exceeds sustainable harvest. For exclusive restoration efforts (no fishing allowed), the model provides a metric for restoration success-namely, shell accretion. Oyster fisheries that remove shell versus reef restorations that promote shell accretion, although divergent in their goals, are convergent in their management; both require vigilant attention to shell budgets.
Directory of Open Access Journals (Sweden)
M. C. Peel
2014-05-01
Full Text Available Two key sources of uncertainty in projections of future runoff for climate change impact assessments are uncertainty between Global Climate Models (GCMs and within a GCM. Within-GCM uncertainty is the variability in GCM output that occurs when running a scenario multiple times but each run has slightly different, but equally plausible, initial conditions. The limited number of runs available for each GCM and scenario combination within the Coupled Model Intercomparison Project phase 3 (CMIP3 and phase 5 (CMIP5 datasets, limits the assessment of within-GCM uncertainty. In this second of two companion papers, the primary aim is to approximate within-GCM uncertainty of monthly precipitation and temperature projections and assess its impact on modelled runoff for climate change impact assessments. A secondary aim is to assess the impact of between-GCM uncertainty on modelled runoff. Here we approximate within-GCM uncertainty by developing non-stationary stochastic replicates of GCM monthly precipitation and temperature data. These replicates are input to an off-line hydrologic model to assess the impact of within-GCM uncertainty on projected annual runoff and reservoir yield. To-date within-GCM uncertainty has received little attention in the hydrologic climate change impact literature and this analysis provides an approximation of the uncertainty in projected runoff, and reservoir yield, due to within- and between-GCM uncertainty of precipitation and temperature projections. In the companion paper, McMahon et al. (2014 sought to reduce between-GCM uncertainty by removing poorly performing GCMs, resulting in a selection of five better performing GCMs from CMIP3 for use in this paper. Here we present within- and between-GCM uncertainty results in mean annual precipitation (MAP, temperature (MAT and runoff (MAR, the standard deviation of annual precipitation (SDP and runoff (SDR and reservoir yield for five CMIP3 GCMs at 17 world-wide catchments
Rasmussen, Teresa J.; Ziegler, Andrew C.; Rasmussen, Patrick P.
2005-01-01
The lower Kansas River is an important source of drinking water for hundreds of thousands of people in northeast Kansas. Constituents of concern identified by the Kansas Department of Health and Environment (KDHE) for streams in the lower Kansas River Basin include sulfate, chloride, nutrients, atrazine, bacteria, and sediment. Real-time continuous water-quality monitors were operated at three locations along the lower Kansas River from July 1999 through September 2004 to provide in-stream measurements of specific conductance, pH, water temperature, turbidity, and dissolved oxygen and to estimate concentrations for constituents of concern. Estimates of concentration and densities were combined with streamflow to calculate constituent loads and yields from January 2000 through December 2003. The Wamego monitoring site is located 44 river miles upstream from the Topeka monitoring site, which is 65 river miles upstream from the DeSoto monitoring site, which is 18 river miles upstream from where the Kansas River flows into the Missouri River. Land use in the Kansas River Basin is dominated by grassland and cropland, and streamflow is affected substantially by reservoirs. Water quality at the three monitoring sites varied with hydrologic conditions, season, and proximity to constituent sources. Nutrient and sediment concentrations and bacteria densities were substantially larger during periods of increased streamflow, indicating important contributions from nonpoint sources in the drainage basin. During the study period, pH remained well above the KDHE lower criterion of 6.5 standard units at all sites in all years, but exceeded the upper criterion of 8.5 standard units annually between 2 percent of the time (Wamego in 2001) and 65 percent of the time (DeSoto in 2003). The dissolved oxygen concentration was less than the minimum aquatic-life-support criterion of 5.0 milligrams per liter less than 1 percent of the time at all sites. Dissolved solids, a measure of the
Estimating Corn Yield in the United States with Modis Evi and Machine Learning Methods
Kuwata, K.; Shibasaki, R.
2016-06-01
Satellite remote sensing is commonly used to monitor crop yield in wide areas. Because many parameters are necessary for crop yield estimation, modelling the relationships between parameters and crop yield is generally complicated. Several methodologies using machine learning have been proposed to solve this issue, but the accuracy of county-level estimation remains to be improved. In addition, estimating county-level crop yield across an entire country has not yet been achieved. In this study, we applied a deep neural network (DNN) to estimate corn yield. We evaluated the estimation accuracy of the DNN model by comparing it with other models trained by different machine learning algorithms. We also prepared two time-series datasets differing in duration and confirmed the feature extraction performance of models by inputting each dataset. As a result, the DNN estimated county-level corn yield for the entire area of the United States with a determination coefficient (R2) of 0.780 and a root mean square error (RMSE) of 18.2 bushels/acre. In addition, our results showed that estimation models that were trained by a neural network extracted features from the input data better than an existing machine learning algorithm.
Brigolin, Daniele; Maschio, Gabriele Dal; Rampazzo, Federico; Giani, Michele; Pastres, Roberto
2009-04-01
The fluxes of carbon, nitrogen and phosphorus through an off-shore long-line Mytilus galloprovincialis farm during a typical rearing cycle were estimated by combining a simple population dynamic model, based on a new individual model, and a set of field data, concerning the composition of the seston, as well as that of mussel meat and faeces. The individual model, based on an energy budget, was validated against a set of original field data, which were purposely collected from July 2006 to May 2007 in the North-Western Adriatic Sea (Italy) and was further tested using historical data. The model was upscaled to the population level by means of a set of Monte Carlo simulations, which were used for estimating the size structure of the population. The daily fluxes of C, N and P associated with mussel filtration, excretion and faeces and pseudo-faeces production were integrated over the 10-month-long rearing cycle and compared with the total amount of C, N and P removed by harvesting. The results indicate that the individual model compares well with an existing literature model and provides reliable estimations of the growth of mussel specimen over a range of trophic conditions which are typical of the Northern Adriatic Sea coastal area. The results of the budget calculation indicate that, even though the harvest represents a net removal of phosphorus and nitrogen from the ecosystem, the mussel farm increases the retention time of both nutrients in the coastal area, via the deposition of faeces and pseudo-faeces on the sea-bed. In fact, the amount of nitrogen associated with deposition is approximately twice the harvested one and the amount of phosphorus is approximately five times higher. These findings are in qualitative agreement with the results of literature budget and model calculations carried out in a temperate coastal embayment. This agreement suggests that the proper assessment of the overall effect of long-line mussel farming on both the benthic and pelagic
Continuous Time Model Estimation
Carl Chiarella; Shenhuai Gao
2004-01-01
This paper introduces an easy to follow method for continuous time model estimation. It serves as an introduction on how to convert a state space model from continuous time to discrete time, how to decompose a hybrid stochastic model into a trend model plus a noise model, how to estimate the trend model by simulation, and how to calculate standard errors from estimation of the noise model. It also discusses the numerical difficulties involved in discrete time models that bring about the unit ...
Real-time yield estimation based on deep learning
Rahnemoonfar, Maryam; Sheppard, Clay
2017-05-01
Crop yield estimation is an important task in product management and marketing. Accurate yield prediction helps farmers to make better decision on cultivation practices, plant disease prevention, and the size of harvest labor force. The current practice of yield estimation based on the manual counting of fruits is very time consuming and expensive process and it is not practical for big fields. Robotic systems including Unmanned Aerial Vehicles (UAV) and Unmanned Ground Vehicles (UGV), provide an efficient, cost-effective, flexible, and scalable solution for product management and yield prediction. Recently huge data has been gathered from agricultural field, however efficient analysis of those data is still a challenging task. Computer vision approaches currently face diffident challenges in automatic counting of fruits or flowers including occlusion caused by leaves, branches or other fruits, variance in natural illumination, and scale. In this paper a novel deep convolutional network algorithm was developed to facilitate the accurate yield prediction and automatic counting of fruits and vegetables on the images. Our method is robust to occlusion, shadow, uneven illumination and scale. Experimental results in comparison to the state-of-the art show the effectiveness of our algorithm.
Estimating yield gaps at the cropping system level.
Guilpart, Nicolas; Grassini, Patricio; Sadras, Victor O; Timsina, Jagadish; Cassman, Kenneth G
2017-05-01
Yield gap analyses of individual crops have been used to estimate opportunities for increasing crop production at local to global scales, thus providing information crucial to food security. However, increases in crop production can also be achieved by improving cropping system yield through modification of spatial and temporal arrangement of individual crops. In this paper we define the cropping system yield potential as the output from the combination of crops that gives the highest energy yield per unit of land and time, and the cropping system yield gap as the difference between actual energy yield of an existing cropping system and the cropping system yield potential. Then, we provide a framework to identify alternative cropping systems which can be evaluated against the current ones. A proof-of-concept is provided with irrigated rice-maize systems at four locations in Bangladesh that represent a range of climatic conditions in that country. The proposed framework identified (i) realistic alternative cropping systems at each location, and (ii) two locations where expected improvements in crop production from changes in cropping intensity (number of crops per year) were 43% to 64% higher than from improving the management of individual crops within the current cropping systems. The proposed framework provides a tool to help assess food production capacity of new systems (e.g. with increased cropping intensity) arising from climate change, and assess resource requirements (water and N) and associated environmental footprint per unit of land and production of these new systems. By expanding yield gap analysis from individual crops to the cropping system level and applying it to new systems, this framework could also be helpful to bridge the gap between yield gap analysis and cropping/farming system design.
Estimation of rice yield affected by drought and relation between rice yield and TVDI
Hongo, C.; Tamura, E.; Sigit, G.
2016-12-01
Impact of climate change is not only seen on food production but also on food security and sustainable development of society. Adaptation to climate change is a pressing issue throughout the world to reduce the risks along with the plans and strategies for food security and sustainable development. As a key adaptation to the climate change, agricultural insurance is expected to play an important role in stabilizing agricultural production through compensating the losses caused by the climate change. As the adaptation, the Government of Indonesia has launched agricultural insurance program for damage of rice by drought, flood and pest and disease. The Government started a pilot project in 2013 and this year the pilot project has been extended to 22 provinces. Having the above as background, we conducted research on development of new damage assessment method for rice using remote sensing data which could be used for evaluation of damage ratio caused by drought in West Java, Indonesia. For assessment of the damage ratio, estimation of rice yield is a key. As the result of our study, rice yield affected by drought in dry season could be estimated at level of 1 % significance using SPOT 7 data taken in 2015, and the validation result was 0.8t/ha. Then, the decrease ratio in rice yield about each individual paddy field was calculated using data of the estimated result and the average yield of the past 10 years. In addition, TVDI (Temperature Vegetation Dryness Index) which was calculated from Landsat8 data in heading season indicated the dryness in low yield area. The result suggests that rice yield was affected by irrigation water shortage around heading season as a result of the decreased precipitation by El Nino. Through our study, it becomes clear that the utilization of remote sensing data can be promising for assessment of the damage ratio of rice production precisely, quickly and quantitatively, and also it can be incorporated into the insurance procedures.
Using SPOT data and leaf area index for rice yield estimation in Egyptian Nile delta
Directory of Open Access Journals (Sweden)
M. Aboelghar
2011-12-01
Full Text Available The objective of the current work is to generate statistical empirical rice yield estimation models under the local conditions of the Egyptian Nile delta. The methodology is based on regressing measured yield with satellite derived spectral information or leaf area index (LAI. LAI field measurements and spectral information from SPOT data collected during two crop seasons are examined against measured yield to generate the yield models. Near-infrared and red bands, six vegetation indices and LAI of 100 points are used as the main inputs for the modeling process while 20 points of the same are used for validation process. Nine models are generated and tested against the observed yield. Comparing the generated models show relatively higher superiority of (LAI-yield and (infrared-yield models over the rest of the models with (0.061 and (0.090 as a standard error of estimate and (0.945 and (0.883 as coefficient of determinations between modeled and observed yield. The models are applicable a month before harvest for similar regions with same conditions.
Rice yield forecasting models using satellite imagery in Egypt
Directory of Open Access Journals (Sweden)
N.A. Noureldin
2013-06-01
Full Text Available Ability to make yield prediction before harvest using satellite remote sensing is important in many aspects of agricultural decision-making. In this study, canopy reflectance band and different band ratios in form of vegetation indices (VI with leaf area index (LAI were used to generate remotely sensed pre-harvest empirical rice yield prediction models. LAI measurements, spectral data derived from two SPOT data acquired on August 24, 2008 and August 23, 2009 and observed rice yield were used as main inputs for rice yield modeling. Each remotely sensed factor was used separately and in combination with LAI to generate the models. The results showed that green spectral band, middle infra-red spectral band and green vegetation index (GVI did not show sufficient capability as rice yield estimators while other inputs such as red spectral band, near infrared spectral band and vegetation indices that are algebraic ratios from these two spectral bands when used separately or in combined with leaf area index (LAI produced high accurate rice yield estimation models. The validation process was carried out using two statistical tests; standard error of estimate and the correlation coefficient between modeled and predicted yield. The validation results indicated that using normalized difference vegetation index (NDVI combined with leaf area index (LAI produced the model with highest accuracy and stability during the two rice seasons. The generated models are applicable 90 days after planting in any similar environmental conditions and agricultural practices.
Sugarcane yield estimation for climatic conditions in the state of Goiás
Directory of Open Access Journals (Sweden)
Jordana Moura Caetano
Full Text Available ABSTRACT Models that estimate potential and depleted crop yield according to climatic variable enable the crop planning and production quantification for a specific region. Therefore, the objective of this study was to compare methods to sugarcane yield estimates grown in the climatic condition in the central part of Goiás, Brazil. So, Agroecological Zone Method (ZAE and the model proposed by Scarpari (S were correlated with real data of sugarcane yield from an experimental area, located in Santo Antônio de Goiás, state of Goiás, Brazil. Data yield refer to the crops of 2008/2009 (sugarcane plant, 2009/2010, 2010/2011 and 2011/2012 (ratoon sugarcane. Yield rates were calculated as a function of atmospheric water demand and water deficit in the area under study. Real and estimated yields were adjusted in function of productivity loss due to cutting stage of sugarcane, using an average reduction in productivity observed in the experimental area and the average reduction in the state of Goiás. The results indicated that the ZAE method, considering the water deficit, displayed good yield estimates for cane-plant (d > 0.90. Water deficit decreased the yield rates (r = -0.8636; α = 0.05 while the thermal sum increased that rate for all evaluated harvests (r > 0.68; α = 0.05.
Mayberry, Dianne; Ash, Andrew; Prestwidge, Di; Godde, Cécile M; Henderson, Ben; Duncan, Alan; Blummel, Michael; Ramana Reddy, Y; Herrero, Mario
2017-07-01
Livestock provides an important source of income and nourishment for around one billion rural households worldwide. Demand for livestock food products is increasing, especially in developing countries, and there are opportunities to increase production to meet local demand and increase farm incomes. Estimating the scale of livestock yield gaps and better understanding factors limiting current production will help to define the technological and investment needs in each livestock sector. The aim of this paper is to quantify livestock yield gaps and evaluate opportunities to increase dairy production in Sub-Saharan Africa and South Asia, using case studies from Ethiopia and India. We combined three different methods in our approach. Benchmarking and a frontier analysis were used to estimate attainable milk yields based on survey data. Household modelling was then used to simulate the effects of various interventions on dairy production and income. We tested interventions based on improved livestock nutrition and genetics in the extensive lowland grazing zone and highland mixed crop-livestock zones of Ethiopia, and the intensive irrigated and rainfed zones of India. Our analyses indicate that there are considerable yield gaps for dairy production in both countries, and opportunities to increase production using the interventions tested. In some cases, combined interventions could increase production past currently attainable livestock yields.
Ethiopian Wheat Yield and Yield Gap Estimation: A Spatial Small Area Integrated Data Approach
Mann, M.; Warner, J.
2015-12-01
Despite the collection of routine annual agricultural surveys and significant advances in GIS and remote sensing products, little econometric research has been undertaken in predicting developing nation's agricultural yields. In this paper, we explore the determinants of wheat output per hectare in Ethiopia during the 2011-2013 Meher crop seasons aggregated to the woreda administrative area. Using a panel data approach, combining national agricultural field surveys with relevant GIS and remote sensing products, the model explains nearly 40% of the total variation in wheat output per hectare across the country. The model also identifies specific contributors to wheat yields that include farm management techniques (eg. area planted, improved seed, fertilizer, irrigation), weather (eg. rainfall), water availability (vegetation and moisture deficit indexes) and policy intervention. Our findings suggest that woredas produce between 9.8 and 86.5% of their potential wheat output per hectare given their altitude, weather conditions, terrain, and plant health. At the median, Amhara, Oromiya, SNNP, and Tigray produce 48.6, 51.5, 49.7, and 61.3% of their local attainable yields, respectively. This research has a broad range of applications, especially from a public policy perspective: identifying causes of yield fluctuations, remotely evaluating larger agricultural intervention packages, and analyzing relative yield potential. Overall, the combination of field surveys with spatial data can be used to identify management priorities for improving production at a variety of administrative levels.
Directory of Open Access Journals (Sweden)
Rodrigo Rizzi
2007-01-01
Full Text Available O objetivo deste trabalho foi estimar a produtividade de soja no Rio Grande do Sul, nas safras de 2000/2001 a 2002/2003, por meio de um modelo agronômico implementado em um Sistema de Informação Geográfica (SIG. Duas abordagens foram utilizadas: o modelo agronômico (AGRO, com valores de índice de área foliar (IAF obtidos da literatura; e o modelo agronômico-espectral (AGROESPEC, com valores de IAF estimados a partir das imagens MODIS (moderate resolution imaging spectroradiometer. As estimativas de produtividade obtidas pelo modelo foram comparadas àquelas fornecidas pelo Instituto Brasileiro de Geografia e Estatística (IBGE, com o uso do teste t para pares de observação. Nas safras 2000/2001 e 2001/2002, não foram observadas diferenças significativas. Para 2002/2003, o modelo subestimou o valor de produtividade em 7,87 e 7,04%, nas abordagens AGRO e AGROESPEC, respectivamente, em comparação à produtividade fornecida pelo IBGE. Ambas as abordagens do modelo permitiram avaliação objetiva e quantitativa do efeito das condições meteorológicas sobre a produtividade de soja. Entretanto, o AGROESPEC forneceu estimativas mais detalhadas, no que se refere à variação espacial da produtividade, em razão do emprego dos valores de IAF estimados a partir das imagens MODIS.The objective of this work was to estimate soybean yield in Rio Grande do Sul State, Brazil, for crop years of 2000/2001 to 2002/2003 through an agronomic crop yield model implemented in a geographic information system (GIS. Two approaches were used: an agronomic model (AGRO, with leaf area index (LAI obtained from literature, and an agronomic-spectral model (AGROESPEC, with LAI estimated from MODIS (moderate resolution imaging spectroradiometer images. Results were compared with the official estimates provided by Instituto Brasileiro de Geografia e Estatística (IBGE, using the t test for paired observations. For crop years 2000/2001 and 2001/2002, there were no
Evaluation of trends in wheat yield models
Ferguson, M. C.
1982-01-01
Trend terms in models for wheat yield in the U.S. Great Plains for the years 1932 to 1976 are evaluated. The subset of meteorological variables yielding the largest adjusted R(2) is selected using the method of leaps and bounds. Latent root regression is used to eliminate multicollinearities, and generalized ridge regression is used to introduce bias to provide stability in the data matrix. The regression model used provides for two trends in each of two models: a dependent model in which the trend line is piece-wise continuous, and an independent model in which the trend line is discontinuous at the year of the slope change. It was found that the trend lines best describing the wheat yields consisted of combinations of increasing, decreasing, and constant trend: four combinations for the dependent model and seven for the independent model.
Regression Models For Saffron Yields in Iran
S. H, Sanaeinejad; S. N, Hosseini
Saffron is an important crop in social and economical aspects in Khorassan Province (Northeast of Iran). In this research wetried to evaluate trends of saffron yield in recent years and to study the relationship between saffron yield and the climate change. A regression analysis was used to predict saffron yield based on 20 years of yield data in Birjand, Ghaen and Ferdows cities.Climatologically data for the same periods was provided by database of Khorassan Climatology Center. Climatologically data includedtemperature, rainfall, relative humidity and sunshine hours for ModelI, and temperature and rainfall for Model II. The results showed the coefficients of determination for Birjand, Ferdows and Ghaen for Model I were 0.69, 0.50 and 0.81 respectively. Also coefficients of determination for the same cities for model II were 0.53, 0.50 and 0.72 respectively. Multiple regression analysisindicated that among weather variables, temperature was the key parameter for variation ofsaffron yield. It was concluded that increasing temperature at spring was the main cause of declined saffron yield during recent years across the province. Finally, yield trend was predicted for the last 5 years using time series analysis.
George, Kerry A.; Rhone, J.; Chappell, L. J.; Cucinotta, F. A.
2011-01-01
To date, cytogenetic damage has been assessed in blood lymphocytes from more than 30 astronauts before and after they participated in long-duration space missions of three months or more on board the International Space Station. Chromosome damage was assessed using fluorescence in situ hybridization whole chromosome analysis techniques. For all individuals, the frequency of chromosome damage measured within a month of return from space was higher than their preflight yield, and biodosimetry estimates were within the range expected from physical dosimetry. Follow up analyses have been performed on most of the astronauts at intervals ranging from around 6 months to many years after flight, and the cytogenetic effects of repeat long-duration missions have so far been assessed in four individuals. Chromosomal aberrations in peripheral blood lymphocytes have been validated as biomarkers of cancer risk and cytogenetic damage can therefore be used to characterize excess health risk incurred by individual crewmembers after their respective missions. Traditional risk assessment models are based on epidemiological data obtained on Earth in cohorts exposed predominantly to acute doses of gamma-rays, and the extrapolation to the space environment is highly problematic, involving very large uncertainties. Cytogenetic damage could play a key role in reducing uncertainty in risk estimation because it is incurred directly in the space environment, using specimens from the astronauts themselves. Relative cancer risks were estimated from the biodosimetry data using the quantitative approach derived from the European Study Group on Cytogenetic Biomarkers and Health database. Astronauts were categorized into low, medium, or high tertiles according to their yield of chromosome damage. Age adjusted tertile rankings were used to estimate cancer risk and results were compared with values obtained using traditional modeling approaches. Individual tertile rankings increased after space
Directory of Open Access Journals (Sweden)
Krešimir Kuterovac
2010-01-01
Full Text Available The aim of this study was to compare different statistical methods for the estimation of daily and 305-day lactation milk, fat and protein yields of Holstein and Simmental cattle breeds using an alternative milk recording scheme. Data included 6,824 individual test-day milk yield records collected according to the A4 milk recording method on 668 cows reared on 15 family farms. Daily milk, fat and protein yields were estimated using several statistical methods with regard to breed. The 305-day lactation yields were calculated from estimated daily yields using the Test Interval Method. The correlation between estimated and true yields, as well as the mean difference among estimated and true yield were used as the evaluation criteria for estimation methods. The linear regression of daily to partial milk, fat and protein yields while taking into account the interval between successive milkings was shown to be the most accurate model for estimating daily values, either from morning or evening records. The simple doubling of morning or evening records overestimated and underestimated the daily yields, respectively. When 305-day lactation milk, fat and protein yields were compared no difference between evaluated methods were found. Also, a separate estimation of daily and 305-day lactation yields according to breeds did not result in increased estimation accuracy.
Estimating for Sediment Yield During Storm Based on Soil and Watershed Geomorphology Characteristics
Lee, K.; Yang, C.
2008-05-01
Concentrated rainfall usually results in serious soil erosion on steep hillslopes. Since the itinerary of the eroded sediment is complicated and measure for temporal sediment concentration is a laborious work, estimating for watershed erosion during storm is considered as difficulty in practice. In this study, a simple method for estimating sediment yield during storm was derived. By using soil data and watershed geomorphologic information, analytical solutions for sediment travel times and delivery ratios for different orders of overland areas and channels were derived to form an instantaneous unit sedimentgraph. Consequently, sediment yield during storm can be estimated by convoluting the rainfall intensities with the proposed instantaneous unit sedimentgraph. In this study, the proposed model has been verified using the data from Goodwin Experimental Watershed located in Mississippi of the United States. A digital elevation model was adopted to obtain the watershed geomorphologic factors for subsequent runoff routing and sediment concentration estimations. The simulated and the measured sediment yields were in good agreement for the test storms. It is therefore promising for the proposed model to be used for sediment yield estimation in gauged and ungauged watersheds for water resources design work.
A numerical integration-based yield estimation method for integrated circuits
Institute of Scientific and Technical Information of China (English)
Liang Tao; Jia Xinzhang
2011-01-01
A novel integration-based yield estimation method is developed for yield optimization of integrated circuits. This method tries to integrate the joint probability density function on the acceptability region directly.To achieve this goal, the simulated performance data of unknown distribution should be converted to follow a multivariate normal distribution by using Box-Cox transformation (BCT). In order to reduce the estimation variances of the model parameters of the density function, orthogonal array-based modified Latin hypercube sampling (OA-MLHS) is presented to generate samples in the disturbance space during simulations. The principle of variance reduction of model parameters estimation through OA-MLHS together with BCT is also discussed. Two yield estimation examples, a fourth-order OTA-C filter and a three-dimensional (3D) quadratic function are used for comparison of our method with Monte Carlo based methods including Latin hypercube sampling and importance sampling under several combinations of sample sizes and yield values. Extensive simulations show that our method is superior to other methods with respect to accuracy and efficiency under all of the given cases. Therefore, our method is more suitable for parametric yield optimization.
Functional dynamic factor models with application to yield curve forecasting
Hays, Spencer
2012-09-01
Accurate forecasting of zero coupon bond yields for a continuum of maturities is paramount to bond portfolio management and derivative security pricing. Yet a universal model for yield curve forecasting has been elusive, and prior attempts often resulted in a trade-off between goodness of fit and consistency with economic theory. To address this, herein we propose a novel formulation which connects the dynamic factor model (DFM) framework with concepts from functional data analysis: a DFM with functional factor loading curves. This results in a model capable of forecasting functional time series. Further, in the yield curve context we show that the model retains economic interpretation. Model estimation is achieved through an expectation- maximization algorithm, where the time series parameters and factor loading curves are simultaneously estimated in a single step. Efficient computing is implemented and a data-driven smoothing parameter is nicely incorporated. We show that our model performs very well on forecasting actual yield data compared with existing approaches, especially in regard to profit-based assessment for an innovative trading exercise. We further illustrate the viability of our model to applications outside of yield forecasting.
An analytic technique for the estimation of the light yield of a scintillation detector
Segreto, Ettore
2011-01-01
A simple model for the estimation of the light yield of a scintillation detector starting from the knowledge of its optical parameters and under general assumptions is developed. It is also shown how to take into account the effects related to Rayleigh scattering and absorption of the photons. The predictions of the model are benchmarked with the outcomes of a Monte Carlo simulation of a specific scintillation detector. The case of a real scintillation detector with internal surface covered by wavelength-shifter is explicitly treated and the model prediction is compared with the measured light yield.
Estimation of the yield of poplars in plantations of fast-growing species within current results
Directory of Open Access Journals (Sweden)
Martin Fajman
2009-01-01
Full Text Available Current results are presented of allometric yield estimates of the poplar short rotation coppice. According to a literature review it is obvious that yield estimates, based on measurable quantities of a growing stand, depend not only on the selected tree specie or its clone, but also on the site location. The Jap-105 poplar clone (P. nigra x P. maximowiczii allometric relations were analyzed by regression methods aimed at the creation of the yield estimation methodology at a testing site in Domanínek. Altogether, the twelve polynomial dependences of particular measured quantities approved the high empirical data conformity with the tested regression model (correlation index from 0.9033 to 0.9967. Within the forward stepwise regression, factors were selected, which explain best examined estimates of the total biomass DM; i.e. d.b.h. and stem height. Furthermore, the KESTEMONT’s (1971 model was verified with a satisfying conformity as well. Approving presented yield estimation methods, the presented models will be checked in a large-scale field trial.
Directory of Open Access Journals (Sweden)
Daniel S. Grohs
2009-03-01
Full Text Available Áreas com diferentes potenciais de rendimento dentro de uma lavoura necessitam ser manejadas separadamente, para fins de aplicação da adubação nitrogenada em cobertura. O equipamento baseado em sensoriamento remoto terrestre (GreenSeeker é um dos instrumentos utilizados para separar diferentes zonas de manejo. Para fazer isso, o sensor permite a definição de classes para estimar o potencial produtivo de forma ágil, precisa e em tempo real. Com o instrumento, foi desenvolvido um modelo para estimativa do potencial produtivo em trigo e cevada, correlacionando o Índice de Vegetação por Diferença Normalizada (NDVI com a biomassa seca acumulada na parte aérea, por ocasião da emissão da sexta folha do colmo principal. A base do modelo foi a formação de classes de potencial produtivo correspondentes a zonas específicas de manejo da lavoura. Essas classes não necessitam ser específicas para diferentes cultivares e/ou espécies, visto que não se detectaram diferenças que justificassem a formação de grupos para elas. As superfícies de fundo (resíduos de restevas de soja e milho tiveram efeitos significativos nas leituras do sensor. O modelo continua válido mesmo se as leituras de NDVI forem feitas antes ou após o período recomendado para tal, podendo ser ajustado com sub ou superestimação. As análises de variabilidade espacial, futuramente, podem avaliar se, as zonas de potencial produtivo estimadas pelas classes de NDVI propostas pelo modelo, correspondem à flutuação espacial da biomassa, doses de N aplicadas e rendimento de grãos.Areas with different yield potential within a field need to be managed separately as for nitrogen application in small grain cereals. Terrestrial remote sensing-based equipment such as the GreenSeeker sensor is one of the tools available to handle different management zones. To do this, the sensor allows the definition of classes to estimate yield potential. A model which correlated the
Estimation of the sustainable yields of boreholes in fractured rock formations
van Tonder, G. J.; Botha, J. F.; Chiang, W.-H.; Kunstmann, H.; Xu, Y.
2001-01-01
The simplest way to derive an estimate for the sustainable yield of a borehole is to study the behaviour of drawdowns observed during a hydraulic (also known as a pumping test) of the borehole, through an appropriate conceptual model. The choice of this model is probably the most difficult choice that the analyst of such a hydraulic test has to make, since a wrong model can only lead to the wrong conclusions and failure of the borehole. This paper discusses a semi-analytical and two numerical methods that can be used to simplify the analyses of hydraulic tests in fractured rock formations. The first method, called the Method of Derivative Fitting (MDF), uses a new approach to identify the conceptual model needed in such analyses. This is achieved by characterizing the various flow periods in fractured rock aquifers with numerical approximations of the first logarithmic derivative of the observed drawdown (the derivative of the drawdown with respect to the logarithm of the time). Semi-analytical expressions are used to estimate the influence that boundaries may have on the observed drawdown and the sustainable yield of a borehole — the rate at which a borehole can be pumped without lowering the water level below a prescribed limit. An effort has also been made to quantify errors in the estimates introduced by uncertainties in the parameters, such as the transmissivity and storativity, through a Gaussian error propagation analysis. These approximations and the MDF, called the Flow Characteristics Method (FCM) have been implemented in a user-friendly EXCEL notebook, and used to estimate the sustainable yield of a borehole on the Campus Test Site at the University of the Orange Free State. The first numerical method, a two-dimensional radial flow model, is included here because it allows the user more freedom than the FCM, although it requires more information. One particular advantage of the method is that it allows one to obtain realistic estimates of the
Consistent estimates of (56)Ni yields for type Ia supernovae
Stritzinger, M; Sollerman, J; Benetti, S; Stritzinger, Maximilian; Mazzali, Paolo; Sollerman, Jesper; Benetti, Stefano
2006-01-01
We present (56)Ni mass estimates for seventeen well-observed type Ia supernovae determined by two independent methods. Estimates of the (56)Ni mass for each type Ia supernova are determined from (1) modeling of the late-time nebular spectrum and (2) through the combination of the peak bolometric luminosity with Arnett's rule. The attractiveness of this approach is that the comparison of estimated (56)Ni masses circumvents errors associated with the uncertainty in the adopted values of reddening and distance. We demonstrate that these two methods provide consistent estimates of the amount of (56)Ni synthesized. We also find a strong correlation between the derived (56)Ni mass and the absolute B-band magnitude (M(B)). Spectral synthesis can be used as a diagnostic to study the explosion mechanism. By obtaining more nebular spectra the Nif--M(B) correlation can be calibrated, and be used to investigate any potential systematic effects this relationship may have upon the determination of cosmological parameters, ...
Battude, Marjorie; Bitar, Ahmad Al; Brut, Aurore; Cros, Jérôme; Dejoux, Jean-François; Huc, Mireille; Marais Sicre, Claire; Tallec, Tiphaine; Demarez, Valérie
2016-04-01
Water resources are under increasing pressure as a result of global change and of a raising competition among the different users (agriculture, industry, urban). It is therefore important to develop tools able to estimate accurately crop water requirements in order to optimize irrigation while maintaining acceptable production. In this context, remote sensing is a valuable tool to monitor vegetation development and water demand. This work aims at developing a robust and generic methodology mainly based on high resolution remote sensing data to provide accurate estimates of maize yield and water needs at the watershed scale. Evapotranspiration (ETR) and dry aboveground biomass (DAM) of maize crops were modeled using time series of GAI images used to drive a simple agro-meteorological crop model (SAFYE, Duchemin et al., 2005). This model is based on a leaf partitioning function (Maas, 1993) for the simulation of crop biomass and on the FAO-56 methodology for the ETR simulation. The model also contains a module to simulate irrigation. This study takes advantage of the SPOT4 and SPOT5 Take5 experiments initiated by CNES (http://www.cesbio.ups-tlse.fr/multitemp/). They provide optical images over the watershed from February to May 2013 and from April to August 2015 respectively, with a temporal and spatial resolution similar to future images from the Sentinel-2 and VENμS missions. This dataset was completed with LandSat8 and Deimos1 images in order to cover the whole growing season while reducing the gaps in remote sensing time series. Radiometric, geometric and atmospheric corrections were achieved by the THEIA land data center, and the KALIDEOS processing chain. The temporal dynamics of the green area index (GAI) plays a key role in soil-plant-atmosphere interactions and in biomass accumulation process. Consistent seasonal dynamics of the remotely sensed GAI was estimated by applying a radiative transfer model based on artificial neural networks (BVNET, Baret
Billings, S. A.
1988-03-01
Time and frequency domain identification methods for nonlinear systems are reviewed. Parametric methods, prediction error methods, structure detection, model validation, and experiment design are discussed. Identification of a liquid level system, a heat exchanger, and a turbocharge automotive diesel engine are illustrated. Rational models are introduced. Spectral analysis for nonlinear systems is treated. Recursive estimation is mentioned.
Estimating climate change, CO2 and technology development effects on wheat yield in northeast Iran
Bannayan, M.; Mansoori, H.; Rezaei, E. Eyshi
2014-04-01
Wheat is the main food for the majority of Iran's population. Precise estimation of wheat yield change in future is essential for any possible revision of management strategies. The main objective of this study was to evaluate the effects of climate change, CO2 concentration, technology development and their integrated effects on wheat production under future climate change. This study was performed under two scenarios of the IPCC Special Report on Emission Scenarios (SRES): regional economic (A2) and global environmental (B1). Crop production was projected for three future time periods (2020, 2050 and 2080) in comparison with a baseline year (2005) for Khorasan province located in the northeast of Iran. Four study locations in the study area included Mashhad, Birjand, Bojnourd and Sabzevar. The effect of technology development was calculated by fitting a regression equation between the observed wheat yields against historical years considering yield potential increase and yield gap reduction as technology development. Yield relative increase per unit change of CO2 concentration (1 ppm-1) was considered 0.05 % and was used to implement the effect of elevated CO2. The HadCM3 general circulation model along with the CSM-CERES-Wheat crop model were used to project climate change effects on wheat crop yield. Our results illustrate that, among all the factors considered, technology development provided the highest impact on wheat yield change. Highest wheat yield increase across all locations and time periods was obtained under the A2 scenario. Among study locations, Mashhad showed the highest change in wheat yield. Yield change compared to baseline ranged from -28 % to 56 % when the integration of all factors was considered across all locations. It seems that achieving higher yield of wheat in future may be expected in northeast Iran assuming stable improvements in production technology.
Estimating climate change, CO2 and technology development effects on wheat yield in northeast Iran.
Bannayan, M; Mansoori, H; Rezaei, E Eyshi
2014-04-01
Wheat is the main food for the majority of Iran's population. Precise estimation of wheat yield change in future is essential for any possible revision of management strategies. The main objective of this study was to evaluate the effects of climate change, CO2 concentration, technology development and their integrated effects on wheat production under future climate change. This study was performed under two scenarios of the IPCC Special Report on Emission Scenarios (SRES): regional economic (A2) and global environmental (B1). Crop production was projected for three future time periods (2020, 2050 and 2080) in comparison with a baseline year (2005) for Khorasan province located in the northeast of Iran. Four study locations in the study area included Mashhad, Birjand, Bojnourd and Sabzevar. The effect of technology development was calculated by fitting a regression equation between the observed wheat yields against historical years considering yield potential increase and yield gap reduction as technology development. Yield relative increase per unit change of CO2 concentration (1 ppm(-1)) was considered 0.05 % and was used to implement the effect of elevated CO2. The HadCM3 general circulation model along with the CSM-CERES-Wheat crop model were used to project climate change effects on wheat crop yield. Our results illustrate that, among all the factors considered, technology development provided the highest impact on wheat yield change. Highest wheat yield increase across all locations and time periods was obtained under the A2 scenario. Among study locations, Mashhad showed the highest change in wheat yield. Yield change compared to baseline ranged from -28 % to 56 % when the integration of all factors was considered across all locations. It seems that achieving higher yield of wheat in future may be expected in northeast Iran assuming stable improvements in production technology.
Shin, Y.; Lee, E.
2015-12-01
Under the influence of recent climate change, abnormal weather condition such as floods and droughts has issued frequently all over the world. The occurrence of abnormal weather in major crop production areas leads to soaring world grain prices because it influence the reduction of crop yield. Development of crop yield estimation method is important means to accommodate the global food crisis caused by abnormal weather. However, due to problems with the reliability of the seasonal climate prediction, application research on agricultural productivity has not been much progress yet. In this study, it is an object to develop long-term crop yield estimation method in major crop production countries worldwide using multi seasonal climate prediction data collected by APEC Climate Center. There are 6-month lead seasonal predictions produced by six state-of-the-art global coupled ocean-atmosphere models(MSC_CANCM3, MSC_CANCM4, NASA, NCEP, PNU, POAMA). First of all, we produce a customized climate data through temporal and spatial downscaling methods for use as a climatic input data to the global scale crop model. Next, we evaluate the uncertainty of climate prediction by applying multi seasonal climate prediction in the crop model. Because rice is the most important staple food crop in the Asia-Pacific region, we assess the reliability of the rice yields using seasonal climate prediction for main rice production countries. RMSE(Root Mean Squire Error) and TCC(Temporal Correlation Coefficient) analysis is performed in Asia-Pacific countries, major 14 rice production countries, to evaluate the reliability of the rice yield according to the climate prediction models. We compare the rice yield data obtained from FAOSTAT and estimated using the seasonal climate prediction data in Asia-Pacific countries. In addition, we show that the reliability of seasonal climate prediction according to the climate models in Asia-Pacific countries where rice cultivation is being carried out.
Soybean Area and Yield Estimation Using MODIS and Landsat Data in the Conterminous United States
Song, X. P.; Hansen, M.; Potapov, P.; Stehman, S. V.; Krylov, A.; King, L.; Adusei, B.
2015-12-01
The world's population is projected to grow to 9 billion by 2050. The increasing population, amplified by people's increasing consumption of animal products will create a massive demand for food and feed from grain production. As such, global food security will remain a worldwide concern for the next half century. Addressing the food security issue requires data and information support, including research and operational programs for crop monitoring, modeling and yield forecasting. Satellite observations, owing to their synoptic and repetitive nature, have the unique advantage of providing timely information on crop growth at regional to global scales. However, it remains a challenge to accurately identify crop type, estimate areal extent and forecast crop yield with satellite data. Here we employ a stratified random sampling framework for estimating soybean area and yield in the conterminous United States using satellite data collected by the MODIS and Landsat sensors. Complementing each other, the temporally-rich MODIS data are used to capture rapid phenological transitions of soybean crops, whereas the moderate-resolution Landsat data are used to delineate more spatial details for accurate area estimation. For every sample, we derive generic phenological metrics from MODIS and Landsat data and employ machine learning algorithms to identify soybean pixels with reference data generated from RapidEye images and verified by extensive field visits. We also characterize empirical relationships between satellite metrics and soybean yield compiled by the USDA National Agricultural Statistics Service (NASS). Preliminary results suggest that MODIS data alone underestimate soybean area considerably, whereas Landsat data can provide accurate estimate on soybean area. However, soybean yield can be predicted using MODIS-based reflectance metrics. Our sample depict well the spatial variation of soybean yield over the conterminous United States. In addition, the area
Use of Vegetation Health Data for Estimation of Aus Rice Yield in Bangladesh
Directory of Open Access Journals (Sweden)
Mohammad Nizamuddin
2009-04-01
Full Text Available Rice is a vital staple crop for Bangladesh and surrounding countries, with interannual variation in yields depending on climatic conditions. We compared Bangladesh yield of aus rice, one of the main varieties grown, from official agricultural statistics with Vegetation Health (VH Indices [Vegetation Condition Index (VCI, Temperature Condition Index (TCI and Vegetation Health Index (VHI] computed from Advanced Very High Resolution Radiometer (AVHRR data covering a period of 15 years (1991–2005. A strong correlation was found between aus rice yield and VCI and VHI during the critical period of aus rice development that occurs during March-April (weeks 8–13 of the year, several months in advance of the rice harvest. Stepwise principal component regression (PCR was used to construct a model to predict yield as a function of critical-period VHI. The model reduced the yield prediction error variance by 62% compared with a prediction of average yield for each year. Remote sensing is a valuable tool for estimating rice yields well in advance of harvest and at a low cost.
High-biomass sorghum yield estimate with aerial imagery
Sui, Ruixiu; Hartley, Brandon E.; Gibson, John M.; Yang, Chenghai; Thomasson, J. Alex; Searcy, Stephen W.
2011-01-01
To reach the goals laid out by the U.S. Government for displacing fossil fuels with biofuels, high-biomass sorghum is well-suited to achieving this goal because it requires less water per unit dry biomass and can produce very high biomass yields. In order to make biofuels economically competitive with fossil fuels it is essential to maximize production efficiency throughout the system. The goal of this study was to use remote sensing technologies to optimize the yield and harvest logistics of high-biomass sorghum with respect to production costs based on spatial variability within and among fields. Specific objectives were to compare yield to aerial multispectral imagery and develop predictive relationships. A 19.2-ha high-biomass sorghum field was selected as a study site and aerial multispectral images were acquired with a four-camera imaging system on July 17, 2009. Sorghum plant samples were collected at predetermined geographic coordinates to determine biomass yield. Aerial images were processed to find relationships between image reflectance and yield of the biomass sorghum. Results showed that sorghum biomass yield in early August was closely related (R2 = 0.76) to spectral reflectance. However, in the late season the correlations between the biomass yield and spectral reflectance were not as positive as in the early season. The eventual outcome of this work could lead to predicted-yield maps based on remotely sensed images, which could be used in developing field management practices to optimize yield and harvest logistics.
Estimation of sediment yield during storms based on soil and watershed geomorphology characteristics
Lee, Kwan Tun; Yang, Chi-Cheng
2010-03-01
SummaryConcentrated rainfall usually results in serious soil erosion on steep hillslopes. Since the itinerary of the eroded sediment is complicated, estimating watershed erosion during storms is practically difficult. A physically-based approach for sediment yield estimation during storms was proposed in this study. By using soil and watershed geomorphologic information, analytical solutions for sediment travel time in different orders of overland areas and channels were derived to develop a geomorphologic instantaneous unit sedimentgraph (GIUS) which showed the temporal distribution of sediment discharge resulting from an instantaneous rainfall excess input. The resultant GIUS was a function of the rainfall excess intensity and sediment delivery ratio. The linearity restriction of the unit hydrograph theory was relaxed. Sediment yields during storm events were calculated by convoluting rainfall intensities with the proposed GIUS, which had been verified by using data from the Goodwin Creek Experimental Watershed in Mississippi, the United States. The simulated and the measured sediment yields were in good agreement for the test storms. Sensitivity of the sedimentgraph to the model parameters was also investigated. The proposed model was considered a promising application for sediment yield estimation in the field of water resources design.
Directory of Open Access Journals (Sweden)
Patrick D. SHAW
2010-08-01
Full Text Available Runoff or water yield is an important input to the Steady-State Water Chemistry (SSWC model for estimating critical loads of acidity. Herein, we present site-specific water yield estimates for a large number of lakes (779 across three provinces of western Canada (Manitoba, Saskatchewan, and British Columbia using an isotope mass balance (IMB approach. We explore the impact of applying site-specific hydrology as compared to use of regional runoff estimates derived from gridded datasets in assessing critical loads of acidity to these lakes. In general, the average water yield derived from IMB is similar to the long-term average runoff; however, IMB results suggest a much larger range in hydrological settings of the lakes, attributed to spatial heterogeneity in watershed characteristics and landcover. The comparison of critical loads estimates from the two methods suggests that use of average regional runoff data in the SSWC model may overestimate critical loads for the majority of lakes due to systematic skewness in the actual runoff distributions. Implications for use of site-specific hydrology in regional critical loads assessments across western Canada are discussed.
Directory of Open Access Journals (Sweden)
Kehinde Anthony Mogaji
2016-07-01
Full Text Available This study developed a GIS-based multivariate regression (MVR yield rate prediction model of groundwater resource sustainability in the hard-rock geology terrain of southwestern Nigeria. This model can economically manage the aquifer yield rate potential predictions that are often overlooked in groundwater resources development. The proposed model relates the borehole yield rate inventory of the area to geoelectrically derived parameters. Three sets of borehole yield rate conditioning geoelectrically derived parameters—aquifer unit resistivity (ρ, aquifer unit thickness (D and coefficient of anisotropy (λ—were determined from the acquired and interpreted geophysical data. The extracted borehole yield rate values and the geoelectrically derived parameter values were regressed to develop the MVR relationship model by applying linear regression and GIS techniques. The sensitivity analysis results of the MVR model evaluated at P ⩽ 0.05 for the predictors ρ, D and λ provided values of 2.68 × 10−05, 2 × 10−02 and 2.09 × 10−06, respectively. The accuracy and predictive power tests conducted on the MVR model using the Theil inequality coefficient measurement approach, coupled with the sensitivity analysis results, confirmed the model yield rate estimation and prediction capability. The MVR borehole yield prediction model estimates were processed in a GIS environment to model an aquifer yield potential prediction map of the area. The information on the prediction map can serve as a scientific basis for predicting aquifer yield potential rates relevant in groundwater resources sustainability management. The developed MVR borehole yield rate prediction mode provides a good alternative to other methods used for this purpose.
DEFF Research Database (Denmark)
Guo, Gang; Lund, Mogens Sandø; Zhang, Y;
2010-01-01
This study compared genomic predictions using conventional estimated breeding values (EBV) and daughter yield deviations (DYD) as response variables based on simulated data. Eight scenarios were simulated in regard to heritability (0.05 and 0.30), number of daughters per sire (30, 100, and unequal......), the EBV and DYD approaches provided similar genomic estimated breeding value (GEBV) reliabilities, except for scenarios with unequal numbers of daughters and half of sires without genotype, for which the EBV approach was superior to the DYD approach (by 1.2 and 2.4%). Using a Bayesian mixture prior model...
Modelling crop yield in Iberia under drought conditions
Ribeiro, Andreia; Páscoa, Patrícia; Russo, Ana; Gouveia, Célia
2017-04-01
The improved assessment of the cereal yield and crop loss under drought conditions are essential to meet the increasing economy demands. The growing frequency and severity of the extreme drought conditions in the Iberian Peninsula (IP) has been likely responsible for negative impacts on agriculture, namely on crop yield losses. Therefore, a continuous monitoring of vegetation activity and a reliable estimation of drought impacts is crucial to contribute for the agricultural drought management and development of suitable information tools. This works aims to assess the influence of drought conditions in agricultural yields over the IP, considering cereal yields from mainly rainfed agriculture for the provinces with higher productivity. The main target is to develop a strategy to model drought risk on agriculture for wheat yield at a province level. In order to achieve this goal a combined assessment was made using a drought indicator (Standardized Precipitation Evapotranspiration Index, SPEI) to evaluate drought conditions together with a widely used vegetation index (Normalized Difference Vegetation Index, NDVI) to monitor vegetation activity. A correlation analysis between detrended wheat yield and SPEI was performed in order to assess the vegetation response to each time scale of drought occurrence and also identify the moment of the vegetative cycle when the crop yields are more vulnerable to drought conditions. The time scales and months of SPEI, together with the months of NDVI, better related with wheat yield were chosen to perform a multivariate regression analysis to simulate crop yield. Model results are satisfactory and highlighted the usefulness of such analysis in the framework of developing a drought risk model for crop yields. In terms of an operational point of view, the results aim to contribute to an improved understanding of crop yield management under dry conditions, particularly adding substantial information on the advantages of combining
Estimated breeding values and genetic trend for milk yield in Nili Ravi buffaloes
Directory of Open Access Journals (Sweden)
M. Ahmad
2010-02-01
Full Text Available Data on 263 pedigrees, breeding and performance records of 98 Nili-Ravi Buffaloes maintained at Livestock Experiment Station, Bahadurnagar, Okara, Punjab, Pakistan during the period 1991 to 2002 were utilized in this study to identify the high yielding elite buffaloes/bull mothers (dams to retain for further breeding for the on going progeny testing program in the country. The lactation records up to 6th parity were used for the analysis. The data were analyzed through Best Linear Unbiased Predictions (BLUP procedure. The breeding values were estimated by using Restricted Maximum Likelihood (REML procedure fitting Individual Animal Model. The least squares mean for lactation milk yield was 2462.92 ± 195.93 kg. The average lactation length was 340.57 ± 61.70 days. Out of 98 buffaloes, 48 had positive breeding values (EBVs+. Within these 48 buffaloes (EBVs+, 16 were declared as elite buffaloes. The estimated breeding values for milk yield from animal model evaluations ranged from –922 to +2954 kg. The over all genetic trend for milk yield was found positive.
Buffalos milk yield analysis using random regression models
Directory of Open Access Journals (Sweden)
A.S. Schierholt
2010-02-01
Full Text Available Data comprising 1,719 milk yield records from 357 females (predominantly Murrah breed, daughters of 110 sires, with births from 1974 to 2004, obtained from the Programa de Melhoramento Genético de Bubalinos (PROMEBUL and from records of EMBRAPA Amazônia Oriental - EAO herd, located in Belém, Pará, Brazil, were used to compare random regression models for estimating variance components and predicting breeding values of the sires. The data were analyzed by different models using the Legendre’s polynomial functions from second to fourth orders. The random regression models included the effects of herd-year, month of parity date of the control; regression coefficients for age of females (in order to describe the fixed part of the lactation curve and random regression coefficients related to the direct genetic and permanent environment effects. The comparisons among the models were based on the Akaike Infromation Criterion. The random effects regression model using third order Legendre’s polynomials with four classes of the environmental effect were the one that best described the additive genetic variation in milk yield. The heritability estimates varied from 0.08 to 0.40. The genetic correlation between milk yields in younger ages was close to the unit, but in older ages it was low.
Directory of Open Access Journals (Sweden)
Kleber Gustavo Andrioli
2009-07-01
Full Text Available The objective of this work was to determine the sensitivity of maize (Zea mays genotypes to water deficit, using a simple agrometeorological crop yield model. Crop actual yield and agronomic data of 26 genotypes were obtained from the Maize National Assays carried out in ten locations, in four Brazilian states, from 1998 to 2006. Weather information for each experimental location and period were obtained from the closest weather station. Water deficit sensitivity index (Ky was determined using the crop yield depletion model. Genotypes can be divided into two groups according to their resistance to water deficit. Normal resistance genotypes had Ky ranging from 0.4 to 0.5 in vegetative period, 1.4 to 1.5 in flowering, 0.3 to 0.6 in fruiting, and 0.1 to 0.3 in maturing period, whereas the higher resistance genotypes had lower values, respectively 0.2-0.4, 0.7-1.2, 0.2-0.4, and 0.1-0.2. The general Ky for the total growing season was 2.15 for sensitive genotypes and 1.56 for the resistant ones. Model performance was acceptable to evaluate crop actual yield, whose average errors estimated for each genotype ranged from -5.7% to +5.8%, and whose general mean absolute error was 960 kg ha-1 (10%.O objetivo deste trabalho foi determinar a sensibilidade de genótipos de milho (Zea mays ao deficit hídrico, pelo uso de um modelo agrometeorológico simples de estimativa de produtividade. Dados de produtividade real e agronômicos de 26 genótipos foram obtidos dos Ensaios Nacionais de Milho, em dez localidades, em quatro estados brasileiros, entre 1998 e 2006. Os dados meteorológicos, para cada experimento e período, foram obtidos das estações mais próximas de cada local. O índice de sensibilidade ao deficit hídrico (Ky dos genótipos foi determinado por meio do modelo de depleção da produtividade. Os genótipos de milho podem ser classificados em dois grupos de resistência ao deficit hídrico. Os de resistência normal tiveram Ky entre 0,4 e 0
A scheme for regional rice yield estimation using ENVISAT ASAR data
Institute of Scientific and Technical Information of China (English)
LE; TOAN; Thuy
2009-01-01
Information on rice growing areas and rice production is critical for most rice growing countries to make state and economic policies. However, the areas where rice crop is cultivated are often cloudy and rainy, which entails the use of radar remote sensing data for rice monitoring. In this paper, a practical scheme to integrate multi-temporal and multi-polarization ENVISAT ASAR data into rice crop model for regional rice yield estimation has been presented. To achieve this, rice distribution information should be obtained first by rice mapping method to retrieve rice fields from ASAR images, and then an assimilation method is applied to use the observed multi-temporal rice backscattering coefficients which are grouped for each rice pixel to re-initialize ORYZA2000 to predict rice yield. The assimilation method re-initializes the model with optimal input parameters, allowing a better temporal agreement between the rice backscattering coefficients retrieved from ASAR data and the rice backscattering coefficients simulated by a coupled model, i.e., the combination of ORYZA2000 and a semi-empirical rice backscatter model through LAI. The SCE-UA optimization algorithm is employed to determine the optimal set of input parameters. After the re-initialization, rice yield for each rice pixel is calculated, and the yield map over the area of interest is produced. The scheme was validated over Xinghua study area located in the middle of Jiangsu Province of China by using the data set of an experimental campaign carried out during the 2006 rice season. The result shows that the obtained rice yield map generally overestimates the actual rice production by 13% on average and with a root mean square error of approximately 1133 kg/ha on validation sites, but the tendency of rice growth status and spatial variation of the rice yield are well predicted and highly consistent with the actual production variation.
Yield models for commercial willow biomass plantations in Sweden
Energy Technology Data Exchange (ETDEWEB)
Mola-Yudego, Blas [Faculty of Forestry, University of Joensuu, P.O. Box 111, FI-801 01 Joensuu (Finland); Aronsson, Paer [Department of Crop Production Ecology, Swedish University of Agricultural Sciences (SLU), P.O. Box 7016, S-750 07 Uppsala (Sweden)
2008-09-15
A yield model for willow plantations for bioenergy production in Sweden was developed based on recorded production of 2082 commercial plantations during the period 1989-2005. The model predicts yield for the first, second and third harvest using oats (avena) production as agro-climatic index. The mean annual yields were 2.6, 4.2 and 4.5 oven dry tonnes (odt) per hectare during the first, second and third cutting cycles, respectively. The yield correlated inversely with the length of the cutting cycle. The results of the study show significant differences between growers, which suggest the importance of proper management in the establishment and tending of the plantations. Model estimates for 25% of the best growers vary from 4.0 to 6.3 odt ha{sup -1} yr{sup -1} in 5-year-rotation plantations during the first cutting cycle, and from 5.4 to 7.1 odt ha{sup -1} yr{sup -1} in 4-year-rotations for the second cutting cycle. The proposed model can be applied in policy making and for management planning. (author)
Statistical modelling and deconvolution of yield meter data
DEFF Research Database (Denmark)
Tøgersen, Frede Aakmann; Waagepetersen, Rasmus Plenge
2004-01-01
This paper considers the problem of mapping spatial variation of yield in a field using data from a yield monitoring system on a combine harvester. The unobserved yield is assumed to be a Gaussian random field and the yield monitoring system data is modelled as a convolution of the yield and an i...
Similar estimates of temperature impacts on global wheat yield by three independent methods
Liu, Bing; Asseng, Senthold; Müller, Christoph; Ewert, Frank; Elliott, Joshua; Lobell, David B.; Martre, Pierre; Ruane, Alex C.; Wallach, Daniel; Jones, James W.; Rosenzweig, Cynthia; Aggarwal, Pramod K.; Alderman, Phillip D.; Anothai, Jakarat; Basso, Bruno; Biernath, Christian; Cammarano, Davide; Challinor, Andy; Deryng, Delphine; Sanctis, Giacomo De; Doltra, Jordi; Fereres, Elias; Folberth, Christian; Garcia-Vila, Margarita; Gayler, Sebastian; Hoogenboom, Gerrit; Hunt, Leslie A.; Izaurralde, Roberto C.; Jabloun, Mohamed; Jones, Curtis D.; Kersebaum, Kurt C.; Kimball, Bruce A.; Koehler, Ann-Kristin; Kumar, Soora Naresh; Nendel, Claas; O'Leary, Garry J.; Olesen, Jørgen E.; Ottman, Michael J.; Palosuo, Taru; Prasad, P. V. Vara; Priesack, Eckart; Pugh, Thomas A. M.; Reynolds, Matthew; Rezaei, Ehsan E.; Rötter, Reimund P.; Schmid, Erwin; Semenov, Mikhail A.; Shcherbak, Iurii; Stehfest, Elke; Stöckle, Claudio O.; Stratonovitch, Pierre; Streck, Thilo; Supit, Iwan; Tao, Fulu; Thorburn, Peter; Waha, Katharina; Wall, Gerard W.; Wang, Enli; White, Jeffrey W.; Wolf, Joost; Zhao, Zhigan; Zhu, Yan
2016-12-01
The potential impact of global temperature change on global crop yield has recently been assessed with different methods. Here we show that grid-based and point-based simulations and statistical regressions (from historic records), without deliberate adaptation or CO2 fertilization effects, produce similar estimates of temperature impact on wheat yields at global and national scales. With a 1 °C global temperature increase, global wheat yield is projected to decline between 4.1% and 6.4%. Projected relative temperature impacts from different methods were similar for major wheat-producing countries China, India, USA and France, but less so for Russia. Point-based and grid-based simulations, and to some extent the statistical regressions, were consistent in projecting that warmer regions are likely to suffer more yield loss with increasing temperature than cooler regions. By forming a multi-method ensemble, it was possible to quantify `method uncertainty’ in addition to model uncertainty. This significantly improves confidence in estimates of climate impacts on global food security.
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Piyawat Wuttichaikitcharoen
2014-08-01
Full Text Available Predicting sediment yield is necessary for good land and water management in any river basin. However, sometimes, the sediment data is either not available or is sparse, which renders estimating sediment yield a daunting task. The present study investigates the factors influencing suspended sediment yield using the principal component analysis (PCA. Additionally, the regression relationships for estimating suspended sediment yield, based on the selected key factors from the PCA, are developed. The PCA shows six components of key factors that can explain at least up to 86.7% of the variation of all variables. The regression models show that basin size, channel network characteristics, land use, basin steepness and rainfall distribution are the key factors affecting sediment yield. The validation of regression relationships for estimating suspended sediment yield shows the error of estimation ranging from −55% to +315% and −59% to +259% for suspended sediment yield and for area-specific suspended sediment yield, respectively. The proposed relationships may be considered useful for predicting suspended sediment yield in ungauged basins of Northern Thailand that have geologic, climatic and hydrologic conditions similar to the study area.
Multilevel systematic sampling to estimate total fruit number for yield forecasts
DEFF Research Database (Denmark)
Wulfsohn, Dvora-Laio; Zamora, Felipe Aravena; Tellez, Camilla Potin
2012-01-01
Early forecasting of fruit orchard yield is important for market planning and for growers and exporters to plan labour, bins, storage and purchase of packing materials. Large variations in tree yield pose a challenge for accurate yield estimation. We evaluated a three-level systematic sampling...
Directory of Open Access Journals (Sweden)
Michele Meroni
2013-01-01
Full Text Available Multitemporal optical remote sensing constitutes a useful, cost efficient method for crop status monitoring over large areas. Modelers interested in yield monitoring can rely on past and recent observations of crop reflectance to estimate aboveground biomass and infer the likely yield. Therefore, in a framework constrained by information availability, remote sensing data to yield conversion parameters are to be estimated. Statistical models are suitable for this purpose, given their ability to deal with statistical errors. This paper explores the performance in yield estimation of various remote sensing indicators based on varying degrees of bio-physical insight, in interaction with statistical methods (linear regressions that rely on different hypotheses. Performances in estimating the temporal and spatial variability of yield, and implications of data scarcity in both dimensions are investigated. Jackknifed results (leave one year out are presented for the case of wheat yield regional estimation in Tunisia using the SPOT-VEGETATION instrument. Best performances, up to 0.8 of R2, are achieved using the most physiologically sound remote sensing indicator, in conjunction with statistical specifications allowing for parsimonious spatial adjustment of the parameters.
Hydrograph estimation with fuzzy chain model
Güçlü, Yavuz Selim; Şen, Zekai
2016-07-01
Hydrograph peak discharge estimation is gaining more significance with unprecedented urbanization developments. Most of the existing models do not yield reliable peak discharge estimations for small basins although they provide acceptable results for medium and large ones. In this study, fuzzy chain model (FCM) is suggested by considering the necessary adjustments based on some measurements over a small basin, Ayamama basin, within Istanbul City, Turkey. FCM is based on Mamdani and the Adaptive Neuro Fuzzy Inference Systems (ANFIS) methodologies, which yield peak discharge estimation. The suggested model is compared with two well-known approaches, namely, Soil Conservation Service (SCS)-Snyder and SCS-Clark methodologies. In all the methods, the hydrographs are obtained through the use of dimensionless unit hydrograph concept. After the necessary modeling, computation, verification and adaptation stages comparatively better hydrographs are obtained by FCM. The mean square error for the FCM is many folds smaller than the other methodologies, which proves outperformance of the suggested methodology.
Soybean yield modeling using bootstrap methods for small samples
Energy Technology Data Exchange (ETDEWEB)
Dalposso, G.A.; Uribe-Opazo, M.A.; Johann, J.A.
2016-11-01
One of the problems that occur when working with regression models is regarding the sample size; once the statistical methods used in inferential analyzes are asymptotic if the sample is small the analysis may be compromised because the estimates will be biased. An alternative is to use the bootstrap methodology, which in its non-parametric version does not need to guess or know the probability distribution that generated the original sample. In this work we used a set of soybean yield data and physical and chemical soil properties formed with fewer samples to determine a multiple linear regression model. Bootstrap methods were used for variable selection, identification of influential points and for determination of confidence intervals of the model parameters. The results showed that the bootstrap methods enabled us to select the physical and chemical soil properties, which were significant in the construction of the soybean yield regression model, construct the confidence intervals of the parameters and identify the points that had great influence on the estimated parameters. (Author)
Crop yield, genetic parameter estimation and selection of sacha inchi in central Amazon
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Mágno Sávio Ferreira Valente
2017-06-01
Full Text Available In Brazil, sacha inchi oil is produced by hand from plant materials with no breeding or detailed information about the chemical composition of seeds. In addition, most of the current information on the agronomic traits of this species originates from research carried out in the Peruvian Amazon. In order to promote the research and cultivation of sacha inchi in the Brazilian territory, this study aimed to analyze, in the central Amazon region, different accessions of this oilseed for characteristics of production and quality of fruits and seeds, as well as to estimate genetic parameters, through mixed models, with identification of superior accessions, for breeding purposes. A total of 37 non-domesticated accessions were evaluated in a randomized block design, with five replications and two plants per plot. The average oil content in seeds was 29.07 % and unsaturated fatty acids amounted to 91.5 % of the total fat content. For the yield traits, the estimates of individual broad-sense heritability were moderate (~0.33, while the heritability based on the average of progenies resulted in a selective accuracy of approximately 0.85. The use of the selection index provided simultaneous gains for yield traits (> 40 % and oil yield. A high genetic variability was observed for the main traits of commercial interest for the species, as well as promising perspectives for the development of superior varieties for agro-industrial use.
Temperature Increase Reduces Global Yields of Major Crops in Four Independent Estimates
Zhao, Chuang; Liu, Bing; Piao, Shilong; Wang, Xuhui; Lobell, David B.; Huang, Yao; Huang, Mengtian; Yao, Yitong; Bassu, Simona; Ciais, Philippe;
2017-01-01
Wheat, rice, maize, and soybean provide two-thirds of human caloric intake. Assessing the impact of global temperature increase on production of these crops is therefore critical to maintaining global food supply, but different studies have yielded different results. Here, we investigated the impacts of temperature on yields of the four crops by compiling extensive published results from four analytical methods: global grid-based and local point-based models, statistical regressions, and field-warming experiments. Results from the different methods consistently showed negative temperature impacts on crop yield at the global scale, generally underpinned by similar impacts at country and site scales. Without CO2 fertilization, effective adaptation, and genetic improvement, each degree-Celsius increase in global mean temperature would, on average, reduce global yields of wheat by 6.0%, rice by 3.2%, maize by 7.4%, and soybean by 3.1%. Results are highly heterogeneous across crops and geographical areas, with some positive impact estimates. Multi-method analyses improved the confidence in assessments of future climate impacts on global major crops and suggest crop- and region-specific adaptation strategies to ensure food security for an increasing world population.
Temperature increase reduces global yields of major crops in four independent estimates.
Zhao, Chuang; Liu, Bing; Piao, Shilong; Wang, Xuhui; Lobell, David B; Huang, Yao; Huang, Mengtian; Yao, Yitong; Bassu, Simona; Ciais, Philippe; Durand, Jean-Louis; Elliott, Joshua; Ewert, Frank; Janssens, Ivan A; Li, Tao; Lin, Erda; Liu, Qiang; Martre, Pierre; Müller, Christoph; Peng, Shushi; Peñuelas, Josep; Ruane, Alex C; Wallach, Daniel; Wang, Tao; Wu, Donghai; Liu, Zhuo; Zhu, Yan; Zhu, Zaichun; Asseng, Senthold
2017-08-29
Wheat, rice, maize, and soybean provide two-thirds of human caloric intake. Assessing the impact of global temperature increase on production of these crops is therefore critical to maintaining global food supply, but different studies have yielded different results. Here, we investigated the impacts of temperature on yields of the four crops by compiling extensive published results from four analytical methods: global grid-based and local point-based models, statistical regressions, and field-warming experiments. Results from the different methods consistently showed negative temperature impacts on crop yield at the global scale, generally underpinned by similar impacts at country and site scales. Without CO2 fertilization, effective adaptation, and genetic improvement, each degree-Celsius increase in global mean temperature would, on average, reduce global yields of wheat by 6.0%, rice by 3.2%, maize by 7.4%, and soybean by 3.1%. Results are highly heterogeneous across crops and geographical areas, with some positive impact estimates. Multimethod analyses improved the confidence in assessments of future climate impacts on global major crops and suggest crop- and region-specific adaptation strategies to ensure food security for an increasing world population.
Growth and yield models for Dahurian larch plantations
Institute of Scientific and Technical Information of China (English)
Yuan Jinlan
1999-01-01
Several equations were seiected using nonlinear regression analysis for setting up growth and yield models of Dahurian larch (Larix gmelinii Rupr.) plantations. Data of 405 stem analysis trees were collected from 336 temporary plots throughout the Daxing'an Mountains. Results showed that the Richards equation was the best model for estimating tree height, stand mean height and stand dominant height by age; the Power equation was the fittest model for predicting tree volume by DBH and tree height, and the Logarithmic stand volume equation was good for predicting stand volume from age, mean height, basal area and other stand variables. These models can be used to construct volume tables, site index table and other forestry tables for Dahurian plantations.
Evaluation of weather-based rice yield models in India
Sudharsan, D.; Adinarayana, J.; Reddy, D. Raji; Sreenivas, G.; Ninomiya, S.; Hirafuji, M.; Kiura, T.; Tanaka, K.; Desai, U. B.; Merchant, S. N.
2013-01-01
The objective of this study was to compare two different rice simulation models—standalone (Decision Support System for Agrotechnology Transfer [DSSAT]) and web based (SImulation Model for RIce-Weather relations [SIMRIW])—with agrometeorological data and agronomic parameters for estimation of rice crop production in southern semi-arid tropics of India. Studies were carried out on the BPT5204 rice variety to evaluate two crop simulation models. Long-term experiments were conducted in a research farm of Acharya N G Ranga Agricultural University (ANGRAU), Hyderabad, India. Initially, the results were obtained using 4 years (1994-1997) of data with weather parameters from a local weather station to evaluate DSSAT simulated results with observed values. Linear regression models used for the purpose showed a close relationship between DSSAT and observed yield. Subsequently, yield comparisons were also carried out with SIMRIW and DSSAT, and validated with actual observed values. Realizing the correlation coefficient values of SIMRIW simulation values in acceptable limits, further rice experiments in monsoon (Kharif) and post-monsoon (Rabi) agricultural seasons (2009, 2010 and 2011) were carried out with a location-specific distributed sensor network system. These proximal systems help to simulate dry weight, leaf area index and potential yield by the Java based SIMRIW on a daily/weekly/monthly/seasonal basis. These dynamic parameters are useful to the farming community for necessary decision making in a ubiquitous manner. However, SIMRIW requires fine tuning for better results/decision making.
Tanaka, Mirai; Yamashita, Takashi; Sano, Natsuki; Ishigaki, Aya; Suzuki, Tomomichi
2017-01-01
The convolution of a series of events is often observed for a variety of phenomena such as the oscillation of a string. A photochemical reaction of a molecule is characterized by a time constant, but materials in the real world contain several molecules with different time constants. Therefore, the kinetics of photochemical reactions of the materials are usually observed with a complexity comparable with those of theoretical kinetic equations. Analysis of the components of the kinetics is quite important for the development of advanced materials. However, with a limited number of exceptions, deconvolution of the observed kinetics has not yet been mathematically solved. In this study, we propose a mathematical optimization approach for estimating the quantum yield distribution of a photochromic reaction in a polymer. In the proposed approach, time-series data of absorbances are acquired and an estimate of the quantum yield distribution is obtained. To estimate the distribution, we solve a mathematical optimization problem to minimize the difference between the input data and a model. This optimization problem involves a differential equation constrained on a functional space as the variable lies in the space of probability distribution functions and the constraints arise from reaction rate equations. This problem can be reformulated as a convex quadratic optimization problem and can be efficiently solved by discretization. Numerical results are also reported here, and they verify the effectiveness of our approach.
Directory of Open Access Journals (Sweden)
Mirai Tanaka
2017-01-01
Full Text Available The convolution of a series of events is often observed for a variety of phenomena such as the oscillation of a string. A photochemical reaction of a molecule is characterized by a time constant, but materials in the real world contain several molecules with different time constants. Therefore, the kinetics of photochemical reactions of the materials are usually observed with a complexity comparable with those of theoretical kinetic equations. Analysis of the components of the kinetics is quite important for the development of advanced materials. However, with a limited number of exceptions, deconvolution of the observed kinetics has not yet been mathematically solved. In this study, we propose a mathematical optimization approach for estimating the quantum yield distribution of a photochromic reaction in a polymer. In the proposed approach, time-series data of absorbances are acquired and an estimate of the quantum yield distribution is obtained. To estimate the distribution, we solve a mathematical optimization problem to minimize the difference between the input data and a model. This optimization problem involves a differential equation constrained on a functional space as the variable lies in the space of probability distribution functions and the constraints arise from reaction rate equations. This problem can be reformulated as a convex quadratic optimization problem and can be efficiently solved by discretization. Numerical results are also reported here, and they verify the effectiveness of our approach.
Crop Yield Forecasted Model Based on Time Series Techniques
Institute of Scientific and Technical Information of China (English)
Li Hong-ying; Hou Yan-lin; Zhou Yong-juan; Zhao Hui-ming
2012-01-01
Traditional studies on potential yield mainly referred to attainable yield： the maximum yield which could be reached by a crop in a given environment. The new concept of crop yield under average climate conditions was defined in this paper, which was affected by advancement of science and technology. Based on the new concept of crop yield, the time series techniques relying on past yield data was employed to set up a forecasting model. The model was tested by using average grain yields of Liaoning Province in China from 1949 to 2005. The testing combined dynamic n-choosing and micro tendency rectification, and an average forecasting error was 1.24%. In the trend line of yield change, and then a yield turning point might occur, in which case the inflexion model was used to solve the problem of yield turn point.
Hodrius, M.; Migdall, S.; Bach, H.; Hank, T.
2015-04-01
Yield Maps are a basic information source for site-specific farming. For sugar beet they are not available as in-situ measurements. This gap of information can be filled with Earth Observation (EO) data in combination with a plant growth model (PROMET) to improve farming and harvest management. The estimation of yield based on optical satellite imagery and crop growth modelling is more challenging for sugar beet than for other crop types since the plants' roots are harvested. These are not directly visible from EO. In this study, the impact of multi-sensor data assimilation on the yield estimation for sugar beet is evaluated. Yield and plant growth are modelled with PROMET. This multi-physics, raster-based model calculates photosynthesis and crop growth based on the physiological processes in the plant, including the distribution of biomass into the different plant organs (roots, stem, leaves and fruit) at different phenological stages. The crop variable used in the assimilation is the green (photosynthetically active) leaf area, which is derived as spatially heterogeneous input from optical satellite imagery with the radiative transfer model SLC (Soil-Leaf-Canopy). Leaf area index was retrieved from RapidEye, Landsat 8 OLI and Landsat 7 ETM+ data. It could be shown that the used methods are very suitable to derive plant parameters time-series with different sensors. The LAI retrievals from different sensors are quantitatively compared to each other. Results for sugar beet yield estimation are shown for a test-site in Southern Germany. The validation of the yield estimation for the years 2012 to 2014 shows that the approach reproduced the measured yield on field level with high accuracy. Finally, it is demonstrated through comparison of different spatial resolutions that small-scale in-field variety is modelled with adequate results at 20 m raster size, but the results could be improved by recalculating the assimilation at a finer spatial resolution of 5 m.
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Davinia Font
2015-04-01
Full Text Available This paper presents a method for vineyard yield estimation based on the analysis of high-resolution images obtained with artificial illumination at night. First, this paper assesses different pixel-based segmentation methods in order to detect reddish grapes: threshold based, Mahalanobis distance, Bayesian classifier, linear color model segmentation and histogram segmentation, in order to obtain the best estimation of the area of the clusters of grapes in this illumination conditions. The color spaces tested were the original RGB and the Hue-Saturation-Value (HSV. The best segmentation method in the case of a non-occluded reddish table-grape variety was the threshold segmentation applied to the H layer, with an estimation error in the area of 13.55%, improved up to 10.01% by morphological filtering. Secondly, after segmentation, two procedures for yield estimation based on a previous calibration procedure have been proposed: (1 the number of pixels corresponding to a cluster of grapes is computed and converted directly into a yield estimate; and (2 the area of a cluster of grapes is converted into a volume by means of a solid of revolution, and this volume is converted into a yield estimate; the yield errors obtained were 16% and −17%, respectively.
Regionalization for Rice Yield Estimation by Remote Sensing in Zhejiang Province
Institute of Scientific and Technical Information of China (English)
无
2001-01-01
In order to provide a scientific basis for rice yield estimation and improve the accuracy of yield estimation in Zhejiang Province, regionalization indices for rice yield estimation by remote sensing (RS) in the province were determined by considering the special features of yield estimation by RS,and based on analysis of the natural conditions of Zhejiang Province.The indices determined included rice cropping system,agroclimate,landform,surface feature structure and rice yield level,where rice planting system was considered as the main one.Then regionalization for rice yield estimation by RS was completed by spatial neighboring analysis with the Geographical Information System (GIS) technology combined with using of tree algorithm.The province was divided into two regions,i.e.,the single-cropping rice region which was subdivided into 3 regions including those in mountains of northwest Zhejiang,water network area of north Zhejiang and mountains of south Zhejiang,and double-cropping rice region which was subdivided into 5 regions including those on plain of north Zhejiang,coastal plains and hills of southeast Zhejiang,Jin-Qu Basin of middle Zhejiang,hills of east Zhejiang,and hills and mountains of northwest Zhejiang.This regionalization took the county borders as the region boundaries,kept the regions connective and made the administrative regions integrity and,then,could meet the requirements of rice yield estimation by RS,showing that the results were quite satisfying.
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S. M. Hsu
2012-06-01
Full Text Available A comprehensive approach estimating sediment yield from a watershed is needed to develop better measures for mitigating sediment disasters and assessing downstream impacts. In the present study, an attempt has been made to develop an integrated method, considering sediment supplies associated with soil erosion, shallow landslide and debris flow to estimate sediment yield from a debris-flow-prone watershed on a storm event basis. The integrated method is based on the HSPF and TRIGRS models for predicting soil erosion and shallow landslide sediment yield, and the FLO-2D model for calculating debris flow sediment yield. The proposed method was applied to potential debris-flow watersheds located in the Sioulin Township of Hualien County. The available data such as hourly rainfall data, historical streamflow and sediment records as well as event-based landslide inventory maps have been used for model calibration and validation. Results for simulating sediment yield have been confirmed by comparisons of observed data from several typhoon events. The verified method employed a 24-h design hyetograph with the 100-yr return period to simulate sediment yield within the study area. The results revealed that the influence of shallow landslides on sediment supply as compared with soil erosion was significant. The estimate of landslide transport capacity into a main channel indicated the sediment delivery ratio on a typhoon event basis was approximately 38.4%. In addition, a comparison of sediment yields computed from occurrence and non-occurrence of debris flow scenarios showed that the sediment yield from an occurrence condition was found to be increasing at about 14.2 times more than estimated under a non-occurrence condition. This implied watershed sediment hazard induced by debris flow may cause severe consequences.
Cotton Area and Yield Estimation at Zhanhua County of China Using HJ-1 EVI Time Series
Directory of Open Access Journals (Sweden)
Liu Qing-Sheng
2016-01-01
Full Text Available Cotton is a significant cash crop of China. Timely and accurate cotton area and yield estimation is useful for management decisions related to the cotton procurement and sales. This study is a first research on cotton area and yield estimation based on remote sensing at Zhanhua County which is one of the high-quality cotton production demonstration bases of China. After normalization of Enhanced Vegetation Index (EVI time series derived from Huanjin 1 A/B satellite (HJ-1 A/B, decision tree classifier was used to identify the cotton, and then K-Means classifier was applied to estimate cotton yield. The results indicated an overall accuracy of 95% for the cotton area estimation and 91% for the cotton yield classification. With further validation, it suggests that this method can be used to timely achieve the cotton area and growth information of this region.
Estimations of neutron yield from beryllium target irradiated by SPring-8 hard synchrotron radiation
Gryaznykh, D A; Plokhoi, V V
2000-01-01
The possibility of creating a neutron source based on ''SPring-8'' synchrotron radiation interaction with beryllium targets is discussed. The possible neutron yield is estimated to be of order 10 sup 1 sup 2 s sup - sup 1 .
Methods of statistical model estimation
Hilbe, Joseph
2013-01-01
Methods of Statistical Model Estimation examines the most important and popular methods used to estimate parameters for statistical models and provide informative model summary statistics. Designed for R users, the book is also ideal for anyone wanting to better understand the algorithms used for statistical model fitting. The text presents algorithms for the estimation of a variety of regression procedures using maximum likelihood estimation, iteratively reweighted least squares regression, the EM algorithm, and MCMC sampling. Fully developed, working R code is constructed for each method. Th
Estimating variability in grain legume yields across Europe and the Americas
Cernay, Charles; Ben-Ari, Tamara; Pelzer, Elise; Meynard, Jean-Marc; Makowski, David
2015-01-01
Grain legume production in Europe has recently come under scrutiny. Although legume crops are often promoted to provide environmental services, European farmers tend to turn to non-legume crops. It is assumed that high variability in legume yields explains this aversion, but so far this hypothesis has not been tested. Here, we estimate the variability of major grain legume and non-legume yields in Europe and the Americas from yield time series over 1961–2013. Results show that grain legume yields are significantly more variable than non-legume yields in Europe. These differences are smaller in the Americas. Our results are robust at the level of the statistical methods. In all regions, crops with high yield variability are allocated to less than 1% of cultivated areas. Although the expansion of grain legumes in Europe may be hindered by high yield variability, some species display risk levels compatible with the development of specialized supply chains. PMID:26054055
Poss, J A; Russell, W B; Grieve, C M
2006-01-01
In arid irrigated regions, the proportion of crop production under deficit irrigation with poorer quality water is increasing as demand for fresh water soars and efforts to prevent saline water table development occur. Remote sensing technology to quantify salinity and water stress effects on forage yield can be an important tool to address yield loss potential when deficit irrigating with poor water quality. Two important forages, alfalfa (Medicago sativa L.) and tall wheatgrass (Agropyron elongatum L.), were grown in a volumetric lysimeter facility where rootzone salinity and water content were varied and monitored. Ground-based hyperspectral canopy reflectance in the visible and near infrared (NIR) were related to forage yields from a broad range of salinity and water stress conditions. Canopy reflectance spectra were obtained in the 350- to 1000-nm region from two viewing angles (nadir view, 45 degrees from nadir). Nadir view vegetation indices (VI) were not as strongly correlated with leaf area index changes attributed to water and salinity stress treatments for both alfalfa and wheatgrass. From a list of 71 VIs, two were selected for a multiple linear-regression model that estimated yield under varying salinity and water stress conditions. With data obtained during the second harvest of a three-harvest 100-d growing period, regression coefficients for each crop were developed and then used with the model to estimate fresh weights for preceding and succeeding harvests during the same 100-d interval. The model accounted for 72% of the variation in yields in wheatgrass and 94% in yields of alfalfa within the same salinity and water stress treatment period. The model successfully predicted yield in three out of four cases when applied to the first and third harvest yields. Correlations between indices and yield increased as canopy development progressed. Growth reductions attributed to simultaneous salinity and water stress were well characterized, but the
Yield model for unthinned Sitka spruce plantations in Ireland
Energy Technology Data Exchange (ETDEWEB)
Omiyale, O.; Joyce, P.M.
1982-01-01
Over the past few decades the construction of yield models, has progressed from the graphical through mathematical and biomathematic approach. The development of a biomathematical growth model for Sitka spruce plantations is described. It is suggested that this technique can serve as a basis for general yield model construction of plantation species in Ireland. (Refs. 15).
Fauziana, F.; Danoedoro, P.; Heru Murti, S.
2016-11-01
Remote sensing has been utilized especially for agriculture yield estimation. Tea yield is effected by biology characteristic including crown density. The challenge of tea yield estimation uses multispectral remote sensing data is the presence of object beside tea. This mixed pixel problem can disturb spectrally to recognize tea tree, so it is necessary to use pixel approach. The aims of this research are (1) to determine fraction of tea and non-tea; (2) to estimate crown density percentage based on tea Normalized Difference Vegetation Index (NDVI); (3) to estimate tea yield based on crown density. SPOT-7 was utilized for this application. Linear Spectral Mixture Analysis (LSMA) has applied to determination fraction percentage each pixel. Each pure endmember was read the NDVI value. NDVI of tea tree has sensitivity with crown density. Counting tea NDVI was applied for NDVI mixed pixel. Linear regression analysis has applied for estimating crown density and tea yield. The results of this research are SPOT -7 which can recognize tea, tree shade, impervious and soil each pixel with accuracy 99,84%. Although it produced high accuracy, it has overestimate at certain tea estate because of the attendance of impervious. Regression analysis of crown density and NDVI showed coeffisien determination 52%. This model result 4-100% crown density percentage, where crown density 4-55% were located beside tea tree or pruned-tea block. Regression analysis of crown density and tea yield relation showed coeffisien determination 45%. This model produced 161,34-1296,8 kg/ha. Each this model resulted Root Mean Square Error (RMSE) 14,27% and 551,52 kg/ha.
Estimating Functions and Semiparametric Models
DEFF Research Database (Denmark)
Labouriau, Rodrigo
1996-01-01
The thesis is divided in two parts. The first part treats some topics of the estimation theory for semiparametric models in general. There the classic optimality theory is reviewed and exposed in a suitable way for the further developments given after. Further the theory of estimating functions...... contained in this part of the thesis constitutes an original contribution. There can be found the detailed characterization of the class of regular estimating functions, a calculation of efficient regular asymptotic linear estimating sequences (\\ie the classical optimality theory) and a discussion...... of the attainability of the bounds for the concentration of regular asymptotic linear estimating sequences by estimators derived from estimating functions. The main class of models considered in the second part of the thesis (chapter 5) are constructed by assuming that the expectation of a number of given square...
ACCURACY OF MILK YIELD ESTIMATION IN DAIRY CATTLE FROM MONTHLY RECORD BY REGRESSION METHOD
Directory of Open Access Journals (Sweden)
I.S. Kuswahyuni
2014-10-01
Full Text Available This experiment was conducted to estimate the actual milk yield and to compare the estimation accuracyof cumulative monthly record to actual milk yield by regression method. Materials used in this experimentwere records relating to milk yield and pedigree. The obtained data were categorized into 2 groups i.e. AgeGroup I (AG I that was cow calving at < 36 months old as many as 33 cows with 33 lactation records andAG II that cows calving e” 36 months old as many as 44 cows with 105 lactation records. The first three toseven months data were used to estimate actual milk yield. Results showed that mean of milk yield/ head/lactation at AG I (2479.5 ± 461.5 kg was lower than that of AG II (2989,7 ± 526,8 kg. Estimated milk yieldsfor three to seven months at AG I were 2455.6±419.7; 2455.7±432.9; 2455.5±446.4; 2455.6±450.8; 2455,64± 450,8; 2455,5 ± 459,3 kg respectively, meanwhile at AG II was 2972.3±479.8; 2972.0±497.2; 2972.4±509.6;2972.5±523.6 and 2972.5±535.1 respectively. Correlation coefficients between estimated and actual milkyield at AG I were 0.79; 0.82; 0.86; 0.86 and 0.88, respectively, meanwhile at AG II were 0.65; 0.66; 0.67;0.69 and 0.72 respectively. In conclusion, the mean of estimated milk yield at AG I was lower than AG II.The best record to estimate actual milk yield both at AG I and AG II were the seven cumulative months.
Johnson, D. M.; Dorn, M. F.; Crawford, C.
2015-12-01
Since the dawn of earth observation imagery, particularly from systems like Landsat and the Advanced Very High Resolution Radiometer, there has been an overarching desire to regionally estimate crop production remotely. Research efforts integrating space-based imagery into yield models to achieve this need have indeed paralleled these systems through the years, yet development of a truly useful crop production monitoring system has been arguably mediocre in coming. As a result, relatively few organizations have yet to operationalize the concept, and this is most acute in regions of the globe where there are not even alternative sources of crop production data being collected. However, the National Agricultural Statistics Service (NASS) has continued to push for this type of data source as a means to complement its long-standing, traditional crop production survey efforts which are financially costly to the government and create undue respondent burden on farmers. Corn and soybeans, the two largest field crops in the United States, have been the focus of satellite-based production monitoring by NASS for the past decade. Data from the Moderate Resolution Imaging Spectroradiometer (MODIS) has been seen as the most pragmatic input source for modeling yields primarily based on its daily revisit capabilities and reasonable ground sample resolution. The research methods presented here will be broad but provides a summary of what is useful and adoptable with satellite imagery in terms of crop yield estimation. Corn and soybeans will be of particular focus but other major staple crops like wheat and rice will also be presented. NASS will demonstrate that while MODIS provides a slew of vegetation related products, the traditional normalized difference vegetation index (NDVI) is still ideal. Results using land surface temperature products, also generated from MODIS, will also be shown. Beyond the MODIS data itself, NASS research has also focused efforts on understanding a
DEFF Research Database (Denmark)
Diouf, Abdoul Aziz; Hiernaux, Pierre; Brandt, Martin Stefan;
2016-01-01
Quantitative estimates of forage availability at the end of the growing season in rangelands are helpful for pastoral livestock managers and for local, national and regional stakeholders in natural resource management. For this reason, remote sensing data such as the Fraction of Absorbed Photosyn......Quantitative estimates of forage availability at the end of the growing season in rangelands are helpful for pastoral livestock managers and for local, national and regional stakeholders in natural resource management. For this reason, remote sensing data such as the Fraction of Absorbed...... evapotranspiration satellite gridded data to estimate the annual herbaceous yield in the semi-arid areas of Senegal. It showed that a machine-learning model combining FAPAR seasonal metrics with various agrometeorological data provided better estimations of the in situ annual herbaceous yield (R2 = 0.69; RMSE = 483...
Kiviet, J.F.; Phillips, G.D.A.
2014-01-01
In dynamic regression models conditional maximum likelihood (least-squares) coefficient and variance estimators are biased. Using expansion techniques an approximation is obtained to the bias in variance estimation yielding a bias corrected variance estimator. This is achieved for both the standard
Institute of Scientific and Technical Information of China (English)
Liang Tao; Jia Xinzhang
2012-01-01
The problem of yield estimation merely from performance test data of qualified semiconductor devices is studied.An empirical formula is presented to calculate the yield directly by the sample mean and standard deviation of singly truncated normal samples based on the theoretical relation between process capability indices and the yield.Firstly,we compare four commonly used normality tests under different conditions,and simulation results show that the Shapiro-Wilk test is the most powerful test in recognizing singly truncated normal samples.Secondly,the maximum likelihood estimation method and the empirical formula are compared by Monte Carlo simulation.The results show that the simple empirical formulas can achieve almost the same accuracy as the maximum likelihood estimation method but with a much lower amount of calculations when estimating yield from singly truncated normal samples.In addition,the empirical formula can also be used for doubly truncated normal samples when some specific conditions are met.Practical examples of yield estimation from academic and IC test data are given to verify the effectiveness of the proposed method.
Mladenova, Iliana E.; Bolten, John D.; Crow, Wade T.; Anderson, Martha C.; Hain, C. R.; Johnson, David M.; Mueller, Rick
2017-01-01
This paper presents an intercomparative study of 12 operationally produced large-scale datasets describing soil moisture, evapotranspiration (ET), and or vegetation characteristics within agricultural regions of the contiguous United States (CONUS). These datasets have been developed using a variety of techniques, including, hydrologic modeling, satellite-based retrievals, data assimilation, and survey in-field data collection. The objectives are to assess the relative utility of each dataset for monitoring crop yield variability, to quantitatively assess their capacity for predicting end-of-season corn and soybean yields, and to examine the evolution of the yield-index correlations during the growing season. This analysis is unique both with regards to the number and variety of examined yield predictor datasets and the detailed assessment of the water availability timing on the end-of-season crop production during the growing season. Correlation results indicate that over CONUS, at state-level soil moisture and ET indices can provide better information for forecasting corn and soybean yields than vegetation-based indices such as normalized difference vegetation index. The strength of correlation with corn and soybean yields strongly depends on the interannual variability in yield measured at a given location. In this case study, some of the remotely derived datasets examined provide skill comparable to that of in situ field survey-based data further demonstrating the utility of these remote sensing-based approaches for estimating crop yield.
Detection and Counting of On-Tree Citrus Fruit for Crop Yield Estimation
Directory of Open Access Journals (Sweden)
Zeeshan Malik
2016-05-01
Full Text Available In this paper, we present a technique to estimate citrus fruit yield from the tree images. Manually counting the fruit for yield estimation for marketing and other managerial tasks is time consuming and requires human resources, which do not always come cheap. Different approaches have been used for the said purpose, yet separation of fruit from its background poses challenges, and renders the exercise inaccurate. In this paper, we use k-means segmentation for recognition of fruit, which segments the image accurately thus enabling more accurate yield estimation. We created a dataset containing 83 tree images with 4001 citrus fruits from three different fields. We are able to detect the on-tree fruits with an accuracy of 91.3%. In addition, we find a strong correlation between the manual and the automated fruit count by getting coefficients of determination R2 up to 0.99.
Directory of Open Access Journals (Sweden)
C. Adati
2006-01-01
Full Text Available As plantas daninhas acarretam reduções no rendimento das culturas agrícolas. Os modelos matemáticos de estimativa de perda de rendimento na cultura devido à interferência dessas plantas podem ser instrumentos úteis à tomada de decisão de manejo. Se for possível prever as perdas de rendimento, será possível decidir se é viável ou não a aplicação de uma medida de controle. Há na literatura vários modelos matemáticos empíricos de regressão lineares, não-lineares e polinomiais usados para estimar as perdas de rendimento devido às plantas daninhas. O presente trabalho teve como objetivo apresentar uma análise dos modelos matemáticos presentes na literatura utilizados para estimar as perdas de rendimento que as plantas daninhas acarretam à cultura, considerando o ajuste matemático às observações e a descrição biológica do comportamento dessas perdas.The presence of weeds in any production system involving plants causes crop yield losses. Mathematical models for crop yield loss estimation due to the interference of weeds can be useful tools for decision-making management strategies. If it is possible to predict crop yield loss, it will be possible to decide whether it is viable to apply control measures. There are several empirical models in the literature used to estimate crop yield loss due to the presence of weeds, which are linear, non linear and polynomial. The goal of this work is to present an analysis of the existing mathematical models used to estimate crop yield loss due to weeds by considering both their mathematical fit and biological behavior.
Mode choice model parameters estimation
Strnad, Irena
2010-01-01
The present work focuses on parameter estimation of two mode choice models: multinomial logit and EVA 2 model, where four different modes and five different trip purposes are taken into account. Mode choice model discusses the behavioral aspect of mode choice making and enables its application to a traffic model. Mode choice model includes mode choice affecting trip factors by using each mode and their relative importance to choice made. When trip factor values are known, it...
Graphical user interface for yield and dose estimations for cyclotron-produced technetium.
Hou, X; Vuckovic, M; Buckley, K; Bénard, F; Schaffer, P; Ruth, T; Celler, A
2014-07-07
The cyclotron-based (100)Mo(p,2n)(99m)Tc reaction has been proposed as an alternative method for solving the shortage of (99m)Tc. With this production method, however, even if highly enriched molybdenum is used, various radioactive and stable isotopes will be produced simultaneously with (99m)Tc. In order to optimize reaction parameters and estimate potential patient doses from radiotracers labeled with cyclotron produced (99m)Tc, the yields for all reaction products must be estimated. Such calculations, however, are extremely complex and time consuming. Therefore, the objective of this study was to design a graphical user interface (GUI) that would automate these calculations, facilitate analysis of the experimental data, and predict dosimetry. The resulting GUI, named Cyclotron production Yields and Dosimetry (CYD), is based on Matlab®. It has three parts providing (a) reaction yield calculations, (b) predictions of gamma emissions and (c) dosimetry estimations. The paper presents the outline of the GUI, lists the parameters that must be provided by the user, discusses the details of calculations and provides examples of the results. Our initial experience shows that the proposed GUI allows the user to very efficiently calculate the yields of reaction products and analyze gamma spectroscopy data. However, it is expected that the main advantage of this GUI will be at the later clinical stage when entering reaction parameters will allow the user to predict production yields and estimate radiation doses to patients for each particular cyclotron run.
ZNJPrice/Earnings Ratio Model through Dividend Yield and Required Yield Above Expected Inflation
Directory of Open Access Journals (Sweden)
Emil Mihalina
2010-07-01
Full Text Available Price/earnings ratio is the most popular and most widespread evaluation model used to assess relative capital asset value on financial markets. In functional terms, company earnings in the very long term can be described with high significance. Empirically, it is visible from long-term statistics that the demanded (required yield on capital markets has certain regularity. Thus, investors first require a yield above the stable inflation rate and then a dividend yield and a capital increase caused by the growth of earnings that influence the price, with the assumption that the P/E ratio is stable. By combining the Gordon model for current dividend value, the model of market capitalization of earnings (price/earnings ratio and bearing in mind the influence of the general price levels on company earnings, it is possible to adjust the price/earnings ratio by deriving a function of the required yield on capital markets measured by a market index through dividend yield and inflation rate above the stable inflation rate increased by profit growth. The S&P 500 index for example, has in the last 100 years grown by exactly the inflation rate above the stable inflation rate increased by profit growth. The comparison of two series of price/earnings ratios, a modelled one and an average 7-year ratio, shows a notable correlation in the movement of two series of variables, with a three year deviation. Therefore, it could be hypothesized that three years of the expected inflation level, dividend yield and profit growth rate of the market index are discounted in the current market prices. The conclusion is that, at the present time, the relationship between the adjusted average price/earnings ratio and its effect on the market index on one hand and the modelled price/earnings ratio on the other can clearly show the expected dynamics and course in the following period.
Directory of Open Access Journals (Sweden)
K. Ono
2011-01-01
Full Text Available The objective of this study was to estimate the potential sediment yield distribution in Japan attributed to extreme-rainfall-induced slope failures in the future. For this purpose, a regression relationship between the slope failure probability and the subsequent sediment yield was developed by using sediment yield observations from 59 dams throughout Japan. The slope failure probability accounts for the effects of topography (as relief energy, geology and hydro-climate variations (hydraulic gradient changes due to extreme rainfall variations and determines the potential slope failure occurrence with a 1-km resolution. The applicability of the developed relationship was then validated by comparing the simulated and observed sediment yields in another 43 dams. To incorporate the effects of a changing climate, extreme rainfall variations were estimated by using two climate change scenarios (the MRI-RCM20 Ver.2 model A2 scenario and the MIROC A1B scenario for the future and by accounting for the slope failure probability through the effect of extreme rainfall on the hydraulic gradient. Finally, the developed slope failure hazard-sediment yield relationship was employed to estimate the potential sediment yield distribution under a changing climate in Japan.
Time series analyses of annual sediment yields covering 15–20 years in 59 dams reveal that extreme sedimentation events have a high probability of occurring on average every 5–7 years. Therefore, the extreme-rainfall-induced slope failure probability with a five-year return period has a statistically robust relationship with specific sediment yield observations (with r^{2} = 0.65. The verification demonstrated that the model is effective for use in simulating specific sediment yields with r^{2} = 0.74. The results of the GCM scenarios suggest that the sediment yield issue will be critical in Japan in the future. When the spatially averaged sediment
Directory of Open Access Journals (Sweden)
K. Ono
2010-09-01
Full Text Available The objective of this study was to estimate the potential sediment yield distribution in Japan attributed to extreme-rainfall-induced slope failures in the future. For this purpose, a regression relationship between the slope failure hazard probability and the subsequent sediment yield was developed by using sediment yield observations from 59 dams throughout Japan. The slope failure hazard probability accounts for the effects of topography (as relief energy, geology and hydro-climate variations (hydraulic gradient changes due to extreme rainfall variations and determines the potential slope failure occurrence with a 1-km resolution. The applicability of the developed relationship was then validated by comparing the simulated and observed sediment yields in another 43 dams. To incorporate the effects of a changing climate, extreme rainfall variations were estimated by using two climate change scenarios (the MRI-RCM20 Ver.2 model A2 scenario and the MIROC A1B scenario for the future and by accounting for the slope failure hazard probability through the effect of extreme rainfall on the hydraulic gradient. Finally, the developed slope failure hazard-sediment yield relationship was employed to estimate the potential sediment yield distribution under a changing climate in Japan.
Time series analyses of annual sediment yields covering 15–20 years in 59 dams reveal that extreme sedimentation events have a high probability of occurring on average every 5–7 years. Therefore, the extreme-rainfall-induced slope failure probability with a five-year return period has a statistically robust relationship with specific sediment yield observations (with r^{2} = 0.65. The verification demonstrated that the model is effective for use in simulating specific sediment yields with r^{2} = 0.74. The results of the GCM scenarios suggest that the sediment yield issue will be critical in Japan in the future. When the spatially
Ono, K.; Akimoto, T.; Gunawardhana, L. N.; Kazama, S.; Kawagoe, S.
2011-01-01
The objective of this study was to estimate the potential sediment yield distribution in Japan attributed to extreme-rainfall-induced slope failures in the future. For this purpose, a regression relationship between the slope failure probability and the subsequent sediment yield was developed by using sediment yield observations from 59 dams throughout Japan. The slope failure probability accounts for the effects of topography (as relief energy), geology and hydro-climate variations (hydraulic gradient changes due to extreme rainfall variations) and determines the potential slope failure occurrence with a 1-km resolution. The applicability of the developed relationship was then validated by comparing the simulated and observed sediment yields in another 43 dams. To incorporate the effects of a changing climate, extreme rainfall variations were estimated by using two climate change scenarios (the MRI-RCM20 Ver.2 model A2 scenario and the MIROC A1B scenario) for the future and by accounting for the slope failure probability through the effect of extreme rainfall on the hydraulic gradient. Finally, the developed slope failure hazard-sediment yield relationship was employed to estimate the potential sediment yield distribution under a changing climate in Japan. Time series analyses of annual sediment yields covering 15-20 years in 59 dams reveal that extreme sedimentation events have a high probability of occurring on average every 5-7 years. Therefore, the extreme-rainfall-induced slope failure probability with a five-year return period has a statistically robust relationship with specific sediment yield observations (with r2 = 0.65). The verification demonstrated that the model is effective for use in simulating specific sediment yields with r2 = 0.74. The results of the GCM scenarios suggest that the sediment yield issue will be critical in Japan in the future. When the spatially averaged sediment yield for all of Japan is considered, both scenarios produced an
Strategic test-day recording regimes to estimate lactation yield in tropical dairy animals
McGill, D.M.; Thomson, P.C.; Mulder, H.A.; Lievaart, J.J.
2014-01-01
Background In developing dairy sectors, genetic improvement programs have limited resources and recording of herds is minimal. This study evaluated different methods to estimate lactation yield and sampling schedules with fewer test-day records per lactation to determine recording regimes that (1) e
Estimates of Sputter Yields of Solar-Wind Heavy Ions of Lunar Regolith Materials
Barghouty, Abdulmasser F.; Adams, James H., Jr.
2008-01-01
At energies of approximately 1 keV/amu, solar-wind protons and heavy ions interact with the lunar surface materials via a number of microscopic interactions that include sputtering. Solar-wind induced sputtering is a main mechanism by which the composition of the topmost layers of the lunar surface can change, dynamically and preferentially. This work concentrates on sputtering induced by solar-wind heavy ions. Sputtering associated with slow (speeds the electrons speed in its first Bohr orbit) and highly charged ions are known to include both kinetic and potential sputtering. Potential sputtering enjoys some unique characteristics that makes it of special interest to lunar science and exploration. Unlike the yield from kinetic sputtering where simulation and approximation schemes exist, the yield from potential sputtering is not as easy to estimate. This work will present a preliminary numerical scheme designed to estimate potential sputtering yields from reactions relevant to this aspect of solar-wind lunar-surface coupling.
Cereal Yield Modeling in Finland Using Optical and Radar Remote Sensing
Directory of Open Access Journals (Sweden)
Jouko Kleemola
2010-09-01
Full Text Available During 1996–2006, the Ministry of Agriculture and Forestry in Finland (MAFF, MTT Agrifood Research and the Finnish Geodetic Institute performed a joint remote sensing satellite research project. It evaluated the applicability of optical satellite (Landsat, SPOT data for cereal yield estimations in the annual crop inventory program. Four Optical Vegetation Indices models (I: Infrared polynomial, II: NDVI, III: GEMI, IV: PARND/FAPAR were validated to estimate cereal baseline yield levels (yb using solely optical harmonized satellite data (Optical Minimum Dataset. The optimized Model II (NDVI yb level was 4,240 kg/ha (R2 0.73, RMSE 297 kg/ha for wheat and 4390 kg/ha (R2 0.61, RMSE 449 kg/ha for barley and Model I yb was 3,480 kg/ha for oats (R2 0.76, RMSE 258 kg/ha. Optical VGI yield estimates were validated with CropWatN crop model yield estimates using SPOT and NOAA data (mean R2 0.71, RMSE 436 kg/ha and with composite SAR/ASAR and NDVI models (mean R2 0.61, RMSE 402 kg/ha using both reflectance and backscattering data. CropWatN and Composite SAR/ASAR & NDVI model mean yields were 4,754/4,170 kg/ha for wheat, 4,192/3,848 kg/ha for barley and 4,992/2,935 kg/ha for oats.
Estimating winter wheat yield through the decreasing phase of its green area
Kouadio, Amani Louis; Djaby, Bakary; Grégory, Duveiller; El Jarroudi, Moussa; TYCHON Bernard
2012-01-01
A large number of agrometeorological models for crop yield assessment are available with various levels of complexity and empiricism. However, the current development of models for wheat yield forecasting does not always reflect the inclusion of the loss of valuable green area and its relation to biotic and abiotic processes in production situation. In this study the senescence phase of winter wheat (Triticum aestivum L.) is monitored through the GAI (Green Area Index), calculated from digita...
Kouadio, Louis; Duveiller, Grégory; Djaby, Bakary; El Jarroudi, Moussa; Defourny, Pierre; Tychon, Bernard
2012-08-01
Earth observation data, owing to their synoptic, timely and repetitive coverage, have been recognized as a valuable tool for crop monitoring at different levels. At the field level, the close correlation between green leaf area (GLA) during maturation and grain yield in wheat revealed that the onset and rate of senescence appeared to be important factors for determining wheat grain yield. Our study sought to explore a simple approach for wheat yield forecasting at the regional level, based on metrics derived from the senescence phase of the green area index (GAI) retrieved from remote sensing data. This study took advantage of recent methodological improvements in which imagery with high revisit frequency but coarse spatial resolution can be exploited to derive crop-specific GAI time series by selecting pixels whose ground-projected instantaneous field of view is dominated by the target crop: winter wheat. A logistic function was used to characterize the GAI senescence phase and derive the metrics of this phase. Four regression-based models involving these metrics (i.e., the maximum GAI value, the senescence rate and the thermal time taken to reach 50% of the green surface in the senescent phase) were related to official wheat yield data. The performances of such models at this regional scale showed that final yield could be estimated with an RMSE of 0.57 ton ha-1, representing about 7% as relative RMSE. Such an approach may be considered as a first yield estimate that could be performed in order to provide better integrated yield assessments in operational systems.
Predicting the Yield Stress of SCC using Materials Modelling
DEFF Research Database (Denmark)
Thrane, Lars Nyholm; Hasholt, Marianne Tange; Pade, Claus
2005-01-01
A conceptual model for predicting the Bingham rheological parameter yield stress of SCC has been established. The model used here is inspired by previous work of Oh et al. (1), predicting that the yield stress of concrete relative to the yield stress of paste is a function of the relative thickness...... of excess paste around the aggregate. The thickness of excess paste is itself a function of particle shape, particle size distribution, and particle packing. Seven types of SCC were tested at four different excess paste contents in order to verify the conceptual model. Paste composition and aggregate shape...... and distribution were varied between SCC types. The results indicate that yield stress of SCC may be predicted using the model....
MODELING OF SEDIMENT AND NONPOINT SOURCE POLLUTANT YIELD
Institute of Scientific and Technical Information of China (English)
Huai'en LI; Xiaokang Hong; Bing SHEN
2001-01-01
For water and soil conservation and water pollution control, it is very important to simulate and predict the load of sediment and pollutant during storm-runoff. On the basis of analyzing the simultaneous measurements of flow, sediment and pollutants observed at watershed outlet, a practical sediment yield model is developed by standardizing the load rate. The results show that the standardized pollutant yield equals effective rainfall and the process of effective load yield is the same as effective rainfall hyetograph. Comparison with measured data show that this model is applicable to various pollutants.
Nizamuddin, Mohammad; Akhand, Kawsar; Roytman, Leonid; Kogan, Felix; Goldberg, Mitch
2015-06-01
Rice is a dominant food crop of Bangladesh accounting about 75 percent of agricultural land use for rice cultivation and currently Bangladesh is the world's fourth largest rice producing country. Rice provides about two-third of total calorie supply and about one-half of the agricultural GDP and one-sixth of the national income in Bangladesh. Aus is one of the main rice varieties in Bangladesh. Crop production, especially rice, the main food staple, is the most susceptible to climate change and variability. Any change in climate will, thus, increase uncertainty regarding rice production as climate is major cause year-to-year variability in rice productivity. This paper shows the application of remote sensing data for estimating Aus rice yield in Bangladesh using official statistics of rice yield with real time acquired satellite data from Advanced Very High Resolution Radiometer (AVHRR) sensor and Principal Component Regression (PCR) method was used to construct a model. The simulated result was compared with official agricultural statistics showing that the error of estimation of Aus rice yield was less than 10%. Remote sensing, therefore, is a valuable tool for estimating crop yields well in advance of harvest, and at a low cost.
Liu, P.; Bongiovanni, T. E.; Monsivais-Huertero, A.; Bindlish, R.; Judge, J.
2013-12-01
Accurate estimates of crop yield are important for managing agricultural production and food security. Although the crop growth models, such as the Decision Support System Agrotechnology Transfer (DSSAT), have been used to simulate crop growth and development, the crop yield estimates still diverge from the reality due to different sources of errors in the models and computation. Auxiliary observations may be incorporated into such dynamic models to improve predictions using data assimilation. Active and passive (AP) microwave observations at L-band (1-2 GHz) are sensitive to dielectric and geometric properties of soil and vegetation, including soil moisture (SM), vegetation water content (VWC), surface roughness, and vegetation structure. Because SM and VWC are one of the governing factors in estimating crop yield, microwave observations may be used to improve crop yield estimates. Current studies have shown that active observations are more sensitive to the surface roughness of soil and vegetation structure during the growing season, while the passive observations are more sensitive to the SM. Backscatter and emission models linked with the DSSAT model (DSSAT-A-P) allow assimilation of microwave observations of backscattering coefficient (σ0) and brightness temperature (TB) may provide biophysically realistic estimates of model states and parameters. The present ESA Soil Moisture Ocean Salinity (SMOS) mission provides passive observations at 1.41 GHz at 25 km every 2-3 days, and the NASA/CNDAE Aquarius mission provides L-band AP observations at spatial resolution of 150 km with a repeat coverage of 7 days for global SM products. In 2014, the planned NASA Soil Moisture Active Passive mission will provide AP observations at 1.26 and 1.41 GHz at the spatial resolutions of 3 and 30 km, respectively, with a repeat coverage of 2-3 days. The goal of this study is to understand the impacts of assimilation of asynchronous and synchronous AP observations on crop yield
Directory of Open Access Journals (Sweden)
Jonathan Van Beek
2015-08-01
Full Text Available Yield and quality estimations provide vital information to fruit growers, yet require accurate monitoring throughout the growing season. To this end, the temporal dependency of fruit yield and quality estimations through spectral vegetation indices was investigated in irrigated and rainfed pear orchards. Both orchards were monitored throughout three consecutive growing seasons, including spectral measurements (i.e., hyperspectral canopy reflectance measurements as well as yield determination (i.e., total yield and number of fruits per tree and quality assessment (i.e., fruit firmness, total soluble solids and fruit color. The results illustrated a clear association between spectral vegetation indices and both fruit yield and fruit quality (|r| > 0.75; p < 0.001. However, the correlations between vegetation indices and production variables varied throughout the growing season, depending on the phenological stage of fruit development. In the irrigated orchard, index values showed a strong association with production variables near time of harvest (|r| > 0.6; p < 0.001, while in the rainfed orchard, index values acquired during vegetative growth periods presented stronger correlations with fruit parameters (|r| > 0.6; p < 0.001. The improved planning of remote sensing missions during (rainfed orchards and after (irrigated orchards vegetative growth periods could enable growers to more accurately predict production outcomes and improve the production process.
Estimating grain yield losses caused by septoria leaf blotch on durum wheat in Tunisia
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Samia Berraies
2014-12-01
Full Text Available Septoria leaf blotch (SLB, caused by Zymoseptoria tritici (Desm. Quaedvlieg & Crous, 2011 (teleomorph: Mycosphaerella graminicola (Fuckel J. Schrot., is an important wheat disease in the Mediterranean region. In Tunisia, SLB has become a major disease of durum wheat (Triticum turgidum L. subsp. durum [Desf.] Husn. particularly during favorable growing seasons where significant yield losses and increase of fungicides use were recorded over the last three decades. The objectives of this study were to evaluate the effect of SLB severity on grain yield of new elite durum wheat breeding lines and to measure the relative effect of fungicide control on grain yield. Experiments were conducted during 2007-2008 and 2008-2009 cropping seasons. A set of 800 breeding lines were screened for reaction to SLB under natural infection at Beja research station. To estimate the disease effect, correlation between disease severity at early grain filling stage and grain yield was performed. Results showed that susceptible varieties yield was significantly reduced by SLB. Average yield reduction was as high as 384 and 325 kg ha-1 for every increment in disease severity on a 0-9 scale in both seasons, respectively. A negative correlation coefficient varied between -0.61 and -0.66 in both seasons. Treated and untreated trials conducted during 2008-2009 and 2009-2010 showed that yield of treated plots increased by 50% on the commonly cultivated susceptible varieties. The results of this investigation suggested that septoria incidence is related to large grain yield losses particularly on susceptible high yielding cultivars. However, appropriate fungicide application at booting growth stage could be beneficial for farmers. The development and use of more effective fungicide could be sought to alleviate the disease effects and therefore could be considered as a part of the integrated pest management and responsible use strategy on septoria leaf blotch in Tunisia.
Estimated yield of double-strand breaks from internal exposure to tritium.
Chen, Jing
2012-08-01
Internal exposure to tritium may result in DNA lesions. Of those, DNA double-strand breaks (DSBs) are believed to be important. However, experimental and computational data of DSBs induction by tritium are very limited. In this study, microdosimetric characteristics of uniformly distributed tritium were determined in dimensions of critical significance in DNA DSBs. Those characteristics were used to identify other particles comparable to tritium in terms of microscopic energy deposition. The yield of DSBs could be strongly dependent on biological systems and cellular environments. After reviewing theoretically predicted and experimentally determined DSB yields available in the literature for low-energy electrons and high-energy protons of comparable microdosimetric characteristics to tritium in the dimensions relevant to DSBs, it is estimated that the average DSB yields of 2.7 × 10(-11), 0.93 × 10(-11), 2.4 × 10(-11) and 1.6 × 10(-11) DSBs Gy(-1) Da(-1) could be reasonable estimates for tritium in plasmid DNAs, yeast cells, Chinese hamster V79 cells and human fibroblasts, respectively. If a biological system is not specified, the DSB yield from tritium exposure can be estimated as (2.3 ± 0.7) × 10(-11) DSBs Gy(-1) Da(-1), which is a simple average over experimentally determined yields of DSBs for low-energy electrons in various biological systems without considerations of variations caused by different techniques used and obvious differences among different biological systems where the DSB yield was measured.
French, V. (Principal Investigator)
1982-01-01
An evaluation was made of Thompson-Type models which use trend terms (as a surrogate for technology), meteorological variables based on monthly average temperature, and total precipitation to forecast and estimate corn yields in Iowa, Illinois, and Indiana. Pooled and unpooled Thompson-type models were compared. Neither was found to be consistently superior to the other. Yield reliability indicators show that the models are of limited use for large area yield estimation. The models are objective and consistent with scientific knowledge. Timely yield forecasts and estimates can be made during the growing season by using normals or long range weather forecasts. The models are not costly to operate and are easy to use and understand. The model standard errors of prediction do not provide a useful current measure of modeled yield reliability.
Performance evaluation of selected crop yield-water use models for wheat crop
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H. E. Igbadun
2001-10-01
Full Text Available Crop yield-water use models that provide useful information about the exact form of crop response to different amounts of water used by the crop throughout its growth stages and those that provide adequate information for decisions on optimal use of water in the farm were evaluated. Three crop yield models: Jensen (1968, Minhas et al., (1974 and Bras and Cordova (1981 additive type models were studied. Wheat (Triticum aestivum was planted at the Institute for Agricultural Research Farm during the 1995/96 and 1996/97 irrigation seasons of November to March. The data collected from the field experiments during the 1995/96 planting season were used to calibrate the models and their stress sensitivity factors estimated for four selected growth stages of the wheat crop. The ability of the model to predict grain yield of wheat with the estimated stress sensitivity factors was evaluated by comparing predicted grain yields by each model with those obtained in the field during the 1996/97 season. The three models performed fairly well in predicting grain yields, as the predicted results were not significantly different from the field measured grain yield at 5% level of significance.
Lazzari, Maurizio; Danese, Maria; Gioia, Dario; Piccarreta, Marco
2013-04-01
Sedimentary budget estimation is an important topic for both scientific and social community, because it is crucial to understand both dynamics of orogenic belts and many practical problems, such as soil conservation and sediment accumulation in reservoir. Estimations of sediment yield or denudation rates in southern-central Italy are generally obtained by simple empirical relationships based on statistical regression between geomorphic parameters of the drainage network and the measured suspended sediment yield at the outlet of several drainage basins or through the use of models based on sediment delivery ratio or on soil loss equations. In this work, we perform a study of catchment dynamics and an estimation of sedimentary yield for several mountain catchments of the central-western sector of the Basilicata region, southern Italy. Sediment yield estimation has been obtained through both an indirect estimation of suspended sediment yield based on the Tu index (mean annual suspension sediment yield, Ciccacci et al., 1980) and the application of the Rusle (Renard et al., 1997) and the USPED (Mitasova et al., 1996) empirical methods. The preliminary results indicate a reliable difference between the RUSLE and USPED methods and the estimation based on the Tu index; a critical data analysis of results has been carried out considering also the present-day spatial distribution of erosion, transport and depositional processes in relation to the maps obtained from the application of those different empirical methods. The studied catchments drain an artificial reservoir (i.e. the Camastra dam), where a detailed evaluation of the amount of historical sediment storage has been collected. Sediment yield estimation obtained by means of the empirical methods have been compared and checked with historical data of sediment accumulation measured in the artificial reservoir of the Camastra dam. The validation of such estimations of sediment yield at the scale of large catchments
Comparing Sediment Yield Predictions from Different Hydrologic Modeling Schemes
Dahl, T. A.; Kendall, A. D.; Hyndman, D. W.
2015-12-01
Sediment yield, or the delivery of sediment from the landscape to a river, is a difficult process to accurately model. It is primarily a function of hydrology and climate, but influenced by landcover and the underlying soils. These additional factors make it much more difficult to accurately model than water flow alone. It is not intuitive what impact different hydrologic modeling schemes may have on the prediction of sediment yield. Here, two implementations of the Modified Universal Soil Loss Equation (MUSLE) are compared to examine the effects of hydrologic model choice. Both the Soil and Water Assessment Tool (SWAT) and the Landscape Hydrology Model (LHM) utilize the MUSLE for calculating sediment yield. SWAT is a lumped parameter hydrologic model developed by the USDA, which is commonly used for predicting sediment yield. LHM is a fully distributed hydrologic model developed primarily for integrated surface and groundwater studies at the watershed to regional scale. SWAT and LHM models were developed and tested for two large, adjacent watersheds in the Great Lakes region; the Maumee River and the St. Joseph River. The models were run using a variety of single model and ensemble downscaled climate change scenarios from the Coupled Model Intercomparison Project 5 (CMIP5). The initial results of this comparison are discussed here.
Top ten models constrained by b {yields} s{gamma}
Energy Technology Data Exchange (ETDEWEB)
Hewett, J.L.
1994-05-01
The radiative decay b {yields} s{gamma} is examined in the Standard Model and in nine classes of models which contain physics beyond the Standard Model. The constraints which may be placed on these models from the recent results of the CLEO Collaboration on both inclusive and exclusive radiative B decays is summarized. Reasonable bounds are found the parameters in some of the models.
Institute of Scientific and Technical Information of China (English)
无
2002-01-01
Water yield and sediment yield in the Teba catchment, Spain, were simulated using SWRRB (Simulator for Water Resources in Rural Basins) model. The model is composed of 198 mathematical equations. About 120 items (variables) were input for the simulation, including meteorological and climatic factors, hydrologic factors, topographic factors, parent materials, soils, vegetation, human activities, etc. The simulated results involved surface runoff, subsurface runoff, sediment, peak flow, evapotranspiration, soil water, total biomass,etc. Careful and thorough input data preparation and repeated simulation experiments are the key to get the accurate results. In this work the simulation accuracy for annual water yield prediction reached to 83.68%.``
Satellite Estimates of Crop Area and Maize Yield in Zambia's Agricultural Districts
Azzari, G.; Lobell, D. B.
2015-12-01
Predicting crop yield and area from satellite is a valuable tool to monitor different aspects of productivity dynamics and food security. In Sub-Saharan Africa, where the agricultural landscape is complex and dominated by smallholder systems, such dynamics need to be investigated at the field scale. We leveraged the large data pool and computational power of Google Earth Engine to 1) generate 30 m resolution cover maps of selected provinces of Zambia, 2) estimate crop area, and 3) produce yearly maize yield maps using the recently developed SCYM (Scalable satellite-based Crop Yield Mapper) algorithm. We will present our results and their validation against a ground survey dataset collected yearly by the Zambia Ministry of Agriculture from about 12,500 households.
Sediment Yield Modeling in a Large Scale Drainage Basin
Ali, K.; de Boer, D. H.
2009-05-01
This paper presents the findings of spatially distributed sediment yield modeling in the upper Indus River basin. Spatial erosion rates calculated by using the Thornes model at 1-kilometre spatial resolution and monthly time scale indicate that 87 % of the annual gross erosion takes place in the three summer months. The model predicts a total annual erosion rate of 868 million tons, which is approximately 4.5 times the long- term observed annual sediment yield of the basin. Sediment delivery ratios (SDR) are hypothesized to be a function of the travel time of surface runoff from catchment cells to the nearest downstream channel. Model results indicate that higher delivery ratios (SDR > 0.6) are found in 18 % of the basin area, mostly located in the high-relief sub-basins and in the areas around the Nanga Parbat Massif. The sediment delivery ratio is lower than 0.2 in 70 % of the basin area, predominantly in the low-relief sub-basins like the Shyok on the Tibetan Plateau. The predicted annual basin sediment yield is 244 million tons which compares reasonably to the measured value of 192.5 million tons. The average annual specific sediment yield in the basin is predicted as 1110 tons per square kilometre. Model evaluation based on accuracy statistics shows very good to satisfactory performance ratings for predicted monthly basin sediment yields and for mean annual sediment yields of 17 sub-basins. This modeling framework mainly requires global datasets, and hence can be used to predict erosion and sediment yield in other ungauged drainage basins.
Remote Sensing and GIS Based wheat Crop Acreage and Yield Estimation of District Hyderabad, Pakistan
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Altaf Ali Siyal
2015-01-01
Full Text Available Pre-harvest reliable and timely yield forecast and area estimates of cropped area is vital to planners and policy makers for making important and timely decisions with respect to food security in a country. The present study was conducted to estimate the wheat cropped area and crop yield in Hyderabad District, Pakistan from the Landsat 8 satellite imagery for Rabi 2013-14 and ground trothing. The required imagery of district Hyderabad was acquired from GLOVIS and was classified with maximum likelihood algorithm using ArcGIS 10.1. The classified image revealed that in district Hyderabad wheat covered 10,210 hectares (9.74% of total area during Rabi season 2013-14 against 15,000 hectares (14.3% of total area reported by Crop reporting Services (CRS, Sindh which is 30% less than that of reported by CRS. A positive linear relation between the wheat crop yield and the peak NDVI with coefficient of determination R2 = 0.91 was observed. Crop area and yield forecast through remote sensing is easy, cost effective, quick and reliable hence this technology needs to be introduced and propagated in the concerned government departments of Pakistan
A dynamic tomato growth and yield model (TOMGRO).
Jones, J.W.; Dayan, E.; Allen, L.H.; Keulen, van H.; Challa, H.
1991-01-01
Models of the greenhouse environment and of crops are needed to determine optimal strategies for environment control in regions where new greenhouse industries are developing. In this research, a physiological model of tomato crop development and yield was developed. A series of differential
Modelling biomass production and yield of horticultural crops: a review.
Marcelis, L.F.M.; Heuvelink, E.; Goudriaan, J.
1998-01-01
Descriptive and explanatory modelling of biomass production and yield of horticultural crops is reviewed with special reference to the simulation of leaf area, light interception, dry matter (DM) production, DM partitioning and DM content. Most models for prediction of harvest date (timing of produc
Institute of Scientific and Technical Information of China (English)
无
2002-01-01
Simulated results of water yield, sediment yield, surface runoff, subsurface runoff, peak flow, evapotranspiration, etc., in the Teba catchment, Spain, using SWRRB (Simulator for Water Resources in Rural Basins) model are presented and the related problems are discussed. The results showed that water yield and sediment yield could be satisfactorily simulated using SWRRB model The accuracy of the annual water yield simulation in the Teba catchment was up to 83.68%, which implied that this method could be effectively used to predict the annual or inter-annual water yield and to realize the quantification of geographic elements and processes of a river basin.``
Estimating the Impact and Spillover Effect of Climate Change on Crop Yield in Northern Ghana.
Botchway, E.
2016-12-01
In tropical regions of the world human-induced climate change is likely to impact negatively on crop yields. To investigate the impact of climate change and its spillover effect on mean and variance of crop yields in northern Ghana, the Just and Pope stochastic production function and the Spatial Durbin model were adopted. Surprisingly, the results suggest that both precipitation and average temperature have positive effects on mean crop yield during the wet season. Wet season average temperature has a significant spillover effect in the region, whereas precipitation during the wet season has only one significant spillover effect on maize yield. Wet season precipitation does not have a strong significant effect on crop yield despite the rainfed nature of agriculture in the region. Thus, even if there are losers and winners as a result of future climate change at the regional level, future crop yield would largely depend on future technological development in agriculture, which may improve yields over time despite the changing climate. We argue, therefore, that technical improvement in farm management such as improved seeds and fertilizers, conservation tillage and better pest control, may have a more significant role in increasing observed crop productivity levels over time. So investigating the relative importance of non-climatic factors on crop yield may shed more light on where appropriate interventions can help in improving crop yields. Climate change, also, needs to be urgently assessed at the level of the household, so that poor and vulnerable people dependent on agriculture can be appropriately targeted in research and development activities whose object is poverty alleviation.
Gastric yield pressure and gastric yield volume to assess anti-reflux barrier in a porcine model.
Duarte, Marcos E S; Freitag, Carmen P F; Fornari, Fernando; Kruel, Cleber R P; Sanches, Paulo R S; Thomé, Paulo R O; Callegari-Jacques, Sidia M; Möllerke, Roseli O; Vicente, Yvone A M V A; Goldani, Helena A S; Barros, Sérgio G S
2013-04-01
Anti-reflux barrier (ARB) resistance may be useful to test new treatments for gastroesophageal reflux (GER). The ARB has been estimated by increasing gastric yield pressure (GYP) and gastric yield volume (GYV) in animal models but has not been validated. This study aimed to develop an experimental model suitable for assessing the ARB resistance to increasing intragastric pressure and volume and its reproducibility in a seven-day interval. Ten two-month-old female Large-White swine were studied. Intragastric pressure and volume were recorded using a digital system connected to a Foley catheter inserted through gastrostomy into the stomach. GYP and GYV were defined as the gastric pressure and volume able to yield gastric contents into the esophagus detected by esophageal pH. A sudden pH drop below 3 sustained during 5 min was considered diagnostic for gastric yield. Animals were studied again after seven days. On days 0 and 7, there were no significant differences for GYP (mean ± SD = 7.66 ± 3.02 mmHg vs. 7.07 ± 3.54 mmHg, p = .686) and GYV (636.70 ± 216.74 ml vs. 608.30 ± 276.66 ml; p = .299), respectively. Concordance correlation coefficient (ρc) was significant for GYP (ρc = 0.634, 95% CI = 0.141-0.829, p = .006), but not for GYV (ρc = 0.291, 95% CI = -0.118 to 0.774, p = .196). This study demonstrated an experimental model, assessing the ARB resistance. GYP seems to be a more reliable parameter than GYV for assessment of ARB resistance.
Simulation Models of Leaf Area Index and Yield for Cotton Grown with Different Soil Conditioners.
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Lijun Su
Full Text Available Simulation models of leaf area index (LAI and yield for cotton can provide a theoretical foundation for predicting future variations in yield. This paper analyses the increase in LAI and the relationships between LAI, dry matter, and yield for cotton under three soil conditioners near Korla, Xinjiang, China. Dynamic changes in cotton LAI were evaluated using modified logistic, Gaussian, modified Gaussian, log normal, and cubic polynomial models. Universal models for simulating the relative leaf area index (RLAI were established in which the application rate of soil conditioner was used to estimate the maximum LAI (LAIm. In addition, the relationships between LAIm and dry matter mass, yield, and the harvest index were investigated, and a simulation model for yield is proposed. A feasibility analysis of the models indicated that the cubic polynomial and Gaussian models were less accurate than the other three models for simulating increases in RLAI. Despite significant differences in LAIs under the type and amount of soil conditioner applied, LAIm could be described by aboveground dry matter using Michaelis-Menten kinetics. Moreover, the simulation model for cotton yield based on LAIm and the harvest index presented in this work provided important theoretical insights for improving water use efficiency in cotton cultivation and for identifying optimal application rates of soil conditioners.
Setyowati, H. A.; S, S. H. Murti B.; Widyatmanti, W.
2016-06-01
Oil palm plantations consist of diverse plant density level that influence the appearance of soil surface or commonly in remote sensing terms called as soil background. Choosing the right density coefficient of vegetation transformation can decrease the noise of soil background for estimating oil palm yield. This research aims 1) to examine the accuracy of SPOT-6 to identify the oil palm l plant growth level and to estimate their yield 2) to know the variation of oil palm yield based on SAVI index vegetation using different density coefficient; and 3) to determine the best density coefficient to estimate the yield of oil palm. This research was held in part of Air Molek, Indragiri Hulu Regency, Riau, one of the largest oil palm plantations in Indonesia. This research method utilises SAVI transformation with density coefficient L-0, L-0.5, and L-1, and regression statistics analysis. The land-cover primary data is derived from SPOT-6 imagery archived in 13rd June 2013. The field survey was conducted in the same month of image's acquisition time and 120 sample areas were taken during that time. Two steps of regression analyses were applied to see the correlation between, first, vegetation index value and oil palm plant; and second, oil palm plant, vegetation index values, and oil palm yield from field observation. These steps produced a model to estimate the oil palm yield based on the index values of yield and vegetation, and the productivity estimation. The result shows that SPOT-6 imagery has 96% accuracy level which is considered high for identifying the oil palm variation. The R value for L-0 density coefficient is 0.8, for L-5 is 0.81 whereas for L-1 is 0.82. The best plant's density coefficient for estimating oil palm yield/yield is L-0 with yield estimation accuracy of 83.33%.
Model Selection Through Sparse Maximum Likelihood Estimation
Banerjee, Onureena; D'Aspremont, Alexandre
2007-01-01
We consider the problem of estimating the parameters of a Gaussian or binary distribution in such a way that the resulting undirected graphical model is sparse. Our approach is to solve a maximum likelihood problem with an added l_1-norm penalty term. The problem as formulated is convex but the memory requirements and complexity of existing interior point methods are prohibitive for problems with more than tens of nodes. We present two new algorithms for solving problems with at least a thousand nodes in the Gaussian case. Our first algorithm uses block coordinate descent, and can be interpreted as recursive l_1-norm penalized regression. Our second algorithm, based on Nesterov's first order method, yields a complexity estimate with a better dependence on problem size than existing interior point methods. Using a log determinant relaxation of the log partition function (Wainwright & Jordan (2006)), we show that these same algorithms can be used to solve an approximate sparse maximum likelihood problem for...
Antwi, Philip; Li, Jianzheng; Boadi, Portia Opoku; Meng, Jia; Shi, En; Deng, Kaiwen; Bondinuba, Francis Kwesi
2017-03-01
Three-layered feedforward backpropagation (BP) artificial neural networks (ANN) and multiple nonlinear regression (MnLR) models were developed to estimate biogas and methane yield in an upflow anaerobic sludge blanket (UASB) reactor treating potato starch processing wastewater (PSPW). Anaerobic process parameters were optimized to identify their importance on methanation. pH, total chemical oxygen demand, ammonium, alkalinity, total Kjeldahl nitrogen, total phosphorus, volatile fatty acids and hydraulic retention time selected based on principal component analysis were used as input variables, whiles biogas and methane yield were employed as target variables. Quasi-Newton method and conjugate gradient backpropagation algorithms were best among eleven training algorithms. Coefficient of determination (R(2)) of the BP-ANN reached 98.72% and 97.93% whiles MnLR model attained 93.9% and 91.08% for biogas and methane yield, respectively. Compared with the MnLR model, BP-ANN model demonstrated significant performance, suggesting possible control of the anaerobic digestion process with the BP-ANN model.
Modeling temporal and spatial variability of crop yield
Bonetti, S.; Manoli, G.; Scudiero, E.; Morari, F.; Putti, M.; Teatini, P.
2014-12-01
In a world of increasing food insecurity the development of modeling tools capable of supporting on-farm decision making processes is highly needed to formulate sustainable irrigation practices in order to preserve water resources while maintaining adequate crop yield. The design of these practices starts from the accurate modeling of soil-plant-atmosphere interaction. We present an innovative 3D Soil-Plant model that couples 3D hydrological soil dynamics with a mechanistic description of plant transpiration and photosynthesis, including a crop growth module. Because of its intrinsically three dimensional nature, the model is able to capture spatial and temporal patterns of crop yield over large scales and under various climate and environmental factors. The model is applied to a 25 ha corn field in the Venice coastland, Italy, that has been continuously monitored over the years 2010 and 2012 in terms of both hydrological dynamics and yield mapping. The model results satisfactorily reproduce the large variability observed in maize yield (from 2 to 15 ton/ha). This variability is shown to be connected to the spatial heterogeneities of the farmland, which is characterized by several sandy paleo-channels crossing organic-rich silty soils. Salt contamination of soils and groundwater in a large portion of the area strongly affects the crop yield, especially outside the paleo-channels, where measured salt concentrations are lower than the surroundings. The developed model includes a simplified description of the effects of salt concentration in soil water on transpiration. The results seem to capture accurately the effects of salt concentration and the variability of the climatic conditions occurred during the three years of measurements. This innovative modeling framework paves the way to future large scale simulations of farmland dynamics.
A crop model-based approach for sunflower yields
Directory of Open Access Journals (Sweden)
João Guilherme Dal Belo Leite
2014-10-01
Full Text Available Pushed by the Brazilian biodiesel policy, sunflower (Helianthus annuus L. production is becoming increasingly regarded as an option to boost farmers' income, particularly under semi-arid conditions. Biodiesel related opportunities increase the demand for decision-making information at different levels, which could be met by simulation models. This study aimed to evaluate the performance of the crop model OILCROP-SUN to simulate sunflower development and growth under Brazilian conditions and to explore sunflower water- and nitrogen-limited, water-limited and potential yield and yield variability over an array of sowing dates in the northern region of the state of Minas Gerais, Brazil. For model calibration, an experiment was conducted in which two sunflower genotypes (H358 and E122 were cultivated in a clayey soil. Growth components (leaf area index, above ground biomass, grain yield and development stages (crop phenology were measured. A database composed of 27 sunflower experiments from five Brazilian regions was used for model evaluation. The spatial yield distribution of sunflower was mapped using ordinary kriging in ArcGIS. The model simulated sunflower grain productivity satisfactorily (Root Mean Square Error ≈ 13 %. Simulated yields were relatively high (1,750 to 4,250 kg ha-1 and the sowing window was fairly wide (Oct to Feb for northwestern locations, where sunflower could be cultivated as a second crop (double cropping at the end of the rainy season. The hybrid H358 had higher yields for all simulated sowing dates, growth conditions and selected locations.
Yield Model Development (YMD) implementation plan for fiscal years 1981 and 1982
Ambroziak, R. A. (Principal Investigator)
1981-01-01
A plan is described for supporting USDA crop production forecasting and estimation by (1) testing, evaluating, and selecting crop yield models for application testing; (2) identifying areas of feasible research for improvement of models; and (3) conducting research to modify existing models and to develop new crop yield assessment methods. Tasks to be performed for each of these efforts are described as well as for project management and support. The responsibilities of USDA, USDC, USDI, and NASA are delineated as well as problem areas to be addressed.
Diversity and genetic parameter estimates for yield and its components in Jatropha curcas L.
Freitas, R G; Dias, L A S; Cardoso, P M R; Evaristo, A B; Silva, M F; Araújo, N M
2016-03-24
Jatropha curcas L. is one of the most promising oilseeds for biodiesel and biokerosene production, but few basic studies or breeding programs have been conducted for the species. We estimated genetic parameters and diversity based on 10 yield traits in 77 half-sib progenies of J. curcas after 52 months in the field, and evaluated correlations between them and the oil content of the seeds. The mean grain yield per plant was 377.9 g (ranging from 169.8 to 772.1 g) and the mean oil content was 36.2% (ranging from 30 to 39.6%). Moderate estimates of heritability at the mean progeny level were obtained for the length of the fruit (84.7%), length (69.1%) and width (68.2%) of the seed, and grain yield per plant (62.2%). Oil content was only positively and significantly correlated with 100-seed weight. Our study revealed a range of possible crosses to be investigated in J. curcas. Progeny production should be evaluated over several crop seasons for the accurate selection of the best progenies.
Modelling daily sediment yield from a meso-scale catchment, a case study in SW Poland
Energy Technology Data Exchange (ETDEWEB)
Keesstra, S. D.; Schoorl, J.; Temme, A. J. A. M.
2009-07-01
For management purposes it is important to be able to assess the sediment yield of a catchment. however, at this moment models designed for estimating sediment yield are only capable to give either very detailed storm-based information or year averages. The storm-based models require input data that are not available for most catchment. However, models that estimate yearly averages, ignore a lot of other detailed information, like daily discharge and precipitation data. There are currently no models available that model sediment yield on the temporal scale of one day and the spatial scale of a meso-scale catchment, without making use of very detailed input data. To fill this scientific and management gap, landscape evolution model LAPSUS has been adapted to model sediment yield on a daily basis. This model has the water balance as a base. To allow calibration with the discharge at the outlet, a subsurface flow module has been added to the model. (Author) 12 refs.
Yield estimation is a critical task in crop management. A number of traditional methods are available for crop yield estimation but they are costly, time-consuming and difficult to expand to a relatively large field. Remote sensing provides techniques to develop quick coverage over a field at any sc...
Estimating the effect of urease inhibitor on rice yield based on NDVI at key growth stages
Directory of Open Access Journals (Sweden)
Kailou LIU,Yazhen LI,Huiwen HU
2014-06-01
Full Text Available The effect of the urease inhibitor, N-(n-butyl thiophosphoric triamide (NBPT at a range of application rates on rice production was examined in a field experiment at Jinxian County, Jiangxi Province, China. The normalized difference vegetation index (NDVI was measured at key growth stages in both early and late rice. The results showed that the grain yield increased significantly when urea was applied with NBPT, with the highest yield observed at 1.00% NBPT (wt/wt. NDVI differed with the growth stage of rice; it remained steady from the heading to the filling stage. Rice yield could be predicted from the NDVI taken at key rice growing stages, with R2 ranging from 0.34 to 0.69 in early rice and 0.49 to 0.70 in late rice. The validation test showed that RMSE (t·hm-2 values were 0.77 and 0.87 in early and late rice, respectively. Therefore, it was feasible to estimate rice yield for different amounts of urease inhibitor using NDVI.
An estimation of the yield and response functions for the mini neutron monitor
Caballero-Lopez, R. A.
2016-08-01
The present study estimates the yield and response functions of the mini neutron monitor (miniNM). This relatively new cosmic ray detector is the mobile version of the standard NM64. It can be use not only to calibrate the NM64 but also to study the modulation processes. Due to its portability, the miniNM can be easily placed in a suitable location to measure secondary particles, which give information about the intensity variations of galactic and solar cosmic rays. In order to perform these modulation studies with miniNMs, it is crucial to know their sensitivity to detect secondary cosmic ray flux, i.e., we must know their yield function. A previous study found that miniNM and NM64 have slightly different response functions. This work analyzes the observed counting rate ratio (miniNM to NM64) and gives for the first time an useful expression for the yield function of the miniNM. The results found here will allow to interpret the new measurements with this mobile neutron monitor. For comparison, a brief summary of the NM64 yield functions reported by other authors is presented.
Runoff and sediment yield modeling in a medium-size mediterranean watershed
Directory of Open Access Journals (Sweden)
Ossama M.M. Abdelwahab
2013-09-01
Full Text Available The AnnAGNPS model was used to estimate runoff, peak discharge and sediment yield at the event scale in the Carapelle watershed, a Mediterranean medium-size watershed (506 km2 located in Apulia, Southern Italy. The model was calibrated and validated using five years of runoff and sediment yield data measured at a monitoring station located at Ordona – Ponte dei Sauri Bridge. A total of 36 events was used to estimate the output of the model during the period 2007-2011, in comparison to the corresponding observations at the watershed outlet. The model performed well in predicting runoff, as was testified by the high values of the coefficients of efficiency and determination during the validation process. The peak flows predictions were satisfactory especially for the high flow events; the prediction capability of sediment yield was good, even if a slight over-estimation was observed. Finally, the model was used to evaluate the effectiveness of different Management practices (MPs on the watershed (converting wheat to forest, using vegetated streams, crop rotation corn/soybean, no tillage. While the maximum reduction in sediment yield was achieved converting wheat to forest, the best compromises between soil conservation and agriculture resulted to be crop rotations.
Algebraic Lens Distortion Model Estimation
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Luis Alvarez
2010-07-01
Full Text Available A very important property of the usual pinhole model for camera projection is that 3D lines in the scene are projected to 2D lines. Unfortunately, wide-angle lenses (specially low-cost lenses may introduce a strong barrel distortion, which makes the usual pinhole model fail. Lens distortion models try to correct such distortion. We propose an algebraic approach to the estimation of the lens distortion parameters based on the rectification of lines in the image. Using the proposed method, the lens distortion parameters are obtained by minimizing a 4 total-degree polynomial in several variables. We perform numerical experiments using calibration patterns and real scenes to show the performance of the proposed method.
Yield stress of duplex stainless steel specimens estimated using a compound Hall–Petch equation
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Noriaki Hirota, Fuxing Yin, Tsukasa Azuma and Tadanobu Inoue
2010-01-01
Full Text Available In this study, the 0.2% yield stress of duplex stainless steel was evaluated using a compound Hall–Petch equation. The compound Hall–Petch equation was derived from four types of duplex stainless steel, which contained 0.2–64.4 wt% δ-ferrite phase, had different chemical compositions and were annealed at different temperatures. Intragranular yield stress was measured with an ultra-microhardness tester and evaluated with the yield stress model proposed by Dao et al. Grain size, volume fraction and texture were monitored by electron backscattering diffraction measurement. The kγ constant in the compound equation for duplex stainless steel agrees well with that for γ-phase SUS316L steel in the temperature range of 1323–1473 K. The derived compound Hall–Petch equation predicts that the yield stress will be in good agreement with the experimental results for the Cr, Mn, Si, Ni and N solid-solution states. We find that the intragranular yield stress of the δ-phase of duplex stainless steel is rather sensitive to the chemical composition and annealing conditions, which is attributed to the size misfit parameter.
Quantum molecular dynamics approach to estimate spallation yield from + 208Pb reaction at 800 MeV
Indian Academy of Sciences (India)
P K Sarkar; Maitreyee Nandy
2003-10-01
The spallation yield of neutrons and other mass fragments produced in 800 MeV proton induced reaction on 208Pb have been calculated in the framework of quantum molecular dynamics (QMD) model. The energy spectra and angular distribution have been calculated. Also, multiplicity distributions of the emitted neutrons and kinetic energy carried away by them have been estimated and compared with the available experimental data. The agreement is satisfactory. A major contribution to the neutron emission comes from statistical decay of the fragments. For mass and charge distributions of spallation productsthe QMD process gives rise to target-like and projectile-like fragments only.
Dynamic modelling of pectin extraction describing yield and functional characteristics
DEFF Research Database (Denmark)
Andersen, Nina Marianne; Cognet, T.; Santacoloma, P. A.
2017-01-01
A dynamic model of pectin extraction is proposed that describes pectin yield, degree of esterification and intrinsic viscosity. The dynamic model is one dimensional in the peel geometry and includes mass transport of pectin by diffusion and reaction kinetics of hydrolysis, degradation and de-esterification....... The model takes into account the effects of the process conditions such as temperature and acid concentration on extraction kinetics. It is shown that the model describes pectin bulk solution concentration, degree of esterification and intrinsic viscosity in pilot scale extractions from lime peel...
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H. Tonhati
2010-02-01
Full Text Available The aim of this study was analyze the (covariance components and genetic and phenotypic relationships in the following traits: accumulated milk yield at 270 days (MY270, observed until 305 days of lactation; accumulated milk yield at 270 days (MY270/ A and at 305 days (MY305, observed until 335 days of lactation; mozzarella cheese yield (MCY and fat (FP and protein (PP percentage, observed until 335 days of lactation. The (covariance components were estimated by Restricted Maximum Likelihood methodology in analyses single, two and three-traits using animal models. Heritability estimated for MY270, MY270/A, MY305, MCY, FP and PP were 0.22; 0.24, 0.25, 0.14, 0.29 and 0.40 respectively. The genetic correlations between MCY and the variables MY270, MY270/A, MY305, PP and FP was: 0.85; 1.00; 0.89; 0.14 and 0.06, respectively. This way, the selection for the production of milk in long period should increase MCY. However, in the search of animals that produce milk with quality, the genetic parameters suggest that another index should be composed allying these studied traits.
Statistical rice yield modeling using blended MODIS-Landsat based crop phenology metrics in Taiwan
Chen, C. R.; Chen, C. F.; Nguyen, S. T.; Lau, K. V.
2015-12-01
Taiwan is a populated island with a majority of residents settled in the western plains where soils are suitable for rice cultivation. Rice is not only the most important commodity, but also plays a critical role for agricultural and food marketing. Information of rice production is thus important for policymakers to devise timely plans for ensuring sustainably socioeconomic development. Because rice fields in Taiwan are generally small and yet crop monitoring requires information of crop phenology associating with the spatiotemporal resolution of satellite data, this study used Landsat-MODIS fusion data for rice yield modeling in Taiwan. We processed the data for the first crop (Feb-Mar to Jun-Jul) and the second (Aug-Sep to Nov-Dec) in 2014 through five main steps: (1) data pre-processing to account for geometric and radiometric errors of Landsat data, (2) Landsat-MODIS data fusion using using the spatial-temporal adaptive reflectance fusion model, (3) construction of the smooth time-series enhanced vegetation index 2 (EVI2), (4) rice yield modeling using EVI2-based crop phenology metrics, and (5) error verification. The fusion results by a comparison bewteen EVI2 derived from the fusion image and that from the reference Landsat image indicated close agreement between the two datasets (R2 > 0.8). We analysed smooth EVI2 curves to extract phenology metrics or phenological variables for establishment of rice yield models. The results indicated that the established yield models significantly explained more than 70% variability in the data (p-value 0.8), in both cases. The root mean square error (RMSE) and mean absolute error (MAE) used to measure the model accuracy revealed the consistency between the estimated yields and the government's yield statistics. This study demonstrates advantages of using EVI2-based phenology metrics (derived from Landsat-MODIS fusion data) for rice yield estimation in Taiwan prior to the harvest period.
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B. N. Patel, S. S. Jaiwar, N. A. Patel, V. R. Akbari and P. B. Dave
2014-12-01
Full Text Available A line x tester analysis was undertaken to estimates the magnitude of heterosis and dominance deviation in Gossypium hirsutum L. for yield, its components and other matricate characters in 60 test entries including (44 F1s along with 15 parents and 1 standard check hybrid. Analysis of variance indicated the significant difference among the parents and hybrids for all 12 characters studied which revealed existence of variability among the genotypes. Studies revealed that out of 44 cross combinations, only 3 hybrids viz., BC-68-2 x MCU 11, BC-68-2 x AC 738 and BN 1 x Reba-B-50 depicted significant and positive heterosis over standard check hybrid G. Cot. Hy. 12. The hybrid BC-68-2 x MCU 11 exhibited significant positive standard heterosis for seed cotton yield per plant and other attributing characters i.e. total number of bolls per plant, average boll weight, lint yield per plant and lint index. The mean values of potence ratio in all twelve characters suggested that degree of dominance was governed by over dominance genes for the expression of all the characters under study.
Image Based Mango Fruit Detection, Localisation and Yield Estimation Using Multiple View Geometry
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Madeleine Stein
2016-11-01
Full Text Available This paper presents a novel multi-sensor framework to efficiently identify, track, localise and map every piece of fruit in a commercial mango orchard. A multiple viewpoint approach is used to solve the problem of occlusion, thus avoiding the need for labour-intensive field calibration to estimate actual yield. Fruit are detected in images using a state-of-the-art faster R-CNN detector, and pair-wise correspondences are established between images using trajectory data provided by a navigation system. A novel LiDAR component automatically generates image masks for each canopy, allowing each fruit to be associated with the corresponding tree. The tracked fruit are triangulated to locate them in 3D, enabling a number of spatial statistics per tree, row or orchard block. A total of 522 trees and 71,609 mangoes were scanned on a Calypso mango orchard near Bundaberg, Queensland, Australia, with 16 trees counted by hand for validation, both on the tree and after harvest. The results show that single, dual and multi-view methods can all provide precise yield estimates, but only the proposed multi-view approach can do so without calibration, with an error rate of only 1.36% for individual trees.
Gitterman, Y.; Hofstetter, R.
2014-03-01
yield estimator. The delay data of the 2009 shot with IMI explosives, characterized by much higher detonation velocity, are clearly separated from ANFO data, thus indicating a dependence on explosive type. This unique dual Sayarim explosion experiment (August 2009/January 2011), with the strongest GT0 sources since the establishment of the IMS network, clearly demonstrated the most favorable westward/eastward infrasound propagation up to 3,400/6,250 km according to appropriate summer/winter weather pattern and stratospheric wind directions, respectively, and thus verified empirically common models of infrasound propagation in the atmosphere.
Neural Network in Modeling Malaysian Oil Palm Yield
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Zuhaimy Ismail
2011-01-01
Full Text Available Problem statement: Forecasting of palm oil yield has become an important element in the management of oil palm industry for proper planning and decision making. The importance of yield forecasting has led us to explore modeling of palm oil yield for Malaysia using the most recent development of Artificial Neural Network (ANN. The main issue in yield forecasting is to predict the future value with the minimum error. Approach: Artificial neural networks are computing systems containing many interconnected nonlinear neurons, capable of extracting linear and nonlinear regularity in a given data set. It is an artificial intelligence model originally designed to replicate the human brains learning process, a network with many elements or neurons that are connected by communications channels or connectors. The ANN can perform a particular function when certain values are assigned to the connections or weights between elements. In this study, a secondary data set from the Malaysian Palm Oil Board (MPOB on the foliar nutrient composition, fertilizer trials and Fresh Fruit Bunch (FFB yield were taken and analyzed. The foliar nutrient composition variables are the nitrogen N, phosphorus P, potassium K, calcium Ca and magnesium Mg concentration, while the fertilizer trials data are the N, P, K and Mg fertilizers and are measured in kg per palm per year. The foliar composition data was presented in the form of measured values whiles the fertilizer data in ordinal levels, from zero to three. Results: Two experiments were conducted to demonstrate the implementation ANN and for both experiment, the result demonstrated that the number of hidden nodes produces an effect to the overall forecast performance of the ANN architecture. From the first experiment, it shows that the number of runs does not affect the ANN performance, but changing the momentum to learning rates, due to shows a significant improvement in the forecast result. The experimental result will be
A GUIDED SWAT MODEL APPLICATION ON SEDIMENT YIELD MODELING IN PANGANI RIVER BASIN: LESSONS LEARNT
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Preksedis Marco Ndomba
2008-12-01
Full Text Available The overall objective of this paper is to report on the lessons learnt from applying Soil and Water Assessment Tool (SWAT in a well guided sediment yield modelling study. The study area is the upstream of Pangani River Basin (PRB, the Nyumba Ya Mungu (NYM reservoir catchment, located in the North Eastern part of Tanzania. It should be noted that, previous modeling exercises in the region applied SWAT with preassumption that inter-rill or sheet erosion was the dominant erosion type. In contrast, in this study SWAT model application was guided by results of analysis of high temporal resolution of sediment flow data and hydro-meteorological data. The runoff component of the SWAT model was calibrated from six-years (i.e. 1977–1982 of historical daily streamflow data. The sediment component of the model was calibrated using one-year (1977–1988 daily sediment loads estimated from one hydrological year sampling programme (between March and November, 2005 rating curve. A long-term period over 37 years (i.e. 1969–2005 simulation results of the SWAT model was validated to downstream NYM reservoir sediment accumulation information. The SWAT model captured 56 percent of the variance (CE and underestimated the observed daily sediment loads by 0.9 percent according to Total Mass Control (TMC performance indices during a normal wet hydrological year, i.e., between November 1, 1977 and October 31, 1978, as the calibration period. SWAT model predicted satisfactorily the long-term sediment catchment yield with a relative error of 2.6 percent. Also, the model has identified erosion sources spatially and has replicated some erosion processes as determined in other studies and field observations in the PRB. This result suggests that for catchments where sheet erosion is dominant SWAT model may substitute the sediment-rating curve. However, the SWAT model could not capture the dynamics of sediment load delivery in some seasons to the catchment outlet.
A GUIDED SWAT MODEL APPLICATION ON SEDIMENT YIELD MODELING IN PANGANI RIVER BASIN: LESSONS LEARNT
Directory of Open Access Journals (Sweden)
Preksedis M. Ndomba
2008-01-01
Full Text Available The overall objective of this paper is to report on the lessons learnt from applying Soil and Water Assessment Tool (SWAT in a well guided sediment yield modelling study. The study area is the upstream of Pangani River Basin (PRB, the Nyumba Ya Mungu (NYM reservoir catchment, located in the North Eastern part of Tanzania. It should be noted that, previous modeling exercises in the region applied SWAT with preassumption that inter-rill or sheet erosion was the dominant erosion type. In contrast, in this study SWAT model application was guided by results of analysis of high temporal resolution of sediment flow data and hydro-meteorological data. The runoff component of the SWAT model was calibrated from six-years (i.e. 1977¿1982 of historical daily streamflow data. The sediment component of the model was calibrated using one-year (1977-1988 daily sediment loads estimated from one hydrological year sampling programme (between March and November, 2005 rating curve. A long-term period over 37 years (i.e. 1969-2005 simulation results of the SWAT model was validated to downstream NYM reservoir sediment accumulation information. The SWAT model captured 56 percent of the variance (CE and underestimated the observed daily sediment loads by 0.9 percent according to Total Mass Control (TMC performance indices during a normal wet hydrological year, i.e., between November 1, 1977 and October 31, 1978, as the calibration period. SWAT model predicted satisfactorily the long-term sediment catchment yield with a relative error of 2.6 percent. Also, the model has identified erosion sources spatially and has replicated some erosion processes as determined in other studies and field observations in the PRB. This result suggests that for catchments where sheet erosion is dominant SWAT model may substitute the sediment-rating curve. However, the SWAT model could not capture the dynamics of sediment load delivery in some seasons to the catchment outlet.
The effect of flow data resolution on sediment yield estimation and channel design
Rosburg, Tyler T.; Nelson, Peter A.; Sholtes, Joel S.; Bledsoe, Brian P.
2016-07-01
The decision to use either daily-averaged or sub-daily streamflow records has the potential to impact the calculation of sediment transport metrics and stream channel design. Using bedload and suspended load sediment transport measurements collected at 138 sites across the United States, we calculated the effective discharge, sediment yield, and half-load discharge using sediment rating curves over long time periods (median record length = 24 years) with both daily-averaged and sub-daily streamflow records. A comparison of sediment transport metrics calculated with both daily-average and sub-daily stream flow data at each site showed that daily-averaged flow data do not adequately represent the magnitude of high stream flows at hydrologically flashy sites. Daily-average stream flow data cause an underestimation of sediment transport and sediment yield (including the half-load discharge) at flashy sites. The degree of underestimation was correlated with the level of flashiness and the exponent of the sediment rating curve. No consistent relationship between the use of either daily-average or sub-daily streamflow data and the resultant effective discharge was found. When used in channel design, computed sediment transport metrics may have errors due to flow data resolution, which can propagate into design slope calculations which, if implemented, could lead to unwanted aggradation or degradation in the design channel. This analysis illustrates the importance of using sub-daily flow data in the calculation of sediment yield in urbanizing or otherwise flashy watersheds. Furthermore, this analysis provides practical charts for estimating and correcting these types of underestimation errors commonly incurred in sediment yield calculations.
Stellar yields from metal-rich asymptotic giant branch models
Karakas, Amanda I
2016-01-01
We present new theoretical stellar yields and surface abundances for three grids of metal-rich asymptotic giant branch (AGB) models. Post-processing nucleosynthesis results are presented for stellar models with initial masses between 1$M_{\\odot}$ and 7.5$M_{\\odot}$ for $Z=0.007$, and 1$M_{\\odot}$ and 8$M_{\\odot}$ for $Z=0.014$ (solar) and $Z=0.03$. We include stellar surface abundances as a function of thermal pulse on the AGB for elements from C to Bi and for a selection of isotopic ratios for elements up to Fe and Ni (e.g., $^{12}$C/$^{13}$C), which can be obtained from observations of molecules in stars and from the laboratory analysis of meteoritic stardust grains. Ratios of elemental abundances of He/H, C/O, and N/O are also included, which are useful for direct comparison to observations of AGB stars and their progeny including planetary nebulae. The integrated elemental stellar yields are presented for each model in the grid for hydrogen, helium and all stable elements from C to Bi. Yields of Li are al...
FREQUENTIST MODEL AVERAGING ESTIMATION: A REVIEW
Institute of Scientific and Technical Information of China (English)
Haiying WANG; Xinyu ZHANG; Guohua ZOU
2009-01-01
In applications, the traditional estimation procedure generally begins with model selection.Once a specific model is selected, subsequent estimation is conducted under the selected model without consideration of the uncertainty from the selection process. This often leads to the underreporting of variability and too optimistic confidence sets. Model averaging estimation is an alternative to this procedure, which incorporates model uncertainty into the estimation process. In recent years, there has been a rising interest in model averaging from the frequentist perspective, and some important progresses have been made. In this paper, the theory and methods on frequentist model averaging estimation are surveyed. Some future research topics are also discussed.
Development of a Coupled Hydrological/Sediment Yield Model for a Watershed at Regional Level
Rajbhandaril, Narayan; Crosson, William; Tsegaye, Teferi; Coleman, Tommy; Liu, Yaping; Soman, Vishwas
1998-01-01
Development of a hydrologic model for the study of environmental conservation requires a comprehensive understanding of individual-storm affecting hydrologic and sedimentologic processes. The hydrologic models that we are currently coupling are the Simulator for Hydrology and Energy Exchange at the Land Surface (SHEELS) and the Distributed Runoff Model (DRUM). SHEELS runs continuously to estimate surface energy fluxes and sub-surface soil water fluxes, while DRUM operates during and following precipitation events to predict surface runoff and peak flow through channel routing. The lateral re-distribution of surface water determined by DRUM is passed to SHEELS, which then adjusts soil water contents throughout the profile. The model SHEELS is well documented in Smith et al. (1993) and Laymen and Crosson (1995). The model DRUM is well documented in Vieux et al. (1990) and Vieux and Gauer (1994). The coupled hydrologic model, SHEELS/DRUM, does not simulate sedimentologic processes. The simulation of the sedimentologic process is important for environmental conservation planning and management. Therefore, we attempted to develop a conceptual frame work for coupling a sediment yield model with SHEELS/DRUM to estimate individual-storm sediment yield from a watershed at a regional level. The sediment yield model that will be used for this study is the Universal Soil Loss Equation (USLE) with some modifications to enable the model to predict individual-storm sediment yield. The predicted sediment yield does not include wind erosion and erosion caused by irrigation and snow melt. Units used for this study are those given by Foster et al. (1981) for SI units.
Climate Change Modelling and Its Roles to Chinese Crops Yield
Institute of Scientific and Technical Information of China (English)
JU Hui; LIN Er-da; Tim Wheeler; Andrew Challinor; JIANG Shuai
2013-01-01
Climate has been changing in the last fifty years in China and will continue to change regardless any efforts for mitigation. Agriculture is a climate-dependent activity and highly sensitive to climate changes and climate variability. Understanding the interactions between climate change and agricultural production is essential for society stable development of China. The first mission is to fully understand how to predict future climate and link it with agriculture production system. In this paper, recent studies both domestic and international are reviewed in order to provide an overall image of the progress in climate change researches. The methods for climate change scenarios construction are introduced. The pivotal techniques linking crop model and climate models are systematically assessed and climate change impacts on Chinese crops yield among model results are summarized. The study found that simulated productions of grain crop inherit uncertainty from using different climate models, emission scenarios and the crops simulation models. Moreover, studies have different spatial resolutions, and methods for general circulation model (GCM) downscaling which increase the uncertainty for regional impacts assessment. However, the magnitude of change in crop production due to climate change (at 700 ppm CO2 eq correct) appears within ±10%for China in these assessments. In most literatures, the three cereal crop yields showed decline under climate change scenarios and only wheat in some region showed increase. Finally, the paper points out several gaps in current researches which need more studies to shorten the distance for objective recognizing the impacts of climate change on crops. The uncertainty for crop yield projection is associated with climate change scenarios, CO2 fertilization effects and adaptation options. Therefore, more studies on the fields such as free air CO2 enrichment experiment and practical adaptations implemented need to be carried out.
Estimating Stochastic Volatility Models using Prediction-based Estimating Functions
DEFF Research Database (Denmark)
Lunde, Asger; Brix, Anne Floor
In this paper prediction-based estimating functions (PBEFs), introduced in Sørensen (2000), are reviewed and PBEFs for the Heston (1993) stochastic volatility model are derived. The finite sample performance of the PBEF based estimator is investigated in a Monte Carlo study, and compared to the p......In this paper prediction-based estimating functions (PBEFs), introduced in Sørensen (2000), are reviewed and PBEFs for the Heston (1993) stochastic volatility model are derived. The finite sample performance of the PBEF based estimator is investigated in a Monte Carlo study, and compared...... to the performance of the GMM estimator based on conditional moments of integrated volatility from Bollerslev and Zhou (2002). The case where the observed log-price process is contaminated by i.i.d. market microstructure (MMS) noise is also investigated. First, the impact of MMS noise on the parameter estimates from...
Simulation of winter wheat yield and its uncertainty band; A comparison of two crop growth models
Javad Khordadi Varamini, Mohammad; Nassiri Mahallati, Mehdi; Alizadeh, Amin
2016-04-01
In this study, we used the WOFOST and AquaCrop crop growth simulation models to examine crop yield responses to a set of plausible scenarios of climate change in Mashhad region, located in Ghareghom basin, northeast of Iran up to 2040. We selected winter wheat as an indicator crop. Also six AOGCMs including GFCM21, HADCM3, INCM3, IPCM4, MPEH5 and NCCCSM under A2 and B1 emission scenarios are used. LARS-WG statistical method for downscaling is utilized. In the present research, using 7-year observed crop data, the crop models were calibrated and then validated. Evaluation of WOFOST and AquaCrop models confirmed the models are able for simulating the yield of wheat grown in the study area. The results showed that average potential yield of wheat ranged from 3.43 to 8.42 and 2.76 to 6.49 ton.ha-1, in AquaCrop and WOFOST models, respectively. Finally, the uncertainty band due to the six AOGCMs for estimating crop yield is drawn and investigated. These bands show possible changes for the yield in the future period to the past one. It can be concluded the positive effects of climate warming and elevated CO2 concentrations on the production in the studied region.
NEST: A Comprehensive Model for Scintillation Yield in Liquid Xenon
Szydagis, M; Kazkaz, K; Mock, J; Stolp, D; Sweany, M; Tripathi, M; Uvarov, S; Walsh, N; Woods, M
2011-01-01
A comprehensive model for explaining scintillation yield in liquid xenon is introduced. We unify various definitions of work function which abound in the literature and incorporate all available data on electron recoil scintillation yield. This results in a better understanding of electron recoil, and facilitates an improved description of nuclear recoil. An incident gamma energy range of O(1 keV) to O(1 MeV) and electric fields between 0 and O(10 kV/cm) are incorporated into this heuristic model. We show results from a Geant4 implementation, but because the model has a few free parameters, implementation in any simulation package should be simple. We use a quasi-empirical approach, with an objective of improving detector calibrations and performance verification. The model will aid in the design and optimization of future detectors. This model is also easy to extend to other noble elements. In this paper we lay the foundation for an exhaustive simulation code which we call NEST (Noble Element Simulation Tech...
Model for Predicting Climatic Yield of Sugarcane in Nanning City
Institute of Scientific and Technical Information of China (English)
Zhanggui; LAN; Guanghai; LI; Yulian; LIANG; Yuhong; YANG; Xiaoping; LI
2014-01-01
According to spatial distribution of climate disasters in Nanning City and physiological and ecological indicator demands of sugarcane,with the aid of HJ- 1 CCD satellite remote sensing images,basic meteorological data and geographic information data,this paper established the model for predicting climatic yield of sugarcane in Nanning City,to predict total yield of sugarcane in Nanning City. Results indicated that the distribution of sugarcane in Nanning City is greatly influenced by drought. In 2010,regions suffered from drought had sugarcane planting area of 346. 20 km2,accounting for 18.88% of the total sugarcane planting area. The influence of frost disaster on distribution of sugarcane in Nanning City is limited. Regions suffered from frost had sugarcane planting area of only 67. 1 km2,taking up 3.75% of the total sugarcane planting area. In 2010,the climatic yield of sugarcane in Nanning City was 8. 8446 million tons. It proved that the prediction accuracy of the model is up to 90%.
Directory of Open Access Journals (Sweden)
Khanchoul Kamel
2014-01-01
Full Text Available Knowledge of sediment yield and the factors controlling it provides useful information for estimating erosion intensities within river basins. The objective of this study was to build a model from which suspended sediment yield could be estimated from ungauged rivers using computed sediment yield and physical factors. Researchers working on suspended sediment transported by wadis in the Maghreb are usually facing the lack of available data for such river types. Further study of the prediction of sediment transport in these regions and its variability is clearly required. In this work, ANNs were built between sediment yield established from longterm measurement series at gauging stations in Algerian catchments and corresponding basic physiographic parameters such as rainfall, runoff, lithology index, coefficient of torrentiality, and basin area. The proposed Levenberg-Marquardt and Multilayer Perceptron algorithms to train the neural networks of the current research study was based on the feed-forward backpropagation method with combinations of number of neurons in each hidden layer, transfer function, error goal. Additionally, three statistical measurements, namely the root mean square error (RMSE, the coefficient of determination (R², and the efficiency factor (EF have been reported for examining the forecasting accuracy of the developed model. Single plot displays of network outputs with respect to targets for training have provided good performance results and good fitting . Thus, ANNs were a promising method for predicting suspended sediment yield in ungauged Algerian catchments.
Estimating parameters for generalized mass action models with connectivity information
Directory of Open Access Journals (Sweden)
Voit Eberhard O
2009-05-01
Full Text Available Abstract Background Determining the parameters of a mathematical model from quantitative measurements is the main bottleneck of modelling biological systems. Parameter values can be estimated from steady-state data or from dynamic data. The nature of suitable data for these two types of estimation is rather different. For instance, estimations of parameter values in pathway models, such as kinetic orders, rate constants, flux control coefficients or elasticities, from steady-state data are generally based on experiments that measure how a biochemical system responds to small perturbations around the steady state. In contrast, parameter estimation from dynamic data requires time series measurements for all dependent variables. Almost no literature has so far discussed the combined use of both steady-state and transient data for estimating parameter values of biochemical systems. Results In this study we introduce a constrained optimization method for estimating parameter values of biochemical pathway models using steady-state information and transient measurements. The constraints are derived from the flux connectivity relationships of the system at the steady state. Two case studies demonstrate the estimation results with and without flux connectivity constraints. The unconstrained optimal estimates from dynamic data may fit the experiments well, but they do not necessarily maintain the connectivity relationships. As a consequence, individual fluxes may be misrepresented, which may cause problems in later extrapolations. By contrast, the constrained estimation accounting for flux connectivity information reduces this misrepresentation and thereby yields improved model parameters. Conclusion The method combines transient metabolic profiles and steady-state information and leads to the formulation of an inverse parameter estimation task as a constrained optimization problem. Parameter estimation and model selection are simultaneously carried out
WFIRST-AFTA Coronagraph Science Yield Modeling with EXOSIMS
Savransky, Dmitry
2015-01-01
We present and discuss the design details of an extensible, modular, open source software framework called EXOSIMS, which creates end-to-end simulations of space-based exoplanet imaging missions. We motivate the development and baseline implementation of the component parts of this software with models of the WFIRST-AFTA coronagraph, and present initial results of mission simulations for various iterations of the WFIRST-AFTA coronagraph design. We present and discuss two sets of simulations: The first compares the science yield of completely different instruments in the form of early competing coronagraph designs for WFIRST-AFTA. The second set of simulations evaluates the effects of different operating assumptions, specifically the assumed post-processing capabilities and telescope vibration levels. We discuss how these results can guide further instrument development and the expected evolution of science yields.
This is a presentation titled Estimating the Effect of Climate Change on Crop Yields and Farmland Values: The Importance of Extreme Temperatures that was given for the National Center for Environmental Economics
Stellar Models and Yields of Asymptotic Giant Branch Stars
Karakas, Amanda I
2007-01-01
We present stellar yields calculated from detailed models of low and intermediate-mass asymptotic giant branch (AGB) stars. We evolve models with a range of mass from 1 to 6Msun, and initial metallicities from solar to 1/200th of the solar metallicity. Each model was evolved from the zero age main sequence to near the end of the thermally-pulsing AGB phase, and through all intermediate phases including the core He-flash for stars initially less massive than 2.5Msun. For each mass and metallicity, we provide tables containing structural details of the stellar models during the TP-AGB phase, and tables of the stellar yields for 74 species from hydrogen through to sulphur, and for a small number of iron-group nuclei. All tables are available for download. Our results have many applications including use in population synthesis studies and the chemical evolution of galaxies and stellar systems, and for comparison to the composition of AGB and post-AGB stars and planetary nebulae.
Directory of Open Access Journals (Sweden)
Nathaniel K. Newlands
2014-06-01
Full Text Available We present a novel forecasting method for generating agricultural crop yield forecasts at the seasonal and regional-scale, integrating agroclimate variables and remotely-sensed indices. The method devises a multivariate statistical model to compute bias and uncertainty in forecasted yield at the Census of Agricultural Region (CAR scale across the Canadian Prairies. The method uses robust variable-selection to select the best predictors within spatial subregions. Markov-Chain Monte Carlo (MCMC simulation and random forest-tree machine learning techniques are then integrated to generate sequential forecasts through the growing season. Cross-validation of the model was performed by hindcasting/backcasting it and comparing its forecasts against available historical data (1987-2011 for spring wheat (Triticum aestivum L.. The model was also validated for the 2012 growing season by comparing its forecast skill at the CAR, provincial and Canadian Prairie region scales against available statistical survey data. Mean percent departures between wheat yield forecasted were under-estimated by 1-4 % in mid-season and over-estimated by 1 % at the end of the growing season. This integrated methodology offers a consistent, generalizable approach for sequentially forecasting crop yield at the regional-scale. It provides a statistically robust, yet flexible way to concurrently adjust to data-rich and data-sparse situations, adaptively select different predictors of yield to changing levels of environmental uncertainty, and to update forecasts sequentially so as to incorporate new data as it becomes available. This integrated method also provides additional statistical support for assessing the accuracy and reliability of model-based crop yield forecasts in time and space.
An Instructional Cost Estimation Model for the XYZ Community College.
Edmonson, William F.
An enrollment-driven model for estimating instructional costs is presented in this paper as developed by the Western Interstate Commission for Higher Education (WICHE). After stating the principles of the WICHE planning system (i.e., various categories of data are gathered, segmented, and then cross-tabulated against one another to yield certain…
Soltani Largani, Afsaneh; Bakker, M.M.; Veldkamp, A.; Stoorvogel, J.J.
2016-01-01
The need for more comparisons among models is widely recognized. This study aimed to compare three different modelling approaches for their capability to simulate and predict trends and patterns of winter wheat yield in Western Germany. The three modelling approaches included an empirical model, a p
Kalman filter estimation model in flood forecasting
Husain, Tahir
Elementary precipitation and runoff estimation problems associated with hydrologic data collection networks are formulated in conjunction with the Kalman Filter Estimation Model. Examples involve the estimation of runoff using data from a single precipitation station and also from a number of precipitation stations. The formulations demonstrate the role of state-space, measurement, and estimation equations of the Kalman Filter Model in flood forecasting. To facilitate the formulation, the unit hydrograph concept and antecedent precipitation index is adopted in the estimation model. The methodology is then applied to estimate various flood events in the Carnation Creek of British Columbia.
Discrete Choice Models - Estimation of Passenger Traffic
DEFF Research Database (Denmark)
Sørensen, Majken Vildrik
2003-01-01
), which simultaneously finds optimal coefficients values (utility elements) and parameter values (distributed terms) in the utility function. The shape of the distributed terms is specified prior to the estimation; hence, the validity is not tested during the estimation. The proposed method, assesses...... for data, a literature review follows. Models applied for estimation of discrete choice models are described by properties and limitations, and relations between these are established. Model types are grouped into three classes, Hybrid choice models, Tree models and Latent class models. Relations between...... the shape of the distribution from data, by means of repetitive model estimation. In particular, one model was estimated for each sub-sample of data. The shape of distributions is assessed from between model comparisons. This is not to be regarded as an alternative to MSL estimation, rather...
Ozaki, Vitor A.; Ghosh, Sujit K.; Goodwin, Barry K.; Shirota, Ricardo
2009-01-01
This article presents a statistical model of agricultural yield data based on a set of hierarchical Bayesian models that allows joint modeling of temporal and spatial autocorrelation. This method captures a comprehensive range of the various uncertainties involved in predicting crop insurance premium rates as opposed to the more traditional ad hoc, two-stage methods that are typically based on independent estimation and prediction. A panel data set of county-average yield data was analyzed for 290 counties in the State of Paraná (Brazil) for the period of 1990 through 2002. Posterior predictive criteria are used to evaluate different model specifications. This article provides substantial improvements in the statistical and actuarial methods often applied to the calculation of insurance premium rates. These improvements are especially relevant to situations where data are limited. PMID:19890450
Ozaki, Vitor A; Ghosh, Sujit K; Goodwin, Barry K; Shirota, Ricardo
2008-11-01
This article presents a statistical model of agricultural yield data based on a set of hierarchical Bayesian models that allows joint modeling of temporal and spatial autocorrelation. This method captures a comprehensive range of the various uncertainties involved in predicting crop insurance premium rates as opposed to the more traditional ad hoc, two-stage methods that are typically based on independent estimation and prediction. A panel data set of county-average yield data was analyzed for 290 counties in the State of Paraná (Brazil) for the period of 1990 through 2002. Posterior predictive criteria are used to evaluate different model specifications. This article provides substantial improvements in the statistical and actuarial methods often applied to the calculation of insurance premium rates. These improvements are especially relevant to situations where data are limited.
Yin, X.; Belay, D.; Putten, van der P.E.L.; Struik, P.C.
2014-01-01
Maximum quantum yield for leaf CO2 assimilation under limiting light conditions (UCO2LL) is commonly estimated as the slope of the linear regression of net photosynthetic rate against absorbed irradiance over a range of low-irradiance conditions. Methodological errors associated with this estimation
Yin, X.; Belay, D.; Putten, van der P.E.L.; Struik, P.C.
2014-01-01
Maximum quantum yield for leaf CO2 assimilation under limiting light conditions (UCO2LL) is commonly estimated as the slope of the linear regression of net photosynthetic rate against absorbed irradiance over a range of low-irradiance conditions. Methodological errors associated with this estimation
Energy Technology Data Exchange (ETDEWEB)
Shevenell, L.A. [Univ. of Nevada, Reno, NV (United States). Nevada Bureau of Mines and Geology
1994-11-01
Hydrograph analysis techniques have been well developed for hydrographs obtained from streams and springs, where data are cast in terms of total discharge. The data obtained from well hydrographs provide water level versus time; hence, a method of hydrograph analysis is required for situations in which only water level data are available. It is hypothesized here that three segments on a recession curve from wells in a karst aquifer represent drainage from three types of storage: conduit (C), fracture (F), and matrix (M). Hydrographs from several wells in a karst aquifer at the U.S. Department of Energy Oak Ridge Y-12 Plant are used to estimate the specific yields (S{sub y}) associated with each portion of the aquifer (C, F, M), as well as continuum transmissivities (T). Data from three short injection tests at one well indicate continuum T at this well bore is {approximately} 5m{sup 2}/d, and tests at numerous other wells in the aquifer yield results between 1 and 7 M{sup 2}/d. The T estimated with well hydrographs from two storm events indicates a T of 9.8 m{sup 2}2/d. Well developed conduit systems in which water levels in wells show a flashy response typically show S{sub y} values of 1{times}10{sup -4}, 1{times}10{sup -3}, and 3{times}10{sup -3}, for C, F, and M. Less well developed conduit areas show more nearly equal S{sub y} values (8.6{times}10{sup -4}, 1.3{times}10{sup -3}, 3{times}10{sup -3}). Areas with no evidence for the presence of conduits have only one, or in some cases two, slopes on the recession curve. In these cases, water level responses are slow. Recession curves with a single slope represent drainage from only the lower T matrix. Those with two slopes have an additional, more rapid response, segment on the recession curve, which represents drainage from the higher T, lower S{sub y}, fractures in the system.
Using operational data to estimate the reliable yields of water-supply wells
Misstear, Bruce D. R.; Beeson, Sarah
The reliable yield of a water-supply well depends on many different factors, including the properties of the well and the aquifer; the capacities of the pumps, raw-water mains, and treatment works; the interference effects from other wells; and the constraints imposed by ion licences, water quality, and environmental issues. A relatively simple methodology for estimating reliable yields has been developed that takes into account all of these factors. The methodology is based mainly on an analysis of water-level and source-output data, where such data are available. Good operational data are especially important when dealing with wells in shallow, unconfined, fissure-flow aquifers, where actual well performance may vary considerably from that predicted using a more analytical approach. Key issues in the yield-assessment process are the identification of a deepest advisable pumping water level, and the collection of the appropriate well, aquifer, and operational data. Although developed for water-supply operators in the United Kingdom, this approach to estimating the reliable yields of water-supply wells using operational data should be applicable to a wide range of hydrogeological conditions elsewhere. Résumé La productivité d'un puits capté pour l'adduction d'eau potable dépend de différents facteurs, parmi lesquels les propriétés du puits et de l'aquifère, la puissance des pompes, le traitement des eaux brutes, les effets d'interférences avec d'autres puits et les contraintes imposées par les autorisations d'exploitation, par la qualité des eaux et par les conditions environnementales. Une méthodologie relativement simple d'estimation de la productivité qui prenne en compte tous ces facteurs a été mise au point. Cette méthodologie est basée surtout sur une analyse des données concernant le niveau piézométrique et le débit de prélèvement, quand ces données sont disponibles. De bonnes données opérationnelles sont particuli
BioSTAR, a New Biomass and Yield Modeling Software
Kappas, M.; Degener, J.; Bauboeck, R.
2013-12-01
, sorghum, sunflower and, sugar beet. Calibrations for rye grass, cup plant, poplar and willow still need to be performed. A Comparison of simulated and observed biomass yields for sites in Lower Saxony has rendered good results with errors (RMSE) ranging from below 10% (winter wheat, n= 102) and 18.6 % (sunflower, n=8) (Bauböck, unpublished). Because simulations can be made with limited soil data (soil type or texture class) and a limited climate data set (smallest set can be either monthly means of precipitation, temperature and, radiation or precipitation, temperature and, humidity) and the software is capable of processing large datasets, the model appears to be a promising tool for mid or large scale biomass and yield predictions. Up to now the model has only been used for yield predictions with current state climate and climate change scenarios in Lower Saxony, but comparisons with output data of the model AquaCrop (Steduto, et al., 2009) have shown good performance in arid and semi-arid climates (Bauböck, 2013).
Blainey, J.B.; Ferre, T. P. A.; Cordova, J.T.
2007-01-01
Pumping of an unconfined aquifer can cause local desaturation detectable with high-resolution gravimetry. A previous study showed that signal-to-noise ratios could be predicted for gravity measurements based on a hydrologic model. We show that although changes should be detectable with gravimeters, estimations of hydraulic conductivity and specific yield based on gravity data alone are likely to be unacceptably inaccurate and imprecise. In contrast, a transect of low-quality drawdown data alone resulted in accurate estimates of hydraulic conductivity and inaccurate and imprecise estimates of specific yield. Combined use of drawdown and gravity data, or use of high-quality drawdown data alone, resulted in unbiased and precise estimates of both parameters. This study is an example of the value of a staged assessment regarding the likely significance of a new measurement method or monitoring scenario before collecting field data. Copyright 2007 by the American Geophysical Union.
Grogan, D. S.; Zhang, F.; Li, C.; Frolking, S.
2011-12-01
Irrigated agriculture is an important part of China's population and economic growth. Currently, water needed to irrigate crops can be drawn from surface runoff, streams, reservoirs, renewable groundwater, or fossil groundwater. Fossil groundwater is not sustainable over long time periods, and therefore regions that rely on this source for irrigation water could face water shortages in the future, and may already be experiencing water stress today. This study uses two models, one to calculate water balance and the other to simulate crop yields, to address the question: how much unsustainable water is currently used for irrigation in China, and by how much would the use of only sustainable water reduce crop yields? The amount of sustainable water available for irrigation is determined using the WBMplus model. This model uses precipitation and temperature drivers, along with gridded data of soils, cropping, and irrigation, to simulate soil moisture, potential evapotranspiration, surface runoff, stream flow, and reservoir storage, in 30 min grid cells. The model also computes demand for irrigation water, and the capacity of various sources to supply that demand in each grid cell. The DNDC model, which has been evaluated against crop yield in a number of studies in China, is used to predict crop yield for ~50 crop types involved in ~100 cropping systems across China, under zero and full irrigation for each grid cell. Yields using only the sustainable irrigation water capacity will be calculated by weighing the zero and full irrigation yields based on the water availability results of WBMplus for each grid cell. With this methodology, we estimate how national-scale food production would be changed by limiting agricultural water use.
Directory of Open Access Journals (Sweden)
Emanuela Tullo
2014-12-01
Full Text Available In tropical environments, lactation curves with lower peaks and higher persistency (PS might be desirable from both an economical and a physiological point of view. The objective of this study was to obtain genetic parameters for test day (TD yields, and PS for the tropical breed Carora and to compare these with results from a standard 305-d-milk yield animal model. Four random regression models (RRM were used on a dataset composed of 95,606 TD records collected in Venezuela and tested to find the best fitting the data. Estimated daily heritabilities for milk yields ranged from 0.21 to 0.30, with the lowest values around the peak of lactation. Lactation repeatabilities ranged from 0.50 to 0.56. Correlations between the breeding values obtained with the RRM and the lactation model currently used in Venezuela [single trait Animal Model (stAM] are quite high and positive (Pearson correlation=0.71 and Spearman correlation=0.72. Correlations between PS and 305-d-milk yield estimated breeding values (EBV ranged from -0.18 (PS as the deviation of daily productions in the interval 50-279 days in milk from a point at the end of lactation to 0.52 (PS as EBV difference between the second and the first stage of lactation. The use of PS indexes accounting for milk yield may allow the selection of individuals able to express their potential genetic values in tropical environment, without incurring in excessive heat stress losses.
Directory of Open Access Journals (Sweden)
Julien Morel
2014-07-01
Full Text Available Estimating sugarcane biomass is difficult to achieve when working with highly variable spatial distributions of growing conditions, like on Reunion Island. We used a dataset of in-farm fields with contrasted climatic conditions and farming practices to compare three methods of yield estimation based on remote sensing: (1 an empirical relationship method with a growing season-integrated Normalized Difference Vegetation Index NDVI, (2 the Kumar-Monteith efficiency model, and (3 a forced-coupling method with a sugarcane crop model (MOSICAS and satellite-derived fraction of absorbed photosynthetically active radiation. These models were compared with the crop model alone and discussed to provide recommendations for a satellite-based system for the estimation of yield at the field scale. Results showed that the linear empirical model produced the best results (RMSE = 10.4 t∙ha−1. Because this method is also the simplest to set up and requires less input data, it appears that it is the most suitable for performing operational estimations and forecasts of sugarcane yield at the field scale. The main limitation is the acquisition of a minimum of five satellite images. The upcoming open-access Sentinel-2 Earth observation system should overcome this limitation because it will provide 10-m resolution satellite images with a 5-day frequency.
DEFF Research Database (Denmark)
Hagnestam-Nielsen, Christel; Østergaard, Søren
2009-01-01
losses was investigated by comparing the results obtained using the potential yield of mastitic cows, had they not developed CM, with those obtained using the yield of non-mastitic cows. The yearly maximum avoidable cost of CM at herd level was estimated at €14 504, corresponding to 6.9% of the net...... and the conventional modelling strategy, with the exception of the cost per case of CM. Similarities between the results obtained using the two methods were particularly evident when the mastitic cows' own yield level, had they not developed CM, was used as the reference for production in healthy cows when yield...
Directory of Open Access Journals (Sweden)
D. K. Henze
2008-05-01
Full Text Available Formation of SOA from the aromatic species toluene, xylene, and, for the first time, benzene, is added to a global chemical transport model. A simple mechanism is presented that accounts for competition between low and high-yield pathways of SOA formation, wherein secondary gas-phase products react further with either nitric oxide (NO or hydroperoxy radical (HO_{2} to yield semi- or non-volatile products, respectively. Aromatic species yield more SOA when they react with OH in regions where the [NO]/[HO_{2}] ratios are lower. The SOA yield thus depends upon the distribution of aromatic emissions, with biomass burning emissions being in areas with lower [NO]/[HO_{2}] ratios, and the reactivity of the aromatic with respect to OH, as a lower initial reactivity allows transport away from industrial source regions, where [NO]/[HO_{2}] ratios are higher, to more remote regions, where this ratio is lower and, hence, the ultimate yield of SOA is higher. As a result, benzene is estimated to be the most important aromatic species with regards to global formation of SOA, with a total production nearly equal that of toluene and xylene combined. Global production of SOA from aromatic sources via the mechanisms identified here is estimated at 3.5 Tg/yr, resulting in a global burden of 0.08 Tg, twice as large as previous estimates. The contribution of these largely anthropogenic sources to global SOA is still small relative to biogenic sources, which are estimated to comprise 90% of the global SOA burden, about half of which comes from isoprene. Uncertainty in these estimates owing to factors ranging from the atmospheric relevance of chamber conditions to model deficiencies result in an estimated range of SOA production from aromatics of 2–12 Tg/yr. Though this uncertainty range affords a significant anthropogenic contribution to global SOA, it is evident from comparisons to recent observations that additional pathways for
Directory of Open Access Journals (Sweden)
Daniel De la Torre
2007-01-01
Full Text Available The approach developed by Fuhrer in 1995 to estimate wheat yield losses induced by ozone and modulated by the soil water content (SWC was applied to the data on Catalonian wheat yields. The aim of our work was to apply this approach and adjust it to Mediterranean environmental conditions by means of the necessary corrections. The main objective pursued was to prove the importance of soil water availability in the estimation of relative wheat yield losses as a factor that modifies the effects of tropospheric ozone on wheat, and to develop the algorithms required for the estimation of relative yield losses, adapted to the Mediterranean environmental conditions. The results show that this is an easy way to estimate relative yield losses just using meteorological data, without using ozone fluxes, which are much more difficult to calculate. Soil water availability is very important as a modulating factor of the effects of ozone on wheat; when soil water availability decreases, almost twice the amount of accumulated exposure to ozone is required to induce the same percentage of yield loss as in years when soil water availability is high.
A regression model to estimate regional ground water recharge.
Lorenz, David L; Delin, Geoffrey N
2007-01-01
A regional regression model was developed to estimate the spatial distribution of ground water recharge in subhumid regions. The regional regression recharge (RRR) model was based on a regression of basin-wide estimates of recharge from surface water drainage basins, precipitation, growing degree days (GDD), and average basin specific yield (SY). Decadal average recharge, precipitation, and GDD were used in the RRR model. The RRR estimates were derived from analysis of stream base flow using a computer program that was based on the Rorabaugh method. As expected, there was a strong correlation between recharge and precipitation. The model was applied to statewide data in Minnesota. Where precipitation was least in the western and northwestern parts of the state (50 to 65 cm/year), recharge computed by the RRR model also was lowest (0 to 5 cm/year). A strong correlation also exists between recharge and SY. SY was least in areas where glacial lake clay occurs, primarily in the northwest part of the state; recharge estimates in these areas were in the 0- to 5-cm/year range. In sand-plain areas where SY is greatest, recharge estimates were in the 15- to 29-cm/year range on the basis of the RRR model. Recharge estimates that were based on the RRR model compared favorably with estimates made on the basis of other methods. The RRR model can be applied in other subhumid regions where region wide data sets of precipitation, streamflow, GDD, and soils data are available.
COMPARISON OF THREE MODELS TO PREDICT ANNUAL SEDIMENT YIELD IN CARONI RIVER BASIN, VENEZUELA
Directory of Open Access Journals (Sweden)
Edilberto Guevara-Pérez
2007-06-01
Full Text Available Caroní River Basin is located in the south-eastern part of Venezuela; with an area of 92.000 km², 40% of which belongs to the main affluent, the Paragua River. Caroní basin is the source of 66% of energy of the country. About 85% of the hydro electrical energy is generated in Guri reservoir located in the lower part of the watershed. To take provisions to avoid the reservoir silting it is very important the study of sediment yield of the basin. In this paper result of three empirical sediment yield models: LangbeinSchumm, Universal Soil Loss Equation-USLE and Poesen, are compared with observed data from five sub basins with records of twenty to thirty years. Men values of sediment yield for low, middle and upper Caroní are of 27, 76, 17 t/km²-year, respectively; and 46 and 78 t/km²-year for low and upper Paragua sub basins are. Standard errors of estimates vary between 13 and 29 for Langbein-Schumm model; between 8 and 32 for USLE procedure; and between 9 and 79, for Poesen model. Sediment yield predictions by Langbein-Schumm model seem to the best in Caroní basin.
COMPARISON OF THREE MODELS TO PREDICT ANNUAL SEDIMENT YIELD IN CARONI RIVER BASIN, VENEZUELA
Directory of Open Access Journals (Sweden)
Edilberto Guevara-Pérez
2007-01-01
Full Text Available Caroní River Basin is located in the south-eastern part of Venezuela; with an area of 92.000 km2, 40% of which belongs to the main affluent, the Paragua River. Caroní basin is the source of 66% of energy of the country. About 85% of the hydro electrical energy is generated in Guri reservoir located in the lower part of the watershed. To take provisions to avoid the reservoir silting it is very important the study of sediment yield of the basin. In this paper result of three empirical sediment yield models: Langbein- Schumm, Universal Soil Loss Equation-USLE and Poesen, are compared with observed data from five sub basins with records of twenty to thirty years. Men values of sediment yield for low, middle and upper Caroní are of 27, 76, 17 t/km2-year, respectively; and 46 and 78 t/km2-year for low and upper Paragua sub basins are. Standard errors of estimates vary between 13 and 29 for Langbein-Schumm model; between 8 and 32 for USLE procedure; and between 9 and 79, for Poesen model. Sediment yield predictions by Langbein-Schumm model seem to the best in Caroní basin.
Outlier Rejecting Multirate Model for State Estimation
Institute of Scientific and Technical Information of China (English)
无
2006-01-01
Wavelet transform was introduced to detect and eliminate outliers in time-frequency domain. The outlier rejection and multirate information extraction were initially incorporated by wavelet transform, a new outlier rejecting multirate model for state estimation was proposed. The model is applied to state estimation with interacting multiple model, as the outlier is eliminated and more reasonable multirate information is extracted, the estimation accuracy is greatly enhanced. The simulation results prove that the new model is robust to outliers and the estimation performance is significantly improved.
Directory of Open Access Journals (Sweden)
Lisa J. Samuelson
2012-12-01
Full Text Available Longleaf pine (Pinus palustris Mill. is an important tree species of the southeast U.S. Currently there is no comprehensive stand-level growth and yield model for the species. The model system described here estimates site index (SI if dominant height (Hdom and stand age are known (inversely, the model can project Hdom at any given age if SI is known. The survival (N equation was dependent on stand age and Hdom, predicting greater mortality on stands with larger Hdom. The function that predicts stand basal area (BA for unthinned stands was dependent on N and Hdom. For thinned stands BA was predicted with a competition index that was dependent on stand age. The function that best predicted stand stem volume (outside or inside bark was dependent on BA and Hdom. All functions performed well for a wide range of stand ages and productivity, with coefficients of determination ranging between 0.946 (BA and 0.998 (N. We also developed equations to estimate merchantable volume yield consisting of different combinations of threshold diameter at breast height and top diameter for longleaf pine stands. The equations presented in this study performed similarly or slightly better than other reported models to estimate future N, Hdom and BA. The system presented here provides important new tools for supporting future longleaf pine management and research.
Statistical modelling and deconvolution of yield meter data
DEFF Research Database (Denmark)
Tøgersen, Frede Aakmann; Waagepetersen, Rasmus Plenge
Data for yield maps can be obtained from modern combine harvesters equipped with a differential global positioning system and a yield monitoring system. Due to delay and smoothing effects in the combine harvester the recorded yield data for a location represents a shifted weighted average of yiel...
Institute of Scientific and Technical Information of China (English)
Sher Khan Panhwar; LIU Qun; Fozia Khan; Pirzada J. A. Siddiqui
2012-01-01
Using surplus production model packages of ASPIC (a stock-production model incorporating covariates) and CEDA (Catch effort data analysis),we analyzed the catch and effort data of Sillago sihama fishery in Pakistan.ASPIC estimates the parameters of MSY(maximum sustainable yield),Fmsy (fishing mortality),q (catchability coefficient),K(carrying capacity or unexploited biomass) and Bl/K(maximum sustainable yield over initial biomass).The estimated non-bootstrapped value of MSY based on logistic was 598 t and that based on the Fox model was 415 t,which showed that the Fox model estimation was more conservative than that with the logistic model.The R2 with the logistic model (0.702) is larger than that with the Fox model (0.541),which indicates a better fit.The coefficient of variation (cv) of the estimated MSY was about 0.3,except for a larger value 88.87 and a smaller value of 0.173.In contrast to the ASPIC results,the R2 with the Fox model (0.651-0.692) was larger than that with the Schaefer model (0.435-0.567),indicating a better fit.The key parameters of CEDA are:MSY,K,q,and r (intrinsic growth),and the three error assumptions in using the models are normal,log normal and gamma.Parameter estimates from the Schaefer and Pella-Tomlinson models were similar.The MSY estimations from the above two models were 398 t,549 t and 398 t for normal,log-normal and gamma error distributions,respectively.The MSY estimates from the Fox model were 381 t,366 t and 366 t for the above three error assumptions,respectively.The Fox model estimates were smaller than those for the Schaefer and the Pella-Tomlinson models.In the light of the MSY estimations of 415 t from ASPIC for the Fox model and 381 t from CEDA for the Fox model,MSY for S.sihama is about 400 t.As the catch in 2003was 401 t,we would suggest the fishery should be kept at the current level.Production models used here depend on the assumption that CPUE(catch per unit effort) data used in the study can reliably quantify
Sharma-Poudyal, D; Chen, X M
2011-05-01
Climatic variation in the U.S. Pacific Northwest (PNW) affects epidemics of wheat stripe rust caused by Puccinia striiformis f. sp. tritici. Previous models only estimated disease severity at the flowering stage, which may not predict the actual yield loss. To identify weather factors correlated to stripe rust epidemics and develop models for predicting potential yield loss, correlation and regression analyses were conducted using weather parameters and historical yield loss data from 1993 to 2007 for winter wheat and 1995 to 2007 for spring wheat. Among 1,376 weather variables, 54 were correlated to yield loss of winter wheat and 18 to yield loss of spring wheat. Among the seasons, winter temperature variables were more highly correlated to wheat yield loss than the other seasons. The sum of daily temperatures and accumulated negative degree days of February were more highly correlated to winter wheat yield loss than the other monthly winter variables. In addition, the number of winter rainfall days was found correlated with yield loss. Six yield loss models were selected for each of winter and spring wheats based on their better correlation coefficients, time of weather data availability during the crop season, and better performance in validation tests. Compared with previous models, the new system of using a series of the selected models has advantages that should make it more suitable for forecasting and managing stripe rust in the major wheat growing areas in the U.S. PNW, where the weather conditions have become more favorable to stripe rust.
Canaza-Cayo, Ali William; Lopes, Paulo Sávio; da Silva, Marcos Vinicius Gualberto Barbosa; de Almeida Torres, Robledo; Martins, Marta Fonseca; Arbex, Wagner Antonio; Cobuci, Jaime Araujo
2015-10-01
A total of 32,817 test-day milk yield (TDMY) records of the first lactation of 4,056 Girolando cows daughters of 276 sires, collected from 118 herds between 2000 and 2011 were utilized to estimate the genetic parameters for TDMY via random regression models (RRM) using Legendre's polynomial functions whose orders varied from 3 to 5. In addition, nine measures of persistency in milk yield (PSi) and the genetic trend of 305-day milk yield (305MY) were evaluated. The fit quality criteria used indicated RRM employing the Legendre's polynomial of orders 3 and 5 for fitting the genetic additive and permanent environment effects, respectively, as the best model. The heritability and genetic correlation for TDMY throughout the lactation, obtained with the best model, varied from 0.18 to 0.23 and from -0.03 to 1.00, respectively. The heritability and genetic correlation for persistency and 305MY varied from 0.10 to 0.33 and from -0.98 to 1.00, respectively. The use of PS7 would be the most suitable option for the evaluation of Girolando cattle. The estimated breeding values for 305MY of sires and cows showed significant and positive genetic trends. Thus, the use of selection indices would be indicated in the genetic evaluation of Girolando cattle for both traits.
Efficient Estimation in Heteroscedastic Varying Coefficient Models
Directory of Open Access Journals (Sweden)
Chuanhua Wei
2015-07-01
Full Text Available This paper considers statistical inference for the heteroscedastic varying coefficient model. We propose an efficient estimator for coefficient functions that is more efficient than the conventional local-linear estimator. We establish asymptotic normality for the proposed estimator and conduct some simulation to illustrate the performance of the proposed method.
Estimating Canopy Dark Respiration for Crop Models
Monje Mejia, Oscar Alberto
2014-01-01
Crop production is obtained from accurate estimates of daily carbon gain.Canopy gross photosynthesis (Pgross) can be estimated from biochemical models of photosynthesis using sun and shaded leaf portions and the amount of intercepted photosyntheticallyactive radiation (PAR).In turn, canopy daily net carbon gain can be estimated from canopy daily gross photosynthesis when canopy dark respiration (Rd) is known.
Yield-Ensuring DAC-Embedded Opamp Design Based on Accurate Behavioral Model Development
Jang, Yeong-Shin; Nguyen, Hoai-Nam; Ryu, Seung-Tak; Lee, Sang-Gug
An accurate behavioral model of a DAC-embedded opamp (DAC-opamp) is developed for a yield-ensuring LCD column driver design. A lookup table for the V-I curve of the unit differential pair in the DAC-opamp is extracted from a circuit simulation and is later manipulated through a random error insertion. Virtual ground assumption simplifies the output voltage estimation algorithm. The developed behavioral model of a 5-bit DAC-opamp shows good agreement with the circuit level simulation with less than 5% INL difference.
Maize Yield Estimation Through the Simulation of Radiation Penetration Into the Canopy.
Musembi, David Kasina
It is essential for planning purposes to be able to assess or predict the output of vital crops in order to develop strategies to cope with shortfalls or surplusses with sufficient lead time. This is usually done by developing yield prediction models based on well specified production factors. Crop yield results from the interaction among plant, physical and environmental factors. Radiation is a key environmental factor in this regard and the principal driver of the biological system. When all the other conditions are optimum, productivity is a function of the available energy and the size of the plant surface able to trap it. However, the distribution of radiation within the plant canopy is uncertain. The objectives of this study were to determine whether the light extinction coefficients of three maize hybrids of different morphological description are different; whether these coefficients change with time during the growing season; and attempt to establish the nature of the relationships between biomass accumulation and the observed morphological plant characters. The extinction coefficients of the three hybrids were not significantly different. They increased with vegetative growth to reach maxima at anthesis and decreased subsequently as the crops approached physiological maturity. These coefficients were used to fit Beer-Bougher type regressions for predicting light penetration into the various canopy levels. There were significant differences in light penetration, among the hybrids, during the growing season as well as within each day. The largest leaf area (index) and the highest light absorption occurred at the top layer of the canopies. Moreover, the highest light penetration into the canopies does not appear to take place at zenith but seems to occur when the solar elevation and leaf angles reach certain critical values. Leaf area index (LAI) had the greatest effect on light penetration during the growing season while the effect of leaf angle was
Directory of Open Access Journals (Sweden)
Syeda Refat Sultana
2014-01-01
Full Text Available For estimation of grain yield in wheat, Normalized Difference Vegetation Index (NDVI is considered as a potential screening tool. Field experiments were conducted to scrutinize the response of NDVI to yield behavior of different wheat cultivars and nitrogen fertilization at agronomic research area, University of Agriculture Faisalabad (UAF during the two years 2008-09 and 2009-10. For recording the value of NDVI, Green seeker (Handheld-505 was used. Split plot design was used as experimental model in, keeping four nitrogen rates (N1= 0 kg ha−1, N2= 55 kg ha−1, N3=110 kg ha−1, and N4= 220 kg ha−1 in main plots and ten wheat cultivars (Bakkhar-2001, Chakwal-50, Chakwal-97, Faisalabad-2008, GA-2002, Inqlab-91, Lasani-2008, Miraj-2008, Sahar-2006, and Shafaq-2006 in subplots with four replications. Impact of nitrogen and difference between cultivars were forecasted through NDVI. The results suggested that nitrogen treatment N4 (220 kg ha−1 and cultivar Faisalabad-2008 gave maximum NDVI value (0.85 at grain filling stage among all treatments. The correlation among NDVI at booting, grain filling, and maturity stages with grain yield was positive (R2 = 0.90; R2 = 0.90; R2 = 0.95, respectively. So, booting, grain filling, and maturity can be good depictive stages during mid and later growth stages of wheat crop under agroclimatic conditions of Faisalabad and under similar other wheat growing environments in the country.
PARAMETER ESTIMATION OF ENGINEERING TURBULENCE MODEL
Institute of Scientific and Technical Information of China (English)
钱炜祺; 蔡金狮
2001-01-01
A parameter estimation algorithm is introduced and used to determine the parameters in the standard k-ε two equation turbulence model (SKE). It can be found from the estimation results that although the parameter estimation method is an effective method to determine model parameters, it is difficult to obtain a set of parameters for SKE to suit all kinds of separated flow and a modification of the turbulence model structure should be considered. So, a new nonlinear k-ε two-equation model (NNKE) is put forward in this paper and the corresponding parameter estimation technique is applied to determine the model parameters. By implementing the NNKE to solve some engineering turbulent flows, it is shown that NNKE is more accurate and versatile than SKE. Thus, the success of NNKE implies that the parameter estimation technique may have a bright prospect in engineering turbulence model research.
Analysis of Empirical Software Effort Estimation Models
Basha, Saleem
2010-01-01
Reliable effort estimation remains an ongoing challenge to software engineers. Accurate effort estimation is the state of art of software engineering, effort estimation of software is the preliminary phase between the client and the business enterprise. The relationship between the client and the business enterprise begins with the estimation of the software. The credibility of the client to the business enterprise increases with the accurate estimation. Effort estimation often requires generalizing from a small number of historical projects. Generalization from such limited experience is an inherently under constrained problem. Accurate estimation is a complex process because it can be visualized as software effort prediction, as the term indicates prediction never becomes an actual. This work follows the basics of the empirical software effort estimation models. The goal of this paper is to study the empirical software effort estimation. The primary conclusion is that no single technique is best for all sit...
MODELLING GROWTH AND YIELD OF Pinus taeda L. USING DIFUSION PROCESS
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Rozane de Loyola Eisfeld
2005-06-01
Full Text Available This work tested a methodology for growth and yield modeling. The diffusion process is not yet widely used incommercial plantations in Brazil, but it can provide predictions comparable to others methodologies, producing satisfactory resultsto simulate growth and yield. For this purpose, 325 permanent samples established in unthinned Pinus taeda L. (loblolly pine standsowned by the International Paper of Brazil Co were used. The diffusion process methodology consists in connecting growthincrement and mortality models in Kolmogorov equation . Seventy sample plots were randomly chosen in order to make thecomparison among the observed and predicted values. In general, the diffusion process provided satisfactory estimates of number oftrees, basal area per hectare and stem volume.
Modeling hypoxia in the Chesapeake Bay: Ensemble estimation using a Bayesian hierarchical model
Stow, Craig A.; Scavia, Donald
2009-02-01
Quantifying parameter and prediction uncertainty in a rigorous framework can be an important component of model skill assessment. Generally, models with lower uncertainty will be more useful for prediction and inference than models with higher uncertainty. Ensemble estimation, an idea with deep roots in the Bayesian literature, can be useful to reduce model uncertainty. It is based on the idea that simultaneously estimating common or similar parameters among models can result in more precise estimates. We demonstrate this approach using the Streeter-Phelps dissolved oxygen sag model fit to 29 years of data from Chesapeake Bay. Chesapeake Bay has a long history of bottom water hypoxia and several models are being used to assist management decision-making in this system. The Bayesian framework is particularly useful in a decision context because it can combine both expert-judgment and rigorous parameter estimation to yield model forecasts and a probabilistic estimate of the forecast uncertainty.
On parameter estimation in deformable models
DEFF Research Database (Denmark)
Fisker, Rune; Carstensen, Jens Michael
1998-01-01
Deformable templates have been intensively studied in image analysis through the last decade, but despite its significance the estimation of model parameters has received little attention. We present a method for supervised and unsupervised model parameter estimation using a general Bayesian...... method is based on a modified version of the EM algorithm. Experimental results for a deformable template used for textile inspection are presented...
Parameter Estimation, Model Reduction and Quantum Filtering
Chase, Bradley A
2009-01-01
This dissertation explores the topics of parameter estimation and model reduction in the context of quantum filtering. Chapters 2 and 3 provide a review of classical and quantum probability theory, stochastic calculus and filtering. Chapter 4 studies the problem of quantum parameter estimation and introduces the quantum particle filter as a practical computational method for parameter estimation via continuous measurement. Chapter 5 applies these techniques in magnetometry and studies the estimator's uncertainty scalings in a double-pass atomic magnetometer. Chapter 6 presents an efficient feedback controller for continuous-time quantum error correction. Chapter 7 presents an exact model of symmetric processes of collective qubit systems.
Wang, Dan; LeBauer, David; Dietze, Michael
2013-06-01
Hybrid poplar (Populus spp.) is an important biomass crop being evaluated for cellulosic ethanol production. Predictions of poplar growth, rotation period, and soil carbon sequestration under various growing conditions, soils, and climates are critical for farmers and managers planning to establish short-rotation forestry (SRF) plantations. In this study, we used an ecoinformatics workflow, the Predictive Ecosystem Analyzer (PEcAn), to integrate literature data and field measurements into the Ecosystem Demography 2 (ED2) model to estimate yield potential of poplar plantations. Within PEcAn 164 records of seven different traits from the literature were assimilated using a Bayesian meta-analysis. Next, variance decomposition identified seven variables for further constraint that contributed > 80% to the uncertainty in modeled yields: growth respiration, dark respiration, quantum efficiency, mortality coefficient, water conductance, fine-root allocation, and root turnover rate. Assimilation of observed yields further constrained uncertainty in model parameters (especially dark respiration and root turnover rate) and biomass estimates. Additional measurements of growth respiration, mortality, water conductance, and quantum efficiency would provide the most efficient path toward further constraint of modeled yields. Modeled validation demonstrated that ED2 successfully captured the interannual and spatial variability of poplar yield observed at nine independent sites. Site-level analyses were conducted to estimate the effect of land use change to SRF poplar on soil C sequestration compared to alternate land uses. These suggest that poplar plantations became a C sink within 18 years of conversion from corn production or existing forest. Finally, poplar yields were estimated for the contiguous United States at a half degree resolution in order to determine potential productivity, estimate the optimal rotation period, and compare poplar to perennial grass yields. This
Mineral resources estimation based on block modeling
Bargawa, Waterman Sulistyana; Amri, Nur Ali
2016-02-01
The estimation in this paper uses three kinds of block models of nearest neighbor polygon, inverse distance squared and ordinary kriging. The techniques are weighting scheme which is based on the principle that block content is a linear combination of the grade data or the sample around the block being estimated. The case study in Pongkor area, here is gold-silver resource modeling that allegedly shaped of quartz vein as a hydrothermal process of epithermal type. Resources modeling includes of data entry, statistical and variography analysis of topography and geological model, the block model construction, estimation parameter, presentation model and tabulation of mineral resources. Skewed distribution, here isolated by robust semivariogram. The mineral resources classification generated in this model based on an analysis of the kriging standard deviation and number of samples which are used in the estimation of each block. Research results are used to evaluate the performance of OK and IDS estimator. Based on the visual and statistical analysis, concluded that the model of OK gives the estimation closer to the data used for modeling.
Nagano, M; Sakaki, N; Ando, K
2004-01-01
The air fluorescence technique is used to detect ultra-high energy cosmic rays (UHECR), and to estimate their energy. Of fundamental importance is the photon yield due to excitation by electrons, in air of various densities and temperatures. After our previous report, the experiment has been continued using a Sr90 $\\beta$ source to study the pressure dependence of photon yields for radiation in nitrogen and dry air. The photon yields in 15 wave bands between 300 nm and 430 nm have been determined. The total photon yield between 300 nm and 406 nm (used in most experiments) in air excited by a 0.85 MeV electron is 3.81+-0.13 (+-13 % systematics) photons per meter at 1013 hPa and 20 $^{\\circ}$C. The air density and temperature dependencies of 15 wave bands are given for application to UHECR observations.
Estimation of heterosis for grain yield and quality traits in sweet corn (Zea mays var. sacharata L.
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M. C. Dagla, R. N. Gadag, O. P. Sharma, Narendra Kumar
2014-12-01
Full Text Available The study was carried out to estimate heterosis for yield and biochemical quality traits in sweet corn (Zea mays var. sacharata crosses. Forty five hybrids using diallel mating design excluding reciprocals were generated. These hybrids along with their ten parents and one standard check (Madhuri were grown at IARI, New Delhi during kharif-2008 in randomized block design (RBD. Estimation of heterosis over standard check (HSC ‘Madhuri’, mid-parent (HMP, and better parent (HBP was calculated. The significant heterosis over standard check for grain yield was found in five crosses, and for sugar content in twelve crosses out of forty five. The HMP for grain yield was found in eighteen crosses and for sugar content in nine crosses. The HMP for grain yield and sugar content was found in IPSA-6134 × IPSA-6141, IPSA-6135 × IPSA-6136, and IPSA-6141 × IPSA-6142. One cross IPSA-6137 × IPSA-6139 showed HMP for protein content and grain yield. One cross IPSA-6136 × IPSA-6139 showed HMP for sugar content and protein content. The HBP was observed in IPSA-6135 × IPSA-6136, IPSA-6136 × IPSA-6139, IPSA-6138 × IPSA-6141, IPSA-6139 × IPSA-6140 and IPSA-6140 × IPSA-6141 for sugar content and IPSA-6134 × IPSA-6137, IPSA-6134 × IPSA-6141, IPSA-6134 × IPSA-6142, IPSA-6135 × IPSA-6139, IPSA-6140 × IPSA-6142, and IPSA-6141 × IPSA-6142 for grain yield. Two crosses IPSA-6135 × IPSA-6136 and IPSA-6141 × IPSA-6142 have the HBP for grain yield and sugar content. One cross IPSA 6137 × IPSA 6139 showed the HBP for protein and grain yield.
Estimation of Wind Turbulence Using Spectral Models
DEFF Research Database (Denmark)
Soltani, Mohsen; Knudsen, Torben; Bak, Thomas
2011-01-01
The production and loading of wind farms are significantly influenced by the turbulence of the flowing wind field. Estimation of turbulence allows us to optimize the performance of the wind farm. Turbulence estimation is; however, highly challenging due to the chaotic behavior of the wind....... In this paper, a method is presented for estimation of the turbulence. The spectral model of the wind is used in order to provide the estimations. The suggested estimation approach is applied to a case study in which the objective is to estimate wind turbulence at desired points using the measurements of wind...... speed outside the wind field. The results show that the method is able to provide estimations which explain more than 50% of the wind turbulence from the distance of about 300 meters....
Institute of Scientific and Technical Information of China (English)
WANG Yingbin; ZHENG Ji; WANG Yang; ZHENG Xianzhi
2012-01-01
We used generalized additive models (GAM) to analyze the relationship between spatiotemporal factors and catch,and to estimate the monthly marine fishery yield of single otter trawls in Putuo district of Zhoushan,China.We used logbooks from five commercial fishing boats and data in government's monthly statistical reports.We developed two GAM models:one included temporal variables (month and hauling time) and spatial variables (longitude and latitude),and another included just two variables,month and the number of fishing boats.Our results suggest that temporal factors explained more of the variability in catch than spatial factors.Furthermore,month explained the majority of variation in catch.Change in spatial distribution of fleet had a temporal component as the boats fished within a relatively small area within the same month,but the area varied among months.The number of boats fishing in each month also explained a large proportion of the variation in catch.Engine power had no effect on catch.The pseudo-coefficients (PCf) of the two GAMs were 0.13 and 0.29 respectively,indicating the both had good fits.The model yielded estimates that were very similar to those in the governmental reports between January to September,with relative estimate errors (REE) of ＜18％.However,the yields in October and November were significantly underestimated,with REEs of 36％ and 27％,respectively.
Forecasting Moroccan Wheat Yields using Two Statistical Models
Wechsung, F.; Childers, K.; Frieler, K.; Hoffmann, P.
2015-12-01
The economy of Morocco is highly dependent on fluctuations in wheat yield. Since very little of the Moroccan wheat harvest is irrigated, this leaves the annual wheat yield dependent on precipitation fluctuations and large scale weather patterns over the north Atlantic. Here we suggest two predictors of the annual change in Moroccan wheat yield based on these relationships. The first, pre-planting indicator relies on the sea surface temperature (SST) anomalies of the north Atlantic in September through November and are reinforced by a mid-season predictor based on the weighted precipitation from October through February. Partial least squares regression is used to determine the three most relevant patterns of Atlantic SST which offer an early indication of the upcoming wheat yield. The prediction is greatly enhanced by the inclusion of the cumulative monthly precipitation weighted by the wheat cultivation areas, from October through the wheat harvest. It is not surprising that the total precipitation in Morocco influences the annual wheat yield, however it is remarkable the degree to which early season precipitation sums are able to forecast the national wheat yield. Higher resolution precipitation reanalysis products from AgMERRA and NOAA have coefficients of determination greater than 0.5 by February (r2=0.78 and 0.57, respectively). The more frequently updated NOAA 0.5° resolution precipitation product has a slightly lower but still significant correlation (r2=0.48).
Sediment Yields Revealed and Fluid Modelling by Twice LiDAR Surveys in Active Tectonics Area
Hsieh, Y.; Chan, Y.; Hu, J.; Lin, C.
2010-12-01
LiDAR technique allows rapid acquisition of high resolution and high precision topographic data. The technique has found considerable use in the earth sciences, for example for fluvial morphology and flood modelling. These developments have offered new opportunities for investigating spatial and temporal patterns of morphological change in gravel-bed river and have contributed to develop in two points: (1)morphometric estimates of sediment transport and sediment yields ;(2)boundary conditions for numerical models, including computational fluid dynamics and modelling. This topographic research funded by the Taiwan Central Geological Survey, surveyed the terrain of the Lanyang River before and after the typhoon season using Airborne LiDAR technique, and computed the terrain variations. The Lanyang River is one of main rivers in Taiwan and often suffers the influence of typhoon during summer. Most of sediments generated from slump and soil erosion into river were transported from the upstream watershed and resulted in the riverbed changes during the typhoon season. In 2008, there are four significant typhoon events influencing this area, including the Kalmaegi, Fung-wong, Sinlaku, and Jangmi typhoons. At present, sediment yield calculation often used empirical or theoretical formula as well as data collected at hydrological stations, and rarely had the actual measured value through high-resolution topography. The variations of the terrain on the riverbed may be regarded as the sediment yield of the bed load transported during the typhoon season. This research used high-resolution terrain models to compute sediment yield of the bed load, and further discussed volumes of sediment yield in watershed during the typhoon season. In the Lanyang River we discovered that the upstream and midstream channel still had the characteristics of erosion and transportation during the typhoon season. The results imply significant sediment yield and transportation from the upstream
Bayesian estimation of the network autocorrelation model
Dittrich, D.; Leenders, R.T.A.J.; Mulder, J.
2017-01-01
The network autocorrelation model has been extensively used by researchers interested modeling social influence effects in social networks. The most common inferential method in the model is classical maximum likelihood estimation. This approach, however, has known problems such as negative bias of
Takahashi, Ribeka; Fullwood, David T.; Adams, Brent L.
2014-09-01
This study employs a novel stress-based Hybrid-Bishop-Hill yield model approach to evaluate the yield surface of oxygen-free electronic copper samples. The local yield surface is determined from three parameters of crystal orientation and one parameter of geometrically necessary dislocation (GND). All four local state variables can be rapidly determined by analysis of measured electron backscatter diffraction patterns. Estimates for the polycrystalline yield surface are obtained by standard averaging procedures. The shape of the yield surface is most influenced by the texture of the material, while the volume of the envelope scales with the average GND density. However, correlations between crystal orientation and GND content modify the yield surface shape and size. While correlations between GND density and crystal orientation are not strong for most copper samples, there are sufficient dependencies to demonstrate the benefits of the detailed four-parameter model. The four-parameter approach has potential for improving estimates of elastic-yield limit in all polycrystalline materials.
Bayesian inference of baseline fertility and treatment effects via a crop yield-fertility model.
Directory of Open Access Journals (Sweden)
Hungyen Chen
Full Text Available To effectively manage soil fertility, knowledge is needed of how a crop uses nutrients from fertilizer applied to the soil. Soil quality is a combination of biological, chemical and physical properties and is hard to assess directly because of collective and multiple functional effects. In this paper, we focus on the application of these concepts to agriculture. We define the baseline fertility of soil as the level of fertility that a crop can acquire for growth from the soil. With this strict definition, we propose a new crop yield-fertility model that enables quantification of the process of improving baseline fertility and the effects of treatments solely from the time series of crop yields. The model was modified from Michaelis-Menten kinetics and measured the additional effects of the treatments given the baseline fertility. Using more than 30 years of experimental data, we used the Bayesian framework to estimate the improvements in baseline fertility and the effects of fertilizer and farmyard manure (FYM on maize (Zea mays, barley (Hordeum vulgare, and soybean (Glycine max yields. Fertilizer contributed the most to the barley yield and FYM contributed the most to the soybean yield among the three crops. The baseline fertility of the subsurface soil was very low for maize and barley prior to fertilization. In contrast, the baseline fertility in this soil approximated half-saturated fertility for the soybean crop. The long-term soil fertility was increased by adding FYM, but the effect of FYM addition was reduced by the addition of fertilizer. Our results provide evidence that long-term soil fertility under continuous farming was maintained, or increased, by the application of natural nutrients compared with the application of synthetic fertilizer.
Parameter and Uncertainty Estimation in Groundwater Modelling
DEFF Research Database (Denmark)
Jensen, Jacob Birk
The data basis on which groundwater models are constructed is in general very incomplete, and this leads to uncertainty in model outcome. Groundwater models form the basis for many, often costly decisions and if these are to be made on solid grounds, the uncertainty attached to model results must...... be quantified. This study was motivated by the need to estimate the uncertainty involved in groundwater models.Chapter 2 presents an integrated surface/subsurface unstructured finite difference model that was developed and applied to a synthetic case study.The following two chapters concern calibration...... and uncertainty estimation. Essential issues relating to calibration are discussed. The classical regression methods are described; however, the main focus is on the Generalized Likelihood Uncertainty Estimation (GLUE) methodology. The next two chapters describe case studies in which the GLUE methodology...
Directory of Open Access Journals (Sweden)
D. K. Henze
2007-10-01
Full Text Available Formation of SOA from the aromatic species toluene, xylene, and, for the first time, benzene, is added to a global chemical transport model. A simple mechanism is presented that accounts for competition between low and high-yield pathways of SOA formation, wherein secondary gas-phase products react further with either nitrogen oxide (NO or hydroperoxy radical (HO_{2} to yield semi- or non-volatile products, respectively. Aromatic species yield more SOA when they react with OH in regions where the [NO]/[HO_{2}] ratios are lower. The SOA yield thus depends upon the distribution of aromatic emissions, with biomass burning emissions being in areas with lower [NO]/[HO_{2}] ratios, and the reactivity of the aromatic with respect to OH, as a lower initial reactivity allows transport away from industrial source regions, where [NO]/[HO_{2}] ratios are higher, to more remote regions, where this ratio is lower and, hence, the ultimate yield of SOA is higher. As a result, benzene is estimated to be the most important aromatic species with regards to formation of SOA, with a total production nearly equal that of toluene and xylene combined. In total, while only 39% percent of the aromatic species react via the low-NO_{x} pathway, 72% of the aromatic SOA is formed via this mechanism. Predicted SOA concentrations from aromatics in the Eastern United States and Eastern Europe are actually largest during the summer, when the [NO]/[HO_{2}] ratio is lower. Global production of SOA from aromatic sources is estimated at 3.5 Tg/yr, resulting in a global burden of 0.08 Tg, twice as large as previous estimates. The contribution of these largely anthropogenic sources to global SOA is still small relative to biogenic sources, which are estimated to comprise 90% of the global SOA burden, about half of which comes from isoprene. Compared to recent observations, it would appear there are additional pathways beyond those
INTEGRATED SPEED ESTIMATION MODEL FOR MULTILANE EXPREESSWAYS
Hong, Sungjoon; Oguchi, Takashi
In this paper, an integrated speed-estimation model is developed based on empirical analyses for the basic sections of intercity multilane expressway un der the uncongested condition. This model enables a speed estimation for each lane at any site under arb itrary highway-alignment, traffic (traffic flow and truck percentage), and rainfall conditions. By combin ing this model and a lane-use model which estimates traffic distribution on the lanes by each vehicle type, it is also possible to es timate an average speed across all the lanes of one direction from a traffic demand by vehicle type under specific highway-alignment and rainfall conditions. This model is exp ected to be a tool for the evaluation of traffic performance for expressways when the performance me asure is travel speed, which is necessary for Performance-Oriented Highway Planning and Design. Regarding the highway-alignment condition, two new estimators, called effective horizo ntal curvature and effective vertical grade, are proposed in this paper which take into account the influence of upstream and downstream alignment conditions. They are applied to the speed-estimation model, and it shows increased accuracy of the estimation.
Model error estimation in ensemble data assimilation
Directory of Open Access Journals (Sweden)
S. Gillijns
2007-01-01
Full Text Available A new methodology is proposed to estimate and account for systematic model error in linear filtering as well as in nonlinear ensemble based filtering. Our results extend the work of Dee and Todling (2000 on constant bias errors to time-varying model errors. In contrast to existing methodologies, the new filter can also deal with the case where no dynamical model for the systematic error is available. In the latter case, the applicability is limited by a matrix rank condition which has to be satisfied in order for the filter to exist. The performance of the filter developed in this paper is limited by the availability and the accuracy of observations and by the variance of the stochastic model error component. The effect of these aspects on the estimation accuracy is investigated in several numerical experiments using the Lorenz (1996 model. Experimental results indicate that the availability of a dynamical model for the systematic error significantly reduces the variance of the model error estimates, but has only minor effect on the estimates of the system state. The filter is able to estimate additive model error of any type, provided that the rank condition is satisfied and that the stochastic errors and measurement errors are significantly smaller than the systematic errors. The results of this study are encouraging. However, it remains to be seen how the filter performs in more realistic applications.
Kukush, A.; Markovsky, I.; Van Huffel, S.
2002-01-01
Consistent estimators of the rank-deficient fundamental matrix yielding information on the relative orientation of two images in two-view motion analysis are derived. The estimators are derived by minimizing a corrected contrast function in a quadratic measurement error model. In addition, a consistent estimator for the measurement error variance is obtained. Simulation results show the improved accuracy of the newly proposed estimator compared to the ordinary total least-squares estimator.
Reidsma, P.; Ewert, F.; Boogaard, H.; Diepen, van K.
2009-01-01
Impacts of climate variability and climate change on regional crop yields are commonly assessed using process-based crop models. These models, however, simulate potential and water limited yields, which do not always relate to observed yields. The latter are largely influenced by crop management, wh
Similar estimates of temperature impacts on global wheat yield by three independent methods
DEFF Research Database (Denmark)
Liu, Bing; Asseng, Senthold; Müller, Christoph
2016-01-01
The potential impact of global temperature change on global crop yield has recently been assessed with different methods. Here we show that grid-based and point-based simulations and statistical regressions (from historic records), without deliberate adaptation or CO2 fertilization effects, produ...
Similar estimates of temperature impacts on global wheat yield by three independent methods
Liu, Bing; Asseng, Senthold; Müller, Christoph; Ewert, Frank; Elliott, Joshua; Lobell, David B.; Martre, Pierre; Ruane, Alex C.; Wallach, Daniel; Jones, James W.; Supit, Iwan; Wolf, Joost
2016-01-01
The potential impact of global temperature change on global crop yield has recently been assessed with different methods. Here we show that grid-based and point-based simulations and statistical regressions (from historic records), without deliberate adaptation or CO 2 fertilization effects,
Similar estimates of temperature impacts on global wheat yield by three independent methods
Liu, Bing; Asseng, Senthold; Müller, Christoph; Ewert, Frank; Elliott, Joshua; Lobell, David B.; Martre, Pierre; Ruane, Alex C.; Wallach, Daniel; Jones, James W.; Supit, Iwan; Wolf, Joost
2016-01-01
The potential impact of global temperature change on global crop yield has recently been assessed with different methods. Here we show that grid-based and point-based simulations and statistical regressions (from historic records), without deliberate adaptation or CO 2 fertilization effects, prod
Estimating impact on clover-grass yield caused by traffic intensities
DEFF Research Database (Denmark)
Jørgensen, Rasmus Nyholm; Sørensen, Claus Grøn; Green, Ole
2009-01-01
Traffic intensities have a significant influence on a range of crop and soil parameters (Hamza & Anderson, 2005; Raper, 2005). For grass and especially clover, the yield response is negative as a function of traffic intensity (e.g. Frost, 1988). During the growing season, conventional grass-clov...
Directory of Open Access Journals (Sweden)
Fernando Pérez-Rodríguez
2016-07-01
Full Text Available Aim of the study: To introduce and describe FlorNExT®, a free cloud computing application to estimate growth and yield of maritime pine (Pinus pinaster Ait. even-aged stands in the Northeast of Portugal (NE Portugal. Area of study: NE Portugal. Material and methods: FlorNExT® implements a dynamic growth and yield modelling framework which integrates transition functions for dominant height (site index curves and basal area, as well as output functions for tree and stand volume, biomass, and carbon content. Main results: FlorNExT® is freely available from any device with an Internet connection at: http://flornext.esa.ipb.pt/. Research highlights: This application has been designed to make it possible for any stakeholder to easily estimate standing volume, biomass, and carbon content in maritime pine stands from stand data, as well as to estimate growth and yield based on four stand variables: age, density, dominant height, and basal area. FlorNExT® allows planning thinning treatments. FlorNExT® is a fundamental tool to support forest mobilization at local and regional scales in NE Portugal. Keywords: forest management; maritime pine; forest modelling; knowledge transfer tool.
Directory of Open Access Journals (Sweden)
Luis Gabriel González Herrera
2008-09-01
of Gyr cows calving between 1990 and 2005 were used to estimate genetic parameters of monthly test-day milk yield (TDMY. Records were analyzed by random regression models (MRA that included the additive genetic and permanent environmental random effects and the contemporary group, age of cow at calving (linear and quadratic components and the average trend of the population as fixed effects. Random trajectories were fitted by Wilmink's (WIL and Ali & Schaeffer's (AS parametric functions. Residual variances were fitted by step functions with 1, 4, 6 or 10 classes. The contemporary group was defined by herd-year-season of test-day and included at least three animals. Models were compared by Akaike's and Schwarz's Bayesian (BIC information criterion. The AS function used for modeling the additive genetic and permanent environmental effects with heterogeneous residual variances adjusted with a step function with four classes was the best fitted model. Heritability estimates ranged from 0.21 to 0.33 for the AS function and from 0.17 to 0.30 for WIL function and were larger in the first half of the lactation period. Genetic correlations between TDMY were high and positive for adjacent test-days and decreased as days between records increased. Predicted breeding values for total 305-day milk yield (MRA305 and specific periods of lactation (obtained by the mean of all breeding values in the periods using the AS function were compared with that predicted by a standard model using accumulated 305-day milk yield (PTA305 by rank correlation. The magnitude of correlations suggested differences may be observed in ranking animals by using the different criteria which were compared in this study.
Regional fuzzy chain model for evapotranspiration estimation
Güçlü, Yavuz Selim; Subyani, Ali M.; Şen, Zekai
2017-01-01
Evapotranspiration (ET) is one of the main hydrological cycle components that has extreme importance for water resources management and agriculture especially in arid and semi-arid regions. In this study, regional ET estimation models based on the fuzzy logic (FL) principles are suggested, where the first stage includes the ET calculation via Penman-Monteith equation, which produces reliable results. In the second phase, ET estimations are produced according to the conventional FL inference system model. In this paper, regional fuzzy model (RFM) and regional fuzzy chain model (RFCM) are proposed through the use of adjacent stations' data in order to fill the missing ones. The application of the two models produces reliable and satisfactory results for mountainous and sea region locations in the Kingdom of Saudi Arabia, but comparatively RFCM estimations have more accuracy. In general, the mean absolute percentage error is less than 10%, which is acceptable in practical applications.
Directory of Open Access Journals (Sweden)
H. Tonhati
2010-02-01
Full Text Available The objectives of this study were to estimate (covariance functions for additive genetic and permanent environmental effects, as well as the genetic parameters for milk yield over multiple parities, using random regressions models (RRM. Records of 4,757 complete lactations of Murrah breed buffaloes from 12 herds were analyzed. Ages at calving were between 2 and 11 years. The model included the additive genetic and permanent environmental random effects and the fixed effects of contemporary groups (herd, year and calving season and milking frequency (1 or 2. A cubic regression on Legendre orthogonal polynomials of ages was used to model the mean trend. The additive genetic and permanent environmental effects were modeled by Legendre orthogonal polynomials. Residual variances were considered homogenous or heterogeneous, modeled through variance functions or step functions with 5, 7 or 10 classes. Results from Akaike’s and Schwarz’s Bayesian information criterion indicated that a RRM considering a third order polynomial for the additive genetic and permanent environmental effects and a step function with 5 classes for residual variances fitted best. Heritability estimates obtained by this model varied from 0.10 to 0.28. Genetic correlations were high between consecutive ages, but decreased when intervals between ages increased
Parameter Estimation of Partial Differential Equation Models
Xun, Xiaolei
2013-09-01
Partial differential equation (PDE) models are commonly used to model complex dynamic systems in applied sciences such as biology and finance. The forms of these PDE models are usually proposed by experts based on their prior knowledge and understanding of the dynamic system. Parameters in PDE models often have interesting scientific interpretations, but their values are often unknown and need to be estimated from the measurements of the dynamic system in the presence of measurement errors. Most PDEs used in practice have no analytic solutions, and can only be solved with numerical methods. Currently, methods for estimating PDE parameters require repeatedly solving PDEs numerically under thousands of candidate parameter values, and thus the computational load is high. In this article, we propose two methods to estimate parameters in PDE models: a parameter cascading method and a Bayesian approach. In both methods, the underlying dynamic process modeled with the PDE model is represented via basis function expansion. For the parameter cascading method, we develop two nested levels of optimization to estimate the PDE parameters. For the Bayesian method, we develop a joint model for data and the PDE and develop a novel hierarchical model allowing us to employ Markov chain Monte Carlo (MCMC) techniques to make posterior inference. Simulation studies show that the Bayesian method and parameter cascading method are comparable, and both outperform other available methods in terms of estimation accuracy. The two methods are demonstrated by estimating parameters in a PDE model from long-range infrared light detection and ranging data. Supplementary materials for this article are available online. © 2013 American Statistical Association.
Hann, Damjan
2016-09-01
This study presents an innovative approach for determining the unconfined yield strength σc during the excavation of coal from the earth's crust by using an equipment that was developed for measuring the mechanical properties of bulk materials stored in silos. Highly productive excavation of coal with a hanging wall top caving leads to intensive deformations in the hanging wall and the broken coal can be considered as bulk material. In this research, the shear tester Johanson Hang-Up Indicizer was used to measure the unconfined yield strength of the tested samples, even though such a tester cannot produce stress-strain conditions similar to those occurring during the excavation. An attempt was made to estimate the real unconfined yield strength of broken coal deep under the surface through a combination of measured data and extrapolation.
Directory of Open Access Journals (Sweden)
J. De Klerk
2001-07-01
Full Text Available The application of fire as a management tool is often used to change the species composition of the vegetation and its cover to maintain plant communities in a specific successional stage. This study investigates the influence of two fire treatments (a head and a back fire on the plateau grassland communities in the Mountain Zebra National Park (MZNP. The production of herbage yield on grazed areas and areas protected from grazing which were subjected to two fire treatments, were compared with that of an unburnt control area subjected to grazing in the same homogenous grassland over two growing seasons. No differences were found in herbage production between the two fire treatment areas. After the burn the grazing exclosures achieved the same herbage yield as the control area within two growing seasons. In comparison, the grazed areas could after the burn only achieve a herbage yield equal to 55.7 of that of the control area. The results indicate that fire stimulates active vegetation growth on the plateau grasslands in MZNP leading to a higher production rate and better utilisation by game.
Conditional shape models for cardiac motion estimation
DEFF Research Database (Denmark)
Metz, Coert; Baka, Nora; Kirisli, Hortense
2010-01-01
We propose a conditional statistical shape model to predict patient specific cardiac motion from the 3D end-diastolic CTA scan. The model is built from 4D CTA sequences by combining atlas based segmentation and 4D registration. Cardiac motion estimation is, for example, relevant in the dynamic...
Statistical Model-Based Face Pose Estimation
Institute of Scientific and Technical Information of China (English)
GE Xinliang; YANG Jie; LI Feng; WANG Huahua
2007-01-01
A robust face pose estimation approach is proposed by using face shape statistical model approach and pose parameters are represented by trigonometric functions. The face shape statistical model is firstly built by analyzing the face shapes from different people under varying poses. The shape alignment is vital in the process of building the statistical model. Then, six trigonometric functions are employed to represent the face pose parameters. Lastly, the mapping function is constructed between face image and face pose by linearly relating different parameters. The proposed approach is able to estimate different face poses using a few face training samples. Experimental results are provided to demonstrate its efficiency and accuracy.
Missing data estimation in fMRI dynamic causal modeling.
Zaghlool, Shaza B; Wyatt, Christopher L
2014-01-01
Dynamic Causal Modeling (DCM) can be used to quantify cognitive function in individuals as effective connectivity. However, ambiguity among subjects in the number and location of discernible active regions prevents all candidate models from being compared in all subjects, precluding the use of DCM as an individual cognitive phenotyping tool. This paper proposes a solution to this problem by treating missing regions in the first-level analysis as missing data, and performing estimation of the time course associated with any missing region using one of four candidate methods: zero-filling, average-filling, noise-filling using a fixed stochastic process, or one estimated using expectation-maximization. The effect of this estimation scheme was analyzed by treating it as a preprocessing step to DCM and observing the resulting effects on model evidence. Simulation studies show that estimation using expectation-maximization yields the highest classification accuracy using a simple loss function and highest model evidence, relative to other methods. This result held for various dataset sizes and varying numbers of model choice. In real data, application to Go/No-Go and Simon tasks allowed computation of signals from the missing nodes and the consequent computation of model evidence in all subjects compared to 62 and 48 percent respectively if no preprocessing was performed. These results demonstrate the face validity of the preprocessing scheme and open the possibility of using single-subject DCM as an individual cognitive phenotyping tool.
Arbones, B.; Figueiras, F. G.; Varela, R.
2000-09-01
Spectral and non-spectral measurements of the maximum quantum yield of carbon fixation for natural phytoplankton assemblages were compared in order to evaluate their effect on bio-optical models of primary production. Field samples were collected from two different coastal regions of NW Spain in spring, summer and autumn and in a polar environment (Gerlache Strait, Antarctica) during the austral summer. Concurrent determinations were made of spectral phytoplankton absorption coefficient [ aph( λ)], white-light-limited slope of the photosynthesis-irradiance relationships ( αB), carbon uptake action spectra [ αB( λ)], broad-band maximum quantum yields ( φm), and spectral maximum quantum yields [ φm( λ)]. Carbon uptake action spectra roughly followed the shape of the corresponding phytoplankton absorption spectra but with a slight displacement in the blue-green region that could be attributed to imbalance between the two photosystems PS I and PS II. Results also confirmed previous observations of wavelength dependency of maximum quantum yield. The broad-band maximum quantum yield ( φm) calculated considering the measured spectral phytoplankton absorption coefficient and the spectrum of the light source of the incubators was not significantly different form the averaged spectral maximum quantum yield [ overlineφ max(λ) ] ( t-test for paired samples, P=0.34). These results suggest that maximum quantum yield can be estimated with enough accuracy from white-light P- E curves and measured phytoplankton absorption spectra. Primary production at light limiting regimes was compared using four different models with a varying degree of spectral complexity. No significant differences ( t-test for paired samples, P=0.91) were found between a spectral model based on the carbon uptake action spectra [ αB( λ) — model a] and a model which uses the broad-band φm and measured aph( λ) (model b). In addition, primary production derived from constructed action spectra [ ac
Complex oscillatory yielding of model hard-sphere glasses.
Koumakis, N; Brady, J F; Petekidis, G
2013-04-26
The yielding behavior of hard sphere glasses under large-amplitude oscillatory shear has been studied by probing the interplay of Brownian motion and shear-induced diffusion at varying oscillation frequencies. Stress, structure and dynamics are followed by experimental rheology and Browian dynamics simulations. Brownian-motion-assisted cage escape dominates at low frequencies while escape through shear-induced collisions at high ones, both related with a yielding peak in G''. At intermediate frequencies a novel, for hard sphere glasses, double peak in G'' is revealed reflecting both mechanisms. At high frequencies and strain amplitudes a persistent structural anisotropy causes a stress drop within the cycle after strain reversal, while higher stress harmonics are minimized at certain strain amplitudes indicating an apparent harmonic response.
Modeling Long Term Corn Yield Response to Nitrogen Rate and Crop Rotation
Directory of Open Access Journals (Sweden)
Laila Alejandra Puntel
2016-11-01
Full Text Available Improved prediction of optimal N fertilizer rates for corn (Zea mays L. can reduce N losses and increase profits. We tested the ability of the Agricultural Production Systems sIMulator (APSIM to simulate corn and soybean (Glycine max L. yields, the economic optimum N rate (EONR using a 16-year field-experiment dataset from central Iowa, USA that included two crop sequences (continuous corn and soybean-corn and five N fertilizer rates (0, 67, 134, 201, and 268 kg N ha-1 applied to corn. Our objectives were to: a quantify model prediction accuracy before and after calibration, and report calibration steps; b compare crop model-based techniques in estimating optimal N rate for corn; and c utilize the calibrated model to explain factors causing year to year variability in yield and optimal N. Results indicated that the model simultaneously simulated well long-term crop yields response to N (relative root mean square error, RRMSE of 19.6% before and 12.3% after calibration, which provided strong evidence that important soil and crop processes were accounted for in the model. The prediction of EONR was more complex and had greater uncertainty than the prediction of crop yield (RRMSE of 44.5% before and 36.6% after calibration. For long-term site mean EONR predictions, both calibrated and uncalibrated versions can be used as the 16-yr mean differences in EONR’s were within the historical N rate error range (40 to 50 kg N ha-1. However, for accurate year-by-year simulation of EONR the calibrated version should be used. Model analysis revealed that higher EONR values in years with above normal spring precipitation were caused by an exponential increase in N loss (denitrification and leaching with precipitation. We concluded that long term experimental data were valuable in testing and refining APSIM predictions. The model can be used as a tool to assist N management guidelines in the US Midwest and we identified five avenues on how the model can add
Modeling Long-Term Corn Yield Response to Nitrogen Rate and Crop Rotation.
Puntel, Laila A; Sawyer, John E; Barker, Daniel W; Dietzel, Ranae; Poffenbarger, Hanna; Castellano, Michael J; Moore, Kenneth J; Thorburn, Peter; Archontoulis, Sotirios V
2016-01-01
Improved prediction of optimal N fertilizer rates for corn (Zea mays L.) can reduce N losses and increase profits. We tested the ability of the Agricultural Production Systems sIMulator (APSIM) to simulate corn and soybean (Glycine max L.) yields, the economic optimum N rate (EONR) using a 16-year field-experiment dataset from central Iowa, USA that included two crop sequences (continuous corn and soybean-corn) and five N fertilizer rates (0, 67, 134, 201, and 268 kg N ha(-1)) applied to corn. Our objectives were to: (a) quantify model prediction accuracy before and after calibration, and report calibration steps; (b) compare crop model-based techniques in estimating optimal N rate for corn; and (c) utilize the calibrated model to explain factors causing year to year variability in yield and optimal N. Results indicated that the model simulated well long-term crop yields response to N (relative root mean square error, RRMSE of 19.6% before and 12.3% after calibration), which provided strong evidence that important soil and crop processes were accounted for in the model. The prediction of EONR was more complex and had greater uncertainty than the prediction of crop yield (RRMSE of 44.5% before and 36.6% after calibration). For long-term site mean EONR predictions, both calibrated and uncalibrated versions can be used as the 16-year mean differences in EONR's were within the historical N rate error range (40-50 kg N ha(-1)). However, for accurate year-by-year simulation of EONR the calibrated version should be used. Model analysis revealed that higher EONR values in years with above normal spring precipitation were caused by an exponential increase in N loss (denitrification and leaching) with precipitation. We concluded that long-term experimental data were valuable in testing and refining APSIM predictions. The model can be used as a tool to assist N management guidelines in the US Midwest and we identified five avenues on how the model can add value toward
Balderama, O. F.
2013-12-01
Peanut is a major upland crop in Cagayan Valley and a leguminous crop that requires less water and therefore, considered an important crop in improving productivity of upland and rainfed areas. However, little information is available on the potential productivity of the crop and analysis on the production constraints including climate change sensitivity. This study was aimed to determine yield potential and production constraints of peanut crop in Cagayan Valley through the use of Decision Support System for Agrotechnology Transfer (DSSAT) simulation modeling; analyze yield gaps between simulated and actual yield levels and to provide decision support to further optimize peanut production under climate change condition. Site of experiment for model calibration and validation was located on-station at Isabela State University, Echague, Isabela. Rainfall and other climatic variables were monitored using a HOBO weather station (Automatic Weather Station) which is strategically installed inside experimental zone.The inputs required to run the CSM model include information on soil and weather conditions, crop management practices and cultivar specific genetic coefficients. In the first step,a model calibration was conducted to determine the cultivar coefficients for certain peanut cultivar that are normally grown in Cagayan Valley. Crop growth and yield simulation modeling was undertaken using the Decision Support System for Agro-Technology Transfer (DSSAT) for small seeded peanut (Pn9). An evaluation of the CSM-CROPGRO-peanut model was performed with data sets from peanut experiment conducted from December 2011 to April 2012. The model was evaluated in the estimation of potential yield of peanut under rainfed condition and low-nitrogen application. Yield potential for peanut limited only by temperature and solar radiation and no-water and nutrient stress, ranged from 3274 to 4805 kg per hectare for six planting dates (October 1, October 15, November 1, November 15
Development of a remote sensing-based rice yield forecasting model
Energy Technology Data Exchange (ETDEWEB)
Mosleh, M.K.; Hassan, Q.K.; Chowdhury, E.H.
2016-11-01
This study aimed to develop a remote sensing-based method for forecasting rice yield by considering vegetation greenness conditions during initial and peak greenness stages of the crop; and implemented for “boro” rice in Bangladeshi context. In this research, we used Moderate Resolution Imaging Spectroradiometer (MODIS)-derived two 16-day composite of normalized difference vegetation index (NDVI) images at 250 m spatial resolution acquired during the initial (January 1 to January 16) and peak greenness (March 23/24 to April 6/7 depending on leap year) stages in conjunction with secondary datasets (i.e., boro suitability map, and ground-based information) during 2007-2012 period. The method consisted of two components: (i) developing a model for delineating area under rice cultivation before harvesting; and (ii) forecasting rice yield as a function of NDVI. Our results demonstrated strong agreements between the model (i.e., MODIS-based) and ground-based area estimates during 2010-2012 period, i.e., coefficient of determination (R2); root mean square error (RMSE); and relative error (RE) in between 0.93 to 0.95; 30,519 to 37,451 ha; and ±10% respectively at the 23 district-levels. We also found good agreements between forecasted (i.e., MODIS-based) and ground-based yields during 2010-2012 period (R2 between 0.76 and 0.86; RMSE between 0.21 and 0.29 Mton/ha, and RE between -5.45% and 6.65%) at the 23 district-levels. We believe that our developments of forecasting the boro rice yield would be useful for the decision makers in addressing food security in Bangladesh. (Author)
Sawko, Robert; Thompson, Chris P.
2010-09-01
This paper presents a series of numerical simulations of non-Newtonian fluids in high Reynolds number flows in circular pipes. The fluids studied in the computations have shear-thinning and yield stress properties. Turbulence is described using the Reynolds-Averaged Navier-Stokes (RANS) equations with the Boussinesq eddy viscosity hypothesis. The evaluation of standard, two-equation models led to some observations regarding the order of magnitude as well as probabilistic information about the rate of strain. We argue that an accurate estimate of the rate of strain tensor is essential in capturing important flow features. It is first recognised that an apparent viscosity comprises two flow dependant components: one originating from rheology and the other from the turbulence model. To establish the relative significance of the terms involved, an order of magnitude analysis has been performed. The main observation supporting further discussion is that in high Reynolds number regimes the magnitudes of fluctuating rates of strain and fluctuating vorticity dominate the magnitudes of their respective averages. Since these quantities are included in the rheological law, the values of viscosity obtained from the fluctuating and mean velocity fields are different. Validation against Direct Numerical Simulation data shows at least an order of magnitude discrepancy in some regions of the flow. Moreover, the predictions of the probabilistic analysis show a favourable agreement with statistics computed from DNS data. A variety of experimental, as well as computational data has been collected. Data come from the latest experiments by Escudier et al. [1], DNS from Rudman et al. [2] and zeroth-order turbulence models of Pinho [3]. The fluid rheologies are described by standard power-law and Herschel-Bulkley models which make them suitable for steady state calculations of shear flows. Suitable regularisations are utilised to secure numerical stability. Two new models have been
Bayesian mixture models for spectral density estimation
Cadonna, Annalisa
2017-01-01
We introduce a novel Bayesian modeling approach to spectral density estimation for multiple time series. Considering first the case of non-stationary timeseries, the log-periodogram of each series is modeled as a mixture of Gaussiandistributions with frequency-dependent weights and mean functions. The implied model for the log-spectral density is a mixture of linear mean functionswith frequency-dependent weights. The mixture weights are built throughsuccessive differences of a logit-normal di...
Energy Technology Data Exchange (ETDEWEB)
Barreiro, S.; Rua, J.; Tomé, M.
2016-07-01
Aim of the study. To introduce and describe FlorNExT®, a free cloud computing application to estimate growth and yield of maritime pine (Pinus pinaster Ait.) even-aged stands in the Northeast of Portugal (NE Portugal). Area of study: NE Portugal. Material and methods: FlorNExT® implements a dynamic growth and yield modelling framework which integrates transition functions for dominant height (site index curves) and basal area, as well as output functions for tree and stand volume, biomass, and carbon content. Main results: FlorNExT® is freely available from any device with an Internet connection at: http://flornext.esa.ipb.pt/. Research highlights: This application has been designed to make it possible for any stakeholder to easily estimate standing volume, biomass, and carbon content in maritime pine stands from stand data, as well as to estimate growth and yield based on four stand variables: age, density, dominant height, and basal area. FlorNExT® allows planning thinning treatments. FlorNExT® is a fundamental tool to support forest mobilization at local and regional scales in NE Portugal. (Author)
Torres, F E; Teodoro, P E; Rodrigues, E V; Santos, A; Corrêa, A M; Ceccon, G
2016-04-29
The aim of this study was to select erect cowpea (Vigna unguiculata L.) genotypes simultaneously for high adaptability, stability, and yield grain in Mato Grosso do Sul, Brazil using mixed models. We conducted six trials of different cowpea genotypes in 2005 and 2006 in Aquidauana, Chapadão do Sul, Dourados, and Primavera do Leste. The experimental design was randomized complete blocks with four replications and 20 genotypes. Genetic parameters were estimated by restricted maximum likelihood/best linear unbiased prediction, and selection was based on the harmonic mean of the relative performance of genetic values method using three strategies: selection based on the predicted breeding value, having considered the performance mean of the genotypes in all environments (no interaction effect); the performance in each environment (with an interaction effect); and the simultaneous selection for grain yield, stability, and adaptability. The MNC99542F-5 and MNC99-537F-4 genotypes could be grown in various environments, as they exhibited high grain yield, adaptability, and stability. The average heritability of the genotypes was moderate to high and the selective accuracy was 82%, indicating an excellent potential for selection.
Estimation and uncertainty of reversible Markov models
Trendelkamp-Schroer, Benjamin; Paul, Fabian; Noé, Frank
2015-01-01
Reversibility is a key concept in the theory of Markov models, simplified kinetic models for the conforma- tion dynamics of molecules. The analysis and interpretation of the transition matrix encoding the kinetic properties of the model relies heavily on the reversibility property. The estimation of a reversible transition matrix from simulation data is therefore crucial to the successful application of the previously developed theory. In this work we discuss methods for the maximum likelihood estimation of transition matrices from finite simulation data and present a new algorithm for the estimation if reversibility with respect to a given stationary vector is desired. We also develop new methods for the Bayesian posterior inference of reversible transition matrices with and without given stationary vector taking into account the need for a suitable prior distribution preserving the meta-stable features of the observed process during posterior inference.
Developing Physician Migration Estimates for Workforce Models.
Holmes, George M; Fraher, Erin P
2017-02-01
To understand factors affecting specialty heterogeneity in physician migration. Physicians in the 2009 American Medical Association Masterfile data were matched to those in the 2013 file. Office locations were geocoded in both years to one of 293 areas of the country. Estimated utilization, calculated for each specialty, was used as the primary predictor of migration. Physician characteristics (e.g., specialty, age, sex) were obtained from the 2009 file. Area characteristics and other factors influencing physician migration (e.g., rurality, presence of teaching hospital) were obtained from various sources. We modeled physician location decisions as a two-part process: First, the physician decides whether to move. Second, conditional on moving, a conditional logit model estimates the probability a physician moved to a particular area. Separate models were estimated by specialty and whether the physician was a resident. Results differed between specialties and according to whether the physician was a resident in 2009, indicating heterogeneity in responsiveness to policies. Physician migration was higher between geographically proximate states with higher utilization for that specialty. Models can be used to estimate specialty-specific migration patterns for more accurate workforce modeling, including simulations to model the effect of policy changes. © Health Research and Educational Trust.
Error estimation and adaptive chemical transport modeling
Directory of Open Access Journals (Sweden)
Malte Braack
2014-09-01
Full Text Available We present a numerical method to use several chemical transport models of increasing accuracy and complexity in an adaptive way. In largest parts of the domain, a simplified chemical model may be used, whereas in certain regions a more complex model is needed for accuracy reasons. A mathematically derived error estimator measures the modeling error and provides information where to use more accurate models. The error is measured in terms of output functionals. Therefore, one has to consider adjoint problems which carry sensitivity information. This concept is demonstrated by means of ozone formation and pollution emission.
Parameter Estimation for Thurstone Choice Models
Energy Technology Data Exchange (ETDEWEB)
Vojnovic, Milan [London School of Economics (United Kingdom); Yun, Seyoung [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
2017-04-24
We consider the estimation accuracy of individual strength parameters of a Thurstone choice model when each input observation consists of a choice of one item from a set of two or more items (so called top-1 lists). This model accommodates the well-known choice models such as the Luce choice model for comparison sets of two or more items and the Bradley-Terry model for pair comparisons. We provide a tight characterization of the mean squared error of the maximum likelihood parameter estimator. We also provide similar characterizations for parameter estimators defined by a rank-breaking method, which amounts to deducing one or more pair comparisons from a comparison of two or more items, assuming independence of these pair comparisons, and maximizing a likelihood function derived under these assumptions. We also consider a related binary classification problem where each individual parameter takes value from a set of two possible values and the goal is to correctly classify all items within a prescribed classification error. The results of this paper shed light on how the parameter estimation accuracy depends on given Thurstone choice model and the structure of comparison sets. In particular, we found that for unbiased input comparison sets of a given cardinality, when in expectation each comparison set of given cardinality occurs the same number of times, for a broad class of Thurstone choice models, the mean squared error decreases with the cardinality of comparison sets, but only marginally according to a diminishing returns relation. On the other hand, we found that there exist Thurstone choice models for which the mean squared error of the maximum likelihood parameter estimator can decrease much faster with the cardinality of comparison sets. We report empirical evaluation of some claims and key parameters revealed by theory using both synthetic and real-world input data from some popular sport competitions and online labor platforms.
Li, T.; Hasegawa, T.; Yin, X.; Zhu, Y.; Boote, K.; Adam, M.; Bregaglio, S.; Buis, S.; Confalonieri, R.; Fumoto, T.; Gaydon, D.; Marcaida III, M.; Nakagawa, H.; Oriol, P.; Ruane, A.C.; Ruget, F.; Singh, B.; Singh, U.; Tang, L.; Yoshida, H.; Zhang, Z.; Bouman, B.
2015-01-01
Predicting rice (Oryza sativa) productivity under future climates is important for global food security. Ecophysiological crop models in combination with climate model outputs are commonly used in yield prediction, but uncertainties associated with crop models remain largely unquantified. We evaluat
Model suitability to assess regional potato yield patterns in northern Ecuador
Soltani Largani, A.; Stoorvogel, J.J.; Veldkamp, A.
2013-01-01
A wide range of scenario studies aiming at rural development require regional patterns of crop yield. This study aims to evaluate three different modeling approaches for their suitability to assess regional potato yield patterns. The three model approaches include (1) an empirical model; (2) a proce
Event-Based Modeling of Driver Yielding Behavior to Pedestrians at Two-Lane Roundabout Approaches.
Salamati, Katayoun; Schroeder, Bastian J; Geruschat, Duane R; Rouphail, Nagui M
2014-01-01
Unlike other types of controlled intersections, drivers do not always comply with the "yield to pedestrian" sign at the roundabouts. This paper aims to identify the contributing factors affecting the likelihood of driver yielding to pedestrians at two-lane roundabouts. It further models the likelihood of driver yielding based on these factors using logistic regression. The models have been applied to 1150 controlled pedestrian crossings at entry and exit legs of two-lane approaches of six roundabouts across the country. The logistic regression models developed support prior research that the likelihood of driver yielding at the entry leg of roundabouts is higher than at the exit. Drivers tend to yield to pedestrians carrying a white cane more often than to sighted pedestrians. Drivers traveling in the far lane, relative to pedestrian location, have a lower probability of yielding to a pedestrian. As the speed increases the probability of driver yielding decreases. At the exit leg of the roundabout, drivers turning right from the adjacent lane have a lower propensity of yielding than drivers coming from other directions. The findings of this paper further suggest that although there has been much debate on pedestrian right-of-way laws and distinction between pedestrian waiting positions (in the street versus at the curb), this factor does not have a significant impact on driver yielding rate. The logistic regression models also quantify the effect of each of these factors on propensity of driver yielding. The models include variables which are specific to each study location and explain the impact size of each study location on probability of yielding. The models generated in this research will be useful to transportation professionals and researchers interested in understanding the factors that impact driver yielding at modern roundabouts. The results of the research can be used to isolate factors that may increase yielding (such as lower roundabout approach speeds
Directory of Open Access Journals (Sweden)
G. Bussi
2013-08-01
Full Text Available Soil loss and sediment transport in Mediterranean areas are driven by complex non-linear processes which have been only partially understood. Distributed models can be very helpful tools for understanding the catchment-scale phenomena which lead to soil erosion and sediment transport. In this study, a modelling approach is proposed to reproduce and evaluate erosion and sediment yield processes in a Mediterranean catchment (Rambla del Poyo, Valencia, Spain. Due to the lack of sediment transport records for model calibration and validation, a detailed description of the alluvial stratigraphy infilling a check dam that drains a 12.9 km2 sub-catchment was used as indirect information of sediment yield data. These dam infill sediments showed evidences of at least 15 depositional events (floods over the time period 1990–2009. The TETIS model, a distributed conceptual hydrological and sediment model, was coupled to the Sediment Trap Efficiency for Small Ponds (STEP model for reproducing reservoir retention, and it was calibrated and validated using the sedimentation volume estimated for the depositional units associated with discrete runoff events. The results show relatively low net erosion rates compared to other Mediterranean catchments (0.136 Mg ha−1 yr−1, probably due to the extensive outcrops of limestone bedrock, thin soils and rather homogeneous vegetation cover. The simulated sediment production and transport rates offer model satisfactory results, further supported by in-site palaeohydrological evidences and spatial validation using additional check dams, showing the great potential of the presented data assimilation methodology for the quantitative analysis of sediment dynamics in ungauged Mediterranean basins.
Modelling of the parametric yield in decananometer SRAM-Arrays
Directory of Open Access Journals (Sweden)
Th. Fischer
2006-01-01
Full Text Available In today's decananometer (90 nm, 65 nm, ..., CMOS technologies variations of device parameters play an ever more important role. Due to the demand for low leakage systems, supply voltage is decreased on one hand and the transistor threshold voltage is increased on the other hand. This reduces the overdrive voltage of the transistors and leads to decreasing read and write security margins in static memories (SRAM. In addition, smaller dimensions of the devices lead to increasing variations of the device parameters, thus mismatch effects increase. It can be shown that local variations of the transistor parameters limit the functionality of circuits stronger than variations on a global scale or hard defects. We show a method to predict the yield for a large number of SRAM devices without time consuming Monte Carlo simulations in dependence of various parameters (Vdd, temperature, technology options, transistor dimensions, .... This helps the designer to predict the yield for various system options and transistor dimensions, to choose the optimal solution for a specific product.
Miller, Cherie V.; Gutierrez-Magness, Angelica L.; Feit Majedi, Brenda L.; Foster, Gregory D.
2007-01-01
concentrations of total phosphorus and total nitrogen had lower values of multiple R2 than suspended sediment, but the estimated bias for all the models was similar. The models for total nitrogen and total phosphorus tended to under-predict high concentrations and to over-predict low concentrations as compared to measured values. Annual yields (loads per square area in kilograms per year per square kilometer) were estimated for suspended sediment, total nitrogen, and total phosphorus using the U.S. Geological Survey models ESTIMATOR and LOADEST. The model LOADEST used hourly time steps and allowed the use of turbidity, which is strongly correlated to concentrations of suspended sediment, as a predictor variable. Annual yields for total nitrogen and total phosphorus were slightly higher but similar to previous estimates for other watersheds of the Chesapeake Bay, but annual yields for suspended sediment were higher by an order of magnitude for the two Anacostia River stations. Annual yields of suspended sediment at the two Anacostia River stations ranged from 131,000 to 248,000 kilograms per year per square kilometer for 2004 and 2005. LOADEST estimates were similar to those determined with ESTIMATOR, but had reduced errors associated with the estimates.
Estimating Model Evidence Using Data Assimilation
Carrassi, Alberto; Bocquet, Marc; Hannart, Alexis; Ghil, Michael
2017-04-01
We review the field of data assimilation (DA) from a Bayesian perspective and show that, in addition to its by now common application to state estimation, DA may be used for model selection. An important special case of the latter is the discrimination between a factual model - which corresponds, to the best of the modeller's knowledge, to the situation in the actual world in which a sequence of events has occurred-and a counterfactual model, in which a particular forcing or process might be absent or just quantitatively different from the actual world. Three different ensemble-DA methods are reviewed for this purpose: the ensemble Kalman filter (EnKF), the ensemble four-dimensional variational smoother (En-4D-Var), and the iterative ensemble Kalman smoother (IEnKS). An original contextual formulation of model evidence (CME) is introduced. It is shown how to apply these three methods to compute CME, using the approximated time-dependent probability distribution functions (pdfs) each of them provide in the process of state estimation. The theoretical formulae so derived are applied to two simplified nonlinear and chaotic models: (i) the Lorenz three-variable convection model (L63), and (ii) the Lorenz 40- variable midlatitude atmospheric dynamics model (L95). The numerical results of these three DA-based methods and those of an integration based on importance sampling are compared. It is found that better CME estimates are obtained by using DA, and the IEnKS method appears to be best among the DA methods. Differences among the performance of the three DA-based methods are discussed as a function of model properties. Finally, the methodology is implemented for parameter estimation and for event attribution.
Yield gap analysis of cumin in nine regions of Khorasan provinces using modelling approach
Directory of Open Access Journals (Sweden)
behnam kamkar
2009-06-01
Full Text Available There are three hierarchical steps to fill the yield gaps in agricultural systems. These steps are determination of potential yield, yield gaps and system optimization to fill yield gaps. In this study a simple mechanistic model was developed and tested to determine potential yield and yield gaps of Cumin (Cuminum cyminum in nine regions of Khorasan provinces (including Bojnourd, Qaeen, Mashhad, Neishabour, Sabzewar, Gonabad, Ferdous, Kashmar and Birjand. Collected data of related year from 228 fields were used to calculate yield gaps. Results indicated variable potential yields in different climatic conditions (the areas with cooler climate and higher radiation had higher potential yields. Also, yield gaps varied considerably between regions (from 2.42 ton.ha-1 in Bojnourd to 0.68 ton.ha-1 in Sabzewar. The highest value for potential yield belonged to Bojnourd (3.7 ton.ha-1. The collected data from studied fields and sensitivity analysis on sowing date (based on common sowing dates range showed that inappropriate sowing dates was one of the most important yield reducing factors in all regions. Results revealed that if the yield gaps can be filled based on appropriate management option, yield can be increased by two to three folds in some regions.
Development on electromagnetic impedance function modeling and its estimation
Energy Technology Data Exchange (ETDEWEB)
Sutarno, D., E-mail: Sutarno@fi.itb.ac.id [Earth Physics and Complex System Division Faculty of Mathematics and Natural Sciences Institut Teknologi Bandung (Indonesia)
2015-09-30
Today the Electromagnetic methods such as magnetotellurics (MT) and controlled sources audio MT (CSAMT) is used in a broad variety of applications. Its usefulness in poor seismic areas and its negligible environmental impact are integral parts of effective exploration at minimum cost. As exploration was forced into more difficult areas, the importance of MT and CSAMT, in conjunction with other techniques, has tended to grow continuously. However, there are obviously important and difficult problems remaining to be solved concerning our ability to collect process and interpret MT as well as CSAMT in complex 3D structural environments. This talk aim at reviewing and discussing the recent development on MT as well as CSAMT impedance functions modeling, and also some improvements on estimation procedures for the corresponding impedance functions. In MT impedance modeling, research efforts focus on developing numerical method for computing the impedance functions of three dimensionally (3-D) earth resistivity models. On that reason, 3-D finite elements numerical modeling for the impedances is developed based on edge element method. Whereas, in the CSAMT case, the efforts were focused to accomplish the non-plane wave problem in the corresponding impedance functions. Concerning estimation of MT and CSAMT impedance functions, researches were focused on improving quality of the estimates. On that objective, non-linear regression approach based on the robust M-estimators and the Hilbert transform operating on the causal transfer functions, were used to dealing with outliers (abnormal data) which are frequently superimposed on a normal ambient MT as well as CSAMT noise fields. As validated, the proposed MT impedance modeling method gives acceptable results for standard three dimensional resistivity models. Whilst, the full solution based modeling that accommodate the non-plane wave effect for CSAMT impedances is applied for all measurement zones, including near-, transition
Robust estimation procedure in panel data model
Energy Technology Data Exchange (ETDEWEB)
Shariff, Nurul Sima Mohamad [Faculty of Science of Technology, Universiti Sains Islam Malaysia (USIM), 71800, Nilai, Negeri Sembilan (Malaysia); Hamzah, Nor Aishah [Institute of Mathematical Sciences, Universiti Malaya, 50630, Kuala Lumpur (Malaysia)
2014-06-19
The panel data modeling has received a great attention in econometric research recently. This is due to the availability of data sources and the interest to study cross sections of individuals observed over time. However, the problems may arise in modeling the panel in the presence of cross sectional dependence and outliers. Even though there are few methods that take into consideration the presence of cross sectional dependence in the panel, the methods may provide inconsistent parameter estimates and inferences when outliers occur in the panel. As such, an alternative method that is robust to outliers and cross sectional dependence is introduced in this paper. The properties and construction of the confidence interval for the parameter estimates are also considered in this paper. The robustness of the procedure is investigated and comparisons are made to the existing method via simulation studies. Our results have shown that robust approach is able to produce an accurate and reliable parameter estimates under the condition considered.
Cecchinato, A; Penasa, M; De Marchi, M; Gallo, L; Bittante, G; Carnier, P
2011-08-01
The aim of this study was to estimate heritabilities of rennet coagulation time (RCT) and curd firmness (a(30)) and their genetic correlations with test-day milk yield, composition (fat, protein, and casein content), somatic cell score, and acidity (pH and titratable acidity) using coagulating and noncoagulating (NC) milk information. Data were from 1,025 Holstein-Friesian (HF) and 1,234 Brown Swiss (BS) cows, which were progeny of 54 HF and 58 BS artificial insemination sires, respectively. Milk coagulation properties (MCP) of each cow were measured once using a computerized renneting meter and samples not exhibiting coagulation within 31 min after rennet addition were classified as NC milk. For NC samples, RCT was unobserved. Multivariate analyses, using Bayesian methodology, were performed to estimate the genetic relationships of RCT or a(30) with the other traits and statistical inference was based on the marginal posterior distributions of parameters of concern. For analyses involving RCT, a right-censored Gaussian linear model was used and records of NC milk samples, being censored records, were included as unknown parameters in the model implementing a data augmentation procedure. Rennet coagulation time was more heritable [heritability (h(2))=0.240 and h(2)=0.210 for HF and BS, respectively] than a(30) (h(2)=0.148 and h(2)=0.168 for HF and BS, respectively). Milk coagulation properties were more heritable than a single test-day milk yield (h(2)=0.103 and h(2)=0.097 for HF and BS, respectively) and less heritable than milk composition traits whose heritability ranged from 0.275 to 0.275, with the only exception of fat content of BS milk (h(2)=0.108). A negative genetic correlation, lower than -0.85, was estimated between RCT and a(30) for both breeds. Genetic relationships of MCP with yield and composition were low or moderate and favorable. The genetic correlation of somatic cell score with RCT in BS cows was large and positive and even more positive were
Chahbi, Aicha; Zribi, Mehrez; Lili-Chabaane, Zohra; Mougenot, Bernard
2015-10-01
In semi-arid areas, an operational grain yield forecasting system, which could help decision-makers to plan annual imports, is needed. It can be challenging to monitor the crop canopy and production capacity of plants, especially cereals. Many models, based on the use of remote sensing or agro-meteorological models, have been developed to estimate the biomass and grain yield of cereals. Remote sensing has demonstrated its strong potential for the monitoring of the vegetation's dynamics and temporal variations. Through the use of a rich database, acquired over a period of two years for more than 60 test fields, and from 20 optical satellite SPOT/HRV images, the aim of the present study is to evaluate the feasibility of two approaches to estimate the dynamics and yields of cereals in the context of semi-arid, low productivity regions in North Africa. The first approach is based on the application of the semi-empirical growth model SAFY "Simple Algorithm For Yield estimation", developed to simulate the dynamics of the leaf area index and the grain yield, at the field scale. The model is able to reproduce the time evolution of the LAI of all fields. However, the yields are under-estimated. Therefore, we developed a new approach to improve the SAFY model. The grain yield is function of LAI area in the growth period between 25 March and 5 April. This approach is robust, the measured and estimated grain yield are well correlated. Finally, this model is used in combination with remotely sensed LAI measurements to estimate yield for the entire studied site.
Robertson, Dale M.; Schwarz, Gregory E.; Saad, David A.; Alexander, Richard B.
2009-01-01
Excessive loads of nutrients transported by tributary rivers have been linked to hypoxia in the Gulf of Mexico. Management efforts to reduce the hypoxic zone in the Gulf of Mexico and improve the water quality of rivers and streams could benefit from targeting nutrient reductions toward watersheds with the highest nutrient yields delivered to sensitive downstream waters. One challenge is that most conventional watershed modeling approaches (e.g., mechanistic models) used in these management decisions do not consider uncertainties in the predictions of nutrient yields and their downstream delivery. The increasing use of parameter estimation procedures to statistically estimate model coefficients, however, allows uncertainties in these predictions to be reliably estimated. Here, we use a robust bootstrapping procedure applied to the results of a previous application of the hybrid statistical/mechanistic watershed model SPARROW (Spatially Referenced Regression On Watershed attributes) to develop a statistically reliable method for identifying “high priority” areas for management, based on a probabilistic ranking of delivered nutrient yields from watersheds throughout a basin. The method is designed to be used by managers to prioritize watersheds where additional stream monitoring and evaluations of nutrient-reduction strategies could be undertaken. Our ranking procedure incorporates information on the confidence intervals of model predictions and the corresponding watershed rankings of the delivered nutrient yields. From this quantified uncertainty, we estimate the probability that individual watersheds are among a collection of watersheds that have the highest delivered nutrient yields. We illustrate the application of the procedure to 818 eight-digit Hydrologic Unit Code watersheds in the Mississippi/Atchafalaya River basin by identifying 150 watersheds having the highest delivered nutrient yields to the Gulf of Mexico. Highest delivered yields were from
PARAMETER ESTIMATION IN BREAD BAKING MODEL
Hadiyanto Hadiyanto; AJB van Boxtel
2012-01-01
Bread product quality is highly dependent to the baking process. A model for the development of product quality, which was obtained by using quantitative and qualitative relationships, was calibrated by experiments at a fixed baking temperature of 200°C alone and in combination with 100 W microwave powers. The model parameters were estimated in a stepwise procedure i.e. first, heat and mass transfer related parameters, then the parameters related to product transformations and finally pro...
Adaptive Covariance Estimation with model selection
Biscay, Rolando; Loubes, Jean-Michel
2012-01-01
We provide in this paper a fully adaptive penalized procedure to select a covariance among a collection of models observing i.i.d replications of the process at fixed observation points. For this we generalize previous results of Bigot and al. and propose to use a data driven penalty to obtain an oracle inequality for the estimator. We prove that this method is an extension to the matricial regression model of the work by Baraud.
Error Estimates of Theoretical Models: a Guide
Dobaczewski, J; Reinhard, P -G
2014-01-01
This guide offers suggestions/insights on uncertainty quantification of nuclear structure models. We discuss a simple approach to statistical error estimates, strategies to assess systematic errors, and show how to uncover inter-dependencies by correlation analysis. The basic concepts are illustrated through simple examples. By providing theoretical error bars on predicted quantities and using statistical methods to study correlations between observables, theory can significantly enhance the feedback between experiment and nuclear modeling.
Estimating an Activity Driven Hidden Markov Model
Meyer, David A.; Shakeel, Asif
2015-01-01
We define a Hidden Markov Model (HMM) in which each hidden state has time-dependent $\\textit{activity levels}$ that drive transitions and emissions, and show how to estimate its parameters. Our construction is motivated by the problem of inferring human mobility on sub-daily time scales from, for example, mobile phone records.
Solar energy estimation using REST2 model
Directory of Open Access Journals (Sweden)
M. Rizwan, Majid Jamil, D. P. Kothari
2010-03-01
Full Text Available The network of solar energy measuring stations is relatively rare through out the world. In India, only IMD (India Meteorological Department Pune provides data for quite few stations, which is considered as the base data for research purposes. However, hourly data of measured energy is not available, even for those stations where measurement has already been done. Due to lack of hourly measured data, the estimation of solar energy at the earth’s surface is required. In the proposed study, hourly solar energy is estimated at four important Indian stations namely New Delhi, Mumbai, Pune and Jaipur keeping in mind their different climatic conditions. For this study, REST2 (Reference Evaluation of Solar Transmittance, 2 bands, a high performance parametric model for the estimation of solar energy is used. REST2 derivation uses the two-band scheme as used in the CPCR2 (Code for Physical Computation of Radiation, 2 bands but CPCR2 does not include NO2 absorption, which is an important parameter for estimating solar energy. In this study, using ground measurements during 1986-2000 as reference, a MATLAB program is written to evaluate the performance of REST2 model at four proposed stations. The solar energy at four stations throughout the year is estimated and compared with CPCR2. The results obtained from REST2 model show the good agreement against the measured data on horizontal surface. The study reveals that REST2 models performs better and evaluate the best results as compared to the other existing models under cloudless sky for Indian climatic conditions.
Dose estimations of fast neutrons from a nuclear reactor by micronuclear yields in onion seedlings.
Fujikawa, K; Endo, S; Itoh, T; Yonezawa, Y; Hoshi, M
1999-12-01
Irradiations of onion seedlings with fission neutrons from bare, Pb-moderated, and Fe-moderated 252Cf sources induced micronuclei in the root-tip cells at similar rates. The rate per cGy averaged for the three sources, , was 19 times higher than rate induced by 60Co gamma-rays. When neutron doses, Dn, were estimated from frequencies of micronuclei induced in onion seedlings after exposure to neutron-gamma mixed radiation from a 1 W nuclear reactor, using the reciprocal of as conversion factor, resulting Dn values agreed within 10% with doses measured with paired ionizing chambers. This excellent agreement was achieved by the high sensitivity of the onion system to fast neutrons relative to gamma-rays and the high contribution of fast neutrons to the total dose of mixed radiation in the reactor's field.
Li, Tao; Hasegawa, Toshihiro; Yin, Xinyou; Zhu, Yan; Boote, Kenneth; Adam, Myriam; Bregaglio, Simone; Buis, Samuel; Confalonieri, Roberto; Fumoto, Tamon; Gaydon, Donald; Marcaida, Manuel, III; Nakagawa, Hiroshi; Oriol, Philippe; Ruane, Alex C.; Ruget, Francoise; Singh, Balwinder; Singh, Upendra; Tang, Liang; Tao, Fulu; Wilkens, Paul; Yoshida, Hiroe; Zhang, Zhao; Bouman, Bas
2014-01-01
Predicting rice (Oryza sativa) productivity under future climates is important for global food security. Ecophysiological crop models in combination with climate model outputs are commonly used in yield prediction, but uncertainties associated with crop models remain largely unquantified. We evaluated 13 rice models against multi-year experimental yield data at four sites with diverse climatic conditions in Asia and examined whether different modeling approaches on major physiological processes attribute to the uncertainties of prediction to field measured yields and to the uncertainties of sensitivity to changes in temperature and CO2 concentration [CO2]. We also examined whether a use of an ensemble of crop models can reduce the uncertainties. Individual models did not consistently reproduce both experimental and regional yields well, and uncertainty was larger at the warmest and coolest sites. The variation in yield projections was larger among crop models than variation resulting from 16 global climate model-based scenarios. However, the mean of predictions of all crop models reproduced experimental data, with an uncertainty of less than 10 percent of measured yields. Using an ensemble of eight models calibrated only for phenology or five models calibrated in detail resulted in the uncertainty equivalent to that of the measured yield in well-controlled agronomic field experiments. Sensitivity analysis indicates the necessity to improve the accuracy in predicting both biomass and harvest index in response to increasing [CO2] and temperature.
ICA Model Order Estimation Using Clustering Method
Directory of Open Access Journals (Sweden)
P. Sovka
2007-12-01
Full Text Available In this paper a novel approach for independent component analysis (ICA model order estimation of movement electroencephalogram (EEG signals is described. The application is targeted to the brain-computer interface (BCI EEG preprocessing. The previous work has shown that it is possible to decompose EEG into movement-related and non-movement-related independent components (ICs. The selection of only movement related ICs might lead to BCI EEG classification score increasing. The real number of the independent sources in the brain is an important parameter of the preprocessing step. Previously, we used principal component analysis (PCA for estimation of the number of the independent sources. However, PCA estimates only the number of uncorrelated and not independent components ignoring the higher-order signal statistics. In this work, we use another approach - selection of highly correlated ICs from several ICA runs. The ICA model order estimation is done at significance level ÃŽÂ± = 0.05 and the model order is less or more dependent on ICA algorithm and its parameters.
Directory of Open Access Journals (Sweden)
M.H. Gharineh
2016-05-01
Full Text Available By application of climatic zoning methods, it is possible to study different agricultural aspects and then with harmony this aspects, determined similar states in a zone. Today, simulation models are widely used around the world in agricultural research and education and cropland management. Due to the vast extent of the agricultural activities in Iran, application of such models seems to be quite necessary for optimization objectives. The primary focus of this research was climatic zoning of Khouzestan region based on the results from wheat yield potential by means of WOFOST model. First, model performance and the accuracy of its results were evaluated. The findings showed that WOFOST model can adequately simulate phenological phases and grain and dry matter yields. The calculated Root Mean Square Error (RMSE values from blooming and physiologic maturity of crop were 1.97 per day and for seed and dry matter performances 810 and 810 kg ha-1, respectively. Also, one-to-one linear regression values for these stages were 0.96, 0.97, 0.93 and 0.91, respectively. The results of simulations indicated that the potentials of crop yield and the actual yield of farmlands are considerably different. Determination of the yield potentials of crop and its restricting factors were considered as the first step toward higher yield of crop. The results emphasized the fact that maximum and minimum yield potentials were found near the cities of Izeh (9247 kg. ha-1 and Shushtar (7538 kg. ha-1. A comparison of potential and actual crop yield trends revealed that the latter has been decreased might be due to the global warming phenomena resulting from green gases release into atmosphere while the increase of the farmer has been related to genetic modification of crop and management strategic. The results also showed that the poor yield of Mahshahr croplands (65.8% was because of unsuitable soil and high level ground water resources. The lowest performance was found in
Efficiently adapting graphical models for selectivity estimation
DEFF Research Database (Denmark)
Tzoumas, Kostas; Deshpande, Amol; Jensen, Christian S.
2013-01-01
of the selectivities of the constituent predicates. However, this independence assumption is more often than not wrong, and is considered to be the most common cause of sub-optimal query execution plans chosen by modern query optimizers. We take a step towards a principled and practical approach to performing...... cardinality estimation without making the independence assumption. By carefully using concepts from the field of graphical models, we are able to factor the joint probability distribution over all the attributes in the database into small, usually two-dimensional distributions, without a significant loss......Query optimizers rely on statistical models that succinctly describe the underlying data. Models are used to derive cardinality estimates for intermediate relations, which in turn guide the optimizer to choose the best query execution plan. The quality of the resulting plan is highly dependent...
Mrg: A Magnitude Scale for 1 s Rayleigh Waves at Local Distances with Focus on Yield Estimation
2016-08-23
Bache, T. (1982), Estimating the yield of underground nuclear explosions, Bull. Seism . Soc. Am., 72, pp. S131-S168. Cho, K. H., R. B. Herrmann, C. J...Ammon, and K. Lee (2007), Imaging the upper crust of the Korean peninsula by surface-wave tomography, Bull. Seism . Soc. Am., 97, pp. 198-207. Denny...Monitoring the earthquake source process in North America, Bull. Seism . Soc. Am., 101, pp. 2609-2625. Kennett, B.L.N., E. R. Engdahl, and R. Buland
Directory of Open Access Journals (Sweden)
H. Khalilzade
2016-02-01
Full Text Available Introduction Around the world maize is the second crop with the most cultivated areas and amount of production, so as the most important strategic crop, have a special situation in policies, decision making, resources and inputs allocation. On the other side, negative environmental consequences of intensive consumption of agrochemicals resulted to change view concerning food production. One of the most important visions is sustainable production of enough food plus attention to social, economic and environmental aspects. Many researchers stated that the first step to achieve this goal is optimization and improvement of resources use efficiencies. According to little knowledge on relation between soil electrical conductivity and yield of maize, beside the environmental concerns about nitrogen consumption and need to replace chemical nitrogen by ecological inputs, this study designed and aimed to evaluate agroecological characteristics of corn and some soil characteristics as affected by application of organic and biological fertilizers under field conditions. Materials and Methods In order to probing the possibility of grain yield and soil nitrogen estimation via measurement of soil properties, a field experiment was conducted during growing season 2010 at Research Station, Ferdowsi University of Mashhad, Iran. A randomized complete block design (RCBD with three replications was used. Treatments included: 1- manure (30 ton ha-1, 2-vermicompost (10 ton ha-1, 3- nitroxin (containing Azotobacter sp. and Azospirillum sp., inoculation was done according to Kennedy et al., 4- nitrogen as urea (400 kg ha-1 and 5- control (without fertilizer. Studied traits were soil pH, soil EC, soil respiration rate, N content of soil and maize yield. Soil respiration rate was measured using equation 1: CO2= (V0- V× N×22 Equation 1 In which V0 is the volume of consumed acid for control treatment titration, V is of the volume of consumed acid for sample treatment
Modeling of secondary organic aerosol yields from laboratory chamber data
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M. N. Chan
2009-04-01
Full Text Available A product-specific model for secondary organic aerosol (SOA formation and composition based on equilibrium gas-particle partitioning is evaluated. The model is applied to represent laboratory data on the ozonolysis of α-pinene under dry, dark, and low-NO_{x} conditions in the presence of ammonium sulfate seed aerosol. Using five major identified products, the model is fit to the chamber data. From the optimal fitting, SOA oxygen-to-carbon (O/C and hydrogen-to-carbon (H/C ratios are modeled. The discrepancy between measured H/C ratios and those based on the oxidation products used in the model fitting suggests the potential importance of particle-phase reactions. Data fitting is also carried out using the volatility basis set, wherein oxidation products are parsed into volatility bins. The product-specific model is best used for an SOA precursor for which a substantial fraction of the aerosol-phase oxidation products has been identified.
DEFF Research Database (Denmark)
Pedersen, Marie; Siroux, Valérie; Pin, Isabelle
2013-01-01
BACKGROUND: Spatially-resolved air pollution models can be developed in large areas. The resulting increased exposure contrasts and population size offer opportunities to better characterize the effect of atmospheric pollutants on respiratory health. However the heterogeneity of these areas may a...
High-dimensional model estimation and model selection
CERN. Geneva
2015-01-01
I will review concepts and algorithms from high-dimensional statistics for linear model estimation and model selection. I will particularly focus on the so-called p>>n setting where the number of variables p is much larger than the number of samples n. I will focus mostly on regularized statistical estimators that produce sparse models. Important examples include the LASSO and its matrix extension, the Graphical LASSO, and more recent non-convex methods such as the TREX. I will show the applicability of these estimators in a diverse range of scientific applications, such as sparse interaction graph recovery and high-dimensional classification and regression problems in genomics.
Parameter estimation, model reduction and quantum filtering
Chase, Bradley A.
This thesis explores the topics of parameter estimation and model reduction in the context of quantum filtering. The last is a mathematically rigorous formulation of continuous quantum measurement, in which a stream of auxiliary quantum systems is used to infer the state of a target quantum system. Fundamental quantum uncertainties appear as noise which corrupts the probe observations and therefore must be filtered in order to extract information about the target system. This is analogous to the classical filtering problem in which techniques of inference are used to process noisy observations of a system in order to estimate its state. Given the clear similarities between the two filtering problems, I devote the beginning of this thesis to a review of classical and quantum probability theory, stochastic calculus and filtering. This allows for a mathematically rigorous and technically adroit presentation of the quantum filtering problem and solution. Given this foundation, I next consider the related problem of quantum parameter estimation, in which one seeks to infer the strength of a parameter that drives the evolution of a probe quantum system. By embedding this problem in the state estimation problem solved by the quantum filter, I present the optimal Bayesian estimator for a parameter when given continuous measurements of the probe system to which it couples. For cases when the probe takes on a finite number of values, I review a set of sufficient conditions for asymptotic convergence of the estimator. For a continuous-valued parameter, I present a computational method called quantum particle filtering for practical estimation of the parameter. Using these methods, I then study the particular problem of atomic magnetometry and review an experimental method for potentially reducing the uncertainty in the estimate of the magnetic field beyond the standard quantum limit. The technique involves double-passing a probe laser field through the atomic system, giving
National Research Council Canada - National Science Library
Zhou, Annan; Sheng, Daichao
2009-01-01
The model recently presented by Sheng, Fredlund, and Gens, known as the SFG model, provides a consistent explanation of yield stress, shear strength, and volume change behaviour of unsaturated soils...
Event-Based Modeling of Driver Yielding Behavior at Unsignalized Crosswalks.
Schroeder, Bastian J; Rouphail, Nagui M
2011-07-01
This research explores factors associated with driver yielding behavior at unsignalized pedestrian crossings and develops predictive models for yielding using logistic regression. It considers the effect of variables describing driver attributes, pedestrian characteristics and concurrent conditions at the crosswalk on the yield response. Special consideration is given to 'vehicle dynamics constraints' that form a threshold for the potential to yield. Similarities are identified to driver reaction in response to the 'amber' indication at a signalized intersection. The logit models were developed from data collected at two unsignalized mid-block crosswalks in North Carolina. The data include 'before' and 'after' observations of two pedestrian safety treatments, an in-street pedestrian crossing sign and pedestrian-actuated in-roadway warning lights.The analysis suggests that drivers are more likely to yield to assertive pedestrians who walk briskly in their approach to the crosswalk. In turn, the yield probability is reduced with higher speeds, deceleration rates and if vehicles are traveling in platoons. The treatment effects proved to be significant and increased the propensity of drivers to yield, but their effectiveness may be dependent on whether the pedestrian activates the treatment.The results of this research provide new insights on the complex interaction of pedestrians and vehicles at unsignalized intersections and have implications for future work towards predictive models for driver yielding behavior. The developed logit models can provide the basis for representing driver yielding behavior in a microsimulation modeling environment.
Setiyono, T. D.
2014-12-01
Accurate and timely information on rice crop growth and yield helps governments and other stakeholders adapting their economic policies and enables relief organizations to better anticipate and coordinate relief efforts in the wake of a natural catastrophe. Such delivery of rice growth and yield information is made possible by regular earth observation using space-born Synthetic Aperture Radar (SAR) technology combined with crop modeling approach to estimate yield. Radar-based remote sensing is capable of observing rice vegetation growth irrespective of cloud coverage, an important feature given that in incidences of flooding the sky is often cloud-covered. The system allows rapid damage assessment over the area of interest. Rice yield monitoring is based on a crop growth simulation and SAR-derived key information, particularly start of season and leaf growth rate. Results from pilot study sites in South and South East Asian countries suggest that incorporation of SAR data into crop model improves yield estimation for actual yields. Remote-sensing data assimilation into crop model effectively capture responses of rice crops to environmental conditions over large spatial coverage, which otherwise is practically impossible to achieve. Such improvement of actual yield estimates offers practical application such as in a crop insurance program. Process-based crop simulation model is used in the system to ensure climate information is adequately captured and to enable mid-season yield forecast.
Extreme gust wind estimation using mesoscale modeling
DEFF Research Database (Denmark)
Larsén, Xiaoli Guo; Kruger, Andries
2014-01-01
through turbulent eddies. This process is modeled using the mesoscale Weather Forecasting and Research (WRF) model. The gust at the surface is calculated as the largest winds over a layer where the averaged turbulence kinetic energy is greater than the averaged buoyancy force. The experiments have been......Currently, the existing estimation of the extreme gust wind, e.g. the 50-year winds of 3 s values, in the IEC standard, is based on a statistical model to convert the 1:50-year wind values from the 10 min resolution. This statistical model assumes a Gaussian process that satisfies the classical...... done for Denmark and two areas in South Africa. For South Africa, the extreme gust atlases from South Africa were created from the output of the mesoscale modelling using Climate Forecasting System Reanalysis (CFSR) forcing for the period 1998 – 2010. The extensive measurements including turbulence...
Entropy Based Modelling for Estimating Demographic Trends.
Directory of Open Access Journals (Sweden)
Guoqi Li
Full Text Available In this paper, an entropy-based method is proposed to forecast the demographical changes of countries. We formulate the estimation of future demographical profiles as a constrained optimization problem, anchored on the empirically validated assumption that the entropy of age distribution is increasing in time. The procedure of the proposed method involves three stages, namely: 1 Prediction of the age distribution of a country's population based on an "age-structured population model"; 2 Estimation the age distribution of each individual household size with an entropy-based formulation based on an "individual household size model"; and 3 Estimation the number of each household size based on a "total household size model". The last stage is achieved by projecting the age distribution of the country's population (obtained in stage 1 onto the age distributions of individual household sizes (obtained in stage 2. The effectiveness of the proposed method is demonstrated by feeding real world data, and it is general and versatile enough to be extended to other time dependent demographic variables.
Model-based estimation of individual fitness
Link, W.A.; Cooch, E.G.; Cam, E.
2002-01-01
Fitness is the currency of natural selection, a measure of the propagation rate of genotypes into future generations. Its various definitions have the common feature that they are functions of survival and fertility rates. At the individual level, the operative level for natural selection, these rates must be understood as latent features, genetically determined propensities existing at birth. This conception of rates requires that individual fitness be defined and estimated by consideration of the individual in a modelled relation to a group of similar individuals; the only alternative is to consider a sample of size one, unless a clone of identical individuals is available. We present hierarchical models describing individual heterogeneity in survival and fertility rates and allowing for associations between these rates at the individual level. We apply these models to an analysis of life histories of Kittiwakes (Rissa tridactyla ) observed at several colonies on the Brittany coast of France. We compare Bayesian estimation of the population distribution of individual fitness with estimation based on treating individual life histories in isolation, as samples of size one (e.g. McGraw & Caswell, 1996).
Directory of Open Access Journals (Sweden)
Lynch J.P.
2017-09-01
Full Text Available Although Irish winter wheat yields are among the highest globally, increases in the profitability of this crop are required to maintain its economic viability. However, in order to determine if efforts to further increase Irish wheat yields are likely to be successful, an accurate estimation of the yield potential is required for different regions within Ireland. A winter wheat yield potential model (WWYPM was developed, which estimates the maximum water-limited yield achievable, within the confines of current genetic resources and technologies, using parameters for winter wheat growth and development observed recently in Ireland and a minor amount of daily meteorological input (maximum and minimum daily temperature, total daily rainfall and total daily incident radiation. The WWYPM is composed of three processes: (i an estimation of potential green area index, (ii an estimation of light interception and biomass accumulation and (iii an estimation of biomass partitioning to grain yield. Model validation indicated that WWYPM estimations of water-limited yield potential (YPw were significantly related to maximum yields recorded in variety evaluation trials as well as regional average and maximum farm yields, reflecting the model’s sensitivity to alterations in the climatic environment with spatial and seasonal variations. Simulations of YPw for long-term average weather data at 12 sites located at spatially contrasting regions of Ireland indicated that the typical YPw varied between 15.6 and 17.9 t/ha, with a mean of 16.7 t/ha at 15% moisture content. These results indicate that the majority of sites in Ireland have the potential to grow high-yielding crops of winter wheat when the effects of very high rainfall and other stresses such as disease incidence and nutrient deficits are not considered.
Fission yield calculation using toy model based on Monte Carlo simulation
Energy Technology Data Exchange (ETDEWEB)
Jubaidah, E-mail: jubaidah@student.itb.ac.id [Nuclear Physics and Biophysics Division, Department of Physics, Bandung Institute of Technology. Jl. Ganesa No. 10 Bandung – West Java, Indonesia 40132 (Indonesia); Physics Department, Faculty of Mathematics and Natural Science – State University of Medan. Jl. Willem Iskandar Pasar V Medan Estate – North Sumatera, Indonesia 20221 (Indonesia); Kurniadi, Rizal, E-mail: rijalk@fi.itb.ac.id [Nuclear Physics and Biophysics Division, Department of Physics, Bandung Institute of Technology. Jl. Ganesa No. 10 Bandung – West Java, Indonesia 40132 (Indonesia)
2015-09-30
Toy model is a new approximation in predicting fission yield distribution. Toy model assumes nucleus as an elastic toy consist of marbles. The number of marbles represents the number of nucleons, A. This toy nucleus is able to imitate the real nucleus properties. In this research, the toy nucleons are only influenced by central force. A heavy toy nucleus induced by a toy nucleon will be split into two fragments. These two fission fragments are called fission yield. In this research, energy entanglement is neglected. Fission process in toy model is illustrated by two Gaussian curves intersecting each other. There are five Gaussian parameters used in this research. They are scission point of the two curves (R{sub c}), mean of left curve (μ{sub L}) and mean of right curve (μ{sub R}), deviation of left curve (σ{sub L}) and deviation of right curve (σ{sub R}). The fission yields distribution is analyses based on Monte Carlo simulation. The result shows that variation in σ or µ can significanly move the average frequency of asymmetry fission yields. This also varies the range of fission yields distribution probability. In addition, variation in iteration coefficient only change the frequency of fission yields. Monte Carlo simulation for fission yield calculation using toy model successfully indicates the same tendency with experiment results, where average of light fission yield is in the range of 90yield is in about 135
Holland, Alexandra D; Wheeler, Dean R
2011-05-01
For non-inhibitory irradiances, the rate of algal biomass synthesis was modeled as the product of the algal autotrophic yield Φ(DW) and the flux of photons absorbed by the culture, as described using Beer-Lambert law. As a contrast to earlier attempts, the use of scatter-corrected extinction coefficients enabled the validation of such approach, which bypasses determination of photosynthesis-irradiance (PI) kinetic parameters. The broad misconception that PI curves, or the equivalent use of specific growth rate expressions independent of the biomass concentration, can be extended to adequately model biomass production under light-limitation is addressed. For inhibitory irradiances, a proposed mechanistic model, based on the photosynthetic units (PSU) concept, allows one to estimate a target speed νT across the photic zone in order to limit the flux of photons per cell to levels averting significant reductions in Φ(DW) . These modeled target speeds, on the order of 5-20 m s(-1) for high outdoor irradiances, call for fundamental changes in reactor design to optimize biomass productivity. The presented analysis enables a straightforward bioreactor parameterization, which, in-turn, guides the establishment of conditions ensuring maximum productivity and complete nutrients consumption. Additionally, solar and fluorescent lighting spectra were used to calculate energy to photon-counts conversion factors.
Correlation Models for Light Olefin Yields from Catalytic Pyrolysis of Petroleum Residue
Institute of Scientific and Technical Information of China (English)
DongXiaoli; MengXianghai; GaoJinsen; XuChunming
2005-01-01
Correlation models for light olefin yields from residue catalytic pyrolysis are studied. Experiments are carried out in a confined fluidized bed reactor for Daqing (China) atmospheric residue catalytic pyrolysis over LCM-5 pyrolyzing catalyst. The influences of reaction temperature, residence time and the weight ratios of catalyst-to-oil and steam-to-oil on light olefin yields are researched. Correlation models for light olefin yields are established, and the model parameters obtained, with the least square method. Results for error analysis and the F-statistical test show that the correlation models have high calculation precision.
Hidden Markov models estimation and control
Elliott, Robert J; Moore, John B
1995-01-01
As more applications are found, interest in Hidden Markov Models continues to grow. Following comments and feedback from colleagues, students and other working with Hidden Markov Models the corrected 3rd printing of this volume contains clarifications, improvements and some new material, including results on smoothing for linear Gaussian dynamics. In Chapter 2 the derivation of the basic filters related to the Markov chain are each presented explicitly, rather than as special cases of one general filter. Furthermore, equations for smoothed estimates are given. The dynamics for the Kalman filte
Generalized elastic model yields a fractional Langevin equation description.
Taloni, Alessandro; Chechkin, Aleksei; Klafter, Joseph
2010-04-23
Starting from a generalized elastic model which accounts for the stochastic motion of several physical systems such as membranes, (semi)flexible polymers, and fluctuating interfaces among others, we derive the fractional Langevin equation (FLE) for a probe particle in such systems, in the case of thermal initial conditions. We show that this FLE is the only one fulfilling the fluctuation-dissipation relation within a new family of fractional Brownian motion equations. The FLE for the time-dependent fluctuations of the donor-acceptor distance in a protein is shown to be recovered. When the system starts from nonthermal conditions, the corresponding FLE, which does not fulfill the fluctuation-dissipation relation, is derived.
On Bayes linear unbiased estimation of estimable functions for the singular linear model
Institute of Scientific and Technical Information of China (English)
ZHANG Weiping; WEI Laisheng
2005-01-01
The unique Bayes linear unbiased estimator (Bayes LUE) of estimable functions is derived for the singular linear model. The superiority of Bayes LUE over ordinary best linear unbiased estimator is investigated under mean square error matrix (MSEM)criterion.
Assessments of Maize Yield Potential in the Korean Peninsula Using Multiple Crop Models
Kim, S. H.; Myoung, B.; Lim, C. H.; Lee, S. G.; Lee, W. K.; Kafatos, M.
2015-12-01
The Korean Peninsular has unique agricultural environments due to the differences in the political and socio-economical systems between the Republic of Korea (SK, hereafter) and the Democratic Peoples' Republic of Korea (NK, hereafter). NK has been suffering from the lack of food supplies caused by natural disasters, land degradation and failed political system. The neighboring developed country SK has a better agricultural system but very low food self-sufficiency rate (around 1% of maize). Maize is an important crop in both countries since it is staple food for NK and SK is No. 2 maize importing country in the world after Japan. Therefore evaluating maize yield potential (Yp) in the two distinct regions is essential to assess food security under climate change and variability. In this study, we have utilized multiple process-based crop models capable of regional-scale assessments to evaluate maize Yp over the Korean Peninsula - the GIS version of EPIC model (GEPIC) and APSIM model that can be expanded to regional scales (APSIM regions). First we evaluated model performance and skill for 20 years from 1991 to 2010 using reanalysis data (Local Data Assimilation and Prediction System (LDAPS); 1.5km resolution) and observed data. Each model's performances were compared over different regions within the Korean Peninsula of different regional climate characteristics. To quantify the major influence of individual climate variables, we also conducted a sensitivity test using 20 years of climatology. Lastly, a multi-model ensemble analysis was performed to reduce crop model uncertainties. The results will provide valuable information for estimating the climate change or variability impacts on Yp over the Korean Peninsula.
Estimating Structural Models of Corporate Bond Prices in Indonesian Corporations
Directory of Open Access Journals (Sweden)
Lenny Suardi
2014-08-01
Full Text Available This paper applies the maximum likelihood (ML approaches to implementing the structural model of corporate bond, as suggested by Li and Wong (2008, in Indonesian corporations. Two structural models, extended Merton and Longstaff & Schwartz (LS models, are used in determining these prices, yields, yield spreads and probabilities of default. ML estimation is used to determine the volatility of irm value. Since irm value is unobserved variable, Duan (1994 suggested that the irst step of ML estimation is to derive the likelihood function for equity as the option on the irm value. The second step is to ind parameters such as the drift and volatility of irm value, that maximizing this function. The irm value itself is extracted by equating the pricing formula to the observed equity prices. Equity, total liabilities, bond prices data and the irm's parameters (irm value, volatility of irm value, and default barrier are substituted to extended Merton and LS bond pricing formula in order to valuate the corporate bond.These models are implemented to a sample of 24 bond prices in Indonesian corporation during period of 2001-2005, based on criteria of Eom, Helwege and Huang (2004. The equity and bond prices data were obtained from Indonesia Stock Exchange for irms that issued equity and provided regular inancial statement within this period. The result shows that both models, in average, underestimate the bond prices and overestimate the yields and yield spread. ";} // -->activate javascript
Computing arbitrage-free yields in multi-factor Gaussian shadow-rate term structure models
Marcel A. Priebsch
2013-01-01
This paper develops a method to approximate arbitrage-free bond yields within a term structure model in which the short rate follows a Gaussian process censored at zero (a "shadow-rate model" as proposed by Black, 1995). The censoring ensures that model-implied yields are constrained to be positive, but it also introduces non-linearity that renders standard bond pricing formulas inapplicable. In particular, yields are not linear functions of the underlying state vector as they are in affine t...
DEFF Research Database (Denmark)
Lühr, Armin; Priegnitz, Marlen; Fiedler, Fine;
2014-01-01
In ion beam cancer therapy, range verification in patients using positron emission tomography (PET) requires the comparison of measured with simulated positron emitter yields. We found that (1) changes in modeling nuclear interactions strongly affected the positron emitter yields and that (2) Monte...
Estimation of runoff and sediment yield in the Redrock Creek watershed using AnnAGNPS and GIS
Institute of Scientific and Technical Information of China (English)
Tsou Ming-Shu; ZHAN Xiao-yong
2004-01-01
Sediment has been identified as a significant threat to water quality and channel clogging that in turn may lead to river flooding. With the increasing awareness of the impairment from sediment to water bodies in a watershed, identifying the locations of the major sediment sources and reducing the sediment through management practices will be important for an effective watershed management. The annualized agricultural non-point source pollution(AnnAGNPS) model and newly developed GIS interface for it were applied in a small agricultural watershed, Redrock Creek watershed, Kansas, in this pilot study for exploring the effectiveness of using this model as a management tool. The calibrated model appropriately simulated monthly runoff and sediment yield through the practices in this study and potentially suggested the ways of sediment reduction through evaluating the changes of land use and field operation in the model for the purpose of watershed management.
Bayesian Estimation of a Mixture Model
Directory of Open Access Journals (Sweden)
Ilhem Merah
2015-05-01
Full Text Available We present the properties of a bathtub curve reliability model having both a sufficient adaptability and a minimal number of parameters introduced by Idée and Pierrat (2010. This one is a mixture of a Gamma distribution G(2, (1/θ and a new distribution L(θ. We are interesting by Bayesian estimation of the parameters and survival function of this model with a squared-error loss function and non-informative prior using the approximations of Lindley (1980 and Tierney and Kadane (1986. Using a statistical sample of 60 failure data relative to a technical device, we illustrate the results derived. Based on a simulation study, comparisons are made between these two methods and the maximum likelihood method of this two parameters model.
Hierarchical Boltzmann simulations and model error estimation
Torrilhon, Manuel; Sarna, Neeraj
2017-08-01
A hierarchical simulation approach for Boltzmann's equation should provide a single numerical framework in which a coarse representation can be used to compute gas flows as accurately and efficiently as in computational fluid dynamics, but a subsequent refinement allows to successively improve the result to the complete Boltzmann result. We use Hermite discretization, or moment equations, for the steady linearized Boltzmann equation for a proof-of-concept of such a framework. All representations of the hierarchy are rotationally invariant and the numerical method is formulated on fully unstructured triangular and quadrilateral meshes using a implicit discontinuous Galerkin formulation. We demonstrate the performance of the numerical method on model problems which in particular highlights the relevance of stability of boundary conditions on curved domains. The hierarchical nature of the method allows also to provide model error estimates by comparing subsequent representations. We present various model errors for a flow through a curved channel with obstacles.
Estimation in Dirichlet random effects models
Kyung, Minjung; Casella, George; 10.1214/09-AOS731
2010-01-01
We develop a new Gibbs sampler for a linear mixed model with a Dirichlet process random effect term, which is easily extended to a generalized linear mixed model with a probit link function. Our Gibbs sampler exploits the properties of the multinomial and Dirichlet distributions, and is shown to be an improvement, in terms of operator norm and efficiency, over other commonly used MCMC algorithms. We also investigate methods for the estimation of the precision parameter of the Dirichlet process, finding that maximum likelihood may not be desirable, but a posterior mode is a reasonable approach. Examples are given to show how these models perform on real data. Our results complement both the theoretical basis of the Dirichlet process nonparametric prior and the computational work that has been done to date.
Energy Technology Data Exchange (ETDEWEB)
Daijiro Kaneko [Department of Civil and Environmental Engineering, Matsue National College of Technology, Matsue (Japan); Toshiro Kumakura [Department of Civil and Environmental Engineering, Nagaoka University of Technology, Nagaoka (Japan); Peng Yang [Laboratory of Resources Remote Sensing and Digital Agriculture, Ministry of Agriculture, Beijing (China)
2008-09-30
This study is intended to develop a model for estimating carbon dioxide (CO{sub 2}) fixation in the carbon cycle and for monitoring grain yields using a photosynthetic-sterility model, which integrates solar radiation and air temperature effects on photosynthesis, along with grain-filling from heading to ripening. Grain production monitoring would support orderly crisis management to maintain food security in Asia, which is facing climate fluctuation through this century of global warming. The author improved a photosynthesis-and-sterility model to compute both the crop yield and crop situation index CSI, which gives a percentage of rice yields compared to normal annual production. The model calculates photosynthesis rates including biomass effects, lowtemperature sterility, and high-temperature injury by incorporating solar radiation, effective air temperature, the normalized difference vegetation index NDVI, and the effect of temperature on photosynthesis by grain plant leaves. A decision-tree method classifies the distribution of crop fields in Asia using MODIS fundamental landcover and SPOT VEGETATION data, which include the Normalized Vegetation index (NDVI) and Land Surface Water Index (LSWI). This study provides daily distributions of the photosynthesis rate, which is the CO2 fixation in Asian areas combined with the land-cover distribution, the Japanese geostationary meteorological satellite (GMS), and meteorological re-analysis data by National Centers for Environmental Prediction (NCEP). The method is based on routine observation data, enabling automated monitoring of crop yields.
Crepp, Justin R
2011-01-01
We use Monte Carlo simulations to estimate the number of extrasolar planets that are directly detectable in the solar-neighborhood using current and forthcoming high-contrast imaging instruments. Our calculations take into account the important factors that govern the likelihood for imaging a planet, including the statistical properties of nearby stars, correlations between star and planet properties, observational effects, and selection criteria. We consider several different ground-based surveys and express the resulting yields as a function of stellar mass. Selecting targets based on their youth and visual brightness, we find that strong correlations between star mass and planet properties are required to reproduce high-contrast imaging results to date. Using the most recent empirical findings for the occurrence rate of planets from RV surveys, our simulations indicate that extrapolation of the Doppler planet population to separations accessible to high-contrast instruments provides excellent agreement bet...
A Biomechanical Modeling Guided CBCT Estimation Technique.
Zhang, You; Tehrani, Joubin Nasehi; Wang, Jing
2017-02-01
Two-dimensional-to-three-dimensional (2D-3D) deformation has emerged as a new technique to estimate cone-beam computed tomography (CBCT) images. The technique is based on deforming a prior high-quality 3D CT/CBCT image to form a new CBCT image, guided by limited-view 2D projections. The accuracy of this intensity-based technique, however, is often limited in low-contrast image regions with subtle intensity differences. The solved deformation vector fields (DVFs) can also be biomechanically unrealistic. To address these problems, we have developed a biomechanical modeling guided CBCT estimation technique (Bio-CBCT-est) by combining 2D-3D deformation with finite element analysis (FEA)-based biomechanical modeling of anatomical structures. Specifically, Bio-CBCT-est first extracts the 2D-3D deformation-generated displacement vectors at the high-contrast anatomical structure boundaries. The extracted surface deformation fields are subsequently used as the boundary conditions to drive structure-based FEA to correct and fine-tune the overall deformation fields, especially those at low-contrast regions within the structure. The resulting FEA-corrected deformation fields are then fed back into 2D-3D deformation to form an iterative loop, combining the benefits of intensity-based deformation and biomechanical modeling for CBCT estimation. Using eleven lung cancer patient cases, the accuracy of the Bio-CBCT-est technique has been compared to that of the 2D-3D deformation technique and the traditional CBCT reconstruction techniques. The accuracy was evaluated in the image domain, and also in the DVF domain through clinician-tracked lung landmarks.
Adaptive Estimation of Heteroscedastic Money Demand Model of Pakistan
Directory of Open Access Journals (Sweden)
Muhammad Aslam
2007-07-01
Full Text Available For the problem of estimation of Money demand model of Pakistan, money supply (M1 shows heteroscedasticity of the unknown form. For estimation of such model we compare two adaptive estimators with ordinary least squares estimator and show the attractive performance of the adaptive estimators, namely, nonparametric kernel estimator and nearest neighbour regression estimator. These comparisons are made on the basis standard errors of the estimated coefficients, standard error of regression, Akaike Information Criteria (AIC value, and the Durban-Watson statistic for autocorrelation. We further show that nearest neighbour regression estimator performs better when comparing with the other nonparametric kernel estimator.
Reise, Steven P; Moore, Tyler M; Haviland, Mark G
2010-11-01
The application of psychological measures often results in item response data that arguably are consistent with both unidimensional (a single common factor) and multidimensional latent structures (typically caused by parcels of items that tap similar content domains). As such, structural ambiguity leads to seemingly endless "confirmatory" factor analytic studies in which the research question is whether scale scores can be interpreted as reflecting variation on a single trait. An alternative to the more commonly observed unidimensional, correlated traits, or second-order representations of a measure's latent structure is a bifactor model. Bifactor structures, however, are not well understood in the personality assessment community and thus rarely are applied. To address this, herein we (a) describe issues that arise in conceptualizing and modeling multidimensionality, (b) describe exploratory (including Schmid-Leiman [Schmid & Leiman, 1957] and target bifactor rotations) and confirmatory bifactor modeling, (c) differentiate between bifactor and second-order models, and (d) suggest contexts where bifactor analysis is particularly valuable (e.g., for evaluating the plausibility of subscales, determining the extent to which scores reflect a single variable even when the data are multidimensional, and evaluating the feasibility of applying a unidimensional item response theory (IRT) measurement model). We emphasize that the determination of dimensionality is a related but distinct question from either determining the extent to which scores reflect a single individual difference variable or determining the effect of multidimensionality on IRT item parameter estimates. Indeed, we suggest that in many contexts, multidimensional data can yield interpretable scale scores and be appropriately fitted to unidimensional IRT models.
Estimation of Model Parameters for Steerable Needles
Park, Wooram; Reed, Kyle B.; Okamura, Allison M.; Chirikjian, Gregory S.
2010-01-01
Flexible needles with bevel tips are being developed as useful tools for minimally invasive surgery and percutaneous therapy. When such a needle is inserted into soft tissue, it bends due to the asymmetric geometry of the bevel tip. This insertion with bending is not completely repeatable. We characterize the deviations in needle tip pose (position and orientation) by performing repeated needle insertions into artificial tissue. The base of the needle is pushed at a constant speed without rotating, and the covariance of the distribution of the needle tip pose is computed from experimental data. We develop the closed-form equations to describe how the covariance varies with different model parameters. We estimate the model parameters by matching the closed-form covariance and the experimentally obtained covariance. In this work, we use a needle model modified from a previously developed model with two noise parameters. The modified needle model uses three noise parameters to better capture the stochastic behavior of the needle insertion. The modified needle model provides an improvement of the covariance error from 26.1% to 6.55%. PMID:21643451
Estimation of Model Parameters for Steerable Needles.
Park, Wooram; Reed, Kyle B; Okamura, Allison M; Chirikjian, Gregory S
2010-01-01
Flexible needles with bevel tips are being developed as useful tools for minimally invasive surgery and percutaneous therapy. When such a needle is inserted into soft tissue, it bends due to the asymmetric geometry of the bevel tip. This insertion with bending is not completely repeatable. We characterize the deviations in needle tip pose (position and orientation) by performing repeated needle insertions into artificial tissue. The base of the needle is pushed at a constant speed without rotating, and the covariance of the distribution of the needle tip pose is computed from experimental data. We develop the closed-form equations to describe how the covariance varies with different model parameters. We estimate the model parameters by matching the closed-form covariance and the experimentally obtained covariance. In this work, we use a needle model modified from a previously developed model with two noise parameters. The modified needle model uses three noise parameters to better capture the stochastic behavior of the needle insertion. The modified needle model provides an improvement of the covariance error from 26.1% to 6.55%.
PARAMETER ESTIMATION IN BREAD BAKING MODEL
Directory of Open Access Journals (Sweden)
Hadiyanto Hadiyanto
2012-05-01
Full Text Available Bread product quality is highly dependent to the baking process. A model for the development of product quality, which was obtained by using quantitative and qualitative relationships, was calibrated by experiments at a fixed baking temperature of 200°C alone and in combination with 100 W microwave powers. The model parameters were estimated in a stepwise procedure i.e. first, heat and mass transfer related parameters, then the parameters related to product transformations and finally product quality parameters. There was a fair agreement between the calibrated model results and the experimental data. The results showed that the applied simple qualitative relationships for quality performed above expectation. Furthermore, it was confirmed that the microwave input is most meaningful for the internal product properties and not for the surface properties as crispness and color. The model with adjusted parameters was applied in a quality driven food process design procedure to derive a dynamic operation pattern, which was subsequently tested experimentally to calibrate the model. Despite the limited calibration with fixed operation settings, the model predicted well on the behavior under dynamic convective operation and on combined convective and microwave operation. It was expected that the suitability between model and baking system could be improved further by performing calibration experiments at higher temperature and various microwave power levels. Abstrak PERKIRAAN PARAMETER DALAM MODEL UNTUK PROSES BAKING ROTI. Kualitas produk roti sangat tergantung pada proses baking yang digunakan. Suatu model yang telah dikembangkan dengan metode kualitatif dan kuantitaif telah dikalibrasi dengan percobaan pada temperatur 200oC dan dengan kombinasi dengan mikrowave pada 100 Watt. Parameter-parameter model diestimasi dengan prosedur bertahap yaitu pertama, parameter pada model perpindahan masa dan panas, parameter pada model transformasi, dan
Institute of Scientific and Technical Information of China (English)
无
2005-01-01
Quantitative estimation of fertilizer requirements can help to increase maize (Zea mays L.) yields and improve the fertilizer use efficiency. The model for the Quantitative Evaluation of the Fertility of Tropical Soils (QUEFTS) was calibrated for maize by use of soil fertility data and fertilizer trials at different sites of the Huang Huai Hal river plain in China. The QUEFTS model accounts for interactions between nitrogen (N), phosphorus (P) and potassium (K). It describes the effects of soil characteristics on maize yields in four steps: (1) assessment of the potential supply of N, P and K based on soil chemical data; (2) calculation of the actual uptake of N, P and K, in function of the potential supply as determined in step 1; (3) draft the yield ranges as a function of the actual uptake of N, P and K as determined in step 2; (4) calculation of the maize yield based on the three yield ranges established in step 3. Data of field experiments with different fertilization treatments of various regions in China during the years of 1985 to 1995 were used to calibrate the QUEFTS model for summer maize. In step 1 the N, P and K recovered from their amount applied were described by new equa tions. The minimum and maximum accumulated N, P and K (kg grain kg-1) in summer maize were determined as (21-64), (126-384) and (20-90), respectively. The simulated yields were in good agreement with the observed ones. It was concluded that the calibrated and adjusted QUEFTS model could be useful to improve fertilizer recommendations for maize in the Huang Huai Hai plain of China.
Maro, G.P.; Mrema, J.P.; Msanya, B.M.; Janssen, B.H.; Teri, J.M.
2014-01-01
The aim of this study was to develop a simple and quantitative system for coffee yield estimation and nutrient input advice, so as to address the problem of declining annual coffee production in Tanzania (particularly in its Northern coffee zone), which is related to declining soil fertility. The st
Estimation of Several Turbulent Fluctuation Quantities Using an Approximate Pulsatile Flow Model
Energy Technology Data Exchange (ETDEWEB)
Dechant, Lawrence J. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
2015-12-01
Turbulent fluctuation behavior is approximately modeled using a pulsatile flow model analogy.. This model follows as an extension to the turbulent laminar sublayer model developed by Sternberg (1962) to be valid for a fully turbulent flow domain. Here unsteady turbulent behavior is modeled via a sinusoidal pulsatile approach. While the individual modes of the turbulent flow fluctuation behavior are rather crudely modeled, approximate temporal integration yields plausible estimates for Root Mean Square (RMS) velocity fluctuations. RMS pressure fluctuations and spectra are of particular interest and are estimated via the pressure Poisson expression. Both RMS and Power Spectral Density (PSD), i.e. spectra are developed. Comparison with available measurements suggests reasonable agreement. An additional fluctuating quantity, i.e. RMS wall shear fluctuation is also estimated, yielding reasonable agreement with measurement.
Mattern, Jann Paul; Edwards, Christopher A.
2017-01-01
Parameter estimation is an important part of numerical modeling and often required when a coupled physical-biogeochemical ocean model is first deployed. However, 3-dimensional ocean model simulations are computationally expensive and models typically contain upwards of 10 parameters suitable for estimation. Hence, manual parameter tuning can be lengthy and cumbersome. Here, we present four easy to implement and flexible parameter estimation techniques and apply them to two 3-dimensional biogeochemical models of different complexities. Based on a Monte Carlo experiment, we first develop a cost function measuring the model-observation misfit based on multiple data types. The parameter estimation techniques are then applied and yield a substantial cost reduction over ∼ 100 simulations. Based on the outcome of multiple replicate experiments, they perform on average better than random, uninformed parameter search but performance declines when more than 40 parameters are estimated together. Our results emphasize the complex cost function structure for biogeochemical parameters and highlight dependencies between different parameters as well as different cost function formulations.
Multiaxial yield surface of transversely isotropic foams: Part I-Modeling
Ayyagari, Ravi Sastri; Vural, Murat
2015-01-01
A new yield criterion is proposed for transversely isotropic solid foams. Its derivation is based on the hypothesis that the yielding in foams is driven by the total strain energy density, rather than a completely phenomenological approach. This allows defining the yield surface with minimal number of parameters and does not require complex experiments. The general framework used leads to the introduction of new scalar measures of stress and strain (characteristic stress and strain) for transversely isotropic foams. Furthermore, the central hypothesis that the total strain energy density drives yielding in foams ascribes to the characteristic stress an analogous role of von Mises stress in metal plasticity. Unlike the overwhelming majority of yield models in literature the proposed model recognizes the tension-compression difference in yield behavior of foams through a linear mean stress term. Predictions of the proposed yield model are in excellent agreement with the results of uniaxial, biaxial and triaxial FE analyses implemented on both isotropic and transversely isotropic Kelvin foam models.
Energy Technology Data Exchange (ETDEWEB)
Schmitz, Martin
2011-05-17
A study of the expected sensitivity of the ATLAS experiment to discover the Standard Model Higgs boson produced via vector boson fusion (VBF) and its decay to H{yields} {tau}{tau}{yields} ll+4{nu} is presented. The study is based on simulated proton-proton collisions at a centre-of-mass energy of 14 TeV. For the first time the discovery potential is evaluated in the presence of additional proton-proton interactions (pile-up) to the process of interest in a complete and consistent way. Special emphasis is placed on the development of background estimation techniques to extract the main background processes Z{yields}{tau}{tau} and t anti t production using data. The t anti t background is estimated using a control sample selected with the VBF analysis cuts and the inverted b-jet veto. The dominant background process Z{yields}{tau}{tau} is estimated using Z{yields}{mu}{mu} events. Replacing the muons of the Z{yields}{mu}{mu} event with simulated {tau}-leptons, Z{yields}{tau}{tau} events are modelled to high precision. For the replacement of the Z boson decay products a dedicated method based on tracks and calorimeter cells is developed. Without pile-up a discovery potential of 3{sigma} to 3.4{sigma} in the mass range 115 GeV
Holzkämper, Annelie; Honti, Mark; Fuhrer, Jürg
2015-04-01
Crop models are commonly applied to estimate impacts of projected climate change and to anticipate suitable adaptation measures. Thereby, uncertainties from global climate models, regional climate models, and impacts models cascade down to impact estimates. It is essential to quantify and understand uncertainties in impact assessments in order to provide informed guidance for decision making in adaptation planning. A question that has hardly been investigated in this context is how sensitive climate impact estimates are to the choice of the impact model approach. In a case study for Switzerland we compare results of three different crop modelling approaches to assess the relevance of impact model choice in relation to other uncertainty sources. The three approaches include an expert-based, a statistical and a process-based model. With each approach impact model parameter uncertainty and climate model uncertainty (originating from climate model chain and downscaling approach) are accounted for. ANOVA-based uncertainty partitioning is performed to quantify the relative importance of different uncertainty sources. Results suggest that uncertainty in estimated yield changes originating from the choice of the crop modelling approach can be greater than uncertainty from climate model chains. The uncertainty originating from crop model parameterization is small in comparison. While estimates of yield changes are highly uncertain, the directions of estimated changes in climatic limitations are largely consistent. This leads us to the conclusion that by focusing on estimated changes in climate limitations, more meaningful information can be provided to support decision making in adaptation planning - especially in cases where yield changes are highly uncertain.
Pazhanivelan, S.; Kannan, P.; Nirmala Mary, P. Christy; Subramanian, E.; Jeyaraman, S.; Nelson, A.; Setiyono, T.; Holecz, F.; Barbieri, M.; Yadav, M.
2015-04-01
Rice is the most important cereal crop governing food security in Asia. Reliable and regular information on the area under rice production is the basis of policy decisions related to imports, exports and prices which directly affect food security. Recent and planned launches of SAR sensors coupled with automated processing can provide sustainable solutions to the challenges on mapping and monitoring rice systems. High resolution (3m) Synthetic Aperture Radar (SAR) imageries were used to map and monitor rice growing areas in selected three sites in TamilNadu, India to determine rice cropping extent, track rice growth and estimate yields. A simple, robust, rule-based classification for mapping rice area with multi-temporal, X-band, HH polarized SAR imagery from COSMO Skymed and TerraSAR X and site specific parameters were used. The robustness of the approach is demonstrated on a very large dataset involving 30 images across 3 footprints obtained during 2013-14. A total of 318 in-season site visits were conducted across 60 monitoring locations for rice classification and 432 field observations were made for accuracy assessment. Rice area and Start of Season (SoS) maps were generated with classification accuracies ranging from 87- 92 per cent. Using ORYZA2000, a weather driven process based crop growth simulation model; yield estimates were made with the inclusion of rice crop parameters derived from the remote sensing products viz., seasonal rice area, SoS and backscatter time series. Yield Simulation accuracy levels of 87 per cent at district level and 85- 96 per cent at block level demonstrated the suitability of remote sensing products for policy decisions ensuring food security and reducing vulnerability of farmers in India.
Flores, P.; Duchêne, L.; Lelotte, T.; Bouffioux, C.; El Houdaigui, F.; Van Bael, A.; He, S.; Duflou, J.; Habraken, A. M.
2005-08-01
The bi-axial experimental equipment developed by Flores enables to perform Baushinger shear tests and successive or simultaneous simple shear tests and plane-strain tests. Such experiments and classical tensile tests investigate the material behavior in order to identify the yield locus and the hardening models. With tests performed on two steel grades, the methods applied to identify classical yield surfaces such as Hill or Hosford ones as well as isotropic Swift type hardening or kinematic Armstrong-Frederick hardening models are explained. Comparison with the Taylor-Bishop-Hill yield locus is also provided. The effect of both yield locus and hardening model choice will be presented for two applications: Single Point Incremental Forming (SPIF) and a cup deep drawing.
Evaluation of the Williams-type model for barley yields in North Dakota and Minnesota
Barnett, T. L. (Principal Investigator)
1981-01-01
The Williams-type yield model is based on multiple regression analysis of historial time series data at CRD level pooled to regional level (groups of similar CRDs). Basic variables considered in the analysis include USDA yield, monthly mean temperature, monthly precipitation, soil texture and topographic information, and variables derived from these. Technologic trend is represented by piecewise linear and/or quadratic functions of year. Indicators of yield reliability obtained from a ten-year bootstrap test (1970-1979) demonstrate that biases are small and performance based on root mean square appears to be acceptable for the intended AgRISTARS large area applications. The model is objective, adequate, timely, simple, and not costly. It consideres scientific knowledge on a broad scale but not in detail, and does not provide a good current measure of modeled yield reliability.
Robust estimation of hydrological model parameters
Directory of Open Access Journals (Sweden)
A. Bárdossy
2008-11-01
Full Text Available The estimation of hydrological model parameters is a challenging task. With increasing capacity of computational power several complex optimization algorithms have emerged, but none of the algorithms gives a unique and very best parameter vector. The parameters of fitted hydrological models depend upon the input data. The quality of input data cannot be assured as there may be measurement errors for both input and state variables. In this study a methodology has been developed to find a set of robust parameter vectors for a hydrological model. To see the effect of observational error on parameters, stochastically generated synthetic measurement errors were applied to observed discharge and temperature data. With this modified data, the model was calibrated and the effect of measurement errors on parameters was analysed. It was found that the measurement errors have a significant effect on the best performing parameter vector. The erroneous data led to very different optimal parameter vectors. To overcome this problem and to find a set of robust parameter vectors, a geometrical approach based on Tukey's half space depth was used. The depth of the set of N randomly generated parameters was calculated with respect to the set with the best model performance (Nash-Sutclife efficiency was used for this study for each parameter vector. Based on the depth of parameter vectors, one can find a set of robust parameter vectors. The results show that the parameters chosen according to the above criteria have low sensitivity and perform well when transfered to a different time period. The method is demonstrated on the upper Neckar catchment in Germany. The conceptual HBV model was used for this study.
Monroe, Scott; Cai, Li
2014-01-01
In Ramsay curve item response theory (RC-IRT) modeling, the shape of the latent trait distribution is estimated simultaneously with the item parameters. In its original implementation, RC-IRT is estimated via Bock and Aitkin's EM algorithm, which yields maximum marginal likelihood estimates. This method, however, does not produce the…
Monroe, Scott; Cai, Li
2014-01-01
In Ramsay curve item response theory (RC-IRT) modeling, the shape of the latent trait distribution is estimated simultaneously with the item parameters. In its original implementation, RC-IRT is estimated via Bock and Aitkin's EM algorithm, which yields maximum marginal likelihood estimates. This method, however, does not produce the…
Archfield, Stacey A.; Vogel, Richard M.; Steeves, Peter A.; Brandt, Sara L.; Weiskel, Peter K.; Garabedian, Stephen P.
2010-01-01
Federal, State and local water-resource managers require a variety of data and modeling tools to better understand water resources. The U.S. Geological Survey, in cooperation with the Massachusetts Department of Environmental Protection, has developed a statewide, interactive decision-support tool to meet this need. The decision-support tool, referred to as the Massachusetts Sustainable-Yield Estimator (MA SYE) provides screening-level estimates of the sustainable yield of a basin, defined as the difference between the unregulated streamflow and some user-specified quantity of water that must remain in the stream to support such functions as recreational activities or aquatic habitat. The MA SYE tool was designed, in part, because the quantity of surface water available in a basin is a time-varying quantity subject to competing demands for water. To compute sustainable yield, the MA SYE tool estimates a daily time series of unregulated, daily mean streamflow for a 44-year period of record spanning October 1, 1960, through September 30, 2004. Selected streamflow quantiles from an unregulated, daily flow-duration curve are estimated by solving six regression equations that are a function of physical and climate basin characteristics at an ungaged site on a stream of interest. Streamflow is then interpolated between the estimated quantiles to obtain a continuous daily flow-duration curve. A time series of unregulated daily streamflow subsequently is created by transferring the timing of the daily streamflow at a reference streamgage to the ungaged site by equating exceedence probabilities of contemporaneous flow at the two locations. One of 66 reference streamgages is selected by kriging, a geostatistical method, which is used to map the spatial relation among correlations between the time series of the logarithm of daily streamflows at each reference streamgage and the ungaged site. Estimated unregulated, daily mean streamflows show good agreement with observed
Shape parameter estimate for a glottal model without time position
Degottex, Gilles; Roebel, Axel; Rodet, Xavier
2009-01-01
cote interne IRCAM: Degottex09a; None / None; National audience; From a recorded speech signal, we propose to estimate a shape parameter of a glottal model without estimating his time position. Indeed, the literature usually propose to estimate the time position first (ex. by detecting Glottal Closure Instants). The vocal-tract filter estimate is expressed as a minimum-phase envelope estimation after removing the glottal model and a standard lips radiation model. Since this filter is mainly b...
Directory of Open Access Journals (Sweden)
Maryam Baharizadeh
2012-10-01
Full Text Available Buffalo milk yield records were obtained from monthly records of the Animal Breeding Organization of Iran from 1992 to 2009 in 33 herds raised in the Khuzestan province. Variance components, heritability and repeatability were estimated for milk yield, fat yield, fat percentage, protein yield and protein percentage. These estimates were carried out through single trait animal model using DFREML program. Herd-year-season was considered as fixed effect in the model. For milk production traits, age at calving was fitted as a covariate. The additive genetic and permanent environmental effects were also included in the model. The mean values (±SD for milk yield, fat yield, fat percentage, protein yield and protein percentage were 2285.08±762.47 kg, 144.35±54.86 kg, 6.25±0.90%, 97.30±26.73 kg and 4.19±0.27%, respectively. The heritability (±SE of milk yield, fat yield, fat percentage, protein yield and protein percentage were 0.093±0.08, 0.054±0.06, 0.043±0.05, 0.093±0.16 and zero, respectively. These estimates for repeatability were 0.272, 0.132, 0.043, 0.674 and 0.0002, respectively. Lower values of genetic parameter estimates require more data and reliable pedigree records.
Chahbi, A.; Zribi, M.; Lili-Chabaane, Z.; Duchemin, B.; Shabou, M.; Mougenot, B.; Boulet, G.
2012-04-01
In semi-arid region and especially in irrigated areas, agriculture represents a major contribution to food security. These areas significantly contribute to the increase of global production. A challenging objective is thus to ensure food security. Therefore an operational forecasting system for the grain yields is required and could help decision-makers to make early decisions and plan annual imports. In this context, remote sensing is a very interesting tool for giving information on the development of vegetation. The main objective is to analyze and predict the average grain yield, based on different indices measured or modelled during the growing season. Thus, we used three lines of research: the first is based on analysing a relationship between normalized vegetation index (NDVI) which is determined from optical satellite imagery and the leaf area index (LAI) measured in situ. The second axis is based on the estimation of the relation between wheat yields and normalized vegetation index NDVI. The third axis is based on the application of a growth model SAFY « Simple Algorithm For Yield Estimate » developed to simulate LAI, dry aboveground phytomass (DAM) and the grain yield (GY). For the first axis, we used optical data at high resolution. A series of 7 SPOT / HRV during the 2010-2011 agricultural seasons was acquired in the Merguellil catchment (Tunisia). At the same time we realised experimental measurements made on 27 test plots of dry or irrigated cereals carried out in study area. These measurements are mainly: the water content of the vegetation, the vegetation height, wheat density and leaf area index LAI (estimated using a hemispherical camera). From satellite data, a profile of the normalized difference vegetation index (NDVI) was generated for each pixel. For both types of cereal, a relationship is established between NDVI and leaf area index LAI. This relationship is exponential and it allows connecting the satellite observations with a variable
Modelling Bambara Groundnut Yield in Southern Africa: Towards a Climate-Resilient Future
Karunaratne, A. S.; Walker, S.; Ruane, A. C.
2015-01-01
Current agriculture depends on a few major species grown as monocultures that are supported by global research underpinning current productivity. However, many hundreds of alternative crops have the potential to meet real world challenges by sustaining humanity, diversifying agricultural systems for food and nutritional security, and especially responding to climate change through their resilience to certain climate conditions. Bambara groundnut (Vigna subterranea (L.) Verdc.), an underutilised African legume, is an exemplar crop for climate resilience. Predicted yield performances of Bambara groundnut by AquaCrop (a crop-water productivity model) were evaluated for baseline (1980-2009) and mid-century climates (2040-2069) under 20 downscaled Global Climate Models (CMIP5-RCP8.5), as well as for climate sensitivities (AgMIPC3MP) across 3 locations in Southern Africa (Botswana, South Africa, Namibia). Different land - races of Bambara groundnut originating from various semi-arid African locations showed diverse yield performances with diverse sensitivities to climate. S19 originating from hot-dry conditions in Namibia has greater future yield potential compared to the Swaziland landrace Uniswa Red-UN across study sites. South Africa has the lowest yield under the current climate, indicating positive future yield trends. Namibia reported the highest baseline yield at optimum current temperatures, indicating less yield potential in future climates. Bambara groundnut shows positive yield potential at temperatures of up to 31degC, with further warming pushing yields down. Thus, many regions in Southern Africa can utilize Bambara groundnut successfully in the coming decades. This modelling exercise supports decisions on genotypic suitability for present and future climates at specific locations.
Modelling Bambara Groundnut Yield in Southern Africa: Towards a Climate-Resilient Future
Karunaratne, A. S.; Walker, S.; Ruane, A. C.
2015-01-01
Current agriculture depends on a few major species grown as monocultures that are supported by global research underpinning current productivity. However, many hundreds of alternative crops have the potential to meet real world challenges by sustaining humanity, diversifying agricultural systems for food and nutritional security, and especially responding to climate change through their resilience to certain climate conditions. Bambara groundnut (Vigna subterranea (L.) Verdc.), an underutilised African legume, is an exemplar crop for climate resilience. Predicted yield performances of Bambara groundnut by AquaCrop (a crop-water productivity model) were evaluated for baseline (1980-2009) and mid-century climates (2040-2069) under 20 downscaled Global Climate Models (CMIP5-RCP8.5), as well as for climate sensitivities (AgMIPC3MP) across 3 locations in Southern Africa (Botswana, South Africa, Namibia). Different land - races of Bambara groundnut originating from various semi-arid African locations showed diverse yield performances with diverse sensitivities to climate. S19 originating from hot-dry conditions in Namibia has greater future yield potential compared to the Swaziland landrace Uniswa Red-UN across study sites. South Africa has the lowest yield under the current climate, indicating positive future yield trends. Namibia reported the highest baseline yield at optimum current temperatures, indicating less yield potential in future climates. Bambara groundnut shows positive yield potential at temperatures of up to 31degC, with further warming pushing yields down. Thus, many regions in Southern Africa can utilize Bambara groundnut successfully in the coming decades. This modelling exercise supports decisions on genotypic suitability for present and future climates at specific locations.
Integrated traffic conflict model for estimating crash modification factors.
Shahdah, Usama; Saccomanno, Frank; Persaud, Bhagwant
2014-10-01
Crash modification factors (CMFs) for road safety treatments are usually obtained through observational models based on reported crashes. Observational Bayesian before-and-after methods have been applied to obtain more precise estimates of CMFs by accounting for the regression-to-the-mean bias inherent in naive methods. However, sufficient crash data reported over an extended period of time are needed to provide reliable estimates of treatment effects, a requirement that can be a challenge for certain types of treatment. In addition, these studies require that sites analyzed actually receive the treatment to which the CMF pertains. Another key issue with observational approaches is that they are not causal in nature, and as such, cannot provide a sound "behavioral" rationale for the treatment effect. Surrogate safety measures based on high risk vehicle interactions and traffic conflicts have been proposed to address this issue by providing a more "causal perspective" on lack of safety for different road and traffic conditions. The traffic conflict approach has been criticized, however, for lacking a formal link to observed and verified crashes, a difficulty that this paper attempts to resolve by presenting and investigating an alternative approach for estimating CMFs using simulated conflicts that are linked formally to observed crashes. The integrated CMF estimates are compared to estimates from an empirical Bayes (EB) crash-based before-and-after analysis for the same sample of treatment sites. The treatment considered involves changing left turn signal priority at Toronto signalized intersections from permissive to protected-permissive. The results are promising in that the proposed integrated method yields CMFs that closely match those obtained from the crash-based EB before-and-after analysis. Copyright © 2014 Elsevier Ltd. All rights reserved.
Moore, Frances C.; Baldos, Uris Lantz C.; Hertel, Thomas
2017-06-01
A large number of studies have been published examining the implications of climate change for agricultural productivity that, broadly speaking, can be divided into process-based modeling and statistical approaches. Despite a general perception that results from these methods differ substantially, there have been few direct comparisons. Here we use a data-base of yield impact studies compiled for the IPCC Fifth Assessment Report (Porter et al 2014) to systematically compare results from process-based and empirical studies. Controlling for differences in representation of CO2 fertilization between the two methods, we find little evidence for differences in the yield response to warming. The magnitude of CO2 fertilization is instead a much larger source of uncertainty. Based on this set of impact results, we find a very limited potential for on-farm adaptation to reduce yield impacts. We use the Global Trade Analysis Project (GTAP) global economic model to estimate welfare consequences of yield changes and find negligible welfare changes for warming of 1 °C-2 °C if CO2 fertilization is included and large negative effects on welfare without CO2. Uncertainty bounds on welfare changes are highly asymmetric, showing substantial probability of large declines in welfare for warming of 2 °C-3 °C even including the CO2 fertilization effect.
AMEM-ADL Polymer Migration Estimation Model User's Guide
The user's guide of the Arthur D. Little Polymer Migration Estimation Model (AMEM) provides the information on how the model estimates the fraction of a chemical additive that diffuses through polymeric matrices.
Tonai, Hironori; Sasabe, Norimasa; Uozumi, Takayuki; Kawamura, Naomi; Mizumaki, Masaichiro
2017-09-01
Partial fluorescence yield (PFY) spectroscopy, which corresponds to a high-resolution version of the X-ray absorption spectroscopy (XAS), is experimentally performed at the Ce L3 edge of CeO2, and the result is theoretically analyzed using an impurity Anderson model (IAM). In order to estimate the Ce 4f-5d interaction Ufd, we employ a semi-empirical IAM framework based on the local density approximation+U method; Slater-Koster parameters describing the valence of CeO2 are estimated by band mapping within the linear combination of atomic orbitals scheme, and the resulting realistic valence structure is considered in the IAM analysis. The global structure of the PFY-XAS result, which consists of the Ce 2p3/2 → 5d dipole transition and the Ce 2p3/2 → 4f quadrupole transition, is excellently reproduced by the calculation. The Ufd value is estimated to be 3.0 eV. We emphasize that the sensitivity of PFY-XAS to Ufd makes it a good ruler for obtaining the Ufd values of Ce compounds.
Determinants of the Government Bond Yield in Spain: A Loanable Funds Model
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Yu Hsing
2015-07-01
Full Text Available This paper applies demand and supply analysis to examine the government bond yield in Spain. The sample ranges from 1999.Q1 to 2014.Q2. The EGARCH model is employed in empirical work. The Spanish government bond yield is positively associated with the government debt/GDP ratio, the short-term Treasury bill rate, the expected inflation rate, the U.S. 10 year government bond yield and a dummy variable representing the debt crisis and negatively affected by the GDP growth rate and the expected nominal effective exchange rate.
André, G; Engel, B; Berentsen, P B M; Vellinga, Th V; Lansink, A G J M Oude
2011-09-01
Automation and use of robots are increasingly being used within dairy farming and result in large amounts of real time data. This information provides a base for the new management concept of precision livestock farming. From 2003 to 2006, time series of herd mean daily milk yield were collected on 6 experimental research farms in the Netherlands. These time series were analyzed with an adaptive dynamic model following a Bayesian method to quantify the effect of heat stress. The effect of heat stress was quantified in terms of critical temperature above which heat stress occurred, duration of heat stress periods, and resulting loss in milk yield. In addition, dynamic changes in level and trend were monitored, including the estimation of a weekly pattern. Monitoring comprised detection of potential outliers and other deteriorations. The adaptive dynamic model fitted the data well; the root mean squared error of the forecasts ranged from 0.55 to 0.99 kg of milk/d. The percentages of potential outliers and signals for deteriorations ranged from 5.5 to 9.7%. The Bayesian procedure for time series analysis and monitoring provided a useful tool for process control. Online estimates (based on past and present only) and retrospective estimates (determined afterward from all data) of level and trend in daily milk yield showed an almost yearly cycle that was in agreement with the calving pattern: most cows calved in winter and early spring versus summer and autumn. Estimated weekly patterns in terms of weekday effects could be related to specific management actions, such as change of pasture during grazing. For the effect of heat stress, the mean estimated critical temperature above which heat stress was expected was 17.8±0.56°C. The estimated duration of the heat stress periods was 5.5±1.03 d, and the estimated loss was 31.4±12.2 kg of milk/cow per year. Farm-specific estimates are helpful to identify management factors like grazing, housing and feeding, that affect the
Zhou, Mengzi; Wang, Huijun; Huo, Zhiguo
2017-04-01
Accurate estimations of grain output in the agriculturally important region of Northeast China are of great strategic significance for guaranteeing food security. New prediction models for maize and rice yields are built in this paper based on the spring North Atlantic Oscillation index and the Bering Sea ice cover index. The year-to-year increment is first forecasted and then the original yield value is obtained by adding the historical yield of the previous year. The multivariate linear prediction model of maize shows good predictive ability, with a low normalized root-mean-square error (NRMSE) of 13.9%, and the simulated yield accounts for 81% of the total variance of the observation. To improve the performance of the multivariate linear model, a combined forecasting model of rice is built by considering the weight of the predictors. The NRMSE of the model is 12.9% and the predicted rice yield explains 71% of the total variance. The corresponding cross-validation test and independent samples test further demonstrate the efficiency of the models. It is inferred that the statistical models established here by applying year-to-year increment approach could make rational prediction for the maize and rice yield in Northeast China before harvest. The present study may shed new light on yield prediction in advance by use of antecedent large-scale climate signals adequately.
Weibull Parameters Estimation Based on Physics of Failure Model
DEFF Research Database (Denmark)
Kostandyan, Erik; Sørensen, John Dalsgaard
2012-01-01
Reliability estimation procedures are discussed for the example of fatigue development in solder joints using a physics of failure model. The accumulated damage is estimated based on a physics of failure model, the Rainflow counting algorithm and the Miner’s rule. A threshold model is used...... distribution. Methods from structural reliability analysis are used to model the uncertainties and to assess the reliability for fatigue failure. Maximum Likelihood and Least Square estimation techniques are used to estimate fatigue life distribution parameters....
Institute of Scientific and Technical Information of China (English)
Yee LEUNG; WU Kefa; DONG Tianxin
2001-01-01
In this paper, a multivariate linear functional relationship model, where the covariance matrix of the observational errors is not restricted, is considered. The parameter estimation of this model is discussed. The estimators are shown to be a strongly consistent estimation under some mild conditions on the incidental parameters.
Bittante, G; Cipolat-Gotet, C; Cecchinato, A
2013-01-01
Cheese yield (CY) is an important technological trait in the dairy industry, and the objective of this study was to estimate the genetic parameters of cheese yield in a dairy cattle population using an individual model-cheese production procedure. A total of 1,167 Brown Swiss cows belonging to 85 herds were sampled once (a maximum of 15 cows were sampled per herd on a single test day, 1 or 2 herds per week). From each cow, 1,500 mL of milk was processed according to the following steps: milk sampling and heating, culture addition, rennet addition, gelation-time recording, curd cutting, whey draining and sampling, wheel formation, pressing, salting in brine, weighing, and cheese sampling. The compositions of individual milk, whey, and curd samples were determined. Three measures of percentage cheese yield (%CY) were calculated: %CY(CURD), %CY(SOLIDS), and %CY(WATER), which represented the ratios between the weight of fresh curd, the total solids of the curd, and the water content of the curd, respectively, and the weight of the milk processed. In addition, 3 measures of daily cheese yield (dCY, kg/d) were defined, considering the daily milk yield. Three measures of nutrient recovery (REC) were computed: REC(FAT), REC(PROTEIN), and REC(SOLIDS), which represented the ratio between the weights of the fat, protein, and total solids in the curd, respectively, and the corresponding nutrient in the milk. Energy recovery, REC(ENERGY), represented the energy content of the cheese versus that in the milk. For statistical analysis, a Bayesian animal model was implemented via Gibbs sampling. The effects of parity (1 to ≥4), days in milk (6 classes), and laboratory vat (15 vats) were assigned flat priors; those of herd-test-date, animal, and residual were given Gaussian prior distributions. Intra-herd heritability estimates of %CY(CURD), %CY(SOLIDS), and %CY(WATER) ranged from 0.224 to 0.267; these were larger than the estimates obtained for milk yield (0.182) and milk fat
Vovchenko, Volodymyr
2016-01-01
We analyze the sensitivity of thermal fits to heavy-ion hadron yield data of ALICE and NA49 collaborations to the systematic uncertainties in the hadron resonance gas (HRG) model related to the modeling of the eigenvolume interactions. We find a surprisingly large sensitivity in extraction of chemical freeze-out parameters to the assumptions regarding eigenvolumes of different hadrons. We additionally study the effect of including yields of light nuclei into the thermal fits to LHC data and find even larger sensitivity to the modeling of their eigenvolumes. The inclusion of light nuclei yields, thus, may lead to further destabilization of thermal fits. Our results show that modeling of eigenvolume interactions plays a crucial role in thermodynamics of HRG and that conclusions based on a non-interacting HRG are not unique.
Multivariate Statistical Models for Predicting Sediment Yields from Southern California Watersheds
Gartner, Joseph E.; Cannon, Susan H.; Helsel, Dennis R.; Bandurraga, Mark
2009-01-01
Debris-retention basins in Southern California are frequently used to protect communities and infrastructure from the hazards of flooding and debris flow. Empirical models that predict sediment yields are used to determine the size of the basins. Such models have been developed using analyses of records of the amount of material removed from debris retention basins, associated rainfall amounts, measures of watershed characteristics, and wildfire extent and history. In this study we used multiple linear regression methods to develop two updated empirical models to predict sediment yields for watersheds located in Southern California. The models are based on both new and existing measures of volume of sediment removed from debris retention basins, measures of watershed morphology, and characterization of burn severity distributions for watersheds located in Ventura, Los Angeles, and San Bernardino Counties. The first model presented reflects conditions in watersheds located throughout the Transverse Ranges of Southern California and is based on volumes of sediment measured following single storm events with known rainfall conditions. The second model presented is specific to conditions in Ventura County watersheds and was developed using volumes of sediment measured following multiple storm events. To relate sediment volumes to triggering storm rainfall, a rainfall threshold was developed to identify storms likely to have caused sediment deposition. A measured volume of sediment deposited by numerous storms was parsed among the threshold-exceeding storms based on relative storm rainfall totals. The predictive strength of the two models developed here, and of previously-published models, was evaluated using a test dataset consisting of 65 volumes of sediment yields measured in Southern California. The evaluation indicated that the model developed using information from single storm events in the Transverse Ranges best predicted sediment yields for watersheds in San
Directory of Open Access Journals (Sweden)
Branislava Lalić
2014-12-01
Full Text Available The impact of adverse weather conditions (AWCs on crop production is random in both time and space and depends on factors such as severity, previous agrometeorological conditions, and plant vulnerability at a specific crop development stage. Any exclusion or improper treatment of any of these factors can cause crop models to produce significant under- or overestimates of yield. The analysis presented in this paper focuses on a range of agrometeorological indices (AMI related to AWCs that might affect real yield as well as simulated yield. For this purpose, the analysis addressed four indicators of extreme temperatures and three indicators of dry conditions during the growth period of maize and winter wheat in Austria, Croatia, Serbia, Slovakia, and Sweden. It is shown that increases in the number and intensity of AWCs cannot be unambiguously associated with increased deviations in simulated yields. The identified correlations indicate an increase in modeling uncertainty. This finding represents important information for the crop modeling community. Additionally, it opens a window of opportunity for a statistical (“event scenario” approach based on correlations between agrometeorological indices of AWCs and crop yield data series. This approach can provide scenarios for certain locations, crop types, and AWC patterns and, therefore, improve yield forecasting in the presence of AWCs.
Modeling Uncertainty when Estimating IT Projects Costs
Winter, Michel; Mirbel, Isabelle; Crescenzo, Pierre
2014-01-01
In the current economic context, optimizing projects' cost is an obligation for a company to remain competitive in its market. Introducing statistical uncertainty in cost estimation is a good way to tackle the risk of going too far while minimizing the project budget: it allows the company to determine the best possible trade-off between estimated cost and acceptable risk. In this paper, we present new statistical estimators derived from the way IT companies estimate the projects' costs. In t...
Benefit Estimation Model for Tourist Spaceflights
Goehlich, Robert A.
2003-01-01
It is believed that the only potential means for significant reduction of the recurrent launch cost, which results in a stimulation of human space colonization, is to make the launcher reusable, to increase its reliability, and to make it suitable for new markets such as mass space tourism. But such space projects, that have long range aspects are very difficult to finance, because even politicians would like to see a reasonable benefit during their term in office, because they want to be able to explain this investment to the taxpayer. This forces planners to use benefit models instead of intuitive judgement to convince sceptical decision-makers to support new investments in space. Benefit models provide insights into complex relationships and force a better definition of goals. A new approach is introduced in the paper that allows to estimate the benefits to be expected from a new space venture. The main objective why humans should explore space is determined in this study to ``improve the quality of life''. This main objective is broken down in sub objectives, which can be analysed with respect to different interest groups. Such interest groups are the operator of a space transportation system, the passenger, and the government. For example, the operator is strongly interested in profit, while the passenger is mainly interested in amusement, while the government is primarily interested in self-esteem and prestige. This leads to different individual satisfactory levels, which are usable for the optimisation process of reusable launch vehicles.
Simulating yield response to water of Teff (Eragrostis tef) with FAO's AquaCrop model
Araya, A.; Keesstra, S.D.; Stroosnijder, L.
2010-01-01
In a semi-arid environment, the main challenge for crop production is water deficit. FAO's AquaCrop model, which simulates yield response to water, has been calibrated to explore alternative water management strategies in teff crop. To calibrate and evaluate this model, we used independent data sets
A model for the [gamma]p[yields][pi][sup +][pi][sup -]p reactions
Energy Technology Data Exchange (ETDEWEB)
Gomez Tejedor, J.A. (Dept. de Fisica Teorica, Centro Mixto Universidad de Valencia-CSIC, Burjassot (Spain) IFIC, Centro Mixto Universidad de Valencia-CSIC, Burjassot (Spain)); Oset, E. (Dept. de Fisica Teorica, Centro Mixto Universidad de Valencia-CSIC, Burjassot (Spain) IFIC, Centro Mixto Universidad de Valencia-CSIC, Burjassot (Spain))
1994-05-09
We have studied the [gamma]p[yields][pi][sup +][pi][sup -]p reaction using a model which includes N, [Delta](1232), N[sup *](1440) and N[sup *](1520) intermediate baryonic states and the [rho]-meson as intermediate 2[pi]-resonance. The model reproduces fairly well experimental cross sections below E[sub [gamma
Simulating yield response to water of Teff (Eragrostis tef) with FAO's AquaCrop model
Araya, A.; Keesstra, S.D.; Stroosnijder, L.
2010-01-01
In a semi-arid environment, the main challenge for crop production is water deficit. FAO's AquaCrop model, which simulates yield response to water, has been calibrated to explore alternative water management strategies in teff crop. To calibrate and evaluate this model, we used independent data sets
Lloyd, Simon J; Kovats, R Sari; Chalabi, Zaid
2011-12-01
Global climate change is anticipated to reduce future cereal yields and threaten food security, thus potentially increasing the risk of undernutrition. The causation of undernutrition is complex, and there is a need to develop models that better quantify the potential impacts of climate change on population health. We developed a model for estimating future undernutrition that accounts for food and nonfood (socioeconomic) causes and can be linked to available regional scenario data. We estimated child stunting attributable to climate change in five regions in South Asia and sub-Saharan Africa (SSA) in 2050. We used current national food availability and undernutrition data to parameterize and validate a global model, using a process-driven approach based on estimations of the physiological relationship between a lack of food and stunting. We estimated stunting in 2050 using published modeled national calorie availability under two climate scenarios and a reference scenario (no climate change). We estimated that climate change will lead to a relative increase in moderate stunting of 1-29% in 2050 compared with a future without climate change. Climate change will have a greater impact on rates of severe stunting, which we estimated will increase by 23% (central SSA) to 62% (South Asia). Climate change is likely to impair future efforts to reduce child malnutrition in South Asia and SSA, even when economic growth is taken into account. Our model suggests that to reduce and prevent future undernutrition, it is necessary to both increase food access and improve socioeconomic conditions, as well as reduce greenhouse gas emissions.
Parameters-related uncertainty in modeling sugar cane yield with an agro-Land Surface Model
Valade, A.; Ciais, P.; Vuichard, N.; Viovy, N.; Ruget, F.; Gabrielle, B.
2012-12-01
uptake of water (root profile), and transpiration and respiration (stomatal conductance, growth and maintenance respiration coefficients). We find that the optimal carboxylation rate and optimal photosynthesis temperature parameters contribute most to the uncertainty in NPP and GPP simulations whereas stomatal conductance is the most sensitive parameter controlling SH, followed by optimal photosynthesis temperature and optimal carboxylation rate. The spatial variation of the ranked correlation between input parameters and output variables is well explained by rain and temperature drivers, suggesting that climate mediated regionally different sensitivities of modeled sugarcane yield to the model parameters, for Australia and Brazil.
Ridi Ferdiana; Paulus Insap Santoso; Lukito Edi Nugroho; Ahmad Ashari
2011-01-01
Software estimation is an area of software engineering concerned with the identification, classification and measurement of features of software that affect the cost of developing and sustaining computer programs [19]. Measuring the software through software estimation has purpose to know the complexity of the software, estimate the human resources, and get better visibility of execution and process model. There is a lot of software estimation that work sufficiently in certain conditions or s...
Directory of Open Access Journals (Sweden)
Ridi Ferdiana
2011-01-01
Full Text Available Software estimation is an area of software engineering concerned with the identification, classification and measurement of features of software that affect the cost of developing and sustaining computer programs [19]. Measuring the software through software estimation has purpose to know the complexity of the software, estimate the human resources, and get better visibility of execution and process model. There is a lot of software estimation that work sufficiently in certain conditions or step in software engineering for example measuring line of codes, function point, COCOMO, or use case points. This paper proposes another estimation technique called Distributed eXtreme Programming Estimation (DXP Estimation. DXP estimation provides a basic technique for the team that using eXtreme Programming method in onsite or distributed development. According to writer knowledge this is a first estimation technique that applied into agile method in eXtreme Programming.
Directory of Open Access Journals (Sweden)
Jakob Geipel
2014-10-01
Full Text Available Precision Farming (PF management strategies are commonly based on estimations of within-field yield potential, often derived from remotely-sensed products, e.g., Vegetation Index (VI maps. These well-established means, however, lack important information, like crop height. Combinations of VI-maps and detailed 3D Crop Surface Models (CSMs enable advanced methods for crop yield prediction. This work utilizes an Unmanned Aircraft System (UAS to capture standard RGB imagery datasets for corn grain yield prediction at three early- to mid-season growth stages. The imagery is processed into simple VI-orthoimages for crop/non-crop classification and 3D CSMs for crop height determination at different spatial resolutions. Three linear regression models are tested on their prediction ability using site-specific (i unclassified mean heights, (ii crop-classified mean heights and (iii a combination of crop-classified mean heights with according crop coverages. The models show determination coefficients \\({R}^{2}\\ of up to 0.74, whereas model (iii performs best with imagery captured at the end of stem elongation and intermediate spatial resolution (0.04m\\(\\cdot\\px\\(^{-1}\\.Following these results, combined spectral and spatial modeling, based on aerial images and CSMs, proves to be a suitable method for mid-season corn yield prediction.
DEFF Research Database (Denmark)
Salo, T J; Palosuo, T; Kersebaum, K C
2016-01-01
, Finland. This is the largest standardized crop model inter-comparison under different levels of N supply to date. The models were calibrated using data from 2002 and 2008, of which 2008 included six N rates ranging from 0 to 150 kg N/ha. Calibration data consisted of weather, soil, phenology, leaf area...... index (LAI) and yield observations. The models were then tested against new data for 2009 and their performance was assessed and compared with both the two calibration years and the test year. For the calibration period, root mean square error between measurements and simulated grain dry matter yields...... mineralization as a function of soil temperature and moisture. Furthermore, specific weather event impacts such as low temperatures after emergence in 2009, tending to enhance tillering, and a high precipitation event just before harvest in 2008, causing possible yield penalties, were not captured by any...
Maydeu-Olivares, Alberto; Hernandez, Adolfo
2007-01-01
The interpretation of a Thurstonian model for paired comparisons where the utilities' covariance matrix is unrestricted proved to be difficult due to the comparative nature of the data. We show that under a suitable constraint the utilities' correlation matrix can be estimated, yielding a readily interpretable solution. This set of identification…
Directory of Open Access Journals (Sweden)
Victor Brunini Moreto
2015-10-01
Full Text Available Forecast is the act of estimating a future event based on current data. Ten-day period (TDP meteorological data were used for modeling: mean air temperature, precipitation and water balance components (water deficit (DEF and surplus (EXC and soil water storage (SWS. Meteorological and yield data from 1990-2004 were used for calibration, and 2005-2010 were used for testing. First step was the selection of variables via correlation analysis to determine which TDP and climatic variables have more influence on the crop yield. The selected variables were used to construct models by multiple linear regression, using a stepwise backwards process. Among all analyzed models, the following was notable: Yield = - 4.964 x [SWS of 2° TDP of December of the previous year (OPY] – 1.123 x [SWS of 2° TDP of November OPY] + 0.949 x [EXC of 1° TDP of February of the productive year (PY] + 2.5 x [SWS of 2° TDP of February OPY] + 19.125 x [EXC of 1° TDP of May OPY] – 3.113 x [EXC of 3° TDP of January OPY] + 1.469 x [EXC of 3 TDP of January of PY] + 3920.526, with MAPE = 5.22%, R2 = 0.58 and RMSEs = 111.03 kg ha-1.
The Forecasting Power of the Yield Curve, a Supervised Factor Model Approach
DEFF Research Database (Denmark)
Boldrini, Lorenzo; Hillebrand, Eric Tobias
We study the forecast power of the yield curve for macroeconomic time series, such as consumer price index, personal consumption expenditures, producer price index, real disposable income, unemployment rate, and industrial production. We employ a state-space model in which the forecasting objective......¨urkaynak, Sack, and Wright (2006) and Diebold and Li (2006) and macroeconomic data from FRED. We compare the models by means of the conditional predictive ability test of Giacomini and White (2006). We find that the yield curve has more forecast power for real variables compared to inflation measures...
Lühr, Armin; Priegnitz, Marlen; Fiedler, Fine; Sobolevsky, Nikolai; Bassler, Niels
2014-01-01
In ion beam cancer therapy, range verification in patients using positron emission tomography (PET) requires the comparison of measured with simulated positron emitter yields. We found that (1) changes in modeling nuclear interactions strongly affected the positron emitter yields and that (2) Monte Carlo simulations with SHIELD-HIT10Areasonably matched the most abundant PET isotopes (11)C and (15)O. We observed an ion-energy (i.e., depth) dependence of the agreement between SHIELD-HIT10Aand measurement. Improved modeling requires more accurate measurements of cross-section values.
Energy Technology Data Exchange (ETDEWEB)
Studnicki, M.; Mądry, W.; Noras, K.; Wójcik-Gront, E.; Gacek, E.
2016-11-01
The main objectives of multi-environmental trials (METs) are to assess cultivar adaptation patterns under different environmental conditions and to investigate genotype by environment (G×E) interactions. Linear mixed models (LMMs) with more complex variance-covariance structures have become recognized and widely used for analyzing METs data. Best practice in METs analysis is to carry out a comparison of competing models with different variance-covariance structures. Improperly chosen variance-covariance structures may lead to biased estimation of means resulting in incorrect conclusions. In this work we focused on adaptive response of cultivars on the environments modeled by the LMMs with different variance-covariance structures. We identified possible limitations of inference when using an inadequate variance-covariance structure. In the presented study we used the dataset on grain yield for 63 winter wheat cultivars, evaluated across 18 locations, during three growing seasons (2008/2009-2010/2011) from the Polish Post-registration Variety Testing System. For the evaluation of variance-covariance structures and the description of cultivars adaptation to environments, we calculated adjusted means for the combination of cultivar and location in models with different variance-covariance structures. We concluded that in order to fully describe cultivars adaptive patterns modelers should use the unrestricted variance-covariance structure. The restricted compound symmetry structure may interfere with proper interpretation of cultivars adaptive patterns. We found, that the factor-analytic structure is also a good tool to describe cultivars reaction on environments, and it can be successfully used in METs data after determining the optimal component number for each dataset. (Author)
Directory of Open Access Journals (Sweden)
Marcin Studnicki
2016-06-01
Full Text Available The main objectives of multi-environmental trials (METs are to assess cultivar adaptation patterns under different environmental conditions and to investigate genotype by environment (G×E interactions. Linear mixed models (LMMs with more complex variance-covariance structures have become recognized and widely used for analyzing METs data. Best practice in METs analysis is to carry out a comparison of competing models with different variance-covariance structures. Improperly chosen variance-covariance structures may lead to biased estimation of means resulting in incorrect conclusions. In this work we focused on adaptive response of cultivars on the environments modeled by the LMMs with different variance-covariance structures. We identified possible limitations of inference when using an inadequate variance-covariance structure. In the presented study we used the dataset on grain yield for 63 winter wheat cultivars, evaluated across 18 locations, during three growing seasons (2008/2009-2010/2011 from the Polish Post-registration Variety Testing System. For the evaluation of variance-covariance structures and the description of cultivars adaptation to environments, we calculated adjusted means for the combination of cultivar and location in models with different variance-covariance structures. We concluded that in order to fully describe cultivars adaptive patterns modelers should use the unrestricted variance-covariance structure. The restricted compound symmetry structure may interfere with proper interpretation of cultivars adaptive patterns. We found, that the factor-analytic structure is also a good tool to describe cultivars reaction on environments, and it can be successfully used in METs data after determining the optimal component number for each dataset.
Implications of b{yields}s{gamma} in the Weinberg three-Higgs-doublet models
Energy Technology Data Exchange (ETDEWEB)
Chang, Darwin; Chen, Chuan-Hung; Geng, Chao-Qiang [National Tsing Hua Univ., Hsinchu, TW (China). Dept. of Physics
1996-06-01
Using recent experimental measurements on Br(b{yields}s{gamma}) from CLEO, we study the constraints on the charged Higgs sector in various three-Higgs-doublet models. Some phenomenological implications in these models with emphasis on CP violation are presented. In particular, in some of these models, the CP violating muon polarization in K{sub {mu}3} can be detected using the current KEK experiment E246. (author)
On estimation of survival function under random censoring model
Institute of Scientific and Technical Information of China (English)
JIANG; Jiancheng(蒋建成); CHENG; Bo(程博); WU; Xizhi(吴喜之)
2002-01-01
We study an estimator of the survival function under the random censoring model. Bahadur-type representation of the estimator is obtained and asymptotic expression for its mean squared errors is given, which leads to the consistency and asymptotic normality of the estimator. A data-driven local bandwidth selection rule for the estimator is proposed. It is worth noting that the estimator is consistent at left boundary points, which contrasts with the cases of density and hazard rate estimation. A Monte Carlo comparison of different estimators is made and it appears that the proposed data-driven estimators have certain advantages over the common Kaplan-Meier estmator.
Model of Yield Response of Corn to Plant Population and Absorption of Solar Energy
Overman, Allen R.; Scholtz, Richard V.
2011-01-01
Biomass yield of agronomic crops is influenced by a number of factors, including crop species, soil type, applied nutrients, water availability, and plant population. This article is focused on dependence of biomass yield (Mg ha−1 and g plant−1) on plant population (plants m−2). Analysis includes data from the literature for three independent studies with the warm-season annual corn (Zea mays L.) grown in the United States. Data are analyzed with a simple exponential mathematical model which contains two parameters, viz. Ym (Mg ha−1) for maximum yield at high plant population and c (m2 plant−1) for the population response coefficient. This analysis leads to a new parameter called characteristic plant population, xc = 1/c (plants m−2). The model is shown to describe the data rather well for the three field studies. In one study measurements were made of solar radiation at different positions in the plant canopy. The coefficient of absorption of solar energy was assumed to be the same as c and provided a physical basis for the exponential model. The three studies showed no definitive peak in yield with plant population, but generally exhibited asymptotic approach to maximum yield with increased plant population. Values of xc were very similar for the three field studies with the same crop species. PMID:21297960
Model of yield response of corn to plant population and absorption of solar energy.
Directory of Open Access Journals (Sweden)
Allen R Overman
Full Text Available Biomass yield of agronomic crops is influenced by a number of factors, including crop species, soil type, applied nutrients, water availability, and plant population. This article is focused on dependence of biomass yield (Mg ha(-1 and g plant(-1 on plant population (plants m(-2. Analysis includes data from the literature for three independent studies with the warm-season annual corn (Zea mays L. grown in the United States. Data are analyzed with a simple exponential mathematical model which contains two parameters, viz. Y(m (Mg ha(-1 for maximum yield at high plant population and c (m(2 plant(-1 for the population response coefficient. This analysis leads to a new parameter called characteristic plant population, x(c = 1/c (plants m(-2. The model is shown to describe the data rather well for the three field studies. In one study measurements were made of solar radiation at different positions in the plant canopy. The coefficient of absorption of solar energy was assumed to be the same as c and provided a physical basis for the exponential model. The three studies showed no definitive peak in yield with plant population, but generally exhibited asymptotic approach to maximum yield with increased plant population. Values of x(c were very similar for the three field studies with the same crop species.
A review on statistical models for identifying climate contributions to crop yields
Institute of Scientific and Technical Information of China (English)
SHI Wenjiao; TAO Fulu; ZHANG Zhao
2013-01-01
Statistical models using historical data on crop yields and weather to calibrate relatively simple regression equations have been widely and extensively applied in previous studies,and have provided a common alternative to process-based models,which require extensive input data on cultivar,management,and soil conditions.However,very few studies had been conducted to review systematically the previous statistical models for indentifying climate contributions to crop yields.This paper introduces three main statistical methods,i.e.,time-series model,cross-section model and panel model,which have been used to identify such issues in the field of agrometeorology.Generally,research spatial scale could be categorized into two types using statistical models,including site scale and regional scale (e.g.global scale,national scale,provincial scale and county scale).Four issues exist in identifying response sensitivity of crop yields to climate change by statistical models.The issues include the extent of spatial and temporal scale,non-climatic trend removal,colinearity existing in climate variables and non-consideration of adaptations.Respective resolutions for the above four issues have been put forward in the section of perspective on the future of statistical models finally.
Directory of Open Access Journals (Sweden)
Louis de Grange
2010-09-01
Full Text Available Maximum entropy models are often used to describe supply and demand behavior in urban transportation and land use systems. However, they have been criticized for not representing behavioral rules of system agents and because their parameters seems to adjust only to modeler-imposed constraints. In response, it is demonstrated that the solution to the entropy maximization problem with linear constraints is a multinomial logit model whose parameters solve the likelihood maximization problem of this probabilistic model. But this result neither provides a microeconomic interpretation of the entropy maximization problem nor explains the equivalence of these two optimization problems. This work demonstrates that an analysis of the dual of the entropy maximization problem yields two useful alternative explanations of its solution. The first shows that the maximum entropy estimators of the multinomial logit model parameters reproduce rational user behavior, while the second shows that the likelihood maximization problem for multinomial logit models is the dual of the entropy maximization problem.
Empirical validation of the InVEST water yield ecosystem service model at a national scale.
Redhead, J W; Stratford, C; Sharps, K; Jones, L; Ziv, G; Clarke, D; Oliver, T H; Bullock, J M
2016-11-01
A variety of tools have emerged with the goal of mapping the current delivery of ecosystem services and quantifying the impact of environmental changes. An important and often overlooked question is how accurate the outputs of these models are in relation to empirical observations. In this paper we validate a hydrological ecosystem service model (InVEST Water Yield Model) using widely available data. We modelled annual water yield in 22 UK catchments with widely varying land cover, population and geology, and compared model outputs with gauged river flow data from the UK National River Flow Archive. Values for input parameters were selected from existing literature to reflect conditions in the UK and were subjected to sensitivity analyses. We also compared model performance between precipitation and potential evapotranspiration data sourced from global- and UK-scale datasets. We then tested the transferability of the results within the UK by additional validation in a further 20 catchments. Whilst the model performed only moderately with global-scale data (linear regression of modelled total water yield against empirical data; slope=0.763, intercept=54.45, R(2)=0.963) with wide variation in performance between catchments, the model performed much better when using UK-scale input data, with closer fit to the observed data (slope=1.07, intercept=3.07, R(2)=0.990). With UK data the majority of catchments showed modelled water yield but there was a minor but consistent overestimate per hectare (86m(3)/ha/year). Additional validation on a further 20 UK catchments was similarly robust, indicating that these results are transferable within the UK. These results suggest that relatively simple models can give accurate measures of ecosystem services. However, the choice of input data is critical and there is a need for further validation in other parts of the world. Copyright © 2016 Elsevier B.V. All rights reserved.
Consistent estimators in random censorship semiparametric models
Institute of Scientific and Technical Information of China (English)
王启华
1996-01-01
For the fixed design regression modelwhen Y, are randomly censored on the right, the estimators of unknown parameter and regression function g from censored observations are defined in the two cases .where the censored distribution is known and unknown, respectively. Moreover, the sufficient conditions under which these estimators are strongly consistent and pth (p>2) mean consistent are also established.
Estimation of Wind Turbulence Using Spectral Models
DEFF Research Database (Denmark)
Soltani, Mohsen; Knudsen, Torben; Bak, Thomas
2011-01-01
The production and loading of wind farms are significantly influenced by the turbulence of the flowing wind field. Estimation of turbulence allows us to optimize the performance of the wind farm. Turbulence estimation is; however, highly challenging due to the chaotic behavior of the wind. In thi...
Mechanical model for yield strength of nanocrystalline materials under high strain rate loading
Institute of Scientific and Technical Information of China (English)
朱荣涛; 周剑秋; 马璐; 张振忠
2008-01-01
To understand the high strain rate deformation mechanism and determine the grain size,strain rate and porosity dependent yield strength of nanocrystalline materials,a new mechanical model based on the deformation mechanism of nanocrystalline materials under high strain rate loading was developed.As a first step of the research,the yield behavior of the nanocrystalline materials under high strain rate loading was mainly concerned in the model and uniform deformation was assumed for simplification.Nanocrystalline materials were treated as composites consisting of grain interior phase and grain boundary phase,and grain interior and grain boundary deformation mechanisms under high strain rate loading were analyzed,then Voigt model was applied to coupling grain boundary constitutive relation with mechanical model for grain interior phase to describe the overall yield mechanical behavior of nanocrystalline materials.The predictions by the developed model on the yield strength of nanocrysatlline materials at high strain rates show good agreements with various experimental data.Further discussion was presented for calculation results and relative experimental observations.
A Note on Structural Equation Modeling Estimates of Reliability
Yang, Yanyun; Green, Samuel B.
2010-01-01
Reliability can be estimated using structural equation modeling (SEM). Two potential problems with this approach are that estimates may be unstable with small sample sizes and biased with misspecified models. A Monte Carlo study was conducted to investigate the quality of SEM estimates of reliability by themselves and relative to coefficient…
Radiation risk estimation based on measurement error models
Masiuk, Sergii; Shklyar, Sergiy; Chepurny, Mykola; Likhtarov, Illya
2017-01-01
This monograph discusses statistics and risk estimates applied to radiation damage under the presence of measurement errors. The first part covers nonlinear measurement error models, with a particular emphasis on efficiency of regression parameter estimators. In the second part, risk estimation in models with measurement errors is considered. Efficiency of the methods presented is verified using data from radio-epidemiological studies.
Parameter estimation of hidden periodic model in random fields
Institute of Scientific and Technical Information of China (English)
何书元
1999-01-01
Two-dimensional hidden periodic model is an important model in random fields. The model is used in the field of two-dimensional signal processing, prediction and spectral analysis. A method of estimating the parameters for the model is designed. The strong consistency of the estimators is proved.
Simulation of potato yield in temperate condition by the AquaCrop model
DEFF Research Database (Denmark)
Razzaghi, Fatemeh; Zhenjiang, Zhou; Andersen, Mathias Neumann
2017-01-01
to calculate the soil water balance on a daily basis has become widespread in the last decades. Therefore, this study was performed to simulate potato yield, dry matter and soil water content under different water stress condition using the AquaCrop model. Three levels of irrigation comprising full irrigated...... was simulated using the AquaCrop model. Data from full irrigated treatment of 2014 was used for model calibration and data from 2013 (If, Id, and I0 treatments), 2014 (Id, and I0 treatments) and 2015 (If, Id, and I0 treatments) were used for model validation. The sensitivity analysis of different parameters...... showed that KcTr, HI0, CCX, calendar day from sowing to start of senescence and WP* had the most pronounced influence on tuber yield. The result showed that the AquaCrop model simulated soil water content, canopy cover and above-ground dry matter during the crop growth seasons with acceptable accuracy...
Simple model relating recombination rates and non-proportional light yield in scintillators
Energy Technology Data Exchange (ETDEWEB)
Moses, William W.; Bizarri, Gregory; Singh, Jai; Vasil' ev, Andrey N.; Williams, Richard T.
2008-09-24
We present a phenomenological approach to derive an approximate expression for the local light yield along a track as a function of the rate constants of different kinetic orders of radiative and quenching processes for excitons and electron-hole pairs excited by an incident {gamma}-ray in a scintillating crystal. For excitons, the radiative and quenching processes considered are linear and binary, and for electron-hole pairs a ternary (Auger type) quenching process is also taken into account. The local light yield (Y{sub L}) in photons per MeV is plotted as a function of the deposited energy, -dE/dx (keV/cm) at any point x along the track length. This model formulation achieves a certain simplicity by using two coupled rate equations. We discuss the approximations that are involved. There are a sufficient number of parameters in this model to fit local light yield profiles needed for qualitative comparison with experiment.
A model for the {gamma}N{yields}{pi}{pi}N reaction
Energy Technology Data Exchange (ETDEWEB)
Gomez Tejedor, J.A.; Oset, E. [Departamento de Fisica Teorica Centro Mixto Universidad de Valencia-CSIC, Burjassot, Valencia (Spain)]|[IFIC Centro Mixto Universidad de Valencia-CSIC, Burjassot, Valencia (Spain)
1996-11-01
We have studied the {gamma}N {yields} {pi}{pi}N reaction using a model which includes N, {Delta}(1232), N{sup *}(1440) and N{sup *}(1520) intermediate baryonic states and the {rho}-meson as intermediate {pi}{pi} resonance. The model reproduces fairly well experimental cross sections below E{sub {gamma}} = 800 MeV and invariant-mass distributions even at higher energies. One of the interesting findings of the study is that the {gamma}N{yields}N{sup *}(1520) {yields} {Delta}{pi} process is very important and interferes strongly with the dominant {Delta}-Kroll-Ruderman term to produce the experimental peak of the cross section. (author) 10 refs, 1 fig
A plasticity model with yield surface distortion for non proportional loading
François, Marc Louis Maurice
2010-01-01
In order to enhance the modeling of metallic materials behavior in non proportional loadings, a modification of the classical elastic-plastic models including distortion of the yield surface is proposed. The new yield criterion uses the same norm as in the classical von Mises based criteria, and a "distorted stress" Sd replacing the usual stress deviator S. The obtained yield surface is then ?egg-shaped? similar to those experimentally observed and depends on only one new material parameter. The theory is built in such a way as to recover the classical one for proportional loading. An identification procedure is proposed to obtain the material parameters. Simulations and experiments are compared for a 2024 T4 aluminum alloy for both proportional and nonproportional tension-torsion loading paths.
Efficient estimation of semiparametric copula models for bivariate survival data
Cheng, Guang
2014-01-01
A semiparametric copula model for bivariate survival data is characterized by a parametric copula model of dependence and nonparametric models of two marginal survival functions. Efficient estimation for the semiparametric copula model has been recently studied for the complete data case. When the survival data are censored, semiparametric efficient estimation has only been considered for some specific copula models such as the Gaussian copulas. In this paper, we obtain the semiparametric efficiency bound and efficient estimation for general semiparametric copula models for possibly censored data. We construct an approximate maximum likelihood estimator by approximating the log baseline hazard functions with spline functions. We show that our estimates of the copula dependence parameter and the survival functions are asymptotically normal and efficient. Simple consistent covariance estimators are also provided. Numerical results are used to illustrate the finite sample performance of the proposed estimators. © 2013 Elsevier Inc.
Genotype by environment interaction for seed yield per plant in rapeseed using AMMI model
Directory of Open Access Journals (Sweden)
Ana Marjanović-Jeromela
2011-02-01
Full Text Available The objective of this study was to assess genotype by environment interaction for seed yield per plant in rapeseed cultivars grown in Northern Serbia by the AMMI (additive main effects and multiplicative interaction model. The study comprised 19 rapeseed genotypes, analyzed in seven years through field trials arranged in a randomized complete block design, with three replicates. Seed yield per plant of the tested cultivars varied from 1.82 to 19.47 g throughout the seven seasons, with an average of 7.41 g. In the variance analysis, 72.49% of the total yield variation was explained by environment, 7.71% by differences between genotypes, and 19.09% by genotype by environment interaction. On the biplot, cultivars with high yield genetic potential had positive correlation with the seasons with optimal growing conditions, while the cultivars with lower yield potential were correlated to the years with unfavorable conditions. Seed yield per plant is highly influenced by environmental factors, which indicates the adaptability of specific genotypes to specific seasons.
Estimation of Stochastic Volatility Models by Nonparametric Filtering
DEFF Research Database (Denmark)
Kanaya, Shin; Kristensen, Dennis
2016-01-01
/estimated volatility process replacing the latent process. Our estimation strategy is applicable to both parametric and nonparametric stochastic volatility models, and can handle both jumps and market microstructure noise. The resulting estimators of the stochastic volatility model will carry additional biases......A two-step estimation method of stochastic volatility models is proposed: In the first step, we nonparametrically estimate the (unobserved) instantaneous volatility process. In the second step, standard estimation methods for fully observed diffusion processes are employed, but with the filtered...... and variances due to the first-step estimation, but under regularity conditions we show that these vanish asymptotically and our estimators inherit the asymptotic properties of the infeasible estimators based on observations of the volatility process. A simulation study examines the finite-sample properties...
Mathematical model of transmission network static state estimation
Directory of Open Access Journals (Sweden)
Ivanov Aleksandar
2012-01-01
Full Text Available In this paper the characteristics and capabilities of the power transmission network static state estimator are presented. The solving process of the mathematical model containing the measurement errors and their processing is developed. To evaluate difference between the general model of state estimation and the fast decoupled state estimation model, the both models are applied to an example, and so derived results are compared.
Estimation in the polynomial errors-in-variables model
Institute of Scientific and Technical Information of China (English)
无
2002-01-01
Estimators are presented for the coefficients of the polynomial errors-in-variables (EV) model when replicated observations are taken at some experimental points. These estimators are shown to be strongly consistent under mild conditions.
AMMI Model for Interpreting Clone-Environment Interaction in Starch Yield of Cassava
Directory of Open Access Journals (Sweden)
SHOLIHIN
2011-03-01
Full Text Available The aim of the study was to analyze the interaction between clone and environment for starch yield in six month-old plants of cassava clones based on additive main effects and multiplicative interaction (AMMI model. The experiments were conducted on mineral soil in four different locations: Lumajang (inceptisol, Kediri (entisol, Pati (alfisol, and Tulangbawang (ultisol. The experiments were carried out during 2004-2005, using a split plot design withthree replications. The main plots were the simple and the improved technology. The clones used were fifteen clones. Parameter recorded was starch yield (kg/ha of the 6 month old plants. The data were analyzed using the AMMI model. Based on the AMMI analysis, environmental factors being important in determining the stability of the starch yield were soil density for subsoil, pH of topsoil, and the maximum air humidity four months after planting. The clones of CMM97001-87, CMM97002-183, CMM97011-191, CMM97006-44, and Adhira 4 were identified as stable clones in starch yield within 6 month-old plants. CMM97007-235 was adapted to maximum relative humidity 4 months after planting and to lower pH of topsoil, whereas, MLG 10311 was adapted to lower bulk density. The mean starch yield of MLG 10311 was the highest six months after planting.
Mark H. Eisenbies; M.B. Adams; W. Michael Aust; James A. Burger
2007-01-01
Floods continue to cause significant damage in the United States and elsewhere, and questions about the causes of flooding continue to be debated. A significant amount of research has been conducted on the relationship between forest management activities and water yield, peak flows, and flooding; somewhat less research has been conducted on the modeling of these...
Climate Impacts on Chinese Corn Yields: A Fractional Polynomial Regression Model
Kooten, van G.C.; Sun, Baojing
2012-01-01
In this study, we examine the effect of climate on corn yields in northern China using data from ten districts in Inner Mongolia and two in Shaanxi province. A regression model with a flexible functional form is specified, with explanatory variables that include seasonal growing degree days,
Validation of Crop Weather Models for'Crop Assessment arid Yield ...
African Journals Online (AJOL)
over predict grain yields of maize, sorghum and wheat, a fact that could be attributed to the inadequacy of the model .... Of particular interest in this category is the cesses (Hanks and Hill, 1980). ... ter and basic infiltration rate for soii data. The.
Wit, de A.J.W.; Diepen, van C.A.
2007-01-01
Uncertainty in spatial and temporal distribution of rainfall in regional crop yield simulations comprises a major fraction of the error on crop model simulation results. In this paper we used an Ensemble Kalman filter (EnKF) to assimilate coarse resolution satellite microwave sensor derived soil
Supporting Crop Loss Insurance Policy of Indonesia through Rice Yield Modelling and Forecasting
van Verseveld, Willem; Weerts, Albrecht; Trambauer, Patricia; de Vries, Sander; Conijn, Sjaak; van Valkengoed, Eric; Hoekman, Dirk; Grondard, Nicolas; Hengsdijk, Huib; Schrevel, Aart; Vlasbloem, Pieter; Klauser, Dominik
2017-04-01
The Government of Indonesia has decided on a crop insurance policy to assist Indonesia's farmers and to boost food security. To support the Indonesian government, the G4INDO project (www.g4indo.org) is developing/constructing an integrated platform implemented in the Delft-FEWS forecasting system (Werner et al., 2013). The integrated platform brings together remote sensed data (both visible and radar) and hydrologic, crop and reservoir modelling and forecasting to improve the modelling and forecasting of rice yield. The hydrological model (wflow_sbm), crop model (wflow_lintul) and reservoir models (RTC-Tools) are coupled on time stepping basis in the OpenStreams framework (see https://github.com/openstreams/wflow) and deployed in the integrated platform to support seasonal forecasting of water availability and crop yield. First we will show the general idea about the G4INDO project, the integrated platform (including Sentinel 1 & 2 data) followed by first (reforecast) results of the coupled models for predicting water availability and crop yield in the Brantas catchment in Java, Indonesia. Werner, M., Schellekens, J., Gijsbers, P., Van Dijk, M., Van den Akker, O. and Heynert K, 2013. The Delft-FEWS flow forecasting system, Environmental Modelling & Software; 40:65-77. DOI: 10.1016/j.envsoft.2012.07.010.
Estimation Risk Modeling in Optimal Portfolio Selection: An Empirical Study from Emerging Markets
Directory of Open Access Journals (Sweden)
Sarayut Nathaphan
2010-01-01
Full Text Available Efficient portfolio is a portfolio that yields maximum expected return given a level of risk or has a minimum level of risk given a level of expected return. However, the optimal portfolios do not seem to be as efficient as intended. Especially during financial crisis period, optimal portfolio is not an optimal investment as it does not yield maximum return given a specific level of risk, and vice versa. One possible explanation for an unimpressive performance of the seemingly efficient portfolio is incorrectness in parameter estimates called “estimation risk in parameter estimates”. Six different estimating strategies are employed to explore ex-post-portfolio performance when estimation risk is incorporated. These strategies are traditional Mean-Variance (EV, Adjusted Beta (AB approach, Resampled Efficient Frontier (REF, Capital Asset Pricing Model (CAPM, Single Index Model (SIM, and Single Index Model incorporating shrinkage Bayesian factor namely, Bayesian Single Index Model (BSIM. Among the six alternative strategies, shrinkage estimators incorporating the single index model outperform other traditional portfolio selection strategies. Allowing for asset mispricing and applying Bayesian shrinkage adjusted factor to each asset's alpha, a single factor namely, excess market return is adequate in alleviating estimation uncertainty.
Analysis of shape isomer yields of 237Pu in the framework of dynamical–statistical model
Indian Academy of Sciences (India)
Hadi Eslamizadeh
2012-02-01
Data on shape isomer yield for + 235U reaction at $E^{\\text{lab}}$ = 20–29 MeV are analysed in the framework of a combined dynamical–statistical model. From this analysis, information on the double humped ﬁssion barrier parameters for some Pu isotopes has been obtained and it is shown that the depth of the second potential well should be less than the results of statistical model calculations.
Wang, Qianfeng; Wu, Jianjun; Li, Xiaohan; Zhou, Hongkui; Yang, Jianhua; Geng, Guangpo; An, Xueli; Liu, Leizhen; Tang, Zhenghong
2017-04-01
The quantitative evaluation of the impact of drought on crop yield is one of the most important aspects in agricultural water resource management. To assess the impact of drought on wheat yield, the Environmental Policy Integrated Climate (EPIC) crop growth model and daily Standardized Precipitation Evapotranspiration Index (SPEI), which is based on daily meteorological data, are adopted in the Huang Huai Hai Plain. The winter wheat crop yields are estimated at 28 stations, after calibrating the cultivar coefficients based on the experimental site data, and SPEI data was taken 11 times across the growth season from 1981 to 2010. The relationship between estimated yield and multi-scale SPEI were analyzed. The optimum time scale SPEI to monitor drought during the crop growth period was determined. The reference yield was determined by averaging the yields from numerous non-drought years. From this data, we propose a comprehensive quantitative method which can be used to predict the impact of drought on wheat yields by combining the daily multi-scale SPEI and crop growth process model. This method was tested in the Huang Huai Hai Plain. The results suggested that estimation of calibrated EPIC was a good predictor of crop yield in the Huang Huai Hai Plain, with lower RMSE (15.4 %) between estimated yield and observed yield at six agrometeorological stations. The soil moisture at planting time was affected by the precipitation and evapotranspiration during the previous 90 days (about 3 months) in the Huang Huai Hai Plain. SPEIG90 was adopted as the optimum time scale SPEI to identify the drought and non-drought years, and identified a drought year in 2000. The water deficit in the year 2000 was significant, and the rate of crop yield reduction did not completely correspond with the volume of water deficit. Our proposed comprehensive method which quantitatively evaluates the impact of drought on crop yield is reliable. The results of this study further our understanding
Wang, Qianfeng; Wu, Jianjun; Li, Xiaohan; Zhou, Hongkui; Yang, Jianhua; Geng, Guangpo; An, Xueli; Liu, Leizhen; Tang, Zhenghong
2016-11-01
The quantitative evaluation of the impact of drought on crop yield is one of the most important aspects in agricultural water resource management. To assess the impact of drought on wheat yield, the Environmental Policy Integrated Climate (EPIC) crop growth model and daily Standardized Precipitation Evapotranspiration Index (SPEI), which is based on daily meteorological data, are adopted in the Huang Huai Hai Plain. The winter wheat crop yields are estimated at 28 stations, after calibrating the cultivar coefficients based on the experimental site data, and SPEI data was taken 11 times across the growth season from 1981 to 2010. The relationship between estimated yield and multi-scale SPEI were analyzed. The optimum time scale SPEI to monitor drought during the crop growth period was determined. The reference yield was determined by averaging the yields from numerous non-drought years. From this data, we propose a comprehensive quantitative method which can be used to predict the impact of drought on wheat yields by combining the daily multi-scale SPEI and crop growth process model. This method was tested in the Huang Huai Hai Plain. The results suggested that estimation of calibrated EPIC was a good predictor of crop yield in the Huang Huai Hai Plain, with lower RMSE (15.4 %) between estimated yield and observed yield at six agrometeorological stations. The soil moisture at planting time was affected by the precipitation and evapotranspiration during the previous 90 days (about 3 months) in the Huang Huai Hai Plain. SPEIG90 was adopted as the optimum time scale SPEI to identify the drought and non-drought years, and identified a drought year in 2000. The water deficit in the year 2000 was significant, and the rate of crop yield reduction did not completely correspond with the volume of water deficit. Our proposed comprehensive method which quantitatively evaluates the impact of drought on crop yield is reliable. The results of this study further our understanding
Determining Rheological Parameters of Generalized Yield-Power-Law Fluid Model
Directory of Open Access Journals (Sweden)
Stryczek Stanislaw
2004-09-01
Full Text Available The principles of determining rheological parameters of drilling muds described by a generalized yield-power-law are presented in the paper. Functions between tangent stresses and shear rate are given. The conditions of laboratory measurements of rheological parameters of generalized yield-power-law fluids are described and necessary mathematical relations for rheological model parameters given. With the block diagrams, the methodics of numerical solution of these relations has been presented. Rheological parameters of an exemplary drilling mud have been calculated with the use of this numerical program.
Bayesian Estimation of Categorical Dynamic Factor Models
Zhang, Zhiyong; Nesselroade, John R.
2007-01-01
Dynamic factor models have been used to analyze continuous time series behavioral data. We extend 2 main dynamic factor model variations--the direct autoregressive factor score (DAFS) model and the white noise factor score (WNFS) model--to categorical DAFS and WNFS models in the framework of the underlying variable method and illustrate them with…
Crop model sensitivity to the estimated daily global solar radiation data
Directory of Open Access Journals (Sweden)
Pavel Kapler
2006-01-01
Full Text Available The results of the previous studies have suggested that the estimated RG values are loaded with an error, which might compromise the precision of the subsequent crop model applications. Therefore a detailed analysis of the error propagation was made using two crop models i.e. CERES-Barley and CERES-Wheat. Database of meteorological data originating from 8 stations in Austria and Czech Republic was used in order to carry out the analysis. It has been found that even application of the method based on sunshine duration that yield the lowest bias in RG estimates significantly influences number of key crop model outputs. It has been also noted that in 5–6 seasons out of 100 cases the deviation greater than ±10 % is to be expected whilst the occurrence of ±25% could not be also ruled out. The precision of the yield estimates and other crop model outputs is lower then expected but still acceptable for most application with mean bias error in range of 2.0–4.1% when estimates based on the diurnal temperature range and cloud cover are used. In this case yield deviations over ±10% occurred in about 20% cases (depending on the crop whilst the probability of significant yield departure (±25% doubled of that found for the previous method. The methods based on the diurnal temperature range and daily precipitation sum showed an increase of the systematic bias of yield of winter wheat and considerably higher number of seasons with yield departures over ±25%. Utilisation of the methods based on the diurnal temperature range only for the purposes of seasonal yield forecasting or climate change impact assessment is questionable as the probability of significant yield departure is very high (as well as the systematic error. These findings should act as an incentive to the further research aimed at development of more precise and widely applicable methods of estimating daily RG based more on the underlying physical principles and/or remote sensing approach
Wang, Haijun; Liang, Xiaomin; Wang, Hongzhu
2017-07-01
Next to excessive nutrient loading, intensive aquaculture is one of the major anthropogenic impacts threatening lake ecosystems. In China, particularly in the shallow lakes of mid-lower Changjiang (Yangtze) River, continuous overstocking of the Chinese mitten crab ( Eriocheir sinensis) could deteriorate water quality and exhaust natural resources. A series of crab yield models and a general optimum-stocking rate model have been established, which seek to benefit both crab culture and the environment. In this research, independent investigations were carried out to evaluate the crab yield models and modify the optimum-stocking model. Low percentage errors (average 47%, median 36%) between observed and calculated crab yields were obtained. Specific values were defined for adult crab body mass (135 g/ind.) and recapture rate (18% and 30% in lakes with submerged macrophyte biomass above and below 1 000 g/m2) to modify the optimum-stocking model. Analysis based on the modified optimum-stocking model indicated that the actual stocking rates in most lakes were much higher than the calculated optimum-stocking rates. This implies that, for most lakes, the current stocking rates should be greatly reduced to maintain healthy lake ecosystems.
Wang, Haijun; Liang, Xiaomin; Wang, Hongzhu
2016-08-01
Next to excessive nutrient loading, intensive aquaculture is one of the major anthropogenic impacts threatening lake ecosystems. In China, particularly in the shallow lakes of mid-lower Changjiang (Yangtze) River, continuous overstocking of the Chinese mitten crab (Eriocheir sinensis) could deteriorate water quality and exhaust natural resources. A series of crab yield models and a general optimum-stocking rate model have been established, which seek to benefit both crab culture and the environment. In this research, independent investigations were carried out to evaluate the crab yield models and modify the optimum-stocking model. Low percentage errors (average 47%, median 36%) between observed and calculated crab yields were obtained. Specific values were defined for adult crab body mass (135 g/ind.) and recapture rate (18% and 30% in lakes with submerged macrophyte biomass above and below 1 000 g/m2) to modify the optimum-stocking model. Analysis based on the modified optimum-stocking model indicated that the actual stocking rates in most lakes were much higher than the calculated optimum-stocking rates. This implies that, for most lakes, the current stocking rates should be greatly reduced to maintain healthy lake ecosystems.
Institute of Scientific and Technical Information of China (English)
Omar; Vergara-Diaz; Shawn; C.Kefauver; Abdelhalim; Elazab; Maria; Teresa; Nieto-Taladriz; José; Luis; Araus
2015-01-01
The biotrophic fungus Puccinia striiformis f. sp. tritici is the causal agent of the yellow rust in wheat. Between the years 2010–2013 a new strain of this pathogen(Warrior/Ambition),against which the present cultivated wheat varieties have no resistance, appeared and spread rapidly. It threatens cereal production in most of Europe. The search for sources of resistance to this strain is proposed as the most efficient and safe solution to ensure high grain production. This will be helped by the development of high performance and low cost techniques for field phenotyping. In this study we analyzed vegetation indices in the Red,Green, Blue(RGB) images of crop canopies under field conditions. We evaluated their accuracy in predicting grain yield and assessing disease severity in comparison to other field measurements including the Normalized Difference Vegetation Index(NDVI), leaf chlorophyll content, stomatal conductance, and canopy temperature. We also discuss yield components and agronomic parameters in relation to grain yield and disease severity.RGB-based indices proved to be accurate predictors of grain yield and grain yield losses associated with yellow rust(R2= 0.581 and R2= 0.536, respectively), far surpassing the predictive ability of NDVI(R2= 0.118 and R2= 0.128, respectively). In comparison to potential yield, we found the presence of disease to be correlated with reductions in the number of grains per spike, grains per square meter, kernel weight and harvest index. Grain yield losses in the presence of yellow rust were also greater in later heading varieties. The combination of RGB-based indices and days to heading together explained 70.9% of the variability in grain yield and 62.7% of the yield losses.
Institute of Scientific and Technical Information of China (English)
Omar Vergara-Diaz; Shawn C. Kefauver; Abdelhalim Elazab; Maria Teresa Nieto-Taladriz; José Luis Araus
2015-01-01
The biotrophic fungus Puccinia striiformis f. sp. tritici is the causal agent of the yellow rust in wheat. Between the years 2010–2013 a new strain of this pathogen (Warrior/Ambition), against which the present cultivated wheat varieties have no resistance, appeared and spread rapidly. It threatens cereal production in most of Europe. The search for sources of resistance to this strain is proposed as the most efficient and safe solution to ensure high grain production. This will be helped by the development of high performance and low cost techniques for field phenotyping. In this study we analyzed vegetation indices in the Red, Green, Blue (RGB) images of crop canopies under field conditions. We evaluated their accuracy in predicting grain yield and assessing disease severity in comparison to other field measurements including the Normalized Difference Vegetation Index (NDVI), leaf chlorophyll content, stomatal conductance, and canopy temperature. We also discuss yield components and agronomic parameters in relation to grain yield and disease severity. RGB-based indices proved to be accurate predictors of grain yield and grain yield losses associated with yellow rust (R2=0.581 and R2=0.536, respectively), far surpassing the predictive ability of NDVI (R2=0.118 and R2=0.128, respectively). In comparison to potential yield, we found the presence of disease to be correlated with reductions in the number of grains per spike, grains per square meter, kernel weight and harvest index. Grain yield losses in the presence of yellow rust were also greater in later heading varieties. The combination of RGB-based indices and days to heading together explained 70.9% of the variability in grain yield and 62.7%of the yield losses.
Humbird, Kelli; Peterson, J. Luc; Brandon, Scott; Field, John; Nora, Ryan; Spears, Brian
2016-10-01
Next-generation supercomputer architecture and in-transit data analysis have been used to create a large collection of 2-D ICF capsule implosion simulations. The database includes metrics for approximately 60,000 implosions, with x-ray images and detailed physics parameters available for over 20,000 simulations. To map and explore this large database, surrogate models for numerous quantities of interest are built using supervised machine learning algorithms. Response surfaces constructed using the predictive capabilities of the surrogates allow for continuous exploration of parameter space without requiring additional simulations. High performing regions of the input space are identified to guide the design of future experiments. In particular, a model for the yield built using a random forest regression algorithm has a cross validation score of 94.3% and is consistently conservative for high yield predictions. The model is used to search for robust volumes of parameter space where high yields are expected, even given variations in other input parameters. Surrogates for additional quantities of interest relevant to ignition are used to further characterize the high yield regions. This work performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344, Lawrence Livermore National Security, LLC. LLNL-ABS-697277.
Simultaneous estimation of parameters in the bivariate Emax model.
Magnusdottir, Bergrun T; Nyquist, Hans
2015-12-10
In this paper, we explore inference in multi-response, nonlinear models. By multi-response, we mean models with m > 1 response variables and accordingly m relations. Each parameter/explanatory variable may appear in one or more of the relations. We study a system estimation approach for simultaneous computation and inference of the model and (co)variance parameters. For illustration, we fit a bivariate Emax model to diabetes dose-response data. Further, the bivariate Emax model is used in a simulation study that compares the system estimation approach to equation-by-equation estimation. We conclude that overall, the system estimation approach performs better for the bivariate Emax model when there are dependencies among relations. The stronger the dependencies, the more we gain in precision by using system estimation rather than equation-by-equation estimation.
Kovalchik, Stephanie A; Varadhan, Ravi; Fetterman, Barbara; Poitras, Nancy E; Wacholder, Sholom; Katki, Hormuzd A
2013-02-28
Estimates of absolute risks and risk differences are necessary for evaluating the clinical and population impact of biomedical research findings. We have developed a linear-expit regression model (LEXPIT) to incorporate linear and nonlinear risk effects to estimate absolute risk from studies of a binary outcome. The LEXPIT is a generalization of both the binomial linear and logistic regression models. The coefficients of the LEXPIT linear terms estimate adjusted risk differences, whereas the exponentiated nonlinear terms estimate residual odds ratios. The LEXPIT could be particularly useful for epidemiological studies of risk association, where adjustment for multiple confounding variables is common. We present a constrained maximum likelihood estimation algorithm that ensures the feasibility of risk estimates of the LEXPIT model and describe procedures for defining the feasible region of the parameter space, judging convergence, and evaluating boundary cases. Simulations demonstrate that the methodology is computationally robust and yields feasible, consistent estimators. We applied the LEXPIT model to estimate the absolute 5-year risk of cervical precancer or cancer associated with different Pap and human papillomavirus test results in 167,171 women undergoing screening at Kaiser Permanente Northern California. The LEXPIT model found an increased risk due to abnormal Pap test in human papillomavirus-negative that was not detected with logistic regression. Our R package blm provides free and easy-to-use software for fitting the LEXPIT model.
MESH FREE ESTIMATION OF THE STRUCTURE MODEL INDEX
Directory of Open Access Journals (Sweden)
Joachim Ohser
2011-05-01
Full Text Available The structure model index (SMI is a means of subsuming the topology of a homogeneous random closed set under just one number, similar to the isoperimetric shape factors used for compact sets. Originally, the SMI is defined as a function of volume fraction, specific surface area and first derivative of the specific surface area, where the derivative is defined and computed using a surface meshing. The generalised Steiner formula yields however a derivative of the specific surface area that is – up to a constant – the density of the integral of mean curvature. Consequently, an SMI can be defined without referring to a discretisation and it can be estimated from 3D image data without need to mesh the surface but using the number of occurrences of 2×2×2 pixel configurations, only. Obviously, it is impossible to completely describe a random closed set by one number. In this paper, Boolean models of balls and infinite straight cylinders serve as cautionary examples pointing out the limitations of the SMI. Nevertheless, shape factors like the SMI can be valuable tools for comparing similar structures. This is illustrated on real microstructures of ice, foams, and paper.
A Prototypical Model for Estimating High Tech Navy Recruiting Markets
1991-12-01
Probability, Logit, and Probit Models, New York, New York 1990, p. 73. 37 Gujarati , D., ibid, p. 500. 31 V. MODELS ESTIMATION A. MODEL I ESTIMATION OF...Company, New York, N.Y., 1990. Gujarati , Damodar N., Basic Econometrics, Second Edition, McGraw-Hill Book Company, New York, N.Y., 1988. Jehn, Christopher
Identification and Estimation of Exchange Rate Models with Unobservable Fundamentals
Chambers, M.J.; McCrorie, J.R.
2004-01-01
This paper is concerned with issues of model specification, identification, and estimation in exchange rate models with unobservable fundamentals.We show that the model estimated by Gardeazabal, Reg´ulez and V´azquez (International Economic Review, 1997) is not identified and demonstrate how to spec
Bauer, Alexander; Bösch, Peter; Friedl, Anton; Amon, Thomas
2009-06-01
Agrarian biomass as a renewable energy source can contribute to a considerable CO(2) reduction. The overriding goal of the European Union is to cut energy consumption related greenhouse gas emission in the EU by 20% until the year 2020. This publication aims at optimising the methane production from steam-exploded wheat straw and presents a theoretical estimation of the ethanol and methane potential of straw. For this purpose, wheat straw was pretreated by steam explosion using different time/temperature combinations. Specific methane yields were analyzed according to VDI 4630. Pretreatment of wheat straw by steam explosion significantly increased the methane yield from anaerobic digestion by up to 20% or a maximum of 331 l(N)kg(-1) VS compared to untreated wheat straw. Furthermore, the residual anaerobic digestion potential of methane after ethanol fermentation was determined by enzymatic hydrolysis of pretreated wheat straw using cellulase. Based on the resulting glucose concentration the ethanol yield and the residual sugar available for methane production were calculated. The theoretical maximum ethanol yield of wheat straw was estimated to be 0.249 kg kg(-1) dry matter. The achievable maximum ethanol yield per kg wheat straw dry matter pretreated by steam explosion and enzymatic hydrolysis was estimated to be 0.200 kg under pretreatment conditions of 200 degrees C and 10 min corresponding to 80% of the theoretical maximum. The residual methane yield from straw stillage was estimated to be 183 l(N)kg(-1) wheat straw dry matter. Based on the presented experimental data, a concept is proposed that processes wheat straw for ethanol and methane production. The concept of an energy supply system that provides more than two forms of energy is met by (1) upgrading obtained ethanol to fuel-grade quality and providing methane to CHP plants for the production of (2) electric energy and (3) utility steam that in turn can be used to operate distillation columns in the
Bayesian approach to decompression sickness model parameter estimation.
Howle, L E; Weber, P W; Nichols, J M
2017-03-01
We examine both maximum likelihood and Bayesian approaches for estimating probabilistic decompression sickness model parameters. Maximum likelihood estimation treats parameters as fixed values and determines the best estimate through repeated trials, whereas the Bayesian approach treats parameters as random variables and determines the parameter probability distributions. We would ultimately like to know the probability that a parameter lies in a certain range rather than simply make statements about the repeatability of our estimator. Although both represent powerful methods of inference, for models with complex or multi-peaked likelihoods, maximum likelihood parameter estimates can prove more difficult to interpret than the estimates of the parameter distributions provided by the Bayesian approach. For models of decompression sickness, we show that while these two estimation methods are complementary, the credible intervals generated by the Bayesian approach are more naturally suited to quantifying uncertainty in the model parameters.
The decay b{yields}sg at NLL in the standard model
Energy Technology Data Exchange (ETDEWEB)
Liniger, Patrick [Institut fuer theoretische Physik, Universitaet Bern, Bern (Switzerland). E-mail: liniger@itp.unibe.ch
2001-06-01
I present a standard model calculation of the decay rate {gamma}(b{yields}sg) (g denotes a gluon) at next-to-leading logarithms (NLL). In order to obtain a meaningful physical result, the decay b{yields}sgg and certain contributions of b{yields}sf-barf (where f are the light quark flavours u, d and s) have to be included as well. Numerically we obtain B{sup NLL}(b{yields}sg) (5.0{+-}1.0)x10{sup -3} which is more than a factor of two larger than the leading logarithmic result B{sup LL}(b{yields}sg) = (2.2{+-}0.8)x10{sup -3}. Furthermore, I consider the impact of this contribution on the charmless hadronic branching ratio B{sub c/}, which could be used to extract the CKM ratio vertical bar V{sub ub}/V{sub cb} vertical bar with more accuracy. Finally, I have a brief look at B{sub c/} in scenarios where the Wilson coefficient C{sub 8} is enhanced by new physics. (author)
Directory of Open Access Journals (Sweden)
Pierre Coenraets
1997-01-01
Full Text Available Comparison of computation time between single-trait and multiple-trait evaluations showed that with the use of the canonicat transformation associated with multiple diagonalization of (covariance matrices, multiple-trait analysis for milk, fat and protein yields is not more expensive than three single-trait analyzes. Rank correlations between breeding values for 54,820 cows with records (for their 1,406 sires estimated with the single-trait and multiple-trait models were over .98 (.99 in fat yield and over .99 (.99 in milk and protein yields. The relative gain expressed as reduction in mean prediction error variance was 3% (1% in milk yield, 6% (3% in fat yield, and .4% (.2% in protein yield for cows (for sires. Relative genetic gains were 3% (1%, 6% (2% and .5% (.2% respectively in milk, fat and protein yields for cows (for sires. The use of multiple-trait models bas therefore the advantages of improved precision and reduced selection bics. Multiple-trait analysis could be extended for the analyzes of test-day records. Results show that this or similar multiple-trait animal model could be implemented immediately in Belgium at low computing cost, using the proposed algorithme and could be the first step to new, more advanced evaluation methods.
Estimation of Boundary Conditions for Coastal Models,
1974-09-01
equation: h(i) y ( t — i) di (3) The solution to Eq. (3) may be obtained by Fourier transformation. Because covariance function and spectral density function form...the cross— spectral density function estimate by a numerical Fourier transform, the even and odd parts of the cross—covariance function are determined...by A(k) = ½ [Y ~~ (k) + y (k)] (5) B(k) = ½ [Yxy (k) - y (k) ] (6) from which the co— spectral density function is estimated : k m—l -. C (f) = 2T[A(o
A new elliptic-parabolic yield surface model revised by an adaptive criterion for granular soils
Institute of Scientific and Technical Information of China (English)
无
2010-01-01
An adaptive criterion for shear yielding as well as shear failure of soils is proposed in this paper to address the fact that most criteria,including the Mohr-Coulomb criterion,the Lade criterion and the Matsuoka-Nakai criterion,cannot agree well with the experimental results when the value of the intermediate principal stress parameter is too big.The new criterion can adjust an adaptive parameter based on the experimental results in order to make the theoretical calculations fit the test results more accurately.The original elliptic-parabolic yield surface model can capture both soil contraction and dilation behaviors.However,it normally over-predicts the soil strength due to its application of the Extended Mises criterion.A new elliptic-parabolic yield surface mode is presented in this paper,which introduces the adaptive criterion in three-dimensional principal stress space.The new model can well model the stress-strain behavior of soils under general stress conditions.Compared to the original model which can only simulate soil behavior under triaxial compression conditions,the new model can simulate soil behaviors under both triaxial compression conditions and general stress conditions.
[On-site measurement of landfill gas yield and verification of IPCC model].
Luo, Yu-Xiang; Wang, Wei; Gao, Xing-Bao
2009-11-01
In order to obtain the accurate yield of landfill gas in Yulongkeng Landfill, Shenzhen, improved pumping test was conducted. The methane production rates of the influence region were figured out as 14.67 x 10(-5), 9.46 x 10(-5), 9.55 x 10(-5), and 4.28 x 10(-5) m3/(t x h), respectively. According to the methane production rate, the whole methane yield of Yulongkeng Landfill in 2005 was 322 m3/h, which indicated that Yulongkeng Landfill had went into stationary phase and the recycle of landfill gas was not valuable. IPCC model was verified by the measured data. Degradation half life of the waste was the key parameter concerned to the prediction accuracy of IPCC model. In China, the degradable waste in municipal solid waste was mainly kitchen waste leading to a short degradation period, which caused the degradation half life was shorter than the proposed value in IPCC model. For the improvement in prediction accuracy of landfill gas yield, the model parameters should be adopted reasonably based on a full survey of waste characterization in China, which will boost the applicability of IPCC model.
Parameter estimation and error analysis in environmental modeling and computation
Kalmaz, E. E.
1986-01-01
A method for the estimation of parameters and error analysis in the development of nonlinear modeling for environmental impact assessment studies is presented. The modular computer program can interactively fit different nonlinear models to the same set of data, dynamically changing the error structure associated with observed values. Parameter estimation techniques and sequential estimation algorithms employed in parameter identification and model selection are first discussed. Then, least-square parameter estimation procedures are formulated, utilizing differential or integrated equations, and are used to define a model for association of error with experimentally observed data.
Inflow, Outflow, Yields, and Stellar Population Mixing in Chemical Evolution Models
Andrews, Brett H.; Weinberg, David H.; Schönrich, Ralph; Johnson, Jennifer A.
2017-02-01
Chemical evolution models are powerful tools for interpreting stellar abundance surveys and understanding galaxy evolution. However, their predictions depend heavily on the treatment of inflow, outflow, star formation efficiency (SFE), the stellar initial mass function, the SN Ia delay time distribution, stellar yields, and stellar population mixing. Using flexCE, a flexible one-zone chemical evolution code, we investigate the effects of and trade-offs between parameters. Two critical parameters are SFE and the outflow mass-loading parameter, which shift the knee in [O/Fe]–[Fe/H] and the equilibrium abundances that the simulations asymptotically approach, respectively. One-zone models with simple star formation histories follow narrow tracks in [O/Fe]–[Fe/H] unlike the observed bimodality (separate high-α and low-α sequences) in this plane. A mix of one-zone models with inflow timescale and outflow mass-loading parameter variations, motivated by the inside-out galaxy formation scenario with radial mixing, reproduces the two sequences better than a one-zone model with two infall epochs. We present [X/Fe]–[Fe/H] tracks for 20 elements assuming three different supernova yield models and find some significant discrepancies with solar neighborhood observations, especially for elements with strongly metallicity-dependent yields. We apply principal component abundance analysis to the simulations and existing data to reveal the main correlations among abundances and quantify their contributions to variation in abundance space. For the stellar population mixing scenario, the abundances of α-elements and elements with metallicity-dependent yields dominate the first and second principal components, respectively, and collectively explain 99% of the variance in the model. flexCE is a python package available at https://github.com/bretthandrews/flexCE.
Analysis of S Wave Propagation Through a Nonlinear Joint with the Continuously Yielding Model
Cui, Zhen; Sheng, Qian; Leng, Xianlun
2017-01-01
Seismic wave propagation through joints that are embedded in a rock mass is a critical issue for aseismic issues of underground rock engineering. Few studies have investigated nonlinear joints with a continuously yielding model. In this paper, a time-domain recursive method (TDRM) for an S wave across a nonlinear Mohr-Coulomb (MC) slip model is extended to a continuously yielding (CY) model. Verification of the TDRM-based results is conducted by comparison with the simulated results via a built-in model of 3DEC code. Using parametric studies, the effect of normal stress level, amplitude of incident wave, initial joint shear stiffness, and joint spacing is discussed and interpreted for engineering applications because a proper in situ stress level (overburden depth) and acceptable quality of surrounding rock mass are beneficial for seismic stability issues of underground rock excavation. Comparison between the results from the MC model and the CY model is presented both for an idealized impulse excitation and a real ground motion record. Compared with the MC model, complex joint behaviors, such as tangential stiffness degradation, normal stress dependence, and the hysteresis effect, that occurred in the wave propagation can be described with the CY model. The MC model seems to underestimate the joint shear displacement in a high normal stress state and in a real ground motion excitation case.
On Frequency Domain Models for TDOA Estimation
DEFF Research Database (Denmark)
Jensen, Jesper Rindom; Nielsen, Jesper Kjær; Christensen, Mads Græsbøll
2015-01-01
of a much more general method. In this connection, we establish the conditions under which the cross-correlation method is a statistically efficient estimator. One of the conditions is that the source signal is periodic with a known fundamental frequency of 2π/N radians per sample, where N is the number...
Baudry, Jean-Patrick
2012-01-01
The Integrated Completed Likelihood (ICL) criterion has been proposed by Biernacki et al. (2000) in the model-based clustering framework to select a relevant number of classes and has been used by statisticians in various application areas. A theoretical study of this criterion is proposed. A contrast related to the clustering objective is introduced: the conditional classification likelihood. This yields an estimator and a model selection criteria class. The properties of these new procedures are studied and ICL is proved to be an approximation of one of these criteria. We oppose these results to the current leading point of view about ICL, that it would not be consistent. Moreover these results give insights into the class notion underlying ICL and feed a reflection on the class notion in clustering. General results on penalized minimum contrast criteria and on mixture models are derived, which are interesting in their own right.
Robust Estimation and Forecasting of the Capital Asset Pricing Model
G. Bian (Guorui); M.J. McAleer (Michael); W-K. Wong (Wing-Keung)
2013-01-01
textabstractIn this paper, we develop a modified maximum likelihood (MML) estimator for the multiple linear regression model with underlying student t distribution. We obtain the closed form of the estimators, derive the asymptotic properties, and demonstrate that the MML estimator is more
Robust Estimation and Forecasting of the Capital Asset Pricing Model
G. Bian (Guorui); M.J. McAleer (Michael); W-K. Wong (Wing-Keung)
2010-01-01
textabstractIn this paper, we develop a modified maximum likelihood (MML) estimator for the multiple linear regression model with underlying student t distribution. We obtain the closed form of the estimators, derive the asymptotic properties, and demonstrate that the MML estimator is more
Performance of Random Effects Model Estimators under Complex Sampling Designs
Jia, Yue; Stokes, Lynne; Harris, Ian; Wang, Yan
2011-01-01
In this article, we consider estimation of parameters of random effects models from samples collected via complex multistage designs. Incorporation of sampling weights is one way to reduce estimation bias due to unequal probabilities of selection. Several weighting methods have been proposed in the literature for estimating the parameters of…
PARAMETER ESTIMATION IN LINEAR REGRESSION MODELS FOR LONGITUDINAL CONTAMINATED DATA
Institute of Scientific and Technical Information of China (English)
QianWeimin; LiYumei
2005-01-01
The parameter estimation and the coefficient of contamination for the regression models with repeated measures are studied when its response variables are contaminated by another random variable sequence. Under the suitable conditions it is proved that the estimators which are established in the paper are strongly consistent estimators.
A viscoelastic-plastic constitutive model with Mohr-Coulomb yielding criterion for sea ice dynamics
Institute of Scientific and Technical Information of China (English)
无
2005-01-01
A new viscoelastic-plastic (VEP) constitutive model for sea ice dynamics was developed based on continuum mechanics. This model consists of four components: Kelvin-Vogit viscoelastic model, Mohr-Coulomb yielding criterion, associated normality flow rule for plastic rehololgy, and hydrostatic pressure. The numerical simulations for ice motion in an idealized rectangular basin were made using smoothed particle hydrodynamics (SPH) method, and compared with the analytical solution as well as those based on the modified viscous plastic(VP) model and static ice jam theory. These simulations show that the new VEP modelcan simulate ice dynamics accurately. The new constitutive model was further applied to simulate ice dynamics of the Bohai Sea and compared with the traditional VP, and modified VP models. The results of the VEP model are compared better with the satellite remote images, and the simulated ice conditions in the JZ20-2 oil platform area were more reasonable.
Maximum likelihood estimation of finite mixture model for economic data
Phoong, Seuk-Yen; Ismail, Mohd Tahir
2014-06-01
Finite mixture model is a mixture model with finite-dimension. This models are provides a natural representation of heterogeneity in a finite number of latent classes. In addition, finite mixture models also known as latent class models or unsupervised learning models. Recently, maximum likelihood estimation fitted finite mixture models has greatly drawn statistician's attention. The main reason is because maximum likelihood estimation is a powerful statistical method which provides consistent findings as the sample sizes increases to infinity. Thus, the application of maximum likelihood estimation is used to fit finite mixture model in the present paper in order to explore the relationship between nonlinear economic data. In this paper, a two-component normal mixture model is fitted by maximum likelihood estimation in order to investigate the relationship among stock market price and rubber price for sampled countries. Results described that there is a negative effect among rubber price and stock market price for Malaysia, Thailand, Philippines and Indonesia.
Yu, Xiaolin; Zhang, Shaoqing; Lin, Xiaopei; Li, Mingkui
2017-03-01
The uncertainties in values of coupled model parameters are an important source of model bias that causes model climate drift. The values can be calibrated by a parameter estimation procedure that projects observational information onto model parameters. The signal-to-noise ratio of error covariance between the model state and the parameter being estimated directly determines whether the parameter estimation succeeds or not. With a conceptual climate model that couples the stochastic atmosphere and slow-varying ocean, this study examines the sensitivity of state-parameter covariance on the accuracy of estimated model states in different model components of a coupled system. Due to the interaction of multiple timescales, the fast-varying atmosphere with a chaotic nature is the major source of the inaccuracy of estimated state-parameter covariance. Thus, enhancing the estimation accuracy of atmospheric states is very important for the success of coupled model parameter estimation, especially for the parameters in the air-sea interaction processes. The impact of chaotic-to-periodic ratio in state variability on parameter estimation is also discussed. This simple model study provides a guideline when real observations are used to optimize model parameters in a coupled general circulation model for improving climate analysis and predictions.
Hoffmann, M.; Castaneda Vera, A.; Wijk, van M.T.; Giller, K.E.; Oberthür, T.; Donough, C.; Whitbread, A.M.
2014-01-01
Reducing the gap between water-limited potential yield and actual yield in oil palm production systems through intensification is seen as an important option for sustainably increasing palm oil production. Simulation models can play an important role in quantifying water-limited potential yield, and
Efficient estimation of moments in linear mixed models
Wu, Ping; Zhu, Li-Xing; 10.3150/10-BEJ330
2012-01-01
In the linear random effects model, when distributional assumptions such as normality of the error variables cannot be justified, moments may serve as alternatives to describe relevant distributions in neighborhoods of their means. Generally, estimators may be obtained as solutions of estimating equations. It turns out that there may be several equations, each of them leading to consistent estimators, in which case finding the efficient estimator becomes a crucial problem. In this paper, we systematically study estimation of moments of the errors and random effects in linear mixed models.
Simulating yield response of rice to salinity stress with the AquaCrop model.
Mondal, M Shahjahan; Saleh, Abul Fazal M; Razzaque Akanda, Md Abdur; Biswas, Sujit K; Md Moslehuddin, Abu Zofar; Zaman, Sinora; Lazar, Attila N; Clarke, Derek
2015-06-01
The FAO AquaCrop model has been widely applied throughout the world to simulate crop responses to deficit water applications. However, its application to saline conditions is not yet reported, though saline soils are common in coastal areas. In this study, we parameterized and tested AquaCrop to simulate rice yield under different salinity regimes. The data and information required in the model were collected through a field experiment at the Bangladesh Agricultural Research Institute, Gazipur. The experiment was conducted with the BRRI Dhan28, a popular boro rice variety in Bangladesh, with five levels of saline water irrigation, three replicates for each level. In addition, field monitoring was carried out at Satkhira in the southwest coastal region of Bangladesh to collect data and information based on farmers' practices and to further validate the model. The results indicated that the AquaCrop model with most of its default parameters could replicate the variation of rice yield with the variation of salinity reasonably well. The root mean square error and mean absolute error of the model yield were only 0.12 t per ha and 0.03 t per ha, respectively. The crop response versus soil salinity stress curve was found to be convex in shape with a lower threshold of 2 dS m(-1), an upper threshold of 10 dS m(-1) and a shape factor of 2.4. As the crop production system in the coastal belt of Bangladesh has become vulnerable to climate induced sea-level rise and the consequent increase in water and soil salinity, the AquaCrop would be a useful tool in assessing the potential impact of these future changes as well as other climatic parameters on rice yield in the coastal region.
Modelling the long term water yield impact of fire in Eucalypt forests