A reliable and accurate forecasting model for crop yields is crucial for effective decision-making in every agricultural sector.Machine learning approaches allow for building such predictive models,but the quality of ...A reliable and accurate forecasting model for crop yields is crucial for effective decision-making in every agricultural sector.Machine learning approaches allow for building such predictive models,but the quality of predictions decreases if data is scarce.In this work,we proposed data-augmentation for wheat yield forecasting in the presence of small data sets of two distinct Provinces in Algeria.We first increased the dimension of each data set by adding more features,and then we augmented the size of the data by merging the two data sets.To assess the effectiveness of data-augmentation approaches,we conducted three sets of experiments based on three data sets:the primary data sets,data sets with additional features and the augmented data sets obtained by merging,using five regression models(Support Vector Regression,Random Forest,Extreme Learning Machine,Artificial Neural Network,Deep Neural Network).To evaluate the models,we used cross-validation;the results showed an overall increase in performance with the augmented data.DNN outperformed the other models for the first Province with a Root Mean Square Error(RMSE)of 0.04 q/ha and R_Squared(R^(2))of 0.96,whereas the Random Forest outperformed the other models for the second Province with RMSE of 0.05 q/ha.展开更多
This paper compares analytical and numerical methods by taking the forecasting of water yield of deep-buried iron mine in Yanzhou, Shandong as an example. Regarding the analytical method, the equation of infinite and ...This paper compares analytical and numerical methods by taking the forecasting of water yield of deep-buried iron mine in Yanzhou, Shandong as an example. Regarding the analytical method, the equation of infinite and bilateral water inflow boundary is used to forecast the water yield, and in the case of numerical simulation, we employed the GMS software to establish a model and further to forecast the water yield. On the one hand, through applying the analytical method, the maximum water yield of mine 1 500 m deep below the surface was calculated to be 13 645.17 m3/d; on the other hand, through adopting the numerical method, we obtained the predicted result of 3 816.16 m3/d. Meanwhile, by using the boundary generalization in the above-mentioned two methods, and through a comparative analysis of the actual hydro-geological conditions in this deep-buried mine, which also concerns the advantages and disadvantages of the two methods respectively, this paper draws the conclusion that the analytical method is only applicable in ideal conditions, but numerical method is eligible to be used in complex hydro-geological conditions. Therefore, it is more applicable to employ the numerical method to forecast water yield of deep-buried iron mine in Yanzhou, Shandong.展开更多
Background Cotton is one of the most important commercial crops after food crops,especially in countries like India,where it’s grown extensively under rainfed conditions.Because of its usage in multiple industries,su...Background Cotton is one of the most important commercial crops after food crops,especially in countries like India,where it’s grown extensively under rainfed conditions.Because of its usage in multiple industries,such as textile,medicine,and automobile industries,it has greater commercial importance.The crop’s performance is greatly influenced by prevailing weather dynamics.As climate changes,assessing how weather changes affect crop performance is essential.Among various techniques that are available,crop models are the most effective and widely used tools for predicting yields.Results This study compares statistical and machine learning models to assess their ability to predict cotton yield across major producing districts of Karnataka,India,utilizing a long-term dataset spanning from 1990 to 2023 that includes yield and weather factors.The artificial neural networks(ANNs)performed superiorly with acceptable yield deviations ranging within±10%during both vegetative stage(F1)and mid stage(F2)for cotton.The model evaluation metrics such as root mean square error(RMSE),normalized root mean square error(nRMSE),and modelling efficiency(EF)were also within the acceptance limits in most districts.Furthermore,the tested ANN model was used to assess the importance of the dominant weather factors influencing crop yield in each district.Specifically,the use of morning relative humidity as an individual parameter and its interaction with maximum and minimum tempera-ture had a major influence on cotton yield in most of the yield predicted districts.These differences highlighted the differential interactions of weather factors in each district for cotton yield formation,highlighting individual response of each weather factor under different soils and management conditions over the major cotton growing districts of Karnataka.Conclusions Compared with statistical models,machine learning models such as ANNs proved higher efficiency in forecasting the cotton yield due to their ability to consider the interactive effects of weather factors on yield forma-tion at different growth stages.This highlights the best suitability of ANNs for yield forecasting in rainfed conditions and for the study on relative impacts of weather factors on yield.Thus,the study aims to provide valuable insights to support stakeholders in planning effective crop management strategies and formulating relevant policies.展开更多
Mathematical models have been widely employed for the simulation of growth dynamics of annual crops,thereby performing yield prediction,but not for fruit tree species such as jujube tree(Zizyphus jujuba).The objective...Mathematical models have been widely employed for the simulation of growth dynamics of annual crops,thereby performing yield prediction,but not for fruit tree species such as jujube tree(Zizyphus jujuba).The objectives of this study were to investigate the potential use of a modified WOFOST model for predicting jujube yield by introducing tree age as a key parameter.The model was established using data collected from dedicated field experiments performed in 2016-2018.Simulated growth dynamics of dry weights of leaves,stems,fruits,total biomass and leaf area index(LAI) agreed well with measured values,showing root mean square error(RMSE) values of 0.143,0.333,0.366,0.624 t ha^-1 and 0.19,and R2 values of 0.947,0.976,0.985,0.986 and 0.95,respectively.Simulated phenological development stages for emergence,anthesis and maturity were 2,3 and 3 days earlier than the observed values,respectively.In addition,in order to predict the yields of trees with different ages,the weight of new organs(initial buds and roots) in each growing season was introduced as the initial total dry weight(TDWI),which was calculated as averaged,fitted and optimized values of trees with the same age.The results showed the evolution of the simulated LAI and yields profiled in response to the changes in TDWI.The modelling performance was significantly improved when it considered TDWI integrated with tree age,showing good global(R2≥0.856,RMSE≤0.68 t ha^-1) and local accuracies(mean R2≥0.43,RMSE≤0.70 t ha^-1).Furthermore,the optimized TDWI exhibited the highest precision,with globally validated R2 of 0.891 and RMSE of 0.591 t ha^-1,and local mean R2 of 0.57 and RMSE of 0.66 t ha^-1,respectively.The proposed model was not only verified with the confidence to accurately predict yields of jujube,but it can also provide a fundamental strategy for simulating the growth of other fruit trees.展开更多
Quantifying sugarcane production is critical for a wide range of applications, including crop management and decision making processes such as harvesting, storage, and forward selling. This study explored a novel mode...Quantifying sugarcane production is critical for a wide range of applications, including crop management and decision making processes such as harvesting, storage, and forward selling. This study explored a novel model for predicting sugarcane yield in Bundaberg region from time series Landsat data. From the freely available Landsat archive, 98 cloud free (<40%) Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper (ETM+) images, acquired between November 15th to July 31<sup>st</sup> (2001-2015) were sourced for this study. The images were masked using the field boundary layer vector files of each year and the GNDVI was calculated. An analysis of average green normalized difference vegetation index (GNDVI) values from all sugarcane crops grown within the Bundaberg region over the 15 year period identified the beginning of April as the peak growth stage and, therefore, the optimum time for satellite image based yield forecasting. As the GNDVI is an indicator of crop vigor, the model derived maximum GNDVI was regressed against historical sugarcane yield data, which showed a significant correlation with R<sup>2</sup> = 0.69 and RMSE = 4.2 t/ha. Results showed that the model derived maximum GNDVI from Landsat imagery would be a feasible and a modest technique to predict sugarcane yield in Bundaberg region.展开更多
Using the artificial nerve network′s knowledge, establish the estimate′s mathematics model of the soybean′s yield, and by the model we can increase accuracy of the soybean yield forecast.
Yield forecasting can give early warning of food risks and provide solid support for food security planning.Climate change and land use change have direct influence on regional yield and planting area of maize,but few...Yield forecasting can give early warning of food risks and provide solid support for food security planning.Climate change and land use change have direct influence on regional yield and planting area of maize,but few studies have examined their synergistic impact on maize production.In this study,we propose an analysis framework based on the integration of system dynamic(SD),future land use simulation(FLUS)and a statistical crop model to prefuture maize yield variation in response to climate change and land use change in a region of central Jilin province,China.The results show that the cultivated land is likely to reduce by 862.84 km^(2) from 2030 to 2050.Nevertheless,the total maize yield is expected to increase under all four RCP scenarios due to the promotion of per hectare maize yield.the scenarios,RCP4.5 is the most beneficial to maize production,with a doubled total yield in 2050.Notably,the yield gap between different counties will be further widened,which necessitates the differentiated policies of agricultural production and farmland protection,e.g.,strengthening cultivated land protection and crop management in low-yield areas,and taking adaptation and mitigation measures to coordinate climate change and production.展开更多
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 bui...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.展开更多
文摘A reliable and accurate forecasting model for crop yields is crucial for effective decision-making in every agricultural sector.Machine learning approaches allow for building such predictive models,but the quality of predictions decreases if data is scarce.In this work,we proposed data-augmentation for wheat yield forecasting in the presence of small data sets of two distinct Provinces in Algeria.We first increased the dimension of each data set by adding more features,and then we augmented the size of the data by merging the two data sets.To assess the effectiveness of data-augmentation approaches,we conducted three sets of experiments based on three data sets:the primary data sets,data sets with additional features and the augmented data sets obtained by merging,using five regression models(Support Vector Regression,Random Forest,Extreme Learning Machine,Artificial Neural Network,Deep Neural Network).To evaluate the models,we used cross-validation;the results showed an overall increase in performance with the augmented data.DNN outperformed the other models for the first Province with a Root Mean Square Error(RMSE)of 0.04 q/ha and R_Squared(R^(2))of 0.96,whereas the Random Forest outperformed the other models for the second Province with RMSE of 0.05 q/ha.
文摘This paper compares analytical and numerical methods by taking the forecasting of water yield of deep-buried iron mine in Yanzhou, Shandong as an example. Regarding the analytical method, the equation of infinite and bilateral water inflow boundary is used to forecast the water yield, and in the case of numerical simulation, we employed the GMS software to establish a model and further to forecast the water yield. On the one hand, through applying the analytical method, the maximum water yield of mine 1 500 m deep below the surface was calculated to be 13 645.17 m3/d; on the other hand, through adopting the numerical method, we obtained the predicted result of 3 816.16 m3/d. Meanwhile, by using the boundary generalization in the above-mentioned two methods, and through a comparative analysis of the actual hydro-geological conditions in this deep-buried mine, which also concerns the advantages and disadvantages of the two methods respectively, this paper draws the conclusion that the analytical method is only applicable in ideal conditions, but numerical method is eligible to be used in complex hydro-geological conditions. Therefore, it is more applicable to employ the numerical method to forecast water yield of deep-buried iron mine in Yanzhou, Shandong.
基金funded through India Meteorological Department,New Delhi,India under the Forecasting Agricultural output using Space,Agrometeorol ogy and Land based observations(FASAL)project and fund number:No.ASC/FASAL/KT-11/01/HQ-2010.
文摘Background Cotton is one of the most important commercial crops after food crops,especially in countries like India,where it’s grown extensively under rainfed conditions.Because of its usage in multiple industries,such as textile,medicine,and automobile industries,it has greater commercial importance.The crop’s performance is greatly influenced by prevailing weather dynamics.As climate changes,assessing how weather changes affect crop performance is essential.Among various techniques that are available,crop models are the most effective and widely used tools for predicting yields.Results This study compares statistical and machine learning models to assess their ability to predict cotton yield across major producing districts of Karnataka,India,utilizing a long-term dataset spanning from 1990 to 2023 that includes yield and weather factors.The artificial neural networks(ANNs)performed superiorly with acceptable yield deviations ranging within±10%during both vegetative stage(F1)and mid stage(F2)for cotton.The model evaluation metrics such as root mean square error(RMSE),normalized root mean square error(nRMSE),and modelling efficiency(EF)were also within the acceptance limits in most districts.Furthermore,the tested ANN model was used to assess the importance of the dominant weather factors influencing crop yield in each district.Specifically,the use of morning relative humidity as an individual parameter and its interaction with maximum and minimum tempera-ture had a major influence on cotton yield in most of the yield predicted districts.These differences highlighted the differential interactions of weather factors in each district for cotton yield formation,highlighting individual response of each weather factor under different soils and management conditions over the major cotton growing districts of Karnataka.Conclusions Compared with statistical models,machine learning models such as ANNs proved higher efficiency in forecasting the cotton yield due to their ability to consider the interactive effects of weather factors on yield forma-tion at different growth stages.This highlights the best suitability of ANNs for yield forecasting in rainfed conditions and for the study on relative impacts of weather factors on yield.Thus,the study aims to provide valuable insights to support stakeholders in planning effective crop management strategies and formulating relevant policies.
基金supported by the National Natural Science Foundation of China(41561088 and 61501314)the Science&Technology Nova Program of Xinjiang Production and Construction Corps,China(2018CB020)
文摘Mathematical models have been widely employed for the simulation of growth dynamics of annual crops,thereby performing yield prediction,but not for fruit tree species such as jujube tree(Zizyphus jujuba).The objectives of this study were to investigate the potential use of a modified WOFOST model for predicting jujube yield by introducing tree age as a key parameter.The model was established using data collected from dedicated field experiments performed in 2016-2018.Simulated growth dynamics of dry weights of leaves,stems,fruits,total biomass and leaf area index(LAI) agreed well with measured values,showing root mean square error(RMSE) values of 0.143,0.333,0.366,0.624 t ha^-1 and 0.19,and R2 values of 0.947,0.976,0.985,0.986 and 0.95,respectively.Simulated phenological development stages for emergence,anthesis and maturity were 2,3 and 3 days earlier than the observed values,respectively.In addition,in order to predict the yields of trees with different ages,the weight of new organs(initial buds and roots) in each growing season was introduced as the initial total dry weight(TDWI),which was calculated as averaged,fitted and optimized values of trees with the same age.The results showed the evolution of the simulated LAI and yields profiled in response to the changes in TDWI.The modelling performance was significantly improved when it considered TDWI integrated with tree age,showing good global(R2≥0.856,RMSE≤0.68 t ha^-1) and local accuracies(mean R2≥0.43,RMSE≤0.70 t ha^-1).Furthermore,the optimized TDWI exhibited the highest precision,with globally validated R2 of 0.891 and RMSE of 0.591 t ha^-1,and local mean R2 of 0.57 and RMSE of 0.66 t ha^-1,respectively.The proposed model was not only verified with the confidence to accurately predict yields of jujube,but it can also provide a fundamental strategy for simulating the growth of other fruit trees.
文摘Quantifying sugarcane production is critical for a wide range of applications, including crop management and decision making processes such as harvesting, storage, and forward selling. This study explored a novel model for predicting sugarcane yield in Bundaberg region from time series Landsat data. From the freely available Landsat archive, 98 cloud free (<40%) Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper (ETM+) images, acquired between November 15th to July 31<sup>st</sup> (2001-2015) were sourced for this study. The images were masked using the field boundary layer vector files of each year and the GNDVI was calculated. An analysis of average green normalized difference vegetation index (GNDVI) values from all sugarcane crops grown within the Bundaberg region over the 15 year period identified the beginning of April as the peak growth stage and, therefore, the optimum time for satellite image based yield forecasting. As the GNDVI is an indicator of crop vigor, the model derived maximum GNDVI was regressed against historical sugarcane yield data, which showed a significant correlation with R<sup>2</sup> = 0.69 and RMSE = 4.2 t/ha. Results showed that the model derived maximum GNDVI from Landsat imagery would be a feasible and a modest technique to predict sugarcane yield in Bundaberg region.
文摘Using the artificial nerve network′s knowledge, establish the estimate′s mathematics model of the soybean′s yield, and by the model we can increase accuracy of the soybean yield forecast.
基金National Natural Science Foundation of China,No.42171414,No.41771429The Open Fund of Key Laboratory for Synergistic Prevention of Water and Soil Environmental Pollution,No.KLSPWSEP-A02。
文摘Yield forecasting can give early warning of food risks and provide solid support for food security planning.Climate change and land use change have direct influence on regional yield and planting area of maize,but few studies have examined their synergistic impact on maize production.In this study,we propose an analysis framework based on the integration of system dynamic(SD),future land use simulation(FLUS)and a statistical crop model to prefuture maize yield variation in response to climate change and land use change in a region of central Jilin province,China.The results show that the cultivated land is likely to reduce by 862.84 km^(2) from 2030 to 2050.Nevertheless,the total maize yield is expected to increase under all four RCP scenarios due to the promotion of per hectare maize yield.the scenarios,RCP4.5 is the most beneficial to maize production,with a doubled total yield in 2050.Notably,the yield gap between different counties will be further widened,which necessitates the differentiated policies of agricultural production and farmland protection,e.g.,strengthening cultivated land protection and crop management in low-yield areas,and taking adaptation and mitigation measures to coordinate climate change and production.
基金Supported by the National Natural Science Foundation of China(41210007 and 41421004)Basic Research and Operation Fund of Chinese Academy of Meteorological Sciences(2016Y007)
文摘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.
