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
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.
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
Runoff and sediment yield from an Indian watershed during the monsoon period were forecasted for differ-ent time periods (daily and weekly) using the back propagation artificial neural network (BPANN) modeling techniq...Runoff and sediment yield from an Indian watershed during the monsoon period were forecasted for differ-ent time periods (daily and weekly) using the back propagation artificial neural network (BPANN) modeling technique. The results were compared with those of single- and multi-input linear transfer function models. In BPANN, the maximum value of variable was considered for normalization of input, and a pattern learning algorithm was developed. Input variables in the model were obtained by comparing the response with their respective standard error. The network parsimony was achieved by pruning the network using error sensitiv-ity - weight criterion, and model generalization by cross validation. The performance was evaluated using correlation coefficient (CC), coefficient of efficiency (CE), and root mean square error (RMSE). The single input linear transfer function (SI-LTF) runoff and sediment yield forecasting models were more efficacious than the multi input linear transfer function (MI-LTF) and ANN models.展开更多
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 wa...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.展开更多
During the period spanning the 1970s and1980s, countries in the West African Sahel experienced severe drought. Its impact on agriculture and ecosystems has highlighted the importance of monitoring the Sahelian rainy s...During the period spanning the 1970s and1980s, countries in the West African Sahel experienced severe drought. Its impact on agriculture and ecosystems has highlighted the importance of monitoring the Sahelian rainy season. In Sahelian countries such as Mali, rainfall is the major determinant of crop production. Unfortunately, rainfall is highly variable in time and space. Therefore, this study is conducted to analyze and forecast the impact of climatic parameters on the rain-fed rice yield cultivation in the Office Riz Mopti region. The data were collected from satellite imagery, archived meteorology data, yield and rice characteristics. The study employed Hanning filter to highlight interannual fluctuation, a test of Pettitt and the standardized precipitation index (SPI) to analyze the rainfall variability. Climate change scenarios under the RCP 8.5 scenario (HadGEM-2 ES) and agroclimatic (Cropwat) model are carried out to simulate the future climate and its impact on rice yields. The results of satellite image classifications of 1986 and 2016 show an increase of rice fields with a noticeable decrease of bare soil. The analysis of the SPI reveals that over the 30 years considered, 56.67% of the rainy seasons were dry (1986-2006) and 43.33% were wet (2007-2015). The modelling approach is applied over 1986-2006 and 2007-2015 periods—considered as typical dry and rainy years—and applied over the future, with forecasts of climate change scenarios in 2034. The results show a decrease in potential yield during dry and slightly wet years. The yields of rain-fed rice will be generally low between 2016 and 2027. Deficits are observed over the entire study area, in comparison with the potential yield. Thus, this situation could expose the population to food insecurity.展开更多
Remote-sensing data acquired by satellite imageries have a wide scope in agricultural applications owing to their synoptic and repetitive coverage. This study reports the development of an operational spectro-agromete...Remote-sensing data acquired by satellite imageries have a wide scope in agricultural applications owing to their synoptic and repetitive coverage. This study reports the development of an operational spectro-agrometereological yield model for maize crop derived from time series data of SPOT VEGETATION, actual and potential evapotranspiration and rainfall estimate satellite data for the years 2003-2012. Indices of these input data were utilized to validate their strength in explaining grain yield recorded by the Central Statistical Agency through correlation analyses. Crop masking at crop land area was applied and refined using agro-ecological zones suitable for maize. Rainfall estimates and average Normalized Difference Vegetation Index were found highly correlated to maize yield with the former accounting for 85% variation and the latter 80%, respectively. The developed spectro-agrometeorological yield model was successfully validated against the predicted Zone level yields estimated by Central Statistical Agency (r<sup>2</sup> = 0.88, RMSE = 1.405 q·ha<sup>-1</sup> and 21% coefficient of variation). Thus, remote sensing and geographical information system based maize yield forecast improved quality and timelines of the data besides distinguishing yield production levels/areas and making intervention very easy for the decision makers thereby proving the clear potential of spectro-agrometeorological factors for maize yield forecasting, particularly for Ethiopia.展开更多
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.
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.
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.
文摘Runoff and sediment yield from an Indian watershed during the monsoon period were forecasted for differ-ent time periods (daily and weekly) using the back propagation artificial neural network (BPANN) modeling technique. The results were compared with those of single- and multi-input linear transfer function models. In BPANN, the maximum value of variable was considered for normalization of input, and a pattern learning algorithm was developed. Input variables in the model were obtained by comparing the response with their respective standard error. The network parsimony was achieved by pruning the network using error sensitiv-ity - weight criterion, and model generalization by cross validation. The performance was evaluated using correlation coefficient (CC), coefficient of efficiency (CE), and root mean square error (RMSE). The single input linear transfer function (SI-LTF) runoff and sediment yield forecasting models were more efficacious than the multi input linear transfer function (MI-LTF) and ANN models.
基金Supported by Agricultural Poor-helping Monopoly of Graduate University of Chinese Academy of Science (40641002)
文摘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.
文摘During the period spanning the 1970s and1980s, countries in the West African Sahel experienced severe drought. Its impact on agriculture and ecosystems has highlighted the importance of monitoring the Sahelian rainy season. In Sahelian countries such as Mali, rainfall is the major determinant of crop production. Unfortunately, rainfall is highly variable in time and space. Therefore, this study is conducted to analyze and forecast the impact of climatic parameters on the rain-fed rice yield cultivation in the Office Riz Mopti region. The data were collected from satellite imagery, archived meteorology data, yield and rice characteristics. The study employed Hanning filter to highlight interannual fluctuation, a test of Pettitt and the standardized precipitation index (SPI) to analyze the rainfall variability. Climate change scenarios under the RCP 8.5 scenario (HadGEM-2 ES) and agroclimatic (Cropwat) model are carried out to simulate the future climate and its impact on rice yields. The results of satellite image classifications of 1986 and 2016 show an increase of rice fields with a noticeable decrease of bare soil. The analysis of the SPI reveals that over the 30 years considered, 56.67% of the rainy seasons were dry (1986-2006) and 43.33% were wet (2007-2015). The modelling approach is applied over 1986-2006 and 2007-2015 periods—considered as typical dry and rainy years—and applied over the future, with forecasts of climate change scenarios in 2034. The results show a decrease in potential yield during dry and slightly wet years. The yields of rain-fed rice will be generally low between 2016 and 2027. Deficits are observed over the entire study area, in comparison with the potential yield. Thus, this situation could expose the population to food insecurity.
文摘Remote-sensing data acquired by satellite imageries have a wide scope in agricultural applications owing to their synoptic and repetitive coverage. This study reports the development of an operational spectro-agrometereological yield model for maize crop derived from time series data of SPOT VEGETATION, actual and potential evapotranspiration and rainfall estimate satellite data for the years 2003-2012. Indices of these input data were utilized to validate their strength in explaining grain yield recorded by the Central Statistical Agency through correlation analyses. Crop masking at crop land area was applied and refined using agro-ecological zones suitable for maize. Rainfall estimates and average Normalized Difference Vegetation Index were found highly correlated to maize yield with the former accounting for 85% variation and the latter 80%, respectively. The developed spectro-agrometeorological yield model was successfully validated against the predicted Zone level yields estimated by Central Statistical Agency (r<sup>2</sup> = 0.88, RMSE = 1.405 q·ha<sup>-1</sup> and 21% coefficient of variation). Thus, remote sensing and geographical information system based maize yield forecast improved quality and timelines of the data besides distinguishing yield production levels/areas and making intervention very easy for the decision makers thereby proving the clear potential of spectro-agrometeorological factors for maize yield forecasting, particularly for Ethiopia.
