Accurate estimation of regional winter wheat yields is essential for understanding the food production status and ensuring national food security.However,using the existing remote sensing-based crop yield models to ac...Accurate estimation of regional winter wheat yields is essential for understanding the food production status and ensuring national food security.However,using the existing remote sensing-based crop yield models to accurately reproduce the inter-annual and spatial variations in winter wheat yields remains challenging due to the limited ability to acquire irrigation information in water-limited regions.Thus,we proposed a new approach to approximating irrigations of winter wheat over the North China Plain(NCP),where irrigation occurs extensively during the winter wheat growing season.This approach used irrigation pattern parameters(IPPs)to define the irrigation frequency and timing.Then,they were incorporated into a newly-developed process-based and remote sensing-driven crop yield model for winter wheat(PRYM–Wheat),to improve the regional estimates of winter wheat over the NCP.The IPPs were determined using statistical yield data of reference years(2010–2015)over the NCP.Our findings showed that PRYM–Wheat with the optimal IPPs could improve the regional estimate of winter wheat yield,with an increase and decrease in the correlation coefficient(R)and root mean square error(RMSE)of 0.15(about 37%)and 0.90 t ha–1(about 41%),respectively.The data in validation years(2001–2009 and 2016–2019)were used to validate PRYM–Wheat.In addition,our findings also showed R(RMSE)of 0.80(0.62 t ha–1)on a site level,0.61(0.91 t ha–1)for Hebei Province on a county level,0.73(0.97 t ha–1)for Henan Province on a county level,and 0.55(0.75 t ha–1)for Shandong Province on a city level.Overall,PRYM–Wheat can offer a stable and robust approach to estimating regional winter wheat yield across multiple years,providing a scientific basis for ensuring regional food security.展开更多
Understanding the spatial distribution of the crop yield gap(YG)is essential for improving crop yields.Recent studies have typically focused on the site scale,which may lead to considerable uncertainties when scaled t...Understanding the spatial distribution of the crop yield gap(YG)is essential for improving crop yields.Recent studies have typically focused on the site scale,which may lead to considerable uncertainties when scaled to the regional scale.To mitigate this issue,this study used a process-based and remote sensing driven crop yield model for winter wheat(PRYM-Wheat),which was derived from the boreal ecosystem productivity simulator(BEPS),to simulate the YG of winter wheat in the North China Plain from 2015 to 2019.Yield validation based on statistical yield data revealed good performance of the PRYM-Wheat Model in simulating winter wheat actual yield(Ya).The distribution of Ya across the North China Plain showed great heterogeneity,decreasing from southeast to northwest.The remote sensing-estimated results show that the average YG of the study area was 6400.6 kg ha^(–1).The YG of Jiangsu Province was the largest,at7307.4 kg ha^(–1),while the YG of Anhui Province was the smallest,at 5842.1 kg ha^(–1).An analysis of the responses of YG to environmental factors showed no obvious correlation between YG and precipitation,but there was a weak negative correlation between YG and accumulated temperature.In addition,the YG was positively correlated with elevation.In general,studying the specific features of the YG can provide directions for increasing crop yields in the future.展开更多
基金supported by the National Natural Science Foundation of China(42101382 and 41901342)the Shandong Provincial Natural Science Foundation(ZR2020QD016)the National Key Research and Development Program of China(2016YFD0300101).
文摘Accurate estimation of regional winter wheat yields is essential for understanding the food production status and ensuring national food security.However,using the existing remote sensing-based crop yield models to accurately reproduce the inter-annual and spatial variations in winter wheat yields remains challenging due to the limited ability to acquire irrigation information in water-limited regions.Thus,we proposed a new approach to approximating irrigations of winter wheat over the North China Plain(NCP),where irrigation occurs extensively during the winter wheat growing season.This approach used irrigation pattern parameters(IPPs)to define the irrigation frequency and timing.Then,they were incorporated into a newly-developed process-based and remote sensing-driven crop yield model for winter wheat(PRYM–Wheat),to improve the regional estimates of winter wheat over the NCP.The IPPs were determined using statistical yield data of reference years(2010–2015)over the NCP.Our findings showed that PRYM–Wheat with the optimal IPPs could improve the regional estimate of winter wheat yield,with an increase and decrease in the correlation coefficient(R)and root mean square error(RMSE)of 0.15(about 37%)and 0.90 t ha–1(about 41%),respectively.The data in validation years(2001–2009 and 2016–2019)were used to validate PRYM–Wheat.In addition,our findings also showed R(RMSE)of 0.80(0.62 t ha–1)on a site level,0.61(0.91 t ha–1)for Hebei Province on a county level,0.73(0.97 t ha–1)for Henan Province on a county level,and 0.55(0.75 t ha–1)for Shandong Province on a city level.Overall,PRYM–Wheat can offer a stable and robust approach to estimating regional winter wheat yield across multiple years,providing a scientific basis for ensuring regional food security.
基金the Shandong Key Research and Development Project,China(2018GNC110025)the National Natural Science Foundation of China(41871253)+2 种基金the Central Guiding Local Science and Technology Development Fund of Shandong—Yellow River Basin Collaborative Science and Technology Innovation Special Project,China(YDZX2023019)the Natural Science Foundation of Shandong Province,China(ZR2020QD016)the“Taishan Scholar”Project of Shandong Province,China(TSXZ201712)。
文摘Understanding the spatial distribution of the crop yield gap(YG)is essential for improving crop yields.Recent studies have typically focused on the site scale,which may lead to considerable uncertainties when scaled to the regional scale.To mitigate this issue,this study used a process-based and remote sensing driven crop yield model for winter wheat(PRYM-Wheat),which was derived from the boreal ecosystem productivity simulator(BEPS),to simulate the YG of winter wheat in the North China Plain from 2015 to 2019.Yield validation based on statistical yield data revealed good performance of the PRYM-Wheat Model in simulating winter wheat actual yield(Ya).The distribution of Ya across the North China Plain showed great heterogeneity,decreasing from southeast to northwest.The remote sensing-estimated results show that the average YG of the study area was 6400.6 kg ha^(–1).The YG of Jiangsu Province was the largest,at7307.4 kg ha^(–1),while the YG of Anhui Province was the smallest,at 5842.1 kg ha^(–1).An analysis of the responses of YG to environmental factors showed no obvious correlation between YG and precipitation,but there was a weak negative correlation between YG and accumulated temperature.In addition,the YG was positively correlated with elevation.In general,studying the specific features of the YG can provide directions for increasing crop yields in the future.
