Determining the suitable areas for winter wheat under climate change and assessing the risk of freezing injury are crucial for the cultivation of winter wheat.We used an optimized Maximum Entropy(MaxEnt)Model to predi...Determining the suitable areas for winter wheat under climate change and assessing the risk of freezing injury are crucial for the cultivation of winter wheat.We used an optimized Maximum Entropy(MaxEnt)Model to predict the potential distribution of winter wheat in the current period(1970-2020)and the future period(2021-2100)under four shared socioeconomic pathway scenarios(SSPs).We applied statistical downscaling methods to downscale future climate data,established a scientific and practical freezing injury index(FII)by considering the growth period of winter wheat,and analyzed the characteristics of abrupt changes in winter wheat freezing injury by using the Mann-Kendall(M-K)test.The results showed that the prediction accuracy AUC value of the MaxEnt Model reached 0.976.The minimum temperature in the coldest month,precipitation in the wettest season and annual precipitation were the main factors affecting the spatial distribution of winter wheat.The total suitable area of winter wheat was approximately 4.40×10^(7)ha in the current period.In the 2070s,the moderately suitable areas had the greatest increase by 9.02×10^(5)ha under SSP245 and the least increase by 6.53×10^(5)ha under SSP370.The centroid coordinates of the total suitable areas tended to move northward.The potential risks of freezing injury in the high-latitude and-altitude areas of the Loess Plateau,China increased significantly.The northern areas of Xinzhou in Shanxi Province,China suffered the most serious freezing injury,and the southern areas of the Loess Plateau suffered the least.Environmental factors such as temperature,precipitation and geographical location had important impacts on the suitable area distribution and freezing injury risk of winter wheat.In the future,greater attention should be paid to the northward boundaries of both the winter wheat planting areas and the areas of freezing injury risk to provide the early warning of freezing injury and implement corresponding management strategies.展开更多
Freeze injury is an usual disaster for winter wheat in Shanxi Province, China, and monitoring freeze injury is of important economic significance. The aim of this article is to monitor and analyze the winter wheat fre...Freeze injury is an usual disaster for winter wheat in Shanxi Province, China, and monitoring freeze injury is of important economic significance. The aim of this article is to monitor and analyze the winter wheat freeze injury using remote sensing data, to monitor the occurrence and spatial distribution of winter wheat freeze in time, as well as the severity of the damage. The winter wheat freeze injury was monitored using multi-temporal moderate-resolution imaging spectroradiometer （MODIS） data, combined with ground meteorological data and field survey data, the change of normalized difference vegetation index （NDVI） before and after freeze injury was analyzed, as well as the effect of winter wheat growth recovery rate on yield. The results showed that the NDVI of winter wheat decreased dramatically after the suffering from freeze injury, which was the prominent feature for the winter wheat freeze injury monitoring. The degrees of winter wheat freeze injury were different in the three regions, of which, Yuncheng was the worst severity and the largest freeze injury area, the severity of freeze injury correlates with the breeding stage of the winter wheat. The yield of winter wheat showed positive correlation with its growth recovery rate （r=0.659^＊＊ which can be utilized to monitor the severity of winter wheat freeze injury as well as its impact on yield. It can effectively monitor the occurrence and severity of winter wheat freeze injury using horizontal and vertical profile distribution and growth wheat freeze injury in Shanxi Province. recovery rate, and provide a basis for monitoring the winter展开更多
Winter wheat freeze injury is one of the main agro-meteorological disasters affecting wheat production. In order to evaluate the severity of freeze injury on winter wheat systematically, we proposed a grey-system mod...Winter wheat freeze injury is one of the main agro-meteorological disasters affecting wheat production. In order to evaluate the severity of freeze injury on winter wheat systematically, we proposed a grey-system model （GSM） to monitor the degree and the distribution of the winter wheat freeze injury. The model combines remote sensing （RS） and geographic information system （GIS） technology. It gave examples of wheat freeze injury monitoring applications in Gaocheng and Jinzhou of Hebei Province, China. We carried out a quantitative evaluation method study on the severity of winter wheat freeze injury. First, a grey relational analysis （GRA） was conducted. At the same time, the weights of the stressful factors were determined. Then a wheat freezing injury stress multiple factor spatial matrix was constructed using spatial interpolation technology. Finally, a winter wheat freeze damage evaluation model was established through grey clustering algorithm （GCA）, and classifying the study area into three sub-areas, affected by severe, medium or light disasters. The evaluation model were verified by the Kappa model, the overall accuracy reached 78.82% and the Kappa coefficient was 0.6754. Therefore, through integration of GSM with RS images as well as GIS analysis, quantitative evaluation and study of winter wheat freeze disasters can be conducted objectively and accurately, making the evaluation model more scientific.展开更多
基金supported by the National Natural Science Foundation of China(31201168)the Basic Research Program of Shanxi Province,China(20210302123411)the earmarked fund for Modern Agro-industry Technology Research System,China(2022-07).
文摘Determining the suitable areas for winter wheat under climate change and assessing the risk of freezing injury are crucial for the cultivation of winter wheat.We used an optimized Maximum Entropy(MaxEnt)Model to predict the potential distribution of winter wheat in the current period(1970-2020)and the future period(2021-2100)under four shared socioeconomic pathway scenarios(SSPs).We applied statistical downscaling methods to downscale future climate data,established a scientific and practical freezing injury index(FII)by considering the growth period of winter wheat,and analyzed the characteristics of abrupt changes in winter wheat freezing injury by using the Mann-Kendall(M-K)test.The results showed that the prediction accuracy AUC value of the MaxEnt Model reached 0.976.The minimum temperature in the coldest month,precipitation in the wettest season and annual precipitation were the main factors affecting the spatial distribution of winter wheat.The total suitable area of winter wheat was approximately 4.40×10^(7)ha in the current period.In the 2070s,the moderately suitable areas had the greatest increase by 9.02×10^(5)ha under SSP245 and the least increase by 6.53×10^(5)ha under SSP370.The centroid coordinates of the total suitable areas tended to move northward.The potential risks of freezing injury in the high-latitude and-altitude areas of the Loess Plateau,China increased significantly.The northern areas of Xinzhou in Shanxi Province,China suffered the most serious freezing injury,and the southern areas of the Loess Plateau suffered the least.Environmental factors such as temperature,precipitation and geographical location had important impacts on the suitable area distribution and freezing injury risk of winter wheat.In the future,greater attention should be paid to the northward boundaries of both the winter wheat planting areas and the areas of freezing injury risk to provide the early warning of freezing injury and implement corresponding management strategies.
基金supported by grants from the Key Tech-nologies R&D Program of Shanxi Province, China(20060311140)the Open Project Program of Weather Bureau of Shanxi Province, China (SX053001)
文摘Freeze injury is an usual disaster for winter wheat in Shanxi Province, China, and monitoring freeze injury is of important economic significance. The aim of this article is to monitor and analyze the winter wheat freeze injury using remote sensing data, to monitor the occurrence and spatial distribution of winter wheat freeze in time, as well as the severity of the damage. The winter wheat freeze injury was monitored using multi-temporal moderate-resolution imaging spectroradiometer （MODIS） data, combined with ground meteorological data and field survey data, the change of normalized difference vegetation index （NDVI） before and after freeze injury was analyzed, as well as the effect of winter wheat growth recovery rate on yield. The results showed that the NDVI of winter wheat decreased dramatically after the suffering from freeze injury, which was the prominent feature for the winter wheat freeze injury monitoring. The degrees of winter wheat freeze injury were different in the three regions, of which, Yuncheng was the worst severity and the largest freeze injury area, the severity of freeze injury correlates with the breeding stage of the winter wheat. The yield of winter wheat showed positive correlation with its growth recovery rate （r=0.659^＊＊ which can be utilized to monitor the severity of winter wheat freeze injury as well as its impact on yield. It can effectively monitor the occurrence and severity of winter wheat freeze injury using horizontal and vertical profile distribution and growth wheat freeze injury in Shanxi Province. recovery rate, and provide a basis for monitoring the winter
基金supported by the National Natural Science Foundation of China (41101395, 41101397 and 41001199)the Beijing New Star Project on Science & Technology,China (2010B024)the Key Technologies R&D Program of China during the 12th Five-Year Plan period(2012BAH29B04)
文摘Winter wheat freeze injury is one of the main agro-meteorological disasters affecting wheat production. In order to evaluate the severity of freeze injury on winter wheat systematically, we proposed a grey-system model （GSM） to monitor the degree and the distribution of the winter wheat freeze injury. The model combines remote sensing （RS） and geographic information system （GIS） technology. It gave examples of wheat freeze injury monitoring applications in Gaocheng and Jinzhou of Hebei Province, China. We carried out a quantitative evaluation method study on the severity of winter wheat freeze injury. First, a grey relational analysis （GRA） was conducted. At the same time, the weights of the stressful factors were determined. Then a wheat freezing injury stress multiple factor spatial matrix was constructed using spatial interpolation technology. Finally, a winter wheat freeze damage evaluation model was established through grey clustering algorithm （GCA）, and classifying the study area into three sub-areas, affected by severe, medium or light disasters. The evaluation model were verified by the Kappa model, the overall accuracy reached 78.82% and the Kappa coefficient was 0.6754. Therefore, through integration of GSM with RS images as well as GIS analysis, quantitative evaluation and study of winter wheat freeze disasters can be conducted objectively and accurately, making the evaluation model more scientific.
