Assessing the Impact of Climate Change on Winter Wheat Production in the North China Plain from 1980 to 2020
As a highly variable factor, climate plays a crucial role in winter wheat production. Quantifying its impact on crop yield and determining its relative importance is essential. This study uses the Random Forest (RF) algorithm to evaluate the effects of climate change on winter wheat yields in the No...
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MDPI AG
2025-02-01
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| Series: | Agriculture |
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| author | Jinhui Zheng Shuai Zhang |
| author_facet | Jinhui Zheng Shuai Zhang |
| author_sort | Jinhui Zheng |
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| description | As a highly variable factor, climate plays a crucial role in winter wheat production. Quantifying its impact on crop yield and determining its relative importance is essential. This study uses the Random Forest (RF) algorithm to evaluate the effects of climate change on winter wheat yields in the North China Plain (1980–2020) and assess yield sensitivity to various climate indicators. The results show that the RF model performs well in simulating winter wheat yields across planting regions, with RRMSE values ranging from 12.88% to 22.06%, Spearman’s r from 0.84 to 0.91, and R<sup>2</sup> from 0.69 to 0.83. From 1980 to 2020, climate trends negatively affected winter wheat yields in Beijing, Tianjin, Hebei, Shanxi, and Jiangsu while promoting yield increases in Henan and Anhui. In general, a 10% increase in precipitation tends to enhance yields, except in northern Hebei, northern Shanxi, and Jiangsu. A 10% rise in solar radiation benefits most regions, although it leads to yield reductions in some areas of Anhui and Jiangsu. A 1 °C increase in temperature typically results in yield decreases, except in Beijing, southern Hebei, and parts of Shanxi and Henan. Among the three predictors, temperature is the most influential (33.81–44.19%), followed by solar radiation (29.01–37.47%) and precipitation (23.27–30.88%). These findings highlight the need for temperature-focused management strategies and region-specific approaches to optimize wheat yields and ensure sustainable production under climate change. |
| format | Article |
| id | doaj-art-d8b7287c19fd4d1eb0e717d2a7bc8e8a |
| institution | DOAJ |
| issn | 2077-0472 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
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| series | Agriculture |
| spelling | doaj-art-d8b7287c19fd4d1eb0e717d2a7bc8e8a2025-08-20T02:53:02ZengMDPI AGAgriculture2077-04722025-02-0115544910.3390/agriculture15050449Assessing the Impact of Climate Change on Winter Wheat Production in the North China Plain from 1980 to 2020Jinhui Zheng0Shuai Zhang1Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, ChinaKey Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, ChinaAs a highly variable factor, climate plays a crucial role in winter wheat production. Quantifying its impact on crop yield and determining its relative importance is essential. This study uses the Random Forest (RF) algorithm to evaluate the effects of climate change on winter wheat yields in the North China Plain (1980–2020) and assess yield sensitivity to various climate indicators. The results show that the RF model performs well in simulating winter wheat yields across planting regions, with RRMSE values ranging from 12.88% to 22.06%, Spearman’s r from 0.84 to 0.91, and R<sup>2</sup> from 0.69 to 0.83. From 1980 to 2020, climate trends negatively affected winter wheat yields in Beijing, Tianjin, Hebei, Shanxi, and Jiangsu while promoting yield increases in Henan and Anhui. In general, a 10% increase in precipitation tends to enhance yields, except in northern Hebei, northern Shanxi, and Jiangsu. A 10% rise in solar radiation benefits most regions, although it leads to yield reductions in some areas of Anhui and Jiangsu. A 1 °C increase in temperature typically results in yield decreases, except in Beijing, southern Hebei, and parts of Shanxi and Henan. Among the three predictors, temperature is the most influential (33.81–44.19%), followed by solar radiation (29.01–37.47%) and precipitation (23.27–30.88%). These findings highlight the need for temperature-focused management strategies and region-specific approaches to optimize wheat yields and ensure sustainable production under climate change.https://www.mdpi.com/2077-0472/15/5/449climate changewinter wheatmachine learningsustainable production |
| spellingShingle | Jinhui Zheng Shuai Zhang Assessing the Impact of Climate Change on Winter Wheat Production in the North China Plain from 1980 to 2020 Agriculture climate change winter wheat machine learning sustainable production |
| title | Assessing the Impact of Climate Change on Winter Wheat Production in the North China Plain from 1980 to 2020 |
| title_full | Assessing the Impact of Climate Change on Winter Wheat Production in the North China Plain from 1980 to 2020 |
| title_fullStr | Assessing the Impact of Climate Change on Winter Wheat Production in the North China Plain from 1980 to 2020 |
| title_full_unstemmed | Assessing the Impact of Climate Change on Winter Wheat Production in the North China Plain from 1980 to 2020 |
| title_short | Assessing the Impact of Climate Change on Winter Wheat Production in the North China Plain from 1980 to 2020 |
| title_sort | assessing the impact of climate change on winter wheat production in the north china plain from 1980 to 2020 |
| topic | climate change winter wheat machine learning sustainable production |
| url | https://www.mdpi.com/2077-0472/15/5/449 |
| work_keys_str_mv | AT jinhuizheng assessingtheimpactofclimatechangeonwinterwheatproductioninthenorthchinaplainfrom1980to2020 AT shuaizhang assessingtheimpactofclimatechangeonwinterwheatproductioninthenorthchinaplainfrom1980to2020 |