Assessing the Impact of Climate Change on Winter Wheat Production in the North China Plain from 1980 to 2020

Jinhui Zheng and Shuai Zhang ()
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Jinhui Zheng: Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
Shuai Zhang: Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China

Agriculture, 2025, vol. 15, issue 5, 1-17

Abstract: 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 2 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.

Keywords: climate change; winter wheat; machine learning; sustainable production (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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