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Publishing Language: Chinese

Assessing the impact of climate index anomaly on maize yield in Northeast China using boosted regression tree

Shuang SUN1Chunyi WANG1( )Yanling SONG1Dasui LI2Yi ZHANG1Qing SUN1Dingrong WU1Yanxia ZHAO3
Chinese Academy of Meteorological Sciences, Beijing 100081, China
Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Xiangshan, Beijing 100193, China
Department of Atmospheric and Oceanic Sciences/Institute of Atmospheric Science, Fudan university, Shanghai 200438, China
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Abstract

Northeast China is one of the major maize-producing regions in national food security. Maize is highly sensitive to the inter-annual variations in both average and extreme climatic conditions. It is also crucial to identify the key climatic variables affecting the maize yield. In this study, a fundamental database was constructed to assess the impact of the climate index anomaly on the maize yield in Northeast China using a boosted regression tree. The maize phenology, yield, and meteorological records were then captured from the agro-meteorological observation stations at the three provinces of Northeast China (Heilongjiang, Jilin, and Liaoning) in the period 1981–2022. The boosted regression tree (BRT) model was employed to quantify the impacts of the anomalies in the average climate, extreme temperature, and temperature-moisture compound indicators on the meteorological yield of the spring maize at the different growing periods. The results show that the meteorological yield of the spring maize in the three provinces shared significant inter-annual fluctuations, with great variations over the different decades. The primary driving factors on the regional maize yield were effectively identified with the cross-validation coefficients ranging from 0.61 to 0.75. There was the great variation in the dominant influencing factors on the spring maize meteorological yield in the different periods over the decades: In the period from 1981 to 1990, the anomalies in the relative humidity (average precipitation) were the primary influencing factor on the regional maize yield; In the period from 1991 to 2000, the dominant factor was the heat growing degree days (extreme high temperatures) during the maize growing season; In the periods from 2001 to 2010 and from 2011 to 2022, the key driver of the maize yield was the anomalies in the heat growing degree days (extreme high temperatures) during the reproductive growing stage of maize emerge. Notably, there was a gradually increasing contribution of the anomalies in the extreme low temperatures during the reproductive growing stage to spring maize yield over the decades. Specifically, there was the negative effect of the anomalies in the cold growing degree days during the reproductive growing stage of the spring maize on the yield in the periods from 1991 to 2000 and from 2001 to 2010; In contrast, these anomalies then shifted to a positive effect on the maize yield in the period from 2011 to 2022. Overall, the anomalies in the heat growing degree days were the dominant influencing factor on the maize meteorological yield. Furthermore, the impact of the extreme low-temperature anomalies on the maize yield gradually transformed from a negative to a positive effect, as the anomaly decreased in the cold growing degree days. The extreme low-temperature stress can minimize the harm caused by extreme high temperatures in the future. The finding can provide a strong reference for the high and stable maize yields in Northeast China.

CLC number: S162.5+3 Document code: A Article ID: 1002-6819(2026)-05-0128-09

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Transactions of the Chinese Society of Agricultural Engineering
Pages 128-136

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Cite this article:
SUN S, WANG C, SONG Y, et al. Assessing the impact of climate index anomaly on maize yield in Northeast China using boosted regression tree. Transactions of the Chinese Society of Agricultural Engineering, 2026, 42(5): 128-136. https://doi.org/10.11975/j.issn.1002-6819.202509133

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Received: 15 September 2025
Revised: 29 December 2025
Published: 15 March 2026
© Chinese Society of Agricultural Engineering 2026