Global maize yield responses to essential climate variables: Assessment using atmospheric reanalysis and future climate scenarios

文献类型： 外文期刊

第一作者： Zhao, Zhi-Wei

作者： Zhao, Zhi-Wei;Leng, Pei;Zhao, Zhi-Wei;Shang, Guo-Fei;Han, Xiao-Jing

作者机构：

关键词： Maize yield; Essential climate variables; Climate change; Generalized additive model

期刊名称：COMPUTERS AND ELECTRONICS IN AGRICULTURE （ 影响因子：8.9； 五年影响因子：9.3 ）

ISSN： 0168-1699

年卷期： 2025 年 232 卷

页码：

收录情况： SCI

摘要： Maize is recognized as one of the four major crops in the world and plays an important role in global agri-food systems. Therefore, understanding how maize yield responds to climate change is essential for addressing the challenges of exponential population growth and food security. In this study, maize yield data in 77 countries from 1982 to 2016 and seven essential climate variables (ECVs) were collected to assess the effects of climate change on maize yield variation. To this end, potential ECVs were first divided into three groups: energy availability (net surface solar radiation, air temperature and land surface temperature), water availability (soil moisture and precipitation), and exchange efficiency (relative humidity and wind speed), which are closely related to crop growth. Correlation analysis was conducted to determine the best ECVs for further investigation. Furthermore, the generalized additive model (GAM) was used to express yield as function to the ECVs in each country. Specifically, the first-order difference in maize yield and ECVs were considered in data process to maximize the effects of reductions from other factors such as crop management and cultivars. Finally, the performance of the proposed approach was compared with that of widely used multiple regression method. The results indicate that: (1) a global average of 46% of maize yield variability can be explained by ECVs variability, yet significant discrepancies exist for different countries; (2) over 73% of countries are dominated by more than two groups of ECVs; (3) GAM generally outperforms the traditional multiple regression method in more than 80% of the investigated countries. This study offers a fresh perspective for investigating maize yield responses to climate change.

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