数字农科院2.0

Global meta-analysis and machine learning show that long-term green manure planting in areas with insufficient fertility produces higher grain yields by enhancing soil health

文献类型： 外文期刊

作者： Peng Wu;Qi Wu;Jinyu Yu;Zihui Zhang;Hua Huang;Enke Liu;Kemoh Bangura;Xingli Huo;Haotian Wu;Zhikuan Jia;Peng Zhang;Guangxin Zhang;Jianfu Xue;Chuangyun Wang;Zhiqiang Gao

作者机构：

关键词： Crop yield;Green manure;Machine learning;Meta-analysis;Soil quality

期刊名称： Field Crops Research

ISSN： 0378-4290

年卷期： 2026 年 338 卷

页码：

收录情况： SCIE（2025版）

摘要： Context: Green manure (GM) can potentially increase crop yields by enhancing the soil properties to solve the contradiction between soil degradation and food security. However, the effects of GM on the soil properties and crop yields in variable environments, and the relationships between them remain unclear, and the key factors need to be identified. Moreover, global prediction are lacking of the effects of GM on crop yields. Objectives and methods: Therefore, we conducted a meta-analysis using 5125 pairs of data observations to study the overall effects of GM and environmental variability on the soil properties and subsequent crop yields, and to establish their relationships by introducing the soil quality index (SQI). In addition, we used a machine learning model to predict the global changes in SQI and the yields of maize, wheat, and rice under GM. Results: GM significantly increased the soil C, N, P, and K contents, and enzyme activities by 5.34–40.02 %, 8.81–32.39 %, 4.54–10.02 %, 1.18–8.74 %, and 9.49–19.76 %, respectively, increasing SQI by 16.96 %. The fundamental physical and chemical features of soil, duration of GM application, and climatic conditions significantly affected the improvements in the soil properties under GM. The initial total nitrogen (TN) content of the soil and duration of GM application were the two main factors associated with the effect of GM on SQI. When the initial soil TN content was lower than 0.75 g kg–1 and the GM application duration exceeded 15 years, SQI increased by 26.60 % and 19.94 %, respectively. GM significantly increased SQI by enhancing the soil properties to eventually increase the crop yield by 3.48 %. GM duration was the most important factor associated with the crop yield, and GM significantly increased the crop yield by 18.72 % when the duration exceeded 15 years. However, when the application duration is less than 5 years, the increase is only 2.10 %. The global machine learning model predicted that GM can potentially increase the SQI and crop yield by 23.98 % and 6.35 %, respectively. Conclusion: Therefore, we conclude that applying GM as a green fertilization strategy can increase crop yields by enhancing SQI, and its effects on increasing yields are greater in areas with insufficient soil fertility and long-term planting. Implication: This study highlights the importance of GM for farmland production. SQI was used to establish the relationship between soil quality and crop productivity, and we predicted the potential of GM for improving the global SQI and crop yields.

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