A Methodological Framework for Assessing the Potential Performance of Maize/Soybean Intercropping Under 2050 Climate Scenarios
Year: 2025
Type: article
Abstract: Climate change and increasing uncertainty in global food trade pose major challenges to China’s food security. This study aimed to evaluate the potential of maize/soybean intercropping across China under current and projected 2050 climate scenarios. A database of 637 field experiments from 56 publications, combined with climate and soil datasets, was trained Random Forest machine learning model to predict yield potential and assess the impacts of replacing varying proportions of maize cropland with maize/soybean intercropping under 2050 climate change. Our results indicate that suitable intercropping zones will expand northwards, with the North China Plain and Northeast China showing particular promise, while land equivalent ratios are expected to remain stable or increase, especially in Southwest China. Scenario analyses suggest that converting 30–50% of maize land to intercropping could double soybean production while maintaining maize yields, and full conversion under low-demand scenarios could achieve near self-sufficiency in maize (96.8%) and exceed self-sufficiency in soybean (104.7%). These results demonstrate the strategic potential of maize/soybean intercropping to enhance national food security. Furthermore, this study provides an exploratory and novel methodological framework that integrates multi-source datasets and machine learning to assess the potential performance of maize/soybean intercropping under climate change, offering a useful reference for future research and model refinement.
Source: Agronomy
Authors: Tao Song, Chaochun Zhang
Language: English
Subfield: Agronomy and Crop Science
Domain: Life Sciences
SDGs: Zero hunger, Climate action
Open Access status: gold
Estimated APC paid: $3,137
Awards: G5038444608, G6384450677, G8257166482
OpenAlex ID: W4415615854