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Updated: Jun 3, 2025

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
Published on: December 9, 2012
Improving future agricultural sustainability by optimizing crop distributions in China
Qi Guan1,2,3, Jing Tang4,5, Kyle Frankel Davis6,7
1State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 211135, China.
Abstract:
Improving agricultural sustainability is a global challenge, particularly for China's high-input and low-efficiency cropping systems with environmental tradeoffs. Although national strategies have been implemented to achieve Sustainable Development Goals in agriculture, the potential contributions of crop switching as a promising solution under varying future climate change are still under-explored. Here, we optimize cropping patterns spatially with the targets of enhancing agriculture production, reducing environmental burdens, and achieving sustainable fertilization across different climate scenarios. Compared with current cropping patterns, the optimal crop distributions under different climate scenarios consistently suggest allocating the planting areas of maize and rapeseed to the other crops (rice, wheat, soybean, peanut, and potato). Such crop switching can consequently increase crop production by 14.1%, with accompanying reductions in environmental impacts (8.2% for leached nitrogen and 24.0% for irrigation water use) across three representative Shared Socio-economic Pathways from 2020 to 2100. The sustainable fertilization rates vary from 148-173 kg N ha-1 in 2030 to 213-253 kg N ha-1 in 2070, significantly smaller than the current rate (305 kg N ha-1). These outcomes highlight large potential benefits of crop switching and fertilizer management for improving China's future agricultural sustainability.
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