Under the global challenges of climate change, population growth, resource scarcity, and ecological-environmental pressures, agriculture as a crucial pillar of the national economy faces particularly critical requirements for sustainable development. Against the complex backdrop of intertwined factors including water resources, land resources, and energy supply, balancing agricultural production, environmental protection, and economic benefits has become a major challenge in contemporary agricultural development.

Dr. Li Zhihui’s team at the Institute of Geographic Sciences and Natural Resources Research (IGSNRR) of the Chinese Academy of Sciences (CAS) and their collaborators propose an analytical framework that integrates the water-land-energy-economy-environment-food nexus to optimize agricultural sustainability. Their innovative two-stage modeling approach of planting structures and spatial layouts combines a multi-objective genetic algorithm (NSGA-II) with spatial allocation through an enhanced Hungarian algorithm, effectively balancing ecological and socioeconomic factors in agriculture production. 

The framework is validated through a case study in the Black Soil Region of Northeast China (“China's Granary”), generating optimized planting configurations for rice, maize, and soybean under three scenarios: 1) economic development priority, 2) environmental protection priority, and 3) balanced development. The model outputs provide actionable spatial layouts and transition hotspots while demonstrating strong scalability for global agricultural systems.

The results show that rice cultivation boosts economic returns (+7.25%) but increases water use (+14.46%), energy consumption (+9.59%), and carbon emissions (+2.98%). Expanding soybean planting (area +112% in environmental protection scenario) reduces environmental costs (carbon emissions -31.87%) but lowers economic returns (-14.87%). Maize serves as a buffer crop, balancing environmental and economic goals. Its area moderately decreases (-14.19% in balanced scenario) to adjust rice/soybean ratios. 

Crop transition hotspots concentrate in northeastern (Hegang-Jixi) and central (Harbin-Changchun) regions, where natural suitability differences make them sensitive to planting adjustments. Spatial optimization achieves high precision (R² > 0.9), effectively aligning geographical suitability with policy goals.

The results will support China’s soybean revitalization plan, that is, compensate for soybean’s economic disadvantages via subsidies or technology while concentrating rice in high-suitability zones. The framework is scalable to other agricultural regions, aiding global food security and sustainable agriculture goals.

Reference:

H. Wu, Z. Li, X. Deng, Z. Zhao. Enhancing agricultural sustainability: Optimizing crop planting structures and spatial layouts within the water-land-energy-economy-environment-food nexus. Geogr. Sustain. 6, 100258 (2025).