Optimizing Crop Planting Structures for Water Savings and Grain Revenue in China Considering Different Spatial Scales

Balancing agricultural production to ensure food security and poverty alleviation with natural resource conservation, e.g., water, is the current focus of China’s sustainable agricultural development policy. Previous studies have addressed the issue by adjusting crop planting areas using separate optimization models at the county, regional, or national levels. However, the impact of spatial scale on optimization outcomes and the associated sustainability implications has been overlooked, potentially leading to problematic policy analysis. This study aims to address this issue by comparing optimization results at two distinct scales: regional and national. Modeling objectives include minimizing the blue water footprint (MBW), maximizing grain revenue (MGR), and achieving a balanced multi-objective optimization solution by giving equal weights to both objectives. These objectives are subject to constraints on arable land, available BWF, and reference production levels for staple grains (rice, wheat, corn, and potatoes). Our findings reveal that national-scale optimization generally results in greater total irrigation water savings or higher overall grain revenue, at the expense of the other objective, compared to aggregating regional-scale optimizations. Optimal solutions that meet both objectives are more likely to be achieved at the regional scale, as demonstrated by both single- and multi-objective optimization outcomes. Our findings imply that focusing on narrower spatial scales for optimization may attain more equitable and sustainable outcomes without requiring trade-offs between critical objectives. • This study optimizes crop planting structure at national and regional scales. • We consider blue water footprint, grain revenue, and balancing-objective scenarios. • National-scale optimization produces larger absolute gains in water savings or revenue. • Smaller-scale analysis offers more opportunities for sustainable and actionable strategies. • Balanced-objective optimization yields win-win solutions in both spatial scales.