Spatial Optimization of Agricultural Planting Structure in Heilongjiang Province Using a Water Footprint and Multi-Objective Coupling Framework

Amid climate change and water scarcity, improving water use efficiency and planting structure is critical for food security and sustainable agricultural development. Heilongjiang Province, a key region for China’s food security, faces urgent demands to enhance water use efficiency and adjust planting structures. Based on water footprint (WF) theory, this study established an accounting system for water footprint of crop production (WFCP, including blue, green, and grey water) and yield per unit area. We analyzed the spatiotemporal patterns for rice, maize, and soybean in Heilongjiang from 2000 to 2022. The results reveal significant spatiotemporal heterogeneity and a pronounced spatial dislocation between WFCP and yield per unit area. Temporally, the blue and gray WFCP declined, but their driving mechanisms still require further verification. Spatially, the WFCP was higher in the west and lower in the east, while yield per unit area showed the opposite trend. Based on this spatial diagnosis, we developed a multi-objective optimization model incorporating water conservation, pollution reduction, income increase, and yield enhancement. The model was solved using the Non-dominated Sorting Genetic Algorithm-III (NSGA-III), with the optimal solution selected via entropy-weighted TOPSIS. The optimized scheme reduces the rice planting area and increases the maize planting area. Compared with pre-optimization, it is projected to deliver approximately 2.56% and 0.79% reductions in blue and gray WF, and 2.11% and 0.67% increases in net economic benefit and grain yield. This study provides a scientific basis for improving water use efficiency and sustainable agricultural development in Heilongjiang Province and other similar agricultural regions.