Optimization of cropping structure of staple crops and benefit evaluation based on carbon-water footprint in the black soil region of Northeast China

As climate change and population growth exacerbate ecological crises, sustainable agricultural management is essential to mitigate food security risks. Optimizing cropping structures by balancing economic, social, and ecological benefits based on carbon and water footprints has emerged as a critical pathway in achieving climate-resilient agriculture. However, existing multi-objective optimization models and evaluation methods often produce multiple optimization solutions due to non-linearity and high dimensionality or are limited by issues such as the autocorrelation of input indicators, which can hinder the derivation of sufficiently optimized outcomes. To address these challenges, this study focuses on the black soil region of Northeast China, one of the country's most important grain production bases. For the first time, we have introduced the combination of the NSGA-III genetic optimization algorithm with the entropy weight-TOPSIS method. This approach aims to screen the optimized solution set and determine the optimal cropping structure scheme at the municipal scale within the study region, thereby providing a robust framework for decision-making that supports sustainable agricultural development. The results showed that rice had the highest carbon footprint followed by soybean and maize. Major contributors to carbon emissions in the study region were methane from paddy fields, straw burning, and nitrogen fertilizer input with respective contribution rates of 24.72 %, 23.30 %, and 17.41 %. Soybean had the highest water footprint followed by maize and rice, with green water dominating the major component. After optimization, the area under cultivation in the central part of the study area will increase by at least 12.96 × 10 4 ha, while the area under cultivation in the northern part will decrease by 119.33 × 10 4 ha. The planting areas of maize and rice will decrease by about 11.87 × 10 4 ha and 6.37 × 10 4 ha, respectively, while the planting area of soybean will increase by about 8.84 × 10 3 ha. The study provides a decision-making basis for optimizing the cropping structure in the black soil region of Northeast China, facilitating the promotion of sustainable agricultural production and the development of climate-smart agriculture. • Paddy methane and straw burning were major carbon sources in the study area. • NSGA-III and TOPSIS model enabled optimizing cropping structure. • Optimization reduced carbon (1.50 ×10⁵ tCO₂eq) and water (1.20 ×10⁹ m³) footprints. • Optimization raised the profit (11.01 ×10⁹ yuan) and food output (2.09 ×10 6 tons).