Sustainable Irrigation Strategies for Cotton Production in Xinjiang, China: Balancing Yield, Profitability, and Sustainability under Climate Change

The spatiotemporal co-optimization of irrigation strategies represents a major leap forward in climate-smart agriculture, addressing the complex interactions between climate, crops, and soil management across both temporal and spatial scales. This study introduces a hybrid methodology that combines agricultural system modeling, machine learning, and economic analysis to optimize irrigation practices in Xinjiang, China. The primary goal is to balance crop yield, water use efficiency, and profitability under varying climate conditions, thereby advancing sustainable agricultural practices in one of China’s most arid regions.

Our study establishes three optimization objectives: yield, profit, and water use efficiency. Under the water use efficiency objective, optimized irrigation strategies significantly reduced water demand. During the historical period (2000–2020), water use decreased by an average of 16% (ranging from 14% to 21%), while under future climate scenarios (2051–2070), reductions of up to 25% (16% to 32%) are projected compared to conventional local practices and trial-based recommendations. For the yield objective, cotton yields increased by 8% during the historical period and are expected to rise by 15% under future climate conditions. Finally, under the profit objective, farmers' net incomes grew by 12% during the historical period and are projected to increase by 16% in future scenarios. The study also explores the scalability of the proposed framework, demonstrating its applicability across various sub-regions within Xinjiang, each characterized by distinct climatic and soil conditions. Sensitivity analyses reinforce the robustness of the optimization approach, confirming its potential to improve water management and agricultural sustainability on a regional scale.

This study highlights the transformative potential of spatiotemporal co-optimization for achieving multiple objectives in irrigation management. It introduces a digital framework tailored for site-specific irrigation strategies, setting a new standard for sustainable agricultural practices in Xinjiang. The findings provide a scalable model that can be adapted to other arid and semi-arid regions, supporting global initiatives in sustainable water management in the face of evolving climate conditions.