Optimal cropping patterns can be conducive to sustainable irrigation: Evidence from the drylands of Northwest China

The pressures of frequent extreme weather, water shortages and increasing food demand pose a continued challenge of maintaining the sustainable development of irrigated agricultural systems. Although rational arable land management is fundamental to alleviating these pressures, the relationship between cropping patterns and irrigation sustainability is understudied. Using the Hexi Corridor as an example, a maximum entropy machine learning model was used to determine the optimal cropping pattern based on crop suitability and to explore the impact mechanism of the optimal cropping pattern on the irrigation sustainability index (SI) from the perspective of reliability, resilience, and vulnerability. An optimal cropping pattern was conducive to sustainable irrigation and reduced irrigation water use by 21.03% from 1960 s to 2010 s with no continued agricultural expansion. Thus, the challenges of food security and sustainability for similar regions, and globally, can be met but will require major changes in cropping planning and management.

Automated summary

Frequent extreme weather, water shortages, and increasing food demand pose challenges to the sustainable development of irrigated agricultural systems. Rational arable land management and optimal cropping patterns play a vital role in alleviating these pressures. Using the Hexi Corridor as an example, a study found that an optimal cropping pattern, determined through a maximum entropy machine learning model, could contribute to sustainable irrigation and reduce irrigation water use. This research highlights the importance of changes in cropping planning and management for addressing food security and sustainability challenges.