Dynamic delineation of management zones in arid Northwest China under climate change: Insights from multiple variables

Climate change intensifies the spatiotemporal heterogeneity of agro-environments in arid Northwest China, a major irrigated agricultural base facing severe water scarcity. Traditional static management zone (MZ) delineation is limited in capturing dynamic crop-environment interactions, restricting climate-smart precision agriculture. Focusing on Xinjiang and the Hexi Corridor, we used Fuzzy C-Means (FCM) clustering to delineate dynamic MZs from multi-source data under SSP245 and SSP585 and evaluated zoning performance. Results indicated that climate variability drove dynamic shifts in optimal MZ numbers (2–7 under SSP245 and 3–6 under SSP585), while static soil-based MZs poorly reflected spatiotemporal crop growth and production patterns. The soil-climate combined scenario generated the most stable and yield-consistent MZs, cutting coefficient of variation (CV) of key variables by 25.3–25.7% and significantly enhancing within-zone homogeneity of yield and water use efficiency. Dynamic zoning outperformed single-variable schemes in matching actual agronomic performance. This study confirms that integrating static soil properties and dynamic climate data optimizes climate-adaptive MZs delineation, providing a scientific foundation for precision irrigation, differentiated input management, and regional climate-smart agricultural planning in water-scarce arid regions.