A novel habitat adaptability evaluation indicator (HAEI) for predicting yield of county-level winter wheat in China base on multisource climate data from 2001 to 2020

The effective collaboration between crop habitat adaptability evaluation and yield prediction is of great significance. Climate factors occupy an indispensable role, but the contributions of different climate factors to crop growth present spatiotemporal heterogeneity and confusion, amplifying the challenges associated with large-scale dynamic evaluations. Additionally, the mounting input parameters in yield prediction compound the uncertainty and intricacy of modeling. To address these challenges, a climate-driven dynamic habitat adaptability evaluation indicator (HAEI) was developed, capable of forecasting county-level winter wheat yields in China. First, the distribution characteristics and matching relationship between climate and yield variability were explored from multi-source data in the long time series, and a novel method of multiple-factor adaptive matching habitat membership degree was proposed. Second, considering the interaction and contribution differences between multiple-factor at different phenological periods, a comprehensive HAEI suitable for the entire growth period of winter wheat is constructed. The results showed that HAEI can integrate climate information that has a greater impact on yield variability and has a significant correlation with yield in various regions and periods, with an average correlation of 0.70. Remarkably, the predictive models incorporating HAEI consistently outperformed other yield prediction algorithms, demonstrating superior accuracy (R2 = 0.62–0.76, nRMSE = 0.1517–0.2031). Even in the least favorable scenario, involving a linear model with HAEI input, satisfactory results were achieved. This comprehensive framework effectively mitigates the adverse consequences of widespread agricultural climate heterogeneity and evaluates the habitat adaptability and yield status of wheat at the county level in China.