Abstract:

Understanding how climatic variability influences crop yield is essential for improving agricultural resilience in climate-vulnerable regions. This study develops an interpretable deep learning framework to analyse wheat and maize yield responses in a long-term conservation-based strip intercropping system. By integrating daily meteorological sequences with phenology-aligned climatic summaries, the model captures both short-term weather variability and cumulative stage-specific climate effects while maintaining high predictive accuracy. The results revealed distinct climatic sensitivity structures for the two crops. Wheat yield was most strongly influenced by precipitation during flowering and radiation during jointing to booting, with additional sensitivity to thermal accumulation and hot days during grain filling. In contrast, maize yield was primarily controlled by precipitation during rapid vegetative growth to pre-tasselling and by growing degree days during tasselling, silking, and grain filling. Attention, SHapley Additive exPlanations value, and perturbation analyses consistently showed that climate–yield relationships were strongly nonlinear. Yield responses exhibited clear threshold and saturation behaviours, with diminishing benefits under excessive moisture and rapid declines once thermal conditions exceeded critical limits. These findings demonstrate that yield formation in this intercropping system is governed by stage-specific climatic thresholds rather than simple linear responses. The framework provides a practical tool for identifying vulnerability windows, climatic thresholds, and adaptation limits, offering useful guidance for climate-resilient management in dryland and data-scarce agroecosystems.