Machine Learning Analysis of Weather-Yield Relationships in Hainan Island’s Litchi
2026
Linyi Feng | Chenxiao Shi | Zhiyu Lin | Ruijuan Li | Jiaquan Ning | Ming Shang | Jingying Xu | Lei Bai
Litchi (Litchi chinensis Sonn.) is a pillar of the tropical agricultural economy in southern China, yet its production faces increasing instability due to climate change. Traditional agronomic models often fail to capture the complex, non-linear interactions between meteorological drivers and yield formation in perennial fruit trees. To address this challenge, the study constructed a yield prediction framework using an optimized Random Forest (RF) model integrated with interpretable machine learning (SHAP), based on a comprehensive dataset from 17 major production regions in Hainan Province (2000&ndash:2022). The model demonstrated robust predictive capability at the provincial scale (R2 = 0.564, RMSE = 2.1 t/ha) and high consistency across regions (R2 ranging from 0.51 to 0.94). Feature importance analysis revealed that heat accumulation (specifically growing degree days above 20 °:C) is the dominant driver, explaining over 85% of yield variability. Crucially, scenario simulations uncovered asymmetric climate risks across phenological stages: while moderate warming generally enhances yield by promoting vegetative growth and ripening, it acts as a stressor during the Fruit Development stage, where temperatures exceeding 26 °:C trigger yield decline. Furthermore, the yield penalty for drought during Flowering (&minus:8.09%) far outweighed the marginal benefits of surplus rainfall, identifying this window as critically sensitive to water deficits. These findings underscore the necessity of phenology-aligned adaptation strategies&mdash:specifically, securing irrigation during flowering and deploying cooling interventions during fruit development&mdash:providing a data-driven basis for climate-smart management in tropical agriculture.
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