Machine Learning Analysis of Weather-Yield Relationships in Hainan Island’s Litchi

作者：Linyi Feng, Chenxiao Shi, Zhiyu Lin, Ruijuan Li, Jiaquan Ning, Ming Shang, J. Xu, Lei Bai · 发表于：Agriculture · 年份：2026 · DOI：10.3390/agriculture16020237 · 被引用次数：1 · 研究领域：Plant Physiology and Cultivation Studies、Greenhouse Technology and Climate Control、Horticultural and Viticultural Research

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 (2000–2022). 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 (−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 p...