用卫星气候数据提升塞拉利昂水稻产量预测精度
Can Machine Learning Forecast Rice Yields in Data-Constrained Settings? Satellite Climate Data, National Crop Statistics, and Lessons from Sierra Leone
- 融合卫星气候数据与历史产量，用XGBoost建模预测
- 加入气候数据后误差降低33%（RMSE 284 vs 428 kg/ha）
- 适合农业政策制定者与数据稀缺地区参考
塞拉利昂农业几乎无数据支持，且无公开机器学习研究涉及其作物产量。本文探讨仅用该国现有数据能否预测水稻产量。基于FAOSTAT 2000-2024年九种主要作物的25年生产数据，采用XGBoost、梯度提升和随机森林模型，在严格防泄漏协议下，通过扩展窗口走查评估，对比朴素持续性基准。仅用作物统计数据的模型均未超越基准。但引入免费卫星气候数据（CHIRPS降雨、NASA POWER温度）后，结果逆转：仅气候数据的XGBoost模型将预测误差降低三分之一（RMSE 284 vs 428 kg/ha），线性模型也具相同效果，且对排除异常的2018年数据保持稳健。早期（5-6月）降雨是主导预测因子，表明收获前数月即可识别季节性风险。但所有模型未能预判2018年产量崩溃，因其根源为制度性而非气候因素。研究将成果转化为塞拉利昂‘饲料萨隆’战略的政策建议，并提供全流程开源工具链。
原文摘要 · Abstract (English)
Sierra Leone's agriculture operates with almost no data-driven decision support, and no published machine learning study has examined the country's crop yields. We ask whether rice yield can be forecast from data Sierra Leone currently has. Using 25 years of FAOSTAT production data (2000-2024) for nine major crops, we train XGBoost, Gradient Boosting, and Random Forest under a strict anti-leakage protocol with expanding-window walk-forward evaluation across seven held-out years, benchmarked against naive persistence. No model trained on crop statistics alone outperforms persistence. Augmenting with free satellite climate data (CHIRPS rainfall, NASA POWER temperature) reverses this result: a climate-only XGBoost reduces forecast error by one third (RMSE 284 vs 428 kg/ha), a gain that holds for a linear model and is robust to excluding the anomalous 2018 season. Early-season (May-June) rainfall is the dominant predictor, implying seasonal yield risk is observable months before harvest. No model anticipated the 2018 collapse, whose origins were institutional rather than climatic. We translate the findings into policy recommendations for Sierra Leone's Feed Salone Strategy, with a fully open-source pipeline.
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