用遥感数据和正则化方法，找出影响秘鲁水稻产量的因果因素。
Sparsity, Regularization and Causality in Agricultural Yield: The Case of Paddy Rice in Peru
- 结合遥感数据与弹性网正则化，识别关键变量。
- 引入速度和加速度特征，提升产量预测准确率。
- 适合农业政策制定者和气候敏感作物研究者。
本研究将农业普查数据与遥感时间序列相结合，构建秘鲁各地水稻产量的精准预测模型。通过稀疏回归和弹性网正则化技术，识别出归因于植被指数（NDVI）、降水和温度等遥感变量与产量之间的因果关系。为提升预测精度，引入这些变量的一阶和二阶动态变换（即速度与加速度），捕捉非线性模式及延迟效应。结果表明，结合正则化与气候地理变量可显著改善预测性能，并在格兰杰意义下验证了因果关系的存在，凸显该方法在农业战略管理中的价值，助力水稻生产的高效可持续发展。
原文摘要 · Abstract (English)
This study introduces a novel approach that integrates agricultural census data with remotely sensed time series to develop precise predictive models for paddy rice yield across various regions of Peru. By utilizing sparse regression and Elastic-Net regularization techniques, the study identifies causal relationships between key remotely sensed variables-such as NDVI, precipitation, and temperature-and agricultural yield. To further enhance prediction accuracy, the first- and second-order dynamic transformations (velocity and acceleration) of these variables are applied, capturing non-linear patterns and delayed effects on yield. The findings highlight the improved predictive performance when combining regularization techniques with climatic and geospatial variables, enabling more precise forecasts of yield variability. The results confirm the existence of causal relationships in the Granger sense, emphasizing the value of this methodology for strategic agricultural management. This contributes to more efficient and sustainable production in paddy rice cultivation.
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