Sparsity, Regularization and Causality in Agricultural Yield: The Case of Paddy Rice in Peru
农业产量中的稀疏性、正则化与因果性:秘鲁水稻案例
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-…