Sparsity, Regularization and Causality in Agricultural Yield: The Case of Paddy Rice in Peru

TL;DR

This paper presents a novel method combining sparse regression and causal analysis using remote sensing and census data to improve paddy rice yield predictions in Peru, revealing causal relationships and enhancing forecast accuracy.

Contribution

It introduces an integrated approach using Elastic-Net regularization and dynamic transformations to identify causal factors and improve yield prediction models for Peruvian paddy rice.

Findings

Enhanced yield prediction accuracy with combined regularization and remote sensing data

Identification of causal relationships between climatic variables and rice yield

Demonstration of non-linear effects through velocity and acceleration transformations

Abstract

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.