2024/09/25 by Rita Rocio Guzman-Lopez, Luis Huamanchumo, Guzman-Lopez, Rita Rocio +9
Agricultural and Biological Sciences · Business, Management and Accounting · Economics, Econometrics and Finance · #Agricultural Economics and Policy #Applications (stat.AP) #FOS: Computer and information sciences #Firm Innovation and Growth #Global Trade and Competitiveness #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Methodology (stat.ME)
paper · pdf · doi:10.48550/arxiv.2409.17298
openalex publication_date 2024/09/25 · openalex created_date 2024/10/28 · openalex updated_date 2026/07/28
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.