Abstract
ABSTRACT: This study presents a novel approach that combines agricultural census data with remotely sensed time series to develop accurate predictive models for paddy rice yield across the different regions of Peru. By leveraging sparse regression and Elastic-Net regularization techniques, the study uncovers causal relationships between key remotely sensed variables such as Normalized Difference Vegetation Index (NDVI), precipitation (PREC), temperature (TEMP), and agricultural yield. To further enhance prediction accuracy, first- and second-order dynamic transformations (velocity and acceleration) of these variables were applied to capture non-linear patterns and lagged effects on yield. The findings demonstrate improved predictive performance when integrating regularization techniques with climatic and geospatial variables, allowing for more accurate forecasts of yield variability. The results confirm the presence of causal relationships in the Granger sense, underscoring the value of this methodology to strategic agricultural management. This contributes to more efficient and sustainable production in paddy rice cultivation.
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Claims 3
Claim · model-written summary
Sparse regression uncovered causal relationships between remotely sensed variables (NDVI, precipitation, temperature) and agricultural yield of paddy rice across regions of Peru.
Not reportedDirection
Verbatim
t0“paddy rice”
e0“the study uncovers causal relationships between key remotely sensed variables such as Normalized Difference Vegetation Index (NDVI), precipitation (PREC), temperature (TEMP), and agricultural yield”
e1“across the different regions of Peru”
Claim · model-written summary
Integrating regularization techniques with climatic and geospatial variables improved predictive performance for paddy rice yield.
Schema note from the model: method evaluation stated without comparator or numeric value
Verbatim
t0“paddy rice”
e0“The findings demonstrate improved predictive performance when integrating regularization techniques with climatic and geospatial variables”
Claim · model-written summary
The results confirm causal relationships in the Granger sense between the remotely sensed and climatic variables and paddy rice yield.
- Taxon
- paddy ricet0
- Outcome
- causal relationships in the Granger sensee0
- Qualifier
- Granger sensee0
- Setting
- Perue1
Not reportedDirection
Verbatim
t0“paddy rice”
e0“The results confirm the presence of causal relationships in the Granger sense”
e1“across the different regions of Peru”