Claim · model-written summary
Integrating regularization techniques with climatic and geospatial variables improved predictive performance for paddy rice yield.
Not reportedMaterialGeneCompoundOrgan / stageInteractorInteractor roleSecond interactorSecond interactor roleTreatmentComparatorSettingValueQualifier
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”
Title
Rita Rocio Guzman-Lopez; Luis Huamanchumo de la Cuba; Kevin Anthony Fernandez Molina; et al. Scientia Agricola 2026. doi:10.1590/1678-992x-2024-0267
Other claims from this paper (2)
- Association Sparse regression uncovered causal relationships between remotely sensed variables (NDVI, precipitation, temperature) and agricultural yield of paddy rice across regions of Peru.
- Association The results confirm causal relationships in the Granger sense between the remotely sensed and climatic variables and paddy rice yield.