Maize yield estimation at different growth stage using weather variables by LASSO, elastic net and stepwise multiple linear regression techniques

Maize yield estimation at vegetative, flowering and grain filling stage was performed using four statistical modeling approaches; least absolute shrinkage and selection operator (LASSO), elastic net, stepwise multiple linear regression (SMLR) and principal component analysis in combination with SMLR (PCA-SMLR) techniques. The models were developed using maize yield data and daily weather parameters, including maximum and minimum temperatures, precipitation, morning and evening relative humidity, bright sunshine hours, and evaporation, recorded during the crop growing period for the period of 1984-2021 at ICAR-Indian Agricultural Research Institute, IARI, New Delhi. Yield estimation was carried out for the kharif season of 2020 and 2021 at vegetative, flowering and grain filling stage. Among the evaluated models, the Root Mean Square Error (RMSE) and normalized RMSE (nRMSE) were lowest for the Elastic Net model, followed by LASSO and SMLR, indicating the superior performance of Elastic Net. The percentage deviation between estimated and observed yield ranged from 8.0 to 29.1%, 6.8-22.0%, and 4.8-18.2% at the vegetative, flowering, and grain filling stages, respectively. Overall, temperature and bright sunshine hours were found to be the most influential predictors of maize yield, and based on model accuracy, the Elastic Net model was identified as the most reliable, followed by LASSO and SMLR for maize yield estimation at different growth stages.