Development of groundnut yield forecasting models in relation to weather parameters in Andhra Pradesh, India
Keywords:Groundnut, Stepwise multiple linear regression, Ridge regression, Least absolute shrinkage and selection operator, Elastic net, Artificial neural network
Groundnut is a key oilseed crop in the world and India is one of the largest groundnuts producing country in terms of area and yield. Keeping that in view, five models were developed for five districts of Andhra Pradesh to forecast the groundnut yield viz., Stepwise Multiple Linear Regression (SMLR), Ridge regression, Least Absolute Shrinkage and Selection Operator (LASSO), Elastic Net (ELNET) and Artificial Neural Network (ANN). The historical data on the weather parameters are obtained from NASA POWER web portal and groundnut yields for these districts of the state during both Kharif and Rabi seasons obtained through Season and Crop Report, Government of Andhra Pradesh for the period, 2001 to 2020. In total 30 weather indices were generated through five weather variables. The assessment of models was done by fixing 75 % of the data for calibration and left 25 % data for validation. The findings inferred that based on the values of R2, RMSE, nRMSE and EF, Ridge regression, ELNET and ANN models showed better performance for Ananthapur, Chittoor and Kadapa districts and SMLR and LASSO models showed better performance for Kurnool and Nellore districts during both Kharif and Rabi seasons at calibration and validation stages.
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Copyright (c) 2023 K. NIRAML RAVI KUMAR, ANURAG SATPATHI, M. JAGAN MOHAN REDDY, PARUL SETIYA, AJEET SINGH NAIN
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