Development and Validation of Stoichiometric Model in Groundnut (Arachis hypogaea L.)

Abstract

The experiment was conducted during 2023 in kharif season (June–September) at GKVK, Bangalore, Karnataka, India aimed to develop a stoichiometric model for groundnut. Regression equations were formulated using historical data on key weather parameters, including Growing Degree Days (GDD), Solar Radiation (SR), Actual Evapotranspiration (AET) and pod yield from the years 2001 and 2003–2014. The observed total dry matter at the end of first four stages i.e., 30 DAS, 50% flowering, pod initiation, pod filling and predicted dry matter at harvest which was used as one of the independent variables to predict the pod yield. The model showed good agreement between observed and predicted values with higher coefficient of determination (R²=0.77) at pod filling stage and it was lower at 30 days after sowing stage (R²=0.08). The developed model was validated for two dates of sowing over four years (2015–2018). To assess its reliability, the model was validated over four years (2015–2018) for two different sowing dates. The validation results indicated a strong predictive accuracy for the first sowing date across all years, except in 2018, where the second sowing date exhibited better alignment with observed values. The developed model was as an effective tool for predicting total dry matter production at various growth stages and estimating pod yield well before harvest, with an accuracy of up to 77%.