2023, Vol. 8, Special Issue 4

Accurate forecasting of rice crop yield is crucial for efficient agricultural planning, resource allocation, and food security. This study introduces a different nonlinear approach for predicting rice crop yields (kg/ha) in the Navsari district of Gujarat. Traditional linear methods often struggle to capture the intricate relationships between crop yield and multifaceted environmental factors. The methodology involves the integration of historical rice yield data for the years 1985-2014. To evaluate the proposed approach, different nonlinear growth models have been applied on rice production data. The validation of the best-fitted models was carried out using data from 2012 to 2014. The results demonstrate the superiority of the nonlinear model in comparison to conventional linear models. The study indicated that linear and nonlinear models play significant role in rice yield forecast. The good fit of the model indicated a change of trend in rice yield which helps formulate price and market availability.