Forecasting of groundnut yield using meteorological variables

Agriculture is one of the most significant economic sectors in India, which is strongly dependent on climatic conditions. Groundnut is one of the major oilseed crops of Gujarat and Junagadh has highest production of groundnut in Gujarat. For effective, forward-looking, and current planning, especially in agriculture, which is fraught with uncertainty, reliable and timely forecasts are essential. Therefore, effective yield forecast of such important oilseed crop is necessary for future planning and policy making. In the present investigation, the time series data of groundnut yield and weather parameters of 29 years Junagadh (1991-92 to 2019-20) were used. The week wise weather indices were generated using correlation between de-trend yield and weekly weather variables. Multiple Linear Regression (MLR) and Discriminant Function Analysis were used to develop yield forecasting model using weather indices before three week and one week before harvest, respectively. These models were compared using Coefficient of multiple determination (Adj. R2), Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). The study found that model-1A, developed using MLR technique have high value of adj. R2(78.8%) and low RMSE value (671.72). Multiple linear regression (MLR) was found to be more accurate than the discriminant function analysis approach.