2024, Vol. 9, Issue 5, Part A

A discriminant function analysis was employed to forecast maize yield in Udham Singh Nagar district of Uttarakhand. Maize crop yield data for a period of 21 years (2001-2021) were drawn from the Dacnet website and the weather data were collected from the Meteorological Observatory, Department of Agrometeorology, College of Agriculture, G.B. Pant University of Agriculture and Technology Pantnagar, Uttarakhand. Maximum temperature, Minimum temperature, Relative Humidity A.M, Relative Humidity P.M, Total rainfall, Sunshine hours, Wind velocity and Evapotranspiration were the weather parameters considered for the study. Out of the 21-year data, 17-year data were used for training the model while remaining 4 years data were used for testing the model. Weekly data on weather variables was used to create weather indices (Agrawal et al., 1983). Crop yield has been classified into three groups: congenial, normal and adverse. Taking these three groups as three populations, discriminant function analysis has been carried out. The resulting discriminant scores were used as regressors, along with time trend, to develop statistical models. Five models were generated using various strategies for incorporating weather indices. Model 4, which included all calculated weighted weather indices, emerged as the best-suited model, with the highest adjusted R-squared value and lowest root mean square error (RMSE). Therefore, this model is recommended for forecasting maize yield in the studied region.