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The use of principal component analysis in the development of statistical models for crop yield forecasting has been demonstrated. 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 1. This work involves developing forecasting models using Principal Component Analysis (PCA) and Multiple Linear Regression (MLR). Applied to extract principal components (PCs) from the correlation matrix of predictors. Five models (Model 1 to Model 5) are developed using MLR, with varying numbers of PCs as regressors which also include time trend and maize yield as dependent variable. The model performance was measured using Adjusted R-squared (adj R2) and Root Mean Square Error (RMSE) as goodness of fit criteria. On the basis of adj R2 and RMSE, model 1 which includes all the calculated weather indices, was found to be best suited model with high adj R2 (74.18 %) and least RMSE (276.36). Hence, this model can be used to forecast maize yield for the studied region.
Yadav et al. (Wed,) studied this question.