Application of principal component analysis in developing statistical models to forecast crop yield using weather variables

Abstract

Application of principal component analysis in developing statistical models for forecasting crop yield has been demonstrated. The time series data on wheat yield and weekly weather variables, viz., Minimum and maximum temperature, Relative Humidity, Wind- Velocity and Sun-Shine hours pertaining to the period 1990 to 2010 in Faizabad district of Uttar Pradesh have been used in this study. Weather indices have been constructed using weekly data on weather variables (Agrawal et al., 1983). Four models have been developed using principal component analysis as regressor variables including time trend and wheat yield as regressand. The model 1 and 3 have been found to be most appropriate on the basis of R2adj, percent deviation of forecast, RMSE (%) and PSE for the forecast of wheat yield two months before the harvest of the crop.