An application of principal component analysis for pre- harvest forecast model for rice crop based on biometrical characters

Abstract

An application of principal component analysis for the development of suitable statistical models for preharvest forecast of rice yield based on biometrical characters has been dealt with in the present paper. The data obtained from the two experiments on rice have been utilised to develop the model. The forecast yields of based onthese models have been found to be 24.25, 22.60 and 21.10 q/ha against the actual yield of 28.00, 23.56 and 21.85 q/ha, respectively, in experiment –I. For experiment –II the forecast yields were found to be 24.62, 28.06 and 29.43 q/ha against the actual yield of 28.82, 29.31 and 26.59 q/ha, respectively. These forecast yields are subject to maximum of almost 10 percent standard error. In most of the cases, the forecast yields were found to be close to the actual yield except in some cases. The values of R2, i.e. 79.80 and 72.60 for experiment –I and II, respectively, indicate the validity of the models. Statistical tool like viz. principal component analysis (PCA) has been first time applied to develop pre-harvest forecast model based on experimental data.