A principal component regression analysis in agriculture

This paper consists of a principal component regression (PCR) model. This represents the yield of rice crop in Manipur's agro-climatic conditions. Primary data is used for the analysis. Findings show that the model fits the data well and diagnostic checks confirmed that data do not seem to contradict the general underlying assumptions about the model. Multiple correlation (R=0.881) suggest that out of the total variation 88.1% of variation in the yield is explained by the independent variables (principal components) used in the fitted model.