Principal Component Regression for Crop Yield Estimation

This book highlights the estimation of crop yield in CentralGujarat, especially with regard to the development of Multiple RegressionModels and Principal Component Regression (PCR) models using climatologicalparameters as independent variables and crop yield as a dependent variable.

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This book highlights the estimation of crop yield in Central
Gujarat, especially with regard to the development of Multiple Regression
Models and Principal Component Regression (PCR) models using climatological
parameters as independent variables and crop yield as a dependent variable. It
subsequently compares the multiple linear regression (MLR) and PCR results, and
discusses the significance of PCR for crop yield estimation. In this context,
the book also covers Principal Component Analysis (PCA), a statistical procedure
used to reduce a number of correlated variables into a smaller number of
uncorrelated variables called principal components (PC). This book will be
helpful to the students and researchers, starting their works on climate and
agriculture, mainly focussing on estimation models. The flow of chapters takes
the readers in a smooth path, in understanding climate and weather and impact
of climate change, and gradually proceeds towards downscaling techniques and
then finallytowards development of principal component regression models and
applying the same for the crop yield estimation.