Principal Component Regression For Crop Yield Estimation (springerbriefs In Applied Sciences And Technology)
by T.M.V. Suryanarayana /
2016 / English / PDF
2.2 MB Download
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 finally towards development of
principal component regression models and applying the same for the
crop yield estimation.
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 finally towards development of
principal component regression models and applying the same for the
crop yield estimation.
