Principal Component Regression for Crop Yield Estimation
Nonfiction, Science & Nature, Mathematics, Applied, Science, Earth Sciences, Technology
| Author: | T.M.V. Suryanarayana, P.B. Mistry | ISBN: | 9789811006630 |
| Publisher: | Springer Singapore | Publication: | March 21, 2016 |
| Imprint: | Springer | Language: | English |
| Author: | T.M.V. Suryanarayana, P.B. Mistry |
| ISBN: | 9789811006630 |
| Publisher: | Springer Singapore |
| Publication: | March 21, 2016 |
| Imprint: | Springer |
| Language: | English |
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.
