Principal Component Regression for 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 subsequent...

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Detaylı Bibliyografya
Asıl Yazarlar: Suryanarayana, T.M.V (Yazar), Mistry, P.B (Yazar)
Müşterek Yazar: SpringerLink (Online service)
Materyal Türü: e-Kitap
Dil:İngilizce
Baskı/Yayın Bilgisi: Singapore : Springer Nature Singapore : 2016.
Imprint: Springer,
Edisyon:1st ed. 2016.
Seri Bilgileri:SpringerBriefs in Applied Sciences and Technology,
Konular:
Engineering mathematics.
Engineering > Data processing.
Climatology.
Statistics .
Environmental sciences > Mathematics.
Agriculture.
Water.
Hydrology.
Mathematical and Computational Engineering Applications.
Climate Sciences.
Statistical Theory and Methods.
Mathematical Applications in Environmental Science.
Online Erişim:Full-text access
Diğer Bilgiler
Özet: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.
Fiziksel Özellikler:XVII, 67 p. 12 illus. in color. online resource.
ISBN:9789811006630
ISSN:2191-5318
DOI:10.1007/978-981-10-0663-0

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