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
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T.M.V. Suryanarayana P.B. Mistry
Principal Component Regression for Crop Yield Estimation 123
SpringerBriefs in Applied Sciences and Technology
More information about this series at http://www.springer.com/series/8884
T.M.V. Suryanarayana P.B. Mistry •
Principal Component Regression for Crop Yield Estimation
123
T.M.V. Suryanarayana Water Resources Engineering and Management Institute The Maharaja Sayajirao University of Baroda Vadodara, Gujarat India
P.B. Mistry Vadodara, Gujarat India
ISSN 2191-530X ISSN 2191-5318 (electronic) SpringerBriefs in Applied Sciences and Technology ISBN 978-981-10-0662-3 ISBN 978-981-10-0663-0 (eBook) DOI 10.1007/978-981-10-0663-0 Library of Congress Control Number: 2016934955 © The Author(s) 2016 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made. Printed on acid-free paper This Springer imprint is published by Springer Nature The registered company is Springer Science+Business Media Singapore Pte Ltd.
Preface
Since the start of the present century, climate change has been the topic of discussion in varied fields. In the same line, the topic gained its importance in the areas of water resources and agriculture. The climate with its various important variables, i.e., climatological variables, has a direct or indirect impact on agricultural production. There is a need to study the effect of climatological variables and their dominance in crop yield estimation. Downscaling techniques in general and statistical downscaling method in particular and principal component analysis in detail are discussed. This book will be helpful to the students and researchers, who are starting their works on climate and agriculture with a special focus on estimation models. The flow of chapters takes the readers in a smooth path, starts 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 applies the same for th
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