Principal Component Regression For Crop Yield Estimation by T.m.v. SuryanarayanaPrincipal Component Regression For Crop Yield Estimation by T.m.v. Suryanarayana

Principal Component Regression For Crop Yield Estimation

byT.m.v. Suryanarayana, P.b. Mistry

Paperback | March 30, 2016

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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. Itsubsequently compares the multiple linear regression (MLR) and PCR results, anddiscusses the significance of PCR for crop yield estimation. In this context,the book also covers Principal Component Analysis (PCA), a statistical procedureused to reduce a number of correlated variables into a smaller number ofuncorrelated variables called principal components (PC). This book will behelpful to the students and researchers, starting their works on climate andagriculture, mainly focussing on estimation models. The flow of chapters takesthe readers in a smooth path, in understanding climate and weather and impactof climate change, and gradually proceeds towards downscaling techniques andthen finally towards development of principal component regression models andapplying the same for the crop yield estimation.

Dr. T. M. V. Suryanarayana is serving as Associate Professor and recognized Ph.D. Guide in Water Resources Engineering and Management Institute, The M. S. University of Baroda. He is Executive Committee Member of Indian Water Resources Society, Secretary and Treasurer of Gujarat Chapter of Association of Hydrologists of India and J...
Title:Principal Component Regression For Crop Yield EstimationFormat:PaperbackDimensions:67 pages, 23.5 × 15.5 × 0.17 inPublished:March 30, 2016Publisher:Springer-Verlag/Sci-Tech/TradeLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:9811006628

ISBN - 13:9789811006623


Table of Contents

1.   Introduction

1.1 Climate

1.2 Climate Change

1.3 Impactof Climate Change in Global Context

1.4 Impactof Climate Change on Agriculture

1.5 ClimatologicalParameters Affecting Crop Yeild

1.5 Downscaling

1.6 Types of Downscaling

1.7 Multiple Linear Regression

1.8 PrincipalComponent Analysis

1.9 Objectives

2.PrincipalComponent Analysis

2.1 Statistical Downscaling Methods

2.2 Principal Component Analysis           

      2.2.1    Advantages of PCA

      2.2.2    Disadvantages of PCA

      2.2.3    Applications of Principal ComponentsAnalysis

2.3 PrincipalComponent Regression

      2.3.1    Calculating Principal Components

      2.3.2    Rules for Retaining Principal Components

      2.3.3    Development of Principal ComponentRegression

3.   Reviewof Literature

3.1 Reviewof Works on Climate Change

3.2 Review of Works on Downscaling Techniques

3.3 Reviewof Works on Multiple Linear Regression

3.4 Reviewof Works on Principal Component Analysis/Regression

4.   StudyArea and Data Analysis

4.1 StudyArea

4.2 DataAnalysis

5.   Methodology

5.1 MultipleLinear Regression Model

5.2 Principal Component Regression Model

5.3 PerformanceIndices

5.4 Analysis of MLR and PCR Models

6.   Resultsand Analysis

6.1 MLRModel

      6.1.1MLR During Training

      6.1.2MLR During Validation

6.2 PCR Model

      6.2.1PCR During Training

      6.2.2PCR During Validation

6.3 Comparisonof MLR and PCR Models Using Performance Indices

6.4 Analysis of PCR Model

7.   Conclusions