Principal Component Regression for Crop Yield Estimation (eBook)

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 Joint Secretary of Indian Society of Geomatics_Vadodara Chapter. He has 74 research papers published in various International/National Journals/ Seminars/ Conferences/ Symposiums. Mr. P. B. Mistry has obtained B.E. (Civil-Irrigation Water Management) and M.E. (Civil) in Water Resources Engineering from The M.S. University of Baroda and is presently working as Assistant Professor in Parul University, Vadodara. He is a life member of Indian Society of Geomatics and Indian Society for Hydraulics.

1.   Introduction

1.1 Climate

1.2 Climate Change

1.3 Impact
of Climate Change in Global Context

1.4 Impact
of Climate Change on Agriculture

1.5 Climatological
Parameters Affecting Crop Yeild

1.5 Downscaling

1.6 Types of Downscaling

1.7 Multiple Linear Regression

1.8 Principal
Component Analysis

1.9 Objectives

2.
Principal
Component 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 Components
Analysis

2.3 Principal
Component Regression

      2.3.1    Calculating Principal Components

      2.3.2    Rules for Retaining Principal Components

      2.3.3    Development of Principal Component
Regression

3.   Review
of Literature

3.1 Review
of Works on Climate Change

3.2 Review of Works on Downscaling Techniques

3.3 Review
of Works on Multiple Linear Regression

3.4 Review
of Works on Principal Component Analysis/Regression

4.   Study
Area and Data Analysis

4.1 Study
Area

4.2 Data
Analysis

5.   Methodology

5.1 Multiple
Linear Regression Model

5.2 Principal Component Regression Model

5.3 Performance
Indices

5.4 Analysis of MLR and PCR Models

6.   Results
and Analysis

6.1 MLR
Model

      6.1.1
MLR During Training

      6.1.2
MLR During Validation

6.2 PCR Model

      6.2.1
PCR During Training

      6.2.2
PCR During Validation

6.3 Comparison
of MLR and PCR Models Using Performance Indices

6.4 Analysis of PCR Model

7.   Conclusions