Principal Component Regression for Crop Yield Estimation

ISBN-13: 9789811006623 / Angielski / Miękka / 2016 / 67 str.

Suryanarayana T. M. V.; P. B. Mistry

Opis

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.

Spis treści

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

O autorze

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.