Principal Component Regression for Crop Yield Estimation (eBook)

T.M.V. Suryanarayana, P.B. Mistry (Autoren)

eBook Download: PDF

2016 | 1st ed. 2016
XVII, 67 Seiten
Springer Singapore (Verlag)
978-981-10-0663-0 (ISBN)

Infos

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.

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.

Inhalt

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

Produkt-Details

• Erscheint lt. Verlag: 21.3.2016
• Reihe/Serie: SpringerBriefs in Applied Sciences and Technology
• Reihe/Serie: SpringerBriefs in Applied Sciences and Technology
• Zusatzinfo: XVII, 67 p. 12 illus. in color.
• Verlagsort: Singapore
• Sprache: englisch
• Themenwelt: Mathematik / Informatik ► Mathematik ► Angewandte Mathematik
• Themenwelt: Mathematik / Informatik ► Mathematik ► Statistik
• Themenwelt: Naturwissenschaften ► Biologie ► Ökologie / Naturschutz
• Themenwelt: Technik
• Themenwelt: Weitere Fachgebiete ► Land- / Forstwirtschaft / Fischerei
• Schlagworte: Climate change for crops • Climate change impacts • Climatological parameters • Crop yield Estimation Models • Multi-co-linearity problem • Multiple Linear Regression Models (MLR) • Performance Indices • Principal Component Analysis (PCA) • Principal Component Regression (PCR) • Statistical downscaling methods • Water Policy • Water resource management
• ISBN-10: 981-10-0663-6 / 9811006636
• ISBN-13: 978-981-10-0663-0 / 9789811006630