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Principal Component Regression for Crop Yield Estimation 1st ed. 2016 [Mīkstie vāki]

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  • Formāts: Paperback / softback, 67 pages, height x width: 235x155 mm, weight: 1474 g, 12 Illustrations, color; XVII, 67 p. 12 illus. in color., 1 Paperback / softback
  • Sērija : SpringerBriefs in Applied Sciences and Technology
  • Izdošanas datums: 30-Mar-2016
  • Izdevniecība: Springer Verlag, Singapore
  • ISBN-10: 9811006628
  • ISBN-13: 9789811006623
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Principal Component Regression for Crop Yield Estimation 1st ed. 2016Palielināt
  • Formāts: Paperback / softback, 67 pages, height x width: 235x155 mm, weight: 1474 g, 12 Illustrations, color; XVII, 67 p. 12 illus. in color., 1 Paperback / softback
  • Sērija : SpringerBriefs in Applied Sciences and Technology
  • Izdošanas datums: 30-Mar-2016
  • Izdevniecība: Springer Verlag, Singapore
  • ISBN-10: 9811006628
  • ISBN-13: 9789811006623
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9789811006623.html
Keywords:
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.

1 Introduction
1(16)
1.1 Climate and Weather
1(1)
1.2 Climate Change
2(1)
1.3 Impact of Climate Change in Global Context
3(1)
1.4 Impact of Climate Change on Agriculture
4(1)
1.5 Climatological Parameters Affecting Crop Yeild
5(1)
1.5.1 Maximum and Minimum Temperature
5(1)
1.5.2 Relative Humidity
5(1)
1.5.3 Wind Speed
6(1)
1.5.4 Sunshine Hours
6(1)
1.6 Downscaling
6(3)
1.6.1 Uncertainty
8(1)
1.7 Downscaling Techniques and Their Application
9(6)
1.7.1 Dynamical Downscaling
9(2)
1.7.2 Statistical Downscaling
11(1)
1.7.3 Statistical--Dynamical Downscaling
12(3)
1.8 Multiple Linear Regression
15(1)
1.9 Principal Component Analysis (PCA)
15(1)
1.10 Objectives
15(2)
2 Principal Component Analysis in Transfer Function
17(10)
2.1 Transfer Function/Regression Method
17(2)
2.2 Types of Regressions
19(1)
2.2.1 The Simple Linear Regression Model
19(1)
2.2.2 The Multiple Linear Regression Model
19(1)
2.2.3 Polynomial Regression Models
20(1)
2.2.4 Nonlinear Regression
20(1)
2.3 Principal Component Analysis (PCA)
20(3)
2.3.1 Advantages and Disadvantages of PCA
21(1)
2.3.2 Applications of Principal Components
22(1)
2.4 Principal Component Regression (PCR)
23(4)
2.4.1 Calculating Principal Components
23(1)
2.4.2 Rules for Retaining Principal Components
24(1)
2.4.3 Development of Principal Component Regression (PCR)
25(2)
3 Review of Literature
27(12)
3.1 Review of Works on Climate Change
27(1)
3.2 Review of Works on Downscaling Techniques
28(5)
3.3 Review of Works on Multiple Linear Regressions
33(1)
3.4 Review of Works on Principal Component Analysis and Principal Component Regression
33(6)
4 Study Area and Data Collection
39(6)
4.1 Agroclimatic Zones by the Planning Commission
39(1)
4.2 Subagroclimatic Zones of Gujarat
39(4)
4.2.1 Southern Hills
41(1)
4.2.2 Southern Gujarat
41(1)
4.2.3 Middle Gujarat
42(1)
4.2.4 North Gujarat
42(1)
4.2.5 Northwest Arid
42(1)
4.2.6 North Saurashtra
42(1)
4.2.7 South Saurashtra
42(1)
4.3 Study Area
43(1)
4.4 Data Collection
44(1)
5 Methodology
45(6)
5.1 Multiple Linear Regression Model
45(1)
5.2 Principal Component Regression Model
46(1)
5.3 Performance Indices
47(2)
5.3.1 Root Mean Squared Error (RMSE)
47(1)
5.3.2 Correlation Coefficient (r)
47(1)
5.3.3 Coefficient of Determination (R2)
48(1)
5.3.4 Discrepancy Ratio (D.R.)
48(1)
5.4 Analysis of MLR and PCR Models
49(2)
6 Results and Analysis
51(12)
6.1 MLR Model During Training and Validation
51(4)
6.1.1 Multiple Linear Regression During Training
51(1)
6.1.2 Multiple Linear Regression During Validation
52(3)
6.2 PCR Model During Training and Validation
55(5)
6.2.1 Principal Component Regression During Training
58(1)
6.2.2 PCR During Validation
58(2)
6.3 Comparison of MLR and PCR Models Using Performance Indices
60(1)
6.4 Analysis of MLR and PCR Models Developed
61(2)
7 Conclusions
63(2)
7.1 Conclusions Based on the Study
63(2)
References 65
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.