000			03368nam a22006135i 4500
001			978-981-10-0663-0
003			DE-He213
005			20200421111652.0
007			cr nn 008mamaa
008			160321s2016 si | s |||| 0|eng d
020			_a9789811006630 _9978-981-10-0663-0
024	7		_a10.1007/978-981-10-0663-0 _2doi
050		4	_aTA329-348
050		4	_aTA640-643
072		7	_aTBJ _2bicssc
072		7	_aMAT003000 _2bisacsh
082	0	4	_a519 _223
100	1		_aSuryanarayana, T.M.V. _eauthor.
245	1	0	_aPrincipal Component Regression for Crop Yield Estimation _h[electronic resource] / _cby T.M.V Suryanarayana, P. B Mistry.
250			_a1st ed. 2016.
264		1	_aSingapore : _bSpringer Singapore : _bImprint: Springer, _c2016.
300			_aXVII, 67 p. 12 illus. in color. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aSpringerBriefs in Applied Sciences and Technology, _x2191-530X
505	0		_aIntroduction -- Principal Component Analysis In Transfer Function -- Review of Litrrature -- Study Area and Data Collection -- Methodology -- Conclusions.
520			_aThis 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.
650		0	_aEngineering.
650		0	_aEnvironmental management.
650		0	_aClimate change.
650		0	_aAgriculture.
650		0	_aStatistics.
650		0	_aApplied mathematics.
650		0	_aEngineering mathematics.
650		0	_aEnvironmental sciences.
650	1	4	_aEngineering.
650	2	4	_aAppl.Mathematics/Computational Methods of Engineering.
650	2	4	_aClimate Change/Climate Change Impacts.
650	2	4	_aStatistical Theory and Methods.
650	2	4	_aMath. Appl. in Environmental Science.
650	2	4	_aAgriculture.
650	2	4	_aWater Policy/Water Governance/Water Management.
700	1		_aMistry, P. B. _eauthor.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9789811006623
830		0	_aSpringerBriefs in Applied Sciences and Technology, _x2191-530X
856	4	0	_uhttp://dx.doi.org/10.1007/978-981-10-0663-0
912			_aZDB-2-ENG
942			_cEBK
999			_c54478 _d54478