000			02027nam a22003017a 4500
999			_c59860 _d59852
001			59860
003			MX-TxCIM
005			20240919020950.0
008			181204s2018 sz |||||o||||z||||||eng d
024	8		_ahttps://doi.org/10.1007/978-3-319-91223-3_10
040			_aMX-TxCIM
041			_aeng
100	1		_91932 _aCeron Rojas, J.J.
245	1	0	_aChapter 10. stochastic simulation of four linear phenotypic selection indices
260			_aSwitzerland : _bSpringer, _c2018.
500			_aOpen Access
520			_aStochastic simulation can contribute to a better understanding of the problem, and has already been successfully applied to evaluate other breeding scenarios. Despite all the theories developed in this book concerning different types of indices, including phenotypic data and/or data on molecular markers, no examples have been presented showing the long-term behavior of different indices. The objective of this chapter is to present some results and insights into the in silico (computer simulation) performance comparison of over 50 selection cycles of a recurrent and generic population breeding program with different selection indices, restricted and unrestricted. The selection indices included in this stochastic simulation were the linear phenotypic selection index (LPSI), the eigen selection index method (ESIM), the restrictive LPSI, and the restrictive ESIM.
546			_aText in English
591			_aCeron Rojas, J.J. : Not in IRS staff list but CIMMYT Affiliation
650		7	_2AGROVOC _96025 _aLinear models
650		7	_2AGROVOC _92445 _aSelection criteria
650		7	_2AGROVOC _91130 _aGenetics
650		7	_2AGROVOC _98102 _aPhenotypic variation
700	1		_aCrossa, J. _gGenetic Resources Program _8CCJL01 _959
773	0		_gp. 231-241 _tLinear selection indices in modern plant breeding _w59831 _z978-3-319-91222-6 (Print) 978-3-319-91223-3 (Online)
856	4		_yOpen Access through DSpace _uhttps://repository.cimmyt.org/handle/10883/19809
942			_2ddc _cBP _n0