000			02952nab a22003737a 4500
001			G95975
003			MX-TxCIM
005			20220609210936.0
008			220609s2011 ii |||p|op||| 00| 0 eng d
022	0		_a0019-6363
040			_aMX-TxCIM
041			_aeng
090			_aCIS-6577
100	1		_92252 _aAtlin, G.N.
245	1	0	_aManaging genotype x environment interaction in plant breeding programs : _ba selection theory approach
260			_aIndia : _bIndian Society of Agricultural Statistics, _c2011.
500			_aPeer-review: No - Open Access: Yes|http://www.isas.org.in/html/journal.html
500			_aPeer review
500			_aOpen Access
520			_aTwo forms of genotype - environment interaction (GEI) are of concern to plant breeders. One consists of fixed GEI associated with predictable environmental, geographical, or management factors that can be used to delineate a target population of environments (TPE) for cultivar development and testing. The other consists of random and unexplained rank changes among trials within the TPE which are not associated with any known factor. These two types of GEI must be managed differently by plant breeding programs; fixed GEI is managed by developing or identifying cultivars with adaptation to the specific fixed factor causing the interaction, while random GEI is a noise stratum that is managed through wide-scale testing that adequately samples environmental variation in the TPE, and through the use of best linear unbiased prediction (BLUP). There is substantial evidence that fixed GEI is of limited importance within well-designed TPE. Management of GEI in cultivar development programs, and the estimation of means from multi-environment trials with appropriate measures of precision (METs) has been hampered by the widespread use of inappropriate models that designate trials or trial locations as fixed effects in the combined analysis of cultivar testing data, resulting in unnecessary division of TPEs, identification of putative patterns of adaptation that are not repeated in subsequent testing, and over-estimation of the precision of entry means in multi-environment trials. Mixed model approaches to testing the relative importance of fixed and random GEI in METs are presented.
546			_aText in English
595			_aCSC
650		7	_2AGROVOC _91133 _aGenotype environment interaction
650		7	_2AGROVOC _926493 _aBest linear unbiased predictor
650		7	_2AGROVOC _94859 _aModels
650		7	_2AGROVOC _99128 _aGenetic Correlation
650		7	_2AGROVOC _96026 _aAdaptation
700	1		_aKleinknecht, K. _921121
700	1		_aSingh, K.P. _921122
700	1		_aPiepho, H.P. _9650
773	0		_tJournal of the Indian Society of Agricultural Statistics _gv. 65, no. 2, p. 237-247 _dIndia : Indian Society of Agricultural Statistics, 2011. _x0019-6363
856	4		_uhttp://hdl.handle.net/10883/3150 _yOpen Access through DSpace
942			_cJA _2ddc _n0
999			_c28853 _d28853