000			03066nab a22004097a 4500
001			G90489
003			MX-TxCIM
005			20240919020946.0
008			210804s2008 xxu|||p|op||| 00| 0 eng d
022			_a1435-0653 (Online)
022			_a0011-183X
024	8		_ahttps://doi.org/10.2135/cropsci2007.11.0632
040			_aMX-TxCIM
041			_aeng
090			_aCIS-5323
100	1		_9907 _aBurgueño, J. _gGenetic Resources Program _8INT3239
245	1	0	_aUsing factor analytic models for joining environments and genotypes without crossover genotype x environment interaction
260			_aUSA : _bCSSA : _bWiley, _c2008.
340			_aComputer File|Printed
500			_aPeer review
500			_aPeer-review: Yes - Open Access: Yes|http://science.thomsonreuters.com/cgi-bin/jrnlst/jlresults.cgi?PC=MASTER&ISSN=0011-183X
520			_aGenotype × environment interaction variability can be due to crossover interaction (COI) or to non-COI. Statistical methods for detecting and quantifying COI and for forming subsets of environments and/or genotypes with negligible COI have been based on fixed effects linear–bilinear models. Linear mixed models and the factor analytic (FA) variance–covariance structure offer a more realistic and effective approach for quantifying COI and forming subsets of environments and genotypes without COI. The main objectives of this study are (i) to present an integrated methodology for clustering environments and genotypes with negligible COI based on results obtained from fitting FA to multi-environment trial (MET) data; and (ii) to detect COI using predictable functions based on the linear mixed model with FA and Best Linear Unbiased Prediction (BLUP) of genotypes. Two CIMMYT maize (Zea mays L.) international METs are used to illustrate the method for searching for subsets of environments and genotypes with negligible COI. Results from both data sets showed that the proposed method formed subsets of environments and/or genotypes with negligible COI. The main advantage of the integrated approach is that one unique linear mixed model, the FA model, can be used for (i) modeling the association among environments; (ii) forming subsets of environments without COI; (iii) grouping genotypes into non-COI subsets; and (iv) detecting COI using the appropriate predictable function.
536			_aGenetic Resources Program
546			_aText in English
591			_aCrop Science Society of America (CSSA)
594			_aINT3239|CCJL01
650		7	_2AGROVOC _91133 _aGenotype environment interaction
650		7	_2AGROVOC _92156 _aFactor analysis
650		7	_2AGROVOC _92624 _aStatistical methods
650		7	_2AGROVOC _911710 _aModelling
700	1		_aCrossa, J. _gGenetic Resources Program _8CCJL01 _959
700	1		_99555 _aCornelius, P.L.
700	0		_921657 _aRong-Cai Yang
773	0		_tCrop Science _n635217 _gv. 48, no. 4, p. 1291-1305 _dUSA : CSSA : Wiley, 2008. _wG444244 _x1435-0653
856	4		_yAccess only for CIMMYT Staff _uhttps://hdl.handle.net/20.500.12665/307
942			_cJA _2ddc _n0
999			_c27040 _d27040