000			03042nab a22003977a 4500
001			G93399
003			MX-TxCIM
005			20240919020946.0
008			210804t2009 xxu|||p|op||| 00| 0 eng d
022			_a1435-0653 (Online)
022			_a0011-183X
024	8		_ahttps://doi.org/10.2135/cropsci2008.12.0702
040			_aMX-TxCIM
041			_aeng
090			_aCIS-5580
100	1		_aVarela, M. _920488
245	1	0	_aGeneralizing the sites regression model to three-way interaction including multi-attributes
260			_aUSA : _bCSSA : _bWiley, _c2009.
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			_aWhen a multienvironment trial (MET) is established across several locations and years, the interaction is referred to as a three-way array. Three-way interaction can be studied by means of three-way principal components analysis. In this study, the three-way principal components analysis is adapted to the sites regression model (three-way SREG). The three-way SREG with location and year combines the effects of genotype, genotype x location, genotype x year, and genotype x location x year. The objective of this study is to show how the three-way SREG can be put to practical use in agriculture and breeding. We utilized two wheat (Triticum aestivum L) data sets that have already been used for fitting a three-way additive main effects and multiplicative interaction model. One data set had genotype (25) x location (4) x sowing times (4) and eight attributes, and the other data set included genotype (20) x irrigation level x year on grain yield. The three-way SREG applied simultaneously to eight attributes facilitates the interpretation of genotypic performance for all traits in specific locations and across locations for a selected sowing time. Results of the three-way SREG for both data sets show the different response patterns of genotypes for locations and sowing dates (Data Set 1), as well as genotypic responses across irrigation levels in different years (Data Set 2). Using Data Set 1, we show that fitting a three-way data structure to a three-way SREG model is more effective for detecting important interaction patterns than using the two-way SREG
536			_aGenetic Resources Program|Global Wheat Program
546			_aText in English
591			_aCrop Science Society of America (CSSA)
594			_aINT2917|CCJL01
650		7	_2AGROVOC _95834 _aRegression analysis
650		7	_2AGROVOC _94859 _aModels
650		7	_2AGROVOC _91134 _aGenotypes
700	1		_aCrossa, J. _gGenetic Resources Program _8CCJL01 _959
700	1		_9873 _aJoshi, A.K. _gGlobal Wheat Program _8INT2917
700	1		_99555 _aCornelius, P.L.
700	0		_95665 _aYann Manes
773	0		_tCrop Science _gv. 49, no. 6, p. 2043-2057 _dUSA : CSSA : Wiley, 2009. _wG444244 _x1435-0653
856	4		_yAccess only for CIMMYT Staff _uhttps://hdl.handle.net/20.500.12665/345
942			_cJA _2ddc _n0
999			_c27813 _d27813