000			03670nab a22005057a 4500
001			G76952
003			MX-TxCIM
005			20240919021145.0
008			210906s2003 xxu|||p|op||| 00| 0 eng d
022			_a1435-0653 (Online)
024	8		_ahttps://doi.org/10.2135/cropsci2003.1698
040			_aMX-TxCIM
041			_aeng
072		0	_aE16
072		0	_aF01
090			_aCIS-3750
100	1		_9341 _aTrethowan, R.M.
245	1	0	_aAssociations among Twenty Years of International Bread Wheat Yield Evaluation Environments
260			_aUSA : _bCSSA : _bWiley, _c2003.
340			_aComputer File
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			_aUnderstanding the way different environments differentiate cultivars for yield allows the plant breeder to optimize choice of parents, germplasm screening, yield testing, and resource use within the target region. To determine the associations among yield testing environments, wheat (Triticum aestivum L.) yield data from 963 replicated trials sown across a 20-yr period were analyzed by means of pattern analysis and the shifted multiplicative model (SHMM) to group sites within and across years. Pattern analysis identified four primary clusters of sites and four representative locations within these clusters were identified by squared Euclidean distances. Group-1 represented primarily Mediterranean and West Asian locations and South American sites. Group-2 was comprised of generally warmer sites in southern and eastern Asia. Group-3 comprised higher rainfall locations in South America and eastern Africa and Group-4 represented cooler sites in South America and West Asia. The respective key locations for each of the four groups were Sakha, Egypt; Quezaltenango, Guatemala; Londrina, Brazil; and Pirsabak, Pakistan. The four key sites were then used to examine site clusters within each year by SHMM. The sites at Pirsabak and Sakha associated best across all global wheat-growing regions where a combined total of 700 of 1117 (62%) possible clusters with other global wheat locations were realized. This compared with 52% for Quezaltenango and 38% for Londrina. Factors with a primary influence on site clustering were cropping season moisture availability and temperature. Genotype performance at Pirsabak and Sakha can be used to enhance genetic progress in a range of related wheat growing environments thereby improving the effectiveness of global wheat breeding.
536			_aGenetic Resources Program|Global Wheat Program
546			_aText in English
591			_a0309|Crop Science Society of America (CSSA)|AL-Wheat Program
594			_aINT2585|CCJL01|INT1422
650		7	_91310 _aWheat _2AGROVOC
650		7	_91313 _aYields _2AGROVOC
650		7	_94558 _aEnvironmental factors _2AGROVOC
650		7	_94371 _aData analysis _2AGROVOC
650		7	_93706 _aMathematical models _2AGROVOC
650		7	_92232 _aGenetic improvement _2AGROVOC
650		7	_aPlant breeding _gAGROVOC _2 _91203
700	1		_997 _aGinkel, M. Van
700	1		_9844 _aAmmar, K. _gGlobal Wheat Program _8INT2585
700	1		_aCrossa, J. _gGenetic Resources Program _8CCJL01 _959
700	1		_aPayne, T.S. _gFormerly Genetic Resources Program _8INT1422 _9828
700			_97052 _aCukadar, B.
700	1		_9661 _aRajaram, S.
700	1		_922785 _aHernandez, E.
773	0		_tCrop Science _n632386 _gv. 43, no. 5, p. 1698-1711 _dUSA : CSSA : Wiley, 2003. _wG444244 _x1435-0653
856	4		_yAccess only for CIMMYT Staff _uhttps://hdl.handle.net/20.500.12665/1302
942			_cJA _2ddc _n0
999			_c23054 _d23054