000			02963nab a22004337a 4500
001			G55383
003			MX-TxCIM
005			20240919021131.0
008			121211b |||p||p||||||| |z||| |
022			_a1435-0653 (Revista en electrónico)
040			_aMX-TxCIM
041	0		_aEn
043			_aUS
072		0	_aF30
072		0	_aH50
090			_aLook _bunder journal title
100	1		_aCrossa, J. _gGenetic Resources Program _8CCJL01 _959
245	0	0	_aA shifted multiplicative model fusion method for grouping environments without cultivar rank change
260			_c1995
340			_aPrinted
500			_aPeer-review: Yes - Open Access: Yes|http://science.thomsonreuters.com/cgi-bin/jrnlst/jlresults.cgi?PC=MASTER&ISSN=0011-183X
520			_aGenotype x environment interactions of most concern to plant breeders involve cultivar rank change across environments, i.e., cross-over interaction (COI). When COIs are present, cluster strategies can be used to group environments without significant changes in cultivar ranks. Several methods for classifying environments without COI have recently been proposed. In this study, the shifted multiplicative model (SHMM) fusion method based on a building block principle is used for grouping environments which rank cultivars similarly. The SHMM fusion selects the best action by computing for each new cluster formed a new set of distance measures with the other clusters and with the unclustered individuals. Experimental data were collected on 55 spring wheat cultivars evaluated under 5 irrigation levels in each of 2 CIMMYT trials (10 environments). Groups of environments obtained with SHMM fusion were compared with those obtained using cluster analysis based on Euclidean distances computed from unstandardized and standardized data. The SHMM fusion formed 4 final clusters of environments. The final clusters grouped environments with similar irrigation levels. Conventional cluster analysis based on Euclidean distances produced final clusters with much larger percentage of COIs than SHMM fusion. Thus, the data indicated that SHMM fusion is a useful strategy for classifying environments without cultivar rank change
536			_aConservation Agriculture Program|Genetic Resources Program
546			_aEnglish
591			_aBIO|Crop Science Society of America (CSSA)|WP|R95ANALY|1
594			_aCCJL01|INT1421|CSAY01
595			_aCSC
595			_aSC
650	1	0	_aCrossbreeding
650	1	0	_aEnvironmental factors
650	1	0	_91133 _aGenotype environment interaction _gAGROVOC
650	1	0	_aMethods _91178
650	1	7	_aPlant breeding _gAGROVOC _2 _91203
700	1		_aCornelius, P.L., _ecoaut.
700	1		_aSayre, K.D., _ecoaut.
700	1		_aOrtiz-Monasterio, I. _gFormerly Sustainable Intensification Program _gFormerly Integrated Development Program _gFormerly Sustainable Agrifood Systems _8INT1421 _9827
773	0		_tCrop Science _gv. 35, no. 1, p. 54-62
942			_cJA
999			_c16539 _d16539