000			03652nab a22004577a 4500
001			G82540
003			MX-TxCIM
005			20231018181307.0
008			211101s2005 ne |||p|op||| 00| 0 eng d
022			_a1573-5060 (Online)
022			_a0014-2336
024	8		_ahttps://doi.org/10.1007/s10681-005-0625-4
040			_aMX-TxCIM
041			_aeng
090			_aCIS-4582
100	1		_aSetimela, P.S. _gFormerly Global Maize Program _gFormerly Sustainable Intensification Program _gSustainable Agrifood Systems _8INT2636 _9846
245	1	0	_aEnvironmental classification of maize-testing sites in the SADC region and its implication for collaborative maize breeding strategies in the subcontinent
260			_aDordrecht (Netherlands) : _bSpringer, _c2005.
340			_aPrinted|Computer File
500			_aPeer review
500			_aPeer-review: Yes - Open Access: Yes|http://science.thomsonreuters.com/cgi-bin/jrnlst/jlresults.cgi?PC=MASTER&ISSN=0014-2336
520			_aWhen evaluating genotypes, it is efficient and resourceful to identify similar testing sites and group them according to similarity. Grouping sites ensures that breeders choose as many variable sites as possible to capture the effects of genotype-by-environment (GE) interactions. In order to exploit these interactions and increase testing efficiency and variety selection, it is necessary to group similar environments or mega-environments. The present mega-environments in the Southern African Development Community (SADC) countries are confounded within each country, which limits the exchange of germplasm among them. The objective of this study was to revise and group similar maize-testing sites across the SADC countries that are not confounded within each country. The study was based on 3 years (1999–2001) of regional maize yield trial data and geographical information systems (GIS) parameters from 94 sites. Sequential retrospective (Seqret) pattern analysis methodology was used to stratify testing sites and group them according to their similarity and dissimilarity based on mean grain yield. The methodology used historical data, taking into account imbalances of data caused by changes over locations and years, such as additions and omission of genotypes and locations. Cluster analysis grouped regional trial sites into seven mega-environments, mainly distinguished by GIS parameters related to rainfall, temperature, soil pH, and soil nitrogen with an overall R2 = 0.70. This analysis provides a challenge and an opportunity to develop and deploy maize germplasm in the SADC region faster and more effectively.
536			_aGlobal Maize Program|Socioeconomics Program|Research and Partnership Program
546			_aText in English
591			_aSpringer|0009
594			_aINT1888|INT2636|INT2550
650		7	_2AGROVOC _91173 _aMaize
650		7	_2AGROVOC _91133 _aGenotype environment interaction
650		7	_2AGROVOC _91136 _aGermplasm
650		7	_2AGROVOC _95260 _aGeographical information systems
650		7	_2AGROVOC _98629 _aField Experimentation
650		7	_2AGROVOC _925495 _aLand classification
700	1		_aChitalu, Z. _96587
700	1		_aJonazi, J. _96588
700	1		_aMambo, A. _96589
700	1		_aHodson, D.P. _gSocioeconomics Program _gSustainable Agrifood Systems _8INT2550 _9843
700	1		_aBanziger, M. _gResearch & Partnership Program _gExcellence in Breeding _8INT1888 _9834
773	0		_tEuphytica _n633423 _gv. 145, no. 1-2, p. 123–132 _x0014-2336 _wG444298 _dDordrecht (Netherlands) : Springer, 2005.
856	4		_yAccess only for CIMMYT Staff _uhttps://hdl.handle.net/20.500.12665/961
942			_cJA _2ddc _n0
999			_c25561 _d25561