Peer review

Peer-review: Yes - Open Access: Yes|http://science.thomsonreuters.com/cgi-bin/jrnlst/jlresults.cgi?PC=MASTER&ISSN=0014-2336

When 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.

Global Maize Program|Socioeconomics Program|Research and Partnership Program

Text in English

Springer|0009

INT1888|INT2636|INT2550