Mega-environment Analysis and Test Location Evaluation Based on Unbalanced Multiyear Data
Yan, W. (2015). Mega-environment Analysis and Test Location Evaluation Based on Unbalanced Multiyear Data, 55(1), 113-122. http://dx.doi.org/10.2135/cropsci2014.03.0203
© 2015 Her Majesty the Queen in Right of Canada, as represented by the Minister of Agriculture and Agri-Food Canada. Mega-environment analysis and test location evaluation are two important issues for effective crop variety evaluation through multilocation variety trials. These must be done based on multiyear multilocation variety-trial data, which are usually highly unbalanced. This paper presents a new graphical approach for conducting mega-environment analysis and test location evaluation utilizing unbalanced multiyear variety trial data. It consists of three steps: (i) generating a G (genotypic main effect) plus GE (genotype × environment interaction), or GGE, biplot using a missing-value estimation procedure and treating each location–year combination (trial) as an environment; (ii) summarizing the interrelations among test locations (L) in a GGL + GGE biplot, which is the same GGE biplot imposed with the test locations. The placement of a test location in the biplot is defined by the coordinates of all environments at the location; and (iii) summarizing any subregion (S) (i.e., mega-environment) differentiation revealed in Step 2 in a GGS biplot, which is the same GGE biplot imposed with the subregions. The placement of a subregion in the biplot is defined by the coordinates of all environments in the subregion. The same GGL + GGE biplot can also be used to visualize the ability and stability of each test location to represent a target mega-environment. Yield data from the 2006–2012 Quebec oat (Avena sativa L.) registration and recommendation trials were analyzed as a demonstration.
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