Analyzing genotype-by-environment interaction using curvilinear regression

In the context of multi-environment trials, where a series of experiments is conducted across different environmental conditions, the analysis of the structure of genotype-by-environment interaction is an important topic. This paper presents a generalization of the joint regression analysis for the...

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Bibliographic Details
Published in:Scientia agricola 2012-12, Vol.69 (6), p.357-363
Main Authors: Pereira, Dulce Gamito Santinhos, Rodrigues, Paulo Canas, Mejza, Iwona, Mejza, Stanislaw, Mexia, João Tiago
Format: Article
Language:English
Subjects:
AGRICULTURE, MULTIDISCIPLINARY
joint regression analysis
Scheffé multiple comparison method
test for parallelism
test of coincidence
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Summary:In the context of multi-environment trials, where a series of experiments is conducted across different environmental conditions, the analysis of the structure of genotype-by-environment interaction is an important topic. This paper presents a generalization of the joint regression analysis for the cases where the response (e.g. yield) is not linear across environments and can be written as a second (or higher) order polynomial or another non-linear function. After identifying the common form regression function for all genotypes, we propose a selection procedure based on the adaptation of two tests: (i) a test for parallelism of regression curves; and (ii) a test of coincidence for those regressions. When the hypothesis of parallelism is rejected, subgroups of genotypes where the responses are parallel (or coincident) should be identified. The use of the Scheffé multiple comparison method for regression coefficients in second-order polynomials allows to group the genotypes in two types of groups: one with upward-facing concavity (i.e. potential yield growth), and the other with downward-facing concavity (i.e. the yield approaches saturation). Theoretical results for genotype comparison and genotype selection are illustrated with an example of yield from a non-orthogonal series of experiments with winter rye (Secalecereale L.). We have deleted 10 % of that data at random to show that our meteorology is fully applicable to incomplete data sets, often observed in multi-environment trials.
ISSN:0103-9016
1678-992X
0103-9016
1678-992X
DOI:10.1590/S0103-90162012000600003