Analyzing genotype-by-environment interaction using curvilinear regression



Título del documento: Analyzing genotype-by-environment interaction using curvilinear regression
Revista: Scientia agricola
Base de datos: PERIÓDICA
Número de sistema: 000356920
ISSN: 0103-9016
Autors: 1
2
3
3
2
Institucions: 1Universidade de Evora, Escola de Ciencias e Tecnologia, Evora. Portugal
2Universidade Nova de Lisboa, Faculdade de Ciencias e Tecnologia, Caparica. Portugal
3Poznan University of Life Sciences, Department of Mathematical and Statistical Methods, Wojska Polskiego, Poznan. Polonia
Any:
Període: Nov-Dic
Volum: 69
Número: 6
País: Brasil
Idioma: Inglés
Tipo de documento: Artículo
Enfoque: Analítico
Resumen en inglés 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
Disciplines Biología,
Matemáticas
Paraules clau: Ecología,
Matemáticas aplicadas,
Genotipo-ambiente,
Regresión no lineal,
Método de Scheffe,
Análisis de regresión,
Pruebas de comparación múltiple
Keyword: Biology,
Mathematics,
Ecology,
Applied mathematics,
Genotype-environment,
Nonlinear regression,
Scheffe method,
Regression analysis,
Multiple comparison tests
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