Resumo -O objetivo deste trabalho foi avaliar a influência da interação de genótipos com ambientes (GxA) na produtividade de grãos de um conjunto de linhagens de soja (Glycine max L.). Foram utilizados dados de 11 experimentos (ambientes) realizados no Estado de Goiás. Em cada experimento foram avaliados 18 genótipos, sendo quatro cultivares comerciais como testemunhas. O método de análise da interação foi o procedimento AMMI (modelo de efeitos principais aditivos e interação multiplicativa). O padrão significativo das interações GxA foi captado apenas pelo primeiro eixo principal AMMI, o qual explicou 36% da soma de quadrados GxA original, sugerindo contaminação da matriz de interações clássica por ruídos que prejudicam a qualidade das predições de respostas fenotípicas obtidas pelos métodos tradicionais. Quanto à estabilidade de comportamento, a maioria das linhagens experimentais destacou-se (com menores interações com ambientes) em relação às cultivares testemunhas. Estas, no entanto, foram relativamente mais produtivas, sobretudo a cultivar Conquista. Entre as novas linhagens, os genótipos L-16, L-13 e L-14 mostraram ser os mais promissores para fins de recomendação como cultivares.Termos para indexação: Glycine max, progênie, interação genótipo-ambiente, adaptação, biplot. Application of AMMI analysis in the assessment of yield stability in soybeanAbstract -The objective of this work was to evaluate the influence of the genotype environment (GE) interaction on the grain yield of a soybean (Glycine max L.) lines group. Yield data from 11 trials (environments) conducted in the State of Goiás, Brazil were used. In each trial 18 genotypes were tested, from which four were commercial cultivars as checks. The statistical method was the AMMI analysis (additive main effect and multiplicative interaction analysis). A significant GE interaction pattern was captured only for the first principal AMMI axis, which explained 36% of the original square sum of the GE interaction, suggesting contamination of the classic GE interaction matrix by noise arising from unpredictable factors, assuring that AMMI analysis provides a better prediction of phenotypic responses than traditional methodologies. About yield stability, most experimental lines outstands (with low GE interaction) over checks. However, the checks, mainly the cultivar Conquista, showed higher yield averages. Among the experimental lines, the genotypes L-16, L-13 and L-14 appeared to be the most promising for cultivars recommendation.Index terms: Glycine max, progeny, genotype environment interaction, adaptation, biplot.(1) Aceito para publicação em 17 de dezembro de 2002. IntroduçãoAs interações de genótipos com ambientes (GxA) trazem aos melhoristas dificuldades na identificação de genótipos superiores, seja por ocasião da seleção, seja no momento da recomendação de cultivares. A presença dessas interações indica que o comportamento relativo dos genótipos nos testes depende, fundamentalmente, das condições ambientais a que estão submetidos. Desta forma, a res...
Improving stability of crop yield in a target production environment is an important breeding objective. It is well known that selection for better stability generally results in lower mean yields and, conversely, that selection for higher mean yields may lead to poorer stability. This paper explores the equivalence between the singular value decomposition used in AMMI analysis and the spectral decomposition used in principal components analysis. This equivalence enables scores of a "supplementary genotype" made up of the highest yield value within each environment to be obtained, and these may serve as the ideal check treatment for selection purposes. These scores are used to (1) display this check in a biplot graph, thereby providing a qualitative comparison with the real genotypes related to their interaction with environments; (2) obtain estimates of the squared distances from the projection of each real genotype to the projection of the "supplementary treatment", thereby allowing conclusions to be made on the yield stability of each real genotype. This procedure was effective in identifying the most stable soybean cultivars in an example shown for illustration.
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