Análise AMMI da produtividade de grãos em linhagens de soja selecionadas para resistência à ferrugem asiática

Abstract

Resumo -O objetivo deste trabalho foi quantificar os efeitos da interação genótipo x ambiente (GxE) sobre a produtividade de grãos em progênies de soja pré-selecionadas para resistência à ferrugem asiática (Phakopsora pachyrhizi). Doze ensaios de avaliação de progênies (linhagens F 6 e F 7 ) foram conduzidos em diferentes ambientes (combinação de locais, anos e tratamentos fungicidas para controle de doenças de final de ciclo, incluindo ou não a ferrugem). A análise "additive main effects and multiplicative interaction" (AMMI) capturou, como padrão da interação GxE, 57% da variação associada aos resíduos de não aditividade, dos quais 44% foram retidos no primeiro componente principal de interação e o restante, no segundo. O primeiro componente associou-se a diferenças entre os anos de avaliação, o que denota imprevisibilidade na predição. O segundo componente, no entanto, associou-se ao manejo diferenciado do cultivo, no que se refere ao controle ou não das doenças. Entre os genótipos de ampla adaptabilidade produtiva, as linhagens USP 02-16.045 e USP 10-10 apresentaram desempenho destacado.Termos para indexação: Glycine max, Phakopsora pachyrhizi, biplot, interação genótipo x ambiente. AMMI analysis of grain yield in soybean lines selected for resistance to Asian rustAbstract -The objective of this work was to quantify the effects of genotype x environment (GxE) interaction on grain yield in soybean progenies pre-selected for resistance to Asian soybean rust (Phakopsora pachyrhizi). Twelve trials evaluating progenies (lines F 6 and F 7 ) were carried out in different environments (combination of locations, years, and fungicide treatments to control late season diseases, including or not rust). Additive main effects and multiplicative interaction (AMMI) analysis captured, as GxE interaction pattern, 57% of the variance associated with the residues of non-additivity, of which 44% were retained in the first principal component of interaction, and the remainder in the second. The first component was associated with the differences between the years of evaluation, which denotes unpredictability. The second component, however, was associated with different crop managements, related to the control or not of the diseases. Among genotypes with wide yield adaptability, the USP 02-16.045 and USP 10-10 lineages stood out.

Determination of the adaptability and stability of soybean cultivars in different locations and at different sowing times in Paraná state using the AMMI and Eberhart and Russel methods

“…The magnitude of variation captured by these two components can be considered as the "standard" level associated with the G×E interaction, to the set of data-model assessed. Thus, the other major components were excluded from the analysis because they were mainly composed of "noise" (SILVA; YOKOMIZO et al, 2013.). The sum of the first two components is consistent with the findings of other studies on soybean with the AMMI biplot, where the first two components explained 61 to 88% of the SQ G×E and were significant (CAMPBELL; JONES, 2005;KAYA et al, 2006;TARAKANOVAS;SPRAINAITIS, 2005).…”

“…This can be observed by the greater dispersion of the vectors associated with the environments in both biplots ( Figure 1a and 1b). This behavior has been observed in other studies, and it is important to examine the Silveira, D. A. et al positive effects of the interaction more efficiently (MARTINS et al, 2012;MEOTTI et al, 2012;YOKOMIZO et al, 2013). Regarding the influence of the environment to the interaction noted in the biplot analysis, it contrasts in the dispersion show that the three sowing dates in Assai (A1, A2, and A3) contributed to the G×E interaction; similarly, the three sowing seasons in São Pedro do Ivaí (A4, A5, and A6), Marilândia do Sul (A10, A11, and A12) were essential for the G×E interaction to reveal significance.…”

“…(a) AMMI analysis biplot (productivity vs. IPCA1) (a) and (b) AMMI2 biplot (IPCA1 vs. IPCA2) of grain productivity of 10 soybean cultivars (G1-G10) SYN 1049 (G1), SYN 1152 (G2), SYN 1059 (G3), SYN 3358 (G4), SYN 1163 (G5), SYN 1157 (G6 The AMMI2 biplot (Figure 1b) confirmed the genotypic behavior observed in the previous analysis; therefore, ICPA1 captured much of the standard G×E interaction of the character studied, but the second IPCA2 component was also significant. Thus, the information captured by the latter can correct possible distortions in the interpretation produced under a single dimension (SILVA; YOKOMIZO et al, 2013). Moreover, stable behavior is thus confirmed along with the productive performance of the genotypes SYN 1163, SYN 1059, and V-MAX.…”

“…Medium) during the analysis of only one interaction axis, were found to contribute to the G×E interaction that was captured on the second main axis (IPCA2), which contradicts their productive stabilities, not being indicated to evaluate the phenotypic predictability of productivity only by ICPA1. According to Yokomizo et al (2013), factors related to the G×E interaction ICPA1 and ICPA2 axes are independent; thus, there should be no loss of generality in cases involving more selected axes, where these factors cannot be disregarded in interpretations where significant effects are…”

“…Furthermore, the AMMI and GGE analysis enables the graphical representation of genotypes and environments in a multivariate scattergram (biplot). These diagrams provide information on the stability and phenotypic adaptability, and permit the agronomic zoning and choice of specific environments for the evaluation and selection of cultivars, as they have been used in studies of this nature in soybean (MEOTTI et al, 2012;STÜLP et al, 2009;YOKOMIZO et al, 2013) and in other cultures (MIRANDA et al, 2008;SCHMILDT et al, 2011;SUBEDI et al, 2007). Therefore, this study aimed to investigate the adaptability and phenotypic stability of 10 soybean genotypes in 12 environments in Paraná using the AMMI and Eberhart and Russell models.…”