RESUMO Os índices de seleção agregam informações de múltiplos caracteres e, com isto, conseguem realizar a seleção simultaneamente de um conjunto de variáveis. Objetivou-se verificar o potencial genético de caracteres agronômicos e selecionar progênies F3:4 de soja com base em diferentes estratégias de seleção. Foram semeadas 123 progênies e os genitores, em blocos casualizados com duas repetições. Os ganhos de seleção direta pelos índices, soma de “ranks” e do genótipo-ideótipo, foram inferiores para todos os caracteres quando, comparados aos ganhos da seleção direta e indireta. O índice de soma de ranks se destacou por alcançar o maior ganho total com 37,11%. O índice do genótipo-ideótipo obteve menor ganho (-0,48%) para o caráter número de dias para o florescimento comparado ao índice de soma de “ranks” (-0,54%) e, alcançou ganho negativo para o atributo altura de inserção da primeira vagem com -1,82%. O potencial genético da população F3:4 é elevado e permite aplicar distintas estratégias de seleção no alcance de genótipos superiores. As progênies UFU 72, UFU 116, UFU 86, UFU 45, UFU 117, UFU 56, UFU 5, UFU 106, UFU 6, UFU 4, UFU 73, UFU 101, UFU 96, UFU 90, UFU 123, UFU 116, UFU 88, UFU 65, UFU 70, UFU 3, UFU 69 e UFU 37 foram selecionadas por ambos índices de seleção. As progênies UFU 72, UFU 90, UFU 88 e UFU 69 são agronomicamente superiores tanto na seleção direta e indireta, quanto nos índices de seleção de soma de “ranks” de Mulamba and Mock (1978) e do genótipo-ideótipo.

ABSTRACT The selection indexes aggregate information to multiple characters and, with this, they are able to carry out the selection of a set of variables simultaneously. The objective was to verify the genetic potential of agronomic traits and to select soybean F3:4 progenies based on different selection strategies. 123 progenies and the parents were sown in randomized blocks with two replications. The gains of direct selection by the indexes, the sum of “ranks” and the genotype-ideotype were lower for all characters when compared to the gains of direct and indirect selection. The rank sum index stood out for achieving the highest total gain with 37.11%. The index of the genotype-ideotype obtained a lower gain (-0.48%) for the character number of days for flowering compared to the sum index of “ranks” (-0.54%) and reached a negative gain for the attribute insertion height of the first pod with -1.82%. The genetic potential of the F3:4 population is high and allows different selection strategies to be applied to reach superior genotypes. The progenies UFU 72, UFU 116, UFU 86, UFU 45, UFU 117, UFU 56, UFU 5, UFU 106, UFU 6, UFU 4, UFU 73, UFU 101, UFU 96, UFU 90, UFU 123, UFU 116, UFU 88, UFU 65, UFU 70, UFU 3, UFU 69 and UFU 37 were selected by both selection indexes. The UFU 72, UFU 90, UFU 88 and UFU 69 progenies are agronomically superior both in direct and indirect selection, as in Mulamba and Mock (1978) sum of “ranks” selections and genotype-ideotype.

Resumo: O objetivo deste trabalho foi avaliar a eficiência dos métodos de Eberhart & Russell e Lin & Binns, modificados para automação da tomada de decisão pela lógica difusa, na avaliação da adaptabilidade e da estabilidade de cultivares de feijoeiro-comum (Phaseolus vulgaris). Foram avaliadas 18 cultivares do grupo comercial carioca em 11 ambientes, no Brasil. Todos os resultados foram obtidos por programação no programa R. Os controladores desenvolvidos foram fundamentados no sistema de inferência difusa desenvolvido por Mamdani. Este sistema foi modelado para possibilitar interpretações do método de Eberhart & Russell isoladamente ou em conjunto com o método de Lin & Binns modificado. O controlador baseado em Eberhart & Russell e o baseado em Eberhart & Russell e Lin & Binns identificaram as mesmas cultivares como tendo adaptabilidade geral, mas diferiram quanto à classificação das cultivares adaptadas a ambientes desfavoráveis. As cultivares BRSMG Pioneiro, BRS Pontal, IAC-Carioca Tybatã e IPR Juriti apresentaram adaptabilidade geral, enquanto Campeão, Pérola e BRS Estilo mostraram adaptabilidade específica a ambientes favoráveis. Os métodos de lógica difusa utilizados mostram eficiência e possibilitam a classificação de todas as cultivares avaliadas.

Abstract: The objective of this work was to evaluate the efficiency of the methods of Eberhart & Russell and Lin & Binns, modified for the automation of decision-making by fuzzy logic, in assessing the adaptability and stability of common bean (Phaseolus vulgaris) cultivars. Eighteen cultivars of the “carioca” commercial group were evaluated in 11 environments, in Brazil. All results were obtained by programming in the R software. The developed controllers were based on the fuzzy inference system developed by Mamdani. This system was modeled to enable interpretations of the method of Eberhart & Russell alone or together with the modified method of Lin & Binns. The controller based on Eberhart & Russell and the one based on Eberhart & Russell and Lin & Binns identified the same cultivars as having general adaptability, but differed as to the classification of cultivars adapted to unfavorable environments. The BRSMG Pioneiro, BRS Pontal, IAC-Carioca Tybatã, and IPR Juriti cultivars presented general adaptability, whereas Campeão, Pérola, and BRS Estilo showed specific adaptability to favorable environments. The fuzzy logic methods used are efficient and allow the classification of all evaluated cultivars.

ABSTRACT: The methods of Annicchiarico (1992) and Cruz et al. (1989) are widely used in phenotypic adaptability and stability analyses in plant breeding. In spite of the importance of these methodologies, their parameters are difficult to interpret. The aim of this research was to develop fuzzy controllers to automate the decision-making process employed by adaptability and stability studies following the methods adopted by Annicchiarico (1992) and Cruz et al. (1989) and check their efficiency using experimental data from common bean cultivars. Fuzzy controllers have been developed based on the Mamdani inference system proposed by these two methods of adaptability and stability studies. For the first fuzzy controller parameters were considered favorable environments and the recommendation index for unfavorable environments obtained by Annicchiarico's method (1992). For the second controller the parameters considered were the general mean (β0), coefficient of regression of unfavorable environments (β1) and coefficient of favorable environments (β1i + β2i) and the coefficient of determination of the method of Cruz et al. (1989). To check the performance of these drivers yield data from field trials on 18 common bean cultivars grown in 11 environments were used. The controllers were developed from established routines in the R software and, using the inference system based on the methods proposed by Annicchiarico (1992) and Cruz et al. (1989), classified the 18 genotypes appropriately in accordance with the criteria for each method. Thus, the methods used are effective, and are prescribed for decision-making automation in yield adaptability and stability studies pertaining to recommendation of cultivars.