Fuzzy logic in the simultaneous selection of quantitative and qualitative descriptors for kale
Resumo
Simultaneous selection in genetic improvement presents difficulties in selecting qualitative traits as well as the desired commercial ranges for quantitative traits. Thus, fuzzy logic has become an alternative, enabling the computational modelling of the researcher’s experience. This study aimed to assess the efficiency of fuzzy logic in simultaneous selection considering both qualitative and quantitative descriptors. The developed methodology was applied to data from two experiments with kale half-sibs. The first experiment was carried out in Viçosa in randomised blocks, with 24 families of kale half-sibs, 4 replications, and 5 plants per plot. The second experiment was carried out in Montes Claros in randomised blocks, with 36 kale genotypes, 33 families of half-sibs, and 3 commercial cultivars, with 4 replications and 6 plants per plot. Quantitative and qualitative traits were evaluated, and individual genetic values were obtained using REML/BLUP. Genetic gains were evaluated based on the Mulamba–Mock index and the developed fuzzy systems. The selection gains were similar for quantitative traits, but fuzzy logic also selected qualitative traits, and thus stands out as a potential tool for kale genetic improvement. The selection of individuals by the fuzzy methodology enables estimated selection gains in a favourable direction for qualitative and quantitative traits, enabling the automation of more accurate and standardised decision-making.
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Referências
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