ABSTRACT
The objective of this work was to perform simultaneous selection in sweet potato genotypes and to verify the efficiency of fuzzy systems when compared to the Mulamba & Mock (MM) method. The experiment was carried out in randomized blocks, with 24 sweet potato genotypes, four replications and ten plants per plot. The breeding values were obtained by the mixed model methodology (REML/BLUP), and then the MM index and the gains obtained by the developed fuzzy systems were estimated. There was a predominance of environmental effects over genotypic effects for all traits. These estimates suggest an expressive contribution of the environment for these traits and, consequently, greater difficulty for genetic improvement. Through this, the fuzzy systems stood out in relation to the MM method, as they presented superior selection gains for characters related to human and animal food. The genotypes with potential for human and animal food selected by the fuzzy system were: UFVJM07, UFVJM05, UFVJM09, UFVJM40, UFVJM01, UFVJM25, UFVJM15. The fuzzy logic was efficient in the simultaneous selection of sweet potato genotypes, allowing the selection of plants similar to the desirable ideotype than the MM method.
Keywords:
Ipomoea batatas; multiple selection; computational intelligence
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