ABSTRACT Water stress is one of the limiting factors for obtaining maximum yield in soybean, which makes the identification of tolerant genotypes an important approach in breeding programs. The objectives of this work were evaluate soybean genotypes in conditions of water deficit, determine traits that allow the identification of genotypes tolerant to water deficit, and suggest promising crosses. Sixteen soybean genotypes were submitted to two osmotic potentials (0.0 MPa and -0.2 MPa) induced by PEG 6000 and the traits of germination and growing were evaluated. Genetic dissimilarity analysis was performed. There was genetic variability among genotypes for all traits, and significant interaction for root length, shoot and total fresh matter and dry root matter. Based on root length, it was possible to discriminate genotypes tolerant to water deficit. The cultivars BRS 7270 IPRO, TMG 2158 IPRO, UFUS Tikuna and UFU_P32 showed higher root length and stood out for dry root, fresh shoot and total fresh matter under water stress conditions and are indicated as promising parents in crosses. programs deficit crosses 0.0 00 0 (0. 0.2 02 2 -0. 600 evaluated performed 727 IPRO 215 UFUP32 UFUP UFU P32 P UFU_P3 0. (0 -0 60 72 21 UFUP3 P3 UFU_P ( - 6 7

ABSTRACT. Cotton (Gossypium hirsutum L.) is the world’s leading natural textile fibre and is grown in over 60 countries, including Brazil, where it is an important agricultural commodity. The cultivation area currently covers approximately one million hectares in Brazil and has expanded into every region of the country, especially the Cerrado biome. Because of this expansion, it is necessary to analyse the influence of the environment on the genotype behaviour to optimize yields. Thus, the objective of this study was to compare fuzzy logic to traditional methods for selecting coloured-fibre cotton genotypes with high adaptability and yield stability. The experiment was conducted on the 2013/2014, 2014/2015, 2015/2016, and 2016/2017 crops of the Capim Branco farm at the Federal University of Uberlândia, Uberlândia, Minas Gerais, Brazil. The following methods were used to select genotypes for adaptability and stability: the Lin and Binns model, additive main effects and multiplicative interaction (AMMI) analysis and the Sugeno fuzzy logic controller. An interaction of the genotype with the environment that affected yield was detected. Environment 4 (the 2016/2017 crop) showed to the lowest genotype to environment interaction. The fuzzy logic approach showed agreement with AMMI and the nonparametric Lin and Binns method. The linguistic fuzzy logic used in the Sugeno fuzzy logic controller demonstrated the potential for selecting cotton genotypes in plant breeding programmes. The UFUJP-16 and UFUPJ-17 genotypes were adaptable, stable and showed promising yields within the tested environments. The fuzzy logic method was effective for estimating adaptability and stability.