The use of resistant cultivars is the most effective strategy for controlling coffee leaf rust caused by the fungus Hemileia vastatrix. To assist the development of such cultivars, amplified fragment-length polymorphism (AFLP) markers linked to two loci of coffee resistance to races I and II as well as pathotype 001 of H. vastatrix were converted to sequence-characterized amplified region (SCAR) and cleaved amplified polymorphic site (CAPS) markers. In total, 2 SCAR markers and 1 CAPS marker were validated in resistant and susceptible parents as well as in 247 individuals from the F₂ population. The efficiency of these markers for marker-assisted selection (MAS) was evaluated in F_2:3 and backcross (BCrs₂) populations genotyped with the developed markers and phenotyped with race II of H. vastatrix. The markers showed 90% efficiency in MAS. Therefore, the developed markers, together with molecular markers associated with other rust resistance genes, were used for F_3:4 and BCrs₃ coffee selection. The selected plants were analyzed using two markers associated with coffee berry disease (CBD) resistance, aiming for preventive breeding. MAS of F_3:4 and BCrs₃ individuals with all resistance loci was feasible. Our phenotypic and genotypic approaches are useful for the development of coffee genotypes with multiple genes conferring resistance to coffee leaf rust and CBD.

Flowering is an important agronomic trait that presents non-additive gene action. Genome-enabled prediction allow incorporating molecular information into the prediction of individual genetic merit. Artificial neural networks (ANN) recognize patterns of data and represent an alternative as a universal approximation of complex functions. In a Genomic Selection (GS) context, the ANN allows automatically to capture complicated factors such as epistasis and dominance. The objectives of this study were to predict the individual genetic merits of the traits associated with the flowering time in the common bean using the ANN approach, and to compare the predictive abilities obtained for ANN and Ridge Regression Best Linear Unbiased Predictor (RR-BLUP). We used a set of 80 bean cultivars and genotyping was performed with a set of 384 SNPs. The higher accuracy of the selective process of phenotypic values based on ANN output values resulted in a greater efficacy of the genomic estimated breeding value (GEBV). Through the root mean square error computational intelligence approaches via ANN, GEBV were shown to have greater efficacy than GS via RR-BLUP.

Alfalfa breeding aimed at trait improvement for livestock feed takes longer periods of time, if compared to many other crops. Therefore, better selection methods are necessary for the success of alfalfa breeding programs. Although knowing about selection methods is quite important, there is a notable lack of information, as regards successful solutions. This study aimed to use direct, indirect and simultaneous selection methods for selecting alfalfa cultivars, based on yield traits and nutritive value. The evaluated traits were subdivided into two groups: forage yield and nutritive value. Selection gains were estimated by direct, indirect and simultaneous selection for each group, considering the selection of the 25 % best cultivars. Direct and indirect selections among genotype averages are not efficient to provide the desirable responses to the whole set of traits. The results for simultaneous selection, using the Tai index, provided a more balanced gain distribution to the set of traits in all cuts. The simultaneous selection allowed the identification of the 5681 and Verdor cultivars in the first cut, as well as ProINTA Patricia in the second cut, as superior in the two groups of evaluated traits.