ABSTRACT. The aim of this study was to evaluate repeated measures over the years to estimate repeatability coefficient and the number of the optimum measure to select superior genotypes in Annona muricata L. The fruit production was evaluated over 16 years in 71 genotypes without an experimental design. The estimation of variance components and the prediction of the permanent phenotypic value were performed using REML/BLUP proceedings. The coefficient of determination, accuracy, and selective efficiency increased when measures increased. The coefficient of determination of 80% was reached beyond 8 crop seasons with high accuracy and selective efficiency. Thus, the evaluation of 8 crop seasons can be suitable to select superior genotypes in the A. muricata L. breeding program. Predicted selection gain had a high magnitude for fruit production indicating that it is possible to take a progressive genetic advance for this trait over cycle breeding.

ABSTRACT. Sugarcane (Saccharum sp) is one of the most promising crops and researchers have sought for renewable alternative energy sources to reduce CO 2 emission. The study of strategies, which allow breeders in the selection of superior genotypes for many traits simultaneously, is important. Therefore, the objectives of this study were: i) to apply path analysis to better understand the relationship between the lignocellulosic traits and technological quality traits with total recoverable sugars (TRS) and ii) to use several multivariate selection indexes to predict the genetic gain and to select superior genotypes in the sugarcane breeding. A total of 40 sugarcane genotypes were evaluated in an experimental design using incomplete blocks with two replicates. The follow traits were evaluated: dry matter (DM), total soluble solids (BRIX), apparent sucrose content in the juice (POL), apparent sucrose content in sugarcane (POLS), fiber content (FIB), purity (PUR), TRS, lignin content (LC), cellulose content (CC), hemicellulose content (HC), and ash content (AC). These traits were analyzed by analysis of variance, phenotypic correlation network, path analysis, and selection index. The highest direct effect on TRS was obtained by POLS (0.337), POL (0.299), BRIX (0.227), and FIB (-0.146). The estimates of phenotypic correlation between these characters and TRS were in the same direction, which demonstrated a cause-and-effect relationship. The highest indirect effect was of POL via POLS (0.331) followed by POLS via POL (0.294). BRIX presented high indirect effects via POLS (0.266) and via POL (0.246). On the other hand, FIB presented negative indirect effects via POLS (-0.169) and POL (-0.103). In conclusion, path analysis and index selection are useful strategies to help breeders in the selection of superior genotypes in sugarcane.

ABSTRACT. Jatropha is a species with great potential for biodiesel production, and the knowledge on how the main agronomic traits are correlated will contribute to its improvement. Therefore, the objectives of this study were to estimate the genetic parameters of the traits: plant height at 12 and 40 months, canopy projection on the row at 12 and 40 months, canopy projection between the row at 12 and 40 months, number of branches at 40 months, grain yield, and oil yield; to verify the existence of phenotypic correlation between these traits; to verify the influence of the morphological traits on oil yield by means of path analysis; and to evaluate the relationship between the productive traits in Jatropha and the morphological traits measured at different ages. Sixty-seven half-sib families were evaluated using a completely randomized block design with two replications and five plants per plot. Analysis of variance was used to estimate the genetic value. Phenotypic correlations were given by the Pearson correlation between traits. For the canonical correlation analysis, two groups of traits were established: group I, consisting of traits of economic importance for the culture, and group II, consisting of morphological traits. Path analysis was carried out considering oil yield as the main dependent variable. Genetic variability was observed among Jatropha families. Productive traits can be indirectly selected via morphological traits due to the correlation between these two groups of traits. Therefore, canonical correlations and path analysis are two strategies that may be useful in Jatrophabreeding program when the objective is to select productive traits via morphological traits.

ABSTRACT. Repeatability studies on fruit species are of great importance to identify the minimum number of measurements necessary to accurately select superior genotypes. This study aimed to identify the most efficient method to estimate the repeatability coefficient (r) and predict the minimum number of measurements needed for a more accurate evaluation of soursop (Annona muricata L.) genotypes based on fruit yield. Sixteen measurements of fruit yield from 71 soursop genotypes were carried out between 2000 and 2016. In order to estimate r with the best accuracy, four procedures were used: analysis of variance, principal component analysis based on the correlation matrix, principal component analysis based on the phenotypic variance and covariance matrix, and structural analysis based on the correlation matrix. The minimum number of measurements needed to predict the actual value of individuals was estimated. Principal component analysis using the phenotypic variance and covariance matrix provided the most accurate estimates of both r and the number of measurements required for accurate evaluation of fruit yield in soursop. Our results indicate that selection of soursop genotypes with high fruit yield can be performed based on the third and fourth measurements in the early years and/or based on the eighth and ninth measurements at more advanced stages.