Vinícius Jardel Szareski

Estimates of genetic parameters and genotypic values prediction in maize landrace populations by REML/BLUP procedure

The REML/BLUP statistics are analyses that can be used as selective criteria in the routine of maize breeding programs. The present study aims to determine the genetic potential in crosses of landrace populations applying the REML/BLUP methodology, and to identify populations for the synthesis of new populations and intrapopulation selection for family farming systems, as well as genetic constitutions for use in maize breeding programs. Nine top cross hybrids obtained in the 2012/2013 harvest were evaluated along with their testator, the landraces used as parents, and four commercial hybrids, in a randomized block design, with information taken from the average of each plot. The evaluated traits were: leaf angle, number of ramifications of the tassel, spike insertion height, plant height, spike diameter, number of grains per spike, mass of grains per spike, spike mass, spike length, prolificity, mass of one hundred grains, and grain yield per plot. The data were analyzed using the Selegen-REML/BLUP software. The top cross hybrids Cateto Branco x Planalto, Amarelão x Planalto and the population Cateto Branco are ranked among the ten best crosses, simultaneously, for the traits: leaf angle, number of ramifications of the tassel, spike insertion height, and plant height (Cateto Branco x Planalto), and leaf angle, spike insertion height, and plant height (Amarelão x Planalto and Cateto Branco). The top cross hybrids Criolão x Planalto, Branco 8 Carreiras x Planalto, Caiano Rajado x Planalto, Amarelão x Planalto, Branco Roxo Índio x Planalto stand out for their high genotypic value of the individual BLUP mean components among the ten best genotypes for grain yield, and by combining three or more traits of interest together, being, for effects of selection, the most indicated.

REML/BLUP and sequential path analysis in estimating genotypic values and interrelationships among simple maize grain yield-related traits

Methodologies using restricted maximum likelihood/best linear unbiased prediction (REML/BLUP) in combination with sequential path analysis in maize are still limited in the literature. Therefore, the aims of this study were: i) to use REML/BLUP-based procedures in order to estimate variance components, genetic parameters, and genotypic values of simple maize hybrids, and ii) to fit stepwise regressions considering genotypic values to form a path diagram with multi-order predictors and minimum multicollinearity that explains the relationships of cause and effect among grain yield-related traits. Fifteen commercial simple maize hybrids were evaluated in multi-environment trials in a randomized complete block design with four replications. The environmental variance (78.80%) and genotype-vs-environment variance (20.83%) accounted for more than 99% of the phenotypic variance of grain yield, which difficult the direct selection of breeders for this trait. The sequential path analysis model allowed the selection of traits with high explanatory power and minimum multicollinearity, resulting in models with elevated fit (R2 > 0.9 and ε < 0.3). The number of kernels per ear (NKE) and thousand-kernel weight (TKW) are the traits with the largest direct effects on grain yield (r = 0.66 and 0.73, respectively). The high accuracy of selection (0.86 and 0.89) associated with the high heritability of the average (0.732 and 0.794) for NKE and TKW, respectively, indicated good reliability and prospects of success in the indirect selection of hybrids with high-yield potential through these traits. The negative direct effect of NKE on TKW (r = -0.856), however, must be considered. The joint use of mixed models and sequential path analysis is effective in the evaluation of maize-breeding trials.

Diallel and prediction (REML/BLUP) for yield components in intervarietal maize hybrids

Genetic improvement is essential to achieve increments in maize (Zea mays L.) grain yield components. It may be obtained through crosses, which enable to exploit the effects of intervarietal heterosis, allelic complementarity, as well as gene actions and effects. This study estimated the components of variance and genetic parameters (REML/BLUP) of an intervarietal diallel to select and predict the best genotypes for maize yield components. The experimental design was randomized blocks containing 60 intervarietal maize hybrids arranged in three repetitions. They were obtained through intervarietal crosses and evaluated in a diallel scheme, where 14 open-pollinated varieties were designated as parentals. Thus, 10 crosses were performed for each hybrid combination to obtain the number of seeds necessary for the competition test. The measured traits were: grain volume relative index, the mass of one hundred grains, and grain yield. The male parents and the additive genetic fraction were determinants for grain volume relative index. Mass of one hundred grains and grain yield were defined by the specific combining ability, and female parents revealed low narrow sense heritability. The female parent Taquarão and male parent Argentino Amarelo presented the best general combining abilities for the measured traits. The specific combining abilities were expressed for crosses AL 25 x Dente de Ouro Roxo, AL 25 x BRS Pampeano, and Taquarão x Argentino Branco. Genetic estimates and predictions were consistent and applicable to breeding programs and could be applied in future quantitative genetic studies of maize.

Artificial Neural Network and Multivariate Models Applied to Morphological Traits and Seeds of Common Beans Genotypes

The aimed to characterize common beans genotypes utilizing multivariate models and artificial neural network thru the agronomic attributes and seeds dimensions. The experiment was conducted in the 2017/2018 crop season at the city of Tenente Portela - RS. The experimental design was expanded blocs, were 53 segregating F2 populations and ten cultivars considered checks, disposed in four repetitions. The accurate characterization of bean genotypes can be based in the reproductive period, cycle and seeds length. Genotypes with longer cycle increase the potential of ramifications, legume and seeds magnitude per plant and increase the seeds yield independent of the commercial group. The use of biometric approach allows revealing patterns to the genotype grouping, being the patterns magnitude dependent of the intrinsic premises to the Standardized Average Euclidian Distance, Tocher optimized grouping and Artificial Neural Network with non-supervised learning. It is defined that the Artificial Neural Network are determinant to define associative patterns, being these inferences indispensable to the common beans genotype selection that answer the agronomic attributes and seeds production.

Adaptabilidade e estabilidade de genótipos de trigo de acordo com índice fenotípico de vigor de sementes

The objective of this work was to evaluate the adaptability and multi-trait stability of wheat (Triticum aestivum) genotypes according to the phenotypic index of seed vigor (PIV). Thirty wheat genotypes were grown in seven environments in the state of Rio Grande do Sul, Brazil, during one crop season. In each environment, a randomized complete block design with three replicates was used. The PIV was elaborated from the following traits: first germination count, germination percentage, accelerated aging, and electrical conductivity. The evaluated phenotypic index makes it possible to define macroenvironments for the production of wheat seeds with high physiological potential and to understand the implications of the genotype x environment interaction. The phenotypic index of seed vigor is effective to rank genotypes considering multi-trait selection related to the vigor of wheat seeds produced in Southern Brazil.