Leonardo Lopes Bhering

Genetic parameters and variability in physic nut accessions during early developmental stages

The objective of this work was to estimate the genetic parameters and variability among accessions (half-sib families) of physic nut (Jatropha curcas) during the early stages of development. For this study, 110 accessions in the first year of development of the physic nut germplasm bank, maintained at Embrapa Cerrados, DF, Brazil, were evaluated in situ. The experiment was established in a randomized complete block design, with two replicates and five plants per plot arranged in rows at 4x2 m spacing. Grain yield, total number of branches per plant, plant height, stem diameter, canopy projection on the row, canopy projection between rows, canopy volume, number of days until first flowering and height of the first inflorescence were evaluated. Estimates of vegetative genetic parameters showed the existence of genetic variability in the physic nut germplasm bank. Physic nut accessions of the germplasm bank were grouped into five similarity groups based on character divergence. Although preliminary, the obtained results are promising for showing potential for Jatropha improvement with selective efficiency.

Seleção simultânea de clones de eucalipto de acordo com produtividade, estabilidade e adaptabilidade

The objective of this work was to estimate genetic parameters and to determine the efficiency of the simultaneous selection of eucalyptus clones based on yield, stability, and adaptability. Twenty-one clones, with 36 months of age, from the genetic breeding program of the company Cenibra were used. The experiment was done in a randomized complete block design, in four environments, with 21 replicates of one plant per plot. The clones were evaluated as to the variables: diameter at breast height, plant height, and total volume with bark. The genetic parameters were estimated using the mixed model procedure (REML/BLUP), and selection was based on the harmonic mean of the relative performance of the genetic values (HMRPGV), with three strategies: selection based on the predicted genetic gain, considering the average performance of genotypes in all environments (without interaction effect) or the performance on each environment (with interaction effect); and simultaneous selection based on productivity, stability, and adaptability. Environment particularities influenced the phenotypic expression of the clones. The heritability estimates indicated good perspectives for selecting clones with high yield, stability, and adaptability. Simultaneous selection optimizes clone selection and can be used in the construction of breeding populations and in the recommendation of genetic materials for commercial plantations.

Método alternativo para análise de agrupamento

O objetivo deste trabalho foi propor uma alteracao no procedimento de agrupamento realizado pelo metodo de Tocher, passando-o de metodo simultâneo (original) para sequencial. Para ilustrar e comparar os metodos, foram realizadas simulacoes de colecoes de acessos com diferentes caracteristicas, tanto para avaliacao individual quanto para avaliacao de experimentos com repeticoes. As simulacoes e as analises estatisticas foram realizadas com auxilio do programa GENES. O numero de grupos formados com o metodo de Tocher sequencial foi menor que o numero de grupos formados pelo metodo de Tocher original. No metodo de Tocher sequencial, nao foi verificada influencia dos genotipos ja agrupados, no agrupamento dos genotipos mais distantes. O limite de acrescimo na media da distância intragrupo, estimado apos a formacao de um novo grupo, constitui uma estimativa da dissimilaridade existente entre os acessos dos grupos. O agrupamento dos genotipos com maior dissimilaridade e realizado com maior eficacia pelo metodo de Tocher sequencial do que pelo metodo de Tocher original.

Genetic gains in physic nut using selection indexes

Abstract – The objective of this work was to estimate genetic gains in physic nut ( Jatropha curcas ) using selection indexes and to establish the best selection strategy for the species. Direct and indirect selection was carried out using different selection indexes, totalizing 14 strategies. One hundred and seventy five families from the active germplasm bank of Embrapa Agroenergy, Brasilia, Brazil, were analyzed in a randomized complete block design with two replicates. The evaluated traits were: grain yield; seeds per fruit; endosperm/seed ratio; seed weight, length, width, and thickness; branches per plant at 0.5, 1.0, and 1.5 m; plant height; stem diameter; canopy projection on rows and between lines; canopy volume; juvenility (days to the first flowering); and height of the first inflorescence. Evaluations were done during the second year of cultivation. The use of selection indexes is relevant to maximize the genetic gains in physic nut, favoring a better distribution of desirable traits. The multiplicative and restrictive indexes are considered the most promising for selection.Index terms:

Joint analysis of phenotypic and molecular diversity provides new insights on the genetic variability of the brazilian physic nut germplasm bank

The genetic variability of the Brazilian physic nut (Jatropha curcas) germplasm bank (117 accessions) was assessed using a combination of phenotypic and molecular data. The joint dissimilarity matrix showed moderate correlation with the original matrices of phenotypic and molecular data. However, the correlation between the phenotypic dissimilarity matrix and the genotypic dissimilarity matrix was low. This finding indicated that molecular markers (RAPD and SSR) did not adequately sample the genomic regions that were relevant for phenotypic differentiation of the accessions. The dissimilarity values of the joint dissimilarity matrix were used to measure phenotypic + molecular diversity. This diversity varied from 0 to 1.29 among the 117 accessions, with an average dissimilarity among genotypes of 0.51. Joint analysis of phenotypic and molecular diversity indicated that the genetic diversity of the physic nut germplasm was 156% and 64% higher than the diversity estimated from phenotypic and molecular data, respectively. These results show that Jatropha genetic variability in Brazil is not as limited as previously thought.

Ganhos genéticos preditos por diferentes métodos de seleção em progênies de Eucalyptus urophylla

The objective of this work was to evaluate genetic parameters and to compare predicted gains using different selection methods in half-sib families of Eucalyptus urophylla. Within and between selection, combined selection and selection based on mixed model equations (REML/BLUP) were used for the traits diameter at breast height, total height and total volume with bark. The progeny test used consisted of 100 55-month-old half-sib families distributed in a 3x2-m spacing, in randomized complete block design with five replicates. The progenies showed significant genetic variability and high heritability for the studied traits, which indicates high genetic control and favorable conditions for selection. All the methods tested were efficient in eucalyptus breeding. However, the combined selection and the selection based on mixed models (BLUP) provided gains significantly larger than those obtained with within and between selections, and were more efficient in the selection of the best individuals in the population.

Neural networks for predicting breeding values and genetic gains

Analysis using Artificial Neural Networks has been described as an approach in the decision-making process that, although incipient, has been reported as presenting high potential for use in animal and plant breeding. In this study, we introduce the procedure of using the expanded data set for training the network. Wealso proposed using statistical parameters to estimate the breeding value of genotypes in simulated scenarios, in addition to the mean phenotypic value in a feed-forward back propagation multilayer perceptron network. After evaluating artificial neural network configurations, our results showed its superiority to estimates based on linear models, as well as its applicability in the genetic value prediction process. The results further indicated the good generalization performance of the neural network model in several additional validation experiments.

Artificial neural networks for adaptability and stability evaluation in alfalfa genotypes

The purpose of this work was to evaluate a methodology of adaptability and phenotypic stability of alfalfa genotypes based on the training of an artificial neural network considering the methodology of Eberhart and Russell. Data from an experiment on dry matter production of 92 alfalfa genotypes (Medicago sativa L.) were used. The experimental design constituted of randomized blocks, with two repetitions. The genotypes were submitted to 20 cuttings, in the growing season of November 2004 to June 2006. Each cutting was considered an environment. The artificial neural network was able to satisfactorily classify the genotypes. In addition, the analysis presented high agreement rates, compared with the results obtained by the methodology of Eberhart and Russell.

Adaptabilidade e estabilidade via regressão não paramétrica em genótipos de café

The objective of this work was to evaluate a methodology of phenotypic adaptability and stability analyses of coffee genotypes based on nonparametric regression. The technique used differs from other techniques because it reduces the influence of extreme points resulting from the presence of genotypes whose answers to a certain environment are too different on the estimation of the adaptability parameter. Data from an experiment studying the average yield of 40 coffee (Coffea canephora) genotypes in a randomized block design with six replicates were used to evaluate the method. The genotypes were evaluated along five years (1996, 1998, 1999, 2000 and 2001) in two locations (Sooretama and Marilândia, ES, Brazil), in a total of ten environments. The methodology proposed proved adequate and efficient, since it eliminates the disproportionate effects induced by the presence of extreme points and avoids misleading recommendations of genotypes in terms of adaptability.

Seleção assistida por marcadores para teor de matéria seca e açúcares redutores em tubérculos de batata

Early generation selection for clones with high content of tuber dry matter and low levels of reducing sugars is required for potato (Solanum tuberosum L.) processing. Selection of superior clones at early generations became possible with the deployment of genetic markers, and can precisely identify the superior individuals. Therefore, it is necessary to identify genetic markers closely linked to genes of interest to do assisted selection. The aim of this research was to evaluate the efficiency of marker assisted selection with genetic markers previously identified by ANDREU (2004), which are assumed to be associated with dry matter and reducing sugars content in potato tubers. Clones from 20 families were evaluated during the seedling generation (S), first clonal generation (C1) and second clonal generation (C2). The estimated coefficients of correlation for all traits among generations were significant, even though of low magnitude, confirming that selection at early generation based only on phenotypic traits is inviable. A total of sixteen bands were amplified using these markers. However, by multiple stepwise regression, only seven of these bands showed association with the evaluated traits. Moreover, no markers associated with dry matter and reducing sugars content in the C1 were significantly associated with these traits in the C2, suggesting the existence of QTLs x environment interactions. The marker assisted selection resulted less efficient than the phenotypic selection in all cases studied, and thus is not recommended for early generation selection of clones for the processing industry. These results suggest that the markers used are not closely linked to the genes controlling the traits most important for processing. Therefore, it is important to identify new markers closely linked with such traits of interest that could improve the efficiency of marker assisted selection.