ABSTRACT Among the multi-trait models selected to study several traits and environments jointly, the Bayesian framework has been a preferred tool when constructing a more complex and biologically realistic model. In most cases, non-informative prior distributions are adopted in studies using the Bayesian approach. However, the Bayesian approach presents more accurate estimates when informative prior distributions are used. The present study was developed to evaluate the efficiency and applicability of multi-trait multi-environment (MTME) models within a Bayesian framework utilizing a strategy for eliciting informative prior distribution using previous data on rice. The study involved data pertaining to rice (Oryza sativa L.) genotypes in three environments and five crop seasons (2010/2011 until 2014/2015) for the following traits: grain yield (GY), flowering in days (FLOR) and plant height (PH). Variance components, genetic and non-genetic parameters were estimated using the Bayesian method. In general, the informative prior distribution in Bayesian MTME models provided higher estimates of individual narrow-sense heritability and variance components, as well as minor lengths for the highest probability density interval (HPD), compared to their respective non-informative prior distribution analyses. More informative prior distributions make it possible to detect genetic correlations between traits, which cannot be achieved with non-informative prior distributions. Therefore, this mechanism presented to update knowledge for an elicitation of an informative prior distribution can be efficiently applied in rice breeding programs.

ABSTRACT Identifying Coffea arabica cultivars that are more efficient in the use of nitrogen is an important strategy and a necessity in the context of environmental and economic impacts attributed to excessive nitrogen fertilization. Although Coffea arabica breeding data have a multi-trait structure, they are often analyzed under a single trait structure. Thus, the objectives of this study were to use a Bayesian multitrait model, to estimate heritability in the broad sense, and to select arabica coffee cultivars with better genetic potential (desirable agronomic traits) in nitrogen-restricted cultivation. The experiment was carried out in a greenhouse with 20 arabica coffee cultivars grown in a nutrient solution with low-nitrogen content (1.5 mM). The experimental design used was in randomized blocks with three replications. Six agromorphological traits of the arabica coffee breeding program and five nutritional efficiency indices were used. The Markov Chain Monte Carlo algorithm was used to estimate genetic parameters and genetic values. The agromorphological traits were considered highly heritable, with a credibility interval (95% probability): H2 = 0.9538 – 5.89E-01. The Bayesian multitrait model presents an adequate strategy for the genetic improvement of arabica coffee grown in low-nitrogen concentrations. Coffee arabica cultivars Icatu Precoce 3282, Icatu Vermelho IAC 4045, Acaiá Cerrado MG 1474, Tupi IAC 1669-33, Catucaí 785/15, Caturra Vermelho and Obatã IAC 1669/20 demonstrated greater potential for cultivation in low-nitrogen concentration.

ABSTRACT. The study of traits in crops enables breeders to guide strategies for selecting and accelerating the progress of genetic breeding. Although the simultaneous evaluation of characteristics in the plant breeding programme provides large quantities of information, identifying which phenotypic characteristic is the most important is a challenge facing breeders. Thus, this work aims to quantify the best approaches for prediction and establish a network of better predictive power in flood-irrigated rice via methodologies based on regression, artificial intelligence, and machine learning. Multiple regression, computational intelligence, and machine learning were used to predict the importance of the characteristics. Computational intelligence and machine learning were notable for their ability to extract nonlinear information from model inputs. Predicting the relative contribution of auxiliary characteristics in rice through computational intelligence and machine learning proved to be efficient in determining the relative importance of variables in flood-irrigated rice. The characteristics indicated to assist in decision making are flowering, number of grains filled by panicles and length of panicles for this study. The network with only one hidden layer with 15 neurons was observed to be efficient in determining the relative importance of variables in flooded rice.

ABSTRACT. The development of superior cultivars involves parents with superiority for the traits of interest and wide genetic variability. Efficient plant breeding and selection strategies that allow for the identification of superior genotypes are essential in breeding programs. This work aims to carry out a comparative study between several strategies for choosing parents, for hybridization purposes, based on phenotypic analysis and molecular information. To obtain the phenotypic and genotypic information of the parents, data simulation was used. For genotyping, 2000 single nucleotide polymorphism markers were used, and from all possible gametes to be formed (22000), 5000 were randomly sampled to form each of the 100 individuals of the population of recombinant inbred strains. To obtain the phenotypic information, five characteristics with different levels of complexity were simulated. The comparative study was carried out using data referring to simulated genotypic values of hybrids and parents. Then, aiming to choose the parents destined for hybridization, different traditional selection strategies based on phenotypic analysis and the genome-wide selection methodology were approached. The genomic information resulted in the choice of the best lines and in obtaining superior hybrids when compared with traditional methodologies. The inclusion of the genomic genetic values of the parents in determining the crosses to be carried out increases the probability of generating phenotypically superior hybrids. Thus, the traditional methods of choosing parents for hybridization purposes are effective, but when incorporating the information from genome-wide selection, the choice of parents provides superior and promising results.

ABSTRACT. The importance of rice (Oryza sativa) is indisputable this cereal is the staple food for half of the global population. Genetic progress estimation allows evaluation of the effectiveness of genetic improvement of crops and helps in the planning of breeding programs. This paper aims to estimate the genetic yield progress made by the program in the state of Minas Gerais, Brazil, which is run by the Epamig/UFV/Embrapa consortium. A total of 210 lines were evaluated by value for cultivation and use testing that was conducted in the municipalities of Janaúba, Leopoldina, and Lambari, from 1993 to 2016. Based on Vencovsky’s (1986) method, the genetic gains in Lambari, Janaúba, and Leopoldina were 1.46, 0.14, and 0.11%, respectively. The gain in Leopoldina was considered very low during the evaluated period. According to Breseghello’s (1998) method, the annual average genetic gain was 0.23 and 0.04% in Lambari and Janaúba, respectively. In Leopoldina, a yield gain of - 2.37% per year was observed for the evaluation period from 1994 to 1999, and a yield gain of 0.1% per year was observed from 2000 to 2016. These results can be explained by the focus on quality as a strategy for the breeding program in Minas Gerais State in the 1990s. Vencovsky’s (1986) and Breseghello’s (1998) methods were able to quantify yield gains and demonstrate the dynamics of the irrigated rice genetic improvement program in Minas Gerais State.

Abstract: The aim of this study was to obtain estimates of genetic parameters via meta-analysis and mixed models to evaluate the potential of the germplasm from Embrapa Pecuaria Sudeste for integration in breeding programs. Data on the dry matter yield of 77 accessions evaluated in 24 harvests were used. Descriptive analysis was performed for mixed model and meta-analytical estimates. The variances for each parameter were estimated. For the meta-analysis, variances were used as weighting measures, and the fixed and random models were tested. A diagonal structure was considered for the residual variance matrix. Mean dry matter yield ranged from 1019.5 to 2684 kg ha-1. The phenotypic, permanent environmental, and genotypic variances oscillated widely throughout the harvests, as well as heritability. Meta-analytical estimates proved to be an accurate and complementary approach to the mixed model analysis. Both mixed model and meta-analytical estimates indicated the breeding potential of the alfalfa germplasm.

ABSTRACT Genetic diversity studies are performed based on information on a set of traits measured in a group of genotypes, considering one or more environments. The pattern recognition methods allow classifying genotypes from a set of important agronomic information. Thus, this study aimed to present and compare pattern recognition methods to inquire about the similarity of environments and genotypes in flood-irrigated rice for the recommendation of cultivars. The experim>ents were performed in the municipalities of Leopoldina, Lambari, and Janaúba, state of Minas Gerais, Brazil. To evaluate the pattern of similarity, 25 rice genotypes in three environments belonging to the flood-irrigated rice breeding program were used. Among these genotypes, five cultivars were used as an experimental control for the grain yield, the height of the plant, flowering, panicle length, grains filled by panicles, percentages of grains filled by panicles, in the 2012/2013 agricultural year. The methods us>ed were mixtures of multivariate normal distributions and density-based clustering algorithm. It was observed, therefore, that the genotypes are distributed in three distinct groups, in which there are intragroup homogeneity and intergroup heterogeneity for the agronomic traits of the flooded rice culture. The methods >used to assess the dissimilarity of environments using pattern recognition methods were efficient in classifying flooded rice irrigated environments.

Resumo: O objetivo deste trabalho foi avaliar o potencial da inteligência computacional e de variáveis canônicas para estudos de diversidade genética entre genótipos de sorgo (Sorghum bicolor) biomassa. Os experimentos foram realizados no campo experimental da Embrapa Milho e Sorgo, nos municípios de Nova Porteirinha e Sete Lagoas, no estado de Minas Gerais, Brasil. Foram avaliadas as seguintes características: dias para a floração, altura das plantas, produção de biomassa fresca, biomassa seca total e produção de biomassa seca. O estudo da diversidade genética foi realizado por meio da análise de variáveis canônicas. Para o reconhecimento do padrão de organização da diversidade genética, utilizou-se o mapa auto-organizável de Kohonen. O uso de variáveis canônicas e o mapa auto-organizável foram eficientes no estudo de diversidade genética. A utilização de inteligência computacional com mapa auto-organizável é promissora e eficiente para estudos de diversidade genética entre genótipos de sorgo biomassa.

Abstract: The objective of this work was to evaluate the potential of computational intelligence and canonical variables for studies on the genetic diversity between biomass sorghum (Sorghum bicolor) genotypes. The experiments were carried out in the experimental field of Embrapa Milho e Sorgo, in the municipalities of Nova Porteirinha and Sete Lagoas, in the state of Minas Gerais, Brazil. The following traits were evaluated: days to flowering, plant height, fresh biomass yield, total dry biomass, and dry biomass yield. The study of genetic diversity was performed through the analysis of canonical variables. For the recognition of the organization pattern of genetic diversity, Kohonen’s self-organizing map was used. The use of canonical variables and a self-organizing map were efficient for the study of genetic diversity. The application of computational intelligence using a self-organized map is promising and efficient for studies on the genetic diversity between biomass sorghum genotypes.

Resumo: O objetivo deste trabalho foi avaliar o método gráfico de rede de similaridade na classificação de genótipos de arroz (Orzya sativa) irrigado quanto a sua adaptabilidade e estabilidade. Duas medidas estatísticas foram utilizadas para representar a proximidade de comportamento (baseada na correlação de Pearson) ou de valores (baseada na distância de Gower) entre pares de genótipos ou entre genótipo e ambiente. Foram avaliados dados de produtividade de 18 genótipos de arroz irrigado em três locais de Minas Gerais, nas safras de 2012/2013, 2013/2014, 2014/2015 e 2015/2016, em delineamento em blocos ao acaso. Os genótipos foram previamente avaliados quanto à adaptabilidade e à estabilidade pelos métodos de Eberhart & Russell e centroide. As representações gráficas fornecidas pelas redes de similaridade facilitaram o reconhecimento do padrão de interação genótipo x ambiente, o que permitiu superar as dificuldades de interpretação devido à discordância entre os resultados obtidos com os métodos de Eberhart & Russell e centroide. As redes de similaridade facilitam os estudos de interação genótipo x ambiente.

Abstract: The objective of this work was to evaluate the similarity network graphic methodology for the classification of flood-irrigated rice (Orzya sativa) genotypes regarding their adaptability and stability. Two statistical measures were used to represent the proximity of the behavior (based on Pearson’s correlation) or values (based on Gower’s distance) between pairs of genotypes or between genotype and environment. Productivity data of 18 genotypes were evaluated in three locations in the state of Minas Gerais, Brazil, in the harvests of 2012/2013, 2013/2014, 2014/2015, and 2015/2016, in a randomized complete block design. The genotypes were previously assessed for adaptability and stability by the Eberhart & Russell and centroid methods. The graphical representations provided by the similarity networks allowed to better identify the pattern of the genotype x environment interaction, overcoming the interpretation difficulties due to the disagreements between the results obtained by the Eberhart & Russell and centroid methods. The similarity networks improve genotype x environment interaction studies.