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.

Resumo O objetivo deste trabalho foi avaliar a variabilidade genética e as associações de características produtivas do feijão-bóer, para indicação dessa cultura ao programa de melhoramento em Moçambique. Foram avaliadas 11 características do feijão-bóer, em delineamento de três blocos ao acaso, nos ambientes de Namapa (I) e Montepuez (II), em 2017/2018. Para avaliação das características, utilizaram-se análises de variância, parâmetros genéticos, correlações, rede de correlação e análise de trilha. Em ambos os ambientes, há alta variabilidade genética e fenotípica para ramos primários, ramos secundários, vagens por planta, número de sementes por vagem, largura e comprimento da vagem e produtividade, bem como alta herdabilidade para todas as características, exceto para altura da planta. Os ramos primários e secundários apresentam alta correlação com a produtividade e efeito direto positivo sobre ela. No entanto, altura de planta e vagens por planta apresentam alta variabilidade fenotípica e correlação positiva com a produtividade no ambiente I. Vagens por planta, peso de 100 sementes e ramos primários apresentam altos efeitos positivos diretos no ambiente I, e ramos secundários e número de sementes por vagem, no ambiente II. Os ramos primários e secundários apresentam alta variabilidade e associações com a produtividade em ambos os ambientes e são indicados para o programa de melhoramento do feijão-bóer em Moçambique.

Abstract The objective of this work was to evaluate the genetic variability and associations of yield traits of pigeon pea, for indication of this crop for the breeding program in Mozambique. Eleven pigeon pea traits were evaluated in a experimental design with three randomized complete blocks, in the environments of Namapa (I) and Montepuez (II), in 2017/2018. To evaluate the traits, analyses of variance, genetic parameters, correlations, correlation network, and path analysis were used. In both environments, there is a high genetic and phenotypic variability for primary branches, secondary branches, pods per plant, number of seed per pod, pod width, pod length, and yield, as well as a high heritability for all traits except for plant height. The primary and secondary branches have a high correlation with yield and a positive direct effect on it. However, plant height and pods per plant show a high phenotypic variability and a positive correlation with yield in environment I. Pods per plant, weight of 100 seed, and primary branches show high direct positive effects in environment I, and secondary branches and number of seed per pod, in environment II. The primary and secondary branches show high variability and associations with yield in both environments and are indicated for the pigeon pea breeding program in Mozambique.

ABSTRACT: Sorghum breeding programs are based predominantly on developing homozygous lines to produce single cross hybrids, frequently with relatively narrow genetic bases. The adoption of complementary strategies, such as genetic diversity study, enables a broader vision of the genetic structure of the breeding germplasm. The purpose of this study was to evaluate the genetic diversity of sorghum breeding lines using structure analysis, principal components (PC) and clustering analyses. A total of 160 sorghum lines were genotyped with 29,649 SNP markers generated by genotyping-by-sequencing (GBS). The PC and clustering analyses consistently divided the R (restorer) and B (maintainer) lines based on their pedigree, generating four groups. Thirty-two B and 21 R lines were used to generate 121 single-cross hybrids, whose performances were compared based on the diversity clustering of each parental line. The genetic divergence of B and R lines indicated a potential for increasing heterotic response in the development of hybrids. The genetic distance was correlated to heterosis, allowing for the use of markers to create heterotic groups in sorghum.

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 Normalmente, em experimentos de campo, pressupõe-se a independência entre erros, mas nem sempre esta condição é atendida, comprometendo os resultados obtidos. Uma escolha não apropriada do modelo de análise pode comprometer a eficiência do programa de melhoramento no sentido de os genótipos pouco promissores poderem ser selecionados e mantidos em próximos ciclos seletivos acarretando desperdício de tempo e recursos. O objetivo deste trabalho foi avaliar a dependência espacial entre erros, em experimentos de avaliação de produtividade de grãos de progênies de feijoeiro, considerando análises em látice e em blocos casualizados. E também avaliar a eficiência de modelos geoestatísticos para caracterização da estrutura de variabilidade espacial entre erros. Os dados utilizados nesse estudo foram obtidos de experimentos instalados no delineamento látice e analisados como látice ou blocos casualizados. O teste de Durbin-Watson foi usado para verificar a presença de autocorrelação espacial. O semivariograma teórico foi ajustado por meio dos modelos geoestatísticos (exponencial, esférico e gaussiano) para descrever a variabilidade espacial dos erros. Aplicou-se o teste da razão de verossimilhança para verificar a significância dos parâmetros dos modelos geoestatísticos. Dos oito experimentos avaliados, cinco apresentaram dependência espacial moderada para análise em blocos e um para análise em látice e em blocos. O tamanho dos experimentos não foi fator determinante da dependência espacial. Os modelos geoestatísticos esférico, exponencial e gaussiano com efeito pepita foram adequados para representar a estrutura espacial na análise em blocos. A análise em látice foi eficiente para garantir a independência entre erros.

ABSTRACT In field experiments, it is often assumed that errors are statistically independent, but not always this condition is met, compromising the results. An inappropriate choice of the analytical model can compromise the efficiency of breeding programs in preventing unpromising genotypes from being selected and maintained in the next selection cycles resulting in waste of time and resources. The objective of this study was to evaluate the spatial dependence of errors in experiments evaluating grain yield of bean progenies using analyses in lattice and randomized blocks. And also evaluate the efficiency of geostatistical models to describe the structure of spatial variability of errors. The data used in this study derived from experiments arranged in the lattice design and analyzed as lattice or as randomized blocks. The Durbin-Watson test was used to verify the existence of spatial autocorrelation. The theoretical semivariogram was fitted using geostatistical models (exponential, spherical and Gaussian) to describe the spatial variability of errors. The likelihood ratio test was applied to assess the significance of the geostatistical model parameters. Of the eight experiments evaluated, five had moderate spatial dependence for the randomized blocks analysis and one for both analyses, in lattice and randomized blocks. The area of the experiments was not a determinant factor of the spatial dependence. The spherical, exponential and Gaussian geostatistical models with nugget effect were suitable to represent the spatial structure in the randomized block analysis. The analysis in lattice was efficient to ensure the independence of errors.

Resumo: O objetivo deste trabalho foi avaliar a eficiência da análise estatística espacial, em comparação a análises usuais em blocos ao acaso e em látice, na classificação de famílias de feijoeiro ( Phaseolus vulgaris), sob diferentes cenários de dependência espacial e de precisão experimental. Foram considerados 12 cenários, formados por quatro classes de dependência espacial entre erros (nula, baixa, média e alta) e três classes de precisão experimental (moderada, alta e muito alta). Para as três classes de precisão experimental, foram definidos os valores de acurácia seletiva de: 0,60, 0,80 e 0,95, respectivamente. Já as quatro classes de dependência espacial entre erros assumiram, respectivamente, os seguintes valores de alcance do modelo geoestatístico exponencial: 0, 10, 20 e 40 m. Para experimentos com precisão experimental muito alta ou ausência de dependência espacial, a eficiência da análise espacial para classificação de famílias do feijoeiro é semelhante à eficiência das análises usuais. Para experimentos com alta e, principalmente, com moderada precisão experimental, a análise espacial é mais eficiente que as análises usuais para classificação de famílias de feijoeiro, quando os erros apresentam dependência espacial.

Abstract: The objective of this work was to evaluate the efficiency of spatial statistical analysis, compared to the usual analyzes in randomized blocks and in lattice, for the classification of common bean ( Phaseolus vulgaris) families under different scenarios of spatial dependence and experimental precision. Twelve scenarios were considered, formed by four classes of spatial dependence between errors (null, low, medium, and high) and three classes of experimental precision (medium, high, and very high). For the three classes of experimental precision, the following values of selective accuracy were defined: 0.60, 0.80, and 0.95, respectively. The four classes of spatial dependence between errors assumed, respectively, the following range values for the exponential geostatistical model: 0, 10, 20, and 40 m. In experiments with very high experimental precision or with the absence of spatial dependence, the efficiency of the spatial analysis for the classification of common bean families is similar to that of the usual analyses. In experiments with high and, especially, with medium experimental precision, the spatial analysis is more efficient than the usual analyses for the classification of common bean families, when the errors show spatial dependence.