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 This study aimed to apply selection indexes in situations in which F2 populations and their parents are evaluated simultaneously in field trials to predict population genotypic values and selecting the best populations. Fifteen F2 soybean populations were evaluated for number of days to flowering and to maturity and grain yield. The data was analyzed using restricted maximum likelihood/best linear unbiased prediction (REML/BLUP) to obtain genotypic effects, variance components and accuracies. Four selection methods were compared: genotypic values of the F2 populations (gF2), combined index with the mean genotypic value of parents and F2 population (Ia) and two indexes that add (to gF2 and to Ia) and consider genetic variability within populations (Ib and Ic). The index Ia presented a result similar to that obtained with the gF2 selection method. Selection indexes (Ib and Ic) based on several sources of information were more efficient than selection based on gF2 values only.

Abstract The hybrids UENF N01, N02 and N03 have enhanced nitrogen-use efficiency, particularly in terms of N responsiveness, which makes them ideal for low-input cultivation. Even under abiotic stress, popping expansion is not reduced, indicating these hybrids for growth in the N and NW of Rio de Janeiro State. N01 N0 nitrogenuse nitrogen use efficiency responsiveness lowinput low input cultivation stress reduced State

ABSTRACT. The genotype by environment interaction is the main factor that influences the response of evaluated genotypes in trials of value for cultivation and use. Adaptability and stability analyses are fundamental to understanding the performance of genotypes in a growing region. Some of these methodologies incorporate previous information for recommending an extra group of genotypes denominated as specific ideotypes under certain cultivation conditions. Based on this strategy, the centroid method and its modifications have been widely used due to the simplicity of classification of the evaluated genotypes. However, these methodologies present problems in identifying adaptability patterns of some genotypes. Artificial intelligence techniques, such as fuzzy C-means, can be an alternative to reduce these difficulties, since they use, in addition to distance information between genotypes, memberships (measures quantifying how much an observation belongs to a particular class) to increase discriminatory power. Therefore, our aim was to propose and evaluate the phenotypic adaptability method by fuzzy clustering to assist cultivar recommendations. The adaptation of the fuzzy C-Means method to classify the genotypes was implemented in BioFuzzy software. The grain yield data of black common bean genotypes were used to evaluate the potential of the method. The results obtained by this method were compared with those obtained by the centroid method. The phenotypic adaptability method by fuzzy clustering was effective in identifying the adaptability patterns of common bean genotypes. Moreover, the discriminatory power was higher than that observed with the centroid method. ABSTRACT use region conditions strategy However techniques Cmeans, Cmeans C means, means C-means difficulties measures class Therefore recommendations CMeans Means software Moreover

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.

Resumo O objetivo deste trabalho foi avaliar um procedimento para elicitação de distribuição a priori informativa, comparada à distribuição a priori não informativa, em tamanho amostral reduzido, com uso de 14 caracteres de três genótipos de linhaça (Linum usitatissimum), em sete épocas de semeadura. Os valores dos hiperparâmetros regulam a informatividade da distribuição a priori; portanto, para cada época, foram calculados os hiperparâmetros a serem utilizados na próxima época. As duas distribuições a priori, não informativa e informativa, foram comparadas pelo comprimento dos intervalos de credibilidade e pela variância da distribuição a posteriori. Em geral, quando a distribuição a priori informativa é adotada, os parâmetros genéticos apresentam menor comprimento do intervalo de credibilidade e estimativas mais precisas. O mecanismo de elicitação a priori informativa com uso de informações prévias de programas de melhoramento é eficiente para estimativa de parâmetros genéticos, incluindo herdabilidade e variância genética, mesmo quando o tamanho da amostra é pequeno. Na avaliação genética, o uso da distribuição a priori informativa é melhor do que o da distribuição não informativa para tamanho amostral pequeno. Em geral, os resultados das distribuições a priori informativas indicam que os valores genéticos da primeira época de semeadura são maiores para os seguintes caracteres: comprimento do ciclo, altura da planta, e número de cápsulas não granuladas e de ramos produtivos.

Abstract The objective of this work was to evaluate a procedure for the elicitation of informative prior distribution, compared with non-informative prior distribution, in a small sample size, using 14 traits of three linseed (Linum usitatissimum) genotypes in seven sowing seasons. The values of the hyperparameters regulate the informativeness of the prior distribution; therefore, for each season, the hyperparameters to be used in the next season were calculated. The two prior distributions, non-informative and informative, were compared by the length of the credible interval and variance of the posterior distribution. In general, when the informative prior distribution is adopted, the genetic parameters present a shorter length of the credible interval and more precise estimates. The mechanism for informative prior elicitation using previous information from breeding programs is efficient for the estimation of genetic parameters, including heritability and genetic variance, even when the sample size is small. In genetic evaluation, the use of informative prior distribution is better than that of non-informative distribution for a small sample size. In general, the results of the informative prior distributions are indicative that the genetic values of the first sowing season are greater for the following traits: cycle length, plant height, and number of non-grained capsules and of productive branches.

Resumo O objetivo deste trabalho foi determinar a capacidade geral e a específica de combinação (CGC e CEC, respectivamente) de seis genitores de soja (Glycine max), para identificar aqueles que são promissores e suas melhores combinações para o desenvolvimento de linhagens superiores quanto à precocidade e à produção de grãos, bem como à melhor época de avaliação. Seis genitores e seus 15 híbridos foram avaliados em blocos ao acaso, durante o inverno de 2014 e o verão de 2015/2016, em casa de vegetação. Os dados obtidos para número de dias para florescimento, ciclo e produção foram analisados pelo método 2 de Griffing, modelo 1. Resultados contrastantes foram obtidos para as duas épocas, com ciclo mais curto e maior produtividade no verão. A maior CGC quanto ao ciclo é observada nos progenitores 'MSOY6101' e 'MSOY9144RR', com sinal negativo e positivo, respectivamente. 'TMG123RR' apresenta a maior CGC quanto à produção de grãos. A maior CEC quanto ao número de dias para o florescimento e ao ciclo está associada aos cruzamentos 'SYN9078RR' x 'MSOY9144RR' e 'TMG123RR' x 'MSOY9144RR', respectivamente. No entanto, a maior CEC quanto à produção de grãos é observada em 'MSOY6101' x 'MSOY9144RR', com valor positivo, e 'TMG801' x 'MSOY9144RR', com valor negativo, durante o verão.

Abstract The objective of this work was to determine the general and specific combining ability (GCA and SCA, respectively) of six soybean (Glycine max) parents, in order to identify the promising ones and their best combinations for the development of superior lines for earliness and grain yield, as well as the best evaluation season. Six parents and their 15 hybrids were evaluated in a randomized complete block design, during the 2014 winter and 2015/2016 summer, in a greenhouse. The data obtained for number of days to flowering, cycle, and grain yield were analyzed by Griffing’s method 2, model 1. Contrasting results were obtained for the two seasons, with a shorter cycle and a higher yield in the summer. The highest GCA for cycle is observed for the 'MSOY6101' and 'MSOY9144RR' parents, with negative and positive signs, respectively. 'TMG123RR' shows the highest GCA for grain yield. The highest SCA for days to flowering and cycle is associated with the 'SYN9078RR' × 'MSOY9144RR' and 'TMG123RR' × 'MSOY9144RR' crosses, respectively. However, the highest SCA for grain yield is observed for 'MSOY6101' × 'MSOY9144RR', with a positive value, and for 'TMG801' × 'MSOY9144RR', with a negative value, during the summer.

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. This paper aimed to evaluate the effectiveness of subset selection of markers for genome-enabled prediction of genetic values using radial basis function neural networks (RBFNN). To this end, an F1 population derived from the hybridization of divergent parents with 500 individuals genotyped with 1000 SNP-type markers was simulated. Phenotypic traits were determined by adopting three different gene action models - additive, additive-dominant, and epistatic, representing two dominance situations: partial and complete with quantitative traits having a heritability (h2) of 30 and 60%; traits were controlled by 50 loci, considering two alleles per locus. Twelve different scenarios were represented in the simulation. The stepwise regression was used before the prediction methods. The reliability and the root mean square error were used for estimation using a fivefold cross-validation scheme. Overall, dimensionality reduction improved the reliability values for all scenarios, specifically with h2 =30 the reliability value from 0.03 to 0.59 using RBFNN and from 0.10 to 0.57 with RR-BLUP in the scenario with additive effects. In the additive dominant scenario, the reliability values changed from 0.12 to 0.59 using RBFNN and from 0.12 to 0.58 with RR-BLUP, and in the epistasis scenarios, the reliability values changed from 0.07 to 0.50 using RBFNN and from 0.06 to 0.47 with RR-BLUP. The results showed that the use of stepwise regression before the use of these techniques led to an improvement in the accuracy of prediction of the genetic value and, mainly, to a large reduction of the root mean square error in addition to facilitating processing and analysis time due to a reduction in dimensionality.

RESUMO: A análise química de flores tem sido estudada em algumas culturas. Para o cafeeiro, a análise do tecido floral possibilitaria a antecipação do diagnóstico nutricional das lavouras. O estudo objetivou (i) comparar a composição mineral de flores e de folhas de cafeeiros (ii) e gerar normas para diagnose nutricional do cafeeiro com base na análise de tecidos de flores e folhas das plantas. Para isso, foram avaliados os teores de nutrientes em flores e folhas e a produtividade de café em 26 lavouras comerciais na região de Manhuaçu, MG, durante três anos. As faixas críticas de nutrientes determinadas em flores são: 2,78 - 3,17; 0,23 - 0,28; 2,80 - 3,12; 0,30 - 0,37; 0,24 - 0,30; 0,15 - 0,18 dag kg-1 de N, P, K, Ca, Mg e S, e 17 - 21; 12 - 18; 52 - 80; 26 - 43 e 28 - 48 mg kg-1 para Zn, Cu, Mn, Fe e B, respectivamente. As faixas críticas de nutrientes em folhas foram: 2,63 - 2,86; 0,13 - 0,14; 2,13 - 2,33; 1,04 - 1,22; 0,27 - 0,33; 0,15 - 0,18 dag kg-1 de N, P, K, Ca, Mg e S, e 9 - 14; 15 - 23; 80 - 115; 99 - 148 e 31 - 37 mg kg-1 para Zn, Cu, Mn, Fe e B, respectivamente. A diagnose nutricional do cafeeiro, quanto aos nutrientes N, P, Ca, Fe, Cu e Mn, pode ser antecipada por meio da análise de flores.

ABSTRACT: The chemical analysis of flowers has been studied for some crops. In coffee trees, the flower tissue analysis could anticipate the nutritional diagnosis. This study aimed to: (i) compare the mineral composition of coffee flowers and leaves; and to (ii) generate reference values for nutritional diagnosis of coffee trees, based on flower and leaf analysis. Nutrient content of flowers and leaves and coffee productivity were evaluated in 26 commercial farms located in Manhuaçu, MG, Brazil throughout three years. The critical nutrient content range in flowers are respectively: 2.78 - 3.17, 0.23 - 0.28, 2.80 - 3.12, 0.30 - 0.37, 0.24 - 0.30, 0.15 - 0.18 dag kg-1 of N, P, K, Ca, Mg, and S; and 17 - 21, 12 - 18, 52 - 80, 26 - 43, and 28 - 48 mg kg-1 of Zn, Cu, Mn, Fe, and B. For leaves, the critical nutrient ranges are respectively: 2.63 - 2.86, 0.13 - 0.14, 2.13 - 2.33, 1.04 - 1.22, 0.27 - 0.33, 0.15 - 0.18 dag kg-1 of N, P, K, Ca, Mg, and S; and 9 - 14, 15 - 23, 80 - 115, 99 - 148, and 31 - 37 mg kg-1 of Zn, Cu, Mn, Fe, and B. The nutritional diagnosis of coffee trees for N, P, Ca, Fe, Cu, and Mn can be anticipated using flower analysis.

RESUMO: Padrões empíricos de desequilíbrio de ligação (LD) podem ser utilizados para aumentar o poder estatístico do mapeamento genético. Este trabalho foi realizado com o objetivo de verificar a eficácia da análise de fatores (AF) aplicada a conjuntos de dados de marcadores moleculares do tipo SNP, visando identificar grupos de ligação e blocos de haplótipos. O conjunto de dados SNPs utilizado foi oriundo de um processo de simulação de uma população F2, contendo 2000 marcas com informações de 500 indivíduos. A estimação das cargas fatoriais (loadings) da AF foi feita de duas formas, considerando a matriz de distâncias entre os marcadores (A) e considerando a matriz de correlação (R). O número de fatores (k) a ser utilizado foi estabelecido com base no gráfico scree-plot e com base na proporção da variância total explicada. Os resultados indicam que as matrizes A e R conduzem a resultados similares. Com base no scree-plot considerou-se k igual a 10 e os fatores interpretados como sendo representativos dos grupos de ligação. O segundo critério conduziu a um número de fatores igual a 50, e os fatores interpretados como sendo representativos dos blocos de haplótipos. Isto mostra o potencial da técnica que permite obter resultados aplicáveis a qualquer tipo de população, corroborando a interpretação de estudos genômicos. O trabalho demonstrou que a AF foi capaz de identificar padrões de associação entre marcadores, identificando subgrupos de marcadores que refletem grupos de ligação fatorial e também grupos de desequilíbrio de ligação.

ABSTRACT: Empirical patterns of linkage disequilibrium (LD) can be used to increase the statistical power of genetic mapping. This study was carried out with the objective of verifying the efficacy of factor analysis (AF) applied to data sets of molecular markers of the SNP type, in order to identify linkage groups and haplotypes blocks. The SNPs data set used was derived from a simulation process of an F2 population, containing 2000 marks with information of 500 individuals. The estimation of the factorial loadings of FA was made in two ways, considering the matrix of distances between the markers (A) and considering the correlation matrix (R). The number of factors (k) to be used was established based on the graph scree-plot and based on the proportion of the total variance explained. Results indicated that matrices A and R lead to similar results. Based on the scree-plot we considered k equal to 10 and the factors interpreted as being representative of the bonding groups. The second criterion led to a number of factors equal to 50, and the factors interpreted as being representative of the haplotypes blocks. This showed the potential of the technique, making it possible to obtain results applicable to any type of population, helping or corroborating the interpretation of genomic studies. The study demonstrated that AF was able to identify patterns of association between markers, identifying subgroups of markers that reflect factor binding groups and also linkage disequilibrium groups.

Abstract In this manuscript we discuss the purpose and scope of biometry and its interactions, complementation, and even overlap with several other areas of genetics. We emphasize that biometry is an area of genetics that enables researchers to analyze, process, and interpret biological phenomena from data, usually obtained from experimental tests, in an improved way to guide strategies and decision making for optimization of resources. We also highlight the importance of the biometry professional in the context of breeding and the need for continual training, due to new demands for and challenges from inclusion of different types of information for processing and analysis paradigms to better interpret these paradigms.