ABSTRACT present study aimed to determine the effects of different light restriction levels (shading levels) on soybean genetic parameters using a Bayesian multi-trait model (MTM) and select high-yielding soybean cultivars. Eighteen commercial soybean cultivars bred in a soybean breeding program were evaluated over two agricultural seasons. Three shading levels were used over two agricultural crop seasons, giving six treatments (light restriction × crop season). The experiments were arranged in a randomized complete block design with six treatments replicated thrice. The genetic values and parameters were estimated using a Monte Carlo Markov Chain algorithm. Broad-sense heritability range from 0.2093 to 0.7153. The lowest genotypic variance estimate was observed at the 45 % photosynthetically active radiation level in the 2019/2020 crop season year compared with that of other shading levels. Furthermore, a 40 % selection intensity had the highest soybean yield under different shading levels. The Bayesian MTM combined with the factor analysis and genotype-ideotype distance method can be used to evaluate and select soybean genotypes considering different shading levels. The soybean cultivars 8579RSF, NS8338, NS7901, NS7667, RK8115, and 8473RSF had higher genetic potential than other cultivars under different shading levels. multitrait multi trait (MTM highyielding high yielding seasons season. . season) thrice algorithm Broadsense Broad sense 02093 0 2093 0.209 07153 7153 0.7153 4 20192020 2019 2020 2019/202 Furthermore genotypeideotype genotype ideotype 8579RSF RSF NS8338 NS NS7901 NS7667 RK8115 RK 0209 209 0.20 0715 715 0.715 2019202 201 202 2019/20 NS833 NS790 NS766 RK811 020 20 0.2 071 71 0.71 201920 2019/2 NS83 NS79 NS76 RK81 02 2 0. 07 7 0.7 20192 2019/ NS8 NS7 RK8

ABSTRACT Elephant grass is an allogamous perennial forage crop with asexual propagation, allowing plant breeders to explore heterosis and develop hybrids. However, selecting parents for diallel crossing schemes is a major hurdle in the development of hybrids. In addition, this perennial crop has several harvests, which increases the dataset size and the complexity of the statistical analyses. Here, we propose a diallel analysis based on linear mixed models with repeatability information to identify parents and hybrids, and the optimum number of harvests. We performed a complete diallel crossing of 11 parents with data from five harvests for morphological traits and two harvests for nutritive value traits. The diallel model with repeatability information aids in the estimation of the genetic effect of diallel analysis to infer the ideal number of harvests and genotype × harvest interaction. We observed that the specific combining ability (SCA) is more critical for morphoagronomic traits, while the general combining ability (GCA) has greater importance for traits related to nutritive value. Furthermore, we detected that five harvests were sufficient for the morphoagronomic traits with a coefficient of determination (R2) of 0.8; however, ten harvests were required for an R2 of 0.9. The model is a promising single-analysis alternative for plant breeders to analyze a diallel with repeated measures and to estimate important parameters without incurring in additional financial costs. propagation hybrids However addition analyses Here 1 interaction SCA (SCA GCA (GCA Furthermore R (R2 0.8 08 0 8 however 09 9 0.9 singleanalysis single costs (R 0.

ABSTRACT. One of the domains of genetic enhancement that has extensively employed both simulation and authentic data is Biometrics. Selecting efficient models for the Genome-Wide Selection (GWS) process using molecular markers (SNPs) presents several challenges. Among these challenges is the effective identification of the optimal model for fitting a given dataset. To contribute to this endeavor, this paper's primary objective is to assess the predictive accuracy of nine (9) distinct models, each following different paradigms within the realm of Biometrics. The data employed in this study were generated through simulation, encompassing the primary issues encountered in this field of research, including high dimensionality, nonlinearity, and multicollinearity. As the primary findings, notable observations include the enhancement of predictive efficiency as data noise decreases, the predominance of the tree paradigm (for low noise levels, BOO), and the efficacy of the neural network paradigm (for high noise levels, RBF). ABSTRACT Biometrics GenomeWide Genome Wide GWS (GWS SNPs (SNPs dataset endeavor papers paper s 9 (9 research dimensionality nonlinearity multicollinearity findings decreases levels BOO, BOO , BOO) RBF. RBF . RBF) (

Resumo O objetivo deste trabalho foi avaliar como a herdabilidade e o número de locos de características quantitativas (QTL) que controla a característica podem influenciar na predição do valor genético por meio de métodos de seleção genômica. Uma equação de predição foi estabelecida para estimar a correlação genética baseada na correlação fenotípica, tendo-se utilizado uma população F2 com 1.000 indivíduos, simulados em diferentes cenários. A herdabilidade (5, 20, 40, 60, 80 e 99%) e o número de QTL (60, 120, 180 e 240) variaram em cada cenário. Os quatro seguintes métodos de seleção genômica foram utilizados nas análises: ridge-regression best linear unbiased prediction (RR-BLUP), BLUP genômico (GBLUP), método bayesiano de estimação B (Bayes B) e reproducing kernel Hilbert spaces regression (RKHS). As habilidades preditivas fenotípicas e genotípicas foram calculadas para cada método, e o teste de Tukey foi utilizado para comparação de médias. O efeito da herdabilidade e do número de QTL que controla a característica foi avaliado por análise de regressão. O teste de Tukey revelou diferenças entre os métodos, sendo que Bayes B e RR-BLUP foram superiores aos demais em quase todos os cenários. A herdabilidade apresenta relação linear positiva com a capacidade preditiva fenotípica e relação quadrática positiva com a capacidade preditiva genotípica. O número de QTL controlando a característica não tem relação com a capacidade preditiva fenotípica e genotípica. (QTL tendose tendo se F 1000 1 000 1.00 indivíduos cenários 5, 5 (5 20 40 60 8 99% 99 (60 120 18 240 cenário análises ridgeregression ridge RRBLUP, RRBLUP RR , (RR-BLUP) GBLUP, GBLUP (GBLUP) RKHS. RKHS . (RKHS) médias regressão genotípica 100 00 1.0 ( 2 4 6 9 (6 12 24 (RR-BLUP (GBLUP (RKHS 10 0 1.

Abstract The objective of this work was to evaluate how heritability and the number of quantitative trait loci (QTL) controlling the trait can influence the prediction of genetic value by genomic selection methods. A prediction equation was established to estimate genetic correlation based on phenotypic correlation, using an F2 population with 1,000 individuals, simulated in different scenarios. Heritability (5, 20, 40, 60, 80, and 99%) and QTL number (60, 120, 180, and 240) varied in each scenario. The following four genomic selection methods were used in the analyses: ridge-regression best linear unbiased prediction (RR-BLUP), genomic BLUP (GBLUP), Bayesian estimation method B (Bayes B), and reproducing kernel Hilbert spaces regression (RKHS). The phenotypic and genotypic predictive abilities were calculated for each method, and Tukey’s test was used to compare means. The effect of heritability and of the number of QTL controlling the trait was evaluated by the regression analysis. Tukey’s test revealed differences between the methods, with Bayes B and RR-BLUP being superior to the others in almost all scenarios. Heritability presents a positive linear relationship with phenotypic predictive ability and a positive quadratic relationship with genotypic predictive ability. The number of QTL controlling the trait has no relationship with the phenotypic and genotypic predictive abilities. (QTL F 1000 1 000 1,00 individuals scenarios 5, 5 (5 20 40 60 80 99% 99 (60 120 180 240 scenario analyses ridgeregression ridge RRBLUP, RRBLUP RR , (RR-BLUP) GBLUP, GBLUP (GBLUP) B, B) RKHS. RKHS . (RKHS) Tukeys Tukey s means analysis 100 00 1,0 ( 2 4 6 8 9 (6 12 18 24 (RR-BLUP (GBLUP (RKHS 10 0 1,

ABSTRACT The prediction of traits allows the breeder to guide strategies to select and accelerate the progress of genetic improvement. The objective of this work was to determine the best prediction approach and establish a network with better predictive power for white oat using methodologies based on artificial intelligence, and machine learning. Seventy-eight white oat genotypes were evaluated. The design was randomized blocks with three replications. The models were evaluated with and without fungicide, and prediction models were established using four sets of experiments. The grain yield was used as a response trait the others as explanatory traits. The coefficient of determination was considered to evaluate the proposed methodologies. The importance of the traits was assessed through the impact of destructuring or disturbing the information of a given input on the estimation of R2. For machine learning, decision trees, bagging, random forest, and boosting were used. The traits indicated to assist in decision-making are plant height, leaf rust severity, and lodging percentage. The R2 ranged from 30.14% - 96.45% and 10.57% - 94.61% for computational intelligence and machine learning, respectively. A high estimate of the coefficient of determination, which was larger than the other estimates, was obtained using the bagging technique. improvement learning Seventyeight Seventy eight replications fungicide experiments R trees forest decisionmaking making height severity percentage 3014 30 14 30.14 9645 96 45 96.45 1057 10 57 10.57 9461 94 61 94.61 respectively estimates technique 301 3 1 30.1 964 9 4 96.4 105 5 10.5 946 6 94.6 30. 96. 10. 94.

Resumo O objetivo deste trabalho foi avaliar modelos que utilizam os nucleotídeos de polimorfismo único significativos (SNPs), encontrados por seleção assistida por marcadores e associação genômica, como um efeito fixo em modelos comumente utilizados na seleção genômica ampla para a população F2, em comparação com o modelo que utiliza todos os SNPs. Utilizou-se para todos modelos o método bayesiano de regressão de crista. Para as comparações entre os modelos, avaliaram-se a capacidade de predição fenotípica e genotípica, a acurácia fenotípica, o ganho de seleção, o índice de coincidência e o tempo de processamento. Ambos os métodos não conseguiram identificar com precisão os verdadeiros loci de características quantitativas (QTL). A seleção baseada apenas nos QTL identificados pelos métodos avaliados elegeu indivíduos de baixo valor genético. O uso de um modelo de seleção genômica ampla -com os SNPs significativos encontrados pela associação genômica como um efeito fixo, e os SNPs restantes como um efeito aleatório – foi a estratégia adequada para selecionar indivíduos superiores com alta precisão. A introdução de QTL já descritos para uma dada característica no modelo de seleção genômica ampla permite a seleção de indivíduos superiores com maior precisão. SNPs, , (SNPs) F2 F Utilizouse Utilizou se crista avaliaramse avaliaram genotípica processamento QTL. . (QTL) genético (SNPs (QTL

Abstract The objective of this work was to evaluate models using the significant single nucleotide polymorphisms (SNPs) detected by marker-assisted selection and genome-wide association, as a fixed effect in the models commonly used in genome-wide selection for F2 population, in comparison with models using all SNPs. For all models, the Bayesian ridge regression method was used. Comparisons between the models were carried out to evaluate the phenotypic and genotypic prediction ability, phenotypic accuracy, selection gain, coincidence index, and processing time. Both methods failed to accurately identify true quantitative trait loci (QTL). The selection based only in the QTL identified by the studied methods elected individuals of low genetic value. The use of a genome-wide selection model – with the significant SNPs found by the genome-wide association as a fixed effect, and the remaining SNPs as a random effect – was the suitable strategy to select superior individuals with high accuracy. The introduction of QTL already described for a given trait into the genome-wide selection model allows of the selection of superior individuals with greater precision. (SNPs markerassisted marker assisted genomewide genome wide F population ability accuracy gain index time QTL. . (QTL) value precision (QTL

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. 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

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. fertilization multi structure Thus sense desirable nitrogenrestricted restricted 2 lownitrogen low 1.5 15 1 5 (1. mM. mM . mM) replications values heritable 95% 95 (95 probability probability) H 09538 0 9538 0.953 5.89E01. 589E01 E 5.89E 01. 89E 01 5.89E-01 concentrations 3282 4045 1474 166933, 166933 1669 33, 33 1669-33 78515 785 785/15 166920 1669/2 concentration 1. (1 9 (9 0953 953 0.95 89E01 5.89E01 589E0 589E 5.89E-0 328 404 147 16693 166 3 1669-3 7851 78 785/1 16692 1669/ ( 095 0.9 89E0 5.89E0 5.89E- 32 40 14 16 1669- 7 785/ 09 0. 4

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 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 variability programs used genotyping 200 22000, 22000 , (22000) 500 10 strains Then genomewide genome approached methodologies Thus effective results 20 2200 (22000 50 1 2 220 (2200 5 22 (220 (22 (2 (

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. F yield likelihoodbest likelihood REML/BLUP REMLBLUP REML BLUP (REML/BLUP effects accuracies compared gF2, gF , (gF2) (Ia Ib Ic. Ic . method only (gF2 (gF

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 Thus floodirrigated flood irrigated regression inputs flowering 1

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.

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.