ABSTRACT This work aimed at investigating the viability of near infrared spectrometry (NIR), associated with chemometric methods, in order to identify differences at the levels of vigor of naturally and artificially aged soybean seeds. Seeds of six soybean genotypes were analyzed when freshly harvested, after natural aging in storage for eight months, and after artificial aging at the temperature of 41 °C for 96 hours. The seed moisture content, germination potential and vigor were evaluated. Also, there were measured the content of protein, oil and of the fatty acids: palmitic, stearic, oleic, linoleic and linolenic. The NIR spectra were obtained from the freeze-dried and grinded seeds. The natural and artificial aging of the seeds promote deterioration at distinct levels, reflecting in differences in seed vigor. The regions of the electromagnetic spectrum between wavelengths of 1000-1200 nm, 1350-1450 nm, 1800-1900 nm and 2300-2400 nm are important to distinguish the levels of quality of the soybean seeds. The contents of oil and protein have a relationship with the physiological quality of the seeds. Also, the most relevant wavelengths for the classification of seed vigor present a relationship with these compounds. NIR spectroscopy, in combination with chemometric methods, presents potential to be used in the classification of soybean seeds regarding their physiological quality. NIR, , (NIR) methods harvested months 4 C 9 hours evaluated Also acids palmitic stearic oleic linolenic freezedried freeze dried 10001200 1000 1200 1000-120 13501450 1350 1450 1350-145 18001900 1800 1900 1800-190 23002400 2300 2400 2300-240 compounds spectroscopy (NIR 1000120 100 120 1000-12 1350145 135 145 1350-14 1800190 180 190 1800-19 2300240 230 240 2300-24 100012 10 12 1000-1 135014 13 14 1350-1 180019 18 19 1800-1 230024 23 24 2300-2 10001 1 1000- 13501 1350- 18001 1800- 23002 2 2300-

Abstract The objective of this work was to compare the use of noninformative and informative priors in Bayesian models, as well as to evaluate the viability of including informative priors in the estimation of variance components and genetic values in soybean breeding programs. The used phenotypic data refer to the evaluation of 80 soybean genotypes in ten environments over three years. For each evaluated crop year, informative and noninformative priors were used, and the parameters were estimated using the Gibbs sampler algorithm. Parameter estimates from the previous crop year were used as prior information for the next evaluated crop year. The goodness-of-fit was calculated using the deviance information criterion (DIC). Selective accuracy showed the highest values for the models chosen through DIC for both crop years. However, the intervals of the highest posterior density are narrower for all models that adopted informative priors. Adding information into Bayesian inference does not always result in a better model fitting. programs 8 years algorithm goodnessoffit goodness fit DIC. . (DIC) However fitting (DIC

Resumo O objetivo deste trabalho foi comparar o uso de a priori não informativas e informativas em modelos bayesianos, bem como avaliar a viabilidade da inclusão de a priori informativas na estimativa dos componentes de variância e dos valores genotípicos em programas de melhoramento de soja. Os dados fenotípicos utilizados referem-se à avaliação de 80 genótipos de soja, em dez ambientes, ao longo de três anos. Para cada safra avaliada, foram utilizadas a priori informativas e não informativas, e os parâmetros foram estimados com uso do algoritmo de amostragem de Gibbs. As estimativas dos parâmetros da safra anterior foram utilizadas como informação prévia para a próxima safra avaliada. A qualidade do ajuste foi calculada com uso do critério de informação de desvio (DIC). A acurácia seletiva apresentou maiores valores nos modelos escolhidos por meio do DIC, para ambas as safras. No entanto, os intervalos de maior densidade a posteriori são menores para todos os modelos que adotaram a priori informativas. Adicionar informações à inferência bayesiana nem sempre resulta em melhor ajuste ao modelo. bayesianos soja referemse referem se 8 ambientes anos avaliada Gibbs DIC. DIC . (DIC) safras entanto modelo (DIC

ABSTRACT. It is common to observe conventional methods for estimating soybean crop yields, making the process slow and susceptible to human error. Therefore, the objective was to develop a model based on deep learning to estimate soybean yield using digital images obtained through a smartphone. To do this, the ability of the proposed model to correctly classify pods that have different numbers of grains, count the number of pods and grains, and then estimate the soybean crop yield was analyzed. As part of the study, two types of image acquisition were performed for the same plant. Image acquisition 1 (IA1) included capturing the images of the entire plant, pods, leaves, and branches. Image acquisition 2 (IA2) included capturing the images of the pods removed from the plant and deposited in a white container. In both acquisition methods, two soybean cultivars, TMG 7063 Ipro and TMG 7363 RR, were used. In total, combining samples from both cultivars, 495 images were captured, with each image corresponding to a sample (plant) obtained through methods AI1 and AI2. With these images, the total number of pods in the entire dataset was 46,385 pods. For the training and validation of the model, the data was divided into subsets of training, validation, and testing, representing, respectively, 80, 10, and 10% of the total dataset. In general, when using the data from IA2, the model presented errors of 7.50 and 5.32% for pods and grains, respectively. These values are considerably lower than when the model used the IA1 data, where it presented errors of 34.69 and 35.25% for pod and grain counts, respectively. Therefore, the data used from IA2 provide better results to the model. ABSTRACT yields error Therefore smartphone this grains analyzed study IA (IA1 leaves branches (IA2 container cultivars 706 736 RR 49 captured (plant AI AI2 46385 46 385 46,38 testing representing respectively 80 10 general 750 7 50 7.5 532 5 32 5.32 3469 34 69 34.6 3525 35 25 35.25 counts (IA 70 73 4 4638 38 46,3 8 75 7. 53 3 5.3 346 6 34. 352 35.2 463 46, 5. 35.

Abstract: Creation, adjustments and adoption of tests and tools that help in the prediction of seed storability have been highly demanded. Therefore, this work aimed to analyze the efficiency of different artificial aging times in predicting the performance of soybean seeds after storage, using the GGE biplot method. Seeds of six genotypes were subjected to storage, under refrigerated and non-refrigerated conditions, and artificial aging, being artificially aged for periods of 0, 48, 96 and 144 hours. Seeds freshly harvested and after natural and artificial aging were subjected to germination and vigor tests. The experiments were analyzed separately, using means test, regression analysis and model identity test, and together, using the GGE biplot method. Artificial aging at a temperature of 41 °C for 96 hours has the potential to be used to predict the performance of soybean seeds after eight months of storage. The GGE biplot is a method that can be used as a tool to analyze the relationships between aging environments and visualize the ranking of genotypes regarding the performance of seeds subjected to natural and artificial aging. Abstract Creation demanded Therefore storage nonrefrigerated non conditions 0 48 9 14 separately test together 4 C 1

Resumo: Tem sido altamente requerida a elaboração, ajustes e adoção de testes e ferramentas que auxiliem na predição da armazenabilidade de sementes. Diante disso, este trabalho teve por objetivo analisar a eficiência de diferentes tempos de envelhecimento artificial na predição do desempenho das sementes de soja após o armazenamento, utilizando-se o método GGE biplot. Sementes de seis genótipos foram submetidas ao armazenamento, sob condição refrigerada e não refrigerada, e envelhecimento artificial, sendo envelhecidas artificialmente pelos períodos de 0, 48, 96 e 144 horas. As sementes recém-colhidas e após o envelhecimento natural e artificial foram submetidas a testes de germinação e vigor. Os experimentos foram analisados separadamente, por meio de teste de médias, análise de regressão e teste de identidade de modelos, e em conjunto, utilizando-se o método GGE biplot. O envelhecimento artificial, à temperatura de 41 °C por 96 horas apresenta potencial para ser utilizado na predição do desempenho de sementes de soja após oito meses de armazenamento. O GGE biplot é um método que pode ser utilizado como ferramenta para analisar as relações entre os ambientes de envelhecimento e visualizar o ranqueamento dos genótipos quanto ao desempenho das sementes submetidas ao envelhecimento natural e artificial. Resumo elaboração disso armazenamento utilizandose utilizando se 0 48 9 14 recémcolhidas recém colhidas vigor separadamente médias modelos conjunto 4 C 1

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

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 Este estudo objetivou determinar o número ótimo de repetições e medições necessárias para screening de cultivares de soja submetidas a três condições de disponibilidade hídrica durante o estádio inicial de desenvolvimento. Dois experimentos foram conduzidos em casa de vegetação. Utilizou-se delineamento de blocos ao acaso, em esquema fatorial com dois fatores, seis repetições, dez cultivares e três condições de disponibilidade hídrica (condição controle, na qual a areia foi umedecida em água; condição estresse no potencial hídrico de -0.2 MPa e condição estresse no potencial hídrico de -0.4 Mpa). Aos 20 dias após o plantio, procedeu-se as mensurações, sendo posteriormente estimado do coeficiente de repetibilidade, do número de medições necessárias e do coeficiente de determinação. O coeficiente de repetibilidade foi estimado por distintos procedimentos estatísticos, tais como o método da análise de variância, componentes principais baseada na matriz de variâncias e covariâncias fenotípicas, componentes principais baseada na matriz de correlação e análise estrutural. Para determinação do número de repetições, os coeficientes de repetibilidade variaram de baixo a alto no experimento I, exceto para as variáveis radiculares. Estas, apresentaram altos valores nos dois experimentos. No experimento II, por outro lado, as magnitudes dos coeficientes de determinação foram superiores a 90%. Para altura da planta, número de folhas, comprimento de raiz e matéria seca da parte aérea, seriam necessárias apenas duas repetições para obter confiabilidade de 80%. Em relação ao número de medições por parcela, apenas uma planta poderia ser avaliada em cada parcela, com confiabilidade de 85%, para os dois experimentos. Apenas duas repetições e uma medição por parcela são suficientes para altas estimativas de repetibilidade. Desse modo, têm-se adequada eficiência na seleção de materiais superiores para tolerância ao déficit hídrico, durante os estádios iniciais de desenvolvimento da soja.

ABSTRACT This study aimed at determining the optimal number of replications and measurements necessary for the screening of soybean cultivars subjected to three conditions of water availability during the initial stage of development. Two experiments were carried out in a greenhouse. A randomized block design was used, in a factorial scheme with two factors (cultivar and water availability), six replications, ten cultivars and three conditions of water availability (control condition, in which the sand was moistened with water; stress control in the water potential of -0.2 MPa and stress condition in the water potential of -0.4 MPa). At 20 days after cultivation, measurements were taken, subsequently estimating the repeatability coefficient, of the number of necessary measurements and of the determination coefficient. The repeatability coefficient was estimated by distinct statistical procedures, such as method of analysis of variance, principal components with basis on the matrices of phenotypic variances and covariances; principal components with basis on the matrices of correlation and structural analysis. For the determination of the number of replications, the repeatability coefficients ranged from low to high in experiment I, except for the root variables, which presented high values in both experiments. In experiment II, in turn, the magnitudes of the determination coefficients were above 90%. For plant height, number of leaves, root length and dry matter of the aerial part, only two replications would be necessary to obtain 80% confidence. Regarding the number of measurements per parcel, only one plant could be evaluated in each parcel, with 85% confidence for both experiments. Only two replications and one measurement per parcel are enough for high estimates of repeatability. This way, adequate efficiency is obtained in the selection of superior materials for tolerance to water deficit, during the initial stages of soybean development.

Abstract Bacterial pustule is an important soybean disease caused by Xanthomonas citri pv. glycines, but information about genetic resistance to this pathogen is scarce. This study aimed to investigate soybean genetic resistance to bacterial pustule in the flowering stage through association analysis, characterization of candidate SNP markers, and identification of protein-coding genes potentially regulating defense processes. Therefore, 109 soybean cultivars genotyped with a 6k Illumina platform were assessed for disease severity. A genome-wide analysis revealed a total of 13 SNPs significantly associated. Through protein annotation, we identified three markers located inside the coding regions of uncharacterized protein LOC100779077, histone-lysine N-methyltransferase SUVR4, and ABC-transporter B family member-9. Nucleotide polymorphism on the first two of these markers produces a non-synonymous polymorphism with polarity shift from hydrophobic to polar amino acid. It is convenient to prioritize these three candidate markers for validation procedures with the purpose of using them in marker-assisted soybean breeding programs.

ABSTRACT: Successive cycles of water absorption and loss favor weathering deterioration, one of the main factors that affect the quality of soybean seeds. This study evaluated the physiological, physical, and morpho-anatomical changes in soybean seeds under weathering deterioration at the pre-harvest phase. Six soybean cultivars (BMX Apolo, DM 6563, NS 5959, NA 5909, BMX Potência, and TMG 1175) were produced in a greenhouse and underwent weathering deterioration through a rainfall simulation system, applying 0, 60, 120, and 180 mm of precipitation at pre-harvest phase. Each rainfall level was divided into two applications at an interval of 72 h: 60 mm (30 + 30), 120 mm (60 + 60), and 180 mm (90 + 90). After harvest, the seeds were evaluated for germination, vigor, physical and morpho-anatomical properties. Weathering deterioration induced by simulated rainfall at the pre-harvest phase contributes to the reduction in soybean seed germination and vigor and is conditioned by the soybean genotype. The increase in intensity of simulated rainfall led to a more significant weathering damage in seeds, as evidenced by the X-ray and tetrazolium test. Cultivars DM 6563 and BMX Potência were more susceptible, while NA 5909 was less susceptible to weathering deterioration (especially at the highest level; 120 mm and 180 mm). Anatomical changes caused by weathering deterioration lead to cell compaction and rupture, mainly in the cell layers of the hourglass and parenchyma, forming intracellular spaces. The presence of weathering damage caused a reduction in physiological soybean seed quality.

RESUMO: A interação genótipo × ambiente (G × E) desempenha um papel essencial na expressão fenotípica e pode provocar dificuldades na seleção genética. Assim, o presente estudo teve como objetivo estimar parâmetros genéticos e comparar diferentes estratégias de seleção no contexto de modelos mistos para melhoramento da soja. Para isso, foram utilizados dados referentes à avaliação de 30 genótipos em dez ambientes, referentes à característica produtividade de grãos. Os componentes de variância foram estimados pela máxima verossimilhança restrita (REML) e os valores genotípicos foram preditos pela melhor previsão imparcial linear (BLUP). Efeitos significativos dos genótipos e interação G × E foram detectados pelo teste da razão de verossimilhança (LRT). Correlação genotípica baixa foi obtida entre os ambientes indicando interação G × E do tipo complexa. A acurácia seletiva foi muito alta, indicando alta confiabilidade. Os resultados mostraram que os genótipos de soja mais produtivos apresentam alta adaptabilidade e estabilidade.

ABSTRACT: The genotype × environment (G×E) interaction plays an essential role in phenotypic expression and can lead to difficulties in genetic selection. Thus, the present study aimed to estimate genetic parameters and to compare different selection strategies in the context of mixed models for soybean breeding. For this, data referring to the evaluation of 30 genotypes in 10 environments, regarding the grain yield trait, were used. The variance components were estimated through restricted maximum likelihood (REML) and genotypic values were predicted through best linear unbiased prediction (BLUP). Significant effects of genotypes and G×E interaction were detected by the likelihood ratio test (LRT). Low genotypic correlation was obtained across environments, indicating complex G×E interaction. The selective accuracy was very high, indicating high reliability. Our results showed that the most productive soybean genotypes have high adaptability and stability.

ABSTRACT. The selection of superior genotypes of soybean entails a simultaneous evaluation of a number of favorable traits that provide a comparatively superior yield. Disregarding the population effect in the statistical model may compromise the estimate of variance components and the prediction of genetic values. The present study was undertaken to investigate the importance of including population effect in the statistical model and to determine the effectiveness of the index based on factor analysis and ideotype design via best linear unbiased prediction (FAI-BLUP) in the selection of erect, early, and high-yielding soybean progenies. To attain these objectives, 204 soybean progenies originating from three populations were examined for various traits of agronomic interest. The inclusion of the population effect in the statistical model was relevant in the genetic evaluation of soybean progenies. To quantify the effectiveness of the FAI-BLUP index, genetic gains were predicted and compared with those obtained by the Smith-Hazel and Additive Genetic indices. The FAI-BLUP index was effective in the selection of progenies with balanced, desirable genetic gains for all traits simultaneously. Therefore, the FAI-BLUP index is an adequate tool for the simultaneous selection of important traits in soybean breeding.

Abstract The genotype × environment (G×E) interaction plays an essential role in phenotypic expression and can lead to difficulties in genotypes recommendation. Thus, the objectives of this study were: i) propose the Multi-Environment Index Based on Factor Analysis and Ideotype-Design/Markov Chain Monte Carlo (FAI/MCMC index), and ii) apply it for soybean genotypes recommendation. To this end, a data set with 30 soybean genotypes evaluated in 10 environments for grain yield trait was used. Variance components, genetic parameters and genetic values were estimated through MCMC algorithm. Environmental stratification was conducted by factor analyses and the selection of soybean genotypes was performed using the FAI/MCMC index. The results indicated the existence of genotypic variability and G×E interaction. The environments were grouped into three factors. The predicted genetic gains from indirect selection was 4.81%. Thus, our results suggest that the FAI/MCMC index can be successfully used in soybean breeding.

ABSTRACT Selection of hybrid coffee plants coming from crosses between divergent populations is particularly relevant for the success of breeding programs. This study aimed to outline the best selection strategy in a hybrid population of Coffea canephora var. kouilou and robusta by estimating intrapopulation genetic parameters. Twenty full-sib progenies obtained by North Caroline II were installed in a randomized complete blocks design, with one plant per elementary plot. The following traits were evaluated: vegetative vigor, reaction to rust, plant height, diameter of canopy projection, maturity time, and bean yield. Significant individual genotypic variance and heritability estimates lead to an effective selection. The multi-trait selection index carried out between progenies and at individual level provided 5% and 40% gain, respectively. Thus, intrapopulation selection in a hybrid population is a viable strategy for the selection of superior individuals to compose new crosses and clones for cultivars in the breeding program of C. canephora, even with unbalanced data.

Abstract Phytophthora root and stem rot is one of the most aggressive diseases in soybean crop. The use of resistant cultivars is the main strategy to reduce losses caused by the pathogen. This study aims to identify SNP markers associated with genes or QTLs that provide soybean with partial resistance to Phytophthora sojae. A total of 169 soybean cultivars were inoculated with Phytophthora sojae and genotyped with 3,807 SNP markers. Genome-wide association analysis was carried out via multiple linear models, followed by multiple linear regression and linkage disequilibrium analysis. Four QTLs associated with the characteristic were identified: two on chromosome 3 and two on chromosome 15. The regions containing these QTLs contain genes already annotated as providers of resistance to pathogens, in plants. The use of those markers in the selection of resistant plants can increase the efficiency of breeding programs in the development of soybean varieties resistant to P. sojae.

Abstract The adoption of genetically modified soybean cultivars is increasing annually around the world, which indicates the excellence of these genotypes. For soybean breeding, the use of such cultivars in a backcross program relies on the ability to detect the transgenic trait. The germination test in a moistened substrate with glyphosate is effective and of low cost, however, wonders whether this is a non-destructive method to check cultivars. The objective of this study was to apply a bioassay for detection of herbicide-tolerant seeds and to evaluate the vegetative and productive development of the plants. It was observed that both glyphosate-tolerant and glyphosate-sensitive plants were affected in the initial stages of development in accordance with the herbicide application rate. However, whereas tolerant plants fully recover both in vegetative growth and production during the cycle, sensitive plants recover partially, producing smaller plants with lower yield as a consequence of the herbicide application rate.