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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.

The GxE interaction is one of the major difficulties of plant breeding programs, both in the selection phase and in the recommendation of cultivars. To assess adaptability and stability, various statistical methods are used. The simultaneous use of some methodologies, using multi-information criteria for cultivar’s recommendation, can extract information that cannot be observed using each methodology separately. The aim of this work was to perform a large description of the behavior of flooded-irrigated rice genotypes, responding to environmental variations, using methods already established in the literature, but exploring the particularities of each methodology that together establish an information criterion for cultivar recommendation. To this end, 18 rice genotypes belonging to flood-irrigated rice breeding program were evaluated over four agricultural years, 2012/2013 to 2015/2016, totaling 12 environments (3 sites × 4 years). Multi-information estimates were performed to adaptability and stability analysis. There was no sign for the effect of the genotypes, and there was the significance of the effects of environment and GxE interaction. The aggregation of information and the large description of the behavior of the flooded rice genotypes demonstrated to be an efficient tool for studies of adaptability and stability.

The performance of genotypes in a wide range of environments can be affected by extensive genotype × environment (G × E) interactions, making the subdivision of the testing environments into relatively more homogeneous groups of locations (mega-environments) a necessary strategy. The genotype main effects + genotype × environment interaction biplot method (GGE) allows identification of megaenvironments and selection of stable genotypes adapted to specific environments and mega-environments. The objectives of this study were to identify mega-environments regarding sorghum [Sorghum bicolor (L.) Moench] grain yield and demonstrate that the GGE biplot method can identify essential locations for conducting tests in different mega-environments. A total of 22 competition trials of grain sorghum genotypes were conducted over three crop seasons across several production locations in Brazil. A total of 25, 22, and 30 genotypes were evaluated during the first, second, and third crop seasons, respectively. After identifying the presence of G × E interactions, the data were subjected to adaptability and stability analyses using the GGE biplot method. A phenotypic correlation network was used to express functional relationships between environments. The GGE biplot was found to be an efficient approach for identifying three mega-environments in grain sorghum in Brazil, selecting representative and discriminative environments, and recommending more adaptive and stable grain sorghum genotypes Made available in DSpace on 2021-12-15T12:06:58Z (GMT). No. of bitstreams: 1 Identification-of-megau2010environments-for-grain-sorghum-in-Brazil-using-GGE-biplot.pdf: 2189041 bytes, checksum: 72d181e5628b2eaa3a76f2ed7c73a106 (MD5) Previous issue date: 2021

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. Made available in DSpace on 2020-10-23T09:12:22Z (GMT). No. of bitstreams: 1 Genetic-diversity.pdf: 3512393 bytes, checksum: 61feb781aa0168f39915f29d3bbfcc09 (MD5) Previous issue date: 2021

The present study evaluated the importance of auxiliary traits of a principal trait based on phenotypic information and previously known genetic structure using computational intelligence and machine learning to develop predictive tools for plant breeding. Data of an F2 population represented by 500 individuals, obtained from a cross between contrasting homozygous parents, were simulated. Phenotypic traits were simulated based on previously established means and heritability estimates (30%, 50%, and 80%); traits were distributed in a genome with 10 linkage groups, considering two alleles per marker. Four different scenarios were considered. For the principal trait, heritability was 50%, and 40 control loci were distributed in five linkage groups. Another phenotypic control trait with the same complexity as the principal trait but without any genetic relationship with it and without pleiotropy or a factorial link between the control loci for both traits was simulated. These traits shared a large number of control loci with the principal trait, but could be distinguished by the differential action of the environment on them, as reflected in heritability estimates (30%, 50%, and 80%). The coefficient of determination were considered to evaluate the proposed methodologies. Multiple regression, computational intelligence, and machine learning were used to predict the importance of the tested traits. Computational intelligence and machine learning were superior in extracting nonlinear information from model inputs and quantifying the relative contributions of phenotypic traits. The R2 values ranged from 44.0% - 83.0% and 79.0% - 94.0%, for computational intelligence and machine learning, respectively. In conclusion, the relative contributions of auxiliary traits in different scenarios in plant breeding programs can be efficiently predicted using computational intelligence and machine learning.

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.

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

Mutant genotypes of biomass sorghum for low lignin - bmr (brown midrib) and sensitive to photoperiod are interesting for the production of second-generation ethanol. This research aimed to estimate the combining ability between lines A and R of biomass sorghum, both carrying the bmr-6 allele, for bioenergy potential. Three male-sterile lines (A) and ten fertility restorative lines (R) were used, generating 30 hybrids bmr and sensitive to photoperiod. The 30 hybrids, their parents, and six controls were evaluated in a triple lattice design (7 × 7), in two experimental areas of Embrapa Maize and Sorghum, in Sete Lagoas and Nova Porteirinha (Minas Gerais). These lines were crossed in a partial diallel design, based on the Griffing IV model (1956) adapted for partial diallel by Miranda Filho and Geraldi (1984). The evaluated traits were: days to flowering (DTF, days after sowing), plant height (PH, m), fresh biomass yield (FBY, ton.ha−1), dry matter (DM, %), and dry matter yield (DMY, t. ha−1). In addition, the FAI-BLUP selection Index was used to assist the selection of hybrids that simultaneously meet favorable traits. Results revealed that treatments x environment interaction had a significant effect (p

Sweet sorghum [Sorghum bicolor (L.) Moench] is a type of cultivated sorghum characterized by the accumulation of high levels of sugar in the stems and high biomass accumulation, making this crop an important feedstock for bioenergy production. Sweet sorghum breeding programs that focus on bioenergy have two main goals: to improve quantity and quality of sugars in the juicy stem and to increase fresh biomass productivity. Genetic diversity studies are very important for the success of a breeding program, especially in the early stages, where understanding the genetic relationship between accessions is essential to identify superior parents for the development of improved breeding lines. The objectives of this study were: to perform phenotypic and molecular characterization of 100 sweet sorghum accessions from the germplasm bank of the Embrapa Maize and Sorghum breeding program; to examine the relationship between the phenotypic and the molecular diversity matrices; and to infer about the population structure in the sweet sorghum accessions. Morphological and agro-industrial traits related to sugar and biomass production were used for phenotypic characterization, and single nucleotide polymorphisms (SNPs) were used for molecular diversity analysis. Both phenotypic and molecular characterizations revealed the existence of considerable genetic diversity among the 100 sweet sorghum accessions. The correlation between the phenotypic and the molecular diversity matrices was low (0.35), which is in agreement with the inconsistencies observed between the clusters formed by the phenotypic and the molecular diversity analyses. Furthermore, the clusters obtained by the molecular diversity analysis were more consistent with the genealogy and the historic background of the sweet sorghum accessions than the clusters obtained through the phenotypic diversity analysis. The low correlation observed between the molecular and the phenotypic diversity matrices highlights the complementarity between the molecular and the phenotypic characterization to assist a breeding program. Made available in DSpace on 2017-08-25T19:13:00Z (GMT). No. of bitstreams: 1 Phenotypicmolecular.pdf: 4534558 bytes, checksum: 7e98a0d3b0adcbdf79eb8bd2f106509c (MD5) Previous issue date: 2017-08-24

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. 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.

1024x768 O sorgo granifero tem apresentado significativa expansao em area e em produtividade no Brasil nos ultimos anos, em razao do aumento da demanda de graos para alimentacao animal. O melhoramento de plantas tem contribuido para o aumento de produtividade, com a oferta de novos hibridos adaptados as diversas areas de producao. O presente trabalho teve o objetivo de selecionar aqueles mais produtivos e estaveis em cultivo de verao em tres localidades. Os experimentos foram instalados em Sete Lagoas, MG, Nova Porteirinha, MG e Pelotas, RS. O delineamento experimental foi em blocos casualizados, com tres repeticoes. Os hibridos comerciais BRS 310 e BRS 330 foram usados como testemunhas. Houve diferencas significativas entre os hibridos para as quatro caracteristicas avaliadas, mostrando haver variabilidade entre os hibridos. A interacao genotipos x ambientes foi significativa para indice de colheita de paniculas e produtividade de graos. A produtividade media das testemunhas nos tres ambientes foi de 4,5 t ha 1 , sendo que 11 hibridos foram estatisticamente superiores as mesmas. Os hibridos 1099030 e 1099048 apresentaram desempenho superior aos demais hibridos, com produtividade acima de 6,0 t ha -1 , e ciclo de maturacao mais precoce do que a testemunha mais produtiva: BRS 330.

Sweet sorghum is a special purpose sorghum with a sugar-rich stalk, almost like sugarcane. The objective of this work was to evaluate the phenotypic stability and adaptability of sweet sorghum genotypes, in different Brazilian regions, for the production of bioethanol. Twenty-five sweet sorghum genotypes were evaluated in 10 environments distributed in the Southeast, Midwest, Northeast, and Southern regions of Brazil. The experimental design was a randomized complete block design with three repetitions. The following agroindustrial traits were evaluated: fresh biomass yield (FBY), total soluble solids content (TSS) and tons of Brix per hectare (TBH). The adaptability and stability analyzes were performed with the methods GGEbiplot and Annicchiarico methodologies. The Annicchiarico and GGEbiplot adaptability and stability study methods presented satisfactory and consistent results and can be used separately or together in sweet sorghum breeding programs, and B005 and B008 sweet sorghum genotypes presented superior performance, with similar classification in both methods studied. Made available in DSpace on 2021-12-12T02:16:13Z (GMT). No. of bitstreams: 1 Phenotypic-stability.pdf: 1135726 bytes, checksum: 83244d6f62e2ea4eebea4d4b1283a06e (MD5) Previous issue date: 2022

Sorghum bicolor (L.) Moench is a crop that has high potential to be used for bioenergy generation. The objective of this study was to introgression of the bmr6 allele in elite lines of biomass sorghum and to obtain experimental ??brown midrib?? hybrids. Three genetic materials belonging to the Embrapa Maize and Sorghum Breeding Program were used. Two backcross programs were conducted separately, in which the CMSXS170 line was the donor of the bmr6 allele and the CMSXS652 and IS23777 lines were the recurrent ones. Through molecular markers specific for the bmr6 allele, the assisted selection was utilized for the brown midrib characteristic in the BC1F1 and BC2F1 generations. Polymorphic SNP markers were distributed throughout the genome of sorghum to accelerate the recovery of the recurrent genome. After the confirmation of the bmr genotypes, the lines were crossed with line A (female), to obtain the hybrid seeds and evaluated under field conditions. As result, it was possible to perform the introgression of the bmr6 allele in lines of biomass sorghum. The SNPs markers were efficient in identifying individuals with a higher rate of recurrence of the recurrent genome. Experimental "brown midrib" hybrids were obtained and demonstrated satisfactory potential for bioenergy production. Made available in DSpace on 2020-08-07T11:12:21Z (GMT). No. of bitstreams: 1 Introgression-bmr6.pdf: 885675 bytes, checksum: 25008e7377dd9948cd3f71e5b528a1a6 (MD5) Previous issue date: 2020

The global demand for fuel from renewable sources has expanded rapidly in recent years and sweet sorghum is a crop with high potential for bioenergy production. This study aimed evaluate the performance of sweet sorghum hybrids belonging to the Embrapa Milho e Sorgo breeding program in different edaphoclimatic regions of Minas Gerais and to identify promising hybrids for bioenergy production, considering multiple traits of sweet sorghum. The study was conducted in two experimental areas of Embrapa Milho e Sorgo, in Sete Lagoas and Nova Porteirinha. A total of 36 sweet sorghum genotypes were evaluated, with 28 experimental hybrids and eight controls. The evaluated traits were: days from planting to flowering, plant height, fresh biomass yield, juice extraction, total soluble solids and tons of Brix per hectare. Analysis of variance, multiple comparison test, the Williams index of selection and the index suggested by Mulamba & Mock were performed. The results demonstrated the existence of genetic variability among the genotypes of sweet sorghum, demonstrating the possibility of selecting high-performance genotypes superior. The selection indexes employed were efficient in the selection of sweet sorghum hybrids with higher agroindustrial performance. It was possible to identify hybrids of sweet sorghum promising for bioenergy production. Made available in DSpace on 2020-11-13T19:26:58Z (GMT). No. of bitstreams: 1 Multi-trait-selection.pdf: 1002407 bytes, checksum: 2fa4b796a29640dad27bac5af4d7f131 (MD5) Previous issue date: 2020