Selection of drought-tolerant tomato during the vegetative stage / Seleção de tomate tolerante à seca durante fase vegetativa

Drought stress consists of a significant productivity constraint in tomatoes. Two contrasting crosses were performed to estimate physiological and morphological traits in response to drought stress during the vegetative stage, aiming to identify superior genotypes for drought tolerance. Two genotypes (GBT_2037 - sensitive drought-sensitive and GBT_2016 - intermediate drought-tolerant) were used as female parentals, and a commercial hybrid (drought tolerant) was used as a pollen source in both crosses: C1 (GBT_2037 × Commercial hybrid) and C2 (GBT_2016 x Commercial hybrid). The populations of parentals (P), the first generation of descendants (F1), backcrosses (BC), and the second generation of self-pollination (F2) were exposed to drought stress for 20 days when they were analyzed: physiological traits (relative water content of leaves, proline, and relative chlorophyll content) and morphological (plant height, stem diameter, number of leaves, fresh and dry matter of roots and shoot and classification by wilt scale). The means of chlorophyll, root/shoot ratio, and water content in leaves for the F2 generation of C2 were higher than C1, indicating that C2 resulted in plants with greater capacity to maintain turgor under conditions of water stress and presented minor damage to the photosynthetic structures, consequently showing greater tolerance to drought stress.

O estresse hídrico consiste em uma importante restrição à produtividade do tomateiro. Dois cruzamentos contrastantes foram realizados para estimar características fisiológicas e morfológicas envolvidas na resposta ao estresse hídrico durante a fase vegetativa, visando identificar genótipos superiores para tolerância à seca. Dois genótipos (GBT_2037 - sensível à seca e GBT_2016 - intermediário tolerante à seca) foram utilizadas como parentais femininos e um híbrido comercial (tolerante à seca) foi usado como fonte de pólen em ambos os cruzamentos: C1 (GBT_2037 x Híbrido comercial) e C2 (GBT_2016 x Comercial híbrido). As populações de parentais (P), primeira geração de descendentes (F1), retrocruzamentos (BC) e segunda geração de autopolinização (F2) foram expostas ao estresse hídrico durante 20 dias, quando foram analisadas: características fisiológicas (conteúdo relativo de água das folhas, prolina e teor relativo de clorofila) e morfológicos (altura da planta, diâmetro do caule, número de folhas, matéria fresca e seca de raízes e parte aérea e classificação por nivel de murcha). As médias de clorofila, razão raiz/parte aérea e teor de água nas folhas para a geração F2 de C2 foram superiores a C1, indicando que C2 resultou em plantas com maior capacidade de manter o turgor sob condições de estresse hídrico e apresentou menos danos nas estruturas fotossintéticas, consequentemente apresentando maior tolerância ao estresse hídrico.

Key locations for soybean genotype assessment in South Brazil region / Locais-chave para a avaliação de genótipos de soja na região Sul do Brasil

Mitigating the high costs of soybean breeding programs requires constant improvement of all the involved processes. Identifying representative and discriminating test locations, as well as excluding redundant and/or non-representative locations, makes it possible to select genotypes with more accuracy while reducing the costs of the multi-environment trials (MET). Therefore, this study had three objectives: to evaluate the representativeness and discriminating power of test locations; to identify similar test locations for each Edaphoclimatic Region (ECR) and locations that did not contribute to genotype evaluation; and to recommend the best locations for evaluating MET in order to reduce breeding program costs in the soybean macro regions 1 (M1) and 2 (M2). Grain yield (GY) data from Value-for-Cultivation-and-Use (VCU) trials obtained during the 2012-2016 crop seasons were used, totaling 132 environments (location x year) and 43 genotypes. The experiments were arranged in a randomized complete block design with three replications. Representative and discriminant locations were identified by GGL (genotype main effects plus genotype × location interaction) + GGE (genotype main effects plus genotype × environment interaction) analysis, using GGEbiplot software. Representative and discriminant locations were identified for each ECR and can be used as core locations for breeding programs. Similarly, locations that were not representative and discriminant, or that present redundancy in the results, should be excluded from or replaced in MET. The most recommended locations for conducting VCU trials in M1 were Cachoeira do Sul (ECR 101); Ronda Alta, Passo Fundo, Santa Bárbara do Sul, and Ciríaco (ECR 102); and Castro (ECR 103). For M2, the most suitable locations are Rolândia, Marechal Cândido Rondon, Campo Mourão, Santa Terezinha de Itaipu, Palotina, Floresta, and Londrina (ECR 201); Naviraí (ECR 202); and Ponta Porã and Maracajú (ECR 204).

A redução dos elevados custos dos programas de melhoramento de soja requer melhoria constante detodos os processos envolvidos. A identificação de locais de teste representativos e discriminativos, bemcomo a exclusão de locais redundantes e/ou não representativos, possibilita a seleção de genótipos commaior precisão, e ao mesmo tempo reduz os custos dos ensaios multiambientes (MET). Portanto, esteestudo teve três objetivos: avaliar a representatividade e a discriminância dos locais de teste; identificarlocais de teste semelhantes e que não contribuíram para a avaliação genotípica dentro de cada RegiãoEdafoclimática (ECR); e recomendar os melhores locais para a avaliação de MET, a fim de reduzir oscustos do programa de melhoramento genético nas macrorregiões 1 (M1) e 2 (M2) de adaptação da soja.Dados de rendimento de grãos (RG) de ensaios de “Valor de Cultivo e Uso” (VCU) obtidos duranteos anos agrícolas 2012-2016 foram utilizados, totalizando 132 ambientes (local x ano) e 43 genótipos.Os experimentos foram conduzidos em delineamento de blocos completamente casualizados, comtrês repetições. Locais representativos e discriminantes foram identificados pela análise GGL (efeitoprincipal de genótipo + interação genótipo × local) + GGE (efeito principal de genótipo + interaçãogenótipo × ambiente), utilizando o software GGEbiplot. Locais representativos e discriminantes foramidentificados para cada ECR, e podem ser utilizados como locais-chave em programas de melhoramento.Da mesma forma, locais que não foram representativos e discriminantes, ou que apresentam redundânciaforam identificados e devem ser excluídos ou substituídos no MET. Os locais recomendados para acondução de ensaios de VCU na M1 são: Cachoeira do Sul (ECR 101); Ronda Alta, Passo Fundo, SantaBárbara do Sul e Ciríaco (ECR 102); e Castro (ECR 103). Já para M2, os locais recomendados são:Rolândia, Marechal Cândido Rondon, Campo Mourão...

Key locations for soybean genotype assessment in South Brazil region / Locais-chave para a avaliação de genótipos de soja na região Sul do Brasil

Mitigating the high costs of soybean breeding programs requires constant improvement of all the involved processes. Identifying representative and discriminating test locations, as well as excluding redundant and/or non-representative locations, makes it possible to select genotypes with more accuracy while reducing the costs of the multi-environment trials (MET). Therefore, this study had three objectives: to evaluate the representativeness and discriminating power of test locations; to identify similar test locations for each Edaphoclimatic Region (ECR) and locations that did not contribute to genotype evaluation; and to recommend the best locations for evaluating MET in order to reduce breeding program costs in the soybean macro regions 1 (M1) and 2 (M2). Grain yield (GY) data from Value-for-Cultivation-and-Use (VCU) trials obtained during the 2012-2016 crop seasons were used, totaling 132 environments (location x year) and 43 genotypes. The experiments were arranged in a randomized complete block design with three replications. Representative and discriminant locations were identified by GGL (genotype main effects plus genotype × location interaction) + GGE (genotype main effects plus genotype × environment interaction) analysis, using GGEbiplot software. Representative and discriminant locations were identified for each ECR and can be used as core locations for breeding programs. Similarly, locations that were not representative and discriminant, or that present redundancy in the results, should be excluded from or replaced in MET. The most recommended locations for conducting VCU trials in M1 were Cachoeira do Sul (ECR 101); Ronda Alta, Passo Fundo, Santa Bárbara do Sul, and Ciríaco (ECR 102); and Castro (ECR 103). For M2, the most suitable locations are Rolândia, Marechal Cândido Rondon, Campo Mourão, Santa Terezinha de Itaipu, Palotina, Floresta, and Londrina (ECR 201); Naviraí (ECR 202); and Ponta Porã and Maracajú (ECR 204).(AU)

A redução dos elevados custos dos programas de melhoramento de soja requer melhoria constante detodos os processos envolvidos. A identificação de locais de teste representativos e discriminativos, bemcomo a exclusão de locais redundantes e/ou não representativos, possibilita a seleção de genótipos commaior precisão, e ao mesmo tempo reduz os custos dos ensaios multiambientes (MET). Portanto, esteestudo teve três objetivos: avaliar a representatividade e a discriminância dos locais de teste; identificarlocais de teste semelhantes e que não contribuíram para a avaliação genotípica dentro de cada RegiãoEdafoclimática (ECR); e recomendar os melhores locais para a avaliação de MET, a fim de reduzir oscustos do programa de melhoramento genético nas macrorregiões 1 (M1) e 2 (M2) de adaptação da soja.Dados de rendimento de grãos (RG) de ensaios de “Valor de Cultivo e Uso” (VCU) obtidos duranteos anos agrícolas 2012-2016 foram utilizados, totalizando 132 ambientes (local x ano) e 43 genótipos.Os experimentos foram conduzidos em delineamento de blocos completamente casualizados, comtrês repetições. Locais representativos e discriminantes foram identificados pela análise GGL (efeitoprincipal de genótipo + interação genótipo × local) + GGE (efeito principal de genótipo + interaçãogenótipo × ambiente), utilizando o software GGEbiplot. Locais representativos e discriminantes foramidentificados para cada ECR, e podem ser utilizados como locais-chave em programas de melhoramento.Da mesma forma, locais que não foram representativos e discriminantes, ou que apresentam redundânciaforam identificados e devem ser excluídos ou substituídos no MET. Os locais recomendados para acondução de ensaios de VCU na M1 são: Cachoeira do Sul (ECR 101); Ronda Alta, Passo Fundo, SantaBárbara do Sul e Ciríaco (ECR 102); e Castro (ECR 103). Já para M2, os locais recomendados são:Rolândia, Marechal Cândido Rondon, Campo Mourão...(AU)

Heterosis and genetic parameters for grain quality in oat segregating populations

Improvement of quality-related traits of grains is a constant concern in white oat breeding programs, which challenges breeders to understand their dynamics. The performance of different genetic combinations must be thoroughly evaluated to make high nutritional quality cultivars available. This study aimed to estimate the heterosis on F1 and F2 generations, vigor loss, due to inbreeding, and correlation between the grain chemical components to understand the dynamics of these traits, considering two segregating oat progenies. The populations Albasul × UPF 15 (population 1) and IAC 7 × UFRGS 19 (population 2) were developed. Both populations showed transgressive segregant individuals. The combination Albasul × UPF 15 provided significant heterosis for traits -glucan total and soluble fiber contents, while the population obtained by crossing IAC 7 × UFRGS 19 generated significant gain by heterosis for total fiber, insoluble fibers and non-structural carbohydrate contents. Considering the F2 average for each population, one can observe that population 1 presents higher -glucan and lipid contents than population 2. On the other hand, population 2 has higher protein content than population 1. In both populations, the non-structural carbohydrate content is strongly and negatively correlated whith protein, total and insoluble fibers. Correlations between total fibers and lipids and between total fibers and insoluble fibers were both positive and high in both populations.

Heterosis and genetic parameters for grain quality in oat segregating populations

Improvement of quality-related traits of grains is a constant concern in white oat breeding programs, which challenges breeders to understand their dynamics. The performance of different genetic combinations must be thoroughly evaluated to make high nutritional quality cultivars available. This study aimed to estimate the heterosis on F1 and F2 generations, vigor loss, due to inbreeding, and correlation between the grain chemical components to understand the dynamics of these traits, considering two segregating oat progenies. The populations Albasul × UPF 15 (population 1) and IAC 7 × UFRGS 19 (population 2) were developed. Both populations showed transgressive segregant individuals. The combination Albasul × UPF 15 provided significant heterosis for traits -glucan total and soluble fiber contents, while the population obtained by crossing IAC 7 × UFRGS 19 generated significant gain by heterosis for total fiber, insoluble fibers and non-structural carbohydrate contents. Considering the F2 average for each population, one can observe that population 1 presents higher -glucan and lipid contents than population 2. On the other hand, population 2 has higher protein content than population 1. In both populations, the non-structural carbohydrate content is strongly and negatively correlated whith protein, total and insoluble fibers. Correlations between total fibers and lipids and between total fibers and insoluble fibers were both positive and high in both populations.(AU)