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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 FROM AUTHOR]

Identifying agronomic traits correlated to grain yield can be very useful for soybean [Glycine max (L.) Merr.] breeding, especially if these traits can be measured through unmanned aerial vehicle high‐throughput phenotyping rather than through manual measurements. The objective of the present study was to assess the association between canopy coverage and soybean grain yield through different statistical methodologies. A panel with 97 soybean genotypes was evaluated in two field experiments conducted in Paraná State, Brazil. Canopy coverage was determined by using an RGB camera coupled to a drone. Images taken during flights at phenological stages V3‐V4, V5‐V6, V7‐V8, and V9‐R1 were used to calculate canopy coverage based on the green pixel ratio in each experimental unit. There were significant genotype × environment interactions in all evaluated traits. Selective accuracy values (0.73–0.96) revealed indirect yield selection efficiency based on canopy coverage. High genetic correlation estimates (0.76) were observed between grain yield and canopy coverage at flowering in one of the assessed environments. These results were confirmed through genetic correlation coefficient decomposition in direct and indirect effects and of gain estimates presenting indirect selection. Thus, canopy coverage data remotely collected using drones to soybean indirect selection for grain yield can be a promising strategy to accelerate genetic gains in soybean breeding programs. Core Ideas: Use of high‐throughput phenotyping in soybean breeding programs for yield prediction.High efficiency in the response correlated by indirect selection for grain yield using drones.High estimate of genotypic correlation is observed between canopy coverage and grain yield in soybean breeding. [ABSTRACT FROM AUTHOR]

In breeding of autogamous plants, breeders often deal with the evaluation of progenies derived from multiple populations. We hypothesize that selection strategies that account for the heterogeneous genotypic variability of progenies within these populations associated with the population means may improve progeny selection. Thus, the objective of this work was to use two mixed models with progenies nested to population effects to obtain and evaluate indexes for the genotypic values of progenies. We used simulations and a real dataset from a multipopulation recurrent selection program of common bean (Phaseolus vulgaris L.). Progenies from 20 populations were evaluated for grain yield in two different generations. The three studied indexes consider the merit of populations, but differently account for the genotypic variability of progenies within populations: index gM uses a mean estimate, index gH is based on heterogeneous estimates, and the index gW also uses a mean estimate but is weighted by the selection accuracy within populations. The studied populations were highly diverse in both generations, justifying the implementation of the two models to obtain the population means and specific estimates of genotypic variability of progenies for each population. The comparison among indexes suggested that index gW more appropriately explored the information from the genotypic profiles of the common bean studied populations in the genotypic values of progenies. Thus, the index gW has the potential to help breeders of self-pollinated plant species to improve progeny selection from multiple populations. [ABSTRACT FROM AUTHOR]

Key message: A multiparental random mating population used in sorghum breeding is amenable for the detection of QTLs related to tropical soil adaptation, fine mapping of underlying genes and genomic selection approaches. Tropical soils where low phosphorus (P) and aluminum (Al) toxicity limit sorghum [Sorghum bicolor (L.) Moench] production are widespread in the developing world. We report on BRP13R, a multiparental random mating population (MP-RMP), which is commonly used in sorghum recurrent selection targeting tropical soil adaptation. Recombination dissipated much of BRP13R's likely original population structure and average linkage disequilibrium (LD) persisted up to 2.5 Mb, establishing BRP13R as a middle ground between biparental populations and sorghum association panels. Genome-wide association mapping (GWAS) identified conserved QTL from previous studies, such as for root morphology and grain yield under low-P, and indicated the importance of dominance in the genetic architecture of grain yield. By overlapping consensus QTL regions, we mapped two candidate P efficiency genes to a ~ 5 Mb region on chromosomes 6 (ALMT) and 9 (PHO2). Remarkably, we find that only 200 progeny genotyped with ~ 45,000 markers in BRP13R can lead to GWAS-based positional cloning of naturally rare, subpopulation-specific alleles, such as for SbMATE-conditioned Al tolerance. Genomic selection was found to be useful in such MP-RMP, particularly if markers in LD with major genes are fitted as fixed effects into GBLUP models accommodating dominance. Shifts in allele frequencies in progeny contrasting for grain yield indicated that intermediate to minor-effect genes on P efficiency, such as SbPSTOL1 genes, can be employed in pre-breeding via allele mining in the base population. Therefore, MP-RMPs such as BRP13R emerge as multipurpose resources for efficient gene discovery and deployment for breeding sorghum cultivars adapted to tropical soils. [ABSTRACT FROM AUTHOR]

Correlated information from different genetic sources is absent in most of annual self‐pollinated crops using the recurrent selection strategy, which is a breeding strategy that improves crop traits consistently over years. In common bean (Phaseolus vulgaris L.) breeding programs, progenies coming from multiple biparental populations are evaluated across generations of inbred plants with the assumption that the data are not correlated. In this paper, in addition to the effects of progeny, we evaluate the effects of populations and generations and provide information for the selection process in a self‐pollinated recurrent selection breeding program. Nineteen progenies were extracted from 20 breeding populations and evaluated at different sowing times across F3:4 and F3:5 generations. The evaluated traits were plant architecture, angular leaf spot resistance, grain appearance, and grain yield. Progenies were selected using three methods: means of progenies regardless of generation and population effects; the multigeneration index (MI), which considered the generation effect; and the selection index with parents, populations, progenies, and generations (SIPPPG). We showed that adding variation among progenies correctly weighted for different generations as well as variation among populations yield for an increase in genetic gain. Therefore, selection accuracies of the SIPPPG were the highest for all traits compared with those of MI and when generation and population effects were not considered. [ABSTRACT FROM AUTHOR]

In common bean breeding programs, experiments are conducted in different environments to select plants with high potential for inbred lines extraction and/or recombination. The occurrence of genetic and/or statistical unbalance is common in these experiments. Moreover, there may be (co)variance between genetic and non-genetic effects when treatments are assessed in different environments. Our aim was to (1) test different (co)variance structures between seasons for genetic and non-genetic effects; (2) choose the model with the highest predictive capacity of the genotypic value; and (3) select the superior progenies to mitigate the effects of genotype-by-environment interactions. To this end, two experiments were conducted in the 2015 drought and winter seasons. The grain yield and grain aspect were assessed. Model 4, with an unstructured (co)variance for genetic effects, homogeneous block variance, and heterogeneous residual diagonal variance, was the model that best fit the data. The heritability estimates and their accuracy differed between the different adjusted models, with the most accurate estimates observed in model 4. The genetic correlation between the drought and winter seasons was of low magnitude (− 0.04) for grain yield, which corroborates the strong genotype by environment interaction. The average gain predicted with the recombination of the selected progenies in model 4 was 2.97% for grain yield. The modeling of different (co)variance structures for genetic and non-genetic effects could be applicable for analyses involving statistical unbalance and the assessment of progenies in different environments, with the aim of selecting those with high potential for recombination. [ABSTRACT FROM AUTHOR]

The goal of breeding programs is selection toward the ideal plant type. In this study, field experiments were performed to select common bean inbred progenies that maximize the probability of extracting superior lines. A total of 124 inbred progenies of three consecutive generations (F2:3, F2:4, and F2:5) were conducted in field experiments over three different environments (one generation in each environment). Seven different traits, related to disease severity, commercial acceptance grain, and yield, were evaluated by best linear unbiased prediction. This work underscored the importance of incorporating population information into the statistical model as a means of comparing progenies from different populations with higher efficacy, even when kinship information between populations is not available. Toward the common bean ideotype, 20 inbred progenies of greater potential were selected using the factor analysis and genotype-ideotype distance (FAI-BLUP) index. This index is based on the structural equation models by joining the factor analysis technique (exploratory factor analysis) with the ideotype design (confirmatory factor analysis). The predicted genetic gain was increased for all the traits in all generations. Selection strategies that capture the multitrait information capitalize the progenies × growth season interactions and are based on the ideotype, such as as the FAI-BLUP index, have the potential for use in genetic breeding toward the common bean ideotype and can accelerate the release of more adapted cultivars. [ABSTRACT FROM AUTHOR]

Recommendation of new varieties is supported by value for cultivation and use (Valor de Cultivo e Uso [VCU]) trials. For a more reliable recommendation, it is necessary to identify methodologies that make better use of the genotype‐by‐environment interaction (GEI). The methodology proposed by Dias et al. is an alternative to take advantage of the GEI; it considers concepts of Bayesian models and probability methods of adaptation and stability analysis in a single model, classifying the genotypes regarding possible success based on a defined selection intensity. Thus, the aim of the present study was to explore the use of Bayesian probabilistic method for the purpose of recommend common bean (Phaseolus vulgaris L.) varieties. To that end, we used grain yield data from 15 genotypes of common bean evaluated in 42 environments distributed over different crop seasons, years, and locations in regard to VCU trials conducted from 2016 to 2018. Under a predefined selection intensity of 30%, the genotypes with greater marginal probability of superior performance were G01, G14, G07, G11, and G02. The genotypes with greater marginal probability of superior stability were G06, G07, G04, G03, and G12. Considering the joint probability of superior performance and yield stability, the genotypes G07, G14, G01, G11, and G04 stand out. Therefore, the use of the Bayesian probabilistic method showed promise in recommendation of common bean varieties. [ABSTRACT FROM AUTHOR]

The objectives of this study were to obtain information about genetic parameters of a partial diallel in common bean, as well as to evaluate the effects of growing seasons, locations and generations in these estimates in order to define the best strategies for the use of diallel analysis as methodology of selection of parents and populations in common bean breeding. Twelve parents were crossed in a partial diallel scheme (5 × 7), and the F1 and F2 generations were evaluated in four experiments (I, II, III, and IV). The effects of seasons were determined in the Experiments I and II, in which the F1 generation was evaluated in two distinct growing seasons. To quantify the effect of locations, we used Experiments III and IV, in which the F2 generation was evaluated in the same season, but at different locations, and to quantify the effect of generations, the experiments II (generation F1) and III (F2 generation) were evaluated in the same season and location. As for grain yield the genetic control varied according to the environment. An effect of interaction on the genetic parameters of the diallel was observed for generation, whereas for grain yield, effects of season and location were observed. For plant architecture, parental combining ability should therefore be estimated using the F1 generation, while for grain yield, both the F1 and F2 can be used. The recombination between the best hybrids is a promising strategy for extraction of superior black bean lines. [ABSTRACT FROM AUTHOR]

Studies highlight the hypocotyl diameter (HD) as an effective indicator of plant architecture (PA). Here, we estimated the genetic gain based on HD to improve PA. Twenty populations of cycles zero (C0) and one (C1), both in the F4 generation, were evaluated for PA, grain yield (GY) and HD. Plants with thickest HD in C0 were intercrossed in a circulant diallel mating design. In cycle C1, an estimated genetic gain of 4.93% was achieved for PA and 4.95% for HD. The populations with the highest probability of breeding lines with a thicker HD belong to cycle C1, and this selection strategy did not alter the GY of the populations of this cycle. Thus, indirect selection based on HD is promising for breeding for common bean PA by recurrent mass selection. [ABSTRACT FROM AUTHOR]

The common bean cultivar "BRSMG Realce", recommended for the state of Minas Gerais, has high yield potential and resistance to powdery mildew, bacterial wilt and to anthracnose pathotypes 65, 73, 77, 81, 91, 475 and 479. The plants and pods are high, resulting in lower loss during mechanical harvest. [ABSTRACT FROM AUTHOR]

The bean cultivar BRSMG União as a new option of a bean cultivar with jalo grains for the state of Minas Gerais. The cultivar BRSMG União had an average grain yield of 9.8% above the mean of the controls (Jalo EEP 558 and BRS Radiante) and was resistant to powdery-mildew. [ABSTRACT FROM AUTHOR]