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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. Core Ideas: Reliable recommendations of common bean varieties can be obtained from probabilist Bayesian models.High‐performance and stable common bean varieties were identified for tropical conditions.Probability methods can minimize risk in decision‐making for broad and specific variety recommendation. Plain Language Summary: Recommendation of varieties is supported by multi‐environment trials (MET). In MET, in addition to genetic and environmental effects, there is an interaction between these (GEI). According to the study of Dias et al., the methodology to take advantage of the GEI is based in Bayesian probabilistic models. Thus, the aim of the present study was to explore the use of Bayesian probabilistic method proposed by Dias et al. for recommending common bean (Phaseolus vulgaris L.) varieties. To that end, we used grain yield data from 15 genotypes of common bean evaluated in 42 environments. Under a predefined selection intensity of 30%, the genotypes with greater joint probability of superior performance and yield stability were G07, G14, G01, G11 and G04. The use of the Bayesian probabilistic method showed promise in recommendation common bean varieties. [ABSTRACT FROM AUTHOR]

Diseases such as anthracnose (Colletotrichum lindemuthianum) and angular leaf spot (Pseudocercospora griseola) can occur in common bean and compromise crop production, especially if susceptible cultivars are grown under conditions favorable to these diseases. The most efficient and sustainable alternative for controlling these diseases is the use of resistant cultivars. The identification of sources of resistance and the use of tools that assist in accurate selection of resistant cultivars are crucial to the success of breeding programs. The aim of this study was to characterize 67 carioca common bean cultivars recommended in Brazil regarding reaction to races 65, 73, 81, and 89 of C. lindemuthianum and to races 63‐23 and 63‐63 of P. griseola and to verify the application of factor analysis in the selection of cultivars resistant to both pathogens. The cultivars and resistant controls BRS Esplendor and MAIII.16‐159 were inoculated in a greenhouse in two experiments for each race. Cultivars were assessed for resistance to multiple races of anthracnose and angular leaf spot using artificial inoculation of plants at V2 stage. Factor analysis proved to be a good alternative for accurate selection of resistant cultivars. The percentage of cultivars simultaneously resistant to all races was low (8.95%). The cultivars Rudá R, VC 15, IAC‐Apuã, IAC‐Carioca Pyatã, BRSMG Amuleto, and BRS FC104 selected by factor analysis showed resistance to anthracnose and angular leaf spot. Therefore, these cultivars are potential sources of resistance for incorporation in bean breeding programs. Core Ideas: Tools for selecting resistant lines are crucial for the success in breeding programs aiming resistance.Factor analysis proved to be an alternative for accurate selection of resistant cultivars.Six cultivars were selected by factor analysis as resistant to anthracnose and angular leaf spot. [ABSTRACT FROM AUTHOR]

The performance of inbred lines in advanced endogamous generation is commonly evaluated in successive generations of testing and selection, which we defined as "multistage field trials" (MSFT). MSFT data routinely exhibit heterogeneity of (co)variances at several levels due to genetic and/or statistical imbalance. Nowadays, mixed models have been widely used to deal with unbalanced data. However, few studies on common bean have addressed the use of a mixed model approach with modeling of (co)variance structures for random effects in MSFT. Furthermore, factor analysis and genotype‐ideotype distance (FAI‐BLUP) selection index was originally proposed using best linear unbiased predictions from individual analysis. In this regard, we aimed to study the implications of modeling (co)variance structures for random effects in the estimation of genetic parameters and evaluate the accuracy and efficiency of inbred line selection by the modeled FAI‐BLUP approach. A total of five trials were evaluated from 2018 to 2020. The results revealed that the unstructured covariance matrix fitted better for grain yield, whereas the matrix with uniform correlation and heterogeneity of variances fitted better for grain aspect and plant architecture. The modeled FAI‐BLUP approach increased the values of selection accuracy and selection efficiency. Our results suggest that modeling the different structures of (co)variances and selecting the best‐performing genotypes by modeled FAI‐BLUP approach should be used in common bean assays involving unbalanced data. Core Ideas: Genetic and/or statistical imbalance is a reality of plant breeding programs.Selecting the best fitted (co)variance structure for unbalanced data increased the selection accuracy.Low magnitude estimates of genetic correlation indicated the predominance of the complex part of the G × E interaction.The modeled FAI‐BLUP approach increased the selection efficiency by an average of 13.95%. [ABSTRACT FROM AUTHOR]

At initial breeding stages, using a replicated check design is a viable alternative to reduce experimental field area as well as financial and operational costs. In this situation, spatial modeling could act to increase prediction accuracy of plant genotypic values. The objectives of this study were to demonstrate how spatially adjusted models can be used to reduce experimental error and how to compare statistical models in order to identify the best model for accurate genotype selection. For this purpose, we assessed 200 F3:4 tomato families for their resistance to Phytophthora infestans isolates. NC1CELBR, NC25P, and the cultivar Santa Clara were used as checks. Under field conditions, plants were inoculated with P. infestans isolates and scored according to their level of disease severity. Nine statistical models were adjusted to estimate family genotypic values. The selection of the fittest model was based on residual variance values, accuracy, Akaike and Bayesian information criteria, and the maximum likelihood ratio test. We observed spatial patterns within the experimental field area. Spatial modeling decreased error, which is indicated by the better experimental variation distribution. Residual variance decreased, while genotypic variance increased ~10% when spatial analysis was used. Spatial analysis improved selection accuracy by 19% compared with the traditional analysis. Therefore, we recommend incorporating spatial modeling into data analysis in breeding trials for disease resistance because it can provide higher gains from selection compared with traditional modeling approaches, depending on the experimental condition. [ABSTRACT FROM AUTHOR]

In plant breeding, several multicategorical variables are evaluated using score scales that are treated as interval scales. However, statistics that use ratio operations, such as the experimental CV and the selection gain, are only appropriate for ratio scales data. Thus, this work aimed to propose strategies to mitigate the inconveniences faced in the data analysis of score scales, especially those involving CV and selection gain obtained from different scales. This work proposes the following strategies: (i) the use of a standard score scale with the properties of a ratio scale (ascending scale with 0 as the point of origin); (ii) the conversion of the scores from different scales into a common ratio scale; (iii) the adjustment of the data from an interval scale to a ratio scale using the Delta Scale (Scale δui), and (iv) the use of unbiased estimator for CV and selection gain in score scales data. The proposed strategies resulted in unbiased estimates of CV and selection gain from data of different score scales. These strategies have the potential to be used in the meta‐analysis of data within and between plant breeding programs. [ABSTRACT FROM AUTHOR]