Abstract

Purpose-The purpose of this paper is to determine the demographic characteristics and habits of craft beer consumers, as well as to identify the motivational factors for consumption. Design/methodology/approach-Data were collected through questionnaires applied to 316 Brazilian craft beer consumers, and results were evaluated descriptively and by multivariate statistics. Findings-The results of the survey revealed that there is a growing market segment with different buying habits and behaviors compared to traditional beer consumers. Demographically, it was found that these consumers are an attractive part of the beer market in terms of age, schooling and, more importantly, in terms of income, factors that indicate the probability of continued growth in the sector. Research limitations/implications-The research was limited to craft beer consumers in the metropolitan region of Belo Horizonte/MG, Brazil. Practical implications-The results obtained are important, as they can help new craft breweries, as well as help established industry managers to create strategies related to marketing four Ps in order to increase the consumption of its products, with competitive advantages to the market. Originality/value-This research presents the characteristics of the consumers of craft beer, a market segment in evident rise in Brazil, about which there are few studies. In addition, it provides valuable information to both the new beverage manufacturers as well as to the already established entrepreneurs in the market so that they can increase the consumption of their products in a strategic way.

Resumo -O objetivo deste trabalho foi alterar o método centroide de avaliação da adaptabilidade e estabilidade fenotípica de genótipos, para deixá-lo com maior sentido biológico e melhorar aspectos quantitativos e qualitativos de sua análise. A alteração se deu pela adição de mais três ideótipos, definidos de acordo com valores médios dos genótipos nos ambientes. Foram utilizados dados provenientes de um experimento sobre produção de matéria seca de 92 genótipos de alfafa (Medicago sativa) realizado em blocos ao acaso, com duas repetições. Os genótipos foram submetidos a 20 cortes, no período de novembro de 2004 a junho de 2006. Cada corte foi considerado um ambiente. A inclusão dos ideótipos de maior sentido biológico (valores médios nos ambientes) resultou em uma dispersão gráfica em forma de uma seta voltada para a direita, na qual os genótipos mais produtivos ficaram próximos à ponta da seta. Com a alteração, apenas cinco genótipos foram classificados nas mesmas classes do método centroide original. A figura em forma de seta proporciona uma comparação direta dos genótipos, por meio da formação de um gradiente de produtividade. A alteração no método mantém a facilidade de interpretação dos resultados para a recomendação dos genótipos presente no método original e não permite duplicidade de interpretação dos resultados.Termos para indexação: Medicago sativa, análise gráfica, componentes principais, interação genótipos x ambientes. Alteration of the centroid method to evaluate genotypic adaptabilityAbstract -The objective of this work was to modify the centroid method of evaluation of phenotypic adaptability and the phenotype stability of genotypes in order for the method to make greater biological sense and improve its quantitative and qualitative performance. The method was modified by means of the inclusion of three additional ideotypes defined in accordance with the genotypes' average yield in the environments tested. The alfalfa (Medicago sativa L.) forage yield of 92 genotypes was used. The trial had a randomized block design, with two replicates, and the data were used to test the method. The genotypes underwent 20 cuts, from November 2004 to June 2006. Each cut was considered an environment. The inclusion of ideotypes of greater biological average production in the environments produced an arrow-shaped graphical dispersion directed to the right in which the most productive genotypes were placed near the tip of the arrow. With the alteration only five genotypes were classified into the former classes of the original centroid method. The arrow-shaped figure allowed a direct comparison of genotypes throughout the productivity gradient. The alteration performed in the method preserved the easy interpretation of results for genotype recommendations of the original method, and does leaves no room for ambiguity in interpretation of the results.

Resumo -O objetivo deste trabalho foi propor uma abordagem bayesiana do método de Eberhart & Russell para avaliar a adaptabilidade e da estabilidade fenotípica de genótipos de alfafa (Medicago sativa), bem como avaliar a eficiência da utilização de distribuições a priori informativas e pouco informativas. Foram utilizados dados de um experimento em blocos ao acaso, no qual se avaliou a produção de massa de matéria seca de 92 genótipos. A metodologia bayesiana proposta foi implementada no programa livre R por meio da função MCMCregress do pacote MCMCpack. Para representar as distribuições a priori pouco informativas, utilizaram-se distribuições de probabilidade com grande variância; e, para representar distribuições a priori informativas, adotou-se o conceito de meta-análise, que se caracteriza pela utilização de informações provenientes de trabalhos anteriores. A comparação entre as distribuições a priori foi realizada por meio do fator de Bayes, com a função BayesFactor do pacote MCMCpack, que indicou a priori informativa como a mais adequada nas condições deste estudo.Termos para indexação: Medicago sativa, fator de Bayes, priori informativa, interação genótipo x ambiente, MCMC. Bayesian approach for the evaluation of adaptability and stability of alfalfa genotypesAbstract -The objective of this work was to propose a Bayesian approach for the Eberhart & Russell method to evaluate the phenotypic adaptability and stability of alfafa (Medicago sativa) genotypes, as well as to evaluate the efficiency of the use of prior informative and noninformative distributions. Data from a randomized block design experiment evaluating the forage dry weight of 92 genotypes were used. The Bayesian methodology proposed was implemented in the free software R by the MCMCregress function of the MCMCpack package. To represent the noninformative prior distributions, a probability distribution with high variance was used; and, to represent the informative prior, a meta-analysis concept was adopted, characterized by the use of information provided by previous studies. The comparison between the prior distributions was done using the Bayes Factor, with the BayesFactor function of the MCMCpack package, which indicated that an informative prior is more appropriate under the conditions of this study.

Analysis using Artificial Neural Networks has been described as an approach in the decision-making process that, although incipient, has been reported as presenting high potential for use in animal and plant breeding. In this study, we introduce the procedure of using the expanded data set for training the network. Wealso proposed using statistical parameters to estimate the breeding value of genotypes in simulated scenarios, in addition to the mean phenotypic value in a feed-forward back propagation multilayer perceptron network. After evaluating artificial neural network configurations, our results showed its superiority to estimates based on linear models, as well as its applicability in the genetic value prediction process. The results further indicated the good generalization performance of the neural network model in several additional validation experiments.

At present, single-trait best linear unbiased prediction (BLUP) is the standard method for genetic selection in soybean. However, when genetic selection is performed based on two or more genetically correlated traits and these are analyzed individually, selection bias may arise. Under these conditions, considering the correlation structure between the evaluated traits may provide more-accurate genetic estimates for the evaluated parameters, even under environmental influences. The present study was thus developed to examine the efficiency and applicability of multi-trait multi-environment (MTME) models by the residual maximum likelihood (REML/BLUP) and Bayesian approaches in the genetic selection of segregating soybean progeny. The study involved data pertaining to 203 soybean F 2:4 progeny assessed in two environments for the following traits: number of days to maturity (DM), 100-seed weight (SW), and average seed yield per plot (SY). Variance components and genetic and non-genetic parameters were estimated via the REML/BLUP and Bayesian methods. The variance components estimated and the breeding values and genetic gains predicted with selection through the Bayesian procedure were similar to those obtained by REML/BLUP. The frequentist and Bayesian MTME models provided higher estimates of broad-sense heritability per plot (or heritability of total effects of progeny; ) and mean accuracy of progeny than their respective single-trait versions. Bayesian analysis provided the credibility intervals for the estimates of . Therefore, MTME led to greater predicted gains from selection. On this basis, this procedure can be efficiently applied in the genetic selection of segregating soybean progeny.

Flowering is an important agronomic trait. Quantile regression (QR) can be used to fit models for all portions of a probability distribution. In Genome-wide association studies (GWAS), QR can estimate SNP (Single Nucleotide Polymorphism) effects on each quantile of interest. The objectives of this study were to estimate genetic parameters and to use QR to identify genomic regions for phenological traits (Days to first flower—DFF; Days for flowering—DTF; Days to end of flowering—DEF) in common bean. A total of 80 genotypes of common beans, with 3 replicates were raised at 4 locations and seasons. Plants were genotyped for 384 SNPs. Traditional single-SNP and 9 QR models, ranging from equally spaced quantiles (τ) 0.1 to 0.9, were used to associate SNPs to phenotype. Heritabilities were moderate high, ranging from 0.32 to 0.58. Genetic and phenotypic correlations were all high, averaging 0.66 and 0.98, respectively. Traditional single-SNP GWAS model was not able to find any SNP-trait association. On the other hand, when using QR methodology considering one extreme quantile (τ = 0.1) we found, respectively 1 and 7, significant SNPs associated for DFF and DTF. Significant SNPs were found on Pv01, Pv02, Pv03, Pv07, Pv10 and Pv11 chromosomes. We investigated potential candidate genes in the region around these significant SNPs. Three genes involved in the flowering pathways were identified, including Phvul.001G214500, Phvul.007G229300 and Phvul.010G142900.1 on Pv01, Pv07 and Pv10, respectively. These results indicate that GWAS-based QR was able to enhance the understanding on genetic architecture of phenological traits (DFF and DTF) in common bean.

Genomic selection (GS) emphasizes the simultaneous prediction of the genetic effects of thousands of scattered markers over the genome. Several statistical methodologies have been used in GS for the prediction of genetic merit. In general, such methodologies require certain assumptions about the data, such as the normality of the distribution of phenotypic values. To circumvent the non-normality of phenotypic values, the literature suggests the use of Bayesian Generalized Linear Regression (GBLASSO). Another alternative is the models based on machine learning, represented by methodologies such as Artificial Neural Networks (ANN), Decision Trees (DT) and related possible refinements such as Bagging, Random Forest and Boosting. This study aimed to use DT and its refinements for predicting resistance to orange rust in Arabica coffee. Additionally, DT and its refinements were used to identify the importance of markers related to the characteristic of interest. The results were compared with those from GBLASSO and ANN. Data on coffee rust resistance of 245 Arabica coffee plants genotyped for 137 markers were used. The DT refinements presented equal or inferior values of ApparentError Rate compared to those obtained by DT, GBLASSO, and ANN. Moreover, DT refinements were able to identify important markers for the characteristic of interest. Out of 14 of the most important markers analyzed in each methodology, 9.3 markers on average were in regions of quantitative trait loci (QTLs) related to resistance to disease listed in the literature.