ABSTRACT. The selection of superior sweet potato genotypes using Bayesian inference is an important strategy for genetic improvement. Sweet potatoes are of social and economic importance, being the material for ethanol production. The estimation of variance components and genetic parameters using Bayesian inference is more accurate than that using the frequently used statistical methodologies. This is because the former allows for using a priori knowledge from previous research. Therefore, the present study estimated genetic parameters and selection gains, predicted genetic values, and selected sweet potato genotypes using a Bayesian approach with a priori information. Root shape, soil insect resistance, and root and shoot productivity of 24 sweet potato genotypes were measured. Heritability, genotypic variation coefficient, residual variation coefficient, relative variation index, and selection gains direct, indirect and simultaneous were estimated, and the data were analyzed using Bayesian inference. Data from 11 experiments were used to obtain a priori information. Bayesian inference was a useful tool for decision-making, and significant genetic gains could be achieved with the selection of the evaluated genotypes. Root shape, soil insect resistance, commercial root productivity, and total root productivity showed higher heritability values. Clones UFVJM06, UFVJM40, UFVJM54, UFVJM09, and CAMBRAIA can be used as parents in future breeding programs.

ABSTRACT In agricultural experimentation, use of the optimum plot size is an important way of increasing experimental precision; however, studies of this type are scarce for the chickpea, a legume that has been conquering both the market and consumers throughout the world. The aim of this study, therefore, was to determine the optimum plot size for evaluating experiments with the chickpea, in scenarios comprising combinations of the number of treatments, number of replications and levels of precision. Two blank experiments were carried out, comprising eight crop rows, 7 m in length, at a spacing of 50 cm between rows and 10 cm between plants; the six central rows were evaluated, giving a total of 60 plants per row. The index of soil heterogeneity was determined, and the optimum plot size was estimated using the Hatheway method in scenarios formed by a combination of i treatments (i = 4, 8, 12 and 16), r replications (r = 3, 4, 5, 6, 7 and 8) and levels of precision (LSD = 25%, 30% and 40%). The index of soil heterogeneity was greater than 0.7 for each of the variables under evaluation. The Hatheway method makes it possible to estimate different plot sizes based on the conditions and limitations of the experimental area. In experiments with the chickpea including 4 to 16 treatments, 25% LSD and six replications, plots of 25 basic units are sufficient to identify significant differences between the mean values of the treatments with a probability of 5%.

RESUMO Na experimentação agrícola, a utilização do tamanho ótimo de parcela é uma estratégia importante, para aumentar a precisão experimental, e trabalhos desta natureza são escassos para a cultura do grão-de-bico, leguminosa que vem conquistando o mercado e consumidores em todo o mundo. Assim, o objetivo deste trabalho foi determinar o tamanho ótimo de parcela, para avaliar experimentos com a cultura do grão-de-bico, em cenários formados por combinações de números de tratamentos, números de repetições e níveis de precisão. Dois experimentos em branco foram realizados, compostos por oito linhas de cultivo, de 7 m de comprimento, espaçadas em 50 cm entre linhas e 10 cm entre plantas; foram avaliadas as seis linhas centrais, totalizando 60 plantas por linha de cultivo. Foi determinado o índice de heterogeneidade do solo e o tamanho ótimo de parcela foi estimado por meio do método de Hatheway, em cenários formados pelas combinações de i tratamentos (i = 4, 8, 12 e 16), r repetições (r = 3, 4, 5, 6, 7 e 8) e níveis de precisão (DMS = 25%, 30% e 40%). O índice de heterogeneidade do solo foi superior a 0,7 para todas as variáveis avaliadas. O método de Hatheway possibilita estimar diferentes tamanhos de parcelas, de acordo com as condições e limitações da área experimental. Em experimentos com o grão-de-bico, com 4 a 16 tratamentos, DMS de 25% e seis repetições, as parcelas de 25 unidades básicas são suficientes para identificar, com probabilidade de 5%, diferenças significativas entre as médias dos tratamentos.

Resumo O objetivo deste trabalho foi avaliar a dissimilaridade genética entre cultivares e genótipos de soja para a seleção de genitores, bem como propor um novo método para a utilização de mapas auto-organizáveis de Kohonen (SOMs) e testar sua eficiência por meio da análise discriminante de Anderson. Foram avaliados os descritores morfoagronômicos de cultivares e genótipos de soja. Para análise dos dados, utilizaram-se redes neurais artificiais do tipo SOM. O método proposto permitiu a determinação da melhor arquitetura de rede de forma não subjetiva. Além disso, no início do treinamento, foi possível mitigar o efeito da aleatoriedade dos pesos sinápticos sobre os grupos formados. Foram formados seis grupos dissimilares; portanto, há dissimilaridade genética entre cultivares e genótipos de soja. As cultivares C25, C8 e C13 podem ser combinadas com as C36, C31, C32 e C33, por apresentarem bons atributos de produtividade e alta dissimilaridade. A metodologia proposta é vantajosa em comparação ao uso de SOMs tradicionais e se mostrou eficiente devido à consistência dos agrupamentos de acordo com a análise discriminante de Anderson.

Abstract The objective of this work was to evaluate the genetic dissimilarity between soybean cultivars and genotypes for the selection of parents, as well as to propose a new method for using Kohonen’s self-organizing maps (SOMs) and to test its efficiency through Anderson’s discriminant analysis. The morphoagronomic descriptors of soybean cultivars and genotypes were evaluated. For data analysis, SOM-type artificial neural networks were used. The proposed method allowed the determination of the best network architecture in a nonsubjective way. Furthermore, at the beginning of training, it was possible to mitigate the randomness effect of the synaptic weights on the formed clusters. Six dissimilar clusters were formed; therefore, there is genetic dissimilarity between soybean cultivars and genotypes. Cultivars C25, C8, and C13 can be combined with C36, C31, C32, and C33 because they show good yield-related attributes and high dissimilarity. The proposed methodology is advantageous in comparison with the use of traditional SOMs, besides being efficient due to clustering consistency according to Anderson’s discriminant analysis.

ABSTRACT. Leaf area is one of the most commonly used physiological parameters in plant growth analysis because it facilitates the interpretation of factors associated with yield. The different leaf formats related to soybean genotypes can influence the quality of the model fit for the estimation of leaf area. Direct leaf area measurement is difficult and inaccurate, requires expensive equipment, and is labor intensive. This study developed methodologies to estimate soybean leaf area using neural networks and considering different leaf shapes. A field experiment was carried out from February to July 2017. Data were collected from thirty-six cultivars separated into three groups according to the leaf shape. Multilayer perceptrons were developed using 300 leaves per group, of which 70% were used for training and 30% for validation. The most important morphological measures were also tested with Garson’s method. The artificial neural networks were efficient in estimating the soybean leaf area, with coefficients of determination close to 0.90. The left leaflet width and right leaflet length are sufficient to estimate the leaf area. Network 4, trained with leaves from all groups, was the most general and suitable for the prediction of soybean leaf area.