The identification of superior genotypes in plant breeding programs is not a quick and simple task and requires breeders to become aware of more suitable and efficient tools for evaluating crop performance. Univariate analyses are often too narrow for the scope of plant breeding because it lacks consideration of relations between variables. Therefore, the objective of this study was to select castor bean hybrids based on principal component analysis (PCA). Trials were conducted in 2017 with 31 hybrids in a randomized block design with 4 replications. The following variables were used to evaluate crop performance: plant height (PH), insertion height of the primary raceme (HPR), number of stem nodes (NN), number of racemes (NR), number of seeds (NS), stem diameter (SD), number of fruits (NF), 100-seed weight (S100) and seed oil content (SOC). The first three principal components (PCs) explained approximately 75.01 % of all the variability in the dataset. PC 1, 2 and 3 were particularly related to productivity (NS, NR, S100 and NF), plant size (SD, HPR and PH) and oil production (SOC), respectively. Hybrids 14 and 23 were the most suitable for grain production in commercial scale due to short-height, which favors mechanical harvesting. Commercial hybrid 26 showed high SOC, medium grain yield and medium-height.
The use of seeds with high physiological quality allows rapid growth and establishment of seedlings in the field to be obtained. Therefore, the accuracy of the information obtained during the determination of the physiological quality of seeds is of great importance. The objective was to use generalized linear models, investigating which link function (Probit, Logit and Complementary log-log) is suitable to predict T50 and uniformity during germination of soybean and corn seeds. To perform the experiments, we used seeds from five commercial hybrids and/or cultivars of corn and soybean. The germination speed was calculated by counting the germinated seeds and the results were expressed in the form of proportions. Germination uniformity was calculated by the difference in the times required for germination. The best model was selected according to the criteria of the test of Deviance, AIC and BIC. The Logit model showed accurate results for most cultivars. The evaluation of germination in the form of proportions considering the assumption of binomial response is satisfactory, and the choice of the link function is dependent on the characteristics of each lot and/or species evaluated. The use of this methodology makes it possible to estimate any germination time and uniformity.
RESUMO Objetivou-se comparar os coeficientes alométricos (b) que descrevem o crescimento das partes e dos órgãos de codornas de corte mantidas em diferentes ambientes térmicos. Foram utilizadas 300 codornas distribuídas em delineamento inteiramente ao acaso, com dois tratamentos (ambiente climatizado, AC) com temperatura de 26ºC e ambiente sem climatização (ASC, 32oC) e seis repetições de 25 aves. Ajustaram-se equações alométricas em função do peso em jejum (PJ) para as variáveis: peso do peito (PPEI), coxa (PCX), sobrecoxa (PSCX), asa (PASA), coração (PCOR), fígado (PFÍG), moela (PMOE) e intestino (PINT). Para comparar os “b” das partes e dos órgãos das aves mantidas nos diferentes ambientes, realizaram-se testes de paralelismo. Não houve diferença entre os “b” em nenhuma das partes e dos órgãos das codornas mantidos no ambiente AC ou no ASC. Observou-se que os PPEI e os PSCX apresentaram crescimento heterogônico positivo (b>1), os PCX crescimento isogônico (b=1), os PASA e os órgãos crescimento heterogônico negativo (b<1) em relação ao PJ. Os “b” que descrevem o crescimento das partes e dos órgãos de codornas de corte mantidas nos diferentes ambientes não são influenciados. O peso do peito e o da sobrecoxa apresentaram crescimento tardio, a asa e os órgãos (coração, fígado, moela e intestino) crescimento precoce, e o peso da coxa apresentou crescimento proporcional em relação ao peso em jejum.
The premise in experiments with repeated measures is that observations taken in the same experimental unit are correlated and that correlations decrease proportionally to the increase in the distance between measurements in time or space. Nevertheless, these experiments are often analyzed as if the correlations between the repeated measures were constant or using methods that only consider correlations different, which may impact on the rejection rate of the null hypothesis, and ultimately type I error rate and statistical power. In this context, this study investigated the application of mixed linear models with different assumptions about the covariance matrix in data sets from simulated experiments with repeated measures. 84 scenarios that varied in terms of the covariance matrix pattern (14 structures), number of repeated measurements (4 and 8) and sample size (4, 8 and 12) were evaluated. 10,000 data sets were simulated for each scenario based on a multivariate normal distribution and were subsequently analyzed using mixed linear models. Type I error rate and statistical power for the hypothesis test of the interaction between treatments and repeated measures were estimated as the proportion of p values less than or equal to 0.01 or 0.05 out of a total of 10,000 tests for each scenario. The models were also evaluated for their ability to fit the data using Bayesian Information Criteria (BIC). Thus, the frequency with which the covariance structures were chosen by the selection criteria was computed. Results indicate that the assumption chosen most frequently by the information criteria resulted from the specified covariance structure that corresponded to the empirical covariance structure of the analyzed data sets, particularly for those with larger number of repeated measures and sample sizes. Results also indicate that the use of covariance models that do not recognize heterogeneous correlations between repeated measures can inflate type I error or reduce it to very conservative levels, which may affect the conclusion of agricultural experiments. For a 5% significance level, type I error bias was greater than 2α, while for 1% significance level, bias was over 5α. In addition, the statistical power was reduced when the assumption about the covariance matrix of the data sets did not correspond to the empirical covariance structure, particularly for those data sets with a smaller sample size.
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