ABSTRACT. In a hypothesis test, a researcher initially fixes a type I error rate, that is, the probability of rejecting the null hypothesis given that it is true. In the case of means tests, it is important to present a type I error that is equal to the nominal pre-fixed level, such that this error remains unchanged across various scenarios, including the number of treatments, number of repetitions, and coefficient of variation. The purpose of this study is to analyse and compare the following multiple comparison tests for the control of both conditional and unconditional type I error rates, depending on a significant F-test in the analysis of variance: Tukey, Duncan, Fisher’s least significant difference, Student-Newman-Keuls (SNK), and Scheffé. As an application, we present a motivation study and develop a simulation study using the Monte Carlo method for a total of 64 scenarios. In each simulated scenario, we estimate the comparison-wise and experiment-wise error rates, conditional and unconditional on a significant result of the overall F-test of analysis of variance for each of the five multiple comparison tests evaluated. The results indicate that the application of the means tests based only on the significance of the F-test should be considered when determining the error rates, as this can change them. In addition, we find that Fisher’s test controls for the comparison-wise error rate, the Tukey and SNK tests control for the experiment-wise error rate, and the Duncan and Fisher tests control for the conditional experiment-wise error rate. Scheffé’s test does not control for any of the error rates considered.

ABSTRACT. The orange variety “x11”, which is a spontaneous mutant of the sweet orange, has a short juvenile period with early flowering. The data used in this paper are from a randomized design experiment that aimed to assess the plants' flowering characteristics when grafted onto two different varieties of lemon rootstock. The plants were pruned in each of the four seasons, and on each pruning occasion, the number of branches on each plant was counted and classified into four mutually exclusive flowering categories. The data presented large variability and many zeros. The statistical analysis included the use of generalized linear mixed models with a Bayesian approach. The results showed that flowering is not equal over the seasons, i.e., there are significant differences in the classification of the branches across the four seasons and the two varieties, with interactions between seasonal and branch effects.

Resumo: A análise radiográfica tem sido eficiente na identificação das principais alterações na morfologia interna de sementes, sendo cada vez mais utilizada em programas de controle de qualidade, devido à sua praticidade, objetividade e rapidez na obtenção dos resultados. Apesar dessas vantagens, ainda não existem estudos que comprovem a eficiência dessas técnicas na análise de sementes de café. Assim, o objetivo desta pesquisa foi avaliar a morfologia interna de sementes de café (Coffea arabica L.) através da análise de imagens radiográficas e sua relação com o desempenho germinativo. Foram avaliadas imagens radiográficas de sementes das cultivares Bourbon e Catucaí 20/15, cada uma representada por seis lotes. As sementes foram semeadas e avaliada a emergência de plântulas, comprimento da parte aérea, diâmetro do caule e massa seca da parte aérea. As sementes de Bourbon apresentaram alta porcentagem de sementes intactas, bem como melhor desempenho na germinação dos lotes em relação ao Catucaí 20/15, que apresentou maior incidência de sementes danificadas e menor germinação. As imagens radiográficas permitiram identificar sementes com malformações, deterioração de tecidos e danos causados pela broca do café. Esta técnica de imagens não destrutiva é eficiente na identificação de sementes intactas, com potencial para germinar e produzir plântulas vigorosas.

Abstract: Radiographic analysis has been efficient in identifying the main changes in the internal morphology of seeds, being increasingly used in quality control programs due to their practicality, objectivity and speed in obtaining the results. Despite these advantages, there are still no studies proving the efficiency of these techniques for coffee seed analysis. Thereby, the aim of this research was to evaluate the internal morphology of coffee seeds (Coffea arabicaL.) through the analysis of radiographic images and their relation with germination performance. Radiographic images of seeds of the cultivars Bourbon and Catucaí 20/15, each represented by six lots, were examined and the seeds were seeded and seedling emergence, shoot length, stem diameter and shoot dry mass were evaluated. Seeds of Bourbon presented high percentage of intact seeds, as well as better performance in the germination of the lots compared to Catucaí 20/15, which presented more incidence of damaged seeds and lower germination. The radiographic images allowed identifying seeds with malformations, tissue deterioration and damage caused by the coffee borer. This non-destructive imaging technique is efficient in identifying intact seeds, with potential to germinate and produce vigorus seedlings.

ABSTRACT Experiments where the response is a categorical variable are usually carried out in many fields such as agriculture. In addition, in some situations this response has three or more levels without an order between them characterizing a multinomial (nominal) response. Statistical models for scenarios where the observations of a nominal response can be considered independent have an extensive literature, such as the baseline-category logit models. However, situations where this assumption is violated (as in longitudinal studies) require specific models that take into consideration the dependence between observations. In this paper, a fairly new extension of the generalized estimating equations is applied to analyze an experiment carried out to investigate the type of vegetation observed in an elephant grass pasture, according to some management conditions over time. This extension uses local odds ratios to explain the dependence among the categories of the outcome over the repeated measurements. Two different structures were compared to describe this dependence, and the Wald test was used to select the significant variables. Further, we built confidence intervals for the predicted probabilities of occurrence of each category and assessed the results comparing observed/predicted values and using the diagnostic analysis. The results allowed to conclude that there are various significant effects for treatments and for time. The structure of local odds ratio also proved as a good way to describe the dependence between categorical responses over time.