Influência da temperatura no desempenho germinativo de um lote de sementes de rainha margarida (Callistephus chinensis Nees - Asteraceae)

Com o objetivo de estudar a influência de quatro níveis de temperatura no desempenho germinativo de sementes de rainha margarida (Callistephus chinensis Nees), foi instalado um experimento utilizando-se ger-minadores regulados com as temperaturas constantes de 20, 25, 30 e 35°C. O desempenho germinativo foi avaliado combinando as frequências acumuladas em intervalos de oito horas com o modelo da função de distribuição de Weibull, com três parâmetros, onde Y = M*(l-exp(-(t/b)c)). O melhor desempenho foi obtido com o germinador regulado à temperatura de 20°C. A estimativa da porcentagem máxima de germinação (M) foi de 78,28% e a de c, que avalia a distribuição das germinações no tempo, foi igual a 5,63. O tempo (b) de ocorrência de 63,21% do máximo de germinações M foi igual a 62,17 horas. Os parâmetros que mediram o ajuste do modelo foram simétricos e estáveis. A temperatura de 30°C proporcionou um desempenho com instabilidade e assimetria na estimativa do parâmetro c. As sementes não germinaram a 35°

Stevia rebaudiana (Bert) Bertoni: regression models with mixed effects for investigating seed germination data

We investigated regression models with mixed effects using generalized linear statistics to evaluate germination data from Stevia rebaudiana (Bert) Bertoni. Estimates and validation of statistical parameters were conducted using the “gamlss” package in the R software. Generalized linear mixed effects followed the binomial, the beta-binomial and the multinomial distribution with the logit link to explain data based on the following explanatory variables: seed germinator, plastic tray position on every tier of shelves, illuminance conditions (light and darkness) and seed lots. We did not find differences in proportional responses from seed germinators, but we did find differences in the illuminance conditions, plastic tray position on the tiers of shelves in the seed germinators and seed lots. The estimates of the generalized Akaike information criterion (GAIC), Akaike information criterion (AIC), global deviance (GD) and Bayesian information criterion of Schwarz (BIC) indicate similar goodness-of-fit for the binomial and beta-binomial models. All of the models indicate that the position of the germination tray on every tier of shelves and illuminance conditions affected the proportions of normal seedlings. The seed germination in the plastic tray on the uppermost position under fluorescent day light lamps had an effect on the proportion of normal seedlings of Stevia.

Stevia rebaudiana (Bert) Bertoni: influence of osmotic stress and seed priming on seed germination under laboratory conditions

 The foremost factor necessary for plant growers cultivating large acreages of Stevia rebaudiana (Bert) Bertoni is the production of qualitative bedding plants. The objective of this study was to evaluate the influence of osmotic-priming on the uniformity of seed germination. First, we evaluated the percentage of normal seedlings from two seed samples harvested in 2011 and 2012. The seeds harvested in 2012 produced 71.4% normal seedlings and thus they were used in the next experiments. The seeds were subjected to osmotic stress using five concentrations of polyethylene glycol (PEG-6000) at -0.2, -0.4, -0.6, -0.8, and -1.0 MPa in contrast with distilled water. Based on these first results, only -0.8 and -1.0 MPa were evaluated in the third experiment. The seeds were immersed in both concentrations of polyethylene glycol (PEG-6000) for imbibing at 20ºC for four, five, six, and seven days. Thereafter, we evaluated the time to the first normal seedling (Ti), time to the last normal seedling (Tf), percentage normal seedlings at the initial time (Pi) and percentage of normal seedlings at the end of every treatment (Pf). Osmotic priming increased the percentage of normal seedlings of the Stevia rebaudiana and reduced the time to the first and last germination events.

<b>Applying regression models with skew-normal errors to the height of bedding plants of <i>Stevia rebaudiana</i> (Bert) Bertoni

The experiment had the objective of fitting regression models to data of the height of the bedding plants cultivated in three multicellular Styrofoam trays with three different cell volumes. We proposed two types of models in the current experiment. First, we fit a model with normal errors and next a model with a skew-normal distribution of errors. The skew-normal regression was suitable for modelling both cases. First, when the model included the time covariate and next when the cell size covariate was part of the model. However, the value of the parameter l for the multivariate model was very high, which is an indication that the skew-normal model is also not the best. Thus, we suggest further fitting using the skew regression model of t-Student

Modeling citrus huanglongbing data using a zero-inflated negative binomial distribution

ABSTRACT. Zero-inflated data from field experiments can be problematic, as these data require the use of specific statistical models during the analysis process. This study utilized the zero-inflated negative binomial (ZINB) model with the log- and logistic-link functions to describe the incidence of plants with Huanglongbing (HLB, caused by Candidatus liberibacter spp.) in commercial citrus orchards in the Northwestern Parana State, Brazil. Each orchard was evaluated at different times. The ZINB model with random effects in both link functions provided the best fit, as the inclusion of these effects accounted for variations between orchards and the numbers of diseased plants. The results of this model show that older plants exhibit a lower probability of acquiring HLB. The application of insecticides on a calendar basis or during new foliage flushes resulted in a three times larger probability of developing HLB compared with applying insecticides only when the vector was detected

Modeling the incidence of citrus canker in leaves of the sweet orange variety ‘Pera’

Citrus canker, caused by the bacterium Xanthomonas citri subsp. citri, is one of the most important diseases of citrus. The use of resistant genotypes plays an important role in the management and control of the disease and is the most environmentally sustainable approach to disease control. Citrus canker incidence was recorded in an experiment on nine genotypes of the sweet orange variety ‘Pera’ grafted on four rootstocks. The experiment was started in 2010 and the incidence of citrus canker on the leaves was recorded on a quarterly basis. The incidence data from the experiment were analyzed using a zero-inflated Beta regression model (RBIZ), which is the appropriate method to describe data with large numbers of zeros. Based on the residual analysis, the data fit the model well. The discrete component of the explanatory variable, rootstock, was not significant as a factor affecting the onset of disease, in contrast with the continuous component, genotype, which was significant in explaining the incidence of citrus canker.