Dispersal of conidia of Fusicladium eriobotryae and spatial patterns of scab in loquat orchards in Spain.

Dispersal of conidia of Fusicladium eriobotryae, the causal agent of loquat scab, was investigated in two loquat orchards in Spain from 2010 to 2012. A volumetric spore sampler, horizontally and vertically orientated microscope slides, and rain collectors were placed in loquat fields to trap conidia of F. eriobotryae. No conidia were collected in the volumetric sampler, and highly variable numbers of conidia were collected in the rain gatherers. Large numbers of conidia were collected by microscope slides, particularly by those held in a horizontal orientation compared with those held in a vertical orientation. Approximately 90%of the F. eriobotryae conidia were collected during rainy periods. Based on ROC and Bayesian analysis, using 65 0.2 mm rainfall as a cut-off value resulted in a high probability of correctly predicting actual conidial dispersal, and had a low probability of failing to predict actual conidial dispersal. Based on the index of dispersion and the binary power law, the incidence of loquat scab on fruit was highly aggregated in space between and within trees, and aggregation was influenced by disease incidence. Our results demonstrate, for the first time, that F. eriobotryae is dispersed mainly in rain splash. The results will be integrated into a mechanistic, weather-driven, disease prediction model that should help growers to minimize fungicide application for the management of loquat scab

Comparison of statistical models in a meta-analysis of fungicide treatments for the control of citrus black spot caused by Phyllosticta citricarpa

Meta-analysis has been recognised as a powerful method to synthetize existing published data from different studies through a formal statistical analysis. Several statistical models have been proposed to evaluate the effectiveness of treatments against plant diseases using meta-analysis, but the sensitivity of the estimated treatment effects to the model chosen has not been investigated in detail in the context of plant pathology. In this paper, four different statistical models were defined to analyse fungicide control trials with binary outcomes. These models were used to conduct a meta-analysis on the effectiveness of fungicide treatments against citrus black spot, a fungal disease caused by the quarantine pathogen Phyllosticta citricarpa. The models differed in the assumption made on the variability of the treatment effect (constant or variable between experimental plots) and in the method used for parameter estimation (classical or Bayesian). Odds ratios were estimated for two groups of fungicides, copper compounds and dithiocarbamates, widely applied for CBS control using each model in turn. Classical and Bayesian statistical models led to similar results, but the estimated treatment effectiveness and their associated levels of uncertainty were sensitive to the assumption made about the variability of the treatment effect. Estimated odds ratios were different depending on whether the treatment effect was assumed to be constant or variable between experimental plots. The size of the confidence intervals was underestimated when the treatment effect was assumed constant while it was variable in reality. Because of the strong between-plot variability, the 90 % percentiles of the odds ratios were much higher than the point estimates, and this result revealed that, in some plots, treatment effectiveness could be much lower than expected. Based on our results, we conclude that it is not sufficient to calculate point estimates of odds ratio when the between-plot variability of the treatment effect is strong and that, in such case, it is recommended to compute the predictive distributions of the odds ratio