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UBC Theses and Dissertations

Modeling post-harvest pathogens in apple fruit. Williams, Katrina Anne


Post-harvest disease of apple fruit causes significant loss of fruit during and after storage with a considerable economic impact. Studying the factors that contribute to post-harvest disease and developing predictive models may help growers and packing house workers to make more informed decisions on disease forecasting and duration of storage for apples. Data for three major post-harvest pathogens, Penicillium expansum, Botrytis cinerea and Mucor piriformis, which were monitored and quantified in four orchards over three years during the growing season and then in storage, were available. Contrary to expectation, it was found that environmental data provided little to no explanation of the trends in inoculum detection. It was hypothesized that this lack of relationship between the amount of inoculum present and the environmental factors was due to the manner in which the data were collected. In contrast, it was found that a large proportion of the variation in storage disease outcomes (R2=0.506, p=0.000) could be predicted by the duration of storage, temperature and rainfall two weeks before storage, and the quantity of pathogen DNA detected on the plant tissue at harvest. In order to better understand the relationship between environmental factors and spore detection in the orchard, a Gaussian plume model was developed for describing spore dispersal. Model results had good qualitative agreement with the data that were collected in the field, suggesting that a high level of variability would be expected when only using one receptor location due to the dynamics of spore movement based on wind conditions. The model predicted that increasing the number of receptors, especially when they were evenly placed around the orchard, would decrease the variability of detection results. Based on the model outcomes, it was concluded that five receptors would give the most reasonable results for the least expenditure. This research develops the first predictive model for post-harvest apple disease outcomes in storage based on pre-storage factors, and gives new insight into the dispersal of fungal spores in an orchard setting, providing recommendations for improving future data collection and modeling work.

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