Molecular and machine learning approaches to study the impact of climatic factors on the evolution of cranberry fruit rot

Abstract

Cranberry (Vaccinium macrocarpon) is an important crop grown in the United States and Canada, with the province of Québec being the world’s largest producer of organic cranberry. However, cranberry fruit rot (CFR), caused by 12 fungal species, has become a major issue affecting yield.A molecular detection tool was used to detect the presence of the 12 CFR fungi and evaluate CFR species across three farms with different fungicide strategies in Québec. The incidence and frequency of CFR fungi were evaluated for 2020 and compared with 2018 data from the same farms in Québec. Machine-learning models were used to determine the possibility of explaining CFR disease and species based on climate, and analyze the effects of weather variables on CFR presence andprimary fungal species.The most predominant CFR species remained the same in both years, with Godronia cassandrae and Coleophoma empetri being the two most common, but some species showed changes in relative abundance. Furthermore, this study examined the diversity variations in 2018 and 2020, with data showing an overall increase in diversity over the period. The results showed that fungicide applications impacted the species composition of CFR among the farms. Five weather variables (daily snow on the ground (cm), total daily precipitation (mm), daily atmospheric pressure (kPa), daily relative humidity (%) and daily temperature (°C)) were selected and found to contribute differently to the model with atmospheric pressure being the most important. Surprisingly, temperature and precipitations did not influence much the incidence of fungal pathogen species and each CFR species behaved differently in response to environmental factors.Overall, this study highlights the complexity of predicting CFR disease, as caused by 12 fungi, and of developing effective disease management strategies for CFR.