Resumen:
Alternaria is a common airborne phytopathogenic fungus that may affect crops in the field or can cause decay of plant products. It can also cause respiratory problems (allergy) in animals. The study of airborne Alternaria conidia is a necessary step for the control and prevention of the agricultural damage they can provoke. The aim of this paper is to contribute to model the presence and levels of Alternaria conidia in the air using logistic regression models. Our study is conducted in 12 monitoring stations in Spain corresponding to three geographic regions with different bio-climatic characteristics that have shown three different patterns of Alternaria conidia dynamics along the year: a unique main sporulation season from mid spring to autumn in NE Spain, two defined periods (spring and autumn) in SE Spain, and a uniform and constant presence in the Canary Islands. Regarding the abundance, NE Spain showed the highest values and the Canary Islands the lowest. Daily Alternaria conidia concentration has a positive correlation with daily minimum temperature and a negative correlation with daily precipitation. The occurrence of rain in the three previous days also has a positive effect on Alternaria levels. The three logistic regression models proposed to estimate the probabilities of the presence or absence of Alternaria conidia, of exceeding a threshold of 10 conidia/m³ and of exceeding a threshold of 30 conidia/m³ are able to describe the conidia emission pattern using basic meteorological parameters (minimum daily temperature, daily temperature range, occurrence of rainfall in the same day and occurrence of rainfall in the three previous days). Moreover, we make the logit regression model useful for real-time forecasting by specifying a cut-off point that makes it possible to transform the predicted probabilities into positive/negative predictions
