Decision Support Systems for Detecting Aerial Potato Phytophthora infestans Sporangia in Northwestern Spain

Abstract

Core Ideas Late blight outbreaks have a remarkable socioeconomic and environmental impact. The levels of late blight sporangia are highly dependent of the weather conditions. Aerobiological information is a useful tool to control late blight. Potato late blight caused by Phytophthora infestans (Mont.) de Bary has major economic impacts on this crop worldwide. Forecasting the risk of infection in potato (Solanum tuberosum L.) cultivars is indispensable for the management of this disease. Agronomic, chemical, statistical or mathematical methods are used to reduce the potato blight outbreaks, and decision support systems are considered a useful tool for integrated management of crops. This study aimed to investigate the effectiveness of different decision support systems (Smith periods, negative prognosis model, Fry system, Wallin severity values, WinstelCast system) to predict the risk of late blight on the A Limia plateau, the most important potato production area in northwestern Spain. The relationships between weather conditions and airborne sporangia were statistically analyzed. The study was conducted during seven crop cycles. The level of P. infestans sporangia was determined by conducting aerobiological sampling by using a LANZONI VPPS‐2000 volumetric trap. The maximum levels of sporangia were found during the vegetative stage of the crop, mainly during germination and leaf development. The aerobiological data and meteorological conditions of the zone suggest that the most effective models to forecast late blight in A Limia are negative prognosis and WinstelCast, considering that optimal relative humidity for the disease is more than 75%.

Interrupted Wet Period (IWP) to Forecast the Aerial Alternaria in Potato Crops of A Limia (Spain)

“…Elements such as economic damage thresholds, monitoring, and risk forecasting systems are valuable tools to rationally define these disease management and control strategies [32]. Most decision support systems predict the occurrence of the first symptoms of the disease, monitoring certain environmental conditions favorable to infection [18,32,33]. The most used parameter in forecasting is temperature [21].…”

“…Forecasting the risk of infection by a pathogen in cultivars is indispensable for the correct management of agricultural crops [18]. The susceptibility of the cultivars, the presence of primary inoculum, and the variations in disease sensitivity depending on environmental conditions are fundamental factors for improvement in fungal control [18,[34][35][36]. In recent years, diverse modelling approaches such as control strategies for the prediction of fungal diseases have been proposed.…”

“…Farmers demand tools to make adequate decisions about the management of their crops. One of the ways to regulate the application of fungicides is to use forecasting models or rules to suggest the optimal time when fungicide is actually needed and apply them accordingly [17,18].…”

“…The development of these models to use as early assessments of disease severity can potentially assist potato growers in making economic decisions such as the optimal time to apply fungicides [19]. However, these decision support systems need to be validated considering the particular conditions of the geographical area where potatoes are cultivated [18].…”

“…Interrupted wet periods (IWP) are used to optimize the application of fungicides against early blight in potato [13,18,20,21]. IWP considers the alternation of the relative humidity occurring during the night and day.…”