Spatiotemporal variation in airborne sporangia of Phytophthora infestans: characterization and initiatives towards improving potato late blight risk estimation

Abstract

This study investigated the value of using real-time monitoring of Phytophthora infestans airborne inoculum as a complement to decision support systems (DSS). The experiment was conducted during the 2010, 2011 and 2012 potato production seasons in two locations in New Brunswick, Canada. Airborne sporangia concentrations (ASC) of P. infestans were monitored using 16 rotating-arm spore samplers placed 3 m above the ground. The first cases of late blight (2010 and 2011) were detected 6-7 days after the first ASC peak, and all samplers captured their first sporangia within the same week (at 3-and 9-day periods). The cumulative ASC curve and the risk curves from two DSS (PLANT-Plus and Pameseb Late Blight) had the same shape but different magnitudes. In both locations, the negative binomial distribution fitted the data better than the Poisson distribution, which is indicative of heterogeneity, and based on Taylor's power law, the heterogeneity increased with increasing ASC. Therefore, the present results suggest that spore-sampling network devices may be a suitable approach for early detection of incoming inoculum and, when combined with DSS, represent a potential aid for targeting the optimal time to apply a disease-control product. In this context, cumulative ASC can be a counterweight to the DSS risk estimate: a high risk combined with significant ASC will trigger fungicide spraying. Moreover, spore sampling can be used to assess the efficiency of management strategies by means of examining the area under the inoculum progress curve.

Forecasting the spread of aerially transmitted crop diseases with a binary classifier for inoculum survival

“…It is known that this assumption of ubiquitous inoculum is flawed from spore trap data (e.g. Gregory & Hirst, 1957;Bugiani et al, 1995;Fawcett, 2006;Fall et al, 2015), which demonstrate that the presence of airborne inoculum in any given location is dependent on weather conditions, local topography, and the phenology of the local vegetation and its associated fungal flora. As the classifier for spore survival was derived using data on the predominant P. infestans genotypes in GB and Europe (13_A2 and 6_A1), it would serve as a useful complement to these systems, providing a binary (yes or no) forecast of the risk of between-field spread of disease that could be used to modify spray recommendations based on infection conditions only.…”

Diagnostic tests and their application in the management of soil- and water-borne oomycete pathogen species

“…Early detection allied to key environmental parameters to control disease at the onset can lead to an increase in production, an improvement of resource efficiency and make a substantial contribution to food security. This approach has successfully been applied to measuring the transmission of crop pathogens in bio‐aerosols and the timed application of control measures (Wakeham & Kennedy, ; Gent et al , ; Fall et al , ; West & Kimber, ). In water, growing media and soil progress is hampered by the environmental sample matrix, suitable sampling regimes to reflect the cropping area and target collection efficiency from the sample.…”

“…The transmission of airborne pathogens from both within, and outside target crops can be modelled (Deardon et al , , ) and this approach provides scope for the development of regional disease forecasts. Early detection of incoming inoculum can be effectively achieved using spore sampling network devices (Skelsey et al , ), and such measurements have been used to improve the precision of fungicide decision support systems for potato blight – guiding the timing, and also giving early and precise indications as to the efficacy, of disease management decisions (Fall et al , ). More rapid and precise molecular techniques have allowed similar observations in potato blight and other disease systems (Skottrup et al , ; Kennedy & Wakeham ; Wakeham & Kennedy, ), readily providing sufficient time for the application of targeted protectant control measures (Wakeham, ; Thiessen et al , ; West & Kimber, ).…”

Probability distributions for marketable pods and white mould on snap bean

“…Likewise, there are few applications of the Hermite distribution in the ecological sciences (Nilsen & Wyller, ). The NB remains a popular choice for modelling over‐dispersion (Lynch et al ., ; Van der Heyden et al ., ; Fall et al ., ). The CMP was reintroduced to the scientific community by Shmueli et al .…”