Air sampling has been used to study the epidemiology of crop diseases in order to design a control strategy – how to alter time of sowing or harvest to escape disease (Davidson et al. 2013), when precisely to apply fungicides or other crop protection agents (Brachaczek et al. 2016; Cao et al. 2016; Carisse et al. 2009; Thiessen et al. 2016; West et al. 2002), and whether it is possible to separate susceptible crops from inoculum sources (Maldonado-Ramirez et al. 2005; Marcroft et al. 2003). Spore sampling is useful to monitor changes in pathogen populations for both fundamental research and applied purposes such as providing a direct forecast of imminent disease risk by detecting the inoculum before infection events start.

Air particulate samples are typically a deposit of dust, spores, pollen, plant fragments and other microscopic material, either captured by impaction onto a sticky surface, or collected by devices that produce a rapid change of direction of airflow, which causes particles to be deposited onto a surface or into a tube (West and Kimber 2015). Until the early 2000s, samples were routinely identified by microscopy. This lengthy and skilled process is often only possible for identification of relatively large and visually characteristic spores because spores of many species look very similar or even identical and cannot be identified to the species level with high certainty. As a result, a range of diagnostic methods have been applied to air-particulate samples. Many recent advances in spore samplers have been designed to improve the ease of sampling and to enhance the downstream application of diagnostic methods to the sample. In the past 15 years or so, most spore samples have been processed not for microscopy but have DNA extracted from the mixture of particles and then tested by PCR or qPCR to detect a specific target organism e.g. (Carisse et al. 2009; Gent et al. 2009; Kunjeti et al. 2016); (Fig. 1).

Fig. 1 Stages in the detection of a plant pathogen from airborne samples Full size image

For lab-based studies, qPCR is often preferred as this method produces accurate quantification allowing the amount of pathogen DNA detected to be translated into a number of spores present in the air sample and therefore the concentration of spores per cubic metre of air sampled. DNA-based diagnostics can be designed in some cases to detect specific genotypes of a species such as a race that can infect a certain crop variety (Kaczmarek et al. 2014) or a mutant that is resistant to a class of fungicide. Already DNA of the air spora (i.e. spores and other biological particles in the air) has been sequenced but the process is currently relatively expensive, although costs are reducing (personal communication, Mogens Nicolaisen, December 2016, re: article submitted).

Other isothermal DNA-based diagnostics such as LAMP and TwistDX are now being used as they may be more rapid than PCR, use less-expensive equipment or be less prone to inhibition by chemicals present naturally at times in some of the components of the air spora (Hansen et al. 2016; Thiessen et al. 2016). These isothermal DNA amplification methods offer the prospect of samples to be analysed using relatively simple equipment and even in the field. It is possible to use these methods not only for pathogen detection but even quantification of specific pathogen DNA onsite in a matter of minutes. Sophisticated immunological diagnostics and biosensors can also perform this role relatively cheaply and rapidly (Wakeham et al. 2016).

The optimal deployment of air samplers varies according to how widespread or common the pathogen is, the volume of air sampled by the device used and the importance or value of the crop (West and Kimber 2015). Inoculum-based forecasts are best suited to sporadic but damaging diseases of crops and particularly those that infect at early or later crop growth stages (such as Fusarium) when farmers do not routinely spray. In particular, diseases that cause yield-loss in high-value crops such as vegetables or fruits, are also likely to benefit from inoculum-based disease forecasting. It is also not cost-effective to sample for exotic or unusual pathogens that only cause epidemics very rarely (Madden et al. 2007).

Thresholds of spore concentrations to trigger disease control operations are difficult to define and are different for each pathogen. Spore concentrations in air decline with distance from the source and height above the ground. If the exact location of the source is not known, we cannot infer how much dilution has occurred ahead of the sample being taken, i.e. a relatively high concentration of spores in the sample could be caused by a small source of spores very close to the sampler, or by a very large distant source. Some smoothing or buffering against the effects of proximity of the sampler to the source can be achieved by mounting air samplers on the roof of a tall building or on a drone or UAV. The decrease in signal strength due to dilution can be a problem for roof-top sampling but it can also be counter-acted by the air being better mixed, representing the air spora released from a wider range of microclimates present in the environment upwind of the sampling position. Therefore, a single air sampler located at rooftop height can be used to infer the presence of common plant pathogen airborne inoculum over a regional scale. However, for many sporadic pathogens a denser network of samplers is needed to provide the full picture of inoculum distribution. Further research is needed to understand issues of the spatial variability in spore concentrations and how that relates to subsequent disease risk. The prospect of automated samplers that are networked to send near real-time data could then provide a new aspect of precision agriculture – knowing exactly where and when to apply crop protection agents.