Relative Contribution of Windbreak, Copper Sprays, and Leafminer Control for Citrus Canker Management and Prevention of Crop Loss in Sweet Orange Trees.

The management of citrus canker, caused by Xanthomonas citri subsp. citri, has been widely studied in endemic areas because of the importance of the disease in several citrus-producing countries. A set of control measures is well established, but no study has investigated the efficiency of each measure individually and their combination for disease suppression. This study comprised a 3-year field study to assess the relative contribution of three measures for the control of citrus canker and reduction of crop losses. Windbreak (Wb), copper sprays (Cu), and leafminer control (Lc) were assessed in eight different combinations in a split-split plot design. The orchard was composed of 'Valencia' sweet orange trees grafted onto 'Rangpur' lime. Casuarina cunninghamiana trees were used as Wb. Cu and Lc sprays were performed every 21 days throughout the year. Individually, Cu showed the highest contribution for canker control, followed by Wb. Lc had no effect on reducing citrus canker. Wb+Cu showed the highest efficiency for control of the disease. This combination reduced the incidence of diseased trees by approximately 60%, and the incidence of diseased leaves and fruit by ≥90% and increased the yield in 2.0- to 2.6-fold in comparison with the unmanaged plots. Cu sprays were important for reducing disease incidence and crop losses, whereas Wb had an additional contribution in minimizing the incidence of cankered, non-marketable fruit. The results indicated that the adoption of these measures of control may depend on the characteristics of the orchard and destination of the production.

Optimising risk-based surveillance for early detection of invasive plant pathogens.

Emerging infectious diseases (EIDs) of plants continue to devastate ecosystems and livelihoods worldwide. Effective management requires surveillance to detect epidemics at an early stage. However, despite the increasing use of risk-based surveillance programs in plant health, it remains unclear how best to target surveillance resources to achieve this. We combine a spatially explicit model of pathogen entry and spread with a statistical model of detection and use a stochastic optimisation routine to identify which arrangement of surveillance sites maximises the probability of detecting an invading epidemic. Our approach reveals that it is not always optimal to target the highest-risk sites and that the optimal strategy differs depending on not only patterns of pathogen entry and spread but also the choice of detection method. That is, we find that spatial correlation in risk can make it suboptimal to focus solely on the highest-risk sites, meaning that it is best to avoid 'putting all your eggs in one basket'. However, this depends on an interplay with other factors, such as the sensitivity of available detection methods. Using the economically important arboreal disease huanglongbing (HLB), we demonstrate how our approach leads to a significant performance gain and cost saving in comparison with conventional methods to targeted surveillance.

Sampling for disease absence-deriving informed monitoring from epidemic traits.

Monitoring for disease requires subsets of the host population to be sampled and tested for the pathogen. If all the samples return healthy, what are the chances the disease was present but missed? In this paper, we developed a statistical approach to solve this problem considering the fundamental property of infectious diseases: their growing incidence in the host population. The model gives an estimate of the incidence probability density as a function of the sampling effort, and can be reversed to derive adequate monitoring patterns ensuring a given maximum incidence in the population. We then present an approximation of this model, providing a simple rule of thumb for practitioners. The approximation is shown to be accurate for a sample size larger than 20, and we demonstrate its use by applying it to three plant pathogens: citrus canker, bacterial blight and grey mould.

A method of determining where to target surveillance efforts in heterogeneous epidemiological systems.

The spread of pathogens into new environments poses a considerable threat to human, animal, and plant health, and by extension, human and animal wellbeing, ecosystem function, and agricultural productivity, worldwide. Early detection through effective surveillance is a key strategy to reduce the risk of their establishment. Whilst it is well established that statistical and economic considerations are of vital importance when planning surveillance efforts, it is also important to consider epidemiological characteristics of the pathogen in question-including heterogeneities within the epidemiological system itself. One of the most pronounced realisations of this heterogeneity is seen in the case of vector-borne pathogens, which spread between 'hosts' and 'vectors'-with each group possessing distinct epidemiological characteristics. As a result, an important question when planning surveillance for emerging vector-borne pathogens is where to place sampling resources in order to detect the pathogen as early as possible. We answer this question by developing a statistical function which describes the probability distributions of the prevalences of infection at first detection in both hosts and vectors. We also show how this method can be adapted in order to maximise the probability of early detection of an emerging pathogen within imposed sample size and/or cost constraints, and demonstrate its application using two simple models of vector-borne citrus pathogens. Under the assumption of a linear cost function, we find that sampling costs are generally minimised when either hosts or vectors, but not both, are sampled.