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The risk of between-field spread of disease is typically omitted from crop disease warning systems, as it is difficult to know the number and location of inoculum sources and thus predict the abundance of inoculum arriving at healthy crops. In this study we explored the utility of a simple approach to predicting risk of between-field spread, based on the estimated probability that inoculum will survive the transportation process. Using potato late blight as a case study, the effects of solar radiation on the viability of detached Phytophthora infestans sporangia were assessed. A model to estimate the probability of spore survival was derived using a binomial Generalized Linear Mixed Model (GLMM), and receiver operating characteristic curve (ROC) analysis and cross-validation were used to evaluate the global performance of the model as a binary classifier for discriminating between viable and non-viable sporangia. The model yielded an area under the ROC curve of 0.92 (95% CI = 0.90-0.93), signifying an excellent classification algorithm. Inspection of the curve provided a number of suitable decision threshold (or cut-off) probabilities for discriminating between viable and non-viable sporangia. The classifier was tested as a forecasting system for potato late blight outbreaks using 10 years of outbreak data from across Great Britain. There was a marked differentiation among the cut-offs, but the best prediction outcome was an accuracy of 89% with an alert frequency of 1 in 7 days. Our model can be easily modified or our methodology replicated for other pathosystems characterised by airborne inoculum. This article is protected by copyright. All rights reserved.

The impact of climate change on dispersal processes is largely ignored in risk assessments for crop diseases, as inoculum is generally assumed to be ubiquitous and nonlimiting. We suggest that consideration of the impact of climate change on the connectivity of crops for inoculum transmission may provide additional explanatory and predictive power in disease risk assessments, leading to improved recommendations for agricultural adaptation to climate change. In this study, a crop-growth model was combined with aerobiological models and a newly developed infection risk model to provide a framework for quantifying the impact of future climates on the risk of disease occurrence and spread. The integrated model uses standard meteorological variables and can be easily adapted to various crop pathosystems characterized by airborne inoculum. In a case study, the framework was used with data defining the spatial distribution of potato crops in Scotland and spatially coherent, probabilistic climate change data to project the future connectivity of crop distributions for Phytophthora infestans (causal agent of potato late blight) inoculum and the subsequent risk of infection. Projections and control recommendations are provided for multiple combinations of potato cultivar and CO2 emissions scenario, and temporal and spatial averaging schemes. Overall, we found that relative to current climatic conditions, the risk of late blight will increase in Scotland during the first half of the potato growing season and decrease during the second half. To guide adaptation strategies, we also investigated the potential impact of climate change-driven shifts in the cropping season. Advancing the start of the potato growing season by 1 month proved to be an effective strategy from both an agronomic and late blight management perspective.

Potato blackleg, caused by Pectobacterium and Dickeya species, is one of the most significant bacterial diseases affecting potato production globally. Although it is generally accepted to be a seedborne disease, the processes underlying the spread of disease largely remain unknown. Spatial point pattern analysis was applied to blackleg occurrence in seed potato crops in Scotland during the period of 2010–2013 (approximately 8000 blackleg-affected crops), to assess whether its distribution was random, regular or aggregated, and the spatial scales at which these patterns occurred. Blackleg-affected crops derived from mother stocks with symptoms were omitted from the analyses in order to examine the statistical evidence for horizontal transmission of blackleg. The pair correlation function was used to test for global spatial autocorrelation, and results indicated significant (P

The impacts of climate change on ecosystem services are complex in the sense that effective prediction requires consideration of a wide range of factors. Useful analysis of climate-change impacts on crops and native plant systems will often require consideration of the wide array of other biota that interact with plants, including plant diseases, animal herbivores, and weeds. We present a framework for analysis of complexity in climate-change effects mediated by plant disease. This framework can support evaluation of the level of model complexity likely to be required for analysing climate-change impacts mediated by disease. Our analysis incorporates consideration of the following set of questions for a particular host, pathogen, host–pathogen combination, or geographic region. 1. Are multiple biological interactions important? 2. Are there environmental thresholds for population responses? 3. Are there indirect effects of global change factors on disease development? 4. Are spatial components of epidemic processes affected by climate? 5. Are there feedback loops for management? 6. Are networks for intervention technologies slower than epidemic networks? 7. Are there effects of plant disease on multiple ecosystem services? 8. Are there feedback loops from plant disease to climate change? Evaluation of these questions will help in gauging system complexity, as illustrated for fusarium head blight and potato late blight. In practice, it may be necessary to expand models to include more components, identify those components that are the most important, and synthesize such models to include the optimal level of complexity for planning and research prioritization.

Potato blackleg and soft rot caused by Pectobacterium and Dickeya species are among the most significant bacterial diseases affecting potato production globally. In this study we estimate the impact of future temperatures on establishment of non-indigenous but confirmed Pectobacterium and Dickeya species in Great Britain (GB). The calculations are based on probabilistic climate change data and a model fitted to disease severity data from a controlled environment tuber assay with the dominant potato blackleg and soft rot-causing species in GB (P. atrosepticum), and three of the main causative agents in Europe (P. carotovorum subsp. brasiliense, P. parmentieri, Dickeya solani). Our aim was to investigate if the European strains could become stronger competitors in the GB potato ecosystem as the climate warms, on the basis of their aggressiveness in tubers at different temperatures. Principally, we found that the tissue macerating capacity of all four pathogens will increase in GB under all emissions scenarios. The predominant Pectobacterium and Dickeya species in Europe are able to cause disease in tubers under field conditions currently seen in GB but are not expected to become widely established in the future, at least on the basis of their aggressiveness in tubers relative to P. atrosepticum under GB conditions. Our key take-home messages are that the GB potato industry is well positioned to continue to thrive via current best management practices and continued reinforcement of existing legislation.

Opportunities exist to improve decision support systems through the use of dispersal information gained from epidemiological research. However, dispersal and demographic information is often fragmentary in plant pathology, and this uncertainty creates a risk of inappropriate action whenever such information is used as a basis for decision making. In this article, a scenario-based simulation approach is used to evaluate crop and economic risks and benefits in the use of dispersal information for decision making using the potato late blight pathosystem (Phytophthora infestans–Solanum tuberosum) as a case study. A recently validated spatiotemporal potato late blight model was coupled to submodels for crop growth, tuber dry matter production, and fungicide efficacy. The yield response of a range of management scenarios to a single influx of primary inoculum (the initial spore load) was calculated. Damage curves (relative yield loss versus initial spore load) from a range of combinations of varietal susceptibility and fungicide treatments were used to classify the various management scenarios as either sensitive to initial spore load or tolerant to initial spore load, thus identifying where a high degree of accuracy would be required in dispersal information for appropriate decision making, and where a greater degree of uncertainty could be tolerated. General epidemics, resulting from spatially homogeneous initial spore loads, responded more strongly to the size of the initial spore load than focal epidemics, resulting from an initial spot infection. Susceptible cultivars responded with sizeable yield losses even at low levels of initial spore load, regardless of the fungicide management regime used. These results indicated that, for susceptible cultivars (late cultivars in particular), the degree of accuracy that would be required in dispersal information for appropriate decision making is unlikely to be practically attainable. The results also indicated that, contrary to “folk wisdom,” spore loads of a few hundred spores per square meter do not lead to appreciable crop loss in resistant cultivars and are therefore acceptable. We conclude that scope exists for including dispersal information in decision making for potato late blight with resistant potato cultivars but not for susceptible cultivars. The modeling framework used in this study can be extended to investigate the scope for inclusion of dispersal information in decision support for other aerially transmitted pathogens.

Aerial dispersal of inoculum is the primary means of movement for many plant diseases. one of the challenges of modem decision support for plant health is to provide predictions of the influx of viable pathogen inoculum from sources outside a crop. Such prediction in a practical setting requires prediction tools that have modest computing and input requirements, yet provide sufficiently accurate predictions. In this paper a hybrid dispersion model is developed, combining Taylor's statistical theory of diffusion for horizontal dispersal with the eddy diffusion theory as implemented in the Lagrangian similarity diffusion model of [van Ulden, A.P., 1978. Simple estimates for vertical diffusion from sources near the ground. Atmos. Environ. 12, 2119-2124] and [Gryning, S.E., van Ulden, A.P., Larsen, S.E., 1983. Dispersion from a continuous ground-level source investigated by a K model. Q. J. Roy. Meteor. Soc. 109, 355-364]. The model is extended with a dry deposition method and an effective source strength. Model results are compared with experimental data for the transport of artificially released spores of Lycopodium clavatum above a potato canopy. The numerical results are in close agreement with the experimental data, which cover distances up to 100 In. Numerical predictions are compared to those produced by two alternative model versions and a previously published Gaussian plume model for the transport of spores above potato canopies. The potential for practical implementation of atmospheric dispersion models in plant disease decision support systems is discussed. (C) 2008 Elsevier B.V. All rights reserved.

Summary Th e epidemiological model BLIGHTSPACE is a spatiotemporal integro-diff erence equation model of the potato late blight pathosystem. To test and scrutinize the validity of model predictions, simulations were made and compared to independent data, collected in fi eld trials on the spread of two genotypes of Phytophthora infestans in fi ve potato cultivars in the Netherlands. Cultivar-isolate specifi c interactions were characterized in the model using three quantitative components of resistance: infection effi ciency, lesion growth rate, and sporulation intensity. Th ese were measured on potato leafl ets in the laboratory. System and model were compared visually using disease progress curves, and numerically through a comparison of predicted and observed t 5 and t 50 points (time in days until 5 and 50 % disease severity is reached respectively). For 80 % of the epidemics, performance criteria for both the t 5 and t 50 points were met. Sensitivity analyses with the model revealed a dichotomy in the epidemiological eff ects of fi tness parameters of P. infestans, providing two useful reference curves with which to formulate hypotheses regarding diff erences between observed and predicted epidemics.

Management of potato late blight could benefit from prediction of the risk posed to potato fields from external inoculum sources of Phytophthora infestans. Influx of inoculum depends on a complex interplay of population biological, atmospheric and spore survival processes, and is difficult to predict. This research aims at building tools for such prediction. BLIGHTSPACE is a spatio-temporal model (parameterized for potato late blight) that has been developed and utilized to study the progress of epidemics in individual fields and networks of fields. A quasi-Gaussian plume model was developed to provide long-range transport of spores within BLIGHTSPACE. Numerical results compared favorably with experimental data. A further submodel for the survival of spores during long-range transportation has been added. Integration of these three submodels will create an experimental arena for comparing control options for potato late blight.

Phenotyping trials may not take into account sufficient spatial context to infer quantitative disease resistance of recommended varieties in commercial production settings. Recent ecological theory—the dispersal scaling hypothesis—provides evidence that host heterogeneity and scale of host heterogeneity interact in a predictable and straightforward manner to produce a unimodal (“humpbacked”) distribution of epidemic outcomes. This suggests that the intrinsic artificiality (scale and design) of experimental set-ups may lead to spurious conclusions regarding the resistance of selected elite cultivars, due to the failure of experimental efforts to accurately represent disease pressure in real agricultural situations. In this model-based study we investigate the interaction of host heterogeneity and scale as a confounding factor in the inference from ex-situ assessment of quantitative disease resistance to commercial production settings. We use standard modelling approaches in plant disease epidemiology and a number of different agronomic scenarios. Model results revealed that the interaction of heterogeneity and scale is a determinant of relative varietal performance under epidemic conditions. This is a previously unreported phenomenon that could provide a new basis for informing the design of future phenotyping platforms, and optimising the scale at which quantitative disease resistance is assessed.

In China, the incidence of phoma stem canker observed in pre-harvest surveys from 2005 to 2012 was greater on winter oilseed rape in provinces in central China (in May) than on spring oilseed rape in north China (in August). In all 742 cases when the causal pathogen was isolated from stem cankers, it was identified as Leptosphaeria biglobosa by morphology in culture and/or by species-specific polymerase chain reaction. Both L. biglobosa and Leptosphaeria maculans were detected on crop debris and seed in shipments of oilseed rape seed imported into China through Shanghai or Wuhan ports in 2009–2011. Descriptions of the observed spread of L. maculans into areas previously colonized by L. biglobosa across a spring oilseed rape growing region (Alberta, Canada, westwards, 1984–1998) and across a winter oilseed rape growing region (Poland, eastwards, 1984–2004) were used to estimate the potential westward spread of L. maculans in China across spring oilseed rape growing regions (north China) and winter oilseed rape growing regions (central China, generally provinces along the Yangtze River), respectively. The rates of spread were estimated as 47 km per year across spring oilseed rape in north China and 70 km per year across winter oilseed rape in central China. Dispersal modelling suggested that the rate of spread of L. maculans across Alberta, Canada (c. 17 km per year) could be explained by windborne dispersal of ascospores.

Strategic spatial patterning of crop species and cultivars could make agricultural landscapes less vulnerable to plant disease epidemics, but experimentation to explore effective disease-suppressive landscape designs is problematic. Here, we present a realistic, multiscale, spatiotemporal, integrodifference equation model of potato late blight epidemics to determine the relationship between spatial heterogeneity and disease spread, and determine the effectiveness of mixing resistant and susceptible cultivars at different spatial scales under the influence of weather. The model framework comprised a landscape generator, a potato late blight model that includes host and pathogen life cycles and fungicide management at the field scale, and an atmospheric dispersion model that calculates spore dispersal at the landscape scale. Landscapes consisted of one or two distinct potato-growing regions (6.4-by-6.4-km) embedded within a nonhost matrix. The characteristics of fields and growing regions and the separation distance between two growing regions were investigated for their effects on disease incidence, measured as the proportion of fields with ≥1% severity, after inoculation of a single potato grid cell with a low initial level of disease. The most effective spatial strategies for suppressing disease spread in a region were those that reduced the acreage of potato or increased the proportion of a resistant potato cultivar. Clustering potato cultivation in some parts of a region, either by planting in large fields or clustering small fields, enhanced the spread within such a cluster while it delayed spread from one cluster to another; however, the net effect of clustering was an increase in disease at the landscape scale. The planting of mixtures of a resistant and susceptible cultivar was a consistently effective option for creating potato-growing regions that suppressed disease spread. It was more effective to mix susceptible and resistant cultivars within fields than plant some fields entirely with a susceptible cultivar and other fields with a resistant cultivar, at the same ratio of susceptible to resistant potato plants at the landscape level. Separation distances of at least 16 km were needed to completely prevent epidemic spread from one potato-growing region to another. Effects of spatial placement of resistant and susceptible potato cultivars depended strongly on meteorological conditions, indicating that landscape connectivity for the spread of plant disease depends on the particular coincidence between direction of spread, location of fields, distance between the fields, and survival of the spores depending on the weather. Therefore, in the simulation of (airborne) pathogen invasions, it is important to consider the large variability of atmospheric dispersion conditions.

This study develops and tests a novel approach for including regional risk factors in operational disease risk warnings against potato late blight. The central premise is that fungicide inputs can be reduced by omitting applications on days when conditions are unsuitable for the atmospheric transport of viable sporangia. The decision support system first decides whether a specific day is 'high risk' (suitable for disease development in planta). Simulation studies revealed that on such high risk days, the capacity of the atmosphere to transport sporangia viably over relevant distances varies widely. An additional rule assesses this capacity, which is high when weather conditions allow a large number of spores to be released from the canopy and transported viably over long distances. When this capacity is high the original spray advice is followed, and when it is low a no-spray advice is given. The concept is implemented using the published decision support system SIMCAST complemented with models for spore release from sporangiophores, spore escape from the canopy, a newly developed model for spore dispersal and dry deposition of spores, spore survival during transportation, and weather forecast data from the mesoscale meteorological model MM5. Cultivar resistance was also incorporated into spray advice. The concept was tested in a field experiment in 2007 with three cultivars, representing a range in resistance to potato late blight from susceptible to highly resistant, and compared to a 'stand-alone' version of SIMCAST. In a period with normal 'infection pressure' (risk of disease) one third of the spray recommendations made by SIMCAST alone were modified and negated by the new system for the highly resistant cultivar. These savings came on top of a reduced, resistance-level dependent dose rate of Shirlan (a.i. Fluazinam). The results demonstrate the feasibility of including dispersal modeling and forecasted meteorology in disease warnings against Phytophthora infestans, even if the whereabouts of sources is unknown. The principles can be used in many decision contexts, but further work is needed to test and refine the method before it can be used in practice. (C) 2008 Elsevier B.V. All rights reserved.

A spatiotemporal/integro-difference equation model was developed and utilized to study the progress of epidemics in spatially heterogeneous mixtures of susceptible and resistant host plants. The effects of different scales and patterns of host genotypes on the development of focal and general epidemics were investigated using potato late blight as a case study. Two different radial Laplace kernels and a two-dimensional Gaussian kernel were used for modeling the dispersal of spores. An analytical expression for the apparent infection rate, r, in general epidemics was tested by comparison with dynamic simulations. A genotype connectivity parameter, q, was introduced into the formula for r. This parameter quantifies the probability of pathogen inoculum produced on a certain host genotype unit reaching the same or another unit of the same genotype. The analytical expression for the apparent infection rate provided accurate predictions of realized r in the simulations of general epidemics. The relationship between r and the radial velocity of focus expansion, c, in focal epidemics, was linear in accordance with theory for homogeneous genotype mixtures. The findings suggest that genotype mixtures that are effective in reducing general epidemics of Phytophthora infestans will likewise curtail focal epidemics and vice versa.