Landscape-Scale Disease Risk Quantification and Prediction

Yuen, Jonathan; Mila, A. L.

doi:10.1146/annurev-phyto-080614-120406

Cited by 37 publications

(21 citation statements)

References 68 publications

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“…The issue of interactions between inoculum polyetic build-up, source strength, and dispersal are particularly apparent for diseases where the primary inoculum plays a defining role (Savary 2014), as in the case of FHB (Willyerd et al 2012). This is an area of important progress for collective disease management through collective, landscape-based, action (Bergamin Filho et al 2016;Yuen and Mila 2015).…”

Section: Patterns Of Change In Wheat Diseasesmentioning

confidence: 99%

A White Paper on Global Wheat Health Based on Scenario Development and Analysis

et al. 2017

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Scenario analysis constitutes a useful approach to synthesize knowledge and derive hypotheses in the case of complex systems that are documented with mainly qualitative or very diverse information. In this article, a framework for scenario analysis is designed and then, applied to global wheat health within a timeframe from today to 2050. Scenario analysis entails the choice of settings, the definition of scenarios of change, and the analysis of outcomes of these scenarios in the chosen settings. Three idealized agrosystems, representing a large fraction of the global diversity of wheat-based agrosystems, are considered, which represent the settings of the analysis. Several components of global changes are considered in their consequences on global wheat health: climate change and climate variability, nitrogen fertilizer use, tillage, crop rotation, pesticide use, and the deployment of host plant resistances. Each idealized agrosystem is associated with a scenario of change that considers first, a production situation and its dynamics, and second, the impacts of the evolving production situation on the evolution of crop health. Crop health is represented by six functional groups of wheat pathogens: the pathogens associated with Fusarium head blight; biotrophic fungi, Septoria-like fungi, necrotrophic fungi, soilborne pathogens, and insect-transmitted viruses. The analysis of scenario outcomes is conducted along a risk-analytical pattern, which involves risk probabilities represented by categorized probability levels of disease epidemics, and risk magnitudes represented by categorized levels of crop losses resulting from these levels of epidemics within each production situation. The results from this scenario analysis suggest an overall increase of risk probabilities and magnitudes in the three idealized agrosystems. Changes in risk probability or magnitude however vary with the agrosystem and the functional groups of pathogens. We discuss the effects of global changes on the six functional groups, in terms of their epidemiology and of the crop losses they cause. Scenario analysis enables qualitative analysis of complex systems, such as plant pathosystems that are evolving in response to global changes, including climate change and technology shifts. It also provides a useful framework for quantitative simulation modeling analysis for plant disease epidemiology.

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Section: Patterns Of Change In Wheat Diseasesmentioning

confidence: 99%

A White Paper on Global Wheat Health Based on Scenario Development and Analysis

et al. 2017

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“…One of the ways to assess the risk of spread is to evaluate climatic requirements for disease establishment (Shaw & Osborne, ; Yuen & Mila, ). How environmental variables relate to the spatial occurrence of a disease can be estimated with different modelling techniques and algorithms.…”

Section: Introductionmentioning

confidence: 99%

Local and regional spread of banana xanthomonas wilt (BXW) in space and time in Kagera, Tanzania

et al. 2016

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Banana is an important crop in the Kagera region of Tanzania. Banana xanthomonas wilt (BXW) was first reported in Kagera in 2006, and is now an important limiting factor in banana production, because all cultivars are susceptible and infected plants can fail to produce fruit. BXW is caused by Xanthomonas campestris pv. musacearum (Xcm), which is spread by farm tools, infected planting materials, and pollinating insects. Practices that address Xcm dissemination, such as mat removal, debudding and tool sterilization, have not prevented the spread of BXW in the region. Disease surveys were conducted in Kagera from 2007 to 2011 to assess BXW presence, monitor its intensity and evaluate its socioeconomic impacts. Spatiotemporal clusters of BXW were analysed with ARCGIS and SAS. The relationship between BXW clusters and environmental variables was examined using bivariate correlations in SPSS; two modelling approaches, MaxEnt (maximum entropy) and logistic regression, were used to predict the potential distribution of BXW in Tanzania. Disease progress over time was best described with the Gompertz model. Significant clustering of BXW was observed in all years and hotspots were located in the Muleba, Karagwe, Misenyi and Bukoba rural districts. These findings suggest that BXW spreads rapidly over short distances. BXW clusters were positively correlated with rainfall and negatively with temperature and altitude. According to MaxEnt, precipitation was the main factor associated with BXW development. MaxEnt and logistic regression predicted a wide potential distribution of BXW in Tanzania because the climate in all banana-growing regions is conducive for its establishment.

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“…Mechanistic algorithms are a function-based estimation of conditions for the development and completion of several (or a single) segments of disease development, while in the IR, these segments are limited to a threshold-based prediction of their completion. The threshold selection is often based on estimates by the model developer and may not be an accurate representation of the complex nature of biological processes [87]. Such algorithms have their appeal in their simplicity, although biological processes, such as the developments of disease epidemics, do not have a binary state but are a part of a complex system that encompasses soft transitions between minimum, optimum and maximum states [88].…”

Section: Discussionmentioning

confidence: 99%

“…The exact methodology used in the development of early models, such as the IR, is not always clear, but the assumption is that they were a product of empirical, often trial and error based methodologies and weather data available at the time (Yuen and Mila, 2015). Hence, the recommendation for future development is to explore the possibility of redesigning currently employed models to facilitate the transition from the threshold based binary estimation of stages of host parasite interaction, to a more realistic one, based on a functional relationship between host, parasite and the environment.…”

Section: Discussionmentioning

confidence: 99%

Evaluation of the ‘Irish Rules’: The Potato Late Blight Forecasting Model and Its Operational Use in the Republic of Ireland

et al. 2019

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Potato late blight caused by Phytophthora infestans is one of the most important plant diseases known, requiring high pesticide inputs to prevent disease occurrence. The disease development is highly dependent on weather conditions, and as such, several forecasting schemes have been developed worldwide which seek to reduce the inputs required to control the disease. The Irish Rules, developed in the 1950s and calibrated to accommodate the meteorological network, the characteristics of potato production and the P. infestans population at the time, is still operationally utilized by the national meteorological agency, Met Éireann. However, numerous changes in the composition and dynamics of the pathosystem and the risks of production/economic consequences associated with potato late blight outbreaks have occurred since the inception of the Irish Rules model. Additionally, model and decision thresholds appear to have been selected ad hoc and without a clear criteria. We developed a systematic methodology to evaluate the model using the empirical receiver operating curve (ROC) analysis and the response surface methodology for the interpretation of the results. The methodology, written in the R language, is provided as an open, accessible and reproducible platform to facilitate the ongoing seasonal re-evaluation of the Irish Rules and corresponding decision thresholds. Following this initial analysis, based on the available data, we recommend the reduction of the thresholds for relative humidity and an initial period duration from 90% and 12 h to 88% and 10 h, respectively. Contrary to recent reports, we found that the risk of blight epidemics remains low at temperatures below 12 °C. With the availability of more comprehensive outbreak data and with greater insight into the founder population to confirm our findings as robust, the temperature threshold in the model could potentially be increased from 10 °C to 12 °C, providing more opportunities for reductions of pesticide usage. We propose a dynamic operational decision threshold between four and 11 effective blight hours (EBH) set according to frequency of the disease outbreaks in the region of interest. Although the risk estimation according to the new model calibrations is higher, estimated chemical inputs, on average, are lower than the usual grower’s practice. Importantly, the research outlined here provides a robust and reproducible methodological approach to evaluate a semi-empirical plant disease forecasting model.

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Landscape-Scale Disease Risk Quantification and Prediction

Cited by 37 publications

References 68 publications

A White Paper on Global Wheat Health Based on Scenario Development and Analysis

A White Paper on Global Wheat Health Based on Scenario Development and Analysis

Local and regional spread of banana xanthomonas wilt (BXW) in space and time in Kagera, Tanzania

Evaluation of the ‘Irish Rules’: The Potato Late Blight Forecasting Model and Its Operational Use in the Republic of Ireland

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