Long-Term Prediction of Soybean Rust Entry into the Continental United States (original) (raw)

Predicting Soybean Rust Incursions into the North American Continental Interior Using Crop Monitoring, Spore Trapping, and Aerobiological Modeling

Plant Disease, 2011

Between 2005 and 2009, millions of U.S. and Canadian soybean acres that would have received fungicide application remained untreated for soybean rust due to information disseminated through the Integrated Pest Management Pest Information Platform for Extension and Education (ipmPIPE), increasing North American producers' profits by hundreds of millions of dollars each year. The results of our analysis of Phakopsora pachyrhizi urediniospores in rain collections, aerobiology model output, and observations of soybean rust spread in 2007 and 2008 show a strong correspondence between spore collections and model predictions for the continental interior of North America, where soybean is an important crop. The analysis suggests that control practices based on up-to-date maps of soybean rust observations and associated commentary from Extension Specialists delivered by the ipmPIPE may have suppressed the number and strength of inoculum source areas in the southern states and retarded th...

Models and applications for risk assessment and prediction of Asian soybean rust epidemics

Fitopatologia Brasileira, 2006

Asian rust of soybean [Glycine max (L.) Merril] is one of the most important fungal diseases of this crop worldwide. The recent introduction of Phakopsora pachyrhizi Syd. & P. Syd in the Americas represents a major threat to soybean production in the main growing regions, and significant losses have already been reported. P. pachyrhizi is extremely aggressive under favorable weather conditions, causing rapid plant defoliation. Epidemiological studies, under both controlled and natural environmental conditions, have been done for several decades with the aim of elucidating factors that affect the disease cycle as a basis for disease modeling. The recent spread of Asian soybean rust to major production regions in the world has promoted new development, testing and application of mathematical models to assess the risk and predict the disease. These efforts have included the integration of new data, epidemiological knowledge, statistical methods, and advances in computer simulation to develop models and systems with different spatial and temporal scales, objectives and audience. In this review, we present a comprehensive discussion on the models and systems that have been tested to predict and assess the risk of Asian soybean rust. Limitations, uncertainties and challenges for modelers are also discussed.

Meteorological Factors and Asian Soybean Rust Epidemics - a Systems Approach and Implications for Risk Assessment

2008

Favorable meteorological and environmental conditions are critical components that affect Asian soybean rust (ASR), caused by Phakopsora pachyrhizi, the most damaging fungal disease of soybean. In this review, we used available knowledge on the effect of meteorological factors affecting the disease to construct a systems-based approach to understand the risk of ASR epidemics. The systems approach is based on a hierarchical framework where relevant environmental factors that affect the key stages of the ASR disease cycle are identified and this included both aerobiological and epidemiological components. The formal framework we used examined the following epidemic characteristics: spore release, spore dispersal, spore deposition, infection efficiency, latent period and spore production. It provided the ability to identify the most important meteorologicalrelated factors along with relevant knowledge gaps from which the implications for disease forecasting were described. This is new information that can be used as a guide for further epidemiological research and especially to develop and improve upon both local and regional risk models. RESUMO: Condições meteorológicas e ambientais são componentes críticos nas epidemias de ferrugem asiática da soja (FAS), doença causada pelo fungo Phakopsora pachyrhizi e que causa o maior dano na cultura da soja. Nesta revisão, o conhecimento sobre o efeito de fatores meteorológicos que influenciam nas epidemias foi usado para construir uma abordagem sistêmica para compreender o risco de epidemias de FAS. Esta é baseada em uma estrutura hierárquica onde os fatores relevantes que afetam os estágios chave no ciclo da doença foram delineados, incluindo os componentes aerobiológicos e epidemiológicos. As seguintes características epidemiológicas foram avaliadas: liberação de esporos, dispersão de esporos, deposição de esporos, eficiência de infecção, período latente e produção de esporos. O conhecimento sobre os fatores meteorológicos que afetam cada um dos componentes foi revisado, sendo identificados os fatores mais importantes e as falhas de conhecimento, bem como as implicações para a previsão da doença. A informação é importante para orientar a pesquisa epidemiológica futura e especialmente desenvolver e melhorar modelos de risco da doença em níveis locais a regionais. Palavras-chave: Phakopsora pachyrhizi, epidemiologia de plantas, previsão de risco, climatologia, doenças de plantas

Initial epidemic area is strongly associated with the yearly extent of soybean rust spread in North America

Biological Invasions, 2012

Hosts of soybean rust (Phakopsora pachyrhizi) are sensitive to low temperatures, limiting this obligate parasite in the United States to overwintering sites in a restricted area along the Gulf Coast. This temperature sensitivity of soybean rust hosts allowed us to study spatial spread of epidemic invasions over similar territory for seven sequential years, 2005-2011. The epidemic front expanded slowly from early April through July, with the majority of expansion occurring from August through November. There was a 7.4-fold range of final epidemic extent (0.4 to 3.0 million km 2) from the year of smallest final disease extent (2011) to that of the largest (2007). The final epidemic area of each year was regressed against epidemic areas recorded at one-week intervals to determine the association of final epidemic extent with current epidemic extent. Coefficients of determination for these regressions varied between 0.44 to 0.62 during April and May. The correlation coefficients varied between 0.70 and 0.96 from early June through October, and then increased monotonically to 1.0 by year's end. Thus, the spatial extent of disease when the epidemics began rapid expansion may have been a crucial contributor to subsequent spread of soybean rust. Our analyses used presence/absence data at the county level to evaluate the spread of the epidemic front only; the subsequent local intensification of disease could be strongly influenced by other factors, including weather.

Geographical and seasonal dynamics and air trajectory modeling of air-borne cereal rust fungi: a case study in western Canada

Authorea (Authorea), 2023

Cereal rust diseases are caused by Puccinia spp. urediniospores that travel by air currents to cause losses in wheat, oat, barley, and rye. In western Canada, inoculum can arrive from the USA through the Great Plains or via a Pacific corridor west of the Rocky Mountains. Moreover, climate and land use are known factors in Puccinia dissemination, but their relative effects remain poorly understood. We investigated aeromycobiota in western Canada by weekly air-sampling over four growing seasons (2015-2018) three mixed-crop sites in British Columbia (BC) and one season (2018) at five sites in southern Alberta (AB). ITS2-based metabarcoding and novel bioinformatic comparative analyses to known, especially curated cereal rust fungal sequences, was used for species identification. The overall aeromycobiota and rust fungal community diversity was higher west than east of the Canadian Rockies. This mountain range delineates climate and land use and also creates a barrier to wind flow to prevent the spread of rusts and other plant pathogens (e.g., Bipolaris, Blumeria). We recovered seven major cereal rusts and revealed their geographic and seasonal dynamics at the eight sampling sites. Forward and reverse trajectory HYSPLIT model simulations predicted the potential sources of rust urediniospores and their pathways of movement modulated by air currents, through which some pronounced changes in the abundance of wheat rust pathogens were explained. This study paves the way for a potential application for pathogen monitoring and disease risk forecasting as part of the design and development of an early-warning system for enhanced biovigilance against crop diseases.

Large-Scale Atmospheric Dispersal Simulations Identify Likely Airborne Incursion Routes of Wheat Stem Rust Into Ethiopia

Phytopathology®, 2017

In recent years, severe wheat stem rust epidemics hit Ethiopia, sub-Saharan Africa’s largest wheat-producing country. These were caused by race TKTTF (Digalu race) of the pathogen Puccinia graminis f. sp. tritici, which, in Ethiopia, was first detected at the beginning of August 2012. We use the incursion of this new pathogen race as a case study to determine likely airborne origins of fungal spores on regional and continental scales by means of a Lagrangian particle dispersion model (LPDM). Two different techniques, LPDM simulations forward and backward in time, are compared. The effects of release altitudes in time-backward simulations and P. graminis f. sp. tritici urediniospore viability functions in time-forward simulations are analyzed. Results suggest Yemen as the most likely origin but, also, point to other possible sources in the Middle East and the East African Rift Valley. This is plausible in light of available field surveys and phylogenetic data on TKTTF isolates from E...

Atmospheric Dispersion of Wheat Rust Spores: A New Theoretical Framework to Interpret Field Data and Estimate Downwind Dispersion

Journal of Applied Meteorology and Climatology, 2012

Theoretical predictions for dispersion of heavy particles above an area source are used to formulate a new framework to interpret measurements of spore concentration above an infected field. Experimental measurements of mean spore concentration above an infected wheat field are used to validate theoretical predictions. The framework is then used to estimate total spore flux from the infected field and deposition patterns downwind. Results suggest that for the present case, consisting of a very low open canopy and friction velocity between 0.2 and 0.5 m s 21 , the properties of the spore plume above the source field are mostly determined by the source strength (i.e., spore release rate) and are approximately independent of turbulence properties. Turbulence conditions have a strong effect on the distance downwind from the source traveled by spores, however, and are therefore critical in the spread of the disease. In addition, effects of spore clumping on dispersal are explored, illustrating the strong effect of clumping on reducing spore dispersal distance.

History, Epidemic Evolution, and Model Burn-In for a Network of Annual Invasion: Soybean Rust

Phytopathology, 2015

Ecological history may be an important driver of epidemics and disease emergence. We evaluated the role of history and two related concepts, the evolution of epidemics and the burn-in period required for fitting a model to epidemic observations, for the U.S. soybean rust epidemic (caused by Phakopsora pachyrhizi). This disease allows evaluation of replicate epidemics because the pathogen reinvades the United States each year. We used a new maximum likelihood estimation approach for fitting the network model based on observed U.S. epidemics. We evaluated the model burn-in period by comparing model fit based on each combination of other years of observation. When the miss error rates were weighted by 0.9 and false alarm error rates by 0.1, the mean error rate did decline, for most years, as more years were used to construct models. Models based on observations in years closer in time to the season being estimated gave lower miss error rates for later epidemic years. The weighted mean ...

Movement of Phakopsora pachyrhizi (soybean rust) spores by non-conventional means

European Journal of Plant Pathology, 2009

Soybean, caused by the rust fungus Phakopsora pachyrhizi, is the most important foliar pathogen infecting soybean. Historically, the disease was important only in the Eastern Hemisphere, but since 1994 the disease has been reported in many countries in Africa and the Americas. In the U.S.A., soybean rust has been perceived as a threat to soybean production and monitoring of the disease occurs throughout the country where soybean is grown. The objectives of this study were to show conclusive evidence that soybean rust spores can be transported by non-conventional means such as clothing. The implication may affect how researchers approach monitoring this disease in research and sentinel plots.

Model-Based Forecasting of Agricultural Crop Disease Risk at the Regional Scale, Integrating Airborne Inoculum, Environmental, and Satellite-Based Monitoring Data

Frontiers in Environmental Science, 2018

Crop diseases have the potential to cause devastating epidemics that threaten the world's food supply and vary widely in their dispersal pattern, prevalence, and severity. It remains unclear what the impact disease will have on sustainable crop yields in the future. Agricultural stakeholders are increasingly under pressure to adapt their decision-making to make more informed and efficient use of irrigation water, fertilizers, and pesticides. They also face increasing uncertainty in how best to respond to competing health, environment, and (sustainable) development impacts and risks. Disease dynamics involves a complex interaction between a host, a pathogen, and their environment, representing one of the largest risks facing the long-term sustainability of agriculture. New airborne inoculum, weather, and satellite-based technology provide new opportunities for combining disease monitoring data and predictive models-but this requires a robust analytical framework. Integrated model-based forecasting frameworks have the potential to improve the timeliness, effectiveness, and foresight for controlling crop diseases, while minimizing economic costs and environmental impacts, and yield losses. The feasibility of this approach is investigated involving model and data selection. It is tested against available disease data collected for wheat stripe (yellow) rust (Puccinia striiformis f.sp. tritici) (Pst) fungal disease within southern Alberta, Canada. Two candidate, stochastic models are evaluated; a simpler, site-specific model, and a more complex, spatially-explicit transmission model. The ability of these models to reproduce an observed infection pattern is tested using two climate datasets with different spatial resolution-a reanalysis dataset (∼55 km) and weather station network township-aggregated data (∼10 km). The complex spatially-explicit model using weather station network data had the highest forecast accuracy. A multi-scale airborne surveillance design that provides data would further improve disease risk forecast accuracy under heterogeneous modeling assumptions. In the future, a model-based forecasting approach, if supported with an airborne surveillance monitoring plan, could be made operational to provide agricultural stakeholders with reliable, cost-effective, and near-real-time information for protecting and sustaining crop production against multiple disease threats.