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Western corn rootworm, Diabrotica virgifera virgifera LeConte, biology is tied to the continuous availability of its host (corn, Zea mays L.). Annual rotation of corn with a nonhost, like soybean (Glycine max (L.) Merrill) was a reliable tactic to manage western corn rootworm. Behavioral resistance to annual crop rotation (rotation resistance) allowed some eastern U.S. Corn Belt populations to circumvent rotation by laying eggs in soybean and in cornfields. When active in soybean, rotation-resistant adults commonly consume foliage, in spite of detrimental effects on beetle survival. Rotation-resistant beetle activity in soybean is enabled by the expression of certain proteinases and an adapted gut microbiota that provide limited protection from soybean antiherbivore defenses. We investigated the effects of corn and soybean herbivory on rotation-resistant female survival and initiation of flight using mortality assays and wind tunnel flight tests. Among field-collected females tested with mortality assays, beetles from collection sites in a cornfield survived longer than those from collection sites in a soybean field. However, reduced survival due to soybean herbivory could be restored by consuming corn tissues. Field-collected beetles that fed on a soybean tissue laboratory diet or only water were more likely to fly in a wind tunnel than corn-feeding beetles. Regardless of collection site and laboratory diet, 90.5% of beetles that flew oriented their flights upwind. Diet-related changes in the probability of flight provide a proximate mechanism for interfield movement that facilitates restorative feeding and the survival of females previously engaged in soybean herbivory., (© The Author(s) 2021. Published by Oxford University Press on behalf of Entomological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Sclerotinia stem rot (SSR) epidemics in soybean, caused by Sclerotinia sclerotiorum, are currently responsible for annual yield reductions in the United States of up to 1 million metric tons. In-season disease management is largely dependent on chemical control but its efficiency and cost-effectiveness depends on both the chemistry used and the risk of apothecia formation, germination, and further dispersal of ascospores during susceptible soybean growth stages. Hence, accurate prediction of the S. sclerotiorum apothecial risk during the soybean flowering period could enable farmers to improve in-season SSR management. From 2014 to 2016, apothecial presence or absence was monitored in three irrigated (n = 1,505 plot-level observations) and six nonirrigated (n = 2,361 plot-level observations) field trials located in Iowa (n = 156), Michigan (n = 1,400), and Wisconsin (n = 2,310), for a total of 3,866 plot-level observations. Hourly air temperature, relative humidity, dew point, wind speed, leaf wetness, and rainfall were also monitored continuously, throughout the season, at each location using high-resolution gridded weather data. Logistic regression models were developed for irrigated and nonirrigated conditions using apothecial presence as a binary response variable. Agronomic variables (row width) and weather-related variables (defined as 30-day moving averages, prior to apothecial presence) were tested for their predictive ability. In irrigated soybean fields, apothecial presence was best explained by row width (r = -0.41, P < 0.0001), 30-day moving averages of daily maximum air temperature (r = 0.27, P < 0.0001), and daily maximum relative humidity (r = 0.16, P < 0.05). In nonirrigated fields, apothecial presence was best explained by using moving averages of daily maximum air temperature (r = -0.30, P < 0.0001) and wind speed (r = -0.27, P < 0.0001). These models correctly predicted (overall accuracy of 67 to 70%) apothecial presence during the soybean flowering period for four independent datasets (n = 1,102 plot-level observations or 30 daily mean observations).

A physically based theory for predicting spore deposition downwind from an area source of inoculum is presented. The modeling framework is based on theories of turbulence dispersion in the atmospheric boundary layer and applies only to spores that escape from plant canopies. A "disease resistance" coefficient is introduced to convert the theoretical spore deposition model into a simple tool for predicting disease spread at the field scale. Results from the model agree well with published measurements of Uromyces phaseoli spore deposition and measurements of wheat leaf rust disease severity. The theoretical model has the advantage over empirical models in that it can be used to assess the influence of source distribution and geometry, spore characteristics, and meteorological conditions on spore deposition and disease spread. The modeling framework is refined to predict the detailed two-dimensional spatial pattern of disease spread from an infection focus. Accounting for the time variations of wind speed and direction in the refined modeling procedure improves predictions, especially near the inoculum source, and enables application of the theoretical modeling framework to field experiment design.

The pathogen causing soybean rust, Phakopsora pachyrhizi, was first described in Japan in 1902. The disease was important in the Eastern Hemisphere for many decades before the fungus was reported in Hawaii in 1994, which was followed by reports from countries in Africa and South America. In 2004, P. pachyrhizi was confirmed in Louisiana, making it the first report in the continental United States. Based on yield losses from countries in Asia, Africa, and South America, it was clear that this pathogen could have a major economic impact on the yield of 30 million ha of soybean in the United States. The response by agencies within the United States Department of Agriculture, industry, soybean check-off boards, and universities was immediate and complex. The impacts of some of these activities are detailed in this review. The net result has been that the once dreaded disease, which caused substantial losses in other parts of the world, is now better understood and effectively managed in the United States. The disease continues to be monitored yearly for changes in spatial and temporal distribution so that soybean growers can continue to benefit by knowing where soybean rust is occurring during the growing season.

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 error rate was lower in backcasting than in forecasting, reflecting how the epidemic had evolved. Ongoing epidemic evolution, and potential model failure, can occur because of changes in climate, host resistance and spatial patterns, or pathogen evolution.

Surveying invasive species can be highly resource intensive, yet near-real-time evaluations of invasion progress are important resources for management planning. In the case of the soybean rust invasion of the United States, a linked monitoring, prediction, and communication network saved U.S. soybean growers approximately $200 M/yr. Modeling of future movement of the pathogen (Phakopsora pachyrhizi) was based on data about current disease locations from an extensive network of sentinel plots. We developed a dynamic network model for U.S. soybean rust epidemics, with counties as nodes and link weights a function of host hectarage and wind speed and direction. We used the network model to compare four strategies for selecting an optimal subset of sentinel plots, listed here in order of increasing performance: random selection, zonal selection (based on more heavily weighting regions nearer the south, where the pathogen overwinters), frequency-based selection (based on how frequently the county had been infected in the past), and frequency-based selection weighted by the node strength of the sentinel plot in the network model. When dynamic network properties such as node strength are characterized for invasive species, this information can be used to reduce the resources necessary to survey and predict invasion progress.

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 the northward progress of seasonal soybean rust incursions into continental North America. The analysis further indicates that spore trapping and aerobiological modeling can reduce our reliance on the costly Sentinel Plot Network while maintaining the effectiveness of the ipmPIPE system for soybean rust management.

Urediniospores of Phakopsora pachyrhizi, the soybean rust fungus, have a high probability of being removed from a soybean leaf by water runoff associated with subsequent rainfall after wet deposition. The effects of rainfall intensity, subsequent spore-free rainfall duration, and soybean leaf sample height on uredinia density were used to evaluate the retention of urediniospores on soybean leaf tissue. Rainfall simulations of 45 and 85 mm/h were conducted on potted soybean plants that were inoculated with 2 min of urediniospore-injected simulated rainfall and exposed to 0, 1, and 30 min of subsequent spore-free rainfall. Urediniospore retention was estimated using uredinia density values obtained from a detached leaf bioassay for the sample heights of soil level, mid-canopy, and upper-canopy. Soil level leaflets inoculated with the 45 mm/h rainfall intensity treatment had a higher (P < 0.01) mean number of uredinia/cm 2 than the 85 mm/h treatment, even though they were inoculated with approximately 40% fewer urediniospores. Subsequent spore-free rainfall reduced (P < 0.01) uredinia density by as much as 38 and 91% for the 1- and 30-min durations, respectively. The relationship between uredinia density proportion and depth of rainfall was best fit using an inverse power empirical model. Our results indicate that a majority of the wet deposited P. pachyrhizi urediniospores would be removed from soybean leaf surfaces by subsequent rainfall, but sufficient percentages of spores (10 to 25%) will likely remain on the leaf tissue long enough to germinate and infect during heavy summer rains lasting ≥30 min.

Plant disease cycles represent pathogen biology as a series of interconnected stages of development including dormancy, reproduction, dispersal, and pathogenesis. The progression through these stages is determined by a continuous sequence of interactions among host, pathogen, and environment. The stages of the disease cycle form the basis of many plant disease prediction models. The relationship of temperature and moisture to disease development and pathogen reproduction serve as the basis for most contemporary plant disease prediction systems. Pathogen dormancy and inoculum dispersal are considered less frequently. We found extensive research efforts evaluating the performance of prediction models as part of operation disease management systems. These efforts appear to be greater than just a few decades ago, and include novel applications of Bayesian decision theory. Advances in information technology have stimulated innovations in model application. This trend must accelerate to provide the disease management strategies needed to maintain global food supplies.

Soybean rust, caused by Phakopsora pachyrhizi, may be the most important foliar disease of soybean. Within the last 10 years, the fungus has moved to many new geographical locations via spread of airborne urediniospores. The objective of this study was to determine the relationship between urediniospore viability and exposure to solar radiation. Urediniospores of P. pachyrhizi were exposed in Capitán Miranda, Paraguay, to determine the deleterious effects of sunlight. Concomitant total solar (0.285 to 2.8 μm) and ultraviolet (0.295 to 0.385 μm) irradiance measurements were used to predict urediniospore germination. Urediniospores exposed to doses of solar and ultraviolet (UV) radiation ≥27.3 MJ/m 2 and ≥1.2 MJ/m 2 , respectively, did not germinate. The proportions of urediniospores that germinated, normalized with respect to the germination proportion for unexposed urediniospores from the same collections, were a linear function of solar irradiance (R 2 = 0.83). UV measurements predicted normalized germination proportions equally well. Results of inoculation experiments with exposed P. pachyrhizi urediniospores supported the results of the germination trials, although the effects of moderate levels of irradiance varied. The relationship between urediniospore viability and exposure to solar radiation has been incorporated into the U.S. Department of Agriculture's soybean rust aerobiological model that provides North American soybean growers with decision support for managing soybean rust.

Bean leaf beetles (BLB; Cerotoma trifurcata) were collected in soybean (Glycine max) fields in 58 and 99 Illinois counties surveyed during the 2000 and 2001 growing seasons, respectively. In 2000, BLB counts were highest in the central portion of the state. BLB counts were lower the following year, but were more uniformly distributed throughout the state. BLB tested positive for Bean pod mottle virus (BPMV) in 37 of 41 counties assayed in 2000. In 2001, BLB tested positive for BPMV in 86 of 99 counties sampled. In 2000 and 2001, western corn rootworm (WCR; Diabrotica virgifera virgifera) adults were abundant in soybean fields only in east central Illinois. WCR adults tested positive for BPMV in 21 of 21 east central Illinois counties in 2000 and 20 of 24 sampled in 2001. BPMV was detected in soybean plants in 38 of 46 counties sampled in 2000. Field-collected WCR adults transmitted BPMV to potted soybean plants at low rates either directly from BPMV-infected soybean fields or with prior feeding on BPMV-infected plants. This is the first report of the distribution of BLB, WCR adults, and BPMV in Illinois and of BPMV transmission by adult WCR.

Using Soybean mosaic virus as a model system, the rate, magnitude and timing of epidemics caused by non-persistently transmitted, aphid-borne viruses are examined under various field conditions. Emphasis is placed on the behavioural responses of vectors to environmental cues, although all three biotic components (host plant, vector and virus) are considered. Both single and double manipulations of the cropping system environment are explored using a computer model developed earlier by Ruesink and Irwin (Plant Virus Epidemics: Monitoring, Modelling and Predicting Outbreaks.