Soybean Foliage Consumption Reduces Adult Western Corn Rootworm (Diabrotica virgifera virgifera)(Coleoptera: Chrysomelidae) Survival and Stimulates Flight.

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.

Weather-Based Models for Assessing the Risk of Sclerotinia sclerotiorum Apothecial Presence in Soybean (Glycine max) Fields.

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 Theoretical Model of Spore Deposition for Predicting Spread of Plant Diseases.

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.

From Select Agent to an Established Pathogen: The Response to Phakopsora pachyrhizi (Soybean Rust) in North America.

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.

Identifying highly connected counties compensates for resource limitations when evaluating national spread of an invasive pathogen.

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.

Disease cycle approach to plant disease prediction.

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.