Evaluation of the Ryegrass Stem Rust Model STEMRUST_G and Its Implementation as a Decision Aid.

STEMRUST_G, a simulation model for epidemics of stem rust in perennial ryegrass grown to maturity as a seed crop, was validated for use as a heuristic tool and as a decision aid for disease management with fungicides. Multistage validation had been used in model creation by incorporating previously validated submodels for infection, latent period duration, sporulation, fungicide effects, and plant growth. Validation of the complete model was by comparison of model output with observed disease severities in 35 epidemics at nine location-years in the Pacific Northwest of the United States. We judge the model acceptable for its purposes, based on several tests. Graphs of modeled disease progress were generally congruent with plotted disease severity observations. There was negligible average bias in the 570 modeled-versus-observed comparisons across all data, although there was large variance in size of the deviances. Modeled severities were accurate in >80% of the comparisons, where accuracy is defined as the modeled value being within twice the 95% confidence interval of the observed value, within ±1 day of the observation date. An interactive website was created to produce disease estimates by running STEMRUST_G with user-supplied disease scouting information and automated daily weather data inputs from field sites. The model and decision aid supplement disease managers' information by estimating the level of latent (invisible) and expressed disease since the last scouting observation, given season-long weather conditions up to the present, and it estimates effects of fungicides on epidemic development. In additional large-plot experiments conducted in grower fields, the decision aid produced disease management outcomes (management cost and seed yield) as good as or better than the growers' standard practice. In future, STEMRUST_G could be modified to create similar models and decision aids for stem rust of wheat and barley, after additional experiments to determine appropriate parameters for the disease in these small-grain hosts.

Decision aids for multiple-decision disease management as affected by weather input errors.

Many disease management decision support systems (DSSs) rely, exclusively or in part, on weather inputs to calculate an indicator for disease hazard. Error in the weather inputs, typically due to forecasting, interpolation, or estimation from off-site sources, may affect model calculations and management decision recommendations. The extent to which errors in weather inputs affect the quality of the final management outcome depends on a number of aspects of the disease management context, including whether management consists of a single dichotomous decision, or of a multi-decision process extending over the cropping season(s). Decision aids for multi-decision disease management typically are based on simple or complex algorithms of weather data which may be accumulated over several days or weeks. It is difficult to quantify accuracy of multi-decision DSSs due to temporally overlapping disease events, existence of more than one solution to optimizing the outcome, opportunities to take later recourse to modify earlier decisions, and the ongoing, complex decision process in which the DSS is only one component. One approach to assessing importance of weather input errors is to conduct an error analysis in which the DSS outcome from high-quality weather data is compared with that from weather data with various levels of bias and/or variance from the original data. We illustrate this analytical approach for two types of DSS, an infection risk index for hop powdery mildew and a simulation model for grass stem rust. Further exploration of analysis methods is needed to address problems associated with assessing uncertainty in multi-decision DSSs.

Developmental parameters and seasonal phenology of Calepitrimerus vitis (Acari: Eriophyidae) in wine grapes of western Oregon.

Developmental parameters of protogyne Calepitrimerus vitis (Nalepa) (Acari: Eriophyidae) were determined at 12, 15, 17, 22, 25, 28, 31, and 34 °C to better understand seasonal activity, population growth, and ultimately more effectively manage pest mites in wine grapes. Net reproductive rate (R(o)) was greater than zero at all temperatures with the maximum R(o) (9.72) at 25 °C. The lowest estimated R(o) (0.001) occurred at 34 °C. There was a gradual decrease in mean generation time (T) as temperatures increased from 17 to 31 °C. The shortest and longest generation time was recorded at 31 °C (T = 5.5 d) and 17 °C (T = 17.5 d). Rates of natural increase were lowest at 17°C (0.035) and increased with increasing temperatures, respectively. The peak rate of natural increase value (0.141) was at 25 °C. Estimations for minimum and maximum developmental thresholds were 10.51 and 39.19 °C, respectively, while the optimum developmental temperature was 26.9 °C. The thermal constant for egg to adult development was estimated at 87.7DD. The highest fecundity was observed at 25 °C. These parameters indicated that mites begin feeding at the onset of shoot growth when tissue is most susceptible in spring. Historical weather data showed that vines are in this susceptible growth stage for longer periods in the cool Willamette Valley compared with warmer Umpqua and Applegate/Rogue Valley regions. Estimation of degree-days indicated when deutogyne mites move to overwintering refuge sites. Degree-day accumulations indicated up to 14 generations per growing season.