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United States Department of Agriculture

Agricultural Research Service

Research Project: Modeling the Effects of Weather on Fhb and Don and Developing Robust Strategies to Minimize Losses Project Number: 0500-00053-003-78
Project Type: Grant

Start Date: May 13, 2014
End Date: May 12, 2015

Objective:
Project 1: Robust Integrated Management Guidelines to Minimize Losses due to FHB in Ohio: (1) Evaluate the integrated effects of fungicide and genetic resistance on FHB and DON, with emphasis on different application timings and new genotypes; and (2) conduct a comparative assessment of Prosaro and Caramba for post-anthesis application in FHB and DON integrated management programs Project 2: Influence of Variable Pre-anthesis Rainfall Patterns on FHB and DON in Wheat: Investigate the specific effects of intermittent moisture during the 8-day pre-anthesis window on FHB and DON. Project 3: Fate of Deoxynivalenol in Wheat after Visual Symptom Development: (1) determine and model the effects of temperature and relative humidity on DON accumulation in spikes with known levels of FHB index; and (2) evaluate and model relationships among post-anthesis infection, inoculum density, and DON accumulation, as influenced by temperature and relative humidity. Project 4: Development of Prediction Models for Fusarium Head Blight and Deoxynivalenol: (1) Coordinate the collection of new observations from the IM-CP used in developing and testing future models; (2) Conduct quality checks on the new observations before including them in the expanded dataset; (3) Improve the prediction accuracy of models for FHB and DON by: (i) including predictors from time periods not considered by the current models, and (ii) by using functional data analysis to identify signal locations within the expanded time series; and (4) evaluate the potential value of prediction models as part of the integrated management program for FHB and DON using Bayesian decision theory.

Approach:
Project 1: There will be four replicate blocks in both experiments. FHB, DON, FDK, yield, and test weight data will be collected and analyzed to determine the effect of fungicide treatment (product, rate and application timing), cultivar resistance, and their interaction on each of the measured response variables.Data from both experiments will be combined with data from other MGMT CP experiments, and a technique called meta-analysis will be used to conduct a quantitative synthesis of the effect of integrated management on FHB, DON, yield, and test weight. Project 2: Influence of Variable Pre-anthesis Rainfall Patterns on FHB and DON in Wheat: Experiments using mist-irrigation systems programmed to run on different schedules will be conducted at three locations (North Carolina, Minnesota, and Ohio), two of these representing soft red winter wheat (SRWW) regions with distinct weather patterns, and the third representing a hard red spring wheat-producing region with in-season weather conditions considerably different from those at the SRWW locations. Project 3: Fate of Deoxynivalenol in Wheat after Visual Symptom Development: Replicated experiments will be conducted in temperature- and RH-controlled growth and mist chambers at the OARDC to evaluate the influence of temperature, moisture, inoculum density, and timing of infection on DON accumulation in both symptomatic and asymptomatic wheat grain. Models fitted to the data will help to identify, and quantify the effect of, late-season factors affecting the fate of DON, and will provide estimates of the risk of DON exceeding critical thresholds under the influence of post-anthesis environmental and biological factors. Project 4: Development of Prediction Models for Fusarium Head Blight and Deoxynivalenol: Functional data analysis will be used to look for differences between epidemics and non-epidemics in a predictors' time series up to several months before anthesis. The results of the functional analysis will be transferred to logistic regression models, which are easy to apply on a large geographical scale via the Fusarium Head Blight Prediction Center. All models identified by this analysis will be further evaluated for utility using Bayesian decision theory.

Last Modified: 11/22/2014
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