Performing Department
(N/A)
Non Technical Summary
Stripe rust of wheat, caused by Puccinia striiformis f. sp. tritici, has emerged as a threat to wheat production in the central Great Plains region of the United States. Over the past 20 years, the frequency of stripe rust epidemics continues has increased with statewide yield losses often exceeding $75 Million in Kansas annually. Recent field observations and preliminary experiments indicate that populations of P. striiformis f. sp. tritici are continuing to adapt to the wheat production systems and environment of the Great Plains. These adaptations include the emergence of strains of P. striiformis f. sp. tritici that recover more rapidly from periods of heat stress than isolates collected just a decade ago. In this proposal, we propose integrated research and extension projects that address this threat to wheat production in the United States. The objectives of these projects are to: 1. Evaluate of the variation of heat stress tolerance in a broader sample of historical and contemporary isolates of P. striiformis f.sp. tritici; 2. Develop predictive models describing the probability that high temperatures will suppress stripe rust development and improve the real-time prediction of disease severity; 3. Deploy the predictive models via web-based tools used to evaluate the risk of multiple threats to wheat production in the region; 4. Verify model predictions and communicate the risk of severe stripe rust based on disease forecasting models and observations of disease activity in the Kansas. The research and extension approaches to accomplishing these objectives will include experiments in controlled environments to quantify the response of P. striiformis f.sp. tritici isolates representing both historical and contemporary populations in the state. The isolates tested in these experiments will include those collected between 1991- 2023 as well as, contemporary isolates sampled from research plots and commercial fields during the duration of the study. The results for these experiments will inform temperature thresholds used in the modeling of potentially suppressive nature of heat stress events on stripe rust development. Predictive models estimating the probability that heat stress events will suppress stripe rust development will be based on historical datasets of observations from replicated research plots and regional disease loss estimates collected between 2000 - 2023. The analytical approach used to develop the models will include a combination of regression and machine learning techniques of variable selection, and model fitting. The resulting models will be deployed via a suite of web-based tools known as the "Kansas Wheat Dashboard" that is currently used by extension specialists in Kansas. This dashboard uses hourly weather observations gathered by the National Weather Service and agricultural weather networks to produce maps of disease risk, as well as, multiple abiotic threats to wheat production in Kansas. The model predictions regarding the suppression of stripe rust will be verified using observations collected from replicated research plots and commercial wheat fields throughout the states. The model results and observations of disease will be used to communicate the risk of severe stripe rust and the need for wheat growers to protect their crops with timely fungicide applications. These communications will utilize maps from the Kansas Wheat Dashboard and provide recommendations via electronic newsletters, social media posts, and other established extension resources that reach thousands of growers each week.
Animal Health Component
0%
Research Effort Categories
Basic
(N/A)
Applied
100%
Developmental
(N/A)
Goals / Objectives
In this proposal, we propose integrated research and Extension projects that address this threat to wheat production in the United States. The goals of these projects are to: 1. Evaluate of the variation of heat stress tolerance in a broader sample of historical and contemporary isolates of P. striiformis f.sp. tritici; 2. Develop predictive models describing the probability that high temperatures will suppress stripe rust development and improve the real-time prediction of disease severity; 3. Deploy the predictive models via web-based tools used to evaluate the risk of multiple threats to wheat production in the region; 4. Verify model predictions and communicate the risk of severe stripe rust based on disease forecasting models and observations of disease activity in the Kansas through targeted extension efforts.
Project Methods
The research and extension approaches to accomplishing these objectives will include experiments in controlled environments to quantify the response of P. striiformis f.sp. tritici isolates representing both historical and contemporary populations in the state. The isolates tested in these experiments will include those collected between 1991- 2023 as well as, contemporary isolates sampled from research plots and commercial fields during the duration of the study. The results for these experiments will inform temperature thresholds used in the modeling of potentially suppressive nature of heat stress events on stripe rust development. Predictive models estimating the probability that heat stress events will suppress stripe rust development will be based on historical datasets of observations from replicated research plots and regional disease loss estimates collected between 2000 - 2023. The analytical approach used to develop the models will include a combination of regression and machine learning techniques of variable selection, and model fitting. The resulting models will be deployed via a suite of web-based tools known as the "Kansas Wheat Dashboard" that is currently used by extension specialists in Kansas. This dashboard uses hourly weather observations gathered by the National Weather Service and agricultural weather networks to produce maps of disease risk, as well as, multiple abiotic threats to wheat production in Kansas. The model predictions regarding the suppression of stripe rust will be verified using observations collected from replicated research plots and commercial wheat fields throughout the states. The model results and observations of disease will be used to communicate the risk of severe stripe rust and the need for wheat growers to protect their crops with timely fungicide applications. These communications will utilize maps from the Kansas Wheat Dashboard and provide recommendations via electronic newsletters, social media posts, and other established extension resources that reach thousands of growers each week.