Early Season Environmental Indicators of Wheat Stripe Rust Epidemics in Kansas and the Central Great Plains Region of United States

Author:

De Wolf Erick12,Andersen Onofre Kelsey3,Lollato Romulo4

Affiliation:

1. Kansas State University, 5308, Plant Pathology Dept, Manhattan, Kansas, United States,

2. Manhattan, United States;

3. Kansas State University, 5308, Plant Pathology Dept, Manhattan, Kansas, United States;

4. Kansas State University, 5308, Agronomy Dept, Manhattan, Kansas, United States;

Abstract

During the past two decades, the wheat producing areas of the Great Plains region in North America experienced frequent, severe yield losses to stripe rust (Puccinia striiformis f.sp.tritici). In general, outbreaks of rust diseases in the Southern Great Plains region often precede disease problems in the Central and Northern Great Plains. However, these generalizations provide little information and our objective for this study was to identify weather variables, geographical areas and time periods that influence the early stages of stripe rust epidemics in the Great Plains. Data used in this analysis consisted of monthly summaries of temperature, precipitation and soil moisture from 10 climate districts in the US state of Texas. These environmental variables were paired with estimates of wheat yield losses to stripe rust in Kansas, 2000-2019 with yield loss coded as a binary variable (1 = >4% statewide yield loss). An ensemble of simple models representing weather variables, time periods and geographical locations hypothesized to be influential in the development of stripe rust epidemics. Model performance were verified with observations not used in model development. Results of this study indicated that soil moisture within 2 to 3 climate districts in Texas were particularly influential in regional disease development. These areas of Texas were 700-1000 km away from locations in Kansas where the disease related yield losses were observed, and often preceded disease losses by 3-6 months. In the future, these models could help establish priority locations and time periods for disease scouting, and inform regional estimates of disease risk.

Publisher

Scientific Societies

Subject

Plant Science,Agronomy and Crop Science

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