Integrating real-time edaphics into epidemic models for predicting risk in soilborne pathogen systems
Jensen Hayter: Texas A&M University Department of Plant Pathology and Microbiology
<div>Soil conditions are relevant to pathosystems involving soilborne pathogens, and pathogenesis occurring in the rhizosphere. Epidemic variation in many such pathosystems is economically important, and has been the focus of modeling efforts aimed at predicting risk of disease in these contexts. In many such models, typological information about soils is utilized; however, existing and novel models stand to benefit from increasing descriptions of soils beyond typology by incorporating real-time estimates of soil physical (structural, chemical, <em>etc.</em>) and edaphic (microbial, nutritional) conditions. Means of providing these estimates through the use of weather and other data are introduced: edaphological models are developed through experimentation and statistical methods, and thereafter used to project relevant soil conditions in real time using available weather data. These conditions are then used in the development of epidemic models used to predict disease risk. Examples of how these benefits are realized in predictive performance are provided through the use of historical (pathosystems involving <em>Fusarium oxysporum</em>) and simulated datasets. Significant improvement in the performance of risk prediction following this integration results, and applications to similar systems is discussed.</div>
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