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From year to year and state to state, disease severity and the resulting losses can differ greatly. Disease development is dependent on three factors, which are often called the plant disease triangle: the presence of the pathogen, the susceptibility of the hybrid to that pathogen, and the environment (prevailing weather conditions, agronomic practices, cropping history, etc.). Disease management relies on manipulating these factors in ways that discourage infection or slow the progression of disease. For example, growing a resistant hybrid, managing previous crop residue (often a source of inoculum), crop rotation, or applying a fungicide.

In recent years, many growers across the nation have adopted soil conservation practices including reduced tillage and cover crops to address the resulting threats of soil erosion, rainfall runoff, and soil crusting. Reduced tillage also provides climate change resilience. The practices have helped to improve the sustainability of U.S. agriculture. However, these soil conservation practices have precluded the use of two standard disease controlling tools: 1) crop residue incorporation (i.e., sanitation) and 2) exclusion of alternate hosts.

Historically, post-harvest crop residues were buried to decompose. With conservation tillage, these residues remain on the surface providing climate change resilience and soil health benefits. However, this also can increase pathogens for future infestations. Farmers need tools to predict when pathogens will threaten. Wind- or insect-dispersed pathogens require community efforts to control costs and minimize yield/quality loss. NPMTI is adapting recent advancements in human pathogen detection and modeling to agricultural systems.

Currently, growers rely on scouting to determine the level and severity of disease in a particular field. As farm sizes increase, it becomes increasingly difficult to scout every field. For some crops, models have been developed to predict risk of disease and recommend a management practice. Good examples include the Fusarium head blight risk assessment tool that integrates specific weather variables 15 days prior to flowering to estimate the risk of Fusarium head blight being greater than 10%. Farmers, extension specialists, or crop advisors use the information to schedule targeted fungicide applications to manage disease and thereby reduce losses and ensure grain quality. More recently, a model to predict white mold of soybeans was developed using 30-day weather averages, crop growth stage, and cultural practice data that has been validated and integrated into a smartphone app that producers and consultants use to determine if a fungicide application is warranted.