A Comprehensive Fungicide Application Decision Support System for Control of White Mold

Scierotinia sclerotiorum, or white mold, is generally considered to be one of the two most damaging diseases to the U.S. canola, dry bean, soybean, and sunflower crops. Although controllable with timely fungicide applications, the expense of these fungicides and the lack of reliable information as to the need for the application often prohibit the preemptive action requited for disease control. White mold's life cycle is intricately tied to the weather, requiring specific conditions for infection to occur. However, the nature of the weather information required for monitoring disease potential makes it impractical for producers to monitor the conditions themselves. Consequently, an alternative means of evaluating the potential for infections is desirable. The proposed program is aimed at demonstrating the feasibility of developing a decision support system (DDS) to remotely monitor the potential for white mold infections in producer's fields. This DDS would incorporate the latest technologies in remote sensing, weather analysis and forecasting, and crop growth and disease modeling. The ability to remotely monitor the weather to which a susceptible field is exposed through integration of data collected by various observing platforms is paramount to the success of the DSS, and is the primary focus of this Phase I investigation.