Developing/Validating a Weather-Based Decision Support System for the Management of White Mold (sclerotium rolfsii) in Peanut


University of Florida

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Southeast (GA, FL, AL)

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In general, 2013 was a low disease pressure year for S. rolfsii in Florida. Much of the state had above average rainfall associated with cooler than average temperatures, which led to significant leaf spot disease problems. Because of these conditions, stem rot was not observed until later in the season. The model performed well at most field sites, and did not indicate higher risk situations until a little more than a month after the peanuts had been planted. Out 12 possible early season spray scenarios the model only warned of 3 high risk situations. Overall, the results indicate that in years with non-optimal conditions for white mold the DSS performs well, and can provide growers with more information to reduce the use of early season fungicide sprays for white mold of peanuts. Many factors can affect disease intensity, and continued research to understand these affects is critical to obtaining optimal peanut yields. As new resistant and tolerant varieties are developed, researchers will need to determine disease inputs carefully and over multiple seasons. This study is part of a 3 year study looking at the utility of the new DSS with Peanut RX. Further testing is needed to assess the performance of the model in conditions where a fungicide application is needed, and in conjunction with the Peanut Rx and site specific weather data.

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