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 2014, the model performed as well or numerically better than the calendar based sprays in all but 5 treatments for both Praline and tebuconazole products. However, the results from 2015 did not produce the same results, especially for the tebuconazole programs. Disease pressure in 2014 was significantly higher that observed in 2015 which could account for these discrepancies. Also, the model showed at each site in 2015 that fungicide applications for white mold were generally not need until 60 days after planting (OAP), but praline and tebuconazole treatments were applied at 40 DAP even if the model did not indicate to. It is likely that leaf spot pathogen control was critical to final yield results, which could account for the lack of separation between the chlorothalonil only spray and the treatments. Thus, it appears in a year when the environment is not conducive for white mold, even in high risk situations, that waiting to spray is most beneficial.
Overall, the results indicate the importance of understanding pathogen's population response to environmental conditions when assessing early season disease risk in order to determine the proper management inputs (i.e. fungicides). 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 provides more information about S. rolfsii development in a field setting and provides data for comparison with optimal years. Overall, it indicates that management improvements can be obtained using the DSS for S. rolfsii, especially in high risk situations.

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