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Predicting favorable conditions for early leaf spot of peanut using output from the Weather Research and Forecasting (WRF) model

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Abstract

Early leaf spot of peanut (Arachis hypogaea L.), a disease caused by Cercospora arachidicola S. Hori, is responsible for an annual crop loss of several million dollars in the southeastern United States alone. The development of early leaf spot on peanut and subsequent spread of the spores of C. arachidicola relies on favorable weather conditions. Accurate spatio-temporal weather information is crucial for monitoring the progression of favorable conditions and determining the potential threat of the disease. Therefore, the development of a prediction model for mitigating the risk of early leaf spot in peanut production is important. The specific objective of this study was to demonstrate the application of the high-resolution Weather Research and Forecasting (WRF) model for management of early leaf spot in peanut. We coupled high-resolution weather output of the WRF, i.e. relative humidity and temperature, with the Oklahoma peanut leaf spot advisory model in predicting favorable conditions for early leaf spot infection over Georgia in 2007. Results showed a more favorable infection condition in the southeastern coastline of Georgia where the infection threshold were met sooner compared to the southwestern and central part of Georgia where the disease risk was lower. A newly introduced infection threat index indicates that the leaf spot threat threshold was met sooner at Alma, GA, compared to Tifton and Cordele, GA. The short-term prediction of weather parameters and their use in the management of peanut diseases is a viable and promising technique, which could help growers make accurate management decisions, and lower disease impact through optimum timing of fungicide applications.

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Acknowledgements

This study was funded by a partnership with the USDA Risk Management Agency, by a Seed Grant from the College of Agricultural and Environmental Sciences of the University of Georgia, by grants from the National Peanut Board/Southeast Peanut Research Initiative, the US National Oceanic and Atmospheric Administration-Climate Program Office (NOAA-CPO) and USDA National Institute of Food and Agriculture (USDA-NIFA) and by State and Federal funds allocatedto Georgia Agricultural Experiment Stations Hatch project GEO01654. The computational support from the Computational and Information Systems Laboratory (CISL) of the National Center for Atmospheric Research (NCAR), Boulder, Colorado and the Research and Computing Center (RCC), University of Georgia, Athens, Georgia are acknowledged.

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Authors and Affiliations

  1. Department of Biological and Agricultural Engineering University of Georgia, University of Georgia, Griffin, GA, 30223, USA

    Rabiu O. Olatinwo, Thara V. Prabha, Joel O. Paz & Gerrit Hoogenboom

  2. Indian Institute of Tropical Meteorology (IITM), Pune-8, India

    Thara V. Prabha

  3. Department of Agricultural and Biological Engineering, Mississippi State University, Box 9632, Starkville, MS, 39762, USA

    Joel O. Paz

  4. Washington State University, 24106 North Bunn Road, Prosser, Washington, DC, 99350-8694, USA

    Gerrit Hoogenboom

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  1. Rabiu O. Olatinwo
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  2. Thara V. Prabha
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  3. Joel O. Paz
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  4. Gerrit Hoogenboom
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Correspondence to Rabiu O. Olatinwo.

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Olatinwo, R.O., Prabha, T.V., Paz, J.O. et al. Predicting favorable conditions for early leaf spot of peanut using output from the Weather Research and Forecasting (WRF) model. Int J Biometeorol 56, 259–268 (2012). https://doi.org/10.1007/s00484-011-0425-6

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  • DOI: https://doi.org/10.1007/s00484-011-0425-6

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