Joint Poster Session JP1.30 Assessing the progression of pests and pathogens using the weather data derived from the Weather Research and Forecast model

Tuesday, 29 April 2008
Floral Ballroom Magnolia (Wyndham Orlando Resort)
Rabiu Olatinwo, University of Georgia, Griffin, GA; and T. Prabhakaran, J. O. Paz, and G. Hoogenboom

Handout (1.4 MB)

Peanut is an important crop in the southeastern USA. In Georgia alone, the total losses associated with disease vectors and pests of peanut in 2004 were approximately $17 million. The population development of peanut disease vectors and pests depends strongly on air temperature and associated weather parameters. Thus, weather information is crucial for a pest and disease forecast models, especially for evaluating the progression of the pest and pathogen population. Weather information is typically obtained from a weather network or National Weather Service forecasts. The goal of this study was to develop a decision support tool based on high resolution weather forecasting data to assist peanut growers in their decision making process. We obtained high resolution weather forecast data and used these as input for pests/pathogens models to derive spatial and temporal distributions of imminent disease forecasts and risk assessments. The Weather Research and Forecasting (WRF) model is a state of the art weather prediction system developed by the National Center for Atmospheric Research (NCAR) and National Center for Environmental Prediction (NCEP). The WRF model was adapted to derive high resolution surface sensible weather over Georgia for agricultural and environmental applications. A test case using the WRF data during the period from April 15 until June15, 2007 was considered in this study. The spatial distribution of degree day accumulations and number of generations accumulated for peanut disease vectors and insect pests were analyzed using air temperature. Two peanut disease vectors; Tobacco thrips and Western flower thrips, and two peanut pests; Corn earworm and Two-spotted spider mite were included in this study. A spatial risk distribution of leaf spot infection initiation on peanut plant was also generated based on the Oklahoma peanut leaf spot model. The model was coupled with WRF output to generate spatial map of accumulated “daily infection hours” in peanut using air temperature and relative humidity. The spatial progression of disease vectors and pests, and the accumulated “daily infection hours” were evaluated in comparisons with available observations. The spatial maps indicated that the southwestern region of Georgia is the most favorable for pest development and disease progression. Risk of early occurrence of pest development was also noticed in this region. An accurate prediction of diseases and pests development could assist peanut growers with optimum timing of fungicide and insecticide applications.
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