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

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.

Weather-based Pest Forecasting for Efficient Crop Protection

“…Weather fronts and incipient thunderstorms are reported to discourage the mass flight of thrips, thereby resulting in high densities above the soil surface due to the landing attempts of thrips (Kirk, 2004;Lewis, 1964Lewis, , 1965Lewis, , 1973Lewis, , 1997, while Morsello et al (2008) also found that the number of thrips captured in flight has a positive relationship with the number of wet days or days with precipitation. In peanut, populations of adult thrips vectors F. occidentalis and F. fusca were reported to be greater for early planting (Olatinwo et al, 2012). in April or late planting in June compared to planting in May (Mitchell and Smith, 1991;Todd et al, 1995). Field observations also indicate a higher level of spotted wilt associated with early-and late-planted peanuts compared to those planted during the middle of the planting season (Brown et al, 2005bOlatinwo et al, 2008).…”

“…According to Grichar et al (2005), other systems have used a similar combination of relative humidity/leaf wetness and temperature to forecast favourable conditions for disease development and scheduling of fungicide applications. Olatinwo et al (2012) demonstrated the possibility of coupling the high-resolution WRF data output with a leaf spot disease model, i.e. Oklahoma peanut leaf spot model in predicting favourable conditions for early leaf spot infection.…”

“…The WRF model is suitable for a broad spectrum of applications across scales ranging from metres to thousands of kilometres. Potential applications of WRF in plant disease and insect vectors were recently evaluated (Olatinwo et al, 2011(Olatinwo et al, , 2012, of which two examples are discussed as case studies later in this chapter.…”

“…With significant progress made in the field of science and technology, pest models have been coupled with high-resolution weather forecast data to predict the risk of an impending increase in pest populations or favourable conditions for infection initiation and disease development. Olatinwo et al (2011Olatinwo et al ( , 2012 recently examined the application of this promising scientific approach for managing insect vectors and a peanut disease in the southeastern United States. The disease and insect vector models were coupled with the weather predictions or forecasts provided by the WRF model.…”

“…The disease and insect vector models were coupled with the weather predictions or forecasts provided by the WRF model. A part of this study (Olatinwo et al, 2012) evaluated a potential short-range forewarning concept that triggers an alert and generates a 3 × 3 km grid high-resolution map when a favourable condition for the potential onset of a disease is met.…”