Development and Evaluation of a Computerized Forecasting Method for Cercospora Leafspot of Peanuts

Parvin, David W.

doi:10.1094/phyto-64-385

Cited by 13 publications

(6 citation statements)

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“…Jenson and Boyle determined that the hours of relative humidity (RH) 295% and the minimum temperature (T) during the high humidity period could be used to forecast leaf spot increase and modeled this relationship (10,ll). The Jenson and Boyle model was later computerized and adapted to schedule fungicide sprays by Parvin et al (18). The Parvin, Smith, and Crosby (PSC) advisory was validated for virginia cultivars in Virginia (20) where it was used commercially from [1981][1982][1983][1984][1985][1986][1987][1988].…”

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Evaluation of a Weather-Based Spray Advisory for Management of Early Leaf Spot of Peanut in Oklahoma1

et al. 1994

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A simplified version of the weather-based advisory program developed by Pawin, Smith, and Crosby (PSC) for scheduling fungicide sprays for management of early l e d spot (Cercospora arachidicoh Hori) of peanut (Arachis hypogea L.) was evaluated under various productions systems in Oklahoma from 1990-1992. Over eight trials with spanish cultivars, the number of sprays per season averaged 5.7 for the 14-d schedule and 4.0 for the advisory program. Final disease incidence (symptomatic and defoliated leaflets) with the fungicide chlorothalonil (1.26 k@a) averaged 15% for the 14-d schedule, 34% for the advisoryprogram, and 77% for the control. However, disease incidence (75%) and defoliation (!jO%) approached unacceptable levels on the Spanish cultivars in some trials. In six trials with runner cultivars, the number of sprays averaged 6.7 for the 14-d schedule and 4.7 for the advisory program. Final disease incidence with chlorothalonil on the runner cultivars averaged 5% for the 14-d schedule, 14% for the advisory program, and 68% in the control. Yields did not differ in any of the 14 trials between the advisory and 14-d programs using chlorothalonil. Yields averaged 3015,3003, and2303 kg/ha for Spanish cultivars and 4108, 3855, and 3066 kg/ha for runner cultivars with the 14-d schedule, advisory program, and control, respectively. The advisory program was effective in irrigated trials where weather stations were deployed either under or outside the influence of irrigation. The fungicides tebuconazole at 0.14 k@a and propiconazole at 0.13 kg/ ha generally provided better leaf spot control with the advisory program than chlorothalonil. The post-infection activity of these fungicides was observed in one trial and probably accounts for their improved performance. Yields were reduced and leafspot incidence was high where tank mixes of benomyl(O.28 kg/ha) or thiophanatemethyl (0.38 k@a) plus mancozeb (1.68 kg/ha) were used with the advisory program. Area under the disease progress curve, leaf spot incidence, and defoliation in the controls were lower for runner than for Spanish cultivars at sites where both market types were planted in adjacent trials. Fungicides applied according to the advisory program provided better leaf spot control on the runner cultivars. Results showed the need for a weather-based advisory which allows greater levels of leafspot control on Spanish cultivars than the PSC advisory.'Approved for publication by the Director, Oklahoma Agric. Exp. Stn. This study was funded in part by Hatch project

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Evaluation of a Weather-Based Spray Advisory for Management of Early Leaf Spot of Peanut in Oklahoma1

et al. 1994

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“…Weather variables such as temperature and humidity have been used and tested extensively in several early disease studies (Bailey et al 1994;Nokes and Young 1991;Wharton et al 2008). In some studies, individual computer programs have been developed based on these weather parameters, while other studies have incorporated computer programs into commercial advisory equipment (Cu and Phipps 1993;Grichar et al 2005;Boyle 1965, 1966;Linvill and Drye 1995;Parvin et al 1974;Shew et al 1988;Wu et al 1999). Therefore, a carefully evaluated disease model coupled with the WRF weather output as presented in this study could provide routine spatio-temporal predictions of potential threats for critical diseases for a range of crops, especially those for which integrated pest management is important.…”

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

et al. 2011

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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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“…In some studies, individual computer programs have been developed based on various weather parameters to make predictions, while others studies have incorporated computer programs into commercial advisory equipment (Cu and Phipps, 1993;Grichar et al, 2005;Boyle, 1965, 1966;Linvill and Drye, 1995;Parvin et al, 1974;Shew et al, 1988;Wu et al, 1999).…”

Section: Weather Factors and Derived Variablesmentioning

confidence: 99%

Weather-based Pest Forecasting for Efficient Crop Protection

Olatinwo

Hoogenboom

2014

Integrated Pest Management

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Development and Evaluation of a Computerized Forecasting Method for Cercospora Leafspot of Peanuts

Cited by 13 publications

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Evaluation of a Weather-Based Spray Advisory for Management of Early Leaf Spot of Peanut in Oklahoma1

Evaluation of a Weather-Based Spray Advisory for Management of Early Leaf Spot of Peanut in Oklahoma1

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

Weather-based Pest Forecasting for Efficient Crop Protection

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