Literature DB >> 18943452

A predictive model for spotted wilt epidemics in peanut based on local weather conditions and the tomato spotted wilt virus risk index.

R O Olatinwo1, J O Paz, S L Brown, R C Kemerait, A K Culbreath, J P Beasley, G Hoogenboom.   

Abstract

Tomato spotted wilt virus (TSWV), a member of the genus Tospovirus (family Bunyaviridae), is an important plant virus that causes severe damage to peanut (Arachis hypogaea) in the southeastern United States. Disease severity has been extremely variable in individual fields in Georgia, due to several factors including variability in weather patterns. A TSWV risk index has been developed by the University of Georgia to aid peanut growers with the assessment and avoidance of high risk situations. This study was conducted to examine the relationship between weather parameters and spotted wilt severity in peanut, and to develop a predictive model that integrates localized weather information into the risk index. On-farm survey data collected during 1999, 2002, 2004, and 2005 growing seasons, and derived weather variables during the same years were analyzed using nonlinear and multiple regression analyses. Meteorological data were obtained from the Georgia Automated Environmental Monitoring Network. The best model explained 61% of the variation in spotted wilt severity (square root transformed) as a function of the interactions between the TSWV risk index, the average daily temperature in April (TavA), the average daily minimum temperature between March and April (TminMA), the accumulated rainfall in March (RainfallM), the accumulated rainfall in April (RainfallA), the number of rain days in April (RainDayA), evapotranspiration in April (EVTA), and the number of days from 1 January to the planting date (JulianDay). Integrating this weather-based model with the TSWV risk index may help peanut growers more effectively manage tomato spotted wilt disease.

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Year:  2008        PMID: 18943452     DOI: 10.1094/PHYTO-98-10-1066

Source DB:  PubMed          Journal:  Phytopathology        ISSN: 0031-949X            Impact factor:   4.025


  4 in total

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

Authors:  Rabiu O Olatinwo; Thara V Prabha; Joel O Paz; Gerrit Hoogenboom
Journal:  Int J Biometeorol       Date:  2011-04-16       Impact factor: 3.787

2.  Predictive Models for Tomato Spotted Wilt Virus Spread Dynamics, Considering Frankliniella occidentalis Specific Life Processes as Influenced by the Virus.

Authors:  Pamella Akoth Ogada; Dany Pascal Moualeu; Hans-Michael Poehling
Journal:  PLoS One       Date:  2016-05-09       Impact factor: 3.240

3.  Induction of Plant Resistance in Tobacco (Nicotiana tabacum) against Tomato Spotted Wilt Orthotospovirus through Foliar Application of dsRNA.

Authors:  Naga Charan Konakalla; Sudeep Bag; Anushi Suwaneththiya Deraniyagala; Albert K Culbreath; Hanu R Pappu
Journal:  Viruses       Date:  2021-04-12       Impact factor: 5.048

4.  Predicting Pre-planting Risk of Stagonospora nodorum blotch in Winter Wheat Using Machine Learning Models.

Authors:  Lucky K Mehra; Christina Cowger; Kevin Gross; Peter S Ojiambo
Journal:  Front Plant Sci       Date:  2016-03-30       Impact factor: 5.753

  4 in total

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