2019
DOI: 10.1094/pdis-10-18-1782-re
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Relating Peanut Rx Risk Factors to Epidemics of Early and Late Leaf Spot of Peanut

Abstract: Previous research has demonstrated the efficacy of prescription fungicide programs, based upon Peanut Rx, to reduce combined effects of early leaf spot (ELS), caused by Passalora arachidicola (Cercospora arachidicola), and late leaf spot (LLS), caused by Nothopassalora personata (syn. Cercosporidium personatum), but the potential of Peanut Rx to predict each disease has never been formally evaluated. From 2010 to 2016, non-fungicide-treated peanut plots in Georgia and Florida were sampled to monitor the develo… Show more

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Cited by 11 publications
(3 citation statements)
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“…Durante o cultivo, a incidência de doenças foliares apresenta relevante aspecto, sendo comumente adotado aplicações preventivas para redução dos danos, e potencial de dano associado a fatores genéticos, histórico da área e fatores ambientais (Fulmer et al, 2019) da doença (Amorim et al, 2016).…”
Section: Resultsunclassified
“…Durante o cultivo, a incidência de doenças foliares apresenta relevante aspecto, sendo comumente adotado aplicações preventivas para redução dos danos, e potencial de dano associado a fatores genéticos, histórico da área e fatores ambientais (Fulmer et al, 2019) da doença (Amorim et al, 2016).…”
Section: Resultsunclassified
“…The data were true, reliable, and relatively systematic, but there might be some omissions in their completeness, which might also cause some errors. Furthermore, previous studies have indicated that the occurrence and epidemic of leaf spot disease were not only affected by meteorological factors, altitude, and cultivars, but also closely related to abiotic factors, such as planting density, site conditions, and canopy density [ 14 , 38 , 39 ]. Gonzalez-Dominguez et al [ 40 ] developed a model using weather and host phenology to predict the infection period and disease progression of Phomopsis cane and leaf spot throughout the season, and validated its performance using ROC analysis (AUROC > 0.7).…”
Section: Discussionmentioning
confidence: 99%
“…Traditionally, the prediction of plant disease incidence and severity has predominantly utilized statistical methods and crop growth models [3][4][5]. However, these conventional monitoring techniques are constrained by temporal and spatial limitations [6,7].…”
Section: Introductionmentioning
confidence: 99%