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Biblioteca(s): |
Embrapa Mandioca e Fruticultura. |
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Data corrente: |
06/09/2014 |
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Data da última atualização: |
25/05/2023 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
CUNNIFFE, N. J.; BARBOSA, F. F. L.; NERI, F. M.; DESIMONE, R. E.; GILLIGAN, C. A. |
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Afiliação: |
NIK J. CUNNIFFE, University of Cambridge; FRANCISCO FERRAZ LARANJEIRA BARBOSA, CNPMF; FRANCO M. NERI, University of Cambridge; R. ERIK DESIMONE, University of Cambridge; CHRISTOPHER A. GILLIGAN, University of Cambridge. |
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Título: |
Cost-effective control of plant disease when epidemiological knowledge is incomplete: modelling Bahia bark scaling of citrus. |
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Ano de publicação: |
2014 |
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Fonte/Imprenta: |
Plos Computational Biology, v. 10, Issue 8 , August, 2014. |
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Idioma: |
Inglês |
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Conteúdo: |
A spatially-explicit, stochastic model is developed for Bahia bark scaling, a threat to citrus production in north-eastern Brazil, and is used to assess epidemiological principles underlying the cost-effectiveness of disease control strategies. The model is fitted via Markov chain Monte Carlo with data augmentation to snapshots of disease spread derived from a previouslyreported multi-year experiment. Goodness-of-fit tests strongly supported the fit of the model, even though the detailed etiology of the disease is unknown and was not explicitly included in the model. Key epidemiological parameters including the infection rate, incubation period and scale of dispersal are estimated from the spread data. This allows us to scale-up the experimental results to predict the effect of the level of initial inoculum on disease progression in a typically-sized citrus grove. The efficacies of two cultural control measures are assessed: altering the spacing of host plants, and roguing symptomatic trees. Reducing planting density can slow disease spread significantly if the distance between hosts is sufficiently large. However, low density groves have fewer plants per hectare. The optimum density of productive plants is therefore recovered at an intermediate host spacing. Roguing, even when detection of symptomatic plants is imperfect, can lead to very effective control. However, scouting for disease symptoms incurs a cost. We use the model to balance the cost of scouting against the number of plants lost to disease, and show how to determine a roguing schedule that optimizes profit. The trade-offs underlying the two optima we identify?the optimal host spacing and the optimal roguing schedule? are applicable to many pathosystems. Our work demonstrates how a carefully parameterised mathematical model can be used to find these optima. It also illustrates how mathematical models can be used in even this most challenging of situations in which the underlying epidemiology is ill-understood. MenosA spatially-explicit, stochastic model is developed for Bahia bark scaling, a threat to citrus production in north-eastern Brazil, and is used to assess epidemiological principles underlying the cost-effectiveness of disease control strategies. The model is fitted via Markov chain Monte Carlo with data augmentation to snapshots of disease spread derived from a previouslyreported multi-year experiment. Goodness-of-fit tests strongly supported the fit of the model, even though the detailed etiology of the disease is unknown and was not explicitly included in the model. Key epidemiological parameters including the infection rate, incubation period and scale of dispersal are estimated from the spread data. This allows us to scale-up the experimental results to predict the effect of the level of initial inoculum on disease progression in a typically-sized citrus grove. The efficacies of two cultural control measures are assessed: altering the spacing of host plants, and roguing symptomatic trees. Reducing planting density can slow disease spread significantly if the distance between hosts is sufficiently large. However, low density groves have fewer plants per hectare. The optimum density of productive plants is therefore recovered at an intermediate host spacing. Roguing, even when detection of symptomatic plants is imperfect, can lead to very effective control. However, scouting for disease symptoms incurs a cost. We use the model to balance the cost of scouting against the num... Mostrar Tudo |
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Palavras-Chave: |
Disorders; Plant diseases. |
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Thesagro: |
Doença de planta; Fruta citríca; Modelo matemático. |
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Thesaurus Nal: |
Citrus; Disease control; Mathematical models. |
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Categoria do assunto: |
-- |
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Marc: |
LEADER 02813naa a2200265 a 4500
001 1994376
005 2023-05-25
008 2014 bl uuuu u00u1 u #d
100 1 $aCUNNIFFE, N. J.
245 $aCost-effective control of plant disease when epidemiological knowledge is incomplete$bmodelling Bahia bark scaling of citrus.$h[electronic resource]
260 $c2014
520 $aA spatially-explicit, stochastic model is developed for Bahia bark scaling, a threat to citrus production in north-eastern Brazil, and is used to assess epidemiological principles underlying the cost-effectiveness of disease control strategies. The model is fitted via Markov chain Monte Carlo with data augmentation to snapshots of disease spread derived from a previouslyreported multi-year experiment. Goodness-of-fit tests strongly supported the fit of the model, even though the detailed etiology of the disease is unknown and was not explicitly included in the model. Key epidemiological parameters including the infection rate, incubation period and scale of dispersal are estimated from the spread data. This allows us to scale-up the experimental results to predict the effect of the level of initial inoculum on disease progression in a typically-sized citrus grove. The efficacies of two cultural control measures are assessed: altering the spacing of host plants, and roguing symptomatic trees. Reducing planting density can slow disease spread significantly if the distance between hosts is sufficiently large. However, low density groves have fewer plants per hectare. The optimum density of productive plants is therefore recovered at an intermediate host spacing. Roguing, even when detection of symptomatic plants is imperfect, can lead to very effective control. However, scouting for disease symptoms incurs a cost. We use the model to balance the cost of scouting against the number of plants lost to disease, and show how to determine a roguing schedule that optimizes profit. The trade-offs underlying the two optima we identify?the optimal host spacing and the optimal roguing schedule? are applicable to many pathosystems. Our work demonstrates how a carefully parameterised mathematical model can be used to find these optima. It also illustrates how mathematical models can be used in even this most challenging of situations in which the underlying epidemiology is ill-understood.
650 $aCitrus
650 $aDisease control
650 $aMathematical models
650 $aDoença de planta
650 $aFruta citríca
650 $aModelo matemático
653 $aDisorders
653 $aPlant diseases
700 1 $aBARBOSA, F. F. L.
700 1 $aNERI, F. M.
700 1 $aDESIMONE, R. E.
700 1 $aGILLIGAN, C. A.
773 $tPlos Computational Biology$gv. 10, Issue 8 , August, 2014.
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| 2. |  | PEIXOTO, C. C.; TRINDADE, A. V.; SANTOS, E. G. dos. Bactérias diazotróficas endofíticas no controle da fusariose em bananeira. In: SEMINÁRIO DE PESQUISA DO RECÔNCAVO DA BAHIA, 2.; SEMINÁRIO ESTUDANTIL DE PESQUISA DA UFRB, 2.; SEMINÁRIO DE PÓS-GRADUAÇÃO DA UFRB, 2., 2008, Cruz das Almas, BA. Sustentabilidade ambiental e qualidade de vida. Cruz das Almas: Universidade Federal do Recôncavo da Bahia, 2008.| Tipo: Resumo em Anais de Congresso |
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