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Biblioteca(s): |
Embrapa Mandioca e Fruticultura. |
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Data corrente: |
20/12/2010 |
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Data da última atualização: |
19/01/2011 |
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Tipo da produção científica: |
Resumo em Anais de Congresso |
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Autoria: |
LARANJEIRA, F. F.; NERI, F. M.; BASSANEZI, R. B.; GILLIGAN, C. A. |
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Afiliação: |
FRANCISCO FERRAZ LARANJEIRA BARBOSA, CNPMF; Franco Maria Neri, UCLES; Renato Beozzo Bassanezi, Fundecitrus; Chris A. Gilligan, UCLES. |
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Título: |
A stochastic cellular automaton model to simulate spatial patterns of Citrus Leprosis. |
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Ano de publicação: |
2010 |
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Fonte/Imprenta: |
In: CONFERENCE INTERNATIONAL ORGANIZATION CITRUS VIROLOGISTS, 18., Campinas, SP, 2010. Proceedings... Campinas: IOCV, 2010. 1 CD-ROM. |
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Idioma: |
Inglês |
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Notas: |
057 PSO.
Publicado também em: Citrus Research & Technology, Cordeirópolis, v. 31, Suplemento, 2010 |
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Conteúdo: |
The Brazilian citrus industry is threatened by a number of diseases and among them the Citrus Leprosis. Caused by Citrus Leprosis Virus (CiLV), a Cilevirus transmitted by the mite Brevipalpus phoenicis (Acari: Tenuipalpidae), the disease occurs mainly in South America. Leprosis has been quite severe in sweet oranges; leaf and fruit spots, early fruit or leaf drop and branch dieback can seriously jeopardise not only yield but also the plant itself. Depending on scion variety, up to 100% of yield loss can be reached. After the adoption of a strict leprosis control (reducing both inoculum sources and vector population), recovery of a severely infested and affected plant can take as long as two years. CiLV induces local lesions which are associated to Brevipalpus feeding sites;. There is no systemic infection, but CiLV has a circulative and propagative relationship with the vector. The few studies on Leprosis epidemiology have shown non-coincident spatial patterns between mite infested and symptomatic plants. Due to difficulties to keep infested groves for research, we aimed to develop a model to simulate Leprosis spatial patterns. Besides that we aimed at testing whether such patterns could be represented by considering only the presence of infected plants at the nearest neighbourhood. The model is a Susceptible-Infected stochastic cellular automaton running in a changeable lattice of variable spacing within and between rows. The transition between states (S to I) for each plant was modulated by both number of infected neighbours (Moore's neighbourhood of range 1) and distance to the infected neighbours. An impulse function was used to set the probability of infection for each number of infected neighbours (0 to 8), starting with a very low probability representing background or primary infection. The most striking feature of an impulse function is a sharp rise in the probability value at a given point followed by a slow increase up to an asymptote. Three impulse functions were tested considering sudden increase in probability of infection (from 0.006 to 0.8) if a given healthy plant had one (I1), two (I2) or four (I4) infected plants in its neighbourhood. Two thousand epidemics were run for each probability set in a lattice of 20 rows, each one with 58 plants in a spacing of 7.5m x 3.8m, representing an actual grove were citrus leprosis was continuously evaluated until reaching more than 30% of affected plants. Simulated maps were compared to actual leprosis data by means of binary power law (quadrat size 3x3) and join counts statistics up to the sixth spatial lag and binary power law (quadrat size 3x3). The results show that the impulse function with a sharp raise for one infected plant in the neighbourhood (I1) was the best to represent the field data. For all spatial lags the agreement between simulated and real data was always above 80%. Also, for spatial lag 1 at almost all incidences nd, the field data join counts statistic fell within the Monte Carlo envelope of I1 simulations, even applying toroidal shifts onto the field data. In addition, the parameters of binary power law were very similar for both simulated (log(A) = 2,92 and b=1.28) and field data (log(A) = 2.56 and b=1.28). These results indicates that the presence of a single infected plant in the neighbourhood of a healthy one is sufficient to dramatically increase the probability of infection. Because there was no indication that additional infected plants are very important to nearest-neighbour dispersal, any control strategy should focus on early detection of symptomatic plants. To our knowledge this is the first model to explicitly deal with spatial patterns of citrus Leprosis. It is currently being used to improve early detection by simulating alternative sampling schemes. MenosThe Brazilian citrus industry is threatened by a number of diseases and among them the Citrus Leprosis. Caused by Citrus Leprosis Virus (CiLV), a Cilevirus transmitted by the mite Brevipalpus phoenicis (Acari: Tenuipalpidae), the disease occurs mainly in South America. Leprosis has been quite severe in sweet oranges; leaf and fruit spots, early fruit or leaf drop and branch dieback can seriously jeopardise not only yield but also the plant itself. Depending on scion variety, up to 100% of yield loss can be reached. After the adoption of a strict leprosis control (reducing both inoculum sources and vector population), recovery of a severely infested and affected plant can take as long as two years. CiLV induces local lesions which are associated to Brevipalpus feeding sites;. There is no systemic infection, but CiLV has a circulative and propagative relationship with the vector. The few studies on Leprosis epidemiology have shown non-coincident spatial patterns between mite infested and symptomatic plants. Due to difficulties to keep infested groves for research, we aimed to develop a model to simulate Leprosis spatial patterns. Besides that we aimed at testing whether such patterns could be represented by considering only the presence of infected plants at the nearest neighbourhood. The model is a Susceptible-Infected stochastic cellular automaton running in a changeable lattice of variable spacing within and between rows. The transition between states (S to I) for each plan... Mostrar Tudo |
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Palavras-Chave: |
Citrus Leprosis; Plant disease. |
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Thesagro: |
Brevipalpus Phoenicis; Doença de Planta; Fruta Cítrica. |
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Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
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Marc: |
LEADER 04617nam a2200217 a 4500
001 1870396
005 2011-01-19
008 2010 bl uuuu u00u1 u #d
100 1 $aLARANJEIRA, F. F.
245 $aA stochastic cellular automaton model to simulate spatial patterns of Citrus Leprosis.
260 $aIn: CONFERENCE INTERNATIONAL ORGANIZATION CITRUS VIROLOGISTS, 18., Campinas, SP, 2010. Proceedings... Campinas: IOCV, 2010. 1 CD-ROM.$c2010
500 $a057 PSO. Publicado também em: Citrus Research & Technology, Cordeirópolis, v. 31, Suplemento, 2010
520 $aThe Brazilian citrus industry is threatened by a number of diseases and among them the Citrus Leprosis. Caused by Citrus Leprosis Virus (CiLV), a Cilevirus transmitted by the mite Brevipalpus phoenicis (Acari: Tenuipalpidae), the disease occurs mainly in South America. Leprosis has been quite severe in sweet oranges; leaf and fruit spots, early fruit or leaf drop and branch dieback can seriously jeopardise not only yield but also the plant itself. Depending on scion variety, up to 100% of yield loss can be reached. After the adoption of a strict leprosis control (reducing both inoculum sources and vector population), recovery of a severely infested and affected plant can take as long as two years. CiLV induces local lesions which are associated to Brevipalpus feeding sites;. There is no systemic infection, but CiLV has a circulative and propagative relationship with the vector. The few studies on Leprosis epidemiology have shown non-coincident spatial patterns between mite infested and symptomatic plants. Due to difficulties to keep infested groves for research, we aimed to develop a model to simulate Leprosis spatial patterns. Besides that we aimed at testing whether such patterns could be represented by considering only the presence of infected plants at the nearest neighbourhood. The model is a Susceptible-Infected stochastic cellular automaton running in a changeable lattice of variable spacing within and between rows. The transition between states (S to I) for each plant was modulated by both number of infected neighbours (Moore's neighbourhood of range 1) and distance to the infected neighbours. An impulse function was used to set the probability of infection for each number of infected neighbours (0 to 8), starting with a very low probability representing background or primary infection. The most striking feature of an impulse function is a sharp rise in the probability value at a given point followed by a slow increase up to an asymptote. Three impulse functions were tested considering sudden increase in probability of infection (from 0.006 to 0.8) if a given healthy plant had one (I1), two (I2) or four (I4) infected plants in its neighbourhood. Two thousand epidemics were run for each probability set in a lattice of 20 rows, each one with 58 plants in a spacing of 7.5m x 3.8m, representing an actual grove were citrus leprosis was continuously evaluated until reaching more than 30% of affected plants. Simulated maps were compared to actual leprosis data by means of binary power law (quadrat size 3x3) and join counts statistics up to the sixth spatial lag and binary power law (quadrat size 3x3). The results show that the impulse function with a sharp raise for one infected plant in the neighbourhood (I1) was the best to represent the field data. For all spatial lags the agreement between simulated and real data was always above 80%. Also, for spatial lag 1 at almost all incidences nd, the field data join counts statistic fell within the Monte Carlo envelope of I1 simulations, even applying toroidal shifts onto the field data. In addition, the parameters of binary power law were very similar for both simulated (log(A) = 2,92 and b=1.28) and field data (log(A) = 2.56 and b=1.28). These results indicates that the presence of a single infected plant in the neighbourhood of a healthy one is sufficient to dramatically increase the probability of infection. Because there was no indication that additional infected plants are very important to nearest-neighbour dispersal, any control strategy should focus on early detection of symptomatic plants. To our knowledge this is the first model to explicitly deal with spatial patterns of citrus Leprosis. It is currently being used to improve early detection by simulating alternative sampling schemes.
650 $aBrevipalpus Phoenicis
650 $aDoença de Planta
650 $aFruta Cítrica
653 $aCitrus Leprosis
653 $aPlant disease
700 1 $aNERI, F. M.
700 1 $aBASSANEZI, R. B.
700 1 $aGILLIGAN, C. A.
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| 3. |  | ROCHA, A. dos S.; PATRÍCIO, L. P. C.; COVA, A. K. W.; ALVES, S. S.; RIOS, Í. F.; MACHADO, C. de F. Caracterização e avaliação de recursos genéticos de maracujazeiro em cultivo orgânico. In: JORNADA CIENTÍFICA EMBRAPA MANDIOCA E FRUTICULTURA, 14., 2020. Ciência em tempos de crise: resumos. Cruz das Almas, BA: Embrapa Mandioca e Fruticultura, 2020. 112 p. il.| Tipo: Resumo em Anais de Congresso |
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