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 | Acesso ao texto completo restrito à biblioteca da Embrapa Agricultura Digital. Para informações adicionais entre em contato com cnptia.biblioteca@embrapa.br. |
Registro Completo
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Biblioteca(s): |
Embrapa Agricultura Digital. |
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Data corrente: |
24/09/2018 |
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Data da última atualização: |
07/01/2020 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
A - 1 |
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Autoria: |
HINNAH, F. D.; SENTELHAS, P. C.; MEIRA, C. A. A.; PAIVA, R. N. |
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Afiliação: |
FERNANDO DILL HINNAH, Esalq/USP; PAULO CESAR SENTELHAS, Esalq/USP; CARLOS ALBERTO ALVES MEIRA, CNPTIA; RODRIGO NAVES PAIVA, Procafé Foundation, Varginha. |
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Título: |
Weather-based coffee leaf rust apparent infection rate modeling. |
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Ano de publicação: |
2018 |
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Fonte/Imprenta: |
International Journal of Biometeorology, v. 62, n. 10, p. 1847-1860, Oct. 2018. |
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DOI: |
https://doi.org/10.1007/s00484-018-1587-2 |
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Idioma: |
Inglês |
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Conteúdo: |
Abstract. Brazil is the major coffee producer in the world, with 2 million hectares cropped, with 75% of this area with Coffea arabica and 25% with Coffea canephora. Coffee leaf rust (CLR) is one of the main diseases that cause yield losses by reducing healthy leaf area. As CLR is highly influenced by weather conditions, this study aimed to determine the best linearization model to estimate the CLR apparent infection rate, to correlate CLR infection rates with weather variables, and to develop and assess the performance of weather-based infection rate models to be used as a disease warning system. The CLR epidemic was analyzed for 88 site-seasons, while progress curves were assessed by linear, monomolecular, logistic, Gompertz, and exponential linearization models for apparent infection rate determination. Correlations between CLR infection rates and weather variables were conducted at different periods. From these correlations, multiple linear regressions were developed to estimate CLR infection rates, using the most weather-correlated variables. The Gompertz growth model had the best fit with CLR progress curves. Minimum temperature and relative humidity were the weather variables most correlated to infection rate and, therefore, chosen to compose a CLR forecast system. Among the models developed, the one for the condition of high coffee yield at a narrow row spacing was the best, with only 9.4% of false negative occurrences for all the months assessed. |
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Palavras-Chave: |
Coffee leaf rust; Estimation models; Gompertz; Linearização; Linearization; Taxa de infecção. |
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Thesagro: |
Café; Coffea Arábica; Coffea Canephora; Hemileia Vastatrix. |
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Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
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Marc: |
LEADER 02336naa a2200289 a 4500
001 2096185
005 2020-01-07
008 2018 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.1007/s00484-018-1587-2$2DOI
100 1 $aHINNAH, F. D.
245 $aWeather-based coffee leaf rust apparent infection rate modeling.$h[electronic resource]
260 $c2018
520 $aAbstract. Brazil is the major coffee producer in the world, with 2 million hectares cropped, with 75% of this area with Coffea arabica and 25% with Coffea canephora. Coffee leaf rust (CLR) is one of the main diseases that cause yield losses by reducing healthy leaf area. As CLR is highly influenced by weather conditions, this study aimed to determine the best linearization model to estimate the CLR apparent infection rate, to correlate CLR infection rates with weather variables, and to develop and assess the performance of weather-based infection rate models to be used as a disease warning system. The CLR epidemic was analyzed for 88 site-seasons, while progress curves were assessed by linear, monomolecular, logistic, Gompertz, and exponential linearization models for apparent infection rate determination. Correlations between CLR infection rates and weather variables were conducted at different periods. From these correlations, multiple linear regressions were developed to estimate CLR infection rates, using the most weather-correlated variables. The Gompertz growth model had the best fit with CLR progress curves. Minimum temperature and relative humidity were the weather variables most correlated to infection rate and, therefore, chosen to compose a CLR forecast system. Among the models developed, the one for the condition of high coffee yield at a narrow row spacing was the best, with only 9.4% of false negative occurrences for all the months assessed.
650 $aCafé
650 $aCoffea Arábica
650 $aCoffea Canephora
650 $aHemileia Vastatrix
653 $aCoffee leaf rust
653 $aEstimation models
653 $aGompertz
653 $aLinearização
653 $aLinearization
653 $aTaxa de infecção
700 1 $aSENTELHAS, P. C.
700 1 $aMEIRA, C. A. A.
700 1 $aPAIVA, R. N.
773 $tInternational Journal of Biometeorology$gv. 62, n. 10, p. 1847-1860, Oct. 2018.
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