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Registro Completo |
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Biblioteca(s): |
Embrapa Arroz e Feijão. |
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Data corrente: |
29/11/2021 |
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Data da última atualização: |
30/11/2021 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
ALMEIDA, A. C. de S.; JESUS, F. G. de; HENG-MOSS, T. M.; LANNA, A. C.; BARRIGOSSI, J. A. F. |
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Afiliação: |
ANDRÉ C. DE SOUSA ALMEIDA, INSTITUTO FEDERAL GOIANO, Urutaí-GO; FLÁVIO G. DE JESUS, INSTITUTO FEDERAL GOIANO, Urutaí-GO; TIFFANY M. HENG-MOSS, Uiversity of Nebraska, Lincoln-NE; ANNA CRISTINA LANNA, CNPAF; JOSE ALEXANDRE F BARRIGOSSI, CNPAF. |
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Título: |
Evidence for rice tolerance to Tibraca limbativentris (Hemiptera: Pentatomidae). |
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Ano de publicação: |
2021 |
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Fonte/Imprenta: |
Pest Management Science, v. 77, n. 9, p. 4181-4191, Sept. 2021. |
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ISSN: |
1526-4998 |
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DOI: |
https://doi.org/10.1002/ps.6455 |
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Idioma: |
Inglês |
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Conteúdo: |
The rice stalk stink bug Tibraca limbativentris (Hemiptera: Pentatomidae) is one of the most important rice pests in Brazil. The search for cultivars that tolerate insect injury is necessary to complement other less aggressive methods of pest suppression. The combination of integrated pest management tactics will reduce insecticide applications and improve the safety of food production. Here, we tested the tolerance response of Xingu, Canela de Ferro and Primavera rice genotypes in glasshouse experiments. In addition, we measured tolerance expressed in a variety of physiological responses, including gas exchange rates, leaf chlorophyll content and reactive oxygen species (ROS) detoxification. |
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Palavras-Chave: |
Rice stalk stink bug. |
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Thesagro: |
Arroz; Oryza Sativa; Tibraca Limbativentris. |
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Thesaurus Nal: |
Agronomic traits; Antioxidants; Chlorophyll; Gas exchange; Rice. |
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Categoria do assunto: |
O Insetos e Entomologia |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/228242/1/pms-2021.pdf
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Marc: |
LEADER 01575naa a2200301 a 4500
001 2136773
005 2021-11-30
008 2021 bl uuuu u00u1 u #d
022 $a1526-4998
024 7 $ahttps://doi.org/10.1002/ps.6455$2DOI
100 1 $aALMEIDA, A. C. de S.
245 $aEvidence for rice tolerance to Tibraca limbativentris (Hemiptera$bPentatomidae).$h[electronic resource]
260 $c2021
520 $aThe rice stalk stink bug Tibraca limbativentris (Hemiptera: Pentatomidae) is one of the most important rice pests in Brazil. The search for cultivars that tolerate insect injury is necessary to complement other less aggressive methods of pest suppression. The combination of integrated pest management tactics will reduce insecticide applications and improve the safety of food production. Here, we tested the tolerance response of Xingu, Canela de Ferro and Primavera rice genotypes in glasshouse experiments. In addition, we measured tolerance expressed in a variety of physiological responses, including gas exchange rates, leaf chlorophyll content and reactive oxygen species (ROS) detoxification.
650 $aAgronomic traits
650 $aAntioxidants
650 $aChlorophyll
650 $aGas exchange
650 $aRice
650 $aArroz
650 $aOryza Sativa
650 $aTibraca Limbativentris
653 $aRice stalk stink bug
700 1 $aJESUS, F. G. de
700 1 $aHENG-MOSS, T. M.
700 1 $aLANNA, A. C.
700 1 $aBARRIGOSSI, J. A. F.
773 $tPest Management Science$gv. 77, n. 9, p. 4181-4191, Sept. 2021.
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Registro original: |
Embrapa Arroz e Feijão (CNPAF) |
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Registro |
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Registro Completo
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Biblioteca(s): |
Embrapa Trigo. |
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Data corrente: |
08/08/2023 |
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Data da última atualização: |
08/08/2023 |
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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: |
PALMA, G. R.; GODOY, W. A. C.; ENGEL, E.; LAU, D.; GALVAN, E.; MASON, O.; MARKHAM, C.; MORAL, R. A. |
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Afiliação: |
GABRIEL R. PALMA, Maynooth University; WESLEY A. C. GODOY, Universidade de São Paulo; EDUARDO ENGEL, Universidade de São Paulo; DOUGLAS LAU, CNPT; EDGAR GALVAN, Maynooth University; OLIVER MASON, Maynooth University; CHARLES MARKHAM, Maynooth University; RAFAEL A. MORAL, Maynooth University. |
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Título: |
Pattern-based prediction of population outbreaks. |
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Ano de publicação: |
2023 |
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Fonte/Imprenta: |
Ecological Informatics, v. 77, 102220, nov. 2023. |
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DOI: |
https://doi.org/10.1016/j.ecoinf.2023.102220 |
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Idioma: |
Inglês |
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Conteúdo: |
Resumo: A complexidade e a importância prática dos surtos de insetos tornaram o problema de prever surtos um foco de pesquisa recente. Propomos o método de Previsão Baseada em Padrões (PBP) para prever surtos populacionais. Este método usa informações sobre valores de séries temporais anteriores que precedem um evento de surto como preditores de surtos futuros, o que pode ser útil ao monitorar espécies de pragas. Nós ilustramos o método usando conjuntos de dados simulados e uma série temporal de pulgões obtida em lavouras de trigo no sul do Brasil. Abstract: The complexity and practical importance of insect outbreaks have made the problem of predicting outbreaks a focus of recent research. We propose the Pattern-Based Prediction (PBP) method for predicting population outbreaks. It uses information on previous time series values that precede an outbreak event as predictors of future outbreaks, which can be helpful when monitoring pest species. We illustrate the methodology using simulated datasets and an aphid time series obtained in wheat crops in Southern Brazil. We obtained an average test accuracy of 84.6% in the simulation studies implemented with stochastic models and 95.0% for predicting outbreaks using a time series of aphids in wheat crops in Southern Brazil. Our results show the PBP method's feasibility in predicting population outbreaks. We benchmarked our results against established state-of-the-art machine learning methods: Support Vector Machines, Deep Neural Networks, Long Short Term Memory and Random Forests. The PBP method yielded a competitive performance associated with higher true-positive rates in most comparisons while providing interpretability rather than being a black-box method. It is an improvement over current state-of-the-art machine learning tools, especially by non-specialists, such as ecologists aiming to use a quantitative approach for pest monitoring. We provide the implemented PBP method in Python through the pypbp package. MenosResumo: A complexidade e a importância prática dos surtos de insetos tornaram o problema de prever surtos um foco de pesquisa recente. Propomos o método de Previsão Baseada em Padrões (PBP) para prever surtos populacionais. Este método usa informações sobre valores de séries temporais anteriores que precedem um evento de surto como preditores de surtos futuros, o que pode ser útil ao monitorar espécies de pragas. Nós ilustramos o método usando conjuntos de dados simulados e uma série temporal de pulgões obtida em lavouras de trigo no sul do Brasil. Abstract: The complexity and practical importance of insect outbreaks have made the problem of predicting outbreaks a focus of recent research. We propose the Pattern-Based Prediction (PBP) method for predicting population outbreaks. It uses information on previous time series values that precede an outbreak event as predictors of future outbreaks, which can be helpful when monitoring pest species. We illustrate the methodology using simulated datasets and an aphid time series obtained in wheat crops in Southern Brazil. We obtained an average test accuracy of 84.6% in the simulation studies implemented with stochastic models and 95.0% for predicting outbreaks using a time series of aphids in wheat crops in Southern Brazil. Our results show the PBP method's feasibility in predicting population outbreaks. We benchmarked our results against established state-of-the-art machine learning methods: Support Vector Machines, Deep Neural Ne... Mostrar Tudo |
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Palavras-Chave: |
Alert zone procedure; Aprendizado de máquina; Deep learning; Machine learning; Monitoramento de pragas; Séries Temporais; Sistemas alerta; Sistemas de Suporte à Tomada de Decisão; Time series. |
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Thesagro: |
Afídeo; Dinâmica Populacional; Epidemiologia; Lavoura; Praga de Planta; Trigo. |
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Thesaurus NAL: |
Population dynamics; Time series analysis; Wheat. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/doc/1155768/1/Pattern-based-prediction-of-population-outbreaks.pdf
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Marc: |
LEADER 03206naa a2200433 a 4500
001 2155768
005 2023-08-08
008 2023 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.1016/j.ecoinf.2023.102220$2DOI
100 1 $aPALMA, G. R.
245 $aPattern-based prediction of population outbreaks.$h[electronic resource]
260 $c2023
520 $aResumo: A complexidade e a importância prática dos surtos de insetos tornaram o problema de prever surtos um foco de pesquisa recente. Propomos o método de Previsão Baseada em Padrões (PBP) para prever surtos populacionais. Este método usa informações sobre valores de séries temporais anteriores que precedem um evento de surto como preditores de surtos futuros, o que pode ser útil ao monitorar espécies de pragas. Nós ilustramos o método usando conjuntos de dados simulados e uma série temporal de pulgões obtida em lavouras de trigo no sul do Brasil. Abstract: The complexity and practical importance of insect outbreaks have made the problem of predicting outbreaks a focus of recent research. We propose the Pattern-Based Prediction (PBP) method for predicting population outbreaks. It uses information on previous time series values that precede an outbreak event as predictors of future outbreaks, which can be helpful when monitoring pest species. We illustrate the methodology using simulated datasets and an aphid time series obtained in wheat crops in Southern Brazil. We obtained an average test accuracy of 84.6% in the simulation studies implemented with stochastic models and 95.0% for predicting outbreaks using a time series of aphids in wheat crops in Southern Brazil. Our results show the PBP method's feasibility in predicting population outbreaks. We benchmarked our results against established state-of-the-art machine learning methods: Support Vector Machines, Deep Neural Networks, Long Short Term Memory and Random Forests. The PBP method yielded a competitive performance associated with higher true-positive rates in most comparisons while providing interpretability rather than being a black-box method. It is an improvement over current state-of-the-art machine learning tools, especially by non-specialists, such as ecologists aiming to use a quantitative approach for pest monitoring. We provide the implemented PBP method in Python through the pypbp package.
650 $aPopulation dynamics
650 $aTime series analysis
650 $aWheat
650 $aAfídeo
650 $aDinâmica Populacional
650 $aEpidemiologia
650 $aLavoura
650 $aPraga de Planta
650 $aTrigo
653 $aAlert zone procedure
653 $aAprendizado de máquina
653 $aDeep learning
653 $aMachine learning
653 $aMonitoramento de pragas
653 $aSéries Temporais
653 $aSistemas alerta
653 $aSistemas de Suporte à Tomada de Decisão
653 $aTime series
700 1 $aGODOY, W. A. C.
700 1 $aENGEL, E.
700 1 $aLAU, D.
700 1 $aGALVAN, E.
700 1 $aMASON, O.
700 1 $aMARKHAM, C.
700 1 $aMORAL, R. A.
773 $tEcological Informatics$gv. 77, 102220, nov. 2023.
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Registro original: |
Embrapa Trigo (CNPT) |
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