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| Registros recuperados : 4 | |
| 1. |  | COUTINHO, I. D.; MERTZ-HENNING, L. M.; DOPP, S. A.; NEPOMUCENO, A. L.; MORAES, L. A. C.; MARCOLINO-GOMES, J.; RICHTER, C.; SCHWALBE, H.; COLNAGO, L. A. Identification of primary and secondary metabolites and transcriptome profile of soybean tissues during different stages of hypoxia. Data in Brief, v. 21, p. 1089-1100, 2018.| Biblioteca(s): Embrapa Instrumentação. |
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| 2. | | COUTINHO, I. D.; MERTZ-HENNING, L. M.; DÖPP, S. A.; NEPOMUCENO, A.; MORAES, L. A. C.; MARCOLINO-GOMES, J.; RICHTER, C.; SCHWALBE, H.; COLNAGO, L. A. Identification of primary and secondary metabolites and transcriptome profile of soybean tissues during different stages of hypoxia. Data in Brief, v. 21, p. 1089-100, 2018.| Biblioteca(s): Embrapa Soja. |
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| 3. | | COUTINHO, I. D.; MERTZ-HENNING, L. M.; DÖPP, S. A.; NEPOMUCENO, A.; MORAES, L. A. C.; MARCOLINO-GOMES, J.; RICHTER, C.; SCHWALBE, H.; COLNAGO, L. A. Flooded soybean metabolomic analysis reveals important primary and secondary metabolites involved in the hypoxia stress response and tolerance. Environmental and Experimental Botany, v. 153, p. 176-187, 2018.| Biblioteca(s): Embrapa Soja. |
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| 4. | | COUTINHO, I. D.; MERTZ-HENNING, L. M.; DOPP, S. A.; NEPOMUCENO, A.; MORAES, L. A. C.; MARCOLINO-GOMES, J.; RICHTER, C.; SCHWALBE, H.; COLNAGO, L. A. Flooded soybean metabolomic analysis reveals important primary and secondary metabolites involved involved in the hypoxia stress response and tolerance. Environmental and Experimental Botany, v. 153, p. 176-187, 2018.| Biblioteca(s): Embrapa Instrumentação. |
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| Registros recuperados : 4 | |
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Registro Completo
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Biblioteca(s): |
Embrapa Meio Ambiente. |
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Data corrente: |
15/10/2013 |
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Data da última atualização: |
19/06/2023 |
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Tipo da produção científica: |
Artigo em Anais de Congresso |
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Autoria: |
MAIA, A. de H. N.; PAZIANOTTO, R. A. A.; LUIZ, A. J. B.; PERVEZ, A. |
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Afiliação: |
ALINE DE HOLANDA NUNES MAIA, CNPMA; RICARDO ANTONIO ALMEIDA PAZIANOTTO, CNPMA; ALFREDO JOSE BARRETO LUIZ, CNPMA; AHMAD PERVEZ, Govt. Degree College. |
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Título: |
Modelling influence of quantitative factors on arthropod demographic parameters. |
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Ano de publicação: |
2013 |
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Fonte/Imprenta: |
In: REUNIÃO ANUAL DA REGIÃO BRASILEIRA DA SOCIEDADE INTERNACIONAL DE BIOMETRIA, 58.; SIMPÓSIO DE ESTATÍSTICA APLICADA À EXPERIMENTAÇÃO AGRONÔMICA, 15., 2013, Campina Grande. Modelagem estatística em áreas multidisciplinares: impactos causados pelas mudanças climáticas na Região Nordeste: anais. Campina Grande: Sociedade Internacional de Biometria, 2013. 7 p. |
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Idioma: |
Inglês |
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Conteúdo: |
Fertility life table (FLT) parameters are important quantitative indicators of interactions between arthropod population and the environment. By summarizing information on both fertility and survivorship, they can capture chronicle sub lethal effects not detected by acute survival assays (MARINHO-PRADO, 2011; NASCIMENTO et al., 1998; NARDO et al (2001) LIU et al, 2005; LUMBIERRES et al, 2004). In life table studies, oviposition and survival data are collected over time (usually daily) and summarized into fertility life tables (FLT) for posterior estimation of the following parameters: net reproductive rate (Ro), intrinsic rate of increase (Rm), doubling time (DT), mean generation time (MGT) and finite rate of increase ( ), for each o treatments evaluated. As FLT parameters summarize data from experimental units into a single estimate for each group (treatment), the information on within treatment variance is not readily available, thus requiring the use of computationally intensive methods for its estimation. Among them, jackknife method, as proposed by MEYER (1986), is the most widely used for variance estimation in FLT analysis. Jacknife-based software available for life table analysis (HULTING et al, 1990 ; MAIA et al, 2000) was developed for analysing qualitative treatments, but such approach is frequently misused for contrasting quantitative factors (GANJISAFFAR et al., 2011; PAKYARI et al., 2011; RAZMJOU et al., 2011). Some authors use regression analysis after estimating FLT parameters for each factor level but do not account for the uncertainty of parameter estimates (CONTI et al, 2010). Here we present methods for adequately quantifying the influence of quantitative factors (e.g. temperature, pesticide level) on FLT parameters by combining the jackknife method with a regression analysis framework. MenosFertility life table (FLT) parameters are important quantitative indicators of interactions between arthropod population and the environment. By summarizing information on both fertility and survivorship, they can capture chronicle sub lethal effects not detected by acute survival assays (MARINHO-PRADO, 2011; NASCIMENTO et al., 1998; NARDO et al (2001) LIU et al, 2005; LUMBIERRES et al, 2004). In life table studies, oviposition and survival data are collected over time (usually daily) and summarized into fertility life tables (FLT) for posterior estimation of the following parameters: net reproductive rate (Ro), intrinsic rate of increase (Rm), doubling time (DT), mean generation time (MGT) and finite rate of increase ( ), for each o treatments evaluated. As FLT parameters summarize data from experimental units into a single estimate for each group (treatment), the information on within treatment variance is not readily available, thus requiring the use of computationally intensive methods for its estimation. Among them, jackknife method, as proposed by MEYER (1986), is the most widely used for variance estimation in FLT analysis. Jacknife-based software available for life table analysis (HULTING et al, 1990 ; MAIA et al, 2000) was developed for analysing qualitative treatments, but such approach is frequently misused for contrasting quantitative factors (GANJISAFFAR et al., 2011; PAKYARI et al., 2011; RAZMJOU et al., 2011). Some authors use regression analysis after estimat... Mostrar Tudo |
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Palavras-Chave: |
Artropoda; Fertility life tables; Jackknife method. |
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Thesagro: |
Análise estatística; Arthropoda; Dinâmica populacional; Inseto; Modelo matemático. |
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Thesaurus NAL: |
Insects; Population dynamics. |
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Categoria do assunto: |
O Insetos e Entomologia |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/91012/1/2013AA56.pdf
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Marc: |
LEADER 02940nam a2200265 a 4500
001 1968632
005 2023-06-19
008 2013 bl uuuu u00u1 u #d
100 1 $aMAIA, A. de H. N.
245 $aModelling influence of quantitative factors on arthropod demographic parameters.$h[electronic resource]
260 $aIn: REUNIÃO ANUAL DA REGIÃO BRASILEIRA DA SOCIEDADE INTERNACIONAL DE BIOMETRIA, 58.; SIMPÓSIO DE ESTATÍSTICA APLICADA À EXPERIMENTAÇÃO AGRONÔMICA, 15., 2013, Campina Grande. Modelagem estatística em áreas multidisciplinares: impactos causados pelas mudanças climáticas na Região Nordeste: anais. Campina Grande: Sociedade Internacional de Biometria, 2013. 7 p.$c2013
520 $aFertility life table (FLT) parameters are important quantitative indicators of interactions between arthropod population and the environment. By summarizing information on both fertility and survivorship, they can capture chronicle sub lethal effects not detected by acute survival assays (MARINHO-PRADO, 2011; NASCIMENTO et al., 1998; NARDO et al (2001) LIU et al, 2005; LUMBIERRES et al, 2004). In life table studies, oviposition and survival data are collected over time (usually daily) and summarized into fertility life tables (FLT) for posterior estimation of the following parameters: net reproductive rate (Ro), intrinsic rate of increase (Rm), doubling time (DT), mean generation time (MGT) and finite rate of increase ( ), for each o treatments evaluated. As FLT parameters summarize data from experimental units into a single estimate for each group (treatment), the information on within treatment variance is not readily available, thus requiring the use of computationally intensive methods for its estimation. Among them, jackknife method, as proposed by MEYER (1986), is the most widely used for variance estimation in FLT analysis. Jacknife-based software available for life table analysis (HULTING et al, 1990 ; MAIA et al, 2000) was developed for analysing qualitative treatments, but such approach is frequently misused for contrasting quantitative factors (GANJISAFFAR et al., 2011; PAKYARI et al., 2011; RAZMJOU et al., 2011). Some authors use regression analysis after estimating FLT parameters for each factor level but do not account for the uncertainty of parameter estimates (CONTI et al, 2010). Here we present methods for adequately quantifying the influence of quantitative factors (e.g. temperature, pesticide level) on FLT parameters by combining the jackknife method with a regression analysis framework.
650 $aInsects
650 $aPopulation dynamics
650 $aAnálise estatística
650 $aArthropoda
650 $aDinâmica populacional
650 $aInseto
650 $aModelo matemático
653 $aArtropoda
653 $aFertility life tables
653 $aJackknife method
700 1 $aPAZIANOTTO, R. A. A.
700 1 $aLUIZ, A. J. B.
700 1 $aPERVEZ, A.
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Embrapa Meio Ambiente (CNPMA) |
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