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Registro Completo |
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Biblioteca(s): |
Embrapa Caprinos e Ovinos. |
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Data corrente: |
13/02/2012 |
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Data da última atualização: |
14/07/2021 |
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Tipo da produção científica: |
Resumo em Anais de Congresso |
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Autoria: |
BRANDÃO, F. Z.; SOUZA, J. M. G.; TORRES, C. A. A.; MAIA, A. L. R. S.; DENADAI, R.; OBA, E.; FONSECA, J. F. da. |
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Afiliação: |
Universidade Federal Fluminense, Niterói, RJ; Universidade Federal de Viçoca - UFV - Viçosa, MG; UNESP, Botucatu, SP; JEFERSON FERREIRA DA FONSECA, CNPC. |
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Título: |
Plasma progesterone concentrations in Toggenburg goats submitted to estrous synchronization reusing autoclaved intravaginal progesterone devices. |
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Ano de publicação: |
2011 |
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Fonte/Imprenta: |
In: CONGRESSO BRASILEIRO DE REPRODUÇÃO ANIMAL, 19., 2011, Recife. Anais... Belo Horizonte: CBRA, 2011. p. 212. 1 CD-ROM. |
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Idioma: |
Inglês |
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Conteúdo: |
Abstract: The aim of this study was to evaluate plasma P4 concentration in Toggenburg goats receiving autoclaved progesterone devices for estrous synchronization. Progesterone analysis allows us to infer that the autoclaving process does not influence progesterone availability and so this technique can be a simple and valuable tool to reduce sanitary risks of disease transmission without alterating fertility in goats. [Concentrações de progesterona plasmática em cabras da raça Toggenburg submetidas à sincronização de estro reutilizando dispositivos intravaginais de progesterona autoclavados]. |
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Palavras-Chave: |
Autoclaved CIDR; CIDR autoclavado; Hormônio esteróide; Steroid hormone. |
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Thesagro: |
Caprino; Eficiência Reprodutiva. |
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Thesaurus Nal: |
Goats; Reproductive efficiency. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/53906/1/RAC-Plasma-progesterone.pdf
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Marc: |
LEADER 01552nam a2200277 a 4500
001 1915189
005 2021-07-14
008 2011 bl uuuu u00u1 u #d
100 1 $aBRANDÃO, F. Z.
245 $aPlasma progesterone concentrations in Toggenburg goats submitted to estrous synchronization reusing autoclaved intravaginal progesterone devices.$h[electronic resource]
260 $aIn: CONGRESSO BRASILEIRO DE REPRODUÇÃO ANIMAL, 19., 2011, Recife. Anais... Belo Horizonte: CBRA, 2011. p. 212. 1 CD-ROM.$c2011
520 $aAbstract: The aim of this study was to evaluate plasma P4 concentration in Toggenburg goats receiving autoclaved progesterone devices for estrous synchronization. Progesterone analysis allows us to infer that the autoclaving process does not influence progesterone availability and so this technique can be a simple and valuable tool to reduce sanitary risks of disease transmission without alterating fertility in goats. [Concentrações de progesterona plasmática em cabras da raça Toggenburg submetidas à sincronização de estro reutilizando dispositivos intravaginais de progesterona autoclavados].
650 $aGoats
650 $aReproductive efficiency
650 $aCaprino
650 $aEficiência Reprodutiva
653 $aAutoclaved CIDR
653 $aCIDR autoclavado
653 $aHormônio esteróide
653 $aSteroid hormone
700 1 $aSOUZA, J. M. G.
700 1 $aTORRES, C. A. A.
700 1 $aMAIA, A. L. R. S.
700 1 $aDENADAI, R.
700 1 $aOBA, E.
700 1 $aFONSECA, J. F. da
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Registro original: |
Embrapa Caprinos e Ovinos (CNPC) |
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Registro Completo
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Biblioteca(s): |
Embrapa Solos. |
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Data corrente: |
17/12/2019 |
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Data da última atualização: |
27/04/2022 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
A - 2 |
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Autoria: |
KOTLAR, A. M.; LIER, Q. de J. van; BARROS, A. H. C.; IVERSEN, B. V.; VEREECKEN, H. |
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Afiliação: |
ALI MEHMANDOOST KOTLAR, CENA/USP; QUIRIJN DE JONG VAN LIER, CENA/USP; ALEXANDRE HUGO CEZAR BARROS, CNPS; BO V. IVERSEN, AARHUS UNIV., DENMARK; HARRY VEREECKEN, INSTITUTE OF BIO- AND GEOSCIENCES (IBG-3), AGROSPHERE, FORSCHUNGSZENTRUM JULICH, GERMANY. |
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Título: |
Development and uncertainty assessment of pedotransfer functions for predicting water contents at specific pressure heads. |
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Ano de publicação: |
2019 |
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Fonte/Imprenta: |
Vadose Zone Journal, v. 18, n. 1, 190063, 2019. |
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DOI: |
10.2136/vzj2019.06.0063 |
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Idioma: |
Inglês |
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Conteúdo: |
There has been much effort to improve the performance of pedotransfer functions (PTFs) using intelligent algorithms, but the issue of covariate shift, i.e., different probability distributions in training and testing datasets, and its impact on prediction uncertainty of PTFs has been rarely addressed. The common practice in PTF generation is to randomly separate the dataset into training and testing subsets, and the outcomes of this random selection may be different if the process is subject to covariate shift. We evaluated the impact of covariate shift generated by data shuffling and detected by Kolmogorov-Smirnov test for the prediction of water contents using soil databases from Denmark and Brazil. The soil water contents at different pressure heads were predicted by developing linear and stepwise regression besides machine learning based PTFs including Gaussian process regression and ensemble method. Regression based PTFs for the Brazilian dataset resulted in better predictions compared with machine learning methods, which in their turn estimated high water contents in Danish soils more accurately. One hundred PTFs were developed for water content at specific pressure heads by data shuffling. From these, 100 sets of fitted van Genuchten parameters were obtained representing the generated uncertainty. Data shuffling led to covariate shift, resulting in uncertainty in water content prediction by the PTFs. Inherent variability of data may lead to increased prediction uncertainty. For correlated data, simple regression models performed as good as sophisticated machine learning methods. Using PTF-predicted water contents for van Genuchten retention parameter fitting may lead to a high uncertainty. MenosThere has been much effort to improve the performance of pedotransfer functions (PTFs) using intelligent algorithms, but the issue of covariate shift, i.e., different probability distributions in training and testing datasets, and its impact on prediction uncertainty of PTFs has been rarely addressed. The common practice in PTF generation is to randomly separate the dataset into training and testing subsets, and the outcomes of this random selection may be different if the process is subject to covariate shift. We evaluated the impact of covariate shift generated by data shuffling and detected by Kolmogorov-Smirnov test for the prediction of water contents using soil databases from Denmark and Brazil. The soil water contents at different pressure heads were predicted by developing linear and stepwise regression besides machine learning based PTFs including Gaussian process regression and ensemble method. Regression based PTFs for the Brazilian dataset resulted in better predictions compared with machine learning methods, which in their turn estimated high water contents in Danish soils more accurately. One hundred PTFs were developed for water content at specific pressure heads by data shuffling. From these, 100 sets of fitted van Genuchten parameters were obtained representing the generated uncertainty. Data shuffling led to covariate shift, resulting in uncertainty in water content prediction by the PTFs. Inherent variability of data may lead to increased prediction uncert... Mostrar Tudo |
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Palavras-Chave: |
Funções de pedotransferência. |
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Thesagro: |
Condutividade Hidráulica; Retenção de Água no Solo. |
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Thesaurus NAL: |
Hydraulic conductivity; Pedotransfer functions; Soil water retention. |
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Categoria do assunto: |
P Recursos Naturais, Ciências Ambientais e da Terra |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/207273/1/Development-and-uncertainty-assessment-of-pedotransfer-functions-2019.pdf
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Marc: |
LEADER 02564naa a2200253 a 4500
001 2117100
005 2022-04-27
008 2019 bl uuuu u00u1 u #d
024 7 $a10.2136/vzj2019.06.0063$2DOI
100 1 $aKOTLAR, A. M.
245 $aDevelopment and uncertainty assessment of pedotransfer functions for predicting water contents at specific pressure heads.$h[electronic resource]
260 $c2019
520 $aThere has been much effort to improve the performance of pedotransfer functions (PTFs) using intelligent algorithms, but the issue of covariate shift, i.e., different probability distributions in training and testing datasets, and its impact on prediction uncertainty of PTFs has been rarely addressed. The common practice in PTF generation is to randomly separate the dataset into training and testing subsets, and the outcomes of this random selection may be different if the process is subject to covariate shift. We evaluated the impact of covariate shift generated by data shuffling and detected by Kolmogorov-Smirnov test for the prediction of water contents using soil databases from Denmark and Brazil. The soil water contents at different pressure heads were predicted by developing linear and stepwise regression besides machine learning based PTFs including Gaussian process regression and ensemble method. Regression based PTFs for the Brazilian dataset resulted in better predictions compared with machine learning methods, which in their turn estimated high water contents in Danish soils more accurately. One hundred PTFs were developed for water content at specific pressure heads by data shuffling. From these, 100 sets of fitted van Genuchten parameters were obtained representing the generated uncertainty. Data shuffling led to covariate shift, resulting in uncertainty in water content prediction by the PTFs. Inherent variability of data may lead to increased prediction uncertainty. For correlated data, simple regression models performed as good as sophisticated machine learning methods. Using PTF-predicted water contents for van Genuchten retention parameter fitting may lead to a high uncertainty.
650 $aHydraulic conductivity
650 $aPedotransfer functions
650 $aSoil water retention
650 $aCondutividade Hidráulica
650 $aRetenção de Água no Solo
653 $aFunções de pedotransferência
700 1 $aLIER, Q. de J. van
700 1 $aBARROS, A. H. C.
700 1 $aIVERSEN, B. V.
700 1 $aVEREECKEN, H.
773 $tVadose Zone Journal$gv. 18, n. 1, 190063, 2019.
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Registro original: |
Embrapa Solos (CNPS) |
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