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Registro Completo |
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Biblioteca(s): |
Embrapa Pecuária Sul. |
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Data corrente: |
10/11/2017 |
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Data da última atualização: |
10/11/2017 |
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Tipo da produção científica: |
Comunicado Técnico/Recomendações Técnicas |
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Autoria: |
SOARES, S. M.; GASPAR, E. B.; MINHO, A. P.; SUÑÉ, R. W.; RIZZARDO, J. S. |
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Afiliação: |
Suelen Mendonça Soares, UPF; EMANUELLE BALDO GASPAR, CPPSUL; ALESSANDRO PELEGRINE MINHO, CPPSUL; RENATA WOLF SUNE MARTINS DA SILVA, CPPSUL; Juliana Soares Rizzardo, URCAMP. |
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Título: |
Caracterização da ocorrência de mastite subclínica no rebanho leiteiro da Embrapa Pecuária Sul. |
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Ano de publicação: |
2017 |
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Fonte/Imprenta: |
Bagé: Embrapa Pecuária Sul, 2017. |
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Páginas: |
8 p. |
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Série: |
(Embrapa Pecuária Sul. Comunicado técnico, 92). |
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ISSN: |
1982-5382 |
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Idioma: |
Português |
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Conteúdo: |
O objetivo deste documento foi caracterizar a ocorrência da mastite subclínica, levando em consideração o número de lactações dos animais, bem como analisar a correlação de testes laboratoriais e a campo para o diagnóstico desta enfermidade no rebanho da Embrapa Pecuária Sul. |
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Thesagro: |
Doença animal; Gado leiteiro; Mamite; Teste de mamite. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/166576/1/CoT-92-online.pdf
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Marc: |
LEADER 00997nam a2200241 a 4500
001 2079472
005 2017-11-10
008 2017 bl uuuu u0uu1 u #d
022 $a1982-5382
100 1 $aSOARES, S. M.
245 $aCaracterização da ocorrência de mastite subclínica no rebanho leiteiro da Embrapa Pecuária Sul.$h[electronic resource]
260 $aBagé: Embrapa Pecuária Sul$c2017
300 $a8 p.
490 $a(Embrapa Pecuária Sul. Comunicado técnico, 92).
520 $aO objetivo deste documento foi caracterizar a ocorrência da mastite subclínica, levando em consideração o número de lactações dos animais, bem como analisar a correlação de testes laboratoriais e a campo para o diagnóstico desta enfermidade no rebanho da Embrapa Pecuária Sul.
650 $aDoença animal
650 $aGado leiteiro
650 $aMamite
650 $aTeste de mamite
700 1 $aGASPAR, E. B.
700 1 $aMINHO, A. P.
700 1 $aSUÑÉ, R. W.
700 1 $aRIZZARDO, J. S.
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Registro original: |
Embrapa Pecuária Sul (CPPSUL) |
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Registro Completo
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Biblioteca(s): |
Embrapa Amapá; Embrapa Amazônia Oriental. |
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Data corrente: |
18/11/2019 |
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Data da última atualização: |
07/12/2023 |
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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: |
TOURNE, D. C. M.; BALLESTER, M. V. R.; JAMES, P. M. A.; MARTORANO, L. G.; GUEDES, M. C.; THOMAS, E. |
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Afiliação: |
DAIANA C. M. TOURNE, USP; MARIA V. R. BALLESTER, USP; PATRICK M. A. JAMES, UNIVERSITY OF MONTRÉAL; LUCIETA GUERREIRO MARTORANO, CPATU; MARCELINO CARNEIRO GUEDES, CPAF-AP; EVERT THOMAS, BIOVERSITY INTERNATIONAL, REGIONAL OFFICE FOR THE AMERICAS. |
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Título: |
Strategies to optimize modeling habitat suitability of Bertholletia excelsa in the Pan-Amazonia. |
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Ano de publicação: |
2019 |
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Fonte/Imprenta: |
Ecology and Evolution, v. 9, n. 22, p. 12357-12960, Nov. 2019. |
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DOI: |
https://doi.org/10.1002/ece3.5726 |
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Idioma: |
Inglês |
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Conteúdo: |
Aim: Amazon-nut (Bertholletia excelsa) is a hyperdominant and protected tree species, playing a keystone role in nutrient cycling and ecosystem service provision in Amazonia. Our main goal was to develop a robust habitat suitability model of Amazon-nut and to identify the most important predictor variables to support conservation and tree planting decisions. Localization: Amazon region, South America. Methods: We collected 3,325 unique Amazon-nut records and assembled >100 spatial predictor variables organized across climatic, edaphic, and geophysical categories. We compared suitability models using variables (a) selected through statistical techniques; (b) recommended by experts; and (c) integrating both approaches (a and b). We applied different spatial filtering scenarios to reduce overfitting. We additionally fine-tuned MAXENT settings to our data. The best model was selected through quantitative and qualitative assessments. Results: Principal component analysis based on expert recommendations was the most appropriate method for predictor selection. Elevation, coarse soil fragments, clay, slope, and annual potential evapotranspiration were the most important predictors. Their relative contribution to the best model amounted to 75%. Filtering of the presences within a radius of 10 km displayed lowest overfitting, a satisfactory omission rate and the most symmetric distribution curve. Our findings suggest that under current environmental conditions, suitable habitat for Amazon-nut is found across 2.3 million km2, that is, 32% of the Amazon Biome. Main conclusion: The combination of statistical techniques with expert knowledge improved the quality of our suitability model. Topographic and soil variables were the most important predictors. The combination of predictor variable selection, fine-tuning of model parameters and spatial filtering was critical for the construction of a reliable habitat suitability model. MenosAim: Amazon-nut (Bertholletia excelsa) is a hyperdominant and protected tree species, playing a keystone role in nutrient cycling and ecosystem service provision in Amazonia. Our main goal was to develop a robust habitat suitability model of Amazon-nut and to identify the most important predictor variables to support conservation and tree planting decisions. Localization: Amazon region, South America. Methods: We collected 3,325 unique Amazon-nut records and assembled >100 spatial predictor variables organized across climatic, edaphic, and geophysical categories. We compared suitability models using variables (a) selected through statistical techniques; (b) recommended by experts; and (c) integrating both approaches (a and b). We applied different spatial filtering scenarios to reduce overfitting. We additionally fine-tuned MAXENT settings to our data. The best model was selected through quantitative and qualitative assessments. Results: Principal component analysis based on expert recommendations was the most appropriate method for predictor selection. Elevation, coarse soil fragments, clay, slope, and annual potential evapotranspiration were the most important predictors. Their relative contribution to the best model amounted to 75%. Filtering of the presences within a radius of 10 km displayed lowest overfitting, a satisfactory omission rate and the most symmetric distribution curve. Our findings suggest that under current environmental conditions, suitable habitat for Am... Mostrar Tudo |
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Palavras-Chave: |
Análise de componentes principais; Avaliação de modelo; Conhecimento especializado; Entropia máxima; Expert knowledge; Filtragem espacial; Maximum entropy; Model evaluation; Modelo de distribuição de espécie; Protected Amazonian species; Spatial filtering; Species distribution model. |
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Thesagro: |
Castanha. |
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Thesaurus NAL: |
Principal component analysis. |
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Categoria do assunto: |
K Ciência Florestal e Produtos de Origem Vegetal |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/doc/1159297/1/Strategies-to-optimize.pdf
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/204836/1/CPAF-AP-2019-Strategies-to-optimize-modeling-habitat.pdf
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Marc: |
LEADER 03112naa a2200361 a 4500
001 2159297
005 2023-12-07
008 2019 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.1002/ece3.5726$2DOI
100 1 $aTOURNE, D. C. M.
245 $aStrategies to optimize modeling habitat suitability of Bertholletia excelsa in the Pan-Amazonia.$h[electronic resource]
260 $c2019
520 $aAim: Amazon-nut (Bertholletia excelsa) is a hyperdominant and protected tree species, playing a keystone role in nutrient cycling and ecosystem service provision in Amazonia. Our main goal was to develop a robust habitat suitability model of Amazon-nut and to identify the most important predictor variables to support conservation and tree planting decisions. Localization: Amazon region, South America. Methods: We collected 3,325 unique Amazon-nut records and assembled >100 spatial predictor variables organized across climatic, edaphic, and geophysical categories. We compared suitability models using variables (a) selected through statistical techniques; (b) recommended by experts; and (c) integrating both approaches (a and b). We applied different spatial filtering scenarios to reduce overfitting. We additionally fine-tuned MAXENT settings to our data. The best model was selected through quantitative and qualitative assessments. Results: Principal component analysis based on expert recommendations was the most appropriate method for predictor selection. Elevation, coarse soil fragments, clay, slope, and annual potential evapotranspiration were the most important predictors. Their relative contribution to the best model amounted to 75%. Filtering of the presences within a radius of 10 km displayed lowest overfitting, a satisfactory omission rate and the most symmetric distribution curve. Our findings suggest that under current environmental conditions, suitable habitat for Amazon-nut is found across 2.3 million km2, that is, 32% of the Amazon Biome. Main conclusion: The combination of statistical techniques with expert knowledge improved the quality of our suitability model. Topographic and soil variables were the most important predictors. The combination of predictor variable selection, fine-tuning of model parameters and spatial filtering was critical for the construction of a reliable habitat suitability model.
650 $aPrincipal component analysis
650 $aCastanha
653 $aAnálise de componentes principais
653 $aAvaliação de modelo
653 $aConhecimento especializado
653 $aEntropia máxima
653 $aExpert knowledge
653 $aFiltragem espacial
653 $aMaximum entropy
653 $aModel evaluation
653 $aModelo de distribuição de espécie
653 $aProtected Amazonian species
653 $aSpatial filtering
653 $aSpecies distribution model
700 1 $aBALLESTER, M. V. R.
700 1 $aJAMES, P. M. A.
700 1 $aMARTORANO, L. G.
700 1 $aGUEDES, M. C.
700 1 $aTHOMAS, E.
773 $tEcology and Evolution$gv. 9, n. 22, p. 12357-12960, Nov. 2019.
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Registro original: |
Embrapa Amazônia Oriental (CPATU) |
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