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Registro Completo |
Biblioteca(s): |
Embrapa Pecuária Sudeste. |
Data corrente: |
27/04/2010 |
Data da última atualização: |
09/09/2022 |
Tipo da produção científica: |
Circular Técnica |
Autoria: |
MENDONCA, F. C.; RASSINI, J. B. |
Afiliação: |
FERNANDO CAMPOS MENDONCA, CPPSE; JOAQUIM BARTOLOMEU RASSINI, CPPSE. |
Título: |
Método EPS para manejo da irrigação de forrageiras. |
Ano de publicação: |
2009 |
Fonte/Imprenta: |
São Carlos, SP: Embrapa Pecuária Sudeste, 2009. |
Páginas: |
9 p. |
Série: |
(Embrapa Pecuária Sudeste. Circular Técnica, 63). |
ISSN: |
1981-2086 |
Idioma: |
Português |
Conteúdo: |
Os fatores mais influentes e limitantes ao desenvolvimento das plantas forrageiras referem-se ao solo e ao clima. Ao contrário dos fatores do solo, não se pode modificar a maioria dos fatores climáticos, devendo-se adaptar a eles as espécies, as cultivares e os sistemas de produção. Os principais fatores climáticos que afetam a produção de forragem são a temperatura do ar, a luminosidade e a precipitação pluvial. Aliados à fertilidade e ao armazenamento de água no solo, esses fatores são responsáveis pela maior parte da produtividade das plantas forrageiras. |
Palavras-Chave: |
Forrageira; Método EPS; Produção de forragem. |
Thesagro: |
Irrigação. |
Categoria do assunto: |
F Plantas e Produtos de Origem Vegetal |
URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/CPPSE-2010/19159/1/PROCICircT63FCM2009.00422.pdf
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Marc: |
LEADER 01178nam a2200205 a 4500 001 1737062 005 2022-09-09 008 2009 bl uuuu u0uu1 u #d 022 $a1981-2086 100 1 $aMENDONCA, F. C. 245 $aMétodo EPS para manejo da irrigação de forrageiras.$h[electronic resource] 260 $aSão Carlos, SP: Embrapa Pecuária Sudeste$c2009 300 $a9 p. 490 $a(Embrapa Pecuária Sudeste. Circular Técnica, 63). 520 $aOs fatores mais influentes e limitantes ao desenvolvimento das plantas forrageiras referem-se ao solo e ao clima. Ao contrário dos fatores do solo, não se pode modificar a maioria dos fatores climáticos, devendo-se adaptar a eles as espécies, as cultivares e os sistemas de produção. Os principais fatores climáticos que afetam a produção de forragem são a temperatura do ar, a luminosidade e a precipitação pluvial. Aliados à fertilidade e ao armazenamento de água no solo, esses fatores são responsáveis pela maior parte da produtividade das plantas forrageiras. 650 $aIrrigação 653 $aForrageira 653 $aMétodo EPS 653 $aProdução de forragem 700 1 $aRASSINI, J. B.
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Registro original: |
Embrapa Pecuária Sudeste (CPPSE) |
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Registro Completo
Biblioteca(s): |
Embrapa Meio Ambiente; Embrapa Recursos Genéticos e Biotecnologia. |
Data corrente: |
11/02/2014 |
Data da última atualização: |
10/03/2023 |
Tipo da produção científica: |
Artigo em Periódico Indexado |
Circulação/Nível: |
C - 0 |
Autoria: |
ANDOW, D. A.; LOVEI, G. L.; ARPAIA, S.; WILSON, L.; FONTES, E. M. G.; HILBECK, A.; LANG, A.; TUAT, N. V.; PIRES, C. S. S.; SUJII, E. R.; ZWAHLEN, C.; BIRCH, A. N. E.; CAPALBO, D. M. F.; PRESCOTT, K.; OMOTO, C.; ZEILINGER, A. R. |
Afiliação: |
D. A. ANDOW, University of Minnesota; GABOR L. LOVEI, Aarhus University; SALVATORE ARPAIA, ENEA-Research Centre Trisaia; LEWIS WILSON, CSIRO Cotton Research; ELIANA MARIA GOUVEIA FONTES, CENARGEN; ANGELICA HILBECK, Swiss Federal Institute of Technology; ANDREAS LANG, University of Basel; NGUYEN VAN TUAT, Food Crops Research Institute; CARMEN SILVIA SOARES PIRES, CENARGEN; EDISON RYOITI SUJII, CENARGEN; CLAUDIA ZWAHLEN, University of Minnesota; A. N. E. BIRCH, Ecological Science Group; DEISE MARIA FONTANA CAPALBO, CNPMA; KRISTINA PRESCOTT, University of Minnesota; CELSO OMOTO, ESALQ-USP; ADAM R. ZEILINGER, University of Minnesota. |
Título: |
An ecologically-based method for selecting ecological indicators for assessing risks to biological diversity from genetically-engineered plants. |
Ano de publicação: |
2013 |
Fonte/Imprenta: |
Journal of Biosafety, v. 22, n. 3, p. 141-156, 2013. |
Idioma: |
Inglês |
Conteúdo: |
The environmental risks associated with genetically-engineered (GE) organisms have been controversial, and so have the models for the assessment of these risks. We propose an ecologically-based environmental risk assessment (ERA) model that follows the 1998 USEPA guidelines, focusing on potential adverse effects to biological diversity. The approach starts by (1) identifying the local environmental values so the ERA addresses specific concerns associated with local biological diversity. The model simplifies the indicator endpoint selection problem by (2) classifying biological diversity into ecological functional groups and selecting those that deliver the identified environmental values. (3) All of the species or ecosystem processes related to the selected functional groups are identified and (4) multi-criteria decision analysis (MCDA) is used to rank the indicator endpoint entities, which may be species or ecological processes. MCDA focuses on those species and processes that are critical for the identified ecological functions and are likely to be highly exposed to the GE organism. The highest ranked indicator entities are selected for the next step. (5) Relevant risk hypotheses are identified. Knowledge about the specific transgene and its possible environmental effects in other countries can be used to assist development of risk hypotheses. (6) The risk hypotheses are ranked using MCDA with criteria related to the severity of the potential risk. The model emphasizes transparent, expert-driven, ecologically-based decision-making and provides formal methods for completing a screening level-ERA that can focus ERA on the most significant concerns. The process requires substantial human input but the human capital is available in most countries and regions of the world. MenosThe environmental risks associated with genetically-engineered (GE) organisms have been controversial, and so have the models for the assessment of these risks. We propose an ecologically-based environmental risk assessment (ERA) model that follows the 1998 USEPA guidelines, focusing on potential adverse effects to biological diversity. The approach starts by (1) identifying the local environmental values so the ERA addresses specific concerns associated with local biological diversity. The model simplifies the indicator endpoint selection problem by (2) classifying biological diversity into ecological functional groups and selecting those that deliver the identified environmental values. (3) All of the species or ecosystem processes related to the selected functional groups are identified and (4) multi-criteria decision analysis (MCDA) is used to rank the indicator endpoint entities, which may be species or ecological processes. MCDA focuses on those species and processes that are critical for the identified ecological functions and are likely to be highly exposed to the GE organism. The highest ranked indicator entities are selected for the next step. (5) Relevant risk hypotheses are identified. Knowledge about the specific transgene and its possible environmental effects in other countries can be used to assist development of risk hypotheses. (6) The risk hypotheses are ranked using MCDA with criteria related to the severity of the potential risk. The model emphasizes tra... Mostrar Tudo |
Palavras-Chave: |
Environmental risk assessment; Genetically engineered organisms. |
Thesagro: |
Biodiversidade; Impacto ambiental; Planta transgênica. |
Thesaurus NAL: |
Biodiversity; ecosystem services; Risk assessment; Transgenic plants. |
Categoria do assunto: |
P Recursos Naturais, Ciências Ambientais e da Terra |
URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/97032/1/2013AP47.pdf
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Marc: |
LEADER 03008naa a2200409 a 4500 001 1980027 005 2023-03-10 008 2013 bl uuuu u00u1 u #d 100 1 $aANDOW, D. A. 245 $aAn ecologically-based method for selecting ecological indicators for assessing risks to biological diversity from genetically-engineered plants.$h[electronic resource] 260 $c2013 520 $aThe environmental risks associated with genetically-engineered (GE) organisms have been controversial, and so have the models for the assessment of these risks. We propose an ecologically-based environmental risk assessment (ERA) model that follows the 1998 USEPA guidelines, focusing on potential adverse effects to biological diversity. The approach starts by (1) identifying the local environmental values so the ERA addresses specific concerns associated with local biological diversity. The model simplifies the indicator endpoint selection problem by (2) classifying biological diversity into ecological functional groups and selecting those that deliver the identified environmental values. (3) All of the species or ecosystem processes related to the selected functional groups are identified and (4) multi-criteria decision analysis (MCDA) is used to rank the indicator endpoint entities, which may be species or ecological processes. MCDA focuses on those species and processes that are critical for the identified ecological functions and are likely to be highly exposed to the GE organism. The highest ranked indicator entities are selected for the next step. (5) Relevant risk hypotheses are identified. Knowledge about the specific transgene and its possible environmental effects in other countries can be used to assist development of risk hypotheses. (6) The risk hypotheses are ranked using MCDA with criteria related to the severity of the potential risk. The model emphasizes transparent, expert-driven, ecologically-based decision-making and provides formal methods for completing a screening level-ERA that can focus ERA on the most significant concerns. The process requires substantial human input but the human capital is available in most countries and regions of the world. 650 $aBiodiversity 650 $aecosystem services 650 $aRisk assessment 650 $aTransgenic plants 650 $aBiodiversidade 650 $aImpacto ambiental 650 $aPlanta transgênica 653 $aEnvironmental risk assessment 653 $aGenetically engineered organisms 700 1 $aLOVEI, G. L. 700 1 $aARPAIA, S. 700 1 $aWILSON, L. 700 1 $aFONTES, E. M. G. 700 1 $aHILBECK, A. 700 1 $aLANG, A. 700 1 $aTUAT, N. V. 700 1 $aPIRES, C. S. S. 700 1 $aSUJII, E. R. 700 1 $aZWAHLEN, C. 700 1 $aBIRCH, A. N. E. 700 1 $aCAPALBO, D. M. F. 700 1 $aPRESCOTT, K. 700 1 $aOMOTO, C. 700 1 $aZEILINGER, A. R. 773 $tJournal of Biosafety$gv. 22, n. 3, p. 141-156, 2013.
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Embrapa Recursos Genéticos e Biotecnologia (CENARGEN) |
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