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Registro Completo |
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Biblioteca(s): |
Embrapa Agricultura Digital. |
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Data corrente: |
28/11/2018 |
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Data da última atualização: |
28/11/2018 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
MARTINS, S. C.; ASSAD, E. D.; PAVÃO, E.; LOPES-ASSAD, M. L. R. C. |
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Afiliação: |
SUSIAN CHRISTIAN MARTINS, FGV; EDUARDO DELGADO ASSAD, CNPTIA; EDUARDO PAVÃO, CNPTIA; MARIA LEONOR RIBEIRO CASIMIRO LOPES-ASSAD, UFSCar. |
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Título: |
Inverting the carbon footprint in Brazilian agriculture: an estimate of the effects of the ABC plan. |
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Ano de publicação: |
2018 |
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Fonte/Imprenta: |
Revista Ciência, Tecnologia & Ambiente, Araras, v. 7, n. 1, p. 43-52, 2018. |
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DOI: |
http://dx.doi.org/10.4322/2359-6643.07106 |
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Idioma: |
Inglês |
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Conteúdo: |
The Sectoral Plan of Mitigation and Adaptation to Climate Change, drafted in order to consolidate an Economy of Low Carbon Emission in Agriculture (ABC Plan), integrates the commitments made by Brazil to mitigate its emissions of greenhouse gases (GHG). The objective of this study is to estimate GHG emissions produced by the agriculture and livestock sector considering the adoption of three low carbon emission technologies (LCT) ? pasture recovery, integrated crop-livestock systems (ICLS), and integrated crop-livestock-forest systems (ICLFS). The GHG emissions were estimated considering the growth projections of the production of soybean, corn, rice, beans, cotton, wheat, sugarcane and pastures from 2012 to 2023. Two scenarios were considered: I - without the adoption of LCT; II - with adoption of LCT, as proposed by the ABC Plan. In scenario I, the cumulative emissions estimated were 670.47 million tCO2eq, with only about 22.67 million tCO2eq from agricultural activities. In the scenario II, the stock of carbon in the soil was higher than carbon emissions and amounted to 1.10 billion tCO2eq, with a recovery of 75% of degraded pasture areas and implementation of ICLS and ICLFS in 25% of the area of degraded pastures. It was estimated that 52 million cattle would be added to the Brazilian production system with the adoption of the LCT. We concluded that the technologies proposed by the ABC Plan can mitigate climate change, and the Brazilian agricultural sector can reduce its carbon footprint and become the main sector in mitigating emissions. MenosThe Sectoral Plan of Mitigation and Adaptation to Climate Change, drafted in order to consolidate an Economy of Low Carbon Emission in Agriculture (ABC Plan), integrates the commitments made by Brazil to mitigate its emissions of greenhouse gases (GHG). The objective of this study is to estimate GHG emissions produced by the agriculture and livestock sector considering the adoption of three low carbon emission technologies (LCT) ? pasture recovery, integrated crop-livestock systems (ICLS), and integrated crop-livestock-forest systems (ICLFS). The GHG emissions were estimated considering the growth projections of the production of soybean, corn, rice, beans, cotton, wheat, sugarcane and pastures from 2012 to 2023. Two scenarios were considered: I - without the adoption of LCT; II - with adoption of LCT, as proposed by the ABC Plan. In scenario I, the cumulative emissions estimated were 670.47 million tCO2eq, with only about 22.67 million tCO2eq from agricultural activities. In the scenario II, the stock of carbon in the soil was higher than carbon emissions and amounted to 1.10 billion tCO2eq, with a recovery of 75% of degraded pasture areas and implementation of ICLS and ICLFS in 25% of the area of degraded pastures. It was estimated that 52 million cattle would be added to the Brazilian production system with the adoption of the LCT. We concluded that the technologies proposed by the ABC Plan can mitigate climate change, and the Brazilian agricultural sector can reduce its ... Mostrar Tudo |
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Palavras-Chave: |
Carbono do solo; Emissões de gases de efeito estufa; Mitigação; Mitigation; Mudanças climáticas; Plano ABC; Soil carbon. |
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Thesaurus Nal: |
Climate change; Greenhouse gases. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/187197/1/AP-Inverting-Assad.pdf
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Marc: |
LEADER 02446naa a2200277 a 4500
001 2100241
005 2018-11-28
008 2018 bl uuuu u00u1 u #d
024 7 $ahttp://dx.doi.org/10.4322/2359-6643.07106$2DOI
100 1 $aMARTINS, S. C.
245 $aInverting the carbon footprint in Brazilian agriculture$ban estimate of the effects of the ABC plan.$h[electronic resource]
260 $c2018
520 $aThe Sectoral Plan of Mitigation and Adaptation to Climate Change, drafted in order to consolidate an Economy of Low Carbon Emission in Agriculture (ABC Plan), integrates the commitments made by Brazil to mitigate its emissions of greenhouse gases (GHG). The objective of this study is to estimate GHG emissions produced by the agriculture and livestock sector considering the adoption of three low carbon emission technologies (LCT) ? pasture recovery, integrated crop-livestock systems (ICLS), and integrated crop-livestock-forest systems (ICLFS). The GHG emissions were estimated considering the growth projections of the production of soybean, corn, rice, beans, cotton, wheat, sugarcane and pastures from 2012 to 2023. Two scenarios were considered: I - without the adoption of LCT; II - with adoption of LCT, as proposed by the ABC Plan. In scenario I, the cumulative emissions estimated were 670.47 million tCO2eq, with only about 22.67 million tCO2eq from agricultural activities. In the scenario II, the stock of carbon in the soil was higher than carbon emissions and amounted to 1.10 billion tCO2eq, with a recovery of 75% of degraded pasture areas and implementation of ICLS and ICLFS in 25% of the area of degraded pastures. It was estimated that 52 million cattle would be added to the Brazilian production system with the adoption of the LCT. We concluded that the technologies proposed by the ABC Plan can mitigate climate change, and the Brazilian agricultural sector can reduce its carbon footprint and become the main sector in mitigating emissions.
650 $aClimate change
650 $aGreenhouse gases
653 $aCarbono do solo
653 $aEmissões de gases de efeito estufa
653 $aMitigação
653 $aMitigation
653 $aMudanças climáticas
653 $aPlano ABC
653 $aSoil carbon
700 1 $aASSAD, E. D.
700 1 $aPAVÃO, E.
700 1 $aLOPES-ASSAD, M. L. R. C.
773 $tRevista Ciência, Tecnologia & Ambiente, Araras$gv. 7, n. 1, p. 43-52, 2018.
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Registro original: |
Embrapa Agricultura Digital (CNPTIA) |
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 | Acesso ao texto completo restrito à biblioteca da Embrapa Arroz e Feijão. Para informações adicionais entre em contato com cnpaf.biblioteca@embrapa.br. |
Registro Completo
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Biblioteca(s): |
Embrapa Arroz e Feijão. |
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Data corrente: |
05/05/2020 |
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Data da última atualização: |
06/05/2020 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
B - 5 |
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Autoria: |
AGUIAR, J. T. de; LOBO JUNIOR, M. |
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Afiliação: |
JORDENE TEIXEIRA DE AGUIAR, UFG; MURILLO LOBO JUNIOR, CNPAF. |
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Título: |
Reliability and discrepancies of rainfall and temperatures from remote sensing and Brazilian ground weather stations. |
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Ano de publicação: |
2020 |
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Fonte/Imprenta: |
Remote Sensing Applications: Society and Environment, v. 18, 100301, Apr. 2020. |
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ISSN: |
2352-9385 |
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DOI: |
https://doi.org/10.1016/j.rsase.2020.100301 |
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Idioma: |
Inglês |
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Conteúdo: |
Insufficient ground meteorological stations limit agricultural research in wide geographic areas, but high-quality data from remote sensing may decrease information gaps, when surface stations are scarce. This study compared meteorological datasets, estimated from satellite and ground meteorological stations in latitudes from 0 to 33 oS, to support agricultural research in Brazil. The dataset comprised 3600 records of monthly temperatures and rainfall from 01 Jan 2004 to 31 Dec 2014 in 30 Brazilian municipalities distributed in six regions, labeled according to their precipitation homogeneity. Climatic records from NASA?s Prediction of Worldwide Energy Resource (POWER) online database were compared with data from Brazilian surface stations managed by National Institute of Meteorology (INMET). Monthly rainfall data showed satisfactory correlation coefficients for almost all locations, between 0.75 and 0.95 (p < 0.01), and simple linear models were fit for estimated (satellite) and observed (ground) rainfall relationship (p < 0.001). Complimentary accuracy and precision tests endorsed rainfall satellite-estimated data according to the root mean square error (RMSE) and the modified index of agreement. Maximum and minimum temperatures estimated by remote sensing in the Brazilian South Region were also statistically supported, but unsuitable results were found especially in lower latitudes, based on higher RMSE. The Pearson?s correlation coefficient for temperatures increased proportionally with latitude, while rainfall did not show this correlation. These results showed satellite-data quality varies regionally and is affected by seasonal variation. Remote sensors may not detect extreme climatic events such as heavy rainfall or draught and, therefore, need to be appraised carefully. MenosInsufficient ground meteorological stations limit agricultural research in wide geographic areas, but high-quality data from remote sensing may decrease information gaps, when surface stations are scarce. This study compared meteorological datasets, estimated from satellite and ground meteorological stations in latitudes from 0 to 33 oS, to support agricultural research in Brazil. The dataset comprised 3600 records of monthly temperatures and rainfall from 01 Jan 2004 to 31 Dec 2014 in 30 Brazilian municipalities distributed in six regions, labeled according to their precipitation homogeneity. Climatic records from NASA?s Prediction of Worldwide Energy Resource (POWER) online database were compared with data from Brazilian surface stations managed by National Institute of Meteorology (INMET). Monthly rainfall data showed satisfactory correlation coefficients for almost all locations, between 0.75 and 0.95 (p < 0.01), and simple linear models were fit for estimated (satellite) and observed (ground) rainfall relationship (p < 0.001). Complimentary accuracy and precision tests endorsed rainfall satellite-estimated data according to the root mean square error (RMSE) and the modified index of agreement. Maximum and minimum temperatures estimated by remote sensing in the Brazilian South Region were also statistically supported, but unsuitable results were found especially in lower latitudes, based on higher RMSE. The Pearson?s correlation coefficient for temperatures increased pro... Mostrar Tudo |
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Thesagro: |
Climatologia; Modelo de Simulação; Sensoriamento Remoto. |
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Thesaurus NAL: |
Agriculture; Climatology; Decision support systems; Remote sensing. |
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Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
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Marc: |
LEADER 02613naa a2200241 a 4500
001 2122081
005 2020-05-06
008 2020 bl uuuu u00u1 u #d
022 $a2352-9385
024 7 $ahttps://doi.org/10.1016/j.rsase.2020.100301$2DOI
100 1 $aAGUIAR, J. T. de
245 $aReliability and discrepancies of rainfall and temperatures from remote sensing and Brazilian ground weather stations.$h[electronic resource]
260 $c2020
520 $aInsufficient ground meteorological stations limit agricultural research in wide geographic areas, but high-quality data from remote sensing may decrease information gaps, when surface stations are scarce. This study compared meteorological datasets, estimated from satellite and ground meteorological stations in latitudes from 0 to 33 oS, to support agricultural research in Brazil. The dataset comprised 3600 records of monthly temperatures and rainfall from 01 Jan 2004 to 31 Dec 2014 in 30 Brazilian municipalities distributed in six regions, labeled according to their precipitation homogeneity. Climatic records from NASA?s Prediction of Worldwide Energy Resource (POWER) online database were compared with data from Brazilian surface stations managed by National Institute of Meteorology (INMET). Monthly rainfall data showed satisfactory correlation coefficients for almost all locations, between 0.75 and 0.95 (p < 0.01), and simple linear models were fit for estimated (satellite) and observed (ground) rainfall relationship (p < 0.001). Complimentary accuracy and precision tests endorsed rainfall satellite-estimated data according to the root mean square error (RMSE) and the modified index of agreement. Maximum and minimum temperatures estimated by remote sensing in the Brazilian South Region were also statistically supported, but unsuitable results were found especially in lower latitudes, based on higher RMSE. The Pearson?s correlation coefficient for temperatures increased proportionally with latitude, while rainfall did not show this correlation. These results showed satellite-data quality varies regionally and is affected by seasonal variation. Remote sensors may not detect extreme climatic events such as heavy rainfall or draught and, therefore, need to be appraised carefully.
650 $aAgriculture
650 $aClimatology
650 $aDecision support systems
650 $aRemote sensing
650 $aClimatologia
650 $aModelo de Simulação
650 $aSensoriamento Remoto
700 1 $aLOBO JUNIOR, M.
773 $tRemote Sensing Applications: Society and Environment$gv. 18, 100301, Apr. 2020.
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Embrapa Arroz e Feijão (CNPAF) |
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