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Registro Completo |
Biblioteca(s): |
Embrapa Meio Ambiente; Embrapa Solos. |
Data corrente: |
07/06/2016 |
Data da última atualização: |
14/03/2017 |
Tipo da produção científica: |
Artigo em Periódico Indexado |
Autoria: |
LIEBIG, M. A.; FRANZLUEBBERS, A. J.; ALVAREZ, C.; CHIESA, T. D.; LEWCZUK, N.; PIÑEIRO, G.; POSSE, G.; YAHDJIAN, L.; GRACE, P.; CABRAL, O. M. R.; MARTIN NETO, L.; RODRIGUES, R. de A. R.; AMIRO, B.; ANGERS, D.; HAO, X.; OELBERMANN, M.; TENUTA, M.; MUNKHOLM, L. J.; REGINA, K.; CELLIER, P.; EHRHARDT, F.; RICHARD, G.; DECHOW, R.; AGUS, F.; WIDIARTA, N.; SPINK, J.; BERTI, A.; GRIGNANI, C.; MAZZONCINI, M.; ORSINI, R.; ROGGERO, P. P.; SEDDAIU, G.; TEI, F.; VENTRELLA, D.; VITALI, G.; KISHIMOTO-MO, A.; SHIRATO, Y.; SUDO, S.; SHIN, J.; SCHIPPER, L.; SAVÉ, R.; LEIFELD, J.; SPADAVECCHIA, L.; YELURIPATI, J.; DEL GROSSO, S.; RICE, C.; SAWCHIK, J. |
Afiliação: |
M. A. LIEBIG, USDA-ARS; A. J. FRANZLUEBBERS, USDA-ARS; C. ALVAREZ, National Institute of Agricultural Technology, Manfredi, Cordoba, Argentina; T. D. CHIESA, IFEVA, Facultad de Agronomía; N. LEWCZUK, National Institute of Agricultural Technology, Buenos Aires, Argentina; G. PIÑEIRO, IFEVA, Facultad de Agronomía, Universidad de Buenos Aires; G. POSSE, National Institute of Agricultural Technology, Buenos Aires, Argentina; L. YAHDJIAN, IFEVA, Facultad de Agronomía, Universidad de Buenos Aires; P. GRACE, Queensland University of Technology, Brisbane, Queesland, Australia; OSVALDO MACHADO RODRIGUES CABRAL, CNPMA; LADISLAU MARTIN NETO, DE/P&D; RENATO DE ARAGAO RIBEIRO RODRIGUES, CNPS; B. AMIRO, University of Manitoba, Winnipeg, Manitoba, Canada; D. ANGERS, Agriculture and Agri-Food Canada, Quebec, Quebec, Canada; X. HAO, Agriculture and Agri-Food Canada, Lethbridge, Alberta, Canada; M. OELBERMANN, University of Waterloo, Waterloo, Ontario, Canada; M. TENUTA, University of Manitoba, Department of Soil Science, Winnipeg, Manitoba, Canada; L. J. MUNKHOLM, Dept. of Agroecology, Aarhus University, Denmark; K. REGINA, Natural Resources Institute Finland; P. CELLIER, French National Institute for Agricultural Research (INRA); F. EHRHARDT, French National Institute for Agricultural Research (INRA); G. RICHARD, French National Institute for Agricultural Research (INRA); R. DECHOW, Thünen Institute of Climate-Smart Agriculture, Braunschweig, Germany; F. AGUS, Indonesian Soil Research Institute; N. WIDIARTA, Indonesian Center for Food Crop Research and Development; J. SPINK, Oak Park Crops Research Centre, Teagasc, Oak Park, Carlow, Ireland; A. BERTI, Dipartimento di Agronomia Animali Alimenti risorse Naturali e Ambiente (DAFNAE), Universita di Padova, Agripolis; C. GRIGNANI, Department of Agricultural, Forest and Food Sciences, University of Turin; M. MAZZONCINI, Department of Agronomy and Agroecosystem Management (DAGA), University of Pisa; R. ORSINI, Dipartimento di Scienze Agrarie Alimentari e Ambientali, Universita Politecnica delle Marche; P. P. ROGGERO, Nucleo di Ricerca sulla Desertificazione and Dipartimento di Agraria, Universita di SassarI; G. SEDDAIU, Nucleo di Ricerca sulla Desertificazione and Dipartimento di Agraria, Universita di Sassari; F. TEI, Dept of Agricultural, Food and Environmental Sciences, University of Perugia; D. VENTRELLA, Research Unit for Cropping Systems in Dry Environments (CRA-SCA); G. VITALI, Dipartimento di Scienze agrarie, Alma Mater Studiorum, Universita di Bologna; A. KISHIMOTO-MO, National Institute for Agro-Environmental Sciences, Japan; Y. SHIRATO, National Institute for Agro-Environmental Sciences, Japan; S. SUDO, National Institute for Agro-Environmental Sciences, Japan; J. SHIN, National Academy of Agricultural Science, Seoul, South Korea; L. SCHIPPER, Environmental Research Institute, University of Waikato, New Zealand; R. SAVÉ, Institute of Agrifood Research and Technology (IRTA), Barcelona, Catalonia, Spain; J. LEIFELD, Agroscope, Zurich, Switzerland; L. SPADAVECCHIA, Department for Environment, Food & Rural Affairs, London; J. YELURIPATI, The James Hutton Institute, Craigiebuckler, Scotland; S. DEL GROSSO, USDA Agricultural Research Service; C. RICE, Kansas State University; J. SAWCHIK, National Institute for Agricultural Research Uruguay, Montevideo, Uruguay. |
Título: |
MAGGnet: an international network to foster mitigation of agricultural greenhouse gases. |
Ano de publicação: |
2016 |
Fonte/Imprenta: |
Carbon Management, v. 7, N. 3-4, P. 243-248, 2016. |
DOI: |
10.1080/17583004.2016.1180586 |
Idioma: |
Inglês |
Conteúdo: |
Research networks provide a framework for review, synthesis and systematic testing of theories by multiple scientists across international borders critical for addressing global-scale issues. In 2012, a GHG research network referred to as MAGGnet (Managing Agricultural Greenhouse Gases Network) was established within the Croplands Research Group of the Global Research Alliance on Agricultural Greenhouse Gases (GRA). With involvement from 46 alliance member countries, MAGGnet seeks to provide a platform for the inventory and analysis of agricultural GHG mitigation research throughout the world. To date, metadata from 315 experimental studies in 20 countries have been compiled using a standardized spreadsheet. Most studies were completed (74%) and conducted within a 1-3-year duration (68%). Soil carbon and nitrous oxide emissions were measured in over 80% of the studies. Among plant variables, grain yield was assessed across studies most frequently (56%), followed by stover (35%) and root (9%) biomass. MAGGnet has contributed to modeling efforts and has spurred other research groups in the GRA to collect experimental site metadata using an adapted spreadsheet. With continued growth and investment, MAGGnet will leverage limited-resource investments by any one country to produce an inclusive, globally shared meta-database focused on the science of GHG mitigation. |
Palavras-Chave: |
Gases de efeito estufa; Global Research Alliance; Óxido nitroso; Sequestro de carbono. |
Thesaurus Nal: |
Carbon sequestration; Greenhouse gases; Nitrous oxide. |
Categoria do assunto: |
P Recursos Naturais, Ciências Ambientais e da Terra |
URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/157589/1/2016AP46.pdf
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/144121/1/2016-008.pdf
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Marc: |
LEADER 03383naa a2200769 a 4500 001 2066933 005 2017-03-14 008 2016 bl uuuu u00u1 u #d 024 7 $a10.1080/17583004.2016.1180586$2DOI 100 1 $aLIEBIG, M. A. 245 $aMAGGnet$ban international network to foster mitigation of agricultural greenhouse gases.$h[electronic resource] 260 $c2016 520 $aResearch networks provide a framework for review, synthesis and systematic testing of theories by multiple scientists across international borders critical for addressing global-scale issues. In 2012, a GHG research network referred to as MAGGnet (Managing Agricultural Greenhouse Gases Network) was established within the Croplands Research Group of the Global Research Alliance on Agricultural Greenhouse Gases (GRA). With involvement from 46 alliance member countries, MAGGnet seeks to provide a platform for the inventory and analysis of agricultural GHG mitigation research throughout the world. To date, metadata from 315 experimental studies in 20 countries have been compiled using a standardized spreadsheet. Most studies were completed (74%) and conducted within a 1-3-year duration (68%). Soil carbon and nitrous oxide emissions were measured in over 80% of the studies. Among plant variables, grain yield was assessed across studies most frequently (56%), followed by stover (35%) and root (9%) biomass. MAGGnet has contributed to modeling efforts and has spurred other research groups in the GRA to collect experimental site metadata using an adapted spreadsheet. With continued growth and investment, MAGGnet will leverage limited-resource investments by any one country to produce an inclusive, globally shared meta-database focused on the science of GHG mitigation. 650 $aCarbon sequestration 650 $aGreenhouse gases 650 $aNitrous oxide 653 $aGases de efeito estufa 653 $aGlobal Research Alliance 653 $aÓxido nitroso 653 $aSequestro de carbono 700 1 $aFRANZLUEBBERS, A. J. 700 1 $aALVAREZ, C. 700 1 $aCHIESA, T. D. 700 1 $aLEWCZUK, N. 700 1 $aPIÑEIRO, G. 700 1 $aPOSSE, G. 700 1 $aYAHDJIAN, L. 700 1 $aGRACE, P. 700 1 $aCABRAL, O. M. R. 700 1 $aMARTIN NETO, L. 700 1 $aRODRIGUES, R. de A. R. 700 1 $aAMIRO, B. 700 1 $aANGERS, D. 700 1 $aHAO, X. 700 1 $aOELBERMANN, M. 700 1 $aTENUTA, M. 700 1 $aMUNKHOLM, L. J. 700 1 $aREGINA, K. 700 1 $aCELLIER, P. 700 1 $aEHRHARDT, F. 700 1 $aRICHARD, G. 700 1 $aDECHOW, R. 700 1 $aAGUS, F. 700 1 $aWIDIARTA, N. 700 1 $aSPINK, J. 700 1 $aBERTI, A. 700 1 $aGRIGNANI, C. 700 1 $aMAZZONCINI, M. 700 1 $aORSINI, R. 700 1 $aROGGERO, P. P. 700 1 $aSEDDAIU, G. 700 1 $aTEI, F. 700 1 $aVENTRELLA, D. 700 1 $aVITALI, G. 700 1 $aKISHIMOTO-MO, A. 700 1 $aSHIRATO, Y. 700 1 $aSUDO, S. 700 1 $aSHIN, J. 700 1 $aSCHIPPER, L. 700 1 $aSAVÉ, R. 700 1 $aLEIFELD, J. 700 1 $aSPADAVECCHIA, L. 700 1 $aYELURIPATI, J. 700 1 $aDEL GROSSO, S. 700 1 $aRICE, C. 700 1 $aSAWCHIK, J. 773 $tCarbon Management$gv. 7, N. 3-4, P. 243-248, 2016.
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Registro original: |
Embrapa Meio Ambiente (CNPMA) |
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Registro Completo
Biblioteca(s): |
Embrapa Agricultura Digital. |
Data corrente: |
14/09/2021 |
Data da última atualização: |
14/09/2021 |
Tipo da produção científica: |
Artigo em Periódico Indexado |
Circulação/Nível: |
A - 2 |
Autoria: |
MARÇAL, M. F. M.; SOUZA, Z. M. de; TAVARES, R. L. M.; FARHATE, C. V. V.; OLIVEIRA, S. R. de M.; GALINDO, F. S. |
Afiliação: |
MARIA FERNANDA MAGIONI MARÇAL, FEAGRI/UNICAMP; ZIGOMAR MENEZES DE SOUZA, FEAGRI/UNICAMP; ROSE LUIZA MORAES TAVARES, UNIVERSITY OF RIO VERDE; CAMILA VIANA VIEIRA FARHATE, FEAGRI/UNICAMP, UNESP; STANLEY ROBSON DE MEDEIROS OLIVEIRA, CNPTIA; FERNANDO SHINTATE GALINDO, FEAGRI/UNICAMP, UNESP. |
Título: |
Predictive models to estimate carbon stocks in agroforestry systems. |
Ano de publicação: |
2021 |
Fonte/Imprenta: |
Forests, v. 12, n. 9, p. 1-15, Sept. 2021. |
DOI: |
https://doi.org/10.3390/f12091240 |
Idioma: |
Inglês |
Notas: |
Article 1240. Na publicação: Stanley Robson Medeiros Oliveira. |
Conteúdo: |
Abstract: This study aims to assess the carbon stock in a pasture area and fragment of forest in natural regeneration, given the importance of agroforestry systems in mitigating gas emissions which contribute to the greenhouse effect, as well as promoting the maintenance of agricultural productivity. Our other goal was to predict the carbon stock, according to different land use systems, from physical and chemical soil variables using the Random Forest algorithm. We carried out our study at an Entisols Quartzipsamments area with a completely randomized experimental design: four treatments and six replites. The treatments consisted of the following: (i) an agroforestry system developed for livestock, (ii) an agroforestry system developed for fruit culture, (iii) a conventional pasture, and (iv) a forest fragment. Deformed and undeformed soil samples were collected in order to analyze their physical and chemical properties across two consecutive agricultural years. The response variable, carbon stock, was subjected to a boxplot analysis and all the databases were used for a predictive modeling which in turn used the Random Forest algorithm. Results led to the conclusion that the agroforestry systems developed both for fruit culture and livestock, are more efficient at stocking carbon in the soil than the pasture area and forest fragment undergoing natural regeneration. Nitrogen stock and land use systems are the most important variables to estimate carbon stock from the physical and chemical variables of soil using the Random Forest algorithm. The predictive models generated from the physical and chemical variables of soil, as well as the Random Forest algorithm, presented a high potential for predicting soil carbon stock and are sensitive to different land use systems. MenosAbstract: This study aims to assess the carbon stock in a pasture area and fragment of forest in natural regeneration, given the importance of agroforestry systems in mitigating gas emissions which contribute to the greenhouse effect, as well as promoting the maintenance of agricultural productivity. Our other goal was to predict the carbon stock, according to different land use systems, from physical and chemical soil variables using the Random Forest algorithm. We carried out our study at an Entisols Quartzipsamments area with a completely randomized experimental design: four treatments and six replites. The treatments consisted of the following: (i) an agroforestry system developed for livestock, (ii) an agroforestry system developed for fruit culture, (iii) a conventional pasture, and (iv) a forest fragment. Deformed and undeformed soil samples were collected in order to analyze their physical and chemical properties across two consecutive agricultural years. The response variable, carbon stock, was subjected to a boxplot analysis and all the databases were used for a predictive modeling which in turn used the Random Forest algorithm. Results led to the conclusion that the agroforestry systems developed both for fruit culture and livestock, are more efficient at stocking carbon in the soil than the pasture area and forest fragment undergoing natural regeneration. Nitrogen stock and land use systems are the most important variables to estimate carbon stock from the physic... Mostrar Tudo |
Palavras-Chave: |
Agroforestry systems; Data mining technique; Floresta aleatória; Land use systems; Mineração de dados; Modelo preditivo; Predictive models; Random forest; Sequestro de carbono; Sistemas agroflorestais; Sistemas de uso da terra. |
Thesagro: |
Matéria Orgânica; Uso da Terra. |
Thesaurus NAL: |
Agroforestry; Carbon sequestration; Land use; Organic matter. |
Categoria do assunto: |
-- |
URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/225942/1/AP-Predictive-models-Forests-2021.pdf
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Marc: |
LEADER 03046naa a2200409 a 4500 001 2134318 005 2021-09-14 008 2021 bl uuuu u00u1 u #d 024 7 $ahttps://doi.org/10.3390/f12091240$2DOI 100 1 $aMARÇAL, M. F. M. 245 $aPredictive models to estimate carbon stocks in agroforestry systems.$h[electronic resource] 260 $c2021 500 $aArticle 1240. Na publicação: Stanley Robson Medeiros Oliveira. 520 $aAbstract: This study aims to assess the carbon stock in a pasture area and fragment of forest in natural regeneration, given the importance of agroforestry systems in mitigating gas emissions which contribute to the greenhouse effect, as well as promoting the maintenance of agricultural productivity. Our other goal was to predict the carbon stock, according to different land use systems, from physical and chemical soil variables using the Random Forest algorithm. We carried out our study at an Entisols Quartzipsamments area with a completely randomized experimental design: four treatments and six replites. The treatments consisted of the following: (i) an agroforestry system developed for livestock, (ii) an agroforestry system developed for fruit culture, (iii) a conventional pasture, and (iv) a forest fragment. Deformed and undeformed soil samples were collected in order to analyze their physical and chemical properties across two consecutive agricultural years. The response variable, carbon stock, was subjected to a boxplot analysis and all the databases were used for a predictive modeling which in turn used the Random Forest algorithm. Results led to the conclusion that the agroforestry systems developed both for fruit culture and livestock, are more efficient at stocking carbon in the soil than the pasture area and forest fragment undergoing natural regeneration. Nitrogen stock and land use systems are the most important variables to estimate carbon stock from the physical and chemical variables of soil using the Random Forest algorithm. The predictive models generated from the physical and chemical variables of soil, as well as the Random Forest algorithm, presented a high potential for predicting soil carbon stock and are sensitive to different land use systems. 650 $aAgroforestry 650 $aCarbon sequestration 650 $aLand use 650 $aOrganic matter 650 $aMatéria Orgânica 650 $aUso da Terra 653 $aAgroforestry systems 653 $aData mining technique 653 $aFloresta aleatória 653 $aLand use systems 653 $aMineração de dados 653 $aModelo preditivo 653 $aPredictive models 653 $aRandom forest 653 $aSequestro de carbono 653 $aSistemas agroflorestais 653 $aSistemas de uso da terra 700 1 $aSOUZA, Z. M. de 700 1 $aTAVARES, R. L. M. 700 1 $aFARHATE, C. V. V. 700 1 $aOLIVEIRA, S. R. de M. 700 1 $aGALINDO, F. S. 773 $tForests$gv. 12, n. 9, p. 1-15, Sept. 2021.
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