IDEAS home Printed from https://ideas.repec.org/a/gam/jlands/v12y2022i1p60-d1014845.html
   My bibliography  Save this article

Monitoring of Carbon Stocks in Pastures in the Savannas of Brazil through Ecosystem Modeling on a Regional Scale

Author

Listed:
  • Claudinei Oliveira dos Santos

    (Image Processing and GIS Laboratory (LAPIG), Institute of Socio-Environmental Studies (IESA), Federal University of Goiás (UFG), Goiânia 74001-970, GO, Brazil)

  • Alexandre de Siqueira Pinto

    (Ecology Department, Federal University of Sergipe, Aracaju 49060-108, SE, Brazil)

  • Janete Rego da Silva

    (Tourism and Patrimony, State University of Goiás, Goiás 76600-000, GO, Brazil)

  • Leandro Leal Parente

    (Image Processing and GIS Laboratory (LAPIG), Institute of Socio-Environmental Studies (IESA), Federal University of Goiás (UFG), Goiânia 74001-970, GO, Brazil)

  • Vinícius Vieira Mesquita

    (Image Processing and GIS Laboratory (LAPIG), Institute of Socio-Environmental Studies (IESA), Federal University of Goiás (UFG), Goiânia 74001-970, GO, Brazil)

  • Maiara Pedral dos Santos

    (Ecology Department, Federal University of Sergipe, Aracaju 49060-108, SE, Brazil)

  • Laerte Guimaraes Ferreira

    (Image Processing and GIS Laboratory (LAPIG), Institute of Socio-Environmental Studies (IESA), Federal University of Goiás (UFG), Goiânia 74001-970, GO, Brazil)

Abstract

In 2020, Brazil was the seventh largest emitter of GHG (greenhouse gases), releasing ~2.16 GtCO2e (gigatons of carbon dioxide equivalent) into the atmosphere. Activities related to land use contributed approximately 73% of national emissions in that year. Considering that pastures represent the primary land use in the country, occupying approximately 20% of the territory, the mapping and monitoring of C stocks in these areas is essential to determine their contribution to national emissions. In this study, based on the integrated use of the CENTURY model, georeferenced databases, and the R environment, we mapped and analyzed, for the first time, the C stocks dynamics associated with the pasture areas of the Cerrado biome between 2000 and 2019. The average C stocks in the soil (0–20 cm) and in the aboveground biomass estimated by modeling were ~31 MgC·ha −1 and ~4 MgC·ha −1 , respectively, values close to those observed in the literature for the region. Furthermore, the model results corresponded to the edaphic patterns of the region, with the highest average estimated C stocks in Cambisols (~34 MgC·ha −1 ) and the lowest in Neosols (~29 MgC·ha −1 ). The temporal dynamics of soil C stocks in these areas are directly related to the age of the pastures. In fact, stocks tend to be reduced in recently converted areas and stabilized in areas that have been under this land use for a longer time (≥30 years). As a result, a loss of ~103 MtC (millions of tons of carbon) was estimated in the Cerrado pasture soils in twenty years. The mapping and monitoring of C stocks in this land use type through approaches such as the one presented in this study is essential to support the Brazilian government’s efforts to mitigate C emissions.

Suggested Citation

  • Claudinei Oliveira dos Santos & Alexandre de Siqueira Pinto & Janete Rego da Silva & Leandro Leal Parente & Vinícius Vieira Mesquita & Maiara Pedral dos Santos & Laerte Guimaraes Ferreira, 2022. "Monitoring of Carbon Stocks in Pastures in the Savannas of Brazil through Ecosystem Modeling on a Regional Scale," Land, MDPI, vol. 12(1), pages 1-12, December.
  • Handle: RePEc:gam:jlands:v:12:y:2022:i:1:p:60-:d:1014845
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2073-445X/12/1/60/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2073-445X/12/1/60/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Glen P. Peters & Robbie M. Andrew & Tom Boden & Josep G. Canadell & Philippe Ciais & Corinne Le Quéré & Gregg Marland & Michael R. Raupach & Charlie Wilson, 2013. "The challenge to keep global warming below 2 °C," Nature Climate Change, Nature, vol. 3(1), pages 4-6, January.
    2. Jean Ometto & Ana Aguiar & Talita Assis & Luciana Soler & Pedro Valle & Graciela Tejada & David Lapola & Patrick Meir, 2014. "Amazon forest biomass density maps: tackling the uncertainty in carbon emission estimates," Climatic Change, Springer, vol. 124(3), pages 545-560, June.
    3. Carvalho, André Luiz de & Menezes, Rômulo Simões Cezar & Nóbrega, Ranyére Silva & Pinto, Alexandre de Siqueira & Ometto, Jean Pierre Henry Balbaud & von Randow, Celso & Giarolla, Angélica, 2015. "Impact of climate changes on potential sugarcane yield in Pernambuco, northeastern region of Brazil," Renewable Energy, Elsevier, vol. 78(C), pages 26-34.
    4. Mercedes Bustamante & Carlos Nobre & Roberto Smeraldi & Ana Aguiar & Luis Barioni & Laerte Ferreira & Karla Longo & Peter May & Alexandre Pinto & Jean Ometto, 2012. "Estimating greenhouse gas emissions from cattle raising in Brazil," Climatic Change, Springer, vol. 115(3), pages 559-577, December.
    5. A. Baccini & S. J. Goetz & W. S. Walker & N. T. Laporte & M. Sun & D. Sulla-Menashe & J. Hackler & P. S. A. Beck & R. Dubayah & M. A. Friedl & S. Samanta & R. A. Houghton, 2012. "Estimated carbon dioxide emissions from tropical deforestation improved by carbon-density maps," Nature Climate Change, Nature, vol. 2(3), pages 182-185, March.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. World Bank, 2017. "Brazil’s INDC Restoration and Reforestation Target," World Bank Publications - Reports 28588, The World Bank Group.
    2. Wang, Qiang & Han, Xinyu, 2021. "Is decoupling embodied carbon emissions from economic output in Sino-US trade possible?," Technological Forecasting and Social Change, Elsevier, vol. 169(C).
    3. Usman, Muhammad & Makhdum, Muhammad Sohail Amjad, 2021. "What abates ecological footprint in BRICS-T region? Exploring the influence of renewable energy, non-renewable energy, agriculture, forest area and financial development," Renewable Energy, Elsevier, vol. 179(C), pages 12-28.
    4. Kim, Yeon-Su & Rodrigues, Marcos & Robinne, François-Nicolas, 2021. "Economic drivers of global fire activity: A critical review using the DPSIR framework," Forest Policy and Economics, Elsevier, vol. 131(C).
    5. Paulo Eduardo Teodoro & Luciano de Souza Maria & Jéssica Marciella Almeida Rodrigues & Adriana de Avila e Silva & Maiara Cristina Metzdorf da Silva & Samara Santos de Souza & Fernando Saragosa Rossi &, 2022. "Wildfire Incidence throughout the Brazilian Pantanal Is Driven by Local Climate Rather Than Bovine Stocking Density," Sustainability, MDPI, vol. 14(16), pages 1-16, August.
    6. Joseph Mascaro & Gregory P Asner & David E Knapp & Ty Kennedy-Bowdoin & Roberta E Martin & Christopher Anderson & Mark Higgins & K Dana Chadwick, 2014. "A Tale of Two “Forests”: Random Forest Machine Learning Aids Tropical Forest Carbon Mapping," PLOS ONE, Public Library of Science, vol. 9(1), pages 1-9, January.
    7. Kukkonen, M.O. & Khamis, M. & Muhammad, M.J. & Käyhkö, N. & Luoto, M., 2022. "Modeling direct above-ground carbon loss due to urban expansion in Zanzibar City Region, Tanzania," Land Use Policy, Elsevier, vol. 112(C).
    8. Zepharovich, Elena & Ceddia, M. Graziano & Rist, Stephan, 2021. "Social multi-criteria evaluation of land-use scenarios in the Chaco Salteño: Complementing the three-pillar sustainability approach with environmental justice," Land Use Policy, Elsevier, vol. 101(C).
    9. Rulli, Maria Cristina & Casirati, Stefano & Dell’Angelo, Jampel & Davis, Kyle Frankel & Passera, Corrado & D’Odorico, Paolo, 2019. "Interdependencies and telecoupling of oil palm expansion at the expense of Indonesian rainforest," Renewable and Sustainable Energy Reviews, Elsevier, vol. 105(C), pages 499-512.
    10. Mangani, Andrea, 2021. "When does print media address deforestation? A quantitative analysis of major newspapers from US, UK, and Australia," Forest Policy and Economics, Elsevier, vol. 130(C).
    11. Azhar, Badrul & Nobilly, Frisco & Lechner, Alex M. & Tohiran, Kamil Azmi & Maxwell, Thomas M.R. & Zulkifli, Raja & Kamel, Mohd Fathil & Oon, Aslinda, 2021. "Mitigating the risks of indirect land use change (ILUC) related deforestation from industrial palm oil expansion by sharing land access with displaced crop and cattle farmers," Land Use Policy, Elsevier, vol. 107(C).
    12. Murphy, David M. A. & Berazneva, Julia & Lee, David R., 2015. "Fuelwood Source Substitution and Shadow Prices in Western Kenya," 2015 AAEA & WAEA Joint Annual Meeting, July 26-28, San Francisco, California 205084, Agricultural and Applied Economics Association.
    13. Federico E. Alice‐Guier & Frits Mohren & Pieter A. Zuidema, 2020. "The life cycle carbon balance of selective logging in tropical forests of Costa Rica," Journal of Industrial Ecology, Yale University, vol. 24(3), pages 534-547, June.
    14. Araujo, Rafael & Costa, Francisco J M & Sant'Anna, Marcelo, 2020. "Efficient Forestation in the Brazilian Amazon: Evidence from a Dynamic Model," SocArXiv 8yfr7, Center for Open Science.
    15. Söder, Mareike, 2014. "EU biofuel policies in practice: A carbon map for the Brazilian Cerrado," Kiel Working Papers 1966, Kiel Institute for the World Economy (IfW Kiel).
    16. Yanfeng Wang & Ping Huang, 2022. "Potential fire risks in South America under anthropogenic forcing hidden by the Atlantic Multidecadal Oscillation," Nature Communications, Nature, vol. 13(1), pages 1-9, December.
    17. Yonghua Li & Song Yao & Hezhou Jiang & Huarong Wang & Qinchuan Ran & Xinyun Gao & Xinyi Ding & Dandong Ge, 2022. "Spatial-Temporal Evolution and Prediction of Carbon Storage: An Integrated Framework Based on the MOP–PLUS–InVEST Model and an Applied Case Study in Hangzhou, East China," Land, MDPI, vol. 11(12), pages 1-22, December.
    18. Pedro Macedo & Mara Madaleno, 2022. "Global Temperature and Carbon Dioxide Nexus: Evidence from a Maximum Entropy Approach," Energies, MDPI, vol. 16(1), pages 1-13, December.
    19. Guilló, María Dolores & Magalhaes, Manuela, 2018. "Long-run Sustainability in the Green Solow Model," QM&ET Working Papers 18-2, University of Alicante, D. Quantitative Methods and Economic Theory.
    20. Kênia Barreiro de Souza & Luiz Carlos de Santana Ribeiro & Fernando Salgueiro Perobelli, 2016. "Reducing Brazilian greenhouse gas emissions: scenario simulations of targets and policies," Economic Systems Research, Taylor & Francis Journals, vol. 28(4), pages 482-496, October.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jlands:v:12:y:2022:i:1:p:60-:d:1014845. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.