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On Carbon Sequestration Based on Above Ground Biomass (AGB) Modeling of Selected Tree Species in Nigeria

Received: 26 May 2022     Accepted: 4 July 2022     Published: 29 July 2022
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Abstract

Allometric models are important for quantifying biomass and carbon storage in terrestrial ecosystems. Generalized allometry exists for tropical trees but species- and site-specific models are more accurate. This paper is to investigate forest inventory data extracted from the Forestry Research Institute of Nigeria (FRIN) repository to compute the Above Ground Biomass (AGB) for five tree species namely; Terminalia Superba, Bombax Rhodognaphadon, Gmelina Arborea, Mansonia Altissima, Pinus Caribaea, Khaya Senegalensis, Khaya Grandifoliola and Shorea Robusta. Allometric models were used with the least squares’ parameter estimates derived from the Marquardt algorithm to compute the above ground biomass of the five tree species selected. Descriptive Statistics alongside selected methods in inferential and non-parametric statistics such as Runs, Normality (KS & SW), and F-tests were done. Model selection criteria such as AIC, BIC, R2, MSE, MAE and RSE were used to select the most appropriate models for modeling AGB of the selected tree species. Chave. Model (2005) fitted best the computed AGB for Bombax Rhodognaphadon and Terminalia Superba while Brown. Moist model (1989) fitted best the AGB of Gmelina Arborea, Khaya Senegalensis, Khaya Grandifoliola and Mansonia Altissima.

Published in American Journal of Biological and Environmental Statistics (Volume 8, Issue 3)
DOI 10.11648/j.ajbes.20220803.15
Page(s) 81-92
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2022. Published by Science Publishing Group

Keywords

Above Ground Biomass, Carbon Sequestration, Statistical Modeling, Non-linear Models, Allometric Models

References
[1] IPCC Climate change 2007: impacts, adaptation and vulnerability. Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change; Cambridge university Press, Cambridge, United Kingdom and New York, NY, USA; 2007.
[2] World Bank the costs to developing countries of adapting to climate change new methods and estimate the global report of the economics of adaptation to climate change study consultation draft. Washington D. C.: The World Bank Group; 2010.
[3] TerrAfrica, Land & Climate: The role of sustainable land management (SLM) for climate change adaptation and mitigation in Sub-Saharan Africa (SSA); 2009.
[4] IPCC, Climate Change 2001: The Scientific Basic Contribution of Working Group I to the Third Assessment Report of the Intergovernmental Panel on Climate Change, Houghton JT, Ding Y, Griggs DJ, Noguer M, van der Linden PJ, Dai X, Maskell K. and Johnson CA, Eds., Cambridge University Press, Cambridge, 2001; 881.
[5] Seo SN, Mendelsohn R, Dinar A, Hassan R, Kurukulasuriya. A Ricardian analysis of the distribution of climate change impacts on agriculture across agro-ecological zones in Africa. Environmental and Resource Economics. 2009; 43: 313-332.
[6] OECD Development Centre/African Development Bank. Growth trends and out look for Africa: Time to unleash Africa's huge energy potential against poverty concludes. African Economic Outlook; 2003-2004.
[7] Yohe GW, Lasco RD, Ahmad QK, Arnell NW, Cohen SJ, Hope C, Janetos AC, Perez RT. Perspectives on climate change and sustainability. Climate change 2007: Impacta, adaptation and vaulnerability, contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, Parry ML, Canziani OF, Palutifok JP, van der Linden, PJ and Hanson CE, Eds., Cambridge University Press, Cambridge, UK, 2007; 811-841.
[8] Hoeller P, Dean A, Nicolaisen J. Macroeconomic implications of reducing greenhouse gas emissions: a survey of emprical studies. OECD Economic studies; 199. Retrieved on 17th July, 2012. Avalable: http://www.oecd.org/dataoecd/47/58/34281995.pdf
[9] FAO monitoring the interaction between agriculture and the environment: current status and future directions African commission on agricultural statistics 22nd session Addis Ababa, Ethopia, 30 November - 3 December, 2011. Retrieved on 23/6/2012. Available: http://faostat.fao.org.
[10] Dossou PJ. Evolution and impacts of coastal land use in Benin: The case of Seme-Podji commune. VADID contribution to ILC Collaborative Research Project on Commercial Pressures on Land, Rome: 2011.
[11] African Union Framework and guidelines on land policy in Africa: a framework to strengthen land rights, enhance productivity and secure livelihoods. African Development Bank/African Union/Economic Commission for Africa; 2009.
[12] Markelova H, Meinzen-Dick R. The importance of property rights in climate change mitigation. 2020 Vision Briefs 16 (10), International Food Policy Research Institute (IFPRI); 2009. British Journal of Environment & Climate Change, 4 (1): 83-94, 2014.
[13] Nordhaus WD. To slow or not to slow: the economics of the geeenhouse effect, revision of a paper presented to the 1989 meetings of the International Energy Workshop and the MIT Symposium on Envieonment and Energy; 1990.
[14] Anseeuw WL, Aiden W, Cotula L, Taylor M. Land rights and the rush for land: findings of the global commercial pressures on land research project. ILC, Rome; 2009. Retrieved on 20th May, 2012. Available: http://www.landcoalition.org/cplstudies.
[15] Onoja AO, Idaho C, Adah C. Policy implications of the effects of deforestation on Nigerian economy. Production and Agricultural Technology Journal, PAT. 2008; 4 (2): 114-120. Retrieved on 26th September, 2008. Available: http://www.patnsukjournal.com/currentissue
[16] Botkin DB, Keller EA. Environmental science: earth as a living plant. 2nd Ed. New York: John Wiley Press. 1997; 215.
[17] UNDP, Human Development Report 2007/2008. Fighting climate change; Human solidarity in a divided world, Palgrave Macmillan; 2007-2008.
[18] Feng, Kurkalova, Kling, and Gassman (Burtraw. 2003) Economic and Environmental Co-benefits of Carbon Sequestration inAgricultural Soils.
[19] Vashum KT, Jayakumar S (2012) Methods to Estimate Above-Ground Biomass and Carbon Stock in Natural Forests - A Review. J Ecosyst Ecogr 2: 116. doi: 10.4172/2157-7625.1000116.
[20] FAO, 1999, Above Ground Business Estimates at Saltonstall Ridge, East Haven, CT. 1999.
[21] FAO Corporate Document Repository (2009), Estimating biomass and biomass change in tropical forests. www.fao.org/documents/
[22] B. L. Chavan. Sequestered standing carbon stock in selective tree species grown in University campus at Aurangabad, Maharashtra, India., International Journal of Engineering Science and Technology Vol. 2 (7), 2010, 3003-3007.
[23] Litton. 2006. Allometric Models for Predicting Aboveground Biomass in Two Widespread Woody., the Journal of tropical biology and conservation/Biitropical 40 (3): 313-320 2008.
[24] Mani S, Parthasarthy N (2007) Above-ground biomass estimation in ten tropical dry evergreen forest sites of peninsular India. Biomass and Bioenergy 31: 284-290.
[25] Mohanraj R, Saravanan J, Dhanakumar S (2011) Carbon stock in Kolli forests, Eastern Ghats (India) with emphasis on aboveground biomass, litter, woody debris and soils. iForest 4: 61-65.
[26] Singh V, Tewari A, Kushwaha SPS, Dahwal VK (2011) Formulating ailometric equations for estimating biomass and carbon stock in small diameter trees. Forest Ecology and Management 216: 1945-1949.
[27] FAO, 1993. Forest resources assessment 1990 tropical countries. FAO Forestry Paper 112. Rome Italy.
[28] Brown S and A. E. Lugo, 1990. Tropical secondary forest. Journal of Tropical Ecology 6: 1-32.
[29] Brown S and A. E. Lugo, 1992. Above ground biomass estimates for tropical moist forests of the Brazillian Amazon, Interciencia 17: 8-18.
Cite This Article
  • APA Style

    Oluwafemi Samuel Oyamakin, Peter Shina Adebayo. (2022). On Carbon Sequestration Based on Above Ground Biomass (AGB) Modeling of Selected Tree Species in Nigeria. American Journal of Biological and Environmental Statistics, 8(3), 81-92. https://doi.org/10.11648/j.ajbes.20220803.15

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    ACS Style

    Oluwafemi Samuel Oyamakin; Peter Shina Adebayo. On Carbon Sequestration Based on Above Ground Biomass (AGB) Modeling of Selected Tree Species in Nigeria. Am. J. Biol. Environ. Stat. 2022, 8(3), 81-92. doi: 10.11648/j.ajbes.20220803.15

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    AMA Style

    Oluwafemi Samuel Oyamakin, Peter Shina Adebayo. On Carbon Sequestration Based on Above Ground Biomass (AGB) Modeling of Selected Tree Species in Nigeria. Am J Biol Environ Stat. 2022;8(3):81-92. doi: 10.11648/j.ajbes.20220803.15

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  • @article{10.11648/j.ajbes.20220803.15,
      author = {Oluwafemi Samuel Oyamakin and Peter Shina Adebayo},
      title = {On Carbon Sequestration Based on Above Ground Biomass (AGB) Modeling of Selected Tree Species in Nigeria},
      journal = {American Journal of Biological and Environmental Statistics},
      volume = {8},
      number = {3},
      pages = {81-92},
      doi = {10.11648/j.ajbes.20220803.15},
      url = {https://doi.org/10.11648/j.ajbes.20220803.15},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajbes.20220803.15},
      abstract = {Allometric models are important for quantifying biomass and carbon storage in terrestrial ecosystems. Generalized allometry exists for tropical trees but species- and site-specific models are more accurate. This paper is to investigate forest inventory data extracted from the Forestry Research Institute of Nigeria (FRIN) repository to compute the Above Ground Biomass (AGB) for five tree species namely; Terminalia Superba, Bombax Rhodognaphadon, Gmelina Arborea, Mansonia Altissima, Pinus Caribaea, Khaya Senegalensis, Khaya Grandifoliola and Shorea Robusta. Allometric models were used with the least squares’ parameter estimates derived from the Marquardt algorithm to compute the above ground biomass of the five tree species selected. Descriptive Statistics alongside selected methods in inferential and non-parametric statistics such as Runs, Normality (KS & SW), and F-tests were done. Model selection criteria such as AIC, BIC, R2, MSE, MAE and RSE were used to select the most appropriate models for modeling AGB of the selected tree species. Chave. Model (2005) fitted best the computed AGB for Bombax Rhodognaphadon and Terminalia Superba while Brown. Moist model (1989) fitted best the AGB of Gmelina Arborea, Khaya Senegalensis, Khaya Grandifoliola and Mansonia Altissima.},
     year = {2022}
    }
    

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  • TY  - JOUR
    T1  - On Carbon Sequestration Based on Above Ground Biomass (AGB) Modeling of Selected Tree Species in Nigeria
    AU  - Oluwafemi Samuel Oyamakin
    AU  - Peter Shina Adebayo
    Y1  - 2022/07/29
    PY  - 2022
    N1  - https://doi.org/10.11648/j.ajbes.20220803.15
    DO  - 10.11648/j.ajbes.20220803.15
    T2  - American Journal of Biological and Environmental Statistics
    JF  - American Journal of Biological and Environmental Statistics
    JO  - American Journal of Biological and Environmental Statistics
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    EP  - 92
    PB  - Science Publishing Group
    SN  - 2471-979X
    UR  - https://doi.org/10.11648/j.ajbes.20220803.15
    AB  - Allometric models are important for quantifying biomass and carbon storage in terrestrial ecosystems. Generalized allometry exists for tropical trees but species- and site-specific models are more accurate. This paper is to investigate forest inventory data extracted from the Forestry Research Institute of Nigeria (FRIN) repository to compute the Above Ground Biomass (AGB) for five tree species namely; Terminalia Superba, Bombax Rhodognaphadon, Gmelina Arborea, Mansonia Altissima, Pinus Caribaea, Khaya Senegalensis, Khaya Grandifoliola and Shorea Robusta. Allometric models were used with the least squares’ parameter estimates derived from the Marquardt algorithm to compute the above ground biomass of the five tree species selected. Descriptive Statistics alongside selected methods in inferential and non-parametric statistics such as Runs, Normality (KS & SW), and F-tests were done. Model selection criteria such as AIC, BIC, R2, MSE, MAE and RSE were used to select the most appropriate models for modeling AGB of the selected tree species. Chave. Model (2005) fitted best the computed AGB for Bombax Rhodognaphadon and Terminalia Superba while Brown. Moist model (1989) fitted best the AGB of Gmelina Arborea, Khaya Senegalensis, Khaya Grandifoliola and Mansonia Altissima.
    VL  - 8
    IS  - 3
    ER  - 

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Author Information
  • Biostatistics Unit, Department of Statistics, University of Ibadan, Ibadan, Nigeria

  • Biostatistics Unit, Department of Statistics, University of Ibadan, Ibadan, Nigeria

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