Determinants of tree resource consumption around Mont Sangbé national park in western Côte d’Ivoire
Paper Details
Determinants of tree resource consumption around Mont Sangbé national park in western Côte d’Ivoire
Abstract
Tropical forests constitute essential resources for rural communities in sub-Saharan Africa, providing food, traditional medicine, fuelwood, and income. Nonetheless, their sustainability is increasingly compromised by factors such as population growth, poverty, deforestation, and access restrictions associated with conservation policies. In the proximity of Mont Sangbé National Park in Côte d’Ivoire, food tree species play a crucial role in ensuring household food security and resilience. This research aims to investigate how household socioeconomic, demographic, and environmental characteristics influence tree resource consumption practices. Data were gathered through a survey of 120 households across three villages, with a focus on the types of species consumed, the parts utilized, and the levels of consumption. A mixed typological approach, integrating factorial analyses (PCA and MCA) and hierarchical clustering, identified three distinct household profiles based on their social integration, access to resources, and proximity to the park. Floristic analysis disclosed a notable diversity of species, predominantly dominated by the Fabaceae and Anacardiaceae families. Fruits emerged as the most consumed parts, followed by leaves and seeds, with a marked preference for versatile species such as Parkia biglobosa and Adansonia digitata. Although Pearson’s correlation analysis did not disclose a significant relationship between frequency of use and intensity of consumption, multiple linear regression analysis identified key determining factors, including distance to the park, land reserves, and the possession of durable goods. These findings underscore the importance of incorporating social dimensions into sustainable forest resource management policies.
Anderson D, Burnham K. 2004. Model selection and multi-model inference. Second edition. Springer-Verlag, New York. 63, 10.
Angelsen A, Jagger P, Babigumira R, Belcher B, Hogarth NJ, Börner J, Smith-Hall C, Wunder S. 2014. Environmental income and rural livelihoods: a global-comparative analysis. World Development 64, 12–28.
Arbonier M. 2009. Arbres, arbustes et lianes des zones sèches d’Afrique de l’Ouest. Quae.
Artusi R, Verderio P, Marubini E. 2002. Bravais-Pearson and Spearman correlation coefficients: meaning, test of hypothesis and confidence interval. The International Journal of Biological Markers 17, 148–151. https://doi.org/10.1177/172460080201700213.
Babulo B, Muys B, Nega F, Tollens E, Nyssen J, Deckers J, Mathijs E. 2009. The economic contribution of forest resource use to rural livelihoods in Tigray, Northern Ethiopia. Forest Policy and Economics 11, 109–117. https://doi.org/10.1016/j.forpol.2008.10.007.
Cavendish W. 2000. Empirical irregularities in the poverty–environment relationship of rural households: evidence from Zimbabwe. World Development 28, 1979–2003.
Chirwa PW, Angassa A, Avana-Tientcheu ML, Muledi JI, Syampungani S, Akinnifesi FK, Assogbadjo AE, Chia EL. 2024. Trees in multifunctional landscapes: definition, classification, systems, structure, functionality, examples in Africa. In: Trees in a Sub-Saharan Multi-Functional Landscape: Research, Management, and Policy. Springer Nature Switzerland, Cham. https://doi.org/10.1007/978-3-031-69812-5_2.
Deressa TT, Hassan RM, Ringler C, Alemu T, Yesuf M. 2009. Determinants of farmers’ choice of adaptation methods to climate change in the Nile Basin of Ethiopia. Global Environmental Change 19, 248–255.
Eshetae MA, Wuletawu A, Teklewold L, Mekonnen K, Tsegaye A. 2024. Understanding farm typology for targeting agricultural development in mixed crop-livestock farming systems of Ethiopia. Farming System 2, 100088.
Esperon-Rodriguez M, Sharmin M, Rodriguez DE, Messier C, Svenning J-C, Moore S, Tjoelker MG. 2025. Socio-economic factors, climate, and people’s behaviours determine urban tree health. Urban Forestry & Urban Greening 107, 128801. https://doi.org/10.1016/j.ufug.2025.128801.
Greenacre M. 2017. Correspondence analysis in practice. Chapman and Hall/CRC. https://www.taylorfrancis.com/books/mono/10.1201/9781315369983/correspondence-analysis-practice-michael-greenacre.
Hawthorne W. 2006. Woody plants of Western African forests: A guide to the forest trees, shrubs and lianes from Senegal to Ghana. Royal Botanic Gardens, Kew, Richmond, Surrey, UK.
Husson F, Josse J, Pagès J. 2010. Principal component methods–hierarchical clustering–partitional clustering: why would we need to choose for visualizing data. Applied Mathematics Department 17.
Lykke AM, Kristensen MK, Ganaba S. 2004. Valuation of local use and dynamics of 56 woody species in the Sahel. Biodiversity & Conservation 13, 1961–1990. https://doi.org/10.1023/B:BIOC.0000035876.39587.1a
Maja MM, Ayano SF. 2021. The impact of population growth on natural resources and farmers’ capacity to adapt to climate change in low-income countries. Earth Systems and Environment 5, 71–83. https://doi.org/10.1007/s41748-021-00209-6.
Ndabalishye I. 1995. Agriculture vivrière ouest-africaine à travers le cas de la Côte d’Ivoire: monographie. Institut des Savanes.
Olunga MA. 2013. Assessment of factors influencing utilization of forest resources in Kipini Division of Tana Delta District, Kenya. MSc Thesis, University of Nairobi. http://erepository.uonbi.ac.ke/handle/11295/56524.
Poilecot P. 2001. Inventaire préliminaire du tapis herbacé dans les formations savanicoles du parc national du Mont Sangbé, Côte d’Ivoire: mission du 22/10 au 09/11/2001.
Pouliot M, Treue T, Obiri BD, Ouedraogo B. 2012. Deforestation and the limited contribution of forests to rural livelihoods in West Africa: Evidence from Burkina Faso and Ghana. Ambio 41, 738–750. https://doi.org/10.1007/s13280-012-0292-3.
Rao ND, Min J. 2018. Decent living standards: Material prerequisites for human wellbeing. Social Indicators Research 138, 225–244. https://doi.org/10.1007/s11205-017-1650-0.
Schreckenberg K. 2004. The contribution of shea butter (Vitellaria paradoxa CF Gaertner) to local livelihoods in Benin. In: Forest Products, Livelihoods and Conservation—Case Studies of Non-Timber Forest Product Systems. Center for International Forestry Research, Jakarta, Indonesia, pp. 91–114.
Shackleton CM, Shackleton SE, Cousins B. 2001. The role of land-based strategies in rural livelihoods: The contribution of arable production, animal husbandry and natural resource harvesting in communal areas in South Africa. Development Southern Africa 18, 581–604. https://doi.org/10.1080/03768350120097441.
Shimizu T. 2006. Assessing the access to forest resources for improving livelihoods in West and Central Asia countries. Livelihoods Support Programme. LSP Working Paper 33. Food and Agriculture Organisation of the United Nations, FAO, Rome. 42p.
Torimiro DO, Mosipuri I, Alao OT, Tselaesele NM, Baruti T. 2025. Forest resources utilisation and rural livelihoods in Sub-Saharan Africa: Exploring e-extension model for sustainable forest management. In: Environmental Change and Biodiversity Conservation in Sub-Saharan Africa: Volume 1. Springer Nature Switzerland, Cham. https://doi.org/10.1007/978-3-031-73136-5_10.
World Bank Group. 2002. A revised forest strategy for the World Bank Group. World Bank Group.
Wunder S, Börner J, Shively G, Wyman M. 2014. Safety nets, gap filling and forests: A global-comparative perspective. World Development 64, 29–42. https://doi.org/10.1016/j.worlddev.2014.03.005.
Zuur AF, Ieno EN, Smith GM. 2007. Analysing ecological data. Statistics for Biology and Health. Springer, New York. https://doi.org/10.1007/978-0-387-45972-1.
Kouamé Christophe Koffi, Serge Cherry Piba, Kouakou Hilaire Bohoussou, Naomie Ouffoue, Alex Beda, 2025. Determinants of tree resource consumption around Mont Sangbé national park in western Côte d’Ivoire. J. Biodiv. Environ. Sci., 27(1), 71-81.
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