Fuzzy Cognitive Mapping in Natural Resource Management: A Global Bibliometric Analysis with India-Specific Insights (1995–2025)
7
Authors
-
Rupak Goswami
RKMVERI, Narendrapur
https://orcid.org/0000-0002-5998-4396
-
Kunal Singh
RKMVERI
-
Suchandra Dutta
RKMVERI
-
Sanchayeeta Misra
RKMVERI
Keywords:
Natural resource management, Participatory modelling, Fuzzy cognitive mapping, Bibliometric analysis, Co-occurrence network, Thematic analysisAbstract
Natural Resource Management (NRM) is a complex, multi-dimensional domain in which effective impact assessment demands tools capable of handling subjectivity, uncertainty and stakeholder diversity. Fuzzy Cognitive Mapping (FCM) has emerged as such a tool, enabling the integration of qualitative knowledge and stakeholder perspectives into structured causal models. This study presents a bibliometric analysis of Scopus-indexed documents applying FCM in NRM contexts from 1995 to 2025, examining global trends in publication output, leading countries, institutions, journals, and authors. Bibliometric and network visualisation analyses were performed using Biblioshiny (bibliometrix R package) and VOSviewer. Qualitative thematic analysis of India-specific documents was conducted to complement the quantitative findings. The field has grown at an annual rate of 11.75%, with the United States and Greece leading global output. India, though modest in publication volume, shows a dynamic trajectory, with studies spanning precision agriculture, climate change adaptation, conservation conflicts, and disaster resilience. Collectively, the findings highlight FCM’s expanding role in applied, participatory, and sustainability-oriented NRM projects.
References
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Alam, L., Pradhoshini, K. P., Flint, R. A., & Sumaila, U. R. (2025). Deep-sea mining and its risks for social-ecological systems: Insights from simulation-based analyses. PLoS ONE, 20(3), e0320888. https://doi.org/10.1371/journal.pone.0320888
Anantharaj, G., Thangavelu, A., & Ramasubbian, H. (2015). A predictive analytical approach towards improving the crop growth yield using Fuzzy Cognitive Maps–CROYAN. Institute of Integrative Omics and Applied Biotechnology (IIOAB) Journal, 6(4), 113–118.
Anuranj, P.R., & Vishnu, S. (2026). Exploring global research trends on extension advisory services in climate-smart agriculture. Indian Journal of Extension Education, 62(2), 1-9. https://doi.org/10.48165/IJEE.2026.62201
Aria, M., & Cuccurullo, C. (2017). bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959–975. https://doi.org/10.1016/j.joi.2017.08.007
Baas, J., Schotten, M., Plume, A., Côté, G., & Karimi, R. (2020). Scopus as a curated, high-quality bibliometric data source for academic research in quantitative science studies. Quantitative Science Studies, 1(1), 377–386. https://doi.org/10.1162/qss_a_00019
Banerjee, A., Chatterjee, A., & Acharya, S. K. (2025). Mental modeling of human elephant conflict using fuzzy cognitive mapping and decision ecology for conflict resolution. Environment Systems & Decisions, 45(3). https://doi.org/10.1007/s10669-025-10032-3
Bansal, S., Singh, S., & Nangia, P. (2022). Assessing the role of natural resource utilization in attaining select sustainable development goals in the era of digitalization. Resources Policy, 79, 103040. https://doi.org/10.1016/j.resourpol.2022.103040
Barman B., Singh R., Padaria R N , Nain M S , Quader S W & Praveen K. V. (2026). A qualitative synthesis of barriers to agriculture 4.0 adoption: evidence from a systematic literature review. Discover Agriculture (2026) 4,34 https://doi.org/10.1007/s44279-026-00505-7
Bornmann, L., & Daniel, H. D. (2008). What do citation counts measure? A review of studies on citing behavior. Journal of Documentation, 64(1). https://doi.org/10.1108/00220410810844150
Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., & Lim, W. M. (2021). How to conduct a bibliometric analysis: An overview and guidelines. Journal of Business Research, 133, 285–296. https://doi.org/10.1016/j.jbusres.2021.04.070
Ellili, N. O. D. (2024). Bibliometric analysis of sustainability papers: Evidence from Environment, Development and sustainability. Environment, Development and Sustainability, 26(4), 8183-8209. https://doi.org/10.1007/s10668-023-03067-6
Falk, T., Zhang, W., Meinzen-Dick, R., & Bartels, L. (2021). Games for triggering collective change in natural resource management: A conceptual framework and insights from four cases from India. Discussion Paper 1995, Washington, DC: International Food Policy Research Institute. https://doi.org/10.2499/p15738coll2.134238
Garfield, E. (1955). Citation indexes for science. Science, 122(3159), 108–111.
Garfield, E. (1972). Citation analysis as a tool in journal evaluation: Journals can be ranked by frequency and impact of citations for science policy studies. Science, 178(4060), 471–479. https://doi.org/10.1126/science.178.4060.471
Goswami, R., Roy, K., Dutta, S., Ray, K., Sarkar, S., Brahmachari, K., Nanda, M. K., Mainuddin, M., Banerjee, H., Timsina, J., & Majumdar, K. (2021). Multi-faceted impact and outcome of COVID-19 on smallholder agricultural systems: Integrating qualitative research and fuzzy cognitive mapping to explore resilient strategies. Agricultural Systems, 189, 103051. https://doi.org/10.1016/j.agsy.2021.103051
Goswami, R., Roy, R., Gangopadhyay, D., Sen, P., Roy, K., Sarkar, S., Misra, S., Ray, K., Monjardino, M., & Mainuddin, M. (2024). Understanding resource recycling and land management to upscale zero-tillage potato cultivation in the coastal Indian Sundarbans. Land, 13(1), 108. https://doi.org/10.3390/land13010108
Gray, S. A., Gray, S., Kok, J. de, Helfgott, A., O'Dwyer, B., Jordan, R., & Nyaki, A. (2015). Using fuzzy cognitive mapping as a participatory approach to analyze change, preferred states, and perceived resilience of social-ecological systems. Ecology and Society, 20(2). https://doi.org/10.5751/es-07396-200211
Gray, S., Voinov, A., Paolisso, M., Jordan, R., BenDor, T., Bommel, P., Glynn, P., Hedelin, B., Hubacek, K., Introne, J., Kolagani, N., Laursen, B., Prell, C., Olabisi, L. S., Singer, A., Sterling, E., & Zellner, M. (2017). Purpose, processes, partnerships, and products: four Ps to advance participatory socio-environmental modeling. Ecological Applications, 28(1), 46–61. https://doi.org/10.1002/eap.1627
Gupta, S.K., Gorai, S., & Nain M.S.(2020).Methodologies for Constraints Analysis, Journal of Extension Systems, 36(2),22-27. http://doi.org/10.48165/JES.2020.36205
Gupta Sanjay Kumar, Gorai, S., & Nain, M. S. (2021). Perceptual mapping for agricultural marketing research: concept and methodologies, Journal of Extension Systems, 37(1), 62-66. http://doi.org/10.48165/JES.2021.37109
Haldar, A., Goswami, R., Ghosh, S., Chakraborty, S., Roy, K., Singh, K., ... & Barman, R. R. (2026). Scenario construction and decision-making for sustainable goat farming: Insights from multi-stakeholder fuzzy logic cognitive mapping. Small Ruminant Research, 107757. https://doi.org/10.1016/j.smallrumres.2026.107757
Jain, R., Nisha, N., Kandpal, A., & Nikam, V. (2025). A deep dive into the impact assessment of agricultural, natural resource management, livestock, and fisheries technologies. Discover Agriculture, 3(1). https://doi.org/10.1007/s44279-025-00323-3
Jayashree, L. S., Palakkal, N., Papageorgiou, E. I., & Papageorgiou, K. (2015). Application of fuzzy cognitive maps in precision agriculture: a case study on coconut yield management of southern India's Malabar region. Neural Computing and Applications, 26(8), 1963–1978. https://doi.org/10.1007/s00521-015-1864-5
Jenitha, G. (2017). Hybrid approach for water demand prediction based on fuzzy cognitive maps. Indonesian Journal of Electrical Engineering and Computer Science, 8(2), 567. https://doi.org/10.11591/ijeecs.v8.i2.pp567-570
Jha, A. K., Mehta, B. K., Kumari, M., & Chatterjee, K. (2025). Impact of frontline demonstrations on mustard in Sahibganj district of Jharkhand. Indian Journal of Extension Education, 57(3), 28-31. http://doi.org/10.48165/IJEE.2021.57307
Karavas, C. S., Kyriakarakos, G., Arvanitis, K. G., & Papadakis, G. (2015). A multi-agent decentralized energy management system based on distributed intelligence for the design and control of autonomous polygeneration microgrids. Energy Conversion and Management, 103, 166–179. https://doi.org/10.1016/j.enconman.2015.06.021
Krishna, D., Kumbhare, N., Sharma, J., Rao, D., Sharma, D., Kumar, P., & Bhowmik, A. (2025). A comparison of impact of agri-tourism as perceived by multiple stakeholders in Maharashtra and Goa. Indian Journal of Extension Education, 57(3), 71-76. https://doi.org/10.48165/IJEE.2021.57317
Krithika, S., Karthikeyan, C., & Mansingh, P. J. (2023). Developing a qualitative approach modelling for food security resilient strategies of farm households post COVID-19 using fuzzy logic cognitive mapping and simulation scenario analysis. Univers. J. Agricultural Res, 11(5), 872-881. https://doi.org/10.13189/ujar.2023.110512
MacMillan, G. A., Badry, N. A., Sarmiento, I., Grant, E., Hickey, G. M., & Humphries, M. M. (2024). Cree knowledge, fuzzy cognitive maps, and the social-ecology of moose habitat quality under an adapted forestry regime. Ecology and Society, 29(4). https://doi.org/10.5751/es-15508-290434
Malarkodi, K. P., & Arthi, D. K. (2018). An enriched multi-goal evolutionary algorithm and intuitionistic fuzzy cognitive maps for prediction of crop yield. ARPN Journal of Engineering and Applied Sciences, 13(23).
Miller, M. L., Gale, R. P., & Brown, P. J. (Eds.) (2019). Social Science in Natural Resource Management Systems. New York, Routledge. https://doi.org/10.4324/9780429306372
Natarajan, R., Subramanian, J., & Papageorgiou, E. I. (2016). Hybrid learning of fuzzy cognitive maps for sugarcane yield classification. Computers and Electronics in Agriculture, 127, 147–157. https://doi.org/10.1016/j.compag.2016.05.016
Nichols, J. D., Koneff, M. D., Heglund, P. J., Knutson, M. G., Seamans, M. E., Lyons, J. E., ... & Williams, B. K. (2011). Climate change, uncertainty, and natural resource management. The Journal of Wildlife Management, 75(1), 6-18. https://doi.org/10.1002/jwmg.33
Obiedat, M., & Samarasinghe, S. (2022). Modelling socio-ecological systems: Implementation of an advanced fuzzy cognitive map framework for policy development. arXiv. https://doi.org/10.48550/arxiv.2208.05103
Özesmi, U., & Özesmi, S. L. (2004). Ecological models based on people's knowledge: a multi-step fuzzy cognitive mapping approach. Ecological Modelling, 176(1–2), 43–64. https://doi.org/10.1016/j.ecolmodel.2003.10.027
Panja, A., Garai, S., Maiti, S., Goswami, R., Zade, S., Veldandi, A., ... & Sankhala, G. (2026). Mapping resilience pathway of smallholder farming community to cyclone-led climate disasters in coastal West Bengal, India. Global Environmental Change, 96, 103108. https://doi.org/10.1016/j.gloenvcha.2025.103108
Rahman, M. M. (2026). Unveiling the nexus of governance, stakeholder, and technology through environmental impact assessment for natural resource management. Environmental Management, 76(4), 125. https://doi.org/10.1007/s00267-026-02422-3
Singh, P. K., & Chudasama, H. (2017). Assessing impacts and community preparedness to cyclones: a fuzzy cognitive mapping approach. Climatic Change, 143(3–4), 337–354. https://doi.org/10.1007/s10584-017-2007-z
Singh, P. K., & Chudasama, H. (2020). Pathways for climate change adaptations in arid and semi-arid regions. Journal of Cleaner Production, 284, 124744. https://doi.org/10.1016/j.jclepro.2020.124744
Abramovitz, J., Banuri, T., Girot, P. O., Orlando, B., Schneider, N., Spanger-Siegfried, E., & Hammill, A. (2001). Adapting to climate change: natural resource management and vulnerability reduction. International Institute for Sustainable Development (IISD).
Alam, L., Pradhoshini, K. P., Flint, R. A., & Sumaila, U. R. (2025). Deep-sea mining and its risks for social-ecological systems: Insights from simulation-based analyses. PLoS ONE, 20(3), e0320888. https://doi.org/10.1371/journal.pone.0320888
Anantharaj, G., Thangavelu, A., & Ramasubbian, H. (2015). A predictive analytical approach towards improving the crop growth yield using Fuzzy Cognitive Maps–CROYAN. Institute of Integrative Omics and Applied Biotechnology (IIOAB) Journal, 6(4), 113–118.
Anuranj, P.R., & Vishnu, S. (2026). Exploring global research trends on extension advisory services in climate-smart agriculture. Indian Journal of Extension Education, 62(2), 1-9. https://doi.org/10.48165/IJEE.2026.62201
Aria, M., & Cuccurullo, C. (2017). bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959–975. https://doi.org/10.1016/j.joi.2017.08.007
Baas, J., Schotten, M., Plume, A., Côté, G., & Karimi, R. (2020). Scopus as a curated, high-quality bibliometric data source for academic research in quantitative science studies. Quantitative Science Studies, 1(1), 377–386. https://doi.org/10.1162/qss_a_00019
Banerjee, A., Chatterjee, A., & Acharya, S. K. (2025). Mental modeling of human elephant conflict using fuzzy cognitive mapping and decision ecology for conflict resolution. Environment Systems & Decisions, 45(3). https://doi.org/10.1007/s10669-025-10032-3
Bansal, S., Singh, S., & Nangia, P. (2022). Assessing the role of natural resource utilization in attaining select sustainable development goals in the era of digitalization. Resources Policy, 79, 103040. https://doi.org/10.1016/j.resourpol.2022.103040
Barman B., Singh R., Padaria R N , Nain M S , Quader S W & Praveen K. V. (2026). A qualitative synthesis of barriers to agriculture 4.0 adoption: evidence from a systematic literature review. Discover Agriculture (2026) 4,34 https://doi.org/10.1007/s44279-026-00505-7
Bornmann, L., & Daniel, H. D. (2008). What do citation counts measure? A review of studies on citing behavior. Journal of Documentation, 64(1). https://doi.org/10.1108/00220410810844150
Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., & Lim, W. M. (2021). How to conduct a bibliometric analysis: An overview and guidelines. Journal of Business Research, 133, 285–296. https://doi.org/10.1016/j.jbusres.2021.04.070
Ellili, N. O. D. (2024). Bibliometric analysis of sustainability papers: Evidence from Environment, Development and sustainability. Environment, Development and Sustainability, 26(4), 8183-8209. https://doi.org/10.1007/s10668-023-03067-6
Falk, T., Zhang, W., Meinzen-Dick, R., & Bartels, L. (2021). Games for triggering collective change in natural resource management: A conceptual framework and insights from four cases from India. Discussion Paper 1995, Washington, DC: International Food Policy Research Institute. https://doi.org/10.2499/p15738coll2.134238
Garfield, E. (1955). Citation indexes for science. Science, 122(3159), 108–111.
Garfield, E. (1972). Citation analysis as a tool in journal evaluation: Journals can be ranked by frequency and impact of citations for science policy studies. Science, 178(4060), 471–479. https://doi.org/10.1126/science.178.4060.471
Goswami, R., Roy, K., Dutta, S., Ray, K., Sarkar, S., Brahmachari, K., Nanda, M. K., Mainuddin, M., Banerjee, H., Timsina, J., & Majumdar, K. (2021). Multi-faceted impact and outcome of COVID-19 on smallholder agricultural systems: Integrating qualitative research and fuzzy cognitive mapping to explore resilient strategies. Agricultural Systems, 189, 103051. https://doi.org/10.1016/j.agsy.2021.103051
Goswami, R., Roy, R., Gangopadhyay, D., Sen, P., Roy, K., Sarkar, S., Misra, S., Ray, K., Monjardino, M., & Mainuddin, M. (2024). Understanding resource recycling and land management to upscale zero-tillage potato cultivation in the coastal Indian Sundarbans. Land, 13(1), 108. https://doi.org/10.3390/land13010108
Gray, S. A., Gray, S., Kok, J. de, Helfgott, A., O'Dwyer, B., Jordan, R., & Nyaki, A. (2015). Using fuzzy cognitive mapping as a participatory approach to analyze change, preferred states, and perceived resilience of social-ecological systems. Ecology and Society, 20(2). https://doi.org/10.5751/es-07396-200211
Gray, S., Voinov, A., Paolisso, M., Jordan, R., BenDor, T., Bommel, P., Glynn, P., Hedelin, B., Hubacek, K., Introne, J., Kolagani, N., Laursen, B., Prell, C., Olabisi, L. S., Singer, A., Sterling, E., & Zellner, M. (2017). Purpose, processes, partnerships, and products: four Ps to advance participatory socio-environmental modeling. Ecological Applications, 28(1), 46–61. https://doi.org/10.1002/eap.1627
Gupta, S.K., Gorai, S., & Nain M.S.(2020).Methodologies for Constraints Analysis, Journal of Extension Systems, 36(2),22-27. http://doi.org/10.48165/JES.2020.36205
Gupta Sanjay Kumar, Gorai, S., & Nain, M. S. (2021). Perceptual mapping for agricultural marketing research: concept and methodologies, Journal of Extension Systems, 37(1), 62-66. http://doi.org/10.48165/JES.2021.37109
Haldar, A., Goswami, R., Ghosh, S., Chakraborty, S., Roy, K., Singh, K., ... & Barman, R. R. (2026). Scenario construction and decision-making for sustainable goat farming: Insights from multi-stakeholder fuzzy logic cognitive mapping. Small Ruminant Research, 107757. https://doi.org/10.1016/j.smallrumres.2026.107757
Jain, R., Nisha, N., Kandpal, A., & Nikam, V. (2025). A deep dive into the impact assessment of agricultural, natural resource management, livestock, and fisheries technologies. Discover Agriculture, 3(1). https://doi.org/10.1007/s44279-025-00323-3
Jayashree, L. S., Palakkal, N., Papageorgiou, E. I., & Papageorgiou, K. (2015). Application of fuzzy cognitive maps in precision agriculture: a case study on coconut yield management of southern India's Malabar region. Neural Computing and Applications, 26(8), 1963–1978. https://doi.org/10.1007/s00521-015-1864-5
Jenitha, G. (2017). Hybrid approach for water demand prediction based on fuzzy cognitive maps. Indonesian Journal of Electrical Engineering and Computer Science, 8(2), 567. https://doi.org/10.11591/ijeecs.v8.i2.pp567-570
Jha, A. K., Mehta, B. K., Kumari, M., & Chatterjee, K. (2025). Impact of frontline demonstrations on mustard in Sahibganj district of Jharkhand. Indian Journal of Extension Education, 57(3), 28-31. http://doi.org/10.48165/IJEE.2021.57307
Karavas, C. S., Kyriakarakos, G., Arvanitis, K. G., & Papadakis, G. (2015). A multi-agent decentralized energy management system based on distributed intelligence for the design and control of autonomous polygeneration microgrids. Energy Conversion and Management, 103, 166–179. https://doi.org/10.1016/j.enconman.2015.06.021
Krishna, D., Kumbhare, N., Sharma, J., Rao, D., Sharma, D., Kumar, P., & Bhowmik, A. (2025). A comparison of impact of agri-tourism as perceived by multiple stakeholders in Maharashtra and Goa. Indian Journal of Extension Education, 57(3), 71-76. https://doi.org/10.48165/IJEE.2021.57317
Krithika, S., Karthikeyan, C., & Mansingh, P. J. (2023). Developing a qualitative approach modelling for food security resilient strategies of farm households post COVID-19 using fuzzy logic cognitive mapping and simulation scenario analysis. Univers. J. Agricultural Res, 11(5), 872-881. https://doi.org/10.13189/ujar.2023.110512
MacMillan, G. A., Badry, N. A., Sarmiento, I., Grant, E., Hickey, G. M., & Humphries, M. M. (2024). Cree knowledge, fuzzy cognitive maps, and the social-ecology of moose habitat quality under an adapted forestry regime. Ecology and Society, 29(4). https://doi.org/10.5751/es-15508-290434
Malarkodi, K. P., & Arthi, D. K. (2018). An enriched multi-goal evolutionary algorithm and intuitionistic fuzzy cognitive maps for prediction of crop yield. ARPN Journal of Engineering and Applied Sciences, 13(23).
Miller, M. L., Gale, R. P., & Brown, P. J. (Eds.) (2019). Social Science in Natural Resource Management Systems. New York, Routledge. https://doi.org/10.4324/9780429306372
Natarajan, R., Subramanian, J., & Papageorgiou, E. I. (2016). Hybrid learning of fuzzy cognitive maps for sugarcane yield classification. Computers and Electronics in Agriculture, 127, 147–157. https://doi.org/10.1016/j.compag.2016.05.016
Nichols, J. D., Koneff, M. D., Heglund, P. J., Knutson, M. G., Seamans, M. E., Lyons, J. E., ... & Williams, B. K. (2011). Climate change, uncertainty, and natural resource management. The Journal of Wildlife Management, 75(1), 6-18. https://doi.org/10.1002/jwmg.33
Obiedat, M., & Samarasinghe, S. (2022). Modelling socio-ecological systems: Implementation of an advanced fuzzy cognitive map framework for policy development. arXiv. https://doi.org/10.48550/arxiv.2208.05103
Özesmi, U., & Özesmi, S. L. (2004). Ecological models based on people's knowledge: a multi-step fuzzy cognitive mapping approach. Ecological Modelling, 176(1–2), 43–64. https://doi.org/10.1016/j.ecolmodel.2003.10.027
Panja, A., Garai, S., Maiti, S., Goswami, R., Zade, S., Veldandi, A., ... & Sankhala, G. (2026). Mapping resilience pathway of smallholder farming community to cyclone-led climate disasters in coastal West Bengal, India. Global Environmental Change, 96, 103108. https://doi.org/10.1016/j.gloenvcha.2025.103108
Rahman, M. M. (2026). Unveiling the nexus of governance, stakeholder, and technology through environmental impact assessment for natural resource management. Environmental Management, 76(4), 125. https://doi.org/10.1007/s00267-026-02422-3
Singh, P. K., & Chudasama, H. (2017). Assessing impacts and community preparedness to cyclones: a fuzzy cognitive mapping approach. Climatic Change, 143(3–4), 337–354. https://doi.org/10.1007/s10584-017-2007-z
Singh, P. K., & Chudasama, H. (2020). Pathways for climate change adaptations in arid and semi-arid regions. Journal of Cleaner Production, 284, 124744. https://doi.org/10.1016/j.jclepro.2020.124744
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