Skip to main content
SpringerLink
Log in

Determinants of Farmers’ Decision-Making Processes under Socio-Political Stressors exacerbated by Water Scarcity and Climate Change Adaptation

  • Published:
Water Resources Management Aims and scope Submit manuscript

Abstract

The sustainability of agriculture is increasingly challenged particularly in the context of socio-political stressors exacerbated by water scarcity and climate change, where it can lead to an unbalance in food security and landscape conservation if not well protected. The reversibility of these outcomes relies on the understanding of farmers’ decision-making processes and the drivers lying behind their way of thinking. In this study, we examine farmers’ decision-making processes and logic by developing quantitative and qualitative (probabilistic and mental) models that capture the main drivers behind their stated decisions when faced with the impacts of climate change and water scarcity. We then conduct a comparative assessment of future land cover/land-use generated using both models. The results showed that while the models shared several common determinants, they differed in the weight assigned to each. The probabilistic models were able to map mechanistically the ways of the mind, whereas mental processes were more anchored to motives and experiences that shape farmers’ vision of their surroundings. The comparative assessment showed a high similarity between mental and probabilistic models with minor differences pertaining to agricultural and bare lands. The discrepancies tended to be concentrated mostly in parcels where the probabilistic models predicted changing the crop type or quitting without selling. In closure, we argue that the concomitant use of both probabilistic and mental models can provide a more realistic representation of farmers’ decision-making processes and the impact of their decisions on land cover-land use projections when faced with water scarcity in the context of socio-political stressors exacerbated by climate change.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Availability of Data and Material

This study relies on information and data that was included within the paper with minimal material re-used and only where necessary particularly in describing the study area or comparing with reported work.

Code Availability

All software used in this study are publically available.

Abbreviations

A:

Age

AIC :

Akaike Information Criterion

CC:

Change crop

CCSW:

Change crop and seek additional water

GW:

Groundwater

HiRAM:

High Resolution Atmospheric Model

IPM:

Integrated Pest Management

LCLU:

Land cover Land use

LT:

Land Tenure

MM:

Mental Model

NC:

No change

PM:

Probabilistic Model

Q:

Quit

RA:

Reliance on Agriculture

S:

Sell

SW:

Seek additional water

TC:

Type of Crop grown

WQ:

Satisfaction with Water Quality

WRF:

Weather Research and Forecasting

YF:

Years in Farming

References

  • Adato M, Meinzen-Dick RS (2002) Assessing the impact of agricultural research on poverty using the sustainable livelihoods framework (No. 581-2016-39396)

  • Agidew AMA, Singh KN (2018) Factors affecting farmers’ participation in watershed management programs in the Northeastern highlands of Ethiopia: a case study in the Teleyayen sub-watershed. Ecol Process 7(1):15

    Article  Google Scholar 

  • Akhbari M, Grigg NS (2015) Managing water resources conflicts: modelling behavior in a decision tool. Water Resour Manag 29:5201–5216

    Article  Google Scholar 

  • Alzate M, Arce-Urriza M, Cebollada J (2020) Mining the text of online reviews to explore brand positioning: Emotional and psychological brand associations. (December 22, 2020). Available at SSRN: https://ssrn.com/abstract=3753772 or https://doi.org/10.2139/ssrn.3753772. Last accessed on 22 Mar 2022 at: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3753772

  • Arumugam N, Arshad FM, Chiew FCE, Mohamed Z (2011) Determinants of fresh fruits and vegetables (FFV) farmers’ participation in contract farming in peninsular Malaysia. Int J Agric Manag Dev (IJAMAD) 1(1047–2016–85471):65–71

    Google Scholar 

  • Azizi KT, Zamani GH (2009) Farmer participation in irrigation management: The case of Doroodzan Dam Irrigation Network. Iran Agric Water Manag 96(5):859–865

    Article  Google Scholar 

  • Aznar-Sanchez JA, Belmonte-Urena LJ, Velasco-Munoz JF, Valera DL (2021) Farmers’ profiles and behaviours toward desalinated seawater for irrigation: Insights from South-east Spain. J Clean Prod 296:126568

    Article  Google Scholar 

  • Bardenhagen CJ, Howard PH, Gray SA (2020) Farmer mental models of biological pest control: associations with adoption of conservation practices in blueberry and cherry orchards. Front Sustain Food Syst 4:54

    Article  Google Scholar 

  • Baynes J, Herbohn J, Russell I (2011) The influence of farmers’ mental models on an agroforestry extension program in the Philippines. Small-Scale For 10(3):377–387

    Article  Google Scholar 

  • Bell S (2007) Discovery and change: Themes of mental model development among successful new farmers. Adult Educ Res Conf. https://newprairiepress.org/aerc/2007/papers/5. Accessed Sept 2022

  • Bragg LA, Dalton TJ (2004) Factors affecting the decision to exit dairy farming: a two-stage regression analysis. J Dairy Sci 87(9):3092–3098

    Article  Google Scholar 

  • Callo-Concha D (2018) Farmer perceptions and climate change adaptation in the West Africa Sudan Savannah: Reality check in Dassari, Benin, and Dano. Burkina Faso Climate 6(2):44

    Google Scholar 

  • Carley KM (1997) Extracting team mental models through textual analysis. J Organ Behav: Int J Ind Occup Organ Psychol Behav 18(S1):533–558

    Article  Google Scholar 

  • Carmona G, Varela-Ortega C, Bromley J (2011) The use of participatory object-oriented Bayesian networks and agro-economic models for groundwater management in Spain. Water Resour Manag 25:1509–1524

    Article  Google Scholar 

  • Cotoranu A, Chen LC (2020) Applying text analytics to examination of end users’ mental models of cybersecurity. AMCIS 2020 Proceedings, 10

  • Cramer W, Guiot J, Fader M, Garrabou J, Gattuso JP, Iglesias A, Lange MA, Lionello P, Llasat MC, Paz S, Penuelas J (2018) Climate change and interconnected risks to sustainable development in the Mediterranean. Nat Clim Change 8(11):972–980

  • Darby S, Sear D (Eds.) (2008) River restoration: Managing the uncertainty in restoring physical habitat. John Wiley and Sons. First published: 15 February 2008. Ltd. ISBN: 978–0–470–86706–8. Copyright © 2008 John Wiley and Sons, Ltd. Last accessed on 22 Mar 2022 at: https://onlinelibrary.wiley.com/doi/book/10.1002/9780470867082

  • Davi A, Haughton D, Nasr N, Shah G, Skaletsky M, Spack R (2005) A review of two text-mining packages: SAS TextMining and WordStat. Am Stat 59(1):89–103

    Article  Google Scholar 

  • 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. Glob Environ Chang 19(2):248–255

    Article  Google Scholar 

  • Dewi YA, Istriningsih I (2010) Factors influencing farmers’ decision-making on the adoption of high yielding varieties of rice in Indonesia. Int J Agric Innov Res 6(5):2319–1473

    Google Scholar 

  • Douglas EM, Wheeler SA, Smith DJ, Overton IC, Gray SA, Doody TM, Crossman ND (2016) Using mental-modelling to explore how irrigators in the Murray-Darling Basin make water-use decisions. J Hydrol: Reg Stud 6:1–12

    Google Scholar 

  • Eckert E, Bell A (2005) Invisible force: Farmers’ mental models and how they influence learning and actions. J Ext 43(3):1–10

    Google Scholar 

  • Eckert E, Bell A (2006) Continuity and change: Themes of mental model development among small-scale farmers. J Ext 44(1):1FEA2

    Google Scholar 

  • Ellis-Iversen J, Cook AJ, Watson E, Nielen M, Larkin L, Wooldridge M, Hogeveen H (2010) Perceptions, circumstances and motivators that influence implementation of zoonotic control programs on cattle farms. Prev Vet Med 93(4):276–285

    Article  Google Scholar 

  • El-Samra R, Bou-Zeid E, Bangalath HK, Stenchikov G, El-Fadel M (2017) Future intensification of hydro-meteorological extremes: downscaling using the weather research and forecasting model. Clim Dyn 49(11–12):3765–3785

    Article  Google Scholar 

  • ElSawah S, Mclucas A, Mazanov J (2013) Using a cognitive mapping approach to frame the perceptions of water users about managing water resources: a case study in the Australian capital territory. Water Resour Manag 27:3441–3456

    Article  Google Scholar 

  • Foltz JD (2004) Entry, exit, and farm size: assessing an experiment in dairy price policy. Am J Agr Econ 86(3):594–604

    Article  Google Scholar 

  • Fosu-Mensah BY, Vlek PL, MacCarthy DS (2012) Farmers’ perception and adaptation to climate change: Case study of Sekyedumase district in Ghana. Environ Dev Sustain 14(4):495–505

    Article  Google Scholar 

  • Franzel SC, Scherr SJ (Eds.) (2002) Trees on the farm: Assessing the adoption potential of agroforestry practices in Africa. © CAB (Centre for Research in Agroforestry) International 2002. Published in association with ICRAF, Nairobi, Kenya. Web site: www.cabi-publishing.org. Accessed Sept 2022

  • Ghalehteimouri KJ, Shamsoddini A, Mousavi MN, Ros FBC, Khedmatzadeh A (2022) Predicting spatial and decadal of land use and land cover change using integrated cellular automata Markov chain model based scenarios (2019–2049) Zarriné-Rūd River Basin in Iran. Environ Chall 6:100399

    Article  Google Scholar 

  • Girma R, Fürst C, Moges A (2022) Land use land cover change modeling by integrating artificial neural network with cellular Automata-Markov chain model in Gidabo river basin, main Ethiopian rift. Environ Chall 6:100419

    Article  Google Scholar 

  • Gray S, Mellor D, Jordan R, Crall A, Newman G (2014) Modeling with citizen scientists: Using community-based modeling tools to develop citizen science projects. International Congress on Environmental Modelling and Software 27

  • Gray SA, Gray S, Cox LJ, Henly-Shepard S (2013) Mental modeler: A fuzzy-logic cognitive mapping modeling tool for adaptive environmental management. Hawaii Int Conf Syst Sci 965–973. IEEE

  • Hansson H, Kokko S (2018) Farmers’ mental models of change and implications for farm renewal–A case of restoration of a wetland in Sweden. J Rural Stud 60:141–151

    Article  Google Scholar 

  • Isinkaralar O, Varol C, Yilmaz D (2022) Digital mapping and predicting the urban growth: Integrating scenarios into cellular automata—Markov chain modeling. Appl Geomat 1–11

  • Jabbour R, Zwickle S, Gallandt ER, McPhee KE, Wilson RS, Doohan D (2014) Mental models of organic weed management: Comparison of New England US farmer and expert models. Renew Agric Food Syst 29(4):319–333

    Article  Google Scholar 

  • Jeon J (2015) The strengths and limitations of the statistical modeling of complex social phenomenon: Focusing on SEM, path analysis, or multiple regression models. Int J Soc Behav Educ Econ Bus Ind Eng 9(5):1594–1602

    Google Scholar 

  • Jones N, Ross H, Lynam T, Perez P, Leitch A (2011) Mental models: an interdisciplinary synthesis of theory and methods. Ecol Soc 16(1). http://www.ecologyandsociety.org/vol16/iss1/art46/. Accessed Sept 2022

  • Kanianska R (2016) Agriculture and its impact on land‐use, environment, and ecosystem services. Landscape Ecology-the Influences of Land Use and Anthropogenic Impacts of Landscape Creation 1–26

  • Khadra WM, Stuyfzand PJ (2018) Simulation of saltwater intrusion in a poorly karstified coastal aquifer in Lebanon (Eastern Mediterranean). Hydrogeol J 26(6):1839–1856

    Article  Google Scholar 

  • Khair K, Kassem F, Amacha N (2016) Factors affecting the discharge rate of the streams-case study; Damour River Basin, Lebanon. J Geogr Environ Earth Sci 7(2):1–17

    Google Scholar 

  • Kiersch B, Tognetti S (2002) Land-water linkages in rural watersheds: Results from the FAO electronic workshop. Land Use Water Resour Res 2(1732–2016–140264)

  • Krauss SE, Hamzah A, Omar Z, Suandi T, Ismail IA, Zahari MZ, Nor ZM (2009) Preliminary investigation and interview guide development for studying how Malaysian farmers form their mental models of farming. Qual Rep 14(2):245–260

    Google Scholar 

  • López-Felices B, Aznar-Sánchez JA, Velasco-Muñoz JF, Mesa-Vázquez E (2023a) Farmers’ profiles and attitudes towards the implementation of rainwater harvesting systems in intensive agriculture. Int J Agric Sustain 21(1):2189402

    Article  Google Scholar 

  • López-Felices B, Aznar-Sánchez JA, Velasco-Muñoz JF, Mesa-Vázquez E (2023b) Examining the perceptions and behaviours of farmers regarding the installation of covers over irrigation ponds: Evidence from South-east Spain. Agric Water Manag 275:107999

    Article  Google Scholar 

  • Masciopinto C (2013) Management of aquifer recharge in Lebanon by removing seawater intrusion from coastal aquifers. J Environ Manage 130:306–312

  • McNeish D (2017) Challenging conventional wisdom for multivariate statistical models with small samples. Rev Educ Res 87(6):1117–1151

    Article  Google Scholar 

  • Morse N (2014) Agriculture in a Changing Landscape. Modeling shifts in the geospatial distribution of crops in response to climate change. Masters project submitted in partial fulfillment of the requirements for the Master of Environmental Management degree in the Nicholas School of the Environment, Duke University

  • Nath S, van Laerhoven F (2021) Using power, mental model, and learning to analyze the evolution of water governance in Bangalore. Environ Policy Gov 31(4):270–282

    Article  Google Scholar 

  • Otto-Banaszak I, Matczak P, Wesseler J, Wechsung F (2011) Different perceptions of adaptation to climate change: a mental model approach applied to the evidence from expert interviews. Reg Environ Change 11(2):217–228

    Article  Google Scholar 

  • Palmunen LM, Lainema T, Pelto E (2021) Towards a manager’s mental model: Conceptual change through business simulation. The International Journal of Management Education 19(2):100460

    Article  Google Scholar 

  • Papageorgiou EI (Ed.) (2013) Fuzzy cognitive maps for applied sciences and engineering: from fundamentals to extensions and learning algorithms (Vol. 54). Springer Science and Business Media

  • Papageorgiou K, Carvalho G, Papageorgiou EI, Papandrianos NI, Mendonça M, Stamoulis G (2020, July) Exploring Brazilian photovoltaic solar energy development scenarios using the fuzzy cognitive map wizard tool. In 2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) (pp. 1-8). IEEE

  • Pillutla VS, Giabbanelli PJ (2019) Iterative generation of insight from text collections through mutually reinforcing visualizations and fuzzy cognitive maps. Appl Soft Comput 76:459–472

    Article  Google Scholar 

  • Poppenborg P, Koellner T (2013) Do attitudes toward ecosystem services determine agricultural land use practices? An analysis of farmers’ decision-making in a South Korean watershed. Land Use Policy 31:422–429

    Article  Google Scholar 

  • R Core Team (2021) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3–900051–07–0. http://www.R-project.org/

  • Ren L, Yang X (2023) Adoption and shift of water-saving strategies to policy shock: Based on social-ecological system analysis. Water Resour Manag 1–23

  • Rouse WB (2007) People and organizations: Explorations of human-centered design (Vol. 51). John Wiley and Sons. Wiley Series in Systems Engineering and Management

  • Sabzian H, Shafia MA, Maleki A, Hashemi SMS, Baghaei A, Gharib H (2019) Theories and practice of agent based modeling: Some practical implications for economic planners. arXiv preprint arXiv:1901.08932. Last accessed on 22 Mar 2022 at: https://arxiv.org/pdf/1901.08932.pdf

  • Salliou N, Barnaud C (2017) Landscape and biodiversity as new resources for agro-ecology? Insights from farmers’ perspectives. Ecol Soc 22(2)

  • Sarker MA, Itohara Y, Hoque M (2009) Determinants of adoption decisions: The case of organic farming (OF) in Bangladesh. Ext Farmg Syst J 5(2):39–46

    Google Scholar 

  • Schoell R, Binder CR (2009) System perspectives of experts and farmers regarding the role of livelihood assets in risk perception: Results from the structured mental model approach. Risk Anal Int J 29(2):205–222

    Article  Google Scholar 

  • Seel NM (2001) Epistemology, situated cognition, and mental models: “Like a bridge over troubled water.” Instr Sci 29(4–5):403–427

    Article  Google Scholar 

  • Serrat O, Serrat O (2017) The sustainable livelihoods approach. Knowledge solutions: Tools, methods, and approaches to drive organizational performance 21–26

  • Suit-B Y, Hassan L, Krauss SE, Ramanoon SZ, Ooi PT, Yasmin AR, Epstein J (2020) Exploring the mental model of cattle farmers in disease prevention and control practices. Vet Sci 7(1):27

    Article  Google Scholar 

  • Suvedi M, Ghimire R, Kaplowitz M (2017) Farmers’ participation in extension programs and technology adoption in rural Nepal: A logistic regression analysis. J Agric Educ Ext 23(4):351–371

    Article  Google Scholar 

  • Talib R, Hanif MK, Ayesha S, Fatima F (2016) Text mining: techniques, applications and issues. Int J Adv Comput Sci Appl 7(11):414–418

    Google Scholar 

  • Talukder A, Sakib MS, Islam MA (2017) Determination of influencing factors for integrated pest management adoption: A logistic regression analysis. Agrotechnology 6(163):2

    Google Scholar 

  • Teshome A, de Graaff J, Ritsema C, Kassie M (2016) Farmers’ perceptions about the influence of land quality, land fragmentation and tenure systems on sustainable land management in the north western Ethiopian highlands. Land Degrad Dev 27(4):884–898

    Article  Google Scholar 

  • Tey YS, Li E, Bruwer J, Abdullah AM, Brindal M, Radam A, Ismail MM, Darham S (2014) The relative importance of factors influencing the adoption of sustainable agricultural practices: a factor approach for Malaysian vegetable farmers. Sustain Sci 9(1):17–29

    Article  Google Scholar 

  • Tsai CH, Brusilovsky P (2019) Designing explanation interfaces for transparency and beyond. In Joint Proceedings of the ACM IUI 2019 Workshops, Annual Meeting of The Intelligent Interfaces Community, Los Angeles, USA

  • Tschakert P, Sagoe R (2009) Mental models: Understanding the causes and consequences of climate change. Particip Learn Act 60(1):154–159

    Google Scholar 

  • Uitdewilligen S, Waller MJ, Roe RA, Bollen P (2021) The effects of team mental model complexity on team information search and performance trajectories. Group Organ Manag 10596011211023219

  • UNEP/MAP (2005) MAP CAMP project "Lebanon” final integrated project document. United Nations Environment Programme Mediterranean Action Plan. MAP Tech Rep Ser No. 160

  • Valbuena D, Verburg PH, Veldkamp A, Bregt AK, Ligtenberg A (2010) Effects of farmers’ decisions on the landscape structure of a Dutch rural region: An agent-based approach. Landsc Urban Plan 97(2):98–110

    Article  Google Scholar 

  • Venables WN, Ripley BD (2002) Modern applied statistics with S. Fourth Edition. Springer-Verlag New York 2002. https://doi.org/10.1007/978-0-387-21706-2

  • Verburg PH, Kok K, Pontius RG Jr, Veldkamp A (2006) Modeling land-use and land-cover change. In Land-use and land-cover change: local processes and global impacts (pp. 117–135). Berlin, Heidelberg: Springer Berlin Heidelberg

  • Vermaire JC, Taranu ZE, MacDonald GK, Velghe K, Bennett EM, Gregory-Eaves I (2017) Extrinsic vs. intrinsic regimes shifts in shallow lakes: Long-term response of cyanobacterial blooms to historical catchment phosphorus loading and climate warming. Front Ecol Evol 5:146

    Article  Google Scholar 

  • Vuillot C, Coron N, Calatayud F, Sirami C, Mathevet R, Gibon A (2016) Ways of farming and ways of thinking: Do farmers’ mental models of the landscape relate to their land management practices? Ecol Soc 21(1)

  • Wickham H (2007) Reshaping data with the reshape package. J Stat Softw 21(12):1–20. http://www.jstatsoft.org/v21/i12/. Accessed Sept 2022

  • Wickham H (2016) ggplot2: Elegant graphics for data analysis. Springer-Verlag, New York

    Book  Google Scholar 

Download references

Acknowledgements

Special thanks are extended to Dr. Daniel Goode at the USGS and Dar Al-Handasah (Shair and Partners) Endowment for its support to the graduate programs in Engineering at the American University of Beirut.

Funding

This study was funded by the US Agency for International Development through the US Geological Survey, under the terms of Grant Number G17AC00079. The opinions expressed herein are those of the authors and do not necessarily reflect the views of the U.S. Agency for International Development or the U.S. Geological Survey (USGS).

Author information

Authors and Affiliations

  1. Department of Civil and Environmental Engineering, American University of Beirut, Beirut, Lebanon

    G. Harik, I. Alameddine & M. El Fadel

  2. Department of Landscape Design and Ecosystem Management, American University of Beirut, Beirut, Lebanon

    R. Zurayk

  3. Department of Civil Infrastructure and Environmental Engineering, Khalifa University, Abu Dhabi, UAE

    M. El Fadel

Authors
  1. G. Harik
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. R. Zurayk
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. I. Alameddine
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. M. El Fadel
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Ghinwa Harik: Data curation; Formal analysis; Investigation; Software; Validation; Visualization; Writing—original draft. Rami Zurayk: Conceptualization; Formal analysis; Methodology; Visualization; Writing—review and editing. Ibrahim Alameddine: Conceptualization; Formal analysis; Methodology; Software; Validation; Visualization; Writing—review and editing. Mutasem El-Fadel Funding acquisition; Project administration; Supervision; Conceptualization; Formal analysis; Methodology; Visualization; Writing—review and editing.

Corresponding author

Correspondence to M. El Fadel.

Ethics declarations

Conflicts of Interest/Competing Interests

None.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Harik, G., Zurayk, R., Alameddine, I. et al. Determinants of Farmers’ Decision-Making Processes under Socio-Political Stressors exacerbated by Water Scarcity and Climate Change Adaptation. Water Resour Manage 37, 6199–6218 (2023). https://doi.org/10.1007/s11269-023-03651-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11269-023-03651-5

Keywords

Search

Navigation