Influence of Mental Models on Sustainable Wheat Seed Use: A Comparative Study in Western Iran

Document Type : Research Article-en

Authors

A. Ansari

Sh. Geravandi

F. Rostami

A.H. Alibaygi

Department of Agricultural Extension and Education, Faculty of Agriculture, Razi University, Kermanshah, Iran

https://doi.org/10.22067/jead.2025.90803.1315

Abstract

Understanding farmers' mental models (MM) is crucial for interpreting their behavior; however, these models, are shaped by technical, economic, and social factors, have often been overlooked. Using a case study from Kangavar County, Western Iran, this study investigates the MM of farmers and experts regarding wheat seed usage and proposes optimization strategies using a mixed-methods approach. Qualitatively, phenomenological analysis of interviews with 16 participants (selected via purposive and snowball sampling) identified five key factors shaping farmers' mental models (MM): work context, climate, experienced risks, available resources, and subjective norms. Experts' rationale for seed recommendations centered on three themes: academic knowledge, professional experience, and user characteristics. Quantitatively, a structured questionnaire administered to a separate sample of 255 farmers (stratified proportional sampling) and 50 experts (complete enumeration) revealed significant disparities. Independent samples t-tests revealed statistically significant differences (p < 0.01) across all major mental model (MM) domains. Responses were initially scored as percentages (0–100) based on item agreement, then standardized to a 100-point scale. Mean scores were substantially higher among farmers in experienced risks (74.02 vs. 15.80), subjective norms (66.24 vs. 18.93), and work context (65.67 vs. 35.30), indicating stronger perceived influence of experiential and social factors. Conversely, experts scored higher on academic knowledge (75.28 vs. 41.71), reflecting greater reliance on scientific evidence.The Delphi method identified and prioritized key strategies for optimizing seed use, with "preparing a suitable seedbed" (weighted average: 0.148) and "using certified seeds" (weighted average: 0.143) ranked highest. These findings underscore the critical gap between farmer and expert models and emphasize the need for targeted interventions that integrate experiential knowledge with scientific evidence to bridge this divide, fostering more sustainable seed usage practices.

Keywords

Cognitive decision-making

Delphi technique

Mental models

Mixed-methods

Phenomenological study

Sustainable wheat production

Subjects

• Agricultural Managment

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Authors retain the copyright. This is an open access article distributed under Creative Commons Attribution 4.0 International License (CC BY 4.0).

References

1. Abolhassani, L., Shahnoushi, N., Dourandish, A., Taherpour, H., & Mansouri, H. (2018). An investigation on outcomes and consequences of subsidies-reform law in wheat, flour and bread sectors. Quarterly Journal of Agricultural Economics and Development, 26(2), 145-164. https://doi.org/30490/aead.2018.73555
2. Afzal, N., Shrestha, S., & Turner, J.A. (2025). Using values-informed mental models to understand farmer, water manager, and scientist use and perceptions of hydrologic models. Journal of Hydrology, 634(5-6), 133171. https://doi.org/10.1016/j.jhydrol.2025.133171
3. Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211. https://doi.org/10.1016/0749-5978(91)90020-T
4. Ajzen, I., & Fishbein, M. (2000). Attitudes and the attitude-behavior relation: Reasoned and automatic processes. European Review of Social Psychology, 11, 1-33. https://org/10.1080/14792779943000116
5. Argha, S., Balouchi, H.R., & Yadavi, A. (2021). Seed storage capability of wheat produced in different conditions of drought stress and sources of nitrogen fertilization. Iranian Journal of Seed Science and Technology, 10(2), 15-35. https://org/10.22092/ijsst.2020.124360.1244
6. Azizi, A., Mehrabi Boshrabadi, H., & Zare Mehrjerdi, M. (2022). Impacts of climate change and water scarcity on farmers' irrigation decisions in North-Khorasan Province: Major crops. Journal of Agricultural Economics and Development, 35(4), 367-382. https://doi.org/10.2166/wp. 2023.122
7. Bandura, A. (1977). Social learning theory. Englewood Cliffs, NJ: Prentice-Hall.
8. Bardenhagen C.J., Howard, P.H., & Gray, S.A. (2020). Farmer mental models of biological pest control: Associations with adoption of conservation practices in blueberry and cherry orchards. Frontiers in Sustainable Food Systems, 4(54), 1-11. https://doi.org/10.3389/fsufs.2020.00054
9. Bartaburu, M., Terra, B., García-Breijo, F., Castrejón-Macedo, O., & Alba-Patiño, D. (2022). Decision spaces in agricultural risk management: A mental model approach. Environment, Development and Sustainability, 24(5), 6856–6880. https://doi.org/10.1007/s10668-021-01693-6
10. Bist, P., Bhatt, P., Bist, N.S., & Kafle, R. (2021). Factor affecting wheat seed production and associated constraints in Kailali district, Nepal. International Journal of Social Sciences and Management, 8(1), 299-305. https://doi.org/10.3126/ijssm.v8i1.34224
11. Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. https://doi.org/10.1191/1478088706qp063oa
12. Bryman, A. (2016). Social Research Methods (5th ed.). London: Oxford University Press.
13. Bulte, E., Di Falco, S., Lensink, R., & Beekman, G. (2023). Miracle seeds: Biased expectations, complementary input use, and the benefits of correcting beliefs. Journal of the European Economic Association. https://doi.org/10.1093/jeea/jvad023
14. Capelo, C., & Dias, J. (2009). A feedback learning and mental models perspective on strategic decision making. Educational Technology Research and Development, 57, 629-644. https://doi.org/10.1007/S11423-009-9123-Z
15. Chermack, T. (2003). Mental models in decision making and implications for human resource development. Advances in Developing Human Resources, 5, 408-422. https://doi.org/10.1177/ 1523422303257373
16. Colaizzi, P.F. (1978). Psychological Research as the Phenomenologist Views It. In: Valle, R.S. and Mark, K., Eds., Existential Phenomenological Alternatives for Psychology, Oxford University Press, New York, 48-71.
17. Creswell, J.W. (2013). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches. 4th Edition, SAGE Publications, Inc., London.
18. Cullen, R., Meyer, J.H., & Rhodes, A.K. (2022). Bridging the gap between conservation science and practice: The role of boundary spanners and their mental models. Society & Natural Resources, 35(1), 98–115. https://doi.org/10.1080/08941920.2021.1999271
19. Dalkey, N., & Helmer, O. (1963). An experimental application of the Delphi method to the use of experts. Management Science, 9, 458-467. https://doi.org/10.1287/mnsc.9.3.458
20. Dillman, D.A., Smyth, J.D., & Christian, L.M. (2014). Internet, phone, mail, and mixed mode surveys: The tailored design method (4th ed.). John Wiley & Sons, Inc. https://doi.org/10.1002/9781394260645   
21. Eckert, E., & Bell, A. (2005). Invisible Force: Farmers' Mental Models and How They Influence Learning and Actions. Journal of Extension, 43(3), 1-7. https://tigerprints.clemson.edu/joe/ vol43/iss3/3
22. Edwards, B., Gray, M., & Hunter, B. (2015). The impact of drought on mental health in rural and regional Australia. Social Indicators Research, 121, 177-194. https://doi.org/10.1007/S11205-014-0638-2
23. Eitzinger, A., Binder, C., & Meyer, M. (2018). Risk perception and decision-making: do farmers consider risks from climate change? Climatic Change, 151, 507-524. https://org/10.1007/s10584-018-2320-1
24. Ellis, N., & Albrecht, G. (2017). Climate change threats to family farmers' sense of place and mental wellbeing: A case study from the Western Australian Wheatbelt. Social Science & Medicine, 175, 161-168. https://doi.org/10.1016/j.socscimed.2017.01.009
25. Feola, G., Lerner, A., Jain, M., Montefrio, M., & Nicholas, K. (2015). Researching farmer behaviour in climate change adaptation and sustainable agriculture: lessons learned from five case studies. Journal of Rural Studies, 39, 74-84. https://org/10.1016/J.JRURSTUD.2015.03.009
26. Field, A. (2018). Discovering statistics using IBM SPSS statistics (5th ed.). Sage.
27. Gatersleben, B., Murtagh, N., & Abrahamse, W. (2014). Values, identity and pro-environmental behaviour. Contemporary Social Science, 9, 374-392. https://org/10.1080/21582041.2012. 682086
28. Gebrehiwot, T., & Veen, A. (2020). Farmers’ drought experience, risk perceptions, and behavioral intentions for adaptation: evidence from Ethiopia. Climate and Development, 13, 493-502. https://doi.org/10.1080/17565529.2020.1806776
29. Ghanbari, S., Bazrafshan, J., Toulabinejad, M., & Toulabinejad, M. (2018). The factors influencing decisions of farmers in applying soil and water resource protection methods in Jaidar Plain, Poldokhtar. Human Geography Research, 50(1), 73-92. https://doi.org/10.22059/jhgr. 2016.58924
30. Grenier, R.S., & Dudzinska-Przesmitzki, D. (2015). A conceptual model for eliciting mental models using a composite methodology. Human Resource Development Review, 14(2), 163–184. https://doi.org/10.1177/1534484315575966
31. Grob, A. (1995). A structural model of environmental attitudes and behaviour. Journal of Environmental Psychology, 15, 209-220. https://doi.org/10.1016/0272-4944(95)90004-7
32. Guido, Z., Zimmer, A., Lopus, S.E., Hannah, C., Gower, D.B., Waldman, K., ... & Evans, T. (2020). Farmer forecasts: Impacts of seasonal rainfall expectations on agricultural decision-making in Sub-Saharan Africa. Climate Risk Management, 30, 100247. https://doi.org/10.1016/ j.crm.2020.100247
33. Haghparast, R. (2013). Principles of dryland wheat farming. First edition. Education and Extension Publications.
34. Halbrendt, J., Gray, S.A., Crow, S., Radovich, T., Kimura, A.H., & Tamang, B.B. (2014). Differences in farmer and expert beliefs and the perceived impacts of conservation agriculture. Global Environmental Change, 28, 50–62. https://doi.org/10.1016/j.gloenvcha.2014.05.001
35. Hansson, H., & Kokko, S. (2018). Farmers' mental models of change and implications for farm renewal – A case of restoration of a wetland in Sweden. Journal of Rural Studies, 60, 141-151. https://doi.org/10.1016/J.JRURSTUD.2018.04.006
36. Harik, G., Zurayk, R., Alameddine, I., & Fadel, M. 2023. Determinants of farmers’ decision-making processes under socio-political stressors exacerbated by water scarcity and climate change adaptation. Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer, European Water Resources Association (EWRA), 37(15), 6199-6218.
37. Hasam, H., Kaur, S., Kaur, H., Kaur, N., Kaur, T., Aulakh, C., & Bhullar, M. (2021). Weed management using tillage, seed rate and bed planting in durum wheat (Triticum durum) under an organic agriculture system. Archives of Agronomy and Soil Science, 68, 1959-1973. https://doi.org/10.1080/03650340.2021.1946041
38. Holtrop, J., Scherer, L., Matlock, D., Glasgow, R., & Green, L. (2020). The importance of mental models in implementation science. Frontiers in Public Health, 6(9), https://doi.org/10.3389/fpubh.2021.680316
39. Hoppe, A., Fritsche, I., & Chokrai, P. (2023). The “I” and the “We” in nature conservation—Investigating personal and collective motives to protect one’s regional and global nature. Sustainability. https://org/10.3390/su15054694
40. Hsu, C.-C., & Sandford, B.A. (2007). The Delphi technique: Making sense of consensus. Practical Assessment, Research & Evaluation, 12(10), 1–https://doi.org/10.7275/pdz9-th90
41. Johnson-Laird, P. (2010). Mental models and deduction. Trends in Cognitive Sciences, 5, 434-442. https://doi.org/10.1016/S1364-6613(00)01751-4
42. Jones, N., Ross, H., Lynam, T., Perez, P., & Leitch, A. (2011). Mental models: an interdisciplinary synthesis of theory and methods. Ecology and Society, 16(1), 46. https://doi.org/10.5751/ES-03802-160146
43. Kansiime, M.K., Mastenbroek, A., & Monge, M. (2021). Understanding farmers' mental models of soil and soil health to inform sustainable soil management. Soil Security, 5, 100015. https://doi.org/10.1016/j.soisec.2021.100015
44. Keating, B., & McCown, R. (2001). Advances in farming systems analysis and intervention. Agricultural Systems, 70, 555-579. https://org/10.1016/S0308-521X(01)00059-2
45. Keeney, S., Hasson, F., & McKenna, (2006). Consulting the oracle: Ten lessons from using the Delphi technique in nursing research. Journal of Advanced Nursing, 53(2), 205–212. https://doi.org/10. 1111/j.1365-2648.2006.03716.x
46. Khazaei, M. (2021). Study of seed consumption in Kangavar County. Kermanshah Province Agricultural Jihad Organization, Kangavar County Agricultural Jihad Management, Kangavar County.
47. Kolb, L., Gallandt, E., & Mallory, E. (2012). Impact of spring wheat planting density, row spacing, and mechanical weed control on yield, grain protein, and economic return in maine, 60, 244-253. https://org/10.1614/WS-D-11-00118.1
48. Krejcie, R.V., & Morgan, D.W. (1970). Determining sample size for research activities. Educational and Psychological Measurement, 30(3), 607-610. https://doi.org/10.1177/ 001316447003000308
49. Kumari, P., & Kataria, R. (2023). Influence of sowing pattern and seed rate on plant growth and yield in wheat. International Journal of Plant & Soil Science, 35(18), 574-583. https://org/10.9734/ijpss/2023/v35i183322
50. Lee, M., & Webb, M. (2005). Modeling individual differences in cognition. Psychologic Bulletin & Review, 12, 605-621. https://doi.org/10.3758/BF03196751
51. Lemerle, D., Cousens, R., Gill, G., Peltzer, S., Moerkerk, M., Murphy, C., Collins, D., & Cullis, B. (2004). Reliability of higher seeding rates of wheat for increased competitiveness with weeds in low rainfall environments. The Journal of Agricultural Science, 142, 395-409. https://org/10.1017/S002185960400454X
52. Levy, M., Lubell, M., & McRoberts, N. (2018). The structure of mental models of sustainable agriculture. Nature Sustainability, 1(8), 413–420. https://doi.org/10.1038/s41893-018-0116-y
53. Lincoln, Y.S., & Guba, E.G. (2018). Paradigmatic controversies, contradictions, and emerging confluences, revisited, Denzin, N.K. and Lincoln, Y.S. (Eds), The Sage Handbook of Qualitative Research (5th ed.), Sage, Thousand Oaks, pp. 213-263. https://doi.org/10.1177/109442810 9332198
54. Linstone, H., & Turoff, M. (2002). The Delphi method: Techniques and applications. https://is.njit.edu/pubs/delphibook
55. Lynam, T., & Brown, K. (2011). Mental models in human-environment interactions: Theory, policy implications, and methodological explorations. Ecology and Society, 16(2), 24. https://doi.org/10.5751/es-04257-170324
56. Marques, J. (2014). Mental models and reality. In: Leadership and mindful behavior. Palgrave Macmillan, New York. 73-89. https://doi.org/10.1057/9781137403797_5
57. Marshall, C., & Rossman, G. (2016). Designing qualitative research. 6th Edition, SAGE, Thousand Oaks.
58. Mersha, M., Alamirew, B., & Ayele, L. (2022). Factors determining farmers’ decision and level of participation in terracing as a response to land degradation in Dejen Woreda, North-West Ethiopia. Cogent Social Sciences, 8(1), 1-18. https://doi.org/10.1080/23311886.2022.2126080
59. Moon, K., Guerrero, A., Adams, V., Biggs, D., Blackman, D., Craven, L., Dickinson, H., & Ross, H. (2019). Mental models for conservation research and practice. Conservation Letters, 12(3), 1-11. https://doi.org/10.1111/conl.12642
60. Morrison, T.H., McCormack, P.C., Hughes, T.P., Hughes, A.C., & Bell-James, D. (2024). Utilizing drawings to understand how our backgrounds and experiences influence our mental models of the world. Plants, People, Planet, 6(5), 1234–1245. https://doi.org/10.1002/ppp3.10592
61. Okello, J.J., Zhou, Y., Kwikiriza, N., Ogutu, S., Barker, I., Schulte-Geldermann, E., Atieno, E., & Ahmed, J.T. (2018). Evidence from use of quality seed potato in Kenya. European Journal of Development Research, 30, 1082–1104. https://doi.org/10.3390/agriculture6040055
62. Olivier de Sardan, J.P. (2005). Anthropology and development: Understanding contemporary social change. London, UK: Zed Books.
63. Patton, M. (2015). Qualitative Research and Evaluation Methods. 4th Edition, Sage Publications, Thousand Oaks.
64. Peltonen-Sainio, P., Jauhiainen, L., Sorvali, J., Laurila, H., & Rajala, A. (2018). Field characteristics driving farm-scale decision-making on land allocation to primary crops in high latitude conditions. Land Use Policy, 71, 49-59. https://org/10.1016/J.LANDUSEPOL. 2017.11.040
65. Rajabali Beglou, R., Fattahi, R., & Parirokh, M. (2019). Value co-creation among digital libraries’ software beneficiaries (users, librarians & designers) of Iran. Iranian Journal of Information Processing and Management, 35(1), 261-290. https://doi.org/10.35050/JIPM010.2019.009
66. Rios-Gonzalez, A., Jansen, K., & Sánchez-Pérez, H. (2013). Pesticide risk perceptions and the differences between farmers and extensionists: towards a knowledge-in-context model. Environmental Research, 124, 43-53. https://org/10.1016/j.envres.2013.03.006
67. Saaty, T.L. (1980). The analytic hierarchy process. McGraw-Hill.
68. Statistical Center of Iran. (2021). Agricultural statistics yearbook of crops 2019-2020. Ministry of Agriculture Jihad, Deputy of Economic Planning, Information and Communication Technology Center.
69. Suomala, J., & Kauttonen, J. (2022). Human’s intuitive mental models as a source of realistic, Artificial Intelligence and Engineering. Frontiers in Psychology, 13. https://doi.org/10.3389/ fpsyg.2022.873289
70. Taylor, J., Stewart, T., & Downton, M. (1988). Perceptions of drought in the Ogallala aquifer region. Environment and Behavior, 20, 150-175. https://doi.org/10.1177/0013916588202002
71. Todorovikj, S., Friemann, P., & Ragni, M. (2019). A cross-domain theory of mental models. Environment and Behavior, 184-194. https://doi.org/10.1007/978-3-030-30639-7_16
72. Tzeng, G., & Huang, J. (2011). Multiple attribute decision making methods and applications. CRC Press, Taylor and Francis Group, A Chapman & Hall Book, Boca Raton.
73. Ulrich, M., Harpaintner, M., Trumpp, N., Berger, A., & Kiefer, M. (2022). Academic training increases grounding of scientific concepts in experiential brain systems. Cerebral Cortex, 33(9), 5646-5657. https://doi.org/10.1093/cercor/bhac449
74. Warner, L.A., Stubbs, E., Murphrey, T.P., & Huynh, P. (2016). Identification of the competencies needed to apply social marketing to extension programming: Results of a Delphi study. Journal of Agricultural Education, 57(2), 14-32. https://doi.org/10.5032/jae.2016.02014
75. Xie, H., & Huang, Y. (2021). Influencing factors of farmers' adoption of pro-environmental agricultural technologies in China: Meta-analysis. Land Use Policy, 109, 105622. https://org/10.1016/J.LANDUSEPOL.2021.105622
76. Zareyan, A., Hosna, Sh., Sadeghi, H., & Jazayeri, M.R. (2011). The process of control and certification of wheat seeds and the status of its seed production in Iran. The Second National Conference of Seed Science and Technology.