Calculation of Farmers' Risk Aversion Degree and Its Impact on Crop Diversification (Case Study: Agricultural Lands of Northern Rudpay, Sari County)

Document Type : Research Article

Authors

• T. Usefi Farahabadi
• S. Shirzadi Laskookalayeh
• F. Kashiri Kolaei
• Kh. Abdi Rokni

Department of Agricultural Economics, Faculty of Agricultural Engineering, Sari Agricultural Sciences and Natural Resources University, Sari, Iran

10.22067/jead.2025.92772.1340

Abstract

Introduction
The agricultural sector in Iran holds significant importance due to its substantial capabilities and capacities. This sector is full of risk and uncertainty. Risks and crises significantly influence producers’ behavior, shaping both the income derived from their products and their decisions regarding input use and product supply. Since individuals’ attitudes toward risk vary, effective risk management in the agricultural sector is a critical concern for farmers and stakeholders alike. Risk management encompasses the application of diverse methods, tools, and policies designed to mitigate the adverse impacts of different types of hazards. Risk and crises influence the behavior of producers, and the outcomes of these are reflected in their effect on the income generated from products and farmers' decisions regarding the use of inputs and product supply. People's attitudes toward these risks differ. Therefore, risk management in the agricultural sector is a critical issue for farmers and stakeholders in this field. Risk management refers to the use of various methods, tools, and policies to reduce the negative impacts of various types of hazards. Strategies such as crop diversification, contract farming, producing crops in exchange for guaranteed prices, and intercropping complementary crops can help mitigate their negative effects by spreading or distributing risks among individuals, organizations, products, and different options. Given the importance of this issue, the present study investigates the impact of risk aversion on crop diversification in the northern Rudpay region of Sari County.
 
Materials and Methods   
In this study, the degree of risk aversion is calculated using the Multi-Attribute Utility Function (MAUF) method. This technique is based on calculating the weight of risk and, as a result, the risk aversion coefficient. This method is based on weighted goal programming. In this research, the two-stage cluster analysis method is used to classify the risk aversion coefficient. Finally, to examine the impact of risk aversion on crop diversification, the Tobit model will be used. The degree of crop diversification will be analyzed using the developed Herfindahl index. The data required for the research, such as water, land, fertilizer, and capital, were partly provided by centers affiliated with the Ministry of Jihad Agriculture in Mazandaran Province. The rest, such as the cropping patterns of each farmer, were collected through the completion of questionnaires and using simple random sampling in the northern Rudpay region.
Results and Discussion
Based on the classification, only one farmer is considered risk-neutral. This indicates that this individual among the farmers of the northern Rudpay region is indifferent to risk and chooses activities without considering their level of risk. In other words, the presence or absence of risk in performing activities is not a concern for them. The next category, which includes 24 individuals, indicates that 10% of the farmers have low risk aversion. The final category shows that 225 individuals (90%) of the sample fall into the high-risk aversion category. Therefore, as observed, the dominant tendency among the studied individuals is high risk aversion. This result suggests that farmers in the region are only willing to adopt new phenomena, such as modern programs and technologies, if they expect or anticipate a higher return compared to the current situation.
The results of the Herfindahl index analysis show that the average crop diversification index is 0.57, which, according to various studies, is a reasonable and acceptable value. Nearly 70% of the farmers, with an index below the average Herfindahl value, have crop diversification and include different products in their cropping patterns. Furthermore, the results indicate that there is no significant relationship between crop diversification and the farmer's age, while variables such as the risk aversion coefficient, the farmer's education, farm size, and the share of agricultural income have a significant effect on crop diversification.
 
Conclusion
Considering that agricultural products are generally produced in a risky and uncertain environment, this study aimed to calculate the degree of risk aversion of farmers in the northern Rudpay region of Sari County using the Multi-Attribute Utility Function. The results from determining the risk aversion level of farmers in the northern Rudpay region in the first part of the study show that the majority of farmers in the region have a strong degree of risk aversion. The results of examining the impact of socio-economic variables on crop diversification show that there is no significant relationship between crop diversification and the farmer's age. However, variables such as the risk aversion coefficient, the farmer's education, farm size, and the share of agricultural income have a significant effect on crop diversification. The share of agricultural income had the most significant impact on the choice of management tools for crop diversification. Specifically, as the share of income from agriculture increases, the likelihood of using management tools increases by 0.06%. Given the positive impact of education and income on the use of risk management tools, it is recommended to enhance farmers' awareness through agricultural extension programs and increase farmers' income by improving their cropping patterns.

Keywords

• Certainty equivalent
• Crops
• Multi-characteristic utility
• Risk

Main Subjects

• Agricultural Economics

©2025 The author(s). This is an open access article distributed under Creative Commons Attribution 4.0 International License (CC BY 4.0)

References

1. Agricultural Jihad Center of Rudpay. )2022(. Agronomy department, Statistics and Information Unit. (In Persian .( https://https://jkmaz.ir
2. Ajijola, S., Egbetokun, O.A., & Ogunbayo, I.E. (2011). Impact of risk attitudes on poverty level among rural farmers in Ogun State. Journal of Development and Agricultural Economic, 3(12), 581-587. https://www.academicjournals. org/
3. Amador, F., Sumpsi, J.M., & Romero, C. (1998). A non-interactive methodology to assess farmers’ utility function: An application to large farms in Andalusia, Spain. European Review of Agricultural Economics, 25(1), 92-109. https://doi.org/1093/erae/25.1.92
4. Amin Ravan, M., Mojaverian, M., Hosseini Yekani, A., Joolaie, R., & Viaggi, D. (2018). The effect of income risk on the pattern of optimal cultivation of crops (application of the data coverage analysis model). Journal of Economic Research and Agricultural Development of Iran, 52(3), 641-631. (In Persian). https://doi.org/22059/ijaedr. 2020.292914.668839
5. Bagheri, A. (2013). Risk and risk management from the point of view of potato farmers in Ardabil city. Iranian Agricultural Extension and education Journal, 10(1), 118-101. (In Persian) https://doi.org/20.1001.1.20081758. 1393.10.1.8.5
6. Cochran, C.B. (1977). Sampling Techniques, John Wiley, New York.
7. Dyer, J.S. (1977). On the relationship between goal programming and multi-attribute utility theory. Discussion paper 69, Management Study Center, University of California, Los Angeles.
8. Hao, A. (2010). Uncertainty, Risk Aversion and Risk Management in Agriculture. Agriculture and Agricultural Science Procedia, 1, 152-156. https://doi.org/10.1016/j.aaspro.2010.09.018
9. Hardaker, J.B., Huirne, R.B.M., & Anderson, J.R. (1997). Coping with risk in agriculture. CAB International. Wallingford. UK. 274 pp. https://www.researchgate.net/publication/40195571
10. Hardaker, J.B., Huirne, R.B.M., Anderson, J.R., & Lin, G. (2004). Coping with risk in agriculture. Second Edition. Washington: CABI Publishing. 276 pp. https://doi.org/1079/9780851998312.0000
11. Hossain, A., Saadi, S., & Amin, A. (2023). Does CEO Risk‑Aversion Affect Carbon Emission? Journal of Business Ethics, 182(1), 1171–1198. https://doi.org/10.1007/s10551-021-05031-8
12. Kumar, J.B. (1995). Trade-off between return and risk in farm planning: MOTAD and target MOTAD approach. Indian Journal of Agricultural Economics, 50(2), 193-199.
13. Malik, D., & Singh, I. (2002). Crop diversification-an economic analysis. Indian Journal of Agricultural Resources. 36(1), 61–64. https://arccarticles.s3.amazonaws.com/webArticle/articles/ijar2361012
14. Mohammadi-Amidabadi, S., Yazdani, S., & Mohammadi-Nejad, A. (2023). An investigation of crop diversity’s impact on income risk reduction of selected crops (Case study: Zanjan province). Iranian Journal of Agricultural Economics and Development Research, 54-2(3), 593-604. (In Persian). https://doi.org/22059/ijaedr.2022. 330592.669085
15. Momeni, F., Dashtbani, S., & Banui, A. (2016). The importance of the agricultural sector in maintaining the economic-social balance of the urban and rural structure of Iran. Quarterly Journal of Space Economy & Rural Development, 6(4),17-46. (In Persian). https://ensani.ir/fa/article/381287
16. Nainggolan, D., Moeis, F., & Termansen, M. (2023). "Does risk preference influence farm level adaptation strategies? – Survey evidence from Denmark. Mitigation and Adaptation Strategies for Global Change, Springer 28(40), 1-23. https://doi.org/10.1007/s11027-023-10077-2
17. Nematollahi, Z., Hosseini-yekani, S., & Hosseinzadeh, M. (2015). Estimation of Esfarayen farmers risk aversion coefficient and its influencing factors (nonparametric approach). Agricultural Economics and Development, 29(3), 284-293. (In Persian). https://doi.org/10.22067/jead2.v0i0.46466
18. Ping, Q., Amjed Ighbal, M., Abid, M., Ijaz Ahmed, U., Nazir, A., & Rehman, A. (2016). Adoption of off-farm diversification income sources in managing agricultural risks among cotton farmers in Punjab Pakistan. Journal of Applied Environmental and Biological Sciences, 6(8), 47-53. https://www.researchgate.net/publication/ 305476181
19. Rui, H., Jianjun, J., Haozhou, G., & Yuhong, T. (2019). The role of risk preferences and loss aversion in farmers’ energy-efficient appliance use behavior. Journal of Cleaner Production, 215, 305-314. https://doi.org/1016/j. jclepro.2019.01.076
20. Sani, F., & Dashti, Q. (1400). The effect of risk aversion on the technical efficiency of potato farmers in Sarab city. The 12^th National Specialized Conference on Agricultural Economics. Sanandaj. (In Persian). https://civilica.com/doc/1798406
21. Senapati, A.K. (2020). Evaluation of risk preferences and coping strategies to manage with various agricultural risks: evidence from India. Heliyon, 6(3), e03503. https://doi.org/10.1016/j.heliyon.2020.e03503
22. Sulewski, O., & Kloczko-Gajewska, A. (2014). Farmers' risk perception, risk aversion and strategies to cope with production risk: An empirical study from Poland. Studies in Agricultural Economics, 116(3), 140-147. https://doi.org/7896/j.1414
23. Sumpsi, J.M., Amador, F., & Romero, C. )1997(. On farmers’ objectives, a multi-criteria approach. European Journal of Operational Research, 96(1), 64-71. https://doi.org/10.1016/0377-2217(95)00338-x
24. Ullah, R., Shivakoti, G.P., Rehman, M., & Kamran, M.A. (2015). Catastrophic risks management at farm: The use of diversification, precautionary savings and agricultural credit. Pakistan Journal of Agricultural Sciences, 52(4), 1139-1147. https://www.researchgate.net/publication/288315786