A Promising Model of Farmers' Resilience to Floods: A case study of Sistan Plain

Document Type : Research Paper

Authors

1 Department of Agricultural Extension and Education, Ga.C., Islamic Azad University, Garmsar, Iran

2 Department of Agricultural Extension and Education, Agricultural College, College of Agricultural and Natural Resources, University of Tehran, Karaj, Iran

Abstract

A B S T R A C T
Floods are one of the most common and severe natural hazards worldwide. Due to climate change, the risk of severe floods is expected to increase. Currently, many organizations aim to enhance community resilience and manage flood hazards through educational measures. The purpose of this study is to investigate the resilience of farmers and the factors that affect their resilience, as well as their impact on reducing vulnerability to floods. A mixed-methods research approach was employed to achieve this aim. This approach involved using grounded theory and survey methods in the qualitative and quantitative phases. The qualitative findings were analyzed using Atlas. ti 9 software and the factors influencing farmers' resilience to floods were identified during this phase. Meanwhile, a model for investigating the factors affecting resilience and vulnerability to floods was developed during the quantitative phase. The model was designed and validated using structural equation modeling (SEM) with the partial least squares (PLS) method in the SmartPLS3 software. The findings from the qualitative phase demonstrated that the factors influencing farmers' resilience to floods include economic, extension and educational, legal, environmental, and infrastructure factors. In the quantitative phase, the modeling results revealed that access to basic services (ABS) was among the important dimensions of resilience. Furthermore, educational and extension factors had a significant positive effect on increasing resilience and reducing vulnerability to floods.
Extended Abstract
Introduction
The severe and widespread flooding in recent decades has caused instability in agricultural production and reduced yields, posing a serious challenge to the agricultural sector's ability to provide sustainable food. Academics and programmers are working to reduce the damage caused by natural hazards, including flooding, by developing different approaches and appropriate programs. One such approach is to empower and strengthen the capacities of vulnerable local communities, enabling them to better prepare for, cope with, and recover from the detrimental effects of natural hazards. The lack of coherent policies for coping with natural risks, such as flooding, and implementing partial policies without sufficient attention to resources has significantly strained development policies and has presented serious challenges in areas such as the Sistan Plain.
 
Methodology
The mixed method was used for this research. In the qualitative stage, factors influencing the farmers' resilience in the face of flooding were identified. The statistical population in the qualitative phase consisted of experts from the social working group of Sistan Plain, consisting of 20 managers and experts in plant production, irrigation, agricultural education, and extension, as well as facilitators. The participants were selected through a non-probability purposive sampling method. The data analysis was performed simultaneously with the data collection process using Atlas. ti 9 software in three stages as Open, Axial, and Selective coding. The survey method was used in the quantitative phase. The statistical population consists of 56,639 farmers, of whom 382 were surveyed using the Morgan table and a simple random sampling method. The data collection tool was a questionnaire. Smart-PLS 3 software was applied for the analysis of quantitative data.
 
 
 
Results and discussion
Qualitative data analysis revealed that several factors play a critical role in increasing the resilience of farmers to floods. The main drivers include educational and extension, economic, legal, environmental, and infrastructural factors. The results of the quantitative phase showed that the mean resilience index in all dimensions is below the average limit of 2.5. Therefore, the households did not have adequate resilience to floods.  The structural equation model revealed that educational-extension services, legal, environmental, infrastructural, and economic factors and indicators related to each of these structures accounted for about 24% of the variance in resilience.  The results of the analysis of factors influencing vulnerability to floods indicated that access to basic services, adaptive capacity, assets, stability, and social security networks, economic, educational and extension services, legal, environmental, and infrastructural factors, as well as indicators related to each of these structures, represented a total of approximately 66 percent of the variance in vulnerability. Findings indicated that educational and extension factors not only directly impact resilience to floods but also reduce farmers' vulnerability to this natural hazard. Farmers who have taken advantage of the new findings from agricultural extension programs report a higher ability to cope with floods. Legal, environmental, and infrastructural factors involve discussions, negotiations, mediation, dispute management, elections, public consultations, support, and other decision-making processes. To effectively contribute to enhancing resilience to floods, integrated risk management should be implemented through a multi-level and multi-central governance system. Farmers perceive that public investments in infrastructure play a crucial role in enhancing resilience and decreasing vulnerability. Moreover, access to media (newspapers, television, the internet, and mobile devices) and proximity to appropriate and standardized medical and health centers have a major impact on coping with floods.
 
Conclusion
The results indicate that the most effective approach to flood management involves a combination of structural and non-structural measures. Structural measures include river management projects that consider the frequency of flood occurrences. Non-structural measures, on the other hand, include extension and educational factors. Farmers can overcome numerous challenges by prioritizing legal, environmental, and infrastructural factors, particularly in the strategically important Sistan Plain that protects the country's borders.
 
Funding
There is no funding support.
 
Authors’ Contribution
Zahra Khaki-Firouz: Data collection, preparation, analysis and editing of the original draft. Mehrdad Niknami: Methodology, project management, supervision, review and editing. Marzieh Keshavarz: Conceptualization, software review, validation, writing - review and editing. Mohammad Sadeq Sabouri: Validation, review and editing..
 
Conflict of Interest
Authors declared no conflict of interest.
 
Acknowledgments
 We are grateful to all the scientific consultants of this paper.

Keywords

Main Subjects

References

  1. Akter, M., Chowdhury, A. K., & Chowdhury, M. A. H. (2012). Soil organic matter, mineral nutrients and heavy metals status of some selected regions of Bangladesh. Journal of Environmental Science and Natural Resources, 5(2), 1–9.
  2. Albuquerque, P. H., & Rajhi, W. (2019). Banking stability, natural disasters, and state fragility: Panel VAR evidence from developing countries. Research in International Business and Finance, 50, 430–443. https://doi.org/10.1016/j.ribaf.2019.06.001
  3. Ayanwuyi, E., Kuponiyi, F. A., Ogunlade, I., & Oyetoro, J. O. (2010). Farmers perception of impact of climate changes on food crop production in Ogbomosho agricultural zone of Oyo State, Nigeria. Global Journal of Human Social Science, 10(7), 33–39.
  4. Bruck, T., D Errico, M., & Pietrelli, R. (2019). The effects of violent conflict on household resilience and food security: Evidence from the 2014 Gaza conflict. World Development, 119, 203–223. DOI: 10.1016/j.worlddev.2018.05.008
  5. Bryan, E., Ringler, C., Okoba, B., Roncoli, C., Silvestri, S., & Herrero, M. (2013). Adapting agriculture to climate change in Kenya: Household strategies and determinants. Journal of Environmental Management, 114, 26–35. https://doi.org/10.1016/j.jenvman.2012.10.036.
  6. Cinner, J. E., & Barnes, M. L. (2019). Social Dimensions of Resilience in Social-Ecological Systems. One Earth, 1(1), 51–56. https: doi.org/10.1016/j.oneear.2019.08.003
  7. Deng, P., Zhang, M., Hu, Q., Wang, L., & Bing, J. (2022). Pattern of spatio-temporal variability of extreme precipitation and flood-waterlogging process in Hanjiang River basin. Atmospheric Research, 276, 106258. https://doi.org/10.1016/j.atmosres.2022.106258.
  8. Gangadharappa, H., Pramod, K., & Shiva, K. H. G. (2007). Gastric floating drug delivery systems: a review. Indian Journal of Pharmaceutical Education and Research, 41(4), 295–305.
  9. Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2–24. doi/10.1108/ebr-11-2018-0203/full/html.
  10. Hair Jr, J., Hair Jr, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2021). A primer on partial least squares structural equation modeling (PLS-SEM). Sage publications. https://www.pls-sem.net/pls-sem-books/a-primer-on-pls-sem-3rd-ed/
  11. Hamidi, A. R., Zeng, Z., & Khan, M. A. (2020). Household vulnerability to floods and cyclones in Khyber Pakhtunkhwa, Pakistan. International Journal of Disaster Risk Reduction, 46, 101496. https://doi.org/10.1016/j.ijdrr.2020.101496
  12. Keshavarz, M., & Karami, E. (2014). Farmers’ decision-making process under drought. Journal of Arid Environments, 108, 43–56. https://doi.org/10.1016/j.jaridenv.2014.03.006.
  13. Keshavarz, M., & Moqadas, R. S. (2021). Assessing rural households’ resilience and adaptation strategies to climate variability and change. Journal of Arid Environments, 184, 104323. https://doi.org/10.1016/j.jaridenv.2020.104323.
  14. Khakifirouz, Z., Niknami, M., Keshavarz, M., & Sabouri, M. S. (2024a). From the farmers’ point of view, how threatening and harmful are natural hazards? A Q methodology. International Journal of Disaster Risk Reduction, 102, 104278. https://doi.org/10.1016/j.ijdrr.2024.104278.
  15. Khakifirouz, Z., Niknami, M., Keshavarz, M., & Sabouri, M. S. (2024b). Transition from vulnerability to resilience to dust storms: a mixed-methods research. Natural Hazards. https://doi.org/10.1007/s11069-024-06901-w.
  16. Kurnio, H., Fekete, A., Naz, F., Norf, C., & Jüpner, R. (2021). Resilience learning and indigenous knowledge of earthquake risk in Indonesia. International Journal of Disaster Risk Reduction, 62, 102423. https://doi.org/10.1016/j.ijdrr.2021.102423.
  17. Lee, V.-H., Ooi, K.-B., Choong, C.-K., & Wong, K.-L. (2013). Organizational Learning: A Mediating Factor between Technological Innovation and TQM. Diversity, Technology, and Innovation for Operational Competitiveness: Proceedings of the 2013 International Conference on Technology Innovation and Industrial Management, 2–279.
  18. Leeuwis, C. (2013). Communication for rural innovation: rethinking agricultural extension. John Wiley & Sons. https://modares.ac.ir/uploads/En-Agr.Doc.
  19. Mahmoudzadeh, J., Sabouri, M. S., Niknami, M., & Danaee, E. (2022). Analysis of Scientific–Executive Capability and Up-to-date of Iran’s Parliament Approvals in Drought Crisis Management. International Journal of Agricultural Management and Development (IJAMAD), 12(3), 197–207. [In Persian].
  20. Murendo, C., Kairezi, G., & Mazvimavi, K. (2020). Resilience capacities and household nutrition in the presence of shocks. Evidence from Malawi. World Development Perspectives, 20, 100241.https://doi.org/10.1016/j.wdp.2020.100241
  21. Okunola, O. H., & Olawuni, P. O. (2022). Determinants of household flood resilience strategies in Kaduna Metropolis, Nigeria. Urban Climate, 44, 101216. https://doi.org/10.1016/j.uclim.2022.101216
  22. Rashki, A., Kaskaoutis, D. G., Francois, P., Kosmopoulos, P. G., & Legrand, M. (2015). Dust-storm dynamics over Sistan region, Iran: Seasonality, transport characteristics and affected areas. Aeolian Research, 16, 35–48. https://doi.org/10.1016/j.aeolia.2014.10.003
  23. Scherzer, S., Lujala, P., & Rød, J. K. (2019). A community resilience index for Norway: An adaptation of the Baseline Resilience Indicators for Communities (BRIC). International Journal of Disaster Risk Reduction, 36, 101107. https://doi.org/10.1016/j.ijdrr.2019.101107
  24. Smith, L. C., & Frankenberger, T. R. (2018). Does Resilience Capacity Reduce the Negative Impact of Shocks on Household Food Security? Evidence from the 2014 Floods in Northern Bangladesh. World Development, 102, 358–376. https://doi.org/10.1016/j.worlddev.2017.07.003
  25. Wamalwa, I. W., Mburu, B. K., & Mang’uriu, D. G. (2016). Agro climate and weather information dissemination and its influence on adoption of climate smart practices among small scale farmers of Kisii country, Kenya. J Biol Agric Healthc, 6(10), 14–23. https://kenyaclimatedirectory.org/resources/66fd2b26ea58e.
  26. Wang, C., Guo, J., & Kuo, M. (2020). The building of social resilience in Sichuan after the Wenchuan earthquake: A perspective of the socio-government interactions. Safety Science, 126, 104662. https://doi.org/10.1016/j.ssci.2020.104662.
Journal of Rural Research
Volume 16, Issue 2
August 2025
Pages 107-124

Files

History

  • Receive Date: 03 April 2025
  • Revise Date: 30 May 2025
  • Accept Date: 07 July 2025

Share

How to cite

Statistics