Adaptive capacity assessment of wheat farmers Towards dust (Case Study: dehloran township)

Document Type : Research Paper

Abstract

the purpose of this study was to assess the adaptive capacity of farmers towards dust. The population in this study comprised of wheat farmers (N = 2105) in which 330 wheat farmers from Markazi and Moosian townships were selected using stratified proportionate cluster sampling techniques. The first To assess farmers' adaptive capacity towards dust was used as indicators of adaptive capacity. Using AHP techniques, 15 experts weighted the indicators through 2*2 matrices and Expert Choice Software was utilized as tool for further analysis. Composite indicators were then developed for further assessments. Fuzzy logic and MATLAB Software was used to determine the adaptive capacity of wheat farmers. Result revealed that wheat farmers the Adaptive capacity of Dasht Abas Rural district Farmers with the rate of 0.605 had the highest Adaptive capacity and Nahr anbar Rural district with rate of 0.508 and Anaran Rural district with rate of 0.563 were placed in second and third place. The implication of this study aids policy-makers in Dehloran Township to allocated resources based on farmers vulnerability level. Furthermore, the result of this study helps policy-makers to plan for enhancement of farmers' adaptive capacities and thus lowering their vulnerability towards dust.

Keywords

References

  1. Acosta-Michlik, L., Klein, R.J.T., Kumar, K., Eierdanz, F., Alcamo, J., Kromker, D., Carius, A., & Tanzler, D. (2005). How vulnerable is India to climatic stress? Measuring vulnerability to drought using the Security Diagram concept. An International Workshop Holmen Fjord Hotel, Asker, near Oslo, 21–23.
  2. Alcamo, J., Acosta-Michlik, K., Carius, A., Eierdanz, F., Klein, R., Kromker, D., & Tanzler, D. (2008). A new approach to quantifying and comparing vulnerability to drought. Regional Environmental Change, 8, 137–149.
  3. Acosta, L.A., & Eugenio, J.M.A. (2014). Defuzzification of Fuzzy Concepts to Support Vulnerability Assessments ofClimate Change Impacts in the Philippines. Advances in Environmental Research, New York: Nova Science, 2014, 165-194 .
  4. Buckle, Ph. (2000). New approach to assessing vulnerability and resilience. Australian Journal of Emergency Management. Winter2000
  5. Bartlett. J.E., Kotrlik, J.W., & Higgins, Ch. C. (2001). Organizational Research: Determining Appropriate Sample Size in Survey Research. Information Technology, Learning, and Performance Journal, 19(1),
  6. Bojorquez-Tapia, L.A. (2002). Integrating Fuzzy Logic, Optimization, and GIS for Ecological Impact Assessments. Environmental management, 30)3(, 418–433.
  7. Boochani, M.H., & Fazeli, D. (2011). Environment Challenges and its Consequences Case Study: Dust and its Impact in the West of Iran. Quarterly Of Doctrine of Policy Making, 2(3),  (In Farsi).
  8. Beaula, Th., & Partheeban, J. (2013). Risk Assessment of natural hazards in nagapattinam district using fuzzy logic model. International Journal of Fuzzy Logic Systems (IJFLS), 3(3), 
  9. Cassel- Gintz, M.A., Ludeke, M.K.B., Petschel-Held, G., Reusswig, F., Plochl, M., Lammel, G., & Schellnhuber, H. J. (1997). Fuzzy logic based global assessment of the marainalitv of aaricultural land use. Climate Research, 8, 135-150.

10. CHeung, W.W. L., Pitcher, T.J., &Pauly, D. (2005). A fuzzy logic expert system to estimate intrinsic extinction vulnerabilities of marine fishes to fishing. Biological Conservation, 124, 97–111.

11. Cutter, S.L., Emrich, CH.T., Webb, J.J., & Morath, D. (2009).  Social Vulnerability to Climate Variability Hazards: A Review of the Literature. Hazards and Vulnerability Research Institute Department of Geography University of South Carolina Columbia, SC 29208.

12. Eakin, H., & Bojorquez-Tapia, L.A. (2008). Insights into the composition of household vulnerability from multicriteria decision analysis. Global Environmental Change, 18, 112–127.

13. Gentle, P., & Maraseni, T.N. (2012). Climate change, poverty and livelihoods: adaptation practices by rural mountain communities in Nepal. Environmental Science & Policy, 21 (2012) 24 – 34.

14. Hahn, M.B., Riederer, A.M., & Foster, S.O. (2009). The Livelihood Vulnerability Index: A pragmatic approach to assessing risks from climate variability and change—A case study in Mozambique. Global Environmental Change, 19(1): 74-88.

15. Ingram, J.C., Franco, G., Rumbaitis-del rio, C., & Khazai, B. (2006). Post-disaster recovery dilemmas: challenges in balancing short-term and long-term needs for vulnerability reduction. Environmental Science & Policy, 9, 607– 613.

16. Ifeanyi-obi, C., Etuk, U.R., & Jike-wai, O. (2012). Climate Change, Effects and Adaptation Strategies; Implication for Agricultural Extension System in Nigeria, Greener Journal of Agricultural Sciences, 2(2), 53-60.

17. Khaledi, F. (2014).  Adaptive capacity  Analysis of wheat farmers against Climate change. (case study: Sarpolzohab township). M.Sc.Thesis,  Razi University (In Farsi).

18. Loayza, N.V., Olaberria, E., Rigolini, J., & CHristiaensen, L. (2012). Natural Disasters and Growth: Going Beyond the Averages. World Development, 40(7), 1317–1336.

19. Naseri, H.R., & Sarami nejad, F. (2011). Compare aquifer vulnerability assessment methods and fuzzy logic DRASTIC: A Case Study of Dashte gel gir Masjed Soleyman. Journal Natural Geography, 4(11),   (In Farsi).

20. Pelling, M. & Uitto, J.I. (2001). Small island developing states: natural disaster vulnerability and global change. Environmental Hazards, 3 (2001) 49–62.

21. Smit, B., & Wandel, J. (2006). Adaptation, adaptive capacity and vulnerability. Global Environmental Change, 16, 282–292.

22. Sivanandam, S.N., Sumathi, S., & Deepa, S.N. (2007). Introduction to Fuzzy Logic using Matlab. Springer-Verlag Berlin Heidelberg 2007.

23. Sahn Alizadeh, M., & Heidari, M.A. (2013). Study Of Monthly And Annual Trends In Number Of Dust Days (Abadan, Iran). The first international conference on Dust Haze, management of factors and consequences, lorestan university, 14-15- May 2013 (In Farsi).

24. Thwaites, R., Curtis, A., Mazur, N., & Race, D. (2008). Understanding rural landholder responses to climate change. Institute for Land, Water & Society Charles Strut University Report No. 48.

25. Ueda, M., Matsubatashi, T., & Sawada. Y. (2013). Natural disasters and suicide: Evidence from Japan. Social Science & Medicine, 82, 126-133.

26. Zlateva, P., Pashova, L., Stoyanov, K., &Velev, D. (2011). Social Risk Assessment from Natural Hazards Using Fuzzy Logic. International Journal of Social Science and Humanity, 1,

 

Iranian Journal of Agricultural Economics and Development Research
Volume 47, Issue 4
April 2017
Pages 901-912

Files

Share

How to cite

Statistics