Vulnerability Analysis For Wheat Growers encounter With dust by using the fuzzy logic (Case Study: dehloran township)



Introduction : One of the environmental concerns during the past decade is dust pollutant. This phenomenon is extensively known around the globe. In Iran, this destructive phenomenon has created serious problems among environmental policy-makers. Dust particles has been a dominant phenomenon in western Iran during recent years, which has a wide range of negative impacts on western half of the nation’s territories. Farmers seem to face extensive loss during the onset of dust. Although they are vulnerable towards dust phenomenon, there has been limited study their vulnerability rate. The term ‘vulnerability’ is used in many different ways by various scholarly communities. The resulting disagreement about the appropriate definition of vulnerability is a frequent cause for misunderstanding in interdisciplinary research on climate change and a challenge for attempts to develop formal models of vulnerability. vulnerability is most often conceptualized as being constituted by a components that include exposure and sensitivity to perturbations or external stresses, and the capacity to adapt. Exposure is the nature and degree to which a system experiences environmental or socio-political stress. The characteristics of these stresses include their magnitude, frequency, duration and areal extent of the hazard. Sensitivity is the degree to which a system is modified or affected by perturbations. Adaptive capacity is the ability of a system to evolve in order to accommodate environmental hazards or policy change and to expand the range of variability with which it can cope.
four dimensions are fundamental to describe a vulnerable situation. 1)System: The system of analysis, such as a coupled human–environment system, a population group, an economic sector, a geographical region, or a natural system. Note that some research traditions restrict the concept of vulnerability to social systems or coupled human–environment systems. whereas others apply it to any system that is potentially threatened by a hazard, 2) Attribute of concern: The valued attribute(s) of the vulnerable system that is/are threatened by its exposure to a hazard. Examples of attributes of concern include human lives and health, the existence, income and cultural identity of a community, and the biodiversity, carbon sequestration potential and timber productivity of a forest ecosystem, 3) Hazard: A potentially damaging influence on the system of analysis. United Nations (2004) defines a ‘hazard’ broadly as ‘‘a potentially damaging physical event, phenomenon or human activity that may cause the loss of life or injury, property damage, social and economic disruption or environmental degradation’’. Hence, a hazard is understood as some influence that may adversely affect a valued attribute of a system. A hazard is generally but not always external to the system under consideration. For instance, a community may also be threatened by hazardous business activities or by unsustainable land management practices within this community. Hazards are often distinguished into discrete hazards, denoted as perturbations, and continuous hazards, denoted as stress or stressor, 4)Temporal reference: The point in time or time period of interest. Specifying a temporal reference is particularly important when the risk to a system is expected to change significantly during the time horizon of a vulnerability assessment, such as for long-term assessments of anthropogenic climate change.

Methodology: This research paradigmatically is type of quantitative research and in terms of the facts and data processing is a descriptive study (non-experimental) - survey and in terms of the target is applied Therefore, the purpose of this study was to assess the vulnerability of farmers towards dust. The population in this study comprised of wheat farmers (N = 2105) in which 330 wheat farmers from Markazi and Moosian Rural districts were selected using stratified proportionate cluster sampling techniques. To assess farmers' vulnerability, adaptive capacity and sensitivity towards dust was used as indicators of vulnerability. 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.

Results: At the end by using the fuzzy logic in software Matlab, the Adaptive capacity, Sensitivity and vulnerability of studied farmer Was calculated. Based on the findings of Fuzzy Logic, 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 0.588 and Anaran Rural district with rate of 0.563 were placed in second and third place. the sensitivity of Dasht Abas Rural district and Anaran Rural district Farmers with the rate of 0.790 had the highest sensitivity and Nahr anbar Rural district with rate of 0.742 were placed in second place. At the end the vulnerability of Anaran Rural district Farmers with the rate of 0.600 had the highest vulnerability,and Nahr anbar Rural district with rate of 0.580 and Dasht Abas Rural district with rate of 0.566 were placed in second and third place.

conclusions :Results revealed that wheat farmers had high adaptive capacities and high sensitivity towards dust which in turn made them somewhat vulnerable towards dust. 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, reducing sensitivity of farmers and thus lowering their vulnerability towards dust.


پورطاهری، مهدی؛ پریشان، مجید؛ افتخاری، عبدالرضا رکن‌الدین و عسگری، علی، 1390، سنجش و ارزیابی مؤلفه‌های مبنایی مدیریت ریسک زلزله (مطالعة موردی: مناطق روستایی شهرستان قزوین)، پژوهش‌های روستایی، بهار، شمارة 2 (1) (پیاپی 5)، صص. 150-115.
صحنعلی‌زاده، مهدی و حیدری، محمدامین، 1392، بررسی روند ماهانه و سالانة تعداد روزهای گرد و غباری (ایستگاه آبادان، ایران)، اولین همایش بین‌المللی ریزگردها، مدیریت عوامل و پیامدها، دانشگاه لرستان، اردیبهشت.
ناصری، حمیدرضا و صارمی‌نژاد، فردوس، 1390، مقایسة ارزیابی آسیب‌پذیری آبخوان به روش‌های DRASTIC و منطق فازی (مطالعة موردی: دشت گل‌گیر مسجد سلیمان)، فصلنامة جغرافیای طبیعی، سال 4، شمارة 11، بهار.
Acosta, L.A. & Eugenio, J.M.A., 2014, Defuzzification of Fuzzy Concepts to Support Vulnerability Assessments of Climate Change Impacts in the Philippines, Advances in Environmental Research, New York: Nova Science, PP. 165-194.
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, PP. 21–23.
Adger, W.N., 2006, Vulnerability, Global Environmental Change 16, PP. 268–281.
Adger, W.N., 1999, Social Vulnerability to Climate Change and Extremes in Coastal Vietnam, World Development, Vol. 27, No. 2, PP. 249-269.
Adger, W.N.; Brooks, N.; Bentham, G.; Agnew, M. & Eriksen, S., 2004, New Indicators of Vulnerability and Adaptive Capacity, Tyndall Centre for Climate Change Research, Technical Report 7.
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, Reg Environ Change Vol. 8, PP. 137–149.
Barnett, J. & Adger, W.N., 2007, Climate Change, Human Securityand Violent Conflict,Political Geography, Vol. 26, Issue 6, August, PP. 639–655.
Beaula, Th. & Partheeban, J., 2013, Risk Assessment of Natural Hazards in Nagapattinam District Using Fuzzy Logic Model, International Journal of Fuzzy Logic Systems (IJFLS), Vol. 3, No. 3, July.
Bojorquez-Tapia, L.A., 2002, Integrating Fuzzy Logic, Optimization, and GIS for Ecological Impact Assessments, Environmental management, Vol. 30, No. 3, PP. 418–433.
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 climate res, Vol. 8, PP. 135-150.
Chambers, R., 1989, Vulnerability, Coping and Policy (Editorial Introduction), IDS Bulletin, Vol. 37, No. 4, September.
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, PP. 97–111.
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.
Eakin, H. & Bojorquez-Tapia, L.A., 2008, Insights into the Composition of Household Vulnerability from Multicriteria Decision Analysis, Global Environmental Change 18, PP. 112–127.
Fussel, Hans M., 2007, Vulnerability: A generally applicable conceptual framework for climate change research, Global Environmental Change 17, PP. 155–167.
Gallopin, G.C., 2006, Linkages between Vulnerability, Resilience, and Adaptive Capacity, Global Environmental Change 16, PP. 293–303.
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), PP. 74-88.
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, ISSN: 2276-7770, March, Vol. 2 (2), PP. 053-060.
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, PP. 607– 613.
Loayza, N.V., Olaberria, E., Rigolini, J. & Christiaensen, L., 2012, Natural Disasters and Growth: Going Beyond the Averages, World Development Vol. 40, No. 7, PP. 1317–1336.
Pelling, M. & Uitto, J.I., 2001, Small Island Developing States: Natural disaster vulnerability and global change, Environmental Hazards 3, PP. 49–62.
Sivanandam, S.N., Sumathi, S. & Deepa, S.N., 2007, Introduction to Fuzzy Logic using Matlab, Springer-Verlag Berlin Heidelberg.
Smit, B. & Wandel, J., 2006, Adaptation, Adaptive Capacity and Vulnerability, Global Environmental Change 16, PP. 282–292.
Thwaites, R., Curtis, A., Mazur, N. & Race, D., 2008, Understanding Rural Landholder Responses to Climate Change, Institute for Land, Water & Society Charles Sturt University Report No. 48.
Zhong, L., Liu, L. & Liu, Y., 2010, International Conference on Agricultural Risk and Food Security 2010 Natural Disaster Risk Assessment of Grain Production in Dongting Lake Area China, Agriculture and Agricultural Science Procedia 1, PP. 24–32.
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, Vol. 1, No. 3.