نوع مقاله : مقاله های برگرفته از پایان نامه
نویسندگان
1 دانشجوی دکتری جغرافیا و برنامه ریزی روستایی، دانشکده جغرافیا دانشگاه تهران، تهران، ایران
2 استاد گروه جغرافیا و برنامه ریزی روستایی، دانشکده جغرافیا، دانشگاه تهران، تهران، ایران
3 دانشیارگروه جغرافیا و برنامه ریزی روستایی، دانشکده جغرافیا، دانشگاه تهران، تهران، ایران
4 استادیار گروه جغرافیا و برنامه ریزی روستایی، دانشکده جغرافیا، دانشگاه تهران، تهران، ایران
چکیده
کلیدواژه‌ها
عنوان مقاله [English]
نویسندگان [English]
Since the beginning of the human civilization, drought has had severe and sometimes catastrophic effects on human life activities throughout the world. Drought by itself is not a catastrophe, but its impact on the people and on the environment, determines whether it is a disaster or not. Drought is a natural hazard, having direct and indirect consequences on our planet, particularly on the livelihoods of agricultural utilization. In this regard, in recent decades, policy makers of rural development, have noticed "rural economic resilience" as an important concept. Especially, the economic resilience of agricultural utilization is closely related to natural phenomena, including drought. This study analyses farmer’s economic resilience against droughts. This is a descriptive – analytic research survey. We use questionnaire to collect data and stratified sampling method. Analysis level is 75 villages chosen from Nowbandeghan, Sheshdeh and Gharabolagh, central part & Shibkouh. We use 18 economic resilience criteria. Our population consists of 63409 residents comprising 18855 families. The sample contains 382 families calculated using Kukran model. Findings indicate that diversity in labor with eigenvalue of 3.899 has the most impact out of the four factors. Ranking models used are SWARA & ARAS. Nowbandeghan, Shashdeh and Gharabolagh Central part & Shibkouh are respectably the most economically resilience parts.
Extended Abstract
Introduction:
Since the beginning of human civilization, drought has had profound and sometimes catastrophic effects on human vital activities worldwide. Drought itself is not a disaster; it is its impact on people and the environment that determines whether or not it is catastrophic. Drought is one of the natural hazards that has direct and indirect consequences on the planet, and in particular on the livelihoods of farmers. In this regard, rural economic resilience is one of the concepts that has been of interest to rural development policymakers in recent decades; especially the economic resilience of agricultural holders is closely linked to natural phenomena such as drought. The purpose of this paper is to analyze the economic resilience of farmers to the effects of drought. With respect to the subject under study and the purpose of the study, the research hypothesis is explained by the following application: It seems that there is a relationship between the economic resilience of rural communities and the effects of drought.
Methodology:
The methodology of the present study is descriptive-analytical based on the survey method used in this research. The statistical population of the rural areas of Fasa city is63409 people and the sample size is calculated by Cochran formula of 382 households. The level of analysis in this study is 75 selected villages, located in four central districts: Sixth and Gharablagh, Shibkoh, Fasa city, and Household Analysis Unit. In this study, the available stratified sampling method was used. The study used 18 indicators of economic resilience. Validity - Reliability of the questionnaire was confirmed by experts and rural experts with Cronbach's alpha coefficient of 0.850. After completing the questionnaires and collecting samples, the data were entered into SPSS software package and the technique used in this research is "cluster analysis". They were then ranked by ArcGIS, Excel, ARAS, SWARA and factor analysis techniques.
Findings:
At first, the research variables were obtained through a questionnaire. Before performing the factor analysis, the suitability of the data set for analysis was assessed by BTS and KMO tests. The value of KMO obtained is 0.804 which indicates that the selected indices are satisfactory for using factor analysis technique. Bartlett's value was 1562.624 which was significant at 99% confidence level. The next step in the factor analysis is to determine the values as follows: - Eigenvalues: (a) eigenvalues of non-rotating extractive agents and b) values of rotational extraction agents. As can be seen, the first four factors have values greater than 1 and account for a total of 53.222% of the variance of the set of 18 indicators, which is an acceptable percentage. Therefore, although all of these factors have eigenvalues larger than the unit, for example the importance and role of the first factor is more than several times the fourth factor. The most important factor in this analysis is Factor 1, which alone accounts for 22.933% of the variance. The second, third and fourth factors accounted for 15.589, 7.945 and 6.756% of the variance, respectively. Because the eigenvalues of the following indices are less than 1, they were not significant and could not be used in subsequent analyzes. The results of the T-test for measuring resilience of users show that there is a significant difference (3.5 = sig) between the baseline (3) and the calculated value of 3.51. Since the calculated value is higher than the standard limit, we conclude that the impact of resilience factors on resilience of agricultural use against the effects of drought is evaluated.
Conclusion:
Today, drought is one of the natural disasters that due to its very complex and gradual crawling and overcoming it requires extensive facilities and has greatly affected the livelihoods of the farmers in the area involved. The present study aimed to analyze the economic resilience of agricultural holders against the effects of drought investigated by factor analysis. So the first factor: The specific amount of this factor is 3.899 which alone accounts for 22.933% of the variance of the population and has the most influence among the four factors. This factor includes variables such as "government support for agricultural jobs, diversification of economic activities and employment in the countryside, creation of conversion and supplementary industries in agriculture, start-up of new businesses, diversification of job skills, and increased post-employment." Rural tourism boom "has a positive and high correlation. This can be attributed to" diversity of skills in the workforce and employment ". Factor 2: The Eigenvalue of the second factor is 2.650 which alone accounts for 15.589% of the variance in society. Variables loaded on this factor are: "Increase in service providers, brokers, land use change, deprivation of agricultural land and stabilization in the consumer market", so this factor can be a factor. Named "Retail Performance and Land and Property Performance". Factor 3: The specific amount of this factor is 1.351 which accounts for 7.945% of the variance. Variables of this factor are "the creation of non-agricultural activities among the villagers, the increase in the tendency for economic activities of the handicrafts and the increase of the skills of the workers in other non-agricultural sectors", "the development of employment levels". Factor Four: The Eigenvalue of the second factor is 1.149 which alone accounts for 6.756% of the variance in society. Variables loaded on this factor are: "The reduction of liquidity between agricultural holders and financial credit in drought conditions does not require borrowing from others; hence this factor can be a factor of" flexibility ". Acceptance and Financial Facilities ».
The final weight of the model dimensions was obtained by following the steps of the Swara method. Factors affecting economic resilience of agricultural farmers to the effects of drought in rural areas of Fasa city, diversity and skills factor with a final weight of 0.339, retail and land efficiency with a weight of 0.269 In the second place, the factor of development of employment levels with the weight of 0.221 was in the third place and finally the flexibility and financial facilities with the final weight of 0.177 were in the fourth place. In order to spatially analyze the rural areas of Fasa city and classify it using ARAS Multi-criteria Decision Making Technique. Given that the number of villages surveyed was 75, showing the spatial analysis of the whole village on the map makes it so crowded that the researchers decided to limit the spatial representation to only four parts of the city.
کلیدواژه‌ها [English]
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