اخبار و اعلانات

 آمار

تعداد نشریات283
تعداد نشریات سازمان81
تعداد شماره‌ها2,766
تعداد مقالات31,497
تعداد مشاهده مقاله39,693,394
تعداد دریافت فایل اصل مقاله17,250,008
اردالی, فاطمه, طرازکار, محمدحسن, نصر نیا, فاطمه, شکوهی, زینب. (1402). عوامل مؤثر بر آسیب پذیری معیشتی کشاورزان در مناطق روستایی جنوب دریاچه بختگان. سامانه مدیریت نشریات علمی, 26(4), 65-90. doi: 10.30490/rvt.2023.362242.1525
فاطمه اردالی; محمدحسن طرازکار; فاطمه نصر نیا; زینب شکوهی. "عوامل مؤثر بر آسیب پذیری معیشتی کشاورزان در مناطق روستایی جنوب دریاچه بختگان". سامانه مدیریت نشریات علمی, 26, 4, 1402, 65-90. doi: 10.30490/rvt.2023.362242.1525
اردالی, فاطمه, طرازکار, محمدحسن, نصر نیا, فاطمه, شکوهی, زینب. (1402). 'عوامل مؤثر بر آسیب پذیری معیشتی کشاورزان در مناطق روستایی جنوب دریاچه بختگان', سامانه مدیریت نشریات علمی, 26(4), pp. 65-90. doi: 10.30490/rvt.2023.362242.1525
اردالی, فاطمه, طرازکار, محمدحسن, نصر نیا, فاطمه, شکوهی, زینب. عوامل مؤثر بر آسیب پذیری معیشتی کشاورزان در مناطق روستایی جنوب دریاچه بختگان. سامانه مدیریت نشریات علمی, 1402; 26(4): 65-90. doi: 10.30490/rvt.2023.362242.1525

عوامل مؤثر بر آسیب پذیری معیشتی کشاورزان در مناطق روستایی جنوب دریاچه بختگان

روستا و توسعه
مقاله 3، دوره 26، شماره 4 - شماره پیاپی 104، اسفند 1402، صفحه 65-90    اصل مقاله (662.47 K)
نوع مقاله: مقاله پژوهشی
شناسه دیجیتال (DOI): 10.30490/rvt.2023.362242.1525
نویسندگان
فاطمه اردالی1؛ محمدحسن طرازکار* 2؛ فاطمه نصر نیا3؛ زینب شکوهی3
1دانشجوی دکتری گروه اقتصاد کشاورزی، دانشکده کشاورزی، دانشگاه شیراز، شیراز، ایران.
2دانشیار گروه اقتصاد کشاورزی، دانشکده کشاورزی،دانشگاه شیراز، شیراز، ایران.
3استادیار گروه اقتصاد کشاورزی، دانشکده کشاورزی، دانشگاه شیراز، شیراز، ایران.
چکیده
تغییرات آب و هوایی اثرات قابل توجهی بر منابع آبی در مناطق خشک و نیمه ­خشک داشته است و می­ تواند بر آسیب ­پذیری کشاورزان و رفاه خانوارهای روستایی نیز مؤثر باشد. لذا در پژوهش حاضر، عوامل تعیین کننده میزان آسیب ­پذیری خانوارهای کشاورز در مناطق روستایی جنوب دریاچه بختگان واقع در استان فارس با تأکید بر تغییر اقلیم مورد ارزیابی قرار گرفت. همچنین آسیب ­پذیری خانوارهای کشاورز منطقه مورد مطالعه با استفاده از شاخص آسیب ­پذیری معیشتی (LVI) مورد سنجش قرار گرفت و از مدل رگرسیون بتا جهت ارزیابی عوامل مؤثر بر سطوح آسیب‌پذیری استفاده شد. حجم نمونه بر اساس فرمول کوکران 350 خانوار روستایی تعیین شد. داده­ ها و اطلاعات مورد نیاز از طریق تکمیل پرسش­نامه و مصاحبه حضوری در سال ۱۴۰۱ جمع‏ آوری شد. نتایج مدل رگرسیون بتا نشان داد که سن سرپرست خانوار، بعد خانوار، سطح تحصیلات سرپرست خانوار، دسترسی به اطلاعات هواشناسی، عضویت در تعاونی­ ها، درآمد خارج از مزرعه و همچنین دریافت وام بانکی تأثیر معنی­ داری بر آسیب ­پذیری خانوارهای روستایی منطقه مورد مطالعه دارد. بر اساس نتایج به دست آمده، توصیه می‏ شود سیاست توسعه روستایی بر روی نکات کلیدی که تاب­ آوری و ظرفیت سازگاری خانوار کشاورز را ارتقا می ­دهد از جمله آموزش، درآمدزایی، دسترسی به اطلاعات هواشناسی و عضویت در تعاونی‌ها هدف‌گذاری شود.
کلیدواژه‌ها
رگرسیون بتا؛ شاخص آسیب‌پذیری معیشتی؛ تغییرات اقلیمی؛ دریاچه بختگان
عنوان مقاله [English]
Factors affecting livelihood Vulnerability of Farmers in Rural Areas of Southern Bakhtegan Lake
نویسندگان [English]
F. Ardali1؛ M.H. Tarazkar2؛ F. Nasrniya3؛ Z. Shokoohi3
1PhD Student of Agricultural Economics, Shiraz University, Shiraz, Iran.
2Associate Professor of Agricultural Economics, Shiraz University, Shiraz, Iran.
3Assistant Professor of Agricultural Economics, Shiraz University, Shiraz, Iran.
چکیده [English]
Introduction
Predictions indicated that the quality of household’s life, especially rural households is decreasing according to the change in social factors, lifestyle, political tensions, and population growth. Climate change and drought have aggravated the problem of undernourishment. Considering that the agricultural sector is the most important source of livelihood and food supply for households living in rural areas, the effects of drought on rural household’s income and production are significant. Also, severe drought and water shortages caused by drought, reduced the productivity of agricultural production. Hence, in the present study, the vulnerability of the agricultural households as well as the village of the studied region has been measured using the Livelihood Vulnerability Index (LVI). Also, the impact of various social and economic factors on the vulnerability of households was investigated using the Beta regression model.
Materials and Methods
The LVI was designed to evaluate households’ vulnerability to climatic fluctuation. The LVI method includes various factors that represent the level of exposure of farming households to climate variability, because it provides a framework for analyzing both the key components that make up livelihoods and the contextual factors that influence them. According to the concept of the Intergovernmental Panel on Climate Change (IPCC), a balance-weighted technique was used to estimate the vulnerability of farming households to changing climate by computing the LVI. A balance weighted average is the average of a data set with different associated values or weights. In addition, LVI in this study was calculated based on seven key components and 17 sub-components. Also, the value of the calculated LVI was classified into five groups: not vulnerable (0.0 to 0.30), slightly vulnerable (0.31 to 0.46), moderately vulnerable (0.47 to 0.51), highly vulnerable (0.52 to 0.60), and extremely vulnerable (0.61 to 1.0). Based on the Kochran formula, a sample of 350 farmers was determined, and required data and information were collected through questionnaires and face-to-face interviews in 2022. The LVI is a function of the demographic, social, physical, and policy environment. Therefore, the determinants of LVI were investigated, with an emphasis on climate change using the Beta regression model.
Results and Discussion
Results of LVI showed that 43% of the sampled households in the study area were slightly vulnerable to climate fluctuation. On the other hand, only 6.8% of farming households in southern Bakhtegan Lake were not vulnerable. Also, 22.8% and 23.42% of sampled farming households were moderately and highly vulnerable, respectively. In general, more than 3.4% of sampled households were extremely vulnerable. The results of the Beta regression model showed that the age of the household head, family size, education level of the household head, access to climate information, membership in the cooperatives, off-farm income, and also receiving a bank loan have a significant effect on the vulnerability of rural households.
Conclusions
In the present study, the LVI was used to evaluate households’ vulnerability to climatic fluctuation, based on the IPCC’s concept of vulnerability. Based on the value of calculated LVI, the sampled farming households’ vulnerability is classified into five groups containing not vulnerable, slightly vulnerable, moderately vulnerable, highly vulnerable, and extremely vulnerable. Also, an econometric estimation procedure that involves the use of Beta regression model was employed to identify the factors affecting the LVI. The result shows that 50% of the sample households were slightly vulnerable or not vulnerable. The LVI showed that the vulnerability of farming households in southern Bakhtegan Lake to climatic fluctuation ranges from extremely vulnerable (3.4%) to highly vulnerable (22.8%) and moderately vulnerable (23.42%) levels. According to the positive effect of age on LVI, the government can consider households with a younger head of the household as the target group. Also, it is suggested to provide information about the occurrence of climatic phenomena through virtual channels and other information sources. Considering the impact of LVI from off-farm income, it is suggested that households engage in off-farm activities.
کلیدواژه‌ها [English]
Beta Regression, Livelihood Vulnerability Index, Climate Change, Bakhtegan Lake
مراجع
  1. Abid, M.J. Schilling, J. Scheffran & Zulfiqar, F. (2016). Climate change vulnerability, adaptation and risk perceptions at farm level in Punjab, Pakistan. Journal of Science of the Total Environment, 15(547), 447-460. doi: 10.1016/j.scitotenv.2015.11.125
  2. Adu, D.T., Kuwornu, J.K.M., Anim-Somuah, H. & Sasaki, N. (2018). Application of livelihood vulnerability index in assessing smallholder maize farming households’ vulnerability to climate change in Brong-Ahafo region of Ghana. Kasetsart Journal of social Sciences. 39(1), 22–32. doi:10.1016/j.kjss.2017.06.009.
  3. Amos, E., Akpan, U. & Ogunjobi, K. (2014) Households’ perception and livelihood vulnerability to climate change in a coastal area of Akwa Ibom State, Nigeria. Journal of Environmental Development Sustainable. 17(4), 1-29. doi:1007/s10668-014-9580-3
  4. Ashok, K.R. & Sasikala, C. (2012), Farmers’ vulnerability to rainfall variability and technology adoption in rain-fed Tank irrigated agriculture, Journal of Agricultural Economics Research Association. 25(2), 137371. doi: 10.22004/ag.econ.137371.
  5. Azadi, H., Barati, A.A., Nazari Nooghabi, S. & Scheffran, J. (2022). Climate-related disasters and agricultural land con-version: Towards prevention policies. Journal of Climate and Development, 13(2), 1-15. doi: 1080/17565529.2021.2008291. [In Persian]
  6. Bagheri, M., Bagheri, A. & Sohooli, G.A. (2016). Analysis of changes in the Bakhtegan lake water body under the influence of natural and human factors. Journal of Iran-Water Resources Research, 12(3), 1-11. [In Persian]
  7. Banan, N., Tabiei, O. & Jafari Nezhad, M. (2008). The current state of Bakhtegan Lake and its future. 2nd Conference of Environmental Engineering. Tehran, Iran. )In Persian(
  8. Barati, A.A., Moradi, M., Zhoolideh, M. & Sohrabi Mollayousef, E. (2021). Analysis of multidimensional poverty in Iranian rural communities. Journal of Rural Research, 12(1), 44-61. doi: https://doi.org/10.22059/jrur.2021.296661.1449. [In persian]
  9. Biazin, B. & Sterk, G. (2013). Drought vulnerability drives land-use and land cover changes in the Rift Vally dry lands of Ethiopia agriculture. Journal of Ecosystems and Environment, 164, 100-113. doi:1016/j.agee.2012.09.012.
  10. Brooks, N., Adger, W.N. & Kelly, P.M. (2005). The determinants of vulnerability and adaptive capacity at the national level and the implications for adaptation. Journal of Global Environmental Change, 15(2), 151-163. doi: 10.1016/j.gloenvcha.2004.12.006.
  11. Campana, P.E., Zhang, J., Yao, T., Andersson, S., Landelius, T., Melton, F. & Yan, J. (2018). Managing agricultural drought in Sweden using a novel spatially-explicit model from the perspective of water-food-energy nexus. Journal of Cleaner Production, 197(12), 1382-1393. doi: https://doi.org/10.1016/j.jclepro.2018.06.096
  12. Cochran, W.G. (1977). Sampling techniques. New York: John Wiley & Sons.
  13. Dao, D.M., Lebailly, P., Nguyen, D.H., Burny, P. & Ho, T.M.H. (2019). The dynamics of livelihood vulnerability index at farm household level: An empirical analysis of the coastal sandy zone in Thua Thien Hue Province, Vietnam. Journal of Economics and Financial Issues 9(5), 77–89. doi:10.32479/ijefi.8250.
  14. Darvishi Boloorani, A., Soleimani, M., Neysani Samany, N., Bakhtiari, M., Qareqani, M., Papi, R. & Mirzaei, S. (2023). Assessment of rural vulnerability to sand and dust storms in Iran. Jounal of Atmosphere, 14(2), 281. doi: 10.3390/atmos14020281.
  15. Devkota, R.P., Bajracharya, B., Maraseni, T.N., Cockfield, G. & Upadhyay, B.P. (2011). The perception of Nepal’s Tharu community in regard to climate change and its impacts on their livelihoods. International Journal of Environmental Studies, 68(6), 937–946. doi: 1080/00207233.2011.587282.
  16. Drolet, J.L. & Sampson, T. (2017). Addressingclimate change from a social development approach: Small cities and rural communities’ adaptation andresponse to climate change in British Columbia,International Social Work, 60(1), 61-73. doi:10.1177/0020872814539984.
  17. Fnrapan, (2011). Measuring vulnerability - challenges and opportunities. Food Agriculture and Natural Resource Policy Analysis Network, 2(11), 1-35.
  18. Forouzani, , Yazdanpanah, M. & Taheri, F. (2016). Analyzing the factors affecting optimal management of saline water by application of sustainable livelihoods framework. Rural Development Strategies, 3, 317-334. doi: 10.22048/rdsj.2017.36638.1455 [In persian]
  19. Fotooli, B. (2002). Bakhtegan management integrated plan studies, Environmental Protection Agency.
  20. Fussel, H.M. (2007). Vulnerability: A generally applicable conceptual framework for climate change research. Journal of Global Environmental Change, 17(2), 155-167.
  21. Gallopin, G.C. (2006). Linkages between vulnerability, resilience, and adaptive capacity. Journal of Global Environmental Change, 16(16), 293-303. http://dx.doi.org/10.1016/j.gloenvcha. 2006.02.004.
  22. Ghazali, S. & Zibaei, M. (2018). Households’ perception and livelihood vulnerability to climate change: Nomads in Fars province. Iranian Journal of Agricultural Economics: 12(1), 39-58. doi: 22034/iaes.2018.31373. [In Persian]
  23. 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. Journal of Global Environmental Change, 19(1), 74–88. doi: http://dx.doi.org/10.1016/j.gloenvcha.2008.11.002.
  24. Ho, T.D.N., Kuwornu, J.K.M., Tsusaka, T.W. (2021). Factors influencing smallholder rice farmers’ vulnerability to climate change and variability in the Mekong Delta Region of Vietnam. Eur. Journal of Journal of Development Research 34(1), 272–302. doi:10.1057/s41287-021-00371-7.
  25. Huynh, L.T.M. & Stringer, L.C. (2018). Multi-scale assessment of social vulnerability to cli- mate change: An empirical study in coastal Vietnam. Climate Journal of Risk Manage. 16(20), 165–180. doi:10.1016/j.crm.2018.02.003.
  26. Ifeanyi-Obi, C.C. & Matthews-Njoku, E.C. (2014). Socio- economic factors affecting choice of livelihood activities among rural dwellers in Southeast Nigeria. IOSR Agric. Vet. Sci., 7(4), 52-56. doi:10.9790/2380-07415256.
  27. Iraji, H., Zibaei,M. & Nasrnia,F (2015). Assessing the vulnerability of wheat farmers to drought in north of Fars province, Journal of Economics and Agricultural Development, 29(2), 169-180. doi: 22067/jead2.v0i0.40665. [In Persian]
  28. Keshavarz, M. & Soltani Moqadas, R. (2021). Assessing rural households’ resilience and adaptation strategies to climate variability and change. Journal of Arid Environments, 184,104323, 1-8. doi: 1016/j.jaridenv.2020.104323.
  29. King, T., Cole, M., Farber, J.M., Eisenbrand, G., Zabaras, D., Fox, E.M. & Hill, J.P. (2017). Food safety for food security: Relationship between global megatrends and developments in food safety. Trends in Food Science & Technology, 68, 160-175. doi: https://doi.org/10.1016/j.tifs.2017.08.014
  30. Kolawole, O.D., Motsholapheko, M.R. Ngwenya, B.N., Thakadu, O. Mmopelwa, G. & Kgathi, D.L. (2016). Climate variability and rural livelihoods: How households perceive and adapt to climatic shocks in the Okavango Delta, Botswana. Weather, Climate and Society, 8(2), 131–145. Doi: https://doi.org/10.1175/WCAS-D-15-0019.1
  31. Kuchimanchi, B.R., Paassen, A.V. & Oosting, S.J. (2021). Understanding the vulnerability, farming strategies and development pathways of smallholder farming systems in Telangana, India. Journal of Risk Manage. 31, 100275. doi:10.1016/j.crm. 2021.100275.
  32. Leartlam, C., Praneetvatakul, S. & Bunyasiri, I. (2021). Investment of rice farm-ing households in Thailand and Vietnam. Kasetsart Journal of Social Sciences. 42, 1–8. doi:10.34044/j.kjss. 2021.42.1.01.
  33. Li, L., Cao, R., Wei, K., Wang, V. & Chen, L. (2019). Adapting Climate challenge: A new vulnerability assessment framework from the global perspective. Journal of Cleaner Production 217, 216- 224. doi: 1016/j.jclepro.2019.01.162
  34. Malekmohammadi, B., Abkar, A., Yavari, A., Ramezani Mehrianm., R. & Zohrabi, H. (2011). Environmental change detection of wetlands and protected areas using multi temporal images of TM sensor (Case study: Neyriz Wetland, Iran), Journal of Environmental Studies, 37(1), 65-76.doi: 1001.1.10258620.1390.37.57.8.6. [In Persian]
  35. Moayedi, M., Hayati, D., Karami, E.A. & Rezaei-Moghadam, K. (2022). Analysis of rural women’s vulnerability towards climate variability: The case of Fars province. Journal of Agricultural Extension and Education, 17(2), 81-97. doi: 1001.1.20081758.1400.17.2.6.4. [In Persian]
  36. Martin-Shields, C.P. & Stojetz, W. (2019). Food security and conflict: Empirical challenges and future opportunities for research and policy making on food security and conflict, Journal of World Development, 119(C), 150-164. Doi: 10.1016/j.worlddev.2018.07.011.
  37. Nasrnia, F. & Zibaei, M. (2015). Vulnerability assessment to Drought in Various Provinces, approach towards risk management in the country. Journal of Agricultural Economics and Development, 29(4), 359-373. doi: 10.22067/jead2.v29i4.47932. [In Persian]
  38. Nasrnia, F., Zebaei, M. & Bakhshodeh, M. (2018). Vulnerability of farmers in Bakhtegan basin before and after drought: Econometric approach. Journal of Agricultural Economics, 12(3), 45-69. doi: 10.22034/iaes.2018.33528. [In Persian]
  39. Ngu, N.H., Tan, N.Q., Non, D.Q., Dinh, N.C. & Nhi, P.T.P. (2023). Unveiling urban households’ livelihood vulnerability to climate change: An intersectional analysis of Hue City, Vietnam. Journal ofEnvironmental and Sustainability Indicators, 19(3), 100269. doi: 10.1016/j.indic.2023.100269
  40. Nguyen, A.T. & Hens, L. (2019). Climate change-associated hazards, impacts, and vulnerability at regional level. In book: Human ecology of climate change hazards in Vietnam. Springer, Cham.
  41. Nguyen, L.A., Pham, T.B.V., Bosma, R., Verreth, J., Leemans, R., De Silva, S., & Lansink, A.O. (2018). Impact of climate change on the technical efficiency of Striped Catfish, Pangasianodon hypophthalmus, farming in the Mekong Delta, Vietnam. Journal of World Aquac. 49(3), 570–581. doi:1111/jwas.12488
  42. Ommani, A.R., Chizari, M., Salmanzadeh, C. & Hoseini, F.A., (2009). Predicting adoption behavior of farmers regarding on-farm sustainable water resources management (SWRM): Comparison of models, Journal of Sustainable Agriculture, 33(5), 595-616. doi:1080/10440040902997827
  43. Paolino, P. (2001). Maximum likelihood estimation of models with Beta-distributed dependent variables, Political Analysis, 9(4), 325-346. doi: https://doi.org/10.1093/oxfordjournals.pan.a004873.
  44. Rahimi, M., Ghorbani,M., Malekian,A. & Alambeigi, A. (2021). Determining the mode and regime of the water governance in the face of environmental changes from the perspective of institution and local stakeholders. Journal of Range and Watershed Management Natural Resources, 74(1), 83-102. doi :22059/JRWM.2019.280359.1379. [In Persian]
  45. Sahneh, B. & Nourtaghani, A. (2021). Vulnerability analysis of livelihood capitals of rural families suffered from the flood of Aq Qala. Journal of Geography and Regional Development, 18(2), 272-239. doi: 10.22067/jgrd.2021.68260.1006. [In Persian]
  46. Shah, K.U., Dulal, H.B., Johnson, C. & Baptiste, A. (2013). Understanding livelihood vulnerability to climate change: Applying the livelihood vulnerability index in Trinidad and Tobago. Geoforum, 47(3-4), 125–137. doi:1016/j.geoforum.2013.04.004.
  47. Shahraki, R., Abedi Sarvestani, A. & Lotfi, A. (2022). Livelihood vulnerability of villagers to climate change: The case of Oghan Watershed in Golestan province. Journal of Watershed Engineering and Management, 14(1), 89-101. doi:10.22092/ijwmse.2021.354072. [In Persian]
  48. Sharafi, L. & Zarafshani, K. (2011). Economic and social vulnerability assessment among farmers towards droudht (Case study: Wheat farmers Kermanshah, Sahneh and Ravansar townships). Journal of Rural Research, 1(4), 129-154. [In Persian]
  49. Sietz, D., Edgar, S., Chque, M. & Matthias, K.B. (2012). Ludeke, typical patterns of smallholder vulnerability to weather extremes with regard to food security in the Peruvian Altiplano. Journal of Regional Environmental Change, 12(3), 489-505. doi:1007/s10113-011-0246-5.
  50. Soleymani, A., Afrakhteh, H., Azizpour, f. & Tahmasebi. A. (2016). Assessing the level of adaptive capacity in rural districts of Rawansar city to drought. Journal of Spatial Analysis Environmental Hazarts, 3(2), 65-78. doi: 18869/acadpub.jsaeh.3.2.65. [In Persian]
  51. Statistical Yearbook of Fars Province. Available at http://www.mpo-fr.ir/reports/1.
  52. Tjoe, Y. (2016). Measuring the livelihood vulnerability index of a dry region in Indonesia: A case study of three subsistence communities in West Timor. World Journal of Science Technology and Sustainable Development. 13(4), 250–274 (WASD). doi:1108/WJSTSD-01-2016-0013.
  53. Toufique, K.A. & Islam, A. (2014). Assessing risks from climate variability and change for disaster-prone zones in Bangladesh. International Journal of Disaster Risk Reduction, 10, 236–24. doi:1016/j.ijdrr.2014.08.008.
  54. Tran, D.D., van Halsema, G., Hellegers, P.J.G.J., Hoang, L.P. & Ludwig, F. (2019). Long-term sustainability of the Vietnamese Mekong Delta in question: An economic assessment of water management alternatives. Journal of Agricultural Water Management. 223, 105703. doi:10.1016/j.agwat.2019.105703
  55. Tran, P.T., Vu, B.T., Ngo, S.T., Tran, V.D. & Ho, T.D. (2022). Climate change and livelihood vulnerability of the rice farmers in the North Central region of Vietnam: A case study in Nghe An province, Vietnam. Journal of Environmental Challenges. 12(11), 3282. doi:1016/j.envc.2022.100460.
  56. Udmale, P., Ichikawa, Y., Manandhar, S., Ishidaira, H. & Kiem, A.S. (2014). Farmers׳ perception of drought impacts, local adaptation and administrative mitigation measures in Maharashtra State, India. International Journal of Disaster Risk Reduction, 10, 250-269. doi: 10.1016/j.ijdrr.2014.09.011.
  57. World Bank (2008). World development report 2008: Agriculture for Development. Washington DC: The World Bank.
  58. Zarepour Moshizi, M., Yousefi, A., Amini, A.M. & Shojaei, P. (2022). Rural vulnerability to water scarcity in Iran: An integrative methodology for evaluating exposure, sensitivity and adaptive capacity. Geo Journal, 1-16. doi:1007/s10708-022-10726-0
  59. Zimmerer, K.S. (2017). Geography and the study of human environmentrelations. In D. Richardson, N. Castree, & M.F. Goodchild (Eds.), International encyclopedia of geography: People, the earth, environment, and technology 1-23. Hoboken: John Wiley & Sons, Ltd.
آمار
تعداد مشاهده مقاله: 561
تعداد دریافت فایل اصل مقاله: 219


counter
سامانه مدیریت نشریات علمی. قدرت گرفته از سیناوب