Abstract
The rural sustainability is one of the most important issues in rain-fed agricultural areas; however, because of multiple obvious and latent driving factors, a few models have been suggested covering the fluctuation of sustainability in these sensitive areas. Therefore, this study takes advantage of a combination of multivariate and multi-criteria decision making to analyze the association of rural resources with sustainability in northwest of Iran. The entire demographical, agricultural and natural standard data of 143 villages (21 variables) were weighted and ranked by Shannon–Winner index and simple additive weighting (SAW) method, respectively. The synthesized dataset was grouped into two categories of highly sustainable (ZSAW > 0) and low sustainable (ZSAW < 0) villages. Factor analysis (FA) was separately performed on the grouped data to identify possible interrelationships among the variables. The results of FA illustrated a significant difference between low and highly sustainable villages with respect to type and contribution of the resources in sustainability. In both cases, the first five rotated factors, including FA1 (human), FA2 (agronomical), FA3 (mechanization), FA4 (rangelands) and FA5 (wheat yield) vectors, accounted for about 99% of the variation. At low sustainable villages, the two most important driving factors were agronomical (51.1%) and rangelands (17.3%) which explained 68.9% of the variation; however, for highly sustainable villages the main factors shift to the human resources (22.1%) and agronomical (43.9%) with 66% contribution. These findings show that reliance on natural resources is a sign of unsustainability in the rain-fed agro-ecosystems, whereas, with increasing human resource contribution, villages become more sustainable. The study also confirmed that human resources are significantly and positively correlated with sustainability index (ZSAW) in highly scored villages. Furthermore, among the human resources, population density and women literacy levels have a dominant effect on human resources contribution. In conclusion, it is suggested that the contribution of human resources in the rural community and its correlation with the sustainability index could be used as the preliminary evaluation tools for sustainability in the rain-fed areas. Thus, enhancing the rural economy by empowering human resources, with emphasis on the development of rural women’s knowledge, can reduce reliance on the natural resources and shifts rural toward the sustainability.
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References
Akua-Sakyiwah, B. (2016). Education as cultural capital and its effect on the transitional issues faced by migrant women in the diaspora. Journal of International Migration and Integration, 17(4), 1125–1142.
Allahyari, M. S., Daghighi Masouleh, Z., & Koundinya, V. (2016). Implementing Minkowski fuzzy screening, entropy, and aggregation methods for selecting agricultural sustainability indicators. Agroecology and Sustainable Food Systems, 40(3), 277–294. https://doi.org/10.1080/21683565.2015.1133467.
Altieri, M. A. (2018). Agroecology: The science of sustainable agriculture. Boca Raton: CRC Press.
Anríquez, G., & Stamoulis, K. (2007). Rural development and poverty reduction: Is agriculture still the key? The Electronic Journal of Agricultural and Development Economics, 4, 5–46.
Ayuya, O. I., Gido, E. O., Bett, H. K., Lagat, J. K., Kahi, A. K., & Bauer, S. (2015). Effect of certified organic production systems on poverty among smallholder farmers: Empirical evidence from Kenya. World Development, 67, 27–37.
Barnes, A. P., Hansson, H., Manevska-Tasevska, G., Shrestha, S. S., & Thomson, S. G. (2015). The influence of diversification on long-term viability of the agricultural sector. Land Use Policy, 49, 404–412.
Biriescu, S., & Babaita, C. (2014). Rural education, an important factor of regional development in the context of local government strategies. Procedia-Social and Behavioral Sciences, 124, 77–86.
Burnside, C. (2005). Fiscal sustainability in theory and practice: A handbook. Geneva: The World Bank.
Cloke, P., & Park, C. C. (2013). Rural resource management (Routledge revivals). Abingdon-on-Thames: Routledge.
Dalsgaard, J., Lightfoot, C., & Christensen, V. (1995). Towards quantification of ecological sustainability in farming systems analysis. Ecological Engineering, 4(3), 181–189.
Dankelman, I., & Davidson, J. (2013). Women and the environment in the third world: Alliance for the future. Abingdon-on-Thames: Routledge.
Digbe, A. (2014). Women and literacy. In Women in the third world: An encyclopedia of contemporary issues (p. 418).
Dixon, J., Gulliver, A., & Gibbon, D. (2001). Improving farmers’ livelihoods in a changing world. Rome and Washington DC: FAO/World Bank.
Ellis, F. (2000). Rural livelihoods and diversity in developing countries. Oxford: Oxford University Press.
Fägerlind, I., & Saha, L. J. (2016). Education and national development: A comparative perspective. Amsterdam: Elsevier.
Figueira, J. R., Greco, S., Roy, B., & Słowiński, R. (2013). An overview of ELECTRE methods and their recent extensions. Journal of Multi-Criteria Decision Analysis, 20(1–2), 61–85.
Garibaldi, L. A., Gemmill-Herren, B., D’Annolfo, R., Graeub, B. E., Cunningham, S. A., & Breeze, T. D. (2017). Farming approaches for greater biodiversity, livelihoods, and food security. Trends in Ecology & Evolution, 32(1), 68–80.
Glover, D., & Kusterer, K. (2016). Small farmers, big business: Contract farming and rural development. Berlin: Springer.
Gupta, S., Dangayach, G., Singh, A. K., & Rao, P. (2015). Analytic hierarchy process (AHP) model for evaluating sustainable manufacturing practices in Indian electrical panel industries. Procedia-Social and Behavioral Sciences, 189, 208–216.
Keylock, C. (2005). Simpson diversity and the Shannon–Wiener index as special cases of a generalized entropy. Oikos, 109(1), 203–207.
Lambey, V., Prasad, A., Chouksey, A., & Sahu, I. (2019). Impact of water conservation structures on hydrology of a watershed for rural development. In Proceedings of international conference on remote sensing for disaster management, 2019 (pp. 739–750). Springer.
Meyer, D. F., & Nishimwe-Niyimbanira, R. (2016). The impact of household size on poverty: An analysis of various townships in the Northern Free State. African Population Studies, 30(2), 2283–2295.
Mittal, S., & Kumar, P. (2000). Literacy, technology adoption, factor demand and productivity: An econometric analysis. Indian Journal of Agricultural Economics, 55(3), 490–499.
Navarro, I. J., Yepes, V., & Martí, J. V. (2019). A review of multicriteria assessment techniques applied to sustainable infrastructure design. Advances in Civil Engineering, 2019, 1–16.
Nezami, S., & Khoramshahi, E. (2016). Spatial modeling of crime by using of GWR method. In 2016 Baltic geodetic congress (BGC geomatics), 2016 (pp. 222–227). IEEE.
Papathanasiou, J., Ploskas, N., Bournaris, T., & Manos, B. (2016). A decision support system for multiple criteria alternative ranking using TOPSIS and VIKOR: A case study on social sustainability in agriculture. In International conference on decision support system technology, 2016 (pp. 3–15). Springer.
Peacock, S. H. (2018). Effect of ecosystem literacy on understanding the impact of human population growth on the environment—A multiple case study. Ecopsychology, 10(3), 181–188.
Pourkhabbaz, H., Javanmardi, S., & Sabokbar, H. (2014). Suitability analysis for determining potential agricultural land use by the multi-criteria decision making models SAW and VIKOR-AHP (Case study: Takestan-Qazvin Plain). Journal of Agricultural Science & Technology, 16(5), 1005–1016.
Pudasaini, S. P. (1983). The effects of education in agriculture: Evidence from Nepal. American Journal of Agricultural Economics, 65(3), 509–515.
Ranganathan, T., Tripathi, A., & Rajoriya, B. (2016). Changing sources of income and income inequality among Indian rural households. In National seminar on dynamics of rural labour relations at national institute of rural development and Panchayati Raj (NIRD & PR), 2016.
Saaty, T. L. (2013). The modern science of multicriteria decision making and its practical applications: The AHP/ANP approach. Operations Research, 61(5), 1101–1118.
Tzeng, G.-H., & Huang, J.-J. (2011). Multiple attribute decision making: Methods and applications. London: Chapman and Hall/CRC.
Verkaart, S., Munyua, B. G., Mausch, K., & Michler, J. D. (2017). Welfare impacts of improved chickpea adoption: A pathway for rural development in Ethiopia? Food Policy, 66, 50–61.
Verma, P., Singh, D., Pathania, I. P., & Aggarwal, K. (2019). Strategies to improve agriculture sustainability, soil fertility and enhancement of farmers income for the economic development. In Soil fertility management for sustainable development (pp. 43–70). Springer.
Yi, P., Li, W., & Li, L. (2018). Evaluation and prediction of city sustainability using MCDM and stochastic simulation methods. Sustainability, 10(10), 3771.
Zeng, D., Alwang, J., Norton, G. W., Shiferaw, B., Jaleta, M., & Yirga, C. (2015). Ex post impacts of improved maize varieties on poverty in rural Ethiopia. Agricultural Economics, 46(4), 515–526.
Zhu, Q., & Yuanhong, L. (2006). Rainwater harvesting: The key to sustainable rural development in Gansu, China. Waterlines, 24(4), 4–7.
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Aliloo, A.A., Dashti, S. Rural sustainability assessment using a combination of multi-criteria decision making and factor analysis. Environ Dev Sustain 23, 6323–6336 (2021). https://doi.org/10.1007/s10668-020-00874-z
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DOI: https://doi.org/10.1007/s10668-020-00874-z