Optimizing soil management by identifying and prioritizing the factors affecting soil erosion in Fars province with grey multiple-criteria decision-making approach

Document Type : Complete scientific research article

Authors

1 Assistant Professor, Department of Management, Islamic Azad University, Marvdasht Branch, Marvdasht, Iran.

2 Master Student of Public Administration, Department of Management and Economics, University of Sistan and Baluchestan, Zahedan, Iran

Abstract

Background and Objectives: Soil erosion is one of the main phenomena related to soil degradation that threatens environmental sustainability and soil productivity. It has always been a major environmental challenge worldwide. Soil erosion and its harmful consequences are a serious obstacle to the development and progress of countries and have adverse effects on agricultural production, water quality, and ecosystem health. Soil erosion control intends to promote innovative land management approaches. Soil conservation methods reduce soil erosion, improve soil structure and water uptake, and maintain soil moisture. Therefore, it is necessary to study soil erosion factors and provide management strategies to mitigate it. This study identifies and prioritizes soil erosion factors and strategies to prevent soil erosion in Fars, Iran.
Materials and Methods: This research is applied in terms of purpose, with a descriptive-survey nature. This study presents a combined approach based on Grey SWARA and Grey ARAS methods to identify soil erosion factors and prioritize methods to prevent soil erosion. After literature review and interviewing an expert panel, the soil erosion factors in Fars were identified and classified into technical, chemical, social, environmental, and climatic. The weight of the criteria was calculated by the Grey SWARA method. Then, the suggested methods and strategies were identified and provided to the panel to evaluate based on the criteria by completing a questionnaire. Then, they were finally prioritized using the Grey ARAS approach. In order to consider the uncertainty and indeterminacy of opinions, grey numbers were used in the calculations.
Results: The results indicated the following as the most important factors affecting soil erosion, respectively: "soil erodibility," "low aggregate stability," "deforestation and ecosystem degradation," "cultivation on slopes and plowing of sloping lands," "loss of vegetation," and "reduced organic matter." The most important preventative measures were prioritized as follows: "ratifying and implementing regulations to preserve soil and natural resources and the developing training and promoting workshops, promoting conservation tillage and smart agriculture, adding organic matter to the soil, mulching the soil with organic or artificial material, and adopting bioengineering methods, such as soil stabilizing bacteria" and "rangeland enclosure and fencing and promotion of industrial livestock.

Conclusion: As unfortunately, in Iran, soil erosion is several times the global average, and a large volume of good soil becomes out of reach per year, it requires serious management measures. More specifically, an extensive management plan should address soil erosion in Fars. It can be expected that findings in this research will help water, soil, and agriculture decision-makers make the most appropriate decisions and solutions to solve soil erosion problems.

Keywords

References

1.Das, B., Bordoloi, R., Thungon, L.T., Paul, A., Pandey, P.K., Mishra, M., and Tripathi, O.P. 2020. An integrated approach of GIS, RUSLE and AHP to model soil erosion in West Kameng watershed, Arunachal Pradesh. Journal of Earth System Science. 129: 1. 1-18.
2.Sadhasivam, N., Bhardwaj, A., Pourghasemi, H.R., and Kamaraj, N.P. 2020. Morphometric attributes-based soil erosion susceptibility mapping in Dnyanganga watershed of India using individual and ensemble models. Environmental Earth Sciences. 79: 14. 1-28.
3.Ahmad, N.S.B.N., Mustafa, F.B., and Didams, G. 2020. A systematic review of soil erosion control practices on the agricultural land in Asia. International Soil and Water Conservation Research. 8: 2. 103-115.
4.Bhandari, D., Joshi, R., Regmi, R.R., and Awasthi, N. 2021. Assessment of Soil Erosion and Its Impact on Agricultural Productivity by Using the RMMF Model and Local Perception: A Case Study of Rangun Watershed of Mid-Hills, Nepal. Applied and Environmental Soil Science. 2021.1-10. https://doi.org/10.1155/ 2021/ 5747138.
5.Aslam, B., Maqsoom, A., Alaloul, W.S., Musarat, M.A., Jabbar, T., and Zafar, A. 2021. Soil erosion susceptibility mapping using a GIS-based multi-criteria decision approach: Case of district Chitral, Pakistan. Ain Shams Engineering Journal. 12: 2. 1637-1649.
6.Jin, F., Yang, W., Fu, J., and Li, Z. 2021. Effects of vegetation and climate on the changes of soil erosion in the Loess Plateau of China. Science of the Total Environment. 773. 145514. https://doi.org/ 10.1016/j.scitotenv.2021.145514.
7.Teka, K., Haftu, M., Ostwald, M., and Cederberg, C. 2020. Can integrated watershed management reduce soil erosion and improve livelihoods? A study from northern Ethiopia. International Soil and Water Conservation Research, 8: 3. 266-276.
8.Luetzenburg, G., Bittner, M.J., Calsamiglia, A., Renschler, C.S., Estrany, J. and Poeppl, R. 2020. Climate and land use change effects on soil erosion in two small agricultural catchment systems Fugnitz–Austria, Can Revull–Spain. Science of the Total Environment. 704. 135389. https://doi.org/10.1016/j. scitotenv. 2019.135389.
9.Arabameri, A., Pourghasemi, H.R., and Cerda, A. 2018. Erodibility Prioritization of sub-watersheds using morphometric parameters analysis and its mapping: A comparison among TOPSIS, VIKOR, SAW, and CF multi-criteria decision making models. Science of the Total Environment. 613: 1385-1400.
10.Sinshaw, B.G., Belete, A.M., Tefera, A.K., Dessie, A.B., Bizuneh, B.B., Alem, H.T., Atanaw, S.B., Eshete, D.G., Wubetu, T.G., Atinkut, H.B., and Moges, M.A. 2021. Prioritization of potential soil erosion susceptibility region Using fuzzy Logic and Analytical Hierarchy process, Upper Blue Nile Basin, Ethiopia. Water-Energy Nexus. 4: 10-24.
11.Vahidi, M., Mirabbasi Najafabadi, R., and Ahmadi, M. 2020. Analysis and ranking of soil erosion prevention methods using multi-criteria decision-making methods in rural areas of Darmian County, South Khorasan. Hydrogeomorphology. 7: 23. 233-209. (In Persian)
12.Zangiabadi, M., Rangavar, A., Rafahi, H., Shorafa, M., and Bihamta, M. 2010. Investigation of the most Important Factors Affecting on Soil Erosion in Kalat Semi-Arid Rangelands. Journal of Water and Soil, 24: 4. 737-744. (In Persian)
13.Mohammadi, S., Karimzadeh, H., and Alizadeh, M. 2018. Spatial estimation of soil erosion in Iran using RUSLE model. Iranian Journal of Ecohydrology. 5: 2. 551-569. (In Persian)
14.Goli Mokhtari, L., Hshafiei, N., and Rahmani, A. 2019. The Estimation of Soil Erosion Using the RUSLE Model (Case Study: Noorabad Mamasani Basin). Hydrogeomorphology, 5: 17. 1-21. (In Persian)
15.Eshtaiwi, M.I., Badi, I.A., Abdulshahed, A.M., and Erkan, T.E. 2017. Assessment of Airport Performance Using the Grey Theory Method: A Case Study in Libya. Grey Systems: Theory and Application. 7: 3. 426-436.
16.Mohammadi, A., and Molaei, N. 2010. Applying a Multi Criteria Decision Making Model Based On Grey Theory In Performance Evaluation Of Firms. Industrial Management Journal. 2: 1. 125-142. (In Persian)
17.Voskoglou, M.Gr., and Theodorou, Y.A. 2017. Application of Grey Numbers to Assessment Processes. International Journal of Applications of Fuzzy Sets and Artificial Intelligence. 7: 273-280.
18.Keršuliene, V., Zavadskas, E.K., and Turskis, Z. 2010. Selection of rational dispute resolution method by applying new step‐wise weight assessment ratio analysis (SWARA). Journal of business economics and management. 11: 2. 243-258.
19.Heidary Dahooie, J., Hosseini Dehshiri, S. J., Banaitis, A., and Binkytė-Vėlienė, A. 2020. Identifying and prioritizing cost reduction solutions in the supply chain by integrating value engineering and gray multi-criteria decision-making. Technological and Economic Development of Economy. 26: 6. 1311-1338.
20.Fazli, S., and Jamaati Tafti, R. 2017. Preprocessing Multiple Criteria Decision- Making Using Data Mining (Case Study: Selection of third party logistic in outsourcing warranty services of an electronic facilities company). Modern Research in Decision Making, 2: 3. 215-239. (In Persian)
21.Mavi, R.K., Zarbakhshnia, N., and Khazraei, A. 2018. Bus rapid transit (BRT): A simulation and multicriteria decision making (MCDM) approach. Transport Policy. 72: 187-197.
22.Ghenai, C., Albawab, M., and Bettayeb, M. 2020. Sustainability indicators for renewable energy systems using multi-criteria decision-making model and extended SWARA/ARAS hybrid method. Renewable Energy, 146: 580-597.
23.Büyüközkan, G., and Güler, M. 2020. Smart watch evaluation with integrated hesitant fuzzy linguistic SAW-ARAS technique. Measurement, 153: 107353.
24.Turskis, Z., Zavadskas, E.K., and Kutut, V. 2013. A model based on ARAS-G and AHP methods for multiple criteria prioritizing of heritage value. International Journal of Information Technology & Decision Making. 12: 01. 45-73.
25.Hosseinzadeh Lotfi, F., and Fallahnejad. R. 2010. Imprecise Shannon’s Entropy and Multi Attribute Decision Making. Entropy. 12: 1. 53-62.
26.Huang, Y., Li, P., A, Q., Mao, F., Zhai, W., Yu, K., and He, Y. 2021. Long‐term land use/cover changes reduce soil erosion in an ionic rare‐earth mineral area of southern China. Land Degradation and Development. 32: 14. 4042-4055.
27.Fenta, A.A., Tsunekawa, A., Haregeweyn, N., Tsubo, M., Yasuda, H., Kawai, T., Ebabu, K., Berihun, M.L., Belay, A.S., and Sultan, D. 2021. Agroecology-based soil erosion assessment for better conservation planning in Ethiopian river basins. Environmental Research. 195.110786. https://doi.org/10.1016/j.envres.2021.110786.
Journal of Soil Management and Sustainable Production
Volume 12, Issue 3
October 2022
Pages 1-25

Files

Share

How to cite

Statistics