ECOLOGICALLY AND ECONOMICALLY SUSTAINABLE AGRICULTURAL TRANSPORTATION BASED ON ADVANCED INFORMATION TECHNOLOGIES
DOI:
https://doi.org/10.5937/ekoPolj1702739FKeywords:
agricultural transportation, environmental effects, optimization modelsAbstract
In the modern world there are lots of considerations about transportation in general, including analysis and decision making about the current situation and planning as well,that means preparation for future needs through defning policies, goals and investments to design transportation networks and facilities. The environmental consequences of general transportation and agriculture itself are of special interest as well. Transport activities are given and unavoidable in every society, for any country, but their intensive practice often produces negative effects on surroundings. The quoted problem emerges in a special manner when observing merged - as agricultural transportation: modeling ecological issues here is particularly complex due to great number of variables and random elements dependent on subjectivity in decision making, appearance of unexpected events and often incorrect data. In this paper authors discuss different aspects of agricultural transportation and point out the importance of application of methods and models that are capable of treating uncertainties and are appropriate to keep under control the abundance of ecological effects of transportation in agriculture, making efforts towards the development of sustainable food and raw materials production.
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References
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3. Akumu, C. E., Woods, M., Johnson, J. A., Pitt, D. G., Uhlig, P., McMurray, S. (2016): GIS-fuzzy logic technique in modeling soil depth classes: Using parts of the Clay Belt and Hornepayne region in Ontario, Canada as a case study, Elsevier, Geoderma, CCLXXXIII, pp. 78-87, DOI: 10.1016/j.geoderma.2016.07.028, Amsterdam, The Netherlands
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6. Bezuidenhout, C.N., Lusso, C.D., Lyne, P.W.L., Meyer, E. (2004): Minimising transport costs through optimal upgrading of roads and loading zones. Proceedings of the 78th South African Sugar Technologists Association - SASTA Congress, 2004, pp. 103-112, Durban, South Africa
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8. Chen, C.A. (2016): How can Taiwan create a niche in Asias cruise tourism industry? Tourism Management, 55, pp. 173-183. Elsevier Ltd.
9. Ćiric, Z., Stojic, D., Sedlak, O. (2015): Multicriteria HR allocation based on hesitant fuzzy sets and possibilistic programming. ACTA POLYTECHNICA HUNGARICA, Vol. 12, No. 3, pp. 185-197, Óbuda University, Budapest, Hungary
10. Danka, S. (2011): Robust Resource Constrained Project Scheduling with Fuzzy Activity Durations. Pollack Periodica, Akademiai Kiado, Vol. 3, pp. 131–142, Budapest, Hungary
11. David, P., Pandian, V. (2011): Transportation planning with modifed S-curve membership functions using an interactive fuzzy multi-objective approach. Applied Soft Computing, 11, pp. 2656–2663.
12. Dinu, M.D. (2016): Supply chain performance within agri food sector. Economics of Agriculture, The Balkan Scientifc Association of Agrarian Economists, Belgrade, Institute of Agricultural Economics, Belgrade, Serbia and Academy of Economic Studies, Bucharest, Romania, Vol. 63, No. 3, pp. 919-928, Belgrade
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15. Harris A.J., Bezuidenhout C.N., Lagrange L.F., Lyne, P.W.L. (2008): Development of a sugarcane transport route planning model in a geographical information system. Proceedings of the 81st South African Sugar Technologists Association - SASTA Congress, pp. 430-433, Durban, South Africa
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20. Jovanic, T. (2013): Agri-environmental legislative framework in Serbiain light of the harmonization with EU law, Economics of Agriculture, II, pp. 321-335
21. Klenk, I., Landquist, B., de Imaña, O.R. (2012): The Product Carbon Footprint of EU Beet Sugar, Sugar Industry Journal, CEFS, Vol. 137, No. 62, pp. 1-23, Brusseles, Belgium
22. Leigh, M., Li, X. (2015): Industrial ecology, industrial symbiosis and supply chain environmental sustainability: a case study of a large UK distributor. Journal of Cleaner Production, 106, pp. 632-643. Elsevier Ltd.
23. Lin, M.C., Yang, M.W. (2016): Investment Evaluation of Tourism Industry: A Case Study of Coastal Recreation Areas in Hualien Taiwan. International Business Research, 9(12), pp. 1-12, Canadian Center of Science and Education.
24. Luo, X. W., Zhang, L. Y., (2016): The Optimal Scheduling Model for Agricultural Machinery Resources with Time-Window Constraints, International Journal of Simulation Modelling, DAAAM International Vienna, Vol. 15, No. 4, pp. 721- 731, DOI: 10.2507/IJSIMM15(4)CO17, Wien, Austria
25. Lyne, P.W.L. (2012):Decision support systems for sugarcane production managers, Proceedings of the 85th South African Sugar Technologists Association – SASTA Congress, pp. 206-220, Durban, South Africa
26. MABC – Ministry of Agriculture of British Columbia (2014): Strengthening Farming – Farm Practice – Transportation, No. 870.218-56, Victoria, British Columbia (available at: http://www2.gov.bc.ca/assets/gov/farmingnatural-resources-and-industry/agriculture-and-seafood/agricultural-land-andenvironment/strengthening-farming/farm-practices/870218-56_transportation.pdf)
27. Mamdani, E.H. (1974): Applications of fuzzy algorithms for control of simple dynamic plant, Proceedings of the Institution of Electrical Engineers, Vol. 121, Issue 12, pp. 1584 – 1588, New York, USA2
28. Marx, B.J., Bezuidenhout, C.N., Lyne, P.W.L., van Antwerpen, R. (2006): Soil compaction decision support, Proceedings of the 80th South African Sugar Technologists Association – SSTA Congress, pp. 104-107, Durban, South Africa
29. Max Brown CS (2010): The role of transport in logistics of agriculture, National Agricultural Marketing Council of Australia (available at: http://www.namc.co.za/upload/input_cost_monitoring/The%20Role%20of%20Transport%20in%20Logistics%20of%20Agriculture.pdf)
30. Mayne, C. (2015): Agricultural transportation challenges, NASCO, Windsor, Texas Department of transportation (available at: http://nasconetwork.com/Websites/nasconetwork/images/AGRICULTURE_TRANSPORTATION.pdf)
31. Meixner, O. (2009): Fuzzy AHP Group Decision Analysis and its Application for the Evaluation of Energy Sources. Proceedings of the 10th International Symposium on the Analytic Hierarchy/Network Process Multi-criteria Decision Making, University of Pittsburgh, Pittsburgh, Pennsylvania, USA, (available at: http://www.isahp.org/2009Proceedings/Final_Papers/50_Meixner_Fuzzy_AHP_REV_ FIN.pdf, Feb. 15, 2017)
32. Rossi, R., Gastaldi, M., Gecchele, G. (2012): Sustainability evaluation of transportation policies: a fuzzy-based method in a "what to" analysis. Online Conference on Soft Computing in Industrial Applications, Technical University of Ostrava, Czech Republic
33. Sedlak, O., Kocic Vugdelija, V., Kudumovic, M., Bešic, C., Đorđevic, D. (2011): Management of family farms – Implementation of Fuzzy method in short-term planning. TTEM - Technics Technologies Education Management Journal, Vol. 5 (3), 2010, pp. 710-719. DRUNPP Sarajevo BiH
34. Stutterheim, P. (2006): An integrated sugarcane supply chain model: development and demonstration, MSc dissertation, University of KwaZulu-Natal Pietermaritzburg South Africa
35. Subagadis, Y.H., Schutze, N., Grundmann, J. (2016): A Fuzzy-Stochastic Modeling Approach for Multiple Criteria Decision Analysis of Coupled GroundwaterAgricultural Systems, Water Resources Management, Springer, Vol. 30, No. 6, pp. 2075-2095, DOI: 10.1007/s11269-016-1270-5, New York City, USA
36. Szakacs, T. (2010): Developing Stability Control Theories for Agricultural Transport Systems. Acta Polytechnica Hungarica, Vol. 7, No. 2, pp. 25-37. Obuda University, Budapest, Hungary
37. Tunde, A.M., Adeniyi, E.E. (2012): Impact of road transport on agricultural development: a Nigerian example, Ethiopian Journal of Environmental Studies and Management, V(3), pp. 232-238, Department of Geography and Environmental Studies, Bahir Dar University, Ethiopia (available at: http://www.ajol.info/index.php/ejesm/article/view/77955/68350)
38. Turan, J. (2005): Masovni transport u poljoprivredi. Poljoprivredna tehnika. Poljoprivredni fakultet, Institut za poljoprivrednu tehniku, Novi Sad, Vol. 30, No. 1, pp. 85-89, Novi Sad, Srbija
39. Tzeng, G.H., Teng, J.Y. (1993): Transportation investment project selection with fuzzy multiobjectives, Transportation Planning and Technology, Vol. 17, No. 2, pp. 91-112, Francis and Teylor Online
40. United States Environmental Protection Agency - EPA (2014): U.S. Greenhouse Gas Inventory Report: 1990-2014 (available at: https://www.epa.gov/ghgemissions/us- greenhouse-gas-inventory-report-1990-2014)
41. van den Akker, J.J.H. (2004): SOCOMO: A soil compaction model to calculate soil stresses and the subsoil carrying capacity, Soil and Tillage Research, Elsevier, Vol. 79, No. 1, pp. 113-127, Amsterdam, Netherlands
42. van der Werf, H.M.G., Petit, J. (2002): Evaluation of the environmental impact of agriculture at the farm level: a comparison and analysis of 12 indicator-based methods, Agriculture, Ecosystems & Environment, Vol. 93, No. 1-3, pp. 131-145, Elsevier, Amsterdam, Netherlands, (available at: http://www.sciencedirect.com/science/article/pii/S0167880901003541)
43. Wang, Y., Hao, H. (2016): Research on the Supply Chain risk Assessment of the Fresh Agricultural Products based on the Improved TOPTSIS Algorithm. 3rd International Conference on Applied Engineering, Book Series: Chemical Engineering Transactions, 51, pp. 445-450, Wuhan, PR China
44. World Resources Institute – WRI (2014): Climate Analysis Indicators Tool, Washington, DC (available at: http://cait2.wri.org/)
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Published
2017-06-30
How to Cite
Fazekaš, T., Bobera, D., & Ćirić, Z. (2017). ECOLOGICALLY AND ECONOMICALLY SUSTAINABLE AGRICULTURAL TRANSPORTATION BASED ON ADVANCED INFORMATION TECHNOLOGIES. Economics of Agriculture, 64(2), 739–751. https://doi.org/10.5937/ekoPolj1702739F
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