with the collaboration of Iranian Society of Mechanical Engineers (ISME)

Document Type : Research Article

Authors

Dept. of Mechanization and Agricultural machinery Engineering, Agricultural Sciences and Natural Resources University of Khuzestan, Ahvaz-Mollasani, Iran

Abstract

Introduction
Understanding the status of tractor power in any region is a key factor in setting a mechanization planning to improve the capacity of mechanized operations. For this reason, it is necessary that the available tractor power in each region meet the needs of agricultural operations in the most demanding time of cropping season in terms of operations related to machinery.
Materials and Methods
The objective of this study is needs assessment and prioritizing the power arrival in the agriculture of Khuzestan province. Required data, such as the number of tractors, areas under crop cultivation, size and number of farmlands, and crop yield were collected from the beginning of the first economic, social and cultural development plan until the end of the fifth development plan. Given to peak of operations, working hours per day and the probability of working days, input power required for each county was calculated. To determine the priorities for arrival power to counties, four criteria, including mechanization level shortage, percentage of obsolete tractors, the harmonic mean of production and area (ha) per tractor ratio (ha/tractor) were applied using TOPSIS-AHP based approach. Technique for order preference by similarity to ideal solution (TOPSIS) is one of the strongest methods in multi-criteria decision making. This method is based on the calculation of geometric distance of alternatives from positive ideal solution and negative ideal solution. Analytical hierarchy process (AHP) was used for weighting to criteria. AHP is one of the most famous multi-criteria decision making methods which has been used to estimate a total score for each criterion, compare indices using pairwise comparison and assess their score for one criterion. AHP is based on the decision-maker experience and knowledge. But since decision-makers rely on their mental ability and experience for doing comparisons, for reasons such as inadequate knowledge and information, complexity of the problem, lack of confidence in decision-making environment and lack of a proper scale, they are not able to express their preferences in the form of pure numbers. So conventional AHP has not enough potential for working based on human thinking style. For solving this problem, the theory of fuzzy sets can be used.
Results and Discussion
Based on the results, 6682 tractors with theoretical power equivalent as 75 hp should be added to provincial fleet to ensure timely agricultural operations in Khuzestan province. The required 75-hp tractor units are 1163, 750 and 742 for Dasht-e Azadegan, Andimeshk and Ahvaz, respectively and Abadan, Khoramshahr, Shadegan, Shushtar, Shush, Andika, Bavi, Behbahan and Hendijan did not need to import any new power due to higher theoretical power available compared to required power. The needs difference of counties came from the difference between counties area under cultivation in the peak work area. Almost there was one tractor per 50-ha area under cultivation in the province. Mechanization level was calculated as 1.2 hp ha-1. Based on the tractor classification by Mechanization Development Center, tractors over 13 years age are known as obsolete, so mechanization level could be reached down to 0.7 hp ha-1 by eliminating these tractors that included 40% of total tractors in the province. Coefficient of variation related to the mechanization level of counties was calculated as 47% that indicates imbalance between provincial regions. The average of variation coefficient of farm lands was obtained as 301.96 % for the province. Also correlation between mechanization level and coefficient of variation of farmlands was -0.436 in 5% level. In order to determine the priorities for importing power to each region of Khuzestan province, the ratio of area under cultivation (ha) to tractor unit assigned highest weight (0.3, 0.41, 0.54). Gotvand, Andimeshk, Izeh and Bagh-e Malek, had highest priority for importing power, respectively.
Conclusion
Results indicate an inappropriate distribution of tractors without considering the actual local need for them. Appropriate distribution of power is more important than quantitative distribution of tractors in Khuzestan province, because power in some regions is more than required power that cause wasting capital. In opposite, the shortage of power resources in the peak of workload in other regions, cause timeliness costs for farmer. Based on this, a necessity for regional planning is felt in the provincial strategic plans to make appropriate and coherent environments.

Keywords

1. Abbasi, S., M. Parashkouhi, and M. Rashidi, 2011. Performance capacity of tractor power in Kabudarahang county. 1th national conference on Modern Topics in Agriculture. Islamic Azad University Saveh Branch. (In Farsi).
2. Abbasi, K., M. Almassi, A. M. Borgheyee, and S. Minayie. 2014. Yield model estimation of basic crops based on the agricultural mechanization level index in Iran. Journal of Agricultural Machinery 4 (2): 344-351. (In Farsi).
3. Agricultural Jihad Organization of Khuzestan Province. 2017. Statistical Yearbook. Available at: http://ajkhz.ir/main/index.php/pages/generalinformationbank.html. (In Farsi).
4. Almassi, M., Sh. Kiani, and N. Lovimi. 2008. Principles of agricultural mechanization. Qom: Hazrat Masume Publication.
5. Arsalanbod, M. 2005. Size, Fragmentation and Inefficiency: A single-stage stochastic parametric approach for wheat production in Iran. Iranian Economic Review 10 (13): 151-162.
6. Baja, S., Chapman, D. M., and D. Dragovich. 2002. Fuzzy modeling of environmental suitability index for rural land use systems: an assessment using GIS. Environment and Planning B: Planning and Design 29: 3-20.
7. Bencheikh, A., A. Nourani1, and M. N. Chabacan. 2017. Sustainability evaluation of agricultural greenhouse structures in southern of Algeria using AHP, case of study: Biskra province. CIGR Journal Open access at http://www.cigrjournal.org. 19 (1): 56-64.
8. Biswas, A., and B. Baran pal. 2004. Application of fuzzy goal programming technique to land use planning in agricultural system. Omega The International Journal of management. 33 (5): 391-398.
9. Chang, D. Y. 1996. Theory and Methodology Applications of the extent analysis method on fuzzy AHP. European Journal of Operational Research 95: 649-655.
10. Chen, H. S., G. S. Liu, Y. F. Yang, X. F. Ye, and Z. Shi. 2010. Comprehensive evaluation of tobacco ecological suitability of Henan province based on GIS. Agricultural Sciences in China 9: 583-592.
11. Chen, T. Y., and Ch. Y. Tsao. 2008. The interval-valued fuzzy TOPSIS method and experimental analysis. Fuzzy Sets and Systems 159: 1410-1428.
12. Chia, C. S. 2010. A performance evaluation model by integrating fuzzy AHP and fuzzy TOPSIS methods. Expert Systems with Applications 37: 7745-7754.
13. Dagdeviren, M., and I. Yuksel. 2008 .Developing a fuzzy analytic hierarchy process (AHP) model for behavior-based safety management. Information Sciences 178: 1717-1733.
14. Ertugrul, I., N. Karakaşoglu. 2009. Performance evaluation of Turkish cement firms with fuzzy analytic hierarchy process and TOPSIS methods. Expert Systems with Applications 36: 702-715.
15. Gungor, Z., G. Serhadlıoglub, and S. E. Kesen. 2009. A fuzzy AHP approach to personnel selection problem. Applied Soft Computing 9: 641-646.
16. Gumus, A. T. 2009. Evaluation of hazardous waste transportation firms by using a two-step fuzzy-AHP and TOPSIS methodology. Expert Systems with Applications 36: 4067-4074.
17. Khurram-Ali, H. M., A. Sultan, and B. B. Rana. 2017. Captive Power Plant Selection for Pakistan Cement Industry in Perspective of Current Energy Crises: A Fuzzy-AHP Approach. Mehran University Research Journal of Engineering and Technology 36 (4): 769-780.
18. Lak, M. B., and M. S. Bloki. 2008. Investigation of mechanization level in Hamedan county. Proceedings of the 5th national congress on agricultural machinery and mechanization. Aug. 28-29. Ferdowsi University of Mashhad. (In Farsi).
19. Larki, M., M. A. Asoodar, A. Marzban, and A. Abde-Shahi. 2012. Investigation of power distribution status and estimation of required tractor power in Khuzestan province. 7th national congress on agricultural machinery and mechanization. Ferdowsi University of Mashhad. (In Farsi).
20. Maddahi, Z., A. Jalalian, M. M. Kheirkhah Zarkesh, and N. Honarjo. 2017. Land Suitability Analysis for Rice Cultivation using a GIS-based Fuzzy Multi-criteria Decision Making Approach: Central Part of Amol District, Iran. Soil and Water Research 12(1): 29-38.
21. Meteorological Organization of Khuzestan Province. 2017. Meteorological Quarterly. Available at: www.khzmet.ir. (In Farsi).
22. Mohammadi, O., and S. Zarifiyan. 2008. Factors affecting the mechanization of farm lands (case study: Nishapur county). 5th national congress on agricultural machinery and mechanization. Ferdowsi University of Mashhad. (In Farsi).
23. Mohajer-doust, V., A. Akram, M. Mashhuri-Azar, and F. Vojdani Heris. 2008. Determination of required tractor units and desirable mechanization level in Savojbolagh plain (given to the time of operational pick and tractor management). 5th national congress on agricultural machinery and mechanization. Ferdowsi University of Mashhad. (In Farsi).
24. Moradi, M. A., M. Sadeghi, H. Sadeghi, and L. Moradi. 2014. Development of a model for evaluating the replacement of worn-out tractors in Iran and presenting complementary energy policy in the agriculture and horticulture sub-sector. 2014. Iranian Journal of Energy 17 (1): 1-24. (In Farsi).
25. Nastaran, M., F. Abolhasani, and M. Izadi. 2010. Application of TOPSIS technique in analysis and prioritization of sustainable development of urban areas (case study: urban areas of Isfahan). Journal of Geography and Environmental Planning 2 (38): 83-100. (In Farsi).
26. Organization of Management and Planning of Khuzestan Province. 2017. Agricultural census results. Available at: https://www.amar.org.ir/ General Agricultural Census/Agricultural census results. (In Farsi).
27. Pishbin, S., H. Mohammadi, and A. Ejrayie. 2007. Investigation of problems related to applying agricultural mechanization in Jahrom region. Journal of Development and Productivity 2 (5): 17-29. (In Farsi).
28. Rahman, S., and M. Rahman. 2008. Impact of land fragmentation and resource ownership on productivity and efficiency: The case of rice Producers in Bangladesh. Land Use Policy 26: 95-103.
29. Ranjbaran. H. 2009. Statistics and probability and its using in the economy. Nooreelm and esbaat. Hamadan. (In Farsi).
30. Safari, M., and M. Almasi. 2008. Mechanization Coefficients and indices in tillage operation using conventional tractor and plow in ten provinces of country. Journal of Research and Construction 21: 52-60. (In Farsi).
31. Saiedirad, M. H., and S. A. Parhizgar. 2011. Investigation of mechanization indices in small-scale agriculture and providing appropriate solutions in Khorasan Razavi province. Journal of Agricultural Machinery 1 (1): 48-53.
32. Secme, N. Y., A. Bayrakdaroğlu, and C. Kahraman. 2009. Fuzzy performance evaluation in Turkish banking sector using analytic hierarchy process and TOPSIS. Expert Systems with Applications 36 (9): 11699-11709.
33. Seyedmohammadi, J. F. Sarmadianb, A. A. Jafarzadeha, M. A. Ghorbanic, and F. Shahbazi. 2017. Application of SAW, TOPSIS and fuzzy TOPSIS models in cultivation priority planning for maize, rapeseed and soybean crops. Geoderma 310: 178-190.
34. Sham-Abadi, Z. 2007. Determination of mechanization coefficients and indices in tillage operation using moldboard plow in Shahrud county. 3rd student conference on Agricultural Machinery Engineering. Shiraz University. (In Farsi).
35. Sindhu, S., V. Nehra, and S. Luthra. 2017. Investigation of feasibility study of solar farms deployment using hybrid AHP-TOPSIS analysis: Case study of India. Renewable and Sustainable Energy Reviews 73: 496-511.
36. Sun, C. C. 2010. A performance evaluation model by integrating fuzzy AHP and fuzzy TOPSIS methods. Expert Systems with Applications. 37: 7745-7754.
37. Tana, S., N. Heerink, A. Kuyvenhoven, and F. Quc. 2010. Impact of land fragmentation on rice producers’ technical efficiency in South-East China. Journal of Life Sciences 57: 117-123.
38. Tavakkoli, J. 2012. Level of agricultural mechanization development in Kermanshah province counties and its relationship with Institutional Infrastructure Indicators. Iranian Geography Quarterly 10 (33). (In Farsi).
39. Tabibi, S. J., and M. R. Maleki. 2005. Strategic planning. Termeh publications house, Tehran. Page 354. (In Farsi).
40. Yazdani, M., P. Zarate, A. Coulibaly, and E. Kazimieras Zavadskas, 2017. A group decision making support system in logistics and supply chain management. Expert Systems with Applications. DOI: 10.1016/j.eswa.2017.07.014.
41. Yousefi, R. A. 2015. Determining the number of suitable working days for spraying wheat fields in Qazvin province. Journal of Biosystem Engineering. (In Farsi).
42. Wang, T. C., and H. D. Lee. 2009. Developing a fuzzy TOPSIS approach based on subjective weights and objective weights. Expert Systems with Applications 36: 8980-8985.
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