Skip to main content

Selection of Agricultural Technology: A Multi-attribute Approach

  • Conference paper
  • First Online:
  • 877 Accesses

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 749))

Abstract

The evaluation and selection process of agricultural technology traditionally is focused on economic aspects, delegating to third parties and ignoring several attributes in the analysis. This article presents a multicriteria model that integrates the TOPSIS technique, based on a similarity index to an ideal alternative performed by a decision group, which integrates simultaneously several attributes in the analysis and having different preference levels, thus the farmers can carry out the evaluation process by themselves. The model is validated through a case study applied to the selection of an agricultural tractor, which is evaluated by four members of an agricultural cooperative, including two types of attributes. On the one hand, tangible attributes: initial cost, maintenance cost and engine power. On the other hand, intangible attributes: after-sales service and maintainability. Currently, the model is being integrated in a software to facilitate applications by farmers, avoiding assigning this task to third parties.

This is a preview of subscription content, log in via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Kimoto, R., Ronquillo, D., Caamaño, M.C., Martinez, G., Schubert, L., Rosado, J.L., Garcia, O., Long, K.Z.: Food, eating and body image in the lives of low socioeconomic status rural Mexican women living in Queretaro State, Mexico. Health & Place 25, 34–42 (2014). http://dx.doi.org/10.1016/j.healthplace.2013.10.004

  2. Zeng, D.-Z., Zhao, L.: Globalization, interregional and international inequalities. J. Urban Econ. 67(3), 352–361 (2010). http://dx.doi.org/10.1016/j.jue.2009.11.002

  3. Hua, Y.: Influential factors of farmers’ demands for agricultural science and technology in China. Technol. Forecast. Soc. Chang. 100, 249–254 (2015). http://dx.doi.org/10.1016/j.techfore.2015.07.008

  4. Carter, M.R., Cheng, L., Sarris, A.: Where and how index insurance can boost the adoption of improved agricultural technologies. J. Dev. Econ. 118, 59–71 (2016). http://dx.doi.org/10.1016/j.jdeveco.2015.08.008

  5. Sun, B., Ma, W.: An approach to consensus measurement of linguistic preference relations in multi-attribute group decision making and application. Omega 51, 83–92 (2015). http://dx.doi.org/10.1016/j.omega.2014.09.006

  6. Chuu, S.-J.: Selecting the advanced manufacturing technology using fuzzy multiple attributes group decision making with multiple fuzzy information. Comput. Ind. Eng. 57(3), 1033–1042 (2009). http://dx.doi.org/10.1016/j.cie.2009.04.011

  7. Evans, L., Lohse, N., Summers, M.: A fuzzy-decision-tree approach for manufacturing technology selection exploiting experience-based information. Expert Syst. Appl. 40(16), 6412–6426 (2013). http://dx.doi.org/10.1016/j.eswa.2013.05.047

  8. Ilgin, M.A., Gupta, S.M., Battaïa, O.: Use of MCDM techniques in environmentally conscious manufacturing and product recovery: State of the art. J. Manuf. Syst. 37, Part 3, 746–758 (2015). http://dx.doi.org/10.1016/j.jmsy.2015.04.010

  9. Veisi, H., Liaghati, H., Alipour, A.: Developing an ethics-based approach to indicators of sustainable agriculture using analytic hierarchy process (AHP). Ecol. Ind. 60, 644–654 (2016). http://dx.doi.org/10.1016/j.ecolind.2015.08.012

  10. Yue, Z.: Extension of TOPSIS to determine weight of decision maker for group decision making problems with uncertain information. Expert Syst. Appl. 39(7), 6343–6350 (2012). http://dx.doi.org/10.1016/j.eswa.2011.12.016

  11. Braglia, M., Gabbrielli, R.: Dimensional analysis for investment selection in industrial robots. Int. J. Prod. Res. 38(18), 4843–4848 (2000). doi:10.1080/00207540050205668

  12. Goh, C.-H., Tung, Y.-C.A., Cheng, C.-H.: A revised weighted sum decision model for robot selection. Comput. Ind. Eng. 30(2), 193–199 (1996). http://dx.doi.org/10.1016/0360-8352(95)00167-0

  13. Knott, K., Getto, R.D.: A model for evaluating alternative robot systems under uncertainty. Int. J. Prod. Res. 20(2), 155–165 (1982). doi:10.1080/00207548208947757

  14. Wei, C.-C., Kamrani, A.K., Wiebe, H.: Animated simulation of the robot process capability. Comput. Ind. Eng. 23(1–4), 237–240 (1992). http://dx.doi.org/10.1016/0360-8352(92)90107-U

  15. Offodile, O., Lambert, B., Dudek, R.: Development of a computer aided robot selection procedure (CARSF). Int. J. Prod. Res. 25, 1109–1121 (1987)

    Google Scholar 

  16. Russell, N.P., Milligan, R.A., LaDue, E.L.: A stochastic simulation model for evaluating forage machinery performance. Agric. Syst. 10(1), 39–63 (1983). http://dx.doi.org/10.1016/0308-521X(83)90015-X

  17. Elhorst, J.P.: The estimation of investment equations at the farm level. Eur. Rev. Agric. Econ. 20(2), 167–182 (1993). doi:10.1093/erae/20.2.167

  18. Søgaard, H.T., Sørensen, C.G.: A model for optimal selection of machinery sizes within the farm machinery system. Biosyst. Eng. 89(1), 13–28 (2004). http://dx.doi.org/10.1016/j.biosystemseng.2004.05.004

  19. Camarena, E.A., Gracia, C., Cabrera Sixto, J.M.: A mixed integer linear programming machinery selection model for multifarm systems. Biosyst. Eng. 87(2), 145–154 (2004). http://dx.doi.org/10.1016/j.biosystemseng.2003.10.003

  20. Bartolini, F., Bazzani, G.M., Gallerani, V., Raggi, M., Viaggi, D.: The impact of water and agriculture policy scenarios on irrigated farming systems in Italy: an analysis based on farm level multi-attribute linear programming models. Agric. Syst. 93(1–3), 90–114 (2007). http://dx.doi.org/10.1016/j.agsy.2006.04.006

  21. Hayashida, T., Nishizaki, I., Ueda, Y.: Multiattribute utility analysis for policy selection and financing for the preservation of the forest. Eur. J. Oper. Res. 200(3), 833–843 (2010). http://dx.doi.org/10.1016/j.ejor.2009.01.035

  22. Manos, B., Chatzinikolaou, P., Kiomourtzi, F.: Sustainable optimization of agricultural production. APCBEE Procedia 5, 410–415 (2013). http://dx.doi.org/10.1016/j.apcbee.2013.05.071

  23. Leicht, K.T., Jenkins, J.C.: State investments in high-technology job growth. Soc. Sci. Res. 65, 30–46 (2017). http://dx.doi.org/10.1016/j.ssresearch.2017.03.007

  24. Lee, H., Choi, H., Lee, J., Min, J., Lee, H.: Impact of IT investment on firm performance based on technology IT architecture. Procedia Comput. Sci. 91, 652–661 (2016). http://dx.doi.org/10.1016/j.procs.2016.07.164

  25. Rudnik, K., Kacprzak, D.: Fuzzy TOPSIS method with ordered fuzzy numbers for flow control in a manufacturing system. Appl. Soft Comput. 52, 1020–1041 (2017). https://doi.org/10.1016/j.asoc.2016.09.027

  26. Akbaş, H., Bilgen, B.: An integrated fuzzy QFD and TOPSIS methodology for choosing the ideal gas fuel at WWTPs. Energy 125, 484–497 (2017). https://doi.org/10.1016/j.energy.2017.02.153

  27. Ertuğrul, İ., Karakaşoğlu, N.: Comparison of fuzzy AHP and fuzzy TOPSIS methods for facility location selection. Int. J. Adv. Manuf. Technol. 39(7), 783–795 (2008). doi:10.1007/s00170-007-1249-8

  28. Li, X., Chen, X.: Extension of the TOPSIS method based on prospect theory and trapezoidal intuitionistic fuzzy numbers for group decision making. J. Syst. Sci. Syst. Eng. 23(2), 231–247 (2014). doi:10.1007/s11518-014-5244-y

  29. Mavi, R.K., Goh, M., Mavi, N.K.: Supplier selection with shannon entropy and fuzzy TOPSIS in the context of supply chain risk management. Procedia – Soc. Behav. Sci. 235, 216–225 (2016). https://doi.org/10.1016/j.sbspro.2016.11.017

  30. Mohamed, H., Omar, B., Abdessadek, T., Tarik, A.: An application of OLAP/GIS-Fuzzy AHP-TOPSIS methodology for decision making: location selection for landfill of industrial wastes as a case study. KSCE J. Civ. Eng., 1–11 (2016). doi:10.1007/s12205-016-0114-4

  31. Lorencowicz, E., Uziak, J.: Repair cost of tractors and agricultural machines in family farms. Agric. Agric. Sci. Procedia 7, 152–157 (2015). https://doi.org/10.1016/j.aaspro.2015.12.010

  32. Amini, S., Asoodar, M.A.: Selecting the most appropriate tractor using analytic hierarchy process – an Iranian case study. Inf. Process. Agric. 3(4), 223–234 (2016). https://doi.org/10.1016/j.inpa.2016.08.003

  33. Andrabi, T., Ghatak, M., Khwaja, A.I.: Subcontractors for tractors: theory and evidence on flexible specialization, supplier selection, and contracting. J. Dev. Econ. 79(2), 273–302 (2006). https://doi.org/10.1016/j.jdeveco.2006.01.012

  34. Malaga-Toboła, U., Tabor, S., Kocira, S.: Productivity of resources and investments at selected ecological farms. Agric. Agric. Sci. Procedia 7, 158–164 (2015). https://doi.org/10.1016/j.aaspro.2015.12.011

  35. Mehta, C.R., Singh, K., Selvan, M.M.: A decision support system for selection of tractor–implement system used on Indian farms. J. Terramech. 48(1), 65–73 (2011). doi:https://doi.org/10.1016/j.jterra.2010.05.002

  36. Bojnec, Š., Latruffe, L.: Financing availability and investment decisions of slovenian farms during the transition to a market economy. J. Appl. Econ. 14(2), 297–317 (2011). http://dx.doi.org/10.1016/S1514-0326(11)60016-0

  37. Papageorgiou, A.: Agricultural equipment in greece: farm machinery management in the era of economic crisis. Agric. Agric. Sci. Procedia 7, 198–202 (2015). https://doi.org/10.1016/j.aaspro.2015.12.017

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jorge L. García-Alcaraz .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

García-Alcaraz, J.L., Martínez-Loya, V., Maldonado-Macias, A., Avelar-Sosa, L. (2017). Selection of Agricultural Technology: A Multi-attribute Approach. In: Valencia-García, R., Lagos-Ortiz, K., Alcaraz-Mármol, G., Del Cioppo, J., Vera-Lucio, N., Bucaram-Leverone, M. (eds) Technologies and Innovation. CITI 2017. Communications in Computer and Information Science, vol 749. Springer, Cham. https://doi.org/10.1007/978-3-319-67283-0_24

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-67283-0_24

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-67282-3

  • Online ISBN: 978-3-319-67283-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics