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Future Perspectives of Farm Management Information Systems

  • Zisis Tsiropoulos
  • Giacomo Carli
  • Erika Pignatti
  • Spyros Fountas
Chapter
Part of the Progress in Precision Agriculture book series (PRPRA)

Abstract

Farm Management Information Systems (FMIS) have evolved from simple record keeping to sophisticated solutions able to capture new trends involving spatial and temporal management, distributed sensors involving interoperability of sensing devices, future internet applications and web services. The FMIS were initially designed to deal with the farmer as the main focus of the system, whereas now data flow from and to the tractor information board, and connections with other pieces of equipment such as precision agriculture devices can be managed through an FMIS. This pathway of evolution has led to the inclusion of a rich set of functionalities and opened up the possibility to improve the cost control of farms. In this chapter, we present the state-of-the-art on these topics depicting the new functionalities included in evolved FMIS and how they can connect the farm to the external context and stakeholders. Then, we delve into the costing functionality of FMIS to understand how precision agriculture can improve the allocation of costs to final products. Finally, we conclude our discussion on the process of adoption of FMIS in European farms.

Keywords

Farm management information systems Precision agriculture Stakeholders Adoption 

References

  1. Adrian AM, Norwood SH, Mask PL (2005) Producers’ perceptions and attitudes toward precision agriculture technologies. Comput Electron Agric 48(3):256–271CrossRefGoogle Scholar
  2. Alvarez J, Nuthall P (2006) Adoption of computer based information systems: The case of dairy farmers in Canterbury, NZ, and Florida, Uruguay. Comput Electron Agric 50(1):48–60CrossRefGoogle Scholar
  3. Awa HO, Ojiabo OU, Emecheta BC (2012) Integrating TAM and TOE frameworks and expanding their characteristic constructs for E-commerce adoption by SMEs. In: Proceedings of Informing Science & IT Education conference (InSITE) 2012 (12:571–588). Informing Science InstituteGoogle Scholar
  4. Backman J, Oksanen T, Visala A (2013) Applicability of the ISO 11783 network in a distributed combined guidance system for agricultural machines Original Research Article. Biosyst Eng 114(3):306–317CrossRefGoogle Scholar
  5. Boehlje MD, Eidman VR (1984) Farm management. Wiley, New York, p 806Google Scholar
  6. Carli G, Canavari M (2013) Introducing direct costing and activity based costing in a farm management system: a conceptual model. Procedia Technol 8:397–405CrossRefGoogle Scholar
  7. Carli G, Canavari M, Grandi A (2014) Introducing activity-based costing in farm management. Int J Agric Environ Inf Syst 5(4):69–84CrossRefGoogle Scholar
  8. Cooper R, Kaplan RS (1988) Measure costs right: make the right decisions. Harv Bus Rev 66(5):96–103Google Scholar
  9. Daberkow SG, McBride WD (2003) Farm and operator characteristics affecting the awareness and Adoption of precision agriculture technologies in the US. Precis Agric 4(2):163–177CrossRefGoogle Scholar
  10. Davis FD (1989) Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q 13(3):319–340CrossRefGoogle Scholar
  11. Davis FD, Venkatesh V (2004) Toward preprototype user acceptance testing of new information systems: implications for software project management. IEEE Trans Eng Manag 51(1):31–46CrossRefGoogle Scholar
  12. Ferreira WN (2004) Is activity based costing a blockbuster? extension economic reports. Clemson University, ClemsonGoogle Scholar
  13. Fountas S, Ess D, Sorensen CG, Hawkins S, Blumhoff G, Blackmore S, Lowenberg-DeBoer J (2005) Farmer experience with precision agriculture in Denmark and the US eastern corn belt. Precis Agric 6:121–141CrossRefGoogle Scholar
  14. Fountas S, Wulfsohn D, Blackmore S, Jacobsen HL, Pedersen SM (2006) A model of decision making and information flows for information-intensive agriculture. Agric Syst 87:192–210CrossRefGoogle Scholar
  15. Fountas S, Carli C, Sørensen CG, Tsiropoulos Z, Cavalaris C, Vatsanidou A, Liakos B, Canavari M, Wiebensohn J, Tisserye B (2015a) Farm management information systems: current situation and future perspectives. Comput Electron Agric 115:40–50CrossRefGoogle Scholar
  16. Fountas S, Sorensen CG, Tsiropoulos Z, Cavalaris C, Liakos V, Gemtos T (2015b) Farm machinery management information system. Comput Electron Agric 110:131–138CrossRefGoogle Scholar
  17. Gladwin H (1989) Ethnographic decision tree modelling. Sage Publications Ltd., LondonCrossRefGoogle Scholar
  18. Hines T (2000) An evaluation of two qualitative methods (focus group interviews and cognitive maps) for conducting research into entrepreneurial decision making. Qual Mark Res Int J 3(1):7–16CrossRefGoogle Scholar
  19. Howley P, Donoghue CO, Heanue K (2012) Factors affecting farmers’ adoption of agricultural innovations: a panel data analysis of the use of artificial insemination among dairy farmers in Ireland. J Agric Sci 4(6):171Google Scholar
  20. Johnson HT, Kaplan RS (1987) Relevance lost: the rise and fall of management accounting. Harvard Business Press, BostonGoogle Scholar
  21. Kaplan RS, Anderson SR (2007) Time-driven activity-based costing: a simpler and more powerful path to higher profits. Harvard Business Press, BostonGoogle Scholar
  22. Kempenaar C, van Evert FK, Been T, Kocks CG, Westerduk CE (2016) Towards data-intensive, more sustainable farming: advances in predicting crop growth and use of variable rate technology in arable crops in the Netherlands. ICPA, St. LouisGoogle Scholar
  23. Lawson LG, Pedersen SM, Sørensen CG, Pesonen L, Fountas S, Werner A, Oudshoorn FW, Herold L, Chatzinikos T, Kirketerp IM, Blackmore S (2011) A four nation survey of farm information management and advanced farming systems: a descriptive analysis of survey responses. Comput Electron Agric 77:7–20CrossRefGoogle Scholar
  24. Lewis T (1998) Evolution of farm management information systems. Comput Electron Agric 19:233–248CrossRefGoogle Scholar
  25. Lu Y, Lu Y, Wang B, Pan Z, Qin H (2014) Acceptance of government-sponsored agricultural information systems in China: the role of government social power. IseB 13(2):329–354CrossRefGoogle Scholar
  26. Magne MA, Cerf M, Ingrand S (2010) A conceptual model of farmers’ informational activity: a tool for improved support of livestock farming management. Animal 4:842–852CrossRefPubMedGoogle Scholar
  27. Morgan DL (1996) Focus groups. Annu Rev Sociol 22:129–152CrossRefGoogle Scholar
  28. Murakami E, Saraiva AM, Ribeiro Junior LCM, Cugnasca CE, Hirakawa AR, Correa PLP (2007) An infrastructure for the development of distributed service-oriented information systems for precision agriculture. Comput Electron Agric 58(1):37–48CrossRefGoogle Scholar
  29. Nikkila R, Seilonen I, Koskinenet K (2010) Software architecture for farm management information systems in precision agriculture. Comput Electron Agric 70(2):328–336CrossRefGoogle Scholar
  30. Pedersen SM, Fountas S, Blackmore BS, Gylling M, Pedersen JL (2004) Adoption and perspectives of precision farming in Denmark. Acta Agric Scand Sect B – Soil Plant Sci 54(1):2–8Google Scholar
  31. Pierpaoli E, Carli G, Pignatti E, Canavari M (2013) Drivers of precision agriculture technologies adoption: a literature review. Procedia Technol 8:61–69CrossRefGoogle Scholar
  32. Pignatti E, Carli G, Canavari M (2015) What really matters? a qualitative analysis on the adoption of innovations in agriculture. J Agric Inf 6(4):73–84Google Scholar
  33. Sørensen GC, Fountas S, Nash E, Pesonen L, Bochtis D, Pedersen SM, Basso B, Blackmore SB (2010) Conceptual model of a future farm management information system. Comput Electron Agric 72:37–47CrossRefGoogle Scholar
  34. Siegel JG, Shim JK (2000) Accounting handbook. Barron’s Educational Series, HauppaugeGoogle Scholar
  35. Tsiropoulos Z, Fountas S (2015) Farm management information system for fruit orchards. Precis Agric 15:217–232Google Scholar
  36. Tsiropoulos Z, Fountas S, Gemtos T, Gravalos I, Paraforos D (2013a) Management information system for spatial analysis of tractor-implement draft forces. Precis Agric 13:349–356Google Scholar
  37. Tsiropoulos Z, Fountas S, Liakos V, Tekin B, Aygun T, Blackmore S (2013b) Web-based farm management information system for agricultural robots. EFITA-WCCA-CIGR Turin, ItalyGoogle Scholar
  38. Venkatesh V, Morris MG, Davis GB, Davis FD (2003) User acceptance of information technology: toward a unified view. MIS Q 27(3):425–478Google Scholar
  39. Zhang N, Wang M, Wang N (2002) Precision agriculture—a worldwide overview. Comput Electron Agric 36(2–3):113–132CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Zisis Tsiropoulos
    • 1
  • Giacomo Carli
    • 2
  • Erika Pignatti
    • 3
  • Spyros Fountas
    • 1
  1. 1.Agricultural University of AthensAthensGreece
  2. 2.Department of Strategy and MarketingThe Open University Business SchoolMilton KeynesUK
  3. 3.Check Fruit-NSF ItalyBolognaItaly

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