Advertisement

Analysis of Farm Profitability and the Weighted Upscaling System Using the Self-Organizing Map

  • Mika Sulkava
  • Maria Yli-Heikkilä
  • Arto Latukka
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 198)

Abstract

Profitability and other economic aspects of farming in Finland are analyzed using the self-organizing map. The analysis of profitability bookkeeping data reveals several interesting relationships between the monitored financial variables. A weight optimization system is presented for upscaling financial figures of the sample of profitability bookkeeping farms to the whole country level. The self-organizing map is also used to assess the performance of the weighting system. It is confirmed that the most important large and medium-sized enterprises are represented well by the sample. Furthermore, it seems that the utilized arable area is the key factor in guiding the weight optimization process. These findings may turn out to be useful in developing the sampling of bookkeeping farms in the future.

Keywords

farm profitability bookkeeping self-organizing map upscaling weight sample optimization constraint agriculture 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Rantala, O., Tauriainen, J.: Development of results and profitability of agriculture and horticulture. In: Niemi, J., Ahlstedt, J. (eds.) Finnish Agriculture and Rural Industries 2011. Publications, ch. 4.1, vol. 111a, pp. 52–56. MTT Economic Research, Agrifood Research Finland (2011)Google Scholar
  2. 2.
    MTT EconomyDoctor (June 2012), http://www.mtt.fi/economydoctor
  3. 3.
    Sulkava, M., Luyssaert, S., Zaehle, S., Papale, D.: Assessing and improving the representativeness of monitoring networks: The European flux tower network example. Journal of Geophysical Research – Biogeosciences 116, G00J04 (2011)Google Scholar
  4. 4.
    Eklund, T.: The Self-Organizing Map in Financial Benchmarking. D.Sc. thesis, Åbo Akademi University, Turku, Finland (December 2004)Google Scholar
  5. 5.
    Sulkava, M.: Exploring agricultural data using self-organizing maps. In: Proceedings of the 20th Pacioli Workshop, Rome, Italy (September/October 2012) (accepted for publication)Google Scholar
  6. 6.
    Ryhänen, M.: Input substitution and technological development on Finnish dairy farms for 1965–1991: Empirical application on bookkeeping dairy farms. Agricultural Science in Finland 3(6), 525–601 (1994)Google Scholar
  7. 7.
    Myyrä, S., Pihamaa, P., Sipiläinen, T.: Productivity growth on Finnish grain farms from 1976–2006: a parametric approach. Agricultural and Food Science 18(3-4), 283–301 (2009)Google Scholar
  8. 8.
    Kuosmanen, T., Sipiläinen, T.: Exact decomposition of the Fisher ideal total factor productivity index. Journal of Productivity Analysis 31(3), 137–150 (2009)CrossRefGoogle Scholar
  9. 9.
    Latukka, A.: Predicting Financial Distress of Farms using Neural Network Application. Lic.Sc. thesis, University of Helsinki, Department of Economics and Management No. 22, Production Economics and Farm Management, Helsinki, Finland. (December 1998) (in finnish)Google Scholar
  10. 10.
    Farm accounting data network (June 2012), http://ec.europa.eu/agriculture/rica/index.cfm
  11. 11.
    Rantala, O., Tauriainen, J.: Development of results and profitability of agriculture and horticulture. In: Niemi, J., Ahlstedt, J. (eds.) Finnish Agriculture and Rural Industries 2012. Publications, MTT Economic Research, Agrifood Research Finland, ch. 4.1, vol. 112a, pp. 56–61 (2012)Google Scholar
  12. 12.
    Community Committee for the Farm Accountancy Data Network. Typology handbook. Technical Report RI/CC 1500 rev. 3, European Commission – Directorate-General for Agriculture and Rural Development, Brussels, Belgium (October 2009)Google Scholar
  13. 13.
    Committee for Corporate Analysis. The Guide to the Analysis of Financial Statements of Finnish Companies, Gaudeamus, Helsinki, Finland (2006)Google Scholar
  14. 14.
    Latukka, A., Sulkava, M.: Economic development of finnish agriculture and horticulture. In: Niemi, J., Ahlstedt, J. (eds.) Finnish Agriculture and Rural Industries 2012. Publications, ch. 4. 2, vol. 112a, pp. 62–65. MTT Economic Research, Agrifood Research Finland (2012)Google Scholar
  15. 15.
    Kohonen, T.: Self-Organizing Maps, 3rd edn. Springer Series in Information Sciences, vol. 30. Springer, Berlin (2001)Google Scholar
  16. 16.
    Vesanto, J., Himberg, J., Alhoniemi, E., Parhankangas, J.: SOM Toolbox for Matlab 5. Report A57, Helsinki University of Technology, Laboratory of Computer and Information Science, Espoo, Finland (2000)Google Scholar
  17. 17.
    Ultsch, A., Siemon, H.P.: Kohonen’s self organizing feature maps for exploratory data analysis. In: Proceedings of International Neural Network Conference (INNC 1990), pp. 305–308. Kluwer, Dordrecht (1990)Google Scholar
  18. 18.
    Kiviluoto, K.: Topology preservation in self-organizing maps. In: Proceedings of the International Conference on Neural Networks (ICNN 1996), vol. 1, pp. 294–299. IEEE Neural Networks Council, Piscataway (1996)CrossRefGoogle Scholar
  19. 19.
    Sulkava, M., Tikka, J., Hollmén, J.: Sparse regression for analyzing the development of foliar nutrient concentrations in coniferous trees. Ecological Modelling 191(1), 118–130 (2006)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Mika Sulkava
    • 1
  • Maria Yli-Heikkilä
    • 1
  • Arto Latukka
    • 1
  1. 1.Economic ResearchMTT Agrifood Research FinlandHelsinkiFinland

Personalised recommendations