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Profitability prediction model for dairy farms using the random forest method

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  • Yli-Heikkilä, Maria
  • Tauriainen, Jukka

Abstract

We applied an ensemble learning method known as random forests, which is widely used in supervised machine learning, to predict the profitability ratio of dairy farms based on financial and production related variables. The predictive model was implemented as a web service to enable farmers to calculate the profitability of their business. Hereby, farmers can better assess the sustainability of their business over time, or in comparison to other farms in the sector.

Suggested Citation

  • Yli-Heikkilä, Maria & Tauriainen, Jukka, 2014. "Profitability prediction model for dairy farms using the random forest method," 2014 International Congress, August 26-29, 2014, Ljubljana, Slovenia 182846, European Association of Agricultural Economists.
    • Handle: RePEc:ags:eaae14:182846
    DOI: 10.22004/ag.econ.182846
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    References listed on IDEAS

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    1. Friedman, Jerome H. & Hastie, Trevor & Tibshirani, Rob, 2010. "Regularization Paths for Generalized Linear Models via Coordinate Descent," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 33(i01).
    2. Karatzoglou, Alexandros & Smola, Alexandros & Hornik, Kurt & Zeileis, Achim, 2004. "kernlab - An S4 Package for Kernel Methods in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 11(i09).
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    Keywords

    Farm Management;

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