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Extraction of Patterns to Support Dairy Culling Management

  • M. López-Suárez
  • E. Armengol
  • S. Calsamiglia
  • L. Castillejos
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11144)

Abstract

The management of a dairy farm involves taking decisions such as culling a subset of cows to improve the dairy production. Culling is the departure of cows from the herd due to sale, slaughter or death. Commonly the culling process is based on the farmer experience but there is not a general procedure to carry it out. In the present paper we use both, a method based on indistinguishability relations and the anti-unification concept, to extract patterns that characterise the cows according to their average milk production of the first lactation. Our goal is to identify as soon as possible poorly productive cows during her first lactation, which may be candidates to be culled.

Keywords

Veterinary Dairy farms Milk production Voluntary culling Artificial intelligence Machine learning Indistinguishability relations Anti-unification concept 

Notes

Acknowledgments

The authors acknowledge data support from CONAFE (Confederación Nacional de la Raza Frisona). This research is partially funded by the projects (Project AGL2015-67409-C2-01-R) from the Spanish Ministry of Economy and Competitiveness; RPREF (CSIC Intramural 201650E044); and the grant 2014-SGR-118 from the Generalitat de Catalunya. Authors also thank to Àngel García-Cerdaña his helpful comments.

References

  1. 1.
    Symbolic explanation of similarities in case-based reasoning: Comput. Inf. 25(2–3), 153–171 (2006)Google Scholar
  2. 2.
    Ansari-Lari, M., Mohebbi-Fani, M., Rowshan-Ghasrodashti, A.: Causes of culling in dairy cows and its relation to age at culling and interval from calving in Shiraz, Southern Iran. Vet. Res. Forum 3, 233–237 (2012)Google Scholar
  3. 3.
    Armengol, E., Boixader, D., García-Cerdaña, A., Recasens, J.: \(t\)-generable indistinguishability operators and their use for feature selection and classification. Fuzzy Sets Syst. (2018)Google Scholar
  4. 4.
    Calsamiglia, S., Castillejos, L., Astiz, S., Lopez-DeToro, C., Baucells, J.: A dairy farm simulation model as a tool to explore the technical and economical consequences of management decisions. In: Proceedings of the World Buiatrics Congress 2016 World Association for Buiatrics, p. 406 (2016)Google Scholar
  5. 5.
    Cavero, D., Tölle, K.H., Buxadé, C., Krieter, J.: Mastitis detection in dairy cows by application of fuzzy logic. Livest. Sci. 105, 207–213 (2006)CrossRefGoogle Scholar
  6. 6.
    Fetrow, J., Nordlund, K.V., Norman, H.D.: Culling: nomenclature, definitions, and recommendations. J. Dairy Sci. 89, 1896–1905 (2006)CrossRefGoogle Scholar
  7. 7.
    Grzesiak, W., Blaszczyk, P., Lacroix, R.: Methods of predicting milk yield in dairy cows: predictive capabilities of wood’s lactation curve and artificial neural networks (ANNs). Comput. Electron. Agric. 54(2), 69–83 (2006)CrossRefGoogle Scholar
  8. 8.
    Kamphuis, C., Mollenhorst, H., Heesterbeek, J.A., Hogeveen, H.: Detection of clinical mastitis with sensor data from automatic milking systems is improved by using decision-tree induction. J. Dairy Sci. 93(8), 3616–27 (2010)CrossRefGoogle Scholar
  9. 9.
    Lopez-Suarez, M., Armengol, E., Calsamiglia, S., Castillejos, L.: Using decision trees to extract patterns for dairy culling management. In: Iliadis, L., Maglogiannis, I., Plagianakos, V. (eds.) AIAI 2018. IAICT, vol. 519, pp. 231–239. Springer, Cham (2018).  https://doi.org/10.1007/978-3-319-92007-8_20CrossRefGoogle Scholar
  10. 10.
    Rodriguez-Lujan, I., Huerta, R., Elkan, C., Cruz, C.S.: Quadratic programming feature selection. J. Mach. Learn. Res. 11, 1491–1516 (2010)MathSciNetzbMATHGoogle Scholar
  11. 11.
    Shahinfar, S., Mehrabani-Yeganeh, H., Lucas, C., Kalhor, A., Kazemian, M., Weigel, K.A.: Prediction of breeding values for dairy cattle using artificial neural networks and neuro-fuzzy systems. Comput. Math. Methods Med. Artical ID 127130 (2012)Google Scholar
  12. 12.
    Sun, Z., Samarasinghe, S., Jago, J.: Detection of mastitis and its stage of progression by automatic milking systems using artificial neural networks. J. Dairy Res. 77, 168–175 (2009)CrossRefGoogle Scholar
  13. 13.
    Valverde, L.: On the structure of F-indistinguishability operators. Fuzzy Sets Syst. 17, 313–328 (1985)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Wang, E., Samarasinghe, S.: On-line detection of mastitis in dairy herds using artificial neural networks. In: Proceedings of the International Congress on Modelling and Simulation (MODSIM 2005), Melbourne, Australia (2005)Google Scholar
  15. 15.
    Zhang, L., Coenen, F., Leng, P.H.: An attribute weight setting method for k-NN based binary classification using quadratic programming. In: van Harmelen, F. (ed.), ECAI, pp. 325–329. IOS Press (2002)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • M. López-Suárez
    • 1
  • E. Armengol
    • 2
  • S. Calsamiglia
    • 1
  • L. Castillejos
    • 1
  1. 1.Animal Nutrition and Welfare Service, Department of Animal and Food SciencesUniversitat Autònoma de BarcelonaBarcelonaSpain
  2. 2.Artificial Intelligence Research Institute, (IIIA-CSIC)BarcelonaSpain

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