IDEAS home Printed from https://ideas.repec.org/a/eee/agisys/v173y2019icp27-38.html
   My bibliography  Save this article

Optimizing management of dairy goat farms through individual animal data interpretation: A case study of smart farming in Spain

Author

Listed:
  • Belanche, Alejandro
  • Martín-García, A. Ignacio
  • Fernández-Álvarez, Javier
  • Pleguezuelos, Javier
  • Mantecón, Ángel R.
  • Yáñez-Ruiz, David R.

Abstract

Dairy goat production systems in developed countries are experiencing an intensification process in terms of higher farm size, electronic identification, reproductive intensification, genetic selection and milking automation. This new situation generates “big data” susceptible to be used to aid farmers during the decision making process. This case study describes how the farm management can be improved by the use of the “Eskardillo”, a tool with a smart-phone terminal which relies on three principles: i) systematic individual data recording (milking control, productivity, genetic merit, morphology, phylogeny, etc.), ii) big data processing and interpretation and iii) interactive feedback to the farmer to optimize farm management. This study evaluated the effectiveness of the Eskardillo tool by monitoring the productive parameters from 2013 to 2016 in 12 conventional Murciano-Granadina dairy goat farms which implemented the Eskardillo (ESK) in late 2014. Moreover, 12 conventional farms without Eskardillo were also monitored as control farms (CTL). Results demonstrated that ESK farms were able to better monitor the productivity and physiological stage of each animal and Eskardillo allowed selecting animals for breeding, replacement or culling according to each animal's records. As a result, goats from ESK farms decreased their unproductive periods such as the first partum age (−30 days), and the dry period length (−20 days) without negatively affecting milk yield per lactation. This study revealed an acceleration in the milk yield in ESK farms since this innovation was implemented (+26 kg / lactation per year) in comparison to the situation before (+7.3) or in CTL farms (+6.1). Data suggested that this acceleration in milk yield in ESK farms could rely on i) a greater genetic progress as a result of a more knowledgeable selection of high merit goats, ii) the implementation of a more effective culling off strategy based on the production, reproductive and health records from each animal, and iii) the optimization of the conception timing for each animal according to its physiological stage and milk yield prospects to customize lactation length while keeping a short and constant dry period length (2 months). Moreover, this study demonstrated a decrease in the seasonality throughout the year in terms of percentage of animals in milking and milk yield allowing an increment in the production of off-season milk (+17%) since Eskardillo was applied. In conclusion, it was demonstrated that the implementation of the Eskardillo tool can be considered a useful strategy to optimize farm management and to contribute to the sustainable intensification of modern dairy goat farms.

Suggested Citation

  • Belanche, Alejandro & Martín-García, A. Ignacio & Fernández-Álvarez, Javier & Pleguezuelos, Javier & Mantecón, Ángel R. & Yáñez-Ruiz, David R., 2019. "Optimizing management of dairy goat farms through individual animal data interpretation: A case study of smart farming in Spain," Agricultural Systems, Elsevier, vol. 173(C), pages 27-38.
  • Handle: RePEc:eee:agisys:v:173:y:2019:i:c:p:27-38
    DOI: 10.1016/j.agsy.2019.02.002
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0308521X17311319
    Download Restriction: Full text for ScienceDirect subscribers only

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Wolfert, Sjaak & Ge, Lan & Verdouw, Cor & Bogaardt, Marc-Jeroen, 2017. "Big Data in Smart Farming – A review," Agricultural Systems, Elsevier, vol. 153(C), pages 69-80.
    2. Guimarães, Vinícius Pereira & Tedeschi, Luis Orlindo & Rodrigues, Marcelo Teixeira, 2009. "Development of a mathematical model to study the impacts of production and management policies on the herd dynamics and profitability of dairy goats," Agricultural Systems, Elsevier, vol. 101(3), pages 186-196, July.
    3. Riveiro, J.A. & Mantecón, A.R. & Álvarez, C.J. & Lavín, P., 2013. "A typological characterization of dairy Assaf breed sheep farms at NW of Spain based on structural factor," Agricultural Systems, Elsevier, vol. 120(C), pages 27-37.
    Full references (including those not matched with items on IDEAS)

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:agisys:v:173:y:2019:i:c:p:27-38. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Haili He). General contact details of provider: http://www.elsevier.com/locate/agsy .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.