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Analysis of animal monitoring technologies in Germany from an innovation system perspective

Author

Listed:
  • Busse, M.
  • Schwerdtner, W.
  • Siebert, R.
  • Doernberg, A.
  • Kuntosch, A.
  • König, B.
  • Bokelmann, W.

Abstract

In order to address societal demands (e.g., animal welfare, traceability, environmental aspects), animal monitoring (AM) technologies provide much potential for innovation in animal production. AM means the real time and on-going automatic monitoring by ‘smart’ sensors of physiological, growth and behaviour parameters of individual farming animals, integrated in various areas (e.g., animal milking, feeding, breeding, health). The literature on AM mainly focusses on technologies and their application. The available information about innovation processes in AM is still very fragmentary and not comprehensive. The present article analyses the generation, development and use of AM technologies in Germany from a dynamic innovation system perspective. The analytical framework of the article is based on the sectoral innovation system approach. Qualitative interviews, an expert workshop, and a Delphi survey were conducted to explore the roles and interactions of heterogeneous actors in innovation processes and the interlocking between innovation stages. On the basis of identified fostering and inhibiting factors, opportunities for systemic interventions are suggested to further innovations in AM and in the German animal production sector and in other countries. These interventions consider, on the one hand, recommendations to support AM technologies and their broader implementation (‘hard skill interventions’) and, on the other hand, interventions that are suitable to stimulate innovations in animal production without specific focus on a single technological regime or innovation area (‘soft skill interventions’). The ‘hard skill interventions’ refer to the need for improvements concerning system compatibility and data exchange; the special need for financial support of extensive validation of AM technologies and clear communication of benefits or constraints to farmers. ‘Soft skill interventions’ are related to innovation capacity building of actors in order to coordinate co-development processes and to improve communication: Finding of a common understanding of the innovation system and common language among actors; early involvement of all actors and the reflection of actors' roles; resource-based fostering of network management. Finally, innovation policies should be capable to gather and react appropriately to these requirements. Generally, the study contributes to a better understanding of the complexity of innovation activities in AM and their embedding in the innovation system of animal production. The opportunities for systemic intervention can be used by the sector's actors to enhance the innovativeness of the German sector and to make better use of AM potentials to address the global challenges and societal demands.

Suggested Citation

  • Busse, M. & Schwerdtner, W. & Siebert, R. & Doernberg, A. & Kuntosch, A. & König, B. & Bokelmann, W., 2015. "Analysis of animal monitoring technologies in Germany from an innovation system perspective," Agricultural Systems, Elsevier, vol. 138(C), pages 55-65.
  • Handle: RePEc:eee:agisys:v:138:y:2015:i:c:p:55-65
    DOI: 10.1016/j.agsy.2015.05.009
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    Cited by:

    1. Kenney, Martin & Serhan, Hiam & Trystram, Gilles, 2020. "Digitalization and Platforms in Agriculture: Organizations, Power Asymmetry, and Collective Action Solutions," ETLA Working Papers 78, The Research Institute of the Finnish Economy.
    2. Monteiro Moretti, Débora & Baum, Chad M. & Ehlers, Melf-Hinrich & Finger, Robert & Bröring, Stefanie, 2023. "Exploring actors' perceptions of the precision agriculture innovation system – A Group Concept Mapping approach in Germany and Switzerland," Technological Forecasting and Social Change, Elsevier, vol. 189(C).
    3. Seguin, Rose & Lefsrud, Mark G. & Delormier, Treena & Adamowski, Jan, 2021. "Assessing constraints to agricultural development in circumpolar Canada through an innovation systems lens," Agricultural Systems, Elsevier, vol. 194(C).
    4. Parra-López, Carlos & Reina-Usuga, Liliana & Carmona-Torres, Carmen & Sayadi, Samir & Klerkx, Laurens, 2021. "Digital transformation of the agrifood system: Quantifying the conditioning factors to inform policy planning in the olive sector," Land Use Policy, Elsevier, vol. 108(C).
    5. 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.
    6. Sirkka Schukat & Heinke Heise, 2021. "Towards an Understanding of the Behavioral Intentions and Actual Use of Smart Products among German Farmers," Sustainability, MDPI, vol. 13(12), pages 1-24, June.
    7. Horrillo, A. & Escribano, M. & Mesias, F.J. & Elghannam, A. & Gaspar, P., 2016. "Is there a future for organic production in high ecological value ecosystems?," Agricultural Systems, Elsevier, vol. 143(C), pages 114-125.

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