IDEAS home Printed from https://ideas.repec.org/p/ags/gewi18/275852.html
   My bibliography  Save this paper

Neue Erkenntnisse Zur Milchviehhaltung Unter Zukünftig Restriktiveren Rahmenbedingungen

Author

Listed:
  • Spörri, Martina
  • Hoop, Daniel
  • Heer, Ines

Abstract

Ändernde politische, wirtschaftliche und gesellschaftliche Rahmenbedingungen fordern eine wettbewerbsfähigere und gleichzeitig nachhaltigere Milchviehhaltung. Eine fundierte Folgen-abschätzung dieser Veränderungen, wie sie zum Beispiel während einer politischen Entscheidungsfindung erwünscht wird, ist jedoch erst nach einer Aufschlüsselung der Heterogenität unter den Milchviehbetrieben möglich. Mittels einer auf betrieblichen Buchhaltungsdaten basierenden Clusteranalyse konnten die Autoren zwei Betriebstypen mit eher extensiven Produktionsstrategien sowie vier Betriebstypen mit eher intensiven Produktionsstrategien identifizieren. Während die extensiven Strategien wirtschaftlich erfolg-reich sind, können die intensiven Strategien bis auf eine Ausnahme aus Kostengründen nicht mithalten. Keiner der Betriebstypen entspricht der durchschnittlichen Produktionsstrategie der gesamten Stichprobe. Somit kann gezeigt werden, dass der globale Durchschnitt bei großer Datenvarianz an Aussagekraft verliert.

Suggested Citation

  • Spörri, Martina & Hoop, Daniel & Heer, Ines, 2018. "Neue Erkenntnisse Zur Milchviehhaltung Unter Zukünftig Restriktiveren Rahmenbedingungen," 58th Annual Conference, Kiel, Germany, September 12-14, 2018 275852, German Association of Agricultural Economists (GEWISOLA).
    • Handle: RePEc:ags:gewi18:275852
    DOI: 10.22004/ag.econ.275852
    as

    Download full text from publisher

    File URL: https://ageconsearch.umn.edu/record/275852/files/Vortrag_144.pdf
    Download Restriction: no
    File URL: https://libkey.io/10.22004/ag.econ.275852?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. J. A. Hartigan & M. A. Wong, 1979. "A K‐Means Clustering Algorithm," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 28(1), pages 100-108, March.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Custodio João, Igor & Lucas, André & Schaumburg, Julia & Schwaab, Bernd, 2023. "Dynamic clustering of multivariate panel data," Journal of Econometrics, Elsevier, vol. 237(2).
    2. Xu, Jing & Wang, Xiaoying & Gu, Yujiong & Ma, Suxia, 2023. "A data-based day-ahead scheduling optimization approach for regional integrated energy systems with varying operating conditions," Energy, Elsevier, vol. 283(C).
    3. Carlos Carrasco-Farré, 2022. "The fingerprints of misinformation: how deceptive content differs from reliable sources in terms of cognitive effort and appeal to emotions," Palgrave Communications, Palgrave Macmillan, vol. 9(1), pages 1-18, December.
    4. Roberto Benocci & Giovanni Brambilla & Alessandro Bisceglie & Giovanni Zambon, 2020. "Eco-Acoustic Indices to Evaluate Soundscape Degradation Due to Human Intrusion," Sustainability, MDPI, vol. 12(24), pages 1-19, December.
    5. Felix Mbuga & Cristina Tortora, 2021. "Spectral Clustering of Mixed-Type Data," Stats, MDPI, vol. 5(1), pages 1-11, December.
    6. Emma L. Schultz & David T. Tan & Kathleen D. Walsh, 2010. "Endogeneity and the corporate governance - performance relation," Australian Journal of Management, Australian School of Business, vol. 35(2), pages 145-163, August.
    7. Muxuan Pan & Hao Wang & Jinquan Huang, 2019. "T–S Fuzzy Modeling for Aircraft Engines: The Clustering and Identification Approach," Energies, MDPI, vol. 12(17), pages 1-15, August.
    8. Zhang, Weibin & Zha, Huazhu & Zhang, Shuai & Ma, Lei, 2023. "Road section traffic flow prediction method based on the traffic factor state network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 618(C).
    9. Ting Liu & Nick Shryane & Mark Elliot, 2022. "Attitudes to climate change risk: classification of and transitions in the UK population between 2012 and 2020," Palgrave Communications, Palgrave Macmillan, vol. 9(1), pages 1-15, December.
    10. Renata De Paris & Christian V Quevedo & Duncan D A Ruiz & Osmar Norberto de Souza, 2015. "An Effective Approach for Clustering InhA Molecular Dynamics Trajectory Using Substrate-Binding Cavity Features," PLOS ONE, Public Library of Science, vol. 10(7), pages 1-25, July.
    11. Michal Bernardelli & Zbigniew Korzeb & Pawel Niedziolka, 2021. "The banking sector as the absorber of the COVID-19 crisis’ economic consequences: perception of WSE investors," Oeconomia Copernicana, Institute of Economic Research, vol. 12(2), pages 335-374, June.
    12. Jelle R Dalenberg & Luca Nanetti & Remco J Renken & René A de Wijk & Gert J ter Horst, 2014. "Dealing with Consumer Differences in Liking during Repeated Exposure to Food; Typical Dynamics in Rating Behavior," PLOS ONE, Public Library of Science, vol. 9(3), pages 1-11, March.
    13. Carlos Fernández-Hernández & Carmelo J. León & Jorge E. Araña & Flora Díaz-Pére, 2016. "Market segmentation, activities and environmental behaviour in rural tourism," Tourism Economics, , vol. 22(5), pages 1033-1054, October.
    14. Haiyang Xia & Song Zha & Jijun Huang & Jibin Liu, 2020. "Radio environment map construction by adaptive ordinary Kriging algorithm based on affinity propagation clustering," International Journal of Distributed Sensor Networks, , vol. 16(5), pages 15501477209, May.
    15. Stefania Salvatore & Diana Domanska & Mollie Wood & Hedvig Nordeng & Geir Kjetil Sandve, 2017. "Complex patterns of concomitant medication use: A study among Norwegian women using paracetamol during pregnancy," PLOS ONE, Public Library of Science, vol. 12(12), pages 1-11, December.
    16. Hafid Kadi & Mohammed Rebbah & Boudjelal Meftah & Olivier Lézoray, 2021. "A Data Representation Model for Personalized Medicine," International Journal of Healthcare Information Systems and Informatics (IJHISI), IGI Global, vol. 16(4), pages 1-25, October.
    17. Zhang, Tonglin & Lin, Ge, 2021. "Generalized k-means in GLMs with applications to the outbreak of COVID-19 in the United States," Computational Statistics & Data Analysis, Elsevier, vol. 159(C).
    18. Chen Lin & Ying Zou & Ji Qin & Xiangrong Liu & Yi Jiang & Caihuan Ke & Quan Zou, 2013. "Hierarchical Classification of Protein Folds Using a Novel Ensemble Classifier," PLOS ONE, Public Library of Science, vol. 8(2), pages 1-11, February.
    19. F. Murtagh & M. Hernández-Pajares, 1995. "The Kohonen self-organizing map method: An assessment," Journal of Classification, Springer;The Classification Society, vol. 12(2), pages 165-190, September.
    20. Alan Fernihough & Kevin Hjortshøj, 2021. "Coal and the European Industrial Revolution," The Economic Journal, Royal Economic Society, vol. 131(635), pages 1135-1149.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    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:ags:gewi18:275852. See general information about how to correct material in RePEc.

    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 bibliographic 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.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: AgEcon Search (email available below). General contact details of provider: https://edirc.repec.org/data/gewisea.html .

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

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.