IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v9y2018i1p3-d192368.html
   My bibliography  Save this article

Animal Welfare Payments and Veterinary and Insemination Costs for Dairy Cows

Author

Listed:
  • Basil Odermatt

    (Agroscope, Research Division Competitiveness and System Evaluation, Tänikon 1, CH-8356 Ettenhausen, Switzerland)

  • Nina Keil

    (Federal Food Safety and Veterinary Office, Center for Proper Housing of Ruminants and Pigs, Agroscope Tänikon 1, CH-8356 Ettenhausen, Switzerland)

  • Markus Lips

    (Agroscope, Research Division Competitiveness and System Evaluation, Tänikon 1, CH-8356 Ettenhausen, Switzerland
    GUS Group, Sonnenstrasse 5, CH-9000 St. Gallen, Switzerland)

Abstract

To promote the provision of animal-friendly housing and management exceeding the minimal legal standards, the Swiss government offers direct payments through two programs for several farm animal species. In dairy cows the BTS program pays for group housing systems with a comfortable lying area separated from the feeding area. The other program, the RAUS, requires that cows receive regular exercise in an outdoor run in the winter and a pasture during summer. The aim of the study was to analyze the relationship between the two Swiss direct payment programs and the veterinary and insemination costs for dairy cows. We used a large sample of more than 21,000 dairy farm observations from 2004 to 2014 obtained from the Swiss Farm Accountancy Data Network. A propensity score weighting was combined with a linear regression model to estimate the doubly robust treatment effects of the BTS and/or RAUS programs on dairying and breeding. Compared to the control group, that is, farms participating in neither program, farms in the RAUS tended to reduce their veterinary costs by 2% (CHF 4.71). Participation in both the BTS and RAUS programs resulted in a 10% cost reduction (CHF 19.32). An analysis of the effects of participation in both programs, with farms participating in only the RAUS as the control group, indicated a cost reduction of 7% for the farms participating in both programs (CHF 13.54). In contrast, participation in the RAUS only or in the RAUS and the BTS did not have a significant effect on insemination costs. The results thus indicate that the implementation of higher welfare standards can have a positive effect on the economic situation of a farm.

Suggested Citation

  • Basil Odermatt & Nina Keil & Markus Lips, 2018. "Animal Welfare Payments and Veterinary and Insemination Costs for Dairy Cows," Agriculture, MDPI, vol. 9(1), pages 1-14, December.
    • Handle: RePEc:gam:jagris:v:9:y:2018:i:1:p:3-:d:192368
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/9/1/3/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2077-0472/9/1/3/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Baqun Zhang & Anastasios A. Tsiatis & Eric B. Laber & Marie Davidian, 2012. "A Robust Method for Estimating Optimal Treatment Regimes," Biometrics, The International Biometric Society, vol. 68(4), pages 1010-1018, December.
    2. Heejung Bang & James M. Robins, 2005. "Doubly Robust Estimation in Missing Data and Causal Inference Models," Biometrics, The International Biometric Society, vol. 61(4), pages 962-973, December.
    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. Wei Liu & Zhiwei Zhang & Lei Nie & Guoxing Soon, 2017. "A Case Study in Personalized Medicine: Rilpivirine Versus Efavirenz for Treatment-Naive HIV Patients," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(520), pages 1381-1392, October.
    2. Masahiro Kato, 2021. "Adaptive Doubly Robust Estimator from Non-stationary Logging Policy under a Convergence of Average Probability," Papers 2102.08975, arXiv.org, revised Mar 2021.
    3. Davide Viviano & Jelena Bradic, 2020. "Fair Policy Targeting," Papers 2005.12395, arXiv.org, revised Jun 2022.
    4. Qingyuan Zhao & Dylan S. Small & Ashkan Ertefaie, 2022. "Selective inference for effect modification via the lasso," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(2), pages 382-413, April.
    5. Sant’Anna, Pedro H.C. & Zhao, Jun, 2020. "Doubly robust difference-in-differences estimators," Journal of Econometrics, Elsevier, vol. 219(1), pages 101-122.
    6. Görg Holger & Marchal Léa, 2019. "Die Effekte deutscher Direktinvestitionen im Empfängerland vor dem Hintergrund des Leistungsbilanzüberschusses: Empirische Evidenz mit Mikrodaten für Frankreich," Perspektiven der Wirtschaftspolitik, De Gruyter, vol. 20(1), pages 53-69, June.
    7. Léa Marchal & Clément Nedoncelle, 2019. "Immigrants, occupations and firm export performance," Review of International Economics, Wiley Blackwell, vol. 27(5), pages 1480-1509, November.
    8. Hisaki Kono & Yasuyuki Sawada & Abu S. Shonchoy, 2016. "DVD-based Distance-learning Program for University Entrance Exams: Experimental Evidence from Rural Bangladesh," CIRJE F-Series CIRJE-F-1027, CIRJE, Faculty of Economics, University of Tokyo.
    9. Paul Frédéric Blanche & Anders Holt & Thomas Scheike, 2023. "On logistic regression with right censored data, with or without competing risks, and its use for estimating treatment effects," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 29(2), pages 441-482, April.
    10. Ignaciuk, Ada & Malevolti, Giulia & Scognamillo, Antonio & Sitko, Nicholas J., 2022. "Can food aid relax farmers’ constraints to adopting climate-adaptive agricultural practices? Evidence from Ethiopia, Malawi and the United Republic of Tanzania," ESA Working Papers 324073, Food and Agriculture Organization of the United Nations, Agricultural Development Economics Division (ESA).
    11. Everding, Jakob & Marcus, Jan, 2020. "The effect of unemployment on the smoking behavior of couples," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 29(2), pages 154-170.
    12. Li Liang & Greene Tom, 2013. "A Weighting Analogue to Pair Matching in Propensity Score Analysis," The International Journal of Biostatistics, De Gruyter, vol. 9(2), pages 215-234, July.
    13. Xin Qiu & Donglin Zeng & Yuanjia Wang, 2018. "Estimation and evaluation of linear individualized treatment rules to guarantee performance," Biometrics, The International Biometric Society, vol. 74(2), pages 517-528, June.
    14. Ruoqing Zhu & Ying-Qi Zhao & Guanhua Chen & Shuangge Ma & Hongyu Zhao, 2017. "Greedy outcome weighted tree learning of optimal personalized treatment rules," Biometrics, The International Biometric Society, vol. 73(2), pages 391-400, June.
    15. McFarland, Michael J. & Geller, Amanda & McFarland, Cheryl, 2019. "Police contact and health among urban adolescents: The role of perceived injustice," Social Science & Medicine, Elsevier, vol. 238(C), pages 1-1.
    16. Fan Li & Ashley L. Buchanan & Stephen R. Cole, 2022. "Generalizing trial evidence to target populations in non‐nested designs: Applications to AIDS clinical trials," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(3), pages 669-697, June.
    17. Antonelli Joseph & Cefalu Matthew, 2020. "Averaging causal estimators in high dimensions," Journal of Causal Inference, De Gruyter, vol. 8(1), pages 92-107, January.
    18. Yukun Ma & Pedro H. C. Sant'Anna & Yuya Sasaki & Takuya Ura, 2023. "Doubly Robust Estimators with Weak Overlap," Papers 2304.08974, arXiv.org, revised Apr 2023.
    19. Zhang, Yingheng & Li, Haojie & Ren, Gang, 2025. "Analysing the role of traffic volume as mediator in transport policy evaluation with causal mediation analysis and targeted learning," Transportation Research Part A: Policy and Practice, Elsevier, vol. 192(C).
    20. Delprato, Marcos & Akyeampong, Kwame, 2019. "The effect of working on students’ learning in Latin America: Evidence from the learning survey TERCE," International Journal of Educational Development, Elsevier, vol. 70(C), pages 1-1.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;

    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:gam:jagris:v:9:y:2018:i:1:p:3-:d:192368. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.