IDEAS home Printed from https://ideas.repec.org/a/caa/jnlcjs/v63y2018i10id63-2017-cjas.html
   My bibliography  Save this article

How do herd's genetic level and milk quality affect performance of dairy farms?

Author

Listed:
  • Helis Luik-Lindsaar

    (Institute of Economics and Social Sciences, Estonian University of Life Sciences, Tartu, Estonia)

  • Ants-Hannes Viira

    (Institute of Economics and Social Sciences, Estonian University of Life Sciences, Tartu, Estonia)

  • Haldja Viinalass

    (Institute of Veterinary Medicine and Animal Sciences, Estonian University of Life Sciences, Tartu, Estonia)

  • Tanel Kaart

    (Institute of Veterinary Medicine and Animal Sciences, Estonian University of Life Sciences, Tartu, Estonia)

  • Rando Värnik

    (Institute of Economics and Social Sciences, Estonian University of Life Sciences, Tartu, Estonia)

Abstract

The effects of genetic level and output quality characteristics on technical efficiency (TE) of dairy farms were studied. The average total relative breeding value (RBV) at herd level was considered a parameter of the genetic level and production potential of the main input (dairy cows), while somatic cell count (SCC) and milk composition characterise the quality of the main output (milk) of dairy farms. The analysis was carried out in two stages: data envelopment analysis was used in the first stage and fractional regression model in the second stage, combining the data collected by the Estonian Farm Accountancy Data Network with the data from the Estonian Livestock Performance Recording Ltd. The results showed that the TE of fully efficient dairy farms is positively affected by the total RBV (P < 0.05), number of dairy cows in the herd (P < 0.05), and negatively affected by the SCC (P < 0.001) and costs of purchased feed per kg of produced milk (P < 0.01). Among the inefficient farms, the TE was positively affected by the lifetime daily milk yield (P < 0.05), and average milk fat (P < 0.1) and protein (P < 0.05) contents. The results confirm our hypothesis that the genetic level of dairy herd and milk quality have a positive effect on the TE of dairy farms.

Suggested Citation

  • Helis Luik-Lindsaar & Ants-Hannes Viira & Haldja Viinalass & Tanel Kaart & Rando Värnik, 2018. "How do herd's genetic level and milk quality affect performance of dairy farms?," Czech Journal of Animal Science, Czech Academy of Agricultural Sciences, vol. 63(10), pages 379-388.
    • Handle: RePEc:caa:jnlcjs:v:63:y:2018:i:10:id:63-2017-cjas
    as

    Download full text from publisher

    File URL: http://cjas.agriculturejournals.cz/artkey/cjs-201810-0001_how-do-herd-s-genetic-level-and-milk-quality-affect-performance-of-dairy-farms.php
    Download Restriction: free of charge
    File URL: http://cjas.agriculturejournals.cz/pdfs/cjs/2018/10/01.pdf
    Download Restriction: free of charge
    ---><---

    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. Esmeralda Ramalho & Joaquim Ramalho & Pedro Henriques, 2010. "Fractional regression models for second stage DEA efficiency analyses," Journal of Productivity Analysis, Springer, vol. 34(3), pages 239-255, December.
    2. Esmeralda A. Ramalho & Joaquim J.S. Ramalho & José M.R. Murteira, 2011. "Alternative Estimating And Testing Empirical Strategies For Fractional Regression Models," Journal of Economic Surveys, Wiley Blackwell, vol. 25(1), pages 19-68, February.
    3. Svend Rasmussen, 2010. "Scale efficiency in Danish agriculture: an input distance--function approach," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 37(3), pages 335-367, September.
    4. Sophia Davidova & Laure Latruffe, 2007. "Relationships between Technical Efficiency and Financial Management for Czech Republic Farms," Journal of Agricultural Economics, Wiley Blackwell, vol. 58(2), pages 269-288, June.
    5. David Roibas & Antonio Alvarez, 2012. "The contribution of genetics to milk composition: evidence from Spain," Agricultural Economics, International Association of Agricultural Economists, vol. 43(2), pages 133-141, March.
    6. Joe Zhu, 2009. "Quantitative Models for Performance Evaluation and Benchmarking," International Series in Operations Research and Management Science, Springer, number 978-0-387-85982-8, April.
    7. Timothy J. Coelli & D.S. Prasada Rao & Christopher J. O’Donnell & George E. Battese, 2005. "An Introduction to Efficiency and Productivity Analysis," Springer Books, Springer, edition 0, number 978-0-387-25895-9, February.
    8. Papke, Leslie E & Wooldridge, Jeffrey M, 1996. "Econometric Methods for Fractional Response Variables with an Application to 401(K) Plan Participation Rates," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 11(6), pages 619-632, Nov.-Dec..
    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. Carolyn‐Thi Thanh Dung Tran & Brian Dollery & Subba Reddy Yarram, 2023. "The Influence of Administrative Intensity on Efficiency: An Empirical Analysis of Australian Universities," Economic Papers, The Economic Society of Australia, vol. 42(3), pages 282-305, September.
    2. Alperovych, Yan & Hübner, Georges & Lobet, Fabrice, 2015. "How does governmental versus private venture capital backing affect a firm's efficiency? Evidence from Belgium," Journal of Business Venturing, Elsevier, vol. 30(4), pages 508-525.
    3. Justus Haucap & Johannes Muck, 2015. "What drives the relevance and reputation of economics journals? An update from a survey among economists," Scientometrics, Springer;Akadémiai Kiadó, vol. 103(3), pages 849-877, June.
    4. Gegenfurtner, Dennis Andreas, 2021. "The causes of Original Sin: An empirical investigation of emerging market and developing countries," IPE Working Papers 174/2021, Berlin School of Economics and Law, Institute for International Political Economy (IPE).
    5. Djokoto, Justice Gameli & Afari-Sefa, Victor, 2017. "Alternative functional forms for technology choice: Application to cocoa production technologies," Technology in Society, Elsevier, vol. 50(C), pages 110-120.
    6. Veronese da Silva, Aline & Costa, Marcelo Azevedo & Lopes-Ahn, Ana Lúcia, 2022. "Accounting multiple environmental variables in DEA energy transmission benchmarking modelling: The 2019 Brazilian case," Socio-Economic Planning Sciences, Elsevier, vol. 80(C).
    7. Gutiérrez, Ester & Lozano, Sebastián, 2016. "Efficiency assessment and output maximization possibilities of European small and medium sized airports," Research in Transportation Economics, Elsevier, vol. 56(C), pages 3-14.
    8. Eucabeth Majiwa & Boon L. Lee & Clevo Wilson & Hidemichi Fujii & Shunsuke Managi, 2018. "A network data envelopment analysis (NDEA) model of post-harvest handling: the case of Kenya’s rice processing industry," Food Security: The Science, Sociology and Economics of Food Production and Access to Food, Springer;The International Society for Plant Pathology, vol. 10(3), pages 631-648, June.
    9. Ramalho Esmeralda A. & Ramalho Joaquim J.S. & Coelho Luís M.S., 2018. "Exponential Regression of Fractional-Response Fixed-Effects Models with an Application to Firm Capital Structure," Journal of Econometric Methods, De Gruyter, vol. 7(1), pages 1-18, January.
    10. Joaquim Ramalho & J. Silva, 2013. "Functional form issues in the regression analysis of financial leverage ratios," Empirical Economics, Springer, vol. 44(2), pages 799-831, April.
    11. da Silva e Souza, Geraldo & Gomes, Eliane Gonçalves, 2015. "Management of agricultural research centers in Brazil: A DEA application using a dynamic GMM approach," European Journal of Operational Research, Elsevier, vol. 240(3), pages 819-824.
    12. Wimmer, Lorenz & Kluge, Jan & Zenz, Hannes & Kimmich, Christian, 2023. "Predicting structural changes of the energy sector in an input–output framework," Energy, Elsevier, vol. 265(C).
    13. Esmeralda A. Ramalho & Joaquim J. S. Ramalho, 2017. "Moment-based estimation of nonlinear regression models with boundary outcomes and endogeneity, with applications to nonnegative and fractional responses," Econometric Reviews, Taylor & Francis Journals, vol. 36(4), pages 397-420, April.
    14. Raúl Pérez-Reyes & Beatriz Tovar, 2021. "Peruvian Electrical Distribution Firms’ Efficiency Revisited: A Two-Stage Data Envelopment Analysis," Sustainability, MDPI, vol. 13(18), pages 1-20, September.
    15. Víctor Chang & Beatriz Tovar, 2022. "Efficiency drivers for the South Pacific West coast port terminals: a two-stage non-convex metafrontier dea approach," Operational Research, Springer, vol. 22(3), pages 2667-2701, July.
    16. Adam Hatzikyriakou & Ning Lin, 2017. "Simulating storm surge waves for structural vulnerability estimation and flood hazard mapping," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 89(2), pages 939-962, November.
    17. Jean-François Brun & Constantin Thierry Compaore, 2021. "Public Expenditures Efficiency On Education Distribution in Developing Countries," Working Papers hal-03116615, HAL.
    18. Nikos Chatzistamoulou & Phoebe Koundouri, 2024. "Is Green Transition in Europe Fostered by Energy and Environmental Efficiency Feedback Loops? The Role of Eco-Innovation, Renewable Energy and Green Taxation," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 87(6), pages 1445-1472, June.
    19. Ogundari, Kolawole, 2014. "The Paradigm of Agricultural Efficiency and its Implication on Food Security in Africa: What Does Meta-analysis Reveal?," World Development, Elsevier, vol. 64(C), pages 690-702.
    20. Akua Agyeiwaa-Afrane & Kofi A. A-O. Agyei-Henaku & Charlotte Badu-Prah & Francis Srofenyoh & Ferguson K. Gidiglo & James K. A. Amezi & Justice G. Djokoto, 2023. "Drivers of Ghanaians’ approval of the electronic levy," SN Business & Economics, Springer, vol. 3(1), pages 1-20, January.

    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:caa:jnlcjs:v:63:y:2018:i:10:id:63-2017-cjas. 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: Ivo Andrle (email available below). General contact details of provider: https://www.cazv.cz/en/home/ .

    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.