IDEAS home Printed from https://ideas.repec.org/a/caa/jnlage/v66y2020i7id231-2019-agricecon.html
   My bibliography  Save this article

Drivers of farm performance in Czech crop farms

Author

Listed:
  • Vladimír Kostlivý
  • Zuzana Fuksová

    (Institute of Agricultural Economics and Information, Prague, Czech Republic)

  • Tamara Rudinskaya

    (Institute of Agricultural Economics and Information, Prague, Czech Republic)

Abstract

When analysing drivers affecting the farm performance, the presence of different technologies should be taken into account. We assume that the technology used by crop farms is not the same for all producers and therefore we use latent class model to identify technological classes at first. Class definition is based on multidimensional classification and determination of indices given by the values of individual components. The principal components analysis is applied to estimate significant and robust weights for the index components. FADN (Farm Accountancy Data Network) database, Czech crop farms data from 2005 to 2017 were used and three groups of technology classes of farms were identified with a determinant influence of the structure index and localisation. The other indices characterise sustainability, innovation, technology, diversification, and individual characteristics. Three distinct classes of crop farms were found, one major class and two minor classes. Family driven farms are usually smaller farms in terms of acreage. Highly sustainable crop farms are most likely located in lower altitudes and not in less-favoured areas. Innovative farms are also likely to be more productive. The results indicate that agricultural production farms with a more sustainable way of farming are most likely to be more productive.

Suggested Citation

  • Vladimír Kostlivý & Zuzana Fuksová & Tamara Rudinskaya, 2020. "Drivers of farm performance in Czech crop farms," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 66(7), pages 297-306.
  • Handle: RePEc:caa:jnlage:v:66:y:2020:i:7:id:231-2019-agricecon
    DOI: 10.17221/231/2019-AGRICECON
    as

    Download full text from publisher

    File URL: http://agricecon.agriculturejournals.cz/doi/10.17221/231/2019-AGRICECON.html
    Download Restriction: free of charge

    File URL: http://agricecon.agriculturejournals.cz/doi/10.17221/231/2019-AGRICECON.pdf
    Download Restriction: free of charge

    File URL: https://libkey.io/10.17221/231/2019-AGRICECON?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
    ---><---

    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. Maria Martinez Cillero & Fiona Thorne & Michael Wallace & James Breen, 2019. "Technology heterogeneity and policy change in farm-level efficiency analysis: an application to the Irish beef sector," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 46(2), pages 193-214.
    2. Lukáš ČECHURA, 2010. "Estimation of technical efficiency in Czech agriculture with respect to firm heterogeneity," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 56(4), pages 183-191.
    3. Alvarez, Antonio & del Corral, Julio & Tauer, Loren W., 2012. "Modeling Unobserved Heterogeneity in New York Dairy Farms: One-Stage versus Two-Stage Models," Agricultural and Resource Economics Review, Cambridge University Press, vol. 41(3), pages 275-285, December.
    4. 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, December.
    5. Luis Orea & Subal C. Kumbhakar, 2004. "Efficiency measurement using a latent class stochastic frontier model," Empirical Economics, Springer, vol. 29(1), pages 169-183, January.
    6. Johannes Sauer & Catherine J. Morrison Paul, 2013. "The empirical identification of heterogeneous technologies and technical change," Applied Economics, Taylor & Francis Journals, vol. 45(11), pages 1461-1479, April.
    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. Elizabeth Ahikiriza & Jef Meensel & Xavier Gellynck & Ludwig Lauwers, 2021. "Heterogeneity in frontier analysis: does it matter for benchmarking farms?," Journal of Productivity Analysis, Springer, vol. 56(2), pages 69-84, December.
    2. K Hervé Dakpo & Laure Latruffe & Yann Desjeux & Philippe Jeanneaux, 2021. "Latent Class Modelling for a Robust Assessment of Productivity: Application to French Grazing Livestock Farms," Journal of Agricultural Economics, Wiley Blackwell, vol. 72(3), pages 760-781, September.
    3. Juan Cabas Monje & Bouali Guesmi & Amer Ait Sidhoum & José María Gil, 2023. "Measuring technical efficiency of Spanish pig farming: Quantile stochastic frontier approach," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 67(4), pages 688-703, October.
    4. Garcia, Luis & Laepple, Doris & Dillon, Emma & Thorne, Fiona, 2020. "The role of hired labor in transient and persistent technical efficiency on Irish dairy farms," 2020 Annual Meeting, July 26-28, Kansas City, Missouri 304395, Agricultural and Applied Economics Association.
    5. Sauer, Johannes & Davidova, Sophia & Gorton, Matthew, 2012. "Land fragmentation, market integration and farm efficiency: empirical evidence from Kosovo," 86th Annual Conference, April 16-18, 2012, Warwick University, Coventry, UK 134968, Agricultural Economics Society.
    6. Marta Arbelo-Pérez & Pilar Pérez-Gómez & Antonio Arbelo, 2023. "Profit efficiency and its determinants in the agricultural sector: A Bayesian approach," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 69(11), pages 436-445.
    7. Amer Ait Sidhoum & K Hervé Dakpo & Laure Latruffe, 2022. "Trade-offs between economic, environmental and social sustainability on farms using a latent class frontier efficiency model: Evidence for Spanish crop farms," PLOS ONE, Public Library of Science, vol. 17(1), pages 1-17, January.
    8. Fertő, Imre & Baráth, Lajos, 2013. "Heterogenitás és technikai hatékonyság - a magyar specializált szántóföldi növénytermesztő üzemek esete [Heterogeneity and technical efficiency - the case of Hungarys specialized arable crop produc," Közgazdasági Szemle (Economic Review - monthly of the Hungarian Academy of Sciences), Közgazdasági Szemle Alapítvány (Economic Review Foundation), vol. 0(6), pages 650-669.
    9. Marta Arbelo-Pérez & Pilar Pérez-Gómez & Antonio Arbelo, . "Profit efficiency and its determinants in the agricultural sector: A Bayesian approach," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 0.
    10. K Hervé Dakpo & Laure Latruffe & Yann Desjeux & Philippe Jeanneaux, 2022. "Modeling heterogeneous technologies in the presence of sample selection: The case of dairy farms and the adoption of agri‐environmental schemes in France," Agricultural Economics, International Association of Agricultural Economists, vol. 53(3), pages 422-438, May.
    11. Sauer, J. & Davidova, S. & Gorton, M., 2013. "Land Fragmentation and Market Integration- Heterogenous Technologies in Kosovo," Proceedings “Schriften der Gesellschaft für Wirtschafts- und Sozialwissenschaften des Landbaues e.V.”, German Association of Agricultural Economists (GEWISOLA), vol. 48, March.
    12. Martinez Cillero, Maria & Breen, James & Thorne, Fiona & Wallace, Michael & Hennessy, Thia, 2016. "Technical efficiency and technology heterogeneity of beef farms: a latent class stochastic frontier approach," 90th Annual Conference, April 4-6, 2016, Warwick University, Coventry, UK 236351, Agricultural Economics Society.
    13. A. G. Billé & C. Salvioni & R. Benedetti, 2018. "Modelling spatial regimes in farms technologies," Journal of Productivity Analysis, Springer, vol. 49(2), pages 173-185, June.
    14. Sauer, Johannes & Davidova, Sophia & Gorton, Matthew, 2012. "Heterogeneous Technologies as an Answer to Market and Price Risk: The Case of Kosovo," 123rd Seminar, February 23-24, 2012, Dublin, Ireland 122509, European Association of Agricultural Economists.
    15. Veronika Fenyves & Tibor Tarnóczi & Zoltán Bács & Dóra Kerezsi & Péter Bajnai & Mihály Szoboszlai, 2022. "Financial efficiency analysis of Hungarian agriculture, fisheries and forestry sector," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 68(11), pages 413-426.
    16. Mohamed Chaffai & Patrick Plane, 2017. "Firm Productivity, Technology and Export Status, What Can We Learn from Egyptian Industries?," Working Papers 1134, Economic Research Forum, revised 09 Jun 2017.
    17. Billé, AG & Salvioni, C. & Benedetti, R., 2015. "Spatial Heterogeneity In Production Functions Models," 150th Seminar, October 22-23, 2015, Edinburgh, Scotland 212662, European Association of Agricultural Economists.
    18. Ioannis Skevas, 2019. "A Hierarchical Stochastic Frontier Model for Efficiency Measurement Under Technology Heterogeneity," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 17(3), pages 513-524, September.
    19. repec:blg:reveco:v:69:y:2017:i:6:p:7-17 is not listed on IDEAS
    20. Jerzy Marzec & Andrzej Pisulewski, 2021. "Measurement of technical efficiency in the case of heterogeneity of technologies used between firms - Based on evidence from Polish crop farms," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 67(4), pages 152-161.
    21. Lajos Baráth & Imre Fertő & Heinrich Hockmann, 2020. "Technological Differences, Theoretical Consistency, and Technical Efficiency: The Case of Hungarian Crop-Producing Farms," Sustainability, MDPI, vol. 12(3), pages 1-18, February.

    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:jnlage:v:66:y:2020:i:7:id:231-2019-agricecon. 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.