Drivers of farm performance in Czech crop farmsý V., Fuksová Z., Rudinskaya T. (2020): Drivers of farm performance in Czech crop farms. Agric. Econ. – Czech, 66: 297-306.
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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.

Afifi A., May S., Clark V.A. (2012): Practical Multivariate Analysis. 5th Ed. Boca Raton, CRC Press, Taylor and Francis Group: 517.
Alvarez A., J. del Corral (2010): Identifying different technologies within a sample using a latent class model: extensive versus intensive dairy farms. European Review of Agricultural Economics, 37: 231–50.
Alvarez A., J. del Corral, Tauer L.W. (2012): Modelling unobserved heterogeneity in New York dairy farms: One-stage versus two-stage models. Agricultural and Resource Economics Review, 41: 1–11.
Baráth L., Fertő I. (2015): Heterogeneous technology, scale of land use and technical efficiency: the case of Hungarian crop farms. Land Use Policy, 42: 141–150.
Bojnec S., Latruffe L. (2013): Farm size, agricultural subsidies and farm performance in Slovenia. Land Use Policy, 32: 207–217.
Cillero M.M., Thorne F., Wallace M., Breen J. (2019): Technology heterogeneity and policy change in farm-level efficiency analysis: an application to the Irish beef sector. European Review of Agricultural Economics, 46: 193–214.
Cechura L. (2010): Estimation of technical efficiency in Czech agriculture with respect to firm heterogeneity. Agricultural Economics – Czech, 56: 183–191.
Coelli T.J., Rao D.S.P., O'Donnell C.J., Battese G.E. (2005): An Introduction to Efficiency and Productivity Analysis. 2nd Ed. New York, Springer: 300–302.
CSO, FSS 2016 (2018): Farm Structure Survey – Analytical Evaluation – 2016. Available at (accessed Oct 16, 2018).
Eurostat Archive (2017): Agricultural Output, Price Indices and Income. Eurostat. Available at,_price_indices_and_income (accessed Nov 26, 2018).
FADN CZ Database (2018): Farm Accountancy Data Network Database in the Czech Republic. FADN CZ. Available at
Greene W. (2005): Reconsidering heterogeneity in panel data estimators of the stochastic frontier model. Journal of Econometrics, 126: 269–303.
Hockmann H., Pieniadz A. (2008): Farm heterogeneity and efficiency in Polish agriculture: A stochastic frontier analysis. In: Proceedings 104th EAAE-IAAE Seminar Agricultural Economics and Transition: "What Was Expected, What We Observed, the Lessons Learned". Budapest, Hungary, September 6–8, 2007. Available at
Jackson J.E. (2003): A User’s Guide to Principal Components. New York, Wiley: 63–77.
Matulova K., Cechura L. (2016): Technological heterogeneity, technical efficiency and subsidies in Czech agriculture. Journal of Central European Agriculture, 17: 447–466.
Orea L., Kumbhakar S.C. (2004): Efficiency measurement using a latent class stochastic frontier model. Empirical Economics, 29: 169–83.
Sauer J., Morrison Paul C.J. (2013): The empirical identification of heterogeneous technologies and technical change. Applied Economics, 45: 1461–1479.
Sauer J. (2018): Drivers of Farm Performance (Methodology). OECD Report TAD/CA/APM/WP(2018)16: 3–6. Available at
Tsionas E.G. (2002): Stochastic frontier models with random coefficients. Journal of Applied Economics, 17: 127–47.
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