1. EFFICIENCY OF BIODYNAMIC FARMS Marie Pechrová Czech University of Life Sciences Prague, Faculty of Economics and Management September 17-18, 2013

2. 1. Content  Introduction  Materials and Methods  Results  Parametric approach  Non-parametric approach  Discussion  Conclusion  References 2

3. 2. Introduction (1)  Aim  to introduce theoretical approach to the analysis of the technical efficiency of the biodynamic farms  Biodynamic agriculture  agricultural system with beliefs in quality over quantity and moral growth above traditional market value  beyond organic agriculture, has a certification process  Rudolf Steiner’s lectures in 1924 => anthroposophy  Efficiency of farms  type of efficiencies: technical, allocative and economic  technical efficiency: ability of a farm to produce the maximum feasible output from a given set of inputs  deterministc or stochastic, parametric or non-parametric approaches 3

4. 2. Introduction (2) Taxonomy of the approaches used in efficiency analysis  Parametric approach – assumptions: about the structure of the production possibility set => gives the information about the transformation process of the inputs to outputs the data generation process => explains why actual values differ from production function (inefficiency of the particular farm or noise in the data)  Non-parametric approach – assumptions: about the return to scale (RTS): constant (CRS), decreasing (DRS), increasing (IRS), varying (VRS) and replicability hull (FDH, FRH) models 4 DeterministicStochastic ParametricCorrected Ordinary Least Squares (COLS) Stochastic Frontier Analysis (SFA) Non-parametricData Envelopment Analysis (DEA) Stochastic Data Envelopment Analysis (SDEA)

5. 3. Aim and Materials  Aim: to introduce and compare approaches to the technical of the biodynamic farms => choose appropriate method for further research  Data sources: Albertina database and balanced sheets and profit and loss statements, State Agricultural Interventional Fund for year 2010  Variables:  Production: sales of own products and services and change of the stock of own activity in particular year (in thousands of CZK)  Material: amount of consumed material and energy by farm  Capital: long-term assets  Labour: dividing of wages paid by a farm by average wage in agriculture  Acreage of farmland  Subsidies (all type of subsidies from Ministry of Agriculture) 5

6. 3. Methods  Parametric Estimation of Efficiency  Stochastic frontier analysis (SFA)  decomposition of the error term ε : the inefficiency term u and stochastic error term v ( )  functional form: Cobb-Douglas  distribution of u: half normal  Non-parametric Estimation of Efficiency  return to scale (RTS): constant (CRS), decreasing (DRS), increasing (IRS), varying (VRS) and replicability hull (FDH, FRH) models 6

7. 4. Results (1)  Comparison of OLS, COLS, SFA  The most inefficient in capital and the most efficient in subsidies, land used only from 74.79 % and labor only from 48.67 %  Farm 1 - efficient almost in all inputs (except for land and subsidies and the less inefficient from all  Farm 3 - the most inefficient Parametric approach 7 OLS, COLS and SFA production functions for biodynamic farms Source: Own elaboration

8. 4. Results (2)  Different assumptions about RTS reflected in a shape of production function  CRS: only firm 1 is 100 % efficient in usage of all inputs except for a land  Farm 1: the most efficient (lies at the frontier in most of the cases)  Farm 2: achieves 100 % in usage of all production factors (DRS, VRS, FDH)  Farm 3: 100 % efficient only in case of IRS, VRS, FDH and FRH assumptions and only in capital, land and subsidies usage  Farm is 4: the less efficient 100 % efficient only in material usage under IRS, VRS, FDH and FRH Non-parametric approach 8 InputCRSDRSIRSVRSFDHFRH 1 Material 26991.000000 2142290.5035521.0000000.5035521.000000 0.569049 343610.128029 0.329623 0.618895 413590.312842 1.000000 1 Capital 48611.000000 2906600.1423401.0000000.1423401.000000 318910.531773 1.000000 4112310.068179 0.168373 1 Labor 11.000000 21170.0226901.0000000.0226901.000000 0.025641 360.034478 0.166667 430.052507 0.333333 1 Land 3860.897379 0.898046 1.0000000.933124 29191.000000 3720.994716 1.000000 41500.363832 0.480344 1 Subsidies 32701.000000 2328320.2644361.0000000.2644361.000000 0.298832 316980.398549 1.000000 421550.239006 0.787553 Efficiency of biodynamic farms using DEA approach; Source: Own calculations

9. 5. Discussion (1)  Non-parametric approach tends to predict higher efficiency than parametric  SFA: farms around 50 % efficient in usage of material and capital, 74.79% in land usage, 73.78% subsidies  DEA: efficiency of 72.31 % in material, 67.32 % in capital usage, only in labor usage lower efficiency (46.85 %), 84.01% in labour and 76.05% in subsidies  The labour efficiency under DEA is more equally distributed.  Several firms with a DEA efficiency of 1 have lower SFA efficiency. Comparison of parametric and non-parametric methods 9

10. 6. Conclusion  Comparison of the results of parametric and non-parametric approach => SFA efficiency in interval from 48.67 % to 74.79 %, DEA from 46.85 % to 84.01 %  The most efficient - farm 1, the less efficient - farm 4  Farm 2 is using the highest amount of inputs, but non-efficiently  In DEA the input changed for an inefficient firm will not change the efficiency of other firms, in SFA it might influence the random error and a difference in efficiency  Data set is enlarged, the efficiency in DEA will only change if the new firms change the frontier, in SFA, efficiency will change the distinction between random errors and inefficiency will be different  More inputs and/or outputs are added, an increasing number of firms will get DEA efficiency of 1  In our sample when all five inputs included into the model, all farms 100 % efficient => SFA approach more feasible 10

11. 7. References (1)  Battese, G. and T. Coelli (1988) ‘Prediction of Firm-Level Technical Efficiencies with a Generalised Frontier Production Function and Panel Data’, Journal of Econometrics, vol. 38, pp. 387-399.  Bogetoft, P., Otto, L. (2011) Benchmarking with DEA, SFA, and R. New York: Springer. ISBN 978-1- 4419-7960-5.  Čechura, L. (2009) Zdroje a limity růstu agrárního sektoru: analýza efektivnosti a produktivity českého agrárního sektoru – aplikace SFA (Stochastic Frontier Analysis). Prague: Wolters Kluwer ČR. ISBN 978-80-7357-493-2.  Farrell, M. J. (1957) ‘The Measurement of Productive Efficiency’, Journal of the Royal Statistical Society, vol. 120, no. 3, pp. 253-290.  Greene, W. (2005) ‘Reconsidering heterogeneity in panel data estimators of the stochastic frontier model’, Journal of Econometrics, vol. 126, pp. 269–303.  Jondrow, J., Lovell, C. A. K, Materov, I. S., Schmidt, P. (1982) ‘On the Estimation of Technical Inefficiency in the Stochastic Frontier Production Function Model’, Journal of Econometrics, vol. 19, pp. 233–238.  Kumbhakar, S. C., Lien, G., Hardaker J. B. (2012) ‘Technical efficiency in competing panel data models: a study of Norwegian grain farming’, Journal of Productivity Analysis, vol. 19 September 2012, pp. 1-17. 11

12. 7. References (2)  Mathijs, E., Swinnen, J. (2001) ‘Production organization and efficiency during transition: an empirical analysis of east-German agriculture’, The Review of economics and Statistics, vol. 83, pp. 100-107.  Phillips, J. C., Rodriguez, L. P. (2006) ‘Beyond Organic: An Overview of Biodynamic Agriculture with Case Examples’, Selected paper prepared for presentation at the American Agricultural Economics Association Annual Meeting, Long Beach, California, July 23 – 26.  Pitt, M. M., Lee, L-F. (1981) ‘The Measurement and Sources of Technical Inefficiency in the Indonesian weaving Industry’, Journal of Development Economics, vol. 9, pp. 43-64.  Singh, I. P., Grover, D. K. (2011) ‘Economic Viability of Organic farming: An Empirical Experience of Wheat Cultivation in Punjab’, Agricultural economics Research Review, vol. 24, pp. 275-281.  Speelman, S., D’Haese, M., Buysse, J., D’Haese, L. (2008) ‘A measure for the efficiency of water use and its determinants, a case study of small-scale irrigation schemes in North-Wet Province, South Africa’, Agricultural economics, vol. 98, pp. 31-39.  Steiner, R. (1993) Spiritual Foundation for the Renewal of Agriculture: A Course of Lectures, Kimberton, PA: Biodynamic Farming and Gardening Association. 12

13. Thank you for your attention. 13