EFFICIENCY OF BIODYNAMIC FARMS

Marie PechrováCzech University of Life Sciences Prague, Faculty of Economics and Management

September 17-18, 2013

1. Content

Introduction Materials and Methods Results

Parametric approach Non-parametric approach

Discussion Conclusion References

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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

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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

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  Deterministic StochasticParametric Corrected Ordinary Least

Squares (COLS)Stochastic Frontier Analysis (SFA)

Non-parametric Data Envelopment Analysis (DEA)

Stochastic Data Envelopment Analysis (SDEA)

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)

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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

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uv

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

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itititititititit uvxxxxxy ,55,44,33,22,11 lnlnlnlnlnln

OLS, COLS and SFA production functions for biodynamic farmsSource: Own elaboration

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 efficient100 % efficient only in material usage under IRS, VRS, FDH and FRH

Non-parametric approach

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Efficiency of biodynamic farms using DEA approach; Source: Own calculations

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

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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

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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.

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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.

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Thank you for your attention.

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