Farm management systems: Technical efficiencies differences and technology gap of Uruguay’s dairy farms

Authors

  • Federico García Suárez Universidad de la República, Facultad de Agronomía.Uruguay
  • Gabriela Pérez Quesada Universidad de la República, Facultad de Agronomía. Uruguay

DOI:

https://doi.org/10.52292/j.estudecon.2019.1661

Keywords:

Technical Efficiency, Technology Gaps, Meta-Frontier, Dairy Production

Abstract

This study analyzes technological differences between two groups of dairy farms in Uruguay, family and business-managed. The meta-frontier methodology is applied to estimate and compare technical effi ciencies between these two groups. Although business-managed farms are more technically effi cient than family farms (0.702 and 0.487, respectively) both groups of farmers could obtain productivity gains improving their technical effi ciency. Moreover, the two groups are operating under different technology conditions. The estimated average meta-technology ratio for BMF is 0.911 and 0.807 for FF. Therefore, BMF should adopt and invest in new technologies to shift the production function upward and reduce the technology gap while FF could try to implement the prevailing practices that are being used by BMF.

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References

Aigner, D., Lovell, C. A. K., & Schmidt, P. (1977). Formulation and estimation of stochastic frontier production function models. Journal of Econometrics,6(1), 21-37 DOI: https://doi.org/10.1016/0304-4076(77)90052-5

Battese, G. E., & Coelli, T. J. (1988). Prediction of fi rm-level technical effi ciencies with a generalized frontier production function and panel data. Journal of Econometrics, 38(3), 387-399 DOI: https://doi.org/10.1016/0304-4076(88)90053-X

Battese, G. E., & Coelli, T. J. (1992). Frontier production functions, technical efficiency and panel data: with application to paddy farmers in India. Journal of Productivity Analysis, 3(1), 153-169 DOI: https://doi.org/10.1007/BF00158774

Battese, G. E., & Coelli, T. J. (1995). A model for technical ineffi ciency effects in a stochastic frontier production function for panel data. Empirical Economics, 20(2), 325-332 DOI: https://doi.org/10.1007/BF01205442

Battese, G. E., & Corra, G. S. (1977). Estimation of a production frontier model: with application to the pastoral zone of eastern Australia. Australian Journal of Agricultural Economics, 21(3), 169-179 DOI: https://doi.org/10.1111/j.1467-8489.1977.tb00204.x

Battese, G. E., & Prasada Rao, D. S. (2002). Technology gap, effi ciency, and a stochastic metafrontier function. International Journal of Business and Economics, 1(2), 87-93

Battese, G. E., Prasada Rao, D. S., & O’Donell, C. J. (2004). A metafrontier production function for estimation of technical effi ciencies and technology gaps for fi rms operating under different technologies. Journal of Productivity Analysis, 21(1), 91-103 DOI: https://doi.org/10.1023/B:PROD.0000012454.06094.29

Cabrera, V. E., Solís, D., & del Corral, J. (2010). Determinants of technical effi ciency among dairy farms in Wisconsin. Journal of Dairy Science, 93(1), 387-393 DOI: https://doi.org/10.3168/jds.2009-2307

Chaddad, F. R. (2009). El sector lechero en Uruguayo en un contexto internacional: organización y estrategia sectorial. Informe técnico. Ministerio de Ganadería Agricultura y Pesca del Uruguay-FAO.

Chen, Z., & Song, S. (2008). Effi ciency and technology gap in china’s agriculture: a regional meta-frontier analysis. China Economic Review, 19(2), 287-296 DOI: https://doi.org/10.1016/j.chieco.2007.03.001

Coelli, T. J. (1995). Estimators and hypothesis tests for a stochastic frontier function: a Monte Carlo analysis. Journal of Productivity Analysis, 6(3), 247-268 DOI: https://doi.org/10.1007/BF01076978

Donnet, M.L, López, I.D, Black, J.R, & Hellin, J. (2017). Productivity differences and food security: a meta-frontier analysis of rain-fed maize farmers in MasAgro in Mexico. AIMS Agriculture and Food, 2(2), 129-148 DOI: https://doi.org/10.3934/agrfood.2017.2.129

Gatti, N., Lema, D., & Brescia, V. (2015). A meta-frontier approach to measuring technical efficiency and technology gaps in beef cattle production in Argentina. In W. Martin (President), International Conference of Agricultural Economists, Milan: International Agricultural Economists Association

Green, W. (2005a). Fixed and random effects in stochastic frontier models. Journal of Productivity Analysis, 23(1), 7-32 DOI: https://doi.org/10.1007/s11123-004-8545-1

Green, W. (2005b). Reconsidering heterogeneity in panel data estimators of the stochastic frontier model. Journal of Econometrics, 126(2), 269-303 DOI: https://doi.org/10.1016/j.jeconom.2004.05.003

Hayami, Y. (1969). Sources of agricultural productivity gap among selected countries. American Journal of Agricultural Economics, 51(3), 564-575 DOI: https://doi.org/10.2307/1237909

Hayami, Y., & Ruttan, V. W. (1970). Agricultural productivity differences among countries. The American Economic Review, 60(5), 895-911

Hayami, Y., & Ruttan, V. W. (1971). Agricultural development: an international perspective. Baltimore: John Hopkins University Press

Henningsen, A., Mpeta, D. F., Adem, A. S., Kuzilwa, J. A., & Czekaj, T. G. (2015). A meta-frontier approach for causal inference in productivity analysis: the effect of contract farming on sunfl ower productivity in Tanzania. Selected Paper prepared for presentation at the 2015 Annual Meeting of Agricultural and Applied Economics Association. Department of Agricultural, Environmental, and Development Economics, San Francisco, CA, July 26-28. Recuperado de http://eprints.lancs.ac.uk/89119/1/productivityPaper.pdf

Hernández, A. (2011). Complejo Lechero En Vassallo, M. (Ed.) Dinámica y competencia intrasectorial en el agro, Uruguay 2000-2010, (pp. 53-71). Montevideo, Uruguay: Facultad de Agronomía

Jiang, N., & Sharp, B. (2015). Technical efficiency and technological gap of New Zealand dairy farms: a stochastic meta-frontier model. Journal of Productivity Analysis, 44(1), 39-49 DOI: https://doi.org/10.1007/s11123-015-0429-z

Jondrow, J., Konx Lovell, C. A., Materov, I. S., & Schmidt, P. (1982). On the estimation of technical ineffi ciency in the stochastic frontier production function model. Journal of Econometrics, 19(2), 233-238 DOI: https://doi.org/10.1016/0304-4076(82)90004-5

Kodde, D. A., & Palm, F. (1986). Wald criteria for jointly testing equality and inequality restrictions. Econometrica, 54(5), 1243-1248 DOI: https://doi.org/10.2307/1912331

Kompas, T., & Che, T. N. (2006). Technology choice and effi ciency on Australian dairy farms. Australian Journal of Agricultural and Resource Economics, 50(1), 65-83 DOI: https://doi.org/10.1111/j.1467-8489.2006.00314.x

Kumbhakar, S. C., & Knox Lovell, C. A. (2000). Stochastic frontier analysis. Cambridge, UK: Cambridge University Press DOI: https://doi.org/10.1017/CBO9781139174411

Kumbhakar, S. C., Line, G., & Hardker, J. B. (2012). Technical efficiency in competing panel data models: A study of Norwegian grain farming. Journal of Productivity Analysis, 41(2), 321-337 DOI: https://doi.org/10.1007/s11123-012-0303-1

Meeusen, W., & van den Broeck, J. (1977). Effi ciency estimation from Cobb-Douglas production functions with composed error. International Economic Review, 18(2), 435-444 DOI: https://doi.org/10.2307/2525757

Mondelli, M. P., Lanzilotta, B., Picasso, V., Ferreira, G., Vairo, M., & Cazulo, P. (2013). Encuesta de actividades de innovación agropecuaria (2007-2009). (ANII Colección de Indicadores y Estudios Nro. 8) Recuperado de http://www.cinve.org.uy/wp-content/uploads/2013/09/ENCUESTA_ACTIVIDADES_INNOVACION_AGROPECUARIA_2007_2009-1.pdf

Moreira, V. H., & Bravo-Ureta, B. E. (2010). Technical efficiency and metatechnology ratios for dairy farms in three southern cone countries: a stochastic meta-frontier model. Journal of Productivity Analysis, 33(1), 33-45 DOI: https://doi.org/10.1007/s11123-009-0144-8

O’Donnell, C. J., Prasada Rao, D. S., & Battese, G. E. (2008). Metafrontier frameworks for the study of fi rm-level effi ciencies and technology ratios. Empirical Economics, 34(2), 231-255 DOI: https://doi.org/10.1007/s00181-007-0119-4

Rao, E. J. O., Brümmer, B., & Qaim, M. (2012). Farmer participation in supermarket channels, production technology, and effi ciency: The case of vegetables in Kenya. American Journal of Agricultural Economics, 94(4), 891-912 DOI: https://doi.org/10.1093/ajae/aas024

Villano, R., Bravo-Ureta, B., Solís, D., & Fleming, E. (2015). Modern rice technologies and productivity in the Philippines: disentangling technology from managerial gaps. Journal of Agricultural Economics, 66(1), 129-154 DOI: https://doi.org/10.1111/1477-9552.12081

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Published

2019-06-13 — Updated on 2021-09-25

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How to Cite

García Suárez, F., & Pérez Quesada, G. (2021). Farm management systems: Technical efficiencies differences and technology gap of Uruguay’s dairy farms. Estudios económicos, 36(72), 91–115. https://doi.org/10.52292/j.estudecon.2019.1661 (Original work published June 13, 2019)

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