Comparison between productivity and profitability of organic and conventional production: does the period of analysis matter?

Authors

  • Moisés Resende Filho Universidade de Brasi­lia

Keywords:

Average treatment effects on the treated, profitability, productivity

Abstract

Brazil is the largest market for organic products in Latin America, but only cultivates 0.5% of its land in organic production systems. This may be due to the lower productivity and profitability of organic production compared to conventional production, which was the research objective of the present study. Using data  collected in the 2019 agricultural year via face-to-face application of a semi-structured questionnaire to 79  strawberry producers in the Federal District, the average effect of treatment on the treated (ATT) for productivity, profitability and costs using the methods of nearest neighbor/propensity score matching and endogenous switching regression. ATT estimates indicated equal productivity and profitability, but higher costs and revenue for the organic production, suggesting that the absence of economic incentives may be an important barrier to conversion to organic production. As the ATT estimates of Resende Filho et al. (2019) for the 2015 agricultural year indicated equal productivity and costs, but higher revenue and  profitability from organic production, it is concluded that the period of analysis matters. Therefore, future studies should seek to control for fixed time effects, possibly using panel data.

References

Abadie, A., & Imbens, G. W. (2006). Large sample properties of matching estimators for average treatment effects. Econometrica, 74(1), 235-267. https://doi.org/10.1111/j.1468-0262.2006.00655.x

Abadie, A., & Imbens, G. W. (2011). Bias-corrected matching estimators for average treatment effects. Journal of Business & Economic Statistics, 29(1), 1-11. https://doi.org/10.1198/jbes.2009.07333

Abadie, A., & Imbens, G. W. (2016). Matching on the estimated propensity score. Econometrica, 84(2), 781-807. https://doi.org/10.3982/ECTA11293

Amare, M., Asfaw, S., & Shiferaw, B. (2012). Welfare impacts of maize–pigeonpea intensification in Tanzania. Agricultural Economics, 43(1), 27-43. https://doi.org/10.1111/j.1468-0262.2006.00655.x

Antunes, L.E.C., Bonow, S., & Reisser Júnior, C. (2020). Morango: crescimento constante em área e produção. Anuário Campo & Negócios HF, 37: 88–92. Disponível em: https://www.alice.cnptia.embrapa.br/alice/bitstream/doc/1122535/1/Anuario-HF-2020-

LEC-Antunes.pdf

Badgley, C., Moghtader, J., Quintero, E., Zakem, E., Chappell, M. J., Aviles-Vazquez, K., Samulon, A., & Perfecto, I. (2007). Organic agriculture and the global food supply. Renewable agriculture and food systems, 22(2), 86-108. https://doi.org/10.1017/S1742170507001640

Becker, S. O., & Ichino, A. (2002). Estimation of average treatment effects based on propensity scores. The Stata Journal, 2(4), 358-377. https://doi.org/10.1177/1536867X0200200403

Caliendo, M., & Kopeinig, S. (2008). Some practical guidance for the implementation of propensity score matching. Journal of economic surveys, 22(1), 31-72. https://doi.org/10.1111/j.1467-6419.2007.00527.x

Dalcin, D., Souza, A. R. L., Freitas, J. B., Padula, A. D., & Dewes, H. (2014). Organic products in Brazil: from an ideological orientation to a market choice. British Food Journal, 116(12), 1998-2015. https://doi.org/10.1108/BFJ-01-2013-0008

De Ponti, T., Rijk, B., & van Ittersum, M. K. (2012). The crop yield gap between organic and conventional agriculture. Agricultural systems, 108, 1-9. https://doi.org/10.1016/j.agsy.2011.12.004

Emater-DF (2016). Informativo da Produção agrícola. Sistemas de acompanhamento das ações de assistência técnica e extensão rural. Emater, Brasília, Distrito Federal, Brasil.

Fagherazzi, A. F., Cocco, C., Antunes, L. E. C., Souza, J. A., & Rufato, L. (2014). La fragolicoltura brasiliana guarda avanti. Rivista di Frutticoltura e di Ortofloricoltura, 76(6), 20-25.

Finckh, M. R., Van Bruggen, A. H., & Tamm, L. (Eds.). (2015). Plant diseases and their management in organic agriculture. American Phytopathological Society. St. Paul, Minnesota: APS Press. https://doi.org/10.1094/9780890544785

Fitzgerald, J., Gottschalk, P., & Moffitt, R. (1998). An analysis of the impact of sample attrition on the second generation of respondents in the Michigan panel study of income dynamics. Journal of Human Resources, 33(2), 300-344. https://doi.org/10.2307/146434

Froehlich, A. G., Melo, A. S., & Sampaio, B. (2018). Comparing the profitability of organic and conventional production in family farming: Empirical evidence from Brazil. Ecological economics, 150, 307-314. https://doi.org/10.1016/j.ecolecon.2018.04.022

Goklany, I. M. (2002). The ins and outs of organic farming. Science, 298(5600), 1889-1891. https://doi.org/10.1126/science.298.5600.1889b

Gregório, L.S., Gurgel, H., Dessay, N., Sousa, G. M., & Roux, E. (2019). Estimativa populacional pelo modelo people in pixel aplicado ao estudo da dengue no Distrito Federal-Brasil. Confins, 42, 1-21. https://doi.org/10.4000/confins.22922

Heckman, J. (1997). Instrumental variables: A study of implicit behavioral assumptions used in making program evaluations. The Journal of Human Resources, 32(3), 441. https://doi.org/10.2307/146178

Heckman, J. J. (1976). The common structure of statistical models of truncation, sample selection andlimited dependent variables and a simple estimator for such models. Annals of Economic and Social Measurement, 5(4), 475-492.

Heckman, J. J. (1978). Dummy Endogenous Variables in a Simultaneous Equation System. Econometrica: Journal of the Econometric Society, 46(6), 931-959. https://doi.org/10.2307/1909757

Holland, P. W. (1986). Statistics and causal inference. Journal of the American statistical Association, 81(396), 945-960.

Imbens, G. W. (2004). Nonparametric estimation of average treatment effects under exogeneity: A review. Review of Economics and statistics, 86(1), 4-29. https://doi.org/10.1162/003465304323023651

Kremen, C., & Miles, A. (2012). Ecosystem services in biologically diversified versus conventional farming systems: benefits, externalities, and trade-offs. Ecology and society, 17(4): 40. http://dx.doi.org/10.5751/ES-05035-170440

Leakey, R. R. (2014). The role of trees in agroecology and sustainable agriculture in the tropics. Annual review of phytopathology, 52, 113-133. https://doi.org/10.1146/annurev-phyto-102313-045838

Maddala, G. S. (1983). Limited-dependent and qualitative variables in econometrics. Cambridge University Press.

Nemes, N. (2009). Comparative analysis of organic and non-organic farming systems: A critical assessment of farm profitability. Food and Agriculture Organization of the United Nations, Rome, 33. https://www.fao.org/3/ak355e/ak355e.pdf

Oliveira, V. C., la Rosa Massahud, R. T., Oliveira Costa, J. F., Andrade Melo, L. D. F., Grugiki, M. A., Melo Junior, J. L. D. A., Melo, M. F. V., Melo, E. F., & Silva, J. C. R. (2024). Avanços da produção orgânica brasileira: estudo a partir do Cadastro Nacional de

Produtores Orgânicos. Contribuciones a Las Ciencias Sociales, 17(1), 4689-4705. https://doi.org/10.55905/revconv.17n.1-280

Reeve, J. R., Hoagland, L. A., Villalba, J. J., Carr, P. M., Atucha, A., Cambardella, C., & Delate, K. (2016). Organic farming, soil health, and food quality: considering possible links. Advances in Agronomy, 137, 319-367. https://doi.org/10.1016/bs.agron.2015.12.003

Resende Filho, M. A., Andow, D. A., Carneiro, R. G., Lorena, D. R., Sujii, E. R., & Alves, R. T. (2019). Economic and productivity incentives to produce organically in Brazil: Evidence from strawberry production in the Federal District. Renewable Agriculture and Food Systems, 34(2), 155-168. https://doi.org/10.1017/S1742170517000412

Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1), 41-55. https://doi.org/10.1093/biomet/70.1.41

Roy, A. D. (1951). Some thoughts on the distribution of earnings. Oxford economic papers, 3(2), 135-146. https://doi.org/10.1093/oxfordjournals.oep.a041827

Rubin, D. (1974). Estimating Causal Effects to Treatments in Randomised and Nonrandomised Studies. Journal of Educational Psychology, 66(5), 688-701. https://doi.org/10.1037/h0037350

StataCorp. (2017). Stata: Release 15. Statistical Software. College Station, TX: StataCorp LLC.

Uematsu, H., & Mishra, A. K. (2012). Organic farmers or conventional farmers: Where’s the money? Ecological Economics, 78, 55-62. https://doi.org/10.1016/j.ecolecon.2012.03.013

Willer, H., Trávníček, J., Meier, C., & Schlatter, B. (2021). The World of Organic Agriculture 2021: Statistics and Emerging Trends 2021. Research Institute of Organic Agriculture FiBL, Frick, and IFOAM – Organics International, Bonn. Available from:<https://www.fibl.org/en/shop-en/1150-organic-world-2021>.

Wooldridge, J. M. (2010). Econometric analysis of cross section and panel data. MIT press.

Published

2024-07-03

How to Cite

RESENDE FILHO, Moisés. Comparison between productivity and profitability of organic and conventional production: does the period of analysis matter?. Organizações Rurais & Agroindustriais, [S. l.], v. 26, p. e2078, 2024. Disponível em: https://www.revista.dae.ufla.br/index.php/ora/article/view/2078. Acesso em: 9 may. 2025.

Issue

Section

Economy and foreign trade