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Economic and productivity incentives to produce organically in Brazil: Evidence from strawberry production in the Federal District

Published online by Cambridge University Press:  31 August 2017

M. A. Resende Filho ,
D. A. Andow ,
R. G. Carneiro ,
D. R. Lorena ,
E. R. Sujii  and
R. T. Alves
M. A. Resende Filho*
Affiliation:
Department of Economics, University of Brasília, Brazil
D. A. Andow
Affiliation:
Department of Entomology, University of Minnesota, USA
R. G. Carneiro
Affiliation:
Agroecology Coordination, Emater DF – Technical Assistance and Rural Extension Corporation of Distrito Federal, Brazil
D. R. Lorena
Affiliation:
Department of Agronomy, University of Brasília, Brazil
E. R. Sujii
Affiliation:
Embrapa Genetic Resources and Biotechnology, Brazil
R. T. Alves
Affiliation:
Emater Brazlândia, Emater DF – Technical Assistance and Rural Extension Corporation of Distrito Federal, Brazil
*
*Corresponding author: [email protected]
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Abstract

Brazil has the largest market for organic products in Latin America, but only 1.04% of its agricultural land is utilized for organic production (OP). We compared organic and conventional production (CP) in economic and productivity terms using data from a randomized survey of 86 organic and conventional strawberry growers (response rate 85.2%) in Brazlândia, Federal District, Brazil. Probit model selection estimates showed that the use of technical assistance from rural extension and producer gender had no effect, but growers with greater age, higher indebtedness, smaller strawberry production area, more education and less experience on growing strawberries were more likely to produce organically. For growers with more than 5.6 years of education and less than 13.54 years of experience, more education and experience make them less likely to produce strawberry organically. Thus, we expect growers’ probabilities of conversion for strawberry OP will remain about the same over time in the study area. The average treatment effect for the treated (ATT) was estimated using nearest neighbor/propensity score matching and endogenous switching regression (ESR). These showed that producing strawberry organically had no effect on productivity or total cost per box, but increased revenue and profit per box, probably as a result of the price premium for organic strawberries. As only 4.8% of farmers had converted to organic production, conversion costs and non-economic factors, such as psychological factors and social capital, may be barriers to conversion.

Keywords

incentives for conversionaverage treatment effect for the treated (ATT)propensity score matching (PSM)nearest neighbor matching (NNM)endogenous switching regression (ESR)
Type
Research Paper
Information
Renewable Agriculture and Food Systems , Volume 34 , Issue 2 , April 2019 , pp. 155 - 168
Copyright
Copyright © Cambridge University Press 2017 

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References

Abadie, A. and Imbens, G.W. 2006. Large sample properties of matching estimators for average treatment effects. Econometrica 74:235–267.Google Scholar
Abadie, A. and Imbens, G.W. 2011. Bias-corrected matching estimators for average treatment effects. Journal of Business and Economic Statistics 29:1–11.Google Scholar
Abadie, A. and Imbens, G.W. 2016. Matching on the estimated propensity score. Econometrica 84:781–807.Google Scholar
Amare, M., Asfaw, S., and Shiferaw, B. 2012. Welfare impacts of maize-pigeon pea intensification in Tanzania. Agricultural Economics 43:27–43.Google Scholar
Andow, D.A., Resende Filho, M.A., Carneiro, R.G., Lorena, D.R., Sujii, E.R., and Alves, R.T. 2017. Heterogeneity in intention to adopt organic strawberry production practices among growers in the Federal District, Brazil. Ecological Economics 140:177–189.Google Scholar
Antunes, L.E.C. and Peres, N.A. 2013. Strawberry production in Brazil and South America. International Journal of Fruit Science 13:156–161.Google Scholar
Antunes, L.E.C. and Reisser Junior, C. 2008. Produção Integrada de Morango: oportunidade de mercado. Anais de Palestras e Resumos do IV Simpósio Nacional do Morango e III Encontro de Pequenas Frutas e Frutas Nativas do Mercosul. Embrapa Clima Temperado, Pelotas.Google Scholar
Anvisa (Agência Nacional de Vigilância Sanitária) 2011. Programa de Análise de Resíduos de Agrotóxicos em Alimentos - PARA. Minuta de nota técnica para divulgação de relatório de atividades de 2010. Anvisa, Brasília.Google Scholar
Badgley, C., Moghtader, J., Quintero, E., Zakem, E., Chappell, M.J., Viles-Vazquez, K., Samulon, A., and Perfecto, I. 2007. Organic agriculture and the global food supply. Renewable Agriculture and Food Systems 22:86–108.Google Scholar
Becker, S.O. and Ichino, A. 2002. Estimation of average treatment effects based on propensity scores. Stata Journal 2:358–377.Google Scholar
Burton, M., Rigby, D., and Young, T. 2003. Modelling the adoption of organic horticultural technology in the UK using duration analysis. Australian Journal of Agricultural and Resource Economics 47:29–54.Google Scholar
Busso, M., DiNardo, J., and McCrary, J. 2014. New evidence on the finite sample properties of propensity score reweighting and matching estimators. Review of Economics and Statistics 96:885–897.Google Scholar
Caliendo, M. and Kopeinig, S. 2008. Some practical guidance for the implementation of propensity score matching. Journal of Economic Surveys 22:31–72.Google Scholar
Carneiro, R.G. 2014. Como classificar as propriedades em relação às ações de agroecologia. EMATER-DF, Brasília, Distrito Federal, Brasil.Google Scholar
Chase, C. 2009. Making the transition from conventional to organic. Ag Decision Maker File A1–26, 1–4. Available at Web site https://www.extension.iastate.edu/agdm/crops/pdf/a1-26.pdf (verified 30 May 2017).Google Scholar
Dalcin, D., Souza, A.R.L., Freitas, J.B., Padula, A.D., and Dewes, H. 2014. Organic products in Brazil: From an ideological orientation to a market choice. British Food Journal 116:1998–2015.Google Scholar
Darolt, M.R. 2008. Morango orgânico: opção sustentável para o setor. Revista Campo & Negócios 34:58–61.Google Scholar
de Carvalho, S.P. 2005. Boletim do morango: cultivo convencional, segurança alimentar, cultivo orgânico. CeasaMinas, Belo Horizonte, 160 p.Google Scholar
Dutoit, L. 2007. Heckman's selection model, endogenous and exogenous switching models, a survey. The Selected Works of Laure C Dutoit. Available at Web site https://works.bepress.com/laure_dutoit/3/ (verified 13 August 2016).Google Scholar
EMATER-DF. 2016. Informativo da Produção agrícola. Sistemas de acompanhamento das ações de assistência técnica e extensão rural. EMATER, Brasília, Distrito Federal, Brasil.Google Scholar
EMATER-DF. 2017. Informações agropecuárias do Distrito Federal - 2016. Available at Web site http://www.emater.df.gov.br/index.php?option=com_phocadownload&view=category&download=1183:producao-agropecuaria-2016&id=72:arquivos-relacionados-relatorios-e-informacoes-agropecuarias&Itemid=55 (verified 25 May 2017).Google Scholar
Fagherazzi, A.F., Grimaldi, F., Kretzschmar, A.A., Molina, A.R., Gonçalves, M.A., Antunes, L.E.C., Baruzzi, G., and Rufato, L. 2017. Strawberry production progress in Brazil. Acta Horticulturae 1156:937–940.Google Scholar
Falcão, J.V., Lacerda, M.P.C., Carvalho Mendes, I., Leão, T.P., and Carmo, F.F. 2013. Qualidade do solo cultivado com morangueiro sob manejo convencional e orgânico. Pesquisa Agropecuária Tropical (Agricultural Research in the Tropics) 43:450–459.Google Scholar
Finckh, M.R., Bruggen, A.H.C., and Tamm, L. 2015. Plant Diseases and their Management in Organic Agriculture. APS Press, St. Paul, Minnesota, USA.Google Scholar
Fitzgerald, J., Gottschalk, P., and Moffitt, R. 1998. An analysis of sample attrition in panel data: The Michigan panel study of income dynamics. Journal of Human Resources 33:251–299.Google Scholar
Goklany, I.M., Mäder, P., Fließbach, A., Dubois, D., Gunst, L., Fried, P., and Niggli, U. 2002. The ins and outs of organic farming. Science 298:1889.Google Scholar
Heckman, J. 1976. The common structure of statistical models of truncation, sample selection and limited dependent variables and a simple estimator for such models. Annals of Economic and Social Measurement 5:475–492.Google Scholar
Heckman, J. 1978. Dummy endogenous variables in a simultaneous equation system. Econometrica 46:931–959.Google Scholar
Heckman, J. 1997. Instrumental variables – a study of the implicit behavioral assumptions used in making program evaluations. Journal of Human Resources 32:441–462.Google Scholar
Henz, G.P. 2010. Desafios enfrentados por agricultores familiares na produção de morango no Distrito Federal. Horticultura Brasileira 28:260–265.Google Scholar
Holland, P. 1986. Statistics and causal inference. Journal of the American Statistical Association 81:945–960.Google Scholar
Imbens, G.W. 2004. Nonparametric estimation of average treatment effects under exogeneity: A review. Review of Economics and Statistics 86:4–29.Google Scholar
Kleemann, L. and Abdulai, A. 2013. Organic certification, agro-ecological practices and return on investment: Evidence from pineapple producers in Ghana. Ecological Economics 93:330–341.Google Scholar
Kremen, C. and Miles, C. 2012. Ecosystem services in biologically diversified versus conventional farming systems: Benefits, externalities, and trade-offs. Ecology and Society 17:40.Google Scholar
Leakey, R.R.B. 2014. The role of trees in agroecology and sustainable agriculture in the tropics. Annual Review of Phytopathology 52:113–133.Google Scholar
Lin, B.H., Smith, T.A., and Huang, C.L. 2008. Organic premiums of U.S. fresh produce. Renewable Agriculture and Food Systems 23:208–216.Google Scholar
Lopes, H.R.D., Silva, B.C., Nascimento, E.F., Ramos, L.X., Pereira, M., and Carneiro, R.G. 2005. A cultura do morangueiro no Distrito Federal. EMATER-DF, Brasília.Google Scholar
Maddala, G.S. 1983. Limited-Dependent and Qualitative Variables in Econometrics. Cambridge University Press, Cambridge, UK.Google Scholar
Meirelles, L. 2016. Country report: Organic agriculture in Brazil. In Willer, H. and Lernoud, J. (eds). The World of Organic Agriculture: Statistics and Emerging Trends 2016. Research Institute of Organic Agriculture (FiBL), Frick, and IFOAM – Organics International, Bonn. p. 240–243. Available at Web site http://www.organic-world.net/yearbook/yearbook-2016.html (verified 17 June 2016).Google Scholar
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. Natural resources management environment department. Available at Web site ftp://ftp.fao.org/docrep/fao/011/ak355e/ak355e00.pdf (verified 30 August 2016).Google Scholar
Oelofse, M., Høgh-Jensen, H., Abreu, L.S., Almeida, G.F., Hui, Q.Y., Sultan, T., and Neergaard, A. 2010. Certified organic agriculture in China and Brazil: Market accessibility and outcomes following adoption. Ecological Economics 69:1785–1793.Google Scholar
Oshita, D. and Jardim, I.C.S.F. 2012. Morango: uma preocupação alimentar, ambiental e sanitária, monitorado por cromatografia líquida moderna. Scientia Chromatographica 4:52–76.Google Scholar
Ponti, T., Rijk, B., and Ittersum, M.K. 2012. The crop yield gap between organic and conventional agriculture. Agricultural Systems 108:1–9.Google Scholar
Reeve, J.R., Hoagland, L.A., Villalba, J.J., Carr, P.M., Atucha, A., Cambardella, C., Davis, D.R., and Delate, K. 2016. Organic farming, soil health, and food quality: Considering possible links. Advances in Agronomy 136:319–367.Google Scholar
Research Institute of Organic Agriculture-FiBL. 2016. Organic world: Global organic farming statistics and news. Available at Web site http://www.organic-world.net/statistics/statistics-data-tables/ow-statistics-data-key-data.html (verified 17 June 2016).Google Scholar
Rosenbaum, P.R. and Rubin, D.B. 1983. The central role of the propensity score in observational studies for causal effects. Biometrika 70:41–55.Google Scholar
Roy, A. 1951. Some thoughts on the distribution of earnings. Oxford Economic Papers 3:135–145.Google Scholar
Rubin, D. 1974. Estimating causal effects to treatments in randomised and nonrandomised studies. Journal of Educational Psychology 66:688–701.Google Scholar
Santos Neto, J., Schwan-Estrada, K.R.F., Sena, J.O.A., Jardinetti, V.A., and Alencar, M.S.R. 2016. Qualidade de frutos de tomateiro cultivado em sistema de produção orgânico e tratados com subprodutos de capim limão. Revista Ciência Agronômica 47:633–642.Google Scholar
Seagri (Secretaria de Agricultura, Abastecimento de Desenvolvimento Rural). 2015. Lista de produtos e preços a serem praticados no Programa de Aquisição de Alimentos/Termo de Adesão. Anexo II – Chamada Pública 001/2015. Seagri, Brasília, Distrito Federal, Brasil.Google Scholar
Seagri (Secretaria de Agricultura, Abastecimento de Desenvolvimento Rural). 2016. Lista de produtos e preços a serem praticados no Programa de Aquisição de Alimentos/Termo de Adesão. Anexo II – Chamada Pública 001/2016. Seagri, Brasília, Distrito Federal, Brasil.Google Scholar
StataCorp. 2015. Stata: Release 14. Statistical Software, StataCorp LLC, College Station, TX.Google Scholar
Uematsu, H. and Mishra, A.K. 2012. Organic farmers or conventional farmers: Where's the money? Ecological Economics 78:55–62.Google Scholar
Willer, H. and Lernoud, J. (eds) 2016. The World of Organic Agriculture: Statistics and Emerging Trends 2016. Research Institute of Organic Agriculture (FiBL), Frick, and IFOAM – Organics International, Bonn. Available at Web site http://www.organic-world.net/yearbook/yearbook-2016.html (verified 17 June 2016).Google Scholar
Wooldridge, J.M. 2010. Econometric Analysis of Cross Section and Panel Data. 2nd ed. The MIT Press, Cambridge, Massachusetts.Google Scholar