Hostname: page-component-cd9895bd7-jkksz Total loading time: 0 Render date: 2024-12-26T20:38:04.692Z Has data issue: false hasContentIssue false
  • English
  • Français

Productivity differences between organic and other vegetable farming systems in northern Thailand

Published online by Cambridge University Press:  13 August 2013

Pranthanthip Kramol ,
Renato Villano ,
Paul Kristiansen  and
Euan Fleming
Pranthanthip Kramol
Affiliation:
Department of Agricultural Economics and Agricultural Extension and Centre for Agricultural Resource System Research (CARSR), Faculty of Agriculture, Chiang Mai University, Thailand.
Renato Villano*
Affiliation:
UNE Business School, University of New England, Armidale, New South Wales, Australia.
Paul Kristiansen
Affiliation:
School of Environmental and Rural Science, University of New England, Armidale, New South Wales, Australia.
Euan Fleming
Affiliation:
UNE Business School, University of New England, Armidale, New South Wales, Australia.
*
* Corresponding author: rvillan2@une.edu.au
Get access Rights & Permissions [Opens in a new window]

Abstract

We analyzed the productivity levels of smallholder farms in northern Thailand practicing different ‘clean and safe’ vegetable farming systems or conventional vegetable (CV) production. ‘Clean and safe’ farmers are categorized into three groups based on their use of synthetic chemicals: organic, pesticide-free and safe-use. Farm-level data on vegetable production were collected from random samples of farms operating these farming systems. A standard stochastic production frontier model and a metafrontier model were estimated for each system to obtain estimates of technical efficiency (TE) with respect to their cohorts, metatechnology ratios (MTRs, showing the extent of technology gaps between farming systems) and overall productivity measures. Productivity levels were found to vary moderately between farming systems. ‘Clean and safe’ farms achieved a higher mean TE score than conventional farms, indicating a more efficient use of inputs in producing a certain level of output within their system. However, their MTRs were significantly lower than those of conventional farmers, indicating greater production technology constraints because of the need to conform to strict guidelines. All four farming systems had at least one farmer who could overcome the technological constraints to achieve the highest possible output regardless of the technology used. Effective assistance providers were found to be crucial for farmers to achieve high productivity in the organic farming system. Improvements are needed to raise low productivity levels through technology transfer, value chain improvement and farmer capacity in production and marketing. The required improvement strategies differ among farming systems.

Keywords

organic farmingtechnical efficiencystochastic metafrontiernorthern Thailand
Type
Research Papers
Information
Renewable Agriculture and Food Systems , Volume 30 , Issue 2 , April 2015 , pp. 154 - 169
Copyright
Copyright © Cambridge University Press 2013 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1 Vanit-Anunchai, C. and Schmidt, E. 2005. Consumer purchase decisions for pesticide-safe vegetables using logistic regression: The case of Thailand. In Batt, P.J. and Jayamangkala, N. (eds). Acta Horticulturae. ISHS Commission Economics and Marketing, Chiang Mai, Thailand. p. 457–464.Google Scholar
2 Posri, W., Shankar, B. and Chadbunchachai, S. 2007. Consumer attitudes towards and willingness to pay for pesticide residue limit compliant ‘safe’ vegetables in northeast Thailand. Journal of International Food and Agribusiness Marketing 19:81–101.Google Scholar
3 Johnson, G.I., Weinberger, K., and Wu, M.-H. 2008. The Vegetable Industry in Tropical Asia: An Overview of Production and Trade, with a Focus on Thailand. World Vegetable Center, Shanua, Taiwan.Google Scholar
4 Kramol, P., Thong-ngam, K., Gypmantasiri, P., and Davies, W.P. 2005. Challenges in developing pesticide-free and organic vegetable markets and farming systems for smallholder farmers in north Thailand. In Batt, P.J. and Jayamangkala, N. (eds). Acta Horticulturae. ISHS Commission Economics and Marketing, Chiang Mai, Thailand. p. 243–252.Google Scholar
5 Lorlowhakarn, S., Boonyanopakun, K., Ellis, W., Panyakul, V., Vildozo, D., and Kasterine, A. 2008. Strengthening the Export Capacity of Thailand's Organic Agriculture. National Innovation Agency, Bangkok.Google Scholar
6 IFOAM. 2008. Definition of Organic Agriculture. International Federation of Organic Agriculture Movements, Bonn. Available at Web site http://ifoam.org/growing_organic/definitions/doa (verified September 21, 2012).Google Scholar
7 Vanit-Anunchai, C. 2006. Possibilities and constraints of marketing environmentally friendly produced vegetables in Thailand. PhD thesis, University of Hannover, Hannover.Google Scholar
8 Panyakul, V. 2003. Organic Agriculture in Thailand, Production and Export of Organic Fruit and Vegetables in Asia. Food and Agriculture Organization of the United Nations, International Federation of Organic Agriculture Movement, and EarthNet Foundation, Bangkok.Google Scholar
9 National Organic Agriculture Development Board. 2008. The National Organic Agriculture Development Plan Issue I. National Organic Agriculture Development Board, Bangkok.Google Scholar
10 Gypmantasiri, P., Puangmanee, J., Thong-ngam, K., Chowsilpa, N., and Limnirankul, B. 2000. Development Process of Pesticide-Free Vegetable Production Systems in Chiang Mai Province. The Multiple Cropping Centre, Chiang Mai.Google Scholar
11 Kramol, P., Thong-ngam, K., Villano, R., and Kristiansen, P. 2009. ‘Clean and Safe’ Agriculture in Northern Thailand: Driving Forces, Current Situation and Challenges. ISSAAS Congress and Annual Meeting 2009, Bangkok, Thailand.Google Scholar
12 McCoy, S. and Parlevliet, G. 2000. Export Market Potential for Clean and Organic Agricultural Products. Rural Industries Research and Development Corporation, Canberra.Google Scholar
13 Salakpetch, S. 2007. Quality management system: Good agricultural practice (GAP) in Thailand. In Hu, S., and Bejosano-Gloria, G. (eds). Good Agricultural Practice (GAP) in Asia and Oceania. Food and Fertilizer Technology Center, Taipei. p. 91–98.Google Scholar
14 Kristiansen, P., Taji, A., and Reganold, J. 2006. Overview of organic agriculture. In Kristiansen, P., Taji, A., and Reganold, J. (eds). Organic Agriculture: A Global Perspective. CSIRO Publishing, Collingwood. p. 1–23.Google Scholar
15 Setboonsarng, S., Leung, P.S., and Cai, J. 2008. Impacts of Institutional Arrangements on the Profitability and Profit Efficiency of Organic Rice in Thailand. Contributed paper to the Cultivating the Future Based on Science. Volume 2: Livestock, Socio-Economic and Cross Disciplinary Research in Organic Agriculture, 18–20 June 2008. International Society of Organic Agriculture Research, Research Institute of Organic Agriculture, Modena.Google Scholar
16 Songsrirote, N. and Singhapreecha, C. 2007. Technical efficiency and its determinants on conventional and certified organic jasmine rice farms in Yasothon province. Thammasat Economic Journal 25(2):96–133.Google Scholar
17 Kramol, P. 2011. Adoption and performance of ‘clean and safe’ vegetable farming systems in Northern Thailand. PhD thesis, University of New England, Armidale.Google Scholar
18 Oude Lansink, A., Pietola, K., and Backman, S. 2002. Efficiency and productivity of conventional and organic farms in Finland 1994–1997. European Review of Agricultural Economics 29:51–65.CrossRefGoogle Scholar
19 Kumbhakar, S.C., Tsionas, E.G., and Sipiläinen, T. 2008. Joint estimation of technology choice and technical efficiency: An application to organic and conventional dairy farming. Journal of Productivity Analysis 31:151–161.Google Scholar
20 Madau, F.A. 2007. Technical efficiency in organic and conventional farming: Evidence from Italian cereal farms. Agricultural Economics Review 8(1):5–21.Google Scholar
21 Sipiläinen, T. and Oude Lansink, A. 2005. Learning in Switching to Organic Farming. NJF-Seminar 369, Organic Farming for a New Millennium: Status and Future Challenges. Nordic Association of Agricultural Scientists, Alnarp, Sweden.Google Scholar
22 Heckman, J.J. 1979. Sample selection bias as a specification error. Econometrica 47:153–161.Google Scholar
23 Aigner, D., Lovell, C.A.K., and Schmidt, P. 1977. Formulation and estimation of stochastic frontier production function models. Journal of Econometrics 6:21–37.Google Scholar
24 Hayami, Y. 1969. Sources of agricultural productivity gap among selected countries. American Journal of Agricultural Economics 51:564–575.Google Scholar
25 Hayami, Y. and Ruttan, V.W. 1970. Agricultural productivity differences among countries. American Economic Review 60:895–911.Google Scholar
26 Hayami, Y. and Ruttan, V.W. 1971. Agricultural Development: An International Perspective. Johns Hopkins University Press, Baltimore.Google Scholar
27 Battese, G.E. and Rao, D.S.P. 2002. Technology gap, efficiency, and a stochastic metafrontier function. International Journal of Business and Economics 1:1–7.Google Scholar
28 Battese, G.E., Rao, D.S.P., and O'Donnell, C.J. 2004. A metafrontier production function for estimation of technical efficiencies and technology gaps for firms operating under different technologies. Journal of Productivity Analysis 21:91–103.Google Scholar
29 O'Donnell, C.J., Rao, D.S.P., and Battese, G.E. 2008. Metafrontier frameworks for the study of firm-level efficiencies and technology ratios. Empirical Economics 34:231–255.Google Scholar
30 Villano, R., Mehrabi Boshrabadi, H., and Fleming, E. 2010. When is metafrontier analysis appropriate? An example of varietal differences in pistachio production in Iran. Journal of Agricultural Science and Technology 12:379–389.Google Scholar
31 Stevenson, R.E. 1980. Likelihood functions for generalized stochastic frontier estimation. Journal of Economics 13:57–66.Google Scholar
32 Kumbhakar, S., Ghosh, S., and McGuckin, J. 1991. A generalized production frontier approach for estimating determinants of inefficiency in U.S. dairy farms. Journal of Business and Economic Statistics 9:279–286.Google Scholar
33 Battese, G.E. and Coelli, T.J. 1995. A model for technical inefficiency effects in a stochastic frontier production function for panel data. Empirical Economics 20:325–332.CrossRefGoogle Scholar
34 Mariano, M.J., Villano, R., and Fleming, E. 2011. Technical efficiency of rice farms in different agroclimatic zones in the Philippines: An application of a stochastic metafrontier model. Asian Economic Journal 25:245–269.Google Scholar
35 Coelli, T.J., Rao, D.S.P., and Battese, G.E. 2005. An Introduction to Efficiency and Productivity Analysis. Springer, New York.Google Scholar
36 Schultz, T.W. 1964. Transforming Traditional Agriculture. Yale University Press, New Haven.Google Scholar
37 Haji, J. 2006. Production efficiency of smallholder's vegetable dominated mixed-farming systems in eastern Ethiopia: A non-parametric approach. Journal of African Economies 16(1):1–27.Google Scholar
38 Rios, A. and Shively, G.E. 2005. Farm size and nonparametric efficiency measurements for coffee farms in Vietnam. Paper presented at the American Agricultural Economics Association Annual Meeting, Rhode Island, 24–27 July.Google Scholar
39 Liu, Y. 2006. Model selection in stochastic frontier analysis: Maize production in Kenya. American Agricultural Economics Association Annual Meeting in Long Beach, California, 23–26 July.Google Scholar
40 Piesse, J. 1999. Efficiency Issues in Transitional Economies: An Application to Hungary. Ashgate, Aldershot.Google Scholar
41 Studenmund, A.H. 2001. Using Econometrics: A Practical Guide. 4th ed. Addisson Wesley Longman, Inc., New York.Google Scholar
42 Battese, G.E. 1997. A note on the estimation of Cobb-Douglas production functions when some explanatory variables have zero values. Journal of Agricultural Economics 48:250–252.Google Scholar
43 Coelli, T.J. 1996. A Guide to FRONTIER Version 4.1: A Computer Program for Stochastic Frontier Production and Cost Function Estimation. CEPA Working Papers, No. 7/96. School of Economics, University of New England, Armidale.Google Scholar
44 Giller, K.E., Rowe, E.C., de Ridder, N., and van Keulen, H. 2006. Resource use dynamics and interactions in the tropics: Scaling up in space and time. Agricultural Systems 88(1):8–27.Google Scholar
45 Henderson, C.W.L. and Bishop, A.C. 2000. Vegetable weed management systems. In Sindel, B.M. (ed.). Australian Weed Management Systems. R.G. and F.J. Richardson, Melbourne. p. 355–372.Google Scholar
46 Lee, D.R. 2005. Agricultural sustainability and technology adoption: Issues and policies for developing countries. American Journal of Agricultural Economics 87(5):1325–1334.Google Scholar
47 Mehrabi Boshrabadi, H., Villano, R., and Fleming, E. 2008. Technical efficiency and environmental-technological gaps in wheat production in Kerman Province of Iran: A meta-frontier analysis. Agricultural Economics 38:67–76.Google Scholar
48 Moreno, G. and Sunding, D.L. 2005. Joint estimation of technology adoption and land allocation with implications for the design of conservation policy. American Journal of Agricultural Economics 87(4):1009–1019.Google Scholar