An illustrated review of some farmer participatory research techniques

Editor’s Invited Paper


A review of farmer participatory research is given highlighting its variety and wide range of terminologies. Two types of methodology are discussed, rapid rural appraisals (RRA) and participatoryrural appraisals (PRA). Some methods included in the PRA group generate quantitative and qualitative data suitable for statistical analysis. The need to ensure rigor in these studies is emphasized, but this is often shunned by researchers in the belief that their studies are simple and require no more than an elementary summary. Yet the multidisciplinary nature of participatory studies, the inherent data variability, and their small sample sizes render them complex in design, analysis, and interpretation. Using three “simple” sets of ranking data from PRA studies, it is shown that statistical methods must be simple to be adopted, that data are often poor in quality, and that parametric assumptions might not always hold. The complex underlying structure of many datasets demands a flexible approach to analysis and interpretation. Greater collaboration between researchers and statisticians will improve the supply of good datasets that can be used to improve statistical techniques for this type of research and ultimately generate greater confidence in the use of statistical methodologies.

Key Words

Collaboration Data quality Nonparametric On-farm Participatory rural appraisal Rapid rural appraisal 


  1. Ashby, J. (1990), Evaluating Technology With Farmers: A Handbook, Cali, Colombia: Centro Internacional de Agricultura Tropical.Google Scholar
  2. Best, D. J. (1993), “Extended Analysis for Ranked Data” Australian Journal of Statistics, 35, 257–262.CrossRefGoogle Scholar
  3. Biggs, S. (1989), “Resource Poor Farmer Participation in Research: A Synthesis of Experinces from Nine National Agricultural Research Systems,” OFCOPR Comparative Study Paper 3, International Service for National Agricultural Research, The Hague.Google Scholar
  4. Chambers, R. (1988), “Direct Matrix Ranking in Kenya and West Bengal,” RRA Notes, 1, 13–16.MathSciNetGoogle Scholar
  5. Chambers, R. (1994), “The Origins and Practice of Participatory Rural Appraisal,” World Development, 22, 953–969.CrossRefGoogle Scholar
  6. Chambers, R., Pacey, A., and Thrupp, L. A. (eds.) (1989), Farmer First: Farmer Innovation and Agricultural Research, London: Intermediate Technology Publications.Google Scholar
  7. Collinson, M. . (1981), “The Exploratory Survey: Content, Methods and Detailed Guidelines for Discussions With Farmers,” CIMMYT East Africa Economic Programme Farming Systems Newsletter, April–June, 1981.Google Scholar
  8. Cornwall, A., and Jewkes, R. (1995), “What is Participatory Research?,” Social Sciences in Medicine, 41, 1667–1676.CrossRefGoogle Scholar
  9. Duggan, B. (1994), “Rapid Rural Appraisal Training for Baseline Data Collection and Target Group Identification,” RRA Notes, 19, 79–84.Google Scholar
  10. Fielding, W. J. (1992), “Damage Assessment by Eye: Some Caribbean Observations,” Field Crops Research, 30, 183–186.CrossRefGoogle Scholar
  11. Fielding, W. J., and Crowder, L. V. (1995), “Sweet Potato Weevils in Jamaica: Acceptable Pests?,” Journal of Sustainable Agriculture, 5, 105–117.CrossRefGoogle Scholar
  12. Fielding, W. J., and Riley, J. (2000), “Scoring Methods: A Cautionary Tale From Papua New Guinea (With Comment by R. Chambers),” PLA Notes, 37, 113–117.Google Scholar
  13. Garonna, P., and Triacca, U. (1999), “Social Change: Measurement and Theory,” International Statistical Review, 67, 49–62.CrossRefGoogle Scholar
  14. Gill, G. J. (1993), O. K., the Data’s Louy, But It’s All We’ve Got (Being a Critique of Conventional Method), Gatekeeper Series 38, London: International Institute for Environment and Development.Google Scholar
  15. Hildebrand, P. E. (1981), “Combining Disciplines in Rapid Appraisal: The Sondeo Approach,” Agricultural Administration, 8, 423–432.CrossRefGoogle Scholar
  16. Inglis, A. (1991), “Harvesting Local Forestry Knowledge: A Comparison of RRA and Conventional Surveys,” RRA Notes, 12, 32–40.Google Scholar
  17. Lama Sherpa, N. G., Ojha, P.R., and Sharma, A.R. (1997), “Why Farmers Adopt or Reject Agricultural Technologies? (A Case of Improved Maize and Wheat Varieties in the Ex-Local Target Area of Pakhribas Agricultural Centre at Dhankuta District of Eastern Nepal),” PAC Technical Paper 177, Pakhribas Agricultural Centre, Pokhara, Nepal.Google Scholar
  18. Leach, M., and Kamangira, J. (1997), “Shotgun Wedding or Happy Marriage? Integrating PRA and Sample Surveys in Malawi,” PLA Notes, 28, 42–46.Google Scholar
  19. Mathema, S. B., and Galt, D. L. (1989), “Appraisal by Group Trek,” in Farmer First: Farmer Innovation and Agricultural Research, eds. R. Chambers, A. Pacey, and L. A. Thrupp. London: Intermediate Technology Publications, pp.68–73.Google Scholar
  20. Maxwell, S., and Bart, C. (1995), “Beyond Ranking: Exploring Relative Preferences in P/RRA,” PLA Notes, 22, 28–34.Google Scholar
  21. Maxwell, S., and Frankenberger, T. R. (1995), Household Food Security: Concepts, Indicators, Measurements, Washington, D.C.: UNICEF/IFAD.Google Scholar
  22. McCracken, J. A., Pretty, J. N., and Conway, G. R. (1988), An Introduction to Rapid Rural Appraisal for Agricultural Development, London: International Institute for Environment and Development.Google Scholar
  23. Moore, M. (1979), “Denounce the Gang of Statisticians. Struggle Against the Sample Line. Unite the Researching Masses Against Professional Hegemony,” Conference on Rapid Rural Appraisal, December 4–7, 1979, Institute of Development Studies, University of Sussex, Brighton, UK.Google Scholar
  24. Pa, G., and Pa, A. (1991), Farmers Participatory Research in North Onto, Ethiopia, Report of a Training Course in Rapid Rural Appraisal, Addis Ababa, Ethiopia: IIED/Farm Africa.Google Scholar
  25. Peterson, W. (1993) “Development-Agency M&E,” in Monitoring and Evaluatings Agricultural Research. A Sourcebook, eds. D. Horton, P. Ballantyne, W. Peterson, B. Uribe, D. Gapasin, and K. Sheridan, Wallingford, UK: CAB International, pp. 58–64.Google Scholar
  26. Riley, J. (1998), “Strengthening Biometry and Statistics in Agricultural Research,” Study Report, Technical Centre for Agricultural and Rural Cooperation, Wageningen.Google Scholar
  27. Riley, J., and Alexander, C. (1997), “Statistical Literature for Participatory On-Farm Research,” Experimental Agriculture, 33, 73–82.CrossRefGoogle Scholar
  28. Schach, S. (1979), “An Alternative to the Friedman Test With Certain Optimality Properties,” Annals of Statistics, 7, 537–550.MATHCrossRefMathSciNetGoogle Scholar
  29. Siegel, S., and Castellan, N. J. (1988), Nonparametric Statistics for the Behavioural Sciences, New York: McGraw-Hill.Google Scholar
  30. Sumberg, J., and Okali, C. (1997), Farmers’ Experiments. Creating Local Knowledge. London: Lynne Reiner.Google Scholar
  31. Taplin, R. H. (1997), “The Statistical Analysis of Preference Data,” Applied Statistics, 46, 493–512.MathSciNetGoogle Scholar
  32. Theis, J., and Grady, H. M. (1991), Participatory Rapid Appraisal for Community Development: A Training Manual Based on Experiences in the Middle East and West Africa, London: International Institute for Environment and Development/Save the Children Fund.Google Scholar

Copyright information

© International Biometric Society 2001

Authors and Affiliations

  1. 1.Overseas Biometrics UnitIACR-RothamstedHarpendenUK
  2. 2.NassauThe Behamas

Personalised recommendations