Skip to main content

Identifying superior rainfed barley genotypes in farmers’ fields using participatory varietal selection

We’re sorry, something doesn't seem to be working properly.

Please try refreshing the page. If that doesn't work, please contact support so we can address the problem.

Abstract

This study was carried out to identify superior barley genotypes for the rainfed areas of western Iran using a participatory varietal selection (PVS) approach. Three field experiments were conducted in two randomly selected farmers’ fields and in one rainfed research station in the 2006–07 cropping season with 69 genotypes (including one local and one improved check). Several univariate and multivariate methods were used to analyze qualitative (farmers’ scores) and quantitative (grain yield) data. Individual farmers’ scores in each village were positively correlated, indicating that the farmers tended to discriminate genotypes in similar fashion, although the genotypes actually selected by farmers were different in the two villages. In recent years, a greater number of farmers in western Iran preferred the improved variety (Sararood-1) over the local barley (Mahali), while in this project the farmers preferred the new genotypes over the two checks. This was also verified by the quantitative data showing that the checks were outyielded by the new genotypes. Farmers were efficient in identifying the best genotypes for their specific environment, as shown by biplot analysis, indicating their competence in selection. The genotypes selected by the breeder and farmers were almost similar but some differences existed. In conclusion, PVS is a powerful way to involve farmers for selecting and testing new cultivars that are adapted to their needs, systems and environments.

This is a preview of subscription content, access via your institution.

References

  1. Abay F, Bjornstad A. 2008. Specific adaptation of barley varieties in different locations in Ethiopia. Euphytica 167: 181–195

    Article  Google Scholar 

  2. Almekinders CJM, Elings A. 2001. Collaboration of farmers and breeders: Participatory crop improvement in perspective. Euphytica 122: 425–438

    Article  Google Scholar 

  3. Ashby JA, Sperling L. 1995. Institutionalizing participatory, client-driven research and technology development in agriculture. In MR Bellon, J Reeves, eds, 2002. Quantitative Analysis of Data from Participatory Methods in Plant Breeding, CIMMYT, Mexico, DF, pp 753–770

    Google Scholar 

  4. Atlin GN, Baker RJ, McRae KB, Lu X. 2000. Selection response in subdivided target regions. Crop Sci. 40: 7–13

    Article  Google Scholar 

  5. Byerlee D, Husain T. 1993. Agricultural research strategies for favored and marginal areas, the experience of farming system research in Pakistan. Expl. Agric. 29: 155–171

    Article  Google Scholar 

  6. Ceccarelli S. 1994. Specific adaptation and breeding for marginal conditions. Euphytica 77: 205–219

    Article  Google Scholar 

  7. Ceccarelli S. 1996. Adaptation to low/high input cultivation. Euphytica 92: 203–214

    Article  Google Scholar 

  8. Ceccarelli S, Grando S. 2007. Decentralized-participatory plant breeding: An example of demand driven research. Euphytica 155: 349–360

    Article  Google Scholar 

  9. Chambers R, Pacey A, Thrupp LA. 1989. Farmers first: Farmer innovation and agricultural research. Intermediate Technology Publications, London, UK, pp 218.

    Google Scholar 

  10. Cleveland DA, Soleri D, Smith SE. 1999. Farmer plant breeding from a biological perspective: Implications for Collaborative Plant Breeding. CIMMYT Economics Work Paper No. 10, Mexico, DF

  11. Danial D, Parlevliet J, Almekinders C, Thiele G. 2007. Farmers participation and breeding for durable disease resistance in the Andean region. Euphytica 153: 385–396

    Article  Google Scholar 

  12. De Groote H, Siambi M, Friesen D, Diallo A. 2002. Identifying farmers’ preferences for new maize varieties in eastern Africa. In MR Bellon, J Reeves, eds, Quantitative Analysis of Data from Participatory Methods in Plant Breeding. CIM MYT, Mexico, DF, pp82–102

    Google Scholar 

  13. DeLacy IH, Basford KE, Cooper M, Bull JK, Mclaren CG 1996. Analysis of multi-environment trials- an historical perspective. In M Cooper, GL Hammer, eds, Plant Adaptation and Crop Improvement,,CAB International, Wallingford, pp 39–124

    Google Scholar 

  14. Flores F, Moreno MT, Cubero JI. 1998. A comparison of univariate and multivariate methods to analyze environments. Field Crops Res. 56: 271–286

    Article  Google Scholar 

  15. Fox PN, Skovmand B, Thompson BK, Braun HJ, Cormier R. 1990. Yield and adaptation of hexaploid spring triticale. Euphytica 47: 57–64

    Article  Google Scholar 

  16. Francis TR, Kannenberg LW. 1978. Yield stability studied in short-season maize. I. A descriptive method for grouping genotypes. Can. J. Plant Sci. 58: 1029–1034

    Article  Google Scholar 

  17. Gabriel KR. 1971. The biplot graphic display of matrices with application to principal component analysis. Biometrika 58: 453–467

    Article  Google Scholar 

  18. Haghparast R, Rahmanian M, Roeentan R, Rajabi R, Khodadoost F et al. 2009. Review on participatory bread wheat breeding program in Kermanshah, Iran under rainfed codition: Importance, opportunities and challenges. In R Mohammadi, R Haghparast, eds, Plant Science in Iran. Middle Eastern Russ. J. Plant Sci. Biotechnol. 3: 1–4

  19. Hohls T. 2001. Conditions under which selection for mean productivity tolerance to environment stress, or stability should be used to improve year across a range of contrasting environments. Euphytica 120: 235–245

    Article  Google Scholar 

  20. Huehn M. 1979. Beitrage zur erfassung der phanotypischen stabilitat. EDV Med. Biol. 10: 112–117

    Google Scholar 

  21. Kroonenberg PM. 1995. Introduction to biplots for G × E tables. Department of Mathematics, Research Report 51. Univ. of Queensland, Australia

    Google Scholar 

  22. Mohammadi R, Pourdad SS, Amri A. 2008. Grain yield stability of spring safflower (Carthamus tinctorius L.). Aust. J. Agric. Res. 59: 546–553

    Article  Google Scholar 

  23. Morris ML, Bellon MR. 2004. Participatory plant breeding research: opportunities and challenges for the international crop improvement system. Euphytica 136: 21–34

    Article  Google Scholar 

  24. Nassar R, Huehn M. 1987. Studies on estimation of phenotypic stability: Tests of significance for non-parametric measures of phenotypic stability. Biometrics 43: 45–53

    Article  Google Scholar 

  25. Ortiz-Ferrara G, Bhatta MR, Pokharel TP, Mudwari A, Thapa DB, Joshi AK, Chand R, Muhammad D, Duveiller R, Rajaram S. 2001. Farmer participatory variety selection in South Asia. In Research Highlights of the Wheat Program 1999–2000, Centro Internacional de Mejoramiento de Mai’zy Trigo, Mexico DF, pp33–37

    Google Scholar 

  26. Ortiz-Ferrara G, Joshi AK, Chand R, Bhatta MR, Mudwari A et al. 2007. Partnering with farmers to accelerate adoption of new technologies in South Asia to improve wheat productivity. Euphytica 157: 399–407

    Article  Google Scholar 

  27. Snapp S. 1999. Mother and baby trials: a novel trial design being tried out in Malawi. In TARGET. The Newsletter of the Soil Fertility Research Network for Maize-Based Cropping Systems in Malawi and Zimbabwe, CIMMYT, Harare, Zimbabwe (January 1999 issue)

    Google Scholar 

  28. Snapp SS, Rohrbach DD, Simtowe F, Freeman HA. 2002. Sustainable soil management options for Malawi: Can small holder farmers grow more legumes? Agric. Ecosys. Environ. 91: 159–174

    Article  Google Scholar 

  29. Thapa DB, Sharma RC, Mudwari A, Ortiz-Ferrara G, Sharma S, Basnet RK, Witcombe JR, Virk DS, Joshi KD. 2009. Identifying superior wheat cultivars in participatory research on resource poor farms. Field Crops Res. 112: 124–130

    Article  Google Scholar 

  30. Witcombe JR, Joshi A, Goyal SN. 2003. Participatory plant breeding in maize: A case study from Gujarat, India. Euphytica 130: 413–422

    Article  Google Scholar 

  31. Witcombe JR, Joshi A, Joshi, KD, Sthapit BR. 1996. Farmer participatory crop improvement. I. Varietal selection and breeding methods and their impact on biodiversity. Exp. Agric. 32: 445–460

    Article  Google Scholar 

  32. Yan W. 2001. GGEBiplot-A Windows application for graphical analysis of multi-environment trial data and other types of two-way data. Agron. J. 93: 1111–1118

    Article  Google Scholar 

  33. Yan W. 2002. Singular-value partitioning in biplot analysis of multienvironment trial data. Agron. J. 94: 990–996

    Article  Google Scholar 

  34. Yan W, Kang MS. 2003. GGE biplot analysis: A graphical tool for breeders, geneticists, and agronomists. CRC Press, Boca Raton, FL

    Google Scholar 

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to Reza Mohammadi.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Mohammadi, R., Mahmoodi, K.N., Haghparast, R. et al. Identifying superior rainfed barley genotypes in farmers’ fields using participatory varietal selection. J. Crop Sci. Biotechnol. 14, 281–288 (2011). https://doi.org/10.1007/s12892-010-0106-8

Download citation

Key words

  • barley
  • farmers
  • participatory varietal selection
  • quantitative and qualitative data