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Determinants of breeders’ participation to an indigenous cattle breeding program

  • Younouss Camara
  • Mamadou Ciss
  • Nassim Moula
  • Mouhamadou Moustapha Sissokho
  • Frédéric Farnir
  • Nicolas Antoine-MoussiauxEmail author
Research Article

Abstract

Many cattle breeding programs were initiated in Africa to increase the productivity of indigenous cattle breeds. Most of these programs have failed, partly due to the lack of involvement of breeders. The present case study contributes to the understanding of such failures. The N’Dama cattle breeding program in Senegal was taken as a case study for an in-depth analysis of participation using mixed methods. Semi-structured interviews were conducted with 52 breeders: 26 who participated, 15 of whom recently resigned, and 27 who had never participated. Content and statistical analyses were conducted to evaluate the motivations of breeders and the factors influencing their participation in the breeding program. Results more particularly highlight the complexity of social issues within a breeding project, in face of classical determinants of adoption that are distance or production systems features. It pinpoints crucial levers of improvement, i.e., the management of animal property rights between the nucleus management and the participating breeders, the legitimacy of participants’ representatives in cooperatives, and the strategic mobilization of member social networks. Also, adding on previous works of the authors, this study highlights the need to take better account of the dynamics of production systems, then paying sufficient attention to the objectives, preferences, and ongoing strategies of the breeders for the future. The present study is the first to highlight the added value of mixed methods to analyze innovation adoption and participation in a livestock breeding program, taking both into account the overall innovation drivers and dynamics tied to actors’ strategies.

Keywords

N’Dama cattle Genetic improvement Breeders’ motivations Mixed methods Senegal 

Notes

Acknowledgments

The authors wish to thank all participating farmers and investigators. They acknowledge the support of the West Africa Agricultural Productivity Program (WAAPP), the Senegalese Institute of Agricultural Research (ISRA), and Wallonie-Bruxelles International, Belgium. The authors are grateful to the journal’s editor and the anonymous referees for useful suggestions on earlier versions of this manuscript.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

References

  1. Abdulai A, Huffman WE (2005) The diffusion of new agricultural technologies: the case of crossbred-cow technology in Tanzania. Amer J Agr Econ 87:645–649.  https://doi.org/10.1177/0160017604266026 CrossRefGoogle Scholar
  2. Agyemang K, Dwinger R, Little D, Rowlands GJ (1997) Village N’Dama cattle production in West Africa: six years of research in The Gambia. International Livestock Research Institute, Nairobi, Kenya, and International Trypanotolerance Centre, Banjul, The GambiaGoogle Scholar
  3. Babeau O, Chanlat JF (2011) Déviance ordinaire, innovation et gestion. Rev Française Gest Lavoisier 37:35–50 doi: halshs-00641100CrossRefGoogle Scholar
  4. Bandiera O, Rasul I (2006) Social networks and technology adoption in Northern Mozambique. Econ J 116:869–902.  https://doi.org/10.1111/j.1468-0297.2006.01115.x CrossRefGoogle Scholar
  5. Baškarada S, Koronios A (2018) A philosophical discussion of qualitative, quantitative, and mixed methods research in social science. Qual Res J 18:2–21.  https://doi.org/10.1108/QRJ-D-17-00042 CrossRefGoogle Scholar
  6. Borges JAR, Tauer LW, Lansink AGJMO (2016) Using the theory of planned behavior to identify key beliefs underlying Brazilian cattle farmers’ intention to use improved natural grassland: a MIMIC modelling approach. Land Use Policy 55:193–203.  https://doi.org/10.1016/j.landusepol.2016.04.004 CrossRefGoogle Scholar
  7. Bosso NA, Corr N, Njie M et al (2007) The N’Dama cattle genetic improvement programme: a review. Anim Genet Resour Inf 40:65–69.  https://doi.org/10.1017/S1014233900002200 CrossRefGoogle Scholar
  8. Camara Y (2012) Analyse génétique des performances zootechniques des bovins de race N’Dama et étude du système d’amélioration génétique à noyau ouvert. Institut Vétérinaire et Agronomique Hassan II, Rabat, MarocGoogle Scholar
  9. Camara Y, Moula N, Sow F et al (2019) Analysing innovations among cattle smallholders to evaluate the adequacy of breeding programs. Animal 13(2):417–426.  https://doi.org/10.1017/S1751731118001544 CrossRefPubMedGoogle Scholar
  10. Castaneda D (2005) Les organisations d’éleveurs et de pasteurs au Sénégal. In: Reflexions et Perspectives/Institut Sénégalais de Recherche Agricole (ISRA), vol 6, p 64Google Scholar
  11. Charbonneau M, Poinsot Y (2008) De l’individuel au collectif: Les modes de gestion de l’élevage dans la puna péruvienne. Etud Rurales 181:39–60.  https://doi.org/10.4000/etudesrurales.8614 CrossRefGoogle Scholar
  12. Crozier M, Friedberg E (1977) L’acteur et le système, Edition du Seuil, Points Essais N°248, Paris, FranceGoogle Scholar
  13. Diop M, Sissokho MM, Niang S (1993) Mise en Place D’un schéma de sélection à noyau Ouvert pour l’amelioration génétique du taurin Ndama : Resultats du “Screening” des vaches exceptionnelles dans le département de Kolda (Sénégal).Google Scholar
  14. Fall A, Diop M, Sandford J et al (1982) Evaluation of the productivities of Djallonke sheep and N’Dama cattle at the Centre de Recherches Zootechniques, Kolda, Senegal. In: ILCA Research Report 3. ILCA (International Livestock Centre for Africa), Addis Ababa, Ethiopia 70 ppGoogle Scholar
  15. Flyvbjerg B (2006) Five misunderstandings about case-study research. Anthropol Notebooks 12:219–245.  https://doi.org/10.1177/1077800405284363 CrossRefGoogle Scholar
  16. Hadush M (2017) Exploring farmers’ seasonal and full year adoption of stall feeding of livestock in Tigrai region. Ethiopia Econ Agric 085:919–944.  https://doi.org/10.5937/ekoPolj1703919H CrossRefGoogle Scholar
  17. Kosgey IS, Baker RL, Udo HMJ, van AJAM (2006) Successes and failures of small ruminant breeding programmes in the tropics: a review. Small Rumin Res 61:13–28 doi: 0921-4488CrossRefGoogle Scholar
  18. Leroy G, Baumung R, Notter D et al (2017) Stakeholder involvement and the management of animal genetic resources across the world. Livest Sci 198:120–128.  https://doi.org/10.1016/j.livsci.2017.02.018 CrossRefGoogle Scholar
  19. Limon G, Lewis EG, Chang YM et al (2014) Using mixed methods to investigate factors influencing reporting of livestock diseases: a case study among smallholders in Bolivia. Prev Vet Med 113:185–196.  https://doi.org/10.1016/j.prevetmed.2013.11.004 CrossRefPubMedPubMedCentralGoogle Scholar
  20. Mankad A, Loechel B, Measham PF (2017) Psychosocial barriers and facilitators for area-wide management of fruit fly in southeastern Australia. Agron Sustain Dev 37:1–12.  https://doi.org/10.1007/s13593-017-0477-z CrossRefGoogle Scholar
  21. Mueller JP, Rischkowsky B, Haile A et al (2015) Community-based livestock breeding programmes: essentials and examples. J Anim Breed Genet 132:155–168.  https://doi.org/10.1111/jbg.12136 CrossRefPubMedGoogle Scholar
  22. Palinkas LA, Aarons GA, Horwitz S et al (2011) Mixed method designs in implementation research. Adm Policy Ment Health Ment Health Serv Res 38:44–53.  https://doi.org/10.1007/s10488-010-0314-z CrossRefGoogle Scholar
  23. R Core Team (2017) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/
  24. Roschinsky R, Kluszczynska M, Sölkner J et al (2015) Smallholder experiences with dairy cattle crossbreeding in the tropics: from introduction to impact. Animal 9:150–157.  https://doi.org/10.1017/S1751731114002079 CrossRefPubMedGoogle Scholar
  25. Senger I, Borges JAR, Machado JAD (2017) Using structural equation modeling to identify the psychological factors influencing dairy farmers’ intention to diversify agricultural production. Livest Sci 203:97–105.  https://doi.org/10.1016/j.livsci.2017.07.009 CrossRefGoogle Scholar
  26. St. Pierre EA (2014) A brief and personal history of post qualitative research toward post inquiry. J Curric Theor 30:2–19Google Scholar
  27. Stear MJ, Bishop SC, Mallard BA, Raadsma H (2001) The sustainability, feasibility and desirability of breeding livestock for disease resistance. Res Vet Sci 71:1–7.  https://doi.org/10.1053/rvsc.2001.0496 CrossRefPubMedGoogle Scholar
  28. Strauss A, Corbin J (1990) Grounded theory, procedured, canons and evaluative criteria. Qual Sociol 13(1):3–21.  https://doi.org/10.1007/BF00988593 CrossRefGoogle Scholar
  29. Suvedi M, Ghimire R, Kaplowitz M (2017) Farmers’ participation in extension programs and technology adoption in rural Nepal: a logistic regression analysis. J Agric Educ Ext 23:351–371.  https://doi.org/10.1080/1389224X.2017.1323653 CrossRefGoogle Scholar
  30. Traoré SA, Reiber C, Zárate AV (2018) Productive and economic performance of endemic N’Dama cattle in southern Mali compared to Fulani Zebu and their crossbreds. Livest Sci 209:77–85.  https://doi.org/10.1016/j.livsci.2018.01.013 CrossRefGoogle Scholar
  31. Triomphe B, Floquet A, Letty B et al (2016) Mieux évaluer et accompagner l’innovation agricole en Afrique. Leçons d’une analyse transversale de 13 cas d’études. Cah Agric 25:1–11.  https://doi.org/10.1051/cagri/2016050 CrossRefGoogle Scholar
  32. Windows (2016) Microsoft Office: Excel version 2016Google Scholar

Copyright information

© INRA and Springer-Verlag France SAS, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Fundamental and Applied Research for Animals & Health (FARAH), Sustainable Animal Production, Faculty of Veterinary MedicineUniversity of LiegeLiegeBelgium
  2. 2.Institut Sénégalais de Recherche AgricoleDakarSénégal
  3. 3.Tropical Veterinary Institute, Faculty of Veterinary MedicineUniversity of LiegeLiegeBelgium

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