Hostname: page-component-586b7cd67f-l7hp2 Total loading time: 0 Render date: 2024-11-24T08:12:10.037Z Has data issue: false hasContentIssue false
  • English
  • Français

Understanding farmers’ preferences for artificial insemination services provided through dairy hubs

Published online by Cambridge University Press:  08 November 2016

I. A. Omondi ,
K. K. Zander ,
S. Bauer  and
I. Baltenweck
I. A. Omondi*
Affiliation:
International Livestock Research Institute (ILRI), P.O. Box 30709, Nairobi 00100, Kenya Justus-Liebig University Giessen, Department of Project and Regional Planning, Diezstr.15, 35390 Giessen, Germany
K. K. Zander
Affiliation:
Charles Darwin University, Northern Institute, Ellengowan Drive, Darwin, 0909 NT, Australia
S. Bauer
Affiliation:
Justus-Liebig University Giessen, Department of Project and Regional Planning, Diezstr.15, 35390 Giessen, Germany
I. Baltenweck
Affiliation:
International Livestock Research Institute (ILRI), P.O. Box 30709, Nairobi 00100, Kenya
*
†E-mail: [email protected]
Get access

Abstract

Africa has a shortage of animal products but increasing demand because of population growth, urbanisation and changing consumer patterns. Attempts to boost livestock production through the use of breeding technologies such as artificial insemination (AI) have been failing in many countries because costs have escalated and success rates have been relatively low. One example is Kenya, a country with a relatively large number of cows and a dairy industry model relevant to neighbouring countries. There, an innovative dairy marketing approach (farmer-owned collective marketing systems called dairy hubs) has been implemented to enhance access to dairy markets and dairy-related services, including breeding services such as AI. So far, the rate of participation in these dairy hubs has been slow and mixed. In order to understand this phenomenon better and to inform dairy-related development activities by the Kenyan government, we investigated which characteristics of AI services, offered through the dairy hubs, farmers prefer. To do so, we applied a choice experiment (CE), a non-market valuation technique, which allowed us to identify farmers’ preferences for desired characteristics should more dairy hubs be installed in the future. This is the first study to use a CE to evaluate breeding services in Kenya and the results can complement findings of studies of breeding objectives and selection criteria. The results of the CE reveal that dairy farmers prefer to have AI services offered rather than having no service. Farmers prefer AI services to be available at dairy hubs rather than provided by private agents not affiliated to the hubs, to have follow-up services for pregnancy detections, and to use sexed semen rather than conventional semen. Farmers would further like some flexibility in payment systems which include input credit, and are willing to share the costs of any AI repeats that may need to occur. These results provide evidence of a positive attitude to AI services provided through the hubs, which could mean that AI uptake would improve if service characteristics are improved to match farmer preferences. The dairy hubs concept is currently in the implementation phase with most hubs at startup phase, hence understanding which AI service characteristics farmers prefer can inform the design of high-quality and cost-effective AI services in the future.

Keywords

breeding technologieschoice experimentcowsdairy hubsEast Africa
Type
Research Article
Information
animal , Volume 11 , Issue 4 , April 2017 , pp. 677 - 686
Copyright
© The Animal Consortium 2016 

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

Abebe, GK, Bijman, J, Kemp, R, Omta, O and Tsegaye, A 2013. Contract farming configuration: smallholders’ preferences for contract design attributes. Food Policy 40, 14–24.Google Scholar
Baltenweck, I, Ouma, R, Anunda, F, Okeyo, M and Romney, D 2004. Artificial or natural insemination: the demand for breeding services by smallholders. Kenya Agricultural Research Institute, Nairobi, Kenya.Google Scholar
Bellemare, MF 2010. Agricultural extension and imperfect supervision in contract farming: evidence from Madagascar. Agricultural Economics 41, 507–517.Google Scholar
Bett, RC, Bett, HK, Kahi, AK and Peters, KJ 2009. Evaluation and effectiveness of breeding and production services for dairy goat farmers in Kenya. Ecological Economics 68, 2451–2460.Google Scholar
Chang, JB. and Lusk, JL 2011. Mixed logit models: accuracy and software choice. Journal of Applied Econometrics 26, 167–172.Google Scholar
Food and Agricultural Organisation (FAO) of the United Nations 2011. Dairy development in Kenya. Dairy Reports by H.G. Muriuki. FAO, Rome, Italy.Google Scholar
Food and Agricultural Organisation (FAO) of the United Nations 2014. FAOSTAT Database FAO STAT. FAO, Rome, Italy.Google Scholar
Fiebig, D, Keane, M, Louviere, J and Wasi, N 2010. The generalized multinomial logit: accounting for scale and coefficient heterogeneity. Marketing Science 29, 393–421.Google Scholar
Godfray, HCJ, Beddington, JR, Crute, IR, Haddad, L, Lawrence, D, Muir, JF, Pretty, J, Robinson, S, Thomas, SM and Toulmin, C 2010. Food security: the challenge of feeding 9 billion people. Science 327, 812–818.Google Scholar
Greene, WH and Hensher, DA 2010. Does scale heterogeneity across individuals matter? An empirical assessment of alternative logit models. Transportation 37, 413–428.Google Scholar
Hensher, DA, Rose, JM and Greene, WH 2005. Applied choice analysis: a primer. Cambridge University Press, Cambridge.Google Scholar
Herrero, M, Grace, D, Njuki, J, Johnson, N and Rufino, M 2013. The roles of livestock in developing countries. Animal 7, 3–18.Google Scholar
Karanja, AM 2003. The dairy industry in Kenya: the post-liberalization agenda. Dairy Industry Stakeholder’s Workshop. Presented at the Dairy Industry Stakeholder’s Workshop, 27 August 2002, Nairobi, Kenya.Google Scholar
Karuga, S 2009. Dairy value chain analysis – Timau milk shed. Market Economies Development Ltd, Nairobi, Kenya.Google Scholar
Kosgey, IS, Mbuku, SM, Okeyo, AM, Amimo, J, Philipsson, J and Ojango, JM 2011. Institutional and organizational frameworks for dairy and beef cattle recording in Kenya: a review and opportunities for improvement. Animal Genetic Resources 48, 1–11.Google Scholar
Kragt, ME 2013. Stated and inferred attribute attendance models: a comparison with environmental choice experiments. Journal of Agricultural Economics 64, 719–736.Google Scholar
Kydd, J, Dorward, AR and Poulton, C 2003. Institutional dimensions of trade liberalisation and poverty. In Agricultural trade and poverty: making policy analysis count (ed. J Brooks), pp. 261–286. Organisation for Economic Co-operation and Development (OECD), Paris, France.Google Scholar
Luce, RD and Tukey, JW 1964. Simultaneous conjoint measurement: a new type of fundamental measurement. Journal of Mathematical Psychology 1, 1–27.CrossRefGoogle Scholar
Makokha, S, Karugia, J, Staal, S and Oluoch-Kosura, W 2006. Valuation of cow attributes by conjoint analysis: a case study in Western Kenya. Presented at the IAAE Conference, International Association of Agricultural Economists, Gold Coast, Australia.Google Scholar
Mubiru, SL, Tenywa, JS, Halberg, N, Romney, D, Nanyeenya, W, Baltenweck, I and Staal, S 2007. Categorisation of dairy production systems: a strategy for targeting meaningful development of the systems in Uganda. Livestock Research for Rural Development 19, Article #100. Retrieved from http://www.lrrd.org/lrrd19/7/mubi19100.html Google Scholar
Muia, JMK, Kariuki, JN, Mbugua, PN, Gachuiri, CK, Lukibisi, LB, Ayako, WO and Ngunjiri, WV 2011. Smallholder dairy production in high altitude Nyandarua milk-shed in Kenya: status, challenges and opportunities. Livestock Research for Rural Development 23, Article #108. Retrieved from http://www.lrrd.org/lrrd23/5/muia23108.htm Google Scholar
Murage, AW and Ilatsia, ED 2011. Factors that determine use of breeding services by smallholder dairy farmers in central Kenya. Tropical Animal Health and Production 43, 199–207.Google Scholar
Muriuki, HG 2003. A review of the small scale dairy sector – Kenya. Food and Agricultural Organization (FAO), Rome, Italy.Google Scholar
Mutinda, G, Baltenweck, I and Omondi, I 2015. Setting up sustainable dairy business hubs: a resource book for facilitators. ILRI Manual 21, International Livestock Research Institute (ILRI), Nairobi, Kenya.Google Scholar
Mwanyumba, PM, Mwang’ombe, A, Lenihan, E, Olubayo, F, Badamana, MS, Wahome, RG and Wakhungu, JW 2010. Participatory analysis of the farming system and resources in Wundanyi location, Taita district, Kenya: a livestock perspective. Livestock Research for Rural Development 22, article #26.Google Scholar
Ngigi, M 2005. The case of smallholder dairying in Eastern Africa. EPT Discussion Paper No. 131. International Food Policy Research Institute (IFPRI), Washington, DC, USA.Google Scholar
Ojango, JM and Pollott, G 2002. The relationship between Holstein bull breeding values for milk yield derived in both the UK and Kenya. Livestock Production Science 74, 1–12.Google Scholar
Owango, M, Staal, S, Kenyanjui, M, Lukuyu, B, Njubi, D and Thorpe, W 1998. Dairy co-operatives and policy reform in Kenya: effects of livestock service and milk market liberalization. Food Policy 23, 173–185.Google Scholar
Reardon, T, Barrett, CB, Berdegué, JA and Swinnen, JFM 2009. Agrifood industry transformation and small farmers in developing countries. World Development 37, 1717–1727.CrossRefGoogle Scholar
Rogers, EM 2003. Diffusion of innovations, 5th edition. Free Press, New York, USA.Google Scholar
Rosegrant, M, Fernandez, M and Sinha, A 2009. Looking into the future for agriculture and AKST (Agricultural Knowledge Science and Technology). In Agriculture at Crossroads (ed. BD McIntyre, HR Herren, J Wakhungu and RT Watson), pp. 307–376. Island Press, Washington, DC, USA.Google Scholar
Rwelamira, J 2015. Strengthening farmers organizations and civil society organizations. An action plan for African agricultural transformation. African Development Bank, Dakar, Senegal, Retrieved on 27 October 2016 from http://www.afdb.org/fileadmin/uploads/afdb/Documents/Events/DakAgri2015/Strengthening_Farmers_Organizations_and_Civil_Society_Organizations.pdf Google Scholar
Scarpa, R, Ferrini, S and Willis, K 2005. Performance of error component models for status-quo effects in choice experiments. In Applications of simulation methods in environmental and resource economics (ed. R Scarpa and A Alberini), pp. 247–273. Springer, Dordrecht, The Netherlands.Google Scholar
Schipmann, C and Qaim, M 2011. Supply chain differentiation, contract agriculture, and farmers’ marketing preferences: the case of sweet pepper in Thailand. Food Policy 36, 667–677.Google Scholar
Schulz, N, Breustedt, G and Latacz-Lohmann, U 2014. Assessing farmers’ willingness to accept ‘greening’: insights from a discrete choice experiment in Germany. Journal of Agricultural Economics 65, 26–48.CrossRefGoogle Scholar
Staal, SJ, Pratt, AN and Jabbar, M 2008. Dairy development for the resource poor part 2: Kenya and Ethiopia dairy development case studies. Pro-poor Livestock Policy Initiative Working Paper No. 44-2. International Livestock Research Institute (ILRI), Nairobi, Kenya.Google Scholar
Stevenson, JS 2014. Impact of reproductive technologies on dairy food production in the dairy industry. Advances in Experimental Medicine and Biology 752, 115–129.Google Scholar
Train, K and Weeks, M 2005. Discrete choice models in preference space and willingness-to-pay space. In Applications of simulation methods in environmental and resource economics (ed. R Scarpa and A Alberini), pp. 1–16. Springer, Dordrecht, The Netherlands.Google Scholar
Tschirley, D, Reardon, T, Dolislager, M and Snyder, J 2014. The rise of a middle class in East and Southern Africa: implications for food system transformation. Working Paper 2014/119. World Institute for Development Economics Research (WIDER), Helsinki, Finland.Google Scholar
World Bank 2013. Growing Africa: unlocking the potential of agribusiness. The World Bank, Washington, DC, USA.Google Scholar
Yorobe, J, Birol, E and Smale, M 2010. Farmer preferences for BT maize, seed information and credit in the Philippines. In Choice experiments in developing countries: implementation, challenges and policy implications (ed. J Bennett and E Birol), pp. 225–243. Edward Elgar, Cheltenham, UK.Google Scholar
Zander, KK, Mwacharo, JM, Drucker, AG and Garnett, ST 2013. Constraints to adopting livestock improvement technologies in Kenya: the case of Narok and Nakuru districts. Journal of Arid Environments 96, 9–18.CrossRefGoogle Scholar
Supplementary material: PDF

Omondi supplementary material

Omondi supplementary material 1

Download Omondi supplementary material(PDF)
PDF 484.1 KB