Factors Affecting Adoption of Climate-Smart Agricultural Technologies: Evidence from Nandi County, Kenya
Keywords:Adoption, Climate Smart Agricultural Technologies, Extension, Agro-forestry, Biogas
Adoption of Climate Smart Agricultural Technologies (CSAT) such as Biogas production, silage making, agroforestry, and water conservation help in improving smallholder production. However, in rural areas of Nandi County of Kenya, the adoption of CSAT amongst smallholder dairy farmers is low. In this study, we analyze factors that affect the adoption of CSAT using information drawn from 350 smallholder dairy farmers participating in the East Africa Dairy Development program. Using the ordered Logit model, we find that the intensity of adoption of CSAT is partly affected by access to extension and credit services. Specifically, we showed that farmers who had access to extension services and credit lines were more likely to adopt drought-resistant crops but not biogas production, agro-forestry, and silage making. Moreover, our result showed that owning a stable tenure system allows farmers to adopt technologies that require more land and take more time like biogas production, drought crops, agroforestry, water storage, and silage making. Finally, we showed that distance between a farmer’s home and the farm is an important factor in adopting agricultural technology. This effect is more pronounced amongst technologies that are heavy to transport like biogas production, water storage and conservation, zero-grazing, and silage making.
Agarwal, B. (1983). Diffusion of rural innovations. Some analytical issues and the case of woodburning stoves. World Development Journal, (12) (7), 359-376.
Easterling, D.R., Meehl, G.A., Parmesan, C., Changnon, S.A. & Mearns, L.O. (2000). Climate extremes: observations, modeling, and impacts. Science, 2000 Sep 22; 289(5,487): 2,068-2,074. DOI: 10.1126/science.289.5487.2068.
Frank, J., & Penrose Buckley, C. (2012). Small Scale Farmers and Climate Change. How Can Farmer Organisations and Fairtrade build the Adaptive Capacity of Small Holders? IIED.
GoK. (2009). Kisii Central District Development Plan. Nairobi: Government Printer.
Howley, P., Cathal, O. D., & Heanue, K. (2012). Factors Affecting Farmers Adoption of Agricultural Innovations:A Panel Data Analysis of the Use of Artificial Insemination among Dairy Farmers in Ireland. Journal of Agricultural Science, 4(6), 171-179.
Jones, L., Lundi, E., & Levine, S. (2010). Towards a Characterization of Adaptive Capacity: A Framework for Analyzing Adaptive Capacity at the Local Level. Overseas Development Institute, UK.
Kockelman, K., & Kweon, Y. (2002). Driver injury severity; Application of Ordered profit models. Accident Analysis and Prevention 34(3);313-321.
Lopez-Ridaura, S., Frelat, R., van Wijk, M., Valbuena, D., Krupnik, T.J. & Jat, M.L. (2018). Climate-smart agriculture, farm household typologies, and food security: An ex-ante assessment from Eastern India. Agricultural Systems, 159; Pp. 57-68
Maddison, D. (2006). The perception of and adaptation to climate change in Africa. (CEEPA Discussion Paper No. 10). Centre for Environmental Economics and Policy in Africa,
Mujeyi, A., Mudhara, M. and Mutenje, M. (2021). The impact of climate-smart agriculture on household welfare in smallholder integrated crop-livestock farming systems: Evidence from Zimbabwe. Agriculture and Food Security, 10, 4(2021).
Ngeno, K., Omasaki, S. & Babe, B. (2013). Assessment of the vulnerability and adaptation strategies to climate variability and change of the Bosi Taurus Dairy genotypes under diverse production environments in Kenya. Journal of Veterinary Advances, 50-67.
O’Donnell C. & Connor, D. (1996). Predicting the Severity of Motor Vehicle Accident Injuries Using Models of Ordered Multiple Choice. Accident Analysis and Prevention, Vol. 28 (6), pp. 739-753
Okuthe, I., Kioli, F., & Abuom, P. (2013). Socio-Cultural Determinants of the Adoption of Integrated Natural Resource Management Technologies by Small Scale Famers in Ndhiwa Division, Kenya. Current Research Journal of Social Sciences, 5(6), 203-218.
Quedraogo, M., Houessionon, P., Zougmore, R. and Partey, S.T. (2019). Uptake of climate-smart agricultural technologies and practices: Actual and potential adoption rates in the climate-smart village site of Mali. Sustainability, 11(17); 4710.
Rodgers , E.M. (2003). Diffusion of innovations. 3rd edition, Collier Macmillan, Canada, Inc.
Tay, R. & Rifaat, S.M. (2010). Factors contributing to the severity of our cohhisions. Journal of Advanced Transportation, 44(1);34-41.
Thornton, P., Van, D., Notenbaert, A., & Herrero, M. (2009). The impacts of climate change on livestock and livestock systems in developing countries. Journal of Agricultural Systems, 101(3); 113-27. Retrieved from https://doi.org/10.1016/j.agsy.2009.05.002
Tran, N.L.D., Ronola, R.F, Ole Sander, B., Reiner, W., Nguyen, D.T. & Nong, N.K.N. (2020). Determinants of adoption of climate-smart agriculture technologies in rice production in Vietnam. International journal of climate change strategies and management, 12(2); pp. 238-256.
UNDP. (2011). Development of Short Term Training Modules to Respond to Selected Skills Gaps for Agribusiness. Retrieved March 28, 2016.Available at: www.ke.undp.org.
Zagst, L. (2011). Socio-economic survey of the East African Dairy Development project/Mitigation of climate change in Agriculture program: a pilot project in Kaptumo. Nairobi: Kenya FAO.
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Copyright (c) 2021 Mokoro, A. Nyasimi, Ochola W. Adede, Omasaki, S. Kemboi, Basweti A Evans
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