Impact of the Meru KOPIA project on poultry farmers’ income in Meru County, Kenya
DOI:
https://doi.org/10.51867/ajernet.6.4.105Keywords:
Impact, Indigenous Chicken, Kenya, Meru County, Smallholder farmersAbstract
Most farming households in Kenya keep indigenous chickens in scavenging systems characterized by low productivity. In response to this, the Korea Program on International Agriculture (KOPIA) center in Nairobi collaborated with the County Government of Meru to disseminate localized technologies and provide technical support to indigenous poultry farmers using the model village approach. The aim of this paper is to assess the impact of the project on participating poultry farmers’ income in Meru County, Kenya, as promoted by the KOPIA. Descriptive cross-sectional survey research was adopted to achieve the objectives of the paper. Data was collected from beneficiaries and a control group through a survey of 236 households using researcher-administered questionnaires sampled from a population of 400 farmers drawn from the 4 participating villages. The study was based on the rational choice theory. Propensity score matching was used to compute the average treatment effect on the treated. Using the nearest neighbor, caliper-based, and kernel-based matching methods, the results showed evidence that program beneficiaries increased their annual income from poultry production, ranging from Kshs 66,616 to 81,674. Being located in Mbaria, Ng’onyi, and Ntalami model villages, the number of livestock enterprises, the number of eggs sold, the number of hens sold, and egg production per hen influenced the impact of the project. The study recommends the establishment of more model villages so as to spread the benefits to a wider area. Future efforts should include addressing constraints in marketing and group cohesion so as to increase the benefits to the participating farmers. Assisting farmers to form a marketing cooperative will not only increase market participation but also benefit farmers from collective procurement of inputs as well as credit for enterprise expansion.
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Abadi, T., Gezahegn, M., & Teklehaimanot, A. (2018). Assessment of factors affecting adoption of exotic chicken breed production in North Western Zone of Tigray, Ethiopia. International Journal of Livestock Production, 9(11), 293-299.
https://doi.org/10.5897/IJLP2017.0392
Acosta, A., Nicolli, F., & Karfakis, P. (2021). Coping with climate shocks: The complex role of livestock portfolios. World Development, 146, Article 105546. https://doi.org/10.1016/j.worlddev.2021.105546
Addison, M., Ohene-Yankyera, K., & Acheampong, P. P. (2022). The impact of uptake of selected agricultural technologies on rice farmers' income distribution in Ghana. Agriculture & Food Security, 11(2), 1-16. https://doi.org/10.1186/s40066-021-00339-0
Ali, A., & Abdulai, A. (2010). The adoption of genetically modified cotton and poverty reduction in Pakistan. Journal of Agricultural Economics, 61(1), 175-192. https://doi.org/10.1111/j.1477-9552.2009.00227.x
Ayieko, M. O. D., Bett, E. K., & Kabuage, L. W. (2015). Analysis of indigenous chicken marketing participation decisions: The case of producers from Makueni County, Kenya. East African Agricultural and Forestry Journal, 81(1), 12-17.
https://doi.org/10.1080/00128325.2015.1040643
Baker, J. (2000). Evaluating the impact of development projects on poverty: A handbook for practitioners. World Bank.
https://doi.org/10.1596/0-8213-4697-0
Barbara, S. (2009). Propensity score matching. Institute for Fiscal Studies, UCL. http://www.esrc.ac.uk
Brookes, G. (2022). Farm income and production impacts from the use of genetically modified (GM) crop technology 1996-2020. GM Crops & Food, 13(1), 171-195. https://doi.org/10.1080/21645698.2022.2105626
Chege, S. M., Wang, D., Leparan, S., & Kyetuza, O. (2019). Influence of technology transfer on performance and sustainability of standard gauge railway in developing countries. Technology in Society, 56, 79-92. https://doi.org/10.1016/j.techsoc.2018.09.007
County Government of Meru. (2018). Meru County Integrated Development Plan, 2018-2022. County Government of Meru.
Delgado, C. L., Mark, W. R., & Meyer, S. (2001). Livestock revolution to 2020: The revolution continues. Paper presented at the Annual Meeting of the International Agricultural Trade Research Consortium (ATRC), Auckland, New Zealand, January 18-19, 2001.
Dilley, L., Mausch, K., Crossland, M., & Harris, D. (2021). What's the story on agriculture? Using narratives to understand farming households' aspirations in Meru, Kenya. The European Journal of Development Research, 33, 1091-1114. https://doi.org/10.1057/s41287-021-00361-9
Government of Kenya. (2012). National Agricultural Sector Extension Policy (NASEP). Government Printer.
Guèye, E. F. (1998). Village egg and fowl meat production in Africa. World's Poultry Science Journal, 54, 73-86. https://doi.org/10.1079/WPS19980007
Hailu, B. K., Abrha, B. K., & Weldegiorgis, K. A. (2014). Adoption and impact of agricultural technologies on farm income: Evidence from Southern Tigray, Northern Ethiopia. International Journal of Food and Agricultural Economics, 2(4), 91-106.
Heckman, J., Ichimura, H., Smith, J., & Todd, P. (1998). Characterizing selection bias using experimental data. Econometrica, 66(5), 1017-1098.
https://doi.org/10.2307/2999630
Hulme, D. (2000). Impact assessment methodologies for microfinance: Theory, experience and better practice. World Development, 28(1), 79-98.
https://doi.org/10.1016/S0305-750X(99)00119-9
Jaetzold, R., Schmidt, H., Hornet, Z. B., & Shisanya, C. A. (2007). Farm management handbook of Kenya: Natural conditions and farm information (Vol. 11/C, 2nd ed.). Ministry of Agriculture/GTZ.
Kamau, C. N., Kabuage, L. W., & Bett, E. K. (2018). Impact of improved indigenous chicken breeds on productivity: The case of smallholder farmers in Makueni and Kakamega counties, Kenya. Cogent Food & Agriculture, 4(1), Article 1477231.
https://doi.org/10.1080/23311932.2018.1477231
Kamau, C. N., Kabuage, L. W., & Bett, E. K. (2019). Analysis of improved indigenous chicken adoption among smallholder farmers: Case of Makueni and Kakamega counties, Kenya. International Journal of Agricultural Extension, 7(1), 21-37. https://doi.org/10.33687/ijae.007.01.2809
KARI. (2011). Kenya Agricultural Research Institute annual report. KARI.
Kejela, Y., Banerjee, S., & Taye, M. (2019). Some internal and external egg quality characteristics of local and exotic chickens reared in Yirgalem and Hawassa towns, Ethiopia. International Journal of Livestock Production, 10(5), 135-142.
https://doi.org/10.5897/IJLP2018.0547
Kenya National Bureau of Statistics. (2021). Economic survey 2021. Kenya National Bureau of Statistics.
Khalil, C. A., Conforti, P., Ergin, I., & Gennari, P. (2017). Defining small-scale food producers to monitor Target 2.3. of the 2030 Agenda for Sustainable Development. Rome: FAO.
Kingori, A., Wachira, A., & Tuitoek, J. (2010). Indigenous chicken production in Kenya: A review. International Journal of Poultry Science, 9(4), 309-316. https://doi.org/10.3923/ijps.2010.309.316
Kirui, O. K., Okello, J. J., Nyikal, R. A., & Njiraini, G. W. (2013). Impact of mobile phone-based money transfer services in agriculture: Evidence from Kenya. Quarterly Journal of International Agriculture, 52(2), 141-162.
Kono, H., & Takahashi, K. (2010). Microfinance revolution: Its effects, innovations, and challenges. The Developing Economies, 48(1), 15-73. https://doi.org/10.1111/j.1746-1049.2010.00098.x
Mensah, A., Asiamah, M., Wongnaa, C. A., Adams, F., Etuah, S., Gaveh, E., & Appiah, P. (2021). Adoption impact of maize seed technology on farm profitability: Evidence from Ghana. Journal of Agribusiness in Developing and Emerging Economies, 11(5), 578-598. https://doi.org/10.1108/JADEE-06-2020-0120
Meru County Government. (2019). Meru Vision 2040: A prosperous, united and happy society. County Government of Meru.
Milkias, M., Molla, M., & Tilahun, S. (2019). Productive and reproductive performance of indigenous chickens in Gena Bossa District of Dawro Zone, Ethiopia. International Journal of Livestock Production, 10(1), 24-32.
https://doi.org/10.5897/IJLP2018.0551
Ministry of Agriculture, Livestock and Fisheries. (2018). Economic review of agriculture. Government Printers.
Ministry of Agriculture, Livestock and Fisheries. (2019). Agricultural census. https://statistics.kilimo.go.ke/en/2_3c/
Mottet, A., & Tempio, G. (2017). Global poultry production: Current state and future outlook and challenges. World's Poultry Science Journal, 73(2), 245-256. https://doi.org/10.1017/S0043933917000071
Mwendia, S., Ohmstedt, U., Peters, M., & Notenbaert, A. (2020). Livestock feeds assessment report from Meru County, Kenya. International Center for Tropical Agriculture.
Mwiti, M. E., Njiri, N. S., & Chege, M. S. (2021). Production of indigenous poultry among smallholder farmers in Tigania West, Meru County, Kenya. African Journal of Agricultural Research, 17(5), 705-713. https://doi.org/10.5897/AJAR2021.15465
Neumann, J., & Morgenstern, O. (2007). Theory of games and economic behavior: 60th anniversary commemorative edition. Princeton University Press. https://doi.org/10.1515/9781400829460
Palee, W., Sirisunyaluck, R., Chalermphol, J., & Limnirankul, B. (2024). Factors affecting farmer's participation on agricultural extension of the longan collaborative farming project in Lamphun Province, Thailand. International Journal of Agricultural Technology, 20(3), 1165-1176.
Republic of Kenya. (2013). Meru County Integrated Development Plan 2013-2017. Government Printers.
Rosenbaum, P., & Rubin, D. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70, 41-55. https://doi.org/10.1093/biomet/70.1.41
Samuel, J., Rao, C. A. R., Raju, B. M. K., Reddy, A. A., Pushpanjali, Reddy, A. G. K., Kumar, R. N., Osman, M., Singh, V. K., & Prasad, J. V. N. S. (2022). Assessing the impact of climate resilient technologies in minimizing drought impacts on farm incomes in drylands. Sustainability, 14, 382. https://doi.org/10.3390/su14010382
Streatfield, D., & Markless, S. (2009). What is impact assessment and why is it important? Performance Measurement and Metrics, 10(2), 134-141. https://doi.org/10.1108/14678040911005473
Stuart, E. A. (2010). Matching methods for causal inference: A review and a look forward. Statistical Science, 25(1), 1-21. https://doi.org/10.1214/09-STS313
Tadelle, D., Alemu, Y., & Peters, K. J. (2000). Indigenous chicken in Ethiopia: Genetic potential and attempts at improvement. World's Poultry Science Journal, 56, 45-54.
https://doi.org/10.1079/WPS20000005
White, H. (2006). Impact evaluation: The experience of the Independent Evaluation Group of the World Bank. The World Bank. https://mpra.ub.uni-muenchen.de/1111/
Wiebe, K., Sulser, T. B., Dunston, S., Rosegrant, M. W., Fuglie, K., & Willenbockel, D. (2021). Modeling impacts of faster productivity growth to inform the CGIAR initiative on crops to end hunger. PLoS ONE, 16(4), e0249994. https://doi.org/10.1371/journal.pone.0249994
World Bank. (2008). The growth report: Strategies for sustained growth and inclusive development. The World Bank.
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