Farmers’ Social capital, Sources of Finances, Information and their implications on Maize Yields in a Rural Highland, Kenya
Maize (Zea mays L.) is a crop of livelihood, nutritional, economic, and political importance in Kenya. Its productivity growth is estimated at 2% annually, with average yields of 2 tons/ha against a potential 6 tons/ha. Annual production lags behind demand. This study was conducted in a typically rural location of Nandi County in Kenya to investigate smallholder farmers’ social capital, sources of finances, information, and their implications on maize yields. Data from 502 farmers, collected ex post facto, was analysed by use of descriptive and inferential statistics. Brown-Forsythe ANOVA showed highly significant differences between groups; based on social capital as measured by their membership to social common-interest groups (F* (2,499) = 23.826, P = .000), based on main sources of finances for farm operations (F* (4, 60.649) = 8.519, P = .000) and main sources of technical information (F (3,498) = 38.738, P = .000). A Games-Howell post hoc test showed that the ‘no group’ category had significantly lower yields compared to members of social groups (P = .000). Farmers who mainly financed farm operations through ‘sale of farm produce’ had significantly lower yields compared to ‘non-farm trade’ and ‘salaries from off-farm employment’ categories (P = .001 and .000). The farmer category that relied mainly on ‘mass media’ for information had significantly lower yields (P = .000) compared to those who relied on Extension (P = .000) and ‘digital sources’ (P = .016). The mix of ‘extension and digital sources’ category showed a significantly higher mean compared to ‘Extension only’ (P = .000). In conclusion, farmer organizations and the associated social capital, funding of farm operations and information sources that guarantee quality have a positive impact on maize productivity and food security. This study is of value for practitioners and policy-makers on farmer organizations, seasonal credits, and extension information delivery
Balogun, O. L., Ogunsina, I. J., Ayo-Bello, T. A., & Afodu, O. J. (2018). Effect of social capital on the productivity of cassava farmers in Ogun State, Nigeria. Journal of Agricultural Sciences (Belgrade), 63(1), 99-112.
Ben-Hador, B. (2022). Levels of social capital in organizations [online presentation], May, 13th 2022. https://www.socialcapitalresearch.com/event/webinar-batia-ben-hador/
Cheruiyot, J. K. (2020). Links between Farmers’ Socio-demographics and Adoption of Soil Conservation Technologies in Hilly Terrains of Nandi County, Kenya. Journal of Agriculture and Ecology Research International, 21(5), 9–21. https://doi.org/10.9734/jaeri/2020/v21i530143
Chune, M. D. (2022). Determinants of Maize Production Income in Western Uganda. East African Journal of Agriculture and Biotechnology, 5(1), 1– 13. https://doi.org/10.37284/eajab.5.1.532
County Government of Nandi. (2018). County Integrated Development Plan 2018-2023.Kenya: County Government of Nandi
Delacre, M., Leys, C., Mora, Y. L., & Lakens, D. (2020). Taking parametric assumptions seriously: Arguments for the use of welch’s f-test instead of the classical f-test in one-way ANOVA. International Review of Social Psychology, 32(1), 1– 12. https://doi.org/10.5334/IRSP.198
Government of Kenya. (2007). The Kenya Vision 2030. Government of the Republic of Kenya, 2. http://www.vision2030.go.ke/cms/vds/Popular_Version.pdf
Heemskerk, W., & Wennink, B. (2004). Building social capital for agricultural innovation: Experiences with farmer groups in Sub-Saharan Africa. Amsterdam: The Netherlands, Royal Tropical Institute
Karagöz, D., & Saraçbasi, T. (2016). Robust Brown-Forsythe and Robust Modified Brown-Forsythe ANOVA Tests Under Heteroscedasticity for Contaminated Weibull Distribution. Revista Colombiana de Estadística, 39(1), 17– 32. https://doi.org/10.15446/rce.v39n1.55135
Kehinde, A. D., Adeyemo, R., & Ogundeji, A. A. (2021). Does social capital improve farm productivity and food security? Evidence from cocoa-based farming households in Southwestern Nigeria. Heliyon, 7(July 2020), e06592. https://doi.org/10.1016/j.heliyon.2021.e06592
Kilimo Trust. (2017). Characteristics of Maize Markets in East Africa. https://www.kilimotrust.org/reacts/files/Maize_markets_X-tisation.pdf
KNBS Census. (2019). 2019 Kenya Population and Housing Census Volume I: Population By County and Sub-County: Vol. I (Issue November). Republic of Kenya
Mucheru-Muna, M. W., Ada, M. A., Mugwe, J. N., Mairura, F. S., Mugi-Ngenga, E., Zingore, S., & Mutegi, J. K. (2021). Socio-economic predictors, soil fertility knowledge domains and strategies for sustainable maize intensification in Embu County, Kenya. Heliyon, 7(2), e06345. https://doi.org/10.1016/j.heliyon.2021.e06345
Mwalupaso, G. E., Wang, S., Rahman, S., Alavo, E. J. P., & Tian, X. (2019). Agricultural informatization and technical efficiency in maize production in Zambia. Sustainability (Switzerland), 11(8), 2451. https://doi.org/10.3390/su11082451
Onono, P. A., Wawire, N. W. H., & Ombuki, C. (2013). The response of maize production in Kenya to economic incentives. International Journal of Development and Sustainability, 2(2), 530–543.
Sang, N. C., & Cheruiyot, J. K. (2020). Farmers’ Information Literacy and Productivity Performance of Smallholder Horticulture in a Highland Zone, Kenya. Journal of Scientific Research and Reports, 26(6), 89–99. https://doi.org/10.9734/jsrr/2020/v26i630274
Statistics How To. (2022). W or F Statistic. Available: https://www.statisticshowto.com/brown-forsythe-test/
Wangui, M.L. (2019). An evaluation of the impact of access to credit on maize output amongst smallholder farmers in Kenya (MA Thesis). Kenya: University of Nairobi.http://erepository.uonbi.ac.ke/bitstream/handle/11295/109630/
Copyright (c) 2022 Joseph Kipkorir Cheruiyot, Festus Kipkorir Nge’tich
This work is licensed under a Creative Commons Attribution 4.0 International License.