dc.description.abstract | Water is a critical resource in environmental sustainability, agricultural production as
well as for improved livelihoods. Climate variability hinders crop and livestock
production in Sub-Saharan African countries. Rainwater harvesting (RWH) is a climate
smart agricultural practice to revert this. Rainwater harvesting has been practiced among
smallholder farmers for centuries in many parts of the world. Recently, it has gained
more attention due to the reported increasing water demand and the need for sustainable
water management hence the research was conducted to evaluate the utilization of
rainwater harvesting technologies (RWHTs) as a climate smart agricultural practice in
Murang’a County, Kenya. Drawing on data from a cross sectional survey of 384
households, our research evaluated the adoption of RWHTs, intensity of crop and
livestock enterprises adoption under RWHTs and the determinants for RWH among
smallholder farmers in Murang’a County, Kenya. Multistage random sampling and
proportionate to size technique was employed to sample farmers in three wards namely:
Murarandia, Mugoiri and Wangu. The KOBO kit a phone application was used during
data collection. To assess the adoption of RWHTs, descriptive statistics and analysis of
variance (ANOVA) were applied. The results found that rooftop water harvesting
technology (93a ± 22), infiltration pits (81a
± 21), furrows (68a ± 16), deep ploughing (67a
± 21), terraces (54a ± 14), mulching (51a ± 17), retention ditches (23a ± 18) and water pans
(17a ± 5) water harvesting technologies had statitistical significant differences among
smallholder farmers (P<0.05), while negarims, water bunds and dams water harvesting
technologies were not statistically significant (P<0.05) adopted at a mean ± S.D of 11 ±
4, 6 ± 2 and, 1 ± 1 smallholder farmers, respectively. The findings exhibited that
households that practiced livestock production including: dairy cattle farming, goat
rearing, sheep farming, beef cattle rearing, pig production, and poultry farming, watered
their livestock using rooftop harvested rainwater at a rate of 12%, 10%, 9%, 6%, 3% and
5%, respectively while, 1% practiced aquaculture. Multivariate probit model (MVP)
analysis showed that crop enterprises adopted (macadamia, maize, coffee, tea, avocado,
fodder, arrowroots, beans, bananas, mangoes and sweet potatoes) among household heads
were key crop enterprises that influenced adoption of these RWHTs. The MVP model
also pointed out that household head’s access to credit facilities, landownership, age,
level of income, education level, gender, family size, source of income, membership to
farmers’ groups and access to training services were statistical significant (P<0.05) thus,
influenced RWH adoption. Membership to farmers group had merits including: support
in farmers’ training, social ties, source of information and source of credit which were
also key determinants to RWH adoption. The study recommends relevant stakeholders
and policy makers to consider promotion or up scaling of RWHTs for crop and livestock
enterprises among household heads in consideration of the determinants influencing
adoption rate in Murang’a County. | en_US |