INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNOVATION (IJRSI)  
ISSN No. 2321-2705 | DOI: 10.51244/IJRSI |Volume XII Issue XI November 2025  
Water Scarcity Impact of Rural Livelihood Choices in Kieni Sub  
Counties, Kenya  
Fred K. Wamalwa  
Catholic University of Eastern Africa, Kenya  
DOI: https://dx.doi.org/10.51244/IJRSI.2025.12110151  
Received: 04 December 2025; Accepted: 09 December 2025; Published: 20 December 2025  
ABSTRACT  
Poverty in developing countries escalates environmental predicaments among rural populations in arid and semi  
(ASAL) communities. A key understated outcome of livelihood occupations on human wellbeing is their  
consequence on water scarcity. Yet, most studies involving poverty and the environment overlook implications  
of rural livelihood options on the environment in marginal areas. The objective of this study was to examine  
water scarcity impact of household livelihoods in Kieni East and West sub counties of Nyeri County in Kenya.  
This was essential as rural ASAL populations are most affected by scarcity of water resources. The study adopted  
cross sectional mixed method approach that applied household survey for quantitative data collection. The  
qualitative data gathering techniques included semi structured interviews, focused group discussions and desk  
reviews. Proportionate stratified random sampling technique was used to inaugurate a 400 sample size from a  
targeted population of 51,304 households. The study used independent T-Test to test statistical significance at  
p<0.05 for the two sites. Multiple regression techniques were applied to determine the influence of livelihood  
options on water scarcity in the study area. Based on the analysis, the study found linkage between livelihoods  
and water scarcity to be significant. Overall results at the two sites revealed water scarcity as predominantly  
instigated by household participation in forest based activities [B=0.264], livestock activities [B=0.184], and  
crop based activities [B=0.169] respectively. The results also demonstrated a higher impact of forest activities  
on water scarcity in Kieni East [B=0.313] than in Kieni West [B=0.231] at p<0.001; while livestock activities  
impact on water scarcity was more substantial in Kieni West [B=0.233] at P<0.05, compared to Kieni East  
[B=0.154] at p<0.05. The study concluded with some recommendations for policy and research consideration.  
Key words: Arid and Semi-Arid lands; Cropping activities, Forest activities; Household Livestock activities;  
Off farm activities.  
INTRODUCTION  
In general, poverty is associated with the rural populations because they are largely deprived of both basic and  
economic livelihood opportunities. Present day concerns about the level of poverty in rural areas have caused  
significant interests in research. In an effort to improve living standards of populations in developing countries,  
rural development objective over the last decades has been closely associated with the continuous evolution of  
development strategies. These include poverty reduction strategies, food security programmes, sustainable  
agriculture and rural development, the Millennium Development Goals (MDGs) and from 2015, sustainable  
development goals (UN, 2015). However, poverty remains a significant issue (Shepherd, et. al., 2014).  
For some time now, promotion of rural livelihoods to enhance household welfare by rural development agents  
has mainly focused on simplistic universal approaches of adopting sustainable livelihoods in developing  
countries. Consequently, a lot has been learnt about poverty reduction and environmental conservation in the  
last decade (2014 -2024), in terms of the relationship between poverty and environmental degradation and vice  
versa. Regardless of advances in the development and promotion of sustainable development, rural households’  
motivation to take up new sustainable livelihoods has remained minimal. This has led to the realization that  
livelihood adoption is not only a technical problem but also a socioeconomic problem, which in recent times,  
has directed attention to the influence of socioeconomic and behavioural factors in rural households’ livelihood  
choices. Like in most contemporary developing countries, the fundamental characteristic of rural households in  
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Kenya is the ability to adapt, through rural livelihoods diversification. Rural livelihoods diversification is a  
survival strategy in which factors of both threat and opportunity cause the rural household to adapt intricate and  
diverse livelihood strategies in order to survive (Ellis, 2000). While participation in multiple activities by rural  
households is not new, there is relative neglect of diverse dimensions of rural livelihoods other than access to  
farming until mid-1980s. The dominant strategy for improving rural welfare was therefore small farm output  
growth.  
World Bank (2007) showed that poverty reduction in sub-Saharan Africa can be achieved through livelihood  
diversification in rural areas. Coherent with this finding, rural households have four possible options to choose  
livelihoods for their wellbeing. They practice farming, raise livestock, and engage in small businesses. The last  
option though not appealing to the poor households is the access to common forest resources when the need to  
survive arises. As an active social process, livelihood diversification involves the maintenance and continuous  
adaptation of diverse portfolio of activities over time in order to secure survival and improve living standards  
(Ellis, 2000b). However, livelihood diversification has consequences for the rural communities, and therefore  
the overall process of structural transformation impacts on the use of resources and the environment in general  
(Loison, 2015). Since the environment is a critical input for rural households, environmental degradation in turn  
implies a shrinking input base for the poor households that increases severity of poverty. From this discourse, it  
has been argued that poor people are concentrated in fragile land (Barbier, 2008, 2010), consistent with evidence  
that poverty has positive correlation with fragility of lands (Dasgupta, et. Al., 2005); and that the role of  
environmental resources in the share of aggregate income of the poor is strong (Hogarth, et. al., 2012; Kamanga,  
et. al., 2009).  
According to Babbier (2010, 2013), poverty is the main obstacle to promoting environmental conservation and  
some of the environmental problems faced in developing countries are water shortage and contamination,  
deforestation, land degradation, air pollution and the loss of biodiversity. Over the last decades, interest in  
sustainable development (Babbier, 2003) has been out of above concerns. Although current economic  
development may be leading to enhanced rural household welfare, it is at the expense of excessive depletion and  
degradation of natural capital. Moreover, human development and environmental issues have generally been  
articulated as separate issues (Nunan, et. al., 2002). For instance, in a study on poverty and environmental links,  
Comim, et. al., (2009) demonstrate that although many studies have focused on poverty as an impediment for  
economic development, the debates on poverty reduction often concentrate on the concept of poverty and its  
measurement.  
Although poor environmental condition is a determinant of poverty (Shyamsundar, 2002), environmental  
degradation such as deforestation, land degradation and limited water supply worsens the condition of the poor.  
One of the strategies employed by rural folks in quest to diversity from farming livelihood is dependence on  
water resources, which in many ways results in water dearth. Therefore, water as a resource becomes important  
as an additional natural resource to define household survival. In developing countries rivers provide a direct  
source of water for domestic use with minimal or no treatment at all. For water scarce countries like Kenya  
(WRI, 2007), this means that water catchment areas should be managed properly so as to retain their capacity to  
supply good quality water all year round.  
The battle against poverty remains an important priority on Kenya’s development agenda as articulated in Vision  
2030, the country’s development blueprint for the period 2008 to 2030 (Government of Kenya, 2007). The  
Vision aims to make Kenya a “middle” income country providing high quality life for Kenyans by the year 2030.  
However, the majority of the poor continue to be concentrated in rural areas, where their livelihoods (Lufumpa,  
2005) depend on subsistence agriculture, making poor farmers encroach on water catchment areas leading to  
water losses. Therefore, Kenya faces the challenge of improving its economic performance and the lives of its  
citizens without undermining the environment upon which its national earnings and individual people’s  
livelihoods depend (Government of Kenya, 2007). This study aims to determine the impact of livelihood  
activities of rural households in Kieni East and West Sub counties on water scarcity so that development  
programmes aim to reduce poverty and promote sustainable water use practices can be achieved.  
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METHODS AND DATA  
In order to understand fully the phenomenon of this study, a mix of quantitative and qualitative approaches were  
adopted. This is because from past studies (Cruz-Trinidad, et. al., 2009; Simpson, 2007) the approach is effective  
for livelihood investigations. Survey techniques were used to collect quantitative information while desk review,  
focused group discussions, key informant interviews and observations methods assisted to collect qualitative  
data.  
Two sites were used in this study – Kieni East and Kieni West sub counties, in Nyeri County in Kenya. The two  
sites depict similar farming systems and socio-cultural settings. The study area comprises of four wards in each  
sub county i.e.  
Mweiga, Mwiyoyo/Endarasha, Mugunda and Gatarakwa wards of Kieni West; and  
Naromoru/Kiamathaga, Thegu River, Kabaru, and Gakawa wards of Kieni East Sub County. The area is  
sandwiched between two major water towers in Kenya i.e. Mt. Kenya and The Aberdares Ranges in Kieni East  
and Kieni West sub counties respectively. The area is characterized by high temperatures in low altitude areas  
and low temperatures for areas adjustment to the two water towers.  
Fig. 3.1 Geographical location of Kieni East and Kieni West sub counties  
Source: Author, 2017.  
According to the 2009 population census (KNBS, 2010), the population of Kieni, was estimated at 175,812  
(51,304 households). Ten sub locations for this study were randomly selected from a total 59 sub locations  
(clusters) in the eight wards (strata).  
Based on Yamane (1967) formula, a sample size of 400 households (200 households for each of the two sites)  
of study site was considered adequate to balance required level of reliability and cost.  
In order to represent the population with sufficient accuracy and to infer the sample results to the population, the  
target sample households were selected in a random two stage sampling process. In the first stage, the study sub  
locations were randomly selected using proportionate stratified random sampling technique. This resulted in the  
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ISSN No. 2321-2705 | DOI: 10.51244/IJRSI |Volume XII Issue XI November 2025  
selection of 10 sub locations; see Table I, appendix 1, each with 40 households according to their respective  
population strengths. Accordingly, the probability of selecting each of the ten selected sub locations based on  
population size was determined and varied between 11.1% for Gakanga sub location, and 56.8% for Kamatongu  
sub location, see Table I. The probability of selecting each household in the selected sub locations based on the  
population was also determined, and varied from 1.4% for Kamatongu to 10.9% in Bondeni sub location (Table  
I.). The constant overall weight of 1.3 (see Table I) demonstrate that each household in the population had an  
equal chance of being selected for the household survey interview. In the second stage, using random sampling  
techniques, individual households units in the selected sub locations were randomly selected in relation to  
population. Household lists provided by the local administrators (area Assistant Chiefs) of the sampled sub  
locations were used as sampling frame for selecting households. Accordingly, 400 households (40 households  
for each of the ten sub locations) were randomly selected for the study (Table I).  
Instruments and Data Collection Procedures  
A survey using structured questionnaire was the primary method of investigation employed for this study.  
However, focus group interviews, key informant interviews, and direct personal observations were also used in  
order to enrich the investigation with relevant qualitative information. The questionnaire was administered in  
Kikuyu, the local language which households of both sites speak between April and July, 2017. A team of 5  
enumerators was recruited and trained for each study site to collect the data from the sampled households. Two  
separate focus group discussions were conducted for each study site, with male and female household members.  
The focus groups composed of between 6 and 9 members of households in both sites. The participants were  
identified in purposeful selection among the survey samples that were thought to express their views actively in  
consultation with the enumerators. Village and major town markets in the area were visited to gather information  
on prices of major traded agricultural, livestock and forest products, including off farm activities. Farm field  
observation was conducted on some household farms to observe livelihood activities, management practices,  
and spatial locations in the farmers’ land holding.  
Data Organisation and Analysis  
The data was coded and entered into SPSS in three separate data files; one for Kieni East, the second for Kieni  
West, and the third for pooled data. To estimate impact of livelihood activities on water scarcity, multiple  
regression analysis technique was applied to predict the unknown values of water scarcity variables from the  
known values of the four livelihood activity variables, also called the predictors (see Table II). As shown in  
Table II, independent sub variable for forest activities (FA) included household annual income from forest  
activities and proportion (%) that depends on forest for a livelihood. The second category was crop activities  
(CA) with sub variables that consisted of household annual crop income, and average number of crop varieties  
per household. In regard to household livestock activities (LA), annual income from livestock sales and livestock  
numbers in tropical livestock unit (TLU) variables were studied as the third category, while the fourth category  
of variables consisted of off farm (OA) sub variables that included annual average income from off farm  
activities and proportion of households who engage in off farm activities. Dependent variables for water scarcity  
(WS) comprised of the following sub variables i.e. proportion of households who perceived forest tree cover had  
reduced over the last 5 years; household proportion that belief tree cutting is prevalent in the area, and household  
proportion that belief timber extraction from forest is by villagers.  
Table II. Descriptive statistics of Kieni East, Kieni West, and Pooled Data (all surveyed households)  
Kieni East (N= 200)  
Kieni West (N= 200)  
Pooled Data (N= 400)  
Variable Description  
Mean  
St. Dev.  
Mean  
St. Dev.  
Mean  
St. Dev.  
Household activities  
% household who engage in  
39.2  
52.2  
45.8  
forest activities[FA]  
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% household who engage in crop  
activities[CA]  
64.5  
47.0  
55.0  
88.5  
32.5  
66.5  
76.5  
39.8  
60.5  
% household who engage in  
livestock activities[LA]  
% household who engage in off  
farm activities[OA]  
Independent variables(livelihood activities)  
[FA]Annual Household income  
from forest activities (KShs)**  
10,459.55 11,653.17 20,995.45  
37383.35  
31,455.20  
98.4  
21,554.19  
63,077.08  
41472.23  
[FA]% of household who depend  
on forest for a livelihood***  
96.2  
100.0  
[CA]Annual household income  
from agriculture (KShs)***  
23,056.62 52,615.09 81,033.08 175,790.46 34,430.73  
[CA]Average number of crop  
varieties grown per household  
4.8  
3.8  
37783.08  
7.97  
4.3  
[LA]Annual Household income  
from livestock (KShs)**  
29064.89  
12.48  
37175.48  
46821.33  
32628.93  
10.23  
[LA]Average  
household  
livestock number in TLU***  
[OA]Average annual household  
income from off farm activities  
(KShs) **  
63,672.73 70,353.60 68,490.91 142,522.19 66,300.83 115,263.53  
[OA]% of households who  
55.0  
66.0  
60.5  
engage in off farm activities **  
Dependent variables (Water Scarcity)  
% of households experiencing  
water shortage**  
93.3  
88.4  
87.3  
86.6  
90.34  
87.5  
% households who experience  
crop failure due to inadequate  
water**  
Variables in which sample households of Kieni East have significant differences from those of Kieni West:  
*** = at 0.01 level of significance ** = at 0.05 level of significance.  
MULTIPLE REGRESSION MODELS  
Based on general regression model, regression of livelihood activities on water scarcity is noted as follows (Eq.  
1):  
Yws=B0+BFAXFA+BCAXCA+BLAXLA+BOA XOA…………….1  
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where: Yws = Water scarcity variable; B0 = Regression intercept coefficient; BFA = Forest activity regression  
coefficient; XFA= Forest activity variable; BCA= Crop activity on regression coefficient; XCA = Crop activity  
variable; BLA= Livestock activity regression coefficient; XLA = Livestock activity variable; BOA = Off farm  
activity regression coefficient, and XOA = off farm activity variable.  
Considering water scarcity factors identified in this study, regression coefficients for four livelihood activity  
variables were computed as shown below in the regression models (2,3, &4) for the water scarcity variables in  
Kieni East, Kieni West, and overall study area. It is therefore a 3-step hierarchical regression, which involves  
the interaction between four continuous scores. In this case, water scarcity variables for Kieni East were entered  
at Step 1 (Model 1). In the second model, variables for Kieni West  
Entered (Model 2), while pooled data for the first two models (Model 3) was for the overall water scarcity in the  
study area.  
Model 1:  
Ywske = B0 +BFA XFA+BCA XCA+ BLA XLA+ BOA XO.………2  
Model 2:  
Ywskw=B0+BFAXFA+BCAXCA+BLAXLA+BOA XOA…………..3  
Model 3:  
Yws=B0+BFAXFA+BCAXCA+BLAXLA+BOA XOA…………….4  
where: Ywske = water scarcity variable in Kieni East; Yswkw= water scarcity variables in Kieni West; and Yws =  
overall water scarcity variable.  
The data obtained from all respondents (200 from each site including their livelihood activities and water  
scarcity) were considered in the models. The explanatory variables (Xi) included in the model were household:  
forest activities (FA), crop activities (CA), livestock activities (LA), and off farm activities (OA). FA, CA, LA,  
and OA are categorical variables. The dependent variable used in this multiple regression analysis was water  
scarcity experienced by households. Like explanatory variable, dependent variables are also categorical. In Table  
III regression analysis results are shown of livelihood activities on water scarcity.  
RESULTS AND DISCUSSION  
Overall, results (Table III) indicate that the three out of four livelihood activities in the area cause water scarcity.  
Results show that off farm activities had insignificant effect on water scarcity.  
Table III. Hierarchical regression analysis coefficients of livelihood activities predicting water scarcity for Kieni  
East and West and pooled data  
Variables  
Kieni East  
Kieni West  
Pooled Data  
Model 1: Water Scarcity  
Model 2: Water Scarcity  
Model 3: Water Scarcity  
B
t
Sign.  
B
t
Sign.  
.162  
B
t
Sign.  
Const.  
-2.127 .035  
-1.403  
3.019  
1.846  
-.129  
-2.542  
5.053  
2.641  
.011**  
.000***  
.009***  
Forestactivities[FA]  
.313  
.112  
4.092  
1.718  
.000*** .231  
.087 .126  
.003*** .264  
.066 .169  
Cropsactivities[CA]  
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Livestockactivities[LA] .154  
2.021  
.129  
.045**  
.898  
.233  
2.962  
-1.428  
9.08  
.003*** .184  
.155 -.045  
3.579  
-.897  
.000***  
.370  
Offfarmactivities[OA]  
.009  
-.107  
F
10.51  
.160  
Adjusted R²  
.140  
a. Dependent Variables: Water scarcity.  
b. *** Significant at 1% level ** Significant at 5% level  
* Significant at 10% Level  
Forest activities (FA)  
Regression results in Table III indicate that forest activities have the greatest impact on water scarcity (B=0.264,  
t-values=5.053, p<0.01). Results also show that the effect of forest activities on water scarcity in both sites was  
positive and significant (Kieni East[B=.313, t-values=4.092, p˂0.01], Kieni West[B=.231, t-values=3.019,  
p˂0.01]). Consistent with this finding, studies have shown that fuelwood collection is often found in tropical  
forests and degraded forest areas (Repetto, 1988; 1990; Rowe, et. al., 1992) and increases water scarcity in  
affected areas. Trees help prevent excessive evaporation of water bodies, and so destruction of forests exposes  
soil moisture to the sun’s intense heat, leaving them dried out. Also, farming in the forest involves clearing forest  
trees and bushes which in turn exposes the soil to direct sunlight leading to evaporation of water from the soil.  
As a result of these activities, one of the FGD participants aptly noted….......…this area was named  
“Kamburaini” because those days it was a rainy place. But now, the name is meaningless because rain is no  
longer a frequent occurrence!….(FGD Participant, Kamburaini Sub Location, Kieni East).  
This finding is also consistent with World Bank (2007) report that major water catchment areas in Kenya,  
including the Aberdare Ranges and Mt. Kenya have lost their forest cover over the years with the closed canopy  
forest cover currently standing at a dismal 2.0%. Furthermore, Mati, et. al., (2008) reported that between 1973  
and 2000, there was a 32% decrease in forest cover and a 203% increase in agricultural cover in the Mara River  
basin in Kenya. This affects water source downstream due to exposure of the forest as water catchment. Also  
grazing of livestock in the forest has a similar negative effect to water availability like crop activities. This is  
because over grazing leads exposure of the soil in the forest resulting in water evaporation from the soil.  
Livestock activities (LA)  
Regression results in Table III show that livestock activities result to water scarcity (B=0.184, t-values=3.579,  
p<0.01). Results in Table 4.8 further show that the effect of livestock activities on water scarcity in both sites  
was significant (Kieni East [B=.154, t-values=2.021, p˂0.05], Kieni West [B=.233, t-values=2.962, p˂0.01]).  
The positive relationship of animal husbandry and water scarcity has been previously studied (Pallas, 1986), in  
which it is shown that in extensive grazing systems, the water contained in forages is significantly lost to meeting  
water requirements for livestock upkeep. In dry climates, the situation is even worse as water content of forages  
decreases from 90 percent during the growing season to about 10 to 15 percent during the dry season (Pallas,  
1986). FGD results revealed that some of the households in the area practice zero grazing mode of livestock  
husbandry, mainly for milk production. Diets for these animals are water intensive because of the huge quantities  
of water required for their upkeep, exacerbating water availability challenges in the area. In his study on livestock  
water consumption in Australia, Luke (1987) reported that water requirements per animal can be high, especially  
for highly productive animals under warm and dry conditions. Furthermore, water scarcity becomes worse in  
the study area where livestock are allowed to wander free in search of food and water. In extensive systems, the  
effort expended by animals in search of feed and water increases the need for water considerably, compared to  
intensive systems where animals do not move around much.  
Cropping activities (CA)  
Results in Table III show that crop activities cause water scarcity (B=0.169, t-values=2.641, p<0.05). As crop  
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farming is mostly accomplished by opening up the soil in preparation of planting, it exposes the soil to water  
evaporation. Reports from key informants from the Ministry of Agriculture and Livestock Development  
(MoA&LD) revealed that approaches that could minimise this loss like minimum tillage are hardly practiced in  
the area. By opening up the soils, farmers also destroy trees and bushes that provide cover to the soil as protection  
from evaporation. Cropping practices also encourage higher water losses (Clay, 2004) mainly through leaky  
irrigation systems; wasteful field water application methods; pollution by agrichemicals; and cultivation of  
‘thirsty’ crops not suited to the environment. According to FGDs results, the situation is even compounded by  
the fact that the area is ASAL where water scarcity is prevalent. Some innovative and resourceful household  
individuals and horticultural firms/farms have established minor and major irrigation systems, which abstract  
water from either the forest and under the ground. This has augmented the water scarcity problem in the area,  
FGD participants argued. However, with continuing population growth and limited potential to increase suitable  
cropland, as other studies have demonstrated, irrigation has become increasingly important to food security  
strategies (Wichelns & Oster, 2006). Unfortunately though, increasing levels of irrigation as practiced by  
horticultural farms and household farmers in the area only augments the cost of water and, this may escalate  
problems of water scarcity in the area further.  
CONCLUSION AND RECOMMENDATIONS  
This study investigates the impact of household livelihood choices on water scarcity. It is established that three  
out of the four commonly practiced livelihoods significantly contribute to water scarcity in the study area, but at  
different levels of significance at the two sites, vis a viz forest, livestock, and cropping activities. The impact on  
water scarcity of forest activities in Kieni East was greater than experienced in Kieni West, while livestock  
activities had a greater impact on water scarcity in Kieni West than in Kieni East Sub County. Consequently. It  
is concluded that forest and livestock activities are vital components for policy making strategies that aim to  
promote water conservation in Kieni East and Kieni West respectively.  
Therefore policies that target the regulation of these activities can contribute immensely towards water  
conservation in the area. These may be achieved by focusing interventions in the forest and on household farms  
respectively. First, policies that target current forest based interventions need review and reformulation. This is  
particularly important in Kieni East, where forest activities had a higher impact on water scarcity. Such policies  
should be spearheaded by relevant institutions led by the Ministry of Environment & Forests (MoE&F), Kenya  
Forest Service (KFS), and the local County government agencies. It is suggested that these institutions take  
measures to regulate and promote sustainable forest activities that take place in the forest. Therefore, water  
conservation strategies should focus on regulating activities that lead to forest cover depletion like logging,  
farming and livestock grazing. This is because these activities expose the forest soils hence leading to  
evapotranspiration, resulting in water loss. Also logging and grazing leads to loss of vegetation from the forest  
resulting in water loss through biomass exportation. Secondly and more importantly, in Kieni West, interventions  
to improve livestock keeping on household farms should be prioritized too, especially by the Ministry of  
Agriculture and Livestock Development, including the relevant County government agencies. For instance,  
households ought to be encouraged to adopt intensive rather than extensive modes of livestock husbandry to  
minimize on water demand by nomadic livestock units.  
Conflict of interest  
We declare that there is no conflict of interest whatsoever by the authors of the manuscript or any other entities  
by submitting this paper and by the publication of the same.  
ACKNOWLEDGEMENT  
This research was executed as part of PhD research at Jomo Kenyatta University of Agriculture and Technology  
(JKUAT), Department of Development Studies, Nairobi, Kenya. We hereby acknowledge contribution of  
JKUAT in ensuring this research was undertaken.  
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APPENDIX 1  
Table I. Sub locations and number of Households randomly selected for questionnaire survey  
Strata/Ward  
Cluster/  
Sub location  
Naromoru  
Ndiriti  
Sub  
Location  
Size  
Cumulative  
Sum(a)  
Clusters  
sampled  
Probability  
1
Household  
per Sub  
Location  
Probability  
2
Overall  
weight  
1161  
1094  
1063  
989  
1661  
2755  
3818  
4807  
6620  
1200  
32.4%  
40  
2.4%  
2.2%  
1.3  
Naromoru/  
Kiamathiga  
Gaturiri  
Rongai  
Kamburaini  
1813  
6330  
35.3%  
40  
1.3  
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Thigithi  
Murichu  
Gikamba  
Kabendera  
Kirima  
666  
7286  
762  
8048  
1098  
830  
9146  
9976  
1505  
1719  
1961  
1020  
1811  
605  
11481  
13200  
15161  
16181  
17992  
18597  
20043  
20915  
22524  
23887  
29012  
29379  
30573  
31351  
34272  
35398  
36764  
37981  
39790  
40691  
42147  
42716  
44623  
45324  
45895  
47389  
47860  
11460  
16590  
29.3%  
35.3%  
40  
40  
2.7%  
2.2%  
1.3  
1.3  
Kabaru  
Ndaathi  
Kimahuri  
Munyu  
Thungari  
Lusoi  
Thegu  
Thirigitu  
Maragima  
Gathiuru  
Githima  
Kahurura  
Bondeni  
Amboni  
Njengu  
1446  
872  
1609  
1363  
5125  
367  
21720  
26850  
31980  
37110  
31.4%  
7.2%  
40  
40  
40  
40  
2.5%  
10.9%  
1.4%  
3.3%  
1.3  
1.3  
1.3  
1.3  
Gakawa  
Mweiga/Mw  
eiga  
1194  
784  
Kamatongu  
Watuka  
2915  
1126  
1366  
1217  
1809  
901  
56.8%  
23.7%  
Gatarakwa  
Lamuria  
Embaringo  
Kamariki  
Mitero  
Endarasha/  
Mwiyogo  
Charity  
1456  
569  
Gakanga  
Endarasha  
Kabati  
42240  
47370  
11.1%  
29.1%  
40  
40  
7.0%  
2.7%  
1.3  
1.3  
1907  
701  
Muthuini  
Labura  
571  
1494  
471  
Mwiyogo  
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Karemeno  
Ruirii  
538  
993  
722  
1191  
48398  
Mugunda  
49391  
Kamiruri  
Nairutia  
10  
50113  
51304(b)  
400  
TOTAL  
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