









































Pa
ge

 
1



Pa
ge

 
51

American Journal of   
Environment and Climate (AJEC)

Socio-Economic Determinants for Adoption of  Adaptation Strategies to Climate Change 
and Variability among Smallholder Farmers in Igambang’ombe, Sub-County, Kenya

Mwendandu I. W.1, Rithaa J. N2*, Wambongo C. S. R3

Volume 2 Issue 3, Year 2023
ISSN: 2832-403X (Online) 

DOI: https://doi.org/10.54536/ajec.v2i3.1786
https://journals.e-palli.com/home/index.php/ajec

Article Information ABSTRACT

Received: June 25, 2023

Accepted: July 28, 2023

Published: September 16, 2023

Climate change and variability have been identified globally as a major challenge to food 
security especially in Arid and Semi-Arid lands (ASALs). Agriculture and smallholder 
farmers have also been identified as the most vulnerable, mainly due to the former being 
rain-fed and the latter having inadequate adaptive capacity to the dynamics of  climate change 
and variability. This study, therefore, sought to determine the social-economic factors that 
influence the adoption of  adaptation strategies for climate change and variability, among 
smallholder farmers in Igambang’ombe Sub-County, Tharaka Nithi County. A descriptive 
research design was applied. Systematic random sampling was used to select the respondents 
and a semi-structured questionnaire with open and closed-ended questions was used for 
data collection. Observations and interviews were carried out, while systematic random 
sampling was used to select the respondents. Correlation analysis was used to assess which 
socio-economic factors influenced the adoption of  adaptation strategies to climate change 
and variability. Some of  the adaptation strategies considered included; accessed weather 
information Ox-ploughing and herbicide use for preparation, terracing and intercropping, 
crop and variety diversification The study showed that age (42%), farmer’s farming 
experience (40%), and level of  education of  the household head (24%), were the key 
socio-economic factors influencing the adoption of  adaptation strategies to climate change 
and variability. Adaptation of  climate change and variability adaptation strategies was also 
associated to access and frequency of  extension services. Access to farm subsidy and credit 
was also important. Farmers and agriculture stakeholders needed to upscale training, guided 
by specific socio-economic farmer characteristics. 

Keywords
Adaptation, Smallholder Farmer, 
Climate Change and Variability, 
Adaptation Strategies, and Socio-
Economic

1 Chuka University Affiliate, Department of  Environmental studies and Resources Development, P.O. Box 1519-60100, Embu, Kenya
2 Chuka University, Department of  Environmental studies and Resources Development, P.O. Box 109-60400, Chuka, Kenya
3 Bomet University College, P.O Box 701-20400, Bomet, Kenya
* Corresponding author’s e-mail: jnrithaa@chuka.ac.ke

INTRODUCTION
Climate change and variability have been identified 
as a major global challenge to the development of  
communities, thus requiring great attention (IPCC, 
2014) The United Nations identified climate as a key 
causal factor affecting food security globally with 
agricultural production being most vulnerable both 
locally, and globally (UN, 2007, IPCC, 2001). Various 
socio-economic, demographic, political, institutional, 
and policy trends have been noted to limit the abilities 
of  smallholder farmers to adapt to climatic variations 
(Rosenzweig & Hillel, 2000). 
Adger et al. (2003) indicated that future climate change 
adaptation will be a function of  an individuals’ capacity, 
that is, that of  the smallholder farmers, their social 
networks, and the state; and nations from which these 
individual farmers hail from. This calls for progressive 
and deliberate enhancement of  the smallholder farmer’s 
resilience and capacity to cope and adapt to climate 
change by all stakeholders of  adaptation. Kerandi 
and Omotosho (2008) identified farmer knowledge 
enhancement, for example, on the rainfall onset dates and 
the length of  the growing season as one of  the capacities 
that farmers need. They argued that this would go a long 
way in enabling farmers to make timely decisions on-farm 
operations thereby helping mitigate the effect of  climate 
change on the adaptation of  crop production. According 

to Camberlin and Okoola (2003), this knowledge is key 
in the determination of  the tactics farmers will employ 
in adapting to the varying and unpredictable climate 
scenarios (Hawkind et al., 2022). 
Kandji and Mackensen (2006) proposed that policymakers 
themselves and other agencies that assist farmers need 
to be adequately informed of  the specific local farmer 
circumstances that influence decision-making in climate 
change adaptation. Reliable information has to be 
generated from time to time to inform these decision-
makers and agencies which empower the communities 
towards adaptation. They need to specifically determine 
the drivers of  adaptation in each local scenario these 
will go a long way in helping them face the uncertainties 
posed by drought and other climate threats ( Sharafin et 
al., 2021). In response to the experienced and perceived 
impacts of  climate change and variability, the Kenya 
Government  enacted a Climate Change law (GoK, 2010). 
In the application of  the Act, it was envisioned that the 
actions or response to the impacts of  climate change 
were to be translated into decisions and development 
plans, and implementation strategies. This included 
building resilience and enhancing the adaptive capacity of  
the Kenyan people. As a result, the National Government 
and other development agencies have initiated support 
programs aimed at strengthening the adaptive capacity of  
smallholder farmers in semi-arid Kenya. 



Pa
ge

 
52

https://journals.e-palli.com/home/index.php/ajec

Am. J. Environ. Clim. 2(3) 51-60, 2023

Statement of  the Problem
Climate change and variability affects agricultural 
productivity, and is a major cause of  food insecurity and 
loss of  livelihoods in Kenya. The National Government 
and other development agencies efforts have initiated 
support programs aimed at strengthening the adaptive 
capacity of  smallholder farmers programs which 
include demand-driven extension services, provision of  
drought-tolerant crop cultivars, and dissemination of  
climate forecasts, input subsidies, and provision of  farm 
implements among others 
Despite these efforts, farmers in Igambang’ombe Sub- 
County continue to experience low farm productivity, 
remain food insecure, relatively poor, and vulnerable to 
climate variability. This situation pointing at a possibility 
of  a disconnection between the smallholder farmer’s 
knowledge on how to adapt to a varying climate and 
the decisions they make at the farm-level.  Against 
this background, the study sought to determine socio-
economic factors; that influence the adoption of  
adaptation strategies of  climate change and variability 
in Igambang’ombe sub- County. Study findings will 
inform the smallholder farmer, policymakers and 
other agricultural stakeholders on what to consider for 
improved uptake of  adaptation strategies to climate 
change and variability thereby improving the farm 
incomes and livelihoods of  smallholder farmers.

Objectives
The specific objectives of  the study were to determine 
the social-economic factors of  the smallholder farmers 
that influences the adoption of  adaptation strategies to 
climate change and variability in Igambang’ombe Sub-
County, Kenya. 

LITERATURE REVIEW
Overview of  Socio-economic Factors Affecting 
Adaptation to Climate Change
The social and economic factors that are most likely 
to influence or affect the adoption of  climate change 
adaptation strategies include; demographic factors as 
household head gender, age, the farming experience, level 
of  education, household type, income, and size of  the 
household. Other factors that  support social economic 
systems for adoption of  adaptation to climate change 
strategies include access to markets and market linkages, 
agricultural and climate extension services, financial 
support in form of  credits, subsidies and, remittances.

Social Factors
While investigating the adaptation of  climate change in 
the light of  the social factors of  communities, Adler et 
al., (2005) point out that the social construction of  the 
adaptive capacity of  the smallholder farmers is very 
important when thinking about the risks and impacts 
of  a varying climate. Furthermore, Adler et al., (2005) 
asserted that the adaptive capacity of  smallholder 
farmers at a local scale is constrained by large-scale social 

processes operating within the community. In addition, 
the researchers observed that it will not only depend on 
access to physical resources within the community, which 
allow a crop to be grown successfully but also the social 
factors that operate at a very local scale. These factors 
include access to decision-making and the structure of  
the social fabric and relationships within a community. 
Besides, it is not just the change in climate which will 
affect vulnerability and livelihoods, but the way that these 
changes are negotiated through complex social systems. 
Due to these social complexities in dealing with the 
impacts of  climate change, gender has become a concern 
for mainstreaming, in every policy, legal, and development 
projects of  the United Nations Development Program 
(UNDP, 2010). Researchers have observed that major 
sources of  variation in the adaptive capacity, is the level of  
education and gender of  individual smallholder farmers 
(Fischer and Qaim, 2012). Furthermore, the researchers 
observed that in some countries as Kenya women make 
a significant contribution to agricultural systems yet their 
main domain as allowed by culture is subsistence crop 
production; any commercial production remains the 
man’s domain. As a result, women have limited access to 
production resources. These generally affect their ability to 
make decisions and adapt to climate change and variability. 
In other some cultures, women are denied education 
for reproductive reasons, which adversely affects their 
ability to contribute to climate change and variability 
adaptation agenda at both the household and community 
level. Since women have very little exposure to the world 
outside their scope of  knowledge and training; they tend 
rather be restrained by their cultural beliefs handed down 
through the ages. 
While reviewing ways of  improving women’s access to 
extension services, Jiggins, (1989) noted that educated 
female farmers, (was the same for males) tend to be more 
willing to take up technologies than uneducated ones, 
hence less vulnerable to climate changes. With examples 
from communities in Zimbabwe, Brown et al., (2012) 
noted that gender considerations also affected technology 
uptake. They found that women had more constrained 
access to credit than men, which resulted in the men being 
more willing to adopt high-yielding varieties (HYVs) 
of  maize (which required high capital investments and 
fertilizers) than their women counterparts who culturally 
have no access to resources. Getting women to adopt 
these HYVs required additional interventions, as getting 
access to some form of  a credit to make them afford 
these crop varieties.  
The dynamics of  these social characteristics are complex 
and require investigation so that meaningful interventions 
by relevant stakeholders can be instituted which assist the 
communities to adapt to climate change and variability 
through technology adoption. According to Otzelberger, 
(2011) gender needs, time use, roles, and poverty greatly 
influence adaptation; the way each gender is affected by 
and negotiate the climate change and variability vary with 
time and space. Globally, 70% of  the people living below 



Pa
ge

 
53

https://journals.e-palli.com/home/index.php/ajec

Am. J. Environ. Clim. 2(3) 51-60, 2023

the poverty line are women, which is a major contributing 
factor to their vulnerability to climate change and 
variability, especially the adverse ones, (Dankelman et al., 
2008). A study carried out in Kenya by MET scientists 
revealed that a farmers’ level of  education and access to 
climate information are two major factors that determine 
whether climate change and variability adaptation 
strategies would be adopted by smallholder farmers 
(Masieyi et al., 2012). 
A new social dynamic to community transformation in 
response to climate variability has also emerged according 
to Masika, (2016); this is the individual mindset of  
a farmer on who is responsible to get him out of  the 
threats of  a variable climate.  Furthermore, according 
to Masika, (2016) where communities look outwards 
to donors, nothing changes. He argues that where each 
farmer takes responsibility, by the change of  mindset, 
utilizes individual capacities and available resources, 
adaptation technologies are adopted without a struggle. 
The researcher in this study endeavoured to investigate 
whether farmers in Igambang’ombe have taken their 
role to deal with negative climate impacts. While Kenyan 
smallholder farmers have no alternative but to adapt 
to climate change and variability, the social barriers 
have been limiting. With the costs of  future adaptation 
not yet fully understood, and these social barriers need 
to be overcome first if  any  meaningful adoption of  
technologies is to occur among the smallholder farmers 
(Karanja, 2006)

Economic Factors
Farm Revenue
The vulnerability of  communities to climate change is 
generally influenced also by their economic status which 
is responsive to the location of  where the community lives 
(Mendelsohn et al., 2006). In their survey in 11 African 
countries, Mendelsohn et al.,(2006) further observed 
that net farm revenues from dry-land crop production 
dwindled with decreasing of  seasonal precipitation in 
rain-fed agriculture noting that food crops are also cash 
crops in ASALs. This assertion was also made by IPCC 
(2000) admitting that developing countries; especially 
those in the tropics are most vulnerable, and likely to be 
hit hard by climate change than the developed ones; and 
again, that the ASALs are more vulnerable to drought 
events, affected more by poverty and have a low adaptive 
capacity due to lack of  sustainable livelihoods. This is 
because this situation affects the macro-scale economic 
processes, on the price received for the crops grown, 
which is normally lower than they should, translating into 
low household incomes leading to cyclic poverty. 

Cost of  Production
To a great extent the cost of  production especially labour 
and input costs, farm, and family size determine whether 
adaptation strategies will be adopted.  These form a range 
of  the many considerations for the family’s resources 
allocation, among many qualifying uses. Family welfare 

is considered the priority for resource allocation while 
investment and other needs get the remainder and the 
bigger the family the more resources it absorbs. 

Income Diversification
According to Kelly and Adger (2000), four priority areas 
for action to improve this kind of  economic situation will 
need to be focused: poverty reduction through engaging 
in meaningful livelihoods; risk-spreading through income 
diversification; respecting common property management 
rights; and promoting collective security. Schneider et al., 
(2007) noted that the household income levels determined 
how farming communities adapt since the cost of  each 
technology for adaptation has to be factored in. In light of  
this observation, therefore, the study assessed the various 
sources of  income, both on- and off- farm sources for the 
smallholder farmers of  Igambang’ombe.
For this to happen IPCC, (2000), suggests that for a 
sustainable response to climate change and variability 
adaptation to be obtained, underlying causes of  social 
vulnerability, including the inequitable distribution of  
resources need to be addressed in the adaptation. The 
study endeavoured to find how available resources were 
allocated to the adaptation activities, while other needs 
were competing for the same. 
An observation by Adger et al., (2005) indicated that 
adaptations at one scale can create externalities, and at 
another reduce the adaptive capacity of  other actors, 
often in the light of  broad cost-benefit analysis. Schneider 
et al., (2007) while highlighting the challenges of  climate 
change, a global assessment, admits that economic 
trends as the GDP and national trade levels, influence 
the level of  climate change and variability adaptation by 
communities at the household level. The findings will be 
useful to any government agency or partner that would 
want to assist theses farmers in improving their adoption 
of  climate change and variability adaptation strategies. 
ICRISAT, (2007) suggests given that these communities 
urgently require to have their adaptive capacity enhanced 
sustainably, and should be a continuous process. For this 
to happen,  Kandji and Mackensen (2006)  proposes that 
policymakers themselves and other agencies that assist 
farmers, need to be adequately informed of  the location-
specific, farmer circumstances that influence their decision 
to adopt or not climate change and variability adaptation 
strategies available to them. This study, therefore, is 
proposed to provide part of  that information which can 
be used by farmers and agriculture stakeholders; firstly, to 
determine the economic status of  the smallholder farmers 
in question and secondly the adaptation technology costs 
that need be factored in the adaptation agenda for these 
farmers to adopt the technologies sustainably.

MATERIALS AND METHODS
The study adopted a descriptive research design which 
involved a household survey and interviews (Kothari, 
2012). Through systematic random sampling, 100 
farmer households were sampled from a total of  7,139 



Pa
ge

 
54

https://journals.e-palli.com/home/index.php/ajec

Am. J. Environ. Clim. 2(3) 51-60, 2023

households from Igambang’ombe Sub-County. Closed 
and open-ended questionnaires were used in this study; to 
collect both qualitative and quantitative data from the field.

RESULTS AND DISCUSSION
Socio-Economic Factors That Influence Adaptation 
to Climate Change and Variability
The results in this section highlight the various socio-
economic factors, of  the smallholder farmer that influence 
climate change and variability adaptation strategies. These 
include; Gender, Age, Education, farming experience, 
household size, income and re-investment, access to 
credit, access to subsidy and remittances, market linkages, 
land size and use, Access to extension.

Gender and Age of  Household Head
The findings in table 1 showed that (84%) of  households 
was male-headed and (16%) were female headed. On 
exploring the age distribution, it was observed that 72% 
of  the farmers were over 39 years and youthful farmers 
were only 28%. Of  the over 39 years 61% were males 
and 11% were females. Abdul-razak et al., (2017), noted in 
Ghana, that the adaptive capacity of  farming communities 
was influenced by social factors like age and education, 
with Muthoni & Wangui, (2015) on women and climate 
change, pointed out that the use of  climate information 
in Tanzania varied between men and women, with the 
women requiring and using the information more to 
alleviate food insecurity.

Table 1: The Farmer’s Age per Gender
Age in Years % Male % Female
<18 1 0
19-28 0 2
29-38 16 5
39-48 18 4
49-59 22 4
>60 21 6
 Total 78 21

Figure 1: Farmer Education per Gender

Farmers Education Level 
The results showed that 58% of  the farmers had primary 
level education and below of  which 42% were males. Of  
the remaining, 35% had secondary and 7% with tertiary 
education level with 30% and 6% males, respectively 
(Figure 1). This can potentially undermine climate change 
adaptation training. According to Nhemachena & Hassan, 
(2007), educated farmers were more likely to adopt 
technologies than uneducated ones. This is because they 
understand better the implications of  climate variability 
and change and also easily learned new skills. In the case 

of  Igambang’ombe, few farmers are likely to benefit or 
have the capacity to utilize climate change training due to 
the prevalent low level of  education.
The concern for the low level of  education has also 
been raised by the County Government of  Tharaka 
Nithi as indicated in the integrated development plan 
CIDP (TNC, 2018). The County plan report estimated 
the population with no formal education at 17%, with 
the majority found in Tharaka South and North Sub- 
Counties. Figure 7 displays the level of  education of  the 
farmers per gender.

Household Head Farming Experience
The results of  the study indicated that 54 % of  the 
household heads had a farming experience of  21-50 
years. Out of  the remaining 46%, 17% had a farming 
experience of  1-5 years (Figure 2). Generally, the more the 
experience, the higher the chances of  good performance, 
but in farming, smallholders tend to be antagonized by 
experience and are not able to free themselves from it; 
to embrace new ideas and technologies. Ainembabazi 
et al., (2015) admitted that indeed there is a positive 

relationship between the adoption of  agricultural 
technologies and the farming experience of  a farmer. 
Nevertheless,  Ainembabazi et al., (2015) further observed 
that the relationship is convex, in that, it influences 
adoption during the early stages of  adoption, up to and 
until the time when the farmers perceive the usefulness 
of  technology, after which it starts to decrease.  This 
happens until skill retraining on the technology is done. 
Hence extension training becomes an accelerator of  
technology adoption.



Pa
ge

 
55

https://journals.e-palli.com/home/index.php/ajec

Am. J. Environ. Clim. 2(3) 51-60, 2023

Farmer Response on Household Size
Table 2 shows the household sizes of  the smallholder 
farmers in the study area. The majority, (87%), had 3-10 
members. Out of  these, 46% had 3-5 members, and 41%, 
6-10 members. There is a possibility that a large family 
strains household resources and limits climate change 
adaptation activities. A study by Oyekale and Oladale, (2012) 

in Ghana observed that larger households adapt more; 
taking into account that the household members provide 
labour for adaptation activities. This agrees with Ali & 
Erenstein, (2017) who observed that there was a positive 
association between the adoption of  technologies regarding 
crop or variety choice, adjusting of  planting date with socio-
economic factors as household or family size, and wealth.

Table 2: The Household’s Family size
Household size Frequency Percent
1-2 Members 11 10
3-5 Members 48 46
6-10 Members 43 41
10 and above 3 3
Total 95 100

Table 3: Farmer Response on Annual Household Income and Re- investment to Adaptation Activities in KES
Annual income in 
KES

<or = 10000 10,001-30,000 30,001-40,000 40,000-50,000 > 50,000 Total

1,000-50,000 22 5 2 0 1 30
51,000-100,000 28 17 2 0 0 47
101,000-150,000 2 3 1 2 0 8
151,000-200,000 6 0 1 0 0 7
201,000-300,000 3 1 0 0 1 5
301,000-400,000 2 1 0 0 0 3
Total  63 27 6 2 2 100

Annual Household Income and Re-investment for 
Adaptation Activities
Table 3 shows the estimated annual income and the 
corresponding re- investment into adaptation activities of  
the respondents. It showed that 77% of  the households 
earned less than KES 101, 000 annually, and only 63% 
of  them re-invested up to KES 10,000 back to the farm 
for adaptation activities. Generally and in practical terms, 
if  there was to be an improvement in climate change 
adaptation among smallholder farmers, they need to 
invest more of  their income in climate change adaptation 
strategies and activities than is currently observed. 
Macharia (2009) observed that in Meru South, low farm 
income was a major constraint in the implementation of  

soil and water conservation among smallholder farmers 
growing coffee. Oyekale & Oladele, (2012) in their study 
Ghana showed that household incomes were related 
positively with adaptation; where households with high 
incomes were likely to re-invest to adaptation than 
those with low ones. Thus, the low farm income and re-
investment among smallholder farmers are likely to limit 
the adaptation strategies of  climate variability and change. 
Observation of  low income and re-investment among 
Igambang’ombe farmers was reported in the National 
Bureau of  Statistics Census of  2009 report on the 
poverty level index, as noted in Tharaka Nithi (CIDP), 
2013 where the poverty index for Igambang’ombe was 
estimated at 50% high.

Figure 2: Farmer Farming Experience

Income Diversification (Off-farm Livelihood)
The study sought to determine whether respondents 
engaged in any off-farm activity for income 
diversification. Figure 9 shows the result were as follows 
the off-farm sources of  income that they were engaged 

in 54% of  the respondents did not engage in any off-
farm livelihoods, 19% owned businesses, 6% engaged in 
the sale of  handicraft and 6% in firewood sale, 1% got 
pension remittances and 7% were employed hence drew 
a salary. The findings of  the study confirmed that farmers 



Pa
ge

 
56

https://journals.e-palli.com/home/index.php/ajec

Am. J. Environ. Clim. 2(3) 51-60, 2023

look for alternative livelihoods out of  the farm when 
their cropping business is threatened by climate change 
and variability. Agricultural productivity in the tropics 
is equated to the reduction of  crop yield, resulting in 
loss or reduction of  household incomes and livelihoods 
(Fischer et al., 2004 and Tubiello, 2014). This explains the 
reason why household have off-farm activities and low 
re-investment to adaptation activities.

Table 4: Farmer response on Source of  Credit 
Source of  credit Frequency Percent
Farmers Self  Help Group 48 50
Farmers SACCO 33 35
GoK 5 5
Commercial Banks 10 10

Access to Credit and Farm Subsidies
The study sought to determine whether farmers had 
access to credit facilities for any agricultural activities; 
72% said yes while 28% said no (Figure 10). The main 
sources of  credit were farmers groups (51%), farmers’ 
SACCO ((35%) commercial banks (10%), and National 
Government (5%) as shown in Table 4.

Figure 5: Access to Subsidies

Table 5: Farmer response on Source of  Subsidies 
Access subsidies Frequency Percent
Local NGO 20 21
Foreign NGO 10 11
GoK 29 30
County Government of  
Tharaka Nithi

10 10

Figure 3: Off-farm Livelihoods

Figure 4: Percentage Farmers Accessing Credit

Access to Farm Subsidies
The study sought to determine whether farmers had 
access to agricultural subsidies; 67% said yes while 33% 
said no (Figure 3). The study also established that farmers 
obtained the subsidies as follows; 30% said from GoK 
(National Government), 21% said from local NGO (One 
operating only in Tharaka Nithi), 11% said from other 

foreign NGOs while 10% said from COTN (County 
Government). The results of  the study agree with 
Kurukulasuriya & Rosenthal, (2003) who argued that for 
households to reduce their vulnerability and increase their 
adaptive capacity, they need to source for farming loans 
from commercial entities and input subsidies.
The subsidies obtained by farmers included fertilizers by 
57%, seeds by 52%, farm implements storage by 26% and 
farm storage structure by 12%  as shown in (Figure 4).



Pa
ge

 
57

https://journals.e-palli.com/home/index.php/ajec

Am. J. Environ. Clim. 2(3) 51-60, 2023

Access to Remittances
The study sought to determine whether farmers 
received remittances from friends or relatives to finance 
farm activities. The results show that farmers received 
remittances from relatives and friends, as follows; 58% 
said no and 42% yes Figure 5. Out of  the 42 % who 
received remittances 53% used the remitted cash for 
other purposes other than farm-oriented ones, 24% was 
to buy seed, 12% said for buying fertilizer, 7% said for 
ploughing and 4% said for pesticides purchase as shown 
in Table 6. Like subsidies, remittances are a social system 
where vulnerable communities living in the rural areas 
are supported by their relations earning their living in the 
urban areas, thereby enhancing them to be resilient to 
adverse climatic variation and change.

farming. In agreement with this observation, most 
households (61%) in Igambang’ombe have allocated 
land to crop and grazing, evidence that the people here 
are agro-pastoralists. The findings further corroborate 
with those of  Ali & Erenstein, (2017) who observed 
that land size and use is positively associated with crop-
related adaptation technologies adopted by farmers. Ali 
& Erenstein, (2017) observed that those with larger land 
size and committed to crop production are more likely 
to be keen on choosing crop types and varieties that 
are adapted to the climate. The guiding factor of  these 
choices being tolerance to prevailing weather conditions, 
diseases and pests, and easily try out new crops; to get 
returns to their land investment and use.

Figure 6: Type of  Subsidies Accessed by Farmers

Figure 7: Access to remittances

Table 6: Activities Supported by Remittances
Activity supported by 
remittances

Frequency Percent

Buying Seeds 16 24
Buying Fertilizer 12 12
Ploughing 7 7
Non –farm activities 50 53
Pesticides 4 4

Household Land Size and Use
The results of  the study showed that 97% of  the 
households owned land of  10 acres and below (Table 
16). On land use, 62% of  the farmers allocated their 
land to crop farming, while 61% had allocated some 
land to grazing and 42% allocated land to forest. This 
agrees with findings by Cecchi (2010) that, households 
in mixed arid and semi-arid environments of  Eastern 
Africa practiced pastoral, agro-pastoral, and mixed 

Table 7: Farmer Response on Household Land Size 
and Use
Land 
Use

Land size Frequency Percent

Total 
land 
owned

Less than 2.5 Acres 45 47
2.5-10 Acres 48 50
More than 10 Acres 2 3

Land 
under 
crops

Less than 2.5 Acres 59 61
2.5-10 Acres 34 36
More than 10 Acres 2 3

Grazing 
land

Less than 2.5 Acres 59 61
2.5-10 Acres 10 11
More than 10 Acres 1 1
none 25 27

Forest 
land 

Less than 2.5 Acres 40 42
2.5-10 Acres 7 9
More than 10 Acres 1 1
none 46 48

Access to Extension Services
Table 8 shows responses on the access to and frequency 
of  extension services, where 81% of  the respondents 
indicated that they had access to extension service (Figure 
6). Out of  the total, 69% indicated that they received it 
weekly or fortnightly. A review on the impacts of  climate 
change to agriculture acknowledged that the type, and 
the availability or access to both appropriate technology 
and extension service, are major drivers, and are factors 



Pa
ge

 
58

https://journals.e-palli.com/home/index.php/ajec

Am. J. Environ. Clim. 2(3) 51-60, 2023

of  agricultural productivity (Kurukulasuriya, 2003). This 
concurred with  Haregeweyn et al., (2015) that relevant 
stakeholder support services, to smallholder farmers is 
necessary for meaningful climate change and variability 
adaptation process in agriculture.
It was also observed from the results that, the training 
by extension agents, focused mainly on energy saving, 
conservation agriculture (CA), and soil and water 
conservation technologies. Kimaro et al., (2014) identified 
CA as a technology that would greatly increase agricultural 
productivity in fragile ecosystems as ASALs, and as earlier 
realized in this study, it is being practiced by a few farmers 

and may require scaling up for its impacts to be substantial 
in Igambang’ombe.
The study sought to determine the main crops that 
farmers grew for com¬mercial purposes to earn income. 
The results were as follows; the combination of  millet, 
green grams, and cowpeas was grown by 40% of  the 
farmers, green grams, pigeon peas, and maize is grown by 
23% of  farmers and cowpeas, green grams, and sorghum 
by 21% and maize, bananas and mangoes combination by 
16% of  the farmers as shown in Figure 15. Food crops in 
Igambang’ombe were the main cash crops and hence the 
main source of  household income.

Figure 8: Access to Extension Service

Table 6: Access and Frequency of  Extension Services by Farmers
Frequency of  accessing extension services Frequency Percent
Weekly 43 45
Fortnightly 23 24
Monthly 17 18
Occasional 12 13

Besides, the study sought to determine where the farmers 
sell their produce and the results were as follows; 92% 
internally within the county while 8% export outside 
the county. On who links the farmers to markets, 95% 
indicated that they were linked by government agencies 
and 5% by the private sector as shown in Table 9. 
Agricultural markets and terms of  trade are often affected 
by this climate variability; according to Kurukulasuriya 

& Rosenthal, (2003), how agricultural markets interact 
with climate variability should concern researchers and 
policymakers.  The results agreed with (Ketiem et al., 2007) 
that market availability and activity are a part of  climate 
change adaptation in that they become an assurance 
for steady household incomes thereby improving the 
resilience of  otherwise vulnerable communities living in 
the ASALs areas.

Figure 9: Percent Responses on Three Main Crops Linked to Markets

Table 9: Percent Responses on Linkage to Market
Frequency Percentage 

Market type Internal within county 87 92
Export outside county 8 8

Who links farmers to 
markets

Government Agency 90 95
Private Sector 5 5



Pa
ge

 
59

https://journals.e-palli.com/home/index.php/ajec

Am. J. Environ. Clim. 2(3) 51-60, 2023

Correlation between Adaptation Strategies and 
Socio-Economic Factors of  the Farmer
In the correlation analysis, the study focused on 
the relationship between the independent variables; 
gender, household size and income, farm size, age, 
education, farming experience, and the climate change 
adaptation strategies; dependent variables that include: 
land preparation methods and use, weather forecasting 
and information access, soil and water management 
as mulching and terracing), and crop enterprise 
diversification for drought and disease- pest resistance 
and/or tolerance. From the correlation analysis, the 
most important socio-economic factors significantly 
associated with the adoption of  climate change strategies 
included age, education, and farming of  the household 
head. Other factors with a significant association with the 
adoption of  climate change adaptation strategies were 
found to be access to extension and frequency, access 
to farm subsidy, and credit (Valizadeh et al., 2022). On 
the contrary, education however influenced significantly 
but negatively in weather forecasting probably due to the 
prevailing low literacy levels, with 58% being the primary 
level and below. 

CONCLUSION 
It was concluded that in Igambang’ombe, the socio-
economic factors affecting the adoption of  climate 
change adaptation strategies included the age of  the 
household head (HH), which was a key determinant of  
the adoption of  SWC strategies and on weather and 
climate forecasting access To 2-3 days forecasting. The 
latter being also affected by education level, and farming 
experience. On the other hand access to 2-3 months 
forecast was influenced by HH education, farming 
experience, and household farm size. This study revealed 
that farmer training through extension service played a 
key role in influencing the adoption of  climate change 
and variability adaptation strategies. The training of  
farmers in the Igambang’ombe Sub-County should 
be cognizant of  the existing household and farmer 
demographic characteristics and should be frequent and 
well-structured to meet specific identified farmer needs. 
The credit and subsidy services from stakeholders also 
contributed greatly in supporting the adoption and the 
adaptation of  climate change and variability and hence 
should be enhanced.

REFERENCES
Abdul-Razak, M., & Kruse, S. (2017). Climate Risk 

Management The Adaptive Capacity Of  Smallholder 
Farmers To Climate Change In The Northern Region 
Of  Ghana. Climate Risk Management, 17, 104–122. 
https://doi.org/10.1016/J.Crm.2017.06.001 

Adger, W. N., Huq, S., Brown, K., Conway, D., & Hulme, 
M. (2003). Adaptation to climate change in the 
developing world. Progress in Development Studies, 3, 179–
195. https://doi.org/10.1191/1464993403ps060oa

Adger, W. N., Huq, S., Brown, K., Conway, D., & Hulme, 

M. (2003). Adaptation to climate change in the 
developing world. Progress in Development Studies, 3, 179–
195. https://doi.org/10.1191/1464993403ps060oa

Ainembabazi, J. H., & Mugisha, J. (2014). The role of  
farming experience on the adoption of  agricultural 
technologies: Evidence from smallholder farmers in 
Uganda. Journal of  Development Studies, 50(5), 666-679. 
https://doi.org/10.1080/00220388.2013.874556

Ali, A., & Erenstein, O. (2017). Assessing Farmer Use 
Of  Climate Change Adaptation Practices And 
Impacts On Food Security And Poverty In Pakistan. 
Climate Risk Management, 16, 183–194. https://doi.
org/10.1016/J.Crm.2016.12.001

Camberlin, P., Moron, V., Okoola, R., Philippon, N., 
& Gitau, W. (2009). Components of  rainy seasons’ 
variability in Equatorial East Africa: Onset, cessation, 
rainfall frequency and intensity. Theoretical and 
Applied Climatology, 98(3–4), 237–249. https://doi.
org/10.1007/s00704-009-0113-1

Erenstein, O. (2003). Smallholder Conservation Farming 
In The Tropics And Sub-Tropics: A Guide To The 
Development And Dissemination Of  Mulching 
With Crop Residues And Cover Crops. Agriculture, 
Ecosystems And Environment, 100, 17–37. https://doi.
org/10.1016/S0167-8809(03)00150-6

Fischer, E., & Qaim, M. (2012). Gender, Agricultural 
Commercialization, And Collective Action In Kenya. 
Food Security, 4(3), 441–453. https://doi.org/10.1007/
S12571-012-0199-7 

GOK. (2010). National Climate Change Response Strategy., 1 
§ (2010). 

Government, of  Tharaka. Nithi. (2018). C. I. D. P. C. 
(2018). Development Plan Cidp 2018-2022 3. 

Hawkins, P., Geza, W., Mabhaudhi, T., Sutherland, C., 
Queenan, K., Dangour, A., et al. (2022). Dietary and 
agricultural adaptations to drought among smallholder 
farmers in South Africa: a qualitative study. Weather 
Clim. Extrem. 35, 100413. https://doi.org/10.1016/j.
wace.2022.100413

IPCC: (2001). Climate Change 2001: Impacts, 
Adaptation, and Vulnerability, Contribution ofWork- 
ing Group II to the Third Assessment Report 
ofthe Intergovernmental Panel on Climate Change, 
Cambridge University Press, 1032.

IPCC, (2014). Climate change 2014 synthesis report 
Longer report. (November).

Kandji, S. T., Verchot, L., & Mackensen, J. (2006). Climate 
change and variability in the Sahel region : impacts 
and adaptation strategies in the agricultural sector. In 
Environment. 

Kerandi, N. M., & Omotosho, (2008). Seasonal Rainfall 
Prediction in Kenya Using Empiric al Methods. 
Journal of  Kenya Meteorogical Society, 2(2)(11), 114–124.

Kimaro, D. N., Msita, H. B., Kabanza, A., Dondeyne, 
S., Deckers, J. A., & Poesen, J. (2014). Adaptation 
By Smallholders In Eastern Africa To Climate 
Change Through Conservation Agriculture. In Soil 
Management Of  Smallholder Agriculture. https://



Pa
ge

 
60

https://journals.e-palli.com/home/index.php/ajec

Am. J. Environ. Clim. 2(3) 51-60, 2023

doi.org/10.1201/B17747
Kothari, C. R. (2012). Research Methodology: An 

Introduction. In Research Methodology: Methods And 
Techniques (P. 418). Retrieved From http://www.
Newagepublishers.Com/Samplechapter/000896.Pdf

Kumar, A., Dudhia, J., Rotunno, R., & Mohanty, U. C. 
(2008). Analysis Of  The 26 July 2005 Heavy Rain 
Event Over Mumbai , India Using The Weather Research 
And Forecasting ( WRF ). 1910(July 2005), 1897–1910. 
https://doi.org/10.1002/Qj

Kurukulasuriya, P., R. (2013). Joint Proceedings Of  The 27th 
Soil Science Society Of  East Africa And The 6th African Soil 
Science Society Theme : “ Transforming Rural Livelihoods In 
Africa : How Can Land And Water Management Contribute 
To. (October).

Kurukulasuriya, P., & Rosenthal, S. (2003). Climate 
Change And Agriculture: A Review of  Impacts And 
Adaptations Published Jointly With The Agruiculture 
And Rural Development Department. Retrieved From 
http://www.C-Ciarn.Uoguelph.Ca/Updates_
Archived/World_Bank_Paper.Pdf

Muthoni, J. W., & Wangui, E. E. (2013). Women and 
climate change: Strategies for adaptive capacity in 
Mwanga District, Tanzania. African Geographical Review, 
32(1), 59-71. https://doi.org/10.1080/19376812.201
2.756766

Nduti, L. W. (2014). .Viability of  Indegenous Knwoledge 
based Stratefies in Mitigation of  Drought in Gachoka 
Divion Embu county- Kenya

Ngugi, K., Kimani, W., Kiambi, D., & Mutitu, E. W. 
(2013). Improving Drought Tolerance In Sorghum 
Bicolor L. Moench: Marker-Assisted Transfer Of  
The Stay-Green Quantitative Trait Loci (QTL) From 
A Characterized Donor Source Into A Local Farmer 
Variety. International Journal Of  Scientific Research In 

Knowledge, 1(6), 154–162. https://doi.org/10.12983/
Ijsrk-2013-P154-162

Nhemachena, C., & Hassan, R. M. (2007). Micro-Level 
Analysis of  Farmers’ Adaptation To Climate Change 
In Southern Africa. In Africa. Retrieved From http://
www.Ifpri.org/Publication/Micro-Level-Analysis-
Farmers-Adaptation-Climate-Change-Southern-Africa

Haregeweyn, N., Tsunekawa, A., Nyssen, J., Poesen, J., 
Tsubo, M., Tsegaye Meshesha, D., ... & Tegegne, F. 
(2015). Soil erosion and conservation in Ethiopia: a 
review. Progress in Physical Geography, 39(6), 750-774. 
https://doi.org/10.1177/0309133315598725

Oyekale, A. S., & Oladele, O. I. (2012.). ARPN Journal 
Of  Science And Technology:Determinants of  
Climate Change Adaptation Among Cocoa Farmers 
In Southwest Nigeria. ICESR 2012 ARPN Journal of  
Science and Technology, 2. Retrieved from http://www.
ejournalofscience.org 

Sharafi, L., Zarafshani, K., Keshavarz, M., Azadi, H., 
and Van Passel, S. (2021). Farmers’ decision to 
use drought early warning system in developing 
countries. Sci. Total Environ. 758, 142761. https://doi.
org/10.1016/j.scitotenv.2020.142761

United Nations. (2007). Rural households’ livelihood 
and well-being: Statistics on rural development and 
agriculture household income. In Statistics on rural 
development and agriculture household income 
(Vol. 1993).

Valizadeh, N., Karimi, V., Fooladi Heleileh, B., Hayati, 
D., and Bijani, M. (2022). Formulating of  small-scale 
farmers’ perception towards climate change in arid 
areas: facilitating social interventions for agricultural 
sustainability. Water Environ. J. 36, 199–213. https://
doi.org/10.1111/wej.12741


