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ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue X October 2025
Assessment of Risk Attitudes and Mitigation Strategies Among Arable
Crop Farmers in Semi-Arid Makueni County, Kenya.
Katungu, S.W.
1
and
Elega, J.O
2
1
Department of Agricultural and Resource Economics, Kangwon National University, Chuncheon-si,
24341 Korea.
2
Department of Agricultural Economics and Farm Management, Federal University of Technology,
Minna, Niger State, Nigeria.
DOI:
https://dx.doi.org/10.47772/IJRISS.2025.910000766
Received: 28 October 2025; Accepted: 03 November 2025; Published: 24 November 2025
ABSTRACT
This study assessed risk attitudes and mitigation strategies of arable crop farmers in Semi-Arid Makueni
County, Kenya. A multi-stage sampling technique was adopted in the selection of 163 respondents. Data were
collected using research questionnaire and analyzed using frequency distribution and percentages, Ordinary
Least Square (OLS) regression, safety first model and a 5-point likert scale. The results revealed that the major
types of risk faced by arable crop farmers are production (93.3%) and climatic (89.6%), followed by market
(74.2%), financial (66.9%) and institutional (50.9%) risks; about 65.6% of farmers were risk-averse, 23.9%
risk-neutral and 10.4% risk-seeking. The OLS model indicated that education, farming experience, farm size
and access to credit significantly influenced farmers’ economic performance while risk attitude coefficient had
a negative effect. The result also showed that the farmers adopted low-cost, experience-based strategies (crop
diversification, intercropping, drought-tolerant varieties and soil and water conservation), however, uptake of
formal instruments like crop insurance (11%) remained low. The study concludes that smallholder farmers in
semi-arid Kenya are highly risk-averse and rely on adaptive strategies grounded in social capital and
indigenous knowledge and recommended strengthening agricultural extension, access to affordable credit and
insurance literacy.
Keywords: Arable crop, Kenya, Makueni, Risk attitude, Risk management, Safety-first model.
INTRODUCTION
Background to the Study
Agriculture remains the backbone of Kenya’s rural economy, employing over 70 percent of the population and
contributing significantly to food security and household income [6]. Within this sector, arable crop farming
plays a central role, particularly in semi-arid regions such as Makueni County, where smallholder farmers
dominate production. However, agricultural activities in these areas are inherently risky due to frequent
droughts, erratic rainfall, pest outbreaks and volatile market prices [10]. These risks have profound effects on
farm productivity, income stability and food availability, especially among smallholders who have limited
resources to absorb shocks.
The risk attitude of farmers; risk-averse, risk-neutral or risk-seeking is strongly influenced by their production
decisions, technology adoption and choice of management strategies [5]. Understanding these attitudes is
crucial for designing interventions that promote resilience and sustainable agricultural practices. In Kenya,
previous studies have examined risk perception and management in various contexts, but there is limited
empirical evidence focusing on how arable crop farmers in Makueni County perceive and respond to risk
within their unique agroecological and socio-economic setting [2].
Problem Statement
Despite the growing recognition of agricultural risk and vulnerability in Kenya’s semi-arid counties, many
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arable crop farmers continue to experience substantial yield fluctuations and income losses. Unpredictable
weather patterns, declining soil fertility and pest infestations have increased production uncertainty, while
limited access to credit, extension services and insurance restrict farmers’ ability to manage these risks
effectively [11]. Although farmers employ a range of coping mechanisms such as diversification, savings
groups or soil conservation, the effectiveness and determinants of these strategies remain poorly understood
[6]. Without this understanding, policy interventions may fail to target the real behavioral and structural
constraints that shape farmers’ responses to agricultural risk. Therefore, closing this gap in the Makueni
context is essential for interventions that are both technically sound and socially acceptable. Based on the
aforementioned, this study seeks to fill the research gap by providing answers to the following research
questions: (i). what are the socio-economic characteristics of arable crop farmers in Semi-Arid Makueni
County? (ii). what are the major types of risks faced by arable crop farmers in Semi-Arid Makueni County?
(iii). What is the risk attitude of arable crop farmers in the County? and (iv). what are the risk management
strategies adopted by arable crop farmers in the County?
Aim and Objectives of the Study
The aim of the study is to analyze risk attitudes and management strategies among arable crop farmers in
Semi-Arid Makueni County, Kenya. The specific objectives are to: (i). describe the socioeconomic
characteristics of arable crop farmers, (ii). identify the major types of risks faced by arable crop farmers, (iii).
determine the risk attitude of arable crop farmers and (iv). identify the risk mitigation strategies adopted by
arable crop farmers in the study area.
Justification of the Study
Assessing farmers’ risk attitudes and management strategies provides valuable insight into their decision-
making processes and the constraints they face in mitigating farm-level uncertainties [10]. This study therefore
fills important empirical and policy gaps; by quantitatively analyzing risk attitudes among arable crop farmers
in Makueni, it adds to the growing but still limited body of literature that integrates attitudes and perceptions
with socioeconomic and farm-level factors in East Africa. It also helps to identify which risk management
strategies are most likely to be adopted, providing evidence for more targeted agricultural policies and
extension efforts in the county. The results can also inform scaling of innovations (organic fertilizers,
conservation agriculture or index insurance) by revealing which strategies align with existing attitudes and
constraints in similar environments.
METHODOLOGY
The Study Area
The study was conducted in Semi-Arid Makueni County, Kenya. Makueni County is located in the south-
eastern region of Kenya, between latitudes 1°35′ and 3°00′ South and longitudes 37°10′ and 38°30′ East. It
borders Machakos County to the north, Kajiado County to the west, Kitui County to the east and Taita Taveta
County to the south. The county has an estimated population of about 987,653 persons, covers an area of
approximately 8,008 km² and is administratively divided into six sub-counties ([8], [9]).
Makueni lies within the Arid and Semi-Arid Lands (ASALs), with mean annual rainfall ranging from 250mm
in the lowlands to 1,200mm in the highlands, while temperatures vary between 20°C and 30°C [9]. The main
economic activity is smallholder rainfed farming which is complemented by livestock production; however,
productivity is constrained by climate variability, recurrent droughts, soil degradation, pest infestations and
limited access to extension and financial services ([10], [6]). Therefore, the county’s agro-ecological diversity
and exposure to climatic shocks make it an ideal setting for analyzing farmers’ risk attitudes and mitigation
strategies for arable crop production.
Sampling Techniques
A multistage sampling technique was adopted in the selection of respondents for the study. Stage one involved
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the stratification of Semi-Arid Makueni county into three agro-ecological zones; the lower, middle and upper
zones. Stage two involved random selection of two sub-counties from each zone to make a total of six sub-
counties. In the third stage, two wards were randomly selected from the sub-counties selected to make 12
wards. The final stage also involved random selection of two villages from each of the wards to make up 24
villages in total where arable crop farmer lists were prepared for sampling. The final sample allocation
produced 22 villages with seven respondents each and one village with nine respondents, resulting in 163
interviewed farmers. To allow for unbiased population estimates, sampling weights were computed as the
inverse of the overall inclusion probability and a design effect of 1.5 was assumed for variance estimation
given the clustered design.
Methods of data collection
Primary data was used for this study and a well-structured research questionnaire was used to elicit
information from the arable crop farmers. The questionnaire was pre-tested to ensure its validity and reliability
for the purpose of this research.
Method of data analysis
Descriptive and econometric tools were used to analyze the data collected in line with the stated objectives.
Objectives i and ii were achieved using frequency distribution and percentages, objective iii was achieved with
the use of Ordinary Least Square (OLS) Regression and Safety-First models while a 5-point likert scale rating
technique was used to achieve objective iv.
Model Specification
OLS Regression Model: To analyze the risk attitude of arable crop farmers, the OLS model was used to
identify the farm specific factors affecting the economic performance (proxied as income) of arable crop
farmers under risk. The explicit form of the model is specified in equation 1.







(1)
Where;
Y
i
= Annual Income (KES),
X
1i
= Age (Years),
X
2i
= Farm Size (Hectares),
X
3i
= Farming Experience (Years),
X
4i
= Years of Schooling (Years),
X
5i
= Risk Attitude (Risk averse = 1, Otherwise = 0),
X
6i
= Access to Credit (Amount Received),
X
7i
= Extension Contact (Yes = 1, No = 0), and
= Error Term.
Safety First Model: The safety-first model was then used to classify the arable crop farmers into risk categories
based on the risk attitude coefficient and complement the OLS analysis by linking income variability and
minimum survival thresholds to behavioral decision patterns [7]. The safety-first model criterion is that
farmers seek to minimize the probability that their income reduces below an acceptable minimum level (Y
m
)
expressed as: Minimize P (Yi ≤ Y
m
).
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The standardized form of the model is presented in equation 2:

(2)
Where;
= Safety-first Index for farmer i,
= Mean income of farmer i,
= Minimum acceptable income level, and
= Standard deviation of farmer’s income.
Farmers were classified into different risk categories based on the safety-first index values as:
Z
i
< 0 = High Risk Averse
0 ≤ Z
i
< 1 = Moderate Risk Averse
1 ≤ Z
i
< 2 = Risk Neutral
Z
i
≥ 2 = Risk Seeking
RESULTS AND DISCUSSION
Socio-economic Characteristics of Arable Crop Farmers in Makueni County The socioeconomic characteristic
of arable crop farmers considered in this study includes age, gender, marital status, educational level,
household size, years of experience, farm income, farm size and primary occupation as presented in Table 1.
The result revealed that about 33% of the arable crop farmers were in their middle age (41 – 50), with a mean
age of 46years. This implies that the farmers in Makueni were within their active age of farming activities
which is consistent with Kenya’s smallholder farmer’s profile [6]. Also, majority of the farmers were male
(56%) who were married (81%) with moderate levels of education; secondary (38.7%) and tertiary (16.6%) as
indicated by the respondents suggesting that the farmers were relatively educated and as such understand the
concept of risk and management strategies; which corroborate [2]. The mean farming experience was 16years
which shows that the farmers had ample experience and practical knowledge of local farming risks and
uncertainties. Majority of the farmers had small to medium farm sizes between 1 – 2 hectares with a mean
farm size of 1.8 hectares which is consistent with land fragmentation in Makueni County ([8], [9]). Majority
(48.5%) had household sizes of 4 – 6 persons with mean household size of 5 persons, which is typical of a
rural demographic setting while the mean income was KES162,000 which is an indication of low profitability
level of a typical Kenyan arable crop farmer.
Table 1 Socio-Economic Characteristics of Arable Crop Farmers in Makueni County
Variables
Frequency
Percentage
Mean
Age (years)
≤ 31
21
12.90
31 – 40
35
21.50
41 – 50
54
33.10
51 – 60
38
23.30
> 60
15
9.20
46
Gender
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Male
92
56.40
Female
71
43.60
Marital Status
Single
12
7.40
Married
132
81.00
Divorced
9
5.60
Widowed
10
6.00
Farming Experience (years)
1 – 10
69
42.30
11 – 20
58
35.60
> 20
36
22.10
16
Educational Level
No Formal
14
8.60
Primary
59
36.20
Secondary
63
38.70
Tertiary
27
16.20
Household Size
1 – 3
28
17.20
4 – 6
79
48.50
> 6
56
34.40
5
Farm Income (KES)
< 50,000
28
17.20
50,001 – 100,000
42
25.80
100,001 – 200,000
57
35.00
162,000
> 200,000
36
22.10
Farm Size (Ha)
< 1
39
23.90
1 – 2
71
43.60
1.8
> 2
53
32.50
Primary Occupation
Farming
111
68.10
Artisan/Businesses
37
22.70
Civil Service
15
9.20
Source: Data Analysis, (2025).
Major Types of Risk Faced by Arable Crop Farmers in Makueni County
The distribution of the major types of risks faced by arable crop farmers is presented in Table 2. The result
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reveled that production and climatic risks are the most predominant type among arable crop farmers in
Makueni County as indicated by 93.3% and 89.6% respectively suggesting the level of risk exposure of the
arable crop farmers to production and climatic factors that affects their yield and income directly. [10] asserted
in their study that production and climatic risks were the key drivers of yield loss among smallholder farmers
in Kenya. The result further revealed that market (74.2%), financial (66.9%) and institutional (50.9%) risks
were the other types of risk faced by arable crop farmers in the study area. These factors underscore the limited
market linkages in rural areas, restricted access to credit as well as gaps in agricultural extension services in the
county. Therefore, farmers in Makueni county faces multi-dimensional risk environment dominated by
production and climatic challenges but exacerbated by weak institutional support and financial constraints.
This finding reflects the similar pattern reported by [11] in Machakos and Kitui Counties.
Table 2 Major Types of Risk Faced by Arable Crop Farmers in Makueni County
Type of Risk Faced
Percentage (%)
Production
93.3
Market
74.2
Financial
66.9
Institutional
50.9
Climatic
89.6
Source: Data Analysis (2025)
(*) indicates Multiple Responses
Risk Attitude of Arable Crop Farmers in Makueni County
The OLS regression result for the risk attitudes of arable crop farmers in the study area is presented in Table 3.
The result shows a coefficient of determination (R
2
) of 0.62 which implies that about 62% of the economic
performance of the farmers was explained by the variables in the model while 38% was accounted for by the
error term and other unidentified variables. The result further revealed that education (P < 0.01), farming
experience (P < 0.05), farm size (P < 0.01) and access to credit (P < 0.05) were statistically significant and
increased economic performance of arable crop farmers in the county. This implies that these factors helped
the farmers to properly mitigate risk through informed decision making. Consistent with the findings of [1] and
[10], the risk attitude (P < 0.01) was negatively significant, implying that risk averse farmers had lower
economic performance than risk neutral and risk seeking farmers in the study area usually because they tend to
avoid uncertain technologies with high-return thereby leading to reduced profitability.
Table 3 Ols Regression Result for the Safety-First Model
Variables
Coefficient
Standard Error
t – value
Age (X
1
)
620.50
248.70
2.49**
Farm Size (X
2
)
28902.60
5936.70
4.87***
Farming Experience (X
3
)
1015.20
415.20
2.45**
Education (X
4
)
3458.40
1212.30
2.85***
Risk Attitude (X
5
)
-22134.80
8107.80
2.73***
Access to Credit (X
6
)
18745.90
7993.30
2.34**
Extension Contact (X
7
)
12856.40
6774.60
1.90
Constant
45213.00
18774.00
2.41**
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R squared
Adjusted R squared
0.62
0.59
F-statistic
19.84***
Source: Data Analysis, (2025).
*** and ** implies significant at 1% and 5% respectively.
The risk attitudes of arable crop farmers were then made the basis for categorizing the farmers into groups of
high risk averse, moderate risk averse, risk neutral and risk seeking farmers as presented in Table 4. This
categorization formed a necessary condition for improving the typology of arable crop farmers, which was
hypothesized to be influenced by socio-economic, demographic and other extrinsic risk factor. The findings
revealed that about 65.6% of the farmers were risk averse, 23.9% were risk neutral and 10.4% were risk
seeking thereby corroborating with empirical findings of Kenyan studies ([12], [10]) that smallholder farmers
are highly risk averse. This implies that arable crop farmers in the county were highly averse to risk suggesting
that farmers prioritize income stability over profit maximization. Figure 1 further shows the proportion of
arable crop farmers in the county based on their risk behavior as determined by the safety-first model with
majority being risk averse, thereby showing a cautious behavior under risk and uncertainties which is typical of
farmers in the semi-arid regions.
Table 4 Result of The Risk Attitude of Arable Crop Farmers in Makueni County
Categories of Risk Attitude
Frequency
Percentage
High Risk Averse
46
28.20
Moderate Risk Averse
61
37.40
Risk Neutral
39
23.90
Risk Seeking
17
10.40
Source: Data Analysis, (2025).
Fig. 1: Proportion of Arable Crop Farmers by Risk Attitudes.
High Risk Averse,
28.2
Moderate Risk
Averse, 37.2
Risk Neutral,
23.9
Risk Seeking,
10.4
PROPORTION OF ARABLE CROP FARMERS BY RISK
ATTITUDE
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Risk Mitigation Strategies Adopted by Arable Crop Farmers in Makueni County
The risk mitigation strategies adopted by arable crop farmers was analyzed using a 5-point likert scale and the
result is presented in table 5. The result revealed a clear preference for production and knowledge-based
strategies of risk mitigation among the arable crop farmers in Makueni County. The highest levels of strong
agreement were recorded by crop diversification (
= 4.29) and intercropping (
= 4.19) suggesting that arable
crop farmers relied more on crop diversification as a low-cost and effective strategy against risk as supported
by [11]. Also, the high levels of agreement of the use of drought-tolerant varieties (
= 4.11) and soil and water
conservation (
= 3.93) measures falls in line with the adaptation behaviors of smallholder farmers in the semi-
arid region of Kenya as reported by [2]. Membership of savings group (
= 3.52) and diversification of off-
farm income (
= 3.63) were the major institutional and financial measures adopted by the arable farmers. This
shows further buttress the role of social capital and livelihood diversification in risk mitigation [12].
Furthermore, at the least of agreement were the use of market/weather information services (
= 3.06) and crop
insurance (
= 2.02) suggesting a low uptake of insurance which is consistent with empirical reports of low
crop insurance uptake in sub-Saharan Africa due to high cost and trust issues as asserted by [4] and [3].
Therefore, the result presented in Table 5 suggests that arable crop farmers in Makueni County prefer risk
measures with immediate, tangible and low entry costs.
Table 5 Risk Mitigation Strategies Adopted by Farmers in Makueni County.
Mitigation Strategy
SA
%
A
%
N
%
D
%
SD
%
WS
Crop Diversification
92
56.4
40
24.5
20
12.3
8
4.9
3
1.8
4.29
Intercropping /Crop Rotation
80
49.1
50
30.7
20
12.3
10
6.1
3
1.8
4.19
Drought-Tolerant / Early Varieties
78
47.9
45
27.6
25
15.3
10
6.1
5
3.1
4.11
Soil & Water Conservation
70
42.9
40
24.5
30
18.4
18
11.0
5
3.1
3.93
Membership in Savings Groups
50
30.7
40
24.5
30
18.4
30
18.4
13
8.0
3.52
Off-farm Income Diversification
48
29.4
52
31.9
30
18.4
20
12.3
13
8.0
3.63
Use of Organic Manure / Compost
45
27.6
40
24.5
40
24.5
25
15.3
13
8.0
3.48
Collective Marketing / Cooperatives
35
21.5
45
27.6
40
24.5
30
18.4
13
8.0
3.36
Small-scale Irrigation / Water harvesting
25
15.3
40
24.5
30
18.4
40
24.5
28
17.2
2.96
Storing Produce for Off-Season Sales
30
18.4
35
21.5
40
24.5
38
23.3
20
12.3
3.10
Use of Weather/Market Information
20
12.3
40
24.5
50
30.7
30
18.4
23
14.1
3.02
Crop/Weather-Index Insurance
5
3.1
14
8.6
30
18.4
50
30.7
64
39.3
2.06
Source: Data Analysis (2025).
Note: SA – Strongly Agree, A – Agree, N – Neutral, D – Disagree and SD – Strongly Disagree and WS –
Weighted Score.
CONCLUSION AND RECOMMENDATIONS
The study concludes that arable crop farmers in Semi-Arid Makueni County operate within a highly uncertain
production environment dominated by climatic and production risks. Majority of the farmers are risk-averse,
traditional practices that ensure income stability over profit maximization. Education, experience and access to
financial and extension services significantly improve their ability to cope with risk while lack of institutional
support and credit constraints limit their adaptive capacity. Various informal and agronomic strategies are
widely adopted but the low utilization of insurance and market information tools
shows the persistent institutional and financial challenges.
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Based on these findings, it was therefore recommended that stakeholders should develop affordable farmer-
friendly credit and micro-insurance schemes for arable crop farmers, scale-up farmers training on risk-reducing
technology, strengthen farmer group and cooperative to improve access to input, and foster on-and off-farm
diversification initiatives through agribusiness development.
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