Risk Analysis of Vegetables in Rwanda - A Case of Carrots and Cabbages

Risk Analysis of Vegetables Production in Rwanda - A Case of Carrots and Cabbages Produced in Rubavu District
AJAERD
Risk Analysis of Vegetables Production in Rwanda - A Case
of Carrots and Cabbages Produced in Rubavu District
KUBWIMANA Jean Jacques
Department of Agribusiness, College of Agriculture and Veterinary medicine (CAVM), University of Rwanda, Rwanda
E-mail: kubwimanajeanjacques@gmail.com Tel: +250788836726/+250783950480
ORCID: https://orcid.org/0000-0002-5718-1661
In Rwanda, smallholders’ farmers and agricultural cooperatives produce vegetable crops in
various agro-ecological zones across the country through the commercial initiative. Vegetable
productions take place in a highly biophysical and economic environment, which poses various
types of risks. As follows, this study identifies, measures and analyzes the key sources of risks
in vegetable production, based on vegetable farmers’ perceptions who typically produced the
cabbages and carrots in volcanic regions whereas vegetables produced basing on rain-fed only,
without any irrigation system adopted. A simple random sampling technique was used in the
selection of 208 smallholder vegetable farmers in Rubavu District. Primary data collected through
structured questionnaires and secondary data were preferentially used. Data collected were
analyzed using frequency distribution, arithmetic mean, and multiple regression analysis. The
independent t-test and chi-square test used to specify the majors’ sources of risks among the
cabbages and carrots farmers by using a five-point Likert-scale. The mean scores results derived
based on Likert-scale indicated that crop seasonality, natural disaster, pests and diseases, lack
of farmers linkage and price fluctuation were instantly identified to be the most important sources
of risk. This study recommends the training for vegetable farmers on risk management
mechanisms, price supports mechanisms, providing the required infrastructure and the use of
vegetable varieties that tolerates for natural disasters and pests/disease resistance.
Keywords: Agriculture, Risk, Uncertainty, Risk Analysis, Vegetable Production Risk, and Risk Management.
INTRODUCTION
Rwanda is one of the fastest-growing economies in Sub-
Saharan Africa. Although still poor and mostly agricultural
(90% of the population is engaged in agriculture.) Rwanda
has made significant progress in recent years. According
to the World Bank figures, the GDP has rebounded with an
average annual growth rate of seven to eight percent since
2003 with inflation reduced to single digits. Despite these
achievements, a significant share of the population still
lives below the official poverty line: 45% in 2016 compared
to 57% in 2006 (Youri et al., 2016).
The agricultural sector stands as a key contributor to
Rwanda’s national economy. A significant share of this
contribution is coming from the horticultural sector. The
Government of Rwanda has an intense focus on
increasing horticultural production and is simultaneously
supporting the development of the export market. Rwanda
enjoys a mild tropical highland climate, suitable for
horticulture production, with lower temperatures than are
typical for equatorial countries because of its higher
elevation (Youri et al., 2016). Agricultural techniques in
Rwanda are still based on rain-fed production systems,
with less than 6% of the cultivated land currently irrigated,
and agricultural production is still largely for subsistence
(IFAD, 2014).
Many Sub-Saharan African countries experience recurring
negative agricultural growth because of various shocks,
however, Rwanda had only one year (in 2003) of negative
growth in the 20 years since the genocide of Tutsi in 1994.
In 2003, agricultural value-added growth was negative
because of the drought that strikes the country. One yearly
assumption, agricultural production misfortunes for
sustenance yields and exports where the period of 1995-
2012, crops arrived at the average of US$65 million, at
least an average of 2.2 % national aggregate yearly
Research Article
Vol. 6(2), pp. 761-772, April, 2020. © www.premierpublishers.org, ISSN: 2167-0477
Journal of Agricultural Economics and Rural Development

Kubwimana 762
agricultural income value (PSTAIIIa, 2013). Risk
characterizes life for many of the world’s poorest family
households. They are more likely to be located in
environments where livelihoods are highly susceptible to
weather and variability of the prices and where health risks
are pervasive. When these risks are uninsured, they not
only reduce the current welfare of rural family households
but also threaten future income growth and thus
perpetuate poverty. Reducing the risks faced by poor
households’ family, and enabling poor households’ family
to better with tremendous events when they do occur, is
essential to improve their welfare in short-run and their
opportunities for income growth in the long run (Asa et al.,
2015).
In Rwanda vegetable products are cultivated in different
agro-ecological zones through business by vegetable
farmers and also for vast scale producers both as sources
from farm income, for markets exports and additionally
food. Anyway, the sort is restricted to few yields, and
vegetable production is concentrated in some sash region
(Dawit, A., and Abera, 2004). The riskiness of crop
production may be attributed to several factors that are
beyond the control of vegetable farmers. Biological
processes of plant growth and climatic conditions inherent
in agricultural production cause random production shocks
such as harvest failure as a result of drought, frost, floods,
and other adverse climatic events policy shocks (Dencon,
2002). The sources of risk and level of its severity can vary
according to farming systems, geographic location,
weather conditions, supporting government policies and
farm types. The risks remain to be an overriding concern
in developing countries where farmers elicit imperfect
information to forecast things such as farm input prices,
product prices, and weather conditions, that might impact
the farms in the future (Hazell, P.B.R. and Norton, 1986;
Nyikal, R.A., and Kosura, 2005; Pannell et al., 2000).
The EICV results show that 74.3% of households have
less than 0.3 ha in Rubavu District. This size of cultivated
the land is little compared to land size used for agricultural
production at the national level. The households with the
land of over 3 ha are estimated to 2 per 1 000 against 19
per 1 000 in the country. This is the main factor, which can
be analyzed to explain the poverty in Rubavu District
(MININFRA, 2016). National yields are comparable for
both yet the size planted for cabbage is more noteworthy,
mirroring its lower per hectare yields. Nearby eggplant
positioned as third as far as used production size and
weight. Carrots and onions are also of importance (NAEB,
2014). The spatial distribution of the production of
cabbages reflects their need for relatively cool growing
conditions. Somewhere in the range of 87% of the
country's carrots is obtained from the western region,
Rubavu district accounting for over half of national
production (NAEB, 2014). Therefore, it is necessary to
provide required information on the risks vegetables
farmers in Rwanda perceive as being more important and
the strategies farmers rely on to manage these risks.
There are different types of risks and uncertainties
involved in different vegetable crops, as has been proven
by several studies. According to Jabir A. and Sanjeev K.
(2008), the perceived priorities of farmers about major
sources of risks in production of fruits and vegetables have
been reported the expensive inputs and lack technical
knowledge on production, processing and quality control
as main sources of risks while risks due to pests and
diseases in the fruits and vegetables have also emerged
as a critical concern in farmers’ responses(Jabir, A. and
Sanjeev, 2008). Another study undertaken by Anju Duhan
(2018) found pests and disease, losses due to animals,
market risks, and price fluctuation as main risk factors in
vegetable production (Anju, 2018). According to the study
conducted by Ahsan & Roth (2010) proven that depending
on the climatic and other factors affecting production
agriculture. The vegetable farmers in Prairies were
frequently experienced with drought, and some were more
prone to excess moisture during the seedlings and harvest
(Ahsan, D.A., and Roth, 2010). Delayed harvest due to
excess moisture can significantly affect the quality and
price will be less severe on the general (Anthon et al.,
2011).
This study seeks to provide updated information on
perceived sources of risk, specifically related to vegetable
production in Rwanda, volcanic region whereas the
farmers produce the vegetables without any kind of
irrigation system. The research studies have revealed
farmers respond differently to policies and farms issues
based on the personal values (Maybery et al., 2005) and
production-oriented behavior of farmers can be explained
by their characteristics (Austin et al., 2001). According to
Hanson & Lagerkvist (2012), ‘farmers’ risk preferences
may be more associated with their characteristics and how
they manage their farms rather than with various external
sources of risk” (Hanson, H. and Lagerkvist, 2012).
Perceptions sources of risks are starting the point for
producers when making risks management decisions. The
enormous differences in perceptions of sources of risks
may be determined the farmers and farm business
characteristics like sex, age, farming experience, farm
size, farm diversification, marketing channel used to sell
the products, as well as personality, beliefs and culture
(Ahsan, D.A., and Roth, 2010; Kisaka_Lwayo, M. and Obi,
2010). The authors suggested confirmation of this result
would be necessary to ensure that designing of farm risk
management tools will consider the individual running of
the farm (Hanson, H. and Lagerkvist, 2012). Therefore, the
interest of this study is to investigate, measure, and
analyze the risk level of vegetable production in Rwanda.

Int. J. Agric. Econs. Rural Dev. 763
RESEARCH METHODOLOGY
a. Description of the Study Areas
This study was conducted in RUBAVU district, Rwanda.
Rwanda is located in East Africa; its capital city is Kigali
located approximately to the center of the country.
Rwanda is bordered by Burundi to South, Uganda to the
North, Tanzania to the East and RDC to the West. Rubavu
District located in the western province of Rwanda, which
is a higher mountains zone; the leading part of this District
is located in volcanic regions. It lies in the western part at
approximately 145km from Kigali city, and the sole point
exists to the DRC in Northern Rwanda. Rainfall in Rubavu
District varies between 1200mm and 1500 mm per year.
The Land of North-West part of the District has an
enormous productive soil, but shallow, volcanic ash and
lava decomposed, while land in the South East has deep
soil but poor, often acidic, sandy clay and leached by high
erosion (MININFRA, 2016).
b. Method of Sampling
To undertake this study, Rubavu District was selected
purposively since it has dominated by vegetable
production in Rwanda. The total vegetable producers who
involved in cabbages and carrots production for the
market-oriented were 1,155 farmers’ cooperatives and
organizations in Rwanda (NAEB, 2014). There were 71
farmers cooperatives and companies’ vegetable farmers
with a total population of 435 involved especially in
cabbages and carrots production in RUBAVU District.
Purposive random sampling was employed to classify a
particular group of respondents from a certain portion of
the population. The sample size in this study was
calculated from the following formula given by Yamane
(1973): 𝑛 = 𝑁
1+𝑁𝑒2
Where: n = sample size; N = population size; and e =
acceptable error (5%) (Yamane, 1973). Using a 5 percent
acceptable error, the sample size, n, is approximately 208
vegetable farmers, for the market-oriented. However, the
sample size can be different from that calculation based
on not producing the vegetables for the market-oriented
and other limitations (Scheaffer et al., 2006).
c. Method of Data collection
The methodology employed in this study was both
qualitative and quantitative research approaches. The use
of a qualitative approach enabled to reach an in-depth
analysis of the risks related to vegetable production and
perception of the farmers on the main sources of risks
associated with vegetable farming in Rubavu District. In
contrast to the quantitative approach, which focuses on
statistics and figures, the qualitative approach focuses on
the words of the respondents and the themes emerging
from their narratives. The two qualitative research
techniques were applied to gather primary data, namely
group interviews and face-to-face individual in-depth
interviews.
The structured interview questionnaire method was
employed to elicit information from the vegetable
smallholder farmers. The questionnaires had four main
parts, 1st section relating to general information. The 2nd
section was designed to obtain information about
agricultural activities on the farm. The 3rd focused on the
sources of on-farm risk and section 4th focused on risk
management strategies. The 3rd and 4th sections measure
how important the sources of risks and risk management
strategies. A five-point Likert-scale ranged between ‘1’ not
important, to ‘5’ extremely important through ‘3’ quite
important for getting the information on the sources of risks
and risks management. The field survey was conducted
from March up to June 2017. Face-to-face interviews were
employed to gather relevant information from the
respondents. The secondary data were collected from
NAEB and MINAGRI libraries. Other materials, especially
the published and unpublished materials and websites
were consulted to generate relevant secondary data.
d. Methods of Data Analysis
The data collected from respondents were analyzed
through STATA 14. Descriptive statistics (frequency
distribution, arithmetic mean, and standard deviation) were
employed to describe farm, vegetable farmers’
characteristics, farmer business, and vegetables
marketing characteristics in Rwanda. One-way ANOVA
and t-test were used to determine the difference between
the farmers’ socio-economic characteristics.
The sum score of the self-assessment scale’s statements
used to determine vegetable farmers’ risk perceptions
level. The reliability test evaluates the contribution of
individual scale items in the common underlying construct.
A measurement that frequently used to evaluate the
reliability is Cronbach’s coefficient alpha (DeVellis, 1991;
Hair et al., 2010; Nunnally, J.C. and Bernstein, 1994;
Peter, 1979). Coefficient alpha measures the proportion of
communal variation due to true differences in farmer’s risk
management toward the risk. It is measured as:
∝=
𝐾
𝐾 − 1
(1 −
∑ 𝜎𝑖
2
𝜎 𝑦
2
Where ∝ is Cronbach’s coefficient alpha, 𝜅 is the number
of statements in the scale, 𝜎𝑖
2
is the variance of the ith
statement, and 𝜎 𝑦
2
is the variance of the k-statement scale.
The coefficient ranges between 0 and 1. In the explanatory
factor analysis, Cronbach’s coefficient alpha value of 0.6
approaches the lower limit accepted by (Cox, S., and Flin,
1998; Hair et al., 2010; Harvey et al., 2002).

Kubwimana 764
The reliability test objective is to generate alpha as high as
possible. Scale optimization can be established by the
statement refinement procedure. The statements, which
have negative or very low Corrected Item-Scale
Correlation (CISC) values, were excluded to generate an
improved Cronbach’s coefficient alpha. CISC represented
as:
𝑟(𝑦−1) =
𝑟𝑦1 𝜎 𝑦 − 𝜎1
√𝜎1
2
+𝜎 𝑦
2 + 2𝜎1 𝜎 𝑦 𝑟𝑦1
Where 𝑟𝑦1 is the correlation of item x1 with total score Y, 𝜎 𝑦
represent the standard deviation of total score Y, 𝜎1 is the
standard deviation of variable x1, and 𝑟(𝑦−1) is the
correlation of item x1 with the sum scores of all variables,
Y, exclusive of item x1. Rules of Thumb suggest the critical
threshold of 0.5 is acceptable for CISC (Hair et al., 2010).
The aggregated score of the refined statement for each
farmer refers to his risk perceptions. This score was used
in the subsequent multiple regressions under the name of
the risk perception scale. Vegetables farmers’ perceptions
of risk sources and risk management strategies were
studied by descriptive analysis. Before that, factor analysis
was used to reduce the number of variables belonging to
risk sources and risk management strategies. Explanatory
variable analysis (EFA) is an essential empirical tool used
in various subjects like economics, social, psychology and
political science. Factors with latent root criterion
(eigenvalues) greater than 1 were considered in this study,
which means of each factor contributes to more
considerable variance than had been possible by any one
of its variables. About factor loadings, a minimum
threshold of 0.3 is typically accepted in the literature, even
though other authors suggest the minimal range between
0.4-0.5 for practical purposes (Von-Pork, 2007). In this
study, values of greater or equal to 0.4 were employed to
determine the inter-correlation among the original
variables (Stevens, 1992).
The Kaiser-Meyer-Olkin (KMO) method measures
sampling adequacy and varies from 0 to 1. KMO with 1
value means that each variable is perfectly predicted
without error by the other variables. The KMO result of 0.6
or greater is recommended (Hair, 2006). Von Pock (2007)
has illustrated that KMO value of greater or equal to 0.50
is hitherto considered to meet the minimum level in the
literature (Von-Pork, 2007). To investigate the factors of
results attitudes and perceptions, based on the study’s
approaches, multiple regressions were used. The Enter
method was used to explain the conventional approaches
about the size of the overall relationship between the
socio-economic characteristics as independent and each
of vegetables farmers’ risk attitudes and their perception of
risk sources and risk management strategies. Multiple
regression analyses using a stepwise method to explain
the multidirectional approach that provide the ability to
evaluate the extent of contribution of the objective and
subjective variables within the best combination. The
regressions performed at 5% as a maximum level of
significance.
The settled binary, Y=1 for situations vegetable producers
had positive perception sources of risk or risk
management, and Y=0 if vegetable producers had
negative perception sources of risk or risk management.
𝐿𝑖𝑛𝑒𝑎𝑟 𝑒𝑞𝑢𝑎𝑡𝑖𝑜𝑛 𝐸(𝑌𝑖) = 𝛽1𝑋𝑖1 + 𝛽2𝑋𝑖2 + ⋯ + 𝛽𝑝𝑋𝑖𝑝
… (1)
For the outcome Yi to take a binary value, a special
function f(E(Yi)), which is called the linear function, has to
be found.
𝐿𝑖𝑛𝑒𝑎𝑟 𝑓𝑢𝑛𝑐𝑡𝑖𝑜𝑛 𝑓(𝐸(𝑌𝑖)) = 𝛼′
+ 𝛽1 𝑋𝑖1 + 𝛽2 𝑋𝑖2 + ⋯ + 𝛽 𝑝 𝑋𝑖𝑝
… (2)
Logistic regression model formula with the outcome Yi
𝐿𝑖𝑛𝑒𝑎𝑟 𝑟𝑒𝑔𝑟𝑒𝑠𝑠𝑖𝑜𝑛 𝑚𝑜𝑑𝑒𝑙 𝑓𝑜𝑟𝑚𝑢𝑙𝑎 𝑤𝑖𝑡ℎ 𝑡ℎ𝑒 𝑜𝑢𝑡𝑐𝑜𝑚𝑒 𝑌𝑖: 𝐿𝑖𝑛𝑒𝑎𝑟 (𝑃𝑖)
= ln (
𝑝𝑖
1
− 𝑝𝑖)
= 𝛽0 + 𝛽1 𝑋𝑖1 + 𝛽2 𝑋𝑖2 + ⋯ + 𝛽 𝑝 𝑋𝑖𝑝 + 𝜖 𝑡 … … … … … (3)
With: ln(𝑝𝑖/1 − 𝑝_𝑖)= Linear for vegetables sources of risk/
risk management decisions, 𝑝𝑖= perception of sources of
risk/ risk management, 1 − 𝑝𝑖= no perception of sources of
risk/ risk management, 𝛽𝑜 = Intercept, 𝛽1 𝛽 𝑛 =coefficient,
X=independent variables and 𝜖=Error term.
RESULTS AND DISCUSSION
The descriptive analysis employed to describe the socio-
demographic characteristics of sampled households,
structure conduct and performance profitability of
cabbages and carrots producers are discussed.
Comparisons of the vegetable farmers’ socio-economic
characteristics between two commodities (both t-test and
Chi-squares) are statistically significantly different, except
for gender, marital status, and education level. The
findings indicated that the cabbages and carrots producers
were mostly differing accord to farming experience, and
family participation in vegetable farming. Results indicated
the elderly persons were more likely to involve in vegetable
farming more than young (62.97% had more than 40
years.) This implies that a little number of younger (3.7%
had less than 30 years) was only interested in the
production of vegetables. This implies the younger farmers
are rare especially in vegetable farming. This may be
positively associated with six challenges identified by
youth in Rwanda themselves to burry them to involve in
the agricultural sector like (1) access to knowledge,
information and education (2) access to land (3) access to
financial services (4) access to green jobs (5) access to
markets (6) and engagements in policy dialogue (FAO,
2014). The results of carrots and cabbages farmers’ socio-
economics characteristics are presented in table 1 below.

Table 1: Carrots and Cabbages farmers’ socio-economic Characteristics
Vegetable type
Items Carrots (N=144) Cabbages (N=64) Overall N=208 P-Value
Age group Frequency % Frequency % Frequency %
20-30 6 4.23 3 4.55 9 4.55
30-40 47 33.10 23 34.85 70 33.65
40-50 74 52.11 30 45.45 104 50.00 0.0743**
>50 15 10.56 10 15.15 25 12.02
Gender
Male 107 75.35 52 78.79 159 76.44 0.587
Female 35 24.65 14 21.21 49 23.56
Level of education
Illiteracy 22 15.49 6 9.09 28 13.46
Primary 78 54.93 52 78.79 130 62.50
Secondary 14 9.86 5 7.58 19 9.13 0.005***
VTC 27 19.72 3 4.55 31 14.90
Vegetable Farming Experiences
<= 10 49 34.51 16 24.24 65 31.25
11-20 57 40.14 31 46.97 88 42.31 0.0033**
21-30 36 25.35 19 28.79 55 26.44
Farmers marital status
Single 3 2.11 6 9.09 9 4.33
Married 134 94.37 54 81.82 183 90.38 0.014**
Widower 5 3.52 6 9.09 11 5.29
Family participation
Yes 99 69.72 46 69.70 145 69.70 0.000***
No 43 30.28 20 30.30 63 30.30
Family members Involvement
Spouse 41 28.87 43 65.15 84 40.38
Children 26 18.31 12 18.18 38 18.27
Relatives 7 4.93 3 4.55 10 4.81 0.000****
Brothers and sisters 28 19.72 2 3.03 30 14.42
Spouse and children 40 28.17 6 9.09 46 22.12
Production areas
Less 10acre 23 16.20 0 0.00 23 11.06
10-50 acre 26 18.31 13 19.70 39 18.75
50-75 acre 62 43.66 45 68.18 107 51.44 0.000***
75-100 acre 28 19.72 3 4.55 31 14.90
Above 100 acres 3 2.11 5 7.58 8 3.85
Land Ownership
Owner Self Operated 40 28.17 13 19.70 53 25.48
Owner and self-lease operated 51 35.92 19 28.79 70 33.65
Lease self-operated 36 25.35 16 24.24 52 25.00 0.000***
Tenant 15 10.56 0 0.00 15 7.21
Other 0 0.00 18 27.27 18 8.65
Off-farm Activities
Yes 128 90.14 6 9.09 134 64.42 0.000***
No 14 9.86 60 90.91 74 35.58
Types of off farm activities
Private 0wne business 17 65.38 5 100 22 70.97 0.118
Special craft man 9 34.62 0 0.00 9 29.03
Net off farm income
100,001-250,000rwf 9 34.62 0 0.00 9 29.03
250,001-500,000rwf 6 23.08 0 0.00 6 19.35 0.005***
500,001-750,000rwf 6 23.08 0 0.00 6 19.35
<750,000rwf 5 19.23 5 100 10 32.26
The sign in table means: *** P-value < 0.01, ** P-value < 0.05 and * P-value < 0.1. Test differences for vegetable farmers
socio-economic characteristics through independents t-test and chi-square.
Source: Primary data, 2018

Kubwimana 766
The results pointed out that the majority (41,8%) of farmers
had the vegetable production experience, which was less
than 10 years in carrots and cabbage production, as the
results indicated, the carrots farmers had more experience
than the cabbages farmers (p<0.0246). An intensely
experienced farmer was reasonably expected to perform
better more than inexperienced farmers in terms of farm
management skills and farm resource allocation to
maximize farm profitability. The average cabbages and
carrots production areas, production quantity, losses
quantity, unit price and farmer net income are presented in
table 2 below.
Table 2: The Average production area, production quantity, loss quantity, unit price, and farmer net income
Items Average
Production
Area (in a)
Average
Production
Quantity
Average
Losses
Quantity
Average
Unit
Price
Average
Production
cost
Net
Farm
Income
Average
Production
ratios
Average Net
Income
ratios
SeasonA
Carrots
(n=108)
67,32 11528.33 10.2 112,82 297215 1003416 171,246732 3,376064297
Cabbage
(N=43)
67,058 9825.882 38.58 71,944 237117 469800 146,52811 1,981302808
SeasonB
Carrots
(N=15)
70,833 12811 0 129 330000 1322619 180,8620276 4,007936364
Cabbage
(N=13)
47 7725 0 98,928 178600 585623 164,3617021 3,278965302
SeasonC
Carrots
(N=67)
67,5 12700 16.34 105 290153 1043347 188,1481481 3,595851154
Cabbage
(N=36)
66,42 12125 16.64 58 286000 417250 182,5504366 1,458916084
Source: Field survey, 2018
The survey results showed the average net farm income
of carrots farmers was significantly higher than for the
cabbage farmers in Rubavu district. In 2017 Season B, the
carrots farmers had an optimum average net farmer
income of 1,322,619rwf the same also for the cabbages
farmers with net farmers’ income of 585,625rwf with a
higher ratio compared to all others agricultural seasons
and higher seasonal agriculture price per local unit
(rwf/kg).
The agricultural season B characterized by heavy rainfall.
The higher number of agriculture farmers feared to involve
in vegetable production, they predicted to invest much
more. The high rainfall and storm pushed the carrots and
cabbages, farmers, to spray many effective pesticides to
prevent the diseases. This finding reflects a widening gap
in income distribution among the carrots and cabbages
smallholder farmers in Rubavu District, Rwanda. The
vegetable farmers in Rubavu District claimed to be highly
exploited by middlemen who assemble consignments
locally for sale in Kigali.
Farmers’ perceptions of credible sources of risk and
risk management strategies of vegetable farmers in
Rubavu District
The results of the perceived sources of risk were
summarized in table 3, whereas the mean scores of each
source of risk were ranked. Standards deviation was used
to indicate the variation in the ratings. In addition, the
independent t-test was employed to compare mean score
differences between cabbages and carrots farmers’
results. The results of the perceived sources of risk were
summarized in table 3, whereas the mean scores of each
source of risk were ranked. Standards deviation was used
to indicate the variation in the rating.
The table 3 shows that the vegetable production risk
associated with the storm, lack of markets contracts, weak
coordination among vegetable farmers’, pests and
diseases, higher variability of market prices and high level
of rainfall as most top 5 sources of risks for carrots farmers
with mean scores of 4.625, 4.357, 4.285, 4,267 and 4,250
respectively. In contrast, the ranked top 5 sources of risks
for cabbages vegetables farmers were natural disaster like
storm, Deficiency in rainfall that causing drought, weak
coordination among vegetable farmers, higher variability of
market prices, the higher level of rainfall, pests and
disease and crops seasonality with the mean scores of
4.45, 3.71, 3.68, and 3,66 respectively.
This finding is consistent with the findings of Patrick et al.
(1985), Martin (1996) and Flaten et al. (2005) who argued
that marketing risks associated with the variability of
product and input prices were the most important sources
of risk considered by farmers in their respective study
areas (Flaten et al., 2005; Martin, 1996). The standard
deviations (SDs) of both sources in each group were less
than one and this indicates that those higher affected risks
gained a high level of consensus among the cabbages and
carrots smallholders farmers’ (Meuwissen, M.P.M., and
Hardarker, 2001). Furthermore, the perception of these top
5 sources of risk for both carrots and cabbages vegetables
smallholders’ farmers were statistically significant (P0.01

Int. J. Agric. Ext. Rural Dev. 767
Table 3: Ranking of perceptions of sources of risk by carrots and cabbages vegetables producers of Rubavu
District.
Carrots (N=144) Cabbages (N=64)
Production risk level Mean SD [95%
Conf.Int]
Rank Mean SD [95%
Conf.Int]
Rank P-Value
Deficiency in rainfall causing Drought 4.14 0.94 3.98-4.29 (7) 3.71 1.52 3.34-4.08 (2) 0.0067***
High Level of rainfall 4.18 0.93 4.03-4.34 (6) 3.66 1.58 3.28-4.06 (5) 0.0018***
Strom 4.59 0.61 4.49-4.69 (2) 4.45 0.66 4.29-4.62 (1) 0.0716*
Pests and diseases 4.22 0.93 4.07-4.38 (3) 3.66 1.58 3.27-4.05 (5) 0.0008***
Unexpected yields Variability 2.84 1.09 2.66-3.02 (9) 2.88 1.18 2.60-3.18 (7) 0.6154
Higher variability of Market Prices 4.19 0.93 4.04-4.35 (5) 3.68 1.59 3.31-4.09 (4) 0.0026***
Unsustainability of input market prices 1.69 0.73 1.57-1.82 (13) 1.62 0.74 1.43-180 (10) 0.2425
High level of Debt 1.75 1.15 1.56-1.95 (12) 1.29 0.46 1.17-1.40 (11) 0.0009***
Changing of national agricultural
policies
2.31 1.27 2.09-2.52 (10) 1.97 0.93 1.74-2.19 (8) 0.0262**
Variability of agricultural land polices 1.84 1.17 1.65-2.04 (11) 1.29 0.76 1.10-1.47 (12) 0.0002***
Theft 3.72 0.99 3.55-3.88 (8) 3.26 1.08 2.99-3.52 (6) 0.0014***
Lack of markets contacts 1.66 0.86 1.52-1.80 1.78 0.85 1.58-1.99 (9) 0.8394
Weak coordination among vegetables
farmers
4.22 0.92 4.07-4.38 (4) 3.71 1.54 3.33-4.09 (3) 0.0016***
Crops seasonality 4.91 0.07 4.17-4.45 (1) 3.66 1.58 3.27-4.26 (5) 0.0001***
characteristics through independents t-test and chi-square.
Table 4: Ranking of perceptions of risk management’s strategies by carrots and cabbages producers of Rubavu
District.
Carrots (N=142) Cabbages (N=66)
Risk Management Level Mean SD [95%
Conf.Int]
Rank Mean SD [95%
Conf.Int]
Rank P-Value
Enterprise and crop diversification 3.04 1.64 2.77-3.31 (10) 1.69 1.19 1.40-199 (14) 0.0000***
Apply pesticides and Insecticides 4.12 0.93 3.96-4.27 (4) 3.57 1.40 3.23-3.92 (6) 0.0006***
Ability to adjust to weather and other
economic factors
1.55 0.49 1.47-1.64 (16) 1.48 0.50 1.36-1.60 (15) 0.1692
Selection of crops varieties ale to resist to
pests and diseases
4.20 0.87 4.06-4.35 (3) 3.85 1.49 3.48-4.21 (1) 0.0159
Adoption of new farming techniques 2.16 1.30 1.94-2.38 (13) 1.76 1.12 1.46-2.05 (12) 0.0170**
Family Network 3.67 1.40 3.24-3.70 (6) 3.68 1.40 3.33-4.03 (4) 0.8418
Crop diversification 2.39 1.30 2.18-2.61 (12) 1.97 0.98 1.73-2.21 (10) 0.0099***
Maintain goods relationships with traders 4.29 0.87 4.15-4.44 (1) 3.66 1.58 4.15-4.44 (5) 0.0001***
Crop planning and time management 1.65 1.08 1.47-1.83 (15) 1.91 1.32 1.58-2.23 (11) 0.9337
Use of improved inputs 4.27 0.89 4.12-4.42 (2) 3.71 1.45 3.35-4.07 (3) 0.0004***
Risk sharing 2.97 0.90 2.83-3.13 (11) 2.97 0.94 2.74-3.20 (8) 0.4732
Reduce debt level 3.47 0.94 3.52-3.83 (7) 3.26 1.08 2.99-3.52 (7) 0.0025***
Investing in non-farm investments/Business 1.96 1.30 1.74-2.17 (14) 1.39 0.55 1.26-1.53 (16) 0.0005***
Formal approaches 4.11 0.88 3.96-4.26 (5) 3.73 1.46 3.37-4.08 (2) 0.0098***
Informal borrowings 3.10 1.03 2.93-3.28 (9) 2.66 0.99 2.42-2.91 (9) 0.0021***
Crop divarication 3.08 1.66 2.81-3.36 (8) 1.73 1.26 1.42-2.04 (13) 0.0000***
and 0.05, respectively). This indicates that these top 5
sources of risks for both cabbages and carrots farmers
were the key specific risks that affected the smallholder’s
farmers’ concern in Rubavu district. Table 4 summarizes
the results of risk management implemented by cabbages
and carrots vegetables farmers in Rubavu district,
whereas the production and financial strategies were
considered as the more important managerial measures
undertaken to risk than other strategies.

Kubwimana 768
Maintaining good relationship with traders, use of the
vegetable hybrids seeds higher resistance to pest and
disease, Apply the pesticides and the insecticides (use of
improved inputs) and formal serving and lending were
ranked as the 5 top strategies adopted by vegetable
farmers with a mean rank of 4.29, 4.27, 4.20, 4.12 and 4.11
respectively. In contrast, cabbages farmers considered the
applying of pesticides and insecticides, formal
approaches, use of improved inputs, strengthening family
network and maintaining good relationship with traders
with the mean value of 3.85, 3.73, 3.71, 3.68 and 3.66
respectively as important key sources of risk. These top 5
strategies were considered as important for cabbage
farmers, contrary to carrots farmers who consider them as
very important. The findings support Martin (1996) who
argued that the farmers’ selection criteria for risk
management strategies varied depending on farm type,
climatic conditions, marketing factors and agriculture rules
and regulations. Furthermore, the perception of these top
5 risk management strategies for both carrots and
vegetables smallholders’ farmers were statistically
significant (P<0.01 and 0.1, respectively) (Martin, 1996).
Factor analysis
The results of the factor analysis of the sources of risk and
risk management strategies are discussed. Explanatory
factor analysis with varimax orthogonal rotation was
applied to the data using STATA version 14. Explanatory
factor analysis is used to reduce the number of sources of
risk and risk management strategies for the cabbages and
carrots farmers. The Kaiser-Meyer-Oklin (KMO) and a
Cronbach’s Alpha value were assessed to ensure the
appropriateness for factor analysis of each data set and to
yield a satisfactory result in the reliability of the factor,
according to Hair (2006) the value which is greater than
0.6 is recommended (Hair, 2006). The test of internal
consistency reliability of each factor was assessed and a
cut-off of
+
−
0.4 was employed for the factor loadings the
inter-correlation among the original variables and the
interpretation purposes of this research (Hair, 2006).
The results in table 5 represent the risk factor analysis for
sources of risk for both cabbages and carrots farmers. The
preliminary results indicated six sources of risk including
“accidents or problems with health, risk from change in
country’s economic, risk from bank’s increase of interest
rate and higher costs of vegetables improved inputs” to be
eliminated from factor analysis because of their low
communalities (< 0.4)(Hair, 2006).
Table 5: Varimax rotated factor loading of sources of risk for Vegetable producers of RUBAVU District
Sources of Risk Factors Communality
F1 F2 F3 F4 F5 F6
Deficiency in rainfall causing drought 0.913 0.110 0.120 0.061 0.212 0.033 0.858
Excess rainfall 0.872 0.150 0.282 0.025 0.176 0.062 0.717
Storm 0.781 0.050 0.041 0.082 0.112 0.049 0.720
Pests and Disease that, affect vegetables 0.724 0.048 0.090 0.082 0.166 -0.073 0.656
High level of debt -0.011 0.702 0.097 0.076 0.055 0.159 0.573
Risk from theft 0.209 0.658 0.037 0.137 0.100 -0.063 0.436
Changes in land prices 0.346 0.557 -0.038 0.235 -0.055 -0.099 0.466
Absence of coordination among vegetable
farmers to expand bartering power
0.179 0.550 -0.231 -0.014 -0.188 0.332 0.530
Changes in governments law and policies 0.054 0.016 0.899 0.102 -0.006 -0.078 0.854
Unexpected yields variability 0.080 0.092 0.082 0.856 0.086 -0.121 0.736
Higher variability of market prices 0.057 0.248 -0.042 0.047 0.823 0.082 0.775
Lack of Market contracts 0.101 0.054 -0.016 0.160 0.842 -0.019 0.736
Eigenvalues 3.64 1.75 1.68 1.33 1.08 1.01
Total variance (%) 24.29 11.69 11.19 8.89 7.23 6.71
Variance explained (%) cumulative 24.92 36.98 48.71 57.05 66.28 69.35
Cronbach‟s Alpha 0.839 0.675 0.784 0.678 0.514 _
Number variables 4 4 1 2 2 0
Factor 1: Natural disasters, Factor 2: Personal and Business environment, Factor 3: The factor related political issues,
Factor 4: Seasonal productivity, Factor 5: Market price fluctuations and Fact 6: Input prices
The sign in table means: *** P-value < 0.01, ** P-value < 0.05 and * P-value < 0.1. Test differences for vegetable farmers’

The factor loadings obtained from the varimax rotations
grouped the 12 sources of risk into six factors for both
cabbages and carrots farmers. Factors one (F1) and two
(F2) had 4 significant loading variables respectively,
factors three and four (F3&F4) had 1 significant variable
and; factor five had 2 significant variables. The six factors
explained at least 70 percent of the total variance. The
Cronbach’s Alpha values for factors F1-4 ranged from
0.678 to 0.839, which were reliable among these factors.
The factor F1-5 are named according to each factor
structure as follows:
Factor one (F1): This factor has a relatively high loading of
the sources of risk variables related to deficiency rainfall
causing drought, excess rainfall, storm and pests and
diseases. The test of internal consistency reliability ranged
from 0.724 up to 0.913. This factor named “Natural
disaster.”
Factor two (F2): The factor is described as “Personal and
Business environment” which is concerned with “High level
of debt, the risk from theft, changes in land policies and
weak coordination among the vegetable farmers” with the
test of internal reliability ranged from 0.550 to 0.701.
Factor three (F3): This factor is loaded highly with one
variable only named “change in government law and
policies” with a higher test of internal consistency reliability
equal to 0.899 and named as “Factor related with political
issues.”
Factor four (F4): This factor is loaded highly with one
variable only named “unexpected yields variability” with a
test of internal consistency reliability equal to 0.852 and
named as “Seasonal productivity.”
Factor five (F5): This factor described as “market price
fluctuations” because there were significant loadings of
sources of risk variables related to “higher variability of
market price and lack of market contracts.”
The association between vegetable farmer’s
characteristics and source of risk and risk
management perceptions.
Table 6 shows the relationship between the carrots and
cabbages farmers’ socioeconomic status and the different
perceptions of sources of risk components; multiple
regression analysis was employed to investigate that
relationship. Marital status, sources financial, vegetable
farming experience and off-farm activities are negatively
related to natural disasters. These implied that the
unmarried vegetable farmers, those who produced on
lower areas, those who borrowed money from the bank
and those who didn’t off-farm activities are likely to
perceive natural disaster as significantly more important
than those who were married, who used large areas, who
didn’t borrow money from the bank and who did the off-
farm jobs. This finding was supported by the result in the
study conducted by Ahmad and Isvilanonda (2003),
whereas natural disaster affecting the farmer with low size
and farm size is one of the constraints to diversification,
that is, farmers with a smallholding have limited ability to
diversify their farm activities (Ahmda, A., and Isvilonda,
2003).
Table 6: Multivariate regression of the source of risk components and vegetable farmer’s characteristics of
Rubavu District.
Independents components Production Risks Sources Components
Factor 1 Factor 2 Factor 3 Factor 4 Factor 5 Factor 6
Agea -0.026 -0.154** -0.149** -0.303*** -0.2745** -0.2647**
Genderb -0.044 0.153 0.141* -0.342** -0.3253** -0.2735*
Education Levelc -0.002 0.006 -0.06 0.097 0.3283*** 0.1903*
Marital Statusd 0.095*** -0.110 -0.147*** 0.102 0.1573 0.086
Family participatione -0.0517 0.045 0.135 -0.427** -0.3451* 0.1818
Production Areasf -0.091*** -0.167** -0.008 -0.315*** 0.4823*** -0.0341
Ownership Land Statusg 0.024 0.208*** 0.098 0.212* 0.0815 -0.0025
Vegetable Farming Experienceg -0.257*** 0.235** 0.027 -0.475*** 0.4216** 0.1639
Sources of farm financialg 0.016*** 0.056 0.019 0.0534 -0.3976** -0.9735***
Loan Rate used in Vegetable productionh 0.028*** 0.277*** 0.204*** 0.309*** -0.1102 -0.1160
Off Farm Activitiesi -1.143*** -0.241** 0.025 0.0888 0.5264*** -0.518***
Net Off Farm Incomej -0.535*** -0.476*** -0.102 0.340 0.5318** -0.4362**
Constant 1.802*** 0.972*** 0.879*** -0.5044*** -0.504 1.1279***
R2 0.8447*** 0.4015** 0.2463*** 0.4052*** 0.4144*** 0.4530***
F1: Natural disaster, F2: Personal and Business environment, F3: Factor related to political Issues, F4: Seasonal productivity
price, F5: Market prices Fluctuations and F6: Financial situations. The sign in table means: *** P-value <0.01%, ** P-value
<0.05 and * P-value <0.1%. Test differences for vegetable farmers characteristics through independents t-test and chi-square.
[(a
1 if farmer’s age over 40 years old, 0 otherwise), (b
1 if farmer is male, 0 if female), (c
1 if farmer’s education is higher than
primary, 0 otherwise), (1d
if married, 0 if unmarried), (e
1 if family members participate, 0 if not) (f
1 if production areas is greater
than 0,5, 0 if less) (g
1if used money from bank, 0 otherwise) (h
1if farmer get affordable net off income, 0
otherwise) (i
1 if farmer’s experience over 10 years, 0 otherwise), (j
1 if the farmer has the off-farm income, 0 if no off farm
income)] Source: Primary data, 2018

Kubwimana 770
Risks related to low personal and farmers who had off-farm
activities perceived farm business strategy and agricultural
diversification as highly important. The off-farm work
coefficient shows a positive significant association with
markets prices fluctuations. The cabbages and carrots
farmers with no stronger background education were
highly concerned about the financial situation. This finding
is similar to that of Mustafa (2006) who argued that more
educated farmers performed better in managing their farm
business than the less educated farmers (Mustafa, 2006).
Table 7 shows the relationship between the cabbages and
carrots farmers’ socio-economic characteristics status and
the different perceptions of risk management strategies.
Table 7: Multivariate regression of the risk strategy components and vegetable farmers of Rubavu District.
Production Risks Sources Components
Independent variables F1 F2 F3 F4
Agea 0.0552 0.1924** 0.1027 -0.1710**
Genderb -0.1441 0.2748** -0.0738 0.1198
Education Levelc 0.0490 -0.0246 -.0008 0.1614**
Marital Statusd 0.0591 0.0513 -0.1425 -0.2077**
Family participatione -0.2977** 0.2190 0.1242 -0.0580
Production Areasf -0.1873*** -0.0645 -0.1626** -0.1772**
Ownership Land Statusg -0.6889 -0.1353* -0.0105 -0.1291
Vegetable Farming Experienceg -0.0242 0.06105 -0.0404*** -0.1503
Sources of farm financialg 0.0343 -0.061063 -0.0666 -01467
Use of Loan in Vegetable productionh 0.2398** 0.1283 -0.0060 0.1773
Off Farm Activitiesi -0.0476 -0.0675 0.0171 0.2152**
Net Off Farm Incomej 0.0076 0.1152 0.2835*** -0.0486
Constant 0.9461*** 0.3309* 1.005*** 1.2381***
R2 0.1441*** 0.1397*** 0.1923*** 0.39699***
F1: Personal and farm business strategy, F2: Agricultural Diversification, F3: Agricultural income, and F4: proper Financial
management
The sign in table means: *** P-value <0.01%, ** P-value <0.05 and * P-value <0.1%. Test differences for vegetable farmers
[(a1 if farmer’s age over 40 years old, 0 otherwise), (b1 if farmer’s is male, 0 if female), (c1 if farmer’s education is higher
than primary, 0 otherwise), (1d if male, 0 if female), (e1 if family members participate, 0 if not) (f1 if production areas is
greater than 0,5, 0 if less) (g1if used money from bank, 0 otherwise) (h1if farmer get affordable net off income, 0 otherwise)
(i1 if farmer’s experience over 10 years, 0 otherwise), (j1 if the farmer has the off-farm income, 0 if no off-farm income)]
The goodness-of-fit coefficients of all models were rather
low, except for proper financial management where the
coefficient explained around 40% of the variation of the
dependent variable. The off-farm activities were negatively
related to proper financial management, which means the
vegetable farmers who didn’t the off-farm activities
perceived the proper financial management as the more
important strategy rather than those who had off-farm
activities. The use of a loan from the bank was positively
related to proper financial management, and this might
due to the farmers who used the bank loan to work hard to
enhance their farm income. The vegetable farmers who
had higher net off-farm incomes perceived the personal
and farm business strategy as highly important.
CONCLUSIONS AND RECOMMENDATION
The perceptions of the sources of risk and risk
management strategies were ranked at a different level
among vegetable farmers in Rubavu District. The
vegetable production risk associated with the storm, lack
of markets contracts, weak coordination among vegetable
farmers’, pests and diseases, Higher variability of market
prices, high level of rainfall, deficiency in rainfall that
causing drought and crops seasonality were ranked as
most top sources of risks for carrots and cabbages
farmers. The carrots farmers ranked them as very
important while the cabbages farmers ranked them as
important sources of risks.
The results of the factor analysis of the sources of risk and
risk management strategies assessed proven that all
factors explained at least 70 percent of the total variance.
Natural disaster factor was highly associated with the
sources of risk like deficiency rainfall causing drought,
excess rainfall, storm and pests and diseases with the
higher test of internal consistency reliability. The factor
described as “Personal and Business environment” which
is concerned with “High level of debt, the risk from theft,
changes in land policies and weak coordination among the
vegetable farmers” with the test of internal reliability
ranged from 0.550 to 0.701. The off-farm work coefficient
shows a positive significant association with markets
prices fluctuations.

The results from the perceptions of risk management
strategies suggested that the production and financial
strategies were more important to overcome the faced
risks. Use of improved inputs, maintain goods relationship
with traders, use of the vegetable hybrids seeds higher
resistance to pest and disease, formal serving and lending;
and use of improved insecticides and pesticides and
strengthening of the family network were considered as
important strategies to adopt. These strategies were
ranked as the 5 top strategies adopted by various
vegetable farmers in Rubavu District. In addition to this,
the vegetable producers should use cultural and biological
methods and chemicals/pesticides to control pests and
diseases.
Strengthening the role of vegetable farmers, cooperatives
should be considered as part of vegetable production risk
reduction in Rubavu District. This because farmers’ groups
or cooperatives can help the vegetable farmers to improve
their negotiating power. Training initiatives that would
enable vegetable farmers to use formal risk management
mechanisms, allocation of financial resources, higher
product price and input prices can then be achieved more
easily, due to economies of scale, than for the individual
farmer. Agriculture insurance should be a proficient tool in
managing farmers’ risks related to natural disasters and
can facilitate an effort to protect farmers from either the
loss of their crops or farm income caused by perishability.
The government of Rwanda should continuously invest in
agricultural research to improve new technologies that
would enhance productivity and prevent epidemics of
pests and diseases in cabbages and carrots production,
especially by producing drought-tolerant vegetable
varieties, and pest and diseases resistant.
REFERENCES
Ahmda, A., and Isvilonda, S. (2003). Rural Poverty and
Agriculture Diverisfication in Thailand. Unpublished
Working Paper: Department of Economics. Land
Univeristy.
Ahsan, D.A., and Roth, E. (2010). Farmers’ Perceived
Risks and Risks Management Strategies in an
Emerging Mussel Aquaculture Industry in Denmark.
Marine Resources Economics, 25(3), 309–323.
Anju, D. (2018). Vegetables Crops: Risks and losses faced
by farmers, a case study of Haryana State, India.
Advances in Research, 12(6), 1–8.
Anthon, J., Kimura, S., and Martin, R. (2011). ’ Risk
Management in Agriculture in Canada,’ OECD Food,
Agriculture and Fisheries Papers. Publishing, Paris.
Http://Dx.Doi.Org/10.1787/5kgj0d6189wg-Cn.
Asa, G., George, G., Mohinder, S.M., Rhoda, R., and
Diana, G. (2015). Rwanda Agricultural Risk
Assessment. Agricultural Global Practice Technical
Assistance Paper: World Bank, 96290. Retrieved from
96290-RW.
Austin, E.J., Deary, I.J, and Willock, J. (2001). Personality
and Intelligence as Predictors of Economic Behaviors
in Scottish Farmers. European Journal of Personality,
15(51), S123-137.
Cox, S., and Flin, R. (1998). Safety Culture: Philosopher’s
Stone or Man of Straw? Work and Stress. 12(3), 941–
952.
Dawit, A., and Abera, D. (2004). Domestic Vegetables
Seeds Production and Marketing. EARO, Ethiopia, 5,
17P.
Dencon, S. (2002). Income Risk, Coping Strategy, and
Safety Nets: The World Bank Research Observer. The
International Bank for Reconstruction and
Development, 17(2), 141–166.
DeVellis, R. F. (1991). Scale Development: Theory
Application. Newbury Park, CA: Sage Publications,
Social Research Methods Series, 26.
FAO. (2014). Youth and Agriculture: KeyChalenges and
Concretes Solutins. Food and Agriculture Organization
of United Nations (FAO) in Collaborationwith the
Technical Centre for Agricultural and Rural Cooperation
and International Fund for the Agricultural Development
(IFAD).
Flaten, O., Lien, G., Koesling, M., Valle, P.S., and
Ebbesvik, M. (2005). Comparing risk perception and
risk management and conventional dairy farming:
Empirical results from Norway. Livestock from
Production Science, 95(1–2), 11–25.
Hair, J.F., Black, W.C., Babin, B.J. and Anderson, R. .
(2010). Multivariate data analysis. Pearson Prentice
Hall.
Hair, J. F. (2006). Multivariate Analysis (6th Edition). New
Jersy: Pearson Prentice Hall.
Hanson, H. and Lagerkvist, C. J. (2012). Measuring the
farmers’ preferences for risk: A domain_Specific risk
preference scale. Journal of Risk Research., 15(7),
737–753.
Harvey, J., Erdos, G., Bolam, H., Cox, M.A., Kennedy,
J.N., and Gregory, D. T. (2002). An analysis of safety
culture attitudes in a highly regulated environment.
Work and Stress., 16(1), 18–36.
Hazell, P.B.R. and Norton, R. D. (1986). Mathematical
Programming for Economic Analysis in Agriculture.
New_York: MacMillan.
IFAD. (2014). “Investing in rural people in Rwandda,”
Rome: International Agriculture for Rural Development.
Jabir, A. and Sanjeev, K. (2008). Consumers’ Perceptions
on Fruits and Vegetables Packaging in India. 18th
Annual World Symposium of International Food and
Agribusiness Management Association on “Meeting
Food Systems Challenges through Innovations and
Entrepreneurship.” In Indian Institute of Management,
Lucknow-226013.
Kisaka_Lwayo, M. and Obi, A. (2010). Risk Perceptions
and Risk Management Strategy by Smallholders
Farmers in Kwazulu-Natal Province, South Africa.
International Journal of Agriculture Management, 1(3),
21–33.

Kubwimana 772
Martin, S. (1996). Risk Management Strategiein New
Zealand Agriculture and Horticulture. Review of
Marketing and Agricultre Economics., 64(1), 31–44.
Maybery, D., Crase, L., and Gullife, C. (2005).
Categorizing Farming Values as Economic,
Conservation and Lifestyle. Journal of Economic
Psychology, 26(1), 59–72.
Meuwissen, M.P.M., and Hardarker, J. B. (2001). Sources
of Risk and Risk Management: An empirical analysis of
Dutch Livestocks analysis. Livestocks Productions
Sciences, 69(1), 521–525.
MININFRA. (2016). Environmental and Social Impact
Assessment (ESIRA). Ministry of Infrastructure, 37.
Mustafa, R. H. (2006). Risk Management in the rain-fed
sector of Sudan. Case study, Gedaref area Eastern
Sudan. Unpublished Doctoral Thesis, Justus Leiberg
Univeristy, Giessen, Germany.
NAEB. (2014). National Horticulture Policy and Strategic
Implementation Plan, 12-13.
Nunnally, J.C. and Bernstein, I. . (1994). Psychometric
theory ( 3rd Edition). New_York: MacMillan.
Nyikal, R.A., and Kosura, W. O. (2005). Risk Preferences
and Optimal Entrprise Combinations in Kahuro division
of Muranga District, Kenya. Agriculture Economics.,
32(2), 131–140.
Pannell, D.J., Malcom., and Kingwell, R. S. (2000). No
Title. Agriculture Economics., 23(1), 69–79.
Peter, J. P. (1979). Reliability: A review of Psychometric
basis and recent marketing practices. Journal of
Markerting Research (JMR), 16(1).
PSTAIIIa. (2013). Strategic Plan for the transformation of
Agriculture in Rwanda, Phase III.
Scheaffer, R.L., Mendenhall, W., and Ott., L. (2006).
Elementary Survey Sampling (6th Edition). In Califonia:
Thomson Books/Cole.
Stevens, J. (1992). Applied Multivariate Statistics for The
Social Sciences. 2nd Edition, Hillsdale (NJ): Lawrence
Erlbaum Associates.
Von-Pork, A. (2007). Strategic Management in Islamic
Finance.
Youri, D., Yeray, S.G., and Lucas, G. (2016). Horticultural
and Floriculture in Rwanda. Identification of focus areas
for sector evelopment. LEI Wageningen UR,
Netherlands Entreprise Agency (RVO).
Accepted 10 April 2020
Citation: Kubwimana JJ (2020). Risk Analysis of
Vegetables Production in Rwanda - A Case of Carrots and
Cabbages Produced in Rubavu District. Journal of
Agricultural Economics and Rural Development, 6(2): 761-
772.
Copyright: © 2020: Kubwimana. This is an open-access
article distributed under the terms of the Creative
Commons Attribution License, which permits unrestricted
use, distribution, and reproduction in any medium,
provided the original author and source are cited.

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Risk Analysis of Vegetables in Rwanda - A Case of Carrots and Cabbages