research article risk analysis of vegetables production in ... · and other adverse climatic events...

Risk Analysis of Vegetables Production in Rwanda - A Case of Carrots and Cabbages Produced in Rubavu District

AJAERD


KUBWIMANA Jean Jacques Department of Agribusiness, College of Agriculture and Veterinary medicine (CAVM), University of Rwanda, Rwanda E-mail: [email protected] 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

mailto:[email protected]


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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 −

∑ 𝜎𝑖2

𝜎𝑦2

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 𝜎𝑦2 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).


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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) =𝑟𝑦1𝜎𝑦 − 𝜎1

√𝜎12+𝜎𝑦

2 + 2𝜎1𝜎𝑦𝑟𝑦1

Where 𝑟𝑦1 is the correlation of item x1 with total score Y, 𝜎𝑦

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′ + 𝛽1𝑋𝑖1 + 𝛽2𝑋𝑖2 + ⋯ + 𝛽𝑝𝑋𝑖𝑝

… (2) Logistic regression model formula with the outcome Yi 𝐿𝑖𝑛𝑒𝑎𝑟 𝑟𝑒𝑔𝑟𝑒𝑠𝑠𝑖𝑜𝑛 𝑚𝑜𝑑𝑒𝑙 𝑓𝑜𝑟𝑚𝑢𝑙𝑎 𝑤𝑖𝑡ℎ 𝑡ℎ𝑒 𝑜𝑢𝑡𝑐𝑜𝑚𝑒 𝑌𝑖: 𝐿𝑖𝑛𝑒𝑎𝑟 (𝑃𝑖)

= ln (𝑝𝑖

1− 𝑝𝑖)

= 𝛽0 + 𝛽1𝑋𝑖1 + 𝛽2𝑋𝑖2 + ⋯ + 𝛽𝑝𝑋𝑖𝑝 + 𝜖𝑡 … … … … … (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, 𝛽1𝛽𝑛 =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.


Int. J. Agric. Econs. Rural Dev. 765 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


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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


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***

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. Source: Primary data, 2018 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***

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. Source: Primary data, 2018

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.


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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


Int. J. Agric. Econs. Rural Dev. 769 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


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


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

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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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