Constraints and Determinants of Compliance With EurepGap Standards: A Case of Smallholder French Bean Exporters in Kirinyaga District, Kenya

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1 Constraints and Determinants of Compliance With EurepGap Standards: A Case of Smallholder French Bean Exporters in Kirinyaga District, Kenya Beatrice Wambui Muriithi International Livestock Research Institute (ILRI), P.O. Box , Nairobi. beatomuriithi@yahoo.com John Mburu Department of Agricultural Economics, University of Nairobi, P.O. Box , Nairobi. jmburu@yahoo.com Margaret Ngigi Department of Agricultural Economics and Management, Egerton University, P.O. Box 536, Egerton, Njoro, Kenya. mngigi@yahoo.com ABSTRACT The authors identify constraints and critical factors that influence compliance with EurepGap standards among French bean smallholder exporters in Kirinyaga District, Kenya. A probit model was used to determine the factors influencing the EurepGap compliance decision while descriptive statistics were used to identify the major constraints to compliance. Results indicate that the high initial cost of compliance is a major constraint on compliance. This arises from the need to purchase recommended agro-chemicals and fertilizers, and the hiring of additional labor. The key factors that are likely to enhance compliance with the standards include socioeconomic and farm characteristics such as area under French beans, household size, total number of farm enterprises and access to extension services. However, compliance is also negatively influenced by access to off-farm income. The authors make several policy recommendations that could be implemented to enhance and upscale compliance with EurepGap standards in the study area. [EconLit citations: D230; Q130; Q180]. r 2010 Wiley Periodicals, Inc. 1. INTRODUCTION Kenya s horticultural subsector has grown in the last decade to become a major foreign exchange earner, employer, and contributor to food needs. Currently, the industry is the fastest growing agricultural subsector in the country and is ranked first in terms of foreign exchange earnings. The subsector has undoubtedly contributed to increased rural incomes and reduced rural poverty, through both direct production effects and linkage effects, as horticultural incomes are respent in rural areas (Mutuku, Tschirley, & Michel, 2004). More than 90% of Kenyan farmers are involved in horticultural production on an estimated 250,000 hectares (McCulloch & Ota, 2002; Mutuku et al., 2004). However, only about 15% of these farmers (both large- and small-scale) produce for the export market. The main vegetable crops grown in Kenya for export include French beans, garden peas, sugar snap peas, mange tout, and baby corn (Horticultural Crops Development Authority [HCDA], 2002). The major export market is the European Union, taking 80% of the exports; with the United Kingdom (UK), Netherlands, and France being the main importers. Other markets include the Middle East, South Africa, Norway, United States, Canada, Japan, and South Africa (HCDA, 2007; Minot & Ngigi, 2004). There is a large demand for French beans in both fresh and processed form in the UK and other European countries. In local markets, there is a limited but growing demand for this vegetable (Tineke, 2003). French bean exports have grown steadily over the last decade. In 1998, they accounted for 18% or 13, tons of the total volume of exported horticultural products. This was equivalent to 44.6% of the total volume of vegetable exports and it, Vol. 27 (2) (2011) Published online in Wiley Online Library (wileyonlinelibrary.com/journal/agr). r 2010 Wiley Periodicals, Inc. DOI: /agr

2 194 MURIITHI, MBURU, AND NGIGI contributed over US $30 million in foreign exchange. In 2005, the volumes increased to about 38, tons, contributing an equivalent income of about US $117.7 million. The introduction of EurepGap standards towards the end of 2005, however, saw the volume of French beans reducing by about 6 tons in 2006 to about 32,000 tons (HCDA, 2008). Kenyan smallholders who have succeeded in producing for the export market are facing new challenges related to new consumer demand for food quality and safety. Another challenge comes because of the transformation of the food retail market in Europe through consolidation, which has led to increased market power, and more control over production practices. European Union (EU) retailers increasingly ask for produce certified according to specific food safety and quality standards. The European Retailer Produce Working Group Good Agricultural Practices (EurepGap) is the most widely known example of a common EU supermarket standard. Currently, this standard is being adopted worldwide as GLOBALGAP. Though it is a private standard, GLOBALGAP is regarded as a condition of entry to EU markets and does not provide price premiums (Graffham, Karehu, & MacGregor, 2007; Zoss & Pletziger, 2007). Compliance to these standards for smallholders entails costly investments in variable inputs (for example, approved pesticides) and long-term structures (e.g., grading shed, disposal pit, and pesticide store; European Retail Produce Working Group Good Agricultural Practice [EurepGap], 2004). These investments are lumpy and mostly specific to the fresh export vegetable business (Aloui & Kenny, 2005; Mausch, Dagmar, Asfaw, & Waibel, 2006; Muaz et al., 2005). It is questionable whether smallholder farmers have the resources and skills to comply with them. The costs of implementing these standards may drive them out of the lucrative export market for French beans and other horticultural produce. Researchers, development partners, and the government are concerned that these changes in requirements by the international supply chains for horticultural and other high-value agricultural products will make it increasingly difficult for smallholders to maintain their position in the export market trade (Dolan & Humphrey, 2000; Dolan, Humphrey, & Harris, 1999; Jaffee, 2003). There are limited studies that have looked at constraints and factors specifically influencing compliance of EurepGap standards. Most of the factors documented so far are related to studies on other food safety standards such as Hazard Analysis and Critical Control Points (HACCP) and Sanitary and Phytosanitary Standards (SPS; e.g., Antle, 1995; Charlotte & Fairman 2003; Henson & Loader, 1999). Further, these few studies, with the exception of that of Okello (2005) and Graffham et al. (2007), have mainly focused on determinants of compliance of standards among farmers in developed countries and in the Middle East (see, e.g., Charlotte & Fairman, 2006; Muaz et al., 2005). Other studies such as Jaffee (2003) have focused on government policies and institutional environment in developing countries while ignoring microeconomic factors that influence smallholder households to comply with food safety standards. Our objective here was to analyze constraints and determinants of compliance with EurepGAP standards among smallholder farmers in the Kirinyaga district. Assessing these constraints and factors will inform policy on what kinds of smallholder farmers can be targeted in scaling up the adoption of the standards in the study area. Moreover, an understanding of the determinants of compliance can enable policy makers to target their investments on specific measures or factors that can enhance adoption of EurepGap standards. 2. STUDY METHODOLOGY 2.1. Study Area The study was done in the Mwea Division of the Kirinyaga district, a central province of Kenya. The Division lies in the mid-altitude range, 1,489 to 2,000 meters above sea level. It has an estimated population of 135,266 with a density of 236 persons per square kilometer (Ministry of Agriculture, 1996). The division falls under three agro-ecological zones: upper midland, midland, and lower midland. The zones are suitable for growing maize, cotton, and

3 COMPLIANCE WITH EurepGap STANDARDS 195 sunflowers depending on rainfall levels and soil types. Most of the Division is covered by black cotton soil, which is suitable for rice production; the rest is covered by red, sandy, and loam soils. Their fertility varies considerably from one location to another. Rice growing is the major economic activity in the area. Food crops such as beans and maize are interplanted in the red soil on small hills, which cannot retain water and are unsuitable for rice growing. Horticulture is emerging as an activity with high potential prospects in this district. The main horticulture crops in the area are French beans and tomatoes, which are grown mainly in the red soil. This study area was selected specifically for its unique agricultural practices. First, the area grows the largest quantity of French beans that are exported to international markets more than any other division in the Kirinyaga district, one of the leading districts in the country in terms of French bean exports. Second, the area qualified for this study because French bean production is done by smallholder farmers. Although land in Mwea is principally utilized for rice production under the National Irrigation Board (NIB), horticultural farming by smallholder farmers is the main competitor for land use in the red soil. Third, French beans are a short-growing-period crop that matures early, ensuring that farmers get income within a short period compared to tomatoes and rice, whose growing periods are 4 and 12 months, respectively. Thus, the crop is a major income earner for smallholder households in the study area Data Sources The study adopted a survey design for collecting primary data among the Mwea smallholder French bean producers. A semistructured questionnaire was used to elicit data on French bean outputs and prices, farm and household characteristics, labor resources, quality characteristics (storage facilities, record keeping, and inputs delivery), membership in local groups, investments when adopting EurepGap requirements, compliance costs and benefits and constraints of compliance. Information such as general perceptions of the requirements was obtained through informal discussions with farmers and exporters. A list of farmers who grow French beans was obtained through the assistance of the Community Development Agency (CDA), which is involved in the registration of community development groups with the Ministry of Gender, Sports, Culture and Social Services. A sampling frame consisting of these farmers who grow French beans from both locations was then developed. One-hundred three farmers were then randomly selected and interviewed. The interviews were conducted in April Theoretical Framework This study uses utility theory to explain the behavior of households. A household faces two alternatives: to comply or not to comply with GlobalGap standards. We assume that a household s utility resulting from either alternative depends upon several characteristics of the household. The utility of an alternative is a function of the attributes of the household, which is given by U 0 ¼ b0 0 X1e 0 ð1þ where U0 is the utility from choosing an alternative; X is a vector containing the characteristics of household/farmer; b 0 0 is a parameter vector, and e 0 is the error term, capturing the uncertainty. Then the utility of complying with EurepGap can be specified as U A ¼ b0 A X1e A ð2þ where U A, b0 A,ande A are the utility, parameter vector, and stochastic part of complying with EurepGap standards, respectively.

4 196 MURIITHI, MBURU, AND NGIGI If the farmer does not comply with the standards, we have UN ¼ b0 N X1e N ð3þ where UN, b0 N, and e N are the utility, parameter vector and stochastic part of not complying with EurepGap standards, respectively. Therefore, the farmer s net utility between complying and not complying is U ¼ UA U N ¼ðb 0 A b0 N ÞX1ðe A e N Þ ¼ b 0 X1e ð4þ where U, b 0,ande are the net utility, parameter vector to be estimated, and the stochastic part, respectively. As the farmer s net utility is a latent variable, we cannot observe it directly. But if U 40, the observed choice will be compliance with EurepGap standards (or compliance 5 1) and if U r0, the observed choice will be the noncompliance with EurepGap standards (compliance 5 0) Empirical Model Specification Guided by the above theoretical model, the estimation that follows is to assess how specific farm and household characteristics, market characteristics, support services, and policy environment variables influence compliance with the EurepGap standards. To account for self-selection bias and to investigate the robustness of the econometric estimates, three alternative models are applied in the analysis whose findings would detect whether there are specification problems. The first model is the linear probability model (LPM). Due to the functional form of LPM, binary response models (probit and logit models) are also estimated. The binary response model has an S-shaped relationship between the independent variables and the probability of an event, which addresses the problem with the functional form in the LPM (Long, 1997). The dependent variable in this multiple regression is a dummy of the compliance with EurepGap requirements. It is assumed that the decision of the ith farmer to comply with the EurepGap requirements or not depends on an unobservable variable I i that is determined by more than one explanatory variable, represented by X j. The regression model can be illustrated as follows: I i ¼ b 1 1b 2 X ij ð5þ where X ij represents a set of independent variables influencing the decision of the ith farmer to adopt the EurepGap requirements. The unobservable variable I i (also known as a latent variable) is related to the actual decision to comply with the EurepGap standards: Y 5 1 if the farmer complies and Y 5 0 otherwise, such that Yi ¼ 1 if I i 40 0 Otherwise Assuming that the unobservable variable I i is normally distributed with the same mean and variance, the probability that the farmer will decide to make any of the above decisions (to comply or not to comply) can be expressed as: P i ¼ PðY ¼ 1=XÞ ¼PðZ i b 1 1b 2 X ij Þ ð6þ ¼ Fðb 1 1b 2 X ij Þ ð7þ where P(Y 5 1/X) is the probability that a farmer will comply given the values of the explanatory variables and Z i is the standard normal variable, ZN(0,s 2 ). F is the standard normal cumulative distribution function (CDF), while b 1 is the constant term and b 2 is the coefficient to be estimated (Gujarati, 2004).

5 COMPLIANCE WITH EurepGap STANDARDS 197 If X represents a vector of determinants of the farmer s decision then the basic form of binomial probit model is reduced to: Y i ¼ b 0 1b 1 X 1 1b 2 X 2 11b j X j 1e i ð8þ where b 0 is the constant term, b 1 y.b j are the coefficients to be estimated, e i is the error term, and X 1 X j are the explanatory variables. As mentioned earlier, the decision to comply with the EurepGap standards varies across households according to many contextual factors. In other words, these factors determine the ability of the farmers to meet the costs of complying with EurepGap requirements. The compliance decision model to be estimated can thus be specified as follows: COMP i ¼ a 0 1 X d FM i 1 X j SOEC i 1 X b MKT i 1 X g SUPSVS i 1e i ð9þ where COMP i is the decision made by farmer i to comply or not with the EurepGap standards. This takes the binary probit expression, COMP 5 1 have complied 0 5 otherwise. a 0 is the constant term, d, f, b, and g are the coefficients to be estimated, FM i is a vector of farm characteristics variables of farmer i, SOCIOECO i is a vector of socioeconomics factors, MKT i is a vector of market characteristics such distance to the nearest market, SUPSVS i is a vector of support services available to the farmer, and e i is the error term. For nonlinear models such as probit, besides the estimation of parameters, obtaining average partial effects (APEs) is necessary to assess the effects of any change in the covariates on the dependent variables (Wooldridge, 2002). This is important because the model can be used to evaluate policies. To demonstrate the APEs, let s assume a structural model, P(Y 5 1/X,q), where X is a vector of observed explanatory variables and q is an observable random variable the unobserved heterogeneity. When X j is continuous the partial effect ¼ 1=X; qþ=@x j ð10þ For continuous changes, this derivative could be given as y j (X,q); for discrete changes, this could be defined as the difference in PðY ¼ 1=X; qþ at two difference values of X j In this study, we relax the distributional assumption of unobserved individual heterogeneity term, and assume the distribution is normal. Therefore, holding the vector of unobservable heterogeneity (q) and other independent variables constant (w), the APE of X j can be calculated j ðx 0 Þ¼Pðy j ðx 0 jwþ ð11þ where w denotes other variables that are held constant. By obtaining the APEs, which is the partial effect averaged across the population distribution of the unobserved heterogeneity, the unobserved heterogeneity is integrated out. In this study, for example, the APE of access to extension services (EXTENSION) among the surveyed sample of French bean growers would be denoted j ðextension 0 Þ¼Pðy j ðextension 0 jhhgender; EDU HH; AREA FRB; DIST MARKET; HHSIZE; OFF-FARM; ENTERPRISES; GRP WATER; PRICEPREMIUMÞ ð12þ A description of the above variables utilized for analysis and their expected signs is provided in Table Hypothesized Factors That Influence Compliance With EurepGap Standards Various studies have cited different factors or characteristics as major barriers to compliance with food safety standards. Charlotte and Fairman (2006), in their study in the UK, indicate that lack of adequate knowledge/ information and lack of finances are major barriers to compliance with food safety standards. Muaz et al. (2005), in his study in Jordan, reveals that SPS and EurepGap regulations favor farmers with large farms. Antle (1995) also agrees that

6 198 MURIITHI, MBURU, AND NGIGI TABLE 1. Description of Variables and their Hypothesized Signs Variable symbol Description Expected signs HHGENDER Gender of the household head (1 5 Male, 0 5 Female) 1 EDUC_HH Education of the household head (number of years of schooling) 1 AREA_FRB Area under French beans (hectare) 1 EXPERIENCE Experience in French bean production (years) 1 DIST_MARKT Total distance from farm to the market (km) EXTENSION Dummy variable for extension training received in the last 1 12 months (1 5 Yes, 0 5 No) HHSIZE Household size in number counts 1 OFF_FARM Dummy for access to off-farm income (1 5 Yes, 0 5 No) 1 ENTERPRISES Total number of farm enterprises 1 GRP_WATER Dummy variable for membership of a water organization 1 (1 5 Yes, 0 5 No) PRICEPREMIUM Dummy variable for whether price premium is expected (1 5 Expected, 0 5 Not expected) 1 size of the farm could explain the importance of the cost of implementation as an incentive to adopt food safety and quality practices. High social capital built through group membership (North, 1990) could also influence adoption of new technologies such as food safety standards. Groups facilitate exchange of information and investment in infrastructure, and bargaining is easier for favorable certification deals (Guenther, 2006). Support services provided to farmers through extension are likely to influence positively their decision to comply with food safety standards. Favorable government policies and investments in infrastructure, education, information access, market access, and credit access are expected to positively influence marketing of horticultural products (Jaffee, 2003). Other variables that could influence adoption of new technologies or innovations such as food safety standards include expectation of a price premium, gender of the household head, size of household, accessibility of off-farm income and existence of many enterprises in the farm which enables farmers to mitigate risks (Adesina & Chianu, 2002; Ellis, 1993). 3. DESCRIPTIVE STATISTICS AND FARMERS CONSTRAINTS WHEN COMPLYING WITH EUREPGAP 3.1. Descriptive Statistics About 79% of the sampled farmers had complied with EurepGap. As presented in Table 2, the mean level of education of the household heads was 10.1 years; the highest education level acquired by the other members of the family living permanently in the homestead was 12.2 years. This was an indication that most of the farmers are literate. The average farm size owned and total size of land cultivated (including rented land) was 0.88 hectare and 1.38 hectare, respectively, indicating that all the farmers in the study area can be classified as small-scale farmers (Graffham et al., 2007). The average price of French beans was $0.66 per kilogram with maximum of $1.64 and minimum of $0.39 per kilogram. The high price differentials are influenced by the market forces in the international market (Tineke, 2003). Most of the respondents had been engaged in French bean production for a long time with an average experience of 9.3 years. EurepGap compliant farmers had the longest experience in French bean production with an average of 10 years. About 64% of the respondents were members of production and marketing farmer organizations (PMOs) or farmer groups with an average of 3.8 years of group membership. About 80.6% of respondents produced under market contract. About 52% of the farmers who had complied with the EurepGap standards had actually received the certificates with an average of 1.5 years of certification. Other variables are as given in Table 2.

7 COMPLIANCE WITH EurepGap STANDARDS 199 TABLE 2. Descriptive Statistics of Selected Variables of Smallholder French Bean Producers in Kirinyaga District Variable n Mean Std. Deviation Min. Max. Age of household head (years) Farming experience (years) Level of education of household head (years) Highest level of education of all members of the family living permanently in the homestead (years) Total land owned (hectares) Total land size (owned and rented, hectares) Total Household size (number counts) Area under French beans (hectares) Amount produced per hectare (Kgs) 103 3, ,362 9,080 Average price of extra and fine beans sold (US,$) Experience in French bean production (years) Years of group membership Contract farming (years) Export production (years) Distance from the farm to grading shed (Km) Distance from the farm to market (Km) Number of years the farmer has been certified Gross income obtained from French bean production (US, $) 103 1, , ,000.0 Total cost of production (US, $) 103 1, , , Net income obtained from French bean production (US, $) , Note: Data is coded 1 compliant and 0 otherwise (non-compliant). Source: Author s computation. Statistical significance at 0.01 level of probability. Statistical significance at 0.05 level of probability. The study went further to compare the above household and farm characteristics between EurepGap compliant and noncompliant farmers using t test (quantitative characteristics) and chi-square (qualitative characteristics). Farming experience, total land under cultivation and area under French beans, household size, gross income obtained from French bean production, and cost of French beans production were significantly different between the two categories of farmer, as shown in Table 2. All these are related to gross production of French beans. A large household, for example, is expected to supply more labor as demanded by French bean production and a high number of enterprises on the farm can provide the required capital and mitigation from risks when complying with EurepGap standards. The number of famers producing French beans on contract, assessing extension services and credit, and participating in production and marketing groups was significantly different between EurepGap compliance and concompliant famers. For example, 99% of the compliant farmers produced French beans under contract; only 14% of the noncompliant were farming under contract. About 83% of compliant farmers had access to extension services with the same services reaching about 36% of the noncompliant farmers. About 24% of noncompliant farmers where reported to access credit whereas no one accessed credit among the compliant farmers Constraints Encountered in Compliance With EurepGap Standards Discussed under this section are constraints encountered at the initial stage of complying with EurepGap as well as the constraints of maintaining the standards. Constraints that hinder compliance, as reported by noncompliant farmers are also discussed. Figure 1 shows the percentage farmers reporting different constraints encountered at the initial stage of compliance. Lack of finances to set up required buildings and facilities was reported as the major constraint experienced at this stage, reported by about 49% of total compliant farmers. The buildings included grading sheds, bathrooms, chemical store, and charcoal cooler among

8 200 MURIITHI, MBURU, AND NGIGI Figure 1 Percentage of EurepGap Compliant Farmers Reporting Constraints Encountered at the Initial Stage of the EurepGap Certification Process. Source: Author s computation. Figure 2 Percentage of EurepGap Compliant Farmers Reporting Constraints Encountered in Maintaining EurepGap Certificate. Source: Author s computation. others. Complex requirements were also cited as a constraint. A good example was the recommended chemical spraying program, which farmers considered difficult and expensive to implement. As earlier indicated, smallholder farmers own very small pieces of land, monocropping as one of the standards requirement was therefore regarded as a unrealistic condition that farmers could not be able to meet. High labor costs were cited by the highest percentage of compliant farmers as a major constraint experienced in maintaining the standards (see Fig. 2). This included employment of additional staff members such as produce graders, field supervisors, sprayers, and clerks. The low prices of the produce were also mentioned by a number of farmers as well as high transaction costs in terms of time required for training. A few farmers reported that the quality of their produce had deteriorated, mainly due to inefficient chemicals that led to high levels of produce rejects and hence low returns. Further research, however, needs to be done to establish whether recommended chemicals have contributed to low-quality produce. Also cited was increased cost of farm inputs, for example the high cost of newly recommended chemicals, which raised the cost of production. All the farmers who had not complied with EurepGap standards stated lack of finance as the major constraint hindering compliance as stated in Table 3. Due to lack of finances, some farmers had been forced to pull out from contract farming where they were required to comply to maintain the farming arrangements. Noncompliance was also associated with lack of information and technical assistance as regards to compliance with EurepGap standards. High pests and disease infestations were also mentioned as a constraint hindering

9 COMPLIANCE WITH EurepGap STANDARDS 201 TABLE 3. Percentage of EurepGap Noncompliant Farmers Reporting Constraints Hindering Certification With EurepGap Certification Process N 5 22% Lack of technical assistance 9.5 High pest and diseases affecting returns 47.6 Lack of finances to carry out auditing and training 42.9 Lack of finances for constructing required facilities Low production hence low returns 28.6 High cost of involved labor 66.7 Lack of information about the standards 66.7 Low prices of produce 33.3 Complex conditions which are difficult to implement 47.6 Lack of time required for trainings 14.3 Source: Author s computation. TABLE 4. Average Partial Effects (APEs) Probit Model Estimates for EurepGap Adoption in Kenya Variable APEs SE z P4z PRICEPREMIUM HHGENDER EDUC_HH AREA_FRB EXPERIENCE DIST_MARKT EXTENSION HHSIZE OFF_FARM ENTERPRISES GRP_WATER y 5 Pr(EUREPGAPADOPTION) Number of observations LR w 2 (11) Prob4w Pseudo R Log likelihood Significant at 10%; significant at 5%; significant at 1%. compliance. Farmers related the reduced French beans production over the recent years to these infestations, hence discouraging them from complying with the standards. 4. DETERMINANTS OF COMPLIANCE WITH EUREPGAP STANDARDS As explained in Section 2, a probit model was estimated to investigate factors that influence the decision of farmers to comply with the EurepGap standards. Further APEs were estimated to assess the effect of any change in the covariates on the dependent variables. The results of the APEs estimated after binomial probit model are presented in Table 4 (see definitions of variables in Table 1). The dependent variable is a dummy variable with two categories of choices: 1 if the farmer is compliant with the EurepGap standards and 0 if otherwise. The results show the APEs of the independent variables, standard error, and Z and P values and the 95% confidence interval of the coefficients. The results of the goodness of fit (log likelihood ratio chi-square of with a P-value of 0.000) showed that the model as a whole is statistically significant in explaining the farmers decision to comply with EurepGap standards. The pseudo R 2 is given as

10 202 MURIITHI, MBURU, AND NGIGI From Table 4 total area under French beans, access to extension training, access to off-farm income, distance to the nearest market, household size, and total number of farm enterprises owned by the farmer had a significant influence on farmer decision to comply with the EurepGap standards Discussion of Model Results In this section, we discuss the results of the significant variables. Results of APEs in Table 4 show that, as total farm area under French bean increases, the probability of compliance increases as predicted. Increase of land by one hectare increases the probability of compliance by about 9%. Thus, farmers with large farms under French beans production are likely to comply than those producing in small pieces of land. This result is similar to that found in developing countries by Charlotte and Fairman (2006). Exposure to information through extension services reduces subjective uncertainty and therefore increases the likelihood of adoption of a new technology. The approach used to capture the impact of information in this study was to determine whether a farmer had access to extension training in a given time. This factor had a positive influence on the decision of the farmer to comply with EurepGap standards. Access to extension training increased the probability of complying with the standards by about 23%. Farmers who had received extension training in the last 12 months were more informed, and hence had higher chances of complying with EurepGap standards than their counterparts. The results agree with Okello (2005), who found out that access to extension services increases the likelihood of adoption of international food standards among small-scale farmers. Access to off-farm income was found to be negatively associated with the decision of the farmer to comply. This factor reduces the probability of compliance by about 14%. This was contrary to Okello (2005), that extra income from off-farm activities could act as a catalyst to compliance where farmers can access the capital required to set up the necessary facilities. From the study area, it was observed that farmers engaged in other nonfarm businesses did not pay much attention to the farming business. They did not consider farming as a commercial enterprise and hence were unwilling to comply with the standards. Farmers are rational and they will not comply with the standard if it is not worthwhile. However, to our surprise, expectation of price premium does not explain the compliance decision: the variable was insignificant. This agrees with a number of studies, for example Graffham et al. (2007), who noted that most of the farmers who withdrew from GlobalGap complained of lack of or inadequate price premium. In this study, it was observed that farmers comply mainly for market access, that is, acceptance of their product in the market and not for price increment. Compliance promotes contract farming with the exporters. The primary benefit for farmers of contract farming is a reduction of economic risk, while contractors are guaranteed a steady source of supply. However, this does not imply higher prices of their products. Distance to the nearest market was found to be positively related to compliance decision, against prior prediction. The study observed that collection centers of French beans are located within the proximity of the farmers belonging to a certain farmer group and not at the nearest market center. Similarly, brokers collected French beans at farm gate from the noncompliant farmers. This could explain the unexpected relationship between compliance and distance to the nearest market. As predicted, household size was found to be statistically significant and positively influencing compliance decision. French bean production is an intensive activity that is highly labor demanding. A larger household therefore implies easy access to required labor and therefore a higher probability of compliance. The result suggests that, an increase of family size by one adult-equivalent individual increases the probability of compliance by about 5%. To spread the uncertainties and risks involved in the agricultural industry, farmers invest in more than one enterprise. The total number of farm enterprises in the farm was tested to determine their influence on farmers decisions to comply with the EurepGap standards. An increase of the number of farm enterprises by one increased the probability of compliance by

11 COMPLIANCE WITH EurepGap STANDARDS 203 about 9%. The results show a positive influence on the decision of the farmer to comply with EurepGap standards, agreeing with the role of diversification of enterprises on minimization of effects of risk (Ellis, 1993). 5. CONCLUSIONS AND POLICY IMPLICATIONS This study has empirically analyzed factors influencing compliance with EureGap standards in a smallholder set-up in Kenya. It has revealed that compliance with the standards is positively influenced by socioeconomic and farm characteristics such as area under French beans, household size, total number of farm enterprises, distance to the nearest market, and availability of external support from extension services, but negatively by access to off-farm income. It has also examined some of the major constraints farmers face when complying with the standards. Availability of capital required for the high cost of compliance was found to be a major constraint on compliance. In some cases, some farmers were reported to have dropped out from the farmer groups or contract farming because they could not raise the money required for compliance. Other constraints pointed out by farmers included the high cost of the recommended chemicals and fertilizers, increased pests attack and disease, and increased cost of hiring extra personnel. The results of this study have three major policy implications: first, the policy makers need to look at how compliance with EurepGap standards could be made cheaper for the farmers. This would call for mobilization of government trade partners and other stakeholders to come together to explore ways of making compliance affordable to the smallholder farmers. Second, increased access to extension services by farmers in the study area will contribute to more compliance with the standards. Hence, government and nongovernmental organizations can invest more in the provision of these services. Third, the research offers suggestions for targeting farmers who are likely to comply with the standards. This could be useful for any future promotional efforts to enhance compliance. Farmers with larger acreage under French beans and those with more crop and animal enterprises could be targeted to upscale compliance with the standards. However, government agencies and development partners would not reap good results if they focus their promotional efforts on farmers involved in offfarm activities. REFERENCES Adesina, A.A., & Chianu, J. (2002). Determinants of farmers adoption and adaptation of alley farming technology in Nigeria. The Journal of Agroforestry Systems, 55, Aloui, O., & Kenny, L. (2005). The cost of compliance with SPS standards for Moroccan exports: A case study. Washington, DC: The World Bank. Antle, J.M. (1995). Choice and efficiency in food safety policy. Washington, DC: American Enterprise Institute. Charlotte, Y., & Fairman, R. (2006). Factors affecting food safety compliance within small and medium-sized enterprises: Implications for regulatory and enforcement strategies. The Journal of Food Control, 17(1), Dolan, C., & Humphrey, J. (2000). Governance and trade in fresh vegetables: Impact of UK supermarkets on the African horticultural industry. The Journal of Development Studies, 37(2), Dolan, C., Humprey, J., & Harris-Pascal, C. (1999). Horticulture commodity chains: The impact of the UK market on the African fresh vegetable industry. Working Paper 96. Brighton, UK: Institute for Development Studies. Ellis, F. (1993). Peasant economics. Farm households and agrarian development (2nd ed.). Cambridge, UK: University of Cambridge Press. European Retail Produce Working Group Good Agricultural Practices (EUREPGAP). (2004). General Regulations Fruits and Vegetables (Versions 1.1 and 2.1). Retrieved January 12, 2007, from Graffham, A., Karehu, E., & MacGregor, J. (2007). Impact of EurepGap on small-scale vegetable growers in Kenya, Fresh Insights 6. Kent, UK: Natural Resources Institute. Retrieved April 11, 2008, from Guenther, D. (2006, March). Building up an internal control system (ICS) for certification to EUREPGAP option 2 in the horticultural sector. Paper presented at the Trade Standards Working Group Meeting, Washington, DC. Gujarati, D.N. (2004). Basic Econometrics (4th ed.). New York: McGraw-Hill. Henson, S., & Loader, R. (1999). Impact of sanitary and phytosanitary standards on developing countries and the role of the SPS Agreement., 15(3), Horticultural Crops Development Authority. (2002). Horticultural news: Horticulture export performance. The Official News Bulletin of HCDA, 28, 5 6.

12 204 MURIITHI, MBURU, AND NGIGI Horticultural Crops Development Authority. (2007, March). Horticulture data: Validation report. Nairobi, Kenya: Author. Horticultural Crops Development Authority. (2008). Export statistics, Retrieved February 13, 2009, from Jaffee, S. (2003). From challenge to opportunity: The transformation of the Kenyan fresh vegetable trade in the context of emerging food safety and other standards. Agriculture and Rural Development Working Paper No. 10. Washington, DC: The International Bank for Reconstruction and Development, Agriculture and Rural Development Department. Long, J.S. (1997). Regression models for categorical and limited dependent variables. Sage Series for Advanced Quantitative Techniques. Thousand Oaks, CA: Sage. Mausch, K., Dagmar, M.D., Asfaw, S., & Waibel, H. (2006, October). Impact of EurepGap standards in Kenya: Comparing smallholder to large-scale vegetable producers. Paper presented at the Conference on International Agricultural Research for Development, University of Bonn, Germany. McCulloch, N., & Ota, M. (2002). Export horticulture and poverty in Kenya. Working Paper 174. Brighton, UK: Institute for Development Studies. Ministry of Agriculture. (1996). District Annual Report. Kerugoya district, Kenya: Author. Minot, N., & Ngigi, M. (2004). Are horticultural exports a replicable success story? Evidence from Kenya and Cote devoire (EPTD Discussion Paper No. 120, and MTID Discussion Paper No. 73). Washington, DC: International Food Policy Research Institute. Muaz, S., Jabarin, A., Assaf, L., Sahawneh, M., AL-Rwadan, O., Allam, H., et al. (2005). An economic analysis of food safety standards and its implication on agricultural trade in the context of EU-MED partnership. The case of SPS standards and EUREPGAP requirements. Al-Jubaiha: Royal Scientific Society of Jordan, Femise Research Programme. Retrieved April 11, 2007, from Mutuku, M., Tschirley, D., & Michel, T.W. (2004). Improving Kenya s domestic horticultural production and marketing systems: Current competitiveness, forces of change, and challenges for the future. Volume II. Horticultural marketing. Working paper No. 08 B/2004. Nairobi: Tegemeo Institute of Agricultural Policy and Development. North, D.C. (1990). Institutions, institutional change and economic performance. Cambridge, UK: University of Cambridge Press. Okello, J.J. (2005). Compliance with international food safety standards: The case of green bean production in Kenyan family farms. Unpublished doctoral dissertation, Michigan State University, East Lansing, MI. Tineke, V.D. (2003). Export chain of French beans from Kenya. Unpublished masters thesis, Wageningen University, Germany. Wooldridge, J.M. (2002). Econometric analysis of cross section and panel data. Cambridge, MA: MIT University Press. Zoss, M., & Pletziger, S. (2007, October). Linking African vegetable smallholders to high value markets: Potentials and constraints in smallholders integration into GLOBALGAP-certified and/or domestic African high-value supply-chains. Paper presented at the International Agricultural Research for Development Conference, University of Kassel-Witzenhausen, Witzenhausen, Germany. Beatrice Wambui Muriithi is a research assistant at the International Livestock Research Institute (ILRI) in Nairobi. She received her B.S. in Agricultural Economics from Egerton University, Kenya in 2005 and a M.S. in Agricultural and Applied Economics from Egerton University and the University of Pretoria, South Africa in Her research interests include policy analysis, livelihood analysis including gender, economic modeling, impact assessment of agricultural research, and cost-benefit analysis. John Mburu is a senior lecturer in the Department of Agricultural Economics at the University of Nairobi, Kenya. He received his B.S. in Agriculture from the University of Nairobi in 1990, his M.S. in Agriculture with a specialization in socio-economics of rural development, production economics, and rural sociology from the University of Goettingen, Germany, in 1999, and his Ph.D. in Environment and Resource Economics from the University of Goettingen in His research interests include economic valuation of forests, wildlife, and agro-biodiversity; analysis of the efficient and sustainable approaches of conservation of natural resources; cost-benefit analysis; incentives for conservation of biodiversity and natural resources; market chain analysis; microfinance; and poverty. Margaret Ngigi is a senior lecturer in the Department of Agricultural Economics and Management at Egerton University. She received her B.S. in Agriculture in 1985, her M.S. in Agricultural Economics in 1989, and her Ph.D. in Agricultural Economics in 2002 from the University of Nairobi, Kenya. Her research interests include agricultural policy, market chain analysis, economic modeling, costbenefit analysis, and institutional economics.