Who Offers Veterinary Services to Smallholder Dairy Farmers in Western Kenya? Lessons from Kakamega County

ABSTRACT

Liberalization of both clinical and artificial insemination services in Kenya allowed many players into the livestock service sector. This study examines key providers of veterinary services and the factors that influence the delivery of veterinary services among smallholder dairy farmers in Kakamega County. Socio-economic data were collected through a household survey of 128 randomly selected households and 30 purposively selected service providers. The descriptive statistics revealed that 59% of all cases were attended to by animal health assistants, with private animal health assistants attending to 38% of all the cases. The results of the multinomial logit econometric model estimated with self-treatment as the base category revealed a negative influence of distance on the choice of government service providers but a positive relationship with tropical livestock unit, treatment cost and education level of the farmer.

KEYWORDS:

• Animal health
• service delivery
• smallholder dairy
• Kakamega

Introduction

Livestock production plays a critical role in the livelihood of rural populations in Kenya. Privatization of clinical veterinary services in the early 1990s resulted in the emergence of different livestock health service delivery systems and players (Oruko et al., 2000). Some of the players included private and government veterinary surgeons, private and government animal health assistants (AHAs), community based animal health workers (CAHWs) and non-formally trained para-vets (Irungu et al., 2006). However, the key players of the livestock service delivery system among the smallholder dairy farmers in medium potential areas are still not clearly understood, notwithstanding that this cadre of farmers contribute about 60–80% of Kenya's milk output (Oruko et al., 2000). This study aims to examine the key providers of veterinary services and the factors that influence demand and delivery of veterinary services among smallholder dairy farmers in Kenya, taking the case of Kakamega County as an example.

Materials and Methods

The study was conducted in Kakamega North District, Kakamega County in Western Kenya. A total of 128 randomly selected households and 30 purposively selected livestock service providers were interviewed. Both descriptive statistics and econometric methods were employed in the data analysis. A multinomial logit model was used to analyse the data due to its ability to analyse a number of response categories at the same time, taking into account the interactions between various categories of the variables (Hailpern and Visintainer, 2003).

Model specifications

The choice of service provider (Spp) is a nominal outcome variable with categories 1=Government for government veterinary service providers, 2=Private for private veterinary service providers and 3=Self for those who treated their livestock. The independent variables include the distance to the government veterinary office (dist), the value of the herd size (tlu), the cost of accessing veterinary service (tr_cost1), the education level of the household head (Educ2) and the nature of the disease (Endemic), whether it is endemic in the area or not.(1) Where b is the base category or the comparison group; m is the other categories in the comparison group; Ω is the multinomial logit function; β is the column vector for the parameters to be estimated. Since , it must hold that (Long and Freese, 2006).

Results

The results indicate that East Coast Fever (ECF) is the most prevalent disease in the area, accounting for 32% of all the reported cases, as shown in Figure 1. This is followed by anaplasmosis, which accounts for 16% of all reported cases. Tick-borne diseases account for 65% of all reported cases.

Figure 1. Prevalence of livestock diseases in Kakamega North District. ECF = East Coast Fever, FMD = foot and mouth disease, LSD = lumpy skin disease.

Private animal health assistants (Private AHAs) are the key providers of clinical veterinary services to the farmer, attending to 38% of all the cases as shown in Table 1. The government animal health assistants (Govt AHAs) only attend to 21% of all the cases. About 63% of the service providers in the district are animal health assistants who have a certificate or diploma level of education and only 10% were veterinary surgeons.

Table 1. The proportion of cases attended to by various service providers.

N=71	Category	Frequency	Percent
Valid	Private animal health assistants (AHAs)	27	38
	Non-trained para-vets	19	27
	Government AHAs	15	21
	Treatment with local medicine	7	10
	Private veterinary surgeon	2	3
	Drug seller	1	1
Total		71	100

Empirical multinomial logit estimates revealed a negative influence of distance variable (dist) on the choice of the government service provider but a positive influence on the choice of private veterinary service providers. This is significant at (p<0.05 and p<0.10) for government veterinary service providers. The tropical livestock unit variable (tlu) has a positive effect and is significant (p<0.10) for government service providers but not significant for private vets. Those with secondary education and above (Educ2) had a positive effect and was significant at p<0.10 for government veterinary service but not significant for private veterinary service (Table 2).

Table 2. The estimates of the multinomial logit model analysis

	Estimation coefficients – multinomial logit model
Variable	Government	Private	Self-treated
Dist	−0.1829*	−0.0180	This is the
Tlu	0.4387*	0.1847	Base
tr_cost1	0.0099***	0.0046***	Outcome
Educ2	2.2262*	0.9633
Endemic	0.9600	2.3241**
Constant	−10.8987***	−4.6134***

* ,** , ***= significant at 10%, 5% and 1%, respectively.

The treatment costs variable (tr_cost1) is significant at (p<0.01) with a positive coefficient in the choice of both government and private veterinary service providers. The variable (Endemic) is only significant at p<0.05 for private veterinary service providers.

Discussion

The prevalence of tick-borne diseases is one of the major constraints to livestock production in Kakamega County, as is the case in many parts of Kenya and many tropical countries especially in Sub Saharan Africa, confirming the findings of Thornton (2010). The most prevalent disease is East Coast Fever (ECF), a situation similar to the observations made by Oruko et al. (2000) among dairy farmers in Kilifi District and also by Bebe et al. (2003) among smallholder dairy farmers in the Kenya highlands. Liberalisation was a deliberate policy to enhance private players’ participation in delivery of livestock related services to the farmers (Government of Kenya, 1996 cited in Oruko et al., 2000). This study has revealed that private practising animal health assistants are the key providers of veterinary services in the County with very low participation of veterinary surgeons. In a related study, Musalia et al. (2010) observed that most smallholder dairy farmers preferred to hire the services of less experienced animal production and animal health technicians because they charged less compared to the more qualified veterinary surgeons. This confirms that a great majority of the rural poor still do not enjoy the range and quality of services required to support the growing livestock industry, as observed by Cheneau et al. (2004).

In a study on access to breeding services by smallholder farmers, Murage and Ilatsia (2011) reported increased access to AI (artificial insemination) services as a result of close proximity to the technicians. Long distances coupled with poor infrastructure can be very instrumental in determining demand and delivery of veterinary services (Owango et al., 1998; Kathiravan et al., 2009). More often, smallholder dairy farmers would prefer non-trained paraprofessionals because they are readily available and flexible in service charges (Irungu et al., 2006; Musalia et al., 2010).

The tropical livestock unit (tlu) had a positive relationship with the demand for government service providers. These results are consistent with the findings of Tambi et al. (1999), which showed that farmers with large herd sizes are more likely to respond to herd health service needs. The results predicted a marginal increase in the probability of choosing both government and private service providers, despite the increase in treatment cost. These findings corroborate the findings of Ahuja and Redmond (2004), which found a high willingness of the farmers to pay for livestock services, cost notwithstanding. Education increases the farmer's management capacity and his/her ability to tackle complicated information related to modern livestock production techniques, hence influencing the management decisions taken at the farm. The significant potential increase in the probability of choosing government veterinary service is a clear indication that better educated farmers seek services of government veterinary services, which is consistent with the observations by Irungu et al. (2006) who noted that a shift from informal to formal education would have a significant effect on the choice of the service provider. However, the case of endemic diseases predicted a high increase in the probability of farmers going for private veterinary service, which is understandable because they are closer to the livestock keepers. Any complications would then be referred to providers in the higher cadre. This type of arrangement is similar to that observed by Rubyogo et al. (2005) among the community animal health workers and the AHAs in Mwingi District.

Conclusion

As the title of the paper, we posed the question “who offers veterinary services to smallholder dairy farmers?” Results show that private animal health assistants are the key service providers, which can be an indication that privatization is taking root. The role of the government should focus more on disease surveillance and supervision of the private service providers to ensure the quality of livestock service delivery.

Acknowledgement

This research was financially supported by Church Development Service (EED), Bonn, Germany.