Determinants of utilisation of artificial insemination (AI) services among Ugandan dairy farmers

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1 African Crop Science Conference Proceedings, Vol. 7. pp Printed in Uganda. All rights reserved ISSN X/2005 $ , African Crop Science Society Determinants of utilisation of artificial insemination (AI) services among Ugandan dairy farmers H. KAAYA, B. BASHAASHA 1 & D. MUTETIKKA 2 Department of Veterinary Services and Animal Industry, P.O. Box 72, Mukono, Uganda 1 Department of Agric. Econ. and Agribusiness, Faculty of Agriculture, Makerere University, P.O. Box 7062, Kampala,Uganda 2 Department of Animal Science, Faculty of Agriculture, Makerere University, P.O. Box 7062, Kampala, Uganda Abstract Cross-sectional data were collected from a total of 180 randomly selected households in Mukono, Wakiso and Kayunga districts of Central Uganda, to establish the determinants of utilisation of Artificial Insemination (AI) technology among Ugandan dairy farmers. The dairy farmers were categorized into AI-users and non-users (using natural service). The data included characteristics, of the household and household head, farm and farm management and attributes of the AI service itself. Data were summarised to generate means, frequencies and percentages and a chi-square test was applied to test for differences observed between means. Data were also subjected to analysis using the Tobit Model to establish the relationship between variables that significantly influenced the utilisation of AI technology/service by dairy farmers. The percentage of farmers using AI technology was found to be average Age of the farmer, years of awareness of the AI technology, total farm milk production and sales, extension visits per year, and quality of AI services provided to the farmers were positively associated with adoption and use of AI technology. Besides farm level cost of AI services, farming experience, herd size, breed of animals were negatively associated with adoption and use of AI technology. Intensifying extension in form of farm visits, improvement in the quality and, reduction in the cost of AI services and increased availability of skilled AI technicians, were identified as avenues to enhance adoption and use of Artificial Insemination. Key words: Artificial insemination, cattle breeding, dairy farmers, Uganda Résumé Des données des éléments croisés étaient collectés à partir d un total de 180 foyers sélectionnés au hasard dans les districts de Mukono, Wakiso et Kayunga, dans le centre de l Ouganda, pour établir les déterminants de l utilisation de la technologie de l insémination artificielle (AI) entre les fermiers laitiers ougandais. Les fermiers laitiers étaient catégorisés en utilisateurs de AI et non utilisateurs de AI mais utilisant le service naturel. Les données avaient inclus les caractéristiques des foyers, des chefs des familles, de fermes, de la gestion des fermes et des attributs de service AI. Les données étaient résumées pour donner les moyens, les fréquences et les pourcentages, et aussi un test carré était utilisé pour évaluer les différences observées entre les moyens. Les données étaient aussi analysées en utilisant le Model Tobit pour établir les relations entre les variables qui d une manière significative influencent l utilisation de la technologie AI/Service des fermes laitiers. Le pourcentage des fermiers utilisant la technologie AI était en moyenne de 36,1. L âge du fermier, les années de la connaissance de la technologie AI, la production laitière totale de la ferme et la vente, l extension des visites par an, et la qualité des services AI fournie aux fermiers étaient positivement associes avec l adoption et l utilisation de la technologie AI. Malgré le niveau du prix de service AI par les fermes, l expérience des fermiers, le niveau de troupeau, l élevage des animaux étaient négativement associés avec l adoption et l utilisation de la technologie AI. En intensifiant l extension sous forme des visites, l amélioration de la qualité et la réduction dans le prix des services AI et l augmentation des techniciens formés en AI, étaient identifies comme moyen d augmenter l adoption et l utilisation de l Insémination Artificielle. Mots clés: Insémination artificielle, troupeau d élevage, fermiers laitiers, Ouganda Introduction Artificial insemination is the single most important technique ever devised for genetic improvement of animals in all aspects including milk and beef production (Dana et al., 1998). While more than 70% of animals are bred using AI in the developed world, the technology is almost practically not available in 25 developing countries 16 of which are found in Africa. In Uganda AI technology was introduced in 1960 first as a public sector service. Currently private players have come in to offer and improve AI usage. Despite implementation of the cattle breeding projects of , by the end of 2000 less than 4% of the country s 6 million heads of cattle were products of AI. Estimates by the Ministry of Agriculture Animal Industry and Fisheries (MAAIF) show that the country has the potential of breeding more than 1 million heads of cattle per year using AI. There is a need to establish the factors which determine the adoption and utilization of AI technology in Uganda. The present study identifies factors which influence usage of AI services among Ugandan dairy farmers and establishes the relationship that exists among the factors. It is hoped that the information generated will be useful to private service providers and policy makers to improve the marketing of the services. Recommendations on how to promote adoption and sustain usage of AI technology are suggested. Materials and methods Description of the study area. The study was conducted in three districts of Central Uganda namely Mukono, Kayunga and Wakiso. The area has a total cattle

2 562 population of 197,887 of which 76% were indigenous and 24% exotic or their crosses (Uganda Bureau of Statistics, 2002). The area lies in the nucleus where breeding of cattle using AI was first introduced in 1960, and these districts were among the pilot districts for the Danish International development Agency (DANIDA) supported Cattle Breeding Project in The area is currently served by a total of 22 AI technicians. Mixed crop livestock farming is the main economic activity. Sampling procedure and data collection. Multi stage sampling was used to select parishes/villages with cattle being targeted as a priority. In addition, stratified sampling techniques were used to target concentration of cattle production. Using information from the district extension staff and local leaders, a list of all sub-counties was compiled. Lists of dairy farmers were generated. A semistructured questionnaire was used to collect relevant information. The questionnaire was pre-tested on 20 farmers (5 farmers per district) after which it was revised to incorporate farmers concerns. A total of 180 farmers were interviewed. Both quantitative and qualitative data were collected during the study. Information was sought on age of farmer, years of schooling, major economic activity, marital status, size of household, farming experience, prior acquaintance with AI, membership of farmer organization, access to credit, farm size, method of breeding used, herd size, management system, available animal management structures, level of farm record keeping, general management of the cattle, contact with extension personnel, method of communication, distance to nearest AI technician, cost of AI services, and constraints to use of AI. Secondary data were collected from The National Animal Genetic Resources Centre Entebbe, District Veterinary Offices, Ministry of Agriculture Animal Industry and Fisheries, Farmers Organisations, and Milk Collecting centres. Econometric model. In this research, the dependent variable which is the proportion of cattle born using AI breeding technology is truncated and is treated as a latent variable (y * ) (Maddala, 2001). We let y * denote the proportion of cattle born using AI breeding technology and X denote a vector of the following 19 explanatory variables: age of household head, sex, marital status, family size, years in school, farming experience, farming occupation, herd size, access to credit, breed of cattle, time spend caring for the herd, management system, milk sold per month, annual extension visits, period of knowing AI, distance to nearest AI technician, distance to nearest source of bull, cows conceived after one AI service, and cost per AI service. µ is the error term assumed to be normally distributed with zero mean and variance. H. KAAYA et al. follow Tobin s (1958) argument that in such an instance, we should use a censored regression model also called the tobit model (or Tobin s probit). This model is also known in the literature as a censored normal regression model because some observations on y* (the dependent * y 0 are censored, meaning that variable) for which we are not allowed to see them. The tobit model is also called the limited dependent variable regression model. Our objective was to use this model to estimate the parameters β and. The censored regression model we used is specified as follows: for farmers with a positive proportion of cattle born using AI technology = 0 for farmers with a proportion of zero for cattle born using AI technology (farmers not using AI breeding technology) We estimated the model using a maximum likelihood method. The analysis involves maximising the likelihood function with respect to and to get maximum likelihood (ML) estimates of the parameters reported. The coefficients generated under this formulation can be interpreted like any other regression coefficients (Gujarati, 2003). Like any other model, the tobit model is not without limitations. The most important limitation being the need for the latent variable to, in principle, be able to take on negative values so the observed zero values are a consequence of censoring and nonobservability (Maddala, 2001). Nonetheless it has been extensively used in empirical studies. An excellent summary of such empirical applications is provided by Amemiya (1984). Data analysis. Data were subjected to Statistical analysis to generate descriptive statistics which included means, standard deviations and percentages. Quantitative data were coded and analysed using descriptive statistics. Qualitative data were organised categorically, reviewed repeatedly and continually and conclusions made. Skewnwess, kurtosis and distribution of the data on continuous explanatory variables like farmer s age, herd size, extension visits, years of schooling, milk production/ sold were checked using exploratory data analysis (EDA). Where continuous variables were not normally distributed, log transformation was used. Correlation was used to test for multicollineality. Data were further subjected to analysis using the Tobit Model For AI users the proportion of cattle born using AI technology for breeding as a dependent variable to identify the factors which determine the adoption and use of AI technology. Results and discussion We postulate a regression equation of the form: y = β x + µ ; * i i i µ σ 2 i N(0, ) However, in this study, we have a large number of observations on non AI users for which the proportion of cattle born using AI breeding technology is zero. We The farmer characteristics. A summary of the effect of characteristics of the household head on adoption and use of AI is presented in Table 1. Thirty six percent of the farmers use AI for breeding their animals. Age of farmer had a positive relationship with use of AI as farmers who used AI to breed their animals were significantly older

3 than non-users by about 5 years on average. In a study on the factors affecting dairy production in the peri-urban areas of Kampala, Tumutegyereize et al. (1999) reported that dairy farmers who had adopted zero grazing were older than those who had not. In addition, farmers who used AI for breeding had been involved in farming for a longer period (21 years) than those using natural service (17 years). Prior experience with the technology was also a factor where AI users had had prior experience of 13.2 years compared to non users who averaged 3 years. Household heads in the AI user group devoted more time to their herds per week than those in the other group. In the high potential areas of Kenya, Tambi et al. (1998) found a greater likelihood of using AI services among livestock farmers who devoted more time to the care of the breeding herd. Households in which AI was used tended to have more members than those using natural service. This is probably because breeding using AI introduces additional labor demands like routine observation of animals, communication with the AI technicians, restraint of the animal during insemination all of which call for more labour. Tumutegereize et al. (1999) estimated that family labour accounted for 80% of the total labour used in dairy farming. In another study in Kenya, International Livestock Research Institute (ILRI, 1999), total household membership was found to be positively associated with adoption of keeping of grade cattle. The level of education attained by the household head had an influence on the likelihood of using AI. AI users had spent a longer period of time at school (11.6 years) than non-users who spent 2.5 years. Successful breeding of animals using AI involves knowledge of record keeping, observation for signs of heat which requires a minimum level of education. In a similar study, Tambi et al. (1998) reported that improving the educational status of producers led to an increase in the likelihood that they would use AI. Education increases the farmers capacity to comprehend technical recommendations that require a certain level of literacy or numeracy (CIMMYT, 1998). As expected Farmers using AI tended to know more AI technicians than their counterparts who used the services of a bull. Therefore AI users stood a better chance of acquiring information on AI technology, accessing AI services and choosing the best technician to use. Reynolds (1996) reported that a successful AI program Determinants of utilisation of artificial insemination (AI) services 563 depends on availability of well trained, skilled and devoted AI technicians in the field. In Table 2 is presented the descriptive statistics of categorical variables of the AI users and non-users and their influence on adoption and use of the technology. Marital status did not seem to have an influence on the use of AI technology. The proportion of households heads who were married tended to be higher (but not significant) among farmers who used AI technology. A large proportion of AI users (84.6%) did not have off farm employment. This means that being their major source of income AI users devoted more resources to the dairy farming activity than non-users. More farmers using AI belonged to farmer groups probably because such groups increase information acquisition through interaction, extension meetings etc. This agrees with the findings of Fader et al. (1985) who reported that agricultural knowledge is affected by communal learning and doing. More AI users had accessed credit facilities. Access to a credit is very important reason for adoption or non-adoption of smallholder dairy technology by households (ILRI, 1999). Farm and animal management characteristics. The effect of farm and animal management characteristics on adoption and use of AI are summarized in Table 3. Nonusers had larger farm sizes of 7.5 ha on average than users. This may be because most users are located close to urban centres where land holdings are small. Similarly farmers who use AI had smaller herd sizes of 14 heads on average. Because use of AI requires a higher level of management input in aspects like feeding, routine cattle/herd observation, and communication with the AI technicians it appears that as herd size is increased the farmers capacity to manage and pay for AI services is constrained. Wyne and Cranfield (1995) found that large commercial beef producers did not use AI contrary to the belief that small farmers are more risk averse. It appears smaller farmers will adopt certain types of innovations more easily than large farmers. Farmers in the AI user group managed their herds intensively and kept records more regularly probably because animals need to be kept under very close supervision. A farmer who adopts the keeping of exotic/ crossbred cattle is also more likely to use AI technology for breeding. Ninety six percent of farmers who used AI had grade cattle. These findings are supported by those Table 1. Descriptive statistics on household head (dairy farmer) characteristics. Variable (mean) AI-users (N=65) Bull-users (N=115) t-value p-value Household head age in years Years spent in school Persons per household Years spent as a farmer Years of knowing AI technology Weekly hours on the cattle herd

4 564 of Babu (1997) who found that diffusion of AI technology resulted in adoption of crossbred cattle in the Kerara province of India. With respect to feeding, a large proportion of the farmers who used AI were more likely to feed improved pastures in addition to providing supplements of concentrates and minerals to the animals. Malnutrition is an important factor responsible for the failure of AI service (Madhumeet and Pant (1998). Tambi et al (1998) also reported that livestock producers with knowledge of improved husbandry practices were more likely to use private AI service providers in Kenya. A large number of farmers using AI (98.2%) reported possession of a crush for handling and restraining cattle at their farms. Non users who had constructed crushes and were not using ropes for restraint were only 54.8%. This shows that use of a crush for restraint is necessary if adoption and use of AI is to be sustained. Wyne and Cranfield (1995) also reported that the type of facilities for restraint installed at a farm affected the breeding decisions of beef producers in Ontario, Canada. The number of extension visits to farmers using AI was significantly higher than to those using natural service (Table 3). AI use by its nature entails extension visits from the technician. The more frequently a dairy farm is visited by extension personnel, the more likely that the H. KAAYA et al. Table 2. Descriptive statistics of the categorical variables of the AI users and bull users. Characteristic Frequency (%) corresponding proprietors will access information on the benefits of AI, and how to manage an AI program at all levels. Such farmers are more likely to adopt and use AI for breeding their animals. In an adoption and impact survey on smallholder dairy production in Coastal Kenya by ILRI (1999), extension agents were reported to be the most frequently used source of information about the benefits of ownership of grade cattle. Ownership and or use of a telephone was higher among AI users (89.2%) than non-users. Access to a telephone is likely to ease communication with technicians and may be less costly in the longer run. The quantity of milk produced and sold on a monthly basis which translates into more disposable income was higher on farms which used AI compared to non-users. In addition 72.3 % of AI users and 29.6% of non-users reported sale of other income generating farm products. Since use of AI entails a cost, farmers with high milk production and incomes are more likely to afford the service. In India, Das (1997) reported that economically constrained farmers did not adopt costly livestock technologies. Characteristics related to AI technology. In Table 4 is presented the influence of the characteristics of AI technology and its attributes on the adoption and use of AI-users (N=65) Bull-users (N=115) x 2 -value p-value Sex (males) Household head married Major occupation farming Organization membership Credit facility access % of the herd pure/graded Intensive/Semi-intensive system Presence and use of a crush facilities Use of ropes for restraining cattle Farm record keeping Cattle feeding on improved pastures Cattle feeding on concentrates Provision of mineral licks to cattle Other income generating farm products Possession and use of telephone facilities Table 3. Descriptive statistics on farm and management characteristics. Variable (mean) AI -users (N=65) Bull-users (N=115) t-value p-value Farmland size (hectares) Cattle herd size Annual extension visits Milk sold per month (litres) Income from milk per month (Ush) Cattle herd born using AI (%)

5 AI. Farmers who were using AI were reported to have acquaintance with at least two or more technicians than non-users. Such farmers were in a better position to compare and make a choice on the best technician to use and were more likely to access reliable AI services. Reynolds et al. (1996) reported that in addition to poor heat detection, unreliability of the service constrained the efficiency of AI on zero grazing farms. Dairy farmers in the non-user category had to travel longer distances to report animals on heat as distance to an AI centre was found to be shorter for AI users than non users by an average of 0.5 km. This means that non-users had to spend more time and money to get to a technician thus making the service less accessible and more expensive. While studying the determinants of service use among rural households in Zambia, Wanmali and Jane (1995) found that services that were farthest from the household were least used. The price of AI at farm level was found to be significantly lower for users than for non-users. Users traveled shorter distances and sometimes reduced cost by using a telephone to communicate to the technician. The cost of using a bull was similar for both categories of farmers. In Kenya, Israelson (1985) observed that using AI was constrained by unreliability, non-effectiveness and Table 4. Descriptive statistics on AI technology and its related attributes. Determinants of utilisation of artificial insemination (AI) services 565 lack of continuity besides high cost at the farm level. More than 67% of farmers in the user category reported conception at first service while the corresponding figure for non-users was 3.5%. This could have been another factor limiting adoption and use because when an animal does not conceive at first service, there is a loss in terms of number of calves and amount of milk produced per unit time. Besides, the repeat services on the same animal are also charged at the same rate as the first. A significantly higher proportion of farmers in the user category (90.8%) reported having received information on AI from the technicians. This was probably because such farmers frequently met the technicians and were more likely to get information from these service providers than the nonusers. Econometric results. A summary of Tobit Model estimates of the determinants of utilization of AI services among dairy farmers is presented in Table 5. Of the 19 variables under consideration, only 9 were significant. Years of exposure to the AI technology, age of the household head, the quantity of milk produced and sold, the number of extension visits and the quality of AI service as measured by number of cows conceiving at first service had a positive and significant coefficient while farming Variable (mean) AI-users Bull-users t-value p-value AI technicians known Farm to AI technician (Kms) Per AI service cost at farm (Ushs) Farm per bull service cost Table 5. Tobit model estimates of the determinants of utilization of AI among dairy farmers. Variable Coefficient Robust standard error p-value Age (years) Sex Marital status Family size Years in school Farm experience Farming occupation Herd size Credit access Cattle breed Hours on herd per week Management system Milk sold per month Annual extension visits Years of knowing AI Distance to AI technician (Kms) Distance to bull (Kms) Cows served (AI) once to conceive Farm cost per AI service Constant

6 566 experience, breed of cattle on the farm and herd size had a negative but significant coefficient. Age of the household head years of knowing AI technology by the household head and number of extension visits were found to be important factors influencing the utilisation of AI by dairy farmers. The results imply that once a dairy farmer has been exposed to AI they are more likely to continue using the technology. It was estimated that for a dairy farmer an additional year of using AI increased the probability of adoption and using AI by 4.8%. These results are consistent with those of Wayne and Cranfield (1995) and Hashakimana (1996). The higher the amount of milk produced and sold from the farm, the higher the probability of using AI. Such farms have more disposable income and were willing to pay for AI services. When all other factors are held constant, increasing the monthly farm milk production levels and sales by 100 litres, increased the probability that a dairy farmer would use AI by 0.2%. Previous studies have also indicated that farm income positively influence adoption and use of new agricultural innovations (Fader et al., 1985). A study by ILRI (1999) found that dairy income constituted more than 33% of the total cash income for farmers adopting grade cattle. When the number of extension visits per year is considered, farmers who received more extension visits were more likely to access information on the benefits and procedures of cattle breeding using AI. It can be derived that ceteris paribus, for each additional extension visit a farmer received, the probability that the farmer in question would use AI was about Extension as a source of agricultural information has been reported to increase adoption and use of new agricultural technologies by other scholars (Fader et al., 1985; CIMMYT, 1993). The number of services per conception is an indicator of the quality of the insemination service. When an animal does not conceive at first service there is a loss in terms of milk production, delayed conception and calving. At farm level AI service is charged and paid for on per service basis. This explains why the number of cows conceiving at the first service is a crucial variable for adoption and use of AI. The negative but significant relationship of experience of the farmer means that it was not necessarily the farming experience which was relevant for a farmer to adopt use of AI. Farmers who had been engaged in farming for a long time were less inclined to breed their cattle using AI. Use of AI is more demanding in terms of management. Decreasing the herd size by one animal increased the probability that a farmer would use AI by These results are consistent with those of other researchers (Fader et al., 1985; Shiyani et al., 2000) who reported that smaller farmers adopt and use new innovations at a faster rate especially if additional gains are substantial. The probable reason for the negative relationship of breed of animals on the farm is that use of AI is more likely to be adopted on farms with unimproved cattle. Initially, farmers adopt and use AI for the purpose of improving their dairy cattle productivity and when such herds are improved to the farmers satisfaction AI is only used for routine breeding. Such a farmer is likely to use natural service as H. KAAYA et al. it is less costly and readily available. In this case farmers with graded cattle herds were found to be more likely to use the bull instead of AI. The price of AI service at farm level had a negative but significant relationship. The higher the cost of AI the less likely the farmer will breed their cattle using AI. In this study, reducing farm level price of AI service by Shs. 10,000 increases the probability that a farmer will breed their animals by 30%. Attributes like cost can limit the adoption of a technology (Fader et al., 1985, Das, 1997; CIMMYT, 1998). Conclusions The adoption rate for AI technology among dairy farmers in Uganda is about 36%. Adoption and use of the technology is influenced by farmer characteristics, farm related attributes, and attributes related to the technology itself. These attributes play a role in enhancing demand, adoption and use of the technology. Farmers who used AI also had smaller herd sizes but more milk production and sales. They were therefore better able to pay for the service. Smaller herds are easier to manage and well managed animals tend to conceive on first service. The results have implication for the providers. The quality of the service has an influence on adoption and use. Farmers need more information on the benefits of the technology and other aspects of husbandry. Private AI providers can enhance acquisition of information on AI by farmers through promotion and constant monitoring besides self evaluation of the quality of service they provide to the farmers. There is a need to train more technicians and regulate the AI service providers by the Government to ensure high standards of service. Additional research work needs to be conducted to establish the status of AI technology use for other enterprises like pigs, beef, goats. Work also needs to be conducted to establish the demand and economics of AI in different parts of the country. References Amemiya, T Tobit Models. A survey. Journal of Econometrics 24, Babu, P. 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