The difference between AI and bull service users on farmer characteristics
Figures 3, 4, 5, 6 and 7 and Table 2 summarize results obtained on farmer’s characteristics. The average dairy farming experience was similar for both AI and bull service farmers, ranging between 5 and 10 years. Majority of AI farmers (more than 70%) mentioned to kept records in all countries except Ethiopia, where only 12% of AI farmers kept records (Fig. 3). Farmers utilizing AI were more likely to keep records (P < .001) compared to those who used bulls in all study countries except for farmers in Ethiopia. The type of records claimed to be kept by farmers were predominantly for animal breeding, health, calving dates, and milk yield (Fig. 4). The majority of farmers (more than 50%) preferred visual observation to other methods for both estrus detection and ensuring timely service/insemination for their cows (Fig. 5 and Fig. 6).
Additionally, in all study countries, farmers who used AI had on average longer formal schooling years (up to 1 year more) than those who used bull service. Further, irrespective of breeding method utilized, most farmers (80%) did not belong to any farmer groups, even though these groups existed. There was a clear grouping of farmers into spatial clusters as shown in Fig. 7, indicating the influence of neighbors in dairying. These clusters coincided with the preferred method of breeding given that farmers in very close clusters tended to choose similar methods of breeding.
Farmer characteristics influencing breeding methods
The farmer’s characteristics associated with choice of breeding method are presented in Table 3. There was a significant relationship between records keeping and the use of AI. Farmers, who used AI, kept animal breeding, health, calving, and milk yield records. This positive relationship between records keeping and AI was observed in Kenya (p < 0.001), Tanzania (p < 0.001), and Uganda (p = 0.004) but not in Ethiopia. While dairy farming experience had a significant impact on choice of AI in Ethiopia (p = 0.001) and Tanzania (p = 0.003), there was no relationship between AI adoption and the level of education of the particular farmer in all four countries.
The difference between AI and bull service users on farm characteristics
Generally, in all countries, farmers preferred two types of feeding systems: grazing and stall feeding. Ethiopia and Tanzania shared the same pattern, where farmers preferred stall feeding (more than 57%) while in Kenya and Uganda, more farmers (more than 40%) preferred grazing to stall feeding (less than 20%). This pattern was observed in both rainy and dry seasons. However, it is important to note that for Kenya, the sites targeted for the study were in one part of the country where land sizes were relatively large, and also, the traditional socio-cultural behaviors favored grazing. Other major dairying regions in Kenya mainly practice stall feeding. There was a marginal effect of seasonality on the chosen feeding system. Generally, increases of between 5 and 8% were observed for stall feeding during the dry season in Ethiopia and Uganda. In all countries, farmers using AI preferred stall feeding to grazing compared to farmers using bulls as shown in Table 4.
For Kenya and Uganda, there was no significant difference between AI and bull users in terms of the total land size per farmer. However, significant differences were observed for Ethiopia (P < .001) and Tanzania (P = 0.03). In Ethiopia, farmers who kept bulls had on average 1.5 acres of land more than those who had adopted AI. In the case of Tanzania, the opposite was true with farmers that used AI having 0.4 acres of land more than bull service users. In terms of average herd size per household, significant differences (P < 0.001) were observed between the two groups in Ethiopia and Uganda. Farmers who used bull service tended to have a larger herd size, having on average two more animals than those who used AI. The number of milking cows tended to be equal among both groups and a slight difference for farmers in Uganda where bull service users had one more milking cow than AI users (Table 5).
In general, farmers who used AI had higher milk yields compared to those who used bull service. There was a significant difference (P < .001) in peak milk yield between the two groups in all countries except for Tanzania. On average, cows of farmers who used AI yielded more (14.42 ± 6.3 L) during peak lactation than cows from farms using bulls (12.73 ± 5.8 L). In Kenya, cows from farmers who used AI yielded 13.08 ± 4.9 L at peak compared to those from bull bred farms which yielded 12.15 ± 5.2 L at peak. In Contrast, the trends in Tanzania were reversed, with cows from AI farmers yielding lower at peak than cows from bull breeding farms, at 11.77 ± 0.9 L and 13 ± 0.7 L, respectively. In Uganda, farmers who used AI averaged 15.09 ± 5.4 L at peak while those who used bulls had peak yields almost 45% lower at 8.24 ± 4.1 L. Additionally, for farmers in Uganda, there was a huge difference (approximately 90 days) in cow lactation length between the two groups (P < 0.001), with farmers who used AI service experiencing longer lactation periods than those who used bulls. There were no significant differences between the two groups in lactation lengths for the other countries (Table 7).
In addition, farmers who used AI engaged their workers for longer periods, on average. Their workers put in between 3 to 7 h/day while workers in bull service farms worked 1 to 4 h/day. Farmers who used AI paid their workers more than those who used bull service.
Farm characteristics influencing breeding methods
A number of farm-related factors were evaluated for their effect on AI adoption as shown in Table 6. Results indicate a strong inverse relationship between AI use and herd sizes in Ethiopia and Uganda. Farmers with large herd sizes tended to use bull mating while small farms used AI. The number of months for fodder purchases had a significant relationship with AI use in Ethiopia, Kenya, and Tanzania. Husbandry practices such as frequency of watering had a positive (Ethiopia) and negative (Tanzania) relationship with AI usage. Similarly, the frequency of deworming and treatment showed a positive relationship with AI usage in Tanzania.
The difference between AI and bull service users on institution settings
Table 7 summarizes the results obtained on institution setting of the farm. Results indicate that access to the preferred service was higher for AI than for bull service. This was true for all surveyed countries with more than 88% of AI service users being able to access the AI service: (Ethiopia, AI = 86.33%; bull = 56.3%), (Kenya, AI = 88.21%; bull = 64.7%), (Tanzania, AI = 94.99; bull = 62.13%), and (Uganda, AI = 99.42%; bull = 63.13%).
In terms of breeding service fees, AI users paid double the price paid by bull users (p < 0.001) and their service providers traveled long distances (p < 0.001) to provide the service. However, farmers who used bull service had to travel long distances (p < 0.001) of up to 2 Km more, to access water and market services. With regard to extension services and capacity development, very few farmers (24%) had attended any training session within the year of the study. This illustrates the scarcity of extension agents and services.
Institutional settings influencing breeding methods
Table 8 presents the results obtained on institutional setting. Factors, such as the breeding method that led to recently calved, the number of times a farmer had used AI (frequency of using AI), and accessibility to breeding method, all had positive significant association to the breeding decision the farmer adopted in all countries. The number of services before conception was negatively associated with choice of AI as a breeding method in Ethiopia, Tanzania, and Uganda. Distance to the service provider had a negative correlation with AI use in Ethiopia and Tanzania. In all study countries, cost of service was associated with the choice of breeding method.
Only in Tanzania was the amount of money a farmer spent to purchase water and the distance traveled to the watering point negatively associated with the farmer’s breeding choices. Water availability and choice of breeding method had a positive association in Ethiopia but a negative one in Kenya and Uganda. The cost of transporting milk to market and the distance traveled to these markets had no relationship with choice of breeding method. Given the negative correlation coefficient despite lack of significance, there is a trend to suggest a negative effect of distance to market on AI choice.
The impact of using AI and bull services on farm income
Table 9 presents the results on farm income. Comparing the total amount of milk sold per day by a farm, farmers who used AI sold more milk than those who used bull service in Ethiopia, Kenya, and Tanzania (difference of 3 L, P < 0.001), Kenya (difference of 2 L, P = 0.01), and Tanzania (difference of 2 L, P = < 0.001). However, there was no significant difference between the two groups except in Tanzania in the amount of income obtained from selling the product.
Farm income influencing breeding methods
Income from setting dairy products had a positive relationship with the use of AI in Ethiopia but had a negative association in Tanzania, Uganda, and Kenya.