Assessment of the status of artificial insemination and its constraints in East Arsi Zone, Oromia Region, Ethiopia

Ethiopia's genetic enhancement efforts have included directly importing exotic cattle
from other countries or introducing genes from an external source via artificial
insemination (AI) to enhance the breed composition of local cattle. The study aimed
to evaluate the status of artificial insemination and identify its constraints in the
selected districts of East Arsi Zone, Oromia regional state. The data were collected
from 301 farmers and 9 AI technicians (AIT) using semi-structured questionnaires.
Five-year secondary data were used from the annual summary of the casebook to
evaluate AI status. Data on AI status, satisfaction, breeding method, controlled
mating and AI delivery were analyzed using Statistical Analysis Systems (SAS) chi-
square procedures. Secondary data were analyzed using General Linear Model SAS
methods. The ranking coefficient was analyzed using the R software Plackett–Luce
model procedure. The study found that 43.52% of participants were dissatisfied with
AI services, while 56.48% were satisfied. Furthermore, 72.43% of respondents
indicated an increase in AI services, whereas 13.62% reported a decrease and
13.95% no change. The respondents' satisfaction with AI and AI status differed
(P<0.05) among districts, but no difference (P>0.05) between the production
systems. The secondary data revealed a gradual increase in AI delivery from 2018
to 2022. The average AI delivery was 2281.5±275.6 per year. While AI services did
not differ (P > 0.05) across districts, there was a significant (P< 0.05) variation over
time. About 55.48% of the districts' dairy producers used AI for breeding. Most
respondents (61.79%) received AI from government administrations, while 36.21%
received from government and private and only 1.99% obtained it from private
suppliers. Breeding methods varied significantly across production systems
(P<0.0001), but controlled mating and AI provision were non-significant. The farmers
preferred neighbor bulls with estimated coefficients of 2.24 followed by their bulls
(1.05) for breeding purposes. The respondents indicated that conception failure
(0.72) and poor conception rates (0.56) were the biggest challenges for AI in the
study areas. Transportation (2.89) was the main constraint in delivering AI services
identified by AITs. Despite these challenges, there has been an increase in the use
of AI in study areas over the past five years. Because AI is the only accessible
technology for increasing dairy cow performance in the country, it is vital to address
these challenges to increase AI utilization in the study regions. Focusing on semen
quality is necessary to achieve a high conception rate per service. Supporting
commercial AI businesses could improve farmers' access to services.