The performance of artificial insemination delivery system in Amhara, Oromia, SNNP and Tigray Regions of Ethiopia (original) (raw)
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A study on reproductive performance of 18 artificial insemination (AI) bulls, which were in semen production during the study period and on the efficiency of AI operations were conducted at the National Artificial Insemination Center (NAIC) and in ten purposively selected areas of five regional states. The study was further conducted to assess problems and constraints associated with the AI service in Ethiopia. Evaluations of number of services per conception and conception rates to first insemination were carried out. Pregnancy diagnosis was performed on 375 cows in the selected areas. Besides, 114 straws of semen comprising of 61 from regions and 53 from NAIC were checked for semen motility to see if there were any differences in motility due to handling between the center and the regions. Thirty AI technicians, 17 technical staff of NAIC and 246 farmers were used for the collection of data using questionnaire surveys. Moreover, fifty-two high-level professionals who have stakes d...
REVIEW ON EFFICIENCY OF ASSISTED REPRODUCTIVE TECHNOLOGIES IN ETHIOPIA
Journal, 2023
This review deals with the efficiency of assisted reproductive technologies (ART) in Ethiopia, with a particular emphasis on artificial insemination and estrus synchronization of cattle. In Ethiopia, artificial insemination was the first and most widely used ART, followed by estrous synchronization and fixed-time artificial insemination. Artificial insemination was introduced to Ethiopia in the early 1930s, whereas estrus synchronization in cattle began in the late 1980s. The conception rate (CR), number of service per conception (NSPC), pregnancy rate (PR) for first insemination of dairy cows/heifers ranged from 27.1% to 79.69%, 1.67 to 42.9, and 34.4 to 66.7 %, respectively. The average calving rate of inseminated cow/heifer in Ethiopia was 54.8%. The efficiency of estrus synchronization for dairy cow/heifers in Ethiopia measured by estrous response rate (ERR), conception rate, conception rate for first service, number of service per-conception, and pregnancy rate (PR), which ranged from 65.5% to 96.6%, 7.1% to 42.9%, 1.44 to 2.65, 28.9% to 69.6 % and 20.18 % to 66.67 %, respectively. The efficiency of AI and estrous synchronization depends on structural linkage and collaboration among stakeholders, resources and facilities, cow related factors, quality of semen, skills of the inseminators, heat detection system, embryonic death and handling of animals.
Ethiopian Veterinary Journal, 2017
A cross sectional study was conducted from November 2015 to May 2016 with the objective of assessing the problems associated with artificial insemination service in dairy cattle in five selected peasant associations of Tullo district, west Hararghe, Ethiopia. A structured questionnaire was used for 389 dairy cattle owners, 3 artificial insemination technicians and 8 animal health professionals. The result of the study showed that 219 (56.3%) of the dairy farmers get artificial insemination services regularly and without interruption while 170 (43.7%) do not use this service regularly due to shortage of artificial insemination technicians (18.2%), discontinuation of service on weekends and holidays (51.2%) and shortage of inputs (30.6.0%). Conception failure (34.9%) was identified by dairy cattle owners as a major problem of AI followed by death of embryo or dystocia (28%). About 71.1% of the owners wait for the next oestrus as they are unable to get AI service during heat period while 28.9% use natural mating if they do not get the service. The questionnaire survey indicated that artificial insemination service faces several constraints and needs improvement for increasing the productivity of dairy cattle in term of genetic improvement in the study area.
Agriculture & food security, 2022
Background: The study was focused on the adoption and intensity of adoption of artificial insemination (AI) Technology in Saesie-tsaedaemba District of Tigray Region, Ethiopia. AI is one of the most important and valuable dairy technology that has been used for genetic improvement for several years in the study area. However, there was little empirical information about major factors affecting adoption decision and intensity of AI in the study area. The purpose of this study was to evaluate the status of AI technology adoption and its intensity and to identify major factors influencing the adoption and intensity of use of AI technology. Methods: A multistage sampling technique was applied to select study sites and sample households. A structured interview was used to collect data from a total of 204 sample farmers. Besides, key informants interview was used to triangulate, validate, and enrich the findings of the household interview. Results: Results of the tobit model regression revealed that households' level of literacy, milk yield, income, training, access to extension service, mobile ownership, supplementation of concentrated feed and hybrid cattle ownership were found to have a positive and statistically significant relationship with adoption and intensity of AI technology, whereas distance to farmer training centre (FTC) office had shown a negative relationship. Conclusions: Adoption of context-based AI technology plays a paramount importance in achieving farm household's food security. The extension system should give more emphasis to the capacity building which is pivotal for introducing, adoption, and scaling out of best practices of dairy technologies. Besides the effort of the government, the participation of the private sector in AI technology is important to achieve wider adoption of AI technology.