BANGLADESH JOURNAL OF MULTIDISCIPLINARY SCIENTIFIC RESEARCH 7(1) (2023), 44-49 44 MULTIDISCIPLINARY SCIENTIFIC RESEARCH BJMSR VOL 7 NO 1 (2023) P-ISSN 2687-850X E-ISSN 2687-8518 Available online at https://www.cribfb.com Journal homepage: https://www.cribfb.com/journal/index.php/BJMSR Published by CRIBFB, USA A REVIEW ON EFFICIENCY OF ARTIFICIAL INSEMINATION IN CATTLE BREEDING IN ETHIOPIA Teweldemedhn Mekonnen (a)1 Selam Meseret (b) (a) Tigray Agricultural Research Institute-Humera Begait Animals Research Center, Tigray, Ethiopia; E-mail: teweldem2004@gmail.com (b) International Livestock Research Institute, Box 5689, Addis Ababa, Ethiopia; E-mail: S.Meseret@cgiar.org A R T I C L E I N F O Article History: Received: 9th August 2023 Revised: 19th October 2023 Accepted: 27th October 2023 Published: 10th November 2023 Keywords: Artificial Insemination, Efficiency, Cattle, Conception Rate, Calving Rate, Breeding A B S T R A C T Ethiopia has large cattle population in Africa. The output of decades of crossbreeding programme in Ethiopia through AI service was quite insignificant because the exotic breeds and their crossbreds of the country accounted for about 1.44%. The objective of this review is to present the efficiency of Artificial Insemination (AI) service in cattle breeding in Ethiopia. The optimum recommended mean number of services per conception (NSC) for profitable dairy cow is about 1.4. However, conventional cattle AI breeding indicated that the mean NSC of different studies ranged from 1.14 in local cows up to 2.47 in different genotypes of cows kept under different management systems and conception rate at first insemination (CR1) ranged from 7.14% to 75.5% in different genotypes of dairy cows kept under extensive and intensive management systems. Estrus synchronization followed AI breeding indicated that CR1 ranged from 24.69% to 70.6% in Zebu cows kept under semi-intensive management system. Calving rate (CR) is the most appropriate measure of fertility of dairy cows which is defined as the number of calves born per 100 services. The poor efficiency of the country cattle AI service is a huge biological economic loss in cattle production and managerial monetary losses. Strategic interventions on cattle AI service efficiency improvement options should be identified and practiced considering breed type, production system and agro-ecology. © 2023 by the authors. Licensee CRIBFB, U.S.A. This open-access article is distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). INTRODUCTION Livestock production provided approximately 35 to 49% of the total agricultural GDP and 16 to 17% of national foreign currency earnings of Ethiopia (Metaferia et al., 2011). The output of decades of crossbreeding programme in Ethiopia through Artificial Insemination (AI) was quite insignificant because the total number of exotic and crossbred female cattle are few (CSA, 2013). About 98.56% of the total Ethiopian cattle populations are indigenous Zebu (Bos indicus) cattle while exotic breeds and their crossbreds account for about 1.44% (CSA, 2016). However, crossbreeding of indigenous cattle with highly productive exotic cattle have been considered a realistic solution to improve the low productivity of indigenous cattle (Tadesse, 2002). For example, the types of exotic cattle breeds used for crossbreeding through the use of AI in Tigray region, Ethiopia comprised of Holstein Friesian (HF) and Jersey, and 50% crossbred of HF and the indigenous Begait cattle (Ashebir et al., 2016). AI is one of the most important techniques ever devised for the genetic improvement of farm animals (Bearden et al., 2004). The greatest advantage of AI is maximum use of superior sires whilst the use of one bull is limited to less than 100 mating per year. The use of AI enabled one dairy sire to provide semen for more than 60,000 services in one year (Webb, 2003). AI service enables maximum use of outstanding males, rapid dissemination of superior genetic material, improve the rate and efficiency of genetic selection, introduction of new genetic material by import of semen rather than live animals (Verma et al., 2012). The critical factors in artificial reproductive management are estrus detection and insemination of the cow at the correct time in the estrus cycle. Estrus detection and conception rates are the main determinants of reproductive efficiency (Bekana et al., 2005). Reproductive failure is a major source of economic loss in dairy and beef industry (Perry, 2005). The application of reproductive technologies accelerates genetic progress and enhance the reproductive performance of farm animal genetic resources (Gizaw et al., 2016). AI has a good potential to improve cattle productivity in Ethiopia. However, its use is limited 1Corresponding author: ORCID ID: 0000-0002-9655-4096 © 2023 by the authors. Hosting by CRIBFB. Peer review under the responsibility of CRIBFB, U.S.A. https://doi.org/10.46281/bjmsr.v7i1.2115 To cite this article: Mekonnen, T., & Meseret, S. (2023). A REVIEW ON EFFICIENCY OF ARTIFICIAL INSEMINATION IN CATTLE BREEDING IN ETHIOPIA. Bangladesh Journal of Multidisciplinary Scientific Research, 7(1), 44-49. https://doi.org/10.46281/bjmsr.v7i1.2115 https://orcid.org/0000-0002-9655-4096 http://creativecommons.org/licenses/by/4.0/) http://creativecommons.org/licenses/by/4.0/) https://doi.org/10.46281/bjmsr.v7i1.2115 https://orcid.org/0000-0002-1178-1821 Mekonnen & Meseret, Bangladesh Journal of Multidisciplinary Scientific Research 7(1) (2023), 44-49 45 due to the challenges related to infrastructure and the availability and skills of AI technicians (Ndambi et al., 2017). Infrastructure, managerial and financial constraints, poor heat detection, improper timing of insemination and embryonic death were the factors which resulted in very low level efficiency of the AI service in Ethiopia (Shiferaw et al., 2003). Although the use of cattle AI service is increasing in Ethiopia, oestrus detection is difficult in Zebu breeds due to their poorly expressed estrus (Bekuma & Ketema, 2019). Failure to accurately detect estrus is the major factor limiting optimum reproductive performance on many farms (Graves, 2012). Reproductive management tools such as estrus synchronization involves induction of estrous in a group of females to breed relatively in around the same time (Schafer et al., 2007; Rick, 2013). Estrus synchronization (ES) and AI are influential technologies for cattle producers in terms of genetic improvement, reproductive management and performance (Jinks et al., 2013). Mean number of services per conception (NSC), conception rate at first insemination (CR1) and calving rate (CR) are essential parameters which enable breeders to determine the efficiency of AI service. Therefore, the objective of this review is to present the efficiency of AI service in cattle breeding in Ethiopia. DISCUSSIONS The major determinants of efficiency of AI in cattle breeding in Ethiopia are presented below (Table 1). The mean NSC, CR1 and CR mainly determine fertility and efficiency of dairy cattle. Number of services per conception (NSC) NSC as an indicator of reproductive efficiency has been defined as the number of services required for a successful conception (Albero, 1993; Haile Mariam et al., 1993; Negussie et al., 1998; Shiferaw et al., 2003). The optimum recommended NSC for profitable dairy cow ranges from 1-1.7 (Evelyn, 2001). The NSC under conventional cattle AI breeding ranged from 1.14 in local dairy cows kept under mixed crop-livestock production up to 2.47 in different genotypes kept under mixed crop livestock and Urban dairying whilst under fixed time AI breeding the NSC ranged from 1.44 in crossbred cows kept under extensive production system up to 2.36 in local cows kept under extensive production system (Table 1). The differences in NSC could be due to intrinsic (genotype, age, parity, body condition score, semen quality) and extrinsic (ecology, production system, heat detection, time of insemination, skill of inseminator, type of insemination, semen handling procedures) factors. Conception rate at first insemination (CR1) In most African countries, poor semen quality, poor semen handling procedure, inadequate insemination skill, poor estrus detection and wrong time of insemination resulted in low CR1 (Tegegne et al., 1995). The CR1 under conventional cattle AI breeding ranged from 7.14% in different genotypes of cows kept under mixed crop livestock and Urban dairying up to 75.5% in crossbred dairy cows kept under extensive and intensive production systems whilst under timed AI breeding the CR1 ranged from 24.69% in Zebu cows kept under unknown production system up to 70.6% in Boran cows kept under semi-intensive production system (Table 1). The differences in CR1 could be due to ecology, genotype, parity, body condition score, skill of inseminator, insemination time, production system and semen quality. Calving Rate (CR) The most appropriate measure of fertility is CR which is defined as the number of calves born per 100 services (Mohamed, 2004). The CR under conventional cattle AI breeding ranged from 22.0% in local dairy cows kept under mixed crop- livestock production system up to 54.8% in HF x Zebu crossbred cows kept under Urban and Peri-urban production system whilst under fixed time AI breeding the CR ranged from 10.67% in dairy cows kept under extensive production system up to 13.58% in Zebu cows kept under unknown production system (Table 1). The differences in CR could be due to prevalence of reproductive diseases, production system and number of services provided per cow. Therefore, as per the reviewed publications, average NSC is the same in conventional cattle AI breeding (1.74) and fixed time AI breeding (1.74) whilst the average CR in conventional cattle AI breeding (38.4%) and fixed time AI breeding (12.1 %) are not comparable which reveal poor AI efficiency. Table 1. Efficiency of Artificial Insemination in different parts of Ethiopia AI type Cattle breed group Management system NSC (mean) CR1 (%) CR (%) Author(s) Conventional AI Local dairy cows Extensive and intensive systems - 72.9 - Befkadu et al., 2019 Crossbred dairy cows Extensive and intensive systems - 75.5 - Befkadu et al., 2019 Local dairy cow Extensive and intensive systems - 53.5 - Hamid et al., 2021 Crossbred cows Extensive and intensive systems - 69.1 - Hamid et al., 2021 Local Zebu cows Extensive system - 48.9 - Abdula and Bilal, 2022 Local Zebu cows Extensive system - 37.4 - Abdula and Bilal, 2022 Zebu cows Extensive system - 24 - Hamid, 2012 Mekonnen & Meseret, Bangladesh Journal of Multidisciplinary Scientific Research 7(1) (2023), 44-49 46 HF x Zebu crossbred cows Extensive system - 60 - Hamid, 2012 Zebu x Holstein-Friesian Farmer’s management system 1.8 - - Kumar et al., 2017 HF x Zebu, Jersey x Zebu cows Intensive system - 62.5 - Abdula and Bilal, 2022 HF x Zebu, Jersey x Zebu cows Intensive system - 41.5 - Abdula and Bilal, 2022 Not specified Mixed crop livestock 1.78 34.29 - Gebreegziabiher, 2008 Not specified Mixed crop livestock, Urban dairying 1.91 33.33 - Gebreegziabiher, 2008 Not specified Mixed crop livestock, Urban dairying 2.47 7.14 - Gebreegziabiher, 2008 Not specified Mixed crop livestock, Urban dairying 1.55 20.31 - Gebreegziabiher, 2008 Local cows Mixed crop-livestock production 1.14 - - Tadesse et al., 2022 Crossbred cows Mixed crop-livestock production 1.15 - - Tadesse et al., 2022 Local and crossbred cows Mixed crop-livestock production - 51.03 - Tadesse et al., 2022 Indigenous cattle Mixed crop-livestock production system 23.36 22 Bayew, 2019 82.3% crossbred dairy cows Not specified - 64.8 - Yehalaw et al., 2018 Fogera x Holstein Friesian Not specified 1.56 - - Sena et al. 2014 >80% crossbred cows and <20% local cows Not specified - 17.64 - Ashebir et al., 2016 >80% crossbred cows and <20% local cows Not specified - 30.12 - Ashebir et al., 2016 >80% crossbred cows and <20% local cows Not specified - 48.47 - Ashebir et al., 2016 HF x Zebu crossbred Urban and Peri-urban 1.67 64.6 54.8 Birhanemeskel et al., 2017 Dairy cows Urban dairy farming 1.6 48.1 - Engidawork, 2018 Not specified Urban dairying 1.7 40.23 - Gebreegziabiher, 2008 Local and crossbred cows Extensive and intensive system - 47.8 - Woldu et al., 2011 Local and crossbred cows Extensive system 1.95 49.3 - Jemal et al., 2016 local cows 65% Mixed crop-livestock system 2.15 - - Yousuf, 2022 F1 crossbred 65% Mixed crop-livestock system 2.0 - - Yousuf, 2022 F2 crossbred 65% Mixed crop-livestock system 1.6 - - Yousuf, 2022 Average 1.74 45.0 38.4 Fixed time AI Local cows Extensive system - 45 - Abiyot and Eyob, 2019 Crossbred cows Extensive system - 50.2 - Abiyot and Eyob, 2019 Local cows Extensive system 1.85 54 - Belay et al., 2016 Crossbred cows Extensive system 1.44 69.6 - Belay et al., 2016 Dairy cattle Extensive system - 34.61 10.67 Zewude, 2018 Local dairy cow Extensive system 1.7 59 - Duro, 2022 Crossbred cows Extensive system 1.5 67 - Duro, 2022 Zebu cows Extensive system - 48.4 - Hamid, 2012 HF x Zebu crossbred cows Extensive system - 46.7 - Hamid, 2012 Boran cows Intensive system - 28.6 - Demisse, 2018 HF x Boran crossbred cows Intensive system - 31.3 - Demisse, 2018 Local cows Not specified 1.71 58.49 - Worku, 2015 Crossbred cows Not specified 1.78 56.1 - Worku, 2015 Zebu (95.8%), Sheko (2.8%) and crossbred (1.4%) Not specified - 24.69 13.58 Fantahun, and Admasu, 2017 Local cows Not specified 1.7 59.5 - Shanku, 2022. Crossbred cows Not specified 1.5 65.0 - Shanku, 2022 Local cows Not specified - 40.8 - Haile et al., 2023 Crossbred cows Not specified - 64.8 - Haile et al., 2023 Boran cows Semi-intensive - 70.6 - Tilahun, 2018 Mekonnen & Meseret, Bangladesh Journal of Multidisciplinary Scientific Research 7(1) (2023), 44-49 47 Zebu x Holstein cross Semi-intensive - 50 - Tilahun, 2018 Local cows Extensive system 2.2 45.7 - Amanuel and Amanuel, 2023 Crossbred cows Extensive system 1.4 70.5 - Amanuel and Amanuel, 2023 Local cows Extensive system 2.36 42.3 - Amanuel and Amanuel, 2023 Crossbred cows Extensive system 1.7 60.2 - Amanuel and Amanuel, 2023 Average 1.74 51.8 12.1 NSC=Number of Services per Conception, CR1=Conception Rate at first insemination, CR=Calving Rate CONCLUSIONS Cattle AI service was widely practiced in Ethiopia, however, the efficiency of cattle AI service in Ethiopia was extremely poor due to different intrinsic and extrinsic factors. Mean number of services per conception (NSC), conception rate at first insemination (CR1) and calving rate (CR) under both conventional cattle AI breeding and fixed time AI breeding indicated poor efficiency of AI service. The poor efficiency of the country cattle AI service is a biological economic loss in cattle production and managerial monetary losses. Strategic interventions on cattle AI service efficiency improvement should be identified and practiced. Author Contributions: Conceptualization, T.M.; Data Curation, N/A.; Methodology, N/A.; Validation, N/A.; Visualization, N/A.; Formal Analysis, T.M. and S.M.; Investigation, T.M. and S.M.; Resources, T.M.; Writing - Original Draft, T.M.; Writing - Review & Editing, T.M. and S.M.; Supervision, T.M.; Software, N/A.; Project Administration, T.M.; Funding Acquisition, N/A. All authors have read and agreed to the published version of the manuscript. Institutional Review Board Statement: Ethical review and approval were waived for this study because the research does not deal with vulnerable groups or sensitive issues. Funding: The authors received no direct funding for this research. Acknowledgement: The authors would like to thank to the researchers who involved in studying the efficiency of AI in cattle breeding in Ethiopia. Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. Data Availability Statement: The data presented in this study are available on request from the corresponding author. The data are not publicly available due to restrictions. Conflicts of Interest: The authors declare no conflict of interest. REFERENCES Abdula, A. M., & Bilal, Z. M. (2022). Effect of breed and risk factors affecting conception rate to artificial insemination in dairy cows of Tullo District Western Haraghe, Ethiopia. Vet Med Open J., 7(1), 16-21. https://dx.doi.org/10.17140/VMOJ-7-163. 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