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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. 

 

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