Journal of Veterinary Healthcare

Journal of Veterinary Healthcare

Journal of Veterinary Healthcare

Current Issue Volume No: 3 Issue No: 1

Research Article Open Access Available online freely Peer Reviewed Citation Provisional

Assessment of Reproductive Performances and Sex Ratio of Newborn in Cross Breed Dairy Cattle of Hawassa City, Ethiopia

Tewabe Hailu 1,   Ararsa Duguma 1 2  

1Haramaya University, College of Veterinary Medicine P.O.Box. 138, Diredawa, Ethiopia.

2Holeta Livestock Development Center, P.O.Box. 28, Holeta Ethiopia

Abstract

Reproductive performance is an essential trait of cows in dairy cattle. The effect of different factors on reproductive performances and sex ratio difference in dairy cattle were not well documented in Ethiopia. In this research, cross-sectional study was conducted from December 2021 to June 2022 in Hawassa city to assess reproductive performances and effect of breeding methods on sex ratio difference of newborn. Questionnaires and observational study were used to collect data. In this study, the mean+SE of age at first service was 17.53+0.035 with difference of relatively one month from onset of puberty. The mean number of services per conception was higher in animals breed naturally (1.87+0.056) as compared to AI (1.69+0.049). Higher conception rate of 74.26% was obtained in age group of <4years as compared to old age >6years at rate of 4.11% with statistically significant difference (P<0.05). Animals with medium level of milk production (15-20L/day) more conceived to first service 43.14% than higher milk production group (20-25L/day) with 35.59%. The presence of reproductive problems significantly affect conception rate to first service with higher conception rate of 54.47% in negative animals and 22.41% in animals with reproductive problems. Conception rate was increased with increasing number of services in both breeding types. Sex of newborn was not significantly affected by breeding methods used and difference in the study area. Generally this research found that reproductive performances and use of AI in the study area were satisfactory even though age, level of milk production and presence of reproductive health problems had significant impacts on performances. In order to sustain and improve reproductive performances age, reproductive health problems and breeding management should be considered. Therefore, reproductive health management and application of AI for genetic improvement should be well practiced to improve dairy cattle reproductive performances.

Author Contributions
Received 18 Oct 2023; Accepted 16 Dec 2023; Published 26 Dec 2023;

Academic Editor: Mohammed A Elmetwally, Professor of Theriogenogy.

Checked for plagiarism: Yes

Review by: Single-blind

Copyright © 2023 Tewabe Hailu, et al.

License
Creative Commons License     This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Competing interests

The authors have declared that no competing interests exist.

Citation:

Tewabe Hailu, Ararsa Duguma (2023) Assessment of Reproductive Performances and Sex Ratio of Newborn in Cross Breed Dairy Cattle of Hawassa City, Ethiopia. Journal of Veterinary Healthcare - 3(1):49-61. https://doi.org/10.14302/issn.2575-1212.jvhc-23-4785

Download as RIS, BibTeX, Text (Include abstract )

DOI 10.14302/issn.2575-1212.jvhc-23-4785

Introduction

Reproductive performance is one of the most important concerns of the modern dairy industry worldwide 54, 3. Improved fertility maximizes profit by decreasing culling costs and by elevating incomes from milk sales and shorter calving intervals 33. Therefore, economics of dairy enterprise is based on the efficient and effective reproductive performance of dairy cattle 21, 35.

The most common indicators of reproductive performance are age at the first service, age at first calving, calving interval, number of services per conception (NSPC), and breeding efficiency. High rates of breeding efficiency in dairy cattle are achieved through regular calving of one viable calf per breeding cow in a year 20, 49. Reduced breeding efficiency rates can be resulted from the long calving interval of a dairy cow which is mainly due to either low conception rate and/or high early embryonic mortality 20. Some studies in Ethiopia indicated that, overall means age at first service for crossbred female was 30.3±4.4 months. Age at first calving was 39.3±3.25 months, NSPC was 1.8 and calving interval was 17.1±4.5 months in Sidama zone southern Ethiopia 4. In another study, the mean age at first service, age at first calving, calving interval, days open and number of service per conception were 26.8 months, 37.4 months, 476 days, 197 days and 1.8, respectively in cross breed dairy cattle at Holleta agricultural research center 17.

In order to fulfill future needs and to be able to sustain livestock production, livestock research and its applications needs to use all emerging technologies, one of which is artificial insemination (AI) to obtain a large number of offspring from genetically superior males in addition to disease control and prevention 34, 19. From previous studies, it has been found that AI service is weak and even declining due to inconsistent service in the smallholder livestock production systems of Ethiopia 13. And also lack of recording scheme, wrong selection procedures and poor management with poor motivations and skills of inseminators 16, 30.

A number of factors have contributed to the reproductive performance of dairy cattle including age of animals, breed body condition, post parturient problem, milk yield 22 diseases, nutrition, housing facilities, methods of breeding and number of services per-conception 45. Accordingly, reproductive health problems causes considerable economic loss to the dairy industry due to prolonged calving intervals 12, 5.

It was found that different animal related and other factors have an effect on reproductive performances in dairy cattle and there was variation in report of research outputs in producing replacement heifers while using different methods of breeding: AI and natural services. But, some complaint from farm owners showed as more male than female new born produced when AI is used than natural services. Moreover, the sex ratio difference between natural services and AI was not well documented in Ethiopia. Therefore, this study was conducted to assess reproductive performances and its determinant factors, and to estimate rate of conception and sex ratio produced under different methods of breeding in dairy cattle in Hawassa city administration.

Material and Methods

Study area

The study was conducted in Hawassa city, Sidama Regional sate, Southern Ethiopia. The area is situated at 275 km south of Addis Ababa at a latitude of 7°04'N and a longitude 38°31'E on the escarpment of the Great Rift Valley. The altitude ranges from 1650 to 1700 m above sea level. The mean annual rainfall and temperature are 900-1100 mm and 27°C, respectively. The total livestock population of Sidama zone is estimated to constitute 1,721,341 cattle, 228,941 goats, 457,465 sheep, 204,460 equines, 725,540 poultry and 44,492 beehives. Specifically, Hawassa city administration has 155 small to large scale dairy farms with total population of 40,163 animals (Hawassa city administration, 2021).

Study animals and management

There were one hundred fifty five (155) dairy cattle farms in six sub cities of Hawassa. The study animals were Holstein Fresian cross breed owned by both privates and government in Hawassa city. All study farms were grouped under intensive dairy farming system. The sources of feed for animals include grass, crop residue, concentrates and agro industrial byproducts. The animals are given feed and water three and two times per day respectively. Treatment, vaccination and management of health problems were done by veterinarians that have employed in each sub-city. Culling in the farms was practiced as a result of aging, decreased production and reproduction as well as incurable diseases. All the farms have space for exercising for the animals. Both AI and natural service methods are used in the area for breeding.

Study design

A cross-sectional study was used to collect information with observational and questionnaire survey from December 2021 to June 2022. Questionnaire survey was conducted at eighty four (84) dairy farms in six sub cities of Hawassa, namely: Tula, Haykdar, Meneharia, Mehal-ketema, Tabor and Misrak.

Data collection methods

Semi-structured questionnaire was prepared and used to interview 84 farm owners. The questionnaire was prepared in English language and translated to Amharic language for easy understating by respondents. During the interview, the respondents included in the study were briefed about the objective of study before presenting the actual questions. Different farm records were used for collections of individual animals’ related data.

In the survey, data like address of owner, breed, parity, body condition score, heat detection, time of insemination, number of services pre-conception, sex of previous newborn, average milk yield, reproductive performances indicators, management factors and reproductive health problems were included. The animals’ body condition score were recorded on the scale of 1-5 based on criteria set by 55. Age of animals was recorded per year that was collected from farm records as well as by interview of owners and individual animal examination. Further, age classified as <4, 4-6 and > 6 years. Parity number and daily milk yield/cow/day were recorded from data of the farm in addition to questioning owners.

Sample size determination and sampling techniques

The sample size was determined by using the formula described by Yamane, (1973) as follows.

Sample size n=N/1+N (e)2, N=total population, e=allowable error (5% precision).

The total number of animals found in 155 farms of Hawassa city was 40,163. Therefore sample size was calculated as 396 animals; but 409 animals’ data were collected to represent all farms proportionally. In individual animal selection, sampling was done proportionally in which female cows that previously have at least one parity and having calves with the age of less than six months to collect clear data in determining sex of newborn. Therefore, 2-6 animals were sampled proportionally based on herd size from selected farm.

All owners of selected farms were interviewed for questionnaire data collections. For this sample size were calculated by using the formula given by 15 which is = 0.25/SE2, Where: N= sample size, SE (standard error) = 5%. Therefore, by using the above formula, the sample size was calculated as 100 farm owners to be interviewed. However, based on owners interest and easy of accessibility with available time and resources, only 84 farms out of 155 were examined for this study.

Data analysis

All data were entered in to Ms-Excel spread sheet, coded and analyzed using STATA version 12. The data were summarized using descriptive statistics of percentage and mean to determine status of reproductive performances in the study area. The variation between different factors and its effect on rate of conception after services was analyzed by using Chi-square test (χ2) and odd ratio (OR) with confidence level of 95%, whereas P < 0.05 were considered for significant value.

Results

Reproductive performances of dairy cows in the current study was evaluated by age at onset of puberty, age at first service, post-partum period, calving interval and number of service per conception (NSPC) (Table 1). This research indicated that the mean age at first services of study animals was 17.53+0.035 with difference of relatively one month from onset of puberty. The postpartum period/days was more than standard with mean +SE of 84.35+0.819. The NSPC was higher in animals breed naturally (1.87+0.056) as compared to using AI (1.69+0.049).

Table 1. Mean reproductive performances of cross breed dairy cows in Hawassa city
Indicators No. of examined Mean + SE Min Max 95% CI
Age at onset of puberty /months 409 16.67+0.036 15 25 16.601-16.744
Age at first service /months 409 17.53+0.035 16 27 17.457-17.594
Postpartum period/days 409 84.35+0.819 60 120 82.741-85.963
Calving Interval /months 381 12.88+0.046 12 24 12.792-12.972
NSPC with AI 204 1.69+0.049 1 3 1.595-1.788
NSPC with NS 205 1.87+0.056 1 4 1.763-1.983

In the result of this study, the relationship between animal related factors (such as age, parity, BCS, average milk yield per day, previous history of reproductive health problems as well as methods of breeding) with conception to first service was assessed. Accordingly, age, parity and history of reproductive health problems were significantly (P<0.05) affect the conception to the first service, whereas BCS, average daily milk yield and breeding mothods have non-significant effect on conception to first service (p>0.05). Conception to first service was decreasing with increasing age of animals significantly. Female cows with parity 2 and 3 have more success rate as compared to parity 1 (Table 2). Animals with reproductive health problems were significantly affected (P=0.001) in conception to first service than animals with absence of previous history of reproductive health problems.

Table 2. Factors affecting first services per conceptions
Factors Number examined Number conceived Percentage (%) OR p-value 95% CI
Age/year
<4 101 75 74.26 - - -
4-6 235 89 37.87 0.0035 0.001 0.00083- 0.01456
>6 73 3 4.11 0.00001 0.001 0.000013-0.00011
Parity
1 34 10 29.41 - - -
2 223 80 35.87 79.640 0.001 17.054-371.914
3 148 76 51.35 6937.57 0.001 1070.09-44977.5
4 4 1 25 136476.9 0.001 4306.95-4324624
BCS
<2 147 50 34.01 - - -
3 258 115 44.57 1.699 0.190 0.769- 3.754
>4 4 2 50 0.8278 0.910 0.032- 21.69
Average milk yield/day/Liter
5-10 17 6 35.29 - - -
10-15 78 30 38.46 6.699 0.030 1.199- 37.421
15-20 255 110 43.14 8.146 0.011 1.612- 41.179
20-25 59 21 35.59 5.335 0.075 0.846-33.634
Methods of Breeding
AI 204 91 44.61 - - -
NS 205 76 37.07 0.6334 0.199 0.316-1.271
Previous history of reproductive health problem
Negative 235 128 54.47 - - -
Positive 174 39 22.41 0.2415 0.001 0.156-0.375

Age and parity of animals showed strong relationship with NSPC. An increase in age of animal in year cause increase NSPC significantly with odds of 1.39. Similarly, the NSPC increases as number of party increases with odds 0.56. (Table 3).

Table 3. Effect of Animal related factors on number of services per conception
Factors No. examined OR SE P-value 95% CI
Age 409 1.39097 0.0290098 0.001 1.33525-1.449007
Parity 409 0.5687712 0.0207298 0.001 0.5295588-0.6108872
BCS 409 0.9290172 0.0309773 0.027 0.8702442-0.9917595
average milk yield/day 409 0.9988624 0.0053838 0.833 0.98836-1.00947
age at First services/Month 409 1.019821 0.0207437 0.335 0.9799638-1.061299

The percentage of animals conceived to the first service differs among different sub-cities. However there is no significant difference (P>0.05). Mehal-ketema and Meneharia sub-cities showed higher conception rate as compared to others (Figure 1). From the figure the lower conception rate to first service in Misrak and Tula (Figure 1a) resulted in higher number of service per conception with in the same sub-cities (Figure 1b).

Figure 1.Effect of study area on first services per conception and NSPC
Effect of study area on first services per conception and NSPC

This study indicated, the rate of conception was increased with increasing number of services in both natural services and AI. Higher conception rate to first services of 44.61% were reported when AI used than natural services with 37.07% (Table 4).

Table 4. Effect of breeding methods on NSPC
Methods of Breeding No. Examined No. Conceived Percentage (%)
NSPC with AI
1 204 91 44.61
2 113 85 75.22
3 28 28 100.00
NSPC with NS
1 205 76 37.07
2 129 83 64.34
3 46 42 91.30
4 4 2 100.00

There was no significant effect of study area and breeding methods on sex ratio of newborn calves (P>0.05). However, a slight difference in producing female newborn when a natural service used with 53.17% as compared to AI with 51.47% of female newborn were reported (Table 5).

Table 5. Sex ratio of new born among breeding methods and study areas
No. Examined Sex of newborn N (%) χ2  p-value
Breeding methods   Female Male    
AI 204 105(51.47%) 99(48.53%) 0.1185 0.731
NS 205 109(53.17%) 96(46.83%)    
Sub city          
Haykdar 62 32(51.61%) 30(48.39%)    
Mehal-ketema 18 9(50%) 9(50%)    
Meneharia 24 11(45.83%) 13(54.17%) 0.6301 0.987
Misrak 5 3(60%) 2(40%)    
Tabor 85 45(52.94%) 40(47.06%)    
Tula 215 114(53.02%) 101(46.98%)    

Discussion

In this study, reproductive performances of dairy cows were assessed in terms of age at onset of puberty, age at first service, post-partum period, calving interval and NSPC. The mean+SE of ages at onset of puberty of heifers, age at first service, post-partum period and calving interval in this study were 16.67+0.036 months, 17.53+0.035 months, 84.35+0.819 days, 12.88+0.046 months respectively. An average NSPC under AI and natural services were 1.69+0.049 and 1.87+0.056 respectively. The overall mean of age at first service is lower than the previous studies of 36.8±0.8, 24.3±8.01, 24 ±4.16 and 26.8 ± 0.34 months reported by 15, 4, 2, 17 respectively in different study areas of Ethiopia. On the other hand, the current report of age at first service was higher than the results that has been shown in previous studies in well-nourished temperate heifer that have potential to conceive at 14-15 months of age 25, 27. The variation between current study and previous report will be due to breed differences, study area, management of animals, nutrition, environmental stress, herd size and social interactions as scientifically confirmed to affect the age at first onset of puberty in heifers 36, 43, 1. In this study, urban dairy farming practices, cross breed animals of the study and improved management as compared to some rural areas were the major suggested factors for early onset of puberty as compared to different study areas of Ethiopia.

The postpartum period of cows assessed in this study was lower than previous study of 32 which was 141 days. The mean+SE of calving interval of cows in this study was 12.88+0.046 months which was shorter than previous studies of 21.36±3.84 14, 13.4 ±5.1 months 41 and 14.64±2.2 2 in different study areas of Ethiopia. On the other hand the current finding was slightly longer than 12.4 months reported by 24. Age and parity of individual cows, as well as transition cows managements will bring difference in postpartum recovery period and further calving interval as well 31.The difference of calving interval might be due to poor heat detection skills of farmers, improper timing and techniques of breeding/service, season of insemination, breeding system, and milk yield level, nutrition and climate conditions may also affect the success of breeding/services that extend calving intervals 36, 51.

In the present study age of the cows significantly (P<0.05) affect conception to first service with high conception rate of 74.26% in animal age group of <4 years than older cows. This was fairly agreed with the result obtained by 29 and Mufti et al. (2010) who reported 77.8% and 72.73% respectively in similar age groups. The decline in fertility with advancing age might be due to a decrease in ovulation rate as a result of lack of gonadotropin release from the pituitary, a deterioration in the quality of eggs ovulated with subsequent fertilization, resulting in embryonic loss or uterine failure due to hormonal imbalance or deficiency 47, 11.

The rate of conception to first service was significantly (P=0.001) increased with parity number from 1-3. However, conception to the first service becomes decrease as parity increase to 4 and more which was decline from 51.35% to 25%. This is in agreement with 52 which was reported as an increased conception rate with advancing parity from 2 to 6, and then declined with more parities and 29 was reported increased conception rate significantly in both parity 2 and 3 (P≤ 0.05). The decreased conception rate in advanced parity of the cows might be due to the aforementioned factors related to age. Furthermore, an increase in parity brings reproductive problems and can also lead to serious locomotive, mammary and metabolic disorders 8.

In present study, the presence of reproductive health problems significantly affect first service to conception rate P=0.001. This might be due to the disturbance of metabolic and hormonal processes of the cow due to health problems. This problem is more prevalent when age and parity number of cows increasing that affect rate of conceptions as it was confirmed in this study. Age and parity of animals showed strong relationship with NSPC. An increase in age of animal in year cause increase NSPC significantly with odds of 1.39. Similarly, the NSPC increases as number of party increases with odds of 0.56. This was confirmed by the study of 18, 53 and 48 that reported an increase in age and parity of cows, and presence of reproductive problems affect conception rate to services and further increase number of services per-conception which resulted in calving-to-conception interval.

The NSPC in the present study was 1.69 for AI and 1.87 for NS. The NSPC in this study while using AI was lower than 1.95 ±0.41 28 and 2.1 ±1.3 30 while the NSPC with NS was higher than 1.7±0.9 30. Even nutritional status of animals mainly affect success in breeding, NSPC might be influenced by breeding system, estrous detection and time of insemination, environmental heat stress and health problems 50, as well as herd size, housing, level of production, age and parity 26. In the current study area, the major practices in selecting breeding method was primarily AI for genetic improvement and only animals with suspected different health problems and repeat breading were allowed for natural services. This makes higher average number of services for individual animals during natural services than AI.

In both natural services and AI, conception rate was increased with increase in number of services. This is disagreed with studies of 10, 42 who reported that cows that were artificially inseminated several times were found to be less likely to conceive than cows inseminated once. The current study was again supported by previous study of 40 who states that heifers and cows allocated to bulls for natural mating needed fewer services to conceive than those under AI. The difference in the current findings from previous studies might be due to limited number of bulls and high female to male ratio for natural services. In addition to this, general practices in use of natural services in the study area were done when female animals were suspected of certain reproductive problems and previous history of repeat breading.

There was no significant difference between breeding methods (AI and NS) on sex ratio of new born (P>0.05). However, slightly more female calves (53.17%) were born with NS and 51.47% with AI. This finding agreed with previous study of 6 who was found 55.4% female calves from NS and lower when AI used. It was different from the reports which states, there is a greater probability of male calves being born when conceived through AI 56, 7, 46. This difference might be due to time of insemination with regard to estrous onset. In the present study area, AI was given early after onset of behavioral estrus that will give probability to have female newborn. Therefore, AI given early or late relative to time of ovulation changes sex ratio as late insemination gives chance to have male and early insemination to have female newborns. Sex ratio has no significant difference among study sites (sub-cities). However, slight difference in percentage might be due to overall management and breeding time difference.

Conclusion

The final goal of each dairy farm is to have lower age at which puberty onset and first service, decreased number of services per conception and shorter calving interval to improve reproductive performances of the farm. According to the result of this study the overall mean reproductive performance of cross bred dairy cows was satisfactory relative to the standard. Slightly more number of services per conceptions was required when natural breeding used than AI due to animals selectively allowed for natural services when there was certain reproductive problems. Age, parity and previous history of reproductive health problems were significantly affect conception to first service. The number of services per conception was increasing with age and number of parity, or presence of reproductive problems. Sex ratio of newborn were not significantly affected by breeding methods used (natural services or AI) which need to look the advantage of AI for genetic improvement. Therefore, reproductive health management and application of AI for genetic improvement should be well practiced to improve dairy cattle reproductive performances.

Acknowledgements

The authors acknowledged Haramaya University for financial support and farm owners of the study area for their interest in providing all necessary information to finalize this research.

References

  1. 1.Abanikannda O T F, Olutogun O. (2019) Genetic and Non-Genetic factors affecting age at first calving of N’Dama Heifers. , Nigerian Journal of Animal Science 21(2), 18-29.
  1. 2.Abera Z, Mengiste B, Demise T. (2015) Reproductive and Lactation Performance of Crossbreed Dairy Cows in Bishoftu. , Ada’a Distict of East Shoa, Eastern Ethiopia. Science, Technology and Arts Research Journal 4(4), 113-119.
  1. 3.Abraham H, Pal S. (2014) Animal biotechnology options in improving livestock production in the horn of Africa. , International Journal Interdisciplinary Multidisciplinary Studies 1-1.
  1. 4.D L Belay. (2016) Assessment of reproductive performance of local and crossbred Dairy cattle in Sidama Zone, Southern Ethiopia. , Journal of Natural science Research 6(9).
  1. 5.A D Benti, Zewdie W. (2014) Major reproductive health problems of indigenous Borena cows in Ethiopia. , Journal of Advanced Veterinary and animal research 1(4), 182-188.
  1. 6.Berihun K. Mekelle University) (2014) Assessment of Factors Affecting Agricultural Production: Evidence from Smallholder Farmers of Southern Tigray, Northern Ethiopia (Doctoral dissertation.
  1. 7.D P Berry, F E Madalena, A R Cromie, P R Amer. (2006) Cumulative discounted expressions of dairy and beef traits in cattle production systems. , Livestock Science 99(2), 159-174.
  1. 8.Bonneville-Hébert A, Bouchard E, D, Lefebvre R. (2011) Effect of reproductive disorders and parity on repeat breeder status and culling of dairy cows in Quebec. , Canadian Journal of Veterinary Research 75(2), 147-151.
  1. 9. (2009) Central Static Authority (CSA), Agricultural sample survey. Statistical Bulletin (2008/09) 302, Addis Ababa. , Ethiopia
  1. 10.R C Chebel, J E Santos, J P Reynolds, R L Cerri, S O Juchem et al. (2004) Factors affecting conception rate after artificial insemination and pregnancy loss in lactating dairy cows. Animal reproduction science. 84-3.
  1. 11.Currin L, Michalovic L, A M Bellefleur, Gutierrez K, Glanzner W et al. (2017) The effect of age and length of gonadotropin stimulation on the in vitro embryo development of Holstein calf oocytes. , Theriogenology 104, 87-93.
  1. 12.Dawit T, Ahmed S. (2013) Reproductive health problems of cows under different management systems. in Kombolcha, Northeast Ethiopia. Advances in Biological Research 7(3), 104-108.
  1. 13.Dekeba A, Ayalew W, P B Hedge, Taddese Z. (2006) Performance of the Abernossa Ranch in the production of Ethiopian Boran x Holstein crossbred dairy heifers in Ethiopia. , Ethiopian Journal of animal production 6(1), 33-53.
  1. 14.Duguma B, Kechero Y, Janssens G P J. (2012) Productive and reproductive performance of Zebu X Holstein-Friesian crossbred dairy cows in Jimma town. , Oromia, Ethiopia, Global Veterinaria 8(1), 67-72.
  1. 15.Gebeyehu G, Asmare A, Asseged B. (2005) Reproductive performances of Fogera cattle and their Friesian crosses in Andassa ranch. , Northwestern Ethiopia. Parity 305(10), 158.
  1. 16.GebreMedhin D, Bekana M, Tegegne A, Belihu K. (2009) Status of artificial insemination service in Ethiopia.
  1. 17.Getahun K, Hunde D, Tadesse M, Tadesse Y. (2019) Reproductive performances of crossbred dairy cattle at Holetta Agricultural Research Center. Livestock Research for Rural Development. 31(9).
  1. 18.Grimard B, Freret S, Chevallier A, Pinto A, Ponsart C et al. (2006) Genetic and environmental factors influencing first service conception rate and late embryonic/foetal mortality in low fertility dairy herds. Animal reproduction science. 91, 31-44.
  1. 19.Hadgu A, Fesseha H. (2020) Reproductive biotechnology options for improving livestock production: A review on. Advanced Food Technology of Nutrition Science Open Journal 6-13.
  1. 20.M H Hammoud, El-Zarkouny Z, Oudah E Z M. (2010) Effect of sire, age at first calving, season and year of calving and parity on reproductive performance of Friesian cows under semiarid conditions in Egypt. , Archiva Zootechnica 13, 60.
  1. 21.Hennessy D, Delaby L, Dasselaar A van den Pol-van, Shalloo L. (2020) Increasing grazing in dairy cow milk production systems in Europe. , Sustainability 12(6), 2443.
  1. 22.C D Hudson, A J Bradley, J E Breen, M J Green. (2012) Associations between udder health and reproductive performance in United Kingdom dairy cows. , Journal of Dairy Science 95(7), 3683-3697.
  1. 23.Hundera S. Addis Ababa University, Bishoftu (2004) Evaluation of semen parameter in Ethiopia indigenous bulls kept at Kality artificialinsemination center, In Thesis of degree of masterscience in tropical veterinary medicine: Faculty of veterinary medicine.
  1. 24.Hunduma D. (2012) Reproductive performance of crossbred dairy cows under smallholder condition in Ethiopia. , International Journal of Livestock Production 3(3), 25-28.
  1. 25.Ibrahim M N M, Zemmelink G.A comparative evaluation of integrated farm models with the village situation in the Forest-Garden area of Kandy, Sri Lanka. , Asian-Australasian Journal of Animal Sciences 13(1), 53-59.
  1. 26.Ibrahim N, Seid A. (2017) Review on reproductive performance of crossbred dairy cattle in Ethiopia. , Journal of Reproduction and Infertility 8(3), 88-94.
  1. 27.M R Jainudeen, Hafez E S E. (2000) Gestation, prenatal physiology, and parturition. Reproduction in farm animals. 140-155.
  1. 28.Jemal H, Lemma A, Bekana M. (2016) Assessment of the reproductive performance of dairy cows in smallholder dairy farms using artificial insemination. Livestock Research for Rural Development.
  1. 29.Khan M R K, Uddin J, M R Gofur. (2015) Effect of age, parity and breed on conception rate and number of service per conception in artificially inseminated cows. , Age (year) 2(3), 19.
  1. 30.Lemma A, Kebede S. (2011) The effect of mating system and herd size on reproductive performance of dairy cows in market oriented urban dairy farms in and around Addis Ababa. Revue Médicine Véternaria. 162(11), 526-530.
  1. 31.Lin Y, Yang H, M J Ahmad, Yang Y, Yang W et al. (2021) Postpartum uterine involution and embryonic development pattern in Chinese Holstein dairy cows. , Frontiers in Veterinary Science 7, 604-729.
  1. 32.Lobago F, Bekana M, Gustafsson H, Kindahl H. (2007) Longitudinal observation on reproductive and lactation performances of smallholder crossbred dairy cattle in Fitche, Oromia region, central Ethiopia. Tropical Animal Health and Production. 39(6), 395-403.
  1. 33.Gatius Lopez-, F. (2003) Is fertility declining in dairy cattle .a retrospective study in north eastern spain.Theriogenology 60(1),pp.89-99.
  1. 34.Mapletoft R. (2018) History and perspectives on bovine embryo transfer. Animal Reproduction (AR). 10-168.
  1. 35.Mebratu B, Fesseha H, Goa E. (2020) Embryo Transfer in Cattle Production and Its Principle and Applications. , International Journal of Pharmacology and Biomedical Research 7, 40-54.
  1. 36.Menale M, Mekuriaw Z, Mekuriaw G, Taye M. (2011) Reproductive performances of Fogera cattle at Metekel Cattle Breeding and Multiplication Ranch, north-west Ethiopia. , Journal of Animal and Feed Research 1(3), 99-106.
  1. 37.Mohammed A. (2018) Artificial Insemination and its Economical Significancy in Dairy Cattle. , International Journal of Research Studies in Microbiology and Biotechnology 4(1).
  1. 38.Morrell J, Rodriguez-Martinez H. (2011) Practical applications of sperm selection techniques as a tool for improving reproductive efficiency. Veterinary medicine international. 1-10.
  1. 39.Mufti M M R, M K Alam, M S Sarker, Bostami A B M R, N G Das. (2010) Study on factors affecting the conception rate in Red Chittagong cows. , Bangladesh Journal of Animal Science 39(1), 52-57.
  1. 40.H W Mwatawala, G C Kifaro.Reproductive performance of artificially and naturally bred Boran heifers and cows under ranch conditions in Tanzania. , Journal of Animal & Plant Sciences 4(1), 267-275.
  1. 41.Nibret M. (2012) Study on reproductive performance of crossbred dairy cows under small holder conditions in and around Gondar, North Western Ethiopia. , Journal of Reproduction and Infertility 3(3), 38-41.
  1. 42.M W Overton, W M Sischo. (2005) Comparison of reproductive performance by artificial insemination versus natural service sires in California dairies. , Theriogenology 64(3), 603-613.
  1. 43.G A Perry. (2016) Factors affecting puberty in replacement beef heifers. , Theriogenology 86(1), 373-378.
  1. 44.C E Pope. (2000) Embryo technology in conservation efforts for endangered felids. , Theriogenology 53(1), 163-174.
  1. 45.M A Rahman, N S Juyena, Shamsuddin M, Bhuiyan M M U. (2021) . Genomic Tools and Genetic Improvement of Crossbred Friesian Cattle. Research in Agriculture Livestock and Fisheries 8(1), 89-107.
  1. 46.J F Roche. (2006) The effect of nutritional management of the dairy cow on reproductive efficiency. Animal reproduction science. 96-3.
  1. 47.P R Shorten, C A Morris, N G Cullen. (2015) The effects of age, weight, and sire on pregnancy rate in cattle. , Journal of Animal Science 93(4), 1535-1545.
  1. 48.Siddiqui M, Das Z, Bhattacharjee J, Rahman M, Islam M et al. (2013) Factors affecting the first service conception rate of cows in smallholder dairy farms in Bangladesh. Reproduction in Domestic Animals. 48(3), 500-505.
  1. 49.Tadesse G, Tegegne A. (2018) Reproductive Performance and Wastage in Large Ruminant (Cattle) in Ethiopia-Review. , Journal of Dairy and Veterinary Sciences 8(1), 555729.
  1. 50.Taverne M, D E Noakes. (2018) Pregnancy and its diagnosis. Arthur's Veterinary Reproduction and Obstetrics-E-Book. 78.
  1. 51.M Y Temesgen, A, T, B A Minalu, A Y Mersha. (2022) Factors affecting calving to conception interval (days open) in dairy cows located at Dessie and Kombolcha towns. , Ethiopia. PloS one 17(2), 0264029.
  1. 52.Than H, Aung M, Daw S S K, H W Ye. (2001) Improvement of cattle production in Myanmar through the use of progesterone RIA to increase efficiency and quality of artificial insemination services (No. IAEA-TECDOC--1220). International Atomic Energy Agency (IAEA) 32(8), 1-50.
  1. 53.Tillard E, Humblot P, Faye B, Lecomte P, Dohoo I et al. (2008) Postcalving factors affecting conception risk in Holstein dairy cows in tropical and sub-tropical conditions. , Theriogenology 69(4), 443-457.
  1. 54.O P Verma, Kumar R, Kumar A, Chand S. (2012) Assisted Reproductive Techniques in Farm Animal-From Artificial Insemination to Nanobiotechnology. 5-301.
  1. 55.E, G M Jones, P E Wagner, R L Boman, Jr Troutt et al. (1982) A dairy cow body condition scoring system and its relationship to selected production characteristics. , Journal of dairy science 65(3), 495-501.
  1. 56.Z, L J Burton. (2000) Estrus synchronization of lactating dairy cows with GnRH, progesterone, and prostaglandin F2α. , Journal of dairy science 83(3), 471-476.
  1. 57.J F Yadeta. (2020) Review on Potential of Reproductive Technology to Improved Ruminant Production in Ethiopia. , Journal of Biology, Agriculture and Healthcare 10(8), 1-24.
  1. 58.Yamane T. Research Methodology/Sample Size. Florida: University Of Florida. Research (1973) .