Success rate of Artificial Insemination, Reproductive Performance and Economic Impact of failure of first service insemination: A Retrospective Study

Abstract

Background

A retrospective cohort study using a 10 year AI and cow reproductive performance data was conducted to study the success rate of AI; associations between effectiveness of AI and breed, AI season and, number of service per conception, and economic impact of failure of FSC in Dessie town, Dessie zuria and Kutaber districts. A total of 3480 dairy cows’ AI and reproductive performance records which were performed between 1995 and 2005 in the three selected districts of South Wollo were used. The economic losses and costs for cows that failed to conceive at their first AI associated with the larger number of days open were estimated.

Result

The prevalence of conception has a statistically significant difference between breeds of cows (P = 0.019). The non-return rate for first service was 58.54%. The median DFS, ISI and GL were 126, 30 and 278 days respectively. Whereas, the mean ± SD days open, CI, NOI and NSPC were 147.2 ± 60.26, 424.5 ± 60.55, 1.14 ± 0.38 and 1.15 ± 0.39 respectively. Based on AI season there was a significant difference in conception between winter and spring (P = 0.021). There is a 45.04 days extension in the mean calving to conception interval in cows that did not conceive at their first AI but conceived by 2nd and 3rd AI than in cows that did conceive at their first AI. A total of 28685.3 ETB extra costs was spent on reproductive treatment and other management for cows that failed to conceive at their first AI but conceived by second and third service. In cows that did not conceive totally the owner losses on average 473.7 ETB per cow per day extra costs until the cows will be culled.

Conclusion

Therefore to increase the conception rate and decrease the economic loss the owners of the dairy cows should supervise the cows regularly and should be trained on how to identify cows on estrous, the AI technicians should be trained to conduct the AI service accurately.

Background

Ethiopia has the largest livestock population in Africa, even if its productivity remains low [1]. From the total cattle population, 99.4%, 0.5% and 0.1% were indigenous, cross and exotic breeds [2] respectively. In order to improve the low productivity of local cattle, cross breeding of these indigenous breed with highly productive exotic cattle have been considered a realistic solution [3]. Now a days, artificial insemination (AI) is recognized as the best technique for increasing reproductive capacity and has received widespread application in farm animals in Ethiopia [4].

Conception at the first service after calving is crucial to improve the reproductive performance in dairy cows to increase the profit [5]. The success of first service conception (FSC) has been reported in a range between 26.7% and 50.7% in previous studies [6, 7]. A decrease in the first service conception results in an increase in the numbers of insemination, number of days open, feeding cost, culling loss, and replacement heifers cost [8, 9]. Therefore, identification of factors that potentially limit the success of FSC is useful to improve reproductive performance in dairy cows.

Several factors like parity, AI season, calving to first service interval, and peripartum disorders (dystocia, metritis, and retained placenta) have been decreasing the efficiency of FSC [6, 7, 10]. According to Quintela et al. [11] higher milk yield (> 39 kg/d), genetic values and cow parities (four or greater) were associated with a higher risk of a low FSC rate in the west-central region of France [11]. Clinical ketosis, metritis, retained placenta, stillbirth, dystocia and birth of twins were associated with a moderate decrease in FSC rate [5, 12]. According to Rearte et al. [13] report in northwest Spain, the higher risk of a low FSC rate is associated with short calving to first AI intervals, dystocia, parity of five and postpartum disorders autumn calving.

In Ethiopia dairy farmers plan to produce one calf per cow per year to maximize milk production and guarantee dairy herd replacement. However, most of the farms lack improved breeding programmes, nutritional strategies and data management strategies. In a dairy production good herd management practices are very important to know the reproductive performance of cows and also assist decision-making process and economic evaluation [14, 15]. Low pregnancy rates results in a reduction in milk production and calves born per year, which reduces the economic profitability of the dairy farms and the country [16].

AI has a main importance in improving local breeds in our country to increase the milk production and the total gain from dairy cows. The efficiency of AI were reported by different researchers ranging from 48.1–86.4% in different parts of Ethiopia [17, 18, 19, 20, 21, 22] However, the efficiency of first service insemination and impact of the AI has not been well-documented in Ethiopia. Thus identification of risk factors limiting FSC in dairy herds, determining the reproductive efficiency and success rate of AI and estimating the economic impact of the failure of FSC might provide useful information for dairy farmers. Therefore, this study was conducted with the objective of assessing association of FSC with some risk factors, determining the reproductive efficiency and success rate of AI, estimating the non-return rate for each service and estimating the economic impacts of failure of FSC.

Methods

Description of the Study Area

A 10 year AI and pregnancy diagnosis routine record book of Dessie town, Dessie zuria district and Kutaber districts (Figure 1) were obtained from South Wollo Zonal Liquid Nitrogen Production and Semen Distribution Centre (SWLNPSDC), Dessie, Ethiopia. Dessie is located in the north eastern part of the country at a distance of 401 km north of Addis Ababa. It is placed at latitude and longitude of 11′8°N and 39′38°E respectively with an altitude range of 2470 to 2550 meters above sea level. The area has an average annual rainfall of 1145mm and a mean annual temperature of 15.2°C. Both crop and livestock production is the main farming system of the districts. The total cattle population of the study districts was 194889 [23].

Study Design

A retrospective cohort study design using a 10 year AI and cow reproductive performance data  was conducted to study the success rate of AI, associations between effectiveness of AI and breed, AI season, number of service per conception, and economic impact of failure of FSC. 

Description of the Data 

A 10 year retrospective AI and reproductive performance data of the three districts was obtained from SWLNPSDC. All the AI and reproductive performance data recorded in the three districts within the 10 years (from 1995- 2005) was used in this study. During obtaining the data consent was taken with the SWLNPSDC to use the data for this scientific study. The original data was comprised of 3500 dairy cows’ AI and reproductive performance records which were performed between 1995 and 2005 in the selected districts of South Wollo. The data includes the following reproductive performance variables: owners name and address, cow ID, cow breed (local = 517, cross = 2983), Sire ID (n = 39), last calving date (heifers = 499, cows with last calving date = 1005), first AI date (n = 3048), second AI date (n =410), third AI date (n = 40) and fourth AI date (n = 2) and their corresponding insemination bull number; pregnancy diagnosis date and outcome of AI (pregnant = 1894, aborted = 81, sold = 348, non-pregnant = 1149, dead = 14, twin birth = 12), calving date and calf sex.

Data Exploration and Editing

Data exploration and editing was done using Microsoft Excel. Several new variables as measures of reproduction efficiency and performance were derived from the aforementioned original data. These includes number of services per conception (NSPC), postpartum conception (PC), calving interval (CI), gestation length (GL), Inter service interval (ISI), days to first service (DFS), number of inseminations (NOI), calving type,   fertility level, parity, season of AI and season of calving.

A data exclusion criterion was conducted according to the Damaso et al. [24]. Thirteen sires (n = 13) which had less than 6 mating records and number of cows with fourth insemination (n = 2) were removed from the data. In addition, cows which had gestation length greater than 295 days (n = 5) were also discarded from the data, as it is biologically less likely [24]. Hence, a total of 3480 records were finally used for this study.

Measure of Conception rate and Reproductive Efficiency

The conception rate was estimated by dividing conceived cows by the number of inseminated cows during that period. Different factors that may affect the conception rate were also assessed. The days open, calving interval, services per conception, days to first service and inter-calving intervals were also estimated from the data.

Definition of Terms

Calving interval (CI): The number of days between the birth of a calf and the birth of a subsequent calf, both from the same cow. 

Inter-service interval (ISI): number of days between two successive services.

Days to first service (DFS): Number of days from last calving to first service/ becoming to heat.

Days to calving (DC): is defined as the number of days from the start of joining to the day of calving.

Days open (DO): Number of days from calving to conception.       

Pregnancy Rate (PR): Number of cows conceived per inseminated cows during the 10 year periods.

Non-return rate (NRR): This is the number of cows bred that do not come back in heat and are thus assumed to have conceived. 

Artificial insemination submission rates in < 85 days postpartum (AIS): were defined as the percentages of cows inseminated within 85 days postpartum in cows to be bred during the same period [25]. 

Season of calving and first AI were grouped as:

Winter: December to February; Spring: March to May; Summer: June to August; Autumn: September to November.

Evaluation of the Economic Impact of Failure of First Service Conception 

The costs associated with the success or failure of first service conception by AI includes the costs of AI and pregnancy diagnosis (PD) both for the cows that conceived and those that failed to conceive at their first AI, and the costs of extra management procedures for cows that failed to conceive at their first AI, incurred because of a higher number of days open than for cows that did conceive at their first AI [26]. The cost of AI and PD was calculated using the total costs of semen, AI technician, and PD until conception occurred. The extra economic losses and costs for cows that failed to conceive at their first AI comprised the costs of replacement heifers, Value of extra feed fed in additional days, Value of extra labor used for management of Animal, value of extra breeding, value of calf loss and value of milk loss associated with the larger number of days open. 

The milk loss due to longer number of days open was estimated based on the average milk yield of that cow, number of days from first fail of AI to conception and the price of milk per litter in the town. The Feed cost per cow was estimated based on the recorded daily consumption and local market price of the feed. A calf price was set based on the value of a calf in the local market. The costs and losses from different factors were estimated using the following formulas according to Ill Hwa Kim and Jae Kwan Jeon [27].

1. Mean number of Extra days of calving to conception= (Total number of days from calving to conception in cows conceived by 2^nd and 3^rd service AI – total number of days from calving to first service AI)/ the number of cows conceived by 2^nd and 3^rd service AI.

2. Replacement cost = Replacement cost per cow/day*extra days of calving to conception = [(difference b/n the price of replaced cow and culled cow*% of culling due to infertility)* extra days of calving to conception /calving interval].

3. Calf price = Calf price per cow/day* extra days of calving to conception= (price of calf/ calving interval)* extra days of calving to conception

4. Cost of nutrition = Cost of nutrition per cow/day*extra days of calving to conception

5. Labor cost= extra days of calving to conception*daily labor cost

6. Milk cost= extra days of calving to conception*average daily milk yield of that cow*price of milk/litter

7. AI cost= number of insemination*cost of single insemination

8. Palpation (PD) cost= no. of PD*single PD cost

Data Management and Analysis

The collected data were entered into Microsoft Excel spread Sheet, edited and analyzed using Stata Version 13. Accordingly, descriptive statistics such as percentages and frequency distribution were used to determine the efficiency of pregnancy with different factors and the association of conception with different factors has been tested using multiple logistic regressions. A value of p<0.05 was considered as significant. The economic losses were analyzed descriptively. 

Results

Non Return Rate 

The non return rate for each service and the AI submission rates in ≤ 85 days postpartum were indicated in Table 2. The non-return rate for first service was 58.54%.

Reproductive Parameters 

The median DFS, ISI and GL were 126, 30 and 278 days respectively. Whereas, the mean ± SD days open, CI, NOI and NSPC were 147.2 ± 60.26, 424.5 ± 60.55, 1.14 ± 0.38 and 1.15 ± 0.39 respectively (Table 3).

From the calculated inter service intervals 9% and 38.3% were distributed in the range of 4 to 18 days and 19 to 26 days respectively. Whereas 30% of the ISI falls in greater than 50 days and 61.7% had greater than 26 days ISI which indicates that there was a gap in the ability of estrous detection (Figure 2). 

The estimated mean gestation length varies significantly (p < 0.005) between calving type, NSPC, AI season and calving season. Whereas, estimated mean calving interval varies significantly (p < 0.001) between breed/genotype, fertility and NSPC. The estimated mean postpartum day varies significantly (p < 0.005) between breed, fertility and NSPC (Table 4).

Days to first service varies significantly between breed and fertility, whereas inter service interval varies significantly between fertility, NSPC, calving season and AI season. The estimated means of NSPC varies significantly between calving type, fertility and parity (p< 0.05) (Table 5).

Association of Conception with Season of AI, Breed of Cows and Parity

Based on multiple logistic regression analysis conception of cows was statistically significantly different between breed of cows (p = 0.030); whereas, there was no any significant difference in conception based on season of AI and parity (p > 0.05) (Table 6). Cross breed cows have a higher probability of conception than local breed. 

Economic Impact of Failure of First Service Conception 

The culling rate owing to infertility in cows that did not conceive at their first AI was 80.2% (279/348), whereas no cows were culled because of infertility if they did conceive at their first AI (0/1776).

The analysis showed that 41.03% of the cows were censored because they were sold, died, or had not conceived until the end of the study years. There is a 45.04 days extension in the mean calving to conception interval in cows that did not conceive at their first AI but conceived by 2nd and 3rd AI than in cows that did conceive at their first AI.

The expense of reproductive treatment required until conception in cows that did or did not conceive at their first AI was shown on Table 7. Cows that failed to conceive at first AI (i.e. conceived by second and third service) required an extra 137.5 ETB due to extra semen and palpation cost than cows that did conceive at their first AI. A total of an additional expense of 28547.8ETB was incurred for other reproductive management procedures required to achieve conception (replacement heifers, nutrition, calf price, milk, and labor) in cows that failed to conceive at their first AI (Table 8). Thus, a total of 28685.3 ETB extra costs was spent on reproductive treatment and other management for cows that failed to conceive at their first AI but conceived by second and third service. In cows that did not conceive totally the owner losses on average 473.7 ETB per cow per day extra costs until conception (Table 8).

ETB= Ethiopian birr

a) Culling due to infertility in cows that failed to conceive at first service: 279/348 (80.2%).

Discussion

In the current study breed wise the conception rate were 54.28 (279/514) and 59.757 (1773/2966) in local and cross breed cows respectively. This finding is lower as compared to the overall conception rate of 74.67% and 64.8% in dairy cows in and around Kombolcha town [18] and in Dairy Cows in and Around Bishoftu [19] in Ethiopia. This result is also lower than the report of Shiferaw et al. [20], Jemal et al. [21], Arthur et al. [22], Balachandran [28], Basuro et al. [29], and who reported a pregnancy rate of 65.6%, 62.1%, 84.66%, 86.4% and 63%-71% respectively. Whereas it is higher than the 48.1% conception rate reported by Engidawork [17] in selected districts of Harari region. The difference in the conception rate could be due to difference in the composition of cows, number of cows, production system, type of semen, environment, inseminator potential and other managemental conditions.

Cross breed cows had 1.25 (CI = 1.04–1.51) times higher odds of occurrence of conception than local breed cows. This agrees with the finding of Befkadu et al. [18] and Yehalaw et al. [19], who reported a higher conception rate in cross breed cows in dairy cows in and around Kombolcha town in Ethiopia. The abortion rate found in the current study is 3.92%, which is higher than the 1.4% reported by Lobago et al. [30] in Sellale, Central Ethiopia.

The non return rate at first insemination in the current study was 86.55%. The result obtained in this study is higher than the 48.1% [17], 75% [31] and 84.03% [32] reported in Hareri, North Gondar, showa and North Gondar zone respectively. The variability on the value of non return rate might be due to difference in semen handling practices, AI technicians, breed, geography and differences in semen quality used for insemination

The mean number of service per conception in this study was 1.15 ± 0.39. This is lower than the 1.6 services per conception reported in central highlands of Ethiopia [41] and Harari [17]. It is also lower than the 1.88 [8], 1.7 [17] and 2.2 [18] reported in north Gonder zone, in and around Zeway and Eastern Lowlands of Ethiopia respectively. The number of service per conception higher than 2.0 were considered as poor [33]. Thus, the result found in the current study can be considered as good.

The estimated mean NSPC varies significantly between calving type, fertility and parity. The finding was in agreement with findings reporting the significant effect of parity of dam on number of service per conception [34, 35, 36]. However, according to the study reported by Engidawork et al. [31], Number of services per conception was not significantly affected by previous calving season and parity. NSPC was dependent on a large number of factors such as the oestrus display, oestrus detection, timing of service, sire fertility and sperm quality, subclinical diseases, and management features. Other studies are needed to investigate all aspects of increased NSPC.

In the current study 9% and 38.3% of the ISI were distributed in the range of 4 to 18 days and 19 to 26 days respectively. In addition 29.5% of interservice intervals were greater than 50 days. This is higher than the report of Softic et al. [37], who reported that a total of 9.6% of interservice intervals were longer than 48 days. Remnant et al. [38] reported that ISI of 19–26 days indicated that this period is the true latent distribution for the ISI with the optimal reproductive outcome, suggesting day-22 with the increased probability of conception [38]. However in our study 75% of the cows had 56.73 ISI and the mean is 39.73 ± 23.72 days which indicates the need of targeted monitoring of cows in order not to miss cows on estrous. This shows that there was a problem in the detection of cows on oestrous.

The median (± SD) CI of 424.5 ± 60.55 in the current study is higher than the report of 385 day by Softic et al. [37] and 12.6 months [39] in Dairy Farms in Una-Sana Canton, Bosnia and Herzegovinathe and Norwegian Red cattle respectively. However the CI is calculated retrospectively and represents the sum of all previous reproductive measures, it could be influenced by wide individual variations within the cows included in the study. Since there was a difference in the management, feed, and blood levels of cows.

The median DFS in this study was 126 days with variations between individual cows. This is highly greater than the 62.5 days reported by Softic et al. [37] in Dairy Farms in Una-Sana Canton, Bosnia and Herzegovinathe. It is also lower as compared to the report for Norwegian Red cattle (85.3 days, SD ± 41.9) [40]. The variations in DFS between individual cows and different studies can be explained by several factors such as nutrition [39, 40, 41], endometritis [42], and poor oestrus detection. According to Elkjær et al. [43] and [31] report uterine infection was associated with poor reproductive performance.

The median and mean days open in this study were 134 and 147.2 ± 60.26 respectively. The median days open in this study was higher than the 101 days open reported by Softic et al. [37] in Una-Sana Canton. The high median and mean days open in this study could be due to ability of detection of estrous, quality of semen and management of semen and cows. To reduce the mean open days, strengthening the heat detection ability and timed AI could be an alternative cost-effective measure. Cows with chronic reproductive problems could also be culled from the dairy herd and replaced by other cows [44, 45].

The economic loss/extra cost due to the failure of FSC in the current study was 28685.3 ETB due to extra costs of reproductive treatment and other management for cows that failed to conceive at their first AI but conceived by second and third service. It is found that a greater economic loss was resulted from management of cows (replacement heifers, nutrition, calf price, and labor) necessitated by the larger number of days open (81 days) than reproductive treatment (including semen, and palpation). In a previous study reported by Ill Hwa Kim and Jae Kwan Jeon [27] a total economic loss of $622.40 per animal was reported due to the failure of FSC. In another study in cows that needs three or more inseminations per conception the profit was decreased by >$205/year per cow [46].

The findings of the current and the previous studies showed that larger numbers of services per conception results in greater economic loss. The magnitude of the economic loss may differ depending on the reproductive efficiency and the amount of other expenses associated with management on dairy cows with extra days open [47]. The estimate of economic loss due to the failure of FSC in the current study and in the previous reports showed that dairy managers and owners should consider the impact of failure of FSC and the requirement to adopt strategies to improve FSC in dairy herds.

Conclusion

Relatively a moderate conception rate was encountered in this study. The conception rate differs between breed of cows and season of AI. Relatively higher average days to service and non-return rate to first conception were estimated. A total of 28685.3 ETB was incurred on cows that failed to conceive at their first AI but conceived by second and third service. Whereas in cows that did not conceive totally the owner losses on average 473.7 ETB per cow per day extra costs until the cow will, return to estrous or will be culled. Therefore to increase the conception rate and the economic loss the owners of the dairy cows should supervise the cows regularly, the owners should be trained on how to identify cows on estrous, the AI technicians should be trained to conduct the AI service accurately, the government should actively involved in the improvement of the local breeds and a cost-benefit analysis should be implemented in dairy farm activity.

Declarations

Acknowledgements 

We would like to acknowledge the AI experts in the selected districts and SWLNPSDC for their cooperations for the retrospective data. 

Competing interests 

The authors declare that they have no competing interests.

Funding 

This research was not funded by any organization.

Contributions

All authors participated in the data collection and preparation of the manuscript, read the final version, and agreed to submit the manuscript for publication.

Consent for publication

Not applicable

Data availability

The data are available from the corresponding author based on reasonable request

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