Age at First Service and Calving, Calving Interval, Open Days, and Number of Services Per Conception of Dairy Cows Under Small Holder in Siltie Zone, Ethiopia

The present study aims to assess age at rst service and calving, calving interval, open days, and number of service per conception of dairy cows under small holder in Siltie zone, Ethiopia. To do so, a total of one hundred eighty sample size was selected from three selected districts of Siltie zone purposively. Data were collected through interviews with household heads using a detailed and pre-tested questionnaire. All data collected from demographic information and management practices of dairy cows were analyzed using SPSS software, version 27.0 while all data obtained from reproduction performances of dairy cows were analyzed using SAS version 9.4 in the General Linear Model (GLM). In this investigation, we found that long age at rst service (41.34±12.23, 37.561±85 and 30.87±8.65), for indigenous and (31.12±10.23, 27.24±7.35 and 25.45±8.45) for crossbred dairy cows in rural, peri ‐ urban and urban, months, respectively. Generally, the dairy cow were characterized by long time taken to reach age at rst services and calving, long calving interval and, open days in the study area. Therefore, selective breeding program should be applied rather than rely only on AI/Bull service crossbreeding. Moreover, supportive materials that used for grinding and mixing feed such as fodder and crop residue should be provided for small holder producers by respective institutions.


Introduction
Milk is one of the most important sources of animal protein for human diets and dairy production is a key element of agriculture worldwide (FAO, 2018). Milk from dairy cow represents nearly 100% of the milk produced in Latin America and Oceania (FAO, 2019).
In Africa, above 75% of the milk comes from cow grazing natural pastures (Gebrekidan et al., 2019). Despite the economic importance of dairy production systems and their major role for the household security of millions of people, the productivity is low (Lobago et al., 2007;FAO, 2019). The total numbers of dairy cattle in the world are in Africa (77%) and Asia and (33%), in Europe and North America (21% and 51%, respectively) of the world's milk production (FAO, 2018).
Ethiopia is the rst country in rank that keeping and producing largest population of livestock in Africa (Metaferia et  Despite the largest population and these importance, the production and reproduction performances of dairy cows are very low due to a number of reasons such, the low genetic capacity of the indigenous cattle for milk and meat production, low adaptation ability of exotic and hybrid dairy cows, substandard feeding, poor health care and other management practices (Belay et  In Ethiopia, dairy cows play a crucial role in development and represents a signi cant part of the urban, peri-urban and rural economy (Azage et al., 2013;Alemu, 2019). Unfortunately, the reproductive e ciency of crossbred and indigenous dairy cow is poor in most cattle production systems, mainly because of cows fail to become pregnant primarily due to management problems, shortage of feed and high prevalence rate of reproductive diseases as well as high calf mortality (Belay et al., 2012;Ayneshet et al., 2018). In the study area, there has been a substantial effort to holding crossbred and indigenous dairy cow by smallholder farmers under urban, peri-urban and rural production system (SZLFRD, 2020). However, farmers are being troubled due to the factors such; shortage of feed, feed resources and problem of reproductive diseases. Besides, the reproduction performance of dairy cow has not been studied in the study area. Moreover, the milk demands of the society is still not enough ful lled in Werabe town of the zone and its surrounded towns. Consequently, there is a need to assess the current reproductive performance of dairy cows. Therefore, the aim of the present study was to assess age at rst service and calving, calving interval, open days, and number of service per conception of dairy cows under small holder in study area.

Description of the Study Area
The current study was conducted in Siltie Zone, Southern Ethiopia. Siltie zone has a total area of 3000 sq.km and for administrative purpose; it is structured in to ten districts and three urban town. These include Alicho, Dalocha, Hulbareg, Lanfro, Western Azernet, Eastern Azernet, Eastern Silti, Mito, Sankurra, Silti, Tora, Kebet and Werabe town. Werabe town is the administrative center of the zone which is found 173 kms from Addis Ababa. The land scape of the zone is fairly level and found in northern part of South Nations, Nationalities and People Region (SNNPR) and located in North West of Alaba Zone, North East of Hadiya Zone, West of Oromia and South, South East and South West of Gurage Zone. The zone can be classi ed into three major climatic zones on the basis of altitude, rainfall and temperature: 20.6% highland, 74.4% Midland and 5% Lowland. Mean annual temperature is between 12-26 0 C. The rainfall is between 700 and 1818 mm. Agriculture is the main economic activity and the zone has varied ecological zones that range from lowland to highland, which makes possible the cultivation of various crop (SZFEDD, 2020). The main economic source of livelihood is based on both crop production and livestock rearing. Crops which are grown for food consumption as well as for income source in the area are enset, wheat, barley, maize, bean, pea, haricot bean, beetroot, potato, tomato, chills, onion, garlic, cabbage, and some other garden spices.

Study Design, Sampling method and Sample size
Cross-sectional study was conducted to assess age at rst service and calving, calving interval, open days, and number of service per conception of dairy cows. In order to get representative sample size from small holder, a three stage sampling technique was used. In the rst stage, three districts were selected purposively based on dairy cow production potential. Correspondingly, Dalocha, Lanfro and Sankura were selected from Siltie zone. Dairy cows were strati ed into rural, peri-urban and urban dairy production systems per each district. Secondly, nine categorized dairy production systems were selected purposively based on their dairy cow production potential. The selected production systems were: (Gute kutiyo, Shanka Tufa and Andasha Zeko) from rural, (Burka Dilapa, Warsha Shanka and M/Gumbi) from Peri-Urban and (Dalocha town, Tora town and Alem Gebiya) from Urban. Thirdly, a total of one hundred and eighty dairy cow's herd owners small holders were selected to be 55, 53 and 72 from rural, peri-urban and urban, respectively. Secondary data, Key Informant Interviews (KIIs), Focus Group Discussions (FGDs), and Observations were also employed to triangulate and support the primary data which obtained from the sample household head interviews. For this study, sample size was determined according to the formula given by Arsham (2020), [N = 0.25/SE 2 ] Where, N = Sample size, SE = Standard error. Therefore, using the standard error of 0.0373% with 95% con dence level, the total number of dairy cow's herd owners small holders sample size was one hundred and eighty.

Data Types and Collection Method Primary Data
The primary data were collected from structured smallholder dairy cow farmers' interviews via pre-tested questionnaire. The study was based on smallholder farms mainly found in rural, peri-urban and urban areas. Data were collected during interviews with household heads using a detailed and pre-tested questionnaire, which was previously developed and checked for clarity of the questions prior the interview and respondents were briefed to the objective of the study. For this study the questionnaire was adopted according to prevailing circumstances before data collection. Structured sample household head interviews employed to generate household level data on the small holder demographic information, dairy cow management practices, production performance of dairy cows.

Secondary data
Secondary data were collected from zonal, districts and each production systems agricultural administrates' documents, review of different documents including research works, books, journals, articles, report that had been written by different scholars on related issues. documents from various o cial websites such as; Ministry of Agriculture, Livestock Resource (MoALR), Ethiopian Institute of Agricultural Research (EIAR), Central Statistical Agency (CSA), National Metrological Agency (NMA) were reviewed.

Focused Group Discussions (FGDs)
A focus group discussion was hold with those who have been holding crossbred and indigenous dairy cows and organized in each production systems; youngsters, women, leaders, and socially respected individuals who are known to have a better knowledge on the present and past social and economic status of the study area. In order to gain more detail information, and triangulate the data obtained from the questionnaire-based household interviews, nine FGDs were conducted at Gute kutiyo, Shanka Tufa, Andasha Zeko, Burka Dilapa, Warsha Shanka, M/Gumbi, Dalocha town, Tora town and Alem Gebiya. In all the nine FGDs, a total of fty six dairy cow's herd owners participated to discuss on current status of milk production and management practices of their dairy cows. Six persons, as representatives of dairy cows herd owners were selected by development agents in consultation with livestock experts of each district. Members of the FGDS were selected from dairy producers who were reported to be capable of answering questions related to production performance and management practices of dairy cows in order to collect accurate information or data in the study area. The researchers have facilitated and monitored the discussions, and take note via closely followed the discussions. The discussants were allowed to freely express themselves with minimal interruptions on issues raised and the facilitated ensured that every member of the group was given fairly equal chances to express their ideas. A checklist guided the sequence of information to be collected from the FGDs. Discussion started with introduction of the study team and explanation of the purpose of the study. Participants were then asked to discuss the challenges and constraints they face in their dairy cow's herds. At the beginning, participants were asked to identify management practices that they were offering. At the end, possible means to increase the milk production e ciency of their dairy cows were discussed.

Key Informant's Interviews (KII)
In this study there were a total of 12 KII(3 from zonal livestock and shery resource o ce and 9 from three districts, 3 from each district). In all sub-sectors, 1 person was selected to be a head o ce, and the left two persons were selected from dairy breed improvement and feed resource classi cations.

Observation
In addition to the above data collection methods, the eld observation was carried out to validate the information provided through primary and secondary data collection tools. As well as information like socioeconomic condition of the study area was explored by eld visit.

Data-Analysis System
All data collected from demographic information and management practices of dairy cows were analyzed using Statistical Package for Social Sciences (SPSS) software, version 27.0. Descriptive statistics such as mean and percentages were used. Besides, Chi-square tests were performed to test the signi cance difference existence between categorical variables via cross tabulations. Otherwise, One-way ANOVA was performed for continuous variables. On the other hand, all data obtained from production performances of dairy cows were analyzed using Statistical Analysis Software, SAS version 9.4 in the General Linear Model (GLM). Furthermore, Dancan was used to examine the differences between levels of signi cance between the effect of interaction between breed and production systems. Statistical signi cance between variables was examined using P-values at critical probability of P < 0.05. In case of rank, index calculation was performed using Cambria Math equation in Excell. 2010.
Models Used for the current Study open days, and number of service per conception) in rural, peri-urban and urban of j th production systems and k th interaction between breeds and production systems. µ = is the overall mean, pi = is xed effect of production system that affects performance of cows (i = rural, peri urban and urban) bj = is xed effect of breed that affects performance of cows (i = crossbred and indigenous) pbijk = is xed effect of interaction between production systems and breeds. εijk = is the residual error. Table 1 shows the demographic information of respondents (gender, age, house hold size, educational status, total land holding, land holding for crop/forage production and land holding for pasture). There is no signi cance difference exist within gender however, age of the respondents differed signi cantly (P < 0.05) among the three production systems with the mean values (40.31 ± 0.95, 40.09 ± 0.72 and 41.40 ± 0.95, in rural, peri-urban and urban, respectively). And also, the total mean of respondent's educational status was fall with 8.40 ± 0.23. The total land holding of the small holders differed signi cantly (P < 0.05) in all production systems with the values (1.43 ± 0.10, 1.19 ± 0.08, 0.85 ± 0.06, in rural, periurban and urban, respectively).

Discussions
The current study is the rst to provide information on the reproductive performances of dairy cows in various production systems under small holder dairy cow's herd owners in Siltie zone, Ethiopia. In our investigation, we found the level of reproductive performance of dairy cows.

Demographic Characterization of the Respondents
There is no signi cance difference exist within gender however, age of the respondents differed signi cantly (P < 0.05) among production systems with the mean values (40.31 ± 0.95, 40.09 ± 0.72 and 41.40 ± 0.95, in rural, peri-urban and urban, respectively). And also, the total mean of respondent's educational status was fall within 8.40 ± 0.23. The total land holding of the small holders differed signi cantly (P < 0.05) in all production systems with the values (1.43 ± 0.10, 1.19 ± 0.08, 0.85 ± 0.06, in rural, periurban and urban, respectively). The overall, 58.89 and 41.11% of the respondents were male and female-headed households, respectively, however, there was no signi cance difference (P > 0.05) among production systems (Table 1). Female-headed household's proportion in the current study was lower than the 47.7 % and higher than 24.1 % of the results reported from Hawassa town (Haile et al. 2012), Jimma town (Duguma and Janssen, 2016, respectively). Overall, mean age of the household heads was found to be 40.68 ± 0.52 years. The present result indicated that farmers with 60-70 ages were involved in dairy production in the study area (Table 1). The overall mean household size was 7.02 ± 0.15 (Table 1). The mean educational status of respondents differed signi cantly (P < 0.05) in all production systems with the values (6.53 ± 51, 8.11 ± 0.25 and 10.04 ± 0.27, in rural, peri urban and urban, respectively)( Table 1). Majority of the respondents in this study had formal education that is very important to understand extension messages and to realize the importance of new technologies within a moment. The total land holding of the small holders differed signi cantly (P < 0.05) in all production systems with the values (1.43 ± 0.10, 1.19 ± 0.08, 0.85 ± 0.06, in rural, peri urban and urban, respectively). This indicates that there is an opportunities to increase dairy production to medium-scale production systems.

Dairy cows' Herd Structures
In the present study, there is no signi cance difference exist among herd structures of the dairy cows except only for calves ( Table 2). A larger number of cattle might be kept under Peri-Urban and Rural production systems, relatively than urban production system. On the other hand, the total number of crossbred and indigenous cattle was lower in rural than Urban and peri-urban production system i.e. the proportion of crossbred cattle is very low in rural dairy production system, better in peri-urban and higher in urban dairy production system (Azage et al., 2013)  Factors like delayed resumption of ovarian activity after calving, longer interval to rst estrus and brief shorter duration of estrus along with its silent symptoms, scarcity and deterioration of available feeds, might have contributed to di culty in heat detection and timely insemination of the cows resulting in prolonged OD (Melaku et al., 2011, Abunna et al., 2018and Mebratu et al., 2018. The variation could be attributed to differences in management practices like lack of giving attention for local animal; feed shortage and lack of proper heat detection might be contributory factors for long day open in local dairy cows reported in this study Table 8 shows the number of services per conception of dairy cows.

Number of services per conception
Number of services per conception for indigenous dairy cows differed signi cantly (P < 0.05) in all production systems with the mean values (2.07 ± 0.63, 1.81 ± 0.44 and 2.29 ± 0.78 in rural, peri-urban and urban, respectively). Similarly, number of services per conception for crossbred dairy cows differed signi cantly (P < 0.05) in all production systems with the mean values (2.29 ± 0.66, 1.77 ± 0.42 and 1.64 ± 0.54 in rural, peri-urban and urban, respectively)( Table 8)

Conclusion And Recommendation
Although dairy production is the most important activities in the study area, the production and reproduction performance of dairy cows have been limited for a long period of time due to a number of constraints. Consequently, the dairy cow were characterized by long time taken to reach age at rst services and calving, long calving interval and, open days, and low daily milk yield in the study area while daily milk demands and, price of one liter of milk is increasing dramatically every day. In this investigation, we found that long age at rst services and calving interval and open days. Based on these results, the following recommendation should be forwarded for better future supplementation of dairy cows.
It had better if selective breeding would be applied rather than rely only on AI/Bull service crossbreeding in order to mitigate low genetic potential of breeds.
Supportive materials that used for grinding and mixing feed such as fodder and crop residue should be provided for small holder producers by Governmental as much also by Non-governmental institutions to increase the quantity and quality of animal feed.

Consent to Participate
Consents from study participants were obtained.

Consent for Publication
Not applicable.

Con ict of Interest
The authors have declared that no con ict of interest.
Code Availability Not applicable.