Livestock Research for Rural Development 27 (3) 2015 Guide for preparation of papers LRRD Newsletter

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Factors affecting the reproductive performance of smallholder dairy cows in two regions of Ethiopia

A Tesfaye, L Alemayehu, Y Tefera and A Endris

Samara University, College of Veterinary Medicine, P.O.Box 132, Samara, Ethiopia


The objective of this study was to investigate the effect of some factors on the reproductive performance of smallholder dairy cows under artificial insemination (AI) in two regions of Ethiopia. A cross-sectional study and retrospective data analysis were conducted on 428 farm characteristics and management, 644 cow reproductive histories and 613 inseminations by examining records and a questionnaire survey.

Mean days for calving to first service interval (CFSI) and calving to conception interval (CCI) were 222 (n=320) and 257(n=234) days, respectively. Service per conception (SPC) was 1.54 and first service conception rate (FSCR) was 41.8%. The relationship between reproductive performance and risk factors was described by using proportion of submitted cows to first service by day 201 after calving (SUB201), pregnant cows by day 228 after calving (PREG228) and non pregnant cows by day 305 after calving (NPREG305). The proportions for SUB201, PREG228 and NPREG305 were 35%, 28% and 52%, respectively. Site, body condition score (BCS) and management systems were associated to SUB201, PREG228 and NPREG305. The odds for BCS <3 to that of BCS>3 were 0.379, 0.297 and 2.03 for SUB201, PREG228 and NPREG305, respectively. Farms with intensive management system had odds of 1.99, 1.77 and 0.533 for SUB201, PREG228 and NPREG305, respectively to that of extensively managed farms. The performance of the AI service in the area had association to SUB201 and NPREG305. BCS, age of the cow, management system and AI service performance were factors to affect the reproductive performance of the smallholder dairy farms. Thus, increasing reproductive performance should overcome the challenge of nutritional and AI management.

Key words: breed, body condition, insemination, pregnancy


Ethiopian dairy production systems generally characterized as a year round calving system with low nutritional input and a limited use of mixed rations. Optimizing reproductive performance needs measurement of current performance, assessment of areas in which performance is less than desirable and subsequent suitable interventions (Hare et al 2006). Almost all Ethiopian researchers has been used are mean interval from calving to first service (CFSI) and calving to conception (CCI) as a major reproductive performance indices (Gebeyehu et al 2007, Lobago et al 2007, Mureda and Mekuriaw 2007, Yifat et al, 2009, Mekonnen et al 2010, Tadesse et al 2010). However, these measurements have their own limitation due to their usual skewed distribution and their means are affected by small numbers of extreme values (particularly in small herds). Furthermore, they exclude non inseminated non pregnant cows and inseminated non pregnant cows (Eddy 1980, Morton 2011). Thus, measuring reproductive performance of herds through a mean value of CFSI and CCI may underestimate the variation on reproductive performance between populations. The limitations of CCI should be addressed by evaluating proportion of pregnant and non pregnant cows by specified time periods after their last calving date such as proportions of cows pregnant or non pregnant by 80 days (Uchida et al 2001), 100 days (Morton 2011), 115 days (Ferguson 1996), 150 days (Raizman and Santos 2002), 210 days (Ferguson 1996) and 320 days (Raizman and Santos 2002) after their calving date. The other method measuring reproductive performance was conception efficiency based on either conception rate (CR) or services per conception (SPC) for those inseminations that ends with pregnancy (Fetrow et al 1990).

Dairy production systems vary internationally due to differences in management system, physical environment, social-economic status of producers, relative cost of labor, nutrition economics, available reproductive technologies and breeding costs, infrastructure availability and the regulatory environment with adaptability and genetic composition of cattle (Thatcher et al 2010a,b). However, energy balance is the most likely non-management factor to influence reproductive performance (Stockdale 2001). Factors affecting reproductive performance are associated to either to the management factors (such as methods of husbandry, feeding, estrus detection, semen handling and transition cow management) or to the cow factors (such as age, body condition score (BCS), postparturient problem, disease events, milk yield, and genetics) (Lucy 2001, Hudson et al 2012). While it is debatable that social status of farm owners and attendants (such as education level among farmer owners and attendants, years involved in farming and AI breeding) is a potential factor for poor reproductive performance of cows and hindrance in the effectiveness of AI programs. The objective of this study was to investigate the effect of some factors on the reproductive performance of smallholder dairy cows under AI in two regions of Ethiopia.

Materials and methods

A cross-sectional and retrospective data analysis was conducted on smallholder dairy farms in four sites that were substantially vary by geographical location. The sites were selected purposely by considering their cattle population and annual milk production, access to sufficient numbers of farms practicing AI breeding and wide range of agro-ecological systems. The study had enrolled only AI breeding smallholder dairy farms and their cows. Cows were 60 days and more since last calving and never bred by natural mating. Data of 428 farms (farm size 1-5) were recorded about their farm characteristics and management strategies by examining records and a survey questionnaire. Reproductive history data of 644 cows and their 613 inseminations were also recorded. Pregnancy diagnosis was done by rectal examination by 80 to 90 days post insemination and the last insemination date was considered as conception date for pregnant cows. Cows were classified non pregnant if and only if they were not inseminated 60 days post calving, returned to estrus within 56 days of insemination and diagnosed as non pregnant under rectal examination.

Data entry and editing were done on Microsoft Excel 2007(Microsoft Corporation, Richmond, WA). Further editing and statistical analyses were performed using SPSS 15 for Windows evaluation version (SPSS 2006, LEAD Technologies). Analyses were done for descriptive statistics, mean comparisons and general linear models. Graphs of multiple line and simple box plots were used to summarize categories of pregnancy success and median proportion of pregnancy respectively. Different days were considered as specific time in industrialized dairy herds; however, no standard days were given as specific point of time for smallholder dairy producers. By such instance, we thoroughly reviewed our own data for submitted and pregnant cows and the median values were selected as to be the specific point of time. Thus, the dependent variables investigated were submission by day 201 after calving (SUB201) which is a median of CFSI, pregnancy by day 228 after calving (PREG228) which is the median of CCI. Thus, SUB201 considers those animals that were submitted to first service where as PREG200 and NPREG305 were used to evaluate pregnant and non-pregnant cows, respectively. Conception rates were described for first inseminations and for all inseminations.

In view of large number of putative factors (Table 1), it was necessary to screen the most important factors through adjustment of location as a fixed effect during evaluation of factors associated to reproductive performance. Multinomial logistic regression was used to associate the effect putative factors on the outcomes of SUB201, PREG228 and PREG305. First, each cow level variables were screened with a series of univariate general linear models with district as fixed factor and variables P≤0.1 were retained for backward stepwise multinomial logistic regression model. Similarly the social status and herd level variables were screened and variables with P≤ 0.2 were selected. Then, multinomial logistic regression was done for selected cow and herd level factors using district as forced entry term.

Table 1. Putative risk factors considered for analysis
Cow level factors Social status factors Management level factors
Breed (Cross/Zebu) Owner sex (Male, Female) Keeping bull in the herd (Yes/No)
Age (<4, 5,6,7,>8) Owner educational status (Preprimary school, Basic education and Tertiary education) Production management system(Intensive and Extensive management)
Parity (1, 2, 3, >4) Who attends the farm?(Owner, Family member, Employed laborer) Do you get AI service regularly?(Yes/No)
BCS (<3, >3) Farm attendant educational status(Preprimary school, Basiceducation and Tertiary education) Do you have problems to perform AI other than regularity? (Yes/No)
Age group(<7, >7) Years involved in farming(<6, 7-10, >10 years) How do you evaluate cooperativeness of AI technician? (Cooperative/Non-cooperative)


The cumulative proportions of pregnant and non pregnant cows from days after calving are shown in Figure 1. The median duration of CFSI was201 days (range = 47–583 days; 25th = 136 days; 75th percentiles= 285 days) and CCI was 228 (range = 48–577 days; 25th= 161 days; 75th percentiles = 349 days). The median days after last calving (DALC) for non pregnant cows were 242 days (range = 62–698 days; 25th= 165 days; 75th percentiles = 331 days). Cow mean SPC were 1.55 (range = 1–5; 25th = 1; 75th percentiles= 2) and CR and FSCR for pregnant cows were 75.0% and 43.8%, respectively. Mean SPC for cows/heifers were 1.54 (range = 1–5; 25th = 1; 75th percentiles= 2) and CR and FSCR for pregnant animals were 73.4% and 42.3%, respectively.

Figure 1. Proportion of pregnant and non pregnant cows post calving

The proportion of SUB201, PREG228, NPREG305 and FSCR among study sites were shown in Table 2. Only 35% of cows were submitted to first service by day 201 after calving which was also variable between sites. The proportion of pregnant cows by day 228 post calving was very low (28% of cows were pregnant). Relatively cows in Dale site achieved highest proportions PREG228.The cumulative proportions of cows those were pregnant by days after calving across sites is shown in Figure 2. There was no important variation in PREG228 between sites. Higher numbers (52%) of cows were not pregnant by day 305 post calving and variations remained between sites and it was highest in Aletawendo. Even if there were great variations between FSCR, 42% of first inseminations ended in result pregnancy.

Table 2. Comparison for proportion of cows at specific aims among study sites
Fogera 0.37 0.30 0.57 0.25
Guangua 0.33 0.23 0.53 0.44
Aletawendo 0.25 0.25 0.60 0.48
Dale 0.48 0.36 0.38 0.49
Cumulative 0.35 0.28 0.52 0.42
P-value 0.003 0.194 0.022 0.002

Figure 2. Commutative proportion of pregnant cows across sites

Out of 470 cows included in SUB201 model only 35% (165/470) of cows were submitted to first service by day 201 after calving. BCS, age of the cow, management system and problems related to AI were associated to SUB201. BCS ≥3 and cows at age of 7 year have better SUB201 (Table 3). On the other hand intensively managed farms and farms without AI service problems had an increased likelihood of SUB201 (OR = 1.86, Table 3).

Table 3. Descriptive statistics and model estimates for SUB201
Variable No. animals Proportion
B (SE) Odds ratio
(95% CI)
Fixed factor
Intercept -2.13(0.447)
Fogera 119 0.37 1.85(0.420) 6.36(2.79-14.5) 0.000
Guangua 106 0.33 1.28(0.343) 3.62(1.85-7.10)
Dale 106 0.48 1.23(0.300) 3.41(1.90-6.14)
Aletawendo 139 0.25 0 1
Body condition score
< 3 303 0.29 -0.969(0.254) 0.379(0.231-0.624) 0.000
>3 167 0.47 0 1
< 4 81 0.42 0.305(0.321) 1.36(0.723-2.54) 0.025
5 77 0.31 -0.024(0.333) 0.977(0.508-1.88)
6 99 0.29 0.023(0.313) 1.02(0.554-1.89)
7 85 0.47 0.896(0.314) 2.45(1.33-4.53)
> 8 128 0.30 0 1
Management system
Intensive 270 0.41 0.688(0.232) 1.99(1.26-3.13) 0.000
Extensive 200 0.21
Problems in using AI
No 311 0.39 0.620(0.329) 1.86(0.976-3.54) 0.045
Yes 159 0.29 0 1  

Pregnancy by day 228 after calving was associated to location, BCS and management. Cows that had a very BCS <3 have a reduced likelihood of PREG228 than that of BCS ≥3 (OR= 0.297, Table 4). Whereas intensively managed cows had greater odds compared to cows kept under extensive management system (OR= 1.77, Table 4).

Table 4. Descriptive statistics and model estimates for PREG228
Variable No. animals Proportion
B (SE) Odds ratio
(95% CI)
Fixed effects
Intercepts -0.510(0.370)
Fogera 108 0.30 0.471(0.342) 1.60(0.818-3.13) 0.011
Dale 98 0.36 0.034(0.366) 1.04(0.505-2.12)
Aletawendo 129 0.25 -0.64(0.371) 0.527(0.254-1.09)
Guangua 88 0.23 0 1
Body condition score
< 3 270 0.21 -1.21(0.276) 0.297(0.173-0.511) 0.000
>3 153 0.28 0 1
Management system
Intensive 242 0.34 0.569(0244) 1.77(1.10-2.85) 0.000
Extensive 181 0.21 0 1  

NPREG305 was strongly associated with BCS of cows, management system and AI service regularity. Cows with BCS <3 had a higher chance of non pregnancy by day 305 post calving (OR= 2.03, Table 5). Farms with interrupted AI services had higher NPREG305 (OR = 2.26, Table 5) and cows under intensive management system were negatively correlated with NPREG305 (OR= 0.533, Table 5).

Table 5. Descriptive statistics and effect estimates for selected variables for NPREG305
Variable No. of animals Proportion
B (SE) Odds ratio
(95% CI)
Fixed effects
Intercepts 0.208(0.477)
Fogera 93 0.57 -0.745(0.357) 0.475(0.236-0.955) 0.003
Guangua 70 0.53 -0.821(0.366) 0.440(0.215-0.901)
Dale 82 0.38 -1.17(0.330) 0.310(0.162-0.593)
Aletawendo 98 0.59 0 1
Body condition score
< 3 211 0.59 0.707(0.279) 2.028(1.18-3.50) 0.010
>3 132 0.42 0 1
Management system
Intensive 196 0.45 -0.629(0.245) 0.533(0.329-0.862) 0.010
Extensive 147 0.61 0 1
AI service regularity
No 179 0.53 0.814(0.372) 2.26(1.089-4.678) 0.027
Yes 164 0.46 0 1  


The reproductive performance of Ethiopian smallholder dairy cows under AI was very poor. This study showed that CFSI and CCI were much extended. Even if it were highly variable across site, the number of cows submitted to first service and pregnant by day 201 and 228 after calving were very small which is also highly varied across sites. Postpartum anestrus is normal phenomenon; however, too long postpartum anestrus may result reproductive failure due to delay of day for first service and pregnancy time (Chagas et al 2007, Cummins et al 2012).

Reproductive performance was the poorest in Aletawendo site with low proportion of pregnancy by day 201 after calving and higher proportion of non-pregnant cows by day 305 after calving. The high NPREG305 in Aetawendo may be associated to the low SUB201 this is similar to that of Morton (2011) who reported that the odds of pregnancy by week 6 were positively associated with ovulation detection probability which was mediated by substantially increased odds of submission by 3 week (Morton 2011).

Reproductive performance was significantly affected by sites, BCS and age of the cow, management system and regularity of the AI services and problems in practicing AI. The farm management influences reproductive performance of cows because management decisions had an influence on cows when to be submitted to first service after days calving and when to be inseminated post start of estrus that influence SUB201 and CR, respectively. Inconsistency in application of herd management factors between cows or over time may also contribute to variation between lactations within a herd (Dohoo et al 2001).

Different studies showed that BCS during calving and at different stages of lactation have an effect on reproductive performance traits. Similarly to others studies, this study showed that BCS at any stage postpartum was negatively associated withSUB201, PREG228 and NPREG 305. Low submission to first service was observed in cows with BCS<3 (OR=1.99). Poor body condition within 201 days postpartum had lowered the submission to service by two times which is also reported by Suriyasathaporn et al (1998) and Pryce et al (2001) where extended CFSI is observed amongst cows losing excessive body condition during postpartum periods. Negative correlations between BCS and days to first service or days to first heat were reported by Dechow et al (2001), Pryce et al (2001) and Berry et al (2003). Change of postpartum body condition results extended days to first estrus due its negative effect on delayed ovarian activity, infrequent luteinizing hormone pulses, poor follicular response to gonadotropins and reduced functional competence of the follicle (Chagas et al 2007). In addition, the probability of successful pregnancy within 228 days post calving for BCS <3 cows was decreased by 3 times than BCS≥3 which agrees with that of Buckley et al (2003) and Roche et al (2007) who reported odds of pregnancy by 6-wk in-calf rate were reduced by 1.28 times and 1.62 times with each 0.5-unit decrease in BCS. Besides Roche et al (2007) also reported that odds for successful pregnancy at 12 wk were 1.7 times lower in cows that were 0.5 BCS units lower at nadir. The likelihood of NPREG305 for BCS <3 were 2.03 times than BCS≥3. There were similar reports by different researchers such as losing over 10% of body weight have prolonged calving to conception intervals (Heinonen et al 1988), losing 0.3 BCS unit (5-point scale) decreases the probability of pregnancy by 1.17 times (Roche et al 2007), low body condition scores (less than 2) in early lactation delays conception (Fagan et al 1988, Suriyasathaporn et al 1998) and cows BCS<3cows had reduced chance of pregnancy by week 6 of the mating period (Buckley et al 2003). The high proportion of cows by day 305 post calving in smallholder dairy farms may be attributed to delay for first service, poor conception rate, and embryo and fetal loss. There are several reasons for prolonged conception among low body condition cows such as prolonged intervals to first service (Suriyasathaporn et al 1998), delayed onset of postpartum ovarian cycling postpartum (Cartmill et al 2001, Moreira et al 2001, Stevenson 2001, Gumen et al 2003). There is also poor conception rate to first service for BCS of <2.75 (Loeffler et al 1999). Excessive body condition loss has also been associated with reduced conception rates and increased embryonic mortality rates (Gillund et al 2001, Pryce et al 2001, Lopez-Gatius et al 2003). The effect of BCS on reproductive performance seems associated to negative energy balance (Butler and Smith, 1989) where BCS is a subjective, visual and tactile monitoring system to measure nutritional and health status of cows (Berry et al 2003).

Cows managed under intensive management system have better reproductive performance where the odds for SUB201, PREG228 and NPREG305 were 1.99, 1.77 and 0.533 times than for cows managed under extensive management system. This result agrees with that of Woldu et al (2011) who reported the proportion of cows pregnant were 52.8% and 43.8% for intensive and extensive management system respectively. The Extensive management was not guaranteed with enough feed for the cows unless a comprehensive supplementary program supports. As reported by Obese et al (1999) and Domecq et al (1997), lack of supplementary feeding in extensively grazed dairy cows affect their reproductive performance. In addition the estrus activities were suppressed in extensively grazed cows due to heat stress (Jordan 2003; Rensis et al 2003; Windig et al 2005) and cows exposed to heat stress 1-3 days post insemination accustomed to embryonic death that leads poor conception rate and repeat breeding (Ealy et al 1995). Besides the nutritional deficiency, parasitic load and allowing calves to suckle their dams in extensive management systems interfere with ovarian function, thereby prolonging the days open (Ball and Peters 2004). Cows reared under very limited resources and unfavourable climate of extensive management systems may fail to become pregnant during the breeding season and lose another year before being capable of conceiving especially in the semi-arid tropics. Severe environmental and nutritional stresses imposed makes cows to spent an excessive proportion of their lives non-productively (Ball and Peters 2004).

BCS and management system of this study were associated to the nutritional status of the cow. Nutrition has a significant impact on numerous reproductive functions including hormone production, folliculogenesis, fertilization, and early embryonic development (Boland et al 2001, Armstrong et al 2003, Boland and Lonergan 2005) Reproduction function of cows inhibited during low food availability or energy demands are not met by compensatory food intake such as in short-term and chronic withdrawal of nutrients (Schneider 2004). Long-term (chronic) and short-term (acute) undernutrition has been observed to suppress female reproduction through the suppression of Gonadotrophin-Releasing Hormone (GnRH) secretion, the delay of onset of puberty, the interference with normal estrous cycles, and the alteration of endocrine function ( Short and Bellows 1971). Also, undernutrition affects ovarian follicle development, ovulation, blastocyst formation and fertility rates (Evans and Anderson 2012). Nutrition before after calving has an effect on pregnancy rates. Inadequate nutrition prior to calving, results in cows being thin at calving which delays the onset of ovarian activity. This delay in onset of cycling activity will influence the percent of cows available to be bred during the breeding season, thus reducing overall conception rates. The level of energy fed after calving will influence conception rates during the breeding season. The amount of energy fed will influence the percent of cows cycling, but even more dramatically influence first service and overall conception rates during the breeding season.

The odds of SUB201 cows in farms having problems in the AI services were 1.86 times than farms having no problems in AI services. The odds of NPREG305 in farms with AI service irregularity were 2.26 times than farms that had regular service. As known absence of AI service postponed the insemination of cows came to estrus to the next cycle. Thus, the irregularity of the AI service and problems associate to the AI service such as irregularity of AI service, uncooperative AI technicians and failure to several inseminations had increased the number of cows not to be submitted to firs service in early postpartum period and not to be pregnant in 305 days after calving. This may attributed the inconsistent and reliable services since more than 60% of the farms did not get reliable and regular services. The main reasons for irregularity of AI service were shortage of inputs, absence of service on weekends & holidays and shortage of AI technicians. Thus the poor SUB201 may also be involved for the highest proportion of non PREG305.



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Received 10 November 2014; Accepted 18 February 2015; Published 3 March 2015

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