Livestock Research for Rural Development 26 (6) 2014 Guide for preparation of papers LRRD Newsletter

Citation of this paper

Reproductive and health constraints of dairy cattle in the peri-urban areas of semi-arid eastern Kenya

E O Mungube, D M G Njarui, M Gatheru, J Kabirizi* and J Ndikumana**

KARI Katumani Research Centre, P.O. Box 340-90100 Machakos, Kenya
* National Livestock Resources Research Institute (NaLIRRI), P.O. Box 96 Tororo, Uganda
** Association for strengthening Agricultural Research in Eastern and Central Africa (ASARECA), Mpigi Road, P.O. Box 765, Entebbe Uganda


The dairy sub-sector in the semi-arid zones of Kenya is constrained by inadequate feeds, inappropriate breeds, inaccessibility and high cost of artificial insemination (AI) services, high incidence of animal diseases and inaccessible credit services. A study with the objective of identifying important reproductive and animal health challenges in smallholder dairy farms of semi-arid Kenya was conducted between September and December 2013. A pre-tested questionnaire was developed and administered to 73 respondents in the three study sites.


The mean age of heifers at first service was 28.1±10.8 months, 25.5±9.0 months and 22.4±5.8 months in Machakos, Wote and Wamunuyu, respectively. There was significant difference (P<0.05) between mean age of heifers at first service from Wamunyu compared to those from Machakos and Wote. Artificial insemination charges per cow/insemination averaged Ksh 1620 ((USD$ 20) in the study areas. Heat detection was done by herd owners (89.2%), bulls/other cattle (1.4%) and both herd owners and cattle (9.5%). Mean calving intervals (CI) in the studied sites were 12.7±1.1, 13.7±3.0 and 14.3±3.7 months for Machakos, Wote and Wamunyu, respectively. The CI in the 3 sites were not significantly different (P>0.005). The average milk production for the lactating dairy cows in the three sites was 6 litres/cow/day. Milk productivity was negatively correlated with lactation phase stage. East Coast fever and anaplasmosis, pneumonia, mastitis, foot and mouth disease and eye conditions were reported as prevalent diseases in the study herds. Further prospective studies should be carried out to evaluate other key reproductive parameters and relationship between nutrition and infertility.

Key words: smallholder dairy cattle, breeding method, calving intervals and heat detection


The livestock sub-sector contributes 40% of the agricultural Gross Domestic Product (GDP) and about 10% of Kenya’s total GDP (KARI 2009). The dairy industry is the single largest agricultural sub-sector in Kenya, larger even than tea (MoLD 2010). It contributes 14% of the agricultural GDP and 3.5% of total GDP (GOK 2008).


Kenya is the leading milk producer in Eastern Africa and produces an estimated 4 to 5 billion litres of milk annually from a herd of about 4 million dairy cows (Wambugu et al 2011). Much of this milk is produced by smallholder dairy farmers who account for 80% of the national milk production (MoLD 2010; Wambugu et al 2011). Smallholder dairy production systems range from stall-fed cut-and-carry systems, supplemented with commercial concentrate, to free grazing on unimproved natural pastures in the more marginal areas. Upgraded (crossbred) dairy cow breeds are kept under the zero grazing system or under the semi-zero-grazing systems (Wambugu et al 2011). The production systems are influenced by the agro-climatic characteristics of the area, land productivity potential and prevalence of animal diseases.


Although Kenya’s dairy sector significantly contributes to the national economy, household incomes and food security, the sector and particularly in the semi-arid zones is constrained by factors such inadequate feeds and water owing to the prolonged droughts, inappropriate breeds, inaccessibility and high cost of artificial insemination (AI) services, high incidence of animal diseases and inaccessible credit services (MoLD 2010; Wambugu et al 2011).


To ensure optimal dairy productivity, it is essential to evaluate the productive and reproductive performance indicators of the dairy cattle reared under various production systems for informed decision making concerning interventions that may be required. Studies on dairy farming have already been undertaken in various regions of Kenya (Muia et al 2011; Lanyasunya et al 2006; MoLD 2010, Wambugu et al 2011). Unfortunately many of these studies have been conducted in the high potential areas with only a few studies conducted in the semi-arid eastern Kenya (Njarui et al 2009, 2011 and 2012). The studies done within the semi-arid areas were focussed mainly on feeding and nutrition practices, milk production and marketing. Consequently, limited information is available on the reproductive and health performance indicators of dairy cattle in the semi-arid eastern Kenya. This study was hence conducted to appreciate the key reproductive and animal diseases constraints in dairy cows of the semi-arid areas to facilitate the recommendation of targeted interventions aimed at stimulating dairy productivity in this region.


Study area


The study was conducted within 10-15 km radius around the urban centres of Machakos, Wote and around Wamunyu trading centre. Wote town in Makueni County is located about 70km south-east of Machakos town while Wamunyu town in Machakos County is also located about 40 km north-east of Machakos town (Figure 1). Farmers in Wote and Wamunyu have slightly large land sizes compared with those in Machakos cluster.

Figure 1. A map showing geographical the location of the clusters of Machakos, Wote and Wamunuyu

These clusters had been preselected as detailed by Njarui et al (2012). The study clusters fall within the lower midland four (LM4) thus are categorized as semi-arid. Semi-arid areas are generally drier and experience erratic and unreliable rainfall which is bimodal (Rao et al 2012). The long rains (LR) season occurs between March to May with peaks in April and the short rains season (SR) starts from October to December with peaks in November. The annual precipitation in the semi arid zones is quite varied with the hilly masses being wetter than the generally low lying areas. Overall, the SR tends to be more reliable for crop and pasture production as it receives more rainfall than the LR.


Production system


Mixed crop-livestock subsistence farming system is predominantly practiced within the semi-arid eastern Kenya (Njarui and Mureithi 2006). Under this system, livestock are kept for manure, draught power, milk, meat and as security against crop failure during drought times. Indigenous zebu is kept mainly for draught power purposes although the breed also secondarily provides meat and milk. Most of the land particularly fertile sections of the farms within LM 4 are set aside for the production of a variety of drought tolerant food crops including pulses, maize and fruit trees while the less fertile pieces of land are left for pasture production.

Smallholder dairy farming is slowly becoming wide spread in the LM 4 where households own between 1 to 3 dairy cows (Njarui et al 2009). The dairy cows are mainly crosses between exotic dairy breeds like Friesian, Aryshire, Guernsey and Jersey and indigenous zebu (Njarui et al 2012).


Sampling and data collection


The inclusion criterion of dairy herds was ownership of at least a grade dairy cow. Proportional stratified sampling method described in detail by Njarui et al (2012) was used to select study dairy herds. A sampling frame composed of a list of dairy farmers situated to the east, west, north and south of each site was constructed before simple random sampling was used to pick the study farms. A total of 19, 33 and 23 dairy small-scale dairy farms from Machakos, Wote and Wamunyu which met the set criteria were included in this study.


This was a cross-sectional study conducted on varying dates between September 2012 and early December 2012. A pre-tested structured questionnaire was used as the sole data collection tool. Key information captured included lactation duration, inter-calving intervals, heat detection practices, method of breeding and herd health parameters among others. A one-off visit was made to every selected dairy herd during which time the herd owner or his/her appointed representative in which a face to face interview sessions to respond to questions in the questionnaire addressing on the above aspects was conducted. Informal discussions and visual observations were also used to collect additional information.


Data analysis


Data were coded and entered in a spreadsheet and checked for errors before analysis. Descriptive statistics including mean and standard deviations as well as proportions of the various reproductive and health indicators were calculated. Mean differences were established using T-test. Data were analyzed using the Statistical Procedures for Social Scientists (SPSS) for Windows version 18 (SPSS 2010).


Dairy herd structure


The study dairy farms had a mixture of male and female cattle (Table 1). The female cattle were mainly crosses although in a few farms pure exotic breeds mainly Friesian was also encountered. Bulls were either grade type reared for breeding purposes or zebu type reared mainly for draught power.

Table 1. The number of female and male cattle in the sites of Machakos, Wote and Wamunyu

Category of female cattle

No of female cattle

Category of male cattle

No of male cattle

Dry cows




Lactating cows






















Dairy farms had between 2 to 30 dairy animals. The farms in peri-urban Machakos had the smallest herd sizes whereas Wote and Wamunyu had moderately bigger herd sizes.


Age of heifers at first service


Although record keeping was not common in the dairy herds studied, the age when heifers were first served varied across the clusters. In Wamunyu had the least maximum age (22.4 ± 5.8 months) at which heifers were first served compared to Machakos and Wote (Table 2). Sometimes, it took some heifers almost 60 months before they were first served.

Table 2. The mean age of heifers at first service in Machakos, Wote and Wamunyu


Households interviewed

Age at first service (months)

Mean ± STDev





28.1 ± 10.8a





25.5 ± 9.0 a





22.4 ± 5.8 b





25.1 ± 8.7*



STDev=standard deviations

*parameter mean

Mean values with different letter superscripts along rows differ at p<0.05

Breeding method

The choice of breeding method used by dairy herd owners differed from site to site. However, in all the three sites, both bull (natural) service and artificial insemination (A.I) were used singly or in combination to breed cows (Figure 2).

Figure 2. Method of breeding cows in the sites of Machakos, Wote and Wamunuyu

Artificial insemination was used most in Machakos and Wamunyu compared to Wote where bull service was mostly used. Repeat breeding (a cow requiring another service almost month after being served) was reported in 61.7% of the studied herds. This was commonly reported in the herds that used A.I as the sole method of breeding. On average, a cow required 1.4 inseminations before conception.


Artificial insemination practitioners


Insemination services were provided through private A.I practitioners, co-operative societies and Ministry of Livestock Development (MoLD) personnel (Figure 3). In Machakos site, private inseminators were the sole providers of A.I services whereas of Wamunyu, A.I services were predominantly provided through the co-operative societies. In Wote site, there was a mix of all the three A.I service providers. Like in Wamunyu, A.I services in Wote cluster were mostly offered through the co-operative societies followed closely by private inseminators. In this cluster unlike in Machakos and Wamunyu, A.I services through MoLD personnel were still being provided.

Figure 3. Artificial insemination service providers in the sites of Machakos, Wote and Wamunuyu

Artificial insemination charges

Insemination charges were also varied based on the area and also type of semen used. Generally, locally produced semen was cheaper than imported semen. Locally produced semen was predominantly used for breeding purposes compared with imported semen. The mean cost of a single insemination across the three study sites was Ksh. 1660 (US$ 20). On average farmers in Wote site were charged Ksh. 2190 (US $ 26) per insemination which was the highest among the 3 sites studied. In the site of Machakos, the farmers were charged an average of Ksh.1850 (US$ 22) per a single insemination. Wamunyu had the lowest inseminatiom charges as farmers paid a mean of Ksh 1060 (US$ 13) per single insemination. 


Sexed semen was slightly more costly as farmers paid an average of Kshs. 6000-8000 (US$ 71-94) per insemination for imported sexed semen in Machakos and Wote. Where A.I was totally lacking, bull service was used. The farmers who did not own breeding bulls paid between Ksh 500 to 1000 (5-9-11.8) per cow per service.


Heat detection practices


In the study clusters, heat detection was done either by humans or cattle themselves. Herd owners/herdsmen dominated (89.2%) in heat detection while bulls or other cows accounted for only 1.4% of heat detections. In some cases, it was reported that both humans and cattle (9.5%) were also used as a method of detecting heat in cows.


A majority (73%) of the respondents had no training on heat detection. The few with training were trained by MoLD officials (50%), Co-operative societies (30%), non-governmental organizations (5%), Private AI practitioners (5%), other experienced farmers (5%) and self learning (5%)


Calving intervals


Overall, the mean inter-calving interval was about 14 months for cows in the three clusters. However, it took some cows a minimum of 11 months to calf down again while some others took up to 53 months to calf down again. Machakos site had the least range of calving intervals and Wamunyu the highest calving interval (Table 3).

Table 3. Calving intervals for the dairy cows in Machakos, Wote and Wamunyu sites


Households interviewed

Calving intervals (months)

Mean ± STDev





12.7 ± 1.1





13.7 ± 3.0





14.3 ± 3.7





13.6 ± 2.9*



STDev=standard deviations

*parameter mean

Milk production

Milk production per cow per day in the three study sites was quite varied. Milk production averaged about 6 litres per cow per day in the three sites studied. Wamunyu had the highest mean milk production at 6.8 litres per cow per day followed by Machakos which had 6.6 litres per cow per day. Wote with a mean of 5.8 litres of milk per cow per day had the lowest milk productivity.


Overall, cows with in the early lactation stage duration produced more milk compared to those in the late lactation stage (Figure 4).

Figure 4. Relationship between lactation duration and milk production
for cows in Machakos, Wote and Wamunyu sites
Infectious diseases

A number of infectious diseases were reported in the study herds. Tick borne diseases like East Coast fever (ECF) and anaplasomosis were the most dominant diseases followed by others like pneumonia, mastitis, foot and mouth disease (FMD), eye conditions and others (Figure 5).

Figure 5. Infectious diseases commonly reported in studied
dairy herds in Machakos, Wote and Wamunyu

It was reported that occurrence of diseases like mastitis, pneumonia, eye conditions, ECF and anaplasmosis had a seasonal pattern as they increasingly occurred during the rainy season whereas others like FMD occurred during the dry season mostly.


The results of the study showed that small-scale dairy farming is predominant in the semi-arid areas of Machakos, Wote and Wamunyu. Generally, the herd sizes averaged 7 cattle per household with between 2 to 3 lactating cows. This agreed well with what was reported by Wambugu et al (2011) who reported that most small-scale dairy farmers in Kenya have two to three lactating cows. On the contrary, herd sizes are slightly smaller (5 cattle/household) in the high potential areas (Muia et al 2011). This variation is explained by the fact that  mixed crop-livestock production system are predominantly practiced in the semi-arid eastern Kenya hence high demand for oxen draught power as opposed to the high potential areas where the available small pieces of land are mostly tilled by hand.


The fertility of dairy cows is multi-factorial and many factors influence the reproductive performance. Such factors include management regime (Bielfeldt et al 2006), environment (Windig et al 2005), genetics (Roxstrom 2001), nutrition (Butler 2003), and biological and health status (Fourichon et al 2000). Successful reproduction, starting with oogenesis and ending with the birth of a calf, relies on complex physiological dynamics and is the result of a chain of events. The resumption of ovarian cyclicity, oestrus, and ovulation are all events that need to precede conception and failure at one stage results in failure of the whole process (Garnsworthy et al 2008).


Studies have shown that well-nourished heifers can attain puberty at the age of 10-12 months and be ready for first service and conception at 14-15 months of age (Hafez and Hafez 2000; Ibrahim and Zemmelink 2000). The present study established that heifers were first served when they were much older perhaps because of reduced growth rates and delayed puberty attributed to the low plane of nutrition which is quite common in the semi-arid eastern Kenya (Njarui et al 2009). According to Hafez (2000), a high plane of nutrition hastens puberty through increased growth rate of heifers.


The ease and success of heat detection is crucial for optimal reproductive performance of dairy cattle (Löf 2012). Although respondents in the present study reported that herd owners or herdsmen detect heat in cows, proper skills necessary for heat detection were lacking. Improper heat detection lowers conception rates due to the wrong insemination timing which results into conception failure. This prolongs the calving intervals (CI) thus negatively impacting on the productive and reproductive performance of dairy cattle. Herds which practice good heat detection are able to attain impressive reproductive performance indicators (Mayne et al 2002). The voluntary waiting period (VWP) of cows that is the time between calving and when a cow is again ready for breeding is another factor of reproductive performance which is likely to affect the CI (Löf 2012). The VWP for cows in the semi-arid areas is likely to be long due to low plane of nutrition in the semi-arid areas. This means cows have longer calving to first insemination (CFI) duration which may unnecessarily prolong the CI in herds where cows are not supplemented. The VWP duration indirectly affects reproductive performance owing to the occurrence of most common metabolic and reproductive disorders around parturition or in early lactation (Erb et al 1984).


The average national CI in Kenya is estimated to be between 15 to 17 months (MoLD 2010) which is slightly higher than the 14 months established by this study. In well managed dairy herds, the CI is approximately 12-13 months (Roberts 1986). This study reported slightly higher CI than the national averages although these seemed to be in agreement with those reported elsewhere (Abdalla et al 1999; Sattar et al 2005; Moges 2012). Prolonged CI results in loss of substantial amounts of milk (Hafez and Hafez 2000). Estimates put annual milk loss in Kenya attributed to prolonged CI at between 450 and 500 million litres worth over Ksh 4 billion (MoLD 2010). The long CI in cows is related to the inadequate feeding, poor heat detection, herd health, the unreliability of AI and /or bull services and the lack of herd recording for decision making (Moges 2012; Duguma et al 2012). Poor breeding management particularly poor semen handling and semen-deposition techniques have also been suspected to lead to the unnecessary prolongation of CI (Alejandrino et al (1999). Similarly, nutritional stress particularly during critical periods of the cow’s reproductive life is also likely to prolong CI especially in the smallholder dairy farms (Ibrahim and Zemmelink 2000). It has been reported that high-yielding dairy cows have high nutritional requirements, which predisposes them to negative energy balance (Butler 2003). This physiologically and nutritionally affects follicular growth and ovulation (Garnsworthy et al 2008).


The push for huge profits has made A.I services to remain inaccessible and unaffordable to most smallholder dairy farmers. Our study established that where private practitioners dominated the AI services market, services were comparatively more expensive compared to where it is provided through co-operative societies or government. The findings by this study are comparable to the estimated national insemination costs that range between Ksh 800-3000 (US$ 10 to 38) per cow per insemination for the locally produced semen and up to Ksh 10000 (US$ 125) for imported semen (Muriuki 2011). Poor artificial insemination technique results into repeated inseminations which increases the cost of breeding in most smallholder dairy farms. It has been projected that a dairy cow in Kenya conceives after every 1.5 inseminations (MoLD 2010). This means that only about 0.33 million dairy cows out of the 4 million dairy cows can be served by the 500,000 semen doses produced per year by the Central Artificial Insemination Station (CAIS). The shortfall in semen production partly explains why smallholder dairy farmers in Kenya have increasingly shifted from AI to bull service although given a chance they would still prefer AI to bull service an indication of the collapsed AI service in Kenya.


This study established that infectious diseases and particularly tick borne diseases are quite prevalent in the semi-arid eastern Kenya. Tick borne diseases (TBDs) including East Coast fever (ECF), babesiosis (red water) and anaplasmosis constitute the largest component of all animal diseases that impact negatively on the dairy industry in Kenya (MoLD 2010). Prevalence of ECF is particularly high under the extensive free grazing and the semi-intensive grazing systems in the lowland areas where the ECF risk reaches 30% per year and account for over half of all clinical cases encountered in the smallholder dairy farms (MoLD 2010). The TBDs cause high mortality rates and are associated with high cost of control through the use of acaricides and chemo-therapy. The increasing risk of TBDs in Kenya is attributed to the collapse of dipping services following the withdrawal of government support in 1993. The delivery support used by most smallholder farmers is hand spraying of acaricides to cattle.


Diseases like foot and mouth (FMD) among other transboundary animal diseases (TADS) are a threat to the sustainable productivity and viability of the dairy industry. The direct economic losses attributed to the TADs are through mortality, reduced productivity, lowered product quality and lost trade opportunities (FAO 2006). Reproductive diseases reported by this study included mastitis which has a huge influence on the productive and reproductive performance of dairy herds as reported by Dubuc et al (2011). Both clinical and sub-clinical mastitis have been associated with poorer reproductive performance (Ahmadzadeh et al 2009). Hertl et al (2010) demonstrated that clinical mastitis occurring any time between 14 days before and 35 days after AI reduced chances of conception in cows. Reduced reproductive performance due to mastitis may be related to extension of the interval from calving to first postpartum AI, reduced pregnancy rate to insemination, prolonged days open, and increased late embryonic mortality after pregnancy diagnosis (Santos et al 2004). Peake et al (2011) found prolongation of the interval from calving to onset of the first luteal phase for cows with one or more of three production stressors: lameness, subclinical mastitis, and body condition score loss.



Further prospective studies are required to establish the relationship between nutrition and age of heifers at first insemination, prolonged CI and low conception rates. Data on other important reproductive parameters like the voluntary waiting period a proxy indicator for days open, calving to first insemination and number of parities of cows is also needed to inform decision making on dairy farming. Dairy farmers need capacity building on proper heat detection skills to minimize incidence of repeated services.


We thank the farmers who willingly accepted to participate on the study and provide information, Special mention also goes to Mr. Olonde, Mr. Mwikya and Mrs. Mary Sila who assisted in data collection and data entry. This study was funded by the Association for Strengthening Agriculture Research in Eastern and Central Africa (ASARECA). The views expressed in this report are not necessarily those of ASARECA.


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Received 1 March 2014; Accepted 28 March 2014; Published 1 June 2014

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