Reproductive performance indicators of dairy cattle in selected small-scale dairy farms in semi-arid Eastern Kenya

E O Mungube, D M G Njarui¹, M W Maichomo, M O Olum, P N Ndirangu, J Kabirizi², J Ndikumana³ and G Mwangi¹

KALRO Muguga North, Veterinary Research Centre; P.O. Box 32-00902 Kikuyu, Kenya
erickmungube@gmail.com
¹ KALRO Agricultural Mechanization Research Institute (AMRI), Katumani P.O. Box 340-90100 Machakos, Kenya
² National Livestock Resources Research Institute, (NaLIRRI), Uganda, P.O. Box 5704 Wakiso Uganda
³ ILRI Nairobi, P.O. Box 30709 Nairobi, Kenya

Abstract

Dairy farming is an emerging enterprise in the semi-arid parts of Eastern Kenya and is constrained by inadequate feeding, poor heat detection methods, unreliable and expensive artificial insemination services, high incidence of infectious diseases among others. A cross-sectional study to establish reproductive and fertility performance indicators of dairy cows was undertaken between May and June 2014 in three peri-urban areas of Wote, Machakos and Wamunyu towns.

A total of 53 peri-urban small-scale dairy farms participated in the study, from which 121 cows aged at least 2 years were randomly sampled. One-off visits to the selected farms were made and pregnancy diagnosis conducted on all inseminated cows. The history of reproductive disorders for the study cows was undertaken using a questionnaire.

Both artificial insemination and bull service were used as breeding methods with the latter as the preferred method of breeding in Machakos. Overall crude preganant rate was 45.5% with Machakos having a crude pregnancy rate of 37.5% which was the lowest and significantly (p<0.05) different from the 46.5% estimated for cows in Wamunyu. Specific pregnancy rates from amongst served cows were 71.4%, 80.9% and 91% for cows in Machakos, Wote and Wamunyu, respectively. These were not significantly (p>0.05) different. Overall, 7.4% cows returned to heat post-insemination with Machakos having the highest rate of return to heat (9.5%) followed by Wamunyu at 9.1% and Wote at 3.7%. Repeat breeding was significantly (p<0.05) higher in Wote with a mean estimate of 1.8 ± 1.3 inseminations compared to both Machakos and Wamunyu in which cows received a mean insemination of 1.3 ± 0.5 before conception. Overall, the mean number of inseminations per cow before conception for the three clusters was 1.5. The ovaries of 54.5% (66/121) non-pregnant cows had growing follicles on 12.1% (8/66) of them. Wote havd the highest proportion (39%) of the non-pregnant cows having growing follicles. Two of the non-pregannt cows exhibited anoestrous signs since there were no palpable structures on their ovaries.

Findings from this study clearly show that the relatively low pregnancy rates and repeated services warrant further investigations to establish likely causes to inform appropriate intervention measures.

Key words: small-scale dairy, heat detection, fertility, pregnancy, repeated services

Introduction

Kenya is the leading milk producer in Eastern Africa with an estimated 4 to 5 billion litres of milk produced annually from about 4 million dairy cows (KNBS 2017, Odero-Waitituh 2017)). Much of this milk is produced by small-scale dairy farmers who account for 80% of the national milk production (Odero-Waitituh 2017). Small-scale dairy production systems range from stall-fed cut-and-carry systems supplemented with commercial concentrate in high potential areas to free grazing on unimproved natural pastures in the marginal areas. In Kenya, dairy production is an emerging enterprise in the semi-arid zones (KNBS 2017).

Despite Kenya’s huge dairy potential, the average daily milk production in Kenya is 8-10 litres per cow per day slightly less than 12.7 litres per cow per day or approximately 4 590 litres per cow per lactation reported in South Africa (Theron and Mostert 2008). The main causes of the low milk productivity include inadequate feeding, high incidence of infectious animal diseases and low genetic potential (Belay et al 2012). Other key constraints to milk productivity include ovarian dysfunction, poor heat detection methods, long inter-calving intervals, unreliable and expensive artificial insemination and reproductive wastage (Hafez and Hafez 2000). High herd replacement costs, high veterinary intervention costs and reduced annual milk yield associated with long inter-calving intervals greatly affect profitability of small-scale dairy farmers (Löf 2012).

The birth of a calf is the starting point of a dairy cow’s productive life. For the cow to continue to produce milk, it must continue to calve at regular intervals. The fertility of dairy cows which is the ability to become pregnant and carry a pregnancy to term – is therefore indispensable for natural milk production. Herd health and herd production advisory services often have reproductive efficiency as a target, using various measures and indicators to monitor and benchmark reproductive performance in dairy cows and herds (Löf 2012). To optimize 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 regarding timely interventions when necessary.

A lot of work on small-scale dairy farming in the semi-arid Kenya has been restricted to forage production interventions (Njarui et al 2009, 2011 and 2012) ignoring other important aspects like reproductive interventions which equally affect milk productivity. There is hence a dearth of knowledge on the reproductive performance indicators of dairy cattle in this region. In order to help increase milk production, there is urgent need to also understand key reproductive performance indicators of the dairy cattle in the semi-arid parts of Eastern Kenya in order to design reproductive interventions which are likely to positively impact on milk productivity. This study was conducted to establish pregnancy rates as a proxy for measuring reproductive performance among the participating smallholder dairy farms in dairy emerging areas in lower eastern Kenya.

Methodology

Study area description

The study was conducted from May to June 2014 within small-scale dairy farms in three peri-urban areas of Machakos, Wote and Wamunyu towns located approximately 5 to 15 km away from these towns. 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). The distance of Machakos, Wote and Wamunyu from Nairobi is 65, 78 and 70 km, respectively.

Figure 1. A map showing geographical the location of the sites of Machakos, Wote and Wamunyu

Households in Wote and Wamunyu have slightly large land sizes estimated at 5-10 acres compared with those in Machakos which are 2-3 acres per households (ASDSP 2014).

These sites had been pre-selected as described in details of study conducted by Njarui et al (2012). All three study sites fall within the lower midland four (LM4) agro-ecological zone thus 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 with total seasonal rains of about 200 mm occurs between March to May with peaks in April and the short rains season (SR) starts from October to December with peaks in November with total precipitation of 300-350 mm (Gichangi et al 2015). Although the annual precipitation amounts in the semi-arid zones are quite varied, the SR tend to be more reliable for crop and pasture production as it receives more rainfall than the LR. According to Rao et al (2012), the temperature in the semi-arid areas ranges from as low as 10^°C in the cold months of June to September to as high as 33^°C in the hotter months (January to March). Due to high evapo-transpiration levels semi-arid areas have generally very low relative humidity (<10%) except during the rainy seasons when it rises to 50% and above.

Production system

In the study areas, agro-pastoralism is practiced where drought tolerant crops like cowpeas, pigeon peas green grams and cassava are grown. Drought tolerant livestock species such as zebu cattle and zebu exotic dairy breed crosses are reared. The number of cattle in the area was approximated to be 650,000 with less than 10% being dairy cattle from the national census figures of 2009 (GoK 2009).

Selection of study farms and cows

This study was conducted in all dairy farms which had already been recruited and were participating in other research activities of the ASARECA funded crop-livestock integration project like production of fodder and vegetables. Of the participating farmers, only those who owned at least a crossbred or pure exotic dairy animal were recruited to participate in the study. Further, only farms with cows aged at least 2 years of age and thus assumed to be reproductively mature with a history of cows on the farm having been inseminated/served qualified for inclusion as study animals. Study design and data collection This study was cross-sectional in nature. One-off visits to the selected dairy farms were made. The purpose of these visits was explained to farm owners and consent to participate in the study and have their cows examined was sought. Data collection was done at the individual cow level as well as management practices of the farm. Information on the lactation status, history of reproductive disorders, cow service status, service mode and if there were repeat services (return to heat three to four weeks post-service) of each cow was captured using a short questionnaire.

Thereafter, pregnancy status of each cow was established through rectal palpations that were conducted only on cows which had been served at least three months prior to the visit. Arm long sleeves were worn by the investigating team of trained veterinarians and para-veterinarians during the rectal palpations (Figure 2).

Figure 2. Performing rectal palpation on the study cows

During rectal palpation, key anatomical features including the cervix, uterine horns and ovaries were relied upon to determine the pregnancy status of the study cows. Where rectal palpations showed the cow was pregnant, aging of the pregnancy was done relying on the size of uterine caruncles, size and location of the foetus and fremitus (flow of blood and pulsation in the ovarian artery).

In non-pregnant cows, the ovaries were palpated for presence of developing follicles, presence of corpus luteum (CL), presence of cysts both follicular and luteal, scar tissue and abscesses. Ovaries with no palpable structures on them were also recorded and such cows were classified as acyclic (anoestrous).

Data analysis

Data was entered in to Ms Excel (Microsoft, USA) and crude pregnancy rates calculated as a percentage of the total number of animals examined. The pregnancy rates were then standardized by only considering the proportion of cows pregnant among the inseminated ones. Similarly, returns to heat post-service rates were calculated as a percentage of animals previously inseminated. Comparison in proportions was done through Pearson Chi square test (p<0.05). The significance for continuous data such as mean inseminations before conception was done through Student T test (p<0.05). Other indices estimated included the frequency of occurrence of reproductive disorders like repeat breeding, abortions, retained placentas and dystocia.

Results

Farm Characteristics In total, of 53 dairy farms (Machakos 17, Wote 16 and Wamunyu 20 farms) participated in the study. In these dairy farms, Friesian crosses were the most preferred cattle breed and accounted for about 77.7% (n = 121) of all the cows studied. Other breeds included Ayrshire crosses, Jersey crosses and the local zebus. All the 53 farms were smallholder farms with herd sizes ranging between 1 and 8 cows (Table 1). Although the age of cows studied ranged from 3 to 16 years in Machakos, the mean age was about 6 years with mean parity of 3.

In the 3 study areas, the body condition scores ranged from 3 to 6 with a mean 4. Machakos had the highest percentage of lactating cows followed by Wote (Table 1). In paradoxical contrast, Machakos had the least percentage of served cows while Wote had the largest percentage of cows served.

Breeding method and pregnancy rates

In the three areas studied, cows were bred using artificial insemination (AI) as well as by natural service with the latter as the most preferred method of service in Machakos compared to Wote and Wamunyu where both AI and bull service were equally used (Table 1). The crude pregnancy rates were varied in the three clusters. Wamunyu had the highest pregnancy of 46.5% followed by Wote at 42% and lastly Machakos which had the lowest crude pregnancy rate of 37.5% (Figure 3). Comparatively, cows in Machakos had a significantly (p<0.05) lower crude pregnancy rate compared to those in Wamunyu. The overall crude pregnancy rate irrespective of service status was 45.5 % (95% CI: 36.1 – 54.4).

Figure 3. Crude pregnancy rates for Machakos, Wote and Wamunyu clusters

When pregnancy was standardized by service status, the overall pregnancy rate amongst the cows which had been inseminated was about 81% (55/68) as is shown in Table 2. Wamunyu cluster registered the highest pregnancy rates (90.9 %) while Machakos cluster had the lowest (71.4%) pregnancy rate. Despite this, there were no significant (p>0.05) differences in pregnancy rates of cows in the three clusters.

Of the served cows which were not pregnant, 7.4% (5/68) had shown heat signs after service. Comparatively a higher proportion of cows returned to heat post-insemination in Machakos (9.5%) and Wamunyu (9.1%) than in Wote cluster (3.7%) as shown in Figure 4.

Figure 4. The proportion of cows returning to heat post-service in Machakos, Wote and Wamunyu

Repeated inseminations were reported in the three clusters. In Machakos, some cows required a minimum of 1 insemination and a maximum of 2 inseminations to conceive while in Wamunyu, some cows conceived after 6 inseminations (Table 3). Overall, a mean of 1.5 inseminations was needed for cows to conceive in the three clusters.

Although there was no significant (student t test; p>0.05) difference between study sites in regard to the number of inseminations per conception, cows in Wote site conceived after slightly more repeated inseminations compared with those in Machakos and Wamunyu.

Ovarian palpation conducted on the non-pregnant cows revealed the presence of structures on the ovaries including growing follicles, corpus luteum (CL) and in some case lack of any palpable structures which was indicative of non-cyclicity (anoestrous). In total, 12.1% (8/121) cows had palpable developing follicles on their ovaries as shown in Table 4.

Only two non-pregnant cows; one from Machakos and the other from Makueni exhibited signs of anoestrous since they had no palpable structures on their ovaries (Table 4). Corpus luteum was not detected in any of the non-pregnant cows.

Discussion

This study was conducted as a baseline with which to the status of reproductive performance indicators in selected ASAL zones in lower Eastern where dairy farming is an emerging enterprise with those areas where dairy is well established. The results of the study showed that dairy farming was still practiced on small-scale since the number of cows which had attained reproductive age was on average 3 to 4 cows. This agrees quite well with what was reported in most smallholder dairy farms in Kenya (Wambugu et al 2011).

Fertility of dairy cows is influenced by many factors including management levels (Bielfeldt et al 2006), environment (Windig et al 2005), genetics (Roxstrom 2001), nutrition (Butler 2003), and biological and health status (Fourichon et al 2000). In the semi-arid eastern Kenya, poor plane of nutrition has been singled out as the most important factor that limits dairy productivity (Njarui et al. 2009; 2011). In fact, it was clear from the findings of this study that almost half of the cows in the study farms had not been inseminated, yet they had reached the reproductive age. This was perhaps as a deliberate move by farmers not to serve their cows for fear of increasing herd sizes relative to the insufficient available feed resources (Njarui et al 2011). It is also likely that since feeds are seasonal, farmers delay serving their animals for fear of them calving down during the time of low feeds.

Inaccessible and costly AI services contribute to delayed inseminations which may affect conception in dairy cows (MoLD 2010). Where artificial insemination is the sole method of breeding, heat detection must be properly done to avoid problems like return to heat by cows since this lowers conception rates as was evidently clear from this study. It is estimated that the national average of inseminations per cow per conception is 1.5 inseminations in Kenya (MoLD 2010) which was comparatively similar to the 1.5 inseminations per cow per conception by this study. A study in Ethiopia reported that cows conceived after 3 to 4 inseminations (Mitiku et al 2012) which was slightly higher than what has been reported by this study. In the India, repeat breeding has also been reported with conceptions occurring after an average of 3.1 inseminations (Sattar et al 2005). In many instances, cows are not served even where owners can afford and access AI services since many cows are in a state of anoestrous (non-cyclicity) related to nutritional inadequacies. Cows in the semi-arid areas are hence likely to suffer infertility owing to anoestrous because of interruptions in feeding which commonly occurs in the semi-arid areas (Njarui et al 2009; Njarui et al 2011). Reproductive diseases like neosporosis in dairy cows (Shaapan 2016), bovine virus diarrhoea (Nikbakht et al 2015), brucellosis and campylobacteriosis (Mai et al 2015) are among reproductive disease which affect the reproductive performance of dairy cattle.

Return to heat post-service and repeat services contribute to economic losses in dairy herds. Causes of infertility in cows with repeat breeding syndrome are usually unclear, but may probably include management, environmental and animal factors (Peters 1996; Levine 1999). Usually, early embryonic loss as a result of unfavourable uterine environment associated with endocrine disorders that include ovarian steroid hormone concentrations may predispose to repeat breeding.

Repeated services and generally poor reproductive performance in dairy cows leads to lose of substantial amount of milk. Ordinarily, calving intervals should not exceed 365 days for cows within a well performing dairy herd (Roberts 1986). It has reported that poor reproductive performance attributed to interrupted breeding causes long inter-calving intervals of 450 to 500 days with estimated loss of milk at between 450 and 500 million litres worth over Ksh 4 billion (approximately US$40 million) have been observed in Kenya (MoLD 2010). In Ethiopia, inter-calving intervals of 412 days have also been reported (Yifat et al 2009). In the semi-arid areas of Kenya, inadequate feeding is responsible for the infertility experienced in dairy cows. Other contributory factors like poor heat detection techniques, poor herd health and lack of herd recording for decision making may also be responsible for the poor reproductive performance.

It has been reported that the loss of pregnancy after early diagnosis is a factor that contributes to decreased reproductive efficiency (Starbuck et al 2004). Evidence from studies shows that 7% to 33% of pregnancies in lactating dairy cows are lost between 28 and 98 days of gestation (Silke et al 2002; Nation et al 2003). Dailey et al (2002) postulated that most loss of pregnancy occurs prior to day 45 of gestation meaning that pregnancy tests are done around this time to ensure cows that lose their pregnancy are closely monitored for timely inseminations.

Certain ovarian structures play an important role in the cyclicity of dairy cows. The presence of growing follicles on the ovaries of some cows was a sign that normal cyclicity in these cows was happening. Cows with corpus luteum (CL) on the ovaries is manifestation normal cycling in such cows. The CL also is present in pregnant cows and assists in maintaining embryonic health and the establishment of pregnancy in cows. However, its persistence can in non-pregnant cows is cause of worry owing to the substantial levels of progesterone in the plasma especially during the dioestrus stage of oestrous cycle which inhibits growth of follicles and hence failure of ovulation through progesterone block (Vernunft et al 2013). It is hence of absolute necessity to shorten the duration of the CL in non-pregnant cows to shorten the inter-calving intervals. Where cows have smooth ovaries (anoestrous), it is a sign that such cows are not cycling. Nutritional deficiency accounts for a large portion of anoestrous in cows (Hafez & Hafez 2000). Anoestrous is likely to be a major problem in the semi-arid Kenya which are prone to inadequate nutrition much of the year (Njarui et al 2009; Njarui et al 2011). In order to stimulate cows to resume their normal cyclicity it may help to consider supplementary feeding to cover for the nutrient deficits. They are caused by a number of factors such as reproductive diseases, nutritional factors, and sometimes hormonal imbalances (Hafez and Hafez 2000; Vernunft et al 2013).

Conclusions

• Repeat breeding was encountered as a challenge for the dairy farmers using A.I as the sole method of breeding.
  
  
• In each dairy herd studied, less than 50% of the cows aged above two years were pregnant.

Recommendations

Arising from the findings of this study, the following are possible recommendations:

• There is real need to build the capacity of herd owners on heat detection methods to minimize repeated services.
  
  
• There is need to undertake regular and frequent examination of all cows aged >2 yrs irrespective of their pregnancy status to detect persistent CL or anoestrus for timely intervention to shorten the reproductive cycles.
  
  
• There is need for further follow-up studies to establish causes of infertility and anoestrous in dairy cows.

Acknowledgements

The authors acknowledge ASARECA LFP 12 for funding this study. Director KARI but now Director General KALRO is thanked for support and provision of logistics. Special mention goes to the dairy farmers who willingly accepted to participate in the study.

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Received 18 January 2019; Accepted 22 April 2019; Published 4 June 2019

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