Changes in milk protein content as indicator of energy balance and fertility in dairy cows

C Alphonsus, G N Akpa, B I Nwagu¹, M Orunmuyi and P P Barje¹

Department of Animal Science, Ahmadu Bello University, Zaria.
mcdyems@gmail.com
¹ National Animal Production Research, Ahmadu Bello University, Shika-Zaria

Abstract

The purpose of this study was to evaluate the potentials of using changes in milk protein content (dmPc) as indicator of energy balance and fertility in dairy cows. Milk samples and fertility traits of 13primiparous and 47 multiparous (F₁) Friesian x Bunaji dairy cows were analyzed. Four  fertility traits: days to first insemination (DFI), days open (DO), non-return rate 56 days after first insemination (NRR56) and numbers of insemination per conception (NIC) were evaluated.The milk samples were analysed weekly for milk fat content (MFC) and milk protein content (MPC). These variables were used to calculate the other milk composition variables; milk fat yields (MFY), milk protein yield (MPY), fat-protein ratio (FPR) and the change variables. These are the current minus the previous values of the milk measures in question. That is change in milk fat (dmFc), change in milk protein (dmPc), and change in fat-protein ratio (dFPR ). Yield values were in kilograms/day (kg/day); content values were in percentages (%) and ratios were unitless. The energy balance (EB) was calculated from milk yield and milk composition measures.

The results showed that the single most informative milk composition variable that could be used as suitable indicator of energy balance and fertility in dairy cows is the changes in milk protein content (dmPc). It has strong relationship with both EB and fertility of the cows. It is therefore concluded that  the suitable indicator for evaluation of EB status and fertility in low-medium yield dairy cows is changes in milk protein content (dmPc).  

Key words: body composition, Friesian x Bunaji cows, milk composition, milk fat, milk protein

Introduction

Energy balance (EB) is difficult to measure in large populations. Therefore, there is interest in other traits, which could served as indicators of EB (Coffey et al 2001) and may subsequently be related to the health and fertility status of an animal. Body condition score (BCS) is one of those measures (de Vries and Veerkamp, 2000; Veerkamp et al 2001), which is widely used in many species to assess body composition and energy status of animals (de Vries and Veerkamp, 2000). However, BCS is subjective and routine recording of BCS is not a common practice on most dairy farms, especially in the developing countries. Also, BCS though feasible in practice, is not well suited for assessing short term changes in energy status of animals due to the relative insensitivity of condition score scales to short term changes. Another alternative measuresof EB status in dairy cows is the use of various blood metabolites, such as β-hydroxybutyrate (BHB), insulin, leptin and non- esterified fatty acids (NEFA) which had been reported to be strongly correlated with energy balance (Konigsson et al 2000; Reist et al 2002; Clark et al 2005). However, analyses of these blood metabolites are currently not commercially available in developing countries, but only feasible on experimental farms.

 Therefore, there is need to identify an easy, cheap but reliable method that has practical applicability and can be use in a commercial scale. Thus, another option that has been put forward for evaluating the EB status of dairy cows during lactation is changes in milk composition measures (Friggens et al 2007). If this option has adequate level of accuracy it will be an attractive one, because it could provide easy and reliable estimation of EB.

The advantage of milk-based EB is its easy integration with automated on-farm in linear sensor-based monitoring system, allowing for frequent monitoring of EB (Lφvendahl et al 2010). Also, the ease and convenience of obtaining milk samples as opposed to blood samples make this method especially helpful for use at the farm level.

The use of milk composition measures to indicate energy status and fertility in dairy cows has been applied in dairy cows for some times (Heuer et al 2000; 2001; Reist et al 2002). Several studies have indicated that in early lactation, milk composition changes are related to health, fertility and other physiological effects associated with energy balance (Loeffler et al 1999; Buckley et al 2000; Lucy 2001; Butler 2005; Mantysaari and Mantysaari 2010;  Buttchereit et al2010). Precisely, the relationship between fertility and protein percentage in milk has been studied previously in Australia (Morton 2000; Fahey et al 2003), New Zealand (Harris and Pryce 2004) and some other European countries, including Ireland (Buckley et al 2003). Most of these studies were conducted on high yielding dairy cows of the temperate region. Despite the well-known relationship between milk composition measures with fertility and EB of high yielding dairy cows of the temperate latitudes, the expected beneficial effect of using these relationships as a management tool for evaluating the EB status and fertility of low- medium dairy cows of the tropics is still unknown. Therefore, this study was focused on the low- medium yield dairy cows of the tropics.

Based on the forgoing, a possible relationship between energy balance, milk composition and fertility in dairy cows can be postulated. It is therefore, hypothesized that changes in milk composition measures can be used to monitor the EB status and reproductive performance of dairy cows during lactation. One way of validating this hypothesis is to assess the relationship between the milk composition variables and energy balance at different stages of lactation. A clear understanding of this relationship in dairy cows would enable the development of low cost indicator of energy balance status and fertility of dairy cows.

Therefore, the objective of this study was to determine the relationship of milk composition measures with energy balance and fertility in the low-medium dairy cows. If association between milk composition measures, EB and fertility exist, this association will provide a valuable insight in the underlying mechanisms of energy balance-fertility-milk production relationship and results may be used for dairy management decision and for future breeding programme.

Materials and Methods

Experimental site

The study was conducted on the dairy herd of National Animal Production Research Institute (NAPRI) Shika, Nigeria, located between latitude 11⁰ and 12⁰N at an altitude of 640 m above sea level, and lies within the Northern Guinea Savannah Zone (Oni et al 2001). The mean annual rainfall in this zone is 1,100mm, which commenced from May and last till October, with 90% falling between June and September. (Malau–Aduli and Abubakar1992).

Animals and managements

Thirteen primiparous and 47 multiparous (F₁) Friesian x Bunaji cows were used for this study.The cows were raised during the rainy season on both natural and paddock–sown pasture, while hay or silage supplemented with concentrate mixture of undelinted cotton seed cake and grinded maize, were offered during the dry season. They had access to water and salt lick ad-libitum. Unrestricted grazing was allowed under the supervision of herdsmen for 7 – 9 hours per day. Routine spraying against ticks and other ecto-parasites was observed, while vaccination was carried out against endemic diseases.

Sample collection and analysis

Milk samples to determine milk composition variables were collected weekly from the cows starting from 4 days post-partum to the end of lactation of each cow. The milk samples were frozen immediately after collection and stored at -20^oC until analysed. The milk composition analysis was carried out at the Food Science Laboratory of Institute of Agricultural Research, Ahmadu Bello University, Zaria-Nigeria. The milk composition traits analysed were milk fat content (MFC) and milk protein content (MPC). These variables were used to calculate the yield variables {milk fat yield (MFY), milk protein yield (MPY)}, the ratio (fat-protein ratio)and the change variables {change in milk fat content (dmFc), change in milk protein content (dmPc), change in fat-protein ratio (dFPR)}.The change variables “d” are the current minus the previous values of the milk measures in question. Yield values are in kilograms/day (kg/day) whilethe content values are in percentages and ratios are unitless (Friggens et al 2007; Lφvendahl et al 2010).

Measurement of fertility traits

In the National Animal Production Research Institute (NAPRI), artificial insemination records are well kept, therefore insemination dates are reliable and accurate; thus these records were used to calculate the fertility traits. The fertility traits measured were the number of inseminations per conception (NI), days from calving to first insemination (DFI), non-return rate 56 days after first insemination (NRR56) and days open (DO), these were computed using insemination and calving records as follow: non-return rate was a binary trait, coded 1 if a cow had only the first insemination date and no second insemination within 56 days after first insemination in a given lactation. Otherwise, NRR56 was coded zero “0” if a cow had two consecutive inseminations within 12days, those inseminations were considered to be for the same heat and code remained as 1. Days to first insemination (DFI) was computed as the number of days between calving and first insemination date in a given lactation (Kadarmiden et al 2003). Non return rate (NRR56) and DFI covers the two most important aspects of female fertility: the ability of cow to cycle and conceived normally (Kadarmiden et al 2000) and has been recommended by European union (EU) concerted action on “Genetic Improvement of Functional Traits in cattle FIFT”( Groen et al 1997) for National Genetic Evaluation.

In addition, number of insemination per conception (NIC) and days open (DO) was also recorded. The NIC was defined as the number of times a cow was inseminated before conception in a particular lactation, while days open (DO) was taken as the number of days from calving to successful conception.

Calculation of energy balance

The energy balance (EB) was calculated weekly from milk measures using the equation described by Lφvendahl et al (2010):

EBmilk = 132.769 + 13.0675 x MFC – 140.304 x F/P – 95.1219 x diff (MY) -172.65 x diff(F/P)+ 802.306 x diff(mPy),

Where : MFC=Milk fat content, F/P=fat: protein ratio, and  MY = milk yield, together with 3 change   “diff()” variables. These are the current minus the previous value of the milk measures in question: diff(MY), diff(F/P), and the difference in milk protein yield, diff(mPy).

Data analysis

The relationships between energy balance and milk composition measures were calculated using the Pearson’s correlation procedure of SAS (2000).

Results and Discussion

The correlation coefficients between energy balance (EB) and milk composition measures both within and across lactation stages are shown in Table 1. The   milk protein content (MPC) and the change in milk protein content (dmPc) were strongly correlated (P< 0.001) with EB both within and across lactation stages. The correlation coefficients between MPC and EB were -0.649, 0.645 and 0.754 for early-, mid- and late-lactation, respectively, while the correlation coefficients between dmPc and EB in early-, mid- and late-lactation were 0.988, 0.999 and 0.999, respectively. The correlation coefficients of MPC and dmPc with EB across lactation stages were -0.616 and 0.996, respectively. The strong relationship observed between the changes in milk protein content and EB suggested that changes in milk protein content could be used to monitor the EB status of dairy cows both within and across lactation stages. This observation agreed with the earlier findings of Hüttman (2007) who reported strong positive genetic correlation between EB and milk protein content. The magnitudes of the correlation coefficients of the other milk composition variables were low at the early lactation but stronger as the lactation progresses, with the strongest correlation coefficient observed at the late-lactation. Also the direction of the correlations between the milk composition variables and the EB were generally negative at the early lactation but positive at the late lactation. These changes in magnitudes and directions of the correlation coefficients between the milk composition variables and EB across lactation stages probably indicate the changes in the EB status of the cows during lactation. It has been reported that most of the dairy cows experiences low or negative energy balance (NEB) during early lactation, and could recovers  from the NEB by  mid or late lactation (Ellis et al 2006). However, the magnitude of the NEB and the recovery rate from the NEB differs with individual cows. Cows with higher genetic merit for EB will mobilize body reserves for a longer period of time to offset the deficit than those with low genetic potential. A work done by Wall et al (2007) showed that substantial genetic variation exists for cumulative EB through the entire lactation of dairy cows. 

The magnitude of the correlation coefficients of fertility traits with milk composition variable was low (Table 2); except for days open (DO), which was strongly correlated with milk composition yield variables. However, changes in milk protein content (dmPc) was significantly (P < 0.05 – 0.01) correlated with all the fertility traits measured; DFI, DO, NRR56 and NIC. The magnitude and direction of the correlation coefficients between dmPc and the fertility traits suggested that dairy cows with high milk protein content would tend to have shorter DFI ( -0.283) and may required least NIC (-0.390) and are more likely to become pregnant within 56 days after first insemination (0.457). This observation corroborate the findings of many other researchers who reported that cows with high milk protein percentage in early lactation had substantially better reproductive performance (Morton 2000; 2001; Opsomer et al 2000; Fahey et al 2003; Harris and Pryce 2004; Patton et al 2007; Yang, 2009).

Taking the correlation structure of the milk composition variables with EB and fertility traits, it was obvious that the single most informative milk composition variable that could be used as suitable indicator of energy balance and fertility in dairy cows is the changes in milk protein content (dmPc). It has strong relationship with EB within and across lactation stages, as well as with all the fertility traits measured in this study. The magnitude and direction of the correlation coefficients between dmPc with EB and the fertility traits suggested that high milk protein is associated with positive EB and good reproductive performance, while low milk protein is associated with negative EB and poor fertility. It has been reported that decrease in milk protein percentage is associated with negative EB and poor reproductive performance in dairy cows (de Vries and Veerkamp 2000; Opsomer et al 2000; Fahey 2008). However, Opsomer et al (2000) concluded that low milk protein does not cause poor fertility, and that the results indicates that there is a simply association between the two, whereby, cows with low milk protein percentage are likely to have poor fertility.The genetic relationship between milk protein content with energy balance and fertility are areas of continuing research.

The underlying context of using milk composition measures, especially changes in milk protein content to indicate EB status and reproductive performance in dairy cows is well known; inadequate intake of fermentable carbohydrate (especially in the low concentrate pasture based management) can cause shortage of glucose for efficient rumen microbial function, this will affects the EB of the cows. In the same vein, the shortage of glucose can leads to insufficient protein synthesis by the rumen microbes and this will compromise the flow of amino acid to the udder hence, a decrease in the milk protein content (Gutler and Schweigert 2005). Fermentable carbohydrates increase the energy density of a diet, which improves the energy supply and determines the amount of bacterial protein produced in the rumen. In addition, fermentable carbohydrates yield more VFA (i.e., more energy) because they are fermented faster and more completely.The extent to which ammonia is used to synthesize microbial protein is largely dependent upon the availability of energy generated by the fermentation of carbohydrates. Therefore, glucose may be the key linkage between the milk protein content, energy balance and fertility in dairy cows.

Conclusion

• Given that changes in milk protein content (dmPc) reflects the energy balance (EB) status and fertility of a cow, it could be a useful variable on which energy balance and reproductive performance of dairy herd could be assess, especially because dmPc is an easy measurable trait that can be obtained from routine milk performance testing.

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Received 6 September 2014; Accepted 29 October 2014; Published 1 December 2014

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