Effect of sires on wide scale of milk indicators in first calving Czech Fleckvieh cows

The possible genetic impact of sire on cattle populations, herd milk yield and milk traits (fat and protein) have been described in the literature along with its impact on some milk indicators (MIs) as somatic cell count, urea and ketones. There is a dearth of information on the impact on a series of other MIs (physical, chemical, health, technological). The goal of this study was to assess the possible effect of sire on a wide range of MIs including technological properties in Czech Fleckvieh to suggest future possible breeding trends. A series of MIs (n=37) was investigated in individual milk samples (MSs). 191 effective daughters (MSs) were included. The sire groups (n=13) were well balanced in terms of herd, lactation stage and sampling season. Only sires with >5 daughters were ranked. A linear model of analysis variance with the fixed effects, sire and combined factor (herd × year × season) was used. 19 MIs as log count of streptococci in fermentation ability of milk (log FAM–CS), FAM–CS, log total fine microflora count in FAM (log FAM–TCM), FAM–TCM, solids non fat (SNF), iodine content, citric acid (CA), titration acidity in FAM, lactose (L), crude protein (CP), true protein (TP), casein (CAS), dry matter, Mg and P content, milk alcohol stability, electrical conductivity (EC), titration acidity, casein numbers (for CP and TP), log count of lactobacilli in FAM (log FAM–CL), FAM–CL and pH in FAM were influenced by sire (P<0.05). However SNF, CA, L, CAS and perhaps EC could be newly reflected as information for genetic improvement of dairy cattle with connection to dairy milk recovery and cow health. CA (10.08±1.92 mmol×l−1) deserves special attention. The model variablity explanation moved from 6.97 (SCC) over 29.51 (CA) to 48.32 % (pH) for MIs. This is one of few studies to assess the impact of sire over a wide range of MIs and the results warrant careful evaluation and further study.


Introduction
Milk yield and milk composition (milk indicators, MIs), reproduction performance (Bezdíček et al. 2007, Parland et al. 2007), health status (Hinrichs et al. 2006) and other functional traits are important data for the genetic improvement of dairy cattle (Bergfeld & Klunker 2002, Distl 2001).The genetic impact of sire on cattle populations, herd milk yields and some MIs have been desribed in a number of papers (Thompson et al. 2007a, 2007b, Biedermann et al. 2003, 2004, Bezdíček et al. 2008).These are usually called milk traits and most of the research has been carried out for such milk traits as milk fat and protein yield and percentage, dry matter (Schutz et al. 1990, Yazgan et al. 2010) and also somatic cell count (Schutz et al. 1990, Xu et al. 2006) as indicators of mammary gland health status.Some authors have also studied the effects of genetics on health and nutrition as MIs such as lactose, urea and ketones (Gravert et al. 1991, Wood et al. 2004, Miglior et al. 2006, Stoop et al. 2007).From a genetic point of view, there is still a dearth of information on a range of other important MIs.The goal of this study was therefore to assess the possible effects of sire on a wide range of important milk indicators including technological properties in Czech Fleckvieh to suggest a future possible trend in breeding work.

Animals and milk samples
Individual milk sample (MSs) collection was carried out at five commercial dairy farms of the Czech Fleckvieh (CF) breed.Only the first calved dairy cows were sampled from 90 to 180 days in milk.The sampling period was two years and 191 MSs were collected.The dairy cows were kept in binding free stables with milking parlours and all were milked twice a day.The dairy cow nutrition was composed of TMR, which is typical for the country conditions and consisted of: maize silage; alfalfa silage; trifolium silage; whole spindle maize silage (LKS); brewery draff; alfalfa hay; concentrates.TMR and concentrates were fed according to milk yield and standard demands.

Analyses of the individual milk samples
The list of analyses is shown in Table 1 for 37 MIs.The analyses were carried out according to the following milk analytical procedures.The fat (F), L and SNF indicators were measured using MilkoScan 133B (Foss Electric, Denmark) calibrated according to reference method results (standard CSN 57 0536 and CSN 57 0530).The protein fractions CP, TP and CAS (N×6.38) were determined by Kjeldahl´s method using the instrument line Tecator with Kjeltec Auto Distillation unit 2 200 (Foss-Tecator AB, Sweden) according to CSN 57 0530.The macro-and microelement milk contents were investigated by atom absorption spectrophotometry using a Spectrometer SOLAAR S4 and 6F S97 Thermo Elemental (England).The SCC was determined using Fossomatic 90 instrument (Foss Electric, Denmark) according to CSN EN ISO 13366-2.The U was determined by spectrophotometry at 420 nm wavelength.The specific reaction solution was prepared as a sour mixture with the p-dimethylaminobenzaldehyde (Hanuš et al. 1995) using Spekol 11 instrument (Carl Zeiss Jena, Germany).The AC was investigated by spectrophotometry at 485 nm wavelength.The AC was absorbed in alkali solution of KCl with salicylaldehyde after to 24 hours microdiffusion (O´Moore 1949, Vojtíšek 1986) using Spekol 11.The CA was determined by spectrophotometry at 428 nm wavelength.Milk was coagulated by trichloracetic acid and the adventitious filtrate then allowed to react with pyridine and acetanhydride (30 min at 32 °C).Used instrument was Spekol 11 (Hanuš et al. 2009).The MFP values were analysed using a top cryoscope Cryo-Star automatic Funke-Gerber (Germany) which was calibrated by standard NaCl solutions (Funke-Gerber).The EC was measured using OK 102/1 (Radelkis, Hungary) conductometer at 20 °C (in mS×cm −1 ) with bell glass electrode which was calibrated by salt (KCl) solution (10.2 mS×cm −1 ).The active acidity (pH) was measured using the pH-meter CyberScan 510 (Eutech Instruments) which was calibrated by buffer solutions (pH 4.0 and 7.0) at 20 °C.The TA was measured by milk titration (100 ml) using alcaline solution according to CSN 57 0530.The AS was determined with the help of the milk titration (5 ml) by 96 % ethanol (ml) to the creation of protein precipited flakes.The FAM-CL, -CS and -TCM (carried out according to standard ON 57 0534 by slightly modified procedure with thermophilic yoghurt culture YC-180-40-FLEX=Streptococcus thermophilus, Lactobacillus delbrueckii subsp.lactis and L. d. subsp.bulgaricus) were investigated by calculating of the colony forming units (CFU) using the classical plate cultivation method (at 30 °C for 72 h) with GTK M (Milcom Tabor) agar according to CSN ISO 6610.TA: ml 0.25 mol×l −1 NaOH solution for the titration of 100 ml of milk (CSN 57 0530), AS: consumption of 96 % ethanol in ml to protein coagulation in 5 ml of milk, CQ: subjective estimation determined by inspection and touch from 1st (excellent) to 4th (poor) class, CF: depth of penetration of the corpuscle falling into curd cake in the standard way expresses the opposite relationship to firmness, WV: whey which was ejected during rennet curd cake creation for 60 min, FAM-T: with microbial culture (by titration acidity in ml of 0.25 mol×l −1 NaOH×100ml −1 ); all the previous parameters at FAM were measured after the yoghurt test fermentation.

Design of the investigation and statistical procedures
13 sires with more than five daughters were included in the data set for statistical evaluation.It means, 191 effective daughters (MSs) were included into investigation.The sire daughter result groups were well balanced in terms of herd, lactation stage and sampling season.Analysis of variance with fixed effects as sire and lactation stage was used for statistical evaluation of data set according to model as follows: where y is the investigated milk indicator, µ is the general average, hys is the herd × year × season effect (combined effect including the impact of herd, year of calving and season of calving) for i from 1 to 6 combinations (this effect used for elimination of major part of systematic environmental variation), s is the sire effect for j from 1 to 13 (Figure 1) sires and e is the random effect.The SAS v9 programme package was used for the calculation.Means and GLM procedures were performed.
As the usual evaluation of milk yield traits was not main object of this evaluation fat and protein yields were not calculated and only fat and protein percentage were evaluated according to analytical results.The other milk indicators were the main goal of investigation.Therefore, SCC, AC and hygienic indicator values (FAM-CL, FAM-CS, FAM-TCM; Table 1) were logarithmically transformed (log) on a decimal basis because of lack of normal frequency distribution in most cases (Shook 1982, Reneau 1986, Meloun & Militký 1994) and after that geometrical averages were also used in results evaluation.This data set had a smaller range than is usual for genetic evaluation of milk traits in the population.In contrast this result set was quite large in terms of evaluating such a wide spectrum of MIs which is not often case.

Main statistic characteristics of milk indicators
The statistics for the MIs are shown in Table 2.The means and variability are mostly very comparable to our previous results in CF (Hanuš et al. 2007) and not very different from results in Holstein (H) (Janů et al. 2007).Differences (compared to CF) are probably connected to first lactation effectshere for instance for L and SNF contents which are markedly higher (5.11>4.96% and 9.11>8.91%; than CF results; Hanuš et al. 2007).Some differences (compared to H) are probably connected, apart from to first lactation also with breed and milk yield (H higher) effects for example especially for main protein fractions.The CP, TP and CAS contents are markedly higher for CF: 3.42>3.24%; 3.23>3.07%; 2.75>2.57% (compared to H results; Janů et al. 2007).

Sire effects on milk indicators in general
Statistic model efficiency and the significance of sire and combined effects for MIs are shown in Table 3.The model variablity explanation moved from 6.97 (SCC) over 29.51 (CA) to 48.32 % (pH) along individual MIs.According to evaluation of model determination coefficients and model significance and significance of both effects (sire and combined effect) it is possible to mark MIs which are important for interpretation of incidental sire impacts (Table 3, bold letters).The most important were L, CP, TP CAS, SNF, DM, P, Mg, I, CA, AS, TA, EC, CAS-CP, FAM-T, log FAM-CS and log FAM-TCM in this data set.There was a lower explanation of variability by model, no significant effect of sire and/ or too essential impact of combined effect (environmental conditions) for MIs.The model explained most of the variability for pH, CF, log FAM-CS, log FAM-TCM, AC, SNF, I, CA and FAM-T but only at log FAM-CS, log FAM-TCM, SNF, I, CA and FAM-T was this possible to explain by sire effect.

Sire effects on main milk composition and nitrogen fraction indicators
Fat content, which is normally included in genetic improvement programmes was not influenced by sire in this smaller data set, probably due to the greater variability of this MI.CP, which is also normally included in genetic improvement, TP and CAS was significantly influenced by sire (Table 3).The most significant effect was at CAS (Figure 1, CAS), which could be newly routinely determined in dairy analytical systems (Broutin 2006 a; MIR-FT under certain circumstances) and in preference included in dairy herd improvement programmes.Lactose content as an indicator of udder health (Hanuš et al. 1993), which is connected with high milk yield (higher L; Janů et al. 2007, Hanuš et al. 2007) was influenced significantly by sire (Table 3).Miglior et al. (2006) reported marked difference between Ayrshire and H dairy cows (L 4.49 and 4.58 %; anhydride) in Canada, probably as a genetic factor.SNF and DM were also influenced by sire (Table 3).It was logically in links with protein fractions and L.

Sire effects on some milk mineral components
Microelement I and macroelements as Mg and P were significantly influenced by sire (Table 3) in this data set.Total P is logically partly connected with casein fraction.Owing to high variability there could be a systematic interference effect caused by the presence (diffusion effect) and or absence of iodine from udder teat disinfection in daughters for I results (Table 2) in practice.Mg and I are minerals important to human nutrition from milk products in connection with nerve and overall metabolism.However in practical terms including these minerals into dairy cattle improvement is improbable.

Sire effect on dairy cow health state milk indicators
SCC is a well-known indicator of mammary gland health in lactating mammals (Shook 1982, Reneau 1986, Amin 2001, Amin et al. 2002, Kühn et al. 2008).The geometric average of SCC was 81 thousand×ml −1 (Table 2) and this confirmed that only relatively healthy dairy cows (clinical mastitis free) were used in this evaluation.In this paper the SCC was not influenced by sire effect (Table 3) although SCC is evaluated routinely in terms of mastitis resistance determination in dairy cattle populations, for instance as sire herd book values.Nevertheless, the coefficient heritability for somatic cell score was also low 0.1 (Schutz et al. 1990).The log SCC was influenced by combined environmental effect in this paper which is practically quite logical (Table 3).Milk urea content is good indicator of nitrogen matter/energy metabolism in dairy cows (Larson et al. 1997, Panicke et al. 2000, Godden et al. 2001a, 2001b, Mottram & Masson 2001, Rajala-Schultz et al. 2001, Guo et al. 2004, Jílek et al. 2006, Miglior et al. 2006, Zhai et al. 2006, Stoop et al. 2007, Bezdíček et al. 2009, Řehák et al. 2009) mostly with negative relation to reproductive performance and longevity.Urea was not significantly influenced by either used model factors in this paper (Table 3; 36.6±6.4 mg×100ml −1 ).Also the model variability explanation was lower.Nevertheless, the sire effect was almost significant at alpha 0.05 (P=0.054;Table 3).The possibility of including U into routine milk recording and dairy cattle improvement in terms of nitrogen matter utilization from feeding rations could be a topic of further research.Miglior et al. (2006) mentioned lower milk U nitrogen in H dairy cows than Ayrshire (11.11<12.20 mg×100ml −1 ).Lower U was found along with lower milk yield between breeds.This is in good accordance with previous results (Hanuš et al. 2007 andJanů et al. 2007), where U was lower in H breed than CF and also was lower along with lower milk yield within both breeds and between breeds.Also Godden et al. (2001 b) found a positive nonlinear association between milk urea and milk yield and energy corrected milk in Ontario H cattle. Stoop et al. (2007) reported milk U nitrogen heritability 0.14, which is lower.Nevertheless, they allowed for the possibility of influencing urea by through genetic selection apart from herd practice.
Milk acetone was evaluated as a suitable indicator of animal energy metabolism.The higher AC the lower energy support or ketosis according to a number of authors (Gravert et al. 1986, 1991, Enjalbert et al. 2001, Mottram et al. 2002, Wood et al. 2004).AC was not influenced by sire in this data set although significance was quite close to set limits of probability (P=0.0942;Table 3; geometric mean 2.25 mg×l −1 ).In contrast, a significant impact was found for combined environmental factor (Table 3).Nevertheless, Gravert et al. (1991) in cows with an increased AC in the first third of lactation (0.25 mmol.l−1 ) a negative correlation (r=−0.47 and −0.42, resp.) to the energy quantity received through fodder and also to milk yield (r=−0.30).They reported a heritability coefficient for milk AC 0.30 which was similar to the milk yield coefficient.Therefore, milk AC content was recommended as assessment of energy balance and to be included in milk recording and breeding value determination in terms of genetic control of energy nutrient utilization.Wood et al. (2004) reported heritability of AC less than 1 %, therefore they mentioned a genetic evaluation based on milk acetone recording on a monthly basis as having little use as a selection tool to decrease the incidence of ketosis.
Milk citric acid could be also a good indicator of animal energy metabolism (low CA means low energy maintenance) according to more papers (Khaled et al. 1999, Baticz et al. 2002, Garnsworthy et al. 2006, Kubešová et al. 2009).The lower milk CA the higher the energy deficiency and vice versa.The physiological range is between 8 and 10 mmol×l −1 .Garnsworthy et al. (2006) confirmed the hypothesis that variation in CA with stage of lactation was related to de novo synthesis of fatty acids and that the relationship was independent of diet and milk yield.This is promising for genetical possibilities.CA (10.08±1.92mmol×l −1 ) was significantly influenced only by sire along good model variability explanation (Table 3).This is the most interesting result in this work (Figure 1, CA).The CA as an energy metabolism MI could be suitable for milk recording and dairy herd improvement as it is practical.This deserves special attention from the genetic evaluation point of view.Similarly metioned by Gravert et al. (1991) for milk acetone and control of feeding rations (energy nutrients) in dairy cows.Also routine series CA analyses are now accessible for effective milk laboratory work (Hanuš et al. 2009).
The F/CP ratio could be a suitable indicator of animal energy metabolism (Geishauser & Ziebell 1995, Heuer et al. 1999, 2001).The higher the F/CP, the lower the metabolism energy support and possible ketosis.In contrast, a low F/CP ratio could provide information on low cow maintenance by structure fiber.F/CP ratio was not influenced by either sire or combined effect in this material.

Sire effects on physical indicators
Out of the physical MIs investigated the EC as a possible indicator of udder health (Hanuš et al. 1993) was statistic significantly influenced only by sire (Table 3, Figure 1, EC) and the model variability explanation was quite successful in this case.EC included into routine milk recording and dairy herd improvement is feasible.There is also a possibility of routine series measurements in dairy cow herds.The pH and MFP were not influenced by sire.However, pH was significantly influenced by combined environmental effect.

Sire effects on milk technological properties
Milk indicators as AS, TA, CAS-CP (Figure 1), CAS-TP, FAM-T, log FAM-CS (Figure 1), FAM-CS, log FAM-TCM (Figure 1), FAM-TCM, log FAM-CL, FAM-CL and FAM-pH (Table 3) were influenced by sire significantly.This refers to almost all MIs of milk fermentation ability.The cheeseability indicators as RTC, CQ, CF and WV, which were significantly influenced by milk yield and differed between cattle breed (CF and H;Hanuš et al. 2007, Janů et al. 2007) were not influenced by sire in this data set (Table 3).On the other hand these were significantly influenced by combined effect.
This paper is one of few studies to assess sire impact over such wide range of MIs.Therefore the results for the technological properties need to be evaluated carefully.For example apropos fermentation ability, there are a number of interference interactions among milk components, properties and fine culture activities.Nevertheless, out including these technological properties in dairy cattle improvement is improbable in practice with the exception of AS and TA.However, their routine series determination is problematic.In conclusion 19 variables (Table 3) as log FAM-CS, FAM-CS, log FAM-TCM, FAM-TCM, SNF, I, CA, FAM-T, L, CP, TP, CAS, DM, Mg, P, AS, EC, TA, CAS-CP, CAS-TP, log FAM-CL, FAM-CL and FAM-pH from 37 MIs (Table 2) were significantly influenced by sire effect in this data set.
Of these, only the SNF, CA, L, CAS and perhaps EC reflect new information for the genetic improvement of dairy cattle with connection to dairy milk recovery and cow health.Care is necessary in the results interpretation.Further studies are also necessary.Nevertheless, the importance of new genetic assessments for a whole series of MIs in terms of future cattle breeding work is currently increasing.This refers to rapid dissemination of modern effective technologies and milk analytical methods like NIR-FT and MIR-FT (near and mid infra-red spectrophotometry with Fourier´s transformations) and other methods mostly on the basis of biosensors which are able to measure new MIs like casein, citric acid, urea, acetone (ketones), free fatty acids, milk freezing point and electrical conductivity with high efficiency (Koops et al. 1989, Hansen 1999, Heuer et al. 2000, Tsenkova et al. 2000, Kukačková et al. 2000, Broutin 2006a, b, Mottram & Masson 2001, Mottram et al. 2002, Jankovská & Šustová 2003, Miglior et al. 2006, Roos et al. 2006, Bijgaart 2006, Šustová et al. 2007, Hanuš et al. 2008).These results could contribute to reliable data about milk indicators for official milk recording for possible inclusion into breeding and dairy cattle improvement.Possible trends were indicated in this paper.It could be used for next research trend in dairy cattle genetic improvement procedure.

Figure 1
Figure 1 Graphical rendering of sire effects on selected milk indicators in Czech Fleckvieh first calving dairy cows

Table 2
Main statistical characteristics of milk indicators in Czech Fleckvieh first calving dairy cows