Volume 175, 15 May 2015, Pages 609–618
- a Nafferton Ecological Farming Group, School of Agriculture Food and Rural Development, Newcastle University, Nafferton Farm, Stocksfield, Northumberland NE43 7XD, UK
- b Agri-Food and Biosciences Institute, Sustainable Agri-Food Sciences Division, Agriculture Branch, Large Park, Hillsborough, Co Down BT26 6DR, UK
- c FiBL Research Institute of Organic Agriculture, Department of Livestock Sciences, Ackerstrasse 113, P.O. Box 219, CH-5070 Frick, Switzerland
- d University of Padua, Department of Animal Medicine, Production and Health, Viale dell’ Università 16, Agripolis, 35020 Legnaro, Padua, Italy
- e Qualitas AG, Chamerstrasse 56, CH-6300 Zug, Switzerland
- f Agri-Food and Biosciences Institute, Finance & Corporate Affairs Division, Biometrics & Information Systems Branch, 18a Newforge Lane, Belfast, Co Antrim BT9 5PX, UK
- Received 29 January 2014, Revised 21 October 2014, Accepted 13 November 2014, Available online 18 November 2014
Highlights
- •
- Breeding by feeding interactions in milk quality were studied on low-input dairy farms.
- •
- Crossbreeding with traditional breeds did not affect milk yield from low-input systems.
- •
- Grazing natural pastures improved milk fatty acid profile.
- •
- Omega-3 content was maximised by combining traditional genetics with high grazing diets.
Abstract
This
study investigated the effect of, and interactions between, contrasting
crossbreed genetics (US Brown Swiss [BS] × Improved Braunvieh
[BV] × Original Braunvieh [OB]) and feeding regimes (especially grazing
intake and pasture type) on milk fatty acid (FA) profiles.
Concentrations of total polyunsaturated FAs, total omega-3 FAs and trans
palmitoleic, vaccenic, α-linolenic, eicosapentaenoic and
docosapentaenoic acids were higher in cows with a low proportion of BS
genetics. Highest concentrations of the nutritionally desirable FAs, trans
palmitoleic, vaccenic and eicosapentaenoic acids were found for cows
with a low proportion of BS genetics (0–24% and/or 25–49%) on high
grazing intake (75–100% of dry matter intake) diets. Multivariate
analysis indicated that the proportion of OB genetics is a positive
driver for nutritionally desirable monounsaturated and polyunsaturated
FAs while BS genetics proportion was positive driver for total and
undesirable individual saturated FAs. Significant genetics × feeding
regime interactions were also detected for a range of FAs.
Keywords
- Milk;
- Fatty acid;
- Low-input;
- Brown Swiss;
- Original Braunvieh
1. Introduction
The
current Brown Swiss dairy cattle population consists of (i) Original
Braunvieh (OB) which is the traditional Brown Swiss pure breed that has
maintained relatively high levels of genetic diversity, (ii) US Brown
Swiss (BS) which originates from a small population of OB exported to
the USA between 1869 and 1910, and strongly selected for milk yield and
(iii) Improved Braunvieh (BV; crosses between BS and OB), which
represents the largest population of Swiss Brown cattle in Switzerland (Hagger, 2005).
In low-input systems, crossbreeding of BV and OB with BS genotypes is
widely practised in Switzerland to improve productivity (milk yield and
total milk solids) and robustness (including fertility, longevity and
ease of calving), as well as the overall economic performance of dairy
farms (Sorensen et al., 2008 and Weigel and Barlass, 2003).
Crossbreeding of Holstein–Friesian genotypes with other breeds is known
to affect both milk yield and fatty acid (FA) composition (Stergiadis et al., 2012 and Stergiadis et al., in press);
however, there is limited information on the effect of crossbreeding OB
with BS on nutritionally relevant milk quality parameters. Although
crossbreeding is practised mainly by low-input, grazing-based dairy
farms, benefits have also been demonstrated in more intensive production
systems (Kargo, Madsen, & Norberg, 2012).
Feeding
regimes used in organic and other low-input, pasture-based dairy
production systems are recognised to increase concentrations of
nutritionally desirable monounsaturated FAs (MUFAs), such as vaccenic
acid (VA; t11 C18:1), polyunsaturated FAs (PUFAs), such as rumenic acid
(RA; c9t11 C18:2 conjugated), α-linolenic acid (ALA; c9c12c15 C18:3),
eicosapentaenoic acid (EPA; c5c8c11c14c17 C20:5), docosapentaenoic acid
(DPA; c7c10c13c16c19 C22:5) and antioxidants/vitamins in milk (Butler et al., 2008, Slots et al., 2009, Stergiadis et al., 2012 and Stergiadis et al., in press). There is also evidence that the type of pasture affects FA profile in milk from cows on high forage diets (Dewhurst, Shingfield, Lee, & Scollan, 2006).
However, interactions between contrasting cow genotypes and feeding
regimes (including grazing intake and pasture type) used in low-input
systems have rarely been investigated (Stergiadis et al., 2012, Stergiadis et al., in press and Yin et al., 2012).
Therefore,
the aim of this study was (i) to investigate the effects of, and
interactions between, dairy cow genotypes (proportion of BS genetics in
crossbreed cows) and feeding regimes (grazing intake and pasture type)
on milk yield and FA profiles, (ii) to identify the relative impact of
dairy genotype (proportion of BS, BV, OB genetics) and individual
dietary components (proportion of intake from different types of
pasture, conserved forages and concentrate feeds) on milk FAs, using
redundancy analysis.
The
study focusses on saturated FA (SFA), MUFA and PUFA profiles in milk
linked to deleterious and beneficial impacts on human health (Givens, 2010 and Haug et al., 2007).
The SFAs in milk have been associated with increased risk of
cardiovascular diseases (CVD) although more recent studies suggest that
specific SFAs, including lauric (C12:0), myristic (C14:0) and palmitic
acid (C16:0), are the primary drivers for CVD (Givens, 2010 and Haug et al., 2007). For example, a recent FAO consultation highlights the need to focus on specific FA rather than FA groups (Food, 2008).
However, in addition to SFA, milk fat also contains the MUFA oleic acid
(OA; c9 C18:1) and VA and PUFAs RA, ALA, EPA and DPA, linked to
positive impacts on human health, such as reduced risk of CVD, certain
cancers and obesity and improved immune system, foetal development and
cognitive function (Belury, 2002, Haug et al., 2007, Mozaffarian et al., 2010 and Swanson et al., 2012). Recent studies (Mozaffarian et al., 2010 and Mozaffarian et al., 2013) showed that high plasma concentrations of the dairy MUFA trans-9
palmitoleic acid (TPA; t9 C16:1) were associated with 48–62% reduced
incidence of type-2 diabetes; however, although there was a positive
link between dairy consumption and plasma TPA levels, the causal effect
on the reduction of diabetes risk has not been proven.
2. Materials and methods
2.1. Experiment/survey design
In
the current study, milk samples were collected from 865 individual cows
on 38 low-input farms in the north east of Switzerland during the
grazing season in 2010 as part of the standard milk recording scheme.
Cows were sampled once during the grazing period, aiming to represent a
wide range of grazing intakes (25–100% of dry matter intake [DMI]). All
farms were members of the Swiss Brown cattle herd book, carried out
regularly milk recording through Braunvieh Schweiz (Zug, Switzerland)
and kept detailed breeding value records, determined by Qualitas AG
(Zug, Switzerland). Herds had between 12 and 57 cows with a mean size of
23 and a standard deviation of 10 cows. Most recorded cows were crosses
between US Brown Swiss (BS), Improved Braunvieh (BV; BS × OB cross) and
Original Braunvieh (OB) genotypes, except for 24 purebred OB cows and 5
which also had Holstein (HO), Simmental (SI), Swiss Fleckvieh (SF) and
Red Holstein (RH) genetics. Selected animals (i) represented crosses
typically used in low-input farms in this area and were considered as 4
groups over a range of BS genetic contribution (BS1, 75–99%; BS2,
50–74%; BS3, 25–49%; BS4, 0–24%) covering 77.5%, 12.4%, 4.3% and 5.7% of
total cows in the present study, respectively, (ii) managed with
contrasting grazing practises (high, 75–100% DMI; medium, 50–74% DMI;
low, 25–49% DMI) and the pasture types (natural and improved). A
detailed questionnaire to record management and feeding regimes during
the summer grazing period was completed with farmers on the day milk
samples were collected. Details recorded included grazing regime and
type of pastures used by lactating cows, types and amounts of conserved
forages, concentrate feeds and feed supplements used (Table 1 and Table 2). Estimated DMI and grazing intake (by difference) were calculated as described by Butler et al. (2008).
- Table 1. Estimated dry matter intake (DMI) and dietary components (% of DMI) of cows from different crossbreed groups (BS) and grazing intakes (GI) from 38 dairy farms in Switzerland during the outdoor grazing season.
Crossbreed group (% of US Brown Swiss genetics)
BS1 BS2 BS3 BS4 Grazing intake (% of DMI)
(75–99%) (50–74%) (25–49%) (0–24%) (75–100%) (50–74%) (25–49%) ANOVA P-valuesa
Parameters assessed n = 670 n = 109 n = 37 n = 49 n = 256 n = 244 n = 365 BS GI BS × GI Estimated DMI (kg/cow/day) 20.2 ± 0.0 20.3 ± 0.1 20.1 ± 0.1 20.1 ± 0.1 20.3 ± 0.1A 20.1 ± 0.1B 20.2 ± 0.0AB ns ∗ ns Estimated grazing 59.3 ± 0.8C 63.3 ± 2.0B 65.0 ± 4.7AB 76.2 ± 2.0A 86.7 ± 0.4A 66.2 ± 0.5B 39.5 ± 0.3C ∗∗∗ ∗∗∗ ∗∗∗ Fresh cut grass/“zero grazing”b 8.4 ± 0.6B 9.1 ± 1.6B 15.8 ± 3.8A 1.4 ± 1.0C 0.0 ± 0.0C 8.6 ± 0.9B 14.2 ± 1.1A ∗∗∗ ∗∗∗ ∗∗∗ Grass silage 7.0 ± 0.5 6.7 ± 1.0 1.9 ± 1.1 7.2 ± 1.1 1.8 ± 0.3C 6.6 ± 0.7B 10.3 ± 0.7A † ∗∗∗ ns Grass/clover silage 1.2 ± 0.2AB 0.7 ± 0.3BC 2.3 ± 0.9A 0.0 ± 0.0C 0.0 ± 0.0B 0.0 ± 0.0B 2.6 ± 0.3A ∗ ∗∗∗ ns Maize silage 8.3 ± 0.4A 5.3 ± 0.9B 3.4 ± 1.1B 2.2 ± 1.0B 1.6 ± 0.3C 6.6 ± 0.6B 11.9 ± 0.6A ∗∗∗ ∗∗∗ † Other silage 0.1 ± 0.0 0.1 ± 0.1 0.2 ± 0.2 0.0 ± 0.0 0.0 ± 0.0B 0.0 ± 0.0B 0.3 ± 0.1A ns ∗∗∗ ns Hay/straw 12.8 ± 0.5A 11.1 ± 1.0AB 10.4 ± 1.7AB 8.0 ± 0.5B 8.0 ± 0.4B 7.8 ± 0.4B 18.0 ± 0.8A ∗ ∗∗∗ ns Wholecropc 0.7 ± 0.1B 1.9 ± 0.5A 0.0 ± 0.0B 0.2 ± 0.2B 1.1 ± 0.2A 1.3 ± 0.2A 0.2 ± 0.1B ∗∗∗ ∗∗∗ ns Cereals 0.4 ± 0.1 0.2 ± 0.1 0.2 ± 0.2 0.5 ± 0.3 0.0 ± 0.0B 0.1 ± 0.1B 0.9 ± 0.1A ns ∗∗∗ ∗ Concentrates 0.6 ± 0.1B 0.7 ± 0.2B 0.0 ± 0.0B 3.9 ± 0.6A 0.0 ± 0.0C 2.1 ± 0.3A 0.5 ± 0.1B ∗∗∗ ∗∗∗ ∗∗∗ Other feeds 0.7 ± 0.1 0.7 ± 0.3 0.6 ± 0.5 0.0 ± 0.0 0.4 ± 0.1B 0.3 ± 0.1B 1.1 ± 0.2A ns ∗∗∗ ∗ Minerals/vitamins 0.4 ± 0.0A 0.4 ± 0.0AB 0.3 ± 0.0B 0.4 ± 0.0A 0.4 ± 0.0 0.4 ± 0.0 0.4 ± 0.0 ∗ ns ∗ -
- a
- Significances were declared at ⁎⁎⁎, P < 0.001; ⁎⁎, P < 0.01; ⁎, P < 0.05; †, 0.05 < P < 0.10 (trend); ns, P > 0.10 (non-significant). Means for crossbreed group or grazing intake within a row with different upper case letters are significantly different according to Tukey’s honestly significant difference test (P < 0.05).
- b
- Fresh cut grass provided within 1–2 days after harvest.
- c
- Ensiled whole wheat plants (stem, leaves and immature grain), harvested approximately 1 month before grain maturity.
- Table 2. Estimated dry matter intake (DMI) and dietary components (% of DMI) of cows from different crossbreed groups (BS) and pasture types (PT) from 35 dairy farms in Switzerland during the outdoor grazing season.
Crossbreed group (% US Brown Swiss genetics)
BS1 BS2 BS3 Pasture type
(76–99%) (51–75%) (26–50%) Natural Improved ANOVA P-valuesa
Parameters assessed n = 674 n = 98 n = 33 n = 690 n = 115 BS PT BS × PT Estimated DMI (kg/cow/day) 20.1 ± 0.0 20.2 ± 0.1 20.1 ± 0.1 20.1 ± 0.0 20.4 ± 0.1 ns ∗∗∗ ns Estimated grazing 57.2 ± 0.8 59.7 ± 2.0 50.4 ± 4.6 56.5 ± 0.8 61.6 ± 1.9 ns ∗ ns Fresh cut grass/“zero grazing”b 8.1 ± 0.6B 8.3 ± 1.6B 23.9 ± 4.4A 10.3 ± 0.7 0.0 ± 0.0 ∗∗∗ ∗∗∗ ns Grass silage 8.5 ± 0.5A 7.4 ± 1.1AB 2.1 ± 1.2B 8.9 ± 0.5 3.4 ± 0.6 ∗ ∗∗∗ ns Grass/clover silage 1.2 ± 0.2B 0.8 ± 0.3B 4.3 ± 1.1A 1.5 ± 0.2 0.0 ± 0.0 ∗∗∗ ∗∗∗ ns Maize silage 9.1 ± 0.4A 6.0 ± 1.0B 4.1 ± 1.2B 7.1 ± 0.4 17.2 ± 1.4 ∗∗∗ ∗∗∗ ns Other silage 0.2 ± 0.0 0.2 ± 0.1 0.2 ± 0.2 0.2 ± 0.0 0.0 ± 0.0 ns ∗ ns Hay/straw 12.8 ± 0.5 12.9 ± 1.4 11.8 ± 1.8 13.2 ± 0.5 10.1 ± 0.2 ns ∗ ns Wholecropc 0.7 ± 0.1B 2.5 ± 0.6A 1.0 ± 1.0B 0.5 ± 0.1 3.2 ± 0.7 ∗∗∗ ∗∗∗ ∗∗∗ Cereals 0.4 ± 0.1 0.4 ± 0.1 0.5 ± 0.3 0.3 ± 0.1 1.1 ± 0.3 ns ∗∗∗ ∗∗∗ Concentrates 0.6 ± 0.1 0.6 ± 0.2 0.3 ± 0.3 0.6 ± 0.1 0.5 ± 0.1 ns ns ns Other feeds 0.7 ± 0.1 0.9 ± 0.4 1.1 ± 0.7 0.5 ± 0.1 2.5 ± 0.7 ns ∗∗∗ ∗∗∗ Minerals/vitamins 0.4 ± 0.0A 0.4 ± 0.0AB 0.3 ± 0.0B 0.4 ± 0.0 0.5 ± 0.0 ∗ ∗∗∗ ns -
- a
- Significances were declared at ⁎⁎⁎, P < 0.001; ⁎⁎, P < 0.01; ⁎, P < 0.05; †, 0.05 < P < 0.10 (trend); ns, P > 0.10 (non-significant). Means for crossbreed group or pasture type within a row with different upper case letters are significantly different according to Tukey’s honestly significant difference test (P < 0.05).
- b
- Fresh cut grass provided within 1–2 days after harvest.
- c
- Ensiled whole wheat plants (stem, leaves and immature grain), harvested approximately 1 month before grain maturity.
2.2. Milk analysis
Forty
ml of milk were collected from each cow included in the survey,
immediately frozen and then stored in a −20 °C freezer prior to
analysis. Basic composition (fat, protein, lactose and urea contents)
and somatic cell count (SCC) analysis of milk were performed by the
company Braunvieh Schweiz (Zug, Switzerland) by Fourier transform
infrared spectrophotometry, using The MilkoScan™ FT+(FOSS, Hilleroed,
Denmark) and by flow cytometry, using Fossomatic™ FC, respectively
(FOSS, Hilleroed, Denmark). The equipment was validated weekly with
commercially available reference material (QSE GmbH, Germany) and, if
necessary, the intercept was adjusted. All analytical equipment was
routinely calibrated every three months (adjustment of slope and
intercept).
For milk FA profiling, 130 μg of lyophilized milk were methylated and esterified, as described by Chilliard, Martin, Rouel, and Doreau (2009).
Analysis of FAMEs was carried out with a gas chromatograph (Shimadzu,
GC-2014, Kyoto, Japan) equipped with a flame ionisation detector and by
using a Varian CP-SIL 88 fused silica capillary column (100 m × 0.25 mm
ID, 0.2 μm film thickness). Modifications in the chromatographic
conditions and gradient in the original method of Chilliard et al. (2009) were applied in our equipment to ensure optimum peak separation, as previously described (Stergiadis et al., 2014), and previously published chromatograms (Loor et al., 2004 and Shingfield et al., 2006); chromatographic separation of the peaks is shown in the appendix (Fig. A1).
Chemicals used in methylation and esterification of FAs, analytical
standards and literature sources to identify chromatogram peaks and
correction factors for short chain FAs (C4:0-C10:0) have been previously
described (Stergiadis et al., 2014).
2.3. Statistical analysis
Prior
to analyses, variables calculated as proportions (individual FAs, SFAs,
MUFAs, PUFAs, as proportions of total FA and individual feed intakes as
proportions of total DMI) were arcsine-transformed, SCC values were
cube root-transformed, with other variables used untransformed. Two
separate analyses of variance (ANOVA) were derived from linear
mixed-effects models (Pinheiro & Bates, 2000).
The first ANOVA considered BS contribution in the cows’ genetics (4
levels: BS1, 75–99%; BS2, 50–74%; BS3, 25–49%; BS4, 0–24%) and grazing
intake (3 levels: low, 25–49%; medium, 50–74%; high, 75–100% of DMI from
grazing) as factors. The second ANOVA considered BS contribution in the
cows’ genetics and pasture type (natural and improved) as fixed
factors. Cows from BS1, BS2, BS3 and BS4 groups were distributed on 37,
31, 12, and 11 farms, respectively. Each ‘genetic by grazing intake’
sub-group included in the study was represented by a minimum of two cows
on different farms. Tukey’s honest significant difference test was used
for pairwise comparisons of means (P < 0.05) where appropriate, based on a mixed-effects model. Analyses were performed in R statistical environment (R Development Core team., 2009) and residual normality was assessed using the qqnorm function (Crawley, 2007), with no data showing deviation from normality.
Multivariate
redundancy analysis (RDA) assesses relationships between variables and
the responses they evoke, using datasets containing both the measured
variables (in this case relating to milk quality) and variables thought
to influence these responses, here the dietary and breeding parameters.
This contrasts with factorial analyses where each response variable is
treated separately. RDAs produce biplots, where arrows indicate the
relative effects of driver variables in relation to the response
variables. RDA was carried out to assess the influence of cow crossbreed
pedigrees, individual diet components and type of pasture on milk FA
profile. Individual FAs were active response variables in the analysis,
with totals derived from these data as supplementary variables. The
genetics components were proportions of each breed in animal genotype:
BS, BV, OB, HO, SI, SF, RH. Feed components comprised dietary
proportions of estimated grazing from natural pastures (NAT), estimated
grazing from improved pastures (IMP), fresh cut grass
intake/“zero-grazing” (ZGRA; fresh cut grass provided within 1–2 days of
harvest), grass silage (GS), grass/clover silage (GCS), maize silage
(MS), other silage (OS), hay/straw (HS), whole crop (WC; ensiled whole
wheat plants, harvested approximately 1 month before grain maturity),
cereals (CER), concentrate feed (CON), other feeds (OTH), as well as oil
supplements (OS), minerals and vitamins (VIT). The analysis was
performed using the CANOCO package (Ter Braak & Smilauer, 1998), with automatic forward selection of variables and significances calculated using Monte Carlo permutation tests.
3. Results
3.1. General
The
data available allowed all 4 levels of BS contribution to the cows’
genetics (BS1, 75–99%; BS2, 50–74%; BS3, 25–49%; BS4, 0–24%) and 3
levels of grazing intake (low, 25–49%; medium, 50–74%; high, 75–100% of
DMI from grazing) to be included in the first 2-factor ANOVA (Table 1 and Table 3).
However, in the second 2-factor ANOVA, only 3 levels of BS contribution
to the cows’ genetics (BS1, 75–99%; BS2, 50–74%; BS3, 25–49%) could be
included in combination with the 2 types of pasture (natural and
improved), since there were no farms that grazed BS4 cows on improved
pasture (Table 2 and Table 4).
- Table 3. Means ± SE and ANOVA P-values for the effect of crossbreed group (BS) and grazing intake (GI) on the yield, basic composition and fatty acid (FA) profile (g/kg total FA) of milk collected from individual cows from 38 dairy farms in Switzerland during the outdoor grazing season.
Crossbreed group (% US Brown Swiss genetics)
BS1 BS2 BS3 BS4 Grazing intake (% of DMI)
(75–99%) (50–74%) (25–49%) (0–24%) (75–100%) (50–74%) (25–49%) ANOVA P-valuesa
Parameters assessed n = 670 n = 109 n = 37 n = 49 n = 256 n = 244 n = 365 BS GI BS × GI Yield (kg/cow/day) 21.2 ± 0.2 22.1 ± 0.7 20.9 ± 0.9 21.6 ± 1.0 22.0 ± 0.4A 20.6 ± 0.5B 21.3 ± 0.3AB ns ∗ ns Fat (g/kg milk) 38.3 ± 0.3 37.1 ± 0.6 37.0 ± 1.0 38.1 ± 0.7 38.0 ± 0.5 38.5 ± 0.4 37.9 ± 0.3 ns ns ns Protein (g/kg milk) 33.3 ± 0.1 33.5 ± 0.4 32.3 ± 0.6 32.9 ± 0.5 33.4 ± 0.3 33.5 ± 0.2 33.0 ± 0.2 ns † ns Lactose (g/kg milk) 47.3 ± 0.1B 47.2 ± 0.2B 47.6 ± 0.2AB 48.3 ± 0.2A 47.2 ± 0.1B 47.3 ± 0.1AB 47.5 ± 0.1A ∗∗ ∗ ns Urea (g/kg milk) 0.24 ± 0.00B 0.26 ± 0.01A 0.26 ± 0.01AB 0.27 ± 0.01A 0.24 ± 0.01B 0.27 ± 0.01A 0.23 ± 0.00B ∗∗ ∗∗∗ ∗∗∗ SCCb (×103) 201 ± 18 290 ± 66 198 ± 45 121 ± 18 203 ± 20 222 ± 24 201 ± 32 ns † ns SFAc C12:0 31.6 ± 0.3 32.7 ± 0.6 31.4 ± 0.9 30.5 ± 0.8 33.4 ± 0.4A 30.7 ± 0.4B 31.1 ± 0.4B ns ∗∗∗ † C14:0 120 ± 0.7A 120 ± 1.6A 118 ± 2.6AB 112 ± 2.0B 124 ± 0.9A 116 ± 1.0B 118 ± 1.0B ∗∗ ∗∗∗ ns C16:0 311 ± 1.5A 299 ± 3.2B 308 ± 6.4AB 303 ± 4.9AB 312 ± 2.4A 303 ± 2.5B 311 ± 1.8A ∗ ∗ ns C18:0 106 ± 0.9 106 ± 2.0 109 ± 3.4 110 ± 3.4 103 ± 1.5C 111 ± 1.5A 106 ± 1.2B ns ∗∗∗ ∗ MUFAd TPA 4.43 ± 0.04B 4.51 ± 0.09AB 4.89 ± 0.19A 4.61 ± 0.14AB 4.67 ± 0.07A 4.56 ± 0.07A 4.26 ± 0.05B ∗ ∗∗∗ ∗ VA 27.1 ± 0.4B 28.8 ± 0.9AB 32.1 ± 1.8A 32.4 ± 1.6A 30.2 ± 0.7A 30.0 ± 0.7A 24.8 ± 0.5B ∗∗∗ ∗∗∗ ∗ OA 199 ± 1.4 203 ± 3.4 194 ± 5.6 203 ± 4.4 189 ± 2.0B 203 ± 2.2A 205 ± 1.9A ns ∗∗∗ ns PUFAe LA 12.4 ± 0.1 13.0 ± 0.4 12.5 ± 0.6 12.9 ± 0.5 11.9 ± 0.2B 12.7 ± 0.2A 12.7 ± 0.2A ns ∗ ∗∗∗ ALA 7.74 ± 0.10C 8.68 ± 0.25B 9.67 ± 0.45A 10.1 ± 0.30A 9.02 ± 0.15A 8.91 ± 0.15A 6.87 ± 0.12B ∗∗∗ ∗∗∗ ns RA 13.0 ± 0.2 13.7 ± 0.5 13.7 ± 0.8 13.9 ± 0.8 14.5 ± 0.4A 13.7 ± 0.3A 11.9 ± 0.3B ns ∗∗∗ ns EPA 0.51 ± 0.01C 0.48 ± 0.02C 0.96 ± 0.13A 0.70 ± 0.07B 0.57 ± 0.03 0.53 ± 0.01 0.52 ± 0.01 ∗∗∗ ns ∗∗∗ DPA 1.01 ± 0.01C 1.03 ± 0.04C 1.20 ± 0.08B 1.36 ± 0.05A 1.02 ± 0.02B 1.12 ± 0.03A 1.00 ± 0.02B ∗∗∗ ∗∗∗ † DHA 0.05 ± 0.00 0.04 ± 0.00 0.05 ± 0.01 0.05 ± 0.00 0.03 ± 0.00B 0.04 ± 0.00B 0.07 ± 0.00A ns ∗∗∗ ns FA groups SFA 679 ± 1.8A 669 ± 4.3B 675 ± 7.5AB 662 ± 5.7B 684 ± 2.8A 669 ± 2.8C 676 ± 2.5B ∗ ∗∗ ns MUFA 270 ± 1.6 276 ± 3.8 269 ± 6.4 280 ± 5.0 262 ± 2.4B 276 ± 2.5A 274 ± 2.2A ns ∗∗∗ ns PUFA 51.5 ± 0.4C 54.7 ± 0.9B 55.9 ± 1.6AB 58.3 ± 1.3A 54.3 ± 0.6A 55.3 ± 0.6A 49.3 ± 0.4B ∗∗∗ ∗∗∗ ns n-3f 13.4 ± 0.2C 15.0 ± 0.4B 16.8 ± 0.8A 17.3 ± 0.6A 15.2 ± 0.3A 15.4 ± 0.3A 12.1 ± 0.2B ∗∗∗ ∗∗∗ ∗ n-6g 16.0 ± 0.2 16.9 ± 0.4 16.3 ± 0.6 17.0 ± 0.5 15.6 ± 0.2B 16.7 ± 0.2A 16.3 ± 0.2A † ∗∗∗ ∗∗∗ n-3/n-6 0.89 ± 0.01B 0.94 ± 0.03B 1.07 ± 0.06A 1.05 ± 0.04A 1.03 ± 0.02A 0.98 ± 0.02A 0.79 ± 0.02B ∗∗∗ ∗∗∗ ∗∗∗ Indices Δ9h 0.29 ± 0.00 0.30 ± 0.00 0.28 ± 0.01 0.29 ± 0.01 0.28 ± 0.00B 0.29 ± 0.00A 0.29 ± 0.00A ns ∗∗∗ ns C14:1/C14:0 0.077 ± 0.001A 0.073 ± 0.002AB 0.069 ± 0.003B 0.067 ± 0.003B 0.077 ± 0.001 0.073 ± 0.001 0.077 ± 0.001 ∗∗∗ ns ∗ c9 C16:1/C16:0 0.047 ± 0.000 0.049 ± 0.001 0.047 ± 0.002 0.047 ± 0.001 0.047 ± 0.001B 0.046 ± 0.001B 0.049 ± 0.001A ns ∗∗ ns OA/C18:0 1.92 ± 0.015 1.95 ± 0.034 1.83 ± 0.059 1.91 ± 0.053 1.90 ± 0.023 1.89 ± 0.022 1.95 ± 0.021 ns † ∗ RA/VA 0.485 ± 0.004A 0.484 ± 0.011A 0.434 ± 0.017B 0.431 ± 0.015B 0.489 ± 0.007 0.469 ± 0.007 0.480 ± 0.006 ∗∗∗ † ∗ -
- a
- Significances were declared at ⁎⁎⁎, P < 0.001; ⁎⁎, P < 0.01; ⁎, P < 0.05; †, 0.05 < P < 0.10 (trend); ns, P > 0.10 (non-significant). Means for crossbreed group or grazing intake within a row with different upper case letters are significantly different according to Tukey’s honestly significant difference test (P < 0.05).
- b
- SCC: Somatic cell count of milk.
- c
- SFA: C4:0, C5:0, C6:0, C7:0, C8:0, C9:0, C10:0, C11:0, C12:0, C13:0, C14:0, C15:0, C16:0, C17:0, C18:0, C20:0, C22:0, C23:0, C24:0.
- d
- MUFA: c9 C14:1, c10 C15:1, c9 C16:1, t9 C16:1 (TPA), c9 C17:1, t6 + t7 + t8 C18:1, t9 C18:1, t10 C18:1, t11 C18:1 (VA), t12 + t13 + t14 C18:1, c9 C18:1 (OA), t15 C18:1, c11 C18:1, c12 C18:1, c13 C18:1, c14 + t16 C18:1, c15 C18:1, c8 C20:1, c13 C22:1, c15 C24:1.
- e
- PUFA: c9t13 C18:2, t9t12 C18:2, t8c13 C18:2, c9t12 C18:2, t9c12 C18:2, t11c15 C18:2, c9c12 C18:2 (LA), c9c15 C18:2, c12c15 C18:2, c6c9c12 C18:3, c9c12c15 C18:3 (ALA), c9t11 C18:2 (RA), unknown conjugated and non-conjugated C18:2 isomers, c11c14 C20:2, c8c11c14 C20:3, c11c14c17 C20:3, c5c8c11c14 C20:4, c13c16 C22:2, c13c16c19 C22:3, c7c10c13c16 C22:4, c5c8c11c14c17 C20:5 (EPA), c7,10c13c16c19 C22:5 (DPA), c4c7c10c13c16c19 C22:6 (DHA).
- f
- n-3 FA: t11c15 C18:2, c9c15 C18:2, c12c15 C18:2, c9c12c15 C18:3 (ALA), c11c14c17 C20:3, c5c8c11c14c17 C20:5 (EPA), c13c16c19 C22:3, c7c10c13c16c19 C22:5 (DPA), c4c7c10c13c16c19 C22:6 (DHA).
- g
- n-6 FA: t9t12 C18:2, c9t12 C18:2, t9c12 C18:2, c9c12 C18:2 (LA), c6c9c12 C18:3, c11c14 C20:2, c8c11c14 C20:3, c5c8c11c14 C20:4, c13c16 C22:2, c7c10c13c16 C22:4.
- h
- Δ9-desaturase activity index (Δ9): (c9 C14:1 + c9 C16:1 + c9 C18:1 + t11 C18:1)/(c9 C14:1 + c9 C16:1 + c9 C18:1 + t11 C18:1 + C14:0 + C16:0 + C18:0 + c9t11 C18:2 conjugated), as proposed by Kay, Mackle, Auldist, Thomson, and Bauman (2004).
- Table 4. Means ± SE and ANOVA P-values for the effect of crossbreed group (BS) and pasture type (PT) on the basic composition and fatty acid (FA) profile (g/kg total FA) of milk collected from individual cows from 35 dairy farms in Switzerland during the outdoor grazing season.
Crossbreed group (% US Brown Swiss genetics)
BS1 BS2 BS3 Pasture type
(76–99%) (51–75%) (26–50%) Natural Improved ANOVA P-valuesa
Parameters assessed n = 674 n = 98 n = 33 n = 690 n = 115 BS PT BS × PT Yield (kg/cow/day) 21.2 ± 0.2 21.9 ± 0.7 20.7 ± 1.0 20.9 ± 0.2 23.4 ± 0.7 ns ∗∗∗ ns Fat (g/kg milk) 38.4 ± 0.3 37.3 ± 0.7 37.3 ± 1.0 38.5 ± 0.2 36.6 ± 0.7 ns ∗∗ ns Protein (g/kg milk) 33.3 ± 0.1 33.4 ± 0.4 32.0 ± 0.5 33.1 ± 0.1 34.1 ± 0.4 † ∗∗ ns Lactose (g/kg milk) 47.3 ± 0.1 47.4 ± 0.2 47.7 ± 0.2 47.3 ± 0.1 47.4 ± 0.2 ns ns ∗ Urea (g/kg milk) 0.24 ± 0.00 0.25 ± 0.01 0.24 ± 0.01 0.24 ± 0.00 0.26 ± 0.01 ns ∗ ns SCCb (×103) 196 ± 17 288 ± 71 131 ± 28 215 ± 19 147 ± 21 ns ∗ ns SFAc C12:0 31.7 ± 0.3 32.9 ± 0.7 31.4 ± 1.0 31.3 ± 0.3 35.0 ± 0.5 ns ∗∗∗ ns C14:0 120 ± 0.7 121 ± 1.8 118 ± 2.9 119 ± 0.7 129 ± 1.1 ns ∗∗∗ ns C16:0 313 ± 1.5A 301 ± 3.5B 302 ± 6.9B 310 ± 1.5 316 ± 3.2 ∗∗ † ns C18:0 105 ± 0.9 105 ± 2.1 110 ± 3.5 106 ± 0.9 98.5 ± 1.8 ns ∗∗ ns MUFAd TPA 4.37 ± 0.04B 4.53 ± 0.10AB 4.89 ± 0.20A 4.45 ± 0.04 4.19 ± 0.08 ∗ ∗ † VA 26.4 ± 0.4B 28.3 ± 1.0AB 32.4 ± 1.7A 27.1 ± 0.4 25.5 ± 0.8 ∗∗ ns ns OA 198 ± 1.4 202 ± 3.7 199 ± 6.7 201 ± 1.4 188 ± 2.7 ns ∗∗∗ ns PUFAe LA 12.3 ± 0.1 12.8 ± 0.4 13.0 ± 0.6 12.5 ± 0.1 11.8 ± 0.3 ns † ns ALA 7.60 ± 0.10B 8.47 ± 0.27A 8.85 ± 0.44A 7.77 ± 0.09 7.68 ± 0.29 ∗∗∗ ns ns RA 12.7 ± 0.2 13.4 ± 0.5 13.8 ± 0.8 12.8 ± 0.2 13.1 ± 0.5 ns ns ns EPA 0.50 ± 0.01 0.48 ± 0.02 0.55 ± 0.03 0.51 ± 0.01 0.45 ± 0.01 ns ∗∗∗ † DPA 0.99 ± 0.01 1.01 ± 0.04 1.01 ± 0.05 1.02 ± 0.01 0.85 ± 0.03 ns ∗∗∗ ns DHA 0.05 ± 0.00 0.04 ± 0.00 0.05 ± 0.01 0.05 ± 0.00 0.03 ± 0.00 ns ∗∗∗ ns FA groups SFA 680 ± 1.8 671 ± 4.7 670 ± 8.6 676 ± 1.8 692 ± 3.3 ns ∗∗∗ ns MUFA 269 ± 1.6 275 ± 4.2 276 ± 7.5 272 ± 1.6 257 ± 2.9 ns ∗∗∗ ns PUFA 50.9 ± 0.4B 54.1 ± 0.9A 54.2 ± 1.5A 51.5 ± 0.4 51.1 ± 0.8 ∗∗ ns † n-3f 13.1 ± 0.2B 14.8 ± 0.4A 14.9 ± 0.6A 13.4 ± 0.2 13.3 ± 0.4 ∗∗∗ ns † n-6g 16.0 ± 0.2 16.7 ± 0.4 16.7 ± 0.7 16.2 ± 0.2 15.5 ± 0.3 ns † ns n3:n6 0.88 ± 0.01 0.94 ± 0.04 0.92 ± 0.04 0.89 ± 0.01 0.88 ± 0.03 ns ns ns Indices Δ9h 0.29 ± 0.00 0.29 ± 0.00 0.29 ± 0.01 0.29 ± 0.00 0.28 ± 0.00 ns ∗∗ ns C14:1/C14:0 0.078 ± 0.001A 0.075 ± 0.002AB 0.069 ± 0.004B 0.077 ± 0.001 0.078 ± 0.002 ∗ ns ns c9 C16:1/C16:0 0.047 ± 0.000 0.049 ± 0.001 0.050 ± 0.002 0.048 ± 0.000 0.046 ± 0.001 ns ∗ ns OA/C18:0 1.93 ± 0.015 1.96 ± 0.035 1.85 ± 0.066 1.93 ± 0.015 1.96 ± 0.036 ns ns ns RA/VA 0.489±0.004A 0.484±0.011AB 0.437±0.018B 0.482±0.004 0.514±0.010 ∗ ∗∗ ns -
- a
- Significances were declared at ⁎⁎⁎, P < 0.001; ⁎⁎, P < 0.01; ⁎, P < 0.05; †, 0.05 < P < 0.10 (trend); ns, P > 0.10 (non-significant). Means for crossbreed group or pasture type within a row with different upper case letters are significantly different according to Tukey’s honestly significant difference test (P < 0.05).
- b
- SCC: Somatic cell count of milk.
- c
- SFA: C4:0, C5:0, C6:0, C7:0, C8:0, C9:0, C10:0, C11:0, C12:0, C13:0, C14:0, C15:0, C16:0, C17:0, C18:0, C20:0, C22:0, C23:0, C24:0.
- d
- MUFA: c9 C14:1, c10 C15:1, c9 C16:1, t9 C16:1 (TPA), c9 C17:1, t6 + t7 + t8 C18:1, t9 C18:1, t10 C18:1, t11 C18:1 (VA), t12 + t13 + t14 C18:1, c9 C18:1 (OA), t15 C18:1, c11 C18:1, c12 C18:1, c13 C18:1, c14 + t16 C18:1, c15 C18:1, c8 C20:1, c13 C22:1, c15 C24:1.
- e
- PUFA: c9t13 C18:2, t9t12 C18:2, t8c13 C18:2, c9t12 C18:2, t9c12 C18:2, t11c15 C18:2, c9c12 C18:2 (LA), c9c15 C18:2, c12c15 C18:2, c6c9c12 C18:3, c9c12c15 C18:3 (ALA), c9t11 C18:2 (RA), unknown conjugated and non-conjugated C18:2 isomers, c11c14 C20:2, c8c11c14 C20:3, c11c14c17 C20:3, c5c8c11c14 C20:4, c13c16 C22:2, c13c16c19 C22:3, c7c10c13c16 C22:4, c5c8c11c14c17 C20:5 (EPA), c7c10c13c16c19 C22:5 (DPA), c4c7c10c13c16c19 C22:6 (DHA).
- f
- n-3 FA: t11c15 C18:2, c9c15 C18:2, c12c15 C18:2, c9c12c15 C18:3 (ALA), c11c14c17 C20:3, c5c8c11c14c17 C20:5 (EPA), c13c16c19 C22:3, c7c10c13c16c19 C22:5 (DPA), c4c7c10c13c16c19 C22:6 (DHA).
- g
- n-6 FA: t9t12 C18:2, c9t12 C18:2, t9c12 C18:2, c9c12 C18:2 (LA), c6c9c12 C18:3, c11c14 C20:2, c8c11c14 C20:3, c5c8c11c14 C20:4, c13c16 C22:2, c7c10c13c16 C22:4.
- h
- Δ9-desaturase activity index (Δ9): (c9 C14:1 + c9 C16:1 + c9 C18:1 + t11 C18:1)/(c9 C14:1 + c9 C16:1 + c9 C18:1 + t11 C18:1 + C14:0 + C16:0 + C18:0 + c9t11 C18:2 conjugated), as proposed by Kay et al. (2004).
3.2. Milk yield and basic composition
There was no significant effect of crossbreed group on milk yield per cow, fat and protein content and SCC in milk (Table 3);
however, milk produced by BS4 cows had higher lactose concentrations
than milk from BS1 and BS2 cows. Grazing intake had a significant effect
on milk yield, urea and lactose contents. Milk yield per cow was
numerically higher in herds with high grazing intake when compared to
herds with a medium and low grazing intake, but only the difference
between high and medium grazing intake was significant. Lactose content
of milk increased with decreasing grazing intake, while milk urea
contents were higher in cows with medium grazing intake.
Pasture type had a significant effect on milk yield, fat, protein and urea concentrations and SCC (Table 4).
Cows grazing on natural pasture produced less milk with higher fat
content and SCC than cows on improved pasture. The use of improved
pasture increased both milk protein and urea content when compared with
natural pasture. A significant crossbreed group × grazing intake
interaction was detected for urea and there was a crossbreed
group × pasture type interaction for lactose content.
3.3. Fatty acid composition
When
2-factor ANOVA was used to analyse effects of crossbreed genetics and
grazing intake, crossbreed genetics had a significant effect on
concentrations of total SFAs and PUFAs and a range of individual SFAs
(C14:0 and C16:0), MUFAs (TPA and VA) and omega-3 (n-3) PUFAs (ALA, EPA
and DPA) (Table 3).
Significant effects were also detected for the omega-3/omega-6 FA ratio
(n-3/n-6), C14:1/14:0 and RA/VA ratios. Although significant
differences between crossbreed groups were detected for both total and
some individual SFAs, these were relatively small (<3%).
Concentrations of total SFAs were higher in the BS1 than in the BS2 and
BS4 crossbreed groups. Concentrations of C14:0 in BS1 and BS2 were
higher than in BS4, while C16:0 concentrations were higher in BS1 than
BS2. Concentrations of the MUFAs, TPAs and VA were higher in the BS3
and/or BS4 crossbreed groups than in the BS1 group although the
difference between BS4 and BS1 was not significant for TPA; relative
differences between the crossbreed group with the highest and lowest
concentration were 9.4% for TPA and 16.4% for VA (Table 3).
Concentrations of total PUFAs and n-3, and the individual n-3 PUFAs,
ALA and DPA, and the n-3/n-6 increased with decreasing proportion of BS
genetics (BS1 ⩽ BS2 ⩽ BS3 ⩽ BS4) although some differences between
individual crossbreed groups were not statistically significant.
Concentrations of EPA were highest in the BS3 group, slightly lower in
the BS4 and lowest in the BS1 and BS2 crossbreed groups. Relative
differences between the crossbreed group with the highest and lowest
concentration were 11.7%, 22.5%, 26.6%, 50.0%, and 25.7% for total
PUFAs, n-3, ALA, EPA and DPA concentrations, respectively. The
C14:1/14:0 and RA/VA ratios decreased with decreasing proportions of BS
genetics although not all differences between the groups were
significant.
Grazing intake (Table 3) and pasture type (Table 4)
also had significant effects on FA profiles in milk. Milk from cows
with high grazing intake had more C12:0 and C14:0, less MUFA, n-6, OA,
linoleic acid (LA; c9c12 C18:2) and lower Δ9-desaturase
index, when compared with milk from medium and lower grazing intake.
Milk from low grazing intake had less PUFA, n-3, TPA, VA, ALA, RA and a
lower n-3/n-6 but higher docosahexaenoic acid (DHA; c4c7c10c13c16c19
C22:6) and c9 C16:1/C16:0 ratio when compared with milk from medium and
high grazing intake. Milk stearic acid (C18:0) was highest when grazing
intake was medium, lowest when grazing intake was high and intermediate
when grazing intake was low. Milk DPA was also highest in the medium
grazing intake group. Total SFA were highest when grazing intake was
high, lowest when grazing intake was medium and intermediate when
grazing intake was low.
Significant
interactions between crossbreed groups and grazing intake were detected
for concentrations of C18:0, TPA, VA, LA, EPA, n-3, n-6 and n-3/n-6 and
ratios of C14:1/C14:0, RA/VA (Fig. 1)
and OA/C18:0 in milk. Concentrations of C18:0 were higher in cows with
the lowest proportion of BS genetics (BS4) than in BS1, BS2 and BS3
cows, but only when low grazing intake-based diets were used.
Concentrations of TPA were highest in milk from BS4 cows when high
forage intake diets and BS3 cows when low forage diets were used, when
compared with BS1 and BS2 cows. VA concentrations in milk increased with
decreasing proportion of BS genetics when high grazing intake diets
were used, although not all differences between the groups were
statistically significant while, on low forage intake diets, BS3 cows
produced milk with the highest VA concentrations. LA concentrations were
highest in milk from BS2 cows when high forage intake diets were used,
but not in milk from BS3 cows when forage intake was intermediate. EPA
concentrations were twice as high in milk from crossbreed group cows
with low (BS3 and BS4) than that in groups with high (BS1 and BS2)
proportions of BS genetics on farms with high grazing diets. A similar
trend was observed on farms using diets based on low grazing intakes,
differences between crossbreed groups were much smaller and only the
difference between the BS2 and BS3 group was significant. When
intermediate grazing intake diets were used no significant difference
between crossbreed groups could be detected. Similar to EPA,
concentration of n-3 was higher (approximately 28%) in milk from
crossbreed groups with low (BS3 and BS4) than in cows with high (BS1 and
BS2) proportions of BS genetics when grazing intake was high. When
intermediate grazing intake diets were used, the highest n-3
concentrations were found in milk from BS2 cows, but differences between
crossbreed groups were relatively small and only significant between
BS1 and BS2. Concentrations of n-6 were higher in BS3 cows than in the
three other crossbreed groups when intermediate grazing intake diets
were used. When low grazing diets were used, milk from BS2 and BS4 cows
had higher n-6 concentrations than had milk from BS1 cows. The n-3/n-6
was higher in BS3 and BS4 than BS1 and BS2 cows when high grazing diets
were used. In contrast, when intermediate grazing intake diets were
used, the n-3/n-6 was higher in milk from BS1 and BS2 than in that from
BS3 and BS4 cows, the difference between BS1 and BS4 was not
significant. The C14:1/C14:0 ratio decreased with decreasing proportion
of BS genetics, when high grazing intake diets were used, but only the
differences between BS1 and the three other groups were significant.
When grazing intake was low, milk from BS4 cows had a lower C14:1/C14:0
ratio when compared to the other 3 crossbreed groups. Very similar
trends were found for the RA/VA ratio in milk.
- Fig. 1.
Interaction means ± SE for the effects of crossbreed group (% US Brown Swiss genetics; BS1, 75–99%; BS2, 50–74%; BS3, 25–49%; BS4; 0–24%) and grazing intake (GI; % of DMI; 75–100%, 50–74%, 25–49%) on the concentrations of stearic acid (C18:0), trans-9 palmitoleic acid (TPA), vaccenic acid (VA), linoleic acid (LA), eicosapentaenoic acid (EPA), total omega-3 fatty acids (n-3), total omega-6 fatty acids (n-6), the omega-3/omega-6 ratio (n-3/n-6) and the Δ9-desaturase activity indicators (C14:1/C14:0 and RA/VA) of milk collected from individual cows from 38 dairy farms in Switzerland during the outdoor grazing season. P represents the ANOVA P-value for the interaction. Bars labelled with different lower case letter are significantly different within the same grazing intake group; bars labelled with different upper case letter are significantly different within the same crossbreed group (Tukey’s honestly significant difference test, P < 0.05). No lower or upper case letters indicate that there are no differences between individual grazing intake and crossbreed groups respectively.
Fewer
significant differences due to the crossbreed genetics factor were
detected when 2-factor ANOVA was performed to analyse its effect in
combination with pasture type (Table 4).
However, where significant effects were detected, the results were
consistent with those identified when grazing intake was used as the
second factor in the ANOVA (Table 3).
Pasture type had a significant effect on FA profiles in milk (Table 4).
Milk from cows on natural pasture had higher concentrations of MUFA,
C18:0, TPA, OA, EPA, DPA and DHA, while milk from cows on improved
pasture had higher concentrations of SFA, C12:0 and C14:0. Significant
interactions between crossbreed groups and pasture type were not
detected.
Results from the RDA (Fig. 2)
showed that both feeding (grazing on natural and improved pasture,
maize, grass, grass/clover and other silage, wholecrop) and breed
(proportions of OB and HO genetics in cows) composition parameters were
strong (P < 0.05) drivers of FA composition. In contrast,
intakes of concentrate feeds, hay/straw, other feeds and mineral/vitamin
supplements, and the proportion of BS, BV and SI genetics were
relatively weak drivers of FA composition. Concentrations of MUFA, PUFA,
n-6, C18:0, TPA, VA, OA, LA, EPA, DPA (and to a lesser extent n-3, ALA,
RA, DHA) were positively associated with OB and HO genetics, and
feeding fresh cut grass/“zero-grazing”, other silage and wholecrop
intake, but negatively associated with intakes of improved pasture, and
maize, grass and grass/clover silage along axis 1. Concentrations of
SFA, C12:0, C14:0, C16:0 were positively associated with intakes of
improved pasture, and maize, grass and grass/clover silage, but
negatively associated with OB and HO genetics, and feeding fresh cut
grass/“zero-grazing”, other silage and wholecrop intake along axis 1.
Also, PUFA, n-3, TPA, VA, ALA, RA and, to a lesser extent, EPA and DPA,
were positively associated with OB intakes along axis 2.
- Fig. 2.
Biplot deriving from the redundancy analysis showing the relationship between milk fatty acid profile (shown as dots; c12 = lauric acid, c14 = myristic acid, c16 = palmitic acid, c18 = stearic acid, tpa = trans-9 palmitoleic acid, va = vaccenic acid, oa = oleic acid, la = linoleic acid, ala = α-linolenic acid, ra = rumenic acid, epa = eicosapentaenoic acid, dpa = docosapentaenoic acid, dha = docosahexaenoic acid, sfa = saturated fatty acids, mufa = monounsaturated fatty acids, pufa = polyunsaturated fatty acids, n3 = omega-3 fatty acids, n6 = omega-6 fatty acids, 36 = omega-3/omega-6 ratio) and production management variables. Continuous variables (shown as arrows): OS = other silage (F = 13.48, P = 0.002), IMP = improved pasture (F = 9.47, P = 0.002); CER = cereals (F = 8.18, P = 0.002); MS = maize silage (F = 11.37, P = 0.004); NAT = natural pasture (F = 11.24, P = 0.004); WC = wholecrop (ensiled whole wheat plants [stem, leaves and immature grain], harvested approximately 1 month before grain maturity; F = 8.80, P = 0.004); HO = proportion of Holstein breed in the genetics (F = 7.56, P = 0.006); GCS = grass/clover silage (F = 5.45, P = 0.010); OB = proportion of Original Braunvieh in the genetics (F = 4.51, P = 0.016); GS = grass silage (F = 4.79, P = 0.026); ZGRA = zero-grazing (fresh cut grass provided within 1–2 days after harvest; F = 3.22, P = 0.044); VIT = minerals/vitamins (F = 2.99, P = 0.052); BV = proportion of Improved Braunvieh in the genetics (F = 2.28, P = 0.106); OTH = other feeds (F = 1.50, P = 0.160); HS = hay/straw (F = 1.39, P = 0.232); CON = concentrate feeds (F = 1.36, P = 0.244); SI = proportion of Simmental breed in the genetics (F = 0.58, P = 0.484); BS = proportion of US Brown Swiss breed in the genetics (F = 0.28, P = 0.748). Axis 1 explained 7.8% of the variation and axis 2 a further 1.9%.
4. Discussion
4.1. General
Here
we report for the first time the effect of US Brown Swiss crossbreed
genetics and interactions between crossbreed genetics and feeding
regimes (especially grazing intake and type of pasture), on (i) milk
yield, (ii) basic milk composition and (iii) FA profiles, by comparing
the performance and milk composition of cows from low-input farms in
Switzerland with contrasting breeding and feeding regimes.
4.2. Milk yield and basic composition
Previous
studies report that Original Braunvieh (OB) cows have lower yields than
both US Brown Swiss (BS) and Improved Braunvieh (BV) in Switzerland (BRUNA., 2012).
Here we show that milk yield and both fat and protein content were
similar in the four different crossbreed groups and there were no
interactions between these groups and feeding regimes for these
parameters. This suggests that the wide range of different crossbreeds
(BS × BV × OB combinations) compared in this study have very similar
productivities in low-input systems, irrespective of the grazing intake
and type of pasture used. It is important to note that the average milk
yield of purebred OB in Switzerland is lower than that of crossbred cows
(BS × BV × OV crosses), as reported by the Swiss Brown Breeders
Federation (Braunvieh CH, 2012)
and that 24 pure bred OB were included in the BS4 group (0–24% BS
genetics). However, this report makes no mention of feeding and other
management likely to influence productivity of the cows and it is
probable that crossbred cows with a high proportion of US Brown Swiss
genetics would have been managed in a more intensive way than those
recorded on low-input farms in the current study. In the present work,
where different crossbreds were compared within similar nutritional
intensity groups, these differences were not confirmed. Ferris (2007)
concludes that grazing alone is insufficient to fully exploit the
potential of high yielding cows (BS1 in our case); implying that some
cows may underperform in high grazing systems, supporting the use of
traditional genetics in low-input systems.
The
finding that improved pasture results in higher yields and protein
content, but lower fat concentrations, compared with natural pastures,
confirmed results from previous studies which also suggested that such
differences were mainly due to sward botanical composition and
associated protein and energy intakes (Stergiadis et al., 2012 and Van Dorland et al., 2006).
In contrast, the finding that cows with the highest grazing intakes
(76–100% of DMI) had the highest yields was surprising, since many
previous studies showed that replacing grazing with concentrate and/or
silage (especially maize silage) will result in higher milk yields per
cow in low-input pasture-based dairy systems (Butler et al., 2008 and Slots et al., 2009).
However, it is important to note that mean grazing intakes were mainly
balanced with feeding fresh cut grass/“zero-grazing” (up to 14% of DMI),
grass silage (up to 10% of DMI), maize silage (up to 12% of DMI) or
hay/straw (up to 18% of DMI), but only small amounts (<2.5% of DMI)
of concentrate feeds in this study, while previous studies showed that
high concentrate feeding is among the main reasons for higher yields in
highly intensive dairy production (Stergiadis et al., 2012).
4.3. Fatty acid composition
4.3.1. Effect of crossbreed group
While the impact of feeding regimes on milk FA composition has been studied in detail (Butler et al., 2011, Butler et al., 2008, Dewhurst et al., 2006 and Stergiadis et al., in press),
there is limited information on the effect of dairy breeds and
crossbreeds on milk composition. In contrast to yield, milk FA
composition was significantly affected by crossbreed group and
crossbreed genetics, with lower proportions of BS genetics tending to
result in higher concentrations of nutritionally desirable FAs including
total MUFAs and PUFAs, VA, ALA, EPA and DPA and a higher n-3/n-6.
Redundancy analyses confirmed that both genetic composition and feeding
regimes were significant drivers for FA composition and that there was a
positive association between OB genetics and nutritionally desirable
FAs (including total MUFAs, PUFAs, and n-3, and TPA, VA, OA, ALA, RA,
EPA and DPA), while BS and BV genetics had the opposite effect.
For
several milk fat parameters, significant interactions between the
genetic compositions of crossbreeds and grazing intensity were detected.
Examination of these interactions indicates that the optimum crossbreed
choice for specific FAs depends on the feeding regime used. Most
importantly, the use of cows with low levels of BS genetics resulted in
significantly higher concentrations of certain nutritionally desirable
FAs (total n-3, VA, TPA and EPA) and a higher n-3/n-6 only when high
grazing diets were used. Both EPA and DPA are produced from ALA by the
enzyme Δ6-desaturase (Bauman & Lock, 2010).
The finding that significant differences between crossbreed groups for
EPA were only found at high grazing intakes may suggest that genotype
differences in Δ6-desaturase activity only become apparent when high fresh forage diets increase ALA intake (Glasser, Doreau, Maxin, & Baumont, 2013). TPA has recently been found to be produced from VA in humans with an average conversion rate of 17% (Jaudszus et al., 2014);
the finding that TPA appears very close to VA in RDA may imply a
similar production mechanism in ruminants, as well as a consequent
positive relation of TPA with fresh grass intake. Appearance of TPA in
the same cluster as PUFA, n-3, VA, ALA and RA, indicates that the same
management and breeding practises that increased these nutritionally
desirable FA may also raise milk contents of TPA. Given that there was
no significant difference in milk yield between crossbreed groups, this
suggests that the use of traditional OB cattle can be recommended for
extensive grazing systems from a milk quality point of view. Notably,
despite the relatively low numbers of BS3 and BS4 cows on dairy farms in
Switzerland, these two groups were comprised of 38 cows on 6 farms,
thus confirming that the recordings represent the potential performance
under different management practises with a high reliance on grazing
(75–100% of DMI).
The
finding that cows with a low level of BS genetics (BS3 and BS4) produced
milk with higher VA, but similar RA contents, compared to cows with
higher levels of BS genetics (BS1 and BS2), suggests a lower Δ9-desaturase activity (the enzyme responsible for VA desaturation to RA; Griinari et al., 2000)
in the mammary gland of BS3 and BS4 cows. This view is supported by the
finding that both the RA/VA and the C14:1/C14:0 (considered to be the
best indicator of Δ9-desaturase activity; Griinari et al., 2000)
ratios decreased with decreasing proportion of BS genetics
(BS1 > BS2 > BS3 > BS4). This would imply that RA
concentrations in milk cannot be increased solely via changes in
crossbreeding strategies based on the three breeds examined in this
study. It also confirms previous studies reporting differences in Δ9-desaturase activity between dairy cattle genotypes (Carroll et al., 2006 and Stergiadis et al., 2013).
Interactions
for C18:0, LA and total n-6 FA were observed but this may be a
consequence of the low number of cows in the groups showing significant
differences. For example, results for subgroups BS3, under intermediate
grazing intake, and BS4, under low grazing intake, were averages of only
two and three cows, respectively, (on two and three farms). Mean
composition in these situations could be strongly influenced by
particular practises such as the feeding of maize silage, cereals or
other feeds.
Current
dietary recommendations are to increase the intake of longer chain n-3
(e.g. EPA, DPA and DHA), since these have been associated with health
benefits, such as action against CVD, development of neuronal, retinal
and immune function in foetuses, improvement of cognitive function and
weight management (Swanson et al., 2012).
The finding that changes in crossbreed choice may increase EPA and DPA
concentrations in milk is therefore of particular nutritional
significance, since other strategies to increase long chain FA
concentrations in milk were either shown to be very expensive (e.g.
fishmeal or seaweed supplementation; (Shingfield, Bonnet, & Scollan, 2012) or not effective (e.g. oilseed supplementation; (Glasser et al., 2008 and Stergiadis et al., 2014).
4.3.2. Effect of grazing intake and pasture type
Previous
studies demonstrated the beneficial effects of using high grazing
intake and natural pasture-based diets on nutritionally desirable PUFA
concentrations (Butler et al., 2008, Stergiadis et al., 2012, Stergiadis et al., in press and Van Dorland et al., 2006).
This was confirmed here, where concentrations of nutritionally
desirable FAs (including VA, ALA, RA, n-3, PUFA) and the n-3/n-6
increased with increasing proportion of grazing-based DMI. The use of
natural pastures had a further effect on milk composition and
simultaneously increased concentrations of total MUFAs and long chain
n-3 (EPA, DPA, DHA) compared with improved pastures. This suggests that
the combined use of high grazing intake from natural pastures will
further improve milk quality.
Previous
reviews concluded that bovine milk/dairy products are a relatively
minor dietary source of long chain n-3 FAs with limited potential to
increase concentrations via changes to breed selection and feeding
regimes (Givens, 2010 and Lock and Bauman, 2004). Recent studies reported that EPA in milk rarely exceeds 1 g/kg FA, with only traces of DHA being detectable (Shingfield et al., 2012), but Swiss milk, produced in summer alpine pasture-based systems, has been reported by Kraft, Collomb, Mockel, Sieber, and Jahreis (2003)
to exceed this value (1.05 g/kg fat). In this study, we demonstrated
that the use of crossbred cows with a low proportion of BS genetics,
together with high fresh forage intake, can increase concentrations of
EPA, DPA and DHA to 1.28, 1.35 and 0.09 g/kg FA, respectively. In
regions with a relatively high consumption of dairy products (e.g.
Finland, where average dairy fat intakes are approximately 50 g per
person per day; Wollf & Precht, 2002),
dairy products would therefore account for approximately 25% of the
daily recommended intake of long-chain n-3 FAs (EPA + DHA), if milk from
such Swiss pasture-based systems was consumed.
Most
previous studies have also reported that concentrations of
nutritionally undesirable SFAs decrease with increasing grazing intake (Butler et al., 2008 and Stergiadis et al., 2012).
In contrast, this study found that milk from cows with a high grazing
intake had the highest concentrations of total SFAs and palmitic acid,
while the lowest levels resulted from the intermediate grazing intake.
However, the differences between groups were small and may not be
biologically important in terms of health-related impact of SFA intake.
This anomaly may be due to confounding effects of pasture type and
breed, since the RDA identified improved pasture use and BS and BV
genetics as the strongest drivers for high SFA concentrations. This
finding supports previous studies reporting strong impacts of breed
choice and pasture type on milk SFA (Carroll et al., 2006 and Stergiadis et al., 2013).
Also, clear effects of breed choice can be explained by genetic
differences in the expression of saturation enzymes in the mammary
gland, responsible for the production of the majority of SFAs (C4:0 to
C14:0 and approximately 50% of C16:0) found in milk (Bauman & Lock, 2010).
Previous
studies have shown that VA and RA concentrations increase with
increasing botanical diversity of pastures, and also environmental
conditions and husbandry practises in alpine grazing systems (Van Dorland et al., 2006). However, since these parameters were not recorded in this study, their relative confounding effect cannot be estimated.
5. Conclusion
The
present study provides evidence that, in Swiss low-input production
systems, the nutritional composition of milk fat can be improved by (i)
greater use of traditional OB genetics, combined with high pasture
intakes, without any negative impact on milk yield and (ii) use of
natural rather than improved pastures, although this option had lower
productivity in this study.
Acknowledgements
The authors gratefully acknowledge financial support from the European Community
under the 7th framework project LowInputBreeds, FP7-project No KBBE 222
632. Special thanks go to all the dairy producers who took part in the
study and Mrs. Dominique Mahrer for her help with the questionnaire data
completion.
Appendix A. Supplementary data
References
- Belury, 2002
- Dietary conjugated linoleic acid in health: physiological effects and mechanisms of action
- Annual Review of Nutrition, 22 (2002), pp. 505–531
- | |
- Butler et al., 2008
- Fatty acid and fat-soluble antioxidant concentrations in milk from high- and low-input conventional and organic systems: Seasonal variation
- Journal of the Science of Food and Agriculture, 88 (2008), pp. 1431–1441
- | |
- Butler et al., 2011
- Fat composition of organic and conventional retail milk in northeast England
- Journal of Dairy Science, 94 (2011), pp. 24–36
- | | | |
- Carroll et al., 2006
- Milk composition of Holstein, Jersey, and Brown Swiss cows in response to increasing levels of dietary fat
- Animal Feed Science and Technology, 131 (2006), pp. 451–473
- | | |
- Chilliard et al., 2009
- Milk fatty acids in dairy cows fed whole crude linseed, extruded linseed, or linseed oil, and their relationship with methane output
- Journal of Dairy Science, 92 (2009), pp. 5199–5211
- | | | |
- Crawley, 2007
- The R book
- John Wiley & Sons Ltd., Chichester, UK (2007)
- Dewhurst et al., 2006
- Increasing the concentrations of beneficial polyunsaturated fatty acids in milk produced by dairy cows in high forage systems
- Animal Feed Science and Technology, 131 (2006), pp. 168–206
- | | |
- Ferris, 2007
- Sustainable pasture based dairy systems – Meeting the challenges
- Canadian Journal of Plant Science, 87 (2007), pp. 723–738
- | |
- Givens, 2010
- Milk and meat in our diet: Good or bad for health?
- Animal, 4 (2010), pp. 1941–1952
- | |
- Glasser et al., 2013
- Fat and fatty acid content and composition of forages: A meta-analysis
- Animal Feed Science and Technology, 185 (2013), pp. 19–34
- | | |
- Glasser et al., 2008
- Oilseed lipid supplements and fatty acid composition of cow milk: A meta-analysis
- Journal of Dairy Science, 91 (2008), pp. 4687–4703
- | | | |
- Griinari et al., 2000
- Conjugated linoleic acid is synthesized endogenously in lactating dairy cows by Δ9-desaturase
- Journal of Nutrition, 130 (2000), pp. 2285–2291
- |
- Hagger, 2005
- Estimates of genetic diversity in the brown cattle population of Switzerland obtained from pedigree information
- Journal of Animal Breeding and Genetics, 122 (2005), pp. 405–413
- | |
- Haug et al., 2007
- Bovine milk in human nutrition
- Lipids in Health and Disease, 6 (2007), p. 25
- Jaudszus et al., 2014
- Trans palmitoleic acid arises endogenously from dietary vaccenic acid
- American Journal of Clinical Nutrition, 99 (2014), pp. 431–435
- | |
- Kargo et al., 2012
- Short communication: Is crossbreeding only beneficial in herds with low management level?
- Journal of Dairy Science, 95 (2012), pp. 925–928
- | | | |
- Kay et al., 2004
- Endogenous synthesis of cis-9, trans-11 conjugated linoleic acid in dairy cows fed fresh pasture
- Journal of Dairy Science, 87 (2004), pp. 369–378
- | | | |
- Kraft et al., 2003
- Differences in CLA isomer distribution of cow’s milk lipids
- Lipids, 38 (2003), pp. 657–664
- | |
- Lock and Bauman, 2004
- Modifying milk fat composition of dairy cows to enhance fatty acids beneficial to human health
- Lipids, 39 (2004), pp. 1197–1206
- | |
- Loor et al., 2004
- Biohydrogenation, duodenal flow, and intestinal digestibility of trans fatty acids and conjugated linoleic acids in response to dietary forage:concentrate ratio and linseed oil in dairy cows
- Journal of Dairy Science, 87 (2004), pp. 2472–2485
- | | | |
- Mozaffarian et al., 2010
- Trans-palmitoleic acid, metabolic risk factors, and new-onset Diabetes in U.S. adults: A cohort study
- Annals of Internal Medicine, 153 (2010), pp. 790–800
- Mozaffarian et al., 2013
- trans-palmitoleic acid, other dairy fat biomarkers, and incident diabetes: the Multi-Ethnic Study of Atherosclerosis (MESA)
- American Journal of Clinical Nutrition, 97 (2013), pp. 854–861
- | |
- Pinheiro and Bates, 2000
- Mixed-effects models in S and S-Plus
- Springer Verlag, New York, U.S.A. (2000)
- Shingfield et al., 2012
- Recent developments in altering the fatty acid composition of ruminant-derived foods
- Animal, 7 (S1) (2012), pp. 132–162
- Shingfield et al., 2006
- Examination of the persistency of milk fatty acid composition responses to fish oil and sunflower oil in the diet of dairy cows
- Journal of Dairy Science, 89 (2006), pp. 714–732
- | | | |
- Slots et al., 2009
- Potential to differentiate milk composition by different feeding strategies
- Journal of Dairy Science, 92 (2009), pp. 2057–2066
- | | | |
- Sorensen et al., 2008
- Invited Review: Crossbreeding in dairy cattle: A Danish perspective
- Journal of Dairy Science, 91 (2008), pp. 4116–4128
- | | | |
- Stergiadis et al., 2012
- Effect of feeding intensity and milking system on nutritionally relevant milk components in dairy farming systems in the north east of England
- Journal of Agricultural and Food Chemistry, 60 (2012), pp. 7270–7281
- | |
- Stergiadis et al., 2014
- Improving the fatty acid profile of winter milk from housed cows with contrasting feeding regimes by oilseed supplementation
- Food Chemistry, 164 (2014), pp. 293–300
- | | |
- Stergiadis et al., 2013
- Variation in nutritionally relevant components in retail Jersey and Guernsey whole milk
- Food Chemistry, 139 (2013), pp. 540–548
- | | |
- Swanson et al., 2012
- Omega-3 fatty acids EPA and DHA: Health benefits throughout life
- Advances in Nutrition, 3 (2012), pp. 1–7
- | |
- Ter Braak and Smilauer, 1998
- Reference manual and user’s guide to Canoco for Windows: Software for canonical community ordination (version 4)
- Centre for Biometry, Wageningen, the Netherlands (1998)
- Van Dorland et al., 2006
- Species-rich swards of the alps
- A. Elgersma, J. Dijkstra, S. Tamminga (Eds.), Fresh herbage for dairy cattle, Springer, Wageningen, the Netherlands (2006), pp. 27–43
- Weigel and Barlass, 2003
- Results of a producer survey regarding crossbreeding on US dairy farms
- Journal of Dairy Science, 86 (2003), pp. 4148–4154
- | | | |
- Wollf and Precht, 2002
- Reassessment of the contribution of bovine milk fats to the trans-18:1 isomeric acid consumption by European populations. Additional data for rumenic (cis-9, trans-11 18:2) acid
- Lipids, 37 (2002), pp. 1149–1150
- Yin et al., 2012
- Genetic parameters for gaussian and categorical traits in organic and low input dairy cattle herds based on random regression methodology
- Livestock Science, 147 (2012), pp. 159–169
- | | |
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