Summer Pasture in Mountainous Area Affects Milk Fatty Acid Profile of Dual-Purpose Cows
by
, , , , , ,
Giovanni Niero
1
,
Tania Bobbo
1,
Simone Callegaro
1,
Giulio Visentin
2,*,
Cristina Pornaro
1,
Martino Cassandro
1,3,
Giulio Cozzi
4,
Massimo De Marchi
1 and
Mauro Penasa
1 1
Department of Agronomy, Food, Natural Resources, Animals and Environment, University of Padova, Viale dell’Università 16, 35020 Legnaro, Italy
2
Department of Veterinary Medical Sciences, Alma Mater Studiorum University of Bologna, Via Tolara di Sopra 50, 40064 Ozzano dell’Emilia, Italy
3
Italian Holstein, Brown Swiss and Jersey National Breeders Association (ANAFIBJ), Via Bergamo 292, 26100 Cremona, Italy
4
Department of Animal Medicine, Production and Health, University of Padova, Viale dell’Università 16, 35020 Legnaro, Italy
*
Author to whom correspondence should be addressed.
Agriculture 2021, 11(10), 928; https://doi.org/10.3390/agriculture11100928
Submission received: 27 August 2021
/
Revised: 22 September 2021
/
Accepted: 24 September 2021
/
Published: 27 September 2021
(This article belongs to the Section Farm Animal Production)
Abstract
:The change in feeding regime of dairy herds using summer grazing in mountainous areas has several implications on cows’ productivity and milk composition. The present study quantified the effect of summer grazing on the milk fatty acids (FA) profile of Simmental and crossbred cows in an alpine holding. A total of 616 milk samples of 71 cows were collected 3 days before the onset of the grazing season until 91 days of grazing. Individual and groups of FA were quantified through mid-infrared spectroscopy. Data were analysed using a linear mixed model including the fixed effects of breed, stage of lactation, parity, sampling period, and the random effects of cows nested within breed and the residual. The stage of lactation and the sampling period were the most significant factors associated with milk FA. The variance explained by the random cow effect ranged from 15.10% (C18:1) to 25.31% (medium-chain FA). The concentration of C14:0, C16:0, and short- and medium-chain FA decreased across the summer season. Long-chain FA and polyunsaturated FA concentrations were greater in milk obtained at pasture compared with milk obtained indoors. Given these outcomes, the present study demonstrated the positive effect of grazing on milk FA composition from a nutraceutical point of view.
1. Introduction
A common practice of dairy farmers in mountainous areas is to move cows to pasture, typically from late spring until the end of summer. This management strategy may have positive implications not only on farm profitability but also on landscape maintenance, touristic appeal of the territory, and the quality of animal-derived products [1,2]. The use of pasture is perceived as a practice to improve animal welfare compared to indoors housing, and possible benefits of this practice are related to both animal health and behaviour [3]. However, lactating cows may experience a period of negative energy balance in grazing conditions due to insufficient energy intake, which is reflected in a loss of productivity and body condition. Therefore, pasture supplementation with some concentrates is generally advised for lactating cows [4,5]. The risk of energy deficit is more common in high-producing cows regardless of their farming conditions; therefore, cows of specialized dairy breeds, such as Holstein, are generally more difficult to be kept in good health when grazing in the mountains [6]. Outdoor grazing requires robust cattle breeds that are well-adapted to low-input feeding and farming systems. Although alpine, and generally mountainous areas, have been a pool of cattle biodiversity for the number of breeds present today, many farmers have directed their choice towards Simmental cows [7]. Simmental is a dual-purpose cattle breed known for its robustness and for the capacity to adapt to extensive farming conditions, including summer grazing in mountain areas [8]. In Italy, the breeding goal of Simmental sets an emphasis of 24% and 44% on beef and milk traits, respectively [9]. The remaining emphasis is divided into conformation (19.5%, including feet and legs and udder conformation) and fitness (12.5%, including milkability and somatic cell count).
In Western countries, milk and dairy products are important sources of a plethora of nutrients, including proteins of high biological value, minerals, vitamins, and fatty acids (FA) [10]. Bovine milk fat is generally composed of about 70% saturated FA (SFA), 25% monounsaturated FA (MUFA), and 5% polyunsaturated FA (PUFA) [11]. The milk FA profile is affected by several genetic and non-genetic factors, including individual genotype, breed, lactation stage, parity, metabolic status of the animal, and feeding [12,13,14]. The measurement of milk FA at the population level has been hampered by expensive and time-consuming gold standard analysis, i.e., gas chromatography. However, mid-infrared (MIR) spectroscopy is an effective high-throughput tool to collect several animal characteristics and milk quality traits on a large scale with moderate to high accuracy, including the FA profile [15].
Given the importance of milk FA intake from a nutritional point of view, the hypothesis of the present study was to test the effect of summer grazing on the concentration of individual FA and groups of FA, predicted through MIR spectroscopy, in the milk of dual-purpose cows farmed in a mountain holding. The hypothesis was that the milk FA profile obtained from grazing cows is closer to consumer’s needs, and, given the importance of FA intake from a nutritional point of view, this could be a strategy to valorize milk production in mountain areas.
2. Materials and Methods
2.1. Herd
The commercial dairy farm involved in this study was located in the Veneto Region (Cansiglio, BL, Italy) at 1100 m above sea level and comprised 55 Simmental (SI), 3 Holstein (HO), and 22 HOxSI crossbreed (CR) cows. During autumn, winter, and spring (i.e., between October and late May), animals were housed indoors, fed a total mixed ration based on meadow hay (9.5 kg as fed), alfalfa hay (3 kg), high moisture corn (7 kg), cereals meal (3 kg), and protein-mineral-vitamin premix (3.5 kg). Milking took place in the morning (5.00 to 7.30 a.m.) and in the evening (5.00 to 7.30 p.m.). During late-spring and summer (i.e., between late May and September), animals had access to pasture adjacent to the barn. Cows grazed fresh herbage following the Voisin rotational grazing system [16], implying that animals utilised the entire herbage production of a paddock and moved to a different paddock every day. This grazing management scheme is based on a short grazing period, a high stock density, and sufficient resting time for plants to recover after grazing. In the present study, lactating cows grazed 24 h in each paddock with a stocking rate between 50 and 70 animals per hectare over one day. During milking, which took place twice per day, cows received 2 kg of an energy supplement based on high-moisture corn and cereal meals. A comprehensive description of farm management and diets’ chemical composition can be retrieved from Niero et al. [17].
2.2. Sample Collection and Chemical Analysis
Repeated milk samples were collected on all cows from late May to late August 2020, exclusively during evening milking through an automatic sampler installed in the milking parlour. The experiment was designed to characterise milk production before the onset and during the grazing season. Milk sampling was performed 3 days and 1 day prior to the beginning of the grazing period to characterise milk quality under indoors farming conditions, hereinafter called barn farming (BF). Similarly, milk samples were collected 2, 3, 7, 10, and 14 days after the beginning of the grazing period to characterise milk quality in the early grazing (EG) season. Further milk samples were collected 21, 49, and 91 days after the beginning of the grazing period to characterise milk quality in the mid-late grazing (MLG) season.
Immediately after sampling, 200 μL of preservative (Bronopol; 2-bromo-2-nitropropan-1,3-diol) was added to 40 mL of milk and transferred at 4 °C to the laboratory of the Breeders Association of Veneto Region (ARAV, Padova, Italy) for the determination of gross chemical composition (fat, protein, casein, and lactose), individual FA, and groups of FA content using the MilkoScan FT6000 (FOSS Analytical A/S, Hillerød, Denmark). The somatic cell count (SCC, cells/μL) was determined using the Fossomatic 7 DC (FOSS Analytical A/S, Hilleroed, Denmark). Values of SCC were transformed to the somatic cell score (SCS) to achieve the normality and homogeneity of variances using the formula SCS = 3 + log2(SCC/100) [18]. The accuracies of MIR prediction models to determine individual and groups of FA are reported in Gottardo et al. [13]. Briefly, the coefficient of determination in validation of individual FA ranged from 0.55 (C16:0) to 0.81 (C18:1), varying from 0.72 (PUFA) to 0.98 (SCFA) for FA groups.
2.3. Data Editing and Statistical Analysis
The initial dataset comprised 658 test day records of 55 SI, 3 HO, and 22 CR (first-generation HOxSI). Records of HO cows were discarded due to the very few test days available. Moreover, cows with only one test day record were removed from the dataset. Days in milk (DIM) were restricted to be between 5 and 560 d; parity was between 1 and 8, and parity numbers greater than 5 were grouped into a unique class.
Records with milk yield (MY) < 2 kg/milking were also discarded. Within breed, samples exceeding three standard deviations from the mean of each trait were considered as missing values. After editing, the dataset comprised 616 test days of 52 SI and 19 CR cows. The number of records, before and after edits, for each parity number is reported in Table 1. To assess sources of variation of the studied traits, the following linear mixed model was implemented in the GLIMMIX procedure of SAS software v. 9.4 (SAS Institute Inc., Cary, NC, USA):
where yijklm is the dependent variable (MY, composition trait, individual FA or group of FA); μ is the overall intercept of the model; periodi is the fixed effect of the ith period of sampling (i = BF, EG, and MLG); breedj is the fixed effect of the jth cow breed (j = SI and CR); stagek is the fixed effect of the kth class of stage of lactation of the cow (k = 1 to 8, the first being a class from 5 to 45 DIM, followed by 7 classes of 45 DIM each, and the last class including DIM > 315 days); parityl is the fixed effect of the lth class of parity of the cow (l = 1 to 6, with the last class including parities from 6 to 8); cowm(breedj) is the random effect of the mth cow nested within breed; and eijklm is the random residual. Differences between least-squares means of the fixed effects were tested using the Tukey post-hoc test (p < 0.05). Pearson correlation coefficients (r) between the residuals of milk composition, the SCS, individual FA, and groups of FA were estimated.
yijklm = μ + periodi + breedj + stagek + parityl + cowm(breedj) + eijklm,
3. Results and Discussion
3.1. Descriptive Statistics and Analysis of Variance
Descriptive statistics of MY, gross composition, and SCS are reported in Table 2. Detailed discussion of such traits can be retrieved from Niero et al. [17], who analysed changes in health-related milk indicators during summer grazing in the same herd. Among individual FA, C14:0 and C18:1 had the lowest and the greatest concentration, averaging 0.32 and 1.01 g/100 mL of milk, respectively (Table 2). The content of C18:0 ranged from 0.02 to 0.66 g/100 mL milk and had the greatest coefficient of variation (30.69%), followed by C18:l with a coefficient of variation of 29.22%. On the other hand, C14:0 and C16:0 had the lowest coefficients of variation (23.03% and 22.18%, respectively). Two recent studies on the milk FA profile of dual-purpose Pinzgauer [19] and SI [20] cattle breeds predicted through MIR spectroscopy reported greater proportions of C14:0 (+26 and +27%, respectively), C16:0 (+23 and +28%, respectively), and C18:0 (+8 and +3%, respectively) and a lower proportion of C18:1 (−25 and −27%, respectively). Saturated FA were the most abundant among FA groups, averaging 2.31 g/100 mL of milk, followed by MUFA (0.97 g/100 mL of milk) and PUFA (0.11 g/100 mL of milk; Table 2). Compared to the results of the present experiment, Bobbo et al. [21] reported a greater concentration of SFA (+13%) and lower concentrations of PUFA (−10%) in a study that aimed at estimating genetic parameters of the milk FA profile of Italian HO. Similar considerations can be resumed by comparing the findings of the present study with those of Manuelian et al. [19,20], who again observed a greater content of SFA and a lower content of MUFA and PUFA in milk of dual-purpose Pinzgauer and SI cattle breeds farmed in the northeast of Italy. Such a lower proportion of SFA in turn of a greater amount of MUFA and PUFA may be explained by two reasons. The first may rely on different genetic backgrounds, due to the specific breeds involved in the present experiment and those considered in previous studies. The second is likely due to different feeding regimes, with particular regard to the fresh herbage grazed by cows enrolled in the present study, which is known to reduce SFA in favour of unsaturated FA [22,23].
Results from the analysis of variance for the concentration of milk FA are summarized in Table 3. The stage of lactation was significant in explaining the variation of individual FA and groups of FA (p < 0.001), with only the exception of short-chain FA (SCFA). The sampling period was significant for C14:0, C16:0, and the majority of groups of FA (p < 0.05). Among individual FA, only C14:0 exhibited significant variation due to the parity effect (p < 0.001). Individual FA and groups of FA were not significantly affected by breed. The random effect of the cow nested within breed accounted for a relatively low proportion of variance of the studied traits, namely, 15.10 to 21.78% for individual FA and 17.02 to 25.31% for groups of FA.
3.2. Phenotypic Correlations
Pearson correlation coefficients of individual FA and groups of FA with milk chemical composition and SCS, and between FA, all adjusted for the systematic effects of the statistical model, are presented in Table 4. As expected, all FA groups were strongly correlated to fat concentration (r > 0.75), except for trans FA (TFA; r = 0.57). Correlations of individual FA and groups of FA with other composition traits were generally weak and, in some cases, did not differ from zero.
The associations between individual FA and groups of FA, as well as those between groups of FA, were significant, positive, and generally greater than 0.50. Moreover, the correlation between TFA and groups of FA increased with the degree of unsaturation of FA, ranging from 0.57 (TFA and SFA) to 0.75 (TFA and PUFA). These results agreed with the findings of Soyeurt et al. [24] and Petrini et al. [25] who estimated phenotypic and genetic correlations between milk FA predicted by MIR spectroscopy and other milk quality traits (i.e., fat, protein, lactose concentration, and SCS) in the Holstein breed in Belgium and under tropical conditions, respectively. Management and breeding strategies aiming at increasing total milk fat concentration are expected to increase not only the unsaturated fraction of FA but also SFA, with different implications. Indeed, a reduction in the dietary ingestion of SFA and in medium-chain FA (MCFA) is encouraged in humans to reduce the risk of cardiovascular diseases, atherosclerosis, and hyperlipidaemia [14]. Moreover, Vanrobays et al. [26] quantified both the phenotypic and genetic correlations between milk FA and methane emissions throughout the lactation using random regression models, demonstrating that methane production (g/d) was positively correlated to C14:0 (average phenotypic and genetic r values of 0.14 and 0.24, respectively), C16:0 (average phenotypic and genetic r values of 0.16 and 0.31, respectively), C18:0 (average phenotypic and genetic r values of 0.11 and 0.32, respectively), and SFA (average phenotypic and genetic r values of 0.16 and 0.30, respectively) concentration in milk. These correlations, however, had a different magnitude and pattern depending on the lactation stage. Chillard et al. [27] reported that the association between milk FA and methane production could be due to the common biochemical pathways between methane, acetate, and butyrate in the rumen but also due to the effect of some dietary FA (SCFA, MCFA, and polyunsaturated C18) on the reduction in archaea methanogens in the rumen. Given the correlations quantified in the present study between individual and groups of FA, strategies to increase milk fat concentration per se should also consider the milk FA profile, which is now predictable with moderate to high accuracy on a large-scale using MIR spectroscopy.
3.3. Effect of Grazing on Milk Fatty Acid Composition
Least-squares means of FA profiles across different sampling periods are displayed in Table 5. Stoop et al. [28] reported that milk FA composition is mainly determined by four factors, which may act independently or together: (i) animal diet, (ii) de novo synthesis in mammary gland, (iii) rumen biohydrogenation, and (iv) mobilization of body fat reserves. It is likely that variations observed in the present study were due to the change of animal diet and to the mobilization of body fat in response to metabolic stress that animals might have experienced while moving from BF to grazing. Among individual FA, C14:0 and C16:0 exhibited a progressive decline, with the greatest values in the BF period (0.36 and 0.95 g/100 mL milk, respectively) and the lowest in the MLG period (0.29 and 0.76 g/100 mL milk, respectively). Such a decrease mirrors the decrease in groups of FA, with particular regard to MCFA and SFA concentration towards late summer. The concentration of PUFA increased across the grazing season compared with BF. Similar results were reported by Gottardo et al. [13], who observed lower MCFA and SFA in favour of greater unsaturated FA proportion in milk produced during the summer season. From a nutraceutical perspective, a lower proportion of MCFA and SFA in favour of PUFA is desirable in human diet to reduce the risk of cardiovascular diseases and atherosclerosis [14]. Therefore, the outcomes of the present study suggest that, considering the degree of unsaturation and carbon chain length, milk produced by grazing cows could better fulfil human dietary recommendations in terms of the FA profile. The content of C18:1 did not change between indoors and grazing periods; again, among groups of FA, this was reflected in the MUFA content, which did not vary significantly across BF, EG, and MLG. The greatest concentration of TFA was recorded in the EG period, when cows were more susceptible to metabolic stress entailed by augmented motility and swing in feeding regime and farming conditions. Such results agree with those of Manuelian et al. [19], who reported that negative energy balance hampers the biohydrogenation capacity of the rumen, leading to a greater production of TFA.
4. Conclusions
The present study aimed at investigating the effect of grazing on the concentration of individual FA and groups of FA in milk of SI and CR cows. The effect of breed was not significant in explaining the variation of the studied traits. Milk produced during EG and MLG periods was characterized by lower concentration of MCFA and SFA, and partially a greater amount of PUFA, compared to milk obtained indoors. The results from the present study suggest that, in general, pasture grazing has a positive impact on milk FA composition, especially in the view of human nutrition and health. This information, which needs to be deepened by future large-scale studies, could be considered for market segmentation and to promote and facilitate the selling of milk produced by grazing cows, which potentially has positive implications also on the promotion of the territory and on the warranty of a right and proper income to farmers operating in low-input dairy systems.
Author Contributions
Conceptualization, G.N., G.C., M.P. and M.C.; methodology, G.N., S.C. and C.P.; software, G.N., T.B. and G.V.; validation, M.P., M.D.M., G.C. and M.C.; formal analysis, T.B., G.V. and M.P.; investigation, G.N., T.B., S.C. and G.V.; resources, M.C.; data curation, T.B. and G.V.; writing—original draft preparation, G.N. and G.V.; writing—review and editing, S.C., T.B., C.P. and M.P.; visualization, M.D.M. and G.C.; supervision, M.P., G.C. and M.C.; project administration, G.N. and M.C.; funding acquisition, M.C. All authors have read and agreed to the published version of the manuscript.
Funding
This research was funded by Veneto Region with the DGR n. 376/2018, Misura 16.1.1 and 16.1.2, FITOCHE project.
Institutional Review Board Statement
Ethical review and approval were waived for this study, since experimental procedures used in this trial were not invasive.
Data Availability Statement
The data used in this study are available upon request from the corresponding author.
Acknowledgments
Mirko Breda (“Lissandri” farm, Pian del Cansiglio, Belluno, Italy) is gratefully acknowledged for the opportunity to collect samples used in the study.
Conflicts of Interest
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
References
- Mack, G.; Walter, T.; Flury, C. Seasonal alpine grazing in Switzerland: Economic importance and impact on biotic communities. Environ. Sci. Pol. 2013, 32, 48–57. [Google Scholar] [CrossRef]
- Niero, G.; Koczura, M.; De Marchi, M.; Currò, S.; Kreuzer, M.; Turille, G.; Berard, J. Are cheese-making properties of dual purpose cattle impaired by highland grazing? A case study using Aosta Red Pied cows. Ital. J. Anim. Sci. 2018, 17, 827–834. [Google Scholar] [CrossRef] [Green Version]
- Arnott, G.; Ferris, C.P.; O’Connell, N.E. Review: Welfare of dairy cows in continuously housed and pasture-based production systems. Animal 2016, 11, 261–273. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Bovolenta, S.; Ventura, W.; Malossini, F. Dairy cows grazing and alpine pasture: Effect of pattern of supplement allocation on herbage intake, body condition, milk yield and coagulation properties. Anim. Res. 2002, 51, 15–23. [Google Scholar] [CrossRef] [Green Version]
- Romanzin, A.; Corazzin, M.; Piasentier, E.; Bovolenta, S. Concentrate supplement modifies the feeding behaviour of Simmental cows grazing in two high mountain pastures. Animals 2018, 8, 76. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Leiber, F.; Kreuzer, M.; Leuenberger, H.; Wettstein, H.R. Contribution of diet type and pasture conditions to the influence of high altitude grazing on intake, performance and composition and renneting properties of the milk of cows. Anim. Res. 2006, 55, 37–53. [Google Scholar] [CrossRef]
- Senczuk, G.; Mastrangelo, S.; Ciani, E.; Battaglini, L.; Cendron, F.; Ciampolini, R.; Crepaldi, P.; Mantovani, R.; Bongioni, G.; Pagnacco, G.; et al. The genetic heritage of Alpine local cattle breeds using genomic SNP data. Genet. Sel. Evol. 2020, 52, 40. [Google Scholar] [CrossRef]
- Cesarani, A.; Hidalgo, J.; Garcia, A.; Degano, L.; Vicario, D.; Masuda, Y.; Misztal, I.; Lourenco, D. Beef traits genetic parameters based on old and recent data and its implications for genomic predictions in Italian Simmental cattle. J. Anim. Sci. 2020, 98, skaa242. [Google Scholar] [CrossRef]
- ANAPRI. Available online: https://www.anapri.eu/it/indice-ida.html (accessed on 10 August 2021).
- Haug, A.; Høstmark, A.T.; Harstad, O.M. Bovine milk in human nutrition—A review. Lipids Health Dis. 2007, 6, 25. [Google Scholar] [CrossRef] [Green Version]
- Grummer, R.R. Effect of feed on the composition of milk fat. J. Dairy Sci. 1991, 74, 3244–3257. [Google Scholar] [CrossRef]
- Poulsen, N.A.; Gustavsson, F.; Glantz, M.; Paulsson, M.; Larsen, L.B.; Larsen, M.K. The influence of feed and herd of fatty acid composition in 3 dairy breeds (Danish Holstein, Danish Jersey, and Swedish Red). J. Dairy Sci. 2012, 95, 6362–6371. [Google Scholar] [CrossRef] [Green Version]
- Gottardo, P.; Penasa, M.; Righi, F.; Lopez-Villalobos, N.; Cassandro, M.; De Marchi, M. Fatty acid composition of milk from Holstein-Friesian, Brown Swiss, Simmental and Alpine Grey cows predicted by mid-infrared spectroscopy. Ital. J. Anim. Sci. 2017, 16, 380–389. [Google Scholar] [CrossRef] [Green Version]
- Hanuš, O.; Samková, E.; Křížová, L.; Hasoňová, L.; Kala, R. Role of fatty acids in milk fat and the influence of selected factors on their variability—A review. Molecules 2018, 23, 1636. [Google Scholar] [CrossRef] [Green Version]
- De Marchi, M.; Toffanin, V.; Cassandro, M.; Penasa, M. Invited review: Mid-infrared spectroscopy as phenotyping tool for milk traits. J. Dairy Sci. 2014, 97, 1171–1186. [Google Scholar] [CrossRef] [PubMed]
- Voisin, A. Grass Productivity; Philosophical Library Inc.: New York, NY, USA, 1959. [Google Scholar]
- Niero, G.; Bobbo, T.; Callegaro, S.; Visentin, G.; Pornaro, C.; Penasa, M.; Cozzi, G.; De Marchi, M.; Cassandro, M. Dairy cows’ health during alpine grazing as assessed by milk traits, including differential somatic cell count: A case study from Italy. Animals 2021, 11, 981. [Google Scholar] [CrossRef] [PubMed]
- Ali, A.K.A.; Shook, G.E. An optimum transformation for somatic cell concentration in milk. J. Dairy Sci. 1980, 63, 487–490. [Google Scholar] [CrossRef]
- Manuelian, C.L.; Visentin, G.; Penasa, M.; Cassandro, M.; De Marchi, M. Phenotypic variation of milk fatty acid composition of Pinzgauer cattle breed. Ital. J. Anim. Sci. 2018, 17, 574–577. [Google Scholar] [CrossRef]
- Manuelian, C.L.; Penasa, M.; Visentin, G.; Benedet, A.; Cassandro, M.; De Marchi, M. Multi-breed herd approach to detect breed differences in composition and fatty acid profile of cow milk. Czech J. Anim. Sci. 2019, 64, 11–16. [Google Scholar] [CrossRef] [Green Version]
- Bobbo, T.; Penasa, M.; Cassandro, M. Genetic parameters of bovine milk fatty acid profile, yield, composition, total and differential somatic cell count. Animals 2020, 10, 2406. [Google Scholar] [CrossRef] [PubMed]
- Heck, J.M.L.; van Valenberg, H.J.F.; Dijkstra, J.; van Hooijdonk, A.C.M. Seasonal variation in the Dutch bovine raw milk composition. J. Dairy Sci. 2009, 92, 4745–4755. [Google Scholar] [CrossRef]
- Elgersma, A. Grazing increases the unsaturated fatty acid concentration of milk from grass-fed cows: A review of the contributing factors, challenges and future perspectives. Eur. J. Lipid Sci. Technol. 2015, 117, 1345–1369. [Google Scholar] [CrossRef]
- Soyeurt, H.; Gillon, A.; Vanderick, S.; Mayeres, P.; Bertozzi, C.; Gengler, N. Estimation of heritability and genetic correlations for the major fatty acids in bovine milk. J. Dairy Sci. 2007, 90, 4435–4442. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Petrini, J.; Iung, L.H.S.; Rodriguez, M.A.P.; Salvian, M.; Pértille, F.; Rovadoscki, G.A.; Cassoli, L.D.; Coutinho, L.L.; Machado, P.F.; Wiggans, G.R.; et al. Genetic parameters for milk fatty acids, milk yield and quality traits of a Holstein cattle population reared under tropical conditions. J. Anim. Breed. Genet. 2016, 133, 384–395. [Google Scholar] [CrossRef] [PubMed]
- Vanrobays, M.-L.; Bastin, C.; Vandenplas, J.; Hammami, H.; Soyeurt, H.; Vanlierde, A.; Dehareng, F.; Froidmont, E.; Gengler, N. Changes throughout lactation in phenotypic and genetic correlations between methane emissions and milk fatty acid contents predicted from milk mid-infrared spectra. J. Dairy Sci. 2016, 99, 7247–7260. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Chillard, Y.; Martin, C.; Rouel, J.; Doreau, M. Milk fatty acids in dairy cows fed whole crude linseed, extruded linseed, or linseed oil, and their relationship with methane output. J. Dairy Sci. 2009, 92, 5199–5211. [Google Scholar] [CrossRef] [PubMed]
- Stoop, W.M.; Bovenhuis, H.; Heck, J.M.; van Arendonk, J.A. Effect of lactation stage and energy status on milk fat composition of Holstein–Friesian cows. J. Dairy Sci. 2009, 92, 1469–1478. [Google Scholar] [CrossRef]
Table 1.
Number of records for each parity number prior and post editing.
| Parity Number | N, Prior Editing | N, Post Editing |
|---|---|---|
| 1 | 120 | 118 |
| 2 | 194 | 192 |
| 3 | 96 | 83 |
| 4 | 80 | 79 |
| 5 | 104 | 92 |
| 6 | 32 | 21 |
| 7 | 10 | 10 |
| 8 | 22 | 21 |
Table 2.
Descriptive statistics of milk yield, gross composition, somatic cell score (SCS), individual fatty acids (FA), and groups of FA 1.
Table 2.
Descriptive statistics of milk yield, gross composition, somatic cell score (SCS), individual fatty acids (FA), and groups of FA 1.
| Trait | N | Mean | SD | CV 2, % | Minimum | Maximum |
|---|---|---|---|---|---|---|
| Milk yield, kg/milking | 608 | 13.53 | 4.63 | 34.18 | 2.00 | 27.50 |
| Milk gross composition, % | ||||||
| Fat | 584 | 3.58 | 0.75 | 21.10 | 2.05 | 6.36 |
| Protein | 607 | 3.46 | 0.34 | 9.76 | 2.54 | 4.44 |
| Casein | 606 | 2.73 | 0.27 | 10.04 | 1.95 | 3.46 |
| Lactose | 610 | 4.85 | 0.19 | 4.01 | 3.76 | 5.30 |
| SCS, units | 605 | 2.81 | 1.70 | 60.42 | −1.06 | 8.76 |
| Individual FA, g/100 mL milk | ||||||
| C14:0 | 610 | 0.32 | 0.07 | 23.03 | 0.13 | 0.66 |
| C16:0 | 604 | 0.86 | 0.19 | 22.18 | 0.35 | 1.40 |
| C18:0 | 605 | 0.33 | 0.10 | 30.69 | 0.02 | 0.66 |
| C18:1 | 601 | 1.01 | 0.30 | 29.22 | 0.06 | 2.12 |
| Groups of FA, g/100 mL milk | ||||||
| SCFA | 604 | 0.46 | 0.12 | 26.56 | 0.09 | 0.86 |
| MCFA | 606 | 1.34 | 0.29 | 21.65 | 0.54 | 2.23 |
| LCFA | 601 | 1.23 | 0.40 | 32.77 | 0.01 | 2.66 |
| SFA | 605 | 2.31 | 0.54 | 23.63 | 0.62 | 4.13 |
| MUFA | 601 | 0.97 | 0.28 | 29.19 | 0.10 | 1.86 |
| PUFA | 607 | 0.11 | 0.03 | 25.19 | 0.03 | 0.25 |
| TFA | 576 | 0.11 | 0.04 | 40.20 | 0.00 | 0.25 |
1 SCFA: short-chain fatty acids, MCFA: medium-chain fatty acids, LCFA: long-chain fatty acids, SFA: saturated fatty acids, MUFA: monounsaturated fatty acids, PUFA: polyunsaturated fatty acids, TFA: trans fatty acids. 2 CV: coefficient of variation.
Table 3.
Results from analysis of variance (F-values and significance) of individual fatty acids (FA) and groups of FA 1.
Table 3.
Results from analysis of variance (F-values and significance) of individual fatty acids (FA) and groups of FA 1.
| Trait | Fixed Effects | Cow Variance, % | RSD 2 | |||
|---|---|---|---|---|---|---|
| Sampling Period | Breed | Days in Milk | Parity | |||
| Individual FA, g/100 mL milk | ||||||
| C14:0 | 56.15 *** | 0.05 | 11.58 *** | 4.18 *** | 21.78 | 0.06 |
| C16:0 | 71.27 *** | 0.07 | 11.07 *** | 1.30 | 21.50 | 0.14 |
| C18:0 | 2.21 | 0.01 | 14.60 *** | 1.31 | 21.55 | 0.08 |
| C18:1 | 0.56 | 0.03 | 16.87 *** | 1.59 | 1.50 | 0.23 |
| Groups of FA, g/100 mL milk | ||||||
| SCFA | 99.88 *** | 0.46 | 1.90 | 1.33 | 22.72 | 0.09 |
| MCFA | 100.15 *** | 0.20 | 9.19 *** | 2.17 | 25.31 | 0.21 |
| LCFA | 8.48 *** | 0.03 | 16.51 *** | 1.53 | 17.97 | 0.31 |
| SFA | 70.55 *** | 0.15 | 9.83 *** | 2.07 | 17.53 | 0.41 |
| MUFA | 1.56 | 0.01 | 20.77 *** | 2.42 * | 17.02 | 0.21 |
| PUFA | 3.99 * | 0.07 | 11.70 *** | 1.60 | 22.48 | 0.02 |
| TFA | 28.17 *** | 1.01 | 7.81 *** | 2.29 * | 21.12 | 0.03 |
1 SCFA: short-chain fatty acids, MCFA: medium-chain fatty acids, LCFA: long-chain fatty acids, SFA: saturated fatty acids, MUFA: monounsaturated fatty acids, PUFA: polyunsaturated fatty acids, TFA: trans fatty acids. 2 RSD: residual standard deviation. * p < 0.05, *** p < 0.001.
Table 4.
Pearson correlation coefficients between milk yield and composition, somatic cell score (SCS), individual fatty acids (FA), and groups of FA 1.
Table 4.
Pearson correlation coefficients between milk yield and composition, somatic cell score (SCS), individual fatty acids (FA), and groups of FA 1.
| Trait | Individual FA | Groups of FA | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| C14:0 | C16:0 | C18:0 | C18:1 | SCFA | MCFA | LCFA | SFA | MUFA | PUFA | TFA | |
| Milk yield | 0.01 | 0.01 | 0.03 | 0.03 | 0.14 *** | 0.01 | 0.05 | 0.06 | 0.03 | 0.05 | 0.10 * |
| Milk gross composition | |||||||||||
| Fat | 0.63 *** | 0.79 *** | 0.87 *** | 0.85 *** | 0.85 *** | 0.77 *** | 0.88 *** | 0.95 *** | 0.85 *** | 0.79 *** | 0.57 *** |
| Protein | 0.22 *** | 0.13 *** | 0.01 | −0.01 | 0.11 *** | 0.21 *** | −0.05 | 0.12 *** | −0.02 | 0.03 | 0.00 |
| Casein | 0.26 *** | 0.19 *** | 0.11 * | 0.08 * | 0.19 *** | 0.26 *** | 0.05 | 0.20 *** | 0.07 | 0.09 * | 0.07 |
| Lactose | −0.18 *** | −0.17 *** | 0.01 | −0.01 | −0.02 | −0.22 *** | 0.00 | −0.13 ** | −0.03 | −0.01 | −0.02 |
| SCS | 0.26 *** | 0.30 *** | 0.22 *** | 0.16 *** | 0.15 *** | 0.30 *** | 0.19 *** | 0.26 *** | 0.21 *** | 0.17 *** | 0.09 * |
| Individual FA | |||||||||||
| C14:0 | 0.86 *** | 0.47 *** | 0.31 *** | 0.63 *** | 0.93 *** | 0.42 *** | 0.76 *** | 0.37 *** | 0.36 *** | 0.19 *** | |
| C16:0 | 0.69 *** | 0.51 *** | 0.73 *** | 0.96 *** | 0.59 *** | 0.89 *** | 0.55 *** | 0.47 *** | 0.30 *** | ||
| C18:0 | 0.89 *** | 0.67 *** | 0.61 *** | 0.94 *** | 0.81 *** | 0.91 *** | 0.81 *** | 0.57 *** | |||
| C18:1 | 0.68 *** | 0.45 *** | 0.96 *** | 0.76 *** | 0.98 *** | 0.88 *** | 0.73 *** | ||||
| Groups of FA | |||||||||||
| SCFA | 0.76 *** | 0.66 *** | 0.93 *** | 0.64 *** | 0.71 *** | 0.64 *** | |||||
| MCFA | 0.54 *** | 0.89 *** | 0.48 *** | 0.44 *** | 0.29 *** | ||||||
| LCFA | 0.78 *** | 0.98 *** | 0.86 *** | 0.63 *** | |||||||
| SFA | 0.75 *** | 0.73 *** | 0.57 *** | ||||||||
| MUFA | 0.86 *** | 0.66 *** | |||||||||
| PUFA | 0.75 *** | ||||||||||
1 SCFA: short-chain fatty acids, MCFA: medium-chain fatty acids, LCFA: long-chain fatty acids, SFA: saturated fatty acids, MUFA: monounsaturated fatty acids, PUFA: polyunsaturated fatty acids, TFA: trans fatty acids. * p < 0.05, ** p < 0.01, *** p < 0.001.
Table 5.
Least-squares means (with standard errors) of individual and groups of fatty acids (FA) 1.
| Trait | Barn Farming | Early Grazing | Mid-Late Grazing |
|---|---|---|---|
| Individual FA, g/100 mL milk | |||
| C14:0 | 0.36 (0.007) a | 0.33 (0.006) b | 0.29 (0.006) c |
| C16:0 | 0.95 (0.02) a | 0.89 (0.01) b | 0.76 (0.02) c |
| C18:0 | 0.32 (0.01) | 0.34 (0.01) | 0.34 (0.01) |
| C18:1 | 1.00 (0.03) | 1.03 (0.02) | 1.02 (0.02) |
| Groups of FA, g/100 mL milk | |||
| SCFA | 0.51 (0.01) a | 0.49 (0.01) a | 0.37 (0.01) b |
| MCFA | 1.49 (0.03) a | 1.40 (0.02) b | 1.16 (0.02) c |
| LCFA | 1.18 (0.04) b | 1.23 (0.03) b | 1.33 (0.03) a |
| SFA | 2.53 (0.05) a | 2.40 (0.04) b | 2.00 (0.04) c |
| MUFA | 0.96 (0.03) | 0.98 (0.02) | 1.01 (0.02) |
| PUFA | 0.105 (0.003) b | 0.111 (0.002) a | 0.106 (0.002) ab |
| TFA | 0.108 (0.004) b | 0.117 (0.003) a | 0.091 (0.004) c |
1 SCFA: short-chain fatty acids, MCFA: medium-chain fatty acids, LCFA: long-chain fatty acids, SFA: saturated fatty acids, MUFA: monounsaturated fatty acids, PUFA: polyunsaturated fatty acids, TFA: trans fatty acids. Least-squares means with different letters within a row are significantly different (p < 0.05).
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Niero, G.; Bobbo, T.; Callegaro, S.; Visentin, G.; Pornaro, C.; Cassandro, M.; Cozzi, G.; De Marchi, M.; Penasa, M. Summer Pasture in Mountainous Area Affects Milk Fatty Acid Profile of Dual-Purpose Cows. Agriculture 2021, 11, 928. https://doi.org/10.3390/agriculture11100928
AMA Style
Niero G, Bobbo T, Callegaro S, Visentin G, Pornaro C, Cassandro M, Cozzi G, De Marchi M, Penasa M. Summer Pasture in Mountainous Area Affects Milk Fatty Acid Profile of Dual-Purpose Cows. Agriculture. 2021; 11(10):928. https://doi.org/10.3390/agriculture11100928
Chicago/Turabian StyleNiero, Giovanni, Tania Bobbo, Simone Callegaro, Giulio Visentin, Cristina Pornaro, Martino Cassandro, Giulio Cozzi, Massimo De Marchi, and Mauro Penasa. 2021. "Summer Pasture in Mountainous Area Affects Milk Fatty Acid Profile of Dual-Purpose Cows" Agriculture 11, no. 10: 928. https://doi.org/10.3390/agriculture11100928
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