Husbandry management, herd productive performances and milk quality.
All the farms produced their own fodders in the investigated lowland area. Maize silage (late variety, such as FAO class 600-700) and the permanent meadows (mix of perennial ryegrass, meadow fescue with a minor presence of red and white clover) were produced in optimal pedoclimatic and irrigated conditions with an average annual yield of 21 and 10 t DM/ha, respectively. The third main fodder, used in the MCS group, was a mix of ensiled forages such as sorghum (25%), lucerne (25%), wheat (20%), perennial grass (15%) and Italian ryegrass (15%), with a medium-high productive yield.
The herd characteristics and productive performances of the five experimental FG are reported in Table 1. Although no statistical analysis was carried out on this data, the highest herd daily DMI was observed in the MMS and MCS groups (24.4 and 23.8 kg/cow, respectively), meanwhile the lowest in the GRG group (21.8 kg/cow) and, as expected, the milk yield was strongly linked to intake. However, the potential production of the maize-silage groups might have been reduced as a strategy to enhance milk quality because also these farms destinated their milk to Protected Designation of Origin (PDO) hard cheese production.
Milk proximate composition and chemical traits characterizing the five FG are reported in Table 2, and they show only little differences among them. The feeding system affected CP, casein and lactose content with the lowest (p < 0.05) values recorded for GRG. The lower CP and casein in GRG samples may be due to an inbalance in ruminal degradability between the highly fermentable fiber and N-sources, which are a typical metabolic condition in the case of early-stage grass intake 33. A similar metabolic process may occur in MCS cows as they too had lower values of both CP and casein compared to the maize silage-fed cows (HMS and MMS), although it may be also related to the higher FPCM milk yield for this group. As reported by Riuzzi, et al. 25, a lower lactose content was found in milk from GRG cows probably because of both their lowest recorded intake and the lower level of energetic concentrates, potentially leading to less ruminal propionate synthesis compared with other groups. Since proprionate is the main precursor of gluconeogenesis in ruminants, this can lead to a decrease in glucose and hence lactose 34.
All the 74 profiled FA are reported in a supplementary table and they had already been detected in similar studies 22. Table 3 includes only the FA that are abundant in milk and that were expected to be influenced by the four roughage sources considered in this study.
Major differences between the FG existed for the concentration of most nutritionally relevant FA, driven by the amount and type of forage in the diets. These often reached significance between GRG and HMS milk, although in some cases GRG milk also differed from MMS, MCS and/or HAY groups with no consistency in the pattern of variation seen for the individual FA. VA, CLA9, C20:5n-3 c-5, c-8, c-11, c-14, c-17 (EPA), total CLA concentrations were higher (p < 0.05) for GRG than HMS milk and concentrations of SFA and SCFA were lower (p < 0.05). These differences also reached significance in comparing GRG and MMS milk for CLA9, total CLA and SCFA whereas for CLA9, GRG milk was significantly higher than for all other groups except HAY and SFA concentrations were less than all groups except MMS. Differences were also significant in comparing GRG with MCS and HAY milk, where C16:0 (palmitic acid, PA) was lower and PUFA concentrations were higher in GRG milk. Other differences also existed when comparing HAY milk with the other groups; C18:2n-3 (linoleic acid, LA) had a tendancy (p = 0.066) to be lower than in GRG milk, and milk from the HAY group had more (p < 0.05) C18:3n-3 (a-linolenic acid, ALA) and total n-3 than HMS milk and the ratio n-6:n-3 was lower (p < 0.05).
One noticeable outcome from our study is a significant (p < 0.05) effect of hay and fresh-grass based diets on the concentrations of total CLA, CLA9 and its precursor VA in milk (especially if compared to HMS), in line with previous studies 10,11,19,35. Indeed, the use of a dairy cow feeding based on fresh grass as the main forage source led to significantly higher concentrations in VA, CLA9 and total CLA 36. The concentration of CLA in milk is enhanced thanks to intake of a polyphitic forage rich in LA and ALA as precursors, which undergo less extensive hydrogenation to the intermediate vaccenic acid rather than fully into C18:0 (stearic acid, SA). Both these hydrogenation products (VA and SA) are subsequently desaturated in the mammary gland secreting CLA9 and C18:1 c-9 (oleic acid, OA) respectively into milk 16,37. A study by Akbaridoust, et al. 15, confirmed that the partial replacement of lowland grass grazing with maize silage leads to a decrease of CLA9, as in the present study.
Both HAY and GRG forages in this study originated as polyphitic vegetation from permanent meadow also leading to high concentrations of ALA, EPA and n-3 compared with other diets, although differences between GRG and other groups do not always reach significance. Many strong linear relationaships between the content of PUFAn-3 and specific botanical families of lowland permanent meadow have been reported, such as those observed for the consumption of forage with an high proportion of Fabaceae (legume) and Ranunculaceae 10 compared to those with a prevalence of Poaceae such as Timothy and perennial ryegrass 16,20. Also Stergiadis, et al. 14, report high concentrations of n-3 in milk from cows grazing legume dominated pastures.
The use of maize silage as the dominant roughage source caused higher concentrations of SCFA (C4:0 and C6:0) and PA content, resulting in a higher amount of total SFA in milk along with a higher ratio of n-6:n-3. The elevated n-6:n-3 ratios found here (mostly driven by less n-3 rather than more n-6) is in common with other studies using maize silage 12,23. However not all findings here are in line with previous studies. With respect to C4:0 and C6:0, Yang, et al. 38, report maize silage diets lead to lower concentrations of these FA but another study by Coppa, et al. 21, highlighted a decrease of C4:0 with an increase of fresh grass in the cows’ diet. Moreover, this latter study observed a significant increase in the proportion of SCFA (from C8:0 to C12:0) with the increase of maize silage in the TMR. Other studies using maize silage in the diet resulted also in higher secretion of PA in milk 16,17,22. Short chain and some medium chain FA are mainly produced by de novo synthesis in the mammary gland, using acetate and butyrate from the ruminal fibrolytic bacteria activity, even if they can also derive from the diet, especially PA, which is reported to increase with the use of maize silage. With the exception of the HAY group, all cows in the study ate diets with a very similar in NFD content (an indication of digestible forages), varying by less than 1%age unit across groups. This might explain why SCFA were not lower with maize silage but does not explain the apparent slightly higher de novo synthesis compared with other forages.
Factorial Discriminant Analysis.
The main purpose of this study was to evaluate the influence of the dairy cow feeding system on the lipidic fingerprinting considering TMR based on five main roughage sources. Thus, a factorial discriminant analysis (FDA) was carried out using the 70 milk FA profiles to have an insight into the changes occurring due to the replacement of maize silage with a mix of ensiled, dried or fresh forages. The FDA resulted in two main significant functions (F1 and F2; Wilks’s λ = 0.002), which accounted for 59.0% and 20.1% of the total variance, respectively. The FDA results are based on 9 most significantly (p < 0.05) discriminative FA: C9:0, C10:0, C16:1 c-9 (for brevity, only C16:1 as in Table 3), C17:0, C17:1 c-9, C18:0 (SA), C18:3n-3 (ALA), C18:2 c-9, t-11 (CLA 9), C20:0. These FA also have a correlation coefficient in absolute value greater than 0.25 with at least one of the two main functions (F1 and F2). Figure 1 shows a scattergram of the FDA model based on F1 and F2 along with the 9 most discriminative FA. The FA that contributed the most to the separation among FG were found to be only partially in agreement with the significant differences outlined by the univariate statistical analysis; indeed, only ALA and CLA9 are shared with the multivariate targeted FDA. As reported in Figure 1, the GRG and HAY milk FA profiles clearly differ from those from the silage-based TMR and between each other, meanwhile there are considerable overlaps among HMS, MMS and MCS samples. HMS and MCS seemed to be similar and only partially overlapping with the MMS group. The analysis of Figure 1 confirmed that HAY-milk samples are correlated with ALA (C18:3 n-3 on the chart), which proves once more to be a specific strong biomarker of hay-based diets 39, with a minor contribution of the long chain SFA C20:0. GRG, instead, seemed to be characterized by higher contents of CLA9 and C17:0, even if these FA can also be used to discriminate the HAY samples. Both of them had already been identified as specific biomarkers of fresh grass-based milk by Butler, et al. 36, Stergiadis, et al. 14, and Paredes, et al. 19, respectively. As mentioned, the finding of CLA9 having discriminative capacity is due to the transformation of dietary LA and ALA, through the metabolic pathways in the rumen and mammary gland. As regards the odd chain FA C17:0, it derives largely from the ruminal microbial activity and its transfer into milk is reported to be enhanced in cows fed hays and fresh grass rich in C18 FA, such as the grass and legume species that were used to formulate the HAY and GRG diet in our study 19,40. Whereas, C16:1 can be indentified as a weak lipidic biomarker of MCS and HMS milk samples, even if it has only a minor discriminative capacity and is only slightly correlated with F1. As for C17:1 c-9, it appears to be associated with both MCS and GRG because of its spatial position along the positive F1 axis and negative ones of F2. Identifying the reasons for their feeding discriminative role is not an easy task. From the literature, C16:1 seems to indicate both the use of maize-based diets 22 and the adoption high concentrates diets 39. C17:1 c-9 was reported to be associated with the use of fresh grass by Coppa, et al. 22, as it is the results of the D9-desaturation of C17:0 in the mammary gland. The samples of the three FG receiving silages tended to have similar FDA loadings that make them spatially overlap into one cluster located in the left-centre of the scattergram, associated with C9:0, C10:0 and SA. However, MMS group is slightly separated from the other two groups (along the negative F2 axis) because of the influence of C10:0 and SA. Compared to rations based on a large inclusion of grass, feeding strategies involing silages, as highly nutritional forages, seemed to significantly increase the proportion of SFA, such as C10:0 23,39 and SA 15, due to a higher ruminal biohydrogenation rate.
The cross-validation used to assess the reliability of the FDA confirmed the accuracy of this supervised targeted model for a correct classification of milk from HAY and GRG groups (Matthews correlation coefficient values of 1.00), meanwhile there was a noticeable misclassification rate (7 out of 49) of the silage-based milk samples, especially between MCS and HMS (Table 4). However, if the silage samples were considered as one cluster, as suggested by the FDA, the predictive performances would be enhanced, thus proving the effective role of FA profile to trace the dairy products according to the adopted feeding system. Extending this approach to the large-scale distribution, it may be effective to consider a labelling system of dairy products based on at least the three feeding strategies investigated in the present study: ensiled (HMS, MMS, MCS) vs. dried (HAY) vs. fresh (GRG) forages; even if they are all produced in the same intensive lowland area. Indeed, milk FA profile can be a powerful, reliable and accurate metabolomics tool to discriminate among rations including high amounts of cereal-derived silages or a mix of grass and legume-derived hays, which affect the milk nutritional value (incidence of beneficial FA), contamination risk (i.e., presence of clostridium bacteria) and the degree of sustainability of the farm (ratio between input and output of human edible energy).
Prediction of milk FA composition with stepwise regression models based on the main dietary roughage source. T
able 5 reports the multiple linear regressions of the most predictive FA according to the four forage sources (maize silage, other silages, hays, fresh grass). The findings discussed in the previous sections are mostly confirmed by the predictive equations. Indeed, although the silages (both maize and others) slightly influence the individual FA, they significantly increased the total SFA and, consequently, reduced PUFA, especially ALA and CLA9. As regards dried forages, they are correlated positively with C17:0 and CLA9 and negatively with SA, resulting in higher levels of PUFA. Also feeding hays seems to play a role in the increase of SFA, even though their effect is weaker than with silages, especially the mixed-crop ones. Feeding fresh grass seems to mildly modify the FA profile, even if it too contributes to higher concentrations of two beneficial FA - C17:0 and CLA9. In the case of OCFA there was no significant predictive capacity by any of the roughage sources.
By looking at these results, it seems the main consequence of replacing maize or other crop silages with hay or fresh grass is the increase in PUFA that are beneficial for human health especially CLA and ALA. However, as shown in figure 2, the levels of ALA and total PUFA do vary a lot within milk from the HAY group, probably because of the variability of botanical composition and phenological stage at the time of harvesting across the investigated farms. Furthermore, the use of silages, especially from cereals other than maize, seem to increase the SFA content of milk, more than feeding hays do, as shown by their higher regression coefficients. Conversely to HAY and GRG groups, the silage-based diets are characterised by greater uniformity within their groups, as showcased in the boxplots of figure 2. The feeding system that has the greatest effect on milk FA composition is GRG, which leads to an increase in beneficial FA, such as CLA and C17:0, despite the highest presence of outliers within this group (Figure 2) probably due to the stronger impact of fresh grass on the ruminal activities.