Resource selection by Sarda cattle in a Mediterranean silvopastoral system.

Knowledge of how grazing cattle utilize heterogeneous landscapes in Mediterranean silvopastoral areas is scarce. Global positioning systems (GPS) to track animals, together with geographic information systems (GIS), can relate animal distribution to landscape features. With the aim to develop a general spatial model that provides accurate prediction of cattle resource selection patterns within a Mediterranean mountainous silvopastoral area, free-roaming Sarda cows were fitted with GPS collars to track their spatial behaviors. Resource selection function models (RSF) were developed to estimate the probability of resource use as a function of environmental variables. A set of over 500 candidate RSF models, composed of up to five environmental predictor variables, were fitted to data. To identify a final model providing a robust prediction of cattle resource selection pattern across the different seasons, the 10 best models (ranked on the basis of the AIC score) were fitted to seasonal data. Prediction performance of the models was evaluated with a Spearman correlation analysis using the GPS position data sets previously reserved for model validation. The final model emphasized that watering point, elevation, and distance to fences were important factors affecting cattle resource-selection patterns. The prediction performances (as Spearman rank correlation scores) of the final model, when fitted to each season, ranged between 0.7 and 0.94. The cows were more likely to select areas lower in elevation and farther from the watering point in winter than in summer (693 ± 1 m and 847 ± 13 m vs. 707 ± 1 m and 635 ± 21 m, respectively), and in spring opted for the areas furthest from the water (963 ± 12). Although caution should be exercised in generalizing to other silvopastoral areas, the satisfactory Spearman correlations scores from the final RSF model applied to different seasons indicate resource selection function is a powerful predictive model. The relative importance of the individual predictors within the model varied among the different seasons, demonstrating the RSF model's ability to interpret changes in animal behavior at different times of the year. The RSF model has proven to be a useful tool to interpret the spatial behaviors of cows grazing in Mediterranean silvopastoral areas and could therefore be helpful in managing and preserving ecosystem services of these areas.

Effect of glucose infusion on glucose and insulin metabolism in early- and mid-lactation ewes and goats fed diets differing in starch and highly digestible fiber concentration.

This study aimed to test possible metabolic differences between ewes and goats in response to an intravenous glucose infusion. Thirty-six animals, 18 mature Sarda dairy ewes and 18 mature Saanen goats (from 15 to 150 ± 11 d in milk [DIM], mean ± SD; body weight: 49.8 ± 6.8 kg for ewes, 60.6 ± 7.3 kg for goats) were compared simultaneously. In early lactation, both species received the same high-starch diet (HS: 20.4% starch, 35.4% neutral detergent fiber [NDF], on dry matter [DM] basis), whereas from 92 ± 11 DIM both species were randomly allocated to 2 dietary treatments: HS (20.0% starch, 36.7% NDF, on DM basis) and low-starch (LS: 7.8% starch, 48.8% NDF, on DM basis) diets. At 50 and 150 ± 11 DIM, ewes and goats were challenged with an intravenous glucose tolerance test and peripheral concentrations of glucose and insulin were determined 15 min before and 5, 10, 15, 30, 45, 90, and 180 min after glucose infusion. In early lactation, baseline plasma glucose and insulin concentrations tended to be higher in ewes than in goats (glucose: 55.8 vs. 42.9 ± 7.3 mg/dL; insulin: 0.13 vs. 0.05 ± 0.04 µg/L). After glucose infusion, glucose and insulin concentrations were higher in ewes than in goats (278.6 vs. 247.6 ± 13.1 mg/dL; 0.82 vs. 0.46 ± 0.12 µg/L). In mid-lactation, the dietary treatment (HS vs. LS) did not affect glucose and insulin metabolism. Baseline plasma glucose was numerically highest in ewes, while baseline insulin was higher in ewes than in goats (0.39 vs. 0.12 ± 0.099 µg/L). After glucose infusion, glucose concentration did not differ between ewes and goats, while insulin concentration was highest in ewes. Compared with goats, ewes showed in both periods a higher peak insulin, insulin increment, linear insulin area under the curve, insulin resistance index, and lower insulin sensitivity indices. In conclusion, despite the limitations associated with the use of intravenous glucose tolerance test to assess glucose regulation mechanisms, this study indicated large species differences in both early and mid-lactation and a more evident anabolic status in the ewes compared with the goats.

Does the timing of pasture allocation affect rumen and plasma metabolites and ghrelin, insulin and cortisol profile in dairy ewes?

A study was undertaken to assess the impact of the timing of grazing on rumen and plasma metabolites and some metabolic hormones in lactating dairy sheep allocated to an Italian ryegrass (Lolium multiflorum Lam) pasture in spring for 4 h/d. Twenty-four mid lactation Sarda ewes stratified for milk yield, body weight, and body condition score, were divided into four homogeneous groups randomly allocated to the treatments (2 replicate groups per treatment). Treatments were morning (AM, from 08:00 to 12:00) and afternoon pasture allocation (PM, from 15:30 to 19:30). Samples of rumen liquor (day 39) and blood plasma (days 17 and 34 of the experimental period) were collected before and after the grazing sessions. Moreover, on days 11 and 35, grazing time was assessed by direct observation and herbage intake measured by the double weighing procedure. Grazing time was longer in PM than AM ewes (P < 0.001) but herbage intake was undifferentiated between groups. The intake of water-soluble carbohydrates at pasture was higher in PM than AM ewes (P < 0.05). The post-grazing propionic and butyric acid concentration, as measured on day 39, were higher in PM than AM ewes (P < 0.05). The basal level of glucose on day 34 and insulin (on both sampling days) were higher in PM than AM (P < 0.05). The opposite trend was detected for non-esterified fatty acids (P < 0.05, day 34) and urea (both days). Pasture allocation in the afternoon rather than in the morning decreased plasma concentration of ghrelin (P < 0.001) and cortisol (P < 0.001), with a smoothed trend on day 34 in the latter variable. To conclude, postponing the pasture allocation to afternoon increased the intake of WSC, favoring a glucogenic pattern of rumen fermentation and a rise of glucose and insulin levels in blood, although these effects were not consistent across the whole experimental period. Moreover, the afternoon grazing decreased the level of cortisol and ghrelin, suggesting a higher satiation-relaxing effect.

Dairy Sheep Grazing Management and Pasture Botanical Composition Affect Milk Macro and Micro Components: A Methodological Approach to Assess the Main Managerial Factors at Farm Level.

The fatty acid profile, vitamins A and E, cholesterol, antioxidant power colour and the phenols profile of Sarda sheep milk from 11 commercial sheep flocks managed under permanent grassland were investigated. In each farm, the structural and managerial data and milk samples were collected during four periods (sampling dates, SD): January, March, May, and July. Data from the milk composition (fat, protein, casein, lactose, and somatic cell count), 68 fatty acids, 7 phenols, 1 total gallocatechin equivalent, ferric reducing antioxidant power, vitamins A and E, cholesterol, degree of antioxidant protection, and the colour (b *, a * and L *) were analyzed by multivariate factorial analysis using a principal component analysis approach. A proc mixed model for repeated measurement to point out the studied factors affecting significant macro and micro milk composition was also used. Only the first five components were detailed in this paper, with approximately 70% of the explained variance detected. PC1 presented the highest positive loadings for milk lactose, de novo FA synthesis and the BH intermediate, whereas OBCFA had negative loadings values. The PC2, LCFA, UFA, MUFA, vitamins E, and DAP showed positive loadings values, while SFA had a negative value. The PC3 showed a high positive loading for total phenols and non-flavonoids. PC4 presented a high positive loading for the milk macro-composition and negative values for n-3 FAs. The PC5 is characterized by high positive loadings for the a * and L * colour parameters whereas negative loadings were detected for the milk flavonoids content. These preliminary results could help to establish future threshold values for the biomarkers in milk sourced from grazing dairy sheep in natural, permanent pasture-based diets.

Spatial Distribution and Habitat Selection of Sarda Cattle in a Silvopastoral Mediterranean Area.

The beef livestock system in Sardinia is based on suckler cows, often belonging to autochthonous breeds, such as the Sarda breed, and they often graze silvopastoral areas. Besides beef meat, silvopastoral systems (SPSs) provide several Ecosystem Services (ESs), such as timber provision, harvested as wood, and watershed protection. Livestock distribution is a critical factor for the sustainable use of SPSs (e.g., to avoid uneven grazing patterns) and information on patterns of spatial use are required. A study was conducted to determine: (i) the spatial distribution and (ii) the habitat selection of Sarda cattle grazing in a Mediterranean silvopastoral area. Over different seasons, 12 free-roaming adult Sarda cows were fitted with Global Positioning System (GPS) Knight tracking collars to calculate an index mapping of the incidence of livestock in the landscape (LRI) and a preference index (PI) for different areas. Since the PI data were not normally distributed, the Aligned Rank Transform (ART) procedure was used for the analysis. LRI was able to represent the spatial variability in resource utilization by livestock as a LRI map. Overall, the areas where the animals drank and received supplementation were strongly preferred by the cows, reaching PI values in the summer of 19.3 ± 4.9 (median ± interquartile range), whereas areas with predominantly rocks were strongly avoided (the worst PI value in the spring was 0.2 ± 0.6). Grasslands were, in general, used in proportion to their presence in the area, with slightly increased use in the spring (PI 1.1 ± 0.5). Forest area was avoided by cows, except in the spring when it was used in proportion to their presence in the area.

Pre- and Post-Slaughter Methodologies to Estimate Body Fat Reserves in Lactating Saanen Goats.

This work aimed to compare pre- and post-slaughter methodologies to estimate body fat reserves in dairy goats. Twenty-six lactating Saanen goats ranging from 43.6 to 69.4 kg of body weight (BW) and from 1.84 to 2.96 of body condition score (BCS; 0-5 range) were used. Fifteen pre-slaughter and four post-slaughter measurement values were used to estimate the weight of fat in the omental (OM), mesenteric (MES), perirenal (PR), organ (ORG), carcass (CARC), and non-carcass components (NC) and total (TOT, calculated as the sum of CARC and NC) depots in goats. The pre-slaughter measurements were withers height; rump height; rump length; pelvis width; chest depth; shoulder width; heart girth; body length; sternum height; BW; BCS assessed in the lumbar (BCSl) and sternal (BCSs) regions; and fat thickness measured by ultrasound in the lumbar (FTUSl), sternal (FTUSs), and perirenal (FTUSpr) regions. The post-slaughter measurements were hot carcass weight (HCW), empty body weight (EBW), and fat thickness measured by digital caliper in the lumbar (FTDCl) and sternal (FTDCs) regions. Linear and multiple regressions were fit to data collected. BW, BCS (from lumbar and sternal regions), all somatic measurements, and fat thickness measured by ultrasound in the lumbar and sternal regions were not adequate to estimate the weight of total fat in lactating Saanen goats (R2 ≤ 0.55). The best pre-slaughter and post-slaughter estimators of OM, MES, PR, ORG, NC, and TOT fat were FTUSpr and EBW, respectively. Among pre- and post-slaughter measurements, BCSl (R2 = 0.63) and HCW (R2 = 0.82) provided the most accurate predictions of CARC fat, respectively. Multiple regression using the pre-slaughter variables FTUSpr, BW, and BCSl yielded estimates of TOT fat with an R2 = 0.92 (RSD = 1.14 kg). On the other hand, TOT fat predicted using the post-slaughter variables HCW and FTDCs had an R2 = 0.83 (RSD = 1.41 kg). These results confirm that fat reserves can be predicted in lactating Saanen goats with high precision using multiple regression equations combining in vivo measurements.

Can FT-Mid-Infrared Spectroscopy of Milk Samples Discriminate Different Dietary Regimens of Sheep Grazing With Restricted Access Time?

Milk obtained from sheep grazing natural pastures and some forage crops may be worth a plus value as compared to milk obtained from stall-fed sheep, due to their apparently higher content of beneficial fatty acids (FAs). Fourier transformed mid-infrared (FT-MIR) analysis of FA can help distinguish milk from different areas and diverse feeding systems. The objective was to discriminate milk from sheep and milk from dairy sheep rotationally grazing Italian ryegrass or berseem clover for 2, 4, or 6 h/day. To test this hypothesis, a data-mining study was undertaken using a database of 1,230 individual milk spectra. Data were elaborated by principal component analysis (PCA) and analyzed by linear discriminant analysis (LDA) with or without the use of genetic algorithm (GA) as a variable selection tool with the primary aim to discriminate grazed forages (grass vs. legume), access time (2, 4, or 6 h/day), grazing day (first vs. last grazing day during the 7-day grazing period), and the milking time (morning vs. afternoon milking). The best-fitting discriminant models of FT-MIR spectra were able to correctly predict 100% of the samples differing for the pasture forage, 91.9% of the samples differing for grazing day, and 97.1% of the samples regarding their milking time. The access time (AT) to pasture was correctly predicted by the model in 60.3% of the samples, and the classification ability was improved to 77.0% when considering only the 2 and 6 h/day classes.

Dietary Starch Concentration Affects Dairy Sheep and Goat Performances Differently during Mid-Lactation.

Evolution of milk production, body reserves and blood metabolites and their relationships with dietary carbohydrates were compared in 30 Sarda dairy ewes and 26 Saanen dairy goats in mid-lactation. From 92 to 152 ± 11 days in milk (DIM), each species was allocated to two dietary treatments: high-starch (HS: 20.0% starch, on DM basis) and low-starch (LS: 7.8% starch, on DM basis) diets. In mid-lactating goats, the HS diet increased fat-corrected milk yield (FCM (3.5%); 2.65 vs. 2.53 kg/d; p = 0.019) and daily milk net energy (NEL; p = 0.025), compared to the LS diet. The body condition score (BCS) was not affected. In mid-lactating ewes, the LS diet increased FCM (6.5%) (1.47 vs. 1.36 kg/d; p = 0.008), and NEL (p = 0.008), compared to the HS diet. In addition, BCS was greater in HS than in LS ewes (3.53 vs. 3.38; p = 0.008). Goats had a higher growth hormone (GH) and lower insulin concentration than ewes (GH: 2.62 vs. 1.37 ng/mL; p = 0.04; insulin: 0.14 vs. 0.38 µg/L; p < 0.001 in goats and ewes, respectively). In conclusion, in mid-lactation, the two species responded differently to dietary carbohydrates, probably due to differences in the concentration of GH and insulin. The HS diet favored milk yield in goats and body reserve accumulation in ewes. In ewes, the partial replacement of starch with highly digestible fiber increased energy partitioning in favor of milk production.

A Note on the Tracing of Herbage Contribution to Grazing Sheep Diet Using Milk and Feces Biomarkers.

Milk from grazing ruminants is usually rich in beneficial components for human health, but distinguishing milks sourced from grazing is difficult, and this hinders the valuing of the grazing benefit. This study aimed at evaluating the ability of milk biomarkers (1) to trace milks sourced from sheep submitted to different access times (ATs) to pasture and (2) to estimate sheep herbage dry matter intake (HDMI, g DM ewe-1 d-1) and herbage percentage (HP, % DM) in sheep diet. Animal data derive from a published experiment in which six replicated groups of mid-lactation Sarda sheep had ATs of 2, 4, or 6 h d-1 to a ryegrass pasture. Sheep HDMI and HP of each group were measured on four dates in April 2013. Group milk was sampled, and milk fatty acids (FAs) and n-alkanes were determined by gas chromatography. The latter markers were also measured in feces samples bulked by group. The data (N = 24 records) were submitted to Linear Discriminant Analysis (LDA) aimed at distinguishing the AT to pasture based on biomarkers previously selected by Genetic Algorithms (GA). Partial Least Square Regression (PLSR) models were used to estimate HDMI and HP using biomarkers selected by GA. Based on one milk alkane and six milk FAs as biomarkers, estimates of the AT using GA-LDA were 95.8% accurate. The estimation of HDMI by GA-PLSR based on five milk FAs was moderately precise [explained variance = 75.2%; percentage of the residual mean square error of cross-validation over the mean value (RMSECV%) = 15.0%]. The estimation of HP by GA-PLSR based on 1 milk alkane and 10 FAs was precise (explained variance = 80.8%; RMSECV% = 7.4%). To conclude, these preliminary results suggest that milks sourced from sheep flocks with AT to pasture differentiated by 2 h in the range 2-6 h d-1 can be precisely discriminated using milk biomarkers. The contribution of herbage to sheep diet can also be precisely estimated.

Milk Urea Concentration in Dairy Sheep: Accounting for Dietary Energy Concentration.

In dairy sheep milk urea concentration (MUC) is highly and positively correlated with dietary crude protein (CP) content and, to a lesser extent, with protein intake. However, the effect of dietary energy and carbohydrate sources on MUC of lactating ewes is not clear. Thus, the objective of this study was to assess the effects of diets differing in energy concentration and carbohydrate sources on MUC values in lactating dairy ewes. Two experiments were conducted (experiment 1, E1, and experiment 2, E2) on Sarda ewes in mid and late lactation kept in metabolic cages for 23 d. In both experiments, homogeneous groups of five ewes were submitted to four (in E1) or three (in E2) dietary treatments, consisting of pelleted diets ranging from low energy (high-fiber diets: 1.2-1.4 Mcal of net energy for lactation (NEL)) to high energy (high-starch diets: 1.7-1.9 Mcal of NEL) contents, but with a similar CP concentration (18.4% dry matter (DM), on average). Each diet had a different main ingredient as follows: corn flakes, barley meal, beet pulp, or corn cobs in E1 and corn meal, dehydrated alfalfa, or soybean hulls in E2. Regression analysis using treatment means from both experiments showed that the best predictor of MUC (mg/100 mL) was the dietary NEL (Mcal/kg DM, MUC = 127.6 - 51.2 × NEL, R2 = 0.85, root of the mean squared error (rmse) = 4.36, p < 0.001) followed by the ratio CP/NEL (g/Mcal, MUC = -14.9 + 0.5 × CP/ NEL, R2 = 0.83, rmse = 4.63, p < 0.001). A meta-regression of an extended database on stall-fed dairy ewes, including the E1 and E2 experimental data (n = 44), confirmed the predictive value of the CP/ NEL ratio, which resulted as the best single predictor of MUC (MUC = -13.7 + 0.5 × CP/NEL, R2 = 0.93, rmse = 3.30, p < 0.001), followed by dietary CP concentration (MUC = -20.7 + 3.7 × CP, R2 = 0.82, rmse = 4.89, p < 0.001). This research highlights that dietary energy content plays a pivotal role in modulating the relationship between MUC and dietary CP concentration in dairy sheep.

Extensive Ruminant Production Systems and Milk Quality with Emphasis on Unsaturated Fatty Acids, Volatile Compounds, Antioxidant Protection Degree and Phenol Content.

Dairy products from grazing ruminant have numerous positive effects on human health thanks to their higher content essential fatty acids, vitamins, and polyphenols. Compared to livestock fed a conventional maize silage- and/or grain-based diet, grass-fed livestock produce milk with higher levels of n-3 fatty acids, vitamins A, E, carotenoids, and phenols. The effect is even more pronounced if animals are grazing on legume/forbs-rich grasslands. This review argues, based on the available literature, about the effect of grazing ruminant on milk and cheese quality, including the hedonistic aspects, pointing out the link between territory and dairy products quality (Protected Designation Origin; Protected Geografic Origin; namely PDO and PGI labels). Moreover, it points out the main plant biomarkers which can be used to discriminate grazing sourced from stall-fed sourced milk and dairy products. Overall milk and cheese sourced from grazing animals (cows, sheep and goat) showed higher levels (compared to stall system) of FA, vitamins, phenols, putatively beneficial for consumers' health. FA and plant secondary metabolites can also affect flavor and some nutritional and technological features of dairy products such as their antioxidant protection degree. This would favour a fair pricing of dairy products sourced from grazing systems and the persistence of viable and sustainable extensive production systems.

The influence of plant polyphenols on lipolysis and biohydrogenation in dried forages at different phenological stages: in vitro study.

BACKGROUND: It is known that forage legumes show a higher transfer efficiency of polyunsaturated fatty acids (PUFA) to ruminant dairy products in comparison with grasses. Legumes are usually characterised by moderate levels of plant secondary metabolites, which can have an effect on lipolysis and biohydrogenation in the rumen. An in vitro study was carried out to compare two species with different plant phenol compositions, Vicia sativa (VS, common vetch, cv. Jose) and Trifolium incarnatum (TI, crimson clover, cv. Viterbo) cut at the vegetative (Veg) and reproductive (Rep) stages, on lipolysis and PUFA biohydrogenation in the rumen. RESULTS: The study showed that forage species and phenological stage affected the levels of bound phenols (BP) and tannic polyphenols (TP). VS was characterised by a higher level of TP than TI at both Veg and Rep stages, whereas BP levels were low in both forages. BP and TP had a negative effect on lipolysis and biohydrogenation, but TP showed a greater negative correlation than BP for both forages. CONCLUSION: These results showed that lipolysis and biohydrogenation of PUFA could be affected by plant phenols, particularly TP.

Determination of poly(ethylene glycol)-binding to browse foliage, as an assay of tannin, by near-infrared reflectance spectroscopy.

Nutritionists are interested in functional assays of tannins that do not require time-consuming and expensive extraction, such as the (14)C-labeled poly(ethylene glycol) (PEG)-binding (PEG-b) assay. This paper reports the application of near-infrared (NIR) spectroscopy to determine the percentage of PEG binding, in place of the (14)C-labeled PEG-b assay of tannin, in Mediterranean woodland vegetation. Calibration was done with 53 samples from 14 species and was validated on 25 samples from 10 species. PEG-b ranged between 1.4 and 20.7% in the samples. The calibration obtained by using the modified partial least-squares (MPLS) method, with all wavelengths in the 1100-2500 nm range combined, and the validation were reasonably linear (R (2) = 0.96 and 0.91, respectively). The accuracies, estimated from the standard errors of cross-validation and prediction, were +/-1.6 and +/-1.7% PEG-b, respectively. The NIRS-aided procedure proposed here can serve as an accurate, inexpensive, time-saving, and environment-friendly functional assay of tannin in Mediterranean browse.