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Article

Dietary White Grape Pomace Silage for Goats: Assessing the Impact of Inclusion Level on Milk Processing Attributes

by
Marina Galvez-Lopez
1,
Manuel Viuda-Martos
1,
Jordi Saldo
2,
Esther Sendra
1,
Gema Romero
1,* and
José Ramón Díaz
1
1
Institute of Agri-Food and Agro-Environmental Research (CIAGRO-UMH), Miguel Hernández University of Elche, Ctra. De Beniel, km 3.2, 03312 Orihuela, Spain
2
Centre de Innovació, Recerca i Transferència en Tecnologia dels Aliments (CIRTTA), MALTA Consolider Team, Animal and Food Science Department, Facultat de Veterinària, Universitat Autònoma de Barcelona, 08193 Bellaterra, Spain
*
Author to whom correspondence should be addressed.
Appl. Sci. 2025, 15(23), 12791; https://doi.org/10.3390/app152312791
Submission received: 30 October 2025 / Revised: 20 November 2025 / Accepted: 29 November 2025 / Published: 3 December 2025
(This article belongs to the Special Issue Recent Applications of Plant Extracts in the Food Industry)
Versions Notes

Abstract

Grape pomace is the principal by-product of the winemaking industry, with an estimated global production of 14 million tonnes annually. Traditional livestock systems often incorporate local agroindustrial by-products into ruminant diets, and grape pomace is particularly notable for its high concentrations of bioactive compounds. These grape-derived molecules may exert beneficial effects on animal oxidative balance, biochemical status and productive performance, offering an environmentally and economically sustainable alternative to conventional feed ingredients that may be incorporated into the milk produced. This study evaluated the impact of incorporating varying inclusion levels (0, 5, 10 and 15% DM) of ensiled white grape pomace (WGP) into isoenergetic and isoproteic diets on the nutritional and technological characteristics of goat milk. Eighty-eight Murciano-Granadina dairy goats were selected and allocated into eight homogeneous batches (n = 11 per batch) based on physiological traits. Following a pre-experimental sampling, each diet was randomly assigned to two batches, and the feeding trial lasted eight weeks. After a two-week dietary adaptation period, four biweekly samplings were conducted to obtain representative bulk tank milk samples from each batch. Milk samples were analysed for gross composition, pH, mineral profile, fatty acid composition, coagulation properties, colorimetric parameters and antioxidant capacity. WGP consumption significantly increased milk fat content, improved the lipid profile from a human health perspective, accelerated curd aggregation and elevated the yellowness index. Moreover, notable changes were observed in the antioxidant activity of the milk. Despite these effects, the overall composition of the milk remained largely unchanged, which is a key factor in preserving its technological properties. Nevertheless, the final product demonstrated enhanced biological quality, reinforcing its value as a functional food for human consumption.

1. Introduction

The global food production and processing industry is undergoing continuous expansion to meet the demands of a growing population and the evolving dietary habits of consumers [1,2]. Given that a substantial portion of livestock production relies on cultivated cereals, legumes and forages, the sector is facing a marked decline in competitiveness [2]. Concurrently, from harvest to final product transformation, an estimated 1.3 billion tonnes of food are wasted annually, with fruits and vegetables accounting for approximately 60% of this loss [3]. The strategic use of agroindustrial by-products and alternative forages could provide locally sourced feed for livestock, thereby reducing dependence on imported feedstuffs. Moreover, their incorporation into animal diets contributes to minimizing waste generated by the food industry, lowering disposal costs and decreasing the land and other resources allocated to feed crop production—thus supporting the principles of a circular economy. However, the pronounced seasonality of harvests limits the year-round availability of these feed resources and their high moisture content results in a short shelf life. Previous studies have demonstrated that ensiling such by-products under controlled fermentative conditions effectively extends their preservation while maintaining the nutritional and hygienic standards required [4,5,6] for their inclusion in small ruminant diets [7,8,9].
Changes in milk composition—particularly in fat, protein and mineral content—can significantly influence its processing attributes and technological performance. For instance, variations in casein fractions and calcium levels affect coagulation kinetics and curd firmness, which are critical for cheese-making [10,11]. Similarly, shifts in fat content and fatty acid profile may alter heat stability and homogenization behaviour, impacting the quality of pasteurized and fermented products [12,13]. Therefore, compositional changes resulting from dietary interventions can have meaningful implications for the industrial suitability and nutritional value of milk [14,15]. Moreover, recent evidence indicates that, when diets are properly formulated to meet nutritional requirements, the inclusion of agroindustrial by-products neither compromises animal performance nor impairs product quality [16,17,18], and may even confer economic benefits [19,20].
An additional advantage of utilizing agroindustrial by-products is their frequent richness in bioactive compounds, whose intake has been associated with functional properties that benefit animal physiology and enhance the biological quality of their products [1]. One of the most significant by-products generated globally—estimated at 14 million tonnes annually—is grape pomace, a residue from grape processing for winemaking [21,22]. This by-product has attracted considerable interest in animal nutrition, as many of the grape’s bioactive compounds remain in the pomace and are not transferred to the wine [23]. Grape pomace is a rich source of phytochemicals (including phenolics, flavonoids and tocopherols), antioxidants and polyunsaturated fatty acids [24,25,26,27]. Ruminants can tolerate higher inclusion levels of fibrous material and poorly digestible bioactive compounds—characteristic of plant-based by-products—than monogastric animals [28,29,30]. For this reason, ruminant livestock exhibits a remarkable ability to produce high-quality food from feedstuffs of limited biological value for human consumption.
Goat milk, characterized by its high total solids content and regarded as a complete and highly nutritious food, has gained recognition in developed countries as a functional food due to its multiple nutritional benefits, hypoallergenic properties and positive health effects—factors that have driven a sustained increase in its demand [31,32,33]. Currently, fresh goat milk ranks third in global consumption after cow and buffalo milk [34]. Europe accounts for 16% of global goat milk production, with Spain being the second-largest European producer, contributing 22% of the total (535.7 thousand tonnes) [35]. Although references in the literature regarding the effects of feeding silages derived from these by-products to sheep on milk quality, composition and animal health are scarce, they suggest the suitability of such practices for this purpose [36,37,38,39]. However, studies evaluating the effects of agroindustrial by-product consumption on the nutritional and technological properties of goat milk remain inconclusive and appear to be dose dependent. Muelas et al. [40] reported no significant effects on the chemical composition or technological characteristics of milk from Murciano-Granadina goats fed up to 25% DM of artichoke by-products (plant and outer bracts). The same study noted a change in milk colorimetric parameters at this inclusion level, whereas a lower dose (12.5% DM) did not elicit this effect. Arco-Pérez et al. [41] evaluated the inclusion of 20% DM of ensiled tomato by-product and 20% DM of ensiled olive by-product, observing no impact on milk yield or gross composition in the same breed, although both by-products altered the fatty acid profile. Specifically, milk from goats fed these by-products showed increased levels of C18:1 trans-11, C20:2 and conjugated linoleic acid (CLA). However, prolonged consumption of ensiled tomato by-product increased saturated fatty acids (SFAs) while reducing monounsaturated fatty acids (MUFAs) and CLA. Monllor et al. [8] found that feeding 25% DM of ensiled artichoke plant improved the lipid and mineral profile of goat milk, and that a 40% DM inclusion of ensiled artichoke by-product increased polyunsaturated fatty acid (PUFA) and omega-6 fatty acid (n-6) content while reducing atherogenic (IA) and thrombogenic indices (TIs) [42]. In a full-lactation study, the same authors reported that feeding 40% DM of ensiled artichoke plant reduced the n-6/n-3 ratio in milk, whereas the same inclusion level of ensiled broccoli by-product appeared excessive, leading to selective feeding behaviour and reduced intake [43]. A concentrate mixture containing corn dried distillers’ grains, dried citrus pulp and exhausted olive cake successfully replaced 44% of cereal grains in the concentrate for dairy goats, resulting in higher milk fat and protein yields and a more unsaturated milk fatty acid profile [44]. Huanca et al. [45] reported that milk and cheese from goats fed 19% DM of lemon tree leaves had higher fat and DM content, while medium-chain fatty acids and total free fatty acids were lower compared to a control diet.
Against this background, the present study aimed to evaluate the effects of including different proportions of white grape pomace (WGP) silage in the diets of lactating dairy goats on milk quality traits. Specifically, we assessed gross composition (including somatic cell count), mineral and fatty acid profiles, milk coagulation properties, colorimetric parameters and antioxidant activity. The ultimate goal was to identify the optimal inclusion level of WGP silage based on the nutritional and technological characteristics of the milk.

2. Materials and Methods

2.1. Animals and Facilities

The research was conducted using a dairy goat herd located at the teaching and experimental facilities of Miguel Hernández University in Spain. The trial extended from the end of April—when preliminary sampling was performed—until the close of June. This farm houses a herd of Murciano-Granadina goats, kept in straw-bedded pens under a free-stall housing system. Each goat was provided with 1.5 m2 of individual space, 35 cm of linear access to the feeding area and unrestricted access to fresh water. Feeding was scheduled twice daily, at 9:00 a.m. and 3:00 p.m., and goats were milked once a day (Casse milking parlour, 2 × 12 × 12, GEA, Bönen, Germany), in accordance with standard practices in the region. This study was approved by the Office of Responsible Research at Miguel Hernández University (UMH.DTA.JDS.04.22).

2.2. Experimental Design

From an initial group of 120 goats at the five weeks postpartum stage of lactation, and fed a conventional diet, 88 animals were selected based on parity, body weight (37.70 ± 5.48 kg), milk yield (2.89 ± 0.84 kg/day) and somatic cell count (LSCC, 2.96 ± 3.30 103 cells/mL). The animals were distributed into 8 homogeneous batches (11 goats/batch), ensuring balance across the selection criteria. After a pre-experimental sampling, every diet was randomly assigned to 2 batches: Control (conventional diet without by-products), 5_WGP (conventional diet including 5% WGP silage on a dry matter basis), 10_WGP (diet including 10% DM of WGP silage) and 15_WGP (diet including 15% DM of WGP silage). Following a two-week dietary adaptation period, four biweekly samplings were conducted to obtain representative bulk tank milk samples from each dietary group. Bulk tank milk was maintained at 4 °C under continuous agitation during sampling to ensure homogeneity. A representative volume was collected and immediately divided into uniform aliquots. Gross composition, somatic cell count (SCC), milk coagulation properties, pH and colorimetric parameters were determined immediately. The remaining aliquots were stored at −20 °C until analysis of mineral content, fatty acid profile and antioxidant capacity.
Milk sampling was carried out sequentially as lactation progressed across the scheduled sampling weeks. To disentangle the effects of lactation stage from those of dietary treatment, two complementary strategies were implemented: a control group receiving the conventional diet was maintained consistently throughout the trial, and the statistical interaction between sampling week and dietary treatment was explicitly tested. This dual approach enabled the identification of stage-dependent dietary effects while minimizing potential confounding associated with the temporal structure of the experimental design. The diets were the same as those previously used by Galvez-Lopez et al. [9]. Briefly, all diets were formulated according to the recommendations of Fernández et al. [46] for goats producing 2.5 kg of milk/day, ensuring isoenergetic and isoproteic rations tailored to their production level. Animals were fed fixed amounts twice daily, with no ad libitum access. Details regarding the ingredients, chemical composition and daily feed offered (Table 1) are presented below. The WGP silage used in the study was sourced from the quality and stability trials of this agroindustrial by-product conducted by Galvez-Lopez et al. [5].

2.3. Variables Analysed

The composition of the diets (Table 1) was determined according to AOAC (1999) methods [48]: dry matter (DM, g/kg; method 930.5), organic matter (OM, g/kg DM; method 942.05), ether extract (EE, g/kg DM; method 920.39), crude protein (CP, g/kg DM; method 984.13) and total sugars (g/kg DM; method 974.06). Neutral detergent fibre (NDF, g/kg DM), acid detergent fibre (ADF, g/kg DM) and acid detergent lignin (ADL, g/kg DM) were analysed according to Van Soest et al. [49]. Starch content was determined using the polarimetric method of Ewers [50]. Total polyphenol content (PT, g GAE eq./kg DM) was analysed using the Folin–Ciocalteu method as described by Kim et al. [51]. The mineral content in the diets was determined using an Inductively Coupled Plasma Mass Spectrometer (ICP-MS; Agilent 7700x, Santa Clara, CA, USA) following acid digestion of the samples according to González Arrojo et al. [52]. The lipid profile determined by the separation of isomers of PUFAs was performed through direct methylation of the lyophilised sample, without prior fat extraction, according to Kramer et al. [53]. Identification of the methyl esters of fatty acids (FAMEs) was carried out using Shimadzu GC-2030 coupled with a flame ionization detector (FID) and an automatic injector AOC-20i (Shimadzu, Kyoto, Japan) equipped with a capillary column (CP Sil 88 100 m × 0.25 mm, 0.20 µm particle size, Agilent, Santa Clara, CA, USA). A mix of FAMEs (18912-1AMP; Sigma-Aldrich, Saint Louis, MO, USA) was used as a standard for identifying the peaks in the fatty acid profile of the sample. Results were calculated as a percentage of each fatty acid in total fatty acids profile.
Gross composition of bulk tank milk—including fat, CP, lactose, useful DM (UDM), non-fat dry matter (NFDM) and milk urea—was analysed using mid-infrared spectroscopy (CombiFossTM 7 DC, Foss, Hillerød, Denmark). Moreover, somatic cell count (LSCC, 103 cells/mL) was determined via flow cytometry (CombiFossTM 7 DC, Foss, Hillerød, Denmark). Total cholesterol content was determined following saponification and methylation of milk samples. Quantification was performed using high-performance liquid chromatography (HPLC 1200, Agilent Technologies, Santa Clara, CA, USA) equipped with a diode array detector (DAD) set at 208 nm. The separation was carried out under isocratic conditions using a mobile phase of 2-propanol:hexane (2:98, v/v) at a flow rate of 1 mL/min, according to the method described by Domínguez et al. [54].
The mineral content in the diets was determined following acid digestion of the samples according to González Arrojo et al. [52]. Quantification was performed using an Inductively Coupled Plasma Mass Spectrometer (ICP-MS; Agilent 7700x, Santa Clara, CA, USA) equipped with an Octopole Reaction System (ORS). To account for potential physical and matrix interferences, an internal standard was employed throughout the analysis.
The milk fatty acid profile was determined following the fat extraction method described by Romeu-Nadal et al. [55] and fatty acid methylation was performed according to Nudda et al. [56]. Fatty acid methyl esters (FAMEs) were separated using the same equipment and column previously described for the diet’s fatty acid profile. The fatty acids were individually identified by comparison with the relative retention times of a standard mix of external standards (37FAME mix, Supelco, Bellefonte, PA, USA). Results were calculated as percentage of each fatty acid in the total fatty acids profile. Nutritional and health-related indices of fatty acids were calculated following the methodology proposed by Chen and Liu [57].
To determine the milk coagulation properties, the samples were pre-conditioned at 32 °C for 15 min in a water bath. Recombinant chymosin (Larbus S.A., Madrid, Spain) was diluted 1:100 in distilled water. Ten mL of milk was placed in each well of the Optigraph (Ysebaert, Frépillon, Francia) and 100 µL of the diluted chymosin was added to start the test at 32 °C and follow the changes of optical properties for 50 min. Accordingly, the variables rennet coagulation time (RCT, min), aggregation rate (min) and curd firmness (mm) were determined using the Optigraph system under controlled conditions. pH measurements were performed using a pH meter (Model pH/Ion 510, Eutech Instruments Pte Ltd., Singapore).
Colour evaluation was performed using the CIE 1976 L*a*b* (CIELab) coordinate system with a spectrophotocolorimeter (CM-700, Minolta Camera Co., Osaka, Japan), set to a 10° standard observer angle and D65 illuminant under SCI (Specular Component Included) mode. To minimize measurement interference, a low-reflectance glass cube (Minolta CR-A51/1829-752; Konica Minolta, Tokyo, Japan) was used as a measuring instrument. The measured CIELab parameters included lightness (L*), red/green coordinate (a*) and yellow/blue coordinate (b*). Based on these values, the psychophysical attributes hue angle (h*) (Equation (1)) and chroma (C*) (Equation (2)) were calculated using the following equations [58]:
h* = tan−1 (b*/a*)
C* = √(a*2 + b*2)
Moreover, whiteness (WI) (Equation (3)) and yellowness (YI) (Equation (4)) indices were determined according to the following equations [59]:
WI = 100 − √ [(100 − L*)2 + a*2 + b*2]
YI = 142.86 × (b*/L*)
To evaluate the antioxidant activity, ABTS scavenging and FRAP reducing power were determined. For the ABTS analysis (reduction of the 2,2’-azino-bis(3-ethylbenzothiazoline-6-sulfonate) radical), the method described by Leite et al. [60] was followed. The FRAP analysis (Ferric Reducing Antioxidant Power, i.e., reduction of ferric ion to ferrous state) was conducted according to the protocol by Oyaizu (1986) [61]. The results of these reactions were measured by spectrophotometry using a UV–visible spectrophotometer (Zuzi 4255/50, Auxilab, Arlegui, Navarra, Spain) at wavelengths of 734 and 593 nm for ABTS and FRAP, respectively. The antioxidant capacity value of the sample was expressed in mg Trolox eq/100 mL of milk.

2.4. Statistical Analysis

The SCC values were transformed into base ten logarithms for statistical analysis.
The statistical evaluation of the measured variables was performed using a general linear model (PROC GLM, SAS v9.4, 2022), considering the fixed effects of diet, sampling time and their interaction, according to the following Equation (5):
Y = μ + Di; + Tk + (Di × Tk) + e
where Y is the dependent variable, μ is the intercept, Di represents the fixed effect of diet (i = Control, 5-WGP, 10-WGP, 15-WGP), Tk is the fixed effect of sampling (k = 0, 1, 2, 3, 4), Di × Tk represents the interaction between both effects (8 levels) and e is the residual error.
Least squares means were calculated to interpret differences between levels of the fixed effects. The null hypothesis was rejected at p < 0.05, indicating statistically significant differences between levels.

3. Results

3.1. Gross Composition

The inclusion of white grape pomace (WGP) in the diet significantly influenced fat and CP content, NFDM, UDM and milk urea, as can be observed in Table 2. Fat content increased progressively with higher levels of WGP, reaching its peak in the WGP_15 milk (4.422%), which was significantly higher than both the control (4.061%) and the other WGP milks (p = 0.001). Similarly, CP content showed a positive trend, with WGP_5 exhibiting the highest value (3.395%), though differences with the other milks were not considered relevant. The NFDM content was significantly greater (p < 0.0024) in milk from the control and WGP_5 groups (8.8%) than in milk from WGP_10 and WGP_15 groups (8.6%). The UDM increased significantly in WGP_15 (7.716%) compared to the control (7.443%), indicating an overall enhancement in milk solids (p < 0.0001). Milk urea was also influenced by dietary treatment, with WGP_15 milk showing the lowest concentration (608.400 mg/L). All WGP milks presented significantly lower urea levels than control milk (638.600 mg/L) (p = 0.0008), although no consistent dose-dependent pattern was observed. Lactose, cholesterol content and LSCC remained unaffected across treatments (p > 0.05).

3.2. Mineral Profile

Regarding the mineral profile, only minor differences were found in the K milk content (Table 3), which was higher in WGP_10 (0.163 g/100 g) and WGP_15 (0.153 g/100 g) (p = 0.0065). The other minerals analysed did not show significant differences between WGP silage levels.

3.3. Fatty Acids Profile

The major milk fatty acids quantified in this experiment are listed in Table 4. C12:0, C:14, C16:0, C18:3n3, SFA, IA, TI, HFA, OLESTE and CLAVACC significantly decreased (p < 0.0001) as the proportion of WGP in the diet increased. Conversely, MUFA, n6/n3, LA/ALA, UI, HH and HPI significantly increased (p < 0.0001) with higher dietary inclusion of WGP. C18:2n6 (p < 0.0001) and PUFA (p = 0.0265) were consistently higher in WGP milks compared to the control. Moreover, C4:0, C18:0, C18:1c9 and C18:1t11 were significantly higher (p < 0.0001) in WGP_15 milk, while C16:1c9 (p < 0.0001) and C24:0 (p = 0.0195) were significantly lower in WGP_15 milk.

3.4. Milk Coagulation Properties

As shown in Table 5, the aggregation rate was significantly lower in WGP_10 (1.359 mm/min) and WGP_15 (1.383 mm/min), without significant differences to the control (1.404 mm/min), while WGP_5 exhibited the highest aggregation rate (p = 0.0127). No significant differences (p < 0.05) were obtained between dietary treatments in rennet coagulation time nor in curd firmness.
Table 5 also presents milk pH values, with control milk showing the highest pH (6.749), and WGP_5 milk presenting the lowest pH values (6.697) (p < 0.0001).

3.5. Colorimetric Parameters

Colorimetric parameters are presented in Table 6. The inclusion of WGP in the diet significantly affected milk colour, as reflected in the a, b, h*, C* coordinates and YI. Values for a*, b* and C* decreased significantly in all WGP milks compared to the control (p < 0.0001), while h* was significantly higher in all WGP treatments (p < 0.0001). The YI showed a dose-dependent response, decreasing significantly with increasing dietary WGP levels (p < 0.0001). Control milk exhibited the highest YI compared to all WGP milks (p < 0.0001).

3.6. Antioxidant Activity

The ABTS radical scavenging potential was significantly higher (p < 0.0001) in WGP_10 milk (6.557 mg Trolox eq/100 mL) and WGP_15 milk (6.318 mg Trolox eq/100 mL) compared to control milk (6.041 mg Trolox eq/100 mL). In contrast, WGP_5 milk exhibited the lowest ABTS antioxidant capacity (5.721 mg Trolox eq/100 mL) (p < 0.0001). A similar trend was observed for the FRAP analysis, as shown in Table 7. FRAP values were significantly higher (p < 0.0001) in WGP_10 (5.175 mg Trolox eq/100 mL) and WGP_15 (5.416 mg Trolox eq/100 mL) milks compared to the control (5.057 mg Trolox eq/100 mL) and WGP_5, which again showed the lowest FRAP potential (4.984 mg Trolox eq/100 mL).
The sampling-related significant differences observed in milk parameters may be attributed to the physiological progression of lactation, which is known to influence milk composition and physical properties independently of dietary treatment.

4. Discussion

Regarding milk chemical composition, slight numerical improvements were observed following increased intake of WGP. It is well established that enhancing dietary fibre content promotes milk fat synthesis [62], and in the present study, WGP inclusion effectively increased the fibre content of the diets. This effect was further reinforced by the concomitant rise in dietary fat content associated with higher WGP inclusion levels, a factor directly correlated with milk fat concentration [63]. As a result, milk from WGP-fed animals exhibited a higher fat content. Variations in UDM were directly linked to the changes observed in milk fat content. Nevertheless, the observed differences were minor, as their magnitude was limited and they had no detectable influence on the technological properties of the milk. In this study, WGP silage inclusion was strictly regulated (up to 15%), and although significant differences were detected, further research is warranted to evaluate the effects of higher inclusion rates and to better characterize the potential of WGP to enhance gross composition and especially milk fat yield.
The scientific literature reports heterogeneous findings regarding the effects of incorporating various agricultural by-products into dairy goat diets on milk composition. Studies involving non-ensiled by-products—such as tomato fruits, citrus pulp, brewer’s grain and yeast—have documented significant differences in milk composition, including increased levels of protein, casein and total solids compared to milk from goats fed conventional diets, upon replacement of 47% of the cereal-based concentrate [64]. Previously, the same authors [65] had only observed changes in lactose content when tomato and cucumber by-products were included in the diet, replacing 35% of the cereal-based concentrate. Monllor et al. [17] reported that milk from goats fed diets containing 40% ensiled artichoke by-product exhibited similar average values for fat, CP, UDM, NFDM, total solids (TS), casein and lactose compared to control milk. Although the literature on grape pomace inclusion and its effects on milk quality and composition remains inconclusive, most studies support its suitability for this purpose. Antunovic et al. [66] observed increased protein and fat content in milk from goats fed diets supplemented with up to 10% grape seed cake; however, as in the present study, these differences were not considered technologically relevant. This finding is consistent with Nudda et al. [67] and Manso et al. [68], who reported no increase in milk fat content when 300 g/day of grape seeds and up to 100 g/kg of grape pomace were incorporated, respectively. In contrast, Badiee Baghsiyah et al. [69] documented a reduction in milk fat and protein percentages in Saanen goats fed diets containing 10% dried grape pomace.
The milk mineral profiles observed across all dietary treatments were consistent with those previously reported by Guo [70] for goat milk. Accordingly, the inclusion of ensiled white grape pomace (WGP) had no discernible impact on the mineral composition of the milk, mirroring the findings of Monllor et al. [42], who reported similar outcomes when incorporating silages derived from other agroindustrial by-products also into the diets of Murciano-Granadina dairy goats.
Lower milk urea concentrations observed in the WGP-fed groups represent a relevant physiological response, which may be primarily attributed to enhanced nitrogen utilization—likely resulting from improved synchrony between rumen-degradable protein and fermentable carbohydrates. However, multiple factors may contribute to this outcome. The main determinants of milk urea formation are CP intake and the dietary protein-to-energy ratio [71]. In the present study, both diets were formulated to be isoenergetic and isoproteic, thereby minimizing confounding effects from these variables. Another factor influencing milk urea concentration is the dietary content of NDF, which was higher in diets with greater WGP inclusion [72]. According to Bonanno et al. [73], NDF and milk urea levels are negatively correlated—under isoproteic conditions—, which may explain the lower urea values observed in the WGP_15 group. Additionally, WGP is rich in tannins [24,27], which bind to proteins in the rumen, forming complexes that hinder ruminal protein degradation [29,30]. This interaction reduces ruminal ammonia (NH3) production, which is subsequently metabolised in the liver, with urea as the primary excretory product of this pathway [28].
The fatty acid profile of milk from goats reported in this experiment was comparable to that reported by Stergiadis et al. [74] for typical goat milk. Moreover, the fatty acid composition observed in WGP-derived milk was closely linked to the lipid profile of the corresponding WGP-based diets, as previously described by Nudda et al. [75] and Correddu et al. [76]. The inclusion of WGP in the diet increased the concentration of vaccenic acid (C18:1 t11) in milk, primarily due to the higher oleic acid content in the feed, which serves as a precursor for Δ9-C18-desaturase activity involved in vaccenic acid synthesis [77]. This increase represents an improvement in the milk’s lipid profile, which became more pronounced with higher levels of WGP inclusion. The elevated concentration of total polyphenols in WGP diets also contributed to increased levels of vaccenic acid and polyunsaturated fatty acids (PUFAs), likely due to their inhibitory effect on ruminal fatty acid biohydrogenation [78]. Additionally, the higher concentrations of other fatty acids in milk, such as linoleic acid (C18:2n6), were directly associated with their increased presence in the WGP-based diets [77]. These findings support the notion that the lipid profile of WGP exerts a direct influence on milk fatty acid composition.
Considering that WGP milk exhibited lower values for AI, TI, HFA and HH, alongside higher levels of UI, HH and HPI, it is noteworthy that WGP consumption improved milk quality in terms of cardiovascular and metabolic health potential [79,80,81]. The lower saturated fatty acid and higher unsaturated fatty acid content in WGP milk compared to the control further support this improvement [57,82,83]. These effects were dose dependent, becoming more pronounced with increasing levels of WGP inclusion in the diet. These results agree with other studies incorporating grape pomace into animal diets that have also reported a positive trend in the lipid profile of milk, with implications for human nutrition and health [68,84,85,86,87].
Regarding milk coagulation properties, the following differences were observed: milk from goats fed the WGP-5 diet exhibited a lower pH and a faster curd aggregation rate compared to other treatments. This behaviour is consistent with the enhanced enzymatic efficiency under acidic conditions, which reduces electrostatic repulsion between casein micelles and promotes κ-casein hydrolysis by chymosin [88,89]. Given the micellar structure of goat milk—more sensitive to pH shifts than bovine milk—, this effect may be further amplified [90].
Milk colour is influenced by factors such as the dispersion state of milk fat globules [91] and the concentration of natural milk pigments [92]. Moreover, the dispersion state of milk fat directly affects the physicochemical properties of dairy products [91]. The L* coordinate is primarily determined by the physical structure of milk, including the dispersion of casein micelles and fat globules [93]; therefore, WGP consumption did not appear to alter milk luminosity. The a* and b* coordinates are associated with the concentration of natural pigments in goat milk. The main pigments include riboflavin (green hue), β-carotene (yellow colouration), and, to a lesser extent, lutein [93]. In goat and sheep milk, the characteristic white colour results from the complete conversion of β-carotene into retinol [91]. Differences in a*, b* and chroma (C*) values between diets may be attributed to slight variations in pigment concentration. Notably, the yellow coordinate (b*) and colour saturation (C*) decreased in WGP milk, contributing to the lower YI observed in these samples. These findings suggest that replacing conventional feed ingredients with WGP may influence expected milk pigmentation. However, it is important to note that such differences (>0.5 units) are imperceptible to the human eye and therefore considered irrelevant from an organoleptic standpoint.
Milk from goats fed higher levels of white grape pomace (WGP) exhibited slightly enhanced ABTS radical scavenging activity and FRAP. These effects can be directly attributed to the increased total polyphenol content of the WGP diets, resulting from the naturally high concentration of these compounds in grape pomace [23,24,26]. These bioactive compounds are well known for their strong antioxidant properties [25,26,27], which, as previously suggested, may be transferred to animal-derived products through dietary intake [29,30,31]. Similar findings were reported by Muelas et al. [94], who observed improved ABTS radical scavenging capacity in raw milk from the same goat breed when animals were fed diets containing 40% agroindustrial by-products silage. Despite their relevance, antioxidant assays have not been widely applied to fresh goat milk samples.
Most studies report only minor or negligible alterations in commercial milk composition, with outcomes largely influenced by the extent of dietary replacement with agroindustrial by-products. In cases where compositional shifts are minimal, the technological properties of milk are generally preserved.

5. Conclusions

White grape pomace silage can be successfully incorporated into balanced diets for dairy goats, replacing up to 15% of conventional ingredients without inducing significant changes in milk composition or its technological properties. The negligible alterations observed in overall milk composition are the primary factor contributing to the preservation of its processing characteristics. Meanwhile, the milk fatty acid profile was positively influenced by the inclusion of WGP silage, resulting in improved health-related indexes, particularly due to the higher degree of unsaturation in its fatty acid composition. Further research is needed to investigate higher inclusion levels of WGP silage and its long-term effects, to establish solid evidence on its impact on milk and dairy product production for human consumption.

Author Contributions

Conceptualization, E.S., J.R.D. and G.R.; methodology, J.R.D., E.S., J.S. and G.R.; investigation, M.G.-L., J.S. and M.V.-M.; resources, J.R.D. and G.R.; data curation, M.G.-L. and G.R.; formal analysis, M.G.-L. and G.R.; writing—original draft preparation, M.G.-L.; writing—review and editing, G.R. and E.S.; supervision: E.S., G.R. and J.R.D.; funding acquisition, J.R.D. All authors have read and agreed to the published version of the manuscript.

Funding

This research is part of the AGROALNEXT programme, supported by the Ministry of Science and Innovation (MCIN) with funding from the European Union through NextGeneration EU (PRTR-C17.I1) and the Generalitat Valenciana (AGROALNEXT/2022/062-SOSCAPRI).

Institutional Review Board Statement

The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Ethics Committee of Universidad Miguel Hernández de Elche (UMH.DTA.JDS.04.22, approval date 15 November 2022).

Informed Consent Statement

Not applicable.

Data Availability Statement

The data presented in this study are available on request from the corresponding author. The data are not publicly available due to their being part of an ongoing study.

Conflicts of Interest

The authors declare no conflicts 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.

Abbreviations

ABTS 2.2′-azino-bis(3-ethylbenzothiazoline-6-sulfonate)
ADFAcid detergent fibre
ADLAcid detergent lignin
AOACAssociation of Official Analytical Chemists
CHOL Cholesterol
CLA Conjugated linoleic acid
CLAVACC CLA/Vaccenic acid
CPCrude protein
DMDry matter
EEEther extract
FRAP Ferric Reducing Antioxidant Power
HFA Hypercholesterolemic fatty acids
HHHypocholesterolemic/Hypercholesterolemic ratio
HPI Health-promoting index
IA Atherogenic index
ISO International Standardization Organization
LA/ALA Linoleic/Alpha-linolenic ratio
LSCCLog10 somatic cell count
MUFAMonounsaturated fatty acids
NDF Neutral detergent fibre
NFDM Non-fat dry matter
OBCFAOdd- and branched-chain fatty acids
OMOrganic matter
OLESTEOleic acid/stearic acid
PUFA Polyunsaturated fatty acids
SCCSomatic cell count
SEMStandard Error of the Mean
SFA Saturated fatty acids
TI Thrombogenic index
TS Total solids
UDM Useful dry matter
UI Unsaturation index
VFAsVolatile fatty acids
WGPWhite grape pomace
WI Whiteness index
YI Yellowness index

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Table 1. Ingredients and chemical composition of experimental diets containing different inclusions of red grape pomace by-product for dairy goat feeding.
Table 1. Ingredients and chemical composition of experimental diets containing different inclusions of red grape pomace by-product for dairy goat feeding.
ItemDiets
ControlWGP_5WGP_10WGP_15
Ingredients (g/kg DM)
Pelleted grain mix612.0602.0648.0593.0
Alfalfa hay368.0348.0252.0277.0
Barley straw20.0--20.0
WGP silage-50.0100.0150.0
kg DM offered/animal/day2.202.222.212.25
Gross composition (g/kg DM)
DM (g/kg FM)890.38856.70825.72786.30
OM878.95865.83869.36868.32
CP176.00175.00177.00178.00
EE31.0031.0037.0038.00
NDF314.00310.00350.00370.00
ADF158.00171.00183.00196.00
ADL26.3038.1039.7050.05
Starch254.00254.00254.00257.00
Total sugar49.0045.0045.0047.00
Total polyphenols (GAE eq.)6.457.637.408.23
ME 1 (Mcal/kg DM)2.852.812.782.74
Mineral content (g/kg DM)
Ca 11.409.9011.3010.30
P3.703.703.903.70
Na 4.804.404.303.90
K 13.6012.9012.7012.40
Mg 3.203.003.102.90
Fatty acids profile 2
C4:00.280.260.280.10
C6:00.100.090.100.04
C12:00.230.170.170.12
C14:00.510.400.410.31
C16:018.8917.4017.1715.95
C16:1c90.360.370.410.38
C18:02.842.802.733.01
C18:1c921.8222.3222.3022.75
C18:1c110.910.930.951.03
C18:2n643.9446.6847.5148.78
C18:3n37.185.685.465.07
C20:00.400.390.360.40
C20:1n90.220.230.230.24
C22:00.290.270.260.29
C24:00.320.300.250.26
SFA24.9623.0422.5721.18
MUFA23.4623.9724.0324.55
PUFA51.5752.9953.3954.25
UI134.53136.18136.86138.54
WGP: White grape pomace, WGP_5: diet with 5% of WGP, WGP_10: diet with 10% of WGP, WGP_15: diet with 15% of WGP. DM: dry matter; FM: fresh matter; OM: organic matter; CP: crude protein; EE: ether extract; CF: crude fibre; NDF: neutral detergent fibre; ADF: acid detergent fibre; ADL: acid detergent lignin; GAE eq.: gallic acid equivalent; ME: metabolic energy: 1 [47]; 2 percentages of the methylated fatty acid area in the fatty acid profile; SFA: saturated fatty acid; MUFA: monounsaturated fatty acid; PUFA: polyunsaturated fatty acid; UI: unsaturation index.
Table 2. Results of statistical analysis (F- and p-values) and least square means (±SEM) comparison for gross milk composition and total cholesterol content of milk across dietary treatments.
Table 2. Results of statistical analysis (F- and p-values) and least square means (±SEM) comparison for gross milk composition and total cholesterol content of milk across dietary treatments.
ItemDietSEMF-/p-Value
ControlWGP_5WGP_10WGP_15DietSamplingDiet × Sampling
Fat (%)4.061 c4.237 b4.183 bc4.422 a0.0538.13 0.00141.99 <0.00011.39 ns
Protein (%)3.382 ab3.395 a3.264 c3.294 bc0.0304.54 0.01398.51 0.00040.15 ns
Lactose (%)4.6444.6224.6414.6220.0200.37 ns20.64 <0.00010.19 ns
NFDM (%)8.758 a8.773 a8.646 b8.689 b0.0236.80 0.002436.68 <0.00010.35 ns
UDM (%)7.443 b7.632 ab7.447 b7.716 a0.0664.32 0.016836.75 <0.00010.70 ns
Urea (mg/L)638.600 a607.400 b601.000 b608.400 b6.2517.24 0.00187.39 0.00080.65 ns
LSCC (103 cells/mL)2.7772.8462.7792.8580.1030.18 ns1.21 ns0.28 ns
CHOL (mg/100 g)18.30418.12018.11318.4310.2210.48 ns6.39 0.00021.89 ns
WGP: white grape pomace, WGP_5: diet with 5% of WGP, WGP_10: diet with 10% of WGP, WGP_15: diet with 15% of WGP; SEM: standard error of the mean; NFDM: non-fat dry matter; UDM: useful dry matter (% fat + % protein); LSCC: Log10 somatic cell count; CHOL: total cholesterol content. a–c: Different letters in the same row indicate significant difference between diets; ns: non-significant (p > 0.05). F- and p-values are reported for the significant effects.
Table 3. Results of statistical analysis (F- and p-values) and least square means (±SEM) comparison for mineral profile of milk across dietary treatments.
Table 3. Results of statistical analysis (F- and p-values) and least square means (±SEM) comparison for mineral profile of milk across dietary treatments.
ItemDietSEMF-/p-Value
ControlWGP_5WGP_10WGP_15DietSamplingDiet × Sampling
Ca (g/100 g)0.1760.1790.2050.1890.0092.40 ns3.59 0.0115.69 <0.0001
P (g/100 g)0.1510.1560.1670.1550.0080.77 ns1.12 ns2.39 0.003
K (g/100 g)0.137 a0.141 ab0.163 c0.153 bc0.0064.50 0.00655.42 0.00096.09 <0.0001
Na (g/100 g)0.0400.0460.0460.0860.0220.95 ns0.88 ns1.03 ns
Mg (g/100 g)0.0150.0160.0170.0330.0081.00 ns0.9 ns1.04 ns
WGP: white grape pomace, WGP_5: diet with 5% of WGP, WGP_10: diet with 10% of WGP, WGP_15: diet with 15% of WGP. SEM: Standard Error of the Mean. a–c: Different letters in the same row indicate significant difference between diets; ns: non-significant (p > 0.05). F- and p-values are reported for the significant effects.
Table 4. Results of statistical analysis (F- and p-values) and least square means (±SEM) comparison for fatty acids profile (percentages of the methylated fatty acid area in the fatty acid profile) and nutritional and health-related indices of milk across dietary treatments.
Table 4. Results of statistical analysis (F- and p-values) and least square means (±SEM) comparison for fatty acids profile (percentages of the methylated fatty acid area in the fatty acid profile) and nutritional and health-related indices of milk across dietary treatments.
ItemDietSEMF-/p-Value
ControlWGP_5WGP_10WGP_15DietSamplingDiet × Sampling
C4:01.110 a1.067 a1.059 a1.172 b0.0254.31 0.00812.66 0.0411.01 ns
C6:01.9031.7771.8161.9050.0402.53 ns1.06 ns1.45 ns
C12:05.302 a4.932 b4.861 b4.485 c0.07221.84 <0.00012.15 ns1.68 ns
C14:09.867 a9.534 b9.393 bc9.258 c0.06217.61 <0.00013.57 0.01112.95 0.0028
C16:028.857 a27.778 b27.682 b26.930 c0.13236.36 <0.000111.80 <0.00014.95 <0.0001
C16:1c90.557 a0.580 b0.555 a0.492 c0.00726.00 <0.00013.33 0.01653.51 0.0006
C18:07.023 a7.982 b7.961 b9.089 c0.12347.42 <0.00018.06 <0.00014.31 <0.0001
C18:1c912.713 a13.559 b13.239 b14.072 c0.13916.82 <0.00018.36 <0.00011.91 ns
C18:1c110.5080.5510.5450.5730.0141.95 ns4.25 0.00430.96 ns
C18:2n63.295 a3.431 b3.602 c3.563 c0.03317.99 <0.00019.84 <0.00011.50 ns
C18:3n30.331 a0.313 b0.270 c0.257 d0.00478.49 <0.000145.67 <0.00017.57 <0.0001
C18:1 t112.373 a2.716 b2.528 b2.946 c0.06424.04 <0.000113.72 <0.00013.05 0.0021
CLA c9 t110.942 a1.021 b0.966 a0.984 ab0.0238.71 <0.000120.16 <0.00011.90 ns
C20:00.1380.1510.1400.1520.0051.98 ns5.280.00100.46 ns
C20:1n90.0540.0600.0610.0590.0031.49 ns0.91 ns1.00 ns
C22:00.0310.0390.0380.0360.0031.75 ns6.86 0.00012.52 0.0094
C24:00.016 a0.019 a0.017 a0.013 b0.0023.56 0.019513.53 <0.00013.48 0.0006
SFA72.165 a70.049 b69.749 b69.472 b0.28518.56 <0.00012.31 ns1.69 ns
MUFA21.694 a23.607 b23.959 b24.253 b0.26319.19 <0.00012.51 ns1.45 ns
PUFA5.478 a5.733 b5.676 b5.705 b0.0643.29 0.026513.15 <0.00011.52 ns
n6n310.656 a11.738 b14.363 c15.017 d0.154182.45 <0.000192.70 <0.000111.31 <0.0001
LA/ALA10.085 a11.086 b13.578 c14.234 d0.147181.68 <0.000190.66 <0.000111.83 <0.0001
UI31.319 a33.512 b34.067 b34.180 b0.38212.20 <0.00013.02 0.02461.06 ns
IA2.728 a2.4162 b2.378 bc2.292 c0.04121.89 <0.00012.33 0.0661.64 ns
TI3.359 a3.094 b3.063 b3.057 b0.04112.58 <0.00014.06 0.00561.47 ns
HFA44.026 a42.246 b41.937 b40.674 c0.14293.18 <0.00013.58 0.0118.33 <0.0001
HH0.437 a0.485 b0.478 b0.515 c0.00631.05 <0.00018.48 <0.00012.63 0.0069
HPI0.370 a0.414 b0.423 bc0.439 c0.00626.35 <0.00012.78 0.03472.24 0.0204
OLESTE1.825 a1.712 b1.680 b1.557 c0.02618.01 <0.000122.24 <0.00012.36 0.0145
CLAVACC0.436 a0.413 a0.418 a0.360 b0.00815.43 <0.000118.80 <0.00011.28 ns
OBCFA3.567 a3.352 b3.276 b2.966 c0.02782.59 <0.000119.25 <0.00014.33 <0.0001
WGP: white grape pomace, WGP_5: diet with 5% of WGP, WGP_10: diet with 10% of WGP, WGP_15: diet with 15% of WGP; SEM: Standard Error of the Mean. CLA: conjugated linoleic acid; SFA: saturated fatty acid; MUFA: monounsaturated fatty acid; PUFA: polyunsaturated fatty acid; n6n3: ω-6/ω-3 fatty acid ratio; LAALA: linoleic acid/α-linolenic acid ratio; UI: unsaturation index; IA: Index of Atherogenicity; IT: Index of Thrombogenicity; HFA: Hypercholesterolemic Index; HH: hypocholesterolemic/hypercholesterolemic ratio; HPI: health-promoting index; OLESTE: oleic to stearic acid ratio; CLAVACC: conjugated linoleic acid to vaccenic acid ratio; OBCFAs: odd- and branched-chain fatty acids. SEM: Standard Error of the Mean. a–d: Different letters in the same row indicate significant difference between diets; ns: non-significant (p > 0.05). F- and p-values are reported for the significant effects.
Table 5. Results of statistical analysis (F- and p-values) and least square means (±SEM) comparison for milk coagulation properties and pH across dietary treatments.
Table 5. Results of statistical analysis (F- and p-values) and least square means (±SEM) comparison for milk coagulation properties and pH across dietary treatments.
ItemDietSEMF-/p-Value
ControlWGP_5WGP_10WGP_15DietSamplingDiet × Sampling
Rennet coagulation time (min)8.668.788.978.770.4430.54 ns46.33 <0.00010.63 ns
Curd firmness (mm)9.54810.3759.7029.3630.0991.12 ns12.24 <0.00010.26 ns
Aggregation rate (mm/min)1.404 a1.591 b1.359 a1.383 a0.3053.76 0.012752.74 <0.00010.25 ns
pH6.749 a6.697 c6.723 b6.730 b0.0079.55 <0.00018.48 <0.00012.36 0.0148
WGP: white grape pomace, WGP_5: diet with 5% of WGP, WGP_10: diet with 10% of WGP, WGP_15: diet with 15% of WGP. SEM: Standard Error of the Mean. a–c: Different letters in the same row indicate significant difference between diets; ns: non-significant (p > 0.05). F- and p-values are reported for the significant effects.
Table 6. Results of statistical analysis (F- and p-values) and least square means (±SEM) comparison for colorimetric parameters across dietary treatments.
Table 6. Results of statistical analysis (F- and p-values) and least square means (±SEM) comparison for colorimetric parameters across dietary treatments.
ItemDietSEMF-/p-Value
ControlWGP_5WGP_10WGP_15DietSamplingDiet × Sampling
L*86.03386.15886.10485.9580.0791.20 ns8.97 <0.00011.31 ns
a*−1.309 a−1.347 b−1.356 b−1.417 c0.01310.94 <0.000139.84 <0.00012.47 0.01
b*5.107 a4.699 b4.988 c4.831 d0.03821.98 <0.000119.75 <0.00012.98 0.001
h*104.425 a106.094 b105.292 c106.387 b0.21816.37 <0.000131.06 <0.00012.86 0.0015
C*5.274 a4.890 b5.173 c5.037 d0.03621.77 <0.000119.31 <0.00013.02 0.0009
WI85.0785.3285.1785.080.0722.57 ns10.48 <0.00011.14 ns
YI8.480 a8.390 b8.275 c8.029 d0.06123.70 <0.000121.19 <0.00012.93 0.0012
WGP: white grape pomace, WGP_5: diet with 5% of WGP, WGP_10: diet with 10% of WGP, WGP_15: diet with 15% of WGP; SEM: Standard Error of the Mean; L*: lightness; a*: red/green coordinate; b*: yellow/blue coordinate; h*: angle; C*: chroma; WIs: whiteness indices; YIs: yellowness indices. SEM: Standard Error of the Mean. a–d: Different letters in the same row indicate significant difference between diets; ns: non-significant (p > 0.05). F- and p-values are reported for the significant effects.
Table 7. Results of statistical analysis (F- and p-values) and least square means (±SEM) comparison for antioxidant activity of milk across dietary treatments expressed in mg Trolox eq/100 mL.
Table 7. Results of statistical analysis (F- and p-values) and least square means (±SEM) comparison for antioxidant activity of milk across dietary treatments expressed in mg Trolox eq/100 mL.
ItemDietSEMF-/p-Value
ControlWGP_5WGP_10WGP_15DietSamplingDiet × Sampling
ABTS6.041 a5.721 b6.557 c6.318 c0.11510.1 <0.000138.67 <0.00012.19 0.0153
FRAP5.057 a4.984 b5.175 c5.416 d0.020216.61 <0.000141.08 <0.000125.25 <0.0001
WGP: white grape pomace, WGP_5: diet with 5% of WGP, WGP_10: diet with 10% of WGP, WGP_15: diet with 15% of WGP; a–d: Different letters in the same row indicate significant difference between diets. SEM: Standard Error of the Mean.
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MDPI and ACS Style

Galvez-Lopez, M.; Viuda-Martos, M.; Saldo, J.; Sendra, E.; Romero, G.; Díaz, J.R. Dietary White Grape Pomace Silage for Goats: Assessing the Impact of Inclusion Level on Milk Processing Attributes. Appl. Sci. 2025, 15, 12791. https://doi.org/10.3390/app152312791

AMA Style

Galvez-Lopez M, Viuda-Martos M, Saldo J, Sendra E, Romero G, Díaz JR. Dietary White Grape Pomace Silage for Goats: Assessing the Impact of Inclusion Level on Milk Processing Attributes. Applied Sciences. 2025; 15(23):12791. https://doi.org/10.3390/app152312791

Chicago/Turabian Style

Galvez-Lopez, Marina, Manuel Viuda-Martos, Jordi Saldo, Esther Sendra, Gema Romero, and José Ramón Díaz. 2025. "Dietary White Grape Pomace Silage for Goats: Assessing the Impact of Inclusion Level on Milk Processing Attributes" Applied Sciences 15, no. 23: 12791. https://doi.org/10.3390/app152312791

APA Style

Galvez-Lopez, M., Viuda-Martos, M., Saldo, J., Sendra, E., Romero, G., & Díaz, J. R. (2025). Dietary White Grape Pomace Silage for Goats: Assessing the Impact of Inclusion Level on Milk Processing Attributes. Applied Sciences, 15(23), 12791. https://doi.org/10.3390/app152312791

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