Tannin Supplementation Alters Foraging Behavior and Spatial Distribution in Beef Cattle

Abstract

Beef production on chemically uniform grass monocultures can limit nutrient synchrony and contribute to uneven pasture use. We evaluated whether supplementing tannins with bioactive plant secondary compounds improves foraging dynamics and landscape use by beef cattle grazing a meadow bromegrass monoculture in ways aligned with rangeland sustainability. Twenty-four Angus cow–calf pairs were allocated to six 3.6-ha paddocks (four pairs/paddock), randomly assigned to Control (Ctrl; n = 3) or Tannin treatment (TT; n = 3). Animals received 1 kg/cow/day of DDGs, with TT receiving an added 0.4% tannins (2:1 condensed:hydrolyzable). Grazing occurred during four 15-day periods (July–September) across two years. Data were analyzed with mixed-effects models. Tannins did not alter biomass removal or cow weight loss (p > 0.05). However, TT cows exhibited longer evening grazing (2.9 vs. 2.1 h), fewer standing-to-lying transitions (5.7% vs. 7.3%), and more even spatial grazing distribution (CV = 1.861 vs. 2.13; p < 0.05), and greater water consumption (147 vs. 121 L/day; p < 0.01). Average daily gain of calves was numerically greater in TT compared to Ctrl (1.03 vs. 0.93 kg/day; p = 0.27). Collectively, these shifts promoted by tannins point to enhanced evening intake opportunities and reduced patch overuse, outcomes consistent with improved welfare and more uniform pasture utilization two pillars of sustainable grazing. Increased water demand under tannins highlights a management consideration for arid systems. Overall, moderate tannin inclusion was compatible with sustainable grazing by promoting even pasture use and potentially improving nutrient use efficiency without compromising intake.

1. Introduction

Rangelands are extensive natural landscapes that cover approximately 40–50% of the Earth’s terrestrial surface [1]. In the western U.S., these landscapes represent about 31% of the national land area and are fundamental for livestock production, supporting about eight million beef calves produced annually [2]. These areas are dominated by grass monocultures, such as intermediate, tall, and crested wheatgrass, which provide essential spring forage for cow-calf pairs [3]. However, the nutritional quality of these grasses declines during mid-summer, requiring protein supplementation to maintain cattle productivity [4]. In this context, protein supplementation enhances diet quality and grazing efficiency [5,6]. Among plant secondary compounds, tannins have been shown to improve protein utilization in cattle [7]. Tannins are polyphenolic compounds broadly classified into two groups: condensed tannins (CTs) and hydrolyzable tannins (HTs). Both types can reduce ruminal protein degradation, thereby increasing protein use efficiency in ruminants and concurrently lowering methane emissions, contributing to environmental sustainability [8,9].

Beyond their effects on ruminal protein metabolism and environmental outcomes, tannins also exert direct influences on animal performance and foraging dynamics in grazing systems. For instance, heifers grazing sainfoin, a tannin-containing legume, gained more weight than those on non-tannin forages [10], with similar improvements reported in lambs [11,12]. Tannin consumption has also been associated with increased water and mineral intake in confinement studies, suggesting physiological adjustments that may alter spatial foraging dynamics [13,14]. Ingesting plant secondary compounds including tannins, terpenoids, and phenolic resins—elicits diuretic-like responses in herbivores such as Neotoma stephensi and N. albigula, characterized by elevated water intake, greater urine output, and reduced urine osmolarity [15,16]. Detoxification often requires nitrogen conjugation, increasing urinary N losses and creating trade-offs between toxin elimination and nutrient conservation [17,18,19]. Herbivores may mitigate these costs by selecting forages with favorable protein-to-tannin ratios or mixing diets to dilute phytochemicals while meeting nutritional demands [20,21,22]. Such strategies are particularly relevant in heterogeneous rangelands where phytochemical profiles vary across time and space [23]. Thus, intake of secondary compounds reflects a complex integration of behavioral, physiological, and biochemical mechanisms through which herbivores balance nutrient acquisition with detoxification. Within this framework, increased water intake plays a central role in offsetting osmotic and excretory costs [15], ultimately shaping grazing behavior, spatial distribution, and performance in variable landscapes.

Although the benefits of tannin-rich forages on livestock performance are well-documented under confined feeding conditions, supplemental effects on free-grazing cattle remain largely unexplored. Recently, commercially processed tannin extracts have gained attention in the livestock sector due to their consistent quality, standardized dosage and scalability for livestock management [9]. Unlike naturally occurring tannins in forages, which vary widely in structure and concentration, commercial extracts provide a reliable means of supplementation that can enhance feed efficiency and promote animal health. These extracts may reproduce some of the functional benefits of tannin-containing forages, including improved grazing efficiency and modified foraging behavior across heterogeneous rangelands [9].

Despite this potential, limited information exists on how supplemental tannins influence performance, grazing behavior, spatial distribution and water intake of free-ranging cattle-factors closely linked to both animal performance and ecosystem-level processes. We therefore hypothesized that supplementing cattle with a commercial tannin extract would mimic some of the behavioral and nutritional functions of grazing diverse phytochemical containing forages by enriching a chemically uniform pasture through post-ingestive feedback and rumen nitrogen dynamics. To test this hypothesis, we examined the effects of supplemental tannins on performance, grazing behavior, spatial distribution, and water consumption in cows grazing meadow bromegrass-dominated rangeland.

2. Materials and Methods

2.1. Study Area

This study was conducted at the Utah State University Richmond Research Farm located in northern Utah, USA and managed by Utah State University (41.9227° N, 111.8136° W). The elevation at the farm is 1511 m, with an annual temperature range between −9 °C and 16 °C, annual rainfall is 52.6 cm and snowfall of 177.8 cm [24]. The soil type is silt loam. The experimental pasture is delineated by a five-strand barbed wire perimeter fence, encompassing 22.26 ha of meadow bromegrass (Bromus inermis) (MBG) pasture monoculture (~2500 kg/ha; [25]) with less even distribution of intermediate wheatgrass (Thinopyrum intermedium). A semi-permanent electric fence divided the pasture into 2 equal blocks. Temporary electric fences, perpendicular to this fence, divide the pasture into 6 paddocks of 3.64 ha each. The study procedures described herein were approved by the Utah State University Institutional Animal Care and Use Committee (approval number 2566).

2.2. Animal and Grazing Protocol

Three of the six paddocks were randomly assigned to the Control treatment (Ctrl), where cattle grazed Bromus inermis and received daily a supplement of Distillers Dried Grains with Solubles (DDGs). The remaining three paddocks were assigned to the Tannin Treatment (TT), where cattle received the same DDGs supplement and feeding protocol, with the addition of a tannin extract blend (in a powder form, ByPro, Silvafeed, Cuneo, Italy) mixed in the supplement. The blend was composed of one-third chestnut tannin extract and two-thirds quebracho tannin extract. Both extracts were analyzed by matrix-assisted laser desorption/ionization-time of fight (MALDI-TOF) mass spectrometry [26]. Quebracho tannin composition was: 84.3% condensed favan-3-ols (predominantly profsetinidin), 10.7% oligomers of favan3-ols (catechin and epicatechin dimers), and 5% carbohydrate derivate (dimers of pentose, monocarboxylic acid of hexose, and 6-carbon sugars) on a dry matter (DM) basis. Chestnut tannin composition was (DM basis): 7.9% digalloyl glucose, 5.0% trigalloyl glucose, 16.5% pentagalloyl glucose, and 70.6% oligomers of digalloyl glucose, trigalloyl glucose, and pentagalloyl glucose [26].

Twenty-four Angus cow-calf pairs were assigned to the paddocks in two consecutive years (cows: 614 ± 20 kg, calves: 244 ± 4 kg initial body weight (BW) in 2023; cows: 539 ± 27 kg, calves: 167 ± 6 kg in 2024). Pairs were stratified by initial cow weight and randomly assigned to Ctrl or TT (N = 12 pairs/treatment) and then pairs (n = 4) were randomly assigned to each of the six paddocks. In each paddock, cow-calf pairs received their DDGs supplement in two troughs (3 m long × 0.61 m wide × 0.43 m tall) at 1 kg/cow for both treatments and with the addition of the tannin blend that represented approximately 0.4% of the diet (400 g of the tannin blend added daily to 1 kg of DDGs/cow).

Grazing occurred from July to September during 2023 and 2024, separated into four consecutive experimental periods of 15 days each (Periods 1, 2, 3 and 4), with the first period being an adaptation period (no tannins; Baseline) with only DDGs supplementation for both Ctrl and TT treatments (

Table A1. Grazing periods for 6 groups of cows grazing in six paddocks (n = 3 supplemented with tannins; n = controls) on the start/end dates for each experimental period.

Periods	Start Date	End Date	Number of Days
Period 1	13 July	27 July	15
Period 2	28 July	11 August	15
Period 3	12 August	26 August	15
Period 4	27 August	10 September	15

). Cow supplements were provided daily from 6 to 8 a.m., and two water troughs of approximately 380 L within each paddock were filled every day at the same time. Ten grams of DDGs were collected daily during supplement, stored in plastic bags and then composited by period for chemical analyses. Analytical procedures for DDGs and pasture composition (DM, CP, NDF, ADF, ash) are described in Section 2.4, and the estimation of cows’ forage intake from pre- and post-grazing biomass (biomass removal) is detailed in Section 2.3.1. Because the study focused mainly on the primary fiber fractions (NDF, ADF), indigestible NDF (iNDF) was not analyzed.

2.3. Measurements of the Impact of Tannin Supplementation

2.3.1. Herbage Availability

Herbage dry matter (DM) availability for bromegrass was assessed per unit area in each paddock before animals entered the paddocks (pre-grazing herbage biomass) on 10 July 2023 and on 13 July 2024. These paddocks were grass monocultures with minimal structural variation; therefore, no specific entry and exit sward heights were defined, and sampling could be initiated from any paddock. Herbage availability was then evaluated after animals grazed (post-grazing herbage biomass) by taking 150 readings in each paddock using a rising plate pasture meter (Electronic Plate Meter Jenquip EC-10, Agriworks Ltd., Manawatu, New Zealand). Each paddock was sampled in a “lazy W” pattern and every 5 steps the plate meter was lowered vertically onto the herbage until 150 reads per paddock were achieved. Simultaneously, and using a similar walking pattern, quadrant frames of the same area as the plate meter (0.10-m²) were randomly tossed and all forage within the frame was cut to the ground (10 samples/paddock). Samples were placed in individual paper bags, weighed and dried at 65 °C to constant weight. Linear relationships for each experimental period were estimated from calibration curves of DM herbage biomass on plate meter readings from pre- and post-grazing events, i.e., Period 1 = Pre-grazing biomass and end of Period 1, Period 2 = end of Periods 1 and 2, Period 3 = end of Periods 2 and 3, Period 4 = end of Periods 3 and 4 [27]. Calibration curves were plotted using the respective plate readings and biomass for the 10 samples per paddock, creating a regression equation for the relationship between height and biomass. This equation derived estimates of dry matter (DM) per gram per square centimeter (DM/g/cm²) using the respective 150 heights readings per period. Subsequently, the estimated biomass was extrapolated to DM per kilogram per hectare (DM/kg/ha) and used to determine forage availability per paddock. This protocol was repeated every 15 days across 4 periods over two consecutive years of the experiment. Tracking changes in biomass between periods provided information on biomass removal by cow-calf pairs, thereby estimating biomass disappearance within the whole period and per day, which was calculated by dividing total periods values by 15 days. The collected dried samples of bromegrass were also ground and sieved through a 1 mm screen (Wiley mill (model 4; Thomas Scientific Swedesboro, NJ, USA) before chemical analyses.

2.3.2. Foraging Behavior

In year two (2024), 18 mother cows (9 TT; 9 CT) were randomly selected within their respective paddocks and fitted with LiteTrack Iridium 750+ GPS collars equipped with accelerometers to monitor foraging behavior by recording locations and accelerometry at 5-min intervals. GPS accuracy was independently validated within the study site under open-sky, stationary conditions consistent with ION STD-101 (1997) [28] protocol. Following this protocol, five random collars were selected and mounted on a fixed stand at a surveyed benchmark, and logged positions were compared with the benchmark to compute horizontal error, yielding approximately 10 m. GPS (Teltonika Telematics, Vilnius, Lithuania) devices weighed 1.75 kg (0.3% to 0.4% of the average body weight of the monitored cattle), which did not affect behavioral patterns [29]. Each animal’s collar serial number and cow IDs were recorded for reference. After retrieval, collar data were cleaned following the protocol outlined in Muzzo et al. [30]. The cleaned GPS collar data for all 18 animals were then used to create a predicted dataset (PD) containing their respective predicted foraging behaviors (PFB).

The PFB in the PD were generated using the highest-accuracy predictor sets for foraging behaviors reported in Muzzo et al. [30]. Accordingly, model training and validation were conducted in the same six-paddock (3 Ctrl; 3 TT) experimental setting at the same site, using the same integrated GPS-accelerometer collars set at a 5-min epoch length, as in the present study, ensuring that the training and application contexts matched. The study randomly selected three focal cows across the paddocks during the Baseline period (no tannins) and then continuously observed and video-recorded them for 12-h daytime sessions over three consecutive days to create time-stamped ground-truth behavior labels. Classifiers were trained and selected under two data partition strategies: a 70:30 random test split (RTS) and five-fold cross-validation (5FCV). The importance of predictors was assessed using a random-forest variable-importance procedure (

Table A2. Selected predictors for classifying animal foraging behaviors and their definitions.

Predictors	Definition
x	left-right cows’ head movement
y	forward-backward cow’s head movement
z	up-down cow’s head movement
Actindex	The cows’ activity levels, where higher values indicate more active behaviors and lower values suggest rest periods
Distance	Distance traveled between two consecutive GPS points.
Speed	Distance the cows traveled over time during the observation period.

), yielding parsimonious models that were applied to all 18 collars in this study (

Table A3. The table presents the overall accuracy (%) of machine-learning (ML) models Random Forest (RF), and XGBoost (XGB) in classifying cattle behaviors and postures under two data-partition strategies: Random Train–Test Split (RTS) and Five-Fold Cross-Validation (CV).

Classification	Model	Behavior(s)	RTS Accuracy (%)	CV Accuracy (%)	Notes
Activity state	XGB	Active vs. Static	74.5	74.2	RTS > CV
Foraging behaviors	RF	GR, RE, RU	56.4	62.9	CV > RTS
Behavior × posture	RF	RU_SU, RU_LD, RE_SU, RE_LD	56.4	58.8	CV > RTS

Activity states (active vs. static), foraging behaviors (grazing (GR), resting (RE), walking (W), ruminating (RU)), and behavior by posture where animals were standing up (SU) and lying down (LD) while RU and RE. The best results for a given metric are bold to highlight which model was optimal for that analysis or task.

). Thereafter, we computed the same predictors (Table A2) for all 18 animals cleaned collar data and applied the highest respective classification models in Table A3 trained on a dataset containing the observed predictors and behaviors of the three ground truth animals, to generate each PFB in our present study that was used for further analysis.

Geographic coordinates collected by all GPS collars in the PD were projected from World Geodetic System 1984 (WGS84) latitude/longitude coordinates to WGS84 Universal Transverse Mercator (UTM) coordinates for zone 12 north that covers our study area. This was done to be consistent with our geospatial database as well as to more easily summarize the data by calculating distances between points, distance traveled by individual cows, foraging behavior, and position of cows at four time of day (TOD) intervals (

Table A4. Categorized intervals of the time of day (TOD) used for behavioral analysis.

Time of Day Interval	Hour Range
1	4:00 a.m.–10:00 a.m.
2	10:00 a.m.–4:00 p.m.
3	4:00 p.m.–10:00 p.m.
4	10:00 p.m.–4:00 a.m.

Time of day (TOD) was divided into four 6-h intervals to assess diurnal variation in animal behaviors.

). TOD intervals were identified by examining the hourly grazing frequency spanning a 24-h period. Hourly frequency was determined by counting all grazing events for a given hour across the four grazing periods (Table A1). The frequency distribution per hour identified 4 distinct 6-h grazing intervals: 4:00 a.m.–10:00 a.m. (Morning), 10:00 a.m.–4:00 p.m. (Afternoon), 4:00 p.m.–10:00 p.m. (Evening), and 10:00 p.m.–4:00 a.m. (Night) (Table A3).

2.3.3. Grazing Distribution

Grazing distribution in the present study was defined as the spatial pattern of GPS points where animals actively removed herbage through biting, representing the ecological footprint of herbivory. The definition followed our previous machine learning approach [30], in which GPS and accelerometer data were integrated to classify foraging behaviors and isolate grazing events from other activities, such as walking, resting, and ruminating. Extracting only grazing points from the dataset allowed spatial analyses to focus specifically on forage removal rather than general occupancy. In year two, grazing distribution was assessed across treatments and periods. GPS points corresponding only to grazing activity were extracted in PFB from PD dataset created using the highest Random Forest (RF) trained model accuracy obtained under 5FCV data partition strategy of foraging behavior [30]. Non-foraging points that inflate occupancy-based measures were excluded. In addition, locations within a 10-m buffer of water troughs, feeders, mineral sites, and fence lines were removed, as these features disproportionately attracted cattle and could bias estimates of forage use. The refined dataset was then used to calculate spatial metrics, including grazing point density and the coefficient of variation (CV), which enabled comparisons between treatments and periods. ArcGIS Pro version 4.4 was used for all spatial data visualization and analysis. Kernel Density analysis was applied to grazing points using a 5 m search radius, producing grids with a 1 m cell resolution. Grid values represented the number of grazing visits per m², and paddock boundaries were used as barriers to confine calculations within each paddock. Density outputs were classified into five categories: no grazing (0), light (0.1–1.5), moderate (1.6–3.0), heavy (3.1–4.5), and very heavy (4.6–6.0). Visualization with a gradient scale was used to display output rasters for periods 1 (baseline, no tannins) and 4 (tannin supplementation), illustrating shifts in the overall distribution of cows’ grazing patterns across treatments. Zonal statistics were then performed to assess and compare grazing distribution [31] between treatments across periods.

2.3.4. Activity Level Estimates

For years 1 and 2, each cow was also equipped with an Icetag3D^TM pedometer (IceRobotics, Roslin, UK) on its left rear leg to record activity every 15 min. The devices were attached upon the cows’ entry into each paddock and removed upon exit. Data were retrieved using the IceRobotics software (version 2023) which continuously recorded activity and posture metrics (e.g., the number of steps taken, the motion index score, and the duration of lying, standing, and transitioning up and down) at 15-min intervals across the period. The IceRobotics software also computed a motion index score, which provides a broader measure of the animal’s activity level and complements the step count by incorporating both the intensity and frequency of movement. Specifically, it quantifies the magnitude of 3-dimensional acceleration and is related to the total amount of energy used by the animal over a given period [32]. The motion index is calculated per second and then summed to provide the total activity per minute (in G’s/10), which is subsequently averaged over each 15-min recording interval. Standing and lying measurements identified rest and activity periods. Transitions between lying and standing estimated the probability of discomfort or restlessness by the cow. The activity summary for steps, motion, standing, lying minutes, and transition up and down was computed by treatment, time of the day, per period and year, thus proportions of standing, lying, and transitions were also calculated.

2.3.5. Water Consumption Estimates

During year 2, two water troughs were used to determine water consumption in mid-summer from 29 July to 5 September 2024 when animals were well adapted to tannin supplementation. Water depth and remaining volume were recorded once per day throughout this entire 38-day period to capture daily intake patterns consistently across paddocks. Troughs, each with approximately 380 L capacity (135 cm long, 79 cm wide, 64 cm high) were filled with water every morning. Prior to refilling, the remaining water depth was measured with a ruler and documented. Water depth in centimeters was converted to volume in liters based on trough dimensions. Daily records included the date, paddock type (treatment or control), paddock number, water depth (in cm), remaining water (in liters), estimated water consumed (in liters), and additional information, such as weather conditions and animal behavior during experimental periods. To account for water loss due to evaporation, a trough of similar dimensions was placed outside the paddock, free from disturbance or animal access. Similar daily measurements were recorded for the control trough to estimate water loss due to evaporation. The water consumed by animals was then calculated by subtracting the remaining volume, adjusted for evaporation, from the total capacity of the troughs (760 L) per treatment paddock.

2.3.6. Animal Performance

During both years, individual cow/calf pairs were weighed separately in a load cell scale (Rice Lake Weighing Systems, Rice Lake, WI, USA) located under a squeeze chute before and after the experiment. Animals were weighed in the morning without prior feed restriction, as they had continuous access to water and forage. Weighing was conducted at the same time of day to minimize variation due to gut fil. The change in weight (i.e., performance) was calculated by subtracting the initial weight recorded before the experiment from the final weight measured after the experiment. The weight gain (or loss) data were then analyzed across treatments, periods and years, to assess the effects of the experimental variables.

2.4. Chemical Analyses

Dry matter, total nitrogen (N) concentration, acid detergent fiber (ADF), and detergent fiber (NDF) were determined according to the Association of Official Analytical Chemists (AOAC, 1990; 930.04) method. DM for the collected grass samples and DDGs was determined after drying samples at 105 °C for 3 h in a forced-air drying oven. Total N concentration was analyzed using a Leco FP-528 N combustion analyzer (AOAC, 2000; 990.03 method) (LECO Corporation, St. Joseph, MI, USA) with crude protein (CP) concentration computed as N concentration × 6.25. Concentration of ADF was determined as per the AOAC (2000) 973.18 method modified by using Whatman 934-AH glass microfiber filters (Cytiva, Marlborough, MA, USA) with 1.5 μm particle retention and a Buchner funnel in place of a fritted glass crucible. In determining NDF, the study adopted procedures modified by Robertson and Van Soest [33].

2.5. Statistical Analysis

The biomass removal, nutrient composition of grass and DDGs, animal performance, and behavioral variables (distance traveled, foraging behaviors, and positions) were analyzed using a two-way factorial design (year × period × TOD) within a completely randomized block design under generalized linear mixed model. Although the number of paddocks was limited (n = 6; three per treatment), replication across two consecutive grazing seasons (2023 and 2024) improved temporal robustness and reduced year-specific bias. All analyses were performed in R statistical software (version 4.4.2; R Core Team, 2024) using the glmmTMB function from the glmmTMB package with a normal distribution. For behavioral variables, the year effect was excluded from the models. The two treatments (Ctrl, TT), four spatial repetitions, four TOD intervals, and their interactions were treated as fixed factors. Random factors included block, block × treatment, block × treatment × period, and higher-order interactions up to block × treatment × period × TOD. Paddocks were treated as experimental units in every analysis with cows nested within paddocks to account for within- and between-group variation, ensuring reliable treatment-level inference despite moderate replication. All animal activity levels were also analyzed similarly, except that the proportion of standing and lying minutes and transitions up and down were analyzed using a beta family model with a zero-inflation component. Residual diagnostics for hierarchical regression models (DHARMa package; Hartig and Lohse, [34]) were used to test model adequacy, ensuring that the assumptions of normality, constant variance, zero inflation, and independence of residuals were met. Additionally, t-tests were conducted to compare means across treatments and periods in spatial grazing distribution. When necessary, data were transformed to meet model assumptions, with the appropriate transformation identified through Box–Cox procedures [35] and residual plots evaluated. Back-transformed least squares means (LSmeans) and standard errors (SE) were then reported.

3. Results

3.1. Herbage Availability, Water Consumption, and Animal Performance

There was no difference (p = 0.475) in biomass removal between cows supplemented with tannins and those in the control group (Figure 1). However, a decline (p < 0.05) in available biomass (kg/ha) was observed across periods for both years (Figure 1). The initial average biomass available was 4985 ± 49 kg/ha per paddock in Period 1, which decreased following grazing to 2114 ± 16 kg/ha by the end of Period 4. In 2023, the average biomass of 5272 ± 31 declined to 4669 ± 26 kg/ha in 2024, a reduction of 603 kg/ha per paddock. Despite these changes in forage availability, the nutritional composition of the pastures remained similar across years. Across all periods and years, the average forage disappearance was 971 kg of dry matter (DM) per paddock for 4 cow–calf pairs. Daily DM disappearance per cow–calf pair remained consistent between years (16.5 kg/day in 2023 vs. 16.0 kg/day in 2024; p = 0.812) and did not vary across periods (p = 0.914), indicating stable forage intake patterns throughout the grazing period. These findings collectively show that despite declines in biomass availability across periods and years, daily forage intake per cow–calf pair was not affected.

Cow-calf pairs in TT drank more water (147 ± 8 L/d) than Ctrl animals (121 ± 8/d; p = 0.0008), with an average water evaporation loss of 2.6 ± 0.00009 L/day. When averaged across animals within a day, daily means fluctuated widely (TT 65.1–225 L/d; Ctrl 62.5–208 L/d), reflecting day effects (p < 0.0001).

There were no differences in cow initial or final BW between treatments (p = 0.197) or between years (p = 0.432). In both years, the average initial BW of mother cows in the control was 535 ± 25 kg, which decreased to a final weight of 477 ± 21 kg, indicating an Average Daily Loss (ADL) of 0.98 kg/day over the 60-day experimental period. Cows in TT had an initial BW of 542 ± 29 kg and a final weight of 492 ± 24 kg, corresponding to an ADL of 0.83 kg/day. Although cows in TT lost numerically less body weight (~9 kg) than those in the control group, this difference was not significant (p = 0.416). Likewise, no differences (p = 0.406) in initial or final BW of calves were detected between treatment groups. Control calves had an initial weight of 165 ± 6 kg and a final BW of 221 ± 6 kg, resulting in an Average Daily Gain (ADG) of 0.93 kg/day. Calves in TT had an initial weight of 169 ± 7 kg and a final weight of 230 ± 7 kg, achieving an ADG of 1.03 kg/day. Again, differences between treatments were non-significant (p = 0.27).

3.2. Nutritional Composition

Results for the nutritional composition of supplement and grass samples from pastures with cows supplemented or not with tannins are presented in

Table 1. Nutritional composition (%) of meadow bromegrass in paddocks grazed by Black Angus cow–calf pairs receiving either a distillers dried grains diet containing tannins (TT) or without tannins (Control, CT) across grazing periods (1–4) in 2023 and 2024. Each paddock (n = 3 per treatment) was 9 acres and grazed by four cow–calf pairs in a completely randomized design.

2023	Tannins				No Tannin				SE (±)	p Values
	Periods
	1	2	3	4	1	2	3			Treatment	Year	Periods	Interactions
DM (%)	95.50 a	96.87 b	97.47 c	97.77 c	95.87 ab	96.13 ab	97.33 c	97.37 c	0.233	0.257	0.561	0.004	ns
CP (%)	7.03 c	5.30 b	4.77 a	3.90 a	6.47 c	5.13 b	4.60 a	3.83 a	0.194	0.272	0.419	0.003	ns
ADF (%)	38.8 a	38.6 a	41.9 b	43.6 b	38.5 a	39.5 a	42.5 b	44.2 b	0.477	0.638	0.846	0.005	ns
NDF (%)	63.6 ab	63.4 ab	64.6 ab	64.8 ab	62.2 a	63.8 ab	64.7 ab	65.4 b	0.692	0.269	0.334	0.001	ns
2024
DM (%)	95.60 a	96.37 ab	96.83 b	98.00 c	95.53 a	96.07 ab	96.73 b	98.20 c	0.184	0.243	0.853	0.004	ns
CP (%)	7.07 c	3.93 b	3.40 b	2.47 a	7.00 c	4.07 b	3.43 b	2.43 a	0.161	0.260	0.349	0.002	ns
ADF (%)	37.7 a	41.5 bc	41.9 b	43.9 d	37.8 a	41.7 bc	40.8 b	43.0 cd	0.386	0.676	0.857	0.005	ns
NDF (%)	59.3 ab	63.0 c	64.0 c	68.4 d	57.8 a	63.1 bc	64.4 c	65.5 cd	0.77	0.432	0.858	0.001	ns

Values are means ± standard error (SE) for dry matter (DM), crude protein (CP), acid detergent fiber (ADF), and neutral detergent fiber (NDF). Different superscript letters within the same row, treatment, and year indicate significant differences among periods (p < 0.05). ns = No significant.

. No significant differences in grass nutritional values were observed between the two treatments (p > 0.243) or across years (p > 0.334). However, significant (p < 0.005) variations were detected across periods (1 to 4) for all measured parameters, including dry matter (DM), crude protein (CP), acid detergent fiber (ADF), and neutral detergent fiber (NDF). The nutritional composition of grass changed significantly across periods (p < 0.05). In 2023, NDF increased from 63.6% to 64.8% ± 0.7%, ADF rose from 38.8% to 43.6% ± 0.5%, while CP declined from 7.03% to 3.90% ± 0.2%. DM content increased from 95.50% to 97.77% ± 0.2%. In 2024, DM, NDF, and ADF increased from 59.3% to 68.4% ± 0.8%, 37.7% to 43.9% ± 0.4%, and 95.60% to 98.00% ± 0.2%, respectively, while CP declined from 7.07% to 2.47% ± 0.2%. The nutritional quality for DDGs as a protein source did not differ (p > 0.309) across the year. In addition to assessing the nutritional quality of grass, a similar evaluation was conducted for DDGs. The overall means for DM, CP, ADF, and NDF were 94.2 ± 0.314, 38.9 ± 0.294, 12.6 ± 0.35, and 32.3 ± 0.113, respectively. The DM, CP and ADF showed no significant differences (p > 0.233) across periods and years. while NDF varied (p < 0.05) in both periods and years.

3.3. Distance Travelled

The study investigated the impact of tannin supplementation on the distance traveled across the time of day (TOD) (night, morning, afternoon, and evening) and period. Results showed no significant difference in the distance traveled between cows supplemented with tannins and the Control treatment (p = 0.315); the average distance (meters) for the tannin-supplemented group (TT) was 44 m/day. Additionally, the three-way interaction between treatment, period, and TOD was not different (p = 0.098). However, a significant interaction was observed between period and TOD (p < 0.0001) (Figure 2). During night, no difference was observed between Periods 1 and 2 (12.7 and 12.5 ± 0.2 m; p = 0.582), but there was a decline between Periods 1 and 3 (p = 0.002), diminishing to ~11 m by Period 4 (p < 0.0001). Likewise, a decrease in distance traveled was detected (1.0 ± 0.14 m) between Periods 1 and 4 in the morning (p < 0.0001), and during afternoons there was a gradual decrease from Period 1 (10.3 ± 0.18 m) to 4 (10.1 ± 0.18 m) (p < 0.0001). For the evening, the distance traveled followed an opposite trend relative to that observed at night: it was lowest in Period 1 (9.1 ± 0.18 m) and gradually increased across subsequent periods (p < 0.0001), increasing by 1 ± 0.01 m from Period 1 to Period 4. These findings show a decline in distance traveled across periods for night, morning and afternoon, whereas there was an increase across periods during evenings.

3.4. Impact of Tannin on States and Foraging Activities

The study evaluated the impact of tannin supplementation on cows’ activity states (active: grazing, static: resting and rumination) across periods and times of day (TOD). No treatment difference occurred on active states (p = 0.974), or interactions with period and TOD (p = 0.095) (Figure 3). Cows were active for approximately 8.4 h during the day and 3.2 h at night, totaling 11.6 h per day, with no differences between groups in overall activity states (p > 0.05). In foraging activities, there was treatment × TOD effect in grazing, RE, and RU times (p = 0.018). Cows in TT increased their evening grazing time (2.9 vs. 2.1 h; p = 0.042) (Figure 4). However, cows in CT tended to increase their evening rumination time (2.6 vs. 2 h, p = 0.062). These results suggested that tannin supplementation did not affect animal being active, but increased grazing patterns with potential to increase their intake.

3.5. Impact of Tannins on Foraging Behavior When Discriminating Static Activities

When analyzing static activities (resting, RE; rumination, RU), no significant differences were detected between treatment groups regardless of body posture (lying down (LD) or standing up (SU)) for RE_LD, RE_SU, RU_LD, and RU_SU (p = 0.828). Similarly, no treatment × period × time of day (TOD) interactions were observed (p = 0.572; Figure 5), while differences were observed across all activity types between periods and TOD (p < 0.0001). The treatment group exhibited greater probabilities of activity, particularly during the morning and afternoon (p < 0.03): for RE_LD, Ctrl cows showed no variation (p = 0.653), while treatment cows consistently had greater probabilities (~0.6–0.7) across periods and TOD (p = 0.041); for RE_SU, the treatment group showed greater activity in the morning (~0.25–0.3) (p < 0.001) with similar probabilities at night and in the afternoon (p > 0.05). The TT group also had greater probabilities for RU_LD in the morning (p = 0.05) with no RU_LD differences in the afternoon, evening, and night between treatment groups (p > 0.252) and across periods (p > 0.420); for RU_SU, the treatment group consistently had greater activity across all periods, especially in the morning (~0.3) and afternoon (~0.3), and even at night (~0.2–0.25) (p < 0.001). The most pronounced effects were observed in earlier periods (1 and 2), with probabilities converging by Period 4, and over time the treatment group showed increased activity in RE_SU and RU_LD, while reducing RE_LD and increasing RU_SU (p < 0.041).

3.6. Behavioral Levels of Activity

There was no significant effect of treatment on the number of steps taken per day (p = 0.891), motion index (p = 0.899) and proportional percentage of standing time (p = 0.388). Similarly, no differences in behavioral levels of activity were observed across years (p > 0.05) (

Table 2. p-values for the fixed effects of treatment, time of the day (TOD: night, morning, afternoon, evening), grazing period (1–4), year (2023–2024), and their interactions on standing time, number of steps, motion index, and transitions from lying to standing in Black Angus beef cows. Cows received either a distillers dried grains diet without tannins (Control, CT) or the same diet containing tannins (TT) while grazing 9-acre meadow bromegrass paddocks (four cow–calf pairs per paddock, n = 3 paddocks per treatment).

	p Values
Parameters	% Standing Time	Number of Steps	Motion Index	% Transition Up
Treatment	0.388	0.891	0.899	0.012
Time of the day	0.000	0.000	0.000	0.058
Period	0.008	0.001	<0.0001	0.581
Year	0.618	0.150	0.062	0.526
Treatment × Time of the day	0.815	0.163	0.737	0.118
Treatment × Period	0.142	0.354	0.094	0.265
Treatment × Year	0.547	0.150	0.547	0.255
Time of the day × Period	0.052	0.390	0.052	0.390
Time of the day × Year	0.000	0.000	0.000	0.000
Period × Year	0.000	0.055	0.000	0.055
Treatment × Time of the day × Period	0.401	0.799	0.401	0.265
Treatment × Time of the day × Year	0.193	0.112	0.193	0.112
Time of the day × Period × Year	0.004	0.440	0.000	0.001
Treatment × Time of the day × Period × Year	0.376	0.805	0.376	0.805

Significant effects (p < 0.05) indicate differences in activity patterns or posture-related behaviors based on the respective factors or their interactions.

). Mother cows took an average of 2573 ± 1.9 steps per day, and they spent 58 ± 0.01% of their time standing. The results reveal that tannin supplementation did not affect activity patterns, movement, or posture-related behaviors in mother cows. Proportions of standing bouts increased from morning to afternoon and then decreased, while lying proportions followed the opposite trend, reflecting a shift from active to less active behavior as the day progressed (Figure 5). The percentage proportion of transitions from lying to standing differs (p = 0.012) between groups, with tannin-fed cows exhibiting fewer transitions (5.7% ± 0.53%, 95% CI: 4.7–6.78%) compared to the control group (7.3% ± 0.66%, 95% CI: 6.1–8.73%). The motion index averaged 9552 ± 6.39 per day. However, both period (steps: p = 0.0005; motion index: p = 0.0001; standing time: p = 0.008) and time of day (steps: p < 0.0001; motion index: p < 0.0001; standing time: p = 0.0001) influenced these parameters. The findings show that tannin supplementation had no effect on overall activity levels, but cows fed tannins exhibited fewer lying-to-standing transitions while step count and motion index varied by period and time of day.

Figure 5. The percentage of standing time relative to lying time in beef cows that received a DDGs supplement (Control—Ctrl; no tannins) or the same supplement containing a mix of condensed and hydrolyzable tannins (Tannins), averaged across four periods (panels, n = 3 paddocks per treatment) of the study. The 24-h day was divided into four 6-h periods: Time 1 (morning), Time 2 (afternoon), Time 3 (evening), and Time 4 (night). Standing proportions decreased, and lying proportions increased as time progressed, reflecting a shift from active to less active behavior throughout the day. Cows receiving tannins exhibited lower standing proportions than those without tannins, particularly during earlier periods. Distinct patterns in standing bouts emerged across periods, with some showing higher standing activity early in the day and others transitioning to lying bouts more quickly.

Figure 5. The percentage of standing time relative to lying time in beef cows that received a DDGs supplement (Control—Ctrl; no tannins) or the same supplement containing a mix of condensed and hydrolyzable tannins (Tannins), averaged across four periods (panels, n = 3 paddocks per treatment) of the study. The 24-h day was divided into four 6-h periods: Time 1 (morning), Time 2 (afternoon), Time 3 (evening), and Time 4 (night). Standing proportions decreased, and lying proportions increased as time progressed, reflecting a shift from active to less active behavior throughout the day. Cows receiving tannins exhibited lower standing proportions than those without tannins, particularly during earlier periods. Distinct patterns in standing bouts emerged across periods, with some showing higher standing activity early in the day and others transitioning to lying bouts more quickly.

3.7. Animal Distribution

Animals primarily grazed on brome grass in period 1 (baseline; no tannins). In period 4, these animals gained experience with tannin supplementation and changed their grazing behavior. In Period 4, the TT paddocks had a lower coefficient of variation (CV) (1.861 vs. 2.13 in Ctrl; p = 0.043, t-test;

Table 3. Grazing Intensity and Distribution Metrics for cows (n = 3/treatment) receiving a supplement with tannins (TT) or without tannins; Control (Ctrl) and Tannin-Supplemented (TT) Paddocks Across Periods.

Period	Treatment	Min	Max	Mean	STD	Coeff. Var	Count
P1	Tannin	0.00010	1.246	0.070	0.081	1.151	254,184
P1	Control	0.00010	1.783	0.087	0.106	1.220	244,770
P4	Tannin	0.00010	2.593	0.068	0.127	1.861	254,184
P4	Control	0.00010	4.658	0.097	0.208	2.130	244,770

), indicating that tannin supplementation led to a more evenly distributed grazing pattern compared with the control group. These results indicate that tannin supplementation led to a more evenly distributed grazing pattern in TT compared to the control group (Figure 6).

4. Discussion

In free range grazing systems, plant diversity provides ruminants not only with a broad range of nutrients but also access to a variety of plant secondary compounds (PSCs) including terpenoids, alkaloids, flavonoids, saponins, and tannins. These compounds can regulate intake, support metabolic efficiency, and enhance nutrient utilization [36,37].

Among these PSCs, condensed and hydrolysable tannins are particularly well studied for their ability to form complexes with dietary proteins, reduce ruminal proteolysis, and enhance post-ruminal amino acid absorption, thereby improving nitrogen retention and reducing urinary nitrogen losses [38,39,40]. Tannin-rich forages such as sainfoin (Onobrychis viciifolia) and birdsfoot trefoil (Lotus corniculatus) alter grazing behavior, enhance animal performance, and reduce greenhouse gas and nitrogen emissions from grazing cattle [41].

We hypothesized that supplementing cattle with a commercial tannin extract would mimic some of the functional benefits of grazing phytochemically diverse forages by enriching a chemically uniform pasture with bioactive compounds. Specifically, we tested whether supplementing the diet of cows grazing a bromegrass (Bromus inermis) monoculture with a combination of hydrolyzable and condensed tannins would affect their foraging behavior, activity patterns, water intake, performance, and spatial use of the landscape during grazing. This approach enabled us to assess whether targeted phytochemical supplementation can emulate key ecological functions of plant diversity and improve animal responses in grass monoculture systems.

4.1. Forage Biomass and Nutritional Value

Meadow bromegrass availability declined substantially from the beginning to the end of the grazing period in both years, with an average standing biomass that decreased from 4985 kg/ha in Period 1 to 2114 kg/ha in Period 4. Such decline reflects the cumulative effects of grazing pressure and seasonal constraints, particularly the region’s limited mid-summer rainfall, which suppressed forage regrowth [42,43]. In support of this, a grazing trial with smooth brome, crested wheatgrass, and tall wheatgrass species ecologically similar to those in the present study showed that limited mid-summer rainfall restricted regrowth and amplified the effects of continued grazing pressure [44]. A moderate interannual decline was also observed, with biomass higher in 2023 (5272 kg/ha) than in 2024 (4669 kg/ha). This difference likely reflects both the rest period before grazing in 2023 and greater precipitation that year, when the Bear River Basin received up to 212% of the median snowpack and above-average rainfall, compared with the drier conditions of 2024 [45]. Izaurralde et al. [46] revealed that reduced precipitation limits forage productivity, patterns consistent with the trends observed in the present study.

Notably, crude protein (CP) content dropped markedly from ~7.0% to below 4.0% in both years, while fiber components (ADF and NDF) increased steadily. These seasonal shifts in forage quantity and quality are consistent with the advanced maturity of plants and declining forage digestibility during summer [47]. Similar trends have been reported across cool season grasses in U.S. rangelands and pasturelands [48,49]. Collectively, these findings indicate that both forage availability and nutritional value declined during the summer grazing period, constraining cattle performance and underscoring the importance of adaptive management, including protein-tannin supplementation strategies, under variable precipitation regimes.

4.2. Supplementation and Forage Disappearance

Reductions in CP content limits rumen microbial efficiency and overall nutrient utilization, ultimately reducing cattle performance. Such effect can be mitigated by protein supplementation as high-protein supplements support microbial activity and enhance fiber digestion [4,50,51]. In this study, cattle were supplemented with DDGs, contributing to sustain nutrient intake and rumen function as forage quality declined. Moreover, condensed tannin-enriched supplements may enhance nitrogen use efficiency and reduce methane emissions in livestock grazing low-quality forages [52,53]. This is because moderate concentrations of condensed and hydrolizable tannins in the diet reduce proteolysis, improving the supply of dietary amino acids for intestinal absorption [54]. In addition, tannins reduce methanogenesis which enhances the efficiency of energy use in ruminants [55]. Thus, these results support the integration of high-protein and tannin-containing supplements as effective strategies to counteract seasonal forage quality declines and improve nutrient utilization efficiency.

Despite a decreased biomass availability and nutritional value across periods, daily dry matter (DM) disappearance per cow–calf pair remained steady at approximately 16 kg/day, indicating that animals sustained intake levels throughout the study. Such consistency in intake likely reflects compensatory foraging behavior as nutritional quality and biomass supply declined. Villalba et al. [56] observed a similar pattern in cattle grazing chemically uniform pastures, where animals adjusted their behavior to maintain intake. This behavioral plasticity is consistent with the role of post-ingestive feedback at modifying foraging behavior by livestock [57,58,59]. Likewise, the present study shows that tannin supplementation did not influence the total amount of biomass removed. This is consistent with earlier findings suggesting that low-to-moderate concentrations of condensed tannins do not suppress voluntary feed intake [60,61,62]. These results support the integration of high-protein and tannin-containing supplements as effective strategies to counteract seasonal declines in forage quality. Provision of protein-rich supplements compounded with the addition of tannins, would help sustain microbial activity and nutrient use efficiency (e.g., through reductions in ruminal proteolysis and methanogenesis) without compromising forage intake.

4.3. Impact of Tannin on Water Consumption and Performance

Water intake revealed physiological adjustments associated with tannin metabolism. Cow–calf pairs in TT drank significantly more water (147 L/day) than controls (121 L/day), consistent with reports that ruminants consuming tannin-rich diets increase water consumption to facilitate renal clearance of tannin–protein complexes and maintain ruminal osmotic balance [13,14,63,64]. Orzuna-Orzuna et al. [13] observed that cattle modulated water intake to counteract tannin astringency, while Besharati et al. [65] showed that water availability influences detoxification efficiency and animal health under tannin-rich diets. Elevated intake dilutes ruminal toxins, enhances excretion, and sustains favorable fmicrobial activity [66,67,68]. Nevertheless, such increases present physiological and logistical challenges in arid and semi-arid rangelands where water is limited and rainfall unpredictable [69,70,71]. Excessive demand can increase travel distance and energy expenditure, especially for lactating cows [72], whereas restricted access reduces dry-matter intake, elevates blood urea nitrogen, and disrupts nitrogen balance [73]. Hence, greater water intake reflects both a metabolic cost and an adaptive detoxification mechanism that supports nitrogen utilization and microbial stability. When managed strategically, tannin supplementation can enhance nitrogen efficiency, reduce methane emissions, and support animal health, though these benefits must be weighed against water constraints in dry environments.

In assessing animal performance, both Ctrl and TT cows lost weight over the 60-day summer grazing period, consistent with an anticipated seasonal forage deficit of quality and abundance. TT cows experienced a slightly lower average daily weight loss (~0.83 kg/day) compared to control cows (~0.98 kg/day), while TT calves gained slightly more weight (1.03 kg/day vs. 0.93 kg/day). Although these differences were not statistically significant, the numerical trends point to potential improvements in energy partitioning in favor of TT cows. The relatively short duration of this study (60 days) may have constrained the expression of significant improvements in body weight gains in calves. In longer-term studies (≥90 days), more consistent and meaningful gains have been observed with tannin supplementation. For example, Lagrange and Villalba [9] reported greater average daily gain (ADG) in calves grazing sainfoin for 120 days than those grazing on non-tannin containing forages. Similarly, Chung et al. [74] and Ebert et al. [12] showed improved nitrogen retention and weight gain in heifers and steers supplemented with tannin-rich sainfoin hay over 90 to 105-day periods. These findings suggest that prolonged exposure to bioactive tannins beyond the 60-day window used in the present study may be necessary to realize their full physiological potential, as improvements in weight gain and metabolic adaptation often require extended timeframes to manifest under free grazing systems.

4.4. Impact of Tannin on Animal Foraging Behaviors

Tannin supplementation induced notable changes in the foraging behavior of cow–calf pairs grazing meadow bromegrass in the present study, reflecting shifts in how animals interact with both their physiology and the forage environment. Although the total duration of daily activity (~11.6 h) did not differ between treatments, TT cows allocated more time to evening grazing and less to rumination. These temporal adjustments align with findings by Provenza et al. [19] and Villalba et al. [58], who reported that altered post-ingestive feedback improves synchronization between metabolic demand and forage opportunity, ultimately enhancing microbial efficiency.

In the present study, evening grazing by TT cows coincided with peaks in forage sugars and soluble proteins generated through photosynthate accumulation [75,76,77]. Tannins likely amplified this effect by stabilizing rumen nitrogen through protein–tannin complexes, slowing proteolysis and ammonia release, delaying satiety, and extending intake [66,78,79]. This more consistent nitrogen pool enhanced microbial activity when soluble carbohydrates were highest, improving protein yield and nutrient extraction [80]. Supporting this mechanism, TT cows in the present trial exhibited a 28% reduction in blood urea nitrogen (BUN), indicating improved nitrogen retention and reduced ammonia burden [54,55]. Comparable effects have been observed in dairy cattle [81], sheep [82], and goats [83,84] while Min et al. [85] showed that dosage strongly influences nitrogen retention under controlled diets. Free-grazing systems such as the present study add further complexity, as tannin effects are shaped by species-specific foraging strategies and fluctuating forage chemistry across plant maturity and seasonal growth. Evening grazing in the present study also conferred thermoregulatory and metabolic benefits. Cooler conditions reduce heat load while maximizing access to sugar-rich forage [86], whereas rumination during hot periods elevates core body temperature [87]. Stabilized ammonia concentrations and rumen pH under tannin diets may have created more favorable fermentation conditions, lowering the risk of subacute acidosis [88,89] and improving overall microbial balance.

Although TT cows in the present study traveled similar daily distances (~44 m/day) as controls, they shifted more grazing activity into evening hours, aligning intake with cooler conditions and higher forage quality [90,91]. Such behavioral plasticity mirrors patterns in cattle [92], sheep [93,94] and wild ungulates such as elk and deer [95,96]. Collectively, reduced evening rumination and increased evening grazing in TT cows reflect a coordinated strategy integrating thermoregulation, energy efficiency, and microbial optimization, ultimately supporting more efficient grazing, improved nitrogen partitioning, and enhanced animal performance.

4.5. Impact of Tannin on Animal Grazing Distribution

Understanding livestock–landscape interactions requires distinguishing animal distribution from grazing distribution. Animal distribution reflects the general occupancy of the landscape, using all GPS points without distinguishing behavioral states [97,98], which indicates movement but not forage removal [99,100]. Reliance on occupancy alone misrepresents grazing impact [101] highlighting the need to link animal position with actual plant use. A behavior-classification approach integrating multi-sensor data and machine learning was therefore used to redefine grazing distribution [30] in the present study. The approach aligns with foraging ecology theory, which emphasizes that herbivory depends not only on where animals go but also on what they do [96,102], and supports arguments that behavior-based classification is essential for identifying actual grazing hotspots [103,104]. The present study applied this approach to evaluate how tannin–protein supplementation influenced animal grazing distribution.

Tannin supplementation reduced variation in grazing distribution, creating a more uniform and spatially extensive pattern. Grazing pressure spread more evenly across paddocks, reducing overuse of preferred patches and promoting regrowth and long-term pasture health [105]. Uneven grazing, usually driven by selective use of nutrient-rich patches, favorable slopes, or familiar areas [106,107], was moderated through both metabolic and behavioral changes. These spatial adjustments may also be associated with post-ingestive feedback linked to rumen nitrogen dynamics. When tannin–protein complexes reduce rumen-available crude protein below the 6–8% threshold for optimal microbial function in ruminants [62,63], the microbiome–gut–brain axis signals nitrogen deficiency via hormones such as GLP-1 and leptin [77]. These cues adjust motivational states and guide cattle toward patches richer in crude protein and soluble nutrients, restoring rumen microbial efficiency and nitrogen balance. This foraging mechanism integrates internal nutrient feedback with spatial decision-making, providing a physiological basis for the observed redistribution. Consistent with this mechanism, tannin–protein complexes improved nitrogen-use efficiency, lowering the drive to target high-protein patches [108,109]. Grazing dynamics shifted in line with marginal value foraging theory, with earlier patch departure, fewer revisits, and greater use of intermediate-quality areas [110,111,112]. The integration of these metabolic and behavioral processes explained the more even grazing patterns observed under supplementation and provided empirical support for mechanisms that had previously been suggested but not directly tested. More uniform grazing also linked spatial redistribution to ecological outcomes, with more impacts in monoculture systems where limited structural and phytochemical diversity amplify uneven use. While in diverse pastures phytochemical variety allowed animals to balance intake through self-selection, reducing the relative influence of tannins on distribution [113,114]. Overall, tannins acted as both metabolic and spatial modulators of grazing behavior and distribution. By reducing uneven patch use, broadening forage coverage, and improving nutrient synchrony, supplementation enhanced pasture-use efficiency and strengthened ecological resilience. Behavior-based spatial analysis provided a methodological advance [115] and reframed phytochemicals from anti-nutritional compounds to ecological tools that can guide sustainable and climate-resilient grazing systems.

4.6. Impact of Tannins on Animal Activity Levels

In this study, cows supplemented with a blend of condensed and hydrolizable tannins exhibited no differences in gross activity metrics such as step count, motion index, or time spent standing, indicating that moderate tannin inclusion (i.e., approximately 0.4% in the diet) did not disrupt general movement patterns. These findings are consistent with previous research showing that phytochemically enriched forages or tannin exposure, when provided at moderate levels or through self-selection, do not elicit marked changes in locomotor behavior [116,117]. The lack of significant variation in these activity indicators suggests that cattle were able to maintain normal daily routines without increased physical effort or behavioral compensations, further supporting the non-detrimental effects of tannins on animal activity relative to control animals. This stability likely reflects a balance between post-ingestive feedback, microbial adaptation, and nitrogen conservation pathways that minimize the need for changes in foraging effort or movement.

Moreover, tannin-supplemented cows displayed fewer posture transitions specifically, shifts from standing to lying than controls. This reduction may signal improved internal comfort, as frequent posture changes are often linked to discomfort, digestive stress, or physiological agitation [118]. Ethological studies in livestock have used posture-based measures to detect subtle shifts in welfare status, with more stable postural behavior interpreted as a sign of enhanced well-being [119,120,121]. In this context, fewer transitions may reflect reduced ruminal ammonia load and better nitrogen partitioning, consistent with tannins’ ability to bind dietary protein and moderate rumen fermentation [122]. These results highlight that while overt activity patterns remained unchanged, posture-related behaviors provided a more sensitive window into the animals’ internal state. Importantly, the findings point to the potential of moderate tannin supplementation as a welfare supportive tool under uniform, low-diversity grazing conditions helping livestock maintain metabolic efficiency and behavioral stability without the need for increased physical activity or stress-linked adjustments.

4.7. Study Limitations and Future Directions

This study provides new insights into how tannin supplementation modulates grazing behavior, spatial distribution, and associated physiological responses in free-grazing cattle. Several limitations warrant consideration. The small number of paddock replicates (n = 6) and the 60-day duration may have limited power to detect subtle performance differences, while mid-summer rainfall and temperature variability could have influenced forage quality and responses. Although behavioral and spatial metrics showed consistent treatment effects, key physiological indicators (blood metabolites, rumen fermentation parameters, thermal stress markers) were not measured. Inclusion of these parameters is proposed to validate welfare and metabolic mechanisms. Given the higher water intake under tannins, evaluating water-provisioning logistics and trade-offs in water-limited rangelands is suggested. A numerical increase in calf weight under tannin supplementation was observed. Extending trials to ~120 days and explicitly testing milk yield and composition (protein, fat, lactose, urea-N, bioactives, lipidome, phenolics/metabolites) is proposed to determine whether milk-mediated pathways link maternal supplementation to calf growth and sustainable productivity. We recommend evaluating tannin interactions with other plant secondary metabolites (saponins, terpenoids, flavonoids) on nutrient-use efficiency, performance, and environmental outcomes, and integrating targeted and untargeted metabolomics to elucidate underlying mechanisms and biochemical pathways. To identify where supplementation remains beneficial, ecosystem-scale validation across contrasting grazing regimes is needed to define benefit thresholds. In parallel, incorporating multi-sensor behavioral datasets into Hidden Markov Models and resource selection functions is proposed to improve predictions of grazing dynamics under variable climate conditions. Finally, techno-economic and sustainability assessments are proposed that integrate supplement cost, water requirements, animal performance, and ecosystem-service outcomes to determine net benefits across diverse production systems.

5. Conclusions

Although total forage intake, herbage disappearance, and cow body weight were not significantly affected (p > 0.05), tannin-fed animals exhibited longer evening grazing periods, more even spatial grazing distribution, and fewer posture transitions (p < 0.05). These shifts promoted by tannins point to enhanced evening intake and reduced patch overuse, outcomes consistent with improved welfare and more uniform pasture utilization two pillars of sustainable grazing. Increased water intake (p < 0.001) under tannin supplementation highlights an adaptive physiological response that may require management consideration in water-limited rangelands. Calf average daily gain was numerically higher with tannin supplementation (p = 0.27), but the difference was not statistically significant. We recommend that future investigations extend observation periods to at least 120 days to detect performance responses and quantify milk yield and composition, thereby testing milk-mediated pathways that link maternal tannin supplementation to calf growth. Additional priorities include evaluating water-provisioning logistics in water-limited rangelands, establishing dose–response relationships to identify optimal tannin inclusion across contrasting production systems. Examining interactions between tannins and other plant secondary metabolites using targeted and untargeted metabolomics to resolve the mechanisms behind these interactions. We also recommend integrating multi-sensor behavioral datasets into Hidden Markov models and resource selection functions to predict grazing dynamics under variable climatic conditions. Additionally, we suggest conducting techno-economic and sustainability assessments that jointly consider supplement costs, water needs, animal performance, and ecosystem-service outcomes.