Effects of brown seaweed supplementation on methane and carbon dioxide emissions and productivity indicators in pregnant replacement heifers

1 Introduction

Cattle account for approximately two-thirds of the greenhouse gas (GHG) emissions from the livestock sector, and beef production alone contributes approximately 6% of total global methane (CH₄) emissions (Chung and Hadi, 2025). In Canada, the agriculture sector is a major source of methane emissions, with a substantial proportion arising from enteric fermentation in cattle (Environment and Climate Change Canada, 2023). Consequently, reducing enteric CH₄ emissions from cattle is important for improving the environmental sustainability of livestock production and supporting national climate targets. Therefore, developing strategies to reduce CH₄ emissions in cattle without compromising their health or performance is of considerable interest. In addition to CH₄, evaluating CO₂ emissions during gas exchange may provide further insight and complementary information on how dietary strategies influence overall gaseous outputs in cattle. Among short-term mitigation strategies, both synthetic and natural feed additives have received increasing attention.

However, further research is needed to better understand their effect and optimal supplementation levels as potential CH₄ mitigants in ruminant production systems.

Research has shown that feed additives including 3-nitrooxypropanol (Hristov et al., 2015), seaweeds such as Asparagopsis spp (Kinley et al., 2020), ionophores (Guan et al., 2006), vegetable oils (Hristov et al., 2013), and essential oils (Silvestre et al., 2022) in cattle diets can lead to a sustained reduction in enteric CH₄ emissions in dairy and beef cattle, although the extent of reduction varies by additive and production context. However, a major challenge remains in ensuring an adequate and consistent supply of these additives for their widespread use as a viable and sustainable option as well as overcoming regulatory barriers for synthetic additives. There is a growing interest in marine macroalgae due to their biomass and potential to mitigate CH₄ emissions in ruminants. An in vivo study showed that supplementation of Asparagopsis taxiformis, a bromoform-containing red seaweed, in the diets of beef steers reduced CH₄ production by 80% (Roque et al., 2021). These in vivo findings suggest the potential of a seaweed-based feed additive to reduce the carbon footprint of ruminant livestock and enhance production efficiency, without affecting meat quality or taste preferences (Roque et al., 2021). However, the main challenge is having enough local supply to meet the industry demand.

Therefore, regionally available seaweed products may provide a practical alternative. One such product available in Atlantic Canada is storm-tossed kelp and rockweed, specifically the species Laminaria longicruris and Fucus vesiculosus, commercially known as Atlantic GRO^®. Atlantic GRO^® is a feed additive made from a dried and ground 100% storm-tossed kelp and rockweed (North Atlantic Organicsn.d). Unlike Asparagopsis, whose methane-reducing effect is largely attributed to bromoform, the mechanism by which brown seaweed products may influence CH₄ production is less clearly established. The CH₄ mitigation potential of this supplement was evaluated in vitro using artificial rumen systems, where supplementation with 1% seaweed demonstrated up to a 16% reduction in CH₄ emissions compared to the control (Kinley and Fredeen, 2015). However, these in vitro findings have yet to be validated in live animals, and the optimal dosage of Atlantic GRO^® for CH₄ reduction in live cattle remains unknown despite its use in the region by beef and dairy producers as a nutritional supplement. A scientific assessment of Atlantic GRO^®’s impacts on cattle productivity, CH₄ and CO₂ emissions, and optimal supplementation level is essential to support its widespread adoption in ruminant diets. Such an assessment supports not only environmental preservation and agricultural resilience by lowering GHG emissions and reducing the ecological footprint but also sustainable livestock management through improved performance.

Therefore, the objective of this study is to determine the optimal inclusion rate of North Atlantic GRO^® in terms of CH₄ reduction, while evaluating its effects on CO₂ emissions, O₂ consumption, and animal productivity in beef replacement heifers.

2 Materials and methods

All experimental procedures (Animal Use Protocol #2022-032) were approved by the Animal Care and Use Committee, Dalhousie University, Nova Scotia, Canada, and the animals were cared for in accordance with the guidelines of the Canadian Council on Animal Care (CCAC, 2009).

This study was carried out in two phases: a performance trial followed by a GHG emission trial. The second phase was a follow-up trial conducted on a randomly selected subset of heifers from the first phase to evaluate gas emissions under chamber conditions.

2.1 Experiment I: performance testing

2.1.1 Animals and treatments

Twenty 18-month-old pregnant replacement heifers, with an average weight of 383 kg, were included in the experiment for 50 days from the mixed breed beef herd at Nappan Research Farm (Nappan, NS). Heifers were synchronized and then bred by artificial insemination in the second week of June. Therefore, they were in early gestation at the start of the trial. The heifers were of mixed breed composition of Black Angus, Shorthorn, Hereford, and Red Angus. They were randomly allocated into one of four seaweed supplement treatment groups: 0.0% (control), 0.5%, 1.0%, and 2.0%, on a dry matter basis. Heifers within each treatment group (n = 5 per group) were penned together at the Maritime Beef Test Station (Nappan, NS). Heifers assigned to the same dietary treatment were housed together within treatment-group pens. Animals had ad libitum access to feed and water, and individual feed intake data were recorded using the Growsafe system (Vytelle LLC). A half-duplex RFID ear tag was used by the system to identify the individual animals and log the weight of feed consumed (as-fed) by each animal. Feed intake recorded on an as-fed basis was converted to a dry matter basis using daily feed samples collected from the feed wagon. Animal weights were recorded at the Nappan Research Farm animal handling facility at the beginning, bi-weekly during the trial, and at the end of the performance test.

2.1.2 Experimental diets and sample analysis

The basal ration consisted primarily of bunker silage and was formulated to meet the nutritional requirements of pregnant replacement beef heifers. Nutrient composition of the diet is summarized in Table 1. The diet included 83.8% grass silage, 13.2% barley, 2.9% soybean meal, and 0.01% mineral–vitamin premix. Prior to commencing the trial, bunker silage samples were submitted to the Nova Scotia Department of Agriculture (NSDA) feed analysis lab in Truro, Nova Scotia, Canada, for nutrient analysis. The results were used to formulate the experimental diet. During the trial period, samples were collected daily directly from the feed wagon and were subsequently dried to determine dry matter content, facilitating weekly adjustments of the seaweed supplement to the diet. Dried samples were ground and mixed weekly, and then each composite was subsampled and sent for nutritional analysis, including dry matter, crude protein, and total digestible nutrients, at the NSDA (Table 1). The seaweed supplement was subjected to nutritional analysis at the Dairy One Forage Laboratory in Ithaca, New York, USA. Samples were also submitted to A&L Laboratories (Ontario, Canada) for heavy metal analysis and to Michigan State University (East Lansing, MI, USA) for iodine determination (Table 1).

Table 1. Nutrient composition of seaweed supplement and the experimental diet composition over 7 weeks of the performance trial (mean ± standard error).

TMR, total mixed ration.

2.1.3 Adaptation and diet preparation

Prior to the initiation of the trial, heifers underwent a 2-week adaptation period to the treatment diets at the Nappan Research Farm. The base diet was prepared at the Research Farm feed mill facility. Seaweed supplement was incorporated by top dressing and mixing by hand into the feed of each group accordingly after placement into the bunk by the feed cart. Daily feed intake and refusals were measured to determine total diet to mix moving forward. Dry matter samples were collected daily, and weekly averages were used to maintain target seaweed % per group. After the adaptation period, seaweed was added directly to the feed cart to simplify the mixing process. This change in delivery method was implemented after the 2-week adaptation period to improve operational efficiency and achieve more uniform treatment distribution. The formal performance test began after adaptation and continued for 50 days while maintaining the target inclusion rates based on dry matter adjustment.

2.1.4 Seaweed total phenolic analyses

2.1.4.1 Sample preparation and extraction

The seaweed supplement used in this trial was characterized for its total phenolic content by the National Research Council of Canada (NRC), Aquatic and Crop Resource Development, using two samples collected in June 2024 and October 2024. Seaweed materials were ground to a fine powder (<425 μm) prior to extraction. For total phenolic content analysis, triplicate extractions were performed by weighing 10 mg of seaweed powder and extracting it with 2 mL of 80% methanol (MeOH) under constant stirring at room temperature. After centrifugation, the supernatant was removed, and the extraction was repeated. The combined extract volume was adjusted to 4 mL.

2.1.4.2 Total phenolic content determination

A 1:20 dilution of the 80% MeOH extract was used for total phenolic determination. The Folin–Ciocalteu assay was adapted to a 96-well microplate format, using phloroglucinol (Sigma #79330, 99%) to generate a calibration curve. Total phenolic content was expressed as mmol phloroglucinol equivalents (PGE) per 100 g of seaweed. All analyses were performed in triplicate, and results are presented as mean ± standard deviation.

2.2 Experiment II: GHG emissions and gas flux testing

2.2.1 Trial setup

Experiment II was conducted as a follow-up gas emission phase on a randomly selected subset of animals from experiment I. Due to the logistical capacity of the chambers, 16 of the 20 heifers from the performance test (n = 4 per group) were randomly selected to continue in the second trial after the 50-day performance trial. Animals were housed in floor pens, two heifers per pen, in the Metabolism building at the Nappan Research Farm. They remained on their assigned treatment diets from the performance test, with feed mixing continuing using the feed cart. They were fed ad libitum, and daily samples of the base diet were collected for DM determination, averaged per week, and used to maintain the seaweed supplement % for each group. Nutrition compositions of the diet are presented in Table 2. Throughout the trial, a routine feeding schedule was implemented to standardize management and synchronize feed intake. The daily ration was divided into five equal portions delivered at five specific times, i.e., 9:30 a.m., 10:30 a.m., 11:30 a.m., 12:30 p.m., and 1:30 p.m. Feeding time accounted for the 1-h delay in start time each day, compared to the day prior, due to time required to remove four animals from the hoods and replace with four new animals. This multilevel feeding strategy was adopted specifically to promote a more stable and continuous rumen fermentation pattern.

Table 2. Nutritional composition of the diet offered to the replacement heifers during the greenhouse gas emission trial, averaged over 2 weeks.

GHG, greenhouse gases.

2.2.2 GHG measurement

Four ventilated headbox-style metabolic chambers (Qubit Systems Inc., Ontario, Canada) were set up in a room adjacent to the floor pen rooms (Figure 1). Headboxes were connected to two vacuum pumps (two headboxes per pump) located outside of the building which pulled air through the chambers at a rate of 600 L/min. Two of the headboxes (one attached to each vacuum pump) were equipped with an additional CO₂ monitor (Greystone Energy Systems Inc., Moncton, New Brunswick, Canada) attached to a Web600 central hub (Sensaphone, Aston, Pennsylvania, USA), which would remotely notify staff via text message if CO₂ levels in the headbox exceeded 8,000 ppm. The gas flux measurement software was programmed to cycle through all four chambers and a reference air channel, in sequence, every 30 min for 24 h. A reference air channel was included in each measurement cycle to account for background gas concentrations, and the headbox system was operated according to manufacturer guidelines and established chamber procedures. The system dwelt on each headbox for 6 min: 2.5 min for flushing and 3.5 min for data measurement and logging.

2.2.3 Heifers adaption and data collection

Heifers were habituated to the metabolic chambers over a 2-week period by cycling them through the chambers three times. In brief, four heifers were habituated to the chambers for a period of 2 h in the morning and then returned to their floor pens. In the afternoon, four different heifers were habituated to the chambers for 2 h. The following day, this was repeated for the remaining eight heifers. On the third day, the first eight heifers were cycled through the chambers for 2-h periods, the same as the first day. On the fourth day, the remaining eight heifers were again cycled through for 2 h. The following week, the first four heifers were habituated for a period of 24 h. The following day, those animals were removed and the next four heifers were habituated to the chamber for a period of 24 h, and so on until all 16 heifers had experienced a 24-h period in the chambers. This marked the completion of the habituation period. At the end of the habituation period, one of the heifers aborted her fetus and was removed from the study, reducing the control group to three animals. The calf was sent for autopsy, and the cause of death was not related to the study. The following week, heifers were cycled through the chambers for a period of 24 h, the same as the week prior, but this time oxygen consumption and CO₂ and CH₄ emissions data were collected. This 24-h data collection was repeated for a second week so that each animal was measured twice. They were weighed prior to entering the chamber. While in the chambers, heifers had ad libitum access to their treatment diet and water. All feed given and refused was also recorded during the data collection periods in the chambers. Feed samples were collected from the feed cart daily, dried, ground and pooled per week, and sent to the NSDA for nutritional analysis (Table 2).

2.2.4 Trait calculations and statistical analysis

2.2.4.1 Performance test analysis

Individual average daily gain (ADG) was calculated over the 50-day period based on the difference between ending and beginning weight, and feed conversion ratio (FCR) was calculated over the 50-day period as the average of individual feed intake over 50 days divided by ADG. Repeated body weight (BW) and daily dry matter intake (DMI; kg DM/day) were analyzed as repeated measures with treatment, days in test (DIT), and TRT × DIT, plus covariates using PROC MIXED in SAS (Version 9.4; SAS Institute, 2013), Equation 1 (below).

However, ADG and FCR were analyzed as whole-period summary traits with treatment, breed, initial BW, and age using Equation 2. Results of p < 0.05 were considered statistically significant. Least squares means were used to compare treatment groups, where significant differences existed.

(1) Yijkl=μ+Ti+DITj+(T×DIT)ij+ Breedk+β1(A)+β2(SW) +Ak+eijkl

(2) Yijk=μ+Ti+ Breedj+β1(A)+β2(SW)+eijk

Where Y_ijkl = observed response variable, μ = overall mean, T_i = fixed effect of treatment (seaweed), DIT_j = fixed effect of days in test (DIT), (T × DIT)_ij = fixed effect of the treatment × DIT interaction, β₁(A) = regression effect of age, β₂(SW) = regression effect of initial body weight, A_k = random effect of animal, and e_ijk = residual error.

2.2.4.2 Gas emissions

2.2.4.2.1 Trait calculations

The CH₄ concentration was continuously measured and logged at 1-s intervals using a Qubit large animal gas exchange system and recorded as a volumetric mixing ratio in parts per million by volume (ppmv). During each measurement cycle, chamber air was compared with a reference air channel, and corrected CH₄ concentration was obtained by subtracting the corresponding reference channel reading from the chamber reading. Corrected CH₄ concentration was then converted to a volumetric emission rate (mL/min) using the chamber airflow rate as follows:

CH4(mL/min)=([CH4]corrected(ppmv)/106)×Flow (L/min)×1,000

Where [CH₄] corrected (ppmv) represents the CH₄ concentration after manually subtracting the reference channel reading, which was used to account for background CH₄ concentration during each measurement cycle. Flow is the volumetric airflow through the chamber, 106 converts ppmv to a volumetric fraction, and 1,000 converts liters to milliliters.

Subsequently, for total CH₄ production analysis, expressed as a mass flow rate in grams per hour (g/h) using the ideal gas law, adjusted for chamber temperature and atmospheric pressure, as follows:

CH4(g/h)=[CH4](mL/min)×(1 L/1,000 mL)×(16.04 g/mol/22.4 L/mol)×(273 K/(273 K+Temp°C))×(AtmPress(hPa)/1,013 hPa)×60 min/h

16.04 g/mol is the molecular weight of CH₄, 22.4 L/mol is the molar volume of an ideal gas at 0 °C and 1 atm, Temp is the chamber temperature in °C, AtmPress is the atmospheric pressure in hPa, and 60 converts minutes to hours. Thus, CH₄ concentration was first corrected for background air and then converted to volumetric and mass emission rates under the observed chamber conditions. The background-corrected CH₄ emission values were subsequently aligned across animals using cubic spline interpolation to estimate CH₄ production at common predicted time points prior to statistical analysis.

2.2.4.2.2 Statistical analysis

Methane emission and gas exchange data were analyzed using the SAS statistical software via a linear mixed-effects model fitted by restricted maximum likelihood (REML). Because measurements were collected repeatedly within animals across time, the animal was considered the experimental unit, and a repeated-measures structure was implemented to account for within-animal temporal correlation. Different covariance structures were tested, and the covariance structure for repeated observations was modeled using a first-order autoregressive structure [AR(1)] given a lower AIC among the tested covariance structures, which assumes that observations closer in time are more strongly correlated than those further apart. Moreover, denominator degrees of freedom were adjusted using the Kenward–Roger method to improve inference in the unbalanced repeated-measures design.

Measurements within each feeding cycle were recorded at slightly different time points among animals. Therefore, a cubic spline interpolation was first applied to the raw CH₄ data to estimate CH₄ production at a set of common predicted time points across animals. These predicted time points were then used as the time variable in the repeated-measures mixed model.

The final statistical model included seaweed treatment level, predicted time point, and their interaction as fixed effects. Because seaweed was provided at graded inclusion levels, orthogonal polynomial contrasts were used to test linear and quadratic dose–response relationships across seaweed treatments.

Potential covariates, including initial body weight, age, feeding time relative to measurement, and time since the start of the test, were evaluated to account for biological and temporal variation among animals. Covariates that were not statistically significant were removed from the final model.

The statistical model used for the analysis was as follows:

Yijkl=μ+Ti+Timej+(T×Time)ij+Animalk+eijkl

where Y_ijkl represents the observed CH₄ emission or gas exchange variable, μ is the overall mean, T_i is the fixed effect of seaweed treatment level, Time_j is the fixed effect of predicted time point within the feeding cycle, +(T × Time)_ij is the interaction between treatment and time, Animal_k is the random effect of animal, and e_ijkl is the residual error with repeated observations modeled using an AR(1) covariance structure. Statistical significance was declared at p ≤0.05, and tendencies were discussed at 0.05 < p ≤ 0.10.

3 Results

3.1 Seaweed composition—phenolic content

The total phenolic content of seaweed is presented in Table 3 as descriptive compositional data. The mean phenolic content, expressed as PGE, ranged from 20.98 to 25.16 mmol PGE/100 g dry weight.

Table 3. Total phenolic content of seaweed supplement samples.

3.2 Growth parameters and feed efficiency

The impacts of seaweed supplementation as well as other factors on heifers’ growth parameters, including ADG, FCR, DMI, and BW change (initial and final weights), are summarized in Table 4. The DIT was the only variable among the listed fixed effects that had a significant effect on DMI (p < 0.001), such that DMI increased with increasing DIT. In general, no clear treatment-related differences in growth performance or efficiency in heifers were detected during the study period.

Table 4. Effects of seaweed supplementation on growth performance and feed efficiency in replacement heifers (n = 5/treatment).

ADG, average daily gain; FCR, feed conversion ratio; DMI, dry matter intake; BWG, body weight gain; DIT, day in test; NS, not significant; NA, not applicable.

Standard errors are indicated in parentheses.

3.3 Greenhouse gas emission

Table 5 summarizes the results for the greenhouse gas emissions. Methane emissions were significantly affected by seaweed supplementation (p < 0.001). Mean CH₄ production decreased from 11.89 g/h in the control treatment to 10.93 and 10.86 g/h in the 1% and 2% seaweed treatments, corresponding to reductions of approximately 8.2% and 8.7%, respectively, compared to the control group. The highest CO₂ emissions were observed in the 0% seaweed group (342.2 g/h), followed closely by 0.5% (337.6 g/h), while the 1.0% and 2.0% groups showed lower levels (328.5 and 329.3 g/h, respectively; p < 0.001), compared to the control group. O₂ consumption was also significantly affected by diet, with the highest values observed in the 0.0% group (250 g/h) and slightly lower levels in the 0.5% group, while the 1.0% and 2.0% groups showed reduced consumption (242–243 g/h; p < 0.001) compared to the control group.

Table 5. Effects of seaweed supplementation on greenhouse gas emissions in replacement heifers.

Letters (a, b, c, d) indicate statistically significant differences between treatments (p < 0.05), as determined by Tukey’s test.

SE, standard error; CH₄, methane; g/h, grams per hour; NA, not applicable.

Gas emission phase sample size was n = 4 for the 0.5%, 1.0%, and 2.0% groups and n, 3 for the control group after removal of one control heifer following abortion.

Polynomial contrasts indicated a significant linear dose–response relationship (p < 0.001) between seaweed inclusion and CH₄ emissions, indicating progressive CH₄ mitigation with increasing seaweed supplementation. A modest but significant quadratic component was also detected (p = 0.046), suggesting diminishing marginal reductions in CH₄ emissions at higher inclusion levels.

Significant CH₄ mitigation was observed at ≥1% seaweed inclusion, while the difference between the 1% and 2% treatments was small, indicating a plateau in CH₄ reduction at higher supplementation levels. No significant treatment × time interaction was detected, suggesting that the CH₄ mitigation effect of seaweed supplementation remained consistent across sampling periods during the measurement cycle.

4 Discussion

4.1 Evaluating the benefits of seaweed supplementation on beef production parameters

Seaweed supplementation has received increasing attention as a potential strategy to influence cattle productivity. Contrary to our results, some previous studies evaluating dietary seaweed inclusion (Roque et al., 2019; Wan et al., 2019; Magnusson et al., 2020) have reported improvements in performance traits such as ADG and FCR. The ADG (0.52–0.63 kg/day) and FCR (15.0–25.86) observed in our study were comparable to those reported in replacement heifer studies without seaweed, such as Lancaster et al. (2021) (ADG: 0.64 kg/day; FCR: 12.28). However, these values were lower than those reported in studies using higher-energy diets or different production classes mainly at the finishing stage, such as Basarab et al. (2007) and Mu et al. (2016), who reported ADG values exceeding 1.3 kg/day. This difference (lower ADG in our study) is consistent with the production goals of replacement heifers, where controlled growth is preferred over maximizing weight gain. Importantly, seaweed supplementation did not negatively affect growth performance in the present study. Similar findings have been reported in beef and dairy cattle supplemented with red (A. taxiformis) or brown (Ascophyllum nodosum) seaweeds, where no significant effects on ADG were observed (Gardiner et al., 2020; Lean et al., 2021; Min et al., 2021; Roque et al., 2021). Likewise, in the present study, seaweed supplementation did not significantly affect ADG.

Seaweed supplementation did not significantly affect DMI, although a non-linear intake trend was observed. Previous studies have also reported inconsistent effects of seaweed on feed intake. For example, supplementation with A. taxiformis at 0.25%–0.5% of diet DM did not alter DMI in beef cattle (Kinley et al., 2020; Stefenoni et al., 2021; Roque et al., 2021). Similarly, Grabez et al. (2023) reported no change in DMI when Saccharina latissima was included at up to 1.5% of diet DM, although intake declined at higher inclusion levels. A dairy study also reported no negative effects of low-level supplementation with S. latissima or Chondrus crispus on feed intake (Muizelaar et al., 2023). These findings suggest that seaweed supplementation may influence intake only above a certain inclusion level, whereas lower inclusion levels may be insufficient to produce a measurable response. In our study, heifers appeared to tolerate moderate inclusion levels without substantial changes in intake, suggesting adaptation to the supplement. Seaweed contains bioactive compounds such as polyphenols, phlorotannins, and polysaccharides that may influence rumen fermentation and digestibility (Allen et al., 2001; Makkar, 2016). In addition, the relatively high mineral content of seaweeds may affect palatability or water intake, potentially influencing feeding behavior (Siddik et al., 2023). However, because DMI was not significantly affected in the present study, these factors did not appear to meaningfully influence feed intake under the conditions and inclusion levels evaluated.

The relatively high FCR observed in this study likely reflects the intentionally moderate growth rates targeted for replacement heifers. Seaweed supplementation did not significantly affect FCR over the 50-day performance phase, which agrees with previous reports showing no effect of A. taxiformis or A. nodosum on feed efficiency in beef cattle (Allen et al., 2001; Anderson et al., 2006; Kinley et al., 2020; Antaya et al., 2019). Similarly, Muizelaar et al. (2023) reported no effect of several seaweed species on feed efficiency in dairy cattle. In contrast, Bach et al. (2008) reported improved feed efficiency in lambs supplemented with brown seaweed, suggesting that responses may vary depending on species, diet, and seaweed processing. In our study, FCR remained unchanged, even as CH₄ reduction was observed at the 1%–2% inclusion levels (discussed in the next section), suggesting that some bioactive compounds may reduce methanogenesis without substantially altering fermentation pathways affecting feed efficiency. Future studies evaluating the effects of this locally produced seaweed supplement in finishing cattle may help clarify potential benefits for growth performance and feed efficiency.

4.2 Effects of seaweed supplementation on methane emissions

4.2.1 Greenhouse gas emission characteristics

The study showed a linear relationship between seaweed inclusion levels and CH₄ emission. The most effective reduction was achieved with 1.0% and 2.0% supplementation without compromising animal performance. In our study, this response may be linked to the phytochemical composition of the supplement. Chemical analysis of the seaweed revealed variation in total phenolic content among samples. The presence of other phenolic compounds, which are known for their antimicrobial and antimethanogenic properties, may also have contributed to the CH₄ reductions observed at the effective inclusion levels (Barnes et al., 2025). It is therefore possible that the CH₄ mitigation observed at the 1.0% and 2.0% inclusion rates may be partly associated with the phenolic composition of the supplement, which may reflect the presence of compounds reported to inhibit methanogenesis and exert antimicrobial effects. However, because only two seaweed samples were analyzed, these data should be interpreted as descriptive rather than as direct evidence of mechanism. In addition, because rumen fermentation, archaeal abundance, volatile fatty acid profiles, and specific phenolic fractions were not measured in the present study, any mechanistic explanation for the CH₄ response should be considered hypothetical.

Our findings align with several studies reporting CH₄ mitigation from brown seaweeds. For example, Yergaliyev et al. (2024) reported CH₄ reductions in vitro when F. vesiculosus was included at 2.5%, aligning with our in vivo results for the same species, and Ramos et al. (2025) observed a 12.2% reduction in CH₄ yield using a blend of F. vesiculosus and L. longicruris at a 5% inclusion rate without altering volatile fatty acid profiles, supporting our observation that specific brown seaweeds can reduce emissions while maintaining rumen function, although the effective inclusion level may differ between in vitro and in vivo systems. Seaweed supplementation has also been shown to reduce CH₄ production by altering rumen microbial populations and inhibiting methanogenesis pathways (De Bhowmick and Hayes, 2023; McGurrin et al., 2023; Vijn et al., 2020). An in vitro investigation demonstrated that the application of A. taxiformis resulted in a reduction in CH₄, linked to a reduction in the overall archaeal community and shifts in key methanogen clades (Yergaliyev et al., 2024). De Bhowmick and Hayes (2023) found that adding red seaweed A. taxiformis, which contains bromoform (a compound known to inhibit methanogenesis), to ruminant diets could reduce CH₄ production by up to 90%. The effectiveness of CH₄ reduction can be influenced by factors such as the type of seaweed used, the composition of the basal diet, and the effective bioactive compounds present in the seaweed (De Bhowmick and Hayes, 2023). However, CH₄ mitigation responses are not consistent across studies. Muizelaar et al. (2023), for example, reported no reduction in CH₄ emissions in dairy cattle supplemented with C. crispus, S. latissima, or Fucus serratus, and Thorsteinsson et al. (2024) found no CH₄ reduction when dairy cows were supplemented with Fucus species at a 4% inclusion rate.

These contrasting results highlight that CH₄ mitigation from seaweed is influenced by multiple factors, including seaweed species, inclusion level, animal type, and diet composition. This is underscored by the in vitro result that A. nodosum and F. vesiculosus harvested in Scotland significantly reduced CH₄, while the same species from Iceland did not, highlighting the critical importance of seaweed source and biogeography (Yergaliyev et al., 2024). Our phytochemical results further indicate that variability in bioactive compounds may play a critical role. Factors such as seasonal variation, harvest location, or processing conditions can influence the concentration of key metabolites.

The lack of CH₄ reduction at the 0.5% inclusion level also suggests a threshold response, where lower supplementation levels may provide insufficient concentrations of antimethanogenic compounds. Lower seaweed intake may not provide enough inhibitory substances to effectively reduce CH₄ emissions but may provide more substrates for methanogenesis (De Bhowmick and Hayes, 2023). In our chemical analysis, the 0.5% inclusion may have provided a small amount of phenolic compounds that was insufficient to inhibit methanogens but enough to change fermentable substrate availability. The in vitro study by Ramos et al. (2025) shows a similar pattern, showing that for many seaweeds including species similar to ours, such as F. vesiculosus and L. longicruris, an inclusion rate greater than 1% was necessary to observe significant anti-methanogenic effects, with the response being highly dose-dependent. Their finding that higher inclusion levels were effective (5%), whereas our in vivo study observed the strongest response at 1%–2%, highlights the complexity of applying in vitro results to live animals, where palatability, rumen adaptation, and the diet all play major roles. These findings emphasize the importance of both dosage and chemical composition when evaluating seaweed as a CH₄ mitigation strategy.

4.2.2 Effects of seaweed supplementation on CO₂ and O₂ exchange

In addition to reducing CH₄ emissions, seaweed supplementation was also associated with differences in other gas exchange parameters. The highest CO₂ emissions and O₂ consumption were observed in the control group, whereas all seaweed-supplemented treatments showed lower values. These outcomes are presented as secondary gas-exchange observations under the chamber conditions of this study. Although previous studies have primarily focused on CH₄ mitigation, these results suggest that seaweed supplementation may also be associated with broader gas-exchange responses and fermentation processes.

Changes in CO₂ production may reflect differences in rumen fermentation or other metabolic processes (Vijn et al., 2020), although these were not directly evaluated in the present study. The modest reduction observed in the 0.5% treatment suggests that even low levels of seaweed may affect metabolic activity, although higher inclusion levels appeared to produce a stronger response. One possible explanation is that higher seaweed inclusion altered fermentation processes associated with overall gas production (Romero et al., 2023), although this was not directly evaluated in the present study.

Monitoring O₂ consumption provided additional insight into potential metabolic effects of seaweed supplementation. While reductions in O₂ consumption were observed in seaweed-supplemented animals, further investigation is needed to determine whether these changes reflect altered fermentation dynamics or other physiological adjustments. Previous studies have rarely evaluated O₂ consumption, highlighting the need for further research to determine whether reduced CH₄ and CO₂ emissions correlate with changes in O₂ use and overall energy metabolism, ensuring that CH₄ mitigation does not compromise animal health or performance (Terry et al., 2023).

Finally, initial body weight, age, feeding time, and time since the start of the test did not significantly influence gas emissions in this study. This suggests that the observed differences in greenhouse gas production were primarily attributable to the seaweed treatments rather than underlying animal characteristics or measurement timing. Overall, our findings indicate that seaweed supplementation at 1%–2% inclusion can reduce enteric CH₄ emissions without compromising productivity, although the magnitude of the response appears to depend on the concentration of bioactive compounds and the production system. However, the gas emission phase was conducted on a subset of animals from the performance phase under different management conditions; therefore, the findings from experiment II should be interpreted as follow-up observations under chamber conditions rather than as directly equivalent to those from experiment I.

5 Conclusion

The inclusion of the North Atlantic brown seaweed F. vesiculosus and L. longicruris (GRO^®) in the diet of pregnant mixed-breed beef heifers was associated with lower enteric CH₄ emissions, alongside decreases in CO₂ emissions and O₂ consumption at the 1.0% and 2.0% inclusion levels. These results highlight the dose-dependent nature of seaweed’s impact on greenhouse gas emissions. Given that the 1.0% and 2.0% inclusion levels did not differ significantly and both were associated with lower enteric CH₄ emissions in this trial without negatively affecting production parameters, these levels may warrant further investigation in future studies with the final inclusion level potentially guided by product availability and economic considerations. Further research is warranted to clarify the underlying mechanisms and evaluate the effectiveness of this supplementation strategy.

Generative AI statement

The author(s) declared that generative AI was not used in the creation of this manuscript.

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Footnotes

Edited by: Doctor Mziwenkosi Nhlanhla Mthiyane, North-West University, South Africa

Reviewed by: Svetlana Malyugina, Mendel University in Brno, Czechia

Luz Y. Peña-Avelino, Universidad Autónoma de Tamaulipas, Mexico

Bornalee Handique, ICAR-IARI, India

Footnotes

ADG, average daily gain; AIC, Akaike information criterion; AR(1), first-order autoregressive covariance structure; AtmPress, atmospheric pressure; BW, body weight; CCAC, Canadian Council on Animal Care; CH₄, methane; CO₂, carbon dioxide; DIT, days in test; DM, dry matter; DMI, dry matter intake; ECCC, Environment and Climate Change Canada; FCR, feed conversion ratio; GHG, greenhouse gas(es); hPa, hectopascal; kg, kilogram; kt, kiloton; L/min, liters per minute; MeOH, methanol; mL/min, milliliters per minute; mmol, millimole; N₂O, nitrous oxide; NS, Nova Scotia; NSDA, Nova Scotia Department of Agriculture; NRC, National Research Council (Canada); O₂, oxygen; PGE, phloroglucinol equivalents; ppm, parts per million; PROC MIXED, SAS mixed procedure; REML, restricted maximum likelihood; RFID, radio frequency identification; SAS, statistical analysis system; Temp, temperature.