The ruminant livestock sector considerably contributes to global greenhouse gas emissions. This study investigates the effectiveness of pelleted bromoform-containing seaweed ( Asparagopsis taxiformis ) (Brominata) as an enteric methane (CH ₄ ) inhibitor in grazing beef cattle. The primary objective was to assess the impact of this antimethanogenic additive on enteric CH ₄ emissions under real-world farm conditions. Twenty-four beef steers, crossbreeds of Wagyu and Angus, with an average liveweight of 399 ± 21.7 kg, were allocated to two treatment groups: Control and Brominata. These animals underwent regular weigh-ins every 14 d, and measurements of CH ₄ , carbon dioxide (CO ₂ ), and hydrogen (H ₂ ) emissions were conducted using the GreenFeed system. Statistical analysis was conducted using SAS 9.4, wherein the model incorporated fixed effects for treatment, time, their interaction, and a covariate, while accounting for animal variations as a random effect within each phase. Three phases of bromoform intake were identified: a 3-wk ramp-up phase, a 3-wk optimal phase, and a 2-wk decreasing phase. No differences were observed between the weekly initial and final liveweight, average daily gain, and predicted dry matter intake. During optimal and decreasing phases, average enteric CH ₄ emissions were significantly reduced in steers that received Brominata supplementation compared to those without supplementation (115 vs. 185 g/d, respectively). Additionally, both groups had similar CO ₂ emissions (6.8 vs. 7.2 kg/d), while H ₂ emissions were lower in the control group (3.4 vs. 1.8 g/d). The findings suggest that pelleted bromoform-containing feed additive has the potential to reduce enteric CH ₄ emissions from grazing beef cattle.

Methane (CH₄) emissions from ruminants contribute significantly to global GHG emissions. Bromoform (CHBr₃)-containing feed ingredients, such as Asparagopsis seaweed, have emerged as promising tools to reduce enteric CH₄ emissions. This meta-analysis quantitatively assessed the effects of CHBr₃-containing seaweeds and synthetic CHBr₃-based additives on CH₄ production (g/d), CH₄ yield (g/kg DMI), and DMI, as well as CH₄ intensity (g/kg product) and production (milk or daily gain in dairy and beef cattle, respectively). Data were collected from 14 studies, with 39 treatment mean differences for CH₄ production, comprising both beef and dairy cattle fed CHBr₃-containing ingredients, while accounting for dose, DMI, and diet composition. The random-effects model's estimates revealed that at an average CHBr₃ dose of ≈28.3 mg/kg DM, CH₄ production was reduced by 47.3%, CH₄ yield by 43.3%, and CH₄ intensity by 39.0%, with increases in the CHBr₃ dose resulting in larger efficacy in mitigating CH₄ emissions. The efficacy of CHBr₃ was influenced by cattle type, with greater mitigation effects in beef cattle than dairy catle, and by dietary composition, with greater reductions observed in diets higher in starch, whereas higher NDF levels attenuated the effect of CHBr₃. At the average CHBr₃ dose, estimated DMI was significantly reduced by 6.45% and 3.26% in dairy and beef cattle, respectively, with increases in CHBr₃ dose resulting in larger DMI reductions. The significant reduction in DMI did translate into a significant effect on milk yield (-4.60%) at average CHBr₃ dose. Carrier type (oil vs. biomass), measurement technique, and cattle type influenced the results. The use of an oil carrier was associated with more pronounced reductions, particularly in CH₄ intensity. Additionally, respiration chambers yielded significantly greater CH₄ reductions compared with other measurement methods. This study highlights the CH₄-mitigating potential of feed ingredients containing CHBr₃, providing predictive models to optimize CH₄ reduction strategies under diverse production conditions. Future research should address long-term effects, dietary optimization, and practical implementation.

This study highlights the importance of monitoring cattle feeding behavior using the YOLO algorithm for object detection. Videos of six Charolais bulls were recorded on a French farm, and three feeding behaviors (biting, chewing, visiting) were identified and labeled using Roboflow. YOLOv8 and YOLOv10 were compared for their performance in detecting these behaviors. YOLOv10 outperformed YOLOv8 with slightly higher precision, recall, mAP50, and mAP50-95 scores. Although both algorithms demonstrated similar overall accuracy (around 90%), YOLOv8 reached optimal training faster and exhibited less overfitting. Confusion matrices indicated similar patterns of prediction errors for both versions, but YOLOv10 showed better consistency. This study concludes that while both YOLOv8 and YOLOv10 are effective in detecting cattle feeding behaviors, YOLOv10 exhibited superior average performance, learning rate, and speed, making it more suitable for practical field applications.

This research introduces a systematic framework for calculating sample size in studies focusing on enteric methane (CH₄, g/kg of DMI) yield reduction in dairy cows. Adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we conducted a comprehensive search across the Web of Science, Scopus, and PubMed Central databases for studies published from 2012 to 2023. The inclusion criteria were: studies reporting CH₄ yield and its variability in dairy cows, employing specific experimental designs (Latin Square Design (LSD), Crossover Design, Randomized Complete Block Design (RCBD), and Repeated Measures Design) and measurement methods (Open-circuit respirometry chambers (RC), the GreenFeed system, and the sulfur hexafluoride tracer technique), conducted in Canada, the United States and Europe. A total of 150 studies, which included 177 reports, met our criteria and were included in the database. Our methodology for using the database for sample size calculations began by defining 6 CH₄ yield reduction levels (5, 10, 15, 20, 30, and 50%). Utilizing an adjusted Cohen's f formula and a power analysis we calculated the sample sizes required for these reductions in balanced LSD and RCBD reports from studies involving 3 or 4 treatments. The results indicate that within-subject studies (i.e., LSD) require smaller sample sizes to detect CH₄ yield reductions compared with between-subject studies (i.e., RCBD). Although experiments using RC typically require fewer individuals due to their higher accuracy, our results demonstrate that this expected advantage is not evident in reports from RCBD studies with 4 treatments. A key innovation of this research is the development of a web-based tool that simplifies the process of sample size calculation (samplesizecalculator.ucdavis.edu). Developed using Python, this tool leverages the extensive database to provide tailored sample size recommendations for specific experimental scenarios. It ensures that experiments are adequately powered to detect meaningful differences in CH₄ emissions, thereby contributing to the scientific rigor of studies in this critical area of environmental and agricultural research. With its user-friendly interface and robust backend calculations, this tool represents a significant advancement in the methodology for planning and executing CH₄ emission studies in dairy cows, aligning with global efforts toward sustainable agricultural practices and environmental conservation.

Methane (CH ₄ ) emissions from ruminants contribute significantly to global GHG emissions. Bromoform (CHBr ₃ )-containing feed ingredients, such as Asparagopsis seaweed, have emerged as promising tools to reduce enteric CH ₄ emissions. This meta-analysis quantitatively assessed the effects of CHBr ₃ -containing seaweeds and synthetic CHBr ₃ -based additives on CH ₄ production (g/day), CH ₄ yield (g/kg DMI), and DMI, as well as CH ₄ intensity (g/kg product) and production (milk or daily gain in dairy and beef cattle, respectively). Data were collected from 14 studies, with 39 treatment mean differences for CH ₄ production, comprising both beef and dairy cattle fed CHBr ₃ -containing ingredients, while accounting for dose, DMI, and diet composition. The random-effects model’s estimates revealed that at an average CHBr ₃ dose of ≈28.3 mg/kg DM, CH ₄ production was reduced by 47.3%, CH ₄ yield by 43.3%, and CH ₄ intensity by 39.0%, with increases in CHBr ₃ dose resulting in larger efficacy in mitigating CH ₄ emissions. The efficacy of CHBr ₃ was influenced by cattle type, with greater mitigation effects in beef cattle than dairy catle, and by dietary composition, with greater reductions observed in diets higher in starch, whereas higher NDF levels attenuate its effect. At the average CHBr ₃ dose, estimated DMI was significantly reduced by 6.45% and 3.26% in dairy and beef cattle, respectively. The significant reduction in DMI did translate into a significant effect on milk yield (-4.60%) at average CHBr ₃ dose. Carrier type (oil vs. biomass), measurement technique and cattle type influenced the results. Oil carrier potentially leading to more pronounced reductions, particularly for CH ₄ intensity, respiration chambers yielded significantly greater CH ₄ reductions compared to other methods, and beef cattle showed stronger mitigation effects than dairy cattle. This study highlights the CH ₄ mitigating potential of CHBr ₃ -containing feed ingredients, providing predictive models to optimize CH ₄ reduction strategies under diverse production conditions. Future research should address long-term effects, dietary optimization, and practical implementation.

The use of collars, pedometers or activity tags is expensive to record cattle's behavior in short periods (e.g. 24h). Under this particular situation, the development of low-cost and easy-to-use technologies is relevant. Similar to smartphone apps for human activity recognition, which analyzes data from embedded triaxial accelerometer sensors, we develop an Android app to record activity in cattle. Four main steps were followed: a) data acquisition for model training, b) model training, c) app deploy, and d) app utilization. For data acquisition, we developed a system in which three components were used: two smartphones and a Google Firebase account for data storage. For model training, the generated database was used to train a recurrent neural network. The performance of training was assessed by the confusion matrix. For all actual activities, the trained model provided a high prediction (> 96 %). The trained model was used to deploy an Android app by using the TensorFlow API. Finally, three cell phones (LG gm730) were used to test the app and record the activity of six Holstein cows (3 lactating and 3 non-lactating). Direct and non-systematic observations of the animals were made to contrast the activities recorded by the device. Our results show consistency between the direct observations and the activity recorded by our Android app.