The pivotal roles of regulatory jurisdictions in the feed additive sector cannot be over-emphasized. In the European Union (EU), antioxidant substances are authorized as feed additives for prolonging the shelf life of feedstuffs based on their effect for preventing lipid peroxidation. However, the efficacy of antioxidants transcends their functional use as technological additives in animal feeds. Promising research results have revealed the in vivo efficacy of dietary antioxidants for combating oxidative stress in production animals. The in vivo effect of antioxidants is significant for enhancing animal health and welfare. Similarly, postmortem effect of dietary antioxidants has been demonstrated to improve the nutritional, organoleptic and shelf-life qualities of animal products. In practice, dietary antioxidants have been traditionally used by farmers for these benefits in livestock production. However, some antioxidants particularly when supplemented in excess could act as prooxidants and exert detrimental effects on animal well-being and product quality. Presently, there is no exclusive legislation in the EU to justify the authorization of antioxidant products for these in vivo and postmortem efficacy claims. To indicate these efficacy claims and appropriate dosage on product labels, it is important to broaden the authorization status of antioxidants through the appraisal of existing EU legislations on feed additives. Such regulatory review will have major impact on the legislative categorization of antioxidants and the efficacy assessment in the technical dossier application. The present review harnesses the scientific investigations of these efficacy claims in production animals and, proposes potential categorization and appraisal of in vivo methodologies for efficacy assessment of antioxidants. This review further elucidates the implication of such regulatory review on the practical application of antioxidants as feed additives in livestock production. Effecting these regulatory changes will stimulate the innovation of more potent antioxidant products and create potential new markets that will have profound economic impacts on the feed additive industry. Based on the in vivo efficacy claims, antioxidants may have to contend with the legislative controversy of either to be considered as veterinary drugs or feed additives. In this scenario, antioxidants are not intended to diagnose or cure diseases as ascribed to veterinary products. This twisted distinction can be logically debated with reference to the stipulated status of feed additives in Commission Regulation (EC) No 1831/2003. Nonetheless, it is imperative for relevant stakeholders in the feed additive industry to lobby for the review of existing EU legislations for authorization of antioxidants for these efficacy claims.

This review aims to elucidate the contemporary methods of measuring and estimating methane (CH₄) emissions from ruminants. Six categories of methods for measuring and estimating CH₄ emissions from ruminants are discussed. The widely used methods in most CH₄ abatement experiments comprise the gold standard respiration chamber, in vitro incubation, and the sulfur hexafluoride (SF₆) techniques. In the spot sampling methods, the paper discusses the sniffer method, the GreenFeed system, the face mask method, and the portable accumulation chamber. The spot sampling relies on the measurement of short-term breath data adequately on spot. The mathematical modeling methods focus on predicting CH₄ emissions from ruminants without undertaking extensive and costly experiments. For instance, the Intergovernmental Panel on Climate Change (IPCC) provides default values for regional emission factors and other parameters using three levels of estimation (Tier 1, 2 and 3 levels), with Tier 1 and Tier 3 being the simplest and most complex methods, respectively. The laser technologies include the open-path laser technique and the laser CH₄ detector. They use the laser CH₄ detector and wireless sensor networks to measure CH₄ flux. The micrometeorological methods rely on measurements of meteorological data in line with CH₄ concentration. The last category of methods for measuring and estimating CH₄ emissions in this paper is the emerging technologies. They include the blood CH₄ concentration tracer, infrared thermography, intraruminal telemetry, the eddy covariance (EC) technique, carbon dioxide as a tracer gas, and polytunnel. The emerging technologies are essential for the future development of effective quantification of CH₄ emissions from ruminants. In general, adequate knowledge of CH₄ emission measurement methods is important for planning, implementing, interpreting, and comparing experimental results.

A meta-analysis based on an individual-cow data set was conducted to investigate between-cow variations in the components and measurements of feed efficiency (FE) and to explore the associations among these components. Data were taken from 31 chamber studies, consisting of a total of 841 cow/period observations. The experimental diets were based on grass or corn silages, fresh grass, or a mixture of fresh grass and straw, with cereal grains or by-products as energy supplements, and soybean or canola meal as protein supplements. The average forage-to-concentrate ratio across all diets on a dry matter basis was 56:44. Variance component and repeatability estimates of FE measurements and components were determined using diet, period, and cow within experiment as random effects in mixed procedures of SAS (SAS Institute Inc., Cary, NC). The between-cow coefficient of variation (CV) in gross energy intake (GE; CV = 0.10) and milk energy (El) output as a proportion of GE (El/GE; CV = 0.084) were the largest among all component traits. Similarly, the highest repeatability estimates (≥0.50) were observed for these 2 components. However, the between-cow CV in digestibility (DE/GE), metabolizability [metabolizable energy (ME)/GE], methane yield (CH4E/GE), proportional urinary energy output (UE/GE), and heat production (HP/GE), as well as the efficiency of ME use for lactation (kl), were rather small. The least repeatable component of FE was UE/GE. For FE measurements, the between-cow CV in residual energy-corrected milk (RECM) was larger than for residual feed intake (RFI), suggesting a greater possibility for genetic gain in RECM than in RFI. A high DE/GE was associated with increased CH4E/GE (r = 0.24), HP/GE (r = 0.12), ME/GE (r = 0. 91), energy balance as a proportion of GE (EB/GE; r = 0.35), and kl (r = 0.10). However, no correlation between DE/GE and GE intake or UE/GE was observed. Increased proportional milk energy adjusted to zero energy balance (El(0)/GE) was associated with increases in DE/GE, ME/GE, EB/GE, and kl but decreases in UE/GE, CH4E/GE, and HP/GE, with no effect on GE intake. In conclusion, several mechanisms are involved in the observed differences in FE among dairy cows, and reducing CH4E yield (CH4E/GE) may inadvertently result in reduced GE digestibility. However, the selection of dairy cows with improved energy utilization efficiencies offers an effective approach to lower enteric CH4 emissions.

The objective of the present study was to investigate factors related to variation in feed efficiency (FE) among cows. Data included 841 cow/period observations from 31 energy metabolism studies assembled across 3 research stations. The cows were categorized into low-, medium-, and high-FE groups according to residual feed intake (RFI), residual energy-corrected milk (RECM), and feed conversion efficiency (FCE). Mixed model regression was conducted to identify differences among the efficiency groups in animal and energy metabolism traits. Partial regression coefficients of both RFI and RECM agreed with published energy requirements more closely than cofficients derived from production experiments. Within RFI groups, efficient (Low-RFI) cows ate less, had a higher digestibility, produced less methane (CH₄) and heat, and had a higher efficiency of metabolizable energy (ME) utilization for milk production. High-RECM (most efficient) cows produced 6.0 kg/d more of energy-corrected milk (ECM) than their Low-RECM (least efficient) contemporaries at the same feed intake. They had a higher digestibility, produced less CH₄ and heat, and had a higher efficiency of ME utilization for milk production. The contributions of improved digestibility, reduced CH₄, and reduced urinary energy losses to increased ME intake at the same feed intake were 84, 12, and 4%, respectively. For both RFI and RECM analysis, increased metabolizability contributed to approximately 35% improved FE, with the remaining 65% attributed to the greater efficiency of utilization of ME. The analysis within RECM groups suggested that the difference in ME utilization was mainly due to the higher maintenance requirement of Low-RECM cows compared with Medium- and High-RECM cows, whereas the difference between Medium- and High-RECM cows resulted mainly from the higher efficiency of ME utilization for milk production in High-RECM cows. The main difference within FCE (ECM/DMI) categories was a greater (8.2 kg/d) ECM yield at the expense of mobilization in High-FCE cows compared with Low-FCE cows. Methane intensity (CH₄/ECM) was lower for efficient cows than for inefficient cows. The results indicated that RFI and RECM are different traits. We concluded that there is considerable variation in FE among cows that is not related to dilution of maintenance requirement or nutrient partitioning. Improving FE is a sustainable approach to reduce CH₄ production per unit of product, and at the same time improve the economics of milk production.

High feed costs make feed conversion efficiency a desirable target for genetic improvement. Residual feed intake (RFI), calculated as the difference between observed and predicted intake, is a commonly used estimate of feed efficiency. However, determination of feed efficiency in dairy herds is challenging due to difficulties in measuring feed intake of individual animals reliably. Using residual CO2 (RCO2) production as an estimate of feed efficiency would allow ranking the cows according to feed efficiency, provided that CO2 production is closely related to heat production and feed intake. The objective of this study was to evaluate the potential of RCO2 as an index of feed efficiency using data from respiration calorimetry studies (289 cow per period observations). Heat production was precisely predicted from CO2 production [root mean square error (RMSE)] adjusted for random effects was 1.5% of observed mean]. Dry matter intake (DMI) was better predicted from energy-corrected milk (ECM) yield and CO2 production than from ECM yield and body weight in the model (adjusted RSME = 0.92 vs. 1.39 kg/d). Residual CO2 production estimated as the difference between actual CO2 production and that predicted from ECM yield, metabolic body weight was closely related to RFI (adjusted RMSE = 0.42) that was calculated as the difference between actual DMI and that predicted from ECM, metabolic body weight, and energy balance (EB). When the cows were categorized in 3 groups of equal sizes on the basis of RCO2 (low, medium, and high), low RCO2 cows had lower DMI, RFI, methane production and intensity (g/kg ECM), and heat production, but higher efficiency of metabolizable energy utilization for lactation than high RCO2 cows. When RFI was predicted from RCO2, the residuals (observed - predicted) were negatively related to EB and digestibility. Predicting RFI with a 2-variable model based on RCO2 and digestibility, adjusted RMSE decreased to 0.23 kg/d, and residuals were not significantly related to EB. The cows in low RCO2 group had a higher energy digestibility than the cows in the high RCO2 group, and differences in EB were observed between the groups. Error of the model predicting residual ECM production from RCO2 was 1.41 kg/d. The residuals were positively related to ECM yield and energy digestibility. Predicting residual ECM from RCO2 and ECM yield decreased adjusted RMSE to 1.07 kg/d, and further to 0.78 kg/d when digestibility was included in the 2-variable model. It is concluded that RCO2 has a potential for ranking individual cows based on feed efficiency.

Automated measurements of the ratio of concentrations of methane and carbon dioxide, [CH₄]:[CO₂], in breath from individual animals (the so-called "sniffer technique") and estimated CO₂ production can be used to estimate CH₄ production, provided that CO₂ production can be reliably calculated. This would allow CH₄ production from individual cows to be estimated in large cohorts of cows, whereby ranking of cows according to their CH₄ production might become possible and their values could be used for breeding of low CH₄-emitting animals. Estimates of CO₂ production are typically based on predictions of heat production, which can be calculated from body weight (BW), energy-corrected milk yield, and days of pregnancy. The objectives of the present study were to develop predictions of CO₂ production directly from milk production, dietary, and animal variables, and furthermore to develop different models to be used for different scenarios, depending on available data. An international dataset with 2,244 records from individual lactating cows including CO₂ production and associated traits, as dry matter intake (DMI), diet composition, BW, milk production and composition, days in milk, and days pregnant, was compiled to constitute the training dataset. Research location and experiment nested within research location were included as random intercepts. The method of CO₂ production measurement (respiration chamber [RC] or GreenFeed [GF]) was confounded with research location, and therefore excluded from the model. In total, 3 models were developed based on the current training dataset: model 1 ("best model"), where all significant traits were included; model 2 ("on-farm model"), where DMI was excluded; and model 3 ("reduced on-farm model"), where both DMI and BW were excluded. Evaluation on test dat sets with either RC data (n = 103), GF data without additives (n = 478), or GF data only including observations where nitrate, 3-nitrooxypropanol (3-NOP), or a combination of nitrate and 3-NOP were fed to the cows (GF+: n = 295), showed good precision of the 3 models, illustrated by low slope bias both in absolute values (-0.22 to 0.097) and in percentage (0.049 to 4.89) of mean square error (MSE). However, the mean bias (MB) indicated systematic overprediction and underprediction of CO₂ production when the models were evaluated on the GF and the RC test datasets, respectively. To address this bias, the 3 models were evaluated on a modified test dataset, where the CO₂ production (g/d) was adjusted by subtracting (where measurements were obtained by RC) or adding absolute MB (where measurements were obtained by GF) from evaluation of the specific model on RC, GF, and GF+ test datasets. With this modification, the absolute values of MB and MB as percentage of MSE became negligible. In conclusion, the 3 models were precise in predicting CO₂ production from lactating dairy cows.