Increasing consumer concern in greenhouse-gas (GHG) contributions from cattle is pushing the livestock industry to continue to improve their sustainability goals. As populations increase, particularly in low-income countries, the demand for animal-sourced foods will place further pressure to reduce emission intensity. Enteric methane (CH4) production contributes to most of the GHG from livestock; therefore, it is key to mitigating such emissions. Feed additives have primarily been used to increase animal productivity, but advances in understanding the rumen has resulted in their development to mitigate CH4 emissions. The present study reviewed some of the main feed additives with a potential to reduce enteric CH4 emissions, focusing on in vivo studies. Feed additives work by either inhibiting methanogenesis or modifying the rumen environment, such that CH4 production (g/day) is reduced. Feed additives that inhibit methanogenesis or compete with substrate for methanogens include 3-nitroxypropanol (3NOP), nitrates, and halogenated compounds containing organisms such as macroalgae. Although 3NOP and macroalgae affect methyl–coenzyme M reductase enzyme that is necessary in CH4 biosynthesis, the former is more specific to methanogens. In contrast, nitrates reduce CH4 emissions by competing with methanogens for hydrogen. However, nitrite could accumulate in blood and be toxic to ruminants. Rumen modifiers do not act directly on methanogens but rather on the conditions that promote methanogenesis. These feed additives include lipids, plant secondary compounds and essential oils. The efficacy of lipids has been studied extensively, and although supplementation with medium-chain and polyunsaturated fatty acids has shown substantial reduction in enteric CH4 production, the results have been variable. Similarly, secondary plant compounds and essential oils have shown inconsistent results, ranging from substantial reduction to modest increase in enteric CH4 emissions. Due to continued interest in this area, research is expected to accelerate in developing feed additives that can provide options in mitigating enteric CH4 emissions.

Supplementing a diet with nitrate is regarded as an effective and promising methane (CH₄) mitigation strategy by competing with methanogens for available hydrogen through its reduction of ammonia in the rumen. Studies have shown major reductions in CH₄ emissions with nitrate supplementation, but with large variation in response. The objective of this study was to quantitatively investigate the effect of dietary nitrate on enteric CH₄ production and yield and evaluate the variables with high potential to explain the heterogeneity of between-study variability using meta-analytical models. A data set containing 56 treatments from 24 studies was developed to conduct a meta-analysis. Dry matter (DM) intake, nitrate dose (g/kg of DM), animal body weight, roughage proportion of diet, dietary crude protein and neutral detergent fiber content, CH₄ measurement technique, and type of cattle (beef or dairy) were considered as explanatory variables. Average DM intake and CH₄ production for dairy cows (16.2 ± 2.93 kg/d; 311 ± 58.8 g/d) were much higher than for beef cattle (8.1 ± 1.57 kg/d; 146 ± 50.9 g/d). Therefore, a relative mean difference was calculated and used to conduct random-effect and mixed-effect model analysis to eliminate the large variations between types of animal due to intake. The final mixed-effect model for CH₄ production (g of CH₄/d) had 3 explanatory variables and included nitrate dose, type of cattle, and DM intake. The final mixed-effect model for CH₄ yield (g of CH₄/kg of DM intake) had 2 explanatory variables and included nitrate dose and type of cattle. Nitrate effect sizes on CH₄ production (dairy: -20.4 ± 1.89%; beef: -10.1 ± 1.52%) and yield (dairy: -15.5 ± 1.15%; beef: -8.95 ± 1.764%) were significantly different between the 2 types of cattle. When data from slow-release nitrate sources were removed from the analysis, there was no significant difference in type of cattle anymore for CH₄ production and yield. Nitrate dose enhanced the mitigating effect of nitrate on CH₄ production and yield by 0.911 ± 0.1407% and 0.728 ± 0.2034%, respectively, for every 1 g/kg of DM increase from its mean dietary inclusion (16.7 g/kg of DM). An increase of 1 kg of DM/d in DM intake from its mean dietary intake (11.1 kg of DM/d) decreased the effect of nitrate on CH₄ production by 0.691 ± 0.2944%. Overall, this meta-analysis demonstrated that nitrate supplementation reduces CH₄ production and yield in a dose-dependent manner, and that elevated DM intake decreases the effect of nitrate supplementation on CH₄ production. Furthermore, the stronger antimethanogenic effect on CH₄ production and yield in dairy cows than in beef steers could be related to use of slow-release nitrate in beef cattle.

The livestock industry is one of the main contributors to greenhouse gas emissions and there is an increasing demand for the industry to reduce its carbon footprint. Several studies have shown that feed additives 3-nitroxypropanol and nitrate to be effective in reducing enteric methane emissions. The objective of this study was to estimate the net mitigating effect of using 3-nitroxypropanol and nitrate on total greenhouse gas emissions in California dairy industry. A life cycle assessment approach was used to conduct a cradle-to-farm gate environmental impact analysis based on dairy production system in California. Emissions associated with crop production, feed additive production, enteric methane, farm management, and manure storage were calculated and expressed as kg CO₂ equivalents (CO₂e) per kg of energy corrected milk. The total greenhouse gas emissions from baseline, 3-nitroxypropanol and nitrate offered during lactation were 1.12, 0.993, and 1.08 kg CO₂e/kg energy corrected milk, respectively. The average net reduction rates for 3-nitroxypropanol and nitrate were 11.7% and 3.95%, respectively. In both cases, using the feed additives on the whole herd slightly improved overall carbon footprint reduction compared to limiting its use during lactation phase. Although both 3-nitroxypropanol and nitrate had effects on decreasing the total greenhouse gas emission, the former was much more effective with no known safety issues in reducing the carbon footprint of dairy production in California.

Livestock manure is widely applied onto agriculture soil to fertilize crops and increase soil fertility. However, it is difficult to provide real-time manure nutrient data based on traditional lab analyses during application. Manure sensing using near-infrared (NIR) spectroscopy is an innovative, rapid, and cost-effective technique for inline analysis of animal manure. This study investigated a NIR sensing system with reflectance and transflectance modes to predict N speciation in dairy cow manure using a spiking method. In this study, 20 dairy cow manure samples were collected and spiked to achieve four levels of ammoniacal nitrogen (NH₄-N) and organic nitrogen (Org-N) concentrations that resulted in 100 samples in each spiking group. All samples were scanned and analyzed using a NIR system with reflectance and transflectance sensor configurations. NIR calibration models were developed using partial least square regression analysis for NH₄-N, Org-N, total solid (TS), ash, and particle size (PS). Coefficient of determination (R²) and root mean square error (RMSE) were selected to evaluate the models. A transflectance probe with a 1 mm path length had the best performance for analyzing manure constituents among three path lengths. Reflectance mode improved the calibration accuracy for NH₄-N and Org-N, whereas transflectance mode improved the model predictability for TS, ash, and PS. Reflectance provided good prediction for NH₄-N (R² = 0.83; RMSE = 0.65 mg mL⁻¹) and approximate predictions for Org-N (R² = 0.66; RMSE = 1.18 mg mL⁻¹). Transflectance was excellent for TS predictions (R² = 0.97), and provided good quantitative predictions for ash and approximate predictions for PS. The correlations between the accuracy of NH₄-N and Org-N calibration models and other manure parameters were not observed indicating the predictions of N contents were not affected by TS, ash, and PS.