Methane emissions from cattle: estimates from short-term measurements using a Green Feed system compared with measurements obtained using respiration chambers or sulphur hexafluoride tracer

The Green Feed (GF) system (C-Lock Inc., Rapid City, USA) is used to estimate total daily methane emissions of individual cattle using short-term measurements obtained over several days. Our objective was to compare measurements of methane emission by growing cattle obtained using the GF system with measurements using respiration chambers (RC)or sulphur hexafluoride tracer (SF6). It was hypothesised that estimates of methane emission for individual animals and treatments would be similar for GF compared to RC or SF6 techniques. In experiment 1, maize or grass silage-based diets were fed to four growing Holstein heifers, whilst for experiment 2, four different heifers were fed four haylage treatments. Both experiments were a 4 × 4 Latin square design with 33 day periods. Green Feed measurements of methane emission were obtained over 7 days (days 22–28) and com-pared to subsequent RC measurements over 4 days (days 29–33). For experiment 3, 12growing heifers rotationally grazed three swards for 26 days, with simultaneous GF and SF6 measurements over two 4 day measurement periods (days 15–19 and days 22–26).Overall methane emissions (g/day and g/kg dry matter intake [DMI]) measured using GF in experiments 1 (198 and 26.6, respectively) and 2 (208 and 27.8, respectively) were similar to averages obtained using RC (218 and 28.3, respectively for experiment 1; and 209 and 27.7, respectively, for experiment 2); but there was poor concordance between the two methods (0.1043 for experiments 1 and 2 combined). Overall, methane emissions measured using SF6 were higher (P<0.001) than GF during grazing (186 vs. 164 g/day), but there was significant (P<0.01) concordance between the two methods (0.6017). There were fewer methane measurements by GF under grazing conditions in experiment 3 (1.60/day) com-pared to indoor measurements in experiments 1 (2.11/day) and 2 (2.34/day). Significant treatment effects on methane emission measured using RC and SF6 were not evident for GF measurements, and the ranking for treatments and individual animals differed using the GF system. We conclude that under our conditions of use the GF system was unable to detectsignificant treatment and individual animal differences in methane emissions that were identified using both RC and SF6techniques, in part due to limited numbers and timing ofmeasurements obtained. Our data suggest that successful use of the GF system is reliant on the number and timing of measurements obtained relative to diurnal patterns of methane emission

Effect of selected plant species within biodiverse pasture on in vitro fatty acid biohydrogenation and tissue fatty acid composition of lamb

The effect of botanical diversity on supply of polyunsaturated fatty acids (PUFA) to ruminants in vitro, and the fatty acid (FA) composition of muscle in lambs was investigated. Six plant species, commonly grown as part of UK herbal ley mixtures (Trifolium pratense, Lotus corniculatus, Achillea millefolium, Centaurea nigra, Plantago lanceolata and Prunella vulgaris), were assessed for FA profile, and in vitro biohydrogenation of constituent PUFA, to estimate intestinal supply of PUFA available for absorption by ruminants. Modelling the in vitro data suggested that L. Corniculatus and P. Vulgaris had the greatest potential to increase 18:3 n-3 supply to ruminants, having the highest amounts escaping in vitro biohydrogenation . Biodiverse pastures were established using the six selected species, under-sown in a perennial ryegrass-based sward. Lambs were grazed (~50 days) on biodiverse or control pastures and the effects on the FA composition of m. longissimus thoracis (lean and subcutaneous fat) and m. semimembranosus (lean) were determined. Biodiverse pasture increased 18:2 n-6 and 18:3 n-3 contents of m. semimembranosus (+14.8 and +7.2 mg/100g tissue respectively) and the subcutaneous fat of m. l. thoracis (+158 and +166 mg/100g tissue respectively) relative to feeding a perennial ryegrass pasture. However, there was no effect on total concentrations of saturated FA in the tissues studied. It was concluded that enhancing biodiversity had a positive impact on muscle FA profile reflected by increased levels of total PUFA

Evaluation and prediction of nitrogen use efficiency and outputs in faeces and urine in beef cattle

Beef cattle production is valuable to food security, contributing meat of high nutritional value. However, beef cattle are rather inefficient in utilising dietary nitrogen (N), thus excreting substantial amounts of N in their urine and faeces and imposing an environmental burden. The aim of this study was to evaluate the main dietary factors affecting N use efficiency (NUE) in beef cattle and develop prediction models for N excretion in manure, faeces and urine. This knowledge is essential for the development and evaluation of cost-effective N mitigation strategies. A database of 289 treatment means was constructed from 69 published studies and 1194 animals. Data included diet contents of N, dry matter (DM), organic matter (OM), neutral-detergent fibre (NDF), acid-detergent fibre (ADF), ether extract, starch, ash, gross energy (GE), metabolisable energy (ME), and outputs of N in manure, in urine or in faeces. Regression equations to predict N outputs in manure (MNO), urine (UNO) and faeces (FNO), as well as various NUE indicators, were developed using residual maximum likelihood analysis. Evaluation of new and existing models was performed using the mean prediction error (MPE) to describe prediction accuracy. Manure, urine and faeces N outputs were predicted with improved accuracy (MPE from 0.557 to 0.162; from 0.764 to 0.208; and from 0.458 to 0.177, respectively) when DM or OM digestibilities, and/or diet contents of N, NDF, ADF, Starch, OM, GE, ME, and/or forage proportion in the diet were added as predictors in different equations already containing either DM intake, N intake or body weight as primary predictor. New and existing models displayed an under-prediction of N outputs at the highest range of actual N outputs (when MNO > 207 g/d, UNO > 109 g/d). However, some of the new equations had improved overall accuracy (best MPE for MNO, UNO and FNO being 0.162, 0.208 and 0.177, respectively) and, when DM digestibility, and contents of N, NDF, Starch and ME were added as predictors in different equations, the extent of this under-prediction was also reduced (occurring when MNO > 208 g/d, UNO > 132 g/d). The regression models for NUE, demonstrated that diets which are more digestible and contain less N and fibre and more ME, may reduce N excretions, but mitigation strategies will also need to account for the potential effect on animal productivity and health

Effects of diet forage source and neutral detergent fiber content on milk production of dairy cattle and methane emissions determined using GreenFeed and respiration chamber techniques

Strategies to mitigate greenhouse gas emissions from dairy cattle are unlikely to be adopted if production or profitability is reduced. The primary objective of this study was to examine the effects of high maize silage (MS) vs. high grass silage (GS) diets, without or with added neutral-detergent fiber (NDF) on milk production and methane emission of dairy cattle, using GreenFeed (GF) or respiration chamber (RC) techniques for methane emission measurements. Experiment 1 was 12-wks in duration with a randomized block continuous design and 40 Holstein cows (74 d in milk; DIM) in free-stall housing, assigned to 1 of 4 dietary treatments (n = 10 per treatment), according to calving date, parity and milk yield. Milk production and dry matter intake (DMI) were measured daily, and milk composition measured weekly, with methane yield (g/kg DMI) estimated using a GF unit (wks 10 to 12). Experiment 2 was a 4 × 4 Latin Square Design with 5-wk periods and 4 dairy cows (114 DIM) fed the same 4 dietary treatments as in experiment 1. Measurements of DMI, milk production and composition occurred in wk 4, and DMI, milk production and methane yield were measured for 2 d in RC during wk 5. Dietary treatments for both experiments were fed as TMRs offered ad libitum and containing 500 g silage/kg DM comprised of either 75:25 MS:GS (MS) or 25:75 MS:GS (GS), without or with added NDF from chopped straw and soy hulls (+47 g NDF/kg DM; MSNDF and GSNDF). In both experiments, compared to high GS, cows fed high MS had a higher (P = 0.01) DMI, greater (P = 0.01) milk production, and lower (P = 0.02) methane yield (24% lower in experiment 1 using GF and 8% lower in experiment 2 using RC). Added NDF increased (or tended to increase) methane yield for high MS, but not high GS diets (P = 0.02 for experiment 1 and P = 0.10 for experiment 2, forage type × NDF interaction). In the separate experiments the GF and RC methods detected similar dietary treatment effects on methane emission (expressed as g/d and g/kg DMI), although the magnitude of the difference varied between experiments for dietary treatments Overall methane emission and yield were 448 g/d and 20.9 g/kg DMI using GF for experiment 1 using GF and 458 g/d and 23.8 g/kg DMI for experiment 2 using RC, respectively

Prediction of portal and hepatic blood flow from intake level data in cattle

There is growing interest in developing integrated post-absorptive metabolism models for dairy 30 cattle. An integral part of linking a multi-organ post-absorptive model is the prediction of nutrient 31 fluxes between organs, and thus blood flow. It was the purpose of this paper to use a multivariate 32 meta-analysis approach to model portal blood flow (PORBF) and hepatic venous blood flow 33 (HEPBF) simultaneously, with evaluation of hepatic arterial blood flow (ARTBF; ARTBF = 34 HEPBF – PORBF) and PORBF/HEPBF (%) as calculated values. The database used to develop 35 equations consisted of 296 individual animal observations (lactating and dry dairy cows and beef 36 cattle) and 55 treatments from 17 studies, and a separate evaluation database consisted of 34 37 treatment means (lactating dairy cows and beef cattle) from 9 studies obtained from the literature. 38 Both databases had information on DMI, MEI, body weight and a basic description of the diet 39 including crude protein intake and forage proportion of the diet (FP; %). Blood flow (L/h or L/kg 40 BW0.75/h) and either DMI or MEI (g or MJ/d or g or MJ/kg BW0.75/d) with linear and quadratic 41 fits were examined. Equations were developed using cow within experiment and experiment as 42 random effects, and blood flow location as a repeated effect. Upon evaluation with the evaluation 43 database, equations based on DMI typically resulted in lower root mean square prediction errors, 44 expressed as a % of the observed mean (rMSPE%) and higher concordance correlation coefficient 45 (CCC) values than equations based on MEI. Quadratic equation terms were frequently non-46 significant, and the quadratic equations did not out-perform their linear counterparts. The best 47 performing blood flow equations were: PORBF (L/h) = 202 (± 45.6) + 83.6 (± 3.11) × DMI (kg/d) and HEPBF (L/h) = 186 (± 45.4) + 103.8 (± 3.10) × DMI (kg/d), with rMSPE% values of 17.5 and 49 16.6 and CCC values of 0.93 and 0.94, respectively. The residuals (predicted – observed) for 50 PORBF/HEPBF were significantly related to the forage % of the diet, and thus equations for 51 3 PORBF and HEPBF based on forage and concentrate DMI were developed: PORBF (L/h) = 210 52 (± 51.0) + 82.9 (± 6.43) × Forage (kg DM/d) + 82.9 (± 6.04) × Concentrate (kg DM/d), and 53 HEPBF (L/h) = 184 (± 50.6) + 92.6 (± 6.28) × Forage (kg DM/d) + 114.2 (± 5.88) × Concentrate 54 (kg DM/d), where rMSPE% values were 17.5 and 17.6 and CCC values were 0.93 and 0.94, 55 respectively. Division of DMI into forage and concentrate fractions improved the joint Bayesian 56 Information Criterion (BIC) value for PORBF and HEPBF (BIC = 6512 vs. 7303), as well as 57 slightly improved the rMSPE and CCC for ARTBF and PORBF/HEPBF. This was despite 58 minimal changes in PORBF and HEPBF predictions. Developed equations predicted blood flow 59 well, and could easily be used within a post absorptive model of nutrient metabolism. Results also 60 suggest different sensitivity of PORBF and HEPBF to the composition of DMI, and accounting 61 for this difference resulted in improved ARTBF predictions

Influence of ruminal methane on digesta retention and digestive physiology in non-lactating dairy cattle

Enteric methane (CH4) production is a side-effect of herbivore digestion, but it is unknown whether CH4 itself influences digestive physiology. We investigated the effect of adding CH4 to, or reducing it in, the reticulorumen (RR) in a 4 × 4 Latin square experiment with rumen-fistulated, non-lactating cows, with four treatments: (i) control, (ii) insufflation of CH4 (iCH4), (iii) N via rumen fistula, (iv) reduction of CH4 via administration of bromochloromethane (BCM). DM intake (DMI), apparent total tract digestibility, digesta mean retention times (MRT), rumen motility and chewing activity, spot breath CH4 emission (CH4exhal, litre/kg DMI) as well as CH4 dissolved in rumen fluid (CH4RRf, μg/ml) were measured. Data were analysed using mixed models, including treatment (or, alternatively, CH4exhal or CH4RRf) and DMI relative to body mass0·85 (rDMI) as covariates. rDMI was the lowest on the BCM treatment. CH4exhal was highest for iCH4 and lowest for BCM treatments, whereas only BCM affected (reduced) CH4RRf. After adjusting for rDMI, CH4RRf had a negative association with MRT in the gastrointestinal tract but not in the RR, and negative associations with fibre digestibility and measures of rumination activity. Adjusting for rDMI, CH4exhal had additionally a negative association with particle MRT in the RR and a positive association with rumen motility. Thus, higher rumen levels of CH4 (CH4exhal or CH4RRf) were associated with shorter MRT and increased motility. These findings are tentatively interpreted as a feedback mechanism in the ruminant digestive tract that aims at mitigating CH4 losses by shortening MRT at higher CH4

The impact of using novel equations to predict nitrogen excretion and associated emissions from pasture-based beef production systems

Excretion of nitrogen (N) in faeces and urine from beef cattle contributes to atmospheric pollution through greenhouse gas and ammonia emissions and eutrophication of land and aquatic habitats through excessive N deposition and nitrate leaching to groundwater. As N excretion by beef cattle is rarely measured directly, it is important to accurately predict losses utilising a combined knowledge of diet and production parameters so that the effect of dietary changes on the potential environmental impact of beef production systems can be estimated. This study aimed to identify differences between IPCC and more detailed country-specific models in the prediction of N excretion and N losses at a system level and determine how the choice of model influences the interpretation of differences in diet at the system scale. The data used in this study were derived from a farm-scale experimental system consisting of three individual grazing farms, each with a different sward type: permanent pasture, a high sugar ryegrass monoculture, and a high sugar ryegrass with white clover (~30% groundcover). Data were analysed using a mixed linear model (residual maximum likelihood analysis). The IPCC methods demonstrated significantly lower estimates of N excretion than country-specific models for the first housing period and significantly greater losses for the grazing and second housing periods. The country-specific models enabled prediction of N partitioning to urine and faeces, important for estimation of subsequent N losses through the production system, although the models differed in their estimates. Overall, predicted N losses were greater using the IPCC approaches compared to using more detailed country-specific approaches. The outcomes of the present study have highlighted that different models can have a substantial impact on the predicted N outputs and subsequent losses to the environment for pasture-based beef finishing systems, and the importance, therefore, of using appropriate models and parameters

The impact of using novel equations to predict nitrogen excretion and associated emissions from pasture-based beef production systems.

Publication history: Accepted - 6 June 2022; Published online - 14 June 2022The excretion of nitrogen (N) in faeces and urine from beef cattle contributes to atmospheric pollution through greenhouse gas and ammonia emissions and eutrophication of land and aquatic habitats through excessive N deposition and nitrate leaching to groundwater. As N excretion by beef cattle is rarely measured directly, it is important to accurately predict losses by utilising a combined knowledge of diet and production parameters so that the effect of dietary changes on the potential environmental impact of beef production systems can be estimated. This study aimed to identify differences between IPCC and more detailed country-specific models in the prediction of N excretion and N losses at a system level and determine how the choice of model influences the interpretation of differences in diet at the system scale. The data used in this study were derived from a farm-scale experimental system consisting of three individual grazing farms, each with a different sward type: a permanent pasture, a high sugar ryegrass monoculture, and a high sugar ryegrass with white clover (~30% groundcover). Data were analysed using a mixed linear model (residual maximum likelihood analysis). The IPCC methods demonstrated significantly lower estimates of N excretion than country-specific models for the first housing period and significantly greater losses for the grazing and second housing periods. The country-specific models enabled prediction of N partitioning to urine and faeces, which is important for estimation of subsequent N losses through the production system, although the models differed in their estimates. Overall, predicted N losses were greater using the IPCC approaches compared to using more detailed country-specific approaches. The outcomes of the present study have highlighted that different models can have a substantial impact on the predicted N outputs and subsequent losses to the environment for pasture-based beef finishing systems, and the importance, therefore, of using appropriate models and parametersThe authors would like to acknowledge funding support from the University of Reading, Rothamsted Research, and UK Biotechnology and Biological Sciences Research Council (BBS/E/C/000I0320). The NWFP is a UK National Capability, also supported by the Biotechnology and Biological Sciences Research Council (BBS/E/C/000J0100)

Challenges and opportunities to capture dietary effects in on-farm greenhouse gas emissions models of ruminant systems

This paper reviews existing on-farm GHG accounting models for dairy cattle systems and their ability to capture the effect of dietary strategies in GHG abatement. The focus is on methane (CH4) emissions from enteric and manure (animal excreta) sources and nitrous oxide (N2O) emissions from animal excreta. We identified three generic modelling approaches, based on the degree to which models capture diet-related characteristics: from ‘none’ (Type 1) to ‘some’ by combining key diet parameters with emission factors (EF) (Type 2) to ‘many’ by using process-based modelling (Type 3). Most of the selected on-farm GHG models have adopted a Type 2 approach, but a few hybrid Type 2 / Type 3 approaches have been developed recently that combine empirical modelling (through the use of CH4 and/or N2O emission factors; EF) and process-based modelling (mostly through rumen and whole tract fermentation and digestion). Empirical models comprising key dietary inputs (i.e., dry matter intake and organic matter digestibility) can predict CH4 and N2O emissions with reasonable accuracy. However, the impact of GHG mitigation strategies often needs to be assessed in a more integrated way, and Type 1 and Type 2 models frequently lack the biological foundation to do this. Only Type 3 models represent underlying mechanisms such as ruminal and total-tract digestive processes and excreta composition that can capture dietary effects on GHG emissions in a more biological manner. Overall, the better a model can simulate rumen function, the greater the opportunity to include diet characteristics in addition to commonly used variables, and thus the greater the opportunity to capture dietary mitigation strategies. The value of capturing the effect of additional animal feed characteristics on the prediction of on-farm GHG emissions needs to be carefully balanced against gains in accuracy, the need for additional input and activity data, and the variability encountered on-farm

Prediction of enteric methane production, yield and intensity in dairy cattle using an intercontinental database

Enteric methane (CH4) production from cattle contributes to global greenhouse gas emissions. Measurement of enteric CH4 is complex, expensive and impractical at large scales; therefore, models are commonly used to predict CH4 production. However, building robust prediction models requires extensive data from animals under different management systems worldwide. The objectives of this study were to (1) collate a global database of enteric CH4 production from individual lactating dairy cattle; (2) determine the availability of key variables for predicting enteric CH4 production (g/d per cow), yield [g/kg dry matter intake (DMI)], and intensity (g/kg energy corrected milk) and their respective relationships; (3) develop intercontinental and regional models and cross-validate their performance; and (4) assess the trade-off between availability of on-farm inputs and CH4 prediction accuracy. The intercontinental database covered Europe (EU), the US (US), Chile (CL), Australia (AU), and New Zealand (NZ). A sequential approach was taken by incrementally adding key variables to develop models with increasing complexity. Methane emissions were predicted by fitting linear mixed models. Within model categories, an intercontinental model with the most available independent variables performed best with root mean square prediction error (RMSPE) as a percentage of mean observed value of 16.6, 14.4, and 19.8% for intercontinental, EU, and US regions, respectively. Less complex models requiring only DMI had predictive ability comparable to complex models. Enteric CH4 production, yield, and intensity prediction models developed on an intercontinental basis had similar performance across regions, however, intercepts and slopes were different with implications for prediction. Revised CH4 emission conversion factors for specific regions are required to improve CH4 production estimates in national inventories. In conclusion, information on DMI is required for good prediction, and other factors such as dietary NDF concentration, improve the prediction. For enteric CH4 yield and intensity prediction, information on milk yield and composition is required for better estimation