Predict Methane Emissions Research Articles

Performance of Crossbred Heifers Supplemented with Linseed Oil, Either Alone or in Combination with Myristic Acid

In order to assess the impact of supplementing linseed oil (LO) and myristic acid (MA) on intake, digestibility and growth, fifteen Karan-Fries heifers (6–12 months of age and 120.15 ± 17.5 kg average body weight) were assigned randomly into three groups of five animals each. Group T1 served as control, fed with hand-mixed basal total mixed ration (TMR) containing concentrate mixture, wheat straw and green berseem (Trifolium alexandrinum) in proportion of 40, 40 and 20 %, respectively. In addition to basal TMR, animals in group T2 were supplemented with 2 % LO and group T3 with 1 % LO + 1 % MA for 120 days. Results revealed that, supplementation of fat sources in both the treatment groups (T2 and T3) did not exhibit any effect on intake of dry matter and crude protein in comparison with group T1. Digestibility coefficients of nutrients did not differ due to treatments, except that of EE, which was greater (P < 0.05) in groups T2 and T3 as compared with control. Furthermore, average daily gain and feed conversion ratio were not influenced by dietary treatments. Additionally, predicted methane emission was decreased up to 20.3 and 18.3 % in groups T2 and T3, respectively than that of group T1. Based on these findings, it was concluded that supplementing either 2 % LO or 1 % LO + 1 % MA may not be a viable nutritional strategy for achieving faster growth response in tropical crossbred dairy heifers at field level.

Prediction of methane emission from lactating dairy cows using milk fatty acids and mid-infrared spectroscopy.

Enteric methane (CH4 ) production is among the main targets of greenhouse gas mitigation practices for the dairy industry. A simple, robust and inexpensive measurement technique applicable on a large scale to estimate CH4 emission from dairy cattle would therefore be valuable. Milk fatty acids (MFA) are related to CH4 production because of the common biochemical pathway between CH4 and fatty acids in the rumen. A summary of studies that investigated the predictive power of MFA composition for CH4 emission indicated good potential, with predictive power ranging between 47% and 95%. Until recently, gas chromatography (GC) was the principal method used to determine the MFA profile, but GC is unsuitable for routine analysis. This has led to the application of mid-infrared (MIR) spectroscopy. The major advantages of using MIR spectroscopy to predict CH4 emission include its simplicity and potential practical application at large scale. Disadvantages include the inability to predict important MFA for CH4 prediction, and the moderate predictive power for CH4 emission. It may not be sufficient to predict CH4 emission based on MIR alone. Integration with other factors, like feed intake, nutrient composition of the feed, parity, and lactation stage may improve the prediction of CH4 emission using MIR spectra. © 2016 Society of Chemical Industry.

Equations to predict methane emissions from cows fed at maintenance energy level in pasture-based systems

Ruminant production is a vital part of food industry but it raises environmental concerns, partly due to the associated methane outputs. Efficient methane mitigation and estimation of emissions from ruminants requires accurate prediction tools. Equations recommended by international organizations or scientific studies have been developed with animals fed conserved forages and concentrates and may be used with caution for grazing cattle. The aim of the current study was to develop prediction equations with animals fed fresh grass in order to be more suitable to pasture-based systems and for animals at lower feeding levels. A study with 25 nonpregnant nonlactating cows fed solely fresh-cut grass at maintenance energy level was performed over two consecutive grazing seasons. Grass of broad feeding quality, due to contrasting harvest dates, maturity, fertilisation and grass varieties, from eight swards was offered. Cows were offered the experimental diets for at least 2 weeks before housed in calorimetric chambers over 3 consecutive days with feed intake measurements and total urine and faeces collections performed daily. Methane emissions were measured over the last 2 days. Prediction models were developed from 100 3-day averaged records. Internal validation of these equations, and those recommended in literature, was performed. The existing in greenhouse gas inventories models under-estimated methane emissions from animals fed fresh-cut grass at maintenance while the new models, using the same predictors, improved prediction accuracy. Error in methane outputs prediction was decreased when grass nutrient, metabolisable energy and digestible organic matter concentrations were added as predictors to equations already containing dry matter or energy intakes, possibly because they explain feed digestibility and the type of energy-supplying nutrients more efficiently. Predictions based on readily available farm-level data, such as liveweight and grass nutrient concentrations were also generated and performed satisfactorily. New models may be recommended for predictions of methane emissions from grazing cattle at maintenance or low feeding levels.

Nordic dairy cow model Karoline in predicting methane emissions: 1. Model description and sensitivity analysis

Decreasing methane (CH4) emissions is necessary both environmentally, as CH4 has a strong greenhouse gas effect and nutritionally as CH4 represents a loss of feed energy. Karoline is a whole dairy cow mechanistic, dynamic model predicting nutrient supply and milk production. The objectives of this study were to revise the digestion and CH4 emissions modules of the Karoline model. In addition, a sensitivity analysis was conducted to evaluate the importance of the accuracy of input data required in predicting CH4 emissions. Modifications were made in the equations predicting digesta passage kinetics, microbial cell synthesis, digestion in the hind-gut, and utilization of hydrogen. The Karoline model predicted similar decreases in both organic digestibility (OMD) and neutral detergent fibre digestibility (NDFD) and improvements in the efficiency of microbial nitrogen synthesis with increasing dry matter intake (DMI) as reported in the literature. The proportion of ruminal digestion of total NDFD (0.95) and fecal metabolic and endogenous output (98g/kg DMI) also agree with the literature data. Predicted total CH4 emissions increased with a diminishing rate by increased DMI. Predicted CH4 emissions as a proportion of GE intake decreased linearly with increased DMI. The relationships between feeding level and CH4 emissions (a decrease of 7.8kJ/MJ gross energy per multiple of maintenance) were in good agreement with experimental data. The sensitivity analysis suggested that feed variables related to digestion kinetics of NDF [indigestible NDF (iNDF) and digestion rate of potentially digestible NDF] have a strong influence on predicted CH4 emissions; for example, predicted CH4 emissions decreased with increasing iNDF concentration. Digestion rates of starch and insoluble protein had smaller effects on predicted CH4 emissions than NDF digestion rates. Fat had a strong negative influence on predicted CH4 emissions (0.27kJ/MJ gross energy per 1g fat/kg DM). The sensitivity analysis suggested that accurate values of digestion kinetic variables are required for satisfactory predictions of CH4 emissions with mechanistic models. Based on reliable predictions of digestibility, microbial protein synthesis and CH4 emissions, it can be concluded that the revised Karoline model is a promising tool for predicting CH4 emissions and understanding the underlying mechanisms.

Short-Term Predictions Of Methane Emissions During Longwall Mining

Abstract The prediction of methane emissions to the longwall environment carried out at the design stage may include subjective errors resulting from the underestimation of input values for calculations and in particular, methane saturation of underlying and overlying seams, their thickness and distance from the mined seam. The method of a short-term prediction of methane emissions to the longwall environment during its excavation which is presented in this article and which was developed as a part of a strategic research project (Krause et al., 2013), allows for verification of discrepancies between the predicted methane emissions during excavation for the designed longwall and the actual one. The method of short-term prediction allows for variant pre-estimation of methane emissions for changing operational progress and changes in methane saturation of the mined seam and surrounding deposit at the panel length. It can be used by the Mine Ventilation Department continuously during the longwall mining.

Hot topic: Innovative lactation-stage-dependent prediction of methane emissions from milk mid-infrared spectra

The main goal of this study was to develop, apply, and validate a new method to predict an indicator for CH4 eructed by dairy cows using milk mid-infrared (MIR) spectra. A novel feature of this model was the consideration of lactation stage to reflect changes in the metabolic status of the cow. A total of 446 daily CH4 measurements were obtained using the SF6 method on 142 Jersey, Holstein, and Holstein-Jersey cows. The corresponding milk samples were collected during these CH4 measurements and were analyzed using MIR spectroscopy. A first derivative was applied to the milk MIR spectra. To validate the novel calibration equation incorporating days in milk (DIM), 2 calibration processes were developed: the first was based only on CH4 measurements and milk MIR spectra (independent of lactation stage; ILS); the second included milk MIR spectra and DIM information (dependent on lactation stage; DLS) by using linear and quadratic modified Legendre polynomials. The coefficients of determination of ILS and DLS equations were 0.77 and 0.75, respectively, with standard error of calibration of 63g/d of CH4 for both calibration equations. These equations were applied to 1,674,763 milk MIR spectra from Holstein cows in the first 3 parities and between 5 and 365 DIM. The average CH4 indicators were 428, 444, and 448g/d by ILS and 444, 467, and 471g/d by DLS for cows in first, second, and third lactation, respectively. Behavior of the DLS indicator throughout the lactations was in agreement with the literature with values increasing between 0 and 100 DIM and decreasing thereafter. Conversely, the ILS indicator of CH4 emission decreased at the beginning of the lactation and increased until the end of the lactation, which differs from the literature. Therefore, the DLS indicator seems to better reflect biological processes that drive CH4 emissions than the ILS indicator. The ILS and DLS equations were applied to an independent data set, which included 59 respiration chamber measurements of CH4 obtained from animals of a different breed across a different production system. Results indicated that the DLS equation was much more robust than the ILS equation allowing development of indicators of CH4 emissions by dairy cows. Integration of DIM information into the prediction equation was found to be a good strategy to obtain biologically meaningful CH4 values from lactating cows by accounting for biological changes that occur throughout the lactation.

Nordic dairy cow model Karoline in predicting methane emissions: 2. Model evaluation

Models are widely used to predict methane (CH4) emissions, and can be used to develop mitigation options and policies. The objective of the current study was to evaluate the performance of the Nordic dairy cow model Karoline in predicting CH4 emissions. Karoline is a dynamic, deterministic and mechanistic simulation model that describes the digestion and metabolism of nutrients, and production in a dairy cow. The model was evaluated against observed data from studies reporting CH4 emissions from respiration chamber studies. The dataset included a total of 184 treatment means from 31 published papers. The dietary and animal characteristics used for the model evaluation represent the typical range of diets fed to dairy cattle. When analyzed with a fixed model regression, there was a good relationship between predicted and observed CH4 emissions measured from respiration chamber studies with a small root mean square error of prediction (R2=0.93, RMSPE=10.1% of the observed mean). The mean bias was small (1.9%) but statistically significant, and there was no slope bias. Most of the error was due to random variation (96.4%), whereas the contributions of mean and slope bias were small. By considering study as the random effect in the model (mixed model regression analysis), the fit improved to R2= 0.98 and RMSPE decreased to 6.1% of the observed mean. The influence of some input variables such as total dry matter intake, proportion of concentrate, dietary concentrations of crude protein, neutral detergent fiber and ether extract, and organic digestibility (OMD) on the residuals (observed–predicted) of CH4 emissions were not significant. The residuals of both CH4 emissions and OMD were significantly related to each other, indicating the Karoline model requires accurate estimates of digestion kinetic parameters as input variables. When the laboratory was used as a class variable in the model, the residuals of CH4 emissions were significantly different both between the laboratories and also between experiments within individual laboratories. It is concluded that the Nordic dairy cow model Karoline is a useful tool in predicting CH4 emissions and understanding the system behavior. The model can also be used in developing mitigation strategies for the national inventories of CH4 emissions.

Addition of Purified Tannin Sources and Polyethylene Glycol Treatment on Methane Emission and Rumen Fermentation in Vitro

The objectives of this experiment were (1) to observe the effects of purified tannins and polyethy-lene glycol (PEG) on in vitro rumen fermentation and methanogenesis, and (2) to assess the accuracy of volatile fatty acid (VFA) profiles in predicting methane emission. Hydrolysable and condensed tannins were extracted and purified from chestnut, sumach, mimosa and quebracho. Hay and concentrate mixture (70:30 w/w, 380 mg) was incubated in Hohenheim glass syringe containing 10 mL rumen liquor + 20 mL buffer. The purified tannins were injected into the syringes at a concentration of 1.0 mg/mL each, either without or with PEG 6,000 addition in three replicates. Results revealed that a decrease of methane emission (20%-27%) was observed when the purified tannins were added into basal diet as compared to control (P<0.05), and PEG addition increased methane emission (P<0.05). All purified tannins decreased total gas and total VFA production (P<0.05). The H 2 recovery of the treatments ranged from 86.7% to 95.3%. Estimation of methane emission by using VFA profiles revealed an accurate result with a very low root mean square prediction error (1.75%). It is concluded that tannins mitigate methane emission while PEG neutralize such effect, and VFA profiles are accurate predictors of the emission. Key words: tannin, polyethylene glycol, methane, rumen, stoichiometry

Genetic parameters for predicted methane production and laser methane detector measurements.

Enteric ruminant methane is the most important greenhouse gas emitted from the pastoral agricultural systems. Genetic improvement of livestock provides a cumulative and permanent impact on performance, and using high-density SNP panels can increase the speed of improvement for most traits. In this study, a data set of 1,726 dairy cows, collected since 1990, was used to calculate a predicted methane emission (PME) trait from feed and energy intake and requirements based on milk yield, live weight, feed intake, and condition score data. Repeated measurements from laser methane detector (LMD) data were also available from 57 cows. The estimated heritabilities for PME, milk yield, DMI, live weight, condition score, and LMD data were 0.13, 0.25, 0.11, 0.92, 0.38, and 0.05, respectively. There was a high genetic correlation between DMI and PME. No SNP reached the Bonferroni significance threshold for the PME traits. One SNP was within the 3 best SNP for PME at wk 10, 20, 30, and 40. Genomic prediction accuracies between dependent variable and molecular breeding value ranged between 0.26 and 0.30. These results are encouraging; however, more work is required before a PME trait can be implemented in a breeding program.

Predicting methane emissions from livestock in Malaysia using the ARIMA model

Purpose – The purpose of this paper is to compute the amount of methane generated from the waste of livestock from 1980 to 2008; then use the information in forecasting subsequent methane emissions by the sector in Malaysia from 2009 to 2020. Design/methodology/approach – The research study employed two approaches; computing methane emissions from 1980 to 2008 using the IPCC guidelines, and forecasting methane emissions for the animals from 2009 to 2020 using the autoregressive integrated moving average (ARIMA) model from the predictive analysis software (PASW-SPSS 18.0). Findings – Methane emissions from cattle, buffaloes and pigs accounted for over 95 per cent of total emissions and emissions from cattle are predicted to increase from 67.0 Gg in 2009 to 77.0 Gg by 2020. Emissions from the others will not be appreciable although poultry emissions will rise to 11.0 Gg by 2020. Attempt by the Malaysian Government to increase cattle production is not necessary at the moment as protein requirement has been met. Research limitations/implications – ARIMA model suffers from linear and data limitation: the future value of a variable assumed to be a linear function of several past observations in ARIMA is sometimes unrealistic. Large amounts of historical data are needed in ARIMA models in order to get desired results. The inventory of the animals was taken from 1980 to 2008.This needs to be improved upon by updating it to cover up to 2011 so that the forecast will start from 2012. Practical implications – The chosen ARIMA method has demonstrated its correctness in being adequate as a predicting tool for animal methane emissions. Policy makers can apply it so as to take practical steps to avoid these emissions. Originality/value – This is a novice idea as animal methane emission forecasting tool. This model will be of immense use and help in predicting methane emissions from livestock.

Archaeal abundance in post-mortem ruminal digesta may help predict methane emissions from beef cattle.

Methane produced from 35 Aberdeen-Angus and 33 Limousin cross steers was measured in respiration chambers. Each group was split to receive either a medium- or high-concentrate diet. Ruminal digesta samples were subsequently removed to investigate correlations between methane emissions and the rumen microbial community, as measured by qPCR of 16S or 18S rRNA genes. Diet had the greatest influence on methane emissions. The high-concentrate diet resulted in lower methane emissions (P < 0.001) than the medium-concentrate diet. Methane was correlated, irrespective of breed, with the abundance of archaea (R = 0.39), bacteria (−0.47), protozoa (0.45), Bacteroidetes (−0.37) and Clostridium Cluster XIVa (−0.35). The archaea:bacteria ratio provided a stronger correlation (0.49). A similar correlation was found with digesta samples taken 2–3 weeks later at slaughter. This finding could help enable greenhouse gas emissions of large animal cohorts to be predicted from samples taken conveniently in the abattoir.

Improving the prediction of methane production and representation of rumen fermentation for finishing beef cattle within a mechanistic model

Ellis, J. L., Dijkstra, J., Bannink, A., Kebreab, E., Archibeque, S., Benchaar, C., Beauchemin, K. A., Nkrumah, J. D. and France, J. 2014. Improving the prediction of methane production and representation of rumen fermentation for finishing beef cattle within a mechanistic model. Can. J. Anim. Sci. 94: 509–524. The purpose of this study was to evaluate prediction of methane emissions from finishing beef cattle using an extant mechanistic model with pH-independent or pH-dependent volatile fatty acid (VFA) stoichiometries, a recent stoichiometry adjustment for the use of monensin, and adaptation of the underlying model structure, to see if prediction improvements could be made for beef cattle. The database used for independent evaluation of methane predictions consisted of 74 animal means from six studies. For the “Bannink” stoichiometries, pH-dependence of stoichiometry improved the root mean square prediction error (RMSPE) statistic (38.8 to 36.6%) but the concordance correlation coefficient (CCC) statist...

Development of correction factors for landfill gas emission model suiting Indian condition to predict methane emission from landfills

Methane emission from landfill gas emission (LandGEM) model was validated through the results of laboratory scale biochemical methane potential assay. Results showed that LandGEM model over estimates methane (CH4) emissions; and the true CH4 potential of waste depends on the level of segregation. Based on these findings, correction factors were developed to estimate CH4 emission using LandGEM model especially where the level of segregation is negligible or does not exist. The correction factors obtained from the study were 0.94, 0.13 and 0.74 for food waste, mixed un-segregated municipal solid waste (MSW) and vegetable wastes, respectively.

Does diverse grazing behavior of suckler cows have an impact on predicted methane emissions?1

A modeling study based on a dataset from a large-scale grazing study was used to identify the potential impact of grazing behavior and performance of diverse cow genotypes on predicted methane (CH4) emissions. Lactating cows grazing extensive seminatural grassland and heath vegetation were monitored with Global Positioning System collars and activity sensors. The diet selected by cows of 3 different genotypes, Aberdeen Angus cross Limousin (AxL), Charolais (CHA), and Luing (LUI), was simulated by matching their locations during active periods with hill vegetation maps. Measured performance and activity were used to predict energy requirements, DMI, and CH4 output. The cumulative effect of actual performance, diet selection, and actual physical activity on potential CH4 output and yield was estimated. Sensitivity analyses were performed for the digestibility of intake, energy cost of activity, proportion of milk consumed by calves, and reproductive efficiency. Although with a better performance (P < 0.05), LUI required less total energy than the other genotypes (P < 0.001) as the other 2 spent more energy for maintenance (P < 0.001) and activity (P < 0.001). By selecting a better quality diet (P < 0.03), estimated CH4 of CHA cow-calf pairs was lower than AxL (P = 0.001) and slightly lower than LUI (P = 0.08). Energy lost as CH4 was 0.17 and 0.58% lower for LUI than AxL and CHA (P < 0.002). This study suggests for the first time that measured activity has a major impact on estimated CH4 outputs. A 15% difference of the cow-calf pair CH4 was estimated when using different coefficients to convert actual activity into energy. Predicted CH4 was highly sensitive to small changes in diet quality, suggesting the relative importance of diet selection on heterogeneous rangelands. Extending these results to a farm systems scale, CH4 outputs were also highly sensitive to reductions in weaning rates, illustrating the impact on CH4 at the farm-system level of using poorly adapted genotypes on habitats where their performances may be compromised. This paper demonstrates that variations in grazing behavior and grazing choice have a potentially large impact on CH4 emissions, illustrating the importance of including these factors in calculating realistic national and global estimates.

Modelling methane emissions from remotely collected liveweight data and faecal near-infrared spectroscopy in beef cattle

The objective of the present study was to develop a model-data fusion approach using remotely collected liveweight (LW) data from individual animals (weighing station placed at the water trough) and evaluate the potential for these data from frequent weighing to increase the accuracy of estimates of methane emissions from beef cattle grazing tropical pastures. Remotely collected LW data were used to calculate daily LW change (LWC), i.e. growth rate on a daily basis, and then to predict feed intake throughout a 342-day grazing period. Feed intake and diet dry matter digestibility (DMD) from faecal near-infrared spectroscopy analysis were used to predict methane emissions using methods for both tropical and temperate cattle as used in the Australian national inventory (Commonwealth of Australia 2014). The remote weighing system captured both short- and long-term environmental (e.g. dry and wet season, and rainfall events) and management effects on LW changes, which were then reflected in estimated feed intake and methane emissions. Large variations in all variables, measured and predicted, were found both across animals and throughout the year. Methane predictions using the official national inventory model for tropical cattle resulted in 20% higher emissions than those for temperate cattle. Predicted methane emissions based on a simulation using only initial and final LW and assuming a linear change in LW between these two points were 7.5% and 5.8% lower than those using daily information on LW from the remote weighing stations for tropical and temperate cattle, respectively. Methane emissions and feed intake can be predicted from remotely collected LW data in near real-time on a daily basis to account for short- and long-term variations in forage quality and intake. This approach has the potential to provide accurate estimates of methane emissions at the individual animal level, making the approach suitable for grazing livestock enterprises wishing to participate in carbon markets and accounting schemes.

Erratum to: “Predicted methane emission on the East Siberian shelf”

Erratum to: “Predicted methane emission on the East Siberian shelf”

Comparative evaluation of equations predicting methane production of dairy cattle from feed characteristics

Techniques that allow direct measurements on animals to quantify methane (CH4) emissions are costly and difficult to transfer to herd level. Mathematical approaches have been developed to predict methane emissions of cattle based on diet and intake characteristics, which were calibrated against largely varying calorimetry data. In this study, nine CH4 prediction equations were applied to five typical Central European dairy cow diets in order to compare their applicability. The five diets differed in respect of forage proportion and type. In a first attempt, regression equations were selected containing easily accessible data such as dry matter intake (DMI, kg/d) forage proportion, as well as neutral and acid detergent fibre that can also be extracted from on-farm datasets. Smallest differences to mean values were observed with the application of equations using neutral detergent fibre, while standard deviations were highest. Therefore, the best capability to differentiate between diets was shown, when using equations that operated with forage proportion and DMI. Nevertheless, the role of CH4 prediction equations should not be overestimated. The differences in CH4 estimates show that frequently used equations are still inaccurate and may only serve as implications to locate trends. It should be taken into consideration to expand datasets, involving future CH4 measurements, on animal and herd level, feeding typical, up-to-date regional diets in order to get more precise equations, suitable for a greater range of estimations. To ease and simplify the future applications, the prediction equations could be classified into groups, clearly stating by which data they were derived, for example, regional origin and diet composition.

Comparing local and cosmopolitan cattle breeds on added values for milk and cheese production and their predicted methane emissions

The aim of this study was to compare added values for milk yield and predicted methane emissions of cows from local and cosmopolitan breeds reared in Italian circumstances. A number of studies showed that milk from local breeds is more suitable to be processed into cheese than that of cosmopolitan populations, so that the development of payment systems which account for the added values of milk destined to cheese production could support the conservation and valorization of local animal genetic resources. Also, the link of local breeds with traditions and the environment might partially compensate their low milk production. Breeders of Burlina, Reggiana, Rendena and Valdostana cows sell products in niche markets and they realize an extra added values per kg of milk of 66 percent, compared with Holstein Friesian. Market-oriented strategies to payment systems that include the added values of milk yield could enhance profitability and interest in rearing and safeguarding of local animal genetic resources; but, not all countries can apply these market strategies. Therefore, other alternative strategies to enrich the added values of milk production might be based on the differences in greenhouse gases emissions among breeds. Indeed, local animal genetic resources are expected reducing the greenhouse gases emissions because of their lowest metabolic body weight, respect to high selected animals. Individual methane emissions might be indirectly calculated using dry matter intake; the latter can be estimated using milk and fat production, and body weight. A reduction of 10 percent of daily methane emissions per kg of metabolic body weight is expected for local compared with cosmopolitan breeds. In average, no differences were found among local and cosmopolitan breeds in terms of daily methane emission per kg of milk yield. In conclusion, animal genetic resources needs to be evaluated not only per unit of output but for other direct and indirect units of output related to social and human returns.

Development of equations for predicting methane emissions from ruminants

Ruminants contribute to global warming by releasing methane (CH4) gas by enteric fermentation. This has increased interest among animal scientists to develop and improve equations predicting CH4 production. The objectives of the current study were to collect a data set from respiration studies and to evaluate the effects of dietary and animal factors on CH4 production from diets that can safely be fed to dairy cows, using a mixed model regression analysis. Therefore, diets containing more than 75% concentrate on a dry matter (DM) basis were excluded from the analysis. The final data set included a total of 298 treatment means from 52 published papers with 207 cattle and 91 sheep diets. Dry matter intake per kilogram of body weight (DMIBW), organic matter digestibility estimated at the maintenance level of feeding (OMDm), and dietary concentrations of neutral detergent fiber (NDF), nonfiber carbohydrates (NFC), and ether extract (EE) were the variables of the best-fit equation predicting CH4 energy (CH4-E) as a proportion of gross energy intake (GE): CH4-E/GE (kJ/MJ)=−0.6 (±12.76) − 0.70 (±0.072) × DMIBW (g/kg) + 0.076 (±0.0118) × OMDm (g/kg) − 0.13 (±0.020) × EE (g/kg of DM) + 0.046 (±0.0097) × NDF (g/kg of DM) + 0.044 (±0.0094) × NFC (g/kg of DM), resulting in the lowest root mean square error adjusted for random study effect (adj. RMSE=3.26 kJ/MJ). Total CH4 production (L/d) in the cattle data set was closely related to DM intake. However, further inclusion of other variables improved the model: CH4 (L/d)=−64.0 (±35.0) + 26.0 (±1.02) × DM intake (kg/d) − 0.61 (±0.132) × DMI2(centered) + 0.25 (±0.051) × OMDm (g/kg) − 66.4 (±8.22) × EE intake (kg of DM/d) − 45.0 (±23.50) × NFC/(NDF + NFC), with adj. RMSE of 21.1 L/d. Cross-validation of the CH4-E/GE equation [observed CH4-E/GE=0.96 (±0.103) × predicted CH4-E/GE + 2.3 (±7.05); R2=0.85, adj. RMSE=3.38 kJ/MJ] indicated that differences in CH4 production between the diets could be predicted accurately. We conclude that feed intake is the main determinant of total CH4 production and that CH4-E/GE is negatively related to feeding level and dietary fat concentration and positively to diet digestibility, whereas dietary carbohydrate composition has only minor effects.

Towards a benchmarking tool for minimizing wastewater utility greenhouse gas footprints

A benchmark simulation model, which includes a wastewater treatment plant (WWTP)-wide model and a rising main sewer model, is proposed for testing mitigation strategies to reduce the system's greenhouse gas (GHG) emissions. The sewer model was run to predict methane emissions, and its output was used as the WWTP model input. An activated sludge model for GHG (ASMG) was used to describe nitrous oxide (N(2)O) generation and release in activated sludge process. N(2)O production through both heterotrophic and autotrophic pathways was included. Other GHG emissions were estimated using empirical relationships. Different scenarios were evaluated comparing GHG emissions, effluent quality and energy consumption. Aeration control played a clear role in N(2)O emissions, through concentrations and distributions of dissolved oxygen (DO) along the length of the bioreactor. The average value of N(2)O emission under dynamic influent cannot be simulated by a steady-state model subjected to a similar influent quality, stressing the importance of dynamic simulation and control. As the GHG models have yet to be validated, these results carry a degree of uncertainty; however, they fulfilled the objective of this study, i.e. to demonstrate the potential of a dynamic system-wide modelling and benchmarking approach for balancing water quality, operational costs and GHG emissions.