Estimating enteric methane production for beef cattle using empirical prediction models compared with IPCC Tier 2 methodology

Escobar-Bahamondes, Paul; Oba, M.; Kröbel, Roland; McAllister, Tim A.; Macdonald, Douglas J.; Beauchemin, K. A.

doi:10.1139/cjas-2016-0163

Cited by 3 publications

(2 citation statements)

References 7 publications

(17 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For instance, the Beef Cattle Nutrient Requirements Model (BCNRM) by the NASEM (2016) provided empirical and mechanistic options to predict the CH 4 emissions of beef cattle. The BCNRM’s empirical option was developed based on selected empirical equations for typical beef cattle production scenarios in North America ( Escobar-Bahamondes et al, 2017 ), whereas the BCNRM’s mechanistic option was developed based on mechanistic and empirical approaches to model the rumen functions ( NRC, 2000 ; Fox et al, 2004 ), often called functional models because they simultaneously have empirical and mechanistic elements in support of a specific predictive goal ( Tedeschi and Fox, 2020a ). Unfortunately, few mathematical nutrition models have explicitly modeled the CH 4 emission from the hindgut of ruminants, in part because the rumen represents close to 90% of the CH 4 emission ( Murray et al, 1976 ; Tedeschi and Fox, 2020a ), and there is a lack of interest in predicting the fermentation dynamics in the hindgut because they contribute little, if any, to ruminant animal performance and production.…”

Section: Methods To Estimate Methanementioning

confidence: 99%

Quantification of methane emitted by ruminants: a review of methods

Tedeschi

Abdalla

Álvarez

et al. 2022

Journal of Animal Science

View full text Add to dashboard Cite

The contribution of greenhouse gas (GHG) emissions from ruminant production systems varies between countries and between regions within individual countries. The appropriate quantification of GHG emissions, specifically methane (CH4), has raised questions about the correct reporting of GHG inventories and, perhaps more importantly, how best to mitigate CH4 emissions. This review documents existing methods and methodologies to measure and estimate CH4 emissions from ruminant animals and the manure produced therein over various scales and conditions. Measurements of CH4 have frequently been conducted in research settings using classical methodologies developed for bioenergetic purposes, such as gas exchange techniques (respiration chambers, headboxes). While very precise, these techniques are limited to research settings as they are expensive, labor-intensive, and applicable only to a few animals. Head-stalls, such as the GreenFeed system, have been used to measure expired CH4 for individual animals housed alone or in groups in confinement or grazing. This technique requires frequent animal visitation over the diurnal measurement period and an adequate number of collection days. The tracer gas technique can be used to measure CH4 from individual animals housed outdoors, as there is a need to ensure low background concentrations. Micrometeorological techniques (e.g., open-path lasers) can measure CH4 emissions over larger areas and many animals, but limitations exist, including the need to measure over more extended periods. Measurement of CH4 emissions from manure depends on the type of storage, animal housing, CH4 concentration inside and outside the boundaries of the area of interest, and ventilation rate, which is likely the variable that contributes the greatest to measurement uncertainty. For large-scale areas, aircraft, drones, and satellites have been used in association with the tracer flux method, inverse modeling, imagery, and lidar, but research is lagging in validating these methods. Bottom-up approaches to estimating CH4 emissions rely on empirical or mechanistic modeling to quantify the contribution of individual sources (enteric and manure). In contrast, top-down approaches estimate the amount of CH4 in the atmosphere using spatial and temporal models to account for transportation from an emitter to an observation point. While these two estimation approaches rarely agree, they help identify knowledge gaps and research requirements in practice.

show abstract

Section: Methods To Estimate Methanementioning

confidence: 99%

Quantification of methane emitted by ruminants: a review of methods

Tedeschi

Abdalla

Álvarez

et al. 2022

Journal of Animal Science

View full text Add to dashboard Cite

show abstract

“…Several attempts, either empirical or mechanistic (Jose et al, 2016;Kebreab et al, 2008), to predict beef cattle GHG emissions, were based on research with cattle in temperate climates (Ellis et al, 2009;Escobar-Bahamondes et al, 2017;IPCC, 2006;Kebreab et al, 2006;Yan et al, 2009). A key barrier to mitigate emissions from beef production systems is regional and local variation in conditions and production practices, leading to a complicated and problematic process of capturing an optimum value (Opio et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Modelling alternative management scenarios of economic and environmental sustainability of beef finishing systems

Kamilaris

Dewhurst

Sykes

et al. 2020

Journal of Cleaner Production

View full text Add to dashboard Cite

The livestock industry, and particularly beef production, is recognised as an important source of greenhouse gas (GHG) emissions linked to climate change. The complexity of beef systems means that appropriate GHG mitigating strategies depend on local conditions, requiring tailored entry points to be identified and evaluated. Using Scotland as a case study, here we combine a bio-economic simulation model and farm-level carbon footprinting tool to study the environmental impact of a range of beef production scenarios, and trade-offs generated between mitigating emissions and increasing farm profitability. To measure the environmental impact of finishing duration, type and gender selection of beef fattening systems, emissions were grouped into five categories: (1) land and crops, (2) enteric emissions, (3) manure, (4) feed and bedding, and (5) fuel and electricity. Results suggest that more intensive shorter duration systems have the lowest environmental impact of all the systems investigated. However, medium duration (i.e. 18-24 months) pasture-based beef production systems in Scotland were found to achieve a balance between financial returns and environmental performance.

show abstract

Prediction method of methane content change in cyclic hydrogen after desulfurization

Feng

Yang

2020

Journal of King Saud University - Science

View full text Add to dashboard Cite

Estimating enteric methane production for beef cattle using empirical prediction models compared with IPCC Tier 2 methodology

Cited by 3 publications

References 7 publications

Quantification of methane emitted by ruminants: a review of methods

Quantification of methane emitted by ruminants: a review of methods

Modelling alternative management scenarios of economic and environmental sustainability of beef finishing systems

Prediction method of methane content change in cyclic hydrogen after desulfurization

Contact Info

Product

Resources

About