Evaluating Dispersion Modeling Options to Estimate Methane Emissions from Grazing Beef Cattle

Abstract

Enteric methane (CH) emission from cattle is a source of greenhouse gas and is an energy loss that contributes to production inefficiency for cattle. Direct measurements of enteric CH emissions are useful to quantify the magnitude and variation and to evaluate mitigation of this important greenhouse gas source. The objectives of this study were to evaluate the impact of stocking density of cattle and source configuration (i.e., point source vs. area source and elevation of area source) on CH emissions from grazing beef cattle in Queensland, Australia. This was accomplished using nonintrusive atmospheric measurements and a gas dispersion model. The average measured CH emission for the point and area source was between 240 and 250 g animal d over the entire study. There was no difference ( > 0.05) in emission when using an elevated area source (0.5 m) or a ground area source (0 m). For the point-source configuration, there was a difference in CH emission due to stocking density; likewise, some differences existed for the area-source emissions. This study demonstrates the flexibility of the area-source configuration of the dispersion model to estimate CH emissions even at a low stocking density.

Herd-scale measurements of methane emissions from cattle grazing extensive sub-tropical grasslands using the open-path laser technique

“…Data were also removed from the analysis if the value was negative or >2 SD from the mean. Nevertheless a 66% success rate is considered acceptable and higher than those recorded by McGinn et al (2011) of 34% andMcGinn et al (2015) of 40% under similar northern Australian field conditions. This discrepancy may have been related to the basis for calculating operational measurement periods which inadvertently removed some failed measurement periods; the operational measurement period on each day commenced and ended with the first and last successful measurement observation.…”

“…(2010) and McGinn et al (2015). In brief, line averaged concentration of CH 4 is recorded by a number of sensors relative to a source area at each site.…”

“…Following morning grazing animals were confined to an area (Table 3), established around the only water point in the study paddock. This confinement assumes a uniform distribution of animals within the area and surface-source assumptions were used in a BLS dispersion model to derive CH 4 flux (McGinn et al, 2015). The surrounding area at each site was flat and considered to present no major impediments to wind characteristics.…”

“…As an alternative, we measured CH 4 emissions at a herd scale during a time of day when free-ranging cattle tend to congregate around an attractant, usually water (Tomkins et al, 2009). It is well known that there are diurnal variations in CH 4 emissions, with peaks in CH 4 output following grazing bouts (Tomkins et al, 2011;McGinn et al, 2015). Behavioural observations on cattle grazing under northern Australian conditions have demonstrated a crepuscular grazing pattern with a rest period during the hottest part of the day (Tomkins et al, 2009).…”

“…The method itself contributes to this as data filtering contributes to random missing values, thus influencing hourly mean values for each day. Using similar filtering methods to those employed in our study McGinn et al (2015) recorded 53% useable data, but the variation in useable data across their treatments ranged from a low of 19% to a maximum of 72% useable values. CH 4 emissions are related to feeding bouts, with CH 4 peaks typically occurring within 2 h of feeding.…”