Login | Create Account (DAF staff only)

Applicability of Eddy Covariance to Estimate Methane Emissions from Grazing Cattle

Coates, T. W., Benvenutti, M. A., Flesch, T. K., Charmley, E., McGinn, S. M. and Chen, D. (2018) Applicability of Eddy Covariance to Estimate Methane Emissions from Grazing Cattle. Journal of Environment Quality, 47 (1). p. 54. ISSN 0047-2425

Full text not currently attached. Access may be available via the Publisher's website or OpenAccess link.

Article Link(s): https://doi.org/10.2134/jeq2017.02.0084

Abstract

Grazing systems represent a significant source of enteric methane (CH4), but available techniques for quantifying herd scale emissions are limited. This study explores the capability of an eddy covariance (EC) measurement system for long-term monitoring of CH4 emissions from grazing cattle. Measurements were made in two pasture settings: in the center of a large grazing paddock, and near a watering point where animals congregated during the day. Cattle positions were monitored through time-lapse images, and this information was used with a Lagrangian stochastic dispersion model to interpret EC fluxes and derive per-animal CH4 emission rates. Initial grazing paddock measurements were challenged by the rapid movement of cattle across the measurement footprint, but a feed supplement placed upwind of the measurements helped retain animals within the footprint, allowing emission estimates for 20% of the recorded daytime fluxes. At the water point, >50% of the flux measurement periods included cattle emissions. Overall, cattle emissions for the paddock site were higher (253 g CH4 m-2 adult equivalent [AE]-1 d-1, SD = 75) and more variable than emissions at the water point (158 g CH4 AE-1 d-1, SD = 34). Combining results from both sites gave a CH4 production of 0.43 g kg-1 body weight, which is in range of other reported emissions from grazing animals. With an understanding of animal behavior to allow the most effective use of tower placement, the combination of an EC measurement platform and a Lagrangian stochastic model could have practical applications for long-term monitoring of fluxes in grazing environments.

Item Type:Article
Business groups:Animal Science
Subjects:Agriculture > Agriculture (General) > Agriculture and the environment
Animal culture > Cattle
Animal culture > Rangelands. Range management. Grazing
Deposited On:23 Jan 2018 01:32
Last Modified:23 Jan 2018 01:32

Repository Staff Only: item control page