43 citations as recorded by crossref.

1. Herd position habits can bias net CO2 ecosystem exchange estimates in free range grazed pastures L. Gourlez de la Motte et al. https://doi.org/10.1016/j.agrformet.2019.01.015
2. Assessing nitrous oxide emissions in time and space with minimal uncertainty using static chambers and eddy covariance from a temperate grassland R. Murphy et al. https://doi.org/10.1016/j.agrformet.2021.108743
3. Greenhouse-gas budgets for irrigated dairy pasture and a winter-forage kale crop J. Laubach & J. Hunt https://doi.org/10.1016/j.agrformet.2017.04.013
4. Invited review: Nitrogen in ruminant nutrition: A review of measurement techniques A. Hristov et al. https://doi.org/10.3168/jds.2018-15829
5. Combining eddy covariance measurements with process-based modelling to enhance understanding of carbon exchange rates of dairy pastures M. Kirschbaum et al. https://doi.org/10.1016/j.scitotenv.2020.140917
6. Methane emissions from animal agriculture: Micrometeorological solutions for challenging measurement situations J. Laubach et al. https://doi.org/10.1016/j.agrformet.2024.109971
7. Continuous CO2/CH4/CO measurements (2012–2014) at Beromünster tall tower station in Switzerland E. Satar et al. https://doi.org/10.5194/bg-13-2623-2016
8. Supervised Machine Learning to Assess Methane Emissions of a Dairy Building with Natural Ventilation S. Hempel et al. https://doi.org/10.3390/app10196938
9. Ammonia emissions from a grazed field estimated by miniDOAS measurements and inverse dispersion modelling M. Bell et al. https://doi.org/10.5194/amt-10-1875-2017
10. Nitrous oxide fluxes determined by continuous eddy covariance measurements from intensively grazed pastures: Temporal patterns and environmental controls L. Liang et al. https://doi.org/10.1016/j.agee.2018.09.010
11. The challenge of reconciling bottom-up agricultural methane emissions inventories with top-down measurements R. Desjardins et al. https://doi.org/10.1016/j.agrformet.2017.09.003
12. Methane efflux from an American bison herd P. Stoy et al. https://doi.org/10.5194/bg-18-961-2021
13. Combining two complementary micrometeorological methods to measure CH4 and N2O fluxes over pasture J. Laubach et al. https://doi.org/10.5194/bg-13-1309-2016
14. Assessment of Despiking Methods for Turbulence Data in Micrometeorology D. Starkenburg et al. https://doi.org/10.1175/JTECH-D-15-0154.1
15. Ammonia emission measurements of an intensively grazed pasture K. Voglmeier et al. https://doi.org/10.5194/bg-15-4593-2018
16. Accounting for Field-Scale Dry Deposition in Backward Lagrangian Stochastic Dispersion Modelling of NH3 Emissions C. Häni et al. https://doi.org/10.3390/atmos9040146
17. Reconciling annual nitrous oxide emissions of an intensively grazed dairy pasture determined by eddy covariance and emission factors A. Wecking et al. https://doi.org/10.1016/j.agee.2019.106646
18. Methane balance of an intensively grazed pasture and estimation of the enteric methane emissions from cattle P. Dumortier et al. https://doi.org/10.1016/j.agrformet.2016.09.010
19. Combining the Eddy Covariance Method and Dry Matter Intake Measurements for Enteric Methane Emission Estimation from Grazing Dairy Cows M. Eismann et al. https://doi.org/10.3390/atmos15111269
20. Direct whole-farm greenhouse gas flux measurements for a beef cattle operation A. Taylor et al. https://doi.org/10.1016/j.agee.2017.01.011
21. How to measure, report and verify soil carbon change to realize the potential of soil carbon sequestration for atmospheric greenhouse gas removal P. Smith et al. https://doi.org/10.1111/gcb.14815
22. Small methane emissions from pasture observed with eddy covariance and flux-gradient methods: are they real or artefacts? J. Laubach et al. https://doi.org/10.1016/j.agrformet.2025.110976
23. Measurements of methane emissions from a beef cattle feedlot using the eddy covariance technique P. Prajapati & E. Santos https://doi.org/10.1016/j.agrformet.2016.09.001
24. Are there memory effects on greenhouse gas emissions (CO2, N2O and CH4) following grassland restoration? L. Merbold et al. https://doi.org/10.5194/bg-18-1481-2021
25. Country-scale greenhouse gas budgets using shipborne measurements: a case study for the UK and Ireland C. Helfter et al. https://doi.org/10.5194/acp-19-3043-2019
26. Assessing Management Tools to Mitigate Carbon Losses Using Field-Scale Net Ecosystem Carbon Balance in a Ley-Arable Crop Sequence M. Eismann et al. https://doi.org/10.3390/atmos16101190
27. Validation of the Swiss methane emission inventory by atmospheric observations and inverse modelling S. Henne et al. https://doi.org/10.5194/acp-16-3683-2016
28. Assessment of the inverse dispersion method for the determination of methane emissions from a dairy housing M. Bühler et al. https://doi.org/10.1016/j.agrformet.2021.108501
29. Temperate grazed grassland carbon balances for two adjacent paddocks determined separately from one eddy covariance system A. Wall et al. https://doi.org/10.1016/j.agrformet.2020.107942
30. Using open-path dual-comb spectroscopy to monitor methane emissions from simulated grazing cattle C. Weerasekara et al. https://doi.org/10.5194/amt-17-6107-2024
31. Greenhouse gas fluxes over managed grasslands in Central Europe L. Hörtnagl et al. https://doi.org/10.1111/gcb.14079
32. Determination of the carbon budget of a pasture: effect of system boundaries and flux uncertainties R. Felber et al. https://doi.org/10.5194/bg-13-2959-2016
33. Estimating methane emissions from beef cattle in a feedlot using the eddy covariance technique and footprint analysis P. Prajapati & E. Santos https://doi.org/10.1016/j.agrformet.2017.08.004
34. Grazing-related nitrous oxide emissions: from patch scale to field scale K. Voglmeier et al. https://doi.org/10.5194/bg-16-1685-2019
35. Estimating Herd‐Scale Methane Emissions from Cattle in a Feedlot Using Eddy Covariance Measurements and the Carbon Dioxide Tracer Method P. Prajapati & E. Santos https://doi.org/10.2134/jeq2018.09.0332
36. Field testing two flux footprint models T. Coates et al. https://doi.org/10.5194/amt-14-7147-2021
37. Comparing methane emissions estimated using a backward-Lagrangian stochastic model and the eddy covariance technique in a beef cattle feedlot P. Prajapati & E. Santos https://doi.org/10.1016/j.agrformet.2018.04.003
38. Discerning the cows from the pasture: Quantifying and partitioning the NEE of a grazed pasture using animal position data R. Felber et al. https://doi.org/10.1016/j.agrformet.2015.09.018
39. Point source emission estimation using eddy covariance: Validation using an artificial source experiment P. Dumortier et al. https://doi.org/10.1016/j.agrformet.2018.12.012
40. Beef cattle methane emissions measured with tracer-ratio and inverse dispersion modelling techniques M. Bai et al. https://doi.org/10.5194/amt-14-3469-2021
41. Beef cattle methane emission estimation using the eddy covariance technique in combination with geolocation P. Dumortier et al. https://doi.org/10.1016/j.agrformet.2020.108249
42. Methane Emissions from Livestock Operations: Sources, Sinks, and Mitigation Strategies B. Manono https://doi.org/10.3390/methane5010007
43. Soil greenhouse gas budget of two intensively managed grazing systems K. Voglmeier et al. https://doi.org/10.1016/j.agrformet.2020.107960