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A novel approach to rapidly tracking whole-farm methane emissions

Bibliographic Data

ID15546201
AuthorsHuilin Chen (0000-0001-8040-3472, University of Groningen, corresponding author), Katarina Vinković (0000-0002-0445-5462, University of Groningen), Chu Sun (Nanjing University), Wouter Peters (0000-0001-8166-2070, University of Groningen), A Hensen (0000-0002-8467-7785, Netherlands Organisation for Applied Scientific Research), Hugo Denier van der Gon (0000-0001-9552-3688, Netherlands Organisation for Applied Scientific Research), M C van Zanten (0000-0003-0010-7839, National Institute for Public Health and the Environment), Pim van den Bulk (Netherlands Organisation for Applied Scientific Research), Ilona Velzeboer (0000-0003-3225-9335, Netherlands Organisation for Applied Scientific Research), Theo Van Der Zee (0000-0002-3748-0912, National Institute for Public Health and the Environment), Tim van der Zee
Year2025
Volume20
Issue3
Pages034016-034016
Publication date2025-02-04
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueEnvironmental Research Letters (JOURNAL)
Journal identifiersISSN: 1748-9326 • E-ISSN: 1748-9326
PublisherIOP Publishing (PUBLISHER • GB)
DOI10.1088/1748-9326/adb1f6
OpenAlexW4407131307
LanguageEN
References cited24

Enteric fermentation and manure from livestock farming are major sources of methane (CH 4 ) emissions and have a large potential for emissions reduction. However, there is a lack of effective methods for evaluating future emissions reduction efforts, especially at the farm scale. We developed a rapid analysis method to evaluate CH 4 emissions from a large number of dairy cow farms in the Netherlands based on single-transect mobile van measurements of CH 4 concentrations downwind of farms located between 80 and 750 m from the road. Methane emissions from 51 dairy cow farms were determined on four campaign days within a total of 7 measurement hours between November 2017 and November 2018 using an inverse Gaussian approach combined with two different wind datasets and their composite. We found a range of moderate to high correlation ( R 2 : minimum 0.42, maximum 0.86) between the estimated CH 4 emission rates for 11–16 farms on each measurement day and the number of animal units (AUs, 1 AU equals 500 kg of animal weight) across four individual days. The whole-farm CH 4 emission factors (including both enteric fermentation and manure) for the four separate campaign days were estimated using the slope between the CH 4 emission rates derived from the composite of two distinct wind datasets and the number of AUs. Daily emission factors for the four campaign days were estimated to be in the range of 0.18–0.50 kgCH 4 /d/AU. From the dataset, averaged over each of the four campaign days, we derived an estimate of the whole-farm CH 4 emission factor, with a 95% confidence interval of 0.47 [0.13–0.81] kgCH 4 /d/AU. Our results demonstrate that CH 4 emissions from a large number of dairy cow farms can be rapidly estimated, providing an independent way to evaluate country-specific emission factors and a potential way to monitor future emission reductions

Atmospheric sciences · Greenhouse gas · Methane · Methane emissions · Tracking (education · Agriculture Sustainability and Environmental Impact · Chemistry · Environmental Science · Odor and Emission Control Technologies · Geology

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    Open Access•Marielle Saunois, Ann R Stavert et al.•Earth System Science Data•2020

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