Discrepancies and Uncertainties in Bottom-up Gridded Inventories of Livestock Methane Emissions for the Contiguous United States

Abstract

In this analysis we used a spatially explicit, simplified bottom-up approach, based on animal inventories, feed dry matter intake, and feed intake-based emission factors to estimate county-level enteric methane emissions for cattle and manure methane emissions for cattle, swine, and poultry for the contiguous United States. Overall, this analysis yielded total livestock methane emissions (8916 Gg/yr; lower and upper 95% confidence bounds of ±19.3%) for 2012 (last census of agriculture) that are comparable to the current USEPA estimates for 2012 and to estimates from the global gridded Emission Database for Global Atmospheric Research (EDGAR) inventory. However, the spatial distribution of emissions developed in this analysis differed significantly from that of EDGAR and a recent gridded inventory based on USEPA. Combined enteric and manure methane emissions from livestock in Texas and California (highest contributors to the national total) in this study were 36% lesser and 100% greater, respectively, than estimates by EDGAR. The spatial distribution of emissions in gridded inventories (e.g., EDGAR) likely strongly impacts the conclusions of top-down approaches that use them, especially in the source attribution of resulting (posterior) emissions, and hence conclusions from such studies should be interpreted with caution.

Top‐Down Constraints on Methane Point Source Emissions From Animal Agriculture and Waste Based on New Airborne Measurements in the U.S. Upper Midwest

“…For example, Cui et al () applied airborne measurements over the San Joaquin Valley to infer regional methane sources that were 1.7 times the bottom‐up estimates, with livestock accounting for ~75% of the total flux. Hristov et al () showed that current inventories differed significantly in their spatial allocation of agricultural methane emissions: within the contiguous United States, the Emission Database for Global Atmospheric Research v4.2 FT2010 (EDGAR, ) and Gridded Environmental Protection Agency (EPA) (Maasakkers et al, ) estimates correlated to only R 2 = 0.1 and 0.5 for manure and enteric fluxes, respectively. Hristov et al () further developed a county‐level emission inventory based on animal population, with some of the largest discrepancies relative to existing inventories occurring over our study region, the Upper Midwest.…”

“…Hristov et al () showed that current inventories differed significantly in their spatial allocation of agricultural methane emissions: within the contiguous United States, the Emission Database for Global Atmospheric Research v4.2 FT2010 (EDGAR, ) and Gridded Environmental Protection Agency (EPA) (Maasakkers et al, ) estimates correlated to only R 2 = 0.1 and 0.5 for manure and enteric fluxes, respectively. Hristov et al () further developed a county‐level emission inventory based on animal population, with some of the largest discrepancies relative to existing inventories occurring over our study region, the Upper Midwest. Furthermore, a recent study based on tall tower measurements in the U.S. Upper Midwest concluded that livestock methane emissions were underestimated 1.8‐fold in the Gridded EPA inventory (Chen et al, ).…”

Short-term methane emissions from 2 dairy farms in California estimated by different measurement techniques and US Environmental Protection Agency inventory methodology: A case study

“…Top-down inventory estimates are based on inverse modeling and use gridded bottom-up (BU) inventories as prior estimates. Thus, errors associated with gridded BU inventories can lead to errors in the TD inventory (Hristov et al, 2017); this makes TD estimates not ideal to evaluate US EPA methodology. Measurements of facilities that provide activity and management data avoid this potential error, and thus are more suited to evaluate US EPA methodology.…”

Emisi Metana dari Pengelolaan Kotoran Ternak di Yogyakarta – Inventarisasi

“…Perhitungan emisi metana dari kotoran ternak jauh lebih rumit dibandingakan dengan emisi metana dari ruminansia karena terkait dengan komposisi kotoran ternak, tipe fasilitas penyimpan kotoran ternak, sistem perkandangan, berapa kali hewan mengeluarkan kotoran dan suhu lingkungan merupakan contoh faktor yang sulit untuk di inventarisir (Hristov et al, 2017). Kemudian, pentingnya perhitungan inventarisasi emisi dari sektor peternakan harus dilakukan setiap tahun dengan tidak menggunakan faktor emisi yang konstan karena meningkatnya laju populasi ternak dan sistem manejemen perkandangan yang semakin maju (Yu et al, 2018).…”