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Livestock contributes approximately one-third of global anthropogenic methane (CH 4 ) emissions. Quantifying the spatial and temporal variations of these emissions is crucial for climate change mitigation. Although country-level information is reported regularly through national inventories and global databases, spatially explicit quantification of century-long dynamics of CH 4 emissions from livestock has been poorly investigated. Using the Tier 2 method adopted from the 2019 Refinement to 2006 IPCC guidelines, we estimated CH 4 emissions from global livestock at a spatial resolution of 0.083° (~9 km at the equator) during the period 1890-2019. We find that global CH 4 emissions from livestock increased from 31.8 [26.5-37.1] (mean [minimum-maximum of 95% confidence interval) Tg CH 4 yr -1 in 1890 to 131.7 [109.6-153.7] Tg CH 4 yr -1 in 2019, a fourfold increase in the past 130 years. The growth in global CH 4 emissions mostly occurred after 1950 and was mainly attributed to the cattle sector. Our estimate shows faster growth in livestock CH 4 emissions as compared to the previous Tier 1 estimates and is ~20% higher than the estimate from FAOSTAT for the year 2019. Regionally, South Asia, Brazil, North Africa, China, the United States, Western Europe, and Equatorial Africa shared the majority of the global emissions in the 2010s. South Asia, tropical Africa, and Brazil have dominated the growth in global CH 4 emissions from livestock in the recent three decades. Changes in livestock CH 4 emissions were primarily associated with changes in population and national income and were also affected by the policy, diet shifts, livestock productivity improvement, and international trade. The new geospatial information on the magnitude and trends of livestock CH 4 emissions identifies emission hotspots and spatial-temporal patterns, which will help to guide meaningful CH 4 mitigation practices in the livestock sector at both local and global scales., (© 2022 John Wiley & Sons Ltd.)

Recent developments in diffuse reflectance soil spectroscopy have increasingly focused on building and using large soil spectral libraries with the purpose of supporting many activities relevant to monitoring, mapping and managing soil resources. A potential limitation of using a mid-infrared (MIR) spectral library developed by another laboratory is the need to account for inherent differences in the signal strength at each wavelength associated with different instrumental and environmental conditions. Here we apply predictive models built using the USDA National Soil Survey Center-Kellogg Soil Survey Laboratory (NSSC-KSSL) MIR spectral library ( n = 56,155) to samples sets of European and US origin scanned on a secondary spectrometer to assess the need for calibration transfer using a piecewise direct standardization (PDS) approach in transforming spectra before predicting carbon cycle relevant soil properties (bulk density, CaCO 3 , organic carbon, clay and pH). The European soil samples were from the land use/cover area frame statistical survey (LUCAS) database available through the European Soil Data Center (ESDAC), while the US soil samples were from the National Ecological Observatory Network (NEON). Additionally, the performance of the predictive models on PDS transfer spectra was tested against the direct calibration models built using samples scanned on the secondary spectrometer. On independent test sets of European and US origin, PDS improved predictions for most but not all soil properties with memory based learning (MBL) models generally outperforming partial least squares regression and Cubist models. Our study suggests that while good-to-excellent results can be obtained without calibration transfer, for most of the cases presented in this study, PDS was necessary for unbiased predictions. The MBL models also outperformed the direct calibration models for most of the soil properties. For laboratories building new spectroscopy capacity utilizing existing spectral libraries, it appears necessary to develop calibration transfer using PDS or other calibration transfer techniques to obtain the least biased and most precise predictions of different soil properties.

Nitrous oxide (N 2 O), like carbon dioxide, is a long-lived greenhouse gas that accumulates in the atmosphere. Over the past 150 years, increasing atmospheric N 2 O concentrations have contributed to stratospheric ozone depletion 1 and climate change 2 , with the current rate of increase estimated at 2 per cent per decade. Existing national inventories do not provide a full picture of N 2 O emissions, owing to their omission of natural sources and limitations in methodology for attributing anthropogenic sources. Here we present a global N 2 O inventory that incorporates both natural and anthropogenic sources and accounts for the interaction between nitrogen additions and the biochemical processes that control N 2 O emissions. We use bottom-up (inventory, statistical extrapolation of flux measurements, process-based land and ocean modelling) and top-down (atmospheric inversion) approaches to provide a comprehensive quantification of global N 2 O sources and sinks resulting from 21 natural and human sectors between 1980 and 2016. Global N 2 O emissions were 17.0 (minimum-maximum estimates: 12.2-23.5) teragrams of nitrogen per year (bottom-up) and 16.9 (15.9-17.7) teragrams of nitrogen per year (top-down) between 2007 and 2016. Global human-induced emissions, which are dominated by nitrogen additions to croplands, increased by 30% over the past four decades to 7.3 (4.2-11.4) teragrams of nitrogen per year. This increase was mainly responsible for the growth in the atmospheric burden. Our findings point to growing N 2 O emissions in emerging economies-particularly Brazil, China and India. Analysis of process-based model estimates reveals an emerging N 2 O-climate feedback resulting from interactions between nitrogen additions and climate change. The recent growth in N 2 O emissions exceeds some of the highest projected emission scenarios 3,4 , underscoring the urgency to mitigate N 2 O emissions.

Accurate knowledge of 13 C isotopic signature (δ 13 C) of methane from each source is crucial for separating biogenic, fossil fuel and pyrogenic emissions in bottom-up and top-down methane budget. Livestock production is the largest anthropogenic source in the global methane budget, mostly from enteric fermentation of domestic ruminants. However, the global average, geographical distribution and temporal variations of the δ 13 C of enteric emissions are not well understood yet. Here, we provide a new estimation of C3-C4 diet composition of domestic ruminants (cattle, buffaloes, goats and sheep), a revised estimation of yearly enteric CH 4 emissions, and a new estimation for the evolution of its δ 13 C during the period 1961-2012. Compared to previous estimates, our results suggest a larger contribution of ruminants' enteric emissions to the increasing trend in global methane emissions between 2000 and 2012, and also a larger contribution to the observed decrease in the δ 13 C of atmospheric methane.

Much concern has been raised about the increasing threat to air quality and human health due to ammonia (NH 3 ) emissions from agricultural systems, which is associated with the enrichment of reactive nitrogen (N) in southern Asia (SA), home of more than 60% the world's population (i.e., the people of West, central, East, South, and Southeast Asia). Southern Asia consumed more than half of the global synthetic N fertilizer and was the dominant region for livestock waste production since 2004. Excessive N application could lead to a rapid increase of NH 3 in the atmosphere, resulting in severe air and water pollution in this region. However, there is still a lack of accurate estimates of NH 3 emissions from agricultural systems. In this study, we simulated the agricultural NH 3 fluxes in SA by coupling the Bidirectional NH 3 exchange module (Bi-NH 3 ) from the Community Multi-scale Air Quality model with the Dynamic Land Ecosystem Model. Our results indicated that NH 3 emissions were 21.3 ± 3.9 Tg N yr -1 from SA agricultural systems with a rapidly increasing rate of ~0.3 Tg N yr -2 during 1961-2014. Among the emission sources, 10.8 Tg N yr -1 was released from synthetic N fertilizer use, and 10.4 ± 3.9 Tg N yr -1 was released from manure production in 2014. Ammonia emissions from China and India together accounted for 64% of the total amount in SA during 2000-2014. Our results imply that the increased NH 3 emissions associated with high N inputs to croplands would likely be a significant threat to the environment and human health unless mitigation efforts are applied to reduce these emissions., Competing Interests: The authors declare no conflicts of interest relevant to this study., (©2017. The Authors.)

Human demand for livestock products has increased rapidly during the past few decades largely due to dietary transition and population growth, with significant impact on climate and the environment. The contribution of ruminant livestock to greenhouse gas (GHG) emissions has been investigated extensively at various scales from regional to global, but the long-term trend, regional variation and drivers of methane (CH 4 ) emission remain unclear. In this study, we use Intergovernmental Panel on Climate Change (IPCC) Tier II guidelines to quantify the evolution of CH 4 emissions from ruminant livestock during 1890-2014. We estimate that total CH 4 emissions in 2014 was 97.1 million tonnes (MT) CH 4 or 2.72 Gigatonnes (Gt) CO 2 -eq (1 MT = 10 12 g, 1 Gt = 10 15 g) from ruminant livestock, which accounted for 47%-54% of all non-CO 2 GHG emissions from the agricultural sector. Our estimate shows that CH 4 emissions from the ruminant livestock had increased by 332% (73.6 MT CH 4 or 2.06 Gt CO 2 -eq) since the 1890s. Our results further indicate that livestock sector in drylands had 36% higher emission intensity (CH 4 emissions/km 2 ) compared to that in nondrylands in 2014, due to the combined effect of higher rate of increase in livestock population and low feed quality. We also find that the contribution of developing regions (Africa, Asia and Latin America) to the total CH 4 emissions had increased from 51.7% in the 1890s to 72.5% in the 2010s. These changes were driven by increases in livestock numbers (LU units) by up to 121% in developing regions, but decreases in livestock numbers and emission intensity (emission/km 2 ) by up to 47% and 32%, respectively, in developed regions. Our results indicate that future increases in livestock production would likely contribute to higher CH 4 emissions, unless effective strategies to mitigate GHG emissions in livestock system are implemented., (© 2017 John Wiley & Sons Ltd.)