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Nitrous oxide (N2O), like carbon dioxide, is a long-lived greenhouse gas that accumulates in the atmosphere. Over the past 150 years, increasing atmospheric N2O concentrations have contributed to stratospheric ozone depletion1 and climate change2, with the current rate of increase estimated at 2 per cent per decade. Existing national inventories do not provide a full picture of N2O emissions, owing to their omission of natural sources and limitations in methodology for attributing anthropogenic sources. Here we present a global N2O inventory that incorporates both natural and anthropogenic sources and accounts for the interaction between nitrogen additions and the biochemical processes that control N2O 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 N2O sources and sinks resulting from 21 natural and human sectors between 1980 and 2016. Global N2O 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 N2O emissions in emerging economies-particularly Brazil, China and India. Analysis of process-based model estimates reveals an emerging N2O-climate feedback resulting from interactions between nitrogen additions and climate change. The recent growth in N2O emissions exceeds some of the highest projected emission scenarios3,4, underscoring the urgency to mitigate N2O emissions.

Following the recent Global Carbon Project (GCP) synthesis of the decadal methane (CH4) budget over 2000-2012 (Saunois et al., 2016), we analyse here the same dataset with a focus on quasi-decadal and inter-annual variability in CH4 emissions. The GCP dataset integrates results from top-down studies (exploiting atmospheric observations within an atmospheric inverse-modelling framework) and bottom-up models (including process-based models for estimating land surface emissions and atmospheric chemistry), inventories of anthropogenic emissions, and data-driven approaches. The annual global methane emissions from top-down studies, which by construction match the observed methane growth rate within their uncertainties, all show an increase in total methane emissions over the period 2000-2012, but this increase is not linear over the 13 years. Despite differences between individual studies, the mean emission anomaly of the top-down ensemble shows no significant trend in total methane emissions over the period 2000-2006, during the plateau of atmospheric methane mole fractions, and also over the period 2008-2012, during the renewed atmospheric methane increase. However, the top-down ensemble mean produces an emission shift between 2006 and 2008, leading to 22 [16-32]Tg CH4yr-1 higher methane emissions over the period 2008-2012 compared to 2002-2006. This emission increase mostly originated from the tropics, with a smaller contribution from mid-latitudes and no significant change from boreal regions. The regional contributions remain uncertain in top-down studies. Tropical South America and South and East Asia seem to contribute the most to the emission increase in the tropics. However, these two regions have only limited atmospheric measurements and remain therefore poorly constrained. The sectorial partitioning of this emission increase between the periods 2002-2006 and 2008-2012 differs from one atmospheric inversion study to another. However, all top-down studies suggest smaller changes in fossil fuel emissions (from oil, gas, and coal industries) compared to the mean of the bottom-up inventories included in this study. This difference is partly driven by a smaller emission change in China from the top-down studies compared to the estimate in the Emission Database for Global Atmospheric Research (EDGARv4.2) inventory, which should be revised to smaller values in a near future. We apply isotopic signatures to the emission changes estimated for individual studies based on five emission sectors and find that for six individual top-down studies (out of eight) the average isotopic signature of the emission changes is not consistent with the observed change in atmospheric 13CH4. However, the partitioning in emission change derived from the ensemble mean is consistent with this isotopic constraint. At the global scale, the top-down ensemble mean suggests that the dominant contribution to the resumed atmospheric CH4 growth after 2006 comes from microbial sources (more from agriculture and waste sectors than from natural wetlands), with an uncertain but smaller contribution from fossil CH4 emissions. In addition, a decrease in biomass burning emissions (in agreement with the biomass burning emission databases) makes the balance of sources consistent with atmospheric 13CH4 observations. In most of the top-down studies included here, OH concentrations are considered constant over the years (seasonal variations but without any inter-annual variability). As a result, the methane loss (in particular through OH oxidation) varies mainly through the change in methane concentrations and not its oxidants. For these reasons, changes in the methane loss could not be properly investigated in this study, although it may play a significant role in the recent atmospheric methane changes as briefly discussed at the end of the paper.

Abstract. The global methane (CH4) budget is becoming an increasingly important component for managing realistic pathways to mitigate climate change. This relevance, due to a shorter atmospheric lifetime and a stronger warming potential than carbon dioxide, is challenged by the still unexplained changes of atmospheric CH4 over the past decade. Emissions and concentrations of CH4 are continuing to increase making CH4 the second most important human-induced greenhouse gas after carbon dioxide. Two major difficulties in reducing uncertainties come from the large variety of diffusive CH4 sources that overlap geographically, and from the destruction of CH4 by the very short-lived hydroxyl radical (OH). To address these difficulties, we have established a consortium of multi-disciplinary scientists under the umbrella of the Global Carbon Project to synthesize and stimulate research on the methane cycle, and producing regular (~biennial) updates of the global methane budget. This consortium includes atmospheric physicists and chemists, biogeochemists of surface and marine emissions, and socio-economists who study anthropogenic emissions. Following Kirschke et al. (2013), we propose here the first version of a living review paper that integrates results of top-down studies (T-D, exploiting atmospheric observations within an atmospheric inverse-modelling framework) and bottom-up models, inventories, and data-driven approaches (B-U, including process-based models for estimating land surface emissions and atmospheric chemistry, and inventories for anthropogenic emissions, data-driven extrapolations). For the 2003–2012 decade, global methane emissions are estimated by T-D inversions at 558 Tg CH4 yr−1 (range [540–568]). About 60 % of global emissions are anthropogenic (range [50–65 %]). B-U approaches suggest larger global emissions (736 Tg CH4 yr−1 [596–884]) mostly because of larger natural emissions from individual sources such as inland waters, natural wetlands and geological sources. Considering the atmospheric constraints on the T-D budget, it is likely that some of the individual emissions reported by the B-U approaches are overestimated, leading to too large global emissions. Latitudinal data from T-D emissions indicate a predominance of tropical emissions (~64 % of the global budget,

We describe a new dataset of cropland area circa the year 2020, with global coverage and with data for 221 countries and territories and 34 regional aggregates. Data are generated from geospatial information on the agreement–disagreement characteristics of six open-access high-resolution cropland maps derived from remote sensing. The cropland area mapping (CAM) aggregation dataset provides information on (i) mean cropland area and its uncertainty, (ii) cropland area by six distinct cropland agreement classes, and (iii) cropland area by specific combinations of underlying land cover product. The results indicated that world cropland area is 1500 ± 400 Mha (mean and 95 % confidence interval), with a relative uncertainty of 25 % that increased across regions. It was 50 % in Central Asia (40 ± 20 Mha), South America (180 ± 80 Mha), and Southern Europe (40 ± 20 Mha) and up to 40 % in Australia and New Zealand (50 ± 20 Mha), Southeastern Asia (80 ± 30 Mha), and Southern Africa (16 ± 6 Mha). Conversely, cropland area was estimated with better precision, i.e., smaller uncertainties in the range 10 %–25 % in Southern Asia (230 ± 30 Mha), Northern America (200 ± 40 Mha), Northern Africa (40 ± 10 Mha), and Eastern Europe and Western Europe (40 ± 10 Mha). The new data can be used to investigate the coherence of information across the six underlying products, as well as to explore important disagreement features. Overall, 70 % or more of the estimated mean cropland area globally and by region corresponded to good agreement of underlying land cover maps – four or more. Conversely, in Africa cropland area estimates found significant disagreement, highlighting mapping difficulties in complex landscapes. Finally, the new cropland area data were consistent with FAOSTAT (FAO, 2023) in 15 out of 18 world regions, as well as for 114 out of 182 countries with a cropland area above 10 kha. By helping to highlight features of cropland characteristics and underlying causes for agreement–disagreement across land cover products, the CAM aggregation dataset may be used as a reference for the quality of country statistics and may help guide future mapping efforts towards improved agricultural monitoring. Data are publicly available at 10.5281/zenodo.7987515 (Tubiello et al., 2023a). [ABSTRACT FROM AUTHOR]

Agricultural, fisheries, forestry and agro-processing activities produce large quantities of residues, by-products and waste materials every year. Inefficient use of these resources contributes to greenhouse gas emissions and non-point pollution, imposing significant environmental and economic burdens to society. Since many nations do not keep statistics of these materials, it has not been possible to accurately quantify the amounts produced and potentially available for recycling. Therefore, the objectives of the present work were to provide: (1) definitions, typologies and methods to aid consistent classification, estimation and reporting of the various residues and by23 products; (2) a global organic matter database (OMD) of residues and by-products from agriculture, fisheries, forestry and related industries; and (3) preliminary estimates of residues and by-products potentially available for use in a circular bio-economy. To the best of our knowledge, the OMD is the first of its kind consolidating quantities and nutrient concentrations of residues and by-products from agriculture, fisheries, forestry and allied industries globally. The OMD and its associated products will be continuously updated as new production data are published in FAOSTAT, and this information is expected to contribute to evidence-based policies and actions in support of sustainable utilization and the transition towards a circular economy. The estimates in OMD are available only at the national level. Due to the lack of uniform methodology and data across countries, it was difficult to accurately estimate the quantities of all agricultural, fisheries and forestry residue and by-products. Therefore, we strongly recommend investment in the inventory of agricultural, fisheries and forestry residues, by-products and wastes for use in a circular bio35 economy and as amendments. [ABSTRACT FROM AUTHOR]

Nutrient budgets help to identify excess or insufficient use of fertilizers and other nutrient sources in agriculture. They allow calculation of indicators such as the nutrient balance (surplus or deficit) and nutrient use efficiency that help in monitoring of agricultural productivity and sustainability across the world. We present a global database of country-level budget estimates for nitrogen (N), phosphorus (P) and potassium (K) in cropland. The database, disseminated in FAOSTAT, is meant to provide a global reference, synthesizing and continuously updating the state-of-the-art on this topic. The database covers 205 countries and territories, as well as regional and global aggregates, for the period 1961 to 2020. Results highlight the wide range in nutrient use and use efficiencies across geographic regions, nutrients, and time. For the year 2020, the data show regional average N surpluses that range from about 10 kg N ha-1 year-1 in Africa to more than 90 kg N ha-1 year-1 in Asia. Furthermore, they highlight P and K deficits in Africa in 2020 and K deficits for the Americas. This study introduces improvements over previous work in relation to key nutrient coefficients affecting nutrient budgets and use efficiency estimates, especially for nutrient removal in crop products, manure nutrient content, atmospheric deposition and crop biological N fixation rates. We conclude by discussing future research directions, highlighting the need to align statistical definitions across research groups, as well as to further refine plant and livestock coefficients and expand estimates to all agricultural land, including nutrient flows in meadows and pastures. [ABSTRACT FROM AUTHOR]

Despite recent advancements in cloud processing and modelling and the increasing availability of high spectral- and temporalresolution satellite imagery, mapping the spatial distribution of crop types remains a challenging task. Here, we present CROPGRIDS - a comprehensive global, geo-referenced dataset providing information on areas for 173 crops circa the year 2020, at a resolution of 0.05° (~5.55 km at the equator). It represents a major update of the Monfreda et al. (2008) dataset, the most widely used geospatial dataset previously available, covering 175 crops with reference year 2000 at 10 km spatial resolution. CROPGRIDS updates Monfreda et al. (2008) through the careful evaluation of 26 published gridded datasets covering more recent crop information at regional, national, and global levels, largely over the period 2015 - 2020. The new product successfully updates the area extent for 80 of the 175 crops originally covered, representing an update to 1.2 billion hectares of crop area (i.e., 81% of the total cropland included in CROPGRIDS). CROPGRIDS carries forward the crop type maps originally in Monfreda et al. (2008) for 93 crops as more recent information for these crops is not available. We compared CROPGRIDS harvested area of individual crops against independent national and subnational data from 36 National Statistical Offices (NSOs), national-level crop area data for more than 180 countries and territories from FAOSTAT, as well as geospatially, against a newly available high-resolution (30 m) cropland agreement map (Tubiello et al., 2023). Results indicated robustness against the available independent information, with CROPGRIDS world total harvested and crop areas around 1.5 billion hectares. To the best of our knowledge, CROPGRIDS represents the most comprehensive update of previous work on the subject area, offering a new benchmark of global gridded harvested and crop area data for the year circa 2020. CROPGRIDS dataset can be downloaded at https://doi.org/10.6084/m9.figshare.22491997 (Tang et al., 2023). [ABSTRACT FROM AUTHOR]

The combustion of woodfuel for residential use is often not considered to be a source of greenhouse gas (GHG) emissions in households since emissions from woodfuel combustion can be offset by the CO2 absorbed by the growth of the forest as a carbon sink (IPCC, 2006). However, this only applies to wood that is harvested in a renewable way, i.e., at a rate not exceeding the regrowth rate of the forest from which it is harvested (Drigo et al., 2002). This paper estimates the share of GHG emissions attributable to non-renewable woodfuel harvesting for use in residential food activities. It adds to a growing research base estimating GHG emissions from across the entire agri-food value chain, from the manufacture of farm inputs, through food supply chains, and finally to waste disposal (Tubiello et al., 2021). Country-level information is generated from United Nations Statistics Division (UNSD) and International Energy Agency (IEA) data on woodfuel use in households. We find that, in 2019, annual emissions from non-renewable woodfuel use in household food consumption were about 745 million tonnes (Mt CO2eq yr-1), with uncertainty ranging from -20 % to + 22 %, having increased 6% from 1990. Overall, global trends were a result of counterbalancing effects: the emission increases were largely fuelled from countries in Sub-Saharan Africa, Southern Asia, and Latin America while significant decreases were seen in countries in Eastern Asia and South-eastern Asia. The Food and Agriculture Organisation of the United Nations (FAO) has developed and regularly maintains a database covering GHG emissions from the various components of the agri-food sector, including pre- and post-production activities, by country and world regions. The dataset is developed according to International Panel on Climate Change guidelines (IPCC, 2006), which avoids overlaps across AFOLU and energy components. It relies mainly on UNSD Energy Statistics data, which are used as activity data for the calculation of the GHG emissions (Tubiello et al., 2022). The information used in this work is available as open data with DOI https://doi.org/10.5281/zenodo.7310932 (Flammini et al., 2022). [ABSTRACT FROM AUTHOR]

Excessive anthropogenic nitrogen (N) inputs to the biosphere have disrupted the global nitrogen cycle. To better quantify the spatial and temporal patterns of anthropogenic N enrichments, assess their impacts on the biogeochemical cycles of the planet and other living organisms, and improve nitrogen use efficiency (NUE) for sustainable development, we have developed a comprehensive and synthetic dataset for reconstructing the History of anthropogenic N inputs (HaNi) to the terrestrial biosphere. The HaNi dataset takes advantage of different data sources in a spatiotemporally consistent way to generate a set of high-resolution gridded N input products from the preindustrial to present (1860-2019). The HaNi dataset includes annual rates of synthetic N fertilizer, manure application/deposition, and atmospheric N deposition in cropland, pasture, and rangeland at a spatial resolution of 5-arcmin. Specifically, the N inputs are categorized, according to the N forms and land uses, as ten types: 1) NH4+-N fertilizer applied to cropland, 2) NO3-N fertilizer applied to cropland, 3) NH4 +-N fertilizer applied to pasture, 4) NO3-N fertilizer applied to pasture, 5) manure N application on cropland, 6) manure N application on pasture, 7) manure N deposition on pasture, 8) manure N deposition on rangeland, 9) NHx-N deposition, and 10) NOy-N deposition. The total anthropogenic N (TN) inputs to global terrestrial ecosystems increased from 29.05 Tg N yr-1 in the 1860s to 267.23 Tg N yr-1 in the 2010s, with the dominant N source changing from atmospheric N deposition (before the 1900s) to manure N (the 1910s-2000s), and to synthetic fertilizer in the 2010s. The proportion of synthetic NH4 +-N fertilizer increased from 64% in the 1960s to 90% in the 2010s, while synthetic NO3-N fertilizer decreased from 36% in the 1960s to 10% in the 2010s. Hotspots of TN inputs shifted from Europe and North America to East and South Asia during the 1960s-2010s. Such spatial and temporal dynamics captured by the HaNi dataset are expected to facilitate a comprehensive assessment of the coupled human-earth system and address a variety of social welfare issues, such as climate-biosphere feedback, air pollution, water quality, and biodiversity. [ABSTRACT FROM AUTHOR]

Despite an increasing attention on the role of land in meeting countries' climate pledges under the Paris Agreement, the range of estimates of carbon fluxes from Land Use, Land-Use Change and Forestry (LULUCF) in available databases is very large. A good understanding of the LULUCF data reported by countries under the United Nations Framework Convention on Climate Change (UNFCCC) - and of the differences with other datasets based on country reported data - is crucial to increase confidence in land-based climate change mitigation efforts. Here we present a new data compilation of LULUCF fluxes of carbon dioxide (CO2) on managed land, aiming at providing a consolidated view on the subject. Our database builds on a detailed analysis of data from National Greenhouse Gas Inventories (NGHGIs) communicated via a range of country reports to the UNFCCC, which report anthropogenic emissions and removals based on the IPCC (Intergovernmental Panel on Climate Change) methodology. Specifically, for Annex I countries, data are sourced from annual GHG inventories. For non-Annex I countries, we compiled the most recent and complete information from different sources, including National Communications, Biennial Update Reports, submissions to the REDD+ (Reducing Emissions from Deforestation and Forest Degradation) framework and Nationally Determined Contributions. The data are disaggregated into fluxes from forest land, deforestation, organic soils and other sources (including non-forest land uses). The CO2 flux database is complemented by information on managed and unmanaged forest area as available in NGHGIs. To ensure completeness of time series, we filled the gaps without altering the levels and trends of the country reported data. Expert judgement was applied in a few cases when data inconsistencies existed. Results indicate a mean net global sink of -1.6 Gt CO2/yr over the period 2000-2020, largely determined by a sink on forest land (-6.4 Gt CO2/yr), followed by source from deforestation (+4.4 Gt CO2/yr) and minor fluxes from organic soils (+0.9 Gt CO2/yr) and other land uses (-0.6 Gt CO2/yr). Furthermore, we compare our NGHGI database with two other sets of country-based data: those included in the UNFCCC GHG data interface, and those based on forest resources data reported by countries to FAO and used as inputs into estimates of GHG emissions in FAOSTAT. The first dataset, once gap-filled as in our study, results in a net global LULUCF sink of -5.4 Gt CO2/yr. The difference with the NGHGI database is in this case mostly explained by more updated and comprehensive data in our compilation for non-Annex I countries. The FAOSTAT GHG dataset instead estimates a net global LULUCF source of +1.1 Gt CO2/yr. In this case, most of the difference to our results is due to a much greater forest sink for non-Annex I countries in the NGHGI database than in FAOSTAT. The difference between these datasets can be mostly explained by a more complete coverage in the NGHGI database, including for non-biomass carbon pools and non-forest land uses, and by different underlying data on forest land. The latter reflects the different scopes of the country reporting to FAO, which focuses on area and biomass, and to UNFCCC, which explicitly focuses on carbon fluxes. Bearing in mind the respective strengths and weaknesses, both our NGHGI database and FAO offer a fundamental, yet incomplete, source of information on carbon-related variables for the scientific and policy communities, including under the Global Stocktake. Overall, while the quality and quantity of the LULUCF data submitted by countries to the UNFCCC significantly improved in recent years, important gaps still remain. Most developing countries still do not explicitly separate managed vs. unmanaged forest land, a few report implausibly high forest sinks, and several report incomplete estimates. With these limits in mind, the NGHGI database presented here represents the most up-to-date and complete compilation of LULUCF data based on country submissions to UNFCCC. [ABSTRACT FROM AUTHOR]

We present results from the FAOSTAT agri-food systems emissions database, relative to 236 countries and territories and over the period 1990-2019. We find that in 2019, world-total food systems emissions were 16.5 billion metric tonnes (Gt CO2eq yr-1), corresponding to 31 % of total anthropogenic emissions. Of the agri-food systems total, global emissions within the farm gate -from crop and livestock production processes including on-farm energy use--were 7.2 Gt CO2eq yr-1; emissions from land use change, due to deforestation and peatland degradation, were 3.5 Gt CO2eq yr-1; and emissions from pre- and post-production processes -manufacturing of fertilizers, food processing, packaging, transport, retail, household consumption and food waste disposal--were 5.8 Gt CO2eq yr-1. Over the study period 1990-2019, agri-food systems emissions increased in total by 17 %, largely driven by a doubling of emissions from pre- and post-production processes. Conversely, the FAO data show that since 1990 land use emissions decreased by 25 %, while emissions within the farm gate increased only 9 %. In 2019, in terms of single GHG, pre- and post-production processes emitted the most CO2 (3.9 Gt CO2 yr-1), preceding land use change (3.3 Gt CO2 yr-1) and farm-gate (1.2 Gt CO2 yr-1) emissions. Conversely, farm-gate activities were by far the major emitter of methane (140 Mt CH4 yr-1) and of nitrous oxide (7.8 Mt N2O yr-1). Pre-and post-processes were also significant emitters of methane (49 Mt CH4 yr-1), mostly generated from the decay of solid food waste in landfills and open-dumps. The most important trend over the 30-year period since 1990 highlighted by our analysis is the increasingly important role of food-related emissions generated outside of agricultural land, in pre- and post-production processes along food supply chains, at all scales from global, regional and national, from 1990 to 2019. In fact, our data show that by 2019, food supply chains had overtaken farm-gate processes to become the largest GHG component of agri-food systems emissions in Annex I parties (2.2 Gt CO2eq yr-1). They also more than doubled in non-Annex I parties (to 3.5 Gt CO2eq yr-1), becoming larger than emissions from land-use change. By 2019 food supply chains had become the largest agri-food system component in China (1100 Mt CO2eq yr-1); USA (700 Mt CO2eq yr-1) and EU-27 (600 Mt CO2eq yr-1). This has important repercussions for food-relevant national mitigation strategies, considering that until recently these have focused mainly on reductions of non-CO2 gases within the farm gate and on CO2 mitigation from land use change. The information used in this work is available as open data at: https://zenodo.org/record/5615082 (Tubiello et al., 2021d). It is also available to users via the FAOSTAT database (FAO, 2021a), with annual updates. [ABSTRACT FROM AUTHOR]

Reliable quantification of the sources and sinks of greenhouse gases, together with trends and uncertainties, is essential to monitoring the progress in mitigating anthropogenic emissions under the Paris Agreement. This study provides a consolidated synthesis of CH4 and N2O emissions with consistently derived state-of-the-art bottom-up (BU) and top-down (TD) data sources for the European Union and UK (EU27 + UK). We integrate recent emission inventory data, ecosystem process-based model results and inverse modeling estimates over the period 1990–2017. BU and TD products are compared with European national greenhouse gas inventories (NGHGIs) reported to the UN climate convention UNFCCC secretariat in 2019. For uncertainties, we used for NGHGIs the standard deviation obtained by varying parameters of inventory calculations, reported by the member states (MSs) following the recommendations of the IPCC Guidelines. For atmospheric inversion models (TD) or other inventory datasets (BU), we defined uncertainties from the spread between different model estimates or model-specific uncertainties when reported. In comparing NGHGIs with other approaches, a key source of bias is the activities included, e.g., anthropogenic versus anthropogenic plus natural fluxes. In inversions, the separation between anthropogenic and natural emissions is sensitive to the geospatial prior distribution of emissions. Over the 2011–2015 period, which is the common denominator of data availability between all sources, the anthropogenic BU approaches are directly comparable, reporting mean emissions of 20.8 TgCH4yr-1 (EDGAR v5.0) and 19.0 TgCH4yr-1 (GAINS), consistent with the NGHGI estimates of 18.9 ± 1.7 TgCH4yr-1. The estimates of TD total inversions give higher emission estimates, as they also include natural emissions. Over the same period regional TD inversions with higher-resolution atmospheric transport models give a mean emission of 28.8 TgCH4yr-1. Coarser-resolution global TD inversions are consistent with regional TD inversions, for global inversions with GOSAT satellite data (23.3 TgCH4yr-1) and surface network (24.4 TgCH4yr-1). The magnitude of natural peatland emissions from the JSBACH–HIMMELI model, natural rivers and lakes emissions, and geological sources together account for the gap between NGHGIs and inversions and account for 5.2 TgCH4yr-1. For N2O emissions, over the 2011–2015 period, both BU approaches (EDGAR v5.0 and GAINS) give a mean value of anthropogenic emissions of 0.8 and 0.9 TgN2Oyr-1 , respectively, agreeing with the NGHGI data (0.9 ± 0.6 TgN2Oyr-1). Over the same period, the average of the three total TD global and regional inversions was 1.3 ± 0.4 and 1.3 ± 0.1 TgN2Oyr-1 , respectively. The TD and BU comparison method defined in this study can be operationalized for future yearly updates for the calculation of CH4 and N2O budgets both at the EU + UK scale and at the national scale. The referenced datasets related to figures are visualized at 10.5281/zenodo.4590875 (Petrescu et al., 2020b). [ABSTRACT FROM AUTHOR]

Author(s): Francesco N. TubielloAuthor Affiliations:Make better use of UN food and agriculture stats FAO specialist staff are on hand to advise researchers on any uncertainties or limitations relating to these [...]