Pantropical CO₂ emissions and removals for the AFOLU sector in the period 1990–2018

Nyawira, Sylvia S. et al. Article — Published Version Pantropical CO₂ emissions and removals for the AFOLU sector in the period 1990–2018 Mitigation and Adaptation Strategies for Global Change Provided in Cooperation with: Springer Nature Suggested Citation: Nyawira, Sylvia S. et al. (2024) : Pantropical CO₂ emissions and removals for the AFOLU sector in the period 1990–2018, Mitigation and Adaptation Strategies for Global Change, ISSN 1573-1596, Springer Netherlands, Dordrecht, Vol. 29, Iss. 2, https://doi.org/10.1007/s11027-023-10096-z This Version is available at: https://hdl.handle.net/10419/315334 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. http://creativecommons.org/licenses/by/4.0/ Vol.:(0123456789) Mitig Adapt Strateg Glob Change (2024) 29:13 https://doi.org/10.1007/s11027-023-10096-z 1 3 ORIGINAL ARTICLE Pantropical CO2 emissions andremovals fortheAFOLU sector intheperiod 1990–2018 SylviaS.Nyawira1 · MartinHerold2· KalkidanAyeleMulatu3· RosaMariaRoman‑Cuesta4,6· RichardA.Houghton5· GiacomoGrassi6· JuliaPongratz7,8· ThomasGasser9· LouisVerchot10 Received: 21 June 2022 / Accepted: 11 December 2023 / Published online: 20 February 2024 © The Author(s) 2024 Abstract Transparent, accurate, comparable, and complete estimates of greenhouse gas emissions and removals are needed to support mitigation goals and performance assessments under the Paris Agreement. Here, we present a comparative analysis of the agriculture forestry and other land use (AFOLU) emission estimates from different datasets, including National Greenhouse Gas Inventories (NGHGIs), FAOSTAT, the BLUE, OSCAR, and Houghton (here after updated H&N2017) bookkeeping models; Emissions Database for Global Atmospheric Research (EDGAR); and the US Environmental Protection Agency (EPA). We disaggregate the fluxes for the forestry and other land use (FOLU) sector into forest land, deforestation, and other land uses (including non-forest land uses), while agricultural emissions are disaggregated according to the sources (i.e., livestock, croplands, rice cultivation, and agricultural fires). Considering different time periods (1990–1999, 2000–2010, and 2011–2018), we analyse the trend of the fluxes with a key focus on the tropical regions (i.e., Latin America, sub-Saharan Africa, and South and Southeast Asia). Three of the five data sources indicated a decline in the net emissions over the tropics over the period 1990– 2018. The net FOLU emissions for the tropics varied with values of 5.47, 5.22, 4.28, 3.21, and 1.17 GtCO2 year−1 (for BLUE, OSCAR, updated H&N2017, FAOSTAT, and NGHGIs, respectively) over the recent period (2011–2018). Gross deforestation emissions over the same period were 5.87, 7.16, 5.48, 3.96, and 3.74 GtCO2 year−1 (for BLUE, OSCAR, updated H&N2017, FAOSTAT, and NGHGIs). The net forestland sink was −1.97, −3.08, −2.09, −0.53, and −3.00 GtCO2 year−1 (for BLUE, OSCAR, updated H&N2017, FAOSTAT, and NGHGIs). Continental analysis indicated that the differences between the data sources are much large in sub-Saharan Africa and South and Southeast Asia than in Latin America. Disagreements in the FOLU emission estimates are mainly explained by differences in the managed land areas and the processes considered (i.e., direct vs indirect effects of land use change, and gross vs net accounting for deforestation). Net agricultural emissions from cropland, livestock, and rice cultivation were more homogenous across the FAOSTAT, EDGAR, and EPA datasets, with all the data sources indicating an increase in the emissions over the tropics. However, there were notable differences in the emission from agricultural fires. This study highlights the importance of investing and improving data sources for key fluxes to achieve a more robust and transparent global stocktake. Extended author information available on the last page of the article Mitig Adapt Strateg Glob Change (2024) 29:13 1 3 13 Page 2 of 24 Keywords Agriculture forestry and other land use (AFOLU)· Forestry and other land use (FOLU)· Agricultural emissions· Emissions· Sinks· Tropics 1 Introduction Atmospheric concentrations of greenhouse gases have increased substantially since preindustrial times. The rise in CO2 is mainly associated with fossil fuel burning and contributions from the land sector (IPCC 2022), commonly referred to as ‘agriculture, forestry, and other land uses’ (AFOLU). AFOLU emissions result from changes in the natural fluxes of CO2 and other non-CO2 gases, largely methane (CH4), and nitrous oxide (N2O), through anthropogenic changes in land use/cover (e.g., conversion of forest lands and grasslands to cropland and pasture, afforestation), and management of croplands, forests, and wetlands. AFOLU activities lead to both sources and sinks of CO2, mainly from forestry and landuse change, and to non-CO2 emissions mainly from agriculture, e.g., CH4 from livestock and rice cultivation, N2O from manure storage, and cropland soils and biomass burning (Tubiello etal. 2013, 2014). Globally, the AFOLU sector was responsible for 13 GtCO2 eq. year−1 in 2019, which was about 22% of net anthropogenic greenhouse gas (GHG) emissions, with approximately half from agriculture and the other half from forestry and other land use activities (FOLU) (Pathak etal. 2022). AFOLU emissions for different GHG gases (CO2 and non-CO2) are available through the Food and Agriculture Organization (FAO) Statistical database (Tubiello etal. 2013, 2014). In addition, global and national CO2 emissions for the FOLU sector are also provided by bookkeeping models, i.e., BLUE, Houghton and Nassikas, and OSCAR models (Gasser etal. 2020; Hansis etal. 2015; Houghton etal. 2017), and the estimates have recently been updated for the Global Carbon Budget 2022 (Friedlingstein etal. 2022). Agricultural CO2 and non-CO2 emissions are available through the FAO database, Emissions Database for Global Atmospheric Research (EDGAR) (Crippa etal. 2019; Crippa etal. 2020), and the US Environmental Protection Agency (EPA) databases (US EPA 2019). Past comparative analyses showed reasonably good agreement among these data sources in the total gross emissions at the aggregated level for the tropical regions, but significant disagreements when disaggregating the emissions by sources, continents, and gases, particularly for the forest sector, with fire leading the differences (Roman-Cuesta, Herold, etal., 2016). In addition, emissions from the tropical regions have remained consistently higher compared to global estimates. For instance, for the FOLU sector, Roman-Cuesta etal. (2016a) reported emissions of ca. 6 GtCO2 eq. year−1 for the tropics (2000–2005), with this value being higher than the global AFOLU emission estimates of ca. 5.0 GtCO2 eq. year−1 in the Intergovernmental Panel for Climate Change (IPCC) AR5 synthesis report over the same period (Smith etal. 2014). Besides these global datasets, article 14 of the Paris Agreement includes an enhanced transparency framework (ETF) to periodically track and review the progress of countries towards achieving their Nationally Determined Contributions (NDCs), and a periodic global stocktake process for assessing the countries’ collective progress towards achieving the long-term goals of the agreement (UNFCCC 2016). All parties to the United Nations Framework Convention on Climate Change (UNFCCC) are required to document the anthropogenic emissions and removals of different sectors and report these through the national greenhouse gases inventories (NGHGIs). Recently, a dataset including compilation of individual NGHGIs for the land use, land-use change and forestry (LULUCF) Mitig Adapt Strateg Glob Change (2024) 29:13 1 3 Page 3 of 24 13 sector has become available (Grassi etal. 2022b). Previous studies revealed conceptual inconsistencies between the scientific modelling assessments and countries in estimating the net anthropogenic land related CO2 emission, with a gap of about 4 to 6.7 GtCO2 year−1 globally between the aggregated NGHIs and global model estimates (Grassi etal. 2018, 2021, 2023). The large differences are mostly due to different approaches in estimating the forest sink. While these two studies have made significant progress in reconciling the differences between global models and NGHGIs, their focus was on forestrelated emissions and sinks at the global level. In a recent study, differences between the NGHGIs and FAOSTAT have also been assessed at the global and regional level (Grassi etal. 2022b), and the discrepancy between global models and NGHGIs has been resolved at the country-level as well (Schwingshackl etal. 2022). However, there’s still a need for a thorough comparative analysis of emissions in tropical regions that integrates all the latest available datasets. With all UNFCCC parties expected to adopt a harmonized reporting by 2024 under the Enhanced Transparency Framework (UNFCCC Secretariat 2020), such an analysis would not only be useful in identifying and understanding the causes of differences among the datasets but would also offer information on how countries can improve their reporting using the available datasets. In this study, we present a comparative analysis of AFOLU emission estimates for the tropics for the period 1990–2018 using seven data sets: NGHGIs, FAOSTAT, the BLUE, OSCAR and Houghton and Nassikas booking models (here after updated H&N2017), EPA, and EDGAR. Although the approach for our analysis is in some ways like that used in the comparative study by Roman-Cuesta etal. (2016b), our study goes a step further by including new databases (e.g., NGHGIs), more updated global data sources, longer analysis periods, and disaggregated analysis for FOLU emissions. The objectives of the study are (i) to provide a comprehensive and updated analyses on the global and tropical regions aggregated and disaggregated AFOLU emissions from different data sets (i.e., at continental level and activity), (ii) assess the trend of the emissions, and (iii) understand the reasons for the differences between the datasets. 2 Methods 2.1 Study scope Our study comprises a detailed analysis of global AFOLU datasets and estimates, with a focus on three tropical regions: Latin America (LAM), sub-Saharan Africa, and South and Southeast Asia. We chose to analyse the emissions for these regions because of the rapid land use change in the tropics and the increased reporting requirements on tropical countries under the Paris Agreement. In addition, the tropics have the largest emissions uncertainty and gaps, hence the need to understand the causes of differences between the available AFOLU data sources (Romijn etal. 2012, 2015). Such detailed comparisons alreadyexist for other regions like Europe (Maria etal. 2020). We analysed the annual average emissions over three different time periods (1990–1999, 2000–2010, 2011–2018), which allowed for an assessment of how the reported emissions have changed over time. We focused on the period 1990–2018, because it was the period where emissions were availableinmost of the datasets. The averageemissions over a given period was calculated by summing the annual emissions and dividing by the total number of years in that period. Our analysis includes net FOLU emissions, gross emissions from deforestation, net emissions or sinks from other Mitig Adapt Strateg Glob Change (2024) 29:13 1 3 13 Page 4 of 24 forestland activities (e.g., harvest), and other land uses. Net land use emissions consider the emissions by the sources and the removals by sinks in a final emission balance where the removals are discounted from the emissions. Gross assessments include the contributions of different sources and sinks to the net land use emissions. A key advantage of the gross assessments compared to net AFOLU emissions is that they offer direct information on the sinks and sources that can facilitate the development of more targeted measures and policies to enhance and promote mitigation (Deb Richter and Houghton 2011; Roman-Cuesta etal. 2016a), and provide a better indication of the climate benefits of forests. 2.2 AFOLU data sources 2.2.1 FAOSTAT FAOSTAT covers agriculture, forestry, and other land uses and their associated emissions of CO2, CH4, and N2O (Federici etal. 2015; Tubiello etal. 2013, 2021) . FAOSTAT database includes emission estimates for 242 countries annually for the reference period 1961–2018 (agriculture) and 1990–2019 (FOLU), based on national activity submitted by countries and further compiled by FAO (Food and Agriculture Organization of the United Nations). Estimates are computed following Tier 1 methods and default factors of the 2006 IPCC Guidelines for National Greenhouse Gas Inventories. FAOSTAT includes estimates of carbon emissions from net forest conversion, biomass fires, organic soils in cropland and grasslands, peatland drainage, and emissions from other forestland activities (Table1). These estimates are based on national statistics. Forest carbon stock changes (both emissions and removals) have recently been updated based on the national-level FAO Forest Resources Assessment 2020 (FAO 2020a, 2020b). 2.2.2 Bookkeeping models We used the three bookkeeping models that were part of the 2022 Global Carbon Budget: the OSCAR model, the bookkeeping of land use emissions (BLUE), and the Houghton and Nassikas model (updated H&N2017) (Gasser etal. 2020; Hansis etal. 2015; Houghton etal. 2017). The models are based on the original bookkeeping approach of Houghton (2003) that keeps track of the carbon stored in the vegetation and soils before and after a land-use change. The models use literature-based response curves to describe the decay of vegetation and soil carbon, including transfer of product pools of different lifetimes, as well carbon uptake due to regrowth. Bookkeeping models estimate emissions of CO2 from deforestation, shifting cultivation, wood harvest, woody debris decay, biomass burning (deforestation and peatland fires), and soil organic matter (SOM) from cultivated soils. The three bookkeeping models differ with respect to the land-use change data used to drive the model. The updated H&N2017 bases its estimates on the Forest Resource Assessment (FRA) of the FAO, which provides statistics on forest change and management at intervals of 5 years and is currently updated until 2020 (FAO 2020a). Changes in land use other than forests are based on annual national changes in cropland and pasture areas reported by FAO (FAOSTAT, 2021). BLUE uses the harmonized land-use change data LUH2-GCB2022 covering the entire 850–2021 period, which is an update to the previously released LUH2 v2h dataset (Hurtt etal. 2020). OSCAR was run with both LUH2-GCB2022 and FAO/FRA (as used in the updated H&N2017), where the drivers of the latter were linearly extrapolated to 2021 using their 2015–2020 trends. BLUE Mitig Adapt Strateg Glob Change (2024) 29:13 1 3 Page 5 of 24 13 Table 1 A summary of the characteristics of the four forestry and land use (FOLU) activity data sets analysed in the present study a https:// www. fao. org/ faost at/ en/# data/ GTdow nload edon2 7thAp ril20 22 b https:// zenodo. org/ record/ 63907 39#. YmkC8 9pBw2y c https:// edgar. jrc. ec. europa. eu/ datas et_ ghg60 downl oaded inJan uary2 020 d https:// www. epa. gov/ globalmitig ationnonco2green housegases/ globalnonco2ghgemiss ions19902030 e Also available at pixel level (0.25) FAOSTAT aBLUE H&N2017 and OSCAR NGHGIsbEDGAR cEPAd Spatial disaggregation in present study Country CountryeCountry Country Country Country Period available 1990–2019 1850–2020 1850–2021 2000–2020 1970–2015 1990–2030 Period in present study 1990–2018 1990–2018 1990–2018 2000–2018 1990–2015 1990–2015 Gross/net emissions Gross except forest fluxes Gross Gross Gross Gross Gross Tier 1 3 3 1 or 2 1 1 Sector Agriculture and FOLU FOLU FOLU FOLU Agriculture Agriculture Gas CO2, CH4, N2OCO2CO2CO2CO2, CH4, N2OCO2, CH4, N2O Mitig Adapt Strateg Glob Change (2024) 29:13 1 3 13 Page 6 of 24 and updatedH&N2017 models exclude land ecosystems’ transient response to changes in climate, atmospheric CO2, and other environmental factors, and the carbon densities are based on contemporary data from literature and inventory data. Since carbon densities remain fixed over time, the additional sink capacity that ecosystems provide in response to CO2 fertilization and some other environmental changes is not captured by the models (Pongratz etal. 2014). Although the OSCAR model does account for the transient response associated with environmental changes, the emissions included here are only those associated with land-use change. Emissions from peat burning and peat drainage are added from external datasets (see Appendix C2.1 in Friedlingstein etal. 2022). We obtained the net FOLU emissions for the three bookkeeping models and annual gross fluxes of CO2 (i.e., both sinks and sources) resulting deforestation, forestland, and other land use transitions for the three bookkeeping models. For the three models, annual emissions were available for 218 countries. The FOLU emissions used here are based on the 2022 Global Carbon budget (Friedlingstein etal. 2022). 2.2.3 National Greenhouse Gases Inventories (NGHGIs) fortheLULUCF sector National Greenhouse Gas Inventories (NGHGIs) presented here include estimates of LULUCF CO2 emissions and sinks submitted to the UNFCCC. Figure 1 illustrates the conceptual differences between the NGHGIs, the bookkeeping models, and FAOSTAT in terms of the processes accounted for and the area extent. The data were compiled from various submissions to UNFCCC, prioritizing the most recent and considering the completeness of the information (Grassi etal. 2022a; Grassi etal. 2022b). For non-Annex 1 countries, National Communications (NCs), Biennial Update reports (BURs), and nationally determined contributions were used in compiling the data. Only one submission was used in compiling the data national GHGIs. While NCs are typically submitted every 4 years, BURs provide an update of the information presented in NCs, typically every 2 years. The time of reporting varies for the different non-Annex 1 counties. To ensure a complete time series for the period 2000–2020, gaps were filled using standard statistical methods, with the aim to maintain the levels and trends of the underlying reported raw data (see Tables4 and 5 of the online dataset in Grassi etal. 2022a, showing the original and gapfilled time series, respectively). However, most of the non-Annex 1 counties with the biggest emissions, including Brazil, Indonesia, and Democratic republic of Congo, reported relatively complete time series. The gap-filled data includes all land uses (i.e., forestland, cropland, grassland, wetlands, settlements, and other land) and covers 195 countries. Almost all Annex 1 countries report all land uses, while many non-Annex 1 countries report only on forestland and deforestation. Here, we used the NGHGI data compiled in Grassi etal. (2022b), which include a split of the net LULUCF flux into the categories of forest land, deforestation, organic soils, and other land use fluxes. 2.2.4 EDGAR The Emissions Database for Global Atmospheric Research version 5 (EDGARv5.0) provides emissions of three main greenhouse gases (CO2, CH4, and N2O) per sector at 0.1 deg × 0.1 deg grid maps and country level (Crippa etal. 2019, 2020). The EDGAR database for this version covers the energy, industry, transport, waste management, and agriculture (including agricultural soils, agricultural waste burning, enteric fermentation, manure management) sectors, mainly quantified using the IPCC 2006 guidelines for emissions Mitig Adapt Strateg Glob Change (2024) 29:13 1 3 Page 7 of 24 13 estimations. However, emissions of large-scale biomass burning in savannahs, forest fires, and sources and sinks from LULUCF are excluded in this version. EDGAR emission estimates are mostly calculated using activity data from FAO statistics. However, N2O emission from fertilizer use and CO2 from urea fertilisation are estimated using both the International Fertilizer Association (IFA) and FAO statistics data. Furthermore, CH4 emissions from enteric fermentation for dairy and non-dairy cattle are calculated using Tier 2 emission factors. We downloaded the EDGARv5.0 (January 2020) emissions over the period 1990–2018 on an annual basis, at the country level (Table1) (Crippa etal. 2019). 2.2.5 The US Environmental Protection Agency (EPA) The EPA dataset contains global non-CO2 projected emissions for the period 1990–2050 for the agriculture (livestock, cropland, rice cultivation, and biomass burning), energy, industrial processes, and the waste sectors. EPA provides future projections of nonCO2 GHGs using a combination of country-reported inventory data and EPA-estimated Fig. 1 A conceptual diagram indicating the key differences between the National Greenhouse Gases Inventories (NGHIs), the bookkeeping models and the FAOSTAT in their Forestry and Land Use (FOLU) emissions. Bookkeeping models and NGHIs provide gross emissions of deforestation, while FAOSTAT provides net emissions from forests, where emissions from deforestation are discounted with the gains from afforestation. Bookkeeping models consider as anthropogenic only direct human-induced fluxes on forestland, while NGHGIs and FAOSTAT account for both the human-induced fluxes and the indirect effects resting from environmental changes, e.g., the CO2 fertilization and nitrogen deposition. In addition, the extent of areas differs with NGHGIs also including unmanaged lands. On cropland and grasslands, bookkeeping models only include fluxes from the land use conversion, FAOSTAT include only the fluxes from organic soils, while the NGHIs account for the fluxes from both mineral and organic soils Mitig Adapt Strateg Glob Change (2024) 29:13 1 3 13 Page 8 of 24 calculations consistent with the 2006 Intergovernmental Panel on Climate Change (IPCC) Guidelines for national GHGIs. The EPA data provides information that can be used to understand national contributions of GHG emissions, historical progress on reductions, and mitigation opportunities. The EPA data does not include estimates of FOLU activities. We downloaded annual agricultural emissions offered at a 1-year interval at country level and disaggregated by gas and emission sources from the recently updated data that was used in the 2015–2050 projections (Table1) (US EPA 2019). 2.3 Estimating net andgross emissions andsinks We analysed the data using two major categories: (1) forestry and other land use activities (FOLU) and (2) agriculture. We quantified the net FOLU and agriculture emissions by summing the emissions and sinks for all the activities in the respective category under Table1. We compared the net FOLU, and agriculture emissions estimated from the different data sources at the global level and within the three continents in the tropical regions. To assess the trend of the emissions in the tropical regions, we calculated the average values for three different time periods (1990–1999, 2000–2010, and 2011–2018). Disaggregated values from the NGHGIs were available in four time slices (2000–2005, 2006–2010, 2011–2015, and 2016–2020). For comparability with other data sets, the 2016–2020 emissions were included in the 2011–2018 period. FOLU emissions from all the considered data sets only included CO2 emissions and excluded non-CO2 greenhouse gases. To gain an in-depth understanding of the emissions patterns and identify the possible causes of differences among the data sources, the FOLU emissions were further disaggregated into three categories: deforestation, forestland, and other land uses (emissions and sources). Emissions from organic soils were included in other land use emissions. Emissions for forest fires were included in the forestland category. Any other non-forest-related activities were categorized as other land-use emissions. FAOSTAT do not provide gross deforestation emissions fluxes, but instead include the net forest conversion emissions (i.e., from forest area losses minus area gains). For comparison purposes with the other data sources, the net forest conversion emissions for FAOSTAT were included in the deforestation category. The disaggregation of FOLU emissions into deforestation, forestland, and other land use was chosen for comparability of other data sets to in the FOLU sector recently published national GHGIs (Grassi etal. 2022b). Emissions from agriculture were separated into four categories based on the different sources, i.e., croplands, livestock, rice cultivation, and agricultural fires. CH4 and N2O emissions were converted to CO2eq. 3 Results 3.1 FOLU emissions 3.1.1 Global andregional net emissions Globally, all the data sources show that the net FOLU activities contributed to CO2 emissions over the periods 1990 to 1999 (Table2; Fig.2). However, there was disagreement between NGHGIs and the other data sources on the emissions for the period between 2000 to 2010 and 2011–2018, with the inventories indicating a net sink of Mitig Adapt Strateg Glob Change (2024) 29:13 1 3 Page 15 of 24 13 FOLU emissions, agricultural emissions were generally higher in Southeast Asia than in South Asia across the three data sources. The agricultural emissions from Latin America were mainly from South America. The total EPA emissions for the four sub-regions in subSaharan Africa were consistently higher than the estimates from EDGAR and FAOSTAT, with differences of up to 0.3 GtCO2 eq. year−1 over Central Africa (Fig.S5). 3.2.2 Disaggregated agricultural emissions. Disaggregated values show a good agreement on the global and tropical region emissions from livestock, cropland, and rice cultivation, while there was a discrepancy in the emissions from agricultural fires (Fig.6). All the data sources indicate that the livestock sector is the major contributor to agricultural emissions globally followed by croplands, rice cultivation and biomass burning from agricultural activities. Over the period 2011–2018, the global emissions from livestock were 2.52, 2.41, and 2.56 GtCO2 eq. year−1 for EDGAR, EPA, and FAOSTAT, respectively (Fig.6), with the total emissions for the three tropical regions being 1.49, 1.32, and 1.43 GtCO2 eq. year−1. All the data sources show an increase in the global emissions from cropland cultivation and livestock, while there were no notable changes in the rice cultivation emissions. EDGAR and EPA had higher agricultural fire emissions compared to FAOSTAT globally and for the tropics (Fig 6). Regional analyses showed that the largest livestock emissions were from Latin America followed by South and Southeast Asia, with lower values in sub-Saharan Africa (Fig.7). The increase in the cropland emissions was evident across the three tropical regions, while an increase in the livestock emissions was more evident in sub-Saharan Africa and Latin America. Emissions from rice cultivation remained constant over time, with the South and Southeast Asia region being the major contributor. Although there was generally good agreement on the global emissions caused by agricultural fires among the EPA and EDGAR datasets, regional analyses show substantial differences in the emissions from sub-Saharan Africa with EPA estimates being much higher than EDGAR (Fig.7). Further analyses show that this difference is mainly due to the emissions in Central Africa, with EPA estimating large emissions from agricultural fires compared to EDGAR and FAOSTAT. This also explains why the total agricultural emission estimates for EPA were larger than the other two data sources in Central Africa. 4 Discussion 4.1 Forest‑related emissions Causes of different FOLU estimates in the available global data sources have been assessed in detail in previous studies and can be broadly categorized into two: (1) differences in the system boundaries and (2) different estimates from similar contributing processes. For bookkeeping models and GHGIs, the differences in the system boundaries mainly result from varying spatial areas of managed lands with bookkeeping models having a broader definition of these lands (Grassi etal. 2018). In addition, there are conceptual differences in accounting for the direct and indirect human-induced effects of land-use change: NGHGIs include the indirect effects of CO2 fertilization, N deposition, and changes in climatic variables (e.g., temperature and precipitation), while bookkeeping models only account Mitig Adapt Strateg Glob Change (2024) 29:13 1 3 13 Page 16 of 24 for the direct effects of land-use change, harvest, and management. Differences between FAOSTAT and NGHGIs can be attributed to differences in estimates of the forest land area, incomplete accounting of all the carbon pools in FAOSTAT, and different methodologies in accounting for sink in some of the non-Annex 1 countries (Grassi etal. 2022b). Here, we assessed the differences in these three data sources through analysing both the net emissions and disaggregated emissions for the different FOLU activities. The estimated difference in the net emissions for the tropics between the NGHGIs and bookkeeping models is about 3.82 GtCO2 year−1 in the 2011–2018 period, assuming an average of 4.99 GtCO2 year−1 in the three booking models estimates (5.47, 5.22, and 4.28 GtCO2 year−1 in BLUE, OSCAR, and updated H&N2017, respectively - Table2). The generally higher emissions in BLUE compared to the updated H&N2017 model are largely due to differences in carbon densities between natural or primary vegetation and secondary vegetation, and higher allocation of cleared and harvested material to fast turnover pools in BLUE compared to HN2017 (Bastos etal. 2021). The disaggregated emission data show Fig. 6 (a) Global and (b) Tropical region disaggregated gross emissions from different agricultural activities Mitig Adapt Strateg Glob Change (2024) 29:13 1 3 Page 17 of 24 13 that the differences in the emissions for the tropics between NGHGIs and the H&N2017 model are mainly due to the varying estimates of the forestland sink, which stems from the differences in the systems boundaries explained above. Although the results show a good agreement in the forest sink estimate between NGHGIs and FAOSTAT in Latin America, a difference of about 2 GtCO2 year−1 exists in sub-Saharan Africa and South and Southeast Asia. The differences in the sink between FAOSTAT and NGHGIs are mainly related to the different estimates of the forestland areas and carbon pools. In particular, Grassi etal. (2022a) found that five countries in the tropics (i.e., Malaysia, Philippines, Central African Republic, Mali, and Namibia) report an implausible forest sink due to the inaccurate implementation of the IPCC methodology. In addition, there is a large uncertainty in the carbon stock changes over time in the two data sources. Schwingshackl etal. (2022) investigated differences in NGHGIs and bookkeeping modelestimates that remained even after the sink induced by environmental changes on managed forest land was accounted for in detail for several countries, including Brazil, Indonesia, and the DR Congo. Various shortcomings are discussed, such as the land-use data underlying the bookkeeping model estimates likely underestimating forest cover changes in Brazil or the NGHGIs capturing only parts of the substantial forest degradation in Indonesia and the DR Congo. Fig. 7 Disaggregated gross emissions from different agricultural activities for the three tropical regions Mitig Adapt Strateg Glob Change (2024) 29:13 1 3 13 Page 18 of 24 The disaggregated data show a difference of up to 2 GtCO2 year−1 the deforestation emissions, with the largest difference mainly stemming from the tropical regions of subSaharan Africa and South and Southeast Asia. This difference can be mainly attributed to the differences in the accounting in the considered data sources. FAOSTAT provides net forest conversion fluxes where the net loss emissions are discounted against the gains from forest area increase (Tubiello etal. 2021), while the NGHGIs and bookkeeping models provide gross deforestation fluxes. This explains why the deforestation fluxes in FAOSTAT were lower than the bookkeeping model and NGHGIs estimates. For the NGHGIs, some countries (e.g., India) do not report gross deforestation emissions, but instead provide net fluxes (Grassi etal. 2023). Excluding the gains from forest recovery and regrowth would increase the estimated deforestation emissions and provide a better indication of the absolute mitigation potential in forests. Although the bookkeeping models estimate higher emissions than NGHGIs, a recent study has shown that deforestation emission in the models are much lower when using high resolution data (Ganzenmüller etal. 2022). Overall the gross deforestation emission values of 2.96 to 5.43 GtCO2 eq. year−1 in the three data sources are within the range of the gross forest emission values of 5.3 ± 2.4 GtCO2 eq. year−1 estimated using spatially observed data combined with IPCC factors (Harris etal. 2021). 4.2 Other land use–related emissions Emissions from other land use activities (e.g., shifting cultivation, non-forest related land use conversions, biomass burning, and peatland drainage) in the tropical regions are relatively small compared to those from deforestation. The net emissions for these activities were larger in the bookkeeping models compared to FAOSTAT and NGHGIs. The higher deforestation emissions mainly stem from shifting cultivation, particularly for the tropical regions. Shifting cultivation has a relatively large importance, and a strong sensitivity has been shown towards the choice of underlying land-use dataset and spatial resolution (Ganzenmüller etal. 2022). Excluding emissions from organic soils in the other land use emission estimates in the NGHGIs indicates that croplands, grasslands, and wetlands contribute to a sink of CO2 in the tropical regions. However, most non-Annex 1 countries do not account for all emissions from other land use activities in their national reporting (Grassi etal. 2021; Grassi etal. 2018), which may explain why these are nearly negligible in sub-Saharan Africa and Latin America. A lack of reporting of these values may lead to uncertainties in the global stocktake especially when comparing NGHGIs to other data sources. While emissions from these activities are currently small and do not exhibit a clear trend (Figs.2 and 3), current national and global efforts to curb deforestation are likely to shift the trends in the land-use and land-use change activities. Increased activities in grassland areas and poor management of cropland areas are also likely to increase emissions from other land use; thus, making them equally relevant for the global stocktake. Overall, our regional analyses provided insights on the trend of the three tropical regions. Kondo etal. (2022) found no clear trend in the net FOLU emissions for Southeast Asia with the two bookkeeping models indicating differing trends — the BLUE model indicated an increase while the HN2017 model a decrease. The additional analyses with FAOSTAT and NGHGIs indicate no clear trend in the emissions for the same region. A slight decline in the emissions over Latin America and an increase over sub-Saharan Africa is evident in all the data sources. This is in line with a recent study which shows that the upward trend in emissions over Brazil has likely been overestimated due to artifacts in earlier land-use datasets (Rosan etal. 2021). A recent study covering the same period Mitig Adapt Strateg Glob Change (2024) 29:13 1 3 Page 19 of 24 13 in this study showed that there has been a global increase in the net AFOLU emissions (Lamb etal. 2021). The analyses presented here show that for the tropics, this increase is mainly from agricultural emissions and an increase in FOLU emissions particularly over sub-Saharan Africa. Although the analyses show a decline in the deforestation emissions over Latin America over the period 1990–2018, which is mainly because of government initiatives (Nepstad etal. 2014), this trend is likely to have reversed subsequently, as the 2019 and 2020 season had the highest deforestation rates in the Amazon region since 2008 (Silva Junior etal. 2021). 4.3 Agriculture‑related emissions Total global agricultural emissions for the considered period were more than the emissions from FOLU activities, with fewer discrepancies between the data sources for the emissions from livestock, cropland, and rice cultivation. The high level of agreement on the net emissions for the agricultural sector is because the considered data sources generally rely on the same activity data from FAO statistics. However, there are some small differences in the estimates between the data sources. Agricultural emission estimates from FAOSTAT solely rely on the IPCC Tier 1 methodology (FAO 2022). While most of the estimates in EDGAR are also based on Tier 1 methodology, CH4 emission factors for enteric fermentation of dairy and non-dairy cattle have been updated including the IPCC 2006 Tier 2 methodology (Crippa etal. 2019). In addition, EDGAR uses data from both FAO statistics and International Fertilizer Association (IFA) in calculating N2O and CO2 emissions from fertilizer and urea use (Janssens-maenhout etal. 2019). This explains observed differences in the emission estimates between FAOSTAT and EDGAR. Quantifying agricultural emissions has clearly received less attention than forestry and other land use emissions, and the hidden assumption in estimates based largely on Tier 1 factors is that there is not a lot of spatial or temporal variability in these emissions. This is an argument for a greater diversity of approaches to better estimate the spatial and temporal variation of these emissions. The results show that emissions from biomass burning in agricultural areas are highly uncertain, with FAOSTAT estimating small emissions compared to the other two data sources. The higher emissions in EPA compared to EDGAR, particularly for sub-Saharan Africa, is mainly because EPA estimates include emissions from savannah burning for agricultural expansions and these are not accounted for in EDGAR (Crippa etal. 2019). A lack of detailed and comprehensive reporting of national emissions from agricultural activities like that of FOLU limits a comparative analysis of these emissions. Future comparative analysis could benefit from improved agricultural reporting, and this would help in understanding the limitations of existing agricultural emissions databases. Only FAOSTAT provides both agriculture and FOLU emissions. Summing the values in Table2 and 4 results in 6.14 and 7.00 GtCO2 eq. year−1 AFOLU emissions globally and for the tropics over the period 2011–2018. The global value is much lower than the 12 GtCO2 eq. year−1 estimated by Lamb etal. (2021) for 2018. The latest IPCC AR6 report offers net global emission values of 6.49 GtCO2 eq. year−1 for LULUCF only (11% of the global 59 GtCO2 eq. year−1 GHG emissions) (Figure TS.6 in Pathak etal. (2022)); thus, the emissions presented here are much lower than the AR6 estimate. The main reason for these lower emissions is they do not account for CH4 and N2O emissions from savannah burning and peatland and forest fires. Of the three FOLU emission datasets in our analysis, only FAOSTAT explicitly includes emissions from forest fires, but these are relatively small (0.26 GtCO2 eq. year−1 for 2011–2018) and only account for CO2 emissions only. In Mitig Adapt Strateg Glob Change (2024) 29:13 1 3 13 Page 20 of 24 addition, CH4 and N2O emissions from drained peatland soils and wetlands over managed lands are still not accounted for in the databases and in the recent AR6 estimates, although these emissions can be high (Hergoualc’h etal. 2020; Swails etal. 2021). Integrating emissions from fires, peatlands, and wetlands in the country reporting and global databases would also provide a more complete assessment of the AFOLU emissions. 5 Conclusion This study provided a detailed comparative analysis of the emissions estimates from available global datasets for the AFOLU sector. The total net FOLU emissions for the tropical regions ranged between 1.17 and 5.47 GtCO2 year−1 compared to global values of −2.03 to 5.16 GtCO2 year−1 over the period 2011–2018 in the four analysed datasets. Disaggregated analyses show that these emissions are mainly from deforestation (2.96 to 7.16 GtCO2 year−1) and partly from other land-use activities (0.45 to 1.56 GtCO2 year−1), which exceed the net forestland sink (−0.53 to −3.08 GtCO2 year−1) and make the tropical region a net source of emissions for the FOLU sector. In terms of trend, three of the five data sources (updated H&N2017, FAOSTAT, and NGHGIs) indicate a decline in the net emissions over the tropics, which is also present globally. The net agricultural pantropical emissions range between 3.08 and 3.70 GtCO2 eq. year−1, with all the data sources showing an increase. Emissions from deforestation and biomass burning are the most uncertain for the agricultural sector are highly uncertain for the tropical region. Our analyses show that for global FOLU assessments, efforts have been made to provide more detailed datasets that allow for an assessment of not only net FOLU emissions, but also gross fluxes for the different contributing activities to this sector. Despite these advancements and the availability of more years of data, we find that there are still important gaps and that many emission sources are still not fully accounted for in the global databases. This is not new as Roman-Cuesta etal. (2016a) had already pointed out to the incomplete accounting of sources, such as from forest fires and peatland emissions, and not including non-CO2 in the FOLU estimates, particularly N2O and CH4 emissions from forest fires. Given that most of the considered databases are likely to play a major role in the global stocktake, efforts should shift towards ensuring that all emission sources are fully accounted for, and more consistency is developed. Moreover, there is an urgent need to offer geographically appropriate AFOLU emissions factors, so that land managers, policy makers, and society can efficiently concentrate efforts and resources towards the existing hotspots of emissions. Aggregated country estimates for greenhouse gas emissions remain a barrier for targeted action and improving mitigation governance. In that line, spatial data such as those published by Roman-Cuesta etal. (2016b) and Harris etal. (2021) show a way forward. Nevertheless, our analysis shows that the existing databases can be an important resource in providing a benchmark for NGHGIs in assessing the plausibility of their estimates, while investing in more detailed, complete, and accurate data is a key priority to underpin the current and future global stocktakes, considering both national to global level assessments. Supplementary Information The online version contains supplementary material available at https:// doi. org/ 10. 1007/ s1102702310096-z. Acknowledgements We thank Clemens Schwingshackl for providing us with the net and disaggregated FOLU emissions for the three bookkeeping models. Mitig Adapt Strateg Glob Change (2024) 29:13 1 3 Page 21 of 24 13 Funding This work was funded by USAID through the CGIAR Trust Fund and by Norad through grant # QZA-21/0124 CIFOR under the CGIAR Mitigate+ initiative. Data availability The national GHGIs, FAOSTAT, EDGAR, and EPA used in the present study are openly available through the links (https:// doi. org/ 10. 5281/ zenodo. 63907 39; https:// www. fao. org/ faost at/ en/# data/ GT; https:// edgar. jrc. ec. europa. eu/ datas et_ ghg60; https:// www. epa. gov/ globalmitig ationnonco2green housegases/ globalnonco2ghgemiss ions19902030). The country-level net emissions for the bookkeeping models are available at the Global Carbon Budget webpage (https:// www. globa lcarb onpro ject. org/ carbo nbudg et/), while the disaggregated data are available upon author request. Declarations Conflict of interest The authors declare no competing interests. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http:// creat iveco mmons. org/ licen ses/ by/4. 0/. 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Nyawira [email protected] 1 International Center forTropical Agriculture (CIAT), Nairobi, Kenya 2 Helmholtz GFZ German Research Centre forGeosciences, Potsdam, Germany 3 International Center forTropical Agriculture (CIAT), AddisAbaba, Ethiopia 4 Technical University ofMunich (TUM), Institute ofForest Management, Munich, Germany 5 Woodwell Climate Research Center, Falmouth, USA 6 Joint Research Centre (JRC), European Commission (EC), Ispra, Italy 7 Department für Geographie, Ludwig-Maximilians-Universität Munich, Luisenstr. 37, 80333München, Germany 8 Max Planck Institute forMeteorology, 20146Hamburg, Germany 9 nternational Institute forApplied Systems Analysis, Laxenburg, Austria 10 International Center forTropical Agriculture (CIAT), Cali, Colombia