Multi-gas and multi-source comparisons of six land use emission datasets and AFOLU estimates in the Fifth Assessment Report, for the tropics for 2000–2005

The Agriculture, Forestry and Other Land Use (AFOLU) sector contributes with ca. 20–25 % of global anthropogenic emissions (2010), making it a key component of any climate change mitigation strategy. AFOLU estimates, however, remain highly uncertain, jeopardizing the mitigation effectiveness of this sector. Comparisons of global AFOLU emissions have shown divergences of up to 25 %, urging for improved understanding of the reasons behind these differences. Here we compare a variety of AFOLU emission datasets and estimates given in the Fifth Assessment Report for the tropics (2000–2005) to identify plausible explanations for the differences in (i) aggregated gross AFOLU emissions, and (ii) disaggregated emissions by sources and gases (CO2, CH4, N2O). We also aim to (iii) identify countries with low agreement among AFOLU datasets to navigate research efforts. The datasets are FAOSTAT (Food and Agriculture Organization of the United Nations, Statistics Division), EDGAR (Emissions Database for Global Atmospheric Research), the newly developed AFOLU “Hotspots”, “Houghton”, “Baccini”, and EPA (US Environmental Protection Agency) datasets. Aggregated gross emissions were similar for all databases for the AFOLU sector: 8.2 (5.5–12.2), 8.4, and 8.0 Pg CO2 eq. yr−1 (for Hotspots, FAOSTAT, and EDGAR respectively), forests reached 6.0 (3.8–10), 5.9, 5.9, and 5.4 Pg CO2 eq. yr−1 (Hotspots, FAOSTAT, EDGAR, and Houghton), and agricultural sectors were with 1.9 (1.5– 2.5), 2.5, 2.1, and 2.0 Pg CO2 eq. yr−1 (Hotspots, FAOSTAT, Published by Copernicus Publications on behalf of the European Geosciences Union. 5800 R. M. Roman-Cuesta et al.: AFOLU dataset comparisons EDGAR, and EPA). However, this agreement was lost when disaggregating the emissions by sources, continents, and gases, particularly for the forest sector, with fire leading the differences. Agricultural emissions were more homogeneous, especially from livestock, while those from croplands were the most diverse. CO2 showed the largest differences among the datasets. Cropland soils and enteric fermentation led to the smaller N2O and CH4 differences. Disagreements are explained by differences in conceptual frameworks (carbon-only vs. multi-gas assessments, definitions, land use vs. land cover, etc.), in methods (tiers, scales, compliance with Intergovernmental Panel on Climate Change (IPCC) guidelines, legacies, etc.) and in assumptions (carbon neutrality of certain emissions, instantaneous emissions release, etc.) which call for more complete and transparent documentation for all the available datasets. An enhanced dialogue between the carbon (CO2) and the AFOLU (multi-gas) communities is needed to reduce discrepancies of land use estimates.


Study area
Our study area covers the tropics and the subtropics, including the more temperate regions of 150 South America (33° N to 54° S, 161° E to 117° W). Land use change occurs nowhere more 151 rapidly than in the tropics (Poorter et al., 2016) so its study has global importance. Moreover, 152 the tropics suffer from the largest data and capacity gaps (Romijn et al., 2012; and their 153 need to access AFOLU data and understand their differences is more crucial. We selected the       Unlike other databases all carbon in the ecosystem considered is accounted for: live 213 vegetation, soil, slash (woody debris produced during disturbance), and wood products. We 214 downloaded regional annual emissions from the TRENDS (1850-2005) dataset for the tropics:  estimates referto a tropical area slightly smaller than our study region and they are offered as  11.5 and 11.8 in chapter 11 of the AR5 (Smith et al., 2014). We will contrast our six datasets 245 against the data from these newly produced figures.  Table 1 shows a summary of key similarities and differences of the assessed AFOLU datasets 248 and the data from the AR5. The exact variables used for each database are described in Table   249 S1 in the Supplement.  252 We focus on human-induced gross emissions only, excluding fluxes from unmanaged land 253 (i.e. natural wetlands). We focus on direct emissions excluding indirect emissions whenever   Table S1 the Supplement).

275
For the case of fire, for all the databases, we excluded CO2 emissions that came from biomass 276 burning in non-woody vegetation such as-savannas and agriculture, since they are assumed to 277 be in equilibrium with annual regrowth processes (for CO2 gases only) (IPCC 2003(IPCC , 2006.   302 We found good agreement among datasets for the aggregated tropical scales with AFOLU   Fire and Decay category (5F) ( Table 3, and Table S1 in the Supplement) is used as a proxy for    Table 3, Table S1), which FAOSTAT relocated as net forest conversion emissions, partly included decay. Their dataset considers some undefined "forest fires" (5A) and 445 "wetland/peatland fires and decay" (5D) ( Table 3; Table S1 in the Supplement). Peatland 446 decay probably explains EDGAR's larger emissions in Asia, while we assume that EDGAR's 447 highest fire emissions for CS America might respond to deforestation fires which were not 448 included in the Hotspots to avoid double counting with deforestation, and relocated in 449 FAOSTAT to deforestation emissions ( Figure 3, Table 3). The Hotspots dataset showed

Wood harvesting 487
There is not a unique way to estimate wood harvesting emissions as exposed in the guidelines budgets. In out study, wood harvesting emissions were 1.2 (0.7-1.6), 2.0, 1.7 PgCO2.yr -1 for 492 the Hotspots, FAOSTAT and Baccini data, respectively (Tables 3, Table S1 in the 493 21 Supplement). Harvested wood products derive from FAO's country reports (i.e. FAOSTAT 494 forest products). All datasets included fuel wood and industrial roundwood (Tables 3, Table   495 S1). EDGAR excluded fuelwood from the AFOLU budget and placed it instead into the 496 energy budget (EDGAR, 2012), which explains its absence in Figure 2. Wood harvesting 497 emissions were larger in FAOSTAT than in the Hotspot data (Figure 2) partly due to the 498 inclusion of some extra categories of fuels (i.e. charcoal and residues) that were not included 499 in the Hotspot database ( Table 3, Table S1 in the Supplement). Charcoal represents 26% of   (Table 3, S1 in the Supplement). For this reason, 534 we excluded N2O emissions from grassland soils, drainage of organic soils, and restoration of 535 degraded lands (Table 3). This restrictions resulted in lower emissions than those estimated 536 for cropland soils in the AR5 (Fig. 11.5 in Smith et al., 2014). The Hotspots and EPA showed 537 the lowest and the highest estimates (Figures 2, 3). With the exception of the Hotspots, the 538 other datasets agreed well at the tropical scale, with FAOSTAT and EDGAR being almost 539 identical, also at continental scales. EPA disagreed more than the other datasets at the    the other datasets (Figure 6 b,c). At a global level, wetlands dominates natural CH4 emissions, 594 while agriculture and fossil fuels represent 2/3 of all human emissions, with smaller 595 contributions coming from biomass burning, the oceans, and termites (Montzka et al., 2011).

596
Fire non-CO2 emissions were quite similar among datasets, confirming that FAOSTAT 597 omissions were CO2 related (see section 3.2.3). Thus, as exposed in FAOSTAT's metadata, 598 only N2O and CH4 are considered in forest fires, excluding CO2 from aboveground biomass.  616 Country comparisons showed poor agreement among datasets for all the emission sectors, 617 particularly for the largest emitters (i.e. Brazil, Argentina, India, Indonesia) (Figures 7, 8).

618
Forests led the AFOLU disagreements (as observed by the similarity of Figure 7 a,b). From a 619 continental perspective, Central and South America showed more countries with high levels 620 of disagreement, suggesting the need for further data research.     Research ran by the carbon community is pivotal for AFOLU assessments and while these 714 two research communities overlap, they do not focus on exactly the same topics. The carbon 715 community works with CO2 emissions-only, fully excluding non-CO2 gases, particularly N2O.

716
It moreover rather focuses on forests and associated land use changes, excluding emissions 717 from agriculture. The AFOLU community has, contrarily, a multi-gas approach (CO2, CH4, 718 N2O) and includes emissions from both forests and agriculture. For these reasons, estimates of 719 the carbon community cannot be considered as AFOLU estimates, and certain confusion 720 appears in the IPCC's AR5 with an incorrect AFOLU labelling (Table 11.