Editing IPCC:AR6/WGIII/Chapter-7 (section)

== 7.2 Historical and Current Trends in GHG Emission and Removals; Their Uncertainties and Implications for Assessing Collective Climate Progress ==

<div id="h1-3-siblings" class="h1-siblings"></div>

The biosphere on land and in wetlands is a source and sink of CO 2 and CH 4 , and a source of N 2 O due to both natural and anthropogenic processes that happen simultaneously and are therefore difficult to disentangle ( [[#IPCC--2010|IPCC 2010]] ; Angelo and Du Plesis 2017; [[#IPCC--2019|IPCC 2019]] ). AFOLU is the only GHG sector to currently include anthropogenic sinks. A range of methodological approaches and data have been applied to estimating AFOLU emissions and removals, each developed for their own purposes, with estimates varying accordingly. Since the SRCCL ( [[#Jia--2019|Jia et al. 2019]] ), emissions estimates have been updated (Sections 7.2.2 and 7.2.3), while the assessment of biophysical processes and short-lived climate forcers ( [[#7.2.4|Section 7.2.4]] ) is largely unchanged. Further progress has been made on the implications of differences in AFOLU emissions estimates for assessing collective climate progress ( [[#7.2.2.2|Section 7.2.2.2]] and Cross-Chapter Box 6 in this chapter).

<div id="7.2.1" class="h2-container"></div>

<span id="total-net-ghg-flux-from-afolu"></span>
=== 7.2.1 Total Net GHG Flux from AFOLU ===

<div id="h2-3-siblings" class="h2-siblings"></div>

National greenhouse gas inventory (NGHGI) reporting following the [[#IPCC--1996|IPCC 1996]] guidelines ( [[#IPCC--1996|IPCC 1996]] ), separates the total anthropogenic AFOLU flux into: (i) net anthropogenic flux from Land Use, Land-Use Change, and Forestry (LULUCF) due to both change in land cover and land management; and (ii) the net flux from Agriculture. While fluxes of CO 2 ( [[#7.2.2|Section 7.2.2]] ) are predominantly from LULUCF and fluxes of CH 4 and N 2 O ( [[#7.2.3|Section 7.2.3]] ) are predominantly from agriculture, fluxes of all three gases are associated with both sub-sectors. However, not all methods separate them consistently according to these sub-sectors, thus here we use the term AFOLU, separate by gas and implicitly include CO 2 emissions that stem from the agriculture part of AFOLU, though these account for a relatively small portion.

Total global net anthropogenic GHG emissions from AFOLU were 11.9 ± 4.4 GtCO 2 -eq yr –1 on average over the period 2010–2019, around 21% of total global net anthropogenic GHG emissions (Table 7.1 and Figure 7.3, using the sum of bookkeeping models for the CO 2 component). When using FAOSTAT/NGHGIs CO 2 flux data, then the contribution of AFOLU to total emissions amounts to 13% of global emissions.

'''Table 7.1 | Net anthropogenic emissions (annual averages for''' '''2010–2019''' a ''') from Agriculture, Forestry and Other Land Use (AFOLU).''' For context, the net flux due to the natural response of land to climate and environmental change is also shown for CO 2 in column E. Positive values represent emissions, negative values represent removals.

{| class="wikitable"
|-
! colspan="6"| '''Anthropogenic'''

! '''Natural response'''

! '''Natural and anthropogenic'''

|-
! '''Gas'''

! '''Units'''

! '''AFOLU Net anthropogenic emissions''' h

! '''Non-AFOLU anthropogenic GHG emissions''' d, f

! '''Total net anthropogenic emissions (AFOLU + non-AFOLU)'''

'''by gas'''

! '''AFOLU as a % of total net anthropogenic emissions by gas'''

! '''Natural land sinks including natural response of land to anthropogenic environmental change and climate variability''' e

! '''Net land- atmosphere CO''' 2 '''flux (i.e., anthropogenic AFOLU + natural fluxes across entire land surface'''

|-
!
! '''A'''

! '''B'''

! '''C = A+B'''

! '''D = (A/C) *100'''

! '''E'''

! '''F=A+E'''

|-
| '''CO''' 2

| GtCO 2 -eq yr –1

| 5.9 ± 4.1 b, f (book-keeping models, managed soils and pasture).

0 to 0.8 (NGHGI/ FAOSTAT data)

| 36.2 ± 2.9

| 42.0 ± 29.0

| 14%

| '''–12.5 ± 3.2'''

| '''–6.6 ± 4.6'''

|-
| rowspan="2"| '''CH''' 4

| MtCH 4 yr –1

| 157.0 ± 47.1 c

| 207.5 ± 62.2

| 364.4 ± 109.3

|
| – i

|
|-
| GtCO 2 -eq yr –1

| 4.2 ± 1.3 g

| 5.9 ± 1.8

| 10.2 ± 3.0

| 41%

|
|-
| rowspan="2"| '''N''' 2 '''O'''

| MtN 2 O yr –1

| 6.6 ± 4.0 c

| 2.8 ± 1.7

| 9.4 ± 5.6

|
|-
| GtCO 2 -eq yr –1

| 1.8 ± 1.1 g

| 0.8 ± 0.5

| 2.6 ± 1.5

| 69%

|
|-
| '''Total''' j

| GtCO 2 -eq yr –1

| 11.9 ± 4.4

(CO 2 component based on book-keeping models, managed soils and pasture)

| 44 ± 3.4

| 55.9 ± 6.1

| 21%

|
|}

a Estimates are given until 2019 as this is the latest date when data are available for all gases, consistent with Chapter 2, this report. Positive fluxes are emission from land to the atmosphere. Negative fluxes are removals.

b Net anthropogenic flux of CO 2 are due to land-use change such as deforestation and afforestation and land management, including wood harvest and regrowth, peatland drainage and fires, cropland and grassland management. Average of three bookkeeping models ( [[#Hansis--2015|Hansis et al. 2015]] ; [[#Houghton--2017|Houghton and Nassikas 2017]] ; [[#Gasser--2020|Gasser et al. 2020]] ), complemented by data on peatland drainage and fires from FAOSTAT ( [[#Prosperi--2020|Prosperi et al. 2020]] ) and GFED4s ( [[#van%20der%20Werf--2017|van der Werf et al. 2017]] ). Bookkeeping based CO 2 -LULUCF emissions (5.7±4.0) are consistent with AR6 WGI and [[IPCC:Wg3:Chapter:Chapter-2|Chapter 2]] of this report. The value of 5.9(±4.1) includes CO 2 emissions from urea application to managed soils and pasture. Comparisons with other estimates are discussed in 7.2.2. Based on NGHGIs and FAOSTAT, the range is 0 to 0.8 GtCO 2 yr –1 .

c CH 4 and N 2 O emission estimates and assessed uncertainty of 30 and 60% respectively, are based on Emissions Database for Global Atmospheric Research (EDGAR) data ( [[#Crippa--2021|Crippa et al. 2021]] ) in accordance with Chapter 2, this report (Sections 2.2.1.3 and 2.2.1.4). Both FAOSTAT ( [[#Tubiello--2019|Tubiello 2019]] ; [[#USEPA--2019|USEPA 2019]] ; [[#FAO--2021a|FAO 2021a]] ) and the USA EPA ( [[#USEPA--2019|USEPA 2019]] ) also provide data on agricultural non-CO 2 emissions, however, mean global CH 4 and N 2 O values considering the three databases are within the uncertainty bounds of EDGAR. EDGAR only considers agricultural and not overall AFOLU non-CO 2 emissions. Agriculture is estimated to account for approximately 89 and 96% of total AFOLU CH 4 and N 2 O emissions respectively. See [[#7.2.3|Section 7.2.3]] for further discussion.

d Total non-AFOLU emissions are the sum of total CO 2 -eq emissions values for energy, industrial sources, waste and other emissions with data from the Global Carbon Project for CO 2 , including international aviation and shipping, and from the PRIMAP database for CH 4 and N 2 O averaged over 2007–2014, as that was the period for which data were available.

e The modelled CO 2 estimates include natural processes in vegetation and soils and how they respond to both natural climate variability and to human-induced environmental changes, for example, the response of vegetation and soils to environmental changes such as increasing atmospheric CO 2 concentration, nitrogen deposition, and climate change (indirect anthropogenic effects) on both managed and unmanaged lands . The estimate shown represents the average from 17 Dynamic Global Vegetation Models with 1SD uncertainty ( [[#Friedlingstein--2020|Friedlingstein et al. 2020]] ).

f The NGHGIs take a different approach to calculating ‘anthropogenic’ CO 2 fluxes than the models ( [[#7.2.2|Section 7.2.2]] ). In particular the sinks due to environmental change (indirect anthropogenic fluxes) on managed lands are generally treated as anthropogenic in NGHGIs and non-anthropogenic in models such as bookkeeping and IAMs. A reconciliation of the results between IAMs and NGHGIs is presented in Cross-Chapter Box 6 in this chapter. If applied to this table, it would transfer approximately –5.5 GtCO 2 yr –1 (a sink) from Column E (which would become –7.0 GtCO 2 yr –1 ) to Column A (which would then be 0.4 GtCO 2 yr –1 ).

g All values expressed in units of CO 2 -eq are based on IPCC AR6 100-year Global Warming Potential (GWP100) values with climate-carbon feedbacks (CH 4 = 27, N 2 O = 273) (Chapter 2, Supplementary Material 2.SM.3; IPCC AR6 WGI [[#7.6|Section 7.6]] ).

h For assessment of cross-sector fluxes related to the food sector, see Chapter 12.

i While it is acknowledged that soils are a natural CH 4 sink ( [[#Jackson--2020|Jackson et al. 2020]] ) with soil microbial removals estimated to be 30 ± 19 MtCH 4 yr –1 for the period 2008–2017 (according to bottom-up estimates), natural CH 4 sources are considerably greater (371 (245–488) MtCH 4 yr –1 ) resulting in natural processes being a net CH 4 source (IPCC AR6 WGI [[IPCC:Wg3:Chapter:Chapter-5#5.2.2|Section 5.2.2]] ). The soil CH 4 sink is therefore omitted from Column E.

j Total GHG emissions concerning non-AFOLU sectors and all sectors combined (Columns B and C) include fluorinated gases in addition to CO 2 , CH 4 and N 2 O. Therefore, total values do not equal the sum of estimates for CO 2 , CH 4 and N 2 O.

<div id="_idContainer011" class="_idGenObjectStyleOverride-1"></div>

[[File:861e472c2e2c2f7ec3261677a162c001 IPCC_AR6_WGIII_Figure_7_3.png]]

'''Figure 7.3 | Subdivision of the total AFOLU emissions from Table 7.''' '''1 by activity and gas for the period 1990 to 2019.''' Positive values are emissions from land to atmosphere, negative values are removals. Panel A shows emissions divided into major activity and gases. Note that ‘biomass burning’ is only the burning of agriculture residues in the fields. The indicated growth rates between 1990–2000, 2000–2010, 2010–2019 are annualised across each time period. Panel B illustrates regional emissions in the years 1990, 2000, 2010, 2019 AFOLU CO 2 (green shading) represents all AFOLU CO 2 emissions. It is the mean from three bookkeeping models ( [[#Hansis--2015|Hansis et al. 2015]] ; [[#Houghton--2017|Houghton and Nassikas 2017]] ; [[#Gasser--2020|Gasser et al. 2020]] ) as presented in the Global Carbon Budget ( [[#Friedlingstein--2020|Friedlingstein et al. 2020]] ) and is not directly comparable to LULUCF in NGHGIs ( [[#7.2.2|Section 7.2.2]] ) ''.'' Data on CH 4 and N 2 O emissions are from the EDGAR database ( [[#Crippa--2021|Crippa et al. 2021]] ). See Sections 7.2.2 and 7.2.3 for comparison of different datasets. All values expressed are as CO 2 -eq with GWP100 values: CH 4 = 27, N 2 O = 273.

This AFOLU flux is the net of anthropogenic emissions of CO 2 , CH 4 and N 2 O, and anthropogenic removals of CO 2 . The contribution of AFOLU to total emissions varies regionally with highest in Latin America and Caribbean with 58% and lowest in Europe and North America with each 7% (Chapter 2, [[IPCC:Wg3:Chapter:Chapter-2#2.2.3|Section 2.2.3]] ). There is a discrepancy in the reported CO 2 AFOLU emissions magnitude because alternative methodological approaches that incorporate different assumptions are used ( [[#7.2.2.2|Section 7.2.2.2]] ). While there is ''low agreement'' in the trend of global AFOLU CO 2 emissions over the past few decades ( [[#7.2.2|Section 7.2.2]] ), they have remained relatively constant ( ''medium confidence'' ) (Chapter 2). Average non-CO 2 emission (aggregated using GWP100 IPCC AR6 values) from agriculture have risen from 5.2 ± 1.4 GtCO 2 -eq yr –1 for the period 1990 to 1999, to 6.0 ± 1.7 GtCO 2 -eq yr –1 for the period 2010 to 2019 ( [[#Crippa--2021|Crippa et al. 2021]] ) ( [[#7.2.3|Section 7.2.3]] ).

To present a fuller understanding of land–atmosphere interactions, Table 7.1 includes an estimate of the natural sink of land to atmospheric CO 2 ( [[#Jia--2019|Jia et al. 2019]] ) (IPCC AR6 WGI Chapter 5). Land fluxes respond naturally to human-induced environmental change (e.g., climate change, and the fertilising effects of increased atmospheric CO 2 concentration and nitrogen deposition), known as ‘indirect anthropogenic effects’, and also to ‘natural effects’ such as climate variability ( [[#IPCC--2010|IPCC 2010]] ) (Table 7.1 and [[#7.2.2|Section 7.2.2]] ). This showed a removal of –12.5 ± 3.2 GtCO 2 yr –1 ( ''medium confidence'' ) from the atmosphere during 2010–2019 according to global dynamic global vegetation model (DGVM) models ( [[#Friedlingstein--2020|Friedlingstein et al. 2020]] ) 31% of total anthropogenic net emissions of CO 2 from all sectors. It is likely that the NGHIs and FAOSTAT implicitly cover some part of this sink and thus provide a net CO 2 AFOLU balance with some 5 GtCO 2 lower net emissions than according to bookkeeping models, with the overall net CO 2 value close to being neutral. Model results and atmospheric observations concur that, when combining both anthropogenic (AFOLU) and natural processes on the entire land surface (the total ‘land–atmosphere flux’), the land was a global net sink for CO 2 of –6.6 ± 4.6 GtCO 2 yr –1 with a range for 2010 to 2019 from –4.4 to –8.4 GtCO 2 yr –1 . ( [[#Rödenbeck--2003|Rödenbeck et al. 2003]] , 2018; [[#Chevallier--2005|Chevallier et al. 2005]] ; [[#Feng--2016|Feng et al. 2016]] ; [[#van%20der%20Laan-Luijkx--2017|van der Laan-Luijkx et al. 2017]] ; [[#Niwa--2017|Niwa et al. 2017]] ; [[#Patra--2018|Patra et al. 2018]] ). The natural land sink is ''highly likely'' to be affected by both future AFOLU activity and climate change (IPCC AR6 WGI Box 5.1 and Figure SPM. 7), whereby under more severe climate change, the amount of carbon stored on land would still increase although the relative share of the emissions that land takes up, declines.

<div id="7.2.2" class="h2-container"></div>

<span id="flux-of-co-2-from-afolu-and-the-non-anthropogenic-land-sink"></span>
=== 7.2.2 Flux of CO 2 from AFOLU, and the Non-anthropogenic Land Sink ===

<div id="h2-4-siblings" class="h2-siblings"></div>

<div id="7.2.2.1" class="h3-container"></div>

<span id="global-net-afolu-co-2-flux"></span>
==== 7.2.2.1 Global Net AFOLU CO 2 Flux ====

<div id="h3-1-siblings" class="h3-siblings"></div>

Comparison of estimates of the global net AFOLU flux of CO 2 from diverse approaches (Figure 7.4) show differences on the order of several GtCO 2 yr –1 . When considering the reasons for the differences, and an approach to reconcile them ( [[#Grassi--2021|Grassi et al. 2021]] ) ( [[#7.2.2.3|Section 7.2.2.3]] ), there is ''medium confidence'' in the magnitude of the net AFOLU CO 2 flux. There is a discrepancy in the reported CO 2 AFOLU emissions magnitude because alternative methodological approaches that incorporate different assumptions are used ( [[#7.2.2.2|Section 7.2.2.2]] ). While the mean of the bookkeeping and DGVM model’s show a small increase in global CO 2 net emissions since year 2000, individual models suggest opposite trends ( [[#Friedlingstein--2020|Friedlingstein et al. 2020]] ). The latest FAOSTAT and NGHGI estimates show a small reduction in net emission. Overall, the trends are unclear.

Regionally (Figure 7.5), there is ''high confidence'' of net emissions linked to deforestation in Latin America, Africa and South-East Asia from 1990 to 2019. There is ''medium confidence'' in trends indicating a decrease in net emissions in Latin America since 2005 linked to reduced gross deforestation emissions, and a small increase in net emissions related to increased gross deforestation emissions in Africa since 2000 (Figure 7.5). There is ''high confidence'' regarding the net AFOLU CO 2 sink in Europe due to forest regrowth and known other sinks in managed forests, and ''medium confidence'' of a net sink in North America and Eurasia since 2010.

<div id="_idContainer045x" class="_idGenObjectStyleOverride-1"></div>

[[File:0174dc984ac7ffe86ec25aabb3d15d1b IPCC_AR6_WGIII_Figure_7_4.png]]

'''Figure 7.4 |''' '''Global net CO''' 2 '''flux due to AFOLU estimated using different methods for the period 1960 to 2019 (GtCO''' 2 '''y''' '''r''' –1 ''').''' Positive numbers represent emissions. '''Light-blue line:''' The mean from 17 DGVMs all using the same driving data under TrendyV9 used within the Global Carbon Budget 2020 and including different degrees of management ( [[#Bastos--2020|Bastos et al. 2020]] ; [[#Friedlingstein--2020|Friedlingstein et al. 2020]] ). '''Brown line:''' Data downloaded 6 June 2021 from FAOSTAT ( [[#FAO--2021b|FAO 2021b]] ; http://www.fao.org/faostat/ ) comprising: net emissions from (i) forest land converted to other land, (ii) net emissions from organic soils in cropland, grassland and from biomass burning, including peat fires and peat draining ( [[#Prosperi--2020|Prosperi et al. 2020]] ) and (iii) net emissions from forest land remaining forest land, which includes managed forest lands ( [[#Tubiello--2020|Tubiello et al. 2020]] ). '''Yellow''' '''line:''' Net flux estimate from National Greenhouse Gas Inventories (NGHGI) based on country reports to the UNFCCC for LULUCF ( [[#Grassi--2021|Grassi et al. 2021]] ) which include land-use change, and flux in managed lands. '''Red EO line:''' The 2001–2019 average net CO 2 flux from non-intact forest-related emissions and removals based on ground and Earth Observation data (EO) ( [[#Harris--2021|Harris et al. 2021]] ). Data to mask non-intact forest were used in the tropics ( [[#Turubanova--2018|Turubanova et al. 2018]] ) and extra-tropics ( [[#Potapov--2017|Potapov et al. 2017]] ). '''Dark''' '''blue line:''' the mean estimate and minimum and maximum (dark-blue shading) from three bookkeeping models ( [[#Hansis--2015|Hansis et al. 2015]] ; [[#Houghton--2017|Houghton and Nassikas 2017]] ; [[#Gasser--2020|Gasser et al. 2020]] ). These include land cover change (e.g., deforestation, afforestation), forest management including wood harvest and land degradation, shifting cultivation, regrowth of forests following wood harvest or abandonment of agriculture, grassland management, agricultural management. Emissions from peat burning and draining are added from external datasets (see text). Both the DGVM and bookkeeping global data is available at: https://www.icos-cp.eu/science-and-impact/global-carbon-budget/2020 (accessed on 4 October 2021). Data consistent with IPCC AR6 WGI Chapter 5. Dotted lines denote the linear regression from 2000 to 2019. Trends are statistically significant (P &lt;0.05) with exception for the NGHGI trend (P &lt;0.01).

<div id="_idContainer016" class="_idGenObjectStyleOverride-1"></div>

[[File:43a2b2dbe10c79c51ec748ecfde1f5c0 IPCC_AR6_WGIII_Figure_7_5.png]]

'''Figure 7.5 | Regional net flux of CO''' 2 '''due to AFOLU estimated using different methods for the period''' '''1990–2019''' '''(GtCO''' 2 '''y''' '''r''' –1 ''').''' Positive numbers represent emissions. The upper-central panel depicts the world map shaded according to the IPCC AR6 regions corresponding to the individual graphs. For each regional panel; '''brown''' '''line:''' Total net flux data from FAOSTAT ( [[#Tubiello--2020|Tubiello et al. 2020]] ); '''yellow line:''' Net emissions estimates from National Greenhouse Gas Inventories based on country reports to the UNFCCC for LULUCF ( [[#Grassi--2021|Grassi et al. 2021]] ); '''dark-blue line:''' The mean estimate and minimum and maximum (dark-blue shading) from three bookkeeping models. ( [[#Hansis--2015|Hansis et al. 2015]] ; [[#Houghton--2017|Houghton and Nassikas 2017]] ; [[#Gasser--2020|Gasser et al. 2020]] ). Regional estimates from bookkeeping models are available at: https://zenodo.org/record/5548333#.YVwJB2LMJPY ( [[#Minx--2021|Minx et al. 2021]] ). See the legend in Figure 7.4 for a detailed explanation of flux components for each dataset.

<div id="7.2.2.2" class="h3-container"></div>

<span id="why-do-various-methods-deliver-difference-in-results"></span>
==== 7.2.2.2 Why Do Various Methods Deliver Difference in Results? ====

<div id="h3-2-siblings" class="h3-siblings"></div>

The processes responsible for fluxes from land have been divided into three categories ( [[#IPCC--2006|IPCC 2006]] , 2010): (i) the ''direct human-induced effects'' due to changing land cover and land management; (ii) the ''indirect human-induced effects'' due to anthropogenic environmental change, such as climate change, CO 2 fertilisation, nitrogen deposition, and so on; and (iii) ''natural effects,'' including climate variability and a background natural disturbance regime (e.g., wildfires, windthrows, diseases or insect outbreaks).

Global models estimate the anthropogenic land CO 2 flux considering only the impact of direct effects, and only those areas that were subject to intense and direct management such as clear-cut harvest. It is important to note, that DGVMs also estimate the non-anthropogenic land CO 2 flux (Land Sink) that results from indirect and natural effects (Table 7.1). In contrast, estimates of the anthropogenic land CO 2 flux in NGHGIs (LULUCF) include the impact of direct effects and, in most cases, of indirect effects on a much greater area considered ‘managed’ than global models ( [[#Grassi--2021|Grassi et al. 2021]] ).

The approach used by countries follows the IPCC methodological guidance for NGHGIs ( [[#IPCC--2006|IPCC 2006]] , 2019). Since separating direct, indirect and natural effects on the land CO 2 sink is impossible with direct observation such as national forest inventories ( [[#IPCC--2010|IPCC 2010]] ), upon which most NGHGIs are based, the IPCC adopted the ‘managed land’ concept as a pragmatic proxy to facilitate NGHGI reporting. Anthropogenic land GHG fluxes (direct and indirect effects) are defined as all those occurring on managed land, that is, where human interventions and practices have been applied to perform production, ecological or social functions ( [[#IPCC--2006|IPCC 2006]] , 2019). GHG fluxes from unmanaged land are not reported in NGHGIs because they are assumed to be non-anthropogenic. Countries report NGHGI data with a range of methodologies, resolution and completeness, dependent on capacity and available data, consistent with IPCC guidelines ( [[#IPCC--2006|IPCC 2006]] , 2019) and subject to an international review or assessment processes.

The FAOSTAT approach is conceptually similar to NGHGIs. FAOSTAT data on forests are based on country reports to FAO-FRA 2020 ( [[#FAO--2020a|FAO 2020a]] ), and include changes in biomass carbon stock in ‘forest land’ and ‘net forest conversions’ in five-year intervals. ‘Forest land’ may include unmanaged natural forest, leading to possible overall overestimation of anthropogenic fluxes for both sources and sinks, though emissions from deforestation are likely underestimated ( [[#Tubiello--2020|Tubiello et al. 2020]] ). FAOSTAT also estimate emissions from forest fires and other land uses (organic soils), following IPCC methods ( [[#Prosperi--2020|Prosperi et al. 2020]] ). The FAO-FRA 2020 ( [[#FAO--2020b|FAO 2020b]] ) update leads to estimates of larger sinks in Russia since 1991, and in China and the USA from 2011, and larger deforestation emissions in Brazil and smaller in Indonesia than FRA 2015 ( [[#FAO--2015|FAO 2015]] ; [[#Tubiello--2020|Tubiello et al. 2020]] ).

The bookkeeping models by [[#Houghton--2017|Houghton and Nassikas (2017)]] , [[#Hansis--2015|Hansis et al. (2015)]] , and [[#Gasser--2020|Gasser et al. (2020)]] and the DGVMs used in the Global Carbon Budget ( [[#Friedlingstein--2020|Friedlingstein et al. 2020]] ) use either the LUH2 dataset ( [[#Hurtt--2020|Hurtt et al. 2020]] ), HYDE ( [[#Goldewijk--2017|Goldewijk et al. 2017]] ), FRA 2015 ( [[#FAO--2015|FAO 2015]] ) or a combination. The LUH2 dataset includes a new wood harvest reconstruction, new representation of shifting cultivation, crop rotations, and management information including irrigation and fertiliser application. The area of forest subject to harvest in LUH2 is much less than the area of forest considered ‘managed’ in the NGHGIs ( [[#Grassi--2018|Grassi et al. 2018]] ). The model datasets do not yet include the FAO FRA 2020 update ( [[#FAO--2020a|FAO 2020a]] ). The DGVMs consider CO 2 fertilisation effects on forest growth that are sometimes confirmed from the ground-based forest inventory networks ( [[#Nabuurs--2013|Nabuurs et al. 2013]] ) and sometimes not at all ( [[#van%20der%20Sleen--2015|van der Sleen et al. 2015]] ).

Further, the DGVMs and bookkeeping models do not include a wide range of practices which are implicitly covered by the inventories; for example: forest dynamics ( [[#Pugh--2019|Pugh et al. 2019]] ; [[#Le%20Noë--2020|Le Noë et al. 2020]] ), forest management including wood harvest (Nabuurs, et al. 2013; [[#Arneth--2017|Arneth et al. 2017]] ), agricultural and grassland practices ( [[#Pugh--2015|Pugh et al. 2015]] ; [[#Sanderman--2017|Sanderman et al. 2017]] ; [[#Pongratz--2018|Pongratz et al. 2018]] ); or, for example, fire management ( [[#Andela--2017|Andela et al. 2017]] ; [[#Arora--2018|Arora and Melton 2018]] ).

Increasingly, higher emissions estimates are expected from DGVMs compared to bookkeeping models, because DGVMs include a loss of additional sink capacity of 3.3 ± 1.1 GtCO 2 yr –1 on average over 2009–2018, which is increasing with larger climate and CO 2 impacts ( [[#Friedlingstein--2020|Friedlingstein et al. 2020]] ). This arises because the DGVM methodological setup requires a reference simulation including climate and environmental changes but without any land-use change such as deforestation, so DGVMs implicitly include the sink capacity forests would have developed in response to environmental changes on areas that in reality have been cleared ( [[#Gitz--2003|Gitz and Ciais 2003]] ; [[#Pongratz--2014|Pongratz et al. 2014]] ) (IPCC AR6 WGI Chapter 5).

Carbon emissions from peat burning have been estimated based on the Global Fire Emission Database (GFED4s; [[#van%20der%20Werf--2017|van der Werf et al. 2017]] ). These were included in the bookkeeping model estimates and added 2.0 GtC over 1960–2019 (e.g., causing the peak in South-East Asia in 1998) (Figure 7.5). Within the Global Carbon Budget ( [[#Friedlingstein--2020|Friedlingstein et al. 2020]] ), peat drainage from agriculture accounted for an additional 8.6 GtC from 1960–2019 according to FAOSTAT ( [[#Conchedda--2020|Conchedda and Tubiello, 2020]] ) used by two of the bookkeeping models ( [[#Hansis--2015|Hansis et al. 2015]] ; [[#Gasser--2020|Gasser et al. 2020]] ).

Remote-sensing products provide valuable spatial and temporal land-use and biomass data globally (including in remote areas), at potentially high spatial and temporal resolutions, that can be used to calculate CO 2 fluxes, but have mostly been applied only to forests at the global or even regional scale. While such data can strongly support monitoring reporting and verification, estimates of forest carbon fluxes directly from Earth Observation (EO) data vary considerably in both their magnitude and sign (i.e., whether forests are a net source or sink of carbon). For the period 2005–2017, net tropical forest carbon fluxes were estimated as –0.4 GtCO 2 yr –1 ( [[#Fan--2019|Fan et al. 2019]] ); 0.58 GtCO 2 yr –1 ( [[#Grace--2014|Grace et al. 2014]] ); 1.6 GtCO 2 yr –1 ( [[#Baccini--2017|Baccini et al. 2017]] ) and 2.87 GtCO 2 yr –1 ( [[#Achard--2014|Achard et al. 2014]] ). Differences can in part be explained by spatial resolution of the datasets, the definition of ‘forest’ and the inclusion of processes and methods used to determine degradation and growth in intact and secondary forests, or the changes in algorithm over time ( [[#Palahí--2021|Palahí et al. 2021]] ). A recent global study integrated ground observations and remote sensing data to map forest-related GHG emissions and removals at a high spatial resolution (30 m spatial scale), although it only provides an average estimate of annual carbon loss over 2001–2019 ( [[#Harris--2021|Harris et al. 2021]] ). The estimated net global forest carbon sink globally was –7.66 GtCO 2 yr −1 , being –1.7 GtCO 2 yr −1 in the tropics only.

Remote sensing products can help to attribute changes to anthropogenic activity or natural inter-annual climate variability ( [[#Fan--2019|Fan et al. 2019]] ; [[#Wigneron--2020|Wigneron et al. 2020]] ). Products with higher spatial resolution make it easier to determine forest and carbon dynamics in relatively small-sized managed forests (e.g., Y. [[#Wang--2020|Wang et al. 2020]] ; [[#Heinrich--2021|Heinrich et al. 2021]] ; [[#Reiche--2021|Reiche et al. 2021]] ). For example, secondary forest regrowth in the Brazilian Amazon offset 9 to 14% of gross emissions due to deforestation 1 ( [[#Aragão--2018|Aragão et al. 2018]] ; [[#Silva%20Junior--2021|Silva Junior et al. 2021]] ). Yet disturbances such as fire and repeated deforestation cycles due to shifting cultivation over the period 1985 to 2017, were found to reduce the regrowth rates of secondary forests by 8 to 55% depending on the climate region of regrowth ( [[#Heinrich--2021|Heinrich et al. 2021]] ).

<div id="7.2.2.3" class="h3-container"></div>

<span id="implications-of-differences-in-afolu-co-2-fluxes-between-global-models-and-national-greenhouse-gas-inventories-nghgis-and-reconciliation"></span>
==== 7.2.2.3 Implications of Differences in AFOLU CO 2 Fluxes Between Global Models and National Greenhouse Gas Inventories (NGHGIs), and Reconciliation ====

<div id="h3-3-siblings" class="h3-siblings"></div>

There is about 5.5 GtCO 2 yr –1 difference in the anthropogenic AFOLU estimates between NGHGIs and global models (this number relates to an IAMs comparison for the period 2005–2015 – see Cross-Chapter Box 6 in this chapter; for comparison with other models see Figure 7.4). Reconciling the differences, in other words, making estimates comparable, can build confidence in land-related CO 2 estimates, for example for the purpose of assessing collective progress in the context of the Global Stocktake (Cross-Chapter Box 6 in this chapter). The difference largely results from greater estimated CO 2 in NGHGIs, mostly occurring in forests ( [[#Grassi--2021|Grassi et al. 2021]] ). This difference is potentially a consequence of: (i) simplified and/or incomplete representation of management in global models ( [[#Popp--2017|Popp et al. 2017]] ; [[#Pongratz--2018|Pongratz et al. 2018]] ), for example, concerning impacts of forest management in biomass expansion and thickening ( [[#Nabuurs--2013|Nabuurs et al. 2013]] ; [[#Grassi--2017|Grassi et al. 2017]] ), (ii) inaccurate and/or incomplete estimation of LULUCF fluxes in NGHGIs ( [[#Grassi--2017|Grassi et al. 2017]] ), especially in developing countries, primarily in non-forest land uses and in soils, and (iii) conceptual differences in how global models and NGHGIs define ‘anthropogenic’ CO 2 flux from land ( [[#Grassi--2018|Grassi et al. 2018]] ). The impacts of (i) and (ii) are difficult to quantify and result in uncertainties that will decrease slowly over time through improvements of both models and NGHGIs. By contrast, the inconsistencies in (iii) and its resulting biases were assessed as explained below.

Since changing the NGHGIs’ approach is impractical, an interim method to translate and adjust the output of global models was outlined for reconciling a bookkeeping model and NGHGIs ( [[#Grassi--2018|Grassi et al. 2018]] ). More recently, an improved version of this approach has been applied to the future mitigation pathways estimated by IAMs ( [[#Grassi--2021|Grassi et al. 2021]] ), with the implications for the Global Stocktake discussed in Cross-Chapter Box 6 in this chapter. This method implies a post-processing of current global models’ results that addresses two components of the conceptual differences in the ‘anthropogenic’ CO 2 flux; (i) how the impact of human-induced environmental changes (indirect effects) are considered, and (ii) the extent of forest area considered ‘managed’. Essentially, this approach adds DGVM estimates of CO 2 fluxes due to indirect effects from countries’ managed forest area (using non-intact forest area maps as a proxy) to the original global models’ anthropogenic land CO 2 fluxes (Figure 7.6).

<div id="_idContainer018" class="_idGenObjectStyleOverride-1"></div>

[[File:a3ae7f919a9ba62160e8aaee65c64a80 IPCC_AR6_WGIII_Figure_7_6.png]]

'''Figure 7.6 | Main conceptual differences between global models (bookkeeping models, IAMs and DGVMs) and NGHGIs definitions of what is considered the ‘anthropogenic’ land CO''' 2 '''flux, and proposed solution (from''' '''Grassi''' '''et al.''' '''2021). (a)''' Differences in defining the anthropogenic land CO 2 flux by global models (‘land use’) and NGHGIs (‘LULUCF’), including the attribution of processes responsible for land fluxes ( [[#IPCC--2006|IPCC 2006]] ; 2010) in managed and unmanaged lands. The anthropogenic land CO 2 flux by global models typically includes only the CO 2 flux due to ‘direct effects’ (land-use change, harvest, regrowth). By contrast, most NGHGIs consider anthropogenic all fluxes occurring in areas defined as ‘managed’, including also the sink due to ‘indirect effects’ (climate change, atmospheric CO 2 increase, N deposition etc.) and due to ‘natural effects’ (climate variability, background natural disturbances). '''(b)''' Proposed solution to the inconsistency, via disaggregation of the ‘Land Sink’ flux from DGVMs into CO 2 fluxes occurring in managed and in unmanaged lands. The sum of ‘land use’ flux (direct effects from bookkeeping models or IAMs) and the ‘Land Sink’ (indirect effects from DGVMs) in managed lands produces an adjusted global model CO 2 flux which is conceptually more comparable with LULUCF fluxes from NGHGIs. Note that the figure may in some cases be an oversimplification, in other words, not all NGHGIs include all recent indirect effects.

<div id="cross-chapter-box-6" class="h2-container box-container"></div>

<span id="cross-chapter-box-6-implications-of-reconciled-anthropogenic-land-co-2-fluxes-for-assessing-collective-climate-progress-in-the-g-lobal-stocktake"></span>
=== Cross-Chapter Box 6 | Implications of Reconciled Anthropogenic Land CO 2 Fluxes for Assessing Collective Climate Progress in the Global Stocktake ===

<div id="h2-5-siblings" class="h2-siblings"></div>

'''Authors:''' Giacomo Grassi (Italy/European Union), Joeri Rogelj (Belgium/Austria), Joanna I. House (United Kingdom), Alexander Popp (Germany), Detlef van Vuuren (the Netherlands), Katherine Calvin (the United States of America), Shinichiro Fujimori (Japan), Petr Havlík (Austria/the Czech Republic), Gert-Jan Nabuurs (the Netherlands)

The Global Stocktake aims to assess countries’ collective progress towards the long-term goals of the Paris Agreement in the light of the best available science. Historic progress is assessed based on NGHGIs, while expectations of future progress are based on country climate targets (e.g., NDCs for 2025 or 2030 and long-term strategies for 2050). Scenarios consistent with limiting warming well-below 2°C and 1.5°C developed by IAMs (Chapter 3) are expected to play a key role as benchmarks against which countries’ aggregated future mitigation pledges will be assessed. This, however, implies that estimates by IAMs and country data used to measure progress are comparable.

In fact, there is about 5.5 GtCO 2 yr –1 difference during 2005–2015 between global anthropogenic land CO 2 net flux estimates of IAMs and aggregated NGHGIs, due to different conceptual approaches to what is ‘anthropogenic’. This approach and its implications when comparing climate targets with global mitigation pathways are illustrated in this Box Figure 1a–e.

By adjusting the original IAM output (Cross-Chapter Box 6, Figure 1a) with the indirect effects from countries’ managed forest (Cross-Chapter Box 6, Figure 1b, estimated by DGVMs, see also Figure 7.6), NGHGI-comparable pathways can be derived (Cross-Chapter Box 6, Figure 1c). The resulting apparent increase in anthropogenic sink reflects simply a reallocation of a CO 2 flux previously labelled as natural, and thus does not reflect a mitigation action. These changes do not affect non-LULUCF emissions. However, since the atmosphere concentration is a combination of CO 2 emissions from LULUCF and from fossil fuels, the proposed land-related adjustments also influence the NGHGI-comparable economy-wide (all sector) CO 2 pathways (Cross-Chapter Box 6, Figure 1d).

This approach does not imply a change in the original decarbonisation pathways, nor does it suggest that indirect effects should be considered in the mitigation efforts. It simply ensures that a like-with-like comparison is made: if countries’ climate targets use the NGHGI definition of anthropogenic emissions, this same definition can be applied to derive NGHGI-comparable future CO 2 pathways. This would have an impact on the NGHGI-comparable remaining carbon or GHG budget (i.e., the allowable emissions until net zero CO 2 or GHG emissions consistent with a certain climate target). For example, for SSP2-1.9 and SSP2-2.6 (representing pathways in line with 1.5°C and well-below 2°C limits under SSP2 assumptions), carbon budget is 170 GtCO 2 -eq lower than the original remaining carbon budget according to the models’ approach (Cross-Chapter Box 6, Figure 1e). Similarly, the remaining carbon (or GHG) budgets in [[IPCC:Wg3:Chapter:Chapter-3|Chapter 3]] (this report), as well as the net zero carbon (or GHG) targets, could only be used in combination with the definition of anthropogenic emissions as used by the IAMs (Cross-Chapter Box 3 in Chapter 3). In the absence of these adjustments, collective progress would appear better than it is.

Cross-Chapter Box 6

The UNEP’s annual assessment of the global 2030 ‘emission gap’ between aggregated country NDCs and specific target mitigation pathways ( [[#UNEP--2020|UNEP 2020]] ), is only affected to a limited degree. This is because some estimates of global emissions under the NDCs already use the same land-use definitions as the IAM mitigation pathways ( [[#Rogelj--2017|Rogelj et al. 2017]] ), and because historical data of global NDC estimates is typically harmonised to the historical data of global mitigation pathway projections ( [[#Rogelj--2011|Rogelj et al. 2011]] ). This latter procedure, however, is agnostic to the reasons for the observed mismatch, and often uses a constant offset. The adjustment described here allows this mismatch to be resolved by drawing on a scientific understanding of the underlying reasons, and thus provides a more informed and accurate basis for estimating the emission gap.

The approach to deriving a NGHGI-comparable emission pathways presented here can be further refined with improved estimates of the future forest sink. Its use would enable a more accurate assessment of the collective progress achieved and of mitigation pledges under the Paris Agreement.

<div id="_idContainer020y"></div>

'''Cross-Chapter Box 6, Figure 1 | Impact on global mitigation pathways of adjusting the modelled anthropogenic land CO 2 fluxes to be comparable with National Greenhouse Gas Inventories (NGHGIs) (from Grassi et al. 2021).''' 

'''(a)''' The mismatch between global historical LULUCF CO 2 net flux from NGHGIs (black), and the original (un-adjusted) modelled flux historically and under future mitigation pathways for SSP2 scenarios from Integrated Assessment Models (IAMs, Chapter 3). '''(b)''' Fluxes due to indirect effects of environmental change on areas equivalent to countries’ managed forest (i.e., those fluxes generally considered ‘anthropogenic’ by countries and ‘natural’ by global models). '''(c)''' Original modelled (solid line) LULUCF mitigation pathways adjusted to be NGHGI-comparable (dashed line), for example, by adding the indirect effects in panel b. The indirect effects in panel b decline over time with increasing mitigation ambition, mainly because of the weaker CO 2 fertilisation effect. In panel c, the dependency of the adjusted LULUCF pathways on the target becomes less evident after 2030, because the indirect effects in countries’ managed forest (which are progressively more uncertain with time, as highlighted by the grey areas) compensate the effects of the original pathways. '''(d)''' NGHGI-comparable pathways for global CO 2 emissions from all sectors including LULUCF (obtained by combining global CO 2 pathways without LULUCF – where no adjustment is needed – and the NGHGI-comparable CO 2 pathways for LULUCF ( [[#Gütschow--2019|Gütschow et al. 2019]] ; [[#Grassi--2017|Grassi et al. 2017]] ). '''(e)''' Cumulative impact of the adjustments from 2021 until net zero CO 2 emissions or 2100 (whatever comes first) on the remaining carbon budget.

<div id="7.2.3" class="h2-container"></div>

<span id="ch-4-and-n-2-o-flux-from-afolu"></span>

=== 7.2.3 CH 4 and N 2 O Flux From AFOLU ===

<div id="h2-6-siblings" class="h2-siblings"></div>

Trends in atmospheric CH 4 and N 2 O concentrations and the associated sources, including land and land use are discussed in Sections 5.2.2 and 5.2.3 of the IPCC AR6 WGI. Regarding AFOLU, the SRCCL and AR5 ( [[#Jia--2019|Jia et al. 2019]] ; Smith et al. 2014) identified three global non-CO 2 emissions data sources: EDGAR ( [[#Crippa--2021|Crippa et al. 2021]] ), FAOSTAT ( [[#FAO--2021a|FAO 2021a]] ; [[#Tubiello--2019|Tubiello, 2019]] ) and the USA EPA ( [[#USEPA--2019|USEPA 2019]] ). Methodological differences have been previously discussed ( [[#Jia--2019|Jia et al. 2019]] ). In accordance with Chapter 2, this report, EDGAR data are used in Table 7.1 and Figure 7.3. It is important to note that in terms of AFOLU sectoral CH 4 and N 2 O emissions, only FAOSTAT provides data on AFOLU emissions, while EDGAR and USEPA data consider just the agricultural component. However, the mean of values across the three databases for both CH 4 and N 2 O, fall within the assessed uncertainty bounds (30 and 60% for CH 4 and N 2 O respectively, [[IPCC:Wg3:Chapter:Chapter-2#2.2.1|Section 2.2.1]] , in this report) of EDGAR data. NGHGIs annually submitted to the UNFCCC ( [[#7.2.2.3|Section 7.2.2.3]] ) provide national AFOLU CH 4 and N 2 O data, as included in the SRCCL ( [[#Jia--2019|Jia et al. 2019]] ). Aggregation of NGHGIs to indicate global emissions must be considered with caution, as not all countries compile inventories, nor submit annually. Additionally, NGHGIs may incorporate a range of methodologies for CH 4 and N 2 O accounting (e.g., [[#van%20der%20Weerden--2016|van der Weerden et al. 2016]] ; [[#Ndung’u--2019|Ndung’u et al. 2019]] ; [[#Thakuri--2020|Thakuri et al. 2020]] ), making comparison difficult. The analysis of complete AFOLU emissions presented here, is based on FAOSTAT data. For agricultural specific discussion, analysis considers EDGAR, FAOSTAT and USEPA data.

<div id="7.2.3.1" class="h3-container"></div>

<span id="global-afolu-ch-4-and-n-2-o-emissions"></span>
==== 7.2.3.1 Global AFOLU CH 4 and N 2 O Emissions ====

<div id="h3-4-siblings" class="h3-siblings"></div>

Using FAOSTAT data, the SRCCL estimated average CH 4 emissions from AFOLU to be 161.2 ± 43 MtCH 4 yr –1 for the period 2007–2016, representing 44% of total anthropogenic CH 4 emissions, with agriculture accounting for 88% of the AFOLU component ( [[#Jia--2019|Jia et al. 2019]] ). The latest data ( [[#FAO--2021a|FAO 2021a]] , 2020b) highlight a trend of growing AFOLU CH 4 emissions, with a 10% increase evident between 1990 and 2019, despite year-to-year variation. Forestry and other land use (FOLU) CH 4 emission sources include biomass burning on forest land and combustion of organic soils (peatland fires) ( [[#FAO--2020c|FAO 2020c]] ). The agricultural share of AFOLU CH 4 emissions remains relatively unchanged, with the latest data indicating agriculture to have accounted for 89% of emissions on average between 1990 and 2019. The SRCCL reported with ''medium evidence'' and ''high agreement'' that ruminants and rice production were the most important contributors to overall growth trends in atmospheric CH 4 ( [[#Jia--2019|Jia et al. 2019]] ). The latest data confirm this in terms of agricultural emissions, with agreement between databases that agricultural CH 4 emissions continue to increase and that enteric fermentation and rice cultivation remain the main sources (Figure 7.7). The proportionally higher emissions from rice cultivation indicated by EDGAR data compared to the other databases, may result from the use of a Tier 2 methodology for this source within EDGAR ( [[#Janssens-Maenhout--2019|Janssens-Maenhout et al. 2019]] ).

<div id="_idContainer020" class="_idGenObjectStyleOverride-1"></div>

[[File:3f7a2799eba95bc9c3f99e8531c30ff3 IPCC_AR6_WGIII_Figure_7_7.png]]

'''Figure 7.7 | Estimated global mean agricultural CH''' 4 '''(top), N''' 2 '''O (middle) and aggregated CH''' 4 '''and N''' 2 '''O (using CO''' 2 '''-eq according to GWP100 AR6 values).''' '''(Bottom) emissions for three decades according to EDGAR v6.0 (Crippa''' '''et al.''' '''2021), FAOSTAT ( [[#FAO--2021a|FAO 2021a]] ) and USEPA ( [[#USEPA--2019|USEPA 2019]] ) databases.''' Latest versions of databases indicate historic emissions to 2019, 2019 and 2015 respectively, with average values for the post–2010 period calculated accordingly. For CH 4 , emissions classified as ‘Other Ag.’ within USEPA data, are re-classified as ‘Agricultural Biomass Burning’. Despite CH 4 emissions from agricultural soils also being included, this category was deemed to principally concern biomass burning on agricultural land and classified accordingly. For N 2 O, emissions classified within EDGAR as direct and indirect emissions from managed soils, and indirect emissions from manure management are combined under ‘Agricultural Soils’. Emissions classified by FOASTAT as from manure deposition and application to soils, crop residues, drainage of organic soils and synthetic fertilisers are combined under ‘Agricultural Soils’, while emissions reported as ‘Other Ag.’ under USEPA data are re-classified as ‘Agricultural Biomass Burning’.

The SRCCL also noted a trend of increasing atmospheric N 2 O concentration, with ''robust evidence'' and ''high agreement'' that agriculture accounted for approximately two-thirds of overall global anthropogenic N 2 O emissions. Average AFOLU N 2 O emissions were reported to be 8.7 ± 2.5 MtN 2 O yr –1 for the period 2007–2016, accounting for 81% of total anthropogenic N 2 O emissions, with agriculture accounting for 95% of AFOLU N 2 O emissions ( [[#Jia--2019|Jia et al. 2019]] ). A recent comprehensive review confirms agriculture as the principal driver of the growing atmospheric N 2 O concentration ( [[#Tian--2020|Tian et al. 2020]] ). The latest FAOSTAT data ( [[#FAO--2020b|FAO 2020b]] , 2021a) document a 25% increase in AFOLU N 2 O emissions between 1990 and 2019, with the average share from agriculture remaining approximately the same (96%). Agricultural soils were identified in the SRCCL and in recent literature as a dominant emission source, notably due to nitrogen fertiliser and manure applications to croplands, and manure production and deposition on pastures ( [[#Jia--2019|Jia et al. 2019]] ; [[#Tian--2020|Tian et al. 2020]] ). There is agreement within latest data that agricultural soils remain the dominant source (Figure 7.7).

Aggregation of CH 4 and N 2 O to CO 2 equivalence (using GWP100 IPCC AR6 values), suggests that AFOLU emissions increased by 15% between 1990 and 2019, though emissions showed trend variability year to year. Agriculture accounted for 91% of AFOLU emissions on average over the period ( [[#FAO--2020b|FAO 2020b]] , 2021a). EDGAR ( [[#Crippa--2021|Crippa et al. 2021]] ), FAOSTAT ( [[#FAO--2021a|FAO 2021a]] ) and USEPA ( [[#USEPA--2019|USEPA 2019]] ) data suggest aggregated agricultural emissions (CO 2 -eq) to have increased since 1990, by 19% (1990–2019), 15% (1990–2019) and 21% (1990–2015) respectively, with all databases identifying enteric fermentation and agricultural soils as the dominant agricultural emissions sources.

<div id="7.2.3.2" class="h3-container"></div>

<span id="regional-afolu-ch-4-and-n-2-o-emissions"></span>
==== 7.2.3.2 Regional AFOLU CH 4 and N 2 O Emissions ====

<div id="h3-5-siblings" class="h3-siblings"></div>

FAOSTAT data ( [[#FAO--2020b|FAO 2020b]] , 2021a) indicate Africa (+44%), followed by Southern Asia (+29%) to have the largest growth in AFOLU CH 4 emissions between 1990 and 2019 (Figure 7.8). Eurasia was characterised by notable emission reductions (–58%), principally as a result of a sharp decline (–63%) between 1990 and 1999. The average agricultural share of AFOLU emissions between 1990 and 2019 ranged from 66% in Africa to almost 100% in the Middle East.

In agreement with AR5 (Smith et al. 2014), the SRCCL identified Asia as having the largest share (37%) of emissions from enteric fermentation and manure management since 2000, but Africa to have the fastest growth rate. Asia was identified as responsible for 89% of rice cultivation emissions, which were reported as increasing ( [[#Jia--2019|Jia et al. 2019]] ). Considering classification by ten IPCC regions, data suggest enteric fermentation to have dominated emissions in all regions since 1990, except in South-East Asia and Pacific, where rice cultivation forms the principal source (FAO 2021; [[#USEPA--2019|USEPA 2019]] ). The different databases broadly indicate the same regional CH 4 emission trends, though the indicated absolute change differs due to methodological differences ( [[#7.2.3.1|Section 7.2.3.1]] ). All databases indicate considerable emissions growth in Africa since 1990 and that this region recorded the greatest regional increases in emissions from both enteric fermentation and rice cultivation since 2010. Additionally, FAOSTAT data suggest that emissions from agricultural biomass burning account for a notably high proportion of agricultural CH 4 emissions in Africa (Figure 7.8).

The latest data suggest growth in AFOLU N 2 O emissions in most regions between 1990 and 2019, with Southern Asia demonstrating highest growth (+74%) and Eurasia, greatest reductions (–51%), the latter mainly a result of a 61% reduction between 1990 and 2000 ( [[#FAO--2020b|FAO 2020b]] , 2021a). Agriculture was the dominant emission source in all regions, its proportional average share between 1990 and 2019 ranging from 87% in Africa, to almost 100% in the Middle East (Figure 7.8).

The SRCCL provided limited discussion on regional variation in agricultural N 2 O emissions but reported with ''medium confidence'' that certain regions (North America, Europe, East and South Asia) were notable sources of grazing land N 2 O emissions ( [[#Jia--2019|Jia et al. 2019]] ). The AR5 identified Asia as the largest source and as having the highest growth rate of N 2 O emissions from synthetic fertilisers between 2000 and 2010 (Smith et al. 2014). Latest data indicate agricultural N 2 O emission increases in most regions, though variation between databases prevents definitive conclusions on trends, with Africa, Southern Asia, and Eastern Asia suggested to have had greatest growth since 1990 according to EDGAR ( [[#Crippa--2021|Crippa et al. 2021]] ), FAOSTAT ( [[#FAO--2021a|FAO 2021a]] ) and USEPA ( [[#USEPA--2019|USEPA 2019]] ) data respectively. However, all databases indicate that emissions declined in Eurasia and Europe from 1990 levels, in accordance with specific environmental regulations put in place since the late 1980s ( [[#Tubiello--2019|Tubiello 2019]] ; [[#European%20Environment%20Agency--2020|European Environment Agency 2020]] ; [[#Tian--2020|Tian et al. 2020]] ), but generally suggest increases in both regions since 2010.

<div id="_idContainer022" class="_idGenObjectStyleOverride-1"></div>

[[File:a9990377774a75dd3f6c82c3f50ac205 IPCC_AR6_WGIII_Figure_7_8.png]]

'''Figure 7.8 | Estimated average AFOLU CH''' 4 '''(top) and N''' 2 '''O (bottom) emissions for three decades according to FAOSTAT data by ten global regions, with disaggregation of agricultural emissions ( [[#FAO--2020b|FAO 2020b]] ; 2021a).''' Note for N 2 O: emissions from manure deposition and application to soils, crop residues and synthetic fertilisers are combined under ‘Agriculture: Soils’.

<div id="7.2.4" class="h2-container"></div>

<span id="biophysical-effects-and-short-lived-climate-forcers"></span>
=== 7.2.4 Biophysical Effects and Short-lived Climate Forcers ===

<div id="h2-7-siblings" class="h2-siblings"></div>

Despite new literature, general conclusions from the SRCCL and WGI-AR6 on biophysical effects and short-lived climate forcers remain the same. Changes in land conditions from land cover change or land management jointly affect water, energy, and aerosol fluxes (biophysical fluxes) as well as GHG fluxes (biogeochemical fluxes) exchanged between the land and atmosphere ( ''high agreement'' , ''robust evidence'' ) ( [[#Anderson--2011|Anderson et al. 2011]] ; [[#O’Halloran--2012|O’Halloran et al. 2012]] ; [[#Alkama--2016|Alkama and Cescatti 2016]] ; [[#Naudts--2016|Naudts et al. 2016]] ; [[#Erb--2017|Erb et al. 2017]] ). There is ''high confidence'' that changes in land condition do not just have local impacts but also have non-local impacts in adjacent and more distant areas ( [[#Pielke--2011|Pielke et al. 2011]] ; [[#Mahmood--2014|Mahmood et al. 2014]] ) which may contribute to surpassing climate tipping points ( [[#Nepstad--2008|Nepstad et al. 2008]] ; [[#Brando--2014|Brando et al. 2014]] ). Non-local impacts may occur through: GHG fluxes and subsequent changes in radiative transfer, changes in atmospheric chemistry, thermal, moisture and surface pressure gradients creating horizontal transport (advection) ( [[#de%20Vrese--2016|de Vrese et al. 2016]] ; [[#Davin--2010|Davin and de Noblet-Ducoudré 2010]] ) and vertical transport (convection and subsidence) ( [[#Devaraju--2018|Devaraju et al. 2018]] ). Although regional and global biophysical impacts emerge from model simulations ( [[#Davin--2010|Davin and de Noblet-Ducoudré 2010]] ; [[#de%20Vrese--2016|de Vrese et al. 2016]] ; [[#Devaraju--2018|Devaraju et al. 2018]] ), especially if the land condition has changed over large areas, there is ''very low agreement'' on the location, extent and characteristics of the non-local effects across models. Recent methodological advances, empirically confirmed changes in temperature and precipitation owing to distant changes in forest cover ( [[#Cohn--2019|Cohn et al. 2019]] ; [[#Meier--2021|Meier et al. 2021]] ).

Following changes in land conditions, CO 2 , CH 4 and N 2 O fluxes are quickly mixed into the atmosphere and dispersed, resulting in the biogeochemical effects being dominated by the biophysical effects at local scales ( ''high confidence'' ) (Y. [[#Li--2015|Li et al. 2015]] ; [[#Alkama--2016|Alkama and Cescatti 2016]] ). Afforestation/reforestation ( [[#Lejeune--2018|Lejeune et al. 2018]] ; [[#Strandberg--2019|Strandberg and Kjellström 2019]] ), urbanisation ( [[#Li--2013|Li and Bou-Zeid 2013]] ) and irrigation ( [[#Mueller--2016|Mueller et al. 2016]] and [[#Thiery--2017|Thiery et al. 2017]] ) modulate the likelihood, intensity, and duration of many extreme events including heatwaves ( ''high confidence'' ) and heavy precipitation events ( ''medium confidence'' ) ( [[#Haberlie--2015|Haberlie et al. 2015]] ). There is ''high confidence'' and ''high agreement'' that afforestation in the tropics ( [[#Perugini--2017|Perugini et al. 2017]] ), irrigation ( [[#Alter--2015|Alter et al. 2015]] ; [[#Mueller--2016|Mueller et al. 2016]] ) and urban greening result in local cooling, ''high agreement'' and ''medium confidence'' on the impact of tree growth form (deciduous vs evergreen) ( [[#Naudts--2016|Naudts et al. 2016]] ; [[#Luyssaert--2018|Luyssaert et al. 2018]] and [[#Schwaab--2020|Schwaab et al. 2020]] ), and ''low agreement'' on the impact of wood harvest, fertilisation, tillage, crop harvest, residue management, grazing, mowing, and fire management on the local climate.

Studies of biophysical effects have increased since AR5 reaching ''high agreement'' for the effects of changes in land condition on surface albedo ( [[#Leonardi--2015|Leonardi et al. 2015]] ). ''Low confidence'' remains in proposing specific changes in land conditions to achieve desired impacts on local, regional and global climates due to: a poor relationship between changes in surface albedo and changes in surface temperature ( [[#Davin--2010|Davin and de Noblet-Ducoudré 2010]] ), compensation and feedbacks among biophysical processes ( [[#Bonan--2016|Bonan 2016]] ; [[#Kalliokoski--2020|Kalliokoski et al. 2020]] ), climate and seasonal dependency of the biophysical effects ( [[#Bonan--2016|Bonan 2016]] ), omittance of short-lived chemical forcers ( [[#Unger--2014|Unger 2014]] ; [[#Kalliokoski--2020|Kalliokoski et al. 2020]] ), and study domains often being too small to document possible conflicts between local and non-local effects ( [[#Swann--2012|Swann et al. 2012]] ; [[#Hirsch--2018|Hirsch et al. 2018]] ).

<div id="7.3" class="h1-container"></div>

<span id="drivers"></span>