A spatially explicit visualization of global GHGs for the AFOLU sector
Rosa María Román‐Cuesta, Martin Herold (WUR), Lou Verchont, Mariana Rufino (CIFOR)
Klaus Butterbach‐Bahl (IFU‐KIT), Todd Rosenstock (ICRAF)
Sofia July 2014
Rosa.roman@wur.nl

Fig 1: GHG Emissions from anthropogenic origin excluding land‐use, land use change and forestry (LULUCF).
Source: edgar.jrc.ec.europe.eu

Project background

Main goal: Identifying regions where mitigation of anthropogenic AFOLU GHG
emissions might be most promising in terms of reduction of gross GHG fluxes,reductions of GHG trends, maximized returns on mitigation investments.

CGIAR‐led SAMPLES Project: Focuses on Mitigation Potential from AgriculturalLandscapes and aims to better understand livelihoods and climate in agricultural
systems, to support pro‐poor climate change mitigation options.

Collaborative initiative between University of Wageningen, CIFOR, ICRAF, and theKarlsruhe Institute of Technology (KIT)Project activities
1. To produce an spatially explicit hotspot emission map for the AFOLU sector.
Components: IPCC categories, pools, and selected gases (CO2, CO, CH4, N2O).
Scales: Pantropical, averaged mean fluxes for the largest period available.
Audience: Policy makers + researchers.
Products: disaggregated maps for key sources and gases (CO2 eq) + policy brief for
COP this year.

2. To identify data caveats and data aggregation issues and to work towards animproved AFOLU emission map.

3. To assess and visualize spatially explicit emission uncertainties.

4. To identify plausible regional mitigation potentials and actions.

Why are integrated AFOLU assessments important?
METOFFICE

Timing: For the first time, AFOLU is considered in a single chapter in the 5thAssessment report (AR5). Political momentum is growing. Available databases
start to appear. FAOstats (2013), EDGAR‐JRC (2011).

Better insights of processes: improved understanding of drivers of emissions,feedbacks and interactions among the different land sectors (e.g. agriculture‐forestry).

Improved emission reporting:•AFOLU offers a complete, although highly uncertain view of the emissions of theland sector.

• A minimized risk of double counting and/or inconsistent treatment of land
categories (e.g. definitions, different assumptions about available land, etc).

Mitigation‐adaptation links: A coherent approach for the consideration oftrade‐offs between mitigation and adaptation options.

Identifying AFOLU emission sources, categories, and gases.

IPCC 2006 GPG summary of AFOLU activities: major contributing GHGs

Identify key global source categories for the different AFOLUsectors

Major contributors of GHG emissions from the AFOLU sector
AR5-WGIII
AFOLU
24% emissions 2010
50% agriculture
50% forestry

SourcesAgriculture
FAOSTAT (2013)
FOLUHoughton et al., (2012)

Figure 11.2. AFOLU emissions and subcategories for the last four decades.

Drained peat and peat fires
JRC/PBL (2012)
Hooijer et al. (2010)
van der Werf et al. (2006)
FAOSTAT 2013

Agricultural contribution: The agricultural sector is the largest contributor of global
anthropogenic non‐CO2 GHGs accounting for 10‐12% of the global anthropogenic emissions in2010.

Paddy rice (9-11%) emissions

FOLU contribution: The FOLU sector mainly contributes to global anthropogenic CO2 GHGs
accounting for 12% of the global anthropogenic emissions in 2000‐2009. The sector is a net sink

DATABASES ON DISAGGREGATED AFOLU EMISSIONS ON MANAGED LAND AT A GLOBAL SCALE

Collect and add available disaggregated global AFOLU datasets(spatially explicit) for key sectors and sub‐categories and GHGs.

IPCC Tier 1‐ FAOSTATS‐ Agricultural total emissions

FAOstats‐Agricultural soil emissions

FAOstats‐ Rice emissions

EDGAR Database: Global total CO2e emissions (Mg C. yr‐1)(0.1° x 0.1° ), excluding land use change and forestry (IPCC Tier 1/2)

Mg C. yr‐1 (0.1° x 0.1°)

Source: European Commission, Joint Research Centre (JRC)/PBL Netherlands Environmental
Assessment Agency. Emission Database for Global Atmospheric Research (EDGAR),version 4.2. http://edgar.jrc.ec.europe.eu, 2011

Enteric fermentation and soil management: CH4 and N2O emissions Herrero et al. (2013)

Biomass burning emissions: CO2eq from global fires Van der Werf et al. (2010)

Mean annual fire emissions 1997‐2009 (gC.m‐2.yr‐1)

Paddy Rice: global CH4 emissions (EPA report, Changsheng Li)

Global deforestation emissions: CO2eq Harris et al. (2012)

Mean gross carbon emissions (GgC.km‐1.yr‐1 )Global degradation emissions: CO2eq Hansen et al. (2014)

Data caveats and knowledge gaps
Uncertainties...next steps

Using IPCC 2006 AFOLU GPG

Benefits of using the IPCC 2006‐GPG•

Coherent conceptual framework to quickly identify key sources of emissions for the land
sector, and guided search for available datasets.

•Useful framework to focus on human‐derived emissions only (e.g. CH4 of wetlands notaccounted for)
Human activities/management vs ecosystem approaches.
Guided effort towards mitigation initiatives.

• An improved understanding of drivers of emissions and interactions in different land sectors(e.g. agriculture‐forestry relation).
• Useful to contrast data reported at different Tiers and assessing emission uncertainties.

Difficulties using the IPCC 2006‐GPG• No data difficulties‐‐‐‐‐‐plenty of data for Tier 1 reporting (but uncertainties are large).
• Some land uses are regionally focused in the IPCC 2006 GPG, and difficult to extrapolate topantropical analyses (e.g. wetlands (definition, human activities in managed wetlands(hortocultural activities?, biofuel consumption?, etc)

Difficulties with AFOLU in general• It is not always possible to separate anthropogenic from natural GHG fluxes in the landsector.
• The input data needed to estimate AFOLU GHG emissions globally and regionally are highly
uncertain, and often based on country level statistics or remote sensing information (spatio‐
temporal resolution, definition, methodological issues, data quality, data access, dataconsistency, etc).

QUESTIONS?