REDD-PAC in the Congo Basin

Aline Mosnier, Martin Tadoum, Peguy Tonga, Johannes Pirker, Adeline Makoudjou, Roland Gyscard Ngonga, Didier Bokelo

REDD-PAC steering committee meeting, 14th October 2014, Laxenburg, Austria

1. Timeline

Timeline

3

Kick-off meeting in the

Congo Basin

Timeline

4

Regional workshop with climate and biodiversity

focal points

Recruitment of experts

1st REDD-PAC school in Douala

Timeline

5

Apr May June July Aug Sept Oct 2014 2014 2014 2014 2014 2014 2014

Project mid-term meeting

in Douala

2d REDD-PAC school in Douala

3rd REDD-PAC school at

IIASA

Death of Eustache Awono, expert for

Cameroon

Recruitment of Adeline Makoudjou, new

expert for Cameroon

2. Project setup in the Congo Basin

6

10 COMIFAC countries

4 pilot countries initially:

DRC, Congo, Cameroon and

CAR

Due to the conflict, low

modeling capacity, and

absence of statistics, CAR is

not a pilot country

anymore

7

Geographical scope

Institutional setting

8

COMIFAC

Expert in Cameroon

Expert in DRC

Expert in Congo

Expert in CAR

1 REDD-PAC position

REDD+ National Coordination

team

REDD+ National

Coordination

team

REDD+ National

Coordination

team

IIASA UNEP-WCMC

Bio

div

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ity

foca

l po

ints

C

limate

focal p

oin

ts

3. Improvements of GLOBIOM-Congo Basin

9

Land cover map

10

From GLC, no cropland in Bandundu and

Katanga, 2 important agricultural regions!

Source: GLC2000

Need to find another land cover

map

Land cover map

11

4 land cover maps available for Congo Basin: MODIS, GlobCover, GLC2000 and UCL

Reclassification of initial land cover classes into the same 7 land

cover classes to facilitate comparison

REDD-PAC Geo-Wiki and simplified interface of Quantum GIS with land cover maps distributed to experts

In April 2014, final choice of land cover classes to be used in the

Congo Basin in GLOBIOM and the land cover maps to be used Different land cover maps can be used for each land cover class Different land cover maps can be used in different regions

Hybrid approach to compute a new land cover map using the best

information available

Land cover map

12

The sum of land cover

classes in a pixel should be

equal to the total land area

in the pixel

Hybrid land cover map

13

Cropland Humid forests Dry forests

National statistics

14

Crops

Available harvested area and production at the second

administrative level for Cameroon (departments) and DRC

(districts)

Available production at the second administrative level for

DRC (districts) and some limited information for yields at

the province level

Some limited information for production at the first

administration unit level in Congo

Number of livestock heads

Volumes of harvested timber

Harmonization of land cover map with national statistics

14/10/2014 Name - Title 15

Cross-entropy approach to allocate harvested area by crop to

simulation unit level on available cropland

Takes into account need for fallow

Calculation of prior depends on transportation costs,

population density and productivity potential/suitability

CR

OP

LAN

D

= su

m o

f al

l cro

ps

PAST

UR

E

=su

m o

f al

l ru

min

ants

n

eed

s

Harvested area by crop in DRC

16

Cassava Corn Rice

Groundnut Potatoes Sweet potatoes

Harvested area by crop in Cameroon

17

Beans Cassava Corn Groundnuts

Millet-Sorghum

Oil Palm Potatoes Sweet Potatoes

Oil palm suitability map

18

Green: very suitable

Red: marginally suitable

Model improvements

19

Representation of subsistence agriculture: introduction of fallow

time which depends on the evolution of population density

(dynamic)

Fuel wood can come from 3 sources leading to different impacts

on forest cover (no impact/degradation/deforestation)

Fuel wood demand

Shifting agriculture

Natural forests

Managed Forests

Model improvements

20

Better representation of current agricultural systems

4. Preliminary results

21

NB: These results have been obtained before last improvements in land cover map and harvested crop area maps

BAU- Deforestation

Results: Simulated deforestation over 2010-2030 (in

million ha/year) using different GDP growth projections

22

Historical

deforestation

2000-2010:

0.36 Mha/year

BAU- Emissions from deforestation and forest degradation using different GDP growth and different carbon biomass maps

23

Uncertainty in future

emissions due to

carbon biomass is

the highest

Deforestation risk in REDD+ projects area

24

BAU deforestation rate is almost 2 times higher in REDD+ project areas than the national average in the next two decades

In Mai Ndombe

5. Next steps

25

From the work in the Atlas on the synergies between REDD+ and biodiversity targets, the key issues in the Congo Basin are: Reforestation and forest restauration Sustainable management of forest

concessions Forest conservation including through

protected areas

Test REDD+ policies which could provide incentives to encourage these practices or compute the opportunity costs of implementing targets on these objectives

26

Scenarios and policy options

Scenarios and policy options

27

Most of the Congo Basin countries want to become emerging countries by 2020: Development of agro-industrial plantations e.g. oil palm Expand infrastructure network Achieve food security Development of mining activities Investment in hydro-electricity Increase competitiveness of forest sector

Investigate how some of these objectives would impact

future forest cover, conservation opportunity costs and if they could be compatible with REDD+ policies

Spatial scale of the results

28

Reference level computed at:

National level

Province level

Biome level

REDD+ pilot project area

Flexible: the model is solved at the 50x50km grid resolution

level (nb of hectares in each grid) and can then be

aggregated to any polygon area

29

Merci!

Pour plus d’informations :

www.globiom.org

Contact: mosnier@iiasa.ac.at