Presentation delivered at the CIALCA international conference 'Challenges and Opportunities to the agricultural intensification of the humid highland systems of sub-Saharan Africa'. Kigali, Rwanda, October 24-27 2011.

1. Impacts of improved transportation
infrastructure on agricultural sector in the Congo
Basin
A. Mosnier , P. Havlik, M.
Obersteiner, K. Aoki, E. Schmid
International Institute for Applied Systems
Analysis (IIASA), Laxenburg, Austria
University of Natural Resources and Applied
Life Sciences (BOKU), Vienna, Austria
International CIALCA Conference, Kigali, Rwanda, October 24-27

2. Introduction
Congo Basin
Cameroon
Congo Republic
Democratic Republic of Congo (DRC)
Central African Republic (CAR)
Gabon
Equatorial Guinea

3. Introduction
• Mainly subsistence agriculture
• 80% of the territory is
• Food production per capita has covered by forests =>
decreased over the last decade agriculture main driver
and net imports have increased of deforestation

4. Introduction
• 1/3 of increase in cereal production worldwide has been attributed to
fertilizer related factors
• Average fertilizer use in SSA ~8 kg/ha vs. 54 kg/ha in Latin America
and 80 kg/ha in South Asia.
• In the Congo Basin, only Cameroon is a significant user of fertilizer
• Problem of poor transportation infrastructures in the Congo Basin
Fertilizer use per arable land in 2008
(kg nutrients/ha)
Cameroon Congo DRC Gabon
Nitrogen 3.37 0.06 0.20 0.07
Phosphate 0.79 0.00 0.04 0.03
Potash 1.30 0.02 0.05 1.35
Total 5.45 0.09 0.29 1.45
Source: State of the forest 2008 and
FAOSTAT

5. Introduction
Literature highlights:
 Positive impact of transportation infrastructure on
agriculture
 Negative impact of transportation infrastructure on forest
cover
• What will be the effect of transportation infrastructure
development on agricultural sector in the Congo Basin?

6. METHODOLOGY

7. GLOBIOM: a global, partial equilibrium model
Objective function = Maximization of producer and consumer surplus
DEMAND Exogenous drivers
Population growth, economic growth
28 regions
Wood products Food Bioenergy
Process
PROCESS
Primary wood
products
Crops SUPPLY
HRU = Altitude & Slope & Soil
Aggregation in
larger units
Altitude class, Slope class,
Soil Class
(max 200*200
LUC
PX5
km)
PX5
Altitude class (m): 0 – 300, 300 – 600, 600 – 1200, 1200 – 2500 and > 2500;
Slope class (deg): 0 – 3, 3 – 6, 6 – 10, 10 – 15, 15 – 30, 30 – 50 and > 50;
Soil texture class: coarse, medium, fine, stony and peat;
Biophysical Between 10*10
models EPIC G4M km and 50*50
km

9. GLOBIOM: Cropland - EPIC
Processes
 Hydrology EPIC
 Weather Evaporation
and
 Erosion Rain, Snow, Transpiration
Chemicals
 Carbon sequestration
 Crop growth Subsurface
Flow
 Crop rotations Surface
Flow
 Fertilization
 Tillage Below Root
Zone
 Irrigation etc…
9

10. Productivity change
EPIC Major outputs:
• Crop yields, Input Requirements, Environmental effects (e.g.
soil carbon, )
• For 18 crops (>75% of harvested area)
• And 4 management systems: High input, Low input,
Irrigated, Subsistence
In GLOBIOM, endogenous yield change through:
• Production increase in more (or less) fertile areas
• Change in management practices

11. Internal transportation costs
Compute time from a methodology
developed by Nelson (2006) using
several GIS datasets
Closest market:
for food crops = city above 300 000
inhabitants
for cash crops, timber and fertilizers= port
Transportation cost per simulation unit
(between 10x10 and 50x50km) :
Tc = constant + wage.hours + fuel price.
fuel consumption.distance + other
costs. distance
Parameters from literature review
(Teravaninthorn and Raballand, 2009;
ILO; GTZ)

12. SCENARIOS & RESULTS

13. Baseline
• Period 2000-2030, 10 year-steps (recursive dynamics)
• Demand exogenously driven:
– By GDP for processed wood
– By population for fuel wood
– By both for food = minimum calorie intake p.c. differentiated
between vegetable and animal calories (FAO, 2006a)
– Bioenergy use from POLES model (Russ et al., 2007) and
World Energy Outlook estimates (IEA-2006)
• Fixed crop mix per simulation unit
• No exogenous agricultural productivity growth

14. Scenario
• Realization of the planned infrastructures after 2010

15. Scenarios
• The reduction of transportation costs affect:
1) Farm output prices
2) Fertilizer cost
• Scenarios to distinguish these different effects:
INFRA1 = channel 1) only
INFRA2 = channels 1) and 2)
DIFHI = increase of the productivity in the high input system
by 5% between 2020-2030)

16. Results

17. Results
Baseline INFRA1 INFRA2 DIFHI
High Input and Irrigated 12.7% 12.7% 19.9% 57.1%
DRC
Subsistence- Low input 87.3% 87.3% 80.1% 42.9%
High Input and Irrigated 8.7% 9.2% 12.7% 48.3%
Congo Basin
Subsistence- Low input 91.3% 90.8% 87.3% 51.7%

18. Results
Relative land use change compared to the baseline level by 2030
INFRA1 INFRA2 DIFHI
Cropland 12.9% 17.4% 15.1%
DRC
Forests -4.2% -4.4% -3.9%
Congo Cropland 3.2% 4.7% 2.2%
Basin Forests -0.4% -0.4% 0.0%
Average annual C02 emissions from deforestation over 2020-2030 (in
MtCO2)
Baseline INFRA1 INFRA2 DIFHI
DRC 36 50 51 47
Congo
Basin 68 76 76 68

19. Conclusion
Transportation infrastructure improvement provides incentives:
 to increase agricultural production
 to switch to more intensive systems even if it remains
limited to 13% of the cropped area in the Congo Basin and
20% in the DRC by 2030
=> support to further increase efficiency of fertilizer use could
lead to significant intensification in the region
• However, it is most probably than intensification will not
impede deforestation increase, especially in dense forests
area of DRC
=> land tenure definition and implementation with delineation
of protected area/permanent forests area

20. Concluding remarks
• Transportation infrastructure could not necessarily lead to
transportation cost reduction
• Need for continuous efforts to maintain infrastructures in
good shape
• There are other non-cost factors that play an important role
for new technology adoption
• Other problems related to increase fertilizer use: higher
costs in the future, environmental sustainability,…

21. Thank you !
For more information :
www.globiom.org
Contact: mosnier@iiasa.ac.at