Ephraim NKONYA "Can the poor afford sustainable land management (SLM)? Drivers of SLM in poor countries"
1. Drivers of change and resilience
increase (SLM)
Ephraim Nkonya
International Food Policy Research Institute
(IFPRI)
Washington D.C.
2. The hard facts & figures about change
• SSA has the fastest growing population in the
world
– Consequently the per capita land area in SSA is
decreasing the fastest
• SSA experiences the most severe deforestation
• Globally, 80% of cropland expansion replaces
forests loss of carbon & land degradation
• About 90% of the remaining 1.8 billion ha of
arable land in developing countries is in LAC &
SSA (Bruinsma 2009).
•
3.
4. Annual loss of arable land per
capita, 1961-2009
80
70
60
Sq meters/capita
50
40
30
20
10
0
SSA World Southern Asia LAC South East Asia
7. Theory of agricultural change
• Induced agricultural innovation – Boserup
1965 – necessity mother of invention
– We are fast running out of arable land
• Agricultural Research & Development (R&D) will play
largest role in ensuring food security & SLM
• But such impact will only be possible in regions with
wider yield gaps = SSA, EECA, South Asia
• Contribution of yield increase to ag production smallest
in SSA
• Unfortunately, R&D & ag expenditure in SSA is the
lowest
8.
9. Agricultural orientation index (% ag
expenditure/%agGDP)
1.2
1.0
0.8
0.6
0.4
0.2
0.0
East Asia & Europe & LAC MENA SA SSA
Pacific CA
1980-89 1990-99 2004-04 2005-07
10. Research approach
• We analyze drivers of change of cropland in
SSA because,
– SSA has the most daunting poverty & food
security challenges
– SSA experiences most severe land degradation
– SSA holds largest potential for increasing food
supply in the world due to having largest yield gap
& largest supply of remaining arable land
11. Data of drivers of change of cropland area
Source Resolution Expected sign
Cropland area change Ramankutty et al 0.50 Dependent variable
Land tenure USAID Country Negative
Ag R&D ASTI Country Quadratic (inverted U)
GDP World Bank Country Environmental Kuznet curve
(inverted U)
Time to urban area CIESIN 0.50 Negative
(>50k)
Rural population GPW (CIESIN) 0.50 Positive
Land suitability FAO (GAEZ) 0.50 +/-
Government World Bank Country -
effectiveness
Crop yield FAO (GAEZ) 0.50 U-shaped quadratic
12. Extent of agricultural land in Africa
Key: Green=cropland
Brown=pasture
Source: Ramankutty et al 2011
14. Drivers of cropland change
Drivers Coefficient
∆ R&D (US$ million) 6.2
(∆ R&D)2 -0.7
∆GDP (US$ billion) 7.6
(∆GDP)2 -1.7
∆Maize yield (Tons/ha) 1.18x10-4
(Maize yield)2 -0.10
Time to town (minutes) -44.7
Rural population 712
Land suitability -3.5
Government effectiveness 144
Land tenure security (cf serious concern)
Moderately severe concern land security -420
NB: all coefficients are significant at p=0.01
15. R&D impact on cropland expansion in
SSA
Cropland expansion 000ha
4.4
R&D expenditure (US$ million)
16. Drivers & implications
• Land tenure security show favorable impact to
intensification
• Access to markets enhance intensification – cf
Tiffen et al study
• Land suitability tend to reduce cropland
expansion