Ephraim NKONYA "Can the poor afford sustainable land management (SLM)? Drivers of SLM in poor countries"
Drivers of change and resilience increase (SLM) Ephraim Nkonya International Food Policy Research Institute (IFPRI) Washington D.C.
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).•
Annual loss of arable land per capita, 1961-2009 80 70 60Sq meters/capita 50 40 30 20 10 0 SSA World Southern Asia LAC South East Asia
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
Agricultural orientation index (% ag expenditure/%agGDP)1.21.00.80.18.104.22.168 East Asia & Europe & LAC MENA SA SSA Pacific CA 1980-89 1990-99 2004-04 2005-07
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
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
Extent of agricultural land in Africa Key: Green=cropland Brown=pasture Source: Ramankutty et al 2011
Land tenure security in Africa Source: USAID & ARD inc 2008
Drivers of cropland changeDrivers 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 -0.10Time to town (minutes) -44.7Rural population 712Land suitability -3.5Government effectiveness 144Land tenure security (cf serious concern)Moderately severe concern land security -420 NB: all coefficients are significant at p=0.01
R&D impact on cropland expansion in SSA Cropland expansion 000ha 4.4 R&D expenditure (US$ million)
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