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Impact of Sustainable Land and Watershed Management (SLWM)
Practices in the Blue Nile
Emily Schmidt (IFPRI)
Fanaye Tadesse (IFPRI)
Addis Ababa University: May 18, 2012
Outline of presentation
• Overview of soil and water conservation in the Blue Nile Basin, Ethiopia
• Research questions
• Sample and descriptive statistics
• Methodology
• Results
• Next steps
Ethiopia and the Blue Nile basin
Agriculture in the Blue Nile Basin
• Land degradation in Ethiopia continues to challenge sustainable agricultural development opportunities
• Rainfall is poorly distributed in both spatial and temporal terms. – Moisture stress between rainfall events (dry spells) is
responsible for most crop yield reductions
(Adejuwon, 2005).
– Soil erosion rates are highest when vegetation cover ranges from 0 to 30% (before the rainy season starts).
Agriculture in the Blue Nile Basin (2)
• Land degradation in some arease is estimated to decrease productivity by 0.5 to 1.1% (annual mean).
(Holden et al. 2009)
• Analysis of soil and water conservation on land productivity in Ethiopia suggest mixed results
– Plots with stone terraces experience higher crop yields (Pender and Gebremedhin, 2006)
– Experimental trials of bunds and terraces suggest costs outweigh benefits (Shiferaw and Holden, 2001).
Study focus: Blue Nile (Abbay) Basin
• Evaluate SLWM adoption impact on value of production per hectare
• Understand time horizon of impact (how long does it take to experience a benefit)
• Assess cost-benefit of such investments
Preview of findings • Farmers that implement and sustain SLWM
experience higher value of production in the medium term
• Significant benefits are not experienced until after 7 years of maintenance
• The longer one sustains SLWM, the higher the marginal effect of sustaining an extra year of activity.
• It is not clear that the benefits of investment in SLWM at the private farm-plot level outweigh the labor costs of maintenance
Sample Selection
• 2 regions, 9 woredas (districts): Random sampling of 200 HHs per woreda
• Stratification: Random sample within woredas that have recently started or planned SLM program
– 3 sites (kebeles) per woreda (SLMP woredas)
• Past or Ongoing program
• Planned program (for 2011)
• No formal past program
Watershed Survey Sample Sites
Broad Overview of Survey Sample 9 woredas: 5 Amhara, 4 Oromiya
– Teff as leading crop (4 woredas in Amhara)
• Fogera • Gozamin • Toko Kutaye • Misrak Este
– Maize • Mene Sibu (Oromiya) • Diga (Oromiya) • Alefa (Amhara)
– Wheat / other • Dega Damot (Amhara) • Jeldu (Oromiya)
• Substantial diversity across woredas in terms of production patterns, landholding, agricultural activity
Households Using SLM on Private Land
Yes No Total
Alefa 50% 50% 100%
Fogera 54% 46% 100%
Misrak Estie 54% 46% 100%
Gozamin 21% 79% 100%
Dega Damot 82% 18% 100%
Mene Sibu 7% 93% 100%
Diga 32% 68% 100%
Jeldu 2% 98% 100%
Toko Kutaye 79% 21% 100%
Total 40% 60% 100%
Ongoing SLM activities
Percent of total plots under SLWM on private land (1944-2009 )
0
2
4
6
8
10
12
14
16
18
20
0
5
10
15
20
25
30
35
40
stoneterrace
soil bund check dam treesplanted
drainageditch
grass strips
Most Successful Sustainable Land Management activities (%)
Perception of SLM activities
Methodology
Impact Analysis : matching based on observables
– Nearest Neighbor Matching: measure ATT of adopting SLM on value of production and livestock holdings
– Adopters: 1/3 of private land within the last 15 years (24% of sample)
– Analyze for two time periods – 1992 – 2002 (1985 – 1994 EC)
– 2003 – 2009 (1995 – 2002 EC)
ATT = E (∆│X,D = 1) = E(A1 – A0│X,D = 1) = E(A1│X,D = 1) – E(A0│X,D = 1)
Covariates for Nearest neighbor matching and continuous effects estimation
• Land Characteristics • Land size • Experienced past flood or erosion • Experienced past drought • Slope (flat, steep, mixed) • Fertilizer use (proxy for willingness to invest – unobservables) • Soil quality (fertile, semi, non) • Agro-ecological zone • Rainfall (30 year average) • Rainfall variation
• Household Characteristics • Obtained credit • Received agricultural extension assistance • Person-months on non-farm activity • Distance from a city
• Other HH characteristics (age, sex, education, etc.) • Other village characteristics
Nearest Neighbor Matching – split sample Outcome Variable ATT Observations
1992-2002 (1985 – 1995 E.C.)
Value of Agricultural Production 0.152 ** 1373 (0.071)
Livestock value (in Birr) -0.429 1318
(.100)
2003-2009 (1996 – 2002 E.C.)
Value of Agricultural Production -0.015 1397
(0.062)
Livestock Value (in Birr) -0.158 1327
(0.095)
• Households that adopted SLWM on their private land in the first 10 years of analysis have 15.2% (2,329 birr avg.) greater value of production in 2010 than non-adopters.
• If this is the case, what is the dose effect of SLWM, in other words, what is the marginal benefit of an extra year of SLWM?
Continuous treatment effect
• Follow the work of Hirano and Imbens (2004)
• Potential outcome - plot level value of production per hectare given a certain treatment level
• Get the average dose – response function defined as
• And the treatment effect function (marginal effect)
( )iY t
( ) [ ( )]it E Y t
( ) ( 1) ( )t t t
Treatment Effect function
-0.15
-0.1
-0.05
0
0.05
0.1
0.15
0.2
1 3 5 7 9 11 13 15 17
Treatment level Level of treatment
(years) Marginal
effect 7 0.02 8 0.04 9 0.05
10 0.06 11 0.08 12 0.09 13 0.10 14 0.12 15 0.13 16 0.15 17 0.16
Treatment range with statistically significant impact
Next steps: Benefit-cost of private investment
Initial investment cost 5000 5000 2000 2000 0 0 Shadow wage rate factor 1 0.5 1 0.5 1 0.5
Discount Rate: .05
NPV of Benefits 11,478 11,478 11,478 11,478 11,478 11,478
NPV of Costs 24,794 12,397 17,918
8,959 13,334
6,667 NPV Benefits / NPV Costs
0.46
0.93
0.64
1.28
0.86
1.72
First Year of NB > 0 NA NA NA 2008 NA 2006
First Year of MB > MC 2002 2000 2002 2000 2002 2000
• Wage rate of non-farm labor is very sensitive • Initial investment cost determines profitability
Conclusions
• Households that construct and sustain SLWM for at least 7 years experience higher value of production in the medium term – Unlike technologies such as fertilizer or improved seeds,
benefits may accrue over longer time horizons.
• A mixture of strategies may reap quicker benefits – Physical SWC measures may need to be integrated with soil
fertility management and moisture management
Conclusions (2)
• The longer one sustains SWC, the higher the marginal benefit of sustaining an extra year of activity. – Initially SWC structures slow ongoing degradation
– Nutrient build-up may take more time to show significant impact on value of production.
• Benefits may plateau at a certain treatment level. – As nutrient repletion and erosion control is successful, we
would expect to see diminishing returns as the necessary biophysical components are replaced.
Thank you for your attention.