Ethiopian Development Research Institute and International Food Policy Research Institute (IFPRI/EDRI), Tenth International Conference on Ethiopian Economy, July 19-21, 2012. EEA Conference Hall
Impact of sustainable land and watershed management (slwm) practices in the blue nile
1. ETHIOPIAN DEVELOPMENT
RESEARCH INSTITUTE
Impact of Sustainable Land and
Watershed Management (SLWM)
Practices in the Blue Nile
Emily Schmidt (IFPRI)
Fanaye Tadesse (IFPRI)
IFPRI ESSP-II
Ethiopian Economic Association
Conference
July 19-21, 2012
Addis Ababa
1
2. Outline of presentation
• Overview of Blue Nile basin, Ethiopia
• Brief literature review
• Research questions
• Methodology
• Results
• Next steps
2
3. 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).
3
4. Agriculture in the Blue Nile Basin (2)
• Land degradation 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 found costs
outweigh benefits (Shiferaw and Holden, 2001).
4
5. 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
5
6. Sample Selection
• 2 regions, 9 woredas (districts): Random sampling of 200
HHs per woreda
• Stratification: Random sample within woredas that have
a 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
6
8. 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
8
9. Ongoing SLM activities
Households Using SLM on Private Land
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
Alefa Fogera Misrak Gozamin Dega Mene Diga Jeldu Toko Total
Estie Damot Sibu Kutaye
9
10. Perception of SLM activities
Most Successful Sustainable Land Management activities (%)
40
35
30
25
20
15
10
5
0
stone soil bund check dam trees drainage grass strips
terrace planted ditch
10
11. Percent of total plots under SLWM on private land (1944-
2009 )
20
18
16
14
12
10
8
6
4
2
0
11
12. Methodology
Impact Analysis : matching based on observables
– Nearest Neighbor Matching: measure ATT of adopting specific
SLWM technologies on value of production and livestock
holdings
• 1/3 of private land within the last 15 years (24% of sample)
– 1992 – 2002 (1985 – 1994 EC)
– 2003 – 2009 (1985 – 1994 and 1995 – 2002 EC)
ATT = E (∆│X,D = 1) = E(A1 – A0│X,D = 1) = E(A1│X,D = 1) – E(A0│X,D = 1)
– Continuous Treatment Effect Estimation: estimate response to
a level of treatment; for this study, measured in years SLWM
activity is maintained (Hirano and Imbens, 2004)
12
13. 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
13
14. 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? 14
15. Continuous treatment effect
• Follow the work of Hirano and Imbens (2004)
• Plot level analysis
• Continuous treatment case where a treatment level t T and
lies between a minimum level of treatment (1 year) and a
maximum, on the interval
• Potential outcome Yi (t ) - plot level value of production per
hectare given a certain treatment level [t0 , t1 ]
• Get the average dose – response function defined as
(t ) E[Yi (t )]
• And the treatment effect function
(t ) (t 1) (t )
15
18. 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
• Wage rate of non-farm labor is very sensitive
• Initial investment cost determines profitability
18
19. 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 realized from constructing SLWM structures may accrue
over longer time horizons.
• A mixture of strategies may reap quicker benefits
– Although soil bund, stone terraces, and check dams were
identified as the three most important conservation measures,
they may not give desired results by themselves in the short run
– Physical SWC measures may need to be integrated with soil
fertility management and moisture management
19
20. Conclusions (2)
• The longer one sustains SWC, the higher the marginal
benefit of sustaining an extra year of activity.
– Well maintained SWC structures would begin to slow ongoing
degradation in the initial years of maintenance, but nutrient
build-up may take more time to show significant impact on value
of production.
• Although the marginal benefit increases with each
additional year that the structure is maintained, we
assume that these 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.
20
21. Conclusions (3)
• It is not clear that the benefits of investment in
SLWM at the private farm-plot level outweigh the
labor costs of maintenance - needs further
investigation.
23. Next Steps
Value of production given different investment scenarios
18,000
16,000 No investment
Value of Production
14,000 SLM investment
12,000 Fertilizer and imp.
Seeds investment
10,000 SLM and fert. and
seeds investment
8,000
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
Year 2009
24. Determinants of Household Participation
Variable dy/dx Std. Err.
HH head age (years) -0.013 ** (0.005)
HH head age sq. 0.000 * (0.000)
Land size in hectares 0.019 ** (0.009)
Land size sq. -0.001 (0.000)
Household experienced flood and erosion (yes=1) 0.081 ** (0.034)
Slope (omitted=flat slope)
Steep slope (percentage of plots with steep slope) 0.159 *** (0.055)
Mixed slope (percentage of plots with mixed slope) 0.056 (0.077)
Fertilizer use (yes=1) 0.061 ** (0.028)
Soil Quality (Omitted=fertile land)
Semi-fertile land (percentage of plots that are semi-fertile) 0.066 *** (0.041)
Non fertile land (percentage of plots that are not fertile) 0.149 * (0.050)
Agroecological Zone (Omitted=Dega)
Kolla -0.181 *** (0.026)
Woina Dega -0.176 *** (0.052)
Wurch 0.282 * (0.157)
Kilometer distance from city of at least 20,000 people -0.010 *** (0.003)
Number of observations=1256
Prob > chi2 =0
Pseudo R2 =0.2480
