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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
Ongoing SLM activities
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%
Percent of total plots under SLWM on private land
                         (1944-2009 )
20
18
16
14
12
10
 8
 6
 4
 2
 0
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
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)
 ATT = E (∆│X,D = 1) = E(A1 – A0│X,D = 1) = E(A1│X,D = 1) – E(A0│X,D = 1)

   – Analyze for two time periods
           – 1992 – 2002 (1985 – 1994 EC)
           – 2003 – 2009 (1995 – 2002 EC)
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 Yi (t ) - plot level value of production per
  hectare given a certain treatment level

• Get the average dose – response function defined as
                    (t )  E[Yi (t )]

• And the treatment effect function (marginal effect)
                   (t )   (t  1)   (t )
Treatment Effect function


                Treatment level                        Level of
 0.2                                                  treatment   Marginal
                                                        (years)    effect
0.15                                                       7        0.02
 0.1                                                       8        0.04
                                                           9        0.05
0.05
                                                          10        0.06
   0                                                      11        0.08
                          Treatment range with            12        0.09
-0.05                     statistically significant
                          impact
                                                          13        0.10
 -0.1                                                     14        0.12
-0.15                                                     15        0.13
        1   3    5    7      9    11 13 15 17             16        0.15
                                                          17        0.16
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.

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Impact of Sustainable Land and Watershed Management (SLWM) Practices in the Blue Nile

  • 1. 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
  • 2. 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
  • 3. Ethiopia and the Blue Nile basin
  • 4.
  • 5. 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).
  • 6. 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).
  • 7. 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
  • 8. 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
  • 9. 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
  • 11. 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
  • 12. Ongoing SLM activities 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%
  • 13. Percent of total plots under SLWM on private land (1944-2009 ) 20 18 16 14 12 10 8 6 4 2 0
  • 14. 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
  • 15. 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) ATT = E (∆│X,D = 1) = E(A1 – A0│X,D = 1) = E(A1│X,D = 1) – E(A0│X,D = 1) – Analyze for two time periods – 1992 – 2002 (1985 – 1994 EC) – 2003 – 2009 (1995 – 2002 EC)
  • 16. 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
  • 17. 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?
  • 18. Continuous treatment effect • Follow the work of Hirano and Imbens (2004) • Potential outcome Yi (t ) - plot level value of production per hectare given a certain treatment level • Get the average dose – response function defined as  (t )  E[Yi (t )] • And the treatment effect function (marginal effect)  (t )   (t  1)   (t )
  • 19. Treatment Effect function Treatment level Level of 0.2 treatment Marginal (years) effect 0.15 7 0.02 0.1 8 0.04 9 0.05 0.05 10 0.06 0 11 0.08 Treatment range with 12 0.09 -0.05 statistically significant impact 13 0.10 -0.1 14 0.12 -0.15 15 0.13 1 3 5 7 9 11 13 15 17 16 0.15 17 0.16
  • 20. 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
  • 21. 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
  • 22. 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.
  • 23. Thank you for your attention.