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.