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 ConferenceJuly 19-21, 2012Addis Ababa

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Outline of presentation

• Overview of Blue Nile basin, Ethiopia• Brief literature review• Research questions• Methodology• Results• Next steps

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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).

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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).

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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

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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

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Watershed Survey Sample Sites

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Broad Overview of Survey Sample9 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

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Households Using SLM on Private LandOngoing SLM activities

Alefa

Foge

ra

Misrak

Estie

Gozamin

Dega D

amot

Mene S

ibu Diga

Jel

du

Toko

Kutaye

Total

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

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stone terrace

soil bund check dam trees planted

drainage ditch

grass strips0

5

10

15

20

25

30

35

40

Most Successful Sustainable Land Management activities (%)

Perception of SLM activities

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Percent of total plots under SLWM on private land (1944-2009 )

1944

1963

1973

1975

1977

1979

1981

1983

1986

1988

1990

1992

1994

1996

1998

2000

2002

2004

2006

2008

0

2

4

6

8

10

12

14

16

18

20

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MethodologyImpact 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)

– 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)

ATT = E (∆ X,D = 1) = E(A│ 1 – A0 X,D = 1) = E(A│ 1 X,D = 1) – E(A│ 0 X,D = 1)│

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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

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Nearest Neighbor Matching – split sampleOutcome Variable ATT Observations1992-2002 (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?

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Continuous treatment effect• Follow the work of Hirano and Imbens (2004)• Plot level analysis • Continuous treatment case where a treatment level and

lies between a minimum level of treatment (1 year) and a maximum, on the interval

• 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

t T

( )iY t

0 1[ , ]t t

( ) [ ( )]it E Y t

( ) ( 1) ( )t t t

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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 177.6

7.8

8

8.2

8.4

8.6

8.8Estimated Dose Response Function

Treatment Level (years)

E[ln

valu

epro

d(t)

]

Treatment range with sta-tistically significant im-pact

Dose Response Function

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Treatment Effect function

Level of treatment

(years)Marginal

effect7 0.028 0.049 0.05

10 0.0611 0.0812 0.0913 0.1014 0.1215 0.1316 0.1517 0.16

1 3 5 7 9 11 13 15 17-0.15

-0.1

-0.05

0

0.05

0.1

0.15

0.2Treatment level

Treatment range with statistically significant impact

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Next steps: Benefit-cost of private investmentInitial 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

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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

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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.

• 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.

Conclusions (3)

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Thank you

Next Steps

19992000

20012002

20032004

20052006

20072008

20098,000

10,000

12,000

14,000

16,000

18,000

Value of production given different investment scenarios

No investment

SLM investment

Fertilizer and imp. Seeds investment

SLM and fert. and seeds investment

Year

Valu

e of

Pro

ducti

on

Determinants of Household ParticipationVariable 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