Adoption and Initial Impacts of Sustainable Land and Watershed

Management Practices in the Blue Nile Basin, Ethiopia

Research Strategy and Initial Findings

Emily Schmidt (IFPRI)Fanaye Tadesse (IFPRI)Kibrom Tafere (EDRI)

IFPRI – ESSP2 Brown Bag SeriesDecember 15th, 2010

The Blue Nile (Abbay) Basin• Ethiopia has rich experience in SLM activities in diverse regions of the

country, yet the Abbay (Blue Nile) is one of the least planned and managed sub-basins of the Nile (IWMI, 2008).

• Although Ethiopia’s biophysical potential is significant, land degradation and poverty continue to challenge sustainable agricultural development opportunities (studies on this include: Desta, et al. (2001); Shiferaw and Holden (2001); Tefera, et al. (2002); Zeleke and Hurni (2002); Okumu et al. (2002); Sonneveld (2002)).

• This is further aggravated by high population pressure in rural highlands, climatic variability, limited use of sustainable land management practices, and a high dependence on rain-fed agriculture.

• The on-site effects of land degradation (eg. erosion and loss of top soil), measured in lost agricultural production is estimated to cost 2 to 6.75% of AGDP per annum (Mahmud, et al. 2005)

Study focus: Blue Nile (Abbay) Basin• Assess the determinants of adoption and the impacts of a variety of

SLM interventions – How do poverty levels relate to the capacity/willingness of communities

to invest in SLM activities, and how vulnerable is the village to the disruption of water delivery and access to water?

• Understand the degree to which SLM interventions enhance agricultural production, improve watershed quality and effectiveness and boost overall welfare of beneficiaries. – How do the impacts of SLM interventions vary across household types,

considering differences in resource and asset endowments, gender, and vulnerability?

• Evaluate actual versus perceived benefits at the village level

• Explore policy options for incentivizing local investment and up-scaling of sustainable land watershed management activities– How can SLM activities and program approaches be improved to ensure

greater cost effectiveness, scalability, and sustainability?

Research Links• Hydrological measurements of impacts of SLM investments

(IWMI/ILRI) - Nile Basin Development Challenge – Jeldu, Diga, Fogera

• Hydrological models of watersheds (IWMI/IFPRI, others)– SWAT: simulating rainfall runoff and soil moisture changes;– AquaCrop model: simulating crop and plant growth– WEAP (Water Evaluation And Planning): simulating water

resources and downstream implications.

• Other analyses (spatial and econometric, linking to PSNP Evaluation) – IFPRI and Ethiopian Development Research Institute (EDRI)

• Socio-economic survey – IFPRI and EDRI

Sample Selection• 9 woredas (1810 HHs) within the Blue Nile Basin• Stratification: Random sample within woredas that have a recently

started or planned SLM program (SLMP – GTZ and World Bank)– 3 sites (kebeles) per woreda (SLMP woredas)• Ongoing program (in the last 2 years)• Upcoming / Planned (in the next year)• No formal past program

• Nile Basin Development Challenge (BDC) woredas: 4 sites within each woreda / watershed– 2 upstream from instrumentation– 2 downstream from instrumentation

• Household sampling: random sampling from household rosters within each kebele and stratification

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 cropping patterns, production patterns, agricultural activity

Survey Sample

SLM (World Bank and GTZ Sites) IWMI Nile BDC SitesTotal

Sample

Woreda

Ongoing SLM

ProgramGTZ Planned

SLM Program

No Past or Planned Program Upstream Downstream

Alefa 80 79 41 0 0 200Fogera 0 0 1 160 44 205Misrak Estie 80 80 39 0 0 199Gozamin 80 80 40 0 0 200Dega Damot 142 14 44 0 0 200

Mene Sibu 80 80 40 0 0 200Diga 0 0 0 51 149 200Jeldu 0 0 0 100 101 201Toko Kutaye 83 80 42 0 0 205

Total 545 413 247 311 294 1,810

Watershed Survey Sample Sites

Cropping Patterns of Sample(5 main cereals and potatoes)

Crop Total HectaresShare of

area% farmers

growing crop

Mean hectares of farmers

growing crop

teff 719.9 25.8% 55.3% 0.719

barley 431.3 15.5% 45.9% 0.520

wheat 363.7 13.0% 42.5% 0.473

maize 818.9 29.3% 64.4% 0.703

sorghum 319.5 11.5% 27.9% 0.633

potatoes 136.8 4.9% 30.7% 0.246

Total 2,790.1 100.0% -- --

Cropping Patterns by Woreda (5 main cereals and potatoes)

Share of Cultivated Area

Alefa Fogera Misrak

Estie Gozamin Dega

Damot Mene

Sibu Diga Jeldu Toko

Kutaye Total

Teff 32% 47% 31% 42% 5% 24% 7% 18% 38% 26%

Barley 13% 11% 23% 12% 33% 0% 0% 35% 19% 15%

Wheat 0% 0% 29% 24% 28% 0% 2% 21% 20% 13%

Maize 36% 41% 2% 18% 14% 50% 64% 7% 13% 29%

Sorghum 19% 0% 1% 0% 0% 25% 26% 8% 9% 11%

Potato 1% 1% 14% 4% 21% 1% 1% 10% 0% 5%Total 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%Total Hectares 195.4 287.1 262.4 214.5 194.0 315.2 496.5 398.8 426.2 2,790.1

Production Patterns by Woreda Mean Value Mean Total Mean Total Household Farm Size

Production Expenditure Production/ Expenditure Size (hectares Major crops Woreda (Birr/HH/yr) (Birr/HH/yr) Expenditure (Dollars/HH/yr) (persons) / person) (area)

Alefa

9,474

11,616 0.816 830 6.05

0.99 Maize (36%),

Teff (32%)

Fogera

16,246

7,787 2.086 556 5.41

1.44 Teff (47%),

Maize (41%)Misrak Estie

5,391

11,316 0.476 808 5.54

1.32

Teff (31%), Wheat (29%)

Gozamin

9,120

8,348 1.092 596 5.40

1.08 Teff (42%),

Wheat (24%)

Dega Damot

4,960

8,425 0.589 602 5.64

1.00

Barley (33%), Wheat (28%),

Potatoes (21%)

Mene Sibu

7,503

7,574 0.991 541 6.44

1.58

Maize (50%), Sorghum (25%),

Teff (24%)

Diga

17,580

12,044 1.460 860 5.96

2.48 Maize (64%),

Sorghum (26%)

Jeldu

8,554

13,407 0.638 958 6.62

1.98 Barley (35%), Wheat (21%)

Toko Kutaye

8,142

15,831 0.514 1,131 6.60

2.10

Teff (38%), Wheat (20%),

Barley (19%)

Average

9,663

10,705

0.96 765 5.96

1.55 -

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

Number of households reporting activities implemented in the village

Ongoing SLM activities (2)

Households who received assistance by type of support Type of support Freq Percent.Advice on how to construct bunds or terraces for soil conservation 1,107 61%Advice on when to apply fertilizer 1,092 60%Advice on how to apply fertilizer 1,086 60%Advice on how to build drainage to reduce erosion 1,085 60%Assistance in obtaining fertilizer 1,031 57%Advice on the best time to plant crops 947 52%Assistance in obtaining improved seeds 920 51%Suggest new crops to grow 913 50%Advise on procurement of livestock vaccines 783 43%Advice or support of other veterinary services, including medicines 740 41%Advice on the construction of irrigation or water harvesting systems 705 39%Advice on how best to deal with insect infestations 689 38%

Ongoing SLM activities (3)

Most important 2nd Most important 3rd Most important

Freq Percent Freq Percent Freq Percent

school 410 29.67 90 9.94 32 5.87

stone terrace 275 19.9 89 9.83 50 9.17

soil bund 137 9.91 149 16.46 45 8.26

check dam 104 7.53 50 5.52 30 5.5

access road 94 6.8 73 8.07 73 13.39

health post 82 5.93 77 8.51 18 3.3

trees planted 80 5.79 105 11.6 137 25.14

gully rehabilitation 60 4.34 18 1.99 12 2.2

pipe water 31 2.24 18 1.99 12 2.2

Households' perception of most important infrastructure built by public works or community organized programs

Perception of SLM activities

Households’ Response on Most Important type of Infrastructure Built (Number of Households)

Perception of SLM activities (2)

Households' response on most Successful Sustainable Land Management activities (%)

Perception of SLM activities (3)

Perception of SLM activities (4)

Average number of years the information providers said the households would have to wait to experience a benefit from program

Woreda

Construction of bunds

or terracesBuilding

drainageIrrigation/water

harvesting system

Alefa 2.38 2.10 1.29

Fogera 2.12 2.33 1.38

Misrak Estie 1.59 1.35 1.23

Gozamin 1.70 1.38 1.17

Dega Damot 2.12 1.72 1.60

Mene Sibu 1.74 1.47 1.56

Diga 1.17 1.80 1.14

Jeldu 1.50 1.50 2.00

Toko Kutaye 3.98 3.80 1.33

Perception of SLM activities (5)

Initial Regression Results

• Two regression equations are estimated

– Probit model: to study probability of participation

– Two-Stage Least Squares (2SLS) Instrumental Variable regression: to study the effect of participation on income

Determinants of Household Participation in SLM activities on private land

Probit Model: to study probability of participation• The model is used to describe the probability

of participation in SLM practices.{ 1| , , } ( ' )i i i ji iP SLM hhc lc d F x

Determinants of Household Participation in SLM activities on private land

Marginal Effects of Probit estimates

Probit regression Number of obs=13638LR chi2(36)=12111.12

Prob > chi2=0.000Log likelihood = -3112.6564 Pseudo R2=0.6605

Variable dy/dx Std. Err.Hhsize 0.0093** [0.0039]Skill* -0.0818* [0.0421]Water source* 0.2242*** [0.0659]Land certification* -0.0636*** [0.0242]Plot size 0.3566*** [0.0479]Plot size sq. -0.0658*** [0.0127]Certification & plot size* -0.0844* [0.0481]Expert assistance* 0.8837*** [0.0053]Own telephone, radio* -0.0347** [0.0153]Drought* 0.2241*** [0.0184]Flood* 0.1787*** [0.0186](*) dy/dx is for discrete change of dummy variable from 0 to 1; *, **, and *** are significance level at 10%, 5% and 1%

• Larger households (large hhsize) appear to have higher participation (sufficient labor allocation?)

• Household heads with some skill appear to participate less (they have other sources of income (non-farm)?

• Households with some kind of water structure seem to have higher participation (perhaps they have the structures b/c they are already participating?)

• Participation initially increases with plot size but declines beyond certain size (2.6ha)

Determinants of Household Participation in SLM activities on private land (2)

• Expert assistance seems to have the largest effect on participation. – Households that receive expert assistance have 88% more

probability of participation. • Households who own telephone, radio or television have

lower probability of participation (b/c ownership indicates wealth?)

• Households that have experienced drought or flood/erosion have higher probability to participate.

• Land certification appears to have negative effect on participation (puzzle?)

Determinants of Household Participation in SLM activities on private land (3)

Effect of participating in private SLM activities on per capita expenditure (income) of households

• Two-Stage Least Squares (2SLS) Instrumental Variable regression: – Due to potential endogeneity of SLM

participation, we have instrumented for participation using expert assistance.

• The expenditure equation is specified as:

where

_ ( , , _ )i i i ipc ex f hhc pc SLM hat

_ ( _ , )i i jiSLM hat f ex assis d

Effect of participating in private SLM activities on per capita expenditure (income) of households (2)

2SLS-IV regressionInstrumental Variables (2SLS) regression Number of obs=13638

Wald chi2(36)=3580.04

Prob > chi2=0.000

R-Squared=0.2079

Log likelihood = -3112.6564 Root MSE=0.5721

Variable Coef. Std. Err.SLM_participation 0.0345* [0.0179]Age 0.0076*** [0.0024]Age_sq -0.0001** [0.0001]Sex 0.143*** [0.0369]HHsize -0.0732*** [0.0026]Grade Level Completed 0.0155*** [0.0019]Ln plot size 0.0349*** [0.0052]Own telephone, radio 0.2187*** [0.0107]

• SLM participation has positive effect on per capita expenditure (3.5%)

• Per capita expenditure increases with age but falls after certain age.

• Male headed households have higher per capita expenditure.• Schooling has positive effect on per capita expenditure.• Households with bigger plots have higher expenditure (b/c

they are richer ?)• Households who own telephone, radio of television have

higher per capita expenditure (wealth indicator?)

Effect of participating in private SLM activities on per capita expenditure (income) of households (3)

Next Steps.

• A MAJOR thank you to everyone who supported the Watersheds Survey and Team!!!

• Draft report of findings in January• Further collaboration with IWMI / ILRI on

watershed modeling and socio-economic impacts

• Linking this survey with PSNP evaluation aspects of infrastructure and OFSP / HABP activities

Questions, Comments, Brainstorming