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Adoption and initial impacts of sustainable land and watershed management practices in the blue nile basin, ethiopia
 

Adoption and initial impacts of sustainable land and watershed management practices in the blue nile basin, ethiopia

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Ethiopian Development Reserach Institute (EDRI) and Interational Food Policy Research Institute (IFPRI), Borwn Bag Series, December 15, 2010 at ILRI Campus Addis Ababa

Ethiopian Development Reserach Institute (EDRI) and Interational Food Policy Research Institute (IFPRI), Borwn Bag Series, December 15, 2010 at ILRI Campus Addis Ababa

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  • [add mean hectares overall]
  • (show 100% column sums)
  • Why were the richest sites not using SLM? Initial analysis – income doesn’t correlate with SLM, so wanted to look at this. Mean total expenditure (column 4 varies by site). Think of sources of income: do farmers have larger farm size, higher value of agricultural production, or higher non-agricultural production. So, lets look at production – substantial variation in production: Fogera and Jeldu has very high value of production – Fogera – lot of teff and fairly large farms, and so mean value of production is very high, and the mean value is quite a bit higher than the expenditure value that we have. Possibly unusually good harvest, and invested. Diga also high incomes – it has very large farms – largest farms in sample and growing a lot of maize. Ag incomes are very high relative to expenditures. (bumper harvest?) Further cleaning on yield data which not showing here. Misrak Estie and Dega Damot – same agro-ecological zone, rather small farm sizes and rather large shares of non-farm income.
  • This is an important slide!
  • Percentage of all households? Can we get percentage of households who actually implemented these activities?
  • Explain that dummies have been included for sites (fixed effects)
  • What is average farm size; what percentage of sample has farm sizes greater than 2.6 hectares?
  • I thought the instrumenting equation could use other exogenous variables that are included in the main regression.
  • Add slide that gives mean values and number of observations of variables

Adoption and initial impacts of sustainable land and watershed management practices in the blue nile basin, ethiopia Adoption and initial impacts of sustainable land and watershed management practices in the blue nile basin, ethiopia Presentation Transcript

  • 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 Series December 15 th , 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 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 cropping patterns, production patterns, agricultural activity
  • Survey Sample   SLM (World Bank and GTZ Sites) IWMI Nile BDC Sites Total Sample Woreda Ongoing SLM Program GTZ Planned SLM Program No Past or Planned Program Upstream Downstream   Alefa 80 79 41 0 0 200 Fogera 0 0 1 160 44 205 Misrak Estie 80 80 39 0 0 199 Gozamin 80 80 40 0 0 200 Dega Damot 142 14 44 0 0 200 Mene Sibu 80 80 40 0 0 200 Diga 0 0 0 51 149 200 Jeldu 0 0 0 100 101 201 Toko 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 Hectares Share 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 Ongoing SLM activities   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%
  • Number of households reporting activities implemented in the village Ongoing SLM activities (2)
  • Households who received assistance by type of support Ongoing SLM activities (3) 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%
  • Households' perception of most important infrastructure built by public works or community organized programs Perception of SLM activities   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’ 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 Perception of SLM activities (5) Woreda Construction of bunds or terraces Building drainage Irrigation/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
  • 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.
  • Determinants of Household Participation in SLM activities on private land
    • Marginal Effects of Probit estimates
    (*) dy/dx is for discrete change of dummy variable from 0 to 1; *, **, and *** are significance level at 10%, 5% and 1% Probit regression Number of obs=13638 LR chi2(36)=12111.12 Prob > chi2=0.000 Log 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]
    • 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
  • Effect of participating in private SLM activities on per capita expenditure (income) of households (2)
    • 2SLS-IV regression
    Instrumental 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