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Impact of Sustainable Land and Watershed Management (SLWM) Practices in the Blue Nile
- 1. 1 Impact of Sustainable Land and Watershed Management (SLWM) Practices in the Blue Nile Emily Schmidt and Fanaye Tadesse IFPRI ESSP-II Sustainable Land and Watershed Management Interventions and Impact Workshop May 10, 2013 Hilton Hotel, Addis Ababa
- 2. Outline of presentation • Brief overview of soil and water conservation in the Blue Nile Basin, Ethiopia • Research questions • Sample and descriptive statistics • Methodology • Results • Implications
- 3. Agriculture in the Blue Nile Basin • Land degradation in some areas is estimated to decrease agricultural productivity by 0.5 to 1.1% per year. (Holden et al. 2009) • Moisture stress between rainfall events (dry spells) is responsible for most crop yield reductions (Adejuwon, 2005) • Many programs implemented past and present to improve soil and water conservation in the highlands of Ethiopia (SLMP, GIZ, World Bank, MERET, SUN, Soil Research Conservation Program)
- 4. Agriculture in the Blue Nile Basin (2) • 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). – Depends on agricultural potential: low-agricultural potential areas benefit more from minimum tillage (MT) and conventional farming, whereas MT has no significant effect on high-agricultural potential areas (Kassie et al., 2010).
- 5. Study focus: Blue Nile (Abbay) Basin • Evaluate SLWM investment impact on value of production per hectare • Understand time horizon of impact (how long does it take to experience a benefit?) • Assess benefit-cost of such investments
- 6. Preview of findings • Farmers that implement and maintain 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
- 7. 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
- 8. Study sites: 9 woredas (1800 households) in Amhara and Oromiya
- 9. Agricultural Production 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
- 10. Households Using SLM on Private Land Woreda Percent of woreda Year of first Community Program Most common activity on private land (percent) Alefa 50% 1990 soil bund (64.2) Fogera 54% 1983 stone terrace (65.8) Misrak Estie 54% 1977 stone terrace (36.1) Gozamin 21% 1988 soil bund (40.9) Dega Damot 82% 1986 soil bund (42.8) Mene Sibu 7% 1992 soil bund (89.8) Diga 32% 2000 irrigation canal (2.9) Jeldu 2% na stone terrace (24.0) Toko Kutaye 79% 1989 soil bund (33.7)
- 11. SLWM Investments Soil Bunds Wood check dam Stone terraces Stone check dam
- 12. 0 5 10 15 20 25 30 35 40 stone terrace soil bund check dam trees planted drainage ditch grass strips Perceived Most Successful SLWM activities (% of households)
- 15. Percent of total plots under SLWM on private land (1944-2009 ) 0 2 4 6 8 10 12 14 16 18 20
- 16. Evaluating the impact of SLWM infrastructure on value of production • Need to identify a suitable comparison group: What would plots be like had they not adopted SLWM on their land? • Use propensity score matching technique to compare similar groups: • Match agricultural plots based on observable characteristics, for example: Biophysical characteristics • Plot slope • Soil fertility • Plot size • Experienced past flood / erosion Household characteristics • Age, sex • Education • Official in the village
- 17. Nearest Neighbor Matching: Plot level • Plots that received SLWM investment at any time in the analysis period have 10.4% greater value of production in 2010 than plots without investment. •Plots that received SLWM investment before 2002 have 23.9% greater value of production in 2010 than plots without investment. • If this is the case, what is the marginal benefit of an extra year of SLWM? Outcome variable: Value of Agricultural Production # Observations ATT: Nearest neighbor matching 1992-2009 10108 0.104 *** (1985-2002 E.C.) (.026) 1992-2002 10108 0.239 *** (1985-1995 E.C.) (.036) 2003-2009 10108 0.0132 (1996-2002 E.C.) (.030)
- 18. -0.15 -0.10 -0.05 0.00 0.05 0.10 0.15 0.20 1 3 5 7 9 11 13 15 17 Marginaleffect Years SLWM maintained Number of years SLWM maintained 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 Increase in value of production given maintenance of SLWM
- 19. 3% Discount Rate 5% Discount Rate Initial investment (birr) 2000 0 2000 0 NPV of Benefits 10,621 10,621 11,478 11,478 Shadow wage rate factor 1 0.5 1 0.5 1 0.5 1 0.5 NPV of Costs 14,535 7,267 11,229 5,614 17,918 8,959 13,334 6,667 NPV Benefits / NPV Costs 0.73 1.46 0.95 1.89 0.64 1.28 0.86 1.72 Year Total Net Benefit > 0 NA 2006 NA 2004 NA 2007 NA 2005 First Year of MB > MC 2001 1999 2001 1999 2001 1999 2001 1999 Benefit-cost of investing in SLWM Infrastructure (1992–2009) • Wage rate of non-farm labor is very sensitive • Initial investment cost affects timing of benefit > cost
- 20. • 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. • The longer one sustains SWC, the higher the marginal benefit of sustaining an extra year of activity. • A mixture of strategies may reap quicker benefits – Physical SWC measures may need to be integrated with soil fertility management and moisture management Conclusions
- 21. • Biophysical benefits may plateau at a certain treatment level. – Expect to see diminishing returns of SWC as the necessary biophysical components are replaced. • Benefit-Cost scenarios suggest that benefits do not outweigh costs immediately – Rethink program planning timelines and initial investments – Provide a package of investments including soil and water conservation structures (i.e. fertilizer and improved seed) – Evaluate other market factors influencing farmer adoption (i.e. off-farm labor opportunities, land rental, etc.) Conclusions (2)
- 22. Thank you
- 23. Methodology Impact 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(A1 – A0│X,D = 1) = E(A1│X,D = 1) – E(A0│X,D = 1)
- 24. 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
Editor's Notes
- In the case of the early adopters (those that adopted SLM 1992-2002), those that have adopted SLM on their private land are more likely to have a 15 percent higher value of agricultural production than who did not. However for the late adopters (those adopting later than 2002, there is no statistically significant impact. No impact is observed in the livestock value for both the early and late adoptersThis implies that time of adoption actually matters and calls for a further investigation on how long it actually takes to see an impact from SLM adoption. We also want to see what the marginal benefit of an extra year of SLM adoption is using the continuous treatment effect function.
- In terms of the treatment effect function, which is the marginal effect of sustaining SLM for one more year, we see that for each additional year SLM is maintained on a plot the value of production is increasing and is statically significant after the 7th year. For instance, for a plot that has maintained SLM for 7 years the marginal benefit of maintaining it for an additional year is a 4% increase in value of production. Moreover, for each additional year SLM is maintained, the marginal effect is increasing with every additional year.
- The PSM analysis suggests that SLWM benefits are obtained well into the future, which makes them especially sensitive to the choice of discount rate used in the cost benefit analysis. Therefore, we assess benefits and costs under two scenarios, assuming SLWM investment occurred in 1992 (first year of the early adopter group). We use a three and a five percent discount rate in order to construct the net present value of increased agricultural production, and costs of initial investment and maintenance over time.We assume a constant, real recurrent labor cost for yearly maintenance of structures at 516 birr per year (reported days and wage rate from the household survey). Given the off-season nature of SLWM work, we present estimates of labor costs using both the market wage for construction and maintenance of structures and a shadow wage rate of 50 percent of the market wage. The analysis assumes that in the absence of adoption of SLWM technology, the value of production in previous years is equal to the 2009 level (in real terms). Finally, we use two scenarios of initial investment costs. At a five percent discount rate, the first scenario assumes a 2,000 birr initial investment. In this scenario, benefits outweigh costs beginning in 2007 (benefit: cost ratio of 1.28), assuming a shadow wage rate factor of 50 percent (Table 8). Under the same discount rate, with zero initial investment costs (households still incur annual labor costs for maintenance), benefits exceed costs in 2005 (benefit: cost ratio of 1.72). Under the three percent discount rate scenario, benefits outweigh costs in 2006 and 2004 for the two scenarios (2,000 birr and zero birr initial investment) respectively, and net benefits are 1.46 and 1.89 times that of costs. Thus, assuming an initial investment in 1992, the earliest that benefits would exceed costs is 12 years later, assuming the initial investment is fully subsidized and household expenses are equivalent to annual maintenance labor costs.These estimates are consistent with other cost-benefit studies found in the literature. Shiferaw and Holden (2001) analyzed experimental trials of bunds and terraces in west and east Amhara and found insufficient economic incentives for investment in such structures. Hengsdijk et al. (2005) underlined the tradeoffs of investments in Tigray region whereby bunds slightly increased crop productivity during drier periods when yields were low, but decreased productivity during moist seasons because overall cropped area was reduced for the construction of bunds.
- Estimate Average Treatment Effect on the Treated (ATT): identify a suitable comparison group which provides an unbiased estimate of the result that adopter households would have if they had chosen not to adopt. Each adopter household is matched to a non-adopter household with its closest propensity scoreTwo potential outcomes for each household (i): adoption Ai(1) or no adoption Ai(0)Impact of adopting SLWM: Δ = A1 – A0. (However, for A1 or A0 the counterfactual is unknown)D = 1 if the household adopts SLWM and 0 if the household is a non-adopterX is a vector of control variables