Presented by G. Gebregziabher, L-M. Rebelo, A. Notenbaert, Y. Abebe, K. Ergano and G. Leta at the Nile Basin Development Challenge (NBDC) Science Workshop, Addis Ababa, Ethiopia, 9–10 July 2013

1. Assessment of Farmers’ Adoption of
Rainwater Management Technology in the
Blue Nile Basin
G. Gebregziabher1
; L-M. Rebelo1
; A. Notenbaert2
; Y. Abebe1
; K.
Ergano2
; G. Leta2
1
International Water Management Institute (IWMI);
2
International Livestock Research Institute (ILRI)
Nile Basin Development Challenge (NBDC) Science Workshop
Addis Ababa, Ethiopia, 9–10 July 2013

2. Outline
 Introduction
 Objectives
 Data and Methodology
 Results and Discussions
 Conclusions
2

3. Introduction (1)
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 Like many Sub-Saharan Africa countries, agriculture is the main sector of
the Ethiopian economy.
Fig.1 Contribution of Agriculture

4. Introduction (2) What do we know so far?
 Agricultural production/productivity across much of Sub-Saharan Africa
is constrained by increasing water scarcity and land degradation(FAO,
2005).
 In contrast, in the Ethiopian highlands, agricultural productivity is
constrained by high climate variability rather than low water availability.
 Rainfall distribution is extremely uneven both spatially and temporally.
 Drought frequently results in crop failure,
 High rainfall intensities result in low infiltration and high runoff causing soil
erosion/land degradation.
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5. Introduction (3)
 Access to rainwater management technologies in the Blue Nile Basin
 Can decrease poverty levels by approximately 22% (Awulachew et al. 2012)
 Provide a buffer against production risk (Kato et al. 2009).
 Potential of RWM is substantial (Pender and Gebremedheni, 2007, Kassie et
al. 2008, Awulachew et al. 2010)
 But adoption and success rates remain low (Santini et al. 2011)
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6. Introduction (4)
• Adoption and scaling-up of RWM technologies are influenced by a variety
of factors including biophysical characteristics (Deressa et al. 2009).
• Even when technologies are appropriate to a biophysical setting, they are
not always adopted (Guerin, 1999; Amsalu and Graaff, 2007 ).
• Externally driven solutions are rarely sustained by farmers (McDonald and
Brown, 2000; Merrey and Gebreselassie, 2011).
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7. What knowledge gaps do we have?
 Relatively little empirical work has been done at watershed level, as a
package or combination of technologies
 This means, most past research works have focused on individual
technologies at large (country or regional) scale, while farmers practically
adopt multiple technologies suitable to specific landscapes
 Little research has been done on the economics of RWM technology
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8. Objective
 To understand factors that influence the adoption or dis-adoption and
scaling-up of a particular RWM technologies.
 To understand why farmers do not adopt some technologies despite their
suitability and potential benefits.
 To understand and suggest how “best-bet” interventions overcome the
limited success and impact of practices that are often adopted using
‘blanket’ approaches
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9. Study area, Data and Methodology (1)
Sample Watersheds
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10. Data and Methodology (2)
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Watersheds and sample households
Region Woreda/District Watershed Site Number of
sample households
Data collected
by:
Oromia Jeldu Meja NBDC 120 ERHA
Guder Boke New 90 ORARI
Shambu Laku New 90 ORARI
Diga Diga NBDC 90 ORARI
Amhara Farta Zefe New 90 ARARI
Fogera Mizuwa NBDC 101 ARARI
Gondar Zuria Gumera/Maksinit New 90 ARARI
Total 671

11. Data and Methodology (3)
Methodology
•The methodological framework is based on the premises that farmers are more
likely to adopt a mix of rainwater management technologies simultaneously
and/or sequentially as complements or substitutes to each other.
•Farmers faced with alternative, but correlated technologies implying
interdependence between the technologies.
•We estimate multivariate probit (MVP) model
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12. Results (1)
Test of correlation
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Rainwater management
Technology
Multipurpose
trees
Orchard Bund/Terraces
Orchard 0.448***(0.080)
Bund/Terraces 0.154*(0.084) 0.232***(0.085)
Gully rehabilitation 0.127*(0.069) - 0.006(0.076) 0.156**(0.071)

13. Results (2)
Number of households who adopt rainwater management technology by watershed
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RWH Technology Watersheds Total
Meja Zefi Meksinit Boke Diga Laku Mizwa
Multipurpose Trees 37 18 16 52 52 45 15 235
Orchards 9 25 6 2 32 12 10 96
Bunds/Terraces(without
Vegetation)
28 77 77 65 75 70 85 477
Bunds/Terraces(with
Vegetation)
19 26 21 6 5 4 9 90
Shallow/hand-dug well 16 9 1 1 0 4 1 32
Pond 10 3 2 1 0 2 5 23
River diversion 24 12 11 16 7 12 28 110
Enclosure 17 41 42 5 0 1 15 121
Gully rehabilitation 26 72 71 30 14 27 57 297
Total 186 283 247 178 185 177 225 1481

14. Results (3)
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15. Results (4)
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16. 16
Independent Variables Technologies (Dependent Variables)
Multipurpose trees Orchard Bund/Terraces
Gully rehabilitation
Coefficient Coefficient Coefficient Coefficient
Household characteristics and asset holding
Household head age (years)
-0.011*** -0.018*** -0.013*** -0.0122***
Household head’s gender (1=male) 0.760*** 0.379 0.159 0.056
Household head’s marital status (1=married) -0.555*** -0.242 0.202 -0.0725
Family size in adult equivalent 0.147*** 0.213*** 0.024 0.040
At least one household member participate in off-farm activity (1=yes) 0.172 -0.232 -0.070 -0.281**
At least one household member migrates (1=yes) -0.267 -0.541** -0.436** 0.015
Livestock holding in TLU 0.015 0.031 0.050*** 0.003
Land holding per adult equivalent 0.363** 0.559*** -0.395** -0.145
Household own of land (1=yes, 0=no) 0.179 0.120** 0.516 -0.313
Access to market and services
One way walking distance to all weather road (minutes) 0.002 -0.006 -0.001 -0.004*
One way walking distance to wereda center (minutes) 0.001 -0.006*** 0.003 -0.012
Indicators of social capital
Household participate in Debo (1=yes) -0.112 0.411 0.521*** 0.371*
Household participate in Edir (1=yes) 0.253 0.987*** 0.491** 0.057
Household participate in women association (1=yes) 0.531*** 0.408** -0.030 0.038
District (Wereda) dummies
Woreda is Guder (1=yes, 0=no) 0.243 -2.003*** -0.928*** -0.608***
Woreda is Horro (Shambu) (1=yes, 0=no) 0.282* -0.404* -0.892*** -0.369**
Woreda is Diga (1=yes, 0=no) 0.411*** 0.505*** -0.624*** -1.044***
Woreda is Farta (1=yes, 0=no) -0.211 0.808*** -0.133 0.795***
Woreda is Gonder zuria (1=yes, 0=no) -0.454*** -0.177 -0.037 1.053***
Woreda is Fogera (1=yes, 0=no) -0.690*** -0.093 -0.327** 0.063
Omitted (control) werda is Jeldu - - - -

17. Conclusion (1)
 Rainwater management interventions should focus not only on the
engineering and biophysical performance of conservation measures, but also
on the socio-economic and livelihood benefits;
 Adoption of rainwater management technologies are interdependent hence,
any intervention need to consider such interdependence;
 Targeting women groups to address their constraints can have positive impact
on the adoption and scaling-up leading to improved livelihoods;
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18. Conclusion (2)
 Important to understand the biophysical suitability of technologies instead of
promoting blanket recommendations
 Externally driven solutions are rarely sustained by farmers unless consideration
is given to socio-economic, cultural and institutional, as well as biophysical and
technical factors.
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