Assessment of farmers’ adoption of rainwater management technology in the Blue Nile Basin
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Assessment of farmers’ adoption of rainwater management technology in the Blue Nile Basin

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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

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

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Assessment of farmers’ adoption of rainwater management technology in the Blue Nile Basin Presentation Transcript

  • 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) 3  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. 4
  • 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) 5
  • 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). 6
  • 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 7
  • 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 8
  • 9. Study area, Data and Methodology (1) Sample Watersheds 9
  • 10. Data and Methodology (2) 10 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 11
  • 12. Results (1) Test of correlation 12 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 13 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) 14
  • 15. Results (4) 15
  • 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; 17
  • 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. 18