Targeting Agricultural Water Management Interventions: the TAGMI Tool

Targeting Agricultural Water Management Interventions: the TAGMI Tool

• 1.
  New approach indecision support for technology outscaling in smallholder farming systems: The TAGMI ‘proof of concept’ Dr Jennie Barron ([email protected]) Stockholm Environment Institute (SEI) Challenge Programme Water and Food Volta Basin V1 project and Limpopo Basin L1 project
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  Session outline : - Briefing of TAGMI concept and product - Q&A - TAGMI testing
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  Targeting AGwater ManagementInterventions: PURPOSE : • provide a decision support tool for AWM outscaling PROCESS: • Merging different type of knowledge through Bayes network approach • Show strength of prediction (uncertainty) PRACTISE • 3 AWM technologies for Volta and Limpopo • User modifying input data and relations www.seimapping.org/tagmi
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  Pooling knowledge ina consultative research process • Reviews, literature search • Consultations (PGIS), MSc theses • Meetings, presentations, dialogue • Consultations National public, (private) , NGOs LBDC, VBDC , CPWF MERGED KNOWLEDGE In TAGMI model Existing academic knowledge Farmers , local community
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  STEP 1: Processof consultation : incorporate various sources of knowledge Consultation 2011 Consultation 2012 Synthesis Reg researc h 5% Nat researc Farmer Farmer 81% / Comm unity … CBO 1% Extensi on 5% Public Service s L2o%cal govt 6% NGO 1% Farmer 2% CBO 5% Public Services Local govt 9% 17% NGO 5% Nat govt 6% Reg mgmnt 2% Nat research 52% Intl research 2% CBO Pu3b%lic Service s 7% Local govt 19% NGO 12% Nat govt 11% h 34% Reg mgmnt 8% Intl researc h 1% CBO 2% Public Services 4% Local govt 27% Reg research 2% research 26% NGO 23% Nat Nat govt 12% Reg mgmnt 3% Intl research 1%
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  STEP 2: Decide:What is relevant technologies? What is ‘success’? AWM intervention Initial Consultation (2011) PGIS in depth (2011,2012) TAGMI representation (2013)_ Soil and water conservation /DRS/CES Planting pits (incl zai) Bunding /ridges/contour bunds/ploughing Tied ridges BF BF GH GH GH,BF GH,BF Cover crop Tree planting Mulching GH GH BF Shallow groundwater use Shallow wells Wastewater re-use GH GH. BF GH ,BF Motorised water pumps ()small scale irrigation) Treadle pumps Drip irrigation Punched bag Micro irrigation Supplemental irrigation (rice) GH, BF BF BF GH BF GH,BF GH, BF GH,BF Earth dams Underground (in stream) dams Small dams /reservoirs Ferro cement tanks Roof waterharvesting Large scale irrigation scheme GH. BF GH. BF GH,BF GH,BF GH,BF 3 AWM interventions chosen for TAGMI
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  STEP 3: Mergeinterdisciplinary factors with Bayes approach
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  STEP 3: Mergeinterdisciplinary factors with Bayes approach
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  STEP 3: Mergeinterdisciplinary factors with Bayes approach
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  STEP 4: Developweb based interface in open source and accessible data layers
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  http://www.seimapping.org/tagmi/index.php
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  Example: Data inputand impact
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  RESULTS: Current TAGMIpredictions Volta SWC Small scale irrigation Small reservoirs
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  RESULTS: Testing climatechange impact on potential Volta basin: Potential out-scaling under CC Current --20%
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  Indica tor of succe ss Indicator of success Can we calibrate/validate? CPWF L2: Requires functional institutional structures CPWF L2: Requires adequate ‘resources’ - Money, manpower, skills, equipment, etc. CPWF L3: Improving market access CPWF L3: Poor soil management/ fertility
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  Can we calibrate/ validate ? TAGMI predictions match actual adoption rates for about 75% of the provinces
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  LESSONS FOR RESEARCH • There is opportunity for out-scaling of SWC , smallholder irrigation and small reservoirs but prediction strength is low • Data on social-human layers are critical, but rarely available • High agreement between factors affecting out-scaling across technologies, countries and basins • The importance and benefit of investments in “Best Practice In Implementation” (‘Due diligence’ ) to achieve successful outscaling
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  TAGMI taken topractise: ‘doing research for development’ • CPWF in Volta and Limpopo developed ‘proof of concept’ • Generic approach: easily done for other technologies and scales • Spin-off in new Bayes model for shallow groundwater irrigation N Ghana; AWM opportunities in Niger; livestock –fodder system improvements Volta-Niger • What does it take to embed into decision support process?
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  www.seimapping.org/TAGMI Stockholm EnvironmentInstitute in partnership with WATERNET, University of Witwatersrand, International Water Management Institute (IWMI), University of Ouagadougou, Institut National de l’Environnement et de Récherche Agricole (INERA), Kwame Nkrumah University of Science and Technology (KNUST), Savanna Agricultural Research Institute (CSIR-SARI), We thank all contributors: absent colleagues farmers, boundary partners and participants in consultations and events VBDC and V1 colleagues, and LBDC and L1 colleagues funders
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  Thank you!