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New approach in decision support for technology 
outscaling in smallholder farming systems: 
The TAGMI ‘proof of concept’ 
Dr Jennie Barron 
(jennie.barron@sei-international.org) 
Stockholm Environment Institute (SEI) 
Challenge Programme Water and Food 
Volta Basin V1 project and Limpopo Basin L1 project
Session outline : 
- Briefing of TAGMI concept and product 
- Q&A 
- TAGMI testing
Targeting AGwater Management Interventions: 
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
Pooling knowledge in a 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
STEP 1: Process of 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%
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
STEP 3: Merge interdisciplinary factors with Bayes approach
STEP 3: Merge interdisciplinary factors with Bayes approach
STEP 3: Merge interdisciplinary factors with Bayes approach
STEP 4: Develop web based interface in open source 
and accessible data layers
http://www.seimapping.org/tagmi/index.php
Example: Data input and impact
RESULTS: Current TAGMI predictions Volta 
SWC 
Small scale 
irrigation 
Small 
reservoirs
RESULTS: Testing climate change impact on potential 
Volta basin: Potential out-scaling under CC 
Current 
--20%
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
Can we calibrate / validate ? 
TAGMI predictions 
match actual adoption 
rates for about 75% of 
the provinces
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
TAGMI taken to practise: ‘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?
www.seimapping.org/TAGMI 
Stockholm Environment Institute 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
Thank you!

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Targeting Agricultural Water Management Interventions: the TAGMI Tool

  • 1. New approach in decision support for technology outscaling in smallholder farming systems: The TAGMI ‘proof of concept’ Dr Jennie Barron (jennie.barron@sei-international.org) Stockholm Environment Institute (SEI) Challenge Programme Water and Food Volta Basin V1 project and Limpopo Basin L1 project
  • 2. Session outline : - Briefing of TAGMI concept and product - Q&A - TAGMI testing
  • 3. Targeting AGwater Management Interventions: 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
  • 4. Pooling knowledge in a 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
  • 5. STEP 1: Process of 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%
  • 6. 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
  • 7. STEP 3: Merge interdisciplinary factors with Bayes approach
  • 8. STEP 3: Merge interdisciplinary factors with Bayes approach
  • 9. STEP 3: Merge interdisciplinary factors with Bayes approach
  • 10. STEP 4: Develop web based interface in open source and accessible data layers
  • 12. Example: Data input and impact
  • 13. RESULTS: Current TAGMI predictions Volta SWC Small scale irrigation Small reservoirs
  • 14. RESULTS: Testing climate change impact on potential Volta basin: Potential out-scaling under CC Current --20%
  • 15. 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
  • 16. Can we calibrate / validate ? TAGMI predictions match actual adoption rates for about 75% of the provinces
  • 17. 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
  • 18. TAGMI taken to practise: ‘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?
  • 19. www.seimapping.org/TAGMI Stockholm Environment Institute 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

Editor's Notes

  1. Match: 15 No match: 17 adoption Pred High Med Low High 4 3 9 Med 3 2 2 Low 2 5 4
  2. (evidence: consultations) evidence: consultations, PGIS synthesis) Low strength in prediction (evidence: TAGMI outputs)