5/9/2014 
1 
Decision support for technology uptake in 
smallholder farming systems: 
The example of TAGMI 
Dr Jennie Barr...
5/9/2014 
2 
Agricultural development discourse , e.g. Volta 
Douxchamps et al 2014 
Technologies promoted 
Focus - Concep...
5/9/2014 
3 
TODAY : Good (research) knowledge and evidence in 
technical fixes? 
Example Ag. water synthesis in Limpopo (...
5/9/2014 
4 
www.seimapping.org/tagmi 
Targeting AGwater Management Interventions: 
PURPOSE : 
• provide a decision suppor...
5/9/2014 
5 
STEP 1: Process of consultation : incorporate various sources 
of knowledge 
Consultation Consultation 2011 2...
5/9/2014 
6 
STEP 3: Merge interdisciplinary factors with Bayes approach 
STEP 3: Merge interdisciplinary factors with Bay...
5/9/2014 
7 
STEP 3: Merge interdisciplinary factors with Bayes approach 
STEP 4: Develop web based interface in open sour...
5/9/2014 
8 
http://www.seimapping.org/tagmi/index.php 
Example: Data input and impact
5/9/2014 
9 
RESULTS: Current TAGMI predictions Volta 
SWC 
Small scale 
irrigation 
Small 
reservoirs 
1.RESULTS: current...
5/9/2014 
10 
RESULTS: Testing climate change impact on potential 
Volta basin: Potential out-scaling small reservoirs und...
5/9/2014 
11 
Can we calibrate / validate ? 
TAGMI predictions 
match actual adoption 
rates for about half of 
the provin...
5/9/2014 
12 
LESSON S FOR RESEARCH 
• There is opportunity for out-scaling of SWC , smallholder 
irrigation and small res...
5/9/2014 
13 
www.seimapping.org/TAGMI 
We thank all contributors: 
absent colleagues 
farmers, boundary partners and part...
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Decision support for technology uptake in smallholder farming systems: The example of TAGMI

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Presented by Jennie Barron (University of York, UK) at the Livestock Systems and Environment (LSE) Seminar, ILRI, Nairobi, 8 May 2014

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Decision support for technology uptake in smallholder farming systems: The example of TAGMI

  1. 1. 5/9/2014 1 Decision support for technology uptake in smallholder farming systems: The example of TAGMI Dr Jennie Barron (jennie.barron@sei-international.org) Stockholm Environment Institute (SEI) University of York, UK LSE seminar ILRI , Nairobi 8th May 2014 ‘Business of research’ changing ? 1. Knowledge exist Multiple knowledge systems 2. Real solutions in real time Impact , relevance 3. Engage outside comfort zone ‘Science objectivity’
  2. 2. 5/9/2014 2 Agricultural development discourse , e.g. Volta Douxchamps et al 2014 Technologies promoted Focus - Concept Main actors 1960 1960 1980 1960 2000 TODAY: Agriculture back in national to global policies • Agriculture is now key on more complex policy agenda o Sustainable o Climate smart o Energy • Policies lag behind practice o No clear vision of the future of agriculture (Limpopo basin) • Agriculture contributes to o Meeting the broader policy goals o But, roles of smallholder farmers not well articulated (Limpopo basin)
  3. 3. 5/9/2014 3 TODAY : Good (research) knowledge and evidence in technical fixes? Example Ag. water synthesis in Limpopo (n=1400 references) -100 0 100 200 300 400 500 Reduced tillage In-situ water retention Evaporation suppressants Nutrient only Water harvesting with storage Cropping system and Agroforestry Combination of two or more interventions Yield change (%) Improved AWM technology n= 85 n= 190 n= 130 n= 247 n= 58 n= 195 n= 428 Magombeyi et al (forthcoming) : Agricultural water management systematic review and yield benefits for Limpopo Yield response to ag. water technologies TODAY : Good (research) knowledge and evidence in technical fixes? Example Ag. water synthesis in Limpopo (n=1400 references) -100 0 100 200 300 400 500 Reduced tillage In-situ water retention Evaporation suppressants Nutrient only Water harvesting with storage Cropping system and Agroforestry Combination of two or more interventions Yield change (%) Improved AWM technology n= 85 n= 190 n= 130 n= 247 n= 58 n= 195 n= 428 Magombeyi et al (forthcoming) : Agricultural water management systematic review and yield benefits for Limpopo Research opportunities Yield increase potential
  4. 4. 5/9/2014 4 www.seimapping.org/tagmi 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 • Reviews, literature search • Consultations (PGIS), MSc theses • Meetings, presentations, dialogue • Consultations National public, (private) , NGOs LBDC, VBDC , CPWF Existing academic knowledge Farmers , local community MERGED KNOWLEDGE In TAGMI model Pooling knowledge in a consultative research process
  5. 5. 5/9/2014 5 STEP 1: Process of consultation : incorporate various sources of knowledge Consultation Consultation 2011 2012 Synthesis Farmer 81% Farmer / Comm unity … CBO 1% Extensi on 5% Public Service s L2o%ca l govt 6% NGO 1% Farmer 2% CBO 5% Public Services Local govt 9% 17% NGO 5% Nat govt 6% Nat research 52% Reg mgmnt 2% Intl research 2% CBO Pu3b%lic Service s 7% Local govt 19% NGO 12% Nat govt 11% Nat researc h 34% Reg researc h 5% Reg mgmnt 8% Intl researc h 1% CBO 2% Public Services 4% Local govt 27% NGO 23% Nat govt 12% Nat research 26% Reg research 2% 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
  6. 6. 5/9/2014 6 STEP 3: Merge interdisciplinary factors with Bayes approach STEP 3: Merge interdisciplinary factors with Bayes approach
  7. 7. 5/9/2014 7 STEP 3: Merge interdisciplinary factors with Bayes approach STEP 4: Develop web based interface in open source and accessible data layers
  8. 8. 5/9/2014 8 http://www.seimapping.org/tagmi/index.php Example: Data input and impact
  9. 9. 5/9/2014 9 RESULTS: Current TAGMI predictions Volta SWC Small scale irrigation Small reservoirs 1.RESULTS: current TAGMI predictions # districts High/Med/Low Cropland Total BF: 2846941ha Total GH: 5102661 ha High/Med/Low Strength prediction Small reservoirs Burkina Faso 50%/32/18 47/20/32 Low Ghana 62%/15/23 58/36/7 Low RESULTS: Current TAGMI predictions Volta Example: Small reservoirs out-scaling potentials
  10. 10. 5/9/2014 10 RESULTS: Testing climate change impact on potential Volta basin: Potential out-scaling small reservoirs under CC Current rainfall Burkina Faso 2 846 941 44/33/24 45/41/15 43/32/25 45/38/17 41/34/26 45/36/19 Ghana 5 102 661 56/23/21 50/26/24 53/24/23 41/19/40 51/23/26 35/20/45 Present-day Driest scenario Wettest scenario Volta Total cropland (ha)* # districts (%) High/Med/low Cropland (%) # districts (%) High/Med/low Cropland (%) # districts (%) High/Med/low Cropland (%) Current rainfall -20% Current rainfall +50% 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: Poor soil management/ fertility CPWF L3: Improving market access
  11. 11. 5/9/2014 11 Can we calibrate / validate ? TAGMI predictions match actual adoption rates for about half of the provinces Weighting the factors differently : Does it matter on the results? Sensitivity : Does the world view matter? DfID livelihood framework Social-ecological system Ostrom (2009)
  12. 12. 5/9/2014 12 LESSON S 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 • Requests from funders and development agents for possible development
  13. 13. 5/9/2014 13 www.seimapping.org/TAGMI 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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