This document discusses the Targeting Agricultural water Management Interventions (TAGMI) decision support tool. TAGMI merges different types of knowledge using a Bayesian network approach to provide predictions on suitable areas for adopting various agricultural water management technologies. It describes the consultation process used to gather different sources of knowledge and select relevant technologies. The document also presents example results from TAGMI on potential areas for adopting small reservoirs in the Volta River Basin under current and climate change scenarios. It concludes with lessons learned around improving TAGMI by incorporating more social data and validating predictions against actual adoption rates.

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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’

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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)

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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

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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

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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

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STEP 3: Merge interdisciplinary factors with Bayes approach
STEP 3: Merge interdisciplinary factors with Bayes approach

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STEP 3: Merge interdisciplinary factors with Bayes approach
STEP 4: Develop web based interface in open source
and accessible data layers

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http://www.seimapping.org/tagmi/index.php
Example: Data input and impact

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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

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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

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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)

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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

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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!