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

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 academicknowledge

Farmers , local 

community

MERGED KNOWLEDGE

In TAGMI model

Pooling knowledge in a consultative research process

STEP 1: Process of consultation : incorporate various sources of knowledge

Consultation 2012Consultation 2011 Synthesis

Farmer81%

Farmer/ Community mgmnt4%

CBO1%

Extension5%

Public Services2%

Local govt6%

NGO1%

Farmer2%

CBO6%

Public Services

9%

Local govt17%

NGO6%Nat govt

6%

Nat research52%

Reg mgmnt2%

Intl research2%

CBO3%

Public Services

7%Local govt

19%NGO12%Nat govt

11%

Nat re-

search35%

Reg research5%

Reg mgmnt8%

Intl re-

search1%

CBO2%

Public Services4%Local govt

27%

NGO23%

Nat govt12%

Nat re-

search26%

Reg research2%

Reg mgmnt3% 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/CESPlanting pits (incl zai)Bunding /ridges/contour bunds/ploughingTied ridges

BFBFGHGH

GH,BF GH,BF

Cover cropTree plantingMulching

GHGH BF

Shallow groundwater useShallow wellsWastewater re-use

GHGH. BF GH ,BF

Motorised water pumps ()small scale irrigation)Treadle pumpsDrip irrigationPunched bag Micro irrigationSupplemental irrigation (rice)

GH, BFBFBF

GHBF

GH,BFGH, BF

GH,BF

Earth damsUnderground (in stream) damsSmall dams /reservoirsFerro cement tanksRoof waterharvestingLarge 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%

Indicator 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

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

We thank all contributors: absent colleagues

farmers, boundary partners and participants in consultations and eventsVBDC and V1 colleagues, and LBDC and L1 colleagues

funders

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),

Thank you!