CPWF Volta V1: Targeting and scaling out

CPWF Volta V1: Targeting and
scaling out
Volta Basin Science and Reflection Meeting
Ouagadougou
3‐6 July 2012

Outline

‐ V1 research question and expected outputs, timelines and linkages
‐ Research findings : synthesis of Stakeholder Consultations
‐ Research findings (draft) : do we know how much AWMi already exist?
‐ Research findings (draft): Cases of AWMi in Ghana and Burkina Faso with PGIS
‐ Young Professionals poster presentations with draft results
(LUNCH)
‐ Research findings (draft): Outscaling tool developments using Bayes model,
database and interface
‐ Findings (draft): Innovation and learning
‐ Next steps of V1

Andes • Ganges • Limpopo • Mekong • Nile • Volta

CPWF Volta V1: Targeting and scaling out

Project aim, status and linkages
in VBDC

Objective of V1 is to develop and evidence a knowledge‐based tool to assess
and map the likelihood that a given intervention will be successful in given
locations, at the basin scale. Secondly , evaluate potential environmental
impacts of outscaling at basin‐scale.

“The project seeks to answer the question of what AWMi can work where
and why”

V1 Expected Results & Contribution to Volta BDC

Outscaling tool with improved capacity to target areas of particular
interventions to contribute to poverty alleviation and (resilient ) development
pathways in a collaborative way with boundary partners


An iterative research process in dialogue with stakeholders and potential end users
Several human capacity elements incl. MSc and professional training

Research activity: Research activity:
Develop protocol Assess cases Research activity:
Synthesise data Develop model tool Improve tool and database
CPWF phase 1 and databases Characterise CPWF L/V field sites
Characterise potential outscaling locations

12 m 24 m 36 m

Consultation activity: Consultation activity:
Consultation activity:
Test prototype tool Provide learning events in out-scaling tool
Protocol, identify cases
Demonstrate/share /distribute research outputs

External actors: External actors:
External actors: Local/regional agents Local/regional/national, international agents
local agents in ag. development, in ag. water and rural development,
in ag. development, (public, NGO) public, NGOs, possibly private sector
( public, NGO) Investors in ag. development Investors
Researchers in ag-water National, international decision makers


LINKAGES with other VBDC projects and CPWF basins

Initial plan: Actual interactions:

- Cross-project partners

- V1 contributing to V2 review

- In-kind contribution of information

- Ex-post domain analysis of sites
(outstanding)

- Topic Working Groups

- Initiative by Kizito, and contribution of
resources

- Collaborations external to VBDC



V1 (& L1) Synthesis of initial
stakeholder consultations
By V1 partners prepared by Annemarieke de Bruin

Result: No pattern in success or failure related to complexity of
technology Type of technology
Rainfed Soil and water
Failure Successful

SA ‐ BF
??
BF
conservation/DRS/CES
Planting pits BF‐Zim
Trench technology SA SA
Conservation agriculture SA SA
Bunding Gha Gha BF
Contour
Zim Gha‐Zim
bunds/ridges/ploughing
Tied ridges Sa‐Zim Sa‐Gha‐BF
Cover crop Gha
Tree planting SA‐ Gha‐BF
Bunding Gha Gha
Mulching SA‐Zim SA Zim
Shallow groundwater Gha
Shallow wells Gha BF
Waste water re‐use Zim Zim
Treadle pump SA BF
Water pumps (small scale
Gha Gha
irrigation)
Sprinkler irrigation SA‐Zim Zim Zim
Drip irrigation SA‐Zim SA‐Zim BF‐Zim
Punched bag Zim
Micro irrigation Gha
Supplemental irrigation (rice) BF
Roof Rainwater Harvesting Zim
Ferro‐cement tanks SA
Earth dams SA ‐ Gha BF
Underground level dams SA
Full irrigation Small dams/reservoirs
Andes • Ganges • Limpopo • Mekong • Nile • Volta SA BF BF‐Zim
Large scale irrigation SA

Result: Best practises (‘due diligence’) was stated as having
significant impact on success/failure of AWMi
South Africa (n=11) Zimbabwe (n=13) Ghana (n=16) Burkina Faso (n=6)
Factor Success Failure % Success Failure % Success Failure % Success Failure %
category % % % %

Natural 9 9 27 31 24 10 25 16
Human 26 13 25 28 18 21 14 8
Social 30 6 7 3 3 6 11 24
Physical 4 17 11 14 18 25 11 28
Financial 4 17 9 6 9 15 7 12
Other 26 38 20 19 27 23 32 12
Definition of a success Other= ‘BEST PRACTISE IN IMPLEMENTATION’
• Direct benefit of well-being •Community owns initiative
• Substantial practise beyond •Early engagement with stakeholders
>2 years intervention to end- •Continuous assistance/backstopping, Appropriate implementation
user
•Direct benefit, Clear demand
•Clear objective
•Appropriate design of technology


Do we know how much land
already under AWMi?
Prepared by Joanne Perry, Jennie Barron based on review by
Douxchamps et al,

Learning from the  past – following V2 review Douxchamps et al ,
V1 Presentation  from 3rd IFWF 16 Nov 2011

Farmers already manage rain and have AWMi, but to what extent and
to what impact?

• We have identified 3 major streams of technology adoption and
adaptation
SWC, small reservoirs, irrigation with small electric/diesel pumps

What impact have these changes?

• Look at high level impacts  addressing  desired benefits (yield food
security, income), undesired  externalities  on environment (water
resources ,flows appropriation, ES loss)

Can we measure these benefits and impacts at various scales? Can we
estimate extent of AWMi in use?


Learning from the past: map with sites of documented ground assessment of
SWC adoption


Learning from the past: example of data
AWMi development per region Cereal yield (t/ha) per region

No conclusions yet
More data needed
Verification?
(we don’t agree…..)


Participatory GIS Assessment ‐
Burkina Faso and Ghana
presented by Mariam Balima (INERA) and
Mathias Fosu (CSIR‐SARI)

Purpose of PGIS
• To validate information from
consultation meeting
• To validate the Bayesian
Model


Methodology
• Google maps of selected
towns in Burkina and
Ghana
• High Level stakeholder
consultation and mapping
• Community level
consultation & mapping
• ‐ identification of prominent
features, landmarks & techs.


Methodology cont..
Community level mapping
• ‐ Take GPS
coordinates of
features, landmarks &
techs not captured on
Google map
• ‐ Georeferencing
of features,
landmarks and
technologies.


Technologies GHANA BURKINA FASO
identified
Treadle Pumps
+ +
Shallow wells
+ +
Dugouts
+ +
Small reservoirs
+ +
Stone and Earth bunds
+ +
Water pumps
+ +
Tree /Field crop
integration
‐ +


Résultats préliminaires (Ghana)

1. Petits réservoirs
• Largement rependus et communément utilisé
• Disponible en saison sèche
• Réduction de l’exode rurale
Problème: maintenance des infrastructures

2. Puits de bas-fond utilisable en saison pluvieuse mais
tarissent pendant la saison sèche
3. Pompage nécessite une source d’eau pérenne


Résultats préliminaires (Ghana) (cont)

4. Boulis, spécifiquement pour l’abreuvement des animaux
Problèmes:
• Faiblement construits
• N’est pas adapte pour l’agriculture de contre-saison
• Généralement de petite taille et par conséquent l’eau tarit
rapidement
• Perte de l’eau par infiltration
• Ensablement des ouvrages
5. Digues en terre et en pierre
Communément utilisé en zones dotées en pierres avec une
pente > 20%

Observations générales
Au delà des pompages, toutes les techniques identifiées sont
utilisées
par les hommes et aussi bien les femme


Résultats préliminaires (Burkina Faso)
• Croissance de l’adoption et de l’adaptation des technologies par les
bénéficiaires

• Accès des ouvrages à toutes les couches sociales

• Amélioration du niveau de vie des communautés (sécurité alimentaire,
éducation, santé)

• Diversification des revenus (réduction de la pauvreté)

• Participation à la gestion durable des infrastructures

• Renforcement des capacités des producteurs en terme d’organisation
et d’engagement

• Contribution à la réduction de l’exode rurale

• Amélioration des facteurs de productions

• Meilleure disponibilité de l’eau pour des usages
multiples en saison sèche: irrigation et
l’abreuvement des animaux

• Meilleure rétention d’eau pour l’agriculture pluviale


Similarities and différence

Simalarité
– même technologie utilisée au Ghana et au Burkina
Faso
– Les problèmes des technologies sont identiques
(exemple la gestion et l’utilisation des petits
réservoirs sont identiques)
Différence
‐ 7 technologies (Burkina) et 6 (Ghana)
‐

Conclusion
• Les informations obtenues lors des PGIS confirment
celles des ateliers de consultations
• Les petits barrages sont plus importants dans la
gestion de l’eau
• Toutes les technologies sont accessibles de façon
égale aux hommes et aux femmes à l’exception des
motopompes
• Les technologies ont contribué à la réduction de
l’exode rurale
• Les technologies ont contribué à l’augmentation des
productions et a l’amelioration du niveau de vie des
beneficiaires


Limites

• difficultés d’obtention de données
quantitatives
• Longueur du temps d’enquête (4 à 5 heures)
• Indisponibilité d’image récente et de cartes
topographiques aux échelles adaptées
(1/50 000 eme)


Perspectives

• Utilisation des données PGIS pour valider le
modèle bayésien


Merci pour votre attention



V1 Young professionals
(poster session)


Targeting and scaling out tool
Eric Kemp Benedict with contributions from Annemariekede Bruin, Frank Annor, Douglas
Wang

V1 Project
• Aim: To produce a framework and web‐based “decision support”,
(or targeting and scaling out tool) that will assist in identifying sites
where the introduction of AWM interventions for smallholder
farming systems are likely to be successful.
• Related projects
– Extrapolation domains
– Bayesian poverty mapping
– AgWater Solutions suitability maps
– Nile Basin outscaling tool
• The novelty of this approach
– Including social and institutional factors
– Eliciting information from experts on the ground
– Open‐source infrastructure


CPWF History (1):
Extrapolation Domains


CPWF History (2):
Bayesian Poverty Mapping


Outside of CPWF:
AgWater Solutions
• Suitability map for
small reservoirs
• Data
– Biophysical
– Economic


Current CPWF Work:
Nile BDC N3

• Similar goals
• Similar inputs
Landscape-levelPerhaps less flexible (?)
• analysis
• Outscaling is important, and the world
needs more than one method

Combining expert insights and data
Including social and institutional data

The Targeting Problem

• We want to out‐scale agricultural water
management (AWM) technologies
• We want to pick sites where the chances of
success are relatively good
• A good way to decide is through rapid
assessment in the field at prospective sites
• But where to do the rapid assessments?


The Pre‐targeting Problem
• Decide where conditions are promising
enough that it is worth investing in a rapid
field assessment
• Only use existing and easily accessible data
that is available over a large part of the basin
• Other considerations:
– Make it affordable
– Build on accumulated experience and knowledge


Conceptual Model
• There are factors that contribute to the
success or failure of AWM technologies
– Biophysical
– Social and institutional
– Technological
– Implementation‐specific
• The factors usually cannot be observed
directly, but there is indirect evidence that
that they are present or absent


Realities
• The needed data are incomplete and
imperfect
• No model can capture all the complexities of
agricultural communities and their
environments
• There will be continued learning and
therefore a need to update the model


Structure of Bayesian Model
Evidence

Factors of success

“Pseudo-factors”*

Indicator of success

Implementation score
Implem.

Implementation factors

* Reduce the number of model parameters

Experts Provided Guidance on
Sources of Evidence


Experts Provided Guidance on
Sources of Evidence

…but we still need to collect
most of the data


For the Mathematically Inclined

P ( s | {e j }) = ?
Probability of
success given
observations
(evidence)



P ( s |{e j }) = ∑ P ( s |{ f i }) P ({ f i }|{e j })
{ fi }
Probability of Probability of
success given factor of success
factors of given evidence
success



⎛ n ⎞
P ( s |{e j }) = ∑ A( s |{ f i }) ⎜ ∏ P ( f ′i | f i ) ⎟ P ({ f i } |{e j })
′

{ fi′ }{ fi } ⎝ i =1 ⎠
A “noisy-and” «et-bruyant» relationship:
Factor of success 1 and factor of
success 2 and factor of success 3 …
must be present, but we don’t have
perfect information

Status
• General model design Fully implemented
Partially implemented
• Expert consultation Not implemented (yet)

– Measures and factors of success
– Sources of evidence/data layers
– Detailed model structure These should have
been further along.
• Tool development Otherwise, we are
reasonably on track.
– Components (see next slide)
– User feedback and input


Summary Status of Tool Components

Fully implemented

Partially implemented

Not implemented (yet)

Implementation factors in the
project’s control

Color (hue): estimated likelihood of success
Intensity (value): degree of confidence


Selecting items on the “checklist”
changes likelihood of success


Conclusions and Ways Forward
• Potential scientific outputs
– Demonstrating that social and institutional variables can be
meaningfully included in a targeting tool
– Conceptual framework of evidence factors of success indicator
of success as a way of framing the targeting problem
– Novel elicitation techniques
• Potential practical outputs
– Better‐informed decisions of where in a basin to direct resources
– Clarity on data needs for improved decision‐making
– An open‐source tool with generically useful source code for spatial
Bayesian models
• Next steps during this project
– Build Bayesian models using expert elicitation
– Improve on tool interface through consultation
– Continue developing data base
– Validation with PGIS and case studies


What Would We Like to Do?
• We are aiming at a tool that could have a life of its
own after this project. Some possibilities to help
make this happen:
– Release the code in an open‐source repository (GitHub,
SourceForge)
– Seek funding for further data collection, elaboration,
dissemination, and hosting
– Identify a potential long‐term home for the web‐based
tool and code



Innovation and learning

ACTIONS OUTCOMES
Vision, mission, changes in IMPACTS
inputs, activities, knowledge, changes in
outputs attitudes, skills, conditions,
Researchers actions) Unions/
well‐being
Associations
CP researchers
VBDC
researchers
NGOs
Wider researcher
(DVT agents)
community

Project Team NGO
SEI networks
Communities
INERA National/
SARI V5 Provincial
KNUST Project Government
Univ Ouaga Agric Dept.
Extension services
Planning

Regional
Project Team Government
Beneficiaries CAADP (NEPAD)
Boundary Partners VBA
..
Other stakeholders

Project sphere of influence Project sphere of concern


Partner engagements in 1st project year (2011):

Mostly national level researchers and various gvt

GVT national
GVT national
3% 3%
Devt agents/implementors Devt agents/implementors
(local Gvt, Ngos) (local Gvt, Ngos)
farmers 14% farmers

other local stakeholders / other local stakeholders /
27% private formal‐informal 9% private formal‐informal
private parastatsal
55%
NARS 3%
3% NARS
regional research

71% regional research
12% intl research
intl research
regional policy
regional policy
Burkina Faso international policy
international policy
Ghana


What have we learned about process?

FOR THE V1

We have likely missed important stakeholders in consultations (private sector
actors), and we have not yet tried to correct for these potential limitations, and
we recognise the challenges

‘Adaptive research’ can be rewarding, but also time-consuming and we
underestimated the time required to fully engage in this as team lead and as
team partners



NEXT STEPS AND PRIORITIES

What V1 will do

FIRST PRIORITY:

• Learning events in Ghana and Burkina Faso (Aug-Sep 2012??)
• Revise , and update and improve tool
• Learning events In Ghana and Burkina Faso (Feb-Mar 2012?)

• NEXT PRIORITY
• Synthesise PGIS across cases and sites : writeshop?
• Complement with AWMi Rainfed case in BF?
• Carry out ex-post domain analysis for VBDC projects
• Finalise review on extent of AWMi in adoption over time
• Model potential hydrological impacts at basin scale
• Contribute to VBDC cross-project on story lines and scenario
• Develop documentations and outreach products


What we would like to do :

‘Best practise of AWMi’ needs to be verified more:
what does it mean ? Who is doing it ?
Costs and impacts?

Develop a framework for evidence factors of success indicator of success

Meaningful spatial information layers of social and institutional factors:
review being done for Limpopo L1 also to benefit V1(Improve the tool accordingly

Find a permanent ‘home’ for tool and associated data

Compare findings of tool application across L1 and V1

Test tool fro v2, V3, V4 AWMi


THANK YOU!

MERCI!


CPWF Volta V1: Targeting and scaling out

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CPWF Volta V1: Targeting and scaling out