CPWF Volta V1 Targeting Scaling AWMi

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:
Develop protocol
Synthesise data
CPWF phase 1

Research activity:
Assess cases
Develop model tool
and databases

Research activity:
Improve tool and database
Characterise CPWF L/V field sites
Characterise potential outscaling locations

12 m
Consultation activity:
Protocol, identify cases

External actors:
local agents
in ag. development,
( public, NGO)
Researchers in ag-water

Test prototype tool

External actors:
Local/regional agents
in ag. development,
(public, NGO)
Investors in ag. development


24 m

36 m

Provide learning events in out-scaling tool
Demonstrate/share /distribute research outputs
External actors:
Local/regional/national, international agents
in ag. water and rural development,
public, NGOs, possibly private sector
Investors
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
Failure
Successful
??
technology Type of technology
Rainfed
Soil and water
BF
SA - BF
conservation/DRS/CES
Planting pits

BF-Zim

Trench technology
Conservation agriculture

SA

SA

SA

SA

Bunding
Contour
bunds/ridges/ploughing

Gha

Gha

Zim

Gha-Zim

Sa-Zim

Sa-Gha-BF
Gha
SA- Gha-BF
Gha
SA
Gha
Gha
Zim

Tied ridges
Cover crop
Tree planting
Bunding
Mulching
Shallow groundwater
Shallow wells
Waste water re-use
Treadle pump
Water pumps (small scale
irrigation)
Sprinkler irrigation
Drip irrigation
Punched bag
Micro irrigation
Supplemental irrigation (rice)

Gha
SA-Zim

Zim
SA

Full irrigation
Small dams/reservoirs
Large scale irrigation

Zim

BF
BF

Gha

Gha

SA-Zim

Zim

SA-Zim

SA-Zim

Zim
BF-Zim

Zim
Gha
BF

Roof Rainwater Harvesting
Ferro-cement tanks
Earth dams
Underground level dams

BF

Zim
SA
SA - Gha

BF

BF

BF-Zim

SA
SA
SA

Result: Best practises (‘due diligence’) was stated as having
significant impact on success/failure of AWMi
South Africa (n=11)
Factor
category

Success
%

Failure %

Zimbabwe (n=13)
Success
%

Ghana (n=16)

Failure %

Success
%

Burkina Faso (n=6)

Failure %

Success
%

Failure %

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

26

38

20

19

27

23

32

12

Other

Definition of a success
• Direct benefit of well-being

Other= ‘BEST PRACTISE IN IMPLEMENTATION’
•Community owns initiative

• Substantial practise beyond
>2 years intervention to enduser

•Early engagement with stakeholders
•Continuous assistance/backstopping, Appropriate implementation
•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
identified
Treadle Pumps
Shallow wells
Dugouts
Small reservoirs
Stone and Earth bunds

Water pumps
Tree /Field crop
integration

GHANA

BURKINA FASO

+
+
+
+
+
+
-


+
+
+
+
+
+
+

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
• analysis
Landscape-levelPerhaps less flexible (?)
• 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

Implem.
Implementation score

Implementation factors

* Reduce the number of model parameters

Experts Provided Guidance on
Sources of Evidence
Factor
Accessibilité à l’eau

Indicator
Nombre de retenues d’eau
Nombre de points d’eau
Existance de cours d’eau
Effectif du cheptel local
Encadrement des producteurs Nombre de structures d’encadrement
Nombre de personnel d’encadrement
Nombre de centres de formation
Taux d’alphabetisation
Types/natures de formations
Disponibilité de main d’oeuvre Effectif de population
Densité de population
Repartition par classe d’age et par sexe
Migration de population
proximité d’un marché de consommationaux centres urbains
Distance
Population locale
Etats des routes
Pouvoir d’achat des populations
Circuit de commercialisation
disponibilité de fumure organique
Types d’elevage
Effectif du cheptel
Nombre de fosses fumieres
Production cerealiere (quantité)

Source
DR Agric, INERA, IGB, AMB, VAREK, PAGIRE
DR-elevage, INSD
Etat, ONG, OP
Etat, ONG, OP
Etat, ONG, OP, DR-MEBA
MEBA
OP, ONG, Etat
INSD, Commune
INSD, Commune
INSD, Commune
INSD, Communes
village, commune
DG-route
Plan, MEF
Chambre de commerce, ONG, OP
DR-elevage, ONG, OP
DR-elevage, ONG, OP
ONG, OP
DR-agric

Sources of Evidence
Factor

Indicator

Source of data

Availability of low lands
for rice cultivation in
northern region

Rice valleys

GIDA, MoFA, CSIR-SARI

Provision of technology

Trained farmers are
CSIR-SARI & MoFA
available in all beneficiary
communities, Contour
bunds constructed are
still being used.

Provision of credit (Agro Seeds ( ie. TOX 3107) are
Inputs)
still available.

CSIR-SARI,

Marketing
arrangements made

NASIA Rice company

Buyers such as NASI and

Culture and interest of
the people in rice
cultivation
Previous exposure to

Trained farmers are still

GIDA, MoFA, CSIR-SARI

Sources of Evidence
…but we still need to collect
most of the data


For the Mathematically Inclined
evidence

factors of
success

pseudofactors

Core model
Additional model elements

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


?

success
indicator

factors of
success

evidence

pseudofactors

success
indicator

Core model

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


factors of
success

evidence

pseudofactors

success
indicator

Core model
n

P ( s | { e j })

A ( s | { f i })
{ f i }{ f i }

P( f

i

| f i ) P ({ f i } | { e j })

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 
• Expert consultation
– Measures and factors of success 
– Sources of evidence/data layers 
– Detailed model structure 

• Tool development
– Components  (see next slide)
– User feedback and input 

Fully implemented
Partially implemented
Not implemented (yet)


These should have
been further along.
Otherwise, we are
reasonably on track.

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
Vision, mission,
inputs, activities,
outputs
Researchers

OUTCOMES
changes in
knowledge,
attitudes, skills,
actions)

CP researchers
VBDC
researchers
Wider researcher
community

Project Team
SEI
INERA
SARI
KNUST
Univ Ouaga

Unions/
Associations

NGOs
(DVT agents)

NGO
networks
National/
Provincial
Government
Agric Dept.
Extension services
Planning

V5
Project

Regional
Government

Project Team
Beneficiaries
Boundary Partners
Other stakeholders

IMPACTS
changes in
conditions,
well-being

CAADP (NEPAD)
VBA
..

Project sphere of influence

Project sphere of concern

Communities

Partner engagements in 1st project year (2011):
Mostly national level researchers and various gvt
GVT national

3% 3%

GVT national

Devt agents/implementors
(local Gvt, Ngos)

14%

farmers
other local stakeholders /
private formal-informal

27%
55%

9%
3%
3%

NARS
regional research

12%

intl research

Devt agents/implementors
(local Gvt, Ngos)
farmers
other local stakeholders /
private formal-informal
private parastatsal
NARS
regional research

71%

intl research
regional policy

Burkina Faso

international policy


regional policy

Ghana

international policy

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 Scaling AWMi

Empfohlen

Empfohlen

Weitere ähnliche Inhalte

Ähnlich wie CPWF Volta V1 Targeting Scaling AWMi

Ähnlich wie CPWF Volta V1 Targeting Scaling AWMi (20)

Mehr von International Water Management Institute (IWMI)

Mehr von International Water Management Institute (IWMI) (20)

CPWF Volta V1 Targeting Scaling AWMi