CPWF Volta V1: Targeting and scaling out

CPWF Volta V1: Targeting and scaling out

Volta Basin Science and Reflection MeetingOuagadougou3‐6 July 2012

Andes • Ganges • Limpopo • Mekong • Nile • Volta

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


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 usersSeveral human capacity elements incl. MSc and professional training

Research activity: Develop protocolSynthesise data CPWF phase 1

Consultation activity: Protocol, identify cases

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

Research activity:Assess casesDevelop model tool and databases

Research activity:Improve tool and databaseCharacterise CPWF L/V field sitesCharacterise potential outscaling locations

Consultation activity: Test prototype tool

12 m 24 m 36 m

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

Consultation activity: Provide learning events in out-scaling toolDemonstrate/share /distribute research outputs

External actors: Local/regional/national, international agents in ag. water and rural development,public, NGOs, possibly private sectorInvestorsNational, 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 Failure Successful ??

Rainfed Soil and water conservation/DRS/CES

SA ‐ BFBF

Planting pits BF‐Zim

Trench technology SA SAConservation agriculture SA SA

Bunding Gha Gha BFContour bunds/ridges/ploughing Zim Gha‐Zim

Tied ridges Sa‐Zim Sa‐Gha‐BFCover crop GhaTree planting SA‐ Gha‐BFBunding Gha GhaMulching SA‐Zim SA ZimShallow groundwater GhaShallow wells Gha BFWaste water re‐use Zim ZimTreadle pump SA BFWater pumps (small scale irrigation)

Gha Gha

Sprinkler irrigation SA‐Zim Zim ZimDrip irrigation SA‐Zim SA‐Zim BF‐Zim

Punched bag ZimMicro irrigation GhaSupplemental irrigation (rice) BF

Roof Rainwater Harvesting Zim

Ferro‐cement tanks SAEarth dams SA ‐ Gha BFUnderground level dams SA

Full irrigation Small dams/reservoirs 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 category

Success %

Failure % Success %

Failure % Success %

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

Other 26 38 20 19 27 23 32 12

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

•Early engagement with stakeholders

•Continuous assistance/backstopping, Appropriate implementation

•Direct benefit, Clear demand

•Clear objective

•Appropriate design of technology

Definition of a success• Direct benefit of well-being

• Substantial practise beyond >2 years intervention to end-user


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 dataAWMi development per region Cereal yield (t/ha) per region

No conclusions yetMore 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

• ‐ Georeferencingof features, landmarks and technologies.


Technologies identified

GHANA BURKINA FASO

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 maistarissent pendant la saison sèche3. Pompage nécessite une source d’eau pérenne


4. Boulis, spécifiquement pour l’abreuvement des animauxProblèmes:

• 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 ouvrages5. Digues en terre et en pierreCommunément utilisé en zones dotées en pierres avec une

pente > 20%

Observations généralesAu delà des pompages, toutes les techniques identifiées sont

utilisées par les hommes et aussi bien les femme

Résultats préliminaires (Ghana) (cont)


• Croissance de l’adoption et de l’adaptation des technologies par lesbénéficiaires

• 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’organisationet d’engagement

• Contribution à la réduction de l’exode rurale

• Amélioration des facteurs de productions

Résultats préliminaires (Burkina Faso)


• 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 toolEric Kemp Benedict with contributions from Annemariekede Bruin, Frank Annor, Douglas

Wang

Background


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

Landscape-level analysis

Combining expert insights and dataIncluding social and institutional data

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


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

Specifics


Implem.

Structure of Bayesian ModelEvidence

Factors of success

“Pseudo-factors”*

* Reduce the number of model parameters

Indicator of success

Implementation factors

Implementation score


Experts Provided Guidance on Sources of Evidence


Experts Provided Guidance on Sources of Evidence

…but we still need to collect most of the data


( |{ })jP s e =

For the Mathematically Inclined

Probability of success given observations (evidence)

?


{ }( |{ }) ( |{ }) ({ } |{ })

i

j i i jf

P s e P s f P f e=∑


Probability of success given

factors of success

Probability of factor of success given evidence


1{ }{ }

( |{ }) ( |{ }) ( | ) ({ } |{ })i i

n

j i i i i jif f

P s e A s f P f f P f e′

′

=

⎛ ⎞= ′⎜ ⎟

⎝ ⎠∑ ∏


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


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 implementedPartially implementedNot 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 successIntensity (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


Project TeamSEIINERASARIKNUSTUniv Ouaga

Researchers

CP researchersVBDC researchersWider researcher community

V5 Project

Regional Government

CAADP (NEPAD)VBA..

Communities

NGOs(DVT agents)

National/Provincial

GovernmentAgric Dept.Extension servicesPlanning

Project sphere of influence Project sphere of concern

Boundary PartnersBeneficiaries

Other stakeholders

Project Team

Unions/ Associations

ACTIONSVision, mission, inputs, activities,

outputs

IMPACTSchanges in conditions, well‐being

OUTCOMES changes in knowledge,

attitudes, skills, actions)

NGOnetworks


Partner engagements in 1st project year (2011):

Mostly national level researchers and various gvt

55%

12%

27%

3% 3%GVT national

Devt agents/implementors (local Gvt, Ngos)farmers

other local stakeholders / private formal‐informalNARS

regional research

intl research

regional policy

international policyBurkina FasoGhana

14%

9%3%3%

71%

GVT national

Devt agents/implementors (local Gvt, Ngos)farmers

other local stakeholders / private formal‐informalprivate parastatsal

NARS

regional research

intl research

regional policy

international policy


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

What have we learned about process?


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

Technology

Transcript of CPWF Volta V1: Targeting and scaling out