How to increase effectiveness and efficiency of the DGIS through decision modelling.

1. DGIS
Increasing Effectiveness and Efficiency
through
Probabilistic Decision Modelling
Keith Shepherd, Eike Luedleing, Jan de Leeuw, Grace Muinga
“The quantitative analysis of decisions and the supporting metrics
will have a profound effect on intervention policy”

2. Applied Information Economics Process
Hubbard Hubbard Decision Research

3. Decisions modelled
• Integrated watershed management in the Tana River Basin in Kenya;
• Payment for environmental services scheme for Sasumua Dam in Kenya;
• Alternatives for water management on the Mekong river in Laos;
• Business case for manufacturing fertilizer from human manure in Ghana;
• Evaluation of a government irrigation scheme in Ghana;
• Design of a regional system of seed storage nodes for maintain agrobiodiversity
in the Volta Basin;
• Evaluation of interventions for building resilience to drought in the Horn of
Africa;
• Assessing Risk of Investment in Groundwater Resources in Sub Saharan Africa –
case study of the Merti aquifer in Kenya.

4. Outputs
Replenishment
Irrigation growth
Initial irrigated area
Water use per hectare
Aquifer size
Natural water use
Importance threshold
Identification of high-
value variables

5. p
p p p
p
p
p
p
p
p p
p
p
n
n
nSLO 1
SLO2
SLO3
SLO4
n
n
n
n
Can we estimate these
numbers?

6. Increasing effectiveness and efficiency
of DGIS through decision modelling
• A coherent approach for representing and quantifying
the project impact pathway, including activities and risks
• Entire project level
• Country level
• Individual intervention level
• Development of this approach in the DGIS project could
lead to a whole new generic approach to project
planning, monitoring & evaluation, and impact
assessment.
• Capacity building opportunities

7. Some benefits
• Reveal important uncertainties in intervention design and
impact pathway
• Improve intervention design to maximize impacts, reduce
risks
• Define high value metrics for monitoring during project
implementation
• Guide accumulated evidence for impact attribution
• Provides a solid business case to present to investors,
donors

8. AIE Process
1. Decision Clarification Workshop
2. Calibrated Probability Training Workshop
3. Detailed Decision Modeling Workshop
4. Risk/Return Analysis
5. Value of Information Analysis

9. Review of the Evidence on Indicators,
Metrics and Monitoring Systems
Commissioned by the UK Department for International Development (DFID)
Conducted by the CGIAR Program on Water, Land & Ecosystems
Coordinated by the World Agroforestry Centre (ICRAF)
Authors: Keith D Shepherd1
, Andrew Farrow2
, Claudia Ringler3
, Anja Gassner1
, Devra
Jarvis4
1
World Agroforestry Centre (ICRAF)
2
Consultant for World Agroforestry Centre (ICRAF)
3
International Food Policy Research Institute (IFPRI)
4
Bioversity International
Shepherd KD, Farrow A, Ringler C, Gassner A,
Jarvis A. 2013. Review of the Evidence on
Indicators, Metrics and Monitoring Systems.
Commissioned by the UK Department for
International Development (DFID). Nairobi:
World Agroforestry Centre.
http://r4d.dfid.gov.uk/output/192446/default.
aspx
Clapp A, DauSchmidt N, Millar M, Hubbard D,
and Shepherd K. 2013. A Survey and Analysis
of the Data Requirements for Stakeholders in
African Agriculture
http://r4d.dfid.gov.uk/Output/193813/Default.
aspx
http://www.hubbardresearch.com/training/
A Survey and Analysis of the Data Requirements
for
Stakeholders in African Agriculture
!
An initiative of the CGIAR Program on Water, Land & Ecosystems!
!
Commissioned by the UK Department for International Development (DFID)!
9 August 2013
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