Poster by the World Agroforestry Centre (ICRAF) presented at the CSA15 conference in Montpellier. Read more about the conference: http://ccafs.cgiar.org/3rd-global-science-conference-%E2%80%9Cclimate-smart-agriculture-2015%E2%80%9D#. http://worldagroforestry.org/

1. Evaluating the cost-effectiveness of
development investments
Eike Luedeling1,2, Jan De Leeuw1, Todd Rosenstock1, Christine
Lamanna1, Keith Shepherd1
• Inspired by Applied Information Economics (Hubbard 2014),
with simulations based on Monte Carlo analysis
• We’re also exploring use of Bayesian Networks in a similar
framework (Fenton and Neil 2012)
A water pipeline to Wajir in
Northern Kenya
(Luedeling et al. 2015)
• Safe drinking water for Wajir by tapping the politically sensitive
Merti aquifer in Habaswein (110 km away)
• Controversial decision currently in progress
• What are the costs, benefits and risks, for different stakeholders?
• Multiple stakeholder workshops
• Participatory model building (team of 8 experts), with several
rounds of feedback and updates
• Monte Carlo simulations for different stakeholders
• Identification of important uncertainties
• Projected decision outcomes for all
stakeholders
• Recommendations for further measurements,
monitoring, design improvement
Participatory model development
No
Do results offer sufficient
guidance?
Yes
Identification of input variables
Model parameterization
Model run
Analysis of results
Identification of important variables
Decision framing and stakeholder identification
Range estimates for
input parameters from:
• Historic sources
• Key informants
• Calibrated estimates
Model
refinement
Confounding
effects
Climate
variability
Societal
change
…
Impacts of Climate-Smart Agriculture (CSA) investments
Unclear impacts
Food security
Mitigation
Adaptation
Policies
Culture
and
traditions
Socio-
economic
drivers
• Multi-dimensional (food security, adaptation, mitigation)
• Affected by many factors, across scientific disciplines
• Prediction is a transdisciplinary challenge
• Impact projection with imperfect information and unclear impact
pathways is a common challenge in business decision analysis
• Can we adopt business analysis methods for predicting impacts of
CSA interventions?
Business decision analysis
References
Hubbard D, 2014. How to Measure Anything: Finding the Value of Intangibles in Business. Wiley. --- Fenton N, Neil M,
2012. Risk Assessment and Decision Analysis with Bayesian Networks. CRC Press. --- Luedeling E, Oord AL, Kiteme B,
Ogalleh S, Malesu M, Shepherd KD, De Leeuw L, 2015. Frontiers in Environmental Science 3, article 16.
Important variables in the
decision model (Figure to the left)
• Major uncertainties about how
to value reduced infant
mortality
• Risks of poor project design and
political interference are major
sources of uncertainty
Outcomes
• Analysis enhanced stakeholder understanding, pointed to
improvements in project design, and established research priorities
• Several stakeholders reduced their confidence in the project, and
decision-makers requested additional measurements
Author affiliations
1 World Agroforestry Centre, Nairobi, Kenya (e.luedeling, j.Leeuw, t.rosenstock,
c.lamanna, k.shepherd; all @cgiar.org)
2 Center for Development Research (ZEF), Bonn, Germany (luedeling@uni-bonn.de)
Plausible net present value distributions for all
stakeholder groups (Figure above)
• Risky project with positive and negative outcomes possible for
most stakeholders
• Prospects for Habaswein better than for Wajir
Conclusion
Decision analysis methods have great potential to enhance decision-
making processes in risky and uncertain environments.
These methods can also help in prioritizing and targeting CSA
interventions in the face of data scarcity and uncertainty.