Agroforestry is increasingly being recognized as a good bet for sustainable, climate friendly future, improved livelihoods and household nutrition At the same time Governments, development organizations and donors increasingly looking for evidence on value for money, and evidence for impact

1. Improving Use of Evidence in Decision
Making on Agroforestry Interventions
Keith Shepherd, Cory Whitney, Eike Luedeling
International workshop on Opportunities and challenges for implementing agroforestry systems: Developing a
decision support framework for policy-makers in Central Asia, 11 - 12 March 2019, Eberswalde University for
Sustainable Development (HNEE), Germany

2. Agroforestry and Evidence
• Complex systems (multiple crops, spatial & temporal complexity,
biophysical/economic/social costs & benefits, multiple stakeholders, on-site & offsite
impacts, trade-offs)
• Sparse, disparate, incomplete data
• Few systematic reviews
• Much expert knowledge but not formally captured and used
• Agroforestry models (e.g. WaNuLCAS, Hi-sAFe) data hungry, incomplete, uncertainty
ignored
How then can we possibly make sound decisions on what to
implement where or make a business case for investment?

3. Decision Analysis has the answers
• A structured procedure for discriminating between suitable courses of action
when faced with large complexity, high uncertainty, limited data
• Define the decision – makes the problem tractable. [Einstein – if 1 hour to solve the
world’s problem, would spend 55 minutes defining the problem!]
• Specify the desired set of outcomes of stakeholders – essential for sustainable
action!
• Be creative in searching for alternatives – often a weak point!
• Predict how the alternatives affect outcomes – yes we can!
• Make inferences on best choice or where further information is needed – we
can calculate the value of information!

4. Key Principles of Decision Analysis
Shepherd et al., 2015. Nature 523, 152-154.
• Incorporate all important aspects into models
Luedeling and Shepherd, 2016. Solutions 7(5), 46-54.
• Model system using all sources of information,
including local and expert knowledge
• Explicitly consider uncertainties about inputs,
processes and outputs (probabilistic models)
• Identify key uncertainties for measurement
using ‘Value of Information’ analysis
• Update model, when new information becomes
available

5. Stochastic Impact Evaluation
Shepherd et al (in review)
Iterative feedback loops
Key concepts
• Represent uncertainty in all costs,
benefits and risks
• Causal models (triggers, risk events,
consequences, controls, mitigants)
• Elicit variables & probability
estimation from experts
• Normally everything monetized ->
trade-offs, investment risks
• Utilities for risk preferences of
stakeholder can be included
• Use information value to
determining model complexity and
data needs

6. Monte Carlo simulation tool – risk return modelling
Economic analysis ++++
• Uncertainty / risk
• Value of information
• Resilience & sustainability
• Risk preferences

7. Factors modified from Sayer et
al (2015)
Bayesian Network
for assessing
restoration project
implementation
risk
Information synthesis

8. Reservoir protection in Burkina Faso
• Lagdwenda reservoir in Burkina
Faso threatened by sedimentation
• How can this be prevented?
• Are options cost-effective?
Lanzanova D et al (2019). Improving development efficiency through decision analysis:
Reservoir protection in Burkina Faso. Environmental Modelling & Software 115: 164-175.

9. Reservoir protection in Burkina Faso
Participatory assessment
What management
options are
available?
What are the risks,
costs and benefits
for each option?
What is known
about them?
What is the
plausible range of
outcomes?
What additional
information do
decision-makers
need?
What is the most
promising course of
action?

10. Model structure -1
Lanzanova et al. (2019)

11. Model structure -2
Lanzanova et al. (2019)

12. Reservoir protection in Burkina Faso
Lanzanova et al., in preparation.

13. Reservoir protection in Burkina Faso
Profit margin of
vegetable
production
• Best option is combination
of dredging, check dams and
buffer strips
Lanzanova et al., in preparation.
• Probably positive outcome,
but small risk of net losses
• Additional information on
profitability of vegetable
production would facilitate
decision

14. Impact of Uganda 2040 Policy on household nutritional security
Production by agroforestry homegardens
Whitney et al. 2018. Earth’s Future, 6(3), 359-372.; … 2017. Agricultural Systems 154, 133-144.
???
Nutrition outcomes
Production &
consumption of
healthy food
???
Agricultural
development
decision
??????
????
????
“A Transformed Ugandan Society from a Peasant to a
Modern and Prosperous Country within 30 years”

15. Bayesian Network
Whitney et al. 2018. Earth’s Future, 6(3), 359-372
Model indicated Vision 2040 may have negative implications on the
nutritional status of households - favoured large-scale production
over agroforests

16. Conclusions
• Decision sciences are geared towards solving
problems
• They don’t aim at precision or ultimate
answers, but at offering comprehensive advice
for decisions and system management
• If our goal is to solve problems, support
decisions and facilitate development in
complex systems …
Decision science – a more fitting paradigm for development research
…we should adopt decision analysis thinking as
our research paradigm

17. Central Asia Agroforestry
A Framework for Synthesis and Actionable Evidence
• Regional Level. Synthesize generic factors / lessons into summary,
causal, probabilistic models that can be applied by planners and project
managers to evaluate and improve interventions. An on-line tool.
• National/Project level. Build a library of holistic risk-return models from
individual projects/contexts, to guide plans, adaptive monitoring &
management, assemble evidence for impact.
• Complementarity with MARISCO

18. Examples
• Shepherd K, Hubbard D, Fenton N, Claxton K, Luedeling E, De Leeuw J, 2015. Development goals should enable decision-making. Nature 523, 152-154.
• Luedeling E and Shepherd KD. 2016. Decision-Focused Agricultural Research. The Solutions Journal 7: 46-54.
• Yet, B., Constantinou, A., Fenton, N., Neil, M., Luedeling, E. and Shepherd, K. 2016. A Bayesian Network Framework for Project Cost, Benefit and Risk Analysis
with an Agricultural Development Case Study. Expert Systems With Applications 60: 141–155.
• Rosenstock,T.S., Mpanda, M., Rioux J., Aynekulua, E., Kimaro, A.A., Neufeldt, H., Shepherd. K.D., Luedeling. E. 2014. Targeting conservation agriculture in the
context of livelihoods and landscapes. Agriculture, Ecosystems and Environment 187: 47–51
• Luedeling, E., Oord, A., Kiteme, B., Ogalleh, S., Malesu, M., Shepherd, K. D., De Leeuw, J. (2015). Fresh groundwater for Wajir – ex-ante assessment of
uncertain benefits for multiple stakeholders in a water supply project in Northern Kenya. Frontiers in Environmental Science 3: 16.
• Favretto, N., Luedeling, E., Stringer, L. C., & Dougill, A. J. (2017). Valuing ecosystem services in semi-arid rangelands through stochastic simulation. Land
Degradation and Development 28, 65–73.
• Tamba Y, Muchiri C, Shepherd K, Muinga G, Luedeling E. 2017. Increasing DryDev’s Effectiveness and Efficiency through Probabilistic Decision Modelling. ICRAF
Working Paper No 260. Nairobi, World Agroforestry Centre.
• Whitney CW, Lanzanova D, Muchiri C, Shepherd KD, Rosenstock TS, Krawinkel M, Tabuti JRS, & Luedeling E. (2018).Probabilistic decision tools for determining
impacts of agricultural development policy on household nutrition. Earth’s Future 6, 359–372.
• Tamba Y, Muchiri C, Luedeling E, Shepherd K. 2018. Probabilistic decision modelling to determine impacts on natural resource management and livelihood
resilience in Marsabit County, Kenya. ICRAF Working Paper No 281. Nairobi, World Agroforestry Centre.
• Wafula J, Karimjee Y, Tamba Y, Malava G, Muchiri C, Koech G, De Leeuw J, Nyongesa J, Shepherd K and Luedeling E. (2018). Probabilistic assessment of
investment options in honey value chains in Lamu County, Kenya. Frontiers in Applied Mathematics and Statistics 4: 6-11.
• Lanzanova D, Whitney C, Shepherd K, Luedeling E (2019). Improving development efficiency through decision analysis: Reservoir protection in Burkina Faso.
Environmental Modelling & Software 115: 164-175.
• Shepherd KD, Whitney C, Luedeling E (2019). A decision analysis framework for development planning and performance measurement: application to land
restoration investments. Land Degradation & Development (in review)
http://www.worldagroforestry.org/sd/landhealth/publications-all

19. Software & Guides
•R package “decisionSupport”. https://cran.r-
project.org/web/packages/decisionSupport/index.html
•Value of Information analysis tool incorporated into AgenaRisk Bayesian Network
package. http://www.agenarisk.com
•Whitney C W, Shepherd K, Luedeling E. 2018. Decision analysis methods guide;
Agricultural policy for nutrition. ICRAF Working Paper No. 275. Nairobi, World
Agroforestry Centre.
•Training opportunities in Applied Information Economics and Subjective Probability
Estimation with Hubbard Decision Research through ICRAF.