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Improving Use of Evidence in Decision Making on Agroforestry Interventions

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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

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Improving Use of Evidence in Decision Making on Agroforestry Interventions

  1. 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. 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. 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. 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. 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. 6. Monte Carlo simulation tool – risk return modelling Economic analysis ++++ • Uncertainty / risk • Value of information • Resilience & sustainability • Risk preferences
  7. 7. Factors modified from Sayer et al (2015) Bayesian Network for assessing restoration project implementation risk Information synthesis
  8. 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. 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. 10. Model structure -1 Lanzanova et al. (2019)
  11. 11. Model structure -2 Lanzanova et al. (2019)
  12. 12. Reservoir protection in Burkina Faso Lanzanova et al., in preparation.
  13. 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. 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. 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. 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. 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. 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. 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.

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