Workshop Trade-off Analysis - CGIAR_19 Feb 2013_Overview of TOA_Lotte Klapwijk

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  • Optimization = only modeling? Synergies.
  • Examples / references ?
  • De Wit, C.T., H. van Keulen, N.G. Seligman, I. Spharim, 1988. Application of interactive multiple goal programming techniques for analysis and planning of regional agricultural development. Agricultural Systems 26: 211-230.Rabbinge, R., H. Van Latesteijn, 1992. Long-term optinos for land use in the European community. Agricultural Systems 40, 195-210.Lu, C.H., M.K. van Ittersum, 2004. A trade-off analysis of policy objectives for Ansai, the Loess Plateau of China. Agriculture, Ecosystems and Environment 102: 235-246.Stoorvogel, J.J., J.M. Antle, C.C. Crissman, W. Bowen, 2004. The tradeoff analysis model: integrated bio-physical and economic modelling of agricultural production systems. Agricultural Systems 80, 43-66.Laborte, A.G., M.K. van Ittersum, M.M. van den Berg, 2007. Multi-scale analysis of agricultural development: A modelling approach for Ilocos Norte, Philippines. Agricultural Systems 94: 862-873.
  • Javier: Keynote on the structure of complex systems where humans interact with nature. (Example = Resilience Alliance)Would you define your work research- or development-driven?Raise hands: agronomists, economists, socialWho of you has modeling experience?Who cooperates with social scientists?Who of you used participatory methods?
  • Objective: Maximize income per capita, with minimum damage/loss of environmental indicators.
  • Lastigefiguure:watheb je op de x-asNo point with overlap from all three circles? First different scales + their trade-offs?!Participatory can also be quantitative.
  • To set the stage, unify the language and develop a common understanding of interactions of complex systems. To understand and predict the effect of certain decisions.Beyond farm interactions
  • Reference: QUALUS syllabus. And vision document PPS.Verifiable vs. non-verifiable, explorative, speculativeDeterministic vs. stochastic
  • Don’t forget Roger Kirkby’s offer..
  • Where to focus on in this field, depends on research questions and goals. And on the scale studied. Socio-economic relevant at at least the farm and landscape level. AIS. DEA. MIDAS model. Multi-objective optimization: FarmDESIGN (email: available online). CLUE-model – Verburg (?). MCDM techniques (Argyris). Participatory approaches (Bosch et al., 2007). TOA for sustainability evaluation (Speelman et al., 2006). APSIM (Speelman et al. 2006)COMPASS framework of the Farming System Ecology group (Groot et al. 2012). Multi Criteria Decision-Aid methods (MCDA) (Dore et al.? Sumpsi et al., 1996)Interviews: focus group, key informant, snow-ball and open-ended. DEED is in ‘Communicating complexity’. SMART = in Douthwaite et al., 2007.Integrated Assessment Models (IAM) (Valdivia et al. 2012). Mark: “These heavy computing options could be contrasted nicely to other multicriteria / multiobjective methods.”
  • Participatory approaches not yet included. Fill in with references?
  • Where to go in this field, depends on research questions and goals. And on the scale studied.
  • Workshop Trade-off Analysis - CGIAR_19 Feb 2013_Overview of TOA_Lotte Klapwijk

    1. 1. Analysis of Trade-Offs in Agricultural Systems Methods and their strengths and weaknesses Lotte Klapwijk 19 February 2013
    2. 2. Content• Introduction• History of Trade-Off Analysis• Definition• Methods for Trade-Off Analysis• Strengths + weaknesses• Conclusion
    3. 3. Introduction• Mission: “Reduce poverty and hunger, improve human health and nutrition, and enhance ecosystem resilience”• Holistic view• Sustainability assessment• System Thinking• Trade-off Analysis…
    4. 4. … is very “hot” Scopus-search: Trade-off Analysis 450 400 350 300Occurences 250 200 150 100 50 0 1970 1984 1994 2004 Year
    5. 5. History• Some examples: – de Wit, van Keulen, Seligman and Spharim (1988) – Rabbinge and van Latesteijn (1992) – Stoorvogel, Antle and Crissman (2004) – Laborte, Van Ittersum and Van den Berg (2007) – Naudin et al. (2012)
    6. 6. Laborte et al., 2007
    7. 7. The different scales of analysis Farm types Better-off Average-RF Province Average-IR Municipality Poor Farm
    8. 8. Provincial explorations Optimization Rice production Farmers‟ incomeWithout water-sharingRice Production (10 3 t) 507 221Farmers’ income (10 9 P) 6 50Biocide use (t) 121 192With water-sharingRice Production (10 3 t) 726 273Farmers’ income (10 9 P) 6 58Biocide use (t) 160 251
    9. 9. Naudin et al., 2012
    10. 10. Science / Research• Tasks – Analysis and charecterisation of agricultural systems – Design of agricultural systems / scenarios – Ex-ante integrated assessment of scenarios and policies• Integrated assessment of sustainability
    11. 11. Tackling a (TO) research question:• Define the problem + objective• Describe components + indicators• Set boundaries / focus• Describe assumptions• Set goals (multiple  optimization)
    12. 12. Quantitative EXPERIMENTAL MODELING Qualitative PARTICIPATORY
    13. 13. Trade-offs• Definition Trade-off: “An exchange that occurs as a compromise”• One more example: MSc thesis on viability of „One-cow-per-poor-family‟- program, Rwanda
    14. 14. Trade-Off Analysis:I. QualitativeII. Empirical or experimentalIII. SimulationIV. Optimization
    15. 15. I. Qualitative• Participatory methods: fuzzy cognitive mapping, resource flow mapping, games, role-playing, etc.• Companion modeling• “People are part of farming systems and their views cannot be ignored” (Lawrence, 2011)
    16. 16. II. Empirical-experimental• Frontier analysis• Data Envelopment Analysis• Randomized controlled trial
    17. 17. Modeling• Model = simplified representation of reality• Goal: to show processes, structure and/or function of a system• Qualitative or quantitative• Simulation or optimization• Descriptive or explanatory• Static or dynamic
    18. 18. III. Simulation• Bio-Economic Farm Models• APSIM• Midas• CLUE-model• FarmSIM• …
    19. 19. III. Simulation• Trade-off Analysis model (Stoorvogel et al., 2004)
    20. 20. IV. Optimization• Multiple Goal Linear Programming• Inverse modeling – Pareto optimality• FSSIM
    21. 21. SWOT of methods• Advantages & disadvantages – Qualitative vs. quantitative – Based on expert knowledge, empirical data and/or theory and process knowledge – Which objectives/indicators can be accounted for? – Data demand – Pareto-optimality – Stakeholder involvement
    22. 22. Conclusion….?
    23. 23. Acknowledgements• Martin van Ittersum, PPS• Ken Giller, PPS (and the whole chair group)• Phil Thornton, CCAFS• Piet van Asten, IITA• Everyone interviewed within Wageningen University as well as the CGIAR
    24. 24. Thank you for your attention! Questions? Remarks?One for you:Who‟s interested to help me with a publication?
    25. 25. Scale Quantitative Modeling Qualitative Experiments FarmSIM Participatory: Farm level Measurements FarmDESIGN - Companion modeling Surveys MGLP (agent-based) APSIM - Interviews … - Plenary discussion - Games / role-playing - Resource flow mapping - Feedback workshop - Rural appraisal (?)Landscape + Bio-economic (BEFM) - PIPA (Douthwaite, 2007 ExperimentsWatershed level Inverse modeling-method (NUANCES) (DYNBAL + MOSCEM) Pareto-optimal Farm System Simulator (FFSIM) Multiple criteria MIDAS (?) analysis CLUE modelRegional + Trade-Off Analysis modelGlobal level DEED. Multi-objective SMART optimization DfID.
    26. 26. Achieving impact = next phase, separated.Communication: radio.And where goes semi-quantitative...
    27. 27. APPROACH: Experiments MGLP BEFM Inverse TOA-(MD) modelingTrade-off level (MOSCEM) Farm Landscape + Watershed Regional + Global
    28. 28. Scale Quantitative Modeling Qualitative Farm level Measurements FarmSIM Participatory: Experiments FarmDESIGN - Interviews … - Games “FRAMEWORK” Bio-economic (BEFM) Inverse modeling-methodLandscape + (DYNBAL + MOSCEM)Watershed level Pareto-optimal Farm System Simulator (FFSIM) Trade-Off Analysis modelRegional +Global level

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