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Demonstration of climate-smart agriculture prioritisation toolkit


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Presentation by Alex Dunnet, consultant at CCAFS, at the CCAFS South Asia Workshop on Institutions and Policies to Scale Out Climate Smart Agriculture

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Demonstration of climate-smart agriculture prioritisation toolkit

  1. 1. Prioritization of Climate-Smart Agricultural Technologies at Local Scale Methodology and Assessment CCAFS 4th December 2013
  2. 2. Session Outline 1. Introductions 2. Biophysical model data requirements – Q&A on data aspects 3. Demonstration tool overview – Mathematical Programming Toolkit – Model Overview 4. Tool exploration exercise 5. Comment: Upscaling these approaches 6. Discussion – Are these tools relevant? – Challenges to uptake and implementation – Capacity building
  3. 3. Why Mathematical Programming? • Simulation + what-if? analysis Simulation – What would the farmers select? Optimisation – Select best from constrained option set – If the farmers selected, what would be the outcome? • Optimisation + do-what? Analysis – What should the farmers be using? – Search for and select best portfolio from large (potentially infinite) option set – Manual OR Automated procedure (e.g. LP)
  4. 4. Model Fundamentals (Classical) Classical toolkit of agricultural sector LP modelling tools dating back over 60 years • Activity selection for land-use planning + Technical coefficient generator • • • • • 𝒙𝒄𝒓𝒐𝒑 𝑙,𝑡,𝑐,𝑎,𝑓𝑠,𝑝,𝑘 Linearized market-price effects Discounting and net-present value Risk measures (e.g. TARGET-MOTAD) Returns to capital investment Interactive Multiple Goal Linear Programming • Key Text: Hazel & Norton (1986) [IFPRI]
  5. 5. Model Fundamentals (Extensions) Innovative modelling approaches • Spatially-explicit crop-models, climate-forecasts and greenhouse-gas emissions calculators • Dynamic optimization with technological investment, landuse change and technology uptake • Stochastic-dynamic modelling to support planning with uncertainty in future climate – Minimax / Maximin / Low-Regret – Real Options analysis: Value the wait and see • Multi-objective optimization and identification of the efficient frontier + gradient • CPU++ Computational tractability ++ resolution
  6. 6. Spatial-Dynamic Land-Use Model (3) Multi-Scale Constraints State-Level Constraints District Level Constraints Land-Unit Constraints Domestic Market Demand Export Limits Rate of Land-Use Change Development Targets Water Availability Labour Availability Land Availability Crop Suitability Technological Suitability Farm-Size Technology Access Production Area Protection Land-Units broken down further by rainfed/irrigated area and farm-size categories
  7. 7. CSA Prioritization Toolkit Model Structure Farm Size Breakdown Spatially-Explicit Bio-physical Database Model Input Database Target Demand Forecasts Crop Nutrition Labour Forecasts Prices and Elasticities Investment Cost Land available by Minimum data boundary Module area and type Markets Module + Crop-water demand Growth available + irrigationTargets Multi-Objective Analysis Model Engine Modular Model Constrained Production Code Calibration Crop labour demand Risk-Objectives +(TARGET-MOTAD) population supply COIN-OR CBC LP Solver Post-Solve Output Analysis Crop yields and Spatial Allocation Constraints emission factors
  8. 8. Demo Tool Setup Note: Only tested on Excel 2010+ versions 1. Place CSA Priotization Demo_v1.0 in desired model folder 2. Unload contents of folder OpenSolver21 into same model folder 3. Open blank Excel workbook 4. Double click OR drag OpenSolver.xlam add-in file into open workbook – 5. Activate the default Excel solver add-in – – – 6. This should load the Opensolver menu under Data tab Goto File-Options-Add-Ins Select Manage “Excel Add-Ins” and click Go Activate the Solver Add-In Open CSA Prioritization Demo_v1.0 See:
  9. 9. Tradeoff Analysis: Overview Priority Means AND Ends Run 2: Run 1:Min Emission Max SSR = Min SSR = Max Emissions Optimal Space Efficient Frontier Cannot improve in one objective without sacrificing another
  10. 10. Running Tradeoff Analysis 1. Run model to optimize primary objective – Suggested: Maximize production or margin 2. In sheet <Variables> record the current objective levels (Cells E17:E21) 3. Select tradeoff objective and specify a desired bound level <Variable> (Cells H17:H21) – Example: Record production max level of CO2,eq and set bound at 80% of that level 4. Re-run the model for primary objective - now under additional constraint
  11. 11. Upscaling Tool to Project Resources required: • Minimum data specification • Algebraic Programming Language – Algebraic Modelling Systems, Modeling and Solving Real World Optimization Problems, Josef Kallrath (Ed.) (2012) • Computational tools (NEOS, Kestrel, CPLEX Studio, Solver Studio etc.,) – – • Modelling programme management – Quality Assurance (QA) • Analytically literate policy audience – Structured policy engagement + facilitation
  12. 12. Discussion Points 1. Do people see promise in this approach to support prioritization of climate-smart investment? 2. What do people envisage as the challenges to implementing these approaches more widely? 3. If needed what do people and institutions need to take this approach forward? (Tools? Programming skills? Data?)