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

1. Prioritization of Climate-Smart
Agricultural Technologies at Local Scale
Methodology and Assessment
CCAFS
4th December 2013

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. 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. 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
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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. 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. 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. 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. 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
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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: http://opensolver.org/

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. 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. 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.,)
– http://solverstudio.org/
– http://www.neos-server.org/neos/
• Modelling programme management
– Quality Assurance (QA)
• Analytically literate policy audience
– Structured policy engagement + facilitation

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?)