Prioritization of Climate-Smart Agricultural Technologies at Local Scale

Methodology and Assessment

CCAFS 4th December 2013

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

• Simulation + what-if? analysis

– What would the farmers select?

– 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)

Optimisation

Why Mathematical Programming?

Simulation

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

• 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]

𝒙𝒄𝒓𝒐𝒑𝑙,𝑡,𝑐,𝑎,𝑓𝑠,𝑝,𝑘

Model Fundamentals (Extensions) Innovative modelling approaches

• Spatially-explicit crop-models, climate-forecasts and greenhouse-gas emissions calculators

• Dynamic optimization with technological investment, land-use 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

Land-Unit Constraints Land Availability Crop Suitability

Technological Suitability Farm-Size Technology Access Production Area Protection

State-Level Constraints Domestic Market Demand

Export Limits Rate of Land-Use Change

Development Targets

Spatial-Dynamic Land-Use Model (3) Multi-Scale Constraints

District Level Constraints Water Availability Labour Availability

Land-Units broken down further by rainfed/irrigated area and

farm-size categories

CSA Prioritization Toolkit Model Structure

Spatially-Explicit Bio-physical

Database

Model Input Database

Farm Size Breakdown

Target Demand Forecasts

Crop Nutrition

Prices and Elasticities

Labour Forecasts

Minimum data boundary

Constrained Production

Land available by area and type

Crop-water demand + irrigation available

Crop labour demand + population supply

Crop yields and

emission factors

Modular Model Code

Investment Cost Module

Markets Module + Growth Targets

Risk-Objectives (TARGET-MOTAD)

Spatial Allocation Constraints

Calibration Multi-Objective

Analysis Model Engine

COIN-OR CBC LP Solver

Post-Solve Output Analysis

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 – This should load the Opensolver menu under Data tab

5. Activate the default Excel solver add-in – Goto File-Options-Add-Ins – Select Manage “Excel Add-Ins” and click Go – Activate the Solver Add-In

6. Open CSA Prioritization Demo_v1.0

See: http://opensolver.org/

Efficient Frontier Cannot improve in one objective

without sacrificing another

Optimal Space

Run 2: Min Emission

= Min SSR Run 1: Max SSR = Max Emissions

Tradeoff Analysis: Overview Priority Means AND Ends

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

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

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?)