AgMIP Multiple Crop Model Training Program

K. J. Boote1, C. H. Porter1,

G. Hoogenboom2, J. Hargreaves3 1Univ. of Florida, 2Washington State Univ., 3APSRU, Australia

CIMMYT (Nepal) – March 18-22, 2013

ICRISAT (India) - March 25-29, 2013

• Improve scientific and adaptive capacity for major agricultural regions in the

developing and developed world

• Collaborate with regional experts in agronomy, economics and climate to build

strong basis for applied simulations addressing key climate-related questions

• Incorporate state-of-the-art climate products as well as crop and agricultural

trade model improvements in coordinated regional and global assessments of

future climate impacts

• Include multiple models, scenarios, locations, crops and participants to explore

uncertainty and impact of data and methodological choices

• Develop framework to identify and prioritize adaptation strategies

• Link to key on-going efforts

– CGIAR, CCAFS, Global Futures, Harvest Choice, Yield Gap Atlas, SERVIR

– National Research Programs, National Adaptation Plans, IPCC

2

Objectives

.

.

.

.

Agricultural Economic

Models

Crop

Models

Future climate

scenarios

Historical climate

conditions

Evaluation and

intercomparison

Future agricultural production, trade,

and food security Adaptation, mitigation, and extensions

Model calibration and improvement

Track 1

Track 2

Track 1: Model Improvement and Intercomparison

Track 2: Climate Change Multi-Model Assessment

Cross-Cutting Themes:

Uncertainty, Aggregation and Scaling,

Representative Agricultural Pathways

Driven by Data at

Sentinel Sites

Silver

Gold

Platinum

Two-Track Science Approach

Benefits include:

- Improved capacity for climate, crop and economic modeling to

identify and prioritize adaptation strategies

- Consistent protocols, scenarios and data access

- Improved regional assessments of climate impacts

- Facilitated transdisciplinary collaboration and active partnerships

- Contributions to National Adaptation Plans

= Wheat

= Maize

= Rice

0˚

0˚ 90˚ -90˚

45˚

-45˚ = Sugarcane

Morogoro

Ames

Wongan Hills

Delhi

Ludhiana

Ayr

Los Baños

Piracicaba

Shizukuishi

Rio Verde

La Mercy

Haarweg Lusignan

Balcarce

Nanjing

AgMIP Sentinel Sites

Regions and Crop Model Pilots

Capacity Building

and Decision Making • Regional vulnerability

• Adaptation strategies

• Trade policy instruments

• Technology exchange

Climate Team

Crop Modeling Team

Economics Team

Information

Technology

Team

Improvements and

Intercomparisons •Crop models

•Agricultural economic models

•Scenario construction

•Aggregation methodologies

Cross-Cutting

Themes

• Uncertainty

• Aggregation and

Scaling

• Representative

Agricultural

Pathways

Assessments • Regional

• Global

• Crop-specific

Key

Interactions

• Soils

• Water

Resources

• Pests and

Diseases

• Livestock

and

Grasslands

Teams, Linkages and Outcomes

AgMIP Capacity Building • Transdisciplinary community of climate, crop,

economic, and IT experts

• Model intercomparison and impacts assessment protocols

• IT databases, interface

• Framework for extensions

to grazing/livestock, water, pests and diseases, etc.

• Establishment of field observation standards for crop model applications

• Training programs (AgMIP Multiple Crop Model Program) and special workshops for sharing data and expertise

• Overall objective: Participants knowledgeable of one crop model, will learn how to use a different crop model for conducting integrated assessment of climate impacts, following AgMIP approach (APSIM & DSSAT)

• Plenary Sessions

– Overview of APSIM & DSSAT – Growth and Phenology

– Principles of genetic coefficient calibration

– Initializing soil water, N, residue, SOC, management

– Goals of AgMIP Integrated Assessment

– AgMIP Tools & Procedures for Integrated Assessment

– Seasonal Strategies (multi-year simulations)

• Parallel Sessions (DSSAT & APSIM)

– Model operation, inputting new crop, soil, management and weather data, calibrating genetic coefficients

– Verifying inputs & simulating field survey data, analyzing results and cumulative probability, bias adjustments, multi-year simulations, creating ACMO files for economists.

Training Program Objectives

• Important Assumptions: Participants are already

knowledgeable of crop modeling, the processes involved

and have data.

• What this Multi-Model Training Program IS NOT:

– It is not a beginning crop modeling course

– It will not be aimed at using models for applications

for managing irrigation, fertilization, cultivars, etc.

• We will not be instructing on:

– Processes in crop models, such as photosynthesis,

water balance, N balance, P balance, pest linkage,

genetic.

• We will emphasize use of the crop models to account for

yield variability attributed to farm management, soils, and

long-term weather, which variability will be used by

economists.

Training Program Activities

• Important Assumptions: Participants are knowledgeable

of crop modeling, the processes involved and have data.

• Expectations:

• We expect participants are coming with their own

(AgMIP) sentinel site data for calibrating GC and with

farm survey yield data, already entered and simulated

with their favorite crop model.

– We will evaluate Fast Track results with your favorite

model: How well did you do in calibrating GC from

sentinel site? How well were you able to set up

simulations for farm survey fields? Learn from issues.

– This week, you will take the opportunity is to learn to

use a different crop model, with that same data.

– Then, you will compare and contrast DSSAT and

APSIM simulations, for both genetic coefficients and

ability to simulate the farm survey fields (multi-model

comparison). Report back.

Training Program Activities

• Important Assumptions: Participants are already

knowledgeable of crop modeling, the processes involved

and have data.

• Outcomes:

– Learn to use multiple crop models and the IT Tools.

– Improve ability to conduct integrated assessment,

accounting for management, soils, cultivar, and

weather effects on production. Understand the factors

affecting bias-adjustment and resulting cumulative

yield probabilities among farm sites and weather

years.

– Be ready to conduct additional simulations for other

regions in your country, potentially for larger regions,

in current project.

– Learn how to simulate adaptations (improved

technology/cultivars).

Training Program Activities

Evolution of Several Major Families of Crop

Models (Origins of DSSAT & APSIM)

Dutch & US

Initiative

COTMOD – SIMCOT – GOSSYM – GOSSYM-COMAX @

SUCROS Family of Models (ORYZA, etc.), Various Crop------ Dutch

GLYCIM-----------------------

COTON French

USDA-ARS

SOYMOD ----------- @ Ohio State University

CROCROPGRO

DSSAT ------------------- CSM ---

G Generic CE CERES-Wheat/Maize

SOYGRO/PNUTGRO - Florida, Iowa,

Michigan, Georgia,

Hawaii, Canada,

Arizona, Mississippi

…

APSIM + ------------------ CSIRO, DPI, …

(Australia)

1970 1980 1990 2000

Time, Years----------------------------------

Groups

Acknowledgements re: the Origin and

Evolution of DSSAT

• ICRISAT

– 1983 Workshop in ICRISAT, publication on minimum data sets for

agrotechnology transfer

– Input from P. Singh, G. Algarswamy, S. Virmani, T. Williams,

Sivakumar, others over time

• Joe Ritchie, Henry Nix, Goro Uehara, Fred Beinroth, Barry

Dent, Phil Thornton, S. Jagtap, Walter Bowen, Bill Batchelor,

Tony Hunt, Gerrit Hoogenboom, Paul Wilkens, Cheryl

Porter, Upendra Singh, Jeff White, Gordon Tsuji, and

many others in addition to Ken Boote and Jim Jones

• Many other scientists who provided data, advice, and other

assistance

• Many institutions that encouraged those of us involved

Important Modules in Crop Models

• Plant Process Module (phenology, C balance, N balance, etc.)

• Soil – Water

– Temperature

– Carbon and Nitrogen (Godwin and CENTURY options for DSSAT; APSIM different SOC module)

– Phosphorus (APSIM, present for some crops in DSSAT)

– Potassium (APSIM?, new, and some crops in DSSAT)

• Environment (Weather, Weather Modification, etc.)

• Soil-Plant-Atmosphere (coupling)

• Management inputs

• Pest/Disease

Role and Tasks of Crop Modelers

in AgMIP/DFID Projects

1. Compute genetic coefficients for the desired crop cultivar,

based on sentinel site experimental data, where anthesis,

maturity, yield, and yield component data were collected.

2. Obtain crop data and simulate yields for farm yield survey

sites. This includes farm identity, crop, year, resource inputs,

costs of resource inputs, observed yield, and simulated yield, all

on a per farm basis. [n=50 to 100 farmers]. Calibration and

bias-adjustment issues: genetics, SOC, SOC pools, residue, IC

nitrate/ammonium, IC soil water, rooting, soil fertility.

3. Simulate same farm sites with 30 year baseline weather

and then with 30 year future weather – giving the individual

values as well as the mean over 30 years for each farm.

Economists should be using the weather variability too.

Example Table of Minimal Crop Data and

Simulated Yields

Tuesday morning – FAST Track Presentation

1. Powerpoint (8 slides in 8 minutes): Emphasis on genetic

calibration, with less emphasis on farm survey simulations.

2. Genetic Coefficient Calibration (5-6 slides):

• Data source (crop, cultivar, treatments, site, years).

• How calibrated? Which crop model?

• Simulated results (time series of LAI, biomass, grain)

(end-of-season 1:1 graphs if enough data).

• Genetic Coefficients listed.

3. Simulation of Farm Yield Survey (3 slides):

• Data source (crop, region, number of farms)

• How were missing inputs defined? (SOC, SOC pools,

residue, IC NO3 & NH4, IC soil water, soil fertility).

• Simulated results (1:1 of observed vs. simulated).

• Cumulative probability of exceedance (sim & obs)

Role of Instructors and “Trainers” & Teams

1. We anticipate many multiple activities with different crops

on at least two crop models: We expect voluntary

collaboration groups to form around work with a given crop with

a given model. Instructors will be asking for help from Trainers

to be mentors this week. Meet with 7 trainers at lunch Monday.

2. APSIM Trainers: Dilys MacCarthy, Nageswara Rao, Patricia

Masikati, Balwinder Singh; DSSAT Trainers: Dakshina

Murthy, Dougbedji Fatondji, Guillermo Baigorria. They will be

mentors this week and upon return home, will assist their teams

and regions with training and advice on using multiple crop

models and IT Tools to accomplish project goals.

3. Monday evening (meet briefly with your regional team):

• Plan for Tuesday morning presentation

• Plan for work to be done this week

4. Other evenings (practice, work, review):

18

For up-to-date events and news, visit www.agmip.org

Questions on Training Program? Contact kjboote@ufl.edu chporter@ufl.edu

gerrit.hoogenboom@wsu.edu John.Hargreaves@csiro.au

Introductions: Name, discipline, institution, crop, favorite crop model, and your role in AgMIP project