AgMIP Model Intercomparison & Improvement Teams

February 24, 2015

AgMIP Mission: Provide effective science-based agricultural

decision-making models and assessments of climate

variability and change and sustainable farming systems to

achieve local-to-global food security

• Overview of AgMIP

• AgMIP MIP Teams

• Crop Model Intercomparisons & Improvement

• Global-Gridded Crop MIP

• Global Economic Model Intercomparison

Outline

Current and Prospective Activities

3

Food Security &

Nutrition

Land Use

Education &

Capacity-Building

Natural Resources &

Ecosystems

Gender & Livelihoods

Protocols for

new AgMIP

Teams or

Activities

• Co-Led

• Written Plan with

Short and Long-

Term Goals

• AgMIP Protocols

• External Science

Advisors

• Review &

Attribution

• Budget and

Funding Strategy

• Quality

Assurance

Current

Prospective

Biofuels

Phase 2 Science Approach

4

Track 1: Model Intercomparison and Improvement

Track 2: Climate Change Multi-Model Assessment

Cross-Cutting Themes

Uncertainty, Aggregation and Scaling,

Representative Agricultural Pathways

Regional and Global Scales

AgMIP Sentinel Sites

Silver

Gold

Platinum

Rosenzweig et al., 2013 AgForMet

>NextGen Models

Adaptation and

Sustainable

Farming Systems

Bronze

Data

Agricultural system models

(economics, livestock, crop, pest & disease

Intercomparison and improvement

Platforms for integration of models for specific applications

PROTOCOLSMulti-model assessments

Scope

5

Knowledge products

e.g., policy briefs, dashboards

Usable by stakeholders

Assessments

1. Scientific Integrity

AgMIP projects and activities must be

based on good science and public-

good products.

2. Conflict of Interest/Bias

AgMIP Steering Council, Principle

Investigators, Team Leaders, and

Partner Leads identify possible conflict

of interest (NAS) and biases.

3. Advocacy

AgMIP promotes the best science for

development, evaluation, and

application of agricultural models

4. Open Data and Models

AgMIP endorses the use and

development of open-source/open-

access models, data and methods

5. Participation

AgMIP is committed to community

building and strives to enable its teams

and members in their regions, activities,

and funding applications. AgMIP

activities are open to all interested

researchers and facilitate

transdisciplinary integration.

6. Attribution

AgMIP publications attribute all

intellectual contributions, including

those related to both models and data

7. Flexibility

AgMIP is structured to facilitate the

ongoing evolution of agricultural

systems science

8. Investment in Future of

Systems Research Encourage new

field, younger scientists, uptake of

methods to curricula for education6

Principles and Standards

• Regional projects awarded on competitive basis

• 15 countries in Sub-Saharan Africa

• 5 countries in South Asia

• 60 institutions and 120+ scientists.

• Methods for Regional Integrated Assessment (RIA) of climate change impacts on agriculture

• Link climate, crop, and economics models in protocol approach to

provide distribution of impacts across farm households

• Create and disseminate handbook, climate scenarios guidebook,

and tools to enable research teams

• Train over 250 scientists

• Engage regional stakeholders and national media

• Handbook Series on Climate Change

and Agroecosystems (2012-2013)

• AgMIP regional project partners chapter authors

• Citable IPCC references

• Multiple crop model training

• 10 scientists ‘trained as trainers’ for Africa, Asia

• 50 scientists trained in multiple crop models and analytical methods

• TOA-MD model training

• 25 socio-economic scientists from Africa and S Asia trained

AgMIP Regional Projects

7

Pretoria, 2013

Nepal, 2013

Antle et al., 20138

New Methods for

Regional Integrated Assessment

1. Establish domain and farming system

2. Pose core questions

3. Engage stakeholders on Rep. Agric. Pathways

4. Develop regional climate projections

5. Calibrate crop model genetic coefficients

6. Incorporate on-farm economic survey data

7. Evaluate adaptation strategies

8. Characterize effects on livelihoods

Nikayi, South West Zimbabwe

Regional Integrated Assessment

of Farm Systems

1. Population and strata

Population: 160 HH (20HH per 8 villages) in Nkayi, South West Zimbabwe

Strata: Ownership of ruminants (TLU)

2. Mixed crop livestock sub-systems

Maize and other crops: Grain and residues

Cattle and other livestock: Milk, draft power, manure, milk

3. Crop, livestock and outcome components

Production: Maize grain and residues, cattle milk and meat

Gains and losses: Net returns on maize, other crops, cattle, other livestock

Herd size Thresholds (TLU) % household

No/few ruminants 0-0.49 29.4

Small herd 0.5-5.4 41.3

Large herd >5.4 29.4

CLIP; Patricia Masileti et al., 2013

Interpretation:

• Increases in temperature and reductions in precipitation result on average in 43%

losses of mean net returns on mixed crop livestock farms in Zimbabwe.

• Absolute losses in net returns and proportion of farmers who lose are most substantial

in stratum 3 (large herds), where spatial variability is also higher, not only indicating

higher losses through CC but also higher risks in production.

• Strata 1 and 2 are strongly correlated, within strata correlations among crop and

livestock activities are not strong.

• Result is overall losses in all strata, with larger economic losses and larger percentage

of farmers experiencing losses in stratum 3.

Summary of TOA-MD Economic Model Results: Climate Change ImpactsStratum Gains (%) Losses(%) Net Loss (%) Percent Losers

1 few livestock 11.2 25.7 14.6 64.5

2 small herds 7.8 30.5 22.7 72.6

3 large herds 3.8 57.1 53.3 86.5

Agg. 5.3 48.1 42.8 76.4

Nikayi, South West Zimbabwe

Gains and Losses

of Farm Systems

CLIP; Patricia Masikati et al., 2013

• Translators for producing inputs to run multiple

crop models (currently, for DSSAT, APSIM,

STICS, SALUS, Yan Zhu models; in progress

for CROPSYST, EPIC, WOFOST, others)

• Database harmonization, using ICASA data

dictionary and meta data exposed via API for

discovery and access (first AgMIP-CCAFS-

AgTrials database, next, harmonization of

various USDA databases)

• AgMIP-USDA Workshop at the National

Agricultural Library in July

AgMIP Data Harmonization

11

AgMIP data flow for

Regional Integrated

Assessments

IT Team (C. Porter & S. Janssen, co-leaders)

= Wheat

= Maize

= Rice

0˚

0˚ 90˚-90˚

45˚

-45˚

= Sugarcane

Ames

Morogoro

Wongan Hills

Delhi

Ludhiana

Ayr

Los Baños

Piracicaba

Shizukuishi

Rio Verde

La Mercy

Haarweg

Lusignan

Balcarce

Nanjing

AgMIP Sentinel Sites

North America

South

America

Sub-Saharan

Africa

Europe

South

Asia

Asia*

Australia*

Rosenzweig et al., 2013; Asseng et al., 201313

• Wheat (27 models), Maize (18), Rice (14), Sugarcane Pilots

• Pilots under development for millet/sorghum, soybean, canola, , and potato

• North America, South America, Europe, Sub-Saharan Africa, South Asia, Asia, Australia

Crop Model MIPs

AGMIP MIP Activities and Leaders

14

Global Economics Hermann Lotze-Campen and Keith WiebeRice Tao LiWheat Senthold Asseng, Pierre Martre, Frank EwertMaize Jean-Louis Durand

SugarcaneAbraham Singels, Fabio Marin, Matthew Jones, Peter Thorburn

Bioenergy Crop Models David LeBauer and Gopal KakaniPotato David FleisherLivestock and Grasslands Jean-Francois Soussana and Fiona EhrhardtCanola Enli Wang and Jing Wang Water Resources Jonathan WinterSoils and Crop Rotation Bruno Basso

AgMIP-Wheat

2011 2012 2013 2014 20152010

Improve-

mentTcanopy

AgMIP

Wheat 2(30 models)

EWG

EWG

AgMIP

Wheat 3(34 models)

AgMIP

Wheat

Pilot(27 models)

CO2

Intercomparison Intercomparison/Improvement/Application

Maize-

Rice-

Wheat

Provided by S. Asseng

16

AgMIP Wheat MIP Team Results

Asseng & Ewert et al., 2013

51 authors, 4 sites, 27 wheat modelsUncertainty in simulating wheat yields

under climate change Nature Climate Change

Maize, rice, and sugarcane pilots underwaySugarcane, peanut/groundnut, potato,

sorghum, millet, soybean

Medians of multiple models

perform better than

individual models at

differing environments

Better field data improves

calibration and reduces

uncertainties

Provided by S. Asseng

AgMIP – WheatWheat modelers (>30 models), crop physiologists and field experimentalists

Coordinated by:

Senthold Asseng, UF, Frank Ewert, U Bonn & Pierre Martre, INRA

Provided by S. Asseng

Model improvements

for impact studies

Season mean temperature (°C)

15 20 25 30 35

Gra

in y

ield

(t/

ha)

0

2

4

6

8

10

12

Season mean temperature (°C)

15 20 25 30 35

Gra

in y

ield

(t/

ha)

0

2

4

6

8

10

12

Bruce Kimball

Asseng et al. 2014 Nature CC

Provided by S. Asseng

CIMMYT, El Batan, Texcoco, Mexico

June 1921, 2013

PD Alderman, E Quilligan, S

Asseng,

F Ewert and MP Reynolds (Editors)

AgMIP – Wheat publications

Provided by S. Asseng

20

Global Gridded Crop Model Results

median of 7 GGCMs and 5 GCMs/AgMIP led agricultural contribution to ISIMIP

Lower latitudes are more vulnerable to climate change

Model uncertainty now included

2080sHatched areas indicate

>70% model agreement

Rosenzweig et al., PNAS, 2013

Global Gridded Crop Model Results

The mean AgMIP results with realistic nitrogen fertilization show steadily decreasing yields for wheat, maize, and soybean in mid and high-latitude regions even for small temperature increases. However, there is still a wide range around the mean AgMIP results, indicating

variation across models. 21

Global Economics Models

Effects of climate change on agricultural prices

(S3-S6 results in 2050 relative to S1 results in 2050)

Source: AgMIP model runs, December 2012.

Nelson, Gerald C. et

al., “Agriculture and

Climate Change in

Global Scenarios:

PNAS; Agricultural

Economics,

2013

9 global

economic

models

Climate change is projected to exert upward pressure on

agricultural prices, but with large uncertainty

Phase 2

workshop

in Dublin

April 9,

2013

22

Key Messages

23

Global Gridded Crop Model Assessment

• In contrast to previous assessments, AgMIP global gridded crop model results with realistic nitrogen fertilization show steadily decreasing yields for wheat, maize, and soybean in mid and high-latitude regions even for small temperature increases.

• Crops in lower latitudes show greater vulnerability.

• For the first time, crop model uncertainty has been characterized, highlighting the need for continuing rigorous model evaluation and improvement.

Global Economic Model Assessment

• Climate change is projected to exert upward pressure on agricultural prices, but with large uncertainty.

• Economic responses reduce yield loss, increase crop area, and reduce consumption.

• Economic models differ primarily in assumptions about ease of land use conversion, intensification, and trade.

Possible approaches:

• Apply a damage function of losses by pests/diseases to crop model results,

perhaps using different crop models to get variations

• Predict magnitude and dynamics of pest/disease and apply damage to crops

using coupling points (as Ken Boote talked about)

• Vary crop models

• Vary pest/disease model to study uncertainty

• Datasets would be useful on pest/disease dynamics and crop yield response

• Construct longer-term activities that test the adequacy of this approach. E.g., an

activity for testing the hypothesis that simple approaches are inadequate

• Outputs from these activities will be a more complex, but ‘richer’ protocol to be

used in future RIA’s.

• From what is learnt, improve P&D models to provide better information for RIA (&

maybe develop more sophisticated protocols).

24

What AgMIP Needs?

Please Join AgMIP Listserve – www.agmip.org

Develop a protocol that works for your team, including data that are to be used by

different team members, timetable on provision of data, results, what to compare,

target paper(s), etc.