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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
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• Overview of AgMIP
• AgMIP MIP Teams
• Crop Model Intercomparisons & Improvement
• Global-Gridded Crop MIP
• Global Economic Model Intercomparison
Outline
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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
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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
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Scope
5
Knowledge products
e.g., policy briefs, dashboards
Usable by stakeholders
Assessments
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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
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• 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
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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
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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
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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
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• 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
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AgMIP data flow for
Regional Integrated
Assessments
IT Team (C. Porter & S. Janssen, co-leaders)
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= 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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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.
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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.