What is AgMIP?

CCAFS Science Meeting May 2, 2011

1

Why AgMIP?

• Agricultural risks growing, including climate change

• Consistent approach needed to enable agricultural sector analysis across relevant scales and disciplines

• Long-term process lacking for rigorous agricultural model testing, improvement, and assessment

2

AgMIP Objectives

• Improve scientific and adaptive capacity of major agricultural regions in developing and developed world

• Collaborate with regional experts in agronomy, economics, and climate to build strong basis for applied simulations addressing key regional questions

• Develop framework to identify and prioritize regional adaptation strategies

• Incorporate crop and agricultural trade model improvements in coordinated regional and global assessments of future climate conditions

• Include multiple models, scenarios, locations, crops and participants to explore uncertainty and the impact of methodological choices

• Link to key on-going efforts – CCAFS, Global Futures, MOSAICC, National Adaptation Plans

3

Track 1: Model Improvement and Intercomparison Track 2: Climate Change Multi-Model Assessment

Cross-Cutting Themes: Uncertainty, Aggregation Across Scales*, Representative Agricultural

Pathways

Scales: Regional and Global

AgMIP Two-Track Science Approach Data at

Sentinel Sites

Silver

Gold

Platinum

0˚

0˚ 90˚ -90˚

45˚

-45˚

AgMIP Regions

Benefits include:

- Improved capacity for climate, crop, and economic modeling to

identify and prioritize adaptation strategies

- Consistent protocols and scenarios

- Improved regional assessments of climate impacts

- Facilitated transdisciplinary collaboration and active partnerships

- Contributions to National Adaptation Plans

Crop Model Pilot Activities in

AgMIIP

Crop Modeling Coordinators

K. J. Boote, Univ. of Florida

Peter Thorburn, CSIRO, Australia

Crop Modeling Team Goal

• To evaluate different crop models

– for accuracy of response to climatic, CO2, and other growth and management factors

– so there is confidence in the ability of models to predict global change effects and make consistent scenario-based projections of future crop production for economic analysis.

Learn from intercomparisons and improve the

crop models. 2nd I in AgMIP is “Improvement”.

Crop Modeling Team Activities

• Activity 1 – Inter-compare crop models for methods and accuracy of predicting response to variety of drivers

• Activity 2 – Conduct uncertainty pilot analyses across an ensemble of models

• Want standardized protocols across crops. – Wheat “uncertainty” (Asseng, Ewert)* – Maize “uncertainty” (Bassu, Durand, Lizaso, Boote)* – Sugarcane “uncertainty” (Thorburn, Marin, Singels)* – Rice “uncertainty” (Bouman, Tao, Hasegawa, Zhu, Singh, Yin)* – New teams (sorghum (Rao), peanut (Singh), potato (Quiroz))

*Already at work

Accomplishments Crop Modeling Team AgMIP-South America Workshop

• Calibrated for two Brazilian sites

– three maize models (CERES-Maize, APSIM, & STICS)

– two rice models (APSIM-ORZYA, and CERES-Rice)

• accounting for soils, cultivar, & management

• Used time-series and end-of-season data

Accomplishments Crop Modeling Team AgMIP-South America Workshop

• Conducted climate change uncertainty analyses with three maize and two rice calibrated crop models – Mean temperature (Tmax & Tmin), (-3, 0, +3, + 6, +9 C).

– CO2 levels (360, 450, 540, 630, & 720 ppm)

– Rainfall (-30, 0, +30%)

– N fertilizer (0, 25, 50, 100, 150% of reference N)

• Simulated baseline 30 years and one future scenario!

• Compare how crop biomass, LAI, grain yield, grain number, N accumulation, seasonal T and E respond to these factors across the different crop models.

11

Grain Yield and Biomass Response of DSSAT, APSIM, & STIC maize models to

temperature

Sensitivity analyses examples from AgMIP Workshop, Campinas, Brazil,

August 2011

APSIM

CERES

STICS

12

Days to maturity and ET of DSSAT, APSIM, & STIC maize models in response to temperature. ET

affected by life cycle.

Sensitivity analyses examples from AgMIP Workshop, Campinas, Brazil,

August 2011

Yield Response of

APSIM-ORYZA

and CERES-Rice

to temperature,

CO2, rainfall, and

N fertilization

Alex Heinemann,

Brazil, Aug 2011

APSIM

CERES

Sensitivity analyses

examples from

AgMIP Workshop

Campinas, Brazil

August 2011

-2 0 2 4 6 80.0

0.5

1.0

1.5

Yield

Temperatura

Rela

tive Y

ield

APSIM

DSSATUpland Rice

a)

400 500 600 700

0.0

0.5

1.0

1.5

Yield

CO2 level

Rela

tive Y

ield

APSIM

DSSATUpland Rice

b)

-30 -20 -10 0 10 20 30

0.0

0.5

1.0

1.5

Yield

Precipitation Variation

Rela

tive Y

ield

APSIMDSSAT

Upland Rice

c)

0 50 100 1500.0

0.5

1.0

1.5

Yield

N Levels

Rela

tive Y

ield

APSIMDSSAT

Upland Rice

d)

-2 0 2 4 6 80.0

0.5

1.0

1.5

DOYMaturity

Temperatura

Rela

tive D

OY

Matu

rity

APSIM

DSSATUpland Rice

r)

400 500 600 700

0.0

0.5

1.0

1.5

DOYMaturity

CO2 level

Rela

tive D

OY

Matu

rity

APSIM

DSSATUpland Rice

s)

-30 -20 -10 0 10 20 30

0.0

0.5

1.0

1.5

DOYMaturity

Precipitation Variation

Rela

tive D

OY

Matu

rity

APSIMDSSAT

Upland Rice

t)

0 50 100 1500.0

0.5

1.0

1.5

DOYMaturity

N Levels

Rela

tive D

OY

Matu

rity

APSIMDSSAT

Upland Rice

u)

Maturity Response

of APSIM-ORYZA

and CERES-Rice

to temperature,

CO2, rainfall, and

N fertilization

Alex Heinemann,

Brazil, Aug 2011

APSIM

CERES

-2 0 2 4 6 80.0

0.5

1.0

1.5

BIOMASS

Temperatura

Rela

tive B

IOM

AS

SAPSIM

DSSATUpland Rice

i)

400 500 600 700

0.0

0.5

1.0

1.5

BIOMASS

CO2 level

Rela

tive B

IOM

AS

S

APSIM

DSSATUpland Rice

j)

-30 -20 -10 0 10 20 30

0.0

0.5

1.0

1.5

BIOMASS

Precipitation Variation

Rela

tive B

IOM

AS

S

APSIMDSSAT

Upland Rice

k)

0 50 100 1500.0

0.5

1.0

1.5

BIOMASS

N Levels

Rela

tive B

IOM

AS

SAPSIMDSSAT

Upland Rice

l)

Biomass Response

of APSIM-ORYZA

and CERES-Rice to

temperature, CO2,

rainfall, and N

fertilization

Alex Heinemann,

Brazil, Aug 2011

APSIM

CERES

-2 0 2 4 6 8

0.0

0.5

1.0

1.5

LAI

Temperatura

Rela

tive L

AI

APSIM

DSSATUpland Rice

e)

400 500 600 700

0.0

0.5

1.0

1.5

LAI

CO2 level

Rela

tive L

AI

APSIM

DSSATUpland Rice

f)

-30 -20 -10 0 10 20 30

0.0

0.5

1.0

1.5

LAI

Precipitation Variation

Rela

tive L

AI

APSIMDSSAT

Upland Rice

g)

0 50 100 1500.0

0.5

1.0

1.5

LAI

N Levels

Rela

tive L

AI

APSIM

DSSAT

Upland Rice

h)

LAI Response of

APSIM-ORYZA

and CERES-Rice

to temperature,

CO2, rainfall, and

N fertilization

Alex Heinemann,

Brazil, Aug 2011

APSIM

CERES

Maize Crop Pilot – Preliminary Results Simona Bassu, Jean Louis Durand,

Jon Lizaso, Ken Boote

Baron Christian, Basso Bruno, Boogard Hendrik, Cassman Ken, Delphine

Deryng, De Sanctis Giacomo, Izaurralde Cesar, Jongschaap Raymond,

Kemaniam Armen, Kersebaum Christian, Kumar Naresh, Mueller Christoph,

Nendel Claas, Priesack Eckart, Sau Federico, Tao Fulu, Timlin Dennis,

Jerry Hatfield, Marc Corbeels

Model Behaviour: Maize Crop Pilot

Preliminary Sensitivity Analysis Low input information

….Response to Temperature (6 models)

0

0,2

0,4

0,6

0,8

1

1,2

1,4

1,6

1,8

-5 0 5 10Y

ield

rat

io

T increase

Ames (Us)

0

0,2

0,4

0,6

0,8

1

1,2

1,4

1,6

1,8

-5 0 5 10

yie

ld r

atio

Temperature increase (°C)

Morogoro (Tanzania)

Models Behaviour: Maize Crop Pilot

Preliminary Sensitivity Analysis Low input information

….Response to CO2 (6 models)

0,9

1

1,1

1,2

1,3

1,4

1,5

300 400 500 600 700 800

yie

ld r

atio

[CO2] ppm

Ames (US)

0,9

1

1,1

1,2

1,3

1,4

1,5

300 400 500 600 700 800

Yie

ld r

atio

[CO2] ppm

Morogoro (Tanzania)

20

AgMIP Initiatives – Track 1 Experimenters & Crop Modelers Workshops

− Test against observed data on response to CO2, Temperature,

including Interactions with Water, and Nitrogen Availability

Track 2

Track 1

Model Improvement

Calibration of CERES and APSIM maize models against 4 seasons at Wa, Ghana

y = 0.833 x + 361

R2 = 0.925

0

1000

2000

3000

4000

5000

0 1000 2000 3000 4000 5000

Observed Grain Yield, kg/ha

Sim

ula

ted

Gra

in Y

ield

, k

g/h

a

Simulated versus observed maize yield at Wa, Ghana over 4

years, using CERES-Maize (data courtesy, Jesse Naab)

Tested CROPGRO-Peanut model response to temperature.

Crop grown at 350 ppm CO2. Model mimics observed pattern of

biomass & pod mass vs. temperature with pod failure at 39C.

0

2000

4000

6000

8000

10000

12000

25 30 35 40 45

Mean Temperature, °C

Cro

p o

r P

od

, k

g / h

a

Sim - Pod

Obs - Pod

Sim - Crop

Obs - Crop

AgMIP, test accuracy of

multiple crop models

against data like this.

Arrow is Southern

US crop cycle temp.

Genetic Impr.

Heat tolerance

Simulated Seed

Yield of Dry Bean

Montcalm vs.

Temperature

No change needed

in temp effect on

podset or sd growth 0

1000

2000

3000

4000

20 25 30 35 40

Mean Temperature, °C

Se

ed

Yie

ld, k

g / h

a Predicted - 700

Observed - 700

0

2000

4000

6000

20 25 30 35 40

Mean Temperature, °C

Cro

p o

r P

od

, k

g / h

a Mod Sim

Obs - Crop

Default Sim

Final Biomass of

Dry Bean Montcalm

vs. Temperature

Made leaf Ps less

sensitive to high

temperature

REGIONAL ECONOMIC MODELING

24

Regional Modeling: Motivation

• Research -- and common sense! -- suggest that poor agricultural households are among the most vulnerable to climate change and face some of the greatest adaptation challenges

• Rural households and agricultural systems are heterogeneous, implying CC impacts – and value of adaptation strategies -- will vary within these populations

• Farmers’ choice among adaptation options involves self-selection that must be taken into account for accurate representation of adaptation options

• Impacts of climate change and adaptation depend critically on future technologies and socio-economic conditions

• Goal of AgMIP regional modeling is to advance CC impact and adaptation research through the development of Protocols for systematic implementation of impact and adaptation analysis, inter-comparison and improvement.

26

Regional Modeling Activities

• Regional SSA and SA Teams – All teams use at least one standard modeling approach (TOA-MD and others

according to region, team composition and interests)

– All teams develop RAPs, adaptation scenarios for their regions, consistent with global RCPs, SSPs and RAPs

– Further refine RAPs concepts and protocols

• Linking regional models to national/global models – Methods for coupling global model prices, other variables to regional analysis

– Inter-comparison of global and regional model outputs?

• Linking climate data, crop & livestock models to regional economic models – Developing improved methods for systematic use of climate data, soils and other

biological data with crop & livestock models to characterize spatial and temporal distributions of productivity for use with economic models

• Methods to assess uncertainty in parameters, model structure – Parameter estimation methods based on survey, experimental, modeled and

expert data; functional form and distributional assumptions

– Within and between individual model levels (climate, crop, econ)

Example: New Methods for Linking Crop and Regional Economic Models

• Question: how to quantify the future productivity of ag systems for impact assessment and adaptation analysis, accounting for spatial heterogeneity?

• Answer: use crop models to simulate relative yield distributions: – y2 = (1+ /y1) y1 = r y1 giving r = (1+ /y1) where r = r + r , (0,1)

– Using this model, with observations on one system and plausible bounds on r &

r we can approximate mean, variance and between-system correlations for the other system

– data for r & r can come from crop model simulations

Example: maize relative yield distribution in Machakos, Kenya R = future yield/present yield

28

-100000

-80000

-60000

-40000

-20000

0

20000

40000

60000

80000

100000

0 10 20 30 40 50 60 70 80 90 100

Lo

sses

Percent of Farms

1a 1b 2a 2b 3a 5a 5b

Sensitivity analysis of alternative methods of estimating relative yield distribution with matched and unmatched site-

specific data and averaged data (simulated CC gains and losses, using TOA-MD model for Machakos, Kenya)

1a = time-averaged, matched bio-phys & econ data by site 1b = matched bio-phys & econ data by site (not time averaged) 2a = time-averaged, unmatched bio-phys & econ data by site 2b = unmatched bio-phys & econ data by site (not time averaged) 3a = site-specific bio-phys data, spatially averaged econ data with approximated spatial variance 5a = averaged bio-phys and econ data 5b = averaged bio-phys and econ data, approximated variance of bio-phys data only

Analysis shows critical role that estimation of spatial variance

(heterogeneity) plays in estimation of distributional impacts.

29

Vihiga Machakos

Poverty Rate (% of farm population living on <$1 per day)

Scenario No Dairy Dairy Total No Dairy Dairy Irrigated Total

base 85 38 62 85 43 54 73

CC 89 49 69 89 51 57 78

imz 87 42 65 85 44 50 73

dpsplw 88 42 66 85 44 50 73

dpsp 85 41 63 83 43 50 71

dpsp1 85 36 60 83 41 49 71

dpsp12 85 30 58 83 38 48 70

RAP1 base 65 17 41 72 30 46 60

RAP1 CC 71 18 44 77 33 47 64

RAP1 imz 66 15 41 70 27 40 58

RAP1 dpsp 65 15 40 69 27 40 57

Example: Using TOA-MD and RAPs to simulate distributional impacts of CC and adaptation

strategies using dual-purpose sweet potato, Vihiga and Machakos Districts, Kenya

(note effect of RAPs on base and estimated impacts)

Source: Claessens et al. Agricultural Systems in press 2012

INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE

GLOBAL ECONOMIC MODEL INTERCOMPARISON

30

Why bother? We all have lots to do!

It matters

• Policy makers care if we tell them

Agricultural land use will expand dramatically

Agricultural prices will increase by 100% between now and 2050

Climate change will increase the number of malnourished children by 25%

Increased agricultural research expenditures can cut both of those numbers in half

Policy makers want 1 handed economists

INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE

WHAT DO THE MODELS SAY ABOUT AGRICULTURAL PRICES?

IMPACT: Economy, demography and climate changes increase prices

(price increase (%), 2010 – 2050, Baseline economy and demography)

Page 33

Minimum and maximum

effect from four climate

scenarios

Alternate Perspectives on Price Scenarios (perfect mitigation), 2004-

2050

Page 34

IMPACT has

substantially greater

price increases

Alternate perspectives on agricultural area changes, 2004-2050

Page 35

IMPACT has land use increases in

some countries and decreases

elsewhere

IMPACT has

negative net land

use change

Activities

Phase 1, Single reference scenario

• Single set of common drivers – income, population, agricultural productivity without climate change

• What do models say about key outputs?

• Why do they differ?

Phase 2, Explore relevant scenario spaces

• E.g., RAPs as drivers

• Linkages to crop and regional economic models

INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE

REFERENCE SCENARIO: A ‘TASTE’ OF THE INITIAL RESULTS

World wheat prices, perfect mitigation

World coarse grains price , perfect mitigation

World agricultural land, perfect mitigation