CCAFS Science Meeting A.2 Jerry Nelson - AgMIP
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Transcript of CCAFS Science Meeting A.2 Jerry Nelson - AgMIP
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