1

Frank A. Ewert

Climate change impacts on agriculture and options for adaptation:

Challenges for multiscale modelling and assessment

Institute of Crop Science and Resource Conservation (INRES), University of Bonn, Germany

http://www.lap.uni-bonn.de

2

Outline

BackgroundClimate change factors and impacts

Modelling impacts of climate changeImportance of factors and processesThe role of scale

Assessing options for adaptationOptions of adaptationThe role of scaleWhich role for agronomy

Concluding remarks

3

• Temperature• Annual mean• Variability (seasonal, inter-annual)• Extreme events (heat waves)

• Precipitation• Annual mean• Variability (temporal distribution)• Extreme events (drought periods)

• CO2• Others (Radiation, Ozone, …)

Background

Factors of climate change affecting agriculture

Climate change(real)

5

MODIS data

Climate variability

Temperature anomalyEurope, 2003

0

2

4

6

8

1950 1960 1970 1980 1990 2000 2010

Year

t/ha

Wheat yield, EU151961-2004

FAO, 2007

Background

6Battisti et al. (2009)

7

Direct effects

• Start of season• Season length• Fruit/grain set• Assimilation & respiration• Leaf area development • H2O uptake and transpiration

• Invasions of pests and diseases (insects, viruses, bacteria, Fungi)

• Invasion of weeds

Indirect effects

Background

Type of effects

• Biomass and yield

Bud burst, oak

Badeck et al., 2004

Germany

Figure: Trends in bud burst of oak in Germany in the periods 1951-84 and 1984-98.

Background

Sowing date

Chmielewski et al., 2004

Maize Sugar beet

Germany

Background

Figure: Changes in the seeding dates of maize and sugar beet in the main cropping areas of Germany, 1961–2000 with calculated trends. Anomalies are shown.

• Shift in sowing dates• Change of varieties (season length)• Change in species (stress adapted)• Crop diversity (spread of risks)• Irrigation• Tillage (water saving)• Fertilization (leaf application)

Crop management

• Stress resistance• Temperature, drought• Pests and diseases

• Maturity types• …

Breeding

Pennisi et al., 2008

Assessing adaptation

Factors

• Temperature• Precipitation• Radiation• CO2,

Climate

• Irrigation• Fertilisation • Varieties• Sowing date• Pest/diseases

Management

Soil • H20• C, N, P, K

Dynamic simulation models

Crop

Soil

Climate

Man

agem

ent

Bre

edin

gModelling impacts

Impacts

• Yield• Water demand• Nutrient demand• GHG Emission• …

• Crops• Grassland• Agroforestry• …

• Field• Farm• Region• …

12

Crop

Soil

Climate

Man

agem

ent

Bre

edin

g Radiation interception

Assimilation

Allocation

Organ growth

Leaves, stems, ... Roots

Respiration

Phen

olog

y

Weather and CO2

Soil

H2O

, N a

nd C

Plan

t NET

Radiation interception

Assimilation

Allocation

Organ growth

Leaves, stems, ... Roots

Respiration

Phen

olog

y

Weather and CO2

Soil

H2O

, N a

nd C

Plan

t NET

Radiation interception

Assimilation

Allocation

Organ growth

Leaves, stems, ... Roots

Respiration

Phen

olog

y

Weather and CO2

Soil

H2O

, N a

nd C

Plan

t NET

Radiation interception

Assimilation

Allocation

Organ growth

Leaves, stems, ... Roots

Respiration

Phen

olog

y

Weather and CO2

Soil

H2O

, N a

nd C

Plan

t NET

Crop model structure

Modelling impacts

Dynamic simulation models

Crop/cropping system

Drivers Impact variablesModel

Scenario analysis

• Climatic factors • Crop productivity

• …• Soil

• Management

Modelling impacts

ScenariosPlausible alternative pathways of future development

Modelling impacts

Scenario analysis

Climate

Effects of outputs from different climate modelson national grain yields

Rosenzweig and Parry, 1994

15

Extrapolation

Input Output

Model

Methods of up-scaling

Model

ModelModel

Aggregation of input/output data

Modelling impacts

Manipulation of input and outputdata

Ewert et al, 2007

16

Spatial variability of inputsEnvironmental Stratification (EnS)

Metzger et al, 2007

Climate

Modelling impacts

Spatial variability

Andersen et al., 2009

NUTS 2 Regions

Environmental zones Climate zones

Top-soil organic carbon (%)

Agro-climatic Zones

Soil classes

Modelling impacts • Climate• Management• Soil

ACE-FAST (APES)(re-implementation)•ca. 550 zones•(23 years)Parameters:•Phenology•RUE, LAI, partitioning(Brute force search algorithm)

Yield (Mgha-1)

Observed1983-2005

Simulated1983-2005

Modelling impacts

Model calibration

Angulo et al., 2010

19

Scales of drivers and processes

Soil properties

Management activities

Climatic conditions

Drivers Processes

Transpiration, assimilation

LAI, biomass, yield

Phenology

CO2

Modelling impacts

Figure: Thermographic images of the canopy of a wheat field at four growth stages during the 2004 growth season (cv. Drifter, Dikopshof). Colors display temperatures in °C

Lenthe et al, 2007

Figure: Regional map of sun-induced fluorescence measured from an aircraft sensor. Measurements were performed in September 2007 in Southern France. Only mature corn fields are selected and differences in sun-induced fluorescence are proposed to quantify differences in gross primary productivity (GPP).

Rascher et al, 2009

Fluorecence measurements in maize (air-borne), region

Thermorgraphic images in wheat, field (ca. 300x500 m)

Modelling impacts

Scales of drivers

Van Bussel et al, 2010Figure: Semi-variograms for ear emergence and harvest for the year 1997 of winter wheat grown in Germany.

Figure: Aggregated emergence dates from the year 1995 for the resolutions 10 × 10, 50 × 50, and 100 km × 100 km grid cells

Modelling impacts

Scales of drivers

Phenological development in wheat (Germany)

Crop/cropping system

Drivers ImpactsModel

Options of adaptation

• Climatic factors • Crop productivity

Scenarios

• Climatic factors

• Soil

• Management

Assessing adaptation

• Sowing date• Varieties

• …

Adaptation

• …

23

Multi-goal adaptation

Adaptation option• sowing dates• Change of varieties (season length)• Change in species (stress adapted)• Crop diversity (spread of risks)• Irrigation• Breeding (drought resistance, …)• …

Intensification• Fertilisation• Pest, disease and weed control• Phyto hormones• Mechanisation• Irrigation

Breeding (yield potential, …)…

Assessing adaptation

Realtive importanceof factors

Crop yields in Europe

24

Assessing adaptation

Model simulations ACE-FAST (APES)•7 GCMs•4 IPCC scenarions•6 crops (wwheat)

Angulo et al., 2010

Centered 1990 Centered 2040

25

202020002050

Hermans et al., 2010

Projected land use change

highmediumlow<0.1%

Country

Nuts

Productivity class

Wheat, EU27, A1

Assessing adaptation

Model simulations

26

Farming system

Cropping system

Land use system

Food system(Agr. sector)

Role of scale

Adaptation (impacts)

Farmer

Regional policy maker

Inter-/national policy maker

Assessing adaptation

Actors

Breeder

27

Scale dependency of drivers, impacts and adaptation options and governance

Assessing adaptation

28

Modelling complexity

FSSIM

CAPRI

Supply …Demand

APES Field 1 Field 1 Field n

Type IIType nType I

Market

Farm

Field

FSSIM

CAPRI

Supply …Demand

APES Field 1 Field 1 Field n

Type IIType nType I

Market

Farm

Field

Ewert et al., 2009

Assessing impacts and adaptation

• Data availability• Scaling methods• Uncertainty• Stakeholder inteaction

29

Crop

Plant

OrganTissue

Cell

Gene

…

Biosphere

Farm

Landscape

Role for agronomy and crop science

Ecosystem

Funding and infrastructure development

• Climate change• Food security• Energy security• Sustainable land use• CAP, WTO

Problems of present concern

Assessing impacts and adaptation

30

Climate change is real (changes and impacts are already observed)

Projected impacts differ depending on the drivers, crop and regions

Impact assessment requires consideration of adaptation

Adaptation depends on several factors and is scale-dependent

Impact assessment modelling of climate change effects should consider cross-scale interactions

Modelling frameworks are currently developed but require evaluation

Testing of scaling methods

Agronomy and crop science can and should contribute to bridging the gaps between scales

Concluding remarks

31

Acknowledgements

System for Environmental and Agricultural Modelling; Linking European Science and Society

AdvancedTerrestrialEcosytemAnalysis andModelling

Advanced Terrestrial Ecosystem Analysis and Modelling

AgriAdapt-NLAssessing the adaptive capacity of Agriculture in the Netherlands to the impacts of climate change under different market and policy scenarios

32

Thank You

http://www.lap.uni-bonn.de

Climate change impacts on agriculture and options for adaptation:

Challenges for multiscale modelling and assessment

33

Schär et al. (2004)

Climate variability

Impacts (projections, drivers)

34

Yield (wheat) distribution and relation to temperature

Reidsma and Ewert, 2008

Adaptation

35

Reidsma and Ewert, 2008

Effects of farm diversity on regional yield responses to climate variability

Adaptation

2.7 53.2

SD (%)

a)

3

5

7

9

1990 2000

Whe

at y

ield

(t/h

a)

b)

3

5

7

9

1990 2000

Whe

at y

ield

(t/h

a)c)

-20

-10

0

10

20

1990 2000

Yie

ld a

nom

aly

(%)

0

15

tem

pAp

ril (

°C)

yield anomalytemperature April

e)

-20

-10

0

10

20

1990 2000

Yie

ld a

nom

aly

(%)

0

25

tem

pJu

ly (°

C)

yield anomalytemperature July

d)

0

60

9 21tem p (°C)

SD (%

)

2.7 53.2

SD (%)

a)

3

5

7

9

1990 2000

Whe

at y

ield

(t/h

a)

b)

3

5

7

9

1990 2000

Whe

at y

ield

(t/h

a)c)

-20

-10

0

10

20

1990 2000

Yie

ld a

nom

aly

(%)

0

15

tem

pAp

ril (

°C)

yield anomalytemperature April

e)

-20

-10

0

10

20

1990 2000

Yie

ld a

nom

aly

(%)

0

25

tem

pJu

ly (°

C)

yield anomalytemperature July

d)

0

60

9 21tem p (°C)

SD (%

)

2.7 53.2

SD (%)

a)

3

5

7

9

1990 2000

Whe

at y

ield

(t/h

a)

b)

3

5

7

9

1990 2000

Whe

at y

ield

(t/h

a)c)

-20

-10

0

10

20

1990 2000

Yie

ld a

nom

aly

(%)

0

15

tem

pAp

ril (

°C)

yield anomalytemperature April

e)

-20

-10

0

10

20

1990 2000

Yie

ld a

nom

aly

(%)

0

25

tem

pJu

ly (°

C)

yield anomalytemperature July

d)

2.7 53.2

SD (%)

a)

3

5

7

9

1990 2000

Whe

at y

ield

(t/h

a)

b)

3

5

7

9

1990 2000

Whe

at y

ield

(t/h

a)c)

-20

-10

0

10

20

1990 2000

Yie

ld a

nom

aly

(%)

0

15

tem

pAp

ril (

°C)

yield anomalytemperature April

e)

-20

-10

0

10

20

1990 2000

Yie

ld a

nom

aly

(%)

0

25

tem

pJu

ly (°

C)

yield anomalytemperature July

d)

0

60

9 21tem p (°C)

SD (%

)

36

Scale dependent responses

Integrated assessment of impacts/adaptation

Drivers PolicyClimate Market …

Impact

Performance, Risk, Resilience

Adaptation

Food productionLand useIncome

EnvironmentProductivity

Subsidies Protection …

Global

EU

Country

Region

Farm

Field

AgricultureGlobal (WTO, ..)

EU (CAP, ..)

National

Region

Farm managementCrop management

Ewert et al., 2008

Drivers PolicyClimate Market …Climate Market …

Impact

Performance, Risk, Resilience

Adaptation

Food productionLand useIncome

EnvironmentProductivity

Subsidies Protection …Subsidies Protection …

Global

EU

Country

Region

Farm

Field

AgricultureGlobal (WTO, ..)

EU (CAP, ..)

National

Region

Farm managementCrop management

Ewert et al., 2008

BiophysicalEconomic

SocialLevels of organisation

Earth system

Biosphere

Ecosystem

Community

Population

Global

National

Regional

Farm

Field

Labour

GTAP

Capri

FSSIM

Dev. countries

Landscape

APES

Model

Labour

GTAP

Capri

FSSIM

Dev. countries

Landscape

APES

Labour

GTAP

Capri

FSSIM

Dev. countries

GTAP

Capri

FSSIM

Dev. countries

Landscape

APES

Model