Climate Change Impacts and Adaptation Metrics for Agriculture

Cynthia Rosenzweig and Francesco TubielloGoddard Institute for Space Studies

at Columbia University, New York, NY

www.itdg.org

Annual Temperature Trends 1973-2002

Background and RationaleMetrics = Measurement Tools for Decision-Making

Goals: To develop metrics to assess agricultural risk under climate change in regions and globally, and to improve adaptation planning, i.e., minimize damage/maximize opportunities.

Metrics need to be:

1) Relevant to stakeholders – E.g., farmers, associations, regional and national governments***

2) As simple as possible – Easy to measure and/or compute; easy to communicate; comparable.

***Focus of this paper

Coping Ranges and Thresholds

Ayers et al. 2005

Characterizing thresholds for differentsocial groups, agro-ecosystems, regions, nations:

Comparable metrics – Case-specific thresholds

Current

1973-2002 Annual Temperature TrendsInterpolated

Observed Agriculture Changes Phenology

Management practices, pests and diseases

Yields

Livestock

<-1.2C to >1.2C

Relevancy to Stakeholders• Address economic costs ($) of impacts as well as

intangibles – e.g., people at risk of hunger.• Take account of likely (autonomous) adaptation.• Be useful for evaluating planned adaptation responses

– e.g., enable coping range and cost-benefit analyses. • Consider time component: e.g., rates/magnitudes of

climate changes; discount rates; socio-economic changes.

• Address vulnerability issues:

Vulnerability = F (Exposure, Sensitivity, Adaptive Capacity)

• Sustainability – Contribute to development goals by improving short-term responses to climate extremes as well as long-term responses to climate change

Categories Vulnerability Criteria Indices

BiophysicalIndicators Exposure

Climate Soil

Crop calenderWater storage

Biomass/Production

Socio-Economic Data Sensititivity

Crop insurance programsTrade flow

% Agriculture sector in subsistence (e.g. heath, education)

% Agriculture sector in poverty and inequality, (e.g. wages)

AgriculturalSystem

CharacteristicsAdaptive Capacity

Technological change % Arable land currently in use

% Rainfed crops/Irrigated crops

ClimateMitigationPrograms

Risk ReductionKyoto commitment capacity

Carbon sequestration potential; CDM projects in place, planned

Metrics Developed at Expert and Stakeholder Workshop

• Developed Countries: US Intensive Production

• Developing Countries: Semi-Arid Tropical Agriculture in South-East South America

• Economy in Transition: High-Latitude Agriculture in Eastern Europe

• Mediterranean Semi-Arid Environments

Workshop Examples

‘Exposure’

Biophysical Indicators

Climate, Soil, Crop Calendar,

Water Storage, Biomass/Production

Annual Length of Frost-Free Period Averaged Over U.S. Corn Production Region

Kendall-Thiel Regression

Easterling, 2006

Developed Country

Change in Dates of First Frost and Last Frost(1915-2003) Estanzuela, SW Uruguay

100

120

140

160

180

200

220

240

260

280

300

1905 1915 1925 1935 1945 1955 1965 1975 1985 1995 2005

Day

of th

e ye

ar

First Frost

Last Frost

7 Jun14 Jun

14 Sep

21 Aug

Frost season is now 6 weeks shorter than in early 1900’s

Baethgen, 2006

Developing Country Semi-arid Tropical Agriculture

Cambio entre 1930-1960 y 1970-2000 (%)(JFM = Verano)

+30-50%Increase in Rainfall

Impacts on Land-use ChangesWalter Baethgen, 2006

Developing Country Semi-arid Tropical Agriculture

Change in Precipitation (%) 1930-1960 and 1970-2000January/February/March

Africa: Drought Patterns 1951-2000• Short-term (6-months) accumulated precipitation• Long-term (24-months) accumulated precipitation

SPI – Standardized Precipitation Index moderate drought: δ > 1.5 Data: NOAA

Drought

Frequency

Drought

Duration

Lotsch 2006

Developing Country

‘Sensitivity’

Socio-Economic Factors and Data

Land-use trends, crop insurance programs, trade flow, % agriculture sector in subsistence

(e.g., health, education), % agriculture sector in poverty and inequality

(e.g., wages)

Changes in Agriculture in CEE

• Significant changes in land ownership structure:• No private land ownership before 1989 • Restoration of the private ownership in central

Europe started after 1990 and it is still not completed

• High fragmentation of the land (12 people per 10 ha) and consolidation is not completed

• Compensation schemes do not work adequately (negative environmental impact)

• Socio-economic factors (connection to own land, ageing)

Bizik 2006

Economy in Transition High-Latitude Agriculture

Agricultural Land-Use Change

Hostert et al. 2005

Economy in Transition High-Latitude Agriculture

Land fragmentationCarpathians, Ukraine,

1988-2000

Bizik 2006

‘Climate Extremes Pull the Trigger, but Multiple Stressors Load the Gun’

• Rapid population growth

• Environmental changes

• Unintended consequences of government policy

Crop Reporting Districts with highest gain correlate with highest proportion of nation’s disaster and crop insurance payments

Easterling, 2006

Developed Country

Technological change, % arable land currently in use, % rainfed crops/irrigated crops,

research/education/extension

‘Adaptive Capacity’

Agricultural System Characteristics

Total Climate

Wh 56 13Su 102 12Mz 110 18Sb - 38

Developing Country SemiDeveloping Country Semi--arid Tropical Agriculture:arid Tropical Agriculture: Changes Changes in Crop Yields (Argentina) during 20in Crop Yields (Argentina) during 20thth CenturyCentury

Yield increases (%) Yield increases (%) betweenbetween

19501950--70 and 197070 and 1970--0000

Magrin et al, 2005

Baethgen 2006

a) Climate Projections developed and developing countries

b) Socio-Economic Development pathways – SRES A1, A2, B1, B2

c) Climate Change Impact projections at several levels of adaptation

d) Model-based probabilities

Future Risks

HAD3

CSIRO

CGCM2

NCAR

ECHAM

NCAR

CSIRO

HAD3

CGCM2

ECHAM

HAD3CSIRO

CGCM2

NCAR

ECHAM

FIGURE 2a

HAD3

CSIRO

CGCM2

NCAR

ECHAM

NCAR

CSIROHAD3

CGCM2

ECHAM

HAD3

CSIRO

CGCM2

NCAR

ECHAM

FIGURE 2b

CO2 concentration (ppm) GCM-projected mean annual Temperature change (�C)

G

CM

-projected mean annual

G

CM

-projected mean annual

CO2 concentration (ppm) GCM-projected mean annual Temperature change (�C)

G

CM

-projected mean annual

G

CM

-projected mean annual

Climate Change Projections

Developed Regions

Developing Regions

Developed and developing regions have differential exposure, especially in regard to precipitation

100

110

120

130

140

150

0 1 2 3 4 5Temper

HAD3CSIRCGCMNCARHAD3CGCMNCARCSIR

90

100

110

120

130

0 1 2 3 4 5Temper

HAD3CSIRCGCNCARHAD3NCARCGCCSIR

95

100

105

110

115

0 1 2 3 4 5Temper

HAD3CSIRCGCMNCARHAD3CGCMNCARCSIR

95

100

105

110

115

0 1 2 3 4 5Temper

HAD3CSIRCGCNCARHAD3NCARCGCCSIR

Impacts/Adaptation on Cereal Production

Developed Regions

Developing Regions

No AdaptationCurrent Agricultural Land

With AdaptationAll Arable Land

100 = 1990 Cereal Production

Impacts and Adaptation Methodological Approaches

Dynamic Crop Models/World Food Trade Models

Biophysical representations of agro-ecosystems, CO2 effectsDemand, production, and tradeAllow testing of effects of specific adaptation management practicesFuture State Metrics

Crop yield, mean and variabilityRegional/national production and valueIrrigation water requirementsImpacts of increased frequency of extreme eventsRisk of hungerValue of production at risk

Ricardian Approaches

Statistical models of land use/value as function of climateAllow comprehensive valuation of impacts and adaptationsFuture State Metrics

Regional/national production and valueProduction and land value at risk

Analyze possible impacts and responses: (What if...)

Climate(scenarios)

Interventions•Technologies•Management•Policies

5

4

3

2

1

SimulationModel

Analyze a wide range of alternatives and Possible impacts in different climate scenarios:

Inform Planning and Decision Making

PossibleOutcomes

Climate and Agriculture Metrics • Evidence has begun to show agricultural responses to observed warming. This is emerging across several indices, agro-ecosystems, and regions.

•Metrics need to be relevant to stakeholders, measurable and/or computable, comparable, and as simple as possible.

• Risk and coping range approaches are useful and need to be developed for a range of agricultural systems.

• Climate change metrics can be mapped onto exposure, sensitivity, and adaptive capacity components.

• Dynamic and statistical approaches offer complementary tools for projecting climate metrics.

Proposed MetricsMetric Units (multiple scales)

Crop Yield Ton/ha

Yield Variability, CV Long-term standard deviation divided by mean yield (%)

Production At local to regional and national levels (Ton/yr)

Economic Value at Risk Net production value at local to regional levels. Agricultural GDP at national level ($)

Land Value at Risk Land value of areas most affected ($)

Damage from Extreme Events Impacts of increased frequency of droughts/floods on damage (Ton and/or $)

Nutrition Index Food demand over supply (sum of internal production and trade). Unitless.

Water Requirements/Withdrawals

Irrigation water requirements over available resources. Unitless.

1) Present State of agricultural system at regional and national scales, comprising:

a) Mean Characteristics of production systems;

b) Observed Recent Trends in climate means and variability, and agricultural responses.

2) Future State of Risk, defined in terms of:

a) Socio-Economic Development pathway(s) without climate change;

b) Climate Change Impact projections at several levels of adaptation.

Benchmark Values for Impact Metrics

Metrics need to be measurable and/or computable, and comparable.

Inclusion of both mean and variability; recent past, current trends and projection of risks.

Grappling with uncertainties: Is climate changing? Do agricultural stakeholders perceive that climate is changing? ‘Usual suspects’ in projections related to emissions futures, climate sensitivities, development pathways, adaptation responses.

Scales of verification: Farm, region, nation; statistics, in situ and remote sensing measurements.

Evaluating impacts and adaptation: Crop modeling and Ricardian approaches.

Measurement Issues