Climate Change Impacts and Adaptation Metrics for Agriculture · Climate and Agriculture Metrics...
Transcript of Climate Change Impacts and Adaptation Metrics for Agriculture · Climate and Agriculture Metrics...
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