Transcript Vulnerability and Adaptation Assessments Hands-On Training Workshop Impact, Vulnerability and Adaptation Assessment for the Agriculture Sector – Part 1 Asunción Paraguay.

Vulnerability and Adaptation Assessments
Hands-On Training Workshop
Impact, Vulnerability and Adaptation Assessment
for the Agriculture Sector – Part 1
Asunción Paraguay. August 14-18, 2006
Graciela O. Magrin
INTA-Instituto de Clima y Agua (Argentina)
Outline
1- Climate change, agriculture and food security
2- Climatic variability
Climatic trends
Climate Change
3- Methods and tools
Datasets
Practical applications
Concepts
Climate
Other stresses
Economic
Variability
Change
Social
Demographic
Changes in Land use
Vulnerability
Vulnerability
Where
How Much
Adaptive
Capacity
Vulnerability
Where
How Much
Adaptive
Capacity
Land degradation
Desertification
Vulnerability
Where
How Much
Adaptive
Capacity
Had CM2 model, 2050s
Temperature
Precipitation
Vulnerability
Where
Adaptive
Capacity
How Much
Anual OC-SE Estandarizado 176 Est
-15
Internal
-20
-25
Barros, 2004
-30
-35
-40
-70
-65
-60
-55
-50
-45
-40
Planned
Figure: Percentage of total county area devoted to wheat (Wh), maize (Mz), sunflower (Su) and soybean
(Sb), during 20’ to 90’ decades in the last century.
100
90
80
70
60
50
40
30
20
10
0
Pilar
Wh
Su
Mz
Sb
20' 30' 40' 50' 60' 70' 80' 90'
100
90
80
70
60
50
40
30
20
10
0
100
90
80
70
60
50
40
30
20
10
0
Rosario
100
90
80
70
60
50
40
30
20
10
0
20' 30' 40' 50' 60' 70' 80' 90'
20' 30' 40' 50' 60' 70' 80' 90'
100
90
80
70
60
50
40
30
20
10
0
Santa Rosa
20' 30' 40' 50' 60' 70' 80' 90'
100
90
80
70
60
50
40
30
20
10
0
9 de Julio
20' 30' 40' 50' 60' 70' 80' 90'
Tres Arroyos
Argentina
Pampas Region
20' 30' 40' 50' 60' 70' 80' 90'
Junín
20' 30' 40' 50' 60' 70' 80' 90'
100
90
80
70
60
50
40
30
20
10
0
100
90
80
70
60
50
40
30
20
10
0
Internal Adaptation
Magrin et al., 2005
20' 30' 40' 50' 60' 70' 80' 90'
100
90
80
70
60
50
40
30
20
10
0
Laboulaye
Pergamino
Azul
20' 30' 40' 50' 60' 70' 80' 90'
Vulnerability
Where
Adaptive
Capacity
How Much
Internal
Policy makers
Civil stakeholders
Scientists
Planned
Vulnerability
Where
Adaptive
Capacity
How Much
Internal
Planned
Lobell and Monasterio, 2006
The example shows the
impact of different
irrigation scheduling in
wheat in the Yaqui Valley
of Mexico.
Average wheat yield loss
relative to the five
irrigation regime for each
of the other six regimes,
are plotted as a function
of initial available water
for available water
holding capacity of 15%
(Lobell & Monasterio,
2006).
Limits to Adaptation

Technological limits (e.g., crop tolerance to waterlogging or high temperature; water reutilization)

Social limits (e.g., acceptance of biotechnology)

Political limits (e.g., rural population stabilization may
not be optimal land use planning)

Cultural limits (e.g., acceptance of water price and
tariffs)
Climate IMPACTS
CO2
Temperature Precipitation
POSSIBLE BENEFITS
CO2
CARBON DIOXIDE
FERTILIZATION
LONGER
GROWING
SEASONS
INCREASED
PRECIPITATION
POSSIBLE DRAWBACKS
MORE
FREQUENT
DROUGHTS


PESTS
HEAT
STRESS
FASTER
GROWING
PERIODS
INCREASED
FLOODING AND
SALINIZATION
Changes in biophysical conditions
Changes in socioeconomic conditions in response to
changes in crop productivity (farmers’ income; markets
and prices; poverty; malnutrition and risk of hunger;
migration)
Agriculture and
Climatic Variability
Droughts affecting the agricultural
sector of LA since 2003
Year
Country
Site
Sector
Losses
Production
million US$
2004
Ecuador
Crops
70%
2004
Guatemala
Crops
80%
4
2004/05
Brazil
RG do Sul
Soybean
and others
8 Mt
2200
2004/05
Argentina
NOA-NEA
Soybean
2 Mt
340
2005
Argentina
centre-north and
western Pampas
Main Crops
10 Mt
900
2005
Paraguay
55%
170
2005
Bolivia
Santa Cruz
Hail and floodings
1
2005
Peru
Piura
frost
11
Soybean
Floods affecting the agricultural
sector of LA since 2003
Flood Argentina 2003
Flood Bolivia 2006
Soybean monoculture
Year
Country
Site
Sector
Soybean
Maize & Sorghum
Losses
Production
million US$
0.4 Mt
0.2 Mt
68
16
0.3 Mha
200
2003
Argentina
Santa Fe
2004
Argentina
Chaco
2006
Bolivia
Potosí, Oruro, La
Paz
Crops
70%
15
2006
Guyana
Mahaica, Mahaicony
Cash crops
Rice export
100%
4
Flooding affecting the agricultural sector of LA since 2003
Other extreme events affecting the
agricultural sector of LA since 2003
Year
2004
2005
Event
Hurricane
Catarina
Hurricane
Stan
Country
Site
Brazil
Bannana
Rice
Guatemala
Basic grains
Horticulture
Coffee
Catlle
Mexico
Chiapas
El Salvador
2006
Hail storm
Sector
Argentina
Santa Fe
Coffee
Losses
Production
millionUS$
85%
40%
160
16
30
134
0.23 Mha
Productive lands
9.1%
Soybean & Maize
0.016 Mha
120
5
Impacts of interanual climatic
variability related to ENSO
Impacts of interanual climatic
variability related to ENSO
Soybean yield, Argentina
Probability of having high/low yields
during El Nino/La Nina years
0 - 35
35 - 45
45 - 55
55 - 65
65 - 75
75 - 85
85 - 100
SD
0 - 35
35 - 45
45 - 55
55 - 65
65 - 75
75 - 85
85 - 100
SD
El Niño
La Niña
Impacts of interanual climatic
variability related to ENSO
Maize production Uruguay
Baethgen et al., 1998
Optimizing crop management
100
90
80
Fertilizer amount
Planting dates
Yield
70
Low
Med.
High
60
50
Frequency (%)
40
30
20
10
0
Adaptation:
All Years
El Niño
"Neutral" La Niña
Adaptation: Argentina. Crop mix
Pergamino
Pilar
Santa Rosa
Mod. Risk aversion
100%
75%
50%
25%
0%
Niña Neutro Niño Clim
Niña Neutro Niño Clim
Niña Neutro Niño Clim
ENSO Phases
Maize
Soybean
Wheat – Soybean
Wheat
Sunflower
Peanut
Agriculture and
Climatic Trends
Trends in total and extreme rainfall 1960-2000
Haylock et al., 2006
Total
precipitation
Annual days
RR>20mm
Sign of the linear trend in rainfall indices as measured by Kendall’s Tau. An increase is shown by a plus
symbol, a decrease by a circle. Bold values indicate significant at p 0.05.
Trends in temperature 1960-2000
Indice based on daily
minimum temperature:
cold and warm nights
(Vincent et al, 2005)
Impacts of climatic trends in
SESA
Changes in crop and pastures production (Argentina-Uruguay)
between 1930-1960 and 1970-2000 due to climate change
(Magrin et al, 2005; Baethgen et al, 2006)
Pastures
Crops
8
1930 1960
1970 2000
Climate
Wheat
+56
+13
(-6 to +21)
Sunflower
+102
+12
(+4 to +24)
Maize
+110
+18
(+6 to +31)
Soybean
-
+38
(+4 to +81)
7
+ 3.6%
+ 6.4%
Materia seca (t/ha)
Total
6
+ 9.8%
5
+ 7.0%
4
3
Azul
Pergamino
Tres Arroyos
Uruguay
Impacts of climatic trends in
SESA
Fusarium incidence in La Estanzuela Uruguay
Fusarium incidence
Mauricio Fernandes AIACC-LA27
1931-1965
1966-1999
Agriculture and
Climate Change
How Might Global Climate Change Affect Crop Production?
Maize
MAIZE
MAIZE
-28
-28
-29
-29
-29
-29
-30
-30
-30
-30
-30
-30
-30
-30
-31
-31
-31
-31
-31
-31
-31
-31
-32
-32
-32
-32
-32
-32
-32
-32
-33
-33
-33
-33
-33
-33
-33
-33
-34
-34
-34
-34
-36
-36
-37
-37
-37
-37
-37
-37
-37
-37
-38
-38
-38
-38
-38
-38
-38
-38
-39
-39
-39
-39
-39
-39
-39
-39
-40
-40
-40
-40
-40
-40
-40
-40
-41
-41
-41
-41
-41
-41
-66
-66
-64
-64
-62
-62
Longitud
Longitud
-60
-60
-58
-58
-28
-28
-66
-66
-64
-64
-29
-29
-62
-62
Longitud
Longitud
-60
-60
-28
-28
-58
-58
-41
-41
-66
-66
-64
-64
-29
-29
-62
-62
Longitud
Longitud
-60
-60
-28
-28
-58
-58
-30
-30
-30
-30
-31
-31
-31
-31
-31
-31
-31
-31
-32
-32
-32
-32
-32
-32
-32
-32
-33
-33
-33
-33
-33
-33
-33
-33
-34
-34
-34
-34
-34
-34
-35
-35
-22%
-35
-35
Latitud
Latitud
-8%
-35
-35
Latitud
Latitud
-30
-30
-34
-34
-36
-36
-37
-37
-37
-37
-37
-37
-37
-37
-38
-38
-38
-38
-38
-38
-38
-38
-39
-39
-39
-39
-39
-39
-39
-39
-40
-40
-40
-40
-40
-40
-40
-40
-41
-41
-41
-41
-41
-41
-66
-66
-64
-64
-29
-29
-62
-62
Longitud
Longitud
-60
-60
-58
-58
-28
-28
-66
-66
-64
-64
-29
-29
-62
-62
Longitud
Longitud
-60
-60
-58
-58
-28
-28
-36
-36
-64
-64
-29
-29
-62
-62
Longitud
Longitud
-60
-60
-58
-58
-28
-28
-30
-30
-31
-31
-31
-31
-31
-31
-31
-31
-32
-32
-32
-32
-32
-32
-32
-32
-33
-33
-33
-33
-34
-34
-34
-34
-35
-35
+18%
-35
-35
-34
-34
-35
-35
-36
-36
-36
-36
-36
-36
-36
-36
-37
-37
-37
-37
-37
-37
-37
-37
-38
-38
-38
-38
-38
-38
-38
-38
-39
-39
-39
-39
-39
-39
-39
-39
-40
-40
-40
-40
-40
-40
-40
-40
-41
-41
-41
-41
-41
-41
-28
-28
-29
-29
-66
-66
-64
-64
-62
-62
Longitud
Longitud
-60
-60
-58
-58
-28
-28
-29
-29
-66
-66
-64
-64
-62
-62
Longitud
Longitud
-60
-60
-58
-58
-28
-28
-29
-29
-64
-64
-62
-62
Longitud
Longitud
-60
-60
-58
-58
-28
-28
-29
-29
-30
-30
-30
-30
-30
-30
-31
-31
-31
-31
-32
-32
-32
-32
-33
-33
-33
-33
-33
-33
-33
-33
-34
-34
-34
-34
-35
-35
-36
-36
+2%
-35
-35
-35
-35
+21%
Latitud
Latitud
-31
-31
-32
-32
Latitud
Latitud
-31
-31
-32
-32
-34
-34
-58
-58
-64
-64
-62
-62
Longitud
Longitud
-60
-60
-58
-58
-60
-60
-58
-58
-41
-41
-66
-66
-30
-30
MPI-ds
MPI-ds
MPI-ds
-60
-60
-3%
-33
-33
Latitud
Latitud
-35
-35
Latitud
Latitud
-30
-30
-8%
-66
-66
-29
-29
-30
-30
-34
-34
-62
-62
Longitud
Longitud
-41
-41
-66
-66
-30
-30
-33
-33
-64
-64
-8%
-35
-35
-36
-36
-28
-28
-66
-66
-29
-29
-30
-30
Latitud
Latitud
Latitud
Latitud
-35
-35
-36
-36
Uncertainty?
-3%
-34
-34
-36
-36
-36
-36
Latitud
Latitud
+3%
-35
-35
-36
-36
UKMO
UKMO
Latitud
Latitud
-35
-35
Latitud
Latitud
-35
-35
Latitud
Latitud
-16%
-34
-34
Latitud
Latitud
-28
-28
-29
-29
Latitud
Latitud
Latitud
Latitud
-28
-28
-29
-29
GISS
GISS
GISS
Wheat
WHEAT
WHEAT
-29
-29
-28
-28
UKMO
Soybean
SOYBEAN
SOYBEAN
-28
-28
Latitud
Latitud
GFDL
GFDL
GFDL
Sunflower
SUNFLOWER
SUNFLOWER
-34
-34
-35
-35
-36
-36
-36
-36
-37
-37
-37
-37
-37
-37
-36
-36
-37
-37
-38
-38
-38
-38
-38
-38
-38
-38
-39
-39
-39
-39
-39
-39
-39
-39
-40
-40
-40
-40
-40
-40
-40
-40
-41
-41
-41
-41
-41
-41
-41
-41
-66
-66
-64
-64
-62
-62
Longitud
Longitud
+7%
Magrin & Travasso 2002
How Might Global Climate Change Affect
small farmers Food Production?
Jones & Thornton, 2003
Overall reduction: 10%
Simulated maize yields (baseline) and changes to 2055 for Latin America.
How Might Global Climate Change Affect
small farmers Food Production?
Jones & Thornton, 2003
Eastern Brazil: an area with moderate predicted maize yield changes in 2055, of a size that
could readily be handled through agronomy and/or breeding.
How Might Global Climate Change Affect
small farmers Food Production?
Jones & Thornton, 2003
Venezuela: a case where maize yields to 2055 are predicted to be almost eliminated, indicating that
maize production may have to be shifted into wetter areas (for example, to the south-west).
How Might
Global Climate
Change Affect
Food
Production?
Potential changes (%) in
national cereal yields for
the 2020s, 2050s and
2080s (compared with
1990) under the HadCM3
SRES A2a and B2
scenarios with and without
CO2 effects.
Parry et al., 2004
Developed-Developing
Country Differences
Potential change (%) in national cereal yields for the 2080s
(compared with 1990) using the HadCM3 GCM and SRES
scenarios (Parry et al., 2004)
Scenario
A1FI A2a
A2b A2c
A2c
B1a
B2b
C02 (ppm)
810
709
709
709
527
561
561
World (%)
-5
0
0
-1
-3
-2
-2
Developed (%)
3
8
6
7
3
6
5
Developing (%)
-7
-2
-2
-3
-4
-3
-5
DevelopedDeveloping) (%)
10
10
8
10
7
9
9
Additional Millions of People
Additional People at
Risk of Hunger
80
69
70
60
60
50
50
40
43
30
34
30
20
9
10
7
5
0
2020
2050
2080
Unstabilised
Stabilised at 750ppmv
Stabilised at 550ppmv
Parry et al., 2004
Conclusions




Although global production appears stable . . .
. . . regional differences in crop production are
likely to grow stronger through time, leading to
a significant polarization of effects . . .
. . . with substantial increases in prices and risk
of hunger amongst the poorer nations
Most serious effects are at the margins
(vulnerable regions and groups)
Methods, Tools, and Datasets
1.
The framework
2.
The choice of the research methods and
tools
Frameworks


Adaptation Policy Framework (APF), US
Country Studies, IPCC, seven steps
All have the essential common elements





Problem definition
Selection and testing of methods
Application of scenarios (climate and
socioeconomic)
Evaluation of vulnerability and adaptation
The studies may want to use a framework as
guidance or draw from the best elements of
all of them
Quantitative
Methods and Tools








Experimental
Analogues (spatial and temporal)
Production functions (statistically derived)
Agroclimatic indices
Crop simulation models (generic and crop-specific)
Economic models (farm, national, and regional) –
Provide results that are relevant to policy
Social analysis tools (surveys and interviews) –
Allow the direct input of stakeholders (demanddriven science), provide expert judgment
Integrators: GIS
Experimental: Effect of Increased C02
Near Phoenix, Arizona,
scientists measure the growth
of wheat surrounded by
elevated levels of atmospheric
CO2. The study, called Free Air
Carbon Dioxide Enrichment
(FACE), is to measure CO2
effects on plants. It is the
largest experiment of this type
ever undertaken.
http://www.ars.usda.gov
http://www.whitehouse.gov/media/gif/Figure4.gif
Experimental
Value
Spatial scale of results
Site
Time to conduct analysis
Season to decades
Data needs
4 to 5
Skill or training required
1
Technological resources
4 to 5
Financial resources
4 to 5
Range for ranking is 1 (least amount) to 5 (most
demanding).
Example: growth chambers, experimental fields.
Analogues: Drought, Floods
Uruguay
January 1998
Uruguay Vegetation Index
Vegetation
Source: INIA-IFDC
January 2000
Analogues (space and time)
Value
Spatial scale of results
Site to region
Time to conduct analysis
Decades
Data needs
1 to 2
Skill or training required
1 to 3
Technological resources
1 to 3
Financial resources
1 to 2
Range for ranking is 1 (least amount) to 5 (most demanding).
Example: existing climate in another area or in previous
time
Production Functions
Precipitation
Relationship between wheat yields and precipitation during the period from 60 days before to 10
days after flowering in two sites in Argentina. (Calviño & Sadras, 2002)
Production Functions
Value
Spatial scale of results
Site to globe
Time to conduct analysis
Season to decades
Data needs
2 to 4
Skill or training required
3 to 5
Technological resources
3 to 5
Financial resources
2 to 4
Range for ranking is 1 (least amount) to 5 (most demanding).
Example: Derived with empirical data.
Agroclimatic Indices
Length of the growing periods (reference climate, 1961-1990).
IIASA-FAO, AEZ
Agroclimatic Indices
Value
Spatial scale of results
Site to globe
Time to conduct analysis
Season to decades
Data needs
1 to 3
Skill or training required
2 to 3
Technological resources
2 to 3
Financial resources
1 to 3
Range for ranking is 1 (least amount) to 5 (most demanding).
Example: FAO, etc.
Crop Models
Based on
Understanding of plants,
soil, weather, management
Calculate
Water
Growth, yield, fertilizer &
water requirements, etc
Carbon
Require
Information (inputs):
weather, management, etc
Nitrogen
Models – Advantages


Models are assisting tools, stakeholder
interaction is essential
Models allow to ask “what if” questions, the
relative benefit of alternative management
can be highlighted:



Improve planning and decision making
Assist in applying lessons learned to policy
issues
Models permit integration across scales,
sectors, and users
Models – Limitations



Models need to be calibrated and validated
to represent reality
Models need data and technical expertise
Models alone do not provide an answer,
stakeholder interaction is essential
Can Optimal Management be an
Adaptation Option for Maize Production in
Argentina?
Source Argentina 2º National communication
12
11
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10
9
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8
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7
5
6
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4
2
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1
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Adaptation: Argentina
Adaptation strategies in two locations of Argentina
25
Changes in maize yield (%)
20
HadCM3 B2 2050
15
Increased inputs and
10
improve management:
5
•
Planting date
0
•
Fertilizer
-5
•
Irrigation
Without adaptation
-10
Level 1: Changing planting date and fertilizer amount
Level 2: Level 1 + Irrigation
-15
Tres Arroyos
Santa Rosa
Travasso et al., 2006
Can Adaptation be Achieved by Optimizing Crop Varieties?
Optimizing crop varieties
Maize >P1 Juvenile phase
Wheat >P1D photoperiodic sensitivity
Crop Models
Value
Spatial scale of results
Site to region
Time to conduct analysis
Daily to centuries
Data needs
4 to 5
Skill or training required
5
Technological resources
4 to 5
Financial resources
4 to 5
Range for ranking is 1 (least amount) to 5 (most demanding).
Example: CROPWAT, CERES, SOYGRO, APSIM,
WOFOST, etc.
Economic Models


Consider both producers and consumers of
agricultural goods (supply and demand)
Economic measures of interest include:


How do prices respond to production
amounts?
How is income maximized with different
production and consumption opportunities?
Economic Models




(continued)
Microeconomic: Farm
Macroeconomic: Regional economies
All: Crop yield is a primary input (demand is
the other primary input)
Economic models should be built bottom-up
Agricultural Trade Models
Parry et al., 1999.
Social Sciences Tools


Surveys and interviews
Allow the direct input of stakeholders
(demand-driven science), provide expert
judgment in a rigorous way
Surveys and Interviews
Development of
adaptation options with
stakeholders
Soybean planting dates
Argentina
Economic and Social Tools
Value
Spatial scale of results
Yearly to centuries
Time to conduct analysis
Site to region
Data needs
4 to 5
Skill or training required
5
Technological resources
4 to 5
Financial resources
4 to 5
Range for ranking is 1 (least amount) to 5 (most demanding).
Examples: Farm, econometric, I/O, national economies,
BLS, …
Integrators: GIS
Without CO2 effects:
-9%
With CO2 effects:
+19%
Potential changes in maize
production for the year 2080
under downscaled scenario
based on HadCM3 SRES A2.
Integrators: GIS
Value
Spatial scale of results
monthly to centuries
Time to conduct analysis
region
Data needs
5
Skill or training required
5
Technological resources
5
Financial resources
5
Range for ranking is 1 (least amount) to 5 (most demanding).
Example: …. All possible applications ….
Conclusions



The merits of each approach vary according to the level of
impact being studied, and they may frequently be mutually
supportive
For example, simple agroclimatic indices often provide the
necessary information on how crops respond to varying
rainfall and temperature in wide geographical areas; cropspecific models are use to test alternative management that
can in turn be used as a component for an economic model
that analyses regional vulnerability or national adaptation
strategies
Therefore, a mix of approaches is often the most rewarding
Datasets



Data are required data to define climatic,
nonclimatic environmental, and
socioeconomic baselines and scenarios
Data are limited
Discussion on supporting databases and
data sources
IPCC Working Group 1: “A Collective
Picture of a Warming World”
Valencia - Annual T(C) 1900-2000
Valencia - Dec-Feb T(C) 1900-2000
Valencia - Jun-Aug T(C) 1900-2000
20
13
26
19
12
25
18
11
24
17
10
23
16
9
22
15
8
1880
1900
1920
1940
1960
1980
2000
2020
21
1880
1900
1920
1940
1960
1980
2000
2020
Source of data: GISS/NASA
1880
1900
1920
1940
1960
1980
2000
2020