asunción paraguay. august 14-18, 2006

Vulnerability and Adaptation Assessments Hands-On Training Workshop

Impact, Vulnerability and Adaptation Assessmentfor the Agriculture Sector – Part 1

Asunción Paraguay. August 14-18, 2006

Graciela O. MagrinINTA-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

Vulnerability

Climate

Variability

Change

Other stresses

Economic

Social

Demographic

Changes in Land use

Vulnerability

Where How MuchAdaptive Capacity

ConceptsConcepts

Vulnerability


Land degradation Desertification

Vulnerability


Precipitation

Temperature

Had CM2 model, 2050s

Vulnerability


Internal Planned

-70 -65 -60 -55 -50 -45 -40

Anual O C -SE Estandarizado 176 Est

-40

-35

-30

-25

-20

-15

Barros, 2004

Internal Adaptation

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.

ArgentinaPampas Region

Pilar

0102030405060708090

100

20' 30' 40' 50' 60' 70' 80' 90'

Wh MzSu Sb

Rosario

0102030405060708090

100

20' 30' 40' 50' 60' 70' 80' 90'

Pergamino

0102030405060708090

100

20' 30' 40' 50' 60' 70' 80' 90'

Junín

0102030405060708090

100

20' 30' 40' 50' 60' 70' 80' 90'

9 de Julio

0102030405060708090

100

20' 30' 40' 50' 60' 70' 80' 90'

Azul

0102030405060708090

100

20' 30' 40' 50' 60' 70' 80' 90'

Laboulaye

0102030405060708090

100

20' 30' 40' 50' 60' 70' 80' 90'

Santa Rosa

0102030405060708090

100

20' 30' 40' 50' 60' 70' 80' 90'

Tres Arroyos

0102030405060708090

100

20' 30' 40' 50' 60' 70' 80' 90'

Magrin et al., 2005

Vulnerability


Internal Planned

Scientists

Scientists

Civil stakeholdersCivil stakeholders

Policy makersPolicy makers

Vulnerability


Internal PlannedLobell 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 water-logging 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

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)

POSSIBLE BENEFITS

POSSIBLE DRAWBACKS

CO2

CARBON DIOXIDEFERTILIZATION

LONGERGROWINGSEASONS

INCREASEDPRECIPITATION

MOREFREQUENTDROUGHTS

PESTS

HEATSTRESS

FASTERGROWINGPERIODS

INCREASEDFLOODING ANDSALINIZATION

POSSIBLE BENEFITS

POSSIBLE DRAWBACKS

CO2

CARBON DIOXIDEFERTILIZATION

LONGERGROWINGSEASONS

INCREASEDPRECIPITATION

MOREFREQUENTDROUGHTS

PESTS

HEATSTRESS

FASTERGROWINGPERIODS

INCREASEDFLOODING ANDSALINIZATION

CO2 Temperature Precipitation

Agriculture and Climatic Variability

YearCountry Site Sector

Losses

Production million US$

2004 Ecuador Crops 70%

2004 Guatemala

Crops 80% 4

2004/05 Brazil RG do Sul Soybeanand 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 Soybean 55% 170

2005 Bolivia Santa CruzHail and floodings

1

2005 Peru Piura frost

11

Droughts affecting the agricultural sector of LA since 2003

Flooding affecting the agricultural sector of LA since 2003

YearCountry Site Sector

Losses

Production

million US$

2003 Argentina Santa Fe SoybeanMaize & Sorghum

0.4 Mt0.2 Mt

6816

2004 Argentina Chaco 0.3 Mha 200

2006 Bolivia Potosí, Oruro, La Paz

Crops 70% 15

2006 Guyana Mahaica, Mahaicony

Cash cropsRice export

100% 4

Soybean monoculture

Flood Bolivia 2006

Flood Argentina 2003

Floods affecting the agricultural sector of LA since 2003

YearEvent Country Site Sector

Losses

Production

millionUS$

2004 Hurricane Catarina

Brazil BannanaRice

85%40%

2005Hurricane Stan

Guatemala Basic grainsHorticultureCoffeeCatlle

1601630

134

Mexico Chiapas Coffee 0.23 Mha 120

El Salvador Productive lands 9.1%

2006 Hail storm Argentina Santa Fe

Soybean & Maize

0.016 Mha 5

Other extreme events affecting the agricultural sector of LA since 2003

Impacts of interanual climatic variability related to ENSO


0 - 3535 - 4545 - 5555 - 6565 - 7575 - 8585 - 100SD

El Niño

0 - 3535 - 4545 - 5555 - 6565 - 7575 - 8585 - 100SD

La Niña

Soybean yield, Argentina Probability of having high/low yieldsduring El Nino/La Nina years

Freq

uen

cy (%) 0

10

20

30

40

50

60

70

80

90

100

LowMed.High

All Years El Niño "Neutral" La Niña

Yield


Maize production UruguayBaethgen et al., 1998

Adaptation:

Optimizing crop management

Fertilizer amountPlanting dates

Maize Soybean

Wheat – Soybean

Wheat

Sunflower

Peanut

ENSO Phases

100%

75%

50%

25%

0%

Pergamino

Mod

. Risk

ave

rsio

n

Santa RosaPilar

Niña Neutro Niño Clim Niña Neutro Niño Clim Niña Neutro Niño Clim

Adaptation: Argentina. Crop mix

Agriculture and Climatic Trends

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 total and extreme rainfall 1960-2000

Haylock et al., 2006

Indice based on daily minimum temperature: cold and warm nights(Vincent et al, 2005)

Trends in temperature 1960-2000

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)

Total Climate

Wheat +56 +13 (-6 to +21)

Sunflower +102 +12 (+4 to +24)

Maize +110 +18 (+6 to +31)

Soybean - +38 (+4 to +81)3

4

5

6

7

8

Azul Pergamino Tres Arroyos Uruguay

Ma

teri

a s

ec

a (

t/h

a)

1930 19601970 2000

+ 9.8%

+ 6.4%

+ 7.0%

+ 3.6%

Crops Pastures

Impacts of climatic trends in SESA

1966-19991931-1965

Fu

sari

um

in

cid

en

ce

Fusarium incidence in La Estanzuela Uruguay Mauricio Fernandes AIACC-LA27

Impacts of climatic trends in SESA

Agriculture and Climate Change

Maize Sunflower Soybean Wheat

GFDL

UKMO

GISS -8%

-8%

-16%

+18%

-22%

+3%

-3%

-8%

-3%

How Might Global Climate Change Affect Crop Production?

MPI-ds+2% +21% +7%

Magrin & Travasso 2002

Uncertainty?

Simulated maize yields (baseline) and changes to 2055 for Latin America.

Jones & Thornton, 2003

How Might Global Climate Change Affect small farmers Food Production?

Overall reduction: 10%

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.



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).



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

How Might Global Climate Change Affect

Food Production?

Developed-Developing Country Differences

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

Developed-Developing) (%)

10 10 8 10 7 9 9

Potential change (%) in national cereal yields for the 2080s (compared with 1990) using the HadCM3 GCM and SRES scenarios (Parry et al., 2004)

Additional People at Risk of Hunger

30

9

69

50

7

60

34

5

43

0

10

20

30

40

50

60

70

80

2020 2050 2080

Add

ition

al M

illio

ns o

f Peo

ple

Unstabilised

Stabilised at 750ppmv


30

9

69

50

7

60

34

5

43

0

10

20

30

40

50

60

70

80

2020 2050 2080

Add

ition

al M

illio

ns o

f Peo

ple

30

9

69

50

7

60

34

5

43

0

10

20

30

40

50

60

70

80

2020 2050 2080

Add

ition

al M

illio

ns o

f Peo

ple

Unstabilised



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 (demand-driven 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 Vegetation Index Vegetation

Source: INIA-IFDC

January 1998 January 2000

Uruguay

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




Example: existing climate in another area or in previous time

Production Functions

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)

Precipitation

Production Functions

Value

Spatial scale of results Site to globe


Data needs 2 to 4





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


Data needs 1 to 3





Example: FAO, etc.

Water

Carbon

Nitrogen

Crop Models

Based on

Understanding of plants, soil, weather, management

Calculate

Require

Growth, yield, fertilizer & water requirements, etc

Information (inputs): weather, management, etc

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

Adaptation: Argentina

-15

-10

-5

0

5

10

15

20

25

Tres Arroyos Santa Rosa

Cha

nges

in

mai

ze y

ield

(%

)

Without adaptation

Level 1: Changing planting date and fertilizer amount

Level 2: Level 1 + I rrigation

HadCM3 B2 2050

Adaptation strategies in two locations of Argentina

Increased inputs and

improve management:

• Planting date

• Fertilizer

• Irrigation

Travasso et al., 2006

Optimizing crop varietiesOptimizing crop varieties

Maize >P1 Maize >P1 Juvenile phase

Wheat Wheat >P1D photoperiodic sensitivity>P1D photoperiodic sensitivity

Can Adaptation be Achieved by Optimizing Crop Varieties?

Crop Models

Value

Spatial scale of results Site to region

Time to conduct analysis Daily to centuries

Data needs 4 to 5





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 datesArgentina

Economic and Social Tools

Value

Spatial scale of results Yearly to centuries

Time to conduct analysis Site to region

Data needs 4 to 5





Examples: Farm, econometric, I/O, national economies, BLS, …

Integrators: GIS

Potential changes in maize production for the year 2080 under downscaled scenario based on HadCM3 SRES A2.

Without CO2 effects:-9%

With CO2 effects:+19%

Integrators: GIS

Value

Spatial scale of results monthly to centuries

Time to conduct analysis region

Data needs 5


Technological resources 5

Financial resources 5


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; crop-specific 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

Valencia - Dec-Feb T(C) 1900-2000

8

9

10

11

12

13

1880 1900 1920 1940 1960 1980 2000 2020

Valencia - Jun-Aug T(C) 1900-2000

21

22

23

24

25

26

1880 1900 1920 1940 1960 1980 2000 2020

Valencia - Annual T(C) 1900-2000

15

16

17

18

19

20

1880 1900 1920 1940 1960 1980 2000 2020

IPCC Working Group 1: “A Collective Picture of a Warming World”

Source of data: GISS/NASA

asunción paraguay. august 14-18, 2006

Documents

va assessment

tariffscrop modeling

agricultural sector

agriculture sector

crop tolerance

cultural limits

adaptationtechnological

acceptance of water