1 vulnerability and adaptation assessment agriculture sector jakarta, indonesia 23 march 2006 ana...

1

Vulnerability and Adaptation Assessment

Agriculture Sector

Jakarta, Indonesia23 March 2006

Ana IglesiasUniversidad Politécnica de Madrid

2

Objective

To provide participants with information on V&A assessment for the agriculture sector A general discussion on the impacts of

climate variability and change on agriculture and food security

Methods, tools and issues to assess V&A PC based training on methods, tools,

issues

3

Outline

1. Climate variability and change, agriculture and food security (½ h)

2. Key differential vulnerabilities (½ h)3. Key issues (½ h)

1. Integration and cooperation (social, water)2. Calibration3. Extreme events 4. Uncertainties

4. PC based training: Models, assisting tools for stakeholders, risk management (3 h)

1. Designing the framework (½ h)2. Participatory evaluation and prioritization of adaptation (½ h)3. PC based training (2 h)

Total: (4 ½ h)

4

Agenda9:15 – 10:45 1. Climate variability and change, agriculture,

and food security

2. Key differential vulnerabilities

3. Key issues

10:45 – 11:00 Coffee

11:00 – 12:30 4. Models, assisting tools for stakeholders, risk management

1. Designing the framework

2. Participatory evaluation and prioritization of adaptation

12:30 – 13:30 Lunch


3. PC based training

5

Climate, agriculture, and food security Climate change is one

stress among many affecting agriculture and the population that depends on it

6

Observations: Increased drought Persistent drying trend in parts of Africa has

affected food production, including freshwater fisheries, industrial and domestic water supplies, hydropower generation (Magazda, 1986; Benson and Clay, 1998; Chifamba, 2000; Iglesias and Moneo, 2005)

Maize production, Zimbabwe

7

Drought in the Mediterranean

0

5

10

15

20

1980 1983 1986 1989 1992 1995 1998 2001

Rendement/SE (Qx/ha) Rendement/SR (Qx/ha)

Q/ha Cereal Yields

Source: R. Mougou, INRGREF

Correlation betwen total rainfall and agricultural production r=0.82

50

150

250

350

450

550

650

1951 1955 1959 1963 1967 1971 1975 1979 1983 1987 1991 1995 1999

An

nu

al R

ain

fall

(m

m)

111mm

624mm

Kairouan (Tunisia)

Rainfall

8

Drought in the Mediterranean

Source: Iglesias and Moneo, 2004

Wheat yield in Spain

0%

20%

40%

60%

80%

100%

all years dry years normal years wet years

Pro

ba

bili

ty o

f y

ield

(%

)

high yield

medium yield

low yield

9

Longer growing seasons …

In Australia, climate change appears to have increased wheat yield by about 10 to 20% since 1952 (Nicholls, 1997)

10

Multiple interactions, vulnerability and adaptation

Social vulnerability

Climate change

Economic, social,

demographic, land usechanges

Systems and social groups that need to

adapt

Systems and social groups that need to

adapt

11

Social vulnerability

“Starvation is the characteristic of some people not having enough food to eat. It is not the characteristic of there being not enough food to eat. While the later can cause the former, it is but one of many possible causes.”

A. Sen, Poverty and Famines, An Essay on Entitlement and Deprivation, 1981, pg 1

12

Multiple interactions: Stakeholders define adaptation

ScientistsScientists

Policy makersPolicy

makers

Civil stake-holders

Civil stake-holders

13

Concepts are important: The big picture …

Conclusions for policy

Models

Assumptions

Data

Theory

14

Agriculture: empirical evidence

15

Source: Wei Xiong, Erda Lin, Xiu Yang, et al., 2006

16

POSSIBLE BENEFITS

POSSIBLE DRAWBACKS

CO2

CARBON DIOXIDEFERTILIZATION

LONGERGROWINGSEASONS

INCREASEDPRECIPITATION

MOREFREQUENTDROUGHTS

PESTS

HEATSTRESS

FASTERGROWINGPERIODS

INCREASEDFLOODING ANDSALINIZATION

POSSIBLE BENEFITS

POSSIBLE DRAWBACKS

CO2





PESTS

HEATSTRESS



POSSIBLE BENEFITS

POSSIBLE DRAWBACKS

CO2





PESTS

HEATSTRESS



POSSIBLE BENEFITS

POSSIBLE DRAWBACKS

CO2





PESTS

HEATSTRESS



POSSIBLE BENEFITS

POSSIBLE DRAWBACKS

CO2





PESTS

HEATSTRESS



POSSIBLE BENEFITS

POSSIBLE DRAWBACKS

CO2





PESTS

HEATSTRESS



POSSIBLE BENEFITS

POSSIBLE DRAWBACKS

CO2





PESTS

HEATSTRESS



POSSIBLE BENEFITS

POSSIBLE DRAWBACKS

CO2





PESTS

HEATSTRESS



POSSIBLE BENEFITS

POSSIBLE DRAWBACKS

CO2





PESTS

HEATSTRESS



POSSIBLE BENEFITS

POSSIBLE DRAWBACKS

CO2





PESTS

HEATSTRESS



POSSIBLE BENEFITS

POSSIBLE DRAWBACKS

CO2





PESTS

HEATSTRESS



POSSIBLE BENEFITS

POSSIBLE DRAWBACKS

CO2





PESTS

HEATSTRESS



Possible benefitsPossible benefits

Possible drawbacksPossible drawbacks

17

Weeds, pests and diseases

Weeds, pests, and diseased damage about one half of the potential production every year

18

Climate change affects crop production

Changes in biophysical conditions Changes in socio-economic 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





PESTS

HEATSTRESS



POSSIBLE BENEFITS

POSSIBLE DRAWBACKS

CO2





PESTS

HEATSTRESS



19

Percentage change in average crop yields for the Hadley Center global climate change scenario (HadCM3). Direct physiological effects of CO2 and crop adaptation are taken into account. Crops modeled are: wheat, maize, and rice.Source: NASA/GISS; Rosenzweig and Iglesias, 2002; Parry et al, 2004

2020s

2050s

2080s

Yield Change (%)

-30 -20 -10 -5 -2.5 0 2.5 5 10 20 30 40

How might global climate change affect food production?

20

Limits to adaptation

Technological limits (i.e., crop tolerance to water-logging or high temperature; water reutilization)

Social limits (i.e., acceptance of biotechnology)

Political limits (i.e., rural population stabilization may not be optimal land use planning)

Cultural limits (i.e., acceptance of water price and tariffs)

21

Developed-Developing country differences

Scenario A1FI A2a A2b A2c A2c B1a B2b

CO2 (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)

22

Additional people at risk of hunger

Parry et al., 2004

0

40

80

120

160

200

2020 2050 2080

Ad

ditio

nal

Mill

ion

s o

f Peo

ple

A2 - Regional Enterprise B2 - Local Stewardship

23

Interaction and integration: Water

0

40

80

120

2020 2050 2080

Po

pula

tion

(m

illio

ns)

Additional population under extreme stress of water shortage

University of Southampton

24

Conclusions

While 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)

25

Agenda9:15 – 10:45 1. Climate variability and change, agriculture, and

food security


3. Key issues

10:45 – 11:00 Coffee




12:30 – 13:30 Lunch



26

Key differential vulnerabilities Climate change is one stress among many now

affecting agriculture and the population that depends on it

Integration of results and stakeholder definition of adaptation strategies are essential to formulate assessments relevant to policy

Potential future consequences depend on: The region and the agricultural system [Where?, The

baseline is important] The magnitude [How much? Scenarios are important] The socio-economic response [What happens in response

to change? Adaptive capacity (internal adaptation) and planned stakeholder adaptation and policy]

27

Map of the night-time city lights of the world (DMSP: NASA and NOAA)

Where? Systems and social groups

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How much? Climate and SRES scenarios

Precipitation change

Temperature change

Had CM2 model, 2050s

http://www.grida.no/climate/ipcc/emission/090.htm

29

What happens in response to change?

Adaptive capacity (internal adaptation) Planned adaptation

30

Definition of key vulnerabilities

Expert judgement Stakeholder consultation Empirical evidence Scientific knowledge of processes Models are assisting tools

31

Check list and ranking of potential vulnerabilities - Examples

Components of the farming system particularly vulnerable Stress on water/irrigation systems Domestic agricultural production Food shortages that lead to an increase in hunger Agricultural exports Prices to consumers Government policies such as agricultural pricing, support,

research and development Greater stress on natural resources or contribute to environmental

degradation (e.g., through land-use change, soil degradation, changes in water supply and water quality, pesticide use, etc.)

Research/extension system capability for providing adaptation advice to farmers

Technological options in place

32

Key vulnerabilities

Individuals particularly vulnerable to environmental change are those with ….

• Relatively high exposures to changes• High sensitivities to changes• Low coping and adaptive capacities• Low resilience and recovery potential

Who can adapt?Who is vulnerable?

33


food security


3. Key issues

10:45 – 11:00 Coffee




12:30 – 13:30 Lunch



34

Key issues

Integration and cooperation (social, water)

Calibration Extreme events Uncertainties

35

Key issues: Pressures and solutions

Water Population Economic and social development

Technology (water desalination, reuse, efficiency)

Agricultural technology Cooperation Improved management

36

Water

Agricultural water use % of total (2004)

0

20

40

60

80

100

A

lban

ia

B

aham

as

B

ahra

in

B

angl

ades

h

B

huta

n

C

ambo

dia

C

hina

C

ook

Isla

nds

In

dia

In

done

sia

Ir

an,

Isla

mic

Jo

rdan

K

azak

hsta

n

K

iriba

ti

K

orea

, D

em

K

orea

,

K

uwai

t

K

yrgy

zsta

n

La

os

Le

bano

n

M

alay

sia

M

aldi

ves

M

icro

nesi

a,F

ed

M

ongo

lia

N

auru

N

epal

N

iue

P

akis

tan

P

alau

P

hilip

pine

s

S

amoa

S

inga

pore

S

olom

on

T

ajik

ista

n

T

haila

nd

T

onga

T

urkm

enis

tan

T

uval

u

U

zbek

ista

n

V

anua

tu

V

iet

Nam

Y

emen

37

Population

Rural population change % (1993-2003)

-14

-12

-10

-8

-6

-4

-2

0

2

4

6

A

lban

ia

B

aham

as

B

ahra

in

B

angl

ades

h

B

huta

n

C

ambo

dia

C

hina

C

ook

Isla

nds

In

dia

In

done

sia

Ir

an,

Isla

mic

Jo

rdan

K

azak

hsta

n

K

iriba

ti

K

orea

, D

em

K

orea

,

K

uwai

t

K

yrgy

zsta

n

La

os

Le

bano

n

M

alay

sia

M

aldi

ves

M

icro

nesi

a,F

ed

M

ongo

lia

N

auru

N

epal

N

iue

P

akis

tan

P

alau

P

hilip

pine

s

S

amoa

S

inga

pore

S

olom

on

T

ajik

ista

n

T

haila

nd

T

onga

T

urkm

enis

tan

T

uval

u

U

zbek

ista

n

V

anua

tu

V

iet

Nam

Y

emen

38

Economic and social development

Agricultural trade balance (exportts-imports) value (million $) (2004)

-22,000

-17,000

-12,000

-7,000

-2,000

3,000

8,000

13,000

A

lban

ia

B

aham

as

B

ahra

in

B

angl

ades

h

B

huta

n

C

ambo

dia

C

hina

C

ook

Isla

nds

In

dia

In

done

sia

Ir

an,

Isla

mic

Jo

rdan

K

azak

hsta

n

K

iriba

ti

K

orea

, D

em

K

orea

,

K

uwai

t

K

yrgy

zsta

n

La

os

Le

bano

n

M

alay

sia

M

aldi

ves

M

icro

nesi

a,F

ed

M

ongo

lia

N

auru

N

epal

N

iue

P

akis

tan

P

alau

P

hilip

pine

s

S

amoa

S

inga

pore

S

olom

on

T

ajik

ista

n

T

haila

nd

T

onga

T

urkm

enis

tan

T

uval

u

U

zbek

ista

n

V

anua

tu

V

iet

Nam

Y

emen

GDP 2004 (millions of US dollars)

0200,000400,000600,000800,000

1,000,0001,200,0001,400,0001,600,0001,800,0002,000,000

A

lban

ia

B

aham

as

B

ahra

in

B

angl

ades

h

B

huta

n

C

ambo

dia

C

hina

C

ook

Isla

nds

In

dia

In

done

sia

Ir

an,

Isla

mic

Jo

rdan

K

azak

hsta

n

K

iriba

ti

K

orea

, D

em

K

orea

,

K

uwai

t

K

yrgy

zsta

n

La

os

Le

bano

n

M

alay

sia

M

aldi

ves

M

icro

nesi

a,F

ed

M

ongo

lia

N

auru

N

epal

N

iue

P

akis

tan

P

alau

P

hilip

pine

s

S

amoa

S

inga

pore

S

olom

on

T

ajik

ista

n

T

haila

nd

T

onga

T

urkm

enis

tan

T

uval

u

U

zbek

ista

n

V

anua

tu

V

iet

Nam

Y

emen

39

Integration and cooperation

0

40

80

120

2020 2050 2080

Po

pula

tion

(m

illio

ns)

Source: University of Southampton

Additional population under extreme stress of water shortage

40

Water

The agriculture sector needs water supply scenarios

Policy defines how much water can be used by agriculture

Water policy and rights are extremely hard to change

41

Water conflictsEvolución del balance Demandas - Disponibilidades

El AtazarValmayor

Sequía 1982

Sequía 1992

Nuevos pozos

Imp. Picadas

Tr. S. Juan Valmayor

0

100

200

300

400

500

600

700

800

900

1000

1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020

hm

3

Capacidad de suministro Demanda

42

www.bgr.de/app/whymap/www.bgr.de/app/whymap/

Water can lead to political hostilities and many regions with political conflicts also share water resources

Transboundary surface and groundwater

http://www.bgr.de/app/whymap/

http://www.bgr.de/app/whymap/

43

Political and cultural process

Irrigation Area: 2000 and 2010

0

1000

2000

3000

4000

France Spain Italy Greece Portugal

Irri

g A

rea

(ha

x 10

00)

2000

2010

Source: EEA, CEDEX

The political process reflects the view about future of the resources and economies, therefore defines the range of adaptation options

Cultural impediments to change traditional water management add complexity to the design of adaptation strategies

Current and projected water demand (%)

1996 2030 Drinking 11.5 17.7Irrigation 83.7 73.5Tourism 0.7 1.5Industrial 4.1 7.3

Tunisia: National strategy on water management (Source: R. Mougou)

Resources management Mobilization, storage (over 1,000 hill

reservoirs in 10 years), and transfer of the resources Use of the non conventional resources: saline and waste

water for irrigation (95,400 and 7,600 ha) Desalinization

Demand management Water saving in irrigation (up to 60% Government subsidies)

45

Example: Integrated assessment in Egypt

Source: El-Shaer et al., 1997; Strzpek et al., 1999

Aim Analysis of no regret options for the future

Current vulnerability• Dependence on the Nile as the primary water source• Large traditional agricultural base• Long coastline already undergoing both intensifying

development and erosion• Problems derived from population increase• Agriculture entirely based on irrigation (water from

the Nile, and to lesser degree from groundwater)• Soil conditions and water quality deteriorating

46

Cooperation and integration

Your expert opinion, consultation ……

47

Calibration of models

This afternoon Documentation

48

Extreme events

Your expert opinion, consultation …… Large knowledge based on risk

management of natural disasters Empirical evidence is essential (external

shock, impacts, vulnerability)

49

Uncertainties

Your expert opinion, consultation …… Climate change scenarios Climate variability Stakeholder adaptation Agricultural models Effects of CO2 on crops Issues of scale Socio economic projections

50

Thanks for your attention!

Visit MEDROPLAN on the web www.iamz.ciheam.org/medroplan

[email protected]

51


food security


3. Key issues

10:45 – 11:00 Coffee




12:30 – 13:30 Lunch



52

The process: Example

Set up a Multidisciplinary

Stakeholder Team (Organizational

component)

Set up a Multidisciplinary

Stakeholder Team (Organizational

component)

Evaluate the legal, social, and political

process(Organizational

component)

Evaluate the legal, social, and political

process(Organizational

component)

Identify risk and potential vulnerabilities

(Methodologicalcomponent)

Identify risk and potential vulnerabilities

(Methodologicalcomponent)

Select and identify priority actions, based

on agreed criteria(Operationalcomponent)

Select and identify priority actions, based

on agreed criteria(Operationalcomponent)

Public review and Revision

Public dissemination(Operationalcomponent)

Public review and Revision

Public dissemination(Operationalcomponent)

www.iamz.ciheam.org/medroplanwww.iamz.ciheam.org/medroplan

53


food security


3. Key issues

10:45 – 11:00 Coffee




12:30 – 13:30 Lunch



54

Bottom-up stakeholder adaptation

Objective of the strategy: To minimize impacts of a warmer and drier climate while maintaining rural agricultural production and minimizing the environmental damage

Consideration of effectiveness to minimize the impacts of a warmer and drier climate, cost, and feasibility

Adequacy for situation without climate change (win-win strategy)

55


Possible tool: MCA WEAP

56


Surveys: Adaptation to climate change in Tunisia, Source: R. Mougou

57

Bottom-up stakeholder adaptationStakeholder group

Adaptation

Level 1

Adaptation

Level 2

Adaptation

Level 3

Small-holder farmers or farmers' groups

Tactical advice on changes in crop calendar and water needs

Management of risk in water availability (quantity and frequency)

Education on water-saving practices and changes in crop choices

Commercial farmers

Tactical on improving cash return for water and land units

Investment in irrigation technology; Risk-sharing (e.g., insurance)

Private sector participation in development of agro-businesses

Resource Managers

Education on alternatives for land and water management

Integrated resource management for water and land

Alternatives for the use of natural resources and infrastructure

58

Water harvesting

Source: T. Oweis, 2004

59

Bottom-up stakeholder adaptationExamples

1. Tactical advice crop calendar2. Tactical advice water needs3. Improve cash return for water

and land units4. Management of risk in water 5. Investment 6. Integrated resource

management for water and land

7. Education 8. Private sector participation9. Alternatives for the use of

natural resources and infrastructure

10. Crop residue incorporation11. Access to fertilizer12. Extension services

13. Indigenous knowledge14. Short-duration varieties15. Crop diversification 16. New crop varieties17. New crops18. Agroforestry 19. Food storage 20. Agrometeorological advice21. Construction of a dam22. Irrigation (new scheme)23. Irrigation (improved system)24. Water harvesting25. Water desalination /

reutilization 26. Cease activity

60

Example: Use MCA WEAP

61


food security


3. Key issues

10:45 – 11:00 Coffee




12:30 – 13:30 Lunch



62

Assisting tools to stakeholders

Need quantitative estimates Models are assisting tools Surveys to stakeholders are assisting tools

for designing bottom-up adaptation options

Key variables for agronomic and socio-economic studies: crop production, land suitability, water availability, farm income, …

63

Before getting started ….

Models are assisting tools, stakeholder participation is essential

The use of models requires high degree of technical expertise

The merits of each model and approach vary according to the objective of the study, and they may frequently be mutually supportive

Therefore, a mix of tools and approaches is often the most rewarding

64

Quantitative methods and tools

Experimental Analogues (spatial and temporal) Production functions (statistically derived) Agro-climatic 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

65

Experimental

Value

Spatial scale of results Season to decades

Time to conduct analysis Site

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.

66

Experimental: Effect of Increased CO2

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

http://www.ars.usda.gov/

67

Analogues (space and time)

Value

Spatial scale of results Decades

Time to conduct analysis Site to region

Data needs 1 to 2

Skill or training required 1 to 3




Example: existing climate in another area or in previous time

68

Analogues: drought, floods

Africa vegetation health (VT - index) Vegetation health: Red – stressed, Green – fair, Blue – favorableSource: NOAA/NESDIS

69

Production functions

Value


Time to conduct analysis Site to globe

Data needs 2 to 4





Example: Derived with empirical data.

70

Dryland Yield

Predicted Values

Yr PP Change (%)

150100500-50-100-150

Dry

land

Yie

ld (

kg h

a-1)

8000

6000

4000

2000

0

Irrigation

Predicted Values

Yr PP Change (%)

150100500-50-100-150

Irrig

atio

n (m

m)

400

300

200

100

0

Statistically derived functions (Almeria – Wheat)Yield Irrigation demand

Production functions

Iglesias, 1999; Iglesias et al., 2000

71

Agroclimatic indices

Value


Time to conduct analysis Site to globe

Data needs 1 to 3





Example: FAO, etc.

72

Agroclimatic Indices

Length of the growing periods (reference climate, 1961-1990). IIASA-FAO, AEZ

73

Crop models

Value

Spatial scale of results Daily to centuries


Data needs 4 to 5





Example: CROPWAT, CERES, SOYGRO, APSIM, WOFOST, etc.

74

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

75

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

76

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

77

Economic and social tools

Value

Spatial scale of results Yearly to centuries


Data needs 4 to 5





Example: Farm, econometric, I/O, national economies, MCA WEAP …

78

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? 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

79

Differences in farming systems

Small holder farmer Commercial farmer

Strategy of production

Stabilize food production Maximize income

Risk Malnutrition and migration Debt and cessation of activity

Primary source of risk

Weather Weather, markets and policies

Non-structural risk avoidance mechanisms

Virtually nonexistent Insurance, credit, legislation

Inputs and farm assets

Very low Very significant

80

Social sciences tools

Surveys and interviews Allow the direct input of stakeholders

(bottom-up approach is emphasized) Provide expert judgment in a rigorous

way

81

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 ….

82

Conclusions

The merits of each approach vary according to the level of impact being studied, and they may frequently be mutually supportive

Therefore, a mix of approaches is often the most rewarding

Data are required data to define climatic, non-climatic environmental, and socio-economic baselines and scenarios

Data is limited Discussion on supporting databases and data

sources

83

Irrigation Area Tunisia (1970 - 1998)

50

150

250

350

450

1970 1975 1980 1985 1990 1995Year

Iirrg

Are

a (

ha

x 1

00

0)

FAO Data USDA ERS Data

Data: Scales, Sources, Reliability

84

PC Based examples

DSSAT CROPWAT

85

Can crop models explain observations?2002 Egypt Morocco Spain Tunisia

Area (1000ha) 100,145 44,655 50,599 16,361Population (1000) 70,507 30,072 40,977 9,728Population 2030 (1000) 109,111 42,505 39,951 12,351Population in agriculture (% of total) 35 35 7 24Population in rural areas (% of total) 57 43 22 33Population in rural areas 2030 (% of total) 46 29 15 22

Agricultural Area (% of total) 3 69 58 55Irrigation area (% of agricultural) 100 4 12 4Wheat Yield (kg/ha) (World = 2,678) 6,150 1,716 2,836 3,853

Agricultural Imports (million $) 3,688 1,740 12,953 1,022Agricultural Exports (million$) 774 811 16,452 391Fertiliser Consumption (kg/ha) 392 12 74 12

Crop Drought Insurance No No Yes NoAgricultural Subsidies Low Low High LowAgriculture, value added (% of GDP) 17 14 4 12GDP Per capita (US$) UN derived from purchasing power parity (PPP) 4,000 3,900 21,200 6,800

Data: FAOSTAT

86

Some crops are more complicated than others ….

87

http://www.icasanet.org/

http://www.clac.edu.eg

International Consortium for Agricultural Systems Applications

Practical Applications: DSSAT

http://www.icasanet.org/

http://www.clac.edu.eg/

88

• What components of the farming system are particularly vulnerable, and may thus require special attention?

• What components of the farming system are particularly vulnerable, and may thus require special attention?

Applications of DSSAT to answer adaptation questions

• Can optimal management decrease vulnerability to climate?

• Can optimal management decrease vulnerability to climate?

• What are the characteristics of optimized crop varieties?

• What are the characteristics of optimized crop varieties?

89

DSSAT Decision Support System for Agrotechnology

Transfer

Components Description

DATABASES Weather, soil, genetics, pests, experiments, economics

MODELS Crop models (Maize, wheat, rice, barley, sorghum, millet, soybean, peanut, dry bean, potato, cassava, etc)

SUPPORTING SOFTWARE

Graphics, weather, pests, soil, genetics, experiments, economics

APPLICATIONS Validation, sensitivity analysis, seasonal strategy, crop rotations

90

Input Requirements

WEATHER: Daily precipitation, maximum and minimum temperatures, solar radiation

SOIL: Soil texture and soil water measurements

MANAGEMENT: planting date, variety, row spacing, irrigation and N fertilizer amounts and dates, if any

CROP DATA: dates of anthesis and maturity, biomass and yield, measurements on growth and LAI

91

Source: Iglesias, 1999

ESSENTIAL STEP 1. Crop Model Validation

92

Key issues

Limitations of datasets Limitations of models Lack of technical expertise and resources Limitations of the studies due to lack of

integration with: Water availability and demand Social and economic response

93

Datasets

Data are required data to define climatic, non-climatic environmental, and socio-economic baselines and scenarios

Data is limited Discussion on supporting databases and

data sources

94

Guided examples

1. Effect of management (nitrogen and irrigation) in wet and dry sites (Florida, USA, and Syria)

2. Effect of climate change on wet and dry sites

Sensitivity analysis to changes in temperature and precipitation (thresholds), and CO2 levels

95

Application 1. Management

Objective: Getting started

96

Weather

Syria Florida, USA

SR (MJ m2 day-1) 19.3 16.5

T Max (C) 23.0 27.4

T Min (C) 8.5 14.5

Precipitation (mm) 276.4 1364.3

Rain Days (num) 55.7 114.8

97

Input files needed

Weather Soils Cultivars Management files (*.MZX files)

description of the experiment

98

Open DSSAT …

99

Weather file

Soilfile

Genotype file (Definition of cultivars)

Examine the data files …

100

Location of the cultivar file …

101

Select the cultivar file …

102

Examine the cultivar file …

103

Examine the cultivar file …

104

Location of the weather file …

105

Selection of the weather file …

106

Examine the weather file …

107

Calculate monthly means …

108

Calculate monthly means …

109

Program to generate weather data …

110

Location of the input experiment file …

111

Select the experiment file …

112

Examine the experiment file (Syria)

113

Examine the experiment file (Florida)

114

The experiment file can be edited also with a text editor (Notepad) .…

115

Start simulation …

116

Running …

117

Select experiment …

118

Select treatment …

119

View the results …

120

Select option …

121

Retrieve output files for analysis

C:/DSSAT35/MAIZE/SUMMARY.OUT C:/DSSAT35/MAIZE/WATER.OUT C:/DSSAT35/MAIZE/OVERVIEW.OUT C:/DSSAT35/MAIZE/GROWTH.OUT C:/DSSAT35/MAIZE/NITROGEN.OUT

There are DOS text files Can be imported into Excel

122

Management: Maize Yield Florida and Syria

0

2000

4000

6000

8000

10000

12000

Rainfed Low N Rainfed High N Irrig Low N Irrig High N

Gra

in Y

ield

(k

g/h

a)

Florida

Syria

Analyse and present results

123

Application 2. Sensitivity to climate

Objective: Effect of weather modification

124

Start simulation …

125

Sensitivity analysis …

126

Select option …

127

Climate Change: Maize Yield Florida

0

500

1000

1500

2000

2500

Florida Base Florida -50% pp

Gra

in Y

ield

(k

g/h

a)

Analyse results ….

128

Proposed application: Adaptation

For advanced participants …

129Pioneer, April 00 - 129

Adaptation

Management strategy: Explicit guidance to farmers regarding optimal crop selection, irrigation, and fertilization, and should institute strong incentives to avoid excessive water use

Use the DSSAT models to evaluate the use of alternative existing varieties and changes in the timing of planting to optimize yield levels or water use

130

Applications of CROPWAT to answer adaptation questions

• Can the water/irrigation systems meet the stress of changes in water supply/demand?

• Can the water/irrigation systems meet the stress of changes in water supply/demand?

• Will climate change significantly affect agricultural water demand production?

• Will climate change significantly affect agricultural water demand production?

131

http://www.fao.org/ag/agl/aglw/cropwat.htm

CROPWAT is a decision support system for irrigation planning and management.

http://www.clac.edu.eg

132

Experiments

1. Calculate ET0

2. Calculate crop water requirements

3. Calculate irrigation requirements for several crops in a farm

133

Start CROPWAT …

134

Retrieve climate file …

135

Examine temperature …

136

Examine ET0 …

137

Calculate ET0 …

138

Examine rainfall …

139

Retrieve crop parameters …

140

View progress of inputs …

141

Define and view crop areas selected …

142

Define irrigation method …

143

Input data completed …

144

Calculate irrigation demand …

145

Calculate irrigation schedule …

146

View results …

147

Review

[email protected]

Climate variability and change, agriculture and food security

Key differential vulnerabilities Key issues Models, assisting tools for stakeholders,

risk management Designing the framework Participatory evaluation and prioritization of

adaptation PC based training

148

Review

1. Climate variability and change, agriculture and food security

2. Key differential vulnerabilities3. Key issues

1. Integration and cooperation (social, water)2. Calibration3. Extreme events 4. Uncertainties

4. PC based training: Models, assisting tools for stakeholders, risk management

1. Designing the framework2. Participatory evaluation and prioritization of

adaptation 3. PC based training

1 vulnerability and adaptation assessment agriculture sector jakarta, indonesia 23 march 2006 ana...

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