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![Page 1: 1 Vulnerability and Adaptation Assessment Agriculture Sector Jakarta, Indonesia 23 March 2006 Ana Iglesias Universidad Politécnica de Madrid.](https://reader036.fdocuments.net/reader036/viewer/2022070307/551b275255034607418b5e1f/html5/thumbnails/1.jpg)
1
Vulnerability and Adaptation Assessment
Agriculture Sector
Jakarta, Indonesia23 March 2006
Ana IglesiasUniversidad Politécnica de Madrid
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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
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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)
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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
13:30 – 15:00 4. Models, assisting tools for stakeholders, risk management
3. PC based training
![Page 5: 1 Vulnerability and Adaptation Assessment Agriculture Sector Jakarta, Indonesia 23 March 2006 Ana Iglesias Universidad Politécnica de Madrid.](https://reader036.fdocuments.net/reader036/viewer/2022070307/551b275255034607418b5e1f/html5/thumbnails/5.jpg)
5
Climate, agriculture, and food security Climate change is one
stress among many affecting agriculture and the population that depends on it
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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
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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
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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
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Longer growing seasons …
In Australia, climate change appears to have increased wheat yield by about 10 to 20% since 1952 (Nicholls, 1997)
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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
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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
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Multiple interactions: Stakeholders define adaptation
ScientistsScientists
Policy makersPolicy
makers
Civil stake-holders
Civil stake-holders
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13
Concepts are important: The big picture …
Conclusions for policy
Models
Assumptions
Data
Theory
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14
Agriculture: empirical evidence
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Source: Wei Xiong, Erda Lin, Xiu Yang, et al., 2006
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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
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
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
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
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
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
Possible benefitsPossible benefits
Possible drawbacksPossible drawbacks
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Weeds, pests and diseases
Weeds, pests, and diseased damage about one half of the potential production every year
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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
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
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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?
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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)
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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)
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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
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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
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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)
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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
13:30 – 15:00 4. Models, assisting tools for stakeholders, risk management
3. PC based training
![Page 26: 1 Vulnerability and Adaptation Assessment Agriculture Sector Jakarta, Indonesia 23 March 2006 Ana Iglesias Universidad Politécnica de Madrid.](https://reader036.fdocuments.net/reader036/viewer/2022070307/551b275255034607418b5e1f/html5/thumbnails/26.jpg)
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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]
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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
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What happens in response to change?
Adaptive capacity (internal adaptation) Planned adaptation
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Definition of key vulnerabilities
Expert judgement Stakeholder consultation Empirical evidence Scientific knowledge of processes Models are assisting tools
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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
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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?
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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
13:30 – 15:00 4. Models, assisting tools for stakeholders, risk management
3. PC based training
![Page 34: 1 Vulnerability and Adaptation Assessment Agriculture Sector Jakarta, Indonesia 23 March 2006 Ana Iglesias Universidad Politécnica de Madrid.](https://reader036.fdocuments.net/reader036/viewer/2022070307/551b275255034607418b5e1f/html5/thumbnails/34.jpg)
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Key issues
Integration and cooperation (social, water)
Calibration Extreme events Uncertainties
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Key issues: Pressures and solutions
Water Population Economic and social development
Technology (water desalination, reuse, efficiency)
Agricultural technology Cooperation Improved management
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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
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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
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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
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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
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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
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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
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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
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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
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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)
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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
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Cooperation and integration
Your expert opinion, consultation ……
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Calibration of models
This afternoon Documentation
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Extreme events
Your expert opinion, consultation …… Large knowledge based on risk
management of natural disasters Empirical evidence is essential (external
shock, impacts, vulnerability)
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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
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Thanks for your attention!
Visit MEDROPLAN on the web www.iamz.ciheam.org/medroplan
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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
13:30 – 15:00 4. Models, assisting tools for stakeholders, risk management
3. PC based training
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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
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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
13:30 – 15:00 4. Models, assisting tools for stakeholders, risk management
3. PC based training
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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)
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Bottom-up stakeholder adaptation
Possible tool: MCA WEAP
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Bottom-up stakeholder adaptation
Surveys: Adaptation to climate change in Tunisia, Source: R. Mougou
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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
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Water harvesting
Source: T. Oweis, 2004
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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
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Example: Use MCA WEAP
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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
13:30 – 15:00 4. Models, assisting tools for stakeholders, risk management
3. PC based training
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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, …
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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
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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
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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.
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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
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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
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
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Analogues: drought, floods
Africa vegetation health (VT - index) Vegetation health: Red – stressed, Green – fair, Blue – favorableSource: NOAA/NESDIS
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Production functions
Value
Spatial scale of results Season to decades
Time to conduct analysis Site to globe
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.
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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
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Agroclimatic indices
Value
Spatial scale of results Season to decades
Time to conduct analysis Site to globe
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.
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Agroclimatic Indices
Length of the growing periods (reference climate, 1961-1990). IIASA-FAO, AEZ
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Crop models
Value
Spatial scale of results Daily 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).
Example: CROPWAT, CERES, SOYGRO, APSIM, WOFOST, etc.
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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
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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
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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
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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).
Example: Farm, econometric, I/O, national economies, MCA WEAP …
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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
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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
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Social sciences tools
Surveys and interviews Allow the direct input of stakeholders
(bottom-up approach is emphasized) Provide expert judgment in a rigorous
way
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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 ….
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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
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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
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PC Based examples
DSSAT CROPWAT
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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
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Some crops are more complicated than others ….
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http://www.icasanet.org/
http://www.clac.edu.eg
International Consortium for Agricultural Systems Applications
Practical Applications: DSSAT
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• 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?
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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
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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
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Source: Iglesias, 1999
ESSENTIAL STEP 1. Crop Model Validation
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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
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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
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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
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Application 1. Management
Objective: Getting started
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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
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Input files needed
Weather Soils Cultivars Management files (*.MZX files)
description of the experiment
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Open DSSAT …
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Weather file
Soilfile
Genotype file (Definition of cultivars)
Examine the data files …
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Location of the cultivar file …
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Select the cultivar file …
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Examine the cultivar file …
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Examine the cultivar file …
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Location of the weather file …
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Selection of the weather file …
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Examine the weather file …
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Calculate monthly means …
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Calculate monthly means …
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Program to generate weather data …
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Location of the input experiment file …
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Select the experiment file …
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Examine the experiment file (Syria)
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Examine the experiment file (Florida)
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The experiment file can be edited also with a text editor (Notepad) .…
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Start simulation …
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Running …
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Select experiment …
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Select treatment …
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View the results …
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Select option …
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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
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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
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Application 2. Sensitivity to climate
Objective: Effect of weather modification
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Start simulation …
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Sensitivity analysis …
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Select option …
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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 ….
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Proposed application: Adaptation
For advanced participants …
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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
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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?
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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
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Experiments
1. Calculate ET0
2. Calculate crop water requirements
3. Calculate irrigation requirements for several crops in a farm
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Start CROPWAT …
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Retrieve climate file …
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Examine temperature …
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Examine ET0 …
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Calculate ET0 …
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Examine rainfall …
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Retrieve crop parameters …
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View progress of inputs …
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Define and view crop areas selected …
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Define irrigation method …
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Input data completed …
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Calculate irrigation demand …
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Calculate irrigation schedule …
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View results …
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Review
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
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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