Transcript Agriculture

Vulnerability and Adaptation Assessments
Hands-On Training Workshop
Impact, Vulnerability and Adaptation Assessment
for the Agriculture Sector
Outline
Climate change, agriculture and food security
Methods, tools, and datasets
Practical applications
Integrated assessments
Climate Change, Agriculture,
and Food Security
Climate change is one
stress among many
affecting agriculture and
the population that
depends on it
Multiple Interactions,
Vulnerability, and Adaptation
Climate
change
Systems and
social groups
that needs to
adapt
Economic,
social,
demographic,
land use
changes
Social vulnerability
Mozambique,
floods
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.”
Multiple Interactions, Stakeholders
Define Adaptation
Policy
makers
Scientists
Civil
stakeholders
Multiple Interactions
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Climate change is one stress among many
now affecting agriculture and the population
that depends on it
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Integration of results is essential to formulate
assessments relevant to policy
Potential future consequences depend on:
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The region and the agricultural system
[Where?]
The magnitude [How much? Scenarios are
important.]
The socioeconomic response [What happens
in response to change? Adaptive capacity
(internal adaptation) and planned adaptation.]
Where? Systems and
Social Groups
Cassava production, Mozambique
Coffee production, Kenya
Vegetable production, Egypt
How Much? Climate and
SRES Scenarios
Had CM2 model, 2050s
Temperature change
Precipitation change
What Happens in
Response to Change?
Adaptive capacity (internal adaptation)
Planned adaptation
Climate Change Affects
Crop Production
POSSIBLE BENEFITS
CO2
CARBON DIOXIDE
FERTILIZATION
LONGER
GROWING
SEASONS
INCREASED
PRECIPITATION
POSSIBLE DRAWBACKS
MORE
FREQUENT
DROUGHTS
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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)
How Might Global Climate Change
Affect Food Production?
2020s
Percentage change in
average crop yields for
the Hadley Center global
climate change scenario
(HadCM2). Direct
physiological effects of
CO2 and crop adaptation
are taken into account.
Crops modeled are
wheat, maize, and rice.
2050s
2080s
Source: NASA/GISS; Rosenzweig and
Iglesias, 1994.
Yield Change (%)
-30
-20 -10
-5
-2.5 0
2.5
5
10
20
30
40
Limits to Adaptation
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Technological limits (e.g., crop tolerance to waterlogging or high temperature; water reutilization)
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Social limits (e.g., acceptance of biotechnology)
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Political limits (e.g., rural population stabilization may
not be optimal land use planning)
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Cultural limits (e.g., acceptance of water price and
tariffs)
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
34
30
30
20
9
10
7
5
0
2020
2050
2080
Unstabilised
Stabilised at 750ppmv
Stabilised at 550ppmv
Parry et al., 2004
Additional People at
Risk of Hunger (continued)
Overall, the potential for additional people with risk of hunger
is greater with the “unstabilized” scenario, although there
are decadal variations
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In all decades, the “unstabilized” scenario is the warmest
In the 2020s, the warming is beneficial for aggregated
crop production
In the 2080s, the warming exceeds the threshold of
optimal crop tolerance in many low latitude regions with
more people at risk
Interaction and Integration: Water
Additional population under extreme stress
of water shortage
Population (millions)
120
80
40
0
2020
2050
2080
Conclusions
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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
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3.
Demand-driven methods: responding to
stakeholders
Key characteristics, strengths, weaknesses
Examples
Datasets: sources, scales, reliability
Frameworks
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Adaptation Policy Framework (APF), US
Country Studies, IPCC, seven steps
All have the essential common elements
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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
Demand-Driven Methods
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Need quantitative estimates
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Models are assisting tools
Surveys are assisting tools for
designing adaptation options
Key variables for agronomic and
socioeconomic studies: crop
production, land suitability, water
availability, farm income, …
Quantitative
Methods and Tools
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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
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.
Analogues: Drought, Floods
Africa vegetation health (VT - index)
Vegetation health: Red – stressed, Green – fair, Blue – favorable
Source: NOAA/NESDIS
Analogues: Drought
Wheat yield in Spain
Probability of yield (%)
100%
high
medium
low
80%
60%
40%
20%
0%
All
Dry
Normal
Wet
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
Production Functions
Statistically derived functions (Almeria – Wheat)
Yield
Irrigation demand
400
Irrigation (mm)
Dryland Yield (kg ha-1)
8000
6000
4000
2000
300
200
100
Irrigation
Dryland Yield
0
-150
Predicted Values
-100
-50
0
50
100
150
0
-150
Predicted Values
-100
-50
0
50
100
Yr PP Change (%)
Yr PP Change (%)
Iglesias et al., 1999
150
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.
Agroclimatic Indices
Length of the growing periods (reference climate, 1961-1990).
IIASA-FAO, AEZ
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.
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
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Models are assisting tools, stakeholder
interaction is essential
Models allow to ask “what if” questions, the
relative benefit of alternative management
can be highlighted:
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Improve planning and decision making
Assist in applying lessons learned to policy
issues
Models permit integration across scales,
sectors, and users
Models – Limitations
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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
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.
Economic Models
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Consider both producers and consumers of
agricultural goods (supply and demand)
Economic measures of interest include:
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How do prices respond to production
amounts?
How is income maximized with different
production and consumption opportunities?
Economic Models
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(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
Farm Models – Differences
Small holder farmer
Commercial farmer
Strategy of production
Stabilize food production
Maximize income
Risk
Malnutrition and migration
Debt and cessation of
activity
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
Price of food crops
Local for primary crops and partially
global for industrial crops, with
some interference of governments
Global with some
interference of
policies
Agricultural Trade Models
Parry et al., 1999.
Social Sciences Tools
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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
Surveys to Stakeholders
Designing Adaptation Options
Stakeholder
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; Risksharing (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
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
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
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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
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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
21
8
1880
1900
1920
1940
1960
1980
2000
2020
1880
1900
1920
1940
1960
1980
2000
2020
Source of data: GISS/NASA
1880
1900
1920
1940
1960
1980
2000
2020
Climate
FAOCLIM
Precipitation Annual 1901-1995
Valladolid - Annual Precipitation (mm)
700
600
500
400
300
Source of data: NOAA, NCDC
1999
1995
1991
1987
1983
1979
1975
1971
1967
1963
1959
1955
1951
200
Global Land Cover Classification
De Fries et al., 1998
1: Evergreen needle leaf forests
2: Evergreen broad leaf forests
3: Deciduous needle leaf forests
4: Deciduous broad leaf forests
5: Mixed forests
6: Woodlands
7: Wooded grasslands/shrubs
8: Closed bushlands or shrublands
9: Open shrublands
10: Grasses
11: Croplands
12: Bare
13: Mosses and lichens
Population
Lights are Related to
Income and Population
Map of the night-time city lights of the world
DMSP: NASA and NOAA
Soils: FAO
FAO and the World Bank
Agricultural GDP as share of total GDP
FAO
Food aid received from external sources 2000
Countries facing
exceptional food
emergencies due to
the drought August
2001
USGS, FEWS, USAID
FEWS NET in cooperation
with USGS and US AID
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Botswana – village flood
watch
Carbon sequestration
Environmental monitoring
and information system
Land cover performance
Madagascar conservation
Rift Valley fever
Sahel land use
Sustainable tree crops
The projected change in annual temperature and precipitation for the 2050s compared
to the present day, for two GCMs, when the climate models are driven with an increase
in greenhouse gas concentrations
defined by the IPCC “business-as-usual” scenario.
Data: Scales, Sources, Reliability
Iirrg Area (ha x 1000)
450
Irrigation Area Tunisia (1970 - 1998)
350
250
150
50
1970
1975
1980
1985
1990
Year
FAO Data
USDA ERS Data
1995
Practical Applications Policy Questions
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What components of the farming system are
particularly vulnerable and may thus require
special attention?
Can the water/irrigation systems meet the
stress of changes in water supply/demand?
Will climate significantly affect domestic
agriculture?
Practical Applications: DSSAT
 Question: What components of the farming
system are particularly vulnerable, and
may thus require special attention? – crop
models (e.g., DSSAT)
International Consortium for Agricultural
Systems Applications
http://www.icasanet.org/
http://www.clac.edu.eg
Practical Applications: DSSAT
1. Overview and previous examples of use
2. Guided use of model (three practical
applications to be done by the participants)
DSSAT Decision Support System for
Agrotechnology Transfer
Components
Description
Databases
Weather, soil, genetics, pests,
experiments, economics
Crop models (maize, wheat, rice,
barley, sorghum, millet, soybean,
peanut, dry bean, potato, cassava, etc.)
Graphics, weather, pests, soil, genetics,
experiments, economics
Models
Supporting
software
Applications
Validation, sensitivity analysis,
seasonal strategy, crop rotations
Input Requirements
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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 Leaf Area Index (LAI)
Crop Model Validation
Source: Iglesias et al., 1999
Examples
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Can optimal management be an adaptation
option for maize production in Zimbabwe?
Can adaptation be achieved by optimizing
crop varieties?
Does the start of the rainy season affect
maize yield in Kasungu, Central Malawi?
Can Optimal Management be an Adaptation Option
for Maize Production in Zimbabwe?
Muchena, 1994
Agroclimatic zones
Impacts: Zimbabwe
Gueru
Banket
Chisumbanje
Impacts of climate change: CERES-Maize model
Muchena, 1994
Adaptation: Zimbabwe
Adaptation strategies in Gueru: CERES-Maize model
Increased inputs and
improve management:
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Fertilizer
Fertilizer and irrigation
Muchena, 1994
Crop Coefficients
Corn
.
P1
.
P2
.
P5
G2
.
G5
.
Can Adaptation be
Achieved by Optimizing
Crop Varieties?
Juvenile phase (growing degree days base
8°C from emergence to end of the juvenile
phase)
Photoperiod sensitivity
Grain filling duration (growing degree days
base 8 from silking to physiological
maturity)
Potential kernel number
Potential kernel weight (growth rate)
Does the Start of the Rainy Season Affect Maize
Yield in Kasungu, Central Malawi?
NUMBER OF GROWING DAYS
SIMULATED MEAN MAIZE YIELDS (t/ha)
0.95
1.83
1.38
160
150
EARLY RAINS NORMAL LATE RAINS
RAINS
140
130
120
110
100
90
80
315
325
335
345
355
DAY OF YEAR, START OF RAINS
Practical Applications
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
3. Adaptation: Changes in management to improve
yield under climate change
Application 1. Management
Objective: Getting started
Weather
Syria
Florida, USA
SR (MJ m2 day1)
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
Input Files Needed
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Weather
Soils
Cultivars
Management files (*.MZX files) description of
the experiment
Open DSSAT . . .
Examine the Data Files . . .
Weather file
Soil
file
Genotype file
(Definition of
cultivars)
Location of the Cultivar File . . .
Select the Cultivar File . . .
Examine the Cultivar File . . .
Location of the Weather File . . .
Selection of the Weather File . . .
Examine the Weather File . . .
Calculate Monthly Means . . .
Calculate Monthly Means . . .
(continued)
Program to Generate
Weather Data . . .
Location of the Input
Experiment File . . .
Select the Experiment File . . .
Examine the Experiment File (Syria)
Examine the Experiment File
(Florida)
. . . The Experiment File Can Be
Edited Also With a Text Editor (Notepad)
Start Simulation …
Running . . .
Select Experiment . . .
Select Treatment . . .
View the Results . . .
Select Option . . .
Retrieve Output Files
for Analysis
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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
Analyse and Present Results
Management: Maize Yield Florida and Syria
12000
Grain Yield (kg/ha)
10000
8000
6000
Florida
Syria
4000
2000
0
Rainfed Low N Rainfed High N
Irrig Low N
Irrig High N
Exp 2. Sensitivity to Climate
Objective: Effect of weather modification
Start Simulation . . .
Sensitivity Analysis . . .
Select Option …
Analyze Results . . .
Climate Change: Maize Yield Florida
2500
Grain Yield (kg/ha)
2000
1500
1000
500
0
Florida Base
Florida -50% pp
Exp 3. Adaptation
Objective: For advanced participants …

Can the water/irrigation systems meet
the stress of changes in water
supply/demand? – irrigation models
(e.g., CROPWAT)
CROPWAT is a decision support system for irrigation
planning and management.
http://www.clac.edu.eg
http://www.fao.org/ag/agl/aglw/cropwat.htm
Experiments
1. Calculate ET0
2. Calculate crop water requirements
3. Calculate irrigation requirements for several
crops in a farm
Start CROPWAT …
Retrieve Climate File . . .
Examine Temperature . . .
Examine ET0 . . .
Calculate ET0 . . .
Examine Rainfall . . .
Retrieve Crop Parameters . . .
View Progress of Inputs . . .
Define and View Crop
Areas Selected . . .
Define Irrigation Method . . .
Input Data Completed . . .
Calculate Irrigation Demand . . .
Calculate Irrigation Schedule . . .
View Results . . .
 Will climate significantly affect
domestic agriculture? – model
integration; GIS integration
Integration of Agriculture and Other
Sectors

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Discussion on how to integrate the V&A methods and
tools into comprehensive assessments relevant to
policy
Examples
 Agriculture – land use, water use (Egypt)
 Agriculture – socioeconomic issues
(Mediterranean)
 Agriculture – water (Global)
Integrated Assessment in Egypt
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
Source: Strzepek et al., 1999
Integrated Assessment in Egypt
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Methods
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Scenario development
Vulnerability evaluation using
agronomic, economic, and water
allocation models
Results: Future vulnerability
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Significant decreases in crop yield and
agronomic water use efficiency with
climate change
Overall crop production further
deteriorated as result of a reduction in
agricultural land due to sea-level
intrusion, and population increase
Adaptation: Limits of
Current Technology
2002
Egypt
Morocco
Spain
Tunisia
Area (1000ha)
Population (1000)
Population 2030 (1000)
Population in agriculture (% of total)
Population in rural areas (% of total)
Population in rural areas 2030
(% of total)
100,145
70,507
109,111
35
57
44,655
30,072
42,505
35
43
50,599
40,977
39,951
7
22
16,361
9,728
12,351
24
33
46
29
15
22
Agricultural Area (% of total)
Irrigation area (% of agricultural)
Wheat Yield (kg/ha) (World = 2,678)
3
100
6,150
69
4
1,716
58
12
2,836
55
4
3,853
Agricultural Imports (million $)
Agricultural Exports (million$)
Fertiliser Consumption (kg/ha)
3,688
774
392
1,740
811
12
12,953
16,452
74
1,022
391
12
No
Low
17
No
Low
14
Yes
High
4
No
Low
12
4,000
3,900
21,200
6,800
Crop Drought Insurance
Agricultural Subsidies
Agriculture, value added (% of GDP)
GDP Per capita (US$) UN derived
from purchasing power parity (PPP)
Data: FAOSTAT
Adaptation
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On-farm adaptation: Use of alternative existing
varieties and optimization of the timing of
planting may improve yield levels or water use
(no cost). In Egypt this is a very limited option.
Essential changes in resource management
(crops, water and land) would lead not only to
adaptation to climate change but also to the
overall improvement of the agricultural systems
(no regret options).
Explicit guidance to farmers regarding optimal
crop selection, irrigation, and fertilization. Should
institute strong incentives to avoid excessive
water use.
Pioneer, April 00 - 128
Socioeconomic Issues


Policy, stakeholders
Technology
Understanding the Stakeholder Linkages
and Decision Process
Small Farmers (80%)
Decisions
Extension
Extensionservice
Service
Project
Regional Policy Makers
Technical Policy Makers
Central Policy Maker
(Ministry of agriculture)
National
commissions
V&A Assessment
Policy Decisions
Adaptation is, in part, a
political process, and
information on options
reflects different views
about the long-term
future of resources,
economies, and societies.
(Downing, 2001)
Tunisia: National Strategy
on Water Management
Current and projected water demand (%)
Drinking
Irrigation
Tourism
Industrial

2030
17.7
73.5
1.5
7.3
Resources management




1996
11.5
83.7
0.7
4.1
Mobilization, storage (over 1,000 hill
reservoirs in 10 years), and transfer of the resources
Use of the nonconventional resources: saline and wastewater for irrigation
(95,400 and 7,600 ha)
Desalinization
Demand management

Water saving in irrigation (up to 60% government subsidies), industry, and other
uses
Crop Liberalization
Example: the recent Egyptian policy of crop
liberalization is giving farmers the possibility of adapting
to more suitable crops in each area; as result of this
policy, the area sown with cotton has sharply decreased
in recent years while the cereal area has increased.
Drought Management in the
Mediterranean


Disaster management
could be an effective
adaptation option
Decreasing drought
vulnerability is a “win-win”
adaptation option
Water for Agriculture

WATER is a fundamental requirement for
agriculture. That requirement is certain to
increase along with the growth of population
and living standards, especially in view of the
prospect of a warmer climate imposed by the
enhanced greenhouse effect.
GISS Temperature Change 2050
Methods
Population (millions)
2020
1600
1400
1200
1995
Low
1000
SCENARIOS
GCMs
variability
WATBAL
Streamflow
PET
SCENARIOS
800
Population,
Development,
600
Technology
400
200
0
CLIMATE
Precip.,
Temp.
Solar Rad.
GISS Precipitation Change 2050
CERES
Crop water
demand
CROPWAT
Regional
irrigation
High
Brazil
China
WEAP
Evaluation
Planning
US
Methods
Crop yields, water demands, and
nitrogen leaching are estimated
with process based crop models
(calibrated and validated). The ratios
(Kc) between simulated and actual
crop ET are used to estimate
regional water demand with
CROPWAT, and are then adjusted
by a regional irrigation efficiency.

Working with Different Models:
Consistency, Scales, Calibration
Projections Using the
Suite of Models




Changes in runoff, water demands, and water system
reliability
Actual changes in crop yield based on consistent
projections of changes in water supply and demand
Changes in environmental stress due to human use
of water resources
Changes in water quality