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Vulnerability and Adaptation Assessments
Hands-On Training Workshop
Impact, Vulnerability and Adaptation Assessmentfor 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
Social vulnerability
Climate change
Economic, social,
demographic, land usechanges
Systems and social groups that needs to
adapt
Systems and social groups that needs to
adapt
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
ScientistsScientists
Policy makersPolicy
makers
Civil stake-holders
Civil stake-holders
Multiple Interactions
Climate change is one stress among many now affecting agriculture and the population that depends on it
Integration of results is essential to formulate assessments relevant to policy
Potential future consequences depend on: 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
Vegetable production, Egypt
Coffee production, Kenya
How Much? Climate and SRES Scenarios
Precipitation change
Temperature changeHad CM2 model, 2050s
What Happens in Response to Change?
Adaptive capacity (internal adaptation)
Planned adaptation
Climate Change Affects Crop Production
Changes in biophysical conditions Changes in socioeconomic conditions in response to
changes in crop productivity (farmers’ income; markets and prices; poverty; malnutrition and risk of hunger; migration)
POSSIBLE BENEFITS
POSSIBLE DRAWBACKS
CO2
CARBON DIOXIDEFERTILIZATION
LONGERGROWINGSEASONS
INCREASEDPRECIPITATION
MOREFREQUENTDROUGHTS
PESTS
HEATSTRESS
FASTERGROWINGPERIODS
INCREASEDFLOODING ANDSALINIZATION
POSSIBLE BENEFITS
POSSIBLE DRAWBACKS
CO2
CARBON DIOXIDEFERTILIZATION
LONGERGROWINGSEASONS
INCREASEDPRECIPITATION
MOREFREQUENTDROUGHTS
PESTS
HEATSTRESS
FASTERGROWINGPERIODS
INCREASEDFLOODING ANDSALINIZATION
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.Source: NASA/GISS; Rosenzweig and Iglesias, 1994.
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?
Limits to Adaptation
Technological limits (e.g., crop tolerance to water-logging or high temperature; water reutilization)
Social limits (e.g., acceptance of biotechnology)
Political limits (e.g., rural population stabilization may not be optimal land use planning)
Cultural limits (e.g., acceptance of water price and tariffs)
Developed-Developing Country Differences
Scenario A1FI A2a A2b A2c A2c B1a B2b
C02 (ppm) 810 709 709 709 527 561 561
World (%) -5 0 0 -1 -3 -2 -2
Developed (%) 3 8 6 7 3 6 5
Developing (%) -7 -2 -2 -3 -4 -3 -5
Developed-Developing) (%)
10 10 8 10 7 9 9
Potential change (%) in national cereal yields for the 2080s (compared with 1990) using the HadCM3 GCM and SRES scenarios (Parry et al., 2004)
Additional People at Risk of Hunger
30
9
69
50
7
60
34
5
43
0
10
20
30
40
50
60
70
80
2020 2050 2080
Add
ition
al M
illio
ns o
f Peo
ple
Unstabilised
Stabilised at 750ppmv
Stabilised at 550ppmv
30
9
69
50
7
60
34
5
43
0
10
20
30
40
50
60
70
80
2020 2050 2080
Add
ition
al M
illio
ns o
f Peo
ple
30
9
69
50
7
60
34
5
43
0
10
20
30
40
50
60
70
80
2020 2050 2080
Add
ition
al M
illio
ns o
f Peo
ple
Unstabilised
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
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
0
40
80
120
2020 2050 2080
Po
pula
tion
(m
illio
ns)
Additional population under extreme stress of water shortage
Conclusions
Although global production appears stable . . . . . . regional differences in crop production are
likely to grow stronger through time, leading to a significant polarization of effects . . .
. . . with substantial increases in prices and risk of hunger amongst the poorer nations
Most serious effects are at the margins (vulnerable regions and groups)
Methods, Tools, and Datasets
1. The framework
2. The choice of the research methods and tools
Demand-driven methods: responding to stakeholders
Key characteristics, strengths, weaknesses Examples
3. Datasets: sources, scales, reliability
Frameworks
Adaptation Policy Framework (APF), US Country Studies, IPCC, seven steps
All have the essential common elements Problem definition Selection and testing of methods Application of scenarios (climate and
socioeconomic) Evaluation of vulnerability and adaptation
The studies may want to use a framework as guidance or draw from the best elements of all of them
Demand-Driven Methods
Need quantitative estimates 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
Experimental Analogues (spatial and temporal) Production functions (statistically derived) Agroclimatic indices Crop simulation models (generic and crop-specific) Economic models (farm, national, and regional) –
Provide results that are relevant to policy Social analysis tools (surveys and interviews) –
Allow the direct input of stakeholders (demand-driven science), provide expert judgment
Integrators: GIS
Experimental: Effect of Increased C02
Near Phoenix, Arizona, scientists measure the growth of wheat surrounded by elevated levels of atmospheric CO2. The study, called Free Air Carbon Dioxide Enrichment (FACE), is to measure CO2 effects on plants. It is the largest experiment of this type ever undertaken. http://www.ars.usda.gov
http://www.whitehouse.gov/media/gif/Figure4.gif
Experimental
Value
Spatial scale of results 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 – favorableSource: NOAA/NESDIS
Analogues: Drought
Wheat yield in Spain
0%
20%
40%
60%
80%
100%
All Dry Normal Wet
Pro
babi
lity
of y
ield
(%
) high
medium
low
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
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 et al., 1999
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.
Water
Carbon
Nitrogen
Crop Models
Based on
Understanding of plants, soil, weather, management
Calculate
Require
Growth, yield, fertilizer & water requirements, etc
Information (inputs): weather, management, etc
Models – Advantages
Models are assisting tools, stakeholder interaction is essential
Models allow to ask “what if” questions, the relative benefit of alternative management can be highlighted: Improve planning and decision making Assist in applying lessons learned to policy
issues Models permit integration across scales,
sectors, and users
Models – Limitations
Models need to be calibrated and validated to represent reality
Models need data and technical expertise Models alone do not provide an answer,
stakeholder interaction is essential
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
Consider both producers and consumers of agricultural goods (supply and demand)
Economic measures of interest include: How do prices respond to production
amounts? How is income maximized with different
production and consumption opportunities?
Economic Models (continued)
Microeconomic: Farm Macroeconomic: Regional economies All: Crop yield is a primary input (demand is
the other primary input) Economic models should be built bottom-up
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
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 StakeholdersDesigning 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; 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
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
The merits of each approach vary according to the level of impact being studied, and they may frequently be mutually supportive
For example, simple agroclimatic indices often provide the necessary information on how crops respond to varying rainfall and temperature in wide geographical areas; crop-specific models are use to test alternative management that can in turn be used as a component for an economic model that analyses regional vulnerability or national adaptation strategies
Therefore, a mix of approaches is often the most rewarding
Datasets
Data are required data to define climatic, nonclimatic environmental, and socioeconomic baselines and scenarios
Data are limited Discussion on supporting databases and
data sources
Valencia - Dec-Feb T(C) 1900-2000
8
9
10
11
12
13
1880 1900 1920 1940 1960 1980 2000 2020
Valencia - Jun-Aug T(C) 1900-2000
21
22
23
24
25
26
1880 1900 1920 1940 1960 1980 2000 2020
Valencia - Annual T(C) 1900-2000
15
16
17
18
19
20
1880 1900 1920 1940 1960 1980 2000 2020
IPCC Working Group 1: “A Collective Picture of a Warming World”
Source of data: GISS/NASA
Climate
FAOCLIM
Valladolid - Annual Precipitation (mm)
200
300
400
500
600
700
1951
1955
1959
1963
1967
1971
1975
1979
1983
1987
1991
1995
1999
Precipitation Annual 1901-1995
Source of data: NOAA, NCDC
1: Evergreen needle leaf forests2: Evergreen broad leaf forests3: Deciduous needle leaf forests4: Deciduous broad leaf forests5: Mixed forests6: Woodlands
Global Land Cover Classification
7: Wooded grasslands/shrubs8: Closed bushlands or shrublands9: Open shrublands10: Grasses11: Croplands12: Bare13: Mosses and lichens
De Fries et al., 1998
Population
Map of the night-time city lights of the worldDMSP: NASA and NOAA
Lights are Related to Income and Population
Soils: FAO
Agricultural GDP as share of total GDP
FAO and the World Bank
Food aid received from external sources 2000
Countries facing exceptional food emergencies due to the drought August 2001
FAO
USGS, FEWS, USAID
FEWS NET in cooperation with USGS and US AID
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.
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
Practical Applications Policy Questions
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?
http://www.icasanet.org/ http://www.clac.edu.eg
International Consortium for Agricultural Systems Applications
Question: What components of the farming system are particularly vulnerable, and may thus require special attention? – crop models (e.g., DSSAT)
Question: What components of the farming system are particularly vulnerable, and may thus require special attention? – crop models (e.g., DSSAT)
Practical Applications: DSSAT
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
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
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 Leaf Area Index (LAI)
Source: Iglesias et al., 1999
Crop Model Validation
Examples
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?
Agroclimatic zones
Muchena, 1994
Impacts: Zimbabwe
Gueru Banket Chisumbanje
Impacts of climate change: CERES-Maize model
Muchena, 1994
Adaptation: Zimbabwe
Increased inputs and improve management:
Fertilizer Fertilizer and irrigation
Adaptation strategies in Gueru: CERES-Maize model
Muchena, 1994
Crop Coefficients Corn
. 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)
P1
P2P5
G2G5
P1
P2P5
G2G5
Can Adaptation be Achieved by Optimizing Crop Varieties?
315 325 335 345 355
160
150
140
130
120
110
100
90
80
NU
MB
ER
OF
GR
OW
ING
DA
YS
DAY OF YEAR, START OF RAINS
EARLY RAINS NORMAL LATE RAINSRAINS
SIMULATED MEAN MAIZE YIELDS (t/ha)
1.83 1.38 0.95
Does the Start of the Rainy Season Affect Maize Yield in Kasungu, Central Malawi?
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
Weather Soils Cultivars Management files (*.MZX files) description of
the experiment
Open DSSAT . . .
Weather file
Soilfile
Genotype file (Definition of cultivars)
Examine the Data Files . . .
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
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
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
Exp 2. Sensitivity to Climate
Objective: Effect of weather modification
Start Simulation . . .
Sensitivity Analysis . . .
Select Option …
Climate Change: Maize Yield Florida
0
500
1000
1500
2000
2500
Florida Base Florida -50% pp
Gra
in Y
ield
(k
g/h
a)
Analyze Results . . .
Exp 3. Adaptation
Objective: For advanced participants …
http://www.fao.org/ag/agl/aglw/cropwat.htm
CROPWAT is a decision support system for irrigation planning and management.
Can the water/irrigation systems meet the stress of changes in water supply/demand? – irrigation models (e.g., CROPWAT)
Can the water/irrigation systems meet the stress of changes in water supply/demand? – irrigation models (e.g., CROPWAT)
http://www.clac.edu.eg
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
Will climate significantly affect domestic agriculture? – model integration; GIS integration
Integration of Agriculture and Other Sectors
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
Source: Strzepek 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
Integrated Assessment in Egypt
Methods Scenario development Vulnerability evaluation using
agronomic, economic, and water allocation models
Results: Future vulnerability 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) 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
Pioneer, April 00 - 128
Adaptation
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.
Socioeconomic Issues
Policy, stakeholders Technology
Understanding the Stakeholder Linkages and Decision Process
Small Farmers (80%)
Regional Policy Makers
V&A Assessment
Central Policy Maker
(Ministry of agriculture)
Technical Policy Makers
National commissions
Extension service
Dec
isio
ns
Project
Extension Service
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)
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
Resources management 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.
0
200
400
600
800
1000
1200
1400
1600
Brazil China US
1995
Low
High
Population (millions)2020
WATBALStreamflow
PET
WATBALStreamflow
PET
CLIMATEPrecip., Temp.
Solar Rad.
CLIMATEPrecip., Temp.
Solar Rad.
WEAP
EvaluationPlanning
WEAP
EvaluationPlanning
CERESCrop water
demand
CERESCrop water
demand
CROPWATRegionalirrigation
CROPWATRegionalirrigation
SCENARIOSGCMs
variability
SCENARIOSGCMs
variability
SCENARIOSPopulation, Development,
Technology
SCENARIOSPopulation, Development,
Technology
GISS Precipitation Change 2050
GISS Temperature Change 2050
Methods
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