Vulnerability and Adaptation Assessments Hands-On Training Workshop Impact, Vulnerability and Adaptation Assessment for the Agriculture Sector

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Vulnerability and Adaptation Assessments
Hands-On Training WorkshopImpact,
Vulnerability and Adaptation Assessmentfor the
Agriculture Sector
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Outline

• Climate change, agriculture and food security
• Methods, tools, and datasets
• Practical applications
• Integrated assessments

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Climate Change, Agriculture, and Food Security

• Climate change is one stress among many affecting
  agriculture and the population that depends on it

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Multiple Interactions, Vulnerability, and
Adaptation
Systems and social groups that needs to adapt
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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.

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Multiple Interactions, Stakeholders Define
Adaptation
Policy makers
Scientists
Civil stake-holders
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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.

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Where? Systems and Social Groups
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How Much? Climate and SRES Scenarios
Had CM2 model, 2050s
Temperature change
Precipitation change
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What Happens in Response to Change?

• Adaptive capacity (internal adaptation)
• Planned adaptation

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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)

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How Might Global Climate Change Affect Food
Production?
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.
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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)

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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
Developed-Developing) () 10 10 8 10 7 9 9
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Additional People at Risk of Hunger
Parry et al., 2004
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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

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Interaction and Integration Water
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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)

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Methods, Tools, and Datasets

• The framework
• The choice of the research methods and tools
• Demand-driven methods responding to stakeholders
• Key characteristics, strengths, weaknesses
• Examples
• Datasets sources, scales, reliability

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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

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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,

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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

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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
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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). Range for ranking is 1 (least amount) to 5 (most demanding).
Example growth chambers, experimental fields.
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Analogues Drought, Floods
Africa vegetation health (VT - index) Vegetation
health Red stressed, Green fair, Blue
favorable Source NOAA/NESDIS
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Analogues Drought
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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). 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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Production Functions
Statistically derived functions (Almeria Wheat)
Yield
Irrigation demand
Iglesias et al., 1999
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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). Range for ranking is 1 (least amount) to 5 (most demanding).
Example Derived with empirical data.
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Agroclimatic Indices
Length of the growing periods (reference climate,
1961-1990). IIASA-FAO, AEZ
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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). Range for ranking is 1 (least amount) to 5 (most demanding).
Example FAO, etc.
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Crop Models
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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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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). Range for ranking is 1 (least amount) to 5 (most demanding).
Example CROPWAT, CERES, SOYGRO, APSIM, WOFOST,
etc.
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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?

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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

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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
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Agricultural Trade Models
Parry et al., 1999.
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Social Sciences Tools

• Surveys and interviews
• Allow the direct input of stakeholders
  (demand-driven science), provide expert judgment
  in a rigorous way

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Surveys and Interviews

• Development of adaptation options with
  stakeholders

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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
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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). Range for ranking is 1 (least amount) to 5 (most demanding).
Examples Farm, econometric, I/O, national
economies, BLS,
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Integrators GIS
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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). 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
• 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

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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

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IPCC Working Group 1 A Collective Picture of a
Warming World
Source of data GISS/NASA
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Climate
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FAOCLIM
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Precipitation Annual 1901-1995
Source of data NOAA, NCDC
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Global Land Cover Classification
De Fries et al., 1998
7 Wooded grasslands/shrubs 8 Closed bushlands
or shrublands 9 Open shrublands 10 Grasses 11
Croplands 12 Bare 13 Mosses and lichens
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
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Population
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Lights are Related to Income and Population
Map of the night-time city lights of the
world DMSP NASA and NOAA
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Soils FAO
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FAO and the World Bank
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FAO
Food aid received from external sources 2000
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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

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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.
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Data Scales, Sources, Reliability
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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?

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Practical Applications DSSAT

• Question What components of the farming system
  are particularly vulnerable, and may thus require
  special attention? crop models (e.g., DSSAT)

http//www.icasanet.org/
http//www.clac.edu.eg
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Practical Applications DSSAT

1. Overview and previous examples of use
2. Guided use of model (three practical applications
   to be done by the participants)

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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
  Leaf Area Index (LAI)

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Crop Model Validation
Source Iglesias et al., 1999
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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?

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Can Optimal Management be an Adaptation Option
for Maize Production in Zimbabwe?
Muchena, 1994
Agroclimatic zones
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Impacts Zimbabwe
Impacts of climate change CERES-Maize model
Muchena, 1994
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Adaptation Zimbabwe
Adaptation strategies in Gueru CERES-Maize model

• Increased inputs and improve management
• Fertilizer
• Fertilizer and irrigation

Muchena, 1994
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Can Adaptation be Achieved by Optimizing Crop
Varieties?
Crop Coefficients Corn
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Does the Start of the Rainy Season Affect Maize
Yield in Kasungu, Central Malawi?
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Practical Applications

• Effect of management (nitrogen and irrigation) in
  wet and dry sites (Florida, USA, and Syria)
• Effect of climate change on wet and dry sites
• Sensitivity analysis to changes in temperature
  and precipitation (thresholds) and CO2 levels
• Adaptation Changes in management to improve
  yield under climate change

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Application 1. Management

• Objective Getting started

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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
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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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Examine the Data Files . . .
Weather file
Soil file
Genotype file (Definition of cultivars)
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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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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 . . . (continued)
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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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Analyse and Present Results
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Exp 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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Analyze Results . . .
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Exp 3. Adaptation

• Objective For advanced participants

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• Can the water/irrigation systems meet the stress
  of changes in water supply/demand? irrigation
  models (e.g., CROPWAT)

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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• Will climate significantly affect domestic
  agriculture? model integration GIS integration

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Integration of Agriculture and Other Sectors

• Discussion on how to integrate the VA methods
  and tools into comprehensive assessments relevant
  to policy
• Examples
• Agriculture land use, water use (Egypt)
• Agriculture socioeconomic issues
  (Mediterranean)
• Agriculture water (Global)

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Integrated Assessment in Egypt
Source Strzepek et al., 1999
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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

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Adaptation Limits of Current Technology
Data FAOSTAT
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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.

Pioneer, April 00 - 128
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Socioeconomic Issues

• Policy, stakeholders
• Technology

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Understanding the Stakeholder Linkages and
Decision Process
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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)

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Tunisia National Strategy on Water Management
Current and projected water demand ()
1996 2030 Drinking 11.5
17.7 Irrigation 83.7 73.5 Tourism 0.7
1.5 Industrial 4.1 7.3

• 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

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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.

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Drought Management in the Mediterranean

• Disaster management could be an effective
  adaptation option
• Decreasing drought vulnerability is a win-win
  adaptation option

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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.

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Methods
SCENARIOS Population, Development, Technology
SCENARIOS GCMs variability
WATBAL Streamflow PET
CLIMATE Precip., Temp. Solar Rad.
WEAP Evaluation Planning
CERES Crop water demand
CROPWAT Regional irrigation
137
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.

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Working with Different Models Consistency,
Scales, Calibration
139
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