AGRIDEMA - DSSAT (Ana Iglesias, Vienna 24 December 2005) 1
Agricultural ModelsDSSAT
AGRIDEMA Training CourseVienna, 24 December 2005
Ana IglesiasUniversidad Politécnica de
Madrid, Spain
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Objective
• To provide information on the use of DSSAT for evaluating vulnerability and adaptation to climate in the agriculture sector:– Define the role of models (What can we learn
and what can’t we learn by using models?)– Datasets and other issues– PC-based training, ….. Responding to
stakeholders and policy questions
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www.icasanet.org
International Consortium for Agricultural
Systems Applications
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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 models and approaches is often the most rewarding
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• Some crops are more complicated than others ….
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Agenda
4. Crop model exercises 2: DSSAT(participants’ design of applications of DSSAT)
14:30 – 15:30
3. Crop model exercises 1: DSSAT(guided examples)
13:30 – 14:30
Wednesday pm
2. Lecture 2: Applications of DSSATto answer policy questions
09:45 – 10:30
1. Lecture 1: The role of models (DSSAT) as a component of agricultural planning
09:00 – 09:45
Wednesday am
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Can crop models explain observations?
2 0 0 2 Egypt Morocco Spain Tunisia
Area (1000ha) 100 ,145 44,655 50,599 16 ,361Populat ion (1000) 70 ,507 30,072 40,977 9,728Populat ion 2030 (1000) 109 ,111 42,505 39,951 12 ,351Population in agriculture (% of total) 3 5 35 7 2 4Populat ion in rural areas (% of total) 5 7 43 22 3 3Populat ion in rural areas 2030 (% of total) 4 6 29 15 2 2
Agricultural Area (% of total) 3 69 58 5 5Irr igation area (% of agricultural) 1 0 0 4 12 4Wheat Yie ld (kg/ha) (Wor ld = 2,678) 6 ,150 1,716 2,836 3,853
Agricultural Imports (million $) 3 ,688 1,740 12,953 1,022Agricultural Exports (mill ion$) 7 7 4 811 16,452 391Ferti l iser Consumption (kg/ha) 3 9 2 12 74 1 2
Crop Drought Insurance No No Yes NoAgricultural Subsidies Low Low High LowAgriculture, value added (% of GDP) 1 7 14 4 1 2GDP Per capi ta (US$) UN der ived from purchasing power par i ty (PPP) 4 ,000 3,900 21,200 6,800
Data: FAOSTAT
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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)
Maize production, Zimbabwe
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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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DSSAT Decision Support System for Agrotechnology Transfer
DescriptionComponents
Validation, sensitivity analysis, seasonal strategy, crop rotations
APPLICATIONS
Graphics, weather, pests, soil, genetics, experiments, economics
SUPPORTING SOFTWARE
Crop models (Maize, wheat, rice, barley, sorghum, millet, soybean, peanut, dry bean, potato, cassava, etc)
MODELS
Weather, soil, genetics, pests, experiments, economics
DATABASES
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Models are assisting tools: Stakeholders’ interactions are essential
ScientistsScientistsPolicy
makersPolicy
makers Technical and
applied experts
Technical and
applied experts
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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
Range for ranking is 1 (least amount) to 5 (most demanding)
Value
4 to 5Financial resources
4 to 5Technological resources5Skill or training required
4 to 5Data needsSite to regionTime to conduct analysis
Daily to centuriesSpatial scale of results
Example: DSSAT (CERES, SOYGRO), APSIM, WOFOST, CROPWAT, etc.
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Advantages, Limitations, and Multiple interactions
• Climate is one factor among many affecting agriculture and the population that depends on it
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Use of models: What happens in response to change?
• Adaptive capacity (internal adaptation)• Planned adaptation
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Using DSSAT to Evaluate How Climate Change Affects Crop Production
• Changes in crop productivity in response to changes in biophysical conditions [DSSAT]
• Changes in socio-economic conditions in response to changes in crop productivity (farmers’ income; markets and prices; poverty; malnutrition and risk of hunger; migration) [Other models]
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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Precipitation change
Temperature changeHad CM2 model, 2050s
Example: Climate Change
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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, 2002
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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Interaction and Integration: Water
0
40
80
120
2020 2050 2080
Pop
ulat
ion
(mill
ions
)
Additional population under extreme stress of water shortage
University of Southampton
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Interaction and Integration with Socio-economic Models
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
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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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Valencia - Dec-Feb T(C) 1900-2000
8
9
10
11
12
13
1880 1900 1920 1940 1960 1980 2 0 0 0 2 0 2 0
Valencia - Jun-Aug T(C) 1900-2000
21
22
23
24
25
26
1880 1900 1920 1940 1960 1980 2 0 0 0 2 0 2 0
Valencia - Annual T(C) 1900-2000
15
16
17
18
19
20
1880 1900 1920 1940 1960 1980 2 0 0 0 2 0 2 0
Current Climate
Source of data: GISS/NASA
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FAOCLIM
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Climate Change ScenariosProjected 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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Soils: FAO
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1: Evergreen needle leaf forests2: Evergreen broad leaf forests3: Deciduous needle leaf forests4: Deciduous broad leaf forests5: Mixed forests6: Woodlands
7: Wooded grasslands/shrubs8: Closed bushlands or shrublands9: Open shrublands10: Grasses11: Croplands12: Bare13: Mosses and lichens
De Fries et al., 1998
Global Land Cover Classification
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Population
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Agricultural GDP as share of total GDP
FAO and the World Bank
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USGS, FEWS, USAID• FEWS NET in
cooperation with USGSand 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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Irrigation Area Tunisia (1970 - 1998)
50
150
250
350
450
1970 1975 1980 1985 1990 1995Year
Iirrg
Are
a (h
a x
1000
)
FAO Data USDA ERS Data
Data: Scales, Sources, Reliability
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Thanks for your attention!
Visit MEDROPLAN on the web at
www.iamz.ciheam.org/medroplan
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END of Session 1
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Agenda
4. Crop model exercises 2: DSSAT(participants’ design of applications of DSSAT)
14:30 – 15:30
3. Crop model exercises 1: DSSAT(guided examples)
13:30 – 14:30
Wednesday pm
2. Lecture 2: Applications of DSSAT to answer policy questions
09:45 – 10:30
1. Lecture 1: The role of models (DSSAT) as a component of agricultural planning
09:00 – 09:45
Wednesday am
AGRIDEMA - DSSAT (Ana Iglesias, Vienna 24 December 2005) 36
• 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 Policy 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 food production?
• Will climate change significantly affect food production?
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Applications of DSSAT to Answer Farm Management and Industry
Questions
• Can optimal management decreases vulnerability to climate?
• Can optimal management decreases 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
DescriptionComponents
Validation, sensitivity analysis, seasonal strategy, crop rotations
APPLICATIONS
Graphics, weather, pests, soil, genetics, experiments, economics
SUPPORTING SOFTWARE
Crop models (Maize, wheat, rice, barley, sorghum, millet, soybean, peanut, dry bean, potato, cassava, etc)
MODELS
Weather, soil, genetics, pests, experiments, economics
DATABASES
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Example Applications ofin AFRICA
ReferencesType of Application
Pisani, 1987; Thornton et al., 1997Food security
Phillips et al., 1998Climate variability
Muchena and Iglesias, 1995Climate change
Booltink et al., 2001Irrigation management
Kamel et al., 1995; MacRobert and Savage, 1998
Irrigation management
Jagtap et al., 1999; Singh et al., 1993; Thornton et al., 1995; Keating et al., 1991
Fertilizer management
Fechter et al., 1991; Mbabaliye and Wojtkowski, 1994; Vos and Mallett, 1987; Wafula, 1995
Crop management
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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 (%)
150100500-50-100-150Ir
rigat
ion
(mm
)
400
300
200
100
0
Statistically derived functions (Almeria – Wheat)Yield Irrigation demand
Examples in Spain: Using DSSATto Derive Production Functions
Iglesias, 1999; Iglesias et al., 2000
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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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Muchena and Iglesias, 1994
Can optimal management be an adaptation option for maize production in Zimbabwe?
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Impacts: Zimbabwe
Gueru Banket Chisumbanje
Impacts of climate change: CERES-Maize model
Muchena and Iglesias, 1994
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Adaptation: Zimbabwe
Increased inputs and improve management:– Fertilizer– Fertilizer and
irrigation
Adaptation strategies in Gueru: CERES-Maize model
Muchena and Iglesias, 1994
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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 form silking to physiological maturity)
. Potential kernel number
. Potential kernel weight (growth rate)
P1
P2P5
G2G5
P1
P2P5
G2G5
What are the characteristics of optimized crop varieties?
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END of Session 2
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Agenda
4. Crop model exercises 2: DSSAT(participants’ design of applications of DSSAT)
14:30 – 15:30
3. Crop model exercises 1: DSSAT (guided examples)
13:30 – 14:30
Wednesday pm
2. Lecture 2: Applications of DSSATto answer policy questions
09:45 – 10:30
1. Lecture 1: The role of models (DSSAT) as a component of agricultural planning
09:00 – 09:45
Wednesday am
AGRIDEMA - DSSAT (Ana Iglesias, Vienna 24 December 2005) 49
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
114.855.7Rain Days (num)
1364.3276.4Precipitation (mm)
14.58.5T Min (C)
27.423.0T Max (C)
16.519.3SR (MJ m2 day-1)
Florida, USASyria
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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:/DSSAT/MAIZE/SUMMARY.OUT• C:/DSSAT/MAIZE/WATER.OUT• C:/DSSAT/MAIZE/OVERVIEW.OUT• C:/DSSAT/MAIZE/GROWTH.OUT• C:/DSSAT/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
(kg
/ha)
FloridaSyria
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
(kg
/ha)
Analyse results ….
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END of Session 3
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Agenda
4. Crop model exercises 2: DSSAT (participants’ design of applications of DSSAT)
14:30 – 15:30
3. Crop model exercises 1: DSSAT(guided examples)
13:30 – 14:30
Wednesday pm
2. Lecture 2: Applications of DSSATto answer policy questions
09:45 – 10:30
1. Lecture 1: The role of models (DSSAT) as a component of agricultural planning
09:00 – 09:45
Wednesday am
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Proposed application: Adaptation
• For advanced participants …
AGRIDEMA - DSSAT (Ana Iglesias, Vienna 24 December 2005) 86Pioneer, April 00 - 86
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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END of Session 4