agricultural models dssat

AGRIDEMA - DSSAT (Ana Iglesias, Vienna 24 December 2005) 1

Agricultural ModelsDSSAT

AGRIDEMA Training CourseVienna, 24 December 2005

Ana IglesiasUniversidad Politécnica de

Madrid, Spain


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


www.icasanet.org

International Consortium for Agricultural

Systems Applications


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


• Some crops are more complicated than others ….


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


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


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


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


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


Models are assisting tools: Stakeholders’ interactions are essential

ScientistsScientistsPolicy

makersPolicy

makers Technical and

applied experts

Technical and

applied experts


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

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.


Advantages, Limitations, and Multiple interactions

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


Use of models: What happens in response to change?

• Adaptive capacity (internal adaptation)• Planned adaptation


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


Precipitation change

Temperature changeHad CM2 model, 2050s

Example: Climate Change


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?


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


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


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



Parry et al., 2004


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


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


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


FAOCLIM


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.


Soils: FAO


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


Population


Agricultural GDP as share of total GDP

FAO and the World Bank


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


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


Thanks for your attention!

Visit MEDROPLAN on the web at

www.iamz.ciheam.org/medroplan

[email protected]


END of Session 1


Agenda


14:30 – 15:30


13:30 – 14:30

Wednesday pm

2. Lecture 2: Applications of DSSAT to answer policy questions

09:45 – 10:30


09:00 – 09:45

Wednesday am


• 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?


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?


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


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


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


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]


Source: Iglesias, 1999

ESSENTIAL STEP 1. Crop Model Validation


Muchena and Iglesias, 1994

Can optimal management be an adaptation option for maize production in Zimbabwe?


Impacts: Zimbabwe

Gueru Banket Chisumbanje

Impacts of climate change: CERES-Maize model



Adaptation: Zimbabwe

Increased inputs and improve management:– Fertilizer– Fertilizer and

irrigation

Adaptation strategies in Gueru: CERES-Maize model



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?


END of Session 2


Agenda


14:30 – 15:30

3. Crop model exercises 1: DSSAT (guided examples)

13:30 – 14:30

Wednesday pm


09:45 – 10:30


09:00 – 09:45

Wednesday am


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


Application 1. Management

• Objective: Getting started


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


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 …


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:/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


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


Application 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

(kg

/ha)

Analyse results ….


END of Session 3


Agenda

4. Crop model exercises 2: DSSAT (participants’ design of applications of DSSAT)

14:30 – 15:30


13:30 – 14:30

Wednesday pm


09:45 – 10:30


09:00 – 09:45

Wednesday am


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


END of Session 4

agricultural models dssat

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