Crop modelling with DSSAT allows researchers to: 1) Conduct experiments and analyses that would be impractical, too expensive, or impossible in real world conditions. 2) Study the long-term effects of management options through simulations and predictions. 3) Develop optimal management strategies through analysis of factors like weather, soil conditions, genotypes, and practices. DSSAT is a widely used crop modeling system that incorporates biophysical models of plant growth and development to simulate crop performance under different conditions and management scenarios.

1. Crop modelling with the DSSAT
September 2011

2. Why model?
• Use for manipulations and experiments that are impractical,
too expensive, too lengthy or impossible (in real-world social
and economic systems)
• Address dynamic complexity (“emergent properties”) of
systems in a way that reductionist science may not be able to
do
• Identify “best management” strategies (through optimization)
• Study the long-term effects of options (predictions,
projections)

3. Why model? -2-
• Allow the researcher to control environmental and
experimental conditions
• Allow hypothetical and exploratory situations to be
investigated
• Allow insight to be gained into the relative importance of
different system elements
• Assemble and synthesise what is known about particular
processes
Nicholson (2008)

4. What can models produce?
Inputs Model Outputs
“Predictions”
• Point prediction: temperature in Kathmandu tomorrow
• Behaviour: trends, patterns in space and time
• Differences: system response with/without an intervention
“Understanding”
• Best bet: optimised performance of the system (N application rate)
• Trade-offs: household income and range condition
• Syntheses: what do we know about these processes, and which are still
black boxes?

5. Reduced Oxidised Floodwater
soil layers soil zone
A complicated system …

6. … but it can be modelled to a useful extent
INPUTS CROP MODEL OUTPUTS
Genotype information Based on mechanisms Biomass, yield
Soil information of plant growth and Water use
Weather information development (some Nitrogen use
Management information may be represented Carbon balance
… empirically) …
Things that apply Things that apply to
to one particular the biophysical
Use in
situation (e.g. a world in general some way
field plot)

7. Simulated and observed biomass accretion (kg DM/ha) for cowpea cultivar
TVU 3046 grown in Griffin, Georgia, in 1998
canopy
stem
leaf
Hoogenboom et al., 2000

8. Comparison of observed
and simulated grain
yield for 5 wheat
models
Simulated grain yield (t / ha)
(a) AFRC-WHEAT2
(b) CERES-Wheat
(c) Sirius
(d) SUCROS2
(e) SWHEAT
The solid lines represent the 1:1
relationship
Jamieson et al., 1998 Observed grain yield (t / ha)

9. Production situation
defining factors: CO2
1 potential radiation
temperature
crop characteristics
- physiology, phenology
- canopy architecture
limiting factors: water
2 nutrients
attainable
Yield-increasing measures
reducing factors: weeds
3 actual pests
diseases
Yield-protecting measures pollutants
Production level (t/ha)

10. Production situation
defining factors: CO2
1 potential radiation
temperature
crop characteristics
- physiology, phenology
- canopy architecture
“Realism”factors: water
limiting increases:
nutrients
2 attainable
but so does complexity
Yield-increasing measures
reducing factors: weeds
3 actual pests
diseases
Yield-protecting measures pollutants
Production level (t/ha)

11. Crop modelling is 50 years old: some of it is “mature science”
Crop model water balance in a layered soil (from late 1970s): Ritchie’s tipping bucket
Transpiration
Evaporation
Rainfall,
Irrigation
Runoff
Capillary rise
Plant water uptake
Bypass flow
Deep drainage

12. DSSAT
Decision Support System for
Agrotechnology Transfer

13. DSSAT v2.1 in 1989  DSSAT v4.5 2010
About 2000 users in over 90 countries

14. Components of DSSAT
DATABASES MODELS APPLICATIONS
Weather Validation /
Crop Models Sensitivity
Analysis
Soil
Seasonal Strategy
SUPPORT SOFTWARE Analysis
Genetics DSSAT User Interface
Graphics
Crop Rotation /
Sequence
Pests
Weather Analysis
Experiments Spatial Analysis /
Soil GIS Linkage
Economics
Experiments
Pests
Genetics
Economics

15. DSSAT v4.5
• Windows-based
• Incorporates DSSAT CSM (+ Legacy Models)
• Field scale
• Data management tools
• XBuild: Input crop management information in standard format
• SBuild: Create and edit soil profiles
• GBuild: Display graphs of simulated and observed data, compute
statistics
• ATCreate: Create and edit observations from experiments, formatted
correctly
• WeatherMan: Assist users in cleaning, formating, generating weather
data
• ICSim – Introductory tool to demonstrate potential yield concepts

16. DSSAT v4.5
Several different analytical capabilities
• Sensitivity Analysis: vary soil, weather, management or variety
characteristics for insight
• Seasonal Analysis: multiple-year simulations to evaluate uncertainty
in biophysical and economic responses
• Rotation/Sequence Analysis: long-term simulations to analyze
changes in productivity and soil conditions associated with cropping
systems
• Spatial Analysis: define spatially variable soil, weather, management
characteristics across a field or region for analysis

17. Main window in DSSAT v4.5

18. Selection of maize
experiment, all treatments
selected for simulation.
Circle shows button for
running the model and
for graphing results.

19. DSSAT4.5 graphics screens

20. Assessing Risk and Ways to Reduce it
• Crop simulation models integrate the interaction of
weather, soil, management and genetic factors
• Use the crop simulation models to run “what if”
scenarios
• Develop alternate management practices that will
benefit the farmer
• Risk factors: weather and price uncertainty, two of the
major sources

21. Context
• Next season’s weather is uncertain
• Variability in historical weather data can be assumed to describe
uncertainty in next season’s weather
• “Experiment” is run by specifying a possible management system
over a number of prior years of weather data
• Thus, a distribution of yields (& other outputs) is produced,
converting uncertainty in weather into uncertainty in yield—for the
specific management
• Other management “treatments” can be simulated in the
experiment

22. Using DSSAT to Analyze Uncertainty
q Simulate n years of the management being analyzed,
using historical years of weather data and soil properties
for the site
q Each year starts with the same initial soil conditions
q Each yield value is assumed to have an equal probability
of happening in the future (assuming future weather
statistical properties are the same)
q Create cumulative probability distribution
q Compute statistical properties (mean, variance, etc.)

23. Annual Yield Variability
5
4.5
4
3.5
Yield, t/ha
3
2.5
2
1.5
1
0.5
0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
Years

24. Developing Cumulative Probability Distributions
from Simulated Results
Year Yield (t/ha) prob Cumulative
1 1.83 0.04 Ranked Yield Probability
2 2.78 0.04 0.3 0.04
1
3 1.9 0.04 0.9 0.08
1.1 0.12
0.9
4 2.3 0.04
5 4.7 0.04 1.3 0.16
1.69 0.2
0.8
6 2.4 0.04
7 1.3 0.04 1.75 0.24
1.83 0.28 0.7
8 4.1 0.04
1.9 0.32
9 3.5 0.04 0.6
2.05 0.36
10 0.3 0.04
2.3 0.4
11 2.6 0.04
2.4 0.44 0.5
12 2.05 0.04 Mean=2.65 t/ha
2.6 0.48
Cumulative Probability
13 3.04 0.04 2.75 0.52 0.4
14 3.28 0.04 2.78 0.56
15 1.69 0.04 3 0.6 0.3
16 0.9 0.04 3.04 0.64 Var=1.31 (t/ha)2
17 1.1 0.04 3.24 0.68 0.2
18 3.24 0.04 3.28 0.72
19 3.95 0.04 3.5 0.76 0.1
20 4.2 0.04 3.67 0.8
21 4 0.04 3.95 0.84 0
22 2.75 0.04 4 0.88 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5
23 1.75 0.04 4.1 0.92
24 3.67 0.04 4.2 0.96 Yield, t/ha
25 3 0.04 4.7 1
Raw Ranked Cumulative
Yield Data Yield Data Probability Distribution

25. CPFs of monetary return ($/ha) for three treatments

26. Planting date evaluation
DAS CO32- Rainfed conditions
8000
Yield (kg ha1)
-
6000
4000
Simulated yields for
different planting 2000
dates under rainfed 0
(top) and irrigated DAS CO32- Irrigated conditions
(bottom) conditions 8000
Yield (kg ha )
-1
6000
4000
2000
0
Feb-01 Feb-15 Mar-01 Mar-15 Apr-01 Apr-15
Planting date

27. Yield forecasting
7000 7000
a) AG9010 b) DKB 333B
6000 6000
5000 5000
Yield (kg ha )
Yield (kg ha )
-1
-1
4000 4000
3000 3000
2000 2000
Average forecasted 1000
Simulated yield
Observed yield 1000
Simulated yield
Observed yield
yield and standard 0 0
Mar-01 Apr-01 May-01 Jun-01 Jul-01 Aug-01 Sep-01 Mar-01 Apr-01 May-01 Jun-01 Jul-01 Aug-01 Sep-01
deviation for 2002 Forecast date Forecast date
as a function of the 7000 7000
forecast date and 6000
c) DAS CO32
6000
d) Exceler
observed yield (kg/ 5000 5000
ha) for four maize
Yield (kg ha)
Yield (kg ha )
-1
-1
4000 4000
hybrids
3000 3000
2000 2000
Simulated yield
1000 1000 Observed yield
Simulated yield
Observed yield
0 0
Mar-01 Apr-01 May-01 Jun-01 Jul-01 Aug-01 Sep-01 Mar-01 Apr-01 May-01 Jun-01 Jul-01 Aug-01 Sep-01
Forecast date Forecast date

28. DSSAT and other crop modelling systems
Used in many different ways around the world:
Crop management Precision agriculture
Fertilizer management Sustainability studies
Irrigation management Climate change studies
Pest management Yield forecasting
Tillage management Education
Variety evaluation …

29. International climate change study: implications
• Crop yields in mid- and high-latitude regions are less adversely
affected than yields in low-latitude regions
• Will simple farm-level adaptations in the temperate regions be
able to offset the detrimental effects of climate change?
• For the tropics, appropriate adaptations need to be developed
and tested further at the household level; the role of genetic
resources and information provision?
• Regional impact analyses: discussion tomorrow

30. DSSAT v4.5 training
• DSSAT training course sponsored by the University of
Florida and ICRISAT, Hyderabad, 5-9 December 2011
(open for applicants)
• Possible: DSSAT training course at CRIDA during the
week of 13-17 February 2012

31. Prediction of milk production from cows consuming
tropical diets
Herrero (1997)

33. “All models are wrong, but some are useful”
“… the practical question is, how wrong
do they have to be to not be useful.”
- GEP Box

34. A simple interface for running complex crop models: