1. Crop Models for Decision Support
James W. Jones
University of Florida
November 7, 2000
Crop Models in Research and Practice:
A Symposium Honoring Professor Joe T. Ritchie
American Society of Agronomy Annual Meeting
Minneapolis, MN

2. Crop Models for Decision Support
Some Success Stories
Research and Technology Transfer (DSSAT)
Australian Applications using APSIM
Soybean Industry-Led Applications in the USA
Farmer-Led Applications in Argentina
Sugarcane Industry Model Uses in South Africa
Others…
Characteristics for Success
Challenges
Trends

3. Research & Technology Transfer
USAID Project, (IBSNAT)
DSSAT, Field-Scale DSS
Biophysical Models (Crop, Soil, Weather), 17 Crops
Risk Analysis (Biophysical and Economic)
Data Entry and Manipulation Tools
Utilities (graphics, data entry, management,…)
Crop Rotation Analyzer
GIS Spatial Analysis Products
GIS-DSSAT Linkage for Region Impact Assessment
GIS Precision Agriculture Analyzer
Targeted for use by Researchers

4. Research & Technology Transfer: Process
Network of research users testing and applying models
Network of developers contributing models, analysis tools, utilities, & data
Minimum data set defined
Standard formats, protocols for use, exchange
Packagers, maintainers, distributors
Trainers
DSSAT - Developed by IBSNAT Project of USAID,

5. DSSAT v3.5 screen showing DATA, MODELS and
ANALYSES sections. Data must be entered for weather, soil,
and management before performing analyses.

6. DSSAT Applications Climate Change Effects on Crop Production
Optimize Management using Climate Predictions
Interdisciplinary Research, Understand Interactions
Diagnose Yield Gaps, Actual vs. Potential
Optimize Irrigation Management
Greenhouse Climate Control
Quantify Pest Damage Effects on Production
Yield Forecasting
Precision Farming
Land Use Planning, Linked with GIS
This list is only a small sample of many different ways that researchers around the world are using crop models.

7. Impacts Adopted by ~ 1500 researchers in 90 countries
Impacts of climate change; used in > 8 national & international projects worldwide
Hundreds of applications independent of developers
Spawned teams on every continent, still active
Validated systems approach for technology transfer
Still in use

8. Agricultural Production
Systems Simulator

9. Crop, pasture and tree modules
Currently available
Maize
Wheat
Barley
Sorghum
Sugarcane
Sunflower
Canola
Chickpea
Mungbean, Cowpea, Soybean
Peanut
Stylo pasture
Lucerne
Cotton (OzCot)*
Native pasture (GRASP)
Hemp
Under development
Lentil / faba beans*
GRAZPLAN*
Lupin*
FOREST #
* by arrangement with CSIRO Plant Industry
@ in association with ICRISAT
# In association with CSIRO L&W
From Brian Keating, 2000

10. APSIM Applications Exploring what if questions:
“Discussion Support System”
Exploring what if questions:
Which crop to sow?
When to sow?
How much N to apply?
Which variety to sow?
What density?
Analysis of different starting conditions and seasonal forecasts
From Brian Keating, 2000

11. Private Sector: United Soybean Board
Goals
Evaluate potential for practical, on-farm uses of soybean model for decision support
Create a sustainable process for soybean production technology transfer, tailored to specific fields for optimizing profits
Integrate new research results into the system, enhancing its capabilities in ways important to farmers
Researchers in eight states

12. Early Experience Overly ambitious
Under estimated time, complexities of process
Conflicting objectives in design
Changing computer technologies
Changing model
Failure of a first prototype
“… Can researchers really do this?”, But...
Input from farmers, industry provided guidance for success

13. What We Did
Packaged soybean model with data on soils, weather access to provide information for:
production planning (planting, weed control, variety, planting date, irrigation, profitability)
in-season decisions (irrigation, re-plant, yield forecast)
Worked with farmers, farmer advisors, industry to refine design and test
Independent evaluation by researchers in a number of states, and by industry
Demonstrated value of approach for integrating new research aimed at specific problems identified by farmers

14. PCYield Simple, targeted, graphical user interface
CROPGRO-Soybean simulation model
Field-specific data management
Internet access to weather data
Production risk indicators
In-season yield projections
Compare varieties, planting dates, re-plant decisions
Irrigation timing, yield impacts
With these considerations in mind, PCYield was designed with support from the United Soybean Board (USB).
It supports a targeted range of decisions but also has an extremely simple user interface.
Even so, the software incorporates the key elements of more complex decision support systems, including
(1) the CROPGRO-Soybean model,
(2) field-specific data management,
(3) Internet access to real-time weather data, and
(4) production risk indicators

15. All Needed Data Available
When the update is finished, the main screen returns.
As you can see, the entry in the upper corner now reflects the most recent data available.
To generate a yield forecast, we now only need to press the compute button.
To place what follows in context, you should note that we have set the inputs to simulate dryland production.

16. Targeting Research to Fill Gaps: Ability to analyze commercial varieties
Develop and test methods for estimating genetic coefficients of new varieties as they are released, using yield trial data

17. Weather and soil data were obtained for each location in Georgia where the trial was conducted and for each year (5 locations and 7 years). Coefficients for the crop model were estimated using the variety trial data. Thsi slide shows that the model can correctly capture much of the yield variations among locations as well as the genotype by environment interaction described earlier (that Hutcheson yielded better under high yielding conditions whereas Bryan yielded better under most lower yielding environments). These and other results have confirmed that the soybean model can accurately simulate yields in locations where weather data are accurately provided, soil characteristics are known with good confidence, and variety characteristics are accurate. This provides confidence in its use in research and applications to many of the issues of interest to commercial agriculture.

18. Targeting Research to Fill Gaps: Precision Agriculture
The Problem:
Yield varies considerably in many fields
Spatially varying inputs and management may increase profits and reduce environmental risks
However:
Quantifying what caused yield variability in a specific field is not easy
How does one determine how to vary management across a field to optimize profit and meet other goals?

19. A. Irmak et al., 2000
Keiper Field, Iowa

20. Working with Industry for Adoption
A. Ferreyra et al., 2000
Riffey Field, Illinois

21. Characteristics of Successful Efforts
Address issues of interest to targeted users (farmers, researchers, policy makers)
Target users are clearly identified
Direct involvement of users, intermediaries (input, service suppliers; extension, researchers)
Interdisciplinary teams
Easy access, use (usually by intermediaries, not farmers or policy makers themselves)
Availability of necessary input data
Open process for evaluation, discussion, design, use
Model credibility, process to assess credibility

22. Challenges
It is much more difficult than originally thought, even if models were perfect
Models do not include many factors important for decision support
It is difficult to include other factors, mainly due to difficulty of measuring inputs needed for those factors
Are our current institutions adequate?
Complexity of upgrading models
Intellectual property rights
Public – private sector cooperation
Documentation, maintenance

23. Trends Industry interest, capabilities
Increasing capabilities for measuring inputs
Modular model design, software engineering
Balanced models with more components
Flexible designs for tailoring model to specific needs
Increasing student interest, contributions to components
Long term investments in process
More cooperation in model development, evaluation
Internet tools

24. Thank You

26. Predicted Results
Information on yields and maturity dates for each scenario. The best, worst and average results for the 10 simulated seasons are shown.
These results show that, having planted an MG4-Early cultivar in this field two weeks earlier than normal, the grower can expect, on average, about a 3% yield advantage relative to planting an MG4 on May 1.
However, because this run was done so early in the season, the range from best to worst is quite broad. As the season progresses, the range narrows. This display gives the producer an idea of the uncertainty he or she is currently facing. This enables users to interpret average results in light of their own risk preferences.
The buttons along the bottom are used to generate detailed time series of growth and rainfall.

27. Predicted growth: (1) Average of 10 years, (2) This year

28. Causes of Yield Variability Develop Prescriptions Risk Assessment
Genetics
Weather
Yield
Soil type
Images
Pests
Elevation
Drainage
Fertility
Causes of Yield Variability
Develop Prescriptions
Risk Assessment
Economics
Crop Models & Precision Farming

29. A. Irmak et al., 2000
Keiper Field, Iowa

30. ICASA International Consortium for Agricultural System Applications
Network of individuals and institutions
Cooperating to facilitate development and application of systems approaches and tools
To affect decisions & policies related to human interactions with natural resources

31. Implications: Need for Toolkit
Models, Analysis Tools
Projective, Exploratory, Predictive
Different scales, purposes
Building block, modular approach
Data
Minimum data set, indicators
Standard formats, protocols
Natural resources, Socioeconomic
Purposes
Assessment
Management, Decision Aids
Conflict Resolution
Wide distribution, easy access
International effort, ICASA, CG Centers, etc.

32. This slide shows observed soybean yields taken from public soybean yield trials in Georgia plotted against site average yields (for 5 locations and 7 years). It shows that soybean yield varied from about 1200 kg/ha for the worst environment (year and location) to about 3400 kg/ha for the best environment. The graph also shows that the Hutcheson variety yielded highest under good environments whereas Bryan variety yielded best under most lower yielding environments.
Weather and soil data were obtained for each location in Georgia where the trial was conducted and for each year (5 locations and 7 years). Coefficients for the crop model were estimated using the variety trial data. The next slide shows that the model can correctly capture much of the yield variations among locations as well as the genotype by environment interaction described above (that Hutcheson yielded better under high yielding conditions whereas Bryan yielded better under most lower yielding environments). See next slide.

33. Model-Based DSS Tools
Many are never accepted, used - Why?
Process (failure to include users from the start)
Ownership (N.I.H. principle)
Impractical data requirements
Wrong problem or inadequate scope
Cost vs. benefit
Naïve developers
Naïve funding agencies

34. APSIM - Plug-in / Pull-out modularity
Manager
Report
Soil pH
SoilN
Erosion
Surface Residue
ENGINE
Crop C
Crop B
Maize
Cowpea
Soil P
Arbitrator
SWIM or
Soilwat
From Brian Keating, 2000