Modeling is a large topic!
- What types of models are we talking about?
- Diagnostic vs prognostic
- Empirical vs mechanistic
- Building new models/improving vs how to use existing models
User group:
- Modeling soil carbon - what exists to use for my tool?
- Learning the rigor/math to model your own data
- Hydrology/stormwater
- Interoperability of models
- What types of problems do we need models for?
- How to bring remote sensing/spatial data into models as inputs
- Bringing accessibility to a broad user base of existing models
- What information in our models is already there/can be adapted that farmers would be interested in?
Main tasks:
-
Core concepts
- The guts of DNDC: a series of equations that model the microclimate of soil microbes. The initial parameters of the equations come from empirical research and first principles of science.
- Multiple resolutions of data quality: models can fill in with good guesses where data aren’t available, or can give better outputs if you have a full inventory of data for parameters. AKA how good is good enough for your inputs?
- All models are wrong, but some models are useful.
- Swappable model components: you need to know the interface to do it.
- Interface design is a computer science specialty, we need to share that knowledge.
- How do you go from natural language request for information to a logical workflow that can be coded as inputs and outputs?
- Who are the target audience for models? Farmers, crop consultants, researchers? Mostly researchers so far, but there’s hunger to implement them in consumer-facing tools.
- How do you expand the inference space of a model? They were designed using empirical data from field sites, but not every possible field situation.
- Models need to be plugged into decision support tools, so that you can see outcomes before action.
- We need an infrastructure for plugging them together (farm management platform?)
- Calibration is a fundamental challenge: It’s possible to get the right outputs for the wrong reason when there are components you’re not simulating. Need to be cognizant of double-dipping data so you’re not introducing confounded bias.
- IFSM is a “typical” model, where the code is not open source, but the basic equations are presented textbook-style. Hard to reproduce.
- Who owns a model? A research group? A consortium? A PI at a university or agency? A company?
-
Accessibility
- What is the role of GOAT in making models extensible and interoperable? A working group?
- Do you drive accessibility by creating demand? Making models more economic, so farmers will want to use them.
- What tools are usable to a farmer? They’re familiar with spreadsheets, but they aren’t flexible enough for models.
- Values: farmers are businesspeople, but they’re also people who care about XYZ environmental/social externalities.
- Values can also be dollars-and-cents decisions. Consumers may be willing to pay premiums for organic/dietary change for livestock, etc.
- Understanding year-to-year variability can be a big challenge when looking at model outputs. Short-term development goals can be detrimental to finding realistic values. How does error/uncertainty propagate through a model?
- What data formats are useful for crosstalk between models? Getting data into a prespecified format can be a challenge for users.
Next steps
- Forum thread
- Skills trade: modelers need validation data, and users need model APIs
- Repository for finding models