Predictive Models for Crops, Carbon & Climate
From process-based crop models to neural networks to hybrid approaches—we build the modeling solution that fits your prediction challenge.
The Full Modeling Spectrum
Every project is different. We meet you where you are and build what you need.
Process-Based Models
Mechanistic crop and ecosystem models grounded in plant physiology, soil science, and biogeochemistry.
- APSIM, DSSAT, ecosys implementation
- Crop growth & development simulation
- Calibration & validation
- Scenario analysis & projections
Data-Driven ML
Neural networks and statistical models trained on observational data for pattern recognition at scale.
- Deep learning & neural networks
- Geospatial & remote sensing ML
- Time series forecasting
- Satellite imagery analysis
Hybrid KGML
The best of both: ML that learns from data while respecting physical and biological constraints.
- Physics-constrained neural networks
- Process model surrogates (7000x faster)
- Knowledge-guided pretraining
- Interpretable, scientifically consistent
We recommend the right approach for your data, timeline, and accuracy requirements—or combine them for optimal results.
Peer-Reviewed Research Portfolio
Our methods are grounded in published, peer-reviewed research—ensuring scientific rigor and reproducibility in every project.
Constraining Large-Scale Yield from Crop Models with a Field-Scale Census
Novel approach combining field-scale census data with crop models to constrain regional yield predictions and quantify spatial uncertainty in agricultural forecasting.
Environmental Research Letters
A Model-Data Fusion Approach for Improved Ecosystem Carbon Estimates
Data assimilation methods that fuse process-based ecosystem models with observations to produce improved carbon flux and stock estimates with uncertainty quantification.
Geoscientific Model Development
Spatial Targeting of Agricultural Soil Amendments
Machine learning framework for optimizing biochar application across the US, mapping response heterogeneity and effectiveness probability to guide targeted soil amendment strategies.
Environmental Research Letters
Remote Sensing-Integrated Crop Model Calibration
Integration of satellite-derived LAI data with APSIM crop model through hierarchical parameter data assimilation for improved regional yield prediction.
Remote Sensing (MDPI)These peer-reviewed publications form the scientific foundation for our consulting work—bringing academic rigor to real-world agricultural challenges.
What You Can Predict
Turn your agricultural data into actionable predictions.
Yield Prediction
Field-level and regional yield forecasts accounting for weather, soil, and management.
Carbon Quantification
Soil carbon changes, GPP, and ecosystem carbon flux for sustainability reporting.
Environmental Outcomes
N₂O emissions, nutrient leaching, water use efficiency predictions.
Climate Scenarios
Projections under future climate including drought, heat stress, and extreme events.
Geospatial Analysis
Satellite imagery fusion, spatial variability mapping, remote sensing integration.
In-Season Forecasts
Real-time predictions that update as the season progresses with new data.
Let's Build Your Prediction Solution
Tell us about your data and prediction goals. We'll recommend the right modeling approach for your needs.