About the Vitagri Academy
Data Agronomy is a free, 16-module course that teaches you to build, evaluate, and trust predictive models for nutrient-dense food production. You start with statistics and end with forecasts that real farms can act on — six Foundation modules to get your bearings, ten Advanced modules to make you fluent.
The curriculum
Two carrots from the same field can differ in antioxidants by a factor of two hundred.
That gap is the subject of this course. Each module pulls one thread — soil biology, mycorrhizae, near-infrared spectroscopy, statistical modelling — and ties it back to the same question: what, exactly, makes one carrot more nourishing than the next?
Who it's for
Built for practitioners, not classrooms.
If you grow food, advise people who do, or set the rules that shape what they grow, this course is yours. No statistics background required for the Foundation level. The Advanced level assumes you finished the Foundation — and nothing more.
How it works
Read. Try. Be tested. Earn the title.
Each module pairs short editorial reading with one interactive exercise and a graded quiz. Finish the six Foundation modules and you earn the Foundation Certificate in Data Agronomy. Finish all ten Advanced modules and you carry the title Certified Data Agronomist — the first formal credential in the field.
The 16 modules
What Data Agronomy covers.
Modules span soil biology, mycorrhizal networks, the 200-fold antioxidant variation within the same crop type, regenerative practice, near-infrared spectroscopy, and the statistical modelling that connects farming systems to health outcomes — each drawn from Vitagri's Growing Health Report and 19,000+ peer-reviewed studies.
Foundation — six modules, earning the Foundation Certificate in Data Agronomy:
- Foundations — Before You Begin
- What Is a Predictive Model?
- The Data We Work With
- Statistics Foundations
- Introduction to Machine Learning
- Inspiration For a Healthier Farming Future
Advanced — ten modules, earning the title Certified Data Agronomist:
- Generalised Linear Mixed Models (GLMM)
- Ensemble Methods: XGBoost & Random Forest
- Metabolomics & Microbial Data
- Model Evaluation & Validation
- The Bidirectional Architecture
- From Theory to Practice
- Forecasting — Reading the Future of a Series
- Classical Forecasting — Smoothing and ARIMA
- Machine-Learning Forecasting
- Evaluating and Applying Forecasts
Take what you’ve learned into the evidence itself
Pulse Workshop is the working platform behind this course — a place you work in, not a training day you attend. Interrogate the graded studies, check claims against them, and build evidence-led plans for your organisation.
See where you stand →