Predictive Soil Intelligence

Know where your biological will work — before you trial it

BioMatch predicts establishment probability for your microbial product across Europe's most characterized soil microbiome database. Stop guessing. Start matching.

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450+
European agricultural soil microbiome profiles
65%
of intra-field yield variation predicted by microbiome
26
bacterial genera power our predictive model
7
independent global datasets validated our approach
Up to 50% of global vegetation variation predicted from local training data
Source: Schaks et al. (2024), Science of The Total Environment, Vol. 958
Trusted by agricultural professionals at
N.U. Agrar LWG Bavaria Game Conservancy Yphen Maschinenring
The Problem

The same biological works on Farm A and fails on Farm B. Nobody knows why.

The #1 challenge in agricultural biologicals is inconsistent field performance. The hidden variable? The native soil microbiome determines whether your product can establish.

🎯

Blind site selection

Trial sites are chosen by geography and logistics — not by soil biological compatibility. Result: 30–50% of sites show no significant effect.

💰

Wasted R&D budget

Each incompatible trial site wastes tens of thousands. Across a full registration program of 15–30 sites, the losses compound rapidly.

⏳

Delayed time-to-market

Inconsistent data forces repeated trials, delays regulatory submissions, and can postpone commercial launch by 1–3 years.

How BioMatch Works

From organism profile to field trial confidence

01

Upload your organism

Provide the genus, species, functional traits, and target crop of your biological product.

02

Database matching

BioMatch queries 450+ characterized soil microbiomes for niche compatibility and competitive dynamics.

03

Prediction package

Receive establishment probability scores, optimal soil archetypes, and recommended trial sites.

04

Competitive landscape

See what's already in the soil doing the same job. Know if your product adds value — before you plant.

Peer-Reviewed Science

Locally trained models predict global plant growth differences

Our LASSO regression model, trained on a single German maize field using just 26 bacterial genera, retrospectively predicted yield variation with remarkable accuracy — and generalized across seven independent datasets from Brazil, China, Spain, and beyond.

The core insight: a globally conserved set of soil bacterial taxa correlates with vegetation. The same genera that predict yield locally also predict productivity globally. This means the soil microbiome contains predictive information about biological product performance that transcends geography.

Schaks, M., Staudinger, I., Homeister, L., Di Biase, B., Steinkraus, B.R., Spiess, A.-N. (2024). Local microbial yield-associating signatures largely extend to global differences in plant growth. Science of The Total Environment, 958, 177946.
65%
of intra-field maize yield variation
predicted by soil microbiome
50%
of global vegetation variation predicted by optimized model
450+
European farm soil profiles in proprietary database
What Clients Say
"We started working with Soilytix to characterize the metagenomic profile and biological functions of our soil, particularly to identify species that may be able to remediate it. The dashboard and customer support has been excellent."
Carmen Mirabelli · R&D Valorization Specialist, Yphen (biosolutions for soil remediation)

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Learn how predictive soil microbiome analytics is changing biologicals field trial design. Free, peer-reviewed data included.

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