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CAGRILAB USE CASES

Making Living Lab evidence useful.

Regenerative agriculture does not produce the same results everywhere. Soil, climate, management and local conditions matter.

CAgriLab is exploring how digital tools can make evidence from Agricultural Living Labs easier to capture, compare, discover and understand.

01CAPTUREExperiments + outcomes
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02HARMONISEComparable evidence
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03FINDRelevant contexts
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04UNDERSTANDEvidence in plain language
CORE USE CASES

What could CAgriLab help you do?

The use cases are grounded in challenges encountered when producing, sharing and using evidence across Agricultural Living Labs.

01
CORE USE CASE

Find evidence relevant to your context

What has worked in conditions like mine?
THE CHALLENGE

Regenerative agriculture is highly context-dependent. The same practice can produce different results depending on soil, climate, crop, management history and farm conditions.

WHAT CAGRILAB ENABLES

CAgriLab helps users discover experiments from comparable contexts, using information such as soil, climate, crop, farm type, management practice and measured outcome.

EXAMPLE

Find cover crop experiments conducted on similar soils and under comparable climatic conditions.

02
CORE USE CASE

Record experiments and their actual outcomes

What was tested, where, and what happened?
THE CHALLENGE

Valuable experiments are conducted across Agricultural Living Labs, but information about experimental design, local conditions and measured outcomes can remain fragmented or difficult to reuse.

WHAT CAGRILAB ENABLES

CAgriLab provides a shared structure for recording what was tested, the experimental design, local context and the outcomes that were actually measured.

EXAMPLE

Compare a regenerative practice with its measured yield, soil or biodiversity outcomes rather than relying only on descriptions of the practice.

03
CORE USE CASE

Ask questions of Living Lab evidence

Can I simply ask the evidence?
THE CHALLENGE

Finding useful agricultural evidence can require knowledge of datasets, terminology and specialised search tools.

WHAT CAGRILAB ENABLES

CAgriLab is developing natural-language search so users can ask questions and discover relevant regenerative agriculture knowledge and Living Lab data.

EXAMPLE

Ask: "How have cover crops performed in conditions similar to mine?"

04

Track experiments over time

What is this Living Lab testing now?
THE CHALLENGE

Regenerative processes can take years to become measurable, while individual research projects are often relatively short.

WHAT CAGRILAB ENABLES

Living Lab digital twins can provide a persistent record of research activities, experiments and outcomes, allowing evidence to accumulate over time.

EXAMPLE

Follow changes in soil health across experiments that continue beyond the lifetime of an individual project.

05

Harmonise data from different Living Labs

How can evidence from different sites be compared?
THE CHALLENGE

Living Labs generate data in different formats and structures, making cross-site comparison and reuse difficult.

WHAT CAGRILAB ENABLES

CAgriLab develops harmonisation tools and a common digital twin model to make heterogeneous Living Lab data easier to combine, compare and reuse.

EXAMPLE

Convert Living Lab soil data into common structures such as GLOSIS-compatible formats.

06

Share data while retaining control

Can researchers share data without losing control of it?
THE CHALLENGE

Collaboration requires data sharing, but ownership, access and data sovereignty can make researchers and organisations reluctant to publish valuable datasets.

WHAT CAGRILAB ENABLES

CAgriLab's data infrastructure is designed to allow data owners to control how their Living Lab data are published, accessed and used.

EXAMPLE

Make a dataset discoverable while retaining control over access and permitted uses.

07

AI-supported soil and biodiversity analysis

Can field observations be measured more easily?
THE CHALLENGE

Some agricultural and ecological measurements require considerable time and specialist analysis.

WHAT CAGRILAB ENABLES

CAgriLab partners are developing image-based tools for agricultural and environmental measurements including vegetation, soil structure and biodiversity.

EXAMPLE

Use imagery to support assessment of grass-clover ratios, forage biomass or soil structure.

WHY CONTEXT MATTERS

An agricultural result is only useful if you understand where it came from.

A regenerative practice cannot be separated from the conditions in which it was tested. Good evidence therefore needs more than a result.

SoilClimateCropFarm typeManagement historyExperimental designPracticeMeasured outcome
EXPLORE CAGRILAB

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