Imagine if we only tested a new medicine on one type of person and assumed it worked for everyone. That is how we currently check if pesticides hurt bees. This work creates a smarter system that tests many different insects and uses computer models to predict how chemical mixes affect them in the real world.
Current pesticide risk assessments are too narrow, focusing only on honey bees and single chemicals. This creates a regulatory blind spot for companies whose products may affect a wider range of pollinators in complex, real-world mixtures.
A prototype web-based risk assessment platform and a suite of predictive toxicology and population models (including BufferGUTS).
If you are a pesticide manufacturer dealing with strict EU approval processes — this project developed in silico models and toxicity data for multiple taxa that speed up the prediction of how chemicals affect non-target insects. This reduces the risk of late-stage regulatory rejection.
If you are a crop management firm dealing with biodiversity loss in your fields — this project developed a web-based platform to test landscape-scale scenarios. This allows you to optimize pesticide use while maintaining the 75% of food crops that depend on animal pollination.
If you are a consulting agency dealing with complex environmental impact reports — this project developed a co-monitoring scheme and risk indicators for pesticide mixtures. This provides a more accurate way to measure toxic loads in the field across different cropping systems.
Based on available project data, no pricing or cost information is provided as the project focuses on developing an open database and tools.
The project has developed a prototype web-based platform and in silico models integrated into VEGAHUB, suggesting a transition toward scalable digital tools.
Based on available project data, the project aims to generate an open database for pollinator/pesticide data and tools, implying an open-access approach.
The tools are designed to support EU Environmental Risk Assessment and policy, specifically contributing to the Farm to Fork Strategy and EU biodiversity targets.
The project period runs from 2024-01-01 to 2027-12-31, with initial prototypes already developed in the first 18 months.
The consortium is heavily research-driven with 6 universities and 2 research institutes, but maintains a 17% industry ratio including 2 SMEs. This balance suggests the project is focused on high-level scientific validation while ensuring that the resulting digital tools are grounded in practical industrial needs across 8 different countries.
Contact Aarhus Universitet (DK) regarding the One System framework
Contact us to explore how to integrate these predictive toxicology models into your product pipeline.
