BIODICAPT

Projet financing:

Total budget: 27776294 €

PSH budget: 243000€

Financier: PEPR Agroécologie et Numérique

Abstract:

Agroecological production systems depend on ecosystem services (ES) derived from biodiversity. They also aim to address two major challenges: protecting and enhancing biodiversity, particularly in agricultural areas, and reducing the use of pesticides. These systems combine multiple levers, such as the diversification of production, the enrichment of biodiversity in agricultural landscapes, and the implementation of targeted agronomic practices. To support the agroecological transition towards these complex systems, it is necessary to have standardized methods to assess their multiperformance and improve their design. These methods must be able to describe and monitor multiple components on a large scale:

• Biodiversity, including a large number of taxa.
• Agricultural practices.
• Agroecological infrastructures (AEI).
• Ecological functions that underpin ES.

The BIODICAPT project aims to build a biodiversity monitoring strategy that is both low-cost and energy-responsible, based on several sensors that can be deployed in agricultural landscapes at different scales: local (farm), regional, national. The project will use already available sensors and will improve the capacity of algorithms to characterize biodiversity and associated services (mainly biological control and pollination) in response to agricultural practices and AEI. The sensors:

• Audio recorders to characterize bird, bat, and insect communities (species composition, trait composition and diversity), and associated ES.
• Remote sensing image sensors to characterize plant communities, structure, composition, ecological properties and management of AEI, associated ES, and agricultural practices at the landscape scale. To account for a wide diversity of production systems and biogeographic contexts, these sensors will be deployed on eight regional networks of plots already in place and tested on a larger scale in institutional monitoring networks at the national level. Statistical models will be developed to analyze these multi-taxa and multi-sensor data in relation to agricultural practices, AEI, and landscape, with the aim of producing biodiversity indices. Tools will then be developed to transfer the knowledge produced to farmers, citizens, and policymakers, and support the design and management of agroecological systems beneficial to biodiversity and ES.

Projects Partners:

CNRS, MNHN, Chambres d’Agriculture France.