Enhancing current life cycle biodiversity impact assessment methods across farming systems through environmental DNA analysis – a proof of concept

Agroecological practices have been promoted as a means of mitigating biodiversity loss in farmland. Current life cycle impact assessment (LCIA) methods have a limited ability to account for impacts of agroecological practices because they focus on a limited number of agricultural land use types, mainly annual and permanent crops. Here, we propose to leverage advancements in environmental DNA (eDNA) analysis to enhance current LCIA methods, enabling the integration of agroecological practices. To illustrate the steps necessary for transforming eDNA sample results into characterisation factors (CFs) for agroecological practices we analysed fungal diversity within a regenerative pasture field using eDNA and compared it to the fungal diversity of traditional rural biotope (TRB) as a semi-natural reference system of Finland. From both the regenerative pasture field and the TRB field, nine soil subsamples combined into 3 samples were taken by use of 50 m transect lines following the LIFEPLAN soil sampling protocol. Samples were analysed with a full turnkey analysis using state-of-the-art DNA metabarcoding protocols. We illustrate the implementation steps required to create CFs for biodiversity assessments of agroecological practices caused by land stress. Within these steps, we used eDNA results to calculate CFs by inserting acquired values in the equations presented by and following the distinctly different procedures and logic of the LCIA methods GLAM 3, LC-IMPACT, ReCiPe, and Impact World+. Our illustrative case study on fungal species richness showed that, even with two field measurements, CFs for regenerative farming can be created. The CFs created for regenerative pasture farming were in the expected magnitude of order of the original CFs of the respective methods. They further showed lower impacts than the minimum intensity level of GLAM 3, indicating the need to investigate the CFs for agroecological farming further. We demonstrate the possibility of using eDNA results to create CFs for agroecological farming. A key advantage of eDNA analysis is its ability to identify multiple taxonomic groups from a single sample, saving time and resources and detecting species that might be overlooked or difficult to identify morphologically. Using comprehensive eDNA data to update current land-stress CFs can enhance the accuracy of biodiversity assessments across agroecological farming systems or even those used for conventional farming. We recommend the integration of eDNA results to enhance land-stress CFs through existing equations provided by current LCIA methods.