Enhancing current Life Cycle Biodiversity Impact Assessment Methods across Farming Systems through Environmental DNA Analysis – a Proof of Concept

Globally biodiversity loss is accelerating. The agricultural sector is one of the main contributors to this biodiversity loss occupying about half of the world's habitable land area, thereby replacing natural ecosystems with human-controlled environments that can poorly support the same level of diversity. Agroecological farming systems (e.g. regenerative farming) have been promoted to support a higher level of biodiversity than conventional farming systems can. Yet, life cycle impact assessment (LCIA) methods, that are often used to quantify the biodiversity impacts of agricultural products, cannot or poorly distinguish between the biodiversity impacts caused by conventional systems versus agroecological farming systems. This can lead to poor research conclusions and thus highlights the inability of these methods to support informed decision-making when it comes to transforming the agricultural sector.

In our research we propose to enhance currently available biodiversity-focused LCIA methods by taking advantage of the latest developments in environmental DNA (eDNA). In our approach we elaborate on steps that are required to measure species richness as an indicator of biodiversity levels on-site using eDNA and how to utilize the results to enhance four of the most widely used LCIA methods (GLAM 3, LC-IMPACT, ReCiPe, and Impact World+), thus improving the biodiversity impact estimates of different agricultural farming systems using empirical data.

We applied our new approach to a case study of regenerative farming in Finland as a proof on concept using eDNA data on fungal diversity. Our case study showed the advantages of using eDNA not only by giving more accurate assessments on this previously unrepresented farming system but also by illustrating the possibility of including soil-dwelling organisms that are crucial to agricultural ecosystems but were previously ignored in LCIA methods. Although we focused solely on fungi for illustrative purposes, using eDNA would allow for the inclusion of many taxonomic groups from a single sample without the high level of expertise required in traditional species identification methods.