Improving biodiversity impact assessment for regenerative farming using LCA and eDNA

Application summary

Biodiversity is decreasing at an alarming rate due to land use and land-use change, climate change, direct exploitation, pollution, and invasive species. Agricultural production is one of the main contributors to biodiversity loss. To strengthen farmland biodiversity, agricultural practices will need to change. Regenerative agriculture has been suggested as a potential management practice that could benefit biodiversity through strengthening (soil) ecosystem functions. Life cycle assessment (LCA) has been a commonly used method to assess the biodiversity impact of agricultural practices. Often life cycle impact methods mostly focus on land-stress related biodiversity impact by comparing biodiversity levels of set land-type categories with that of a natural state. However, currently no information is available on the land-stress related biodiversity impact of regenerative farming within LCA-based biodiversity assessment methods inhibiting a fair LCA of regenerative agriculture. Environmental DNA (eDNA) analysis allows for the identification of many species from single samples of soil, vegetation, air or water - including elusive and rare species that are difficult to identify using traditional surveying methods. Applying eDNA to regenerative agriculture could therefore provide us with data necessary to determine biodiversity levels and compare them to conventional farming. In this project we aim to increase our understanding of regenerative farming by utilizing the power of eDNA to improve LCA biodiversity methods. Sampling for eDNA will help us determine the difference between biodiversity levels in regenerative agricultural farms and conventional agricultural farms. These results will be utilized to establish new so-called characterization factors for LCA that are specific to regenerative farming and will be tested on two case studies. Combining eDNA analysis to develop the LCA method is a new but potentially powerful way to develop biodiversity assessment methods.