Synergies and trade-offs in ecosystem services across the Upper Welland catchment

Agricultural intensification is one the principal drivers of environmental decline in agricultural landscapes. Declines in biodiversity, reduced capacity to store and sequester carbon, and degrading water quality are some of the most pressing concerns for managers of these environments. Agri-environment schemes (AES) seek to remediate this environmental damage and are increasingly guided by the natural capital approach and the concept of ecosystem services (ES). This research project aimed to develop a method to quantify ES trade-offs and synergies resulting from the potential land-cover change associated with AES policies, such as the Environmental Land Management Scheme (ELMS), the UK’s latest AES policy, which is currently undergoing pilots. This was performed using fine-resolution land-use/land-cover (LULC) maps, and spatial models of crop production, carbon storage, pollinator abundance, water yield, and water quality. A new, fine-resolution (10 m) LULC map of the Upper Welland Catchment, (UK) offered improvements over existing coarser-resolution maps in terms of the quantification of semi-natural habitats (SNH) (woodland and grassland) across the landscape: estimating 123% higher woodland area, 41% higher grassland area, and 21% reduced arable area over digital maps with a resolution of 100 m.

In collaboration with the Welland Rivers Trust, five land-cover change scenarios were modelled predicting the effects of potential future AES policy using the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) toolkit and earth observation data from the European Space Agency’s Sentinel-2 satellite. These simulated a “three-compartment model” based on land-sparing and land-sharing, where the landscape was split between high intensity farmland, low intensity farmland, and SNH. These scenarios ranged from those focused on crop production, to those seeking to restore extensive areas of SNH to the landscape. Estimated ES provision for wild-bee abundance, carbon storage, and water quality all benefitted from the restoration of SNH across the landscape increasing by 17%, 59%, and 58% respectively between the baseline and most extensive SNH restoration scenario. These ES typically exhibited synergistic interactions and increased in supply with one another across the landscape. In contrast, estimated crop production was reduced by 43% between the baseline and most comprehensive SNH restoration scenarios, and was associated with trade-offs with the increased supply of the other ES. Estimated water yield was minimally affected by the land-cover scenarios and exhibited a mixed dynamic in which trade-offs and synergies increased in approximately equal proportions between the scenarios. When simulating conservation objectives where all ES were weighted equally, the most extensive land-cover change scenario (193% increased woodland area, 21% increased grassland area, and 43% reduced arable land-cover) provided the optimal increase in supply of the ES bundle. When greater priority was given to crop production, the optimal scenario varied, with an intermediate scenario (52% increased woodland area, 19% increased grassland area, 19% reduced arable land-cover) providing the best outcomes for the ES bundle.

Overall, the research results demonstrate the significant contributions that spatial models of ES can make to the design and implementation of future AES policies. Restoring SNH to agricultural landscapes is likely to create substantial benefits to ES supply, particularly when spatially targeted in areas of greatest potential effect. This new method to quantify ES trade-offs and synergies can be easily applied to varied landscapes and is able to account for differing local conservation priorities.