Identifying pathways to more sustainable farming using archetypes and multi-objective optimisation

Elsevier 2024-09

The benchmarking of farm environmental sustainability and the monitoring of progress towards more sustainable farming systems is made difficult by the need to aggregate multiple indicators at the relevant spatial scales. We present a novel framework for identifying alternative pathways to improve environmental sustainability in farming systems that addresses this challenge by analysing the co-variance of indicators within a landscape context. A set of sustainability indicators was analysed within the framework of a published set of Farm Management Archetypes (FMAs) that maps the distribution of farming systems in England based on combinations of environmental and management variables. The archetype approach acknowledges that sustainability indicators do not vary independently and that there are regional constraints to potential trajectories of change. Using Pareto Optimisation, we identified optimal combinations of sustainability indicators (“Pareto nodes”) for each FMA independently, and across all FMAs. The relative sustainability of the archetypes with respect to one another was compared based on the proportion of Pareto nodes in each FMA. Potential for improvement in sustainability was derived from distances to the nearest Pareto node (either within or across FMAs), incorporating the cost of transitioning to another archetype based on the similarity of its environmental variables. The indicators with the greatest potential to improve sustainability within archetypes (and, therefore, should have a greater emphasis in guiding management decisions) varied between FMAs. Relatively unsustainable FMAs were identified that also had limited potential to increase within archetype sustainability, indicating regions where more fundamental system changes may be required. The FMA representing the most intensive system of arable production, although relatively unsustainable when compared to all other archetypes, had the greatest internal potential for improvement without tran

Ecology and Environment; Agriculture and Soil Science; Data and Information; Publication - Article; PeerReviewed

Open access content. Open access content
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UKNRA oai:nora.nerc.ac.uk:537812
Bütikofer, Luca; Goodwin, Cecily E.D. ORCID: https://orcid.org/0000-0003-0093-9838 ; Varma, Varun; Evans, Paul M. ORCID: https://orcid.org/0000-0001-6706-420X ; Redhead, John W. ORCID: https://orcid.org/0000-0002-2233-3848 ; Bullock, James M. ORCID: https://orcid.org/0000-0003-0529-4020 ; Pywell, Richard F. ORCID: https://orcid.org/0000-0001-6431-9959 ; Mead, Andrew; Richter, Goetz M.; Storkey, Jonathan. 2024 Identifying pathways to more sustainable farming using archetypes and multi-objective optimisation. Ecological Indicators, 166, 112433. 11, pp. 10.1016/j.ecolind.2024.112433
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