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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 transitioning to a different farming system. In contrast, the intensive horticulture FMA had limited internal potential to improve sustainability. The FMAs with the greatest potential for system change as a viable pathway to improved sustainability were dairy, beef and sheep, and rough grazing, moving towards more mixed systems incorporating arable. Geographically, these transitions were concentrated in the west of England, introducing diversity into otherwise homogenous landscapes. Our method allows for an assessment of the potential to improve sustainability across spatial scales, is flexible relative to the choice of sustainability indicators, and—being data-driven—avoids the subjectivity of indicator weightings. The results allow decision makers to explore the opportunity space for beneficial change in a target landscape based on the indicators with most potential to improve sustainability.

The use of linear mixed effects models (LMMs) is increasingly common in the analysis of biological data. Whilst LMMs offer a flexible approach to modelling a broad range of data types, ecological data are often complex and require complex model structures, and the fitting and interpretation of such models is not always straightforward. The ability to achieve robust biological inference requires that practitioners know how and when to apply these tools. Here, we provide a general overview of current methods for the application of LMMs to biological data, and highlight the typical pitfalls that can be encountered in the statistical modelling process. We tackle several issues regarding methods of model selection, with particular reference to the use of information theory and multi-model inference in ecology. We offer practical solutions and direct the reader to key references that provide further technical detail for those seeking a deeper understanding. This overview should serve as a widely accessible code of best practice for applying LMMs to complex biological problems and model structures, and in doing so improve the robustness of conclusions drawn from studies investigating ecological and evolutionary questions.