Territorial sustainability of ecological farming

This Deliverable 5.2 of the LIFT project presents a territorial level sustainability assessment of alternative scenarios for the adoption of ecological farming approaches for 16 case study areas across Europe. Given that there are many approaches to sustainability assessment depending on the theoretical framework, the assessment’s aims, and data used, this deliverable begins with a review of the sustainability assessment literature in relation to agriculture and territorial scale issues, to identify the most appropriate methodology for this deliverable. The limited availability of landscape-scale data, the use of scenarios, and the need to rapidly apply a straightforward methodology across diverse case study areas, favoured a qualitative assessment of each adoption scenario in terms of their impacts against a set of regionally-specific sustainability objectives. Moreover, because territorial impacts can reflect many interacting cause-effect relationships, network analysis formed an optional extension to the assessment, to explore the patterns of influence underpinning scenario performance. In order to define the sustainability objectives for assessment, an initial long list of possible objectives was created through a review of the academic literature and relevant regional policy documents, followed by a round of stakeholder consultation to produce a final short list of objectives for each case study area. Performance against these objectives was assessed for four ecological farming adoption scenarios that differed in terms of the rate (either high or low) and distribution (clustered or dispersed) of adoption in 10 years’ time. The ecological practices being adopted in these scenarios were identified based on the output of Delphi exercises with stakeholder panels for each case study area, conducted as part of previous research in LIFT. Drawing on results from LIFT, local literature, and expert knowledge, each scenario was described as the product of a set of drivers of change. The drivers of change were tabulated against the objectives to produce an assessment matrix for each scenario. Groups of experts and stakeholders completed these matrices by deciding whether the state of each driver in each scenario had a positive or negative, strong or weak, impact on each objective. The different driver impacts on each objective were aggregated to show the scenario’s overall performance against each objective. For the High Weald case study area in England, the assessment matrices were also used to create network graphs to show the interacting cause-effect relationships between drivers and impacts, and network analysis was used to identify features of the system that were especially influential in determining overall sustainability performance. Based on this qualitative mapping of impacts against sustainability objectives, across case study areas, territorial sustainability performance was assessed to be strongest when the ecological farming adoption rate was high, and when adoption occurred in a clustered distribution (although the impact of adoption distribution was typically smaller than the impact of adoption rate). The same overall pattern was also reported when considering only the environmental and social dimensions of sustainability, but economic sustainability performance was only impacted by the rate, and typically not the distribution, of adoption. These results suggest that the practices identified by stakeholders as relevant to future ecological adoption scenarios for a given case study area tend to be appropriate for achieving the area’s specific sustainability objectives, and that the spread of ecological farming approaches, at least in some areas, has the potential to deliver ‘win-win-win’ outcomes that reconcile performance across different sustainability dimensions. However, no one scenario was best for every objective in a case study area, so even if high clustered adoption of ecological farming makes an overall positive contribution to sustainability at the territorial level, realising this scenario in practice will still involve navigating some trade-offs between objectives. By extending the methodology to include network analysis, the sustainability assessment for the High Weald highlighted pathways and barriers that could be important in accounting for differences in scenario performance. In particular, information exchange among farmers on the benefits of ecological practices (facilitated by strong organisational and advisory support, and the use of technology) was a key contributor to the strong territorial sustainability performance of a high clustered adoption scenario. The network graphs produced for the High Weald also provided some indication as to how the territorial sustainability performance of ecological farming is due to a combination of farm-level and landscape-level processes, and suggested areas of interaction between these farm and landscape-level processes. As an example, strong interpersonal relationships among farmers could enhance information exchange that promotes ecological practice uptake on individual farms, but also encourage greater coordination of land management and collaboration between farmers. While the combination of scenario analysis, qualitative impact mapping and network analysis has been used for sustainability assessment before, in this case scenario development was built around alternative outcomes for the spread of agricultural land management practices, rather than starting with particular philosophies about the direction of society or policy. A key challenge, and priority area for further innovation, could be to reconcile the approach presented here with farm-level sustainability assessments, helping to identify areas of alignment or disconnect between farm and territorial level performance.