Dynamic trajectories and maturity of farmer collaboration for biodiversity sensitive farming – Insights from the FRAMEwork Farmer Clusters [* Cross-Reference *]

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Bohnet, Iris C.; Hager, Gerid; Rellensmann, Thomas; Hardy, Claire; McHugh, Niamh M.; Ablinger, Daniela; Bagnoni, Virginia; Banks, Gillian; Beyer, Marco; Duijvestijn, Lennard; Franck, Pierre; Janeckova, Kristina; Kaasik, Riina; Martin, Youri; Moonen, Anna Camilla; Salehi, Aliyeh; Sánchez-García, Carlos; Schoone, Martine; Scott, Clare; Travnicek, Jan; van Rijn, Paul; Varas, Gonzalo; Veromann, Eve; Warlop, François; Wohlmuth, Marie Luise; Begg, Graham S., 2026, "Dynamic trajectories and maturity of farmer collaboration for biodiversity sensitive farming – Insights from the FRAMEwork Farmer Clusters [* Cross-Reference *]", https://lore.list.lu/citation?persistentId=perma:LIST.WGVY0G, LIST Open Repository, V1
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CONTEXT: Building on the Farmer Cluster approach, which has evolved over the past decade in England to address ecosystem degradation and biodiversity loss at the landscape scale, FRAMEwork (Farmer clusters for Realising Agrobiodiversity Management across Ecosystems), a Horizon 2020 project, established a network of eleven Farmer Clusters across Europe. OBJECTIVE: To test the effectiveness of the FRAMEwork Farmer Clusters, a new level of technological and scientific support was offered to the clusters providing opportunities for collaboration, co-production of knowledge, co-innovation, peer-to-peer learning, and monitoring. METHODS: We provide an overview of the eleven clusters and an in-depth comparative multiple case study analysis to understand the dynamic trajectories and levels of maturity shaping the development and outcomes of each of the Farmer Clusters. RESULTS AND CONCLUSIONS: We identified five formative dimensions – governance, leadership, facilitation, group characteristics and context – all of which are interdependent and dynamic, affecting the functioning of the Farmer Clusters, and leading to different levels of maturity. Comparing the situation of each cluster regarding the five dimensions and the level of maturity, we found that the clusters started in distinct contexts with diverse initial conditions across Europe – from favourable to unfavourable. This led to different dynamic trajectories on a pathway to biodiversity sensitive farming. SIGNIFICANCE: The maturity assessment matrix offers a valuable tool for Farmer Clusters to reflect on their progress and capacity in achieving their goals, guiding future efforts for effective cluster management. (2026-03-01)
***This entry has been automatically imported via Infodoc(ASO) CSV by LIST harvest scripts. Please refer to https://doi.org/10.1016/j.agsy.2026.104644 for the original and latest version of the dataset and data downloads*** (2026-06-04)
Subject Earth and Environmental Sciences
Keyword Agri-environmental management, Collaborative governance, Farmland biodiversity, Landscape-scale, Living labs, Maturity assessment matrix
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