Success of collaboration for sustainable agriculture: a case study meta-analysis - Sarah Velten; Nicolas W. Jager; Jens Newig (Environment, Development and Sustainability, 2021)
More and better collaboration between farmers and other stakeholders has repeatedly been identified as a key strategy for sustainable agriculture. However, for collaboration to actually benefit sustainable agriculture certain conditions have to be met. In this paper, we scrutinize the conditions that support or hamper the success of collaborative efforts in the context of sustainable agriculture. For this purpose, we conducted an exploratory case study meta-analysis to consolidate insights from 30 case studies on local and regional collaborative groups for a more sustainable agriculture in the EU. Through multiple regression analysis, we evaluated which factors influence the 'success' of such collaboratives. Thereby, we measured success through five explicit and comprehensive success criteria. We found two external, five actor-related, and five organization and management-related factors to decisively influence the different success criteria. Overall, our results highlight that collaboration success requires defining priorities as for each of the success criteria a different set of factors is decisive. Although our results showed trade-offs between the achievement of social and economic goals, it is possible to pursue some success criteria simultaneously. Furthermore, our results give reason to be optimistic about the performance of collaboratives: internal factors, which are in the hand of the collaboratives, are likely to be of greater importance than uncontrollable external conditions. Additionally, conditions encountered at the outset of a collaborative matter less than the way these conditions develop toward later stages. Thus, rather than depending on external and predefined conditions, success largely depends on the agency within the collaboratives.
Keywords: 330; Sustainable development; Farming; Collaboratives; European Union; Local; regional; Case survey method; Multiple regression