The canton of Grisons is a biodiversity hotspot, but as in many places of the world, biodiversity is under pressure (Amt für Natur und Umwelt Graubünden, 2022). Agricultural landscape scale management is seen as a way forward to counteract biodiversity decline (IPBES, 2019; OECD, 2023). Coordinated and collective action by farmers in agri-environmental schemes is expected to alleviate trade-offs between biodiversity conservation, ecosystem service provision, and agricultural production (Montoya et al., 2020; Prager et al., 2012; Westerink et al., 2017). In such approaches the aggregated outcome of a group of farms over a predefined spatial extent determines financial compensation (Nguyen et al., 2022; Prager, 2015).
Collective agri-environmental schemes come with transaction costs. These transaction costs occur for example when farmers invest time and effort into planning measures (decision-making costs), into coordinating the measures within a collective (negotiation costs), or into monitoring the results (monitoring costs) (Splinter and Dries, 2023). While larger groups of farmers working collectively may increase biodiversity benefits, it may also increase transaction costs. However, little is known about the potential trade-offs between biodiversity outcomes of collective agri-environmental schemes and transaction costs under given farmers preferences and production conditions (e.g., Kuhfuss et al., 2019). More specifically, there is currently no ex-ante assessment of how farmers' perceived transaction costs affect the cost-effectiveness of collective agri-environmental schemes for biodiversity.
This paper addresses this gap and investigates the biodiversity outcomes of different policy design options for a collective agri-environmental program when farmers' perceived transaction costs are considered. To answer this question, we use a bio-economic agent-based modelling approach that is parametrized using empirical data on perceived transaction costs, collected via a choice experiment. We model the lower Engadine region as a case study. The results demonstrate how perceived transaction costs influence participation in the program and, consequently, affect biodiversity outcomes at the landscape level.
We use the agent-based model FARMIND to assess the effect of a collective agri-environmental scheme on biodiversity. More specifically, we simulate how working in differently sized collectives influences farmers’ perception of transaction costs and thus the adoption decisions with respect to collective agri-environmental schemes and compare the resulting biodiversity potential. FARMIND is an agent-based model which captures farmer’s behavioural and social factors in a modular framework (Huber et al., 2022). To parameterize our model, we use three different sources. First, we use the bio-economic model FarmDyn as a sub-model to calculate farm-level costs and benefits of participating in a collective agri-environmental scheme (Britz et al., 2021). Second, we use the parcel-level life cycle assessment tool SALCA-Biodiversity (effect of agricultural management) in combination with species distribution models (potential habitat suitability) to assess the potential biodiversity outcomes (“usable potential”) of collaboration between farmers (Klein et al., 2025). Third, we use empirical data on behavioural factors from a survey which included a choice experiment. The choice experiment explicitly elicited farmer’s preferences with regards to the three different design options in collective agri-environmental schemes: group size, monitoring, and discretion (Fahrni et al., 2026a, 2026b). For this novel transdisciplinary model linkage between FARMIND, FarmDyn, and SALCA-Biodiversity, we have updated the models to recent data through extensive data work. We model 125 farms located in the lower Engadine region of the Canton of Grisons as a case study.
Our results show how much biodiversity potential could be harnessed in a case study region under differently designed collective agri-environmental schemes. Different scheme design options are associated with transaction costs and farmer preferences. The different options therefore lead to certain adoption outcomes (How many farmers adopt? Where in space are they located? Which collectives emerge?) and are associated with varying levels of usable biodiversity potential.
Literature
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