Farmers’ social networks and regional spillover effects in agricultural climate change mitigation

Abstract

Climate change poses a severe threat to global agricultural production and rural livelihoods, and since agriculture itself is a significant source of greenhouse gas (GHG) emissions, it can also play an important role in climate change mitigation. This article investigates how farmers’ social networks influence the adoption of on-farm mitigation strategies. More precisely, we use a network autocorrelation model to explore the relationship between a farmer’s own mitigation behavior and the mitigation behavior and knowledge of his fellow farmers. The analysis is based on a regional case study in Switzerland and uses data obtained from personal network interviews combined with survey and census data of 50 farmers. Half of them are members of a local collective action initiative for agricultural climate change mitigation, while the others do not participate in the initiative. We find that, on average, farmers with a larger network adopt more mitigation measures, and furthermore, mitigation adoption is linked with the level of knowledge within farmers’ networks. Indeed, the likelihood that non-members will adopt mitigation measures increases if they are closely associated with members of the collective action, suggesting a local spillover effect. It follows that strengthening knowledge exchange among farmers and supporting local farmers’ initiatives can potentially contribute to the diffusion of agricultural climate change mitigation practices.

Quantifying the impact of farmers' social networks on the effectiveness of climate change mitigation policies in agriculture

“…Seeking conformity and a feeling of belonging has even been found to have stronger implications for behavioural change than financial incentives (Kuhfuss et al, 2013). Moreover, particularly in our case study region, farmers were found to learn from and imitate (perceived) knowledgeable peers (Kreft, Angst et al, 2023) and trust relationships can help to lower the perceived risks of adoption (Sligo & Massey, 2007).…”

“…These are exogenous parameters based on a farm survey (Kreft et al, 2020). (3) A social network between farmers representing personal exchange of knowledge on climate change mitigation derived from a social network analysis based on face‐to‐face interviews (Kreft, Angst et al, 2023; Kreft, Angst, et al, 2021). Most individual and social factors could be taken into account without further transformation.…”

“…The larger the network and the smaller the number of activities within the network, the more likely it is that an agent will be dissimilar to their peers. The connection between the different agents in FARMIND is thereby based on an empirically informed social network (Kreft, Angst et al, 2023).…”

“…On the other hand, strong self‐efficacy (believing in one's own capabilities to successfully fulfil a given task) and a stronger innovation sense have been found to positively affect farmers' adoption of on‐farm measures to reduce GHG emissions (Kreft, Huber, et al, 2021; Niles et al, 2016). Moreover, social learning through knowledge exchange within farmers' social networks and in particular frequent contact with knowledgeable peers can increase mitigation adoption (Kreft, Angst et al, 2023; Moran et al, 2013).…”

“…Understanding farmers' decision‐making with respect to climate change mitigation is crucial for the design and implementation of such policy incentives. However, the role of behavioural factors in general, and social learning in particular, remains poorly understood in this context (Kreft, Angst et al, 2023; Kreft, Finger et al, 2023; Kreft, Huber, et al, 2021; Niles et al, 2016). Although bio‐economic modelling approaches are key tools used for the (ex‐ante) assessment of agricultural policies and their impact on actual GHG reduction potential as well as production and farm incomes (e.g., De Cara et al, 2005; Lengers et al, 2014), they usually lack integration of individual behavioural factors and social interactions.…”