Farmers' Decision Making on Livestock Trading Practices: Cowshed Culture and Behavioral Triggers Amongst New Zealand Dairy Farmers

Abstract

Studies of farmers' failure to implement biosecurity practices frequently frame their behavior as a lack of intention. More recent studies have argued that farmers' behaviors should be conceptualized as emergent from farming experiences rather than a direct consequence of specific intentions. Drawing on the concepts of “cowshed” culture and the “Trigger Change Model,” we explore how farmers' livestock purchasing behavior is shaped by farms' natural and physical environments and identify what triggers behavioral change amongst farmers. Using bovine tuberculosis (bTB) in New Zealand as a case example, qualitative research was conducted with 15 New Zealand dairy producers with varying bTB experiences. We show how farmers' livestock purchasing behavior evolve with culture under a given farm environment. However, established cultures may be disrupted by various triggers such as disease outbreaks, introductions of animals with undesired characteristics, and farm relocation. While dealing with economic and socio-emotional impacts posed by triggers, farmers reorganize their culture and trading behaviors, which may involve holistic biosecurity strategies. Nevertheless, we also show that these triggers instigate only small behavioral changes for some farmers, suggesting the role of the trigger is likely to be context-dependent. Using voluntary disease control schemes such as providing disease status of source farms has attracted great interest as a driver of behavioral change. One hopes such schemes are easily integrated into existing farm practices, however, we speculate such an integration is challenging for many farmers due to path-dependency. We therefore argue that these schemes may fail to bring their intended behavioral changes without a greater understanding of how different types of triggers work in different situations. We need a paradigm shift in how we frame farmers' livestock trading practices. Otherwise, we may not able to answer our questions about farm biosecurity if we continue to approaching these questions solely from a biosecurity point of view.

Generative models of network dynamics provide insight into the effects of trade on endemic livestock disease

“…For example, larger batch sizes (and fewer trades) may inhibit flexibility in adapting to changing conditions. Furthermore, there is evidence that some farmer behaviours are determined by responses to external influences including extreme weather events and socially accepted farming practices [ 45 ]. This suggests that incentives, e.g.…”

Ambition Meets Reality: Achieving GHG Emission Reduction Targets in the Livestock Sector of Latin America

“…For both the dissemination of information and policy formulation, it is likewise important to understand how livestock producers make decisions, i.e., regarding the adoption of technologies and mitigation strategies, and how their decision-making process is influenced by e.g., trust (in the information provided or in its sources), risks, social networks and socio-cultural contexts. Although this is a growing field of research with interesting approaches (e.g., Robert et al, 2016;Singh et al, 2016), evidence has so far mainly been provided for agricultural (e.g., Stuart et al, 2014;de Sousa et al, 2018;Azadi et al, 2019;Gatto et al, 2019) and non-bovine livestock production (e.g., Jones et al, 2013;Ambrosius et al, 2019;Hidano et al, 2019), and only to a limited extent for (bovine) livestock production in Latin American countries (e.g., Martínez-García et al, 2013;Rossi Borges and Oude Lansink, 2016). This indicates a knowledge gap which needs to be addressed in order to assure a more widespread adoption of mitigation strategies.…”

Trading with risk: associating bovine Tuberculosis to cattle commodities in risk-based trading

“…In its progress report on biosecurity, Defra (2018, p. 10) notes that CHeCS lacks 'broad appeal required to make a real difference to TB control'. Researchers suggest that this lack of broad appeal may be due to confidence and support for voluntary schemes being spatially dependent on disease risk (Maye 2017), differing preferences for a statutory over a voluntary scheme (Adkin et al 2016, Little et al 2017, farmer fatalism (Enticott 2008a), practical difficulties of implementing the scheme (ADAS UK Ltd 2012), established trading cultures (Hidano et al 2019) and differing conceptualisations of disease (Enticott 2016). In spite of this academic interest in risk-based trading, only Enticott (2016) has investigated an active risk-based trading scheme and analysed why it had been accepted by farmers.…”