Farmer Identity in Multifunctional Landscapes: using a collective identity construct to explore the nature and impact of occupational identity

ABSTRACT

The use of multifunctionalism to conceptualise contemporary rural landscapes has focused attention on the increased extent and impact of non-farmers in traditionally agriculture-based rural communities. Typologies of rural landholders have been developed as part of that research effort. However, those typologies are not grounded in established theory, including collective identity theory that could provide an important foundation for the study of occupational identity. Indeed, most quantitative studies may be flawed in drawing on only one of the seven elements contributing to collective identity. This paper examines the efficacy of relying on self-declared occupational identity through the analysis of rural landholder survey data in an Australian region. Tests for expected relationships between self-declared identity of farmer/non-farmer and social and farming variables expected to be correlated with occupation, such as property size, profitability, hours worked on- and off-property, enterprise type and membership of farming-related local organisations found that the expected relationships existed. It seems that self-declared occupational identity is a valid and cost-effective way to explore occupational identity amongst rural landholders. Collective identity, however, is complex and it is unlikely that important research questions can be adequately explored without a more holistic approach to the study of farmer identity in multifunctional landscapes. The paper concludes with a discussion of the potential benefits of moving beyond self-declared occupation to include other elements in the collective identity construct.

KEY WORDS:

• Identity
• multifunctional landscapes
• collective identity construct
• agricultural landscapes
• rural restructuring
• occupation

Acknowledgements

The authors thank the Wimmera Catchment Management Authority for funding the survey implementation, the survey respondents who participated in this research and Simon McDonald of the Spatial Data Analysis Network (CSU) for providing statistical support.