Examining the agricultural producer identity: utilising the collective occupational identity construct to create a typology and profile of rural landholders in Victoria, Australia

Abstract

Multifunctional rural landscapes are often characterised by contrasting values, land uses and land management practices of rural property owners. It seems these trends are, in part, an expression of rural landholder's identification as farmers. Existing typologies of rural landholders seldom take into account occupational identity. Research discussed in this paper addresses that gap. The objective was to apply the collective occupational identity construct to address the challenges of profiling rural landholders and test its effectiveness at distinguishing between different types of landholders. A 12-item scale was used to explore the extent rural landholders in south-eastern Australia held an agricultural producer identity. Cluster analysis resulted in the creation of four clusters of rural landholders with distinctive characteristics, suggesting the approach can provide researchers with a theoretically sound construct and practitioners with a useful tool as they attempt to better understand and engage rural landholders in sustainable agriculture.

Keywords:

• cluster analysis
• collective identity construct
• identity theory
• occupation
• rural transition

Acknowledgements

The authors would like to thank Simon McDonald of the Spatial Data Analysis Network (CSU) for providing statistical support. We would also like to thank those involved with the NC CMA, local government agencies and landholders in the NC CMA.

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1. As indicated by Scopus – accessed April 18 2015.

2. Methods tested were Ward's, between groups, and furthest distance linkage methods, using combinations of both Euclidean and squared Euclidean distance as the distance measure. All input variables were transformed where necessary to give a normal distribution.

3. Z-score transformation converts each raw data score into a standardised value with a mean of 0 and a standard deviation of 1, eliminating bias introduced by differences in scales used in the analysis. Both a 5- and 6-point Likert scale were used. Z-score transformation resulted in the same number of clusters as the original unstandardised scores.

4. Hair et al. (2010) states that a very stable solution would be produced with less than 10% of observations being reassigned to a different group; a stable solution would result in between 10% and 20% being reassigned; and a somewhat stable solution between 20% and 25% of observation being reassigned to a different cluster than the initial one.