Animal Health & Welfare

Better understanding of the attitudes and intentions of farmers

When formulating government policy, or preparing publicity material to inform and influence the agricultural community, it is preferable to understand the attitudes and intentions of different types of people, rather than basing plans on unrealistic assumptions about "average" farmers. In recent work with SRUC, data from a survey of Scottish farmers were used to explore current behaviours and intentions as regards uptake of innovative animal health and welfare technologies, including, for example, electronic ID (EID) tagging, wearing activity monitors and application of genomic science.

Using latent class models, we can allow for the possibility that different individuals belong to different subpopulations, each sub-group having its own distribution of opinions and responses. Based on the patterns of responses in the full data set, we found greatest support for there being three sub-populations: non-adopters (75% of the population), who were not particularly interested in any innovations; EID adopters (20%), who were largely interested in making more use of EID technologies; and, enthusiastic adopters (5%), who were interested in all technologies. Age, level of education, budget and other factors were found to influence the probabilities of an individual being assigned to each sub-population. This ability of latent class modelling to identify and better characterise sub-populations has potential to help formulate improved agricultural policies and more precisely target messages to different farmers.

effect of age on technology adoptionThe effect of age on the probability of farmers being assigned membership of the different technology adoption subpopulations.

Further details from: Jiayi Liu

Article date 2015

Consultancy Advice & Collaboration

Plant Science

Animal Health & Welfare

Ecology & Environmental Science

Human Health & Nutrition