Journal of Soil and Water Conservation · 2017 · 26 citations · 23 references
Natural resource user typologies are viewed as a promising way to improve engagement strategies, and prior research on Corn Belt farmers used latent class analysis to develop a six‑class typology of climate‑change perspectives. This paper investigates whether 33 observable farm enterprise characteristics, land‑management practices, and farmer demographics can reveal systematic patterns across the six typology classes to inform audience segmentation. The authors applied latent class analysis to 34 unobservable variables—beliefs, extreme‑weather experience, perceived risks, and action attitudes—to create the typology, then examined the 33 observable measures to assess their discriminative power. The analyses revealed few meaningful observable differences between the classes, showing that farmers with divergent climate‑change views can appear similar, and suggesting that Extension outreach should not rely solely on observable traits but also consider network integration and broaden efforts to reach less engaged farmers.
Development of natural resource user typologies has been viewed as a potentially effective means of improving the effectiveness of natural resource management engagement strategies. Prior research on Corn Belt farmers9 perspectives on climate change employed a latent class analysis (LCA) that created a six-class typology—the Concerned, Uneasy, Uncertain, Unconcerned, Confident, and Detached—to develop a better understanding of farmer perspectives on climate change and inform more effective climate adaptation and mitigation outreach strategies. The LCA employed 34 variables that are generally unobservable—beliefs about climate change, experience with extreme weather, perceived risks of climate change, and attitudes toward climate action—to identify types. The research reported in this paper builds on this typology of Corn Belt farmers by exploring 33 measures of observable farm enterprise characteristics, land management practices, and farmer demographics to assess whether variations in these observable characteristics between the six farmer classes display systematic patterns that might be sufficiently distinctive to guide audience segmentation strategies. While analyses detected some statistically significant differences, there were few systematic, meaningful observable patterns of difference between groups of farmers with differing perspectives on climate change. In other words, farmers who believe that anthropogenic climate change is occurring, that it poses risks to agriculture, and that adaptive action should be taken, may look very much like farmers who deny the existence of climate change and do not support action. The overall implication of this finding is that climate change engagement efforts by Extension and other agricultural advisors should use caution when looking to observable characteristics to facilitate audience segmentation. Additional analyses indicated that the farmer types that tended to be more concerned about climate change and supportive of adaptive action (e.g., Concerned and Uneasy) reported that they were more influenced by key private and public sector actors in agricultural social networks. On the other hand, farmers who were not concerned about climate change or supportive of adaptation (e.g., the Unconcerned, Confident, and Detached groups, comprising between one-third and one-half of respondents) were less integrated into agricultural networks. This suggests that Extension and other agricultural advisors should expand outreach efforts to farmers who are not already within their spheres of influence.
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