No abstract

Development of extension and outreach that effectively engage farmers in climate change adaptation and/or mitigation activities can be informed by an improved understanding of farmers' perspectives on climate change and related impacts. This research employed latent class analysis (LCA) to analyze data from a survey of 4,778 farmers from 11 US Corn Belt states. The research focused on two related research questions: (1) to what degree do farmers differ on key measures of beliefs about climate change, experience with extreme weather, perceived risks to agriculture, efficacy, and level of support for public and private adaptive and mitigative action; and (2) are there potential areas of common ground among farmers? Results indicate that farmers have highly heterogeneous perspectives, and six distinct classes of farmers are identified. We label these as the following: the concerned (14%), the uneasy (25%), the uncertain (25%), the unconcerned (13%), the confident (18%), and the detached (5%). These groups of farmers differ primarily in terms of beliefs about climate change, the degree to which they had experienced extreme weather, and risk perceptions. Despite substantial differences on these variables, areas of similarity were discerned on variables measuring farmers' (1) confidence that they will be able to deal with increases in weather variability and (2) support for public and private efforts to help farmers adapt to increased weather variability. These results can inform segmented approaches to outreach that target subpopulations of farmers as well as broader engagement strategies that would reach wider populations. Further, findings suggest that strategies with specific reference to climate change might be most effective in engaging the subpopulations of farmers who believe that climate change is occurring and a threat, but that use of less charged terms such as weather variability would likely be more effective with a broader range of farmers. Outreach efforts that (1) appeal to farmers' problem solving capacity and (2) employ terms such as "weather variability" instead of more charged terms such as "climate change" are more likely to be effective with a wider farmer audience.

Persistent above average precipitation and runoff and associated increased sediment transfers from cultivated ecosystems to rivers and oceans are due to changes in climate and human action. The US Upper Midwest has experienced a 37% increase in precipitation , leading to increased crop damage from excess water and off-farm loss of soil and nutrients. Farmer adaptive management responses to changing weather patterns have potential to reduce crop losses and address degrading soil and water resources. This research used farmer survey (n = 4778) and climate data to model influences of geophysical context, past weather, on-farm flood and saturated soils experiences, and risk and vulnerability perceptions on management practices. Seasonal precipitation varied across six Upper Midwest subregions and was significantly associated with variations in management. Increased warm-season precipitation (2007)(2008)(2009)(2010)(2011) relative to the past 40 yr was positively associated with no-till, drainage, and increased planting on highly erodible land (HEL). Experience with saturated soils was significantly associated with increased use of drainage and less use of notill, cover crops, and planting on HEL. Farmers in counties with a higher percentage of soils considered marginal for row crops were more likely to use no-till, cover crops, and plant on HEL. Respondents who sell corn through multiple markets were more likely to have planted cover crops and planted on HEL in 2011. This suggests that regional climate conditions may not well represent individual farmers' actual and perceived experiences with changing climate conditions. Accurate climate information downscaled to localized conditions has potential to influence specific adaptation strategies.

Over the last twenty years there have been numerous developments in diagnostic procedures for hierarchical linear models; however, these procedures are not widely implemented in statistical software packages, and those packages that do contain a complete framework for model assessment are not open source. The lack of availability of diagnostic procedures for hierarchical linear models has limited their adoption in statistical practice. The R package HLMdiag provides diagnostic tools targeting all aspects and levels of continuous response hierarchical linear models with strictly nested dependence structures fit using the lmer() function in the lme4 package. In this paper we discuss the tools implemented in HLMdiag for both residual and influence analysis.

In statistical modeling we strive to specify models that resemble data collected in studies or observed from processes. Consequently, distributional specification and parameter estimation are central to parametric models. Graphical procedures, such as the quantile-quantile (Q-Q) plot, are arguably the most widely used method of distributional assessment, though critics find their interpretation to be overly subjective. Formal goodness-of-fit tests are available and are quite powerful, but only indicate whether there is a lack of fit, not why there is lack of fit. In this paper we explore the use of the lineup protocol to inject rigor to graphical distributional assessment and compare its power to that of formal distributional tests. We find that lineups of standard Q-Q plots are more powerful than lineups of de-trended Q-Q plots and that lineup tests are more powerful than traditional tests of normality. While, we focus on diagnosing non-normality, our approach is general and can be directly extended to the assessment of other distributions.