Structural equation modeling facilitates transdisciplinary research on agriculture and climate change

Increasingly, funding agencies are investing in integrated and transdisciplinary research to tackle "grand challenge" priority areas, critical for sustaining agriculture and protecting the environment. Coordinating multidisciplinary research teams capable of addressing these priority areas, however, presents its own unique set of challenges, ranging from bridging across multiple disciplinary perspectives to achieve common questions and methods to facilitating engagement in holistic and integrative thinking that promotes linkages from scholarship to societal needs. We propose that structural equation modeling (SEM) can provide a powerful framework for synergizing multidisciplinary research teams around grand challenge issues. Structural equation modeling can integrate both visual and statistical expression of complex hypotheses at all stages of the research process, from planning to analysis. Three elements of the SEM framework are particularly beneficial to multidisciplinary research teams; these include (i) a common graphical language that transcends disciplinary boundaries, (ii) iterative, critical evaluation of complex hypotheses involving manifest and latent variables and direct and indirect interactions, and (iii) enhanced opportunities to discover unanticipated interactions or causal pathways as empirical data are tested statistically against the model. Using our ongoing multidisciplinary, multisite field investigation of climate change adaptation and mitigation in annual row crop agroecosystems as a case study, we demonstrate the value of the SEM framework for project design, coordination, and implementation and provide recommendations for its broader application as a means to more effectively engage and address issues of critical societal concern.