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Title:   Author  item
                                            SMITH, RICHARD - University Of New Hampshireitem
                                            Davis, Adamitem
                                            KOIDE, ROGER - Brigham Young Universityitem
                                            MORTENSEN, DAVID - Pennsylvania State Universityitem
                                            GRANDY, STUART - University Of New Hampshireitem
                                            HUNTER, MITCH - Pennsylvania State Universityitem
                                            DALY, AMANDA - University Of New Hampshireitem
                                            ATWOOD, LESLEY - University Of New Hampshireitem
                                            JORDAN, NICHOLAS - University Of Minnesotaitem
                                            Spokas, Kurtitem
                                            YANNARELL, ANTHONY - University Of Illinois
  Submitted to: Crop Science
                                    Publication Type: Peer Reviewed Journal
                                    Publication Acceptance Date: 10/24/2013
                                    Publication  Date: 6/1/2014
                                    Citation: Smith, R.G., Davis, A.S., Koide, R., Mortensen, D., Grandy, S., Hunter, M., Daly, A., Atwood, L., Jordan, N., Spokas, K.A., Yannarell, A. 2014. Structural equation modeling facilitates transdisciplinary research on agriculture and climate change. Crop Science. 54:475-483.Interpretive Summary: Given the growing trend toward larger-scale, multi-investigator research projects addressing climate change, how can we best focus our research efforts on the task at hand and ensure that our diverse research teams are functioning in the most effective manner possible? We believe that structural equation modeling (SEM), which integrates both visual and statistical expression of complex hypotheses at all stages of the research process from planning to analysis, may provide a powerful framework for achieving such goals. Here we present a rationale for making structural equation models and other visual thinking methods an explicit component of multidisciplinary agricultural and environmental science research projects focusing on climate change and other environmental challenges, although we expect the approach we outline will be equally applicable to many other kinds of multidisciplinary research programs. Our goal is not necessarily to promote SEM and related techniques as an analytical approach to investigating multivariate relationships, though it is ideally suited to such endeavors, but rather to highlight the properties of SEM that make it a useful framework for focusing and conceptualizing multidisciplinary research questions and refining research activities.
                                Technical Abstract: Climate change is representative of many of the “grand challenges” facing agriculture and the environment—it is complex, spans traditional disciplinary boundaries, and is both a consequence and driver of coupled physical, biological, and socioeconomic processes acting at multiple spatial and temporal scales. Researchers have recognized the need for more integrated and multifaceted approaches to dealing with such challenges, and funding agencies are increasingly investing in the creation of large, multidisciplinary research teams and longer-term (i.e., >3 yrs) integrated research projects. Given the growing trend toward larger-scale, multi-investigator research projects, how can we best focus our research efforts on the task at hand and ensure that our diverse research teams are functioning in the most effective manner possible? We believe that structural equation modeling (SEM), which integrates both visual and statistical expression of complex hypotheses at all stages of the research process from planning to analysis, may provide a powerful framework for achieving such goals. Here we present a rationale for making structural equation models and other visual thinking methods an explicit component of multidisciplinary agricultural and environmental science research projects focusing on climate change and other environmental challenges, although we expect the approach we outline will be equally applicable to many other kinds of multidisciplinary research programs. Our goal is not necessarily to promote SEM and related techniques as an analytical approach to investigating multivariate relationships, though it is ideally suited to such endeavors, but rather to highlight the properties of SEM that make it a useful framework for focusing and conceptualizing multidisciplinary research questions and refining research activities.


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