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ARS Home » Midwest Area » Urbana, Illinois » Global Change and Photosynthesis Research » Research » Publications at this Location » Publication #294622

Research Project: ECOLOGY, MANAGEMENT AND ENVIRONMENTAL IMPACT OF WEEDY AND INVASIVE PLANT SPECIES IN A CHANGING CLIMATE

Location: Global Change and Photosynthesis Research

Title: Structural equation modeling facilitates transdisciplinary research on agriculture and climate change

Author
item Smith, Richard - University Of New Hampshire
item Davis, Adam
item Koide, Roger - Brigham Young University
item Mortensen, David - Pennsylvania State University
item Grandy, Stuart - University Of New Hampshire
item Hunter, Mitch - Pennsylvania State University
item Daly, Amanda - University Of New Hampshire
item Atwood, Lesley - University Of New Hampshire
item Jordan, Nicholas - University Of Minnesota
item Spokas, Kurt
item 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.