A system dynamics modeling framework to evaluate impacts on economic, environmental, and social quality components of a U.S. Midwestern agroecosystem transitioning from row crop agriculture to mixed farming systems

Abstract

Agroecosystems comprise environmental, economic, and social components with complex interactions that affect systemwide performance. Attempts to describe or predict how agroecosystems respond to management must account for these interconnected components, so approaches that are limited to a single discipline cannot capture the complexities necessary for a holistic understanding of performance. The goal of this research is to develop a system dynamics (SD) modeling framework that can provide quantitative measures of consequences of management on each component of an agroecosystem. A SD framework is proposed with a description of model components, as well as an illustration of methodological steps to evaluate model performance through calibration, validation, and sensitivity testing. The model structure is based on a complex web of (i) stocks that describe the system’s state, (ii) flows that represent the direction and rate of change, and (iii) auxiliary parameters that assign quantitative values to each component. The capacity of the model to adequately evaluate agroecosystem response is demonstrated using a case study investigating environmental, economic, and social indicators while manipulating multiple management practices, including cover crops, tillage, and integration of crop and livestock operations. Importantly, the SD model identified tradeoffs in the three indicators that accurately reflect producer experiences when making management decisions. For example, the integration of cash crops, cover crops, and livestock clearly improves economic and environmental endpoints while negatively impacting social quality with reduction in leisure time. These findings suggest the SD modeling framework provides a viable approach for the quantitative evaluation of management interventions that can be adapted to a range of complex agroecosystems.