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

Authors: Papanicolaou, Athanasios; BASNET, KESHAV - Oak Ridge Institute For Science And Education (ORISE); O'Brien, Peter; Wacha, Kenneth; Malone, Robert; Archer, David

Submitted to: Ecological Modelling
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 4/15/2025
Publication Date: 4/22/2025
Citation: Papanicolaou, A.N., Basnet, K., O'Brien, P.L., Wacha, K.M., Malone, R.W., Archer, D.W. 2025. 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. Ecological Modelling. 506. https://doi.org/10.1016/j.ecolmodel.2025.111142.
DOI: https://doi.org/10.1016/j.ecolmodel.2025.111142

Interpretive Summary: Agroecosystems, which are essential to supporting human populations, are complex entities that consist of several interacting components. Key components include food production and economics, available natural resources, and human capital. Understanding how different management decisions and factors such as climate change affect each of these components and the performance of the whole agroecosystem is challenging and impossible to address with single branch of knowledge. A key contribution of our work is the realization that assessment of agroecosystem performance requires an integrated approach that considers how one component of the system affects the other, and in turn the whole agroecosystem. Therefore, an approach developed in Industrial Engineering, namely a systems dynamics modeling approach, is adopted here due to its established performance and simplicity to use for complex integrated systems. Stakeholders need simple modeling tools to forecast agroecosystem productivity and resource sustainability and this systems modeling approach fits into that realm. The developed tool predicts future responses of the agroecosystem components for a typical Midwestern agroecosystem transitioning from row crop agriculture to mixed farming systems with the ultimate goal to sustain available resources without jeopardizing food production. The tool provides managers and other stakeholders with a set of optimal solutions that consider the trade-offs between food production and sustainability of natural resources as one size does not fit all.

Technical 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 structure of the model relies on a complex web of (i) stocks that describe the system status, (ii) flows that represent the directionality and rates of change, and (iii) auxiliary parameters that provide 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 reflects producer experiences when making management decisions. For example, the integration of crops and livestock clearly improves economic and environmental endpoints and the cost of social quality. Thus, this SD modeling framework provides a viable approach to quantitively evaluating management interventions that can be adapted to a range of complex agroecosystems.