Agricultural supply chains are complex systems that consist of four major subsystems: production, processing and manufacturing, distribution, and utilization. The agricultural production subsystem is spatio-temporally explicit and is sensitive to changes in climate and weather patterns. It is not only dependent on local-scale environmental factors such as weather and soil properties, but also affected by farm management and infrastructure capabilities. Fluctuations in agricultural productivity impose significant challenges on the design and operations of integrated supply chain systems that couple productivity with farm management decisions within the context of large-scale supply chains. As a result, a key challenge for supply chain modeling is predicting agricultural production across space and time in order to provide better management decisions at each stage-from growth through processing to distribution of goods. Systems analysis is critical to understand the interactions among subsystems and to improve the efficiency and effectiveness of the whole system. Given increased attentions on how to utilize agricultural resources to improve energy securities, this study aims to develop a holistic approach to provide decision support on biofuel supply chain development considering the changes of weather and crop production. Many computational tools have been developed to simulate independent subsystems of agricultural production. However, our key questions require understanding of the coupled systems. Our research aims to establish a coherent, extensible scientific workflow that enables new scientific discovery by linking domain-specific computer models of complex systems into a hierarchical meta-model of the interacting physical, biological, and engineering systems. To address these questions we are developing an integrated workflow to evaluate the interactions among climate change, crop production, and supply chain systems. The workflow consists of three major modeling tools, weather research and forecasting (WRF) modeling, BioCro crop simulation modeling, and BioScope supply chain optimization (Figure 1).