SAFES integrates agriculturally and environmentally related disciplines to develop holistic approaches for tackling "wicked" challenges.

Credit: Adobe Stock

Credit: Adobe Stock

Programmatic Domains

Achieving agricultural, food, economic, and ecosystem sustainability amid changing political, economic, and environmental climates is inherently a multi-system problem that can be a moving target. The science of agricultural sustainability for landscape-level challenges will require a multi-disciplinary and multi-scale approach involving innovations in integrated modeling, data science, and visualization. 

SAFES establishes an infrastructure of four programmatic domains to address key challenges that impact society within the interconnected contexts of agriculture, food, watersheds, natural resources, the economy, and urban/rural interfaces. These domains of expertise are the scaffold that support the establishment and endurance of areas of expertise, fortifying the University's capacity to effectively respond and provide solutions to emergent issues such as disruption, resilience, regeneration, biodiversity, optimizing ecosystems services, and innovative stakeholder engagement, among others.


The data domain will create a capacity of expertise in the theory and methods of data acquisition and analysis. This domain will identify data sources that should be available to researchers at Penn State and work to make these resources easily accessible to the Penn State research community. The data domain will also identify and develop state of the art techniques to collect original data (sensor technology) and mine data (machine learning or artificial intelligence) for important new insights relevant to multisystem dynamics and landscape scale interactions.  


The models domain will create a capacity of expertise in coupling and translating information across biophysical and socioeconomic models. This framework builds on strengths at Penn State in the development and use of computational models and tools for research and expands this expertise to integrate across models and collaborate across modeling teams and platforms. The model domain will focus on building data and computational tools to help facilitate coupling and translation across individual models.


The discovery domain will create a capacity of expertise in making acquired data and interoperable models accessible to academic and non-academic audiences within and beyond the university. This domain builds strength in specialized translations (visual, technical, verbal, etc.) that can effectively communicate the knowledge and thus extend the societal benefits of the research, evaluation of new technologies, and design of communications around policy options and business management solutions. The discovery domain builds on a strong and unique tradition in the college for “use- and need-inspired" research. The discovery domain will be a “switchboard" for connecting researchers to resources and building synergies across the University to leverage talent and skill within and especially beyond the college.

Landscape-Level Interactions

The landscape-scale interactions domain will build a capacity of expertise in understanding how interactions occur across scales, which is critical to improving our predictability of how a system at one spatial scale will be influenced by an impact occurring at a different spatial scale. This framework may be understood as a cross-cutting research domain that will address fundamental challenges most modelers and data scientists face when attempting to understand multisystem dynamics; namely, how are the different components in the system integrated, and importantly how do these dynamics interact across different landscape scales. 

Affiliated Centers

Affiliated Initiatives and Projects