The goal of SHEAF is to bring together a diverse team of collaborators to examine how soil health can act as a social-ecological feedback to encourage soil enhancing practices through a transdisciplinary integration of biophysical, climatic and social science data at multiple scales.

Our team is made up of scientists and policymakers from University institutions (Oregon State University, University of Nebraska, Iowa State University, University of Maryland) – as well as leaders from the USDA, NRCS and the private sector.

Our workshops are run out of the Socio-Environmental Synthesis Center (SESYNC) – a NSF funded effort thru the University of Maryland.

We use R, python, and Solr to organize, structure, and model data, and then use ESRI’s StoryMaps technology to describe and visualize outcomes.

Below is a brief summary narrative of the initial datasets we are collecting and examining for inclusion in our modeling effort.  You can click on the “+” link Read More

Our modeling efforts use Structural Equation Modeling (SEM) approaches to understand latent constructs in relationship to soil health and climate resilience.

With Esri Story Maps, you can combine your maps with narrative text, images, and multimedia content to create compelling, user-friendly web apps.

The Data Analysis Dashboards organize data for review and display.  The intent is to allow team members to examine data, in order to determine what datasets could be used as part of our modeling process.