1. Earth
Data
Science
Lindsay Barbieri

2. Rubenstein School of Environment & Natural Resources
“Our mission is to understand, nurture, and enrich the interdependence of people with healthy ecological systems.”
Natural Resources = Interdisciplinary Earth Sciences?

3. “At the Gund Institute for Ecological Economics, we integrate natural and social sciences to understand the interactions between people and nature and to help build a sustainable future.”
“In complex physical, biological, social and engineered systems, the self-organizing dynamics of interacting entities (be they molecules, cells, genes, bacteria, plants, birds, humans, nanobots, electrical substations, etc.) give rise to emergent system properties (such as consciousness, cancer, global warming, societies, etc.).
Fortunately, many essential properties of such systems may be studied, modeled and understood using similar approaches, regardless of the application domain”

4. American Geophysical Union:
Sections and Focus Groups connect you with other scientists in your research area.
Historically, Sections are disciplinary while Focus Groups are interdisciplinary.

5. American Geophysical Union:
Sections and Focus Groups connect you with other scientists in your research area.
Historically, Sections are disciplinary while Focus Groups are interdisciplinary.

6. Hydrology Surface Runoff & Collection Stations
Soil Leaching & Lysimeters
Modeling Field Inundation

7. Biogeochemistry Nutrient Cycling Soil Chemistry
Greenhouse Gas Emissions

8. Atmospheric Science Meteorological Stations
Greenhouse Gas Concentrations
How Climate Affects Land

9. GIS & Remote Sensing Drones NLCD & Landsat Imagery & Other “Remote”
Data Collection

10. The Data: Monitoring of Two Hay Fields, Shelburne Farms
Edge of Field Station
Field 1
Non Aerated
“Conventional”
Field 2
Aerated
“Best Management Practice (BMP)”
Air Temperature
Precipitation
Water quality and field runoff
* sampled every 15 minutes
mitigation is important… and becoming more of a theme in the scientific literature (search for “agriculture” and “climate”) -- take out CGIAR

11. The Data: Monitoring of Two Hay Fields, Shelburne Farms
Non Aerated
“Conventional”
Air Temperature
Precipitation
Water quality and field runoff
* sampled every 15 minutes
Edge of Field Station
Static Chambers
Soil Gas Flux
Soil Temperature
Soil Moisture
* sampled ~once a week
June - October 2015
Field 2
Aerated
“Best Management Practice (BMP)”
mitigation is important… and becoming more of a theme in the scientific literature (search for “agriculture” and “climate”) -- take out CGIAR

12. The Data: Two Hay Fields, Two Corn Fields
Non Aerated
“Conventional”
Field 2
Aerated
“Best Management Practice (BMP)”
Field 1
Non Aerated
“Conventional”
Field 2
Aerated
“Best Management Practice (BMP)”
mitigation is important… and becoming more of a theme in the scientific literature (search for “agriculture” and “climate”) -- take out CGIAR

13. The Data: Two Hay Fields, Two Corn Fields
Non Aerated
“Conventional”
Field 2
Aerated
“Best Management Practice (BMP)”
Field 1
Non Aerated
“Conventional”
Field 2
Aerated
“Best Management Practice (BMP)”
mitigation is important… and becoming more of a theme in the scientific literature (search for “agriculture” and “climate”) -- take out CGIAR

14. The Data: Economics and Ecosystem Services Valuation
Biophysical
Data
mitigation is important… and becoming more of a theme in the scientific literature (search for “agriculture” and “climate”) -- take out CGIAR

15. The Data: Full Project Adoption? Livelihoods? Policy? Leverage Points?
Adaptation to Climate Change?
PAR, Surveys, Agent Based Modeling
Economics
Ecosystem Services
Biophysical
Data
mitigation is important… and becoming more of a theme in the scientific literature (search for “agriculture” and “climate”) -- take out CGIAR

16. Lessons Learned
Interdisciplinary work is important to address societal & environmental challenges
Data Management Plan → but researchers still seem lagging in what that means? Is this even the best terminology?
Synthesis? Seems to be the goal, BUT researchers don’t seem to always know what the scaffolding should be to make sure that’s possible

17. Lessons Learned: Important Discussion
What scales? (all!)
Individual Research Project (IE: 5 PI’s on an interdisciplinary grant)
“Big Picture” and synthesis / analytics across all Earth Science data