Location: Rangeland Resources & Systems ResearchTitle: Go with the flow by way of interdisciplinary collaboration: Sharing, integrating and opening access to data from various studies in the Cache la Poudre watershed following fire and flood
|BOOT, CLAUDIA - Colorado State University|
|HALL, EDWARD - Colorado State University|
Submitted to: Popular Publication
Publication Type: Popular Publication
Publication Acceptance Date: 7/20/2016
Publication Date: 8/30/2016
Citation: Kaplan, N.E., Boot, C., Hall, E. 2016. Go with the flow by way of interdisciplinary collaboration: Sharing, integrating and opening access to data from various studies in the Cache la Poudre watershed following fire and flood. Popular Publication. Colorado Water. July/August.
Technical Abstract: The United States Department of Agriculture, Agricultural Research Service Long-Term Agroecosystem Research (LTAR) Network consists of 18 sites across the continental United States. LTAR scientists seek to determine ways to ensure sustainability and enhance food production and ecosystem services at broad regional scales. They are conducting manipulative experiments to compare traditional production methods with innovative methods, to achieve these aspirational goals. Their success requires that LTAR scientists and their collaborators have well-timed access to various data in useable, well documented formats. Scientists at the Central Plains Experimental Range, a site in the LTAR Network, in collaboration with scientists from Texas A&M University, Colorado State University and the University of California-Davis and local ranchers designed a novel, co-designed grazing study, the Adaptive Grazing Management (AGM) experiment (https://www.ars.usda.gov/plains-area/cheyenne-wy/rangeland-resources-research/docs/adaptive-grazing-management/research/) in 2012, to investigate how grazing management can be implemented to achieve livestock, vegetation and wildlife objectives for both production and conservation goals in a manner that responds to changing weather/climatic and rangeland conditions, incorporates active learning and makes decisions based on quantitative, repeatable measurements collected at multiple spatial and temporal scales. Multiple, large (big) data sets are produced in this study including: vegetation production, vegetation composition, vegetation structure, density of key vegetation species, soil water, soil health, soil carbon and nitrogen, livestock production/gain, livestock behavior, livestock energetics, fecal quality of livestock, dietary patterns of livestock, grassland bird numbers and distribution, carbon/energy/water fluxes (from Eddy Covariance towers), vegetation phenology (from Phenocams), vegetation greenness (from NDVI sensors), precipitation from numerous rain gauges and forage residual amounts. Today, data and information are served within static pdf files on a project website, within PowerPoint slides, as journal articles and reports. But, these static documents are limited in showing the extent of the information. So, we are investigating the use of a Geospatial Portal for Scientific Research (GPSR), which has an ESRI geospatial database on the backend that drives an online interface to visualize data and communicate information in more dynamic ways.