Missouri Botanical Garden Open Conference Systems, TDWG 2016 ANNUAL CONFERENCE

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LTAR Research: Aspiring to meet production and conservation objectives on the USDA-ARS Central Plains Experimental Range, Nunn, Colorado, USA
Nicole E. Kaplan, Justin D. Derner, David J. Augustine, Bruce C. Vandenberg

Building: Computer Science
Room: Computer Science 3
Date: 2016-12-06 09:45 AM – 10:00 AM
Last modified: 2016-10-16


The Long-Term Agroecosystem Research (LTAR) Network consists of 18 sites across the continental United States (US) sponsored by the US Department of Agriculture, Agricultural Research Service, universities and non-governmental organizations. LTAR scientists seek to determine ways to ensure sustainability and enhance food production (and quality) and ecosystem services at broad regional scales.  They are conducting common experiments across the LTAR network to compare traditional production strategies (“business as usual or BAU) with aspirational strategies, which include novel technologies and collaborations with farmers and ranchers.  Within- and cross-site network success towards achieving the desired outcomes of enhancing quality food production and reducing environmental impact requires that LTAR scientists and collaborators have well-timed access to various data.  We are striving to provide data and metadata in useable, well documented and consistent formats for them.

Scientists at the Central Plains Experimental Range, 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-production 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. The AGM investigates how rangeland management strategies 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 composition, and structure, soil water, carbon and nitrogen, livestock diet composition, foraging behaviour, energetics, and weight gains, grassland bird numbers and distribution, carbon/energy/water fluxes (from Eddy Covariance towers), vegetation phenology (from Phenocams), vegetation greenness (from Normalized Difference Vegetation Index (NDVI) sensors), and precipitation inputs from numerous rain gauges.

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.  As a result, we are investigating the use of a Geospatial Portal for Scientific Research (GPSR), which has an ESRI geospatial database on the backend, which drives an online interface to visualize data and communicate information in more dynamic ways.