The purpose of the USDA Long-term Agro-ecosystem Research (LTAR) network includes sustaining and enhancing agricultural production at large geographical scales to meet increasing demands for agricultural goods and services. The LTAR initially comprises ten USDA Agriculture Research Service (ARS) sites, three of which are co-located with NEON. The USDA is currently expanding the network to other sites such as long-term research watersheds and experimental ranges.
NEON and the LTAR are aligned on many aspects including data products, measurements, and remote sensing. Both systems are part of an emerging complex ecosystem of national observation and experimental facilities that focus on enabling transformational science through the generation, management, and curation of high-quality data that are discoverable, accessible, and usable. Concomitant to this emerging ecosystem of facilities is an emerging paradigm in the environmental sciences of delineating the responsibilities between (1) the collection and management of high-quality data that are comparable across spatio-temporal scales, and (2) the utilization and transformation of that data for research, education, and applied purposes. These emerging paradigms are driven by a need for enhanced prognostic capabilities to forecast the impacts of large-scale environmental changes that ultimately impact society's well-being.
The process of transforming measurements into calibrated data and into information for science, education, and applied purposes can be depicted as a value-chain, where each subsequent step of processing adds complexity to the resultant product. The “interoperability fabric” enables this value-chain by (1) establishing well documented and traceable scientific requirements, (2) implementing well-documented, tested, and vetted algorithms for models and data products, (3) implementing measurements calibrated using traceable global standards, and (4) conforming to technical standards and data management best practices advocated by the informatics community.
Using this framework, we are designing a conceptual architecture for NEON and LTAR to achieve interoperability on focal axes (e.g. plant productivity, water quality) that maximize the discoverability, accessibility, and usability of NEON and LTAR data within the larger context of the ecosystem of national observation and experimental facilities. The objective is to enable consumers to integrate and fuse data from NEON, LTAR, and other systems in a more seamless manner. For example, consumers will benefit from well-documented data with uncertainty estimates that allows for assessments of fitness of use, data products with information about how they were generated, and metadata that utilizes community vetted vocabularies.