Submitted to: Soil and Water Conservation Society
Publication Type: Abstract Only
Publication Acceptance Date: March 16, 2011
Publication Date: April 1, 2011
Citation: Steiner, J.L., Walthall, C.L., Sadler, E.J., Hatfield, J.L., Wilson, G.J. 2011. Data access and interchange in agronomic and natural resources management [abstract]. Soil and Water Conservation Society, Modeling Summit, March 29-31, 2011, Denver, Colorado. http://www.swcs.org/index.cfm?nodeID=31752&audienceID=1. Interpretive Summary: Abstract only.
Technical Abstract: Challenges related to agriculture and natural resource management have never been greater. Comprehensive agronomic and natural resources data relevant to climate change, food security, bioenergy, and sustainable water supply are rare and in demand. Data used for policy development must be rigorous and legally-defendable and there are legal and policy requirements for US Federal agencies to make data accessible to multiple stakeholders via digital portals. Data needs spans from those with national coverage, to site-specific research data essential for model development, calibration, and validation. Data are needed to represent temporal land use change, diversity of management within land use categories, ecological site descriptions for mixed land uses, and the level of detail across land use types – e.g. crop land, grazing land, wetlands, riparian areas, developed areas – should be balanced. We need incentives for providers of proprietary data to partner with external users to extract useful information while respecting privacy constraints. Data assimilation into models is an evolving technology with great potential to improve model sensitivity to land surface conditions and processes. Climate years represented in assessments should be defined, and climate data bases need systematic updates to maintain recency. Additionally, models need to represent impacts of extreme, versus, mean climate drivers. With increasing focus on optimization of multiple outputs, economic and non-economic criteria across field, farm, landscape scales must be included in modeling assessments. To attain linkage of multiple data sources needed for complex systems science, investments in data management infrastructure is needed along with professional credit for data providers. Many agencies are developing such infrastructure. Such efforts require partnerships to leverage resources, provide interoperability across data sources, and avoid duplication of efforts; support from top management levels; and engagement of scientists, information technologists, and librarians. Success strengthens research capacity, enhances collaborative opportunities for synthesis and integration analyses and development of decision support products for diverse stakeholders, increases return on investment, improves scientific credibility by documenting data quality, and provides accountability at the agency level for investment in research and monitoring programs.