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ARS Home » Midwest Area » Ames, Iowa » National Laboratory for Agriculture and The Environment » Soil, Water & Air Resources Research » Research » Publications at this Location » Publication #311559

Title: Sentinel site data for model improvement – Definition and characterization

item BOOTE, KENNETH - University Of Florida
item PORTER, CHERYL - University Of Florida
item JONES, JAMES - University Of Florida
item THORBURN, PETER - Csiro European Laboratory
item KERSEBAUM, K - Institute Of Landscape Systems Analysis, Leibniz Centre For Agricultural Landscape Research
item HOOGENBOOM, GERRITT - Washington State University
item White, Jeffrey
item Hatfield, Jerry

Submitted to: Advances in Agricultural Systems Modeling
Publication Type: Book / Chapter
Publication Acceptance Date: 9/9/2015
Publication Date: 10/14/2016
Citation: Boote, K.J., Porter, C., Jones, J.W., Thorburn, P.J., Kersebaum, K.C., Hoogenboom, G., White, J.W., Hatfield, J.L. 2016. Sentinel site data for model improvement – Definition and characterization. In: J. L. Hatfield, D. Fleisher, editors. Improving Modeling Tools to Assess Climate Change Effects on Crop Response. Advances in Agricultural Systems Modeling. Volume 7. Madison, WI: American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America, Inc. p. 125-158. doi: 10.2134/advagricsystmodel7.2014.0019.

Interpretive Summary:

Technical Abstract: Crop models are increasingly being used to assess the impacts of future climate change on production and food security. High quality site-specific data on weather, soils, management, and cultivar are needed for those model applications. Also important, is that model development, evaluation, improvement, and calibration require additional high quality site-specific measurements on crop yield, growth, phenology, and ancillary traits. In this paper, we discuss the evolution of minimum data set requirements for agroecosystem modeling and then describe the characteristics and ranking of sentinel site data needed for crop model improvement, calibration, and application. We, in the Agricultural Model Intercomparison and Improvement Project (AgMIP), propose to rank sentinel site datasets as Platinum, Gold, Silver, and Copper, based on the degree of true site-specific measurement of weather, soils, management, crop yield, as well as the quality, comprehensiveness, quantity, accuracy, and value. For example, to be Platinum, the weather and soil characterization must be measured on-site, and all management inputs must be known. Dataset ranking will be lower for weather measured off-site or soil traits estimated from soil mapping. Furthermore, the ranking also depends on the intended purposes for data use in modeling activities such that a dataset may be rated as Platinum for potential yield application purposes even if it is lacking site observations of nutrients. However, if the purpose is to improve a crop model for response to water or N, then additional site observations are necessary, such as initial soil water, initial soil inorganic N levels, and plant N uptake during the growing season, in order to be Platinum. Rankings are also enhanced by presence of multiple treatments and sites that allow testing responses to water, fertility, weather, daylength, and other treatment factors. Examples of Platinum, Gold, Silver, and Copper quality datasets for model improvement and calibration uses are illustrated.