Minimum dataset and metadata guidelines for soil‐test correlation and calibration research. Issue 1 (2nd November 2021)

• Record Type: Journal Article
• Authors: Slaton, Nathan A.; Lyons, Sarah E.; Osmond, Deanna L.; Brouder, Sylvie M.; Culman, Steve W.; Drescher, Gerson; Gatiboni, Luciano C.; Hoben, John; Kleinman, Peter J. A.; McGrath, Joshua M.; Miller, Robert O.; Pearce, Austin; Shober, Amy L.; Spargo, John T.; Volenec, Jeff J.
• Is Part Of: Soil Science Society of America Journal. Volume 86:Issue 1(2022)
• Page Start: 19
• Page End: 33
• Publication Date: 2021-11-02
• Subjects: Soils -- United States -- Periodicals; Soil science -- Periodicals; Periodicals; 631.4973
• DOI: 10.1002/saj2.20338
• Languages: English
• ISSNs: 0361-5995

Abstract

Abstract: Soil‐test correlation and calibration data are essential to modern agriculture, and their continued relevance is underscored by the expansion of precision farming and the persistence of sustainable soil management priorities. In support of transparent, science‐based fertilizer recommendations, we seek to establish a core set of required and recommended information for soil‐test P and K correlation and calibration studies, a minimum dataset, building on previous research. The Fertilizer Recommendation Support Tool (FRST) project team and collaborators are developing a national database that will support a soil‐test‐based nutrient management decision aid tool. The FRST team includes over 80 scientists from 37 land‐grant universities, two state universities, one private university, three federal agencies, two private not‐for‐profit organizations, and one state department of agriculture. The minimum dataset committee developed and vetted a robust set of factors fo minimum dataset consideration that includes information on soil sample collection and processing, soil chemical and physical properties, experimental design and statistical analyses, and metadata about the trial, production system, and field management. The minimum dataset provides guidelines for essential information to meet the primary objective of knowledge synthesis, including meta‐analysis and systemic reviews, but permits researchers the flexibility to satisfy local, state, and regional objectives. Ultimately, this consensus‐driven effort seeks to establish a standard that ensures the maximum utility and impact of modern correlation and calibration studies for developing crop nutrition recommendations that improve productivity and profitability for the crop producer, while reducing environmental impacts of nutrient losses. Core Ideas: Data sharing is needed for transparent soil‐test‐based recommendations. A soil‐test correlation and calibration minimum dataset provides a data quality standard. A minimum dataset will facilitate meta‐analysis for nutrient recommendations.