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Data for Fertilizer Management and Environmental Factors Drive N2O and NO3 Losses in Corn: A Meta-Analysis

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By Alison J Eagle1, Craig F. Drury2, Ardell D. Halvorson3, John P. Hoben4, Bijesh Maharjan5, Timothy B. Parkin6, G. Philip Robertson7, Douglas R. Smith6, Rodney T. Venterea8

1. Environmental Defense Fund 2. Agriculture & Agri-Food Canada 3. USDA-ARS, retired 4. University of Kentucky 5. University of Nebraska-Lincoln 6. USDA-ARS 7. Michigan State University 8. USDA-ARS and University of Minnesota

Data used in Eagle et al. 2017 (Soil Sci. Soc. Am. J. 81:1191–1202; doi:10.2136/sssaj2016.09.0281). Fertilizer Management and Environmental Factors Drive N2O and NO3 Losses in Corn: A Meta-Analysis

Additional materials available

Version 1.0 - published on 12 Nov 2018 doi:10.4231/R7NS0S57 - cite this Archived on 08 Feb 2019

Licensed under CC0 1.0 Universal


The specific aim of this meta‐analysis project was to determine the impact of 4R N management techniques on nitrous oxide (N2O) and nitrate (NO3) losses relative to corn yield. The team collected and synthesized field research data published prior to July 2014 that measured N losses as affected by 4R fertilizer N management (right rate, source, timing, and placement) in North American corn‐based cropping systems.

Core ideas from the paper include: 1) Systematic review and meta-analysis demonstrate key factors for reducing agricultural N losses. 2) Nitrification inhibitors and side-dress fertilizer N each reduce N2O losses by ~30%. 3) Temperature controls N2O emissions and precipitation controls NO3 leaching losses. 4) Higher levels of soil carbon reduce NO3 losses, but increase N2O emissions. 5) Lack of simultaneous data for N2O and NO3 impedes understanding of tradeoffs and synergies.

This data publication includes all observations (management, crop response, and N loss details; for each treatment-site-year) used in meta-analysis models. The dataset contains 789 observations (417 with N2O losses and 388 with NO3 losses) with up to 72 variables each.

The Data Dictionary describes all variables. Because parts of the dataset have been updated/corrected since publication of the journal article, the Data Dictionary also includes notes that detail specific dataset modifications needed to reset a small number of variables to the status used in the meta-analysis models (in order to reproduce the exact regression models from the publication).

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