A geostatistical approach to identify and mitigate agricultural nitrous oxide emission hotspots

P. A. Turner, T. J. Griffis, D. J. Mulla, J. M. Baker, R. T. Venterea

Research output: Contribution to journalArticlepeer-review

9 Scopus citations


Anthropogenic emissions of nitrous oxide (N2O), a trace gas with severe environmental costs, are greatest from agricultural soils amended with nitrogen (N) fertilizer. However, accurate N2O emission estimates at fine spatial scales are made difficult by their high variability, which represents a critical challenge for the management of N2O emissions. Here, static chamber measurements (n = 60) and soil samples (n = 129) were collected at approximately weekly intervals (n = 6) for 42-d immediately following the application of N in a southern Minnesota cornfield (15.6-ha), typical of the systems prevalent throughout the U.S. Corn Belt. These data were integrated into a geostatistical model that resolved N2O emissions at a high spatial resolution (1-m). Field-scale N2O emissions exhibited a high degree of spatial variability, and were partitioned into three classes of emission strength: hotspots, intermediate, and coldspots. Rates of emission from hotspots were 2-fold greater than non-hotspot locations. Consequently, 36% of the field-scale emissions could be attributed to hotspots, despite representing only 21% of the total field area. Variations in elevation caused hotspots to develop in predictable locations, which were prone to nutrient and moisture accumulation caused by terrain focusing. Because these features are relatively static, our data and analyses indicate that targeted management of hotspots could efficiently reduce field-scale emissions by as much 17%, a significant benefit considering the deleterious effects of atmospheric N2O.

Original languageEnglish (US)
Pages (from-to)442-449
Number of pages8
JournalScience of the Total Environment
StatePublished - Dec 1 2016

Bibliographical note

Funding Information:
We thank Jeff Wood, Matt Erickson, Mike Dolan, William Breiter, Ke Xiao, Zichong Chen, and Lucas Rosen for field and laboratory assistance. This work was supported by the U.S. Department of Agriculture (USDA) Grant USDA-NIFA 2013-67019-21364 and the USDA – Agricultural Research Service . We are also appreciative of the private landowner who volunteered their field for our use.

Publisher Copyright:
© 2016 Elsevier B.V.


  • Biogeochemical hotspots
  • Geospatial cokriging
  • Greenhouse gas management
  • LiDAR digital elevation model
  • Nitrous oxide


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