Modeling nitrous oxide mitigation potential of enhanced efficiency nitrogen fertilizers from agricultural systems

Ram B Gurung; Stephen M Ogle; F Jay Breidt; William J Parton; Stephen J Del Grosso; Yao Zhang; Melannie D Hartman; Stephen A Williams; Rodney T Venterea

doi:10.1016/j.scitotenv.2021.149342

Modeling nitrous oxide mitigation potential of enhanced efficiency nitrogen fertilizers from agricultural systems

Sci Total Environ. 2021 Dec 20:801:149342. doi: 10.1016/j.scitotenv.2021.149342. Epub 2021 Aug 8.

Authors

Ram B Gurung¹, Stephen M Ogle², F Jay Breidt³, William J Parton⁴, Stephen J Del Grosso⁵, Yao Zhang⁴, Melannie D Hartman⁴, Stephen A Williams⁴, Rodney T Venterea⁶

Affiliations

¹ Natural Resource Ecology Laboratory, Colorado State University, Fort Collins, CO 80523, USA; Graduate Degree Program in Ecology, Colorado State University, Fort Collins, CO 80523, USA. Electronic address: Ram.Gurung@colostate.edu.
² Natural Resource Ecology Laboratory, Colorado State University, Fort Collins, CO 80523, USA; Department of Ecosystem Science and Sustainability, Colorado State University, Fort Collins, CO 80523, USA.
³ Department of Statistics, Colorado State University, Fort Collins, CO 80523, USA.
⁴ Natural Resource Ecology Laboratory, Colorado State University, Fort Collins, CO 80523, USA.
⁵ Soil Management and Sugar Beet Research, USDA-ARS-SPNR, Fort Collins, CO, 80526, USA.
⁶ Soil and Water Management Research Unit, USDA-ARS, St. Paul, MN 55108, USA.

PMID: 34467931
DOI: 10.1016/j.scitotenv.2021.149342

Abstract

Agriculture soils are responsible for a large proportion of global nitrous oxide (N₂O) emissions-a potent greenhouse gas and ozone depleting substance. Enhanced-efficiency nitrogen (N) fertilizers (EENFs) can reduce N₂O emission from N-fertilized soils, but their effect varies considerably due to a combination of factors, including climatic conditions, edaphic characteristics and management practices. In this study, we further developed the DayCent ecosystem model to simulate two EENFs: controlled-release N fertilizers (CRNFs) and nitrification inhibitors (NIs) and evaluated their N₂O mitigation potentials. We implemented a Bayesian calibration method using the sampling importance resampling (SIR) algorithm to derive a joint posterior distribution of model parameters that was informed by N₂O flux measurements from corn production systems a network of experimental sites within the GRACEnet program. The joint posterior distribution can be applied to estimate predictions of N₂O reduction factors when EENFs are adopted in place of conventional urea-based N fertilizer. The resulting median reduction factors were - 11.9% for CRNFs (ranging from -51.7% and 0.58%) and - 26.7% for NIs (ranging from -61.8% to 3.1%), which is comparable to the measured reduction factors in the dataset. By incorporating EENFs, the DayCent ecosystem model is able to simulate a broader suite of options to identify best management practices for reducing N₂O emissions.

Keywords: Bayesian calibration; Controlled-release nitrogen fertilizer; Enhanced efficiency nitrogen fertilizers; Nitrification inhibitors; Nitrous oxide; Sampling importance resampling; Sensitivity analysis.

MeSH terms

Agriculture
Bayes Theorem
Ecosystem
Fertilizers* / analysis
Nitrogen
Nitrous Oxide* / analysis
Soil

Substances

Fertilizers
Soil
Nitrous Oxide
Nitrogen