Nitrous oxide emissions from subtropical horticultural soils: a time series analysis
Xiaodong Huang A , Peter Grace A B , Keith Weier B and Kerrie Mengersen A B C
+ Author Affiliations
- Author Affiliations
A Science and Engineering Faculty, Queensland University of Technology, GPO Box 2434, Brisbane, Qld 4001, Australia.
B Institute for Future Environments, Queensland University of Technology, GPO Box 2434, Brisbane, Qld 4001, Australia.
C Corresponding author. Email: k.mengersen@qut.edu.au
Soil Research 50(7) 596-606 https://doi.org/10.1071/SR11100
Submitted: 2 May 2011 Accepted: 12 October 2012 Published: 13 November 2012
Abstract
Time series regression models were used to examine the influence of environmental factors (soil water content and soil temperature) on the emissions of nitrous oxide (N2O) from subtropical soils, by taking into account temporal lagged environmental factors, autoregressive processes, and seasonality for three horticultural crops in a subtropical region of Australia. Fluxes of N2O, soil water content, and soil temperature were determined simultaneously on a weekly basis over a 12-month period in South East Queensland. Annual N2O emissions for soils under mango, pineapple, and custard apple were 1590, 1156, and 2038 g N2O-N/ha, respectively, with most emissions attributed to nitrification. The N2O-N emitted from the pineapple and custard apple crops was equivalent to 0.26 and 2.22%, respectively, of the applied mineral N. The change in soil water content was the key variable for describing N2O emissions at the weekly time-scale, with soil temperature at a lag of 1 month having a significant influence on average N2O emissions (averaged) at the monthly time-scale across the three crops. After accounting for soil temperature and soil water content, both the weekly and monthly time series regression models exhibited significant autocorrelation at lags of 1–2 weeks and 1–2 months, and significant seasonality for weekly N2O emissions for mango crop and for monthly N2O emissions for mango and custard apple crops in this location over this time-frame. Time series regression models can explain a higher percentage of the temporal variation of N2O emission compared with simple regression models using soil temperature and soil water content as drivers. Taking into account seasonal variability and temporal persistence in N2O emissions associated with soil water content and soil temperature may lead to a reduction in the uncertainty surrounding estimates of N2O emissions based on limited sampling effort.
Additional keywords: autoregressive process, N2O emission, time series regression.
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