@article{Rowlings2015,
title = {Rainfall variability drives interannual variation in N_{2}O emissions from a humid, subtropical pasture},
author = { D.W Rowlings and P.R Grace and C Scheer and S. Liu},
doi = {10.1016/j.scitotenv.2015.01.011},
year = {2015},
date = {2015-01-06},
journal = {Science of the Total Environment},
volume = {512–513},
pages = {8-18},
abstract = {"Variations in interannual rainfall totals can lead to large uncertainties in annual N_{2}O emission budget estimates from short term field studies. The interannual variation in nitrous oxide (N_{2}O) emissions from a subtropical pasture in Queensland, Australia, was examined using continuous measurements of automated chambers over 2 consecutive years. Nitrous oxide emissions were highest during the summer months and were highly episodic, related more to the size and distribution of rain events than soil water content. Over 48% of the total N_{2}O emitted was lost in just 16% of measurement days.
Interannual variation in annual N_{2}O estimates was high, with cumulative emissions increasing with decreasing rainfall. Cumulative emissions averaged 1826.7 ± 199.9 g N_{2}O-N ha^{− 1} yr^{− 1} over the two year period, though emissions from 2008 (2148 ± 273 g N_{2}O-N ha^{− 1} yr^{− 1}) were 42% higher than 2007 (1504 ± 126 g N_{2}O-N ha^{− 1} yr^{− 1}). This increase in annual emissions coincided with almost half of the summer precipitation from 2007 to 2008. Emissions dynamics were chiefly driven by the distribution and size of rain events which varied on a seasonal and annual basis. Sampling frequency effects on cumulative N_{2}O flux estimation were assessed using a jackknife technique to inform future manual sampling campaigns. Test subsets of the daily measured data were generated for the pasture and two adjacent land-uses (rainforest and lychee orchard) by selecting measured flux values at regular time intervals ranging from 1 to 30 days. Errors associated with weekly sampling were up to 34% of the sub-daily mean and were highly biased towards overestimation if strategically sampled following rain events. Sampling time of day also played a critical role. Morning sampling best represented the 24 hour mean in the pasture, whereas sampling at noon proved the most accurate in the shaded rainforest and lychee orchard."},
keywords = {Science of the Total Environment},
pubstate = {published},
tppubtype = {article}
}

@article{VanSwieten2013,
title = {Pyrolysing poultry litter reduces N_{2}O and CO_{2} fluxes},
author = { L van Zwieten and S.W Kimber and S.G Morris and B.P Singh and P. R. Grace and C Scheer and J Rust and A Downie and A. Cowie},
doi = {10.1016/j.scitotenv.2013.02.054},
year = {2013},
date = {2013-11-01},
journal = {Science of the Total Environment},
volume = {465},
pages = {279-287},
abstract = {Application of poultry litter (PL) to soil can lead to substantial nitrous oxide (N_{2}O) emissions due to the co-application of labile carbon (C) and nitrogen (N). Slow pyrolysis of PL to produce biochar may mitigate N_{2}O emissions from this source, whilst still providing agronomic benefits. In a corn crop on ferrosol with similarly matched available N inputs of ca. 116 kg N/ha, PL-biochar plus urea emitted significantly less N_{2}O (1.5 kg N_{2}O–N/ha) compared to raw PL at 4.9 kg N_{2}O–N/ha. Urea amendment without the PL-biochar emitted 1.2 kg N_{2}O–N/ha, and the PL-biochar alone emitted only 0.35 kg N_{2}O–N/ha. Both PL and PL-biochar resulted in similar corn yields and total N uptake which was significantly greater than for urea alone. Using stable isotope methodology, the majority (~ 80%) of N_{2}O emissions were shown to be from non-urea sources. Amendment with raw PL significantly increased C mineralisation and the quantity of permanganate oxidisable organic C. The low molar H/C (0.49) and O/C (0.16) ratios of the PL-biochar suggest its higher stability in soil than raw PL. The PL-biochar also had higher P and K fertiliser value than raw PL. This study suggests that PL-biochar is a valuable soil amendment with the potential to significantly reduce emissions of soil greenhouse gases compared to the raw product. Contrary to other studies, PL-biochar incorporated to 100 mm did not reduce N_{2}O emissions from surface applied urea, which suggests that further field evaluation of biochar impacts, and methods of application of both biochar and fertiliser, are needed.},
keywords = {Science of the Total Environment},
pubstate = {published},
tppubtype = {article}
}

@article{Morris2013,
title = {Improving the statistical preparation for measuring soil N_{2}O flux by closed chamber},
author = { S. G Morris and S. W Kimber and L van Zwieten and P. R. Grace},
doi = {10.1016/j.scitotenv.2013.02.032},
year = {2013},
date = {2013-11-01},
journal = {Science of the Total Environment},
volume = {465},
pages = {166-172},
abstract = {Nitrous oxide emissions from soil are known to be spatially and temporally volatile. Reliable estimation of emissions over a given time and space depends on measuring with sufficient intensity but deciding on the number of measuring stations and the frequency of observation can be vexing. The question of low frequency manual observations providing comparable results to high frequency automated sampling also arises. Data collected from a replicated field experiment was intensively studied with the intention to give some statistically robust guidance on these issues. The experiment had nitrous oxide soil to air flux monitored within 10 m by 2.5 m plots by automated closed chambers under a 3 h average sampling interval and by manual static chambers under a three day average sampling interval over sixty days. Observed trends in flux over time by the static chambers were mostly within the auto chamber bounds of experimental error. Cumulated nitrous oxide emissions as measured by each system were also within error bounds. Under the temporal response pattern in this experiment, no significant loss of information was observed after culling the data to simulate results under various low frequency scenarios. Within the confines of this experiment observations from the manual chambers were not spatially correlated above distances of 1 m. Statistical power was therefore found to improve due to increased replicates per treatment or chambers per replicate. Careful after action review of experimental data can deliver savings for future work.},
keywords = {Science of the Total Environment},
pubstate = {published},
tppubtype = {article}
}

@article{Huang2013b,
title = {A flexible Bayesian model for describing temporal variability of N_{2}O emissions from an Australian pasture},
author = { X Huang and P. R. Grace and D. W. Rowlings and K. Mengersen},
doi = {10.1016/j.scitotenv.2013.03.013},
year = {2013},
date = {2013-06-01},
journal = {Science of the Total Environment},
volume = {454-455},
number = {5},
pages = {206-210},
abstract = {Soil-based emissions of nitrous oxide (N_{2}O), a well-known greenhouse gas, have been associated with changes in soil water-filled pore space (WFPS) and soil temperature in many previous studies. However, it is acknowledged that the environment–N_{2}O relationship is complex and still relatively poorly unknown. In this article, we employed a Bayesian model selection approach (Reversible jump Markov chain Monte Carlo) to develop a data-informed model of the relationship between daily N_{2}O emissions and daily WFPS and soil temperature measurements between March 2007 and February 2009 from a soil under pasture in Queensland, Australia, taking seasonal factors and time-lagged effects into account. The model indicates a very strong relationship between a hybrid seasonal structure and daily N_{2}O emission, with the latter substantially increased in summer. Given the other variables in the model, daily soil WFPS, lagged by a week, had a negative influence on daily N_{2}O; there was evidence of a nonlinear positive relationship between daily soil WFPS and daily N_{2}O emission; and daily soil temperature tended to have a linear positive relationship with daily N_{2}O emission when daily soil temperature was above a threshold of approximately 19 °C. We suggest that this flexible Bayesian modeling approach could facilitate greater understanding of the shape of the covariate-N_{2}O flux relation and detection of effect thresholds in the natural temporal variation of environmental variables on N_{2}O emission.},
keywords = {Science of the Total Environment},
pubstate = {published},
tppubtype = {article}
}

@article{Huang2011,
title = {Spatio-temporal variation in soil derived nitrous oxide emissions under sugarcane},
author = { X Huang and P. R. Grace and K. Mengersen},
doi = {10.1016/j.scitotenv.2011.07.044},
year = {2011},
date = {2011-10-01},
journal = {Science of the Total Environment},
volume = {21},
number = {21},
pages = {4572-4578},
abstract = {Nitrous oxide (N_{2}O) is a significant greenhouse gas with a global warming potential that is 300 times than that of carbon dioxide. Soil derived N_{2}O emissions usually display a high degree of spatial and temporal variability because of their dependence on soil chemical and physical properties, and climate dependent environmental factors. However, there is little research that incorporates spatial dependence in the estimation of N_{2}O emissions allowing for environmental factors in the same model. This study aims to examine the impact of two environmental factors (soil temperature and soil moisture) on N_{2}O emissions and explore the spatial structure of N_{2}O in the sub-tropical South East Queensland region of Australia. The replicated data on N_{2}O emissions and soil properties were collected at a typical sugarcane land site covering 25 uniform grid points across 3600 m^{2} between October 2007 and September 2008. A Bayesian conditional autoregressive (CAR) model was used to model spatial dependence. Results showed that soil moisture and soil temperature appeared to have substantially different effects on N_{2}O emissions after taking spatial dependence into account in the four seasons. There was a substantial variation in the spatial distribution of N_{2}O emission in the different seasons. The high N_{2}O emission regions were accompanied by high uncertainty and changed in varying seasons in this study site. Spatial CAR models might be more plausible to elucidate and account for the uncertainty arising from unclear variables and spatial variability in the assessment of N_{2}O emissions in soils, and more accurately identify relationships with key environmental factors and help to reduce the uncertainty of the soil parameters.},
keywords = {Science of the Total Environment},
pubstate = {published},
tppubtype = {article}
}