The development of fast-response analysers for the
measurement of nitrous oxide (N

Accurate measurements of nitrous oxide (N

To date, the common method for measuring fluxes of N

Alternative approaches to the static chamber method include the use of
(semi-)automated chambers and micrometeorological techniques that allow

In this study, we tested whether a single field-deployed QCL could be used
for manual injections of gas samples taken from static chambers to allow
nearly concurrent measurements of chamber N

This study was conducted at Troughton Farm, a commercially operating 199 ha
dairy farm in the Waikato region, 3 km east of Waharoa (37.78

One intensive field campaign was conducted between 10 and 16 September 2019.
The campaign's primary purposes were to (1) manually collect gas samples from
static chambers comprising potentially low to high N

The static chamber trial comprised a randomised block design of circular
treatment and control plots, each of which included three replicates per
treatment or control. Ammonium nitrate (AN) fertiliser was used as a treatment
and applied at different rates to ensure production of a wide range of low
to high

Chamber measurements were made on the day of treatment application and
throughout the following 6 d with chamber gas samples collected on nine
occasions (Table S1 in the Supplement). The sampling followed a standardised chamber technique
(de Klein et al., 2003, 2015; Luo et al., 2008b) and was
carried out daily at 10:00 (NZDT) (van der Weerden et al., 2013). Additional
sampling was also conducted at noon on 12 and 15 September. Before sampling, polyvinyl chloride
(PVC) lids were fitted to water-filled base channels that provided a gas-tight
seal over the 10 L headspace of each chamber. Gas samples were taken from
this headspace during a 45 min enclosure period four times –

Gas chromatography was conducted on the first sample batch at the New
Zealand National Centre for Nitrous Oxide Measurements (NZ-NCNM) at Lincoln
University, New Zealand. Automated analysis (GX-271 Liquid Handler, Gilson
Inc., Middleton, WI) was performed using an SRI 8610 GC (SRI Instruments,
Torrance, CA, USA) and a Shimadzu GC-17a (Shimadzu Corp., Kyoto, Japan)
equipped with a

The second batch of N

For the purpose of our analysis, we switched the QCL from its continuous-measurement (EC) mode to an “injection mode”. The injection-mode conversion
took less than 30 min: a stainless-steel three-way valve (Swagelok,
Solon, OH, USA) mounted to the air inlet of the QCL allowed for the redirection of
the airflow from the primary inlet tube of the EC system into a second, 1 m
long Bev-A-Line tube (4 mm internal diameter). At its end, the tube was
connected to a pressure regulator and a bottle of oxygen-free,
industrial-grade N

Schematic illustration of how to use a field-based QCL
for EC measurements and manual injections. (1) The main components of
the QCL EC system; (2) an example of a static chamber from which
N

Once the injection line had been established, the flow rate was reduced from
an initial 15 L min

Standards of certified N

GC and QCL analyses resulted in the output of peak area data from the
injected N

The

The statistical analysis for ^{®} (Version 19, VSN
International, Hemel Hempstead, UK). After testing for normality using a
Shapiro–Wilk test and homogeneity of variance by examining residual and
fitted values, we applied three different statistical approaches to compare
GC with QCL data: (1) orthogonal regression, (2) Bland–Altman and (3) bioequivalence statistics.

The orthogonal regression analysis used standardised

The core of this orthogonal regression was a principal component analysis
which, in contrast to ordinary least-squares regression, allowed for
measurement errors in the response and the predictor variable by minimising
the squared residuals in a vertical and horizontal direction. While
orthogonal regression returned a Pearson correlation coefficient

Still, testing for correlation and agreement did not determine whether GC
and QCL data would effectively and for practical purposes be the same
(termed “equivalent”). We therefore used bioequivalence statistics to
assess the biological and analytical relevance of the difference between the
two methods. The first part of this analysis comprised a one-way analysis of
variance (ANOVA) for

Daily mean air temperatures during the 7 d chamber campaign ranged
from 8.3 to 12.8

Measurements resulted in a wide range of

Fluxes of nitrous oxide (

The correlation between calculated

Orthogonal regression analysis of standardised N

In contrast to the strong comparability of GC and QCL data at AN treatment
sites,

More generally, QCL analysis resulted in slightly higher

Bland–Altman plots showing the difference between the GC
and QCL method expressed as the percentage difference of the standard method
A (

Cumulative N

Cumulative N

The measurement precision of QCL and, particularly, GC has been generally
well-reviewed (de Klein et al., 2015; Lebegue et al., 2016; Rapson and
Dacres, 2014). Gas chromatographs can be as precise as

However, the analytic precision can also depend on factors other than the
technical performance of the analyser itself. Rannik et al. (2015) indicated
that the performance (and thus the precision of

The QCL analysis of our study was conducted in a temperature- and
pressure-controlled environment, where variations in these parameters were
unlikely, and the variation in temperature was expected to be less than 0.02 ppb

Bioequivalence analysis for N

The GC and QCL methods in comparison: details provided in the table relate to this study, and the information provided was not generalised. NZD: New Zealand dollars.

Using the Pearson correlation coefficient and the coefficient of
determination for comparing two or more quantitative methods is a generally
preferred approach in the field of N

An important aspect of statistical hypothesis testing is that the null
hypothesis is never accepted. But failure to reject the null hypothesis is
not the same as proving no difference. A bioequivalence analysis allows the
statistical assessment of whether two methods (e.g. measurement devices,
drug treatment) are effectively the same. Central to a bioequivalence
analysis is the “equivalence range” that defines the size of the
acceptable difference for which the values are similar enough to be
considered equivalent. This becomes important when considering that even
with the most precise analytical design and the most tightly controlled
experimental conditions, e.g.

Overall, our results showed that

To the best of our knowledge, bioequivalence has not been broadly applied in
the greenhouse gas literature to identify and discuss the range at which
a difference in

The employment of a QCL analyser proposes an alternative approach for the
injection of N

Previously, QCL had been used either in conjunction with EC or coupled to
automated chambers. Here, we showed that one QCL device could be used as a
practical tool for the analysis of static-chamber-derived N

Data were deposited at the University of Waikato Research Commons; see

The supplement related to this article is available online at:

ARW, VMC, JL and LAS designed the experiment. ARW performed the fieldwork. ARW conducted the post-processing of GC and QCL data using MATLAB scripts, which are based on the work of ARW and DIC. ARW performed the statistical analysis with inputs and contributions from VMC. VMC and LAS commented on the results of the initial data analysis. ARW wrote and revised the paper with contributions from VMC, ARW, LLL, JL, DIC and LAS.

The authors declare that they have no conflict of interest.

The authors would like to recognise the farm owners, Sarah and Ben Troughton, for their cooperation. Chris Morcom is thanked for his help in the fields and Emily Huang from NZ-NCNM for her all-embracing support regarding gas chromatography. Training notes on the concept of bioequivalence were gratefully received from Neil Cox. We would like to further acknowledge continuous support from Aerodyne Research Ltd. in maintaining and advancing our QCL EC systems. Finally, Cecile A. M. de Klein, Jordan P. Goodrich, Tom P. Moore and two anonymous reviewers are thanked for thoroughly revising this paper.

This research project (grant nos. 17-CAN9.3.4, 19-CAN9.8) was supported by the New Zealand Agricultural Greenhouse Gas Research Centre (NZAGRC), AgResearch Ruakura, DairyNZ and the University of Waikato.

This paper was edited by Daniela Famulari and reviewed by two anonymous referees.