Rossana M. Ferrara1*, Cristina Muschitiello1, Alessandro E. Agnelli2, Alessandra Lagomarsino2
1
Consiglio per la ricerca in agricoltura e l’analisi dell’economia agraria (CRA) – Research Unit for Cropping Systems in Dry Environments (SCA), via C. Ulpiani 5, 70125, Bari, Italy
2 Consiglio per la ricerca in agricoltura e l’analisi dell’economia agraria (CRA) - Centre for Research for Agrobiology and Pedology (ABP), Piazza
Massimo D’Azeglio 30, 50121, Florence, Italy
*rossana.ferrara@entecra.it
Abstract
The closed chamber method for quantifying surface-atmosphere trace gas exchanges is widely employed since it is a low cost technique, simple in operating and it is particularly suitable for studies that require treatment replications. However, against its simplicity in the measurement procedures, the complexity and the uncertainty in flux estimates rely on the approach used for computation. The alteration in the boundary conditions, due to the covering of the soil by the chambers, disturbs the gas exchange processes and, then, the widely diffuse linear approach for the interpretation of the increase or decrease in trace gas concentration over time could be not correct. Non-linear models have been developed for estimating fluxes, showing that the linear approach often underestimate fluxes when compared with these alternative methods. These results have been confirmed in this study, where the linear model has been compared to a non-linear one, for a sub-set of data collected in an Italian paddy cropped in permanent flooding.
Parole chiave Modello HM, CH4, N2O, CO2, riso Keywords HM model, CH4, N2O, CO2, rice Introduction
Trace gas exchanges between ecosystems and atmosphere can be monitored by means of the close chamber method (Livingston and Hutchinson, 1995). This approach is worldwide used for its low cost and ease of use. However, the placement of chambers on the soil perturbs natural conditions and can alter the process involved in the gas exchanges. In particular, the assumed linear gradient between concentration and time can be not satisfied and non-linear models can be employed in order to compensate feedback between the increase of gas concentration inside the chamber and the diffusion process. Kroon et al., (2008) demonstrated that the assumption of linearity may not be justified even over short-time scale in well-sealed chambers and with small rate of fluxes. More rigorous models than the linear one are reported in literature (Hutchinson and Mosier, 1981; Kroon et al., 2008) and they highlight that the linear regression model underestimates static chamber fluxes.
Here, we compare the linear regression model (LR) with a non-linear one for calculation of greenhouse gas (GHG) fluxes by static chambers’ data in a paddy soil.
Materials and Methods
The data reported here are parts of a dataset relative to two- year experiment conducted from 2012 to 2013 in rice fields, located in the province of Bologna (central Italy). The fields were planted with rice cultivar Gladio under continuous flooding. Carbon dioxide (CO2), methane (CH4)
and nitrous oxide (N2O) were monitored by means of
vented, closed chamber method described in Adviento- Borbe et al. (2013). Four chambers were closed for 63 min and four gas samples were collected at 0, 21, 42, and 63 min between 10:00–12:00 a.m.. Concentrations of CO2,
CH4 and N2O were analysed using a GC-2014 gas
chromatograph (Shimadzu Scientific). The GC detection limits were 11.6 ng l-1 for N2O, 84.8 ng l-1 for CH4 and 8.36
gl-1 for CO2. The data are relative to the period around the
peak of N2O emission, from 08 July to 08 August 2013 (10
sampling dates).
GHG fluxes were calculated using: (i) the slope of linear regression of gas concentration versus chamber closure time and the enclosed soil surface area (LR approach); (ii) the HMR approach presented by Pedersen et al., (2010) which starts from the non-linear model by Hutchinson and Mosier (1981), trying to apply it first of all, otherwise it applies the linear model. In the HMR routine (developed in R, www.r.project.org), the parameterization of the HM is:
Ct=ϕ+ f0exp(−κt)
−κh (1)
where Ct is the chamber concentration at time t>0, f0 is the
initial flux, h is the effective chamber height,
ϕ
is the constant source concentration,κ
is the effective gaseous diffusion coefficient and it determines the curvature of the model. The model parameters ϕ,κ
and f0 are estimated bythe least squared method (Seber and Wild, 1989), MSE. Synthetically, the HM model is approximately linear for
small value of
κ
, while it approaches to constant concentration model, indicating no-flux data forκ
tending to infinity.In this study, fluxes were set to zero if the change in gas concentration during chamber enclosure fell below the minimum detection limit determined for the GC both in the LR and the HMR approach (HMR_DL). Moreover, flux values were rejected (i.e. treated as missing data) if they passed the detection test, but had a r2 < 0.90 for LR.
Results and Discussion
An example of the HMR procedure is given in Fig. 1 for two typical situations: best performance by LR (Fig 1a) and by HM (Fig. 1b). In limiting cases, with almost linear data, HMR estimates the same fluxes as the LR. The comparison between the two models on the three gases is reported in Fig. 2. It is clear the fluxes’s underestimation of the LR approach with respect to HM. However, when the HMR routine suggests HM model instead of LR, the maximum percentages of underestimation of LR with respect to HM are 53, 51 and 61% for N2O, CH4 and CO2, respectively.
These values agree with those founded in literature (e.g. Kroon et al., 2008).
Fig.1 – The HMR approach applied to: (a) data with flux well estimated by linear regression. LR; (b) data with HM model flux estimation better than the linear one.
Fig.1- Applicazione della procedura HMR a: (a) dati per i quali il flusso è ben stimato usando una regressione lineare, LR; (b) dati per i quali il modello HM produce una stima di flusso migliore rispetto al modello lineare.
Conclusions
The application of the HMR approach highlights that the LR model underestimates GHG fluxes of static chambers, confirming the results reported in literature. Next step will be its application to all the experimental campaign to test the results obtained by the LR model in the quantification of the Global Warming Potential of two water managements: one under continuous flooding and the other under alternate wetting and drying.
0 200 400 600 0 200 400 600 LR HMR_DL
N
2O
100 300 500 1 0 0 300 500 LR HMR_DLCH
40e+00 2e+05 4e+05 6e+05
0e+00 3e+05 6e+ 0 5 LR HMR_DL
CO
2 N2O: HMR_DL= 1.92 LR CH4: HMR_DL= 1.09 LR CO2: HMR_DL= 1.12 LR Fluxes (g/ha d) fitted 1:1Fig.2 – Scatter plot GHG fluxes estimated by the two models, linear (LR) and no-linear (HMR_DL) with a test on detection limit of the GC measurements (see text).
Fig.2 – Scatter plot dei flussi di GHG stimati mediante i due modelli di regression lineare (LR) e non lineare (HMR_DL) con la condizione sul limite di misura del GC (vedi testo).
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