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Research Article

Partitioning of CH4 and CO2 Production Originating from Rice Straw, Soil and Root Organic Carbon in Rice Microcosms

  • Quan Yuan,

    Affiliation: Max-Planck-Institute for Terrestrial Microbiology, Marburg, Germany

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  • Judith Pump,

    Affiliation: Max-Planck-Institute for Terrestrial Microbiology, Marburg, Germany

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  • Ralf Conrad mail

    conrad@mpi-marburg.mpg.de

    Affiliation: Max-Planck-Institute for Terrestrial Microbiology, Marburg, Germany

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  • Published: November 05, 2012
  • DOI: 10.1371/journal.pone.0049073

Abstract

Flooded rice fields are an important source of the greenhouse gas CH4. Possible carbon sources for CH4 and CO2 production in rice fields are soil organic matter (SOM), root organic carbon (ROC) and rice straw (RS), but partitioning of the flux between the different carbon sources is difficult. We conducted greenhouse experiments using soil microcosms planted with rice. The soil was amended with and without 13C-labeled RS, using two 13C-labeled RS treatments with equal RS (5 g kg−1 soil) but different δ13C of RS. This procedure allowed to determine the carbon flux from each of the three sources (SOM, ROC, RS) by determining the δ13C of CH4 and CO2 in the different incubations and from the δ13C of RS. Partitioning of carbon flux indicated that the contribution of ROC to CH4 production was 41% at tillering stage, increased with rice growth and was about 60% from the booting stage onwards. The contribution of ROC to CO2 was 43% at tillering stage, increased to around 70% at booting stage and stayed relatively constant afterwards. The contribution of RS was determined to be in a range of 12–24% for CH4 production and 11–31% for CO2 production; while the contribution of SOM was calculated to be 23–35% for CH4 production and 13–26% for CO2 production. The results indicate that ROC was the major source of CH4 though RS application greatly enhanced production and emission of CH4 in rice field soil. Our results also suggest that data of CH4 dissolved in rice field could be used as a proxy for the produced CH4 after tillering stage.

Introduction

Flooded rice fields are an important source of the greenhouse gas CH4 [1], [2]. Methane and CO2 are end products of anoxic degradation of organic matter in rice field soil [3]. The organic matter is mainly derived from three sources [4]: (1) soil organic matter (SOM), (2) root organic carbon (ROC) including root exudates and sloughed-off dead root, and (3) dead plant organic matter, such as rice straw (RS), which is often applied in large amounts (up to 12 t ha−1 annually) to maintain soil fertility [5][7]. Methane production is partitioned mainly between these three types of organic matter. Knowledge of partitioning is important for improving process-based modeling of CH4 emission from rice fields [8], [9], which is the basis for predicting methane flux and assessing the impact of agricultural management and global change.

Quantification of carbon partitioning can in principle be achieved by pulse-labeling of rice plant with 13CO2 or 14CO4 [10][12]. Recently, free-air CO2 enrichment (FACE) using 13C-depleted CO2 was used for determining the contribution of ROC to production of CO2 and CH4 in rice field soil [13]. However, pulse-labeling only assesses the immediate contribution of root exudates, while the contribution of sloughed-off dead root cells cannot be fully accounted for [13][16]. Since FACE experiments apply elevated CO2 concentrations, photoassimilation of CO2 may be enhanced and thus increase the contribution of plants and soil organic matter to carbon flux [17][19]. Furthermore, most studies of carbon flux partitioning in rice fields have been done without application of straw, so that full partitioning of the origin of carbon flux into SOM, ROC and RS was not possible [4]. However, application of RS should be taken into account, since RS may not only be used as substrate for CH4 production, but might also enhance CH4 production from other carbon sources [20], [21].

The partitioning of the CH4 production from different sources of organic carbon (SOM, ROC, RS) can be achieved, if these have different isotopic signatures. However, a major difficulty during partitioning the sources of CH4 is caused by the carbon isotopic fractionation during the conversion of organic matter to CH4, which is typically 10–70‰ [22]. Nevertheless, the relative contribution of acetoclastic versus hydrogenotrophic methanogenesis to CH4 production has been determined successfully in environments such as rice field soil [23] and lake sediments [24], after the isotopic fractionation factors in both methanogenic pathways were determined. The δ13C values of CH4 from the two pathways are substantially different, since the isotopic fractionation factors of the two pathways are largely different [22], . Analogously, it is possible to partition the sources of CH4 if the δ13C of CH4 derived from each carbon source in the rice field soil is known. Normally, the CH4 derived from SOM, ROC and RS has similar δ13C values, since all the organic matter has eventually been derived from rice plant material [23], [26]. However, this problem may be solved by cultivation of rice in soil amended with 13C-labeled RS.

The aim of this study was to determine the partitioning of the carbon flux involved in methanogenic degradation of carbon sources by determining the δ13C of CH4 derived from ROC. We therefore prepared rice microcosms with two treatments of 13C-labeled RS, both having the same amount of RS (5 g kg−1 soil, equals about 5 t ha−1) but different content of 13C. We determined the produced CH4 and CO2 by collecting soil cores and incubating samples anoxically [27].

Materials and Methods

Planted and unplanted rice microcosms

Soil samples were provided by the Italian Rice Research Institute in Vercelli. Soil was taken from a drained paddy field in spring 2009 and was air dried and stored at room temperature. The soil was sieved (<2 mm) prior to use. The characteristics of the soil have been described previously [28]. Planting pots (upper diameter = 19 cm; lower diameter = 14 cm; height = 16 cm) were filled with 2 kg dry soil and turned into a slurry with demineralized water.

For planted rice microcosms, in total 48 pots were prepared, 16 pots for the unamended control, and 16 pots each for RS treatment I and RS treatment II. Fertilizer solution (50 ml of a solution containing per liter: 10 g urea, 7.6 g KH2PO4) was added to each pot as basal fertilizer. For both RS treatments, 10 g powder of RS was added to each pot and mixed thoroughly into the soil slurry. The δ13C values of RS added in treatment I and II were 213.0‰ and 474.7‰, respectively. These δ13C values were obtained by adding desired amount of 13C-labeled (δ13C = 1859.9‰) and unlabeled (δ13C = −27.6‰) RS separately into each pot. The 13C-labeled RS was prepared by growing rice plants in the greenhouse until the late vegetative stage. The plants were covered with a 18-L acrylic chamber, 1% 13CO2 (final concentration; 99 atom%, Sigma, Germany) was added to the headspace, incubated for 5 days (12 h light, 25°C), and then harvested. The unlabeled RS was from rice plant grown in the same manner without feeding on 13CO2. These rice plants were dried and ground to powder. After 3 days of incubation in the greenhouse, all the pots were planted with one 12-day old rice seedling (Oryza sativa var. KORAL type japonica), and were flooded with demineralized water to give a water depth of 5 cm above the soil surface. The water depth was maintained throughout the experimental period. The rice microcosms were incubated in the greenhouse with a relative humidity of 70%, a 12-h photoperiod and a 28/22°C day/night temperature cycle. The day of transplantation was taken as day zero. On day 21, a second dose of 30 ml fertilizer solution was added to each microcosm. At each sampling time (day 41, 55, 70 and 90), 12 rice microcosms were sacrificed (4 replicates for control and for each treatment). For unplanted microcosms, the preparation was the same as for planted ones, but without rice plant in the pots. In total, 12 pots were prepared with 4 pots each for the unamended control, RS treatment I and RS treatment II.

CH4 flux, soil pore water and plant parameters

Rates of CH4 emission was measured on day 41, 55, 70 and 90 of incubation in the greenhouse as described previously [27]. For flux measurements, planted rice microcosms were covered by flux chambers, and gas samples were taken every 30 min for 2 h. CH4 emission rates were determined from the slope of the linearly increasing CH4 mixing ratio and expressed in mmol CH4 m−2 h−1.

Samples for the determination of the isotopic signature (δ13CH4) of the emitted CH4 were taken in glass containers (100 ml). The first sample was taken directly after closure of the chambers, the second sample was taken at the end of the 2-h closure period. The isotopic signature of the emitted CH4 was calculated according to [27].

Pore water samples were collected into Venoject blood-collecting tubes (Terumo Europe N.V., Belgium) from the rhizosphere (3 cm depth) and bulk (9 cm depth) soil of rice microcosms using Rhizon pore water samplers (Rhizosphere Research Products, the Netherlands). After heavy shaking by hand, the headspace of the tubes was sampled using a pressure lock syringe and directly analyzed for CH4 and CO2 and δ13C. The CH4 and CO2 concentration in the soil pore water was calculated as described previously [27].

Plant height, tiller number and aboveground biomass were determined. For dry weight determination, samples were dried for 48 h at 60°C.

CH4 and CO2 production

Production rates of CH4 and CO2 and respective δ13C values were determined by collecting soil core samples in rice microcosms on day 41, 55, 70 and 90 of incubation in the greenhouse [27]. After cutting off the rice plant, the surface water layer was removed. Soil cores were taken in each pot with stainless steel corer (Ø 22 mm, 210 mm in length). Two to three soil cores (about 100 g in total) were collected from each pot and transferred into a 250-ml bottle. The soil samples were turned into slurry using N2-gassed deionized sterile water so that the ratio of dry weight of soil to water was 1:1. After flushing the samples with N2, the bottles were sealed with butyl rubber stoppers and, after shaking, flushed again with N2 to remove residual O2 and CH4. Incubation was performed statically at 25°C in the dark for 24 h. Headspace samples were taken every 12 h after shaking the bottles, and analyzed for concentration of CH4 and CO2 and their δ13C. The CH4 and CO2 production from planted soil microcosms was due to decomposition of SOM plus ROC (unamneded control) or of SOM, ROC plus RS (RS treatments). CH4 production rates were calculated by linear regression of the CH4 increase with incubation time, and expressed in nmol CH4 gdw−1 h−1 of soil. The CO2 production rates were determined analogously.

For unplanted soil microcosms, the methods for collection and incubation of soil core samples were similar, but these pots were not sacrificed, but at each sampling day (day 41, 55, 70 and 90), a 60-g soil core was taken from the pot. After removal of the soil core the residual soil in the pot was compacted, and water was added to maintain a water level of 5 cm depth. Using this procedure about 2.1% of the total amount of soil in the pot was collected during each sampling. The CH4 and CO2 production from unplanted soil microcosms was only due to decomposition of SOM (unamneded control) or of SOM plus RS (RS treatments).

Analytical techniques

The gas samples were analyzed for CH4 and CO2 using a gas chromatograph (GC) equipped with flame ionization detector (FID) [29]. Stable isotopic analysis of gas samples (CH4 and CO2) from pore water and soil core incubation were performed directly using the GCC-IRMS, samples from flux measurements (low in CH4) were preconcentrated on a Precon (Finnigan, Bremen, Germany). The principal operation of the GCC-IRMS has been previously described [30], [31]. The isotope reference gas was CO2 (99.998% purity; Messer-Griessheim, Düsseldorf, Germany) calibrated with the working standard methyl stearate (Merck). The latter was intercalibrated at the Max-Planck-Institute for Biogeochemistry, Jena, Germany (courtesy of Dr. W.A. Brand) against NBS 22 and USGS 24, and reported in the delta notation vs. V-PDB: δ13C = 103 (Rsa/Rst −1), with R = 13C/12C of sample (sa) and standard (st), respectively. The precision of repeated analysis was ± 0.2‰, when 1.3 nmol CH4 were injected [23]. The determination of the stable isotopic signatures of dried plant and soil samples was carried out at the Institute for Soil Science and Forest Nutrition (IBW) at the University of Göttingen, Germany.

Calculations

1. Fraction of CH4 production from ROC (fROC).

The fraction of CH4 derived from ROC (fROC) can be determined from the following mass balance equation:(1)
where δ13CCH4 = δ13C of CH4 produced (or dissolved) in the planted rice microcosms at each sampling time; δ13CCH4-ROC = δ13C of CH4 formed from ROC (determination see below); δ13CCH4-SOR = δ13C of CH4 formed from SOM plus RS, i.e. the CH4 produced (or dissolved) in the unplanted soil treated with RS. The equation can be transformed into the following two equations for RS-treatment I and II, respectively:

(2)
(3)

Since fROC and δ13CCH4-ROC should be the same in treatment I and II, δ13CCH4-ROC can be calculated by solving equations (2) and (3):(4)

Then, fROC can be calculated from either equation (2) or (3).

2. Fraction of CH4 production from RS carbon (fRS).

The δ13C values of the CH4 produced (or dissolved) in the two RS treatments are given by the following two mass balance equations:(5)
(6)
with fRS, fSOM and fROC denote fractions of CH4 produced from RS, SOM and ROC, respectively; δ13CRS-I and δ13CRS-II are δ13C of the rice straw carbon in treatment I (213.0‰) and II (474.7‰), respectively; δ13CSOM and δ13CROC are δ13C of SOM (−25.8‰) and of the plant biomass (Fig. 1), respectively; ΔCH4 designates the overall isotopic fractionation factors involved in CH4 production from these organic matters, in case of dissolved CH4 also those involved in oxidation and transfer of CH4 from soil to the atmosphere.

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Figure 1. Values of δ13C of dried rice plants obtained from planted microcosms without (control) and with addition of 13C-labeled RS.

RS I and RS II denote the two treatments, the δ13C of rice straw applied was 213.0‰ and 474.7‰ for RS I and RS II, respectively; means ± standard deviation (SD) (n = 3). The differences between the treatments over time were examined using Duncan post hoc test of a one-way ANOVA. Different letters on the top of bars indicate significant difference (P<0.05) between the data.

doi:10.1371/journal.pone.0049073.g001

Since the terms fSOM δ13CSOM, fROC δ13CROC and ΔCH4 should be the same in treatment I and II, combination of equations (5) and (6) and solving for fRS results in:(7)
of which the δ13C can be determined experimentally. Here, δ13CCH4-I and δ13CCH4-II were determined experimentally, and δ13CRS-I and δ13CRS-II were mixtures of labeled and unlabelled RS, of which the δ13C were determined experimentally (see above). Finally, the fraction of CH4 production from SOM (fSOM) can be calculated, since

(8)
Analogous equations are valid for the fractions of CO2 produced from ROC, SOM and RS in rice field soil.

Statistical analysis

The significance of differences between treatments over time for various variables were determined by one-way analysis of variance (ANOVA) followed by multiple comparisons (Duncan post hoc test) using SPSS 13.0. To test the significance of the differences between contributions to produced and dissolved CH4 or CO2, two-tailed independent t-tests were applied using Microsoft Excel 2007.

Results

1. Stable carbon signature of rice plants

The δ13C of rice plants in the control and RS treatments were almost constant with time (Fig. 1). Rice plants in the RS treatments were enriched in δ13C by about 5‰ compared with the control. The δ13C of rice plants was consistently higher in treatment II than in treatment I, but the difference was not significant.

2. Rates and δ13C of CH4 emitted from planted microcosms

In the rice microcosms without addition of RS, CH4 emission rates increased from the tillering stage (day 41) to the booting stage (day 55) and peaked at the flowering stage (day 70), then decreased again till the ripening stage (day 90) (Fig. 2A). Application of rice straw increased CH4 emission rates throughout the growth period, but particularly during tillering and booting stage (Fig. 2A). The δ13C of the emitted CH4 became gradually more negative during the cultivation period in all the treatments (Fig. 2B). The δ13C of CH4 was substantially higher in RS treatment II > RS treatment I > control, especially during the tillering stage (Fig. 2B).

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Figure 2. Seasonal change of (A) CH4 emission rates and (B) δ13C of CH4 emitted in planted microcosms with and without treatment with 13C-labeled RS; means ± SD (n = 4).

The differences between the treatments over time were examined using Duncan post hoc test of a one-way ANOVA. Different letters on the top of bars indicate significant difference (P<0.05) between the data.

doi:10.1371/journal.pone.0049073.g002

3. Concentrations and δ13C values of CH4 and CO2 dissolved in pore water

Concentrations and δ13C values of dissolved CH4 and CO2 were similar in the pore water sampled from 3 cm and 9 cm soil depth. Therefore, only the data from the 9-cm soil layer are shown (Fig. 3, 4A and B). In the planted microcosms, CH4 concentrations increased steadily from the beginning until the ripening stage. Application of rice straw resulted in elevated CH4 concentrations in the beginning but subsequently became similar to the control (Fig. 3A). The δ13C values of the CH4 dissolved in planted and unplanted microcosms were similar with each other in both RS treatments at tillering stage (Fig. 4A). However, while δ13C values decreased with time in the planted microcosms, they did not decrease much in the unplanted microcosms. The δ13C of the dissolved CH4 was consistently higher (less negative) in RS treatment II > RS treatment I > control for both planted and unplanted microcosms (Fig. 4A). The δ13C values of the dissolved CH4 in planted microcosms (Fig. 4A) were similar to those of the emitted CH4 (Fig. 2B).

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Figure 3. Temporal change of the concentrations of dissolved (A) CH4 and (B) CO2 in planted microcosms with and without addition of 13C-labeled RS; means ± SD (n = 4).

The differences between the treatments over time were examined using Duncan post hoc test of a one-way ANOVA. Different letters on the top of bars indicate significant difference (P<0.05) between the data.

doi:10.1371/journal.pone.0049073.g003
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Figure 4. δ13C of (A) CH4 and (B) CO2 dissolved in microcosms with and without RS application; δ13C of (C) CH4 and (D) CO2 produced in microcosms with and without RS application.

Solid line indicated planted microcosms, dashed lines unplanted microcosms; means ± SD (n = 4).

doi:10.1371/journal.pone.0049073.g004

In the planted microcosms, dissolved CO2 concentrations were between 4.0 and 5.5 mM independently of the treatment and the vegetation period (Fig. 3B). The δ13C of the dissolved CO2 exhibited a temporal pattern similar to that of CH4 and was again consistently higher (less negative) in RS treatment II > RS treatment I > control (Fig. 4B). However, δ13C of dissolved CO2 was in general higher (less negative) than that of CH4.

4. Rates and δ13C of CH4 and CO2 produced in planted and unplanted microcosms

At each time of sampling, soil cores were collected from microcosms with and without rice plants, in order to determine the rates and the δ13C of the CH4 and CO2 produced. Depending on the microcosm tested, CH4 and CO2 were produced from ROC (planted microcosms), SOM (all microcosms) and RS (RS-treated microcosms). In the planted control without RS treatment, CH4 production rates increased steadily during the vegetation period (Fig. 5A). However, treatment with RS resulted in further increase of CH4 production rates. In the unplanted microcosms, CH4 production rates were also enhanced by RS treatments but were lower than in the planted microcosms with RS treatment. The δ13C of produced CH4 was similar in the planted and unplanted control microcosms without RS (Fig. 4C). Treatment with RS resulted in increase of δ13C values of produced CH4, which was higher in treatment II than treatment I. However, the increase was less in the planted than in the unplanted microcosms (Fig. 4C).

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Figure 5. Production rates of (A) CH4 and (B) CO2 in planted and unplanted microcosms with and without RS application; means ± SD (n = 4).

The differences between the treatments over time were examined using Duncan post hoc test of a one-way ANOVA. Different letters on the top of bars indicate significant difference (P<0.05) between the data.

doi:10.1371/journal.pone.0049073.g005

The rates of CO2 production were constant over the vegetation period in the planted microcosms and were similar for the treatments with and without RS, but were at least twice as high in planted as in unplanted microcosms (Fig. 5B). The δ13C values of CO2 exhibited a similar pattern with respect to vegetation period and treatment as that of CH4, but the values were generally higher (Fig. 4D).

5. Partitioning CH4 and CO2 produced in rice microcosms

For calculation of fROC, first of all the δ13C of the CH4 and CO2 produced from ROC had to be determined. The data, which were calculated using eq. (4), are shown in Table 1. The δ13C of CH4 produced from ROC was about −60‰ on average (range of −67 to −49‰) during the whole vegetation period, though fluctuations on individual sampling dates, at tillering stage in particular, were rather high (Table 1). The δ13C values of CO2 produced from ROC were about −31‰ at tillering stage and increased to around −11‰ to −4‰ subsequently (Table 1). Values of fROC were then calculated using eq. (2) and (3). Both equations gave similar values, but those obtained with eq. (2) showed higher standard deviations than those obtained with eq. (3). Only the latter values are shown in Fig. 6 and 7. ROC was found to make a major contribution (41–63%) to CH4 production over the entire vegetation period (Fig. 6A). For CO2 production, ROC had even a higher importance (43–76%) (Fig. 7A).

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Table 1. δ13C values of CH4 and CO2 derived from ROC in planted rice microcosms with RS application.

doi:10.1371/journal.pone.0049073.t001
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Figure 6. Percentage contribution of (A) ROC, (B) SOM and (C) RS to produced and dissolved CH4 in planted microcosms with RS treatment; means ± SD (n = 4).

The differences between contributions to produced and dissolved CH4 were tested by two-tailed independent t-tests, indicated by * when P<0.05.

doi:10.1371/journal.pone.0049073.g006
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Figure 7. Percentage contribution of (A) ROC, (B) SOM and (C) RS to produced and dissolved CO2 in planted microcosms with RS treatment; means ± SD (n = 4).

The differences between contributions to produced and dissolved CH4 were tested by two-tailed independent t-tests, indicated by * when P<0.05.

doi:10.1371/journal.pone.0049073.g007

The fractions of CH4 and CO2 produced from RS (fRS) were calculated using eq. (7). Values of δ13C were obtained from the CH4 (Fig. 4C) and CO2 (Fig. 4D) produced in soil samples from planted microcosms. Values of fRS were determined to be in a range of 12–24% for CH4 production (Fig. 6B) and 11–31% for CO2 production (Fig. 7B).

Finally, fSOM was calculated by difference to fROC and fRS, being in a range of 23–35% of CH4 (Fig. 6C) and 13–26% of CO2 production in soil from planted and straw-treated microcosms (Fig. 7C).

6. Partitioning CH4 and CO2 dissolved in rice microcosms

Similarly as for the production of CH4 and CO2 (see above), the gases dissolved in the rice microcosms were also used for determination of the partitioning of their origin from ROC, RS, and SOM using the equations described above. In this case, values of δ13C were from the CH4 and CO2 dissolved in pore water of planted and unplanted microcosms (Fig. 4A and B). The δ13C of CH4 derived from ROC was −30‰ at tillering stage when calculated with δ13C of CH4 in pore water (Table 2), substantially more positive than that calculated with δ13C of produced CH4 (Table 1). The resulting fROC for CH4 was only 13% (Fig. 6A). In contrast, the relative contribution of RS (fRS) to CH4 dissolved was significantly higher than that for CH4 produced at the tillering stage (Fig. 6B). However, the relative contributions of each carbon source to dissolved and produced CH4 were nearly the same at later season (Fig. 6). For CO2, the δ13C of CO2 derived from ROC was −49‰ at tillering stage, more negative than that calculated with δ13C of produced CO2 (−31‰), but there was no significant difference between the relative contributions of each carbon source to dissolved and produced CO2 (Fig. 7).

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Table 2. δ13C values of CH4 and CO2 derived from ROC in planted rice microcosms with RS application.

doi:10.1371/journal.pone.0049073.t002

Discussion

1. Partitioning of methane production

Our study comprehensively determined the partitioning of CH4 and CO2 production in a rice ecosystem considering all three major carbon sources (i.e., ROC, RS, SOM). In planted and straw-treated rice microcosms, more than 60% of the CH4 was produced from root organic carbon, except on the first sampling date (tillering stage) when it was 41%. Thus, plant photosynthesis was the most important driver of CH4 production. The same was the case for CO2 production. The results are consistent with the observation that CH4 and CO2 production rates were at least twice as high in microcosms with than without rice plants (Fig. 5A and 5B). At the same time, the substantial lower δ13C of CH4 and CO2 produced in planted versus unplanted microcosms also indicated that ROC-derived CH4 and CO2 production diluted the CH4 and CO2 produced from labeled rice straw (Fig. 4C and 4D). Our results are consistent with two earlier experiments reporting 40–60% of the CH4 production being due to plant derived carbon. These experiments were based on pulse-labeling and FACE techniques [11], [13], which potentially influence carbon flux partitioning in a different way than our approach. For instance, pulse-labeling may only account for part of the plant-derived carbon flux and FACE treatment may stimulate carbon flux [13], [14]. Nevertheless, the determined relative contribution of plant derived carbon to production of CH4 and CO2 was rather similar despite the different approaches. Hence, the results that plant-derived carbon is the most important carbon source for CH4 production in flooded rice fields is a rather robust finding.

In contrast to ROC, straw contributed only about 20% to CH4 production. A similar low percentage has previously been found in Japanese rice soil microcosms after 50 days of incubation [4]. Immediately after application of the straw, however, its contribution to CH4 production and emission reached almost 100% [4]. This was likely also the case in our experiments. This conclusion is supported by the following observations: (1) On day 41, δ13C of the produced CH4 was <150‰ albeit the applied rice straw carbon had a δ13C of 474.7‰ (Fig. 4C). The difference is much more than theoretically possible from isotope discrimination during methanogenesis. Therefore, we have to assume that the CH4 produced immediately after straw application had a much higher δ13C as it was derived from straw to a large extent. (2) The analogous observation was made with the produced CO2 (Fig. 4D), although isotope discrimination is much smaller for production of CO2 than of CH4. (3) Still after day 40, δ13C of the produced CH4 and CO2 tended to decrease with vegetation time. Hence, we conclude that contribution of decomposition of straw to CH4 production was very high after straw application and then progressively decreased as the carbon compounds of the straw became increasingly less decomposable. Future studies should further refine the seasonal change in flux partitioning. This will help improving the predictions of CH4 emission rates from rice fields by process-based modeling.

2. Contribution of different carbon sources to the dissolved CH4 and CO2

Previous studies reported that δ13C values of pore water CH4 and emitted CH4 were relatively poor proxies for those of produced CH4 [32], [33]. This assessment is plausible, since in rice field soil pore water CH4 and emitted CH4 are not only affected by CH4 production, but also by CH4 oxidation [34][36] and CH4 transport [37][39], which all undergo carbon isotopic fractionation. Therefore, we primarily used the CH4 produced in soil samples for determining flux partitioning. However, we found that not only the data of the produced CH4 but also of the dissolved CH4 allowed determination of flux partitioning and resulted in similar values. Thus, more than 60% of the CH4 and CO2 dissolved in soil pore water were derived from root organic carbon after tillering stage, nearly the same as for produced CH4 and CO2 (Fig. 6 and 7).

At tillering stage, however, the relative contribution of ROC to the dissolved CH4 was significantly lower and that of RS significantly higher when compared to the contribution to the produced CH4. The difference was probably due to the gas transport limitation of rice plants at the early vegetative stage [32], [40]. The residence time of CH4 in pore water at tillering stage can amount to several days. Therefore, at day 41 the pore water was probably still highly enriched in 13CH4 which had been produced from RS at earlier time. This conclusion is consistent with the substantially higher δ13C values of the dissolved CH4 than those of the produced CH4 at day 41 (Fig 4A and 4C). As a result, the relative contribution of RS to dissolved CH4 was higher than to produced CH4 at day 41 and that of ROC was lower (Fig. 6B).

In contrast, at later growth season, the residence time of CH4 in pore water of planted soil was much shorter (several hours) [32], this was consistent with the rapid decrease of δ13C values of dissolved CH4 and CO2 after tillering stage. Furthermore, the δ13C values of dissolved and produced CH4 were similar with each other after the tillering stage (Fig. 4A and 4C). Therefore, the relative contributions of each carbon source to dissolved and produced CH4 were similar to each other (Fig. 6). This suggested that pore water CH4 could be used as a proxy for produced CH4 and could be suitable for partitioning the CH4 production after tillering stage.

3. Stable carbon isotope fractionation during CH4 production from ROC

The δ13C of the CH4 produced from ROC (δ13CCH4-ROC) were in a range of −67‰ to −49‰. These values are similar to δ13CCH4 values observed in rice field soil or in incubations of soil slurries [23], [33]. Theoretically the value of δ13CCH4-ROC should be equal to the δ13C of ROC plus the overall isotopic enrichment factor (εROC,CH4) for the conversion of ROC to CH4. The δ13CROC should be similar to the δ13C of the rice plant biomass (Fig. 1). Using these values and the δ13CCH4-ROC, the overall enrichment factor εROC,CH4 was in a range of about −24‰ to −42‰. This is a rather large range, but has been observed before (about −20‰ to −75‰) during anaerobic decomposition of straw in paddy soil [41] or anoxic incubations of rice roots [42]. The overall enrichment factor εROC,CH4 is composed of (1) the enrichment factors involved in the conversion of ROC to the methanogenic substrates (i.e., acetate and H2/CO2) and (2) in the enrichment factors involved in the conversion of the methanogenic substrates to CH4. The latter enrichment factors are the larger ones, in particular those involved in the production of CH4 from H2/CO2 [23], [43]. Whereas acetoclastic methanogenesis has relatively moderate enrichment factors (−10‰ to −25‰), those of hydrogenotrophic methanogenesis are often very large (−25‰ to −90‰) [22]. Our data suggest that CH4 production from ROC is dominated by hydrogenotrophic methanogenesis, which is consistent with earlier observations studying CH4 production on rice roots [42], [44], [45].

The δ13C of the CO2 produced from root organic carbon was in a range of −31‰ to −4‰ (Table 1). The overall isotopic enrichment factors involved in CO2 production from organic matter were thus about −6‰ to +21‰. These enrichment factors are much smaller than those involved in CH4 production. Nevertheless, the range is similarly large, which may be due to carbon isotopic fractionation during CO2 consumption by hydrogenotrophic methanogenesis [23] and also during reactions between gaseous CO2 and bicarbonate/carbonate [46].

4. Practical considerations

Our study demonstrated the possibility to determine the partitioning of CH4 and CO2 flux from degradation of straw, soil organic matter, and plant root-derived carbon, by treating soil with 13C-labeled rice straw. The procedure is more practical than labeling of the rice plants with 13CO2 that requires cumbersome incubation techniques or expensive FACE treatment. For calculation of fROC, it was important that the δ13C of the two RS applications were sufficiently different from each other, and in addition were sufficiently different from the δ13C of both ROC and SOM. This was achieved by two RS treatments using the same amount of RS but 13C-labeled to different extent. As a result, the δ13C of emitted CH4 (Fig. 2B), δ13C of dissolved and produced CH4 and CO2 (Fig. 4) were substantially higher than the control without RS, and of course they were always higher in treatment II than treatment I.

Calculation of fRS was simply achieved by using the δ13C values of the applied RS and the CH4 derived from the two RS treatments (Eq. 7) assuming that ROC was not differently affected by the two RS treatments. This assumption was in agreement with the observation that the 13C values of the rice plants in the two RS treatments were not significantly different (Fig 1). Notably, these values were significantly higher than those in the control microcosms without RS, probably because some of the RS carbon was assimilated (probably via CO2) by the plants [20], [21]. However, the difference was only a few permil and did not prevent computation of flux partitioning, since the difference to the δ13C of the labeled RS was quite large.

In summary, application of labeled RS may be a convenient technique to determine flux partitioning in rice fields on a routine basis. The determination requires in total three planted field plots and three unplanted ones, i.e., two RS treatments and one untreated control, everything with appropriate replication. Technical installation is not required. Hence, it should be feasible to increase the data basis on the partitioning of CH4 production from ROC, RS and SOM on a regional and seasonal scale. This will help improving process-based modeling of CH4 emission from rice fields.

Acknowledgments

We thank P. Claus and M. Klose for laboratory technical assistance, R. Angel for help in statistical analysis.

Author Contributions

Conceived and designed the experiments: QY RC. Performed the experiments: QY. Analyzed the data: QY RC. Contributed reagents/materials/analysis tools: JP. Wrote the paper: QY RC.

References

  1. 1. Cai ZC, Tsuruta H, Minami K (2000) Methane emission from rice fields in China: Measurements and influencing factors. Journal of Geophysical Research-Atmospheres 105: 17231–17242. doi: 10.1029/2000jd900014
  2. 2. Lelieveld J, Crutzen PJ, Dentener FJ (1998) Changing concentration, lifetime and climate forcing of atmospheric methane. Tellus Series B-Chemical and Physical Meteorology 50: 128–150. doi: 10.1034/j.1600-0889.1998.t01-1-00002.x
  3. 3. Kimura M, Murase J, Lu YH (2004) Carbon cycling in rice field ecosystems in the context of input, decomposition and translocation of organic materials and the fates of their end products (CO2 and CH4). Soil Biology & Biochemistry 36: 1399–1416. doi: 10.1016/j.soilbio.2004.03.006
  4. 4. Watanabe A, Takeda T, Kimura M (1999) Evaluation of origins of CH4 carbon emitted from rice paddies. Journal of Geophysical Research 104: 23623–23629. doi: 10.1029/1999jd900467
  5. 5. Sass RL, Fisher FM, Harcombe PA, Turner FT (1991) Mitigation of methane emissions from rice fields: Possible adverse effects of incorporated rice straw. Global Biogeochemical Cycles 5: 275–287. doi: 10.1029/91gb01304
  6. 6. Schütz H, Holzapfel-Pschorn A, Conrad R, Rennenberg H, Seiler W (1989) A 3-year continuous record on the influence of daytime, season, and fertilizer treatment on methane emission rates from an Italian rice paddy. Journal of Geophysical Research 94: 16405–16416. doi: 10.1029/jd094id13p16405
  7. 7. Yagi K, Minami K (1990) Effect of organic matter application on methane emission from some Japanese paddy fields. Soil Science and Plant Nutrition 36: 599–610. doi: 10.1080/00380768.1990.10416797
  8. 8. Fumoto T, Kobayashi K, Li C, Yagi K, Hasegawa T (2008) Revising a process-based biogeochemistry model (DNDC) to simulate methane emission from rice paddy fields under various residue management and fertilizer regimes. Global Change Biology 14: 382–402. doi: 10.1111/j.1365-2486.2007.01475.x
  9. 9. Li C, Mosier A, Wassmann R, Cai Z, Zheng X, et al. (2004) Modeling greenhouse gas emissions from rice-based production systems: Sensitivity and upscaling. Global Biogeochemical Cycles 18: GB1043. doi: 10.1029/2003gb002045
  10. 10. Dannenberg S, Conrad R (1999) Effect of rice plants on methane production and rhizospheric metabolism in paddy soil. Biogeochemistry 45: 53–71. doi: 10.1007/bf00992873
  11. 11. Minoda T, Kimura M, Wada E (1996) Photosynthates as dominant source of CH4 and CO2 in soil water and CH4 emitted to the atmosphere from paddy fields. Journal of Geophysical Research 101: 21091–21097. doi: 10.1029/96jd01710
  12. 12. Watanabe A, Machida N, Takahashi K, Kitamura S, Kimura M (2004) Flow of photosynthesized carbon from rice plants into the paddy soil ecosystem at different stages of rice growth. Plant and Soil 258: 151–160. doi: 10.1023/b:plso.0000016545.36421.bc
  13. 13. Tokida T, Adachi M, Cheng WG, Nakajima Y, Fumoto T, et al. (2011) Methane and soil CO2 production from current-season photosynthates in a rice paddy exposed to elevated CO2 concentration and soil temperature. Global Change Biology 17: 3327–3337. doi: 10.1111/j.1365-2486.2011.02475.x
  14. 14. Meharg AA (1994) A critical-review of labeling techniques used to quantify rhizosphere carbon-flow. Plant and Soil 166: 55–62. doi: 10.1007/bf02185481
  15. 15. Ostle N, Briones MJI, Ineson P, Cole L, Staddon P, et al. (2007) Isotopic detection of recent photosynthate carbon flow into grassland rhizosphere fauna. Soil Biology & Biochemistry 39: 768–777. doi: 10.1016/j.soilbio.2006.09.025
  16. 16. Denef K, Bubenheim H, Lenhart K, Vermeulen J, Van Cleemput O, et al. (2007) Community shifts and carbon translocation within metabolically-active rhizosphere microorganisms in grasslands under elevated CO2. Biogeosciences 4: 769–779. doi: 10.5194/bg-4-769-2007
  17. 17. Tokida T, Fumoto T, Cheng W, Matsunami T, Adachi M, et al. (2010) Effects of free-air CO2 enrichment (FACE) and soil warming on CH4 emission from a rice paddy field: impact assessment and stoichiometric evaluation. Biogeosciences 7: 2639–2653. doi: 10.5194/bg-7-2639-2010
  18. 18. Carney KM, Hungate BA, Drake BG, Megonigal JP (2007) Altered soil microbial community at elevated CO2 leads to loss of soil carbon. Proceedings of the National Academy of Sciences of the United States of America 104: 4990–4995. doi: 10.1073/pnas.0610045104
  19. 19. Langley JA, McKinley DC, Wolf AA, Hungate BA, Drake BG, et al. (2009) Priming depletes soil carbon and releases nitrogen in a scrub-oak ecosystem exposed to elevated CO2. Soil Biology & Biochemistry 41: 54–60. doi: 10.1016/j.soilbio.2008.09.016
  20. 20. Chidthaisong A, Watanabe I (1997) Methane formation and emission from flooded rice soil incorporated with 13C-labeled rice straw. Soil Biology & Biochemistry 29: 1173–1181. doi: 10.1016/s0038-0717(97)00034-5
  21. 21. Watanabe A, Yoshida M, Kimura M (1998) Contribution of rice straw carbon to CH4 emission from rice paddies using 13C-enriched rice straw. Journal of Geophysical Research 103: 8237–8242. doi: 10.1029/97jd03460
  22. 22. Conrad R (2005) Quantification of methanogenic pathways using stable carbon isotopic signatures: a review and a proposal. Organic Geochemistry 36: 739–752. doi: 10.1016/j.orggeochem.2004.09.006
  23. 23. Penning H, Conrad R (2007) Quantification of carbon flow from stable isotope fractionation in rice field soils with different organic matter content. Organic Geochemistry 38: 2058–2069. doi: 10.1016/j.orggeochem.2007.08.004
  24. 24. Conrad R, Noll M, Claus P, Klose M, Bastos WR, et al. (2011) Stable carbon isotope discrimination and microbiology of methane formation in tropical anoxic lake sediments. Biogeosciences 8: 795–814. doi: 10.5194/bg-8-795-2011
  25. 25. Whiticar MJ, Faber E, Schoell M (1986) Biogenic methane formation in marine and fresh-water environments: CO2 reduction vs acetate fermentation - isotope evidence. Geochimica Et Cosmochimica Acta 50: 693–709. doi: 10.1016/0016-7037(86)90346-7
  26. 26. Oleary MH (1981) Carbon isotope fractionation in plants. Phytochemistry 20: 553–567. doi: 10.1016/0031-9422(81)85134-5
  27. 27. Krüger M, Frenzel P, Conrad R (2001) Microbial processes influencing methane emission from rice fields. Global Change Biology 7: 49–63. doi: 10.1046/j.1365-2486.2001.00395.x
  28. 28. Holzapfel-Pschorn A, Seiler W (1986) Methane emission during a cultivation period from an Italian rice paddy. Journal of Geophysical Research 91: 11803–11814. doi: 10.1029/jd091id11p11803
  29. 29. Bodelier PLE, Hahn AP, Arth IR, Frenzel P (2000) Effects of ammonium-based fertilisation on microbial processes involved in methane emission from soils planted with rice. Biogeochemistry 51: 225–257.
  30. 30. Brand WA (1995) Precon: a fully automated interface for the pre-GC concentration of trace gases in air for isotopic analysis. Isotopes in Environmental and Health Studies 31: 277–284. doi: 10.1080/10256019508036271
  31. 31. Brand WA (1996) High precision isotope ratio monitoring techniques in mass spectrometry. Journal of Mass Spectrometry 31: 225–235. doi: 10.1002/(sici)1096-9888(199603)31:3<225::aid-jms319>3.0.co;2-l
  32. 32. Conrad R, Klose M (2005) Effect of potassium phosphate fertilization on production and emission of methane and its 13C-stable isotope composition. Soil Biology & Biochemistry 37: 2099–2108. doi: 10.1016/j.soilbio.2005.03.012
  33. 33. Krüger M, Eller G, Conrad R, Frenzel P (2002) Seasonal variation in pathways of CH4 production and in CH4 oxidation in rice fields determined by stable carbon isotopes and specific inhibitors. Global Change Biology 8: 265–280. doi: 10.1046/j.1365-2486.2002.00476.x
  34. 34. Barker JF, Fritz P (1981) Carbon isotope fractionation during microbial methane oxidation. Nature 293: 289–291. doi: 10.1038/293289a0
  35. 35. Templeton AS, Chu KH, Alvarez-Cohen L, Conrad ME (2006) Variable carbon isotope fractionation expressed by aerobic CH4-oxidizing bacteria. Geochimica Et Cosmochimica Acta 70: 1739–1752. doi: 10.1016/j.gca.2005.12.002
  36. 36. Zhang XY, Zhang GB, Ji Y, Ma J, Xu H, et al. (2012) Straw application altered CH4 emission, concentration and 13C-isotopic signature of dissolved CH4 in a rice field. Pedosphere 22: 13–21. doi: 10.1016/s1002-0160(11)60187-2
  37. 37. Chanton JP (2005) The effect of gas transport on the isotope signature of methane in wetlands. Organic Geochemistry 36: 753–768. doi: 10.1016/j.orggeochem.2004.10.007
  38. 38. Chanton JP, Whiting GJ, Blair NE, Lindau CW, Bollich PK (1997) Methane emission from rice: Stable isotopes, diurnal variations, and CO2 exchange. Global Biogeochemical Cycles 11: 15–27. doi: 10.1029/96gb03761
  39. 39. Tyler SC, Bilek RS, Sass RL, Fisher FM (1997) Methane oxidation and pathways of production in a Texas paddy field deduced from measurements of flux, δ13C, and δD of CH4. Global Biogeochemical Cycles 11: 323–348. doi: 10.1029/97gb01624
  40. 40. Watanabe A, Satoh Y, Kimura M (1995) Estimation of the increase in CH4 emission from paddy soils by rice straw application. Plant and Soil 173: 225–231. doi: 10.1007/bf00011459
  41. 41. Conrad R, Klose M, Yuan Q, Lu Y, Chidthaisong A (2012) Stable carbon isotope fractionation, carbon flux partitioning and priming effects in anoxic soils during methanogenic degradation of straw and soil organic matter. Soil Biology and Biochemistry 49: 193–199. doi: 10.1016/j.soilbio.2012.02.030
  42. 42. Penning H, Tyler SC, Conrad R (2006) Determination of isotope fractionation factors and quantification of carbon flow by stable carbon isotope signatures in a methanogenic rice root model system. Geobiology 4: 109–121. doi: 10.1111/j.1472-4669.2006.00075.x
  43. 43. Penning H, Conrad R (2006) Carbon isotope effects associated with mixed-acid fermentation of saccharides by Clostridium papyrosolvens. Geochimica Et Cosmochimica Acta 70: 2283–2297. doi: 10.1016/j.gca.2006.01.017
  44. 44. Chin KJ, Lueders T, Friedrich MW, Klose M, Conrad R (2004) Archaeal community structure and pathway of methane formation on rice roots. Microbial Ecology 47: 59–67. doi: 10.1007/s00248-003-2014-7
  45. 45. Lehmann-Richter S, Grosskopf R, Liesack W, Frenzel P, Conrad R (1999) Methanogenic archaea and CO2-dependent methanogenesis on washed rice roots. Environmental Microbiology 1: 159–166. doi: 10.1046/j.1462-2920.1999.00019.x
  46. 46. Deuser WG, Degens ET (1967) Carbon isotope fractionation in the system CO2(gas)-CO2(aqueous)-HCO3(aqueous). Nature 215: 1033–1035. doi: 10.1038/2151033a0