Past water management affected GHG production and microbial community pattern in Italian rice paddy soils

Soil Biology & Biochemistry 93 (2016) 17e27

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Past water management affected GHG production and microbial community pattern in Italian rice paddy soils A. Lagomarsino a, *, A.E. Agnelli a, R. Pastorelli a, G. Pallara b, D.P. Rasse c, H. Silvennoinen c a

Consiglio per la Ricerca in Agricoltura e l'Analisi dell'Economia Agraria, Centre for Agrobiology and Pedology (CREA-ABP), P.zza M. D'Azeglio 30, Firenze, Italy b Department of Agrifood Production and Environmental Sciences, University of Florence, P.zzale delle Cascine 18, Firenze, Italy c Norwegian Institute for Bioeconomy Research e NIBIO, Høgskoleveien 7, 1430 Ås, Norway

a r t i c l e i n f o

a b s t r a c t

Article history: Received 13 March 2015 Received in revised form 16 October 2015 Accepted 25 October 2015 Available online 6 November 2015

The water management system of cultivated paddy rice soils is one of the most important factors affecting the respective magnitudes of CH4 and N2O emissions. We hypothesized an effect of past management on soil microbial communities and greenhouse gas (GHG) production potential. The objectives of this study were to i) assess the influence of water management history on GHG production and microbial community structure, ii) relate GHG production to the microbial communities involved in CH4 and N2O production inhabiting the different soils. Moreover, the influence of different soil conditioning procedures on GHG production was determined. To reach these aims, we compared four soils with different water management history, using dried and sieved, pre-incubated and fresh soils. Soil conditioning procedures strongly affected GHG production: drying and sieving induced the highest production rates and the largest differences among soil types, probably through the release of labile substrates. Conversely, soil pre-incubation tended to homogenize and level out the differences among soils. The water management history strongly affected microbial community structure, which was itself tightly linked to CH4 and N2O production. N2O production was the highest in aerobic soil, which also exhibited the strongest evidence for active nitrifying communities (NirK). Drying and rewetting aerobic soil enhanced the production of nitrate, which was further reduced to N2O through denitrification. As expected, CH4 production was the lowest in aerobic soil, which showed a less abundant archaeal community. This work supports the hypothesis that microbial communities in paddy soils progressively adapt to water management practices, thereby reinforcing potential differences in GHGs production. © 2015 Elsevier Ltd. All rights reserved.

Keywords: Rice paddies CH4 N2O and CO2 production Archaeal and methanogens communities Nitrifying and denitrifying microbial communities

1. Introduction Agricultural activities significantly contribute to emissions of key greenhouse gases (GHGs): carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O) (Smith et al., 2007). Aerobic arable soils mainly emit CO2 and N2O, the latter being released in direct proportion of N fertilizer input (van Groenigen et al., 2010). By contrast, irrigated rice cropping systems under mostly anaerobic conditions are responsible for 5e20 per cent of CH4 emission from all anthropogenic sources (Tilman et al., 2001), and CH4 emissions

* Corresponding author. Tel.: þ39 055 2491232. E-mail address: [email protected] (A. Lagomarsino). http://dx.doi.org/10.1016/j.soilbio.2015.10.016 0038-0717/© 2015 Elsevier Ltd. All rights reserved.

contribute the majority of global warming potential (GWP) in rice systems (Linquist et al., 2012). The CH4 emission from irrigated rice fields is controlled by production, oxidation and transport processes (Krüger et al., 2001). Following flooding, strictly anaerobic methanogenesis (either acetoclastic or hydrogenotrophic) produces CH4 as terminal product of anaerobic mineralization of soil organic matter (SOM) degradation, in the absence of alternative electron acceptors (O2, NO
3 , Fe(III), and SO2
4 ) or microbes capable of using those. The CH4 produced in anoxic deep soil layers can be oxidized to CO2 by methanotrophic bacteria while diffusing upwards through oxic soil/water layers. By contrast, N2O is produced by soil microbes under both aerobic and anaerobic conditions, through multiple chemical and biological processes, including nitrification (autotrophic and heterotrophic), denitrification, nitrifieredenitrification, co-

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denitrification, nitrate ammonification, chemodenitrification of soil nitrite and abiotic decomposition of ammonium nitrate, as reviewed by Butterbach-Bahl et al. (2013). Of these processes, nitrification has a dual role in direct N2O production (side-product in oxic conditions; end product in nitrifier denitrification in anoxic conditions) and in indirect production by supplying NO
3 to denitrification. Both nitrifying and denitrifying bacteria have genes coding for each of the enzymes participating in the oxidative and reductive steps. For example, nirK is pivotal for N cycle processes, as it plays a role in ammonia oxidization to nitrite in nitrification (Nitrosomonas europaea), and regulates dissimilatory nitrate reduction in denitrification. Hence, nirK abundance by itself cannot be used to apportion in situ N2O emissions from denitrification vs. nitrification sources, but it is a valuable indicator of the potential for one or both of these processes to occur. In depth analysis of the site dependent differences in gas kinetics and in gene abundance, as we attempted in this study, can provide more information on the actual functional differences of the studied sites. However, in general N2O emissions are very low in permanently flooded rice paddies due to inhibited nitrification and usage of ammonium rather than nitrate based fertilizers (Smith, 1982; Zou et al., 2005), whereas peaks occur during alternate wetting and drying or midseason drainage (Cai et al., 1997; Zheng et al., 2000; Zou et al., 2005). The water management system under which rice is grown is therefore one of the most important factors affecting the respective magnitudes of CH4 and N2O emissions. Field drainage, while significantly reducing CH4, may actually increase N2O emissions under conditions promoting nitrification and denitrification (Kudo et al., 2014). Therefore, effects of water management techniques on emissions need to be better understood for developing climate-friendly management techniques for rice production. For example, water management may strongly affect microbial communities present in the soil and thereby C and N cycling processes and their impact on field-scale GHG emissions. Moreover, in the case of rice paddies, previous land use may strongly affect GHG fluxes and the relative importance of CH4 with respect to other gases. Several authors reported delayed or minor CH4 emissions when paddy fields were previously managed under
ska et al., 2014; aerobic conditions (Hatala et al., 2012; Brzezin Pittelkow et al., 2014). Even if methanogen communities can persist in soil during dry periods (Angel et al., 2011), a full development of methanogen communities appears to require a certain
ska et al., 2014). However, as indicated by amount of time (Brzezin Watanabe et al. (2011) the ecology of methanogens in soil under non-flooded condition is still not fully understood. Even less is known about nitrifier/denitrifier communities in soils under different water regimes and crops cultivation. The present manuscript therefore intends to cover this knowledge gap by specifically focusing on the impact of past management on GHG production and soil microbial communities in anoxic environments. To our knowledge no other articles specifically approached this topic on rice paddies, even if legacy effects of past management have been regarded as important for mitigating GHG emissions in croplands (Ogle et al., 2014) and microbial community composition (Jangid et al., 2011). We hypothesized that soil history in terms of i) water management, ii) type of cultivation and iii) duration of flooding affects the microbial community composition, which requires an adaptation period in the response to new management practices. Laboratory approaches are useful to understand soil processes because the controlled conditions allow us to analyze responses that can otherwise be masked by the high level of heterogeneity and variability encountered in the field (Schaufler et al., 2010). However, soil storage and conditioning procedures may strongly affect results, through physico-chemical and microbiological

changes. In order to reduce possible artifacts, soil microbial activity is usually determined in fresh samples (Trasar-Cepeda et al., 2000). Nevertheless, air-drying is preferred for practical reasons allowing soil to be stable during storage with minimum cost and may significantly reduce variability among soil samples collected at different moisture content (Haney et al., 2004). Air drying and sieving can cause a temporary increase of mineralization of organic matter released from broken soil aggregates (Hassink, 1992; Degens, 1998). Moreover, rewetting of air-dried soil causes slaking, i.e. increased breakdown of soil aggregates, which leads to a release of mineralizable organic matter. Thus, the comparison of different preparation methods for soil samples can provide specific information on processes affecting GHG emissions. To assess the influence of soil management on N2O and CH4 potential production, four soils with different history of water management were compared, using three different soil conditioning procedures. The objectives of this study were to i) assess the influence of water management history on GHG production potential and microbial community structure, ii) relate the microbial communities inhabiting the different soils to CH4 and N2O production and iii) determine the influence of different soil conditioning procedures on GHG production measurement. By means of ad hoc laboratory approach, the work aimed to present a comprehensive view of the interactions among GHG production, microorganisms involved and past management. The effect of past water management systems in rice paddies on the CH4 and N2O producers adaptation and consequent GHG production has rarely been addressed, as present literature mainly focuses on seasonal dynamics (Jiao et al., 2006;  et al., 2012; Breidenbach and Conrad, 2014 this last on CH4 Ferre only). 2. Materials and methods 2.1. Experimental design Four fields were selected at the Cantaglia experimental farm  Italiana Sementi-SIS, Bologna, Italy), characterized by (Societa different history of water management: PF-2 (2 years old rice paddy, permanently flooded), PF-1 (1 year old rice paddy, permanently flooded), AF-1 (1 year old rice paddy, alternately flooded and dried), NEVER (aerobic field, never flooded). From each field three replicates were collected at 0e15 cm depth. Soils from the four fields are mesic Thapto-Histic Fluvaquent (Soil Survey Staff, 2010) and show similar texture (14% sand, 42% clay, 44% silt) and pH 8.3. Other chemical characteristics are reported in Table 1. Three different soil conditioning procedures were compared: DRY (dried soils sieved at 2 mm), PREINCUBATED (dried soils sieved at 2 mm with 1 week preincubation 1:1.5 soil:water ratio), FRESH (undisturbed fresh soils). 2.2. GHG analysis For each experiment a set of twelve 120 mL serum flasks were prepared with 1:1.5 soil:water ratio (v:v). The headspace air was replaced with 99% He þ 1% O2. The incubation system is a thermostated water bath at 25  C with positions for crimp-sealed serum flasks (120 mL) with magnetic stirring. Headspace gas was sampled every 4 h by a CTC GC-PAL autosampler and a peristaltic pump and measured by a GC (Model 7890A, Agilent, Santa Clara, CA, US) equipped with 20 m wide-bore (0.53 mm diameter) Poraplot Q column, 30 m 5 Å mol sieve (0.53 mm diameter), 2 HayeSep columns for backflushing water, a thermal conductivity detector (TCD) for analysing CO2, O2 and N2, an electron capture detector

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Table 1 Main chemical characteristics of selected soils. No significant differences were observed among soils. Standard errors are reported in italics. TOC %

PF-2 PF-1 AF-1 NEVER

TN %

C/N

NH4 mg N g
1

pH

NO3

mean

s.e.

mean

s.e.

mean

s.e.

mean

s.e.

Mean

s.e.

Mean

s.e.

1.273 1.408 1.361 1.284

0.05 0.03 0.03 0.07

0.123 0.149 0.145 0.129

0.008 0.001 0.001 0.007

10.4 9.4 9.4 10.0

0.3 0.1 0.2 0.3

8.31 8.20 8.21 8.44

0.01 0.02 0.05 0.10

1.66 2.10 1.81 1.91

0.5 0.3 0.4 0.5

0.91 0.65 0.84 0.81

0.2 0.2 0.2 0.2

Table 2 PCR primers and amplification annealing temperatures used in this study. Target gene

For DGGE analysis 16S rDNA Bacteria 16S rDNA Archaea 16S rDNA Archaea nirK Bacteria amoA Bacteria amoA Archaea For Real time PCR 16S rDNA Archaea 16S rDNA Bacteria a

PCR conditions

Reference

Primers

Annealing

a

986f, UNI1401r 1106F, 1378R a 0357F, 0691R F1aCu, aR3Cu a amoA1F, amoA2R Arch-amoAF, aArch-amoAR

56 55 55 57 57 55

uniMet1F, uniMet1R 341F, 534R

60  C 60  C

a



C C  C  C  C  C 

Felske and Akkermans, 1998 Watanabe et al., 2006 Watanabe et al., 2004 Throb€ ack et al., 2004 Xu et al., 2012 Xu et al., 2012 Zhou et al., 2009 Muyzer et al., 1993

A GC-clamp was added for DGGE analysis.

(ECD) for analysing N2O, a flame injection analyser (FID) for analysing CH4. Gas production rates were computed from raw data following Molstad et al. (2007). 2.3. DNA extraction and denaturing gradient gel electrophoresis (DGGE) analysis DNA was extracted from FRESH undisturbed soil using the FastDNA®SPIN Kit for Soil (MP Biomedicals) and amplified using the primer sets reported in Table 2. For Archaea phylum we used two different primer sets: 1106F/1378R targeting methanogenic as well non-methanogenic archaeal 16S rRNA genes (Feng et al., 2013) was used to detect soil archaeal community whereas 0357F/0691R more specific for methanogenic Archaea 16S rRNA genes, was used to detect soil methanogenic community. Amplification reactions were carried out in a MJ Research PTC200™ thermocycler (Bio-Rad) in a 25-ml mixture containing 2 ml of template DNA, 1.5 mmol L
1 MgCl2, 200 mmol L
1 of each deoxynucleotide triphosphate (dNTP) (Promega Corporation), 10 pmol of each primer (TIB MolBiol), 1  green GoTaq®flexi buffer (Promega), 1 U of GoTaq®polymerase (Promega), under reaction conditions of 94  C for 4 min followed by 35 cycles of denaturation at 95  C for 45 s, annealing (specific temperature are reported Table 2) for 45 s, extension at 72  C for 45 s, and final extension at 72  C for 7 min. Three independent PCR amplifications were performed for each primer set and each soil sample and the triplicate amplification products were pooled to minimize the effect of PCR biases. Amplicon yields were estimated by comparison of amplified DNA to Low DNA mass ladder (Invitrogen) using the Chemidoc Apparatus (Bio-Rad). DGGEs were performed loading 500 ng of amplicons onto a polyacrylamide gel (acrylamide/bis 37,5:1; Euroclone), with a linear denaturing gradients obtained with an 100% denaturing solution containing 40% formamide (Euroclone) and 7 M Urea (Euroclone). The gels were run for 17 h in 1X TAE buffer at constant voltage (80 V) and temperature (60  C), using the INGENY phorU-2 System (Ingeny International BV). At the end, gels were stained with SYBR®GOLD (Molecular Probes) diluted 1:1000 in 1  TAE and the gel images digitalized using the Chemidoc Apparatus.

2.4. Quantitative real-time PCR analysis Real-time PCR amplification for enumeration of methanogenic archaeal 16S rRNA gene was performed using total bacterial 16S rRNA gene as reference gene according to Guo et al. (2008). Amplifications were performed in triplicate using a MJ Research PTC200™ Chromo4 thermocycler (MJ Research) in a 25-ml mixture containing 2  SsoAdvanced Universal SYBR®Green Supermix (BioRad), 400 nmol L
1 of each primer (Table 2) and 5 ng of template DNA. The amplification conditions involved denaturation at 95  C for 3 min, followed by 40 cycles of 95  C for 15 s and 60  C for for 30 s and fluorescence data were collected at the end of the hybridization step. Optimization of assay conditions were performed for both primers and template DNA concentration and a linear regression of the threshold cycle (CT) for different DNA dilution vs the log dilution of pooled DNA was used to estimate PCR efficiency (E ¼ 10
1/slope) for each primer pair (Pfaffl, 2001) where “5” is the fold dilution. Negative DNA standards were included in each qPCR run to prove specificity of the primer pairs. Amplicon specificity was tested with a dissociation curve analysis by increasing the temperature of 0.5  C every 30 s from 65 to 95  C. Data output released by Opticon Monitor software (version 2.03 MJ Research). The relative abundance of methanogenic archaeal community was calculated using the 2
DDCT method (Pfaffl, 2001) because efficiencies of the amplification curves of the bacterial and archaeal 16S genes were equal (E ¼ 1.96). For each sample, a DCT value was calculated subtracting the bacterial 16S CT (reference gene) from the archaeal 16S CT (target gene). DCT values from PF-2 soil samples were averaged and used as calibrator (Pfaffl, 2001) in the equation: DDCT ¼ DCT(given sample)
DCT(calibrator). Soils with a 2
DDCT value below 1 have lower than PF-2 methanogenic archaeal abundance, while soils with a 2
DDCT value above 1 have higher than PF-2 methanogenic archaeal abundance. 2.5. Statistical analysis General Linear Model analysis and Fisher LSD post-hoc test were applied to test for significant differences in soil chemical properties

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and CH4, CO2 and N2O cumulative emissions among different soils and incubation procedures (StatSoft Statistica software package). DGGE gels were normalized and analyzed using GelCompar II software v 4.6 (Applied Maths, Sint-Martens-Latem, Belgium). A hierarchical cluster analysis based on position and presence/ absence of bands in the different profiles was performed using Dice coefficient and the unweighted pair group method using arithmetic average (UPGMA) algorithm by GelCompar. Band-matching data with band intensities were standardized by calculating relative intensity of each band (ratio of intensity of each band versus the total band intensity) and imported into PAST software (Hammer et al., 2001; http://folk.uio.no/ohammer/past) and used for further statistical analysis. Non-metric multidimensional scaling (nMDS) representations generated from BrayeCurtis similarity matrices in PAST were used to represent the distance between each sample in two-dimensional space. nMDS was performed using 9999 random starting configurations of sample points; the accuracy of the nMDS plots was determined by calculating a 2D stress value. One-way analysis of similarity (ANOSIM) and permutational multivariate analysis of variance (PERMANOVA) were used to examine statistical significance between DGGE profiles in order to determine difference in the microbial community structure due to water managements history. ANOSIM and PERMANOVA analyses were performed in PAST with the BrayeCurtis distance measure and 9999 permutation tests. Finally, binary matrices of PCR-DGGEs were related to soil gas production (CO2, CH4, N2O) by canonical correspondence analysis (CCA) performed with PAST in order to find potential connection between potential GHG production and microbial community composition. DGGE bands were used as “species” data (filled symbols), while soil gas production as “environmental” variables (vectors); the statistical significance was assessed using a 999 permutation test. The distances between the symbols reflect their dissimilarity; a symbol's position in relation to a vector head is a function of the correlation between the gas and the microbial species. The length of a vector reflects the relative importance of that gas in discriminating the microbial community of the different soils (Zhang et al., 2008). To identify the taxa that mainly contributes to the separation of the microbial communities of the different soils, DGGE band scores were plotted on the ordination diagrams. A one-way ANOVA analysis was performed to compare the methanogenic 16S gene abundance among soil samples using STATISTICA 7.0 (StatSoft Inc.) statistical software package and means were compared by Least Significant Difference (LSD) test at P < 0.05.

Fig. 1. Kinetic of CH4 production from PF-2, PF-1, AF-1 and NEVER soils with DRY (top), PRE-INCUBATED (middle) and FRESH (bottom) incubation.

3. Results 3.1. GHG kinetics 3.1.1. CH4 CH4 accumulation showed a first order kinetics and increased until the end of incubation (Fig. 1). Among soil conditioning procedures, the maximum cumulated values showed the ranking DRY > FRESH > PREINCUBATED (Table 3). Soils were significantly different, with PF-2 showing the maximum values, significantly different from NEVER soil within all incubations. PF-1 and AF-1 soils showed similar maximum cumulated values, with intermediate ranking. 3.1.2. N2O N2O accumulated during an initial phase followed by a decrease to initial values (Fig. 2). Overall, soil conditioning significantly affected N2O kinetics, and DRY incubation showed the maximum cumulated values of N2O (Table 3). The interaction between soils

and incubations was significant and DRY incubation showed values higher than other incubation methods in NEVER soil only. The NEVER soil displayed the maximum peak of N2O, which was significantly higher than that of the other soils irrespective of incubation procedures. 3.1.3. CO2 CO2 accumulation followed an asymptotic rise towards a plateau, characteristic of first order kinetics (Fig. 3). The highest maximum cumulated values were observed with DRY incubation (Table 3). Within this soil conditioning, soils showed the ranking PF-1 > PF-2 ¼ AF-1 > NEVER, whereas no differences were observed within other incubation procedures. 3.2. Microbial community structure In 16S-DGGE profiles, maximum differentiation was observed for NEVER soil, which was always clearly separated from other soil

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Table 3 Maximum cumulated value of CH4, N2O and CO2 production from PF-2, PF-1, AF-1 and NEVER soils with DRY, PREINCUBATED and FRESH incubation. Capital letters indicate significant differences among incubation procedures within each soil, small letters indicate significant differences among soils within each incubation. Standard errors are reported in italics. Incubation

Soil

CH4 (mg g
1) mean

PF-2 0.173Aa PF-1 0.147 Ab AF-1 0.158 Aab NEVER 0.135 Ab PREINCUBATED PF-2 0.077Ba PF-1 0.074 Bab AF-1 0.074 Bab NEVER 0.055 Bb FRESH PF-2 0.107Ca PF-1 0.091 Cab AF-1 0.094 Cab NEVER 0.075 Bb General linear model (p values) Soil 0.002 Incubation 0.000 Soil  incubation 0.913 DRY

N2O (mg g
1)

CO2 (mg g
1)

s.e.

Mean

s.e.

Mean

s.e.

0.009 0.016 0.008 0.015 0.007 0.000 0.007 0.003 0.006 0.005 0.005 0.003

0.017Aa 0.010Aa 0.011Aa 0.053Ab 0.008Aa 0.003Aa 0.002Aa 0.020Bb 0.003Aa 0.006Aa 0.005Aa 0.017Bb

0.001 0.001 0.001 0.004 0.003 0.000 0.000 0.003 0.001 0.005 0.002 0.013

655.6Aa 1128.7Ab 660.7Aa 451.0Ac 95.0Ba 113.3Ba 116.5Ba 58.5Ba 192.0Ba 142.9Ba 112.2Ba 140.3Ba

125.8 64.9 160.6 68.3 3.8 10.8 9.3 10.3 58.8 29.9 33.9 40.5

0.000 0.000 0.024

0.003 0.000 0.002

types in both UPGMA and nMDS analyses (Fig. 4). A similar trend was observed for nirK-DGGE (Fig. 5) whereas, for amoA-bearing nitrifier communities, the UPGMA and nMDS analyses revealed a main separation between soils under permanent flooding (PF-1 and PF-2) and soils from AF-1 and NEVER (Fig. 5). One-way ANOSIM and PERMANOVA tests (Table 4) showed that in all cases the DGGE bands profiles clustered according to the FRESH undisturbed soils and that the different water managements had a significant effect on final microbial community structure. In order to further characterize the soil microbial communities, canonical correspondence analysis was used to produce a twodimensional plot showing the relationship between microbial community composition obtained from the DGGE banding patterns and GHG production measured on soils. Canonical correspondence analysis reinforced results obtained by multivariate analyses and for both 16S rDNA or N-cycle genes, the first axis clearly separated microbial communities of NEVER soil from the other water managements. The first axis explained a significant proportion of data variation, i.e. 25% (P < 0.03) and 18% (P < 0.01) for 16S rDNA- and N-cycle genes-DGGE, respectively. The relationship among microbial communities composition and explanatory variables differed according to water management history. Thus, N2O production resulted positively correlated to microbial community structure of NEVER soil, while CO2 and CH4 production resulted positively correlated to microbial community structure of permanent flooded soils (PF-1 and PF-2). Moreover, Fig. 6 showed that a large number of archaeal taxa (red and purple dots) are the most important in separating the permanent flooded soils (PF-1 and PF-2) from NEVER soil. 3.3. Relative abundance of methanogenic archaea The correlation coefficients for DNA dilution vs CT were 0.981 and 0.998 for archaeal and bacterial 16S rDNA, indicating no interference of inhibitory substances in purified DNA extracts. The abundance of methanogenic Archaea was estimated by realtime PCR and expressed relative to the abundance of methanogenic community in PF-2 soil (Fig. 8). The methanogenic archaeal community appeared greater in PF-1 and AF-1 soils (increasing by an average of 35% and 39%, respectively, relative to PF-2 soil) and significantly lower in NEVER soil (decreasing by an average of 60% relative to PF-2 soil).

Fig. 2. Kinetic of N2O production from PF-2, PF-1, AF-1 and NEVER soils with DRY (top), PRE-INCUBATED (middle) and FRESH (bottom) incubation.

4. Discussion 4.1. Soil conditioning There was an important effect of soil preparation on GHG production. Drying and sieving soil samples is thought to alter the soil physico-chemical environment and disturbing soil aggregates, may favour gas diffusivity and substrate accessibility (Powlson, 1980; Bottner, 1985). Moreover, drying and rewetting soil is known to influence the size and activity of soil microbial populations (Clark and Hirsch, 2008; Chowdhury et al., 2011), including methanogenic archaea (Conrad, 2002). Higher CO2 fluxes from rewetted dry samples than from moist samples have often been reported (Van Gestel et al., 1991; Franzluebbers and Arshad, 1997); but less is known about the effects of soil conditioning on CH4 and N2O. In our study, higher cumulative production from DRY soil were found for CO2, CH4 and N2O, confirming previous findings. However, the

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observed in the field (Cai et al., 1997; Beare et al., 2009; Kim et al., 2012), but differences due to soil type, N and C availability and microbial community composition may strongly influence the emission response (Harrison-Kirk et al., 2013). NO
3 immobilization can be greater in undisturbed than in physically disturbed soils (Booth et al., 2005), thus less substrate might have been available for denitrification with FRESH incubation. The lack of significant differences in N2O production among incubation procedures from paddy soils and the large peak observed with DRY incubation from aerobic soil therefore suggests an optimal combination between substrate availability and microbial community composition for N2O production. 4.2. Past management history

Fig. 3. Kinetic of CO2 production from PF-2, PF-1, AF-1 and NEVER soils with DRY (top), PRE-INCUBATED (middle) and FRESH (bottom) incubation.

three gases displayed contrasting responses. The largest effects of sieving and drying soils were found for CH4 and CO2, while N2O showed a significant interaction between incubation procedure and soil history. In particular, CH4 sharply increased in the first two days of DRY incubation. The release of physically protected organic matter after sieving, drying and rewetting has been proposed as possible mechanism (Harrison-Kirk et al., 2013) leading to shortterm flush of C mineralization (Franzluebbers et al., 2000; Muhr et al., 2008; Butterly et al., 2010). The consumption of labile substrates during the first week of incubation is consistent with lower maximum cumulated values observed in PREINCUBATED than in DRY and FRESH procedures. Pre-incubation presumably acted at two levels: i) promoted microbial growth and adaptation to incubation conditions and ii) favoured the consumption of the most labile substrates, which were no longer available during the actual incubation period. In contrast, N2O production showed the highest peak and maximum cumulated values with DRY incubation only in NEVER soil. Peaks of N2O with drying/rewetting cycles are commonly

4.2.1. GHG production Water management history significantly affected GHG production rates and the relative contribution of the three gases to the global warming potential (CH4 and N2O in particular). Aerobic soils contribute little to CH4 emissions or may even act as a sink, while they are primary sources of N2O (Linquist et al., 2012). Conversely, in rice systems CH4 is the dominant GHG produced and emitted, largely depending on water and residue management practices (Yagi et al., 1997; Wassmann et al., 2000). Our laboratory incubation confirmed these previous results, as the NEVER soil consistently emitted most N2O irrespective of incubation procedure. Regarding CH4, we observed a consistent trend towards lower production from NEVER soils as compared to soils previously cultivated in anaerobic conditions, especially PF-2 soil, which had the highest emission although not always significantly so as compared to PF-1 and AF-1. However, no clear trend was observed among different water managements (PF-1 and AF-1). This lack of significance can be due to the fairly short duration of our field management systems, and therefore we cannot exclude that a more pronounced response would appear in the longer term. Moreover, when soil is under rotation between aerobic and anaerobic crops, or when water management is implemented to reduce water input, results are often contrasting (Cai et al., 1997; Zou et al., 2007; Lagomarsino et al., in press). Despite the lack of data, some evidence of past water management influences on CH4 and N2O fluxes have been reported. Pittelkow et al. (2013) showed in a rice monoculture that CH4 emissions were higher after winter flooding, assumed to promote early and rapid methanogenesis, primarily due to permanently reduced soil conditions (Yan et al., 2005; Xu and Hosen, 2010). Hatala et al. (2012) reported low CH4 fluxes from a rice paddy and attributed this effect to the lack of labile organic carbon substrate within the soil due to the past land-use history. Besides water management, we cannot exclude an influence of crop type on C and N dynamics and fluxes, or at least an interaction between water management and crop. 4.2.2. Microbial communities and their relationship with GHGs Our results suggest that the soils microbial communities had a specific response to the different water management history. Both bacterial and archaeal communities of NEVER soil always significantly differed in composition from those of flooded soils, even under alternate flooding and drying. Comparable results were obtained by considering the nirK-gene bearing bacterial denitrifying community. The amoA-containing ammonia-oxidizing bacteria and Archaea varied little, showing more similarity between AF-1 and NEVER soils with respect to permanently flooded soils. The amoA Archaeal DGGE profiles resulted in a higher number of bands than amoA bacterial DGGE profiles confirming statements that Archaea are the most abundant ammonia-oxidizing prokaryotes in soils (Leininger et al., 2006; Erguder et al., 2009; Hatzenpichler, 2012).

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Fig. 4. UPGMA and nMDS analyses of 16S rRNA gene from Bacteria (DGGE conditions: 6% acrylamide; 52%e62% denaturing gradient), Archaea (DGGE conditions: 8% acrylamide; 32e55% denaturing gradient) and Methanogens groups (DGGE conditions: 8% acrylamide; 32e55% denaturing gradient).

Ammonia-oxidizing Archaea are considered ubiquitous due to their capacity to adapt to a wide range of growth conditions and, in some € nneke et al., 2005; cases, even to adopt an autotrophic lifestyle (Ko Erguder et al., 2009). Moreover, past management also had community-specific effects on the amount of soil methanogens. In fact, methanogens were less abundant in NEVER than flooded soils, and no significant difference was observed in soil methanogen abundance of permanent flooding (PF-1 and PF-2) and alternate wetting and drying (AF1) soils. Methanogenic Archaea have been reported to be quite resistant to aeration (O2) and desiccation (Fetzer et al., 1993) persisting in soil for a least a year without any significant change in abundance and diversity, even when exposed to oxic and drying conditions (Lueders and Friedrich, 2000; Krüger et al., 2005; Liu and Whitman, 2008). Upon anoxia, by flooding or waterlogging, the methanogenesis process may be initiated as result of increasing methanogenic populations (Mayer and Conrad, 1990). As expected (Le Mer and Roger, 2001), the correlation between archaeal species and CH4 production was stronger in permanently flooded soils (PF-2 and PF-1) than in the other soils (Fig. 6). In contrast, as showed in Fig. 7, microbial species in NEVER soil were positively correlated to N2O production, confirming the results obtained from the GHG production kinetics. The AF-1 soil showed an intermediate response and its microbial community might be affected by either aerobic or anoxic conditions that are created by alternating flooding and drying. Both methanogenesis and the chain of nitrate reduction processes occurred under anoxic environmental conditions after the initial O2 was consumed, competing

with each other (Le Mer and Roger, 2001; Saggar et al., 2004) and favoured in flooded or waterlogged soils. From canonical correspondence analysis and GHG production kinetics, we can hypothesize major contributions of both nitrifying and denitrifying communities to N2O production in NEVER soil. Soil conditions and substrates availability (e.g. O2 and inorganic N content) control reactions producing N2O, but the response to these conditions depends on the abundance and composition of denitrifying and nitrifying communities (Cavigelli and Robertson, 2000; Braker and Conrad, 2011). Under O2-limiting conditions amoA-containing bacteria substitute O2 with NO
2 as the electron acceptor in “nitrifier denitrification” (Lund et al., 2012) and especially at the interface between aerobic and anaerobic habitats “nitrifier denitrification” is thought to produce as much or even more N2O than heterotrophic denitrification (Webster and Hopkins, 1996; Bartossek et al., 2010). In the present experiment, NEVER soil showed a peak of N2O accumulation within 100 h under DRY and FRESH incubation conditioning, and a following decrease to initial values. Given that up to now there are few evidences that nitrifier denitrification carries out the N2O reduction process all the way to N2 (Zhu et al., 2013), our results suggest the involvement of heterotrophic denitrification in consuming the N2O produced. Moreover, under DRY incubation, NEVER soil showed the maximum cumulated values of N2O presumably attributable to coupled nitrification-denitrification activity that could take place in soils where favourable conditions for both nitrification and denitrification are present in neighbouring microhabitats, such as aerobiceanaerobic interface sites (Wrage et al., 2001). Under DRY incubation, we hypothesize a higher NHþ 4

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A. Lagomarsino et al. / Soil Biology & Biochemistry 93 (2016) 17e27

Fig. 5. UPGMA and nMDS analyses of genes involved in nitrification (amoA) and denitrification (nirK) processes. DGGE conditions: acrylamide, 6%; denaturing gradient 52e65%, 30e55%, 50e62% for bacterial amoA, archaeal amoA and nirK, respectively.

Table 4 Analysis of similarity (ANOSIM) and permutational multivariate analysis of variance (PERMANOVA) global test evidencing differences in bacterial and archaeal communities derived from DGGE band profiles. ANOSIM

16S rDNA Bacteria 16S rDNA Archaea 16S rDNA Methanogens NirK Bacteria amoA Bacteria amoA Archaea

PERMANOVA

R

Significance

0.883 0.886 0.975 0.978 1.000 0.389

P P P P P P

< < < < < <

0.001 0.001 0.001 0.001 0.001 0.05

F

Significance

11.8 7.805 14.0 7.856 46.91 3.2

P P P P P P

< < < < < <

0.001 0.001 0.001 0.001 0.001 0.05

availability due to enhanced mineralization triggered by drying and sieving processes. Therefore, high NHþ 4 availability likely promotes nitrifier populations, which ensure the NO
3 supply necessary for heterotrophic denitrification, supporting the coupled nitrificationdenitrification hypothesis. Furthermore, Archaea are thought to be actively involved in N2O and CO2 emissions, through denitrification (Philippot, 2002; Hatzenpichler, 2012) and fermentable anaerobic food chain (Lueders and Friedrich, 2000). In our soils, an abundant and composite archaeal community was found and although their role and ecological importance in N- and C-biogeochemical cycle are still open questions, DGGE and canonical correspondence analysis results suggest that they might be important actors not only for CH4 but also for N2O and CO2 production.

5. Conclusion The approach used in this work highlighted the importance of interactions among GHG production, GHG producers and soil conditions. Taken together, our results suggest that microbial community patterns affected GHG production differently according to different water management history. However, we have to keep in mind that PCR DNA-based techniques reflect the potential denitrifying, nitrifying or methanogenic populations and are expected to detect preferentially dominant population with the contribution of dormant and dead cells (Blagodatskaya and Kuzyakov, 2013) giving us only indication on the putative implication of these microbial communities to GHG emissions. Overall, our results described the legacy effects of past management of rice paddies on microbial communities' adaptation and growth, which in turn control the relative contributions of CH4 and N2O productions. Moreover, new evidences on the prevalent processes involved in CH4 and N2O production under different water management have been provided. Therefore, our results provided practical implications on innovative management of rice paddies. Actual strategies aiming at decreasing CH4 emissions from rice paddies should indeed take into account management options that limit archaeal growth, such as rotation with aerobic crops. Given that reduction in CH4 emission can potentially be offset by a parallel increase in N2O emission with higher GWP, controlling N availability and related N2O producing processes is essential for obtaining optimal combination of water

A. Lagomarsino et al. / Soil Biology & Biochemistry 93 (2016) 17e27

25

Fig. 6. Canonical correspondence analysis (CCA) ordination diagram of microbial communities and gas production variables defined by the first and second axes. The plots were generated by 16S rRNA gene DGGE banding patterns from Bacteria (blue circle), Archaea (purple circle) and Methanogens (red circle) groups. Vectors represent maximum gas production (CO2, CH4, N2O) measured under DRY, PRE-INCUBATED and FRESH soil conditioning procedures. Symbols represent agricultural managements: PF-2, blue square; PF-1, purple circle; AWD, inverted grey triangle; NEVER, green triangle(For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.).

Fig. 7. Canonical correspondence analysis (CCA) ordination diagram of nitrifying and denitrifying microbial communities and gas production variables defined by the first and second axes. The plots were generated by nirK gene DGGE banding patterns (blue circle) and amoA gene DGGE banding patterns from Bacteria (purple circle) and Archaea (purple circle). Vectors represent gas production (CO2, CH4, N2O) measured under DRY, PRE-INCUBATED and FRESH soil conditioning procedures. Symbols represent agricultural managements: PF-2, blue square; PF-1, purple circle; AWD, inverted grey triangle; NEVER, green triangle(For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.).

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A. Lagomarsino et al. / Soil Biology & Biochemistry 93 (2016) 17e27

Fig. 8. Relative abundance of archaeal 16S rRNA genes in soils under different managements. Error bars indicate standard errors (n ¼ 3). The different letters indicate significant differences (P < 0.05).

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