Microbes Environ. Vol. 25, No. 3, 224–227, 2010
http://wwwsoc.nii.ac.jp/jsme2/ doi:10.1264/jsme2.ME09184
Short Communication Changes in Bacterial Community Composition in the System of Rice Intensification (SRI) in Chiang Mai, Thailand THANWALEE SOOKSA-NGUAN1, PHREK GYPMANTASIRI2, NANTAKORN BOONKERD1, JANICE E. THIES3*, and NEUNG TEAUMROONG1* 1School
of Biotechnology, Institute of Agricultural Technology, Suranaree University of Technology, 111 University Avenue, Nakhon-Ratchasima 30000, Thailand; 2Multiple Cropping Center (MCC), Faculty of Agriculture, Chiang Mai University, Chiang Mai 50200, Thailand; and 3Department of Crop and Soil Sciences, Cornell University, 719 Bradfield Hall, Ithaca, NY 14853
(Received December 15, 2009—Accepted May 25, 2010—Published online June 7, 2010)
The factors of alternating flooding and draining during the vegetative growth phase and applying compost to investigate changes in bacterial community composition between the system of rice intensification (SRI) and conventionally managed rice were investigated. 16S rRNA gene T-RFLP analysis showed the major changes in the bacterial communities from the beginning of cultivation to vegetative phase, at which time the groups formed remained consistent until the end of cropping season. Significant and consistent separations of microbial communities between the two systems were revealed. These results suggested that the differences in rice cultivation practice can cause the changes in microbial communities. Key words: soil bacterial community, SRI, system of rice intensification, T-RFLP, 16S rRNA
The system of rice intensification (SRI) is a set of practices for rice cultivation whose combined use is reported to dramatically raise rice yield, without mineral fertilizer inputs or changes in rice cultivar (19). SRI involves a set of five or six practices in paddy management that differ from conventional rice cultivation systems (16). These practices are: (i) transplanting younger seedlings (ii) using a single seedling per hill, (iii) using wider spacing between hills, (iv) managing water by alternating flooding and draining during vegetative growth, (v) applying compost and (vi) mechanically managing weeds. While several studies based on agronomic and scientific approaches have been conducted (1, 9, 14) to understand mechanisms responsible for observed increases in rice productivity, the reasons are still unclear. SRI practices are based on careful water management leading to changes in the oxidation states and form of essential nutrients in soil, the cycle of which is mediated by soil organisms (2), are likely. Our previous study (15) revealed the effect of SRI practice on nitrogen cycling and results indicated the significantly higher nitrification rates in the SRI system comparing to conventionally managed plots. This gave us a clue that soil bacteria are effected by water management. Therefore, investigating back to the changes in microbial community structure might give us more information to understand SRI system. 16S rRNA gene has been used extensively as a molecular marker, enabling the identification or at least a phylogenetic assignment of the microbial community comparisons (6, 17). The introduction of terminal restriction fragment length polymorphism (T-RFLP) to microbial ecology provided a * Corresponding authors. Janice E. Thies; E-mail: jet25@cornell. edu; Tel: +1–607–255–5099; Fax: +1–607–255–8615. and Neung Teaumroong; E-mail:
[email protected]; Tel: +66–44–224–279; Fax: +66–44–224–150.
valuable molecular fingerprinting technique for studying changes in microbial community composition (4, 5, 11). T-RFLP facilitates separation of mixtures of PCR-amplified gene fragments based on terminal restriction fragments (T-RFs) and allows large numbers of samples to be analyzed simultaneously (18). Thus, this technique is well suited for monitoring the changes in microbial communities in response to changes in environmental factors. Accordingly, the objective of this study was to investigate the changes in microbial community structure throughout cropping periods between SRI and conventionally managed plots by performing the T-RFLP analysis of 16S rRNA gene. The research area is located in the Multiple Cropping Center (MCC), Chiang Mai, Thailand. The soil is a sandy loam to sandy clay loam, which has sand:silt:clay content of 61.5:13.8:24.7 (w/w/w), pH 5.0–5.3, organic matter 1.09– 1.16% (w/w), 35–200 mg-P kg−1 and 40 mg-K kg−1. Rice was grown under two types of water management, the conventional system (CS) and SRI. Oryza sativa was planted in nursery plots at the beginning of February and seedlings were transplanted to the conventional and SRI fields when seedling were 24 and 12-d-old, respectively. Plant spacing of 25 cm×25 cm with a single plant per hill was used. Chicken manure (0.5% N) was applied at 12.5 t ha−1 one day before transplanting. In the SRI system, 4–5 d of flooding was alternated with 4–5 d of drained soil management during the vegetative growth period, while rice fields in the conventional system were completely flooded for the whole period. The compost or no-compost treatments were applied within these two water management systems. Replicated four times for each set of treatments, were arranged in a randomized complete block design. Plots measured 5 m×4 m and were separated from each other by bunds. Soils were sampled five times at monthly intervals during
Microbial Community Composition in Rice Field
the dry season, February to June, 2003. The stages of rice development at each sampling time were: i) rice pre-plant (February), ii) vegetative growth phase after the first draining of the SRI plots (March), iii) vegetative growth phase one month after the previous sampling (April), iv) rice panicle initiation (May), and v) rice harvest (June). The rice fields were flooded in both manage systems when the soil samples were collected except during vegetative growth phase in March, when conventionally managed plots were flooded and SRI managed plots were drain but the soil samples still moist. Eight sub-samples per plot were collected and separated into 2 depths (0–10 and 10–20 cm from the soil surface) then pooled to make one composite sample for each soil depth. This was done twice for each plot. Composite samples were mixed well and frozen at −20°C for further DNA extraction (15). Soil microbial DNA was extracted using the BIO 101 FastDNA® Spin Kit for Soil (Qbiogene, Irvine, CA, USA). DNA was diluted to approximately 1–3 ng µL−1 in nucleasefree water (Promega, Madison, WI, USA) prior to being used as the template for PCR amplifications. The 16S rRNA genes of members of the bacterial domain were PCR amplified using the fluorescently labeled forward primer 27f (5'-[6FAM] AGA GTT TGA TCC TGG CTC AG-3') and an unlabeled reverse primer 1492r (5'-GGT TAC CTT GTT ACG ACT T-3') (Integrated DNA Technologies, Coralville, IA, USA), which yielded products of approximately 1,500 base pairs (12). The archaeal 16S rRNA primers were Ar109f (5'-[6FAM] ACG/T GCT CAG TAA CAC GT-3') and Ar912r (5'-CTC CCC CGC CAA TTC CTT TA-3') (10). The reaction mixtures contained 15 ng of soil DNA template, 0.05 U µL−1 Taq polymerase (Applied Biosystems, Foster City, CA, USA), 1×PCR buffer, 2.0 mM MgCl2 (PCR buffer and MgCl2 were supplied with the Taq enzyme), 0.2 mM deoxy-nucleotide triphosphates (dNTPs) (Promega), 0.1 µg µL−1 bovine serum albumin (BSA) (Promega), 0.1 µM of each primer, and nuclease free water. The PCR amplifications were performed using an MJ Research thermal cycler PTC 200 (MJ Research, Waltham, MA, USA) and the following program: 5 min at 94°C, followed by 27 cycles of 94°C for 45 s, 56°C for 45 s, and 72°C for 1 min, and a final extension step at 72°C for 10 min; and 5 min at 94°C, followed by 30 cycles of 94°C for 1 min, 51°C for 1 min, and 72°C for 1.5 min, and a final extension step at 72°C for 6 min; for bacteria and archaea, respectively. Products obtained from each bacteria and archaea PCR amplification were subjected to HhaI and Sau96I (Promega) digestions. Digested products were purified using a Performa® DTR Edge Plate (Edge BioSystems, Gaithersburg, MD, USA). The fragments were mixed with Liz 500 size standard (Applied Biosystems) prior to analyze through a ABI 3730xl electrophoretic capillary sequencer (Applied Biosystems). T-RFLP electropherograms were analyzed by GeneMapper 3.0 software (Applied Biosystems). The presence or absence of each T-RF between 50–500 bp was converted into binary data, then analyzed by the Additive Main Effects and Multiplicative Interaction (AMMI) model using MathmodelTM software (Microcomputer Power, Ithaca, NY, USA)
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(7). The AMMI model [also known as a doubly-centered principal components analysis (PCA)] has been wildly used in agricultural research, especially in analyses of yield trials and is also recommended for most T-RFLP data (4, 5). AMMI combines the additive elements of ANOVA with the multiplicative elements of PCA using ANOVA to partition the variation into main effects and interactions follows by applying PCA to the interactions to create interaction principal components (IPCs) (8). The differential responses were shown in two dimensions, IPC1 and IPC2 on the x-axis and y-axis, respectively). The bacterial communities grouped clearly by sampling period (Fig. 1) and this is in agreement with other studies that show clear changes in bacterial consortia as crop plants grow and mature (4). At rice pre-plant (February), the bacterial communities from all treatments grouped together since apart from the very recent addition of compost, the field plots had been managed in the same way. The bacterial community composition started to change during the vegetative phase (March), just after the practice of alternating wetting and drying had begun and the communities continued to diverge between the two management strategies until April, when water management in both systems was maintained
Fig. 1. Plot diagram AMMI analysis of bacterial T-RFLP data. TRFs derived from digestion of amplified DNA with HhaI. Soil sampled from (A) upper soil depth (0–10 cm) and (B) lower soil depth (10–20 cm) in the conventional and SRI managed plots across the five sampling periods. Number 1, 2, 3, 4 and 5 indicated the data from February, March, April, May and June, respectively. , conventionally managed plots with compost; , conventionally managed plots without compost; , SRI managed plots with compost; and , SRI managed plots without compost.
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Fig. 2. Plot diagram AMMI analysis of archaeal T-RFLP data. T-RFs derived from digestion of amplified DNA with HhaI. Soil sampled from (A) upper soil depth (0–10 cm) and (B) lower soil depth (10–20 cm) in the conventional and SRI managed plots across the five sampling periods. Number 1, 2, 3, 4 and 5 indicated the data from February, March, April, May and June, respectively. , conventionally managed plots with compost; , conventionally managed plots without compost; , SRI managed plots with compost; and , SRI managed plots without compost.
similarly. This indicates that the bacterial communities responded strongly to water management and although in the end both fields were flooded after flowering and dried down toward harvest, the early water management had a lasting effect on soil bacterial communities. The same scenario was observed for the T-RFs derived from the Sau96I digests from both soil depths (data not shown). The bacterial and archaeal datasets were also analyzed by their individual sampling periods. The clear separations of bacterial communities from each cultivation system were observed in all sampling periods from upper and lower soil depths (Fig. S1.B–E and S1.G–J, respectively) except in February, which was the rice pre-plant (Fig. S1.A and S1.F). The only discernable differences found in relation to compost application occurred in both soil depths in April (Fig. S1.C and S1.H). Archaeal communities behaved similarly to the bacterial communities, when examined across the five sampling periods (Fig. 2). That is, the communities were not different at rice pre-plant, but began diverging during the vegetative phase. The environmental factors associated with cultivation systems were more pronounced than those of sampling periods. The differences in archaeal communities between
the two cultivation systems were observed for each individual sampling time in March to June from upper and lower soil depths (Fig. S2.B–E and S2.G–J, respectively). The denaturing gradient gel electrophoresis (DGGE) of archaea 16S rRNA genes in March showed the clear separation between water management systems, and more than 90% similarity in SRI in both soil depths were observed (data not shown). Clearly, both times in the cropping cycle and that in water management were strong drivers of changes in both the bacterial and archaeal communities. These changes might be necessarily reflected as differences in components of yield, although, in our previous study (15) showed that the rice yields from conventional managed plots were significantly higher than those obtained from the SRI-managed plots but there are also other factors that could reflect the rice yields such as nematode infection. The community structures reflected by amplified 16S rRNA genes analyzed by T-RFLP analysis of Bacteria and Archaea were consistent with each other and reflective of changes in soil water status. The results clearly showed that the differences in rice cultivation systems did affect the structures of Bacteria and Archaea communities in both soil depths. A discernable pattern of time was also revealed. As previously reported (15), some Nitrosospira-like sequences were obtained from the SRI-managed plots but not from conventionally managed plots. This finding corresponds with the results of Ceccherini et al. (3) and Innerbner et al. (9). Thus these T-RFLP data may agree with the changes in nitrification activity measured at this site (15). A higher nitrification rate was observed at the start of the alternating flooding and draining practice in the SRI plots during the vegetative growth phase in March in both soil depths, corresponding to the changes in microbial communities, as determined by T-RFLP analysis in this study. The impact of drying and then rewetting of soil on microbial communities have been investigated. Pesaro and colleagues (13) reported that some bacterial groups that survived under drying and rewetting stress became more uniformly active or started to grow. Also the active bacteria may be related to yield improvement. Since the differences in plot management affected the composition of the soil microbial communities and they could also drive other important changes in soil such as functions of soil bacteria, it is worthy of further study in order to understand more about SRI management in order to improve yield production. While previous studies have been undertaken to better understand the practices that increase yield in SRI in terms of their agronomy and scientific bases (16), there was only one study that focused on the soils bacterial activities (15). This study shows the evidence on the effect of the different agricultural practice in rice cultivation on microbial community changes. Acknowledgements This work was supported by the Royal Golden Jubilee Ph.D. Program (PHD/0134/2544) and the Basic Research Grant for RGJ grant holder (BGJ4580013) from the Thailand Research Fund, the Department of Crop and Soil Sciences, Cornell University, Ithaca, NY and Suranaree University of Technology, Thailand. We gratefully acknowledge Chris Jones for his extensive help in setting up
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