The influence of season, agricultural management, and soil properties on gross nitrogen transformations and bacterial community structure.
The aim of this study was to assess the influence of season, farm
management (organic, biodynamic, integrated, and conventional), and soil
chemical, physical, and biological properties on gross nitrogen (N)
fluxes and bacterial community structure in the semi-arid region of
Western Australia. Moisture availability was the dominant factor
mediating microbial activity and carbon (C) and N cycling under this
climate. In general, microbial biomass N, dissolved organic N, and
potentially mineralisable N were greater in organic and biodynamic than
integrated and conventional soil. Our results indicate that greater silt
and clay content in organic and biodynamic soil may also partly explain
these differences in soil N pools, rather than management alone.
Although plant-available N (N[H.sub.4.sup.+] + N[O.sub.3.sup.-]) was
greater in conventional soil, this was largely the result of higher
N[O.sub.3.sup.-]production. Multiple linear modelling indicated that
soil temperature, moisture, soil textural classes, pH, electrical
conductivity (EC), and C and N pools were important in predicting gross
N fluxes. Redundancy analysis revealed that bacterial community
structure, assessed by denaturing gradient gel electrophoresis of 16S
rDNA, was correlated with C and N pools and fluxes, confirming links
between bacterial structure and function. Bacterial community structure
was also correlated with soil textural classes and soil temperature but
not soil moisture. These results indicate that across this semi- arid
landscape, soil bacterial communities are relatively resistant to water
Additional keywords: Western Australia, dryland agriculture, [sup.15]N pool dilution, DGGE.
Soil productivity (Research)
|Publication:||Name: Australian Journal of Soil Research Publisher: CSIRO Publishing Audience: Academic Format: Magazine/Journal Subject: Agricultural industry; Earth sciences Copyright: COPYRIGHT 2006 CSIRO Publishing ISSN: 0004-9573|
|Issue:||Date: Nov, 2006 Source Volume: 44 Source Issue: 7|
|Topic:||Event Code: 310 Science & research|
|Geographic:||Geographic Scope: Australia Geographic Code: 8AUST Australia|
A fundamental understanding of how farming systems alter microbial ecology is increasingly recognised as a key requirement for sustainable soil management (Abbott and Murphy 2003). In agro-ecosystems, microbial function facilitates a variety of biochemical processes that are essential to crop productivity. The activity and growth of soil microorganisms are affected by climatic and soil conditions such as moisture, temperature, organic matter content, texture, and nutrient availability. These factors are in turn influenced by agricultural management practices. However, only recently have attempts been made to examine the ecological importance of microbial community structure on a landscape scale and study the relationships between soil management, seasonality, and soil chemical, physical, and biological properties (Myers et al. 2001).
Climatic conditions in the semi-arid arable zones of Western Australia are characterised by hot, dry summers (surface soil temperatures 30-40[degrees]C) and cool, wet winters (surface soil temperatures 5-15[degrees]C), with annual rainfall ranging from 300 to 600 mm (Rovira 1995) with the majority during May to September. Under these conditions, microbial activity is primarily mediated by moisture availability and not soil temperature (Murphy et al. 1998a, 1998b). Periods of moisture stress affect bacterial communities through induced osmotic stress and by reduced substrate availability, possibly eliciting a strong selective pressure on the structure and functioning of the microbial communities (Strong et al. 1998; Zak et al. 1999).
A growing economic and environmental interest in alternative farming systems has resulted in an increasing number of farmers converting to organic, biodynamic, and integrated management. Previous research in temperate regions has shown that soil chemical, physical, and biological properties may improve under alternative management regimes (reviewed by Stockdale et al. 2001; Stockdale and Cookson 2003). This is normally associated with increases in organic matter content (Stockdale and Cookson 2003). However, the semi-arid regions of Western Australia differ from many other arable areas in that (i) soils have extremely low organic matter content and low clay and silt content, limiting the amount and rate at which organic matter can accumulate and (ii) conventional agriculture is characterised by minimum or zero tillage, which minimises organic matter loss. Therefore, we hypothesise that across a landscape, carbon (C) and nitrogen (N) cycling and bacterial community structure will be more strongly influenced by inherent soil properties than management per se. The aim of this study was to examine the effect of season, farm management, and a range of soil properties on soil N fluxes and bacterial community structure.
Materials and methods
Soil description and treatments
Soils were obtained from 2 organic, 2 biodynamic, 2 integrated, and 6 conventional farms in the wheatbelt region of Western Australia, between Cunderdin (117[degrees]15'E, 31[degrees]40'S), Beverly (116[degrees]55'E, 32[degrees]04'S), and Wickepin (117[degrees]30'E, 32[degrees]47'S). All farms can be generally defined as wheat based with a variety of rotation strategies and have been managed trader these systems continuously for at least 8 years. The organic and biodynamic farms were all certified through the National Association for Sustainable Agriculture Australia (www.nasaa.com.au) or the Bio-dynamic Research Institute (www.demeter.net), respectively, and are generally described by Conacher and Conacher (1998) and Rovira (1995). The management practices in which the conventional and integrated farms range are summarised by Anderson and Garlinge (2000) and Littlewood (2003). Biodynamic and organic farms receive no inorganic fertilisers and pesticides, instead using alternatives such as rock phosphate and fish and seaweed organic fertilisers. Biodynamic and organic farms also often used tillage to control weeds. Biodynamic management differs from organic management mainly in the specific biodynamic amendments applied to crops and soils based on Rudolf Steiner's philosophy (Koepf 1981). These amendments comprise various amounts of silica, cattle manure, and plant extracts (Flielssbach et al. 2000). Conventional and integrated farms were no or minimum tillage. Integrated farm management combined organic and conventional management, minimising inorganic fertiliser and pesticide use. Farms were chosen that had the same rotation strategy. All soils were planted with wheat at the time of sampling. Climate data were obtained from the Western Australia Agriculture Department climate stations at Cunderdin, Beverley, and Wickepin; each farm was within 10 km of a climate station. Soil moisture was measured gravimetrically from each soil sample. Soil was collected in 2002 from the same GPS point (accuracy to 20m) after harvest (January), at sowing (July), tillering (August), and flowering (October) of the wheat crop. Soils were collected from 2 sites from each farm, one recognised by the farmer as having high and the second as low, soil fertility. Three composite (30 samples) surface soil (0-0.05 m) samples (approx. 5 kg) were collected randomly along a 200-m transect at each sampling date and immediately stored at 4[degrees]C. A representative subsample (approx. 30g) of soil was collected and immediately stored below 0[degrees]C in the field and then at -80[degrees]C on return to the laboratory for bacterial community analysis. Field moist soil was transported to the laboratory (within 6 h of collection) and stored at 4[degrees]C for all other analyses. No processing of bacterial community structure soil was undertaken before DNA extraction. Bulk soil samples were passed through a 4-mm sieve and stored at 4[degrees]C for less than 1 week before the assessment of soil chemical and biological properties. Unsieved soils were used for the determination of soil physical properties. Soil texture size classes, total-C, and total-N were only assessed in January 2002. Soil moisture was measured gravimetrically from each soil sample.
EC, pH, and soil texture classes
Soil EC and pH were determined on oven-dried (50[degrees]C) soil (10g) in 50 mL of distilled water, shaken for 1 h and left to stand overnight. Soil texture was determined using particle size analysis and defined according to the Australian classification system (McDonald et al. 1984).
C and N pools and fluxes
Total C and N were determined on oven-dried (50[degrees]C) soil using a combustion analyser (CHN, LECO Corp., USA). Soil mineral-N (N[H.sub.4.sup.+]-N and N[O.sub.3.sup.-]-N) content was determined on 0.5 M [K.sub.2] S[O.sub.4] extracts (1:4 soil:solution ratio) and analysed colorimetrically by automated segmented flow analysis (Skalar Analytical B.V., The Netherlands). Dissolved organic N (DON) in the [K.sub.2]S[O.sub.4] extracts was determined colorimetrically after alkaline persulfate oxidation (Cabrera and Beare 1993).
Microbial respiration was determined by trapping C[O.sub.2] from fresh (15 g oven-dried equivalent) soil in 0.5 M KOH within sealed incubation vessels (Anderson 1982) over a 7-day period at 20[degrees]C. Carbon dioxide concentration was determined by adding Ba[Cl.sub.2] and auto-titration (Mettler-Toledo, Switzerland) with 0.1 M HCl.
Potentially mineralisable N (PMN) was determined by anaerobic incubation (Keeney and Brenmer 1966) at 40[degrees]C for 7 days. Mineral N was extracted with 0.5 M [K.sub.2]S[O.sub.4] and determined colorimetrically as described above.
Microbial biomass N (MB-N) was determined by fumigation extraction technique (Brookes et al. 1985). Ammonium (N[H.sub.4.sup.+]-N) and nitrate (N[O.sub.3.sup.-]-N) concentrations in [K.sub.2]S[O.sub.4] extracts were determined colormetrically as described above after alkaline persulfate oxidation (Cabrera and Beare 1993). A [k.sub.EN] factor of 0.38 was used to calculate the MB-N, as previously determined by Sparling and Zhu (1993) for similar Western Australian soils.
[sup.15]N isotopic pool dilution was used to measure gross mineralisation (GMR), gross nitrification (GNR), and potential gross immobilisation rates (GIR, N[H.sub.4.sup.+]-N+N[O.sub.3.sup.-]-N immobilisation) (Murphy et al. 2003). To measure gross N fluxes, fresh soil subsamples (40 g oven-dried equivalent) from each site were mixed thoroughly with 0.5 mL of [sup.15]N-labelled N[H.sub.4]Cl or KN[O.sub.3] (99% enrichment, l[micro]g [sup.15]N/g soil) in 100-mL incubation vessels. Air-tight lids were placed on the incubation vessels and they were then buried in the soil (0.05m depth) outside the laboratory to achieve diurnal temperature fluctuations. After 2 and 72 h, a subsample of soil (15g oven-dried equivalent) was removed from each incubation vessel and immediately extracted with 0.5M [K.sub.2] S[O.sub.4]. Ammonium-N and N[O.sub.3.sup.-]-N content of extracts were determined colorimetrically as described above. The [sup.15]N content of the N[H.sub.4.sup.+]-N or N[O.sub.3.sup.-]-N in the [K.sub.2]S[O.sub.4] extracts was measured separately using a 2-stage diffusion method described by Brooks et al. (1989), and determined using an automated Roboprep C/N analyser coupled with a VG Micromass Sira 9 mass spectrometer (ANCA-MS). GMR and GNR were calculated using equations described by Kirkham and Bartholomew (1954) and Davidson et al. (1991). GIR was calculated as N[H.sub.4.sup.+]-N immobilisation (N[H.sub.4.sup.+]-N consumption--nitrification) + N[O.sub.3.sup.-]-N immobilisation (N[O.sub.3.sup.-]-N consumption).
Bacterial community structure
Soil bacterial community structure was assessed using denaturing gradient gel electrophoresis (DGGE) (Muyzer et al. 1993; Marschner et al. 2001, 2003). Deoxyribonucleic acid was extracted from the soil using the UltraClean soil DNA isolation kit (Mo Bio Laboratories, Inc., USA) in combination with vortex beating. Bacterial 16S rRNA genes were amplified using the universal bacterial primer set PRBA338f and PRUN518r (Ovreas et al. 1997). For PCR, 3 [micro]L of a 10-fold dilution of the DNA extract was added to 50 [micro]L of PCR reaction mix composed of 0.5 [micro]M each primer, 100 [micro]M each dNTP, 0.1 [micro]L (0.5 U) Taq polymerase, 5 [micro]L 10 x PCR buffer, 3mM Mg[Cl.sub.2], 0.2% bovine serum albumin, and sterile water to volume. PCR amplification was performed using 94[degrees]C for 5 min; 30 cycles of denaturation at 94[degrees]C for 30 s, annealing at 55[degrees]C for 30 s, and extension at 72[degrees]C for 1 min; and a single final extension at 72[degrees]C for 10 min. Successful amplification was verified by electrophoresis in 1.5% (w/v) agarose gels stained with ethidium bromide. DGGE was performed with 8% (w/v) acrylamide gels containing a linear gradient ranging from 30 to 55% denaturant (100% denaturant was defined as 7 M urea and 40% (v/v) formamide). Fifteen [micro]L of PCR products were electrophoresed in 0.5 x TAE buffer at 60[degrees]C at a constant voltage of 200 V for 5 h (BIO-RAD Dcode systems). PCR products were loaded into lanes of the gels in a random order to avoid any potential bias in the later analysis. After electrophoresis, the gels were stained for 15 min with SyBR green I nucleic acid stain (FMC Bio Products) (10 000-fold dilution in 0.5 x TAE) and imaged using a laser scanning system (Molecular Dynamics Fluorimager 573). Band detection and quantification of band intensity were preformed using TotalLab (Nonlinear dynamics, Newcastle upon Tyne, England). Band positions in the different gels were compared by expressing them relative to bands of a standard bacterial mix added to each gel. Deoxyribonucleic acid band intensity was normalised by dividing the band intensity of each band by the mean band intensity of the gel.
Statistical design and analysis
Soil chemical, physical, and biological properties presented are mean values of 4 replicates from each high or low fertility site on each farm. Treatments were compared for season, agricultural management, and high and low site by general and unbalanced analysis of variance (ANOVA). Differences between means were assessed using least significant difference (1.s.d.) calculated at P = 0.05 and standard error of the difference or average standard error of the difference are presented. For unbalanced data the maximum replicate 1.s.d. was used. All the analyses were preformed using GENSTAT 7.1 ([C] 2003).
Ordinary least square (OLS) multiple linear regression was used to evaluate the influence of individual farm soil physical, biological, and chemical properties, soil moisture, and temperature on GMR, GNR, and GIR. Regression modelling was preformed using STATA release 8.0 ([c] 2003). The selection of explanatory variables for all the models was based on the consideration of practical aspects of soil management relevant to soil organic matter management and on expert knowledge of potential effect of the variables on the GMR, GNR, and GIR. The occurrence of multi-colinearity was checked using bi-variate correlation of all explanatory variables. To achieve a strict check of multi-colinearity a stepwise regression at the 0.1 probability was applied to exclude explanatory variables with this problem.
Bacterial community similarities based on relative DGGE band intensity and position were analysed by performing redundancy discrimination analysis (RDA) with Monte Carlo permutation tests using CANOCO 4.5 ([c] 2002). The Monte Carlo tests are based on 199 random permutations of the data. RDA in combination with Monte Carlo permutation tests can be used to determine which environmental variables are significantly (P [less than or equal to] 0.1) correlated with community structure, as well as the relative importance of these variables. Soil moisture, temperature, chemical, physical, and biological properties were used as environmental variables.
Soil temperature and moisture
Average daily soil temperature (0.05 m depth) varied among the Cunderdin, Beverley, and Wickepin weather stations and was highest at harvest (38, 34, and 29[degrees]C, respectively), decreased at sowing (10, 11, and 9[degrees]C, respectively) and tillering (11, 11, and 9[degrees]C, respectively), and increased again at flowering (23, 22, and 19[degrees]C, respectively). Gravimetric soil moisture content was considerably higher at sowing (range 7.9-11.7%) and tillering (range 3.8-8.9%) than at harvest (range 0.4-1.0%) or flowering (range 0.6-1.8%). Soil moisture content was also higher at sowing than at tillering. Although soil moisture content varied considerable among farms, this was not as a result of site or farm management (data not shown).
Soil EC, pH, and texture
Across all soils, EC and pH ranged from 26 to 882 [micro]S/cm and from 4.9 to 6.2, respectively (Table 1). Soil EC and pH were found to be higher in sites identified by farmers as being low fertility (202 [micro]S/cm and 5.8, respectively) than in sites identified as high fertility (102 [micro]S/cm and 5.6, respectively). Whereas soil EC was highest in organic (285 [micro]S/cm) and integrated (300 [micro]S/cm) compared with biodynamic (121 [micro]S/cm) and conventional (69 [micro]S/cm) soil, pH was greater in biodynamic (5.8) and conventional (5.8) compared with organic (5.6) and integrated (5.4) soil. Soil pH and EC were not affected by sampling time (data not presented).
Across all soils, silt, clay, and sand ranged from 3 to 29%, 3 to 29%, and 58 to 93%, respectively (Table 1). Soil clay and silt contents were higher in high fertility (15 and 8%, respectively) than in low fertility (10 and 7%, respectively) sites, whereas the reverse was true for the sand content. Clay content was higher in organic (14%) and biodynamic (15%) soil than in integrated (10%) and conventional (12%) soil, whereas silt was lowest in organic (6%) and highest in biodynamic (8%) soil; biodynamic (76%) soil also had less sand than organic (80%), integrated (81%), and conventional (81%) soil.
C and N pools and processes
Across all soils, total-C and total-N ranged from 0.5 to 3.7% and from 0.04 to 0.28%, respectively (Table 1). Total-C and total-N were greater in the high fertility (1.9 and 0.14%, respectively) than in the low fertility (1.6 and 0.12%, respectively) sites. Total-C and total-N were also generally greater in integrated (2.3 and 0.17%, respectively), than in biodynamic (1.9 and 0.14%, respectively), organic (1.9 and 0.15%, respectively) and conventional (1.5 and 0.11%, respectively) soil.
Microbial respiration, N[H.sub.4.sup.-], N[O.sub.3.sup.-], DON, PMN, and MB-N were higher in high fertility than in low fertility sites and generally higher at tillering and sowing than at harvest and flowering (Table 2). Although there were few obvious patterns in microbial respiration, N[H.sub.4.sup.+], DON, PMN, and MB-N among systems, integrated and conventional soil were generally lower in these parameters than organic and biodynamic soil; this effect varied among samplings (Table 2). Conversely, N[O.sub.3.sup.-] was generally higher in conventional than in organic, biodynamic, and integrated soil; this effect varied among samplings (Table 2).
GMR, GNR, and GIR were greater in high fertility (3.9, 2.5, 3.1 [micro]g N/g soil.day, respectively) than in low fertility (2.1, 1.2, 2.8 [micro]g N/g soil.day, respectively) sites. GMR, GNR, and GIR were higher at sowing and tillering than at harvest and flowering (Fig. 1). GMR was greater in conventional soil than in organic, biodynamic, and integrated soil at sowing; this effect was reversed at tillering (Fig. 1). GNR was greater in conventional soil than in organic, biodynamic, and integrated soil at sowing and tillering; GNR was also greater in integrated soil than organic, biodynamic soil at sowing (Fig. 1). GIR and GIR/GMR were generally greater in organic (0.99), biodynamic (0.95), and integrated (0.94) soil than in conventional (0.80) soil (Fig. 1).
[FIGURE 1 OMITTED]
In general, soil moisture was positively and soil temperature negatively, correlated with C and N pools and fluxes (Table 3). Measures of C and N cycling were consistently correlated with each other and with DON (Table 3). Total soil C and N and PMN were positively correlated with soil clay and silt contents and EC (Table 3).
In all of the model runs, full and reduced models explained the same amount of variation. The reduced model explained 68% of the variability in GMR with significant (P < 0.1) positive influence of soil moisture, EC, total soil C/N ratio and microbial respiration rate and negative influence of soil silt content, clay content, sand content, soil temperature, pH, and total-C (Table 4). The reduced model explained 89% of the variability in GIR with significant (P < 0.1) positive influence of MB-N, microbial respiration rate, and GMR and negative influence of silt content, sand content, soil temperature, and pH (Table 4). The reduced model explained 84% (adjusted [R.sup.2]) of the variability in GNR with significant (P < 0.1) positive effect of sand content, moisture content, EC, total-C, PMN, and GMR and negative relationships with soil temperature, total-N, and MB-N (Table 4).
Bacterial community structure
In total, 24 DGGE banding positions were compared across site, management, and season. On average, we found 9.3 bands per lane, which ranged from 4 to 16 (data not shown). Ordination and discrimination analysis of the DGGE banding patterns was unable to separate or show any significant effect of site, season, or farm management on bacterial community structure (data not shown). RDA showed that bacterial community structure was correlated (P < 0.1) with soil physical (silt, sand, or clay content), chemical (pH, EC, total N and C, and DON) and biological (microbial respiration, PMN, GMR, GNR, GIR) properties (Table 5). RDA showed that bacterial community structure was not significantly correlated (P = 0.12) with soil water content. RDA also showed that bacterial community structure at sowing and tillering was correlated (P < 0.1) with soil temperature (Table 5).
We proposed that across this semi-arid landscape, C and N cycling and bacterial community structure will be more strongly influenced by inherent soil properties than management per se. Our results tend to agree with only part of this hypothesis, as farm management had no effect on bacterial community structure, but significantly affected C and N cycling. However, the effect of farm management on C and N cycling may at least partly be explained by soil properties, such as silt and clay content, which are unlikely to be affected by farm management. These relationships and their importance are discussed in more detail below.
In our study, farm management had no influence on bacterial community structure. This is in contrast to previous research, which has found differences between farming systems receiving different amounts of organic inputs (Zelles et al. 1995; Bossio et al. 1998; Lundquist et al. 1999). Although we found differences in soil C content between management systems, these differences may not have been large enough to produce a change in the bacterial community structure. Recent research has also shown that a distinct microbial community structure develops under crops such as barley, canola, and sweet corn (Larkin 2003). It is well accepted that plants have an important effect on soil microbiology, due primarily to releasing qualitatively and quantitatively different nutrient and organic compounds into the soil (Grayston et al. 1998). Differences in bacterial community structure caused by farm management may therefore be masked by the influence of crop (i.e. wheat) and comparisons between management systems under different crops are possibly invalid.
We found that bacterial community structure was correlated to soil textural classes, gross N fluxes, and C and N pools. This is in agreement with other studies (Bardgett et al. 1999; Marschner et al. 2001; Jackson et al. 2003), which have suggested that changes in soil C and N mineralisation are related to changes in microbial community structure (Calderon et al. 2000; Cookson et al. 2005). Soil texture is also often considered a primary mediating factor on microbial community structure via its influence on the retention of soil moisture and organic matter, and the development of soil structure and pore size distribution (Wardle 1992; Hassink 1997; Murphy et al. 2003). We found that gross N fluxes were negatively correlated to silt and clay content but total-C, total-N, DON, PMN, and MB-N were positively correlated to silt and clay content. This agrees with previous work (Strong et al. 1999a), which found that while soils were moist, high clay content had a negative effect on net N mineralisation as it effectively protected organic-N from microbial decomposition. Under the often water-limited, semi-arid environment of our study, soil clay and silt content seemed to contribute considerably to soil organic matter content.
Rates of N cycling often differ with respect to agricultural management (Stockdale et al. 2001; Stockdale and Cookson 2003). In general, we found that a greater proportion of N mineralised in organic, biodynamic, and integrated soil was re-immobilised compared with conventional soil. Decreasing the ratio between GIR/GMR provides a greater opportunity for N[H.sub.4.sup.+] to be nitrified; combine this with the use of inorganic fertilisers and hence we found greater N[O.sub.3.sup.-] and nitrification in conventional soil. It is generally assumed that where sufficient C substrate exists, heterotrophic microbial growth (immobilisation) will dominate autotrophic growth (oxidation) (Tietema and Wessel 1992). As total C was lower in conventional soil it is possible that C supply has restricted immobilisation in these soils compared with the other management systems. Although these factors provided greater plant-available N (N[H.sub.4.sup.+] + N[O.sub.3.sup.-]) in conventional soil, greater N[O.sub.3.sup.-] production will also increase the risk of N losses compared with organic, biodynamic, and integrated soil.
In general, the silt and/or clay content and the C and N pools and fluxes were greater in organic and biodynamic soils than in integrated and conventionally managed soils. Many studies have also shown soil C and N pools and fluxes are greater under alternative than integrated or conventional management (reviewed by Stockdale et al. 2001). In our study, this is likely to be partly attributed to longer pasture phases in organic and biodynamic managed farms than in integrated and conventional managed farms. However, management differences in soil silt and clay content are probably related to location, rather than management per se. Furthermore, if we consider the relationships between silt and clay content and C and N pools and fluxes proposed above, silt and clay content is also likely to have significantly contributed to the 'management' effect on C and N cycling in these soils. Greater silt and clay content and C and N pools and fluxes in sites identified by farmers as high fertility may also indicate that soil silt and clay content plays an important role in crop productivity. Greater clay and silt content is likely to ease water limitation on crop growth, producing increased biomass and greater organic matter inputs to the soil (Roper 1998). Well-structured, finer textured soils also create more niche environments for microbial colonisation, supporting greater microbial populations due to protection from desiccation (Bushby and Marshall 1977) and predators (Roper and Marshall 1977). Under this often water-limited, semi-arid environment, soil clay and silt content therefore contribute considerably to crop productivity and soil organic matter content, which in turn influence bacterial structure and function.
We found relationships among soil temperature, gross N fluxes, and bacterial community structure under the cool, wet conditions at sowing and tillering. This is in agreement with other work, which has shown that soil temperature can greatly influence gross N fluxes (Cookson et al. 2002) and microbial community structure (Zogg et al. 1997) when water is not limiting. The correlation with temperature within our model was negative, as increased soil temperature across seasons was associated with a decrease in soil moisture. This agrees with the assertion that microbial activity is positively related to soil temperature only when moisture is not limiting (Campbell et al. 1999; Hoyle et al. 2006). Soil moisture can therefore be considered the dominant factor controlling microbial activity in this agro-ecosystem and factors such as temperature and C and N availability influence bacterial community structure only when soil moisture is not limiting.
The results indicate there was no significant (P < 0.1) relationship between soil moisture and bacterial community structure, providing evidence for the proposal that pre-adapted soil bacteria may resist moisture variations by regulation of cellular activity (Meikle et al. 1995; Griffiths et al. 2003). Fierer et al. (2003) found drying-rewetting regimes influenced bacterial structure only in soil that was less frequently exposed to moisture stress. This implies that the structure of the bacterial communities which regularly experience moisture stress, such as those in the present research work, will change little. The ability of soil bacteria to withstand perturbations such as moisture stress is thought to represent a survival strategy for persistence of bacteria in harsh, low-nutrient environments, which is mediated by starvation gene expression, cell shrinkage, or sporulation (Bakken 1997). The majority of the soils we studied can be considered coarse textured (<70% sand) and dominated by large soil pores. Soil dry down and wet up may therefore not favour any particular bacterial groups because niches that remain water filled for extended periods may be limited.
Griffiths et al. (2003) also found no changes in soil bacterial community structure when soil was subjected to different watering regimes. Griffiths et al. (2003) suggested that this was partly explained by the large genotypic diversity of bacteria present in soil and that only dominant templates are detected in DGGE profiles. Differences in bacterial community structure between seasons will also be dependent on the ability of the soil to decompose DNA released from dead bacterial cells. It has been shown that DNA absorbs to soil minerals, including quartz (Chamier et al. 1993), feldspar (Romanowski et al. 1992), and clay minerals (Khanna and Stotzky 1992). Therefore, DGGE banding patterns may at least in part be derived from extracellular DNA masking differences that might exist in the active bacterial community between seasons. Another limitation of DGGE is that a given band may comprise several species whose DNA has similar melting characteristics. Changes in relative abundance of species may therefore remain undetected. Although we found DGGE was spatially sensitive (farms and sites), temporal sensitivity (seasons) is more likely to be affected by the factors described above.
We found a close relationship between gross N fluxes and microbial respiration rates. Others (Hart et al. 1994; Bengtsson et al. 2003) have also found that GMR and GIR in forested soils (Hart et al. 1994; Bengtsson et al. 2003) and in coarse textured agricultural soils (Murphy et al. 1998a) are correlated to microbial respiration rates. This confirms recent research (Bengtsson et al. 2003; Murphy et al. 2003) that has shown that microbial respiration can be used to predict the magnitude of gross N fluxes (Bengtsson et al. 2003; Murphy et al. 2003).
Our research indicates that soil pH and EC were correlated to GMR, GNR, and bacterial community structure. Strong et al. (1999b) stated that pH was not a good predictor of soil N[H.sub.4.sup.+]-N content across a similar pH range (4.4-6.7, Strong et al. 1998). We suggest that GMR and GIR are more sensitive to pH as they provide a rate of both N[H.sub.4.sup.+]-N production and consumption rather than simply the product of the 2 processes. We also found that pH was higher in low fertility than in high fertility sites but this probably reflects the salinity problems identified by greater EC in these low fertility sites (Table 1). The correlation coefficient for EC in our model was zero, reflecting the wide range of EC values (26-882 [micro]s/cm) measured. Within this wide range, positive and negative effects on microbial activity will have occurred and masked each other. The correlation between EC and GMR, GNR, and bacterial community structure agrees with recent research, which showed that EC was a good indicator of organic matter and C and N cycling (Smith et al. 2002) and that induced salinity caused shifts in the microbial community structure (Pankhurst et al. 2001).
The present research has shown that PMN, DON, and MB-N fluctuated during the growing season, with the lowest concentrations at harvest (summer) and the highest at sowing (winter). Murphy et al. (1998a) also found PMN and MB-N to be lowest during late spring and early summer compared with winter in this semi-arid environment under wheat production. Murphy et al. (1998a) concluded that the fluctuations in MB-N coincided with changes in the availability of mineralisable substrates and changes in soil conditions such as moisture content; our results confirm this assertion. We also found that MB-N, N[H.sub.4.sup.+], N[O.sub.3.sup.-], and DON were more highly correlated than MBN and PMN, suggesting that DON is a more sensitive measure of N substrate availability to the majority of the microbial biomass than PMN. This supports the concept that it is the rate of soil organic matter conversion to dissolved organic matter that limits substrate availability to the active microbial population (Cookson and Murphy 2004; Jones et al. 2004).
In conclusion, this study has demonstrated that although soil water determines the timing of microbial activity, the size and magnitude of biological processes, such as gross N fluxes, are largely mediated by differences in soil temperature and texture. We found that changes in bacterial community structure were related to a range of soil chemical, physical, and biological properties but that under the conditions of this research, bacterial communities were resistant to water stress. Although we found no effect of farm management on bacterial community structure, an effect on C and N pools and fluxes was evident. However, this farm management effect can at least partly be explained by differences in soil silt and clay content and their role in mediating the size of C and N pools. Although plant-available N (N[H.sub.4.sup.+] + N[O.sub.3.sup.-]) was also greater in conventional soil, this was largely the result of higher N[O.sub.3.sup.-] production, possibly increasing the risk of N losses compared with organic, biodynamic, and integrated soil. These results indicate that on the coarse textured soils of this semi-arid environment, soil silt and clay content have a central role in ecosystem productivity by mediating water and organic matter availability and therefore influencing bacterial structure and function.
This research was conducted with the support of the New Zealand Foundation of Science and Technology, Australian Research Council, Grains Research and Development Corporation, The University of Western Australia, Rothamsted Research and United Kingdom Biotechnology, and Biological Sciences Research Council. The authors wish to thank Matt Braimbridge for sample collection, Steven McCoy for farm selection, and Chunya Zhu and Ian Fillery for support with [sup.15]N analysis.
Manuscript received 30 March 2005, accepted 18 January 2006
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W. R. Cookson (A,E) P. Marschner (B), I. M. Clark (C), N. Milton (A), M. N. Smirk (A), D. V. Murphy (A), M. Osman (A), E. A. Stockdale (D), and P. R. Hirsch (C)
(A) School of Earth and Geographical Sciences, Faculty of Natural and Agricultural Science, The University of Western Australia, 35 Stifling Highway, Crawley, WA 6009, Australia.
(B) Soil and Land Systems, School of Earth and Environmental Sciences, Faculty of Sciences, DP 637 636, The University of Adelaide, Waite Campus, SA 5005, Australia.
(C) Plant Pathogen Interactions Division, Rothamsted Research, Harpenden, Hertfordshire AL5 2JQ, UK.
(D) Agriculture and the Environment Division, Rothamsted Research, Harpenden, Hertfordshire AL5 2JQ, UK.
(E) Corresponding author. Email: firstname.lastname@example.org
Table 1. Soil electrical conductivity (EC, [micro]S/cm), pH ([H.sub.2]O), clay content (%), silt content (%), sand content (%), total soil carbon (TotC, %), and total soil nitrogen (TotN, %) from low and high fertility sites on organic, biodynamic, integrated, and conventional managed farms in the Western Australian wheatbelt after harvest in January 2002 (n = 3) Farm management Site EC pH Clay Silt Sand TotC TotN Organic 1 Low 749 5.8 9.3 8.8 79.5 1.8 0.13 High 208 4.9 12.8 4.5 82.2 1.9 0.15 Organic 2 Low 85 5.9 5.2 4.9 90.6 1.7 0.13 High 99 5.9 28.5 5.1 66.8 2.0 0.17 Biodynamic 1 Low 104 5.9 10.5 11.3 77.7 1.7 0.12 High 124 6.1 26.9 4.0 72.6 2.9 0.22 Biodynamic 2 Low 186 5.5 12.3 9.6 75.8 1.4 0.11 High 68 5.6 11.8 7.8 78.0 1.6 0.12 Integrated 1 Low 118 5.6 14.0 8.8 77.3 2.0 0.16 High 107 5.0 8.8 5.3 85.0 1.4 0.10 Integrated 2 Low 882 5.8 7.8 8.8 80.3 3.7 0.28 High 94 5.0 10.5 8.0 82.2 2.2 0.16 Conventional 1 Low 60 5.9 11.1 8.0 78.9 1.6 0.11 High 59 5.6 10.3 10.1 78.1 1.4 0.11 Conventional 2 Low 26 5.5 8.3 5.3 83.7 1.9 0.15 High 76 5.7 7.0 3.5 90.8 0.8 0.07 Conventional 3 Low 31 5.6 3.0 5.5 93.3 0.6 0.05 High 64 5.8 12.8 7.8 77.6 1.5 0.11 Conventional 4 Low 40 5.8 7.5 3.0 90.8 0.5 0.04 High 82 5.8 15.1 8.3 74.9 1.7 0.13 Conventional 5 Low 39 6.1 23.6 6.0 69.7 1.8 0.14 High 161 6.2 11.0 28.8 58.1 2.9 0.19 Conventional 6 Low 100 6.2 6.5 2.3 92.9 0.7 0.06 High 84 5.9 21.6 4.5 77.5 2.5 0.20 Table 2. Effect of site, sampling season, and farm management on soil microbial respiration (C[O.sub.2], [micro]g C/g soil.day), ammonium (N[H.sub.4.sup.+], micro]g N/g soil), nitrate (N[O.sub.3.sup.-], [micro]g N/g soil), dissolved organic nitrogen (N) (DON, [micro]g N/g soil), potentially mineralisable N (PMN, [micro]g N/g soil), and microbial biomass N (MB-N, [micro]g N/g soil) s.e.d., Standard error of the difference. Sites, season, or management type means followed by the same letter are not significantly different (P < 0.05) (n = 3). S x M, Sampling x management N[H.sub.4 Treatment Factor C[O.sub.2] .sup.+] Site Low fertility 15.7a 1.1a High fertility 17.3b 1.4b s.e.d. 0.3 0.1 Sampling Harvest 3.4a 0.7a Sowing 35.0c 2.5c Tillering 25.2b 1.1b Flowering 2.5a 0.8ab s.e.d. 1.3 0.2 Management Organic 21.4b 1.2a Biodynamic 16.3ab 1.0a Integrated 11.3a 1.3a Conventional 16.7ab 1.3a [s.e.d.sub.(average)] 3.1 0.2 S x M (harvest) Organic 4.0a 0.8a Biodynamic 3.6a 0.7a Integrated 2.8a 0.6a Conventional 3.3a 0.7a [s.e.d.sub.(average)] 0.5 0.1 S x M (sowing) Organic 45.3c 2.7b Biodynamic 34.4b 2.9b Integrated 21.6a 2.0a Conventional 36.3b 2.5a [s.e.d.sub.(average)] 5.0 0.2 S x M (tillering) Organic 33.8b 1.1a Biodynamic 23.5a 1.5a Integrated 20.1a 0.8a Conventional 24.6a 1.0a [s.e.d.sub.(average)] 3.9 0.4 S x M (flowering) Organic 2.6ab 0.8a Biodynamic 3.6b 0.9a Integrated 0.8a 0.7a Conventional 2.7ab 0.6a [s.e.d.sub.(average)] 0.9 0.1 Treatment Factor N[O.sub.3] DON Site Low fertility 2.4a 21.6a High fertility 3.0b 23.8b s.e.d. 0.2 1.1 Sampling Harvest 0.7a 8.2a Sowing 7.2c 38.3d Tillering 2.1b 29.1c Flowering 0.8a 15.2b s.e.d. 0.3 1.5 Management Organic 2.2a 26.4a Biodynamic 2.0a 25.0a Integrated 2.6ab 19.9a Conventional 3.3b 21.6a [s.e.d.sub.(average)] 0.4 3.0 S x M (harvest) Organic 0.5a 9.5b Biodynamic 0.6a 8.3ab Integrated 0.5a 5.3a Conventional 0.8b 8.7b [s.e.d.sub.(average)] 0.1 1.5 S x M (sowing) Organic 6.2ab 49.5a Biodynamic 5.5a 39.9a Integrated 8.9bc 33.9a Conventional 10.2c 35.5a [s.e.d.sub.(average)] 1.5 6.4 S x M (tillering) Organic 1.8a 33.4a Biodynamic 1.6a 27.8a Integrated 2.0a 28.3a Conventional 2.5a 28.5a [s.e.d.sub.(average)] 0.6 4.1 S x M (flowering) Organic 0.6a 13.2a Biodynamic 0.7a 23.9b Integrated 0.6a 12.2a Conventional 0.9b 14.0a [s.e.d.sub.(average)] 0.1 2.3 Treatment Factor PMN MB-N Site Low fertility 34.3a 25.0a High fertility 41.8b 28.0b s.e.d. 1.4 1.2 Sampling Harvest 33.0a 9.1a Sowing 42.9b 47.9d Tillering 36.3a 32.3c Flowering 40.0b 16.8b s.e.d. 2.0 1.7 Management Organic 40.2bc 26.4a Biodynamic 44.6c 31.3a Integrated 38.6ab 25.3a Conventional 35.0a 25.4a [s.e.d.sub.(average)] 2.4 3.5 S x M (harvest) Organic 30.0a 8.4a Biodynamic 42.6a 9.5a Integrated 30.2a 8.3a Conventional 31.8a 9.5a [s.e.d.sub.(average)] 5.7 1.7 S x M (sowing) Organic 48.2a 41.0a Biodynamic 47.8a 67.8b Integrated 43.2a 45.4a Conventional 39.4a 44.4a [s.e.d.sub.(average)] 4.6 6.0 S x M (tillering) Organic 39.5ab 37.8b Biodynamic 42.6b 26.0a Integrated 38.4ab 34.0ab Conventional 32.5a 32.0ab [s.e.d.sub.(average)] 3.9 4.2 S x M (flowering) Organic 43.1ab 18.2ab Biodynamic 45.5b 21.9b Integrated 42.5ab 13.6a Conventional 36.1a 15.7a [s.e.d.sub.(average)] 4.3 2.6 Table 3. Bivariate correlations between soil gross nitrogen (N) mineralisation (GMR), gross nitrification (GNR), and gross N immobilisation (GIR) rates, silt, clay, and sand content, moisture (Moist), temperature (Temp.), pH, electrical conductivity (EC), total carbon (TotC), total N (TotN), microbial biomass N (MB), potentially mineralisable N (PMN), microbial respiration (C[O.sub.2]), and dissolved organic N (DON) from soil collected at harvest, sowing, tillering, and flowering from organic, biodynamic, integrated, and conventionally managed farms Numbers in bold represent significant (P < 0.05) correlations GMR GIR GNR Silt Clay Sand GMR 1.00 GIR 0.81# 1.00 GNR 0.95# 0.78# 1.00 Silt 0.12 0.06 0.14 1.00 Clay 0.05 0.01 0.07 0.05 1.00 Sand -0.08 -0.07 -0.09 -0.72# -0.71# 1.00 Moist 0.70# 0.65# 0.65# 0.01 0.01 -0.02 Temp. -0.67# -0.67# -0.61# -0.01 -0.01 0.01 pH -0.05 -0.08 -0.02 0.24 0.16 -0.26 EC -0.13 -0.05 -0.14 0.14 -0.13 -0.07 TotC 0.03 0.02 0.04 0.51# 0.20 -0.57# TotN -0.01 0.01 0.01 0.36# 0.25 -0.49# N[H.sub.4] 0.59# 0.48# 0.62# -0.13 -0.09 0.15 N[O.sub.3] 0.30 0.36# 0.27 -0.11 0.01 0.08 MBN 0.59# 0.54# 0.52# 0.07 0.06 -0.06 PMN 0.17 0.15 0.18 0.57# 0.58# -0.79# C[O.sub.2] 0.64# 0.62# 0.58# -0.10 0.06 0.04 DON 0.54# 0.50# 0.48# 0.06 0.02 -0.04 Moist Temp. pH EC TotC TotN GMR GIR GNR Silt Clay Sand Moist 1.00 Temp. -0.76# 1.00 pH 0.01 0.01 1.00 EC -0.01 -0.03 0.03 1.00 TotC 0.03 -0.01 0.14 0.50# 1.00 TotN 0.04 -0.01 0.13 0.47# 0.97# 1.00 N[H.sub.4] 0.44# -0.33# -0.17 0.02 -0.10 -0.09 N[O.sub.3] 0.65# -0.47# -0.07 -0.04 -0.11 -0.09 MBN 0.74# -0.67# -0.09 -0.20 -0.08 -0.09 PMN 0.17 -0.19 0.05 -0.02 0.40# 0.34# C[O.sub.2] 0.83# -0.66# -0.02 -0.11 -0.12 -0.09 DON 0.67# -0.66# -0.05 -0.12 -0.07 -0.08 N[H.sub.4] N[O.sub.3] MBN PMN C[O.sub.2] DON GMR GIR GNR Silt Clay Sand Moist Temp. pH EC TotC TotN N[H.sub.4] 1.00 N[O.sub.3] 0.56# 1.00 MBN 0.38# 0.67# 1.00 PMN -0.01 0.11 0.24 1.00 C[O.sub.2] 0.33# 0.58# 0.70# 0.12 1.00 DON 0.33# 0.50# 0.73# 0.19 0.67# 1.00 Note: Numbers in bold represent significant (P < 0.05) correlations indicated with # Table 4. Full and reduced ordinary least square regression models of gross nitrogen (N) mineralisation rate, gross N immobilisation rate, and gross nitrification rate as influenced by soil moisture, soil temperature, and soil physical, biological, and chemical properties Reduced model, probability of 0.1; CV, coefficient of variation; s.e., standard error; F, F-statistic for testing the overall fit of the models; MDF, model degrees of freedom; all other abbreviations given in Table 3 Gross N mineralisation Soil Full model Reduced model properties CV s.e. CV s.e. Silt -0.04 0.03 -0.05 0.03 * Clay -0.07 0.03 ** -0.07 0.03 *** Sand -0.07 0.03 ** -0.06 0.03 ** Moist 0.06 0.01 **** 0.06 0.01 **** Temp. -0.03 0.00 **** -0.03 0.00 **** pH -0.17 0.08 ** -0.13 0.08 * EC 0.00 0.00 ** 0.00 0.00 * TOW -0.23 0.45 -0.10 0.06 * TotN 1.55 5.59 TotC/TotN 0.07 0.04 0.06 0.02 ** MB-N 0.00 0.00 PMN -0.01 0.00 C[O.sub.2] 0.01 0.00 **** 0.01 0.00 **** DON 0.00 0.00 GMR GNR GIR Constant 8.65 2.94 *** 8.02 2.84 *** Observations 288 288 F 42 59 MDF 14 10 Adjusted 0.67 0.68 [R.sup.2] Gross N immobilisation Soil Full model Reduced model properties CV s.e. CV s.e. Silt -0.03 0.02 * -0.01 0.00 ** Clay -0.01 0.02 Sand -0.02 0.01 * -0.01 0.00 ** Moist 0.00 0.01 Temp. -0.02 0.00 **** -0.02 0.00 **** pH -0.10 0.05 ** -0.09 0.04 ** EC 0.00 0.00 TOW 0.41 0.25 * TotN 5.66 3.11 * TotC/TotN -0.04 0.02 MB-N 0.00 0.00 ** 0.00 0.00 * PMN 0.00 0.00 C[O.sub.2] 0.00 0.00 0.00 0.00 * DON 0.00 0.00 GMR 0.24 0.02 **** 0.22 0.01 **** GNR -0.03 0.03 GIR Constant 8.65 1.66 ** 2.30 0.36 **** Observations 288 288 F 158 356 MDF 16 7 Adjusted 0.90 0.90 [R.sup.2] Gross nitrification Soil Full model Reduced model properties CV s.e. CV s.e. Silt 0.00 0.03 Clay 0.00 0.03 Sand 0.01 0.03 0.01 0.01 * Moist 0.05 0.02 *** 0.05 0.01 **** Temp. -0.02 0.00 **** -0.02 0.00 **** pH 0.01 0.09 EC 0.00 0.00 ** 0.00 0.00 ** TOW 0.21 0.47 0.48 0.19 ** TotN -4.54 5.91 -7.89 2.44 *** TotC/TotN 0.03 0.05 MB-N -0.01 0.00 *** -0.01 0.00 *** PMN 0.01 0.00 *** 0.01 0.00 ** C[O.sub.2] 0.00 0.00 DON 0.01 0.00 ** 0.01 0.00 ** GMR 0.33 0.02 **** 0.33 0.02 **** GNR GIR -0.16 0.04 **** -0.16 Constant -1.72 3.16 -1.53 0.04 **** Observations 288 288 0.69 ** F 92 136 MDF 16 11 Adjusted 0.84 0.84 [R.sup.2] * P < 0.1; ** P < 0.05; *** P < 0.01; **** P < 0.001. Table 5. Soil chemical, physical, and biological properties with significant (P [less than or equal to] 0.1) correlations to bacterial denaturing gradient gel electrophoresis (DGGE) profiles at each sampling determined by Monte Carlo permutation tests Numbers presented are Eigen-values with P values in parentheses. n.s., Not significant (P > 0.1); all other abbreviations given in Table 4 Sampling season Environ. factor Harvest Sowing Tillering Chemical Temp. n.s. 0.05 (0.04) 0.08 (0.04) pH 0.06 (0.03) 0.08 (0.0l) n.s. EC n.s. 0.07 (0.05) 0.08 (0.04) TotN n.s. n.s. 0.09 (0.04) TotC n.s. 0.04 (0.05) n.s. DON n.s. 0.06 (0.04) 0.07 (0.06) Physical Sand n.s. 0.05 0.09 (0.0l) Silt n.s. n.s. n.s. Clay n.s. n.s. 0.08 Biological C[O.sub.2] 0.06 (0.0l) 0.05 (0.06) 0.l3 (0.01) MB-N n.s. 0.05 (0.04) n.s. PMN n.s. n.s. n.s. GMR 0.06 (0.04) n.s. 0.l3 (0.0l) GNR 0.05 (0.08) 0.06 (0.03) 0.l1 (0.0l) GIR 0.03 (0.03) 0.09 (0.02) 0.08 (0.02) Sampling season Environ. factor Flowering Organic Chemical Temp. n.s. n.s. pH n.s. n.s. EC 0.l2 (0.03) n.s. TotN 0.08 (0.04) n.s. TotC n.s. n.s. DON n.s. n.s. Physical Sand n.s. n.s. Silt n.s. n.s. Clay n.s. n.s. Biological C[O.sub.2] 0.08 (0.06) n.s. MB-N n.s. n.s. PMN n.s. n.s. GMR n.s. n.s. GNR n.s. n.s. GIR 0.08 (0.06) 0.10 (0.03) Management system Environ. factor Biodynamic Integrated Conventional Chemical Temp. n.s. n.s. n.s. pH n.s. 0.07 (0.09) 0.04 (0.09) EC n.s. n.s. n.s. TotN n.s. n.s. n.s. TotC 0.11 (0.09) 0.07 (0.08) n.s. DON 0.l5 (0.0l) n.s. 0.09 (0.0l) Physical Sand n.s. n.s. n.s. Silt 0.l3 (0.06) n.s. n.s. Clay n.s. n.s. n.s. Biological C[O.sub.2] n.s. n.s. n.s. MB-N 0.l5 (0.04) 0.08 (0.09) 0.08 (0.0l) PMN 0.l4 (0.03) n.s. n.s. GMR 0.16 (0.0l) n.s. 0.06 (0.04) GNR n.s. n.s. 0.04 (0.09) GIR n.s. 0.08 (0.08) 0.05 (0.09)
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