Seasonal changes in microbial function and diversity associated with stubble retention versus burning.
In low-rainfall dry-land agricultural cropping systems, a significant part of the year is associated with little or no plant growth, and microbial activity in soil is limited by the absence of water and readily available carbon (C) sources (e.g. root turnover, root exudates, shoot residues). In Western Australia (WA), a single rain-fed winter (May November) crop is grown each year and since limited opportunities exist for summer cropping, plant cover for the remainder of the year generally consists only of the stubble remaining after crops are harvested and opportunistic summer germinating weeds. Consequently, soil organic matter (SOM) levels are typically low (<5%; McArthur 1991).
Historically, benefits associated with lower stubble loads such as reduced disease carry over (Mayfield and Clare 1984; Murray et al. 1991), enhanced weed control (Stynes and Wise 1980; Orr et al. 1997), increased availability of nutrients (Turpin et al. 1998), reduced soil erosion, easier passage of seeding equipment, and an even seed bed (Smil 1999) have resulted in the adoption of stubble burning as a management tool. However, the loss of organic residues via stubble burning has also been associated with longer term nutritional and chemical imbalances in soil (Chan et al. 1992), which are often manifested in subsequent crops. In response to this perceived decline in SOM fertility and to address soil erosion problems (Littleboy et al. 1992), strategies such as stubble retention and reduced tillage have evolved and become more widely adopted in WA farming systems with the development of technological capacity (i.e. disc seeders, controlled traffic).
The ability of stubble retention and reduced tillage to increase the amount of labile C in soil, and provide a suitable substrate to promote microbial growth and activity, is clearly demonstrated by Powlson et al. (1987) for a temperate agricultural soil. In their long-term study (18 years), total soil organic C (SOM-C) increased by only 5% despite high plant biomass returns of straw and stubble (5 t/ha.year), whilst microbial biomass-C (MB-C) increased by 37-45%. Similarly, Carter and Mele (1992) demonstrated no change in total SOM-C in retained v. burnt stubble treatments under high crop stubble loads (4-6 t/ha) in north-eastern Victoria. We therefore question the capacity for changes in total SOM-C to occur in the low-input, rainfall-limited system of WA, particularly given that a significant proportion of the total plant biomass (14-38%) that may be contributed from below ground (i.e. roots) is actually retained at harvest (Russell and Fillery 1996; Thorup-Kristensen 2001; Atwell et al. 2002). Since the work by Powlson et al. (1987), new methods to assess aspects of the functional diversity and community structure of the microbial population in soil have become available. Degens and Harris (1997), for example, established that patterns of microbial substrate utilisation (defined here as community level physiological profiles, CLPP) were more sensitive to management effects than either SOM or the microbial biomass pool (Bending et al. 2000).
Environmental regulators such as soil temperature also influence the mineralisation of SOM (Murphy et al. 1998; Recous et al. 1998; Hoyle et al. 2006). Increasing soil temperature has been demonstrated to affect the survival and activity of microorganisms in situ (Joergensen et al. 1990), and a relatively wide range has been reported for the temperature dependence of C mineralisation as measured by C[O.sub.2]-C evolution in field and laboratory studies (Raich and Schlesinger 1992; Kirschbaum 1995; Davidson et al. 1998; Reichstein et al. 2000). Temperature fluctuations therefore have a role in nutrient supply since soil N supply in agricultural systems (Angus 2001) is predominantly determined by microbial decomposition of organic matter. Management strategies such as stubble retention have been shown to influence the capacity of a soil to buffer extremes in temperature, but their effectiveness is likely to be both spatially and temporally variable due to non-uniform distribution (Hatfield et al. 2001). In dry-land, low-rainfall environments, only subtle changes in the soil micro-environment are likely to result from stubble retention due to high soil temperatures and low stubble loads. Therefore, differences in annual residue inputs and residue quality are more likely to influence microbial processes affecting nutrient supply.
Soil enzyme activity has also been shown to alter with stubble management, with increasing enzyme activity associated with incorporation of crop residues attributable to changes in the functional diversity of the microbial community (Bending et al. 2002). In soil, enzymatic activity results from the accumulation of enzymes from microbial turnover (Ladd 1978), plants and organic residues (Tabatabai 1994), as well as from enzymatic activity of active microorganisms (Kiss et al. 1975). Arylsulfatase and phosphatase, for example, are catalysts for biochemical reactions (Bandick and Dick 1999) essential in SOM breakdown and nutrient turnover that becomes plant available. Grierson and Adams (2000) also demonstrate specific associations between plant species and acid phosphatase activity, fungal biomass and microbial biomass-P, thus influencing nutrient availability. Cellulase enzyme activity signals the breakdown of cellulose associated with plant residues into more bio-available compounds, and [beta]-glucosidase is active in the release of low-molecular-weight sugars that serve as an energy source for microorganisms (Bandick and Dick 1999). Enzyme assays may also be useful as indicators of potential N supply, as has been demonstrated for arginine ammonification (Bonde et al. 2001).
The aims of this study were therefore to assess changes in the mass, structure, and function of the microbial community associated with (i) seasonal fluctuations and (ii) stubble management on a low-fertility, sandy loam soil in WA.
Materials and methods
Field site description and experimental design
The study area was located at the Department of Agriculture Merredin Research Station (31[degrees]28'S, 118[degrees] 16'E) on a trial established in 1987 in the low-rainfall region of WA on a Red-Brown Earth (Red Chromosol, 26% clay). The experiment was established for 16 years prior to sampling in 2003 and was in a continuous cropping rotation (predominantly legume--wheat based) with stubble either burnt (SB) prior to sowing (autumn burn) or retained as standing stubble (SR) after harvest. Mean temperature (1987-2002) calculated monthly ranged from a minimum of 5.6[degrees]C to a maximum 34.1 [degrees]C, with mean annual rainfall estimated at 294 mm (Fig. 1). In 2003, temperatures were in the range--1.7 to 42.2[degrees]C and annual rainfall reached 356 mm of which 190 mm fell during the winter cereal-growing season (May-November), indicating a potential wheat yield of 3.80 t/ha based on production of 20 kg grain/mm effective rainfall (French and Schultz 1984).
[FIGURE 1 OMITTED]
The rotation phase at the time of sampling was wheat (Triticum aestivum L.). The trial was seeded in 16-row plots (5.0m in width), 30.0 m in length in a randomised block design with 3 replicates and treatment structures of stubble management and row spacing. Plots were sown (direct drilled) on 4 June (180-mm row spacing), with 150 kg/ha Agras No. 1 (17.5% N, 7.6% P, 17.0% S, 0.06% Zn) basal fertiliser applied at seeding and using 80-mm-wide press wheels with following chains. Wheat cv. Wyalkatchem was sown at 110 kg/ha (after adjustment for seed size and assuming a field emergence of 70%), to achieve a target density of 200 plants/[m.sup.2]. The experiment was conducted using composite soil samples from each field plot (n = 3) for SR and SB treatments collected on 4 occasions under a wheat crop at key growth stages (8 May, pre-sowing; 21 July, terminal spikelet; 15 September, anthesis; 28 November, physiological maturity). Base-line soil sampling (8 May) was conducted for 0-0.05 and 0.05-0.10 m soil layers using a hand 'push-in' auger (70 mm diameter by 50 mm depth), and deeper sampling (0.10-0.20, 0.20-0.30 m) conducted using a 'screw' auger to assess stratification of microbial biomass-C (MB-C) with depth. Subsequent soil sampling (21 July, 15 September, and 28 November) was restricted to the initial 2 soil layers (0-0.05, 0.05-0.10 m) as there was only a significant (P [less than or equal to] 0.05) difference in MB-C between SR and SB in the 0-0.05 m soil layer.
A composite sample of 12 cores was collected from each plot using a 'Z' sampling pattern, sieved (<2 mm), and stored field-moist at 4[degrees]C for <6 days prior to characterising chemical and physical soil properties (Table 1). Soil from each field plot was used to estimate changes in microbial growth and activity, biochemical processes, and gross N transformation rates under SR and SB treatments at 2 soil depths (0-0.05, 0.05-0.10 m). Community level physiological profiles (CLPP) and phospholipid fatty acids (PLFA) were determined only on surface (0-0.05 m) soils. Soils were adjusted to 45-50% water-holding capacity (WHC, approx.--100 kPa, 0.12 g [H.sub.2]O/g soil) and pre-incubated at average field soil temperature (20[degrees]C for 8 May sampling; 10[degrees]C for 21 July sampling; 15[degrees]C for 15 September sampling; 30[degrees]C for 28 November sampling) for 4 days prior to analysis for microbiological parameters.
Soil pH and EC were determined in 1:1 (v/v) soil : Ca[Cl.sub.2] extracts. Cation exchange capacity (CEC) was determined using the silver-thiourea method (Rayment and Higginson 1992). Soil moisture was determined by drying at 105[degrees]C for 24h. Total C and total N were determined on oven-dried, finely ground soil (<4mm) by total combustion using a CHN-2000 analyser (Leco Corp., St Joseph, MI). A commercial DNA analysis service (Predicta-B) was used to determine the risk of crop damage due to the presence of major cereal root diseases or pathogenic nematodes in soil at this site (0-0.10 m).
Microbial biomass-C (Wu et al. 1990) and microbial biomass-N (MB-N; Joergensen et al. 1990) were determined by fumigation extraction. Moist soils (20g dry weight equivalent, 3 laboratory replicates) were fumigated with chloroform containing amylene (0.006% v/v) as the stabilising agent for 24 h to determine the flush of C and N by extraction with 80mL 0.5M [K.sub.2]S[O.sub.4] for 1 h. Filtered extracts (Whatman No. 42) were frozen at -20[degrees]C until analysed for total oxidisable C (Shimadzu Model 5050) and ninhydrin-reactive N (GBC UV-VIS 920 Spectrophotometer). MB-C was calculated from the flush in total oxidisable C, and MB-N from the flush in ninhydrin-reactive N between fumigated and non-fumigated soil. The resulting flush in organic-C and ninhydrin-reactive N was then adjusted by a factor of 2.22 for MB-C (Wu et al. 1990) and 5 for MB-N (Joergensen et al. 1990). Soluble organic C ([K.sub.2]S[O.sub.4]-extractable) was determined from the total amount of oxidisable C measured in non-fumigated soil.
Microbial C[O.sub.2]-C evolution
Soil samples (100 g dry weight; DW) were weighed into air-tight, 523-mL glass containers and sealed using lids modified with gas septum ports. Soils were incubated for 7 days under average field soil temperature at 100% humidity. Headspace C[O.sub.2] concentrations were measured using an infrared gas analyser (IRGA) at intervals ranging from 15 to 72h (depending on the rate of build-up in the headspace), by extracting 1 mL of air after first mixing the headspace gas. Treatments were run against a C[O.sub.2] standard (4.95 [+ or -] 0.10% C[O.sub.2] in helium, BOC Ltd). After each sampling, all containers were opened and the headspace gas exchanged with flesh air. The C[O.sub.2]-C results presented are the average daily rate of evolution measured (1-7 days) for each treatment, minus a control treatment (no soil) prior to the application of [sup.15]N solutions as described below.
Inorganic N and gross N transformation rates
[sup.15]N-enriched solutions (60 atom % excess) were prepared using either [sup.15]N-labelled [(N[H.sub.4]).sub.2]S[O.sup.4] to contain 5[micro]g N/g soil, or [sup.15]N-labelled KN[O.sub.3] to contain 2 [micro]g N/g soil in 4mL of solution and applied on Day 7 of the incubation. Soils (100g DW soil) received 4mL of [sup.15]N-enriched (N[H.sub.4])[sub.2]S[O.sub.4] solution applied as multiple droplets. Following application of treatments, all soils were mixed thoroughly and final soil moisture determined (53-60% WHC, approx. -85 kPa, 0.14g [H.sub.2]O/g soil). Soils continued to be incubated at average field soil temperature, as previously described, prior to analysis. Two extraction times were used ([T.sub.0] = 4 h, [T.sub.1] = 24 h for gross N mineralisation and To = 4 h, [T.sub.2] = 120 h for gross nitrification) to determine inorganic N and obtain samples for [sup.15]N analysis, based on the rate of [sup.15]N enrichment decline previously determined for this soil (Hoyle et al. 2006). Soil inorganic N was determined by extraction with 80 mL 0.5 M [K.sub.2] S[O.sub.4] (soil : solution ratio 1 : 4) for 1 h and filtered (Whatman No. 42). Extracts were analysed for N[H.sub.4.sup.+] concentration colorimetrically using the salicylate-nitroprusside method (Krom 1980), and N[O.sub.3.sup.-] concentration using the hydrazinium reduction method (Kamphake et al. 1967; Kempers and Luft 1988) on a Skalar Auto-analyser (Skalar San plus). Remaining N in soil solution was removed using Buchner funnels under vacuum by repeated extraction twice with 0.5 M [K.sub.2]S[O.sub.4] and twice with double-distilled water in soil labelled with [sup.15]N-enriched [(N[H.sub.4]).sub.2]S[O.sub.4]. Soil remaining after extraction of N was then dried at 40[degrees]C and ground to a fine powder using a Sibertechnic mill.
A modified diffusion technique (Georges and Dittert 1998; Herrmann et al. 2004) was used to prepare [K.sub.2]S[O.sub.4] soil extracts for [sup.15]N / [sup.14]N analysis. In short, 5 mL of extract was placed in a 20-mL plastic scintillation vial and separation of N[H.sub.4.sup.+]-N and N[O.sub.3] -N pools achieved by first diffusing extracts with c. 200 mg MgO, and subsequently with c. 400 mg Devarda's alloy. Soil extracts with low inorganic N[H.sub.4.sup.+] were spiked with 60[micro]g [.sup.15]N at natural abundance as [(N[H.sub.4]).sub.2]S[O.sub.4] prior to diffusion to increase N content and to dilute the [sup.15]N enrichment within range for mass spectrometry (MS) analysis. Soil extracts labelled with [.sup.15]N-enriched KN[O.sub.3] were not spiked. Evolved N[H.sub.3] was trapped by 10 [micro]L 2.5 M KHS[O.sub.4] placed on a glass-fibre filter disk (Whatman GFC) sealed between a double layer of PTFE tape. Samples were shaken for 7 days in a vertical rotary shaker at 20[degrees]C before glass-fibre disks were removed and dried down prior to analyses for [sup.15]N / [sup.14]. Dried disks and [sup.15]N soils (c. 50mg) were then placed into tin capsules and [sup.15]N / [sup.14] isotope ratios for N[H.sub.4.sup.+] and N[O.sub.3.sup.-] fractions determined separately by Tracer Mass Spectrometry (Europa 20 : 20). Standards of [sup.15]N-enriched solution containing N[H.sub.4] at 5 and 10 atom% were also diffused and analysed as internal standards to assess recovery and possible isotopic discrimination in the diffusion process.
Analytical calculations, FLUAZ modelling, and statistical analysis
Gross N mineralisation and N[H.sub.4.sup.+] consumption rates (immobilisation, nitrification, and gaseous loss) were calculated analytically (Kirkham and Bartholomew 1954) after [sup.15]N/[sup.14] labelling with [sup.15]N-enriched (N[H.sub.4])2S[O.sub.4]. Gross nitrification and N[O.sub.3.sup.-] consumption (i.e. immobilisation and gaseous loss) were also estimated analytically (Kirkham and Bartholomew 1954) after [sup.15]N/[sup.14] labelling with [sup.15]N-enriched KN[O.sub.3] solution. Additionally, gross N mineralisation, potential gross nitrification rates (i.e. in the presence of excess N[H.sub.4.sup.+]), and N[H.sub.4.sup.+] and N[O.sub.3.sup.-] immobilisation rates were also simulated using first-order kinetics in the numerical model FLUAZ assuming a N[H.sub.4.sup.+] :N[O.sub.3.sup.-] partition rate ([beta]) of 0.05 (Mary et al. 1998).
The climatic factor model (Eqn 6b from Andren and Paustian 1987) was used to normalise differences in temperature between sampling times to a reference temperature of 20[degrees]C. Daily variation in soil temperature (at constant soil water content) was standardised to the number of 'normalised' days at 20[degrees]C using [Q.sub.10] temperature relationships derived for C mineralistion and gross N mineralisation from a previous study (Hoyle et al. 2006) for this trial site. The [Q.sub.10] temperature relationships for C mineralisation were between 1.75 and 2.65 in SR treatments, and between 1.66 and 3.03 in SB treatments. The [Q.sub.10] temperature relationships for gross N mineralisation rates were between 1.97 and 2.14 in SR, and between 1.51 and 3.28 in SB treatments.
Biochemical enzyme assays
Arginine ammonification activity was assessed following incubation of 1.0g DW soil with 200 [micro]L of 10mM L-arginine and 1800 [micro]L of double-distilled water (final concentration of 1 mM arginine) for 1 h at 20[degrees]C (Bonde et al. 2001). Control samples also received 200gL of 10 mM L-arginine solution following incubation, with mineralisation of arginine terminated after application of 8 mL of cold 2 M KCl (Bonde et al. 2001). Filtered extracts (Whatman No. 1) were analysed for N[H.sub.4.sup.+] concentration colourimetrically as described above and corrected for gravimetric soil water content. Total cellulase activity (endoglucanase, exoglucancase, and [beta]-glucosidase) was determined after incubation of 1.5 g DW soil with 0.5 g Avicel at 40[degrees] C for 16 h (Hope and Burns 1987). Reducing sugars were determined colourimetrically using the method of Nelson and Somogyi (Spiro 1966) on a UV/VIS spectrophotometer (GBC) at 520 nm and adjusted using a correction factor for soil water content. [beta]-glucosidase (Eivazi and Tabatabai 1988) and acid phosphatase activity (Tabatabai and Bremner 1969; Eivazi and Tabatabai 1977) were determined following a 1-h incubation of soil at 37[degrees]C with 0.25 mL of toluene, 4mL of universal buffer, and 1 mL of 25mM p-nitrophenyl-[beta]-D-glucosidase (PNG) or 1 mL of 15 mM sodium p-nitrophenyl phosphate (PNP), respectively. The p-nitrophenyl concentration was determined colorimetrically using a UV/VIS spectrophotometer (GBC) at 400 nm, after extraction of supernatant with 1 mL of Ca[Cl.sub.2] and 4 mL of NaOH (phosphatase activity) or 4mL of Tris (hydroxymethyl) aminomethan (THAM, pH 12) for [beta]-glucosidase and centrifugation at 3500rpm for 3 min. Arylsulfatase activity was determined on soil after a l-h incubation at 37[degrees]C with 0.25mL of toluene, 4mL of acetate buffer (0.5M, pH 5.8), and l mL of 25mM p-nitrophenyl sulfate (Tabatabai and Bremner 1970). After centrifugation at 3500 rpm for 3 min, supernatant was analysed for p-nitrophenol concentration colorimetrically using a UV/VIS spectrophotometer (GBC) at 400nm after extraction of supernatant with 1 mL of Ca[Cl.sub.2] and 4 mL of NaOH. Enzyme activity was calculated from the flush between samples treated with respective substrates and blank samples using a correction factor for soil water content. All measurements and blanks were carried out in duplicate for each of 3 field replicates.
Community level physiological profiles (CLPP)
CLPP were determined by applying a range of 25 organic substrates to soil (Degens et al. 2001). Organic substrate solutions (2mL) and a control solution of double-deionised (DDI) water were each added to separate glass vacutainers (9 mL) containing 1.0 g field-moist soil, and treatments replicated 3 times (Degens and Harris 1997; Degens et al. 2001). Organic substrates were adjusted to a pH of 5.8-6.2 (Degens 1998) and included 2 amines (D-glucosamine, L-glutamine) and six amino acids (L-arginine, L-asparagine, L-glutamic acid, L-histidine, L-lysine, L-serine) added at 10mM; 2 carbohydrates (D-glucose, D-mannose) added at 75mM; and 15 carboxylic acids (L-aseorbic acid, citric acid, fumaric acid, gluconic acid, [alpha]-ketobutyric acid, [alpha]-ketoglutaric acid, [alpha]-ketovaleric acid, DL-malic acid, malonic acid, pantothenic acid, quinic acid, succinic acid, tartaric acid, uric acid, and urocanic acid) added at 100 mM (Degens and Vojvodic-Vnkovic 1999). Immediately after amendment of soil with solutions vacutainers were sealed with a septum, vortexed, and subsequently incubated in the dark for 4 h at 25[degrees]C. Samples were mixed after 2 h and again immediately prior to sampling to homogenise the gas headspace, after which evolved C[O.sub.2]-C was measured by taking a 1-mL syringe sample. C[O.sub.2]-C was analysed for each of the treatments using an infrared gas analyser (Model LI-6252, LI-COR, Inc., Lincoln, Nebraska), calibrated against a C[O.sub.2] standard (4.95 [+ or -] 0.10% C[O.sub.2] in helium, BOC Ltd).
Diversity indices were calculated from the average rate of C[O.sub.2]-C evolution measured after 4 h for each treatment, minus the control (DDI). Catabolic diversity requires both the number of substrates metabolised (richness) and variation in substrate use (evenness) to be analysed (Degens et al. 2000). In this study, richness is the number of substrates metabolised in each treatment. The Simpson diversity index (D) was calculated from the C[O.sub.2]-C evolution of each substrate as a proportion of the total C[O.sub.2]-C response ([p.sub.i]) where D = 1/y[SIGMA][p.sub.i.sup.2] (Magurran 1988) and was used as a measure of catabolic evenness (E). Equitability ([E.sub.D]) can be calculated by taking Simpsons index (D) and expressing it as a proportion of the total number of substrates. If [E.sub.D] = 1, this would indicate that all substrates were used evenly.
Phospholipid fatty acid extraction (PLFA)
Total lipids were extracted using the 1-phase procedure of Zelles and Bai (1993). Extraction of the lipid phase (includes phospholipid fatty acids) was completed on previously freeze-dried soil (6.0 [+ or -] 0.1 g DW) using a single-phase procedure in 28.5 mL of a CH[Cl.sub.3] : MeOH : [K.sub.2]HP[O.sub.4] buffer solution (1.25 : 2 : 1 v/v/v). After sonication of soil solution for 90 s, samples were centrifuged (3500 rpm for 4 min) and phases allowed to separate before supernatant was decanted into a sterile 50-mL centrifuge tube. Both CH[Cl.sub.3] (3 mL) and DDI water (3 mL) were then added to the supernatant, and this solution centrifuged (3000 rpm for 3 min) until separation of phases was achieved and the chloroform (lipid) phase removed. A second extraction phase was undertaken after the addition of a further 3 mL of CH[Cl.sub.3]. This CH[Cl.sub.3] (lipid) phase was also separated, removed, and added to the first volume extracted. Removal of the chloroform (CH[Cl.sub.3]) from the lipid phase was primarily achieved using a rotor evaporator and remaining solution dried down under a continuous flow of [N.sub.2] (Zelles 1997).
Prior to fractionation of the lipid phase into 3 fractions (neutral lipids, glycolipids, and phospholipids), solid-phase silica-bonded extraction columns (SPE-Si, Supleco, Poole, UK) were conditioned sequentially twice with 1 mL of MeOH and twice with 1 mL of CH[Cl.sub.3] under vacuum, prior to conditioning with l mL of CH[Cl.sub.3] under gravitation (Zelles 1997). The lipid material was re-dissolved and transferred to the extraction column in 4 aliquots (4 x 250 [micro]L) of CH[Cl.sub.3]. Lipid material was then sequentially eluted under vacuum twice with 1 mL of CH[Cl.sub.3] and twice with 1 mL of acetone prior to the collection of polar lipids with 2mL of MeOH (Zelles 1997). An internal standard (200 [micro]L of nonadecanoic acid methyl ester solution containing 15 ng hexane/[micro]L) was added and samples were dried under [N.sub.2]. Samples were derivatised by subjecting the phospholipid fraction to mild alkaline hydrolysis by re-suspending in MeOH :toluene (1 : 1, v/v, 200 [micro]L) to liberate the ester-linked (EL) fatty acids, prior to adding 500 [micro]l of 0.2 M KOH : MeOH (Zelles 1997). Samples were then sequentially shaken and heated to 75[degrees]C, cooled to room temperature, and neutralised with acetic acid (0.2 M, 500 [micro]L). One mL each of CH[Cl.sub.3] and DDI water were added to the lipid fraction, vortexed 3 times for 3 s, and centrifuged (3000 rpm for 2 min) until separation of phases was achieved and the CH[Cl.sub.3] phase extracted (Zelles 1997). A second extraction phase was undertaken after the addition of a further 1 mL of CH[Cl.sub.3] after which the extracted lipid phase was dried down under a continuous flow of [N.sub.2] and frozen at -20[degrees]C until analysed. Peak areas were quantified from blanks containing an internal standard (C19:0, 10 ng/[micro]L, Sigma) and the microbial community characterised according to Zelles (1999) and Dickens and Anderson (1999).
Plant biomass and grain yield components
Organic matter inputs were calculated on SR treatments assuming a constant harvest index of 45% (Table 1). The remaining plant biomass (55%) was assumed to have been retained following harvest operations. Due to low residue levels in some years, stubble burning was not complete and was estimated to be 80% effective. In 2003, 6 quadrats (each 0.33 [m.sup.2]) were sampled in each treatment (2 per plot) at anthesis (15 September) and harvest (28 November) to determine plant biomass. Grain yield components measured were total dry matter production, head number, average grain weight, total grain weight, and harvest index. Grain yield was also measured by mechanically harvesting a 1.44-m strip in the centre of each plot to a length of 10 m. Grain quality was assessed for grain protein, weight by volume (hectolitre weight), and small grain screenings (% of grain passing through a 2-mm sieve).
General analysis of variance (ANOVA) was used after testing for normal distribution to determine significant treatment effects for stubble treatment and temperature on average daily C[O.sub.2]-C evolution, inorganic N, and gross N transformation rates using GENSTAT 7th edn. Variation in selected parameters was investigated using regression analyses. Analytical data are given as mean [+ or -] the least significant difference (l.s.d.), and statistical tests were considered significant at P [less than or equal to] 0.05. FLUAZ-derived estimates of gross N transformation rates are given as the simulation value [+ or -] 95% confidence interval. Data were also considered a set of repeated measurements as the same plots were sampled through time (Webster and Payne 2002). Repeat sampling analysis of variance was used after testing for normal distribution to determine significant treatment effects using GENSTAT 7th edn. The focus of the analysis was to model biological trends with time and to examine the effects of stubble management techniques on these soil properties. All multivariate tests of PLFA and CLPP data were based on Bray--Curtis dissimilarities calculated among observations that were 4th-root transformed. Tests of the multivariate null hypotheses of no differences among a priori defined groups (PLFA profiles to the 2-factor design) were examined using permutation multivariate analysis of variance (PERMANOVA; Anderson 2001; McArdle and Anderson 2001) and canonical analysis of principal coordinates (CAP; Anderson and Robinson 2003; Anderson and Willis 2003). The F-ratio constructed in PERMANOVA is analogous to Fisher's F-ratio and is constructed from sums of squared distances within and between groups. CAP accounts for the correlation structure among variables, and provides a constrained ordination that maximises the differences among a priori groups (Anderson and Willis 2003). Relationships between PLFA and CLPP profiles and environmental variables (described in Table 4) were analysed using non-parametric multivariate multiple regression (McArdle and Anderson 2001). Individual variables were analysed separately for their relationship with multivariate species data (ignoring other variables), and variables were then subjected to a step-wise forward selection procedure to develop a model of the species data obtained using 9999 permutations (DISTLM; McArdle and Anderson 2001).
The total amount of above-ground organic matter retained during the 16-year history of this trial differed by approximately 11.7 t/ha with an estimated average annual contribution of 0.91 t/ha in SR treatments and 0.02 t/ha in SB treatments. However, this annual difference in contribution of new plant organic matter (average 0.89 t/ha) did not result in any measurable difference in total soil C (P > 0.05). However, a significant difference (P < 0.007) in the [K.sub.2]S[O.sub.4]-extractable soluble organic C pool was observed in May between SR and SB treatments (Table 1). Total soil N was also significant at P < 0.07 (Table 1). No change in soil [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] or CEC was observed between SR and SB treatments, whilst soil EC was significantly lower (P [less than or equal to] 0.05) in SB treatments measured both in July and September (Table 1). Lower EC concentrations were observed in soil at 0.05-0.10 m depth compared with surface soil, with no treatment differences extending to this layer (data not presented). Cation exchange capacity was higher (P < 0.001) at depth (0.05-0.10 m) than in surface soils. Soil pH did not change with soil depth but declined steadily from an average pH of 5.8 in May, to a pH of 5.3 in November (data not presented).
Microbial biomass-C measured in May was positively (P [less than or equal to] 0.05) influenced by the retention of crop residues, with treatment differences most evident in surface soils (0-0.05 m; Fig. 2). Although approximately 65% of the total microbial biomass (0-0.30 m) was located below a depth of 0.05 m, no differences (P > 0.05) in MB-C were detected between stubble treatments in deeper soil layers (Fig. 2).
[FIGURE 2 OMITTED]
The average mass of microorganisms measured between May and November (0-0.05 m soil depth) was significantly (P < 0.001, l.s.d. = 11) influenced by the retention of crop residues and measured 203 kg MB-C/ha in SR treatments and 143 kg MB-C/ha in SB treatments. Although there was no effect of stubble treatment on MB-C in the 0.05-0.10m soil layer, significant seasonal variability (P < 0.001) in MB-C was observed in both soil layers (Fig. 3a, b). Microbial biomass-C in both SR and SB treatments sampled on 15 September was higher (P < 0.001) than at other sampling times, demonstrating that seasonal variability was of a greater magnitude than management (Fig. 3a, b). There was no significant interaction (P [greater than or equal to] 0.05) observed between stubble treatment and sampling time.
[FIGURE 3 OMITTED]
Higher (P [less than or equal to] 0.05, l.s.d. = 5.7) microbial biomass-N (MBN) was also measured in SR treatments (0-0.05 m depth; 41.2 mg N/kg soil) compared with SB treatments (31.1 mg N/kg soil). However, contrary to observations for MB-C there was no significant change in MB-N (P [greater than or equal to] 0.05) associated with sampling time (Fig. 3c, d). Consequently, the resulting microbial biomass C/N ratio is altered in September. The microbial C/N ratio at this time was measured at 14:1 and differed significantly (P [less than or equal to] 0.05, l.s.d. = 2.3) from other sampling dates (range 6-7 : 1).
Microbial C[O.sub.2]-C evolution
Average C[O.sub.2]-C evolution in SR treatments was significantly higher (P < 0.001) than SB treatments at all sampling dates and in both soil layers (Fig. 4a, b). C[O.sub.2]-C evolution measured in surface soil (0-0.05 m) was higher (P < 0.001) in both SR (Fig. 4a) and SB (Fig. 4b) treatments than at depth (0.05-0.10 m). Seasonal differences in C[O.sub.2]-C evolution associated with sampling date, demonstrated greater variability than treatment effects (Fig. 4a, b). Standardising C transformation rates at variable temperature and moisture to the number of 'normalised' days (Recous et al. 1998) at a reference temperature (20[degrees]C) and moisture (45% WHC) did not reduce the variability associated with sampling periods in C[O.sub.2]-C evolution (data not presented).
[FIGURE 4 OMITTED]
The metabolic quotient (qC[O.sub.2]) was greater (P < 0.002, l.s.d. = 0.0003) in SR treatments compared with SB treatments as demonstrated by the increase in C[O.sub.2]-C evolution per unit MB-C. The metabolic quotient in SR treatments averaged 0.0037 [micro]g C[O.sub.2]-C/[micro]g MB-C.h (range 0.0028-0.0056 [micro]g C[O.sub.2]-C/[micro]g MB-C.h), compared with SB treatments that averaged 0.0029 [micro]g C[O.sub.2]-C/[micro]g MB-C.h (range 0.0015-0.0046 [micro]g C[O.sub.2]-C/[micro]g MB-C.h). The qC[O.sub.2] also varied significantly (P < 0.001) with sampling date and was on average 1.9 times greater on 15 September (0.0051 [micro]g C[O.sub.2]-C/[micro]g MB-C.h) compared with other sampling dates in both SR and SB treatments (0.0023-0.0029 [micro]g C[O.sub.2]-C/[micro]g MB-C.h).
After incubation for 7 days at prescribed temperatures, average soil N[H.sub.4.sup.+] concentration when assessed over all sampling times was significantly higher (P < 0.04, l.s.d. = 0.5) in SR treatments (1.4 [micro]g N/g soil) than in SB treatments (0.9 [micro]g N/g soil). However, this was primarily influenced by differences observed between stubble treatments in September (Fig. 5a-d). In contrast, N[O.sub.3.sup.-] concentrations in SR treatments were significantly lower (P < 0.009) than in SB treatments at the first sampling date, with no significant differences for other sampling times (Fig. 5a-d).
[FIGURE 5 OMITTED]
Nitrate was the dominant form of available N at the first sampling date (Fig. 5a-d). Soil N[H.sub.4.sup.+]-N concentration remained low between May and September (0.2 and 1.3 mg N/kg soil), and increased significantly (P < 0.05) in November (Fig. 5a-d). Prior to plant uptake, N[O.sub.3.sup.-] concentration was greatest in SB treatments. However, a significant decline in N[O.sub.3.sup.-]-N due to strong demand for nutrients by plants during the cropping season resulted in little or no separation between stubble treatment effects for N[O.sub.3.sup.-] during later sampling periods. In contrast, a rapid decline in soil N[O.sub.3.sup.-]-N concentration was observed after May (Fig. 5a, b). No change in the unlabelled N[H.sub.4.sup.+] or unlabelled N[O.sub.3.sup.-] pool size was observed after the application of [sup.15]N-enriched solution as [(N[H.sub.4]).sub.2]S[O.sub.4] or KN[O.sub.3] to soil in SR or SB treatments. Low nitrate consumption observed at all sampling dates also suggests plant uptake of N[O.sub.3.sup.-], although at such low concentrations of inorganic soil N[O.sub.3.sup.-], accurate measurement of N[O.sub.3.sup.-] consumption was difficult.
Gross N transformation rates
Analytical recovery ([[SIGMA].sup.15]N[H.sub.4.sup.+], [sup.15]N[O.sub.3.sup.-], ground soil organic [sup.15]N) of the applied [sup.15]N-enriched [(N[H.sub.4]).sub.2]S[O.sub.4] label was 67-101% for SR treatments, with the recovery derived using FLUAZ between 78 and 86% (mean weighted error, MWE 0.50). In SB treatments, the recovery efficiency of the [sup.15]N-enriched [(N[H.sub.4]).sub.2]S[O.sub.4] label was 68-97% for analytical measurement, and 70-100% using FLUAZ (MWE 0.89). FLUAZ-derived gross N transformation rates have been reported in this study to compare treatment effects.
Due to relatively large confidence intervals, there were no differences (P > 0.05) observed in gross N mineralisation rates between SR and SB treatments, or as a result of sampling date. However, if only the November sampling is considered, significantly greater (P < 0.08, l.s.d. = 0.71) rates of gross N mineralisation were observed in SR treatments (2.85 [micro]g N/g soil.day) compared with SB treatments (2.01 [micro]g N/g soil.day). Normalising gross N mineralisation data for temperature (Hoyle et al. 2006) resulted in higher rates of gross N mineralisation (P [less than or equal to] 0.05) in July under SR treatments (Fig. 6a). Increased rates were also evident for the July sampling in SB treatments but were not significantly (P > 0.05) different from gross N mineralisation rates at other sampling times (Fig. 6b).
[FIGURE 6 OMITTED]
Immobilisation (N[H.sub.4.sup.+] and N[O.sub.3.sup.-]) derived using FLUAZ was significantly (P < 0.021) influenced by sampling time, with the highest rates of N[H.sub.4.sup.+] immobilisation (P < 0.003) occurring in July (1.3 [micro]g N/g soil.day) and September (2.2 [micro]g N/g soil.day) at the same time as N[O.sub.3.sup.-] immobilisation was at its lowest (P < 0.001; 0.1 mg N/kg soil.day). Microbial immobilisation of N[H.sub.4.sup.+] accounted for 60-62% of total immobilisation derived using FLUAZ (N[H.sub.4.sup.+] and N[O.sub.3.sup.-]) in May and November, but increased to 93-96% for July and September (Fig. 6c, d). Gross N mineralisation was greater than N[H.sub.4.sup.+] immobilisation (N[H.sub.4.sup.+] consumption minus gross nitrification), with the exception of soils sampled in May and November, which more closely matched gross N mineralisation rates.
The potential nitrification derived from FLUAZ in the presence of an N[H.sub.4.sup.+] substrate was higher than gross nitrification rates calculated analytically in which gross nitrification has been constrained by N[H.sub.4.sup.+] availability (Fig. 6e, f). Gross nitrification rates in the presence of an N[H.sub.4.sup.+] source were greatest (P < 0.057) from May to September in both SR and SB treatments (Fig. 6e, f). The potential for gross nitrification was also greater (P < 0.09, l.s.d. = 0.91) in SR treatments (2.91 [micro]g N/g soil.day) in November compared with SB treatments (1.94 [micro]g N/g soil.day). The ratio of nitrification to immobilisation (N/I ratio) was used to assess the relative dominance of N[H.sup.+] consumptive processes (Tietema and Wessel 1992; Stockdale et al. 2002). This resulted in an approximate N/I ratio nearing 6 : 1 in both the SR and SB treatment sampled in May, which subsequently decreased to approximately 2 : 1 between July and November. This indicates significant potential for N[O.sub.3.sup.-] losses associated with leaching rainfall events received prior to sowing a crop.
Biochemical enzymatic assays
Stubble-retained treatments demonstrated significantly greater cellulase (P = 0.051), acid phosphatase (P < 0.004) and [beta]-glucosidase enzyme activity (P < 0.002) than SB treatments when measured in the 0-0.05m soil layer (Table 2). Arginine ammonification (P < 0.035) was also higher in SR treatments when averaged over both soil depths (Table 2). With the exception of arylsulfatase in which no treatment differences were observed (P> 0.05), enzyme activity decreased at depth (Table 2). Seasonal fluctuations were observed, with increased activity in both SR and SB treatments in arginine ammonification (P < 0.012; l.s.d. = 49) and [beta]-glucosidase (P < 0.001; l.s.d. = 25) activity during the September sampling (Table 2). At this sampling time, arginine ammonification was 121 [micro]g N[H.sub.4.sup.+]-N/g soil.h compared with 36-38 [micro]g N[H.sub.4.sup.+]-N/g soil.h at other sampling periods, and [beta]-glucosidase was 155 [micro]g p-nitrophenol/g soil.h compared with 75-97g p-nitrophenol/g soil.h. Significant variability in acid phosphatase activity was also noted across sampling times (P < 0.001), with the lowest activity in September (Table 2).
Repeat sampling ANOVA demonstrated a significant interaction (P < 0.001) between substrate and sampling time on C[O.sub.2]-C evolution (Fig. 7). A significant (P < 0.001) decline in C[O.sub.2]-C evolution was observed in September compared with July for 22 of the 25 substrates tested, with the remainder declining in the November sampling (Fig. 7). The largest responses (P < 0.001) observed were associated with a subset of the carboxylic acids (citric acid, ([alpha]-ketoglutaric acid, [alpha]-ketobutyric acid, [alpha]-ketovaleric acid, DL-malic acid, fumaric acid and L-ascorbic acid) in at least 2 of 3 sampling dates (Fig. 7). Substrate utilisation generally decreased after the July sampling date, with the exception of glucose, which increased in September (Fig. 7). On average, SR treatments (7.1 [micro]g C[O.sub.2]-C/g soil.4h) evolved significantly (P < 0.001, l.s.d. = 0.28) more C[O.sub.2]-C than SB treatments (6.4 [micro]g C[O.sub.2]-C/g soil.4h).
[FIGURE 7 OMITTED]
All organic substrates (n = 25) were used in this study, indicating no difference in the catabolic richness (number of substrates used) between stubble treatments (Fig. 7). No significant (P > 0.05) difference in catabolic evenness was observed between SR and SB treatments as determined by both Shannons H' index (range 2.90-2.95), and Simpson's D index (range 14.9-15.9) for which a maximum possible value of 25 is possible. Equitability ([E.sub.D]) in substrate metabolism was significantly higher (P < 0.035, l.s.d. = 0.05) in November (0.66) than in July (0.60), with an ED value of 1 indicating complete equitability in substrate utilisation. However, permutational multivariate analysis of variance identified a significant effect (P < 0.001) of sampling time on CLPP, whilst no effect of stubble management was observed. Canonical analyses of principal coordinates indicate that 93.9% of variability in catabolic response profiles between stubble treatments and sampling time was explained by 2 principal axes (Fig. 8a). Significant predictor variables (P [less than or equal to] 0.05) relating to the variation in CLPP include soil inorganic N concentration (N[H.sub.4.sup.+], N[O.sub.3.sup.-]), MB-C, microbial activity, qC[O.sub.2], gross N mineralisation, immobilisation, cellulase activity, arginine ammonification, and [beta]-glucosidase activity (Table 3).
[FIGURE 8 OMITTED]
Microbial community composition (PLFA analyses) was estimated using canonical analyses of principal coordinates with 2 principal coordinate axes accounting for 81.8% of variation between treatments (Fig. 8b). Permutational multivariate analyses of variance indicate that the community structure was dependent on sampling time (P < 0.001), with a shift in the population structure observed between each pair of sample dates. Significant predictor variables (P < 0.05) associated with the variation in community structure included inorganic N concentration (N[H.sub.4.sup.+], N[O.sub.3.sup.-]), MB-C, microbial C/N ratio, microbial activity, qC[O.sub.2], potential nitrification, cellulase activity, arginine ammonification, and [beta]-glucosidase (Table 3). A detailed analysis of taxonomically relevant phospholipid groups further supported the influence of sampling time on microbial community structure, and indicated that the microbial community composition was most different during September (Table 4). No significant effect of stubble treatment was observed on PLFA analyses.
A 2-way crossed analysis of distance-based similarities resulted in a significant separation of data points for both stubble treatments (P < 0.001) and time of sampling (P < 0.001) based on soil physiochemical properties, resources, and function (Table 5). Non-metric, multidimensional scaling based on Euclidean distance of normalised data indicates a cyclical (seasonally based) change in similarity between different treatment groups (Fig. 9). Greater dissimilarity (represented by greater distance between points) was observed in SB treatments between sampling times compared with SR treatments (Fig. 9).
[FIGURE 9 OMITTED]
Although no difference (P > 0.05) in plant biomass was measured between stubble treatments at anthesis, harvest plant biomass in SR treatments was significantly (P < 0.029) greater than in SB treatments (Table 6). Grain yield was also significantly higher (P < 0.05) in SR than in SB treatments (Table 6). Grain protein and total N uptake were similar in SR and SB treatments. A significant difference (P < 0.018) was observed between treatments in grain weight (Table 6). A water-use efficiency of 20 kg grain/mm growing-season rainfall was attained in SR treatments, 33% higher than in SB treatments, which achieved 15 kg grain/mm.
In a low-rainfall environment, plant growth is often limited, resulting in relatively low annual inputs of crop residues that contribute to the SOM pool. Physiological adaptations such as leaf drop in legume crops (Heenan et al. 2004) and the relative contribution of above-ground v. below-ground (i.e. root) biomass also serve to reduce the differences in organic material 'lost' through burning. Partial burns resulting from low residue loads or spatial dislocation of stubble, and leaching of labile C and nutrients from stubble between harvest and autumn burning (Amato et al. 1987) may also negate the relative losses associated with burning stubble. Therefore, although stubble burning reduces C input (De-Bano and Conrad 1978), it is not surprising that negligible changes to total SOM-C content may result in this low-production system when considered over extended time periods; since it compares the relative contribution of a small change in the amount of new crop residues (which are rapidly decomposing) with a very large, historically derived SOM-C pool. This is clearly demonstrated in this study, with no change in total SOM-C measured after 16 years of stubble retention v. stubble burning. This agrees with previous studies in a similar environment (Carter and Mele 1992) and in some temperate systems (Powlson et al. 1987), but contrasts to other studies that have demonstrated increases in SOM-C associated with retaining stubble (Chart et al. 1992). Thus, to consider the effect of stubble burning on microbial processes and nutrient turnover it is more appropriate to distinguish changes in factors that influence biological processes, such as the short-term flux of labile SOM pools (i.e. dissolved organic matter, light fraction SOM), for which changes due to management may be more readily measured and which has been linked to N mineralisation in this environment (Cookson and Murphy 2004; Cookson et al. 2005). The resulting difference in MB-C as a component of the labile or available pool of SOM is therefore likely to be a more sensitive indicator of changes in management practice compared with total SOM-C in retained v. burnt stubble treatments in both a temperate and semi-arid environment.
Significant declines in soil MB-C (P < 0.001, 0-0.05 m) and C[O.sub.2]-C evolution (P < 0.001) in SB treatments reflect a greater effect of burning on microbial population growth and activity in surface soils compared with soils below 0.10m, in which no significant differences were observed between stubble treatments. In the WA rain-limited environment, low crop production and residual plant biomass may have resulted in a low-intensity burn unlikely to cause significant microbial death. Andersson et al. (2004) determined that soil temperature measured at 0.01 m depth increased from 25[degrees]C to 37[degrees]C when the equivalent of 4.8t/ha grassland (approx. 4 times the stubble remaining in SR treatments) was burnt. Since the average soil temperature in a WA environment can reach more than 40[degrees]C, we hypothesise that the established microbial community is resistant to these burn intensities. Therefore a decrease in the microbial biomass is more likely an indirect effect of substrate limitations in SB treatments. Further evidence of a substrate limitation to growth is demonstrated in the seasonal biomass fluctuations observed in both SR and SB treatments, in which MBC increases during the September sampling period. This supports previous work in which microbial biomass measured in the rhizosphere increased during periods of rapid plant development due to high rates of root exudation (Gardner et al. 1983). Since C mineralisation is also dependent on substrate availability, this supports the increased rates of C[O.sub.2]-C evolution measured in September. The increase in MB-C could therefore reflect microbial assimilation and storage of C prior to utilisation (Barz 1970), rather than an increase in the mass or efficiency of microorganisms per se, since a corresponding increase in MB-N was not observed. Subsequently, seasonal changes in C[O.sub.2]-C evolution may therefore not result directly from an increase in microbial biomass but through increased utilisation of rapidly assimilated C substrates associated with the production of root exudates. This is further supported by a corresponding increase in the qC[O.sub.2] (metabolic quotient) values associated with higher microbial biomass (Anderson and Domsch 1985), which indicate that the ratio of metabolically active to dormant portions of a biomass experienced significant seasonally related fluxes, or alternatively suggests a rapid turnover of C during these periods.
Typically, the oxidisation of SOM and turnover of heat-sensitive microorganisms associated with burning contributes to a pulse of inorganic N (Raison 1979; Diaz-Ravina et al. 1996) and the release of soluble sugars (Choromanska and DeLuca 2001). Raison (1979) suggests that these fire-induced changes are in part related to the intensity of soil heating contributing to the chemical oxidation of SOM and therefore its chemical composition (Fernandez et al. 1997; Choromanska and DeLuca 2001). The relative sensitivity of microorganisms to heat is reported in the range 50-120[degrees]C, with bacteria considered more resistant to heat stress (Neary et al. 1999). Thus, fire may influence microbial community composition and N turnover, as demonstrated by Andersson et al. (2004) where nitrifying bacteria were stimulated by fire. However, a lack of change in the composition of the microbial community between SR and SB treatments suggests that the fire intensity at this site was not sufficient to influence microbial diversity. The adaptation of microorganisms through increased microbial turnover (Zogg et al. 1997) or modification of transformation pathways for dissolved organic C (Dalias et al. 2001; Marschner and Bredow 2002) may also cause changes in community composition and thus changes in C and N mineralisation rates in soils held at a constant moisture content and incubated over a range of temperatures. In this study, changes in substrate utilisation and community composition (determined by CLPP and PLFA, respectively) largely resulted from seasonal modifications in environment, resources, and processes as demonstrated between sampling times. The change in SOM-C quality between SR and SB treatments did not alter the community level physiological profile. It was, however, apparent that the microbial community structure determined by PLFA exhibited a strong cyclic seasonal pattern (Fig. 9). This was more noticeable in the SB treatment and may infer less stability/resilience of this microbial population with respect to seasonal fluctuations in temperature and soil moisture.
Edaphic factors are likely to be rate-limiting factors associated with biological activity and gross N transformation rates in this environment. Differences in gross N mineralisation during November were consistent with previous studies (Hoyle et al. 2006) in which differences between stubble treatments were most evident when incubated above 20[degrees]C. Normalising C[O.sub.2]-C data to a reference temperature (20[degrees]C) did not result in reduced variability between sampling dates, indicating that temperature was not the only factor influencing microbial activity. Since incubation of soils was conducted at a constant 45-50% WHC, the fluctuations in C[O.sub.2]-C observed are more likely to be a result of C depletion and availability, reflecting changes associated with plant growth and crop residues. Normalising gross N mineralisation data for temperature resulted in higher rates of gross N mineralisation in July under SR treatments, which may be attributable to increased C substrate availability. This contrasts to findings of Recous et al. (1998) where the majority of seasonal fluctuation in gross N mineralisation for a temperate environment could be attributed to temperature and moisture. However, their field incubation study was conducted in the absence of growing plants. In contrast, we found a strong influence of growing plants on normalised gross N mineralisation rates. Normalised gross N mineralisation rates were 2-6 times greater in the presence of a growing plant (July, September) compared with periods during which there was no active plant growth (May, November). We attribute this to greater microbial activity associated with the presence of a root rhizosphere. During periods of active plant growth, N[H.sup.4+] immobilisation was greater than actual gross nitrification rates, implying that sufficient C was available for heterotrophic microorganisms to out-compete autotrophic nitrifiers for N[H.sup.4+], as demonstrated by Ross et al. (2001). However, in the absence of growing plants and under drying soil conditions, heterotrophic assimilation of N[H.sup.4+] was low and actual gross nitrification dominated N[H.sup.4+] consumption. Actual gross nitrification rates were constrained by low N[H.sup.4+] availability, as demonstrated by the significantly larger potential gross nitrification rates in the presence of excess N[H.sup.4+].
Cellulase activity results in the release of a readily available energy source for microorganisms and is therefore considered an indicator of microbial biomass turnover (Gander et al. 1994). Differences in organic matter inputs due to the retention of stubble in this experiment were sufficient to cause changes in cellulase activity in the surface (0-0.05m) layer. Cookson et al. (1998) suggest that the influence of crop residues on microbial activity and N supply is influenced by residue quality and pre-conditioning of the microbial community, resulting in the development of substrate-adapted microorganisms (Killham et al. 1988). Therefore we expect cellulase function in SB treatments to be constrained due to low amounts of fresh residue. Since [beta]-glucosidase activity is the rate-limiting enzyme in the microbial degradation of cellulose to glucose (Alef and Nannipieri 1995), an increase in [beta]-glucosidase activity is likely to reflect greater C availability, thus influencing biological turnover of N. However, contrary to previous research demonstrating reasonable seasonal stability in [beta]-glucosidase activity (Bandick and Dick 1999), sampling time significantly (P < 0.05) influenced [beta]-glucosidase, indicating a possible C substrate limitation to activity caused by a depletion of fresh plant material (Caravaca et al. 2002) in SB treatments. This suggests that the potential to mineralise organic matter in SB treatments is reduced compared with SR treatments.
Arginine ammonification has been proposed as an index of gross N mineralisation (Bonde et al. 2001), and is considered a key process for determining N availability to the crop. Arginine ammonification activity successfully discriminated between stubble treatments at all sampling times and appeared to be a more suitable technique than gross N mineralisation in reflecting changes to N cycling under different management practices. The peak activity in September was associated with increased [beta]-glucosidase activity, successfully linking biological turnover of C and N. Hoyle et al. (2006) found no differences in gross N mineralisation between SR and SB treatments at temperatures less than 200C. However, larger differences were apparent between 30[degrees]C and 40[degrees]C. This implies a C substrate difference between treatments, which is only important at high temperature. Therefore the main difference in N cycling between SR and SB treatments is likely to occur not during the cropping season but over summer months (if soil moisture is available).
In this study, separation between stubble treatments for acid phosphatase activity ([micro]mol PNP/g soil.h) was possible only during periods of high soil moisture in surface soils (0-0.05 m), but in comparison with previous studies in natural ecosystems, did not strictly reflect seasonal wetting/drying patterns (Grierson and Adams 2000). Thus its use as an early indicator of available P may be limited by a lack of available soil moisture. Although arylsulfatase activity has previously been correlated with SOM-C content, total N, and CEC (Tabatabai and Bremner 1970), differences in arylsulfatase activity could not be determined on either a seasonal or soil treatment basis. Large within-season variation in enzyme activity emphasises the importance of adequate sampling in determining treatment and/or seasonal effects on biological turnover of OM and nutrient cycling (Grierson and Adams 2000). Despite changes in soil enzyme activity through the growing season, the variability observed for different enzymes demonstrated similar fluctuations, as described previously by Kirchner et al. (1993).
(i) Stubble retention increased microbial biomass, C[O.sub.2]-C evolution, and some enzyme activities.
(ii) No effect of stubble treatment was observed on community diversity or substrate utilisation at temperatures assessed.
(iii) Differences in gross N cycling between SR and SB treatments were only apparent at high (30[degrees]C) temperature.
(iv) Biochemical measurements demonstrated greater seasonal variability than under different stubble management treatments.
(v) Estimates of management-induced changes in microbial and/or biochemical processes as a result of burning stubble were most apparent between July and September when rapid crop growth and development was likely to have influenced root exudation and hence C availability.
(vi) The increase in grain size and dilution of grain protein content associated with SR treatments, suggests it is possible that differences in plant N uptake influenced crop development and allowed greater assimilation and redistribution of carbohydrates during grain development.
This work was funded by the Grains Research and Development Corporation (Soil Biology Initiative), with grant support from the Department of Agriculture and Food Western Australia and the University of Western Australia. The authors thank Glen Reithmuller for access to and maintenance of the field trial, Jaymie Norris for GC analyses of PLFA samples, and Dr Richard Cookson for multivariate statistical advice.
Manuscript received 21 November 2005, accepted 21 February 2006
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F. C. Hoyle (A,B) and D. V. Murphy (A)
(A) School of Earth and Geographical Science, Faculty of Natural and Agricultural Sciences, University of Western Australia, Crawley, WA 6009, Australia.
(B) Corresponding author. Email: email@example.com
Table 1. Soil properties determined under a long-term (16-year) rotational trial comparing stubble retention (SR) and stubble burning (SB) at 4 sampling times Sample Stubble Stubble date retained burnt Total C (%) 8/05/2003 1.363 1.163 Total N (%) 8/05/2003 0.11 0.10 Soluble 8/05/2003 72.9 55.0 organic C 21/07/2003 63.4 59.9 ([K.sub.2]S[O.sub.4]- 15/09/2003 65.7 58.6 extractable) 28/11/2003 73.8 70.30 Dry bulk density 8/05/2003 1.41 1.37 of soil 21/07/2003 1.37 1.38 (0-0.10 m; 15/09/2003 1.32 1.22 Mg/[m.sup.3]) 28/11/2003 1.37 1.32 Soil [MATHEMATICAL 8/05/2003 5.83 5.93 EXPRESSION NOT 21/07/2003 5.66 5.76 REPRODUCIBLE IN ASCII] 15/09/2003 5.80 5.73 28/11/2003 5.39 5.22 [EC.sub.1:1] ([micro]S/cm) 8/05/2003 137.7 106.1 21/07/2003 81.8 63.8 15/09/2003 70.7 54.3 28/11/2003 43.6 45.9 CEC (cmol/kg) 8/05/2003 9.77 10.18 21/07/2003 10.29 9.84 15/09/2003 9.62 9.34 28/11/2003 10.97 11.10 Sample Significance date Total C (%) 8/05/2003 n.s. Total N (%) 8/05/2003 0.01 (P=0.069) Soluble 8/05/2003 8.8 ** organic C 21/07/2003 n.s. ([K.sub.2]S[O.sub.4]- 15/09/2003 n.s. extractable) 28/11/2003 n.s. Dry bulk density 8/05/2003 n.s. of soil 21/07/2003 n.s. (0-0.10 m; 15/09/2003 n.s. Mg/[m.sup.3]) 28/11/2003 n.s. Soil [MATHEMATICAL 8/05/2003 n.s. EXPRESSION NOT 21/07/2003 n.s. REPRODUCIBLE IN ASCII] 15/09/2003 n.s. 28/11/2003 n.s. [EC.sub.1:1] ([micro]S/cm) 8/05/2003 n.s. 21/07/2003 5.55 ** 15/09/2003 10.15 * 28/11/2003 n.s. CEC (cmol/kg) 8/05/2003 n.s. 21/07/2003 n.s. 15/09/2003 n.s. 28/11/2003 n.s. * P < 0.05; ** P < 0.01; n.s., not significant. Table 2. Effect of stubble retention (SR) v. stubble burning (SB) on a range of enzymatic processes at 2 sampling depths Data are the average of all sampling times. Within rows, values followed by the same letter are not significantly different at P = 0.05 Stubble retained Stubble burnt 0-0.05 m 0.05-0.10m 0-0.05 m 0.05-0.10m Acid phosphatase ([micro]g PNP/g soil.h) 243.4b 151.0a 161.0a 120.9a Arginine ammonification ([micro]g N[H. sub.4.sup.+]- N/g soil.h) 105.7b 63.8ab 54.7ab 34.5a Arylsulfatase ([micro]g PNP/g soil.h) 18.0a 8.5a 5.3a 2.2a Cellulase ([micro]g glucose/g soil.h) 124.5b 65.0a 82.4ab 55.7a [beta]-Glucosi- dase ([micro]g PNP/g soil.h) 158.9c 90.6ab 122.8b 62.4a l.s.d. (P = 0.05) Acid phosphatase ([micro]g PNP/g soil.h) 42.6 Arginine ammonification ([micro]g N[H. sub.4.sup.+]- N/g soil.h) 51.4 Arylsulfatase ([micro]g PNP/g soil.h) 19.6 Cellulase ([micro]g glucose/g soil.h) 39.7 [beta]-Glucosi- dase ([micro]g PNP/g soil.h) 34.7 Table 3. Influence of soil physiochemical properties, soil resources, and soil processes on phospholipid fatty acid (PLFA) profiles and community level physiological profiles (CLPP) of stubble management treatments analysed using permutational multivariate analysis of variance (Anderson 2001) Numbers are t-values (F), with associated P-value and percentage of variation attributable to each variable (Var.) CLPP Variable F P Var. Full model (groups) Soil physiochemical 9.26 <0.001 0.912 Pools (resources) 1.69 0.163 0.601 Processes (function) 6.07 0.002 0.918 Soil physiochemical Bulk density 0.39 0.615 0.024 % Clay 0.42 0.598 0.026 EC 0.40 0.737 0.024 pH 1.87 0.179 0.105 CEC 0.96 0.353 0.057 Soil N[H.sub.4.sup.+] cone. 38.74 <0.001 0.708 Soil N[O.sub.3.sup.-] cone. 3.92 0.032 0.197 Pools (resources) Light fraction OM (%C) 2.59 0.108 0.139 Light fraction OM (%N) 0.85 0.376 0.050 Light fraction ON ratio 1.97 0.164 0.110 Soil total C (%) 1.69 0.188 0.095 Soil total N (%) 0.89 0.373 0.053 Soil C/N ratio 1.09 0.307 0.064 Soluble organic C 0.39 0.626 0.024 MB-C 7.36 0.006 0.315 MB-N 1.77 0.182 0.100 MB C/N ratio 1.51 0.221 0.086 Processes (function) C[O.sub.2]-C evolution 9.90 <0.001 0.310 gC[O.sub.2] (99 C[O. sub.2]-C/[micro]g MB-C) 8.73 <0.002 0.284 Gross N mineralisation 2.91 0.042 0.117 Immobilisation 4.01 0.011 0.154 Potential nitrification 2.34 0.083 0.096 Cellulase 5.06 0.005 0.187 Phosphatase 1.76 0.155 0.074 Arginine ammonification 6.97 <0.001 0.241 Arylsulfatase 0.58 0.668 0.026 [beta]-glucosidase 7.50 <0.001 0.254 PLFA Variable F P Var. Full model (groups) Soil physiochemical 2.38 0.005 0.605 Pools (resources) 2.22 0.012 0.542 Processes (function) 1.61 0.070 0.596 Soil physiochemical Bulk density 0.91 0.421 0.040 %Clay 0.68 0.564 0.030 EC 2.81 0.025 0.113 pH 2.87 0.049 0.116 CEC 0.83 0.468 0.036 Soil N[H.sub.4.sup.+] cone. 8.12 <0.001 0.270 Soil N[O.sub.3.sup.-] cone. 7.09 <0.001 0.244 Pools (resources) Light fraction OM (%C) 0.32 0.827 0.015 Light fraction OM (%N) 0.22 0.914 0.010 Light fraction ON ratio 0.22 0.918 0.010 Soil total C (%) 0.63 0.580 0.028 Soil total N (%) 0.64 0.578 0.028 Soil C/N ratio 0.45 0.725 0.020 Soluble organic C 0.37 0.799 0.017 MB-C 9.73 <0.001 0.307 MB-N 1.73 0.156 0.073 MB C/N ratio 4.92 0.006 0.183 Processes (function) C[O.sub.2]-C evolution 6.89 0.009 0.301 gC[O.sub.2] (99 C[O. sub.2]-C/[micro]g MB-C) 12.76 <0.001 0.444 Gross N mineralisation 1.59 0.213 0.090 Immobilisation 1.31 0.269 0.075 Potential nitrification 8.45 0.005 0.346 Cellulase 5.73 0.016 0.264 Phosphatase 1.51 0.223 0.086 Arginine ammonification 7.97 0.008 0.333 Arylsulfatase 0.56 0.586 0.034 [beta]-glucosidase 13.07 <0.001 0.450 Table 4. Concentration of key phospholipids fatty acid groups (ng PLFA/ g soil) in soil sampled at 4 times from SB and SR treatments Data presented for saturated (Sat.), hydroxylated (Hydrox.), branched (Branch), mono-unsaturated (Mono-unsat.), poly-unsaturated (Poly-unsat.), and cyclopropyl (Cycloprop.) fatty acids, the ratio of saturated to mono-unsaturated fatty acids, and total fatty acids. Within columns, values followed by the same letter are not significantly different at P= 0.05 Treatment Sample date Sat. (A) Hydrox. Branch Stubble retained 8/05/2003 59.9a 5.0a 33.0a 21/07/2003 396.46 21.6a 209.9a 15/09/2003 628.4c 61.76 494.86 28/11/2003 125.6a 12.4a 66.7a Stubble burnt 8/05/2003 79.2a 6.4a 53.3a 21/07/2003 619.26 44.36 40.7a 15/09/2003 935.7c 98.9c 646.0b 28/11/2003 181.2a 19.8ab 144.7a l.s.d. (P = 0.05) 217.3 *** 30.3 ** 218.2 ** Treatment Sample date Mono-unsat. Poly-unsat. Cycloprop (B) Stubble retained 8/05/2003 7.5a 29.5a 0 21/07/2003 54.3ab 248.76 0 15/09/2003 121.1b 607.1c 0 28/11/2003 15.6a 76.8ab 0 Stubble burnt 8/05/2003 11.1a 53.8a 0 21/07/2003 96.5a 452.96 0 15/09/2003 333.66 693.1c 0 28/11/2003 31.3a 165.9a 0 l.s.d. (P = 0.05) 108.5 * 199.5 ** -- Treatment Total Ratio A : B Stubble retained 8/05/2003 157.7a 8.046 21/07/2003 1073.26 7.426 15/09/2003 2299.3c 5.18a 28/11/2003 336.4ab 8.386 Stubble burnt 8/05/2003 236.1a 7.46bc 21/07/2003 1920.96 7.59c 15/09/2003 3281.8c 3.31a 28/11/2003 613.1a 5.956 l.s.d. (P = 0.05) 851.5 ** 1.62 ** * P < 0.05; ** P < 0.01; *** P < 0.001. Table 5. A 2-way crossed analysis of distance-based similarities based on soil physiochemical properties, resources, and function Variable Stubble treatment Sampling time Soil physiochemical ** ** Pools (resources) *** n.s. Processes (function) ** *** All data combined *** *** * P < 0.05; ** P < 0.01; ***P < 0.001; n.s., not significant. Table 6. Plant yield and yield components measured under a long- term (16-year) rotational strategy involving stubble retention (SR) or stubble burning (SB) Stubble Stubble l.s.d. retained burnt (P = 0.05) Plant anthesis biomass (t/ha) 6.3 5.7 n.s. Plant harvest biomass (t/ha) 7.8 6.1 0.9 Grain yield (t/ha) 3.8 2.9 0.7 Grain protein (%) 8.2 9.5 n.s. N uptake (kg N/ha) 292 247 n. s. Average grain weight (mg) 42.3 36.3 2.4 n.s., Not significant.
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|Author:||Hoyle, F.C.; Murphy, D.V.|
|Publication:||Australian Journal of Soil Research|
|Date:||Nov 1, 2006|
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