Relating soil microbial activity to water content and tillage-induced differences in soil structure

Geoderma 163 (2011) 256–264

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Geoderma j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / g e o d e r m a

Relating soil microbial activity to water content and tillage-induced differences in soil structure Per Schjønning a,⁎, Ingrid K. Thomsen a, Søren O. Petersen a, Kristian Kristensen b, Bent T. Christensen a a b

Aarhus University, Department of Agroecology, P.O. Box 50, DK-8830 Tjele, Denmark Aarhus University, Department of Molecular Biology and Genetics, P.O. Box 50, DK-8830 Tjele, Denmark

a r t i c l e

i n f o

Article history: Received 14 July 2010 Received in revised form 15 April 2011 Accepted 16 April 2011 Available online 20 May 2011 Keywords: Soil structure Net nitrification Water Gas diffusion Reduced tillage

a b s t r a c t Several studies have identified optima in soil water content for aerobic microbial activity, and this has been ascribed to a balance between gas and solute diffusivity as limiting processes. We investigated the role of soil structure, as created by different tillage practices (moldboard ploughing, MP, or shallow tillage, ST), in regulating net nitrification, applied here as an index of aerobic microbial activity. Intact soil cores were collected at 0–4 and 14–18 cm depth from a fine sandy (SAND) and a loamy (LOAM) soil. The cores were drained to one of seven matric potentials ranging from − 15 to − 1500 hPa and subjected to measurements of gas diffusivity prior to incubation at 20 °C for 31 days. Net nitrification was calculated from nitrate accumulation during incubation. The upper layer of ST and MP soil had similar physical properties in terms of bulk density, pore size distribution, and relative gas diffusivity (RD). The soil from the 14–18 cm layer under ST had reduced air-filled pore space and RD at any given matric potential, and MP soil had a significantly higher volume of pores not active in gas diffusion (blocked pores). Net nitrification in the 0–4 cm layer was significantly higher for ST than for MP soil, while the opposite was true for the 14–18 cm layer. The net tillage effect calculated per mass of soil across the same area of land was negligible (e.g. SAND: 20.8 and 21.9 kg NO3– N ha − 1 for the MP and ST soils, respectively). Net nitrification generally increased with water content to a maximum and then decreased. This relationship was modelled with a second order polynomium. Model parameters did not show any tillage effect on the optimum water content, but the optimum coincided with a lower matric potential in ST (SAND: − 140 to –197 hPa; LOAM: − 37 to − 65 hPa) than in MP soils (SAND: − 42 to − 67 hPa; LOAM: − 15 to − 22 hPa). Net nitrification for a given soil and depth peaked at about the same level of RD irrespective of tillage treatment (SAND, 0–4 cm: 0.040; SAND, 14–18 cm: 0.015; LOAM, 0– 4 cm: 0.010; LOAM, 14–18 cm: 0.003). Our study suggests that gas diffusivity may become one indicator of conditions for aerobic microbial activity, but more studies are needed to reveal soil type dependent drivers in play. © 2011 Elsevier B.V. All rights reserved.

1. Introduction Aerobic soil microbial activity has been shown to peak at specific water contents (e.g. Franzluebbers, 1999). Skopp et al. (1990) suggested that such an optimum reflects a balance between the rates of substrate and gas diffusion, i.e., that lower water contents limit the diffusion of soluble substrates in soil, while higher water contents limit oxygen diffusion. Schjønning et al. (2003) found net nitrification in each of three texturally contrasting soils to correlate with gas diffusivity in bulk soil under wet conditions. Empirical evidence relating the diffusion of solutes to aerobic activity under dry conditions is scarce, although aerobic microbial activity has been reported to increase with water content in dry soil (e.g. Thomsen et al., 1999).

⁎ Corresponding author. Tel.: + 45 8999 1766; fax: + 45 8999 1200. E-mail address: [email protected] (P. Schjønning). 0016-7061/$ – see front matter © 2011 Elsevier B.V. All rights reserved. doi:10.1016/j.geoderma.2011.04.022

Moldboard ploughing (MP) fragments the bulk soil to aggregates throughout the upper ~20 cm layer, whereas shallow tillage (ST) leaves a variable part of the former plough layer undisturbed. This in turn changes the density and pore size distribution and hence the conditions for gas exchange. Consequently, the physical environment for organic matter (OM) turnover can be very different depending on tillage practice. Differences in soil structure induced by contrasting tillage practices have been shown to influence gas diffusivity (e.g. Schjønning, 1989). Gas diffusivity has also been shown to differ among structurally intact soils of contrasting texture, while gas diffusivity was identical for remoulded and repacked soils (Schjønning et al., 1999). This indicates that soil structure has a decisive influence on gas diffusivity (Gregorich et al., 2006). The tillage-induced change in soil structure may hence be a feasible object for studies on the diffusional constraints that regulate aerobic and anaerobic microbial activity in soil. Only a few studies have addressed soil microbial processes in undisturbed bulk soil under controlled water regimes. Hence, most

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models simulating OM turnover in arable soils use rather crude, empirical relationships to describe water effects. Rodrigo et al. (1997) and later Bauer et al. (2008) compared a number of simulation models and found major differences in model outputs due to differences in temperature and water response functions. The adequacy of response functions applied in OM turnover models are of substantial importance because such models are widely used in evaluating the impacts of agricultural production on the environment (e.g. Shaffer et al., 2001). Hence, there is an urgent need to improve the experimental basis for such models. Here we investigate physical properties and microbial activity of structurally intact soil samples from two differently located field experiments with two tillage systems. Net nitrification was taken to express the prevalence of aerobic microbial activity. Our aim was to determine the optimum water regime for aerobic microbial activity and to identify the soil physical characteristic best describing this optimum. Assuming that the effects on solute diffusivity were negligible within the range of soil water employed (Laegdsmand et al., submitted for publication), we hypothesize this to be the bulk soil gas diffusivity. A study of nitrous oxide evolution from the same cores was reported by Petersen et al. (2008).

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2.2. Soil sampling

2. Materials and methods

In spring 2004 when the soil water content reached field capacity, intact soil cores of 100 cm 3 were collected in steel cylinders (Ø 61 mm, height 34.2 mm) from the 0–4 and 14–18 cm depths of ST and MP treated soil. Steel cylinders were inserted into the soil by a hammer with a special flange to ensure vertical introduction. Soil surfaces with visible earthworm channels were avoided. When sampling the 0–4 cm depth, the metal cylinders were pushed a few millimetres below the soil surface and the surface litter was discarded. The soil cores were then trimmed with a knife to give an effective sampling depth of ~0.5–4 cm. We sampled 42 cores for each tillage treatment and depth with seven cores per experimental plot at Dronninglund, and 14 cores per plot at Nakskov. Within each experimental plot, cores were collected from three randomly selected sampling points. A total of 336 cores were sampled (2 locations × 2 depths × 2 tillage treatments × 42 cores). Additional cores (28) for each combination of location, depth and tillage treatment were sampled for calibration purposes (see below). Bulk soil from the 0–20 cm depth of each plot was sampled with a 20 cm long, 2-cm diameter corer. At each plot, eight replicate subsamples were bulked, but for ST the subsamples were separated to represent soil above and below the tillage depth.

2.1. Soils and treatments

2.3. Experimental setup

We selected two field experiments (Dronninglund: 57°08N, 10°17E and Nakskov: 54°53N, 11°10E), where MP and ST had been practised for 4–5 years prior to sampling. The Dronninglund soil is developed on marine sediments from the Yoldia post-glacial period and is classified as a Humic Dystric Cambisol. The Nakskov soil is derived from Weichselian glacial till and classified as a Gleyic Luvisol according to the WRB(FAO) system (Krogh and Greve, 1999). The contrasting origin of the two soils is reflected in their textural composition: the Dronninglund soil is a sorted soil with most primary particles in the fine sand fractions, while the Nakskov is a graded soil with mineral particles being more equally distributed between the particle size fractions (Table 1). The Dronninglund soil is high in OM due to a high water table before the initiation of arable management (gradual upheaval of the land). The Nakskov soil is depleted in OM after centuries of arable cropping. At Dronninglund, ST was stubble tillage to 5 cm depth by a Horsch Flachgrubber, and the seeding was performed by a Horsch Airseeder. At Nakskov ST was stubble tillage to 10 cm depth followed by a Wäderstad Rapid disc drill. At both locations MP included annual ploughing to approximately 20 cm depth and drilling with a Wäderstad Rapid disc drill. The Dronninglund site grew winter wheat throughout the experimental period. At Nakskov the crop sequence included springand winter-sown cereals, and sugar beets. Winter wheat was grown in the year of sampling and the year before. At Dronninglund, all plots received pig slurry (~30 tonnes per ha) supplemented with N, P and K in mineral fertilizers. The wheat straw was mulched every year. At Nakskov, all nutrients were applied in mineral fertilizers, and crop residues were removed. At both locations, the experiments were based on a randomized block design with six and three replicate blocks at Dronninglund and Nakskov, respectively.

The set of 42 soil cores from each combination of location, depth and tillage treatment was split into seven groups for subsequent adjustment to different water potentials. During this procedure, we ensured that the six replicate cores selected for each of the seven drainage groups included one core from each of six defined weight classes (on a wet bulk density basis). Thereby the chance of a similar stochastic variability and average value of dry bulk density within each drainage group was improved. However, we also ensured that all field blocks were represented in each drainage group. The soil cores were placed on tension tables and saturated with water from beneath. Two ceramic discs (Ø 11 mm, height 3 mm) were placed on the soil surface at full water saturation for estimation of preincubation NO3–N content (Strong et al., 1997; Thomsen and Schjønning, 2003). The ceramic discs were allowed to equilibrate with the soil water for 4 h. Previous studies have shown that this procedure provides reliable estimates of the NO3 concentration in the soil water (Thomsen and Schjønning, 2003). The soil cores in the seven groups were then drained to matric potentials (Ψ) of −15, −30, − 60, − 100, −200, −500 or − 1500 hPa, respectively. These potentials were achieved by tension tables (sand or ceramics) except at −1500 hPa, where pressure plates were used. After the specified matric potential had been reached, the ceramic discs were removed for extraction. The soil volume within each cylinder was calculated using the actual height of the soil core measured by a purpose-built calliper. The soil cores were analyzed for air diffusivity with the transient state method suggested by Taylor (1949) applying the technique described by Schjønning (1985). Gas diffusion was determined at 20 °C with O2 as the test gas. Before the measurement, the soil at the edge of the cylinder was gently pressed to minimize gas by-pass at the soil/cylinder interface. The cores were brought in air-tight contact to

Table 1 Textural composition of the 0–20 cm layer of the two experimental areas. Location

Years of repeated treatment

Org. mattera kg kg

Dronninglund Nakskov a

4 5

Average for the experimental field.

0.088 0.023

Clay b2 μm

Silt 2–20 μm

Fine sand 20–200 μm

Coarse sand 200–2000 μm

0.092 0.176

0.095 0.154

0.619 0.484

0.106 0.163

−1

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diffusion chambers that were flushed with N2 free of O2 prior to the measurements. The chamber O2 concentrations were monitored for a period of ca. 2 h, and the diffusion coefficient calculated as described by Schjønning (1985). Previous calculations indicated that we could ignore O2 consumption in the cores during diffusion measurements. The O2 diffusion coefficient in soil, DS, is presented relative to that in free air, D0, i.e., the relative gas diffusivity (RD) = DS/D0. In this study, the relation between DS/D0 and the air-filled pore space, εa, is described by a simple power function n

DS = D0 = mεa

ð1Þ

Subsequently, the cylinders were capped with perforated plastic lids and placed on a metal mesh in sealed containers and kept in the dark at 20 °C for 31 days. In order to minimize desiccation, precautions were taken to ensure a high relative humidity in the containers (moist rubber foam glued to the inner side of the top cover). Based on sample weights before and after incubation the water losses were found to range from 1.2 to 2.3 cm 3 (4.6 to 7.6% of initial water content). At day 31, the soil was removed from the cylinder, and a 30 g subsample was immediately extracted in 100 ml of 1 M KCl. Other subsamples were used for determination of soil parameters described by Petersen et al. (2008). The remaining soil was dried at 105 °C for 24 h for determination of water content. 2.4. Determination of soil NO3 contents by the use of ceramic discs Additional soil cores from each combination of location, sampling depth, tillage treatment, and matric potential were supplied with ceramic discs at full water saturation before drainage. The ceramic discs were allowed to equilibrate for 4 h and then drained to −15, −30, − 60, −100, − 200, −500, −1500 hPa. When the matric potential was reached, the ceramic discs were removed for extraction of NO3. The soil from each cylinder was then removed, mixed, and subsamples extracted for determination of soil NO3 concentration. The remaining soil was dried at 105 °C for determination of water content. A calibration equation was established to calculate the soil NO3 concentration at day zero from measurements of NO3 concentrations in the ceramic discs. Net nitrification during the incubation was calculated by subtracting the estimated NO3 content at day zero from that measured at the end of incubation (see Thomsen and Schjønning (2003) for further details). 2.5. Analyses Soil C was determined on ball-milled subsamples using a LECO CNS-1000 analyzer with IR detector (LECO Corporation, St. Joseph, MI). The soil was tested for free carbonates and soil organic C calculated as the difference between total C and carbonate-C. Soil particle density was determined by the pycnometer method and ranged from 2.47 to 2.49 g cm − 3 and 2.63 to 2.64 g cm − 3 for the Dronninglund and Nakskov soils, respectively. Water retained at the −1.5 MPa matric potential (equivalent to the ‘permanent wilting point’) was obtained by draining remoulded soil samples on 15 bar ceramic plates. After removal from the soil, the ceramic discs (from the main study as well as the calibration study) were weighed and shaken end-overend in 10 ml of 1 M KCl for 4 h (90 rpm). The discs were removed from the KCl, dried (80 °C) and reweighed. The NO3 content in the KCl extracts was determined on a Technicon Autoanalyser II. The content of mineral particles N2 mm was determined by wet-sieving, and reported results are based on soil b2 mm.

2.6. Calculations and statistics Soil porosity was calculated for each individual core, combining dry bulk density and average soil particle density for a given combination of location, depth and tillage treatment. Soil air content at a given matric potential was calculated as the difference between total pore volume and the volume of water retained at that potential. The statistical significance for treatment differences in soil properties was tested for each combination of location and soil depth. Data on net nitrification were log-transformed to provide a normal distribution. We applied a mixed model accounting for the fixed effects of tillage treatment, and the random effects of plot and sampling point (nested within plot). The test for treatment effects on parameters measured at specific drainage potentials adopted the same model, but were applied to each matric potential separately. We fitted the power function, Eq. (1), to measured data by linear regression using the equation log(DS/D0) = log(m) + n log(εa) and employing average data at each matric potential. Treatment effects on the slope, n, the intercept, log(m), and the intercept on the εa-axis at DS/D0 = 10 − 4 were tested by linear models. The relation between net nitrification and soil water content was modelled using second order polynomials. For each combination of location and soil depth, the parameters of the two polynomials (one for each treatment) were estimated using a mixed model that included the fixed effect of intercept, linear and quadratic effects of soil water content, and the random effects of plot and sampling point (nested within plot). Based on the estimated parameters, the soil water content providing the maximum net nitrification was calculated by differentiation of the polynomials and equating the expressions to zero. Then the net nitrification was calculated at these soil water contents. From this and the variance–covariance matrix for the fixed effect parameters of the mixed model, the standard error of the soil water content giving maximum net nitrification was calculated by using a first order Taylor approximation. The standard error of the estimated net nitrification was calculated for the given soil water content. Based on the approximate standard errors, approximate ttests were performed to test the hypotheses of no difference between treatments in the soil water contents at maximum net nitrification, and for no difference in maximum net nitrification. We used the SAS 8.2 package (SAS Institute Inc.; Cary, NC., USA) for these calculations. 3. Results and discussion 3.1. Organic C, bulk density, and water content Four to five years of ST increased the C concentration in the topsoil compared to MP, although the difference was significant only at Nakskov (Table 2). For ST at Nakskov, the organic C concentration was also significantly higher in the topsoil than in the 14–18 cm depth. This effect of shallow tillage is in accordance with a previous screening across 11 Danish tillage trials (Schjønning and Thomsen, 2006), and with studies reviewed by West and Post (2002). The dry bulk density of the 0–4 cm soil layer of both soils was unaffected by the MP/ST treatments (Table 2). This is attributed to the homogenizing effect of secondary tillage operations for seedbed preparation under MP as well as ST. In contrast, a highly significant increase in dry bulk density was observed in the 14–18 cm layer of ST compared to MP. As opposed to texturally well-sorted soils (e.g. the marine-deposited Dronninglund soil), morainic soils like Nakskov tend to densify if not tilled frequently (Ehlers and Claupein, 1994). This was reflected in the considerable 300 kg m− 3 increase in bulk density of ST compared to MP soil for the 14–18 cm layer (Table 2) and is in accordance with previous studies (Ball et al., 1998; Riley et al., 1994). The volumetric water content at sampling was higher for ST than for MP soil although significant only at Dronninglund (0–4 cm) and Nakskov (14–18 cm) (Table 2). This is in accordance with other

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Table 2 The effects of tillage treatment on soil density, organic C, field water content at sampling, selected pore size classes, and pre-incubation soil water nitrate concentration. Figures followed with the same letter within a specific soil depth are not significantly different (for organic C, indications of significant differences are across both soil layers) (P = 0.05). Location

Depth

Tillage treatment

cm Dronninglund

0–4 14–18

Nakskov

0–4 14–18

a b c d

MP ST MP ST MP ST MP ST

Dry bulk density

Organic Ca

Field water content

Field air-filled pore space

Water content at − 100 hPab

Field water contentc

Soil water NO3d

kg m− 3

g kg− 1

m3 m− 3

m 3 m− 3

m3 m− 3

kg kg− 1

g NO3–N m− 3 soil

1009a 952a 1113a 1265b 1262a 1221a 1308a 1606b

58.4a 65.8a 58.4a 59.8a 15.2a 17.6b 15.2a 16.0a

0.374a 0.418b 0.394a 0.406a 0.228a 0.253a 0.258a 0.295b

0.219a 0.196a 0.156b 0.087a 0.294a 0.284a 0.246b 0.096a

0.366a 0.406a 0.374a 0.387a 0.262a 0.307b 0.267a 0.312b

0.373a 0.442b 0.357a 0.324a 0.181a 0.207b 0.197a 0.184a

7.7a 13.2b 5.7b 3.7a 2.6a 5.5b 2.6b 2.0a

For MP treated soil, organic C was analysed for a bulked plough layer sample. Based on only 6 replicate cores drained to the − 100 hPa potential (the other variables are averages of 42 cores). Calculated from the dry bulk density and the field water content in volumetric units. Pre-incubation NO3 concentration in soil water; geometric means.

observations for North-European countries (e.g. Ball et al., 1998) and may be attributed to a higher volume of pores retaining water at field capacity in ST soil. However, it is noticeable that this trend was reversed for the 14–18 cm layer – although not significantly – if water was expressed on a gravimetric basis (Table 2). This suggests that the amount of water retained on an area basis is not increased by shifting from MP to ST. We sampled the soil cores in the spring and consider the water content at sampling to be ‘field capacity’. For the present soil types, the matric potential at field capacity is normally around −100 hPa (Madsen, 1976). The estimated water content at this matric potential was close to that actually measured when the Dronninglund soil was sampled, while the field water content in the Nakskov soil at sampling was generally lower than determined at −100 hPa (Table 2), especially for the ST soil. 3.2. Pore size distribution The tillage system had only a slight effect on pore size distribution in the MP and ST topsoil, both treatments being tilled similarly for seedbed preparation (Fig. 1, Table 2). No significant effects between tillage systems were found at Dronninglund, whereas the ST soil at Nakskov retained more water at −100 hPa (Table 2) and had a somewhat smaller volume of N30 μm pores than the MP soil (Fig. 1). The effect of tillage system on the pore size distribution was much more pronounced in the 14–18 cm layer (Fig. 1 and Table 2). The smaller pore volume in the ST soil originated from a significant reduction in large pores, especially N50 μm pores. Compared to ploughed soil, the ST treatment reduced the volume of N30 μm pores

by 0.079 and 0.156 m 3 m − 3 for the Dronninglund and Nakskov sites, respectively (Fig. 1). This reflected similar differences in the volume of air-filled pore space at sampling (Table 2). The reduction in the volume of large pores was accompanied by significant increases in the volume of pores b0.2 μm not accessible for soil microbes (van Veen and Kuikman, 1990) (Fig. 1). 3.3. Gas diffusivity Table 3 lists values of RD for combinations of location, depth and drainage potential. The only significant tillage effect for the 0–4 cm layer was a higher RD for MP soil at Dronninglund when drained to −500 hPa matric potential. Thus, conditions for gas diffusion in the upper seedbed layer were almost similar for the two tillage treatments. This is in accordance with the results for the pore size distribution (Fig. 1) for that soil layer. At Nakskov there was a tendency of higher diffusivities for ploughed soil at intermediate matric potentials (P ~ 0.05–0.10). For the 14–18 cm layer at both locations, considerably higher diffusivities were found in the MP than the ST treatment (Table 3). We note that – especially at Nakskov – RD was for several matric potentials below the 0.005–0.02 range often considered critical to aerobic biotic activity in soil (Table 3; Fig. 2, inserted) (e.g. Stepniewski, 1981). Fig. 2 shows RD related to the soil air-filled pore space. The lines indicate the best fit for the power function model DS/D0 = mεan. Table 4 lists the model parameters for each tillage treatment at all four combinations of location and soil depth. The n-parameter is an expression of the ‘rate of opening up’ of new pores for diffusion with

Fig. 1. Pore size distribution calculated from water retention data. The lower lines linking the MP and ST columns indicate the volume that is water-filled at − 100 hPa (b30 μm pores), and the figures above this line are the MP-ST differences in N30 μm pores (ns: not significant; **: P b 0.01; ***: P b 0.001). Significant tillage effects on a specific pore size class are indicated by contrasting letters, while significant effects on the total porosity is indicated by contrasting letters on top of the columns (P = 0.05).

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Table 3 Measured values of relative gas diffusivity (RD = Ds/D0), expressed as 1000 × Ds/D0. Estimates for tillage treatments within the same combination of location, depth and matric potential are significantly different (P = 0.05) if they are followed by different letters, whereas those without letters are not significantly different. The averages are estimated by a least squares procedure taking into account field block effects and may – due to unbalanced data – deviate slightly from the mean values plotted in Fig. 2. Location

Depth (cm)

Matric water potential (− hPa) 15

Dronninglund Nakskov

0–4 14–18 0–4 14–18

30

60

100

200

500

1500

MP

ST

MP

ST

MP

ST

MP

ST

MP

ST

MP

ST

MP

ST

2.8 2.7 3.7 3.6

7.1 1.0 3.7 0.3

9.7 8.2 12.1 14.1b

13.1 1.9 11.3 1.8a

38.6 25.6b 31.5 30.9b

31.3 4.4a 16.4 2.2a

37.5 29.3b 52.2 44.5b

32.7 5.8a 26.5 3.0a

63.6 55.1b 67.7 57.4b

57.8 13.7a 47.6 6.9a

81.9b 72.3b 98.1 81.9b

66.5a 28.5a 75.4 12.6a

115.3 85.1b 116.7 90.3b

100.1 40.1a 80.5 22.0a

increasing drainage, while the term 10 −(log(m) + 4)/n (the interception on the εa-axis at DS/D0 = 10 − 4) can be interpreted as the volume fraction of soil pores that is not contributing to the diffusion pathway (Schjønning et al., 2002) since a relative gas diffusivity below 10 − 4 reflects diffusion in water films (e.g. Broecker and Peng, 1974). The results of the analyses indicated that the relation between gas diffusivity and air-filled pore space did not differ significantly between tillage treatments for any of the locations at the 0–4 cm depth (Table 4). Table 4 and Fig. 2 show that the MP treatment at Dronninglund for both soil layers displayed a higher ‘rate’ of opening of soil pores than the ST soil (higher n-value and more pronounced increase in diffusivity with εa). The effect was, however, not significant for the top layer as just mentioned. At the 14–18 cm layer, significantly higher estimates of blocked pore volumes (the 10 −(log(m) + 4)/n-term) were found for the MP than the ST treatment at both locations (Table 4). This may be interpreted as a more complex soil structure in the MP treatment (Schjønning et al., 2002). 3.4. Net nitrification Fig. 3 shows the net NO3 production (net nitrification) during the 31 d incubation period for each individual soil core as related to

volumetric water content. The relationship was described reasonably well by a second-order polynomium model. For each combination of location and depth, we first compared a model with individual fit for each tillage treatment to a model with no differentiation of treatments. A Likelihood Ratio Test showed that for all four combinations of location and depth, the model with different polynomials for tillage treatments was significantly better than a common polynomial (analyses not shown). The polynomium model generally explained more of the variation in data for the Nakskov environments than for Dronninglund (Table 5). An uncertain estimate of maximum net nitrification and the corresponding water content was obtained for the MP treatment in 14–18 cm depth at Nakskov, for which the estimate of water content for maximum nitrification was outside the range of measured values (Fig. 3). For the other environments, the water content at which net nitrification peaked was estimated with standard errors in the range 0.009 − 0.032 m 3 m − 3 (Table 6, Fig. 4). This means that the optima in water content – due to the large number of individual cores included in the studies – were estimated rather accurately despite the scatter observed in Fig. 3. Standard error of maximum net nitrification estimates ranged from 1.7 to 5.9% of the estimates (log scale; data in original scale included in Table 6). Again, the value for the 14–18 cm MP

Fig. 2. Relative gas diffusivity, DS/D0, as related to air-filled pore space for decreasing (more negative) water potentials (from left to right). Bars indicate +/− 1 standard error of the mean. The lines give the best fit for the model DS/D0 = mεan. Consult Table 4 for model parameters.

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Table 4 Results from the model expressed by Eq. (1). The values of 10−(log(m) + 4)/n give model predictions of air-filled pore space (blocked pores, m3 m− 3), εa, at Ds/D0 = 10− 4. Log refers to the logarithm with base 10. See text for further details. Figures with the same letters within each combination of location and depth are not significantly different (P = 0.05). Standard error of estimates in parentheses. Location

Dronninglund

Depth (cm)

Tillage treatment

log(m)

0–4

MP ST MP ST MP ST MP ST

14–18 Nakskov

0–4 14–18

a

Model predictions: log(Ds/D0) = log(m) + n log(εa) n

0.578a 0.054a 0.691b − 0.075a 0.476a 0.482a 0.378b 0.314a

(0.31) (0.14) (0.22) (0.11) (0.12) (0.11) (0.07) (0.28)

3.29a 2.38a 2.92b 2.00a 3.13a 3.30a 2.85a 2.51a

(0.47) (0.22) (0.28) (0.10) (0.19) (0.18) (0.10) (0.25)

R2

10−(log(m) + 4)/n

0.89 0.95 0.95 0.99 0.98 0.98 0.99 0.94

0.041a 0.020a 0.025b 0.011a 0.037a 0.044a 0.029b 0.019a

(0.099)a (0.093)a (0.076)a (0.047)a (0.048)a (0.039)a (0.031)a (0.063)a

Standard error for log10-transformed data.

environment at Nakskov was estimated with relatively large uncertainty. Although no consistent difference in the physical conditions for soil microbial activity could be detected from the gas diffusivity measurements in the 0–4 cm layer (Fig. 2), a significantly higher net nitrification was found for the ST treatment in this layer at both sites (Fig. 3 and Table 6). This was probably due to higher contents of labile OM in ST soil (Table 2). At 14–18 cm depth the highest net nitrification rates were found for MP, although statistically significant at the P = 0.05 level only at Dronninglund (Fig. 3, Table 6). These effects of depth and site on net nitrification are in close agreement with the treatment effects on potential ammonium oxidation reported by Petersen et al. (2008). At Dronninglund, the potential net nitrification over the 31 day period (0–20 cm depth) at optimum water content (Table 2 and Table 6) corresponds to 20.8 and 21.9 kg NO3–N ha − 1 for the MP and ST soil, respectively. These results support the conclusion by Oorts et al. (2007) that the top layer of shallow tilled soil may have higher N 100 50

mineralization than ploughed soil, but that the net effect when integrated across the same soil mass is negligible. This is also supported by a study including five soils, where neither dry matter yield nor N-uptake by crops were affected by tillage method (Thomsen and Christensen, 2007). The uncertain estimate for the 14–18 cm MP environment at Nakskov (Fig. 3) prevented a comparison of potential net nitrification under ST and MP systems at this site. 3.5. Soil physical measures for optimum net nitrification This study focused on the physical conditions affecting net nitrification. Our working hypothesis was that gas and solute diffusion determines the extent and prevalence of aerobic microbial activity, as suggested by Skopp et al. (1990), who interpreted these physical processes in the context of Liebig's law of the minimum: the aerobic microbial activity – here represented by net nitrification – will be constrained by the physical process being the most limiting. We assumed that solute diffusivity remains largely unaffected by

MP ST

Dronninglund 0-4 cm

Dronninglund 14-18 cm

20

Net nitrification (g NO3-N m-3 soil)

10 5 2 100 50

RD<0.005 0.005<=RD<0.02 0.02<=RD

Nakskov 0-4 cm

Nakskov 14-18 cm

20 10 5

2 1 0.1

0.2

0.3

0.4

0.5

0.2

0.3

0.4

0.5

Soil water content (m3m-3) Fig. 3. Net nitrification for individual soil cores related to their volumetric water content. Lines give model predictions. Model parameters are given in Table 5, while model predictions and their variation are given in Table 6. RD is relative gas diffusivity.

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Table 5 Coefficients a, b and c and coefficient of determination, R2, for the model log(Y) = a + b × x + c × x2, where Y is net nitrification (g NO3–N m− 3 soil) and x is water content (m3 m− 3). See Fig. 3 for model predictions. Location

Dronninglund Nakskov

Depth (cm)

0–4 14–18 0–4 14–18

Model coefficients Moldboard ploughing (MP)

Shallow tillage (ST) 2

a

b

c

R

− 0.180 − 0.516 − 0.463 − 0.308

6.16 7.42 7.22 5.39

− 7.94 − 8.98 − 10.02 − 5.92

0.213 0.307 0.706 0.587

differences in soil structure. This is based on previous results (e.g. Olesen et al., 2000) as well as a recent study indicating that tillage has only a minor impact on solute diffusivity (Laegdsmand et al., submitted for publication). Our results confirm that aerobic microbial activity peaks at an intermediate water content (Skopp et al., 1990). Similar trends have been observed in other studies (e.g. Franzluebbers, 1999; Linn and Doran, 1984). Comparing three differently textured soils of the same geological origin, we found a highly significant effect of soil texture on the optimum volumetric water content for net nitrification (Schjønning et al., 2003). Also Paul et al. (2003) found that the water content per se was a poor predictor of net nitrogen mineralization across soil types. In the present study, for both locations and depths, net nitrification peaked at higher water contents for MP than for ST soil, but the trend was not significant for any of the environments (Table 6, Fig. 3). The water content at maximum nitrification tended to be higher for the sandy Dronninglund soil than for the loamy soil at Nakskov (disregarding the poor estimate for MP, 14–18 cm). Petersen et al. (2008) found that RD explained the observed variation in nitrous oxide emissions better than volumetric water content or water-filled pore space (WFPS). Similarly, Mutegi et al. (2010) found that N2O emissions from winter barley on soil under no-till, shallow tillage or ploughing were better predicted by RD than by WFPS. It is relevant to also evaluate, which expression of the soil water regime best identifies the optimum for aerobic microbial activity across soil type, soil depth and tillage system. Fig. 4 shows different expressions of the soil water status at maximum net nitrification: a) volumetric water content (the same data as in Table 6), b) the water content relative to water held at pF2 (field capacity) (RWC), c) water content held in ‘active pores’ relative to ‘active pores’ at pF2 (RAWC), d) water-filled pore space (WFPS), e) matric water potential, and f) relative gas diffusivity, RD. More specifically, RWC, RAWC and WFPS were calculated as follows: RWC = θ = θpF2

ð2Þ



 RAWC = θ−θpF4:2 = θpF2 −θpF4:2

ð3Þ

WFPS = θ × 100 = Φ

ð4Þ

where θ is the water content (m 3m − 3), θpF2 is water content retained at a matric potential of − 100 hPa (pF2) (corresponding to field

a

b

c

R2

0.279 − 1.572 − 1.440 − 1.82

6.21 12.47 15.94 14.40

− 8.29 − 16.54 − 23.01 − 21.91

0.129 0.224 0.788 0.200

capacity), θpF4.2 is water content at − 1.5 MPa, and Φ is soil total porosity (m 3m − 3). The matric potentials and RD values displayed in Fig. 4 were found by manual interpolation on curves relating water content to matric potential and RD, respectively. Standard errors for the volumetric water content (Fig. 4a) are those estimated by the polynomium models for fitting data in Fig. 3 (also listed in Table 6). The standard errors displayed for the other parameters in Fig. 4 were found by recalculation of mean ± SE values in Fig. 4a. For the matric potential and relative gas diffusivity (Fig. 4e,f), this involved a manual interpolation as for the estimates of means. We note higher RWC and RAWC estimates of the optimum for nitrification for MP relative to ST soil. The RWC has been suggested as a normalized expression of the soil water content relevant for microbial activity (Reichstein et al., 2002). For the sandy soil at Dronninglund, the optimum water content for aerobic microbial activity was close to field capacity (RWC ~ 1), while it was higher than field capacity for the loamy soil at Nakskov (Fig. 4b). The RAWC parameter is a relative expression of the water-filled pore space that is habitable to soil microbes (van Veen and Kuikman, 1990). Based on the considerable deviation across the different environments (Fig. 4c), this parameter seems to be a poor predictor of the optimum for net nitrification although the opposite has been reported (Paul et al., 2003). A range of studies have highlighted WFPS as a predictor of microbial processes in soil, and a threshold value of 60% for aerobic conditions has been suggested (e.g. Linn and Doran, 1984). In this study, we found WFPS at optimum net nitrification in the range ~60– 90% (Fig. 4d). This is in close agreement with a previous study (Schjønning et al., 2003) and aligns with other studies (e.g. Paul et al., 2003; Rodrigo et al., 1997). Soil water potential was also not a universal predictor of optimum conditions for aerobic microbial activity. Especially for the sandy soil at Dronninglund, the matric potential was much lower (more negative) for ST than MP soil (Fig. 4e). This is noteworthy as the soil water potential has in some studies been suggested to be the most probable regulator of microbial activity (e.g. Castellano et al., 2009; Ilstedt et al., 2000; Schroll et al., 2006). However, the direct effects of soil water potential upon microbial activity in undisturbed soil are important only at rather low potentials (Griffin, 1981). Thus, Stark and Firestone (1995) found that matric potential became rate

Table 6 Polynomium model estimates of maximum net nitrification and the corresponding water contents for the data shown in Fig. 3. Means followed by the same letter in a row are not significantly different (P = 0.05). Data in parantheses next to the nitrification estimates indicate estimates minus and plus the standard error (skewed range due to log-normal distribution). Data in parentheses for water content is standard error of estimate. Location

Dronninglund Nakskov

Depth (cm)

0–4 14–18 0–4 14–18

Maximum nitrification (g NO3–N m− 3 soil)

Water content at maximum nitrification (m3 m− 3)

MP

ST

MP

ST

(25.2) 27.6b (30.2) (5.5) 6.0a (6.5) (20.0) 21.0b (22.1) (3.3) 3.5a⁎(3.8)

0.388a (0.032) 0.413a (0.028) 0.360a (0.025) 0.455⁎⁎a (0.117)

0.374a 0.377a 0.346a 0.329a

(9.5) (9.7) (6.5) (6.4)

10.3a (11.2) 10.4b (11.2) 6.9a (7.3) 8.3a⁎(10.7)

⁎ P ~ 0.1. ⁎⁎ Estimate not reliable (outside range of measured data).

(0.018) (0.010) (0.009) (0.022)

P. Schjønning et al. / Geoderma 163 (2011) 256–264

0.45

d

100 Ptillage=0.15 CVtillage=0.06

80 70 0.35

60

b

1.8

Ptillage=0.10 CVtillage=0.31

-250

e

Ptillage=0.09 CVtillage=1.22

1.6

-200 -150

1.4 -100 1.2 -50

1.0 2.2 2.0

RAWC (m3m-3)

90

0.40

2.0

RWC (m3m-3)

Ptillage=0.17 CVtillage=0.17

c

Ptillage=0.11 CVtillage=0.45

f

Ptillage=0.45 CVtillage=0.22

1.8 1.6

0.05 0.04 0.03

1.4

0.02

1.2 0.01

1.0 0.8

MP ST MP ST 14-18 0-4 Dronninglund

MP ST MP ST 0-4 14-18 Nakskov

MP ST MP ST 14-18 0-4 Dronninglund

MP ST MP ST 0-4 14-18 Nakskov

WFPS (m3 100m-3)

a

Relative gas diffusivity (-) Matric water potential (hPa)

Water content (m3 m-3)

0.50

263

0.00

Fig. 4. Expressions of the physical conditions at the water content yielding maximum net nitrification. The data for water content was estimated by the polynomial model (Fig. 3 and Table 6). Bars indicate +/− 1 standard error (SE) of these estimates. The relative water content (RWC), relative active water content (RAWC) and water-filled pore space (WFPS) expressions were calculated from these data, while the matric water potential and the relative gas diffusivity were estimated by manual interpolation on curves relating these parameters to water. The SE for these parameters is related to the SE for water contents through similar interpolations. The very high SE for the Nakskov soil, MP, 14–18 cm is because the water content estimated was outside the range of measured data. The P-values indicate the probability that the tillage effects on the parameters are random (pairwise t-tests), while the CVtillage values are coefficients of variation relating MP and ST normalized across the four environments.

limiting for nitrification only at water potentials more negative than −6000 hPa. We hypothesized that the interaction between diffusion constraints for solutes and gases controls aerobic microbial activity in soil. Assuming that any constraints on the diffusion of solutes is little affected by soil structural conditions (Laegdsmand et al., submitted for publication), we expected RD to be a good predictor of the optimum conditions for net nitrification. However, the scatter in data across location and soil layers indicates that RD was also not a universal predictor (Fig. 4f). The higher threshold values found for the sandy soil at Dronninglund as compared to the loamy soil at Nakskov are in accordance with a previous study, where net nitrification peaked approximately at RD = 0.030 for a sandy soil and at RD ~ 0.005 for more clayey soils (Schjønning et al., 2003). Based on Fig. 4 and the discussion above, it is obvious that our data do not identify a ‘universal’ soil physical predictor of optimum conditions for aerobic microbial activity. Possible reasons for this include treatment effects on substrate diffusion in the water phase (which we had assumed to be negligible). The estimates of the optimum water content may also be biased because we used simple second order polynomials to express the relation between water content and net nitrification. Tillage is expected to affect primarily the physical properties of the soil, while basic soil properties like textural composition etc. remain the same. It may hence be relevant to focus especially the optimum water content and related water regime expressions across the differently tilled soil at identical soil and depth.

The Ptillage-values shown in Fig. 4 are from the results of pairwise ttests of MP vs ST treatments (four pairs in terms of two locations times two soil layers). A high Ptillage-value would indicate that tillage with a high probability has no influence on the expression's (e.g. WFPS) effect on net nitrification. The CVtillage shown in Fig. 4 was calculated as follows:

CVtillage =

sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi, ! 2  1 4
1 4
∑ xiðMPÞ −xiðST Þ ∑ xiðMPÞ + xiðST Þ 4 i=1 8 i=1

ð5Þ

where x is one of the parameters (water content, RWC etc.) in Fig. 4 (MP or ST treatment), and subscript i (1..4) is one of the four combinations of location and soil layer. CVtillage is an expression (coefficient of variation) relating MP and ST normalized across the four environments. The CVtillage measure shows that the magnitude of the tillage effect was least for the WFPS parameter, while the highest tillage-induced variation was observed for the matric potential (Fig. 4). We further note that by far the highest Ptillage-value (P = 0.450) was found for the RD parameter (Fig. 4). This means that the relative gas diffusivity was the parameter that was least affected by tillage system. The Ptillage values for the other parameters indicate a rather strong effect of tillage on the estimated optima for aerobic conditions (e.g. Ptillage = 0.09 for the matric water potential). For all soil water content related expressions (water content per se, and RWC, RAWC and WFPS) lower optima were observed for ST than MP soils for all four

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environments, the only exception being WFPS in the 14–18 cm depth at Dronninglund (Fig. 4). For the matric potential, numerically higher (more negative) values were observed for the ST treatment, and this actually corresponds to the water quantity expressions. Models simulating OM turnover in arable soils are essential tools in the development of sustainable management systems, but most existing models use rather crude, empirical relationships to describe the water effects (Bauer et al., 2008; Rodrigo et al., 1997). We intended to add a refinement to existing simulation models, albeit not on a fully mechanistic basis. Our results indicate that none of the expressions of the soil–water regime fulfilled the role as a ‘universal’ descriptor of net nitrification. However, WFPS and RD tended to perform better than the other variables examined in our study, and previously published studies on the same soil cores have indicated that gas diffusivity is a better predictor of nitrous oxide evolution than other expressions of the soil water status (Petersen et al., 2008). Nevertheless, more studies are needed and should include more soil types and measurements of solute as well as gas diffusivity. 4. Conclusions Shallow tillage as opposed to ploughing had only minor influence on soil physical characteristics of the top 0–4 cm soil, while ST dramatically increased bulk density and changed the pore size distribution of the 14–18 cm depth, especially at the morainic soil at Nakskov. At 0–4 cm depth significantly higher net nitrification was found in ST compared to MP soil. The opposite trend was found for the 14–18 cm layer, but integrated for the same soil mass of the upper ~ 20 cm soil the tillage effects tended to be negligible. Maximum net nitrification occurred at lower matric potentials (drier soil) for the ST than the MP soil. Irrespective of tillage treatment the maximum net nitrification occurred at higher relative gas diffusivities for the sandy soil at Dronninglund than for the loamy soil at Nakskov. We found no ‘universal’ predictor of maximum net nitrification across soil type, soil depth and tillage treatment. Water-filled pore space and especially relative gas diffusivity tended to be the parameters that were least affected by tillage when used to predict the optimum conditions for maximum net nitrification. Acknowledgements Bodil B. Christensen, Karin Dyrberg, Michael Koppelgaard and Stig T. Rasmussen are acknowledged for their technical assistance in carrying out the measurements. This study was financially supported by the Danish Research Council for Technology and Production Sciences (project no. 274-05-0031). References Ball, B.C., Tebrügge, F., Sartori, L., Giráldez, J.V., González, P., 1998. Influence of no-tillage on physical, chemical and biologecal soil properties. In: Tebrügge, F., Böhrnsen, A. (Eds.), Experience with the applicability of no-tillage crop production in the WestEuropean Countries. Final report, Concerted Action No AIR 3-CT 93–1464, pp. 7–27. Bauer, J., Herbst, M., Huisman, J.A., Weihermüller, L., Vereecken, H., 2008. Sensitivity of simulated soil heterotrophic respiration to temperature and moisture reduction functions. Geoderma 145, 17–27. Broecker, W.S., Peng, T.-H., 1974. Gas exchange rates between air and sea. Tellus 26, 21–35. Castellano, M.J., Schmidt, J.P., Kaye, J.P., Walker, C., Graham, C.B., Lin, H., Dell, C.J., 2009. Hydrological and biogeochemical controls on the timing and magnitude of nitrous oxide flux across an agricultural landscape. Glob. Change Biol. 1–9. doi:10.1111/ j.1365-2486.2009.02116.x Published Online: 23 Oct 2009. Ehlers, W., Claupein, W., 1994. Approaches toward conservation tillage in Germany. In: Carter, M. (Ed.), Conservation Tillage in Temperate Agroecosystems. Lewis Publishers, pp. 141–165. Franzluebbers, A.J., 1999. Microbial activity in response to water-filled pore space of variably eroded southern Piedmont soils. Appl. Soil Ecol. 11, 91–101. Gregorich, E.G., Rochette, P., Hopkins, D.W., McKim, U.F., St-Georges, P., 2006. Tillageinduced environmental conditions in soil and substrate limitations determine biogenic gas production. Soil Biol. Biochem. 38, 2614–2628.

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