Biochar differentially affects the cycling and partitioning of low molecular weight carbon in contrasting soils

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Biochar differentially affects the cycling and partitioning of low molecular weight carbon in contrasting soils Q8

Mark Farrell*, Lynne M. Macdonald, Jeff A. Baldock Agriculture Flagship, CSIRO, PMB 2, Glen Osmond, SA, 5064, Australia

a r t i c l e i n f o

a b s t r a c t

Article history: Received 24 March 2014 Received in revised form 16 July 2014 Accepted 22 September 2014 Available online xxx

Biochar application to soils has received much attention due to the potential for dual benefits of improved fertility and carbon (C) sequestration. Whilst its effect on C and nitrogen (N) cycling in soils has been investigated previously, this has usually either focussed on the bulk soil organic matter, or a single compound such as glucose. Five low molecular weight dissolved organic C (LMWDOC) substrates (three sugars, one amino acid, one organic acid) were selected for a 14C-CLPP experiment from which turnover rate (t1/2) and immediate carbon use efficiency (CUE) of the substrate were estimated. We demonstrated that whilst soil type had the greatest effect on soil microbial function, the addition of biochar also influenced microbial turnover and CUE of the substrates, most notably in the lowest fertility soil. We also identified that the relationship between turnover and CUE of the five substrates differed substantially, and the effect of biochar and soil type was more pronounced in the amino acid than the organic acid. This effect tended to be greatest in biochars produced at 450
C, and less pronounced with the addition of biochars produced at 550
C, though these trends were not consistent for all compounds in all soils tested. We conclude biochars and soils interact to manifest non-systematic differences in turnover rates of LMWDOCs, and thus a variety of mechanisms are likely responsible for this observation. As these compounds are most commonly found in the rhizosphere and can contribute a significant portion of photosynthetically-fixed C, and plant roots have been observed to grow preferentially around biochar particles, it is apparent that biochar may significantly affect the flow of LMWDOC through the microbial community in soils. © 2014 Elsevier Ltd. All rights reserved.

Keywords: Char LMWOC C-14 Substrate induced respiration Black carbon Australian soils Root exudates Carbon use efficiency

1. Introduction Agricultural soils receive a variety of organic amendments, including biosolids (Tian et al., 2009), composts (Farrell and Jones, 2009), and pyrolysed organic material, known as biochar (Woolf et al., 2010). These are applied both for the delivery of nutrients and improvement of soil bio-physicochemical properties (Bulluck et al., 2002), and more recently, the sequestration of carbon (C) derived from atmospheric CO2 in soils (Thangarajan et al., 2013). Recent observations of significant quantities of charcoal present across Australian soils (Lehmann et al., 2008; Baldock et al., 2013), and earlier data from the Amazonian Terra Preta soils (Glaser et al., 2001), demonstrate a long residence time for black C in soils. Thus, potential exists for biochar additions to sequester additional C in soil organic matter (SOM). Biochar has been demonstrated to

Q1

* Corresponding author. CSIRO Land & Water, PMB 2, Glen Osmond, SA, 5064, Australia. Tel.: þ61 8 8303 8664; fax: þ61 8303 8550. E-mail address: [email protected] (M. Farrell).

increase productivity in some agricultural systems (Atkinson et al., 2010; Jeffery et al., 2011), potentially mitigate nutrient losses (Chen et al., 2010), and to influence the soil microbiota (Anderson et al., 2011; Parvage et al., 2013; Farrell et al., 2013a) and resultant ecosystem services including C and nutrient cycling (Nelissen et al., 2012; Biederman and Harpole, 2013; McCormack et al., 2013; Farrell et al., 2014). However, the behaviour of biochars in soil, their effect on soil biogeochemical functioning, and their persistence can be varied. This is ultimately a function of both feedstock type, and pyrolysis process and temperature (Harvey et al., 2012), coupled with interactions with soil chemistry. Increasing pyrolysis temperatures increase the amount of aryl and O-aryl aromatic structures and concurrently decrease microbial degradability of biochar-C (Baldock and Smernik, 2002). Biochars produced from labile, nutrient rich materials such as manures can be expected both to degrade quicker, and provide more labile C and nutrients to the soil microbial community than those produced from cellulosic and lignin-rich materials such as wood (Singh et al., 2012). Variations in biochar composition are considered to account for the contrasting

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Please cite this article in press as: Farrell, M., et al., Biochar differentially affects the cycling and partitioning of low molecular weight carbon in contrasting soils, Soil Biology & Biochemistry (2014), http://dx.doi.org/10.1016/j.soilbio.2014.09.018

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reports on the effect of biochar on various indicators of biogeochemical function (Sohi et al., 2010). Given that the soil microbial biomass was identified by Jenkinson (1977) as the “eye of the needle” through which all organic matter entering the soil must pass, any effects of biochars on the function of the microbial community must be understood to ensure that negative effects which reduce the function of the soil biota are avoided should large-scale biochar application occur (Anderson et al., 2011). In order for soil microorganisms to take up and process organic matter, it must be in the dissolved phase, and this dissolved organic C (DOC) pool forms an integral part of terrestrial C cycling. Although when measured as a standing pool, high molecular weight (HMW) materials may appear to dominate DOC (Farrell et al., 2011a), it is the smaller yet much faster cycling low molecular weight (LMW) components of DOC that may be of greater importance, with some components estimated to turn over more than 4000 times annually, with half-lives of <2 h (van Hees et al., 2005; Boddy et al., 2007). LMWDOC is typically a spatially and temporally variable resource in soils, with concentrations typically orders of magnitudes higher in hot spots of root exudates and microbial secretions, especially in the rhizosphere, where rhizodeposits are known to regulate ecosystem function (Jones et al., 2009). Exudate compounds can constitute a significant proportion of photosynthetically fixed C (Lynch and Whipps, 1990), and serve as readily available energy sources for the soil microbiota. Exudates may influence microbial growth and metabolism, with flow on effects for the cycling of C in soil (Shi et al., 2013), though it has been observed that the carbon use efficiency (CUE) of these compounds can differ markedly (Bradford et al., 2013). Recent observations that plants may actively grow their roots around biochar particles when they encounter them in the soil (Prendergast-Miller et al., 2014) mean that LMWDOC exudate compounds containing a mixture of sugars, organic acids, and also N-bearing compounds such as amino acids (Jones et al., 2009; Shi et al., 2013) are liable to come into direct contact with biochar particles in the soil, and thus be influenced by the biochar surface. Secondly, as biochar contains both nutrients and small amounts of available C (Farrell et al., 2013a; Gomez et al., 2014; Watzinger et al., 2014), these labile components of the biochar itself will also directly affect microbial function upon addition to the soil as microbes respond rapidly to this fresh resource. Finally, the chemical structure of the biochar, and in particular aryl-C and carbonyl-C content have been demonstrated to significantly affect soil microbial community structure and function respectively (Ng et al., 2014). Given that different biochars and their interactions with various soils have been observed to have significantly divergent effects on plant growth (Jeffery et al., 2011), it is likely that there are also significant differences in how soil microbial responses, and in particular, the turnover of LMWDOC are affected. The aim of this study was to investigate the microbial cycling of five LMWDOC compounds in three differing soils, as affected by six biochars produced from three feedstocks at two pyrolysis temperatures. 2. Materials and methods 2.1. Soil and biochar characterisation 2.1.1. Soil Independent field replicates (n ¼ 4) of three contrasting soils (0e10 cm depth) were collected in the southern hemispheric spring (September 2011), and were express shipped in cool boxes to the laboratory in Adelaide where they were immediately refrigerated. The aridic arenosol was taken from under Triticum aestivum (wheat) rotation at the Wongan Hills Research Station,

approximately 182 km north-east of Perth, WA, Australia. The rhodic ferralsol was obtained from under Lolium perenne (perennial ryegrass) pasture at the New South Wales Department of Primary Industries Wollongbar Research Station, approximately 750 km north of Sydney, NSW, Australia. The pellic vertisol was sourced from Hermitage Research Station in Queensland, Australia, approximately 150 km south-west of Brisbane. These three soil types are representative of major agricultural soils in Australia and globally. The chemical properties of the three soils are presented in Table 1, and were determined on each independent replicate (n ¼ 4). Total C (TC) and total N (TN) were determined by dry combustion (2000-CNS, Leco Australia Pty. Ltd., Castle Hill, NSW, Australia). pH and electrical conductivity were determined in 0.01 M CaCl2 and ultra-pure water respectively using standard electrodes (1:5 soil:solution ratio). A 0.5 M K2SO4 extract (1:5, soil:solution) was used to assess soluble C and N chemistry. Dissolved organic C (DOC) and total dissolved N (TDN) mere measured on a Thermalox TOC-TN analyser (Analytical Sciences, Cambridge UK). Nitrate and ammonium were measured colourimetrically on the extracts using the methods of Miranda et al. (2001) and Mulvaney (1996) respectively on a SynergyMX microplate reader (Biotek, Winooski, VT), and dissolved organic nitrogen (DON) was calculated by difference. Microbial biomass C/N (MBC/N) was determined following Voroney et al. (2008), and extracts of the fumigated soils were analysed on the same TOC-TN analyser as above. Available P was estimated using the method of Olsen et al. (1954) followed by colourimetric analysis using the malachite green method (Ohno and Zibilski, 1991) using the same microplate reader as for mineral N. All data are reported on a dry weight basis. 2.1.2. Biochar Three biochar feedstocks were obtained for use in this study. Poultry manure (PM) comprising a mixture of wood chips and chicken faeces was collected from near Gosford, NSW, in early 2010. Wheat straw (WS) was purchased from Coastal Rural Trader, Ourimbah, NSW in early 2010, and consisted mainly of dry T. aestivum straw with some seed husks and leaves present. Oil mallee (the woody residue from Eucalyptus oleosa trees after eucalypt oil steam extraction; OM) was collected from western NSW in July 2010. Biochars were produced from these three feedstocks at 450 and 550
C in a Pyrochar300 continuous reactor with a mean residence

Table 1 Soil chemical properties (mean ± SEM, n ¼ 4). Within each variable, significant differences at the 0.05 level are denoted by different letters between soil types. Where no differences were observed for a variable, no letters are present.

Location Total C (g kg1) Total N (g kg1) C:N ratio pH Electrical conductivity (ms cm1) Dissolved organic C (mg kg1) Dissolved organic N (mg kg1) Microbial biomass C (mg kg1) Microbial biomass N (mg kg1) NH4þeN (mg kg1) 1 NO3 eN (mg kg ) Olsen P (mg kg1)

Aridic arenosol

Pellic vertisol

Rhodic ferralsol

30
890 S, 116
720 E 7.49 ± 0.41a 0.538 ± 0.032a 13.9 ± 0.2b 7.02 ± 0.01a 30.1 ± 1.0a

28
120 S, 150
060 E 20.2 ± 1.3b 1.29 ± 0.11b 15.8 ± 0.4c 7.13 ± 0.07a 48.9 ± 1.7b

28
290 S, 153
140 E 48.1 ± 2.1c 4.42 ± 0.22c 10.9 ± 0.1a 4.95 ± 0.12b 25.5 ± 3.4a

56.7 ± 1.5b

27.5 ± 3.3a

137 ± 11c

8.62 ± 0.72b

0.304 ± 0.304a

18.6 ± 1.1c

191 ± 9a

189 ± 33a

647 ± 111b

34.6 ± 3.2a

26.4 ± 3.9a

219 ± 19b

1.50 ± 0.11 4.49 ± 0.09a 18.8 ± 1.6a

2.41 ± 1.38 9.71 ± 1.73ab 47.9 ± 3.7b

0.841 ± 0.207 13.1 ± 2.1b 16.3 ± 1.8a

Please cite this article in press as: Farrell, M., et al., Biochar differentially affects the cycling and partitioning of low molecular weight carbon in contrasting soils, Soil Biology & Biochemistry (2014), http://dx.doi.org/10.1016/j.soilbio.2014.09.018

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time of 20 min (Pacific Pyrolysis Pty. Ltd., Somersby, NSW, Australia). Total C, N, and electrical conductivity were determined as for the soils (Section 2.1.1). pH of the biochars was measured in ultra-pure water at a 1:5 soil:solution ratio, and total phosphorus (P) was analysed by ICP-OES (720-ES, Agilent Technologies, Santa Clara, CA) after digestion in HClO4/HNO3 in a heated block (Farrell et al., 2013b). All data are presented in Table 2. 2.1.3. NMR analysis To highlight the differences in chemistry of the C within the three soils and six biochars studied, 13C-CP/MAS (cross-polarisation/magic angle spinning) NMR spectroscopy was employed following the procedures detailed in Baldock et al. (2013) and Farrell et al. (2013a). Briefly 13C NMR spectra were acquired using a Bruker Avance 200 MHz spectrometer equipped with a 4.7 T widebore superconducting magnet operating at a 13C resonance frequency of 50.33 MHz. Known weights of each biochar and soil (84.8e479.4 mg) were packed into 7 mm diameter zirconia rotors with Kel-F end caps and spun at 5 kHz. Chemical shift values were calibrated to the methyl resonance of hexamethylbenzene at 17.36 ppm and a 50 Hz Lorentzian line broadening was applied to all spectra. Between 2712 and 30000 scans (dependent upon C content and signal intensity) were collected using a 3.2 ms, 195 w, 90
pulse with a contact time of 1 ms and a recycle delay of 1 s. Initial inversion recovery analyses confirmed that saturation was avoided by the 1 s recycle delay. Signal intensity was integrated and apportioned to a range of chemical shift regions as defined by Baldock et al. (2013). 2.2. Mineralisation characteristics of

C-labelled compounds

Using the soils and biochars outlined above, an incubation experiment was carried out as detailed below. For each of the three soils, the six biochars were added individually at a rate equivalent to 30 t ha1 to 5 g field-moist soil in a 50 mL centrifuge tube, and were pre-incubated at 22
C for two weeks in the dark. The four field replicates were retained, and a control treatment consisting of no biochar addition was included. This resulted in 28 individual samples per soil type per substrate, 84 samples across all three soil types, and 420 individual incubations for the entire experiment which used five 14C-labelled substrates. The sugars glucose, sucrose, and fructose; the organic acid oxalate, and the amino acid L-phenylalanine were selected for use in the 14C-SIR assay. Each of the five 14C-labelled substrates was added in solution to individual pre-incubated samples (outlined above) and incubated for 7 day at 22
C according to Farrell et al. (2013c). Briefly, 500 mL of 50 mM substrate (2 kBq m L1) was added to 5 g fresh soil for each of the four replicates. To quantify rates of respired 14 CO2, NaOH traps were removed 0.25, 0.5, 1, 3, 6, 24, 48, 72, 96 and 168 h after 14C label addition. Trapped 14CO2 was quantified by liquid scintillation counting, after mixing with HighSafe 3 scintillation cocktail (PerkinElmer Inc.), in a Tri-Carb 3110 TR scintillation counter (PerkinElmer Inc.). After incubating for 168 h, the soil was shaken with 25 mL 0.5 M Na2SO4 for 30 min to recover any 14C label Table 2 Biochar characteristics.

1

Total C (g kg ) Total N (g kg1) Total P (g kg1) pH Electrical conductivity (ms cm1)

remaining in the solution or the exchangeable phase (Kuzyakov and Jones, 2006). A double first-order exponential decay equation was fitted to the inverse of the mineralisation data using a least squares optimisation routine in SigmaPlot v11.0 (Systat Software Inc., Chicago, IL) where:



y ¼ Yr  expk1 t þ Yb  expk2 t

Wheat straw

Poultry manure

Oil mallee

450
C

550
C

450
C

550
C

450
C

550
C

531 22.4 3.3 8.39 9.18

566 21.0 11.7 9.00 7.66

383 20.4 9.8 7.69 5.19

441 17.1 16.5 9.57 7.68

564 3.8 0.4 7.23 0.93

670 5.4 0.2 7.51 0.85

(1)

in which y is the amount of 14C remaining in the soil (%) and t is time (h). Yr and Yb represent the amount of 14C-substrate (%) partitioned into microbial respiration and biomass production and subsequent mineralisation of decay products, respectively. k1 and k2 are the rate constants (h1) for pools Yr and Yb, respectively (Boddy et al., 2007; Farrar et al., 2012; Farrell et al., 2013c). The first, rapid phase of respiration (pool Yr) represents microbial catabolism, and has a high rate of turn-over (k1). The second phase of respiration (pool Yb) represents anabolic metabolism of the labelled substrate, with a subsequent slower rate of turnover (k2) as a result of degradation of secondary products (Boddy et al., 2007; Farrar et al., 2012). Substrate half-life in the soil solution (t1/2) and microbial C assimilation efficiency i.e. the proportion of 14C used for microbial growth (C use efficiency; CUE; Yc) were calculated (Boddy et al., 2007; Farrell et al., 2011b; Farrar et al., 2012) as


. t1=2 ¼ ln 2 k1

(2)

and

Yc ¼ Yb =ðYb þ Yr Þ

14

3

(3)

respectively. This CUE equation makes the assumption that all utilised substrate not mineralised was incorporated into microbial biomass or metabolites (Thiet et al., 2006). It should also be noted that CUE as assessed by this method may not consider true maintenance costs as it is only a reflection of the utilization of the 50 mM aliquot of substrate (Sinsabaugh et al., 2013). 2.3. Statistical analysis Principal components analysis (PCA) was used to analyse the 13C NMR data to highlight similarities and differences between the biochars and soils in terms of their organic C chemistry. Nonparametric multivariate methods were used to assess the relative overall effect of soil and of biochar on both the t1/2 and Yc. Data variables (normalised) were used to create a Euclidean based similarity index, before carrying out non-metric multi-dimensional scaling (nMDS) in PRIMER (PRIMER-E, Plymouth Routines in Multivariate Ecological Research v. 6). The nMDS ordination provides a means of assessing general relationships between samples, with the level of confidence in the 2D representation of the multidimensional relationship indicated by the associated ‘stress’: 0.2 or less provides good representation (Clarke and Gorley, 2006). Permutational Multivariate ANOVA (PERMANOVA) analysis was carried out in the PERMANOVA þ add-on to PRIMER-E (Anderson et al., 2008) to assess the effect of soil type and biochar addition on both t1/2 and CUE of the five added substrates. 3. Results 3.1. Soil and biochar properties The three soils differed greatly in most of the chemical properties measured (Table 1). The rhodic ferralsol contained more than twice as much C as the pellic vertisol and more than six times that

Please cite this article in press as: Farrell, M., et al., Biochar differentially affects the cycling and partitioning of low molecular weight carbon in contrasting soils, Soil Biology & Biochemistry (2014), http://dx.doi.org/10.1016/j.soilbio.2014.09.018

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in the aridic arenosol. The C:N ratios of the three soils followed the order pellic vertisol (15.7) > aridic arenosol (13.8) > rhodic ferralsol (10.9). The rhodic ferralsol also contained 3.5 times the MBC and greater than six times the MBN of the other two soils, indicating a higher degree of N availability to soil microorganisms in the rhodic ferralsol than might be reflected by the DON and DIN concentrations. Despite its lower extractable N concentrations, the pellic vertisol contained three times the amount of extractable P, highlighting the P binding nature of the rhodic ferralsol. The six biochars, produced from three different feedstocks and two different pyrolysis temperatures had markedly different chemical properties (Table 2). There was a wide range of gravimetric C:N:P ratios across the six biochars, with the two PM biochars having the narrowest ratios (PM450: 39.1:2.1:1; PM550 26.7:1:1), and the two OM biochars having the highest ratios (OM450: 1410:9.5:1; OM550 3350:27:1). The OM biochars also

differed notably in their electrical conductivity, having ca. ten times lower EC values than the WS and PM biochars. 3.2. Organic C chemistry The organic C chemistry of the six biochars and three soils used in the present study was examined using 13C-CP/MAS NMR spectroscopy, and the spectra are presented in Fig. 1. There was a significant systematic shift of the C from a complex mixture of C species in the three biochars produced at 450
C towards a single dominant peak near 130 ppm in the biochars produced at 550
C. Allocation of the acquired signal intensity to different chemical shift regions revealed that aryl C accounted for 60% of the total CP-observable C in the biochars produced at 550
C. This was in contrast to all three biochars produced at 450
C, where alkyl and O-alkyl C contribute significantly to the overall NMR signal.

Fig. 1. 13C-CP/MAS NMR spectra of the biochars and soils used in the present study. WS ¼ wheat straw, PM ¼ poultry manure, OM ¼ oil mallee. 450 ¼ pyrolysis temperature of 450
C, 550 ¼ pyrolysis temperature of 550
C. Note the spinning side bands at 25 and 225 ppm in the biochars.

Please cite this article in press as: Farrell, M., et al., Biochar differentially affects the cycling and partitioning of low molecular weight carbon in contrasting soils, Soil Biology & Biochemistry (2014), http://dx.doi.org/10.1016/j.soilbio.2014.09.018

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Principal components analysis (Fig. 2) revealed similarity between the three biochars produced at 550
C, and dissimilarity between those produced at the lower temperature of 450
C. Like the lowertemperature biochars, the three soils show a heterogeneous mixture of C moieties. 3.3. Effect of biochar on microbial metabolism of LMWOCs

Q2

The evolution of 14CO2 from the five added LMWOCs fitted a double exponential decay model (Equation (1), R2  0.973), and examples of the measured and modelled cumulative amount of 14 CO2 mineralised from glucose are presented in Fig. 3. The Na2SO4 extraction of soluble C completed at the end of the 168 h incubation demonstrated that for glucose, phenylalanine and sucrose, uptake of the labelled substrate (and re-uptake of labile by-products) was complete (>97% label removed from the extractable pool). However, in the incubations with oxalate and fructose, an average of 8.5% and 15.7% of the 14C-label was recovered respectively in the extractable phase of the aridic arenosol, with lower proportions (3.95e11.5%) recovered in the other two soils. Multi-dimensional scaling plots (Fig. 4) revealed that soil type was a driver of substrate 14C half-life, with a smaller secondary trend evident as a factor of biochar treatment. Similarly, the pattern of microbial CUE of the labelled substrates was influenced by soil type and biochar addition, although clear distinction between soils was not as evident as for the half-life. Permutational multivariate ANOVA (Anderson et al., 2008) confirmed a highly significant (P < 0.001) effect of soil and biochar type on both t1/2 and CUE when the mineralisation data of the five substrates is analysed together (Table 4). However, while there was also a significant interaction

5

(F ¼ 4.74, P < 0.001) observed between biochar and soil type for t1/2, indicating that the effect of biochar addition on the turnover rate of LMWOCs differed between soil types. In contrast to this, no significant (F ¼ 1.74, P ¼ 0.441) interaction term was observed for CUE, highlighting that the influence of biochar on CUE was uniform across soil types. By comparing turnover rate of the added substrates expressed as half-life, and CUE, it is apparent that the five substrates behaved differently across the soil and biochar treatments (Fig. 5). The comparison of these two metrics of soil microbial LMWOC metabolism revealed strong separation on the basis of soil type for phenylalanine, and for glucose when comparing the sandy aridic arenosol to the two more clayey soils. But, much less discrimination was demonstrated for the other three compounds. Overall, CUE of glucose decreased as t1/2 increased, whereas the opposite trend was seen for fructose, and no obvious relationship was demonstrated for sucrose or oxalate. Across the tight clustering by soil type for phenylalanine, CUE also decreased with increased t1/2. The effects of biochar treatments on the relationship between turnover rate and CUE were most visible in the aridic areonsol, which was the poorest soil in terms of its structure and chemistry (Table 1). Here, the two oil mallee biochars increased the t1/2 induced the greatest increase in t1/2 relative to the control (no biochar) treatment. 4. Discussion 4.1. Biochar and soil chemistry The biochars and the soils used in this study varied in chemical composition. The differences in soil type were supported by their

Fig. 2. Principal components analysis of C chemistry in the six biochars and three soils. WS ¼ wheat straw, PM ¼ poultry manure, OM ¼ oil mallee. 450 ¼ pyrolysis temperature of 450
C, 550 ¼ pyrolysis temperature of 550
C. AA ¼ aridic arenosol, PV ¼ pellic vertisol, RF ¼ rhodic ferralsol.

Please cite this article in press as: Farrell, M., et al., Biochar differentially affects the cycling and partitioning of low molecular weight carbon in contrasting soils, Soil Biology & Biochemistry (2014), http://dx.doi.org/10.1016/j.soilbio.2014.09.018

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Fig. 3. 14C- mineralisation data for the five substrates in the three soils used in this study, as affected by biochar treatment. Data points are means ± SEM, n ¼ 4, lines are fitted using a double first-order exponential decay approach (Equation (1)), and are means of the fitted line to each individual replicate of each treatment (n ¼ 4). a) glucose in rhodic ferralsol, b) glucose in pellic vertisol, c) glucose in aridic arenosol, d) fructose in rhodic ferralsol, e) fructose in pellic vertisol, f) fructose in aridic arenosol, g) sucrose in rhodic ferralsol, h) sucrose in pellic vertisol, i) sucrose in aridic arenosol, j) oxalate in rhodic ferralsol, k) oxalate in pellic vertisol, l) oxalate in aridic arenosol, m) phenylalanine in rhodic ferralsol, n) phenylalanine in pellic vertisol, o) phenylalanine in aridic arenosol. WS ¼ wheat straw, PM ¼ poultry manure, OM ¼ oil mallee. 450 ¼ pyrolysis temperature of 450
C, 550 ¼ pyrolysis temperature of 550
C.

Please cite this article in press as: Farrell, M., et al., Biochar differentially affects the cycling and partitioning of low molecular weight carbon in contrasting soils, Soil Biology & Biochemistry (2014), http://dx.doi.org/10.1016/j.soilbio.2014.09.018

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woody biochars and those produced at temperatures in excess of 500
C (Sohi et al., 2010; McBeath et al., 2011, 2014). The two OM biochars also had the highest C:N:P ratios and the lowest soluble salts (measured as EC), indicating C-rich and nutrient poor biochars. 4.2. Microbial metabolism of LMWOCs

Fig. 4. nMDS distributions of a) t1/2 and b) Yc of the five 14C-labelled substrates, as affected by soil and biochar type. : ¼ aridic arenosol, - ¼ pellic vertisol,  ¼ rhodic ferralsol; biochar treatments are defined by shading. WS ¼ wheat straw, PM ¼ poultry manure, OM ¼ oil mallee. 450 ¼ pyrolysis temperature of 450
C, 550 ¼ pyrolysis temperature of 550
C. Two-dimensional stress for t1/2 ¼ 0.075; Yc ¼ 0.095.

differences in chemistry, notably in their C:N ratios, microbial biomass, and available P (Table 1). NMR analysis revealed that all three soils differed in terms of their organic C chemistry (Figs. 1 and 2). In that regard, it is surprising that the pellic vertisol and the rhodic ferralsol are not more similar with regard to their organic C chemistry, given their relative geographical and climatic proximity, and the fact that both soil types bind and protect C from degradation, albeit by different mechanisms (Leinweber et al., 1999; Conçalves et al., 2003). The main chemical difference between the organic C in the pellic vertisol compared to the other two soils in this study was the relative increase in Aryl-C and decrease in OAlkyl-C, indicating a greater proportion of the SOC was in a charlike form (Schmidt and Noack, 2000; Baldock and Smernik, 2002; Lehmann et al., 2008). A significant proportion of the SOC was expected to be char-like, given the widespread presence of charcoal in Australian soils (Lehmann et al., 2008). Biochars from all three feedstocks were extensively impacted by pyrolysis temperature, with an increase in aryl- and O-aryl- C relative to all other C moieties at the 550
C pyrolysis temperature relative to the 450
C treatment. The change was greatest in the PM biochar, with a six-fold reduction in alkyl-C in PM550 compared to PM450. Large differences in biochar C chemistry have been observed both between feedstocks and also in the same feedstock at different pyrolysis temperatures, with aromaticity increasing in

Microbial turnover and partitioning of the suite of LMWOCs used in this study was significantly affected by both soil type and the addition of biochars (Fig. 4, Table 3). All five compounds (carbohydrates: glucose, sucrose and fructose, amino acid: L-phenylalanine, organic acid: oxalate) are relevant to the soil and rhizosphere (van Hees et al., 2005; Jones et al., 2009). Our use of the 14 C label avoids confounding results through both priming (Kuzyakov et al., 2000) and concentration-specific responses (Hill et al., 2012), allowing insight into specific biochemical processes. The strong effect of soil type on microbial catabolism and CUE of the labelled substrates (Fig. 4) was expected, given the dissimilarity in chemical and physical properties between the soils (Table 1). Within each soil type, biochar also exerted significant effect (P < 0.001) on both turnover rate and CUE (with the exception of CUE of phenylalanine), with half-life being the more discriminant metric across all five LMWOCs. Organic acids in particular are known to sorb to soils (Jones, 1998), and this possibly explains the overall longer half-life of oxalate in the rhodic ferralsol, which is a soil type with a high sorption capacity (Conçalves et al., 2003). Biochar contains labile C components, and its application can affect the cycling of soil C through a range of biological, chemical and physical mechanisms, and that these effects and mechanisms are affected by both soil and biochar type (Anderson et al., 2011; Ameloot et al., 2013; Farrell et al., 2013a). With the exception of oxalate, the effect of biochar was greatest in the aridic arenosol (Fig. 5), with an intermediate response to biochar observed in the pellic vertisol. The lowest response to biochar treatment was observed in the rhodic ferralsol which, with the exception of oxalate, also resulted in the shortest t1/2 of the LMWOCs. Feoxalate binding, which would render the oxalate less accessible to microorganisms is most likely responsible for the decrease in turnover rate of this compound. There have been several recent studies investigating the effects of biochar application on the mineralisation of LMWOCs, with differing results presented. Dempster et al. (2012) investigated the effect of a hardwood biochar on a nutrient-rich eutric cambisol from the UK, and the same wheat straw biochar (pyrolysed at 450
C) used in the present study on a similar aridic arenosol, and found minimal biochar effect on the mineralisation of amino acids and a peptide. Conversely, when specifically investigating the soil surrounding biochar particles (dubbed the charsophere), Quilliam et al. (2013) observed reduced mineralisation rates of glucose at the biochar surface, and substantially reduced CUE relative to their control soil treatment. Though glucose had the shortest half-life of the five LMWOCs used here, sucrose, oxalate and L-phenylalanine turnover rates were affected by biochar application to a greater extent (Fig. 5). This was especially apparent in the aridic arenosol for sucrose and Lphenylalanine, notably with the OM550 biochar. We tentatively ascribe this to the intrinsically low chemical, biological and physical fertility of this biocharesoil combination causing microbial immobilisation of the substrate, noting the very coarse sandy structure of this soil. Additionally in the case of phenylalanine in the aridic arenosol, turnover time increased systematically with pyrolysis temperature for all three biochar feedstocks used in this study. With the exception of oxalate, the OM550 biochar treatment

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Fig. 5. Relationship between turnover (expressed as t1/2) and carbon utilisation efficiency (CUE) for a) glucose, b) fructose, c) sucrose, d) oxalate, and e) phenylalanine as affected by soil type and biochar addition. : ¼ aridic arenosol, - ¼ pellic vertisol,  ¼ rhodic ferralsol; biochar treatments are defined by shading. WS ¼ wheat straw, PM ¼ poultry manure, OM ¼ oil mallee. 450 ¼ pyrolysis temperature of 450
C, 550 ¼ pyrolysis temperature of 550
C.

consistently tended towards the slowest turnover of the 14Clabelled substrates. It is known that some LMWOCs can trigger a response in the microbial community at much lower concentrations than others, notably fructose (Jaeger et al., 1999), which showed a lower discriminatory power in terms of turnover. There appeared to be a common effect of biochar addition across the three soils on the relationship between turnover and CUE of fructose (Fig. 5), with CUE increasing as rate of catabolism decreases. This is in contradiction to Quilliam et al. (2013), and indicates a shift towards anabolic utilisation of fructose-C with the addition of biochar, though a possible cause for this observation is unknown.

4.3. A conceptual model All LMWOCs in this study are associated with root exudates (Jones, 1998; van Hees et al., 2005) and the half-lives indicate rapid turnover of all substrates by the broad soil microbial community. However, the results from this work clearly demonstrate variation in the metabolism of LMWOCs as affected by soil and biochar type. Using data from the present study, and from the literature, the figure provided in the online Graphical abstract synthesises this Q3 information. It demonstrates the interactions between biochar and soil, and the effects of different biochar and soil parameters on the turnover rate of LMWOCs by the soil microbial community. There

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Q6

Table 3 Outputs of the univariate ANOVA (GLM) for individual14C-labelled substrates, and the permutational multivariate ANOVA for the data when considered in multivariate analysis to show overall effects of soil and biochar on LMWOC turnover. Univariate ANOVA

Half-life F

Glucose Soil Biochar Soil  Biochar Fructose Soil Biochar Soil  Biochar Sucrose Soil Biochar Soil  Biochar Oxalate Soil Biochar Soil  Biochar Phenylalanine Soil Biochar Soil  Biochar PERMANOVA Soil Biochar Soil  Biochar

CUE P

F

P

34.7 5.42 0.944

<0.001 <0.001 0.512

380 7.97 2.67

<0.001 <0.001 0.007

47 12.5 2.76

<0.001 <0.001 0.005

87.7 30.9 2.15

<0.001 <0.001 0.028

29.4 14.3 8.01

<0.001 <0.001 <0.001

278 6.71 2.65

<0.001 <0.001 0.007

63.2 12.8 2.85

<0.001 <0.001 <0.001

84.5 6.63 1.96

<0.001 <0.001 0.047

<0.001 <0.001 <0.001

639 0.758 0.391

<0.001 0.606 0.961

656 23.9 10.9 Pseudo-F

P

Pseudo-F

P

108 12.5 4.74

<0.001 <0.001 <0.001

189 6.71 1.74

<0.001 <0.001 0.441

are compound-specific exceptions to this model, such as the oxalate/ferralsol combination highlighted earlier. However, generally: the higher the pyrolysis temperature and aromaticity of the biochar, the lower the labile C and nutrient availability, and the lower the microbial biomass size and activity, the result will be a lower rate of turnover of LMWOCs. 4.4. Conclusions We conclude that biochars and soils interact to manifest nonsystematic differences in turnover rates of LMWDOCs, and thus a variety of mechanisms are likely responsible for this observation. Plant roots have been observed to grow preferentially around biochar particles (Prendergast-Miller et al., 2014), and thus rhizosphere may preferentially form close to biochar particles. As LMWOCs are concentrated in the rhizosphere and can contribute a significant portion of photosynthetically-fixed C (Lynch and Whipps, 1990), it is apparent that biochar may significantly affect the flow of this C in soils. Uncited reference

Q7

Dijkstra et al., 2011; Hamer et al., 2004; Keiblinger et al., 2010; Tucker et al., 2013. Acknowledgements

Q4

This work was facilitated by the CSIRO Land & Water capability development fund. Mr Thomas Carter and Mrs Janine McGowan of CSIRO are thanked for technical support, and Prof Daniel Murphy and Dr Lukas van Zwieten are acknowledged for the provision of fresh soil samples. References Ameloot, N., Graber, E.R., Verheijen, F.G.A., De Neve, S., 2013. Interactions between biochar stability and soil organisms: review and research needs. European Journal of Soil Science 64, 379e390.

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