Soil carbon and nitrogen pools, their depth distribution and stocks following plantation establishment in south east Queensland, Australia

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Soil carbon and nitrogen pools, their depth distribution and stocks following plantation establishment in south east Queensland, Australia J. Bernhard Wehra, , Tom Lewisb,c, Ram C. Dalala, Neal W. Menziesa, Luke Verstratenc, Scott Swiftd, Philippa Bryantc, Neil Tindalec, Tim E. Smithb,c ⁎

a

The University of Queensland, School of Agriculture and Food Sciences, St Lucia, Qld 4072, Australia Department of Agriculture and Fisheries, Queensland Government, University of Sunshine Coast, Sippy Downs, QLD 4556, Australia c University of Sunshine Coast, Sippy Downs, QLD 4556, Australia d Department of Agriculture and Fisheries, Queensland Government, Gympie, QLD 4570, Australia b

ARTICLE INFO

ABSTRACT

Keywords: Land use Pasture Reforestation Cropping Mid infrared spectroscopy

Changes in land use affect the soil organic carbon (SOC) stocks and pools. This is currently of great interest as soils are a major pool of carbon in the biosphere and can sequester atmospheric carbon. This study investigated the effect of pasture conversions into reforestation or cropping land on SOC, humic organic C (HOC), particulate organic C (POC) and resistant organic C (ROC) stocks, and soil nitrogen, to 0.5 m depth using mid infrared spectroscopy and combustion analyses. The ten study sites in south east Queensland, Australia were cleared of native vegetation to pasture decades earlier and were more recently (6–19 years ago) converted to either forest plantations (eucalypts) or cropping land. The SOC was significantly affected by land use and site location. Conversion of pasture to forest plantation had no significant effect on SOC compared to pasture up to 19 years since reforestation, but SOC increased slightly with plantation age at a rate of 3.03 t ha−1 per year. Conversion of pasture to cropping land significantly decreased SOC. The stocks of SOC ranged from 47.0 to 107.6 t ha−1. The HOC pool contributed the most towards the total SOC, and ranged from 17.7 to 67.8 t ha−1 across all sites. The POC pool ranged from 2.1 to 17.8 t ha−1 and the ROC pool varied between 5.3 and 22.3 t ha−1. The total SOC stock determined by MIR was closely related to SOC determined by dry combustion analysis. Soil N was significantly affected by land use and location, but trends were not consistent. On pasture sites, total N was higher likely due to fertiliser applications and total N ranged between 3.7 and 15.0 t ha−1 across sites. In summary, conversion of pasture to forest plantation had no significant effect on SOC stocks and different soil carbon pools over 19 years in the subtropical climate of south east Queensland.

1. Introduction Soil organic carbon (SOC) is the largest terrestrial carbon (C) pool in the biosphere (Lal, 2008) and may play a role in mitigating global atmospheric CO2 concentrations. The SOC plays multiple important roles in the environment (Murphy, 2014) such as maintaining soil aggregate stability. Consequently, SOC will affect the soil bulk density or porosity of the soil and its hydraulic properties (Murphy, 2014; Paul, 2016). Furthermore, the presence of positive and negative charges on SOC has beneficial effects on the cation and anion exchange capacity of soil and acts as a pH buffer and nutrient store (Murphy, 2014). The SOC is decomposed by soil microbes, supplying the soil microbes with energy

and releasing nutrients for plant use (Blume et al., 2016). Most SOC is derived from root biomass (Kogel-Knabner, 2017). As plants grow and fix atmospheric CO2 in the biomass, plant roots and root exudates deposit C into the soil (Blume et al., 2016; KogelKnabner, 2017). This form of C is referred to as particulate organic carbon (POC) and is relatively labile unless it becomes entrapped in soil aggregates and clay minerals (von Lutzow et al., 2007; Paul, 2016). Part of the POC will be degraded by microorganisms and converted to more stable humic organic carbon (HOC) (Blume et al., 2016). The turn-over of HOC is slower and some of the HOC can be converted to resistant organic carbon (ROC) which contains aromatic structures and some ROC can also be attributed to charcoal in the soil (Page et al., 2013).

Abbreviations: SOC, soil organic carbon; HOC, humic organic carbon; POC, particulate organic carbon; ROC, resistant organic carbon; TOC, total organic carbon; DBH, diameter at breast height; MIR, mid infrared spectroscopy; C, carbon; N, nitrogen; s.e.m., standard error of the mean; CWD, coarse woody debris ⁎ Corresponding author. E-mail address: [email protected] (J.B. Wehr). https://doi.org/10.1016/j.foreco.2019.117708 Received 13 June 2019; Received in revised form 30 August 2019; Accepted 22 October 2019 0378-1127/ © 2019 Elsevier B.V. All rights reserved.

Please cite this article as: J. Bernhard Wehr, et al., Forest Ecology and Management, https://doi.org/10.1016/j.foreco.2019.117708

Forest Ecology and Management xxx (xxxx) xxxx

2

151.8292 151.8291 151.4691 151.4693 152.3363 152.3347 151.1812 151.1815 151.6913 151.6911 152.5130 152.5136 151.8292 151.8283 151.7545 151.7553 151.7076 151.7087 151.7074 151.7089 152.6316 152.6315 4.96 2.4 240 8

7.97 3.41 146 224

9.21 302

9.75 200

2.71 662

6.54 188

8.18 504

16.61 387

246

11.13

−26.4766 −26.4757 −25.4904 −25.4919 −27.5454 −27.5429 −24.1480 −24.1471 −26.5968 −26.5976 −27.1416 −27.1411 −26.5021 −26.5026 −26.6618 −26.6600 −26.6201 −26.6220 −26.6219 −26.6203 −28.1112 −28.1120 12.25 210

12.4 0 12.3 0 19 0 9.4 0 7.6 0 6 0 13.5 0 10.2 0 12.2 5.9 0 0 13.2 0 9

8

7

6

5

4

3

2

5.5–7.5 5.5–7.5 LC-MHC LC-MHC mid 0.57 1563 892 18.9 (11.9–26.0) Dermosol (Mollisol) 10

5.5 CL mid 0.46 1669 765 17.8 (11.0–24.7)

mid 0.46 1669 765 17.8 (11.0–24.7)

lower 0.53 1566 828 17.3 (10.8–23.9)

19.2 (13.1–25.3)

954

1561

0.61

mid

CL-LC CL-LC SL-MC SL-LC CL-LC CL-LC LC-MC mid 0.46 1669 764 17.8 (11.0–24.7)

lower 0.50 1834 916 21.9 (16.3–27.6)

lower 0.45 1745 781 19.8 (12.9–26.7)

mid 0.42 1818 764 20.1 (13.1–27.1)

5.5–5.0 5.0 4.5–7.0 6.0–7.0 6.0–6.5 5.5–6.5 6.5

Plantation (SPG) Pasture Plantation (SPG) Pasture Plantation (mixed Eucs) Cultivation Plantation (mixed Eucs) Pasture Plantation (SPG) Pasture Plantation (mixed Eucs) Pasture Plantation (SPG) Pasture Plantation (SPG) Pasture Plantation (SPG) Plantation (SPG) Cultivation Pasture Plantation (SPG) Pasture 6.5–5.5 6.5–5.5 6.0–6.5 6.0–6.5 7.0–7.5 7.0–8.0 6.5–7.0 CL-LC CL-LC CL-LC CL-LC MC-MHC MC-MHC SCL-LC upper 0.53 1566 828

Ferrosol (Oxisol) Ferrosol (Oxisol) Vertosol (Vertisol) Chromosol (Alfisol) Ferrosol (Oxisol) Chromosol (Alfisol) Ferrosol (Oxisol) Ferrosol (Oxisol) Ferrosol (Oxisol)

17.3 (10.8–23.9)

Longitude Latitude Tree BA (m2/ha) Tree density (St/ha) Age (years) Vegetation pH texture Slope position Wet index Evap (mm)

1

Ten paired sites with contrasting land uses were selected in south east Queensland, Australia (Table 1). Site selection was based on the same soil type and position in the landscape. Cores to least 1 m depth were inspected in each land use to ensure the soils were comparable at a given site. Sites were cleared of native vegetation (woodlands) prior to 1959 and used for cropping or pasture. Land use changes occurred between 6 and 19 years ago from pasture to either forest plantings or cropping. Reforestation plantings ranged in age from 6 to 19 years (Table 1) and consisted of spotted gum (Corymbia citriodora ssp. variegata) ((F. Muell.) A.R. Bean & M.W. McDonald) or of mixed hardwood Eucalyptus species including spotted gum. The seven spotted gum plantation sites were established as commercial plantings and received similar management. They were planted using tubestock at a density of around 833–1000 stems per hectare. The planting rows are ripped to a depth of 60–70 cm prior to planting. Fertiliser was added at planting (Phosphorus at around 15 kg/ha, and trace elements Boron and Zinc

Rain (mm)

2.1. Site details

Mean temp (°C) (min–max)

2. Methods

Soil type

The conversion of C from POC to HOC to ROC is dependent on climatic and biological conditions in soil and is important to our understanding of the capacity of soil to sequester C (Paul, 2016). The extent of C sequestration in soil is a balance between C inputs and C outputs. The C inputs in soil are increased with annual rainfall and decrease with mean annual temperatures (Webb, 2002; Wynn et al., 2006). Carbon sequestration is increased with soil clay content, and decreased with sand content (Paul, 2016; Luo et al., 2017; Singh et al., 2017; Xu et al., 2018). These factors directly control plant growth and stability of carbon sequestered in soil. Increasing atmospheric CO2 may lead to greater plant growth and sequestration of C in the roots and thus soil, but the downside is that land use changes can increase CO2 emissions from soil by degradation of SOC (Dalal et al., 2005; LopezDiaz et al., 2017; Oso and Rao, 2017). Removal of plants from soil (as in cropping systems) and changing from a perennial vegetation cover to a seasonal vegetation cover will decrease the ability of the soil to sequester C due to decreased C inputs (Post and Kwon, 2000; Tang et al., 2018). In addition, soil disturbance by tillage is known to expose SOC to microbial degradation, and thus increase CO2 emissions from soil and loss of SOC (Dalal et al., 2005). Changing from native vegetation to either perennial pasture or forest plantation can have variable effects on SOC. In some cases, perennial pastures have been found to increase SOC over native woody vegetation (Wilson et al., 2008; Wilson et al., 2011), in other cases, SOC decreased (Bolstad and Vose, 2005; Dalal et al., 2005; Nath et al., 2018). Similar effects were observed when changing vegetation from native to planted forests. The direction and magnitude of changes in SOC are often determined by the standing biomass, recalcitrance of the SOC contributed by the vegetation, climate effects (temperature and rainfall) and soil mineralogy (Orgill et al., 2014; Blume et al., 2016; Kogel-Knabner, 2017;Lu et al., 2017). Therefore, to better understand changes in SOC it is necessary to generate data on a local-scale, taking into account local vegetation types, land uses and local climate and soil effects. The baseline data on SOC pools in Australia are being progressively elucidated through ongoing research to help support C policy development in Australia. Due to the large spatial variability of climate and soil types in Australia, determination of local SOC stocks and pools are required and this information can also contribute to a better understanding of global SOC stocks. The aim of this study is to quantify total stocks of C and nitrogen (N) in various soils, and determine the effect of land use changes, in particular reforestation, on the total stocks and on pools of C. The hardwood plantation industry in subtropical Queensland is relatively young, with establishment essentially beginning in the late 1990s, so there is currently a poor understanding of the influence of this reforestation on soil and ecosystem carbon stocks.

Site

Table 1 Description of study sites used in this study. Land uses were either improved pastures, forest plantations with either spotted gum (SPG) or mixed eucalypt hardwoods, or regular cultivation. Soil types are classified according to the Australian Soil Classification or the USDA classification (names in brackets). BA, basal area; tree density is expressed as stems per hectare.

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added, if necessary). Sites had been non-commercially thinned (all debris retained on-site) to 150–300 stems per hectare stems per hectare 3–5 years after planting. The three mixed hardwood plantings were non-commercial plantings and no thinning took place in these plantings. Tree densities and basal areas at the time of sampling varied among sites depending on the type of planting and plantation age (Table 1). Livestock grazing took place in the understorey of the plantings in most cases after the plantation was established (all sites except sites 3 and 6). Soil types at the sites (Table 1) were classified using the Australian Soil Classification (Isbell, 2016). At eight sites, the agricultural land use consisted of pasture, at one site it was cropping and at one site it was a three-way comparison between reforestation, cropping and pasture. The pastures were dominated by perennial grass species, including African lovegrass (Eragrostis curvula), blue grass (Bothriochloa sp), Rhodes grass (Chloris gayana), wire grass (Aristidia sp.), red Natal grass (Melinia repens), blady grass (Imperata cylindrica), thatch grass (Hyparrhenia rufa) and paspalum (Paspalum sp.). Slopes of sites were ≤5° for all sites except site 10, which had a maximum slope of approximately 10°. Drainage varied among sites depending on the soil types and textures; being poor at the Vertosol site (site 3), imperfect in some cases (e.g. Chromosols) through to well drained in mid-upper slope position Ferrosols (National Committee on Soil and Terrain, 2009). Following clearing of the original vegetation (from the early 1900s), most sites would have been cropped (crops such as maize, sorghum, millet, beans, peanuts and cereal crops), until conversion to pasture. Mean annual rainfall across the study sites varied from 765 mm to 954 mm (Table 1), with most rainfall occurring during summer months (Nov–Mar), whereas evapotranspiration was almost double the rainfall, leading to wetness indices for the sites of around 0.5. Mean minimum temperature ranged from 10.8 °C to 16.3 °C while mean maximum temperature ranged from 23.9 °C to 27.6 °C. Climatic data for the study area were based on spatially interpolated Bureau of Meteorology observational data from 1889 to 2013 (Jeffrey et al., 2001).

For each plot, the diameter at breast height (DBH) of trees ≥0.10 m diameter was measured. The DBH was used to provide an estimation of the above- and below-ground biomass. Above- and below-ground biomass was estimated using general allometric relationships (Paul et al., 2016; Paul et al., 2019). These allometrics were developed based on existing biomass datasets in Australia for single-stemmed eucalypt individuals: Above-ground biomass (kg) = exp [−2.016 + 2.375 ln (DBH)] × 1.067 (based on n = 6004 trees, DBH range = 0.05–1.69 m) Below-ground biomass (kg) = exp [−2.682 + 2.212 ln (DBH)] × 1.096 (based on n = 810 trees, DBH range = 1.1–139 cm) Tree biomass was converted to C using a C concentration of 49%. No assessment of biomass was made for grasses in the pasture, but we assumed C stocks in above-ground biomass as 1.1 t ha−1 for grassdominated tropical pastures (Yonekura et al., 2010). 2.4. Soil sampling Hollow cylindrical steel sampling tubes (0.70 × 0.05 m) with a cutting head 0.042 m in diameter were used to collect soil to a depth of 0.5 m. Soil volume and bulk densities were calculated on four separate soil cores collected from each sampling site and land use. Sampling tubes were driven into the ground with an electrical jackhammer and retrieved with a manual lifter. While it would be preferable to measure SOC to greater depth, practical limitations at these sites (depth to parent material) limited sampling to 0.5 m. Furthermore, this study was part of an Australia-wide investigation of soil C stocks under the National Soil Carbon Program, and the decision was made to standardise sampling depth to 0.3 m (IPCC, 2006) and 0.5 m. Soil cores were pushed out of the sampling tubes and divided into the five sampling depths of 0–0.05, 0.05–0.1, 0.1–0.2, 0.2–0.3 and 0.3–0.5 m. Soil samples collected within each of the eight 10 × 10 m sub-plots were bulked together for each depth. Therefore, there were eight bulked soil samples for each depth for each plot. Once collected, soils were kept in a cool dark location until the samples were air dried, processed and sent to the laboratory. Soil core mass and bulk density samples were dried at 105 °C to constant weight for calculation of bulk density.

2.2. Plot layout Selection of paired sites was based on the procedure outlined by Lewis et al. (2019). Briefly, paired-comparison sites were chosen based on sufficient area of the target vegetation being on the same soil type, with the same slope position. Plots were 0.3–0.5 ha in both the agricultural land and reforestation, and were separated by < 200 m at each site. Each plot was divided into 30–50 sub-plots of 10 × 10 m and eight sub-plots were randomly selected for sampling (stratified simple random sampling). Plots and sub-plots were established using tape measures, optical squares and sighting posts to ensure right-angles. Each sub-plot contained one hundred 1 × 1 m squares, of which 10 were randomly selected for sampling. Each selected sub-plot and square was marked with line-marking paint to delineate the sampling positions. The positions of sub-plots and sampling squares were referenced from the plot corner positions to determine their coordinates and to allow future sampling within the same locations.

2.5. Soil processing and analysis All soil samples were processed as outlined by Lewis et al. (2019). Briefly, samples were weighed after air drying and sieved to 2 mm. The soil bulk density was calculated for each soil sample using the ovendried weight of the < 2 mm size fraction and the volume of the soil coring tube at the individual samples depth interval. The soil sample was also inspected for the presence of carbonates using a fizz test (with a few drops of HCl placed on the sample), but only one site had carbonates in some horizons, requiring pre-treatment with H2SO3 to remove carbonates using the method of Baldock et al (2013). Sub-samples of the < 2 mm fraction were separated from the whole sample by passing repeatedly through a riffle box sample splitter. Total C and N concentrations were then determined by dry-combustion with a LECO CNS-2000 analyser (LECO Corporation, MI, USA). Soil organic C fractions for particulate (POC), humus (HOC), resistant (ROC) and total (TOC) fractions were estimated using mid-infrared spectroscopy (MIR) following the methods of Baldock et al. (2013). Visible charcoal was removed during sieving and was determined to be less than 0.04% of soil dry weight and was therefore excluded from further calculation of soil C stocks. SOC stocks for each depth interval were calculated using: SOC (t ha−1) = %C × ρ × V × (1 − f) where %C is the C concentration (g 100 g−1); ρ the soil bulk density (g m−3); and V the volume (m3) of soil per hectare (depth in m × 104 m2) in the samples depth interval, after the volume fraction (f) of the organic material, charcoal and rocks have been subtracted.

2.3. Litter sampling and above-ground tree carbon estimates As outlined by Lewis et al (2019), a steel quadrat (0.5 × 0.5 m square) was placed in the centre of each 1 × 1 m sample square, and all dead and detached vegetation (litter) was collected down to the soil surface, being careful to exclude mineral soil. Litter material collected in paper bags, air-dried and transported to the lab, where it was ovendried at 65 °C and weighed. Litter C concentration was determined by dry-combustion with a LECO CNS-2000 analyser (LECO Corporation, MI, USA). Material ≥100 mm was defined as coarse woody debris (CWD) and was assessed using the line-intersect method (van Wagner, 1968), as outlined by Lewis et al. (2019). 3

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2.6. Data analysis

significant effect on SOC stocks when measured to 0.3 m depth at six sites. At one site, SOC stock was 9% higher under forest plantation than under pasture and at two sites, SOC stock was higher under pasture. At the two sites with a cropping-plantation contrast, cropping resulted in significant lower SOC stocks to 0.3 m depth. The SOC stocks to 0.5 m depth were approximately 33% higher than when measured to 0.3 m depth. Highest SOC stocks to 0.5 m depth were recorded at sites 3 and 8 (107.6 t ha−1 in the 19 year old reforestation at site 3 and 100.6 and 101.7 t ha-1 in the pasture and ten year old reforestation at site 8), and lowest stocks at site 4 (plantation 47.0 t ha−1 and pasture 51.8 t ha−1) (Table 2). When expressing SOC stocks to 0.5 m depth, stocks were not significantly different between pasture and reforestation land use at eight sites; at one site, soil C was higher under pasture than under forest and at two sites, soil C was lower under cropping (Table 2). Nevertheless, there was a weak positive correlation between total SOC to 0.5 m depth and plantation tree age (total SOC (t ha−1) = 3.03 × age (y) + 44.96 ; r2 = 0.378, P = 0.0444).

Data were analysed with SAS (version 7.1) by Analysis of Covariance (ANCOVA) using log-transformed depths as covariate to determine the effect of land use on soil C and N concentrations and C pools at the ten sites. The ANCOVA determines if slopes (i.e. change in concentration with depths) differ significantly between land use. Concentrations of total C and N, and C pools were expressed on an equivalent soil mass basis (Ellert and Bettany, 1995), to compensate for possible changes in soil bulk density between land use (e.g. caused by soil compaction due to cattle grazing, tillage, etc.). Equivalent soil mass for each depth interval was calculated using the cubic spline method (Wendt and Hauser, 2013). In addition to analysing soil variables at individual depths, variables were also analysed for the total 0–0.3 m (IPCC, 2006) and 0–0.5 m depths. A two-way ANOVA was used to determine significant land-use × site effects. Differences in response variables were considered significant at P < 0.05 using Tukey’s HSD test. Response variables (total soil C, N, and C pools) were log10 + 1 transformed to compensate for possible deviations in the normal distribution of the values and to meet the requirement of homoscedasticity prior to ANOVA.

3.2. Change in SOC pools with land use The HOC, POC, ROC and TOC pools were determined by MIR for nine sites only (they were not measured at site 10). The TOC was dominated by the HOC pool (contributing between 39% and 89% to the TOC), whereas the ROC and POC pools were small across all sites (Table 3). These C pools showed a significant interaction between land use and location (P = 0.0001). The HOC pool to 0.3 m depth was smallest at site 4 (14.2 t ha−1 in plantation and 15.4 t ha−1 in pasture) and greatest at site 8 (47.6 t ha−1 in plantation and 48.4 t ha−1 in pasture) (Table 3). The pool of HOC to 0.3 m depth was not affected by land use at seven of the nine sites; at site 9, HOC was smallest under cultivation and at site 7, HOC was smaller under forest plantation than under pasture. While HOC pools were greater when measured to 0.5 m depth, the effect of land use was the same as for the 0.3 m depth. The POC pool to 0.3 m depth was smallest at site 9 (1.7 t ha−1 under

3. Results 3.1. Changes in soil organic C stocks with land use Total SOC stocks showed a statistically significant interaction between land use and location (P = 0.0059). Highest C stocks under forest plantation were at sites 3 and 8 (73.8 t ha−1 and 74.8 t ha−1) (Table 2). Highest SOC stocks to 0.3 m depths under pasture were observed at sites 2 and 7 (both 74.4 t ha−1). Lowest C stocks under pasture were recorded at sites 4 and 6 (36.0 and 37.0 t ha−1) and lowest stocks under reforestation were measured at sites 4 and 6 (34.1 and 35.6 t ha−1). Conversion of pasture to forest plantation did not have a

Table 2 Stocks of soil organic carbon and nitrogen (in t ha−1) and the C/N ratios to 0.3 or 0.5 m depths for the 10 sites. The soil organic carbon and total nitrogen were determined by dry combustion analysis. Means ± s.e.m. followed by the same letter per site and depth at a site are not significantly different (Tukey’s HSD, P = 0.05). Site

Vegetation

Soil C (t ha−1)

Soil N (t ha−1)

C/N ratio

0–0.3 m

0–0.5 m

0–0.3 m

0–0.5 m

0–0.3 m

0–0.5 m

1

Plantation Pasture

51.5 ± 1.3 a 47.3 ± 1.3 b

67.0 ± 2.0 a 62.1 ± 2.0 a

5.8 ± 0.1 a 5.5 ± 0.1 a

7.6 ± 0.2 a 7.3 ± 0.2 a

8.9 a 8.6 b

8.8 a 8.5 a

2

Plantation Pasture

69.2 ± 3.2 a 74.4 ± 3.2 a

86.0 ± 3.9 a 91.1 ± 3.9 a

4.9 ± 0.1 a 5.2 ± 0.1 a

6.5 ± 0.2 a 6.9 ± 0.2 a

14.1 a 14.3 a

13.2 a 13.2 a

3

Plantation Cultivation

73.8 ± 1.1 a 51.4 ± 1.1 b

107.6 ± 1.7 a 74.8 ± 1.7 b

5.5 ± 0.1 a 4.2 ± 0.1 b

8.0 ± 0.2 a 6.4 ± 0.2 b

13.4 a 12.2 b

13.5 a 11.7 b

4

Plantation Pasture

34.1 ± 2.1 a 36.0 ± 2.1 a

47.0 ± 5.7 a 51.8 ± 5.7 a

4.6 ± 0.8 a 5.8 ± 0.8 a

6.3 ± 1.2 a 8.5 ± 1.2 a

7.4 a 6.2 a

7.5 a 6.1 a

5

Plantation Pasture

60.5 ± 2.2 a 54.2 ± 2.2 a

83.9 ± 2.9 a 76.3 ± 2.9 a

5.4 ± 0.2 a 4.8 ± 0.2 a

7.4 ± 0.3 a 6.6 ± 0.3 a

11.2 a 11.3 a

11.3 a 11.6 a

6

Plantation Pasture

35.6 ± 1.0 a 37.0 ± 1.0 a

49.6 ± 1.8 a 52.0 ± 1.8 a

2.8 ± 0.7 b 5.5 ± 0.7 a

3.7 ± 1.0 b 7.6 ± 1.0 a

12.7 a 6.7 b

13.4 a 6.8 b

7

Plantation Pasture

62.9 ± 2.0 b 74.4 ± 2.0 a

84.9 ± 3.5 b 98.7 ± 3.5 a

6.8 ± 0.2 b 8.1 ± 0.2 a

9.1 ± 0.4 b 10.8 ± 0.4 a

9.3 a 9.2 a

9.3 a 9.1 a

8

Plantation Pasture

74.8 ± 2.8 a 70.5 ± 2.8 a

101.7 ± 3.1 a 100.6 ± 3.1 a

5.9 ± 0.2 a 5.8 ± 0.2 a

7.7 ± 0.2 a 8.1 ± 0.2 a

12.7 a 12.2 b

13.2 a 12.4 b

9

Plantation young Plantation old Pasture Cultivation

50.8 54.8 57.3 44.1

73.0 77.2 79.4 66.0

5.0 5.3 5.6 4.9

7.0 7.4 7.6 7.4

10.2 a 10.3 a 10.2 a 9.0 b

10.4 a 10.4 a 10.4 a 8.9 b

10

Plantation Pasture

67.5 ± 3.1 a 68.2 ± 3.1 a

5.7 a 5.9 a

5.7 a 5.9 a

± ± ± ±

1.4 1.4 1.4 1.4

b ab a b

± ± ± ±

2.0 2.0 2.0 2.0

ab a a b

85.4 ± 3.7 a 85.6 ± 3.7 a

± ± ± ±

0.1 0.1 0.1 0.1

ab ab a b

11.8 ± 3.0 a 11.6 ± 3.0 a

4

± ± ± ±

0.2 0.2 0.2 0.2

a a a a

15.0 ± 3.7 a 14.4 ± 3.7 a

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Table 3 Stocks (in t ha−1) of humic (HOC), particular (POC), resistant (ROC) and total (TOC) organic C pools to either 0.3 or 0.5 m depth. Means ± s.e.m. followed by the same letter per site and depth are not significantly different (Tukey’s HSD, P = 0.05). Site

Vegetation

HOC t ha−1

POC t ha−1

ROC t ha−1

TOC t ha−1

0–0.3 m

0–0.5 m

0–0.3 m

0–0.5 m

0–0.3 m

0–0.5 m

0–0.3 m

0–0.5 m

1

Plantation Pasture

33.6 ± 0.9 a 31.4 ± 0.9 a

45.9 ± 1.4 a 42.4 ± 1.4 a

6.5 ± 0.3 a 6.3 ± 0.3 a

6.9 ± 0.3 a 6.8 ± 0.3 a

5.5 ± 0.2 a 5.1 ± 0.2 a

6.0 ± 0.3 a 5.7 ± 0.3 a

47.9 ± 1.3 a 45.2 ± 1.3 a

60.1 ± 2.0 a 56.9 ± 2.0 a

2

Plantation Pasture

34.5 ± 1.3 a 37.9 ± 1.3 a

45.5 ± 1.6 a 49.3 ± 1.6 a

6.2 ± 0.3 a 6.9 ± 0.3 a

6.9 ± 0.4 a 7.9 ± 0.4 a

9.7 ± 0.7 a 10.6 ± 0.7 a

10.3 ± 0.8 a 11.3 ± 0.8 a

60.7 ± 2.8 a 66.8 ± 2.8 a

73.5 ± 3.5 a 80.5 ± 3.5 a

3

Plantation Cultivation

41.3 ± 1.2 a 39.1 ± 1.2 a

61.5 ± 2.0 a 58.2 ± 2.0 a

13.4 ± 0.4 a 8.1 ± 0.4 b

17.8 ± 0.4 a 11.3 ± 0.4 b

15.2 ± 0.4 a 12.1 ± 0.4 b

22.3 ± 0.6 a 16.6 ± 0.6 b

76.3 ± 1.8 a 64.0 ± 1.8 b

112.6 ± 2.5 a 92.6 ± 2.5 b

4

Plantation Pasture

14.2 ± 0.7 a 15.4 ± 0.7 a

17.7 ± 1.1 b 22.3 ± 1.1 a

8.1 ± 0.3 a 8.7 ± 0.3 a

11.1 ± 0.4 a 10.5 ± 0.4 a

8.1 ± 0.3 a 8.3 ± 0.3 a

8.7 ± 0.4 a 9.0 ± 0.4 a

8.1 ± 0.4 a 8.3 ± 0.4 a

45.6 ± 1.2 b 50.1 ± 1.2 a

5

Plantation Pasture

41.2 ± 1.6 a 37.5 ± 1.6 a

57.4 ± 2.0 a 53.5 ± 2.0 a

11.3 ± 0.9 a 8.2 ± 0.9 b

12.4 ± 1.2 a 8.4 ± 1.2 b

8.9 ± 0.4 a 7.7 ± 0.4 a

11.7 ± 0.7 a 9.8 ± 0.7 a

71.4 ± 2.8 a 62.8 ± 2.8 b

94.7 ± 4.0 a 83.4 ± 4.0 a

6

Plantation Pasture

20.0 ± 0.8 a 20.2 ± 0.8 a

30.6 ± 1.6 a 29.8 ± 1.6 a

9.1 ± 0.5 a 10.1 ± 0.5 a

13.1 ± 1.3 a 12.0 ± 1.3 a

9.5 ± 0.4 a 9.4 ± 0.4 a

13.2 ± 1.1 a 11.4 ± 1.1 a

41.9 ± 1.4 a 43.1 ± 1.4 a

61.9 ± 3.5 a 57.5 ± 3.5 a

7

Plantation Pasture

42.1 ± 1.0 b 49.5 ± 1.0 a

60.1 ± 2.1 b 68.9 ± 2.1 a

3.4 ± 0.5 b 5.7 ± 0.5 a

3.9 ± 0.5 b 6.2 ± 0.5 a

5.4 ± 0.4 b 7.3 ± 0.4 a

6.1 ± 0.6 b 8.2 ± 0.6 a

52.1 ± 2.2 b 64.5 ± 2.2 a

67.2 ± 3.9 b 80.8 ± 3.9 a

8

Plantation Pasture

48.4 ± 1.8 a 47.6 ± 1.8 a

64.8 ± 2.0 a 67.8 ± 2.0 a

7.3 ± 0.6 b 10.1 ± 0.6 a

7.5 ± 0.6 b 10.3 ± 0.6 a

7.3 ± 0.2 a 7.5 ± 0.2 a

9.9 ± 0.2 a 9.7 ± 0.2 a

74.3 ± 3.0 a 74.9 ± 3.0 a

96.5 ± 3.1 a 99.3 ± 3.1 a

9

Plantation young Plantation old Pasture Cultivation

38.3 40.5 40.3 30.5

56.6 58.8 59.1 46.6

2.6 3.7 4.7 1.7

2.8 3.9 4.9 2.1

5.5 7.1 7.2 3.7

7.3 9.1 9.2 5.3

50.3 57.9 58.9 39.1

69.3 78.2 80.1 57.5

10

Plantation Pasture

No data No data

± ± ± ±

1.1 1.1 1.1 1.1

a a a b

± ± ± ±

No data No data

1.7 1.7 1.7 1.7

a a a b

± ± ± ±

0.3 b 0.3 a 0.3 a 03 b

No data No data

± ± ± ±

0.3 b 0.3 a 0.3 a 0.3c

No data No data

cultivation and 3.2 t ha−1 in the older plantation) and greatest at site 3 (8.1 t ha−1 in pasture and 13.4 t ha−1 in plantation) (Table 3). Land use had no effect on POC pools at four sites; at sites 3 and 5, POC was higher under forest plantation than under pasture, at sites 7, 8 and 9, POC was greater under pasture than under forest plantation. Extending the sampling depth to 0.5 m increased the POC levels but did not affect the land use effects. The ROC pools to 0.3 m depth ranged from 3.7 t ha−1 under cultivation at site 9 to 15.2 t ha−1 under forest plantation at site 3 (Table 3). There was no effect of land use on ROC pools at six sites; at site 3 and 9, ROC was greater under plantation or pasture than under cultivation, and at site 7, ROC was greater under pasture than plantation. The agreement between TOC determined by MIR and SOC by LECO was strong: TOC (t ha−1) = 0.8588 × SOC (t ha−1) + 0.382 (r2 = 0.8239) across all sites. While the TOC pool by MIRS is determined independently of the ROC, POC and HOC pools, the relationship between TOC and the sum of HOC, ROC and POC was good (TOC (t ha−1) = 1.121 × (HOC + POC + ROC (t ha−1)) − 0.0292, r2 = 0.946, P < 0.001).

± ± ± ±

0.2 b 0.2 a 0.2 a 0.2c

No data No data

± ± ± ±

0.4 b 0.4 a 0.4 a 0.4c

No data No data

± ± ± ±

1.9 b 1.9 a 1.9 a 1.9c

No data No data

± ± ± ±

2.9 2.9 2.9 2.9

a a a b

No data No data

forest plantation at three sites (sites 1, 3 and 5); greater under pasture at three sites (site 6, 8 and 9) and unaffected by land use at three sites (site 2, 4 and 7) (Fig. 1). In the subsoil (0.3–0.5 m), POC was greater under forest plantation at two sites (sites 3 and 4), and unaffected by land use at six sites. At site 9, POC in the subsoil was greater under cultivation than under pasture or plantation. The ROC content of the topsoil was not affected by land use at five sites (sites 2, 4, 5, 6, 7), was higher under forest plantation at two sites (sites 1 and 3) and higher under pasture at two sites (sites 8 and 9) (Fig. 1). Subsoil content of ROC was not affected by land use at seven sites (sites 1, 2, 4, 5, 6, 7, 9) and was higher under plantation at two sites (sites 3 and 8). 3.4. Litter and tree biomass C The surface litter C (which included collected litter < 100 mm in diameter and CWD ≥ 100 mm in diameter) at the forest plantation sites was greater than at pasture sites and least at cropped (cultivated) sites. Litter C stock ranged from 28.5 t ha−1 under forest plantation at site 1, to 0.1 t ha−1 under cropping at site 9 (Table 4). Total C in aboveground biomass at plantation sites ranged from 12.8 t ha−1 in the young plantation at site 9, to 81.7 t ha−1 at site 3. The above ground C in trees increased with tree age at a rate of 3.7 t ha−1 y−1; as the plantation trees had only reached an age of 19 years, the standing biomass increased linearly over this time (r2 = 0.622, P < 0.001). Likewise, below-ground biomass (i.e. roots) was estimated to range from 2.8 t ha−1 in the pasture containing trees at site 10, to 15.8 t ha−1 at site 3, and increased linearly at a rate of 0.75 t ha−1 y−1 (r2 = 0.572, P < 0.001).

3.3. Changes in soil C pools with depth In all plots, soil C pools decreased logarithmically with depth, but in cultivated plots, the mixing of soil in the 0–0.10 m depth layer changed the pattern in the topsoil (Fig. 1). There were site specific differences in the distribution of C pools. The HOC in the topsoil (0–0.05 m) was significantly greater under pasture at six sites (site 1, 4, 6, 7, 8 and 9); at two sites (site 2 and 5), HOC in the topsoil was not affected by land use (Fig. 1). Cropping land (site 3 and 9) had less HOC in the topsoil than pastures or forest plantations. In the subsoil (0.3–0.5 m), HOC content was unaffected by land use at eight sites, and was greater under pasture at one site only (site 4) (Fig. 1). The POC content in the 0–0.05 m topsoil layer was greater under

3.5. Changes in soil N stocks with land use and depth Soil N stocks to 0.3 or 0.5 m depth showed significant interaction between land use and location: soil N was higher under pasture than under reforestation at three sites, decreased under cultivation 5

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Fig. 1. Variation in soil organic carbon pools with depth and land use. One site (10) had no data on carbon pools. The pools are denoted by the following symbol shapes: HOC: ▲; POC: ▾; ROC: ●; TOC: ■. The symbol fill style represents land uses: black - pasture, white – forest, grey – cropping. For site 9, symbols with dot and dotted line represent the old forest, and symbols with cross and dashed line represent the young forest. Some symbols and lines are obscured due to overlaps.

compared to forest at one site and unaffected by land use at six sites (Table 2). Greatest soil N stocks were measured at site 10 (11.6 t ha−1 under pasture to 11.8 t ha−1 under plantation to 0.3 m depth) and lowest N stocks at site 6 (2.8 t ha−1 under plantation to 5.5 t ha−1 under pasture to 0.3 m depth (Table 2). The soil N stocks to 0.5 m were 24–52% greater when compared to 0–0.3 m depths. Topsoil N content (0–0.05 m) was significantly greater under pasture than under forest plantation at four sites (sites 6, 7, 8 and 9), but

greater under plantation than under pasture or cropping at sites 1 and 3 (Fig. 2). Differences in subsoil N (0.3–0.5 m) content were only observed at three sites. At site 6, subsoil N was greater under pasture than forest plantation; at site 9, the cultivated field had greater subsoil N than either the pasture or plantation, and at site 8, subsoil N was greater under forest plantation than pasture (Fig. 2). The C/N ratios differed between sites, depth and land use (Fig. 2) and C/N ratios ranged from 5.7 to 14.1 6

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plantation soils, showing an increase in SOC of ~0.001% per year to 30 cm (r2 = 0.3773, P = 0.0444). Land use changes may initially result in a decrease in SOC due to soil disturbance (Turner and Lambert, 2000; Zhang et al., 2012). For instance, Zhang et al (2018) have observed that SOC decreases in the first 17 y after reforestation, reaches the same SOC as native forests after 30 y, and after 37 y, SOC in reforested sites was greater than in native forests. In one of our earlier studies, we observed that the response of SOC to land use change is highly site specific and we found that conversion of native forest to exotic pine plantation either increased, decreased, or had no effect on SOC (Lewis et al., 2016). A study by Pulrolnik et al. (2009) established that TOC under 15–20 y old eucalypt plantations was the same as for pasture or native forest, indicating that a considerable time is required for SOC stocks to restore to pre-clearing levels. Since conversion of pasture to plantation often leads to a transient decrease in SOC during the first few years (Zhang et al., 2012; Lebenya et al., 2018), likely due to disturbance of the soil during site preparation and initially lower C input into the soil, it takes many years of C input by forest trees to return the SOC stock to predisturbance levels. In addition to age of trees, vegetation characteristics are known to affect SOC stocks. For instance, Zhang et al. (2016) suggested that evergreen broadleaf forests accumulate more SOC than conifer forests, whereas Diaz-Pines et al. (2011) found that pine stands have higher SOC stocks than oak stands and attributed this to the greater litter fall under pine and slower degradation of pine litter compared to oak litter. Since litter quality and temperature affect litter turn-over (Jiang and Xu, 2006), it is possible that, at our sites, turn-over of eucalypt litter is similar to that of grass litter, or that grass litter is produced in greater quantities than eucalypt leaf litter and eucalypt litter is slower to turn over. Eucalypt roots are mostly thicker than grass roots and do not turnover as quickly as grass roots (Gill and Jackson, 2000) and they contribute to SOC for longer periods. Work by Kumar et al. (2010) and Lodge and Murphy (2006) has shown that pasture grass sites have greater root length density (0.5–1.6 m m−3) at 0.5 m depth than spotted gum plantations (0.04–0.8 m m−3) (Falkiner et al., 2006; Grant et al., 2012). Thus, it appears that pasture grasses provide greater SOC inputs than plantation trees but we hypothesise that the pasture organic matter is degraded more rapidly, resulting in no net effect of land use on SOC stocks at depth. Cropping land use at our sites in south east Queensland had lower SOC than either pasture or forest plantations and this can be attributed to soil disturbance by tillage which is known to have a negative effect on SOC stocks and lower C inputs due to seasonality of crop growth (Dalal and Mayer, 1986b; Lopez-Diaz et al., 2017; Oso and Rao, 2017; Tang et al., 2018). We suspect that some of our pasture or plantation sites may have been used for cropping shortly after land-clearing and were then retired to pasture decades ago when cropping yields were uneconomical. In the intervening years, SOC stocks would have recovered under plantations or pasture, but continue to decline under regular cultivation (Dalal and Mayer, 1986b; Richards et al., 2007). While detailed management history was unavailable for the sites sampled here, it is acknowledged that the complex history of cropping and pasture establishment likely had some influence on the SOC stocks at the time of sampling. The varied management histories among the ten sites undoubtedly influenced our ability to detect clear trends in SOC pools across sites. The ‘paired’ sampling approach utilised should limit the differences in land use history between individual pairs of sites, but it is possible that in some cases historic land use differed either side of a fence line. It is possible that some loss in SOC occurred at these sites through soil erosion. While most sites only had gentle slope inclination (< 5°), most were located in the mid-slope position of the landscape, and given the potential for soil erosion in the study region, particularly on the Ferrosol soils in summer months (Smith et al., 1993; Bell et al., 1999), there is a chance that some SOC has been lost historically (e.g. tillage erosion) and losses may be more likely in cultivated areas than in areas

Table 4 Stocks (in t ha−1) of surface litter (including coarse woody debris), below ground C in tree roots (BGC) and above ground carbon (AGC) in tree biomass. Values for total below and above ground tree carbon were estimated from allometric relationships and only one value was calculated for each land use, precluding a statistical analysis on the shown values. Site

Vegetation

Surface litter C

Total BGC

Total AGC

1

Plantation Pasture

28.5 0.8

12.3

63.4

2

Plantation Pasture

7.5 2.4

10.4

51.7

3

Plantation Cultivation

9.1 2.2

15.8

81.7

4

Plantation Pasture

3.9 2.0

7.8

35.9

5

Plantation Pasture

3.6 0.3

6.0

28.7

6

Plantation Pasture

1.8 1.2

7.9

27.3

7

Plantation Pasture

9.0 2.9

9.1

45.9

8

Plantation Pasture

6.6 2.9

8.2

38.6

9

Plantation young Plantation old Pasture Cultivation

5.0 17.4 0.5 0.1

3.1 7.8

12.8 40.2

10

Plantation Pasture*

2.7 0.7

4.5 2.8

19.6 18.8

* Pasture at site 10 contained a few scattered large trees.

(Table 2). The C/N ratio under forest plantation was greater than under pasture or cropping at five sites (sites 3, 4, 6, 8 and 9), and unaffected by land use at the remaining five sites (sites 1, 2, 5, 7, 10). At site 2, the C/ N ratio decreased with depth, whereas the C/N ratio increased with depth at site 6. At the remaining sites, differences in C/N ratios with depth were not marked. 4. Discussion Establishment of forest plantations on former pastures has not resulted in increases in SOC at our trial sites in south east Queensland over a timeframe of 6–19 years. The average SOC stock in the 10 paired pasture sites was 57.7 t ha−1 (0–0.3 m depth), while the average SOC stock in the forest plantation sites was 58.3 t ha−1. The measured SOC stocks are similar to those reported by Lebenya et al. (2018) for South Africa (25–65 t ha−1 to 0.3 m). Our SOC stocks were slightly lower than those measured by Wilson et al. (2011) for similar soils in NSW or by Lewis et al. (2019) for North Queensland. By contrast, SOC stocks at our sites were considerably lower than values measured by Nath et al. (2018) in tropical parts of India (106–130 t ha−1) or by Oso and Rao (2017) in the humid lowlands of Papua New Guinea (208–218 t ha−1). These differences likely reflect climatic differences and thus standing biomass. The SOC pool is a function of C input and microbial decomposition, with inputs derived from surface litter, roots, and rhizodeposition, and outputs controlled by soil properties and climatic influences (water availability and temperature) which affect microbial degradation of SOC (Paul, 2016; Kogel-Knabner, 2017). Since our sites had high temperatures and low rainfall (Table 1), it was expected that SOC stocks would be relatively low due to low C input and high SOC turn-over. It is likely that SOC would increase under forest plantations with increasing age (Zhang et al., 2012; Zhang et al., 2016; Zhou et al., 2017; Nath et al., 2018; Lewis et al., 2019). Indeed, we observed a weak positive correlation between plantation age and SOC in the forest 7

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Fig. 2. Variation in SOC and nitrogen concentrations, and the C/N ratio, with soil depth for various land uses and sites. Soil C: ▲; soil N: ▾; C/N ratio: ●. Symbol colours represent land use: black – pasture; white – forest; grey – cropping. For site 9, symbols with dot and dotted line represent the old forest, and symbols with cross and dashed line the young forest. Some symbols and lines are obscured due to overlaps.

with vegetation cover (Scanlan et al., 1996; Carroll et al., 2000). Soil mineralogy can also affect SOC (Orgill et al., 2014; Deiss et al., 2017; Porras et al., 2017) and it has been reported that SOC is greatest in soils derived from basalt (e.g. Ferrosols (Oxisols) and Vertosols (Vertisols)) or sediment, and lowest in soils derived from granite (e.g. Chromosols (Alfisols), Kandosols (Ultisols)) (Wilson et al., 2011). It has been suggested that the SOC increases with clay content, in particular Fe/Al oxide content (Richards et al., 2009) and allophane (Singh et al.,

2017) as this controls mineralization of SOC, and SOC is more controlled by Fe/Al than vegetation type (Toriyama et al., 2015). In our study sites, soil types were predominantly Ferrosols (Oxisols) which are rich in Fe and Al oxides, with a few Chromosols (Alfisols), Dermosols (Molisols) and Vertosols (Vertisols) which are high in aluminosilicate minerals. Although we had an insufficient number of different soil types to statistically confirm an effect of soil type on SOC stocks for our sites in south east Queensland, our data show that sites with highest SOC 8

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were located on Ferrosols (Oxisols) and sites with lowest SOC were on Chromosols (Alfisols) (Table 2), confirming results by Wilson et al (2011). Changes in the pools of SOC are considered to be important because their rate of degradation differs. The POC pool is considered to be an active pool of C derived from litter input and rhizodeposition, whereas the HOC is considered to be a slow turn-over pool (Page et al., 2013), derived from breakdown of POC. In the subtropics, the active SOC pool has a mean residence time of 219 d, whereas the slow C pool has residence time of 78 y and makes up 25–65% of SOC (Yang et al., 2007). Although the ROC pool is considered inert and derived from charcoal, Page et al. (2013) have shown that this pool decreased in cropped land and could not be considered inert. We found only small quantities of visible charcoal during soil preparation, and measured pools of ROC were low at most sites. Pools of POC were small in the pasture and forest plantation sites and POC pools decreased only slightly with depth. Land use affected POC pools at some sites but not at other sites. The surface litter pool was greater in the plantations than in pasture (Table 2) and this is caused by litterfall (leaf/twigs/branches) from trees (Bolstad and Vose, 2005). Surface litter is considered to turn over quickly and has a relatively minor contribution to POC as it does not readily become incorporated into the soil (Jackson et al., 2017; Bonini et al., 2018). Thus, POC pools in the 0–0.05 m layer were generally greater in forest plantation and pasture sites, and smaller under cropping (Table 3). The POC pool at depth is a reflection of root distribution in the subsoil (Kogel-Knabner, 2017) which is affected by soil properties such as bulk density, shrink-swell properties, salinity and water table (Falkiner et al., 2006; Grant et al., 2012), and vegetation type. A study by Grant et al. (2012) has shown that spotted gum root density decreases linearly with depth and it could be expected that POC pools in soil would also decrease linearly with depth under plantation soils. Pools of HOC were larger than the POC and ROC pools, and decreased with depth (Table 3). The HOC pool was affected by land use, especially in the topsoil layer. However, there was no consistent effect of land use on HOC pools between pasture and plantation, in some sites HOC was greater under pasture and at other sites HOC was greater under plantation. Cropping had a significant negative effect on HOC, POC and TOC pools, especially in the topsoil. This can be attributed to the mixing of soils by cultivation, lower C inputs and increased breakdown of organic matter by soil microbes (Dalal and Mayer, 1986b; van Veen and Kuikman, 1990). Yeasmin et al. (2017) suggested that POC is bound mainly to phyllosilicate clays whereas sesquioxide clays bind less POC. Labile forms of SOC (such as POC) decrease with depth, whereas stable forms of SOC (e.g. HOC) increase with depth (Soucemarianadin et al., 2018) and this would suggest a vertical movement of SOC down the soil profile. At our sites, vertical movement of SOC is likely to be limited due to low rainfall and high evapotranspiration (wetness index ~0.5) (Table 1). Nitrogen stocks mirrored the SOC stocks (Table 2), with whole profile stocks ranging from 2.9 to 11.9 t ha−1 and the N content measured here is similar to values reported by Dalal and Mayer (1986a). The C:N ratio between sites differed (Fig. 2) and this may be caused by addition of N in fertiliser or presence of legumes at some of the sites and the average C/N varied between 5.7 (site 10) to 13.5 (site 3). For instance, Wilson et al (2011) have measured higher C/N ratios in granite-derived soils and lowest C/N ratio on metamorphic sediment derived soils. The C/N ratios also changed markedly with depth at some sites (site 2, 4, 6), whereas at most sites, the C/N ratio varied only slightly with depth (Fig. 2) and this is similar to the observation made by Wilson et al (2011). However, we measured no significant effect of land use on C/N ratios, whereas Wilson et al. (2011) determined that the C/N ratio is higher under remnant forest (> 100 years old) than pasture or cultivation.

5. Conclusion In short-to-medium timescales (0–19 y), conversion of pastures to hardwood plantation forests does not have an effect on soil C sequestration in the subtropical/subhumic climate of south east Queensland, with losses due to land preparation (site preparation before planting trees) the same magnitude as C sequestration, resulting in a zero net effect. We have only compared pasture-forest or pasture-cropping conversions, not native-forest to forest or pasture conversion, but earlier work in different climates and soils showed little effect of forest to plantation conversion on SOC stocks. Based on the relationship detected between SOC and plantation age it is likely that a longer term (> 19 years) forest plantation may result in higher SOC stocks than that under pasture land use. Together with the carbon stored in the living trees and debris pools, forest plantations on former agricultural land offer opportunities for carbon sequestration, particularly on land that has become degraded through repeated cultivation. Acknowledgments Funding for this work was granted by the Commonwealth of Australia Government under the Filling the Research Gap program. We thank Angelique Woods and Kerrilyn Catton for technical assistance. Student volunteers (from USC), including Dotty Timms, Benjamin Francis, Rhianna Robinson, Lisa Hunt, Joe McNeal, Doug Mohr, Nick Grant Nevison and Anthony Staniland assisted with field data collection and soil processing. Anton Zbonak and Bruce Hogg (DAF) provided assistance with field work and data storage and processing. We are grateful to Ian Last and Russell Vance (HQPlantations Pty Ltd) and individual landholders (or leasees) for providing access to the sites and information on the sites sampled. References Baldock, J.A., Hawke, B., Sanderman, J., Macdonald, L.M., 2013. Predicting contents of carbon and its component fractions in Australian soils from diffuse reflectance midinfrared spectra. Soil Res. 51, 577–583. Bell, M.J., Moody, P.W., Yo, S.A., Connolly, R.D., 1999. Using active fractions of soil organic matter as indicators of the sustainability of Ferrosol farming systems. Soil Res. 37, 279–288. Blume, H., Brummer, G., Fleige, H., Horn, R., Kandeler, E., Kogel-Knabner, I., Kretzschmar, R., Star, K., Wilke, B., 2016. Scheffer/Schachtschnabel Soil Science. Springer, Heidelberg. Bolstad, P., Vose, J., 2005. Forest and pasture carbon pools and soil respiration in the southern Appalachian Mountains. For. Sci. 51, 372–383. Bonini, I., Marimon, B., Matricardi, E., Phillips, O., Petter, F., Oliveira, B., Marimon, B., 2018. Collapse of ecosystem carbon stocks due to forest conversion to soybean plantations at the Amazon-Cerrado transition. For. Ecol. Manage. 414, 64–73. Carroll, C., Merton, L., Burger, P., 2000. Impact of vegetative cover and slope on runoff, erosion, and water quality for field plots on a range of soil and spoil materials on central Queensland coal mines. Soil Res. 38, 313–328. Dalal, R., Mayer, R., 1986a. Long term trends in fertility of soils under continuous cultivation and cereal cropping in southern Queensland. V. Rate of loss of total nitrogen from the soil profile and changes in carbon : nitrogen ratios. Aust. J. Soil Res. 24, 493–504. Dalal, R., Mayer, R., 1986b. Long term trends in fertility of soils under continuous cultivation and cereal cropping in southern Queensland. II. Total organic carbon and its rate of loss from the soil profile. Soil Res. 24, 281–292. Dalal, R.C., Harms, B., Krull, E., Wang, W., 2005. Total soil organic matter and its labile pools following mulga (Acacia aneura) clearing for pasture development and cropping 1. Total and labile carbon. Aust. J. Soil Res. 43, 13–20. Deiss, L., Franzluebbers, A., Amoozegar, A., Hesterberg, D., Polizzotto, M., Cubbage, F., 2017. Soil carbon fractions from an alluvial soil texture gradient in North Carolina. Soil Sci. Soc. Am. J. 81, 1096–1106. Diaz-Pines, E., Rubio, A., Van Miegroet, H., Montes, F., Benito, M., 2011. Does tree species composition control soil organic carbon pools in Mediterranean mountain forests? For. Ecol. Manage. 262, 1895–1904. Ellert, B.H., Bettany, J.R., 1995. Calculation of organic matter and nutrients stored in soils under contrasting management regimes. Can. J. Soil Sci. 75, 529–538. Falkiner, R., Nambiar, E., Polglase, P., Theiveyanathan, S., Stewart, L., 2006. Root distribution of Eucalyptus grandis and Corymbia maculata in degraded saline soils of south-eastern Australia. Agrofor. Syst. 67, 279–291. Gill, R., Jackson, R., 2000. Global patterns of root turnover for terrestrial ecosystems. New Phytol. 147, 13–31. Grant, J., Nichols, J., Yao, R., Smith, R., Brennan, P., Vanclay, J., 2012. Depth distribution

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