Parent material and climate affect soil organic carbon fractions under pastures in south-eastern Australia.
Agricultural soil in Australia is estimated to store 12.76 Gt of organic carbon (OC) in the 0-30 cm layer (Viscarra Rossel et al. 2014). Over the past 20 years, research has increasingly focused on land management options to increase the stock and permanence of OC in soil. In particular, the south-eastern agricultural zone of Australia has been identified as having potential to increase its store of OC in soil (Luo et al. 2010; Zhao et al. 2013). Pastures arc reported to offer a considerable opportunity for OC accumulation in agricultural soil (Conant et al. 2001). Globally, improved pasture management, such as species selection, irrigation, nutrient application, lime application and grazing management, has been suggested to increase the stock of OC in soil by an average of 0.541C [ha.sup.-1] [year.sup.-1] (0-30 cm layer), ranging between 0.11 and 3.04tC [ha.sup.-1] [year.sup.-1] (Conant et al. 2001). In south-eastern Australia, long-term field trials have demonstrated increases of up to 0.70 t C [ha.sup.-1] [year.sup.-1] under dryland perennial pastures, with the highest sequestration rates associated with converting cropped fields with low OC concentrations to fertilised perennial pastures (Conyers et al. 2015).
In recent years, there have been several studies of the potential for C sequestration in soil on the Australian continent (Chan et al. 2010; Badgery et al. 2013, 2014; Davy and Koen 2013; Schwenke et al. 2013; Orgill et al. 2014; Rabbi et al. 2014; Robertson et al. 2016). The emphasis has generally been on land use and management practices, driven by the desire to promote C sequestration in soil by farmers. Results from these field surveys highlight the variable and often non-significant response of OC in soil to pasture management; notably, there was little difference in OC stocks under annual versus perennial, introduced versus native and rotationally versus continuously grazed pastures (Chan et al. 2010; Schwenke et al. 2013; Orgill et al. 2014; Sanderman et al. 2015). An example of management increasing OC in soil was where phosphorus (P) applied to introduced perennial pastures in south-eastern Australia increased OC stocks by between 0.26 and 0.72 tC [ha.sup.-1] [year.sup.-1] (mean 0.41 tC[ha.sup.-1] [year.sup.-1]; Chan et al. 2010). One explanation for little response of OC in soil to pasture management is that the growth potential of perennial pastures may be limited by the pasture sward composition or inadequate soil nutrition (Badgery et al. 2014).
However, studies that also evaluated climate and inherent soil factors concluded that these have a greater effect on OC stocks than do management practices. For example, several studies found that OC stocks were primarily controlled by climate-related variables, such as annual average rainfall and vapour pressure deficit (VPD), followed by soil texture, with OC stock declining as the percentage of sand increased (Badgery et al. 2013; Page et al. 2013; Robertson et al. 2016). Land use and management practices had smaller or non-significant effects. Similarly, Davy and Koen (2013) found that climate was the dominant factor, with OC stocks increasing with annual average rainfall and decreasing with annual average daily temperature. That study also highlighted the effect of topography on OC stocks, with slopes of 7-10% favouring a higher stock of OC in soil (Davy and Koen 2013).
Some of the previously mentioned field surveys in southeastern Australia reported promising correlations between land management and the type of OC in soil. The different components of OC can be categorised into biologically significant fractions, ordered from the most vulnerable to biological degradation to the least vulnerable, and include particulate OC (POC), humus and char-like components (Skjemstad et al. 1996; Baldock et al. 2013). This order of vulnerability of OC fractions in soil also reflects the order in which land management most readily influences OC in soil. For example, more POC has been observed under perennial pastures compared with continuously cropped fields (Schwenke et al. 2013; Badgery et al. 2014; Rabbi et al. 2014), although others have reported no difference (Davy and Koen 2013). Although pastures may increase POC compared with cropped fields, it has been argued that increasing these active fractions does not necessarily result in a net increase in the total stock of OC in soil (Chan et al. 2002).
Parent material, as defined by geology, and climate affect soil properties, the processes that regulate soil properties and net primary production. Despite this, few studies have investigated the effects of parent material on OC fractions. The present study compared OC in soil under perennial pastures with contrasting parent material and climate in south-eastern Australia, assessed the effects of these two environmental factors on total OC stock and OC fractions and sought to identify opportunities for land management to increase OC accumulation in soil. The present study is therefore unique in the Australian context and clearly shows the contrasting effects of granite and basalt geology on the potential for sequestering C in soil.
Materials and methods
Three study regions with contrasting climates were selected in southern New South Wales (NSW), Australia: the Monaro, Boorowa and Coleambally regions (Table 1). The Monaro and Boorowa regions are located on the southern tablelands of NSW, where the primary land use is livestock grazing (sheep and cattle) on dryland perennial pastures. The Coleambally region is located in the Riverina district of south-western NSW, where previously irrigated crops were grown. However, reduced water allocations have meant livestock grazing on both dryland annual and perennial pastures has become more common.
Soils were classified according to the World Reference Base for Soil Resources (WRB; IUSS Working Group WRB 2015) and the Australian Soil Classification System (ASC; Isbell 2002). Soil chemical data are summarised in Table 2. In the Monaro region, 19 sites with granite-derived duplex soils (WRB: Lixisols and Acrisols; ASC: Chromosols and Kurosols) and 13 sites with basalt-derived gradational soils (WRB: Phaeozems and Nitisols; ASC: Dermosols) were sampled. Granite-derived soils were divided into deep granite-derived soil (13 sites), where the C horizon was deeper than 50 cm, and shallow granite-derived soil (six sites), where the C horizon was within 50 cm of the soil surface. In the Boorowa region, 20 sites with deep granite-derived duplex soils (WRB: Lixisols and Acrisols; ASC: Chromosols and Kurosols) were sampled. Soil descriptions for sites in the Monaro and Boorowa regions have been reported previously by Orgill et al. (2014).
The Coleambally region is located on an alluvial plain consisting of 100-200 m of non-indurated sediments, plains, prior streams and Aeolian clay deposits (Hornbuckle et al. 2008). The parent material for this region is referred to as 'plains' and relates to the typically weakly self-mulching Coleambally clays (Hombuckle et al. 2008). The 20 sites sampled in the Coleambally region were Vertisols and Calcisols (WRB; ASC: Vertosols). The plains soil had a uniform and high clay content (>35%) throughout the soil profile with moderate soil structure, strong vertic properties expressed as cracks >5 mm wide at the surface (typically >20 mm) and some carbonates in the B horizon.
Rainfall and temperature data (1980-2010) for each site were extracted from the SILO climate database (http://www.longpaddock.qld.gov.au/silo/index.html, accessed 1 August 2013).
Paired study sites (<500 m distance) with similar soil and landscape attributes were selected in each of the three regions. Native and introduced perennial pastures and grazing management for sites in the Monaro and Boorowa regions have been described previously (Orgill et al. 2014). Briefly, sites were rain-fed, native pastures had never been cultivated and introduced pastures were established between 1955 and 1998, with a mean establishment age of 31 years (median 29 years). Sites ranged from continuously grazed, where stock were present all the time, to rotationally grazed, where stock were removed and fields rested to encourage pasture regeneration. Most landholders reported belowaverage applications of mineral fertilisers for more than 10 of the 15 years covered by the management survey period due to below-average rainfall. In general, mineral phosphate fertilisers, such as single superphosphate (SSP), are applied to pastures on granite-derived soil (typically 125 kg SSP [ha.sup.-1] [year.sup.-1]), whereas gypsum is applied to address inherent S deficiencies on basalt-derived soil (typically 125 kg gypsum [ha.sup.-1] [year.sup.-1]).
In the Coleambally region, nine pasture and 10 cropped sites were sampled. Pasture sites had been under annual and perennial pasture for at least 5 years and were occasionally irrigated. The main pasture species included lucerne (Medicago sativa), fescue (Festuca arundinacea Schreb.), phalaris (Phalaris aquatica), annual ryegrass (Lolium rigidum), balsana clover (Trifolium michelianum) and subterranean clover (Trifolium suhterraneum). Most pastures were either annually or occasionally fertilised with P fertilisers (SSP; typically 125 kg [ha.sup.-1]), P and N fertilisers (diammonium phosphate; typically 125 kg [ha.sup.-1]) and/or N fertilisers (urea; typically 125 kg [ha.sup.-1]). Cropped sites had been in crop for at least 5 of the 10 years preceding and were barley, wheat or fallow at the time of sampling. Stubble management in cropped fields depended on seasonal conditions and included stubble remaining standing, stubble incorporated by ploughing or stubble burned. All crops were fertilised with P- and/ or N-based fertilisers (diammonium phosphate, ranging from 120 to 200 kg [ha.sup.-1], or urea, ranging from 100 to 400 kg [ha.sup.-1]).
Five remnant native vegetation sites across the three study regions (Table 2) were selected to indicate the likely stock of OC in soil before agriculture, or without agricultural management. These sites had not been used for agriculture for at least 30 years and were conservation areas. Because these sites were not replicated, they only provide an indication of OC stocks in the absence of agricultural management and may not represent a true baseline of OC stocks under native ecosystems.
Sites were sampled in August 2009 in the Monaro region, October 2009 in the Coleambally region and April 2010 in the Boorowa region. Soil samples were collected from a 40 m x 40 m representative area within each field. At each site, 16 cores were collected to a depth of at least 40 cm for chemical analyses and four cores were collected for bulk density (BD) determination. Soil cores were divided into four soil layers: 0-5,5-10, 10-20 and 20-30 cm. Samples for chemical analyses were composited in the field, whereas cores for BD were analysed separately.
Soil samples were oven dried at 40[degrees]C, passed through a 2-mm sieve and ground to <0.5 mm using a single puck mill head (Rayment and Lyons 2011; Method 17A1 sample grinding used for X-ray fluorescence (XRF) analysis. The oven-dried equivalent (ODE) mass of soil was calculated using moisture content measured from subsamples dried at 105[degrees]C for 48 h. Results for the present study are on an ODE of soil mass.
BD (g[cm.sup.-3]) was determined on samples dried at 105[degrees]C as described previously (Dane and Topp 2002).
Soil chemical analyses were conducted according to Rayment and Lyons (2011): Colwell phosphorus (P Col; mg [kg.sup.-1]; Method 9B2), extractable sulfur (S K[Cl.sub.40]; mg[kg.sup.-1]; Method 10D1), cation exchange capacity (CEC; [cmol.sub.c][kg.sup.-1]; Method 15E1) and soil pH (pH Ca[Cl.sub.2]; Method 4B2).
Samples were screened for inorganic carbon using hydrochloric acid (Rayment and Lyons 2011; Method 19D1). Samples that exhibited effervescence were pretreated before total OC (TOC) analysis with sulfurous acid (Sanderman et al. 2011). TOC (mg[g.sup.-1]) and total N (TN; mg[g.sup.-1]) were determined on finely ground soil using a LECO combustion furnace (LECO TruMac CN, Australia) (Rayment and Lyons 2011; Method 6B2b). Labile C (LC; mg[g.sup.-1]) was determined using the Blair et al. (1995) method, which involved oxidation with 333 mM KMn[O.sub.4]. POC, humic OC (HUM) and resistant OC (ROC) fractions were predicted using mid-infrared spectroscopy (MIR) with a spectral range of 7800-400 [cm.sup.-1] at 8 [cm.sup.-1] resolution in a Thermo Nicolet 6700 Fourier transform infrared (FTIR) spectrometer (ThermoFisher Scientific, Australia), as described previously (Baldock et al. 2013). Predictions for TOC, POC, HUM, ROC, TN, pH, clay, Si[O.sub.2], Al and Fe from individual soil spectra were generated using partial least-squares regression (PLSR) and based on calibrated datasets (Baldock et al. 2013).
Calculating OC stocks
Carbon stocks (tC [ha.sup.-1]) based on an equivalent mass of soil to 10 and 30 cm were calculated using Eqn 1 to calculate the stock to a depth standard (DS) for the soil layers and then using Eqn 2 to calculate stock based on an equivalent soil mass (ESM) for either the 0-10 or 0-30 cm soil layer. The reference soil mass (for ESM) was calculated as the 10th percentile of soil mass values for the 0-10 or 0-30 cm soil layer of the parent material class in each region.
DS OC stock (g per 100 [cm.sup.2]) = OC (g per 100g) x BD (g [cm.sup.-3]) x depth (cm) (1) x (1 - proportion gravel)
where the OC stock (g per 100[cm.sup.2]) = OC stock (tC[ha.sup.-1]). ESM OC stock (tC[ha.sup.-1],0 - 30cm) = [DS.sub.0-20] + ([DS.sub.20-30] x ([ESM.sub.ref] - Soil [mass.sub.0-20])/Soil mas20-30)) (2)
where [DS.sub.0-20] is the sum of OC stock for all soil layers to 20 cm calculated using Eqn 1, [DS.sub.20-30] is the OC stock for the 20-30 cm soil layer calculated using Eqn 1, [ESM.sub.ref] is the reference soil mass for the 0-30 cm soil layer, Soil [mass.sub.0-20] is the actual mass of the 0-20 cm soil layer and Soil [mass.sub.20-30] is the actual mass of the 20-30 cm soil layer. Eqn 2 was adjusted for the ESM OC stock 0-10 cm calculations.
The effects of parent material and land use (i.e. vegetation type) on OC stock were estimated by linear models. Variation in OC stock was described by effects due to parent material and vegetation plus the interaction effect where estimable. Significance tests were conducted for each effect by F-ratio tests.
Estimates of the mean OC stock for each parent material were derived by aggregating over the vegetation effects. Standard errors were calculated for each parent material because the number of observations varied. Paired comparison tests were conducted by calculating the least significant difference at 5% critical value specific to each pairing of the parent material. The mean for each vegetation class within parent material was also calculated from the linear models.
Measured soil chemical factors were aggregated over the subcores from 0 to 20 cm and a different set of factors determined by MIR spectra were aggregated over 0-30 cm. The strength of association between soil factors in each set was assessed by estimating correlations. The effects of parent material and vegetation on the multivariate data were likely to cause spurious correlations between the soil chemical factors if ignored, so the correlation matrix was estimated by multivariate linear model. The data matrix from each set was modelled in response to parent material and vegetation assuming an unstructured covariance matrix. The estimated covariance matrix was then transformed to correlation form.
All data analyses and graphs were constructed in the R environment (R Foundation for Statistical Computing, Vienna, Austria).
Soil OC stocks: parent material and climate
Parent material significantly (P< 0.05) affected the concentration of TOC and OC fractions in soil (Fig. 1), and the stock of OC in the 0-10 and 0-30 cm soil layers (Table 3). Basalt-derived soil had a significantly (P< 0.05) greater stock of TOC, ROC and HUM for the 0-10 and 0-30 cm soil layers compared with all other parent material (Table 3). Deep granite-derived soil (Monaro) had a significantly (P<0.05) greater stock of TOC and OC fractions in the 0-10 and 0-30 cm soil layers compared with shallow granite-, deep granite- (Boorowa) and plains-derived soil (Table 3). There was a statistically similar stock of POC in basalt- and deep granite-derived soil (0-30 cm) in the Monaro region (12.8 vs 13.3 t[ha.sup.-1]; s.e.m. 0.8; Table 3). This suggests that although environmental factors are affecting ROC and HUM in soil, POC may be affected by other factors, such as land management. When grouped by parent material, there was no statistically significant difference in the stock of TOC or OC fraction in the 0-10 or 0-30 cm soil layers with vegetation type for any region, native versus introduced pastures in the Monaro and Boorowa regions or pastures versus crops in the Coleambally region (Table 4).
Total OC stock decreased with an increase in temperature (Fig. 2). Variability in total OC stocks in the Monaro region (mean annual temperature 3-9[degrees]C) could be explained, in part, by parent material. Although there was large variability in TOC stocks when mean annual rainfall was compared, higher OC stocks were associated with higher spring and summer rainfall (Fig. 2).
Soil OC and soil chemistry
TOC was positively and significantly (P<0.01) correlated with the POC, ROC and HUM fractions (Table 5). TOC and OC fractions were compared with MIR spectra-determined soil properties (Table 5) and were significantly (P<0.01) and positively correlated with TN and negatively (P<0.05) correlated with soil pH. The HUM fraction was positively (P < 0.05) correlated with A1 and negatively (P< 0.05) correlated with Si[O.sub.2]. When TOC concentration and OC fractions were compared with chemically measured soil properties (Table 5), they were positively correlated with TN (P < 0.01), LC (P< 0.01) and extractable S (P<0.05). There was also a significant (P < 0.05) positive correlation between TOC, POC and ROC fractions and measured CEC. LC was positively correlated (P< 0.05) with TN and extractable S. In contrast with the significant correlations between both TOC and OC fractions and pH determined by MIR, there was no significant correlation with soil pH based on measured values (Table 5).
Environmental factors: parent material and climate
The stock of TOC and OC fractions in soil under perennial pastures in the present study was significantly affected by parent material as defined by geology and climate. Parent material of soil formed in situ determines soil texture and mineralogy. Soil with a high proportion of clay-sized particles, such as basalt-derived soil, is associated with high CEC and nutrient and waterholding capacity (Hudson 1994) compared with soil dominated by sand-sized particles, such as the surface layers of granite-derived soil. Under the same land use, clay-rich soils may potentially grow more biomass and protect more organic matter (OM) through soil aggregation and organomineral associations, leading to a greater stock of OC in the soil (Baldock and Skjemstad 2000). In contrast, soils with a high proportion of sand-sized particles may produce a negative correlation between the proportion of sand and OC in the soil (Badgery et al. 2013; Rabbi et al. 2014). Badgery et al. (2013) used Si[O.sub.2] content determined by MIR spectra as a surrogate for the sand proportion and reported a significant negative correlation with TOC stock in the 20-30 cm soil layer. The present data demonstrate a significant negative correlation between Si[O.sub.2] content and HUM concentration in the 0-30 cm soil layer (Table 5), similar to the findings reported by Rabbi et al. (2014). As confirmed in the present study (Table 3), humus is typically the largest component of OC in the soil (Kogel-Knabner 2002; Stevenson 1994) and is generally associated with clay and silt-sized particles (Baldock and Skjemstad 2000). Although parent material largely determines soil texture and CEC, increasing OC in the soil can also increase CEC. In the present study, CEC was significantly (P<0.01) and positively correlated with TOC, POC, ROC and TN concentration (Table 5). This is likely to reflect the positive feedback between the effects of CEC on the supply and accumulation of OM in the soil and the role of OC in enhancing CEC in the soil.
In addition to parent material and the associated major soil properties, the present field survey highlights the effects of climate on the stock of OC in soil (Fig. 2). TOC stock decreased with an increase in temperature. Although there was large variability in TOC stocks when mean annual rainfall was compared, higher OC stocks were associated with higher spring and summer rainfall (Fig. 2). For example, despite similar pasture composition, there was a significantly (P<0.05) greater stock of TOC and OC fractions in the deep granite-derived soil from the Monaro region compared with similar soil from the Boorowa region (51.5 vs 31.51C [ha.sup.-1] to 30 cm; Table 3). This is likely due to more favourable soil conditions for OM decomposition in the Boorowa region; that is, less seasonal variability in soil moisture and temperature as a consequence of higher annual rainfall, more even rainfall distribution and warmer temperatures (Orgill et al. 2014).
The interaction of parent material-derived soil properties, climate and OC stocks is evident when the plains (Coleambally) and basalt-derived (Monaro) soils were compared. Both soils had a high clay content (>35% clay), comparable clay mineralogy (mainly smectite and illite) and high CEC (>33 [cmol.sub.c][kg.sup.-1]). However, the mean stock of OC fractions in the 0-30 cm soil was significantly less in the plains compared with the basalt-derived soil (Table 3). There are three likely explanations for this. First, despite similar clay content, the plains soils were mostly Vertisols prone to swelling and shrinking with wetting and drying cycles, which cause vertical cracks in the soil. During dry conditions, these vertical cracks can accumulate OM deeper than the 30 cm soil depth observed in the present study. Second, soil in the Monaro region was under long-term perennial pasture (mean >31 years), whereas the sites located in the Coleambally region had a short pasture history (<5 years). Third, the drier and hotter climate of the Coleambally region compared with the Monaro region (Table 1; Fig. 2) suggests a lower net primary productivity, thus a lower supply of OM to plains soil. Other studies have reported little difference in OC in soil with land management in low-rainfall (<500 mm) areas of south-eastern Australia (Chan et al. 2003; Davy and Koen 2013).
Opportunity for management to increase OC stock in soil: type of vegetation and soil fertility
When grouped by parent material, there was no difference in the stock of TOC or OC fraction in the 0-10 or 0-30 cm soil layers with vegetation type for any region (Table 4). This is despite the 0-10 cm soil layer (Davy and Koen 2013) and OC fractions (Chan et al. 2002) being suggested to be more sensitive to land management than the 0-30 cm soil layer and TOC. In the Coleambally region, statistically similar OC stocks between crop and pasture fields are likely due to higher nutrient and water inputs to crops resulting in a greater OM supply to the soil, and the short pasture history (<5 years). In the Monaro and Boorowa regions, similar stocks of OC fractions under introduced and native perennial pastures are likely to be the result of underperforming introduced pastures as a consequence of the preceding 10 years of dry conditions and low fertiliser application rates (Orgill et al. 2014).
Because the remnant sites were not replicated, they only provide an indication of OC stocks in the absence of agricultural management. These sites suggest a role for agricultural management in increasing OC accumulation in some but not all regions. The considerably greater mean stock of OC in soil under agricultural management in the Monaro region compared with the Monaro reference sites (Table 4) implies that agricultural management may increase OC accumulation in the soil beyond native ecosystems. However, it is also important to note that the two remnant sites in the Monaro region had lower soil pH compared with the mean soil pH values for soil under agricultural management (Table 2) and this may affect the OC stocks (Rabbi et al 2014). In the Boorowa region, although the reference sites had a greater stock of TOC, the mean HUM stocks were greater under agricultural management, suggesting greater permanence of OC in agricultural soil (Table 4). In the Coleambally region, the reference site had a higher stock of TOC and all OC fractions, and this may indicate a decline in OC associated with agriculture in this low-rainfall environment.
Ensuring adequate crop and pasture nutrition will increase OM supply to the soil through increased biomass production. In contrast with other studies, available P was not significantly correlated with TOC or any OC fraction in the present study (once region and parent material were accounted for), although the aggregated data were supportive of a general trend, as has been observed previously (Chan et al. 2010; Davy and Koen 2013; Badgery et al. 2014). Short-term changes in P management will affect available P, but will have little effect on OC fractions accumulated over greater periods of time. However, TN (P<0.01) and available S (P<0.05) were positively correlated with TOC and OC fractions (Table 5). Both nutrients are essential for plant growth and are important components of OM in soil (Himes 1997). Nitrogen is particularly important for pasture production and is often the most limiting nutrient in grass-dominated pastures. Sulfur promotes nodulation in legumes, and dryland perennial pastures in the Monaro and Boorowa regions rely on N fixation by legumes to supply N in soil. Based on the present field survey, nutrient management programs ensuring adequate available N and S may increase OC stocks in south-eastern Australia. However, the large stock of OC in soil under perennial pastures, and the considerable effects of parent material and climate on this stock, may mean that any modest increases in OC accumulation due to N and S additions go undetected.
The present study demonstrated that the stocks of TOC and OC fractions under perennial pastures in south-eastern Australia were significantly affected by parent material, as defined by geology, and climate, but not necessarily by land use. The results indicate that parent material is an effective aggregator of soil properties that have a demonstrated effect on OC accumulation in soil under perennial pastures and thus should be considered when interpreting field surveys. The present field survey indicates that nutrient management programs that ensure adequate N and S may increase OC stocks under pastures in these regions. In the low-rainfall zone, the low OC stock in soil and high clay content suggests there is potential to accumulate more OC in the soil, and pastures may help achieve this increase. However, the costs associated with nutrient management in the higher-rainfall zone and with land use change in the lower-rainfall zone mean that these management practices may need to be incentivised. Further research is required to indicate whether these soils have reached their upper TOC limit as defined by soil type and climate.
Received 8 November 2016, accepted 15 May 2017, published online 9 June 2017
The authors thank Robert Smith, Jo Powells and Kieran O'Keeffe (NSW Department of Primary Industries) for field assistance. Dr Lachlan Ingram (University of Sydney) and Dr Graeme Schwenke (NSW Department of Primary Industries) are acknowledged for comments on the manuscript. Dr Jeff Baldock and his team at the CSIRO Land and Water laboratory in Adelaide are acknowledged for providing access to MIR spectroscopy. Landholders in the study regions whose properties were sampled are gratefully acknowledged. This research was part of Susan Orgill's PhD project through Charles Sturt University.
Badgery WB, Simmons AT, Murphy BM, Rawson A, Andersson KO, Lonergan VE, van de Ven R (2013) Relationship between environmental and land-use variables on soil carbon levels at the regional scale in central New South Wales, Australia. Soil Research 51, 645-656. doi:10.1071/ SR 12358
Badgery WB, Simmons AT, Murphy BW, Rawson A, Andersson KO, Lonergan VE (2014) The influence of land use and management on soil carbon levels for crop-pasture systems in Central New South Wales, Australia. Agriculture, Ecosystems & Environment 196, 147-157. doi: 10.1016/j.agee.2014.06.026
Baldock JA, Skjemstad JO (2000) Role of the soil matrix and minerals in protecting natural organic materials against biological attack. Organic Geochemistry 31, 697-710. doi:10.1016/S0146-6380(00)00049-8
Baldock JA, Hawke B, Sanderman J, Macdonald LM (2013) Predicting contents of carbon and its component fractions in Australian soils from diffuse reflectance mid-infrared spectra. Soil Research 51, 577-595. doi: 10.1071/SR 13077
Blair GJ, Lefroy RDB, Lisle L (1995) Soil carbon fractions based on their degree of oxidation, and the development of a carbon management index for agricultural systems. Australian Journal of Agricultural Research 46, 1459-1466. doi:10.1071/AR9951459
Chan KY, Heenan DP, Oates A (2002) Soil carbon fractions and relationship to soil quality under different tillage and stubble management. Soil & Tillage Research 63, 133-139. doi: 10.1016/SO167-1987(01)00239-2
Chan KY, Heenan DP, So HB (2003) Sequestration of carbon and changes in soil quality under conservation tillage on light-textured soils in Australia: a review. Australian Journal of Experimental Agriculture 43, 325-334. doi: 10.1071/EA02077
Chan KY, Oates A, Li GD, Conyers MK, Prangnell RJ, Poile G, Liu DL, Barchia 1M (2010) Soil carbon stocks under different pastures and pasture management in the higher rainfall areas of south-eastern Australia. Soil Research 48, 7-15. doi:10.1071/SR09092
Conant RT, Paustian K, Elliott ET (2001) Grassland management and conversion into grassland: effects on soil carbon. Ecological Applications 11, 343-355. doi: 10.1890/1051-0761(2001)011 [0343:GMAC1G]2.0.CO;2
Conyers M, Liu DL, Kirkegaard J, Orgill S, Oates A, Li G, Poile G, Kirkby C (2015) A review of organic carbon accumulation in soils within the agricultural context of southern New South Wales, Australia. Field Crops Research 184, 177-182. doi:10.1016/j.fcr.2014.07.013
Dane JH, Topp CG (Eds) (2002) 'Methods of soil analysis. Part 4: physical methods.' Agronomy No. 9. (Soil Science Society of America: Madison, WI)
Davy MC, Kocn TB (2013) Variations in soil organic carbon for two soil types and six land uses in the Murray Catchment, New South Wales, Australia. Soil Research 51, 631-644. doi: 10.1071/SR12353
Himes F (1997) Nitrogen, sulfur, and phosphorus and the sequestering of carbon. In 'Soil processes and the carbon cycle'. (Eds R Lai, JM Kimble, RF Follett, BA Stewart) pp. 315-320. (CRC Press: Boca Raton, FL)
Hombuckle J, Christen E, Thacker J, Muirhead W, Stein TM (2008) Soils of the Murrumbidgee, Coleambally and Murray irrigation areas of Australia, II: physical properties. CSIRO Land and Water Science Report 23/08, CRC for Irrigation Futures Technical Report No. 02/ 08, CSIRO Land and Water, Canberra, ACT, Australia.
Hudson BD (1994) Soil organic matter and available water capacity. Journal of Soil and Water Conservation 49, 189-194.
Isbell RF (2002) 'The Australian soil classification.' Revised edn. (CSIRO Publishing: Melbourne, Vic.)
IUSS Working Group WRB (2015) 'World reference base for soil resources 2014, update 2015. International soil classification system for naming soils and creating legends for soil maps.' (FAO: Rome, Italy)
Kogel-Knabner I (2002) The macromolecular organic composition of plant and microbial residues as inputs to soil organic matter. Soil Biology & Biochemistry 34, 139-162. doi: 10.1016/S0038-0717(01)00158-4
Luo Z, Wang E, Sun OJ (2010) Soil carbon change and its responses to agricultural practices in Australian agro-ecosystems: a review and synthesis. Geoderma 155, 211-223. doi:10.1016/j.geoderma.2009.12.012
Orgill SE, Condon JR, Conyers MK, Greene RSB, Morris SG, Murphy BW (2014) Sensitivity of soil carbon to management and environmental factors within Australian perennial pasture systems. Geoderma 214-215, 70-79. doi: 10.1016/j.geoderma.2013.10.001
Page KL, Dalai RC, Dang YP (2013) How useful are MIR predictions of total, particulate, humus, and resistant organic carbon for examining changes in soil carbon stocks in response to different crop management? A case study. Soil Research 51, 719-725. doi: 10.1071 /SRI3064
Peel MC, Finlayson BL, McMahon TA (2007) Updated world map of the Koppen-Geiger climate classification. Hydrology and Earth System Sciences 11, 1633-1644. doi: 10.5194/hess-11-1633-2007
Rabbi SMF, Tighe M, Cowie A, Wilson BR, Schwenke G, McLeod M, Badgery W, Baldock J (2014) The relationships between land uses, soil management practices, and soil carbon fractions in South Eastern Australia. Agriculture, Ecosystems & Environment 197, 41 52. doi: 10.1016/j.agee.2014.06.020
Rayment GE, Lyons D (2011) 'Soil chemical methods--Australasia.' (CSIRO Publishing: Melbourne, Vic.)
Robertson F, Crawford D, Partington D, Oliver 1, Rees D, Aumann C, Armstrong R, Perris R, Davey M, Moodie M, Baldock J (2016) Soil organic carbon in cropping and pasture systems of Victoria, Australia. Soil Research 54, 64-77. doi:10.1071/SR15008
Sanderman J, Baldock J, Hawke B, MacDonald L, Massis-Puccini A, Szarvas S (2011) 'National soil carbon research programme: field and laboratory methodologies.' (CSIRO: Adelaide, SA)
Sanderman J, Reseigh J, Wurst M, Young M-A, Austin J (2015) Impacts of rotational grazing on soil carbon in native grass-based pastures in southern Australia. PLoS One 10, eO136157. doi:10.1371/journal. pone.0136157
Schwenke GD, McLeod MK, Murphy SR, Harden S, Cowie AL, Lonergan VE (2013) The potential for sown tropical perennial grass pastures to improve soil organic carbon in the north-west slopes and plains of New South Wales. Soil Research 51, 726-737. doi:10.1071/SRI3200
Skjemstad JO, Clarke P, Taylor JA, Oades JM, McClure SG (1996) The chemistry and nature of protected carbon in soil. Australian Journal of Soil Research 34, 251-271. doi: 10.1071 /SR9960251
Stevenson FJ (1994) 'Humus chemistry: genesis, composition, reactions.' 2nd edn. (John Wiley and Sons: New York, NY)
Viscarra Rossel RA, Webster R, Bui EN, Baldock JA (2014) Baseline map of organic carbon in Australian soil to support national carbon accounting and monitoring under climate change. Global Change Biology 20, 2953-2970. doi:10.1111/gcb. 12569
Zhao G, Bryan BA, King D, Luo Z, Wang E, Song X, Yu Q (2013) Impact of agricultural management practices on soil organic carbon: simulation of Australian wheat systems. Global Change Biology 19, 1585-1597. doi: 10.1111/gcb. 12145
Susan E. Orgill (A,B,F), Jason R. Condon (B), Mark K. Conyers (A,B), Stephen G. Morris (C), Brian W. Murphy (D), and Richard S. B. Greene (E)
(A) NSW Department of Primary Industries, Wagga Wagga Agricultural Institute, Pine Gully Rd NSW 2650, Australia.
(B) Graham Centre for Agricultural Innovation, School of Agricultural and Wine Sciences, Charles Sturt University, Pugsley Place, Wagga Wagga, NSW 2650, Australia.
(C) NSW Department of Primary Industries, 1243 Bruxner Highway, Wollongbar, NSW 2477, Australia.
(D) NSW Office of Environment and Heritage, Evans Street, Cowra, NSW 2794, Australia.
(E) Australian National University, Acton, ACT 2601, Australia.
(F) Corresponding author. Email: Susan.Orgill@dpi.nsw.gov.au
Caption: Fig. 1. Correlation scatterplots of total (TOC), particulate (POC), resistant (ROC) and humic (HUM) organic carbon for soil derived from different parent material classes.
Caption: Fig. 2. Total organic carbon (TOC) stocks for the 0-30 cm soil layer plotted against to mean annual daily temperature and mean annual total, spring plus summer and autumn plus winter rainfall for the period 1980-2010.
Table 1. Summary of climate and landscape data for the Monaro, Boorowa and Coleambally regions over a 30-year period (1980-2010) Koppen-Geiger climate classification based on Peel et at. (2007). Rainfall, temperature and annual vapour pressure deficit (VPD) data show mean 30-year values for each region based on the sites sampled in the present study obtained from the SILO database (http://www.longpaddock.qld.gov.au/silo/index.html, accessed 1 August 2013). Mean elevation and slope for sites within each region were obtained from the Smoothed Digital Elevation Model of Australia (ANZCW0703014016; http://doi.org/10.4225/08/5689DA774564A, accessed 1 August 2013.) Attribute Monaro Boorowa Location 36[degrees]14'S, 34[degrees]44'S, Koppen-Geiger 149[degrees]07'E 48[degrees]72'E climate Cfa (temperate, Cfa (temperate, without classification without dry season, dry season, warm summer) warm summer) Rainfall (mm) Annual (range) 548 (490-657) 654 (610-713) Autumn-Winter 275 401 Spring Summer 272 252 Temperature ([degrees]C) Annual 11 14 Autumn-W inter 7 10 Spring-Summer 16 20 Other VPD (mm) 0.52 0.71 Elevation (m) 958 582 Slope (%) 5 6 Attribute Coleambally Location 34[degrees]48'S, Koppen-Geiger 145[degrees]53'E climate BSh (arid, steppe, classification hot mean annual temperature) Rainfall (mm) Annual (range) 404 (389-7418) Autumn-Winter 247 Spring Summer 157 Temperature ([degrees]C) Annual 17 Autumn-W inter 13 Spring-Summer 23 Other VPD (mm) 1.03 Elevation (m) 121 Slope (%) 0 Table 2. Summary of major soil attributes in the Monaro, Boorowa and Coleambally regions Unless indicated otherwise, data are given as the mean [+ or -] s.d. for the 0-5 and 5-10 cm soil layers. Parent material classes were classified as follows: basalt; deep granite (Granite D); shallow granite (Granite S); plains. Vegetation types were classified as: introduced pasture (Pasture I); native pasture (Pasture N); remnant vegetation (Remnant; single sites); crop. Soils were classified according to the World Reference Base for Soil Resources (WRB; IUSS Working Group WRB 2015) as follows: Phaeozems (PH); Nitisols (NT); Lixisols (LX); Acrisols (AC); Vertisols (VR); Calcisols (CL). TOC, total organic carbon; TN, total N; CEC, cation exchange capacity; Col P, Colwell P; S [KCl.sub.40], extractable sulfur Class No. Soil type (n) Soil sites depth (cm) Monaro Basalt Pasture I 6 PH (3), NT (3) 0-5 5-10 Pasture N 6 PH (3), NT (3) 0-5 5-10 Remnant 1 PH 0-5 5-10 Monaro - Granite D Pasture 1 6 LX (4), AC (2) 0-5 5-10 Pasture N 6 LX (3), AC (3) 0-5 5-10 Remnant 1 AC 0-5 5-10 Monaro - Granite S Pasture N 6 LX (6) 0-5 5-10 Boorowa - Granite D Pasture I 9 LX (8), AC (1) 0-5 5-10 Pasture N 9 LX (8), AC (1) 0-5 5-10 Remnant 2 LX (1), AC (1) 0-5 5-10 Coleambally - Plains Crop 10 VR (6), CL (4) 0-5 5-10 Pasture 1 9 VR (6), CL (3) 0-5 5-10 Remnant 1 CL 0-5 5-10 Class TOC TN CEC (mg [g.sup.-1]) (mg [g.sup.-1]) ([cmol.sub.c] [kg.sup.-1]) Monaro Basalt Pasture I 68.9 [+ or -] 14.9 6.5 [+ or -] 1.4 33 [+ or -] 9 41.5 [+ or -] 8.2 3.7 [+ or -] 0.8 33 [+ or -] 9 Pasture N 60.4 [+ or -] 17.7 5.4 [+ or -] 1.5 33 [+ or -] 9 39.7 [+ or -] 12.5 3.3 [+ or -] 0.9 34 [+ or -] 9 Remnant 46.8 3.1 33 38.7 2.7 33 Monaro - Granite D Pasture 1 39.3 [+ or -] 12.5 3.7 [+ or -] 1.4 9 [+ or -] 4 20.7 [+ or -] 6.3 1.8 [+ or -] 0.5 6 [+ or -] 2 Pasture N 44.9 [+ or -] 22.4 3.6 [+ or -] 1.3 9 [+ or -] 2 22.9 [+ or -] 13.1 1.9 [+ or -] 1.0 6 [+ or -] 2 Remnant 46.7 2.3 6 17.6 1.1 4 Monaro - Granite S Pasture N 17.6 [+ or -] 8.3 1.5 [+ or -] 0.8 6 [+ or -] 1 10.8 [+ or -] 1.3 0.8 [+ or -] 0.2 5 [+ or -] 1 Boorowa - Granite D Pasture I 25.0 [+ or -] 5.1 2.1 [+ or -] 0.5 8 [+ or -] 3 11.7 [+ or -] 2.8 0.9 [+ or -] 0.2 4 [+ or -] 1 Pasture N 25.2 [+ or -] 4.3 2.1 [+ or -] 0.4 6 [+ or -] 1 11.5 [+ or -] 1.3 0.9 [+ or -] 0.2 4 [+ or -] 1 Remnant 32.1 2.2 10 13.9 0.9 5 Coleambally - Plains Crop 13.4 [+ or -] 3.6 1.3 [+ or -] 0.4 18 [+ or -] 7 10.2 [+ or -] 1.9 1.1 [+ or -] 0.2 19 [+ or -] 9 Pasture 1 19.5 [+ or -] 5.7 l.7 [+ or -] 0.4 25 [+ or -] 8 11.7 [+ or -] 4.1 1.1 [+ or -] 0.3 25 [+ or -] 8 Remnant 19.5 1.4 19 12.2 0.9 19 Class Col P S [KCl.sub.40] Soil pH (mg (mg (1:5 Ca[Cl.sub.2]) [kg.sup.-1]) [kg.sup.-1]) Monaro Basalt Pasture I 112 [+ or -] 28 123 [+ or -] 33 5.0 [+ or -] 0.4 62 [+ or -] 20 93 [+ or -] 29 5.1 [+ or -] 0.3 Pasture N 98 [+ or -] 27 102 [+ or -] 37 5.2 [+ or -] 0.3 59 [+ or -] 22 77 [+ or -] 30 5.3 [+ or -] 0.3 Remnant 117 34 4.7 81 32 3.9 Monaro - Granite D Pasture 1 58 [+ or -] 36 73 [+ or -] 26 4.6 [+ or -] 0.2 30 [+ or -] 20 57 [+ or -] 28 4.5 [+ or -] 0.3 Pasture N 43 [+ or -] 8 71 [+ or -] 41 4.7 [+ or -] 0.2 21 [+ or -] 6 50 [+ or -] 30 4.7 [+ or -] 0.2 Remnant 17 31 4.2 9 21 4.0 Monaro - Granite S Pasture N 41 [+ or -] 20 31 [+ or -] 7 4.8 [+ or -] 0.2 22 [+ or -] 11 26 [+ or -] 8 4.8 [+ or -] 0.4 Boorowa - Granite D Pasture I 31 [+ or -] 13 24 [+ or -] 10 5.3 [+ or -] 0.5 16 [+ or -] 4 16 [+ or -] 3 4.7 [+ or -] 0.4 Pasture N 17 [+ or -] 4 25 [+ or -] 10 4.8 [+ or -] 40.3 8 [+ or -] 2 17 [+ or -] 5 4.6 [+ or -] 0.3 Remnant 16 20 5.2 7 10 5.2 Coleambally - Plains Crop 67 [+ or -] 50 50 [+ or -] 31 5.8 [+ or -] 0.8 46 [+ or -] 35 53 [+ or -] 46 5.7 [+ or -] 0.8 Pasture 1 69 [+ or -] 30 52 [+ or -] 10 5.9 [+ or -] 0.8 31 [+ or -] 13 34 [+ or -] 17 5.9 [+ or -] 0.9 Remnant 128 75 5.1 79 118 5.6 Class Texture Monaro Basalt Pasture I Clay loam (A) Clay loam (B) Pasture N Clay loam (C) Light clay (D) Remnant Monaro - Granite D Pasture 1 Sandy loam (A) Sandy loam (B) Pasture N Sandy loam (C) Coarse, sandy clay (D) Remnant Monaro - Granite S Pasture N Sandy loam (A) Sandy loam (B) Sandy loam (C) Coarse sandy clay (D) Boorowa - Granite D Pasture I Fine sandy loam (A) Fine sandy loam (B) Pasture N Sandy loam (C) Sandy clay loam (D) Remnant Coleambally - Plains Crop Light clay (A) Light clay (B) Pasture 1 Light clay (C) Medium clay (D) Remnant (A) 0-5cm soil layer. (B) 5-10cm soil layer. (C) 10-20cm soil layer. (D) 20-30cm soil layer. Table 3. Mean ([+ or -] s.e.m.) stock of total (TOC), particulate (POC), resistant (ROC) and humic (HUM) organic carbon for the 0-10 and 0-30 cm soil layers based on parent material and region Parent material was classified as basalt-, deep granite (Granite D)-, shallow granite (Granite S)- and plains (non-indurated sediments (-derived soil. Within rows, values with different letters differ significantly (P<0.05) Monaro Parent material Basalt Granite D TOC (t [ha.sup.-1]) 0-10cm 33.4 [+ or -] 1.7d 25.5 [+ or -] 1.7c 0-30 cm 77.1 [+ or -] 3.3c 51.5 [+ or -] 3.3b POC (t [ha.sup.-1]) 0-10 cm 6.5 [+ or -] 0.6c 8.6 [+ or -] 0.6d 0-30 cm 12.8 [+ or -] 0.8c 13.3 [+ or -] 0.8c ROC (t [ha.sup.-1]) 0-10 cm 7.3 [+ or -] 0.3d 5.6 [+ or -] 0.3c 0-30 cm 18.6 [+ or -] 0.7e 12.5 [+ or -] 0.7d HUM (t [ha.sup.-1]) 0 10 cm 16.7 [+ or -] 0.7d 12.8 [+ or -] 0.7c 0-30 cm 44.9 [+ or -] 1.7d 29.3 [+ or -] 1.7c Boorowa Parent material Granite S Granite D TOC (t [ha.sup.-1]) 0-10cm 12.9 [+ or -] 2.4a 18.8 [+ or -] 1.4b 0-30 cm 27.2 [+ or -] 4.6a 31.5 [+ or -] 2.7a POC (t [ha.sup.-1]) 0-10 cm 4.4 [+ or -] 0.8b 4.6 [+ or -] 0.5b 0-30 cm 6.4 [+ or -] 1.1b 5.7 [+ or -] 0.7b ROC (t [ha.sup.-1]) 0-10 cm 4.3 [+ or -] 0.5b 3.6 [+ or -] 0.3b 0-30 cm 9.1 [+ or -] 1.0c 6.3 [+ or -] 0.6b HUM (t [ha.sup.-1]) 0 10 cm 9.6 [+ or -] 1.0b 7.9 [+ or -] 0.6ab 0-30 cm 20.5 [+ or -] 2.5b 14.6 [+ or -] 1.5a Coleambally Parent material Plains TOC (t [ha.sup.-1]) 0-10cm 14.0 [+ or -] 1.4a 0-30 cm 24.5 [+ or -] 2.6a POC (t [ha.sup.-1]) 0-10 cm 2.1 [+ or -] 0.4a 0-30 cm 3.1 [+ or -] 0.6a ROC (t [ha.sup.-1]) 0-10 cm 2.6 [+ or -] 0.3a 0-30 cm 4.6 [+ or -] 0.5a HUM (t [ha.sup.-1]) 0 10 cm 6.7 [+ or -] 0.6a 0-30 cm 12.7 [+ or -] 1.4a Table 4. Mean stock of total (TOC), particulate (POC), resistant (ROC) and humic (HUM) organic carbon for the 0-10 and 0-30 cm soil layers according to vegetation type within parent material and region Parent material was classified as basalt-, deep granite (Granite D)-, shallow granite (Granite S)- and plains (non-indurated sediments (-derived soil. Vegetation classes were classified as introduced perennial pastures (IPP), native perennial pastures (NPP), introduced pasture (IP), crop and remnant vegetation (Rem; single sites only, except for the Boorowa region, where two Rem sites (Rem 1, Rem 2) were sampled) Monaro Basalt Granite D IPP NPP Rem IPP NPP Rem TOC (t [ha.sup.-1]) 0-10 cm 36.1 30.7 28.0 24.2 26.9 21.6 0-30 cm 82.1 72.2 66.1 51.2 51.9 37.9 POC (t [ha.sup.-1]) 0-10cm 7.2 5.8 3.7 8.4 8.9 4.2 0-30 cm 13.7 12.0 8.1 13.4 13.3 6.5 ROC (t [ha.sup.-1]) 0-10cm 7.7 6.9 6.6 5.3 5.9 3.4 0-30 cm 19.7 17.5 16.2 12.0 12.9 7.6 HUM (t [ha.sup.-1]) 0-10cm 17.7 15.8 13.4 12.3 13.2 6.1 0-30cm 46.7 43.0 37.3 28.7 29.8 14.9 Monaro Boorowa Granite S Granite D NPP IPP NPP Rem 1 Rem 2 TOC (t [ha.sup.-1]) 0-10 cm 12.9 19.1 18.5 25.3 20.2 0-30 cm 27.2 32.3 30.7 36.7 37.1 POC (t [ha.sup.-1]) 0-10cm 4.4 4.4 4.7 4.6 3.3 0-30 cm 6.4 5.6 5.9 5.6 4.2 ROC (t [ha.sup.-1]) 0-10cm 4.3 3.6 3.5 4.3 4.3 0-30 cm 9.1 6.5 6.1 6.4 7.6 HUM (t [ha.sup.-1]) 0-10cm 9.6 7.8 7.9 7.7 8.0 0-30cm 20.5 14.6 14.7 11.1 14.2 Coleambally Plains Crop IP Rem s.e.m. TOC (t (^) [ha.sup.-1]) 0-10 cm 12.1 16.0 16.2 2.2 0-30 cm 22.9 26.1 26.7 4.2 POC (t [ha.sup.-1]) 0-10cm 1.6 2.7 3.4 0.7 0-30 cm 2.7 3.6 4.9 1.0 ROC (t [ha.sup.-1]) 0-10cm 2.5 2.8 4.2 0.4 0-30 cm 4.7 4.5 6.0 0.9 HUM (t [ha.sup.-1]) 0-10cm 6.2 7.3 9.6 1.0 0-30cm 12.9 12.6 15.8 2.3 (^) The standard error of the mean (s.e.m.) was derived from the error variance as estimated by a linear model. As the survey was unbalanced, each group had a different standard error of the mean and therefore the average standard error is presented. Table 5. Correlations of total organic carbon (TOC), determined using the dry combustion method, and organic carbon fractions, determined by mid-infrared spectroscopy (MIR), with selected soil factors as determined by MIR for the 0-30cm layer and chemically for the 0-20 cm layer Asterisks indicate significant values (* P< 0.05, ** P< 0.01). POC, particulate organic carbon; ROC, resistant organic carbon; HUM, humic organic carbon; TN, total nitrogen; Col P, Colwell phosphorus Correlation coefficient (r) MIR analysis 0-30 cm TOC POC HUM POC 0.85 ** 1 HUM 0.86 ** 0.82 ** 1 ROC 0.91 ** 0.87 ** 0.81 ** TN 0.99 ** 0.85 ** 0.90 ** PH -0.43 * -0.33 * -0.38 * Clay -0.12 0.03 0.19 Silica -0.14 -0.26 -0.53 ** (Si[O.sub.2]) A1 -0.03 0.18 0.40 * Fe -0.09 -0.07 0.28 Chemical analysis 0-20 cm TOC POC HUM POC 0.84 ** 1 HUM 0.87 ** 0.83 ** 1 ROC 0.91 ** 0.86 ** 0.84 ** Col P 0.11 0.16 0.11 Available S 0.32 * 0.31 * 0.40 * CEC 0.31 * 0.30 * 0.20 TN 0.81 ** 0.83 ** 0.82 ** Labile C 0.60 ** 0.68 ** 0.61 ** PH -0.23 -0.18 -0.27 Correlation coefficient (r) MIR analysis 0-30 cm ROC TN pH POC HUM ROC 1 TN 0.89 ** 1 PH -0.30 * -0.40 * 1 Clay -0.08 -0.02 0.36 * Silica -0.16 -0.23 -0.24 (Si[O.sub.2]) A1 0.05 0.08 0.32 * Fe -0.15 -0.02 0.21 Chemical analysis 0-20 cm ROC Col P Available S POC HUM ROC 1 Col P 0.29 1 Available S 0.33 * 0.18 1 CEC 0.44 * 0.24 0.24 TN 0.74 ** 0.13 0.36 * Labile C 0.59 ** -0.01 0.47 * PH -0.17 0.27 0.08 Correlation coefficient (r) MIR analysis 0-30 cm Clay Silica Al (Si[O.sub.2]) POC HUM ROC TN PH Clay 1 Silica -0.65 ** 1 (Si[O.sub.2]) A1 0.68 ** -0.94 ** 1 Fe 0.54 ** -0.87 ** 0.75 ** Chemical analysis 0-20 cm CEC TN Labile C POC HUM ROC Col P Available S CEC 1 TN 0.36 * 1 Labile C 0.22 0.74 ** 1 PH 0.50 ** -0.2 -0.26
|Printer friendly Cite/link Email Feedback|
|Author:||Orgill, Susan E.; Condon, Jason R.; Conyers, Mark K.; Morris, Stephen G.; Murphy, Brian W.; Greene,|
|Date:||Nov 1, 2017|
|Previous Article:||Tree-based techniques to predict soil units.|
|Next Article:||Carbon and nitrogen molecular composition of soil organic matter fractions resistant to oxidation.|