Quantification of soil carbon from bulk soil samples to

Geoderma 267 (2016) 207–214

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Quantification of soil carbon from bulk soil samples to predict the aggregate-carbon fractions within using near- and mid-infrared spectroscopic techniques M.P.N.K. Henaka Arachchi, D.J. Field ⁎, A.B. McBratney Department of Environmental Sciences, Faculty of Agriculture and Environment, The University of Sydney, NSW 2006, Australia

a r t i c l e

i n f o

Article history: Received 31 May 2015 Received in revised form 16 December 2015 Accepted 28 December 2015 Available online 17 January 2016 Keywords: Soil aggregates Soil organic carbon NIR MIR Aggregate fractionation

a b s t r a c t There is a persistent general concern with carbon sequestration and modeling of soil carbon change affecting global issues, such as climate change and food security. To address these concerns requires the measurement of carbon everywhere and routinely, but the rate limiting step is the need to physically fraction the soil carbon to establish; where it is stored in soil, to model the formation of soil aggregates that physically protect soil carbon, and in-turn to populate soil carbon models. To remove the need for this fractionation pretreatment, commonly done by wet-sieving, this study scopes the notion of the efficacy of using near- (NIR) and mid- (MIR) infrared derived spectra taken of bulk soil samples to predict carbon in the separated aggregate fractions contained within. Forty five surface soil samples were collected from three bioregions of New South Wales providing for a range of soil types and associated soil carbon. The carbon content was measured of the bulk soil samples and their aggregate fractions of b 63 μm, 63–250 μm, and N 250 μm subsequently separated by wet-sieving. The bulk soil samples were scanned in the spectral ranges 800–2500 nm (NIR region) and 2500–25,000 nm (MIR region). The Cubist regression tree model was used to predict the carbon content in the aggregate fractions scanned from the bulk soil samples. The cross-validation results reveal that the MIR demonstrated the strongest correlation between measured and predicted carbon of the aggregate fractions demonstrated by high R2 (0.63–0.85) and ration of performance to inter-quintile distance (RPIQ, 0.53–0.93). The wavelengths selected in the Cubist model coincide with wavelengths identified as characterizing adsorption due to chemistry of soil carbon in some recently published works in this area of research. © 2016 Elsevier B.V. All rights reserved.

1. Introduction Soil carbon has been a major focus globally in the last 5 to 10 years and this is partly driven by its sequestering potential in soil having a significant and achievable impact on mitigating rises in atmospheric carbon (Bellon-Maurel and McBratney, 2011; Knox et al., 2015). This is because SOC is the largest terrestrial store of carbon making it the second largest to the ocean (Lal, 2009; Stockmann et al., 2013). The singling out of soil carbon as one of the seven unique functions of soil is in the further evidence of wide ranging interest of soil carbon (CEC, 2006; Bouma and Droogers, 2007; McBratney et al., 2014). Also, there remains an on-going need to integrate knowledge of SOC and SOC models into existing models, such as those focusing on hydrology and predicting ecosystem change (Karim Malamoud et al., 2009; Bouma and McBratney, 2013). Finally, the fact that carbon responds quickly to changes in the soil makes it a amenable indictor of soil change, and the fact that the carbon is already recognized by the broader community ⁎ Corresponding author at: Department of Environmental Sciences, Faculty of Agriculture and Environment, Suite 103, Biomedical Building, C81, Australian Technology Park, NSW 2006, Australia. E-mail address: damien.fi[email protected] (D.J. Field).

http://dx.doi.org/10.1016/j.geoderma.2015.12.030 0016-7061/© 2016 Elsevier B.V. All rights reserved.

means it could be easily understood by policy makers and the general public (Schmidt et al., 2011; Koch et al., 2014), makes it a amenable indictor of soil change. In response to these recent observations there is an urgent need to be able to routinely, efficiently, and cheaply measure carbon everywhere (Grunwald, 2009) so as to effectively monitor its change. This not only includes the assessment of total soil carbon but a rapid assessment of soil fractions that are routinely used in soil carbon models. Equally important is the need to quantify where the carbon is stored within soil aggregate fractions providing for their integrity and in-turn it being physically protecting from immediate degradation. Although there has been progress in expediting the quantification of SOC of bulk soil using spectroscopic techniques, such as near- (NIR) and midinfrared (MIR) spectroscopy, (Janik et al., 2007; Reeves, 2010; Stenberg et al., 2010; Bellon-Maurel and McBratney, 2011) the rate limiting step of having to physically fraction the soil carbon (Reeves et al., 2006; Zimmermann et al., 2007) to address where it is stored and to populate current carbons models, such as Struc-C and CAST, remains (Karim Malamoud et al., 2009; Stamati et al., 2013). The natural variation of soil types and the impact of land-use result in variation of soil carbon both spatially and temporarily (Knox et al., 2015). This is because the distribution of carbon is partly determined

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Fig. 1. Sample selection for aggregates fractionation and spectroscopy measurements.

by the potential sources and nature of soil carbon provided by the associated above ground biomass and microbial residues (Gregorich et al., 2006; Clemente et al., 2011). Within soil SOC is distributed throughout soil aggregate fractions and is implicated in their formation and stabilization. The smallest of these aggregates are composed of organomineral associations, which are in-turn bound with bacterial and fungal debris to form microaggregates, and the clustering of these into macroaggregates (Emerson, 1959; Edwards and Bremner, 1967; Tisdall and Oades, 1982; Six et al., 2000). Over time the degradation of organic carbon binding agents in macroaggregates results in the release of the more stable microaggregates, and these microaggregates have the potential to form the building blocks for the next cycle of macroaggregate formation (Six et al., 2000). Up to 90% of SOC in surface soils is found to be located within aggregates (Six et al., 2002) and 20–40% of the SOC is intra-microaggregates (Jastrow et al., 1996; Carter, 1996). To obtain these fractions requires separation using pre-treatments of wet sieving and density separation which is expensive, time consuming, and therefore preclude this from being part of most routine soil analysis procedures (Ashman et al., 2003; McBratney et al., 2006; Viscarra Rossel and Hicks, 2015). It has been shown that spectroscopic methods can be used to measure soil carbon rapidly, inexpensively, and nondestructively (Janik et al., 1998; Viscarra Rossel et al., 2006). A number of databases now exist where the SOC has been determined by mid-infrared spectroscopy (Reeves et al., 2006; Zimmermann et al., 2007; Bornemann et al., 2008; Yang et al., 2012) and recently Viscarra Rossel and Hicks (2015) reported on the use of vis-NIR to predict carbon fractions used to populate soil carbon models associated with the measured SOC. A few studies have also reported on the prediction of soil structural properties, such as soil aggregation, using NIR and MIR spectra (Chang et al., 2001; Madari et al., 2005; Minasny and McBratney, 2008; Sarkhot et al., 2011), but the aggregate associated carbon still has not been predicted without fractionation. This research will investigate the potential to predict soil carbon fractions from bulk soil scanned using NIR and MIR spectroscopy. Table 1 Basic soil properties of the surface horizon of the soil orders sampled. Soil order

Number of samples

pH

Clay (%)

Silt (%)

Sand (%)

TOC (g/kg)

Chromosol Kandosol Kurosol Rudosol Sodosol Vertosol

4 12 2 2 21 9

6.70 7.08 5.54 6.35 7.21 7.09

26 13 10 7 17 31

11 12 13 24 13 12

63 75 77 24 70 56

11.20 10.60 6.05 6.72 8.62 8.90

2. Material and methods 2.1. Soil sampling Soil samples were obtained from a soil survey conducted in 2010 (Singh et al., 2012) which focused on developing a soil spectral library for the prediction of soil carbon, primarily to be used to populate soil carbon turnover models. It is known that variation in soil carbon is strongly influenced by soil type and its associated variation with climate and land use (Lou et al., 2010) so to maximize the variation in SOC in the 2010 soil survey of three major bio-regions of New South Wales (NSW), Australia, namely the South Eastern highlands, NSW South Western Slopes and South Brigalow Belt covering an area of 158,000 km2 (Fig. 2) were sampled. The mean annual rainfall of the area sampled varies from 412 to 987 mm and the land-use types range from cropping, grazing of modified pasture, and natural vegetation. The sample sites were identified using Conditioned Latin Hypercube sampling (Minasny and McBratney, 2006) which enabled effective sampling of the regional soil variation with a minimal number of sampling sites. This resulted in 150 samples collected to a depth of 30 cm and the covariates and sample locations are reported in Singh et al. (2012). Due to the large number of samples that is generated when separating aggregates to study the size distribution and associated SOC the sample selection for this study was reduced from 150 to 50 samples. To maximize the changes in SOC and the variation in soil aggregation these parameters were used to cluster the 150 soil samples collected in the 2010 survey into 10 clusters from which the 50 samples were randomly selected (Fig. 1), using the influential properties of total organic carbon (TOC%) the quantity of clay, silt and changes in the cation exchange capacity (CEC) (Amézketa, 1999; Six et al., 2002; Bronick and

Table 2 The total organic carbon (TOC), pH, particle size distribution, mass recovery, and carbon recoveries of bulk soil. Character

Mean

St. dev.

Max.

Min.

Skewness

pH Clay (%) Silt (%) Sand (%) Bulk carbon (g/kg) Macroaggregate-C (g/kg) Microaggregate-C (g/kg) Organo-mineral-C (g/kg) Mass recovery (%) Carbon recovery (%)

7.06 19 12 68 10.21 3.09 2.456 4.08 96.63 98.25

0.91 9.94 4.55 9.33 6.51 2.22 1.45 2.27 3.27 19.22

8.66 43 24 89 29.80 10.90 7.10 10.50 102.67 128.20

5.09 4 3 38 2.53 0.80

–0.31 0.50 0.98 –0.40 1.72 2.35 1.23 1.10

1.10 90.08 74.84


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Fig. 2. Wet sieving procedure used to obtain aggregate fractions and the combined use of the spectroscopic data.

Lal, 2005)and the mean percentage of sand, silt, clay, total organic carbon, and pH are presented in Table 1.

Carbon recoveries% ¼

Carbon% ðMA þ MIA þ OMAÞ Carbon%of total soil

ð2Þ

2.2. Aggregate fractionation Aggregate fractionation was achieved using the wet-sieving method described by Six et al. (2000) as illustrated in Fig. 1. Some 100 g of airdried soil was submerged for 5 min on a 2000 μm sieve, and subsequently the aggregates were separated by moving the sieve up and down 3 cm with 50 repetitions for 2 min. The N 2000 μm aggregates were collected and sieving was repeated for the b2000 μm size fractions with the 250 μm and 63 μm sieves. For this study the three aggregate size fractions recovered are identified as macroaggregates (250– 2000 μm), microaggregates (63–250 μm) and organo-mineral associations (b 63 μm). The collected fractions were oven dried at 40 C and weighed. The TOC of the bulk soil and aggregate fractions were measured using an isotope ratio mass spectrometer (IRMS Delta V Thermo Finnigan, Bremen & Germany). Mass recoveries and carbon recoveries of bilk soil and aggregate fractions were calculated as follows: Dry mass ðMA þ MIA þ OMAÞ Mass recoveries% ¼ Dry mass of total soil

ð1Þ

Table 3 Cubist cross-validation of total organic carbon (TOC) using the near- (NIR) and mid-infrared (MIR) bulk spectra in aggregate fractions. Fraction

Macroaggregates NIR MIR Microaggregates NIR MIR Organo-mineral associations NIR MIR

Derivatives

Calibration

Validation

R2

RMSE

R2

RMSE

RPIQ

First First

0.69 0.91

1.31 0.70

0.59 0.88

1.42 0.70

0.35 0.69

None None

0.69 0.74

0.37 0.34

0.58 0.55

0.60 0.44

0.68 0.91

None None

0.67 0.78

1.12 1.32

0.42 0.35

1.94 1.90

0.41 0.39

where, MA as macroaggregates, MIA are microaggregates, and OMA are organo-mineral associations.

Table 4 A comparison of the carbon fractions measured, the spectroscopic ranges including vis-, near-(NIR) and mid-infrared (MIR), and analysis of the spectra, of selected recent publications. Selected recent publications

Measured

Method & analysis

Baldock et al. (2013)

Total inorganic (TIC) and organic carbon (TOC) and carbon pools; particulate (POC), humic (HUM) and resistant (ROC) carbon fractions Total carbon (TC) and selected carbon pools including; organic (SOC) recalcitrant (RC), hydrolysable (HC), carbon fractions Organic carbon (SOC) and carbon pools; particulate (POC), humic (HUM) and resistant (ROC) carbon fractions. Particulate organic carbon (POC) pool carbon (C) and nitrogen (N) contents in particle size fractions, 2000–53 μm, 53–2 μm, b2 μm Dissolved (DOC), particulate (POC), and resistant (ROC) carbon, and organic carbon associated with sand & stable aggregates, and silt & clay, fractions

Spectroscopy MIR; partial least squares regression (PLSR)

Knox et al. (2015)

Viscarra Rossel and Hicks, (2015)

Yang et al. (2012)

Zimmermann et al. (2007)

Spectroscopy vis-NIR & MIR; partial least squares regression (PLSR) and random forest (RF) Spectroscopy vis-NIR; decision-tree algorithm

Spectroscopy NIR and MIR; partial Least Squares regression (PLSR) Spectroscopy MIR; partial least squares regression (PLSR)

210


Fig. 3. Histograms, outlier box-plots and of carbon (g/kg) distribution of macroaggregates, microaggregates and organo-mineral associations.

2.3. Mid- and near-infrared diffuse reflectance spectroscopy Spectra of bulk soils were recorded using a Bruker Tensor 37 spectrophotometer equipped with an automatic high throughput device (Bruker HTS-XT GmbH, Ettlingen, Germany) with OPUS software version 6.5. This is operating with a liquid N2 cooled mercury–cadmium telluride (MCT) detector. About 20 mg of bulk soil sample was transferred to micro-plates and compacted to leave a plain and dense surface for measurement of the spectra using Diffuse Reflectance Infrared Fourier Transform (DRIFT). The spectra were collected at a resolution of 4 cm− 1 from 800 to 2500 nm (4000–124,500 cm− 1) with nearinfrared (NIR) and from 2500 to 25,000 nm (600–4000 cm−1) with mid-infrared (MIR) detectors. KBr powder was used to set a baseline

and 60 scans were done on every sample to minimize errors in spectroscopic measurement.

2.4. Spectral analysis MIR and NIR spectra taken from the Bruker Tensor 37 spectrophotometer were measured in the reflectance mode and were reprocessed to a resolution of 2 nm. The best fitting derivative was performed to correct for baseline differences between spectra and weak spectral signals. The spectra were then smoothed using the Savitsky–Golay algorithm with a window size of 11 and a second order polynomial (Savitzky and Golay, 1964).

Fig. 4. Calibration scatter plots the measured and cubits predicted total organic carbon (TOC) (g/kg) by near- (NIR) and mid-infrared (MIR) spectra.


2.5. Cubist regression model The Cubist model (Kuhn et al., 2013) was used to calibrate the spectra against the measured determined C content of bulk soil and aggregate fractions. The Cubist model was introduced as an alternative method for handling soil spectra by Minasny and McBratney (2008). The adoption of this approach is shown to result in high accuracy, easy interpretation and variable selection, parsimony, and respects the upper and lower boundary values of the predicant (Minasny et al., 2013; Viscarra Rossel and Hicks, 2015). The data were split randomly into two parts: 75% for developing the calibration model and 25% for validation model. The accuracy of the predictions was assessed using the coefficient of determination (R2), root mean squared error (RMSE), standard error of prediction (SEP), and ratio of the interquartile distance of the validation set to the standard error of prediction (RPIQ) (Bellon-Maurel et al., 2010). rn

RMSE ðSEPÞ ¼ RPIQ ¼

ðY YiÞ2 N i¼1

ð3Þ

IQ SEP

ð4Þ

Where, Yi = observed value, Y = predicted value, IQ = interquartile distance of the validation set (IQ = Q3 − Q1) and N is the number of

211

samples. Used as general quality parameters, larger R2 and RPIQ values combined with smaller RMSE values are considered to be good predictions for the models being developed. 3. Results and discussion 3.1. Basic soil properties and carbon distribution of aggregate fractions As can be seen in Table 2 there is a large range in the silt (3% to 24%) and clay (4 to 40%) and the TOC of the soils varies from 2.53 to 29.80 gkg−1. On average there was a recovery of 98.25% carbon after the wet sieving of the soils. Many studies show that the soil carbon concentrations when distributed into size fractions have a positive skew (Minasny et al., 2013), which are approximately log normal and the same is found here (Fig. 3). Macroaggregate-C shows a highly positive skewness compared to the microaggregate-C and organo-mineral associated-C. The relative C concentration for each aggregate fraction observed follows organo-mineral associations Nmacroaggregates N microaggregates (Table 2) and this generally agrees with the work of Sarkhot et al. (2011) who observed that the particle size C greater in the b53 μm. Generally, this relative concentration of carbon in aggregate fractions is explained by the notion that macroaggregates are clusters of microaggregates, intra-aggregate bonds and microbial biomass meaning they are carbon enriched (Cambedella and Elliot, 1994; Jastrow et al., 1996; Kleber et al., 2007; Puget et al., 1995; Six et al., 2000; Six et al., 2002).

Fig. 5. Variables used in the Cubist model for bulk soil near-infrared (NIR) spectra to enable the prediction of carbon in; a) macroaggregates, b) microaggregates, and c) organo-mineral associations.

212


3.2. Quantification of bulk soil TOC The comparison of the measured and predicted TOC of bulk soil samples is presented in Fig. 2. There are no derivatives required to get the best fit model by the MIR however, the first derivative was used for the NIR to enhance weak signals. The NIR and MIR successfully quantified the bulk soil TOC where the MIR calibration

resulted in a better prediction of TOC compared to the NIR. This is supported by the validation model with larger R 2 value and smaller RMSE for the MIR (R2 = 0.88, RMSE = 2.257 g kg− 1) compared to the NIR (R 2 = 0.90, RMSE = 3.11 g kg − 1 ). According to the literature MIR performs better compared to NIR in predicting SOC (Soriano-Disla et al., 2014), with prediction errors generally 10 to 40% lower, and this is attributed to MIR peaks being better

Fig. 6. Variables used in the Cubist model by bulk soil mid-infrared (MIR) spectra to predict carbon in; a) macroaggregates, b) microaggregates, and c) organo-mineral associations.


resolved, more absorbance providing more spectral information (Reeves, 2003).

213

Table 5 Comparison of time required for to prepare samples; a) using and b) without fraction procedures, before analysis of soil carbon for forty-five samples.

3.3. Quantification of C in aggregate fractions

Processing activity

a) Fractionation procedure (days)

The physically fractionated aggregate-carbon was predicted using NIR and MIR spectroscopy and the validation models were assessed using R2, RMSE and RPIQ (Table 3). The R2 values for macroaggregateC and microaggregate-C indicate that the carbon was approximated based on the criteria proposed by William (2003) where values between 0.66–0.81 indicate an approximate ‘quantitative’ prediction while values N 0.91 suggest an ‘excellent prediction’ and those inbetween ‘good’ predictions (Table 3). The RMSE for the NIR and MIR (Table 3) was the smallest for the microaggregates and when compared to the range of carbon values for the aggregate related carbon (Table 2) the RMSE is reasonable. Considering that the carbon distributions of the aggregate fractions are log-normal (Table 2) RPIQ values, as described by Bellon-Maurel et al. (2010), indicate that it was the largest for microaggregate-C predicted by MIR spectra and the smallest for organo-mineral -C. The aim to relate high loadings of IR frequencies in models to aggregate composition (i.e. functional groups) is ambitious and important work. This has been done previously, both indirectly as in now (via high predictive power models) and directly by infrared of isolated aggregates (Calderón et al., 2011; Verchot et al., 2011; Jindalunag et al., 2013) and, the prediction of soil carbon and nitrogen fractions is established by Yang et al. (2012). Also, much of the recent work focusing on assessing soil carbon using infrared spectroscopy has focused on measuring soil carbon relating to the conceptual pools describing soil carbon (Table 4) and the analysis of the spectra has is usually completed using a partial least squares approach. Here the output of the Cubist model provides the percentage of variables used in the model conditions and the wavelengths used as a predictor in the regression. The blue bars in Figs. 3 & 4 highlight the wavelengths used in the rule conditions and the purple refer to the wavelengths used in the regression model to predict carbon in the aggregate fractions. Both the NIR and MIR bulk soil spectra have used similar spectral bands to obtain the Cubist rule conditions and regression. The macroaggregate-C was predicted mainly from the aliphatic regions from NIR and MIR spectra, as well as the iron, aluminum, polysaccharide and hydroxyl bands (as attributed by Jastrow and Miller, 1998). The hydroxyl compounds are mainly alcoholic, carbohydrates and phenolic substances. The presence of these carbon bands in the macroaggregate fraction is associated with the presence of fungal hyphae, bacterial cells, algae and other transformation products such as aliphatic compounds (Jastrow and Miller, 1998; Tisdall and Oades, 1982). Thus, when this fraction is predicted from the bulk soil spectra, these functional groups are effectively used as predictors. Also, Calderón et al. (2011) observed a high absorption peak at 10,928 nm for light fraction SOM. Many studies have reported that the light fraction is an important component of macroaggregates and is highly correlated with macroaggregate-C (Jastrow and Miller, 1998; Golchin et al., 1994). This suggests that the presence of light fraction SOM in macroaggregates and this could be due to the effect of plant roots and other temporal binding materials of macroaggregates as proposed by Oades and Waters (1991). MIR wavelengths of 14,306 nm, 12,626 nm, 9416 nm, 3756 nm and 2631 nm were used as predictors in the regression to predict the microaggregate-C; whereas, NIR prediction was based on wavelengths at 825 nm, 950 nm, 1145 nm, 1364 nm and 2402 nm. Figs. 5 & 6.) These wavelengths mainly corresponded to aromatic compounds, polysaccharides and inorganic binding materials. The microaggregates are believed to be stabilized mainly by persistent agents such as aromatic humic materials associated with polyvalent metal cations such as amorphous iron and aluminum oxides and polysaccharides (Jastrow and Miller, 1998). Wavelengths related to amides have shown to be influential in predicting the carbon precentage of organo-mineral associations.

Spectral library generation Drying, sieving Wet-sieving Drying & grinding Grinding and packing Total carbon measurement Total days

0 7 5 5 0 1 18

b) No fractionation procedure (days) ~0.5 6 0 0 1.2 0.04 8

Previous studies revealed that the SOM associated with organo-mineral associations was enriched with various organic functional groups. Amide forms, aliphatic-C and oxidized-C are dominant in phyllosilicate, quartz, feldspar and iron oxides groups follow. In addition, Sarkhot et al. (2011) observed an amide peak for N53 μm wet- and dry-sieved derived aggregate fractions in MIR spectra. The wavelength corresponding to weathered mineral soil is shown by the pronounced iron oxide and clay mineral features near 1382 nm, 969 nm and 2407 nm (Viscarra Rossel and Webster, 2012). MIR wavelengths around 9960 nm and 9587 nm correspond to silicates and clay minerals since this fraction is enriched with coarse silt, coarse clay and fine clay (Christensen, 2001). A time comparison of using a wet-sieving procedure needed to separate aggregate fractions for assessment of their soil carbon using mass spectrometry (IMRS) and the quantification using the NIR and MIR spectroscopic techniques is shown in Table 5. It can be seen the processing of the 45 samples using wet sieving to obtain the macroaggregate, microaggregate and organo-mineral associations required 18 days for in the analysis. Whereas, it required 8 days to quantify the carbon content in the aggregate fractions. The grinding and drying preprocessing that was required to analyze the samples using the IRMS was not required for the spectroscopy, effectively saving the equivalent of 10 days. It should be noted that the development of the spectral data base would require at least 5 days, but if this library is used at least 100 times it could be assumed that this is equivalent to adding 0.05 days to the spectral analysis procedure. 4. Conclusions As has been demonstrated in previous publications the MIR has a greater potential to quantify the aggregate-C compared to the NIR spectroscopy. This is enabled by the use of the Cubist model and the mean adsorption from the aggregate spectra with selected wavelengths, some of which have been identified as functional groups that could characterize carbon fractions in each of the aggregate fractions, but in future will require further analysis to confirm this. The proposed method described in this paper demonstrates that the quantification of aggregate-C is possible from bulk soil NIR and MIR spectra, and this negates the need to for the traditional time-consuming fractionation methods before analyzing carbon which will be beneficial when having to analyze large number of samples. Acknowledgments We are grateful to Dr. Kanika Singh and Michael Short for helping with the soil sampling and we also extend our thanks to Professor Budiman Minasny, Mario Fajardo and Sebastian Campbell for their advice for using the Cubist model. References Amézketa, E., 1999. Soil aggregate stability: a review. J. Sustain. Agric. 14, 83–151. Ashman, M.R., Hallett, P.D., Brookes, P.C., 2003. Are the links between soil aggregates size class, soil organic matter and respiration rate artefacts of the fractionation procedure? Soil Biol. Biochem. 35, 435–444.

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Baldock, J., Hawke, B., Sanderman, J., Macdonald, L., 2013. Predicting contents of carbon and its component fractions in Australian soils from diffuse reflectance midinfrared spectra. Soil Res. 51, 577–595. Bellon-Maurel, V., McBratney, A., 2011. Near-infrared (NIR) and mid-infrared (MIR) spectroscopic techniques for assessing the amount of carbon stock in soils — critical review and research perspectives. Soil Biol. Biochem. 43, 1398–1410. Bellon-Maurel, V., Fernandez-Ahumada, E., Palagos, P., Roger, J.M., McBrateny, A.B., 2010. Critical review of chemometric indicators commonly used for assessing the quality of the prediction of soil attributes by NIR spectroscopy. Trends Anal. Chem. 29, 1073–1081. Bornemann, L., Welp, G., Brodowski, S., Rodionov, A., Amelung, W., 2008. Rapid assessment of black carbon in soil organic matter using mid-infrared spectroscopy. Org. Geochem. 39, 1537–1544. Bouma, J., Droogers, P., 2007. Translating soil science into environmental policy: A case study on implementing the EU soil protection strategy in The Netherlands. Environ. Sci. Pol. 10, 454–463. Bouma, J., McBratney, A.B., 2013. Framing soils as an actor when dealing with wicked environmental problems. Geoderma 200–201, 130–139. Bronick, C.J., Lal, R., 2005. Soil structure and management: a review. Geoderma 124, 3–22. Calderón, F.J., Reeves III, J.B., Collins, H.P., Paul, E.A., 2011. Chemical differences in soil organic matter fractions determined by diffuse-reflectance mid-infrared spectroscopy. Soil Sci. Soc. Am. J. 79, 568–579. Cambedella, C.A., Elliot, E.T., 1994. Carbon and nitrogen dynamics of soil organic matter fractions from cultivated grassland soils. Soil Sci. Soc. Am. J. 58, 123–130. Chang, C.W., Laird, D.A., Mausbach, M.J., Hurburgh, C.R., 2001. Near-infrared reflectance spectroscopy — principle components regression analysis of soil properties. Soil Sci. Soc. Am. J. 65, 480–490. Christensen, B.T., 2001. Physical fractionation of soil and structural and functional complexity in organic matter turnover. Eur. J. Soil Sci. 52, 345–353. Clemente, J.S., Simpson, A.J., Simpson, M.J., 2011. Association of specific organic matter compounds in size fractions of soils under different environmental controls. Org. Geochem. 42, 1169–1180. Commission of European Communities (CEC), 2006. Proposal for a Directive of the European Parliament and of the Council Establishing a Framework for the Protection of Soil and Amending Directive 2004/35/EC. Com 231 Final, Brussels. Edwards, A.P., Bremner, J.M., 1967. Microaggregates in soil. J. Soil Sci. 18, 64–73. Emerson, W.W., 1959. The structure of soil crumbs. J. Soil Sci. 10, 235–244. Golchin, A., Oades, J.M., Skjemstad, J.O., Clarke, P., 1994. Soil structure and carbon cycling. Aust. J. Soil Res. 32, 1043–1068. Gregorich, E.G., Beare, M.H., McKim, U.F., Skjemstad, J.O., 2006. Chemical and biological characteristics of physically uncomplexed organic matter. Soil Sci. Soc. Am. J. 70, 975–985. Grunwald, S., 2009. Multi-criteria characterization of recent digital soil mapping and soil modelling approaches. Geoderma 152, 195–207. Janik, L.J., Merry, H.R., Skjemstad, J.O., 1998. Can mid infrared diffuse reflectance analysis replace soil extractions? An. Prod. Sci. 38, 681–696. Janik, L.J., Skjemstad, J.O., Shepherd, K.D., Spouncer, L.R., 2007. The prediction of soil carbon fractions using mid–infrared–partial least square analysis. Aust. J. Soil Res. 45, 73–81. Jastrow, J.D., Miller, R.M., 1998. Soil aggregate stabilization and carbon sequestration. In: Lal, R., Kimble, J.M., Follett, R.F., Stewart, B.A. (Eds.), Soil Processes and the Carbon Cycle. CRC Press, Boca Raton, FL, pp. 207–233. Jastrow, J.D., Boutton, T.W., Miller, R.M., 1996. Carbon dynamics of aggregate-associated organic matter estimated by carbon-13 natural abundance. Soil Sci. Soc. Am. J. 60, 801–807. Jindalunag, W., Kheoruenromme, I., Suddhiprakarn, A., Singh, B.P., Singh, B., 2013. Influence of soil texture and mineralogy on organic matter content and composition in physically separated fractions soils of Thailand. Geoderma 195–196, 207–219. Karim Malamoud, K., McBratney, A.B., Minasny, B., Field, D.J., 2009. Modelling how carbon affects soil structure. Geoderma 149, 19–26. Kleber, M., Sollins, P., Sutton, R., 2007. A conceptual model of organo–mineral interactions in soils: self assembly of organic molecular fragments into zonal structures on mineral surfaces. Biogeochemistry 85, 9–24. Knox, N.M.S., Grunwald, S., McDowell, M.L., Bruland, G.J., Myers, D.B., Harris, W.G., 2015. Modelling soil carbon fractions with visible near-infrared (VNIR) and mid-infrared (MIR) spectroscopy. Geoderma 239–240, 229–239. Kuhn, M., Weston, S., Keefer, C., Coulter, N., 2013. Cubist Package. Version 0.0.13. Lal, R., 2009. Soil carbon sequestration impacts on global climate change and food security. Science 304, 1623–1627. Lou, Z., Wang, E., Sun, O.J., 2010. Soil carbon change and its responses to agricultural practices in Australian agro-ecosystems: a review and synthesis. Geoderma 155, 211–223. Madari, B.E., Reeves III, J.B., Coelho, M.R., Machado, P.L.O.A., De Polli, H., Coelho, R.J., Benites, V., Souza, M., McCarty, L.F., G. W., 2005. Mid- and near-infrared spectroscopic determination of total and organic carbon in a diverse set of soils from the Brazilian national soil collection. Spectrosc. Lett. 38, 721–740.

McBratney, A.B., Minasny, B., Viscarra-Rossel, R., 2006. Spectral analysis and inference systems: a powerful combination for solving the soil data crisis. Geoderma 136, 272–278. McBratney, A.B., Field, D.J., Koch, A., 2014. The dimensions of soil security. Geoderma 213, 203–213. Minasny, B., McBratney, A.B., 2006. A conditioned Latin hypercube method for sampling in the presence of ancillary information. Comput. Geosci. 32, 1378–1388. Minasny, B., McBratney, A.B., 2008. Regression rules as a tool for predicting soil properties from infrared reflectance spectroscopy. Chemom. Intell. Lab. Syst. 94, 72–79. Minasny, B., McBratney, A.B., Stockmann, U., Hong, Y.S., 2013. Cubist a regression rule approach for use in calibration of NIR spectra. NIR 2013 Proceedings, 2–7 June. LaGrande-Motte, France. Oades, J.M., Waters, A.G., 1991. Aggregate hierarchy in soil. Aust. J. Soil Res. 29, 815–828. Puget, P., Chenu, C., Balesdent, J., 1995. Total and young organic matter distributions in aggregates of silty cultivated soils. Eur. J. Soil Sci. 46, 449–459. Reeves III, J.B., 2003. Mid-infrared diffuse reflectance spectroscopy: is sample dilution with KBr necessary, and if so, when? Am. Lab. 35, 24–28. Reeves, J.B., 2010. Near versus mid-infrared diffuse reflectance spectroscopy for soil analysis emphasizing carbon and laboratory versus on-site analysis: where are we and what needs to be done? Geoderma 158, 3–14. Reeves III, J.B., Follett, R.F., McCarty, G.W., Kimble, J.M., 2006. Can near or mid-infrared diffuse reflectance spectroscopy be used to determine soil carbon pools? Commun. Soil Sci. Plant Anal. 37, 2307–2325. Sarkhot, D.V., Grunwald, S., Ge, Y., Morgan, C.S.I., 2011. Comparison and detection of total and available soil carbon fractions using visible/near infrared diffuse reflectance spectroscopy. Geoderma 164, 22–32. Savitzky, A., Golay, M.J.E., 1964. Smoothing and differentiation of data by simplified least squares procedures. Anal. Chem. 36, 1627–1639. Schmidt, W.I., Tom, M.S., Abiven, S., Dittmar, T., Guggenberger, G., Janssesns, I.A., Kleber, M., Kögel-Knabner, I., Lehmann, J., Manning, D.A.C., Nannipieri, P., Rasse, D.P., Weiner, S., Trumbore, S.E., 2011. Persistence of soil organic matter as an ecosystem property. Nature 478, 49–56. Singh, K., Minasny, B., McBratney, A.B., Short, M.G., Namazi, F.M., 2012. Sampling for field measurement of soil carbon using Vis-NIR spectroscopy. In: Minasny, B., Malone, B., McBratney, A.B. (Eds.), Digital Soil Assessments and Beyond. Taylor & Francis Group, London. Six, J., Paustin, K., Elloit, E.T., Combrink, C., 2000. Soil structure and organic matte: I. distribution of aggregate-size classes and aggregate-associated carbon. Soil Sci. Soc. Am. J. 64, 681–689. Six, J., Conant, R.T., Paul, E.A., Paustin, K., 2002. Stabilisation mechanisms of soil organic matter: implications for C-sequestration of soils. Plant Soil 241, 155–176. Soriano-Disla, J.M., Janik, L.J., Viscarra Rossel, R.A., Macdonald, L.M., McLaughlin, M.J., 2014. The performance of visible, near-, and mid-infrared reflectance spectroscopy for prediction of soil physical, chemical, and biological properties. Appl. Spectrosc. Rev. 49, 141–163. Stamati, F.E., Nikolaidis, N.P., Banwart, S., Blum, W.E.H., 2013. A coupled carbon, aggregation, and structure turnover (CAST) model for topsoils. Geoderma 211–212, 51–64. Stenberg, B., Viscarra Rossel, R.A., Mouazen, A.M., Wetterlind, J., 2010. Visible and near infrared spectroscopy in soil science. Adv. Agron. 107, 163–215. Stockmann, U., Adams, M.A., Crawford, J.W., Field, D.J., Henakarchchi, N., Jenkins, M., Minasny, B., McBratney, A.B., de Courcelles, V., De, R., Singh, K., Wheeler, I., Abbott, L., Angers, A.D., Baldock, J., Bird, M., Brookes, P.C., Chenu, C., Jastrow, J.D., Lal, R., Lehmann, J., O'Donnell, A.G., Parton, W.J., Whitehead, D., Zimmermann, M., 2013. The knowns, known unknowns and unknowns of sequestration of soil organic carbon. Agric. Ecosyst. Environ. 164, 80–99. Tisdall, J.M., Oades, J.M., 1982. Organic matter and water-stable aggregates in soils. J. Soil Sci. 33, 141–163. Verchot, L.V., Dutaur, L., Shepherd, K.D., Albrecht, A., 2011. Organic matter stabilization in soil aggregates: understanding the biogeochemical mechanisms that determine the fate of carbon inputs in soils. Geoderma 161, 182–193. Viscarra Rossel, R.A., Hicks, W.S., 2015. Soil carbon and its fractions estimated by visiblenear infrared transfer functions. Eur. J. Soil Res. 66, 438–450. Viscarra Rossel, R., Webster, R., 2012. Predicting soil properties from the Australian soil visible-near infrared spectroscopic database. Eur. J. Soil Sci. 63, 848–860. Viscarra Rossel, R.A., Walvoort, D.J.J., McBratney, A.B., Janik, L.J., Skjemstad, J.O., 2006. Visible, near infrared, mid-infrared or combined diffuse reflectance spectroscopy for simultaneous assessment of various soil properties. Geoderma 131, 59–75. William, P., 2003. Near-infrared Technology—Getting the Best Out of Light. PDK Projects, Nanaimo, Canada. Yang, X.M., Xie, H.T., Drury, C.F., Reynolds, W.D., Yang, J.Y., Zhang, X.D., 2012. Determination of organic carbon and nitrogen in particulate organic matter and particle size fractions of Brookston clay loam soil using infrared spectroscopy. Eur. J. Soil Sci. 63, 177–188. Zimmermann, M., Leifeld, J., Furhrer, J., 2007. Quantification of soil organic carbon fractions by infrared spectroscopy. Soil Biol. Bichem. 39, 224–231.