Supplementary Information s73

Supplementary Information

The chemical composition of native organic matter influences the response of bacterial

community to input of biochar and fresh plant material

Congying Wang a,b, Craig Anderson c, Manuel Suárez-Abelenda d, Tao Wang a, Marta Camps-

Arbestain a, *, Riaz Ahmad a,e, H.M.S.K. Herath a, f a New Zealand Biochar Research Centre, Institute of Agriculture and Environment, Private

Bag 11222, Massey University, Palmerston North 4442, New Zealand b School of Environmental Science and Resources, Shanxi University, Taiyuan 030006, China c The New Zealand Institute for Plant and Food Research, Private Bag 4704, Christchurch

8140, New Zealand. d Departamento de Edafoloxía e Química Agrícola, Facultade de Bioloxía, Universidade de

Santiago de Compostela, 15782- Santiago de Compostela, Spain e Department of Soil Science & Soil Water Conservation, Pir Mehr Ali Shah Arid Agriculture

University, Rawalpindi, Pakistan f Department of Export Agriculture, Faculty of Animal Science and Export Agriculture, Uva

Wellassa University, Badulla 9000, Sri Lanka

*Corresponding author: Marta Camps-Arbestain

E-mail: [email protected]

Number of Tables: 5

Number of Figures: 7 12 12 ) A ) B % %

e e l l b b a 8 a t 8 t c c a r a t a r t x x e TK-respiration

e TK-plant

e - 6 t

a a a b c EG-respirationt h a a EG-plant p c h s 4 p b o s 4

h a b o

o b bc p r y 2 c o r P

y 2 c P 0 Ctr CS CS-350 CS-550 0 Ctr CS CS-350 CS-550 12 12 C ab b a ab D

) 10 % % ( (

e e l 8 a a l 8 b a a b a a s s i i d d i i

x TK-respiration 6 x TK-plant

o 6 o - - e e t a a

EG-respiration t

a b a EG-plant a m 4 a a m o a 4 o r a r h h c i c i D 2 D 2

0 0 Ctr CS CS-350 CS-550 Ctr CS CS-350 CS-550

12 b 12 b b a a b a a F 10 E 10

8 8 ) ) % % ( (

TK-respiration l 6 TK-plant l 6 a

a b t t

o b EG-respiration o ab T EG-plant a a b b T a 4 4

0 0 Ctr CS CS-350 CS-550 Ctr CS CS-350 CS-550

Figure S1. Pyrophosphate-extractable C (A, B), dichromate-oxidisable C (C, D), and total C (E, F) content (in % w/w basis) of the Alfisol (TK) and the Andisol (EG) samples incubated in the respiration study and in the plant study at time 510 d. CS = corn stover, CS-350 = biochar produced from CS at a peak temperature of 350 °C, CS-550 = biochar produced from CS at a peak temperature of 550 °C. For a specific soil and study type, significant (P<0.05) differences based on Duncan post hoc test between means are denoted by different letters. For a given soil and study type, specific patterns in pyrophosphate-extractable C were observed, which contrast with their relatively similar total C and oxidisable C values. This extractant is considered to mostly release organic C associated to organo-metal complexes and that adsorbed on mineral surfaces, although its specificity is yet to be proven (Kögel- Knabner et al., 2008). These results evidence the existence of differences in the quality of the organic matter between treatments – and similarities among replicates of a specific treatment − and justify the pooling of samples from the same treatment.

Table S1. Percentage of the total quantified peak area of the major pyrolysis products grouped based on their possible sources (Buurman et al. 2007; Suárez-Abelenda et al. 2014) of a sample of a New Zealand soil treated and analysed as the soil samples of this study. The table shows that replicates from the same sample have similar peak areas, with CV generally being <10% (exceptions are products with low abundances). It should be noted that the conclusions of this study are drawn based on the factor analysis and RDA after the centre-log transformation of the data. Multivariate analyses such as these are likely to maximise the effect of the different treatments based on the overall data distribution rather than on the value of a specific compound. Therefore, it is reasonably safe to run a single subsample to represent a well-mixed soil sample via Py-GC/MS technique.

replicate replicate replicate replicate replicate replicate Average SD CV

Group 1 2 3 4 5 6

Polysaccharides 43.24% 43.89% 44.21% 44.77% 42.55% 40.58% 43.21% 1.50% 3.5

Phenol 3.35% 6.72% 7.72% 7.21% 8.36% 8.45% 6.7% 2.0% 29.3

Aromatics 13.99% 13.19% 12.90% 11.35% 12.67% 13.71% 12.8% 1.0% 7.5

Lignin 4.43% 4.34% 4.33% 5.90% 5.95% 4.50% 5.0% 0.9% 17.1

Alkanes 15.31% 13.43% 12.59% 13.90% 12.45% 13.16% 13.5% 1.2% 8.6

Alkenes 10.98% 9.48% 9.02% 7.89% 8.73% 9.78% 9.2% 1.1% 12.4

N-compounds 4.86% 5.20% 5.68% 5.38% 5.95% 6.37% 5.4% 0.4% 7.8

Fatty Acids 0.19% 0.24% 0.23% 0.21% 0.19% 0.27% 0.2% 0.0% 10.4

Other alk 0.43% 0.37% 0.39% 0.42% 0.41% 0.25% 0.4% 0.0% 5.6

Methyl ketones 2.96% 2.76% 2.50% 2.54% 2.33% 2.51% 2.6% 0.2% 9.3

Other 0.28% 0.38% 0.43% 0.42% 0.41% 0.43% 0.4% 0.1% 16.5

Sum 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% Table S2. Pyrolysis products

Pyrolysis product list obtained from the quantification of major peaks, with retention times

(RT), fragments ion used for quantification (m/z) and % of total quantified peak area (mean

and standard deviation for all studied samples).

% (Standard Code compounds M+ m/z RT % (Mean) deviation)

9:0-33:0 alkanes. C9-C33 128-464 57+71 4.20 - 26.24 6.48 0.66

9:1-30:1 alkenes. C9-C30 126-420 55+69 4.31 - 28.19 10.28 1.29 Al1 branched alkene - 55+69 8.735 0.32 0.11 Al2 prist-1-ene 266 56+57 15.440 0.17 0.02 Al3 branched alkene - 55+69 16.230 0.05 0.01 Al4 branched alkene - 55+69 16.775 0.20 0.06 Al5 branched alkene - 55+69 19.620 0.36 0.09 Al6 branched alkene - 55+69 24.330 0.05 0.02 Al7 branched alkene - 55+69 25.425 0.03 0.01 Al8 branched alkene - 55+69 26.060 0.04 0.01 Al9 squalene 410 81+95 36.605 0.07 0.02

K13-K35 methylketones. C13-C35 198-506 58+59 12.69 - 31.86 2.62 0.36

FA5-F24 fatty acids. C5-C24 102-368 60+73 4.48 - 24.72 5.78 0.75

FA15i iso-fatty acid C15 242 60+73 16.530 0.09 0.04

FA15a ante iso-fatty. acid C15 242 60+73 16.615 0.20 0.03

FA16i iso-fatty acid. C16 256 60+73 17.545 0.20 0.07

FA17i iso-fatty acid. C17 270 60+73 18.530 0.04 0.01

FA17a ante iso-fatty acid. C17 270 60+73 18.615 0.07 0.01 Ph1 phenol 94 66+94 5.635 3.89 0.84 Ph2 2-methylphenol 108 107+108 6.665 0.43 0.10 Ph3 3/4-methylphenol 108 107+108 7.020 2.25 0.41 Ph4 3-ethylphenol 122 107+122 7.885 0.04 0.01 Ph5 3.4-dimethylphenol 122 107+122 8.055 0.23 0.05 Ph6 4-ethylphenol 122 107+122 8.380 0.51 0.12 Lg1 guaiacol 124 109+124 7.125 0.69 0.11 Lg2 4-methylguaiacol 138 123+138 8.650 0.40 0.10 Lg3 4-vinylphenol 120 91+120 9.240 1.87 0.67 Lg4 4-ethylguaiacol 152 137+152 9.890 0.15 0.04 Lg5 4-vinylguaiacol 150 135+150 10.405 1.79 0.27 Lg6 syringol 154 154+139 10.925 0.16 0.03 Lg7 4-methylsyringol 168 153+168 12.170 0.11 0.03 Lg8 4-vinylsyringol 180 180+165 13.655 0.20 0.04 Lg9 4-(-prop-1-enyl)syringol 194 194+91 15.240 0.14 0.03 Ar1 benzene 78 77+78 2.210 1.23 0.24 Ar2 toluene 92 91+92 2.900 5.45 0.68 Ar3 ethylbenzene 106 91+106 3.875 0.36 0.07 Ar4 dimethylbenzene 106 91+106 3.980 0.40 0.11 Ar5 styrene 104 78+104 4.240 1.42 2.30 Ar6 dimethylbenzene 106 91+106 4.285 0.36 0.06 Ar7 acetophenone 120 77+105 6.770 0.33 0.04

B3 benzene. C3 120 91+92 5.085 0.12 0.02

B4 benzene. C4 134 91+92 6.580 0.10 0.01

B4:1 benzene. C4:1 (3-butenyl-) 132 91+132 6.390 0.09 0.04 B5-B28 benzenes. C5-C28 148-470 91+92 8.08 - 30.12 0.87 0.18

B17:2 benzene. C17:2 314 91+92 22.320 0.11 0.40 PA1 indene 116 115+116 6.440 0.34 0.06 PA2 1-methyl-1H-indene 130 115+130 8.005 0.15 0.02 PA3 2-methyl-1H-indene 130 115+130 8.095 0.14 0.02 PA4 3-methyl-1H-indene 130 115+130 8.235 0.03 0.00 PA5 biphenyl 154 153+154 11.280 0.04 0.01 PA6 fluorene 166 165+166 13.925 0.07 0.01

PA7 fluorene. C1 180 165+180 14.165 0.01 0.00 PA8 phenanthrene 178 178 16.220 0.03 0.01 PA9 anthracene 178 178 16.330 0.01 0.00 NA naphthalene 128 128 7.045 0.36 0.10

NA1 naphthalene. C1 142 141+142 9.700 0.30 0.05

NA1 naphthalene. C1 142 141+142 10.145 0.14 0.02

NA1 naphthalene. C1 142 141+142 10.380 0.11 0.01

NA2 naphthalene. C2 156 141+156 12.305 0.20 0.03

NA3 naphthalene. C3 170 155+170 13.250 0.01 0.00

NA3 naphthalene. C3 170 155+170 13.455 0.01 0.00

NA3 naphthalene. C3 170 155+170 13.970 0.01 0.00 N1 pyrrole 67 67 2.805 1.24 0.61 N2 pyridine 79 52+79 2.760 1.32 0.26

N3 pyrrole. C1 81 80+81 3.635 0.30 0.12

N4 pyrrole. C1 81 80+81 3.725 0.55 0.19 N5 acetamide 59 59 3.825 2.64 1.05 N6 benzonitrile 103 76+103 5.575 0.07 0.03 N7 benzeneacetonitrile 117 90+117 7.870 0.10 0.08 N8 indole 117 117+90 10.220 0.50 0.06

N9 indole. C1 131 130+131 11.450 0.27 0.07 N10 4-phenylpyridine 155 154+155 12.465 0.03 0.02 N11 diketodipyrrole 186 93+186 15.410 0.45 0.17 N12 diketopiperazine compound 194 70+194 18.045 0.66 0.15

AM20-AM22 amides. C20 -C22 311-339 59+72 23.45 - 24.98 0.15 0.02

AM26 amide. C26 395 59+72 27.750 0.09 0.02

AM28-AM34 amides. C28-C34 451-507 59+72 29.17 - 36.53 0.45 0.13 Ps1 acetic acid 60 60 2.280 6.35 0.92 Ps2 furan-3-one 84 54+84 3.250 3.64 1.16 Ps3 3-furaldehyde 96 95+96 3.315 0.32 0.08 Ps4 2-furaldehyde 96 95+96 3.555 6.91 1.73 Ps5 2-methyl-2-cyclopenten-1-one 96 67+96 4.450 0.12 0.02 Ps6 5-methyl 2-furfuraldehyde 110 109+110 5.240 2.07 0.55 4-hydroxy-5.6-dihydro-(2H)- Ps7 pyran-2-one 114 58+114 5.825 2.25 0.71 2-hydroxy-3-methyl 2- Ps8 cyclopenten-1-one 112 55+112 6.275 0.23 0.06 Ps9 levoglucosenone 126 68+98 6.340 0.10 0.05 Ps10 dianhydrorhamnose 128 113+128 6.430 0.72 0.19 Ps11 7-methylbenzofuran 132 131+132 7.325 0.06 0.01 Ps12 2-methylbenzofuran 132 131+132 7.390 0.11 0.03 Ps13 dimetilbenzofuran 146 145+146 8.860 0.04 0.01 Ps14 dimetilbenzofuran 146 145+146 8.980 0.10 0.02 Ps15 dianhydro-alpha-D-glucopyranose 144 69+57 9.205 1.28 0.16 Ps16 dibenzofuran 168 168+139 13.105 0.04 0.01 Ps17 levoglucosan 162 60+73 14.225 13.59 3.68

Ps18 furan. C14 264 81+95 17.310 0.07 0.01 St1 sterol-derived compound 396 145+147 27.165 0.03 0.01 St2 sterol-derived compound 410 174+161 29.420 0.04 0.02 St3 sterol-derived compound - 397+398 34.740 0.21 0.24 Factors Name Abbrev. Type Levels Soil Type So Fixed 2 Organic Amendment Oa Fixed 4 Study Type St Fixed 2

PERMANOVA table of results Unique Source df SS MS Pseudo-F P(perm) perms So 1 32011 32011 26.346 0.001 998 Oa 3 8592.2 2864.1 2.3572 0.001 999 St 1 10956 10956 9.0175 0.001 999 SoxOa 3 9330.8 3110.3 2.5598 0.001 996 SoxSt 1 10450 10450 8.601 0.001 999 OaxSt 3 4864.8 1621.6 1.3346 0.083 998 SoxOaxSt 3 6184 2061.3 1.6965 0.011 998 Res 73 88696 1215 Total 88 1.727E5

Figure S2. MDS ordination of all ARISA data and 3-factor PERMANOVA of the Bray Curtis similarity measures. Bacterial community profiles were analysed using ARISA and then compared using Bray Curtis similarity. MDS is unconstrained and represents the results from 250 restarts. TK = Tokomaru silt loam soil, EG = Egmont black loam soil, R = respiration study, P = planted study, CS = corn stover, CS-350 = biochar produced from CS at a peak temperature of 350 °C, CS-550 = biochar produced from CS at a peak temperature of 550 °C. Percentage of variation explained by individual axes % explained variation % explained variation out of fitted model out of total variation Axis Individual Cumulative Individual Cumulative 1 47.01 47.01 19.00 19.00 2 27.87 74.88 11.27 30.27 3 13.72 88.60 5.55 35.82 4 4.51 93.11 1.82 37.64 5 3.96 97.07 1.60 39.25 6 2.04 99.11 0.83 40.07 7 0.89 100 0.36 40.43

Figure S3. dbRDA plot and variation partitioning for DISTLM modelling (multiple regression) of the relationship between the multivariate data cloud of ARISA profiles from EG (Andisol) soil and SOM predictor variables from the pyGCMS analysis. R = respiration study, P = planted study, CS = corn stover, CS-350 = biochar produced from CS at a peak temperature of 350 °C, CS-550 = biochar produced from CS at a peak temperature of 550 °C. Thirteen predictor variables were used that represented combinations of similar compounds detected in the pyGCMS analysis. This model was used to investigate specific SOM drivers associated with samples from the respiration study versus samples from the planted study. The model was run using the ‘Best’ procedure which examines the value of the selection criterion for all possible combinations of predictor variables to produce the model with the lowest residual SS. ‘Adjusted R2’ was used as the selection criterion because it only accounts for the combination of predictor variables used. Percentage of variation explained by individual axes % explained variation % explained variation out of fitted model out of total variation Axis Individual Cumulative Individual Cumulative 1 56.04 56.04 18.9 18.90 2 14.03 70.07 4.73 23.63 3 12.09 82.17 4.08 27.70 4 7.09 89.26 2.39 30.10 5 6.13 95.39 2.07 32.16 6 2.98 98.37 1.01 33.17 7 1.63 100 0.55 33.72

Figure S4. dbRDA plot and variation partitioning for DISTLM modelling (multiple regression) of the relationship between the multivariate data cloud of ARISA profiles from TK (Alfisol) soil and SOM predictor variables from the pyGCMS analysis. R = respiration study, P = planted study, CS = corn stover, CS-350 = biochar produced from CS at a peak temperature of 350 °C, CS-550 = biochar produced from CS at a peak temperature of 550 °C. Thirteen predictor variables were used that represented combinations of similar compounds detected in the pyGCMS analysis. This model was used to investigate specific SOM drivers associated with samples from the respiration study versus samples from the planted study. The model was run using the ‘Best’ procedure which examines the value of the selection criterion for all possible combinations of predictor variables to produce the model with the lowest residual SS. ‘Adjusted R2’ was used as the selection criterion because it only accounts for the combination of predictor variables used.

Figure S5. Multiple regression Tree (MRT) comparing the similarity matrix of samples from the Respiration study with SOM predictor variables from the pyGCMS analysis. TK = Tokomaru silt loam soil, EG = Egmont black loam soil, CS = corn stover, CS-350 = biochar produced from CS at a peak temperature of 350 °C, CS-550 = biochar produced from CS at a peak temperature of 550 °C.The LINKTREE procedure was used with a minimum group size of 5 and minimum split size of 4. The LINKTREE procedure splits samples into logical groups based on their similarities and dissimilarities and then matches the ‘best’ variables from a complementary predictor data set to provide possible explanations as to why the samples have been split at each specific tree node. Multiple variables can be used at each node. Nodes are represented by capital letters. LINKTREE outputs a B% value that is a measure of absolute group differences at each tree node using ranks derived from the similarity matrix. B% values are used as a y-axis for the tree with horizontal lines linking the samples split at each node appearing at the appropriate location on the y-axis. LINKTREE also outputs ANOSIM R values (R = 0 means 100% similar, R = 1 means completely dissimilar) to describe the relationships between samples split at each tree node along with threshold values for predictor variables that describe how the chemical data is related to the samples split at each node. Predictor variable thresholds associated with samples to the left of the nodes are not in brackets while those associated with samples to the right of each node are bracketed.

Figure S6. Multiple regression Tree comparing the similarity matrix of samples from the Planted study with SOM predictor variables from the pyGCMS analysis. TK = Tokomaru silt loam soil, EG = Egmont black loam soil, CS = corn stover, CS-350 = biochar produced from CS at a peak temperature of 350 °C, CS-550 = biochar produced from CS at a peak temperature of 550 °C.The LINKTREE procedure was used with a minimum group size of 5 and minimum split size of 4. The LINKTREE procedure splits samples into logical groups based on their similarities and dissimilarities and then matches the ‘best’ variables from a complementary predictor data set to provide possible explanations as to why the samples have been split at each specific tree node. Multiple variables can be used at each node. Nodes are represented by capital letters. LINKTREE outputs a B% value that is a measure of absolute group differences at each tree node using ranks derived from the similarity matrix. B % values are used as a y-axis for the tree with horizontal lines linking the samples split at each node appearing at the appropriate location on the y-axis. LINKTREE also outputs ANOSIM R values (R = 0 means 100% similar, R = 1 means completely dissimilar) to describe the relationships between samples split at each tree node along with threshold values for predictor variables that describe how the chemical data is related to the samples split at each node. Predictor variable thresholds associated with samples to the left of the nodes are not in brackets while those associated with samples to the right of each node are bracketed. Table S3. Variation partitioning for DISTLM modelling (multiple regression) of the relationship between the multivariate data cloud of ARISA profiles from the Respiration Study and SOM predictor variables from the pyGCMS analysis. The model was run using the ‘Best’ procedure which examines the value of the selection criterion for all possible combinations of predictor variables to produce the model with the lowest residual SS. ‘Adjusted R2’ was used as the selection criterion because it only accounts for the combination of predictor variables used.

Percentage of variation explained by individual axes % explained variation % explained variation out of fitted model out of total variation Axis Individual Cumulative Individual Cumulative 1 58.77 58.77 28.42 28.42 2 19.37 78.14 9.37 37.78 3 8.25 86.40 3.99 41.77 4 5.59 91.99 2.70 44.48 5 4.83 96.82 2.33 46.81 6 1.72 98.54 0.83 47.65 7 1.46 100 0.70 48.35

Table S4. Variation partitioning for DISTLM modelling (multiple regression) of the relationship between the multivariate data cloud of ARISA profiles from the Planted Study and SOM predictor variables from the pyGCMS analysis. The model was run using the ‘Best’ procedure which examines the value of the selection criterion for all possible combinations of predictor variables to produce the model with the lowest residual SS. ‘Adjusted R2’ was used as the selection criterion because it only accounts for the combination of predictor variables used.

Percentage of variation explained by individual axes % explained variation % explained variation out of fitted model out of total variation Axis Individual Cumulative Individual Cumulative 1 65.85 65.85 26.58 26.58 2 10.70 76.55 4.32 30.90 3 8.85 85.40 3.57 34.48 4 6.11 91.52 2.47 36.94 5 5.53 97.04 2.23 39.18 6 2.96 100 1.19 40.37 Table S5. C3S:S ratio (C3 side-chain syringol relative to the syringol) of the Alfisol (TK) and

the Andisol (EG) samples incubated in the respiration study and in the plant study at time 510

d. The postfixes “c”, “CS”, “350” and “550” stand for control soil, and soil amended either

with fresh corn stover (CS), biochar CS-350, or biochar CS-550.

Soil type Tokomaru soil (Alfisol) Egmont soil (Andisol)

Code TKc TKCS TKCS350 TKCS550 EGc EGCS EGCS350 EGCS550 C3S:S ratio (Respiration 1.3 1.2 1.2 1.1 0.7 1.1 0.8 0.8 study) C3S:S ratio (Plant study) 0.9 0.8 0.9 1.1 0.6 0.7 0.5 0.7 Figure S7. Illustration of pyrograms obtained from corn stover (CS) and both biochar types (CS350 and CS550). The most representative pyrolysates (represented structures) are included next. CS: acetic acid (Ps1), furaldehyde (Ps3), methyl furaldehyde (Ps6), cyclopenten-1-one (PsX1), 2-methyl-2-cyclopenten-1-one (Ps5), guaiacol (Lg1), methyl guaiacol (Lg2), styrene (Ar5), ethyl guaiacol (Lg4), vinyl guaiacol (Lg5), syringol (Lg6), methyl syringol (Lg7), vinyl syringol (Lg8), levoglucosan (Ps17), propenyl syringol (Ps9), and C16 and C18 fatty acids and some sterol-derived compound. CS350: acetic acid (Ps1), methyl benzene (ArX1), cyclopenten-1-one (PsX1), ethyl benzene (Ar3), 2-methyl-2- cyclopenten-1-one (Ps5), trimethyl benzene (ArX3), phenol (Ph1), methyl phenol (Ph3), guaiacol (Lg1), ethyl phenol (Ph4), naphthalene (NA), vinylphenol (Lg3), ethyl methyl phenol (PhX1), trimethyl phenol (PhX2), vinyl guaiacol (Lg5), syringol (Lg6), levoglucosan (Ps17), fluorene (PA6), and C16 and C18 fatty acids. CS550: Benzene (Ar1), toluene (Ar2), dimethyl benzene (ArX2), ethyl benzene (Ar3), trimethyl benzene (ArX3), naphthalene (NA), methyl naphthalene (NA1), biphenyl (PA5), anthracene (PA9) and fluorene (PA6). Codes (excluding PsX, ArX and PhX) are indicated in Table S1.

Technical specifications of Py-GC/MS analysis for samples in Fig. S2. The chemical signature of finely milled OA material (i.e. CS, CS350 and C550) was determined using a Py- GC/MS analyser type Pyroprobe 5000 (CDS Analytical Inc., Oxford, PA, USA) coupled to a 6890 GC and 5975 MS (Agilent Technologies, Palo Alto, CA, USA). Samples were pyrolysed at 600ºC for 10s (heating rate 10ºC/ms). The pyrolysis interface was set at 300ºC and the GC inlet at 325ºC. The oven of the GC was heated from 50 to 325ºC at 10ºC/min and held isothermal for 5 min. The GC/MS transfer line was held at 325ºC, the ion source (in electron impact mode, 70eV) at 230ºC and the quadrupole detector at 150ºC, measuring fragments in the m/z 50–500 range. The GC was equipped with a (non-polar) HP-1 100% dimethylpolysiloxane column. Helium was used as the carrier gas (constant gas flow, 1mL min–1). The major peaks in the total ion current of all samples were identified using the NIST 05 library and Py-GC/MS literature. References

Buurman P, Peterse F, Almendros Martin G (2007) Soil organic matter chemistry in allophanic soils:

a pyrolysis-GC/MS study of a Costa Rican Andosol catena. Eur J Soil Sci 58: 1330-1347

Kögel-Knabner I, Guggenberger G, Kleber M, Kandeler E, Kalbitz K, Scheu S, Eusterhues K,

Leinweber P (2008) Organo-mineral associations in temperate soils: integrating biology,

mineralogy, and organic matter chemistry. J Plant Nutr Soil Sci 171:61-82

Suárez-Abelenda M, Kaal J, Camps-Arbestain M, Knicker H, Macías F (2014) Molecular

characteristics of permanganate- and dichromate-oxidation-resistant soil organic matter from

a black-C-rich colluvial soil. Soil Res 52: 164-179