THE FATE OF ABOVE GROUND CARBON INPUTS AS ......The Fate of Above-ground Carbon Inputs as Stable SOM and GHGs ii Abstract Soil organic matter (SOM), the largest terrestrial carbon

THE FATE OF ABOVE-GROUND CARBON INPUTS AS STABLE SOM AND GHGS

Elaine Mitchell B.Sc. M.Sc.

Submitted in fulfilment of the requirements for the degree of Doctor of Philosophy

School of Biology and Environmental Science

Science and Engineering Faculty

and

Institute for Future Environments

Queensland University of Technology

2020

The Fate of Above-ground Carbon Inputs as Stable SOM and GHGs i

Keywords

The carbon cycle, soil organic carbon, stable isotopes, litter decomposition, microbial

decomposition, soil carbon sequestration, soil organic carbon pools, soil organic

carbon fractionation, stable carbon, soil organic carbon models, agricultural best

management practices, tillage, residue retention, organic residue addition.

The Fate of Above-ground Carbon Inputs as Stable SOM and GHGs ii

Abstract

Soil organic matter (SOM), the largest terrestrial carbon (C) pool, is fundamental to

soil and ecosystem functions across a wide range of scales, from site-specific soil

fertility and water holding capacity, to global biogeochemical processes that influence

C-climate feedbacks. Carbon sequestration in agricultural soils has been promoted as

a means to reduce atmospheric greenhouse gas (GHG) concentration, whilst improving

soil productivity. Although there is broad agreement on management practices that

increase C stocks, uncertainty remains on the magnitude and permanence of these

gains.

This study tracked the fate of isotopically labelled residue into SOM fractions that

varied in their degree of protection (and therefore residence time) in the mineral soil.

The effect of climate, management and soil properties on the fate of residue inputs as

soil organic carbon (SOC) and GHGs was examined across 7 sites over 12 months of

in situ decomposition. The effect of climate was examined at all 7 sites along a 3,600

km climate gradient, spanning 16° in latitude from; Kidman Springs, NT (Mean

Annual Temperature (MAT) = 28°C, Mean Annual Precipitation (MAP) = 946mm) to

Tamworth, NSW (MAT = 16°C, MAP = 448 mm). Two treatments were established

at all sites to mimic common agricultural practices; (1) varying the rate of C input (e.g.

residue retention), and (2) incorporating residue with the topsoil (i.e. tillage). In order

to understand the influence of soil properties on SOC stabilisation, one site (Crows

Nest) with contrasting soil texture and mineralogical properties within close proximity

(< 2 km) was examined.

Findings demonstrate that labile residue inputs can be rapidly incorporated into

mineral-associated organic matter, forming an immediate and long-term sink for C.

This finding aligns with the emerging understanding of SOC formation; that mineral-

associated organic matter does not require the successive depolymerisation of

particulate organic matter. The incorporation of residue (i.e. tillage) increased the

transfer of residue-C to all SOC fractions, whilst reducing the C priming effect,

resulting in a more positive C balance over 12 months than residues applied at the soil

surface. An increase in the input of residue-C resulted in an increase in GHG losses,

particularly from C-priming, which offset SOC gain in a finer textured soil. Climate

The Fate of Above-ground Carbon Inputs as Stable SOM and GHGs iii

was the main driver of above-ground residue decomposition and the accumulation of

residue-SOC as particulate organic matter (POM), but soil properties dominated the

accumulation of C in mineral-associated SOC fractions. Bio-physical limits to SOC

accumulation within the mineral-associated organic matter fraction were

demonstrated, with no significant response of mineral-associated organic matter to an

increase in C input at high input levels. In contrast, there were no limits to residue-

SOC accumulation as POM, which is readily mineralised and lost from the system.

Overall, this research improves our mechanistic understanding of SOC stabilisation,

particularly in semi-arid and sub-tropical biomes, that are currently underrepresented

in decomposition studies. A quantitative understanding of SOC stabilisation will

improve the next generation of SOC models, which mechanistically reflect SOC

stabilisation, generating more robust predictions to inform future climate change and

land-use policies.

The Fate of Above-ground Carbon Inputs as Stable SOM and GHGs iv

Table of Contents Keywords .................................................................................................................................. i

Abstract .................................................................................................................................... ii

List of Abbreviations .............................................................................................................. xv

Statement of Original Authorship ......................................................................................... xvi

Acknowledgements .............................................................................................................. xvii

1. Introduction ......................................................................................................... 18 1.1 Research framework .................................................................................................... 20

2. Literature Review ................................................................................................ 21 2.1 Section 1: Decomposition and SOC formation ............................................................ 21

2.2 Section 2: Agricultural management and SOC content ............................................... 38

2.3 Section 3: SOC models ................................................................................................ 42

2.4 Key research gaps ........................................................................................................ 48

3. Methodology ......................................................................................................... 51 3.1 Research sites ............................................................................................................... 51

3.2 Isotopically labelled residue production ...................................................................... 54

3.3 Experimental design ..................................................................................................... 56

3.4 The effect of management on SOC stabilisation and GHG fluxes............................... 57

3.5 The effect of soil type on SOC stabilisation and GHG fluxes ..................................... 58

3.6 The effect of temperature on SOC stabilisation and GHG fluxes ................................ 60

3.7 Residue and soil collection .......................................................................................... 60

3.8 Soil fractionation .......................................................................................................... 61

3.9 GHG sampling and isotopic analyses .......................................................................... 62

4. The influence of above-ground residue input and incorporation on GHG fluxes and stable SOM formation in a sandy soil .................................................. 65 Abstract .................................................................................................................................. 67

4.1 Introduction .................................................................................................................. 68

4.2 Materials and methods ................................................................................................. 70

4.3 Statistics ....................................................................................................................... 75

4.4 Results .......................................................................................................................... 76

4.5 Discussion .................................................................................................................... 85

4.6 Conclusion ................................................................................................................... 87

5. Amount and incorporation of plant residue inputs modify stabilisation dynamics in soil organic matter fractions .............................................................. 88 5.1 Abstract ........................................................................................................................ 90

5.2 Introduction .................................................................................................................. 91

The Fate of Above-ground Carbon Inputs as Stable SOM and GHGs v

5.3 Materials and methods .................................................................................................. 93

5.4 Results .......................................................................................................................... 99

5.5 Discussion ................................................................................................................... 109

5.6 Conclusion .................................................................................................................. 113

6. Limits on input-driven C sequestration in sub-tropical grassland soils: ‘new’ SOC sequestration is offset by native SOC priming in a finer textured soil. ... 114 6.1 Abstract ....................................................................................................................... 116

6.2 Introduction ................................................................................................................ 117

6.3 Methods ...................................................................................................................... 120

6.4 Results ........................................................................................................................ 127

6.5 Discussion ................................................................................................................... 137

6.6 Acknowledgments ...................................................................................................... 141

6.7 Supplementary Material .............................................................................................. 142

7. Climate controls decomposition of litter at a regional scale, but soil properties are the dominant control on litter-derived SOC stabilisation at a local scale. ............................................................................................................... 143 7.1 Introduction ................................................................................................................ 143

7.2 Materials and methods ................................................................................................ 146

7.3 Results ........................................................................................................................ 152

7.4 Discussion ................................................................................................................... 157

8. Overall Discussion ............................................................................................. 161 8.1 Introduction ................................................................................................................ 161

8.2 Summary of findings .................................................................................................. 168

8.3 Future research ........................................................................................................... 170

8.4 Conclusion .................................................................................................................. 171

9. References .......................................................................................................... 173

The Fate of Above-ground Carbon Inputs as Stable SOM and GHGs vi

List of Figures Figure 1: Schematic representation of biochemical and physical pathways

leading to SOM formation from progressive loss of litter chemical components. Each component loss is in the percentage of its initial weight (Cotrufo et al., 2015). ....................................................................... 24

Figure 2: The soil continuum model (Lehmann and Kleber, 2015). This model attempts to reconcile the current conceptual models for the fate of organic debris into a consolidated view of organic matter cycles and ecosystem controls in soil. In the proposed model, a continuum of organic fragments is continually processed by the decomposer community from large plant and animal residues towards smaller molecular size. At the same time, greater oxidation of the organic materials increases solubility in water as well as the opportunity for protection against further decomposition through greater reactivity towards mineral surfaces and incorporation into aggregates. Dashed arrow lines indicate mainly abiotic transfer, solid lines denote mainly biotic transfer; thicker lines indicate more rapid rates; larger boxes and end of wedges illustrate greater pool sizes; all differences are illustrative. All arrows represent processes that are a function of temperature, moisture and the biota present. ............................................... 28

Figure 3: Schematic representation of microbial processes involved in C cycling in terrestrial ecosystems (Liang et al., 2017). Primary production inputs to soils occur through two pathways (1) in vivo turnover and (2) ex vivo modification that jointly explains soil C dynamics driven by microbial catabolism and/or metabolism before entering the stable soil C pool. ..................................................................... 32

Figure 4: Theoretical steady-state relationships between C input, physico-chemically stabilised SOC fractions, and the non-protected SOC fraction. In this conceptualised by Castellano et al. (2015) stable SOC fractions are subdivided into two pools that are both known to exhibit saturation behaviour; mineral associated and microaggregate occluded. ...................................................................................................... 33

Figure 5: A schematic diagram of the priming effect (a) acceleration of SOM decomposition = positive priming effect; (b) slowing down of SOM decomposition = negative priming effect (Kuzyakov, 2000). ..................... 40

Figure 6: Total new soil C (A) and sequestration rates (B) following a 2 Mg C ha-1 yr-1 increase in inputs with a cessation of the new inputs after 60 years for 3 scenarios: #1 – all new C enters and stays in soil as unprotected POC (MRT = 3 yrs); #2 – all new C enters as POC but a fraction of the POC becomes protected within aggregates (MRT = 30 yrs); and #3 – as #2 with an additional fraction becoming stabilised by adsorption to minerals (MRT = 120 yrs). Model structure shown in (C), with the 3 pools shown as boxes (turnover time, τ, given for each pool), solid arrows represent transfers, and wavy arrows represent losses as CO2. Steady state distribution of SOC amongst pools is ~25% as POC, ~15% as aggregate protected and ~60% as sorbed C.

The Fate of Above-ground Carbon Inputs as Stable SOM and GHGs vii

This model is mathematically very similar to RothC and Century models, but the 3 pools are represented by specific stabilisation processes (Sanderman et al., 2009). ............................................................. 46

Figure 7: Study sites along the North to South temperature gradient: Kidman Springs, Brigalow, Samford, Crows Nest, Tamworth. Crows Nest and Samford = higher intensity sampling sites where weekly GHG measurements were taken. ........................................................................... 52

Figure 8: The growth chamber located at QUT Pilot Plant Facility, Banyo, Brisbane. The Rhodes grass within the illuminated chamber can be seen on the left, whilst the operational area was housed within the remaining area of the container on the right. ............................................... 55

Figure 9: A schematic representation of the Growth Chamber located at the Banyo Pilot Plant Facility. The left-hand side of the diagram represents the controls responsible for maintaining 13CO2 levels and optimum growing conditions within the chamber. On the right of the diagram, the growth chamber is represented where biomass was isotopically enriched with 13CO2 and 15N2O. 15N2O enrichment was achieved by circulating 15N enriched nutrient solution from an external tank. ................................................................................................ 55

Figure 10 (L) PVC cores in ground at Kidman Springs, Northern Territory. (R) At this site, additional wire was spread over the soil cores to prevent damage from wallabies. ............................................................................... 56

Figure 11: A schematic diagram to represent the treatments established to mimic agricultural practices with (a) varying the rate of residue input; LO = 5 t ha-1, MED = 10 t ha-1, HI = 15 t ha-1, C = control where no residue was added, and (b) incorporating residue with the topsoil to mimic tillage (MIX) versus left remaining on the soil surface (SUR), CM = control mix where no residue was added but soil was mixed to a depth of 10 cm. The rate of input in MIX was the same as the MED treatment (10 t ha-1). ..................................................................................... 57

Figure 12: Fractionation methodology using the approach of Zimmermann et al. (2006). ..................................................................................................... 62

Figure 13: Total recovery of applied residue-derived C across different treatments (expressed as % of applied residue-derived C recovered). C recovered in bulk soil 10 – 20 cm, C in stable C pools (C associated with silt and clay and C in microaggregates) 0-10 cm, particulate organic matter 0-10 cm, residues remaining on the soil surface after 12 months (only applicable to surface applied treatments LO, MED, HI) and residue-derived CO2 flux. 100% recovery was not achieved. This is most likely due to the removal of coarse fragments of organic matter when the soil was sieved < 2mm prior to fractionation. ................... 77

Figure 14: Temporal dynamics of GHG fluxes (CO2, CH4 and N2O), Samford, September 2013- September 2014 for input treatment (LO, MED, HI) and tillage (MIX). LO = low input (5t ha-1), MED = medium input (10t ha-1), HI = high input (15t ha-1), MIX = mixed treatments where residues were mixed with top 10 cm of soil at same input as MED (10t ha-1). (a) Temperature at 10 cm depth (solid line), precipitation (bars),

The Fate of Above-ground Carbon Inputs as Stable SOM and GHGs viii

and WFPS % (dotted line). (b) Total CO2 flux in all treatments in comparison to Control. Horizontal line indicate the length of time over which the priming effect was most significant for MED, HI and MIX treatments (c) CH4 flux in all treatments in comparison to Control. (d) N2O flux in all treatments in comparison to Control, indicating 3 major emissions pulses during experiment (P1-P3) and soil mineral N levels; nitrate (triangles) and ammonium (squares). The values are means of four replicates (±SE). .................................................................... 79

Figure 15: An increase in cumulative C flux with increasing input of residues (LO → HI) and with mixing (MIX). Cumulative C loss over 12 months is partitioned into residue-derived C loss and soil-derived C loss. Soil-derived C loss was higher than the Control in MED, HI and MIX treatments, indicated by the dotted line. This difference (i.e. the priming effect) was only significant in MIX treatment over 12 months. The values are means of four replicates (±SE). SE is shown for residue and soil-derived C flux for treatments, and for Controls is SE of total C flux. Letters indicate significant differences across treatments for total CO2 flux. .............................................................................................. 80

Figure 16: The variation in the relative contribution of residue-derived C to total CO2 flux (expressed as a % of total CO2 flux, f residues) over 12 months in residue input (LO, MED, HI) and mixed (MIX) treatment showing an increase in residue-derived CO2 flux corresponding to periods of heavy rainfall (shown in bars). The values are the mean of four replicates (±SE). ................................................................................... 81

Figure 17: An increase in cumulative N2O flux with increasing input (LO→HI) and with mixing (MIX). N2O flux is partitioned into residue-derived N2O flux and soil-derived N2O flux. Difference between soil-derived N flux and N flux in control is shown by horizontal dotted line (i.e. priming effect). A positive PE is shown in MED, HI and MIX treatments, but is only significant in HI input treatment. A lower soil-derived N flux than control is shown in LO input treatment, but this difference is not significant. The values are means of four replicates (±SE). SE is shown for residue and soil-derived N flux for treatments, and for Controls is SE of total N flux. Letters indicate significant difference between treatments. .................................................................... 82

Figure 18: The recovery of residue-derived C across residue input (LO, MED, HI) and mixed treatment (MIX) in SOC fractions (bars). Active C fraction = particulate organic matter (POM), stable C fractions = silt and clay associated C and C in microaggregates. Letters indicate significance (one-way ANOVA) between fractions across different input levels (LO, MED and HI). Values are means of 4 replicates (±SE). ........................................................................................................... 84

Figure 19: Temperature (line), precipitation (bars) and sampling time intervals (T1 to T4) over 12 month experimental period (February 2014-February 2015) at Crows Nest, QLD, Australia. The highest rates of SOM formation were experienced in T1 and T4 when climatic conditions were warmer and wetter. ............................................................ 94

The Fate of Above-ground Carbon Inputs as Stable SOM and GHGs ix

Figure 20: Dynamics of Rhodes grass C loss from the soil surface and recovery of residue-derived C in the mineral soil (bulk 13C measurement i.e. total SOM = sum of fractions, 0-10 cm) at sampling intervals at 95 (T1), 197 (T2), 286 (T3) and 378 (T4) days. (a) Surface C loss and recovery of residue-derived C as SOM in LO input treatment (input rate = 5t ha-1). (b) Surface C loss and recovery of residue-derived C as SOM in MED input treatment (input rate = 10t ha-1). (c) Surface C loss and recovery of residue-derived C as SOM in HI input treatment (input rate = 10t ha-1). (d) MIX treatment; undecomposed residue (>2mm) was separated by sieving prior to soil fractionation. Data for surface residue at 95 days (LO, MED, HI input treatments) was not available. Data are means (n=4) with standard errors. ............................... 100

Figure 21: Total recovery of applied residue-C and N across different treatments (expressed as a % of applied residue-derived C and N recovered) at sampling period T4 after 378 of in situ decomposition. Residue-derived C and N recovered as: undecomposed residue (>2mm) on soil surface in LO, MED, HI input treatments and below-ground in MIX treatment; particulate organic matter (POM), sand-sized fraction (SA) and silt and clay sized fraction (SC). 13C and 15N recovery in bulk soil (5-10 cm and 10-20cm) is also shown. .................... 103

Figure 22: Residue-derived C at sampling intervals T1-T4 (T1= 95 days, T2 = 197 days, T3= 286 days and T4 = 378 days) for three measured SOM fractions; (a) particulate organic matter (POM), (b) sand-sized OM (SA) and (c) silt and clay associated residue-C (SC). Values represent the mean of 4 replicates (± SE). POM fraction accumulated the largest proportion of residue-derived C, followed by SC fraction and the SA fraction. ...................................................................................................... 104

Figure 23: Residue-derived SOC recovery in different fractions at each sampling interval T1-T4. % SOC recovery in fractions expressed as ∆SOC gain fraction/∆SOC gain total for each time period (T1 to T4). ..... 106

Figure 24: Residue-derived N recovery in different SOM fractions at sampling intervals T1-T4 (T1= 95 days, T2 = 197 days, T3= 286 days and T4 = 378 days) (a) particulate organic matter (POM), (b) sand-sized OM (SA) and (c) silt and clay (SC) associated residue-N. Values represent the mean of 4 replicates (± SE). ................................................................. 108

Figure 25: Residue-derived C recovery in different compartments in 3 soil types (CLAY, LOAM, SAND) for the surface applied treatment (SUR) and the incorporation treatment (MIX) after 365 days of in situ decomposition 0-30 cm. C compartments: (1) residue-derived CO2 (2) undecomposed residue on soil surface and coarse particulate organic matter > 2mm in soil, and (3) residue-derived SOC. Averages with standard errors are reported (n=4). Letters indicate significance (p < 0.05) between soil types via analysis of variance (ANOVA). ................... 130

Figure 26: Cumulative residue and soil-derived CO2 flux for three soil types (CLAY, LOAM, SAND) and placement (SUR, MIX) over 12 months of in situ decomposition. C = control, CM = control mix. (A) = significant C priming effect (p

The Fate of Above-ground Carbon Inputs as Stable SOM and GHGs x

significant priming effect in CLAY MIX (i.e. the difference between soil-derived CO2 flux and control mix. (C) indicates the difference in CO2 flux between control (no residue applied, no soil mixing) and control mix (no residue applied and soil mixed/ disrupted to a depth of 10 cm). Therefore (C) indicates the difference in C mineralisation with the disturbance of soil. Averages with standard errors are reported (n=4). ........................................................................................... 131

Figure 27: (a) Rainfall and temperature during the study period (February 2014- February 2015); (b) temporal dynamics for CO2; (c) residue-derived CO2 flux (% contribution to total CO2 flux); (d) temporal dynamics for N2O; and (e) temporal dynamics for CH4. GHG dynamics are shown for 3 soil types (CLAY, LOAM, SAND) and 2 treatments (surface applied residue = SUR and incorporated residue= MIX). Average value of SUR and MIX are shown for CH4 as residue incorporation had no significant effect on CH4 flux. ................................. 133

Figure 28: 13C recovery in SOC fractions. Particulate organic matter (POM), sand and aggregate associated OM (SA), silt and clay sized fraction (SC) 0-10 cm. Letters indicate statistical significance (p2mm, particulate organic matter (POM), and mineral-associated organic matter (MAOM) after 12 months of in situ decomposition. Sites are displayed along the temperature gradient from Kidman (K) with a mean annual temperature of 27°C, Brigalow (B), Samford (S), Crows Nest (clay), Crows Nest (loam), Crows Nest (sand), Tamworth (T) with a mean annual temperature of 10°C. .................................................. 152

Figure 30 (a) Litter-derived C measured in different C fractions (0-10 cm) after 12 months of in situ decomposition at all sites (K= Kidman, B = Brigalow, C (C) = Crows Nest clay, C (L) =Crows Nest loam, C (S) = Crows Nest sand, S = Samford, T = Tamworth; (a) litter-derived C in litter and > 2mm within the mineral soil in the case of MIX treatment (d) litter-derived C in particulate organic matter < 2mm, POM (g) litter-derived C in MAOM (accounting for litter-derived C in SA and SC fractions). The relative importance of predictor variables [(% silt clay, CEC, C, pH, Precip (Precipitation), Temp (temperature) and placement] are represented as a % contribution to the model fit (i.e. relative importance) and are shown in (b) (e) and (h). The model fit (predicted values versus observed values) are shown in (c) (f) and (i) with the overall variance explained by model expressed in %. ................. 154

Figure 31: The efficiency of mineral-associated organic matter (MAOM) formation calculated as MAOM gain/litter lost. Black circles = litter incorporated within mineral soil to 10 cm, open circles = litter placed on soil surface. ........................................................................................... 155

Figure 32: A schematic representation of the path analyses used to identify the controls on the fate of litter-derived C in different C compartments (litter, POM, SA and SC). Latent variables were constructed (loading scores are shown in diagram) for soil (C, CEC, silt and clay%, pH)

The Fate of Above-ground Carbon Inputs as Stable SOM and GHGs xi

and climate (temperature and precipitation) to account for the range of measured indicators. .................................................................................. 156

Figure 33: Accumulation of above-ground inputs in SOC fractions (average across all experimental sites), 2013-2014. Expressed as a % of C added. POM = particulate organic matter, displays a linear increase in residue-derived C content from LO to HI input of residue. In contrast SA (sand and aggregates) and SC (silt and clay) displayed no significant increase in residue-derived C content from LO to HI input. ... 163

The Fate of Above-ground Carbon Inputs as Stable SOM and GHGs xii

List of Tables Table 1: A selection of the most relevant decomposition studies using stable

isotopes ......................................................................................................... 36 Table 2: A summary of research site characteristics .................................................. 53 Table 3: Selected properties for the 3 soil types at Crows Nest site used for this

study (CLAY, LOAM, SAND). Soil particle size distribution: Clay (

The Fate of Above-ground Carbon Inputs as Stable SOM and GHGs xiii

USDA soil textural triangle (Soil Survey Division Staff, 1993). *Detailed soil mineralogy data available in the supplementary material. ..................................................................................................... 128

Table 11: A General Linear Model was used to determine the statistical significance of the effect of soil type (CLAY, LOAM, SAND) and placement of residue (surface applied, SUR and incorporated, MIX), and their interaction, on dependent variables. ........................................... 132

Table 12: The overall GHG balance over 12 months showing the balance between SOC gain (in all fractions, POM, SA, SC) versus GHG losses from C priming, N2O and CH4 all expressed in (kg CO2e ha-1). Overall GHG balance is expressed in (kg CO2e ha-1) with a net positive balance in yellow and net negative balance in green. ................................ 134

Table 13: Selected site properties (0-10 cm) along North-South temperature transect; from Kidman Springs, NT (16°S) to Tamworth, NSW (31°S) ... 151

The Fate of Above-ground Carbon Inputs as Stable SOM and GHGs xiv

List of Publications Mitchell, E., Scheer, C., Rowlings, D. W., Conant, R. T., Cotrufo, M. F., van Delden,

L., & Grace, P. R. (2016). The influence of above-ground residue input and

incorporation on GHG fluxes and stable SOM formation in a sandy soil. Soil Biology

and Biochemistry, 101, 104-113.

Mitchell, E., Scheer, C., Rowlings, D., Conant, R.T., Cotrufo, M.F. and Grace, P.,

2018. Amount and incorporation of plant residue inputs modify residue stabilisation

dynamics in soil organic matter fractions. Agriculture, ecosystems & environment,

256, pp.82-91

Mitchell, E., Scheer, C., Rowlings, D. W., Conant, R. T., Cotrufo, M. F., & Grace, P.

R. (2020). Limits on input-driven C sequestration in sub-tropical grassland soils.

Biogeochemistry (under review).

The Fate of Above-ground Carbon Inputs as Stable SOM and GHGs xv

List of Abbreviations

ADF Acid detergent fibre ANOVA Analysis of variance AUR Acid-unhydrolyzable residue BD Bulk density C Carbon CO2 Carbon dioxide CDI Climate Decomposition Index DOM Dissolved organic matter GHG Greenhouse gas GWP Global warming potential mA Microaggregate fraction (paper 1). MAOM Mineral associated organic matter (SA + SC) MAP Mean annual precipitation MAT Mean annual temperature MO Microorganisms N Nitrogen N2O Nitrous oxide OM Organic matter POM Particulate organic matter SA Sand and aggregates SC Silt and clay SD Saturation deficit SEM Structural equation model WLS Weighted least squares WFPS Water filled pore space PE Priming effect SOC Soil organic carbon SOM Soil organic matter

The Fate of Above-ground Carbon Inputs as Stable SOM and GHGs xvi

Statement of Original Authorship

The work contained in this thesis has not been previously submitted to meet

requirements for an award at this or any other higher education institution. To the best

of my knowledge and belief, the thesis contains no material previously published or

written by another person except where due reference is made.

Signature: QUT Verified Signature

Date: 6/03/20

The Fate of Above-ground Carbon Inputs as Stable SOM and GHGs xvii

Acknowledgements

I would like to thank my supervisors Dr. Clemens Scheer, Dr. David Rowlings and

Professor Peter Grace, who have provided advice and guidance over the past 5 years.

They have been extremely supportive and understanding of my life balancing a PhD

and motherhood. I have also been fortunate to receive support from Dr. Francesca

Cotrufo and Dr. Richard Conant from Colorado State University. Their knowledge and

insight has been invaluable.

The work I present in this thesis would not have been possible without the help of the

laboratory and field staff. In particular, I would like to thank Sarah Carrick who

worked tirelessly in the initial stages of this project to complete the majority of the soil

fractionation. In addition, I would like to thank; Stephen Leo, Charles Winchester, Ben

Vickery, Lauren Martinez, Chris Chandler, and Tom Swain for their help in the

laboratory and field; Johannes Friedl and Alice Strazzabosco for their help with IR-

MS; and the Rowlings family for access to their property and GHG sample collection.

Finally, I would like to thank my husband Ewan for his patience and understanding

over the years. It has been a long journey, but we got there in the end.

I would also like to thank the following organisations for their contributions towards

my PhD; (1) the Australian Government’s Department of Agriculture, and Water

Resources as part of the National Soil Carbon Programme (Filling the Research Gap

FtRG) (project number 01203.073) and, (2) The Central Analytical Research Facility

(CARF) operated by the Institute of Future Environments (QUT). The data reported in

this thesis were obtained at CARF. Access to CARF is supported by generous funding

from the Science and Engineering Faculty (QUT).

18

1. Introduction

Soil organic matter (SOM), the largest terrestrial (C) pool, is fundamental to soil and ecosystem

functions across a wide range of scales, from site-specific soil fertility and water holding

capacity to global biogeochemical processes that influence carbon-climate feedbacks (Paustian

et al., 2016). Human appropriation of land for agriculture has decreased soil organic carbon

(SOC) by ~25% to 75% depending on climate, soil type and the management of soil resulting

in the emission of at least 150 Pg of carbon dioxide to the atmosphere (Lal, 2011). Recapturing

even a small fraction of these legacy emissions through improved land management would

represent significant GHG mitigation, whilst restoring soil health (Sanderman et al., 2017).

In agroecosystems, SOC content is determined by the balance between inputs of C through

plant residue and the return of C to the atmosphere through the decomposition of organic matter

i.e. heterotrophic soil respiration (Paustian et al., 2000). Therefore any measure that acts to

increase C input (e.g. residue retention, fertilisation, frequency of fallow, crop type and pasture

management), or reduce the rate of decomposition (e.g. reduce soil disturbance through no-

tillage) will typically increase SOC stocks (Paustian et al., 2000). Improved soil management

will also result in multiple co-benefits to support plant productivity (e.g. nutrient cycling) to

increase farm productivity and profitability (Lal, 2004a; Pacala and Socolow, 2004).

The majority of plant C inputs are mineralised by soil microorganisms and respired to the

atmosphere over short time scales. However, a proportion cycles through the soil slowly,

persisting for centuries to millennia as it is ‘protected’ from microbial degradation (Krull et al.,

2003; Trumbore, 2000). Plant C inputs can be protected (or stabilised) within the mineral soil

through strong physicochemical sorption to the solid phase (e.g. ligand exchange) and/or

spatial separation from soil microorganisms (e.g. via occlusion within microaggregates)

(Dungait et al., 2012; Lützow et al., 2006). Understanding the response of this slowly cycling

pool to environmental change will help to determine whether the soil acts as a C sink that

recovers previously lost C, or instead acts as a C source to the atmosphere, further accelerating

climate change (Bradford et al., 2016).

Despite significant progress on the understanding of SOC stabilisation, critical conceptual gaps

remain. These gaps impede the development of a unified theoretical model of how plant C

inputs enter the soil and are subsequently transformed to stable SOM (Sokol et al., 2018). In

19

the context of this research, for example, there are contrasting accounts on the contribution of

above-ground C inputs to SOC stabilisation. Some recent studies highlight the primacy of

below-ground C input in stable SOC formation (e.g. Austin et al., 2017), whereas others have

demonstrated that above-ground inputs, especially dissolved organic matter from litter

leachate, can instead dominate mineral SOC stocks in certain environments (Michalzik et al.,

2003). This research will further recent advances through the use of isotopically labelled

residue to trace its fate in SOC fractions that vary in their turnover time.

Beyond improving our mechanistic understanding of SOC formation, this study will advance

modelling capabilities by quantifying key processes. Modelling SOC dynamics is a valuable

tool for predicting SOC stocks under future scenarios of management and climate. However,

confidence in these predictions is limited as current models (e.g. CENTURY, Roth C) do not

represent our mechanistic understanding of SOC stabilisation; that chemically labile plant

inputs can be physico-chemically protected in the mineral soil matrix. Instead commonly

applied SOC models continue to use conceptual, kinetically-defined SOC pools that are not

directly measureable, and rest on the assumption that decomposition creates increasingly

complex ‘humus’ molecules that resist decomposition. A lack of mechanistic representation of

the decomposition process produces disagreement among SOC models (Todd-Brown et al.,

2013) and between model predictions and observational data (Bonan et al., 2013). As our

contemporary understanding of stable SOM moves away from the humification theory, so too

must the way we represent SOM stabilisation pathways in biogeochemical models (He et al.,

2016; Robertson et al., 2019).

This study quantifies the contribution of above-ground inputs to SOC stabilisation across a

broad spatial scale (covering arid, semi-arid and subtropical biomes) that are currently

underrepresented in decomposition studies. The fate of above-ground C inputs into functionally

relevant SOC pools (that vary in their degree of protection in the soil matrix) was quantified

through the use of isotopically labelled plant material. This was coupled with an analysis of the

impact of C inputs on existing SOM and GHG fluxes (which very few studies have done) to

gain a holistic understanding of whether C inputs results in an overall C sink or source. This

unique data set will advance our process level understanding of SOC formation from above-

ground inputs and contribute to the next generation of SOC models that mechanistically reflect

SOC stabilisation allowing more robust predictions in the development of future climate

change and land-use policies.

20

1.1 RESEARCH FRAMEWORK

Literature Review 21

2. Literature Review

Section 1 of the literature review highlights the importance of SOC in ensuring the future

sustainability of agricultural system s and its potential role in climate change mitigation and

adaptation. It introduces the terrestrial carbon cycle and outlines how carbon is cycled through

the system, starting as CO2 fixed during photosynthesis, which is then transferred to SOC

through the decomposition of plant biomass. This section outlines the processes and

mechanisms responsible for the decomposition of organic matter, and factors that control the

rate of this process. It concludes by discussing how SOC persists in the soil in the long-term

allowing SOC storage on decadal to millennial timescales.

Section 2 examines how land can be actively managed for SOC gain in agroecosystems. This

section explores the uncertainty surrounding the magnitude of SOC gain in agroecosystems, in

particular the bio-physical limits to SOC storage and the potential trade-off that exists between

SOC gain and increased GHG fluxes. It concludes by introducing the current global framework

for soil carbon sequestration; the United Nations ‘4 per mille’ initiative and the potential for

Australia to operate within this framework.

Finally, section 3 discusses the importance of SOC models in predicting SOC stocks under

future management and climate scenarios. This section highlights current model limitations;

that they do not represent our current understanding of SOC stabilisation mechanisms. It

concludes by discussing ways in which SOC models can be improved through the use of

measurable pools that can be verified by field experiments.

2.1 SECTION 1: DECOMPOSITION AND SOC FORMATION

2.1.1 The Soil Carbon Pool

Terrestrial ecosystems store most organic carbon in soils where it has the potential to become

stable soil carbon that can be sequestered over long time periods.

The global soil carbon pool (2500 Pg C) is 3.3 times the size of the atmospheric pool and 4.5

times the size of the biotic pool (Lal, 2004b). Organic C is found in soils in the form of various

organic compounds, collectively called soil organic matter (SOM). SOM includes all living

and non-living organic material in the soil (Baldock and Skjemstad, 2000). The living


component includes plants, soil fauna and microbial biomass. The non-living component,

representing the bulk of SOM, includes a spectrum of material from fresh residues and simple

monomeric compounds to highly condensed, irregular polymeric structures with residence

times varying from days to millennia (Sanderman et al., 2009).

Human conversion and use of land for agriculture has greatly reduced SOC stocks, creating a

large carbon debt of approximately 133 Pg C (Sanderman et al., 2017). Around 50 million km2

of soils are currently managed to some degree by humans for food, fibre and livestock

production. The consequences of human domination of soil resources are far-reaching and

include; nutrient depletion, desertification, salinization, compaction, accelerated erosion, and

loss of SOM (Montanarella et al., 2016). Of these threats, the loss of SOM has received growing

attention in recent decades, due to critical role it plays in the global carbon cycle (Crowther et

al., 2016; Schlesinger, 2005), and as a key component in maintaining healthy and productive

soils (Oldfield et al., 2015).

The depletion of SOM has numerous adverse ecological and economic consequences. SOM

loss is accompanied by the depletion of plant nutrients, increased bulk density, loss of

aggregate structure, decreased water-holding capacity, decreased CEC, increased surface

erosion, and a decline in soil biological activity and diversity – which ultimate results in a

decline in crop yield and quality (Verrell and O'Brien, 1996; Whitbread et al., 1998). The large

historic SOC losses can be regained through improved land management, which will not only

sequester large amount of CO2, but restore soil fertility for agricultural productivity

(Sanderman et al., 2009).

2.1.2 Decomposition and SOM formation

The decomposition and transformation of above and below-ground plant detritus is the main

process by which SOM is formed. Decomposition is a biological process that includes the

physical breakdown and biochemical transformation of complex organic molecules of dead

material into simpler organic and inorganic molecules (Swift et al., 1979). The decomposition

of plant biomass results in the sustained and continual influx of carbon-rich plant litter, invested

with the sun’s energy via photosynthesis, and enriched with nutrients absorbed from the soil

(Amundson, 2001). Decomposition is critical for maintaining a supply of most plant essential

nutrients; in natural ecosystems, nutrient recycling via decomposition often accounts for >90%

of plant-available N and P (Chapin et al., 2011).


Decomposition in most ecosystems largely results from the activities of microorganisms and

animals which breakdown non-living organic matter into simpler forms, to gain energy and

matter to build and maintain their biomass (Swift et al., 1979). Plant residues must be degraded

by enzymes to a relatively small size before they can be actively transported across the cell

wall of microorganisms (Weiss et al., 1991). In terrestrial ecosystems, exo-enzymes perform

this function outside the microorganism. Controls on the activities of these organisms therefore

influence the rate at which energy and matter flow through decomposer food webs, and regulate

the supply of nutrients into available forms for plant uptake and growth (Swift et al., 1979).

The decomposition of organic material occurs through a number of mechanisms;

fragmentation, leaching of water-soluble compounds, and microbial catabolism (Sanderman

and Amundson, 2003). One of these processes (catabolism) results in an immediate loss of C

from the terrestrial biosphere back to the atmosphere as CO2, while the other two

(fragmentation and leaching) contribute to the formation of soil organic matter (Cotrufo et al.,

2009). Soil fauna fragment and partially solubilise fresh plant residues, facilitating the

establishment of saprotrophic (organisms that digest food externally and then absorb the

products) microorganisms (Sanderman et al., 2009). Ultimately, given enough time and

optimum environmental conditions, most natural compounds will be fully mineralised to

inorganic forms (Marschner et al., 2008).

The decomposition pathways of leaching and fragmentation have a distinct temporal variation.

During the initial, fast phase of litter decomposition, C and N enters soil through leaching of

dissolved organic matter (DOM) (Klotzbücher et al., 2011). This lead to the formation of

mineral-associated organic matter (MAOM) either through direct associations with minerals or

after being metabolised by microbes (Liang et al., 2017). Evidence suggests that 6–39 % of C

loss during litter decay enters the soil in the form of dissolved organic C (DOC) during the

early phases of decomposition when litter loses mostly non-structural compounds (Don and

Kalbitz, 2005; Magill and Aber, 2000; Qualls et al., 1991). Later in the decomposition process,

the remaining residue structural components fragment into residues of increasingly smaller

molecular size and contribute to SOM primarily in the form of particulate organic matter

(Haddix et al., 2016). This POM can remain unprotected in the mineral soil, or become

encapsulated within soil aggregates of different class sizes (Christensen, 2001).

Microaggregates (250-53 µm) have been consistently found to resist disturbance and are

therefore believed to offer protection from further mineralisation (Six et al., 2000; Tisdall and

Oades, 1982).


The decomposition pathways are therefore important in determining the mechanism by which

SOC is stabilised (or remains unprotected) in the mineral soil. This was summarised in a

conceptual model by Cotrufo et al. (2015) which shows; (1) a DOM-microbial pathway, which

is significant in the early stages of decomposition where labile components are leached from

litter and stabilised by adsorption to mineral surfaces, and (2) a physical transfer pathway of

particulate organic matter during the latter stages of decomposition, where stabilisation may

occur via inherent chemical recalcitrance (e.g. plant structural components, such as lignin) or

possible physical occlusion within microaggregates.

Figure 1: Schematic representation of biochemical and physical pathways leading to SOM formation from progressive loss of litter chemical components. Each component loss is in the percentage of its initial weight (Cotrufo et al., 2015).

2.1.3 Modelling above-ground mass loss

The gradually slowing absolute rate of mass loss from decaying litter over time has been

captured by the negative exponential decay model:

Mt = M0e –kt

Where Mt is litter mass at time t, M0 is initial litter mass, and k is the rate constant of loss.

However, this model often does not capture the initial very rapid loss phase adequately (Chen


et al., 2002), but has worked well for the majority of data from short-term experiments (Harmon

et al., 2009). Long-term data indicate that a relatively slow phase of decomposition is

eventually reached (Berg et al., 1984; Chen et al., 2002; Lousier and Parkinson, 1978;

McClaugherty et al., 1985) and sometimes an asymptote in mass is reached (Berg et al., 2001).

For example, Cotrufo et al. (2000) in a litter bag (2 mm mesh size) decomposition study

demonstrated that beech leaf litter lost only around 40% of the original weight after five years

of decomposition on the forest floor. Mass loss was best described by a single exponential

model towards a protected asymptotic values of 54%.

2.1.4 Decomposition pathways and SOC formation

There is an emerging consensus that slow-cycling SOM consists of relatively low-molecular-

weight organic carbon substrates that enter the mineral soil as dissolved organic matter which

then associates with mineral surfaces (MOAM) (Sokol et al., 2018), as outlined in the model

presented by Cotrufo et al. (2015). However, whilst Cotrufo et al. (2015) presented this initial

transfer as a DOM-microbial pathway, contradictory evidence persists around whether C

substrates directly sorb to mineral surfaces or undergoes microbial transformation before

incorporation into MAOM (Sokol et al., 2018).

The in vivo microbial pathway (Liang et al., 2017) suggests that low-molecular-weight C

compounds in the mineral soil will be assimilated by the soil microbial community, providing

there are no constraints on microbial activity, such as very low temperatures or oxygen

availability (Schmidt et al., 2011). Following substrate assimilation, then biosynthesis, growth

and turnover, soil microbes can directly contribute to the MAOM pool through the deposition

of senesced microbial biomass (necromass), exudates (e.g. extracellular polymeric substances)

and other by-products (e.g. stress compounds such as osmolytes) (Schimel and Schaeffer,

2012). These microbial residues are prime targets for mineral sorption due to the close spatial

association that exists between minerals and microbes (Or et al., 2007; Pett-Ridge and

Firestone, 2017). However, there is also evidence for a direct sorption pathway for

accumulation in the MAOM pool, whereby low molecular weight compounds can be

translocated as dissolved organic matter from a plant source to a mineral surface either intact,

or after more complex compounds are partially oxidised by extracellular enzymes (Sanderman

and Amundson, 2008).


2.1.5 Factors controlling decomposition.

Organic matter decomposition controls the size of soil C pools, thereby regulating the rate and

magnitude of CO2 fluxes from the biosphere to the atmosphere, acting as the dominant control

over the recycling of plant nutrients in terrestrial ecosystems (Chapin et al., 2011; Swift et al.,

1979). As such, identifying the controls over decomposition is critical for understanding

terrestrial ecosystem function, both now and in the future (Cleveland et al., 2014). An extensive

body of research has shown that decomposition rates are regulated by climate, litter quality and

decomposer organisms (e.g. Aerts, 1997; Gholz et al., 2000; Swift et al., 1979), but that the

relative importance of these factors varies with scale.

Over large spatial scales, climate dominates C losses from soils. Soil respiration and

decomposition rates both increase exponentially with increasing temperature, approximately

doubling with every 10°C increase in temperature (Conant et al., 2008; Kirschbaum, 2010;

Trumbore et al., 1996). However, this temperature response will be moderated if moisture

becomes limiting (Davidson and Janssens, 2006) and many seasonally dry ecosystems

experience large pulses of respiration following intermittent rain (Sanderman et al., 2009). At

smaller spatial scales, over which climate remains relatively constant, litter quality explains

much of the variation in decomposition rates (Aerts, 1997; Cornwell et al., 2008; Melillo et al.,

1982).

When examining decomposition rates, it is important to distinguish between surface litter

decomposition and decomposition of SOC once it is incorporated in the mineral soil. The

dominant controls over initial litter decomposition likely differ from the dominant controls

over stabilisation once litter-derived OM is within the soil matrix (Hicks Pries et al., 2017) due

to the different soil moisture, oxygen and thermal regimes of the mineral soil (Cotrufo et al.,

2009). Furthermore, once OM is within the mineral soil, it interacts with mineral surfaces,

which acts to slow SOM decomposition relative to total SOM due to physicochemical

protection by mineral association and microaggregate occlusion (Six et al., 2002; von Lützow

et al., 2006). Therefore, findings from litter decomposition studies cannot be extrapolated to

represent the decomposition of OM in the mineral soil (Cotrufo et al., 2009).

In fresh unprotected detritus, microbial access is not generally a constraint on decomposition

(Schimel and Schaeffer, 2012). In mineral soils, however, the situation is different as much of

the substrate is in protected forms, either occluded in aggregates or sorbed on mineral surfaces.

Therefore the rate at which this C can be metabolised is limited by microbes’ ability to access


it (Schmidt et al., 2011). When aggregates occlude organic debris (Six et al., 2000), two steps

are required for microbes to process the material – first a physical step of aggregate disruption,

and then possibly a second step in which exoenzymes break up the polymers (Navarro-García

et al., 2012). Therefore, OM breakdown in mineral soils is not limited by the catabolic capacity

of the microbes, but rather physical factors that limit microbial access to it (Schimel and

Schaeffer, 2012).

2.1.6 SOC stabilisation

The humification model is a traditional model of SOC stabilisation. This assumes

transformation or synthesis of the initial decomposition products into large, dark-coloured

macromolecules that are resistant to decomposition and consequently have a longer residence

time in the soil (Lehmann and Kleber, 2015). The suite of hypothetical transformation

processes, that became collectively known as humification, is also referred to as the ‘synthesis

concept of the genesis of humic substances’ or ‘secondary synthesis of humic substances’ (e.g.

Burdon, 2001; Guggenberger, 2005). Humic polymers were thought to be large polymers

(Sutton and Sposito, 2005), molecules so complex it was possible no two were identical

(Schimel and Schaeffer, 2012). In such a model, molecules require extracellular enzymes to

fragment them, but with no repeated structures, decomposition requires specialised

exoenzymes.

Selective preservation of plant inputs is another model of SOC stabilisation, which was

informed primarily by studies of decomposing litter (Aber et al., 1990; Melillo et al., 1982) and

the accumulation of plant fragments (with perceived recalcitrance, e.g. lignin, suberin, cutin)

(Baldock et al., 1992). This concept assumes that certain compounds ‘selectively accumulated’

due to their complex chemical structure (von Lützow et al., 2006), making them energetically

costly for microorganisms to degrade, at least during the initial stages of decomposition

(Kögel-Knabner et al., 2008) until more labile substrates are exhausted. However, evidence suggests that beyond the decadal timeframe, selective preservation of relatively unaltered

plant-derived compounds due to biochemical recalcitrance is not an important long-term

stabilisation mechanism (Ekschmitt et al., 2008; Marschner et al., 2008; Schmidt et al., 2011).

Scientists now believe that appropriately adapted decomposer organisms are able to degrade

highly recalcitrant materials, including e.g. polycondensed aromatics (Gramss et al., 1999) and

fire-derived carbon (Hamer et al., 2004).


Advances in molecular techniques have led to substantial progress in understanding how plant

C inputs are transformed to SOM. These advances include the recognition that the slowest

cycling fraction of SOM primarily consists of small and recognisable biomolecules which

interact with mineral surfaces forming mineral-associated organic matter (Sokol et al., 2018).

The soil continuum model (SCM), presented by Lehmann and Kleber (2015) attempts to

consolidate the developments in how scientists view stable SOM (Figure 2). In the SCM

concept, a continuum of organic fragments is continually processed by the decomposer

community from large plant and animal residues towards smaller molecular size; “The

breakdown of large molecules leads to a decrease in the size of primary plant material with

concurrent increases in polar and ionisable groups, and thus to increased solubility in water.

At the same time, the opportunity for protection against further degradation increases through

greater reactivity towards mineral surfaces and incorporation into aggregates….

Consequently, the SCM does not require microbial or abiotic generation of recalcitrance

through the formation of specific organic compounds and is in agreement with the stated need

to focus on the spatial arrangement of soil organic matter and environmental controls such as

temperature, moisture or soil mineralogy (Schmidt et al., 2011).” (Lehmann and Kleber, 2015)

Figure 2: The soil continuum model (Lehmann and Kleber, 2015). This model attempts to reconcile the current conceptual models for the fate of organic debris into a consolidated view of organic matter cycles and ecosystem


controls in soil. In the proposed model, a continuum of organic fragments is continually processed by the decomposer community from large plant and animal residues towards smaller molecular size. At the same time, greater oxidation of the organic materials increases solubility in water as well as the opportunity for protection against further decomposition through greater reactivity towards mineral surfaces and incorporation into aggregates. Dashed arrow lines indicate mainly abiotic transfer, solid lines denote mainly biotic transfer; thicker lines indicate more rapid rates; larger boxes and end of wedges illustrate greater pool sizes; all differences are illustrative. All arrows represent processes that are a function of temperature, moisture and the biota present.

However, this model is contested in a study by Haddix et al. (2020) which demonstrated a lack

of consistent formation of new mineral-associated organic matter across all sites later in the

decomposition continuum, indicating that mineral-associated organic matter forms

independently of particulate organic matter depolymerisation. It also provides evidence that

SOC models (e.g. CENTURY, Parton et al., (1993)), which have plant-derived C passing

through a sequence of pools and becoming progressively more stable, may not be

mechanistically correct (Haddix et al., 2020). Therefore the mechanisms and pathways for

stable SOC formation continue to be contested.

2.1.7 SOC stabilisation mechanisms

The mechanisms that act to slow the mineralisation of C compounds in soil were summarised

by Sanderman et al. (2009); (1) spatial inaccessibility of substrate to microbes and enzymes

and (2) interactions of organic material with minerals, metal ions, and other organic substances.

Both of these categories will impart perceived recalcitrance due to the persistence of the

stabilised material, but if that stabilisation process is interrupted (e.g. aggregate disruption

through tillage), the substrate may be easily decomposed even if it is thousands of years old

(Ewing et al., 2006).

The substrate can be spatially isolated from microbes and enzymes through the aggregation of

the soil at multiple scales (Six et al., 1999; Tisdall and Oades, 1982). Two major mechanisms

have been put forward to explain the formation of microaggregates and the consequent long-

term stabilisation of OM. Edwards and Bremner (1967) proposed that organo-mineral

microaggregates (20-250 µm) form by interactions of polyvalent metals and organic ligands

with mineral surfaces. The nature and binding of organo-mineral interactions depend on the

type (Kaiser et al., 2002) and the surface area of the mineral particles (Kaiser and

Guggenberger, 2003). Others argue for a mechanism in which microaggregates form when

organic debris becomes surrounded by fine mineral particles (Golchin et al., 1994; Jastrow,

1996; Tisdall and Oades, 1982). In this view, aggregate formation begins with the introduction

of plant litter to the soil, which is progressively broken down by the soil micro and macrobiota

(Kong et al., 2005). The smaller compounds produced by this microbial processing bind with


mineral surfaces, creating high density, highly stable clay and silt-size microaggregates (


critical, contrasting roles in controlling terrestrial C fluxes; promoting the release of C to the

atmosphere through their catabolic activities, but also preventing release by stabilising C into

a form that is not readily decomposed through anabolic activity i.e. senesced microbial biomass

(Schimel and Schaeffer, 2012).

Liang et al. (2017) categorised two major pathways in which MO influence SOM formation:

ex vivo (extracellular) modification, in which extracellular enzymes attack and transform plant

residues, resulting in deposition of plant-derived C that is not readily assimilated by

microorganisms; and in vivo turnover of organic substrates via cell uptake-biosynthesis-

growth-death resulting in the deposition of microbial-derived C. In the ex vivo modification

pathway, transformations of plant material and residues occur without actual assimilation by

microorganisms. These compounds are often partially oxidised and mobilised by the action of

microbial extracellular enzymes prior to mineral-sorption, but they do not pass through a

microbial body, and thus display a clear plant-derived signature within the MAOM pool (Sokol

et al., 2018).

In contrast, in vivo turnover leads to the deposition of microbial-derived C (Figure 3). Even

though the relative importance of in vivo turnover (red lines) and ex vivo modification (green

lines) may vary with different environmental scenarios, Liang et al. (2017) argues that the

majority of C that is persistent in soils occurs through the in vivo turnover pathway generated

microbial necromass and metabolites that are the precursors for persistent soil C.


Figure 3: Schematic representation of microbial processes involved in C cycling in terrestrial ecosystems (Liang et al., 2017). Primary production inputs to soils occur through two pathways (1) in vivo turnover and (2) ex vivo modification that jointly explains soil C dynamics driven by microbial catabolism and/or metabolism before entering the stable soil C pool.

2.1.9 Limits to C storage

Important biophysical issues that possibly limit SOC storage potential are related to the

inherent capacity of soil to store carbon in a stable form and the longevity of the additional

carbon stored (Chenu et al., 2019). Most current models of SOM dynamics assume first-order

kinetics for the various conceptual pools of organic matter, which means that equilibrium C

stocks are linearly proportional to C inputs. However, a growing body of evidence from a

diverse array of ecosystems indicates that soils have a finite capacity to store C within relatively

stable pools in the mineral soil matrix (Castellano et al., 2015). This limits the capacity for soils

to retain new litter inputs into stable SOC (Chung et al., 2008; Hassink, 1997; Stewart et al.,

2007).

In contrast to physico-chemically stabilised SOC pools, evidence for saturation of non-

protected SOC pools is weak (Gulde et al., 2008). This led to the development of a two-pool

model for C saturation that includes a stable slow-turnover SOC pool that exhibits saturation

behaviour and a relatively labile fast-turnover SOC that does not exhibit saturation behaviour

(Stewart et al., 2007). This model is summarised by Castellano et al. (2015) Figure 4; plant

litter is decomposed to the point that it is < 2 mm and thus enters the labile non-protected SOC

pool, which is dominated by plant residues that are not physico-chemically stabilised (i.e. non-

protected SOC). Decomposition products from the labile non-protected pool are then

transferred to the physico-chemically stabilised SOC or mineralised. As C inputs increase, SOC

in the physico-chemically stabilised pool asymptotically increases to a maximum (i.e.

saturation). A consequence of this relationship is that the stabilisation efficiency of new litter

inputs declines in proportion to the amount of physico-chemically stabilised SOC already

present. New litter inputs not transferred to the physico-chemically stabilised SOC pool

accumulate in the non-protected SOC pool or are mineralised.

Generally, soil organic matter stabilisation increases with increasing clay content due to the

greater reactive surface area of clay particles, providing greater capacity to chemically stabilise

SOM through clay SOM interactions but also physically protect SOM by incorporation within

aggregates (Feller and Beare, 1997; Six et al., 2002). Additionally as clay content increases,

the proportion of small pores increases, which stabilise soil organic C due to the exclusion of

decomposer microbes (Mtambanengwe et al., 2004; Strong et al., 2004). Hassink (1997)


examined the relationship between SOM fractions and soil texture and found a relationship

between the silt- and clay-associated C and soil texture, though he did not find any correlation

between texture and amount of C in the sand-sized fraction. Based on these findings, he defined

the capacity of soil to preserve C by its association with silt and clay particles. Studies

investigating the retention of specific microbial products (i.e. amino sugars) corroborate the

proposition of Hassink (1997) that C associated with primary organo-mineral complexes are

chemically protected and the amount of protection increased with an increased silt plus clay

proportion of the soil (Chantigny et al., 1997; Guggenberger et al., 1999; Puget et al., 2000).

In addition to the clay content, clay type (i.e. 2:1 versus 1:1 versus allophanic clay minerals)

influences the stabilization of organic C and N (Feller and Beare, 1997; Torn et al., 1997).

As our ability to increase SOC stocks (even beyond native levels) through greater C inputs and

improved management practices advances, it is crucial to determine what, if anything, limits

the amount and rate of SOC stabilisation. Much of the research on stabilisation limits has been

conducted in temperate climates, which do not face the edaphic and climatic constraints of

Australian soils. Therefore it is important to identify areas with greater potential for SOC

sequestration (that are not approaching C saturation) as a first step in SOC sequestration policy.

Figure 4: Theoretical steady-state relationships between C input, physico-chemically stabilised SOC fractions, and the non-protected SOC fraction. In this conceptualised by Castellano et al. (2015) stable SOC fractions are subdivided into two pools that are both known to exhibit saturation behaviour; mineral associated and microaggregate occluded.


2.1.10 Methods for measuring decomposition

Traditionally, decomposition studies have focused on litter mass loss rates from litter bags with

no consideration of where that mass ends up (Cotrufo et al., 2009). The guiding paradigm has

been that litter mass loss represents losses from the system, with the amount of litter which

does not decompose at measurable rate contributing to stable SOM (humus) formation (Berg

and McClaugherty, 2008). In other studies, litter decomposition is considered as the

mineralisation of carbon from litter residue due to microbial respiration and is thus measured

as CO2 production. Cotrufo et al. (2009) identified the short-comings of these approaches; we

describe litter decomposition as the result of three interrelated processes (i.e. leaching,

fragmentation and catabolism). However, we study decomposition by measuring one of the

three processes (i.e. catabolism – by microbial respiration), or by a rough estimate of the

combined effect of leaching and fragmentation by mass loss measurement, with mesh bags

restricting the occurrence of the latter.

Methods that allow the determination of decay rates without affecting decomposition processes (i.e. leaching, fragmentation) should be used (Cotrufo et al., 2009). This can be achieved through the use of isotopically labelled plant material, which acts as a tracer differentiating between added ‘new C’ and ‘existing C’ in the soil. The use of 13C labelled plant material has been used in a relatively small number of decomposition studies (see


Table 1) with wider application limited by the time and cost associated with its production. By

combining the use of labelled litter with SOM fractionation techniques, the contribution of

litter decay to SOM pools with varying turnover times, can provide a detailed insight into

carbon dynamics (Cotrufo et al., 2009).


Table 1: A selection of the most relevant decomposition studies using stable isotopes

Author

Title Ecosystem Region and climatic zone

Angers et al. (1997)

Fate of carbon and nitrogen in water-stable aggregates during decomposition of 13C15N-labelled wheat straw in situ

Agroecosystem France

Ladd et al. (1983)

Decomposition of plant material in Australian soils. I. The effect of quantity added on decomposition and on residual microbial biomass

Agroecosystem Australia

Amato et al. (1984)

Decomposition of plant material in Australian soils. II. Residual organic 14C and 15N from legume plant parts decomposing under field and laboratory conditions


Ladd et al. (1985)

Decomposition of plant material in Australian soils. III. Residual organic and microbial biomass C and N from isotope-labelled legume material and soil organic matter, decomposing under field conditions


Bird et al. (2003)

Stabilization of 13C-Carbon and Immobilization of 15N-Nitrogen from Rice Straw in Humic Fractions

Agroecosystem USA

Bird and Torn (2006)

Fine Roots vs. Needles: A Comparison of 13C and 15N Dynamics in a Ponderosa Pine Forest Soil

Forest USA

Denef and Six (2006)

Contributions of incorporated residue and living roots to aggregate-associated and microbial carbon in two soils with different clay mineralogy

Agroecosystem USA

Bird et al. (2008)

13C and 15N stabilization dynamics in soil organic matter fractions during needle and fine root decomposition

Forest USA

Rubino et al. (2010)

Carbon input belowground is the major C flux contributing to leaf litter mass loss: Evidences from a 13C labelled-leaf litter experiment

Forest Italy

Kammer (2012) Decomposition pathways of 13C depleted plant litter

Forest Switzerland


Bimüller et al. (2012)

Rapid transfer of 15N from labelled beech leaf litter to functional soil organic matter fractions in a Rendzic Leptosol

Forest Germany

Helgason et al. (2014)

Long-term microbial retention of residue C is site-specific and depends on residue placement

Agroecosystem Canada

Cotrufo et al. (2015)

Formation of soil organic matter via biochemical and physical pathways of litter mass loss

Agroecosystem USA

Soong et al. (2016)

Soil microarthropods support ecosystem productivity and soil C accrual: evidence from a litter decomposition study in the tallgrass prairie

Agroecosystem USA

Hicks Pries et al. (2017)

Long term decomposition: the influence of litter type and soil horizon on retention of plant carbon and nitrogen in soils

Forest USA


2.2 SECTION 2: AGRICULTURAL MANAGEMENT AND SOC CONTENT

Human appropriation of land for agriculture has greatly altered the terrestrial carbon balance,

creating a large carbon debt in soils (Sanderman et al., 2017). The rate and extent of the decline

in SOC stocks vary greatly across the world, due to differences in soil properties, climate, and

type of land-use conversion, but is estimated as a global loss of ~133 Pg C (Sanderman et al.,

2017). Following land use change, SOC losses generally occur due to reasons; (1) reduced

inputs due to, for example, harvest and stubble burning; and (2) increased decomposition rates

through soil disruption which changes porosity that creates a more favourable environment for

decomposition. Additionally, cultivation, especially when followed by fallow conditions,

creates favourable conditions for both wind and water erosion (Packer et al., 1992). The amount

of SOC that has been historically lost can be thought of as a future carbon sink potential and

with the adoption of best management practices, it is estimated that ~two-thirds of lost SOC

can be recovered (Lal, 2004b).

2.2.1 Conservation Tillage

Conservation or no-tillage has been promoted as a means to reduce C losses in agroecosystems.

Numerous studies have demonstrated that tillage enhanced CO2 evolution (e.g. Beare et al.,

1994; Beyaert and Paul Voroney, 2011; Burgess et al., 2002), which is likely the result of tillage

(1) improving soil aeration and breaking up aggregate structures exposing new surfaces to

microbial decomposition (2) mixing litter into the soil, allowing better microbial access to fresh

substrate. However, there is wide variation in the experimental outcomes.

Numerous studies have reported greater SOC stocks in the surface of no-tillage systems

(e.g.Chan et al., 2002; Dalal et al., 2011; Pankhurst et al., 2002; Thomas et al., 2007), but other

studies report little impact (e.g.Armstrong et al., 2003; Standley et al., 1990). This may be an

artefact of experimental design where studies vary greatly in their timescale and soil depths

examined. For example, Baker et al. (2007) examined how the depth of sampling could affect

the outcome of experiments. They contended that in a majority of cases where reduced/ no-

tillage appeared to store carbon, only the top 30 cm or less of soil had been sampled. Very few

tillage studies have sampled below 30 cm, with most studies sampling between 15 to 30 cm

depth (Alvarez, 2005; VandenBygaart et al., 2003; West and Post, 2002).

In studies where deeper samples were included, the difference in SOC content between tilled/

no-tilled areas disappeared. Baker et al. (2007) argued that due to the differences in root

distribution and soil physical properties, SOC under no-tillage tended to be concentrated near


the surface, while in conventional tillage systems the carbon was distributed deeper in the soil

profile. The variation in experimental outcomes for the effects of tillage demonstrates that there

is the need to obtain more data on the long-term effects of different tillage systems on carbon

and nitrogen mineralisation and immobilisation in field situations. Continuous monitoring of

long-term changes in the SOC and soil quality under conservation tillage in different agro-

ecological zones is essential.

2.2.2 Increasing C Inputs

In general, any practice that increases C input (e.g. organic amendments, cover crops, residue

retention) should feedback into the soil system as increased SOC storage (Denef et al., 2008;

Gregorich et al., 1996; Kong et al., 2005). However, practices that increase C inputs will also

stimulate microbial activity leading to increased decomposition rates – the so-called C priming

effect (Kuzyakov, 2000). The balance between increased inputs and increased decomposition

will dictate how much, if any, C will be sequestered under these management practices

(Sanderman et al., 2009).

Globally, the return of crop residues in soils has been recommended in field practices to

increase SOC stocks, soil structure, and plant available nutrients (Conteh et al., 1998; Han et

al., 2016; Kumar and Goh, 1999). However, there are numerous indications that plant inputs

can decrease SOM levels, particularly through the priming of existing SOC (Fontaine et al.,

2003) (Figure 5). The inputs of organic compounds can stimulate the soil microbial community

to decompose more SOM (Bingeman et al., 1953) by; (i) promoting microbial grounds that

target complex compounds of SOM (Fontaine et al., 2003), (ii) by providing energy to break

down these compounds (Blagodatskaya and Kuzyakov, 2008), or (iii) by providing C for

microbial growth, thus increasing microbial N demand and facilitating N mining, i.e., the

microbial breakdown of SOM to get access to N (Craine et al., 2007).

Land management changes leading to increased soil C may also increase or decrease fluxes of

powerful greenhouse gases such as N2O or methane. In some, perhaps many, situations, these

changes may be far more significant than changes in SOC stocks because of the very large

global warming potentials of these gases. N2O and methane, have, respectively, 298 and 25

times the GWP of CO2 when considered on a 100- year time scale (IPCC, 2007). An increase

in residue inputs may stimulate nitrous oxide emissions or suppress CH4 uptake (Bodelier and

Laanbroek, 2004). Crop residues may play multiple roles in mediating soil N2O emissions. As

an organic N fertilizer, they are subject to microbial N mineralization and nitrification, leading


to N2O production. Furthermore, crop residues can serve as an energy provider for denitrifiers,

enhancing denitrification, and N2O production under anaerobic conditions. Besides these direct

impacts, crop residues can further modify soil aeration by enhancing soil aggregation as well

as microbial O2 demand, therefore the level of soil micro-site anaerobicity. Therefore,

understanding the balance of ‘new’ SOM accrual and how this interacts with native SOC and

GHG fluxes is critical to assessing the effectiveness of land management practices as a climate

change mitigation strategy.

Figure 5: A schematic diagram of the priming effect (a) acceleration of SOM decomposition = positive priming effect; (b) slowing down of SOM decomposition = negative priming effect (Kuzyakov, 2000).

2.2.3 SOC sequestration policy

The COP21 or 21st Conference of the Parties to the United Nations Framework Convention on

Climate Change in Paris (2015) produced the Paris Agreement. This is a global agreement on

the reduction of climate change, limiting global warming to less than 2ºC compared to pre-

industrial levels. At the COP21, the French government introduced an ambitious international

research program, the ‘4 per mille Soils for Food Security and Climate’ of the Lima-Paris

Action Agenda. This strategy aspires to increase global soil organic matter stocks by 0.4 % per

year as compensation for the global emissions of greenhouse gases by anthropogenic sources

(Minasny et al., 2017). The annual greenhouse gas emissions from fossil carbon are estimated

at 8.9 Gt C, and a global estimate of soil C stock to 2 m of soil depth of 2400 Gt C (Batjes,

1996). Taking the ratio of global anthropogenic C emissions and the total SOC stock


(8.9/2400), results in the value of 0.4%. If we take the land are of the world as 149 million km2,

it would be estimated that on average there are 161 t C ha-1. Therefore there would need to be

an average sequestration rate to offset emissions at 0.6 t C ha-1 yr-1.

The Australian carbon stocks has been estimated to be 25 G t in the top 0.3 m (Viscarra Rossel

et al., 2014). Due to its large land mass, Australia could potentially play an important role in

the global ‘4 per mille’ initiative. If we consider the area of agricultural land to be 470 million

ha, a 0.4% increase means, on average, a sequestration rate of 0.22 t C ha−1 year−1. This

suggested sequestration rate falls within measured rates of C sequestration after the adoption

of best-management practices in Australia, where water is not severely limiting. For example,

crop rotations, particularly ones that include a leguminous species, stubble retention and

reduced tillage achieved an annual increase of soil C in the Australian environments studied;

with sequestration rates of 0.20 C ha−1 year-1 (Sanderman et al., 2009).


2.3 SECTION 3: SOC MODELS

Soil organic carbon models are increasingly being used as a component of what Jasanoff (2003)

refers to as predictive methods; a general term for integrating scientific knowledge within a

framework to support policy goals. Predictive methods help simplify complexities at the

interface of policy and science, particularly in the areas of natural resources and the

environment. In these areas, SOM models can generate quantitative evaluations of SOM and

ecosystem dynamics which can then be used as a decision making tool to create change.

2.3.1 SOC models

Soil organic matter is challenging to bring into a single comprehensive analytical framework

as direct measurements do not easily account for SOM’s extreme physical, chemical, spatial

and temporal complexity (Dungait et al., 2012). Current SOC models attempt to deal with the

complexity of soil composition through the creation of conceptual pools of carbon that cycle

at different rates (Jenkinson, 1999; Parton, 1996). SOC typically contain three conceptual

pools; (1) an active pool that includes the majority of fresh plant residues with a residence time

of, at most, a few years (2) a slow pool with turnover times from decades to centuries, and (3)

a passive pool with residue times of centuries to millennia. Pools in these mo

Documents

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