Characterization of soil aggregation and soil organic matter in European agricultural soils

Taru Lehtinen

Faculty of Life and Environmental Sciences University of Iceland

2014

Characterization of soil aggregation and soil organic matter in European

agricultural soils

Taru Lehtinen

Dissertation submitted in partial fulfilment of a Philosophiae Doctor degree in Geography

Advisors

Prof. Guðrún Gísladóttir (University of Iceland) Prof. Kristín Vala Ragnarsdóttir (University of Iceland)

PhD Committee

Prof. Guðrún Gísladóttir (University of Iceland) Prof. Kristín Vala Ragnarsdóttir (University of Iceland)

Prof. Rattan Lal (Ohio State University)

Opponents

Prof. Bjarni Diðrik Sigurðsson (Agricultural University of Iceland) Prof. Thomas Kätterer (Swedish University Agricultural Sciences)

Faculty of Life and Environmental Sciences School of Engineering and Natural Sciences

University of Iceland

Reykjavik, October 2014

Characterization of soil aggregation and soil organic matter in European agricultural soils

Soil aggregation and soil organic matter in agricultural soils

Dissertation submitted in partial fulfilment of a Philosophiae Doctor degree in Geography

Copyright © 2014 Taru Lehtinen

All rights reserved

Faculty of Life and Environmental Sciences

School of Engineering and Natural Sciences

University of Iceland

Sturlugata 7

101, Reykjavik

Iceland

Telephone: 525 4000

Bibliographic information:

Lehtinen, Taru, 2014, Characterization of soil aggregation and soil organic matter in

European agricultural soils, PhD dissertation, Faculty of Life and Environmental

Sciences, University of Iceland, 139 pp.

ISBN 978-9935-9164-9-5

Printing: Háskólaprent

Reykjavik, Iceland, October 2014

Abstract

This thesis presents the results of studies of the dynamics of soil aggregates and soil

organic matter (SOM), and of studies of the effects of crop residue (CR) incorporation on

soil organic carbon (SOC) and greenhouse gas emissions. An improved method for

macroaggregate breakdown using low-energy ultrasonication and density fractionation was

used to investigate the soil aggregate dynamics and SOM in European agricultural soils.

The greatest aggregate breakdown was observed in Andisol and Entisol, followed by

Alfisol, Ultisol, and Inceptisol. The stability of macroaggregates was influenced by

particle-size distribution and the amounts of exchangeable Mn and Mg. In Iceland and

Austria, evidence of diminished aggregate hierarchy was observed. Mn oxides in Iceland

and Fe oxides in Austria were positively correlated with macroaggregation, as was fungal

biomass. In Iceland, in low SOM concentration sites macroaggregates contributed 40-70%

of the organic carbon and nitrogen to bulk soil, whereas in high SOM concentration sites

free particulate organic matter contributed up to 70% of the OC and N to bulk soil. In

Austria, the slightly different SOM distributions between the sites were most likely caused

by differences in soil texture and maybe soil age. Analyses of published data on the effect

of CR incorporation on SOC and greenhouse gas emissions in Europe indicate a 7%

increase in SOC. In contrast, CO2 and N2O emissions were six and twelve times higher,

respectively. The processes linking CR incorporation to soil aggregate and SOM dynamics

needs to be clarified in future studies.

Útdráttur

Lífrænt kolefni í jarðvegi og samkornun jarðvegs eru mikilvæg fyrir gæði hans. Þessi

ritgerð fjallar um gagnkvæma virkni lífræns efnis og samkorna í jarðvegi á ræktarlandi í

Evrópu, en beitt var nýrri aðferð við sundrun stórra samkorna til að auðvelda

rannsóknirnar. Ritgerðin fjallar einnig um áhrif blöndunar lífrænna leifa af ræktarlandi í

jarðveg á lífrænt kolefni í jarðvegi og losun gróðurhúsalofttegunda. Samkornin sundruðust

mest í Andisol og Entisol, en einnig í Alfisol, Ultisol og Inceptisol, í þeirri röð. Stöðugleiki

stórra samkorna var háður kornastærðardreifingu og einnig magni skiptanlegra Mn og Mg-

jóna. Niðurstöður benda til þess að stigskipting samkornunar í jarðvegi á Íslandi og í

Austurríki sé minni en sýnt hefur verið fram á annars staðar. Jákvæð fylgni var milli stórra

samkorna og Mn-oxíða í íslenskum jarðvegi og Fe-oxíða í austurrískum, og milli stórra

samkorna og lífmassa sveppa í jarðvegi beggja landanna. Þegar lífrænt innihald íslensks

jarðvegs var lítið var um 40-70% af lífrænu kolefni og köfnunarefni í stórum samkornum,

en þegar hlutur lífræns efnis var mikill var um 70% þess lítt niðurbrotið lífrænt efni.

Breytileiki í lífrænu kolefni og köfnunarefni í jarðvegi í Austurríki réðst af

kornastærðardreifingu og sennilega af aldri jarðvegs. Áhrif íblöndunar lífrænna leifa í

jarðveg víða í Evrópu leiddi til 7% aukningar á lífrænu kolefni, en sex sinnum meiri losun

á CO2 og tólf sinnum meiri á N2O. Þörf er á frekari rannsóknum til að skilja þau ferli sem

tengja íblöndun lífrænna leifa við samkornun jarðvegs sem og gagnkvæma virkni

samkorna við lífrænt efni í jarðvegi.

Dedicated to Hans,

for being the most positive himself, loving me to pieces, trying to teach me to face

challenges with laughter, pushing me to believe in my skills, encouraging me to be myself,

supporting me like the most wonderful loving partner, and for taking me to exciting

adventures when I the least felt it was the time for them.

And now, the end is near

And so I face the final curtain

My dear, I'll say it clear

I'll state my case, of which I'm certain

I've lived a life that's full

I travelled each and every highway

And more, much more than this, I did it my way

Regrets, I've had a few

But then again, too few to mention

I did what I had to do, I saw it through without exemption

I planned each charted course, each careful step along the highway

And more, much more than this, I did it my way

Yes, there were times, I'm sure you knew

When I bit off more than I could chew

And through it all, when there was doubt

I ate it up and spit it out

I faced it all and I stood tall and did it my way

I've loved, I've laughed and cried

I've had my fill, my share of losing

And now, as tears subside, I find it all so amusing

To think I did all that

And may I say, not in a shy way,

"Oh, no, oh, no, not me, I did it my way"

For what is a woman, what has she got?

If not herself, then she has naught

The right to say the things she feels and not the words of one who kneels

The record shows I took the blows and did it my way!

Slightly modified from Frank Sinatra´s “My Way”

List of Papers

This thesis is an amalgamation of four papers. Appendix I includes author contributions to

the papers, and appendix II lists published papers and a popular science paper outside of

this PhD thesis. The papers will be referred in the text as follows:

Chapter 2. Lehtinen, T., Lair, G.J., Mentler, A., Gisladóttir, G., Ragnarsdóttir, K.V.,

Blum, W.E.H. 2014. Soil Aggregate Quantification Using Low Dispersive Ultrasonic

Energy Levels. Soil Science Society of America Journal, 78: 713-723. Reprint is published

with kind permission of the journal.

Chapter 3. Lehtinen, T., Gísladóttir, G., Lair, G.J., van Leeuwen, J., Blum, W.E.H.,

Bloem, J., Steffens, M., Ragnarsdóttir, K.V., 2014. Aggregation and organic matter in

subarctic Andosols under different grassland management. Acta Agriculturae

Scandinavica, Section B – Soil & Plant Science (submitted 12.08.2014).

Chapter 4. Lehtinen, T., Lair, G.J., van Leeuwen, J.P., Gísladóttir, G., Bloem, J.,

Ragnarsdóttir, K.V., Steffens, M., Blum, W.E.H. 2014. Characterization of soil

aggregation and soil organic matter under intensive cropping on Austrian Chernozems.

Journal of Plant Nutrition and Soil Science (to be submitted).

Chapter 5. Lehtinen, T., Schlatter, N., Baumgarten, A., Bechini, L., Krüger, J., Grignani,

C., Zavattaro, L., Costamagna, C., Spiegel, H. 2014. Effect of crop residue incorporation

on soil organic carbon (SOC) and greenhouse gas (GHG) emissions in European

agricultural soils. Soil Use and Management (in press). Manuscript included in the thesis

with kind permission of the journal.

xi

Table of Contents

List of Papers ...................................................................................................................... ix

List of Figures ................................................................................................................... xiv

List of Tables ..................................................................................................................... xvi

Abbreviations .................................................................................................................. xviii

Acknowledgements ........................................................................................................... xxi

1 Introduction ..................................................................................................................... 1 1.1 Background .............................................................................................................. 1

1.1.1 Soil structure in agricultural soils .................................................................. 1 1.1.2 Soil organic matter in agricultural soils ......................................................... 4 1.1.3 Greenhouse gas (GHG) emissions following crop residue

incorporation .................................................................................................. 7 1.2 Aims of the research ................................................................................................ 9 1.3 Methodology .......................................................................................................... 10

1.3.1 Study sites .................................................................................................... 10 1.3.2 Methodology summary ................................................................................ 16

1.4 Results .................................................................................................................... 19 1.4.1 Aggregate breakdown in European soils ..................................................... 19 1.4.2 Soil aggregates and soil organic matter in Icelandic grasslands and

Austrian croplands ....................................................................................... 19 1.4.3 Effect of crop residue incorporation on SOC and GHG emissions ............. 20

1.5 Discussion .............................................................................................................. 20 1.5.1 Aggregate dynamics in European soils ........................................................ 20 1.5.2 Soil organic matter in European agricultural soils ....................................... 22 1.5.3 Effect of crop residue incorporation GHG emissions .................................. 23 1.5.4 Conclusions .................................................................................................. 24

References ....................................................................................................................... 26

2 Soil Aggregate Stability in Different Soil Orders Quantified by Low

Dispersive Ultrasonic Energy Levels........................................................................... 39

3 Aggregation and organic matter in subarctic Andosols under different

grassland management ................................................................................................. 53 Abstract ........................................................................................................................... 54 3.1 Introduction ............................................................................................................ 55 3.2 Material and methods ............................................................................................. 56

3.2.1 Site description............................................................................................. 56 3.2.2 Soil sampling ............................................................................................... 59 3.2.3 Physicochemical and biological characterization of soils at the

grassland sites .............................................................................................. 59

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3.2.4 Density and aggregate fractionation ............................................................ 60 3.2.5 Solid-state

13C NMR spectroscopy .............................................................. 62

3.2.6 Statistical analyses ....................................................................................... 62 3.3 Results .................................................................................................................... 62

3.3.1 Physicochemical and biological characterization of soils at the

grassland sites .............................................................................................. 62 3.3.2 Distribution of soil fractions in the grassland sites ...................................... 65 3.3.3 Distribution of OC and Nt in the grassland sites ......................................... 69

3.4 Discussion ............................................................................................................... 70 3.4.1 Soil structure in the grassland sites .............................................................. 70 3.4.2 SOM in the grassland sites .......................................................................... 71 3.4.3 SOM distribution and chemical quality in the grassland sites ..................... 72

3.5 Conclusions ............................................................................................................ 73 Acknowledgements ......................................................................................................... 73 References ....................................................................................................................... 75

4 Characterization of soil aggregation and soil organic matter under intensive

cropping on Austrian Chernozems .............................................................................. 82 Abstract ............................................................................................................................ 83 4.1 Introduction ............................................................................................................ 84 4.2 Material and methods ............................................................................................. 85

4.2.1 Site description ............................................................................................ 85 4.2.2 Soil sampling ............................................................................................... 86 4.2.3 Physicochemical soil properties .................................................................. 86 4.2.4 Soil microbiology ........................................................................................ 86 4.2.5 Density and aggregate fractionation ............................................................ 87 4.2.6 Solid-state

13C NMR spectroscopy .............................................................. 87

4.2.7 Statistical analyses ....................................................................................... 88 4.3 Results .................................................................................................................... 88

4.3.1 Soil characteristics ....................................................................................... 88 4.3.2 Distribution of soil fractions ........................................................................ 92 4.3.3 Distribution and chemical quality of SOM .................................................. 94

4.4 Discussion ............................................................................................................... 97 4.4.1 Soil structure in the cropland sites ............................................................... 97 4.4.2 SOM in the cropland sites ........................................................................... 98 4.4.3 SOM distribution and chemical quality in the cropland sites ...................... 98

4.5 Conclusions ............................................................................................................ 99 Acknowledgements ......................................................................................................... 99 References ..................................................................................................................... 100

5 Effect of crop residue incorporation on soil organic carbon (SOC) and

greenhouse gas (GHG) emissions in European agricultural soils ........................... 107 Abstract .......................................................................................................................... 108 5.1 Introduction .......................................................................................................... 109 5.2 Material and methods ........................................................................................... 110

5.2.1 Data sources ............................................................................................... 110 5.2.2 Data preparation ........................................................................................ 117 5.2.3 Data aggregation ........................................................................................ 117 5.2.4 Data analysis .............................................................................................. 117

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5.3 Results .................................................................................................................. 117 5.3.1 Effect of environmental zone ..................................................................... 120 5.3.2 Effect of clay content ................................................................................. 120 5.3.3 Effect of experiment duration .................................................................... 120 5.3.4 Effect of experiment and crop residue type on RR for GHG emissions .... 120 5.3.5 Correlation between SOC concentration and crop yields .......................... 120

5.4 Discussion ............................................................................................................ 125 5.4.1 Effect of environmental zone ..................................................................... 126 5.4.2 Effect of clay content ................................................................................. 126 5.4.3 Effect of experiment duration .................................................................... 126 5.4.4 Effect of experiment and crop residue type on RR for GHG emissions .... 127 5.4.5 Correlations between crop yields and SOC concentrations ....................... 128 5.4.6 Possible improvements of the data set for future analyses ........................ 128

5.5 Conclusions .......................................................................................................... 128 Acknowledgements ....................................................................................................... 129 References ..................................................................................................................... 130

Appendix I ........................................................................................................................ 137 Author contributions to the papers ................................................................................ 137

Appendix II ...................................................................................................................... 139 Publications ................................................................................................................... 139

5.5.1 Scientific publications outside of the PhD thesis ....................................... 139 5.5.2 Popular science publications outside of the thesis ..................................... 139

xiv

List of Figures

Figure 1.1 Hierarchical conceptual model of aggregation as first described by

Tisdall and Oades (1982). The loop describes the chronological

formation of aggregates, from clay particles that forms domains that get

gradually larger into microaggregates and macroaggregates, as well as

formation of microaggregates from macroaggregates. Modified from

Ghezzehei (2012). ............................................................................................... 2

Figure 1.2 The conceptual model of the Life cycle of macroaggregate. T1 to t4 denote

different time steps in the life cycle. Source: Six et al. (2000), with kind

permission from Wiley. ....................................................................................... 3

Figure 1.3 Simplified schematic of the carbon dynamics in a cropland, showing three

different SOC pools and the various sources of CO2 from the agricultural

soil system. Source: Janzen (2006), with kind permission from Wiley. ............. 8

Figure 1.4 Schematic diagram on the effects of crop residue incorporation on soil

N2O emissions. IP1 and IP3 denote higher emissions (due to nitrification

and denitrification, respectively); IP2 lower emissions (due to

nitrification); IP4 and IP5 higher or lower emissions (depending on

electron donors and acceptor, and anaerobicity, respectively). Source:

Chen et al. (2013), with kind permission from Wiley. ........................................ 9

Figure 1.6 Map of the selected farms in Iceland. For acronyms, see text above. Map

composed by Friðþór Sófus Sigurmundsson (Faculty of Life and

Environmental Sciences, University of Iceland). ............................................. 12

Figure 1.7 Pictures from the selected improved farming sites (HaAorg, HaAcon

HiAorg, and HiAcon) from Icelandic organic (HaAorg, HiAorg) and

conventional farms (HaAcon, HiAcon). ........................................................... 13

Figure 1.8 Map of the selected farms in Austria, at Obersiebenbrunn and Lassee.

Map composed by Helene Pfalz-Schwingenschlögl (BOKU). .......................... 14

Figure 1.9 Sampled organic (Org76, Org95) and conventional farms in Austria

(Con76, Con95). ............................................................................................... 15

Figure 1.10 The selected European long-term experiments. Map composed by Janine

Krüger (Leibniz-Institute of Vegetable and Ornamental Crops,

Grossbeeren, Germany).Map is a black and white version of the map in

Chapter 5 and in this PhD thesis with the kind permission of Soil Use

and Management. ............................................................................................. 16

Figure 3.1 Schematic of the fractionation procedure. Gray circles represent fractions

for further analyses. ......................................................................................... 61

xv

Figure 3.2 The distributions of micro- (<20 µm, and 20-250 µm) and

macroaggregates (>250 µm) of soils of different grassland sites. A 0-10

cm, B 10-20cm. ................................................................................................. 67

Figure 3.3 The C and N distribution within particle-size fractions and C/N ratios of

different soil fractions of soils of different grassland sites. (Note: The C

concentration of each fraction was calculated by taking total soil C as

the sum of the C associated with all separate particle-size fractions,

including POM fractions). A, C, E= 0-10 cm, B, D, F=10-20 cm. .................. 69

Figure 4.1 The distributions of micro- (<20 µm, and 20-250 µm) and

macroaggregates (>250 µm) in A) 0-15 cm, and B) 30-40cm. ........................ 92

Figure 4.2 The C and N distribution within particle-size fractions and C/N ratios of

different soil fractions in A, C, E) 0-15 cm, and B, D, F) 30-40 cm. (Note:

The C content of each fraction was calculated by taking total soil C as

the sum of the C associated with all separate particle-size fractions,

including POM fractions). ................................................................................ 95

Figure 5.1 Map of the experiment locations and their distribution across the

aggregated environmental zones (Nemoral, Atlantic, Continental,

Mediterranean). .............................................................................................. 115

Figure 5.2 Response ratios (RRs) of SOC concentrations across A environmental

zones (ENZs), B) clay contents (%), and C) experiment durations (years).

The left vertical line of the box represents the first quartile, median is

shown as a thick line, and the right vertical line represents the third

quartile. Horizontal bars show the minimum and maximum values. The

(°) and (*) denote outliers. The figure is based on the original data on

response ratios, without any weighting procedure. The numbers of RR

(and experiments) are presented for each category along the y-axis.

Different letters indicate significant differences according to Tukey´s as a

Post Hoc test (p<0.05). ................................................................................... 118

Figure 5.3 Correlation between RR for SOC concentration and crop yields A) across

the sites, B) across the aggregated environmental zones, C) across the

clay contents, and D) across the experiment durations. The figure is

based on the original data on response ratios, without any weighting

procedure ........................................................................................................ 124

xvi

List of Tables

Table 1.1 Summary of methods used in this PhD thesis. Letters indicate where the

analyses were carried out. ................................................................................ 17

Table 3.1 Background information of the studied sites. ...................................................... 58

Table 3.2 Key physicochemical and biological properties of the studied soils. ................. 63

Table 3.3 Means (standard deviations) of free particulate organic matter (fPOM),

occluded particulate organic matter (oPOM) and mean weight diameter

(MWD) in the studied sites. .............................................................................. 66

Table 3.4 Pearson correlation coefficients between the mean weight diameter

(MWD), particulate organic matter and soil aggregate fractions, and the

key physicochemical and biological soil properties1. ...................................... 68

Table 3.5 Integrated chemical shift regions (% of total signal intensity) obtained by 13

C CPMAS NMR spectroscopy for the extracted free particulate organic

matter (fPOM), occluded particulate organic matter (oPOM), and bulk

soil. ................................................................................................................... 70

Table 4.1 Means and standard deviations of physicochemical and biological

properties of the bulk soils studied (n=3). Different letters indicate

significant differences according to Tukey´s as a Post Hoc test. ..................... 89

Table 4.2 Results of two-way analyses of variance (ANOVA) showing the level of

significance for each significant variation source associated with the soil

properties (n=24 for physicochemical soil properties at both soil depths

(0-15 cm and 30-40 cm), n=12 for fungal biomass, active fungi, bacterial

biomass, mineralisable N, and hot water extractable carbon (HWC) at

the 0-15 cm soil depth). .................................................................................... 91

Table 4.3 Means (standard deviations) of mean weight diameter (MWD) of

ultrasound stable sand corrected aggregates (<5 mm), free particulate

organic matter (fPOM) and occluded particulate organic matter (oPOM)

in the studied sites (n=3). Different letters indicate significant differences

according to Tukey´s as a Post Hoc test (p<0.05). .......................................... 93

Table 4.4 Integrated chemical shift regions (% of total signal intensity) obtained by 13

C CPMAS NMR spectroscopy for the extracted free particulate organic

matter (fPOM), occluded particulate organic matter (oPOM), and bulk

soil for the studied sites. ................................................................................... 96

Table 5.2 Aggregated variables and specific levels of each variable. .............................. 116

Table 5.3 Significant results of multiple regressions. ....................................................... 119

xvii

Table 5.4 Mean response ratios of GHG emissions in crop residue incorporation

management practices compared to crop residue removal management

practices in different aggregated environmental zones (ENZs), clay

contents (%), and experiment durations (years). The values have been

calculated from average data from each experiment and were weighted

based on the amount of response ratios calculated into the average.

Different letters indicate significant differences according to Tukey´s as a

Post Hoc test (p<0.05). ................................................................................... 122

xviii

Abbreviations

ANOVA: analysis of variance

C: carbon

CEC: cation exchange capacity

CO2: carbon dioxide

CR: crop residue

CZO: Critical Zone Observatory

ENZ: environmental zone

Exo1: thermally labile soil organic matter

Exo2: thermally more stable organic matter

Exo3: refractory soil organic matter

fPOM: free particulate organic matter

GHG: greenhouse gas

K: potassium

MWD: mean weight diameter

N: nitrogen

Nt: total nitrogen

N2O: nitrous oxide

OC: organic carbon

OM: organic matter

oPOM: occluded particulate organic matter

P: phosphorous

POM: particulate organic matter

RR: response ratio

SOC: soil organic carbon

xix

SOM: soil organic matter

STA: simultaneous thermal analysis

NMR: nuclear magnetic resonance

WEOC: water-extractable organic carbon

WFPS: water filled pore space

WRB: world reference base

XRD: X-ray diffraction

xxi

Acknowledgements

Completing a PhD is a journey that requires Finnish sisu, enthusiasm, passion for science,

determination, lots of running and yoga, and an amazing personal and academic support

network. Due to the importance of the personal support network, I will thank one person

from the personal support group first. I have dedicated this work to my beloved Hans

Göransson because without him I would not have made it! You came to my life like a

wonderful surprise, literally completely out of the blue! I know we could have met even

though you wouldn´t have moved to Vienna, but words can´t describe how happy and

privileged I am that you did decide to take a job in Vienna. We have seen and explored

Vienna, Austria and Europe together and we can even understand each other on a

professional level. You may think we are from completely different sides of science, but at

least we are both trying to explore the “not-so-black-box” of soil and trying to understand

and explain what happens in there. Your red pants and humour made me think you´re from

the Best coast of Sweden and so you were. A piece of Göteborg came to me, and I could

not even have imagined what kind of a pearl was been thrown at me. Life with you is a

constant adventure as well as wonderful everyday life with home-brewed beer, and I hope

it never ends. I mean the life, not the beer, even though you´re a talented brew master! I am

ever grateful for the enormous support you have given me during my PhD. I am truly a

lucky woman to have you standing behind my back. I hope I´ll be able to support you as

much in your endeavours, and can´t wait to see and experience our life without me

finishing a PhD! Please understand that it will be emotional when this PhD is over, tears of

joy and happiness will flow. Please see it as a little river of thankfulness and proudness of a

chapter finalized. Afterwards, we can laugh as much as you want!

PhD Supervisors and Committee. My supervisors have done an amazing job, which I can´t

thank them enough for. Prof. Guðrún Gísladóttir: You have supervised me about science

and life, and always stood behind me. You have been the one to remind me of the real life

when my dreams fly a bit too high. Thanks to you I now have an experience box, which I

can open and close whenever I want. It will be strange not to be skyping to you almost

every week. I admire your enthusiasm and will power in making the University of Iceland

a dream place for a soil scientist to be. I am proud to be a piece in your soil puzzle! Prof.

Kristín Vala Ragnarsdóttir: You have always supported me in my scientific and personal

successes and challenges. Thank you! My motivation to study in the first place was to see

the world and learn more about the environment. You gave me the possibility to do so, and

I hope my educational journey has benefited you as much as me. I will never forget how

you helped me to move and gave my boxes a home for a while, as well as our trip around

Iceland in the summer of 2010! I highly respected a dean, you, that said yes to my wish to

stay and volunteer at a farm for a couple of days. That was wonderful! I´m looking forward

to seeing your next sustainability projects! Prof. Rattan Lal: You came into my life as a

PhD Committee member, and I want to thank you very much for your guidance and

valuable input. You made a big impression on me with your talk in Iceland in the summer

of 2013. Your way to look at soils from the global perspective with the whole planet Earth

and its people involved is truly unique. I would like to see many soil scientists to follow

that path, and remind us all, that many crucial daily things in life such as food on our plate

and safety depend on healthy and well-functioning soils. It´s easy to extract an aggregate

xxii

from a soil sample, but to connect it to the bigger picture and to the biggest environmental

challenges of today and the future is a task you have really mastered.

PhD coauthors and supporters. Dr. Georg J. Lair and Prof. Windfried E.H. Blum: Thank

you for helping me to come to Austria, and for financially supporting my soil organic

matter course in Freising, Germany. Especial thank you for Georg for helping me to plan

my research and for helping me to find the right farmers to cooperate with and the right

fields to sample in Austria. Dr. Axel Mentler: without you I wouldn´t know half as much

about ultrasonication and its details. Thank you for your guidance and for saving me with

some drinks every now and then! Jeroen van Leeuwen: It has been a pleasure to have you

as a PhD colleague, co-author and a cooperating partner. You always smile, and focus on

the good things in life. You were the first Dutch person I know to climb a huge mountain

with me (Iceland´s highest peak), and were brave enough to join for morning swims in the

North Italian lake in the late fall weather. Highly respected by a Finnish person ;) I wish

we´ll stay in touch in the future; please do invite me to your defence! Dr. Jaap Bloem:

Thank you for your wise advice and wonderful personality. It has been great to cooperate

with you and exchange ideas with you. Prof. Peter de Ruiter: Thanks for seeing Iceland as

an interesting research opportunity and cooking lamb for the vegetarian before climbing

the highest peak of Iceland, I may not have made it without the meat! Dr. Markus Steffens:

thank you for a fantastic month in Bavaria! I learned a lot about NMR, got to meet great

people, got lots of new ideas for the future, and of course, fell in love with the Bavarian

beer! PI Dr. Heide Spiegel:, I feel privileged to have gotten the chance to work with you!

You shine positive energy, one can hear you laugh every day, and that creates an excellent

working environment for the whole crew. Thank you for giving me a great job, for your

continuous support and your way to make me feel I can learn more than I thought! Norman

Schlatter, you are a great colleague and teacher, and oh, so well organized with excel! It

has been great to share an office with you, and work with you on several issues! Dr.

Andreas Baumgarten, thank you for having me at the institute for sustainable plant

production and trusting my skills and knowledge! Dr. Luca Bechini: Thank you for all the

skype meetings about crop residue incorporation, and your invaluable support in writing

my 4th

manuscript! I´m happy we´re back in the same project, and am looking very much

forward to continuing to work with you. Janine Krüger: thank you for your beautiful maps

and such a nice cooperation. I hope it will continue in the future! Prof. Carlo Grignani, Dr.

Laura Zavattaro, Chiara Costamagna: thank you for your great effort in productivity side

and excellent constructive comment on my writing! Dr. Loredana Saccone: thanks for

excellent help in the field in Iceland, and for initiating the research cooperation with Lund

University, Sweden, about silica in Icelandic soils! Dr. Wim Clymans and Prof. Daniel

Conley, it has been great to learn about silica cycling from you, and hopefully we will meet

in person soon! Reynir Smári Atlason, I´ve learned a lot about economic perspectives on

farming from you. Thanks for excellent cooperation! Dr. Joost Keuskamp, Bas Dingemans,

Dr. Mariet Hefting and Dr. Judith Sarneel, thank you for the cooperation with the Tea Bag

Index and for bringing a lot of joy and enthusiasm into my life! Dr. Rannveig Anna

Guicharnaud and Dr. Anu Mikkonen, thank you for your support! I´m looking forward to a

conference where we´ll all three will be present and will get the chance to open a big bottle

of red wine! EUROSOIL Istanbul 2016?

Farmers and data providers. A very warm thank you for all of our eight farmers and their

families, who opened their doors to us, told us the history of their farm and allowed us to

sample from their properties. It almost comes without saying that without your

xxiii

contributions there would not have been any thesis. I´m always ready to discuss more

about the results and to go through them with you! A big thank you also for the researchers

of the 50 experiments who made it possible to write my manuscript about crop residue

incorporation in Europe.

SoilTrEC and Catch-C consortiums. Special thanks to Brynhildur Davidsdóttir for

encouraging me to think about SoilTrEC! Thanks Inge Regelink for great discussions and

article support whenever I have been wondering about what my results really mean. Dr.

Manoj Menon, it has been a pleasure to work with you, I do hope we´ll stay in touch in the

future! I´m looking forward to many discussions about aggregates Special thank you for

Prof. Steve Banwart for leading the project and for good discussion. Eydís Mary

Jónsdóttir, it was a real pleasure to work with you and to be able to help you with your

MSc. You´re sunshine, don´t let it go away Dr. Utra Mangasingh: thank you so much

for listening to all of my worries and making me believe in myself. Please remember that

this works the other way around as well, my office and phone are always open for you! Big

thanks for the whole SoilTrEC group for many memorable moments during the project!

Thanks for the whole Catch-C consortium and AGES for having me in the group, it has

been very educational and I think the almost best thing about the project is the wonderful

atmosphere! The communication and feeling in the group is great, and make it to such a

pleasure to be part of it. Please keep it up Helene Berthold: you are such a specialist in

all soil related happenings going on in Vienna! It has been a pleasure to work with you,

and lucky me, it will continue!

PhD students and coworkers at the University of Iceland. Martin Nouza and Sófus

Sigurmundsson, I shared an office with you in the beginning of my PhD, which I´ll never

forget. It took you two months to tell me that I sing when it gets too loud in the room and I

get annoyed. I bet you had fun, I enjoyed every bit of sharing an office with you guys

Sófus, thanks for the beautiful map on my Icelandic farms! Harald, Virgile, Edda, Olga,

Sigrún, María, and my other wonderful colleagues and group members; we got a bit less

time together than expected but the time we had was great! Special thanks for Harald

Schaller for trying to prepare me for moving to Central Europe. I did not understand your

wise words and why my wardrobe looked “too Finnish”, but I´ve got good laughs for my

Nordic way of thinking and how different countries in Europe can be. Dr. Sigrún Maria

Kristínsdóttir: thanks for all the skype chats and talks during our PhDs! It was great to feel

that there was a sister on the same boat Dr. Hanna Sisko Kaasalainen: thanks for all our

great discussions and guidance on the way to a PhD. Sandra Ósk Snæbjörnsdóttir: a dear

friend and now a PhD colleague as well. What a dream that we would be working in the

same place! Unfortunately our offices are thousands of kilometres apart, but the telepathy

still works. Your cat, my dear Logi, kept me company for two important weeks in the

beginning of my PhD, which I will always remember. I´m looking forward to support you

in your PhD! Dr. Pacifica Ogola, thank you for your wise words and life wisdom. Your

painting on my wall reminds me of you every day.

PhD students, scientific staff and technical staff at the Soil Research Institute at BOKU,

Vienna, Austria. Prof. Sophie Zechmeister- Boltenstern: thank you for having me at the

institute. You have a great group, a great institute, and millions of exciting opportunities.

My PhD would not be the same without the support from the institute. PhD students didn´t

have an organized support group to begin with, but we created one. Christine Gritsch:

without your enthusiasm we wouldn´t have made it, thank you! I´m also thankful for our

great discussion Dr. Stefanie Kloss: thanks for our yoga and discussion times! Sonja,

xxiv

Sumitra, Leo, Elsa, Joshua, Jasmin: thank you for taking time to meet up to discuss PhD

related issues and to be there to boost each other’s self-confidence and presentation skills.

Dr. Axel Mentler, Ewald Brauner, Astrid Hobel, Elisabeth Kopecky, Karin Hackl, Angelika

Hromatka: without you I would not be defending my PhD. Axel is a man that everybody

needs and appreciates. He knows the most about any machine at the institute and always

has ways and will power to help people. Thank you for showing and teaching me things in

the lab, and solving many many of my problems. Ewald, Astrid, Elisabeth, Karin,

Angelika, a million thanks to all the work you have done to get my samples through the lab

and for your help and guidance in the lab. Heider Naschimento, Guilhem Heranney, Flora

Brocza: I´m ever grateful for your help in the lab! Flora, you made my farmer interviews in

Austria possible, thank you! Your enthusiasm, positive attitude, and thoroughness are

qualities everybody wants. Make sure you´re surrounded by good people and that you get

enough stimulation in your studies, you will go long! Dr. Ika Djukic: I enjoyed sharing an

office with you and getting to know a person from much more south of Europe than I am.

Thanks for our discussions, your help in life and statistics, our wonderful evening at

Hofburg, and the friendship that was created and that hopefully will be life-long. Dr.

Katharina Keiblinger: thank you for introducing me to Hans, taking time to help all the

PhD students with their CVs, managing the breakfast, managing the lab meetings and

doing so much for the institute. Prof. Franz Zehetner: thank you for your comments on my

questions and always emailing to the ZID regarding my requests. Thanks for the whole

staff for the great moments shared!

Soil Science Group at the Technical University of Munich, Freising, Germany. Prof. Ingrid

Kögel-Knabner: I really enjoyed the soil organic matter course in March 2012 and knew

that I wanted to come back and learn more. Thank you for supporting my dream to come

for a visit to your group! Dr. Markus Steffens: as said before, I really liked to work with

you and appreciate your teaching during my time in Freising! Dr. Carten Müller: thank

you for answering a lot of questions about density fractionation even before my coming to

Freising, for showing me around at the institute, and encouraging me to come for a longer

time! I still want to hear more about your work in Alaska! Caroline Bimüller, Cordula

Vogel, Dominik Christophel: thanks for showing me the student life and joys of Freising, I

hope we will meet many many times more! Thank you for the whole group for making me

feel like one of the group from the very beginning, and for having such a great working

atmosphere!

Dear Friends. Laukki: when I had challenges adjusting to a new environment in Vienna

you came and lightened up my everyday life with some step dance. You booked tickets to

operas, musicals, operettas, ballets and what not during the days, and in the evenings we

enjoyed the cultural delights of Vienna. Luckily you´ve come back a couple of times,

please do come again! I already have a list of things you should see ;) Jaana: when I felt

like there was no nature in Vienna, I could come and see you in Paris. After three trips to

Paris I saw that Vienna was a city full of green space and surrounded by a big big forest

and countless amounts of vineyards. And the mountains are only a bit more than an hour

away by train. So what a place! Iceland is a paradise on Earth, but Vienna is fantastic in its

own way. Thanks for helping me to see that, and for always being there for me. You´re like

a sister, and so you will always be. Leena: you are always in my heart even though I can´t

see you as much as I would like. Let’s do another ladies trip to Tallinn or somewhere else

soon! Marianne: you´ve entered the same PhD boat, take a deep breath and think of

positive thoughts. Lots of yoga, and karma on! Thanks for all our running times and

xxv

discussions, they have been priceless. James, Marco and Maria: without you I wouldn´t

have tasted half as many wines and biked through so many Austrian places. Thank you!

Louise and Pad, thanks for hosting me and Hans in Iceland for such a long time and Louise

for reading through my English. I hope to get you to the boat soon again Sandra,

Steinþór, Agla, and Logi: you´re my Icelandic family and I couldn´t imagine a life without

you! Thank you for all my dear friends in Iceland, Austria, Finland, Sweden and around

the world who have supported me during this time, and shared joys and sorrows in so

many lovely occasions!

Family. Family IS the BEST. Äiti: kiitos sun mä oon opiskellu ja toteuttanu unelmiani. Ja

suurella suomalaisella sisulla sitä on tehty. Sä oot aina sanonu, että vaikkei rahaa ole niin

opiskella voi kuka tahansa ja kuinka paljon vaan haluaa. Oot myös sanonu että elämässä

pitää olla unelmia, pieniä ja suuria. Ja näitä oppeja olen sitten toteuttanut. Anteeksi, että se

on vienyt mut kauas pois, mutta kun maailmassa on vielä niin paljon nähtävää, ja opittava

kun ei lopu koskaan. Kiitos sun, mä oon saanu enkeleitä matkalle. Niitä on tarvittu, ja ne

on mua suojannu. Vieläkään ei oo yhtään luuta saatu rikki (kop kop koputan puuta!),

vaikka väikkärin aikanakin sitä on triathlonikisoissa kokeiltu. Siitä oon kiittänyt sua, ja

korttienkeliä joka matkaa rahapussissa arjessa mukana. Onneksi matka meijän välillä ei

tällä hetkellä ole kovin pitkä. Ja mun ovet on aina rakkaalle äidille auki. Isä: kiitos ihanista

juttutuokioista skypessä ja sun tuesta urheilun suhteen. Kyllä mä vielä joku päivä sun

kanssa kilometrikisaan pääsen Sami: sanat eivät riitä sua kiittämään. Sä oot ollu aina

mun tukena, kuunnellut ilot ja surut, auttanut muuttamaan Ruotsiin, tullut joka maahan

kylään ja ollut ihana itsesi. Oot kuunnellut ja tukenut isoissa päätöksissä. Kiitos kiitos

kiitos! Vaikkakin Hans on vienyt suuren osan mun huolien ja unelmien kuuntelusta, niin

sinun paikkaasi ei kukaan voi viedä. Raila: kiitos kun pidät mun rakkasta veljestä niin

hyvää huolta ja olet tuonut iloa koko Lehtisen perheeseen! Sari: onneksi mut on siunattu

myös siskolla. Mun lapsuuden idoli, joka onneksi on vihdoinkin saanut asennettua skypen

jotta me voidaan olla enemmän puheyhteyksissä. Sydämessä ja ajatuksissa olet aina. Tero:

oot järjestäny perheen parhaat löylyt ja vielä kauniilla maisemalla. Pidäthän hyvän huolen

mun rakkaasta siskosta ja neljästä kullannupusta. Riku: mitä mä ikinä tietäisin

suomalaisesta urheilusta ja uutisista jos mulla ei olisi sua? Meijän skypet on yksi mun

arkipäivän tukipilareista! Sisarustenlapset. Karoliina, Samuel, Johanna, Eemil, Annika,

Oskari ja Wilhelm. Te ootte mulle kultaa kalliimpia ja kiitos teidän oon saanu vaihtaa

monta vaippaa, tuntea itseni lapseksi keskellä huvipuistoa, oppinut Mangasta, kuullut ihan

uusia sanoja, saanut miljoonia pusuja ja haleja, ja niin paljon iloa elämään. Samuel ja

Eemil, mä jo innolla odotan uutta yhteistä lomaa, tuntu hiukka tyhjältä kun kesä 2013 ja

2014 ei tuonutkaan teitä luokseni. Saattaapi olla että tulevaisuudessa mä lennän Samun

luokse ulkomaille ;) Eemil, muista että kummipojalla on aina paikka kummitädin luona,

kylään saa tulla milloin vaan. Karoliina, sä oot matkannu mun perässä joka kaupunkiin ja

maahan. Toivottavasti näin jatkossakin, ja jos ei reissuja niin skypen voisi asentaa!

Johanna, mä kaipaan sua jo Wieniin takaisin. Kun siltä tuntuu, niin tänne vaan Palapeli

on joutunut takaisin laatikkoon, ja kaipaa tekijää. Annika, onneksi koripallo toi sut kanssa

Wieniin niin kummitäti pääsi näyttämään sulle parhaat shoppailukadut ja sä veit mut

kauppakeskuksiin mitä en ollu edes nähnyt. Oskari, mun pitäisi kyllä taas päästä sun

lätkämatsiin. Wilhelm, meillä on vielä monta yhteistä matkaa ja kokemusta edessä!

Margareta och Bo: tack för fantastiska stunder tillsammans! Jag har njutit av all vår til

tidsammans; i Wien, på Djursten, på båten, och jag hoppas att vi kommer att få mycket

mycket mer tid tillsammans! Ett mycket speciellt tack för er som har visat mig hur vackert

det kan vara när två personer älskar varandra så mycket som ni gör! Olle, Amanda, Samuel,

xxvi

Naomi, Daniel: det har varit jättefint att ni har kommit i min värld och att vi har kunnat

redan uppleva saker tillsammans. Jag ser fram emot nästa äventyr!

Funding. No research can be done without funding. My project was financially supported

by the European Commission FP7 Collaborative Project “Soil Transformations in

European Catchments” (SoilTrEC), Grant Agreement N° 244118. I also want to

acknowledge the support I received from the European Science Foundation (ESF) for the

activity entitled 'Natural molecular structures as drivers and tracers of terrestrial C fluxes'

to conduct NMR measurements at the Technical University of Munich in Freising. I thank

the FemTech grant I got to work at the Austrian Agency for Health and Food Safety

(AGES) for half a year in 2013. The last part of my research, carried out at AGES, was

funded under the CATCH-C project (Grant Agreement N° 289782) within the 7th

Framework Programme for Research, Technological Development and Demonstration,

Theme 2 – Biotechnologies, Agriculture & Food.

This PhD has been full of experiences and one of the best things I´ve done in my life. I´ve

sometimes had my doubts about whether science is for me, but I can´t deny it; this is just

the best job on Earth! In which other job can you try out your crazy ideas, dream a little,

travel around the world and meet lots and lots of interesting new people? Thanks to my

amazing personal and academic support network I will defend my PhD today, and fulfil

my dream of becoming a researcher! I wish the coming years will bring me many more soil

research experiences, not to forget my dream to run a full marathon when this work is

done! Thank you my support team, you are worth more than any piece of gold (kultaa

kalliimpia!) and I would not have made it without you!

“Learn from yesterday, live for today, hope for tomorrow. The important thing is to not

stop questioning” – Albert Einstein

1

1 Introduction

1.1 Background

Agricultural soil systems of today are under great stress and global production is expected

to need to double by the end of the century in order to meet the food demands for the

increasing population. Current agricultural practices have developed highly productive

food and biomass-producing systems based on industrial principles (Manlay et al., 2007).

However, the agricultural soil system is facing diverse challenges, such as loss of natural

ecosystems, degradation of soils and pollution due to population growth, and increasing

demand for food high up in the food chain (i.e. meat), energy, water and land for

industrialization (Lal, 2007). Arable land covers approximately one fourth of the global

land area, but only half of it can be used efficiently for cultivation to feed the growing

population and most of the best quality land is already in use (Tilman et al., 2002). In order

to face these challenges the EC Thematic Strategy for Soil Protection listed the key and

most essential soil functions including food and other biomass production; filtering,

buffering and transformation of water, nutrients and contaminants; storage of carbon; and

acting as a biological habitat and gene pool (European Commission, 2006). Soil fertility

denotes the ability of a well-structured soil to store and supply essential plant nutrients in

sufficient amounts, while maintaining preferable living conditions for soil biotic

communities and enabling effective soil organic matter dynamics (Mäder et al., 2002).

Sustainable multifunctional agricultural systems aim to meet the requirements for

increased net primary productivity per unit input, but to keep the production within the

limits of natural resources available and to maintain the ecosystem services for future

generations (Brussaard et al., 2007; Kibblewhite et al., 2008). In terms of soil management

this means maintaining and enhancing the soil carbon pool and its biodiversity (Lal, 2009).

1.1.1 Soil structure in agricultural soils

Soil structure represents the organization and arrangement of soil particles and pore

networks in the soil (Ghezzehei, 2012). Soil aggregate formation and stability are

fundamental for soil structure, and are essential controls of soil fertility and agronomic

productivity (Bronick and Lal, 2005). In agriculture, soils are ploughed in order to provide

a favourable physical state for agriculture including good infiltration and water retention,

well-aerated soil for optimal root growth, and favourable conditions for microbial activity

(Hadas, 1997).

According to the hierarchical aggregate model, which was first described by Tisdall and

Oades (1982), macroaggregates (> 250 µm) are constructed of microaggregates (< 250

µm), sand, and particulate organic matter (POM) bound together by transient or temporary

binding agents (Figure 1.1). Transient binding agents are microbial- and plant-derived

polysaccharides that decompose rapidly, whereas temporary binding agents include roots

and fungal hyphae. In contrast, microaggregates consist of associations of free primary

particles bound together by persistant binding agents such as organic molecules, metal

oxy(hydr)oxides, polyvalent cations, Ca- and Mg- carbonates, and CaSO4 (Tisdall and

Oades, 1982; Amézketa, 1999). The lowest hierarchical order of aggregates is clay

2

particles (< 2 µm). They are bound together by electrostatic bonding that causes

flocculation and forms clay domains in the soil (Ghezzehei, 2012). The flocculation is

dependent on various factors including the type of clay minerals, organic matter content

and cation exchange capacity (Ghezzehei, 2012).

Figure 1.1 Hierarchical conceptual model of aggregation as first described by Tisdall and

Oades (1982). The loop describes the chronological formation of aggregates, from clay

particles that forms domains that get gradually larger into microaggregates and

macroaggregates, as well as formation of microaggregates from macroaggregates.

Modified from Ghezzehei (2012).

Six et al. (2000) proposed another conceptual aggregate model (Figure 1.2), which

included the important change to aggregate hierarchy model by Oades (1984) that

describes the formation of microaggregates within macroaggregates. They postulate that

macroaggregates form around fresh (particulate) organic matter when microbially derived

organic molecules bind mineral particles. The fresh residue acts as a source of carbon for

microbial activity; thus, microbially-derived binding agents will be produced (Golchin et

al., 1994; Six et al., 2000). Since the binding agents of macroaggregates are weaker than in

microaggregates, macroaggregates are easily influenced by management practices such as

tillage (Ghezzehei, 2012). Microaggregates are formed within macroaggregates, as clay

and silt particles become encrusted with soil organic matter (SOM) and microbial waste

products (Six et al., 2000). Stable macroaggregates are formed when bridges between

primary and secondary particles coated with oxy(hydro)oxides are formed (Six et al., 2000;

2004). When the physical soil disturbance is reduced, e.g. in connection to no tillage,

stable microaggregates are formed within macroaggregates, and the turnover of

macroaggregates becomes longer, thus, carbon (C) can be stabilized within the

microaggregates and consequently SOM contents may increase (Six et al., 2000).

Clay particles Domains + silt particles Microaggregates Macroaggregates

<2 µm 2-20 µm 20-250 µm >250 µm

ë

3

Figure 1.2 The conceptual model of the Life cycle of macroaggregate. T1 to t4 denote

different time steps in the life cycle. Source: Six et al. (2000), with kind permission from

Wiley.

Soil aggregate stability is an indicator of the ability of the coagulated soil matrix to

withstand disruptive, physical forces. It is governed by various biotic and abiotic factors

and their interactions (Ghezzehei, 2012); the five most important being microorganisms,

roots, soil fauna, inorganic binding agents and environmental variables (Six et al., 2004).

Soil properties such as particle size distribution (Lehrsch et al., 1991), Fe and Al

oxy(hydr)oxide contents (Römkens et al., 1977; Le Bissonnais and Singer, 1993; Six et al.,

2004), and SOM level (Tisdall and Oades, 1982; Churchman and Tate, 1987; Deviren

Saygin et al., 2012) are influencing the aggregate stability. Roots govern aggregates by

mechanical effects of root penetration, moisture dynamics induced by plants, root

exudates, C inputs as well as entanglement by roots (Ghezzehei, 2012). Earthworms

influence the soil aggregates the most of any other soil fauna since they transport and break

down organic matter (OM) in the soil. Wetting and drying cycles may affect the aggregates

through a number of processes, namely swelling and shrinkage of clays, physical transport,

4

deposition and hardening of organic and inorganic binding agents as well capillary stresses

(Ghezzehei, 2012). Further, a wide range of soil properties and functions are influenced by

aggregate stability; including aeration, compacting ability, sealing, soil porosity, hydraulic

conductivity, resistance to erosion, and organic carbon (OC) stabilization by physical

protection (Fristensky and Grismer, 2008; An et al., 2010; Schmidt et al., 2011). For

agricultural soils, soil structure provides nutrient storage and cycling as well as governs the

accessibility, through chemical and physical protection, of the nutrients to the microbial

communities (van Veen and Kuikman, 1990). Even though concepts of soil structure are

numerous, a precise definition has yet to be defined in soil research and currently the

multiple scales in time and space are not fully understood (Ghezzehei, 2012).

Connection between OM inputs, such as manure and compost, and increased aggregate

stability is supported by several studies (Siegrist et al. 1998; Shepherd et al. 2002;

Williams and Petticrew, 2009; Karami et al. 2012). Organic inputs entering the soil provide

substrate for soil fungi, which further physically stabilize soil particles into larger

aggregates when fungal growth increases and hyphae enmesh soil particles (Eash et al.

1994) and fungi exude polysaccharides (Saccone et al., 2012; Gazze et al., 2013) onto

mineral surfaces which can further stabilize aggregates. The findings of Tisdall (1991) that

show the highest aggregate stability in the topsoil is explained by the high concentrations

of fine roots, organic matter and fungi that provides a favorable environment for

macroaggregation in the topsoil. Fungal hyphae and extracellular polysaccharides

produced by fungi enhance formation and stabilization of aggregates. OM inputs may also

attribute to higher microbial activity and production of microbial decomposition products

that bind the soil particles into microaggregates and microaggregates further into

macroaggregates (Sodhi et al., 2009). Compost is generally seen to enhance soil structure,

but its influence has also been described as short-lived (Debosz et al., 2002). Manure

inputs have been connected to significant and non-significant differences in soil structure

when using horse manure (Roldán et al., 1996). As a summary, a recent review (Abiven et

al., 2009) found no clear global trend on effects of diverse organic inputs on aggregate

stability. According to their review, manure and compost both affect aggregate stability by

a rather small magnitude but only after several months or even years of application.

Fertilizer application in conventional farming practice may also increase macroaggregation

through increasing the yields and subsequently the return of OM (Ladha et al., 2011),

which acts as an aggregating agent (Haynes and Naidu, 1998). This has been shown

especially for phosphorous fertilizers, which can enhance aggregation by the formation of

Al or Ca phosphate binding agents (Haynes and Naidu, 1998).

1.1.2 Soil organic matter in agricultural soils

Soils store approximately twice as much carbon (2 500 Pg) than the atmospheric and biotic

pool together (1 320 Pg); only the oceanic (38 000 Pg) and geologic pool (5000 Pg) store

more carbon than soils (Lal, 2004). SOM and its turnover play a pivotal role in the

biogeochemical cycling of nutrients and in the response of terrestrial C to future climate

scenarios (Schlesinger, 1995; Marzaioli et al., 2010). SOM originates primarily from plant

litter and microbial biomass and consists of many different compounds with varying

structure, content, and recalcitrance (Kögel-Knabner, 2002). Root-derived OM originates

from root biomass as well as root exudates that are a result of passive diffusion or plant-

regulated exudates with functional significance (Stockmann et al., 2013). Approximately

50 % of SOM is C, 40 % oxygen (O), 3 % nitrogen (N), and small amounts of phosphorous

(P), potassium (K), calcium (Ca), magnesium (Mg) and micronutrients are also present.

5

The fate and dynamics of SOM are mainly governed by its properties, the substrate

availability, biological conditions and environmental conditions (von Lützow and Kögel-

Knabler, 2009; Schmidt et al., 2011). Chemical recalcitrance of SOM itself and

physicochemical stabilization processes govern the protection of SOM in the soil matrix

(Stockmann et al., 2013). The chemical composition of SOM has been shown not to be the

governing factor of decomposition, and it affects the decomposition only in the short-term

(Amelung et al., 2008). Microbial ecology and activity, enzyme kinetics, environmental

drivers, and matrix protection mainly govern decomposition of SOM (Kleber, 2010). SOM

can be divided into three pools with different turnover times: i) labile or active (1-2 years),

ii) intermediate (10-100 years), and iii) slow (100 to >1000 years) (von Lützow et al.,

2006; 2008). The physicochemical stabilization of SOM occurs through protection within

aggregates causing spatial inaccessibility or through interactions with mineral surfaces and

metal ions (von Lützow et al., 2006). Spatial inaccessibility means that SOM is not

available for microbes and enzymes due to being occluded in aggregates, intercalated

within phyllosilicates, encapsulated in macromolecules or being hydrophobic (von Lützow

et al., 2006). Interactions between SOM and mineral surfaces and metal ions include ligand

exchange such as anion exchange, polyvalent cation bridges such as electrostatic cation

bridges, weak interactions such as Van der Waals forces, and complexation when metal

ions and SOM interact (von Lützow et al., 2008). In addition, microbial activity is an

important agent in SOM stabilization (Chabbi and Rumpel, 2009).

Separation of organic matter fractions, based on density fractionation in combination with

ultrasonic dispersion, enables separation of free particulate organic matter (fPOM,

consisting of undecomposed plant residue, hyphae and their partial decomposition

products), occluded particulate organic matter (oPOM, consisting of POM occluded in

aggregates), and organo-mineral associations with more processed SOM in the heavy

fraction (sediment of the density fractionation procedure) (Christensen, 1992; Golchin et

al., 1994; Kölbl and Kögel-Knabner, 2004). Density fractionation is one of the physical

fractionation methods used in soil science; others include aggregate fractionation, particle

size fractionation and high-gradient magnetic separation (Christensen, 1992; von Lützow

et al., 2007). Chemical fractionation methods include extraction procedures such as

microbial biomass carbon, hydrolysis of OM with hot water or acids, oxidation of OM (e.g.

with potassium permanganate (KMnO4)), and destruction of mineral phases with e.g.

hydrofluoric acid (HF) (Christensen, 1992; von Lützow et al., 2007). The separated SOM

fractions may function as early indicators for changes in SOM under varying soil

management (Leifeld and Kögel-Knabner, 2005).

SOM increase has numerous benefits for soils, including better plant nutrition, aggregate

stability and greater soil porosity, facilitating cultivation and seedbed preparation, reduced

bulk density as well as earlier warming by heat absorption in spring that enables earlier soil

management in the season (Carter and Steward, 1996). Loss of SOM is considered as one

of the biggest threats to soils according to the European Commission (European

Commission, 2006), and the benefits of soil carbon and the ecosystems provided by it are

well known (Victoria et al. (2012)). SOM provides society with i) supporting ecosystems

services such as soil formation and nutrient cycling; ii) regulating ecosystem services

including retention and decomposition of agrochemicals and contaminants, and climate

regulation; iii) provisioning services such as being basis for food and fibre production; and

iv) cultural ecosystem services, including preservation of archaeological remains (Victoria

et al., 2012; Robinson et al., 2014). The loss of SOM causes concomitant losses of

6

ecosystems services, especially loss of soil structure and soil nutrients (Malomoud et al.,

2009). Furthermore, the SOM can vanish quickly but the build-up takes considerably

longer time and requires long-term investment and management (Victoria et al., 2012).

Loveland and Webb (2003) suggested a critical SOM threshold of 2 % SOC for temperate

agricultural soils, but more quantitative evidence is required to define such thresholds for

different soil types.

In agricultural context, three historical periods regarding theories about SOM can be

distinguished: i) the humic until 1840, ii) the mineralist from 1840 until 1940, and iii) the

ecological since 1940 (Manlay et al., 2007). The humic period was characterized by

Thaer´s theory (Manley et al., 2007) of the importance of organic inputs in soil fertility.

Based on this theory, soil fertility index was based on soil texture, content of lime and

humus, as well as information on yield and organic fertilization. Thus, the soil fertility

index was used to define sustainable cropping systems (Feller et al., 2003). The mineralist

period started when Liebig in 1840 (Manley et al., 2007) concluded that a plant takes C

from carbon dioxide (CO2), hydrogen from water and other essential nutrients from

solubilized salts in soil and water (Manlay et al., 2007). This theory, together with other

agricultural field studies, led to modern agriculture and the use of mineral fertilizers to

compensate for soil mineral depletion (Manlay et al., 2007). The use of organic inputs

gradually decreased, especially following the hygienic movement that wanted to move

away from recycling organic matter in the farms and began in the 1880s (Mårald, 2002). In

current day intensive agriculture, all biomass is harvested in most cases since agricultures

started to specialize either in livestock or crop production – in addition to bioenergy

production - and therefore SOC stocks are reduced over large areas globally (Powlson et

al., 2011; Victoria et al., 2012). Losses of up to 50 % SOC after only 30-50 years of

farming have been reported (Post and Kwon, 2000). Another factor reducing SOC in

agricultural soils is tillage that subsequently decreases aggregate stability. Tillage disrupts

macroaggregates, produces better aeration and thus enhances decomposition of SOM and

releases CO2 (Elliott 1986; Victoria et al., 2012). Soil erosion due to conventional farming

practices accounts for 100 times greater losses of soil compared to natural soil formation

processes (Brantley et al., 2007; Montgomery, 2007). The concern of loss of soil

ecosystem services started the ecological period, which is characterized by ideas of

nutrient cycling, energy transfer and the importance of organic inputs into the soil systems

(Manlay et al., 2007).

Alternative farming practices, such as organic farming, aim to increase the content of SOM

by applying organic inputs such as manure into the soil (Siegrist et al., 1998) that

subsequently supports aggregation and even may increase crop yields (Loveland and

Webb, 2003). Organic amendments, such as animal manure, green manure, compost,

biochar and/or crop residues, may improve physical, chemical and biological soil

properties, that result in enhanced soil fertility (e.g. increased plant nutrients, OM, and soil

structure) (Watson et al. 2002; Sodhi et al. 2009; Diacono and Montemurro 2011; Sun and

Lu, 2014). Other methods that aim to increase SOM and soil fertility are diverse crop

rotations that include cover and catch crops, shelter beds, contour cultivation, crop residue

incorporation as well as intercropping (Watson et al., 2002; Victoria et al., 2012). In

addition, organic farming practice uses legumes to supply the soil with nitrogen, and uses

neither mineral fertilizers nor synthetic chemicals for plant protection (Leifeld and Fuhrer,

2010). A recent review by Leifeld and Fuhrer (2010) showed that increases in SOC (2.2 %

annual increase) were obtained due to high amounts of organic inputs such as manure and

7

compost. An amount of 6-7 t ha-1

year -1

of biowaste compost has been suggested to be

sufficient to maintain the SOM content in Pannonian climate (Erhart and Hartl, 2010),

whereas up to 16 t ha-1

year-1

may be required when aiming to maintain Norg levels (Hartl

and Erhart, 2005). A more recent review of 74 farming studies (Gattinger et al., 2012)

revealed significantly higher soil organic carbon (SOC) concentrations and stocks in

farming practice using organic inputs compared to farming practice using mineral

fertilizers. The suggested drivers for carbon accumulation were the external carbon inputs

as well crop rotation with forage legumes (Gattinger et al., 2012). However, it should be

noted that SOC accumulation cannot continue indefinitely following a change in

management, but is rapid at the beginning and slows down until a new equilibrium is

reached (Johnston et al., 2009; Stockmann et al., 2013). Management-induced changes

may be sooner detected in the distribution of SOM between the particulate organic matter

(POM) and aggregate fractions (20 µm, 20-250 µm, and >250 µm) than in the bulk SOM

(Christensen, 1992; Golchin et al. 1994; Chan et al. 2002; Steffens et al. 2009). Soil

aggregates can physically protect the incorporated OM from decomposition, especially in

soil systems in which physical disturbance is low (Six et al., 2000). POM fractions (free

POM, occluded POM) represent plant and animal residues undergoing decomposition and

have been observed to respond more sensitively to farming practice changes than total OC

(Golchin et al., 1994; Chan et al., 2002), especially occluded POM that may be lost from

soil aggregates due to intense cultivation (Golchin et al., 1994).

Approximately four billion tons of crop residues are produced globally (Chen et al., 2013).

In the US, approximately 2/3 of crop residues produced are cereals, and 1/5 are legumes

and sugar crops (Lal, 2005). Removal of crop residues has been shown to have a negative

effect on soil organic carbon (SOC), although it has been estimated that between 25 % and

50 % of crop residues could be harvested for other uses without endangering soil

functioning (Blanco-Canqui, 2013). The harvesting of crop residues may be beneficial for

farmers since residues can be used as livestock bedding, residues can be sold or thermally

utilized, and harvesting residues fits reduced or no-tillage operations better than

incorporation of residues. Incorporation of crop residues may be a sustainable and cost-

efficient management practice to maintain the ecosystem services provided by soils, the

SOC levels and to increase soil fertility in European agricultural soils (Perucci et al., 1997;

Powlson et al., 2008). Especially Mediterranean soils that have low SOC concentrations

(Aguilera et al., 2013), and areas where stockless croplands predominate (Kismányoky and

Tóth, 2010; Spiegel et al., 2010) could benefit of this management practice. Crop residue

incorporation has been observed to increase SOC concentrations and stocks, although to a

minor extent if compared to farmyard manure (Cvetkov et al., 2010) or to slurry (Triberti

et al., 2008). For GHG emissions, both positive and negative effects have been observed

following crop residue incorporation (e.g. Abalos et al., 2013).

1.1.3 Greenhouse gas (GHG) emissions following crop residue

incorporation

Globally, approximately 25% of CO2 and 70% of N2O anthropogenic emissions are

coming from agricultural lands (Stavi and Lal, 2013). According to Jenkinson and

Ayanaba (1977), approximately 1/3 of plant material added to soil is retained after the first

year whereas 2/3 is emitted to the air as CO2, in temperate climate conditions. The amount

of SOC depends on photosynthetic C added to the soil and the decay rate (Figure 1.3;

Janzen, 2006). In agricultural soils, SOC usually declines due to lower C inputs compared

8

to the outputs, as well as increase of biological activity and erosion which both deplete

SOC (Janzen, 2006). GHG emissions have been observed to both increase and decrease

following crop residue incorporation (e.g. Abalos et al., 2013). The increased CO2

emissions account for increased microbial activity in the soils in question (Iqbal et al.,

2009).

Figure 1.3 Simplified schematic of the carbon dynamics in a cropland, showing three

different SOC pools and the various sources of CO2 from the agricultural soil system.

Source: Janzen (2006), with kind permission from Wiley.

For N2O, which has 298 times higher global warming potential compared to CO2 (IPCC,

2007), huge variation and thus uncertainty exists in results of measured emissions

following crop residue incorporation as well as in estimations of emission factors for crop

residues (Chen et al., 2013). Figure 1.4 presents the numerous ways crop residues are

involved in N2O emissions. First of all, crop residues undergo microbial N mineralization

and nitrification that leads to N2O emissions. Secondly, Crop residues also function as OC

substrate for microbial growth thus stimulating microbial N assimilation (Chen et al.,

2013). Thirdly, energy is provided for denitrificators from crop residues that increases N2O

emissions under anaerobic conditions (Chen et al., 2013). According to Khalil and Baggs

(2005) nitrification is a major source of N2O emissions at 30-60% water filled pore space

(WFPS), whereas denitrification dominates at 50-90% WFPS. The rate of emissions

depends on residue composition, quality, and quantity (e.g. Baggs et al., 2000), and soil

properties such as pH (Chen et al., 2013), soil structure (Chen et al., 2013), and soil

temperature and water content (Stott et al., 1986).

9

Figure 1.4 Schematic diagram on the effects of crop residue incorporation on soil N2O

emissions. IP1 and IP3 denote higher emissions (due to nitrification and denitrification,

respectively); IP2 lower emissions (due to nitrification); IP4 and IP5 higher or lower

emissions (depending on electron donors and acceptor, and anaerobicity, respectively).

Source: Chen et al. (2013), with kind permission from Wiley.

1.2 Aims of the research

The overall objective of this PhD research was to characterize SOM and soil aggregates in

European agricultural soils, as well as investigate GHG emissions from crop residue

incorporation experiments. Two specific agricultural areas, Iceland and Austria, were

selected for studying the specifics of soil structure and SOM dynamics. Iceland is of

interest as an agricultural area since it is likely to become more used for agriculture due to

global warming of climate on a global scale. Austria is of interest since it is currently under

intensive farming and therefore an important farming area for the future as well. In order to

study how long-term management affects soil, SOC and GHG emissions in particular, a

European scale meta-analysis study on effects of crop residue incorporation from

published experiments was also carried out.

The specific objectives of this PhD study were:

To study macroaggregate breakdown by ultrasonication in soil orders with wide

range of stabilities and formed from diverse parent materials: alluvial calcareous

sediments (Entisol, Austria), volcanic ash and basalt (Andisol, Iceland),

serpentinite (Alfisol, Czech Republic), schist (Ultisol, Greece), and granite

(Inceptisol, Switzerland). All soils were from Critical Zone Obervatories (CZO)

and related to areas in the SoilTrEC project. The outcomes of these evaluations are

presented in Chapter 2. These results were used to determine how much ultrasonic

10

energy should be used for density fractionation in the studies presented in

Chapters 3 and 4.

To study aggregation and organic matter in sub-arctic grassland soils of Iceland,

and specifically macroaggregate stability and soil organic matter (SOM) quantity,

quality and distribution between different fractions with a density fractionation.

The outcomes of these evaluations are shown in Chapter 3.

To characterize soil structure and soil organic matter in cropland soils of the

agricultural area of Marchfeld, in Austria, and particularly macroaggregate stability

and SOM quantity, quality and distribution between different fractions were studied

following a density fractionation. The outcomes of these evaluations are shown in

Chapter 4.

To investigate the effect of crop residue incorporation on soil organic carbon (SOC)

and greenhouse gas (GHG) emissions in European agricultural soils with a meta-

analyses approach. The outcomes of these evaluations are shown in Chapter 5.

1.3 Methodology

1.3.1 Study sites

Critical Zone Observatories

For the study on aggregate breakdown following ultrasonication (Chapter 2) soils were

selected to represent different stages in soil pedogenesis, including organic matter

accumulation, and mineral weathering. For this purpose, soils from the SoilTrEC Critical

Zone Observatories (CZOs) were sampled (Figure 1.5):

- An Andisol from Iceland was selected to represent a very young soil with volcanic

ash and basalt as parent material.

- An Inceptisol from Alpine grassland located close to the chronosequence of the

Damma Glacier forefield in Switzerland was selected to represent a more

developed soil compared to the Andosol.

- An Entisol expected to develop into a Mollisol on alluvial Danube River floodplain

sediments in Austria was selected to represent a typical soil from an agricultural

area in Central Europe.

- An Ultisol from an agricultural soil cultivated for thousands of years, and

developed on schist in Greece was selected to represent a more developed soil.

- As a fifth soil an Alfisol from an intensively managed Norway spruce (Picea abies)

forest on serpentinite bedrock in the Czech Republic was selected.

For these study sites, the US Soil Taxonomy has been used as a requirement from the

journal where the study was published, whereas in the study sites in the next descriptions

WRB soil classification has been used. Therefore, both Andisol and Andosol soil

11

classifications are being used in this introduction. Andisol refers to Chapter 2 and

Andosol to Chapter 3.

Farm sites in Iceland and Austria

Farms studied on Andosols in Chapter 3 and on Chernozems in Chapter 4 were located

in Iceland (Figure 1.6, 1.7) and Austria (Figure 1.8, 1.9). In Iceland, six sites were selected

(Chapter 3):

- Grass1 is unimproved grassland that is used as a pasture for young cattle and sheep

for a short time in the autumn.

- HaAorg is improved grassland where organic fertilizers (manure, compost, and

cattle urine) and biodynamic preparations are used.

- HaAcon is improved grassland where organic (manure) and supplemental inorganic

fertilizers are used.

- Grass2 is unimproved grassland that is not used for pasture.

- HiAorg is improved grassland that receives organic fertilizers (manure, and

compost).

Figure 1.5 The selected study sites. Map composed by Janine Krüger (Leibniz-Institute of

Vegetable and Ornamental Crops, Grossbeeren, Germany).

12

- HiAcon is improved grassland that receives organic (manure) and supplementary

inorganic fertilizers.

Figure 1.6 Map of the selected farms in Iceland. For acronyms, see text above. Map

composed by Friðþór Sófus Sigurmundsson (Faculty of Life and Environmental Sciences,

University of Iceland).

13

Figure 1.7 Pictures from the selected improved farming sites (HaAorg, HaAcon HiAorg,

and HiAcon) from Icelandic organic (HaAorg, HiAorg) and conventional farms (HaAcon,

HiAcon).

In Austria (Chapter 4), four cropland sites located in the agricultural area of Marchfeld,

southeast of Vienna, in the fluvial terrace of the river Danube were selected (Figure 1.8

and 1.9):

- Org76 is an organic farm that has been managed according to the Austrian

guidelines for organic farming (BIO AUSTRIA, 2010) since 1976. The studied

field receives biowaste compost as an organic fertilizer.

- Con76 is a field at a conventional farm that receives mineral fertilizers according to

the Austrian fertilization recommendations (BMLFUW, 2006).

- Org95 is a field at an organic farm that receives horse manure as an organic

fertilizer and was converted to organic management according to the Austrian

guidelines for organic farming (BIO AUSTRIA, 2010) in 1995.

- Con95 is a field at a conventional farm that receives mineral fertilizers according to

the Austrian fertilization recommendations (BMLFUW, 2006).

HiAorg HiAcon

HaAcon HaAorg

14

Figure 1.8 Map of the selected farms in Austria, at Obersiebenbrunn and Lassee. Map

composed by Helene Pfalz-Schwingenschlögl (BOKU).

15

Figure 1.9 Sampled organic (Org76, Org95) and conventional farms in Austria (Con76,

Con95).

Long-term field experiments

For Chapter 5, a detailed literature review was conducted concerning scientific

publications that had reported on long-term agricultural experiments in Europe. This

yielded a total of 50 experiments in 15 countries (Figure 1.10, see more detailed

information about individual experiments in Chapter 5), from 39 publications. The

selected publications report on measurements of SOC concentration, and CO2 and N2O

emissions from pairwise comparisons of crop residue incorporation and crop residue

removal management practices. Our data came from 46 field experiments and four

laboratory experiments that covered 10 European Environmental Zones (ENZs), as defined

by Metzger et al. (2005).

Org76 Con76

Con95 Org95

16

Figure 1.10 The selected European long-term experiments. Map composed by Janine

Krüger (Leibniz-Institute of Vegetable and Ornamental Crops, Grossbeeren,

Germany).Map is a black and white version of the map in Chapter 5 and in this PhD thesis

with the kind permission of Soil Use and Management.

1.3.2 Methodology summary

In Table 1.1, all the methods used in this PhD thesis are summarized. For detailed method descriptions, see Chapters 2, 3, 4, and 5. The main physical and chemical characterisation of the soils were carried out at the Soil Research Institute at the University of Natural Resources and Life Sciences (BOKU) in Austria and the biological characterisations were carried out at Wageningen University, in the Netherlands. For Chapter 2, analyses were also carried out at the Institute of Applied Geology (BOKU) in Austria. Custom-made ultrasonic soil dispersion equipment that was developed in the laboratory of Soil Research Institute at BOKU (Schomakers et al., 2011a, 2011b) was used in Chapters 2, 3, and 4. For detailed method description, see the above-mentioned chapters. In Chapter 2 the method was tested for 5 common soil orders (all European soil CZOz). In Chapters 3 and 4 the instrument was used to study aggregate stability and quantities of occluded particulate organic matter in Andisols (Icelandic farms) and Chernozems (Austrian farms). The solid-state

13C

NMR spectroscopy measurements (Chapters 3 and 4) were carried out

at the Chair of Soil Science in Freising at the Technical University of Munich, Germany. Solid-state

13C

NMR spectroscopy (DSX 200 NMR spectrometer, Bruker, Karsruhe,

Germany) was used to study the relative changes in carbon distribution in the samples from farmed study sites (Chapter 3 and 4). For Chapter 5, analyses of already published data from long-term agricultural experiments from Europe were carried out at the Austrian Agency for Health and Food Safety. Meta-analysis of the data collected is described in Chapter 5.

17

Table

1.1

Sum

mary

of

met

hods

use

d i

n t

his

PhD

th

esis

. L

ette

rs i

ndic

ate

wh

ere

the

an

aly

ses

wer

e ca

rrie

d o

ut.

Aim

of

the

met

hod

Ref

eren

ce

Use

d i

n

Ch

apte

r

P

hys

ical

met

ho

ds

Ult

raso

nic

soil

aggre

gat

e st

abil

ity (

US

AS

)a S

chom

aker

s et

al.

, 20

11

a an

d 2

011

b

2

Mea

n W

eight

Dia

met

er (

MW

D)a

Kem

per

and

Ro

senau

19

86

2

Min

eral

og

yb

M

oore

an

d R

eyn

old

s 1

99

7

2

Par

ticl

e si

ze d

istr

ibuti

on

a S

oil

Su

rvey

Sta

ff,

20

04

2

, 3,

4

Den

sity

fra

ctio

nat

ion

a

mo

dif

ied

fro

m M

uel

ler

et a

l., 2

00

9 a

nd

Ste

ffen

s et

al.

, 2

00

9,

ult

raso

nic

atio

n b

ased

on C

hap

ter

2

3,

4

Bulk

den

sity

(B

D)a

S

oil

Su

rvey

Sta

ff,

20

04

3

, 4

C

hem

ical

met

hods

Soil

pH

a S

oil

Su

rvey

Sta

ff,

20

04

2

, 3,

4

Tota

l C

and N

conte

nts

a T

abat

abai

and

Bre

mner

, 19

91

2

, 3,

4

Car

bonat

e (C

aCO

3)

conte

nta

Soil

Su

rvey

Sta

ff,

20

04

2

, 4

Cat

ion e

xch

ange

capac

ity (

CE

C)a

S

oil

Su

rvey

Sta

ff,

20

04

2

, 3,

4

Am

moniu

m-o

xal

ate

extr

acta

ble

Fe,

Mn, A

l, (

Si)

a

Sch

wer

tman

n, 1

96

4

2,

(3),

4

Dit

hio

nit

e-ci

trat

e-bic

arbo

nat

e-ex

trac

table

Fe,

Mn,

Al,

(S

i)a

Meh

ra a

nd J

ackso

n 1

96

0

2,

(3),

4

All

ophan

e co

nte

nta

P

arfi

tt 1

990

3

Fer

rih

yd

rite

conte

nta

P

arfi

tt a

nd C

hil

ds

198

8

3

Cal

cium

-ace

tate

-lac

tate

ex

trac

table

P a

nd K

a Ö

NO

RM

L10

87

3

, 4

B

iolo

gic

al

met

hods

Wat

er e

xtr

acta

ble

org

anic

C (

WE

OC

)c B

ran

dst

ette

r et

al.

, 19

96

2

, 3,

4

Mic

robia

l bio

mas

s C

a V

ance

et

al., 1

98

7

3,

4

18

Hot

wat

er e

xtr

acta

ble

Cc

Ghan

i et

al.

, 20

03

3

, 4

Min

eral

izab

le N

c C

anal

i an

d B

ened

etti

20

06

3

, 4

Hyph

al l

ength

and b

acte

rial

num

ber

sc B

loem

an

d V

os

20

04

3

, 4

Bac

teri

al b

iom

assc

Blo

em e

t al

. 19

95

3

, 4

Bac

teri

al a

ctiv

ity

c B

loem

an

d B

olh

uis

20

06

3

, 4

M

ethods

to s

tudy

SO

M

Sim

ult

aneo

us

ther

mal

anal

ysi

s (S

TA

)b

Bar

ros

et a

l., 2

00

7

2

Chem

ical

qual

ity o

f S

OM

(so

lid

-sta

te 1

3C

NM

R

spec

trosc

op

y)d

K

nic

ker

et

al., 2

00

5

3,

4

aIn

stit

ute

of

Soil

Res

earc

h, U

niv

ersi

ty o

f L

ife

and

En

vir

on

men

tal

Sci

ence

s (B

OK

U),

Vie

nn

a, A

ust

ria.

bIn

stit

ute

of

Appli

ed G

eolo

gy, U

niv

ersi

ty o

f L

ife

and E

nv

iron

men

tal

Sci

ence

s (B

OK

U),

Vie

nn

a, A

ust

ria.

c Wag

enin

gen

Univ

ersi

ty,

the

Net

her

lands.

dC

hai

r of

Soil

Sci

ence

, T

echnic

al U

niv

ersi

ty o

f M

un

ich

, G

erm

any (

Fre

isin

g).

19

1.4 Results

1.4.1 Aggregate breakdown in European soils

Chapter 2. Lehtinen, T., Lair, G.J., Mentler, A., Gísladóttir, G., Ragnarsdóttir, K.V.,

Blum, W.E.H. 2014. Soil Aggregate Quantification Using Low Dispersive Ultrasonic

Energy Levels. Soil Science Society of America Journal, 78: 713-723. Reprint is published

with kind permission of the journal.

The study in Chapter 2, showed that aggregate breakdown at low energy levels was

greatest in the Andisol and the Entisol, followed by the Alfisol, Ultisol and Inceptisol. The

stability of macroaggregates was influenced by particle size distribution, the amounts of

exchangeable Mn (influenced mean weight diameter (MWD) positively) and exchangeable

Mg (influenced MWD negatively). The results demonstrate that aggregate breakdown is

strongly depending on the amount of energy applied, as well as of soil properties, which

influence defined aggregate size classes differently. The results also confirmed that

Andosol and Entisol behaved similarly, and therefore the density fractionation for Chapter

3 and 4 was designed with the same amount of energy (8 J ml-1

) for both soil types,

Andodols and Chernozems, due to their similar behaviour under vibrational energy

application. This confirmed recommendations from previous studies (e.g. Amelung and

Zech, 1999; Schmidt et al., 1999), that guide researchers to carefully select the amount of

ultrasonic energy used, based on the soils being studied.

1.4.2 Soil aggregates and soil organic matter in Icelandic

grasslands and Austrian croplands

Chapter 3. Lehtinen, T., Gísladóttir, G., Lair, G.J., van Leeuwen, J., Blum, W.E.H.,

Bloem, J., Steffens, M., Ragnarsdóttir, K.V., 2014. Aggregation and organic matter in

subarctic Andosols under different grassland management. Acta Agriculturae

Scandinavica, Section B – Soil & Plant Science (submitted 12.08.2014).

Chapter 4. Lehtinen, T., Lair, G.J., van Leeuwen, J.P., Gísladóttir, G., Bloem, J.,

Ragnarsdóttir, K.V., Steffens, M., Blum, W.E.H. 2014. Characterization of soil

aggregation and soil organic matter under intensive cropping on Austrian Chernozems.

Journal of Plant Nutrition and Soil Science (to be submitted).

In Chapter 3, it was shown that macroaggregate stability in Icelandic topsoils was

approximately twice as high in organically managed compared to conventionally managed

sites, and had a closer resemblance to unimproved grasslands. This was probably due to

organic inputs (manure, compost, and cattle urine) in the organically managed sites.

Macroaggregates (>250 µm) were most prominent aggregates in the topsoils of the

unimproved and organically managed grasslands, whereas 20-250 µm aggregates were the

most prominent ones in the conventionally managed grasslands. The organic matter

distribution differed between the sites based on SOM concentrations. Macroaggregates

contributed between 40-70% of SOM in soil of low SOM concentration and free

particulate organic matter (fPOM) contributed up to 70% in soils with high SOM

concentration. Oxalate-extractable Mn and fungal biomass correlated positively with the

macroaggregates, and were main aggregating agents of macroaggregates. In neither Iceland

20

(Chapter 3) nor Austria (Chapter 4) could aggregate hierarchy be proven. In Austria

(Chapter 4), no significant differences in magroaggregation were found between the

studied sites. This may be due to small amount of organic inputs in the organically

managed sites and beneficial effects of fertilizer usage on aggregation. Iron oxides content

and active fungal biomass were positively correlated with the amount of the

macroaggregates and the mean weight diameter (MWD). The soil fractions that were

observed in the highest proportion to the bulk soil (<20 µm aggregates at the sites Org76

and Con76, and 20-250 µm aggregates at the sites Org95 and Con95) contained the most

OC and total N (Nt). The distribution and dynamics of Nt content paralleled those of the

OC content. Macroaggregates are important in protecting SOM, which is a prerequisite for

adequate soil functioning in agricultural areas. Thus, further studies on a quantitative basis

for evaluating whether it may be beneficial to use organic inputs in order to increase SOM

content and macroaggregation are needed.

1.4.3 Effect of crop residue incorporation on SOC and GHG emissions

Chapter 5. Lehtinen, T., Schlatter, N., Baumgarten, A., Bechini, L., Krüger, J., Grignani,

C., Zavattaro, L., Costamagna, C., Spiegel, H. 2014. Effect of crop residue incorporation

on soil organic carbon (SOC) and greenhouse gas (GHG) emissions in European

agricultural soils. Soil Use and Management (in press). Manuscript included in the thesis

with kind permission of the journal.

Chapter 5, I showed that the SOC increased by 7 % following crop residue incorporation.

In contrast, in a subsample of cases, CO2 emissions were six times and N2O emissions 12

times higher following CR incorporation. The ENZ had no significant influence on RRs.

For SOC concentration, soils with a clay content >35 % showed 8 % higher RRs compared

to soils with clay contents between 18 and 35 %. As the experiment progressed, RR for

SOC concentration and stock increased. For N2O emissions, RR was significantly higher in

experiments with duration of <5 years compared to 11-20 years. No significant correlations

were found between RR for SOC concentration and yields, but differences between sites

and study durations were detected. In summary, the incorporation of crop residues increase

SOC, but its effect on GHG emission should be quantified in more detail in order to

investigate the effect of this management practice on the whole carbon and nitrogen cycle

in agricultural soils.

1.5 Discussion

1.5.1 Aggregate dynamics in European soils

The aggregate hierarchy was both confirmed and not confirmed in this PhD thesis. On one

hand, we observed different binding mechanisms for microaggregates and

macroaggregates (Chapters 2, 3, and 4). On the other hand, macroaggregates were not

always correlated with SOM but with other properties that is not in accord with

identification of aggregate hierarchy based on Elliott (1986), and Oades and Waters

(1991). Instead of the expected correlations with SOM, manganese proved to play a role in

macroaggregation as demonstrated in Chapters 2 and 3. So far little is known about the

role of Mn in (macro)aggregation in the scientific literature. In Chapter 2, higher amounts

21

of exchangeable Mn increased the proportion of macroaggregates when various energy

levels were applied. Manganese is highly reactive as Mn(III, IV) oxy(hydr)oxide coatings

on mineral soil particles and in complexes with SOM. It can cause oxidative

polymerization of organic molecules containing phenolic functional groups (Heintze and

Mann, 1947; Navrátil et al., 2007), thereby playing a role in the formation and stabilization

of soil structure (e.g., Alekseeva et al., 2009). In Chapter 3, macroaggregates were

correlated with oxalate and dithionite-extractable Mn. The role of Mn in aggregation may

be explained by it being associated with SOM (Navrátil et al., 2007), which in turn

functions as an aggregating agent. In future studies, these relationships would be

interesting to explore further. In Chapter 4, the amount of macroaggregates was

significantly positively correlated with the content of Fed, which supports the strong

aggregating power of oxides (Amézketa, 1999). According to Amézketa (1999), soil

structure is improved in the presence of oxides due to them acting as flocculants in

solution, their ability to bind clay particles to OM, and their ability to precipitate as gels on

clay surfaces. Very stable aggregates can be formed when amorphous Fe3+

(Feo in this

study) and SOM interact (Barral et al., 1998). Oxy(hydr)oxides have high surface areas and

can adsorb organic material on their surface by electrostatic binding and thereby enhance

aggregation (Six et al., 2004). Thus, both Chapter 3 and 4 showed a diminished

expression of the hierarchical model of aggregates due to other binding agents than SOM

(Oades and Waters, 1991), in the studied Andosols and Chernozems.

In Chapter 2, macroaggregate breakdown followed the increasing order: Inceptisol <

Ultisol < Alfisol < Entisol < Andosol, which contradicted our hypothesis and confirmed

that SOM, oxy(hydr)oxides, and clay content were not the main explaining factors of

macroaggregate stability. The Andisol was similar to the Andosols presented in Chapter 3

and the Entisol was similar to the Chernozems sampled in Chapter 4. The breakdown of

the macroaggregates (>250 µm) started already at low energy inputs, 2 J mL-1

for the

Andisol and Entisol, that confirm results by Mentler et al. (2004). Based on the aggregate

breakdown pattern expressed by the MWD (see Fig. 1 in Chapter 2) the strongest

macroaggregate breakdown had been reached at the end of the experiment at 40 J mL-1 in

the Alfisol, Andisol, Entisol; whereas more energy would have been necessary in the

Inceptisol and Ultisol to get a similar loss of macroaggregates. The variability between

different soil orders in breakdown of macroaggregates agrees with the review by Bronick

and Lal (2005) who concluded that in different soil orders, aggregation is controlled by

different properties. The authors stated that aggregation in Alfisols, Entisols, and Ultisols

is mainly governed by SOM, whereas in Andisols and Inceptisols the amount and type of

clay mineral plays a bigger role in aggregation. However, we did not find any significant

correlations between clay content and aggregate-size distribution, and the only soil where

clay type could play a bigger role in aggregation was the Inceptisol. High CEC clay

vermiculite was present in small amounts in the Inceptisol in Chapter 2, which may have

contributed to the greater aggregate stability in this soil order (Amézketa, 1999; Schulten

and Leinweber, 2000). Due to the clay minerals not having a major role in aggregation in

the Andisol and Entisol, they were not analysed for Chapters 3 and 4 in this PhD thesis.

Fungal biomass (Chapter 3) and active fungi (Chapter 4) were proved to be major

aggregating agents in the Icelandic and Austrian agricultural sites. This may be explained

by the input of OM into the soils, in the form of manure, compost and urea (Chapter 3),

foremost at the organically managed sites. These OM inputs provide substrate for the soil

fungi, which further physically stabilize soil particles into larger aggregates when fungal

22

growth increases and hyphae enmesh soil particles (Eash et al., 1994). Fungi exude

polysaccharides that adhere to minerals in the soil (Sacconi et al., 2012; Gazzé et al.,

2013). This will physically aid the association of soil particles into larger aggregates when

fungal growth increases and hyphae enmesh soil particles (Eash et al., 1994). The compost

input may also have attributed to the higher microbial activity and the production of

microbial decomposition products that bind the soil particles into microaggregates, and

microaggregates further into macroaggregates (Sodhi et al., 2009). Macroaggregate

stability was highest in the topsoil, as explained by Tisdall (1991). The concentrations of

fine roots, OM and fungi are the highest in the topsoil and provide therefore a favorable

environment for macroaggregation. Fungal hyphae and extracellular polysaccharides

produced by fungi enhance formation and stabilization of aggregates. The conventional

farms in Chapter 3 also used manure as soil improver, but the amount did not seem to be

sufficient to increase macroaggregation. In Chapter 4, the aggregation did not differ

significantly among the sites with different fertilization, but the farms that received only

mineral fertilizers had slightly more aggregated soils. The conventional farming practice

had higher alkyl-C (lipids) contents, which has in previous studies been shown to improve

aggregation due to their hydrophobic nature (Monreal et al., 1995; Dinel et al., 1997; Pare

et al., 1999). This may partly explain the slightly higher aggregation on the conventional

farming practice sites, compared to organic farming sites. However, the differences

between the farming practices in amount of macroaggregates and MWD were not

significant.

1.5.2 Soil organic matter in European agricultural soils

In both Chapter 3 and 4, the soil fractions that were observed in the highest proportion to

the bulk soil contained the most C and Nt, following results by Poll et al. (2003). Also, the

distribution and dynamics of Nt followed those of OC. In Iceland (Chapter 3), differences

were observed between two types of Andosols, Haplic and Histic, fPOM associated OC

and Nt fraction having by far the greatest storage capacity in the more OM rich Histic

Andosols. Sites with only organic inputs favored macroaggregate-associated OM fraction

and had a closer resemblance to unimproved grassland sites (Grass 1 and Grass 2) sites

compared to sites that received mineral fertilizers. The increase in fPOM associated OC

and Nt concentration measured in the cultivated sites compared to the unimproved

grassland sites is consistent with other studies that have shown that animal manure can

increase the particulate OM fraction (Whalen and Chang, 2002; Courtier-Murias et al.,

2013). In Austria (Chapter 4), macroaggregate-associated OM fraction was the most

significantly differentiating factor between the farming practices (organic vs.

conventional), being higher in conventional compared to the organic. The changes in OM

associated with >250 µm aggregates are in accordance with those having been reported as

being the most sensitive aggregate size for differences in farming practice (Kandeler et al.,

1999; Six et al., 2004). The small differences in C/N ratios between the different aggregate

classes in both Iceland and Austria further support the diminished aggregate hierarchy in

our studied soils (Six et al., 2004).

The solid-state 13

C NMR spectroscopy of all analyzed fractions (Chapters 3 and 4)

showed an increasing degree of decomposition in the order fPOM < oPOM < bulk soil,

shown as increased Alkyl-C to O-Alkyl-C ratio (Baldock et al., 1997). In Iceland (Chapter

3), no differences in chemical characteristics of OM were found between the sites. Golchin

et al. (1994) observed a similar trend when five different soils with different environmental

conditions and vegetation were compared. Also Courtier-Murias et al. (2013) showed that

23

organic amendments did not necessarily affect the composition of the OM stabilized in

agricultural soils. In contrast, in Austria (Chapter 4), a higher proportion of alkyl-C of the

total OC in fPOM in Con76 compared to Org76 was observed. This is likely to reflect the

fertilization differences. In Org76, biowaste compost that consists of humic substances

(Erhart and Hartl, 2010) was used and therefore explains lower proportion of alkyl-C,

which represents lipids and hemicelluloses (Golchin et al., 1994), compared to Con76.

In Chapter 5, 50 European experiments (46 field, and 4 laboratory) investigating the effect

of crop residue incorporation on SOC were analysed. The observed increases in both SOC

concentration and stock are well in agreement with previous meta-analyses where organic

inputs were incorporated into the soil (Lemke et al., 2010; Powlson et al., 2012).

Incorporation of crop residues is one of the few methods applied by farmers to maintain

SOC and to sustain soil functions (Powlson et al., 2008). This addition makes it a very

important management tool. Even a small increase in SOC can improve soil

physicochemical and biological properties such as soil structure and ecosystem services

such as nutrient cycling and possibly even increase in yields (Loveland and Webb, 2003;

Bhogal et al., 2009; Blanco-Canqui, 2013). The observed higher response ratios for SOC

concentration and stock for longer experiment durations agree with previous studies

(Körschens et al., 1998). As experiment duration increases, more interactions between clay

minerals and SOC may take place (von Lützow et al., 2006); this is accompanied by a

more marked accumulation of resistant crop residue C that is not mineralised (De Neve and

Hofman, 2000), especially in soils without mechanical tillage (Six et al., 2000). Long-term

experiments can reliably demonstrate how a farming practice affects soil and when many

of them are analysed together, a clearer picture of the effects can be drawn. According to

Johnston (2009), the effects of agricultural practices and its sustainability can be assessed

properly only in long-term experiments, where small changes can accumulate over the

years before they become detectable (as often occurs in SOC changes), and interaction

with meteorological variability can be assessed. Several authors have stated that response

of soil to farming practices is not a fast process and therefore long-term monitoring would

be a necessary tool for identifying responses to management changes (e.g. Loveland and

Webb, 2003; Leifeld and Fuhrer, 2010). According to Leifeld and Fuhrer (2010), an

optimal experiment for studying and evaluating management differences in soils should i)

be controlled in terms of having known initial conditions (soil properties), ii) last more

than 20 years, iii) study both topsoil and subsoil, iv) measure bulk density and SOC

concentrations to enable calculations of SOC stocks, and v) have similar organic

fertilization and crop rotation. These above mentioned guidelines should be borne in mind,

when new experiments investigating quantitative effects of different organic inputs on

SOC and SOM are set up.

1.5.3 Effect of crop residue incorporation GHG emissions

In my research (Chapter 5), GHG emissions were significantly increased following crop

residue incorporation, N2O up to twelve times higher compared to crop residue removal,

which demonstrates a need for more research where the whole soil carbon and nitrogen

cycles would be studied at once. With crop residue incorporation, CO2 emissions will

increase compared to crop residue removal due to more easily available C that enhances

microbial activity (Meijide et al., 2010). In contrast, if crop residues are removed, they will

be decomposed elsewhere, used as bedding and incorporated into farmyard manure or

burned, releasing approximately the same amount of CO2 (Blanco-Canqui, 2013). Thus,

crop residue incorporation is not primarily a way to decrease CO2 emissions. Emissions of

24

N2O occur both during the nitrification process and as a result of anaerobic denitrification.

The increase of the RR for N2O following crop residue incorporation in a study by Baggs

et al. (2003) was explained by mineral N fertilization and an increased denitrification

capacity stimulated by the added substrate. In our analysis, the limited number of data most

likely enabled us to find such relationships. The soil respiration process may create

anaerobic microsites in the soil and thereby increase N2O emissions through denitrification

(Garcia-Ruiz and Baggs, 2007; Abalos et al., 2013). Nonetheless, the N2O emissions

caused by the crop residues should be put in relation to the fact that not all removed crop

residues are decomposed or burned with no N2O emissions.

The factors that were found to have an influence on GHG emissions were the type of

experiment (field vs. laboratory), type of crop residue (vegetative vs. cereal) and duration.

The higher response ratios of N2O emissions in vegetative material laboratory experiments

compared to field experiments agree with a meta-analysis that studied N2O emissions

following crop residue incorporation into the soil (Chen et al., 2013). Those authors

explained the difference by the smaller size and subsequent increase of surface area of the

crop residues in the laboratory experiments compared to field-scale applications.

Moreover, under laboratory conditions moisture and temperature are stable and optimised

for microbial activity, thus promoting higher emissions compared to field experiments

(Chen et al., 2013). Higher RR of GHG emissions were observed in vegetative material

crop residue incorporation experiments compared to cereal crop residue incorporation

experiments, which is supported by observed higher N2O emissions following low C/N

ratio crop residues in previous studies (e.g. Alexander, 1977; Shan and Yan, 2013). This

may be explained by immobilisation of N with increasing C/N ratio of the crop residues

(Abalos et al., 2013). The oxidation rate is higher immediately after the incorporation of

vegetative material (versus cereal residues) due to quick decomposition, thus possibly

promoting higher denitrification rates (Nicolardot et al., 2001; Rizhiya et al., 2011). The

experiment duration lowered the RR, supporting a study by Chen et al. (2013). Peak

microbial activity when easily available organic inputs (crop residues) are added into the

soil (Recous et al., 1995) may explain this response (Powlson et al., 2011). Several authors

(e.g. Loveland and Webb, 2003; Leifeld and Fuhrer, 2010) have stated that response of soil

to farming practices is not a fast process and therefore long-term monitoring would be a

necessary tool for identifying responses to these management changes. Another potential

factor may be N fertilisation, which increased GHG emissions as has been presented in

several studies (e.g. Garcia-Ruiz and Baggs, 2007; Meijide et al., 2010; Sanz-Cobena et al.,

2014). Nevertheless, the data set in this research (Chapter 5) did not reveal any significant

correlations between N2O emissions and mineral N fertilisation. This may be due to limited

data accessibility and differences in the set-up of the experiments investigated. Chapter 5

suggests a win-win scenario to be crop residue incorporation for a long duration in a

continental climate. Due to a limited amount of data available, general conclusions of

GHG emissions following crop residue incorporation on a European scale are too early to

be drawn. In order to close the knowledge gap and to give better-informed

recommendations to farmers, further field-scale research focusing on in situ carbon and

nitrogen balances are required.

1.5.4 Conclusions

The aim of this thesis was to investigate the dynamics of soil aggregates and soil organic

matter (SOM) that are important for soil quality, as well as the effect of crop residue (CR)

incorporation on soil organic carbon (SOC) and greenhouse gas emissions. Our study on

25

macroaggregate breakdown (Chapter 2) in 5 different soil orders showed that all soils do

not respond the same to disturbance and generalizations in methodology cannot be made

before comparing different soils. The fractionation method designed in this PhD thesis

(Chapters 3 and 4, based on results from Chapter 2) could be used to study SOM and

aggregate distributions in numerous future studies, for example changes in SOM and

aggregate distributions in time could be investigated. Aggregate hierarchy was not

observed when e.g. oxy(hydr)oxides are governing factors and diminish the effect of SOM

on macroaggregation.

In order to gain knowledge on temporal changes following management changes, based on

our analyses, it would be advisable to monitor the following soil properties in future

studies:

Soil physical properties: soil aggregates (distribution, mean weight diameter

(MWD))

Soil chemical properties: pH, plant-available nutrients (P, K), carbon and nitrogen,

SOM distribution

Soil biological properties: fungal and bacterial biomass, active fungi

Chapter 5 showed that crop residue incorporation does increase SOC but as a trade off

also increase GHG emissions. The recommended win-win scenario between yield and SOC

would be crop residue incorporation over the longer term (>20 years) in a continental

climate. Data availability from field experiments on GHG emissions is still scarce, and the

data do not allow for selection of win-win and worst-case scenarios for these parameters.

Thus, more long-term field studies are needed to better assess the CO2 and N2O emissions

following crop residue incorporation, specifically from the same studies in which SOC is

measured. Crop residue incorporation can be regarded as an important management

practice to maintain SOC concentrations and stocks and to sustain soil functioning.

However, its influence on GHG emissions should be considered. GHG emissions as well

as complete in situ carbon and nitrogen balances should be measured in on-going long-

term field experiments in order to close the knowledge gap and to give better-informed

recommendations to farmers.

26

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2 Soil Aggregate Stability in Different Soil Orders Quantified by Low

Dispersive Ultrasonic Energy Levels

Soil Science Society of America Journal

Soil Sci. Soc. Am. J. 78:713–723 doi:10.2136/sssaj2013.02.0073 Received 22 Feb. 2013. *Corresponding author: (georg.lair@uibk.ac.at). © Soil Science Society of America, 5585 Guilford Rd., Madison WI 53711 USA All rights reserved. No part of this periodical may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. Permission for printing and for reprinting the material contained herein has been obtained by the publisher.

Soil Aggregate Stability in Different Soil Orders Quantified by Low Dispersive Ultrasonic Energy Levels

Soil Physics

Soil aggregate formation and stability are fundamental for soil structure, and are essential controls of soil fertility and agronomic productivity (Bronick and Lal, 2005). Soil aggregate stability is a measure of the ability of the co-

agulated soil matrix to withstand disruptive, physical forces. A wide range of soil properties are influenced by aggregate stability; including aeration, compactabil-ity, sealing, soil porosity, hydraulic conductivity, resistance to erosion, and organic carbon (C) stabilization by physical protection (Fristensky and Grismer, 2008; An et al., 2010; Schmidt et al., 2011). Aggregate stability is closely related to and gov-erned by soil properties such as particle-size distribution (Lehrsch et al., 1991), Fe and Al (hydr)oxide contents (Römkens et al., 1977; Le Bissonnais and Singer, 1993; Six et al., 2004), and SOM level (Tisdall and Oades, 1982; Churchman and Tate, 1987; Deviren Saygin et al., 2012).

According to the hierarchical aggregate model of Tisdall and Oades (1982), macroaggregates (>250 mm) are constructed of microaggregates (<250 mm),

Taru LehtinenInstitute of Soil ResearchUniv. of Natural Resour. and Life Sci. (BOKU)Peter Jordan Straße 82a1190 ViennaAustria and Faculty of Life and Environ. Sci.Univ. of IcelandSturlugötu 7101 ReykjavíkIceland Faculty of Earth SciencesUniv. of IcelandSturlugötu 7101 ReykjavíkIceland

Georg J. Lair*Institute of Soil ResearchUniv. of Natural Resour. and Life Sci. (BOKU)Peter Jordan Straße 82a1190 Vienna Austria

Institute of Ecology Univ. of Innsbruck Sternwartestraße 15 6020 Innsbruck Austria

Axel MentlerInstitute of Soil Research Univ. of Natural Resour. and Life Sci. (BOKU) Peter Jordan Straße 82a 1190 ViennaAustria

Guðrún GísladóttirFaculty of Life and Environ. Sci. Univ. of Iceland Sturlugötu 7 101 Reykjavík Iceland

Kristín Vala RagnarsdóttirFaculty of Earth SciencesUniv. of IcelandSturlugötu 7101 ReykjavíkIceland

Winfried E.H. BlumInstitute of Soil ResearchUniv. of Natural Resour. and Life Sci.(BOKU)Peter Jordan Straße 82a1190 ViennaAustria

Ultrasonic dispersion of soil aggregates in water-based solutions is commonly used in soil science, because it is possible to quantify the amount of energy applied to the solutions. However, currently available instrumentation does not provide precisely controlled low ultrasonic energy; thus, the study of weakly aggregated soils is still a challenge. The aim of this study was to apply amplitude controlled, low energy ultrasonic dispersion to study macroaggregate breakdown in soil orders with wide range of stabilities and formed on diverse parent materials: alluvial calcareous sediments (Entisol), volcanic ash and basalt (Andisol), serpentinite (Alfisol), schist (Ultisol), and granite (Inceptisol). Aggregates were exposed to increasing ultrasonic energy levels in six steps from 0 to 40 J mL-1, and the resulting macro- and microaggregate masses were measured. Subsequently, the aggregate distribution was correlated with various physicochemical properties of the 250- to 1000-µm macroaggregates. The study showed that aggregate breakdown at low energy levels was greatest in the Andisol and the Entisol, followed by the Alfisol, Ultisol, and Inceptisol. Stability of macroaggregates was influenced by particle-size distribution, the amounts of exchangeable Mn (influenced mean weight diameter [MWD] positively) and exchangeable Mg (influenced MWD negatively). In contrast, stable microaggregates in the range of 63- to 250-µm were positively correlated with oxalate extractable Fe and Al as well as with soil organic matter (SOM) content. The results demonstrate that aggregate breakdown is strongly depending on the amount of energy applied, as well as of soil properties, which influence defined aggregate-size classes differently.

Abbreviations: MWD, mean weight diameter; POM, particulate organic matter; RMSE, root mean square error; SOM, soil organic matter; STA, simultaneous thermal analysis; XRD, x-ray diffraction; WEOC, water-extractable organic matter.

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sand, and particulate organic matter (POM) bound together by transient or temporary binding agents. Transient binding agents are microbial- and plant-derived polysaccharides that decompose rapidly, whereas temporary binding agents include roots and hyphae. In contrast, microaggregates, consist of associations of free primary particles bound together by organic molecules, (hydr)oxides, polyvalent cations, Ca- and Mg- carbonates, and CaSO4 (Tisdall and Oades, 1982; Amézketa, 1999). Six et al. (2000) proposed another conceptual aggregate model. They posit that macroaggregates form around fresh (particulate) organic matter when microbially derived organic molecules bind mineral particles. Microaggregates are formed within macroaggregates, as clay and silt particles become encrusted with SOM and microbial waste products (Six et al., 2000). However, the build-up of different aggregate-size classes as well as their stabilities and life cycles are still not fully understood.

Ultrasound has become an extensively used method to study aggregate stability (e.g., North, 1976; Amézketa, 1999; Mentler et al., 2004; Schomakers et al., 2011a) since H2O can be used as a natural solvent and suspending medium (Mentler et al., 2004; Ashman et al., 2009; Zhu et al., 2010), and since the level of mechanical energy applied to the sample can be regulated (North, 1976; Raine and So, 1993; Zhu et al., 2009). Macroaggregates are disrupted and dispersed usually at relatively low absorbed energies (as low as 2 J mL-1; Edwards and Bremner, 1967; North, 1976; Amelung and Zech, 1999; Field and Minasny, 1999; Mentler et al., 2004; Six et al., 2004). In contrast, ultrasonic energy levels higher than 800 J mL-1 are needed to cause nearly complete microaggregate dispersion (Kaiser et al., 2012). Commercial ultrasound equipment often is inadequate for low energy dispersion. This is because the vibration amplitude and acoustic pressure at the lowest instrumental settings are still relatively high, and cannot be precisely controlled (Schomakers et al., 2011a, 2011b). Zhu et al. (2009) found that the displayed energy of commercial equipment differed 10 to 20% from the actual output energy. The custom-made ultrasonic soil dispersion equipment that was developed in our laboratory (Schomakers et al., 2011a, 2011b), and used in this study, has the advantage of using vibration amplitude instead of power to control the ultrasonic magnitude of the equipment and it has been shown to be successful in breakdown of macroaggregates. However, the method has only been tested on two soil orders (Inceptisol, Mollisol), but not on soils with larger variation in properties and expected diverse macroaggregate stabilities.

Ultrasonic dispersion may enhance leaching and redistribution of SOM, and its bioavailability (Amelung and Zech, 1999; Mueller et al., 2012), depending on the soil properties (Cerli et al., 2012). During ultrasonication, leaching of SOM can be investigated by analyzing the water extractable organic C (WEOC; Schomakers et al., 2011b). Redistribution of SOM can be investigated by analyses on the aggregate sizes gained, for example, by thermal analyses. Thermal stability of SOM measured as a function of temperature depends on the composition of the SOM itself, and can be used to quantify

various SOM compounds (e.g., De la Rosa et al., 2008; Plante et al., 2009).

The focus of this study was to monitor the breakdown of macroaggregates to measure how soil macrostructure in various soil orders respond to increasing ultrasonic energies, and further, which physical and chemical soil properties are mainly responsible for the stability of the aggregate-size classes collected. Knowing such relationships would help to understand soil structure formation, but also to define levels of ultrasonic dispersion energies to obtain various fractions of SOM such as particulate or mineral-associated SOM. Justification for the ultrasonic energy levels applied to different soil orders is rarely given in literature (Griepentrog and Schmidt, 2013).

Our objectives were (i) to characterize macroaggregate (250–1000 mm) stability and breakdown into microaggregates (<250 mm) across five soil orders with increasing dispersive ultrasonic energy levels, (ii) to identify the main physical and chemical properties influencing the measured aggregate stabilities, and (iii) to document the release of SOM (i.e., WEOC) from macroaggregates as a function of the ultrasonic energy level. We hypothesized that in all studied soil orders the contents of SOM, clay and oxides are the main determinants for a stable soil structure. Therefore, we selected five soil orders of different physicochemical and mineralogical characteristics sampled across Europe (namely Alfisol, Andisol, Entisol, Inceptisol, and Ultisol according to the U.S. Soil Taxonomy [Soil Survey Staff, 2004]). Macroaggregates were selected because they determine soil functioning (e.g., Stavi et al., 2011), and the size range of 250 to 1000 mm had previously been shown to be the dominant water-stable macroaggregate size in the studied soils (Kercheva et al., 2011; G. Lair, unpublished data, 2011).

MATERIALS AND METHODSStudy Site, Soil Sampling, and Preparation

The sampled soils selected represent different stages in pedogenesis, including organic matter accumulation, and mineral weathering: (i) an Andisol from Iceland as a very young soil with volcanic ash and basalt as parent material, (ii) an Inceptisol from an alpine grassland located close to the chronosequence of the Damma Glacier forefield in Switzerland, (iii) an Entisol expected to develop into a Mollisol on alluvial Danube River floodplain sediments in Austria, (iv) an Ultisol from an agricultural soil cultivated for thousands of years, and developed on schist in Greece; and (v) an Alfisol from an intensively managed Norway spruce (Picea abies) forest on serpentinite bedrock in the Czech Republic (Table 1). Samples were collected from the A-horizons of each soil below the densest rooting (5–20 cm, see exact sampling depths in Table 1) between April and July 2010, except the Andisol was sampled in June 2011. Before sampling, soils were described and classified (World Reference Base guidelines [IUSS Working Group WRB, 2006]; and U.S. Soil Taxonomy [Soil Survey Staff, 2004]). The freshly excavated soil samples were gently broken by hand into smaller aggregates (Ø < 15 mm) in the field, transported to the laboratory in plastic boxes, air-

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dried at room temperature and carefully dry-sieved to 250- to 1000-mm sized aggregates before this study.

Physicochemical and Mineralogical Characterization of the 250- to 1000-mm Aggregates

Aggregate pH was measured potentiometrically (Microprocessor pH Meter pH196 WTW, Weilhem, Germany) in H2O at an aggregate to solution ratio of 1:2.5 (Soil Survey Staff, 2004). Total carbon (Ct) and total nitrogen (Nt) were quantified by dry combustion (Tabatabai and Bremner, 1991) using an elemental analyzer (Carlo Erba NA 1500 Nitrogen Analyzer 1500, Milan, Italy, detection limit for C: 1 mg kg-1; N: 10 mg kg-1). Carbonate content was measured gas-volumetrically (CO2 evolution; Soil Survey Staff, 2004). Organic C (OC) was calculated as the difference of total C and carbonate C. Cation-exchange capacity (CEC) and exchangeable cations were determined using an unbuffered 0.1 M BaCl2 extraction (Soil Survey Staff, 2004). Extracted exchangeable cations (exchangeable K, Na, Ca, Fe, Mg, Mn, and Al) were measured by flame atomic absorption spectrophotometry (PerkinElmer 2100). Ammonium oxalate-extractable Fe, Mn, and Al (Feo, Mno, Alo) were determined according to Schwertmann (1964). Dithionite-citrate-bicarbonate-extractable Fe, Mn, and Al (Fed, Mnd, Ald) were quantified according to Mehra and Jackson (1958). Particle-size distribution was determined by a combination of wet sieving and sedimentation (Soil Survey Staff, 2004). All analyses of the measured aggregate properties were performed in duplicate.

Simultaneous thermal analysis (STA) was conducted according to Barros et al. (2007), using 50 mg of oven dried (60°C) aggregates (Netzsch STA 409 PC). The samples were heated from 25 to 1000°C at a rate of 5°C min-1 in a reaction atmosphere of synthetic air (flow rate: 50 mL min-1). According to De la Rosa et al. (2008) thermal analysis allows the distinction between the amount of total SOM (decomposes between 200 and 650°C), as well as thermally labile SOM (Exo 1, decomposes between 200 and 380°C), thermally more stable SOM (Exo 2,

decomposes between 380 and 475°C), and refractory SOM (Exo 3, decomposes between 475 and 650°C). In the Exo1 fraction, SOM consists mainly of carbohydrates and proteins (De la Rosa et al., 2008), whereas in the Exo2 fraction polyphenolic and aromatic organic structures get oxidized (Lopez-Capel et al., 2005). The black carbon in soil burns at higher temperatures within the Exo3 fraction (De la Rosa et al., 2008).

Identification of aggregate mineralogy was performed according to Moore and Reynolds (1997). The samples were studied using X-ray diffraction (XRD) on a Panalytical XPert Pro MPD diffractometer with an automatic divergent slit, a Cu LFF tube at 45 kV and, 40 mA, and with an X´Celerator detector (PANalytical, the Netherlands). The measuring time was 25 s, with a step size of 0.017°. Semi-quantitative mineral identification of the samples was estimated using the method described by Schultz (1964).

Ultrasonic Soil Aggregate StabilityUltrasonic dispersion of 250- to 1000-mm aggregates was

performed according to Schomakers et al. (2011a and 2011b), with minor modifications. Four grams of the 250- to 1000-mm aggregate-size fraction and 70 mL deionized water were put into a Plexiglas beaker (i.d. 44 mm, height 90 mm). A cylindrical titanium alloy probe (diam. 30 mm, insertion depth 2 mm) with constant vibration amplitude of 2.5 mm and a frequency of approximately 20 kHz was used in all experiments. The 30-mm diam. ultrasonic probe tip that we used generated a more homogeneous pressure field compared with the commonly used 10- to 19-mm size (Schmidt et al., 1999; Schomakers et al., 2011b). The suspension was maintained during sonication with a magnetic stir plate and bar (2 Hz and, cylindrical 25 mm × 8 mm bar, with rounded caps of 10 mm in diameter at both ends to minimize damage to the aggregates at the bottom of the beaker). The stirring started simultaneously with the ultrasonic vibration, and it continued throughout the course of the experiment to obtain a homogeneous distribution of soil in suspension and to ensure complete ultrasound absorption. All of

Table 1. Site information of the studied soil macroaggregates.

Andisol† Inceptisol‡ Entisol§ Ultisol¶ Alfisol#

Location Iceland Switzerland Austria Greece Czech Republic

Coordinates N 64°02¢917² N 46°38¢487² N 48°08¢685² N 35°23¢397² N 50°03¢504²

W 20°10¢782² E 08°28¢494² E 16°41¢660² E 24°05¢607² E 12°46¢545²Elevation (masl) 119 2019 158 552 773

Mean annual temperature (MAT) 6.3°C 2.7°C 9°C 18°C 6.2°CMean annual precipitation (MAP) 1113 mm 2400 mm 550 mm 969 mm 844 mm

Soil type (WRB) Haplic Andosol Haplic Cambisol Mollic Fluvisol Haplic Leptosol Luvic Stagnosol

Soil parent material Volcanic ash and basalt Granite Alluvial sediments Schist Serpentinite

Land use Grazing land Grassland Hardwood forest Olive plantation Spruce forest

Sampled soil depth (Horizon) 10–20 cm (A) 5–11 cm (AB) 5–10 cm (A) 5–10 cm (ACp) 5–12 cm (AE)† Icelandic Meteorological Office database, 2012.‡ Bernasconi et al., 2011.§ Lair et al., 2009. ¶ Nikolaidis et al., 2012; Moraetis, D. personal communication, 2011.# Bencoková et al., 2011.

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the aggregates were subjected to a constant vibration amplitude of the ultrasonic probe of 2.5 mm, which was determined using an electromagnetic induction coil and strain gauges (Mayer et al., 2002; Mentler et al., 2004). Closed-loop control of vibration amplitude and resonance frequency guarantees maximum deviations of ± 1% between pre-selected and actual vibration amplitude (Schomakers et al., 2011b).

Time steps used were 0, 15, 30, 60, 180, and 300 s, a range that resulted in six energy levels of 0, 2, 4, 8, 24, and 40 J mL-1. The output power was calibrated calorimetrically according to North (1976). Cooling the suspension during ultrasonication was not deemed necessary over maximum 300-s run, since the temperature of the suspension increased by only 2.9°C (± 0.2, n = 3). After each ultrasonification step, a liquid sample (5 mL) was taken with a syringe for determination of WEOC (filtered to <0.45 mm) by UV absorption at 254 nm (Brandstetter et al., 1996). Thereafter, different aggregate mass fractions were determined by gentle wet sieving with standard sieves (1000–630, 630–250, 250–63, and <63 mm sizes). Determination of each mass fraction was performed after drying the aggregates at 60°C to a constant weight (accuracy 0.001 g). The weights of the aggregates were corrected for the sand content (for aggregate size classes 63–250, 250–630, and >630 mm), to exclude a sand particle to be weighted as an aggregate (Six et al., 2000). Mean weight diameter (MWD, mm) of the sand-corrected aggregates was calculated according to Kemper and Rosenau (1986) as follows:

1

MWD = n

iii

w X=∑

where iX is the geometric mean of aggregate size on sieve i, and wi is the fraction of aggregates on sieve i. Standard deviations for the ultrasonic dispersion-aggregate distribution results were determined for 15 samples from the Inceptisol and Entisol. The standard deviations varied for individual aggregate-size fractions between 1.6 and 4.3% for the Inceptisol and between 0.4 and 1.8% for Entisol. The standard deviation for the released WEOC was determined for 50 samples from the Inceptisol (5 replicates at 10 different energy levels), and was less than ±0.8% between the individual replicates. This standard deviation was used as an estimate for the laboratory error in the different soil orders investigated.

The <63-mm aggregates were analyzed by STA after energy levels of 2 and 40 J mL-1 had been applied, as previously described. The ratio of the different amounts of SOM fractions (total SOM, Exo1, Exo2, Exo3) in <63-mm sized aggregates after the application of the energy treatment of 40 J mL-1, compared with the amounts after energy treatment of 2 J mL-1 was calculated as follows:

A

B

SOM fractionEnrichment ratio =SOM fraction

Where SOM fractionA is the quantity of the SOM fraction at higher energy application and SOM fractionA is the quantity of the SOM fraction at subsequent lower energy application.

Statistical AnalysesStatistical analyses were performed using IBM SPSS

Statistics 20 software package for Mac (IBM Corp., 2011). The results were calculated as arithmetic means of two analytical replicates of the same field sample. Correlations between the physicochemical properties of the 250- to 1000-mm aggregates and the aggregate distributions and MWDs were calculated with the Pearson correlation coefficient. Different mathematical models (polynomial, exponential decay, power and rational) using SigmaPlot 12.0 (Systat Software, Inc., 2010) were tested to describe the changes in the experimental data for MWD, aggregate breakdown, and percentage of total OC as WEOC over the range of ultrasonic energy applied. The best model was selected by comparing the resulting root mean square errors (RMSE) and r2 values.

RESULTSPhysicochemical Characterization of the 250- to 1000-mm Aggregate Fraction

The physicochemical properties of the 250- to 1000-mm aggregates are summarized in Table 2. The aggregate pH ranged from 4.1 to 7.3, increased in the order Inceptisol (granite as parent material) » Alfisol (serpentinite as parent material) << Ultisol (schist as parent material) < Andisol (volcanic ash and basalt as parent material) » Entisol (calcareous alluvial sediments as parent material), indicating the influence of the parent material and environmental conditions (see Table 1) on soil acidity. For OC contents of the studied macroaggregates the following increasing order was observed: Ultisol < Alfisol < Entisol < Inceptisol << Andisol. The macroaggregates of the Andisol and the Inceptiosol had the highest concentrations of SOM in Exo1 and the lowest concentrations in Exo3 compared with the other studied soil orders. Cation-exchange capacity was highest in the macroaggregate fractions with highest pH, and with the highest clay and SOM contents (Entisol, Andisol). Quartz and plagioclase feldspar presented the main minerals in all of the 250- to 1000-mm aggregate samples, except in the Andisol, which was dominated by amorphous minerals. The swelling clay, vermiculite, was only identified in small amounts in the Inceptisol (Table 3).

Ultrasonic Soil Aggregate StabilityThe aggregates in the Inceptisol were the most stable in

terms of MWD among all of the studied aggregates, and the MWD decreased gradually with increasing energy supply. The other soils showed a faster and different breakdown pattern (Fig. 1). The Alfisol, Entisol, and Andisol showed a faster decrease of the MWD in the first two steps of ultrasound absorption. At the end of the experiment, MWDs for the Andisol and Entisol were approximately 4 and 6%, respectively, of the MWD at 0 J mL-1 (Fig. 1); whereas for the Inceptisol, it was 51%. We found MWD to be negatively correlated with silt content (r = between −0.912 and −0.967, p < 0.05, depending on the energy level applied) and a positively correlated with sand content (r = between

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0.943 and 0.973, p < 0.05, depending on the energy level applied) (Table 4). Exchangeable Mg was also strongly and negatively correlated with MWD (r = between -0.931 and -0.968, p < 0.05, depending on the energy level applied) whereas exchangeable Mn had a strong positive correlation (r = between 0.905 and 0.993, p < 0.05, depending on the energy level applied) (Table 4).

The 250- to 1000-mm aggregates showed an overall decrease in the >630-mm and 250- to 630-mm aggregate size fractions, and an overall increase of the <63-mm size aggregate fraction with increasing absorbed energy (Fig. 2). For all of the 250- to 1000-mm sized aggregates, except for those in the Entisol, the mass of 63- to 250-mm sized aggregates increased with increasing ultrasonic energy applied until reaching a maximum either at 4 or at 8 J mL-1, and then it levelled off (Fig. 2). For the Entisol, the mass of these aggregates declined from a maximum at 4 J mL-1.

The changes in the >630 mm and 250- to 630-mm aggregate fractions throughout the experiment followed the MWD pattern, with the highest aggregate breakdown in the Andisol and Entisol; and the lowest in Inceptisol. The largest initial aggregate-size fraction after soil wetting (0 J mL-1) in the Andisol and Entisol was the size fraction of >630 mm (54.4 and 49.7% of soil used, respectively); whereas for the Inceptisol, Ultisol and Alfisol, it was the 250- to 630-mm size fraction (38.0, 36.5, and 33.1% of soil used, respectively).

The amount of <63-mm aggregates followed an inverse pattern of the MWDs (Fig. 2). Similar behaviour was also pronounced in the 63- to 250-mm aggregates in the Andisol and Entisol. In contrast to the macroaggregate size fractions (250–630 mm, >630 mm), there was a high increase in this 63- to 250-mm size fraction after low amounts of ultrasonic energy were applied (4 J mL-1) which then levelled off. In the Inceptisol, the increase in this size fraction was observed at a higher energy level (8 J mL-1), and it was not as pronounced. In the Ultisol

and Alfisol the increase in the 63- to 250-mm aggregates was small, and the distribution of this aggregate-size class was stable throughout the different absorbed ultrasonic energies applied to the macroaggregates.

The amounts of the collected aggregates corrected for entrained sand was significantly correlated with the selected 250- to 1000-mm aggregate properties (Table 4). The amounts of macroaggregates (250–630, and >630 mm) had a strong positive correlation with the sand content (e.g., at energy level 8 J mL-1, r = 0.970 and 0.956 for 250–630 mm, and >630 mm, respectively, p < 0.05) and the exchangeable Mn concentration (e.g., at energy level 8 J mL-1, r = 0.997 and 0.991 for 250- to 630-mm and >630 mm, respectively, p < 0.01). Significant, negative correlations were found between quantities of macroaggregates and exchangeable Mg concentration (e.g., at energy level

Table 2. Key physicochemical properties† of the studied soil macroaggregates (250–1000 mm).

Andisol Inceptisol Entisol Ultisol Alfisol

pH (H2O) 7.0 4.0 7.3 6.1 4.1

Texture (WRB) silt loam loamy sandloamy silt

loamy sandloamy sand

Clay, g kg-1 160 90 180 120 90

Silt, g kg-1 530 280 610 340 550

Sand, g kg-1 310 630 210 540 360

CaCO3, g kg-1 0 0 237 6 0

Feo, g kg-1 37.3 3.3 2.5 0.9 3.8

Fed, g kg-1 34.9 4.9 8.4 37.1 8.8

Mno, g kg-1 0.6 0.3 0.2 0.3 0.07

Mnd, g kg-1 0.6 0.5 0.4 0.5 0.1

Alo, g kg-1 22.0 1.2 0.9 0.3 0.9

Ald, g kg-1 9.4 1.0 0.5 4.3 1.0

CEC, mmolc kg-1 148 42 279 80 52

Exchangeable K, mmolc kg-1 0.7 1.8 3.7 1.4 0.9

Exchangeable Na, mmolc kg-1 5.0 0.2 0.1 1.3 0.4

Exchangeable Ca, mmolc kg-1 112.1 2.1 253.8 69.2 1.8

Exchangeable Fe, mmolc kg-1 0.03 0.2 0.01 0.02 1.9

Exchangeable Mg, mmolc kg-1 30.5 1.9 21.2 7.3 21.2

Exchangeable Mn, mmolc kg-1 0.3 2.0 0.1 1.1 0.6

Exchangeable Al, mmolc kg-1 0.0 33.3 0.0 0.1 25.2

OC, g kg-1 58.9 39.6 35.4 17.8 27.3

C/N 14.0 15.2 14.5 16.7 28.3

SOM (200–650°C) , g kg-1 129 54 78 56 52

Exo1 (200–380°C), g kg-1 99.7 35.1 41.1 27.4 27.7

Exo2 (380–450°C), g kg-1 22.7 12.9 15.7 10.9 11.2

Exo3 (450–650°C), g kg-1 6.4 6.0 20.7 17.8 13.5† CEC = cation exchange capacity; Feo, Mno, Alo = ammonium-oxalate extractable; Fed, Mnd, Ald =

dithionite-citrate-bicarbonate-extractable.

Table 3. Minerals present in the 250- to 1000-mm aggregate fraction of the soils studied.

Mineralogical composition

Soil major medium small Traces

Andisol amorphous solids plagioglace feldspar and pyroxeneInceptisol plagioglace feldspar quartz K-feldspar, biotite, muscovite, and vermiculite epidote, chlorite, and kaolinite

Entisol quartz, dolomite, and mica plagioglace feldspar, chlorite, and calcite,K-feldspar, kaolinite, and mixed-layer minerals

Ultisol quartz Muscovit eand paragonite plagioglace feldspar and kaolinite K-feldspar and chlorite

Alfisol Amphibole and quartzplagioglace feldspar, kaolinite, chlorite, talc,and serpentinite,

K-feldspar, mixed-layer minerals, epidote, and goethite

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8 J mL-1, r = -0.892 and -0.936 for 250 to 630, and >630 mm, respectively, p < 0.05) and with the silt content (e.g., at energy

level 8 J mL-1, r = -0.949 and r = -0.940 for 250–630 mm, and >630 mm, respectively, p < 0.05) in the soil aggregates.

The amount of thermally quantified SOM fractions (total SOM, Exo1, Exo2) and Feo and Alo affected positively the amount of microaggregates in the size range of 63 to 250 mm (r = between 0.891 and 0.983, p < 0.05, depending on energy level and aggregate property), whereas the amount of <63 mm sized aggregates correlated negatively with the sand content and exchangeable Mn concentration (at energy level 24 J mL-1, r = -0.924 and -0.948, p < 0.05, for exchangeable Mn and sand content, respectively) (Table 4). No significant correlations between the aggregate size distributions after macroaggregate breakdown and the Exo3 fraction or the clay content were found.

The total thermally quantified SOM content in the <63-mm aggregate size fraction was rising in the soil orders Andisol < Entisol < Alfisol < Inceptisol with increasing ultrasound application, but was declining in the Ultisol (Table 5). The highest enrichment factors in the thermally quantified SOM fractions were observed for Exo1 or Exo2, depending on the soil order.

Soil Organic Matter Release during UltrasonicationIn general, WEOC release increased strongly after the first

steps of ultrasonication and levelled off at the energy level of >8 J mL-1, with the exception of the Inceptisol and Alfisol, in

Table 4. Correlations between mean weight diameter (MWD), aggregate fractions at ultrasonic application of 8 and 24 J mL-1 and selected soil aggregate properties (n = 5). Measured soil macroaggregate properties not included in the matrix showed no significant correlations.

sand silt Feo Alo Mg2+ Mn2+ SOM Exo 1 Exo2

Silt, % -0.983**

Feo, g kg-1 -0.324 0.266

Alo, g kg-1 -0.311 0.246 0.999***

Mg2+, mmolc kg-1 -0.874 0.868 0.693 0.677

Mn2+, mmolc kg-1 0.965** -0.931* -0.398 -0.391 -0.896*

SOM, 200–650°C), g kg-1 -0.519 0.424 0.943* 0.948* 0.757 -0.578

Exo1 (200–380°C), g kg-1 -0.383 0.301 0.982** 0.985** 0.689 -0.437 0.981**

Exo2 (380–450°C), g kg-1 -0.499 0.406 0.921* 0.925* 0.715 -0.514 0.984** 0.976**

MWD 2 J mL-1 0.973** -0.967** -0.500 -0.483 -0.958* 0.947* -0.636 -0.532 -0.614

MWD 4 J mL-1 0.965** -0.960** -0.487 -0.469 -0.931* 0.905* -0.630 -0.533 -0.633

MWD 8 J mL-1 0.966** -0.946* -0.548 -0.534 -0.968** 0.962** -0.688 -0.581 -0.656

MWD 24 J mL-1 0.966** -0.938* -0.481 -0.471 -0.941* 0.993*** -0.639 -0.513 -0.583

MWD 40 J mL-1 0.943* -0.912* -0.551 -0.542 -0.960** 0.983** -0.691 -0.575 -0.630

8 J mL-1 < 63µm -0.834 0.854 -0.092 -0.103 0.621 -0.857 0.080 -0.079 0.000

8 J mL-1 63–250µm -0.272 0.198 0.956* 0.957* 0.585 -0.289 0.933* 0.979** 0.960**

8 J mL-1 250–630µm 0.970** -0.949* -0.358 -0.348 -0.892* 0.997** -0.532 -0.391 -0.469

8 J mL-1 > 630µm 0.956* -0.940* -0.447 -0.436 -0.936* 0.991*** -0.589 -0.465 -0.523

24 J mL-1 < 63µm -0.948* 0.944* 0.039 0.028 0.717 -0.924* 0.259 0.095 0.218

24 J mL-1 63–250µm -0.290 0.258 0.983** 0.975** 0.676 -0.329 0.891* 0.954* 0.891*

24 J mL-1 250–630µm 0.989** -0.970** -0.454 -0.441 -0.934* 0.970** -0.621 -0.501 -0.595

24 J mL-1 > 630µm 0.976** -0.960** -0.451 -0.439 -0.942* 0.988** -0.603 -0.480 -0.553* significant at P < 0.05.** significant at P < 0.01.*** significant at P < 0.001..

Fig. 1. Mean weight diameters (MWD) of sand-corrected aggregates at different absorbed ultrasonic energies (J mL-1, vibration amplitude of 2.5 mm). Curves were fitted to measured data with r2 > 0.98. The standard deviation for the ultrasonic dispersion-aggregate distribution procedure was determined for 15 samples from both the Inceptisol and Entisol, and they varied for individual aggregate size fractions from 1.6 to 4.3% for the Inceptisol and from 0.4 to 1.8% for the Entisol.

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which WEOC did not reach a plateau with increasing ultrasound application (Fig. 3). Release of WEOC as a percentage of total OC was between 0.6 and 2.7% after the highest energy input, and decreased in the order of: Ultisol > Alfisol > Entisol > Inceptisol > Andisol (Fig. 3).

DISCUSSIONStability and Breakdown of Macroaggregates

Contrary to our initial hypothesis, the BaCl2–extractable (exchangeable) cations Mg and Mn had significant impacts on the stability and breakdown of macroaggregates in this study (Table 4). Higher exchangeable Mg concentrations

decreased the MWDs and the amount of macroaggregates in the size classes >630 mm and 250–630 mm, whereas higher amounts of exchangeable Mn increased the proportion of these aggregate size classes when various energy levels were applied. Approximately, the MWD was increasing by a factor of 2 when the exchangeable Mg/Mn ratio was decreasing to one quarter in our studied soils (Mg/Mn ratio = 0.08MWD-2.05; r2 = 0.85; p < 0.05). However, little is known about the role of exchangeable Mn in (macro)aggregation. Manganese is highly reactive as Mn(III, IV) (hydr)oxide coatings on mineral soil particles and in complexes with SOM. It can cause oxidative polymerization of organic molecules containing phenolic

Fig. 2. Distribution of different sand-corrected aggregate-size fractions of soils at different absorbed ultrasonic energies (J mL-1, vibration amplitude of 2.5 mm). Measured values were connected with straight lines. The standard deviation for the ultrasonic dispersion-aggregate distribution procedure was determined for 15 samples from both the Inceptisol and Entisol, and they varied for individual aggregate-size fractions from 1.6 to 4.3% for the Inceptisol and from 0.4 to 1.8% for the Entisol.

720 Soil Science Society of America Journal

functional groups (Heintze and Mann, 1947; Navrátil et al., 2007), thereby playing a role in the formation and stabilization of soil structure (e.g., Alekseeva et al., 2009).

Strong significant negative correlations between exchangeable Mg concentration and MWDs and macroaggregates were in accordance with De-Campos et al. (2009). Zhang and Norton (2002) hypothesized that Mg2+ promotes disaggregation, and their study showed Mg2+ to be a strong dispersing agent in leaching column studies. Exchangeable Mg2+ has a 50% greater hydration radius than for example Ca2+, which enables the soil to absorb more water, and the surface diffuse layer thickness and the surface potential are greater than in a Ca2+–saturated clay. These attributes of exchangeable Mg2+ weaken the van der Waals interactions that hold the soil particles together, thereby decreasing aggregate stability. The strength of the interactions with exchangeable Mg may also be dependent on the type of clay mineral and electrolyte concentration of the soil (Zhang and Norton, 2002). In our study, the other BaCl2–extractable cations (exchangeable K, Na, Ca, Fe, Al) and the CEC did not have any significant correlations with MWDs or with the distribution of aggregate-size classes, even though a higher CEC has previously been correlated with higher aggregate stability (e.g., Kay, 1998, Six et al., 2000).

The breakdown of the macroaggregates (>250 mm) started already at low energy inputs, 2 J mL-1 for the Andisol and Entisol that confirm results by Mentler et al. (2004). Based on the aggregate breakdown pattern expressed by the MWD (Fig. 1) the strongest macroaggregate breakdown had been reached at the end of the experiment at 40 J mL-1 in the Alfisol, Andisol, Entisol; whereas more energy would have been necessary in the Inceptisol and Ultisol to get a similar loss of macroaggregates. In comparison, 60 J mL-1 was needed for complete macroaggregate breakdown in a previous study (Amelung and Zech, 1999), in which prairie soils from different temperature regimes across Canada were studied.

The breakdown of macroaggregates in our study varied between the different soil orders (Fig. 1 and 2). This agrees with the review by Bronick and Lal (2005) who concluded that in different soil orders, aggregation is controlled by different properties. The authors stated that aggregation in Alfisols, Entisols, and Ultisols is mainly governed by SOM, whereas in Andisols and Inceptisols the amount and type of clay mineral plays a bigger role in aggregation. We did not find any significant correlations between clay content and aggregate-size distribution. Clay content affects aggregation through swelling

and dispersion, and the high surface area of clay minerals results in higher levels of aggregation (Kay, 1998; Bronick and Lal, 2005). High CEC clays such as smectite and vermiculite have large surface areas, which may result in higher aggregate stability (Amézketa, 1999; Schulten and Leinweber, 2000). Vermiculite was present in small amounts in the studied Inceptisol (Table 3), which may have contributed to the greater aggregate stability in this soil order.

In our investigation, the silt content of the 250- to 1000-mm aggregates correlated negatively with the macroaggregates and MWDs. Silt-sized particles have usually much lower charged surface areas than clay particles (Brady and Weil, 2008); therefore, they are not as active aggregating agents as clay-sized particles. In our study, macroaggregates and MWDs were positively correlated with sand content in the 250- to 1000-mm aggregate-size fraction (Table 4). This contradicts recent research that reported sand to correlate negatively with soil stability (Dimoyiannis, 2012). However, in our investigation, the widely varied sand contents in the 250- to 1000-mm aggregates could have influenced the correlations.

Stability and Breakdown of MicroaggregatesIn this study we observed correlations between the

measured properties of the 250- to 1000-mm aggregate fraction and distribution of aggregates collected after ultrasonication. The correlations show that ammonium-oxalate extractable Fe and Al oxides act as strong aggregating agents in the size range of 63 to 250 mm (Table 4). Amézketa (1999) explained the action of such agents through their ability to act as flocculants of suspensions, and, by being able to bind clay particles to the organic molecules through bridging. The ammonium-oxalate extraction gives an estimate of the paracrystalline Fe and Al oxide contents (Schwertmann, 1964). The paracrystalline Fe

Fig. 3. Release of water extractable organic C (<0.45 mm) from 250- to 1000-mm aggregates using stepwise ultrasonic energy application (J mL-1, amplitude 2.5 mm). Curves were fitted to measured data with r2 > 0.98. The standard deviation for the release of WEOC (absorbance at 254 nm) was determined for 50 samples (5 replicates for 10 different ultrasonication steps), and it was <0.8% between the individual replicates.

Table 5. Enrichment ratios of SOM fractions between 2 and 40 J mL-1 of ultrasonic energy application in <63 mm aggregate fraction.

Andisol Inceptisol Entisol Ultisol Alfisol

< 63 µmSOM 1.08 1.38 1.16 0.93 1.27

Exo 1 1.06 1.41 1.19 0.94 1.35

Exo 2 1.17 1.43 1.19 0.90 1.34Exo 3 1.08 1.11 1.10 0.94 1.10

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(hydr)oxides have a much larger and more reactive surface area compared with crystalline Fe (hydr)oxides; they therefore have higher aggregating power (Duiker et al., 2003).

In our study the correlations between mass of collected aggregates and SOM were only observed for microaggregates in the size class of 63 to 250 mm (Table 4). The fact that the amount of microaggregates correlate strongly with total SOM, as well as both with Exo1 and Exo2 (Table 4), reveals the importance of SOM for the formation and stability of this microaggregate size class. Carbohydrates and proteins (Exo1; De la Rosa et al., 2008) as well as polyphenolic and aromatic organic structures (Exo2; Lopez-Capel et al., 2005) seem to be of high importance for the formation and stability of these microaggregates. This impact of SOM on microaggregates is corroborated also by other studies (e.g., Tisdall and Oades, 1982; Six et al., 2004; Kleber et al., 2007; Schmidt et al., 2011), which highlight, that SOM compounds can closely associate with mineral surfaces, and get physically protected through aggregation processes against microbial decay. This protection of SOM within aggregates leads to a stabilization and aging of even easily decomposable OC compounds like amino acids and sugars in soils (Kleber et al., 2007; Schmidt et al., 2011).

The enrichment of SOM fractions in <63-mm aggregates during the ultrasonication varied for the different soils (Table 5), revealing an accumulation of organic matter-rich particles (Inceptisol > > Alfisol > Entisol > Andisol) or organic matter-free particles (Ultisol). A highest accumulation ratio in both SOM fractions of Exo1 and Exo2 can be noticed (Table 5), which reflects the importance of these SOM fractions acting as binding agents within the formation of microaggregates. Polyphenolic and aromatic organic structures (Exo2; Lopez-Capel et al., 2005) seem to be most involved in the microaggregation in Andisol (strongest increase of 17% in Exo2; Table 5), but also in the other soil orders when taking the relative SOM distribution in the macroaggregtes into account (i.e., Exo1 >> Exo2 in all soils; Table 2). A relatively small increase in the Exo3 fraction by approximately 10% in the <63-mm sized aggregates during the ultrasound application supports a previous study by Brodowski et al. (2006), in which it was shown that also black carbon acts as a binding agent in aggregates. In our studied soil orders (with the exception of the Ultisol), all of the thermally measured SOM fractions contributed to the formation of microaggregates in the order Exo2 ³ Exo1 > Exo3.

Soil Organic Matter Release during Ultrasonication

The WEOC released from the aggregates during sonication differed greatly between the soil orders (Fig. 3), with the aggregates from the Ultisol releasing the most WEOC when the energy applied increased. The aggregates of the Andisol contained the highest OC content, but released the least amount of WEOC, which can be explained by the strong binding of OC to the paracrystalline allophane and imogolite in this soil (Table 3, Shoji et al., 1993). The Inceptisol in our study contained

vermiculite, a swelling clay mineral, which could also explain a strong binding of OC and a consequently low release of WEOC (Fig. 3). In contrast, the higher WEOC released from the aggregates of the Entisol, Ultisol, and Alfisol, can be explained by SOM presenting the main aggregating factor (Bronick and Lal, 2005). We found a maximum of 2.7% of the total OC content as WEOC during the ultrasonication (Fig. 3) and no significant correlations with the clay content in the studied soils.

CONCLUSIONSIn this study we followed differences in macroaggregate

breakdown with increasing low energy, amplitude controlled ultrasonic application (2– 40 J mL-1) across a wide range of soil orders and properties. Following results were found:

·Macroaggregate breakdown followed the increasing order: Inceptisol < Ultisol < Alfisol < Entisol < Andosol, which contradicts our hypothesis and confirms that SOM, oxides, and clay content were not the main explaining factors of macroaggregate stability.

·Macroaggregate breakdown started with low amounts of ultrasonic energy applied (2 J mL-1), nevertheless, one universal amount of energy for aggregate breakdown does not exist, and it has to be adjusted to the soils or soil fractions under study.

·Macroaggregate breakdown was positively correlated with the amounts of exchangeable Mg, and negatively correlated with the amounts of exchangeable Mn. A decreasing Mg/Mn ratio below 30 increased macroaggregate stability in the studied soils. However, further research is needed to understand the role of exchangeable Mn in aggregate stability.

·The quantity of microaggregates (63–250 mm), were governed by oxalate extractable Fe and Al as well as SOM (mainly carbohydrates, proteins, polyphenolic and aromatic organic structures) and this is consistent with the hierarchical aggregate model by Tisdall and Oades (1982).

ACKNOWLEDGMENTSThe project was financially supported from the European Commission FP7 Collaborative Project “Soil Transformations in European Catchments” (SoilTrEC), Grant Agreement no. 244118. We thank N.P. Nikolaidis from the Technical University of Crete, Greece; S. Bernasconi from ETH Zürich, Switzerland; and P. Kram from Czech Geological Survey for valuable background information of the field sites. F. Ottner (Inst. of Applied Geology, BOKU, Austria) is acknowledged for mineralogical measurement and assistance with STA measurements. We are grateful to R. Schuller (Inst. of Physics and Materials Science, BOKU, Austria) for his help with the ultrasound equipment. Analytical assistance was provided by E. Brauner, K. Hackl, A. Hobel, A. Hromatka and E. Kopecky (Inst. of Soil Research, BOKU, Austria). We thank Bruce R. James (University of Maryland, USA) as well as the three anonymous reviewers for their valuable scientific inputs to improve the manuscript.

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53

3 Aggregation and organic matter in subarctic Andosols under different

grassland management

54

Taru Lehtinen, Guðrún Gísladóttir, Georg J. Lair, Jeroen van Leeuwen, Winfried E.H.

Blum, Jaap Bloem, Markus Steffens, and Kristín Vala Ragnarsdóttir. 2014. Aggregation

and organic matter in aubarctic Andosols under different grassland management. Acta

Agriculturae Scandinavica, Section B – Soil & Plant Science (submitted 12.8.2014).

Referencing style according to the journal guidelines.

Abstract

Quantity and quality of soil organic matter (SOM) affect physical, chemical and biological

soil properties; and are pivotal to productive and healthy grasslands. Thus, we analyzed the

distribution of soil aggregates and assessed quality, quantity and distribution of SOM in a

total of six unimproved (Grass 1, Grass 2) and improved (organic (HaAorg, HiAorg) and

conventional (HaAcon, HiAcon)) grasslands in subarctic Iceland. We also evaluated

principal physicochemical and biological soil properties, which influence soil aggregation

and SOM dynamics. Macroaggregates (>250 µm) in topsoils were most prominent in

unimproved (62-77 %) and organically (58-69 %) managed sites, whereas 20-250 µm

aggregates were the most prominent in conventionally managed sites (51-53 %).

Macroaggregate stability in topsoils, measured as mean weight diameter (MWD), was

approximately twice as high in organically managed (12-20 mm) compared to the

conventionally managed (5-8 mm) sites, possibly due to higher organic inputs (e.g.

manure, compost, and cattle urine). In unimproved grasslands and one organic site

(HaAorg), macroaggregates contributed between 40-70 % of organic carbon (OC) and

nitrogen (N) to bulk soil, whereas in high SOM concentration sites free particulate organic

matter (fPOM) contributed up to 70 % of the OC and N to bulk soil. Oxides were one of

the binding agents of macroaggregates, which was shown by macroaggregates and MWD

being correlated positively with fungal biomass (r = 0.87, r = 0.80, p<0.01) and oxalate-

extractable Mn (r = 0.46, r = 0.46, p<0.01) and dithionite-extractable Mn (r = 0.46, r =

0.46, p<0.01). Thus, evidence of diminished aggregate hierarchy was shown. The higher

macroaggregate stability in organic farming practice compared to conventional farming is

of interest due to the importance of macroaggregates in protecting SOM, which is a

prerequisite for soil functions in grasslands that are envisaged for food production in the

future.

Keywords: aggregate hierarchy; Andosols; grassland management; Iceland; particulate

organic matter (POM); solid-state 13

C NMR spectroscopy.

55

3.1 Introduction

Physical, chemical and biological properties are strongly influenced by quantity and

quality of soil organic matter (SOM). Being the key attributes of soil quality and

productivity (Manlay et al. 2007; Feller et al. 2012), SOM-induced changes in soil quality

strongly affect productivity and health of grasslands. Land misuse and soil mismanagement

may deplete SOM, while sustainably managed farms can maintain and enhance SOM

levels and soil structure such as soil application of organic amendments (Siegrist et al.

1998). Stable aggregates physically protect the encapsulated SOM against decomposition,

especially in soil systems with less physical disturbance such as no-tillage or conservation

agriculture (Six et al. 2000). Formation and stabilization of aggregates is influenced by

several factors such as input of biomass-C, soil fauna (most importantly earthworms)

(Siegrist et al. 1998), soil microorganisms such as fungi and bacteria and their activity

(Eash et al. 1994), roots (Six et al. 2004), inorganic binding agents such as carbonates and

oxy(hydr)oxides (Six et al. 2004), nutrient availability (Six et al. 2004) and environmental

variables such as freeze/thaw and drying/wetting cycles (Six et al. 2000; Six et al. 2004).

Tisdall and Oades (1982) proposed the hierarchical model of soil aggregates, and

hypothesized that macroaggregates (>250 µm) are comprised of microaggregates (<250

µm), mineral particles, and particulate organic matter (POM). These components are

bound together and stabilized by transient or temporary binding agents such as microbial-

and plant-derived polysaccharides, roots and fungal hyphae. Microaggregates are

associations of free primary particles bound together by organic molecules,

oxy(hydr)oxides, polyvalent cations, Ca- and Mg carbonates, and CaSO4 (Tisdall and

Oades 1982; Amézketa 1999). The hierarchical order indicates that macroaggregates are

less stable and more influenced by farming practices (e.g., tillage) than microaggregates

(Six et al. 2004).

SOM originates primarily from plant litter and microbial biomass and consists of numerous

compounds with varying structure, concentration, and recalcitrance (Kögel-Knabner 2002).

Mechanical tillage is one of the most important factors that may reduce soil organic carbon

(SOC) stocks and also decrease aggregate stability in Icelandic grasslands. Approximately

96% of farmland in Iceland is under grassland management for hay production

(Jóhannesson 2010). Normally, the soils are ploughed when the fields are renewed from

several to tens of years interval, and the fields are fertilized annually. Tillage may disrupt

macroaggregates and thus aggravate decomposition of SOM (Elliott 1986). Farming

practices that apply organic amendments (e.g., animal manure, green manure, compost,

and/or crop residues) may improve physical, chemical and biological soil properties, and

result in enhanced overall soil fertility (e.g., increased plant nutrients, SOM, and soil

structure) (Watson et al. 2003; Sodhi et al. 2009; Diacono and Montemurro 2011).

Regardless of few comparative land management studies on soil aggregate stability,

previous research by Siegrist et al. (1998) and Shepherd et al. (2002) suggests that organic

farming practices increase macroaggregate stability.

Separation of SOM fractions, based on density fractionation in combination with ultrasonic

dispersion, enables separation of free particulate organic matter (fPOM, consisting of

undecomposed plant residue, hyphae and their partial decomposition products), occluded

particulate organic matter (oPOM, consisting of POM occluded in aggregates), and

organo-mineral associations with more processed SOM in the heavy fraction (sediment of

the density fractionation procedure) (Christensen 1992; Golchin et al. 1994; Kölbl and

56

Kögel-Knabner 2004). In general, POM fractions respond more sensitively to management

changes than total organic carbon (OC) (Golchin et al. 1994; Chan et al. 2002; Steffens et

al. 2009). Marriott and Wander (2006) observed increased oPOM in organic farming

systems over conventionally managed farming systems, and oPOM was more decomposed

in manure+legume than in legume-based organic systems. Most of the studies on

agricultural soils focus on temperate cropland soils (e.g. Gättinger et al. 2012), whereas

grassland soils from sub-arctic and of volcanic origin have received less attention, as well

as on-farm studies are lacking in the research literature (Siegrist et al. 1998; Stavi et al.

2011).

Therefore, the objective of this research was to assess: 1) distribution of soil aggregates, 2)

quality, quantity and distribution of SOM in unimproved and improved grasslands in

subarctic Iceland, and 3) principal physicochemical and biological soil properties

influencing soil aggregation and SOM dynamics. We hypothesized that 1) unimproved

grasslands without ploughing would have higher SOM levels and thus higher distribution

of macroaggregates compared to improved grasslands, 2) organically managed grassland

have a closer resemblance to unimproved grassland than to conventionally managed

grassland, and that 3) organic matter (OM) and fungi are the major aggregating agents.

3.2 Material and methods

3.2.1 Site description

The sites were selected to represent the major grassland soil types in Iceland (Haplic

Andosol and Histic Andosol), and a range of grassland management practices (Table 3.1).

Samples obtained from the first four sites were from Haplic Andosols (according to WRB,

according to Icelandic classification Brown Andosols (Arnalds 2004), hereafter referred to

as HaA). Samples obtained from the two last sites were from Histic Andosols (according to

WRB, hereafter referred to as HiA). All together, six grassland sites located in South and

Southwest Iceland comprised of (Table 3.1):

1) Grass1: unimproved grassland that has not been ploughed or fertilized. The field is

lightly used as a pasture for young cattle and sheep for a short time in the autumn. The site

is located in between HaAorg and HaAcon.

2) HaAorg: improved grassland where organic fertilizers (manure, compost, and cattle

urine) and biodynamic preparations are used (Table 3.1). Biodynamic preparations are

used to enhance sprouting and early spring growth, and to enhance ripeness of the crop and

to protect from fungal diseases, and to enhance plant availability of phosphates (Table 3.1).

Ploughing of HaAorg was done during the first three consecutive years of the crop

rotation, most recently in 2001, 2002, and 2003. The field has not been ploughed since.

Since 1996 the field has been managed according to Icelandic guidelines for organic and

sustainable production and resource utilization, which are based on the guidelines from

Soil Association (TÚN 2013). Before 1996, the field had never been ploughed or fertilized.

3) HaAcon: improved grassland where organic (manure) and supplemental inorganic

fertilizers are used. The last ploughing was done in 1995 when the field was renewed; the

cultivation of the field started in the 1960s.

57

4) Grass2: unimproved grassland that has neither been ploughed nor fertilized. The site is

located in between HiAorg and HiAcon, and is not used for pasture.

5) HiAorg: improved grassland that receives organic fertilizers (manure, and compost) and

is managed according to the Icelandic guidelines for organic and sustainable production

and resource utilization (TÚN 2013) since 1994. During the first three consecutive years of

the crop rotation (1994 - 1996), the field was ploughed but has not been ploughed since

1996. Before 1994, the studied field had never been ploughed or fertilized.

6) HiAcon: improved grassland that receives organic (manure) and supplementary

inorganic fertilizers. The soil is ploughed when the field is renewed, which was ploughed

in 1998. Cultivation of the field started in the 1960s

58

Table

3.1

Back

gro

un

d i

nfo

rmati

on o

f th

e st

udie

d s

ites

.

H

apli

c A

nd

oso

l H

apli

c A

nd

oso

l H

apli

c A

nd

oso

l H

apli

c A

nd

oso

l H

isti

c A

nd

oso

l H

isti

c A

nd

oso

l

G

rass

lan

d

Org

anic

C

on

ven

tion

al

Gra

ssla

nd

O

rgan

ic

Con

ven

tion

al

Ab

bre

via

tion

G

rass

1

HaA

org

H

aAco

n

Gra

ss2

H

iAorg

H

iAco

n

Coord

inat

es

N 6

4°3

´1´´

N

64

°02

´34

´´

N 6

4°3

´0´´

N

64

°20

´38

´´

N 6

4°2

0´4

3´´

N

64

°20

´33

´´

W

20

°10

´46

´´

W 2

0°1

2´1

4´´

W

20

°10

´44

´´

W 2

1°3

6´1

6´´

W

21

°36

´14

´´

W 2

1°3

4´5

4´´

Mea

n

An

nu

al

Tem

per

atu

re

(MA

T)a

3.6

¨C

3.6

¨C

3.6

°C

4.3

°C

4.3

°C

4.3

°C

Mea

n

An

nu

al

Pre

cipit

atio

n

(MA

P)a

1120

mm

1

120

mm

1

120

mm

8

00

mm

8

00

mm

8

00

mm

Far

m s

ince

9

th c

entu

ry

9th c

entu

ry

9th c

entu

ry

9th c

entu

ry

9th c

entu

ry

9th c

entu

ry

Dra

inag

e N

o d

rain

age

No d

rain

age

No d

rain

age

No d

rain

age

1960

1968

Lat

est

plo

ugh

ing

N/A

2

003

, 15

-20

cm

1

995

, 15

-20

cm

N

/A

1996

, 15

-20

cm

1

998

, 15

-20

cm

Con

ver

sion

to o

rgan

ic f

arm

ing

N/A

1

996

N/A

N

/A

1994

N/A

Cro

p r

ota

tion

b

N/A

o,

ob

, 8

*gc,

gc

g

N/A

v,b

c,6

*gc,

gc

g

Org

anic

fer

tili

zers

N

o f

erti

liza

tion

No f

erti

liza

tion

- M

anu

re

S

pri

ng 3

5 t

ha-1

S

pri

ng 2

0 t

ha

-1

S

pri

ng 3

0 t

ha-1

S

pri

ng 3

0 t

ha

-1

- C

om

post

Fal

l 3

5 t

ha-1

F

all

10

t h

a-1

-

Cat

tle

uri

ne

S

pri

ng 5

0 t

ha-1

-

Tota

l N

(k

g N

ha

-1 y

ear-1

)e

970

kg N

ha-1

4

0 k

g N

ha-1

260

kg N

ha-1

6

0 k

g N

ha-1

- T

ota

l C

(t

C h

a-1

yea

r-1)e

8

.6 t

C h

a-1

0.8

t C

ha-1

3.2

t C

ha-1

1

.2 t

C h

a-1

Inorg

anic

fer

tili

zers

-

Tota

l N

(k

g N

ha

-1 y

ear-1

)

S

pri

ng 8

0 k

g N

ha

-1

Sp

rin

g 3

00

kg N

ha

-1

- T

ota

l P

(k

g P

ha-1

yea

r-1)

S

pri

ng 2

0 k

g P

ha

-1

- T

ota

l K

(k

g K

ha

-1 y

ear-1

)

S

pri

ng 2

0 k

g K

ha

-1

Sp

ecia

l tr

eatm

ents

Bio

dyn

amic

pre

par

atio

nsc

S

hel

l sa

nd

, fi

shm

eald

aIc

elan

dic

met

eoro

logic

al o

ffic

e d

atab

ase,

201

2

bo d

enote

s oat

s, b

bar

ley,

gc

gra

ss a

nd

clo

ver

, v v

eget

able

s, b

old

let

ter

the

tim

e of

sam

pli

ng.

Gra

ss a

nd

gra

ss a

nd

clo

ver

mix

ture

s at

in

div

idual

far

ms:

BA

org

(P

hle

nu

m p

rete

nse

, P

oa

pra

ten

sis,

Fes

tuca

rub

ra/r

icha

rdso

nii

,

Tri

foli

um

rep

ens,

Tri

foli

um

pre

ten

se a

nd

bar

ley),

Bac

on

(P

hel

enu

m p

rete

nse

(85

%),

Fes

tuca

rih

ard

son

ii/r

ub

ra (

10%

), P

oa

pra

ten

sis

(5%

)),

HA

org

(P

len

um

pre

ten

se a

nd

Tri

foli

um

rep

ens)

, H

Aco

n (

Tri

foli

um

rep

ens

and

Alo

pec

uru

s p

rate

nsi

s).

c Hu

mu

s p

rep

arat

ion

No.

500

to e

nh

ance

sp

rout

and

ear

ly s

pri

ng g

row

th,

Sil

ica

pre

par

atio

n N

o.5

01

to e

nhan

ce r

ipen

ess

and

pro

tec

t fr

om

fun

gal

dis

ease

s, s

pec

ial

pre

par

atio

n o

f V

ale

rian

a o

ffic

ian

ali

s N

o.

507

to l

oose

n t

he

ph

osp

hat

e. A

ll p

rep

arat

ion

s ar

e p

rep

ared

and

use

d a

ccord

ing t

o t

he

bio

dyn

amic

gu

idel

ines

.

dsh

ell

san

d M

gC

a(C

O3) 2

: 1

996

3-5

t h

a-1, 19

97

1.5

t h

a-1;

fish

mea

l: 1

99

5 2

20

kg h

a-1

, 199

6 5

45

kg h

a-1, 199

8 5

45

kg h

a-1, 200

0 2

70

kg h

a-1, 200

3 2

70

kg h

a-1.

e calc

ula

ted a

ccord

ing t

o H

oogen

dorn

et

al.

(20

10

), P

álm

ason (

201

3),

an

d D

yrm

und

sson

(2

013

, p

erso

nal

com

munic

atio

n).

N/A

not

avai

lab

le.

59

For all improved grassland sites (HaAorg, HaAcon, HiAorg, HiAcon), the soils were

ploughed with a moldboard plow to a depth of 15-20 cm. The improved grasslands were

generally cut twice each year for forage production, and on a good year a third time, while

Grass1 and Grass2 sites were not harvested. Thus, principal differences between the

selected grasslands are the start of the cultivation of the grasslands, usage of mineral

fertilizers on the conventional sites and input of organic material as well as crop rotation at

the organically managed sites. The organically managed sites have higher diversity of

grasses and related fodder plants including legumes such as clover (Trifolium repens and

T. pratense). None of the sites received pesticides.

3.2.2 Soil sampling

Soil samples were obtained in June 2011. Samples at each site were obtained in triplicate

from randomly selected 0-10 cm and 10-20 cm soil depth using a core sampler (8 cm

diameter). The replicates were approximately 30-40 m apart. Each replicate comprised of

10-15 subsamples, which were sampled within a 1-2 m radius and were composited. Thus,

there were a total of 36 homogeneous samples. Soils for biological analyses were sampled

only from 0-10 cm depth in a similar fashion to that of other samples, in triplicates. Soil

samples were gently sieved through a 5 mm sieve in the field, and kept at 4°C for the

biological analyses, and air-dried in the laboratory pending other analyses.

3.2.3 Physicochemical and biological characterization of soils at

the grassland sites

Soil pH was measured electrochemically (Microprocessor pH Meter pH196 WTW,

Weilheim, Germany) in H2O at a soil:solution ratio of 1:2.5 (Soil Survey Staff 2004).

Particle size distribution was determined by a combination of sieve and pipette method

after removal of organic matter with 10% hydrogen peroxide and dispersion by reciprocal

shaking with sodium metaphosphate solution for 12 h (Soil Survey Staff 2004). However,

because of the presence of active amorphous materials complete dispersion may not have

been achieved. Ammonium-oxalate-extractable Fe, Mn, Al, and Si (Feo, Mno, Alo, Sio)

were determined according to Schwertmann (1964). Dithionite-citrate-bicarbonate-

extractable Fe, Mn and Al (Fed, Mnd, Ald) were quantified according to Mehra and Jackson

(1960). Allophane concentration was estimated by multiplying Sio by 6, based on an

average Al/Si ratio of 1.5 (Parfitt, 1990), and ferrihydrite concentration was estimated by

multiplying Feo by 1.7 (Parfitt and Childs 1988). Total carbon (Ct) and total nitrogen (Nt)

were quantified by the dry combustion method (Tabatabai and Bremner 1991) using an

elemental analyzer (Carlo Erba Nitrogen Analyser 1500, Milano, Italy). The Icelandic

volcanic soils contain no carbonate minerals (e.g. Gíslason 2008), and therefore, Ct values

obtained were assumed to be organic C (OC). Plant available phosphorous (P) and

potassium (K) were determined by the calcium-acetate-lactate (CAL)-extraction

(ÖNORM, L1087). Cation exchange capacity (CEC) and exchangeable cations were

determined using an unbuffered 0.1 M BaCl2 extraction (Soil Survey Staff 2004).

Extracted exchangeable cations (K, Na, Ca, Fe, Mg, Mn, and Al) were measured by flame

atomic absorption spectrophotometry (Perkin-Elmer 2100).

Dissolved organic carbon (DOC) was determined by UV adsorption at 254 nm

(Brandstetter et al. 1996) and microbial biomass C by chloroform fumigation-extraction

method (Vance et al. 1987). Hot water (16 h at 80°C) extractable C (HWC) was

determined according to Ghani et al. (2003). Mineralizable nitrogen (Min N) was measured

60

as the NH4 production during one week of anaerobic incubation in slurry at 40°C (Canali

and Benedetti 2006). For determination of hyphal length and bacterial numbers,

microscopic slides were prepared as described by Bloem and Vos (2004) after a pre-

incubation period of two weeks at 20°C. The equation of a cylinder with spherical ends (V

= (π/4) W2 (L-(W/3)) where V = volume (µm³), L = length (µm) and W= width (µm)), a

mean hyphal diameter of 2.5 µm and a specific C concentration of 130 fg C µm-³ were

used to estimate fungal biomass. Bacterial biomass was calculated using a specific C

concentration of 320 fg C µm-³ and bacterial cell numbers and volume were determined by

confocal laser scanning microscopy combined with an image analysis system (Bloem et al.

1995). Bacterial activity was estimated by measuring incorporation rates of [³H]thymidine

and [14

C]leucine (Bloem and Bolhuis 2006).

3.2.4 Density and aggregate fractionation

A three-step density and aggregate fractionation procedure, modified from Mueller et al.

(2009) and Steffens et al. (2009), was used in triplicate for all sites (Figure 3.1). Briefly, 20

g of air-dried soil (<5 mm) was capillary-saturated with Na-polytungstate solution (density

of 1.8 g cm-3

) and allowed to settle overnight. The floating particulate organic matter

(referred to as fPOM, 20-5000 µm) was extracted by aspiration via a water jet pump. To

obtain POM occluded in aggregates (referred to as oPOM, 20-5000 µm), the subsequent

heavy fraction (>1.8 g cm-3

) was treated by ultrasound. The application of low energy

ultrasound of 8 J ml-1

was used to maintain stable macroaggregates as well as to minimize

the production of artifacts (Lehtinen et al. 2014). Calibration of the output power of the

sonicator was done calorimetrically according to North (1976). With a subsequent density

fractionation step (Na-polytungstate solution, 1.8 g cm-3

), the oPOM floating on the

suspension was obtained after centrifugation (10 minutes at 4350 rpm). All POM fractions

were washed with deionized water until the electric conductivity dropped below 5 µS cm-1

(Mueller et al. 2009; Steffens et al. 2009) and then freeze-dried for further analyses. The

residues of the density fractionation procedure (mineral particles and organomineral

associations) were sieved at 250 µm and 20 µm to obtain macroaggregates (250-5000 µm)

and microaggregates (20-250 µm and < 20 µm). All aggregate fractions were washed with

deonized water until the electronic conductivity dropped below 5 µS cm-1

, oven dried at

100°C, weighed and ground for further analyses. The weights of aggregates were corrected

for the sand concentration of the same size (for aggregates 20-250 µm, and > 250 µm), in

order to exclude sand particles to be weight as aggregates (Six et al. 2000; Lehtinen et al.

2014). Mean weight diameter (MWD, mm) of the sand-corrected aggregates was

calculated according to Kemper and Rosenau (1986) as follows:

∑ ̅

where ̅ is the geometric mean of aggregate size on sieve i, and is the fraction of

aggregates on sieve i.

61

Figure 3.1 Schematic of the fractionation procedure. Gray circles represent fractions for

further analyses.

soil sample

air-dried

< 5 mm

density fractionation

ρ = 1.8 g cm-3

fPOM

(20-5000µm)

< 1.8 g cm-3

ultrasonication

8 J ml-1

density fractionation

ρ = 1.8 g cm-3

residue

> 1.8 g cm-3

oPOM

(20-5000µm)

< 1.8 g cm-3

residue

> 1.8 g cm-3

sieving

aggregates

(250-5000µm)

aggregates

(20-250µm)

aggregates

(< 20µm)

centrifugation

10 min 4350rpm

62

3.2.5 Solid-state 13C NMR spectroscopy

The chemical quality of selected POM fractions and bulk soils from the improved

grasslands (HaAorg, HaAcon, HiAorg, and HiAcon) was analyzed by solid-state 13

C NMR

spectroscopy (DSX 200 NMR spectrometer, Bruker, Karsruhe, Germany). Composite bulk

soil and POM samples were prepared by mixing equal amounts of the replicates. To

improve the signal-to-noise ratio, bulk soil samples were treated with 10 % HF (Schmidt et

al. 1997). The cross-polarization magic angle spinning (CPMAS) technique with a 13

C-

resonance frequency of 50.32 MHz and a spinning speed of 6.8 kHz was applied. A

ramped 1H-pulse starting at 100% to 50% of the initial power was used during a contact

time of 1 ms in order to circumvent spin modulation during the Hartmann-Hahn contact.

Pulse delays between 0.8 and 1 s were used for all spectra. Depending on the C

concentrations of the samples, between 2000 and 320 000 scans were accumulated and a

line broadening of 50 Hz was applied. The 13

C chemical shifts were calibrated relative to

tetramethylsilane (0 ppm). The relative contributions of the various C groups were

determined by integration of the signal intensity in their following respective chemical

shift regions (Knicker et al. 2005) assignable to alkyl C (-10 to 45 ppm), N-alkyl-C (45 to

60 ppm), O-alkyl C (60 to 110 ppm), olefinic and aromatic C (110 to 160 ppm), and

carbonyl (aldehyde and ketone) and carboxyl/amide C (160 to 220 ppm).

3.2.6 Statistical analyses

Statistical analyses were performed using IBM SPSS Statistics 20 software package for

Mac. Normality was tested with Shapiro-Wilkinson´s test and all data were log-

transformed before analyses to obtain homogeneity of variances. One-way analysis of

variance (ANOVA) followed by Tukey´s´- significant difference (p<0.05) as a post hoc

test (Tukey 1957) was used to compare means of the different soil properties between the

different grassland sites. Correlations between variables were calculated with the Pearson

correlation coefficient.

3.3 Results

3.3.1 Physicochemical and biological characterization of soils at the grassland sites

The physicochemical and biological characteristics of the bulk soils are summarized in

Table 3.2 and in Supplementary Table 3.S1. Clay concentration was higher and soil pH

lower in HiAs compared to HaAs, indicating the differences in parent material between the

soil types. Concentrations of OC and Nt were significantly higher in HiAs compared to

HaAs and CAL-extractable K concentration was higher in the HaAs compared to HiAs.

Significantly higher concentrations of OC and CAL-extractable P in HiAcon compared to

HiAorg were observed. CEC followed OC and clay concentrations, being the highest in

HiAorg and HiAcon with the highest OC and clay concentrations. Fungal biomass was

significantly higher in HiAorg compared to HiAcon, and higher fungal biomass was

detected in HaAs compared to HiAs although the difference was not significant. Bacterial

biomass did not differ among sites, while leucine incorporation (bacterial activity),

mineralizable N and HWC all differed significantly among the soil types but not among the

grassland management practices (Table 3.2).

63

Table

3.2

Key

phys

icoch

emic

al

and b

iolo

gic

al

pro

per

ties

of

the

stu

die

d s

oil

s.

D

epth

(cm

) G

rass

1

HaA

org

H

aAco

n

Gra

ss2

H

iAo

rg

HiA

con

Phys

ical

soil

pro

per

ties

san

d (

g k

g-1

) 0

-10

40

8.1

(89

)a

37

9.8

(22

)a

37

5.1

(66

)a

36

0.5

(11

)a

41

4.7

(20

)a

41

4.7

(20

)a

1

0-2

0

37

1.0

(33

)a

33

5.7

(19

)a

39

8.3

(57

)a

34

0.1

(56

)a

32

4.9

(65

)a

37

2.6

(47

)a

silt

(g k

g-1

) 0

-10

52

8.1

(74

)a

56

5.8

(13

)a

55

9.7

(31

)a

53

5.2

(54

)a

50

2.3

(99

)a

42

5.9

(11

7)a

1

0-2

0

58

5.7

(12

)a

61

5.0

(23

)a

55

8.3

(58

)a

59

3.3

(47

)a

57

2.8

(37

)a

52

1.1

(61

)a

clay

(g k

g-1

) 0

-10

63

.8 (

20

)ab

54

.3 (

11

)a

52

.4 (

13

)a

89

.8 (

14

)ab

1

37.2

(27

)ab

15

9.3

(10

0)b

10

-20

43

.2 (

22

)a

49

.3 (

5)a

4

3.5

(2

)a

66

.6 (

26

)a

10

2.3

(29

)a

10

6.3

(14

)a

Chem

ical

soil

pro

per

ties

pH

(H

2O

) 0

-10

5.9

(0

.1)a

5

.9 (

0.2

)a

5.8

(0

.2)a

b

5.5

(0

.2)a

b

5.2

(0

.4)b

4

.7(0

.5)b

10

-20

5.9

(0

.4)a

5

.9 (

0.0

)a

6.0

(0

.1)a

5

.7 (

0.1

)ab

5

.2 (

0.3

)bc

4.8

(0

.1)c

OC

(g k

g-1

) 0

-10

59

.9 (

4.4

)a

69

.5 (

9.4

)a

60

.3 (

9.2

)a

83

.2 (

18

)ab

1

24.9

(8

.7)b

1

93.2

(46

)c

1

0-2

0

52

.0 (

4.6

)a

59

.7 (

1.9

)a

54

.4 (

12

)a

68

.9 (

15

)a

10

0.0

(6

.1)a

1

55.3

(19

)b

Nt (

g k

g-1

) 0

-10

3.7

(0

.6)a

4

.6 (

0.8

)a

4.0

(0

.8)a

6

.1 (

1.5

)a

8.7

(0

.8)a

1

2.2

(2

.9)b

1

0-2

0

3.3

(0

.5)a

4

.0 (

0.1

)a

3.5

(0

.9)a

4

.9 (

1.0

)ab

6

.1 (

0.2

)b

10

.3 (

1.4

)c

K (

mg k

g-1

) 0

-10

77

.2 (

32

.4)a

3

7.8

(2

9.2

)a

20

.2 (

18

.9)a

1

19.1

(73

.3)a

9

.6 (

1.8

)b

17

.4 (

14

.5)a

b

1

0-2

0

24

.5 (

28

.6)a

1

9.7

(2

8.2

)a

5.1

(7

.6)a

4

6.4

(7

9.2

)a

0.6

(0

.1)a

8

.4 (

9.8

)a

P (

mg k

g-1

) 0

-10

0.8

(0

.1)a

4

.6 (

0.9

)a

4.8

(1

.8)a

0

.2 (

0.2

)a

7.6

(1

.8)b

1

7.7

(4

.9)c

10

-20

0.1

(0

.2)a

2

.8 (

1.1

)a

1.7

(0

.4)a

0

.2 (

0.2

)a

5.1

(1

.5)a

b

8.6

(1

.3)b

CE

C (

mm

olc

kg

-1)

0-1

0

14

8.3

(15

.1)a

1

66.5

(18

.4)a

1

28.3

(9

.8)a

2

15.3

(37

.5)b

3

64.1

(50

.9)c

3

49.8

(40

.8)c

1

0-2

0

55

.3 (

2.1

)a

54

.3 (

10

.8)a

5

1.1

(3

.6)a

9

9.1

(6

.9)a

b

14

1.6

(16

.5)b

1

08.1

(2

.4)a

b

Mn

d (

g k

g-1

) 0

-10

1.1

(0

.4)a

0

.6 (

0.0

3)a

0

.6 (

0.0

2)a

0

.7 (

0.0

6)b

0

.6 (

0.1

)a

0.7

(0

.3)a

1

0-2

0

1.2

(0

.5)a

0

.7 (

0.0

1)a

0

.6 (

0.0

4)a

0

.7 (

0.0

5)b

0

.6 (

0.1

)ac

1.0

(0

.3)a

b

Mn

p (

g k

g-1

) 0

-10

0.1

(0

.01

)a

0.0

3 (

0.0

0)a

0

.02

(0

.01

)a

0.0

3 (

0.0

1)b

0

.1 (

0.0

2)c

0

.1 (

0.0

3)b

c

64

1

0-2

0

0.0

4 (

0.0

2)a

0

.01

(0.0

0)a

b

0.0

1(0

.00

)ab

0.0

1(0

.01

)b

0.1

(0

.03

)c

0.1

(0

.03

)c

Mn

o (

g k

g-1

) 0

-10

1.0

(0

.4)a

0

.5 (

0.0

1)a

0

.7 (

0.1

)a

0.6

(0

.02

)b

0.5

(0

.1)a

0

.5 (

0.2

)a

1

0-2

0

1.0

(0

.4)a

c 0

.6 (

0.0

2)a

c 0

.6 (

0.0

2)a

c 0

.7 (

0.0

1)b

0

.5 (

0.1

)c

0.9

(0

.3)a

b

All

op

han

e (%

) 0

-10

6.6

(0

.2)a

b

7.2

(2

.6)a

b

9.5

(0

.6)a

6

.6 (

0.6

)ab

4

.2 (

0.1

)b

4.8

(2

.6)b

1

0-2

0

8.4

(0

.6)a

9

.6 (

0.4

)a

9.5

(0

.5)a

7

.3 (

0.8

)ab

4

.7 (

0.1

)b

4.4

(0

.3)b

Fer

rih

yd

rite

(%

) 0

-10

5.4

(0

.1)a

b

5.4

(0

.1)a

b

6.3

(0

.7)a

5

.4 (

0.2

)ab

4

.6 (

0.9

)b

5.3

(0

.5)a

b

1

0-2

0

6.4

(0.3

)ac

5.8

(0

.2)a

b

6.1

(0

.6)a

b

5.8

(0

.5)a

b

4.7

(0

.6)b

7

.6 (

0.4

)c

Bio

logic

al

soil

pro

per

ties

Fu

ngi

(µg C

g-1

dry

so

il)

0-1

0

n.d

. 2

2.4

(3

.8)a

2

1.6

(1

1.1

)a

32

.2 (

10

.0)a

1

3.8

(2

.8)a

9

.46

(3

.99

)b

Bac

teri

a (µ

g C

g-1

dry

so

il)

0-1

0

n.d

. 5

1.2

(4

.7)a

3

5.3

(4.3

)a

49

.5 (

13

.1)a

4

7.7

(1

5.3

)a

39

.1 (

13

.8)a

Leu

cin

e in

corp

ora

tion

(p

mo

l g

-1 h

-1)

0-1

0

n.d

. 4

5.4

(2

7.9

)a

-10

.3 (

15

.0)a

5

7.2

(1

9.1

)a

13

3.8

(37

.5)b

1

26.4

5 (

64

.52

)b

Min

eral

izab

le N

(m

g k

g-1

) 0

-10

n.d

. 9

5.9

(7

.7)a

7

4.5

(2

1.4

)a

10

7.6

(40

.6)a

2

57.5

(28

.9)b

3

33.4

4 (

13

6.8

2)b

HW

C (

µg C

g-1

) 0

-10

n.d

. 1

217

.9 (

10

2.5

)a

91

2.8

(15

6.8

)a

13

98

.5 (

32

3.0

)a

33

87

.7 (

38

5.6

)b

42

24

.06

(123

5.6

0)b

Mea

n v

alues

and s

tand

ard d

evia

tions

in p

aren

thes

is (

n =

3).

Dif

fere

nt

lett

ers

wit

hin

row

in

dic

ate

sign

ific

ant

dif

fere

nce

s ap

ply

ing T

ukeys´

signif

ican

t dif

fere

nce

as

a post

hoc

test

at

p<

0.0

5.

65

3.3.2 Distribution of soil fractions in the grassland sites

Total soil recovery after fractionation into fPOM, oPOM, and various aggregate sizes; <20

µm 20-250 µm and >250 µm was between 92% and 97% for all sites, indicating negligible

soil losses during the fractionation procedure. The fPOM concentration in 0-10 cm depth

ranged from the minimum of 41 g kg-1

in HaAorg to the maximum of 556 g kg-1

in HiAcon

(Table 3.3). The amount of oPOM in 0-10 cm depth ranged from 4 g kg-1

to 92 g kg-1

in

Grass2 and Grass1, respectively, and higher values were observed in HiAs than in HaAs.

The mean weight diameter (MWD) was highest in Grass2 and among the lowest in HiAcon

at both soil depths. The aggregate distribution showed that macroaggregates (> 250 m)

were most prominent in Grass1, Grass 2, HaAorg, and HiAorg; followed by HaAcon and

HiAcon (Figure 3.2). The amounts of microaggregates (<20 m) in 0-10 cm depth were

the highest in HaAcon (121 g kg-1

) and the lowest in Grass2 (37 g kg-1

) (Figure 3.2). Stable

macroaggregates (>250 µm) were most strongly positively correlated with fungal biomass

(r = 0.87, p<0.001) and oxalate and dithionite extractable Mn (r = 0.46, r = 0.42, p<0.01

for both), as well as MWD (r = 0.80, p<0.001; r = 0.46, p<0.01, r = 0.46, p<0.01,

respectively). On the contrary, fPOM was most strongly positively correlated with HWC,

mineralisable N and OC concentration (r = 0.88, 0.85 and 0.85, p<0.001, respectively)

(Table 3.4).

66

Table

3.3

Mea

ns

(sta

nd

ard

dev

iati

ons)

of

free

pa

rtic

ula

te o

rga

nic

matt

er (

fPO

M),

occ

luded

part

icu

late

org

an

ic m

att

er (

oP

OM

) a

nd

mea

n

wei

ght

dia

met

er (

MW

D)

in t

he

studie

d s

ites

.

D

epth

(cm

) G

rass

1

HaA

org

H

aAco

n

Gra

ss2

HiA

org

H

iAco

n

fPO

M (

g k

g-1

) 0-1

0

45.8

(49.7

)a*

40.7

(2.2

)a

71.5

(24.6

)a

49.4

(36.2

)a

460.1

(56.5

)b

556.0

(199.6

)b

10-2

0

15.5

(16.4

)a

20.0

(0.8

)a

31.0

(12.7

)a

9.2

(0.2

)a

305.1

(134.9

)b

553.9

(132.4

)c

oP

OM

(g k

g-1

) 0-1

0

91.5

(56.3

)a

12.4

(2.2

)b

12.6

(1.1

)b

4.4

(2.2

)b

39.1

(25.2

)abc

88.9

(56.9

)cd

10-2

0

14.1

(2.4

)a

11.8

(4.2

)a

8.8

(0.5

)a

4.5

(1.9

)a

24.0

(9.0

)a

37.1

(4.6

)a

MW

D (

mm

) 0-1

0

14.7

(1.3

)ab

19.9

(4.1

)ab

8.3

(3.8

)ac

27.3

(2.0

)b

11.7

(0.8

)abc

4.8

(1.2

)c

10-2

0

11.1

(4.3

)ab

7.7

(2.9

)a

10.6

(3.2

)abc

26.2

(7.5

)b

13.3

(5.1

)abc

8.3

(3.7

)ac

67

Figure 3.2 The distributions of micro- (<20 µm, and 20-250 µm) and macroaggregates

(>250 µm) of soils of different grassland sites. A 0-10 cm, B 10-20cm.

68

Table 3.4 Pearson correlation coefficients between the mean weight diameter (MWD),

particulate organic matter and soil aggregate fractions, and the key physicochemical and

biological soil properties1.

Particulate organic

matter Soil aggregate fractions

MWD fPOM oPOM <20 µm

20-250

µm

>250

µm

Nt -0.22 0.81*** 0.41*

-

0.67*** -0.76***

-

0.54***

OC -0.29 0.85*** 0.50**

-

0.65*** -0.74***

-

0.60***

K 0.34* -0.20 0.10 -0.38* -0.28 0.34*

P

-

0.51** 0.71*** 0.54*** -0.05 -0.12

-

0.60***

DOC -0.21 0.83*** 0.49** -0.61** -0.68***

-

0.52***

Biomass C -0.03 0.55*** 0.48** -0.59** -0.63*** -0.27

CEC 0.06 0.61*** 0.41* -0.63** -0.65*** -0.20

pH (H2O) 0.26 -0.81*** -0.35* 0.57** 0.63** 0.56***

Fed -0.15 0.44** 0.04 -0.47** -0.56*** -0.39*

Mnd 0.46** -0.39* -0.62*** -0.10 -0.12 0.42*

Ald -0.10 0.18 0.04 -0.39* -0.42* -0.26

Sid 0.06 -0.75** -0.33* 0.56*** 0.60** 0.36*

Fep -0.24 0.82** 0.40*

-

0.64*** -0.70***

-

0.56***

Mnp -0.02 0.80** 0.30

-

0.68*** -0.70*** -0.35*

Alp -0.16 0.73** 0.31

-

0.66*** -0.70*** -0.48**

Mno 0.46** -0.46** -0.65*** -0.04 -0.05 0.46**

Alo 0.23 -0.64*** -0.64*** 0.29 0.22 0.35*

Sio 0.08 -0.75*** -0.49** 0.71*** 0.75*** 0.40*

Allophane 0.09 -0.72*** -0.53*** 0.75*** 0.74*** 0.39*

Ferrihydrite -0.28 -0.16 -0.14 0.24 0.14 -0.18

Silt 0.13 -0.46** -0.37* 0.31 0.29 0.26

Clay -0.07 0.67** 0.32

-

0.56*** -0.53*** -0.36*

Fungal biomass

0.80**

* -0.71*** -0.72*** 0.03 0.22 0.87***

Fungi/bacteria 0.58* -0.60* -0.70** 0.21 0.49 0.69***

Leucine

incorporation 0.00 0.43 0.33 -0.69** -0.74*** -0.20

Mineralisable N -0.40 0.85*** 0.79*** -0.65** -0.70** -0.63*

HWC -0.42 0.88*** 0.78*** -0.65** -0.68** -0.65** 1n=15 for fungi, fungi/bacteria, leucine incorporation, mineralisable N, and HWC; for

other soil properties n=36. Asterisks indicate significance at 0.05 (*), 0.01 (**), and 0.001

(***) significance levels, respectively.

69

3.3.3 Distribution of OC and Nt in the grassland sites

Total loss of OC and Nt during fractionation was negligible (recoveries between 92% and

99% for all sites). We found that the distribution of OC and N differed between the soil

types (Figure 3.3). In 0-10 cm depth, in Grass1, HaAorg and Grass2 macroaggregates >250

µm contributed the greatest quantities of OC and N to bulk soil (44%, 65% and 70% for

OC; 54%, 65%, and 71% for N, respectively). In comparison, in HiAorg and HiAcon,

fPOM contributed the largest quantities of OC and N to bulk soil (61% and 69%,

respectively). The differences between the soil types remained in 10-20 cm depth, but for

Grass1, HaAorg and HaAcon microaggregates 20-250 µm were the largest contributor of OC

and N to bulk soil (44%, 50%, and 41% for OC; 43%, 48%, and 40% for N, respectively).

The C/N ratio decreased in the following order in both soil layers: fPOM > oPOM

>macroaggregates 250 µm > macroaggregates 20-250 µm > microaggregates <20 µm.

Figure 3.3 The C and N distribution within particle-size fractions and C/N ratios of

different soil fractions of soils of different grassland sites. (Note: The C concentration of

each fraction was calculated by taking total soil C as the sum of the C associated with all

separate particle-size fractions, including POM fractions). A, C, E= 0-10 cm, B, D, F=10-

20 cm.

13C CPMAS-NMR spectra revealed a large contribution of O-alkyl C in all analyzed fractions

(Table 3.5). In HaAorg and HaAcon alkyl C increased in the order fPOM < oPOM < bulk soil

and O-alkyl C decreased in the same order. Thus, alkyl C to O-alkyl C ratios increased in the

order fPOM < oPOM < bulk soil. Aryl-C decreased in the order: fPOM > oPOM > bulk soil.

In HiAorg and HiAcon, similar differences were found in alkyl C to O-alkyl C ratio, whereas

chemical shift regions were fairly similar between fPOM, oPOM and bulk soil. The chemical

quality differed between the soil types, with an especially larger contribution of alkyl C in

HiAorg and HiAcon compared to HaAorg and HaAcon.

70

Table 3.5 Integrated chemical shift regions (% of total signal intensity) obtained by 13

C

CPMAS NMR spectroscopy for the extracted free particulate organic matter (fPOM),

occluded particulate organic matter (oPOM), and bulk soil.

Depth (cm) HaAorg HaAcon HiAorg HiAcon

fPOM

Alkyl-C (%) 0-10 16 17 26 30

10-20 14 13 26 34

O-Alkyl-C (%) 0-10 58 53 47 44

10-20 58 55 45 41

Aryl-C (%) 0-10 15 18 15 15

10-20 17 20 16 14

Carboxyl-C (%) 0-10 11 12 12 11

10-20 10 12 13 11

Alkyl-C/O-Alkyl-C (%) 0-10 0.31 0.33 0.52 0.68

10-20 0.26 0.24 0.56 0.59

oPOM

Alkyl-C (%) 0-10 20 19 25 30

10-20 18 19 28 28

O-Alkyl-C (%) 0-10 52 56 49 45

10-20 55 54 46 42

Aryl-C (%) 0-10 18 16 14 15

10-20 17 18 16 16

Carboxyl-C (%) 0-10 10 10 11 10

10-20 10 9 11 15

Alkyl-C/O-Alkyl-C (%) 0-10 0.34 0.34 0.56 0.73

10-20 0.36 0.39 0.64 0.68

Bulk soil

Alkyl-C (%) 0-10 24 25 26 30

10-20 24 25 28 33

O-Alkyl-C (%) 0-10 50 50 46 41

10-20 49 50 45 41

Aryl-C (%) 0-10 14 14 15 16

10-20 14 14 15 15

Carboxyl-C (%) 0-10 12 11 13 12

10-20 13 12 12 12

Alkyl-C/O-Alkyl-C (%) 0-10 0.49 0.51 0.57 0.73

10-20 0.49 0.50 0.62 0.80

3.4 Discussion

3.4.1 Soil structure in the grassland sites

The hierarchical model of aggregates was confirmed in the studied soils by evidence of

different stabilizing mechanisms for microaggregates (<20 µm, and 20-250 µm) and

macroaggregates (>250 µm). Persistent binding agents Si oxides and allophones were

connected to the microaggregates, whereas macroaggregates were bound together by

71

temporary binding agent fungal biomass. However, macroaggregates were not correlated

with SOM but with oxalate and dithionite-extractable Mn that is not in accord with

identification of aggregate hierarchy based on Elliott (1986) and Oades and Waters (1991).

Our results agree with Lehtinen et al. (2014), who indicated the role of Mn in

macroaggregation. The role of Mn in aggregation may be explained by it being associated

with SOM (Navrátil et al. 2007), which in turn functions as an aggregating agent. Stability

of microaggregates is in agreement with Tisdall and Oades (1982) and Dexter (1988), who

also observed that stability of microaggregates depends on persistent forms of stabilizing

agents such as organic carbon materials and sesqui-oxides and hence tends to be more

resistant to management practices (Six et al. 2004). In Andosols, it is well known that

amorphous inorganic materials such as allophane and ferrihydrite affect the aggregation

(Hoyos and Comerford 2005), for which we found evidence in this study. Thus, we suggest

that in Icelandic grassland soils the aggregate hierarchy does not exist because oxides

diminish the expression of the aggregate hierarchy, as was also suggested by Oades and

Waters (1991) in Oxisols. The main aggregating agents in macroaggregates are rather

fungal biomass and Mn oxides than SOM.

Soil structure, measured, as MWD and amount of macroaggregates was highest in the

unimproved grasslands Grass 1 and Grass 2 that had never been ploughed, followed by the

improved grasslands (Table 3.3, Figure 3.2). It is well known that tillage breaks down

aggregates and subsequently SOM is mineralized when not physically protected in the

aggregates (e.g. Majedon et al. 2007; Laudicina et al. 2011). We further assume that the

application of OM in organic farming practices may have had positive effects on soil

structure (Table 3.3, Figure 3.2). HaAorg received the highest OM inputs (manure,

compost and cattle urine; Table 1), which may have contributed to the closest resemblance

of macroaggregates to the Grass 1 and Grass 2 sites (which have never been disturbed by

tillage and with insignificant grazing intensity) compared to the other sites (Figure 3.2).

Connection between OM inputs and increased aggregate stability is supported by several

previous studies (Siegrist et al. 1998; Shepherd et al. 2002; Karami et al. 2012). In our

study, we link higher amount of macroaggregates and higher MWD with higher fungal

biomass (cf. Tisdall and Oades 1982). Organic inputs entering the soil provide substrate for

the soil fungi, which further physically stabilize soil particles into larger aggregates when

fungal growth increases and hyphae enmesh soil particles (Eash et al. 1994). We found

higher macroaggregate stability in the topsoil, as explained by Tisdall (1991). The

concentrations of fine roots, OM and fungi are the highest in the topsoil and provide

therefore a favorable environment for macroaggregation. Fungal hyphae and extracellular

polysaccharides produced by fungi enhance formation and stabilization of aggregates. In

our study, conventional farms also used manure, but the amount did not seem to be

sufficient to increase macroaggregation. The compost input, used at HaAorg and HiAorg,

may have attributed to the higher microbial activity and the production of microbial

decomposition products that bind the soil particles into microaggregates, and

microaggregates further into macroaggregates (Sodhi et al. 2009).

3.4.2 SOM in the grassland sites

Significantly higher bulk soil OC concentrations were observed in HiAs compared to HaAs

(Table 3.2), as expected for these soil types (Arnalds 2004). The higher OC concentration

in HiAcon compared to HiAorg contradicts previous studies that have found evidence for

significantly more OC in organically managed top soils compared to conventional farming

practice (Leifeld and Fuhrer 2010; Gattinger et al. 2012). A major contributing factor for

72

lower OC concentrations in HiAorg compared to HiAcon could be ploughing history,

which increases decomposition of SOM, damages fungal hyphae and reduces root biomass

(cf. Bolinder et al. 2002). However, duration since the last ploughing does not support this

hypothesis (HiAcon was last ploughed in 1998 and HiAorg in 1994, 1995, and 1996),

while the intensity might. Addition of compost and manure can increase OC concentrations

compared to conventional farming practice (Leifeld and Fuhrer 2010). However, the SOC

concentration at the organic farm (HaAorg) did not differ significantly from the SOC

concentration at the neighboring conventional farm (HaAcon) that both applied manure on

their fields. Historical OC values for the sites could give more insight into the differences

between the farming practices but those were unfortunately not available. The lower OC

and Nt concentrations in the unimproved grassland sites (Grass 1, Grass 2) may be caused

by a number of factors, including lower OM input to the soil and/or lack of inorganic

fertilization (Guo and Gifford 2002). Sites Grass 1 and Grass 2 are both on HaAs, and

therefore have significantly lower OC concentrations than the sites on HiAs that are

organic rich soils (Table 3.2) and low concentration of plant available P in HaAs may limit

plant growth.

Density fractionation revealed that fPOM reflected higher OC concentrations, being the

highest in HiAs and the lowest in HaAs as well as higher (but not significantly) in

conventional sites compared to organic sites (Table 3.3). The higher concentration of

fPOM in these soils may be a result from the timing and frequency of tillage; HaAcon was

last ploughed earlier and less frequent (1995) than HaAorg (2001, 2002, 2003) and the

HiAcon had a lower ploughing frequency compared to HiAorg (1998 for HiAcon; 1994,

1995, and 1996 for HiAorg). The negative effect of tillage on fPOM concentration in the

soil is in accord with some previous studies (Chan et al. 2002; Linsler et al. 2013).

However, we observed no significant differences between unimproved grasslands and

improved grasslands. Presence of allophane, which has a low density, has been observed as

an explanation for a large percentage of POM of the bulk soil (Golchin et al. 1997), but

contradicts this study because HiAcon and HiAorg had significantly lower concentrations

of allophane compared to HaAcon and HaAorg, yet these soils had the highest fPOM

concentrations.

3.4.3 SOM distribution and chemical quality in the grassland sites

The results of the SOM distribution showed that the OC and Nt in oPOM and in <20 µm

aggregates were different between farming practices and soil types (Figure 3.3).

Macroaggregate-associated OM fraction was the highest and less susceptible to

mineralization in the Grass1, HaAorg, HaAcon, and Grass2 sites (Figure 3.3). In HiAorg

and HiAcon, however, the fPOM associated OC and Nt fraction had by far the greatest

storage capacity. The soil fractions that were observed in the highest proportion to the bulk

soil contained the most OC and Nt, which is in accordance to Poll et al. (2003). In general,

the distribution and dynamics of Nt concentration paralleled those of the OC concentration.

Organic farming practices favored macroaggregate-associated OM fraction and had a

closer resemblance to Grass 1 and Grass 2 sites compared to conventional farming

practice. The increase in fPOM associated OC and Nt concentration measured in the

cultivated sites compared to the unimproved grassland sites (Grass 1 and Grass 2) is

consistent with other studies that have shown that animal manure can increase the

particulate OM fraction (Whalen and Chang 2002; Courtier-Murias et al. 2013). However,

73

the fact that the C/N ratio of the aggregate size fractions decreased from POM fractions to

the smallest aggregates suggests that the nitrogen rich organic materials were associated

with mineral particles, and indicates the plant-like character of fPOM and oPOM. This is in

accordance with previous studies (Baldock et al. 1997; Golchin et al. 1997). Because the C

concentrations and C/N ratios were not significantly different between the 20-250 µm and

>250 µm sized aggregates, the SOM distribution in our soils is not in accord to the

aggregate hierarchy model (Six et al. 2004).

The solid-state 13

C NMR spectroscopy of all analyzed fractions showed an increasing

degree of decomposition in the order fPOM < oPOM < bulk soil, shown as increased

Alkyl-C to O-Alkyl-C ratio (Baldock et al. 1997). Despite the differences in farming

practices the chemical characteristics of the OM in soil fractions were similar in organic

and conventional farming practices. A similar trend was reported by Golchin et al. (1994),

where five different soils with different environmental conditions and vegetation were

compared. The organic inputs did not appear to affect the amount (except at 10-20 cm

depth in HiAs) or the composition of the OM in the studied soils. Our results indicate that

the fPOM and oPOM consisted mainly of plant material at different stages of

decomposition, and were less decomposed compared to the SOM in the bulk soil (Table

3.4). Courtier-Murias et al. (2013) showed that organic amendments could affect the

amount but not necessarily the composition of the OM stabilized in agricultural soils.

3.5 Conclusions

This study has demonstrated that macroaggregates were the most prominent soil

aggregates in the topsoils of unimproved and organically managed sites, whereas 20-250

µm aggregates were the most prominent in conventionally managed topsoils. Aggregate

stability decreased under cultivation of grasslands, but probably less so under organic

farming practices compared to conventional farming practice. This may be due to later start

of cultivation at the organic grassland sites and the higher usage of organic fertilizers (e.g.,

manure, compost and urine) compared to the conventional grassland sites.

Macroaggregates in the topsoils were the biggest contributor of OC and N to bulk soil at

Grass1, HaAorg and Grass2 macroaggregates, whereas the fPOM did so for the HiAorg

and HiAcon Stability of microaggregates (<250 µm) was related to allophane and Si oxides

whereas macroaggregates (>250 µm) correlated with higher fungal biomass and higher

concentration of oxalate and dithionite-extractable Mn. Because oxides were one of the

dominant binding agents in macroaggregates, the aggregate hierarchy could not be

confirmed in these studied Icelandic grasslands.

Acknowledgements

The authors thank Dr. Axel Mentler (BOKU) and Dr. Carsten Müller (Technical

University of Munich) for their advice on method development. We express gratitude to E.

Brauner, E. Kopecky, A. Hobel, K. Hackl, A. Hromatka, G. Heranney, and F. Brocza for

technical assistance and laboratory work. Dr. Anu Mikkonen (University of Helsinki,

Finland) is acknowledged for comments on the manuscript and Dr. Hans Göransson and

Dr. Ika Djukic (BOKU) for advice on statistics. Farmers are gratefully acknowledged for

their cooperation and permission to take samples from their properties. Louise Hamilton,

Jo Reilly and James Salter are acknowledged for English proofreading. The project was

financially supported by the European Commission FP7 Collaborative Project “Soil

74

Transformations in European Catchments” (SoilTrEC), Grant Agreement no. 244118.

Alterra was also supported by the research program KB IV “Innovative scientific research

for sustainable green and blue environment” funded by the Netherlands Ministry of

Economic Affairs, Agriculture and Innovation, and carried out by Wageningen University

and Research Centre. We acknowledge the support T. Lehtinen received from the

European Science Foundation (ESF) for the activity entitled 'Natural molecular structures

as drivers and tracers of terrestrial C fluxes' to conduct NMR measurements at the

Technical University of Munich.

75

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80

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0-2

0

2.8

(0

.6)a

1

.7 (

0.3

)a

1.6

(0

.2)a

1

.6 (

0.2

)b

3.3

(0

.8)b

5

.0 (

0.5

)c

Al o

(g k

g-1

) 0

-10

26

.4 (

3.3

)ac

19

.7 (

0.6

)a

22

.9 (

1.3

)ab

24

.6 (

1.7

)b

19

.0 (

2.0

)c

21

.7 (

2.0

)ac

1

0-2

0

26

.7 (

3.2

)ac

24

.0 (

0.9

)ac

23

.5 (

0.7

)ac

24

.2 (

1.7

)a

19

.1 (

1.8

)b

22

.7 (

1.5

)c

Si d

(g k

g-1

) 0

-10

3.0

(0

.2)a

4

.0 (

0.3

)a

4.8

(0

.3)a

4

.7 (

0.3

)b

1.7

(0

.1)c

2

.3 (

0.2

)d

81

1

0-2

0

3.3

(0

.3)a

4

.9 (

0.3

)a

5.0

(0

.3)a

4

.7 (

0.3

)b

1.9

(0

.2)c

2

.9 (

0.1

)bd

Si o

(g k

g-1

) 0

-10

11

.0 (

1.1

)ad

11

.0 (

0.4

)ab

1

3.9

(4

.0)b

1

6.3

(1

.4)a

d

7.0

(0

.2)c

d

7.8

(3

.2)d

1

0-2

0

12

.1 (

1.3

)ab

14

.0 (

1.0

)a

16

.3 (

0.8

)a

16

.5 (

1.2

)b

7.8

(0

.5)c

7

.7 (

0.6

)bc

Soil

bio

logic

al

pro

per

ties

DO

C (

mg L

-1)

0-1

0

3.6

(0

.1)a

3

.4 (

0.1

)a

3.0

(1

.1)a

4

.2 (

0.9

)a

9.5

(0

.4)b

1

0.9

6 (

1.7

5)b

10

-20

2.8

(0

.2)a

2

.4 (

0.7

)a

2.6

(0

.4)a

3

.8 (

1.4

)a

7.6

(1

.7)b

8

.28

(0

.84

)b

Bio

mas

s C

(m

g 1

00

g-1

) 0

-10

78

.4 (

7.9

)a

38

.1 (

24

.3)a

2

6.3

(1

8.5

)a

67

.6 (

29

.4)a

1

14.3

(9

.8)a

b

12

8.9

4 (

47

.04

)b

10

-20

25

.2 (

3.2

)a

21

.2 (

15

.1)a

2

8.5

(9

.4)a

3

2.4

(1

3.7

)a

48

.9 (

6.4

)a

67

.56

(18

.92

)a

82

4 Characterization of soil aggregation and soil organic matter under

intensive cropping on Austrian Chernozems

83

Taru Lehtinen, Georg J. Lair, Jeroen P. van Leeuwen, Guðrún Gísladóttir, Jaap Bloem,

Kristin Vala Ragnarsdóttir, Markus Steffens, Winfried E.H. Blum. 2014. Characterization

of soil aggregation and soil organic matter under intensive cropping on Austrian

Chernozems. To be submitted to Journal of Plant Nutrition and Soil Science. Referencing

style according to the journal guidelines.

Abstract

Cultivation can cause adverse effects on soil structure and soil organic matter (SOM)

quantity and quality due to high frequency of soil disturbance by tillage activities and

harvesting operations. Therefore, we sampled four intensively farmed croplands (Org76,

Con76, Org95, Con95) on Austrian Haplic Chernozems. Soil structure and SOM quantity,

quality and distribution between different particulate organic matter (POM) and aggregate

size fractions (<20 µm, 20-250 µm, 250-5000 µm) were studied following a density

fractionation procedure with low-energy ultrasound vibration. In addition, the effect of the

soil physicochemical and biological properties on soil aggregates and SOM were studied.

There were no significant differences in the mean weight diameter (MWD) or amount of

macroaggregates between the sites. Iron oxides content and active fungal biomass were

positively correlated with amount of the macroaggregates and the mean weight diameter

(MWD). The soil fractions that were observed in the highest proportion to the bulk soil

(<20 µm aggregates at Org76 and Con76, and 20-250 µm aggregates at Org95 and Con95)

contained the most OC and Nt. The distribution and dynamics of Nt content paralleled

those of the OC content. Further studies are required on cultivated Chernozems to

understanding quantitative basis for evaluating whether it may be beneficial to use

biowaste compost and horse manure as organic inputs, in order to increase SOM content

and macroaggregation.

Key words: aggregate stability, solid-state 13

C NMR spectroscopy, particulate organic

matter (POM), aggregate hierarchy.

84

4.1 Introduction

Approximately 12% of the Earth´s ice-free land surface is used for croplands (Ramankutty

et al., 2008), which provide approximately 80% of global food supply (Pimentel and

Wilson, 2004). One of the important farming areas in Europe are the riparian areas, which

cover approximately 2 % of continental Europe and in plain areas are characterized by

agricultural land use (Clerici et al., 2011). The riparian agricultural area of Marchfeld in

Lower Austria, east of Vienna and north of the Danube River, is one of the most important

food production areas in the country. During the last 50 years, farms in the region have

changed to stockless farming systems (Surböck et al., 2006; Spiegel et al., 2010).

Cultivation can cause adverse effects on soil structure and soil organic matter (SOM)

quantity and quality due to high frequency of tillage activities (Lal, 2013) as well as

harvesting of crops such as potatoes (Solanum tuberosum) and sugar beet (Beta vulgaris)

that can also lead to strong soil disturbances and soil compaction in the subsoil due to

heavy machinery (Pulleman et al., 2003). Macroaggregates (>250 µm) are less stable and

more influenced by farming practices than microaggregates (<250 µm), due to

macroaggregates mostly transient or temporary binding agents such as microbial-derived

polysaccharides, and roots and fungal hyphae (Tisdall and Oades, 1982; Amézketa, 1999;

Six et al., 2004). Farmers aim to increase the content of SOM by applying organic inputs

including manure into the soil (Siegrist et al., 1998) in order to maintain the soil functions

such as biomass production, which also supports aggregation. The formation and

stabilization of soil aggregates are influenced by physical, chemical, and biological soil

properties, as well as environmental variables including as drying/wetting cycles (Six et al.,

2000; Six et al., 2004). An amount of 6-7 Mg ha-1

year -1

of biowaste compost has been

suggested to be sufficient to maintain the SOM content in the Marchfeld area (Erhart and

Hartl, 2010), whereas up to 16 Mg ha-1

year-1

may be required when aiming to maintain

Norg levels (Hartl and Erhart, 2005). However, it is still unclear how important the

application of organic inputs is counteracting the detrimental effect of tillage on soil

structure (Abiven et al., 2009). In a study by Williams and Petticrew (2009), soils that

received only chemical fertilizers had less stable macroaggregates compared to soils

receiving organic inputs (Williams and Petticrew, 2009).

Management-induced changes may be sooner detected in the distribution of SOM between

the particulate organic matter (POM) and aggregate fractions (20 µm, 20-250 µm, and

>250 µm) than in the bulk SOM, because cultivation has been shown to relatively increase

SOM in clay-sized particles compared to coarser soil particles (Christensen, 1992). Soil

aggregates can physically protect the incorporated organic matter (OM) from

decomposition, especially in soil systems in which physical disturbance is low (Six et al.,

2000). POM fractions (free POM, occluded POM) represent plant and animal residues

undergoing decomposition and have been observed to respond more sensitively to farming

practice changes than total organic carbon or OC (Golchin et al., 1994; Chan et al., 2002),

especially occluded POM that may be lost from soil aggregates due to intense cultivation

(Golchin et al., 1994). While many researchers have studied organic inputs such as cattle

manure (Abiven et al., 2009), little attention has been given to organic inputs that contain

woody debris or straw including horse manure and biowaste compost (Jannoura et al.,

2014).

85

Therefore, objectives of this study were to assess: 1) soil structure, 2) quality, quantity, and

distribution of SOM, and 3) to link them to physicochemical and biological soil properties

in intensively managed croplands on Haplic Chernozems in the agricultural area of

Marchfeld, Austria. The study is based on the hypotheses that: 1) organic inputs increase

macroaggregate stability due to OM acting as a main aggregating agent compared to soils

receiving only mineral fertilizers, and 2) oxides also play a major role in aggregation of

these soils.

4.2 Material and methods

4.2.1 Site description

The sites were selected to represent one of the major soil types, Haplic Chernozem (WRB),

in the agricultural area of Marchfeld, southeast of Vienna, Austria, in the alluvial terrace of

the river Danube. Sites were chosen that have the same soil type, with the same

pedogenesis and the same main horizons. The mean annual temperature in the sampling

area is approximately 9°C and mean annual precipitation is about 550 mm with dry

summers (Lair et al., 2009). We sampled four cropland sites, in the towns of

Obersiebenbrunn (O) and Lassee (L):

a) Org76 (48°17´087N, 16°41´245E, O) is an organic farm that has been managed

according to the Austrian guidelines for organic farming (BIO AUSTRIA, 2010) since

1976. The studied field receives biowaste compost produced by the city of Vienna (except

the years 2001-2003, details on the biowaste compost in Plahl et al., 2002) as an organic

fertilizer. In 2009, catch crops were sown before tillage activities in the fall;

b) Con76 (48°17´093N, 16°41´209E, O) is located next to Org76 and receives mineral

fertilizers according to the Austrian fertilization recommendations (BMLFUW, 2006);

c) Org95 (48°13´556N, 16°50´051E, L) receives horse manure every five years as an

organic fertilizer and was converted to organic management according to the Austrian

guidelines for organic farming (BIO AUSTRIA, 2010) in 1995. Catch crops were used in

2007, 2008, and 2009 and were incorporated into the soil during the tillage activities in the

fall; and

d) Con95 (48°14´153N, 16°50´090E, L) is located close to Org95 and receives mineral

fertilizers according to the Austrian fertilization recommendations (BMLFUW, 2006).

Catch crops were used before the fall tillage activities in 2002, 2004, 2006, and 2007.

The use of pesticides and herbicides at Con76 and Con95 was done according to Austrian

guidelines for each crop (e.g. AGES, 2013). The crops at the time of sampling were potato

(Org76, Con76) and winter wheat (Triticum aestivum) (Org95, Con95). The soils studied

at all sites are classified as Haplic Chernozems (WRB) with a fine sediment texture.

Tillage at all farms was carried out annually in the fall to a depth of 25 cm (except after

sunflower in 2007 at Con95) and conventional methods for preparing the seedbed for each

crop were used. If catch crops were used, additional seedbed preparation was carried out

and catch crops were ploughed into the soil during the regular tillage activities in the fall.

More details on farm management in Supplementary Table 4.S1.

86

4.2.2 Soil sampling

The soil sampling campaign was carried out in May 2011. At each study field, soil was

sampled randomly in triplicates from 0-15 cm and 30-40 cm depths with a soil corer

(diameter 8 cm, height 15 cm; root corer Eijkelkamp, Agrisearch Equipment, The

Netherlands). Approximately 10-15 cores were taken for each replicate and a total of 24

composite bulk samples were obtained. Soil samples were gently broken by hand and

sieved through a 5 mm sieve in the field. Soils for microbiological analyses were only

sampled from 0-15 cm soil depth. The soil samples were transported in plastic boxes, and

kept at 4°C in the dark for the biological analyses and air-dried in the laboratory prior to all

other analyses.

4.2.3 Physicochemical soil properties

Soil pH was measured electrochemically (Microprocessor pH Meter pH196 WTW,

Weilheim, Germany) in distillied H2O at a soil:water ratio of 1:2.5 (Soil Survey Staff,

2004). Particle size distribution was determined with a combined sieve and pipette method

after removal of SOM with hydrogen peroxide and dispersion by reciprocal shaking with

sodium metaphosphate solution for 12 h (Soil Survey Staff, 2004). Ammonium-oxalate-

extractable Fe, Mn, and Al (Feo, Mno, Alo) were determined according to Schwertmann

(1964). Dithionite-citrate-bicarbonate-extractable Fe, Mn, and Al (Fed, Mnd, Ald) were

determined according to Mehra and Jackson (1960). Total carbon (Ct) and total nitrogen

(Nt) were quantified by dry combustion (Tabatabai and Bremner, 1991) using an elemental

analyzer (Carlo Erba Nitrogen Analyser 1500, Milano, Italy). Carbonate content was

measured gas-volumetrically (Soil Survey Staff, 2004). Organic C (OC) was calculated as

the difference of total and carbonate C. Plant available phosphorous and potassium were

determined by the calcium-acetate-lactate (CAL)-extraction (ÖNORM L1087). Cation

exchange capacity (CEC) and exchangeable cations were determined using an unbuffered

0.1 M BaCl2 extraction (Soil Survey Staff, 2004). Extracted exchangeable cations (K, Na,

Ca, and Mg) were measured by flame atomic absorption spectrophotometry (Perkin-Elmer

2100).

4.2.4 Soil microbiology

For determination of hyphal length and bacterial numbers, microscopic slides were

prepared as described by Bloem and Vos (2004) after a pre-incubation period for 2 weeks

at 20°C. The equation of a cylinder with spherical ends (V = (π/4) W2 (L-(W/3)) where V

= volume (µm³), L = length (µm) and W= width (µm)), a mean hyphal diameter of 2.5 µm

and a specific C content of 130 fg C µm-3

were used to estimate fungal biomass. Total and

active fungi were distinguished using differential fluorescent stain (DFS) where cell walls

(polysaccharides) were stained blue with fluorescent brightener and DNA and RNA

(presumably actively growing hyphae) were stained red with Europium chelate. Bacteria

(proteins) were stained with DTAF. Bacterial biomass was calculated using a specific C

content of 320 fg C µm-3

and bacterial cell numbers and volume were determined by

confocal laser scanning microscopy combined with an image analysis system (Bloem et al.,

1995). Mineralizable nitrogen (Min N) was measured as the accumulation of NH4 during

one week anaerobic incubation in slurry at 40°C (Canali and Benedetti, 2006). Hot water

(16 h at 80°C) extractable C (HWC) was determined according to Ghani et al. (2003).

87

4.2.5 Density and aggregate fractionation

A three-step density and aggregate fractionation procedure, according to Lehtinen et al.

(2014a, submitted), was carried out in triplicate. In short, the free particulate organic

matter (referred to as fPOM, 20-5000 µm) was separated from soil using Na-polytungstate

solution (density of 1.8 g cm-3

). To obtain POM occluded in aggregates (referred to as

oPOM, 20-5000 µm), the subsequent heavy fraction (>1.8 g cm-3

) was treated by

ultrasound. Ultrasonic application of 8 J ml-1

was used in order to disrupt all

macroaggregates and to protect the microaggregates as well as to minimize the production

of artifacts following heavy ultrasonication (Lehtinen et al., 2014b). Calibration of the

output power of the sonicator was done calorimetrically according to North (1976). With a

subsequent density fractionation step (Na-polytungstate solution, 1.8 g cm-3

), the oPOM

floating on the suspension was obtained after centrifugation (10 minutes at 4350 rpm). All

POM fractions were washed with deionized water on a 20 µm sieve until the electric

conductivity dropped below 5 µS cm-1

(Mueller et al., 2009; Steffens et al., 2009) and

thereafter freeze-dried for further analyses. The soil matrix with a density of > 1.8 g cm-3 –

mineral particles and organomineral associations – was sieved at 250 µm and 20 µm to

obtain macroaggregates (250-5000 µm) and two microaggregate fractions (20-250 µm and

< 20 µm). All aggregate fractions were washed in a steel pressure filter apparatus (filter

size 0.45 µm) with deionized water until the electronic conductivity dropped below 5 µS

cm-1

, then oven dried at 100°C, weighed and ground for further analyses. The weights of

aggregates were corrected for their sand content (for aggregates 20-250 µm, and > 250

µm), in order to exclude single sand particles from being weighed as an aggregate (Six et

al., 2000; Lehtinen et al., 2014b). Mean weight diameter (MWD, mm) of the sand-

corrected aggregates was calculated according to Kemper and Rosenau (1986) as follows:

∑ ̅

Where, ̅ is the geometric mean of aggregate size on sieve i, and is the fraction of

aggregates on sieve i.

4.2.6 Solid-state 13C NMR spectroscopy

The chemical quality of selected POM fractions and bulk soils was analyzed by solid-state 13

C NMR spectroscopy (DSX 200 NMR spectrometer, Bruker, Karsruhe, Germany).

Composite samples were prepared by mixing equal amounts of the three replicates. To

improve the signal-to-noise ratio, the bulk soil samples were treated with 10% HF (Schmidt

et al., 1997). The cross-polarization magic angle spinning (CPMAS) technique with a 13

C-

resonance frequency of 50.32 MHz and a spinning speed of 6.8 kHz was applied. A

ramped 1H-pulse starting at 100% to 50% of the initial power was used during a contact

time of 1 ms in order to circumvent spin modulation during the Hartmann-Hahn contact.

Pulse delays between 0.8 and 1 s were used for all spectra. Depending on the C contents of

the samples, between 11000 and 525000 scans were accumulated and a line broadening of

50 Hz was applied. The 13

C chemical shifts were calibrated relative to tetramethylsilane (0

ppm). The relative contributions of the various C groups were determined by integration of

the signal intensity in their following respective chemical shift regions (Knicker et al.,

2005) assignable to alkyl C (-10 to 45 ppm), N-alkyl-C (45 to 60 ppm), O-alkyl C (60 to

110 ppm), olefinic and aromatic C (110 to 160 ppm), and carbonyl (aldehyde and ketone)

and carboxyl/amide C (160 to 220 ppm).

88

4.2.7 Statistical analyses

Statistical analyses were performed using IBM SPSS Statistics 20 software package for

Mac. Normality was tested with Shapiro-Wilkinson´s test and confirmed that no data

transformations were necessary before statistical analyses, except for OC and Nt

distribution in the soil POM and aggregate fractions due to non-normal distribution. Two-

way analyses of variance (ANOVA), with location (Obersiebenbrunn (O), and Lassee (L),

and soil depth (0-15 cm, and 30-40 cm) as factors, followed by Tukey´s- significant

difference (p<0.05) as a post hoc test (Tukey, 1957), was used to study the effects of the

factors with the soil physicochemical properties. Correlations between variables were

calculated with the Pearson correlation coefficient.

4.3 Results

4.3.1 Soil characteristics

The sand contents were significantly higher at both soil depths at Org95 and Con95

compared to Org76 and Con76, whereas silt contents were significantly lower at Org95

and Con95 compared to Org76 and Con76 (Table 4.1). No statistical differences between

the sites were observed for OC and Nt. CaCO3 contents were significantly higher at Org95

and Con95 compared to Org76 and Con76. CAL-extractable K content at 0-15 cm depth

was significantly higher at Con76 compared to Org76, while CAL-extractable P was

higher at Org76 and Con76 compared to Org95 and Con95, although not significantly.

A significantly higher content of Fed at Org76 and Con76 compared to Org95 and Con95

was found, while the difference was similar but not significant for Ald and Alo contents.

Active fungi content was significantly higher at Con76 compared to Org76, whereas

between Con95 and Org95 only an insignificantly higher content was observed at Con95.

Fungal and bacterial biomass did not differ between sites, while contents of mineralizable

N and HWC both differed significantly between the locations (Lassee vs.

Obersiebenbrunn) (Table 4.1). Site locations (Lassee vs. Obersiebenbrunn) differed mainly

in soil pH, contents of OC and CaCO3, sand, silt and clay contents as well as hydroxides

(Fed, Ald, Mnd, Feo, Alo, Mno) (Table 4.2). Significant differences between soil depths were

observed for pH (H2O), CAL-extractable K and P, OC, Nt, hydroxides (Mdo, Fed, Mno),

fPOM, oPOM and 20-250 µm sized microaggregates (Table 4.2).

89

Table

4.1

Mea

ns

and s

tand

ard

dev

iati

ons

of

ph

ysic

och

emic

al

and

bio

logic

al

pro

per

ties

of

the

bulk

soil

s st

udie

d (

n=

3).

Dif

fere

nt

lett

ers

indic

ate

sig

nif

icant

dif

fere

nce

s acc

ord

ing t

o T

uke

y´s

as

a P

ost

Ho

c te

st.

Ob

ersi

eben

bru

nn

Ob

ersi

eben

bru

nn

Las

see

Las

see

Org

76

Co

n76

Org

95

Co

n95

d

epth

(cm

) m

ean

± s

d

mea

n ±

sd

mea

n ±

sd

mea

n ±

sd

Ph

ysic

al

soil

pro

per

ties

sand

(g k

g-1

) 0

-15

21.6

±

4

.5a

19.4

±

3

.8a

44.4

±

3

.1b

41.4

±

2

.2b

30

-40

19.5

±

6

.0a

15.3

±

0

.8a

40.7

±

1

.0b

40.4

±

3

.9b

silt

(g k

g-1

) 0

-15

61.7

±

3

.0a

63.6

±

3

.4a

41.2

±

1

.3b

44.7

±

2

.0b

30

-40

60.2

±

4

.7a

65.3

±

3

.3a

42.0

±

1

.8b

44.2

±

1

.7b

clay

(g k

g-1

) 0

-15

16.7

±

1

.6a

17.0

±

0

.9a

14.4

±

1

.8a

13.9

±

0

.2a

30

-40

20.3

±

2

.8a

19.4

±

2

.6a

15.4

±

2

.0a

15.5

±

2

.2a

Chem

ica

l so

il p

rop

erti

es

pH

(H

2O

) 0

-15

8

.0

±

0.0

a 7

.9

±

0.0

5a

8.1

±

0

.03b

8.0

±

0

.02ab

3

0-4

0

8.1

±

0

.1a

8.1

±

0

.04a

8.4

±

0

.05b

8.2

±

0

.1ab

OC

(g k

g-1

) 0

-15

18.0

±

4

.0a

15.2

±

0

.6a

18.6

±

1

.8a

20.0

±

0

.7a

30

-40

13.1

±

0

.8a

14.0

±

2

.7a

13.7

±

0

.4a

17.3

±

3

.5a

Nt (

g k

g-1

) 0

-15

1.4

±

0

.1a

1.4

±

0

.1a

1.5

±

0

.1a

1.5

±

0

.2a

3

0-4

0

1.1

±

0

.2a

1.2

±

0

.2a

1.0

±

0

.1a

1.4

±

0

.4a

CaC

o3 (

g k

g-1

) 0

-15

68.3

±

1

.8a

65.3

±

1

2.0

a 1

96

.0

±

19.8

b

196

.9

±

8.9

b

30

-40

142

.9

±

55.3

a 1

25

.9

±

26.1

a 2

40

.2

±

17.6

b

219

.2

±

43.7

b

CA

L-e

xtr

acta

ble

K (

mg k

g-1

) 0

-15

70.8

±

1

2.7

a 2

19

.2

±

48.0

b

204

.7

±

18.0

b

114

.7

±

27.5

b

30

-40

41.7

±

1

4.1

a 6

7.9

±

5

1.8

a 9

7.5

±

4

0.0

a 9

7.6

±

4

1.9

a

CA

L-e

xtr

acta

ble

P (

mg k

g-1

) 0

-15

106

.9

±

26.3

a 1

24

.7

±

26.6

a 8

9.4

±

9

.9a

88.5

±

5

.8a

30

-40

44.2

±

3

3.2

a 3

4.6

±

8

.2a

29.3

±

7

.5a

43.8

±

2

5.2

a

CE

C (

mm

olc

kg

-1)

0-1

5

192

.7

±

89.3

a 1

83

.2

±

79.2

a 2

09

.5

±

63.4

a 2

44

.2

±

77.3

a

30

-40

174

.6

±

77.9

a 1

73

.6

±

77.5

a 1

79

.9

±

62.9

a 2

45

.5

±

92.4

a

90

BD

(g c

m-3

) 0

-15

1.5

±

0

.2a

1.5

±

0

.1a

1.4

±

0

.1a

1.4

±

0

.03a

30

-40

1.4

±

0

.1a

1.5

±

0

.1a

1.5

±

0

.02a

1.4

±

0

.2a

Fe d

(g k

g-1

) 0

-15

5085

±

361

.4a

5154

±

278

.0a

3253

±

371

.5b

3912

±

168

.3b

3

0-4

0

4689

±

466

.9a

4868

±

373

.2a

3533

±

173

.9b

3920

±

88.4

b

Mn

d (g k

g-1

) 0

-15

310

±

16.9

a 3

15

±

20.5

a 2

18

±

27.4

a 2

59

±

25.1

a

30

-40

234

±

57.8

a 2

51

±

44.4

a 1

77

±

19.2

a 2

38

±

55.9

a

Al d

(g k

g-1

) 0

-15

554

±

46.7

a 5

75

±

52.7

a 3

96

±

44.4

b

492

±

40.3

ab

30

-40

499

±

20.6

a 5

75

±

104

.5a

435

±

56.0

a 4

80

±

6.0

a

Fe o

(g k

g-1

) 0

-15

972

±

7.1

a 1

062

±

31.3

a 8

44

±

53.4

a 9

40

±

14.1

a

30

-40

749

±

164

.6a

868

±

96.1

a 8

48

±

72.9

a 9

02

±

24.4

a

Mn

o (g k

g-1

) 0

-15

280

±

22.5

a 2

95

±

32.8

a 1

97

±

30.7

a 2

39

±

35.1

a

3

0-4

0

211

±

49.0

a 2

28

±

30.4

a 1

36

±

46.3

a 2

28

±

55.3

a

Al o

(g k

g-1

) 0

-15

1287

±

115

.6a

1382

±

63.2

a 1

011

±

55.9

b

1157

±

109

.2ab

30

-40

1225

±

122

.6a

1291

±

111

.2a

934

±

57.2

b

1135

±

149

.0ab

Bio

logic

al

soil

p

rop

erti

es

Fu

ngi

(µg C

g-1

dry

soil

) 0

-15

12.7

±

4

.5a

12.7

±

1

.8a

10.9

±

2

.2a

15.1

±

6

.6a

Act

ive

fun

gi

(% o

f h

yp

hal

len

gth

) 0

-15

2.5

±

2

.4a

14.0

±

3

.5b

0.7

±

1

.3a

1.8

±

2

.1a

Bac

teri

al b

iom

ass

(µg C

g-1

dry

soil

) 0

-15

44.4

±

1

0.0

a 3

8.3

±

9

.8a

68.9

±

1

6.1

a 3

8.3

±

7

.0a

Min

eral

izab

le N

(m

g k

g-1

) 0

-15

8.1

±

2

.7a

9.4

±

7

.2a

31.0

±

1

0.5

b

15.2

±

5

.3ab

HW

C (

µg C

g-1

) 0

-15

316

.6

±

37.7

a 3

46

.5

±

76.3

a 5

10

.0

±

43.5

b

403

.2

±

24.3

ab

91

Table 4.2 Results of two-way analyses of variance (ANOVA) showing the level of

significance for each significant variation source associated with the soil properties (n=24

for physicochemical soil properties at both soil depths (0-15 cm and 30-40 cm), n=12 for

fungal biomass, active fungi, bacterial biomass, mineralisable N, and hot water

extractable carbon (HWC) at the 0-15 cm soil depth).

Sources of variation

Soil characteristic Location Soil depth

pH (H2O) *** ***

Sand *** NS

Silt *** NS

Clay *** NS

Nt NS **

OC *b **

CAL-extractable K NS ***

CAL-extractable P NS ***

CaCO3 *** NS

Fed *** NS

Mnd **a

**

Ald ***a

NS

Feo ***a

**

Mno **a

**

Alo *** NS

fPOM NS **

oPOM **a

**

20-250 **a

*

>250 µm *a

NS

Mean weight diameter (MWD) *a

NS

Fungal biomass **a

-

Active fungi **a

-

Bacterial biomass NS -

Mineralizable N **a

-

Hot-water extractable carbon (HWC) **a

-

92

4.3.2 Distribution of soil fractions

Total soil losses after fractionation into fPOM, oPOM, <20 µm aggregates, 20-250 µm

aggregates and >250 µm aggregates were negligible, indicated by >99% recoveries for all

the sites. There were higher amounts of macroaggregates (Figure 4.1) and MWD (Table

4.3) at Con76 and Con95 compared to Org76 and Org95 at both soil depths, although the

differences were not significant. The amount of microaggregates (<20 m) was the highest

in Con95 (337 g kg-1

) and lowest in Con76 (260 g kg-1

), the amount of fPOM was the

highest in Org95 (5.9 g kg-1

) and the lowest in Con76 (3.4 g kg-1

, Table 4.3) and the

amount of oPOM was the highest in Org95 (3.8 g kg-1

) and the lowest in Con95 (2.5 g kg-

1) in the 0-15 cm soil depth. Amount of macroaggregates (>250 µm) was most strongly and

positively correlated with active fungi and Fed (r=0.68 and r=0.42, n=12 and n=24,

respectively, both p<0.05), and MWD with active fungi (r=0.71, p<0.05, n=12). In

contrast, fPOM was most strongly and positively correlated with HWC contents and

bacterial biomass (r=0.79, p<0.001 and r=0.68 and p<0.01, respectively, both n=12).

Figure 4.1 The distributions of micro- (<20 µm, and 20-250 µm) and macroaggregates

(>250 µm) in A) 0-15 cm, and B) 30-40cm.

93

Table

4.3

Mea

ns

(sta

nd

ard

dev

iati

ons)

of

mea

n w

eigh

t dia

met

er (

MW

D)

of

ult

raso

und

sta

ble

sa

nd

co

rrec

ted

agg

reg

ate

s (<

5 m

m),

fre

e

part

icula

te o

rganic

matt

er (

fPO

M)

and

occ

lud

ed p

art

icula

te o

rgan

ic m

att

er (

oP

OM

) in

th

e st

udie

d s

ites

(n

=3

). D

iffe

ren

t le

tter

s in

dic

ate

signif

icant

dif

fere

nce

s acc

ord

ing t

o T

uke

y´s

as

a P

ost

Hoc

test

(p<

0.0

5).

Dep

th

Org

76

C

on

76

O

rg9

5

Con

95

cm

fPO

M (

g k

g-1

) 0-1

5

3.6

(0.9

)ab

3

.4 (

1.5

)ab

5.9

(1.3

)b

3.9

(0.4

)ab

30-4

0

1.6

(0.8

)a

2.5

(1.3

)ab

2

.8 (

2.3

)ab

2.2

(0.8

)ab

oP

OM

(g k

g-1

) 0-1

5

3.3

(0.6

)bc

3.1

(0.4

)abc

3.8

(0.6

)bc

2.5

(0.4

)ab

c

30-4

0

1.5

(1.5

)ab

1.9

(0.8

)abc

1.3

(0.2

)a

1.3

(0.5

)a

MW

D (

mm

) 0-1

5

7.6

(2.4

)ab

10

.0 (

4.6

)ab

3.8

(2.2

)a

4.5

(2.4

)ab

30-4

0

6.7

(2.2

)ab

8

.9 (

2.8

)ab

8

.4 (

2.6

)ab

11

.9 (

1.1

)b

94

4.3.3 Distribution and chemical quality of SOM

Total loss of OC and Nt during fractionation was negligible (recoveries >98% for all sites).

However, the distribution of OC and N differed among sites (Figure 4.2). In 0-15 cm soil

depth, at Org95 and Con95 microaggregates 20-250 µm contributed the greatest quantities

of OC and N to bulk soil (46%, and 50% for OC; 45% and 45% for Nt, respectively). In

comparison, in Org76 and Con76, microaggregates <20 µm contributed the largest

quantities of OC and N to bulk soil (51% and 46% for OC; 51% and 47% for Nt,

respectively). The differences among sites were similar at 30-40 cm depth. The C/N ratio

was the highest in fPOM, followed by that in oPOM and the least in different aggregate

size fractions at all sites (Figure 4.2E, 4.2F).

13C CPMAS-NMR spectra revealed a large contribution of O-alkyl C and an increasing

Alkyl-C/O-alkyl C ratio in the order fPOM < oPOM < bulk soil, in all analyzed fractions

(Table 4.4). Aryl-C increased in the order: fPOM < oPOM < bulk soil, except at Org76 and

Con76 the differences were in the opposite direction at 30-40 cm soil depth. Carboxyl-C

increased in the order of fPOM < oPOM < bulk soil at all sites.

95

Figure 4.2 The C and N distribution within particle-size fractions and C/N ratios of

different soil fractions in A, C, E) 0-15 cm, and B, D, F) 30-40 cm. (Note: The C content of

each fraction was calculated by taking total soil C as the sum of the C associated with all

separate particle-size fractions, including POM fractions).

96

Table 4.4 Integrated chemical shift regions (% of total signal intensity) obtained by 13

C

CPMAS NMR spectroscopy for the extracted free particulate organic matter (fPOM),

occluded particulate organic matter (oPOM), and bulk soil for the studied sites.

Depth (cm) Org76 Con76 Org95 Con95

fPOM

Alkyl-C (%) 0-15 15 17 16 16

30-40 15 19 16 17

O-Alkyl-C (%) 0-15 49 49 53 43

30-40 37 38 45 48

Aryl-C (%) 0-15 24 23 21 26

30-40 33 29 27 25

Carboxyl-C (%) 0-15 12 12 10 14

30-40 15 13 12 10

Alkyl-C/O-Alkyl-C (%) 0-15 0.34 0.34 0.30 0.37

30-40 0.41 0.50 0.36 0.35

oPOM

Alkyl-C (%) 0-15 20 19 18 19

30-40 23 25 17 20

O-Alkyl-C (%) 0-15 45 49 46 44

30-40 42 36 36 38

Aryl-C (%) 0-15 24 22 25 27

30-40 24 26 32 30

Carboxyl-C (%) 0-15 11 10 11 10

30-40 12 13 15 12

Alkyl-C/O-Alkyl-C (%) 0-15 0.45 0.40 0.39 0.44

30-40 0.54 0.69 0.47 0.53

Bulk soil

Alkyl-C (%) 0-15 21 23 21 21

30-40 22 23 20 21

O-Alkyl-C (%) 0-15 36 36 36 35

30-40 32 34 30 32

Aryl-C (%) 0-15 26 25 28 28

30-40 28 26 33 31

Carboxyl-C (%) 0-15 17 16 15 15

30-40 18 17 18 16

Alkyl-C/O-Alkyl-C (%) 0-15 0.60 0.65 0.58 0.61

30-40 0.68 0.69 0.66 0.68

97

4.4 Discussion

4.4.1 Soil structure in the cropland sites

Evidence for the hierarchical model of aggregates was not observed since we only found

evidence for the different stabilizing mechanisms for macroaggregates (>250 µm). The

amount of macroaggregates was significantly and positively correlated with active fungal

biomass and the content of Fed (r=0.68 and r=0.47, p<0.05, respectively), and MWD with

active fungal biomass (r=0.71, p<0.05). The correlation with Feo supports the strong

aggregating power of oxides and is supported by a study by Duiker et al. (2003), which

showed the more active role of Feo over Fed in aggregation. Very stable aggregates can be

formed when amorphous Fe3+

(Feo in this study) and SOM interact (Barral et al., 1998).

Oxides have high surface areas and can adsorb organic material on their surface by

electrostatic binding and thereby enhance aggregation (Six et al., 2004). The contents of

Ald and Feo were higher in Con76 and Con95 compared to Org 76 and Org95 (Table 4.1),

which was reflected in the slightly lower pH in these soils (Bronick and Lal, 2005).

According to Amézketa (1999), soil structure is improved in the presence of oxides due to

them acting as flocculants in solution, their ability to bind clay particles to OM, and their

ability to precipitate as gels on clay surfaces.

The positive correlation between active fungal biomass and macroaggregates and MWD

may be explained by organic inputs entering the soil and providing substrate for soil fungi

(Eash et al., 1994) at the 0-15 cm soil depth. Fungi exude polysaccharides that adhere to

minerals in the soil (Sacconi et al., 2012; Gazzé et al., 2013). This will physically aid the

association of soil particles into larger aggregates when fungal growth increases and

hyphae enmesh soil particles (Eash et al., 1994). The soils of this study had a high pH of

approximately 8 (Table 4.1), which was more favourable for the bacterial biomass

compared to fungal biomass. A study by Rousk et al. (2010) confirmed that the relative

abundance and diversity of bacteria were positively related to pH, which agrees with our

results on bacterial biomass. In addition, soils in this study were annually ploughed, which

further decreases the abundance of filamentous fungi. Con76 had higher alkyl-C (lipids)

contents (Table 4.4) compared to Org76, which can improve aggregation due to their

hydrophobic nature (Monreal et al., 1995; Dinel et al., 1997; Pare et al., 1999). This may

partly explain the slightly higher aggregation at Con76, however, the differences in amount

of macroaggregates and MWD were not significant.

Slightly higher macroaggregation (Figure 4.1) and MWD (Table 4.3) were observed in

Con76 and Con95 compared to those in Org76 and Org95. This trend may be explained by

the continuous mineral fertilizer application in Con76 and Con95. Fertilizer application

may have increased the yields and subsequently the return of OM (Ladha et al., 2011),

which may enhance aggregation because of the cementing action of OM (Haynes and

Naidu, 1998). This trend has been shown especially for phosphorous fertilizers, which can

enhance aggregation by the formation of Al or Ca phosphate binding agents (Haynes and

Naidu, 1998).

In our studied farms, Org76 used biowaste compost and Org95 horse manure as organic

inputs to the soil system. Compost application can enhance soil structure, but its influence

can be short-lived (Debosz et al., 2002), and therefore may not have increased aggregation

of the studied soils. In addition, dry climate may limit the positive effects of compost on

98

soil structure (de Leon-Gonzalez et al., 2000). The horse manure did not result in higher

amount of macroaggregates or MWD at Org95 compared to the Con95, supporting the

impact of the low precipitation and accompanying dryness of the soil (soil moisture

content was 14.8%). In general, soil aggregates in manured soils are weak when soil is dry

but strong under wet conditions (Munkholm et al., 2002). In contrast, soils without manure

inputs may have strong aggregates when dry (Munkholm et al., 2002). In some cases,

application of horse manure may have no significant impact on soil structure (Roldán et al.,

1996). In summary, our results are in accord with the conclusion of Abiven et al. (2009)

who did not observe any clear global trends regarding the effects of diverse organic inputs

on aggregate stability. Abiven and colleagues reported that manure and compost both affect

aggregate stability with a rather small magnitude but only after several months of

application.

The difference among sites (Obersiebenbrunn versus Lassee) in amount of

macroaggregates (Table 4.2) at the 0-15 cm soil depth may be a result of the differences in

the concentration of carbonates (Table 4.1). In general, aggregation decreases with increase

in concentration of carbonates (Dimoyiannis et al., 1998; Dimoyiannis, 2012), which is in

accord with results obtained in this study for 0-15 cm soil depth.

4.4.2 SOM in the cropland sites

There were no significant differences in OC and Nt concentrations among sites (Table 4.1),

which contradict recent studies (Leifeld and Fuhrer, 2010; Gattinger et al., 2012) but are in

accord with some earlier studies (Kirchmann et al., 2007; Leifeld et al., 2009; Spiegel et al.,

2010). This anomaly may be explained by the relatively low amounts of organic inputs,

biowaste compost and horse manure, at Org76 and Org95 (10 Mg ha-1

year-1

and 20 Mg ha-

1 every fifth year, respectively). Erhart and Hartl (2010) concluded that 6-7 Mg ha

-1 year

-1

of compost should be sufficient to maintain the SOM content in soils similar to the present

study (16 Mg ha-1

year-1

when aiming to maintain Norg levels (Hartl and Erhart, 2005)). In

addition, the soils at all sites were ploughed annually, which causes oxidation of OC (West

and Post, 2002), and limits OC accumulation. In the present study, potatoes and/or sugar

beet were included in the crop rotation at all sites, which may be an additional explanation

for similar OC and Nt contents. Harvesting of potatoes and sugar beets at all sites causes

severe additional disturbance to the annual ploughing and seedbed preparation and may

have hindered OC and Nt accumulation (Pulleman et al., 2003). Annual ploughing may

also explain the relatively small amounts of POM, and insignificant differences observed

among sites.

4.4.3 SOM distribution and chemical quality in the cropland sites

The slightly different SOM distributions among sites (Obersiebenbrunn versus Lassee) are

most likely caused by the differences in the soil texture and soil age. In the sites with

significantly higher sand content compared to the other sites, Org95 and Con95 compared

to Org76 and Con76, the 20-250 µm aggregate associated OC and Nt fractions were the

highest and less susceptible to mineralization. In contrast, in the sites with significantly

higher silt content and slightly higher clay content, Org76 and Con76, compared to Org95

and Con95, the <20 µm aggregate associated OC and Nt fractions had the greatest OM

storage capacity. The soil fractions that were observed in the highest proportion to the bulk

soil (<20 µm aggregates at Org76 and Con76, and 20-250 µm aggregates at Org95 and

Con95) contained the most OC and Nt. These results are in accord with those reported by

99

Poll et al. (2003). The distribution and dynamics of Nt content paralleled those of the OC

content. fPOM and oPOM associated OC and Nt were the smallest fractions in all soils,

reflecting the annual tillage activities that result in fast decomposition of easily available

OM. Further, the C/N ratio of soil fractions decreased from POM fractions to the

aggregates, indicating that the nitrogen rich organic materials were associated with mineral

particles and reflecting the plant-like character of fPOM and oPOM (Baldock et al., 1997;

Golchin et al., 1997). Since the C/N ratios for the different aggregate classes were fairly

similar (Figure 4.2E, 4.2F), there existed no clear aggregate hierarchy in the studied soils

(Six et al., 2004).

The higher proportion of alkyl-C of the total OC in fPOM in Con76 compared to Org76

(Table 4.4), most likely reflects the fertilization differences. The biowaste compost used as

a fertilizer consists of humic substances (Erhart and Hartl, 2010), and therefore, alkyl-C,

that represents lipids and hemicelluloses (Golchin et al., 1994), was observed in lower

proportion of the total OC in Org76 compared to Con76. The analyzed fractions showed an

increasing degree of decomposition in the order fPOM < oPOM < bulk soil, shown as

increased Alkyl-C to O-Alkyl-C ratio (Baldock et al., 1997). The data presented herein

indicate that the fPOM and oPOM consisted mainly of plant material at different stages of

decomposition, and were less decomposed compared to the SOM in the bulk soil (Table

4.4).

4.5 Conclusions

This study has demonstrated that macroaggregates in the range of 20-250 µm were the

most prominent soil aggregates in both topsoils and subsoils in the studied Chernozem

cropland soils in Austria. The data did not support the aggregate hierarchy model. The

content of macroaggregates was correlated with dithionite-extractable Fe (Fed) and the

fungal activity, and MWD with fungal activity. The soil fractions that were observed in the

highest proportion to the bulk soil (<20 µm aggregates at Org76 and Con76, and 20-250

µm aggregates at Org95 and Con95) contained the most OC and Nt. The distribution and

dynamics of Nt content paralleled those of the OC content. Additional research is needed

on cultivated Chernozems to obtain quantitative basis for evaluating whether it may be

beneficial to use biowaste compost and horse manure as organic inputs, in order to increase

SOM content and macroaggregation.

Acknowledgements We are grateful to E. Brauner, E. Kopecky, A. Hobel, K. Hackl, A. Hromatka, H.

Nascimento, G. Heranney, and F. Brocza for technical assistance and laboratory work. F.

Brocza is also acknowledged for valuable help and translations with farmer interviews.

Farmers are gratefully acknowledged for their cooperation and permission to take samples

from their properties. We are thankful for PI Dr. Adelheid Spiegel (AGES, Austria) and

Dr. Anu Mikkonen (University of Helsinki, Finland) for their valuable comments on the

manuscript. Louise Hamilton is acknowledged for English proofreading. The project was

financially supported from the European Commission FP7 Collaborative Project “Soil

Transformations in European Catchments” (SoilTrEC), Grant Agreement no. 244118. We

acknowledge the support T. Lehtinen received from the European Science Foundation

(ESF) for the activity entitled 'Natural molecular structures as drivers and tracers of

terrestrial C fluxes' to conduct NMR measurements at the Technical University of Munich.

100

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105

Supplementary Table 4.S1 Crop rotation, and fertilization from the studied sites. Crops

written in bold were the crop at the time of sampling.

Org76 Con 76 Org95 Con 95

Cropa Biowaste

compost Crop Fertilizer Cropb Horse

manure Crop Fertilizer

Ye

ar

t ha-1 (kg N

ha-1)c kg ha-1 Year

t ha-1 (kg N

ha-1)d kg ha-1

201

1 Potato

10 (115-

164) Potato

N 95, P 50,

K 130 2011 Winter

wheat - Winter

wheat

N 138, P

21, K 21

201

0

Soy

beans

10 (115-

164)

Sugar

beet

N 118, P

46, K 60 2010 Sugar beet

20 (200-

400) Corn

N 150, P

40, K 40

200

9

Soy

beans

10 (115-

164)

Winter

wheat N 120 2009

Spring

barley - Sugar beet N 126

200

8

Winter

wheat

10 (115-

164) Onion - 2008

Winter

wheat -

Winter

wheat

N 138, P

40, K 40

200

7 Potatoes

10 (115-

164)

Winter

wheat N 120 2007 Peas -

Sun

flowers N 60

200

6

Soy

beans

10 (115-

164) Potato - 2006

Spring

barley&pot

ato -

Winter

wheat

N 128, P

24, K 24

200

5 Corn

10 (115-

164)

Winter

wheat N 120 2005 Sugar beet

20 (200-

400) Corn N 137

200

4

Winter

wheat

10 (115-

164)

Sugar

beet - 2004

Winter

wheat -

Durum

wheat

N 129, P

19, K 19

200

3 Potatoes -

Winter

wheat N 120 2003 Clover mix - Sugar beet

N 133, P

30, K 30

200

2 Poppy - Potato - 2002 Clover mix -

Winter

wheat

N 130, P

25, K 25

200

1

Winter

wheat -

Winter

wheat N 120 2001 Corn - Corn - acrop rotation before 2001 was similar to the crop rotation presented in the table; Org76: potato 30 %, winter

wheat 30 %, soy beans 30 %; Con76: potato 30 %, sugar beet 20 %, winter wheat 45 %; Org95: winter wheat

30 %, sugar beet 30 %, clover mix 20 %, Con95: wheat 45 %, corn 30 %, sugar beet 20 %. Additional to the

annual ploughing and seedbed preparation for the main crop, a seedbed preparation was done in the

following years for the described catch crops: a) Org76: 2009 Vicia and Lathyrus mix; b) Con76: no catch

crops; c) Org95: 2007 Pisum arvense (harvested and sold outside the farm), Fagopyrum esculentum, and

Phacelia, 2008, Pisum arvense (harvested and sold outside the farm), Fagopyrum esculentum, and Phacelia

and 2009 Lathyrus, and Fagopyrum esculentum; d) Con95: 2002 Sinapsis alba, and Fagopyrum esculentum,

2004 Sinapsis alba, and Fagopyrum esculentum, 2006 Pisum arvense, and 2007 Sinapsis alba. bclover mixtures in the crop rotation of Org95 were not harvested but incorporated into the soil as green

manure. ctotal nitrogen, Erhart and Hartl, 2010.

dtotal nitrogen, Koenig et al., 2011.

107

5 Effect of crop residue incorporation on soil organic carbon (SOC) and

greenhouse gas (GHG) emissions in European agricultural soils

108

Lehtinen, T., Schlatter, N., Baumgarten, A.,

Bechini, L., Krüger, J., Grignani, C.,

Zavattaro, L., Costamagna, C., Spiegel, H. 2014. Effect of crop residue incorporation on

soil organic carbon (SOC) and greenhouse gas (GHG) emissions in European agricultural

soils. Soil Use and Management (in press). Manuscript included in the thesis with kind

permission of the journal. Referencing style according to the journal guidelines.

Abstract

Soil organic matter (SOM) improves soil physicochemical and biological properties, and

the sequestration of SOM may mitigate climate change. Soil organic carbon (SOC) often

decreases in intensive cropping systems. Incorporation of crop residues (CR) may be a

sustainable management practice to maintain the SOC levels and to increase soil fertility.

This study quantifies the effects of CR incorporation on SOC and greenhouse gas (GHG)

emissions (CO2 and N2O) in Europe using data from long-term experiments. Response

ratios (RRs) for SOC and GHG emissions were calculated between CR incorporation and

removal. The influences of environmental zones (ENZs), clay content and experiment

duration on the RRs were investigated. We also studied how RRs of SOC and crop yields

were correlated. A total of 475 RRs were derived from 39 publications. The SOC increased

by 7 % following CR incorporation. In contrast, in a subsample of cases, CO2 emissions

were six times and N2O emissions 12 times higher following CR incorporation. The ENZ

had no significant influence on RRs. For SOC concentration, soils with a clay content >35

% showed 8 % higher RRs compared to soils with clay contents between 18 and 35 %. As

the experiment progressed, RR for SOC concentration increased. For N2O emissions, RR

was significantly greater in experiments with a duration <5 years compared to 11-20 years.

No significant correlations were found between RR for SOC concentration and yields, but

differences between sites and study durations were detected. We suggest that a long

duration of crop residue incorporation is a win-win scenario under a continental climate.

We conclude that CR incorporation is important for maintaining SOC, but its influence on

GHG emissions should be taken into account as well.

Keywords: carbon dioxide (CO2), nitrous oxide (N2O), soil organic carbon, response ratio,

crop residue management, climate change.

109

5.1 Introduction

Soil organic matter improves soil physical (e.g. increased aggregate stability), chemical

(e.g. cation exchange capacity) and biological (e.g. biodiversity, earthworms) properties,

and it mitigates climate change by sequestering carbon in soils (Lal, 2013). Currently, as

much as 25-75 % of the SOC in the world’s agricultural soils may have been lost due to

intensive agricultural practices (Lal, 2013), and about 45 % of European soils exhibit low

organic matter contents (European Commission, 2006). The decline of OM is one of the

major threats to soils described by the European Commission (European Commission,

2006).

Globally, approximately four billion tons of crop residues are produced (Chen et al., 2013).

Removal of crop residues has a negative effect on SOC, but an estimated 25-50 % of crop

residues could be harvested without threatening soil functions (Blanco-Canqui, 2013).

Harvesting crop residues may be beneficial for farmers because residues can be used as

livestock bedding, sold or thermally utilized. Harvesting residues also fits reduced or no-

tillage farming operations because the soil will be less disturbed due to no ploughing of

crop residues into the soil. Incorporation of crop residues may be a sustainable and cost-

effective management practice to maintain the ecosystem services provided by soils, the

SOC levels and to increase soil fertility in European agricultural soils (Perucci et al., 1997;

Powlson et al., 2008). In particular, Mediterranean soils with small SOC concentrations

(Aguilera et al., 2013), and areas where stockless croplands predominate (Kismányoky and

Tóth, 2010; Spiegel et al., 2010b), could benefit from this management practice.

Nonetheless, crop residue incorporation increases the SOC concentrations less than does

farmyard manure (Cvetkov et al., 2010) or slurry (Triberti et al., 2008). For GHG

emissions, both positive and negative effects have been observed following crop residue

incorporation (e.g. Abalos et al., 2013). Emissions of CO2 indicate heterotrophic microbial

activity and particularly mineralization (Baggs et al., 2003), whereas N2O emissions

indicate both nitrification and denitrification processes (Chen et al., 2013).

The response of soil properties to management practices may depend on various factors

such as soil temperature and soil moisture content, soil clay content (Körschens, 2006;

Chen et al., 2013) or duration of the experiment (Smith et al., 2012; Chen et al., 2013).

Metzger et al. (2005) presented a stratification of environmental zones (ENZs) in Europe,

which is based on climate, geology and soils, geomorphology, vegetation and fauna. It can

be used to compare the response of soil to management practices across Europe (Jongman

et al., 2006). In their meta-analysis, Chen et al. (2013) showed that the clay content was a

good predictor for N2O emissions following crop residue incorporation. Especially in the

case of soil processes, the experiment duration improves the accuracy of data.

Accordingly, long-term experiments are very important when assessing the impact of a

management practice on soil (Körschens, 2006). Effects of crop residue incorporation on

SOC and GHG emissions have been studied across the world (Chen et al., 2013, Liu et al.,

2014), but the results differ due to the wide range of systems inherent in a global coverage.

The lack of studies focusing on both SOC and GHG emissions (Ingram and Ferdandes,

2001), calls for an analysis of European results. An analysis of long-term experiments

(LTEs) helps integrate current knowledge in Europe and provides guidance for policy

development.

110

This study was designed to quantify the effects of crop residue incorporation on SOC and

GHG emissions in varying environmental zones in Europe, using the published results of

LTEs. Specifically, we addressed the following questions:

i) Are environmental zones important for analysing the effects of crop residue

incorporation on SOC concentration, as well as on GHG emissions (CO2,

N2O)?

ii) Does the effect of crop residue incorporation on SOC and GHG emissions vary

with differences in clay content?

iii) Does the duration of the experiment influence the response ratios of SOC and

GHG emissions following crop residue incorporation?

iv) Does the RR of GHG emissions following residue incorporation vary with

experimental setup and crop residue type?

v) Are RRs for SOC concentrations and yields correlated?

We hypothesised that the response ratios of SOC increase the most in the Nemoral ENZ

due to cool temperatures, particularly in soils with a large clay content due to interactions

between SOC and clay minerals, and furthermore they increase with time. The response

ratios of GHG emissions were expected to be least in the Nemoral ENZ, and to decrease

with time. We expected the response ratios of GHG emissions to be larger in laboratory

than field experiments due to more favourable conditions for the microorganisms, such as

optimal soil water content. The RR of GHG emissions were expected to be greater with

incorporation of low-C/N-ratio crop residues (hereafter referred to as “vegetative material”

such as sugar beet, potato or leafy greens compared to high-C/N-ratio crop residues,

hereafter referred to as “cereal” such as barley, wheat or maize residue incorporation).

Further, we expected to observe a positive correlation between yields and SOC

concentrations, as higher yields would result in more residues and greater accumulation of

SOC.

5.2 Material and methods

5.2.1 Data sources

A detailed literature review was conducted concerning scientific publications that had

reported on long-term agricultural experiments in Europe. This yielded a total of 475

response ratios from 39 publications (Table 5.1), 50 experiments in 15 countries. An online

database was created, which included 46 field experiments and four laboratory experiments

that covered 10 European Environmental Zones (ENZs), as defined by Metzger et al.

(2005), and four aggregated ENZs (Figure 5.1, Table 5.2). Most of the data were published

in peer-reviewed scientific journals, while a smaller fraction were published in national

technical journals and conference proceedings. The publications report on measurements

of SOC concentration and CO2 and N2O emissions from pairwise comparisons of crop

residue incorporation and crop residue removal management practices. The minimum

requirements for data being included were that the studies had i) replicates and ii) paired

treatments that compared crop residue incorporation and removal. Further, we only

included experiments in which crop residue incorporation and removal were investigated

111

under the same climatic and soil conditions, as well as with similar fertilization levels. For

CO2 and N2O emissions, data from long-term experiments were scarce. For these variables,

shorter experiment durations and laboratory experiments were included in the database.

For this analysis, mostly publications reporting data in tables, which could be directly

transferred into the database, were used. Data given in figures were extracted using the

program WebPlotDigitizer (Rohatgi, 2013).

112

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silt

y l

oam

B

agg

s et

al.

, 2

00

3

14

Co

logn

e G

erm

any

50

°56

´N 0

6°5

7´E

A

TC

1

969

silt

M

arsc

hn

er e

t al

., 2

00

3

15

Gem

blo

ux

B

elgiu

m

50

°33

'N 0

4°4

1'E

A

TC

1

959

silt

y l

oam

P

ow

lso

n e

t al

., 2

011

16

Wie

rzch

uci

nek

P

ola

nd

53

°15

´N 1

7°4

7´E

C

ON

1

979

san

dy l

oam

Ja

no

wia

k,

19

95

17

Ro

sto

ck

Ger

man

y

54

°05

'N 1

2°0

8'E

C

ON

1

954

loam

L

ein

web

er &

Reu

ter,

199

2

18

Mü

nch

eber

g

Ger

man

y

52

°30

'N 1

4°0

8'E

C

ON

1

962

silt

y l

oam

R

ogas

ik e

t al

., 2

00

1

Table

5.1

Sum

mary

des

crip

tion o

f th

e si

tes

incl

ud

ed i

n t

he

ana

lysi

s.

113

19

Gro

ssb

eere

n 1

G

erm

any

52

°21

'N 1

3°1

8'E

C

ON

1

972

loam

y s

and

R

üh

lman

n &

Rup

pel

, 2

00

5;

Rüh

lman

n,

20

06

; M

LU

V,

20

09

20

Gro

ssb

eere

n 2

G

erm

any

52

°21

´N 1

3°1

8´E

C

ON

1

972

san

dy l

oam

R

üh

lman

n &

Rup

pel

, 2

00

5;

Rüh

lman

n,

20

06

; M

LU

V,

20

09

21

Gro

ssb

eere

n 3

G

erm

any

52

°21

´N 1

3°1

8´E

C

ON

1

972

silt

R

üh

lman

n &

Rup

pel

, 2

00

5;

Rüh

lman

n,

20

06

; M

LU

V,

20

09

22

Bra

un

sch

wei

g

Ger

man

y

52

°18

'N 1

0°2

7'E

C

ON

1

952

silt

y l

oam

R

ogas

ik e

t al

., 2

00

1

23

Sp

röd

a G

erm

any

51

°32

´N 1

2°2

5´E

C

ON

1

966

san

dy l

oam

A

lber

t &

Gru

ner

t, 2

013

24

Met

hau

G

erm

any

51

°04

´N 1

2°5

1´E

C

ON

1

966

silt

y l

oam

A

lber

t &

Gru

ner

t, 2

013

25

Pu

ch

Ger

man

y

48

°11

´N 1

1°1

3´E

C

ON

1

984

silt

y l

oam

H

ege

& O

ffen

ber

ger

, 2

006

26

Su

chdo

l C

zech

Rep

ub

lic

49

° 5

7'N

15

°09

'E

CO

N

19

97

loam

N

edved

et

al.,

20

08

27

Lu

kav

ec

Cze

ch R

epu

bli

c 4

9°3

3'N

14

°59

'E

CO

N

19

97

san

dy l

oam

N

edved

et

al.,

20

08

28

Alp

envo

rlan

d

Au

stri

a

48

°07

'N 1

5°0

8'E

C

ON

1

986

silt

y l

oam

S

pie

gel

et

al.,

20

10

a

29

Mar

chfe

ld

Au

stri

a

48

°13

'N 1

6°3

6'E

P

AN

1

982

san

dy l

oam

S

pie

gel

et

al.,

20

10

a

30

Vie

nn

a A

ust

ria

48

°11

'N 1

6°4

4'E

P

AN

1

986

loam

y s

and

S

pie

gel

et

al.,

20

10

b

31

Kes

zth

ely

Hu

ngar

y

46

°44

'N 1

7°1

3'E

P

AN

1

960

san

dy l

oam

K

ism

anyo

ky &

To

th,

20

13

32

Tru

tno

v

Cze

ch R

epu

bli

c 5

0°3

3'N

15

° 5

3'E

A

LS

1

966

san

dy l

oam

S

imo

n e

t al

., 2

01

3

33

Rak

ican

S

loven

ia

46

°38

'N 1

6°1

1'E

A

LS

1

993

loam

y s

and

Cvet

ko

v &

Taj

nse

k 2

00

9;

Cvet

ko

v e

t al

., 2

01

0;

Taj

nse

k e

t al

.,

20

13

34

Jab

le

Slo

ven

ia

46

°08

'N 1

4°3

4'E

A

LS

1

993

silt

y l

oam

C

vet

ko

v &

Taj

nse

k 2

00

9

35

Gri

gn

on

F

ran

ce

45

°39

'N 0

6°2

2'E

A

LS

1

963

loam

P

ow

lso

n e

t al

., 2

011

36

Do

azit

F

ran

ce

43

°41

'N 0

0°3

8'W

L

US

1

967

loam

y s

and

P

lén

et e

t al

., 1

99

3

37

Ser

resl

ou

s F

ran

ce

43

°40

´N 0

0°4

0´W

L

US

1

967

silt

y l

oam

P

lén

et e

t al

., 1

99

3;

Lu

bet

et

al., 1

99

3

38

Tet

to F

rati

It

aly

44

°53

'N 0

7°4

1'E

M

DM

1

992

loam

G

rign

ani

et a

l.,

200

7;

Ber

tora

et

al.,

20

09

; Z

avat

taro

et

al., 2

012

39

Pad

ova

Ital

y

45

°21

'N 1

1°5

8'E

M

DN

1

966

clay

lo

am

Lu

gat

o e

t al

., 2

006

114

a En

vir

on

men

tal

zon

e as

sign

ed a

cco

rdin

g t

o M

etzg

er e

t al

. (2

005

): N

EM

, N

emo

ral;

AT

N,

Atl

anti

c N

ort

h;

AT

C,

Atl

anti

c C

entr

al;

CO

N,

Co

nti

nen

tal;

PA

N,

Pan

no

nia

n;

AL

S,

Alp

ine

So

uth

;

LU

S,

Lu

sita

nia

n;

MD

M, M

edit

erra

nea

n M

ou

nta

ins;

MD

N, M

edit

erra

nea

n N

ort

h;

MD

S, M

edit

erra

nea

n S

ou

th.

40

Pap

ian

o

Ital

y

42

°57

´N 1

2°2

0´E

M

DN

1

971

loam

B

ian

chi

et a

l., 19

94

; P

eru

cci

et a

l.,

199

7

41

Fo

ggia

1

Ital

y

41

°27

´N 1

5°3

2´E

M

DN

1

977

clay

Mai

ora

na,

199

8;

Mai

ora

na

et a

l. 2

00

4

42

Fo

ggia

2

Ital

y

41

°27

´N 1

5°3

2´E

M

DN

1

990

clay

M

aio

ran

a, 1

99

8;

Mai

ora

na

et a

l. 2

00

4

43

Alm

acel

les

1

Sp

ain

41

°43

´N 0

0°2

6´E

M

DS

2

010

clay

lo

am

Bia

u e

t al

., 2

01

3

44

Alm

acel

les

2

Sp

ain

41

°43

´N 0

0°2

6´E

M

DS

2

010

loam

B

iau

et

al.,

201

3

45

El

En

cín

Sp

ain

40

°32

'N 0

3°1

7'W

M

DS

2

010

clay

lo

am

Mei

jid

e et

al.

, 20

10

; A

bal

os

et a

l., 2

01

3

46

La

Ch

imen

ea

Sp

ain

40

°03

'N 0

3°3

1'W

M

DS

2

009

silt

y c

lay

loam

S

anz-

Co

ben

a et

al.

, 2

01

4

La

bo

rato

ry

stu

die

s

47

Fle

vo

po

lder

Th

e

Net

her

lan

ds

52

°30

'N 0

5°2

8'E

A

TC

1

999

clay

V

elth

of

et a

l.,

20

02

48

Wag

enin

gen

Th

e

Net

her

lan

ds

51

°58

'N 0

5°3

9'E

A

TC

1

999

san

d

Vel

tho

f et

al.

, 20

02

49

Wij

nan

dsr

ade

Th

e

Net

her

lan

ds

50

°54

´N 0

5°5

2´E

A

TC

N

/A

silt

y l

oam

C

ayu

ela

et a

l.,

201

3

50

Wye

Est

ate

UK

5

1°1

0´N

00

°56

´E

AT

C

19

99

silt

y l

oam

G

arci

a-R

uiz

& B

aggs,

20

07

115

Figure 5.1 Map of the experiment locations and their distribution across the aggregated

environmental zones (Nemoral, Atlantic, Continental, Mediterranean).

116

Table

5.2

Aggre

gate

d v

ari

able

s and s

pec

ific

lev

els

of

each

va

ria

ble

.

Var

iable

S

pec

ific

lev

els

EN

Za

Nem

ora

l (N

EM

) A

tlan

tic

(AT

N, A

TC

, L

US

) C

onti

nen

tal

(CO

N,

PA

N,

AL

S)

Med

iter

ran

ean

(M

DM

, M

DN

, M

DS

)

Cla

y %

<

18 %

18

-35 %

>

35

%

E

xper

imen

t d

ura

tion

b

<5 y

ears

5-1

0 y

ears

1

1-2

0 y

ears

>

20 y

ears

a En

vir

on

men

tal

zon

e as

sign

ed a

cco

rdin

g t

o M

etzg

er e

t al

. (2

005

): N

EM

, N

emo

ral;

AT

N,

Atl

anti

c N

ort

h;

AT

C,

Atl

anti

c C

entr

al;

CO

N,

Co

nti

nen

tal;

PA

N,

Pan

no

nia

n;

AL

S,

Alp

ine

So

uth

;

LU

S,

Lu

sita

nia

n;

MD

M, M

edit

erra

nea

n M

ou

nta

ins;

MD

N, M

edit

erra

nea

n N

ort

h;

MD

S, M

edit

erra

nea

n S

ou

th.

b E

xp

erim

ent

du

rati

on

: yea

rs b

etw

een

th

e b

egin

nin

g o

f th

e ex

per

imen

t an

d t

he

mea

sure

men

t.

117

5.2.2 Data preparation

For each pairwise comparison, a response ratio (RR) was calculated as:

RR = propertyI/propertyR

where propertyI is the SOC concentration, CO2 emission, or N2O emission in crop residue

incorporation management practice, and propertyR is the SOC concentration, CO2

emission, or N2O emission in crop residue removal management practice. RR >1 was

assumed to be an improvement in SOC concentrations, whereas RR >1 for CO2 and N2O

emissions was assumed to be an undesirable increase in GHG emissions.

5.2.3 Data aggregation

In some cases it was possible to derive more than one comparison from an experiment, e.g.

when they report on multiple years or multiple contrasting managements. For stepwise

linear multiple regressions and one-way analyses of variance (ANOVA), we used a single

average of the response ratios for each experiment to aggregate multiple within-experiment

response ratios prior to a between-study analysis (Lajeunesse, 2011). These averages were

weighted based on the number of response ratios (sample size) from the experiments,

because in many publications the standard deviation (SD) and number of samples (n) were

missing.

5.2.4 Data analysis

The statistical analyses were performed using the IBM SPSS Statistics 20 software

package for Mac. The normality of data was checked with Shapiro-Wilk´s test. All data on

SOC concentration and GHG emissions (CO2 and N2O) were not normally distributed, thus

log-transformed before the statistical analyses to obtain homogeneity of variances. A

stepwise linear multiple regression was used to identify the significant continuous

variables (temperature, precipitation, clay content, duration of the experiment were tested)

on RR of SOC concentration and GHG emissions (Table 3). To strengthen our analyses,

the effect of the variables ENZ, clay content, and experiment duration (as aggregated into

specific levels in Table 2) were investigated with ANOVA with Tukey´s significance test

(p<0.05) as a Post Hoc test. Correlations between variables were presented in Pearson

correlation coefficients.

5.3 Results

Crop residue incorporation increased the SOC concentration on average by 7% (Figure

5.2), whereas CO2 emissions were increased almost six fold and N2O emissions more than

twelve fold on average (n = 84 and 97, respectively). Multiple regressions revealed that

experiment duration had highest effect on SOC concentration, explaining 14% of the

variation (Table 5.3). Response ratio (RR) of SOC concentration was 12% greater in

experiments with > 20 years duration, compared to experiments with duration < 5 years.

98% of the variation in RR of CO2 emissions was explained by clay content alone, whereas

approximately 75% of the variation in RR of N2O emissions was explained by clay content

and temperature (Table 5.3).

118

Figure 5.2 Response ratios (RRs) of SOC concentrations across A environmental zones

(ENZs), B) clay contents (%), and C) experiment durations (years). The left vertical line of

the box represents the first quartile, median is shown as a thick line, and the right vertical

line represents the third quartile. Horizontal bars show the minimum and maximum values.

The (°) and (*) denote outliers. The figure is based on the original data on response ratios,

without any weighting procedure. The numbers of RR (and experiments) are presented for

each category along the y-axis. Different letters indicate significant differences according

to Tukey´s as a Post Hoc test (p<0.05).

A

B

C

ENZ

Clay (%)

Duration

220 (41)

11 (3)a

32 (12)a

124 (20)a

53 (6)a

220 (41)

54 (23)ab

148 (14)a

18 (4)b

220 (41)

27 (3)a

11 (5)ab

108 (11)b

74 (22)c

number of RR (experiments)

119

Table 5.3 Significant results of multiple regressions.

LOG RR of SOC concentration

R2 F P n

Model 0.140 34.385 <0.0001 213

Variables Coefficient SEa

95% CIb

T P

Intercept 0.008 0.004 0.001-0.016 2.125 0.035

Duration 0.001 0.0002 0.0006-0.0012 5.864 <0.0001

LOG RR of CO2 emissions

R2 F P n

Model 0.983 1297.063 <0.0001 41

Variables Coefficient SE 95% CI T P

Intercept 0.494 0.012 0.469-0.159 40.608 <0.0001

Clay content -0.018 0.001 -0.019-(-)0.017 -36.015 <0.0001

LOG RR of N2O emissions

R2 F P n

Model 0.752 44.845 <0.0001 37

Variables Coefficient SE 95% CI t P

Intercept 0.5587 0.265 0.048-1.126 2.212 0.034

Clay content 0.098 0.017 0.068-0.133 5.721 <0.0001

Temperature -0.185 0.052 -0.289-(-)0.080 -3.579 0.001

aSE, standard error

bCI, confidence interval

120

5.3.1 Effect of environmental zone

The effect of the aggregated environmental zone (ENZ) on the response ratio of SOC

concentration was not significant (Figure 5.2A). For GHG emissions, data were retrieved

only for Atlantic and Mediterranean ENZs (Table 5.4). The RR for CO2 for the Atlantic

Zone was significantly larger than for the Mediterranean. For N2O emissions, RR was

greater for the Atlantic Zone compared to Mediterranean, although not significantly

probably due to the considerable variability normally associated with this measurement.

5.3.2 Effect of clay content

Among different clay contents, a content >35 % was found to be associated with

significantly greater response ratios for SOC concentration compared to contents between

18 and 35 % (Figure 5.2B). Data for GHG emissions were retrieved only for the clay

contents <18% and 18-35 % (Table 4). The RR for CO2 for <18 % clay content was seven

fold larger compared to that for the 18-35 % clay content. For N2O, the effect of clay was

similar that on CO2, being twice as much in soils with clay contents <18 % compared to

18-35 %. This difference, however, was not significant.

5.3.3 Effect of experiment duration

As the duration of the experiment increased, RR for SOC concentration also became larger

(Figure 5.2C). The RR was statistically greater for experiments lasting >20 years compared

to the other duration periods. For CO2 (Table 5.4), no distinction between duration groups

could be detected because all the RRs were in the <5 years group. For N2O, RR was

significantly larger in experiments lasting <5 years compared to those of 11-20 years

duration. Note, however, that there was only one experiment in the 11-20 years duration

group.

5.3.4 Effect of experiment and crop residue type on RR for GHG

emissions

We observed greater response ratios for CO2 and N2O emissions in laboratory experiments

compared with field experiments (Table 5.4), except for N2O emissions when cereal crop

residues were incorporated. The RR was greater in vegetative material crop residue

incorporation experiments compared with cereal crop residue incorporation experiments

(Table 5.4). In field experiments for N2O emissions, however, the effect was the opposite.

5.3.5 Correlation between SOC concentration and crop yields

The mean RR for yield was 1.06 ± 0.15 (n=71). This means that crop residue incorporation

resulted in an average 6 % yield increase compared to crop residue removal. We expected

to observe an increase in SOC together with an increase in yield due to a positive feedback

between crop residue incorporation, nutrient availability, crop nutrient uptake rate, and

finally crop growth rate. From another perspective, larger crop yields result in more crop

residue production, followed by greater SOC when these crop residues are incorporated.

Unexpectedly, however, no significant correlation (r=0.02, p>0.05) was found between the

RR of SOC concentration and the RR of yield. Differences between the studied sites

(Figure 5.3A), ENZs (Figure 5.3B), and experiment durations were found (Figure 5.3D).

No differences were detected between different clay content groups (Figure 5.3C). No

121

effect of crop type was recorded, but yield data were available only for the crops wheat,

barley and maize. The sites Kesthely, Grossbeeren 2, and Ultuna had the largest RRs in

both SOC concentration and yield, whereas Almacelles 1 and 2 were among the sites with

smallest RRs. As the experiment duration increased, the RRs for yields increased with the

exception of Foggia 1 and Foggia 2, where RRs for yields were less than unity, even when

the experiment had lasted for more than twenty years.

122

Table

5.4

Mea

n r

esponse

rati

os

of

GH

G e

mis

sio

ns

in c

rop

res

idue

inco

rpora

tio

n m

an

ag

emen

t p

ract

ices

com

pa

red t

o c

rop

res

idu

e re

mova

l

managem

ent

pra

ctic

es i

n d

iffe

rent

aggre

gate

d e

nvi

ron

men

tal

zones

(E

NZ

s),

cla

y co

nte

nts

(%

), a

nd

exp

erim

ent

du

rati

ons

(yea

rs).

Th

e va

lues

have

bee

n c

alc

ula

ted f

rom

ave

rage

data

fro

m e

ach

exp

erim

ent

an

d w

ere

wei

gh

ted

base

d o

n t

he

am

oun

t o

f re

spon

se r

ati

os

calc

ula

ted i

nto

th

e

ave

rage.

Dif

fere

nt

lett

ers

indic

ate

sig

nif

icant

dif

fere

nce

s acc

ord

ing

to T

uke

y´s

as

a P

ost

Ho

c te

st (

p<

0.0

5).

Cer

eal

Veg

etat

ive

mat

eria

l

CO

2

CO

2

Mea

n

SD

a n e

xp

b

n R

Rc

Mea

n

SD

n

ex

p

n R

R

O

ver

all

Fie

ld

1.0

a 0.0

8

3

17

1.7

a 0

.50

2

7

Lab

ora

tory

2.4

b

0.4

6

3

15

9.2

b

3.9

3

5

0

EN

Z

Atl

anti

c F

ield

1.0

0.0

0

1

4

2.1

0

.00

1

4

Lab

ora

tory

2.4

0.4

6

3

15

9.2

3

.9

3

50

Med

iter

ran

ean

F

ield

1.0

0.0

9

2

13

1.1

0

.00

1

3

Lab

ora

tory

N

/A

N/A

N

/A

N/A

N/A

N

/A

N/A

N

/A

C

lay

%

<18 %

F

ield

1.0

0.0

0

1

4

2.1

0

.00

1

4

Lab

ora

tory

2.4

0.4

6

3

15

9.2

3

.9

3

50

18-3

5 %

F

ield

1.0

0.0

9

2

13

1.1

0

.00

1

3

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124

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125

5.4 Discussion

The results of this analysis demonstrate an increase in RR of SOC concentration following

crop residue incorporation (Figure 5.2). The same has been demonstrated in previous meta-

analyses for organic inputs (Lemke et al., 2010; Powlson et al., 2012), e.g. in organic

farming (Gattinger et al., 2012; Aguilera et al., 2013). Incorporation of crop residues is one

of the few methods applied by farmers to maintain SOC and to sustain soil functions

(Powlson et al., 2008). This makes it a very important management tool. Even a small

increase in SOC can improve soil physicochemical and biological properties and

ecosystem services such as nutrient cycling and possible increases in yields (Loveland and

Webb, 2003; Bhogal et al., 2009; Blanco-Canqui, 2013). A critical level of 2% SOC was

thoroughly investigated by Loveland and Webb (2003), however, the authors concluded

that a single value cannot be recommended with the evidence available but local conditions

and relationships must be taken into account when desirable ranges for SOC are

recommended.

The overall data for CO2 and N2O emissions were collected from both field and laboratory

experiments as well as from experiments that incorporated cereals and vegetative

materials. Thus, the standard deviation was high for these indicators, possibly due to

spatial heterogeneity driven by variability in soil characteristics. With crop residue

incorporation, CO2 emissions will increase compared to crop residue removal due to more

easily available C that enhances microbial activity (Meijide et al., 2010). In contrast, if

crop residues are removed, they will be decomposed elsewhere, used as bedding and

incorporated into farmyard manure or burned, releasing approximately the same amount of

CO2 (Blanco-Canqui, 2013). Thus, crop residue incorporation is not primarily a way to

decrease CO2 emissions and may not be beneficial for all soil ecosystem services such as

carbon sequestration. To close the knowledge gap and to give better-informed

recommendations to farmers, further field-scale research focusing on in situ carbon balance

is required.

In the case of N2O, emissions from crop residue incorporation are up to twelve times

greater compared to crop residue removal. Emissions of N2O occur both during the

nitrification process and as a result of anaerobic denitrification. The latter process requires

the presence of microbes capable of using nitrates. The increase of the RR for N2O

following crop residue incorporation in a study by Baggs et al. (2003) was explained by

mineral N fertilization and an increased denitrification capacity stimulated by the added

substrate. In our analysis, no distinct relationships were found with mineral N fertilisation

(r=0.08, p>0.05), most likely owing to the limited number of data. The soil respiration

process may create anaerobic microsites in the soil and thereby increase N2O emissions

through denitrification (Garcia-Ruiz and Baggs, 2007; Abalos et al., 2013). Nonetheless,

the N2O emissions caused by the crop residues should be put in relation to the fact that not

all removed crop residues are decomposed or burned with no N2O emissions. Given that

the global warming potential of N2O is 298 on a 100 year time scale, it is of importance to

monitor these emissions in future studies and carry out analyses of gross global warming

potential of crop residue incorporation versus removal, as has already been done in paddy

soils (e.g. Shen et al., 2014).

126

5.4.1 Effect of environmental zone

The aggregated ENZ proved not to be a determining factor when RRs for SOC

concentration, CO2 and N2O emissions were studied (Figure 5.2, Table 5.4). This is in

contrast with concepts in which climate is directly and indirectly linked with carbon

concentrations in soils (e.g. Ingram & Fernandes, 2001). One explanation may be that the

aggregated ENZs in our study were too broad categories to capture the differences between

different climates. ENZ are assigned based on several factors beyond climate, such as

geomorphology, vegetation and fauna (Metzger et al., 2005). Given the large heterogeneity

in these environmental factors across the experimental sites in this study, probably more

data would have been required to detect significant differences between ENZs. In previous

studies, temperature has been found to be one of the driving factors for both N2O (Mutegi

et al., 2010) and CO2 emissions (Meijide et al., 2010). This was also supported by our

multiple regressions, in the case of N2O (Table 5.3).

5.4.2 Effect of clay content

Our results indicated larger RRs for SOC concentration for greater clay content (Figure

5.2B), probably because the clay fraction physically protects organic matter molecules

from mineralization (Lal, 1997). SOM may be physically protected in the clay fraction of

fine-textured soils by chemical bonds due to high surface activity (Six et al., 2000), thereby

being inaccessible for microbial degradation (von Lützow et al., 2006). Nonetheless, the

low clay content (<18 %) soils also showed a positive SOC response to management

changes (Cvetkov and Tajnsek, 2009). This may be explained by SOC being accumulated

as POM in the sand fraction of these soils, and not additionally in the clay fraction, as has

been shown in tropical soils (Feller and Beare, 1997; Chivenge et al., 2007). Furthermore,

the initial SOC concentration of the soil may play a role in how much C is retained in the

fine fraction (Poirier et al., 2013). The authors showed that soils with a small SOC

concentration have a greater capacity to accumulate C in the fine fraction when large

amounts of crop residues are added to the soil.

For GHG emissions the number of experiments and RRs was too small to allow a

representative analysis of differences between clay content groups. Velthof et al. (2002)

compared sandy and clay soils under laboratory conditions and found the N2O emissions to

be much less in the latter than in the former. This is contrary to our analysis of field data on

cereal crop residue incorporation (Table 5.4), but more measurements would be necessary

before generalisations could be made. Indications of smaller RRs for N2O emission in soils

with a small content of clay are in accordance with a recent meta-analysis that confirmed

the influence of texture on N2O emissions (Chen et al., 2013). Soil texture may influence

the response to crop residue incorporation through O2 availability in soil microsites and its

influence on denitrification (Chen et al., 2013).

5.4.3 Effect of experiment duration

The observed larger response ratios for SOC concentration in experiments of longer

duration (Figure 5.2C) agree with previous studies (Körschens et al., 1998). For soils with

clay contents <18 %, there was a positive SOC response to changes in management ten

years after its imposition (Cvetkov and Tajnsek, 2009) but it may be that SOC saturation in

soils with a small clay content is reached faster than in those with a large content (>35 %).

As experiment duration increases, more interactions between clay minerals and SOC may

127

take place (von Lützow et al., 2006); this is accompanied by a more marked accumulation

of resistant crop residue C that is not mineralised (De Neve and Hofman, 2000), especially

in soils without mechanical tillage (Six et al., 2000). Hence, the increase in SOC

concentration has its limits and the accumulation rate becomes smaller when the soil

system is close to a new equilibrium (Powlson et al., 2008).

For GHG emissions, the influence of the experiment duration was the opposite (Table 5.4),

supporting a study by Chen et al. (2013). Those authors analysed experiment durations

above and below 70 days and showed that the RR is initially higher, but as the duration

increases, the RR of GHG emissions is also lower. Peak microbial activity when easily

available organic inputs (crop residues) are added into the soil (Recous et al., 1995) may

explain this response (Powlson et al., 2011).

5.4.4 Effect of experiment and crop residue type on RR for GHG emissions

The greater response ratios of N2O emissions from incorporated vegetative material in

laboratory experiments compared to those from field experiments (Table 5.4) are

consistent with a meta-analysis that studied N2O emissions following crop residue

incorporation (Chen et al., 2013). Those authors explained the difference by the smaller

size and subsequent increase of surface area of the crop residues in the laboratory

experiments compared to field-scale applications. This applies to laboratory experiments in

our analysis (Velthof et al., 2002; Garcia-Ruiz & Baggs, 2007; Cayuela et al., 2013),

compared to the field experiments (Baggs et al., 2003; Mutegi et al., 2010; Abalos et al.,

2013; Sanz-Cobena et al., 2014). Moreover, under laboratory conditions moisture and

temperature are stable and optimised for microbial activity, thus promoting higher

emissions compared to field experiments (Chen et al., 2013).

Previous studies show that N2O emissions decrease at a higher C/N ratio of the residues

(Alexander, 1977; Shan and Yan, 2013). This is in line with the observed higher RR of

GHG emissions (Table 5.4) in vegetative material crop residue incorporation experiments

compared to cereal crop residue incorporation experiments in our study. This may be

explained by immobilisation of N with increasing C/N ratio of the crop residues (Abalos et

al., 2013). The oxidation rate is greater immediately after the incorporation of vegetative

material (compared with cereal residues) due to quick decomposition, thus possibly

promoting larger denitrification rates (Nicolardot et al., 2001; Rizhiya et al., 2011). Greater

GHG emissions from low-C/N-ratio crop residue incorporation were observed in

individual studies under field conditions in our analysis (e.g. Baggs et al., 2000; 2003).

This can be explained by the availability of N being greater, first for nitrification and then

for denitrification, when the C/N ratio of incorporated crop residue is small (Baggs et al.,

2003). Garcia-Ruiz and Baggs (2007), however, stated that more knowledge on the

interactions between organic and inorganic N sources and compounds released from the

crop residues is required before drawing conclusions on how to reduce GHG emissions

following crop residue incorporation.

One additional explanation for the RR of GHG emissions may be the cultivation technique,

which affects the nutrient supply to microorganisms and the aeration (Baggs et al., 2003;

Mutegi et al., 2010). However, soil tillage was not in the scope of this study. Another

potential factor is N fertiliser application, which increased GHG emissions in several

studies (e.g. Garcia-Ruiz and Baggs, 2007; Meijide et al., 2010; Sanz-Cobena et al., 2014).

128

Nevertheless, our analysis did not reveal any significant correlations between N2O

emissions and addition of mineral N fertiliser. This may be due to limited data accessibility

and differences in the set-up of the experiments we investigated. The variation observed

between ENZs, clay content groups and experiment durations within experiment types and

crop residue types most likely reflected differences between experiments and not between

the categories. More data from long-term field experiments are required to enable a study

of such relationships.

5.4.5 Correlations between crop yields and SOC concentrations

The slight positive influence of crop residue incorporation on crop yield (Figure 5.3A)

contradicts previous studies reporting yield decreases (Swan et al. 1994; Nicholson et al.,

1997), but agrees with Wilhelm et al. (2004). The positive influence of crop residue

incorporation may be explained by the increase in SOC and the experiment duration

(Figure 5.3A, D). Crop residues act as a continuous source of soil nutrients and soil organic

matter (Liu et al., 2014), which improves soil functioning (Bhogal et al., 2009) and thereby

yields. Thus, a positive feedback, initiated by incorporation of crop residues, occurs. In the

case of the Foggia experiment (Figure 5.3A), the incorporation of crop residues lowered

yield because of the poor mineralisation and strong N immobilisation due to arid climate

and the low soil N status (Maiorana, 1998). Mineral N fertilization did not increase yields

at Almacelles even though SOC concentrations were sufficient, possibly due to the short

duration of the experiment and the arid climate (Biau et al., 2013).

5.4.6 Possible improvements of the data set for future analyses

Long-term experiments with data on SOC concentrations and GHG emissions from the

same experiments are lacking in our dataset. To reach sustainable agricultural management

with a positive soil carbon budget, both SOC and GHG emissions should be taken into

account (Ingram & Ferdandes, 2001; Lal, 2013). This calls for long-term field experiments

to study these interactions and possible trade-offs between management practices

(Körschens, 2006). The present study was based on measurements from the topsoil (<30

cm), in the future it would be important to investigate SOC concentrations also in the

deeper soil layers (Aguilera et al., 2013; Lal, 2013).

5.5 Conclusions

This analysis indicates that the impacts of crop residue incorporation on SOC

concentration are positive, but the CO2 and N2O emissions are increased. Even a small

decrease in SOC may have detrimental effects on other soil properties such as aggregate

stability. Thus, maintaining or even increasing SOC levels is crucial for agricultural soils.

We show that long-term crop residue incorporation may increase crop yields. A win-win

scenario between yield and SOC is for crop residue incorporation over a longer term (>20

years) under a continental climate. Data availability from field experiments on GHG

emissions is still scarce, and the data do not allow for selection of win-win scenarios for

these parameters. Thus, more long-term field studies are needed to better assess the CO2

and N2O emissions following crop residue incorporation, specifically from the same

studies in which SOC is measured. We conclude that crop residue incorporation is an

important management practice to maintain SOC concentrations and to sustain soil

functioning, but that its influence on GHG emissions should be considered. GHG

129

emissions should be measured in on-going long-term field experiments to more accurately

calculate trade-offs such as in situ SOC and GHG balances following crop residue

management in agricultural systems.

Acknowledgements

We thank the authors of the 50 experiments whose extensive field and laboratory work

enabled us to conduct our analysis. This study was part of the European CATCH-C project

that is funded within the 7th Framework Programme for Research, Technological

Development and Demonstration, Theme 2 – Biotechnologies, Agriculture & Food (Grant

Agreement N° 289782). Taru Lehtinen is thankful for the FEMtech programme grant from

the Federal Ministry for Transport, Innovation and Technology (BMVIT), Austria, to carry

out this study. Jo Reilly and Michael Stachowitsch are acknowledged for English

proofreading.

130

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Appendix I

Author contributions to the papers

Chapter 2. I planned the laboratory study with Georg J. Lair, conducted the ultrasonic

experiments under the supervision of Axel Mentler and measured STA under the

supervison of Franz Ottner. I analysed all the data and wrote the manuscript. All co-

authors, Prof. Bruce James and three anonymous reviewers provided valuable comments

and suggestions on the manuscript.

Chapter 3. I planned the study with Guðrún Gisladóttir, Kristín Vala Ragnarsdóttir and

Georg J. Lair. I was responsible for the field sampling, prepared the samples for

measurements, conducted the density fraction, and the NMR measurements. Background

information in form of in depth interviews with the farmers were conducted with Guðrún

Gísladóttir. I analysed all the data and wrote the manuscript. All co-authors provided

comments on the manuscript.

Chapter 4. I planned the study with Georg J. Lair and Guðrún Gisladóttir. I did the

sampling, prepared samples to for measurements, conducted the density fraction, NMR

measurements, analysed the data and wrote the manuscript. I collected background

information on the farms studied together with Flora Brozca. All authors provided

comments on the manuscript.

Chapter 5. I collected data from 50 long-term field experiments in Europe from the

literature, which was done together with the other authors and other FP7 Catch-C project

partners as a group effort to collect the data into an online database. The idea and

framework of the manuscript was created with Heide Spiegel, and also discussed in depth

with Luca Bechini, Janine Krüger, and Norman Schlatter during the writing process. I was

responsible for collecting and analysing the data, as well as to write the manuscript. All

authors provided comments on the manuscript.

139

Appendix II

Publications

5.5.1 Scientific publications outside of the PhD thesis

Clymans, W., Lehtinen, T., Gísladóttir, G., Lair, G.J., Barão, L., Ragnarsdóttir, K.V.,

Struyf, E., Conley, D.J. 2014. Si precipitation during weathering in different Icelandic

Andosols. Procedia Earth and Planetary Science, 10, 260-265.

Lehtinen, T., Mikkonen, A., Sigfusson, B., Ólafsdóttir, K.., Ragnarsdóttir, K.V.,

Guicharnaud, R. 2014. Bioremediation trial on aged PCB polluted soils – A bench study in

Iceland. Environmental Science and Pollution Research 21(3), 1759-1768.

Keuskamp1, J.A., Dingemans, B.J.J., Lehtinen, T., Sarneel, J.M., Hefting, M.M. 2013.

Tea Bag Index: a novel approach to collect uniform decomposition data across ecosystems.

Methods in Ecology and Evolution 4(11), 1070-1075.

Wallenius, K., Lappi, K., Mikkonen, A., Wickström, A., Vaalama, A., Lehtinen, T.,

Suominen, L. 2012. Simplified MPN method for enumeration of soil naphthalene

degraders using gaseous substrate. Biodegradation 23, 47-55.

5.5.2 Popular science publications outside of the thesis

Dinkemans, B., Keuskamp, J., Sarneel, J., Lehtinen, T., Hefting, M. 2012. De Tea Bag

Index. Bodem - Tijdschrift over duurzaam bodembeheer, 6, 8-9.