1. business and industrial
2. agriculture and forestry
3. crops and seed

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 Mgjóna. 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.
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
xi
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
xii
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
xiii
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
xiv
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
xv
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
xvi
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
xvii
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
xviii
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
xix
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
xxi
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
xxii
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,
xxiii
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
xxiv
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,
xxv
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
xxvi
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
1
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).
Clay particles Domains + silt particles Microaggregates
<2 µm
2-20 µm
20-250 µm
Macroaggregates
>250 µm
ë
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).
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.
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,
3
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 plantregulated 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.
4
The fate and dynamics of SOM are mainly governed by its properties, the substrate
availability, biological conditions and environmental conditions (von Lützow and KögelKnabler, 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
5
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
6
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 costefficient 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
7
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).
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.
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
9
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 metaanalyses 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
10
classifications are being used in this introduction. Andisol refers to Chapter 2 and
Andosol to Chapter 3.
Figure 1.5 The selected study sites. Map composed by Janine Krüger (Leibniz-Institute of
Vegetable and Ornamental Crops, Grossbeeren, Germany).
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).
11
-
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).
12
HaAorg
HaAcon
HiAorg
HiAcon
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).
13
Figure 1.8 Map of the selected farms in Austria, at Obersiebenbrunn and Lassee. Map
composed by Helene Pfalz-Schwingenschlögl (BOKU).
14
Org76
Con76
Org95
Con95
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).
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.
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.
16
2, (3), 4
3
3
3, 4
Mehra and Jackson 1960
Parfitt 1990
Parfitt and Childs 1988
ÖNORM L1087
Brandstetter et al., 1996
Vance et al., 1987
Biological methods
Water extractable organic C (WEOC)c
Microbial biomass Ca
2, 3, 4
3, 4
2, 3, 4
2, 3, 4
2, 4
2, 3, 4
2, (3), 4
3, 4
3, 4
2
2
2
2, 3, 4
Used in
Chapter
Soil Survey Staff, 2004
Tabatabai and Bremner, 1991
Soil Survey Staff, 2004
Soil Survey Staff, 2004
Schwertmann, 1964
Schomakers et al., 2011a and 2011b
Kemper and Rosenau 1986
Moore and Reynolds 1997
Soil Survey Staff, 2004
modified from Mueller et al., 2009 and Steffens et al., 2009,
ultrasonication based on Chapter 2
Soil Survey Staff, 2004
Reference
Chemical methods
Soil pHa
Total C and N contentsa
Carbonate (CaCO3) contenta
Cation exchange capacity (CEC)a
Ammonium-oxalate extractable Fe, Mn, Al, (Si)a
Dithionite-citrate-bicarbonate-extractable Fe, Mn,
Al, (Si)a
Allophane contenta
Ferrihydrite contenta
Calcium-acetate-lactate extractable P and Ka
Density fractionationa
Bulk density (BD)a
Physical methods
Ultrasonic soil aggregate stability (USAS)a
Mean Weight Diameter (MWD)a
Mineralogyb
Particle size distributiona
Aim of the method
Table 1.1 Summary of methods used in this PhD thesis. Letters indicate where the analyses were carried out.
17
2
3, 4
Knicker et al., 2005
18
b
3, 4
3, 4
3, 4
3, 4
3, 4
Barros et al., 2007
Ghani et al., 2003
Canali and Benedetti 2006
Bloem and Vos 2004
Bloem et al. 1995
Bloem and Bolhuis 2006
Institute of Soil Research, University of Life and Environmental Sciences (BOKU), Vienna, Austria.
Institute of Applied Geology, University of Life and Environmental Sciences (BOKU), Vienna, Austria.
c
Wageningen University, the Netherlands.
d
Chair of Soil Science, Technical University of Munich, Germany (Freising).
a
Methods to study SOM
Simultaneous thermal analysis (STA)b
Chemical quality of SOM (solid-state 13C NMR
spectroscopy)d
Hot water extractable Cc
Mineralizable Nc
Hyphal length and bacterial numbersc
Bacterial biomassc
Bacterial activityc
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
19
(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
20
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
21
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 13C 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
22
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
23
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
24
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 longterm field experiments in order to close the knowledge gap and to give better-informed
recommendations to farmers.
25
References
Abalos, D., Sanz-Cobena, A., Garcia-Torres, L., van Groenigen, J.W. & Vallejo, A. 2013.
Role of maize stover incorporation on nitrogen oxide emissions in a non-irrigated
Mediterranean barley field. Plant and Soil, 364, 357-371.
Abiven, S., Menasseri, S., Chenu, C. 2009. The effects of organic inputs over time on soil
aggregate stability – A literature analysis. Soil Biology & Biochemistry, 41, 1-12.
AGES (Austrian Agency for Health and Food Safety). 2013. Pflanzenschutzmittel. URL:
http://www.ages.at/ages/landwirtschaftlichesachgebiete/pflanzenschutzmittel/.
Accessed
14.11.2013.
Aguilera, E., Lassaletta, L., Gattinger, A. & Gimeno, B.S. 2013. Managing soil carbon for
climate change mitigation and adaptation in Mediterranean cropping systems: A metaanalysis. Agriculture, Ecosystems and Environment, 168, 25-36.
Alekseeva, T.V., Z. Sokolowska, M. Hajnos, A.O. Alekseev, and P.I. Kalinin. 2009. Water
stability of aggregates in subtropical and tropical soils (Georgia and China) and its
relationships with the mineralogy and chemical properties. Eurasian Soil Science, 42, 415–
425.
Alexander, M. 1977. Mineralization and immobilization of nitrogen, In: Introduction to
soil microbiology, 2nd edition (ed Alexander, M.). Wiley, New York, USA, pp. 136-247.
Amelung, W., Brodowski, S., Sandhage-Hofmann, A., Bol, R. 2008. Combining biomarker
with stable isotope analyses for assessing the transformation and turnover of soil organic
matter. Advances in Agronomy, 1000, 155-250.
Amelung, W., Zech, W. 1999. Minimisation of organic matter disruption during particlesize fractionation of grassland epipedons. Geoderma, 92, 73–85.
Amézketa, E. 1999. Soil aggregate stability: A Review. Journal of Sustainable Agriculture
14, 83-151.
An, S., A. Mentler, H. Mayer, and W.E.H. Blum. 2010. Soil aggregation, aggregate
stability, organic carbon and nitrogen in di_erent soil aggregate fractions under forest and
shrub vegetation on the Loess Plateau, China. Catena, 81, 226–233.
Baggs, E.M., Stevenson, M., Pihlatie, M., Regar, A., Cook, H. & Cadisch, G. 2003.
Nitrous oxide emissions following application of residues and fertiliser under zero and
conventional tillage. Plant and Soil, 254, 361-370.
Baggs, E.M., Rees, R.M., Smith, K.A. & Vinten, A.J.A. 2000. Nitrous oxide emission
from soils after incorporating crop residues. Soil Use and Management, 16, 82-87.
Baldock, J.A., Oades, J.M., Nelson, P.N., Skene, T.M., Golchin, A., Clarke, P. 1997.
Assessing the extent of decomposition of natural organic materials using solid-state 13C
NMR spectroscopy, Australian Journal of Soil Research, 35, 1061-1083.
26
Barral, M.T., Arias, M., Guerif, J. 1998. Effects of iron and organic matter on the porosity
and structural stability of soil aggregates. Soil and Tillage Research, 46, 261–272.
Barros, N., J. Salgado, and S. Feijoo. 2007. Calorimetry and soil. Thermochimica Acta,
458, 11–17.
Bhogal, A., Nicholson, F.A., & Chambers, B.J. 2009. Organic carbon additions: effects on
soil bio-physical and physico-chemical properties. European Journal of Soil Science, 60,
276-286.
Bindraban P.S., Van der Velde M., Ye L., Van den Berg M., Materechera S., Kiba D.I.,
Tamene L., Ragnarsdottir K.V., Jongschaap R., Hoogmoed M., Hoogmoed M., van Beek
C., van Lynden G. 2012. Assessing the impact of soil degradation on food production.
Current Opinion in Environmental Sustainability, 4, 478-488.
BIO AUSTRIA, 2010. Produktionsrichtlinien Die Biobäuerinnen und Biobauern
Österreichs, 3rd edition, Linz, Austria.
Blakemore, R.J. 2000. Ecology of earthworms under the ‘Haughley Experiment’ of
organic and conventional management regimes. Biology, Agriculture and Horticulture, 18,
141-159.
Blanco-Canqui, H. 2013. Crop Residue Removal for Bioenergy Reduces Soil Carbon
Pools: How Can We Offset Carbon Losses? Bioenergy Research, 6, 358-371.
Bloem, J., Bolhuis, P.R. 2006. Thymidine and leucine incorporation to assess bacterial
growth rate, in: Microbiological Methods for Assessing Soil Quality, edited by: Bloem, J.,
Hopkins, D.W., Benedetti, A., CABI, Wallingford, UK.
Bloem, J., Vos, A. 2004. Fluorescent staining of microbes for total direct counts, in:
Molecular Microbial Ecology Manual 2nd edition, edited by: Kowalchuk, G.A., de Bruijn,
F.J., Head, I.M., Akkermans, A.D.L., van Elsas, J.D., Kluwer Academic Publishers,
Dordrecht, The Netherlands.
Bloem, J., Veninga, M., Shepherd, J. 1995. Fully-automatic determination of soil
bacterium numbers, cell volumes, and frequencies of dividing cells by confocal laserscanning microscopy and image-analysis, Applied and Environmental Microbiology, 61,
926-936.
BMLFUW, 2006. Richtlinien für die sachgerechte düngung, 6th edition, Vienna, Austria.
Brandstetter, A., Sletten, R.S., Mentler, A., Wenzel, W.W. 1996. Estimating dissolved
organic carbon in natural waters by UV absorbance (254 nm), Zeitschrift für
Pflanzenernährung und Bodenkunde, 159, 605-607.
Brantley S.A., Goldhaber M. and Ragnarsdottir K.V. 2007. Crossing disciplines and scales
to understand the critical zone. Elements, 3, 307-314.
Bronick, C.J., and R. Lal. 2005. Soil structure and management: A review. Geoderma, 124,
3–22.
27
Brussaard, L., de Ruiter, P.C., Brown, G.G. 2007. Soil biodiversity for agricultural
sustainability. Agriculture, Ecosystems and Environment, 121, 233-244.
Canali, S., Benedetti, A. 2006. Soil nitrogen mineralization, in: Microbiological methods
for assessing soil quality, edited by: Bloem, J., Hopkins, D.W., Benedetti, A., CABI
Publishing, Wallingford, UK.
Carter, M.R., Stewart, B.A. (Eds.) 1996. Structure and Organic Matter Storage in
Agricultural Soils. Dvances in Soil Science. CRC Press, Boca Raton, FL, USA.
Chabbi, A:, Rumpel, C. 2009. Guest editors´ introduction. Organic matter dynamics in
agro-ecosystems-the knowledge gaps. European Journal of Soil Science, 60, 153-157.
Chan, K.Y., Heenan, D.P., Oates, A. 2002. Soil carbon fractions and relationship to soil
quality under different tillage and stubble management, Soil and Tillage Research, 63, 133139.
Chen, H., Li, X., Hu, F. & Shi, W. 2013. Soil nitrous oxide emissions following crop
residue addition: a meta-analysis. Global Change Biology, 19, 2956-2964.
Christensen, B.T. 1992. Physical Fractionation of Soil and Organic Matter in Primary
Particle Size and Density Separates. Advances in Soil Science, 20, 1-90.
Churchman, G.J., and K.R. Tate. 1987. Stability of aggregates of di_erent size grades in
allophonic soils from volcanic ash in New Zealand. Journal of Soil Science, 38, 19–27.
Courtier-Murias, D., Simpson, A.J., Marzadori, C., Baldoni, G., Ciavatta, C., Fernández,
J.M., López-de-Sá, E.G., Plaza, C. 2013. Unraveling the long-term stabilization
mechanisms of organic materials in soils by physical fractionation and NMR spectroscopy,
Agriculture, Ecosystems and Environment, 171, 9-18.
Cvetkov, M., Santavec, I., Kocjan Acko, D. & Tajnsek, A. 2010. Soil organic matter
content according to different management system within long-term experiment. Acta
agriculturae Slovenica, 95, 79-88.
Debosz, K., Petersen, S.O., Kure, L.K., Ambus, P. 2002. Evaluating effects of sewage
sludge and household compost on soil physical, chemical and microbiological properties.
Applied Soil Ecology, 19, 237-248.
De Neve, S., Hofman, G. 2000. Influence of soil compaction on carbon and nitrogen
mineralization of soil organic matter and crop residues. Biology and Fertility of Soils, 30,
544-549.
Deviren Saygin, S., W.M. Cornelis, G. Erpul, and D. Gabriels. 2012. Comparison of
di_erent aggregate stability approaches for loamy sand soils. Applied Soil Ecology, 54, 1–
6.
Diacono, M., Montemurro, F. 2011. Long-term effects of organic amendments on soil
fertility, in: Sustainable Agriculture, vol. 2, edited by: Lichtfouse, E., Hamelin, M.,
Navarrete, M., Debaeke, P., Springer, The Netherlands.
28
Dinel, H., Righi, D., Hardy, M., Jambu, P. 1997. Neutral lipids and structural stability of
physically degraded soils. Agrochemica, 41, 97-108.
Droogers, P., Bouma, J. 1996. Biodynamic vs. conventional farming effects on soil
structure expressed by simulated potential productivity. Soil Science Society of America
Journal, 60, 1552-1558.
Dýrmundsson, Ó.R. 2000. Organic Farming in Iceland. Bad Dürkenheim, Germany:
Stiftung
Ökologie
&
Landbau
(SÖL).
Retrieved
from:
www.organiceurope.net/country_reports/pdf/2000/iceland.pdf 11.7.2011.
Eash, N.S., Karlen, D.L., Parkin, T.B. 1994. Fungal Contributions to Soil Aggregation and
Soil Quality, Soil Science Society of America Journal special publication, 35, 221-228.
Elliott, E.T. 1986. Aggregate structure and carbon, nitrogen, and phosphorus in native and
cultivated soils, Soil Science Society of America Journal, 50, 627-633.
Erhart, E., Hartl, W. 2010. Compost Use in Organic Farming, in: Lichtfouse, E. (ed),
Genetic Engineering, Biofertilization, Soil Quality and Organic Farming, Sustainable
Agriculture Reviews 4. Springer, pp. 311-345.
European Commission (EC). 2006. Proposal for a Directive of the European Parliament
and of the Council, Establishing a Framework for the Protection of Soil and Amending
Directive, European Commission 2004/35/EC COM(2006) 232 (2006). Retrieved from:
http://eur-lex.europa.eu/LexUriServ/LexUriServ.do?uri=CELEX:52006PC0232:EN:NOT
Feller, C.L., Thuries, L.J.-M., Manlay, R.J., Robin, P., Frossard, E. 2003. “The principles
of rational agriculture” by Albrecht Daniel Thaer (1752-1828). An approach to the
sustainability of cropping systems at the beginning of the 19th century. Journal of Plant
Nutrition and Soil Science, 166, 687-698.
Fließbach, A., Oberholyer, H.-R., Gunst, L., Mäder, P. 2007. Soil organic matter and
biological soil qualitz indicators after 21 zears of organic and conventional farming.
Agriculture, Ecosystems and Environment, 118, 273-284.
Fließbach, A., Mäder, P. 2000. Microbila biomass and size-density fractions differ between
soils of organic and conventional agricultural systems. Soil Biology & Biochemistry, 32,
757-768.
Fristensky, A., and M.E. Grismer. 2008. A simultaneous model for ultrasonic aggregate
stability assessment. Catena, 74, 153–164.
Garcia-Ruiz, R. & Baggs, E.M. 2007. N2O emission from soil following combined
application of fertilizer-N and ground weed residues. Plant and Soil, 299, 263-274.
Gattinger, A., Muller, A., Haeni, M., Skinner, C., Fliessbach, A., Buchmann, N., Mäder,
P., Stolze, M., Smith, P. 2012. El-Hage Scialabba, N., Niggli, U.: Enhanced top soil carbon
stocks under organic farming. Proceedings of the National Academy of Sciences, 109,
18226-18231.
29
Gazze S.A., Saccone L., Smits M.M., Duran A.L., Leake J.R., Banwart S.A., Ragnarsdottir
K.V. and McMaster T.J. 2013. Nanoscale observations of Extracellular Polymeric
Substances deposition on phyllosilicates by an ectomycorrhizal fungus. Geomicrobilogy
Journal 30(8), 721-730.
Ghani, A., Dexter, M., Perrott, K.W. 2003. Hot-water extractable carbon in soils: A
sensitive measurement for determining impacts of fertilisation, grazing and cultivation,
Soil Biology & Biochemistry, 35, 1231-1243.
Ghezzehei, T.A. 2012. Soil Structure. In: Huang, P.M., Li, Y., Sumner, M.E. (Eds.)
Handbook of Soil Sciences: Properties and Processes, Second Edition. CRC Press, Taylor
& Francis Group, Boca Raton, FL, USA.
Golchin, A., Oades, J.M., Skjemstad, J.O., Clarke, P. 1994. Study of Free and Occluded
Particulate Organic Matter in Soils by Solid state 13C CP/MAS NMR Spectroscopy and
Scanning Electron Microscopy, Australian Journal of Soil Research, 32, 285-309.
Hadas, A. 1997. Soil tilth – The desired soil structural state obtained through proper soil
fragmentation and reorientation process. Soil and Tillage Research, 43, 7-40.
Hartl, W., Erhart, E. 2005. Crop nitrogen recovery and soi lnitrogen dynamics in a 10-year
field experiment with biowaste compost. Journal of Plant Nutrition and Soil Science, 168,
781-788.
Haynes, R.J., Naidu, R. 1998. Influence of lime, fertilizer and manure applications on soil
organic matter content and soil physical conditions: a review. Nutrient Cycling in
Agroecosystems, 51, 123-137.
Heintze, S.G., and P.J.G. Mann. 1947. Divalent manganese in soil extracts. Nature, 158,
791–792.
IPCC (Intergovernmental Panel on Climate Change). 2007. The Physical Science Basis,
Contribution of Working Group I to the Fourth Assessment Report of the
Intergovernmental Panel on Climate Change. In: Solomon, S., Qin, D., Manning, M.,
Chen, Z., Marquis, M., Averyt, K.B., Tignor, M., Miller, H.L. (Eds.), Climate Change
2007. Cambridge University Press, Cambridge, United Kingdom and New York, NY,
USA.
Iqbal, J. Hu, R., Lin, S., Hatano, R., Feng, M., Lu, L., Ahamadou, B., Du, L. 2009. CO2
emission in a subtropical red paddy soil (Ultisol) as affected by straw and N fertilizer
applications: A case study in Southern China. Agriculture, Ecosystems and Environment,
131, 292-302.
Janzen, H.H. 2006. The soil carbon dilemma: Shall we hoard it or use it? Soil Biology &
Biochemistry, 38, 419-424.
Jenkinson, D.S., Ayanaba, A. 1977. Decomposition of 14C-labeled plant material under
tropical conditions. Soil Science Society of America Journal, 41, 912-915.
Jóhannesson, T. 2010. Agriculture in Iceland: Conditions and Characteristics. The
Agricultural University of Iceland, Hvanneyri, Iceland.
30
Johnston, A., Poulton, P.R., Coleman, K. 2009. Soil organic matter: Its importance in
sustainable agriculture and carbon dioxide fluxes. Advances in Agronomy, 101, 1-57.
Kandeler, E., Palli, S., Stemmer, M., Gerzabek, M.H. 1999. Tillage changes microbial
biomass and enzyme activities in particle-size fractions of a Haplic Chernozem. Soil
Biology & Biochemistry, 31, 1253-1264.
Karami, A., Homaee, M., Afzalinia, S., Ruhipour, H., Basitar, S. 2012. Organic resource
management: Impacts on soil aggregate stability and other soil physic-chemical properties,
Agriculture, Ecosystems and Environment, 148, 22-28.
Kemper, W.D., and R.C. Rosenau. 1986. Aggregate stability and size distribution. In: A.
Klute, editor, Methods of soil analysis. Part 1. Agron. Monogr. 9. 2nd ed. ASA, SSSA,
Madison, WI. p. 425–442.
Khalil, M.I., Baggs, E.M. 2005. CH4 oxidation and N2O emissions at varied soil waterfilled pore spaces and headspace CH4 concentrations. Soil Biology and Biochemistry, 37,
1785-1794.
Kibblewhite, M.G., Ritz, K:, Swift, M.J. 2008. Soil health in agricultural systems.
Philosophical Transactions of the Royal Society B, 363, 685-701.
Kismányoky, T., Tóth, Z. 2010. Effect of mineral and organic fertilization on soil organic
carbon content as well as on grain production of cereals in the IOSDV (ILTE) long-term
field experiment, Keszthely, Hungary. Archives of Agronomy and Soil Science, 59, 11211131.
Kleber, M. 2010. What is recalcitrant soil organic matter? Environmental Chemistry, 7,
320-332.
Knicker, H., Gonzalez-Vila, F.J., Polvillo, O., Gonzalez, J.A., Almendros, G. 2005. Fireinduced transformation of C- and N-forms in different organic soil fractions from a Dystric
Cambisol under a Mediterranean pine forest (Pinus pinaster). Soil Biology &
Biochemistry, 37, 701-718.
Kögel-Knabner, I. 2002. The macromolecular organic composition of plant and microbial
residues as inputs to soil organic matter, Soil Biology & Biochemistry, 34, 139-162.
Kölbl, A., Kögel-Knabler, I. 2004. Content and composition of free and occluded
particulate organic matter in a differently textured arable Cambisol as revealed by solidstate 13C NMR spectroscopy, Journal of Plant Nutrition and Soil Science, 167, 45-53.
Körschens, M., Weigel, A. & Schulz, E. 1998. Turnover of Soil Organic Matter (SOM)
and Long-Term Balances – Tools for Evaluating Sustainable Productivity of Soils.
Zeitschrift für Pflanzenernährung und Bodenkunde, 161, 409-424.
Ladha, J.K., Reddy, C.K., Padre, A.T., van Kessel, C. 2011. Role of Nitrogen Fertilization
in Sustaining Organic Matter in Cultivated Soils. Journal of Environmental Quality, 40,
1756-1766.
Lal, R. 2004. Soil carbon sequestration to mitigate climate change. Geoderma, 123, 1-22.
31
Lal, R. 2005. World crop residues production and implications of its use as a biofuel.
Environment International, 31, 575-584.
Lal, R. 2007. Soils and sustainable agriculture. A review. Agronomy for Sustainable
Development, 27, 1-8.
Lal, R. 2009. Laws of sustainable soil management. Agronomy for Sustainable
Development, 29, 7-9.
Le Bissonnais, Y., and M.J. Singer. 1993. Seal formation, runo_ and interrill erosion from
seventeen California soils. Soil Science Society of America Journal, 57, 224–229.
Lehrsch, G.A., R.E. Sojka, D.L. Carter, and P.M. Jolley. 1991. Freezing e_ects on
aggregate stability a_ected by texture, mineralogy, and organic matter. Soil Science
Society of America Journal, 55, 1401–1406.
Lehtinen, T., Lair G.J., Mentler, A., Gísladóttir, G., Ragnarsdóttir, K.V., Blum, W.E.H.
2014. Soil Aggregate Stability in Different Soil Orders Quantified by Low Dispersive
Ultrasonic Energy Levels. Soil Science Society of America Journal, 78, 713-723.
Leifeld, J., Fuhrer, J. 2010. Organic Farming and Soil Carbon Sequestration: What Do We
Really Know About the Benefits? AMBIO, 39, 585-599.
Leifeld, J., Kögel-Knabner, I. 2005. Soil organic matter fractions as early indicators for
carbon stock changes under different land use? Geoderma, 124, 143-155.
Lemke, R.L., Van den Bygaart, A.J., Campbell, C.A., Lafond, G.P. & Grant, B. 2010. Crop
residue removal and fertilizer N: Effects on soil organic carbon in a long-term crop rotation
experiment on a Udic Boroll. Agriculture, Ecosystems and Environment, 135, 42-51.
Loveland, P., Webb, J. 2003. Is there a critical level of organic matter in the agricultural
soils of temperate regions: a review. Soil & Tillage Research, 70, 1-18.
Manlay, R.J., Feller, C., Swift, M.J. 2007. Historical evolution of soil organic matter
consepts and their relationships with the fertility and sustainability of cropping systems.
Agriculture, Ecosystems and Environment, 119, 217-233.
Marzaioli, F., Lubritto, C., Del Galdo, I., D´Onofrio, A., Cotrufo, F., Terrasi, F. 2010.
Comparison of different soil organic matter fractionation methodologies: Evidences from
ultrasensitive 14C measurements. Nuclear Instruments and Methods in Physics Research B,
268, 1062-1066.
Mayer, H., A. Mentler, M. Papakyriacou, N. Rampazzo, Y. Marxer, and W.E.H. Blum.
2002. In␣
uence of vibration amplitude on ultrasonic dispersion of soils. International
Agrophysics, 16, 53–60.
Mehra, O.P., Jackson, M.L. 1960. Iron oxide removal from soils and clays by dithionitecitrate systems buffered with sodium bicarbonate, Clays and Clay Minerals, 7, 317–327.
32
Meijide, A., Cárdenas, L.M., Sánchez-Martín, L. & Vallejo, A. 2010. Carbon dioxide and
methane fluxes from a barley field amended with organic fertilizers under Mediterranean
climatic conditions. Plant and Soil, 328, 353-367.
Mentler, A., H. Mayer, P. Strauß, and W.E.H. Blum. 2004. Characterisation of soil
aggregate stability by ultrasonic dispersion. International Agrophysics, 18, 39–45.
Metzger, M.J., Bunce, R.G.H., Jongman, R.H.G., Mücher, C.A. & Watkins, J.W. 2005. A
climatic stratification of the environment of Europe. Global Ecology and Biogeography,
14, 549-563.
Monreal, C.M., Schnitzer, M., Schulten, H.R., Campbell, C.A., Anderson, D.W. 1995. Soil
organic structures in macro and microaggregates of a cultivated brown chernozem. Soil
Biology & Biochemistry, 27, 845-853.
Montgomery, D.R. 2007. Soil erosion and agricultural sustainability. Proceedings of the
National Academy of Sciences, 104, 13268-13272.
Moore, D.M., Reynolds, R.C. 1997. X–ray diffraction and the identification and analysis of
clay minerals. Oxford Univ. Press, New York.
Mueller, C.W., Brüggemann, N., Pritsch, K., Stoelken, G., Gayler, S., Winkler, J.B.,
Kögel-Knabler, I. 2009. Initial differentiation of vertical soil organic matter distribution
and composition under juvenile beech (Fagus szlvatica L.) trees. Plant and Soil, 323, 111123.
Mäder, P., Fließbach, A., Dubois, D., Gunst, L., Fried, P., Niggli, U. 2002. Soil Fertility
and Biodiversity in Organic farming. Science, 296, 1694-1697.
Mårald, E. 2002. Everything circulates: agricultural chemistry and recycling theories in the
second half of the nineteenth century. Environment and History, 8, 65-84.
Navrátil, T., J.B. Shanley, P. Skrivan, P. Krám, M. Mihaljevic, and P. Drahota. 2007.
Manganese Biochemistry in a Central Czech Republic Cathment. Water, Air & Soil
Pollution, 186, 149–165
Nicolardot, B., Recous, S. & Mary, B. 2001. Simulation of C and N mineralisation during
crop residue decomposition: A simple dynamic model based on the C:N ratio of the
residues. Plant and Soil, 228, 83-103.
North, P.F. 1976. Towards an absolute measurement of soil structural stability using
ultrasound. Journal of Soil Science, 27, 451–459.
Oades, J.M., Waters, A.G. 1991. Aggregate hierarchy in soils, Australian Journal of Soil
Research, 29, 815-828.
Pare, T., Dinel, H., Moulin, A.P., Townley-Smith, L. 1999. Organic matter quality and
structural stability of a Black Chernozemic soil under different manure and tillage
practices. Geoderma 91, 311-326.
33
Parfitt, R.L. 1990. Allophane in New Zealand – A review, Australian Journal of Soil
Research, 28, 343-360.
Parfitt, R.L., Childs, C.W. 1988. Estimation of forms of Fe and Al: A review, and Analysis
of Contrasting Soils by Dissolution and Moessbauer Methods, Australian Journal of Soil
Research, 26, 121-144.
Perucci, P., Bonciarelli, U., Santilocchi, R. & Bianchi, A.A. 1997. Effect of rotation,
nitrogen fertilization and management of crop residues on some chemical, microbiological
and biochemical properties of soil. Biology and Fertility of Soils, 24, 311-316.
Plahl, E., Rogalski, W., Gilnreiner, G., Erhart, E., 2002. Vienna´s biowaste compost –
quality development and effect of input materials. Waste Management & Research, 20,
127-133.
Poll, C., Thiede, A., Wermbter, N., Sessitsch, A., Kandeler, E. 2003. Micro-scale
distribution of microorganisms and microbial enzyme activities in a soil with long-term
organic amendments. European Journal of Soil Science, 54, 715-724.
Post, W.M., Kwon, K.C. 2000. Soil carbon sequestration and land-use change: processes
and potential. Global Change Biology, 6, 317-327.
Powlson, D.S., Bhogal, A., Chambers, B.J., Coleman, K., Macdonald, A.J., Goulding,
K.W.T., Whitmore, A.P. 2012. The potential to increase soil carbon stocks through
reduced tillage or organic material additions in England and Wales: A case study.
Agriculture, Ecosystems and Environment, 146, 23-33.
Powlson, D.S., Glendining, M.J., Coleman, K., Whitmore, A.P. 2011. Implications for Soil
Properties of Removing Cereal Straw: Results from Long-Term Studies. Agronomy
Journal, 103, 279-287.
Powlson, D.S., Riche, A.B., Coleman, K., Glendining, M.J., Whitmore, A.P. 2008. Carbon
sequestration in European soils through straw incorporation: Limitations and alternatives.
Waste Management, 28, 741-746.
Pulleman, M., Jongmans, A., Marinissen, J., Bouma, J. 2003. Effects of organic versus
conventional arable farming on soil structure and organic matter dynamics in a Marine
Loam in the Netherlands. Soil Use and Management, 19, 157-165.
Recous, S., Robin, D., Darwis, D., Mary, B. 1995. Soil inorganic N availability: Effect on
maize residue decomposition. Soil Biology and Biochemistry, 27, 1529–1538.
Rizhiya. E.Y., Boitsova, L.V., Buchkina, N.P., Panova, G.G. 2011. The influence of crop
residues with different C:N ratios on the N2O emission from a loamy sand soddy-podzolic
soil. Eurasian Soil Science, 44, 1144-1151.
Robinson, D.A., Fraser, I., Dominati, E.J., Daviðsdóttir, B., Jónsson, J.O.G., Jones, L.,
Jones, S.B., Tuller, M., Lebron, I., Bristow, K.L., Souza, D.M., Babwart, S., Clothler, B.E.
2014. On the Value of Soil Resources in the Context of Natural Capital and Ecosystem
Service Delivery. Soil Science Society of America Journal, 78, 685-700.
34
Rodale Institute. 2014. The Farming Systems Trial Celebrating 30 years. Retrieved
(22.7.2014)
from:
http://rodaleinstitute.org/our-work/farming-systems-trial/farmingsystems-trial-30-year-report/.
Roldán, A. Albaladejo, J., Thornes, J.B. 1996. Aggregate stability changes in a semiarid
soil after treatment with different organic amendments. Arid Soil Research and
Rehabilitation, 10, 139-148.
Römkens, M.J.M., Roth, C.B., Nelson, D.W. 1977. Erodability of selected clay subsoils in
relation to physical and chemical properties. Soil Science Society of America Journal, 41,
954–960.
Sanz-Cobena, A., García-Marco, S., Quemada, M., Gabriel, J.L., Almendros, P., Vallejo,
A. 2014. Do cover crops enhance N2O, CO2 or CH4 emissions from soil in Mediterranean
arable systems? Science of the Total Environment, 466-467, 164-174.
Saccone, L., Gazzé, S.A., Duran, A.L., Leake, J.R., Banwart, S.A., Ragnarsdóttir, K.V.,
Smits, M.M., McMaster, T.J. 2012. High resolution characterization of ectomycorrhizal
fungal-mineral interactions in axenic microcosm experiments. Biogeochemistry 111, 411425.
Schlesinger, W.H. 1995. An overview of the C cycle. In: Lal, R., Kimble, J., Levin, J.,
Stewart, B.A. (eds) Soils and global change. CRC, Boca Raton, pp. 9-26.
Schmidt, M.W.I., Torn, M.S., Abiven, S., Dittmar, T., Guggenberger, G., Janssens, I.A.,
Kleber, M., Kögel-Knabner, I., Lehmann, J., Manning, D.A.C., Nannipieri, P., Rasse, D.P.,
Weiner, S., Trumbore, S.E. 2011. Persistence of soil organic matter as an ecosystem
property. Nature, 478, 49–56.
Schmidt, M.W.I., Rumpel, C., Kögel-Knabner, I. 1999. Evaluation of an ultrasonic
dispersion procedure to isolate primary organomineral complexes from soils. European
Journal of Soil Science, 50, 87-94.
Schmidt, M.W.I., Knicker, H., Hatcher, P.G., Kogel-Knabner, I. 1997. Improvement of 13C
and 15N CPMAS NMR spectra of bulk soils, particle size fractions and organic material by
treatment with 10 % hydrofluoric acid, European Journal of Soil Science, 48, 319-328.
Schneeberger, W., Eder, M., Darnhofer, I., Walla, C. Zollitsch, W. 2005. Biologisher
Landbau
in
Österreich.
Ländlicher
Raum.
URL:
http://www.wiso.boku.ac.at/fileadmin/_/H73/H733/pub/Biolandbau/2005_LuR_Schneeb.p
df. Accessed 22.11.2013.
Schomakers, J., Mentler, A., Degischer, N., Blum, W.E.H., Mayer, H. 2011a.
Measurement of soil aggregate stability using low intensity ultrasonic vibration. Spanish
Journal of Soil Science, 1, 8–19.
Schomakers, J., Mentler, A., Steurer, T., Klik, A., Mayer, H. 2011b. Characterization of
soil aggragate stability using low intensity ultrasonic vibration. International Agrophysics,
25, 165–172.
35
Schulten, H.-R., Leinweber, P. 2000. New insights into organic-mineral particles:
Composition, properties and models of molecular structure. Biology and Fertility of Soils,
30, 399–432.
Schwertmann, U. 1964. Differenzierung der Eisenoxide des Bodens durch Extraktion mit
Ammoniumoxalat-Lösung, Zeitschrift fuer Pflanzenernaehrung, Duengung und
Bodenkunde, 105, 194-202,.
Scialabba, N. E-H. 2007. Organic agriculture and food security. Food and Agriculture
Organization of the United Nations.
Shan, J., Yan, X. 2013. Effects of crop residue returning on nitrous oxide emissions in
agricultural soils. Atmospheric Environment, 71, 170-175.
Shepherd, M.A., Harrison, R., Webb, J. 2002. Managing soil organic matter – implications
for soil structure on organic farms, Soil Use & Management, 18, 284-292.
Siegrist, S., Schaub, D., Pfiffner, L., Mäder, P. 1998. Does organic agriculture reduce soil
erodability? The results of a long-term field study on loess in Switzerland. Agriculture,
Ecosystems and Environment, 69, 253-264,.
Six, J., Bossuyt, H., Degryze, S., Denef, K. 2004. A history of research on the link between
(micro)aggregates, soil biota, and soil organic matter dynamics, Soil Tillage & Research,
79, 7-31.
Six, J., Elliott, E.T., Paustian, K. 2000. Soil macroaggregate turnover and microaggregate
formation: a mechanism for C sequestration under no-tillage agriculture, Soil Biology &
Biochemistry, 32, 2099-2103.
Sodhi, G.P.S., Beri, V., Benbi, D.K. 2009. Soil aggregation and distribution of carbon and
nitrogen in different fractions under long-term application of compost in rice-wheat
system, Soil Tillage & Research, 103, 412-418.
Soil Survey Staff. 2004. Soil Survey Laboratory Methods Manual. Soil Survey
Investigations Rep. 42. USDA – NRCS, Washington, DC.
Spiegel, H., Dersch, G., Baumgarten, A., Hösch, J. 2010. The International Organic
Nitrogen Long-term Fertilization Experiment (IOSDV) at Vienna after 21 years. Archieves
of Agronomy and Soil Science, 56, 405-420.
Stavi, I., Lal, R. 2013. Agriculture and greenhouse gases, a common tragedy. A review.
Agronomy for Sustainable Development, 33, 275-289.
Steffens, M., Kölbl, A., Kögel-Knabler, I. 2009. Alteration of soil organic matter pools and
aggregation in semi-arid steppe topsoil’s as driven by organic matter input. European
Journal of Soil Science, 60, 198-212.
Stockmann, U., Adams, M.A., Crawford, J.W., Field, D.J., Henakaarchchi, N., Jenkins, M.,
Minasny, B., McBratney, A.B., de Remy de Courcelles, V., Singh, K., Wheeler, I., Abbott,
L., Angers, D.A., Baldock, J., Bird, M., Brookes, P.C., Chenu, C., Jastrow, J.D., Lal, R.,
Lehmann, J., O´Donnell, A.G., Parton, W.J., Whitehead, D., Zimmermann, M. 2013. The
36
knowns, known unknowns and unknowns of sequestration of soil organic carbon.
Agriculture, Ecosystems and Environment, 164, 80-99.
Stott, D.E., Elliott, L.F., Papendick, R.I., Campbell, G.S. 1986. Low temperature or low
water effects on microbial decomposition of wheat residue. Soil Biology and
Biochemistry, 18, 577-582.
Sun, F., Lu, S. 2014. Biochars improve aggregate stability, water retention, and pore-space
properties of clayey soil. Journal of Plant Nutrition and Soil Science, 177, 26-33.
Surböck, A., Friedel, J.K., Heinzinger, M., Freyer, B. 2006. Long-term monitoring of
different management systems within organic farming and their effects o narable land and
landscape. Phase I: Monitoring of the conversion to organic farming, in: Raupp, J., Pekrun,
C., Oltmanns, M., Köpke, U. (Eds.), Long-term field experiments in organic farming.
ISOFAR Scientific Series, Verlag Dr. H. J. Köster, Berlin, Germany, pp. 183-198.
Tabatabai, M. A., Bremner, J. M., 1991. Automated instruments for determination of total
carbon, nitrogen, and sulfur in soils by combustion techniques, in Soil Analysis, edited by
K.A. Smith, pp. 261– 286, Marcel Dekker, New York.
Tilman, D., Cassman, K.G., Matson, P.A., Naylor, R., Polasky, S. 2002. Agricultural
sustainability and intensive production practices. Nature, 418, 671-677.
Tischler, K., Rech, T. 2009. Organic Farming in Austria. Federal Ministry of Agriculture,
Forestry, Environment and Water Management, Vienna, Austria, pp. 36.
Tisdall, J.M. 1991. Fungal hyphae and structural stability of soil, Australian Journal of Soil
Research, 29, 729-743.
Tisdall, J.M., Oades, J.M. 1982. Organic matter and water-stable aggregates in soils.
Journal of Soil Science, 33, 141-163.
Triberti, L., Nastri, A., Giordani, G., Comellini, F., Baldoni, G., Toderi, G. 2008. Can
mineral and organic fertilization help sequestrate carbon dioxide in cropland? European
Journal of Agronomy, 29, 13-20.
TÚN. 2013. Rules for organic and sustainable production and resource utilization, 2nd
edition, Vottunastofan Tún ehf, Reykjavik, Iceland.
Vance, E.D., Brookes, P.C., Jenkinsson, D.S. 1987. An extraction method for measuring
soil microbial biomass C, Soil Biology & Biochemistry, 19, 703-707.
van Leeuwen, J.P., Lehtinen, T., Lair, G.J., Bloem, J., Hemerik, L., Ragnarsdóttir, K.V.,
Gísladóttir, G., Newton, J.S., de Ruiter, P.C. 2014. An ecosystem approach to assess soil
quality in organically and conventionally managed farms in Iceland and Austria. SOIL (in
review).
van Veen, J.A., Kuikman, P.J. 1990. Soil structural aspects of decomposition of organic
matter by micro-organisms. Biogeochemistry, 11, 213-233.
37
Victoria, R., Banwart, S., Black, H., Ingram, J., Joosten, H., Milne, E., Noellemeyer, E.
2012. The Benefits of Soil Carbon (Ch 2). In: UNEP: Year book 2012, pp. 19-33.
von Lützow, M., Kögel-Knabler, I. 2009. Temperature sensitivity of soil organic matter
decomposition – what do we know? Biology and Fertility of Soils, 46, 1-15.
von Lützow, M.V., Kögel-Knabner, I., Ludwig, B., Matzner, E., Flessa, H., Ekschmitt, K.,
Guggenberger, G., Marschner, B., Kalbitz, K. 2008. Stabilization mechanisms of organic
matter in four temperate soils: Development and application of a conceptual model.
Journal of Plant Nutrition and Soil Science, 171,111-124.
von Lützow, M., Kögel-Knabner, I., Ekschmitt, K., Flessa, H., Guggenberger, G.,
Matznere, E., Marschner, B. 2007. SOM fractionation methods: Relevance to functional
pools and to stabilization mechanisms. Soil Biology & Biochemistry, 39, 2183–2207.
von Lützow, M.V., Kögel-Knabner, I., Ekschmitt, K., Matzner, E., Guggenberger, G.,
Marschner, B., Flessa, H. 2006. Stabilization of organic matter in temperate soils:
mechanisms and their relevance under different soil conditions – a review. European
Journal of Soil Science, 57, 426-445.
Watson, C.A., Atkinson, D., Gosling, P., Jackson, L.R., Rayns, F.W. 2002. Managing soil
fertility in organic farming systems. Soil Use and Management, 18, 239-247.
Willer, H., Youssefi, M. 2007. The World of Organic Agriculture – Statistics and
Emerging Trends 2007. International Federation of Organic Agriculture Movements
IFOAM, Bonn, Germany and Research Institute of Organic Agriculture FiBL,
Ackerstrasse, Switzerland.
Williams, N.D., Petticrew, E.L. 2009. Aggregate stability in organically and
conventionally farmed soils. Soil Use and Management, 25, 284-292.
ÖNORM L1087. Retrieved from: http://www.bodeninfo.net/index.php?article_id=75 on
January 28th 2013.
38
2 Soil Aggregate Stability in Different
Soil Orders Quantified by Low
Dispersive Ultrasonic Energy Levels
39
Soil Physics
Soil Aggregate Stability in Different Soil Orders
Quantified by Low Dispersive Ultrasonic Energy Levels
Taru Lehtinen
Institute of Soil Research
Univ. of Natural Resour. and Life Sci. (BOKU)
Peter Jordan Straße 82a
1190 Vienna
Austria
and
Faculty of Life and Environ. Sci.
Univ. of Iceland
Sturlugötu 7
101 Reykjavík
Iceland
Faculty of Earth Sciences
Univ. of Iceland
Sturlugötu 7
101 Reykjavík
Iceland
Georg J. Lair*
Institute of Soil Research
Univ. of Natural Resour. and Life Sci. (BOKU)
Peter Jordan Straße 82a
1190 Vienna
Austria
Institute of Ecology
Univ. of Innsbruck
Sternwartestraße 15
6020 Innsbruck
Austria
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.
Axel Mentler
Institute of Soil Research
Univ. of Natural Resour. and Life Sci. (BOKU)
Peter Jordan Straße 82a
1190 Vienna
Austria
Guðrún Gísladóttir
Faculty of Life and Environ. Sci.
Univ. of Iceland
Sturlugötu 7
101 Reykjavík
Iceland
Kristín Vala Ragnarsdóttir
Faculty of Earth Sciences
Univ. of Iceland
Sturlugötu 7
101 Reykjavík
Iceland
Winfried E.H. Blum
Institute of Soil Research
Univ. of Natural Resour. and Life Sci.(BOKU)
Peter Jordan Straße 82a
1190 Vienna
Austria
Soil Science Society of America Journal
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 macroand 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.
S
oil 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 coagulated soil matrix to withstand disruptive, physical forces. A wide range of soil
properties are influenced by aggregate stability; including aeration, compactability, 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 governed 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),
Soil Sci. Soc. Am. J. 78:713–723
doi:10.2136/sssaj2013.02.0073
Received 22 Feb. 2013.
*Corresponding author: ([email protected]).
© 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.
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
714
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 Methods
Study 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, airSoil Science Society of America Journal
Table 1. Site information of the studied soil macroaggregates.
Location
Andisol†
Inceptisol‡
Entisol§
Ultisol¶
Alfisol#
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
Elevation (masl)
W 20°10¢782²
119
E 08°28¢494²
2019
E 16°41¢660²
158
E 24°05¢607²
552
E 12°46¢545²
773
6.3°C
1113 mm
2.7°C
2400 mm
9°C
550 mm
18°C
969 mm
6.2°C
844 mm
Haplic Cambisol
Mollic Fluvisol
Haplic Leptosol Luvic Stagnosol
Mean annual temperature (MAT)
Mean annual precipitation (MAP)
Soil type (WRB)
Haplic Andosol
Soil parent material
Volcanic ash and basalt Granite
Alluvial sediments Schist
Land use
Grazing land
Hardwood forest
Olive plantation Spruce forest
5–10 cm (A)
5–10 cm (ACp)
Grassland
Sampled soil depth (Horizon)
10–20 cm (A)
5–11 cm (AB)
† 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.
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,
www.soils.org/publications/sssaj
Serpentinite
5–12 cm (AE)
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 Stability
Ultrasonic 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 30mm 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
715
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:
MWD =
n
∑w X
i=1
i
i
where X i 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:
Enrichment ratio =
SOM fraction A
SOM fraction B
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.
716
Statistical Analyses
Statistical 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.
Results
Physicochemical 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 Stability
The 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
Soil Science Society of America Journal
0.943 and 0.973, p < 0.05, depending Table 2. Key physicochemical properties† of the studied soil macroaggregates (250–1000 mm).
on the energy level applied) (Table 4).
Andisol
Inceptisol
Entisol
Ultisol
Alfisol
Exchangeable Mg was also strongly and pH (H2O)
7.0
4.0
7.3
6.1
4.1
loamy
loamy
negatively correlated with MWD (r = Texture (WRB)
silt loam
loamy sand
loamy sand
silt
sand
between -0.931 and -0.968, p < 0.05, Clay, g kg-1
160
90
180
120
90
depending on the energy level applied) Silt, g kg-1
530
280
610
340
550
whereas exchangeable Mn had a strong Sand, g kg-1
310
630
210
540
360
positive correlation (r = between 0.905 CaCO , g kg-1
0
0
237
6
0
3
and 0.993, p < 0.05, depending on the Fe , g kg-1
37.3
3.3
2.5
0.9
3.8
o
energy level applied) (Table 4).
34.9
4.9
8.4
37.1
8.8
Fed, g kg-1
The 250- to 1000-mm aggregates Mn , g kg-1
0.6
0.3
0.2
0.3
0.07
o
showed an overall decrease in the >630- Mn , g kg-1
0.6
0.5
0.4
0.5
0.1
d
mm and 250- to 630-mm aggregate size Al , g kg-1
22.0
1.2
0.9
0.3
0.9
o
fractions, and an overall increase of the Ald, g kg-1
9.4
1.0
0.5
4.3
1.0
<63-mm size aggregate fraction with CEC, mmolc kg-1
148
42
279
80
52
increasing absorbed energy (Fig. 2). Exchangeable K, mmolc kg-1
0.7
1.8
3.7
1.4
0.9
For all of the 250- to 1000-mm sized Exchangeable Na, mmolc kg-1
5.0
0.2
0.1
1.3
0.4
aggregates, except for those in the Exchangeable Ca, mmolc kg-1
112.1
2.1
253.8
69.2
1.8
Entisol, the mass of 63- to 250-mm sized Exchangeable Fe, mmolc kg-1
0.03
0.2
0.01
0.02
1.9
1.9
21.2
7.3
21.2
aggregates increased with increasing Exchangeable Mg, mmolc kg-1 30.5
2.0
0.1
1.1
0.6
ultrasonic energy applied until reaching Exchangeable Mn, mmolc kg-1 0.3
0.0
33.3
0.0
0.1
25.2
a maximum either at 4 or at 8 J mL-1, Exchangeable Al, mmolc kg-1
58.9
39.6
35.4
17.8
27.3
and then it levelled off (Fig. 2). For the OC, g kg-1
14.0
15.2
14.5
16.7
28.3
Entisol, the mass of these aggregates C/N
129
54
78
56
52
declined from a maximum at 4 J mL-1. SOM (200–650°C) , g kg-1
99.7
35.1
41.1
27.4
27.7
The changes in the >630 mm and Exo1 (200–380°C), g kg-1
22.7
12.9
15.7
10.9
11.2
250- to 630-mm aggregate fractions Exo2 (380–450°C), g kg-1
6.4
6.0
20.7
17.8
13.5
throughout the experiment followed Exo3 (450–650°C), g kg-1
the MWD pattern, with the highest † CEC = cation exchange capacity; Feo, Mno, Alo = ammonium-oxalate extractable; Fed, Mnd, Ald =
dithionite-citrate-bicarbonate-extractable.
aggregate breakdown in the Andisol and
Entisol; and the lowest in Inceptisol. The
and Alfisol the increase in the 63- to 250-mm aggregates was
largest initial aggregate-size fraction after soil wetting (0 J mL-1)
small, and the distribution of this aggregate-size class was stable
in the Andisol and Entisol was the size fraction of >630 mm (54.4
throughout the different absorbed ultrasonic energies applied to
and 49.7% of soil used, respectively); whereas for the Inceptisol,
the macroaggregates.
Ultisol and Alfisol, it was the 250- to 630-mm size fraction (38.0,
The amounts of the collected aggregates corrected for
36.5, and 33.1% of soil used, respectively).
entrained sand was significantly correlated with the selected
The amount of <63-mm aggregates followed an inverse
250- to 1000-mm aggregate properties (Table 4). The amounts of
pattern of the MWDs (Fig. 2). Similar behaviour was also
macroaggregates (250–630, and >630 mm) had a strong positive
pronounced in the 63- to 250-mm aggregates in the Andisol
correlation with the sand content (e.g., at energy level 8 J mL-1,
and Entisol. In contrast to the macroaggregate size fractions
r = 0.970 and 0.956 for 250–630 mm, and >630 mm, respectively,
(250–630 mm, >630 mm), there was a high increase in this 63- to
p < 0.05) and the exchangeable Mn concentration (e.g., at
250-mm size fraction after low amounts of ultrasonic energy were
energy level 8 J mL-1, r = 0.997 and 0.991 for 250- to 630-1
mm and >630 mm, respectively, p < 0.01). Significant, negative
applied (4 J mL ) which then levelled off. In the Inceptisol, the
increase in this size fraction was observed at a higher energy
correlations were found between quantities of macroaggregates
level (8 J mL-1), and it was not as pronounced. In the Ultisol
and exchangeable Mg concentration (e.g., at energy level
Table 3. Minerals present in the 250- to 1000-mm aggregate fraction of the soils studied.
Mineralogical composition
Soil
major
medium
small
Andisol
amorphous solids
Inceptisol plagioglace feldspar
quartz
plagioglace feldspar and pyroxene
K-feldspar, biotite, muscovite, and vermiculite
Entisol
quartz, dolomite, and mica
plagioglace feldspar, chlorite, and calcite,
Muscovit eand paragonite
plagioglace feldspar and kaolinite
plagioglace feldspar, kaolinite, chlorite,
talc,and serpentinite,
Ultisol
quartz
Alfisol
Amphibole and quartz
www.soils.org/publications/sssaj
Traces
epidote, chlorite, and kaolinite
K-feldspar, kaolinite, and
mixed-layer minerals
K-feldspar and chlorite
K-feldspar, mixed-layer
minerals, epidote, and goethite
717
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.
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.
Soil Organic Matter Release during Ultrasonication
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
In 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
* significant at P < 0.05.
** significant at P < 0.01.
*** significant at P < 0.001..
0.976**
-0.960**
-0.451
-0.439
-0.942*
0.988**
-0.603
-0.480
-0.553
718
Soil Science Society of America Journal
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.
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).
Discussion
Stability 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
www.soils.org/publications/sssaj
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
719
Table 5. Enrichment ratios of SOM fractions between 2
and 40 J mL-1 of ultrasonic energy application in <63 mm
aggregate fraction.
< 63 µm
SOM
Exo 1
Exo 2
Exo 3
Andisol
Inceptisol
Entisol
Ultisol
Alfisol
1.08
1.06
1.17
1.08
1.38
1.41
1.43
1.11
1.16
1.19
1.19
1.10
0.93
0.94
0.90
0.94
1.27
1.35
1.34
1.10
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
720
Fig. 3. Release of water extractable organic C (<0.45 mm) from 250to 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.
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 1000mm 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 Microaggregates
In 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
Soil Science Society of America Journal
(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 matterfree 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
www.soils.org/publications/sssaj
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.
Conclusions
In 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).
Acknowledgments
The 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.
721
References
Alekseeva, T.V., Z. Sokolowska, M. Hajnos, A.O. Alekseev, and P.I. Kalinin. 2009.
Water stability of aggregates in subtropical and tropical soils (Georgia and
China) and its relationships with the mineralogy and chemical properties.
Eurasian Soil Sci. 42:415–425. doi:10.1134/S1064229309040085
Amelung, W., and W. Zech. 1999. Minimisation of organic matter disruption
during particle-size fractionation of grassland epipedons. Geoderma
92:73–85. doi:10.1016/S0016-7061(99)00023-3
Amézketa, E. 1999. Soil aggregate stability: A Review. J. Sustain. Agric. 14:83–
151. doi:10.1300/J064v14n02_08
An, S., A. Mentler, H. Mayer, and W.E.H. Blum. 2010. Soil aggregation,
aggregate stability, organic carbon and nitrogen in different soil aggregate
fractions under forest and shrub vegetation on the Loess Plateau, China.
Catena 81:226–233. doi:10.1016/j.catena.2010.04.002
Ashman, M.R., P.D. Hallett, P.C. Brookes, and J. Allen. 2009. Evaluating soil
stabilisation by biological processes using step-wise aggregate fractionation.
Soil Tillage Res. 102:209–215. doi:10.1016/j.still.2008.07.005
Barros, N., J. Salgado, and S. Feijoo. 2007. Calorimetry and soil. Thermochim.
Acta 458:11–17. doi:10.1016/j.tca.2007.01.010
Bencoková, A., J. Hruska, P. Krám, and E. Stuchlík. 2011. Future and recent
changes in flow patterns in the Czech headwater catchments. Bodenkultur
62:1–4.
Bernasconi, S.M., A. Bauder, B. Bourdon, I. Brunner, E. Bünemann, I. Christl, et
al. 2011. Chemical and Biological Gradients along the Damma Glacier Soil
Chronosequence, Switzerland. Vadose Zone J. 10:867–883. doi:10.2136/
vzj2010.0129
Brady, N.C., and R.R. Weil. 2008. The nature and properties of soils. 14th ed.
Prentice Hall, Upper Saddle River, N.J.
Brandstetter, A., R.S. Sletten, A. Mentler, and W.W. Wenzel. 1996. Estimating
dissolved organic carbon in natural waters by UV absorbance (254
nm). Z. Pflanzenernaehr. Bodenkd. 159:605–607. doi:10.1002/
jpln.1996.3581590612
Brodowski, S., B. John, H. Flessa, and W. Amelung. 2006. Aggregate-occluded
black carbon in soil. Eur. J. Soil Sci. 57:539–546. doi:10.1111/j.13652389.2006.00807.x
Bronick, C.J., and R. Lal. 2005. Soil structure and management: A review.
Geoderma 124:3–22. doi:10.1016/j.geoderma.2004.03.005
Cerli, C., L. Celi, K. Kalbitz, G. Guggenberger, and K. Kaiser. 2012. Separation of
light and heavy organic matter fractions in soil—Testing for proper density
cut-off and dispersion level. Geoderma 170:403–416. doi:10.1016/j.
geoderma.2011.10.009
Churchman, G.J., and K.R. Tate. 1987. Stability of aggregates of different size
grades in allophonic soils from volcanic ash in New Zealand. J. Soil Sci.
38:19–27. doi:10.1111/j.1365-2389.1987.tb02119.x
De-Campos, A.B., A.I. Mamedov, and C. Huang. 2009. Short-Term reducing
conditions decrease soil aggregation. Soil Sci. Soc. Am. J. 73:550–559.
doi:10.2136/sssaj2007.0425
De la Rosa, J.M., H. Knicker, E. López-Capel, D.A.C. Manning, J.A. GonzálezPerez, and F.J. Conzález-Vila. 2008. Direct detection of black carbon in
soils by Py-GC/MS, carbon-13 NMR spectroscopy and thermogravimetric
techniques. Soil Sci. Soc. Am. J. 72:258–267. doi:10.2136/sssaj2007.0031
Deviren Saygin, S., W.M. Cornelis, G. Erpul, and D. Gabriels. 2012. Comparison
of different aggregate stability approaches for loamy sand soils. Appl. Soil
Ecol. 54:1–6. doi:10.1016/j.apsoil.2011.11.012
Dimoyiannis, D. 2012. Wet aggregate stability as affected by excess carbonate and
other soil properties. Land Degrad Develop. 23:450–455. doi:10.1002/
ldr.1085
Duiker, S.W., F.E. Rhoton, J. Torrent, N.E. Smeck, and R. Lal. 2003. Iron (hydr)
oxide crystallinity effects on soil aggregation. Soil Sci. Soc. Am. J. 67:606–
611. doi:10.2136/sssaj2003.0606
Edwards, A.P., and J.M. Bremner. 1967. Dispersion of soil particles by sonic
vibration. J. Soil Sci. 18:47–63. doi:10.1111/j.1365-2389.1967.tb01487.x
Field, D.J., and B. Minasny. 1999. A description of aggregate liberation and
dispersion in A horizons of Australian Vertisols by ultrasonic agitation.
Geoderma 91:11–26. doi:10.1016/S0016-7061(98)00142-6
Fristensky, A., and M.E. Grismer. 2008. A simultaneous model for ultrasonic
aggregate stability assessment. Catena 74:153–164. doi:10.1016/j.
catena.2008.04.013
Griepentrog, M., and M.W.I. Schmidt. 2013. Discrepancies in utilization of
density fractionation along with ultrasonic dispersion to obtain distinct
722
pools of soil organic matter. J. Plant Nutr. Soil Sci. 176:500–504.
doi:10.1002/jpln.201200469
Heintze, S.G., and P.J.G. Mann. 1947. Divalent manganese in soil extracts.
Nature 158:791–792. doi:10.1038/158791a0
IBM Corp. 2011. IBM SPSS statistics 20.0 software package for Mac. IBM
Corp., New York.
IUSS Working Group WRB. 2006. World reference base for soil resources
2006. A framework for international classification, correlation and
communication. Worlds Soil Resources Reports No 103. FAO, Rome.
Kaiser, M., A.A. Berhe, M. Sommer, and M. Kleber. 2012. Application of
ultrasound to disperse soil aggregates of high mechanical stability. J. Plant
Nutr. Soil Sci. 175:521–526. doi:10.1002/jpln.201200077
Kay, B.D. 1998. Soil structure and organic carbon: A review. In: R. Lal, et al.
editors, Soil processes and the carbon cycle. CRC Press, Boca Raton, FL.
p. 169–197.
Kemper, W.D., and R.C. Rosenau. 1986. Aggregate stability and size distribution.
In: A. Klute, editor, Methods of soil analysis. Part 1. Agron. Monogr. 9.
2nd ed. ASA, SSSA, Madison, WI. p. 425–442.
Kercheva, M., S. Rousseva, E. Dimitrov, M. Nenov, and T. Shishkov. 2011. Soil
aggregation estimates in CZO-Fuchsenbigl. Appl. Geochem. 26:S57–S59.
doi:10.1016/j.apgeochem.2011.03.028
Kleber, M., P. Sollins, and R. Sutton. 2007. A conceptual model of organomineral interactions in soils: Self-assembly of organic molecular fragments
into zonal structures on mineral surfaces. Biogeochemistry 85:9–24.
doi:10.1007/s10533-007-9103-5
Lair, G.J., F. Zehetner, M. Hrachowitz, N. Franz, F.-J. Maringer, and M.H.
Gerzabek. 2009. Dating of soil layers in a young floodplain using iron
oxide crystallinity. Quat. Geochronol. 4:260–266. doi:10.1016/j.
quageo.2008.11.003
Le Bissonnais, Y., and M.J. Singer. 1993. Seal formation, runoff and interrill
erosion from seventeen California soils. Soil Sci. Soc. Am. J. 57:224–229.
doi:10.2136/sssaj1993.03615995005700010039x
Lehrsch, G.A., R.E. Sojka, D.L. Carter, and P.M. Jolley. 1991. Freezing
effects on aggregate stability affected by texture, mineralogy, and
organic matter. Soil Sci. Soc. Am. J. 55:1401–1406. doi:10.2136/
sssaj1991.03615995005500050033x
Lopez-Capel, E., R. Bol, and D.A.C. Manning. 2005. Application of simultaneous
thermal analysis mass spectrometry and stable carbon isotope analysis in a
carbon sequestration study. Rapid Commun. Mass Spectrom. 19:3192–
3198. doi:10.1002/rcm.2145
Mayer, H., A. Mentler, M. Papakyriacou, N. Rampazzo, Y. Marxer, and W.E.H.
Blum. 2002. Influence of vibration amplitude on ultrasonic dispersion of
soils. Int. Agrophys. 16:53–60.
Mehra, O.P., and M.L. Jackson. 1958. Iron oxide removal from soils and clays
by dithionite-citrate systems buffered with sodium bicarbonate. Clays Clay
Miner. 7:317–327. doi:10.1346/CCMN.1958.0070122
Mentler, A., H. Mayer, P. Strauß, and W.E.H. Blum. 2004. Characterisation of
soil aggregate stability by ultrasonic dispersion. Int. Agrophys. 18:39–45.
Moore, D.M., and R.C. Reynolds. 1997. X–ray diffraction and the identification
and analysis of clay minerals. Oxford Univ. Press, New York.
Mueller, C.W., S. Schuld, J. Prietzel, I. Kögel-Knabner, and M. Gutsch. 2012.
Soil aggregate destruction by ultrasonication increases soil organic
matter mineralization and mobility. Soil Sci. Soc. Am. J. 76:1634–1643.
doi:10.2136/sssaj2011.0186
Navrátil, T., J.B. Shanley, P. Skrivan, P. Krám, M. Mihaljevic, and P. Drahota.
2007. Manganese Biochemistry in a Central Czech Republic Cathment.
Water Air Soil Pollut. 186:149–165. doi:10.1007/s11270-007-9474-1
Nikolaidis, N.P., F. Bouraoui, and G. Bidoglio. 2012. Hydrologic and geochemical
modelling of a karstic Mediterranean watershed. Hydrol. Earth Syst. Sci.
Discuss. 9:1–27. doi:10.5194/hessd-9-1-2012
North, P.F. 1976. Towards an absolute measurement of soil structural stability
using ultrasound. J. Soil Sci. 27:451–459. doi:10.1111/j.1365-2389.1976.
tb02014.x
Plante, A.F., J.M. Fernández, and J. Leifeld. 2009. Application of thermal
analysis techniques in soil science. Geoderma 153:1–10. doi:10.1016/j.
geoderma.2009.08.016
Raine, S.R., and H.B. So. 1993. An energy based parameter for the assessment of
aggregate bond energy. J. Soil Sci. 25:34–40.
Römkens, M.J.M., C.B. Roth, and D.W. Nelson. 1977. Erodability of selected
clay subsoils in relation to physical and chemical properties. Soil Sci. Soc.
Soil Science Society of America Journal
Am. J. 41:954–960. doi:10.2136/sssaj1977.03615995004100050030x
Schmidt, M.W.I., C. Rumpel, and I. Kögel-Knabler. 1999. Evaluation of
an ultrasonic dispersion procedure to isolate primary organomineral
complexes from soils. Eur. J. Soil Sci. 50:87–94. doi:10.1046/j.13652389.1999.00211.x
Schmidt, M.W.I., M.S. Torn, S. Abiven, T. Dittmar, G. Guggenberger, I.A.
Janssens, M. Kleber, I. Kögel-Knabner, J. Lehmann, D.A.C. Manning, P.
Nannipieri, D.P. Rasse, S. Weiner, and S.E. Trumbore. 2011. Persistence
of soil organic matter as an ecosystem property. Nature 478:49–56.
doi:10.1038/nature10386
Schomakers, J., A. Mentler, N. Degischer, W.E.H. Blum, and H. Mayer. 2011a.
Measurement of soil aggregate stability using low intensity ultrasonic
vibration. Spanish J. Soil Sci. 1:8–19.
Schomakers, J., A. Mentler, T. Steurer, A. Klik, and H. Mayer. 2011b.
Characterization of soil aggragate stability using low intensity ultrasonic
vibration. Int. Agrophys. 25:165–172.
Schulten, H.-R., and P. Leinweber. 2000. New insights into organic-mineral
particles: Composition, properties and models of molecular structure.
Biol. Fertil. Soils 30:399–432. doi:10.1007/s003740050020
Schultz, L.G. 1964. Quantitative Interpretation of mineralogical composition
from x-ray and chemical data for the Pierre Shales. U.S. Geol. Surv. Prof.
Pap. 391–C. U.S. Gov. Printing Office, Washington, DC.
Schwertmann, U. 1964. Differenzierung der Eisenoxide des Bodens durch
Extraktion mit Ammoniumoxalat-Lösung. Z. Pflanzenernaehr. Dueng.
Bodenkd. 105:194–202. doi:10.1002/jpln.3591050303
Shoji, S., M. Nanzyo, and R.A. Dahlgren. 1993. Volcanic ash soils: Genesis,
properties and utilization. Dev. Soil Sci. 21. Elsevier, Amsterdam, the
Netherlands.
www.soils.org/publications/sssaj
Six, J., E.T. Elliott, and K. Paustian. 2000. Soil macroaggregate turnover and
microaggregate formation: A mechanism for C sequestration under notillage agriculture. Soil Biol. Biochem. 32:2099–2103. doi:10.1016/
S0038-0717(00)00179-6
Six, J., H. Bossuyt, S. Degryze, and K. Denef. 2004. A history of research on
the link between (micro)aggregates, soil biota, and soil organic matter
dynamics. Soil Tillage Res. 79:7–31. doi:10.1016/j.still.2004.03.008
Soil Survey Staff. 2004. Soil survey laboratory methods manual. Soil Survey
Investigations Rep. 42. USDA-NRCS, Washington, DC.
Stavi, I., R. Lal, and L.B. Owens. 2011. On-farm effects of no-till versus occasional
tillage on soil quality and crop yields in eastern Ohio. Agron. Sustain. Dev.
31:475–482. doi:10.1007/s13593-011-0006-4
Systat Software, Inc. 2010. SigmaPlot for Windows, Systat Software, San Jose, CA.
Tabatabai, M.A., and J.M. Bremner. 1991. Automated instruments for
determination of total carbon, nitrogen, and sulfur in soils by combustion
techniques. In: K.A. Smith, editor, Soil analysis. Marcel Dekker, New York.
p. 261–286.
Tisdall, J.M., and J.M. Oades. 1982. Organic matter and water-stable aggregates
in soils. J. Soil Sci. 33:141–163. doi:10.1111/j.1365-2389.1982.tb01755.x
Zhang, X.C., and L.D. Norton. 2002. Effect of exchangeable Mg on saturated
hydraulic conductivity, disaggregation and clay dispersion of disturbed
soils. J. Hydrol. 260:194–205. doi:10.1016/S0022-1694(01)00612-6
Zhu, Z.L., B. Minasny, and D.J. Field. 2009. Measurement of aggregate
bond energy using ultrasonic dispersion. Eur. J. Soil Sci. 60:695–705.
doi:10.1111/j.1365-2389.2009.01146.x
Zhu, Z.L., D.J. Field, and M. Budiman. 2010. Measuring and modeling the actual
energy involved in aggregate breakdown. Catena 82:53–60. doi:10.1016/j.
catena.2010.04.009
723
3 Aggregation and organic matter in
subarctic Andosols under different
grassland management
53
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 oxalateextractable 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 13C NMR spectroscopy.
54
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 microbialand 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
55
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.
56
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
57
No drainage
Drainage
N/A
Spring 20 kg K ha-1
No fertilization
N/A
N/A
N/A
No drainage
9th century
800 mm
4.3°C
W 21°36´16´´
N 64°20´38´´
Grass2
Grassland
Haplic Andosol
3.2 t C ha
-1
260 kg N ha-1
Fall 10 t ha-1
Spring 30 t ha-1
v,bc,6*gc, gc
1994
1996, 15-20 cm
1960
9th century
800 mm
4.3°C
W 21°36´14´´
N 64°20´43´´
HiAorg
Organic
Histic Andosol
Spring 300 kg N ha-1
1.2 t C ha-1
60 kg N ha-1
Spring 30 t ha-1
g
N/A
1998, 15-20 cm
1968
9th century
800 mm
4.3°C
W 21°34´54´´
N 64°20´33´´
HiAcon
Conventional
Histic Andosol
58
Special treatments
Biodynamic preparationsc
Shell sand, fishmeald
a
Icelandic meteorological office database, 2012
b
o denotes oats, b barley, gc grass and clover, v vegetables, bold letter the time of sampling. Grass and grass and clover mixtures at individual farms: BAorg (Phlenum pretense, Poa pratensis, Festuca rubra/richardsonii,
Trifolium repens, Trifolium pretense and barley), Bacon (Phelenum pretense (85%), Festuca rihardsonii/rubra (10%), Poa pratensis (5%)), HAorg (Plenum pretense and Trifolium repens), HAcon (Trifolium repens and
Alopecurus pratensis).
c
Humus preparation No. 500 to enhance sprout and early spring growth, Silica preparation No.501 to enhance ripeness and protect from fungal diseases, special preparation of Valeriana officianalis No. 507 to loosen the
phosphate. All preparations are prepared and used according to the biodynamic guidelines.
d
shell sand MgCa(CO3)2: 1996 3-5 t ha-1, 1997 1.5 t ha-1; fishmeal: 1995 220 kg ha-1, 1996 545 kg ha-1, 1998 545 kg ha-1, 2000 270 kg ha-1, 2003 270 kg ha-1.
e
calculated according to Hoogendorn et al. (2010), Pálmason (2013), and Dyrmundsson (2013, personal communication).
N/A not available.
- Total K (kg K ha year )
-1
Spring 20 kg P ha-1
-1
- Total P (kg P ha-1 year-1)
0.8 t C ha
Spring 80 kg N ha-1
8.6 t C ha
-1
40 kg N ha-1
Spring 20 t ha-1
g
N/A
1995, 15-20 cm
No drainage
9th century
1120 mm
3.6°C
W 20°10´44´´
N 64°3´0´´
HaAcon
Conventional
Haplic Andosol
- Total N (kg N ha-1 year-1)
Inorganic fertilizers
- Total C (t C ha year )
-1
970 kg N ha-1
-1 e
- Total N (kg N ha-1 year-1)e
-1
Spring 50 t ha
- Cattle urine
-1
Fall 35 t ha-1
- Compost
o, ob, 8*gc, gc
Spring 35 t ha-1
No fertilization
1996
2003, 15-20 cm
- Manure
Organic fertilizers
Crop rotation
N/A
Conversion to organic farming
b
N/A
Latest ploughing
No drainage
1120 mm
9th century
1120 mm
9th century
3.6¨C
Farm since
Precipitation
3.6¨C
W 20°12´14´´
W 20°10´46´´
Temperature
Mean
Annual
(MAT)a
Mean
Annual
(MAP)a
HaAorg
N 64°02´34´´
Grass1
N 64°3´1´´
Coordinates
Organic
Grassland
Abbreviation
Haplic Andosol
Haplic Andosol
Table 3.1 Background information of the studied sites.
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-bicarbonateextractable 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
59
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 preincubation 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 [14C]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
aggregates on sieve i.
60
is the fraction of
soil sample
air-dried
< 5 mm
density fractionation
ρ = 1.8 g cm
-3
fPOM
(20-5000µm)
< 1.8 g cm
residue
> 1.8 g cm
-3
-3
ultrasonication
-1
8 J ml
centrifugation
10 min 4350rpm
density fractionation
ρ = 1.8 g cm
oPOM
(20-5000µm)
-3
residue
> 1.8 g cm
< 1.8 g cm
-3
-3
sieving
aggregates
aggregates
aggregates
(250-5000µm)
(20-250µm)
(< 20µm)
Figure 3.1 Schematic of the fractionation procedure. Gray circles represent fractions for
further analyses.
61
3.2.5 Solid-state
13
C 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 13C 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 13Cresonance 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 13C 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 logtransformed 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).
62
-1
Mnp (g kg )
-1
Mnd (g kg-1)
CEC (mmolc kg )
P (mg kg-1)
K (mg kg-1)
Nt (g kg-1)
OC (g kg-1)
pH (H2O)
Chemical soil properties
clay (g kg-1)
silt (g kg )
-1
sand (g kg-1)
Physical soil properties
0.1 (0.01)a
1.2 (0.5)a
10-20
0-10
1.1 (0.4)a
55.3 (2.1)a
10-20
0-10
148.3 (15.1)a
0.1 (0.2)a
10-20
0-10
0.8 (0.1)a
24.5 (28.6)a
10-20
0-10
77.2 (32.4)a
3.3 (0.5)a
0-10
3.7 (0.6)a
10-20
52.0 (4.6)a
0-10
59.9 (4.4)a
10-20
5.9 (0.4)a
0-10
5.9 (0.1)a
10-20
43.2 (22)a
10-20
0-10
63.8 (20)ab
585.7 (12)a
0-10
528.1 (74)a
10-20
371.0 (33)a
0-10
408.1 (89)a
10-20
Grass1
0-10
Depth (cm)
0.03 (0.00)a
0.7 (0.01)a
0.6 (0.03)a
54.3 (10.8)a
166.5 (18.4)a
2.8 (1.1)a
4.6 (0.9)a
19.7 (28.2)a
37.8 (29.2)a
4.0 (0.1)a
4.6 (0.8)a
59.7 (1.9)a
69.5 (9.4)a
5.9 (0.0)a
5.9 (0.2)a
49.3 (5)a
54.3 (11)a
615.0 (23)a
565.8 (13)a
335.7 (19)a
379.8 (22)a
HaAorg
Table 3.2 Key physicochemical and biological properties of the studied soils.
0.02 (0.01)a
0.6 (0.04)a
0.6 (0.02)a
51.1 (3.6)a
128.3 (9.8)a
1.7 (0.4)a
4.8 (1.8)a
5.1 (7.6)a
20.2 (18.9)a
3.5 (0.9)a
4.0 (0.8)a
54.4 (12)a
60.3 (9.2)a
6.0 (0.1)a
5.8 (0.2)ab
43.5 (2)a
52.4 (13)a
558.3 (58)a
559.7 (31)a
398.3 (57)a
375.1 (66)a
HaAcon
0.03 (0.01)b
0.7 (0.05)b
0.7 (0.06)b
99.1 (6.9)ab
215.3 (37.5)b
0.2 (0.2)a
0.2 (0.2)a
46.4 (79.2)a
119.1 (73.3)a
4.9 (1.0)ab
6.1 (1.5)a
68.9 (15)a
83.2 (18)ab
5.7 (0.1)ab
5.5 (0.2)ab
66.6 (26)a
89.8 (14)ab
593.3 (47)a
535.2 (54)a
340.1 (56)a
360.5 (11)a
Grass2
0.1 (0.02)c
0.6 (0.1)ac
0.6 (0.1)a
141.6 (16.5)b
364.1 (50.9)c
5.1 (1.5)ab
7.6 (1.8)b
0.6 (0.1)a
9.6 (1.8)b
6.1 (0.2)b
8.7 (0.8)a
100.0 (6.1)a
124.9 (8.7)b
5.2 (0.3)bc
5.2 (0.4)b
102.3 (29)a
137.2 (27)ab
572.8 (37)a
502.3 (99)a
324.9 (65)a
414.7 (20)a
HiAorg
0.1 (0.03)bc
1.0 (0.3)ab
0.7 (0.3)a
63
108.1 (2.4)ab
349.8 (40.8)c
8.6 (1.3)b
17.7 (4.9)c
8.4 (9.8)a
17.4 (14.5)ab
10.3 (1.4)c
12.2 (2.9)b
155.3 (19)b
193.2 (46)c
4.8 (0.1)c
4.7(0.5)b
106.3 (14)a
159.3 (100)b
521.1 (61)a
425.9 (117)a
372.6 (47)a
414.7 (20)a
HiAcon
0-10
0-10
Mineralizable N (mg kg-1)
HWC (µg C g-1)
n.d.
n.d.
n.d.
n.d.
n.d.
1217.9 (102.5)a
95.9 (7.7)a
45.4 (27.9)a
51.2 (4.7)a
22.4 (3.8)a
5.8 (0.2)ab
5.4 (0.1)ab
9.6 (0.4)a
7.2 (2.6)ab
0.6 (0.02)ac
0.5 (0.01)a
0.01(0.00)ab
912.8 (156.8)a
74.5 (21.4)a
-10.3 (15.0)a
35.3(4.3)a
21.6 (11.1)a
6.1 (0.6)ab
6.3 (0.7)a
9.5 (0.5)a
9.5 (0.6)a
0.6 (0.02)ac
0.7 (0.1)a
0.01(0.00)ab
1398.5 (323.0)a
107.6 (40.6)a
57.2 (19.1)a
49.5 (13.1)a
32.2 (10.0)a
5.8 (0.5)ab
5.4 (0.2)ab
7.3 (0.8)ab
6.6 (0.6)ab
0.7 (0.01)b
0.6 (0.02)b
0.01(0.01)b
3387.7 (385.6)b
257.5 (28.9)b
133.8 (37.5)b
47.7 (15.3)a
13.8 (2.8)a
4.7 (0.6)b
4.6 (0.9)b
4.7 (0.1)b
4.2 (0.1)b
0.5 (0.1)c
0.5 (0.1)a
0.1 (0.03)c
4224.06
(1235.60)b
333.44 (136.82)b
126.45 (64.52)b
39.1 (13.8)a
9.46 (3.99)b
7.6 (0.4)c
5.3 (0.5)ab
4.4 (0.3)b
4.8 (2.6)b
0.9 (0.3)ab
0.5 (0.2)a
0.1 (0.03)c
64
Mean values and standard deviations in parenthesis (n = 3). Different letters within row indicate significant differences applying Tukeys´
significant difference as a post hoc test at p<0.05.
0-10
Leucine incorporation (pmol g h )
0-10
-1 -1
0-10
6.4(0.3)ac
10-20
Bacteria (µg C g-1 dry soil)
5.4 (0.1)ab
0-10
8.4 (0.6)a
10-20
1.0 (0.4)ac
10-20
6.6 (0.2)ab
1.0 (0.4)a
0.04 (0.02)a
0-10
10-20
0-10
Fungi (µg C g-1 dry soil)
Biological soil properties
Ferrihydrite (%)
Allophane (%)
Mno (g kg-1)
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).
65
0-10
10-20
0-10
10-20
oPOM (g kg-1)
MWD (mm)
66
0-10
10-20
Depth (cm)
fPOM (g kg )
-1
*
14.7 (1.3)ab
11.1 (4.3)ab
91.5 (56.3)a
14.1 (2.4)a
45.8 (49.7)a
15.5 (16.4)a
Grass1
19.9 (4.1)ab
7.7 (2.9)a
12.4 (2.2)b
11.8 (4.2)a
40.7 (2.2)a
20.0 (0.8)a
HaAorg
8.3 (3.8)ac
10.6 (3.2)abc
12.6 (1.1)b
8.8 (0.5)a
71.5 (24.6)a
31.0 (12.7)a
HaAcon
27.3 (2.0)b
26.2 (7.5)b
4.4 (2.2)b
4.5 (1.9)a
49.4 (36.2)a
9.2(0.2)a
Grass2
11.7 (0.8)abc
13.3 (5.1)abc
39.1 (25.2)abc
24.0 (9.0)a
460.1 (56.5)b
305.1 (134.9)b
HiAorg
4.8 (1.2)c
8.3 (3.7)ac
88.9 (56.9)cd
37.1 (4.6)a
556.0 (199.6)b
553.9 (132.4)c
HiAcon
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.
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
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
matter
organic
MWD
fPOM
oPOM
Nt
-0.22
0.81***
0.41*
OC
K
0.85***
-0.20
0.50**
0.10
P
-0.29
0.34*
0.51**
0.71***
0.54***
DOC
Biomass C
CEC
pH (H2O)
Fed
Mnd
Ald
Sid
-0.21
-0.03
0.06
0.26
-0.15
0.46**
-0.10
0.06
0.83***
0.55***
0.61***
-0.81***
0.44**
-0.39*
0.18
-0.75**
0.49**
0.48**
0.41*
-0.35*
0.04
-0.62***
0.04
-0.33*
Fep
-0.24
0.82**
0.40*
Mnp
-0.02
0.80**
0.30
Alp
Mno
Alo
Sio
Allophane
Ferrihydrite
Silt
-0.16
0.46**
0.23
0.08
0.09
-0.28
0.13
0.73**
-0.46**
-0.64***
-0.75***
-0.72***
-0.16
-0.46**
0.31
-0.65***
-0.64***
-0.49**
-0.53***
-0.14
-0.37*
Clay
-0.07
0.80**
*
0.58*
0.67**
0.32
Soil aggregate fractions
20-250
>250
<20 µm µm
µm
0.67*** -0.76*** 0.54***
0.65*** -0.74*** 0.60***
-0.38*
-0.28
0.34*
-0.05
-0.12
0.60***
-0.61** -0.68*** 0.52***
-0.59** -0.63*** -0.27
-0.63** -0.65*** -0.20
0.57**
0.63**
0.56***
-0.47** -0.56*** -0.39*
-0.10
-0.12
0.42*
-0.39*
-0.42*
-0.26
0.56*** 0.60**
0.36*
0.64*** -0.70*** 0.56***
0.68*** -0.70*** -0.35*
0.66*** -0.70*** -0.48**
-0.04
-0.05
0.46**
0.29
0.22
0.35*
0.71*** 0.75***
0.40*
0.75*** 0.74***
0.39*
0.24
0.14
-0.18
0.31
0.29
0.26
0.56*** -0.53*** -0.36*
Fungal biomass
-0.71***
-0.72***
0.03
0.22
0.87***
Fungi/bacteria
-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**
1
n=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.
68
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=1020 cm.
13
C 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.
69
Table 3.5 Integrated chemical shift regions (% of total signal intensity) obtained by 13C
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
70
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
71
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,
72
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 13C 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
73
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.
74
References
Amézketa E. 1999. Soil aggregate stability: A Review, Journal of Sustainable Agriculture.
14:83-151.
Baldock JA, Oades JM, Nelson PN, Skene TM, Golchin A, Clarke P. 1997. Assessing the
extent of decomposition of natural organic materials using solid-state 13C NMR
spectroscopy, Aust. J. Soil Res. 35:1061-1083.
Bloem J, Bolhuis PR. 2006. Thymidine and leucine incorporation to assess bacterial
growth rate. In: Bloem J, Hopkins DW, Benedetti A, editors. Microbiological Methods for
Assessing Soil Quality. Wallingford, UK: CABI.
Bloem J, Vos A. 2004. Fluorescent staining of microbes for total direct counts. In:
Kowalchuk GA, de Bruijn FJ, Head IM, Akkermans ADL, van Elsas JD, editors.
Molecular Microbial Ecology Manual 2nd edition. Dordrecht, The Netherlands: Kluwer
Academic Publishers.
Bloem J, Veninga M, Shepherd J. 1995. Fully-automatic determination of soil bacterium
numbers, cell volumes, and frequencies of dividing cells by confocal laser-scanning
microscopy and image-analysis. Appl. Environ. Microbiol. 61:926-936.
Bolinder MA, Angers DA, Bélanger G, Michaud R, Laverdière MR. 2002. Root biomass
and shoot to root ratios of perennial forage crops in eastern Canada. Can. J. Plant Sci.
82:731-737.
Brandstetter A, Sletten RS, Mentler A, Wenzel WW. 1996. Estimating dissolved organic
carbon in natural waters by UV absorbance (254 nm). Zeitschrift für Pflanzenernährung
und Bodenkunde. 159:605-607.
Canali S, Benedetti A. 2006. Soil nitrogen mineralization. In: Bloem J, Hopkins DW,
Benedetti A, editors. Microbiological methods for assessing soil quality. Wallingford, UK:
CABI Publishing.
Chan KY, Heenan DP, Oates A. 2002. Soil carbon fractions and relationship to soil quality
under different tillage and stubble management. Soil Tillage Res. 63:133-139.
Christensen BT. 1992. Physical Fractionation of Soil and Organic Matter in Primary
Particle Size and Density Separates. Adv. Soil Sci. 20:1-90.
Courtier-Murias D, Simpson AJ, Marzadori C, Baldoni G, Ciavatta C, Fernández JM,
López-de-Sá EG, Plaza C. 2013. Unraveling the long-term stabilization mechanisms of
organic materials in soils by physical fractionation and NMR spectroscopy. Agric.,
Ecosyst. Environ. 171: 9-18.
Crochet P, Jóhannesson T. 2011. A data set of gridded daily temperature in Iceland, 19492010. Jökull. 61:1-17.
Diacono M, Montemurro F. 2011. Long-term effects of organic amendments on soil
fertility. In: Lichtfouse E, Hamelin M, Navarrete M, Debaeke P, editors. Sustainable
Agriculture, vol. 2. The Netherlands: Springer.
75
Dexter AR. 1988. Advances in characterization of soil structure. Soil Tillage Res. 11:199238.
Eash NS, Karlen DL, Parkin TB. 1994. Fungal Contributions to Soil Aggregation and Soil
Quality. Soil Sci. Soc. Am. J. special publication. 35:221-228.
Elliott ET. 1986. Aggregate structure and carbon, nitrogen, and phosphorus in native and
cultivated soils. Soil Sci. Soc. Am. J. 50:627-633.
Feller C, Blanchart E, Bernoux M, Lal R, Manlay R. 2012. Soil fertility concepts over the
past two centuries: the importance attributed to soil organic matter in developed and
developing countries. Arch. Agron. Soil Sci. 58:S3-S21.
Gattinger A, Muller A, Haeni M, Skinner C, Fliessbach A, Buchmann N, Mäder P, Stolze
M, Smith P, El-Hage Scialabba N, Niggli U. 2012. Enhanced top soil carbon stocks under
organic farming. Proceedings of the National Academy of Sciences. 109:18226-18231.
Ghani A, Dexter M, Perrott KW. 2003. Hot-water extractable carbon in soils: A sensitive
measurement for determining impacts of fertilisation, grazing and cultivation. Soil Biol.
Biochem. 35:1231-1243.
Gislason SR, Oelkers EH, Eiriksdottir ES, Kardjilov MI, Gisladottir G, Sigfusson B,
Snorrason A, Elefsen S, Hardadottir J, Torssander P, Oskarsson N. 2008. The feedback
between climate and weathering. Mineral. Mag. 72:317-320.
Golchin A, Baldock JA, Clarke P, Higashi T, Oades JM. 1997. The effects of vegetation
and burning on the chemical composition of soil organic matter of a volcanic ash soil as
shown by 13C NMR spectroscopy. II. Density fractions. Geoderma. 76:175-192.
Golchin A, Oades JM, Skjemstad JO, Clarke P. 1994. Study of Free and Occluded
Particulate Organic Matter in Soils by Solid state 13C CP/MAS NMR Spectroscopy and
Scanning Electron Microscopy. Aust. J. Soil Res. 32:285-309.
Guo LB, Gifford RM. 2002. Soil carbon stocks and land use changes: a meta analysis.
Global Change Biology. 8:345–360.
Hoogendoorn CJ, Betteridge K, Costall DA, Ledgard SF. 2010. Nitrogen concentration in
the urine of cattle, sheep and deer grazing a common ryegrass/cocksfoot/white clover
pasture. N. Z. J. Agric. Res. 53:235-243.
Hoyos N, Comerford NB. 2005. Land use and landscape effects on aggregate stability and
total carbon of Andisols from the Columbian Andes. Geoderma 129:268-278.
Jóhannesson T. 2010. Agriculture in Iceland: Conditions and Characteristics. Hvanneyri,
Iceland: Agricultural University of Iceland.
Karami A, Homaee M, Afzalinia S, Ruhipour H, Basitar S. 2012. Organic resource
management: Impacts on soil aggregate stability and other soil physic-chemical properties.
Agric., Ecosyst. Environ. 148:22-28.
76
Knicker H, Gonzalez-Vila FJ, Polvillo O, Gonzalez JA, Almendros G. 2005. Fire-induced
transformation of C- and N-forms in different organic soil fractions from a Dystric
Cambisol under a Mediterranean pine forest (Pinus pinaster). Soil Biol. Biochem. 37:701–
718.
Kögel-Knabner I. 2002. The macromolecular organic composition of plant and microbial
residues as inputs to soil organic matter. Soil Biol. Biochem. 34:139-162.
Kölbl A, Kögel-Knabler I. 2004. Content and composition of free and occluded particulate
organic matter in a differently textured arable Cambisol as revealed by solid-state 13C
NMR spectroscopy. J. Plant Nutr. Soil Sci. 167:45-53.
Lehtinen T, Lair GJ, Mentler A, Gisladóttir G, Ragnarsdóttir KV, Blum WEH. 2014. Soil
Aggregate Stability in Different Soil Orders Quantified by Low Dispersive Ultrasonic
Energy Levels. Soil Sci. Soc. Am. J. 78:713:723.
Leifeld J, Fuhrer J. 2010. Organic Farming and Soil Carbon Sequestration: What Do We
Really Know About the Benefits? AMBIO. 39:585-599.
Linsler D, Geisseler D, Loges R, Taube F, Ludwig B. 2013. Temporal dynamics of soil
organic matter composition and aggregates distribution in permanent grassland after a
single tillage event in a temperate climate. Soil Tillage Res. 126:90-99.
Manlay RJ, Feller C, Swift MJ. 2007. Historical evolution of soil organic matter concepts
and their relationships with the fertility and sustainability of cropping systems. Agric.,
Ecosyst. Environ. 119:217-233.
Marriott EE, Wander M. 2006. Qualitative and quantitative differences in particulate
organic matter fractions in organic and conventional farming systems. Soil Biol. Biochem.
38:1527-1536.
Mehra OP, Jackson ML. 1960. Iron oxide removal from soils and clays by dithionitecitrate systems buffered with sodium bicarbonate. Clays Clay Miner. 7:317–327.
Mueller CW, Brüggemann N, Pritsch K, Stoelken G, Gayler S, Winkler JB, Kögel-Knabler
I. 2009. Initial differentiation of vertical soil organic matter distribution and composition
under juvenile beech (Fagus szlvatica L.) trees. Plant Soil. 323:111-123.
Navrátil T, Shanley JB, Skrivan P, Krám P, Mihaljevic M, Drahota P. 2007. Manganese
Biochemistry in a Central Czech Republic Cathment. Water Air Soil Pollut. 186(1-4):149165.
Pálmason F. 2013. PlöntunæringarLandbúnaðarháskóli Íslands.
og
áburðarfræði.
Hvanneyri,
Iceland:
Oades JM, Waters AG. 1991. Aggregate hierarchy in soils. Aust. J. Soil Res. 29:815-828.
Parfitt RL. 1990. Allophane in New Zealand – A review. Aust. J. Soil Res. 28:343-360.
Parfitt RL, Childs CW. 1988. Estimation of forms of Fe and Al: A review, and Analysis of
Contrasting Soils by Dissolution and Moessbauer Methods. Aust. J. Soil Res. 26:121-144.
77
Poll C, Thiede A, Wermbter N, Sessitsch A, Kandeler E. 2003. Micro-scale distribution of
microorganisms and microbial enzyme activities in a soil with long-term organic
amendments. Eur. J. Soil Sci. 54:715-724.
Schmidt MWI, Knicker H, Hatcher PG, Kogel-Knabner I. 1997. Improvement of 13C and
15
N CPMAS NMR spectra of bulk soils, particle size fractions and organic material by
treatment with 10 % hydrofluoric acid. Eur. J. Soil Sci. 48:319-328.
Schwertmann U. 1964. Differenzierung der Eisenoxide des Bodens durch Extraktion mit
Ammoniumoxalat-Lösung. Z. Pflanzenernaehr., Dueng., Bodenkd. 105:194-202.
Shepherd MA, Harrison R, Webb J. 2002. Managing soil organic matter – implications for
soil structure on organic farms. Soil Use Manage. 18:284-292.
Siegrist S, Schaub D, Pfiffner L, Mäder P. 1998. Does organic agriculture reduce soil
erodability? The results of a long-term field study on loess in Switzerland. Agric., Ecosyst.
Environ. 69:253-264.
Six J, Elliott ET, Paustian K. 2000. Soil macroaggregate turnover and microaggregate
formation: a mechanism for C sequestration under no-tillage agriculture. Soil Biol.
Biochem. 32:2099-2103.
Six J, Bossuyt H, Degryze S, Denef K. 2004. A history of research on the link between
(micro)aggregates, soil biota, and soil organic matter dynamics. Soil Tillage Res. 79:7-31.
Sodhi GPS, Beri V, Benbi DK. 2009. Soil aggregation and distribution of carbon and
nitrogen in different fractions under long-term application of compost in rice-wheat
system. Soil Tillage Res. 103:412-418.
Soil Survey Staff. 2004. Soil Survey Laboratory Methods Manual. Soil Survey
Investigations Rep. 42. Washington, DC: USDA – NRCS.
Stavi I, Lal R, Owens LB. 2011. On-farm effects of no-till versus occasional tillage on soil
quality and crop yields in eastern Ohio. Agron. Sustainable Dev. 31:475-482.
Steffens M, Kölbl A, Kögel-Knabler I. 2009. Alteration of soil organic matter pools and
aggregation in semi-arid steppe topsoil’s as driven by organic matter input. Eur. J. Soil Sci.
60:198-212.
Tabatabai MA, Bremner JM. 1991. Automated instruments for determination of total
carbon, nitrogen, and sulfur in soils by combustion techniques. In: Smith KA, editor. Soil
Analysis. New York, USA: Marcel Dekker.
Tisdall JM. 1991. Fungal hyphae and structural stability of soil. Aust. J. Soil Res. 29:729743.
Tisdall JM, Oades JM. 1982. Organic matter and water-stable aggregates in soils. Eur. J.
Soil Sci. 33:141-163.
Tukey JW. 1957. On the comparative anatomy of transformations. Ann. Math. Stat.
28:602-632.
78
TÚN. 2013. Rules for organic and sustainable production and resource utilization, 2nd
edition. Reykjavik, Iceland: Vottunastofan Tún ehf.
Vance ED, Brookes PC, Jenkinsson DS. 1987. An extraction method for measuring soil
microbial biomass C. Soil Biol. Biochem. 19:703-707.
van Leeuwen JP, Lehtinen T, Lair GJ, Bloem J, Hemerik L, Ragnarsdóttir KV, Gísladóttir
G, de Ruiter RC. 2014. An ecosystem approach to assess soil quality in organically and
conventionally managed farms in Iceland and Austria. SOIL (in review).
Watson CA, Atkinson D, Gosling P, Jackson LR, Rayns FW. 2002. Managing soil fertility
in organic farming systems. Soil Use Manage. 18:239-247.
Whalen JK, Chang C. 2002. Macroaggregate characteristics in cultivated soils after 25
annual manure applications. Soil Sci. Soc. Am. J. 66:1637-1647.
79
80
Sid (g kg-1)
Alo (g kg-1)
Alp (g kg-1)
Ald (g kg-1)
Mno (g kg )
-1
Mnp (g kg-1)
Mnd (g kg-1)
Feo (g kg-1)
Fep (g kg-1)
Fed (g kg-1)
Chemical soil properties
BD (g cm3)
Physical soil properties
3.0 (0.2)a
26.7 (3.2)ac
10-20
0-10
26.4 (3.3)ac
2.8 (0.6)a
10-20
0-10
3.3 (0.5)a
11.8 (2.2)a
10-20
0-10
11.9 (1.8)a
10-20
0-10
1.0 (0.4)a
1.0 (0.4)ac
0-10
0.04 (0.02)a
10-20
1.2 (0.5)a
0.1 (0.01)a
10-20
0-10
1.1 (0.4)a
34.0 (2.7)ac
0-10
10-20
2.8 (0.7)a
31.8 (1.0)ab
10-20
0-10
3.6 (0.2)a
57.6 (6.3)a
0-10
55.1 (4.8)a
10-20
n.d.
10-20
0-10
n.d.
Grass1
0-10
Depth (cm)
4.0 (0.3)a
24.0 (0.9)ac
19.7 (0.6)a
1.7 (0.3)a
2.0 (0.1)a
9.7 (0.5)a
9.2 (0.6)a
0.6 (0.02)ac
0.5 (0.01)a
0.01(0.00)ab
0.03 (0.00)a
0.7 (0.01)a
0.6 (0.03)a
37.6 (1.5)a
31.6 (0.5)a
1.6 (0.4)a
1.6 (0.2)a
36.1 (1.3)a
29.9 (2.1)a
1.0 (0.1)a
0.7 (0.1)a*
HaAorg
4.8 (0.3)a
23.5 (0.7)ac
22.9 (1.3)ab
1.6 (0.2)a
1.7 (0.1)a
7.6 (0.4)ab
7.8 (0.4)ab
0.6 (0.02)ac
0.7 (0.1)a
0.01(0.00)ab
0.02 (0.01)a
0.6 (0.04)a
0.6 (0.02)a
33.8 (0.9)a
32.5 (1.6)b
1.3 (0.1)a
1.5 (0.1)a
37.0 (1.9)a
36.4 (1.4)a
1.1 (0.1)a
0.8 (0.03)a
HaAcon
4.7 (0.3)b
24.2 (1.7)a
24.6 (1.7)b
1.6 (0.2)b
1.8 (0.2)b
8.8 (1.2)b
9.6 (1.2)b
0.7 (0.01)b
0.6 (0.02)b
0.01(0.01)b
0.03 (0.01)b
0.7 (0.05)b
0.7 (0.06)b
37.1 (3.1)a
38.0 (3.3)a
1.3 (0.2)ab
1.6 (0.2)ab
36.3 (1.8)b
37.3 (2.4)b
n.d.
n.d.
Grass2
1.7 (0.1)c
19.1 (1.8)b
19.0 (2.0)c
3.3 (0.8)b
3.4 (0.8)b
8.9 (1.7)a
8.6 (1.5)a
0.5 (0.1)c
0.5 (0.1)a
0.1 (0.03)c
0.1 (0.02)c
0.6 (0.1)ac
0.6 (0.1)a
25.1 (4.2)b
25.4 (4.6)c
4.9 (0.6)b
5.5 (0.9)b
41.8 (5.8)a
38.5 (4.5)ab
0.6 (0.1)b
0.6 (0.2)ab
HiAorg
2.3 (0.2)d
22.7 (1.5)c
21.7 (2.0)ac
5.0 (0.5)c
4.9 (0.7)c
12.7 (1.4)b
12.7 (1.3)bc
0.9 (0.3)ab
0.5 (0.2)a
0.1 (0.03)c
0.1 (0.03)bc
1.0 (0.3)ab
0.7 (0.3)a
39.2 (7.3)c
31.5 (3.5)ad
8.5 (2.6)c
8.2 (2.9)c
72.1 (19.3)c
58.7 (13.0)b
0.7 (0.1)b
0.5 (0.1)b
HiAcon
Supplementary Table 3.S1 key physicochemical and biological properties of the studied soils; mean values and standard deviations in
parenthesis (n = 3).
-1
Biomass C (mg 100g )
DOC (mg L-1)
Soil biological properties
Sio (g kg-1)
78.4 (7.9)a
25.2 (3.2)a
10-20
2.8 (0.2)a
10-20
0-10
3.6 (0.1)a
0-10
12.1 (1.3)ab
10-20
3.3 (0.3)a
11.0 (1.1)ad
0-10
10-20
4.9 (0.3)a
21.2 (15.1)a
38.1 (24.3)a
2.4 (0.7)a
3.4 (0.1)a
14.0 (1.0)a
11.0 (0.4)ab
5.0 (0.3)a
28.5 (9.4)a
26.3 (18.5)a
2.6 (0.4)a
3.0 (1.1)a
16.3 (0.8)a
13.9 (4.0)b
4.7 (0.3)b
32.4 (13.7)a
67.6 (29.4)a
3.8 (1.4)a
4.2 (0.9)a
16.5 (1.2)b
16.3 (1.4)ad
1.9 (0.2)c
48.9 (6.4)a
114.3 (9.8)ab
7.6 (1.7)b
9.5 (0.4)b
7.8 (0.5)c
7.0 (0.2)cd
81
67.56 (18.92)a
128.94 (47.04)b
8.28 (0.84)b
10.96 (1.75)b
7.7 (0.6)bc
7.8 (3.2)d
2.9 (0.1)bd
4 Characterization of soil aggregation
and soil organic matter under
intensive cropping on Austrian
Chernozems
82
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
matter (POM), aggregate hierarchy.
13
C NMR spectroscopy, particulate organic
83
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).
84
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.
85
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-oxalateextractable 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) W 2 (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).
86
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 sandcorrected aggregates was calculated according to Kemper and Rosenau (1986) as follows:
∑
̅
Where, ̅ is the geometric mean of aggregate size on sieve i, and
aggregates on sieve i.
4.2.6 Solid-state
13
is the fraction of
C 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 13Cresonance 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 13C 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).
87
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 N t
distribution in the soil POM and aggregate fractions due to non-normal distribution. Twoway 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).
88
CEC (mmolc kg )
-1
CAL-extractable P (mg kg-1)
CAL-extractable K (mg kg-1)
CaCo3 (g kg-1)
Nt (g kg-1)
OC (g kg )
-1
pH (H2O)
Chemical soil properties
clay (g kg )
-1
silt (g kg-1)
sand (g kg-1)
Physical soil properties
192.7
174.6
30-40
44.2
30-40
0-15
106.9
41.7
30-40
0-15
70.8
142.9
30-40
0-15
68.3
1.1
30-40
0-15
1.4
13.1
0-15
18.0
30-40
8.1
0-15
8.0
30-40
20.3
0-15
16.7
30-40
60.2
30-40
0-15
61.7
19.5
0-15
21.6
30-40
mean ± sd
±
±
±
±
±
±
±
±
±
±
±
±
±
±
±
±
±
±
±
±
77.9a
89.3a
33.2a
26.3a
14.1a
12.7a
55.3a
1.8a
0.2a
0.1a
0.8a
4.0a
0.1a
0.0a
2.8a
1.6a
4.7a
3.0a
6.0a
4.5a
173.6
183.2
34.6
124.7
67.9
219.2
125.9
65.3
1.2
1.4
14.0
15.2
8.1
7.9
19.4
17.0
65.3
63.6
15.3
19.4
mean ± sd
Con76
Org76
0-15
depth (cm)
Obersiebenbrunn
Obersiebenbrunn
±
±
±
±
±
±
±
±
±
±
±
±
±
±
±
±
±
±
±
±
77.5a
79.2a
8.2a
26.6a
51.8a
48.0b
26.1a
12.0a
0.2a
0.1a
2.7a
0.6a
0.04a
0.05a
2.6a
0.9a
3.3a
3.4a
0.8a
3.8a
179.9
209.5
29.3
89.4
97.5
204.7
240.2
196.0
1.0
1.5
13.7
18.6
8.4
8.1
15.4
14.4
42.0
41.2
40.7
44.4
mean ± sd
Org95
Lassee
±
±
±
±
±
±
±
±
±
±
±
±
±
±
±
±
±
±
±
±
62.9a
63.4a
7.5a
9.9a
40.0a
18.0b
17.6b
19.8b
0.1a
0.1a
0.4a
1.8a
0.05b
0.03b
2.0a
1.8a
1.8b
1.3b
1.0b
3.1b
245.5
244.2
43.8
88.5
97.6
114.7
219.2
196.9
1.4
1.5
17.3
20.0
8.2
8.0
15.5
13.9
44.2
44.7
40.4
41.4
mean ± sd
Con95
Lassee
89
±
±
±
±
±
±
±
±
±
±
±
±
±
±
±
±
±
±
±
±
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.
92.4a
77.3a
25.2a
5.8a
41.9a
27.5b
43.7b
8.9b
0.4a
0.2a
3.5a
0.7a
0.1ab
0.02ab
2.2a
0.2a
1.7b
2.0b
3.9b
2.2b
0-15
0-15
0-15
0-15
Bacterial biomass (µg C g-1 dry soil)
Mineralizable N (mg kg-1)
HWC (µg C g-1)
90
0-15
316.6
8.1
44.4
2.5
12.7
1225
30-40
Active fungi (% of hyphal length)
1287
211
30-40
0-15
280
749
30-40
0-15
972
499
30-40
0-15
554
234
30-40
0-15
310
4689
30-40
0-15
5085
1.4
30-40
0-15
1.5
0-15
Fungi (µg C g-1 dry soil)
Biological soil properties
Alo (g kg-1)
Mno (g kg-1)
Feo (g kg-1)
Ald (g kg-1)
Mnd (g kg-1)
Fed (g kg-1)
BD (g cm-3)
±
±
±
±
±
±
±
±
±
±
±
±
±
±
±
±
±
±
±
37.7a
2.7a
10.0a
2.4a
4.5a
122.6a
115.6a
49.0a
22.5a
164.6a
7.1a
20.6a
46.7a
57.8a
16.9a
466.9a
361.4a
0.1a
0.2a
346.5
9.4
38.3
14.0
12.7
1291
1382
228
295
868
1062
575
575
251
315
4868
5154
1.5
1.5
±
±
±
±
±
±
±
±
±
±
±
±
±
±
±
±
±
±
±
76.3a
7.2a
9.8a
3.5b
1.8a
111.2a
63.2a
30.4a
32.8a
96.1a
31.3a
104.5a
52.7a
44.4a
20.5a
373.2a
278.0a
0.1a
0.1a
510.0
31.0
68.9
0.7
10.9
934
1011
136
197
848
844
435
396
177
218
3533
3253
1.5
1.4
±
±
±
±
±
±
±
±
±
±
±
±
±
±
±
±
±
±
±
43.5b
10.5b
16.1a
1.3a
2.2a
57.2b
55.9b
46.3a
30.7a
72.9a
53.4a
56.0a
44.4b
19.2a
27.4a
173.9b
371.5b
0.02a
0.1a
403.2
15.2
38.3
1.8
15.1
1135
1157
228
239
902
940
480
492
238
259
3920
3912
1.4
1.4
±
±
±
±
±
±
±
±
±
±
±
±
±
±
±
±
±
±
±
24.3ab
5.3ab
7.0a
2.1a
6.6a
149.0ab
109.2ab
55.3a
35.1a
24.4a
14.1a
6.0a
40.3ab
55.9a
25.1a
88.4b
168.3b
0.2a
0.03a
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).
Soil characteristic
pH (H2O)
Sand
Silt
Clay
Nt
OC
CAL-extractable K
CAL-extractable P
CaCO3
Fed
Mnd
Ald
Feo
Mno
Alo
fPOM
oPOM
20-250
>250 µm
Mean weight diameter (MWD)
Fungal biomass
Active fungi
Bacterial biomass
Mineralizable N
Hot-water extractable carbon (HWC)
Sources of variation
Location
Soil depth
***
***
***
NS
***
NS
***
NS
NS
**
*b
**
NS
***
NS
***
***
NS
***
**a
***a
***a
**a
NS
**
NS
**
**
***
NS
**a
**a
*a
*a
NS
**
**
*
NS
NS
**a
**a
-
NS
-
a
-
**
**a
91
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 kg1
) 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.
92
0-15
30-40
0-15
30-40
oPOM (g kg-1)
MWD (mm)
fPOM (g kg-1)
Depth
cm
0-15
30-40
3.1 (0.4)abc
1.9 (0.8)abc
10.0 (4.6)ab
8.9 (2.8)ab
7.6 (2.4)ab
6.7 (2.2)ab
3.4 (1.5)ab
2.5 (1.3)ab
Con76
3.3 (0.6)bc
1.5 (1.5)ab
3.6 (0.9)ab
1.6 (0.8)a
Org76
3.8 (2.2)a
8.4 (2.6)ab
3.8 (0.6)bc
1.3 (0.2)a
5.9 (1.3)b
2.8 (2.3)ab
Org95
4.5 (2.4)ab
11.9 (1.1)b
2.5 (0.4)abc
1.3 (0.5)a
3.9 (0.4)ab
2.2 (0.8)ab
Con95
93
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).
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).
13
C 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.
94
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
Table 4.4 Integrated chemical shift regions (% of total signal intensity) obtained by 13C
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)
fPOM
Alkyl-C (%)
O-Alkyl-C (%)
Aryl-C (%)
Carboxyl-C (%)
Alkyl-C/O-Alkyl-C (%)
oPOM
Alkyl-C (%)
O-Alkyl-C (%)
Aryl-C (%)
Carboxyl-C (%)
Alkyl-C/O-Alkyl-C (%)
Bulk soil
Alkyl-C (%)
O-Alkyl-C (%)
Aryl-C (%)
Carboxyl-C (%)
Alkyl-C/O-Alkyl-C (%)
96
Org76
Con76
Org95
Con95
0-15
30-40
0-15
30-40
0-15
30-40
0-15
30-40
0-15
30-40
15
15
49
37
24
33
12
15
0.34
0.41
17
19
49
38
23
29
12
13
0.34
0.50
16
16
53
45
21
27
10
12
0.30
0.36
16
17
43
48
26
25
14
10
0.37
0.35
0-15
30-40
0-15
30-40
0-15
30-40
0-15
30-40
0-15
30-40
20
23
45
42
24
24
11
12
0.45
0.54
19
25
49
36
22
26
10
13
0.40
0.69
18
17
46
36
25
32
11
15
0.39
0.47
19
20
44
38
27
30
10
12
0.44
0.53
0-15
30-40
0-15
30-40
0-15
30-40
0-15
30-40
0-15
30-40
21
22
36
32
26
28
17
18
0.60
0.68
23
23
36
34
25
26
16
17
0.65
0.69
21
20
36
30
28
33
15
18
0.58
0.66
21
21
35
32
28
31
15
16
0.61
0.68
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
97
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 ha1
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
98
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.
99
References
Abiven, S., Menasseri, S., Chenu, C. (2009): The effects of organic inputs over time on soil
aggregate stability – A literature analysis. Soil Biol. Biochem. 41, 1-12.
AGES (Austrian Agency for Health and Food Safety) (2013): Pflanzenschutzmittel. URL:
http://www.ages.at/ages/landwirtschaftlichesachgebiete/pflanzenschutzmittel/.
Accessed
14.11.2013.
Amézketa, E. (1999) Soil aggregate stability: A Review. Journal of Sustainable Agriculture
14, 83-151.
Barral, M.T., Arias, M., Guerif, J. (1998): Effects of iron and organic matter on the
porosity and structural stability of soil aggregates. Soil Tillage Res. 46, 261–272.
BIO AUSTRIA (2010): Produktionsrichtlinien Die Biobäuerinnen und Biobauern
Österreichs, 3rd edition, Linz, Austria.
Bloem, J., Veninga, M., Shepherd, J. (1995): Fully-automatic determination of soil
bacterium numbers, cell volumes, and frequencies of dividing cells by confocal laserscanning microscopy and image-analysis. Appl. Environ. Microbiol. 61, 926-936.
Bloem, J., Vos, A. (2004): Fluorescent staining of microbes for total direct counts, in
Kowalchuk, G.A., De Bruijn, F.J., Head, I.M., Akkermans, A.D.L., Van Elsas, J.D.
(editors): Molecular Microbial Ecology Manual, 2nd edition, Kluwer Academic Publishers,
Dordrecht, pp. 861-874.
BMLFUW (2006): Richtlinien für die sachgerechte düngung, 6th edition, Vienna, Austria.
Bronick, C.J., Lal, R. (2005): Soil structure and management: a review. Geoderma 124, 322.
Canali, S., Benedetti, A. (2006): Soil nitrogen mineralization, in: Bloem, J., Hopkins, D.W.,
Benedetti, A. (editors): Microbiological methods for assessing soil quality, CABI
Publishing, Wallingford, UK, pp. 127-135.
Chan, K.Y., Heenan, D.P., Oates, A. (2002): Soil carbon fractions and relationship to soil
quality under different tillage and stubble management. Soil Tillage Res. 63, 133-139.
Christensen, B.T. (1992): Physical fractionation of soil and organic matter in primary
particle size and density separates. Adv. Soil Sci. 20, 1-90.
Clerici, N., Weissteiner, C.J., Paracchini, M.L., Strobl, P. (2011): Riparian zones: where
green and blue networks meet Pan-European zonation modelling based on remote sensing
and GIS. Publications Office of the European Union, Luxenburg, pp. 59,
doi:10.2788/80271.
Debosz, K., Petersen, S.O., Kure, L.K., Ambus, P. (2002): Evaluating effects of sewage
sludge and household compost on soil physical, chemical and microbiological properties.
Appl. Soil Ecol. 19, 237-248.
100
de Leon-Gonzalez, F., Hernandez-Serrano, M.M., Etchevers, J.D., Payan-Zelaya, F.,
Ordaz-Chaparro, V. (2000): Short-term compost effect om macroaggregation in a sandy
soil under low rainfall in the valley of Mexico. Soil Tillage Res. 56, 213-217.
Dimoyiannis, D.G. (2012): Wet aggregate stability as affected by excess carbonate and
other soil properties. Land Degradation & Development 23, 450-455.
Dimoyiannis, D.G., Tsadilas, C.D., Valmis, S. (1998): Factors affecting aggregate
instability of Greek agricultural soils. Commun. Soil Sci. Plan Anal. 29, 1239-1251.
Dinel, H., Righi, D., Hardy, M., Jambu, P. (1997): Neutral lipids and structural stability of
physically degraded soils. Agrochemica 41, 97-108.
Duiker, S.W., Rhoton, F.E., Torrent, J., Smeck, N.E., Lal, R. (2003): Iron (Hydr)Oxide
Crystallinity Effects on Soil Aggregation. Soil Sci. Soc. Am. J. 67, 606-611.
Eash, N.S., Karlen, D.L., Parkin, T.B. (1994): Fungal Contributions to Soil Aggregation
and Soil Quality. Soil Sci. Soc. Am. J. special publication 35, 221-228.
Erhart, E., Hartl, W. (2010): Compost Use in Organic Farming, in: Lichtfouse, E. (ed):
Genetic Engineering, Biofertilization, Soil Quality and Organic Farming, Sustainable
Agriculture Reviews 4. Springer, pp. 311-345.
Gattinger, A., Muller, A., Haeni, M., Skinner, C., Fliessbach, A., Buchmann, N., Mäder, P.,
Stolze, M., Smith, P., El-Hage Scialabba, N., Niggli, U. (2012): Enhanced top soil carbon
stocks under organic farming. PNAS 109, 18226-18231.
Gazzé, S.A., Saccone, L., Smiths, M.M., Duran, A.L., Leake, J.R., Banwart, S.A.,
Ragnarsdóttir, K.V., McMaster, T.J. (2013): Nanoscale Observations of Extracellular
Polymeric Substances Deposition on Phyllosilicates by an Ectomycorrhizal Fungus.
Geomicrobiol. J. 30, 721-730.
Ghani, A., Dexter, M., Perrott, K.W. (2003): Hot-water extractable carbon in soils: A
sensitive measurement for determining impacts of fertilisation, grazing and cultivation.
Soil Biol. Biochem. 35, 1231-1243.
Golchin, A., Baldock, J.A., Clarke, P., Higashi, T., Oades, J.M. (1997): The effects of
vegetation and burning on the chemical composition of soil organic matter of a volcanic
ash soil as shown by 13C NMR spectroscopy. II. Density fractions. Geoderma 76, 175-192.
Golchin, A., Oades, J.M., Skjemstad, J.O., Clarke, P. (1994): Study of Free and Occluded
Particulate Organic Matter in Soils by Solid state 13C CP/MAS NMR Spectroscopy and
Scanning Electron Microscopy. Aust. J. Soil Res. 32, 285-309.
Hartl, W., Erhart, E. (2005): Crop nitrogen recovery and soi lnitrogen dynamics in a 10year field experiment with biowaste compost. J. Plant Nutr. Soil Sci. 168, 781-788.
Haynes, R.J., Naidu, R. (1998): Influence of lime, fertilizer and manure applications on soil
organic matter content and soil physical conditions: a review. Nutr. Cycl. Agroecosyst. 51,
123-137.
101
Jannoura, R., Joergensen, R.G., Bruns, C. (2014): Organic fertilizer effects on growth,
crop yield, and soil microbial biomass indices in sole and intercropped peas and oats under
organic farming conditions. Eur. J. Agron. 52, 259-270.
Kemper, W.D., Rosenau, R.C. (1986): Aggregate stability and size distribution, in Klute, A.
(Ed): Methods of Soil Analysis. Part 1. Physical and Mineralogical Methods, Agronomy
Monograph 9. 2nd ed. ASA/SSSA, Madison, WI, USA. p. 425-442.
Kirchmann, H., Bergström, L., Kätterer, T., Mattson, L., Gesslein, S. (2007): Comparison
of long-term organic and conventional crop-livestock systems on a previously nutrientdepleted soil in Sweden. Agron. J. 99, 960-972.
Knicker, H., Gonzalez-Vila, F.J., Polvillo, O., Gonzalez, J.A., Almendros, G. (2005): Fireinduced transformation of C- and N-forms in different organic soil fractions from a Dystric
Cambisol under a Mediterranean pine forest (Pinus pinaster). Soil Biol. Biochem. 37, 701–
718.
Ladha, J.K., Reddy, C.K., Padre, A.T., van Kessel, C. (2011): Role of Nitrogen
Fertilization in Sustaining Organic Matter in Cultivated Soils. J. Environ. Qual. 40, 17561766.
Lair, G.J., Zehetner, F., Hrachowitz, M., Franz, N., Maringer, F.-J., Gerzabek, M.H.
(2009): Dating of soil layers in a young floodplain using iron oxide crystallinity.
Quaternary Geochronology 4: 260-266.
Lal, R. (2013): Enhancing ecosystem services with no-till. Renewable Agriculture and
Food Systems 28, 102-114.
Lehtinen, T., Gísladóttir, G., Lair, G.J., van Leeuwen, J., Blum, W.E.H., Bloem, J.,
Steffens, M., Ragnarsdóttir, K.V. (2014a): Aggregation and organic matter in subarctic
Andosols under different grassland management. Acta Agriculturae Scandinavica, Section
B – Soil & Plant Science (submitted).
Lehtinen, T., Lair, G.J., Mentler, A., Gísladóttir, G., Ragnarsdóttir, K.V., Blum, W.E.H.
(2014b): Soil Aggregate Stability in Different Soil Orders Quantified by Low Dispersive
Ultrasonic Energy Levels. Soil Sci. Soc. Am. J. 78, 713-723.
Leifeld, J., Fuhrer, J. (2010): Organic farming and soil carbon sequestration: What do we
really know about the benefits? Ambio 39, 585-599.
Leifeld, J., Reiser, R., Oberholzer, H.R. (2009): Consequences of conventional versus
organic farming on soil carbon: Results from a 27-year field experiment. Agron. J. 101,
1204-1218.
Mehra, O.P., Jackson, M.L. (1960): Iron oxide removal from soils and clays by dithionitecitrate systems buffered with sodium bicarbonate. Clays Clay Miner. 7, 317–327.
Monreal, C.M., Schnitzer, M., Schulten, H.R., Campbell, C.A., Anderson, D.W. (1995):
Soil organic structures in macro and microaggregates of a cultivated brown chernozem.
Soil Biol. Biochem. 27, 845-853.
102
Mueller, C.W., Brüggemann, N., Pritsch, K., Stoelken, G., Gayler, S., Winkler, J.B., KögelKnabler, I. (2009): Initial differentiation of vertical soil organic matter distribution and
composition under juvenile beech (Fagus szlvatica L.) trees. Plant Soil 323, 111-123.
Munkholm, L.J., Schjonning, P., Debosz, K., Jensen, H.E., Christensen, B.T. (2002):
Aggregate strength and mechanical behaviour of a sandy loam soil under long-term
fertilization treatments. Eur. J. Soil Sci. 53, 129-137.
North, P.F. (1976): Towards an absolute measurement of soil structural stability using
ultrasound. J. Soil Sci. 27, 451-459.
Pare, T., Dinel, H., Moulin, A.P., Townley-Smith, L. (1999): Organic matter quality and
structural stability of a Black Chernozemic soil under different manure and tillage
practices. Geoderma 91, 311-326.
Plahl, E., Rogalski, W., Gilnreiner, G., Erhart, E. (2002): Vienna´s biowaste compost –
quality development and effect of input materials. Waste Manage. Res. 20, 127-133.
Pimentel, D., Wilson, A. (2004): World Population, Agriculture, and Malnutrition, in:
Worldwatch
Institute:
Population
and
its
discontents,
URL:
http://www.worldwatch.org/system/files/EP175D.pdf. Accessed 22.11.2013.
Poll, C., Thiede, A., Wermbter, N., Sessitsch, A., Kandeler, E. (2003): Micro-scale
distribution of microorganisms and microbial enzyme activities in a soil with long-term
organic amendments. Eur. J. Soil Sci. 54, 715-724.
Pulleman, M., Jongmans, A., Marinissen, J., Bouma, J. (2003): Effects of organic versus
conventional arable farming on soil structure and organic matter dynamics in a marine
loam in the Netherlands. Soil Use Manage. 19, 157-165.
Ramankutty, N., Evan, A.T., Monfreda, C., Foley, J.A. (2008): Farming the planet: 1.
Geographical distribution of global agricultural lands in the year 2000. Global Biochemical
Cycles 22, GB1003, doi: 10.1029/2007GB002952.
Roldán, A. Albaladejo, J., Thornes, J.B. (1996): Aggregate stability changes in a semiarid
soil after treatment with different organic amendments. Arid Soil Res. Rehabil. 10, 139148.
Rousk, J., Bååth, E., Brookes, P.C., Lauber, C.L., Lozupone, C., Caporaso, J.G., Knight,
R., Fierer, N. (2010): Soil bacterial and fungal communities across a pH gradient in an
arable soil. ISME J. 4, 1340-1351.
Saccone, L., Gazzé, S.A., Duran, A.L., Leake, J.R., Banwart, S.A., Ragnarsdóttir, K.V.,
Smits, M.M., McMaster, T.J. (2012): High resolution characterization of ectomycorrhizal
fungal-mineral interactions in axenic microcosm experiments. Biogeochemistry 111, 411425.
Schmidt, M.W.I., Knicker, H., Hatcher, P.G., Kögel-Knabner, I. (1997): Improvement of
13
C and 15N CPMAS NMR spectra of bulk soils, particle size fractions and organic
material by treatment with 10 % hydrofluoric acid. Eur. J. Soil Sci. 48, 319-328.
103
Schwertmann, U. (1964): Differenzierung der Eisenoxide des Bodens durch Extraktion mit
Ammoniumoxalat-Lösung. Z. Pflanzenernaehr., Dueng., Bodenkd. 105, 194-202.
Siegrist, S., Schaub, D., Pfiffner, L., Mäder, P. (1998): Does organic agriculture reduce soil
erodability? The results of a long-term field study on loess in Switzerland. Agric., Ecosyst.
Environ. 69, 253-264.
Six, J., Bossuyt, H., Degryze, S., Denef, K. (2004): A history of research on the link
between (micro)aggregates, soil biota, and soil organic matter dynamics. Soil Tillage Res.
79, 7-31.
Six, J., Elliott, E.T., Paustian, K. (2000): Soil macroaggregate turnover and microaggregate
formation: a mechanism for C sequestration under no-tillage agriculture. Soil Biol.
Biochem. 32, 2099-2103.
Soil Survey Staff (2004): Soil Survey Laboratory Methods Manual. Soil Survey
Investigations Rep. 42. USDA – NRCS, Washington, DC.
Spiegel, H., Dersch, G., Baumgarten, A., Hösch, J. (2010): The International Organic
Nitrogen Long-term Fertilization Experiment (IOSDV) at Vienna after 21 years. Arch.
Agron. Soil Sci. 56, 405-420.
Steffens, M., Kölbl, A., Kögel-Knabler, I. (2009): Alteration of soil organic matter pools
and aggregation in semi-arid steppe topsoil’s as driven by organic matter input. Eur. J. Soil
Sci. 60, 198-212.
Surböck, A., Friedel, J.K., Heinzinger, M., Freyer, B. (2006): Long-term monitoring of
different management systems within organic farming and their effects o narable land and
landscape. Phase I: Monitoring of the conversion to organic farming, in Raupp, J., Pekrun,
C., Oltmanns, M., Köpke, U. (editors): Long-term field experiments in organic farming.
ISOFAR Scientific Series, Verlag Dr. H. J. Köster, Berlin, Germany, pp. 183-198.
Tabatabai, M.A., Bremner, J.M. (1991): Automated instruments for determination of total
carbon, nitrogen, and sulfur in soils by combustion techniques, in Smith, K.A. (editor): Soil
Analysis. Marcel Dekker: New York, USA, pp. 261– 286.
Tisdall, J.M., Oades. J.M. (1982): Organic matter and water-stable aggregates in soils. J.
Soil Sci. 33, 141-163.
Tukey, J.W. (1957): On the comparative anatomy of transformations. Ann. Math. Stat. 28,
602-632.
West, T.O., Post, W.M. (2002): Soil Organic Carbon Sequestration Rates by Tillage and
Crop Rotation: A Global Data Analysis. Soil Sci. Soc. Am. J. 66, 1930-1946.
Williams, N.D., Petticrew, E.L. (2009): Aggregate stability in organically and
conventionally farmed soils. Soil Use Manage. 25, 284-292.
104
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
Biowaste
Crop
compost
t ha-1 (kg N
ha-1)c
10 (115164)
Potato
Soy
10 (115beans
164)
Soy
10 (115beans
164)
Winter
10 (115wheat
164)
10 (115Potatoes
164)
a
Ye
ar
201
1
201
0
200
9
200
8
200
7
200
6
200
5
200
4
200
3
200
2
200
1
Soy
beans
Corn
Winter
wheat
10 (115164)
10 (115164)
10 (115164)
Potatoes
-
Poppy
Winter
wheat
-
Con 76
Crop
Potato
Sugar
beet
Winter
wheat
Onion
Winter
wheat
Potato
Winter
wheat
Sugar
beet
Winter
wheat
Potato
Winter
wheat
Org95
b
Fertilizer
Crop
Con 95
Horse
manure
t ha-1 (kg N
ha-1)d
kg ha-1
N 95, P 50,
K 130
N 118, P
46, K 60
Year
N 120
2009
-
2008
N 120
2007
-
2006
N 120
2005
-
2004
Sugar beet
Winter
wheat
N 120
2003
Clover mix
-
-
2002
Clover mix
N 120
2001
Corn
2011
2010
Winter
wheat
Sugar beet
Spring
barley
Winter
wheat
Peas
Spring
barley&pot
ato
20 (200400)
-
Crop
Fertilizer
Winter
wheat
kg ha-1
N 138, P
21, K 21
N 150, P
40, K 40
Corn
Sugar beet
Winter
wheat
Sun
flowers
N 126
N 138, P
40, K 40
Winter
wheat
N 128, P
24, K 24
Corn
Durum
wheat
-
Sugar beet
Winter
wheat
N 137
N 129, P
19, K 19
N 133, P
30, K 30
N 130, P
25, K 25
-
Corn
-
-
20 (200400)
-
N 60
a
crop 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.
b
clover mixtures in the crop rotation of Org95 were not harvested but incorporated into the soil as green
manure.
c
total nitrogen, Erhart and Hartl, 2010.
d
total nitrogen, Koenig et al., 2011.
105
5 Effect of crop residue incorporation
on soil organic carbon (SOC) and
greenhouse gas (GHG) emissions in
European agricultural soils
107
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.
108
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 notillage 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 costeffective 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.
109
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
110
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).
111
Øsaker
Ultuna
Foulum
Studsgaard
Askov
Rønhave
Edinburgh
Morley
Gleadthorpe
Woburn
Rothamsted
Wye Estate
Cologne
Gembloux
Wierzchucinek
Rostock
Müncheberg
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
112
Ås
Field studies
Experiment
1
Experimen
t Nr
Germany
Germany
Poland
Belgium
Germany
UK
UK
UK
UK
UK
UK
Denmark
Denmark
Denmark
Denmark
Sweden
Norway
Norway
Country
52°30'N 14°08'E
54°05'N 12°08'E
53°15´N 17°47´E
50°33'N 04°41'E
50°56´N 06°57´E
CON
CON
CON
ATC
ATC
ATC
ATC
51° 48'N
00°21'W
51°10´N 00°56´E
ATC
ATC
ATN
ATN
ATN
ATN
ATN
ATN
NEM
NEM
NEM
Environmental
zonea
51°59'N 00°37'W
53°13´N 01°05´W
52°34´N 01°06´W
55°57´N 03°11´W
54°54'N 09°47'E
55°28'N 09°07'E
56°05'N 08°54'E
56°30'N 09°34'E
59° 00'N 17°00'E
59°23´N 11°02´E
59°39'N 10°47'E
Location
Table 5.1 Summary description of the sites included in the analysis.
1962
1954
1979
1959
1969
1999
1852
1938
1984
1984
1995
1969
1894
1969
1997
1956
1963
1953
Start
year
silty loam
loam
sandy loam
silty loam
silt
silty loam
clay
sandy loam
loamy sand
sandy loam
clay loam
sandy loam
sandy loam
loamy sand
sandy loam
Rogasik et al., 2001
Leinweber & Reuter, 1992
Janowiak, 1995
Powlson et al., 2011
Marschner et al., 2003
Baggs et al., 2003
Powlson et al., 2011
Murphy et al., 2007; Powlson et al., 2011
Nicholson et al., 1997
Nicholson et al., 1997; Powlson et al., 2011
Ball et al., 1990
Powlson et al., 2011
Powlson et al., 2011
Powlson et al., 2011
Mutegi et al., 2010; Petersen et al., 2011
Börjesson et al., 2012
Uhlen, 1991, Børresen, 1999
silty clay
loam
clay loam
Uhlen, 1991
References
clay loam
Soil texture
Grossbeeren 1
Grossbeeren 2
Grossbeeren 3
Braunschweig
Spröda
Methau
Puch
Suchdol
Lukavec
Alpenvorland
Marchfeld
Vienna
Keszthely
Trutnov
Rakican
Jable
Grignon
Doazit
Serreslous
Tetto Frati
Padova
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
Italy
Italy
France
France
France
Slovenia
Slovenia
Czech Republic
Hungary
Austria
Austria
Austria
Czech Republic
Czech Republic
Germany
Germany
Germany
Germany
Germany
Germany
Germany
45°21'N 11°58'E
44°53'N 07°41'E
43°40´N 00°40´W
43°41'N 00°38'W
45°39'N 06°22'E
46°08'N 14°34'E
46°38'N 16°11'E
50°33'N 15° 53'E
46°44'N 17°13'E
48°11'N 16°44'E
48°13'N 16°36'E
48°07'N 15°08'E
49°33'N 14°59'E
49° 57'N 15°09'E
48°11´N 11°13´E
51°04´N 12°51´E
51°32´N 12°25´E
52°18'N 10°27'E
52°21´N 13°18´E
52°21´N 13°18´E
52°21'N 13°18'E
MDN
MDM
LUS
LUS
ALS
ALS
ALS
ALS
PAN
PAN
PAN
CON
CON
CON
CON
CON
CON
CON
CON
CON
CON
1966
1992
1967
1967
1963
1993
1993
1966
1960
1986
1982
1986
1997
1997
1984
1966
1966
1952
1972
1972
1972
clay loam
loam
silty loam
loamy sand
loam
silty loam
loamy sand
sandy loam
sandy loam
loamy sand
sandy loam
silty loam
sandy loam
loam
silty loam
silty loam
sandy loam
silty loam
silt
sandy loam
loamy sand
Lugato et al., 2006
113
Grignani et al., 2007; Bertora et al., 2009; Zavattaro et al., 2012
Plénet et al., 1993; Lubet et al., 1993
Plénet et al., 1993
Powlson et al., 2011
Cvetkov & Tajnsek 2009
Cvetkov & Tajnsek 2009; Cvetkov et al., 2010; Tajnsek et al.,
2013
Simon et al., 2013
Kismanyoky & Toth, 2013
Spiegel et al., 2010b
Spiegel et al., 2010a
Spiegel et al., 2010a
Nedved et al., 2008
Nedved et al., 2008
Hege & Offenberger, 2006
Albert & Grunert, 2013
Albert & Grunert, 2013
Rogasik et al., 2001
Rühlmann & Ruppel, 2005; Rühlmann, 2006; MLUV, 2009
Rühlmann & Ruppel, 2005; Rühlmann, 2006; MLUV, 2009
Rühlmann & Ruppel, 2005; Rühlmann, 2006; MLUV, 2009
Foggia 2
Almacelles 1
Almacelles 2
El Encín
La Chimenea
42
43
44
45
46
Flevopolder
Wageningen
Wijnandsrade
Wye Estate
47
48
49
50
50°54´N 05°52´E
The
Netherlands
51°10´N 00°56´E
51°58'N 05°39'E
The
Netherlands
UK
52°30'N 05°28'E
40°03'N 03°31'W
40°32'N 03°17'W
41°43´N 00°26´E
41°43´N 00°26´E
41°27´N 15°32´E
41°27´N 15°32´E
42°57´N 12°20´E
The
Netherlands
Spain
Spain
Spain
Spain
Italy
Italy
Italy
ATC
ATC
ATC
ATC
MDS
MDS
MDS
MDS
MDN
MDN
MDN
1999
N/A
1999
1999
2009
2010
2010
2010
1990
1977
1971
silty loam
silty loam
sand
Garcia-Ruiz & Baggs, 2007
Cayuela et al., 2013
Velthof et al., 2002
Velthof et al., 2002
Sanz-Cobena et al., 2014
silty clay
loam
clay
Meijide et al., 2010; Abalos et al., 2013
Biau et al., 2013
Biau et al., 2013
Maiorana, 1998; Maiorana et al. 2004
Maiorana, 1998; Maiorana et al. 2004
Bianchi et al., 1994; Perucci et al., 1997
clay loam
loam
clay loam
clay
clay
loam
114
LUS, Lusitanian; MDM, Mediterranean Mountains; MDN, Mediterranean North; MDS, Mediterranean South.
Environmental zone assigned according to Metzger et al. (2005): NEM, Nemoral; ATN, Atlantic North; ATC, Atlantic Central; CON, Continental; PAN, Pannonian; ALS, Alpine South;
a
Foggia 1
41
Laboratory
studies
Papiano
40
Figure 5.1 Map of the experiment locations and their distribution across the aggregated
environmental zones (Nemoral, Atlantic, Continental, Mediterranean).
115
Nemoral (NEM)
<18 %
<5 years
ENZa
Clay %
Experiment durationb
5-10 years
18-35 %
Atlantic (ATN, ATC, LUS)
11-20 years
>35%
Continental (CON, PAN, ALS)
>20 years
Mediterranean (MDM, MDN, MDS)
Experiment duration: years between the beginning of the experiment and the measurement.
116
b
LUS, Lusitanian; MDM, Mediterranean Mountains; MDN, Mediterranean North; MDS, Mediterranean South.
Environmental zone assigned according to Metzger et al. (2005): NEM, Nemoral; ATN, Atlantic North; ATC, Atlantic Central; CON, Continental; PAN, Pannonian; ALS, Alpine South;
a
Specific levels
Variable
Table 5.2 Aggregated variables and specific levels of each variable.
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).
117
number of RR (experiments)
A
220 (41)
11 (3)a
32 (12)a
ENZ
124 (20)a
53 (6)a
B
220 (41)
54 (23)ab
148 (14)a
Clay (%)
18 (4)b
C
220 (41)
27 (3)a
11 (5)ab
Duration
108 (11)b
74 (22)c
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
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
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
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
LOG RR of CO2 emissions
LOG RR of N2O emissions
a
SE, standard error
b
CI, confidence interval
119
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
120
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.
121
122
Duration
< 5 years
18-35 %
Clay %
<18 %
Mediterranean
ENZ
Atlantic
Overall
Field
Laboratory
Field
Laboratory
Field
Laboratory
Field
Laboratory
Field
Laboratory
Field
Laboratory
1.0
2.4
1.0
2.4
1.0
N/A
1.0
2.4
1.0
N/A
1.0a
2.4b
Cereal
CO2
Mean
0.08
0.46
0.00
0.46
0.09
N/A
0.00
0.46
0.09
N/A
0.08
0.46
SDa
3
3
1
3
2
N/A
1
3
2
N/A
3
3
n expb
17
15
4
15
13
N/A
4
15
13
N/A
17
15
n RRc
1.7
9.2
2.1
9.2
1.1
N/A
2.1
9.2
1.1
N/A
1.7a
9.2b
0.50
3.9
0.00
3.9
0.00
N/A
0.00
3.9
0.00
N/A
0.50
3.9
Vegetative material
CO2
Mean
SD
2
3
1
3
1
N/A
1
3
1
N/A
2
3
n exp
7
50
4
50
3
N/A
4
50
3
N/A
7
50
n RR
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).
Field
Laboratory
Field
Laboratory
Field
Laboratory
Field
Laboratory
Field
Laboratory
1.4
2.3
8.4
N/A
1.4
2.3
8.4
N/A
3.7a
2.3a
0.50
2.30
2.34
N/A
0.50
2.30
2.34
N/A
3.60
2.30
SD
2
3
2
N/A
2
3
2
N/A
4
3
n exp
20
15
10
N/A
20
15
10
N/A
30
15
n RR
2.7
21.4
0.9
N/A
2.7
21.4
0.9
N/A
1.9a
21.4b
2
3
N/A
N/A
1
3
1
N/A
1
3
1
N/A
2
3
n exp
7
50
N/A
N/A
4
50
3
N/A
4
50
3
N/A
7
50
n RR
123
N2O emissions in crop residue
0.95
20.4
N/A
N/A
0.00
20.4
0.00
N/A
0.00
20.4
0.00
N/A
0.95
20.4
Vegetative material
N2O
Mean
SD
Field
5.5
3.67
3
18
1.9
Laboratory
2.3
2.30
3
15
21.4
11-20 years
Field
1.0
0.00
1
12
N/A
Laboratory
N/A
N/A
N/A
N/A
N/A
a
SD, standard deviation.
b
n exp, number of experiments.
c
n RR, number of response ratios; RR, CO2 or N2O emissions in crop residue incorporation treatment/CO2 or
removal treatment.
N/A, not available.
Duration
<5 years
18-35%
Clay %
<18%
Mediterranean
ENZ
Atlantic
Overall
Cereal
N2O
Mean
124
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
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 metaanalyses 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 N 2O
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).
125
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
126
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).
127
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
128
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.
129
References
Abalos, D., Sanz-Cobena, A., Garcia-Torres, L., van Groenigen, J.W. & Vallejo, A. 2013.
Role of maize stover incorporation on nitrogen oxide emissions in a non-irrigated
Mediterranean barley field. Plant and Soil, 364, 357-371.
Aguilera, E., Lassaletta, L., Gattinger, A. & Gimeno, B.S. 2013. Managing soil carbon for
climate change mitigation and adaptation in Mediterranean cropping systems: A metaanalysis. Agriculture, Ecosystems and Environment, 168, 25-36.
Albert, E. & Grunert, M. 2013. Wirkung einer langjährig differenzierten mineralischorganischen Düngung auf Ertrag, Humusgehalt, N-Bilanz und Nährstoffgehalte des
Bodens. Archives of Agronomy and Soil Science,
59, 1073-1098.
Alexander, M. 1977. Mineralization and immobilization of nitrogen, In: Introduction to
soil microbiology, 2nd edition (ed Alexander, M.). Wiley, New York, USA, pp. 136-247.
Baggs, E.M., Stevenson, M., Pihlatie, M., Regar, A., Cook, H. & Cadisch, G. 2003.
Nitrous oxide emissions following application of residues and fertiliser under zero and
conventional tillage. Plant and Soil, 254, 361-370.
Baggs, E.M., Rees, R.M., Smith, K.A. & Vinten, A.J.A. 2000. Nitrous oxide emission
from soils after incorporating crop residues. Soil Use and Management, 16, 82-87.
Ball, B.C., Bickerton, D.C. & Robertson, E.A.G. 1990. Straw incorporation and tillage for
winter barley: Soil structural effects. Soil and Tillage Research, 15, 309-327.
Bertora, C., Zavattaro, L., Sacco, D., Monaco, S. & Grignani, C. 2009. Soil organic matter
dynamics and losses in manured maize-based forage systems. European Journal of
Agronomy, 30, 177-186.
Bhogal, A., Nicholson, F.A., & Chambers, B.J. 2009. Organic carbon additions: effects on
soil bio-physical and physico-chemical properties. European Journal of Soil Science, 60,
276-286.
Bianchi, A.A., Guiducci, M. & Bonciarelli, U. 1994. Effect of crop rotation and nitrogen
fertilization on PAR absorption and radiation use efficiency of winter bread wheat.
Proceedings to the Third congress of the European Society for Agronomy, pp. 70-71.
Biau, A., Santiveri, F. & Lloveras, J. 2013. Stover Management and Nitrogen Fertilization
Effects on Corn Production. Agronomy Journal, 105, 1264-1270.
Blanco-Canqui, H. 2013. Crop Residue Removal for Bioenergy Reduces Soil Carbon
Pools: How Can We Offset Carbon Losses? Bioenergy Research, 6, 358-371.
Börjesson, G., Menichetti, L., Kirchmann, H. & Kätterer, T. 2012. Soil microbial
community structure affected by 53 years of nitrogen fertilisation and different organic
amendments. Biology and Fertility of Soils, 48, 245-257.
130
Børresen, T., 1999. The effect of straw management and reduced tillage on soil properties
and crop yields of spring-sown cereals on two loam soils in Norway. Soil & Tillage
Research, 51, 91-102.
Cayuela, M.L., Kuikman, P., Bakkers, R. & van Groenigen, J.W. 2013. Tracking C and N
dynamics and stabilization in soil amended with wheat residue and its corresponding
bioethanol by-product: a 13C/15N study. Global Change Biology Bioenergy,
DOI: 10.1111/gcbb.12102.
Chen, H., Li, X., Hu, F. & Shi, W. 2013. Soil nitrous oxide emissions following crop
residue addition: a meta-analysis. Global Change Biology, 19, 2956-2964.
Chivenge, P.P., Murwira, H.K., Giller, K.E., Mapfumo, P. & Six, J. 2007. Long-term
impact of reduced tillage and residue management on soil carbon stabilization:
Implications for conservation agriculture on contrasting soils. Soil & Tillage Research, 94,
328-337.
Cvetkov, M., Santavec, I., Kocjan Acko, D. & Tajnsek, A. 2010. Soil organic matter
content according to different management system within long-term experiment. Acta
agriculturae Slovenica, 95, 79-88.
Cvetkov, M. & Tajnsek, A. 2009. Soil organic matter changes according to the application
of organic and mineral fertilizers within long-term experiments. Acta agriculturae
Slovenica, 93, 311-320.
De Neve, S. & Hofman, G. 2000. Influence of soil compaction on carbon and nitrogen
mineralization of soil organic matter and crop residues. Biology and Fertility of Soils, 30,
544-549.
European Commisson. 2006. Thematic Strategy for Soil Protection. Commission of the
European Communities, Brussels, Belgium, COM(2006) 231. Available at: eurlex.europa.eu/LexUriServ/LexUriServ.do?uri=COM:2006:0231:FIN:EN:PDF;
accessed
12/16/2013.
Feller, C. & Beare, M.H. 1997. Physical control of soil organic matter dynamics in the
tropics. Geoderma, 79, 69-116.
Garcia-Ruiz, R. & Baggs, E.M. 2007. N2O emission from soil following combined
application of fertilizer-N and ground weed residues. Plant and Soil, 299, 263-274.
Gattinger, A., Muller, A., Haeni, M., Skinner, C., Fliessbach, A., Buchmann, N., Mäder,
P., Stolze, M., Smith, P., El-Hage Scialabba, N. & Niggli, U. 2012. Enhanced top soil
carbon stocks under organic farming. Proceedings of the National Academy of Sciences,
109, 18226-18231.
Grignani, C., Zavattaro, L., Sacco, D. & Monaco, S. 2007. Production, nitrogen and carbon
balance of maize-based forage systems. European Journal of Agronomy, 26, 442-453.
Hege, U. & Offenberger, K. 2006. Effect of differentiated mineral fertilization and organic
manuring on yield, product quality and N balances in the international permanent organic
nitrogen experiment (IOSDV) Puch. Archives of Agronomy and Soil Science, 52, 535-550.
131
Ingram, J.S.I. & Fernandes, E.C.M. 2001. Managing carbon sequestration in soils:
concepts and terminology. Agriculture, Ecosystems & Environment, 87, 111-117.
Janowiak, J. 1995. Influence of manure fertilization with straw addition and different
nitrogen doses on properties of organic matter. Zeszyty Problemowe postepów nauk
rolniczych, 421a, 145-150.
Jongman, R.H.G., Bunce, R.G.H., Metzger, M.J., Mücher, C.A., Howard, D.C. & Mateus,
V.L. 2006. Objectives and applications of a statistical environmental stratification of
Europe. Landscape Ecology, 21, 409–419.
Kismányoky, T. & Toth, Z. 2010. Effect of mineral and organic fertilization on soil
fertility as well as on the biomass production and N utilization of winter wheat (Triticum
aestivum L.) in a long-term cereal crop rotation experiment (IOSDV). Archives of
Agronomy and Soil Science, 56, 473-479.
Körschens, M. 2006. The importance of long-term field experiments for soil science and
environmental research – a review. Plant, Soil and Environment, 52, 1-8.
Körschens, M., Weigel, A. & Schulz, E. 1998. Turnover of Soil Organic Matter (SOM)
and Long-Term Balances – Tools for Evaluating Sustainable Productivity of Soils.
Zeitschrift für Pflanzenernährung und Bodenkunde, 161, 409-424.
Lajeunesse, M.J. 2011. On the meta-analysis of response ratios for studies with correlated
and multi-group designs. Ecology, 92, 2049-2055.
Lal, R. 2013. Intensive Agriculture and the Soil Carbon Pool. Journal of Crop
Improvement, 27, 735-751.
Lal, R. 1997. Residue management, conservation tillage and soil restoration for mitigating
greenhouse effect by CO2-enrichment. Soil & Tillage Research, 43, 81-107.
Leinweber, P. & Reuter, G. 1992. The influence of different fertilization practices on
concentrations of organic carbon and total nitrogen in particle-size fractions during 34
years of a soil formation experiment in loamy marl. Biology and Fertility of Soils, 13, 119124.
Lemke, R.L., Van den Bygaart, A.J., Campbell, C.A., Lafond, G.P. & Grant, B. 2010. Crop
residue removal and fertilizer N: Effects on soil organic carbon in a long-term crop rotation
experiment on a Udic Boroll. Agriculture, Ecosystems and Environment, 135, 42-51.
Liu, C., Lu, M., Cui, J., Li, B. & and Fang, C. 2014. Effects of straw carbon input on
carbon dynamics in agricultural soils: a meta-analysis. Global Change Biology, doi:
10.1111/gcb.12517.
Loveland, P. & Webb, J. 2003. Is there a critical level of organic matter in the agricultural
soils of temperate regions: a review. Soil & Tillage Research, 70, 1-18.
Lubet, E., Plénet, D. & Juste, C. 1993. Effet à long terme de la monoculture sur le
rendement en grain du maïs (Zea mays L) en conditions non irriguées. Agronomie, 13,
673–683.
132
Lugato, E., Berti, A. & Giardini, L. 2006. Soil organic carbon (SOC) dynamics with and
without residue incorporation in relation to different nitrogen fertilization rates. Geoderma,
135, 315-321.
Maiorana, M., Convertini, G. & Fornaro, F. 2004. Gestione del suolo nella omosuccessione
di grano duro. Informatore agrario, 60, 79-82.
Maiorana, M. 1998. Interramento dei residui colturali di frumento duro. Informatore
agrario, 54, 41–45.
Marschner, P., Kandeler, E. & Marschner, B. 2003. Structure and function of the soil
microbial community in a long-term fertilizer experiment. Soil Biology & Biochemistry,
35, 453-461.
Meijide, A., Cárdenas, L.M., Sánchez-Martín, L. & Vallejo, A. 2010. Carbon dioxide and
methane fluxes from a barley field amended with organic fertilizers under Mediterranean
climatic conditions. Plant and Soil, 328, 353-367.
Metzger, M.J., Bunce, R.G.H., Jongman, R.H.G., Mücher, C.A. & Watkins, J.W. 2005. A
climatic stratification of the environment of Europe. Global Ecology and Biogeography,
14, 549-563.
MLUV (Ministerium für Ländliche Entwicklung, Umwelt und Verbrauchershutz des
Landes Brandenburg). 2009. Dauerfeldversuche in Brandenburg und Berlin - Beiträge für
eine
nachhaltige
Landwirtschaftliche
Bodennutzung.
Available
at:
http://lelf.brandenburg.de/sixcms/media.php/4055/Dauerfeldversuche%20f%C3%BCr%20
nachhaltige%20Landwirtschafte%20Bodennutzung.pdf; accessed at 11/6/2013.
Murphy, D.V., Stockdale, E.A., Poulton, P. R., Willison, T. W. & Goulding, K.W.T.
2007. Seasonal dynamics of carbon and nitrogen pools and fluxes under continuous arable
and ley-arable rotations in a temperate environment. European Journal of Soil Science, 58,
1410-1424.
Mutegi, J.K., Munkholm, L.J., Petersen, B.M., Hansen, E.M. & Petersen, S.O. 2010.
Nitrous oxide emissions and controls as influenced by tillage and crop residue management
strategy. Soil Biology & Biochemistry, 42, 1701-1711.
Nedved, V., Balík, J., Cerný, J., Kulhánek, M. & Balíková, M. 2008. The changes of soil
nitrogen and carbon contents in a long-term field experiment under different systems of
nitrogen fertilization. Plant, Soil and Environment, 54, 463-470.
Nicholson, F.A., Chambers, B.J., Mills, A.R. & Strachan, P.J. 1997. Effects of repeated
straw incorporation on crop fertilizer nitrogen requirements, soil mineral nitrogen and
nitrate leaching losses. Soil Use and Management, 13, 136-142.
Nicolardot, B., Recous, S. & Mary, B. 2001. Simulation of C and N mineralisation during
crop residue decomposition: A simple dynamic model based on the C:N ratio of the
residues. Plant and Soil, 228, 83-103.
133
Perucci, P., Bonciarelli, U., Santilocchi, R. & Bianchi, A.A. 1997. Effect of rotation,
nitrogen fertilization and management of crop residues on some chemical, microbiological
and biochemical properties of soil. Biology and Fertility of Soils, 24, 311-316.
Petersen, S.O., Mutegi, J.K., Hansen, E.M. & Munkholm, L.J. 2011. Tillage effects on
N2O emissions as influenced by a winter cover crop. Soil Biology & Biochemistry, 43,
1509-1517.
Plénet, D., Lubet, E. & Juste, C. 1993. Évolution á long terme du statut carboné du sol en
monoculture non irriguée du mais (Zea mays L). Agronomie, 13, 685-698.
Poirier, V., Angers, D.A., Rochette, P. & Whalen, J.K. 2013. Initial soil organic carbon
concentration influences the short-term retention of crop-residue carbon in the fine fraction
of a heavy clay soil. Biology and Fertility of Soils, 49, 527-535.
Powlson, D.S., Bhogal, A., Chambers, B.J., Coleman, K., Macdonald, A.J., Goulding,
K.W.T. & Whitmore, A.P. 2012. The potential to increase soil carbon stocks through
reduced tillage or organic material additions in England and Wales: A case study.
Agriculture, Ecosystems and Environment, 146, 23-33.
Powlson, D.S., Glendining, M.J., Coleman, K. & Whitmore, A.P. 2011. Implications for
Soil Properties of Removing Cereal Straw: Results from Long-Term Studies. Agronomy
Journal, 103, 279-287.
Powlson, D.S., Riche, A.B., Coleman, K., Glendining, M.J. & Whitmore, A.P. 2008.
Carbon sequestration in European soils through straw incorporation: Limitations and
alternatives. Waste Management, 28, 741-746.
Recous, S., Robin, D., Darwis, D. & Mary, B. 1995. Soil inorganic N availability: Effect
on maize residue decomposition. Soil Biology and Biochemistry, 27, 1529–1538.
Rizhiya. E.Y., Boitsova, L.V., Buchkina, N.P. & Panova, G.G. 2011. The influence of crop
residues with different C:N ratios on the N2O emission from a loamy sand soddy-podzolic
soil. Eurasian Soil Science, 44, 1144-1151.
Rogasik, J., Schroetter, S., Schnug, E. & Kundler, P. 2001. Langzeiteffekte ackerbaulicher
massnahmen auf die bodenfruchtbarkeit. Archives of Agronomy and Soil Science, 47, 3-17.
Rohatgi,
A.
2013.
WebPlotDigitizer
Version
http://arohatgi.info/WebPlotDigitizer; accessed at 12/11/2013.
2.6.
Available
at:
Rühlmann, J. 2006. The Box Plot Experiment in Grossbeeren after six rotations: Effect of
fertilization on crop yield. Archives of Agronomy and Soil Science, 52, 313-319.
Rühlmann, J. & Ruppel, S. 2005. Effects of organic amendments on soil carbon content
and microbial biomass – results of the long-term box plot experiment in Grossbeeren.
Archives of Agronomy and Soil Science, 51, 163-170.
Sanz-Cobena, A., García-Marco, S., Quemada, M., Gabriel, J.L., Almendros, P. & Vallejo,
A. 2014. Do cover crops enhance N2O, CO2 or CH4 emissions from soil in Mediterranean
arable systems? Science of the Total Environment, 466-467, 164-174.
134
Shan, J. & Yan, X. 2013. Effects of crop residue returning on nitrous oxide emissions in
agricultural soils. Atmospheric Environment, 71, 170-175.
Shen, J., Tang, H., Liu, J., Wang, C., Li, Y., Ge, T., Jones, D.L. & Wu, J. 2014.
Contrasting effects of straw and straw-derived biochar amendments on greenhouse gas
emissions within double rice cropping systems. Agriculture, Ecosystems and Environment,
188, 264-274.
Simon, T., Mikanova, O. & Cerhanova, D. 2013. Long-term effect of straw and farmyard
manure on soil organic matter in field experiment in the Czech Republic. Archives of
Agronomy and Soil Science, 59, 1193-1205.
Smith, W.N., Grant, B.B., Campbell, C.A., McConkey, B.G., Desjardins, R.L., Kröbel, R.,
& Malhi, S.S. 2012. Crop residue removal effects on soil carbon: Measured and intermodel comparisons. Agriculture, Ecosystems and Environment, 161, 24-38.
Spiegel, H., Dersch, G. & Baumgarten, A. 2010a. Long term field experiments - a basis to
evaluate parameters of soil fertility. In: New Challenges in field crop production 2010,
Proceedings of Symposium (eds D. Kocjan Acko & B. Ceh), Slovensko agronomsko
drustvo, Ljubljana, Slovenia, pp.76-82.
Spiegel, H., Dersch, G.,
Baumgarten, A. & Hösch, J. 2010b. The International
Organic Nitrogen Long-term Fertilisation Experiment (IOSDV) at Vienna after 21 years.
Archives of Agronomy and Soil Science, 56, 405-420.
Swan, J.B., Higgs R.L., Bailey T.B., Wollenhaupt N.C., Paulson W.H. & Peterson A.E.
1994. Surface residue and in-row treatment on long-term no-tillage continuous corn.
Agronomy Journal, 86, 711-718.
Tajnsek, A., Cergan, Z. & Ceh, B. 2013. Results of the long-term field experiment IOSDV
Rakican at the beginning of the 21st century. Archives of Agronomy and Soil Science, 59,
1109-1119.
Triberti, L., Nastri, A., Giordani, G., Comellini, F., Baldoni, G. & Toderi, G. 2008. Can
mineral and organic fertilization help sequestrate carbon dioxide in cropland? European
Journal of Agronomy, 29, 13-20.
Uhlen, G. 1991. Long-Term Effects of Fertilizers, Manure, Straw and Crop-Rotation on
Total-N and Total-C in Soil. Acta Agriculturae Scandinavica, 41, 119-127.
Velthof, G.L., Kuikman, P.J. & Oenema, O. 2002. Nitrous oxide emission from soils
amended with crop residues. Nutrient Cycling in Agroecosystems, 62, 249-261.
von Lützow M., Kögel- Knabner I., Ekschmitt K , Matzner E., Guggenberger G.,
Marschner B. & Flessa H., 2006. Stabilization of organic matter in temperate soils:
mechanisms and their relevance under different soil conditions - a review. European
Journal of Soil Science, 57, 426-445.
Wilhelm, W.W., Johnson, J.M.F., Hatfield, J.L., Voorhees, W.B. & Linden, D.R. 2004.
Crop and soil productivity response to corn residue removal. Agronomy Journal, 96, 1–17.
135
Zavattaro, L., Monaco, S., Sacco, D. & Grignani, C. 2012. Options to reduce N loss from
Maize in intensive cropping systems in Northern Italy. Agriculture, Ecosystems and
Environment, 147, 24-35.
136
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 coauthors, 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.
137
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.
139