Page 1
R E S E A R C H A R T I C L E
Response of maize yield to changes in soil organic matter
in a Swedish long-term experiment
Thomas Kätterer
|
Martin A. Bolinder
Department of Ecology, Swedish
University of Agricultural Sciences (SLU),
Uppsala, Sweden
Correspondence
Thomas Kätterer, Swedish University
of Agricultural Sciences (SLU),
Department of Ecology, Box 7044,
75007 Uppsala, Sweden.
Email:
thomas.katterer@slu.se
Funding information
Stiftelsen Lantbruksforskning,
Grant/Award Number: O-22-23-734;
Horizon 2020 Framework Programme,
Grant/Award Number: 862695; Svenska
Forskningsrådet Formas, Grant/Award
Number: 2022-00214
Abstract
Agricultural practices that lead to soil carbon sequestration may be a win
–
win
strategy for mitigating global warming and improving soil fertility and
resource use efficiency. The mechanisms through which soil organic carbon
(SOC) concentration affects crop yields are numerous but difficult to separate.
The objective of this study was to disentangle these processes and estimate to
what extent the yield response to SOC is mainly driven by changes in physical
or biochemical properties and processes. This was achieved by analysing the
response of yields in continuous maize to SOC concentrations during 20 years
(2000
–
2019), which had evolved in 14 experimental treatments in a Swedish
long-term field experiment at Ultuna since 1956, ranging from 0.94% to 3.65%
in the topsoil (0
–
20 cm). Average maize yields during this period varied
between 1.9 and 8.4 Mg dry mass per hectare in the different treatments. The
treatments comprise applications of different mineral nitrogen (N) fertilizers
and organic amendments and combinations thereof. Our analysis showed that
maize yield in the treatments that were not severely limited by nitrogen supply
or soil acidity increased by 16% for each percentage unit increase in SOC. We
applied the widely used concept of critical N concentration in plant biomass to
diagnose the N status in maize in the different treatments (N nutrition index
[NNI]) and parameterized a response function between yield and pH (
R
pH
).
Dry soil bulk density (BD) was used as a proxy for soil physical properties.
These three variables NNI,
R
pH
and BD explained 95% of the variation in
maize yields among treatments. Further analysis of the relationship between
BD, SOC and plant available water capacity revealed that about two thirds of
the yield increases in response to SOC change could be ascribed to associated
changes in soil physical properties. Our analysis suggests that the extra storage
capacity of water, which increased by up to 15 mm in the topsoil for each unit
percentage increase in SOC, was the main driver for the observed yield
responses. We conclude that measures for increasing SOC in soils most likely
are an effective adaptation strategy for reducing the risk of crop damage during
dry spells, which probably are becoming more frequent in the future due to cli-
mate change, even in relatively humid climates as in Sweden.
Received: 19 December 2023
Revised: 19 March 2024
Accepted: 21 March 2024
DOI: 10.1111/ejss.13482
This is an open access article under the terms of the
Creative Commons Attribution-NonCommercial
License, which permits use, distribution and reproduction in any
medium, provided the original work is properly cited and is not used for commercial purposes.
© 2024 The Authors.
European Journal of Soil Science
published by John Wiley & Sons Ltd on behalf of British Society of Soil Science.
Eur J Soil Sci.
2024;75:e13482.
wileyonlinelibrary.com/journal/ejss
1 of 18
https://doi.org/10.1111/ejss.13482
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K E Y W O R D S
crop productivity, maize, nitrogen nutrition index, nitrogen use efficiency, plant available
water capacity, soil carbon, soil carbon sequestration, soil physical properties
1
|
I N T R O D U C T I O N
Humanity is facing significant challenges such as climate
change, food security, loss of biodiversity and depletion
of natural resources. These challenges are interconnected
and involve multiple trade-offs and synergies. Due to direct
and indirect links to all these challenges, soils, land use and
agricultural practices have come into focus in society. Vari-
ous policies, programmes (such as the 4 per 1000 initiative;
Minasny et al.,
2017
) and carbon farming schemes are
currently being developed and implemented to promote
soil health and climate change mitigation (European
Commission,
2021
). Soil carbon sequestration (SCS) has
gained recognition as a win
–
win strategy and remedy for cli-
mate change adaption and mitigation as well as for restoring
soil health, crop productivity and more efficient use of natu-
ral resources (IPCC WG1,
2021
).
Agricultural practices that diversify crop rotations by
including cover crops, and perennial plants or promote C
inputs to the soil in other ways have been found to
increase soil organic carbon (SOC) and soil organic mat-
ter stocks (Kätterer & Bolinder,
2022
). This can lead to
SCS, that is, the net transfer of carbon from the atmo-
sphere to the soil (Don et al.,
2023
), which, in turn, may
increase yield potentials, improve food security, the effi-
ciency of agricultural inputs and promote the efficient
use of soil, one of our primary natural resources (Rumpel
et al.,
2020
). It was estimated that 0.08
–
0.4 Pg C year
1
or
even more if further technical development in plant breed-
ing is considered, can be globally sequestered in upland
agricultural soils (Paustian et al.,
2016
). On considering only
low-cost measures, Bossio et al. (
2020
) estimated potential
SCS to be 0.14 Pg C, which corresponds to about 0.1 Mg C
ha
1
year
1
(Kätterer & Bolinder,
2022
). Although the mag-
nitude of potential SCS is highly uncertain and disputed
due to methodological inaccuracies, nutrient limitations or
socioeconomic barriers (Moinet et al.,
2024
; Poulton
et al.,
2018
; Van Groenigen et al.,
2017
), the implementation
of practices that increase soil carbon storage can contribute
to achieving several Sustainable Development Goals related
to climate change, reduced hunger, poverty and increased
environmental protection (Soussana et al.,
2019
).
The positive relationship between SOC and crop pro-
ductivity has been acknowledged for a long time (Manlay
et al.,
2007
). This is not surprising since SOC is a master
indicator for soil quality and soil functioning (Bünemann
et al.,
2018
) contributing to at least 12 of the 17 Sustainable
Development Goals of the United Nations (Kopittke et al.,
2022
). Already small increases in SOC at a global scale
have been estimated to enhance farmers' output during
drought years while reducing global temperature warming
(Iizumi & Wagai,
2019
). The mechanisms through which
SOC affects crop biomass production are numerous (Jha
et al.,
2023
; Johnston et al.,
2009
; Meurer, Barron, et al.,
2020
; Rubio et al.,
2021
; Schjønning et al.,
2012
). They
include biogeochemical processes such as increased turn-
over and supply of nutrients to plants from the mineraliza-
tion of soil organic matter, and enhanced retention of
nutrients through increased cation exchange capacity, as
well as physical processes such as better water infiltration,
soil structure and structural stability due to stronger aggre-
gation leading to less soil erosion, alleviation of compac-
tion, facilitation of root growth, increased water storage
and diffusivity of gases. Although SOC accumulation in
agricultural soils is predominantly beneficial, potential
trade-offs have to be accounted for. For example, higher
SOC stocks may result in increased nitrate leaching
(Powlson et al.,
1989
) and N
2
O emissions (Guenet et al.,
2021
). Thus, certain SCS-supporting management prac-
tices may offset some of the benefits of SCS.
Several meta-analyses assessing the relationship
between SOC and crop yields have been published in
recent years. Oldfield et al. (
2019
) presented global-level
predictions for crop yields in response to changes in SOC.
Based on their meta-analysis, they estimated potential
yield increases of 10 ± 11% for maize and 23 ± 37% for
wheat amounting to 32% of the projected yield gap for
maize and 60% of that for wheat. Moinet et al. (
2023
) pre-
sented an insightful review on this topic in which they
analysed 21 publications including 36 meta-analyses
based on global, continental (four continents) or national
datasets. The majority (17) of these reported positive rela-
tionships between SOC and crop yield, 12 reported no
Highlights
•
Maize yield responses to SOC were studied in a
field trial with clay soil in a hemiboreal climate
•
Yields increased by 14%
–
16% for each unit per-
centage of SOC increase
•
About two thirds of the yield response was
likely due to improved soil physical properties
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Page 3
effect and 7 reported negative relationships. Although the
response was positive in most cases, Moinet et al. (
2023
)
concluded that the outcomes are context-specific and
vary with space, time and methods used. They also elabo-
rated on the validity and limitations of these meta-
analyses, addressing problems regarding the causality
between SOC and crop yields and highlighting differ-
ences in the choice of response variables, for example,
actual or potential yields. Indeed, confounding factors
and potential biases make this kind of analysis difficult,
even under similar climatic conditions.
This is illustrated in two Scandinavian studies, where
negative correlations between SOC and crop yields were
found at the national scale in Denmark (Oelofse et al.,
2015
;
Schjønning et al.,
2018
) and Sweden (Kirchmann et al.,
2020
), and potential confounding factors were discussed.
For example, perennial ley is a major crop on dairy and
these farms have higher SOC compared to farms growing
annual cash crops only (Henryson et al.,
2022
). Since their
focus is on milk production, dairy farmers may be less
focused on optimizing yields of their grain crops which also
are part of their rotations. Consequently, grain crop yields
on dairy farms may be lower than on farms with only
annual crops, which results in a negative correlation
between SOC and yields (Schjønning et al.,
2018
). More-
over, leys take up far more cations than anions, leading to
stronger acidification in leys compared with cereals
(Haynes,
1983
). Liming, to compensate for this loss of cat-
ions, may be less frequent on dairy than on cash crop
farms, which would enforce a negative correlation
between SOC and crop yield. Systematic differences in soil
conditions between farming systems may also affect this
relationship since dairy farms are generally located in
areas with poorer, more acidic soils than cash crop farms.
In fact, a significant negative correlation between SOC
and soil pH was found in a Swedish national dataset
(Kirchmann et al.,
2020
). Kirchmann et al. (
2020
) concluded
that low soil pH rather than high SOC was the most proba-
ble reason for the observed yield decline with increasing
SOC. Thus, a proper analysis of the relationship between
SOC and crop yields needs to account for this potential
interaction with other yield-determining variables.
Although the cycling of nutrients increases with SOC,
since nutrient limitations and acidity can be controlled by
fertilizer and lime, the huge interest in the relationship
between SOC and soil fertility is mainly driven by non-
nutrient-constrained yield effects (Hijbeek et al.,
2017
).
These effects relate to soil physical properties and associ-
ated processes. For instance, the effect of SOC on plant-
available water capacity (PAWC) has been investigated in
many studies as reviewed by Lal (
2020
). Whereas most of
the studies he reviewed reported a positive relationship
between SOC and PAWC, several studies reported no or
even negative correlations. He concluded that further
research is needed to understand the mechanisms behind
this relationship that can explain the huge variation
among studies.
Our objective was to analyse the response of biomass
production during the recent 20 years (2000
–
2019) to
changes in SOC content that had evolved in 14 treatments
in the
‘
Ultuna long-term soil organic matter experiment
’
in Sweden for more than five decades. In particular, we
wanted to disentangle the processes behind this response,
that is, to which extent the yield response due to SOC
changes was mainly driven by changes in physical or bio-
chemical properties and processes. We also analysed
changes in soil acidity and crop N supply resulting from
the long-term addition of mineral N fertilizers and
organic amendments. We present new primary data from
the experiment and studied yields of silage maize that
was grown as monoculture since 2000 in response to
changes in soil properties. After about five decades, SOC
concentrations differed by almost a factor of four between
the extreme treatments (Kätterer et al.,
2011
). This wide
range of SOC concentrations established at the same site,
is, except for a few other studies (Kauer et al.,
2019
;
Lal,
2013
), unique and suitable for this kind of analysis
since it minimizes the potential interference with con-
founding factors related to different sites (Moinet
et al.,
2023
). By considering N limitation and soil acidity
in our analysis, we estimated the yield response to a
change in SOC that is governed by soil structure and
plant-accessible water and that cannot be compensated
for by applying more mineral fertilizer.
We hypothesized that biomass production increases
with SOC concentration due to higher N delivery from
the soil and crop N use efficiency, as well as changes in
soil physical properties affecting plant available soil water
capacity. The positive response of crop productivity to
increasing pH in acid soils is not new, but we intended
to quantify this relationship along the range of pH-values
in the treatments (4.18
–
7.27). Such high differences in
soil pH are unique in studies on agricultural practices at
the same site, and we hypothesized that productivity con-
tinues to increase with soil pH at pH-values above 6.5,
which is the target value for clayey arable soils according
to Swedish recommendations (Kirchmann et al.,
2020
).
2
|
M A T E R I A L S A N D M E T H O D S
2.1
|
Site description and treatments
The Ultuna long-term
‘
soil organic matter experiment
’
(FRAME-56) was initiated in 1956 at the Swedish Univer-
sity of Agricultural Sciences (SLU) close to Uppsala
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Page 4
(59.82
o
N, 17.65
E). The major objective of the experi-
ment is to investigate the long-term effect of different
mineral N fertilizers and organic amendments on crop
productivity and soil properties. The site lies within a Dfb
climate (warm summer hemiboreal) according to the
Köppen classification (Peel et al.,
2007
), with a mean
annual (1956
–
2009) air temperature of 5.8
C and an
annual mean total precipitation of 542 mm (Kätterer
et al.,
2011
). The soil is classified as Eutric Cambisol
(IUSS Working Group,
2006
). The parent material con-
sists of post-glacial sediments and illite is the main clay
mineral (Gerzabek et al.,
1997
). In 1956, the topsoil
(0
–
20 cm) had an organic C content of 1.50%, an N
content of 0.17%, a pH (water) of 6.54 and dry soil bulk
density (BD) of 1.44 Mg m
3
. The texture of the topsoil is
clay loam with 36.5% clay, 41.0% silt (0.002
–
0.06 mm)
and 22.5% sand (0.06
–
2 mm).
The experimental design consists of 15 treatments
with four replicate plots in a randomized block design
(Table
1
). Each plot is 2
2 m, separated by 40 cm high
steel frames extending to a depth of about 30 cm. Soil
dry BD differs widely across treatments, which is
reflected in elevation differences between the plots
which varied by up to 5.7
–
7.5 cm between treatments
(due to soil volume expansion with decreasing BD),
depending on the two estimation methods used
(Kätterer et al.,
2011
). Three of the treatments receive
different
types
of
inorganic
N
fertilizers
only
(80 kg N ha
1
year
1
), six receive organic amendments
only, four receive both N fertilizer and organic amend-
ments, and one receives neither N fertilizer nor organic
amendments. A bare fallow, without any crop, is also
included (Table
1
). All plots are regularly weeded by
hand or, during a few years with heavy weed invasion,
treated with an herbicide. Weed biomass production has
not been measured, but was estimated to be less than
50 kg dry matter ha
1
year
1
(Kätterer et al.,
2011
).
In 10 of the treatments receiving different organic
amendments, such as straw, green manure, farmyard
manure, sawdust, peat and sewage sludge (SS), approx-
imately the same amount of C (4 Mg ha
1
) was added
in 1956, 1960, and biannually since 1963 after crop har-
vest before autumn tillage (Table
1
). All organic
amendments were produced ex situ, including straw
and green manure. From 1956 to 1999, annual C3 crops
were cultivated, whereof spring barley and oats were
most frequent (see Kätterer et al.,
2011
for details).
Since 2000, silage maize, a plant with a C4 photosyn-
thetic cycle, is grown every year. The rationale for this
shift in crop type was to change the
13
C signature of
SOC, which can be used to track the fate of maize-
derived C in SOC (Menichetti et al.,
2013
,
2015
) and in
different soil fractions (Ghafoor et al.,
2017
), as
respired CO
2
(Shahbaz et al.,
2019
) or in microbial
biomass (Börjesson et al.,
2016
; Shahbaz et al.,
2020
).
At harvest, all above-ground crop residues are removed
after cutting the crops at the soil surface. Thus, crop
residue inputs are solely from below-ground, including
below-ground stem bases, root tissues and rhizodeposits.
Thereafter, usually in October, the soil is tilled by hand
with a spade to a depth of 20 cm. The effect on topsoil
mixing is similar to that of mouldboard ploughing but
soil compaction by heavy machinery is avoided. In
spring, all plots are fertilized with the same amount of
P and K, that is, 20 kg P ha
1
year
1
as superphosphate
and 35
–
38 kg K ha
1
as KCl with the intention that
P and K should not limit plant growth. This was
followed up with bi-annual soil tests showing very good
P- and K-status in all treatments (data not shown). A
more detailed description of the experiment and results
for the first 35 years was presented by Kirchmann et al.
(
1994
) and Persson and Kirchmann (
1994
) and for the
first 53 years by Kätterer et al. (
2011
).
2.2
|
Biomass and soil sampling and
analysis
In this work, we focus on the period 2000
–
2019 when
maize was grown, which has been sown in rows with a
40 cm distance. For estimating the dry mass of above-
ground biomass, an area of 1.6 m
2
(0.8
2.0 m) in the
centre of each plot was harvested, dried and weighed.
Subsamples were analysed for N and other nutrients
using standard methods and thereafter stored in our sam-
ple archive at SLU.
The soil was sampled at five random locations in each
plot to a depth of 20 cm using a soil corer. Soil sampling
was conducted after crop harvest but before tillage in
autumn. The soil was sampled intermittently between
1956 and 1983, and biannually thereafter. The most
recent sampling was done in 2019. The five samples per
plot were then combined into one composite sample
per plot before drying at 105
C and sieving at 2 mm. Soil
C and N concentrations were measured with dry combus-
tion (LECO instruments) and soil pH was measured in
water. After analysis for plant nutrients, soil samples
were then stored in our sample archive at SLU. The
development of SOC concentrations in the treatments
over time is presented in Figure
1
, and average soil pH
values over the period 1999
–
2019 per treatment are pre-
sented in Table
1
. The BD was measured only occasion-
ally in this experiment. Here, we use the measurements
from 2009 that have been published by Kätterer et al.
(
2011
) that were assumed to be representative of the
period 2000
–
2019.
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Page 5
TABLE
1
Treatments
in
the
Ultuna
long-term
soil
organic
matt
er
experiment.
ID
Ref
§
Treatment
Fertilizer
§§
(80
kg
N
h
a
1
year
1
)
Amendment
(C/N
ratio)
Yield
(Mg
ha
1
)
SOC
(%)
SON
(%)
SOC/SON
BD
(Mg
m
3
)
pH(H
2
O)
R
pH
NNI
BF
A
Bare
fallow
0.94
L
0.094
10.0
de
1.43
6.21
de
Unf
B
Control
2.61
fg
1.09
k
0.106
g
10.3
de
1.40
6.30
d
0.99
0.52
ef
CaN
C
Calcium
nitrate
Ca(NO
3
)
2
6.07
cd
1.33
i
0.127
f
10.4
d
1.28
6.67
b
1.01
0.84
abc
AS
D
Ammonium
sulphate
(NH
4
)
2
SO
4
1.92
g
1.20
j
0.136
e
8.85
f
1.21
4.18
h
0.30
0.65
de
CN
E
Calcium
cyanamide
CaCN
2
6.26
cd
1.40
h
0.135
e
10.4
d
1.25
7.28
a
1.03
0.82
abc
GM
H
Green
manure
Grass
(33)
5.35
de
1.56
f
0.150
d
10.4
d
1.34
6.09
de
0.98
0.74
cd
SS
O
Sewage
sludge
Sew.
Sludge
(8)
7.62
ab
2.66
c
0.274
a
9.71
e
1.02
4.90
g
0.82
0.94
a
FYM
J
Farmyard
manure
Manure
(14)
7.20
bc
2.12
d
0.202
b
10.5
cd
1.24
6.55
bc
1.00
0.79
bc
FYM
+
P
§§§
K
Farmyard
manure
Extra
P
Manure
(14)
7.12
bc
2.16
d
0.203
b
10.6
cd
1.20
6.35
d
0.99
0.80
bc
Peat
I
Peat
Peat
(45)
4.52
e
3.10
b
0.170
c
18.2
a
1.12
5.62
f
0.94
0.55
ef
Peat
+
N
M
Peat
+
N
Ca(NO
3
)
2
Peat
(45)
8.38
a
3.65
a
0.204
b
17.9
a
1.05
5.99
e
0.97
0.89
ab
SD
L
Sawdust
Sawdust
(1865)
2.58
fg
1.82
e
0.129
ef
14.1
b
1.28
6.37
cd
0.99
0.50
f
SD
+
N
N
Sawdust
+
N
Ca(NO
3
)
2
Sawdust
(1865)
6.98
bc
2.15
d
0.158
d
13.6
b
1.23
6.70
b
1.01
0.79
bcd
Str
F
Straw
Straw
(74)
3.49
ef
1.48
g
0.134
e
11.1
c
1.38
6.39
cd
0.99
0.58
ef
Str
+
N
G
Straw
+
N
Ca(NO
3
)
2
Straw
(74)
7.09
bc
1.88
e
0.170
c
11.1
c
1.21
6.60
b
1.00
0.86
abc
Note
:
Type
of
N
fertilizer
and
organic
amendments
(with
C/N
ratio
in
parenthesis),
measured
dry
matter
yields
of
silage
maize,
soil
organic
carbon
(SOC)
an
d
nitrogen
(SON)
concentrations,
soil
C/N
ratios,
dry
soil
bulk
density
(BD),
pH
in
water,
and
estimated
relative
growth
response
to
pH
(
R
pH
),
and
nitrogen
nutrition
index
(
NNI
).
Values
represent
average
values
for
the
period
2000
–
2019
calculated
from
annual
yields
and
biannual
soil
sampling
conducted
in
autumn
after
harvest,
except
for
BD
which
was
measured
only
once,
in
2009,
during
this
period
and
for
which
average
values
were
taken
from
Kätterer
et
al.
(
2011
).
Means
suffixed
by
different
letters
are
significantly
different
at
p
<
0.05
according
to
Tukey's
Studentized
range
test.
§
Reference
to
treatment
IDs
used
in
previous
publications
from
the
Ultuna
trial.
§§
All
treatments
including
the
bare
fallow
receive
20
kg
P
and
35
–
38
kg
K
h
a
1
year
1
.
§§§
Calcium
cyanamide
was
applied
in
FYM
+
P
only
in
1956.
Manures
applied
in
1956
and
1960
derived
from
different
batches
in
FYM
and
FYM
+
P.
Total
applications
of
N
a
s
manure
were
about
60
kg
higher
in
FYM
+
P
than
FYM
during
that
period.
Thereafter,
the
only
differences
between
FYM
and
FYM
+
P
were
double
P-application
(superphosphate)
in
FYM
+
P
compared
to
FYM
and
all
other
treatments.
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Page 6
2.3
|
Yield response to changes in SOC
As N supply differed among experimental treatments,
which also altered soil pH over time (Table
1
), we
grouped the treatments into five classes:
i.
High N treatments
, being those eight treatments receiving
80 kg N ha
1
year
1
from mineral fertilizer (CaN, CN,
Peat
+
N, SD
+
N and Str
+
N) as well as those receiving
more than 80 kg N ha
1
year
1
from organic N inputs
(FYM, FYM
+
P and SS), 140 or 250 kg N ha
1
year
1
from manure or SS, respectively,
ii.
Low pH treatment
AS with an average soil pH value
of 4.18, where toxic effects of Al
3
+
are likely severe
(Rahman & Upadhyaya,
2021
),
iii.
Medium N treatment
, that is, treatment GM that
receives 61 kg N ha
1
year
1
from green manure
and is, therefore, moderately N limited,
iv.
Low N treatments
, that is, the unfertilized control
treatment (Unf) and those receiving organic inputs
with low N content, such as cereal straw (Str) and peat
(Peat) corresponding to 27 and 44 kg N ha
1
year
1
,
respectively, and
v.
Very low N treatment
, that is, treatment SD, where
sawdust is added with a C/N ratio as high as 1865,
which
likely
causes
N
immobilization
during
decomposition.
2.4
|
Yield response to soil pH
For evaluating the effect of soil pH on crop yield, we con-
sidered the three treatments CaN, AS and CN that
received the same amounts (80 kg N ha
1
year
1
) but dif-
ferent types of mineral fertilizers, which over time resulted
in the evolution of significantly different soil pH values
(Table
1
). We used the whole time series (1956
–
2019) to
cover the entire range of soil pH differences between treat-
ments. Since soil pH measurements were scarce during
the first three decades of the time series, we fitted trend
lines to the data series to obtain annual soil pH estimates.
They described the data well, especially for the recent two
decades which are in focus in this paper (Figure
2
). In
treatment CaN, soil pH was quite stable over time. There-
after, we normalized annual yield records in AS and CN
relative to those in CaN by calculating yield ratios, that is,
AS/CaN and CN/CaN. This allowed us to construct
response functions describing the relationship between
annual yield measurements as a function of soil pH
(Figure
3
). The response of these yield ratios to soil pH
(
R
pH
) was then plotted and a Michaelis Menton-type
0
1
2
3
4
5
6
7
8
1950
1970
1990
2010
2030
Soil pH
CN
CaN
AS
F I G U R E 2
Evolution of soil pH(H
2
O) over time in treatments
CaN, AS and CN. The fitted trend lines are for treatments CaN:
y
=
6.54, AS:
y
=
0.000599x
2
–
0.0769x
+
6.54, CN:
y
=
Min(6.54
+
0.051x; 7.26) for
x
=
(year-1956).
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
3
4
5
6
7
8
Relave crop yield
Soil pH
AS
CN
RpH
F I G U R E 3
Yields in treatments AS and CN relative to those in
treatment CaN, which had a relatively stable pH of 6.54. The
estimated parameter values are
R
max
=
1.12,
R
0
=
4.07,
K
=
0.31,
and the calibrated response function is:
R
pH
=
1.12(pH -4.07)/[0.31
+
(pH -4.07)].
0
1
2
3
4
5
1950
1960
1970
1980
1990
2000
2010
2020
SOC % (0-20 cm)
Peat+N
Peat
SS
SD+N
FYM+P
FYM
Str+N
SD
GM
Str
CN
CaN
AS
Unf
BF
F I G U R E 1
Topsoil (0
–
20 cm) carbon concentrations over time
(1956
–
2019) for the 15 treatments in the Ultuna frame trial
(see Table
1
for the description of treatments).
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Page 7
function was fitted to the data by estimating the value of
its three parameters by minimizing root mean square error
using the generalized reduced gradient function imple-
mented in the solver package in MS Excel:
R
pH
¼
R
max
pH
R
0
ð
Þ
K
þ
pH
R
0
ð
Þ
, for
R
0
≤
pH
≤
7
:
26,
ð
1
Þ
where
R
max
defines the asymptote of the function,
R
0
is
the intercept on the
x
-axis corresponding to the soil pH
below which crop growth will vanish and
K
is a fitting
parameter. The sum of
K
+
R
0
corresponds to the soil
pH at which biomass production is expected to be 50%
compared with the reference pH, which here corre-
sponds to the initial soil pH (6.54). The function is
probably only valid in the range of soil pH values con-
sidered here, that is, from
R
0
to pH 7.26, representing
the average value in treatment CN during the period
1999
–
2019.
2.5
|
Nitrogen nutrition index
We applied the widely used concept of critical N con-
centration (
N
c
) to diagnose the N status in maize in the
different treatments (Liu et al.,
2023
). This concept
builds on the principle that plant N concentration
decreases monotonically as the crop grows (Greenwood
et al.,
1986
), and
N
c
is defined as the minimum N con-
centration required for maximum crop growth rate
under given conditions (Ulrich,
1952
). A lot of work has
been done to develop empirical functions describing
N
c
for different crops during the vegetative growth of agri-
cultural crops (Greenwood et al.,
1986
; Lemaire
et al.,
2008
). We adapted here the empirical power func-
tion developed for a German dataset that was shown to
be valid even until silage maturity (Herrmann &
Taube,
2004
):
N
c
¼
3
:
41
DM
0
:
391
,
ð
2
Þ
for maize dry mass (DM)
≥
1 Mg ha
1
and
N
c
=
3.41 for
DM <1 Mg ha
1
. The rationale for a constant
N
c
at low
biomass is that light competition for isolated young
plants is limited during early growth stages (Plénet &
Lemaire,
1999
). Under these conditions, internal nitrogen
concentration is linearly related to the relative growth
rate and exponential growth occurs if the internal con-
centration is constant (Ågren,
1985
; Ingestad,
1982
). To
quantify the degree of N limitation, we calculated the N
nutrition index (
NNI
) according to Lemaire et al. (
2008
)
from the ratio of actual N concentration in maize
biomass (
N
a
) relative to
N
c
,
NNI
¼
N
a
=
N
c
:
ð
3
Þ
NNI
reflects the degree of N limitation that is propor-
tional to plant growth or yield, it varies between 0 and an
upper limit of 1.0, that is, when
N
a
>
N
c
, the nitrogen
supply has been in excess and will not result in further
plant growth.
2.6
|
Nitrogen balances
Nitrogen balance components were compiled to gain
knowledge about the use efficiency of N in fertilizers and
organic amendments in the different treatments and their
potential impact on the environment. Nitrogen in harvested
biomass was calculated from annual records of DM yield
and N concentrations. Nitrogen added in the organic
amendments was calculated from bi-annual measurements
for the period from 1999 to 2019. Air deposition estimates
were adapted from Pihl Karlsson et al. (
2012
). Changes in
soil organic N (SON) stock to a depth of 20 cm were calcu-
lated from the slope of linear regression lines fitted to
bi-annual SON concentrations (1999
–
2019), multiplied by
BD measured in 2009 (Kätterer et al.,
2011
). The changes in
SON stocks were included in our analysis because they con-
stitute important sinks or sources in N balances (Karlsson
et al.,
2003
). The N surplus, a widely used indicator for
potential N losses to the environment, was calculated as the
difference between measured N inputs from fertilizer, seeds
and air deposition, and outputs in the harvested DM. As
indicators of resource use efficiency (outputs/inputs), we
calculated several commonly used indicators (Lahda
et al.,
2005
) of nitrogen use efficiency (NUE) by excluding
(NUE) or including (NUE*) net annual changes in SON
(i.e., N mining). We also calculated fertilizer agronomic effi-
ciency (AE), which is the difference in harvested N in treat-
ment and unfertilized control over inputs (excluding
changes in SON). In addition, we calculated fertilizer use
efficiency (FUE) accounting for differences in harvested N
(
h
) and soil N mining (
m
) between the fertilized treatment
(subscript
t
) and the corresponding unfertilized control
treatment (subscript c): [(
h
t
–
h
c
)
–
(
m
t
–
m
c
)] divided by annual
fertilizer input (80 kg N ha
1
). Control treatments were
those with corresponding amendments, that is, CaN for
treatments AS and CN, Str for Str
+
N, Peat for Peat
+
N
and SD for treatment SD
+
N. For the treatments receiving
amendments but no mineral N, we calculated amendment
N use efficiency (AUE) in the same way as FUE but with
annual N input from organic amendments in the denomi-
nator. The control treatment here was always the unferti-
lized control (Unf). By comparing FUE and AUE, the
equivalent N fertilizer value of organic amendments can be
calculated (Delin et al.,
2012
).
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Page 8
2.7
|
Calculation of plant available water
capacity
Soil water retention characteristics were measured in
1997 in the topsoil (0
–
20 cm) in nine treatments (BF,
Unf, CaN, Str, GM, Peat, FYM, SD and SS) in the wetter
range up to a tension of pF 3.5, corresponding to the
pressure of a 30 m water column (Kirchmann &
Gerzabek,
1999
). Assuming that water content at wilting
point (pF 4.2) was not responsive to experimental treat-
ments, we used the measurement of wilting point from a
soil profile in 1969 adjacent to the experimental plots
(17.2%; Wiklert et al.,
1983
) to calculate PAWC, that is,
the difference in volumetric soil water content between
field capacity (pF 2) and wilting point (pF 4.2).
2.8
|
Data analysis
The variance of target variables such as yield, SOC, SON,
soil pH and
NNI
measured during the two decades was
analysed using mixed models in the SAS software (SAS
Institute Inc., Cary, NC, USA). For yields, plot-wise
annual records for the period 2000
–
2019 were available.
Treatment was included in the statistical model as a fixed
categorical variable, replicate block as a random factor
and year as a repeated measure.
NNI
was calculated per
treatment and year since plot-wise N concentrations in
maize biomass were not available for all years. For the
soil variables, bi-annual measurements of SOC, SON and
soil pH were available (except for 2003) at the plot scale.
The same statistical model as for yields was applied to
these soil variables, but the sampling year was defined as
a numerical variable instead of a repeated measure to
cover potential trends over time. Interactions between
sampling year and treatment were significant for SOC
and SON and were further studied with linear regressions
fitted to the times series of SOC and SON for each treat-
ment. For soil pH, this interaction was not significant
and therefore excluded from the model. Tukey's Studen-
tized range test was used to analyse treatment effects.
Multiple linear regression analysis was used for stepwise
testing the impact of BD,
R
pH
and
NNI
on average maize
yield (2000
–
2019) in the treatments.
3
|
R E S U L T S A N D D I S C U S S I O N
3.1
|
Yield response to changes in SOC
Maize DM yields varied between 1.85 and 8.39 Mg ha
1
across years in the different treatments. Treatment effects
on yield were significant and differences between several
treatments were significant according to Tukey's post hoc
test (Table
1
). Replicate block and interactions with treat-
ment and sampling year had no significant effect on the
variance in crop yield according to the mixed model.
Maize yield increased with SOC in both the
‘
High N
treatments
’
and
‘
Low N treatments
’
(Figure
4
). Yields in
treatment Peat
+
N with the highest SOC concentration
(3.67%; Table
1
) were 39% higher than those in treatment
CaN (with 1.33% SOC) receiving only nitrate fertilizer.
The yield response to SOC was about 1 Mg ha
1
for each
percentage unit increase in SOC in the
‘
High N treat-
ments
’
and about 0.9 Mg ha
1
in the
‘
Low N treatments
’
according to linear regression analysis (R
2
=
0.96). In rela-
tive terms, this corresponds to a yield increase of 16% per
unit of SOC% increase. This yield response can probably
not be extrapolated ad infinitum, but surprisingly, the
yield response to SOC in this study was linear up to 3.67%
SOC and did not level off above 2% SOC as suggested by
the meta-analysis presented by Oldfield et al. (
2019
).
3.2
|
Yield response to soil pH
Fertilization with calcium nitrate could maintain soil
pH in treatment C throughout the experiment (Figure
2
).
Values of soil pH during the recent decade (6.60) were
similar to the initial pH in 1956 (6.54). Fertilization with
calcium cyanamide (treatment CN), which is hydrolysed
in the soil to form cyanamide and bicarbonate, increased
soil pH during the first two decades by about 0.7 pH units
to 7.26. On the contrary, fertilization with ammonium
sulphate in treatment AS strongly acidified the soil over
time (Figure
2
), where the soil pH has dropped to values
y = 0.97x + 4.95
R² = 0.96
y = 0.87x + 1.87
R² = 0.91
0
1
2
3
4
5
6
7
8
9
0
1
2
3
4
Y
ield (Mg
ha
-1
)
SOC%
High N
Low N
Medium N
Very low N
Low pH
F I G U R E 4
Maize yield response to SOC concentration
averaged over 20 years (2000
–
2019 for yields and 1999
–
2019 for
SOC) for the experimental treatments grouped into five classes
depending on N supply and soil pH (see text and tables for details).
The lines are response functions fitted to the eight
‘
High N
treatments
’
(CaN, CN, Str
+
N, FYM, FYM
+
P, SD
+
N, Peat
+
N
and SS) and three
‘
Low N treatments
’
(Unf, Str and Peat).
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Page 9
TABLE
2
Nitrogen
balance
components
(kg
N
h
a
1
year
1
)
and
efficiency
indices
(%)
in
the
experimental
treatments
for
the
period
2000
–
2019.
BF
Unf
CaN
AS
CN
GM
SS
FYM
FYM
+
P
Peat
Peat
+
NS
D
S
D
+
N
Str
Str
+
N
Harvested
biomass
(kg
N
h
a
1
year
1
)
27.9
79.8
29.9
80.5
66.4
116
86.6
88.4
45.4
106
27.3
84.2
39.1
91.3
Mineral
fertilizer
(kg
N
h
a
1
year
1
)8
0
8
0
8
0
8
0
8
0
8
0
Amendments
(kg
N
h
a
1
year
1
)
6
1
250
140
140
44
44
1
1
27
27
Seeds
a
(kg
N
h
a
1
year
1
)
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.4
Air
deposition
b
(kg
N
h
a
1
year
1
)3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
N
mining
c
(kg
N
h
a
1
year
1
)
2
5
3
2
2
7
1
7
2
24
2
2
8
1
0
1
0
1
4
3
2
3
2
2
8
2
92
9
Total
inputs
d
(kg
N
h
a
1
year
1
)
2
8
3
5
110
100
105
107
281
152
153
62
160
36
112
59
139
N
surplus
e
(kg
N
h
a
1
year
1
)3
25
4
5
3
3
2
137
56
55
2
2
2
23
0
91
9
Total
balance
f
(kg
N
h
a
1
year
1
)
2
8
7
30
70
25
40
165
66
65
16
54
9
2
8
2
0
4
8
NUE
g
(%)
821
96
36
97
103
46
61
62
95
83
611
100
129
83
NUE*
h
(%)
79
72
30
76
62
41
57
58
74
66
75
75
66
66
AE
i
(%)
65
2
6
6
6
3
3
5
4
2
4
3
3
9
6
3
57
69
41
59
FUE
j
(%)
71
22
78
52
76
65
AUE
k
(%)
47
37
58
59
a
Estimated
from
standard
values,
28
kg
ha
1
and
9%
protein,
N%
=
protein/6.25.
b
Estimate
according
to
Pihl
Karlsson
et
al.
(
2012
).
c
Annual
decline
in
N
stocks
in
the
topsoil
(0.20
m)
according
to
the
linear
slope
of
a
regression
line
fitted
to
bi-annual
soil
organic
nitrogen
concentr
ation
measurement
(1999
–
2019),
multiplied
by
bulk
density
(measured
in
2009
by
Kätterer
et
al.,
2011
)
and
soil
depth.
d
All
inputs
including
N
mining.
e
Inputs
(excluding
N
mining)
minus
N
in
harvested
biomass.
f
Inputs
(including
N
mining)
minus
N
in
harvested
biomass.
g
Nutrient
use
efficiency,
that
is,
harvested
N
over
inputs
(excluding
N
mining)
in
percentage.
h
Nutrient
use
efficiency,
that
is,
harvested
N
over
inputs
(including
N
mining)
in
percentage.
i
Agronomic
efficieny,
that
is,
difference
in
harvested
N
in
treatment
and
unfertilized
control
over
inputs
(excluding
N
mining)
in
percentage.
j
Fertilizer
use
efficiency
(percentage)
accounting
for
differences
in
harvest
N
(h)
and
N
mining
(m)
between
the
fertilized
treatment
(subscript
t)
a
nd
the
corresponding
unfertilized
control
treatment
(subscript
c):
[(h
t
–
h
c
)
–
(m
t
–
m
c
)]
divided
by
annual
fertilizer
input
(80
kg
N
h
a
1
).
Control
treatments
were
those
with
corresponding
amendments,
that
is,
CaN
for
treatments
AS
and
CN,
Str
for
Str
+
N,
Peat
for
Peat
+
N
and
SD
for
SC
+
N.
k
Amendment
N
use
efficiency
(percentage)
was
calculated
in
the
same
way
as
FUE
but
with
annual
N
input
from
amendments
in
the
denominator.
The
control
tr
eatment
here
was
always
the
unfertilized
control
(Unf
).
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Page 10
around 4.1, which has led to very low maize yields during
recent years.
Yields, relative to those in treatment CaN, strongly
responded to an increase in soil pH between values of
4 and 5 (Figure
3
). According to the calibrated response
function, yields increased from zero at
R
0
at pH 4.07 to
50% at pH 4.38 (
R
0
+
K
) and to 85% at pH 5 of the yield
obtained at soil pH 6.54. An increase in pH from 6.54 in
treatment CaN to 7.26 in treatment CN further stimu-
lated biomass production by 2% according to the response
function. In comparison, yield measurements average
over the period 2000
–
2019 were 3% higher in treatment
CN than in treatment CaN (Table
1
). This response func-
tion was used to estimate
R
pH
for all treatments for the
average soil pH during the targeted period (Table
1
). Pre-
dicted yield responses to soil pH were similar across
11 treatments and deviated by a maximum of 3% from
unity (
R
pH
-values between 0.97 and 1.03), but for three of
the treatments (Peat, SS and AS) with severe acidification
(soil pH: 5.62, 4.90 and 4.18, respectively), corresponding
yield reductions were higher: 6, 18 and 70% (
R
pH
-values
0.94, 0.82 and 0.30).
3.3
|
Nitrogen balances, N efficiencies
and N mining
Input
–
output estimates of nitrogen are essential for
improving nitrogen management in agroecosystems. The
nitrogen balances for the treatments disclosed several
interesting insights about N cycling (Table
2
). The N sur-
plus was naturally most negative in the unfertilized treat-
ment without amendments (Unf) and in the unfertilized
treatment receiving sawdust with low N concentration
(SD). The surplus was highly positive in the acidic treat-
ment AS, in the farmyard manure treatments (FYM and
FYM
+
P), and especially in the SS treatment. Negative
N surplus values were closely related to high NUE, which
was close to 100% in the N fertilized treatments without
amendments, or even above 100% in the control (Unf)
and in treatments without N fertilizer but receiving
amendments with low to moderate N content (Str, GM
and Peat). The NUE was lowest in the acidic treatment
AS, the farmyard manure treatments (FYM and FYM
+
P) and the SS treatment. The high values for NUE,
especially in the low-N treatments, were due to decreases
in SON stocks. Surprisingly, all treatments lost SON dur-
ing 1999
–
2019, between 10 and 42 kg N ha
1
year
1
on
average, and SOC was lost in 11 out of 15 treatments dur-
ing the same period. Thus, net N mineralization of SON
(N mining) was a major N source for the maize, espe-
cially in the low-N treatments. Primarily in the treat-
ments with organic amendments, the positive trend over
time in SOC and SON shifted from positive to negative
around the year 2000 with the onset of maize cultivation
(Figures
1
and
7
). Maize is uncommon in central
Sweden, and in our experiment, it has been sown quite
late in spring. The Ultuna experiment is not intended for
optimizing maize productivity, and crop yields were gen-
erally low, reaching only around 8 Mg DM ha
1
on aver-
age in the best treatment (Table
1
). The potential length
of the growing season for maize in the area is about
140 days and has not been fully utilized. Indeed, the
mean growth period for maize at our site was only
95 ± 10 days, which is also 25 days less than the average
growth period of the C3 crops grown during the 1956
–
1999
period. Furthermore, the root/shoot ratio of the C3 crops
grown at this site (mostly small-grain cereals) is about 0.2
under Scandinavian conditions (Palosuo et al.,
2016
),
whereas typical root/shoot ratios for maize for such a short
growth period are usually lower and around 0.1 or less
(Amos & Walters,
2006
; Hirte et al.,
2018
; Xu et al.,
2020
).
Consequently, C input through maize roots was probably
lower than root inputs during the years before 2000, which
partly may explain the decline in SOC and SON during
recent decades.
The N mining was lowest in the farmyard treatments
(FYM and FYM
+
P) and highest in treatment GM
receiving green manure. A potential interpretation for
high N mining in GM could be that priming was stimu-
lated through the input of fresh plant biomass. This is
supported by recent findings from incubations of soils
from a long-term experiment, where priming in a
manure-amended soil was lower than in a soil receiving
only mineral N or NPK fertilizers (Wu et al.,
2023
). The
increase in N mining was associated with increased ratios
of dissolved organic C and N. Although the soluble C
fraction was similar in the added green and farmyard
manure in our experiment, the former decomposed more
than three times as fast as the latter in a 3-day incubation
study (Peltre et al.,
2012
) and had also lower N concen-
trations (Table
1
).
The frequently used NUE index was well above its
global median value (46% according to Zhang et al.,
2021
),
but was not very helpful in our context where NUE
exceeded 100% in several treatments. When including N
mining as a source of N (NUE*), N use was most efficient
(79%) in the unfertilized control (Unf) and lowest in the
SS treatment (O). Even several of the N-fertilized treat-
ments (CaN, CN and SD
+
N) had NUE*-values above
70%. Except for the acidic treatment (AS), the AE was also
relatively high (59%
–
69%) in all N fertilized treatments
(CaN, CN, Str
+
N, Peat
+
N and SD
+
N).
FUE was highest (78%) in treatment CN. Since crop
uptake was similar (about 80 kg N ha
1
in both CaN and
CN), the higher FUE in CN compared with CaN was
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Page 11
mainly governed by lower N mining in CN, which indi-
cates that the higher soil pH in CN did rather slow down
than accelerate the turnover of soil organic matter. This is
in contrast to Leifeld et al. (
2013
) who observed decreasing
mean residence times of SOC with increasing soil pH
along a natural acidity gradient in alpine grasslands.
The efficiency of N use from N-rich amendments
(AUE) was generally lower than FUE. It varied between
37% in SS and 59% in FYM
+
P. Efficiencies of N use
from treatments receiving low-N amendments are less
relevant to consider since these are representing legacy
effects, where the treatments have resulted in N immobi-
lization rather than N mineralization and the built-up of
soil N stocks.
3.4
|
Nitrogen nutrition index
Nitrogen concentrations in harvested maize biomass var-
ied greatly between years and treatments (Figure
5
). The
calculated
NNI
(Equation
3
) averaged over the period
2000
–
2019 per treatment is presented in Table
1
. It is not
surprising that the unfertilized control treatment (Unf)
was severely N-limited (
NNI
=
0.52) and that the SS
treatment with excessive N input was the least N-limited
treatment (
NNI
=
0.94). The unfertilized treatments (Str,
Peat and SD) that received N-poor organic inputs, such
as straw, peat and sawdust, were also severely N-limited
(
NNI
from 0.50 to 0.58). The treatment with green
manure addition (GM) was moderately N limited
(
NNI
=
0.74). The relatively low
NNI
(0.65) in the acidi-
fied AS treatment may indicate that free aluminium in
the soil solution severely affected root functioning. In the
other treatments receiving mineral fertilizer or farmyard
manure,
NNI
varied between 0.79 and 0.89, indicating a
moderate degree of N limitation.
In this study, the evaluation of efficiency indexes did
not support our hypothesis that nitrogen cycling was
more efficient in high-SOC treatments. Indeed, NUE*,
FUE and AUE correlated negatively with SOC. Correla-
tion coefficients were
0.33,
0.84 and
0.35, respec-
tively. This negative correlation may be related to more
intense N cycling at high SOC, which may result in
increased N leaching and gaseous N losses, especially
during autumn (Guenet et al.,
2021
; Powlson et al.,
1989
).
However,
NNI
was closely related to total N inputs
(Figure
6
; Table
2
). This relationship was well described
by
the
logarithmic
dose/response
relationship
(R
2
=
0.87), which implies that the impact of additional
N input on
NNI
decreased with input rates. However, it
has to be considered that this response includes both
mineral fertilizers and organic amendments, where the N
efficiency of organic amendments (AUE) was generally
lower than that of mineral fertilizers (FUE).
3.5
|
Yield determinants
We used the BD, and the estimated
R
pH
and
NNI
values
(Table
1
) for the 14 treatments in a multiple linear regres-
sion analysis (Figure
7
). The model shows that nitrogen
status in maize was the major determinant of maize
yield, where
NNI
explained 79% of the variation in crop
yield among treatments;
R
pH
and BD explained 12% and
5%, respectively. The impact of all three variables on yield
was significant (
p
< 0.01), and the multiple regression
model explained 95% of the variation in yields. The
model intercept was not significantly different from zero.
This shows that it was possible to separate the positive
0
1
2
3
4
0
5
10
15
Maiz
e
biomass N %
Maize biomass (Mg ha
-1
)
Peat+N
Peat
SS
SD+N
FYM+P
FYM
Str+N
SD
GM
Str
CN
CaN
AS
Unf
Nc
F I G U R E 5
Nitrogen concentrations in harvested maize
biomass across treatments and years (2000
–
2019) as a function of
dry matter yield (see Table
1
for the description of treatments).
The black line represents the critical nitrogen concentration
N
c
=
3.41
DM
0.391
according to Equation (
2
) adapted from
Herrmann and Taube (
2004
).
y = 0.24ln(x) - 0.35
R2 = 0.87
0.00
0.20
0.40
0.60
0.80
1.00
1.20
0
50
100
150
200
250
300
NNI
Total N inputs
F I G U R E 6
Nitrogen nutrition index (NNI) versus total
N inputs from seeds, N deposition, fertilizer, amendments
and N mining for 14 treatments in the Ultuna frame trial
(i.e., bare fallow excluded).
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Page 12
impact of SOC on maize productivity into an effect
related to N supply or uptake efficiency and a physical
effect related to BD. This model describing maize yield
(
Y
)
normalized
for
the
standard
treatment
CaN
(i.e., yields in CaN are equal to 1) is:
Y
¼
0
:
0714
þ
1
:
653
NNI
þ
0
:
775
R
pH
–
0
:
892
BD
:
ð
4
Þ
This implies that a decrease from 1.4 to 1.0 Mg m
3
(the range of BD values in the experimental treatments)
would increase plant productivity by 29% under non-
limiting N conditions at reference soil pH (6.54). At sub-
optimal N supply and soil pH, the predicted relative
impact of BD increases, for example, to a 35% productiv-
ity increase at a simultaneous 10% decrease in
NNI
and
R
pH
(corresponding to pH 5.3).
It is well known that soil organic matter correlates
negatively with the BD of soil (Jeffrey,
1970
). Therefore,
SOC, a common proxy for soil organic matter, is included
as a predicting variable for hydraulic soil properties in
many pedotransfer functions (Rawls et al.,
2003
). In our
experiment, the negative slope of the regression line BD
versus SOC measured in 2009 was 0.122 (
R
2
=
0.67)
according to Kätterer et al. (
2011
), which means that an
increase in SOC by one unit percentage will lead to a
decrease in BD of 0.122 Mg m
3
. This slope is almost
identical to that derived from a database for agricultural
soils across Sweden (Kätterer et al.,
2006
), which
indicates that our results are representative, a least for
Swedish soils although the intercept of this functional
relationship varies between soil types (Kätterer et al.,
2011
). Substitution of measured BD (which is measured
much less frequently than SOC) in equation 4 with its
estimate derived from SOC (BD
est
=
1.469
–
0.122
SOC)
resulted in a similar model fit, explaining 96% of the
variance (not shown). Substitution of measured BD with
measured SOC in the multiple regression model together
with
NNI
and
R
pH
as independent variables resulted in
the following model that explained 97% of the variation
in Y (Figure
7
):
Y
¼
1
:
222
þ
0
:
131
SOC
þ
1
:
841
NNI
þ
0
:
562
R
pH
:
ð
5
Þ
The impact of the three variables on yield was highly
significant (
p
≤
0.0005), and partial
R
2
-values for
NNI
,
R
pH
and SOC were 79%, 11% and 7%, respectively. Inter-
actions between these three variables were not signifi-
cant. This implies that crop yields are predicted to
increase by 0.131 units, or 10% when SOC increases from
1% to 2%, for each unit increase in SOC percentage due
to changes in soil physical properties (i.e., when both
NNI
and
R
pH
=
1). When excluding the treatment with
an extremely low pH value (treatment AS) from the anal-
ysis, the slope of SOC in the regression model slightly
increased from about 10% to 13% yield increase per unit
increase in SOC. Yields (relative to the treatment CaN) in
the high-N treatments in our experiment increased by
16% for each unit change in SOC (Figure
4
). Thus, a
major part, of a least two thirds (10/16
=
63%), of the
SOC effect on crop yield was likely due to processes asso-
ciated with changes in soil physical properties, rather
than nutrient cycling.
3.6
|
Effects of SOC on PAWC
Soil water retention characteristics were measured in
1997 in the topsoil (0
–
20 cm) in nine treatments (BF,
Unf, CaN, Str, GM, SD, FYM, Peat and SS; Kirchmann &
Gerzabek,
1999
). When regressing water content at differ-
ent soil water tensions on SOC concentration, the slopes
of the regressions generally declined with soil water
tension in their study. Thus, water content under dry
conditions was less affected by SOC than under wetter
conditions, which is in accordance with many previous
studies (Hudson,
1994
). At the highest tension measured
by Kirchmann and Gerzabek (
1999
), pF 3.5, correspond-
ing to the pressure of a 30 m water column, the slope of
the regression was insignificant. Assuming that water
content at wilting point (pF 4.2) was not responsive to
SOC, we used the measurement of wilting point from a
y = 1.00x + 0.01
R2 = 0.95
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
0
0.5
1
1.5
Measured yield
Predicted yield
y = 1.00x - 0.00
R2 = 0.97
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
0
0.5
1
1.5
Measured yield
Predicted yield
F I G U R E 7
Measured versus
predicted average maize yields normalized
for the standard treatment C in 14 of the
treatments in the Ultuna frame trial
during the period 2000
–
2019 according to
two models. Left panel: Y
=
0.0714
+
1.653
NNI
+
0.775
R
pH
–
0.892
BD; right
panel:
1.222
+
0.131
SOC
+
1.841
NNI
+
0.562
R
pH
(see the text for details).
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Page 13
soil profile in 1969 adjacent to the experimental plots
(17.2%; Wiklert et al.,
1983
) to calculate plant available
water capacity (PAWC), that is, the difference in volu-
metric soil water content between field capacity (pF 2)
and wilting point (pF 4.2). With this assumption, PAWC
in the upper 20 cm layer varied between 20.2 mm in the
bare fallow and 32.8 mm in the farmyard manure treat-
ment (Figure
8
). For the six treatments BF, Unf, CaN,
Str, GM and FYM, reflecting common agricultural prac-
tices, PAWC increased by 11.1 mm per unit percentage
increase of SOC (
R
2
=
0.94). This relationship was less
clear for the treatments SD, Peat and SS that received
sawdust, peat and SS, which are rather uncommon
amendments in agriculture, and were so probably also
during its historic land use. When including those in the
regression, PAWC increased only by 3.6 mm per unit
SOC%, and the coefficient of determination decreased to
R
2
=
0.51 (Figure
8
). Relative to SOC concentration,
these three treatments seemed to have affected soil pore
space and PAWC less than the others. This may relate to
the findings by Gerzabek et al. (
2001
), who found that
the extra SOC accumulating in these treatments in sam-
ples from 1997 was preferentially recovered in silt and
sand fractions rather than in the clay fraction. Another
fractionation study on samples taken in 1998 also showed
that SOC in the clay-sized fraction was most enriched in
13
C and
15
N, indicating the most intense turnover of soil
organic matter in the farmyard manure treatment and
least in the peat treatment (Kirchmann et al.,
2004
).
Treatment differences in SOC quality were also apparent
according to a size and density fractionation study on
samples from 2004 (Magid et al.,
2010
). According to
their study, a much lower proportion (around 40%) of
total SOC was recovered in the smallest fraction
(<0.05 mm) with the highest density (>1.85 Mg m
3
) in
the sawdust and SS treatments compared with, for exam-
ple, the bare fallow and unfertilized control treatments
(where this fraction contributed by almost 70% to total
SOC). The sawdust and peat had also the highest
cellulose-, lignin- and cutin-like fractions among amend-
ments according to the Van Soest fractionation scheme
(Peltre et al.,
2012
). Furthermore, the relatively high C/N
ratios in peat and sawdust treatments (Table
1
) also indi-
cate that these materials may decompose more slowly
than other organic inputs and therefore accumulate in silt
and sand fractions rather than in mineral-associated
organic matter in clay size fractions. Nevertheless, the
organic matter in the SS treatment had a relatively low
C/N ratio (9.6). The slow decomposition of sludge was
probably masked by its low C/N ratio (8.0). The high con-
tent of iron and aluminium in the sludge, which had been
added to wastewater in the treatment plant for precipitat-
ing phosphorus, and high concentrations of heavy metals
during the early decades of the experiments, may also
have decreased its accessibility for decomposers (Börjesson
et al.,
2014
). The low soil pH in the sludge treatment and
high concentrations of free aluminium and iron may also
have affected decomposition (Xiang et al.,
2023
) as well as
soil structure (
ˇ
Simanský & Jonczak,
2020
).
However, the relatively low impact of SOC on PAWC in
the treatments with high SOC does not reflect the overall
effect of the peat and sludge treatments on water storage
because our analysis was limited to the upper 20 cm of the
soil profile. According to Menichetti et al. (
2015
), these two
treatments significantly affected SOC, also in the upper sub-
soil, to a depth of 35 cm. Since topsoil layer thickness
increases proportionally to changes in BD, which in turn is
closely related to SOC, topsoil layer thickness and SOC are
closely correlated (Meurer, Chenu, et al.,
2020
). This was
clearly shown in samples taken in 2009 in our experiment
based on both plot elevation measurements and mass
balances (Kätterer et al.,
2011
). Therefore, when considering
only the topsoil, the effects of SOC on PAWC in these treat-
ments will be underestimated.
Our estimated increase in topsoil PAWC by 11.1 mm
for each unit increase in SOC% for the most common agri-
cultural practices is relatively high compared with several
previous studies (Bagnall et al.,
2022
; Lal,
2013
,
2020
) but
within the range reported by others (Hudson,
1994
). We
see three major reasons for the relatively strong response
in our study. Firstly, the clay content (36.5%) is compara-
tively high, and thus, aggregation may respond stronger to
SOC in our study than in studies based on more coarse-
textured soils. Secondly, all management in the Ultuna
y = 3.62x + 19.59
R2 = 0.51
y = 11.07x + 8.94
R2 = 0.94
0
5
10
15
20
25
30
35
40
45
50
0
1
2
3
4
PAWC (m
m
)
SOC%
All practices
Common practices
F I G U R E 8
Response of plant available water capacity (PAWC)
in the topsoil (0
–
20 cm) to SOC in nine selected treatments,
calculated based on data from Kirchmann and Gerzabek (
1999
).
The dataset was split into two categories: only common (BF, Unf,
CaN, Str, GM and FYM, in orange) and all agricultural practices
including less common ones (Peat, SD and SS, in black). The trend
lines concern the former group or the whole dataset (both groups).
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Page 14
small-plot frame trial is done by hand, and the soil is not
compacted by vehicles, which likely resulted in a stronger
response in structure to changes in SOC compared to
large plots or on-farm experiments using full-scale
machinery. Thirdly, the impact of SOC and topsoil layer
thickness has not been considered in previous reviews
and meta-analyses. The latter argument has implica-
tions for PAWC in the field. According to Kätterer et al.
(
2011
), topsoil layer thickness increased by 2.3 cm for
each unit of SOC% increase, which means that the same
mass of soil is distributed within a greater soil volume.
Thus, the whole volume of plant available water of this
extra 2.3 cm layer has to be added to the effect of
11.1 mm. For the farmyard manure treatment, for exam-
ple, this corresponds to an additional water storage
capacity of 3.8 mm. Consequently, the total increase in
PAWC was almost 15 mm in our experiment per unit of
SOC% increase. Particularly during dry spells, this extra
storage of water may become decisive for crop produc-
tivity (Liu et al.,
2023
).
4
|
C O N C L U S I O N S
Our analysis showed that maize yield in the treatments
that are not severely limited by nitrogen supply
(i.e., treatments receiving at least 80 kg N ha
1
year
1
as inorganic fertilizer or organic amendments) or soil
acidity (pH <5) increased by 16% for each percentage
unit increase in SOC. Three variables were identified as
yield-determining factors, that is, soil pH, the N status
of the crop and BD (as a proxy for soil physical proper-
ties). These variables together explained 95% of the vari-
ation in maize yields between treatments. According to
the resulting multiple regression model, yield responses
were sensitive to BD. Estimated potential yields that
were neither limited by acidity nor nitrogen, according
to this model, increased by about 10% for each percent-
age unit increase in SOC. Since the model accounted for
the biochemical limitations (nitrogen and soil pH), this
yield response to SOC should represent the response to
changes in soil structure associated with the changes in
SOC. We conclude that a major part, likely as much as
two thirds of yield responses to SOC change, could be
ascribed to associated changes in soil physical proper-
ties. Since the use efficiency of nitrogen was not notice-
ably affected by the level of SOC, the extra storage
capacity of plant available water associated with SOC
(i.e., which increased by almost 15 mm in the topsoil
per unit of SOC% increase) was most likely the main
driver for the observed yield responses. It should, how-
ever, be considered that the yield responses to SOC may
not be directly transferable in a quantitative way from
the Ultuna trial to farm-scale conditions. In farmers'
fields, soils are frequently compacted by agricultural
machinery, which impacts negatively on soil porosity
and water storage capacity. Nevertheless, the positive
effect of SOC on water storage capacity suggests that
measures for increasing SOC in cropland are most likely
an effective adaptation strategy for maintaining crop
productivity under future climatic conditions with more
frequent dry spells, even in relatively humid climates
such as in Sweden.
A U T H O R C O N T R I B U T I O N S
Thomas Kätterer:
Conceptualization; methodology;
software; data curation; investigation; validation; formal
analysis;
funding
acquisition;
visualization;
project
administration; writing
–
original draft; resources.
Martin A. Bolinder:
Writing
–
review and editing;
conceptualization.
A C K N O W L E D G E M E N T S
We dedicate this work to the memory of our colleague
and friend, the late Professor Holger Kirchmann, who
was involved in the early discussions about this work.
We remember him for his kindness and generosity and
as a dedicated scientist seeking to understand carbon
and nutrient cycling in agroecosystems. We acknowledge
the late Olle Gunnarsson and late Hans Nömmik for ini-
tiating the experiment and the late Professor Jan Persson
who safeguarded its continuation. We are thankful to our
faculty for providing resources to maintain the Swedish
long-term experiments, to Per Hillström, who carefully
managed the Ultuna frame trial for the three recent
decades, and to Karl-Gunnar Söderdahl, who took over
the management during recent years.
F U N D I N G I N F O R M A T I O N
This work was funded by the Swedish Government
Research
Council
for
Sustainable
Development
(FORMAS, grant 2022-00214), the EJP SOIL project
SIMPLE,
which
has
received
funding
from
the
European Union's Horizon 2020 research and innova-
tion program (grant agreement No. 862695) and by the
Swedish Farmers' Foundation for Agricultural Research
(SLF, grant O-22-23-734).
D A T A A V A I L A B I L I T Y S T A T E M E N T
The data that support the findings of this study are avail-
able from the corresponding author upon reasonable
request.
O R C I D
Thomas Kätterer
https://orcid.org/0000-0002-1751-
007X
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Page 15
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