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European Journal of Soil Biology 121 (2024) 103624
Available online 17 May 2024
1164-5563/© 2024 The Authors. Published by Elsevier Masson SAS. This is an open access article under the CC BY license
(http://creativecommons.org/licenses/by/4.0/).
Original article
Effects of different soil organic amendments (OAs) on extracellular
polymeric substances (EPS)
Yujia Luo
a
,
b
, Juan Bautista Gonzalez Lopez
b
, H. Pieter J. van Veelen
b
, Dirk-Jan Daniel Kok
c
,
Romke Postma
d
, Dirk Thijssen
d
, Valentina Sechi
a
,
b
,
*
, Annemiek ter Heijne
a
,
T. Martijn Bezemer
e
,
f
, Cees J.N. Buisman
a
,
b
a
Environmental Technology, Wageningen University, P.O. Box 17, 6700 AA, Wageningen, the Netherlands
b
Wetsus, European Centre of Excellence for Sustainable Water Technology, Oostergoweg 9, 8911 MA, Leeuwarden, the Netherlands
c
Institute of Environmental Sciences, Leiden University, P.O. Box 9505, 2300 RA, Leiden, the Netherlands
d
Nutri¨
enten Management Institute BV, Nieuwe Kanaal 7C, 6709 PA, Wageningen, the Netherlands
e
Institute of Biology, Aboveground Belowground Interactions Group, Leiden University, P.O. Box 9505, 2300 RA, Leiden, the Netherlands
f
Netherlands Institute of Ecology (NIOO-KNAW), Department of Terrestrial Ecology, Droevendaalsesteeg 10, 6708 PB, Wageningen, the Netherlands
ARTICLE INFO
Keywords:
Sustainable agricultural management
Three-year eld experiment
Soil bacteria and fungi
Polysaccharides
Protein
Soil aggregation and stability
ABSTRACT
Extracellular polymeric substances (EPS) synthesized by soil microorganisms play a crucial role in maintaining
soil structure by acting as binding agents of soil aggregates. Microbial EPS production is governed by C sources,
soil nutrient availability, pH, and other local environmental factors. Another important factor is soil manage-
ment, and particularly, the addition of organic amendments (OAs), has the potential to inuence soil EPS as it
can change the biotic and abiotic properties of the soil. Yet the response of soil EPS to the addition of OAs,
especially in eld trials, and its subsequent impact on soil aggregation remains unclear. This study aimed to
elucidate the inuence of OAs (including compost from organic residues, mown grass from roadsides and parks,
and cattle manure) on soil EPS content and aggregate stability in a three-year eld experiment with annual OA
application. We further investigated factors that govern EPS production in the soil by exploring the relationship
between soil EPS (i.e., polysaccharide and protein content), soil physicochemical properties (i.e., pH, dissolved
organic carbon, available and total amount of nutrients), and the soil microbial community (i.e., microbial
abundance and taxonomic structure). We found that the addition of grass, manure, and the combination of grass
and manure led to an increase in soil EPS content compared to unamended and compost-amended soils. EPS
content was correlated with soil variables; in particular, a signicant positive correlation was observed between
EPS concentration and available N in the soil. Furthermore, bacterial and fungal biomass contributed to soil EPS.
Specic bacteria (e.g., members of Proteobacteria, Bacteroidetes, and Chloroexi) and fungi (e.g., members of
Ascomycota and Basidiomycota) demonstrated strong and signicant correlations with EPS in the soil. The di-
rection of correlation, whether positive or negative, varied at the order level. In addition, our study revealed
signicant positive correlations between EPS concentration and soil aggregate stability. These ndings offer
insights into designing sustainable agricultural management practices, and whether the application of appro-
priate OAs can enhance soil EPS content and, consequently, soil aggregate stability.
1. Introduction
In their natural environment, microbes are predominantly associated
with surfaces [1]. During the sessile growth mode, microbes produce
extracellular polymeric substances (EPS), which are highly hydrated and
charged [1,2]. While the composition of EPS can vary greatly, poly-
saccharides and proteins are considered to be the major fractions of EPS
[3]. In the soil, EPS are intermixed with cells and other soil organics and
minerals, providing diverse benets to microbes. EPS can affect soil
functioning, as they are viewed as highly responsive transient binding
agents. EPS normally do not persist in the soil; instead, they have a short
turnover time. Research shows that they can be more affected by current
soil management than by legacy effects from previous management,
even when applied continuously for a duration of over 50 years [4,5].
* Corresponding author. Wetsus, European Centre of Excellence for Sustainable Water Technology, Oostergoweg 9, 8911 MA, Leeuwarden, the Netherlands.
E-mail address: valentina.sechi@wetsus.nl (V. Sechi).
Contents lists available at ScienceDirect
European Journal of Soil Biology
journal homepage: www.elsevier.com/locate/ejsobi
https://doi.org/10.1016/j.ejsobi.2024.103624
Received 29 December 2023; Received in revised form 3 May 2024; Accepted 8 May 2024
European Journal of Soil Biology 121 (2024) 103624
2
EPS can facilitate microaggregate formation, increase water holding
capacity, and improve soil structural stability more effectively than bulk
soil organic matter (SOM) [4,5]. This benets agriculture, especially in
environments that are dry and with decit irrigation [6,7]. EPS act as
sponges and retain water, delay drying, and connect microorganisms
with substrates, thereby supporting microbial activity even at low water
potential [7–9]. Due to the wide range of benets that EPS bring to the
soil, the management of soil EPS is gaining increasing interest in agri-
cultural practices. Studies have been conducted to understand EPS dy-
namics inuenced by both previous and current land use, where they
have investigated the effects of agricultural management practices, such
as soil water management and organic input, on EPS and soil aggrega-
tion [5,10–12].
Microbial EPS production can be promoted by changing environ-
mental variables (e.g., soil pH, nutrient availability, carbon-to-nitrogen
(C:N) ratio, and water content) [13–15] or by introducing/stimulating
EPS-producing pure cultures [16,17]. The addition of organic amend-
ments (OAs) is another potential way of promoting EPS production in
the soil since OAs are rich in C substrates and nutrients, yet this
approach has received less attention than the addition of pure cultures,
and only a few studies have investigated EPS in eld trials so far [5,10,
18]. It is currently unclear to what extent OAs can enhance soil EPS and
aggregate stability and which soil microorganisms are potentially
responsible for building the soil EPS matrix in response to the addition of
OAs. Previous studies have found that soil EPS content is positively
correlated with microbial biomass [9,19]. In addition, The availability
of C substrates stimulates microbial EPS production and altering soil N
levels also regulates EPS content [10,20,21]. The availability of different
carbon sources can directly affect microbial EPS production by inu-
encing the precursor molecules necessary for EPS synthesis [12,22]. The
availability of N can inuence the composition of soil EPS and the
quantity of polysaccharides produced by impacting the N metabolism of
the microbial community [23]. Sher et al. [12] found that soil EPS can
be inuenced by root biomass, which potentially regulates fresh C
supply. Hale et al. [10] reported enhancements in soil microbial
biomass, soil EPS, and soil aggregate stability with the addition of
compost.
To understand the inuence of key physicochemical and biological
variables on EPS content in agricultural soil, we conducted a three-year
eld experiment. We tested the effects of different OAs, including
compost in high and low doses, mown grass, cattle manure, and a
mixture of mown grass and cattle manure, on soil EPS concentration and
aggregate stability. Compost, grass, and manure are commonly used in
agriculture as soil amendments to increase SOM and nutrients [24,25].
These OAs vary in nutrient availability, C stability (i.e., bioavailability
and biodegradability of OM), and microbial activity [26], potentially
affecting soil EPS content [10,12]. We expected that soils amended with
OAs would have higher EPS concentrations than unamended soil. Spe-
cically, we expected that the addition of grass, manure, and their
combination, providing more labile organic C and nutrients for micro-
bial growth, would result in a higher absolute quantity of EPS compared
to compost addition. Additionally, since grass typically contains higher
fractions of lignin and cellulose than manure, and manure exhibits
higher biological activity than grass [26,27], we anticipated that the
addition of manure would provide greater support for microbial growth
and consequently, EPS production in the soil compared to grass.
Furthermore, we hypothesized that the addition of OAs would induce
changes in the soil microbial community. Specically, we expected that
the addition of OAs would increase microbial biomass and alpha di-
versity compared to the control due to the provision of C sources and
nutrients. We anticipated that soil EPS concentration would correlate
with specic microbial taxa, potentially contributing to EPS production
or degradation in the soil. By testing these hypotheses, we aim to
identify key physicochemical and biological variables that correlate
with EPS content and soil aggregation in agricultural soil.
2. Material and methods
2.1. Field experiment description and soil sample collection
The experimental eld is located near Heelsum, The Netherlands.
The soil is classied as a coarse sandy Anthrosol (WRB-FAO classica-
tion), comprising 74 % sand, 20 % silt, 2 % clay, and 3.7 % organic
matter. Maize (Zea mays L.) and Lolium multiorum Lam. were grown in
rotation, with the latter acting as a winter catch crop and being grown
during the sampling season (February 2021). Six treatments were tested,
including unamended soil (control, only mineral fertilizers were added),
low-dose compost from organic residue (CL, ~11 ton/ha per year), high-
dose compost of the same type (CH, ~22 ton/ha per year), mown grass
from roadsides and parks (Gra, ~20 ton/ha per year), cattle manure
(Man, ~30 ton/ha per year), and a combination of mown grass and
cattle manure (Gra +Man, ~50 ton/ha per year). The combination
represents a common practice in Dutch agronomic reality, where excess
manure from the livestock industry is often combined with organic
residues for soil amendments. The application rates varied yearly based
on the quality of OAs and were designed in accordance with national
fertilization recommendations and standard application norms [28,29].
We limited the total nutrient input to 120 kg/ha of available N, 50 kg/ha
of P
2
O
5
, and 200 kg/ha of K
2
O. Once one nutrient limit was met, decits
in other nutrients were compensated by adding mineral fertilizers,
ensuring the total input of nutrients from OAs and mineral fertilizers met
the maximum allowable input (Table 1) [24,25]. We had deviations in
the inputs of OAs between years, which we considered acceptable, as
they reected the natural variation in OAs that one can expect in reality.
The OAs were mechanically incorporated into the soil using a disc
harrow to a depth of approximately 15 cm. The treatments were applied
to 10 ×10 m plots in a randomized complete block design across a 30 ×
60 m experimental eld, as shown in Fig. 1. Each treatment was repli-
cated three times, totalling 18 plots. The OAs were applied yearly from
March 2018 until March 2020.
Five soil cores (10–20 cm deep, 3 cm diameter) were collected at
random points from each plot in February 2021, 11 months after the last
application round of the OAs. The soil samples were homogenized per
plot and transported to the laboratory on ice. Upon arrival, soil samples
from each plot were divided into four subsamples for physicochemical
characterization, microbial composition analysis, and EPS visualization
and quantication. One subsample was immediately xed with 3 %
paraformaldehyde buffered with phosphate-buffered saline (PBS 1X) to
prevent cell lysis and to maintain its integrity for later microscopy
visualization [30]. One subsample was immediately stored at 4 ◦C for
water content (WC) and organic matter content (OM) analysis the day
after. One subsample was immediately stored at −20 ◦C for DNA
extraction which was carried out about one week later. The remaining
subsample was immediately dried at 65 ◦C for three days until a constant
weight was achieved for physicochemical characterization.
For the analysis of aggregate stability, a different sampling strategy
was used. Changes in aggregate stability are subject to signicant tem-
poral variability. Yet we do not have the right tools to predict when the
effect of OAs on aggregate stability is greatest (i.e., when it peaks). Since
we did not want to miss the effect of OAs on aggregate stability by
sampling too soon or too late, we decided to sample for aggregate sta-
bility twice per year (in 2019 and 2020), once in late June (three months
after sowing) and once in late November (three months after harvesting/
ploughing). By having sampled for aggregate stability multiple times
within a year, we aimed to increase the robustness of our study and add
more certainty to our conclusions.
2.2. Physicochemical analysis of soil
The WC of the soil was measured after drying in a forced-air oven at
105 ◦C for 8 h, and samples were subsequently burned at 550 ◦C for 2 h
to quantify OM content. For dried soil samples, various parameters were
Y. Luo et al.
European Journal of Soil Biology 121 (2024) 103624
3
assessed following the methods presented in the book “Soil Sampling
and Methods of Analysis” [31] with modications [32]. Given that
water serves as the solvent and transport medium of nutrients for mi-
croorganisms and plants in the soil, we opted to use water, rather than
alternatives such as CaCl
2
, for nutrient extraction to better represent this
reality. Specically, these parameters include pH, electrical conductiv-
ity (EC), water-available nutrients (NO
3
−
, NO
2
−
, NH
4
+
, PO
4
3−
, K
+
), total
nutrients (including total carbon TC, total nitrogen TN, total phosphorus
TP, and total potassium TK), and dissolved organic carbon (DOC). Soil
pH and EC were measured using a Mettler Toledo SevenExcellence™ in
a 1:10 soil/MilliQ water suspension (w/v) following 1 h of shaking at
25 ◦C. Soil WC was determined after drying in a forced-air oven at
105 ◦C for 4 h, with subsequent ignition at 550 ◦C for 2 h to quantify OM.
TC and TN were determined using an elemental analyser (Interscience
FlashSmart CHNSO). For TP and TK analysis, inductive coupled plasma
optical emission spectrometry (PerkinElmer Optima 5300 DV) was
employed after microwave acid digestion (Milestone Ethos Easy SK-15).
Water-soluble nutrients (NO
3
−
, NO
2
−
, NH
4
+
, K
+
, and PO
4
3−
) were measured
in a 1:10 soil/MilliQ water suspension (w/v). The suspension underwent
centrifugation at 3750g after 2 h of shaking at 25 ◦C. The supernatant,
ltered through a 0.45
μ
m membrane lter (Hydrophilic PTFE), was
then analysed using ion chromatography (Metrohm Compact IC 761).
Additionally, DOC was extracted and prepared similarly to available
nutrients and subsequently analysed using a TOC analyzer (Shimadzu
TOC-L).
2.3. EPS quantication and visualization
Soil EPS were extracted using cation-exchange resins (CER)
following the method described by Redmile-Gordon et al. [33]. First, 3 g
of fresh soil was suspended in a 25 mL soluble microbial products (SMP)
extraction solution. The solution consisted of 0.01 M CaCl
2
(local rain-
water ionic equivalent) with pH 7 adjusted by 0.01 M Ca(OH)
2
. The
mixture of soil and the SMP extraction solution was shaken at 4 ◦C for
30 min at a speed of 2 cycles s
−1
. After shaking, the supernatant con-
taining SMP was discarded following centrifugation at 3200g for 30 min.
Second, for EPS extraction, cation-exchange resin (CER, DOWEX
Marathon C, sodium form) was pre-washed in phosphate-buffered saline
(PBS 1X) twice. After pre-washing, CER was added to the centrifuged
pellet along with 25 mL of chilled EPS extraction buffer. The amount of
CER (g) was calculated using the equation: 2.543 x (SOC% x soil sample
mass (g dry weight equivalent)) x 70. 2.543 is the conversion factor from
carbon loss on ignition (LECO) to volatile solids (VS). The quantity of
CER needed for EPS extraction is calculated based on the VS content in
the soil, where sufcient CER equals 70 g of CER per gram of VS. The EPS
extraction buffer contained 2 mM Na
3
PO
4
⋅12H
2
O, 4 mM NaH
2
PO
4
⋅H
2
O,
9 mM NaCl and 1 mM KCl with pH 7 adjusted by 1 M HCl, and cooled to
4 ◦C. The mixture of the centrifuge pellet and EPS extraction buffer was
shaken at 4 ◦C for 2 h, with a speed of 2 cycles s
−1
. The supernatant
containing the extracted EPS was then centrifuged at 4000 g for 30 min,
ltered to remove soil or plant residues, and further puried by dialysis.
Table 1
An overview of the nutrient content in organic amendments (OAs) and the application dose of OAs and mineral fertilizers [24]. OM: organic matter; TN: total nitrogen;
AN: available nitrogen. Control: unamended soil; CL: compost amended soil with low dose; CH: compost amended soil with high dose; Gra: mown grass amended soil;
Man: cattle manure amended soil; Gra +Man: a combination of grass and manure amended soil.
Year Treatment Dose Inputs from OAs kg/ha Mineral fertilizer kg/ha
ton/ha OM TN AN P
2
O
5
K
2
O N P
2
O
5
K
2
O
March 2018 Control 0 0 0 0 0 0 140 50 200
CL 10.6 1657 67 7 48 79 133 2 121
CH 21.3 3329 136 14 96 158 126 0 42
Gra 26.3 3570 373 186 123 208 0 0 0
Man 34 1476 124 75 45 184 65 5 16
Gra+Man 60.3 5046 497 261 168 392 0 0 0
March 2019 Control 0 0 0 0 0 0 106 65 200
CL 12.3 1713 74 7 64 85 98 0 115
CH 22.7 3174 138 14 118 157 92 0 43
Gra 18.8 978 99 49 43 119 56 22 152
Man 30 1386 128 58 42 141 47 23 59
Gra+Man 48.8 2364 227 107 85 260 0 0 0
March 2020 Control 0 0 0 0 0 0 120 50 200
CL 10.6 1593 71 7 39 57 122 11 143
CH 21.8 3266 146 15 80 118 99 0 82
Gra 13.5 2026 151 90 37 117 35 13 83
Man 27.5 1386 114 54 40 113 67 10 87
Gra+Man 41 3412 265 144 77 230 0 0 0
Fig. 1. Treatment arrangements in the eld, located in Heelsum, The
Netherlands. The four coordinates are: Top-right: 51
◦58
′
43.47
″
N, 5◦45
′
59.06
″
E,
Top-left: 51◦58
′
43.57
″
N, 5◦45
′
57.39
″
E, Bottom-right: 51◦58
′
41.50
″
,
5◦45
′
58.45
″
E, Bottom-left: 51◦58
′
41.60
″
N, 5◦45
′
56.85
″
E. CL: compost amended
soil with low dose; CH: compost amended soil with high dose; Gra: mown grass
amended soil; Man: cattle manure amended soil; Gra +Man: a combination of
grass and manure amended soil.
Y. Luo et al.
European Journal of Soil Biology 121 (2024) 103624
4
The dialysis used tubular dialysis membranes with a 12–14 kDa mo-
lecular weight cut-off (Spectra/Por 2) against distilled water to remove
low molecular weight metabolites and salts. The puried EPS solution
was ltered with 0.45
μ
m lters (Hydrophilic PTFE) and then analysed
using liquid chromatography-organic carbon detection (LC-OCD model
8, DOC-LABOR) to determine the amount of EPS carbon (EPS-C) and EPS
nitrogen (EPS-N), representing polysaccharides and proteins,
respectively.
Confocal laser scanning microscopy (CLSM) was employed to visu-
alize EPS on soil particles. First, the paraformaldehyde-xed soil sam-
ples were incubated at 4 ◦C overnight and then washed three times with
Hanks’ Balanced Salt Solution buffer (HBSS, ThermoFisher) before
staining according to the method of Chen et al. [34]. Briey,
α
-poly-
saccharides, cellulose, and DNA from living cells were stained with
concanavalin A (Con A), calcouor white (CW), and SYTO 63, respec-
tively. SYTO 63 (20
μ
M), Con A (250 mg/L), and CW (300 mg/L) were
sequentially added and incubated at 30 ◦C for 30 min after the addition
of each dye. After each stage of the labelling process, the sample was
washed three times with HSBB buffer to remove the excess dye. Before
visualization, the labelled sample was embedded on glass slides and
frozen at −20 ◦C.
2.4. Aggregate formation and stability
The geometric mean diameter (GMD) [mm] and mean weight
diameter (MWD) [mm] of soil samples were determined using the wet
sieving method [35], and as detailed by Kok et al. [25]. GMD reects soil
aggregate formation and the size of the aggregates, while MWD in-
dicates the stability of soil aggregates [36]. Higher values of GMD and
MWD indicate that soil aggregates contain more larger fractions and are
also more stable. Briey, soil samples were air-dried for two weeks and
then oven-dried at 60 ◦C for 24 h before wet sieving. A sieve with a mesh
width of 8 mm was initially used to remove roots, rocks and pebbles, as
well as break large aggregates in the oven-dry samples. Subsequently, a
sieve stack of descending sizes (2 mm, 1 mm, 500
μ
m, 250
μ
m, and 125
μ
m) was submerged into a column of water. Dry soil (20–40 g, the total
mass was indicated as M
T
) was slowly wetted with deionized water and
then gently poured onto the top of the sieve stack. The sample was
sieved under water in a vertical direction for 2 min at a frequency of 30
waves, of a 3 cm amplitude, per minute. Each sieve was then subse-
quently washed, and the dispersed aggregate (the mass of dispersed
aggregate was indicated as M
Ai
) and coarse material remaining (the
mass of coarse was indicated as M
Ci
) in each sieve were recovered and
oven dried at 60 ◦C. GMD and MWD were calculated using the following
equations:
GMD =exp [∑n
i=1(MAi ∗ln (di))
MT−∑n
i=1MCi ](1)
MWD =∑n
i=1(MAi ∗di)
MT−∑n
i=1MCi
(2)
Where i =1, 2, …, n corresponds to each aggregate size fraction (n =5),
and di is the average diameter of each size fraction (i.e., mean inter-sieve
size). It is important to note that we did not analyze the GMD and MWD
indices for the soil receiving the low compost dose. It was physically not
possible to sample all plots within a day for all the properties investi-
gated at the eld site. The decision was made to sample the high
compost dose over the low compost dose given that we expected greater
effect from the higher compost dose. Consequently, we excluded the EPS
data of the soil receiving low compost dose from the correlation analysis
with the GMD and MWD.
2.5. Characterization of bacteria and fungi
DNA was extracted from 0.25 g of soil using the DNeasy Power Soil
Kit (Qiagen) following the manufacturer’s protocol. DNA concentration
and purity (OD
260
and OD
280
) were quantied using the Quant-it dsDNA
kit on a Quantus (Promega) and the Nanodrop 1000 (Thermo Scientic),
respectively. The extracted DNA samples were stored at −20 ◦C before
downstream analysis. For microbial abundance assessment, triplicate
qPCR assays were performed on a CFX96 Real-Time System (Bio-rad) to
quantify bacterial and fungal gene copies using the 16S rRNA and ITS
genes, respectively. The qPCR assays were conducted using a Real-Time
PCR detection system (CFX96 Touch, Bio-Rad). A 2
μ
L DNA sample was
added to 18
μ
L master mix containing 0.6
μ
L of each primer (300 nM),
10
μ
L of iQ™ SYBR® Green Supermix (Bio-Rad), and 6.8
μ
L of Ultra-
Pure™ DNase/RNase-Free distilled water (Invitrogen). For each primer
set (338F/518R for bacteria [37], ITS86F/ITS4R for fungi [38]), a
linearized plasmid standard (gBlocks, IDT technologies) containing the
target region was used to create a standard curve. The thermal prole for
bacterial qPCR was as follows: initial denaturation at 95 ◦C for 5 min,
followed by 40 cycles at 95 ◦C for 15 s, and 64 ◦C for 30 s. The thermal
prole for fungal qPCR was as follows: initial denaturation at 95 ◦C for 5
min, followed by 40 cycles of 95 ◦C for 15 s, 55 ◦C for 30 s, and 72 ◦C for
1 min. Bacterial qPCR efciency was 99.8 %, with R
2
>0.999. The
fungal qPCR efciency was 95.3 %, with R
2
>0.994.
DNA samples were normalized to 20 ng/
μ
L for library preparation
and sequencing at MrDNA (TX, USA) on a MiSeq platform (Illumina).
Libraries for bacteria were constructed using primers 338F (ACTCC-
TACGGGAGGCAGCAG) [39] and 806R (GGACTACHVGGGTWTCTAAT)
[40], and for fungi using primers ITS1F (CTTGGTCATTTA-
GAGGAAGTAA) and ITS2R (GCTGCGTTCTTCATCGATGC) [41]. The
raw sequence data are available at the European Nucleotide Archive
(ENA) at EMBL-EBI under accession number PRJEB47068 (https://www
.ebi.ac.uk/ena/browser/view/PRJEB71609). Raw sequence data were
analysed by QIIME2 (version 2019.10) as described previously [32]. The
downstream analyses of bacterial and fungal communities were per-
formed in RStudio (R version 4.0.4) using the phyloseq package [42]
and the vegan package [43]. Alpha diversity of bacteria and fungi was
assessed on rareed datasets (at a depth of 70,621 and 66,498 reads per
sample, respectively, as shown in Fig. S1) by calculating the Shannon
index and observed OTU richness.
2.6. Statistical analysis
Statistical analysis was performed in RStudio (R version 4.0.4). To
assess the effects of OA addition on soil physicochemical properties, soil
EPS content, and microbial gene abundance, one-way ANOVA (aov()
function, alpha =0.05) was employed, followed by pairwise post-hoc
comparisons (TukeyHSD() function, family-wise error rate 5 %). For
investigating the effects of types of OA addition, seasons, and years on
aggregate formation (GMD) and stability (MWD), a three-way ANOVA
was conducted (alpha =0.05), followed by pairwise post-hoc compar-
isons (TukeyHSD, family-wise error rate 5 %). Given that GMD and
MWD were mainly inuenced by the type of OA addition, we did not
differentiate among seasons and years in the presentation of the results
for GMD and MWD (statistical results are shown in Table S1). As-
sumptions of normality of residuals and equality of variances were
veried for each ANOVA model. To explore EPS patterns and their re-
lationships with soil physicochemical properties, redundancy analysis
(RDA) was employed to identify which properties were signicantly
associated with EPS concentration in the soil (rda() function in vegan
package) [43]. Prior to RDA, forward selection was applied to reduce the
number of soil physicochemical variables that were inter-correlated. The
variance ination factors (VIFs) of the remaining soil variables in the
RDA model were also checked, and VIFs were all lower than 10.
Spearman correlations (cor.test() function), which reveal monotonic
relationships between two continuous or ordinal variables, were calcu-
lated to evaluate potential associations between EPS concentration and
soil aggregation (i.e., between EPS and GMD, and between EPS and
MWD). For the Spearman correlation analysis, the closest sampling time
Y. Luo et al.
European Journal of Soil Biology 121 (2024) 103624
5
point of GMD and MWD to the EPS sampling point was considered, given
different sampling time points. Additionally, Spearman correlations
were explored between soil microorganisms (at the order level, >1 %
relative abundance) and soil EPS. Only robust correlations were
considered (|
ρ
| ≥0.6, P <0.05), based on Barber´
an et al. [44] who used
threshold |
ρ
| ≥0.6 and P <0.01 to indicate robust Spearman correlation
of soil microorganisms. To assess the difference in alpha diversity be-
tween treatments, we applied a nonparametric Kruskal-Wallis test
(kruskal.test() function), followed by Wilcoxon tests for pairwise com-
parisons (pairwise.wilcox.test() function). Adjusted (Holm) P-values
were reported, considering the overall number of comparisons, to con-
trol the ination of Type I errors (false positive results). Principal co-
ordinates analysis (PCoA, ordinate() function in phyloseq package) was
used to visualize Bray-Curtis dissimilarities of bacterial and fungal
communities (presented in the Supplementary Information Fig. S2)
[45]. We analysed order-level taxa to identify differential relative
abundances across treatments using a multinomial regression model.
This method is specically employed to handle compositional data with
sampling zeros [46] and was implemented using Q2-songbird plugin in
QIIME2, following the procedure outlined on GitHub (https://github.
com/biocore/songbird). Fitted multinomial models with experimental
parameters were compared against null models (intercept only) to
explore associations between microbial taxon abundances and OA
treatment.
3. Results
3.1. Soil physicochemical properties after three-year of treatment
The addition of OAs signicantly affected available nitrogen (AN, the
sum of NO
3
−
, NO
2
−
, and NH
4
+
) in the soil (ANOVA, P <0.01, Fig. 2A).
Specically, the Gra +Man treatment exhibited a higher AN concen-
tration compared to the control (P <0.05) and compost amended soil (P
<0.01, irrespective of compost application dose). Moreover, the Gra
Fig. 2. Available nitrogen (A), qPCR results of bacterial (B) and fungal (C) biomass indicated as 16S rRNA gene copies and ITS gene copies, respectively, and relative
abundance of bacteria (D) and fungi (E) at order level after three years of amelioration with different organic amendments (OAs) (mean ±sd; n =3). Within each
panel, boxes with identical letters indicate no signicant difference based on the Tukey HSD test.
Y. Luo et al.
European Journal of Soil Biology 121 (2024) 103624
6
treatment showed a higher AN concentration than the compost-
amended soil (P <0.05, irrespective of compost application dose).
However, other physicochemical properties, including pH, EC, WC,
water available nutrients, total nutrients, and DOC, did not show sig-
nicant differences among the treatments. Additional details regarding
the values of each physicochemical parameter and statistical results are
provided in the supplementary information (Table S2; Table S3).
3.2. Response of microbial abundance and composition to OA application
The abundance of 16S rRNA and ITS gene copies in treatments
exhibited a similar trend to that of AN (Fig. 2B and C). Specically,
compost addition did not signicantly inuence the abundance of soil
bacteria (P <0.05) or fungi (P <0.05) compared to the control, while
the Gra, Man, and Gra +Man tended to have higher gene copies of
bacteria and fungi. Microbial community composition, as indicated by
Bray-Curtis dissimilarities, differed among treatments, although alpha
diversity did not show signicant differences (Fig. S2). The Gra +Man
had relatively fewer Acidobacteriales (phylum Acidobacteria) and
relatively more Cytophagales (phylum Bacteroidetes) compared to the
control soil (Fig. 2D; multinomial regression, Fig. S2D). The relative
abundance of the fungi Sordariales (phylum Ascomycota) was signi-
cantly higher in the Gra +Man than in other treatments (Fig. 2E;
multinomial regression, Fig. S2H). The abundance of Agaricales
(phylum Basidiomycota) was signicantly lower in all OA-amended soils
compared to the control soil. Additionally, the relative abundance of
Cantharellales (phylum Basidiomycota) was higher in the Gra, Man, and
Gra +Man than in the control. In contrast, the soil amended with
compost had similar or lower proportions of Cantharellales than the
control.
3.3. Soil EPS, aggregate formation, and aggregate stability after three-
year of treatment
Confocal laser scanning microscopy (CLSM) imaging revealed the
presence of EPS in the soil. Consistent patterns were observed across all
treatments: certain soil aggregates had living microorganisms without
detectable polysaccharides (Fig. 3B and C), while others had both living
microorganisms and evident polysaccharides (Fig. 3E and F). These
observations underscore the variability among soil microorganisms in
their ability to accumulate EPS, emphasizing the highly heterogeneous
distribution of EPS in the soil. In addition to CLSM images, we quantied
EPS concentrations in different treatments. The Gra, Man, and Gra +
Man treatments had higher amounts of EPS carbon (EPS-C) and nitrogen
(EPS-N) than the control and compost-amended soil (Fig. 4A and B).
EPS-C and EPS-N contents were used as proxies for polysaccharides and
proteins, respectively. Notably, compost, irrespective of application
dose, did not affect the EPS concentration in the soil. The ratio between
the concentration of EPS-C and EPS-N remained consistent across all
treatments (Fig. 4C).
In general, the addition of OAs signicantly increased the percentage
of macroaggregates (>2 mm) compared to the unamended soil (Fig. S3).
Specically, the addition of grass and manure, especially in combina-
tion, enhanced aggregate stability (Fig. 5A and D) compared to the
control, while the addition of compost had no signicant effects on
MWD and GMD. Strong and positive correlations (Spearman correlation
coefcient
ρ
>0.6) were observed between EPS and MWD (Fig. 5B and
C), as well as between EPS and GMD (Fig. 5E and F), suggesting that EPS
content played a supportive role in the formation and stability of soil
aggregates.
3.4. Correlations between soil physicochemical and microbial
characteristics and soil EPS
The RDA was performed on soil physicochemical properties and EPS
(EPS-C and EPS-N contents) after forward selection, retaining AN, DOC,
TK, TP, and EC in the RDA model (Table S4). Soil EPS content exhibited
signicant positive correlations with AN (P <0.001) and EC (P <0.01),
while a signicant negative correlation was observed with DOC (P <
0.05). Additionally, EPS content showed signicant positive correlations
with bacterial (ANOVA, P <0.01) and fungal gene copy numbers
(ANOVA, P <0.01). The relative abundance of nine orders (belonging to
ve phyla) of bacteria and ve orders (belonging to two phyla) of fungi
were strongly (|
ρ
| ≥0.6) and signicantly (P <0.05) correlated with
EPS concentration (Fig. 6). The total relative abundances of the bacterial
groups correlated with EPS was less than 0.5 %, while the total relative
abundances of fungi correlated with EPS reached 22 %.
Fig. 3. Confocal laser scanning microscopy (CLSM) of two soil particles (particle one: A, B, C; particle two: D, E, F). DNA (B and E) and polysaccharides (C and F) of
the particles were stained and are shown in red and blue colors, respectively.
Y. Luo et al.
European Journal of Soil Biology 121 (2024) 103624
7
4. Discussion
4.1. The addition of grass, manure, and their combination enhance soil
EPS and soil aggregation
In this study, we show that soils amended with mown grass (Gra),
cattle manure (Man), and the combination of mown grass and cattle
manure (Gra +Man) exhibited higher EPS concentrations than the
compost-amended and control soils. This aligns with our hypothesis that
the availability and accessibility of C substrates regulate microbial EPS
production, with labile C substrates exerting a greater impact on EPS
production than recalcitrant ones. Compost is an organic material that
has undergone biological breakdown into a relatively homogenous and
stable form, while grass and manure are fresh organic materials that
have not yet degraded. Consequently, compared to grass and manure,
compost contained a higher fraction of recalcitrant OM, which included
aliphatic and aromatic components with high hydrophobicity [26].
Olagoke et al. [47] observed a similar trend, reporting that the addition
of more labile C substrate (i.e., starch) resulted in a higher EPS con-
centration in the soil compared to the addition of less labile C substrate
(i.e., cellulose), especially in soils with high clay content. In such soils,
microorganisms may experience slower utilization rates of C substrates
due to reduced oxygen availability and limited accessibility of C sub-
strates compared to sandy soils [48,49]. The decomposition of OM and
the release of nutrients from compost may play a role in EPS production.
Hale et al. [10] suggested that compost might have prolonged effects on
EPS production by promoting sustained labile C inputs into the soil, and
we believe this merits further investigation. It should be noted that all
treatments received a similar total amount of available nutrients, as
adjustments were made using mineral fertilizers. We recommend that
future experiments include control and OA-amended soils without the
addition of mineral fertilizers. This approach would enable us to explore
Fig. 4. Polysaccharide concentration (
μ
g/g soil) indicated by EPS-C (A) and protein concentration (
μ
g/g soil) indicated by EPS-N (B), and their ratio (C) after three
years of amelioration with different organic amendments (OAs) (mean ±sd; n =3).
Fig. 5. Aggregate stability expressed as mean weight diameter (MWD, A) and its Spearman correlation with EPS-C (B) and EPS-N (C). Aggregation formation
expressed as geometric mean diameter (GMD, D) and its Spearman correlation with EPS-C (E) and EPS-N (F). The box in the boxplot represents the rst and the third
quartile, and the horizontal line in the boxplots represents the median of the 12 replicates.
Y. Luo et al.
European Journal of Soil Biology 121 (2024) 103624
8
the effects of mineral fertilizers and their cross-effects with organic
amendments on the soil.
The addition of grass, manure, and their combination increased the
stability of soil aggregates. We observed a positive correlation between
soil aggregation (as indicated by GMD and MWD) and both EPS-C and
EPS-N. This suggests that the contents of EPS-polysaccharides and EPS-
proteins potentially contribute to the formation and stability of soil
aggregates. In general, EPS-protein content is often considered more
crucial than EPS-polysaccharides for aggregate stability [5,21,47]. The
presence of hydrophobic R groups in amino acids plays a key role in
surface hydrophobicity, providing architectural stability and protecting
fragile polysaccharides against disruption from rapid wetting [50,51].
Additionally, these hydrophobic amino acids (such as phenylalanine and
tyrosine) contribute to the formation of adhesive peptides, enhancing
the cohesive strength of materials produced by soil organisms [52].
However, our ndings reveal that EPS-polysaccharides exhibit stronger
and more signicant correlations with aggregate formation and stability
compared to EPS-proteins. We speculate that this could be attributed to
the formation of rigid bonds between EPS-polysaccharides and soil ions
(e.g., Ca
2+
) and inorganic C (carbonate binding), altering the molecular
structure of EPS and ultimately leading to an increase in soil aggregate
stability [10,53,54]. The robust positive correlation between EPS and
GMD/MWD underscores the pivotal role of microbial EPS in soil ag-
gregation. Managing soil EPS, such as through the addition of OAs with
labile C and fresh plant input, could be a potential strategy for main-
taining and improving soil structure [5,9].
4.2. Soil microbial abundance and N availability contribute to soil EPS
In our eld experiment, a positive correlation was observed between
microbial abundance (indicated by gene copy numbers) and EPS con-
centration. Wu et al. [55] observed enhanced EPS production during the
microbial growth phase in a batch experiment. During the growth phase,
the majority of sugar substrates are phosphorylated into
sugar-6-phosphates and degraded through glycolysis by microorgan-
isms. Some of these sugar-6-phosphates can be converted into
sugar-1-phosphates by phosphoglucomutases [19]. These
sugar-1-phosphates serve as central metabolites for forming sugar nu-
cleotides (such as UDP-glucose, UDP-galactose, and dTDP-rhamnose),
from which the majority of EPS is synthesized [19,56,57]. This mecha-
nism may explain the observed positive correlation between microbial
abundance and soil EPS in our data.
Negative correlations between EPS production and microbial abun-
dance have also been reported, especially under drought conditions or
when temperature and salt stress are applied [15]. These observations
do not necessarily contradict our ndings. The allocation of carbon and
energy resources to microbial growth or EPS formation is a survival
strategy for microorganisms. When exposed to environmental stresses,
microorganisms may allocate more carbon to EPS production than to
growth since EPS advantageously enables the storage of carbon, nutri-
ents, and energy [3]. However, when the growth of microorganisms is
not threatened by environmental stresses, as was the case in the current
eld study, microbial growth and EPS production are not “competing”
processes, and EPS is coupled with microbial growth but probably with
lower EPS production efciency (i.e., C allocated to EPS synthesis vs. C
allocated to microbial cell growth) than would be achieved under
environmental stress.
We expected that there would be more EPS in soils with more
available nutrients. We indeed found that for AN, there was a signicant
positive correlation with soil EPS. N management has been widely used
to regulate EPS production in different research elds (e.g., water
research and molecular microbiology for biosynthesis), but contra-
dicting effects have been reported: a range of low to high N inputs have
all been demonstrated to promote microbial EPS production [55,56,
58–61]. Therefore, it remains difcult to generalize the dependence of
soil EPS on N availability. Wu et al. [55] and More et al. [62] reported
that microbial EPS production was positively related to microbial
growth and N supply. It is important to note that an increase in N in the
soil that causes enhanced microbial growth does not necessarily result in
higher EPS production. Microbial growth depends on the C:N ratio, and
an excess of available N can result in the rapid mineralization of OM
[63]. This can lead to EPS degradation since EPS can serve as C sources.
Furthermore, N starvation can also promote EPS production under the
condition that microbial growth is ensured. Under N limitation or a high
C:N ratio, microorganisms may allocate more C to EPS production than
cellular growth. This is because the availability of N is insufcient for
protein synthesis, and the excess energy from a surplus of C sources also
supports EPS production, particularly polysaccharide production [64,
65]. Ajao and co-workers [59] observed an increase in microbial EPS
production by applying N limitation. Still, they observed a decrease in
microbial EPS production in an environment unsuitable for microbial
growth such as a C:N ratio of 100. Investigating the role of N and the
Fig. 6. Total relative abundance of soil bacterial (A) and fungal (B) orders that signicantly correlated with soil EPS (sum of EPS-C and EPS-N contents). Included
orders showed strong (|
ρ
| ≥0.6) and signicant (P <0.05) correlations with EPS.
Y. Luo et al.
European Journal of Soil Biology 121 (2024) 103624
9
effects of C:N ratio through adding OAs and fresh plant inputs differing
in C:N ratios on EPS production in the soil and its potential application
for managing EPS and soil structure merits further investigation.
4.3. Specic bacterial and fungal taxa that are correlated with EPS
concentration in the soil
We identied several bacterial taxa whose relative abundance posi-
tively correlated with EPS. Among these identied taxa, Pseudomona-
dales and Elusimicrobia were particularly related to EPS concentration
in the soil. Pseudomonadales are well-recognized and commercially
available EPS producers [66–68]. In soil, the genus Pseudomonas can
produce biolms [69]. Pseudomonadales can encode proteins involved
in the metabolism of cyclic dinucleotide (c-di-GMP), which plays a vital
role in the regulation of EPS production [70]. Two orders from the
Elusimicrobia phylum also positively correlated with EPS. Elusimicrobia
is an enigmatic and recently described bacterial phylum [71]. Elusimi-
crobia can x N [72] and are capable of synthesizing common
energy-storage polysaccharides, with several genes encoding enzymes
for starch or glycogen metabolism [71]. This indicates that Elusimi-
crobia may be an EPS producer or indirectly involved in the EPS matrix
through nutrient (particularly N) interactions with other
microorganisms.
Microorganisms that correlate with soil EPS are not necessarily EPS
producers. In some cases, EPS produced by certain bacteria can serve as
resources or act as growth substrates for other bacteria in the soil [73].
For instance, Planctomycetes, Chloroexi, and Flavobacteriales are po-
tential EPS degraders, and they showed positively or negatively corre-
lations with soil EPS in our study. Planctomycetes are considered
K-selected bacteria with efcient cell metabolism and strong competi-
tive ability, growing slowly on recalcitrant complex substances [74].
They are recognized as primary degraders of complex hetero-
polysaccharides in the soil [73]. Chloroexi, particularly the Ktedono-
bacterales, play a role as heterotrophic oligotrophs in soils. They usually
contain numerous exoenzymes, such as chitinase, glucuronidase,
galactosidase [75], and proteases [76]. This suggests that Chloroexi
primarily grow on complex polysaccharides and proteins [77–79]. Fla-
vobacteriales constitute a bacterial group often associated with the ca-
pacity to degrade complex organic compounds or macromolecules in the
soil [80]. Flavobacterium strains can produce glucosamine-6-phosphate
deaminases [80], catalysing the reversible isomerization and deamina-
tion of D-glucosamine 6-phosphate (important metabolites for EPS syn-
thesis [57]) into D-fructose 6-phosphate [81]. Therefore,
Flavobacteriales may either consume EPS or suppress EPS production.
Five fungal orders were identied whose relative abundance corre-
lated with EPS concentration: Sordariales, Cantharellales, Holterman-
niales, Microbotryomycetes, and Helotiales, belonging to two fungal
phyla, Ascomycota and Basidiomycota. Both Ascomycota and Basidio-
mycota are considered saprotrophic fungi. While these fungi have been
shown to produce EPS in other studies (e.g., Rashid et al. [82]) and can
utilize bacterial EPS as C sources [83], the specic roles of the fungal
orders identied in this study in the soil EPS matrix have been rarely
documented. These fungi may be directly or indirectly involved in
building the soil EPS matrix, especially in interactions with
EPS-producing/degrading bacteria. It has been reported that bacteria
can form biolms around fungal hyphae by producing EPS and altering
EPS composition to facilitate subsequent adherence [84].
We emphasize that correlations between soil microorganisms and
EPS concentrations do not reveal the causal mechanisms governing EPS
dynamics in the soil. For future studies, we recommend the utilization of
other statistical methods, such as structural equation modeling [85],
designed to reveal causal connections. Additionally, experimental vali-
dations of these correlations are crucial to uncover the role of soil mi-
croorganisms in regulating EPS production/degradation and soil
aggregation. However, due to the inherent complexity of the soil system,
understanding the functioning of certain microbial groups and their
ecological relevance remains signicantly challenging. To address this,
we recommend employing a synthetic community (SynCom) repre-
senting core soil microbiomes, with the addition or elimination of one
group [86,87], to enable a step-wise investigation of their role in
regulating EPS content in the soil and soil aggregation.
5. Conclusion
In this eld experiment, the addition of grass, manure, and their
combination increased soil EPS concentration compared to the un-
amended soil, while the addition of compost, regardless of the appli-
cation dose, had the least impact on soil EPS concentration compared to
the unamended soil. Grass and manure, especially when combined, also
improved soil aggregation and stability. Soil EPS showed a positive
correlation with AN and microbial biomass. The total relative abun-
dance of bacteria and fungi that positively correlated with EPS was
higher in the soil amended with grass, manure, and their combination
than the unamended and compost-amended soils. The correlation
observed between soil microbes and soil EPS in this study warrants
further experimental validation to elucidate the role of soil microor-
ganisms in building the EPS matrix under various environmental con-
ditions, such as different C sources, nutrient limitations, and extreme
climates. Future research focused on unravelling the interactions be-
tween bacteria and fungi in the EPS production process is also recom-
mended. Our work demonstrates how the addition of various OAs, such
as grass, cattle manure, or their combination, can enhance soil EPS
production and, consequently, improve soil aggregate stability on a eld
scale. These ndings offer valuable insights for designing and imple-
menting sustainable agricultural management practices, particularly
through the reuse of organic residues to regulate EPS production. In the
face of climate extremes, such as heatwaves and periodic droughts, our
results become even more signicant. They highlight the importance of
enhancing soil structure, as this leads to stable soil aggregates and
increased resistance to environmental stress.
CRediT authorship contribution statement
Yujia Luo: Writing – original draft, Visualization, Methodology,
Formal analysis, Data curation, Conceptualization. Juan Bautista
Gonzalez Lopez: Formal analysis, Data curation. H. Pieter J. van
Veelen: Writing – review & editing, Methodology. Dirk-Jan Daniel
Kok: Writing – review & editing, Formal analysis. Romke Postma:
Writing – review & editing, Resources. Dirk Thijssen: Resources. Val-
entina Sechi: Writing – review & editing, Supervision, Conceptualiza-
tion. Annemiek ter Heijne: Writing – review & editing, Supervision,
Conceptualization. T. Martijn Bezemer: Writing – review & editing,
Supervision, Conceptualization. Cees J.N. Buisman: Writing – review &
editing, Supervision, Resources, Funding acquisition,
Conceptualization.
Declaration of competing interest
The authors declare that they have no known competing nancial
interests or personal relationships that could have appeared to inuence
the work reported in this paper.
Acknowledgment
This work was performed in the cooperation framework of Wetsus,
European Centre of Excellence for Sustainable Water Technology (htt
ps://www.wetsus.nl/). Wetsus is co-funded by the Dutch Ministry of
Economic Affairs and Climate Policy, the European Union Regional
Development Fund, the City of Leeuwarden, the Province of Fryslˆ
an, the
Northern Netherlands Provinces, and the Netherlands Organisation for
Scientic Research. We would like to thank the members of the research
soil theme (Agriton, Mulder Agro, Vereniging Afvalbedrijven,
Y. Luo et al.
European Journal of Soil Biology 121 (2024) 103624
10
Netherlands Institute of Ecology [NIOO-KNAW], Koninklijke Oosterhof
Holman, Waterketen Onderzoek Noord [WON], and Waterschap Zui-
derzeeland) for the fruitful discussions and nancial support.
Appendix A. Supplementary data
Supplementary data to this article can be found online at https://doi.
org/10.1016/j.ejsobi.2024.103624.
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