Characterization of the Soil Prokaryotic Community With Respect to Time and Fertilization With Animal Waste–Based Digestate in a Humid Continental Climate

Abstract

There is a renewed global awareness to improve soil health through the intensification and management of organic inputs such as the application of animal waste–based digestate and other types of organic fertilizers to the soil. The objective of this study was to evaluate the influence of different types of animal waste–based digestate application on soil prokaryotic diversity and composition in an agricultural cropping system over a period of 3 years, cultivated with three different annual cereal crops (spring wheat, triticale, and barley). Treatments were laid out in a randomized design with five conditions (three replicates per condition): fertilizer treatments included three different types of digestate (pig manure, chicken manure, and cow manure digestates), synthetic mineral nitrogen, and unfertilized control. Prokaryotic soil communities were characterized by Illumina MiSeq sequencing. The three most abundant phyla identified were Actinobacteria, Acidobacteria, and Proteobacteria, which accounted for over 55% of the total prokaryotic community. Other phylogenetic groups such as Verrucomicrobia and Bacteroidetes were also identified as part of the native soil microbiota. It was observed that the period of digestate application did not significantly influence the prokaryotic diversity in the soil. On the contrary, sampling time was a major factor in driving β-diversity. A correlation with soil pH was also observed for several taxonomic groups, indicating its importance in shaping prokaryotic community composition. Our study showed that the richness and diversity of the soil prokaryotic community were not affected by digestate application, while other factors such as the yearly crop varieties and seasonal/climate changes were the major contributors to differentiating the prokaryotic community composition over time.

Full text

Characterization of the Soil
Prokaryotic Community With Respect
to Time and Fertilization With Animal
Waste–Based Digestate in a Humid
Continental Climate
Skaidre Suproniene
1
, Modupe Olufemi Doyeni
1
, Carlo Viti
2
, Vita Tilvikiene
1
and
Francesco Pini
3
*
1
Institute of Agriculture, Lithuanian Research Centre for Agriculture and Forestry, Akademija, Lithuania,
2
Department of
Agricultural, Food, Environmental and Forestry Sciences and Technologies, University of Florence, Floren ce, Italy,
3
Department of
Biology, University of Bari Aldo Moro, Bari, Italy
There is a renewed global awareness to improve soil health through the intensification and
management of organic inputs such as the application of animal waste–based digestate
and other types of organic fertilizers to the soil. The objective of this study was to evaluate
the influence of different types of animal waste–based digestate application on soil
prokaryotic diversity and composition in an agricultural cropping system over a period
of 3 years, cultivated with three different annual cereal crops (spring wheat, triticale, and
barley). Treatments were laid out in a randomized design with five conditions (three
replicates per condition): fertilizer treatments included three different types of digestate (pig
manure, chicken manure, and cow manure digestates), synthetic mineral nitrogen, and
unfertilized control. Prokaryotic soil communities were characterized by Illumina MiSeq
sequencing. The three most abundant phyla identified were Actinobacteria, Acidobacteria,
and Proteobacteria, which accounted for over 55% of the total prokaryotic community.
Other phylogenetic groups such as Verrucomicrobia and Bacteroidetes were also
identified as part of the native soil microbiota. It was observed that the period of
digestate application did not significantly influence the prokaryotic diversity in the soil.
On the contrary, sampling time was a major factor in driving β-diversity. A correlation with
soil pH was also observed for several taxonomic groups, indicating its importance in
shaping prokaryotic community composition. Our study showed that the richness and
diversity of the soil prokaryotic community were not affected by digestate application, while
other factors such as the yearly crop varieties and seasonal/climate changes were the
major contributors to differentiating the prokaryotic community composition over time.
Keywords: microbiota, sampling time, crops, soil, digestate
Edited by:
María Almagro,
Spanish National Research Council
(CSIC), Spain
Reviewed by:
Jessica Cuartero Moñino,
Spanish National Research Council
(CSIC), Spain
Eva Lloret Sevilla,
Universidad Politécnica de Cartagena,
Spain
*Correspondence:
Francesco Pini
francesco.pini@uniba.it
Specialty section:
This article was submitted to
Soil Processes,
a section of the journal
Frontiers in Environmental Science
Received: 11 January 2022
Accepted: 22 April 2022
Published: 06 June 2022
Citation:
Suproniene S, Doyeni MO, Viti C,
Tilvikiene V and Pini F (2022)
Characterization of the Soil Prokaryotic
Community With Respect to Time and
Fertilization With Animal Waste–Based
Digestate in a Humid
Continental Climate.
Front. Environ. Sci. 10:852241.
doi: 10.3389/fenvs.2022.852241
Frontiers in Environmental Science | www.frontiersin.org June 2022 | Volume 10 | Article 8522411
ORIGINAL RESEARCH
published: 06 June 2022
doi: 10.3389/fenvs.2022.852241

INTRODUCTION
The consumption of animal-derived products is constantly
increasing (Salter, 2017), and in the future years it is expected
to rise; therefore, it is extremely important to develop sustainable
systems for animal-waste product management. Biogas systems
produce clean energy using organic waste such as animal
byproducts and discarded food which are converted into
methane and carbon dioxide (Aydin, 2017). Digestates are the
end-products generated from the anerobic digestion of these
organic substrates (Crolla et al., 2013;Nkoa, 2014). Digestates
could be further used as fertilizer because of their high content of
nutrients such as nitrogen (N), potassium (K), phosphorus (P),
and organic matter, being a sustainable alternative to reduce the
utilization of inorganic fertilizers (Bachmann et al., 2014;Lee
et al., 2021). Moreover, digestates may contain beneficial bacteria
as nitrogen fixers and phosphate solubilizers (Fernandez-Bayo
et al., 2020;Raymond et al., 2020), conferring an additional value
as biofertilizers (Crolla et al., 2013;Insam et al., 2015). The
bacterial composition of the digestate is different with respect to
the microbiota present in the primary feedstocks owing to
anerobic treatments they have undergone (Fernandez-Bayo
et al., 2020). This will also affect the persistence in the soil of
the bacteria present in the digestate which is lower in comparison
to other organic fertilizers such as woodchip compost (Akari and
Uchida, 2021;Dincăet al., 2022). Organic fertilization
contributes to increasing nutrient availability to plants (Chu
et al., 2007), enhances soil microorganism activity (Makdi
et al., 2012;Nkoa, 2014), and in turn improves crop yield
(Šimon et al., 2015). Soil type and the kind of organic
material applied are two critical factors influencing soil
microbial activities such as respiration rate and soil microbial
biomass (Li et al., 2018;Chen et al., 2019). Specifically, several
studies indicated increased microbial biomass due to digestate
application (Fuchs et al., 2008;Alburquerque et al., 2012;Nkoa,
2014). In terms of the time frame of continuous digestate
application, there have been varying reports on the influence
of digestate/organic fertilization on soil microbes from short- to
long-term experiments based on the primary feedstocks and the
mode of experiments (Luo et al., 2015;Möller, 2015;Nielsen
et al., 2020). For instance, Möller (2015) reported that digestate
with a high degradability of organic matter such as clover-grass
has a stronger effect on the short-term soil microbial activity. In
addition, Nielsen et al. (2020) reported in a previous review that
higher effects of digestate application on some soil microbial
activity define parameters such as metabolic content and basal
respiration than those of their individual feedstock in the short-
term involving specific experimental setup. In contrast, Luo et al.
(2015) reported a shift in microbial community structure in a
long-term study involving 33 years of fertilization, which was also
affirmed in other studies with a different long-term period
(Ruppel and Makswitat, 1999;Chu et al., 2007;Guo et al.,
2019). Also, climatic fluctuations over a cultivation year result
in soil bacterial communities being constantly exposed to
changes and adaptations to environmental conditions such as
moisture, resource availability, and temperature (Bardgett and
Caruso, 2020).
The bacterial community may then be significantly altered in
response to the individual components of the added waste and
with respect to time. Therefore, it is important to understand the
major factors in shaping the soil prokaryotic community to pave
the way for improving soil quality and carrying out proper
fertilization using alternative byproducts such as digestate
(Peacock et al., 2001).
In previous studies, we analyzed the effects of three different
types of digestates (from pig, chicken, and cow manure) on soil
features and plant yield. Repeated digestate applications over
3 years of treatment lead to a slight decrease in nitrogen and
carbon soil content, while a considerable increase was observed
for potassium (K
2
O), in particular for soils treated with cow and
chicken manure digestates. Another difference was related to P
content which increased in all treatments (including unfertilized
plots and plots fertilized with synthetic nitrogen), with the
exception of the plots treated with chicken manure digestates.
There were no differences observed in relation to soil pH (Doyeni
et al., 2021b).
In terms of plant quality and productivity, the effects of
fertilization differed depending on the tested crop. Higher
grain density was observed for spring wheat and spring barley
treated with digestates or synthetic nitrogen fertilizers with
respect to the control. The grain protein percentage was
generally higher in all the fertilized plants comparable values
were observed for pig manure digestate and synthetic fertilizer in
spring wheat; in triticale, synthetic fertilizer outperformed with
respect to digestates, while in spring barley, chicken and cow
manure digestates gave better results (Doyeni et al., 2021b).
Moreover, in pot experiments, where a similar soil was used, a
general increase of the soil microbial biomass with all the three
types of digestates was observed (Doyeni et al., 2021a). The aim of
this study was to evaluate the composite effect of fertilization with
these different sources of animal waste–based digestate and
seasonal/annual variation on the soil prokaryotic community
diversity and composition over 3 years.
MATERIALS AND METHODS
Experimental Design and Soil Sampling
The experimental field was located at the Lithuanian Research
Centre for Agriculture and Forestry (55.40 N, 23.87 E), which is
characterized by a humid continental climate (Belda et al., 2014).
The soil in the experimental area is loamy (Endocalcaric Epigleyic
Cambisol) (Baxter, 2007), and the soil chemical composition
exhibited suitable parameters for cereal cultivation: pH (7.03),
organic carbon content (1.3%), and nitrogen content (0.14%). For
a complete characterization of physico-chemical properties, the
details are shown in (Doyeni et al., 2021b). A complete
randomized block design with five treatments was used to
evaluate the effects of fertilization and time on soil microbiota.
The complete randomized design was characterized by 15 plots
(five fertilizer treatments × three replicates). Each treatment plot
was 30 m
2
(3 m × 10 m). Fertilization conditions were as follows:
1) no fertilizers (Control; C), 2) synthetic nitrogen fertilizer
([NH
4
NO
3
]; SN), and three different organic fertilizers
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Suproniene et al. Seasonal Effects on Soil Microbiota

obtained from anerobic digestion of animal manure, 3) pig
manure digestate (PM), 4) chicken manure digestate (ChM),
and 5) cow manure digestate (CoM); for a complete
characterization of the digestates used, see (Doyeni et al.,
2021b). The experiment was carried on for 3 years (1–3) from
April 2018 to August 2020. The samples were collected each year
before fertilization (BF; April-May) and after harvest (AH;
August). At the beginning of the field experiment, soil samples
were randomly collected from five different spots at a depth of
0–20 cm. The samples were thoroughly mixed to form a
composite, and soil specimens were immediately stored at a
temperature of -80 °C. The samples were named according to
fertilization conditions and sampling time. Before the beginning
of the experiment, the field was cultivated with winter wheat
(Triticum aestivum)-cultivar “Skagen”(Nordic seed A/S,
Denmark). In the first, second, and third year, plots were
cultivated with spring wheat (Triticum aestivum) cultivar
“Collada”(Einbeck, Germany), in the second year with spring
triticale, a hybrid between wheat and rye [cultivar “Milkaro”
(Koscian, Poland)], and in the third year, spring barley (Hordeum
vulgare L.) cultivar “Ema DS”(Akademija, Lithuania). The
sowing rate was 270 kg ha
−1
(spring wheat), 250 kg ha
−1
(spring triticale), and 220 kg ha
−1
(spring barley). The seeds
were sown on 19 April 2018, 16 April 2019, and 16 April
2020. The field was fertilized in all years of the 3-year
experiment. In all years (year 1–3), samples before fertilization
(start of the cultivation year) were subdivided into two groups:
control (no fertilization) and control-treated (samples fertilized
the precedent year), while samples after harvest (end of the
cultivation year-month of August of each year) were further
subdivided with respect to the fertilization treatment used.
Analysis was conducted considering two different variables: 1)
the fertilization treatment used, and 2) the sampling time, a
composite parameter influenced by multiple factors.
Total DNA Extraction From Soils
Total DNA was extracted using the FastDNA spin kit for soil (MP
Biomedicals, California, United States). Briefly, around 0.5 g of
soil was weighed, homogenized by bead beating in the FastPrep®-
24 instrument (MP Biomedical) at 6 m/s for 40 s, and DNA was
purified with the aforementioned column-based kit according to
the manufacturer’s instructions. Extracted DNA was checked by
agarose gel electrophoresis. The DNA purity and quantity were
measured using an ND-1000 Spectrophotometer (NanoDrop
Technologies, Wilmington, United States) and standardized to
a concentration of 10 ng μL
−1
.
Sequencing and Data Processing
For each sample, the V3–V4 region of the 16S rRNA gene was
amplified using primers Pro341f and Pro805R (Takahashi et al.,
2014), which allow the amplification of both Bacteria and
Archaea domains, and barcodes were added to the forward
primer. Amplicons for each library were purified and mixed in
equal proportions. Illumina MiSeq v3 chemistry 300 base paired-
end (PE) amplification and sequencing were performed at BMR
genomics (Padova, Italy). Briefly, PCR reactions were prepared
using 0.2 U of Platinum Taq DNA Polymerase HiFi
(Thermofisher, Massachusetts, United States), 10 µM of each
primer, 10 mM dNTPs mix, 1X buffer, 50 mM of MgSO
4
, and
50 ng of genomic DNA in a final volume of 25 µL. Amplification
conditions were 94°C for 1 min, 25 cycles with 94°C for 30 s, 55°C
for 30 s, and 68°C for 45 s, and a final elongation step at 68°C for
7 min. Two samples (C2AH1 and CoM1AH3) were excluded
from further analysis due to sequencing failure. The primer
sequences were removed using Cutadapt (Martin, 2011). Read
quality was evaluated using DADA2 (Callahan et al., 2016), and
reads (R1 and R2) were then trimmed and filtered using the
following parameters: truncLen = c(265,220), maxN = 0, maxEE
=c(2,2), and truncQ = 2. The reads were merged with an overlap
of at least 12 bases, identical to each other in the overlap region.
Chimeras were removed and amplicon sequence variants (ASVs)
were classified against the Silva database v138 (Yilmaz et al.,
2014), using the function assignTaxonomy in the DADA2
package, version 1.18.0 (Callahan et al., 2016) with “R”version
4.0.3 (R Core Team, 2020). ASVs matching with chloroplast and
mitochondria sequences were removed from the ASV table.
Statistical Analysis
The α-diversity measures (number of observed ASVs, Chao1
value, and Shannon index) were calculated using the vegan
package, version 2.5–6(Oksanen et al., 2020)in“R”version
4.0.3 (R Core Team, 2020). Pielou’s evenness index was
calculated as J = H′/ln(S), where H′is Shannon Weiner
diversity and S is the total number of species (ASVs)
(Pielou, 1966). A nonmetric multidimensional scaling
(NMDS) and a permutational multivariate analysis of
variance (PERMANOVA) based on Hellinger-transformed
ASV abundance data were performed using the metaMDS
and the adonis2 functions, respectively. Both the NMDS
and the PERMANOVA were performed with the
Bray–Curtis dissimilarity index. The taxa with different
relative abundances between sampling times and treatments
were identified by using a negative binomial mixed model
(method = nb) using the function mms {y, fixed=~Sampling
Time+Treatment+offset[log(N)],random=~1|plots,min.
p=0.2,method=“nb”}, where y is the matrix with the number
of sequences for each taxonomic group (genus or phylum),
sampling times and treatments were considered fixed
variables, and plots as a random variable; only taxa with a
proportion of nonzero values >0.2 (min p) were included in the
analysis, and differences were considered significant for p<
0.05 (Zhang and Yi, 2020). Shapiro–Wilk and Levene tests
were performed to check normality and homogeneity of
variance, respectively, depending on results of the ANOVA
or Kruskal–Wallis group test with false discovery rate (“fdr”)
p-value adjustment followed by Tukey’sHSDorDunn’spost
hoc test, respectively, were used. All tests were conducted in
“R”version 4.0.3 (R Core Team, 2020). Pearson’s correlations
among different taxa (at phylum and genus level) and soil
chemical features such as N, C, K
2
O, P
2
O
5
,pH,andhumus
(previously measured in Doyeni et al., 2021b) were calculated
in “R”using the package Hmisc (R core Team, 2020); p-values
were adjusted using the Benjamini–Hochberg false discovery
rate procedure (Supplementary Datasheet S1).
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Suproniene et al. Seasonal Effects on Soil Microbiota

RESULTS
Soil Prokaryotic Diversity
Illumina MiSeq v3 sequencing was performed on the variable
V3–V4 region of the 16S rDNA gene, producing a total of
17,989,800 sequences (ranging from 59,530 to 208,305 sequences
per library). Rarefaction curves showed sequencing coverage for all
samples (Supplementary Figure S2), allowing the identification of
12,730 amplicon sequence variants (ASVs), with a range from 1,024
to 3,137 ASVs per sample (Supplementary Figure S2).
The α-diversity was calculated for the number of ASVs
observed, Chao1 value, Shannon diversity, and Pielou’s evenness
indexes. We considered variations in soil prokaryotic relative
abundance, namely: 1) treatment used and 2) sampling time.
The sampling time was indicative as a composite parameter
influenced by multiple variables: 1) different crops/varieties
grown each year, 2) weathering conditions, and 3) the
agricultural techniques used in each segment such as tillage and
split fertilization. Species richness and Shannon diversity were not
significantly different throughout the experiment in terms of
treatment used or sampling time (Supplementary Figure S3).
In contrast, a significant difference related to the species evenness
(Pielou’s evenness index) was observed for sampling time
(Figure 1). Higher evenness values were found for sampling
times 1 and 2, while sampling time 3 showed the lowest value
(Kruskal–Wallis and Dunn test, p<0.05). No differences were
detected with respect to the different fertilizing treatments used.
Sampling time was also the major factor in driving the β-
diversity as observed with the PCoA and PERMANOVA analysis
(p<0.01), with all the sampling times differing from each other
(Figure 2B;Supplementary Table S1). In relation to the
fertilization treatment, significant differences were observed
between the control group (no fertilization, before fertilization)
and all the groups “after harvest”(control after harvest, mineral
nitrogen, chicken manure digestate, cow manure digestate, and
pig manure digestate; PERMANOVA, p<0.01), reflecting
differences observed in relation to sampling time (Figure 2A).
A significant difference was also found between the groups
control and control-treated (PERMANOVA, p<0.01,
Supplementary Table S2).
Soil Prokaryotic Composition
99.81% of the ASVs were identified at least at the phylum level. ASVs
were classified into 47 phyla, 113 classes, 256 orders, 324 families,
and 591 genera. The overall prokaryotic community composition
was similar in all the conditions tested (Figure 3A). The three most
abundant phyla in all the treatments were Actinobacteria,
Acidobacteria, and Proteobacteria, whose relative abundance was
similar within samples: Actinobacteria (18.8 ± 1.9%), Proteobacteria
(19.5 ± 1.5%), and Acidobacteria (19.8 ± 1.5%) (Figure 3). Together,
these three phyla accounted for 55.7 to 60.9% of the total prokaryotic
community. Other phyla whose relative abundance was relatively
high (>5%) were Bacteroidetes (13.1 ± 1.6%), Verrucomicrobia (8 ±
0.9%), and Chloroflexi (6.4 ± 0.7%). The 10 most representative
genera were the group 41 (2.04 ± 0.05%; Acidobacteria); Nitrospira
(1.71 ± 0.02%; Nitrospira); Candidatus Udaeobacter,Candidatus
Xiphinematobacter,andChthoniobacter (1.61 ± 0.04%, 1.05 ± 0.02%,
and 0.99 ± 0.02%, respectively; Verrucomicrobia); Gaiella,
Pseudoarthrobacter,andNocardiodes (1.55 ± 0.02%, 1.52 ±
0.05%, and 1.16 ± 0.02%, respectively; Actinobacteria);
Sphingomonas (1.29 ± 0.05%; Proteobacteria), and Chryseolinea
(1.03 ± 0.02%; Bacteroidetes). However, for many sequences, it
was not possible identifying at the genus rank (54.7 ± 0.3% of
the sequences); in particular within the phylum Acidobacteria, we
observed the higher number of unassigned ASVs (16% of the total
number of sequences), with only the 19.16% of the sequences falling
in the phylum Acidobacteria assigned at the genus rank. Soil
chemical features (nitrogen (N), carbon (C), potassium oxide
(K
2
O), phosphorus pentoxide (P
2
O
5
), pH, and humus content
were previously measured at two time points: the beginning
(sampling time 0) and the end of the trial (sampling time 5)
(Doyeni et al., 2021b). Correlation analyses were performed
between the different taxonomic groups identified (at phylum
and genus level) and soil composition. No significant correlation
was observed at the phylum level, while at the genus rank, 75 groups
(out of 463 analyzed) showed a significant correlation with pH
(p-value adjusted <0.05); among them only two genera, Haliangium
(Mixococcota) and group TM7a (Patescibacteria), showed a negative
correlation. Most of the genera correlating with pH belonged to
Proteobacteria (30 genera) and Firmicutes (15 genera). The five most
represented groups showing a significant correlation with pH were
Sphingomonas (Proteobacteria), Haliangium (Mixococcota),
FIGURE 1 | Pielou’s evenness index. On the X-axis are indicated
sampling times, dots are differently colored with respect to treatments: control
(before fertilization, no fertilization), control-treated (before fertilization, fertilized
the precedent year), control after harvest (after harvest, no fertilization),
mineral nitrogen (after harvest), chicken manure digestate (after harvest), cow
manure digestate (after harvest), and pig manure digestate (after harvest).
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Suproniene et al. Seasonal Effects on Soil Microbiota

Massilia (Proteobacteria), Puia (Bacteroidetes), and Arenimonas
(Proteobacteria) (Supplemental Dataset 1).
Effect of Different Treatments and Sampling
Time on Soil Prokaryotic Composition
The PERMANOVA analysis at the phylum rank showed
significant differences for both variants analyzed, fertilizing
treatments, and sampling time (p<0.001). For sampling time,
differences were detected for all the six groups considered.
Regarding fertilizing treatments, there were significant
differences between control groups before fertilization (control
and control-treated) and groups after fertilization, reflecting
differences observed in relation to sampling time. The group
control-treated showed no significant difference between cow
manure digestate and chicken manure digestate groups.
Considering groups sampled after fertilization, significant
differences (at phylum rank) were found between the control
group and fertilized groups, while within fertilized groups were
observed differences only among plots fertilized with mineral
FIGURE 2 | Non-metric multidimensional scaling (nMDS) plot. nMDS plot based on the Bray–Curtis index. (A) nMDS plot with samples colored with respect to the
treatment used: control (before fertilization, no fertilization), control-treated (before fertilization, fertilized the precedent year), control after harvest (after harvest, no
fertilization), mineral nitrogen (after harvest), chicken manure digestate (after harvest), cow manure digestate (after harvest), and pig manure digestate (after harvest). (B)
nMDS plot with samples colored with respect to sampling time.
FIGURE 3 | Soil prokaryotic composition. Soil prokaryotic community composition with respect to (A) the treatment used or (B) sampling time. The fifteen major
groups are reported at the phylum taxonomic level. Other phyla are collapsed within the group “other”.
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Suproniene et al. Seasonal Effects on Soil Microbiota

nitrogen and pig manure digestate (PERMANOVA, p<0.05). A
negative binomial mixed model was applied to infer differences
related to sampling time and/or treatment used (Figure 4). Most
of the differences (12 phyla) were related to sampling time
(Figure 4), while only three phyla were differently abundant
in relation to the treatment used: Firmicutes, Patescibacteria, and
Dependentiae (Figures 4,5), which accounted for the 2.18, 0.3,
and 0.04% of the soil community, respectively. Significant
differences in Firmicutes relative abundance were observed
during the first year (sampling time 1) among control plots
and plots treated with cow or chicken manure digestates
(Figure 5A). A higher amount of sequences belonging to the
phylum Patescibacteria was detected in plots treated with pig
manure digestate in the second year (Figure 5B), while in the
third year, there was a significant difference for Dependentiae
phylum between the control group and plots treated with mineral
nitrogen (Figure 5C).
Differences at the genus rank reflected what was observed at the
phylum rank with most of the differences (113 genera) associated
with sampling time and only a few genera (18) varying in relation to
the different treatments used (Supplementary Figure S5). The 18
genera belong to eight different phyla, with six genera affiliated with
Proteobacteria, five to Actinobacteria, and two to Firmicutes. We
also analyzed the relative abundance of these groups in the three
different years and for seven of them, we found significant
differences (p<0.05). In the first year, we observed significant
differences for the genera Streptomyces,Paenisporosarcina,the
subgroup 10 of the phylum Acidobacteria and Opitutus,andthe
group TM7a of the phylum Patescibacteria (Figure 6;
Kruskal–Wallis or ANOVA, p<0.05). The subgroup 10 relative
abundance was higher in the control group with respect to the plots
treated with mineral nitrogen and cow manure digestate. For the
other genera, we observed a lower amount in the control group with
respect to one or more fertilized groups, in particular Streptomyces
relative abundance was higher in all the manure digestate groups
(Figure 6B). For the second year, only Acinetobacter showed
significant differences with a higher presence in plots treated with
cow manure digestate with respect to control and chicken manure
digestate groups (Figure 6D;Kruskal–Wallis, p<0.05). In the third
year, two genera, Gaiella and Paenisporosarcina, were characterized
by higher levels of pig and cow manure digestate (respectively)
versus the control group (Figures 6A,C;ANOVA,p<0.05).
Paenisporosarcina was the only genus showing a marked increase
in treated groups in two different years (first and third, Figure 6C).
DISCUSSION
The overall community composition was quite similar for all the
treatments used, and the major phyla detected at the initial sampling
time in the year 2018 were well-known soil dominant phyla
(Proteobacteria, Acidobacteria, and Actinobacteria) which are
commonly found in this type of soil, and they kept their
predominance throughout the duration of the project (Mhete
et al., 2020;Wu et al., 2020;Li et al., 2021). These phyla
accounted for over 55% of the soil’s prokaryotic composition.
Proteobacteria is one the most diverse and abundant phyla
present in the soil; within this phylum, many different microbes
can thrive and adapt to different soil conditions and influence plant
growth either as plant growth–promoting rhizobacteria or as
pathogens (Spain et al., 2009). Actinobacteria are typically
dominant soil microbes partaking in the biogeochemical cycling
of carbon, nitrogen, phosphorus, potassium, and several other
elements in the soil. Furthermore, within Actinobacteria, there
are aerobic saprophytes capable of producing extracellular
hydrolytic enzymes that can degrade complex compounds
(Ranjanietal.,2016). Actinobacteria presence helps in
sustainably improving soil health and providing an effective
FIGURE 4 | Heatmap for the effects of sampling time and fertilization treatments at the phylum level. Differences in p-values at the phylum taxonomic rank for
sampling time or treatment used in samples collected after harvest are indicated in gray (no differences) or different shades of blue (p-value ranging from 0.05 to 0).
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Suproniene et al. Seasonal Effects on Soil Microbiota

FIGURE 5 | Effect of the different fertilizers used at the phylum level after harvest. Each bar is labeled respective to the treatment applied and colored respective to
the year of treatment: black (2018, first year), light gray (2019, second year), and dark gray (2020, third year). (A) Firmicutes, (B) Patescibacteria, and (C) Dependentiae.
Means sharing the same letter within the same year are not significantly different (post hoc Tukey’s HSD test or Dunn’s test).
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Suproniene et al. Seasonal Effects on Soil Microbiota

pathway for nutrient cycling (Bhatti et al., 2017). Acidobacteria is
considered one of the most abundant soil phyla with their relative
abundances ranging from ca. 20–40% in temperate soils such as
forests, grasslands, and pasture soils (Janssen, 2006). Here, we
observed a relative abundance ranging from 16 to 23%, the
relatively low amount of Acidobacteria found could be possibly
linked to the pH (7.3) of the receiving soil used as most
Acidobacteria prefer lower pH (3.0–6.5) (Kalam et al., 2020). The
FIGURE 6 | Effect of the different fertilizers used at the genus level after harvest. Each bar is labeled respective to the treatment applied and colored respective to the
year of treatment: black (2018, first year), light gray (2019, second year), and dark gray (2020, third year). (A) Gaiella,(B) Streptomyces,(C) Paenisporosarcina,(D)
Acinetobacter,(E) Acidobacteria subgroup 10, (F) Opitutus, and (G) TM7a (Patescibacteria). Means sharing the same letter within the same year are not significantly
different (post hoc Tukey’s HSD test or Dunn’s test).
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Suproniene et al. Seasonal Effects on Soil Microbiota

addition of manure digestates did not affect soil pH (Doyeni et el.,
2021b) and Acidobacteria relative abundance was found similar in
the 3-years analysis with no difference with respect to the treatments
used. Indeed, in other similar studies (Xu et al., 2016;Zhang et al.,
2021), the application of the digestate does not have negative effects
on their availability. However, the correlation analysis showed that
several genera (75) were affected by soil pH as a slight decrease
(unrelated to soil treatments) was observed between the beginning
and the end of the trial (from 7.03 to ~6.5) (Doyeni et el., 2021b).
Noticeably, the decrease in pH we observed could be linked to
seasonality (Wolińska et al., 2022). We did not find significant
correlations between Acidobacteria and pH at phylum or genus
ranks; however, this could be biased as it was not possible to assign
most of the sequences falling in the Acidobacteria phylum at low
taxonomic ranks (i.e., genus). Soil pH is a major driver of bacterial
selection and abundance as for bacteria the pH range for optimal
growth is quite narrow (Rousk et al., 2010;Tian et al., 2021). Most of
the taxa influenced by acidification belonged to the Proteobacteria
and Firmicutes phyla and showed a positive correlation with pH.
The most represented genera showing a positive correlation with pH
were Puia (Bacteroidetes) and three proteobacterial taxa
(Sphingomonas,Massilia, and Arenimonas) which decreased
when compared with the first year (before fertilization) and the
third year (after fertilization). Massilia genus has been found to
colonize root surfaces and is relatively abundant in the rhizosphere
(Ofek et al., 2012;Wolińska et al., 2022). In contrast, Haliangium
(Mixococcota) relative abundance increased with a lower pH, and
members of this genus are commonly found in soil [e.g., in the
rhizosphere of melon plants (Ling et al., 2014)] and may have
different effects on soil microbiota: it has been observed that they are
capable of predating on Gram-positive bacteria (Zhang and Lueders,
2017) and also have the potential to inhibit the growth of a wide
spectrum of fungi (Fudou et al., 2001;Ling et al., 2014).Variations in
Massilia and Haliangium relative abundance have been previously
observed in relation to pH and seasonality in a trial using an
intercrop mixture and a maize monoculture (Wolińska et al., 2022).
Soils are complex environments, and perturbations of their
homeostasis may alter the microbial community composition.
Therefore, digestate application may enrich soil phyla already
present in soil and/or influence their relative abundance. Previous
analysis showed a beneficial effect of digestate application on plant
growth (Doyeni et al., 2021b), which was possibly due to an increase
in microbial biomass (Doyenietal.,2021a); however, the lack of
direct measurement of these samples could not directly confirm this
hypothesis. Moreover, it was not clear if this was also due to
alterations in the prokaryotic community and/or to the presence
of novel microorganisms present in the digestates. The samples were
then collected at two different time points, a medium one few
months after fertilization (3–4 months after digestate application,
after harvest) and a long term before the fertilization of the following
year. However, no significant differences were observed between the
treatments. In contrast, the PERMANOVA analysis showed that
most of the differences observed were related to sampling time,
indicating that multiple factors related to this parameter had a major
influencing role on soil bacterial community composition. Indeed,
this parameter was associated to the period of sensitivity in
weathering seasonal changes, cultivation years, and agricultural
practices. The applied agricultural techniques such as annual
tillage before the start of the cultivation season (before
fertilization) and the harvesting activity (after harvest) could have
impacted significant changes in microbial composition (Longepierre
et al., 2021). Also, environmental factors are known to play a
fundamental role in shaping microbial composition and diversity
(Zhang et al., 2019).
Considering only the effects of a few months after fertilization
(after harvest), the overall prokaryotic composition was similar
for all treatments, with few differences observed at phylum and
genus levels. For instance, for samples collected in the first year
(after harvest), Firmicutes relative abundance was different in
control with respect to the other treatments, but no differences
were observed between the digestates and mineral nitrogen
fertilizers, indicating an effect of fertilization on this group.
Furthermore, Patescibacteria and Dependetiae are enriched in
mineral nitrogen–treated soil in comparison to the control in the
second and third years, respectively. Patescibacteria are ultra-
small bacteria mostly uncultivated with reduced genomes and
often found in groundwater environments (Tian et al., 2020);
however, their presence has also been observed in endophytic
communities (Wemheuer et al., 2019). Similarly, the phylum
Dependentiae (formerly known as TM6) is a group of
microorganisms widespread in different environments (mats,
sediments, sulfur springs, and sinks) whose current knowledge
comes from metagenomic data only (Yeoh et al., 2016). The
comparative genomic analysis showed parasitism as a common
feature within this group (Yeoh et al., 2016), suggesting that it
could potentially affect plant growth; however, its presence was
significantly higher in mineral nitrogen–treated plots only.
Regarding the differences among treatments at the genus level,
similar trends (differences among control samples and mineral
nitrogen and/or samples of plots treated with digestates) were
noticeable. Taxa belonging to the genera Gaiella,Streptomyces,
Acinetobacter,Opitutus, Acidobacteria (subgroup 10), and
Streptomycetes showed differences among treatments in 1 year
only, while Paenisporosarcina showed significant differences in the
first and third years. The Paenisporosarcina genus has been
characterized by mostly psychrophilic species (Reddy et al., 2013);
however, it has been found also in soils where it may have a beneficial
effect on plant growth (i.e., rice), inhibiting potential pathogens such
as Rhizoctonia solani owing to VOC production (Wang et al., 2021).
Also, members of the Gaiellales have been found associated to cereals
in the root system of rice (Hernández et al., 2015). Microbes
belonging to the genus Streptomycetes are often detected inside
plant roots and can be beneficial (Olanrewaju and Babalola, 2019)
while sometimes can act as plant pathogens (Seipke et al., 2012);
however, the crop yield and quality of crops were not compromised
in the period of digestate application (Doyeni et al., 2021b).
The identity of the host plant has a significant influence on the
identity of its microbiome (Dastogeer et al., 2020), and promoting
a soil microbiome for high plant production requires
management of microbial abundance and activity, community
composition, and specific functions (Lehmann et al., 2020). In
essence, the different cereal-based crop plants cultivated in the
3 years may have played key roles in the prokaryotic relative
abundance as each plant has unique requirements in terms of
Frontiers in Environmental Science | www.frontiersin.org June 2022 | Volume 10 | Article 8522419
Suproniene et al. Seasonal Effects on Soil Microbiota

needs, uptake, and competitiveness with the soil microbes. These
factors together with other environmental/agricultural factors
were then the major drivers in influencing microbiota
composition rather than digestate application.
CONCLUSION
All the three types of digestate tested gave similar results with the
native prokaryotic community composition not significantly affected
in a medium/long term response over the 3 years of application. The
major effect on community composition was due to the sampling
time possibly related to the changing environmental conditions and
other agricultural management techniques factors such as the tillage
before each year’s cultivation, harvesting during summer, and
different cereal crops grown each year. A pH decrease was
observed between the beginning and the end of the trial, and this
was unrelated to the treatment used and probably linked with
seasonality. However, soil pH probably played a major role in
microbiota relative abundance (positively) correlating with many
taxa at genus rank.
Digestate application showed then a positive effect as the
short- to long-term aim was to prevent the introduction/
increase of potential pathogens in the soil and avoid a
perturbation of the native soil prokaryotic community.
DATA AVAILABILITY STATEMENT
The 16S rRNA gene amplicon sequence data are available at the
National Centre for Biotechnology Information Sequence Read
Archive (SRA; http://www.ncbi.nlm.nih.gov/sra) with SRA
accession from SAMN24344854 to SAMN24344941.
AUTHOR CONTRIBUTIONS
SS and VT conceived the experiment. MD and SS performed soil
sampling and soil DNA extraction. MD, CV and FP analyzed
data. All authors contributed to data interpretation, drafted the
manuscript, agreed with its final version, and revised the manuscript.
FUNDING
This research was funded by the Research Council of Lithuania
(LMTLT), agreement No. S-SIT-20-5. and the APC was funded
by the Research Council of Lithuania (LMTLT), agreement No.
S-SIT-20-5.
ACKNOWLEDGMENTS
The authors wish to thank Ausra Baksinskaite, Urte Stulpinaite,
and the field team of the Plant Nutrition and Agroecology
department (Lithuanian Research Centre for Agriculture and
Forestry) for their technical support.
SUPPLEMENTARY MATERIAL
The Supplementary Material for this article can be found online at:
https://www.frontiersin.org/articles/10.3389/fenvs.2022.852241/
full#supplementary-material
Supplementary Table S1 | P-values of the PERMANOVA test show differences
among sampling times.
Supplementary Table S2 | P-values of the PERMANOVA test show differences
among treatments.
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Frontiers in Environmental Science | www.frontiersin.org June 2022 | Volume 10 | Article 85224112
Suproniene et al. Seasonal Effects on Soil Microbiota