Assessment of soil quality in agroecosystems based on soil fauna

Abstract

Soil arthropods respond sensitively to land management practices and correlate with beneficial soil functions. The aim of this research was to determine soil quality using the QBS index in different types of crops and influence of soil variables (pH soil, soil moisture, potassium, phosphorus and nitrogen) on soil arthropods. Between the years 2018 and 2020, we studied different types of crops (Brassica napus, Pisum sativum, Triticum aestivum, T. spelta, Zea mays, Grass mixture and Hordeum vulgare) and recorded 14 taxa. Our results suggest a higher QBS index value in crops grass mixture, Pisum sativum, Triticum aestivum, T. spelta. The EMI value grew with increasing values of soil moisture, soil pH, phosphorus, potassium and nitrogen; indicating the presence of soil arthropods occurring in higher quality soil. Our results suggest that agricultural intensification affects soil arthropods, which are important for the production of biomass, which also affects crop yields.

Full text

Biosyst. Divers., 2021, 29(4)
Biosystems
Diversity
ISSN 2519-8513 (Print)
ISSN 2520-2529 (Online)
Biosyst. Divers.,
2021, 29(1), 319–325
doi: 10.15421/012140
Assessment of soil quality in agroecosystems based on soil fauna
V. Langraf*, K. Petrovičová**, J. Schlarmannová*, S. David*, T. A. Avtaeva***, V. V. Brygadyrenko****
*Constantine the Philosopher University in Nitra, Nitra, Slovak Republic
**University of Agriculture in Nitra, Nitra, Slovak Republic
***Chechen State Pedagogical University, Grozny, Russia
****Oles Honchar Dnipro National University, Dnipro, Ukraine
Article info: Received 04.11.2021
Received in revised form 26.11.2021
Accepted 28.11.2021
Constantine the Philosopher University in Nitra,
Tr. A. Hlinku, 1, Nitra, 94901, Slovak Republic.
E-mail: langrafvladimir@gmail.com
University of Agriculture in Nitra, Tr. A. Hlinku, 2,
Nitra, 94901, Slovak Republic.
E-mail: kornelia.petrovicova@gmail.com
Chechen State Pedagogical University, Subry Kishie-
voy st., 33, Grozny, 364068, Chechen Republic,
Russia. E-mail: avtaeva1971@mail.ru
Oles Honchar Dnipro National University,
Gagarin av. 72, Dnipro, 49010, Ukraine.
Tel.:+38-050-93-90-788. E-mail: brigad@ua.fm
Langraf, V., Petrovičová, K., Schlarmannová, J., David, S., Avtaeva, T. A., & Brygadyrenko, V. V. (2021).
Assessment of soil quality in agroecosystems based on soil fauna. Biosystems Diversity, 29(4), 319–325.
doi:10.15421/012140
Soil arthropods respond sensitively to land management practices and correlate with beneficial soil functions.
The aim of this research was to determine soil quality using the QBS index in different types of crops and influence
of soil variables (pH soil, soil moisture, potassium, phosphorus and nitrogen) on soil arthropods. Between the years
2018 and 2020, we studied different types of crops (Brassica napus, Pisum sativum, Triticum aestivum, T. spelta,
Zea mays, Grass mixture and Hordeum vulgare) and recorded 14 taxa. Our results suggest a higher QBS index
value in crops grass mixture, Pisum sativum, Triticum aestivum, T. spelta. The EMI value grew with increasing
values of soil moisture, soil pH, phosphorus, potassium and nitrogen; indicating the presence of soil arthropods
occurring in higher quality soil. Our results suggest that agricultural intensification affects soil arthropods, which
are important for the production of biomass, which also affects crop yields.
Keywords: soil arthropods; index QBS; agrosystems; diversity; soil animals.
Introduction
Soil is one of the most species-rich habitats, representing the de-
composition subsystem of terrestrial ecosystems. It reflects ecosystem
quality, where all bio-geo-chemical processes of soil and different eco-
system components are combined. A criterion of evaluating long-term
sustainability of ecosystems is to assess the fluctuations of soil quality
(Schoenholtz et al., 2000). Regarding soil quality indicators, there is a
heavy reliance upon a few appraisals such as soil organic matter among
chemical indicators (Gilley et al., 2001), bulk density (Li et al., 2001)
and aggregate stability (Six et al., 2000). Biological monitoring is re-
quired to correctly assess soil degradation and correlated risks. There is
a need to identify indicators which are capable of expressing soil quality
criteria and can be used as bench-marks in environmental remediation,
as well as to assess and monitor soil quality (Violante, 2000).
Soil fauna is considered to be an important component of the soil
ecosystem for maintaining nutrient cycling and soil fertility (Osler &
Sommerkorn, 2007). It is considered to be a useful indicator of soil
quality because it is sensitive to changes in land management (Yeates,
2003). As indicators of soil quality, the abundance and diversity of soil
fauna integrate the physical, chemical, microbiological properties of
soil, and reflect general ecological change (Menta et al., 2008).
The application of biological indicators is limited by the difficulties
in identifying species of soil arthropods. A simplified eco-morpholo-
gical index (EMI) based on the types of soil microarthropods, does not
require species level identification (Parisi & Menta, 2008). The EMI
index was used to evaluate soil quality using the Qualità Biologica del
Suolo (QBS index). The QBS index is based on the arthropod groups
present in a soil sample and does not include any measure of abun-
dance. It follows that soil arthropods with the same EMIs have the same
effect on the ecosystem when their true effects could differ because of
differences in abundance. Soil quality is positively correlated with the
number of arthropod groups that are well-adapted to soil habitats. The
QBS index is a measure of how well the soil fauna adapts to the particu-
lar soil (Aspetti et al., 2010). Soil invertebrates need the energy obtained
from the microbial degradation of organic matter in order to make a
positive effect on the nutrient recycling processes (Potapov et al., 2017;
Lajtha et al., 2018). They are important in supporting and regulating
ecosystem services (Lavelle et al., 2006). Organic matter forming waste
represents the basis of a detritus based food web whose primary con-
sumers are bacteria and fungi.
The microbial feeders, saprovores and secondary consumers such
as nematodes, oribatid mites and proturans are the most abundant or-
ganisms of the soil fauna. Arachnids, predaceous mites, pseudoscor-
pions, small spiders and centipedes are at the higher trophic levels (Bry-
gadyrenko & Reshetniak, 2014; Ruchin et al., 2018, 2021). In moist
soils rich in organic matter, the total density of soil arthropods can be
hundreds of thousands of specimens/m2 (Coleman & Wall, 2015; Or-
giazzi et al., 2016; Menta & Remelli, 2020). The biocenosis of soil
depends on the organic matter such as undecayed plant and animal
residues and its role is the degradation of such matter in order to make
mineral nutrients available (N, P, K) to plants (Wardle, 2002; Murphy,
2014). This cycle is linked to the sequestration of a large amount of car-
bon and, therefore, soil management and conservation can be conside-
red of global interest from many points of view, from the regulation of
climate processes to the productivity of agroecosystems. The intensity
of land use is also affected by working with the plough. The rate of na-
tural setting of the soil after ploughing depends on the structure, frag-
mentation and amount of precipitation. Therefore, ploughing ensures
favourable conditions and supports the activity of soil arthropods (Lal,
2004; Lajtha et al., 2018; Ondrasek et al., 2019).
The aim of this paper is to evaluate soil quality, based on QBS in-
dex in crops grass mixture, Pisum sativum, Triticum aestivum, T. spelta,
Brassica napus, Zea mays, Hordeum vulgare. It was also intended to
discover the influence of environmental variables (soil moisture, soil
pH, potassium, phosphorus and nitrogen) on the EMI index.
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Biosyst. Divers., 2021, 29(4)
Materials and methods
The research took place during the years 2018 to 2020, during which
we collected soil arthropods from seven types of agricultural crops. These
types of agricultural crops were examined throughout each year, the posi-
tion of crops in the fields changed every year. The fields of the individual
crops were adjacent to each other (Fig. 1).
Fig. 1. Map of the study area
In winter-planted crops (Brassica napus, Pisum sativum, Triticum
aestivum, T. spelta), soil arthropods were collected from November to
July. In the spring-planted crops (Zea mays, Hordeum vulgare), soil arth-
ropods were trapped from April to October. In the grass mixture, soil
arthropods were collected all year round. For crops conventional tillage
was applied based on annual tillage ploughing, incorporating crop residues
and weeds into the soil. The soil was ploughed three times and turned.
Presowing preparation and sowing were combined. Machines with driven
working tools were used in conjunction with a seed drill. When sowing, it
was possible to use seed coulters with an obtuse angle of penetration into
the soil. The study area was located in the Podunajská pahorkatina-
Danubian upland geomorphological unit (South-Western Slovakia) in the
cadastral territory of Nitra. The altitude of the monitored area was approxi-
mately 130 m above sea level with a brown soil type. The study area is
considered a warm arid climate with mild winters. The mean temperature
ranges during each month were as follows: January −5–5 °C, February
−3–6 °C, March 0–12 °C, April 10–20 °C, May 15–22 °C, June 18–
27 °C, July 22–29 °C, August 20–29 °C, September 15–23 °C, October
8–15 °C, November −3–7 °C, December −5–5 °C. The average precipita-
tion for each month was as follows: January 30 mm, February 26 mm,
March 35 mm, April 12 mm, May 65 mm, June 77 mm, July 41 mm,
August 57 mm, September 64 mm, October 54 mm, November 40 mm
and December 55 mm. Each field was sampled ten times for one year
during the monitoring period (years 2018, 2019, 2020). Each time, five
replicates of soil cores (10 × 10 × 10 cm) were collected from each field,
following a linear path at a distance of 20 m between every soil cores. Soil
samples were collected regularly in two-week intervals every year (2018
to 2020). Arthropod extraction was performed by Berlese-Tüllgren funnel
for 10 days. The extracted specimens were collected and preserved in
75% ethyl alcohol and 25% glycerol by volume. The nomenclature of soil
arthropods was established according to the works of Majzlan (2009),
Pokorný & Šifner (2004).
The insecticide FORCE (Syngenta, Basel, Switzerland), a granular
insecticide intended for soil application to control soil pests, was applied to
the crops (Brassica napus, Zea mays, Hordeum vulgare). Insects were
killed through respiratory and tactile poison ingestion. The preparation had
a fast effect and a strong residual (repellent) action against a wide range of
soil pests from the orders of Coleoptera, Aranea and Hymenoptera.
The applied dose was administered uniformly at a concentration of 12–
15 kg per ha each year for the duration of the research. Solinure FX ferti-
lizer (Medilco Hellas S.A., Athens, Greece) containing chlorides and urea,
was applied to the crops and was intended for field fertility. Due to its
acidifying effect, it contributed to lowering the soil pH.
At each pitfall trap location we removed stones, fallen leaves from
crops and sampled the soil to a depth of 15 cm for analysis. Samples were
taken from each field every two weeks over a period of three years. Sub-
sequently, environmental variables (N, P, K, pH, soil and moisture) were
analyzed using soil moisture meter (Rapitest 3 1835, Luster Leaf, Illinois,
USA) and pH meter (Dexxer (PH-03, Luboň Poland). We thoroughly wet
the broken-up soil with water (ideally distilled or deionized water) to a
mud consistency. We wiped the meter probe clean with a tissue or paper
towel. The probe was inserted into the soil up to the probe base (7–10 cm).
We waited one minute and wrote the value. We converted the measured
values into units of mg. The average values of environmental variables for
all crops during the years 2018–2020 are shown in Table 1.
Table 1
Average values of environmental variables
during 2018–2020 for the studied crops
Crops pH
Nitrogen,
mg
Phospho-
rus, mg
Potassi-
um, mg
Moisture,
%
Grass mixture
6.73
13.11
1.05
13.11
32.69
Pisum sativum
6.90
9.81
0.78
9.81
27.77
Hordeum vulgare
6.84
15.69
1.26
15.69
37.13
Zea mays
6.66
12.72
1.02
12.72
32.66
Triticum aestivum
6.98
21.84
1.75
21.84
41.89
Brassica napus
7.06
13.52
1.08
13.52
39.75
Triticum spelta
6.89
12.93
1.03
12.93
28.29
The data obtained by the research has been saved in the Microsoft
SQL Server 2017 database program (Express Edition), consisting of fre-
quency tables for collections and measured environmental variables (pH,
soil moisture, potassium, phosphorus and nitrogen). The database also
consisted of code tables for study sites and their variables (crops, habitat,
locality name, cadastral area, altitude and coordinates of localities). Matri-
ces for statistical calculations using (Microsoft SQL Server 2017; (RTM)
14.0.1000.169 (X64) 2017 Microsoft Corporation Express Edition (64-
bit) on Windows 10 Home 10.0) were programmed.
Multivariate analysis (redundancy analysis – RDA) to determine the
dependencies between objects (soil arthropods and crops, soil variables)
was used. We tested the statistical significance of crops (Brassica napus,
Pisum sativum, Triticum aestivum, T. spelta, Zea mays, grass mixture,
Hordeum vulgare) and soil variables (moisture, pH, phosphorus, potas-
sium and nitrogen) using the Monte Carlo permutation test (iterations =
499) in the Canoco5 program (Ter Braak & Šmilauer, 2012; Canoco
reference manual and user’s guide: software for ordination, version 5.0;
Ithaca USA: Microcomputer Power).
Analysis in the statistical program Statistica (Statsoft, 2004) focused
on linear regression, expressing the relationship between the value EMI of
soil arthropods and the values of potassium, phosphorus, nitrogen, pH and
soil moisture was used.
The biological forms present in a sample were divided into the diffe-
rent adaptation levels of soil environment for every systematic group.
For each higher taxon, the QBS method requires searching for the biologi-
cal form (morphotype) that is most adapted to soil. As a general rule,
euedaphic (i.e. deep soil-living) forms get an EMI = 20, hemiedaphic
(i.e. intermediate) forms are given an index rating proportionate to their
degree of specialization, while epiedaphic (surface-living) forms score
320

Biosyst. Divers., 2021, 29(4)
EMI = 1. EMI values for taxa are given in (Parisi et al., 2005). If two eco-
morphological forms are present in the same taxa group, the final score is
determined by the higher EMI. To calculate the QBS score of fields, it is
sufficient to sum up the EMIs of all collected groups.
Results
A total of 27,943 specimens belonging to 14 taxa were observed.
Taxa of Coleoptera (46%), Collembola (18%) and Diplopoda (13%) had
an eudominant representation of specimens. Araneae (6%) were domi-
nant, the other groups had subdominant to subrecendent representation.
Fields with grass mixture, Pisum sativum, Triticum aestivum, T. spelta had
a high QBS value. Fields with Zea mays, grass mixture, Hordeum vulgare
had a low value (Table 2).
Multivariate analysis of the soil arthropods between the years 2018
and 2020 was determined using the redundancy analysis (RDA, SD
(length of gradient) is 1.80 on the first ordination axis). We observed the
relationship between soil arthropods and crops (grass mixture, Pisum sati-
vum, Triticum aestivum, T. spelta, Brassica napus, Zea mays, Hordeum
vulgare). The values of the explained cumulative variability of taxonomic
data were 48.6% on the first ordination axis and 53.1% on the second
ordination axis. The cumulative variability of the species set explained by
crops was represented in the first ordination axis at 78.8% and in the 2nd
axis at 90.9%. Using the Monte Carlo permutation test, we identified a
statistically significant effect of grass mixture (P = 0.046, F(1.393) = 2.052,
df = 6), Pisum sativum (P = 0.014, F(1.623) = 1.911, df = 6), Triticum aesti-
vum (P = 0.021, F(2.212) = 3.102, df = 6), Triticum spelta (P = 0.042,
F(3.112) = 4.212, df = 6), Hordeum vulgare (P = 0.045, F(1.139) = 1.527, df =
6) on the structure of soil arthropods. No significant effect was found for
Brassica napus (P = 0.156, F(3.937) = 1.452, df = 6), Zea mays (P = 0.394,
F(2.184) = 1.974, df = 6) on the structure of soil arthropods. The selected
variables were not mutually correlated with the maximum value of the
inflation factor 1.714. The ordination graph (biplot) had a predominance
of soil arthropods ordered around the crops grass mixture, Pisum sativum,
Triticum aestivum, T. spelta (cluster I). The second cluster (II) was repre-
sented by taxa linking to Hordeum vulgare. The third cluster (III) con-
sisted of soil arthropods with a preference for Brassica napus, Zea mays
(Fig. 2).
Table 2
Soil arthropod taxa, associated EMI, QBS values and number of individuals for crops
Taxa
Grass mixture
Pisum sativum
Triticum aestivum
Triticum spelta
Hordeum vulgare
Zea mays
Brassica napus
EMI
ind.
EMI
ind.
EMI
ind.
EMI
ind.
EMI
ind.
EMI
ind.
EMI
ind.
Collembola
4
374
–
–
4
–
4
–
4
126
4
–
–
–
Dermaptera
1
1189
1
83
1
11
–
34
–
309
1
74
–
67
Orthoptera
20
2835
20
505
20
463
20
401
20
2738
20
5263
20
539
Hemiptera
1
3814
–
–
1
–
1
–
1
1539
–
2
1
–
Coleoptera
6
1
6
39
6
27
6
–
6
–
6
1
6
–
Hymenoptera
5
3109
5
92
5
167
5
4
5
66
5
42
5
4
Diptera (larvae)
10
519
10
1
10
45
10
–
–
494
10
126
10
13
Other holometabolous
insects (adults)
1 148 – – – – – – – 65 – – – 2
Acari
20
277
20
32
20
18
20
5
20
695
–
53
–
25
Araneae
5
61
5
–
5
–
5
–
5
14
5
72
5
3
Opiliones
10
146
10
30
10
1
10
31
10
5
10
447
–
4
Isopoda
10
40
10
–
10
–
10
–
10
21
10
85
10
–
Chilopoda
20
290
20
27
20
53
20
6
20
36
20
90
20
39
Diplopoda
5
5
5
–
5
–
5
–
5
–
5
–
5
–
QBS index /
∑ individuals
118 12809 112 809 117 785 116 481 106 6108 96 6256 82 695
Note: ind. ‒ individuals.
In the second stage of multivariate analysis using redundancy analysis
(RDA, SD (length of gradient) is 1.80 on the first ordination axis), we
observed relationships between soil arthropod groups and soil variables
(pH of the soil, soil moisture, potassium, phosphorus, nitrogen). The valu-
es of the explained variability in taxonomic data were 48.6% on the first
ordination axis and 53.1% on the second ordination axis. The cumulative
variability of the set of taxa explained by soil variables was on the first
ordination axis 86.4% and on the second axis 94.5%. Using the Monte
Carlo permutation test, we identified a statistically significant effect of soil
moisture (P = 0.004, F(1.221) = 1.451, df = 3), pH of soil (P = 0.018, F(1.714) =
1.899, df = 3), phosphorus (P = 0.032, F(1.112) = 1.392, df = 3), potassium
(P = 0.026, F(1.368) = 1.911, df = 3) and nitrogen (P = 0.016, F(1.614) = 2.045,
df = 3) on the structure of soil arthropods. The selected variables were not
mutually correlated with the maximum value of the inflation factor
1.6266. The ordination graph (biplot) contained of taxa ordered into two
clusters (Fig. 3). The first cluster (I) consisted of soil arthropods correlated
with phosphorus (mg), nitrogen (mg) and potassium (mg). The second
cluster (II) was represented by taxa linking to pH and moisture (%).
The values of EMI of taxa were processed using linear regression.
Using the regression model, we expressed the relationship (correlation)
between values of EMI and potassium (mg/kg), phosphorus (mg/kg),
nitrogen (mg/kg), pH and humidity (%). The correlation coefficient value
was high for the values EMI of taxa and moisture (%, r = 0.851, Fig. 4A),
potassium (r = 0.741, Fig. 4B), phosphorus (r = 0.731, Fig. 4C), nitrogen
(r = 0.891, Fig. 4D) and pH (r = 0.711, Fig. 4F), which indicated a strong
relationship. The reliability coefficient for moisture R2 = 0.781 indicated
the capture of 78.1% variability, potassium R2 = 0.712, phosphorus R2 =
0.851, nitrogen R2 = 0.791 and pH R2 = 0.711. The overall suitability of
the regression model is statistically significant in all cases: moisture (P =
0.038), potassium (P = 0.048), phosphorus (P = 0.047), nitrogen (P =
0.039) and pH (P = 0.037). The results showed that increasing values of
potassium, phosphorus, nitrogen and soil humidity, increased the value of
EMI. Thus, it increases the presence of taxa indicating higher soil quality.
The ideal value for taxa was 15–50 mg/kg potassium, 1.3–4.0 mg/kg phos-
phorus, 15–50 mg/kg nitrogen, 6.8–7.0 pH and 35–75% for moisture.
Discussion
Bioindicators are useful to highlight changes in complex systems.
This is particularly true for environmental soil problems. Environmental
indicators represent synthesized information about the state of the envi-
ronment for management. Ecological indicators have two main functions.
First, to decrease the number of measures that would normally be required
to represent a situation. Second, to simplify the communication process
through which information on collected data is conveyed to final users
(Muller et al., 2000; Brygadyrenko, 2015; Porhajašová et al., 2015). Cal-
culation of QBS index does not require estimates of the number of speci-
mens for every group or the single species present in the sample. Com-
pared with methods that use a single taxon as biological indicators, such as
ants (Wiezik et al., 2017; Purkart et al., 2019), isopods (Paoletti & Hassall,
1999) and mites (Maraun & Scheu, 2000), the QBS index does not re-
quire a species level diagnosis, and is therefore considered an appropriate
tool for large-scale monitoring, where a large number of samples may be
gathered.
Analysis of our results showed significant differences in QBS values
between crops. We recorded higher QBS values in fields with grass mix-
ture, Pisum sativum, Triticum aestivum, T. spelta. The QBS values appear
to increase as arable land use pressure is reduced. These differences are
greater for the intensive crops, such as corn or tomato, and smaller for the
low input crops, such as wheat (Triticum) and barley (Hordeum).
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Biosyst. Divers., 2021, 29(4)
Fig. 2. RDA analysis of soil arthropods with crops
Fig. 3. RDA analysis of soil arthropods with respect to soil variables
322

Biosyst. Divers., 2021, 29(4)
Fig. 4. Linear regression model potassium, phosphorus, nitrogen, pH, moisture on the value EMI
Our results also indicated smaller differences in QBS index values
between Hordeum vulgare, Triticum aestivum and T. spelta. Thus, QBS is
at present considered a useful tool in the fields of ecological risk assess-
ment, environmental impact studies, as well as an early warning device for
soil degradation evaluation, and for that reason may be useful also in areas
prone to desertification (Gardi et al., 2003).
The multivariate analysis of our results, of single soil arthropods in
relation to land use showed that the predominance of taxa is linked to
grass mixture, Pisum sativum, Triticum aestivum, T. spelta. The predo-
minance of soil arthropods in the same crops is noted in (Porhajašová
et al., 2018). We also noted the predominance of soil arthropods with
phosphorus, nitrogen and potassium binding. Thus, our results agreed
with the results of Langellotto & Denno (2004), who observed a decline
in soil arthropods with increasing land use. The response of soil orga-
nisms is a key part of the sustainability of the soil ecosystem (Langraf
et al., 2020) being considered as a bioindicator of the environment (Kru-
mpálová, 2002; Krumpálová et al., 2009; Bote & Romero, 2012; Ma-
gura et al., 2020). Soil arthropods living in agricultural landscapes have
a wider tolerance than the soil arthropods of natural habitats (Lenoir &
Lennartsson, 2010). They also achieve a high local density due to the in-
fluence of agriculture (Holecová et al., 2003). We recorded an arthropod
community dominated by Coleoptera, Collembola and Diplopoda.
The great abundance of these groups influenced the maintenance of the
natural balance and substance cycle of the biogenic elements in ecosys-
tems such as carbon, nitrogen, sulfur and phosphorus (Morris & Cam-
pos, 1999). Their activities accelerated the decomposition of plant resi-
dues, aerated the soil and improved soil structure and quality (Kotroczó
et al., 2020). The presence of other soil arthropods was heterogeneous
and may depend on the management regime and the surrounding vege-
tation (Moço et al., 2010).
Relationships between soil arthropods and chemical parameters have
a positive influence on organic matter supply of soil fauna. The C/N ratio
can be considered as an index of the quality and biodegradation of organic
matter. In soils with a higher C/N ratio, the fungal contribution to biode-
gradation tends to prevail and this has a positive effect on soil arthropod
communities (Laiho et al., 2001). Diversity of soil arthropods can be cor-
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Biosyst. Divers., 2021, 29(4)
related to soil pH, potassium, phosphorus and nitrogen (Brygadyrenko,
2015a, 2015b; Alekseev & Ruchin, 2020). The result has a direct signifi-
cant and positive effect on soil fauna richness and the presence of taxa
indicating higher soil quality (Da Silva et al., 2016). Our results also point
to a strong positive relationship between environmental variables (soil pH,
potassium, phosphorus, nitrogen and moisture) and EMI values. The re-
sults show that as the value of environmental variables increases, so does
the EMI value, indicating the presence of soil arthropods occurring in
higher quality soils. An absence of positive correlation between soil arth-
ropods and soil chemistry was found (Teofilova, 2021). A negative corre-
lation of pH with springtails, was recorded by (Fazekašová & Bobuľov-
ská, 2012), who identified it as the main soil parameter influencing Col-
lembola communities. Soil arthropods are involved in ecosystems in
maintaining the natural balance and material cycle of the biogenic ele-
ments such as carbon, nitrogen, sulfur and phosphorus (Brygadyrenko,
2016) and they have more suitable conditions in sustainable agriculture
(Saranenko, 2011).
Conclusions
Our results have provided new knowledge about the soil quality
based on the QBS index in the conditions of Brassica napus, Pisum sati-
vum, Triticum aestivum, T. spelta, Zea mays and grass mixture in Central
Europe. We confirmed a higher value of the QBS index and also the cor-
relation of more soil arthropods to fields with grass mixture, Pisum sati-
vum, Triticum aestivum, T. spelta. The dispersion of soil arthropods was
also positively influenced by soil moisture, pH of soil, and levels of phos-
phorus, potassium and nitrogen. The values of the EMI index were posi-
tively influenced by soil moisture, pH soil, phosphorus, potassium and
nitrogen. They pointed to an increase in soil arthropods with a higher EMI
value and thus to a higher soil quality. Soil arthropods are important dri-
vers of ecosystem functions such as nutrient cycling, pest control and
maintenance of soil structure. They are important for increasing biomass,
which affects crop yields. Therefore, it is important to promote the strate-
gies for addressing the conservation of soil arthropods in agricultural land-
scapes.
This research was supported by the grants VEGA 1/0604/20 Environmental assess-
ment of specific habitats in the Danube Plain. KEGA No. 019UKF-4/2021 Creation
and innovation of education – Zoology for Ecologists, part – Invertebrates.
The authors declare no conflict of interest.
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