Spatial and Temporal Variability of the Microbiological and Chemical Properties of Soils under Wheat and Oilseed Rape Cultivation

Abstract

The size of the microbial biomass and the activity of soil enzymes are among the most sensitive indicators of agricultural land quality. The aim of this study was to determine the spatial and temporal variability of microbial biomass, the activity of dehydrogenase (DHA) enzyme and the concentration of micro- (Na, Mg and Ca) and macroelements (Cu, Zn, Mn and Fe) in the soil, collected from 37 measurement sites (depth 0–30 cm) in a 40-hectare field during two growing seasons (wheat and oilseed rape). The percentage of nitrogen (%N) in the wheat grain and rapeseeds was also determined. Mapping the spatial distribution of the microbial biomass, the level of DHA activity and the concentration of the selected elements was used to assess the soil productivity. All tested soil parameters exhibited temporal and spatial variability. The creation of raster maps showing the distribution of the tested parameters allowed the observation of a higher nitrogen content in wheat grains in the south-western part of the field, with high values of DHA activity, bacterial biomass and soil pH. In the case of oilseed rape, plants grown in the northern part of the field were characterized by a higher nitrogen content in the grain, where the soil was characterized by a higher content of fungal biomass. On the basis of the obtained research results, a positive, statistically significant correlation was also shown between the biomass of bacteria and the level of DHA in the soil under the cultivation of both wheat and rape. The cultivation of both crops had a significant impact on the size of the microbial biomass pool and on the DHA activity level but did not affect the concentration of the nutrients in the soil. High concentrations of the analyzed elements at the measuring points correlated with a greater %N content in the grain/seeds of the crops harvested at those locations in the field. The results conclude that the mapping of the physicochemical parameters, microbial biomass and activity on the field permits the development of an effective strategy for maintaining sustainable soil productivity through the appropriate management of agricultural practices and the better approximation of mineral fertilization.

1. Introduction

Most soil properties that vary both spatially and temporally are influenced by natural (internal) conditions, such as parental (geological) material, as well as by vegetation and the climate. The distribution of the physico-chemical and biological properties in the agricultural soil is also created by exogenous anthropogenic factors related to plant production, such as plowing, fertilization and crop rotation [1,2]. According to Cavigelli et al. [3], agricultural practices can alter the local soil nutrient heterogeneity and contribute to additional soil property heterogeneity, thereby affecting soil microbial communities.

The productivity of the agroecosystem in a sustainable agriculture system is defined by the efficiency of the transformation processes that occur when (a) organic matter is introduced into the soil in the form of plant residues, or (b) fertilizers are applied [4,5]. According to Daniel [6], soils are the most diverse microbiological environments. However, the determination of the patterns of soil microbial properties is extremely complicated due to the large number of factors that influence microbial activity and its considerable variability over time [7]. According to Martirosyan et al. [8] and Kuzyakov and Blagodatskaya [9], aggregations of microorganisms are produced at various spatial scales even as part of a homogeneously managed system. Correa-Galeote et al. [10] and Barberan et al. [11], while examining the spatial distribution of a soil microbial community structure, noted a high variability of microbial number and activity in the field at a distance of several centimeters.

According to Schulz et al. [12], knowledge of the structure of microbial communities is key to predicting the dynamics of soil organic matter (SOM) decomposition, nutrient cycling, the maintenance of soil structure and the regulation of its biological population.

According to Zhang et al. [13] and Zheng et al. [14], bacteria and fungi are the two most important consumer groups in the soil food web. Plant residues and root exudates provide growing material for microorganisms, which enrich the soil with nutrients (for plants) by breaking down and converting complex compounds [15]. This plant–soil process is a major driver of microbial community succession [16,17]. Strickland and Rousk [18] and Grzyb et al. [19] reported that bacteria tend to predominate in the early stages of the decomposition of plant debris, while fungi predominate in the later stages. The studies of Zhong et al. [20] and Liu et al. [21] showed that fungi exhibit greater variability than bacteria during the decomposition process. According to Dai et al. [22], the size, growth and diversity of the bacterial population in the soil is positively correlated with the organic carbon (C) content and pH, but under conditions where nitrogen (N) is balanced by other nutrients, such as phosphorus (P) and potassium (K).

The decomposition of organic C in the soil is primarily driven by bacterial and fungal activity, while only 10–15% of the soil C flow can be directly attributed to fauna activities [23]. In most soils, more than 90% of the total N and sulfur (S), along with more than 50% of total P, is related to the microbial biomass and organic matter, and therefore the circulation and bioavailability of these key soil nutrients are primarily controlled by microbial activity-related processes [24,25].

According to Bastida et al. [26], the measurement of microbial biomass is an important tool for monitoring the biological balance of the soil ecosystem. Based on the obtained bacterial and fungal biomass, the F:B ratio (F—fungal biomass; B—bacterial biomass) can be determined, which, according to De Vries et al. [27], is an indicator of the direction of organic matter decomposition and N mineralization in the soil. According to those authors, greater fungal biomass is indicative of a lower N leaching potential and a more negative partial N balance (or smaller N surplus).

Conventional agriculture, by simplifying the structure of the cultivated plants, as well as the large applications of mineral fertilizers, especially N, has disrupted the functioning of agroecosystems, significantly changing the chemical composition of plant residues, which significantly affects the mineralization rates of C and N compounds in the soil [28]. According to Scharroba et al. [29], bacteria prefer organic material with a narrow C:N ratio, while fungi prefer a much wider ratio. The intensity of mineral fertilization and the type of plant residues introduced into the soil, especially straw, has led to significant modifications in the composition of soil microorganisms [30,31].

Currently, many countries are striving to reduce their applications of mineral fertilizers [32,33]. Ultimately, this is to reduce costs and losses related to nitrate (NO₃⁻) ion leaching, denitrification (N₂/N₂O) and N volatilization (NH₃) from the soil. However, Kuzyakov and Xu [34] and Trivedi et al. [35] pointed out that a reduction in the use of mineral fertilizers may, however, increase competition for food resources between the soil microorganisms and plant roots, leading to a reduction in crop yield. Therefore, according to Schipanski and Drinkwater [36] and Vasseur et al. [37], it is extremely important to study the model of spatial and temporal variability in the bacterial and fungal biomass in the soil during crop growth as it explains the ecological functions of microorganisms in the soil environment, as well as assisting in the formulation of a fertilizer management strategy.

The spatial variability of microbial biomass is related to the fact that the greatest microbial growth is associated with the places in the soil where most nutrients are available [38].

Soil enzymatic activity is also a sensitive indicator of soil quality changes caused by agricultural management practices (e.g., tillage, plowing and agrochemicals addition) [39]. The activity of this group of enzymes is influenced by the physical and chemical parameters of the soil, such as soil moisture, temperature and pH [40,41]. The level of dehydrogenase (DHA) activity (as an intracellular enzyme) indicates the presence of physiologically active microorganisms, a function of the number and structure of the soil microbial communities. From soil enzyme activities, DHA is related to the C cycle and to SOM, thus reflecting the total range of oxidative activity in the soil microbiome, as well as the activity of other soil enzymes, e.g., catalase and β-glucosidase [42].

The spatial assessment of the aforementioned factors is carried out using geostatistical techniques, which are based on the theory of regionalized variables, and are more useful tools for the description and understanding of the spatial relationships of the measured variables compared to traditional statistical methods [43]. Geostatistics provides a set of statistical tools to model the patterns of the studied features, focusing on the prediction of the variability of soil properties on small and large spatial scales, thus providing the necessary information for kriging, which is a method of data interpolation at unsampled points [44,45]. This technique is used to assess the spatial variability of the physical, microbiological and biochemical properties of the soil [46,47].

A literature review shows that no comprehensive spatial analysis of the microbiological, enzymatic and physicochemical soil parameters or, additionally, plant quality characteristics has been performed to date. This is evidenced by the studies by Piotrowska and Długosz [47], who focused on the spatial analysis of the distribution of microbial biomass and the activity of the selected soil enzymes. Constancias et al. [48], in turn, determined the variability of the content of organic carbon (OC), pH, available nitrogen (N), phosphorus (P), potassium (K) and sulfur (S) and soil pH values. Vasu et al. [49] analyzed the variability of the soil microbial communities and enzymatic activities and their interactions with the pH value and the C/N ratio in the soil. According to Łukowiak [50], the presented soil biological and chemical parameters affect the yield and the amount of N in grain/seeds, and the assessment of its content is one of the main determinants of the nutritional value of grain. However, to date, only single studies have been carried out in this area, linking plant quality parameters with selected factors, such as only one issue, the biomass of microorganisms [51] or the type of fertilization [52]. Therefore, the wide range of research conducted in this study complements the above issues, and additionally, illustrates not only the variability in the distribution of selected soil parameters, but also their mutual interactions, further related to the content of selected plant quality characteristics. The significant spatio-temporal variability characteristics of soil properties, such as DHA activity, microbial biomass, pH and humidity, and the concentration of micro- and macroelements, identified in the conducted research and defined the direction of this variability and the type of interaction between the studied variables. At the same time, it was assumed that the spatio-temporal variability of the biomass of soil microorganisms and DHA activity depend on the physico-chemical parameters of the soil and is related to the qualitative characteristics of the cultivated plants (e.g., N content in grain). Taking into account all the above issues, the following research objectives were set: (1) spatial and temporal exploration of the biomass of soil microorganisms and soil biochemical variability (dehydrogenase activity); (2) determination of the spatial and temporal variability of soil physico-chemical parameters; (3) determination of nitrogen content in grains of tested plants; and (4) evaluation of the relationship between microbiological parameters, physicochemical properties of the soil in the entire test area and the nitrogen content in the grain of the tested sites.

On the basis of raster maps that present the values of the tested soil and crop parameters, we will determine the parts of the field that can yield a potentially greater yield and those that have a lower yield potential, which will allow for the precise application of agrochemicals.

2. Materials and Methods

2.1. Research Objectives

In 2019 and 2020, a field experiment was carried out on a 40-hectare arable field, located in the town of Kobylniki, Poland (52°4′20.4″ N; 16°32′21.3″ E). In the field, 37 points were established in the raster system at strictly defined georeferenced points (Figure 1). The soil was classified as lessive soil, from slightly loamy sands to medium loam. The soil texture was determined by Pruszczyński’s method [53] using a hydrometer.

The experiment was carried out on the sequence of two test plants: winter wheat (Triticum aestivum L.) cultivar Gustaw and winter oilseed rape (Brassica napus L.) cultivar DK Extract at a sowing rate of 350 seeds m⁻² and 50 seeds m⁻², respectively. Agrotechnical and cultivation procedures were carried out in accordance with the principles of good agricultural and experimental practice for the two crop species. For wheat, organic fertilization was used—35 t/ha manure (under the forecrop) and a 20 m³/ha slurry application—followed by mineral fertilization with N (1st—61 kg/ha; 2nd—94 kg/ha; 3rd—40 kg/ha). Pre-sowing fertilizers with P (69 kg/ha), K (120 kg/ha), magnesium (Mg) (18 kg/ha) and sulphur (S) (37.5 kg/ha) were used in the cultivation of oilseed rape. After sowing, N (1st—34 kg/ha; 2nd—63 kg/ha; 3rd—115 kg/ha), Mg (32.5 kg/ha) and S (65 kg/ha) were applied.

2.2. Soil Sampling

For biochemical analyses, soil samples were taken from the topsoil (0–0.3 m) with a soil auger (Eijkelkamp Agrisearch Equipment, Giesbeek, the Netherlands). Around each measurement point, an area of 1 m in diameter was created, from which three sub-plots were randomly selected. Soil samples were collected from each sub-plot, then a cumulative sample was prepared and placed in a sterile plastic bag and a portable cooler (4 °C).

In both years of the study, samples were collected on four dates: before the start of crop growth, during the flowering of the crop, during harvest, before the next growing season (saturation of the soil sorption complex (with cations) (

Table 1. Sampling dates in 2019–2020.

YEAR	2019
TERM	I	II	III	IV
Winter wheat	13.02 before the start of crop growth	03.06 flowering of the crop	09.07 harvest	22.10 saturation of the soil sorption complex
YEAR	2020
TERM	V	VI	VII	VIII
Winter oilseed rape	13.02 before the start of crop growth	21.04 flowering of the crop	07.07 harvest	22.10 saturation of the soil sorption complex

).

2.3. Enzymatic Activity

Biochemical analyses were performed using the spectrophotometric method. Soil enzymatic activity was based on the measurements of DHA activity (EC 1.1.1.1), which was determined according to Camiña et al. [54]. The soil (1 g) was incubated for 24 h with 2,3,5-triphenyltetrazolium chloride (TTC) at 28 °C, at pH 7.4. The triphenylformazane (TPF) that was produced was extracted with 96% ethanol and was spectrophotometrically measured (Rayleigh UV1800, Beijing, China) at a wavelength of 485 nm. The DHA activity was expressed as μmol TPF 24 h⁻¹ g⁻¹ dm soil.

2.4. Biomass of Soil Microorganisms

The bacterial and fungal biomass was determined by estimating the count of bacterial cells or by measuring the hyphae in microscope specimens, and was expressed as mg g⁻¹ DM soil [55]. The biomass was measured by collecting 0.01 mL of the soil suspension with sodium pyrophosphate and Tween 80, using a Breed chamber. Bacterial biomass was measured after the 24 h incubation of specimens in phenol erythrosine by counting cells with an eyepiece graticule micrometer. Bacterial biomass was calculated to allow for the number of cells in 10 μm grids in 40 fields of view, the mean cell volume and the cell specific weight. Fungal biomass was calculated by measuring hyphae (length and width) stained with aniline blue (24 h) in 160 fields of view with an eyepiece micrometer. Mean hypha volume was multiplied by the constant coefficient adjusted to the parameters of the optical microscope used for measurements. Then, it was multiplied by a coefficient of 0.2 (assuming that the dry mass of the mycelium amounted to 20%).

2.5. Determinationof Physico-Chemical Properties

The soil moisture content was determined using a moisture analyzer (Radwag, Poland) (Figure A1). Soil pH was measured using a CX-742 pH meter (Elmetron, Poland) after shaking the suspension of soil and 0.01-M CaCl₂ (soil/solution ratio 2.5:1; m/v) for 2 h (Figure A2).

Micro and macroelements (Na, Ca, Mg, Copper (Cu), Zinc (Zn), manganese (Mn) and iron (Fe)) were determined in samples of dried and sieved soil at the beginning and end of the experiment [56]. Two grams of soil and 20 mL of Mehlich 3 extraction solution (0.2M CH₃COOH, 0.25M NH₄NO₃, 0.015M NH₄F, 0.013M HNO₃, 0.001M EDTA) were combined and shaken for 5 min at 200 rpm on an Eberbach shaker (MI, USA). The mixture was filtered using filter paper and determined by the FAAS method (Flame Atomic Absorption Spectrophotometer, Thermo Scientific iCE 3500 Series, Dreieich, Germany).

2.6. Plant Analyses

Depending on the harvested crop, the collected sample was divided into grain/seed subsamples and harvest residue (straw). The following yield components were analyzed: (i) %N in the grains/seeds, and (ii) %N in the plant biomass. The N content was determined by the Kjeldahl method [57]. The ground samples (0.2 g), 10 mL of concentrated H₂SO₄ and catalyst were added in a Kjeldahl tube, then placed in the digestion unit for an hour, cooled and analyzed using a Kjeltec Auto 1031Analyzer (Foss, Höganäs, Sweden).

2.7. Statistics

Statistical analyses were performed using the Statistica 13.3 software (StatSoft Inc., Cracow, Poland). Two-way ANOVA was applied for the sampling date and place, separately for each year of analysis, because the results between the years were not comparable. Tukey’s test at a significance level p = 0.05 was used to determine the significance of the variability of soil parameters due to the date of sampling and the soil collection point. The Pearson correlation coefficient was used to quantify the strength of the relationships between the microbial biomass and DHA activity. Principal component analysis (PCA) was used to illustrate the relationship among enzyme activity, microbial biomass, %N in the plant, and the micro- and macroelement content to assess the associations of response variables with spatial sampling points. Linear regression was used to determine the relationship between the bacterial biomass and DHA activity in the soil under the cultivation of each plant (Equation (1)).

$${\mathrm{y}}_i={\beta }_0+{\beta }_1\mathrm{x}+{ϵ}_i \mathrm{i}=1,\dots ,n$$

(1)

where y_i is the depended variable, β₀, β₁ is the regression coefficient, x is observation and ϵ_i is the random error.

By fitting experimental semi-variograms, calculated according to the formula given below (Equation (2)), the spatial variability of the selected properties was estimated.

$$\hat{\mathsf{\gamma }}\left(h\right)=\frac{1}{2N\left(h\right)} {\displaystyle \sum }_{i=1}^{N\left(h\right)}{\left[Z\left(X_i\right)-Z\left(X_i+h\right)\right]}^2$$

(2)

where h is the lag distance, N(h) is the number of pairs for distance h, and X_i and X_i + h relate to the value of the variable at locations separated by distance h.

The mapping of the spatial variability was performed using the kriging interpolation technique for each property with a fitted semi-variogram. The procedure was carried out using the R programming language (R Core Team). R library geoR was used, firstly to load spatial data, then after visual plotting, the presence of the trend was determined. If necessary, the first or second order of trend removal was applied. The function plot.variogram was applied to find the optimal variogram for each property (Figure A3), which was used in the Krige function, where either ordinary or universal kriging was selected. The resulting prediction grids were exported as tiff files using the raster library and were imported into QGIS software (QGIS Development Team) where the final maps were prepared.

3. Results

3.1. Spatio-Temporal Variability of Soil Biochemical Activity

The level of DHA activity in the sampling points was greater in the first year of the study compared to 2020. The greatest values occurred during the flowering of the wheat crop (term II), and the lowest in the fifth term of the study (before the oilseed rape crop had started growing).

Statistical analysis also showed the significant influence of soil variable and time (of the analyses) on DHA activity (

Table 2. F test statistics and significance levels of the two-way analysis of variance for the tested parameters associated with research fixed factors: sampling point and terms (*** p = 0.001; ** p = 0.01).

Parameter	Term	Point	Interaction
WHEAT
DHA	1109.63 **	31.01 **	21.40 **
Bacteria	986.98 **	29.73 **	20.53 **
Molds	26,071.07 **	818.21 **	332.84 **
Na	18,987.32 **	234.31 **	121.35 **
Ca	907.41 **	908.53 **	9.58 **
Mg	419.98 **	402.44 **	16.61 **
Cu	391.54 ***	3.07 ***	2.08 ***
Zn	190.77 ***	20.72 ***	1.66 ***
Mn	1091.73 **	134.31 **	53.39 **
Fe	3022.32 **	215.51 **	72.82 **
Seed (%N)	1322.36 ***	343.61 ***	19.27 ***
Straw (%N)	2396.17 ***	353.61 ***	33.94 ***
RAPESEED
DHA	876.96 **	41.93 **	10.48 **
Bacteria	674.70 **	41.52 **	10.29 **
Molds	21,934.64 **	866.60 **	471.89 **
Na	1400.50 **	11.33 **	13.02 **
Ca	2084.08 **	120.13 **	56.98 **
Mg	742.67 **	99.08 **	33.39 **
Cu	37.06 ***	5.19 ***	2.82 ***
Zn	1967.19 **	44.02 **	27.00 **
Mn	214.79 **	38.29 **	16.62 **
Fe	175.17 **	93.75 **	33.86 **
Seed (%N)	629.87 ***	194.86 ***	16.21 ***
Straw (%N)	691.32 ***	171.34 ***	20.20 ***

). Analyses of the spatial distribution of DHA activity under the cultivation of wheat and oilseed rape showing high variability was dependent on the crop growth phase and the physicochemical properties of the soil. Principal components analysis (PCA) and Pearson correlation revealed the existence of varied relationships between DHA activity in the sampling points, microbial biomass and soil chemical parameters (Figure 2,

Table A1. Pearson correlation matrix between microbiological and chemical characteristics of the soil under winter wheat cultivation. *—indicates significance at p = 0.05.

	DHA	Bacteria	Fungi	Na	Ca	Mg	Cu	Zn	Mn	Fe	pH	Moisture (%)	Seed (%N)	Straw (%N)
DHA		1.00 *	−0.27 *	−0.07	−0.35	0.10	−0.16	−0.21 *	−0.14	0.62 *	−0.31 *	−0.37 *	−0.14	−0.16
Bacteria	1.00 *		−0.27 *	−0.07	−0.33	0.10	−0.17	−0.22 *	−0.15	0.61 *	−0.30 *	−0.34 *	−0.13	−0.15
Fungi	−0.27 *	−0.27 *		−0.004	0.07	0.29 *	−0.09	0.17	0.22 *	−0.38 *	0.02	0.23 *	0.23 *	−0.13
Na	−0.07	−0.07	−0.004		0.19 *	0.08	−0.20 *	−0.14	−0.24 *	0.17	0.03	0.08	−0.05	0.46 *
Ca	−0.35 *	−0.33 *	0.07	0.19		−0.15	−0.18	−0.52 *	−0.48 *	−0.36 *	0.71 *	0.82 *	0.18	0.29 *
Mg	0.10	0.10	0.29 *	0.08	−0.15		−0.12	0.20 *	0.14	−0.02	−0.31 *	−0.01	−0.02	0.12
Cu	−0.16	−0.17	−0.09	−0.20	−0.18	−0.12		0.35 *	0.01	−0.005	0.08	−0.24 *	−0.001	−0.05
Zn	−0.21 *	−0.22 *	0.17	−0.14	−0.52 *	0.20 *	0.35 *		0.70 *	−0.35 *	−0.45 *	−0.35 *	−0.09	−0.15
Mn	−0.14	−0.15	0.22 *	−0.24	−0.48 *	0.14	0.01	0.70 *		−0.36 *	−0.50 *	−0.31 *	−0.16	−0.19 *
Fe	0.62 *	0.61 *	−0.38 *	0.17	−0.36 *	−0.02	−0.005	−0.35 *	−0.36 *		−0.19	−0.35 *	0.11	0.11
pH	−0.31 *	−0.30 *	0.02	0.03	0.71 *	−0.31 *	0.08	−0.45 *	−0.50 *	−0.19		0.43 *	−0.04	0.06
Moisture (%)	−0.37 *	−0.34 *	0.23 *	0.08	0.82 *	−0.01	−0.24 *	−0.35 *	−0.31 *	−0.35 *	0.43 *		0.36 *	0.27 *
Seed (%N)	−0.14	−0.13	0.23 *	−0.05	0.18	−0.02	0.002	−0.09	−0.16	0.11	−0.04	0.36 *		0.19
Straw (%N)	−0.16	−0.15	−0.13	0.46	0.29 *	0.12	−0.05	−0.15	−0.19 *	0.11	0.06	0.27 *	0.19

and

Table A2. Pearson correlation matrix between microbiological and chemical characteristics of soil under winter oilseed rape cultivation. *—indicates significance at p = 0.05.

	DHA	Bacteria	Fungi	Na	Ca	Mg	Cu	Zn	Mn	Fe	pH	Moisture (%)	Seed (%N)	Straw (%N)
DHA		1.00 *	−0.03	0.22 *	−0.18	−0.15	0.22 *	−0.09	0.13	0.30 *	0.05	−0.05	−0.51 *	−0.02
Bacteria	1.00 *		−0.03	0.23 *	−0.13	−0.15	0.22 *	−0.14	0.09	0.27 *	0.08	0.01	−0.51 *	−0.03
Fungi	−0.03	−0.03		−0.05	0.01	−0.01	−0.05	0.00	−0.13	−0.26 *	−0.17	−0.01	−0.06	0.06
Na	0.22 *	0.23 *	−0.05		−0.01	0.57 *	0.18	0.00	0.12	0.52 *	−0.14	0.20 *	−0.17	0.29 *
Ca	−0.18	−0.13	0.01	−0.01		−0.08	−0.01	−0.79 *	−0.75 *	−0.63 *	0.64 *	0.84 *	0.05	−0.38 *
Mg	−0.15	−0.15	−0.01	0.57 *	−0.08		−0.01	0.20 *	0.10	0.17	−0.01	−0.05	0.14	0.35 *
Cu	0.22 *	0.22 *	−0.05	0.18	−0.01	−0.01		−0.01	0.02	0.16	−0.01	0.06	0.11	−0.12
Zn	−0.09	−0.14	0.00	0.00	−0.79 *	0.20 *	−0.01		0.82 *	0.54 *	−0.63 *	−0.72 *	0.19 *	0.37 *
Mn	0.13	0.09	−0.13	0.12	−0.75 *	0.10	0.02	0.82 *		0.73 *	−0.59 *	−0.63 *	0.10	0.35 *
Fe	0.30 *	0.27 *	−0.26 *	0.52 *	−0.63 *	0.17	0.16	0.54 *	0.73 *		−0.47 *	−0.44 *	−0.18	0.40 *
pH	0.05	0.08	−0.17	−0.14	0.64 *	−0.01	−0.01	−0.63 *	−0.59 *	−0.47 *		0.51 *	−0.11	−0.16
Moisture (%)	−0.05	0.01	−0.01	0.20 *	0.84 *	−0.05	0.06	−0.72 *	−0.63 *	−0.44 *	0.51 *		−0.001	−0.26 *
Seed (%N)	−0.51 *	−0.51 *	−0.06	−0.17	0.05	0.14	0.11	0.19 *	0.10	−0.18	−0.11	−0.001		−0.09
Straw (%N)	−0.02	−0.03	0.06	0.29 *	−0.38 *	0.35 *	−0.12	0.37 *	0.35 *	0.40 *	−0.16	−0.26 *	−0.09

). In the case of wheat cultivation, the level of DHA activity was positively correlated with bacterial biomass and Fe content, and negatively correlated with fungal biomass, Ca and Zn concentrations, as well as with pH and soil moisture content (Figure 2).

Under wheat cultivation, the mean DHA activity value was at its least in plots 55–57 located in the northern part of the field. Under oilseed rape, the mean DHA activity value was at its least in sampling points 48, 56–58 and 64–66. The greatest values, regardless of crop type, were recorded in soil samples taken from points 33, 38, 39 and 46 (Figure 3).

The spatial distribution of the DHA activity, compared in the period between the disturbance of the crop and harvest, was very variable. In the first period of analysis, the greatest activity was recorded in points 33, 39 and 46. However, wheat cultivation resulted in a modification of the soil conditions, and DHA activity exhibited the greatest values in points 34–36 during the harvesting phase (term III) (Figure 4). The level of DHA activity in the sampling points under the cultivation of oilseed rape was similar. Before the sowing of the oilseed rape crop (term V), the greatest DHA activity was recorded in points 38, 39 and 46, and in points 33, 34, 38, 39 and 54 after the harvest of the oilseed rape crop (Figure 4).

3.2. Spatio-Temporal Variability of the Soil Microbial Biomass

The amount of microbial biomass analyzed throughout the experiment was also significantly related to the timing of the soil sampling and the type of crop grown. Greater bacterial and fungal biomass was found in sampling points under wheat cultivation compared to oilseed rape cultivation. Microbial biomass also increased between the start of crop growth in spring and the flowering of the wheat crop (this was also observed with oilseed rape), and decreased between flowering and harvest.

Under wheat cultivation, the bacterial biomass was positively correlated with the level of DHA activity (Equation (3)) and the concentration of Fe (Figure 2).

$${\mathrm{Bacteria} \mathrm{Biomass} }_{wheat}=0.7765 \mathrm{DHA}+0.8729{ \mathrm{for} \mathrm{R}}^2=0.99; p\le 0.01$$

(3)

During the second year of the study (oilseed rape), bacterial biomass in the soil was also correlated with the concentration of Na and Cu (Equation (4)).

$${\mathrm{Bacteria} \mathrm{Biomass} }_{\mathrm{rapeseed}}=0.7696 \mathrm{DHA}+0.8741{ \mathrm{for} \mathrm{R}}^2=0.98; p\le 0.01$$

(4)

The relationships between fungal biomass and the various soil physio-chemical parameters were different. For wheat cultivation, a positive correlation was found between fungal biomass and Mg and soil moisture content, while in the second year of the study (oilseed rape), fungal biomass was only negatively correlated with Fe concentration (Figure 3). The mean bacterial biomass was the greatest in sampling points 31–33, 36–39 and 46, located in the south-western part of the field. Mean fungal biomass was greatest in points 46, 47, 54 and 60–66 located in the northern part of the field. The lowest mean bacterial biomass values were observed in points 48, 55–60 and 62–66, and the lowest mean fungal biomass values were observed in points 30–33, 35–38 and 42–45 (Figure 3).

The spatial distribution of the microbial biomass was variable before the start of the growth of both crops and during their harvest (Figure 5 and Figure 6). Bacterial biomass was the greatest in the first period of the analyses in points 30–33, 37–39 and 44–48, while fungal biomass was greatest in points 31, 34, 36, 38, 46, 48, 49, 61 and 62 and contributed to the modification of the soil conditions. Bacterial biomass was greatest in sampling points 34–36, 40 and 53, while the fungal biomass was greatest in points 34, 47, 52, 54, 60, 62, 63 and 66. The analyzed microbiological parameters were similar in the points under oilseed rape cultivation. Before the crops were removed (term V), the greatest bacterial biomass values were recorded in sampling points 32, 33, 38, 39, and in 63, 64, 66 for fungal biomass.

As with wheat, the cultivation of oilseed rape changed the spatial distribution of the microorganisms: the greatest bacterial biomass was found in points 37, 38 and 54, and the greatest fungal biomass in points 41, 43, 54, 61, 64 and 65 (Figure 5 and Figure 6).

The F:B value showed variable responses to both the sampling point and date of sampling, and the type of crop grown (Figure 7 and Figure 8). Regardless of the date of the analyses, a greater F/B value was observed in the soil under oilseed rape. In the case of wheat, the mean value of this coefficient was greater at a harvest time than at the beginning of the experiment. An opposite reaction was recorded in the soil under oilseed rape: a lower F/B value at the time of harvest. The soil analyzed from points 36, 46, 47, 57 60 and 65 (mostly in the western part of the field) was characterized by a high F/B value at the time of harvest of both crops.

3.3. Spatio-Temporal Variability of Micro- and Macroelements

The influence of the crop type on the concentrations of micro- and macroelements in the soil was strongest in the sampling points where wheat was grown. In the second year of the study (oilseed rape), only the concentrations of Zn and Fe were greater. The mean Na ion concentration in the sampling points decreased from the start of wheat cultivation (term I) to harvest (term III), and then sharply increased during the flowering of oilseed rape (term VI). The average concentration of Ca and Mg was elevated in the first period of the analysis, while in the subsequent phases of plant development, a decrease and then a re-increase in the concentration of both elements were noted at the start of the oilseed rape crop (term V) (

Table 3. The mean values and standard deviation of the macroelement concentration relative to terms I–VIII. Mean values followed by the same letter do not differ significantly at p = 0.05.

YEAR	2019								2020
TERM	I		II		III		IV		V		VI		VII		VIII
	X	SD	X	SD	X	SD	X	SD	X	SD	X	SD	X	SD	X	SD
Na
	3.98 a	0.06	3.09 ab	0.07	1.63 c	0.07	1.72 c	0.01	1.64 c	0.24	3.30 ab	0.20	2.81 b	0.10	1.39 c	0.03
Ca
	61.22 a	1.38	53.42 b	1.33	54.50 b	1.05	55.06 b	0.47	57.33 b	0.38	52.75 b	2.16	35.39 c	2.88	59.29 ab	4.13
Mg
	9.92 ab	0.24	9.01 ab	0.30	8.14 ab	0.52	7.96 b	0.61	10.64 a	0.42	7.37 b	0.43	7.49 b	0.87	8.04 ab	0.15

Terms: I—before the sowing of wheat crop; II—flowering of wheat crop; III—harvest of wheat crop; IV—soil complex saturation; V—before the sowing of oilseed rape crop; VI—flowering of oilseed rape crop; VII—harvest of oilseed rape crop; and VIII—soil complex saturation.

and

Table 4. Mean values and standard deviation of microelement concentration relative to terms I–VIII. Mean values followed by the same letter do not differ significantly at p = 0.05.

YEAR	2019								2020
TERM	I		II		III		IV		V		VI		VII		VIII
	X	SD	X	SD	X	SD	X	SD	X	SD	X	SD	X	SD	X	SD
Cu
	0.06 a	0.01	0.05 a	0.01	0.02 c	0.004	0.03 b	0.02	0.02 c	0.004	0.02 c	0.01	0.03 b	0.01	0.05 a	0.01
Zn
	0.02 c	0.002	0.02 c	0.002	0.03 b	0.01	0.03 b	0.002	0.04 a	0.003	0.02 c	0.002	0.02 c	0.003	0.02 c	0.01
Mn
	0.76 a	0.05	0.07 c	0.03	0.18 b	0.003	0.05 c	0.01	0.02 c	0.005	0.04 c	0.01	0.04 c	0.01	0.03 c	0.01
Fe
	0.14 d	0.01	0.88 c	0.07	1.01 b	0.11	1.72 a	0.21	0.97 c	0.11	1.55 a	0.20	1.64 a	0.14	1.14 ab	0.74

Terms: I—before the sowing of wheat crop; II—flowering of wheat crop; III—harvest of wheat crop; IV—soil complex saturation; V—before the sowing of oilseed rape crop; VI—flowering of oilseed rape crop; VII—harvest of oilseed rape crop; and VIII—soil complex saturation.

).

The concentrations of Cu and Mn also reached a maximum value in the first period. Afterwards, a significant decrease in the value of these elements was noted during oilseed rape cultivation. The greatest Cu and Mn values in the first period of analyses were found in points 46, 48 and 55–58, while the greatest concentration of Mg was found in points 35, 56 and 63–66. In the first term, the greatest concentration of Cu and Fe were recorded at points 30–33, 37 and 41–43, located in the south-eastern part of the field, and the greatest concentration of Zn and Mn was observed in points 61–64 and 66 in the northern part.

The cultivation of wheat did not contribute to a modification of soil conditions, therefore the concentration of micro- and macroelements (Ca, Mg, Zn, Mn and Fe) during the harvest phase (term III) were similar to concentrations at the beginning of the analysis (Table 3 and Table 4). However, the cultivation of wheat changed the chemical composition of the soil in terms of Na and Cu concentrations, which were greatest at points 30, 32, 35, 56 and points 30, 39, 46 and 64, respectively.

The concentration of nutrients during the cultivation of oilseed rape was similar. Before the sowing (term V), the greatest Na, Ca and Mg concentrations were recorded in points 30, 32, 56 and 65, points 48, 55–57, 56 and 66, and in points 56, 62 and 63, respectively. In the case of micronutrients, such as Cu, Zn, Mn and Fe, the greatest values were observed in points 30, 35 and 41, points 30, 31, 37, 40–42, 61 and 63, points 30, 31, 45 and 61, and in points 30, 31, 42 and 53, respectively. Similarly to wheat, the cultivation of oilseed rape did not change the spatial distribution of Ca, Mg and Fe concentrations in the soil. However, the oilseed rape cultivation contributed to a change in Na, Cu, Zn and Mn concentrations, which were greatest in points 42–45, 47, 48 and 50, points 30, 31, 34 and 39, points 41, 45, 53, 59, 61 and 62 and in points 53, 59 and 61, respectively (see Appendix A).

The statistical analysis showed the significant effect of soil variable on the concentration of micro- and macroelements (Table 2), while PCA revealed different relationships between the soil chemical parameters and the activity of soil enzymes and the microbial biomass (Figure 2). The greatest positive correlation values were noted with the interactions between DHA activity or bacterial biomass and the Fe content in the soil when both crops were cultivated (Table A1 and Table A2).

3.4. Spatial Variability in Nitrogen Content in the Crop Yield

The %N content in the wheat grain ranged from 0.96% to 1.85%, and from 0.48% to 0.92% in the straw. Oilseed rape seeds contained 2.99% to 3.63% N, and between 0.74% to 1.63% N in the biomass (Figure 9 and Figure 10).

In the first year of the study, there was a positive relationship between %N in the grain and the fungal biomass and soil moisture content (Figure 2). In the second year of analyses, this parameter negatively correlated with DHA activity and bacterial biomass.

The greatest %N values in the wheat grain were recorded at points 35, 37, 38, 43 and 53, and the least at points 41, 46 and 61–66. For oilseed rape seeds, the greatest %N values were found at points 49, 58, 60, 61 and 65, and the least at points 33, 50, 63, 64 and 66 (Figure 11).

4. Discussion

4.1. Spatio-Temporal Variability of Enzyme Activity and Soil Microbial Biomass

The timing of the soil sampling, related to the developmental phase of the cultivated crops, significantly influenced the level of DHA activity and bacterial and filamentous fungal biomass (Table 2). The mean values of these parameters were at their greatest under the cultivation of wheat (Figure 12, Figure 13 and Figure 14). In both years of the research, the greatest DHA activity and microbial biomass values were observed during the flowering phase of the crops. The post-harvest residues of the crops introduced into the soil influenced the level of DHA activity during their mineralization. The introduction of the plant residues inhibited DHA activity, which was observed in the fourth and eighth terms of analyses, especially with the oilseed rape residues. Moreover, the fungal biomass was greater than bacterial biomass in all field sampling points during the entire research period, which is in agreement with Vries et al. [27] and Rousk and Bååth [58]. According to Rousk and Bååth [59], the determination of the soil microbial biomass can be used as an indicator of two soil food web pathways. A greater F/B value is an indicator of a more balanced agroecosystem with a lower negative impact on the environment, in which the decomposition of organic matter, including N mineralization, dominates the supply of nutrients to the plants [60]. It has been reported that fertilization with inorganic N reduces the F/B value associated with biomass, while organic matter with a high C:N ratio stimulates fungal growth and thus increases the F/B ratio.

Koch et al. [61] hypothesized that mineralization rates are lower in winter than in summer as an adaptation of the microbial biomass and demonstrated the temporal variability of the composition of microbial communities in the soil caused by seasonal changes in temperature, soil moisture and plant activity, which can be considered important factors in the temporal and spatial heterogeneity of soils. Through their secretions, growing roots change the physico-chemical properties of the soil environment, thus introducing heterogeneity on a small scale [62,63]. During vegetative growth, plant-derived secretions and the availability of C act as factors that stimulate the structure and function of the microbial community. During the aging phase of plants, decaying above-ground biomass and root material may become the most important C source for microorganisms [9,64]. Therefore, both the quantity and quality of C compounds from root exudates vary significantly during the growing season, and this variability strongly affects the performance of microorganisms in the soil [34,64,65]. According to Hinsinger et al. [66] and Galloway et al. [67], the chemical composition of root exudates depends on the plant type, but are nevertheless rich in organic acids, such as citrate, fumarate, malate, oxalate and acetate; carbohydrates, such as glucose, fructose, xylose, maltose, sucrose, galactose and ribose; as well as amino acids and inorganic compounds, such as carbon dioxide (CO₂), inorganic ions, protons and anions associated with the metabolic activity of the roots. In response to a change in the root exudate patterns with plant development, the structure and composition of microbial communities in the rhizosphere also changes over time [68].

Galloway et al. [67] hypothesized that wheat root exudates may contain large amounts of polysaccharide complexes, which are readily available C sources for microorganisms [69]. According to Blagodatskaya and Kuzyakov [70], saccharides can stimulate microorganisms by promoting the release of various types of enzymes, thereby intensifying SOM mineralization. Yang et al. [71] observed greater soil enzymatic activity in the intensive growth phase of cucumber (Cucumis L.), and lesser activity in the early and late growth phases. This is confirmed by the results of our own research, where DHA activity was the greatest during the flowering phase.

Chaparro et al. [72] hypothesized that as a plant ages, it releases specific substrates and potentially antimicrobial compounds in an effort to select for particular microbial inhabitants and noted that the production of root secretions with a specific chemical composition is aimed at stimulating the activity of specific groups of microorganisms that play specific functions in N and P transformations in the rhizosphere, which is also the plant’s response to the demand for these macroelements.

In the case of microorganisms, the greatest bacterial biomass values were recorded in sampling points 33 and 36–39, and the greatest fungal biomass were recorded in points 54 and 60–66. The least bacterial biomass values were observed in points 55–60 and 62–66, and fungal biomass in points 30–33 and 35–39. This allows us to conclude that in sample points with where there was intense DHA activity, there was also a large amount of bacterial biomass and a small amount of fungal biomass. Nayak et al. [73] and Wolinska et al. [74] reported positive correlations between the DHA activity and soil microbial biomass and indicated that greater DHA values in the soil are associated with greater numbers of bacteria. Studies have shown a clear interaction between fungi and bacteria in soil systems. For example, Thiele-Bruhn and Beck [75] and Feeney et al. [76] demonstrated the inhibition of fungal growth by bacteria, while Meidute et al. [77] observed synergistic interactions where the initial degradation of cellulose by fungi appeared to favor bacterial growth.

A visual comparison of the microbial biomass maps and environmental traits by Constancias et al. [48] suggests that microbial abundance is influenced by both land management and soil characteristics, which also correspond to soil organic C distribution and C:N ratio. Liu et al. [78] showed that bacterial communities (similarly to soil nutrients) were spatially distributed in the soil zone in accordance with the pH value and soil C content. Additionally, Song et al. [79] reported a range in microbial biomass values, where bacteria showed an upward trend from the northern part of their research plot to the southern part. According to Philippot et al. [80] and Rousk et al. [81], the soil pH value, soil texture and management are some of the most important factors that determine the biomass and activity of soil microorganisms. This is clearly visible on the basis of both years of our study (especially terms I and V), where the biomass of bacteria and DHA activity were greater in the southern part of the field. The spatial distribution indicates the presence of bacterial hotspot zones, which were probably related to changes in the spatial variability of soil pH and available soil water. In places where soil moisture and pH were greater, the bacterial biomass and DHA activity were also greater. Soil elements at the sampling points were also important, and low concentrations of Na, Ca, Cu and Fe meant low amount and activity of bacteria (Figure 4, Figure 5 and Figure 6). As for the fungal biomass, for most of the time (from term III to VII), the greatest amount was in the opposite, north-west part of the field. This observation can be related to the lower soil pH in this area. Available soil water was also essential for fungal growth. Additionally, a greater concentration of elements positively (Mg and Mn) or negatively (Fe) influences the development of fungi (Figure 4 and Figure 7).

Knowledge of the amount and activity of microorganisms in agricultural soils is crucial for the development of sustainable agricultural practices [82]. Analyses of the activity and biomass of soil microflora during the cultivation of wheat and oilseed rape, such as in our study, will provide information on how to protect the soil and maintain an optimum ecological balance.

4.2. Spatio-Temporal Variability of Micro- and Macroelements

On the basis of our research results, the average concentration of all macroelements (Na, Ca and Mg) and two microelements (Cu, Mn) in the sampling points was significantly greater in the first year of the study than in the second (

Table A4. One-dimensional results for winter rape. Overparameterized model Type III decomposition.

	DHA	Bacteria	Fungi	Na	Ca	Mg	Cu	Zn	Mn	Fe
Constant term	235.47	764.77	24,339.16	2222.15	782,950.07	24,074.79	0.21	0.22	0.45	639.32
Term	29.87	15.39	750.16	81.24	14,870.48	381.71	0.00273	0.01934	0.02	14.46
Sampling point	1.43	0.95	29.64	0.66	857.19	50.92	0.00038	0.00043	0.00309	7.74
Term*sampling point	0.36	0.23	16.14	0.76	406.56	17.16	0.00021	0.00027	0.00134	2.80
Error	0.03	0.02	0.03	0.06	7.14	0.51	0.00007	0.00001	0.00008	0.08

and

Table A5. Mean values and standard deviation of the macroelements concentration for measurement points relative to terms I–VIII.

YEAR	2019								2020
TERM	I		II		III		IV		V		VI		VII		VIII
	X	SD	X	SD	X	SD	X	SD	X	SD	X	SD	X	SD	X	SD
Na
30	4.16	0.02	3.11	0.11	2.62	0.12	2.41	0.06	2.35	0.35	2.74	0.03	2.98	0.09	1.42	0.02
31	4.18	0.02	2.59	0.01	1.99	0.06	2.37	0.02	1.43	0.27	2.86	0.17	2.76	0.09	0.96	0.05
32	4.02	0.02	3.27	0.07	2.54	0.06	1.83	0.00	2.23	0.14	3.11	0.38	2.84	0.03	2.17	0.03
33	3.65	0.02	2.80	0.04	1.60	0.05	1.31	0.01	1.55	0.23	2.83	0.03	2.46	0.03	0.91	0.02
34	3.38	0.01	3.11	0.08	1.67	0.07	1.55	0.01	1.43	0.19	3.76	0.20	2.99	0.08	1.89	0.01
35	4.61	0.03	3.11	0.03	2.51	0.06	1.53	0.00	1.60	0.16	3.33	0.19	2.90	0.03	1.36	0.01
36	3.00	0.01	3.20	0.06	1.71	0.06	1.58	0.00	1.49	0.36	3.19	0.23	2.89	0.06	1.58	0.01
37	3.71	0.03	3.01	0.08	1.90	0.05	1.39	0.00	1.72	0.15	3.25	0.23	2.74	0.07	1.15	0.00
38	3.54	0.03	3.08	0.04	1.14	0.05	1.38	0.01	1.69	0.04	2.79	0.08	2.60	0.05	1.21	0.03
39	3.14	0.02	2.65	0.10	1.43	0.06	1.51	0.00	1.45	0.25	3.77	0.42	2.58	0.04	0.98	0.03
40	3.83	0.05	3.04	0.12	2.06	0.08	1.97	0.02	1.77	0.15	3.11	0.00	2.78	0.05	1.25	0.02
41	4.64	0.04	3.85	0.22	1.60	0.06	1.40	0.00	1.40	0.23	2.97	0.08	2.70	0.08	1.69	0.01
42	3.20	0.03	4.67	0.06	1.19	0.03	1.59	0.02	1.34	0.05	3.23	0.04	3.06	0.10	1.19	0.05
43	3.92	0.02	3.40	0.19	1.49	0.05	2.16	0.01	1.60	0.09	2.91	0.20	3.04	0.07	2.14	0.08
44	3.10	0.05	2.83	0.02	1.46	0.03	1.13	0.00	1.30	0.19	3.22	0.48	3.11	0.06	1.09	0.07
45	3.74	0.05	3.81	0.05	1.42	0.04	1.08	0.01	0.65	0.26	3.31	0.16	3.48	0.12	0.84	0.06
46	4.97	0.08	2.78	0.08	1.18	0.04	2.27	0.02	1.62	0.21	2.96	0.12	2.85	0.03	1.50	0.03
47	4.25	0.08	3.38	0.09	1.92	0.06	1.48	0.00	1.38	0.30	4.31	0.12	3.08	0.08	1.50	0.02
48	3.81	0.05	3.56	0.07	1.70	0.05	2.05	0.00	1.80	0.22	3.52	0.09	3.11	0.07	1.99	0.01
49	4.22	0.08	4.18	0.07	1.39	0.05	1.70	0.01	1.65	0.16	2.95	0.04	2.82	0.02	1.98	0.02
50	3.72	0.07	3.05	0.03	1.28	0.04	1.63	0.02	1.57	0.19	3.19	0.06	4.53	0.38	1.65	0.01
51	5.18	0.10	2.80	0.12	1.05	0.04	1.63	0.01	1.71	0.23	2.91	0.09	2.86	0.21	1.40	0.04
52	4.55	0.09	2.59	0.01	1.24	0.04	1.55	0.01	1.27	0.33	3.63	0.46	2.63	0.06	1.15	0.05
53	3.80	0.08	2.77	0.08	1.08	0.04	1.56	0.02	1.51	0.36	3.44	0.33	2.80	0.06	1.41	0.09
54	4.32	0.08	2.89	0.04	1.24	0.02	3.70	0.03	1.82	0.07	4.05	0.92	3.15	0.45	1.90	0.01
55	4.32	0.06	3.36	0.10	2.00	0.06	2.50	0.00	1.60	0.30	3.12	0.06	2.53	0.09	2.09	0.03
56	7.91	0.31	4.26	0.19	2.40	0.08	2.70	0.02	3.52	0.34	3.17	0.09	3.27	0.18	0.77	0.01
57	4.67	0.05	2.91	0.06	1.43	0.07	1.89	0.03	2.07	0.41	3.10	0.19	2.18	0.03	1.03	0.04
58	5.13	0.12	2.50	0.10	1.86	0.06	1.40	0.00	1.89	0.31	2.90	0.01	2.30	0.08	1.17	0.02
59	3.14	0.05	2.50	0.00	1.79	0.54	1.22	0.01	1.45	0.25	3.27	0.03	2.17	0.03	1.03	0.03
60	4.01	0.06	3.07	0.06	1.76	0.07	1.80	0.00	1.79	0.50	3.01	0.02	2.97	0.25	1.29	0.06
61	3.27	0.05	2.65	0.05	1.22	0.05	1.13	0.00	1.10	0.27	3.15	0.09	2.18	0.01	0.98	0.04
62	3.36	0.05	2.52	0.06	1.17	0.03	1.27	0.01	1.30	0.22	3.24	0.32	2.44	0.05	0.79	0.03
63	3.27	0.06	2.42	0.15	2.13	0.06	1.25	0.01	1.26	0.25	3.00	0.10	2.40	0.06	1.20	0.01
64	2.74	0.03	2.50	0.02	1.23	0.05	1.67	0.02	1.40	0.27	6.01	0.81	2.53	0.03	1.36	0.01
65	2.96	0.05	2.42	0.08	1.44	0.07	1.38	0.00	2.33	0.20	3.01	0.10	2.76	0.38	1.31	0.00
66	3.95	0.07	3.60	0.03	1.42	0.04	1.70	0.01	1.51	0.39	3.80	0.48	2.64	0.18	2.06	0.01
Ca
30	41.91	1.08	42.29	1.06	44.41	0.79	44.88	0.97	48.40	0.45	42.24	1.77	28.51	3.11	45.84	3.19
31	42.82	1.47	37.45	0.56	38.26	0.13	35.50	0.37	40.76	0.28	48.77	11.69	23.85	2.92	38.32	3.06
32	68.68	1.79	60.51	0.75	61.98	0.28	60.98	0.32	66.57	0.45	60.26	4.82	40.69	1.91	62.29	3.42
33	74.96	2.21	65.13	1.38	61.41	0.79	65.08	0.47	66.38	0.12	52.34	1.21	43.46	3.50	64.40	3.72
34	60.63	0.90	51.23	1.11	50.64	0.40	54.08	0.11	57.80	0.23	52.82	0.28	36.66	3.84	56.53	4.16
35	58.98	2.22	53.74	0.64	52.20	1.77	59.87	0.05	59.47	0.74	51.68	1.00	39.28	3.59	59.14	2.84
36	64.53	1.66	58.21	1.20	58.70	1.04	56.72	0.63	60.65	0.54	50.69	1.10	37.16	2.06	64.20	4.66
37	62.57	1.55	54.46	0.26	56.03	1.47	54.86	0.03	57.65	0.69	59.65	2.12	34.74	2.93	58.96	4.03
38	80.00	2.43	66.24	0.80	65.78	1.33	63.07	0.94	66.84	1.01	59.99	1.18	42.20	4.37	67.33	3.41
39	74.87	1.96	64.70	1.44	62.38	1.25	64.08	0.58	68.63	0.44	36.38	1.38	43.95	3.87	66.26	3.79
40	48.93	0.83	41.07	1.27	43.11	0.54	39.32	0.62	41.73	0.37	37.61	0.83	24.93	2.71	40.47	5.22
41	45.69	1.50	40.54	1.47	44.70	1.21	41.04	0.15	42.74	0.21	33.57	0.37	23.70	3.73	41.24	4.38
42	42.91	1.53	33.79	0.95	37.32	0.32	36.20	0.78	37.51	0.48	51.95	1.28	22.55	2.36	41.72	4.35
43	60.65	0.90	54.09	2.10	53.74	0.91	54.99	0.17	56.31	0.46	42.87	1.14	33.25	3.26	54.14	3.87
44	62.13	0.84	49.13	1.90	50.78	0.97	44.56	0.06	49.36	0.28	34.13	1.37	35.90	0.67	47.42	4.15
45	56.79	0.69	50.37	0.30	52.27	1.93	38.03	0.40	39.79	0.24	54.52	2.05	23.28	1.99	40.26	4.39
46	63.45	1.17	58.76	1.50	61.44	0.36	59.75	0.32	60.64	0.45	49.99	11.43	35.86	1.95	58.25	3.25
47	67.15	1.27	55.49	1.38	57.60	0.82	58.00	0.26	61.83	0.12	70.32	1.17	36.21	3.46	67.96	4.95
48	79.41	1.65	73.43	2.11	72.18	1.95	70.35	0.24	77.84	0.12	60.36	2.47	46.00	10.38	85.44	5.62
49	69.91	1.86	62.80	1.57	64.65	1.61	62.52	0.48	65.58	0.24	40.15	1.87	41.18	2.33	63.96	4.08
50	55.88	1.62	45.80	0.29	49.33	1.05	46.51	0.45	44.39	0.29	45.03	0.93	27.33	2.06	49.25	3.86
51	66.04	2.80	57.20	0.48	53.60	0.83	55.01	0.33	53.45	0.34	38.59	0.72	32.03	2.03	53.70	3.45
52	67.54	2.08	58.00	0.96	59.44	1.38	50.64	0.02	54.67	0.20	69.84	4.49	30.54	2.93	51.24	2.28
53	45.23	1.21	38.35	0.88	41.48	0.01	35.96	0.18	42.20	0.41	82.94	1.47	21.38	2.62	37.34	3.97
54	74.02	3.05	66.09	2.30	65.59	2.52	52.39	0.87	52.21	0.74	80.67	3.07	32.32	2.12	103.40	6.86
55	105.12	1.94	89.64	2.89	89.93	3.06	85.99	0.47	84.64	0.37	56.24	0.23	55.79	2.49	96.26	6.60
56	99.29	2.83	88.01	3.74	89.85	2.02	81.86	1.45	89.59	0.36	38.69	1.38	59.09	3.34	63.09	4.39
57	81.69	1.77	71.25	2.80	69.00	0.65	72.67	0.16	81.57	0.27	32.71	0.86	48.76	2.36	54.60	3.65
58	81.32	1.31	62.68	1.39	64.16	2.17	53.54	0.10	53.91	0.46	29.36	0.93	32.50	3.27	49.88	3.29
59	57.17	0.31	48.20	2.44	51.91	1.86	45.45	0.14	48.50	0.10	52.37	2.64	25.99	2.12	61.90	3.71
60	60.23	0.80	54.49	2.54	55.24	1.03	60.15	0.13	59.60	0.06	42.04	2.30	34.28	3.09	28.20	2.48
61	34.65	0.36	29.11	0.80	30.53	0.91	30.38	0.05	30.66	0.25	51.40	1.39	16.77	1.81	34.10	2.49
62	32.29	0.30	31.23	0.89	34.16	0.05	34.27	0.20	34.05	0.85	67.71	2.84	20.29	1.85	40.39	3.32
63	35.55	0.37	32.74	0.04	35.78	0.40	40.01	1.71	41.73	0.73	67.21	1.09	24.12	2.33	64.69	2.73
64	46.29	0.04	44.25	1.34	44.97	0.30	58.43	0.63	59.39	0.30	75.38	2.90	35.99	3.32	91.98	5.36
65	53.63	0.56	50.78	0.97	52.01	0.38	83.59	2.20	80.52	0.42	80.27	0.72	58.47	1.65	92.53	7.69
66	42.05	0.23	35.25	0.62	39.86	0.21	86.52	0.41	83.68	0.14	50.94	1.61	60.38	2.39	96.92	6.09
Mg
30	5.07	0.05	9.24	0.33	8.02	0.71	8.48	0.50	10.15	0.64	7.64	0.36	6.81	1.30	7.88	0.09
31	5.15	0.03	5.36	0.08	5.21	0.42	5.18	0.24	6.55	0.38	4.28	1.18	4.46	0.64	5.22	0.05
32	4.61	0.11	4.18	0.19	3.51	0.43	6.78	0.57	9.50	0.45	5.17	1.52	6.04	0.55	6.67	0.23
33	4.89	0.10	3.67	0.12	4.56	0.39	3.18	0.09	3.46	0.48	8.79	0.49	2.63	0.21	3.18	0.15
34	9.36	0.13	9.15	0.37	9.43	0.54	8.87	0.68	11.41	0.21	8.86	0.08	8.43	1.20	8.62	0.06
35	17.66	0.00	13.94	0.08	12.49	0.76	8.52	0.63	11.48	0.21	7.79	0.07	7.46	0.70	8.61	0.09
36	8.49	0.30	8.97	0.24	8.55	0.61	7.85	0.69	9.49	0.49	8.42	0.44	6.97	0.52	8.40	0.10
37	10.47	0.21	9.27	0.21	8.92	0.51	7.78	0.66	12.44	0.45	5.14	0.17	7.03	0.86	8.28	0.09
38	5.37	0.16	5.40	0.09	4.30	0.35	5.92	0.35	6.13	0.11	2.89	0.07	4.52	0.61	6.90	0.35
39	4.80	0.12	3.84	0.06	4.13	0.36	3.28	0.12	4.36	2.18	6.13	0.26	2.71	0.31	2.80	0.10
40	6.39	0.01	7.20	0.39	6.38	0.55	6.35	0.38	8.57	0.11	5.54	0.27	4.75	0.61	6.26	0.07
41	11.82	0.03	9.88	0.19	9.65	0.32	6.96	0.61	7.44	0.05	7.58	0.27	4.51	0.91	5.55	0.06
42	7.93	0.22	8.54	0.36	6.61	0.52	7.95	0.61	10.42	0.31	7.76	0.62	5.92	0.93	6.98	0.07
43	9.43	0.21	9.34	0.58	9.07	0.49	7.87	0.68	10.44	0.15	6.76	0.30	6.70	0.91	7.05	0.07
44	7.00	0.19	7.11	0.43	7.11	0.69	7.04	0.65	9.37	0.41	8.23	0.56	8.75	0.30	5.82	0.07
45	9.92	0.02	9.16	0.27	9.21	0.43	8.53	0.61	9.63	0.44	8.36	0.21	9.32	0.87	7.71	0.18
46	9.26	0.26	7.32	0.57	7.32	0.68	8.97	0.68	11.60	0.29	7.14	1.94	7.83	0.61	8.94	0.12
47	11.80	0.33	9.74	0.15	9.37	0.46	8.30	0.67	11.15	0.66	7.79	0.26	7.85	0.94	9.07	0.21
48	9.21	0.31	8.54	0.51	7.77	0.56	7.26	0.60	10.42	0.39	8.23	0.34	7.84	0.46	8.66	0.33
49	10.43	0.29	10.49	0.41	8.56	0.49	7.98	0.65	11.53	0.17	7.88	0.49	8.70	0.44	8.00	0.30
50	12.51	0.25	12.53	0.04	9.81	0.23	8.85	0.64	11.53	0.22	8.63	0.26	19.10	1.25	9.99	0.13
51	10.94	0.35	9.40	0.04	8.94	0.37	9.01	0.64	11.21	0.77	7.50	0.20	7.43	0.89	8.60	0.07
52	9.36	0.31	7.06	0.15	7.28	0.60	7.98	0.65	9.39	0.12	8.58	0.67	6.67	1.01	9.77	0.05
53	7.18	0.09	6.29	0.21	7.50	0.49	6.96	0.63	8.47	0.55	4.73	0.32	6.35	0.94	8.47	0.13
54	7.28	0.28	5.68	0.65	4.68	0.29	11.89	1.14	8.32	0.31	7.01	0.84	7.93	0.74	7.98	0.39
55	9.56	0.35	7.95	0.31	6.65	0.61	6.04	0.35	7.52	0.42	9.76	0.24	4.90	0.37	13.94	0.19
56	19.33	0.99	15.68	0.85	12.44	0.60	11.52	1.13	21.49	0.25	11.30	0.37	12.27	1.74	7.71	0.25
57	5.99	0.25	4.73	0.14	5.01	0.31	6.24	0.29	7.48	0.23	9.53	0.33	4.87	0.30	9.34	0.05
58	11.68	0.57	11.44	0.58	9.56	0.36	9.36	0.58	13.26	0.62	9.22	0.28	8.47	1.24	6.84	0.03
59	7.75	0.25	7.58	0.45	6.28	0.43	7.26	0.68	9.41	0.24	8.77	0.40	5.61	0.84	9.17	0.06
60	10.62	0.26	9.76	0.32	8.43	0.34	9.50	0.56	13.99	0.82	6.52	0.37	8.46	1.10	9.05	0.19
61	12.95	0.21	10.23	0.32	8.25	0.55	9.84	0.73	14.14	0.70	8.55	0.28	8.36	1.11	9.61	0.11
62	11.61	0.16	12.63	0.31	9.30	0.15	11.09	0.95	18.74	0.19	5.14	0.26	10.19	1.28	12.46	0.16
63	15.59	0.30	12.83	0.13	10.27	0.14	12.66	1.21	21.60	0.36	4.85	0.10	11.65	1.57	10.55	0.08
64	14.87	0.26	13.28	0.55	10.76	0.05	10.87	0.94	15.53	0.41	11.24	0.59	11.67	1.83	6.97	0.25
65	17.88	0.54	16.19	0.17	14.73	2.44	7.63	0.59	10.64	0.53	5.31	0.11	8.39	0.98	4.42	0.10
66	12.84	0.21	9.73	0.15	11.07	1.14	4.79	0.16	5.51	0.36	5.80	0.56	5.66	1.00	12.18	0.43

), which suggests a greater demand for these elements by the oilseed rape crop. The studies by Kowalska et al. [83] and Ma et al. [84] show that wheat and oilseed rape differ in their response to the availability of elements in the soil. To produce 1 tonne of grain, winter cereals, especially wheat, require 22–28 kg N, 10–13 kg P (as P₂O₅), 22–30 kg K (as K₂O), 3–4 kg of Mg (as MgO) and 2–3 kg S. An average 8–10 g Cu/tonne of grain is required by wheat, while 70–110 g Mn/tonne of grain is required. Zinc and boron (B) are also necessary for wheat development with 65 g and 5 g/tonne of grain with straw, respectively. To produce 1 tonne of seed and straw per hectare, winter oilseed rape requires 50–73 kg N, 9–20 kg P, 33–89 kg K, 4–11 kg Mg and 14–20 kg S. A literature review has shown that nutrient deficiencies, at least 150 g B/ha, 40 g Cu/ha, 200 g Mn/ha and 10 g molybdenum (Mo)/ha, can be expected with oilseed rape in the autumn (during the 4–6 leaf phase) [85,86].

According to Vasu et al. [49], the uptake of nutrients from the soil by plants during their growth and development leads to spatial variability in their distribution. This phenomenon depends on the type of plant and its growth rate. In our study, the differences in the content of the analyzed elements in the sampling points also resulted from the time variability in the experiment, which is in agreement with Santillano-Cázares et al. [87], who showed that the temporal variability of soil nutrients was much greater than its spatial variability. Those authors showed that the concentration of nutrients increased in the autumn–winter period, which they related to the occurrence of rainfall, which caused a temporary increase in the level of soil fertility. This is in agreement with our results, where in the first year of study, the concentration of Na, Ca, Mg, Cu and Mn increased in the winter period. The autumn–winter period of the second year also showed an increase in the Ca, Mg and Zn concentrations. Climatic factors may explain the spatial variability of nutrients, the concentration of which, according to McCauley et al. [88], decreases with decreasing values of temperature and precipitation. According to Huang et al. [89], soil nutrient concentrations are not only influenced by human activity, but are also related to soil type and conditions. Many soil factors, such as pH value, SOM, temperature and humidity influence the availability of the nutrients to the growing crops [90]. Wang et al. [91] analyzed the concentration of 15 tested elements in an arable field and noted that only 5 (P, Cu, Zn, Co and Pb) showed significant geographical variability, while others did not show any variability.

The spatial distribution of the chemical parameters in the soil prior to sowing both crops and during their harvest was found to be variable and depended on the place of soil sampling (Table 2). Concentrations of elements showed spatial variation due to the inherent spatial variability of soil physical properties. In this study, the PCA analysis (Figure 2) showed a negative correlation between the soil moisture value and the elements (Cu, Zn, Mn and Fe) in the soil under both crops. This suggests a higher concentration and availability of elements in the sampling points with a lower level of available water (but not the lack of it), as excess water will cause the leaching of elements.

During both years of our study, greater concentrations of macronutrients (Na, Ca, Mg) were recorded in sampling points with a higher pH, while greater concentrations of micronutrients (Cu, Zn, Mn, Fe) were recorded in points with a lower pH [88] macronutrients (N, K, Ca, Mg and S) are more available in the pH range from 6.5 to 8, while most micronutrients (B, Cu, Fe, Mn, Ni and Zn) are more available from pH 5 to 7. This is also in agreement with Bao et al. [92], where the soil pH showed a highly significantly negative correlation with the concentration of selected microelements (Fe, Mn, Cu) in the soil. Their research also revealed a correlation between the tested elements: the Fe concentration was positively correlated with Mn and Cu, and the Cu concentration with Zn concentration. Similar relationships were observed in the first year of this study, where the Cu concentration was positively correlated with Zn, and in the second year, where Fe concentration was positively correlated with Zn and Mn.

Regardless of the spatial and temporal changes in the chemical and physical soil properties, awareness of how these changes occur is necessary to increase profitability and to develop sustainable agricultural management [93]. In the current era of precision farming, fertilizer application, crop varieties and farming practices should be matched to the variability of soil and climatic conditions. The management of soil nutrients is important to meet the food needs of an ever-growing global population without adversely affecting the environment. Maps that illustrate the geographic distribution of micronutrient availability in the soil provide guidance on the optimal management of soil nutrients. Furthermore, this information is essential for a better understanding of the nature and extent of micronutrient deficiencies in plants. Farmers around the world tend to care more about the use of macronutrients, forgetting the importance of micronutrients in the soil, which results in the increased deficiency of micronutrients [94,95]. The mapping of soil nutrients can indicate the fertility status of the soil, thereby helping to plan precise fertilizer allocation.

4.3. Spatial Variability of N Content in the Yield

Our results indicate lower %N content in the wheat yield (Figure 3) compared to the results obtained by Yue et al. [96], where the average concentration of N in grain was 2.11%, and 0.53% in straw. Such large discrepancies in the content of this element in individual parts of a plant can be explained by a wide range of environmental conditions that prevail in a given field ecosystem and differences in fertilization management, particularly in N application.

According to Łukowiak et al. [50], the content of N in wheat grain, one of the main determinants of the nutritional value of the grain, is the result of complex processes of uptake, assimilation and N use by the plant. Modern high-yielding wheat varieties require an adequate N supply to guarantee a high yield and target grain quality parameters. The main tools for regulating N nutrition are specific fertilization systems that are mainly based on the determined or estimated available amount of mineral N in the soil (N_min), expected N mineralization rates and the N demand of the growing crop. According to Asseng and Milroy [97] and Estrada-Campuzano et al. [98], there are clear genotypic differences in grain nitrogen concentration (GNC) and wheat grain quality parameters, but the uptake and N utilization are also influenced by environmental factors, which are variable over time and place.

The research of Haberle and Svoboda [99], Semenov et al. [100] has shown that water availability is one of the strongest factors that determine the uptake and efficiency of N use by a plant, as well as the yield and quality of the grain. According to Estrada-Campuzano et al. [98], drought in the critical phases of grain filling has a detrimental effect on the quality characteristics. On the other hand, Triboï and Triboï-Blondel [101] reported water shortages are often associated with high ambient temperatures, which contribute to the shortening of the grain growth time and a reduction in the yield. This was confirmed by our own research, which showed that the wheat in sampling points with a greater water content also exhibited greater %N values.

In our study, the %N in oilseed rape was, on average, 3.3%, and was similar to the values reported by Stepaniuk and Głowacka [102] (on average 3.38%). Šidlauskas and Tarakanovas [103] and Narits [104] found that the N concentration in mature seeds and oilseed rape straw can vary widely, even in crops that have received adequate N fertilization. Studies have shown that the N content in seeds and straw after moderate N fertilization can range from 3.35 to 5.14% and from 0.80 to 1.80%, respectively. The results of field experiments by Nowosad et al. [105] showed the influence of weather conditions, especially temperature and precipitation, on the development of plants, seed yield and its composition. According to Bartomeus et al. [106], the soil properties, mainly soil pH, are the key factors that influence the yield of winter oilseed rape and %N content in the grain.

The plant growth and yield in the field show obvious spatial variation due to the inherent spatial variability of the soil chemical and physical parameters [107]. This is well illustrated by the research of Welsh and Wood [108], where the yield level and its characteristics were influenced by, among others, the soil type (sandy, loamy) and water capacity. In our study, the N content in the wheat grain was greater in the central-western part of the field. In the second year, in the north-western part of the field, there was an area with a greater % N content in the oilseed rape seeds. The spatial distribution of N indicates the presence of separate production zones. Spatial patterns in the quality parameters of winter wheat yield were probably related to changes in the spatial variability of available soil water at different stages of plant growth. In places where the soil moisture was greater, the N content in the wheat grain was also greater, which is in agreement with the results of Basso et al. [109] and Song et al. [110]. According to Albrizio et al. [111], a water shortage during the growth and flowering period of wheat causes yield losses due to the reduction in the potential number of grains per unit land area, and drought stress during grain filling may reduce the average weight of the kernels.

For both wheat and oilseed rape crops, N nutrition is generally considered to be the primary factor that influences both yield and grain quality, as it affects protein concentration [110,112]. In our research, we can assume that N was used in different ways by plants at particular sampling points, which translated into different %N values in their yield (Figure 9). The reaction to N, both in terms of yield and quality, may differ depending on the location. According to Delin [113], different uses of N by crops may result from spatial differences in N losses, fungal diseases, competition from weeds or a limited supply of other nutrients. During N fertilization, farmers usually do not take into account the spatial variability of the field and generally apply a uniform dose of N over the entire area of the field.

Our research findings might be used as sensitive and effective tools for the development of precision farming, the concept of which is based on obtaining greater crop yields from plants of greater quality, as well as lowering production costs and limiting environmental damage through the use of sustainable fertilization.

5. Conclusions

Spatial heterogeneity is one of the characteristic features of agricultural soils, which exhibit considerable variability in terms of soil chemistry, soil microbial biomass and enzyme activity. An arable field with uniform management is considered homogeneous with similar soil properties. However, the mean values obtained from homogenized soil samples often do not accurately reflect the actual condition of the soil ecosystems, as one field may contain several types of soil with different pH values and also contain the most important elements.

Based on our research results, it is possible to draw several important conclusions concerning the spatial and seasonal biochemical and physicochemical dynamics of the soil. Our work has shown that the spatial structure of the majority of the tested variables differed depending on the time and place of soil sampling and the type of crop under cultivation. The frequently changing soil conditions that resulted from the cultivation of the two crops favored changes in the activity of the DHA enzyme and the amount of microbial biomass, as well as in the physicochemical composition of the soil. The crops also had a significant impact on microbial parameters and the cultivation of oilseed rape reduced the amount of microbial biomass, which reduced enzymatic activity.

The obtained test results confirm that both the microbial biomass value and DHA activity are shaped by selected soil physicochemical parameters (particularly the Fe, Ca, Zn, Na and Cu contents, as well as the soil pH and moisture). However, it was shown that the nature of these interactions depends on the crop species being cultivated. It was also observed that the content of nitrogen in the grain and straw of the test subjects was mainly positively correlated with the content of selected macro- and microelements and soil moisture.

Nevertheless, the occurrence of characteristic areas in the field was observed, which was characterized by the higher values of the tested parameters. The southern part of the farmland (study site) was characterized by a greater pH and soil moisture resulting in greater bacterial biomass values, and therefore, also to higher DHA activity. In this part of the field, high concentrations of Zn and Mn and a high content of N in the grain of wheat plants were also found. The soil in the north of the site was richer in macronutrients (Na, Ca and Mg), as well as in Cu and Fe, and was characterized by a lower pH, which had a positive effect on the development of fungal biomass, and the greatest N content in rape seeds was also found in this part of the field.

Our research results conclude that the mapping of the spatial distribution of microbial activity and biomass, the soil physicochemical parameters as well as the nitrogen content in plants permits the development of an effective strategy for maintaining sustainable soil productivity through the appropriate management of agricultural practices, particularly, mineral fertilization.