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Article

Influence of Forecrop on Soil Quality Estimated on the Basis of the Growth and Development of Faba Bean and Biochemical Properties of the Soil

by
Małgorzata Baćmaga
*,
Jadwiga Wyszkowska
* and
Jan Kucharski
Department of Soil Science and Microbiology, Faculty of Agriculture and Forestry, University of Warmia and Mazury in Olsztyn, Łódzki 3 Sq., 10-719 Olsztyn, Poland
*
Authors to whom correspondence should be addressed.
Sustainability 2024, 16(17), 7492; https://doi.org/10.3390/su16177492 (registering DOI)
Submission received: 16 July 2024 / Revised: 14 August 2024 / Accepted: 17 August 2024 / Published: 29 August 2024
(This article belongs to the Special Issue Soil Science and the Latest Studies on Sustainable Agriculture)
Browse Figures
Versions Notes

Abstract

:
Proper crop rotation determines soil fecundity, which is pertinent for successor crops. With this problem in mind, a study was performed to evaluate the interference of spring wheat (Triticum aestivum L. subsp. aestivum), winter wheat (T. aestivum L. subsp. aestivum), maize (Zea mays L.), and winter rape (Brassica napus L.) as forecrops on the increase in faba bean (Vicia faba L.) and the biochemical and physicochemical properties of the soil. Tests with faba bean were performed in pots in the vegetation hall. The pots were filled with soil from under the abovementioned four plant species, and faba bean was grown. Soil unsown with faba bean was also tested to measure the rhizosphere effect. At the beginning of the experiment, and after its completion, enzymatic and physicochemical analyses of the soil were performed. On the 120th day of the study, faba bean was harvested and biometry was performed. The forecrop substantially influenced the biometric features of the faba bean and the biochemical activity of the soil. Faba beans grown in soil under spring wheat and winter wheat had the highest seed yield, while those grown in soil under the winter rape had the lowest yield. The geometric mean of the enzyme activity index was only significantly positively correlated with the number of faba bean seeds and the soil pH, as well as with the seed dry matter yield and the faba bean plant height. Faba bean cultivation increased the soil biochemical activity. The values of the biochemical and physicochemical parameters of the unsown soil were lower compared to the soil sown with faba bean. The conducted research can help to estimate the changes occurring in arable soils and maintain their stability thanks to the use of appropriate bioindicators, which are the soil enzymes. Moreover, the use of a diversified crop rotation in soil cultivation can provide a lot of information about its function, which can ultimately be used for planning the plant rotation, leading to the improvement of the soil structure and fertility, as well as its protection.

1. Introduction

Human activity has a very strong impact on soil processes, which is particularly noticeable in agroecosystems [1,2]. Hence, agricultural production should take account of sustainable development so as to minimize soil degradation. Soil is the cornerstone of food production, which provides it with a superior role to play in sustainable land management [3,4]. Its quality means, primarily, its ability to preserve plant productivity. It is evaluated based on soil properties like, the enzymatic activity, the contents of micro- and macro-elements, the contents of C and N, humidity, the pH, the soil structure and texture, and also the population numbers and activities of the soil microorganisms [5]. These parameters enable the observation of changes triggered in the soil environment by anthropogenic activity and natural conditions [6,7,8]. The purpose of plant production is to achieve the maximal mass and quality of the crop yield, usually by means of appropriate cultivation practices, which most often lead to an increase in soil fertility. However, improper land management may hamper the functioning of agroecosystems by, among other things, suppressing the biological activity of the soil [9,10,11].
Due to their sensitivity to varying environmental factors, soil enzymes are well-proven reliability indicators in soil quality diagnosis. They perfectly indicate whether the course of biochemical reactions proceeding in the soil environment has been correct or somehow disrupted [12,13,14,15]. Given their prompt reactions and heightened sensitivity to modifications in the soil conditions induced by arable land management, enzymes have been deemed sensitive and reliable indicators of soil usability for performing ecological functions [16,17,18,19,20,21]. There are many enzymes, e.g., urease, alkaline phosphatase, acid phosphatase, arylsulfatase, β-glucosidase, cellulase, and invertase, in the soil that increase the rate of chemical reactions, as well as biological and biochemical processes. Soil enzymes are implicated in the circulation of elements and the conversations concerning SOM (soil organic matter), which has a huge role in plant development. They are mainly responsible for the synthesis of proteins and nucleic acids, the hydrolysis of nitrogen compounds, the degradation of amino acids, and the metabolism of organic phosphorus species [22,23]. These processes result in the release of elements and ions that become accessible to plants, which is of vital importance to farming production [5]. Bogati et al. [24] demonstrated that extracellular enzymes synthesized by live microorganisms and endoenzymes secreted after their death play important functions in the soil environment, as they enable the release of bioavailable carbon and other biogenic elements, thereby affecting the control of the succession and stabilization of biotic soil communities [25,26]. The synthesis and secretion of enzymes responsible for organic material degradation in the soil strongly depend on the availability and quality of the substrates, which, in turn, are affected by the species abundance and productivity of the plants [27]. Enhanced plant productivity may contribute to the increased carbon content of the soil by producing litter from the plant shoots and roots and releasing root secretions, thereby increasing the substrate availability and enzyme activity [28]. Plant roots produce secretions that include various organic compounds that are used by the rhizosphere microbiota as a source of carbon and energy. These compounds include, among others, phenols, saccharides, organic acids, sterols, and vitamins [29].
It is essential to evaluate the dependence between soil biochemical and physicochemical properties, crop yields, and soil cultivation systems so to evaluate the impact of agriculture on the functioning and productivity of soil ecosystems. Extending the knowledge about the response of these parameters to diversified soil management is deemed necessary because they are the indicators of the qualities and fertility of agricultural soils, which are, in turn, pivotal to sustainable land management. Modern agricultural challenges force the search for innovative solutions that will not only increase plant yields, but also contribute to maintaining long-term soil quality. One of the main tools contributing to the achievement of these goals is proper soil tillage planning with the use of appropriate plants and the estimation of the soil fertility based on biochemical and physicochemical indicators [30].
Holatko et al. [31] reported that the varied use of a suitable forecrop is a desirable practice in crop production, which not only leads to increased crop yields but is also related to sustainability. An example is growing wheat after maize or legumes [32,33]. This forecrop has a beneficial effect on plant growth through the improved yield [34], increased nutrient use efficiency, soil aggregation [34], and the number and diversity of soil microorganisms [35,36]. Fabaceae plants have an important function in crop rotation because they not only enhance the soil fertility and quality but also eliminate weeds, pathogens, and pests. In addition, they help to improve the soil structure, increase the biodiversity, and accelerate nitrogen and phosphorus immobilization in the soil environment [4,37,38]. In their study, Faligowska et al. [39] proved that legumes as a forecrop increased the efficiency of all post-harvest crops in the rotation. The rotation conducted in the 2012–2018 field experiment included the following crops: legumes and spring barley, winter rape, spring wheat, and winter wheat. The study by Szymanska et al. [40] also showed that legumes had a beneficial effect on the growth and development of spring barley, winter triticale, and winter rape.
Soil use has a meaningful influence on the microbiological, biochemical, and chemical processes in the soil, as it modifies the quantity and quality of its organic matter [41,42]. An experiment carried out by Błońska et al. [43] showed differences in the soil biochemical properties, with the lowest dehydrogenases and urease activities found in the agriculturally cultivated soils and enhanced activities of these enzymes—in the pasture and forest soils. This relationship is related to the high SOM content and the respective C:N ratio. In turn, Danuoras et al. [44] reported that landforms, plant biodiversity, and humus content play a crucial role in the soil biochemical activity in grasslands and forests.
The present study aimed to assess the impact of the forecrops (spring wheat, winter wheat, maize, winter rape) and soil use (unsown soil and sown soil) on the soil quality as assessed by its biochemical activity and physicochemical properties during the growth of faba bean. The following hypotheses were proposed: (a) the type of forecrop affects the yielding of faba bean; (b) the type of forecrop modifies the biochemical, chemical, and physical properties of the soil; and (c) soil use modifies its biochemical and physicochemical properties.

2. Materials and Methods

2.1. Soil Material

Soil was brought in from the Krzyżanowo village (eastern part of the Pomeranian Province, Poland NE, 54.0242°N, 19.1216°E). This area belongs to the Vistula Marshland, a macroregion—Gdansk Coast, and subprovince—South Baltic Coast. In line with the IUSS Working Group [45] classification, the soil was categorized as Fluvisols. The soil was collected from an arable field, from the topsoil and humus layer (layer depth from 0 to 20 cm), where the following crops were grown: spring wheat (SW)—Triticum aestivum L. subsp. aestivum “Tybalt” cultivar, winter wheat (WW)—Triticum aestivum L. subsp. aestivum “Apostel” cultivar, maize (M)—Zea mays L. “SY Calo” cultivar, and winter rape (WR)—Brassica napus L., “Kuga F1” cultivar. The crops were amended with the following fertilizer doses (per pure compound in kg ha−1): SW and WW—198 kg N, 60 kg P and 90 kg K; M—310 kg N, 62 kg P and 93 kg K; and WR—264 kg N, 80 kg P and 120 kg K. Table 1 presents the soil physicochemical properties, whereas Table 2 collates data on the soil enzymatic activity.

2.2. Experiment Establishment and Design

The soil was collected from plots where SW, WW, M, and WR were cultivated, and then transported to the greenhouse of the Educational and Experimental Station in Olsztyn (NE Poland, Central Europe). The vegetation pot experiment was conducted for 120 days, from May to September 2021. Before the pot experiment, the collected soil batches were passed through a screen, and then 5 kg portions of each soil type were weighed into plastic pots (7 dm3 in volume) in 6 replications. The experiment was carried out in two variants: with samples of the soil without seeding plants (unsown—Us) (3 repetitions), and with the samples of soil sown (Ss) with faba bean (Fb)—Vicia faba L. (3 repetitions). All soil samples were fertilized with identical doses of P and K (per pure compound), i.e., a P dose of 91.65 mg kg−1 in the form of KH2PO4 and a K dose of 36.14 mg kg−1 in the form of KH2PO4 + KCl. In the second mentioned variant, Fb of the “Albus” cultivar was sown in each pot (10 seeds per pot), and then the soil was brought to a constant humidity (50% of the capillary volume of water). At the sprouting phase of Fb (developmental phase 0), the plants were thinned, and 5 plants were left in each pot. The soil moisture was monitored throughout the experimental period and kept at a stable level (water losses were replenished 3 times a day with deionized water). Over the experimental period, the conditions in the greenhouse were as follows: the mean air humidity—70.0%, the mean air temperature—16.8 °C, and the mean day length—10.2 h. On the day of harvest, faba bean was analyzed for biometric parameters, and the soil material was collected for enzymatic and physicochemical analyses. The soil material was collected from each pot (3 repetitions) separately for each combination (forecrop—Fc and soil use—Su). From each combination, the soil from the 3 pots was combined and thoroughly mixed. Moist soil was collected for the microbiological and enzymatic analyses into sterile plastic bags in the amount of 700 g. In turn, the soil intended for the physicochemical analyses was air-dried, sieved through a screen, and then placed in plastic bags (weight of the soil sample was 700 g). Enzyme activity assay was performed on the samples of moist soil, and physicochemical activity assay was performed on the samples of air-dry soil. A schematic diagram of the vegetation pot experiment is presented in Figure 1.

2.3. Biometry of Faba Bean

The plants were harvested on day 120 of the experiment, at the BBCH 99 developmental stage (harvested product), according to the BBCH scale (Biologische Bundesantalt, Bundessortenamt und Chemische Industrie). The plant biometry was performed for the plants from each pot sown with Fb (each pot contained 5 plants, giving a total of 15 plants):
▪
Stem length—SLe (cm);
▪
Number of pods per plant—PNu;
▪
Number of seeds per pod—SNu;
▪
Dry matter yield of: seeds—YDMSe, leaves—YDMLe, stems—YDMSt and siliques—YDMSi (g d.m. per plant).
In addition, the leaf greenness index (SPAD) was determined on two dates using a chlorophyll meter when Fb was at the following developmental stages:
▪
BBCH 32—2 visibly extended internodes;
▪
BBCH 79—nearly all pods have reached the final length.

2.4. Physicochemical Analysis of the Soil

Basic physicochemical analyses of the soil were carried out before the establishment of the experiment (soil samples collected from the crop field) and after the Fb harvest (after 120 days of the experiment). Before the physicochemical analyses, the soil material was air-dried and sieved through a screen. The soil samples were determined for the following:
▪
Particle size distribution—with the laser diffraction method;
▪
pH—with the potentiometric method;
▪
HAC and EBC—with the Kappen method;
▪
Corg and Ntot—using a Vario Max Cube CN analyzer (Elementar Analysen-systeme GmbH, Germany).
The listed physicochemical features were determined as described in the publication by Wyszkowska et al. [46].

2.5. Enzymatic Analysis of the Soil

The enzymatic analysis of the soil conducted on the day the experiment was established and on day 120 of the experiment included determinations of activities of the following: Deh with 3% C19H15ClN4 [47] as a substrate; Cat—3% H2O2 [48]; Pal and Pac—0.115 M O2NC6H4OP(O)(ONa)2 × 6H2O; Aryl—0.02 M C6H4KNO6S; Glu—0.025 M C12H15NO8; and Ure—10% CH4N2O [49]. The method for determining the enzymatic activity was described in the papers by Kucharski et al. [50] and Wyszkowska et al. [46]. All enzymatic analyses were conducted in 3 repetitions for each experimental variant with the Us and Ss with Fb. The results of the soil enzyme activity determination were then used to compute the GMea based on the equation posited by Paz-Ferreiro et al. [51]:
GMea = (Deh × Cat × Pal × Pac × Aryl × Glu × Ure)1/7
The rhizosphere effect (R:S), a ratio of the enzyme activity in the SS with Fb to that in the Us, was computed as well.

2.6. Statistical Analysis of the Results

The research results were subjected to 2-way analysis of variance (ANOVA; I: forecrop—Fc, II: soil use—Su) at p = 0.05, using Statistica 13.3 package [52]. The following analyses were conducted:
▪
Homogeneous groups (2-way analysis, Tukey t-test p = 0.05)—computed separately for each enzyme and each physical and chemical property of the soil;
▪
Percentage of the observed variability (η2) — determined for enzyme activity and soil chemical properties;
▪
PCA (principal component analysis)—conducted for enzyme activity and soil physicochemical properties;
▪
Pearson’s linear correlations (p = 0.05) between soil biochemical parameters and soil chemical properties — presented as a heat map.

3. Results

3.1. Biometric Traits of Faba Bean

The growth of faba bean (Fb) followed different patterns on the soils with various crops grown as the forecrop (Fc) (Figure 2, Table 3). Fb developed the longest stems (SLe) when grown after winter wheat (WW) (94.943 cm). The highest number of pods (PNu) per plant was noted when it was grown after spring wheat (SW) (6.120 pods per plant, on average), whereas the highest number of seeds (SNu) per pod—when it was cultivated after maize (M) (1.885 seeds per pod, on average). The dry matter yield of seeds (YDMSe) per pod was the highest in the Fb grown after WW (4.099 g seeds per pod, on average). The dry matter content of the individual morphological parts of the Fb was found to depend on the Fc, so its YDMSe was the highest when it was grown after WW (4.099 g d.m. plant−1, on average), whereas the dry matter yield of leaves (YDMLe), dry matter yield of stems (YDMSt), and dry matter yield of siliques (YDMSi)—when it was grown after SW (2.489, 3.554, and 2.213 g d.m. plant−1, on average, respectively).
It was also observed that, by modifying the physicochemical features of the soil, the Fc had an immediate impact on the leaf greenness index (SPAD) of the Fb (Figure 3), which was the highest when it was grown after WW (34.944), whereas the lowest—when it was cultivated after winter rape (WR) (28.023). At both analyzed developmental stages of Fb, i.e., at 2 visibly extended internodes (BBCH 32) and when nearly all pods had reached final length (BBCH 79), the SPAD value was the highest in the variant with WW used as the Fc (39.537 and 30.350, respectively).

3.2. Biochemical Properties of the Soil

The statistical analysis demonstrated (Figure 4) that activity of the soil enzymes was significantly affected by the sowing of the soil with Fb (Ss), i.e., by 84.63% (dehydrogenases—Deh) to 99.87% (catalase—Cat). The Fc and the interactions of the analyzed factors affected enzyme activity to a small extent. The values of the geometric mean of enzyme activities (GMea) were most strongly influenced by the soil use (ƞ2 = 90.69%).
After the Fb harvest (120 days), unsown soil (Us) collected after M cultivation exhibited the highest activity of Deh (13.537 µMol TFF), Cat (0.591 Mol O2), and arylsulfatase—Aryl (0.384 mMol PNP); that collected after SW cultivation exhibited the highest activity of acid phosphatase—Pac (5.603 mMol PNP), whereas the soil after SW cultivation showed the highest activity of alkaline phosphatase—Pal (3.877 mMol PNP), β-glucosidase—Glu (0.403 mMol PNP), and urease—Ure (0.892 mMol N-NH4). The Ss with Fb after M cultivation exhibited the highest activity of Deh (19.249 µMol TFF), Cat (0.651 Mol O2), and Aryl (0.438 mMol PNP); the soil after SW cultivation as the Fc—the highest activity of Glu (0.582 mMol PNP); the soil cultivated after WW—the highest activity of Pal (5.663 mMol PNP) and Ure (1.245 mMol N-NH4); and the soil after WR cultivation—the highest activity of Pac (6.648 mMol PNP). Activities of the soil enzymes were, indeed, lower in the Us with respect to the Ss with Fb, i.e., Deh activity was lower by 15.80% to 71.13%, Cat activity by 7.98% to 12.82%, Pal activity by 5.29% to 41.46%, Pac activity by 2.05% to 37.53%, Glu activity by 1.92% to 42.78%, Aryl activity by 12.33% to 30.50%, and Ure activity by 23.91% to 52.54%. Also, the values of GMea indices were significantly higher in the Ss than Us. The highest value of the GMea index was determined in the Ss with Fb after M cultivation (GMea = 1.853), and the lowest one—in the Us collected also after M cultivation (GMea = 1.210). The GMea value determined for the Us collected after SW cultivation decreased 1.3 times, after WW—1.2 times, after M—1.5 times and after WR—1.4 times, in comparison to the Ss with Fb (Table 4).
Furthermore, the conditions developed in the soil after SW cultivation promoted, to the greatest extent, the activities of Cat (R:S = 1.148), Aryl (R:S = 1.439), and Glu (R:S = 1.750); those developed in the soil after M cultivation—the activities of Deh (R:S = 3.494) and Ure (R:S = 2.108), whereas those found after WR cultivation—the activities of Pal (R:S = 1.712) and Pac (R:S = 1.601) (Figure 5).
The principal component analysis (PCA) results demonstrated (Figure 6) that the parameters such as the pH value, the sorption capacity (CEC) of the soil, and the total nitrogen content (Ntot) were negatively correlated with the PC1, explaining 51.90% of the total variability, and that the activities of Deh and Pac, the hydrolytic acidity (HAC), and the ratio of organic carbon to total nitrogen content (C:N ratio) were negatively correlated with the PC2, explaining 29.93% of the total variability.
The enzyme activity and GMea index were significantly positively correlated with the C:N, whereas the organic carbon content (Corg)—with the Ure activity. The remaining physicochemical parameters of the soil had no significant impact on the activity of the soil enzymes (Figure 7).

3.3. Physicochemical Properties of the Soil

The physicochemical properties of the soil (Figure 8) were most strongly modified by the Fc, as indicated by the percentage of the observed variability (η2) which ranged from 87.79% (pH) to 98.55% (base saturation—BS). The contents of Corg and Ntot, as well as the C:N ratio in the soil, were most strongly modified by the interactions of Fc (η2 reached 44.22%, 47.88%, and 19.57%, respectively). The soil use had the strongest impact on the C:N value (η2 =39.46%), whereas its effect on the remaining analyzed soil properties was less pronounced and ranged from 2.92% (HAC) to 13.28% (Corg).
The pH of the Us soil collected after SW, WW, M, and WR cultivation ranged from 5.833 to 6.333, whereas that of the Ss with Fb—from 5.633 to 6.333. Considering the soil use (Su), it was found that the pH values were higher and the HAC values were lower in the Us compared to the Ss with Fb. In turn, the C:N ratio was higher in the Ss with Fb than in the Us. The Ss with Fb after WW cultivation had the most beneficial values of pH, HAC, the sum of exchangeable base (EBC), CEC, and BS. Opposite observations were made for the Ss with Fb after WR cultivation, i.e., its pH, EBC, CEC, and BS values were the lowest, whereas the HAC value was the highest (Table 5).

3.4. Relationships between the Studied Soil Parameters

Based on the calculated correlation coefficients (Table 6), there were significant positive correlations between the GMea and SNu; soil pH and SLe and YDMSe, Corg and Ntot; SLe and YDMSe and YDMSt; as well as YDMSt and YDMSi. In turn, significant negative correlations were found between forecrop (Fc) and PNu and YDMSt; Deh and Corg;, Aryl and Ure, HAC, and BS.

4. Discussion

4.1. The Influence of Forecrop on the Faba Bean Biometry

Every plant needs appropriate nourishing compounds for its normal development. However, the substances found in soil often fail to cover plant demands for nutrients that would ensure the desired yield amount and quality. For this reason, soil cultivation should make it rich in nutrients available to plants [53]. Hence, the use of appropriate forecrop (Fc) as an additional nutritional source for the crops plays an important role in agriculture [54,55]. The study conducted by Pszczółkowska et al. [56] showed that the Fc had a strong impact on winter wheat (WW) yielding, as its seed yield was observed to increase when it was grown after faba bean (Fb) and blue lupine. In this study, the number of seeds (SNu), dry matter yield of leaves (YDMLe), dry matter yield of stems (YDMSt), and dry matter yield of siliques (YDMSi) were the most positively affected by spring wheat (SW) used as Fc. In turn, the stem length (SLe) and the dry matter yield of seeds (YDMSe) were the most positively affected by winter wheat (WW) used as Fc. These correlations might have been due to the beneficial physicochemical effect of WW which endowed the soil properties which promoted Fb growth and development. The Ss with Fb after WW cultivation had the highest pH value, the sum of exchangeable bases (EBC), the sorption capacity (CEC), the base saturation (BS), and the organic carbon content (Corg), which could increase Fb biomass. The soil cultivated after SW as the Fc also had high contents of Corg and total nitrogen content (Ntot). Faligowska et al. [39] reported that crop yielding might be determined by residues of N and C left after the cultivation of other crops (Fc). These authors found legumes to be appropriate Fc for the cultivation of winter rape (WR) and WW. In our research, the contents of Corg and Ntot, as well as the ratio of organic carbon to total nitrogen content (C:N ratio) were, probably, the major factors determining Fb growth and development on the soils where other various crops had been earlier cultivated. As indicated by Kimbirauskiene et al. [57], the Fc, following a period as well as intensive and reduced soil tillage systems, may strongly affect the N-fixing capability and yield of legumes, including Fb. The chlorophyll content of leaves is an indicator of the satisfactory condition and health of a plant. Insufficient contents of nutrients may reduce the chlorophyll content of young plants and, consequently, deteriorate their quality [58]. Plant yielding is determined by photosynthesis, the rate of which is strongly affected by N, i.e., its low level decreases chlorophyll content and suppresses nitrate reductase activity in a plant, thereby impeding the course of photosynthesis [59]. In the present study, Fb photosynthesis was stimulated to the greatest extent in the soil with WW used as the Fc, as indicated by the leaf greenness index (SPAD) value. In turn, Wanic and Treder [60] observed that WR used as the Fc had a more beneficial effect on the course of photosynthesis of common wheat and spelt compared to field pea, spring barley, and wheat.

4.2. The Influence of Forecrop on the Biochemical Properties of the Soil

The soil enzymes are one of the most activated organic components of the plant rhizosphere. They are biologically active substances that exhibit catalytic properties, which are released mainly from the cells of soil microorganisms but also from plant root secretions and during the degradation of plant and animal residues [61,62]. Through their active involvement in biochemical processes, the soil enzymes effectively aid the metabolic processes in the soil and serve an important function in the circulation of elements and the flow of energy. Enzyme activity is indicative of the intensity and course of biological processes in the soil, as well as the health of soil ecosystems [63]. Activities of all soil enzymes analyzed in the present study were higher in the soil sown (Ss) with Fb than in the bare soil (Us), which suggests that the plant root secretions have a strong impact on the biochemical parameters of the soil. Plant interactions with soil enzymes are determined by the chemical composition of their root secretions, which may vary greatly across plant genera, species, and cultivars [3,64,65]. Woźniak et al. [66] reported that legumes had a more beneficial impact on the soil enzymes compared to cereals. In turn, Meena and Rao [67] reported that soil tillage systems and the extent of soil coverage by vegetation significantly affected soil properties as well as soil organic matter (SOM) content, thereby contributing to changes in the soil quality and fertility. The biological functions of the soil are also influenced by crop genus or cultivar. Soil enzyme activity is regulated by the availability of C sources and SOM degradation, and may be suppressed due to the intensive soil tillage disrupting its structure [68,69,70].
The analyzed enzymes are key indicators of soil health due to their deep involvement in the processes ongoing in the soil. The enzyme activity is strictly linked to the biomass of microorganisms and the Corg content of the soil [67,69]. The enzymes belonging to the group of oxidoreductases, i.e., dehydrogenases (Deh) and catalase (Cat), are often used as indicators of the biological properties of soil because the activity of Deh indicates the intensity of soil microbiota respiration, whereas Cat degrades toxic hydrogen peroxide to water and molecular oxygen [5]. In the present study, the highest activities of Deh and Cat were determined in the Ss with Fb after maize (M) cultivation, which could be due to the greater availability of SOM derived from plant residues and root secretions [70], as well as to the quantity and quality of M root secretions, which had a stimulating effect on the diversity and activity of soil microorganisms. These enzymes are considered indicators of the intensity of soil microbial metabolism, and their activities strongly correlate with the content of SOM in the soil [71]. Noteworthy is also a group of enzymes which aid the nutrient uptake from organic compounds, like arylsulfatase (Aryl), which is involved in the hydrolysis of S-containing organic compounds to produce SO42– [9]. This process enriches the soil with easily available S compounds, which in turn improves crop yielding. In this experiment, the highest Aryl activity was determined in the Ss with Fb after SW and M cultivation. Ure is another important soil enzyme as it participates in urea hydrolysis, which makes it indispensable in providing crops with available nitrogen compounds. Its activity depends on multiple factors, like soil pH, soil type, the abundance of nutrients, and the land management system [8]. The highest Ure activity in the soil from under WW and M crops may have been related to the physicochemical properties of the soil, including, in particular, its pH, Corg, and Ntot contents and C:N ratio, which strongly affect the activity of the analyzed enzyme. The beneficial effect of the Fc after WW and M on urease (Ure) activity may be due to an increase in the content of N-NH4+ ions in the soil. SOM represents a source of energy and offers a suitable environment for the growth of microorganisms that catalyze the activity of extracellular enzymes, including Ure [72]. In addition, these soils were reported to have high values of the sorption capacity (CEC), which may have contributed to the binding of Ure by soil colloids [73]. In our study, its highest activity was determined in the soil sown with Fb after WW and M cultivation, which indicates that the availability of SOM derived from Fb root secretions could enhance the activity of soil microorganisms and, by this means, contribute to increased Ure activity [74,75]. The mineralization of organic P proceeds, among others, in the presence of alkaline phosphatase (Pal) and acid phosphatase (Pac), which make it available to plants. In the present study, the activity of Pal was the highest in the Ss with Fb after WW cultivation, whereas that of Pac—in the Ss with Fb after M. The activities of both these enzymes could be due to the nutrient content and SOM content of the soil, as well as the structure of microbial communities [7,8]. These enzymes are also involved in the degradation of Corg, e.g., cellulose or lignin. In turn, β-glucosidase (Glu) is a reliable indicator of the N-free metabolism of organic compounds [76]. Its highest activity determined in the soil from under the SW crop may be related to the availability of the substrate, which contributes to its production yield and rate. It was also likely that the soil from under the SW crop was rich in carbohydrates that were used by microorganisms, producing large amounts of Glu, as a source of nutrients. This, in turn, may have promoted the synthesis of this enzyme in the soil ecosystems, leading to a significant increase in its activity [66,77]. Beyond that, the rate of enzymatic reactions is linked to the soil pH, which is responsible for the ionization state of the enzyme active centers. A change in the content of H+ ions may trigger changes in the content of inhibitors or activators in the soil, thereby affecting the availability of substrates [77]. The present study showed that its activity was the highest in the Ss with Fb after SW cultivation. In general, Glu activity is associated with SOM degradation in the soil, which provides nutrients to plants and, thus, increases Glu secretion to the soil environment [66]. This enzyme is, therefore, deemed a reliable index of early changes in the soil conditions and the SOM metabolism in the soil. In addition, Glu activity is positively correlated with the soil fertility, the contents of available N and P, and microbiological activity of the soil [78]. Positive correlations were observed between the C:N ratio and activities of the analyzed enzymes, which implies that the SOM availability is the key indicator of their activity. Kwiatkowski et al. [79] also noted a positive correlation between these parameters. The activity of the soil enzymes could have been due to the content of SOM provided by, among others, the grown crops. Jurado et al. [80] reported that the C:N ratio established the course and intensity of the SOM conversion in the soil. In the present study, the geometric mean of enzyme activities (GMea), determined based on the enzymatic activity, was the highest in the soil cultivated after M (GMea = 1.853). The values of this index in this soil reflected the higher activities of most analyzed soil enzymes, i.e., Deh, Cat, Pac, and Aryl, compared to the other soil types. The GMea index is based on the activity of many soil enzymes; hence, it is very sensitive to any changes in the soil environment. In addition, its values are related to the physical and chemical properties of the soil. In our study, the soil from under M cultivation showed higher GMea index values compared to the other soil types, indicating an improvement in the soil conditions. The highest value of the GMea index in the soil after M cultivation was determined for Deh, Cat, Pal, and Aryl [51,81].

4.3. The Influence of Forecrop on the Physicochemical Properties of the Soil

An important factor affecting the quality and amount of crop yield is the soil affluence in nutrients, which in turn greatly affects its physical and chemical properties [82]. In this experiment, the soils collected after SW, WW, M, and WR cultivation differed in their physicochemical properties, which was also reflected in their enzymatic activity and Fb yielding. Woźniak et al. [66] emphasized that both soil use (Su) and Fc led to modifications in the physical and chemical properties of the soil. They also noted a higher content of Corg and pH value in the soil from under the wheat crop compared to the soil from pea cultivation, as well as an opposite correlation in the case of the Ntot content. In the present study, the unsown soil (Us) cultivated after SW as the Fc showed the highest values of Corg, Ntot, and C:N, whereas that cultivated after WW had the highest values of pH, EBC, CEC, and BS. In turn, the Ss with Fb after WW cultivation had the highest values of pH, EBC, CEC, BS, Corg, and Ntot. The secretions of plant root systems are connected to the species and cultivar of plants, which modify the physicochemical properties of the soil, especially in the rhizosphere, which is diverse in terms of soil microbial species. Such a relationship may have occurred in the soil from under SW and WW crops, which translated into the changes observed in the soil physicochemical properties [81,83]. Haruna et al. [84] reported that wheat cultivation could improve the soil structure and increase the contents of N and SOM, which was related to root density. In turn, Wang et al. [82] emphasized that SOM affected soil quality by improving its structure and water-holding capacity and, thereby, enhancing its bioactivity. Such conditions of the soil can be achieved by, among other things, using the Fc.

5. Conclusions

The Fc was found to strongly affect Fb growth as well as the enzymatic and physicochemical properties of the soil. The soil under SW and WW produced the highest yield of Fb seeds, whereas the lowest YDMSe was recorded in the soil under WR. In addition, the soil from under WW had the highest dry matter yield of faba bean leaves, stems, and siliques (YDMLe, YDMSt, and YDMSi). The highest SPAD was recorded for the Fb grown in the soil after SW, and the lowest one—for that grown in the soil after WR. On the grounds of the rhizosphere effect, it was found that the Us from under the SW crop showed the highest activities of Cat, Aryl, and Glu, and the soil from under the M crop had the highest activities of Deh and Ure, whereas the soil from under the WR crop had the highest activities of Pal and Pac. The Ss with Fb after SW cultivation had the highest values of pH, EBC, CEC, BS, Corg, and Ntot, whereas the Ss with Fb after WR as the Fc showed the most beneficial ratio of Corg to Ntot. It was found that WW as the Fc exerted the most positive effect on the physicochemical properties of the soil, which could contribute to slightly better soil conditions for the Fb development than in the case of SW, M, and WR. The parameters determined in the study are very useful in evaluating soil fertility and quality, particularly for agricultural purposes.

Author Contributions

Conceptualization, M.B., J.W. and J.K.; methodology, M.B., J.W. and J.K.; conducted the experiments, M.B.; formal analysis, M.B.; statistical analyses, M.B.; visualization, M.B.; validation, J.W.; investigation, M.B., J.W. and J.K.; writing—original draft preparation, M.B.; writing—review and editing, M.B., J.W. and J.K.; supervision, J.K. All authors contributed significantly to the discussion of the results and the preparation of this manuscript. All authors have read and agreed to the published version of the manuscript.

Funding

This study was funded by the University of Warmia and Mazury in Olsztyn, Faculty of Agriculture and Forestry, Department of Soil Science and Microbiology (grant No. 30.610.006-110), and was financially supported by the Minister of Education and Science under the program “The Regional Initiative of Excellence Program”.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data are available by contacting the authors.

Acknowledgments

The authors would like to thank Łukasz Kijewski for providing the agricultural fields for this study.

Conflicts of Interest

The authors declare no conflicts of interest. The funders had no role in the design of this study; in the collection, analyses, interpretation of data; in the writing of this manuscript; or in the decision to publish the results.

Abbreviations

SW—spring wheat; WW—winter wheat; M—maize; WR—winter rape; Fc—forecrop; Su—soil use; Fc×Su—interaction of factors; Us—unsown soil; Ss—sown soil; Deh—dehydrogenases; Cat—catalase; Pal—alkaline phosphatase; Pac—acid phosphatase; Aryl—arylsulfatase; Glu—β-glucosidase; Ure—urease; GMea—geometric mean of enzyme activities; pH—soil acidity/alkalinity; HAC—hydrolytic acidity; EBC—sum of exchangeable bases; CEC—sorption capacity; BS—base saturation; Corg—organic carbon content; Ntot—total nitrogen content; C:N—ratio of organic carbon content to total nitrogen content; SLe—stem length; PNu—pod number; SNu—seed number; YDMSe—dry matter yield of seeds; YDMLe—dry matter yield of leaves; YDMSt—dry matter yield of stems; YDMSi—dry matter yield of siliques; BBCH 32—2 visibly extended internodes; BBCH 79—nearly all pods have reached final length.

References

  1. Kooch, Y.; Tavakoli, M.; Akbarinia, M. Tree species could have substantial consequences on topsoil fauna: A feedback of land degradation/restoration. Eur. J. For. Res. 2018, 137, 793–805. [Google Scholar] [CrossRef]
  2. Yang, H.; Ma, J.; Rong, Z.; Zeng, D.; Wang, Y.; Hu, S.; Ye, W.; Zheng, X. Wheat straw return influences nitrogen-cycling and pathogen associated soil microbiota in a wheat–soybean rotation system. Front. Microbiol. 2019, 10, 1811. [Google Scholar] [CrossRef] [PubMed]
  3. Jezierska-Tys, S.; Wesołowska, S.; Gałązka, A.; Joniec, J.; Bednarz, J.; Cierpiała, R. Biological activity and functional diversity in soil in different cultivation systems. Int. J. Environ. Sci. Technol. 2020, 17, 4189–4204. [Google Scholar] [CrossRef]
  4. Siczek, A.; Frąc, M.; Kalembasa, S.; Kalembasa, D. Soil microbial activity of faba bean (Vicia faba L.) and wheat (Triticum aestivum L.) rhizosphere during growing season. Appl. Soil Ecol. 2018, 130, 34–39. [Google Scholar] [CrossRef]
  5. Furtak, K.; Gałązka, A. Enzymatic activity as a popular parameter used to determine the quality of the soil environment. Pol. J. Agron. 2019, 37, 22–30. [Google Scholar] [CrossRef]
  6. Acosta-Martínez, V.; Cano, A.; Johnson, J. Simultaneous determination of multiple soil enzyme activities for soil health-biogeochemical indices. Appl. Soil Ecol. 2018, 126, 121–128. [Google Scholar] [CrossRef]
  7. Maharajan, M.; Sanaullah, M.; Razavi, B.S.; Kuzyakov, Y. Effect of land use and management practices on microbial biomass and enzyme activities in subtropical top- and sub-soils. Appl. Soil Ecol. 2017, 113, 22–28. [Google Scholar] [CrossRef]
  8. Moghimian, N.; Hosseini, S.M.; Kooch, Y.; Darki, B.Z. Impacts of changes in land use/cover on soil microbial and enzyme activity. Catena 2017, 157, 407–414. [Google Scholar] [CrossRef]
  9. Filipek-Mazur, B.; Pużyńska, K.; Tabak, M.; Pużyński, S. Enzymatic activity of soil under spelt grown in an organic farming system in Poland’s temperate climate. Agronomy 2020, 10, 930. [Google Scholar] [CrossRef]
  10. Shrestha, P.; Gautam, R.; Ashwath, N. Effects of agronomic treatments on functional diversity of soil microbial community and microbial activity in a revegetated coal mine spoil. Geoderma 2019, 338, 40–47. [Google Scholar] [CrossRef]
  11. Wang, J.; Liu, G.B.; Zhang, C.; Wang, G.L.; Fang, L.C.; Cui, Y.X. Higher temporal turnover of soil fungi than bacteria during long term secondary succession in a semiarid abandoned farmland. Soil Till. Res. 2019, 194, 104305. [Google Scholar] [CrossRef]
  12. Lee, S.H.; Kim, M.S.; Kim, J.G.; Kim, S.O. Use of soil enzymes as indicators for contaminated soil monitoring and sustainable management. Sustainability 2020, 12, 8209. [Google Scholar] [CrossRef]
  13. Nannipieri, P.; Trasar-Cepeda, C.; Dick, R.P. Soil enzyme activity: A brief history and biochemistry as a basis for appropriate interpretations and meta-analysis. Biol. Fertil. Soils 2018, 54, 11–19. [Google Scholar] [CrossRef]
  14. Tiwari, R.; Dwivedi, B.S.; Sharma, Y.M.; Sharma, A.; Dwivedi, A.K. Activities of β-glucosidase, phosphatase and dehydrogenase as soil quality indicators: A review. Int. J. Curr. Microbiol. App. Sci. 2019, 8, 834–846. [Google Scholar] [CrossRef]
  15. Wyszkowska, J.; Borowik, A.; Olszewski, J.; Kucharski, J. Soil bacterial community and soil enzyme activity depending on the cultivation of Triticum aestivum, Brassica napus, and Pisum sativum ssp. arvense. Diversity 2019, 11, 246. [Google Scholar] [CrossRef]
  16. Bueis, T.; Turrión, M.B.; Bravo, F.; Pando, V.; Muscolo, A. Factors determining enzyme activities in soils under Pinus halepensis and Pinus sylvestris plantations in Spain: A basis for establishing sustainable forest management strategies. Ann. For. Sci. 2018, 75, 34. [Google Scholar] [CrossRef]
  17. Erdel, E.; Şimşek, U. Effects of soil conservation management systems on soil enzyme activities under wheat cultivation. Pol. J. Environ. Stud. 2023, 32, 1105–1111. [Google Scholar] [CrossRef] [PubMed]
  18. Garbuz, S.; Mackay, A.; Camps-Arbestain, M.; DeVantier, B.; Minor, M. Biochar increases soil enzyme activities in two contrasting pastoral soils under different grazing management. Crop Pasture Sci. 2022, 74, 101–111. [Google Scholar] [CrossRef]
  19. Holík, L.; Hlisnikovský, L.; Honzík, R.; Trögl, J.; Burdová, H.; Popelka, J. Soil microbial communities and enzyme activities after long-term application of inorganic and organic fertilizers at different depths of the soil profile. Sustainability 2019, 11, 3251. [Google Scholar] [CrossRef]
  20. Rao, D.L.N.; Aparna, K.; Mohanty, S. Microbiology and biochemistry of soil organic matter, carbon sequestration and soil health. Indian J. Fertil. 2019, 15, 124–138. [Google Scholar]
  21. Skowrońska, M.; Bielińska, E.J.; Szymański, K.; Futa, B.; Antonkiewicz, J.; Kołodziej, B. An integrated assessment of the long-term impact of municipal sewage sludge on the chemical and biological properties of soil. Catena 2020, 189, 104484. [Google Scholar] [CrossRef]
  22. Khalaf, A.S.M.; Al-Ansari, A.M.S. The effect of adding some pesticides in inhibiting the activity of the urease enzyme in sandy loam soil treated with different organic sources. Ann. Rom. Soc. Cell Biol. 2021, 25, 529–546. [Google Scholar]
  23. Silva-Olaya, A.M.; Mora-Motta, D.A.; Cherubin, M.R.; Grados, D.; Somenahally, A.; Ortiz- Morea, F.A. Soil enzyme responses to land use change in the tropical rainforest of the Colombian Amazon region. PLoS ONE 2021, 16, e0255669. [Google Scholar] [CrossRef]
  24. Bogati, K.A.; Golińska, P.; Sewerniak, P.; Burkowska-But, A.; Walczak, M. Deciphering the impact of induced drought in agriculture soils: Changes in microbial community structure, enzymatic and metabolic diversity. Agronomy 2023, 13, 1417. [Google Scholar] [CrossRef]
  25. Buckley, S.; Allen, D.; Brackin, R.; Jämtgård, S.; Näsholm, T.; Schmidt, S. Microdialysis as an in situ technique for sampling soil enzymes. Soil Biol. Biochem. 2019, 135, 20–27. [Google Scholar] [CrossRef]
  26. Demkina, E.V.; Shanenko, E.F.; Nikolaev, Y.A.; El’-Registan, G.I. Model of the regulation of activity of immobilized enzymes (amylases) in soil. Microbiology 2017, 86, 231–240. [Google Scholar] [CrossRef]
  27. Zhang, B.; Cai, Y.; Hu, S.; Chang, S.X. Plant mixture effects on carbon-degrading enzymes promote soil organic carbon accumulation. Soil Biol. Biochem. 2021, 163, 108457. [Google Scholar] [CrossRef]
  28. Zheng, L.T.; Chen, H.Y.H.; Yan, E.R. Tree species diversity promotes litterfall productivity through crown complementarity in subtropical forests. J. Ecol. 2019, 107, 1852–1861. [Google Scholar] [CrossRef]
  29. Li, X.; Kang, X.; Zou, J.; Yin, J.; Wang, Y.; Li, A.; Ma, X. Allochthonous arbuscular mycorrhizal fungi promote Salix viminalis L.–mediated phytoremediation of polycyclic aromatic hydrocarbons characterized by increasing the release of organic acids and enzymes in soils. Ecotoxicol. Environ. Saf. 2023, 249, 114461. [Google Scholar] [CrossRef]
  30. Hou, D.; Bolan, N.S.; Tsang, D.C.; Kirkham, M.B.; O’connor, D. Sustainable soil use and management: An interdisciplinary and systematic approach. Sci. Total Environ. 2020, 729, 138961. [Google Scholar] [CrossRef]
  31. Holatko, J.; Brtnicky, M.; Baltazar, T.; Smutny, V.; Frouz, J.; Kintl, A.; Jaskulska, I.; Ryant, P.; Radziemska, M.; Latal, O.; et al. Long-term effects of wheat continuous cropping vs. wheat in crop rotation on carbon content and mineralisation, aggregate stability, biological activity, and crop yield. Eur. J. Agron. 2024, 158, 127218. [Google Scholar] [CrossRef]
  32. Zhang, Q.Q.; Men, X.Y.; Hui, C.; Ge, F.; Ouyang, F. Wheat yield losses from pests and pathogens in China. Agric. Ecosyst. Environ. 2022, 326, 107821. [Google Scholar] [CrossRef]
  33. Zhang, L.; Chu, Q.; Jiang, Y.; Chen, F.; Lei, Y.-D. Impacts of climate change on drought risk of winter wheat in the North China Plain. J. Integr. Agric. 2021, 20, 2601–2612. [Google Scholar] [CrossRef]
  34. Ghosh, T.; Maity, P.P.; Das, T.K.; Krishnan, P.; Bhatia, A.; Bhattacharya, P.; Sharma, D.K. Variation of porosity, pore size distribution and soil physical properties under conservation agriculture. Indian J. Agric. Sci. 2020, 90, 2051–2058. [Google Scholar] [CrossRef]
  35. Hazra, K.K.; Nath, C.P.; Singh, U.; Praharaj, C.S.; Kumar, N.; Singh, S.S.; Singh, N.P. Diversification of maize-wheat cropping system with legumes and integrated nutrient management increases soil aggregation and carbon sequestration. Geoderma 2019, 353, 308–319. [Google Scholar] [CrossRef]
  36. Romero-Salas, E.A.; Navarro-Noya, Y.E.; Luna-Guido, M.; Verhulst, N.; Crossa, J.; Govaerts, B.; Dendooven, L. Changes in the bacterial community structure in soil under conventional and conservation practices throughout a complete maize (Zea mays L.) crop cycle. App. Soil Ecol. 2021, 157, 103733. [Google Scholar] [CrossRef]
  37. Serafin-Andrzejewska, M.; Helios, W.; Jama-Rodzénska, A.; Kotecki, A.; Kozak, M.; Zarzycki, P.; Kaliska, B. Effect of the depth and rate of sowing on the yield and yield components of determinate and indeterminate faba beans (Vicia faba var. minor L.) cultivars under conditions of Southwestern Poland. Agron. Sci. 2022, 77, 27–40. [Google Scholar] [CrossRef]
  38. Karkanis, A.; Ntatsi, G.; Lepse, L.; Fernández, J.A.; Vågen, I.M.; Rewald, B.; Alsin, I.; Kronberga, A.; Balliu, A.; Olle, M.; et al. Faba bean cultivation—Revealing novel managing practices for more sustainable and competitive European cropping systems. Front. Plant Sci. 2018, 9, 1115. [Google Scholar] [CrossRef]
  39. Faligowska, A.; Szymańska, G.; Panasiewicz, K.; Szukała, J.; Koziara, W.; Ratajczak, K. The long-term effect of legumes as forecrops on the productivity of rotation (winter rape-winter wheat-winter wheat) with nitrogen fertilization. Plant Soil Environ. 2019, 65, 138. [Google Scholar] [CrossRef]
  40. Szymańska, G.; Faligowska, A.; Panasiewicz, K.; Szukała, J.; Ratajczak, K.; Sulewska, H. The long-term effect of legumes as forecrops on the productivity of rotation winter triticale–winter rape with nitrogen fertilisation. Acta Agric. Scand B Soil Plant Sci. 2020, 70, 128–134. [Google Scholar] [CrossRef]
  41. Adetunji, A.T.; Lewu, F.B.; Mulidzi, R.; Ncube, B. The biological activities of β-glucosidase, phosphatase and urease as Soil quality indicators: A Review. J. Soil Sci. Plant Nutr. 2017, 17, 794–807. [Google Scholar] [CrossRef]
  42. Komorek, D.; Wyszkowska, J.; Borowik, A.; Zaborowska, M. Microbial activity and diversity in soil sown with Zea mays and Triticosecale. Agriculture 2024, 14, 1070. [Google Scholar] [CrossRef]
  43. Błońska, E.; Lasota, J.; Zwydak, M. The relationship between soil properties, enzyme activity and land use. For. Res. Pap. 2017, 78, 39–44. [Google Scholar] [CrossRef]
  44. Daunoras, J.; Kăcergius, A.; Gudiukaite, R. Role of soil microbiota enzymes in soil health and activity changes depending on climate change and the type of soil ecosystem. Biology 2024, 13, 85. [Google Scholar] [CrossRef]
  45. International Union of Soil Sciences Working Group. World Reference Base for Soil Resources 2014, Update 2015. In International Soil Classification System for Naming Soils and Creating Legends for Soil Maps; World Soil Resources Reports No. 106; FAO: Rome, Italy, 2015. [Google Scholar]
  46. Wyszkowska, J.; Boros-Lajszner, E.; Kucharski, J. The impact of soil contamination with lead on the biomass of maize intended for energy purposes, and the biochemical and physicochemical properties of the soil. Energies 2024, 17, 1156. [Google Scholar] [CrossRef]
  47. Öhlinger, R. Dehydrogenase activity with the substrate TTC. In Methods in Soil Biology; Schinner, F., Ohlinger, R., Kandler, E., Margesin, R., Eds.; Springer: Berlin/Heidelberg, Germany, 1996; pp. 241–243. [Google Scholar]
  48. Johnson, J.L.; Temple, K.L. Some variables affecting the measurement of catalase activity in soil. Soil Sci. Soc. Am. J. 1964, 28, 207–209. [Google Scholar] [CrossRef]
  49. Alef, K.; Nannipieri, P. (Eds.) Methods in Applied Soil Microbiology and Biochemistry; Academic London: London, UK, 1998; pp. 316–365. [Google Scholar]
  50. Kucharski, J.; Tomkiel, M.; Baćmaga, M.; Borowik, A.; Wyszkowska, J. Enzyme activity and microorganisms diversity in soil contaminated with the Boreal 58 WG herbicide. J. Environ. Sci. Health B 2016, 51, 446–454. [Google Scholar] [CrossRef]
  51. Paz-Ferreiro, J.; Gasco, G.; Gutiérrez, B.; Mendez, A. Soil biochemical activities and the geometric mean of enzyme activities after application of sewage sludge and sewage sludge biochar to soil. Biol. Fertil. Soils 2012, 48, 511–517. [Google Scholar] [CrossRef]
  52. TIBCO Software Inc. Statistica, Version 13.3. In Data Analysis Software System; Tibco Software Inc.: Palo Alto, CA, USA, 2024; Available online: https://docs.tibco.com/products/tibco-statistica-document-management-system-13-3-0 (accessed on 15 April 2024).
  53. Lemanowicz, J.; Bartkowiak, A.; Lamparski, R.; Wojewódzki, P.; Pobereżny, J.; Wszelaczyńska, E.; Szczepanek, M. Physicochemical and enzymatic soil properties influenced by cropping of primary wheat under organic and conventional farming systems. Agronomy 2020, 10, 1652. [Google Scholar] [CrossRef]
  54. Litke, L.; Gaile, Z.; Ruža, A. Effect of nitrogen rate and forecrop on nitrogen use efficiency in winter wheat (Triticum aestivum). Agron. Res. 2019, 17, 582–592. [Google Scholar] [CrossRef]
  55. Magyar, Z.; Pepó, P.; Gyimes, E. Effects of agrotechnical factors on the quality and quantity of yield in winter wheat production. Acta Agric. Debreceniensis 2020, 1, 69–75. [Google Scholar] [CrossRef]
  56. Pszczółkowska, A.; Okorski, A.; Olszewski, J.; Fordoński, G.; Krzebietke, S.; Chareńska, A. Effects of pre-preceding leguminous crops on yield and chemical composition of winter wheat grain. Plant Soil Environ. 2018, 64, 592–596. [Google Scholar] [CrossRef]
  57. Kimbirauskiene, R.; Sinkeviciene, A.; Jonaitis, R.; Romaneckas, K. The effect of tillage intensity on faba bean growth and development. Geogr. Res. Bull. 2023, 2, 169–182. [Google Scholar] [CrossRef]
  58. Janusauskaite, D.; Feiziene, D.; Feiza, V. Nitrogen-induced variations in leaf gas exchange of spring triticale under field conditions. Acta Physiol. Plant 2017, 39, 193. [Google Scholar] [CrossRef]
  59. Jiang, C.; Zu, C.; Wang, H. Effect of nitrogen fertilization on growth and photosynthetic nitrogen use efficiency in tobacco (Nicotiana tabacum L.). J. Life Sci. 2015, 9, 373–380. [Google Scholar] [CrossRef]
  60. Wanic, M.; Treder, K. Effect of forecrops on gas exchange and selected agronomic characteristics of wheat and spelt. J. Elem. 2020, 25, 607–619. [Google Scholar] [CrossRef]
  61. Li, Y.; Nie, C.; Liu, Y.; Du, W.; He, P. Soil microbial community composition closely associates with specific enzyme activities and soil carbon chemistry in a long-term nitrogen fertilized grassland. Sci. Total Environ. 2019, 654, 264–274. [Google Scholar] [CrossRef]
  62. Chen, Y.P.; Tsai, C.F.; Rekha, P.D.; Ghate, S.D.; Huang, H.Y.; Hsu, Y.H.; Liaw, L.L.; Young, C.C. Agricultural management practices influence the soil enzyme activity and bacterial community structure in tea plantations. Bot. Stud. 2021, 62, 1. [Google Scholar] [CrossRef] [PubMed]
  63. Goenster, S.; Gründler, C.; Buerkert, A.; Joergensen, R.G. Soil microbial indicators across land use types in the river oasis Bulgan sum center, Western Mongolia. Ecol. Indic. 2017, 76, 111–118. [Google Scholar] [CrossRef]
  64. De Vries, F.T.; Williams, A.; Stringer, F.; Willcocks, R.; McEwing, R.; Langridge, H.; Straathof, A.L. Changes in root–exudate–induced respiration reveal a novel mechanism through which drought affects ecosystem carbon cycling. New Phytol. 2019, 224, 132–145. [Google Scholar] [CrossRef]
  65. Steinauer, K.; Chatzinotas, A.; Eisenhauer, N. Root exudate cocktails: The link between plant diversity and soil microorganisms? Ecol. Evol. 2016, 6, 7387–7396. [Google Scholar] [CrossRef]
  66. Woźniak, A. Chemical properties and enzyme activity of soil as affected by tillage system and previous crop. Agriculture 2019, 9, 262. [Google Scholar] [CrossRef]
  67. Meena, A.; Rao, K.S. Assessment of soil microbial and enzyme activity in the rhizosphere zone under different land use/cover of a semiarid region, India. Ecol. Proces. 2021, 10, 16. [Google Scholar] [CrossRef]
  68. Sarto, M.V.M.; Borges, W.L.B.; Sarto, J.R.W.; Pires, C.A.B.; Rice, C.W.; Rosolem, C.A. Soil microbial community and activity in a tropical integrated crop-livestock system. Appl. Soil Ecol. 2020, 145, 103350. [Google Scholar] [CrossRef]
  69. Silva, É.O.; de Medeiros, E.V.; Duda, G.P.; Junior, M.A.L.; Brossard, M.; de Oliveira, J.B.; dos Santos, U.J.; Hammecker, C. Seasonal effect of land use type on soil absolute and specific enzyme activities in a Brazilian semi-arid region. Catena 2019, 172, 397–407. [Google Scholar] [CrossRef]
  70. Vinhal-Freitas, I.C.; Corrêa, G.F.; Wendling, B.; Bobuľská, L.; Ferreira, A.S. Soil textural class plays a major role in evaluating the effects of land use on soil quality indicators. Ecol. Indic. 2017, 74, 182–190. [Google Scholar] [CrossRef]
  71. Jat, H.S.; Datta, A.; Choudhary, M.; Sharma, P.C.; Dixit, B.; Jat, M.L. Soil enzymes activity: Effect of climate smart agriculture on rhizosphere and bulk soil under cereal based systems of north-west India. Eur. J. Soil Biol. 2021, 103, 103292. [Google Scholar] [CrossRef]
  72. Lasota, J.; Błońska, E.; Piaszczyk, W. State of soil enzymatic activity in relationship to some chemical properties of Brunic Arenosols. Soil Sci. Annual 2021, 72, 140641. [Google Scholar] [CrossRef]
  73. Wang, R.; Dorodnikov, M.; Yang, S.; Zhang, Y.; Filley, T.R.; Turco, R.F.; Zhang, Y.; Xu, Z.; Li, H.; Jiang, Y. Responses of enzymatic activities within soil aggregates to 9-year nitrogen and water addition in a semi-arid grassland. Soil Biol. Biochem. 2015, 81, 159–167. [Google Scholar] [CrossRef]
  74. Bonanomi, G.; D’Ascoli, R.; Antignani, V.; Capodilupo, M.; Cozzolino, L.; Marzaioli, R.; Puopolo, G.; Rutigliano, F.A.; Scelza, R.; Scotti, R.; et al. Assessing soil quality under intensive cultivation and tree orchards in Southern Italy. Appl. Soil Ecol. 2011, 47, 184–194. [Google Scholar] [CrossRef]
  75. Pathan, S.I.; Žifčáková, L.; Ceccherini, M.T.; Pantani, O.L.; Větrovský, T.; Baldrian, P. Seasonal variation and distribution of total and active microbial community of β-glucosidase encoding genes in coniferous forest soil. Soil Biol. Biochem. 2017, 105, 71–80. [Google Scholar] [CrossRef]
  76. De Almeida, R.F.; Naves, E.R.; da Mota, R.P. Soil quality: Enzymatic activity of soil β-glucosidase. Glob. J. Agric. Res. Rev. 2015, 3, 146–450. [Google Scholar]
  77. Dong RAbdelkerim-Ouba, D.; Liu, D.; Ma, X.; Wang, S. Impacts of partial substitution of chemical fertilizer with organic manure on the kinetic and thermodynamic characteristics of soil β–glucosidase. Agronomy 2023, 13, 1065. [Google Scholar] [CrossRef]
  78. Mariscal-Sancho, I.; Ball, B.; McKenzie, B. Influence of tillage practices, organic manures and extrinsic factors on β-glucosidase activity: The final step of cellulose hydrolysis. Soil Systems 2018, 2, 21. [Google Scholar] [CrossRef]
  79. Kwiatkowski, C.A.; Harasim, E.; Feledyn-Szewczyk, B.; Antonkiewicz, J. Enzymatic activity of loess soil in organic and conventional farming systems. Agriculture 2020, 10, 135. [Google Scholar] [CrossRef]
  80. Jurado, M.M.; Suárez-Estrella, F.; Vargas-García, M.C.; López, M.J.; López-González, J.A.; Moreno, J. Evolution of enzymatic activities and carbon fractions throughout composting of plant waste. J. Environ. Manag. 2014, 133, 355–364. [Google Scholar] [CrossRef]
  81. Pobereżny, J.; Wszelaczyńska, E.; Lamparski, R.; Lemanowicz, J.; Bartkowiak, A.; Szczepanek, M.; Gościnna, K. The impact of spring wheat species and sowing density on soil biochemical properties, content of secondary plant metabolites and the presence of Oulema ssp. PeerJ 2023, 11, e14916. [Google Scholar] [CrossRef]
  82. Wang, Y.; Tu, C.; Cheng, L.; Li, C.; Gentry, L.F.; Hoyt, G.D.; Zhang, X.; Hu, S. Long term impact of farming practices on soil organic carbon and nitrogen pools and microbial biomass and activity. Soil Tillage Res. 2011, 117, 8–16. [Google Scholar] [CrossRef]
  83. Yuan, J.; Sadiq, M.; Rahim, N.; Tahir, M.M.; Liang, Y.; Zhuo, M.; Yan, L.; Shaheen, A.; Mahmood, B.; Li, G. Changes in soil properties and crop yield under sustainable conservation tillage systems in spring wheat agroecosystems. Land 2023, 12, 1253. [Google Scholar] [CrossRef]
  84. Haruna, S.I. Influence of winter wheat on soil thermal properties of a Paleudalf. Int. Agrophys. 2019, 33, 389–395. [Google Scholar] [CrossRef]
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Figure 1. Schematic diagram of the vegetation pot experiment.
Figure 1. Schematic diagram of the vegetation pot experiment.
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Figure 2. Faba bean at BBCH 63 (flowers open 3 racemes per plant). Explanations of the abbreviations can be found in Section 2.
Figure 2. Faba bean at BBCH 63 (flowers open 3 racemes per plant). Explanations of the abbreviations can be found in Section 2.
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Figure 3. Leaf greenness index of the faba bean. Explanations of the abbreviations can be found in Section 2. Homogeneous groups marked with letters (a–d) were calculated separately for each phase of bean development.
Figure 3. Leaf greenness index of the faba bean. Explanations of the abbreviations can be found in Section 2. Homogeneous groups marked with letters (a–d) were calculated separately for each phase of bean development.
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Figure 4. The percentage of variability observed (η2) in the soil biochemical parameters. Explanations of the abbreviations can be found in Section 2. Variable factors: Fc—forecrop, Su—soil use, Fc×Su—interaction of factors.
Figure 4. The percentage of variability observed (η2) in the soil biochemical parameters. Explanations of the abbreviations can be found in Section 2. Variable factors: Fc—forecrop, Su—soil use, Fc×Su—interaction of factors.
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Figure 5. The rhizosphere effect (R:S) on the soil enzymatic activity. Explanations of the abbreviations can be found in Section 2. Homogeneous groups, marked with letters (a–d), were compared between soil use methods separately for each enzyme.
Figure 5. The rhizosphere effect (R:S) on the soil enzymatic activity. Explanations of the abbreviations can be found in Section 2. Homogeneous groups, marked with letters (a–d), were compared between soil use methods separately for each enzyme.
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Figure 6. PCA of the soil biochemical and chemical parameters. Explanations of the abbreviations can be found in Section 2.
Figure 6. PCA of the soil biochemical and chemical parameters. Explanations of the abbreviations can be found in Section 2.
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Figure 7. The interaction of soil enzyme activity and soil physicochemical properties, n = 24, *—significant differences at p = 0.05. Explanations of the abbreviations can be found in Section 2.
Figure 7. The interaction of soil enzyme activity and soil physicochemical properties, n = 24, *—significant differences at p = 0.05. Explanations of the abbreviations can be found in Section 2.
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Figure 8. The percentage of variability observed (η2) in the physicochemical properties of the soil. Explanations of the abbreviations can be found in Section 2.
Figure 8. The percentage of variability observed (η2) in the physicochemical properties of the soil. Explanations of the abbreviations can be found in Section 2.
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Table 1. Physicochemical properties of the soil used in the experiment.
Table 1. Physicochemical properties of the soil used in the experiment.
Soil PropertiesUnitForecrop (Fc)
Spring Wheat (SW)Winter Wheat (WW)Maize
(M)		Winter Rape (WR)
Soil type Silty loam clay (Slc)Silty loam clay (Slc)Silty loam clay (Slc)Silty loam clay (Slc)
Particle-size distribution
Clay (<0.002 mm)%1.000 ± 0.0261.000 ± 0.0102.000 ± 0.0211.000 ± 0.030
Silt (0.02–0.05 mm)69.000 ± 2.08265.000 ± 1.00069.000 ± 1.73261.000 ± 2.517
Sand (0.0–2.0 mm)30.000 ± 1.52834.000 ± 2.51729.000 ± 2.08238.000 ± 2.646
Chemical properties
pH1 Mol KCl dm−36.233 ± 0.0586.367 ± 0.0586.233 ± 0.0585.933 ± 0.058
Hydrolytic acidity (HAC)mMol+ kg−1 d.m. soil26.750 ± 0.43318.750 ± 0.75021.250 ± 0.43328.000 ± 0.433
Sum of exchangeable bases (EBC)238.000 ± 2.00298.000 ± 2.00386.667 ± 2.309210.000 ± 2.00
Sorption capacity (CEC)264.750 ± 2.385316.750 ± 1.250407.917 ± 2.554238.000 ± 2.385
Base saturation (BS)%89.896 ± 0.09094.080 ± 0.26095.645 ± 0.07688.236 ± 0.089
Organic carbon content (Corg)g kg−1 d.m. soil18.220 ± 0.02318.770 ± 0.01817.170 ± 0.01318.290 ± 0.012
Total nitrogen content (Ntot)2.460 ± 0.0092.630 ± 0.0042.625 ± 0.0062.745 ± 0.004
C:N ratio 7.406 ± 0.0097.137 ± 0.0356.541 ± 0.0606.663 ± 0.035
Table 2. Enzymatic properties of the soil (kg−1 d.m. soil).
Table 2. Enzymatic properties of the soil (kg−1 d.m. soil).
Forecrop (Fc)Dehydrogenases (Deh)
µMol TFF		Catalase (Cat)
Mol O2		Alkaline Phosphatase (Pal)Acid Phosphatase (Pac)β-Glucosidase (Glu)Arylsulfatase (Aryl)Urease (Ure)
mMol
N-NH4		Geometric Mean of Enzyme Activities (GMea)
mMol PNP
SW22.161 ± 0.3630.592 ± 0.0044.587 ± 0.2306.359 ± 0.1010.518 ± 0.0030.520 ± 0.0211.044 ± 0.0211.951 ± 0.001
WW12.056 ± 0.8720.622 ± 0.0046.419 ± 0.1446.340 ± 0.2520.430 ± 0.00 91.280 ± 0.0321.278 ± 0.0212.153 ± 0.002
M12.172 ± 1.3200.624 ± 0.0024.928 ± 0.1666.940 ± 0.0890.448 ± 0.0020.757 ± 0.0180.731 ± 0.0861.813 ± 0.002
WR20.112 ± 0.8100.554 ± 0.0044.408 ± 0.0837.202 ± 0.0220.475 ± 0.0050.542 ± 0.0221.317 ± 0.0651.981 ± 0.001
Explanations of forecrops can be found in Table 1.
Table 3. Biometry of the faba bean.
Table 3. Biometry of the faba bean.
FcStem Length
(cm)		Pod Number
(Plant−1)		Seed Number (Pod−1)Dry Matter Yield Of
SeedsLeavesStemsSilique
g d.m. Plant−1
SW93.880 ± 1.339 a6.120 ± 0.676 a1.708 ± 0.071 b3.915 ± 0.712 a2.489 ± 0.144 a3.554 ± 0.279 a2.213 ± 0. 092 a
WW94.943 ± 4.253 a5.300 ± 0.817 ab1.782 ± 0.075 b4.099 ± 1.018 a1.993 ± 0.340 ab3.488 ± 0.338 a2.157 ± 0.395 a
M92.307 ± 2.451 b4.967 ± 1.054 ab1.885 ± 0.078 a3.714 ± 0.496 ab2.168 ± 0.51 7 ab3.242 ± 0.554 b2.075 ± 0.372 b
WR89.783 ± 3.172 c4.233 ± 0.446 c1.810 ± 0.044 a3.408 ± 0.915 b1.860 ± 0.421 b2.976 ± 0.804 c1.739 ± 0.455 c
p-value0.0720.0040.0440.5080.0520.0080.029
Explanations of the abbreviations can be found in Section 2. Homogeneous groups marked with letters (a–c) were calculated separately for each parameter.
Table 4. Soil enzymatic activity (kg−1 d.m. soil).
Table 4. Soil enzymatic activity (kg−1 d.m. soil).
FcDeh
µMol TFF		Cat
Mol O2		PalPacGluArylUre
mMol
N-NH4		GMea
mMol PNP
Us
SW13.537 ± 1.118 d0.551 ± 0.004 d3.309 ± 0.018 f5.603 ± 0.157 d0.333 ± 0.006 g0.303 ± 0.004 e0.420 ± 0.018 g1.286 ± 0.009 f
WW8.375 ± 0.598 f0.588 ± 0.004 c3.877 ± 0.102 d3.898 ± 0.039 h0.408 ± 0.029 e0.350 ± 0.007 d0.892 ± 0.018 b1.378 ± 0.002 e
M5.558 ± 0.538 g0.591 ± 0.007 bc2.647 ± 0.056 g4.816 ± 0.049 e0.403 ± 0.014 e0.384 ± 0.014 c0.589 ± 0.034 d1.210 ± 0.017 g
WR15.023 ± 0.733 c0.531 ± 0.003 e2.486 ± 0.156 h4.153 ± 0.027 g0.380 ± 0.006 f0.330 ± 0.035 e0.498 ± 0.017 f1.262 ± 0.008 f
Ss
SW17.408 ± 0.490 b0.632 ± 0.004 a3.494 ± 0.128 e5.720 ± 0.103 c0.582 ± 0.001 a0.436 ± 0.028 a0.552 ± 0.018 e1.632 ± 0.022 d
WW12.273 ± 0.062 e0.639 ± 0.004 a5.663 ± 0.069 a4.375 ± 0.093 f0.416 ± 0.003 d0.411 ± 0.011 b1.245 ± 0.017 a1.702 ± 0.010 c
M19.249 ± 0.775 a0.651 ± 0.004 a4.004 ± 0.033 c6.421 ± 0.028 b0.430 ± 0.015 c0.438 ± 0.007 a1.241 ± 0.017 a1.853 ± 0.010 a
WR17.842 ± 0.838 b0.607 ± 0.007 b4.247 ± 0.001 b6.648 ± 0.135 a0.533 ± 0.002 b0.412 ± 0.007 b0.792 ± 0.017 c1.764 ± 0.004 b
p-value
Fc0.064<0.0010.086<0.001<0.001<0.0010.042<0.001
Su<0.001<0.001<0.001<0.001<0.001<0.001<0.001<0.001
Fc×Su0.032<0.0010.043<0.001<0.001<0.0010.023<0.001
Explanations of the abbreviations can be found in Section 2. Homogeneous groups marked with letters (a–h) were calculated separately for each enzyme.
Table 5. Physicochemical properties of the soil (kg−1 d.m. soil).
Table 5. Physicochemical properties of the soil (kg−1 d.m. soil).
FcpHHACEBCCECBS
(%)		CorgNtotC:N
mmol(+) kg−1 d.m. Soilg kg−1 d.m. Soil
Us
SW6.167 ± 0.058 b27.750 ± 0.750 b240.000 ± 2.000 d267.750 ± 1.750 d89.635 ± 0.299 c20.090 ± 0.230 a2.540 ± 0.090 a7.914 ± 0.190 ab
WW6.333 ± 0.58 a19.250 ± 0.433 e300.667 ± 2.082 b319.917 ± 1.665 b93.982 ± 0.165 a18.770 ± 0.180 c2.415 ± 0.035 bc7.773 ± 0.038 ab
M6.167 ± 0.058 b21.750 ± 0.750 cd228.000 ± 2.000 e249.750 ± 2.750 e91.293 ± 0.204 b19.030 ± 0.130 bc2.520 ± 0.060 ab7.555 ± 0.232 b
WR5.833 ± 0.058 c29.250 ± 0.750 b212.000 ± 2.000 f241.250 ± 1.750 f87.875 ± 0.336 e17.700 ± 0.120 d2.265 ± 0.035 d7.816 ± 0.174 ab
Ss
SW6.067 ± 0.058 b29.250 ± 0.750 b236.000 ± 2.000 d265.250 ± 2.750 d88.974 ± 0.168 d19.430 ± 0.060 b2.455 ± 0.015 ab7.915 ± 0.024 ab
WW6.333 ± 0.058 a20.250 ± 0.750 de334.000 ± 2.000 a354.250 ± 2.750 a94.285 ± 0.167 a20.260 ± 0.360 a2.525 ± 0.005 ab8.024 ± 0.158 a
M6.033 ± 0.058 b23.250 ± 0.750 c256.000 ± 2.000 c279.250 ± 2.462 c91.675 ± 0.222 b18.775 ± 0.325 c2.330 ± 0.020 cd8.058 ± 0.070 a
WR5.633 ± 0.058 d31.250 ± 0.433 a217.333 ± 1.155 f248.583 ± 1.588 e87.429 ± 0.094 c19.405 ± 0.085 b2.405 ± 0.025 bc8.069 ± 0.119 a
p-value
Fc<0.001<0.001<0.001<0.001<0.001<0.001<0.0010.046
Su<0.001<0.001<0.001<0.0010.053<0.0010.032<0.001
Fc×Su<0.001<0.001<0.001<0.0010.001<0.001<0.0010.023
Explanations of the abbreviations can be found in Section 2. Homogeneous groups denoted by letters (a–f) were calculated separately for each physicochemical parameter of the soil.
Table 6. Interactions between the studied soil properties and plant biometry.
Table 6. Interactions between the studied soil properties and plant biometry.
VariableFcDehCatPalPacArylGluUreGMeapHHACEBCCECBSCorgNtotC:NSLePNuSNuYDMSeYDMLeYDMStYDMSi
Fc1.000
Deh0.3501.000
Cat−0.438−0.0651.000
Pal0.083−0.8730.1001.000
Pac0.6090.940−0.340−0.7381.000
Aryl−0.2140.393−0.610−0.7130.3941.000 0.801 ÷ 1.00
Glu−0.3940.6520.578−0.7530.3550.2001.000 0.601 ÷ 0.800
Ure0.269−0.3260.6020.672−0.323−0.997 *−0.1741.000 0.401 ÷ 0.600
GMea0.7520.4740.2450.0040.532−0.5680.1190.6291.000 0.201 ÷ 0.400
pH−0.716−0.6670.7530.505−0.869−0.5210.1040.465−0.3051.000 0.01 ÷ 0.200
HAC0.2270.571−0.784−0.6950.6790.9030.054−0.874−0.220−0.8371.000 –0.200 ÷ 0.00
EBC−0.337−0.8600.5280.865−0.903−0.750−0.3500.698−0.1170.867−0.9091.000 −0.400 ÷ −0.201
CEC−0.346−0.8830.4940.874−0.918−0.726−0.3790.671−0.1530.861−0.8890.999 *1.000 −0.600 ÷ −0.401
BS−0.309−0.6630.7450.734−0.769−0.854−0.1070.8170.0950.885−0.991 *0.9520.9371.000 −0.800 ÷ −0.601
Corg−0.331−0.964 *−0.1780.777−0.861−0.158−0.7310.091−0.6170.496−0.3310.6940.7260.4411.000 −1.00 ÷ −0.801
Ntot−0.542−0.913−0.1330.614−0.8750.010−0.559−0.083−0.7870.552−0.2540.6250.6590.3770.967 *1.000
C:N0.9110.094−0.1200.3930.314−0.597−0.4380.6390.840−0.364−0.1960.0540.0380.111−0.173−0.4201.000
SLe−0.859−0.6540.6490.380−0.873−0.2950.1460.232−0.5180.969 *−0.6760.7640.7660.7460.5340.642−0.5761.000
PNu−0.989 *−0.2080.470−0.218−0.4920.2630.523−0.310−0.6950.657−0.1680.2300.2340.2380.1860.413−0.9270.8031.000
SNu0.7220.3620.3050.1210.419−0.6690.0630.7230.992 *−0.202−0.3350.011−0.0250.215−0.526−0.7080.859−0.429−0.6791.000
YDMSe−0.832−0.7050.6230.442−0.905−0.3250.0770.261−0.5170.973 *−0.6960.8020.8040.7690.5860.680−0.5380.997 *0.766−0.4221.000
YDMLe−0.8210.2300.281−0.638−0.0480.5730.736−0.592−0.5830.2640.223−0.235−0.233−0.182−0.1900.067−0.9290.4470.889−0.6270.3891.000
YDMSt−0.972 *−0.4860.5670.113−0.742−0.0160.315−0.043−0.6590.859−0.4450.5400.5440.5220.4160.589−0.7900.954 *0.943−0.6010.9360.6871.000
YDMSi−0.914−0.3340.7610.041−0.635−0.1520.4950.106−0.4410.880−0.5470.5260.5180.5930.2080.372−0.6930.9310.912−0.3850.9030.6830.962 *1.000
Explanations of the abbreviations can be found in Section 2. n = 12, *—significant differences at p = 0.05.
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Baćmaga, M.; Wyszkowska, J.; Kucharski, J. Influence of Forecrop on Soil Quality Estimated on the Basis of the Growth and Development of Faba Bean and Biochemical Properties of the Soil. Sustainability 2024, 16, 7492. https://doi.org/10.3390/su16177492

AMA Style

Baćmaga M, Wyszkowska J, Kucharski J. Influence of Forecrop on Soil Quality Estimated on the Basis of the Growth and Development of Faba Bean and Biochemical Properties of the Soil. Sustainability. 2024; 16(17):7492. https://doi.org/10.3390/su16177492

Chicago/Turabian Style

Baćmaga, Małgorzata, Jadwiga Wyszkowska, and Jan Kucharski. 2024. "Influence of Forecrop on Soil Quality Estimated on the Basis of the Growth and Development of Faba Bean and Biochemical Properties of the Soil" Sustainability 16, no. 17: 7492. https://doi.org/10.3390/su16177492

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