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Article

Influence of Pesticides and Mineral Fertilizers on the Bacterial Community of Arable Soils under Pea and Chickpea Crops

by
Ludmila Khmelevtsova
1,
Elizaveta Konstantinova
1,
Shorena Karchava
1,
Maria Klimova
1,
Tatiana Azhogina
1,
Elena Polienko
2,
Margarita Khammami
1,
Ivan Sazykin
1 and
Marina Sazykina
1,*
1
Academy of Biology and Biotechnologies, Southern Federal University, 344090 Rostov-on-Don, Russia
2
Federal State Budget Scientific Institution “Federal Rostov Agricultural Research Centre”, Rassvet, 346735 Rostov Oblast, Russia
*
Author to whom correspondence should be addressed.
Agronomy 2023, 13(3), 750; https://doi.org/10.3390/agronomy13030750
Submission received: 4 February 2023 / Revised: 1 March 2023 / Accepted: 2 March 2023 / Published: 4 March 2023
(This article belongs to the Special Issue Impact of Agrochemicals on Soil)
Browse Figures
Versions Notes

Abstract

:
Fertile Chernozems of Southern Russia are of great value, so it is important to study the impact of agricultural activities on the soil quality. Changes in taxonomic composition and α-diversity of microbial communities of agricultural soils occupied by pea (Pisum sativum L.) and chickpea (Cicer arietinum L.) in response of cropland management were studied. A field experiment was conducted under four different conditions: (1) control, (2) mineral fertilizers (NPK) application alone, (3) pesticides application alone, and (4) fertilization combined with pesticides. The taxonomic composition of the soil bacterial community was studied by amplification and sequencing of the 16S rRNA gene. The predominance of Actinobacteria (17.7–32.3%), Proteobacteria (17.7–28.2%), Planctomycetes (10.1–21.3%), Acidobacteria (5.3–11.1%), Chloroflexi (1.0–7.1%), Gemmatimonadetes (2.5–8.0%), Bacteroidetes (3.6–11.3%), and Verrucomicrobia (3.9–9.2%) was noted. Introduction of pesticides led to an increase in the relative abundance of Chlorobi and Gemmatimonadetes. The time of sampling was the main significant factor determining the differences in the structure of soil microbial communities. All treatments did not have a significant effect on the α-diversity of the studied soils. Thus, treatment with mineral fertilizers and pesticides does not have a significant negative effect on the bacterial community of cultivated soils.

1. Introduction

Soil is a key component of the agroecosystem. Intensification of agriculture to meet the growing needs of the society can lead to deterioration, depletion and degradation of the soil cover. Traditional farming practices typically involve plowing and applying various chemicals (pesticides, herbicides, mineral fertilizers, etc.) during the vegetation period of cultivated plants. To date, it has been shown that agricultural practices can significantly affect soil microorganisms and the conditions in which they exist [1]. Bacteria play an important role in maintaining soil health by participating in the nutrient cycle, decomposition of plant residues, and antagonistic activity against soil pathogens [2,3]. In this regard, changes in the structure and functions of the soil microbial community can directly affect soil quality and crop yields.
A number of studies have found that the use of inorganic fertilizers can negatively affect the diversity of soil microbial community, including through soil pH changes [4,5,6]. Nevertheless, a positive effect of mineral fertilizers has also been shown, mainly mediated by an increase in plant biomass and rhizodeposition [7]. The proportion of labile organic C and nutrient content in plant material vary significantly in different cultivated species, which is a key factor in the development of soil microbial communities [8]. Data on the effect of cropland management on the taxonomic composition and diversity of soil microorganisms are also contradictory [9,10,11]. To a large extent, this scatter of data is due to differences in the physicochemical properties of the studied soils, climatic conditions, and other concomitant factors. Thus, in order to fully understand the impact of agricultural practices on soil bacteria, it is important to obtain, accumulate and analyze data on soil microbial communities in various regions.
Despite the fact that the study of soil microbial communities using modern molecular genetic methods is carried out almost all over the world, the microbiomes of extremely diverse soils in Russia are still not studied in such detail [12]. However, in recent years, the problem of studying the soil metagenome has attracted increasing attention of Russian researchers. The geography of these studies is quite diverse—from the swamps of the forest-tundra of the Nenets Autonomous Okrug outside the Arctic Circle [13] to the alkali soils of the Caspian Lowland [14]. The soils of Southern Russia are of great importance, in particular the highly productive fertile chernozems widely distributed in the region, which are the basis for the development of agriculture. There is evidence of the agriculture impact on the cultivated Chernozems of the Kamennaya Steppe Reserve (Voronezh Oblast) [12,15,16] and Stavropol Krai [17].
This study aims to characterize the microbial communities of chernozem agricultural soils after growing peas and chickpeas using inorganic fertilizers and pesticides. It was also of interest to evaluate the impact of agricultural practices on soil physicochemical properties, and how they relate to the composition and diversity of soil bacteria.

2. Materials and Methods

2.1. Study Area and Experimental Design

The field experiment was carried out from April to July 2021 in the Rassvet village of the Rostov region, Russia (47°21′40″ N, 39°52′50″ E). The climate of the territory is a temperate continental. The average annual precipitation is 530–550 mm. The average monthly temperature ranges from −5 °C to −9 °C in winter and from 22 °C to 24 °C in summer. The soils are classified as Calcic Chernozems (Loamic).
The experimental design involved four treatment conditions: control, application of fertilizers alone, application of pesticides alone, and combined use of fertilizers and pesticides, applied in chernozems cultivated with chickpea (Cicer arietinum L.) and pea (Pisum sativum L.). Nitrogen-phosphorus-potassium (NPK) fertilizers grade 15:15:15 (S10) in the dosage of N40P40K40 were used. Detailed information about the applied chemical plant-protection agents (PPA) is given in Table 1. Each treatment was carried out in triplicate. In total, 24 plots with an area of 20 × 12 m2 were organized. Soil samples were taken twice during vegetation before the introduction of PPA (27 May 2021), and at the time of harvest (13 July 2021). Soil samples were taken from the depth of 0–20 cm by the envelope method [18,19]. Soil samples were thoroughly mixed, placed into Falcon plastic tubes (50 mL), and stored at −20 °C.

2.2. Analysis of Soil Physicochemical Properties

Exchangeable ammonium was determined by extraction with potassium chloride and a photoelectric colorimetry [20]. Nitrates were measured by the ionometric method [21]. Mobile compounds of phosphorus and potassium were extracted by ammonium carbonate solution and analyzed using a photoelectric colorimeter according to the Machigin method in modification [22]. The soil organic matter (SOM) in the soils was determined by the dichromate oxidation method using a photoelectric colorimeter [23]. The soil pH was measured in a 1:5 soil-water suspension using a pH meter, and solid residue content of water extracts was measured by weighing after evaporation of the filtrate on a water bath and keeping in a heating block for 3 h at 105 °C [24].

2.3. Total DNA Isolation and Metagenomic Analysis of 16S rRNA Genes

The 16S rRNA sequencing method was used to study soil microorganisms. Total DNA was isolated from soil samples using the innuSPEED Soil DNA Kit (Analytik Jena GmbH, Jena, Germany) according to the manufacturer’s instructions. 16S rRNA sequencing libraries were constructed according to the 16S metagenomic sequencing library preparation protocol (16S metagenomic sequencing library preparation).
Amplification of the V3-V4 region of 16S rRNA was performed using prokaryotic primers: direct−TCGTCGGCAGCGTCAGATGTGTATAAGAGACAGCCTACGGGNGGCWGCAG; reverse−GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAGGACTACHVGGGTATCTAATCC followed by amplicon indexing. Sequences were analyzed by next generation sequencing using the MiSeq system (Illumina, San Diego, CA, USA) in the 2 × 300 bp mode. The readings were processed and analyzed using the QIIME software version 1.9.1 (http://qiime.org/ (accessed on 11 November 2022) [25]. After qualitative filtering, chimera removal, and sparsing steps, the sequences were grouped into operational taxonomic units (OTUs) with a sequence similarity threshold of 97%. The latest version of the GreenGenes 13.8 database was used [26].
To characterize the richness and evenness of the bacterial community, we calculated the phylogenetic distance metric [PD_Whole_Tree], Chao1, Shannon, and Simpson indices. Similarities in bacterial composition of samples was assessed using beta diversity characteristics, and weighted and unweighted Unifrac values were calculated using QIIME software and further visualized using PCoA.

2.4. Statistical Analysis

Statistical analyses were conducted with STATISTICA 12 (Statsoft Inc., Tulsa, OK, USA), and data visualization was performed with Grapher 17 (Golden Software, Golden, CO, USA). Basic descriptive statistics included mean, median, minimum, maximum, standard deviation (SD). The α-diversity indices relative to the soil samples under different experiment conditions were represented by box-plots.
Differences in the distribution of the soil bacterial community from cultivated crops (pea vs. chickpea) and sampling period (before vs. after harvesting) were estimated based on the analysis of the results of the Mann-Whitney U Test. Comparison of the relative abundance of bacteria at the phylum level depending on the method of soil treatment was carried out using Kruskal-Wallis analysis of variance (ANOVA) followed multiple comparisons of mean ranks for all groups. The significance level was set at p < 0.05.
Principal component analysis (PCA) was carried out in order to identify relationship between the structure and diversity of microbial community and soil physicochemical properties by achieving individual component loadings. The relative abundance of the dominant bacterial phyla and soil characteristics were used as active and supplementary variables, respectively. Only principal components with eigenvalues above 1.0 were considered, following the Kaiser criteria.

3. Results

3.1. Soil Physicochemical Properties

The basic physicochemical properties of soils at different treatment conditions are shown in Table 2. At the beginning of the growing season, the content of NH4+–N, NO3−–N, P2O5, and K2O in the treated soils cultivated with pea were generally lower than in the control soils. In the treated soils under chickpea, the reverse trend was observed, with K2O being comparable to the control soils. During the maturation of crops in general, the content of NH4+–N and P2O5 decreased and the content of NO3−–N and K2O increased. The content of SOM in the treated soils with different treatment options at the beginning of the experiment was comparable to the control and slightly increased by the end of the growing season. The reaction of soils, regardless of the cultivated crop and treatment conditions, corresponded to neutral. Values of pH were higher in soils treated with fertilizers and/or pesticides compared to control. During the growing season, in soils, regardless of the cultivated crop, pH decreased only when the soil was treated with mineral fertilizers separately, while in all other variants, pH increased.

3.2. Abundance and Variability of the Bacterial Community in Relation of Experimental Conditions

A total of 225,377 (14,086 ± 311 per sample) 16SV4-V5 bacteria sequences were recovered from 16 soil samples after paired-end alignments, quality filtering, and deletion of chimeric sequences. These were subsequently assigned to a 4412 ± 201 sequence variants per sample. A total of 34 phyla, 106 classes, 144 orders, 180 families, and 235 bacterial genera were detected for all the samples. Actinobacteria was the dominant phylum (17.7–32.3%), followed by Proteobacteria (17.7–28.2%), Planctomycetes (10.1–21.3%) Acidobacteria (5.3–11.1%), Chloroflexi (1.0–7.1%), Gemmatimonadetes (2.5–8.0%), Bacteroidetes (3.6–11.3%), and Verrucomicrobia (3.9–9.2%) (Figure 1A).
The time of sampling was a statistically significant factor in differentiating the relative abundance for Actinobacteria (p = 0.018), FBP (p = 0.041), OD1 (p = 0.003), OP11 (p = 0.040), and SR1 (p = 0.032). During the growth of cultures, the relative abundance of Actinobacteria (from 26.0 to 28.2%) increased, while the relative abundance of FBP (from 0.25 to 0.16%), OD1 (from 1.08 to 0.34%), OP11 (from 0.05 to 0.02%), and SR1 (from 0.004 to 0.0%) decreased. Results of the Mann-Whitney U Test showed no significant differences between crops at the phylum level (p > 0.05). The only exception was TM7 (p = 0.031), the relative abundance of which was higher in soils under pea (0.81%) compared to chickpea (0.58%). The Kruskal-Wallis test revealed statistically significant differences in the relative abundance of Chlorobi (χ2 = 8.360, p = 0.039), and Gemmatimonadetes (χ2 = 8.381, p = 0.035) between soils with different treatment. The relative abundance of Chlorobi decreased in the series: pesticide application (0.09%) > control = combined treatment (0.06%) > fertilization (0.05%). In soils treated with pesticides, the relative abundance of Gemmatimonadetes was greatest (6.3%), less in control samples (5.9%), followed by fertilized soils (5.2%) and combined treated soils (4.0%).
At the genus level, the highest relative abundance was characteristic of Kaistobacter (0.0–2.28%), Sphingomonas (0.01–1.35%), Rhodoplanes (0.0–1.2%), Pseudomonas (0.04–2.95%), and Balneimonas (0.0–1.04%) belonging to Proteobacteria, Opitutus (0.75–1.73%) and DA101 (0.56–1.71%) belonging to Verrucomicrobia, Gemmata (0.67–1.33%) belonging to Planctomycetes, Rubrobacter (0.41–1.02%), Arthrobacter (0.04–1.79%), and Streptomyces (0.01–1.06%) belonging to Actinobacteria, and Flavisolibacter (0.34–1.11%), Flavobacterium (0.05–1.04%), and Niastella (0.0–1.03%) belonging to Bacteroidetes (Figure 1B). Stenotrophomonas (Proteobacteria) (0.0–1.5%) can be considered as a specific genus, since these bacteria were found mainly in samples taken after the harvesting of crops. Lactobacillus (Firmicutes) was the rarest genus found in the studied soils, namely in the control sample before planting pea (3.0%) and in the pesticide-treated sample before planting chickpea (0.09%).
In general, the main significant factor determining differences in the structure of soil microbial communities was the time of sampling, which is confirmed by the results of PCA. Figure 2 shows two separate clusters in the coordinates of the two principal components.

3.3. Effect of Experimental Conditions on Microbial Diversity

The indices of the ɑ-diversity of bacterial communities in the samples of the studied soils are presented in Appendix A Table A1 The values of the ɑ-diversity indices varied slightly depending on the experimental conditions. In general, the values of phylogenetic distance metric were 62.1–90.7 with a mean of 85.8 and SD of 6.6, the Chao1 index varied from 5205.6 to 8132.3 (mean was 7174.1 and SD was 646.8), the Shannon index varied from 10.8 to 11.1 (mean was 11.0 and SD was 0.1), and the Simpson index varied from 0.998 to 0.999 (mean was 0.998 and SD was 0.0003). No statistically significant differences were found in the diversity of microbial communities depending on the experimental conditions (Figure 3).

3.4. Effect of Physicochemical Properties on Microbial Community

The principal component analysis (PCA) was used to reveal the relationship between soil conditions and the structure of microbial communities. The main phyla with a relative abundance of more than 1% were considered (Table A2). Two principal components (PCs) were identified with eigenvalues exceeding one (5.8, and 1.5, respectively) and describing 81.4% of the initial data. The PC1 is the most significant since it determines 64.7% of the total variance. On PC1, Planctomycetes, Chloroflexi, and Actinobacteria have strong positive loadings (0.910, 0.903, and 0.866, respectively), while the strong negative loadings are noted for Verrucomicrobia (−0.936), Bacteroidetes (−0.933), Proteobacteria (−0.830), and Acidobacteria (−0.709). Also, PC1 is characterized by moderate negative loadings of Gemmatimonadetes (−0.689). In PC2, which describes 16.7 % of the total variability of the data, Armatimonadetes have a moderate positive loadings (0.510). The main physicochemical properties of the soils were used as supplementary variables. No significant correlations were found between PC1 and parameters of the soils. Nitrates and solid residue of the water extract have high positive correlations with PC2. The results of the analysis are graphically presented in Figure 4, which shows the projections of the variables onto the factor- plane. In general, soil properties have little effect on the structure of the bacterial community. A statistically significant positive relationship was noted between nitrate content and salinity and the abundance of Proteobacteria.

4. Discussion

4.1. Changes in Taxonomic Composition

The taxonomic structure of microbial community at the phylum level was in general quite typical for agricultural soils. Actinobacteria predominated, followed by Proteobacteria, Planctomycetes, Acidobacteria, Chloroflexi, Gemmatimonadetes, Bacteroidetes and Verrucomicrobia. Similar results were obtained in other studies of bacterial communities of cultivated soils [27,28,29].
According to the results of present study, a significant factor determining differences in the structure of soil microbial communities was the sampling time. This correlates with a number of other studies in which the influence of sampling time on the taxonomic composition and diversity of the soil microbiome was also noted [30,31,32,33]. When treating the soil with natural β-triketone herbicide leptospermon and synthetic sulcotrione, it was found that composition of the bacterial community was significantly influenced by the time of sampling, the application dose (one or 10 recommended doses) and the type of herbicide. At the same time natural herbicide affected the taxonomic composition of the community much stronger than the synthetic [30]. In soils under organic and traditional management, the relative abundance of large phyla (for example, Acidobacteria and Actinobacteria) primarily depended on the year of sampling, and not on crop management [31]. The crop management system affected some of less dominant phyla (Chloroflexi, Nitrospirae, Gemmatimonadetes), which also correlated with pH and organic nitrogen. Leitner et al. showed that abundance of soil microbial community is dynamic on the short-term scale of agricultural crops growing season [32]. Using a multilevel approach, Richter-Heitmann et al. confirmed the significant influence of sampling dates on the composition of the community [33]. The composition of the communities in the soils, sampled in April and June, were the most different, which is related to the influence of vegetation cover on the studied sites.
In the present study, the relative abundance of Actinobacteria increased at the end of the agricultural crops vegetation period, which may be due to intensive development of plant roots, the positive influence of organic substances released by them, and the accumulation of nitrogen as a result of symbiotic nitrogen fixation. In addition, community variability over time in agricultural soils may be due to soil moisture and temperature [34]. The increase in the number of Actinobacteria can indicate a good condition of the investigated soils. In a number of studies, it was shown that the abundance of Actinobacteria decreases in degraded soils compared to healthy ones [35], and their abundance is significantly higher in untreated soils than in agricultural plots [36].

4.2. Pesticides Influence of on the Bacterial Community of Arable Soil

The toxic effect of pesticides on soil microbial diversity as a bioindicator has been considered in a number of studies [37,38,39]. Egbe et al. reported the negative impact of a mixture of organochlorine pesticides on the autochthonous community of agricultural soils, expressed in a decrease in the number of Nitrospirae species and disappearance of Glomeromycota [40]. Agricultural soils in India, chronically polluted with organophosphorus compounds and pyrethroids, had the lowest microbial diversity, limited species richness and homogeneity [28].
We assumed to observe a similar trend of the pesticide negative impact on the microbial community of soils. However, it was shown that introduction of PPA in concentrations recommended by the manufacturer slightly changed the soil microbiome taxonomic composition and did not affect its α-diversity. It was found that in the soils treated with pesticides, the relative abundance of Chlorobi and Gemmatimonadetes was higher. Phylum Gemmatimonadetes was identified not so long ago, and its role in soils is not quite clear. It is known that representatives of this phylum can be especially adapted to arid environments [31].
Interestingly, other researchers noted the complete disappearance of Chlorobi in the soil treated with organochlorine pesticides [40]. At the same time, bacteria belonging to the order Chlorobiales were recognized as insensitive to human agricultural practices [41].
The organochlorine pesticide lindane in concentrations of 50 and 100 mg kg−1 did not affect the α-diversity of the soil microbiome of the arable field in Schierne, Germany [42]. Proteobacteria, Gemmatimonadetes, Actinobacteria, Acidobacteria, Firmicutes, and Bacteroidetes were the dominant phyla both in the main and in the rhizosphere soil. Betaproteobacteriales (especially Burkholderiaceae), Rhizobiales, Sphingomonadales taxa responded positively to lindane contamination. Lindane had a strong negative influence on the number of nitrogen-fixing bacteria in the bulk of soil. In some studies, it is noted that the effects of pesticides depend on their class, for example, application of insecticides significantly increased the number of microbial communities [43]. Hexaconazole fungicide did not affect the overall bacterial diversity and community structure of rice soils in China when applied in the concentrations of 0.6 and 6 mg kg−1. At the same time, hexaconazole temporarily reduced the bacterial biomass in both soil types in all tested doses [44]. When the soil of apple orchards was treated with the dazomet fungicide, its effect on richness and α-diversity of the soil microbiome was not detected after 19 months of treatment [45]. Application of pyraclostrobin fungicide in wheat/maize crop rotation for 2 years did not have a negative effect on soil fungi and bacteria but affected the relative abundance of bacterial phyla in the field [46]. Treatment of greenhouse soils under cucumber crops with fluopyram, triadimenol and penthiopyrad (broad-spectrum foliar fungicides) affected the bacterial community composition [47]. During treatment with pentiopyrad the amount of Chloflexi and Firmicutes decreased, and during treatment with fluopyram and triadimenol—the amount of Bacteroidetes and Acidobacteria.
At the genus level, as shown in Figure 1B, Kaistobacter, Opitutus, DA101, Sphingomonas, Rhodoplanes, Gemmata, Rubrobacter, Flavisolibacter, Arthrobacter, Streptomyces, Pseudomonas, Flavobacterium, Balneimonas, and Niastella were the most abundant. The abundance of the most numerous genus Kaistobacter in the soil cultivated with pea increased after pesticide treatment (separately and as a part of combined treatment). Liu et al. also pointed out that Kaistobacter which has the ability to suppress tobacco diseases, was the most abundant in soils. In addition, these bacteria are able to biodegrade both EPTC (S-ethyldipropylthiocarbamate) and atrazine in soils [48]. This genus belongs to the Sphingomonadaceae family, which also contains destructor strains and nitrogen-fixers.
In a number of studies, it has been shown that Xanthomonadales, Sphingomonadales, Pseudomonadales are known as degraders of chlorinated pesticides [49], and bacteria belonging to the orders Burkholderiales, Rhizobiales, Acidobacteriales are abundant in agricultural soil contaminated with chlorpyrifos [50]. The relative abundance of Sphingomonas in our experiment increased with combined treatment (by 152% in pea and 139% in chickpea), but decreased with separate application of inorganic fertilizers. Interestingly, the relative abundance of nitrogen fixers of the Rhizobiales order also increased in the combined treatment by 114% in pea and 103% in chickpea and decreased in unbalanced treatments (only fertilizers and only pesticides). This may be due to better development of agricultural plants in fertilized soil in the absence of weeds and insect pests. In turn, this leads to an increase of root exudates amount, which has a beneficial effect on soil microorganisms.
Three different insecticides were used in the present study, belonging to the classes of neonicotinoids (thiamethoxam), pyrethroids (alpha-cypermethrin and lambda-cyhalothrin), as well as organophosphorus compounds (dimethoate). As a fungicide, the complex preparation “Ceriax Plus” was used, containing substances of the classes of strobilurins (pyraclostrobin), carboxamides (fluxapyroxad) and azoles (epoxiconazole). In addition to this, a herbicide containing bentazone (class thiadiazine) was applied.
They have the optimal form of suspension concentrates (SC), emulsion (EC) and colloidal solution (CS), which allows them to be applied by spraying with fine-dispersion sprayers. This ensures the maximum coverage of plant organs in tiers and less pollution of the soil cover. This can explain the absence of a significant negative influence of pesticides on the soil bacterial community.
In addition, their action was considered in a relatively short-term perspective (during the growing season of agricultural plants). Cycoń and Piotrowska-Seget [51] showed that after 7–10 days of cultivation of individual strains or mixed microbial communities, the degradation rate of pyrethroids was about 70–93% [52].
A mixture of seven pesticides was degraded by a bacterial consortium for 12 days [52]. Thus, at the moment of sampling, the concentration of pesticides in the soil could be very insignificant.

4.3. Fertilizers Influence on the Bacterial Community of Arable Soils

Application of inorganic fertilizers to arable soils to increase the yield of agricultural crops is widespread. However, there is evidence that this can lead to soil degradation, changes in its pH and the amount of organic carbon [53]. Application of mineral fertilizers (NPK) did not have a significant effect on the taxonomic composition and α-diversity of the soil bacterial community under chickpea and pea crops.
Researchers have obtained rather contradictory data on the influence of inorganic fertilizers on soil microbial community. Guo et al. found that the relative abundance of Actinobacteria and Chloroflexi was higher in the soil treated with NPK than in the soil with organic fertilizers [54]. Metaanalysis of 37 studies, conducted by Bebber and Richards, did not reveal a significant difference in taxonomic diversity between NPK-fertilized soils and control [55].
Taxonomic diversity of bacteria and archaea was on average 2.9% greater in the soil with organic fertilizers (ORG) compared to control and 2.4% greater in ORG compared to NPK. An earlier metaanalysis also found no significant NPK effects overall [56]. Long-term (more than 30 years) application of mineral and organic fertilizers to the black earth soils of China showed that the sampling point location had a greater effect on the soil microbial community than the fertilization regime. Long-term exposure to nitrogen fertilizers did not lead to a noticeable shift in the bacterial community [57]. However, in some studies, a negative effect of mineral fertilizers on the soil microbiome was noted [4,5].
In our study, the weak response of microbial community to application of inorganic fertilizers may be due to the fact that a shorter period of time and/or a lower nitrogen introduction was evaluated than in the indicated studies. In addition, the positive effect of fertilizers on development and productivity of agricultural plants, in turn, could mitigate a part of the possible negative effects for soil microorganisms. It should be noted that usage of inorganic fertilizers is an important factor that ensures the economic benefit of agricultural production.
For example, when growing corn, NPK application significantly (p < 0.05) increased grain yield relative to the control. At the full rate of NPK application, the net profit increased significantly compared to the half rate [58].
Therefore, at the moment, it is difficult to find an alternative of equal value to the use of mineral fertilizers. Thus, it can be said that usage of inorganic fertilizers in compliance with timing and dosage of application does not have a significant negative impact on the microbial community of agricultural soils in the short term.

4.4. Influence of Physicochemical Properties on the Microbial Community

Results of the present study showed that overall soil properties had little effect on bacterial community structure. Physicochemical soil properties were not a factor explaining more than half (64.7%) of the total data variance.
For agricultural soils, this can be explained by the fact that a high supply of nutrients to increase productivity can provide bacteria with a sufficient substrate for growth, which weakens the connection between their abundance and the physicochemical properties of the soil (compared, for example, to natural soils).
In the work of Zhang et al. it was noted that Actinobacteria, Gemmatimonadetes, Chloroflexi, Acidobacteria, Planctomycetes, Proteobacteria and Verrucomicrobia, which make up a large proportion of the bacterial community, showed a weaker connection with the physical and chemical properties of the soil [59].
A significant amount of works show that pH and soil type are important factors shaping the composition and structure of the soil bacterial community [28,55,59,60,61,62].
In our case, various treatments did not cause a significant shift in the pH of the examined black earth soils, therefore, probably, we did not observe a pronounced influence on the soil bacterial communities.
Sampling time also plays its role, as, for example, in a long-term field experiment it was found that pH was an important factor affecting the bacterial community structure only in winter and summer [63]. In addition to control factors (introduction of fertilizers and pesticides) in our work, it is possible to assume a significant influence of other, unaccounted-for factors that can have a stronger influence on the microbial community.

5. Conclusions

This research tackled the effect of mineral fertilizers and pesticides on taxonomic composition and α-diversity of the bacterial community of agricultural soils under chickpea and pea crops.
In general, no negative impact of these agrochemicals on the bacterial community of soils under crops of legumes was noted. It was discovered that pesticides influenced the community taxonomic composition increasing the relative abundance of the Chlorobi and Gemmatimonadetes phyla. Both pesticides and mineral fertilizers had no effect on α-diversity of soil bacteria during the vegetative period of agricultural plants. The soil microbiome of the studied soils was significantly affected not only by agrochemical treatments but also by sampling time.
Thus, the bacterial community under crops of leguminous plants showed resistance to agricultural practices. The obtained results can be useful in planning and carrying out land treatment.

Author Contributions

Conceptualization, L.K. and I.S.; data curation, E.K.; formal analysis, E.K. and T.A.; funding acquisition, L.K.; investigation, S.K., M.K. (Maria Klimova) and E.P.; methodology, I.S.; project administration, L.K. and I.S.; software, E.K.; supervision, M.S.; validation, T.A.; writing—original draft preparation, L.K. and E.K.; writing—review & editing, M.K. (Margarita Khammami), E.K. and M.S. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Russian Science Foundation, grant number 21-76-10048, https://rscf.ru/en/project/21-76-10048/ (accessed on 1 February 2023), at Southern Federal University.

Data Availability Statement

All data supporting the results of this study are available within the paper.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

Table
Table A1. Indices of the ɑ-diversity of bacterial communities.
Table A1. Indices of the ɑ-diversity of bacterial communities.
CropTreatmentPD_Whole_TreeChao1ShannonSimpson
PeaControl86.06843.810.80.998
85.67581.310.90.998
Fertilization90.77316.011.10.999
89.67335.611.10.999
Pesticide application90.67138.411.00.998
87.47873.311.10.998
Fertilization + Pesticide application87.76871.910.90.998
88.48132.311.00.998
ChickpeaControl84.87068.810.90.998
86.07402.111.00.998
Fertilization89.16750.710.90.998
84.47552.310.90.998
Pesticide application86.47538.311.10.999
62.15205.710.80.998
Fertilization + Pesticide application87.77241.511.00.999
86.16933.910.90.998
Table
Table A2. Correlation matrix between principal components (PC) and variables (factor loadings matrix).
Table A2. Correlation matrix between principal components (PC) and variables (factor loadings matrix).
VariablePC 1PC 2
Actinobacteria0.8660.432
Proteobacteria−0.8300.510
Planctomycetes0.910−0.144
Acidobacteria−0.709−0.463
Chloroflexi0.903−0.178
Gemmatimonadetes−0.689−0.370
Bacteroidetes−0.9330.137
Verrucomicrobia−0.936−0.004
Others0.149−0.797
*N-NH40.160−0.210
*N–NO3−0.2840.753
*P2O50.254−0.112
*K2O0.2110.186
*pH0.0490.335
*SR−0.1300.738
*SOM0.0640.176
Notes: An asterisk indicates auxiliary variables. High factor loads are marked in bold, medium factor in italics.

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Agronomy 13 00750 g001 550
Figure 1. Relative abundance of soil bacterial communities at the phylum (A) and genus (B) levels in soils under pea and chickpea before (B_) and after (A_) plant protection application under different treatment conditions: control (c), fertilization (f), pesticide application (p), and combined treatment (f + p). The taxa were classified as “others” when the relative abundance was less than 1%.
Figure 1. Relative abundance of soil bacterial communities at the phylum (A) and genus (B) levels in soils under pea and chickpea before (B_) and after (A_) plant protection application under different treatment conditions: control (c), fertilization (f), pesticide application (p), and combined treatment (f + p). The taxa were classified as “others” when the relative abundance was less than 1%.
Agronomy 13 00750 g001
Agronomy 13 00750 g002 550
Figure 2. Principal component analysis (PCA) of bacterial community composition in soils under different experimental conditions.
Figure 2. Principal component analysis (PCA) of bacterial community composition in soils under different experimental conditions.
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Agronomy 13 00750 g003 550
Figure 3. Effect of experimental conditions on α-diversity indices: phylogenetic distance metric (A), Chao1 (B), Shannon (C), and Simpson (D). The ends of the whiskers represent the minimum and maximum, the box edges are quartiles, and the central line is the median. No significant differences were observed for the group comparisons based on Mann-Whitney U Test and Kruskal-Wallis ANOVA.
Figure 3. Effect of experimental conditions on α-diversity indices: phylogenetic distance metric (A), Chao1 (B), Shannon (C), and Simpson (D). The ends of the whiskers represent the minimum and maximum, the box edges are quartiles, and the central line is the median. No significant differences were observed for the group comparisons based on Mann-Whitney U Test and Kruskal-Wallis ANOVA.
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Agronomy 13 00750 g004 550
Figure 4. Factor loadings plots of the abundance of bacterial phyla and the soil physicochemical properties on main principal components. The length of the arrows approximates the variance of the variables, whereas the angles among them estimate their correlations.
Figure 4. Factor loadings plots of the abundance of bacterial phyla and the soil physicochemical properties on main principal components. The length of the arrows approximates the variance of the variables, whereas the angles among them estimate their correlations.
Agronomy 13 00750 g004
Table
Table 1. Chemical plant protection products used in this study.
Table 1. Chemical plant protection products used in this study.
Plant-Protecting AgentTrade NameCompositionApplicationDose
(L ha−1)		Crop
InsecticidesEuphoria106 g L−1 lambda-Cyhalothrin +
141 g L−1 thiamethoxam		SC0.2Pea/Chickpea
Fascord100 g L−1 alpha-cypermethrinEC0.2Pea
BI-58 New400 g L−1 dimethoateEC1.0Pea
FungicidesCeriax Plus66.6 g L−1 pyraclostrobin + 41.6 g L−1 fluxapyroxad + 41.6 g L−1 epoxiconazoleEC0.4Pea/Chickpea
HerbicidesBenito300 g L−1 bentazoneCC3.0Pea/Chickpea
Notes: SC are suspension concentrates, EC are emulsion concentrates, and CC are colloidal concentrates.
Table
Table 2. The physicochemical characteristics of soil.
Table 2. The physicochemical characteristics of soil.
CropTreatmentSample DateNH4+–N
(mg kg−1)		NO3−–N
(mg kg−1)		P2O5
(mg kg−1)		K2O
(mg kg−1)		pHSolid Residue
(% w/w)		SOM
(%)
PeaControl27 May 20215.26.439.4382.06.590.0523.88
13 July 20213.69.627.2372.56.680.0503.95
Fertilization27 May 20214.84.546.4353.47.310.0473.92
13 July 20214.811.031.7362.96.990.0583.96
Pesticide application27 May 20214.03.818.1334.36.690.0453.91
13 July 20214.48.716.2334.36.840.0484.06
Fertilization + Pesticide application27 May 20218.15.933.2334.36.830.0423.86
13 July 20215.49.432.4343.86.780.0593.95
ChickpeaControl27 May 20214.15.627.9343.86.790.0413.80
13 July 20213.813.525.7367.76.670.0593.91
Fertilization27 May 20216.55.933.0343.87.270.0573.83
13 July 20215.014.125.3353.46.810.0703.82
Pesticide application27 May 20215.15.619.8343.86.840.0453.83
13 July 20214.315.920.0343.87.040.0693.91
Fertilization + Pesticide application27 May 20215.46.830.9334.37.850.0583.88
13 July 20214.814.828.3372.57.830.0813.94
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Khmelevtsova, L.; Konstantinova, E.; Karchava, S.; Klimova, M.; Azhogina, T.; Polienko, E.; Khammami, M.; Sazykin, I.; Sazykina, M. Influence of Pesticides and Mineral Fertilizers on the Bacterial Community of Arable Soils under Pea and Chickpea Crops. Agronomy 2023, 13, 750. https://doi.org/10.3390/agronomy13030750

AMA Style

Khmelevtsova L, Konstantinova E, Karchava S, Klimova M, Azhogina T, Polienko E, Khammami M, Sazykin I, Sazykina M. Influence of Pesticides and Mineral Fertilizers on the Bacterial Community of Arable Soils under Pea and Chickpea Crops. Agronomy. 2023; 13(3):750. https://doi.org/10.3390/agronomy13030750

Chicago/Turabian Style

Khmelevtsova, Ludmila, Elizaveta Konstantinova, Shorena Karchava, Maria Klimova, Tatiana Azhogina, Elena Polienko, Margarita Khammami, Ivan Sazykin, and Marina Sazykina. 2023. "Influence of Pesticides and Mineral Fertilizers on the Bacterial Community of Arable Soils under Pea and Chickpea Crops" Agronomy 13, no. 3: 750. https://doi.org/10.3390/agronomy13030750

APA Style

Khmelevtsova, L., Konstantinova, E., Karchava, S., Klimova, M., Azhogina, T., Polienko, E., Khammami, M., Sazykin, I., & Sazykina, M. (2023). Influence of Pesticides and Mineral Fertilizers on the Bacterial Community of Arable Soils under Pea and Chickpea Crops. Agronomy, 13(3), 750. https://doi.org/10.3390/agronomy13030750

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