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Article

Effects of Long-Term Straw Returning and Nitrogen Fertilizer Reduction on Soil Microbial Diversity in Black Soil in Northeast China

by
Feng Jiao
*,
Dongdong Zhang
,
Yang Chen
,
Jinhua Wu
and
Junying Zhang
College of Agriculture, Heilongjiang Bayi Agricultural University, Daqing 163319, China
*
Author to whom correspondence should be addressed.
Agronomy 2023, 13(8), 2036; https://doi.org/10.3390/agronomy13082036
Submission received: 16 June 2023 / Revised: 24 July 2023 / Accepted: 25 July 2023 / Published: 31 July 2023
(This article belongs to the Section Agroecology Innovation: Achieving System Resilience)
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Abstract

:
Returning straw to the field, coupled with fertilizer application, is an effective means to improve the fertility of black soil in Northeast China. Previous studies have mainly focused on the physical and chemical properties of soil structure and fertility. However, few efforts have been made to study the impact of straw returning on the microbial community of black soil in Northeast China. Here, we studied the typical northeast black soil in Heilongjiang Province to characterize the effects of long-term chemical fertilizer application and straw returning on its bacterial community structure. High-throughput sequencing was conducted to characterize the bacterial community of northeast black soil under different agricultural fertilization treatments, and the main factors affecting the bacterial community of northeast black soil were revealed through bioinformatic analyses. The results of high-throughput sequencing analyses demonstrated that the main bacterial phyla in the black soil in Northeast China were Actinomycetes, Proteobacteria, Acidobacteria, Chloroflexus, and Bacteroidetes. Long-term application of chemical fertilizers significantly increased the fertility and crop yield of black soil in Northeast China but led to significant changes in bacterial community structure and a significant decrease in diversity. Although straw returning improved soil fertility, it did not alleviate the adverse effects of the long-term application of chemical fertilizers on soil bacterial communities. Furthermore, our results demonstrated that changes in soil pH were the main factor leading to variations in soil bacterial communities. Returning straw to the field based on fertilizer application can improve black soil fertility in Northeast China but fails to alleviate the adverse effects of fertilizer-induced soil acidification on the composition and diversity of soil bacterial communities. This suggests that returning straw to the field may not have a significant beneficial impact on the microbial ecology of the black soil of Northeast China. Therefore, further research is needed to establish new straw return strategies to maximize agricultural yields while minimizing ecological impacts.

1. Introduction

Black soil occupies a total area of 1,090,000 km2 in the northeast region of the China Plain and is mainly distributed in the Hulunbeier grassland, the Greater and Lesser Khingan Mountains, the Sanjiang Plain, the Songnen Plain, the Songliao Plain, and the Changbai Mountains [1]. This region is among the only four black soil areas in the world [1]. The black soil region in Northeast China is an important producer of grains, vegetables, and other crops and thus contributes substantially to the food security of China [2]. However, black soil in this region has suffered serious problems, such as soil acidification, soil erosion, and degradation due to unreasonable fertilization utilization and long-term straw removal from farmlands [3]. These irrational applications of fertilizers and the traw removal of black soil in Northeast China poses a serious threat not only to national food security but also to the environment. The application of chemical fertilizers can quickly and effectively supplement the nutrients required for optimal crop growth and significantly increases the content of key elements such as nitrogen (N), phosphorus (P), and potassium (K) in black soil, all of which are known to increase agricultural yields [4]. However, excessive application of chemical fertilizers can lead to serious ecological impacts such as environmental pollution and soil degradation due to soil salinization and acidification, leading to serious damage to the soil structure, thus causing the occurrence of soil crusting [5]. The combined application of chemical fertilizers and organic matter is considered to be an effective strategy to alleviate the adverse effects of large amounts of chemical fertilizer inputs. Crop straw is the most common organic material in agricultural production, and studies have shown that the combination of chemical fertilizers and straw returning can increase the organic matter content of black soil [6]. This strategy can also improve the physical properties of soil. For example, recent studies have demonstrated that straw returning increases soil porosity [7], reduces bulk density [8], improves soil permeability and water and fertilizer retention [9], increases crop yields [10], and reduces the environmental pollution caused by straw burning.
Soil micro-organisms are the most abundant life form in soil and play a crucial role in maintaining ecological functions such as biogeochemical cycles [11,12], litter decomposition, and plant growth [13]. Moreover, due to their sensitivity to environmental changes, shifts in microbial communities can reflect changes in ecological functions [14]. The decomposition and transformation process of straw returning to the soil is carried out under the impetus of soil micro-organisms. Straw returning to the soil provides sufficient carbon sources for the reproduction and growth of soil micro-organisms, increasing the content and types of carbon sources [15,16]. Li et al. [17] reported that straw returning changed the bacterial community structure in the North Plain of China and increased the abundance of bacteria related to the degradation of complex organic matter. Zhao et al. [18] also found that returning straw significantly changed the structure and abundance and diversity of the soil bacterial community. Compared with a single application of chemical fertilizers, straw returning combined with chemical fertilizers significantly improved soil fertility, increased soil enzyme activity and bacterial abundance, and changed the bacterial community structure [19]. Therefore, studying the effect of long-term straw returning on the soil microbial community has important implications for evaluating its impact on soil ecology, as well as the identification of environmentally friendly agricultural practices.
Previous studies have reported that the use of chemical fertilizers and straw returning can significantly change the abundance and composition of soil microbial communities. For example, the application of chemical fertilizers can significantly increase the soil microbial biomass and lead to the differentiation of microbial communities [16]. Moreover, the combination of chemical fertilizers and straw returning can further increase the biomass of soil micro-organisms [20], improve soil microbial activity [21], and improve the diversity of the soil microbiota [22]. However, most previous studies have relied on traditional research methods and techniques that cannot provide accurate insights into the changes in microbial community composition in response to environmental stimuli. Moreover, very few studies have explored the impact of nitrogen fertilizer reduction coupled with long-term straw returning on the soil microbial communities of farmlands. Therefore, this study sought to assess the effects of continuous rice straw return and nitrogen fertilizer reduction on farmland bacterial communities. To this end, high-throughput sequencing technology was used to characterize the effects of combining nitrogen fertilizer application and straw returning on the soil microbial community structure and diversity, as well as their relationship with soil physicochemical properties. Specifically, the present study aimed to (1) characterize the effects of straw returning and nitrogen fertilizer reduction on soil microbial community structure and diversity and (2) assess whether the driving factors that determine the effects of straw returning and nitrogen fertilizer reduction on the soil microbial community structure and diversity were consistent. Therefore, the findings of this study can provide a theoretical basis and technical support for the improvement of existing agricultural management strategies in the black soil region of Northeast China.

2. Materials and Methods

2.1. Experiment Site and Design

This experiment was established in 2013 and performed in Qianjin Farm (43°49′–48°27′ N, 129°11′–135°05′ E), Heilongjiang Province, China. The predominant soil type in the study area is glebe albicans, and the basic properties of the soil are summarized in Table 1 (measured in the first year). The research area was 1050 m2 and was divided into 4 sub-regions, which were randomly arranged. Here, we examined the effects of four experimental treatments (Figure 1): fertilizer (F), straw returning (S), FS (fertilizer × straw returning), and a control treatment (CK, without fertilizer addition and straw returning). The straw returned to the field was rice straw, and all of the removed straw was returned to the field. The rice straw was crushed into 8~10 cm, and the content of total nitrogen (TN), total phosphors (TP), and total potassium (TK) were 0.098, 0.026, and 2.1 g·kg−1, respectively. The nitrogen fertilizer used in this study was urea (N content 46%), the phosphorus fertilizer was double superphosphate (P2O5 content 46%), and the potassium fertilizer was potassium sulfate (K2O content 50%). The nitrogen fertilizer application rate was 120 kg·hm−2, the phosphorus fertilizer application rate was 75 kg·hm−2, and the potassium fertilizer application rate was 90 kg·hm−2. Furthermore, 40% of the tested nitrogen fertilizer was applied as base fertilizer, 30% was used as tiller fertilizer, and 30% was used as ear fertilizer. Water input was strictly controlled for each treatment using a single irrigation and single row approach, and the field management method was consistent with conventional rice production techniques.
The rice was harvested in the fifth year (2018), and three 10 m2 areas were randomly delineated in each plot. Next, five soil samples (0–20 cm top soils) were collected in each area with a soil auger, after which the soil samples were pooled into a single sample for each treatment. Afterward, the soil samples were divided into three parts: one part was crushed, air dried, and passed through a 2 mm sieve for the determination of soil physicochemical properties; the second part was stored at 4 °C for the determination of soil enzyme activity; and the third part was stored at −80 °C for sequencing.
Soil pH was measured using a pH meter and soil to water ratio of 1:2.5 w/v. Soil organic carbon (SOC) was measured according to Yeomans and Bremner [23]. Total nitrogen (TN) was measured using an elemental analyzer (Elementar, Langenselbold, Germany). Available nitrogen (AN) was sequentially digested in H2SO4-HCLO4, 0.05 M NaHCO3, 2.0 M KCL. Available phosphorous (AP) was quantified using a colorimetric method upon extraction with 0.5 M NaCO3 [24]. Total phosphor (AP), total potassium (TK), available nitrogen (AN), and available potassium (AK) were measured using a with a continuous flow analysis (SAN++, Skalar Analytical, Breda, The Netherlands). Soil exchangeable calcium (Ca2+) and magnesium (Mg2+) were detected by ammonium acetate exchange-AAS method [25].

2.2. DNA Extraction and 16S rRNA Sequencing

Soil genomic total DNA was extracted by Power Soil Extraction Kit (Mobio company, catalog number 12888, Carlsbad, CA, USA), and the method was carried out according to the protocol of the Kit. The DNA was detected and purified by 1% agarose gel electrophoresis and Nanodrop 2000 (Thermo Fisher Scientific, Waltham, MA, USA). The concentrations of DNAs were adjusted into 50 ng. μL−1. The bacterial 16S rRNA region was selected to amplified using primer 338F (5′-ACT CCT ACG GGA GGC AGC A-3′) and 806R (5′-GGA CTA CHV GGG TWT CTA AT-3′). The PCR reaction volume was 25 μL, which contained 12.5 μL PCR mix with GC buffer, 1.5 μL forward and reward primers, 1.5 μL DNA template, and added ddH2O to adjust into 25 μL. The PCR amplification process was as follows: ① pre-denaturation at 95 °C for 10 min; ② 32 cycles of denaturation at 95 °C for 45 s, annealing at 52 °C for 45 s, extension at 60 °C for 90 s; and ③ finally an extension step at 72 °C for 8 min. Each PCR had three replicates. The PCR products were detected using 2% agarose gel electrophoresis, and the PCR were purified with the AxyPrep DNA purification kit (Axygen Company, Union City, CA, USA). Three replicates PCR were pooled in equal amounts; the pooled samples were then paired-end sequenced on the Illumina HiSeq 2500. The raw sequences were uploaded to the NCBI Sequence Read Archive database under accession number SUB13342571.
According to the Overlap between PE reads for each soil sample, the paired-end sequence data obtained by Hiseq sequencing were spliced (Merge) into sequence Tags, and the quality of Reads and the effect of Merge were filtered for quality control. There were three main steps, as follows: (1) PE reads splicing: use FLASH v l.2.7 software [26] to splice the reads of each sample through overlap, and the spliced sequence obtained is the original Tags data (Raw Tags); (2) Tags filtering: Use Trimmomatic v0.33 software [27] to filter the spliced Raw Tags to obtain high-quality Tags data (CleanTags); and (3) Removal of chimeras: Use UCHIME (version 4.2) software [28] to identify and remove chimera sequences to obtain the final effective data (Effective Tags). The obtained operational taxonomic units (OTUs) were clustered by Uprase [29] at similarity threshold of 97% and the taxonomy were annotated to the SILVA database (v138.1). Four indices were used to evaluate alpha diversity, including community richness (Chao1 and ACE), community diversity (Shannon and Simpson). Before further analysis of Alpha diversity index (ACE, Chao1, Simpson, and Shannon) and Beta diversity (non-metric multi-dimensional scaling), the reads were normalized according to the lowest number of reads for a single soil sample. ACE, Chao1, Simpson, and Shannon indices were calculated on OTU level by “microeco” package R language (version 4.3.1) [30]. Non-metric multi-dimensional scaling (NMDS) was performed based on OTU level by “microeco” package R language (version 4.3.1) [30]. The indicator species of soil bacteria, i.e., linear discriminant analysis (LDA) analysis were finished to identify bacterial indicator species on the basis of a normalized relative abundance matrix across group using the default parameters on the biocloud platform https://www.bioincloud.tech/ (accessed on 16 May 2023). The soil bacterial functions based on OTU level were performed according to FAPROTAX function classifications on the biocloud platform https://www.bioincloud.tech/standalone-task-ui/faprotax (accessed on 16 May 2023).

2.3. Bioinformatics and Statistical Analysis

Origin 8.5 and GraphPad Prism 5 software were used for data collation and analysis, Canoco 5.0 software was used for redundancy analysis. The one-way analysis of variance was processed by statistical software SPSS 21.0 (SPSS Inc., Chicago, IL, USA) [31], and the significance of difference and multiple comparison analysis were processed by the Duncan test method at p < 0.05. The data in the charts are the average values of 3 or more repeated treatments. The heatmap and cluster analyses based on the Bray–Curtis dissimilarity was conducted on the biocloud platform https://www.bioincloud.tech/ (accessed on 16 May 2023) using the most 20 relative abundance genera. All the figures were performed on the biocloud platform https://www.bioincloud.tech/ (accessed on 16 May 2023). The Spearman’s rank correlation analysis for the relationships between the alpha diversity of soil bacteria and the soil physicochemical properties were finished via SPSS 21.0 [31].

3. Results

3.1. Physicochemical Properties of the Soil Samples under Different Treatments

All of the examined soil physicochemical properties exhibited significant changes between all of the experimental treatments (Table 2, p < 0.05). As summarized in Table 2, the soil TP, Ca, Mg, soil organic carbon (SOC), and TK in the F, S, and FS treatments decreased significantly compared to the CK, whereas AN, AP, and TK increased significantly compared to the CK. The concentrations of soil TN, TK, AP, and Ca were significantly higher in the FS treatment compared to the CK (Table 2, p < 0.05), whereas the concentrations of soil TP, AK, Mg, and SOC were significantly lower compared to the CK (Table 2, p < 0.05). The S treatment significantly increased the concentrations of soil TK and AN but significantly decreased the concentrations of soil TN, TP, AK, Mg, and SOC, whereas the remaining soil parameters did not change compared to the CK (Table 2, p < 0.05). The F treatment significantly increased the concentrations of soil AP and AK. In contrast, soil TP, TK, Mg, SOC, and pH were significantly decreased, whereas the remaining soil parameters were not significantly affected compared to the CK (Table 2, p < 0.05).

3.2. Alpha and Beta Diversity of Soil Bacteria

The soil bacterial alpha diversity indices, including the ACE, Chao1, Simpson, and Shannon indices exhibited distinct variations among all treatments (Table 3). The ACE and Chao1 indices of the soil bacterial community were not different among the four treatments (Table 3, p > 0.05), whereas the Simpson and Shannon indices were significantly different among the four treatments (Table 3, p < 0.05). The S treatment exhibited the highest Shannon index of 6.3, followed by CK and FS, with F exhibiting the lowest value (Table 3).
The relationships between the alpha diversity of soil bacteria and the soil characteristics were evaluated via Spearman’s rank correlation analysis. Soil available N exhibited a significant positive correlation with the Shannon index (p < 0.05, Table 4). Similarly, SOC showed a significant correlation with OTU (p < 0.05, Table 4).
Non-metric multidimensional scaling (NMDS) based on the Bray–Curtis distance indicated that the community structure of the soil bacteria differed among the CK, F, S, and FS treatments (Figure 2, PERMANOVA p < 0.05). Moreover, similar soil bacterial community structures were observed between FS and S and between CK and F (Figure 2).

3.3. Community Structure of Soil Bacteria

The relative abundance of the top 10 soil bacteria phyla (relative abundance > 1%) in all soil samples exhibited the following descending order: Proteobacteria (30–33%), Actinobacteria (14–17%), Chloroflexi (13–14%), Bacteroidetes (11–13%), Acidobacteria (10–11%), Verrucomicrobia (5–6%), Gemmatimonadetes (2–3%), Patescibacteria (2–3%), Nitrospirae (0.8–1%), and Armatimonadetes (0.8–1%) (Figure 3a). The relative abundance of Chloroflexi, Verrucomicrobia, Patescibacteria, and Nitrospirae in the FS treatment decreased significantly compared to the CK (Table 5, Duncan test and p < 0.05). The relative abundances of Bacteroidetes and Acidobacteria in the S and FS treatments increased significantly compared to the CK, respectively (Table 5, Duncan test and p < 0.05).
The relative abundance of the top 10 soil bacteria classes (relative abundance > 1%) in all soil samples exhibited the following descending order: Gammaproteobacteria (18–21%), Actinobacteria (11–16%), Anaerolineae (11–12%), Bacteroidia (10–12%), Deltaproteobacteria (7–7%), Acidobacteriia (5–7%), Alphaproteobacteria (5–6%), Verrucomicrobiae (5–6%), Gemmatimonadetes (2–3%), and Holophagae (1–2%) (Figure 3b). However, the relative abundance of all bacterial classes did not differ significantly compared to the CK (Table 5).
Finally, the relative abundance of the top 10 soil bacteria genera (relative abundance > 1%) in all soil samples exhibited the following descending order: Pseudarthrobacter (7–12%), Massilia (5–7%), Sideroxydans (4–7%), Anaerolinea (3–4%), Candidatus_Udaeobacter (2–3%), Candidatus_Solibacter (2–3%), Cryobacterium (1–3%), Gemmatimonas (1–2%), Bryobacter (1–2%), and Geobacter (1–2%) (Figure 3c). Moreover, the relative abundance of the top 10 genera was analyzed via one-way ANOVA coupled with Duncan’s test (Table 5).
The relative abundance of Candidatus_Udaeobacter in the FS treatment and Cryobacterium in the S and FS treatments decreased significantly compared to the CK (Table 5, p < 0.05), whereas the other dominant genera in the F, S, and FS treatments did not change significantly compared to the CK (Table 5, p < 0.05). The relative abundance of Pseudarthrobacter in the F, Candidatus_Udaeobacter in the FS, and Cryobacterium in the S and FS treatments changed significantly compared to the CK (Table 5, p < 0.05), whereas the other dominant genera in the F, S, and FS treatments did not change significantly compared to the CK (Table 5, p < 0.05). The relative abundance of Candidatus_Udaeobacter, Anaeromyxobacter, Bryobacter, and Sphingomonas exhibited the following decreasing order: CK > F > S > FS. In contrast, the relative abundance of Massilia and Candidatus_Solibacter was significantly higher in the F, S, and FS treatments compared to the CK (Table 5).

3.4. Indicator Species of Soil Bacteria

Based on a linear discriminant analysis (LDA) effect size (LEfSe) score > 2, nine groups of soil bacterial community were found to be significantly different between the F, S, and FS treatments (Figure 3, p < 0.05). Betaproteobacteriales, Gallionellaceae, and Bacteroidales were the indicator taxa in the S treatment. Acidobacteria was the indicator taxon in the FS treatment, and Actinobacteria, Micrococcales, Pseudarthrobacter, and Micrococcaceae were the dominant indicator taxa in the F treatment (Figure 4).
As illustrated in Figure 5, heatmap and cluster analyses based on Bray–Curtis dissimilarity demonstrated that the experimental treatments had a substantial effect on the relative abundance of the different members of the soil bacterial community. The relative abundance of Candidatus_Udaeobacter, Anaeromyxobacter, Bryobacter, and Sphingomonas exhibited the following decreasing order: CK > F > S > FS. In contrast, the relative abundance of Massilia and Candidatus_Solibacter was higher in the F, S, and FS treatments compared to the CK.

3.5. Effect of Soil Physicochemical Properties on Soil Bacterial Community Composition

Redundancy analysis indicated that the soil microbial community structure was significantly correlated with the soil physicochemical properties (Figure 6). At the OTU level, TP, and AK were significantly associated with CK and F (Figure 5, p < 0.05), and TK was significantly associated with FS and S (Figure 6, p < 0.05), whereas other soil parameters were not significantly associated with the community structure of soil bacteria (Figure 6, p > 0.05).

3.6. Inferred Functionality of Soil Bacteria and Differences between the Experimental Treatments

As illustrated in Figure 7, a total of 54 key ecological functions were identified via FAPROTAX analysis. Chemoheterotrophy was the most dominant functional category, followed by aerobic_chemoheterotrophy and nitrification. Furthermore, the functions differed among all different treatments. Chemoheterotrophy, aerobic_chemoheterotrophy, and nitrification were more prominent in CK and F than in S and FS (p < 0.05, Figure 7). In contrast, Chlorate_reducers were more prominent in F than in other treatments, and Plant_pathogen and dark_iron_oxidation were more prominent in S than in other treatments (p < 0.05, Figure 7).

4. Discussion

4.1. Changes in Soil Bacterial Community Diversity under Straw and Fertilizer Treatments

Several studies have sought to identify novel strategies to improve the productivity of black soil in Northeast China, among which straw returning is considered one of the most promising methods [32,33]. Nevertheless, the effects of long-term straw returning on soil microbial community structure and function had remained largely unexplored. Therefore, the present study sought to characterize the effects of the long-term application of chemical fertilizers and straw returning to the field on the bacterial community structure of black soil in Northeast China using high-throughput sequencing technology. Our findings demonstrated that long-term fertilization improved soil fertility but significantly changed the bacterial community structure and decreased bacterial diversity (Table 2). The effect of long-term fertilization combined with straw return on the bacterial community was similar to that of a single application of chemical fertilizers and did not significantly influence the recovery of the bacterial community structure and diversity. Through further analysis, we found that the main factors affecting the changes in the alpha diversity of the black soil bacterial community were soil pH, SOC, and AN. In turn, this indicated that soil acidification and soil nutrient changes caused by fertilization were the main factors that led to changes in the bacterial community structure and the loss of diversity in black soil. Previous studies have reported that soil pH is the main factor affecting the geographical distribution of soil bacterial communities [34,35]. In farmland ecosystems, some studies also demonstrated that pH played a major role in shaping the diversity of bacterial community [35,36]. Our results provide further evidence that the effect of soil pH on the diversity of bacterial communities is universal. Large changes in soil acidity and alkalinity can lead to the extinction of many microbial species, which is manifested as a reduction in bacterial community diversity. In this study, the low pH induced by fertilization increased the proportion of acidophilic, acid-fast bacteria (e.g., Acidobacteria) in the bacterial community (Table 1). At the same time, we found that the increase in soil AN, TK, and TN content caused by long-term straw returning also affected the diversity of the bacterial community to a certain extent, indicating that changes in soil nutritional conditions also play a key role in shaping the bacterial community diversity. Previous studies have found that chemical fertilizer application combined with straw returning can increase the abundance and diversity of ammonifying bacteria, nitrifying bacteria, denitrifying bacteria, and cellulolytic bacteria [37], as chemical fertilizer application and straw returning provide them with the nutrients required for optimal growth. In contrast, excessive nutrient input can also inhibit the growth of some oligotrophic bacteria and lead to the formation of a more active and eutrophic bacterial community [38]. Nevertheless, changing the nutritional conditions of the black soil had a weaker effect on the bacterial communities compared to soil acidification. Soil acidification is a common problem in China’s agroecosystems [39], with high N fertilizer input being one of the main culprits of this phenomenon [15]. Soil acidification can adversely affect crop growth, thus reducing crop yield and quality. Our results demonstrated that although straw returning can improve the fertility of black soil and increase crop yield, it did not alleviate the soil acidification caused by fertilizer application, which may diminish the positive effect of straw returning on crop yield.
We also found that soil nutrient characteristics (e.g., SOC and AN) were key environmental factors affecting the soil bacterial diversity (Table 4). Our result showed that fertilization and straw addition significantly changed the soil physicochemical properties, i.e., soil organic carbon decreased, whereas AN increased under F and S addition compared to CK (Table 2). Moreover, SOC positively correlated with OTU and AN positively correlated with Shannon. Previous studies reported that soil N application significantly changed the soil bacterial diversity [40].
In view of N fertilization, the increased availability of N, such as ammonium nitrogen and ammonium nitrogen, can satisfy the N requirements of micro-organisms and directly increased the diazotrophic community abundance and richness [41]. Similarly, rice straw addition increased the carbon sources for soil bacteria and significantly increased the soil bacterial abundance. In contrast, TP correlated positively but AP negatively with soil bacterial richness (Chao1 and OTU) and Shannon (not significant), which is consistent with the results of [42]. The abundance of soil bacteria decreases under low-P conditions, as P is an important component of nucleotides, energy compounds, and cell membranes [40]. However, some studies reported that demonstrated that soil P deficiency had a more important influence on soil bacteria than N, due to diazotrophs inhabited by P content in barren soils [43]. We guess that black soil is a rich nutrient soil with high P content, and therefore P did not show limitation on soil bacteria.

4.2. Driving Factors of Soil Bacterial Community Composition under Straw and Fertilizer Treatments

In line with our second hypothesis, the main soil parameters that determined the composition of the soil bacterial communities varied between the experimental treatments (Figure 6). The bacterial community compositions of the CK and F treatments were primarily influenced by soil TP and AK, and those of FS and S were mainly affected by SOC and TK (Figure 6). These results indicated that the F and S treatments had different effects on the soil bacterial community composition and the main influencing soil parameters were also different. Our results demonstrated that rice straw returning significantly increased soil N, P, and K, with the FS treatment exhibiting the highest levels of these nutrients compared to the other treatments. Rice straw is composed of complex material components such as hemicellulose, lignin, cellulose, etc. [44], and the soil requires a large number of enzymes, a variety of micro-organisms, and the combined action of soil bacteria and fungi in order to adequately degrade the straw material applied to the soil, thus promoting the nutrient cycle in the soil. After decomposition, the rice straw can provide plants with nutrients necessary for growth, and straw can also increase the effective nutrient content and nitrogen content, improving soil fertility. Therefore, we guess the rice straw input significantly changed the soil nutrients (i.e SOC and TK), but for fertilization treatment, the fertilization only changed the soil TP and TK, but the SOC did not input and thus gave a different response on soil bacterial community composition. This indicated that the combined application of straw and chemical fertilizers would significantly change soil nutrient content. The continuous addition of exogenous nutrients stimulated the activity of soil micro-organisms, strengthened the process of microbial mineralization and immobilization, and promoted the accumulation of nutrients in straw-amended soil. Moreover, the nutrient content in rice straw is significantly more complex than that of a single chemical fertilizer, which likely explains the significant differences in the key factors that determined the soil bacterial community structure in black soil treated with chemical fertilizer or rice straw. These findings were consistent with those of Chen et al. [19] However, Sun et al. [45] studied the effects of the long-term application of chemical fertilizers and straw returning on the soil bacterial community structure of black soil and found that the driving factors were the same under both conditions. Specifically, both conditions were mainly affected by pH and SOC, which is not consistent with our results. We speculate that these discrepancies may be caused by differences in crops and soil types.
From the perspective of microbial community composition, the dominant bacterial phyla under S returning and F application were Proteobacteria, Actinobacteria, Chloroflexi, Bacteroidetes, and Acidobacteria. However, straw returning combined with chemical fertilizer application did not change the relative abundance of the aforementioned bacterial phyla, with only the abundance of Nitrospirae being significantly decreased (Figure 3a, Table 4), which is inconsistent with previous reports [45,46]. Previous studies have shown that the nutrients supplied by straw returning influence the key factors that determine the growth of soil microbial populations. Among them, soil nutrients, as an important determinant of microbial reproduction [47], are the influencing factors that induce changes in microbial communities. Straw returning improves the levels of soil nutrients and also provides a substrate for microbial growth. Proteobacteria, a phylum that encompasses a wide variety of nitrogen-fixing bacteria [48], accounted for a relatively high proportion of the microbial community of the soil treated with rice straw, and these bacteria likely participated extensively in the biochemical cycle of various nutrients such as soil mineral carbon and nitrogen. The organic materials introduced by the returned straw release nitrogen through mineralization and degradation. In turn, this supplements the soil nitrogen pool and promotes the growth and reproduction of nitrogen-fixing bacteria, thereby increasing the relative abundance of nitrogen-fixing microbial populations belonging to the Proteobacteria phylum [49]. In turn, this was conducive to the maintenance of soil fertility and plant growth. The micro-organisms belonging to the Chloroflexi phylum are mainly anaerobic bacteria, which can ferment sugars and polysaccharides into organic acids and hydrogen [50], thereby accelerating the decomposition and absorption of organic materials in the soil. Abdallah et al. [51] demonstrated that the members of this phylum could promote the degradation of hemicellulose in straw. Moreover, Yu et al. [52] showed that after 6 years of continuous straw returning, the relative abundance of Chloroflexi in soil was significantly higher than that without S returning. However, our findings indicated that the abundance of the main bacterial phyla remained largely unaffected by the experimental treatments. This may be because the black soil in Northeast China is uniquely nutrient rich, and the dominant bacteria in these soils may not be able to utilize the nutrients provided by exogenous sources. Therefore, this is likely to be one of the main reasons why the relative abundance of the major bacteria did not change significantly in response to the experimental treatments. Moreover, this may also be due to the high soil moisture content of paddy fields. In this study, the soil samples were collected during summer, and the rice straw had not completely decomposed at this time. Therefore, its nutrients could not enter the soil to be used by the micro-organisms. The rice straw may thus require additional time to fully decompose in order to affect the relative abundance of the main members of the bacterial community. Moreover, our findings demonstrated that neither straw returning nor chemical fertilizer application changed the relative abundance of the major bacterial classes and genera (Figure 3b,c, Table 4).
Dependent on the genus level, the dominant bacterial genus under straw returning and fertilizer application were Pseudarthrobacter, Sideroxydans, Sideroxydans, Candidatus_Udaeobacter, and Anaerolinea (Figure 3c and Table 4). Moreover, rice straw significantly decreased the relative abundance of Pseudarthrobacter and Candidatus_Udaeobacter, but other genera relative abundance of fertilization and straw did not change compared to CK. This indicated that straw returning and fertilizer addition did not change most soil bacteria genera, but only changed the abundance of major degrading bacteria genera. This is consistent with previous studies [42,53]. Pseudarthrobacter and Candidatus_Udaeobacter were common decomposers, which are primary decomposers with well-known cellulolytic activity [54]. Strangely, the increase of these bacteria was inhibited after adding straw, and the abundance of these degrading bacteria was increased by adding fertilizer. This may be that this experiment collected the soils 5 years after the straw was added, and the rice straw may be more difficult to decompose after 5 years. Such bacteria cannot use the straw, but due to the impact of fertilizers, we guess these bacteria may be opportunist due to the presence of exogenous nutrient input; these bacteria genera could use exogenous nutrients. Therefore, further research is needed to explore the function of such bacteria genera.

4.3. Changes of Soil Bacterial Functions between the Experimental Treatments

Analyzing the functions of the bacterial community can help to determine the relationship between the fertilization, straw, and a composite bacterial system. Our study showed that Chemoheterotrophy, aerobic_chemoheterotrophy, and nitrification were prominent in CK and F than in S and FS (Figure 7), but Chlorate_reducers were more prominent in F than in other treatments, and Plant_pathogen and dark_iron_oxidation were more prominent in S than in other treatments (Figure 7). This indicated that long-term fertilization and straw significantly changed the soil bacterial functions, but the soil bacterial function had a different response to fertilization and rice straw. We noted that the phyla Proteobacteria, Actinobacteria, Chloroflexi, and Bacteroidetes could effectively degrade lignocellulose, which significantly correlated with the functions of nitrogen fixation and Chemoheterotrophy. Our results indicated that not only is the degradation of straw directly influenced by the nitrogen fixation, but the fertilization utilization is also affected by Chlorate_reducers, nitrification, and fermentation.

5. Conclusions

Straw returning and fertilizer addition could significantly increase the AN and AK contents and pH of black soil. Straw returning combined with chemical fertilizers significantly increased soil TK and AP contents and pH. Soil pH is the main factor affecting soil bacterial diversity. High-throughput sequencing technology showed that straw returning to the field combined with chemical fertilizers could increase the Shannon diversity index of soil bacteria but had no effect on the Chao1 index. Actinomycetes, Proteobacteria, Acidobacteria, Chloroflexus, and Bacteroidetes are mainly bacterial taxa. Soil pH was the main factor leading to variations in soil bacterial communities. Returning straw to the field based on fertilizer application can improve black soil fertility in Northeast China but fails to alleviate the adverse effects of fertilizer-induced soil acidification on the composition and diversity of soil bacterial communities. Therefore, continuous straw returning combined with chemical fertilizer application can improve the ecological environment of farmland soil in Northeast China and maintain the stability of ecosystem functions, thereby ensuring the sustainable development of agriculture.

Author Contributions

Conceptualization, F.J.; methodology, Y.C. and D.Z.; software, J.W. and J.Z.; validation, F.J., Y.C., J.W. and J.Z.; formal analysis, F.J.; investigation, Y.C. and J.W.; resources, F.J.; data curation, J.Z.; writing—original draft preparation, F.J.; writing—review and editing, F.J., Y.C., J.W. and J.Z.; visualization, Y.C. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

The raw sequences were uploaded to the NCBI Sequence Read Archive database under accession number SUB13342571.

Conflicts of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Figure 1. The experiment design. CK indicates control; F indicates fertilizer application; S indicates straw returning; FS indicates fertilizer × straw returning.
Figure 1. The experiment design. CK indicates control; F indicates fertilizer application; S indicates straw returning; FS indicates fertilizer × straw returning.
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Figure 2. Non-metric multi-dimensional scaling (NMDS) analysis of Bray–Curtis distance between all of the soil samples. CK, control treatment; F, fertilizer; S, straw; FS, fertilizer×straw.
Figure 2. Non-metric multi-dimensional scaling (NMDS) analysis of Bray–Curtis distance between all of the soil samples. CK, control treatment; F, fertilizer; S, straw; FS, fertilizer×straw.
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Figure 3. Composition of soil bacteria at the phylum (a), class (b), and genus (c) level under different fertilizer and straw treatments. CK, control; F, fertilizer; S, straw; FS, fertilizer + straw.
Figure 3. Composition of soil bacteria at the phylum (a), class (b), and genus (c) level under different fertilizer and straw treatments. CK, control; F, fertilizer; S, straw; FS, fertilizer + straw.
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Figure 4. Indicator species of bacterial communities under different treatments with significantly different taxa. F, fertilizer; S, straw; FS, fertilizer + straw.
Figure 4. Indicator species of bacterial communities under different treatments with significantly different taxa. F, fertilizer; S, straw; FS, fertilizer + straw.
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Figure 5. Heatmap and cluster analysis based on the relative abundance of the top 20 genera identified in soil bacterial communities. CK, control; F, fertilizer; S, straw; FS, fertilizer + straw. The color gradient (red, white, blue) represents the relative abundance of the soil genera from high to low in the different treatments.
Figure 5. Heatmap and cluster analysis based on the relative abundance of the top 20 genera identified in soil bacterial communities. CK, control; F, fertilizer; S, straw; FS, fertilizer + straw. The color gradient (red, white, blue) represents the relative abundance of the soil genera from high to low in the different treatments.
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Figure 6. Redundancy analysis of soil bacterial community constrained by soil physicochemical properties. CK, control; F, fertilizer; S, straw; FS, fertilizer + straw.
Figure 6. Redundancy analysis of soil bacterial community constrained by soil physicochemical properties. CK, control; F, fertilizer; S, straw; FS, fertilizer + straw.
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Figure 7. Heatmap of the function composition of soil bacteria in different treatments. The asterisks indicate significant differences in different treatments according to one-way ANOVA at 0.05 level. CK, control; F, fertilizer; S, straw; FS, fertilizer + straw. The color gradient (red, white, blue) represents the relative abundance of the soil genera from high to low in the different treatments.
Figure 7. Heatmap of the function composition of soil bacteria in different treatments. The asterisks indicate significant differences in different treatments according to one-way ANOVA at 0.05 level. CK, control; F, fertilizer; S, straw; FS, fertilizer + straw. The color gradient (red, white, blue) represents the relative abundance of the soil genera from high to low in the different treatments.
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Table
Table 1. Basic soil characteristics.
Table 1. Basic soil characteristics.
Soil Layer
(cm)		pHTN
(g·kg−1)		TP
(g·kg−1)		TK
(g·kg−1)		AN
(mg·kg−1)		AP
(mg·kg−1)		AK
(mg·kg−1)
0–205.921.650.7516.50191.1432.33183.50
TN, total nitrogen; TP, total phosphor; TK, total potassium; AN, available nitrogen; AP, available phosphor; AK, available potassium.
Table
Table 2. Soil physicochemical properties under different treatments.
Table 2. Soil physicochemical properties under different treatments.
TreatmentCKFSFS
TN (g·kg−1)1.77 ± 0.05 b1.72 ± 0.03 b1.59 ± 0.07 c1.93 ± 0.02 a
TP (g·kg−1)0.96 ± 0.02 a0.86 ± 0.00 b0.74 ± 0.01 c0.74 ± 0.03 c
TK (g·kg−1)18.76 ± 0.37 c17.86 ± 0.28 d20.23 ± 0.13 b20.91 ± 0.34 a
AN (mg·kg−1)196.74 ± 4.05 c180.57 ± 3.06 c214.13 ± 1.53 a159.06 ± 8.53 d
AP (mg·kg−1)46.38 ± 0.53 b52.33 ± 0.21 a45.19 ± 3.53 b52.89 ± 1.87 a
AK (mg·kg−1)209.12 ± 1.96 b229.45 ± 1.46 a151.33 ± 2.68 d197.13 ± 4.10 c
Ca (g·kg−1)1.21 ± 0.05 b0.78 ± 0.02 c1.22 ± 0.04 b1.32 ± 0.02 a
Mg (g·kg−1)3.76 ± 0.04 a2.97 ± 0.05 d3.59 ± 0.04 b3.57 ± 0.10 b
SOC (g·kg−1)2.05 ± 0.10 a1.59 ± 0.10 c1.92 ± 0.01 b1.89 ± 0.08 b
pH6.17 ± 0.04 a5.89 ± 0.07 b6.14 ± 0.10 a6.26 ± 0.06 a
Note: different lowercase letters indicate significant differences according to one-way ANOVA coupled with Duncan’s test (p < 0.05). CK, control; F, fertilizer; S, straw; FS, fertilizer + straw. TN, total nitrogen; TP, total phosphor; TK, total potassium; AN, available nitrogen; AP, available phosphor; AK, available potassium.
Table
Table 3. Alpha diversity indices of different treatments.
Table 3. Alpha diversity indices of different treatments.
TreatmentACEChao1SimpsonShannon
CK1710 ± 1.1 a1710 ± 2.9 a0.0089 ± 0.0004 b6.2 ± 0.01 ab
F1710 ± 0.9 a1710 ± 1.1 a0.0112 ± 0.0016 a6.0 ± 0.060 b
S1711 ± 1.4 a1711 ± 1.4 a0.0076 ± 0.0001 bc6.3 ± 0.01 ab
FS1712 ± 2.0 a1712 ± 2.6 a0.0074 ± 0.0002 bc6.2 ± 0.02 a
Note: different lowercase letters indicate significant differences according to one-way ANOVA coupled with Duncan’s test (p < 0.05). CK, control; F, fertilizer; S, straw; FS, fertilizer + straw.
Table
Table 4. Relationship between soil characteristics and soil bacterial alpha diversity indices.
Table 4. Relationship between soil characteristics and soil bacterial alpha diversity indices.
OTUACEChao1SimpsonShannon
TN0.460.470.430.43−0.33
TP0.040.070.08−0.330.50
TK0.420.370.30.43−0.26
AN−0.04−0.05−0.04−0.420.62 *
AP−0.09−0.08−0.06−0.11−0.10
AK−0.17−0.12−0.08−0.220.06
Ca0.59 *0.55 *0.450.56 *−0.23
Mg0.64 *0.61 *0.52 *0.320.15
SOC0.55 *0.510.410.41−0.01
pH0.64 *0.61 *0.540.63 *−0.33
* Correlations were considered significant at p < 0.05. TN, total nitrogen; TP, total phosphor; TK, total potassium; AN, available nitrogen; AP, available phosphor; AK, available potassium.
Table
Table 5. Variations in dominant soil bacteria phyla, classes, and genera under different treatments.
Table 5. Variations in dominant soil bacteria phyla, classes, and genera under different treatments.
PhylaProteobacteriaActinomycetotaChloroflexiBacteroidetesAcidobacteria
CK32,750 ± 4548.5 a16,779 ± 1909.7 a12,890 ± 1356.9 a8775 ± 1722.8 b9944 ± 903.7 b
F28,873 ± 7476.2 a17,700 ± 2355.0 a12,184 ± 1938.2 a10,047 ± 1270.7 ab10,054 ± 2689.0 ab
S32,226 ± 8612.7 a13,867 ± 3567.6 a13,085 ± 3189.9 a12,357 ± 3395.9 a10,968 ± 3333.8 a
FS27,189 ± 3828.5 a13,059 ± 2643.6 a11,926 ± 1899.5 b10,474 ± 1729.0 a10,763 ± 1760.1 a
VerrucomicrobiaPatescibacteriaGemmatimonadetesNitrospiraeArmatimonadetes
CK5584 ± 496.5 a2651 ± 294.6 a3060 ± 571.2 a1279 ± 212.3 a555 ± 42.4 a
F5174 ± 806.9 a2412 ± 260.6 a3166 ± 748.9 a1198 ± 231.3 a502 ± 117.5 a
S5136 ± 1232.7 a2245 ± 720.3 a3092 ± 876.9 a925 ± 254.0 ab581 ± 198.0 a
FS4457 ± 851.7 b1850 ± 310.0 b2602 ± 230.4 a739 ± 124.0 b492 ± 144.6 a
ClassGammaproteobacteriaActinobacteriaBacteroidiaAnaerolineaeDeltaproteobacteria
CK19,438 ± 2770.6 a13,814 ± 1656.4 ab9866 ± 1499.6 a11,359 ± 1131.3 a7347 ± 1006.9 a
F17,030 ± 4502.2 a14,701 ± 1521.4 a9274 ± 1058.8 a10,476 ± 1606.9 a6349 ± 1756.3 a
S19,993 ± 5414.8 a10,904 ± 2887.0 ab11,353 ± 3109.8 a11,419 ± 2793.2 a6997 ± 1851.3 a
FS15,971 ± 1881.3 a10,475 ± 2012.1 b9561 ± 1594.2 a10,354 ± 1687.5 a6140 ± 987.0 a
AlphaproteobacteriaVerrucomicrobiaeAcidobacteriiaGemmatimonadetesHolophagae
CK5965 ± 788.6 a5584 ± 496.5 a5196 ± 538.4 a2754 ± 536.7 a1780 ± 169.5 a
F5494 ± 1259.6 a5174 ± 806.9 a5610 ± 1449.8 a2900 ± 707.1 a1611 ± 436.9 a
S5236 ± 1361.3 a5136 ± 1232.7 a5668 ± 1802.2 a2758 ± 779.1 a2085 ± 599.9 a
FS5078 ± 965.2 a4457 ± 851.7 a5908 ± 776.9 a2321 ± 226.1 a1896 ± 105.1 a
GeneraPseudarthrobacterSideroxydansMassiliaCandidatus_UdaeobacterAnaerolinea
CK7329.67 ± 807.77 ab4634.33 ± 1022.62 a3958.67 ± 729.66 a2640 ± 355.28 a2891.67 ± 310.054 a
F8138 ± 696.79 a3252 ± 984.33 a4339.33 ± 720.7 a2202.67 ± 315.65 ab2694.33 ± 372.22 a
S5587 ± 1601.14 b5180 ± 1261.95 a4686.67 ± 1322.84 a2077 ± 557.43 ab2910.33 ± 644.71 a
FS5196.67 ± 1595.22 b4114.75 ± 1191.49 a3895.33 ± 436.1 a1702 ± 391.28 b2651 ± 440.75 a
Candidatus_SolibacterCryobacteriumGeobacterPedobacterPseudolabrys
CK1860.67 ± 171.45 a1643.67 ± 182.44 a1276.33 ± 282.91 a1106 ± 221.31 a1076.67 ± 214.65 a
F2172.33 ± 507.34 a1850 ± 218.35 a1148 ± 379.92 a1243.67 ± 120.97 a956.67 ± 272.24 a
S1965.33 ± 624.37 a1039.67 ± 230.37 b971.33 ± 255.01 a1083.33 ± 373.94 a1075.67 ± 328.05 a
FS2087.67 ± 181.79 a1102.67 ± 220.93 b975.33 ± 215.63 a777.67 ± 225.22 a1110.67 ± 215.4 a
Note: different lowercase letters indicate significant differences according to one-way ANOVA coupled with Duncan’s test (p < 0.05). CK, control; F, fertilizer; S, straw; FS, fertilizer + straw.
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MDPI and ACS Style

Jiao, F.; Zhang, D.; Chen, Y.; Wu, J.; Zhang, J. Effects of Long-Term Straw Returning and Nitrogen Fertilizer Reduction on Soil Microbial Diversity in Black Soil in Northeast China. Agronomy 2023, 13, 2036. https://doi.org/10.3390/agronomy13082036

AMA Style

Jiao F, Zhang D, Chen Y, Wu J, Zhang J. Effects of Long-Term Straw Returning and Nitrogen Fertilizer Reduction on Soil Microbial Diversity in Black Soil in Northeast China. Agronomy. 2023; 13(8):2036. https://doi.org/10.3390/agronomy13082036

Chicago/Turabian Style

Jiao, Feng, Dongdong Zhang, Yang Chen, Jinhua Wu, and Junying Zhang. 2023. "Effects of Long-Term Straw Returning and Nitrogen Fertilizer Reduction on Soil Microbial Diversity in Black Soil in Northeast China" Agronomy 13, no. 8: 2036. https://doi.org/10.3390/agronomy13082036

APA Style

Jiao, F., Zhang, D., Chen, Y., Wu, J., & Zhang, J. (2023). Effects of Long-Term Straw Returning and Nitrogen Fertilizer Reduction on Soil Microbial Diversity in Black Soil in Northeast China. Agronomy, 13(8), 2036. https://doi.org/10.3390/agronomy13082036

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