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Article

Changes in Soil Chemical Properties and Rhizosphere Bacterial Community Induced by Soil Amendments Associated with Reduction in Cadmium Accumulation by Rice

by
Yu Xin
1,†,
Min Liu
2,†,
Lanchun Wei
1,
Yu Gao
1,
Yunze Ruan
1,
Qing Wang
1,* and
Zhijun Zhang
3,*
1
School of Breeding and Multiplication (Sanya Institute of Breeding and Multiplication), Hainan University, Sanya 572025, China
2
School of Ecology and Environment, Hainan University, Haikou 570208, China
3
Key Laboratory of Cultivated Land Conservation in Hainan Province, Institute of Agricultural Environment and Soil, Hainan Academy of Agricultural Sciences, Haikou 571100, China
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Agronomy 2023, 13(12), 3051; https://doi.org/10.3390/agronomy13123051
Submission received: 29 October 2023 / Revised: 6 December 2023 / Accepted: 11 December 2023 / Published: 13 December 2023
(This article belongs to the Special Issue Remediation of Heavy Metal/Organic Pollutant Contaminated Farmland)
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Abstract

:
Soil amendments have been extensively employed for the purpose of remediating soils contaminated with cadmium (Cd). However, the potential impacts of soil amendments on soil chemical properties, soil Cd bioavailability, total Cd accumulation by rice, and rhizosphere bacterial community in Cd-contaminated paddy fields located in a tropical region is still at its infancy. In this study, a commercial MgO-CaO-SiO2 conditioner (A), biochar (B), and a combination of the commercial MgO-CaO-SiO2 conditioner and biochar with a ratio of 1:1 (C) were applied at two different doses [2250 kg ha−1 (A150, B150, C150), 4500 kg ha−1 (A300, B300, C300)] to investigate their impacts on soil Cd stabilization and total Cd uptake of rice straw and grain in a Cd-contaminated remediation field experiment. Rhizosphere bacterial community diversity and composition were also assessed using high-throughput sequencing based on 16S rRNA genes. Compared with non-amendment treatment (CK), soil pH, cation exchange capacity (CEC), organic matter (OM), total nitrogen (TN), available nitrogen (AN), and nitrate (NO3−) concentrations were significantly elevated, whereas ammonium (NH4+) and soil available Cd concentrations were reduced by soil amendment treatments. Meanwhile, soil amendments significantly decreased concentrations of total Cd in both rice straw and grain, with the lowest Cd concentration in the C300 treatment. Soil pH and CEC were significantly and negatively associated with soil Cd availability and rice straw and grain Cd concentrations, while NH4+ concentration was positively correlated with soil available Cd concentration, and OM, TN, and NO3− concentrations were positively linked with rice grain Cd concentration. Soil amendments significantly increased bacterial Chao 1 and Shannon indexes and altered bacterial community composition in rhizosphere soil, due to changes in the composition of the community primarily influenced by variations in soil pH, CEC, and soil available Cd, NH4+, available phosphorous (AP) and available Potassium (AK) concentrations. Furthermore, the abundant bacterial species (Pseudomonas) and rare bacterial species (Bacillus, Candidatus_Solibacter and Streptomyces) have been up-regulated by different soil amendments, which might be in favour of soil Cd immobilization. A structural equation model also showed that soil amendments could improve bacterial diversity and change soil pH and CEC, which were conducive to hindering the removal and conversion of Cd. Overall, these results indicate that biochar-(MgO-CaO-SiO2) mixed amendments at high dosage exerted better performance compared with single application soil amendment A and B. The changes in soil chemical properties, available Cd content, and rhizosphere bacterial community assembly induced by soil amendments are closely correlated with the decrease in rice’s ability to accumulate Cd.

1. Introduction

The rice (Oryza sativa L.) is widely grown in Asia as an important staple food, and China accounts for a significant portion of the world’s rice production, with approximately one-third of global output originating from this country [1]. It should be emphasized that the rising population will lead to a 20% increase in the domestic requirement for rice yield by 2030, according to projections [2]. Nevertheless, according to a nationwide investigation, heavy metal contamination was detected in approximately 19.4% of agricultural soil samples, with cadmium (Cd) being the most prevalent among these toxic elements at a rate of 7% among heavy metals [3]. Cd is a potential highly mobile toxic element that accumulates and has substantial biological toxicity, and easily transfers from soil to food chain [4]. Cd2+ is readily absorbed by the roots of plants and subsequently accumulates in various plant tissues, with a particular affinity for rice grain [5]. The rice grain’s Cd accumulation seriously threatens the safe use of farmland and human dietary health [6], and approximately per 10% to 20% rice and rice by-products face heavy metals contamination problems in China [7]. Therefore, enhanced awareness on Cd pollution remediation promoted us to screen effective in situ remediation modes to decrease availability of Cd in soil for biological uptake, and then reduce Cd accumulation of rice grains in a safe range.
Chemical stabilization is a regular remediation strategy to immobilize heavy metals to reduce their uptake by crops [8,9]. In situ immobilization using various soil amendments is an economical solution to decrease the movement and accessibility of Cd, especially for their use in the large-scale remediation of Cd-contaminated arable soils [10,11]. Inorganic and organic amendments such as quicklime [12], apatite [11], commercial MgO-CaO-SiO2 conditioner [13], biochar [4], and organic fertilizer [14] have been commonly utilized to immobilize and minimize Cd bioavailability in soils. However, the remediation mechanisms of various soil amendments for soils contaminated with Cd are quite different [15]. For example, biochar is a type of carbonaceous product of materials from a feedstock by pyrolysis at elevated temperatures under low or no oxygen conditions [16]. Biochar contains high pH value, large porosity, and area on the surface, active functional groups, and capacity for exchanging cations, thus, it effectively immobilizes Cd via adsorption, ion exchange, precipitation, and surface complexation [17,18]. Studies have reported that the application of biochar can alter soil chemical properties, decrease in the availability of Cd, and thus decrease Cd uptake of crop [19,20]. Moreover, biochar can improve soil fertility, increase nutrients availability to crops, and thus enhance crop production [21]. As an inorganic soil amendment, the commercial MgO-CaO-SiO2 conditioner regularly consists of multiple alkaline materials and could decrease soil Cd availability and mobility, mainly via increasing soil pH or adsorption, chelation, or precipitation [22,23,24]. In addition, the commercial MgO-CaO-SiO2 conditioner contains Ca2+, Mg2+, and Si2+ elements, which could replenish nutrient deficiency in crops [13,14]. Long-term application of a single remediation material is easy to damage the soil structure and influence soil fertility [25], which has certain limitations in field experiments. Thus, composite amendments may have better remediation effects and have been increasingly used in heavy metal remediation [11,26]. For instance, a single lime (Ca(OH)2) decreased soil Cd bioavailability by 40%, while the composite amendment composed of manure, lime, and sepiolite produced better results in soil Cd availability, by 46% [26].
Soil microorganisms have a vital function in controlling nutrient cycling, crop growth, and facilitating the removal of heavy metals [11]. Soil microorganisms exhibit susceptibility to heavy metal contamination and are always considered as a vital indicator of soil quality [27]. Prior research has indicated that the excessive presence of heavy metals reduced the biomass of microorganisms and microbial diversity, and altered microbial community structure [28]. Changes of soil microbial communities triggered by heavy metal contamination influence nutrient conversion and soil chemical properties, which in turn impact poisonousness and bioavailability of heavy metals [4]. Furthermore, soil microbial communities are also significantly influenced by soil amendments [11,13]. For example, biochar could improve the electron transfer between heavy metals and microorganisms, thus causing the transformation and decreasing the degree of harmfulness exhibited by heavy metals to microbes [29]. Xu [30] found that biochar indirectly reduced soil Cd bioavailability via altering key microbial community composition in Cd-contaminated soils, thus alleviating the stress effect of Cd on rice, and ultimately reducing the Cd concentration in rice grains. In addition, alkaline soil amendments have been reported to enhance microbial diversity and change its community structure [12]. Li [31] pointed out that the application of alkaline soil amendments can regulate the soil micro-ecology to enhance the resistance of microorganisms to heavy metals. Thus, microbial community and functions in response to soil amendments are more and more attracting people’s attention. Nonetheless, the majority of research has concentrated on the impact of single soil amendments on soil Cd stabilization or Cd uptake by crops, while neglecting the conjunct utilization effects of different amendments on heavy metals remediation and the assembly of an associated microbial community [32,33,34].
In this study, a field experiment was carried out using three amendments (CaO-MgO-SiO2, biochar, and CaO-MgO-SiO2 + biochar) with varying levels of application rates in Cd-contaminated paddy fields in Hainan Province, China. The objectives were to: (1) compare the impact of various soil amendments on the chemical properties of soil, soil Cd availability, and total Cd accumulation in rice; (2) evaluate the assembly of the rhizosphere bacterial community in response to different soil amendments; (3) assess the associations between soil Cd availability and other abiotic parameters affected by different soil amendments and rhizosphere bacterial communities in Cd-contaminated paddy fields. We hypothesized that (1) combination of CaO-MgO-SiO2 and biochar would exerted better inhibitory effects on soil Cd availability and Cd uptake by rice, in comparison with sole application CaO-MgO-SiO2 and biochar; and (2) the shifts of soil chemical properties and rhizosphere bacterial community induced by soil amendments were associated with Cd uptake in rice grain.

2. Materials and Methods

2.1. Field Description and Experimental Design

The field trial was conducted in a Cd-contaminated paddy field since 2020 in Gancheng Town, Hainan Province (108°43′ N, 18°52′ E). According to the USDA classification, the soil is classified as sandy loam. The experiment site was classical gold and silver mining area and parts of paddy field in this area was heavily contaminated with Cd. The mean Cd concentration was 1.60 mg kg−1, which was considered as ‘moderately Cd-contaminated’ field according to Soil Environmental Quality Agricultural Land Soil Pollution Risk Control Standard [35]. The basic soil chemical properties were as follows: pH 5.3, cation exchange capacity (CEC) 6.3 cmol kg−1, soil organic matter (OM) 24.5 g kg−1, available nitrogen (AN) 115 mg kg−1, available phosphorous (AP) 15 mg kg−1, available potassium (AK) 120 mg kg−1.
Three commonly used soil amendments were used as materials for the remediation of soil. Soil amendment A (inorganic soil conditioner, mainly composed of CaO, MgO, and SiO2); soil amendment B (biochar, coconut shell pyrolyzed at 550 °C, and kept constant for 1 h); and soil amendment C (a combination of soil amendments A and B with a ratio of 1:1). Relevant information regarding the primary chemical characteristics of chosen amendments can be found in Table S1. There were seven treatment plots with three replicates (each plot area was 5 m × 6 m = 30 m2) in a completed randomized block design: (1) CK: control with no amendment; (2) A150: soil amendment A with a low dose of 2250 kg ha−1; (3) A300: soil amendment A with a high dose of 4500 kg ha−1; (4) B150: soil amendment B with a low dose of 2250 kg ha−1; (5) B300: soil amendment B with a high dose of 4500 kg ha−1; (6) C150: soil amendment C with a low dose of 2250 kg ha−1; and (7) C300: soil amendment C with a high dose of 4500 kg ha−1. The plots were parted by a ridge and covered with mulching film. All soil amendments were applied one time before tilling. The hybrid rice cultivar named Guangyou8 was selected for this study. Field management and fertilization were conducted in accordance with traditional local practices.

2.2. Sample Collection

Soil samples were collected in a farmland in July 2022. The collection of rhizosphere soil samples followed the procedure outlined by Edwards [36]. In each replicate plot, rhizosphere soil samples were gathered as a mixed sample, which was fully blended by stochastically gathered ten-plant samples at the rice maturity stage. Meanwhile, ten rice grain and straw were also casually gathered and blended to form a sample of plant roots for further analysis at the mean time. All soil samples passed through the 2 mm sieve and grouped into two parts: a part of fresh soils was kept at −80 °C for DNA extraction, and another part of air-dried soils was maintained to analyse the soil chemical properties. The plant samples were divided into different tissues (straw and grain), and all plants were dried to a steady weight at 80 °C and pulverized into a fine powder.

2.3. Chemical Analysis

Soil pH was measured in a 1:2.5 soil/water suspension with a pH meter (S975 Seven Excellence, Mettler Toledo, Greifensee, Switzerland). The concentrations of NO3− and NH4+ were analysed using 5 g fresh soil samples with a continuous flow analyser (TRAACS 2000, Bran and Luebbe, Norderstedt, Germany) after extracting with 2 mol/L KCl (1:10, soil:water). Soil TN was measured using an elemental analyser (multi EA® 5000, Analytik Jena, Jena, Germany). Soil AN was determined by alkaline hydrolysis nitrogen diffusion method. Soil AP was extracted with 0.5 M NaHCO3 (Xilong Scientific Co., Ltd., Shantou, China) (pH 8.5) and analysed calorimetrically. Soil AK was analysed using flame photometry after NH4OAc (Xilong Scientific Co., Ltd., Shantou, China) (pH 7.0) extraction. Soil OM was determined using the potassium dichromate oxidation colorimetric method. The CEC was determined by a hexaamminecobalt trichloride solution spectrophotometric method. The concentration of soil available Cd (CaCl2 extractable Cd) were extracted with 0.1 M CaCl2 (Xilong Scientific Co., Ltd., Shantou, China) solution (1:10, soil:solution), and total Cd concentrations in rice straw and grain were digested with a mix of acid solution (HF-HClO4-HNO3) (Xilong Scientific Co., Ltd., Shantou, China), following the Environmental Protection Agency (EPA) 3052 method. Total Cd concentrations in rice straw and grain, and calcium chloride extractable Cd concentration in soil extracts were all analysed using an ICP-MS (PE300X, PerkinElmer, Waltham, MA, USA) [37,38]. For all soils, relevant analyses were conducted to determine the physiochemical properties, following established soil agrochemical analysis protocols [39].
The calculation of the Cd passivation ratio was performed after applying different amendments as follows:
Cd passivation ratio = [(C0 − C1)/C0] × 100%
where C0 represented the initial concentration of Cd in the control treatment, while C1 denoted the concentration of Cd following the implementation of passivation amendments.

2.4. DNA Extraction, PCR Amplification, and High-Throughput Sequencing

Total genome DNA from 21 rhizosphere soil samples were extracted using soil DNA Isolation Kits (MO BIO Laboratories, Carlsbad, CA, USA) according to the product description. Bacterial 16S rRNA gene V3-V4 region was amplified using specific primers 341F/806R with the unique barcode. The PCR reaction was carried out in 50 μL mixtures including 25 μL pre-mixture, 1 μL forward and reverse primers, and 2 μL template. The PCR reactions consisted of initial denaturation at 95 °C for 1 min, followed by 30 cycles of denaturation at 95 °C for 30 s, annealing at 54 °C for 30 s, and elongation at 72 °C for 30 s and finally extension at 72 °C for 5 min. PCR products were purified with Tiangen PCR Purification Kit (Tiangen, Beijing, China). The purified PCR products from all samples were sequenced on an Illumina Miseq platform (Majorbio, Shanghai, China).
Sequence analysis was performed using QIIME (v1.7.0) software. In brief, the primers, low-quality sequences, and barcodes were deleted using USEARCH (v11.0), and the paired-end sequences were merged with FLASH (v1.2.7). Chimera detection and deletion were conducted by the Uchime algothm. The operational taxonomic units (OTUs) were formed at the 3% dissimilarity threshold, and representative sequence of each OTU was annotated based on the SILVA ribosomal RNA gene database. The bacterial α-diversity including Chao 1 and Shannon indexes were calculated using Mothur software. The raw sequences were deposited into the NCBI archive database (No. PRJNA1000091).

2.5. Statistical Analysis

Statistical analysis was implemented using SPSS (v23.0) One-way analysis of variance (ANOVA) followed by Warren–Duncan’s test was used for determining the statistical significance (p < 0.05) of soil parameters among different treatments. Principal coordinate analysis (PCoA) based on the Bray–Curtis distances was used for investigating the effects of soil amendments on bacterial community structure. Redundancy analysis (RDA) was conducted with CANOCO software (v5.0) to examine the relationship between the environment and the abundance of microorganisms. Variation partitioning analysis (VPA) based on db-RDA was used to evaluate the impacts of significant environmental factors on the compositions of microbial community structures by vegan package (R v4.1.2). Additionally, the relative contributions of edaphic factors to the Cd concentrations in soil and rice straw and grain were assessed using Mantel test in the ‘LinkET’ package. Random forest is robust to outliers and non-linear data, we implemented random forest to recognize the most accurate indicators for forecasting the levels of Cd concentration in soil with the randomForest package, as well as rice straw and grain. Meanwhile, the rfPermute package was utilized to assess the importance of each predictor. Structural equation model (SEM) was used to evaluate the latent direct and indirect effects of significant factors, namely soil amendments, soil pH, CEC, bacterial α-diversity (Shannon index), and available Cd concentration in soil, and total Cd concentration in rice grain. Evaluation of model fitness was described as follows: the chi-square test (the model is accepted when p > 0.05), goodness of fit index (GFI; the model was accepted when GFI > 0.9), and the root MSE of approximation (RMSEA; the model is accepted when RMSEA < 0.05 and p > 0.05). The highest 0.1% of OTUs present in all samples underwent filtration, elimination of zero values, and correlation analysis using with the package ‘wgcna’ in R. Next, the correlation among microbial communities was intuitionistic using the Frucherman Reingold algorithms in Gephi software (v0.9.7) co-occurrence network. All the figures were plotted with Origin 2021 software.

3. Results and Discussion

3.1. Effects of Soil Amendments on Soil Chemical Properties and Total Cd Accumulation in Rice

Soil chemical properties and total Cd concentrations in rice straw and grain after soil amendment application are shown in Figure 1 and Figure 2. Soil chemical properties changed significantly by soil amendments multifarious with the type and applied dose (Figure 1). Soil pH values significantly elevated by 0.12–1.26 units after soil amendments applied relative to CK (p < 0.05), while the highest pH value (6.54) was found after applying soil amendment C at high dose (C300). All these soil amendments (MgO-CaO-SiO2, biochar, biochar + MgO-CaO-SiO2) applied in this study were alkaline, and the increase in soil pH with these soil amendments added at high dosage was obviously higher than that at low dosage (p < 0.05). Similarly, soil CEC was enhanced by 12.12–33.58% under most soil amendment-related treatments except A150 and B150, which showed no impact on it. Ca2+ and Mg2+ can exchange with soil colloidal species H+ and Al3+ ions [40,41], thus increasing pH and CEC. The OM concentration was significantly increased by 21.52%, 16.14%, 18.86%, and 7.68% under A300, B150, B300, and C300 treatments, respectively, in comparison with CK, while A150 and C150 treatments had no significant effect on it. A recent study reported that commercial MgO-CaO-SiO2 conditioner increased OM concentration of rhizosphere soil relative to CK [13]. Soil amendments B and C contained a biochar component, which would contribute to the increase in OM [42]. The TN content was significantly elevated with increasing application rates of soil amendments A and C, but was not affected by the different doses of soil amendment B. Compared to CK, all soil amendment-related treatments significantly decreased the NH4+ concentration by 13.33–41.26%, whereas they significantly increased NO3− concentration by 25.92–66.41% (p < 0.05). Previous studies have reported that enhanced pH by alkaline materials could strengthen the transformation of inorganic NH4+ to NO3−, thus accelerated the nitrification process [43]. Soil amendments A300, B150, B300 and C150 significantly increased AN concentration by 6.80–26.15% except A150 and C300 treatments. There was an increment of 11.53–42.26% in AP concentration under A300, B150, B300, and C150 treatments, while this was decreased by 33.19% under A150 treatment. As for AK, B150 and B300 significantly increased the AK concentration by 13.42% and 16.82%, respectively, while C150 and C300 decreased it by 18.76% and 5.45%, respectively. According to the two-way ANOVA results, the types of soil amendments significantly affected soil pH, CEC, NH4+, AP, and AK (F = 45.55, 21.168, 26.451, 11.644, and 40.049, respectively; p < 0.001), while the dosage of soil amendments only affected OM (F = 16.41, p < 0.001) (Table S2). Significant variation in soil available Cd content with types and application rate of soil amendments were found (Figure 2a). Compared with CK, soil amendments with different application rates significantly reduced soil available Cd content by 16.04–81.33%, and the immobilization impact of the soil amendments was more pronounced when added at higher dosages compared to a low dosage (p < 0.05), with the lowest soil available Cd content found under C300 treatment. Xu [44] and Cheng [13] also revealed that the commercial MgO-CaO-SiO2 conditioner and biochar significantly reduced the soil available Cd content. There are several possible mechanisms for this phenomenon. First, CaO and MgO can cause an increase in soil pH, resulting in a significant decrease in the solubility of heavy metals and uptake of Cd by plants [45]. Second, SiO2 colloid can cooperate with a Cd2+ soil solution to form a Cd-Si composite oxide to reduce the activity of Cd in soil and the absorption of Cd by plants [46,47]. Third, the adsorption capacity of biochar for heavy metals can use the reactive surface and a high pore structure to adsorb and stabilize Cd [48,49]. In addition, early studies also found that the soil available Cd concentration was in connection with soil chemical properties, containing soil pH, CEC, organic matter and nutrients availability, of which pH is the most crucial factor [4,14]. In the current study, all soil amendments observably enhanced soil pH and CEC, which showed significant and negative correlations with soil available Cd concentration (p < 0.01). Meanwhile, soil amendments reduced NH4+ availability, which was definitely related with soil available Cd concentration (p < 0.05). Therefore, these soil amendments resulted in a substantial improvement in both soil pH and CEC, and reduced NH4+ availability, and consequently decreased the soil available Cd concentration [13].
Soil amendments were highly efficient in decreasing the total Cd accumulation in rice, but the decrease in total Cd concentration across various rice tissues was observed to be influenced by both the dosage and composition of soil amendments (Figure 2b,c). Compared with CK, total Cd concentration in rice straw was strongly decreased in the A150, A300, B300, and C300 treatments, by 36.03%, 48.55%, 41.62%, and 67.24%, respectively, while the low dosage of soil amendments B150 and C150 showed no inhibitory effect on it (Figure 2b). The rice grain in the CK treatment was 0.25 mg kg−1, which exceeded the Chinese food safety standard for cereal (Cd ≤ 0.2 mg kg−1) [35] (Figure 2c). All soil amendment-related treatments significantly reduced total Cd concentration in rice grains by 49.87–71.33% in comparison with CK, with the lowest Cd content in rice grain was observed under A150, A300, B300 and C300 treatments. The total Cd concentration in rice grain after applying soil amendments was much lower than China’s food safety threshold for Cd, indicating that these selected soil amendments in this study are very effective in impeding Cd transformation from soil to rice grain.
Additionally, the partial Mantel test was used to examine the relationships between soil available Cd and total Cd concentrations in rice straw and grain with environmental variables (Figure 3a). Overall, soil pH and CEC were negatively correlated with soil Cd availability and rice straw and grain Cd concentrations (p < 0.01), while NH4+ concentration showed a strong positive correlation with the soil available Cd concentration (p < 0.05), and OM TN and NO3− concentrations were positively correlated with total Cd concentration in rice grain (p < 0.05). Random forest analysis suggested that pH, NH4+, and CEC are the main variables affecting the soil available Cd (Figure 3b). pH, CEC, and AP were the main predictors determining total Cd concentration in rice straw (Figure 3c). In addition, pH, CEC, and AK were the main predictors for concentration in rice grain (Figure 3d). These results suggested that soil amendments induced the variation in soil chemical properties indirectly reduced total Cd concentration in straw and rice grains [13,14]. Two-way ANOVA showed that the types of soil amendment (F = 16.407, p < 0.001) and interactions between soil amendment types and applied dose (F = 13.892, p < 0.001) influenced the soil available Cd concentration (Table S3). However, the recommended dosage for applying soil amendments only had a considerable impact on Cd accumulation in rice straw and grain (F = 38.337, 33.506; p < 0.001).

3.2. Effects of Soil Amendments on Rhizosphere Bacterial Community Diversity and Composition

Bacterial community α-diversity was assessed by Chao 1 and Shannon indexes based on OTU level (Figure 4a,b). Compared with CK, all soil amendment-associated treatments significantly increased Chao 1 and Shannon indexes, with the most effective treatment was B300. A similar result was also observed in previous studies, indicating that soil alkaline remediation materials enhanced bacterial α-diversity in Cd-contaminated soils [12,50]. However, other studies reported that these selected soil amendments had no obvious effects on bacterial α-diversity in Cd-contaminated soils [13,30]. These findings indicated that the impacts caused by soil amendments on microbial community traits were diverse, which needed widely study in various soil ecosystems. In addition, two-way ANOVA revealed that the types of soil amendment (F = 6.427, 4.759; p < 0.05) and interactions between soil amendment types and additive dose (F = 24.101, 16.727; p < 0.001) influenced Chao 1 and Shannon indexes (Table S4).
Different soil amendments significantly influenced bacterial community composition at a phylum level (Figure 4c). The top five dominant bacterial phylum were Chloroflex, Proteobacteria, Actinobacteria, Acidobacteria, and Verrucomicrobia, accounting for 50.32–90.09% of total bacterial reads. Chloroflexi is the most abundant bacteria, and soil amendments A and B with different application doses significantly decreased the relative abundance of Chloroflexi by 3.27–20.21% compared to CK, which was opposed to a recent research [4], but increased with soil amendment C. Proteobacteria was considered as a strongly metal-tolerant bacteria and could survive in the heavy metal contaminated soil [41]. Soil amendments A, B, and C with different application doses increased the relative abundance of Proteobacteria by 3.68–80.94%, which could reduce soil Cd availability through their complexation and absorption abilities [5,31]. In particular, all soil amendments decreased the relative abundance of Actinobacteria by 1.18–12.41%. Acidobacteria was considered to have a significant impact on the fixation of heavy metals [41]. The relative abundance of Acidobacteria in the A300 and B150 treatments significantly increased by 3.31% and 10.80%, respectively, compared with CK. And all soil amendments increased the relative abundance of Verrucomicrobia by 1.74–14.58%. Overall, soil amendments in this study improved the relative abundance of some microbial species like Proteobacteria, Acidobacteria, and Verrucomicrobia, which might be involved in Cd passivation [4].
A heat map was utilized to visually represent the spatial patterns of the most prevalent bacterial genera among all experimental conditions (Figure 4e). The results showed that both soil amendment types and applied dose significantly changes in soil bacterial community structure at the genera were observed due to alterations in the relative abundance of specific genera, resulting from either reduction or enhancement. For example, Anaerolinea (phylum Chloroflexi) was important in available Cd precipitation [31]. Compared to CK, soil amendments significantly decreased the relative abundance of Anaerolinea by 3.57–20.27%. On the contrary, soil amendments significantly increased the relative abundance of the abundant taxa Pseudomonas (relative abundance > 0.5%) by 19.20–98.83%. The genus Pseudomonas from Proteobacteria phylum is capable of mobilizing heavy metals in soils [51]. In particular, the relative abundance of Rhodoplanes in the A150, B150, and C150 treatments significantly increased by 3.40%, 3.89%, and 2.86%, respectively, while A300, B300, and C300 treatments significantly decreased it by 8.50% 9.70%, and 11.03%. Furthermore, in our study, soil amendments promoted the upregulation of some rare bacterial taxa (Bacillus, Candidatus_Solibacter, and Streptomyces) (relative abundance < 0.1%), which might be helpful for the immobilization of heavy metals. Earlier research has indicated that the presence of Bacillus in pot experiments enhanced the immobilization of Cd in soil [52,53]. Candidatus_Solibacter has been proven to be a core microorganism in soils with different toxic heavy metals [28,54]. Streptomyces can reduce the antioxidant activity of crops, thereby reducing metal stress in polluted soil [55].
We also conducted principal component analysis (PCoA) and PERMONOVA to assess the effects of soil amendments on bacterial community structure at the OTU level (Figure 4d). PCoA analysis based on Bray–Curtis distance matrices showed that the axis1 and axis2 explained 31.43% and 21.37% of bacterial community variability, respectively. The bacterial community structure under A150, A300, B300, C150, and C300 treatments were separated from CK, while no obvious distinction was observed between CK and B150 treatments. Meanwhile, significant differences in the bacterial community structure between A150 and A300, B150 and B300, C150 and C300 were observed, indicating that different application dosages of soil amendments also altered bacterial community structure [12,44]. Furthermore, PERMONOVA analysis also showed that the impacts of different soil amendment-related treatments on bacterial community structure were significant (F = 11.823, p < 0.001) (Table S4). The results revealed that bacterial community structure could be clearly separated by the soil amendment type and applied dose.

3.3. Correlations between Soil Variables and Bacterial Community Diversity and Composition

The partial Mantel test was employed to examine the associations between environmental variables and bacterial community diversity, as measured by the Chao 1 and Shannon indexes (Figure S1a). Overall, the Chao 1 and Shannon indexes were strongly correlated with AK and NH4+ (p < 0.05). Random forest analysis suggested that AK, NH4+, and AP are the main variables affecting the Chao 1 index. In addition, pH, CEC, NH4+, soil available Cd, AP, and NO3− concentrations were the main predictors determining the Shannon index (Figure S1b,c).
The redundancy analysis (RDA) showed that soil pH, CEC, soil available Cd, NH4+, AP, and AK were the primary environmental factors affecting bacterial community structure (Figure 5a), which was supported by a previous study [56]. Variation partitioning analysis (VPA) manifested that soil pH and available Cd explained 7.92% and 5.98% of the variation in bacterial community, respectively (Figure 5b). Meanwhile, other soil edaphic parameters containing NH4+, CEC, AP, and AK together explained 23.25% of the variation in bacterial community. Moreover, Pearson correlation analysis was also conducted to evaluate the relationships between soil chemical properties and bacterial community composition at the phylum level (Figure S2). Specifically, soil available Cd was positively correlated with relative abundances of Actinobacteria, Firmicutes, Crenarchaetota, Gemmatimonadetes, and SAR406 (p < 0.05), while negatively correlated with relative abundances of Verrucomicrobia, Chlorobi, Cyanobacteria, Bacteroidetes, and TPD_58 (p < 0.05), which was inconsistent with recent finding of Zhang [4]. In addition, pH and CEC were significantly and positively correlated with relative abundances of Verrucomicrobia and Chlorobi (p < 0.05), but negatively correlated with relative abundances of Firmicutes, Actinobacteria, Crenarchaetota, and Gemmatimonadetes (p < 0.05). Soil OM, NH4+, NO3−, and TN were positively correlated with the relative abundances of Actinobacteria and Firmicutes. These results suggested that soil amendments with different applied doses altered soil chemical properties, thus affected the relative abundances of main bacterial species [11].
Moreover, a structural equation model (SEM) was also performed to study the correlations among soil chemical properties, bacterial community diversity and structure, soil available Cd content, and total Cd concentration in rice grains (Figure 6). Our results showed that soil amendments directly affected soil pH, CEC, bacterial diversity (Shannon index), and total Cd concentration in rice grains. Furthermore, pH, CEC, and Shannon index directly influenced soil Cd availability. Overall, soil amendments directly changed soil pH, CEC, and Shannon index and then indirectly reduced the soil Cd availability, thus, finally decreased the total Cd concentration in rice grains [11,56].

3.4. Microbial Co-Occurrence Networks

A bacterial symbiosis network was constructed to evaluate the potential ecological interactions among microbial members in all treatments (Figure 7). After implementation of soil amendments, many topological properties of bacterial symbiosis network changed significantly (Table S5); this reveals the reactions of microorganisms to Cd immobilization [57]. The bacterial symbiosis network of C150 treatment was more complex, and the number of nodes entering the network was greater than other treatments. The A150 treatment had the highest positive line, accounting for 55%, indicating that the positive interaction among bacterial communities in the A150 treatment was greater than other treatments. In addition, the B300 treatment had a higher average degree and clustering coefficient, indicating that B300 treatment increased the compactness and complexity of soil bacterial communities. Li [58,59] found that biochar could improve the bacterial changed activity, which was conducive to improving the harbour of soil bacterial communities to Cd. The average path length of C300 treatment was low, which might mean that this treatment was conducive to the information exchange and energy and material transportation of the entire ecosystem. Symbiotic network analysis showed the variations in the component and function of soil bacterial community [22,60], and the composition and dosage of composite amendments have a significant impact on them [20].

4. Conclusions

This study explored the effects of soil amendments (CaO-MgO-SiO2, biochar, and CaO-MgO-SiO2 + biochar) on soil chemical properties, soil Cd bioavailability, total Cd concentrations in rice straw and grain, and rhizosphere bacterial community in a Cd-contaminated paddy field. The commercial Cao-MgO-SiO2 conditioner and/or biochar addition significantly reduced soil Cd availability and total Cd concentrations in rice straw and grain, and their co-application at high dosage exerted a better inhibitory effect. The changes in soil pH, CEC, and nutrient availability after applying soil amendments were the key abiotic factors influencing soil Cd availability, total Cd concentrations in rice straw and grain. Moreover, soil amendments significantly shifted bacterial community structure, with changes in bacterial community structure mostly driven by soil pH, soil available Cd, AP, AK, and NH4+ concentration. In addition, the abundant bacterial species (Pseudomonas) and rare bacterial species (Bacillus, Candidatus_Solibacter, and Streptomyces) have been enhanced by different amendments, which might reduce soil Cd availability. Overall, soil amendments directly affected soil chemical properties and changed bacterial community and then influenced soil Cd bioavailability, which is also conducive to blocking the migration and transformation of Cd from soil to rice.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/agronomy13123051/s1, Figure S1: Multiple soil environmental variables shaping the bacterial community diversity (Chao 1 and Shannon index); Figure S2: Pearson correlation analysis of bacterial communities and soil available Cd (soil ACd) and soil chemical parameters; Table S1: Basic chemical properties of tested amendments; Table S2: Effects of three soil amendments with different application rates on soil properties during rice maturity; Table S3: Effects of three soil amendments with different application rates on soil Cd availability, total Cd accumulation in straw and rice grain at the mature stage of rice; Table S4: Effects of three soil available Cd with different application rates on bacteria α-diversity (chao1 index and Shannon index) and β-diversity (PC1) at the mature stage of rice; Table S5: The network properties of bacterial taxonomic groups in soil at the phylum level by different application of three soil amendments.

Author Contributions

Methodology, Q.W. and Z.Z.; formal analysis, Y.X., M.L., L.W. and Y.G.; investigation, Y.X., M.L., L.W. and Y.G.; writing—original draft preparation, Y.X.; writing—review and editing, Y.R., Q.W. and Z.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This study was financially supported by Hainan Provincial National Science Foundation of China (321RC1022) and (322MS031), Hainan Natural Science Foundation (No. 321MS100) and by the Project of Sanya Yazhou Bay Science and Technology City, Grant No: SCKJ-JYRC-2023-23.

Data Availability Statement

The data presented in this study are available in article.

Acknowledgments

We thank the anonymous reviewers for reviewing our manuscript.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Soil chemical characteristics under various amendment treatments. (a): pH, (b): OM, (c): CEC, (d): TN, (e): NH4+, (f): NO3−, (g): AN, (h): AP, (i): AK. Within each subgraph, values with the same lower-case letters are not significantly different (p = 0.05), different lower-case letters indicate a significant (p < 0.05) difference under different treatments according to Warren–Duncan test. Vertical bars represent standard errors (n = 3).
Figure 1. Soil chemical characteristics under various amendment treatments. (a): pH, (b): OM, (c): CEC, (d): TN, (e): NH4+, (f): NO3−, (g): AN, (h): AP, (i): AK. Within each subgraph, values with the same lower-case letters are not significantly different (p = 0.05), different lower-case letters indicate a significant (p < 0.05) difference under different treatments according to Warren–Duncan test. Vertical bars represent standard errors (n = 3).
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Figure 2. Effect of different amendment treatments on soil available Cd (a) and total Cd concentrations in rice straw (b) and grain (c). Within each subgraph, values with the same lower-case letters are not significantly different (p = 0.05), different lower-case letters indicate a significant (p < 0.05) difference under different treatments according to the Warren–Duncan test. Vertical bars represent standard errors (n = 3).
Figure 2. Effect of different amendment treatments on soil available Cd (a) and total Cd concentrations in rice straw (b) and grain (c). Within each subgraph, values with the same lower-case letters are not significantly different (p = 0.05), different lower-case letters indicate a significant (p < 0.05) difference under different treatments according to the Warren–Duncan test. Vertical bars represent standard errors (n = 3).
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Figure 3. Multiple soil environmental variables shaping soil available Cd and total Cd concentrations in rice straw and grain. (a) The soil available Cd and total Cd concentrations in rice straw and grain based on Bray–Curtis distance, revealing the correlation between environmental factors is demonstrated using Pearson’s correlation coefficient, which is visualized with a colour gradient. The line’s width indicates the partial Mantel’s r statistic, while its colour signifies the statistical significance determined through 999 permutations. Revealing the correlation between environmental factors is demonstrated using Pearson’s correlation coefficient, which is visualized with a colour gradient. Bar plots display the percentage increase in mean squared error (MSE) in soil available Cd due to environmental drivers, as determined by the random forest predictor importance (b), total Cd concentrations in rice straw (c), and grain (d) across the amendment treatments. Significance levels of each predictor are as follows: * p < 0.05, ** p < 0.01, *** p < 0.001.
Figure 3. Multiple soil environmental variables shaping soil available Cd and total Cd concentrations in rice straw and grain. (a) The soil available Cd and total Cd concentrations in rice straw and grain based on Bray–Curtis distance, revealing the correlation between environmental factors is demonstrated using Pearson’s correlation coefficient, which is visualized with a colour gradient. The line’s width indicates the partial Mantel’s r statistic, while its colour signifies the statistical significance determined through 999 permutations. Revealing the correlation between environmental factors is demonstrated using Pearson’s correlation coefficient, which is visualized with a colour gradient. Bar plots display the percentage increase in mean squared error (MSE) in soil available Cd due to environmental drivers, as determined by the random forest predictor importance (b), total Cd concentrations in rice straw (c), and grain (d) across the amendment treatments. Significance levels of each predictor are as follows: * p < 0.05, ** p < 0.01, *** p < 0.001.
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Figure 4. The α-diversity and community structure of soil bacteria undergo alterations when subjected to various amendment treatments. (a) The bacterial Chao1 index, (b) the bacterial Shannon index, (c) the relative abundances which bar colour represents bacterial phylum. The top 15 species are shown and the remaining phylum are designated as ‘Other’. (d) The bacterial PCoA plot at the OUT level of bacteria in bacteria community compositions. (e) The clustering heat map is based on relative abundances of the top 20 genera under the amendment treatments.
Figure 4. The α-diversity and community structure of soil bacteria undergo alterations when subjected to various amendment treatments. (a) The bacterial Chao1 index, (b) the bacterial Shannon index, (c) the relative abundances which bar colour represents bacterial phylum. The top 15 species are shown and the remaining phylum are designated as ‘Other’. (d) The bacterial PCoA plot at the OUT level of bacteria in bacteria community compositions. (e) The clustering heat map is based on relative abundances of the top 20 genera under the amendment treatments.
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Figure 5. Influence of environmental factors on the taxonomic composition of bacteria. (a) Redundancy analyses (RDA) showed the relationship between edaphic factors and microbial phylogenetic composition at OTU quantity (relative abundance > 0.1%). (b) RDA-based variation partitioning analysis (VPA) illustrated the contribution of soil available Cd, pH, and other edaphic factors including OM, CEC, TN, NH4+, AN, AP, and AK to bacterial community structure. * p < 0.05, ** p < 0.01.
Figure 5. Influence of environmental factors on the taxonomic composition of bacteria. (a) Redundancy analyses (RDA) showed the relationship between edaphic factors and microbial phylogenetic composition at OTU quantity (relative abundance > 0.1%). (b) RDA-based variation partitioning analysis (VPA) illustrated the contribution of soil available Cd, pH, and other edaphic factors including OM, CEC, TN, NH4+, AN, AP, and AK to bacterial community structure. * p < 0.05, ** p < 0.01.
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Figure 6. Structural equation model (SEM) indicating the relationships between soil chemical properties, bacterial community diversity (Shannon index) and structure (Bray–Curtis), soil available Cd content, and total Cd concentration in rice grains. Solid red lines indicate significantly positive effects, blue lines indicate significantly negative effects, and dashed lines indicate no significant effects. Numbers on lines are standardized direct path coefficients. *** p < 0.001, * p < 0.05.
Figure 6. Structural equation model (SEM) indicating the relationships between soil chemical properties, bacterial community diversity (Shannon index) and structure (Bray–Curtis), soil available Cd content, and total Cd concentration in rice grains. Solid red lines indicate significantly positive effects, blue lines indicate significantly negative effects, and dashed lines indicate no significant effects. Numbers on lines are standardized direct path coefficients. *** p < 0.001, * p < 0.05.
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Figure 7. Co-occurrence networks of soil bacterial communities under different amendment treatments. The networks are coloured based on bacterial taxonomic information at the phylum level. The nodes are coloured by modules and node size is proportional to its degree.
Figure 7. Co-occurrence networks of soil bacterial communities under different amendment treatments. The networks are coloured based on bacterial taxonomic information at the phylum level. The nodes are coloured by modules and node size is proportional to its degree.
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MDPI and ACS Style

Xin, Y.; Liu, M.; Wei, L.; Gao, Y.; Ruan, Y.; Wang, Q.; Zhang, Z. Changes in Soil Chemical Properties and Rhizosphere Bacterial Community Induced by Soil Amendments Associated with Reduction in Cadmium Accumulation by Rice. Agronomy 2023, 13, 3051. https://doi.org/10.3390/agronomy13123051

AMA Style

Xin Y, Liu M, Wei L, Gao Y, Ruan Y, Wang Q, Zhang Z. Changes in Soil Chemical Properties and Rhizosphere Bacterial Community Induced by Soil Amendments Associated with Reduction in Cadmium Accumulation by Rice. Agronomy. 2023; 13(12):3051. https://doi.org/10.3390/agronomy13123051

Chicago/Turabian Style

Xin, Yu, Min Liu, Lanchun Wei, Yu Gao, Yunze Ruan, Qing Wang, and Zhijun Zhang. 2023. "Changes in Soil Chemical Properties and Rhizosphere Bacterial Community Induced by Soil Amendments Associated with Reduction in Cadmium Accumulation by Rice" Agronomy 13, no. 12: 3051. https://doi.org/10.3390/agronomy13123051

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