Next Article in Journal
Optimized Tomato Production in Chinese Solar Greenhouses: The Impact of an East–West Orientation and Wide Row Spacing
Previous Article in Journal
Yield and Quality Traits of Tomato ‘San Marzano’ Type as Affected by Photo-Selective Low-Density Polyethylene Mulching
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Agronomy
Volume 14
Issue 2
10.3390/agronomy14020316
agronomy-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Straw and Biochar Application Alters the Structure of Rhizosphere Microbial Communities in Direct-Seeded Rice (Oryza sativa L.) Paddies

by
Zhiqiang Tang
1,
Na He
1,
Liying Zhang
1,
Lili Wang
1,
Diankai Gong
1,
Changhua Wang
1,
Hui Wang
2,
Guomin Sui
2,* and
Wenjing Zheng
1,*
1
Rice Research Institute of Liaoning Province, Liaoning Academy of Agricultural Sciences, Shenyang 110101, China
2
Liaoning Academy of Agricultural Sciences, Shenyang 110101, China
*
Authors to whom correspondence should be addressed.
Agronomy 2024, 14(2), 316; https://doi.org/10.3390/agronomy14020316
Submission received: 12 December 2023 / Revised: 21 January 2024 / Accepted: 27 January 2024 / Published: 31 January 2024
(This article belongs to the Section Soil and Plant Nutrition)
Browse Figures
Versions Notes

Abstract

:
A comprehensive understanding of rice straw (RS) and biochar (BC) addition affecting soil quality, enzyme activities, bacterial community structure and grain yield is crucial. The objective of this study was to examine the dynamics of the soil microbial community impacted by the application of rice straw and biochar, and to understand the relationship between the microbial communities, soil enzymes, nutrients and grain yield of paddies. We conducted a field experiment with organic amendments under the direct seeding of paddies. The bacterial community structure in the rhizosphere was characterized using high-throughput 16S rRNA sequencing. The results showed that RS amendment increased grain yields by 8.5 and 9.9% more than with BC and the control without organic amendment (CK), respectively (p < 0.05). The abundance of bacteria associated with nitrate reduction in RS was higher than that in BC and CK, which further showed the significance of the RS-treated soil bacteria in rice nutrient utilization. A cladogram plotted using linear discriminant analysis effect size showed that Proteobacteria (Alphaproteobacteria), Acidobacteria, Firmicutes, Verrucomicrobia and Epsilonbacteraeota in the RS-treated soil increased in comparison with CK. Pearson’s correlation analysis showed that enzymes activities (cellulase activity and protease activity), soil nutrition content (soil hydrolyzable nitrogen), and bacterial phyla (Nitrospinae) were positively correlated with grain yield, suggesting that the RS-treated soil improved enzyme activities, soil nutrition content, and bacterial abundance, which in turn increased grain yield. The results indicated that RS-treated bacterial communities combined with soil enzymatic activities strengthen the transformation of nutrients, suggesting that the interactions play an important role in enhancing the grain yield of paddy rice. These results provide new insights and a theoretical basis for studying the changes in soil microbial communities with the application of RS and BC in Northeastern China.

1. Introduction

Rice is a staple food source in China, such that the total rice planting area and production are directly tied to overall national food security [1]. The total area of rice cultivation has reached 30 million hectares, with a yield of 7100 kg ha−1. High levels of rice production thus inevitably yield large volumes of byproducts, including rice straw [2], and the application of this straw to the soil can help to minimize waste while protecting against soil degradation and increasing the organic matter present therein [3]. Owing to these benefits, straw return can lead to significant improvements in soil porosity, pH, and water content, which ultimately contribute to enhanced fertility and plant productivity [4]. Applying these crop residues can also influence the diversity and composition of soil microbial communities [5]. Indeed, straw application has been reported to benefit the richness of soil microbial communities consistent with the ability of straw return to favor the activity of soil microbes [6]. Biochar is a byproduct generated through the pyrolysis of straw that can also be applied to influence soil water retention, pH, nutrient content, and organic C levels [7]. Biochar is porous, providing a habitat in which certain soil microorganisms can thrive, such that biochar has also been reported to benefit the soil microbes associated with paddy rice [8,9]. Few studies to date, however, have sought to clarify the underlying mechanisms through which organic soil amendments can influence the function and structure of the soil bacterial communities associated with rice production. This study was thus designed with the goal of testing the impact of organic amendments on these microbial communities during different growth stages of rice paddies.
As soil microbes and plants coexist within a shared environment, they are able to directly interact with and influence one another. Many of these microorganisms can provide benefits to proximal crops, enhancing overall soil fertility or aiding their ability to obtain soil nutrients in a manner conducive to healthy growth [10]. Crops frequently rely on a combination of their root systems and associated rhizosphere microorganisms to promote growth and nutrient uptake [11,12]. In prior studies, researchers have documented a balanced relationship between the positive and negative microbial communities present in rhizosphere soil, with shifts in this balance ultimately impacting crop growth [13]. The application of straw with or without straw decomposer could particularly stimulate the copiotrophic bacteria, enhance the soil biological activity, and increase crop yields [14]. Efforts to characterize the relationships more fully between these crops growth stages and associated microbial communities in straw and biochar application may provide valuable insights that can help enhance agricultural productivity and facilitate more sustainable development.
Soil enzymes exhibit a range of active catalytic functions that help shape the flow and recycling of nutrients and mineral elements within the soil microenvironment. Measuring these soil enzyme levels can thus provide important insight into the association between soil microbial communities and nutrient availability [15], given that these enzymes reflect microbial activity that serves as a key determinant of the availability of nutrients within this complex ecological setting [16]. Soil helps bridge the link between roots and the soil microenvironment, and root exudates can directly impact enzymatic activity and the microbial communities present within the soil [17]. There is thus a clear need to explore the impacts of soil organic amendments on nutrient levels and soil enzyme activity as a means of assessing the dynamic characteristics of the soil microbial communities found in rice paddies.
To achieve this goal, this study aimed to assess the effects of rice straw (RS) and biochar (BC) application on soil properties, soil enzyme activity levels, and the diversity and richness of soil microbial communities at different stages of rice growth. We attempted to tackle this question with specific objectives: (1) to evaluate the impact of RS and BC on the grain yield of paddy rice, (2) to determine the response pattern of rhizosphere soil bacteria taxonomy and bacteria composition at different rice growth stages, and (3) to characterize soil microbial responses with soil organic amendments.

2. Materials and Methods

2.1. Experimental Site

This study was performed at the Liaoning Rice Research Institute (40°57′ N, 122°14′ W, altitude: 41.5 m) in Shenyang, Liaoning Province, China. This study site has a mean annual temperature of 8.3 °C and receives an average of 545 mm of precipitation annually. The site is characterized by clayey loam soil with a pH of 5.4 (1:2.5 w/v) and the following properties: organic carbon: 13.2 g kg−1; total nitrogen: 1.23 g kg−1; total phosphorus: 2.42 g kg−1; total potassium: 46.5 g kg−1; available nitrogen: 102.6 mg kg−1; available phosphorus: 22.5 mg kg−1; available potassium: 45.8 mg kg−1.

2.2. Experimental Design and Sample Collection

This study was conducted using the Japonica rice cultivar (Liaoxing 21), with the growing period of 153 days. It belongs to early-maturing varieties and is suitable for direct seeding in the Shenyang area of Liaoning Province. A fully randomized block design with three replicates was used, and three organic amendments were tested including control (CK) without amendment, and with RS (9750 kg ha−1), or BC (3450 kg ha−1). The application rate of RS was based on the average amount of rice straw produced in the region. RS was prepared by chopping dried RS to pass through a 2–5 mm sieve, and it consisted of 0.7% N and 35% C. BC was provided by Jinfu Agricultural Development Co., Ltd., Panzhihua, China. Approximately one-third of RS was prepared in the form of BC consisting of granular particles 2–5 mm in diameter, consisting of 0.8% N and 66% C with a pH of 8.7 (1:2.5 H2O). In April 2019, RS or BC was applied to experimental plots, as required, prior to rice planting. RS and BC were uniformly applied by hand using a rake, followed by the mechanical tilling of all plots to a depth of 200 mm. The soil was further amended with compound fertilizer (750 kg ha−1; 12% N, 12% P, 12% K), and additional urea was added at the four-leaf (270 kg ha−1) and mid-tillering (270 kg ha−1) stages of rice paddies. Before sowing rice seeds, compound fertilizers were evenly applied in the form of basal fertilizer on the surface of the soil and incorporated in the 0–100 mm layer with a hand rake. Rice seeds were applied on 27 April 2019, at 5.5 million ha−1 via mechanized direct dry seeding with a row spacing of 300 mm. Paddy rice was harvested on 20 October 2019. Appropriate measures were employed to regulate water levels, insects, weed growth, and disease, so as to minimize any yield losses.
Soil samples were collected at the jointing (19 July 2019), booting (6 August 2019), heading (20 August 2019), grain filling (9 September 2019), and maturity (2 October 2019) stages of rice growth. For each of these stages, five hills were collected from each plot and a soil sample was collected from each hill covering a 200 × 200 mm area to a depth of 200 mm, with 15 plants per hill. Five soil samples from each plot were pooled to generate a composite soil sample. Any excessive soil was removed by shaking the roots of the plant, after which sterile forceps were used to vigorously shake the root system to separate the roots from the attached soil. These rhizosphere soil samples were then transferred to sterile 500 mL flasks in two aliquots, one of which was transferred to a 50 mL tube for DNA extraction and stored at −80 °C in a 50 mL tube, while the other was stored at 4 °C for the subsequent measurement of enzyme activity levels. Rice plants were collected at the maturity stage to measure crop yields.

2.3. DNA Sequencing

DNA was extracted from 0.5 g samples of soil with a Fast DNA SPIN Kit for Soil (Q-BIOgene, Carlsbad, CA, USA) in accordance with provided instructions. An automated microplate reader (BioTek ELX 800, Vernon Hills, IL, USA) was then used to assess DNA quality and quantity, followed by the amplification of the 16S rRNA gene sequences using the 338F:806R primer pair (ACTCCTACGGGAGGCAGCA/GGACTACHVGGGTWTCTAAT) [18]. Amplification was performed for 25/30 cycles with an ABI GeneAmp 9700 thermocycler and a total reaction volume of 10 μL containing DNA template (5–50 ng), Vn F (10 μM) 0.3 μL, Vn R (10μM) 0.3 μL, KOD FX Neo Buffer 5 μL, dNTP (2 mM) 2 μL, KOD FX Neo 0.2 μL, and ddH2O. Thermocycler settings were the following: 95 °C for 5 min; 25 cycles of 95 °C for 30 s, 50 °C for 30 s, and 72 °C for 40 s; 72 °C for 7 min. Agencourt AMPure XP Beads (Beckman Coulter, Indianapolis, IN, USA) were then used to purify amplified PCR products that were shipped to Biomarker Technologies Co., Ltd., Beijing, China. Paired-end sequencing was performed with an Illumina MiSeq 2500 instrument, and the resultant data were merged with FLASH [19]. Merged tags were compared with primer sequences, and any tags exhibiting more than 6 matches were removed to eliminate low-quality tags, after which mass filtering was performed [20]. Chimeric sequences were then removed to select for high-quality tagged sequences [21], and the remaining sequences were clustered at a 97% similarity level [22], with a 0.005% OTU filtering threshold [23], thereby yielding the final dataset consisting of effective tags.

2.4. Physicochemical Property and Microbial Biomass Analyses

A pH meter was used to assess the pH of soil samples in water at a 1:2.5 soil:water ratio (w/w). Levels of soil hydrolyzable nitrogen (AN) were measured via a NaOH diffusion approach [24]. Levels of soil available phosphorus (AP) were determined using NaHCO3 extraction and then using the molybdenum blue colorimetric method to assess P levels in the filtrate [25]. Ammonium acetate was used to extract soil available potassium (AK) at a pH of 7.0 and measured via flame photometry. Soil organic carbon (OC) levels were measured using the K2Cr2O7-H2SO4 wet oxidation method [26]. Microbial biomass carbon (MBC) and nitrogen (MBN) were measured with a chloroform fumigation-K2SO4 extraction approach [27,28]. MBC and MBN were calculated using a conversion factor of 0.45 [29].

2.5. Enzyme Activity Analyses

Soil enzyme activity was assessed with the methods of Guan et al. [30]. Soil saccharase activity (SA) was analyzed via the 3,5-dinitrosalicylic acid colorimetric method, with one unit of saccharase activity defined by the quantity of NO2 (µmol) produced per gram of soil per day. Urease activity (UA) was assessed using a sodium phenol colorimetric approach, with one unit of urease activity defined by the production of 1 µg of ammonia nitrogen per gram of soil per day. Cellulase activity (CA) was assessed using carboxymethylcellulose as a substrate, with glucose product output assessed after a 72 h incubation at 37 °C. The ninhydrin colorimetric method was used to quantify protease activity (PA), with one unit of protease activity defined by the production of 1 mg of tyrosine per gram of soil per day.

2.6. Statistical Analyses

Data were analyzed using the SAS MIXED procedure [31]. After testing for normality with the Shapiro–Wilk test and assessing the homogeneity of variance for this dataset, results were transformed, where necessary, to fit a normal distribution. p < 0.05 served as the significance threshold for all analyses, and the effects of organic amendments on crop yields, enzyme activity levels, microbial biomass, and soil properties were assessed using least significant difference (LSD). Bioinformatics analyses were conducted using BMKCloud (www.biocloud.net, 1 June 2023) for bioinformatics analysis. The richness and diversity of soil microbial communities were assessed based on alpha diversity indices (Chao1 and Shannon indices), while differences among these communities were assessed via a principal coordinate analysis (PCoA), and significant relationships between the abundance of particular phyla, levels of enzyme activity, soil characteristics, and grain yields of paddies were assessed through Pearson’s correlation analyses.

3. Results

3.1. Grain Yields

The grain yield (GY) with the RS (8.0 t ha−1) increased significantly (p < 0.05), by 8.5% and 9.9%, compared with the BC (7.4 t ha−1) and CK (7.3 t ha−1), respectively (Figure 1). There was no difference in GY between BC and CK.

3.2. Abundance and Diversity of Microbial Communities

The microbial abundance and diversity in soil samples from CK, RS, and BC treatments were assessed based on Chao1 and Shannon index values (Figure 2). The Chao1 index revealed a greater bacterial richness in the CK at the grain filling and maturity stages and in the BC at the heading and maturity stages, while the Shannon index indicated higher levels of microbial diversity in the RS relative to CK at all stages of rice growth. There was no significant difference in Chao1 or Shannon index values between the BC and CK at any stages of rice growth.
A principal coordinate analysis (PCoA) was used to compare the rhizosphere microbial communities of these different organic amendments at the operational taxonomic unit (OTU) level based on Bray–Curtis distances (Figure 3). This approach revealed significant differences among the three treatments, with the bacterial communities for the BC and CK exhibiting a greater similarity at all stages of rice growth relative to the RS (Figure 3A). Dissimilarity distances were computed among these three organic amendments for all stages of rice growth (Figure 3B). These analyses revealed that the bacterial community structures in the RS- and BC-treated soil samples differed from the CK, while a relatively similar bacterial community structure was evident in the RS- and BC-treated soils from the booting to the grain-filling stage. Bacterial community structures in the BC and CK samples were similar at the jointing and maturity stages but differed from those of the RS at these growth stages. Bacterial community structure for the BC-amended soil declined from the jointing to the booting stage but did not change significantly from the heading to the maturity stage.
Bacterial community composition in the RS, BC, and CK is shown in Figure 4. Of the 29 phyla identified, the most abundant phyla were Proteobacteria (29.7%), Acidobacteria (21.4%), Chloroflexi (21.0%), Actinobacteria (9.1%), Gemmaproteobacteria (6.0%), Verrucomicrobia (2.4%), Patescibacteria (1.7%), Bacteroidetes (1.7%), Nitrospirae (1.6%), and Cyanobacteria (1.3%). Total bacterial distributions also varied among organic amendments. Specifically, the relative Proteobacteria, Acidobacteria, Actinobacteria, Gemmatimonadetes, Nitrospirae, and Bacteroidetes abundance in the RS was higher than that in the CK at all stages of rice growth, while Proteobacteria, Chloroflexi, Actinobacteria, Patescibacteria, and Bacteroidetes abundance in the BC-amended soil tended to increase, relative to the CK soil. Proteobacteria, Gemmatimonadetes, Nitrospirae, and Bacteroidetes were also more abundant in the RS-amended soil relative to the BC-amended soil, throughout rice growth stages.
The most abundant soil bacteria included Proteobacteria, Acidobacteria, Actinobacteria, Chloroflexi, Verrucomicrobia, Patescibacteria, Bacteroidetes, Firmicutes, Epsilonbacteraeota, WPS-2, and Cyanobacteria (Figure 5). The relative levels of Proteobacteria (Alphaproteobacteria), Acidobacteria, Firmicutes, Verrucomicrobia, and Epsilonbacteraeota differed significantly when comparing the CK and RS, while WPS-2 abundance differed significantly in the BC-treated soil, relative to the CK and RS.

3.3. Soil Physicochemical Characteristics

Significant differences in pH, OC, AN, AP, AK, MBN, and MBC were evident when analyzing soil samples from the three different organic amendments (p < 0.05) (Table 1). RS increased AN contents relative to the BC and CK at the grain-filling stage. AP contents in the RS were significantly elevated prior to the grain-filling stage, relative to the BC and CK, while BC contributed to increases in AK and MBC contents, relative to the CK and RS at all stages of rice growth. BC also resulted in an increase in soil pH relative to the RS and CK, after the grain-filling stage.

3.4. Soil Enzyme Activities and Microbial Biomass

Significant differences in soil SA, UA, CA, and PA were observed among the organic amendments over the course of rice growth (p < 0.05) (Figure 6). Specifically, significantly higher UA levels were observed in the BC-amended soil as compared to the soil in the RS and CK treatments at all stages of rice growth, while RS application was associated with increased CA and PA levels relative to the BC and CK at all stages of growth. In addition, SA levels in the RS-amended soil significantly increased from the jointing to the heading stage, relative to the BC and CK.

3.5. Associations between Enzyme Activity Levels, Physicochemical Properties, Bacterial Communities, and Crop Yields

A series of Pearson’s correlation analyses were employed to explore relationships among the abundance of rhizosphere bacteria at the phylum level, the physicochemical properties of the soil, and soil enzyme activity levels over the course of rice growth, with an additional focus on grain yields (p < 0.05) (Table 2 and Table 3). As shown in Table 2, GY exhibited a positive correlation with SA, CA, PA, AN, OC, and AP. In contrast, UA was positively correlated with AP, pH, OC, and AP but negatively correlated with MBC. While PA levels and AP were negatively correlated, PA was positively correlated with pH, AN, OC AK, MBN, and MBC. As shown in Table 3, while Proteobacteria abundance and AK levels were positively related to one another, Chloroflexi abundance was negatively correlated with these AK levels. A positive correlation was observed between Acidobacteria abundance and PA, OC, AK, MBN, and MBC, whereas these bacteria were negatively correlated with UA and AP. A negative correlation was also detected between the abundance of Gemmatimonadetes and AK, while these microbes were positively correlated with soil pH. Bacteroidetes levels were negatively correlated with those of AN, OC, MBN, and MBC. A positive correlation was detected between Patescibacteria abundance and pH, MBN, and MBC, while it was negatively correlated with soil SA and AP. Relative Nitrospinae abundance was also positively correlated with GY, CA, PA, AN, MBN, and MBC, while Cyanobacteria levels were negatively correlated with those of PA, AN, OC, and AK.

4. Discussion

Plant roots are associated with beneficial soil bacterial communities that can facilitate the conversion of nutrients into forms that can be more readily utilized by plants to facilitate their growth [32,33]. Recent evidence suggests that crop roots can recruit beneficial microbes in a selective and dynamic manner, and these communities become increasingly specific, to facilitate nutrient availability [34,35,36]. Enzyme activity levels are key indicators associated with soil biochemical activity as they serve as the main drivers of plant-nutrient-cycling–soil-microbe interactions [37]. Soil bacterial communities, enzyme activity levels, and nutrient levels are all closely related to one another, and studies of these relationships thus have the potential to contribute to improvements in rice crop yields.
In this study, rice yields in the RS were significantly (p < 0.05) higher than those in the BC and CK (Figure 1), with no difference in yields between the latter two treatments. Combining RS amendment with chemical fertilizers can contribute to significantly improve grain yields [14,38]. Syamsiyah et al. [39] further observed increases in soil available N, P, and K levels and greater crop nutrient uptake, leading to better crop yields. Similarly, Han and He [40] reported a significant increase in soil cellulase activity levels following crop straw amendment. These prior results are consistent with the present results. Soil enzymes and microbes were able to break down the RS, thereby releasing nutrients into the soil and ultimately contributing to higher rice yields. These results, however, are not universal. Some studies failed to observe any rise in grain yields following crop straw amendment and fertilizer application [3,41,42]. RS was associated with a significant increase in grain yields in the present study, primarily because the RS amendment resulted in significant increases in levels of soil enzyme activity (CA and PA), soil nutrient levels (AN), and changes in soil bacterial community characteristics such as increases in relative Nitrospinae abundance. CA, PA, AN, and Nitrospinae may thus all be important determinants of rice grain yields (Table 2 and Table 3).
The richness and diversity of soil microbial communities are essential to robust and sustainable agronomic productivity, helping to facilitate the effective breakdown and utilization of crop residues [43,44]. The decomposition of rice straw is primarily driven by the soil microbes and the activities of enzymes found within the soil [45]. Distinct microbial community succession has previously been observed over the course of the breakdown of straw [46]. The breakdown of RS can alter the composition of soil microbial communities [47], in line with prior evidence. Substantial variations in the relative abundance of bacterial phyla including Proteobacteria, Gemmatimonadetes, Nitrospirae, and Bacteroidetes were observed over the course of rice growth following the RS amendment (Figure 4), reflecting the effects of RS decomposition. These results revealed clear differences in the makeup and diversity of these rhizosphere microbial communities when comparing among RS, BC, and CK. RS had a significant impact on bacteria richness. This aligns well with results published by Navarrete et al. [48], who found that straw application was related to an increase in soil bacterial abundance and diversity, relative to control conditions. Improvements in soil microbial functional diversity were attributable to the release of nutrients and available C from straw over the course of its decomposition [49]. Significant increases in Chao1 and Shannon index values were observed for the microbial communities in the RS-treated soil samples, consistent with greater levels of microbial diversity and richness. As shown in Figure 5, RS amendment also led to improvements in soil microbial community structure relative to the BC and CK. BC provided high levels of C and an environmental niche compatible with the growth of these root-associated microbes, owing to its stable, porous structure [50]. BC can offer a habitat in which microbes can grow while also influencing the structure and diversity of these rhizosphere soil communities [8]. The composition of these microbial communities can vary as a function of both field-crop residence time and growth stage, while bacterial dynamics are primarily dependent on soil substrates [51].
Prolonged crop growth often results in significant reductions in soil microbial abundance and diversity. However, the application of RS or BC can positively impact such bacterial diversity and abundance, supporting the selective recruitment of beneficial microbes [52]. In this study, these organic amendments were similarly found to impact the bacterial community structure at different stages of rice growth. Gemmatimonadetes abundance was significantly greater in the RS-amended soil, relative to the soil in the BC or CK, whereas Actinobacteria abundance in the BC-treated soil was greater than that in the RS and CK over the course of rice growth. The relatively high Gemmatimonas abundance may have resulted from the decomposition of straw over time, given that these bacteria can serve as indirect drivers of cellulose degradation [53]. BC application has previously been associated with increases in relative Actinobacteria abundance in rice paddies [50,54], and members of this phylum have been shown to be present in soil rich in organic matter, particularly under slightly alkaline or neutral conditions [55]. Biochar can facilitate soil N immobilization, which can then be slowly released over time as nutrients are absorbed by the roots of rice plants. As BC is alkaline, BC amendment can also improve acidic soil pH values [55]. Many gaps exist in realizing the mechanism of biochar interaction with the soil microbial community. Therefore, it is necessary to identify which phyla of microorganisms have increased in abundance after the addition of biochar and to study the functions of these microorganisms. The application of organic matter can alter bacterial communities within the soil, thereby impacting levels of soil enzyme activity [14]. RS amendment can contribute to higher SA, CA, and PA levels in the soil relative to the CK and BC, with multiple enzymes required to facilitate the effective release of the nutrients stored within the straw. These results align well with prior analyses [47,56]. These enzymes can break down RS to yield cellulose and hemicelluloses, which can, in turn, be broken down to produce starches, polysaccharides, carbohydrates, and simple sugars [57]. BC, in contrast, exhibits a porous structure and can fix soil C and N while benefiting local microbial growth [58]. The present results indicated that UA levels in rice paddy soil rose following BC amendment relative to the RS and CK, potentially owing to the ability of BC to fix soil nutrients. The application of RS and BC has been linked to better soil physicochemical properties [59]. In the present study, RS and BC application contributed to higher levels of nutrient content in paddy soil, together with higher levels of microbial biomass at different stages of rice growth (p < 0.05) (Table 1), including significantly increased OC, AN, AP, AK, MBN, and MBC in the rhizosphere soil throughout the growth process. Nutrients were released into the soil following straw decomposition such that these nutrient levels rose, consistent with prior studies [14,39]. Relative to CK, BC resulted in significant increases in soil pH, AK, MBN, and MBC, in line with previous research [36].
Recent studies have primarily focused on the soil factors that contribute to alter soil bacterial community composition and diversity [60]. However, the relationships between soil bacterial community composition, enzyme activity levels, nutrient availability, and grain yields in rice paddies remain to be systematically characterized. In this study, levels of bacterial phyla were positively correlated with soil enzyme activity, physicochemical properties, and grain yields (Table 2 and Table 3). The nutrients released over the course of organic amendment degradation can be utilized to facilitate the various activities of soil microbes [61]. Here, Nitrospinae abundance rose following the RS amendment, relative to the BC and CK, potentially owing to the increase in nutrient release over the course of straw decomposition. Sustained PA and CA activity over time is necessary to effectively break down straw, and Nitrospinae abundance levels were positively correlated with PA, CA, and GY. GY was positively correlated with CA, PA, soil AN, and Nitrospinae abundance during all phases of paddy growth. All four of these parameters were also present at higher levels in the RS relative to the BC and CK, likely explaining the higher grain yields in the RS. RS application may thus provide an environment conducive to more rapid bacterial growth, contributing to improved bacterial community structure, increased levels of soil enzyme activity, better soil fertility, and consequent improvements in rice yields.

5. Conclusions

The results showed that RS increased the grain yield of paddies. RS and BC could improve soil enzyme activities, physicochemical characteristics and microbial community structure. The application of RS also had a positive impact on the bacteria richness. The results indicated that RS released nutrients into the soil during the process of decomposition, which increased available nutrients in the soil, and resulted in improving soil enzymatic activities and nutrition-related bacterial richness, ultimately enhancing the grain yield of paddies.
This study mainly focused on the impact of rice straw and biochar application on microbial abundance and diversity, without studying the functions of these microorganisms. In the future, research will be conducted on the functions of microorganisms.

Author Contributions

Conceptualization, Z.T. and W.Z.; methodology, Z.T. and W.Z.; software, L.W.; validation, L.W., L.Z. and C.W.; formal analysis, Z.T. and C.W.; investigation, L.W., N.H. and L.Z.; resources, N.H.; data curation, Z.T.; writing—original draft preparation, Z.T. and H.W.; writing—review and editing, W.Z., H.W. and G.S.; visualization, D.G.; supervision, D.G.; project administration, W.Z.; funding acquisition, Z.T. and W.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This study was funded by China-Russia Cooperation Rice Breeding Laboratory (2019LHSYS04), Shenyang Scientific and Technological Innovation Talents Support Program (RC210489), the earmarked fund for China Agriculture Research System (CARS-01), Liaoning Province agriculture major project (2022JH1/10200003).

Data Availability Statement

The datasets generated for this study can be accessed from the NCBI Sequence Read Archive (https://www.ncbi.nlm.nih.gov/biosample/?term=PRJNA842547 (1 June 2023)) under accession number PRJNA842547.

Conflicts of Interest

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

References

  1. Zhang, J.; Liu, Y.-X.; Zhang, N.; Hu, B.; Jin, T.; Xu, H.; Qin, Y.; Yan, P.; Zhang, X.; Guo, X.; et al. NRT1.1B is associated with root microbiota composition and nitrogen use in field-grown rice. Nat. Biotechnol. 2019, 37, 676–684. [Google Scholar] [CrossRef]
  2. Liu, H.; Jiang, G.; Zhuang, H.; Wang, K. Distribution, utilization structure and potential of biomass resources in rural China: With special references of crop residues. Renew. Sustain. Energy Rev. 2008, 12, 1402–1418. [Google Scholar] [CrossRef]
  3. Sun, R.; Zhang, X.-X.; Guo, X.; Wang, D.; Chu, H. Bacterial diversity in soils subjected to long-term chemical fertilization can be more stably maintained with the addition of livestock manure than wheat straw. Soil Biol. Biochem. 2015, 88, 9–18. [Google Scholar] [CrossRef]
  4. Franzluebbers, A.J. Achieving Soil Organic Carbon Sequestration with Conservation Agricultural Systems in the Southeastern United States. Soil Sci. Soc. Am. J. 2010, 74, 347–357. [Google Scholar] [CrossRef]
  5. Tian, J.; Wang, J.; Dippold, M.; Gao, Y.; Blagodatskaya, E.; Kuzyakov, Y. Biochar affects soil organic matter cycling and microbial functions but does not alter microbial community structure in a paddy soil. Sci. Total Environ. 2016, 556, 89–97. [Google Scholar] [CrossRef] [PubMed]
  6. Wang, Y.; Zhang, X.; Lou, Z.; An, X.; Li, X.; Jiang, X.; Wang, W.; Zhao, H.; Fu, M.; Cui, Z. The effects of adding exogenous lignocellulose degrading bacteria during straw incorporation in cold regions on degradation characteristics and soil indigenous bacteria communities. Front. Microbiol. 2023, 14, 1141545. [Google Scholar] [CrossRef] [PubMed]
  7. Ren, H.; Guo, H.; Shafiqul Islam, M.; Zaki, H.E.M.; Wang, Z.; Wang, H.; Qi, X.; Guo, J.; Sun, L.; Wang, Q.; et al. Improvement effect of biochar on soil microbial community structure and metabolites of decline disease bayberry. Front. Microbiol. 2023, 14, 1154886. [Google Scholar] [CrossRef]
  8. Zhou, G.; Xu, X.; Qiu, X.; Zhang, J. Biochar influences the succession of microbial communities and the metabolic functions during rice straw composting with pig manure. Bioresour. Technol. 2019, 272, 10–18. [Google Scholar] [CrossRef]
  9. Abdelraouf, R.; Abdou, S.; Mahmoud Abbas, M.; Hafez, M.; Alexander, I.; HAMED, L. Influence of n-fertigation stress and agro-organic wastes (biochar) to improve yield and water productivity of sweet pepper under sandy soils conditions. Plant Arch. 2020, 20, 3208–3217. [Google Scholar]
  10. Wang, P.; Kong, X.; Chen, H.; Xiao, Y.; Liu, H.; Li, X.; Zhang, Z.; Tan, X.; Wang, D.; Jin, D.; et al. Exploration of Intrinsic Microbial Community Modulators in the Rice Endosphere Indicates a Key Role of Distinct Bacterial Taxa Across Different Cultivars. Front. Microbiol. 2021, 12, 629852. [Google Scholar] [CrossRef]
  11. Zhong, Y.; Hu, J.; Xia, Q.; Zhang, S.; Li, X.; Pan, X.; Zhao, R.; Wang, R.; Yan, W.; Shangguan, Z. Soil microbial mechanisms promoting ultrahigh rice yield. Soil Biol. Biochem. 2020, 143, 107741. [Google Scholar] [CrossRef]
  12. Fierer, N. Embracing the unknown: Disentangling the complexities of the soil microbiome. Nat. Rev. Microbiol. 2017, 15, 579–590. [Google Scholar] [CrossRef]
  13. Chen, S.; Waghmode, T.R.; Sun, R.; Kuramae, E.E.; Hu, C.; Liu, B. Root-associated microbiomes of wheat under the combined effect of plant development and nitrogen fertilization. Microbiome 2019, 7, 136. [Google Scholar] [CrossRef]
  14. Zhao, J.; Ni, T.; Xun, W.; Huang, X.; Huang, Q.; Ran, W.; Shen, B.; Zhang, R.; Shen, Q. Influence of straw incorporation with and without straw decomposer on soil bacterial community structure and function in a rice-wheat cropping system. Appl. Microbiol. Biotechnol. 2017, 101, 4761–4773. [Google Scholar] [CrossRef]
  15. Nannipieri, P.; Trasar-Cepeda, C.; Dick, R.P. Soil enzyme activity: A brief history and biochemistry as a basis for appropriate interpretations and meta-analysis. Biol. Fertil. Soils 2017, 54, 11–19. [Google Scholar] [CrossRef]
  16. Cotrufo, M.F.; Soong, J.L.; Horton, A.J.; Campbell, E.E.; Haddix, M.L.; Wall, D.H.; Parton, W.J. Formation of soil organic matter via biochemical and physical pathways of litter mass loss. Nat. Geosci. 2015, 8, 776–779. [Google Scholar] [CrossRef]
  17. Sasse, J.; Martinoia, E.; Northen, T. Feed Your Friends: Do Plant Exudates Shape the Root Microbiome? Trends Plant Sci. 2018, 23, 25–41. [Google Scholar] [CrossRef]
  18. Cui, Y.; Bing, H.; Fang, L.; Wu, Y.; Yu, J.; Shen, G.; Jiang, M.; Wang, X.; Zhang, X. Diversity patterns of the rhizosphere and bulk soil microbial communities along an altitudinal gradient in an alpine ecosystem of the eastern Tibetan Plateau. Geoderma 2019, 338, 118–127. [Google Scholar] [CrossRef]
  19. Magoč, T.; Salzberg, S.L. FLASH: Fast length adjustment of short reads to improve genome assemblies. Bioinformatics 2011, 27, 2957–2963. [Google Scholar] [CrossRef]
  20. Bolger, A.M.; Lohse, M.; Usadel, B. Trimmomatic: A flexible trimmer for Illumina sequence data. Bioinformatics 2014, 30, 2114–2120. [Google Scholar] [CrossRef]
  21. Edgar, R.C.; Haas, B.J.; Clemente, J.C.; Quince, C.; Knight, R. UCHIME improves sensitivity and speed of chimera detection. Bioinformatics 2011, 27, 2194–2200. [Google Scholar] [CrossRef]
  22. Edgar, R.C. UPARSE: Highly accurate OTU sequences from microbial amplicon reads. Nat. Methods 2013, 10, 996–998. [Google Scholar] [CrossRef]
  23. Schloss, P.D.; Westcott, S.L.; Ryabin, T.; Hall, J.R.; Hartmann, M.; Hollister, E.B.; Lesniewski, R.A.; Oakley, B.B.; Parks, D.H.; Robinson, C.J.; et al. Introducing mothur: Open-Source, Platform-Independent, Community-Supported Software for Describing and Comparing Microbial Communities. Appl. Environ. Microbiol. 2009, 75, 7537–7541. [Google Scholar] [CrossRef]
  24. Lu, R.K. Soil Agricultural Chemical Analysis Method; China Agricultural Science and Technology Press: Beijing, China, 2000. [Google Scholar]
  25. Olsen, S.; Sommers, L.; Evans, D.; White, J.; Ensminger, L.; Clark, F. Methods of Soil Analysis. Part 2. Chemical and Microbiological Properties; American Society of Agronomy: Madison, WI, USA, 1982. [Google Scholar]
  26. De Vos, B.; Lettens, S.; Muys, B.; Deckers, J.A. Walkley? Black analysis of forest soil organic carbon: Recovery, limitations and uncertainty. Soil Use Manag. 2007, 23, 221–229. [Google Scholar] [CrossRef]
  27. Brookes, P.C.; Landman, A.; Pruden, G.; Jenkinson, D.S. Chloroform fumigation and the release of soil nitrogen: A rapid direct extraction method to measure microbial biomass nitrogen in soil. Soil Biol. Biochem. 1985, 17, 837–842. [Google Scholar] [CrossRef]
  28. Vance, E.D.; Brookes, P.C.; Jenkinson, D.S. An extraction method for measuring soil microbial biomass C. Soil Biol. Biochem. 1987, 19, 703–707. [Google Scholar] [CrossRef]
  29. Zang, Y.; Hao, M.; Zhang, L.; Zhang, H. Effects of wheat cultivation and fertilization on soil microbial biomass carbon, soil microbial biomass nitrogen and soil basal respiration in 26 years. Acta Ecol. Sin. 2015, 35, 1445–1451. [Google Scholar]
  30. Guan,, S.; Zhang, D.; Zhang, Z. Soil Enzyme and its Research Methods; China Agriculture Press: Beijing, China, 1986. [Google Scholar]
  31. Littell, R.; Milliken, G.; Stroup, W.; Wolfinger, R.; Schabenberger, O. SAS for Mixed Models, 2nd ed.; SAS Institute Inc.: Cary, NC, USA, 2006. [Google Scholar]
  32. Edwards, J.; Santos-Medellín, C.; Liechty, Z.; Nguyen, B.; Lurie, E.; Eason, S.; Phillips, G.; Sundaresan, V. Compositional shifts in root-associated bacterial and archaeal microbiota track the plant life cycle in field-grown rice. PLoS Biol. 2018, 23, 16. [Google Scholar] [CrossRef]
  33. Berendsen, R.L.; Pieterse, C.M.J.; Bakker, P.A.H.M. The rhizosphere microbiome and plant health. Trends Plant Sci. 2012, 17, 478–486. [Google Scholar] [CrossRef]
  34. Breidenbach, B.; Pump, J.; Dumont, M.G. Microbial Community Structure in the Rhizosphere of Rice Plants. Front. Microbiol. 2016, 6, 1537. [Google Scholar] [CrossRef]
  35. Hamonts, K.; Trivedi, P.; Garg, A.; Janitz, C.; Grinyer, J.; Holford, P.; Botha, F.C.; Anderson, I.C.; Singh, B.K. Field study reveals core plant microbiota and relative importance of their drivers. Environ. Microbiol. 2018, 20, 124–140. [Google Scholar] [CrossRef]
  36. Zhang, Y.; Liu, Y.; Zhang, G.; Guo, X.; Sun, Z.; Li, T. The Effects of Rice Straw and Biochar Applications on the Microbial Community in a Soil with a History of Continuous Tomato Planting History. Agronomy 2018, 8, 65. [Google Scholar] [CrossRef]
  37. Nannipieri, P.; Ascher, J.; Ceccherini, M.T.; Landi, L.; Pietramellara, G.; Renella, G. Microbial diversity and soil functions. Eur. J. Soil Sci. 2003, 54, 655–670. [Google Scholar] [CrossRef]
  38. Kumari, K.; Prasad, J.; Solanki, I.S.; Chaudhary, R. Long-term effect of crop residues incorporation on yield and soil physical properties under rice-wheat cropping system in calcareous soil. J. Soil Sci. Plant Nutr. 2018, 18, 27–40. [Google Scholar] [CrossRef]
  39. Syamsiyah, J.; Sunarminto, B.H.; Hanudin, E.; Widada, J.; Setyawati, A. Suntoro, Selected soil nutrient availability, plant nutrient uptake and upland rice yield in response to rice straw and mycorrhiza application. IOP Conf. Ser. Earth Environ. Sci. 2021, 724, 012017. [Google Scholar] [CrossRef]
  40. Han, W.; He, M. The application of exogenous cellulase to improve soil fertility and plant growth due to acceleration of straw decomposition. Bioresour. Technol. 2010, 101, 3724–3731. [Google Scholar] [CrossRef]
  41. Zhang, J.; Li, W.; Zhou, Y.; Ding, Y.; Xu, L.; Jiang, Y.; Li, G. Long-term straw incorporation increases rice yield stability under high fertilization level conditions in the rice–wheat system. Crop J. 2021, 9, 1191–1197. [Google Scholar] [CrossRef]
  42. Yang, H.; Feng, J.; Weih, M.; Meng, Y.; Li, Y.; Zhai, S.; Zhang, W. Yield reduction of direct-seeded rice under returned straw can be mitigated by appropriate water management improving soil phosphorus availability. Crop Pasture Sci. 2020, 71, 134. [Google Scholar] [CrossRef]
  43. Reddy, A.P.; Simmons, C.W.; D’haeseleer, P.; Khudyakov, J.; Burd, H.; Hadi, M.; Simmons, B.A.; Singer, S.W.; Thelen, M.P.; VanderGheynst, J.S. Discovery of Microorganisms and Enzymes Involved in High-Solids Decomposition of Rice Straw Using Metagenomic Analyses. PLoS ONE 2013, 8, e77985. [Google Scholar] [CrossRef]
  44. Zhao, J.; Zhang, R.; Xue, C.; Xun, W.; Sun, L.; Xu, Y.; Shen, Q. Pyrosequencing Reveals Contrasting Soil Bacterial Diversity and Community Structure of Two Main Winter Wheat Cropping Systems in China. Microb. Ecol. 2013, 67, 443–453. [Google Scholar] [CrossRef]
  45. Jing, J.; Cong, W.; Bezemer, T. Legacies at work: Plant–soil–microbiome interactions underpinning agricultural sustainability. Trends Plant Sci. 2022, 21, 781–792. [Google Scholar] [CrossRef] [PubMed]
  46. Wegner, C.-E.; Liesack, W. Microbial community dynamics during the early stages of plant polymer breakdown in paddy soil. Environ. Microbiol. 2015, 18, 2825–2842. [Google Scholar] [CrossRef] [PubMed]
  47. Bao, Y.; Feng, Y.; Stegen, J.C.; Wu, M.; Chen, R.; Liu, W.; Zhang, J.; Li, Z.; Lin, X. Straw chemistry links the assembly of bacterial communities to decomposition in paddy soils. Soil Biol. Biochem. 2020, 148, 107866. [Google Scholar] [CrossRef]
  48. Navarrete, A.A.; Diniz, T.R.; Braga, L.P.P.; Silva, G.G.Z.; Franchini, J.C.; Rossetto, R.; Edwards, R.A.; Tsai, S.M. Multi-Analytical Approach Reveals Potential Microbial Indicators in Soil for Sugarcane Model Systems. PLoS ONE 2015, 10, e0129765. [Google Scholar] [CrossRef] [PubMed]
  49. Yuan, H.-Z.; Zhu, Z.-K.; Wei, X.-M.; Liu, S.-L.; Peng, P.-Q.; Gunina, A.; Shen, J.-L.; Kuzyakov, Y.; Ge, T.-D.; Wu, J.-S.; et al. Straw and biochar strongly affect functional diversity of microbial metabolism in paddy soils. J. Integr. Agric. 2019, 18, 1474–1485. [Google Scholar] [CrossRef]
  50. Tang, Z.; Zhang, L.; He, N.; Liu, Z.; Ma, Z.; Fu, L.; Wang, H.; Wang, C.; Sui, G.; Zheng, W. Influence of planting methods and organic amendments on rice yield and bacterial communities in the rhizosphere soil. Front. Microbiol. 2022, 13, 918986. [Google Scholar] [CrossRef]
  51. Dombrowski, N.; Schlaeppi, K.; Agler, M.T.; Hacquard, S.; Kemen, E.; Garrido-Oter, R.; Wunder, J.; Coupland, G.; Schulze-Lefert, P. Root microbiota dynamics of perennial Arabis alpina are dependent on soil residence time but independent of flowering time. ISME J. 2016, 11, 43–55. [Google Scholar] [CrossRef]
  52. Trivedi, P.; Leach, J.E.; Tringe, S.G.; Sa, T.; Singh, B.K. Plant–microbiome interactions: From community assembly to plant health. Nat. Rev. Microbiol. 2020, 18, 607–621. [Google Scholar] [CrossRef] [PubMed]
  53. Takaichi, S.; Maoka, T.; Takasaki, K.; Hanada, S. Carotenoids of Gemmatimonas aurantiaca (Gemmatimonadetes): Identification of a novel carotenoid, deoxyoscillol 2-rhamnoside, and proposed biosynthetic pathway of oscillol 2,2′-dirhamnoside. Microbiology 2010, 156, 757–763. [Google Scholar] [CrossRef]
  54. Ali, I.; Yuan, P.; Ullah, S.; Iqbal, A.; Zhao, Q.; Liang, H.; Khan, A.; Imran; Zhang, H.; Wu, X.; et al. Biochar Amendment and Nitrogen Fertilizer Contribute to the Changes in Soil Properties and Microbial Communities in a Paddy Field. Front. Microbiol. 2022, 13, 834751. [Google Scholar] [CrossRef]
  55. Tang, Z.; Zhang, L.; He, N.; Gong, D.; Gao, H.; Ma, Z.; Fu, L.; Zhao, M.; Wang, H.; Wang, C.; et al. Soil bacterial community as impacted by addition of rice straw and biochar. Sci. Rep. 2021, 11, 22185. [Google Scholar] [CrossRef] [PubMed]
  56. Wei, T.; Zhang, P.; Wang, K.; Ding, R.; Yang, B.; Nie, J.; Jia, Z.; Han, Q. Effects of Wheat Straw Incorporation on the Availability of Soil Nutrients and Enzyme Activities in Semiarid Areas. PLoS ONE 2015, 10, e0120994. [Google Scholar] [CrossRef] [PubMed]
  57. Hansen, V.; Müller-Stöver, D.; Munkholm, L.J.; Peltre, C.; Hauggaard-Nielsen, H.; Jensen, L.S. The effect of straw and wood gasification biochar on carbon sequestration, selected soil fertility indicators and functional groups in soil: An incubation study. Geoderma 2016, 269, 99–107. [Google Scholar] [CrossRef]
  58. Hagemann, N.; Joseph, S.; Schmidt, H.-P.; Kammann, C.I.; Harter, J.; Borch, T.; Young, R.B.; Varga, K.; Taherymoosavi, S.; Elliott, K.W.; et al. Organic coating on biochar explains its nutrient retention and stimulation of soil fertility. Nat. Commun. 2017, 8, 1089. [Google Scholar] [CrossRef]
  59. Blanco-Canqui, H. Biochar and Soil Physical Properties. Soil Sci. Soc. Am. J. 2017, 81, 687–711. [Google Scholar] [CrossRef]
  60. Zhong, Y.; Yan, W.; Wang, R.; Wang, W.; Shangguan, Z. Decreased occurrence of carbon cycle functions in microbial communities along with long-term secondary succession. Soil Biol. Biochem. 2018, 123, 207–217. [Google Scholar] [CrossRef]
  61. Keeler, B.L.; Hobbie, S.E.; Kellogg, L.E. Effects of Long-Term Nitrogen Addition on Microbial Enzyme Activity in Eight Forested and Grassland Sites: Implications for Litter and Soil Organic Matter Decomposition. Ecosystems 2008, 12, 1–15. [Google Scholar] [CrossRef]
Agronomy 14 00316 g001
Figure 1. Grain yield as impacted by organic amendments. Different letters indicate a significant difference at p < 0.05. RS: rice straw, BC: biochar, and CK: no RS or BC.
Figure 1. Grain yield as impacted by organic amendments. Different letters indicate a significant difference at p < 0.05. RS: rice straw, BC: biochar, and CK: no RS or BC.
Agronomy 14 00316 g001
Agronomy 14 00316 g002
Figure 2. Chao1 index (A) and Shannon index (B) affected by rice straw and biochar addition during various growth stages of paddies. Different letters indicate significant differences at p < 0.05 among RS, BC and CK at each growth stage. RS: rice straw, BC: biochar, and CK: no RS or BC.
Figure 2. Chao1 index (A) and Shannon index (B) affected by rice straw and biochar addition during various growth stages of paddies. Different letters indicate significant differences at p < 0.05 among RS, BC and CK at each growth stage. RS: rice straw, BC: biochar, and CK: no RS or BC.
Agronomy 14 00316 g002
Agronomy 14 00316 g003
Figure 3. (A) Principal component analysis (PCoA) of the bacterial OTUs level in the soil treated with RS, BC and CK from the jointing to maturity stages of rice growth. (B) Dissimilarity distances showing the differences in rhizospheric soil bacterial communities amended with RS, BC and CK. Error bars indicate the standard deviation of the three replicates. Dissimilarity distances were based on the binary Jaccard distance algorithm at the OTU level. RS: rice straw, BC: biochar, and CK: no RS or BC.
Figure 3. (A) Principal component analysis (PCoA) of the bacterial OTUs level in the soil treated with RS, BC and CK from the jointing to maturity stages of rice growth. (B) Dissimilarity distances showing the differences in rhizospheric soil bacterial communities amended with RS, BC and CK. Error bars indicate the standard deviation of the three replicates. Dissimilarity distances were based on the binary Jaccard distance algorithm at the OTU level. RS: rice straw, BC: biochar, and CK: no RS or BC.
Agronomy 14 00316 g003
Agronomy 14 00316 g004
Figure 4. Phylum-level bacterial community composition as affected by organic amendments at various growth stages of paddies. RS: rice straw, BC: biochar, and CK: no RS or BC.
Figure 4. Phylum-level bacterial community composition as affected by organic amendments at various growth stages of paddies. RS: rice straw, BC: biochar, and CK: no RS or BC.
Agronomy 14 00316 g004
Agronomy 14 00316 g005
Figure 5. Cladogram plotted using linear discriminant analysis effect size (LEfSe) showing the significant differences (p < 0.05) in relative abundance of bacterial taxon. Different colors indicate different treatments. The colored dots from inner to outer levels represent phylum, class, order, family, and genus levels. Only taxa with a logarithmic discriminant analysis (LDA) score > 2.5 are shown on the cladogram. RS: rice straw, BC: biochar, and CK: no RS or BC.
Figure 5. Cladogram plotted using linear discriminant analysis effect size (LEfSe) showing the significant differences (p < 0.05) in relative abundance of bacterial taxon. Different colors indicate different treatments. The colored dots from inner to outer levels represent phylum, class, order, family, and genus levels. Only taxa with a logarithmic discriminant analysis (LDA) score > 2.5 are shown on the cladogram. RS: rice straw, BC: biochar, and CK: no RS or BC.
Agronomy 14 00316 g005
Agronomy 14 00316 g006
Figure 6. Saccharase activity (A), urease activity (B), cellulase activity (C), and protease activity (D) affected by rice straw and biochar addition during various growth stages of paddies. Different letters indicate significant differences at p < 0.05 among RS, BC and CK at each growth stage. RS: rice straw, BC: biochar, and CK: no RS or BC.
Figure 6. Saccharase activity (A), urease activity (B), cellulase activity (C), and protease activity (D) affected by rice straw and biochar addition during various growth stages of paddies. Different letters indicate significant differences at p < 0.05 among RS, BC and CK at each growth stage. RS: rice straw, BC: biochar, and CK: no RS or BC.
Agronomy 14 00316 g006
Table 1. Soil physicochemical characteristics and microbial biomass as affected by organic amendments at different growth stages of paddies.
Table 1. Soil physicochemical characteristics and microbial biomass as affected by organic amendments at different growth stages of paddies.
Growth StagesTreatmentspHOrganic CAvailable NAvailable PAvailable KMicrobial Biomass NMicrobial Biomass C
(g kg−1)(mg kg−1)(mg kg−1)(mg kg−1)(mg kg−1)(g kg−1)
JointingRS5.63 a12.20 b112.74 a43.08 a119.88 b141.51 a1.08 b
BC5.65 a12.53 a113.04 a41.03 b127.60 a140.90 a1.13 a
CK5.58 a11.59 c103.03 b35.49 c105.63 c139.03 b1.06 c
LSD(0.05)0.170.231.901.151.001.52<0.01
BootingRS5.82 a11.97 b109.60 a41.70 a110.01 b147.81 b1.31 b
BC5.81 a12.36 a109.92 a40.55 b116.20 a150.00 a1.38 a
CK5.71 a11.19 c98.50 b34.68 c98.32 c145.35 c1.28 c
LSD(0.05)0.110.121.910.841.211.94<0.01
HeadingRS5.85 a12.66 a120.48 a35.99 a96.88 b153.00 b1.39 b
BC5.77 a12.25 b120.24 a35.62 a100.51 a158.44 a1.46 a
CK5.71 a11.88 c98.67 b31.74 b85.30 c150.77 b1.39 c
LSD(0.05)0.200.072.260.901.542.43<0.01
Grain fillingRS5.72 b12.71 a122.16 a34.95 a114.44 b156.81 ab1.63 b
BC5.86 a12.39 b117.44 b33.85 b121.44 a158.38 a1.66 a
CK5.64 c11.95 c106.25 c28.24 c106.64 c155.58 b1.51 c
LSD(0.05)0.070.151.350.962.171.830.01
MaturityRS5.75 b12.29 b124.95 a27.48 b115.20 b168.34 a1.80 b
BC5.85 a12.95 a120.85 b28.47 a122.73 a165.16 b1.88 a
CK5.61 c11.98 c110.54 c25.38 c105.63 c162.25 b1.64 c
LSD(0.05)0.060.101.900.623.913.140.01
Different letters indicate statistical significance at the p < 0.05 level. RS: rice straw, BC: biochar, and CK: no RS or BC.
Table 2. Pearson’s correlation coefficients between enzyme activities, soil physicochemical properties measured at all growth stage of paddies, and grain yield of paddies.
Table 2. Pearson’s correlation coefficients between enzyme activities, soil physicochemical properties measured at all growth stage of paddies, and grain yield of paddies.
GYSAUACAPApHANOCAPAKMBN
SA0.600 **
UA−0.0470.337 *
CA0.786 **0.646 **0.002
PA0.531 **0.014−0.307 *0.517 **
pH0.1930.2560.457 **0.366 *0.359 *
AN0.545 **0.2150.1400.596 **0.794 **0.434 **
OC0.436 **0.2660.325 *0.493 **0.565 **0.429 **0.866 **
AP0.320 *0.759 **0.507 **0.368 *−0.471 **0.087−0.1270.027
AK0.2420.2090.1210.1790.313 *0.0220.454 **0.533 **0.050
MBN0.143−0.344−0.1510.1560.831 **0.387 **0.661 **0.467 **−0.727 **0.102
MBC0.086−0.396 **−0.1410.0990.800 **0.433 **0.611 **0.480 **−0.736 **0.1850.963 **
Asterisks indicate significance level; * p < 0.05 and ** p < 0.01. GY: grain yield, SA: saccharase activity, UA: urease activity, CA: cellulase activity, PA: protease activity, AN: soil hydrolyzable nitrogen, OC: soil organic carbon, AP: soil available phosphorus, AK: soil available potassium, MBN: microbial biomass nitrogen, MBC: microbial biomass carbon.
Table 3. Pearson’s correlation coefficients among enzyme activities, soil physicochemical properties, bacterial abundant phyla measured at all growth stage of paddies, and grain yield of paddies.
Table 3. Pearson’s correlation coefficients among enzyme activities, soil physicochemical properties, bacterial abundant phyla measured at all growth stage of paddies, and grain yield of paddies.
GYSAUACAPApHANOCAPAKMBNMBC
Proteobacteria0.2850.264−0.0300.1470.028−0.2110.0540.1250.1120.401 **−0.106−0.111
Acidobacteria−0.045−0.122−0.310 *−0.1510.405 **−0.2370.2710.310 *−0.476 **0.436 **0.357 *0.349 *
Chloroflexi−0.118−0.1820.0190.016−0.1120.282−0.108−0.0560.043−0.357 *0.0240.074
Actinobacteria0.2090.1370.398 **0.349 *0.0540.2890.387 **0.2860.212−0.2360.1320.031
Gemmatimonadetes0.080−0.013−0.229−0.1140.119−0.310 *−0.034−0.039−0.1830.419 **0.0140.064
Verrucomicrobia0.0540.1660.0030.0540.045−0.0810.1690.2150.0870.0700.003−0.072
Bacteroidetes−0.187−0.052−0.012−0.229−0.299−0.024−0.389 **−0.412 **0.143−0.103−0.336 *−0.309 *
Patescibacteria−0.106−0.331 *0.064−0.1850.2920.310 *0.2700.289−0.468 **−0.0730.549 **0.589 **
Nitrospirae0.423 **0.173−0.1280.506 **0.525 **0.1250.649 **0.505 **−0.0690.0670.349 *0.223
Cyanobacteria−0.026−0.0340.110−0.179−0.311 *−0.012−0.419 **−0.482 **0.149−0.322 *−0.266−0.224
Asterisks indicate significance level; * p < 0.05 and ** p < 0.01. GY: grain yield, SA: saccharase activity, UA: urease activity, CA: cellulase activity, PA: protease activity, AN: soil hydrolyzable nitrogen, OC: soil organic carbon, AP: soil available phosphorus, AK: soil available potassium, MBN: microbial biomass nitrogen, MBC: microbial biomass carbon.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Tang, Z.; He, N.; Zhang, L.; Wang, L.; Gong, D.; Wang, C.; Wang, H.; Sui, G.; Zheng, W. Straw and Biochar Application Alters the Structure of Rhizosphere Microbial Communities in Direct-Seeded Rice (Oryza sativa L.) Paddies. Agronomy 2024, 14, 316. https://doi.org/10.3390/agronomy14020316

AMA Style

Tang Z, He N, Zhang L, Wang L, Gong D, Wang C, Wang H, Sui G, Zheng W. Straw and Biochar Application Alters the Structure of Rhizosphere Microbial Communities in Direct-Seeded Rice (Oryza sativa L.) Paddies. Agronomy. 2024; 14(2):316. https://doi.org/10.3390/agronomy14020316

Chicago/Turabian Style

Tang, Zhiqiang, Na He, Liying Zhang, Lili Wang, Diankai Gong, Changhua Wang, Hui Wang, Guomin Sui, and Wenjing Zheng. 2024. "Straw and Biochar Application Alters the Structure of Rhizosphere Microbial Communities in Direct-Seeded Rice (Oryza sativa L.) Paddies" Agronomy 14, no. 2: 316. https://doi.org/10.3390/agronomy14020316

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop