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Article

Impacts of Corn Straw Compost on Rice Growth and Soil Microflora under Saline-Alkali Stress

by
Shenglin Li
1,2,
Lixin Li
1,*,
Zhigang Wang
2,3,*,
Jing Sun
4 and
Hailong Zhang
1
1
Key Laboratory of Saline-Alkali Vegetation Ecology Restoration, Ministry of Education, College of Life Sciences, Northeast Forestry University, Harbin 150040, China
2
College of Life Sciences and Agriculture and Forestry, Qiqihar University, Qiqihar 161006, China
3
Heilongjiang Provincial Technology Innovation Center of Agromicrobial Preparation Industrialization, Qiqihar 161006, China
4
School of Life Sciences, Jiangsu Normal University, Xuzhou 221116, China
*
Authors to whom correspondence should be addressed.
Agronomy 2023, 13(6), 1525; https://doi.org/10.3390/agronomy13061525
Submission received: 12 May 2023 / Revised: 26 May 2023 / Accepted: 29 May 2023 / Published: 31 May 2023
(This article belongs to the Section Soil and Plant Nutrition)
Browse Figures
Versions Notes

Abstract

:
Saline–alkali soil seriously inhibits crop growth and yields and threatens the sustainable development of agriculture. Corn straw compost can alleviate saline–alkali stress and improve crop growth and development. In this study, we demonstrate that corn straw compost (CSC) improved soil physicochemical properties, e.g., decreased pH and electrical conductivity (EC), but increased soil nutrients, e.g., available nitrogen and phosphorus, and soluble organic carbon, as well as activities of sucrase and urease in saline–alkali soil. CSC affected the structure of water-stable aggregates (WSA) and the composition of soil microflora in saline–alkali soil. With the increase in the content of CSC, the abundances of some genera, e.g., Thermobacillus, Thermopolyspora, and Thermobispora, were significantly increased, suggesting that they play an important role in improving soil nutrient components and physicochemical properties, which subsequently improved plant growth and development. Consequently, the biomass and yields of rice grown in saline–alkali soil were greatly improved. In conclusion, CSC can improve saline–alkali soil activities and microbial communities, thus improving crop growth and yields. Our findings provide a theoretical basis for the sustainable development of modern agriculture.

1. Introduction

Saline–alkali soil is a type of degraded soil with poor agricultural production, which seriously affects global crop yields and ecological development [1]. As a unique ecosystem, saline–alkali land has special soil physicochemical properties and microbial communities [2]. Approximately 11 million hectares of soil worldwide is saline–alkali type [3], and the proportion in China accounts for approximately 10% [4]. Saline–alkali land in Northeast China mainly contains carbonates, such as NaHCO3 and Na2CO3; thus, it is also known as soda saline–alkali land [5]. The characteristics of poor soil structure, high pH value, high salt contents, and lack of fertility in saline–alkali soil seriously inhibit plant growth and development [6]. Improving saline–alkali soils to increase crop yields in saline–alkali lands is critical for meeting China’s food needs and for sustainable ecosystem development [7].
The traditional methods to improve soda saline–alkali land are physical and chemical methods. Recently, biological methods are being developed and have shown better effects, which are environmentally friendly, cost-effective, and sustainable.
Composting is a process that uses microorganisms isolated from nature to compost and convert organic matter in waste into stable humus through a series of biochemical reaction processes [8]. The compost consists of a pure plant-derived ecofertilizer, such as an ecodisease-preventing microbial agent, straw-degrading microbial agent, and microbial organic fertilizer, which is outstanding in solving drug damage, promoting maturity and resistance to adversity, and improving quality and efficiency. Compost can play an important role in improving the stability of the soil structure by increasing its organic matter content, boosting the hydraulic conductivity and improving the water retention [9]. Further, an increase in soil nutrient supply is another advantage of compost-amended soils [10]. It can improve soil organic matter, reduce chemical fertilizer application, and improve crop yield and quality. Rice straw compost could help in both nutrient recycling and soil fertility for a long time [10]. The straw decomposer project takes plant endophytic bacteria as the core and maximizes the efficient circulation of nutrients and organic matter in straw by regulating crop microecology and soil microorganisms. Various composting methods markedly improved the N, P, and K status of all substrates [11]. Supplementation with composts helped promote the availability of soil nitrogen in the cold water paddy field, thereby improving the soil’s productivity and increasing the yield of the rice crop [12]. For example, rice straw compost can increase crop yield by 4%–9%, as the compost contains 1.7%–2.1% of nitrogen, 1.5% of phosphorous, and 1.4–1.6% of potassium, apart from being rich in available silicon [13]. A rice straw compost mediated by Trichoderma was rich in nutrients such as N, P, and K and promoted germination, seedling vigor, and growth of rice plant. Nitrogen derived from a cattle manure compost applied to a paddy field was continuously taken up at a rate of 2%–3% every year for 3 years after application, and 66%–69% of the applied nitrogen remained in the soil after 3 years of cultivation. Long-term application of organic matter to paddy fields accumulates organic nitrogen in the soil and increases the amount of nitrogen taken up by rice [14]. Apart from growth promotion, an enriched compost imparted intrinsic stress tolerance to rice by producing a higher amount of defense enzymes such as catalase, peroxidase, and superoxide dismutase [15]. The use of composted manure in rice cultivation has both economic and environmental benefits, and thus shows great potential for wide application [16]. Salinity and alkalinity have a negative impact on soil microbial growth, microbial biomass and diversity, and associated enzyme activity [17,18,19,20]. As soil salinity increases, the biomass and activity of soil microorganisms decrease [7,21]. Previous studies have shown that environmental factors play an important role in the formation of microbial community structure and composition [22]. Microbial amendment using plant rhizobia-promoting bacteria and phosphate-solubilizing microorganisms is a promising approach for improving calcium-sodic and saline soils due to its high efficiency, low cost, and environmental friendliness [23,24].
Although some studies have investigated saline–alkali soil organisms, there is little information on using CSC to improve saline–alkali soil. Therefore, the aim of this study is to determine the effects of CSC on the physiochemical and biological properties of saline–alkali soil. The results indicate that the application of CSC significantly improved the nutrients and activities of saline–alkali soil, and changed the soil structure by improving the proportion of soil particles with a size of 0.106 mm. Furthermore, the microflora abundance and composition were also significantly affected by CSC. The rice biomass and yields were improved along with increase in CSC. Our findings provide a theoretical basis for the improvement of soda saline–alkali land.

2. Materials and Methods

2.1. Experimental Materials

Saline–alkali soil samples were collected from three regions in Northeast China (Supplementary Table S1). The experiments were conducted at a campus of Qiqihar University (47°16′ N, 123°55′ E), Qiqihar, Heilongjiang Province, China. CSC was generated by the Microbiology Laboratory, Qiqihar University. Gypsum was used as a positive control. The CSC was mixed with soil evenly, and the proportions of the CSC in different treatments are shown in Table 1. Two rice varieties, Tongxi926, a saline–alkali–resistant variety, and Wuyoudao No. 4, a saline–alkali–sensitive variety, were grown in saline–alkali soil from the three places mentioned above (Table 2).
Rice seedlings were provided by the Longping Weak Alkali Experimental Base, and were planted in saline–alkali soil and CSC mixture in pots and grown in natural conditions from the end of April to the middle of October. The experiments were repeated three times (2020 to 2022). The rice rhizosphere soil and rice samples were collected at the tillering and ripening stage.
The soil samples were collected from the rhizosphere of rice, and after liquid nitrogen treatment, the samples were sent for bacteria 16S rRNA sequencing.
The corn straw compost used in this study was generated by our team by screening plant dominant endophytic strains and mixing them with corn straw for fermentation.

2.2. Determination of Soil Physiochemical Properties and Enzymatic Activities

The Kjeldahl method was used to determine the total nitrogen (TN) content. After alkaline hydrolysis, the available nitrogen (AN) content was determined using the microdiffusion technique. After wetting, the total phosphorus (TP) content was determined using molybdenum blue colorimetry and by digestion with sulfuric and perchloric acids. Available phosphorus (AP) was extracted with 0.5 mol·L−1 sodium bicarbonate, and AP content was determined using the molybdenum blue method. Available potassium (AK) content was measured by flame photometry after NH4OAc extraction [25]. All results were calculated based on oven-dry weights (at 105 °C) [26]. The molybdenum–antimony colorimetric method was used to determine the contents of TP, ammonium nitrogen (NH4+-N ammonium nitrogen (NH4+-N), and nitrate nitrogen (NO3−-N), which were extracted with 2 mol·L−1 KCl and analyzed using a dual-channel flow analyzer (AA3, Germany). The total organic carbon (TOC) content of the soil was determined using the potassium dichromate volumetric method, and the NH4+ and NO3− contents of the soil were analyzed using AA3 automatic flow injection analysis [26] (SEAL Analytical GmbH, Norderstedt, Germany).
The soil sucrase and urease activities were measured according to previously reported methods [27]. The 3,5-dinitrosalicylate colorimetric method was used to determine soil sucrase activity at a wavelength of 508 nm. Sodium phenolate was used to determine soil urease activity, and a UV spectrophotometer was used for colorimetric determination at 578 nm. Soil water-stable aggregate (WSA) content was determined using the wet-sieving method [28]. The soil pH was measured using a pH meter (soil/water, 1:5). Soil samples were saturated with water, and the acidity and electrical conductivity (EC) of the saturated soil extract were determined using a pH meter and EC meter (DDS-11A EC meter, Shang Hai Yoke Instrument Co., Ltd., Shanghai City, China) in a soil water suspension (1:5, w/v), respectively. The suspensions were prepared using 10 g of sieved soil and 50 mL of distilled water prior to being centrifuged for 15 min.

2.3. Preparation and 16S rRNA Sequencing of Soil Bacteria

A total of 30 samples from the tillering stage were sent for 16S rRNA sequencing (Supplementary Table S2). There were three replicates per sample. Microbial genomic DNA was extracted from rice rhizosphere soil using the TruSeqTM DNA Sample Prep Kit according to the manufacturer’s instructions. All the samples were sequenced. Distinct regions of the bacterial 16S rRNA genes were amplified using primers. The hypervariable region (V3–V4) of the bacterial 16S rRNA gene was amplified using the primer pair 338F (5′-ACTCCTACGGGAGGCAGCAG-3′) and 806R (5′-GGACTACHVGGGTWTCTAAT-3′), and the ITS gene was amplified using the primer pair ITS1F (5′-CTTGGTCATTTAGAGGAAGTAA-3′) and ITS2R (5′-GCTGCGTTCTTCATCGATGC-3′) in an ABI GeneAmp ® 9700 PCR thermocycler (ABI, CA, USA). The PCR products were extracted from 2% agarose gel, purified using an AxyPrep DNA Gel Extraction Kit (Axygen Biosciences, Union City, CA, USA) according to the manufacturer’s instructions, and quantified using a Quantus™ Fluorometer (Promega, USA). Purified amplicons were pooled in equimolar amounts and paired-end-sequenced on an Illumina MiSeq PE300 platform (Illumina, San Diego, CA, USA) according to the standard protocols of Majorbio Bio-Pharm Technology Co. Ltd. (Shanghai, China). Raw reads were deposited in the NCBI Sequence Read Archive (SRA) database (Accession Number: PRJNA774498).

2.4. Bioinformatics Analyses

A bioinformatics analysis of the soil microbiota was conducted using the Majorbio Cloud platform (https://cloud.majorbio.com).
Graphical representations were generated using GraphPad Prism 8 (GraphPad Software, Inc., La Jolla, CA, USA), and the means and standard deviations of the data were calculated. Tukey’s studentized range (HSD) test was used to identify taxa that were significantly different between soil types at the phylum or family level. A paired Wilcoxon rank-sum test was performed to compare the alpha diversity of different soil types. PERMANOVA was performed to measure effect sizes and significant differences in beta diversity. Analysis of variance (ANOVA) and Tukey’s method were performed using SPSS 22.0 (SPSS Inc., Chicago, IL, USA) to estimate the differences in bacterial community composition. In the results, the different lowercase letters represent significance at p < 0.05 among all the groups, and each value is represented as the mean ± standard deviation of three individuals (n = 3).

3. Results

3.1. Effects of Corn Straw Compost on Soil Nutrients and Structure

At the rice ripening stage, the contents of soil nutrients, e.g., available potassium (AK), total phosphorus (TP), available nitrogen (AN), nitrate nitrogen (NO3−-N), available phosphorus (AP), and total organic carbon (TOC), in all treatments were higher than those in T0 control (p < 0.05, Figure 1 and Figure S1). Except for NH4+-N, the contents of soil nutrients increased with the increase in content of CSC in soil, and T50 had the highest contents of soil nutrients, followed by T30, TS, and T10. The AK content in each type of soil was the highest except for FF and KD in which AP was the highest (p < 0.05), and that of TP and AN was the lowest (Figure 1). For example, the contents of AP, TN, and TOC in soil with CSC were much higher than those in T0 and TS (Supplementary Figure S1). Moreover, ANOVA analyses showed that the activities of soil enzymes, such as sucrase and urease, increased significantly with the increase in CSC in soil, and T50 had the highest soil activities (Supplementary Figure S2).
The analysis of soil aggregates indicated that after adding CSC, the soil aggregate structure changed obviously. The proportion of particles with diameters of 0.25, 0.106, 0.053, and 0.012 mm in T10 to T50 altered significantly (Figure 2). Except for FF and KF, which did not show a regular change, other soil samples had the highest proportion of 0.106 mm particle size, and the proportion increased with the increase in CSC in soil. As a result, T50 had the most proportion of 0.106 mm particle size (Supplementary Figure S2). These results indicate that CSC significantly affected soil structure.

3.2. Effects of Corn Straw Compost on Soil pH Value and Electrical Conductivity

The pH and electrical conductivity (EC) of soil samples of T10, T30, and T50 at the transplanting and tillering stages changed significantly compared with those of T0 and TS. The indexes indicated that pH and EC at the tillering stage reduced more significantly than those at the transplanting stage. Along with the CSC content increasing, pH and EC decreased significantly, reaching the lowest value at T50 (Figure 3).
The magnitude of cation exchange capacity (CEC) can be used as an indicator to evaluate the fertilizer retention and supply capacity of soil, which is one of the important bases for soil improvement and reasonable fertilizer application, as well as an important indicator of the fertility of a high- and stable-yielding farmland [29]. Adding CSC significantly affected CEC in rice rhizosphere soil at the tillering stage, and the CEC indexes decreased with the increase in CSC in soil (Figure 4), indicating the amount of variable charge; i.e., conductivity in soil was reduced. These results demonstrate that CSC can reduce the pH and EC of saline–alkali soil to improve soil properties.

3.3. Effects of Corn Straw Compost on Soil Bacterial Diversity

Analysis of the 16S rDNA genes yielded a total of 4,475,992 optimized sequences and 1,868,089,619 bases. The operational taxonomic unit (OTU) sequence similarity was 97% with a classification confidence of 70% in the optimized sequence reads (with a read length of ≥400 bp) across all 90 samples (30 samples * three replicates). The average length of the sequence was 417 bp, and the sequence length of each sample differed and ranged from 32,729 to 73,708 bp. The coverage rate of all samples was above 97%, indicating that the sequencing results were reliable and that they basically described the soil bacteria distribution.
We further performed alpha diversity, which measures the number of species within a bacterial community and the relative abundance between species. The Chao and Ace indexes indicate that the community richness had significant differences between the treatments except for FF (Figure 5a,b), and the Shannon and Simpson indexes indicate that the community diversity also had significant differences between the treatments except for FD and KZ (Figure 5c,d).

3.4. Effects of Corn Straw Compost on Bacterial Community Composition

The community barplot analysis indicated the composition and proportion of bacterial phyla (top 15) in each soil sample (Figure 6). The most abundant bacterial phyla were Chloroflexi, Actinobacteriota, Firmicutes, Bacteroidota, and Proteobacteria, and the abundance of these phylum changed significantly with the increase in CSC in soil compared with T0 and TS. For example, among the bacteria phylum compositions in FD rhizosphere soil, Actinobacteriota in FDS accounted for 4.24%, in FD0 accounted for 2.74%, in FD10 accounted for 5.55%, in FD30 accounted for 10.44%, and in FD50 accounted for 16.89%, showing a clear increasing trend with the increase in CSC in soil (Figure 6a). Meanwhile, in KD rhizosphere soil, Proteobacteria in KDS accounted for 15.60%, in KD0 accounted for 29.01%, in KD10 accounted for 21.67%, in KD30 accounted for 16.27%, and in KD50 accounted for 13.25%, showing a clear decreasing trend with the increase in CSC in soil (Figure 6d). These results indicate that CSC greatly affected the bacteria composition and proportion in the saline–alkali soil.
Then, the top 50 bacterial genera with the highest abundance were screened out in each soil sample using the OTU analysis. The distribution patterns of these 50 bacterial genera in T10, T30, and T50 soil samples were similar, but were different from those in T0 and TS control groups (Supplementary Figure S3). The dominant bacterial genera were screened, and six most dominant bacterial genera, Thermopolyspora, Thermobacillus, Thermobispora, Micromonosporaceae, VadinHA17, and Alphaproteobacteria, were identified. They only presented in T10, T30, and T50 soil samples rather than the control groups (Table 3). Among these, Thermopolyspora and Thermobispora belong to Actinomycetes, and Thermobacillus belongs to Firmicutes Bacillus. They are speculated to play a key role in the regulation of plant growth, soil nutrient contents, and soil physicochemical properties. Among them, Thermopolyspora and Thermobispora were the top 2 genera with the most abundance in T10, T30, and T50 soil, followed by Thermobacillus and Micromonosporaceae, and their abundance increased with the increase in CSC in soil (Table 3). These results revealed that CSC positively affected soil bacteria composition to improve soil activities.

3.5. Correlation between Soil Properties and Bacterial Genera Levels

Redundancy analysis (RDA) reflects the impacts of environmental factors on the bacterial genera levels (Figure 7). For example, in T30 and T50 soil, bacterial genera levels were significantly positively correlated with the activities of urease and sucrase and the contents TOC, AK, TN, AP, and NO3−-N (p < 0.05, Figure 7). Additionally, the activities of urease and sucrase increased with the increase in CSC in soil (Supplementary Figure S4). Meanwhile, in T0 soil, bacterial genera levels were significantly negatively correlated with pH value except in FF0 and KF0 soil (p < 0.05, Figure 7a). These results indicate that the soil properties closely affected bacterial genera levels.

3.6. Effects of Corn Straw Compost on Rice Biomass and Yields

Due to the significant improvement of soil nutrients and physicochemical properties by CSC, it is definitely beneficial for the growth and development of rice. As a result, the biomass and yields of rice grown in T10, T30, and T50 were significantly higher than those in T0, including fresh and dry weight, plant height, root length, number of tillers, and thousand grain weight; especially T50 had the highest biomass and yields compared with other treatments (Figure 8, Figure 9 and Figure S5). The indexes of the saline–alkali–resistant rice variety Tongxi926 were obviously higher than those of the saline–alkali–sensitive Wuyoudao No. 4 in all treatments; especially the yields of Tongxi926 were almost twice that of Wuyoudao, except in Fularki soil (FF and KF) (Figure 9 and Figure S5). Meanwhile, the indexes of TS and T10 were partially overlapping, as adding gypsum resulted in a decrease in soil pH and a certain recovery of the soil environment (Figure 8 and Figure 9). These results indicate that CSC significantly improved soil properties, thereby increasing the biomass and yields of rice.

4. Discussion

4.1. Corn Straw Compost Improved Soil Microenvironment by Increasing Soil Nutrients and Adjusting pH

Soda saline–alkali land often hardens, with low nutrient contents and many nutrient elements in an insoluble state. Gypsum is a commonly used chemical soil amendment, while, as a biological soil amendment, compost made from plant straw and animal manure is widely used to alleviate saline–alkali stress in plants [30,31]. The application of corn straw compost improves soil fertility by supplying nutrients to the soil, thereby contributing to enhance plant growth [32]. Certain soil-to-CSC ratios have been found to improve soil health and reduce bulk density over time [33]. In this study, compared with the T0 sample, all treatments with CSC significantly improved the physical and chemical properties of the soil. Significant changes in pH and EC, AK, TP, NH4+-N, AN, NO3−-N, AP, TN, TOC, and activities of urease and sucrase were observed with the increase in CSC contents (Figure 1, Figure 3 and Figure S1). Among the treatment soils, T0 had the highest pH and EC, and T50 had the lowest pH and EC. As a positive control, gypsum in TS soil reduced soil pH, thus improving the soil microenvironment to a certain extent; thus the pH value in TS was lower than that in T10 (Figure 3a,b). The reduced value of CEC indicates that the ability of soil colloids to adsorb variable charges was improved, which can be linked with the change of soil structure; that is, the application of CSC adjusted the size of soil colloidal particles to make it easier to adsorb variable charge. This is also consistent with the results of the change in the size of soil particles after the application of CSC, which shows a significant increase in the contents of soil particles with a diameter of 0.106 mm (Figure 2). This result suggests that soil particles with a diameter of 0.106 mm may be the most effective to adsorb variable charges in improved saline–alkali soil using CSC, and a size of 0.106 mm of soil colloidal particles may serve as a standard to measure the effectiveness of improvement of soda saline–alkali paddy fields.
The incorporation of compost can reduce bulk density, increase aggregate stability, and improve soil porosity, which are usually observed simultaneously [34,35], and the improvement of soil aggregate stability is often considered a sign of soil improvement by soil amendments [35,36]. It has been reported that composts play an important role in the formation of larger aggregates, primarily those with a diameter of >1 mm [37]. The difference between this result and ours may be due to the different diameters of the most suitable soil particles in general dry soil and paddy fields.
It is demonstrated that environmental factors play important roles in regulating the structure and composition of microbial communities [38,39,40]. It is known that the decaying organic matter provided by paddy rice straw and other weeds helps to reduce the soil pH by increasing the soil CO2 concentration and releasing H+ when dissolved in water [41,42]. Soil pH is an important factor affecting the chemical reactions in soil and the availability of soil nutrients for plant growth. Notably, pH has been reported to be the most important factor determining the microbial community structure in natural environments [25,43]. In addition, soil EC also significantly affects the bacterial community. In this study, the soil pH was significantly reduced in T10, T30, and T50 treatments compared with T0 soil. The application of CSC can mitigate the negative effects of saline–alkali soils on crops. The organic carbon content of T0 soil treatment was very low, while the application of CSC increased the total organic carbon contents in soil (Figure 1) and promoted the rice biomass and yields (Figure 8 and Figure 9).

4.2. Corn Straw Compost Affected Diversity and Composition of Bacterial Communities

Salinity and alkalinity results in severe nutrient deficiencies that greatly reduce plant growth [29]. Bacteria play an important role in the transformation of organic and inorganic soil matter. Bacterial diversity is affected by many factors, including the soil physiochemical properties, temperature, etc. [44,45]. The sensitivity of soil bacteria to high saline–alkali stress negatively affects ecosystem processes associated with soil bacterial communities, including biomass, soil respiration, organic matter decomposition, etc. [46]. Our study demonstrated that species richness and the diversity of bacteria significantly increased under CSC treatments compared with the controls (T0 and TS) (Figure 6 and Figure 7). The indexes indicated that pH value was negatively correlated with microorganism genera. The T0 control had the highest pH and the lowest microbial diversity and abundance (Figure 7).
In addition, the large CO2 emissions during the decomposition of organic matter and the nitrification process of converting NH4+-N into NO3−-N are key factors contributing to the decline in soil pH [47]. The degradation of organic matter inevitably produces organic acids, which reduce the pH of compost [48]. Conductivity reflects the degree of soil salt and the phytotoxic effects on plants [49]. Our results indicate that CSC significantly reduced pH and EC, i.e., alleviated the saline–alkali stress, which are consistent with the results of the abovementioned studies. Our results showed that soils incorporated with CSC (T10, T30, T50) maintained a range of pH 7.5–8.5, which was significantly lower than the control (T0) (Figure 3a), which significantly affected the soil bacteria community structure and rice growth and yields. Previous reports have suggested that changes in pH may contribute to the abundance and composition of Acidobacteria communities [43,50]. Seven dominant bacterial phyla were found in all soil samples in our study: Firmicutes, Proteobacteria, Actinobacteria, Bacteroidota, Desulfobacterota, Acidobacteria, and Chloroflexi (Figure 6). In addition, the relative abundances of Acidobacteria, Chlorobacteria, Filamentobacteria, and Proteobacteria were significantly positively correlated with CSC content and significantly negatively correlated with soil pH and organic matter content (Figure 7). Increasing in CSC content in the transplanting and tillering stages significantly increased the levels of N, P, and K in the saline–alkali soil, thereby assisting in soil repair and increasing the number of aggregates, stability, and hydraulic conductivity compared with the T0 treatment, thus contributing to the improvement of the saline–alkali soil.
Northeast China is one of the main corn-producing areas, with a large amount of corn straw produced every year. Usually, farmers burn the straw, which not only wastes green resources, but also causes serious environmental pollution and increases the risk of fire. Using corn straw for composting can not only turn the agricultural solid waste into usable substances to generate green fertilizer, but also be environmentally friendly, low-cost, and easy to operate. At the same time, it also effectively increases crop yields. This biological method for the improvement of saline–alkali land is beneficial for sustainable agricultural development.

5. Conclusions

In this study, we demonstrate that the corn straw compost amendment significantly improved the physiochemical properties and WSA structure and stability of saline–alkali soil and increased the diversity and composition of bacterial communities. The bacterial community abundance was tightly correlated with soil properties. Additionally, the improvement of soil nutrients, structure, and bacterial community diversity by the application of CSC significantly accelerated the growth and yields of rice grown in saline–alkali paddy fields. Our research on utilizing CSC to improve saline–alkali soil provides a theoretical basis for the improvement of soda saline–alkali land. Due to its ability to recycle and utilize various straws, as well as its good fertility, low cost, and environmental friendliness, compost has great potential for application.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/agronomy13061525/s1, Figure S1: Effects of CSC treatments on soil physiochemical properties at tillering stage of rice; Figure S2: Changes of enzyme activities in different treatments of rice; Figure S3: Comparison of differences between different treatments of saline soils for bacteria at the genus level; Figure S4: Comparison of the proportion of soil particles with different sizes; Figure S5: Comparison of biomass. Table S1: Basic information of saline-alkali soil samples; Table S2: Information of 30 samples for 16S rRNA sequencing.

Author Contributions

Z.W. and L.L. conceived this project. Z.W., L.L. and S.L. designed the experiments. Z.W., L.L. and S.L. generated all the research materials (rice seedlings, soil samples, experimental b). S.L., H.Z. and J.S. conducted sample collection, observation, plant treatment, data processing, and analysis. Z.W. provided reagents, materials, and analytical platforms. Z.W. and S.L. drafted the manuscript. L.L. revised the manuscript. All authors commented on the manuscript. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the National Natural Science Foundation of China (Nos. 31870493, 32170279); Key Research and Development Projects in Heilongjiang, China (No. GA21B007); and Basic Research Fees of Universities in Heilongjiang Province, China (No. 135409103).

Data Availability Statement

The original contributions presented in the study are included in the article/Supplementary Materials. Further inquiries can be directed to the corresponding authors.

Conflicts of Interest

The authors declare that there are no conflict of interest.

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Figure 1. Effects of different CSC treatments on soil physicochemical properties at the tillering stage of rice. (a) FD, (b) FF, (c) FZ, (d) KD, (e) KF, (f) KZ. Abbreviation: AK, rapidly available potassium; AN, available nitrogen; AP, available phosphorus; NH4+-N, ammonium nitrogen; NO3−-N, nitrate nitrogen; TN, total nitrogen; TOC, total organic carbon; TP, total phosphorus. FD0, Wuyoudao No. 4 in soil from Dumeng, and 0% corn straw compost + 100% saline soil; FD10, Wuyoudao No. 4 in soil from Dumeng, and 10% corn straw compost + 90% saline soil; FD30, Wuyoudao No. 4 in soil from Dumeng, and 30% corn straw compost + 70% saline soil; FD50, Wuyoudao No. 4 in soil from Dumeng, and 50% corn straw compost + 50% saline soil; FDS, Wuyoudao No. 4 in soil from Dumeng, and 7.7% gypsum + 92.3% saline soil (control). The rest of the abbreviations can be deduced from these. T0, 0% corn straw compost + 100% saline soil; T10, 10% corn straw compost + 90% saline soil; T30, 30% corn straw compost + 70% saline soil; T50, 50% corn straw compost + 50% saline soil; TS, 7.7% gypsum + 92.3% saline soil (control). FD, Wuyoudao No. 4 in soil from Dumeng; KD, Tongxi926 in soil from Dumeng; FF, Wuyoudao No. 4 in soil from Fularki; KF, Tongxi926 in soil from Fularki; FZ, Wuyoudao No. 4 in soil from Zhaoyuan; KZ, Tongxi926 in soil from Zhaoyuan.
Figure 1. Effects of different CSC treatments on soil physicochemical properties at the tillering stage of rice. (a) FD, (b) FF, (c) FZ, (d) KD, (e) KF, (f) KZ. Abbreviation: AK, rapidly available potassium; AN, available nitrogen; AP, available phosphorus; NH4+-N, ammonium nitrogen; NO3−-N, nitrate nitrogen; TN, total nitrogen; TOC, total organic carbon; TP, total phosphorus. FD0, Wuyoudao No. 4 in soil from Dumeng, and 0% corn straw compost + 100% saline soil; FD10, Wuyoudao No. 4 in soil from Dumeng, and 10% corn straw compost + 90% saline soil; FD30, Wuyoudao No. 4 in soil from Dumeng, and 30% corn straw compost + 70% saline soil; FD50, Wuyoudao No. 4 in soil from Dumeng, and 50% corn straw compost + 50% saline soil; FDS, Wuyoudao No. 4 in soil from Dumeng, and 7.7% gypsum + 92.3% saline soil (control). The rest of the abbreviations can be deduced from these. T0, 0% corn straw compost + 100% saline soil; T10, 10% corn straw compost + 90% saline soil; T30, 30% corn straw compost + 70% saline soil; T50, 50% corn straw compost + 50% saline soil; TS, 7.7% gypsum + 92.3% saline soil (control). FD, Wuyoudao No. 4 in soil from Dumeng; KD, Tongxi926 in soil from Dumeng; FF, Wuyoudao No. 4 in soil from Fularki; KF, Tongxi926 in soil from Fularki; FZ, Wuyoudao No. 4 in soil from Zhaoyuan; KZ, Tongxi926 in soil from Zhaoyuan.
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Figure 2. Statistics of proportion of soil aggregates with different particle sizes in the soil samples. The proportion of particles with different diameter is shown below each type of particle. (a) FD, (b) FF, (c) FZ, (d) KD, (e) KF, (f) KZ.
Figure 2. Statistics of proportion of soil aggregates with different particle sizes in the soil samples. The proportion of particles with different diameter is shown below each type of particle. (a) FD, (b) FF, (c) FZ, (d) KD, (e) KF, (f) KZ.
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Figure 3. The pH value and electrical conductivity were affected by CSC. (a) pH; (b) electrical conductivity. Values are means ± standard deviation (SD) (n = 3). Three replicates per experiment, three independent experiments per sample. Values are means ± SD (n = 3). Different lowercase letters indicate significances (p < 0.05); EC, electrical conductivity. Please refer to Figure 1 legend for the meaning of FD0, FD10, FD30 etc.
Figure 3. The pH value and electrical conductivity were affected by CSC. (a) pH; (b) electrical conductivity. Values are means ± standard deviation (SD) (n = 3). Three replicates per experiment, three independent experiments per sample. Values are means ± SD (n = 3). Different lowercase letters indicate significances (p < 0.05); EC, electrical conductivity. Please refer to Figure 1 legend for the meaning of FD0, FD10, FD30 etc.
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Figure 4. Comparison of cation exchange properties of different soil samples. (a) FD, (b) FF, (c) FZ, (d) KD, (e) KF, (f) KZ. Values are means ± standard deviation (SD) (n = 3). Three replicates per experiment, three independent experiments per sample. Values are means ± SD (n = 3). Different lowercase letters indicate significances (p < 0.05).
Figure 4. Comparison of cation exchange properties of different soil samples. (a) FD, (b) FF, (c) FZ, (d) KD, (e) KF, (f) KZ. Values are means ± standard deviation (SD) (n = 3). Three replicates per experiment, three independent experiments per sample. Values are means ± SD (n = 3). Different lowercase letters indicate significances (p < 0.05).
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Figure 5. The soil alpha diversity analysis of different treatments. (a) Ace index, (b) Chao index, (c) Shannon index, (d) Simpson index. Values are means ± SD (n = 3). * indicate significance (p < 0.05).
Figure 5. The soil alpha diversity analysis of different treatments. (a) Ace index, (b) Chao index, (c) Shannon index, (d) Simpson index. Values are means ± SD (n = 3). * indicate significance (p < 0.05).
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Figure 6. The community barplot analysis of bacterial phyla in rice rhizosphere soil. (a) FD, (b) FF, (c) FZ, (d) KD, (e) KF, (f) KZ. Abundance of top 15 bacterial phyla is shown.
Figure 6. The community barplot analysis of bacterial phyla in rice rhizosphere soil. (a) FD, (b) FF, (c) FZ, (d) KD, (e) KF, (f) KZ. Abundance of top 15 bacterial phyla is shown.
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Figure 7. Redundancy analysis of correlation of soil properties and bacterial genera levels. (a) FD, (b) FF, (c) FZ, (d) KD, (e) KF, (f) KZ. The values of X- and Y-axis indicate percentage. Arrows represent environmental factors, and the length of arrows represent the tightness of correlations. EC, electrical conductivity; pH; AN, available nitrogen; AP, available phosphorus; AK, rapidly available potassium; TOC, total organic carbon; TP, total phosphorus; TN, total nitrogen; NO3−-N, nitrate nitrogen; NH4+-N, ammonium nitrogen; urease; sucrase.
Figure 7. Redundancy analysis of correlation of soil properties and bacterial genera levels. (a) FD, (b) FF, (c) FZ, (d) KD, (e) KF, (f) KZ. The values of X- and Y-axis indicate percentage. Arrows represent environmental factors, and the length of arrows represent the tightness of correlations. EC, electrical conductivity; pH; AN, available nitrogen; AP, available phosphorus; AK, rapidly available potassium; TOC, total organic carbon; TP, total phosphorus; TN, total nitrogen; NO3−-N, nitrate nitrogen; NH4+-N, ammonium nitrogen; urease; sucrase.
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Figure 8. Comparison of rice biomass. Root length, plant height, fresh/dry weight of aboveground and underground parts were compared by radar chart. (a) FD, (b) FF, (c) FZ, (d) KD, (e) KF, (f) KZ.
Figure 8. Comparison of rice biomass. Root length, plant height, fresh/dry weight of aboveground and underground parts were compared by radar chart. (a) FD, (b) FF, (c) FZ, (d) KD, (e) KF, (f) KZ.
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Figure 9. Comparison of rice yields of different treatments. (a) FD, (b) FF, (c) FZ, (d) KD, (e) KF, (f) KZ. Values are means ± standard deviation (n = 3). Different lowercase letters indicate significances (p < 0.05). ns, no significance. *, p ≤ 0.05, **, p ≤ 0.01, ***, p ≤ 0.001, ****, p ≤ 0.0001.
Figure 9. Comparison of rice yields of different treatments. (a) FD, (b) FF, (c) FZ, (d) KD, (e) KF, (f) KZ. Values are means ± standard deviation (n = 3). Different lowercase letters indicate significances (p < 0.05). ns, no significance. *, p ≤ 0.05, **, p ≤ 0.01, ***, p ≤ 0.001, ****, p ≤ 0.0001.
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Table
Table 1. The proportion of corn straw compost in different treatments.
Table 1. The proportion of corn straw compost in different treatments.
AbbreviationMixture Components
T00% corn straw compost + 100% saline soil
T1010% corn straw compost + 90% saline soil
T3030% corn straw compost + 70% saline soil
T5050% corn straw compost + 50% saline soil,
TS7.7% gypsum + 92.3% saline soil (control)
Table
Table 2. The rice varieties and grown soil.
Table 2. The rice varieties and grown soil.
AbbreviationThe Rice Varieties and Grown Soil
FDWuyoudao No. 4 in soil from Dumeng
KDTongxi926 in soil from Dumeng
FFWuyoudao No. 4 in soil from Fularki
KFTongxi926 in soil from Fularki
FZWuyoudao No. 4 in soil from Zhaoyuan
KZTongxi926 in soil from Zhaoyuan
Table
Table 3. The contents of dominant bacteria in different treatments.
Table 3. The contents of dominant bacteria in different treatments.
Genus of BacteriaTreatmentsGenus of BacteriaTreatments
FD0FD10FD30FD50FDS KD0KD10KD30KD50KDS
thermobacillus0.00%0.00%1.80%2.62%0.00%thermobacillus0.00%0.00%1.84%2.30%0.00%
thermobispora0.00%0.00%3.24%5.35%0.00%thermobispora0.00%0.00%3.35%5.07%0.00%
thermopolyspora0.00%1.24%3.98%8.79%0.00%thermopolyspora0.00%0.00%3.63%5.63%0.00%
micromonosporacease0.00%0.00%1.54%3.19%0.00%micromonosporacease0.00%0.00%1.37%1.84%0.00%
desulfobacterota0.00%0.00%0.00%1.72%0.00%desulfobacterota0.00%0.00%0.00%0.00%0.00%
FF0FF10FF30FF50FFS KF0KF10KF30KF50KFS
thermobacillus0.00%0.00%0.00%1.78%0.00%thermobacillus0.00%0.00%0.00%1.61%0.00%
thermobispora0.00%0.00%2.09%3.67%0.00%thermobispora0.00%0.00%1.97%3.02%0.00%
thermopolyspora0.00%1.22%3.15%5.59%0.00%thermopolyspora0.00%0.00%2.67%4.70%0.00%
micromonosporacease0.00%0.00%1.14%1.63%0.00%micromonosporacease0.00%0.00%1.58%1.42%0.00%
desulfobacterota0.00%0.00%0.00%1.17%0.00%desulfobacterota0.00%0.00%0.00%0.00%0.00%
FZ0FZ10FZ30FZ50FZS KZ0KZ10KZ30KZ50KZS
thermobacillus0.00%0.00%1.62%2.36%0.00%thermobacillus0.00%0.00%1.44%2.66%0.00%
thermobispora0.00%0.00%1.94%3.63%0.00%thermobispora0.00%1.18%2.53%5.42%0.00%
thermopolyspora0.00%0.00%2.08%3.72%0.00%thermopolyspora0.00%1.00%2.76%7.11%0.00%
micromonosporacease0.00%0.00%0.90%1.84%0.00%micromonosporacease0.00%0.00%1.45%3.34%0.00%
desulfobacterota0.00%0.00%0.77%1.13%0.00%desulfobacterota0.00%0.00%0.00%1.64%0.00%
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Li, S.; Li, L.; Wang, Z.; Sun, J.; Zhang, H. Impacts of Corn Straw Compost on Rice Growth and Soil Microflora under Saline-Alkali Stress. Agronomy 2023, 13, 1525. https://doi.org/10.3390/agronomy13061525

AMA Style

Li S, Li L, Wang Z, Sun J, Zhang H. Impacts of Corn Straw Compost on Rice Growth and Soil Microflora under Saline-Alkali Stress. Agronomy. 2023; 13(6):1525. https://doi.org/10.3390/agronomy13061525

Chicago/Turabian Style

Li, Shenglin, Lixin Li, Zhigang Wang, Jing Sun, and Hailong Zhang. 2023. "Impacts of Corn Straw Compost on Rice Growth and Soil Microflora under Saline-Alkali Stress" Agronomy 13, no. 6: 1525. https://doi.org/10.3390/agronomy13061525

APA Style

Li, S., Li, L., Wang, Z., Sun, J., & Zhang, H. (2023). Impacts of Corn Straw Compost on Rice Growth and Soil Microflora under Saline-Alkali Stress. Agronomy, 13(6), 1525. https://doi.org/10.3390/agronomy13061525

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