Next Article in Journal
Foliar Application of Biostimulant Mitigates Water Stress Effects on Soybean
Previous Article in Journal
Genome-Wide Association Analysis of Seed Vigor-Related Traits in Wheat
 
 
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 3
10.3390/agronomy14030413
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

Effects of Different Agricultural Waste Composts on Cabbage Yield and Rhizosphere Environment

by
Jue Wang
1,
Bo Zhang
1,2,
Jie Wang
1,
Guobin Zhang
1,
Zhibin Yue
1,
Linli Hu
1,
Jihua Yu
1,3,* and
Zeci Liu
1,*
1
College of Horticulture, Gansu Agricultural University, Lanzhou 730070, China
2
Dingxi Seed Station, Dingxi Academy of Agricultural Sciences, Dingxi 743000, China
3
Gansu Provincial Key Laboratory of Arid Land Crop Science, Gansu Agricultural University, Lanzhou 730070, China
*
Authors to whom correspondence should be addressed.
Agronomy 2024, 14(3), 413; https://doi.org/10.3390/agronomy14030413
Submission received: 17 January 2024 / Revised: 10 February 2024 / Accepted: 13 February 2024 / Published: 21 February 2024
(This article belongs to the Section Soil and Plant Nutrition)
Browse Figures
Versions Notes

Abstract

:
The return of agricultural waste to the field is one of the most effective strategies of increasing crop yield, improving the soil’s physicochemical properties, and improving the soil rhizosphere environment. In the present study, sheep manure (SM), cow manure (CM), tail vegetable (TV), mushroom residue (MR), and corn straw (CS) were used as raw materials, and no fertilization (CK1) and local commercial organic fertilizer (CK2) treatments were used as controls. Eight composts were set up using specific mass ratios of different compost materials. After fermentation, field experiments were conducted to determine the cabbage yield, soil’s physicochemical properties, and soil rhizosphere conditions. The eight composts increased the soil organic matter and nutrient contents significantly. Among the eight fermentation formulas, T6 (CM:CS:TV:SM = 1:1:2:6), T7 (MR:CS:TV:SM = 1:1:2:6), and T8 (CM:MR:CS:TV:SM = 1:1:1:2:5) were relatively effective. Therefore, high-throughput sequencing was performed on T6, T7, T8, CK1, and CK2. T6, T7, and T8 exhibited increased relative abundance of Proteobacteria, Actinomycetes, and Firmicutes, while the Acidobacteria abundance was decreased. In addition, Ascomycota’s and Basidiomycetes’ relative abundance decreased, and the oil chytrid and mortierella increased. The microbial community structure was affected significantly by pH, electrical conductivity, available potassium, available nitrogen, and organic matter. In general, the three composts increased yield by improving the soil’s physicochemical properties, fertility, and microbial community structure. Among them, T6 had the most significant effect and is the optimal formula for use as a local organic cabbage fertilizer, and it could facilitate sustainable agricultural development.

1. Introduction

Agricultural waste is the non-product output of agricultural production and processing. It is potentially toxic to plants, animals, and human beings in numerous direct and indirect ways and has become one of the main sources of agricultural non-point source pollution, threatening the environmental security of many countries globally. Today, most of the agricultural waste is in the form of crop straw and livestock manure [1,2,3]. In China, the annual output of livestock and poultry manure is up to 3.8 billion tons, and that of crop straw is up to 1 billion tons [4]. However, due to a lack of proper environmental regulations and poor waste treatment systems for agricultural waste, most of the agricultural waste is not disposed of properly, or may be simply thrown away, which not only causes environmental pollution but represents a waste of resources [5,6].
With an increase in people’s environmental awareness, the use patterns of agricultural waste are changing, with carbonization, biodegradation, compost hydrolysis, and pyrolysis being increasingly adopted to create fertilizers, feed, energy, base material, and other products. Such activities improve the economic value of agricultural waste by transforming it into useful products [7,8]. Among them, compost hydrolysis is a sustainable approach that can be used to fix organic matter into material that can be used to promote soil improvement.
Soil ecosystems are vital for the functioning of the Earth’s biosphere [9]. The soil’s microbial community is diverse, and the microbiome structure is one of the indexes for soil health. The microbiome plays vital roles in nutrient cycling and energy flows in soil ecosystems [10,11]. Moreover, soil’s microbial community composition and diversity, which play key roles in soil’s ecosystem function, are sensitive indexes of microenvironmental changes in soil. The functional diversity of the soil’s microbial community is an index that can reflect the ecological characteristics of soil microorganisms, providing a reliable basis for understanding microbial diversity. Organic fertilizer can affect the function of various agroecosystems by altering the composition and abundance of functional genes [12,13].
Previous field studies have concluded that organic fertilizer can maintain soil structure, improve soil’s nutrient contents, and promote pathogen antagonists [14]. In addition, soil’s microbial community structure and diversity can improve following organic fertilizer application, and the amount of plant growth promoting rhizobacteria, such as Azospirillum [15], Enterobacter [16], Pseudomonas [17], Bacillus [18,19], and Trichoderma [20], increases. Organic fertilizer application has been considered a partial substitute for inorganic nitrogen (N) and to provide longer-term nutrient release [21]. Furthermore, organic fertilizer amendment to soil can improve the growth of plant roots and the absorption efficiency of nutrients and water [22,23]. Moreover, organic fertilizers can increase soil’s organic matter contents and, in turn, available nutrients to support crop growth [24].
Previous studies on organic fertilizers have focused mainly on the application of organic fertilizer alone or increased organic fertilizer in combination with reduced chemical fertilizer application [25,26]. Only a few studies have investigated the effects of agricultural waste compost on crop yield and the rhizosphere. In addition, the interactions between crop yield and shifts in microbial community diversity and compost application systems, in addition to the underlying mechanisms, remain unclear.
Lanzhou, China, is an important summer vegetable production area at the northern margin of the Tibetan plateau. The region is also rich in agricultural waste resources. At present, local, commercial, and organic vegetable fertilizer is mainly produced by compost fermentation mixed with tail vegetable/sheep manure = 3.5:6.5 (mass ratio), and there are still other types of crop and animal waste that are not properly recycled. Therefore, in the present study, some of the agricultural waste resources (corn straw, CS; tail vegetable, TV; mushroom residue; MR; sheep manure, SM; cow manure, CM) were used as fermentation materials. The compost fermentation formulas were based on local, commercial, and organic fertilizer formulas. By studying the effects of different agricultural waste composts on cabbage yield and the rhizosphere, the organic fertilizer production formula was optimized, which could provide a reference formula for application in the cultivation of cabbage or other vegetables. The results of the present study could provide a theoretical basis for the sustainable disposal and exploitation of agricultural waste and environmental management.

2. Materials and Methods

2.1. Experimental Site Condition

The present study was conducted in Yuzhong, Gansu Province, China. Average annual temperature, precipitation, and evaporation are 6.6 °C, 300~400 mm, and 1343 mm, respectively. The pH and electrical conductivity (EC) of the surface soil (0~20 cm depth) were 8.12 and 0.242 μS·cm−1, respectively. In addition, the alkali-hydrolyzable N (AHN), available phosphorus (P; AP), available potassium (K; AK), and organic matter (OM) contents were 76.42 mg·kg−1, 117.40 mg·kg−1, 237.70 mg·kg−1, and 14.03 g·kg−1, respectively.

2.2. Composting

Agricultural waste raw materials (corn straw, CS; tail vegetable (leafy vegetable), TV; mushroom residue; MR; sheep manure, SM; cow manure, CM) were crushed and weighed and mixed at specific mass ratios. The basic physicochemical properties of the composted agricultural waste are listed in Table 1. Before composting, the water content of the composting pile was adjusted to 60~65%. Subsequently, 1 kg of compost bacteria (colony forming unit [CFU] ≥ 50 billion/g, protease activity ≥ 70 μg/g, cellulase activity ≥ 60 μg/g) purchased from Renyuan biotechnology company (Hebi, China) was added to every 15 tons of agricultural waste. After the composting materials and bacteria were mixed completely, the mixture was stacked into strip ridges with widths of 1.5~3.0 m and heights of 0.8~1.5 m. During the composting process, the temperature 25~30 cm away from the top of the pile was monitored. After the temperature reached 55~60 °C, stacks were turned for the first time. Subsequently, the stacks were turned every 5~7 d until composting was complete, when the color of the stack was dark brown. The measured composting temperature was close to room temperature (20~30 °C), and the smell of rotten soil was perceived, which took about 38~40 d. The basic physicochemical properties of compost fertilizers with different agricultural waste are listed in Table 2.

2.3. Test Materials and Field Management

“Shuangkang 58” cabbage (an early-maturing variety), purchased from Tianjin Shuofeng Seed Industry Co., Ltd. (Tianjin, China), was used as the experimental material. Seedlings were raised on 15 June 2022 and planted on 25 July 2022.
The field trial was conducted based on a random block design with three replicates. Two control groups were set up, with eight treatments: CK1 (no fertilizer), CK2 (local organic fertilizer fermentation formula, TV:SM = 3.5:6.5), T1 (TV:SM = 4.5:5.5), T2 (CM:TV:SM = 1:3:6), T3 (MR:TV:SM = 1:3:6), T4 (CS:TV:SM = 1:3:6), T5 (MR:CM:TV:SM = 1:1:2:6), T6 (CS:CM:TV:SM = 1:1:2:6), T7 (CS:MR:TV:SM = 1:1:2:6), and T8 (CS:MR:CM:TV:SM = 1:1:1:2:5). The application amounts of all the eight fermented organic fertilizer treatments and the CK2 were 6000 kg/ha. The organic fertilizer was evenly applied in strips, the soil on both sides was turned over for 25~30 cm to cover the base fertilizer, and ridges were set up. Each experimental plot was 20 m long, 1.1 m wide, and 22 m2 in area. The width of each planting row was covered with film, and the width of the planting row was 1 m. Two rows of cabbage were cultivated on each planting row, and the plant spacing and row spacing were 20~25 cm and 35~40 cm, respectively.

2.4. Measurement of Yield and Soil Sampling

The planted cabbage was harvested on 7 October 2022. During the harvest period, 75 cabbage plants from each treatment were selected randomly to measure yield, and the soil samples from the 15~20 cm depth of three labeled cabbages were collected and used for rhizosphere microbial diversity analysis. Soil samples from the three cabbage roots were mixed. Debris such as stones, roots, and film fragments were sieved and removed with a 2 mm sieve. Afterward, the soil was divided into two parts; one part was used for subsequent microbial DNA extraction, and the other part was air-dried and used for the determination of soil’s physicochemical properties.

2.5. Composting Fertilizers and Soil Physicochemical Property Analysis

The pH and EC of the soil were determined using the soil filtrate [16]. PHS-3E (Jinko, Shanghai, China) was used to determine the filtrate pH, and the EC was determined using a DSJ-308A conductivity meter (Jinko, Shanghai, China). After oxidation with potassium dichromate (K2Cr2O7), OM content was measured using the titration method. Before the determination of total N (TN), total P (TP), and total K (TK) contents, wet digestion was performed with the H2SO4-H2O2 method. Subsequently, the TN content was determined using an automatic Kjeldahl N analyzer K1100F, TP was determined using a UV-1780 spectrophotometer (Shimadzu, Suzhou, China), and TK was determined using a ZEEnit 700P flame atomic spectrophotometer (Analytik Jena, Jena, Germany). The determination of AN, AP, and AK contents are in accordance with the methods used in previous studies [27]. All the above indexes were measured in triplicate.

2.6. Soil DNA Extraction and PCR Amplification

Soil microbial DNA was extracted using the EZNA kit (Omega Bio-tek, Norcross, GA, USA). Primers ITS1F and ITS2R were used to amplify the ITS2 region of the fungal DNA, while the s338F and 806R primers were used to amplify the V3–V4 region of bacterial DNA. The reaction mixture volume was 20 µL, including 4 µL buffer, 2 µL dNTPs, 1.5 µL of each primer, 10 ng DNA, and sufficient ddH2O. The ABI GeneAmp 9700 PCR amplifier (Applied Biosystems, Foster City, CA, USA) was used for PCR. The amplification procedure was initial denaturation at 95 °C for 3 min, then denaturation for 30 s, annealing at 55 °C for 30 s, extension at 72 °C for 45 s, and final extension at 72 °C for 10 min, and the amplification cycle was 27. After amplification, the procedure was terminated at 4 °C. PCR products were electrophoresed on 2% agarose gel, purified using an AxyPrep DNA gel extraction kit, and quantified using the Promega quantum fluorometer (Promega, Madison, WI, USA).

2.7. Illumina MiSeq Sequencing

Purified amplified PCR amplification fragments were collected and sequenced by Shanghai Meiji Biomedicine Technology (Shanghai, China) on the Illumina MiSeq sequencing platform. The original 16s rRNA and ITS gene were sequenced through multi-channel separation. Trimmomatic was used to perform quality filtering, and FLASH merge was used to merge the data according to the standard. Sequence similarity > 97% was specified as an Operational Taxonomic Unit and used for UPARSE clustering. The fungal ITS (Unite 8.0) sequences and bacterial 16S rRNA (Silva SSU128) sequences were evaluated using RDP classifier (confidence threshold = 0.7).

2.8. Data Analysis

Microbial data analysis was performed in R v.3.5.2 [28]. Shannon, Simpson, and Chao indexes were calculated using QIIME2, accessed on 11 July 2023 (https://qiime2.org/). Principal Coordinate Analysis (PCoA) was performed based on Bray–Curtis distance to evaluate similarity in microbial community composition. Relationships among physicochemical properties and soil microbial community were analyzed using redundancy analysis (RDA) and Spearman rank correlation. Soil microbial communities in different treatments were evaluated by substitution multiple variance analysis (Per MANOVA). PCoA, RDA, and Per MANOVA analyses were implemented using the vegan package in R (version 2.0-5;) [29]. Circos-0.67-7 was accessed on 16 July 2023 (https://bioweb.pasteur.fr/packages/pack@[email protected]) to draw Circos diagrams (version 0.67-7) [30]. One-way Analysis of Variance and Pearson correlation analysis were performed in IBM SPSS Statistics 21 (IBM Corp., Armonk, NY, USA). p ≤ 0.05 indicated a significant difference among treatments.

3. Results

3.1. Soil’s Physicochemical Properties under Different Composting Treatments

Different agricultural waste composts had significant effects on the physicochemical indexes of the cabbage rhizosphere soil (Figure 1). Compared with CK1 (no fertilizer), the total available and available nutrients in the soil were increased significantly after the organic fertilizer application. TN, TP, and TK contents were the highest under the T6 treatment, at 0.65 g/kg, 1.41 g/kg, and 11.48 g/kg, respectively. Compared with those in the CK2 (local commercial fertilizer) treatment, the TN, TP, and TK in the T6 treatment were 5.36%, 28.24%, and 15.49% higher, respectively. In addition, the AN, AP, and AK contents in T6 were the highest, at 89.86 mg/kg, 98.78 mg/kg, and 173.94 mg/kg, respectively, which were 5.26%, 11.97%, and 18.44% higher than those in the CK2 treatment, respectively.
Both soil pH and EC were the highest under the CK1 treatment and the lowest under the T6 treatment, and the difference was significant. However, the total OM was the highest in the T6 treatment, and the lowest in the CK1 treatment, which was significantly lower than those in the other treatments.

3.2. Cabbage Yield under Different Composting Treatments

The yields of all agricultural waste compost treatments were significantly higher than that in the CK1 treatment. The biological yields (overground weight) and economic yields (sphere weight) of the T6 treatment were the highest, reaching 98.058 ton/ha and 71.263 ton/ha, followed by those in the T4 treatment. The biological and economic yields of the T6 treatment were significantly higher than those of the CK1 treatment, by 79.94% and 88.24%, respectively. In addition, compared with those of the CK2 treatment, the biological and economic yields of the T6 treatment were 7.18% and 5.38% higher, respectively (Figure 2).

3.3. Soil’s Microbial Diversity under Different Treatments

The ACE, Chao 1, Simpson, and Shannon indexes of fungi and bacteria under different treatments were measured to study the microbial community’s diversity. The ACE and Chao 1 indexes were proportional to the microbial abundance, the Simpson index was inversely proportional to the diversity, and the Shannon index was proportional to the diversity.
The soil bacteria’s ACE index of the T6 treatment was the highest, and there was no significant difference between T6 and the other treatments (Figure 3A). The T6 treatment had the highest Chao 1 index, which was 8.13% higher than that of the CK1 treatment, and the difference was significant (Figure 3B). Compared with that of the CK1 treatment, the Simpson index of the bacteria in the CK2, T6, T7, and T8 treatments decreased by 3.70, 12.00, 7.69, and 3.70%, respectively. In addition, the Simpson index in the T6 treatment was the lowest and significantly different from that of the CK1 treatment (Figure 3C). The Shannon index of the soil bacteria in the T6 treatment was the highest and was 2.22% higher than that of the CK1 treatment (Figure 3D).
T6 and T7 had the highest soil fungal ACE indexes, which were 19.12% and 20.36% higher than those in the CK1 treatment, respectively (Figure 3E). The Chao 1 index of soil fungi under the T6 treatment was the highest and 13.18% higher than that of the CK1 treatment (Figure 3F). T6 had the lowest Simpson index, but there were no significant differences among the treatments (Figure 3G). Compared with that of the CK1 treatment, the bacterial Shannon indexes of the CK2, T6, T7, and T8 treatments were 3.75, 4.50, 1.25, and 1.00% higher, respectively. The Shannon index of the T6 treatment was the highest, although there were no significant differences among the treatments (Figure 3H).

3.4. Soil’s Microbial Community Structure under Different Fertilization Conditions

Different compost formulas altered the bacterial and fungal community’s structure significantly. PCoA divided the bacterial community into two groups, namely, CK2, T6, and T7 and CK1 and T8 (Figure 4A). The bacterial community structures under the CK2, T6, and T7 treatments were clustered far from those of the other treatments, indicating that the bacterial community structure was changed significantly by compost application (Figure 4A). PCoA divided the fungal community into three groups, namely, CK2, T6, and T7; CK1; and T8 (Figure 4B). The fungal community structures in the CK2, T6, and T7 treatments were clustered at a distance from those of the other treatments (Figure 4B), while CK1 existed in one quadrant alone, indicating that compost application changed the fungal community structure significantly.

3.5. Relative Abundance of Major Bacterial and Fungal Groups

As shown in Figure 5A,C, the main bacterial phyla under each treatment were Proteobacteria (25.22~29.49%), Actinobacteria (18.46~23.49%), Firmicutes (10.76~17.64%), and Acidobacteria (10.29~14.48%), and their relative abundances in the treatments were in the 64.73~85.10% range. The other bacterial phyla were Chloroflexi (7.51~11.22%), Bacteroidota (3.82~5.10%), Myxococcota (2.89~3.87%), Gemmatimonadetes (2.30~2.77%), and Nitrospirota (1.02–1.24%). The abundances of Proteobacteria, Actinomycetes, and Firmicutes in the T6, T7, and T8 treatments were higher than that in the CK1 treatment, and the Acidobacteria abundances were lower than that in the CK1 treatment. Proteobacteria abundance was the highest in the T7 treatment, accounting for 29.49% of the total bacteria (TB), and its abundance was 16.93% higher than that in the CK1 treatment. The Actinomyces abundance was the highest in the T8 treatment, accounting for 23.49% of the TB, which was 21.96% higher than that in the CK1 treatment. The relative abundance of Firmicutes was the highest in the CK2 treatment, accounting for 17.64% of the TB, and it was 21.96% higher than that in the CK1 treatment. CK1 had the highest Acidobacteria abundance, which accounted for 14.48% of the TB, followed by those in the T8 and T6 treatments, accounting for 12.13% and 12.03% of the abundance, respectively.
The major fungal groups under different treatments were Ascomycota (71.61–81.34%), Olpidiomycota (3.44–15.19%), Mortierellomycota (5.89–10.44%), and Basidiomycota (5.89–10.44%). Basidiomycota (3.35–5.02%) was with lower concentrations in other unclassified phyla (Figure 5B,D). The abundances of Ascomycetes and Basidiomycetes under fertilization treatments were lower than those under the CK1 treatment, whereas Chytromycetes and Sporoides abundances were higher than those under the CK1 treatment. Ascomycota abundances under the CK2, T6, T7, and T8 treatment were lower than those under the CK1 treatment and the lowest in the T6 treatment, with abundances that were 71.61% and 13.59% lower than that in the CK1 treatment. Basidiomycota abundances under the CK2, T6, and T8 treatments were lower than those of the CK1 treatment, and the lowest in the T6 treatment, which was 3.35% and 44.78% lower than that in the CK1 treatment. Ochrochytridia in the CK2, T6, and T7 treatments were higher than those in the CK1 treatment, and was the highest in the T6 treatment, being 15.19% and 238.31% higher than that in the CK1 treatment. Sporinae abundances in the CK2, T6, and T8 treatments were higher than those in the CK1 treatment and the highest in the CK2 treatment. The abundance in the CK2 treatment was 10.44% and 59.15% higher than that in the CK1 treatment.

3.6. Relationships among Soil Microbial Communities and Environmental Factors

The effects of environmental factors on the microbial community composition of the cabbage rhizosphere were evaluated using RDA (Figure 6). The soil properties under different agricultural waste compost applications influenced the microbial community structure. In Figure 6A, the first two axes of the RDA explain 47.37% and 20.81% of the total variation in bacterial community structure, respectively; for fungi, the first two axes explained 53.98% and 15.87% of the total variation, respectively (Figure 6C). Moreover, for bacteria, the first axis was positively correlated with TN, TP, TK, AN, AP, OM, and EC but negatively correlated with AK and pH; the second axis was positively correlated with AN, AK, and EC but negatively correlated with TN, TP, TK, AP, pH, and OM. In fungi, the first axis was positively correlated with TN, TP, TK, AN, AP, OM, and EC and negatively correlated with AK and pH. The second axis was positively correlated with TN, TP, TK, AN, AP, AK, pH, and OM and negatively correlated with EC. The environmental factors were significantly correlated to the bacterial and fungal community composition and were key factors influencing the bacterial community composition.
The relationships among physicochemical properties and bacterial communities were also assessed based on Spearman rank correlation (Figure 6B). We observed that Actinobacteriota was significantly positively correlated with EC, and Cyanobacteria was significantly positively correlated with AK (p ≤ 0.01), whereas Myxococcota, Desulfobacterota, and Cyanobacteria were negatively correlated with EC. In addition, the microbes were correlated with other soil factors. Desulfobacterota was significantly positively correlated with AK and OM; Dependentiae was significantly negatively correlated with AN; Bacteroidota had a negative correlation with AN; Armatimonadota, Abditibacteriota, and Entotheonellaeota were positively correlated with pH; Hydrogenedentes and Dependentiae were negatively correlated with OM and TK; Hydrogenedentes were negatively correlated with AP; Dependentiae was negatively correlated with TN; and Dependentiae and Bacteroidota were negatively correlated with TP.
The Spearman correlation between the fungal community and physicochemical properties showed that unclassified fungi and AK were significantly positively correlated (Figure 6D). Unclassified-k-Fungi and Ascomycota were significantly negatively correlated with EC; Kickxellomycota was positively correlated with EC. Blastocladiomycota and Rozellomycota were negatively correlated with EC. Glomeromycota was positively correlated with pH and negatively correlated with TN, TK, AN, AP, TP, and OM. AK were positively correlated with Blastocladiomycota, Rozellomycota, and Aphelidiomycota.

3.7. Bacterial Function in Agricultural Waste Compost Soil

Tax4Fun transforms the 16S taxonomic lineages based on the Silva database into taxonomic lineages of prokaryotes in the KEGG database and predicts the KEGG function of prokaryote microbial communities by combining the correspondence between the two databases. The first level of the metabolic pathway database classifies biological metabolic pathways into six broad types: cellular processes, environmental information processing, genetic information processing, human diseases, metabolism, and organismal systems. The second level divides the 6 categories into 40 subcategories, and the 21 subclasses in Figure 7 are derived from 4 major classes and selected according to the second-level biological metabolic pathways.
In the genetic information processing pathway, there were four subcategories: cell growth and death, cell motility, cell community—prokaryote, transport, and catabolism. Bacterial communities under the T7 and T8 treatments had the highest functional abundance in the four subcategories. Bacterial communities under the CK2 treatment had the highest functional abundance in cellular motility. There were 11 subclasses of metabolic pathways, and the bacterial community under the CK2 treatment had the highest functional abundance in carbohydrate metabolism, glycan biosynthesis metabolism, and nucleotide metabolism; the bacterial community under the T8 treatment had the highest functional abundance in biosynthesis of other secondary metabolites, energy metabolism, metabolism of other amino acids, and biodegradation and metabolism of heterotrophic organisms. The bacterial community under the T7 treatment had the highest functional predictive abundance in amino acid metabolism, energy metabolism, and cofactor and vitamin metabolism, while carbohydrate metabolism, terpenes, and polyketones metabolism were the highest under the T6 treatment. The results indicated that soil bacteria accounted for the highest proportion and functional abundance in biological metabolic pathways. Meanwhile, the application of agricultural waste compost improved the abundance of cellular processes, genetic information processing, and soil bacteria metabolism significantly.

3.8. Fungal Function in Soil Treated with Agricultural Waste Composts

FUNGuild (Fungi Functional Guild) can classify fungal community function based on the trophic level, and functional prediction was conducted in the present study by associating species classification and functional guild classification using bioinformatics methods. Fungal communities can be divided into seven classes (saprotrophs, pathotrophs, saprotroph-symbiotrophs, symbiotrophs, pathotroph-symbiotrophs, pathotroph-saprotrophs, and pathotroph-saprotroph-symbiotrophs) according to the nutrition mode. A total of 81 functional groups were detected through functional classification and identification. According to the abundance ratio > 0.01 principle, 14 functional groups were identified. FUNGuild was used to predict changes in the composition of functional groups of fungi under different treatments (Figure 8).
Compared with the no fertilization CK1 treatment, the abundances of a variety of microbes changed after fertilization. The undefined saprotroph abundance in the CK2, T7, and T8 treatments increased by 19.81%, 4.98%, and 7.92%, respectively. The plant pathogen abundance in the CK2, T6, and T8 treatments decreased by 22.47%, 16.51%, and 56.77%, respectively. The abundances of Animal Pathogen–Endophyte–Plant Pathogen–Wood in the CK2, T6, and T7 treatments decreased by 223.38%, 289.06%, and 362.83%, respectively. The Animal Parasite–Fungal Parasite abundances increased by 73.16%, 340.52%, and 82.34%, respectively. Moreover, the abundances of Animal Pathogen–Dung Saprotroph–Endophyte–Epiphyte–Plant Saprotroph–Wood Saprotroph under the CK2, T6, T7, and T8 treatments increased by 56.19%, 146.32%, 26.30%, and 85.12%, dung Saprotroph abundances increased by 97.19%, 405.62%, 444.28%, and 140.60%, while the Saprotroph–Wood Saprotroph abundances decreased by 580.44%, 491.26%, 523.23%, and 55.04%, respectively. In addition, Dung Saprotroph–Ectomycorrhizal–Soil Animal Pathogen–Endophyte–Lichen Parasite–Plant Pathogen–Soil Saprotroph–Wood Saprotroph abundances in the T6 and T7 treatments increased by 0.43% and 114.69%, respectively. According to the results, the agricultural waste compost application increased the number of saprophytic bacteria and saprophytic–symbiotic fungi significantly and reduced the number of pathogenic bacteria and pathogenic fungi to some extent.

4. Discussion

4.1. Effects of Agricultural Waste Composts on Cabbage Yield and Soil’s Physicochemical Properties

Fertilization is critical for sustainable agricultural development. The rational application of crop waste compost improves soil’s OM, which influences plant growth and yield by directly providing nutrients or indirectly by altering soil’s physicochemical properties [31,32]. In the present study, the OM content was the highest under the T6 treatment and the lowest under the CK1 (no fertilizer) treatment, which was significantly lower than those in the other treatments, indicating that compost can increase the OM content significantly. Compared with the status in the CK1 treatment, the OM, total available nutrients, and available nutrient contents in the soil were significantly increased following the application of crop waste compost. Organic fertilizer can stimulate the production of root exudates and promote the activation of insoluble nutrients. In addition, organic fertilizers contain high amounts of nutrients, and the constituent carbon (C) and N can be decomposed and released relatively easily [33]. Moreover, biological organic fertilizers have high OM contents, which enhance microbial activities and can promote nutrient transformation and cycling [34]. Compared with those in the CK2 treatment, the OM, total available nutrient, and available nutrient contents in the T6, T7, and T8 treatments were increased significantly. The compost formulas in the T6, T7, and T8 treatments contained corn straw, and the OM, TN, and TP contents of corn straw are high, which could increase soil’s N and P contents, indicating that the addition of appropriate amounts of corn straw can improve soil’s N content and soil’s N fixation ability [35]. After organic materials (including crop residues and organic fertilizers) are applied to the soil, mineral N is fixed by microbes due to the addition of energy materials, and soil N will increase correspondingly [36,37].
pH is one of the key indexes influencing soil’s microbial community diversity; the closer the soil value is to a neutral pH (pH = 7), the greater the bacterial diversity is [38]. In the present study, the soil pH was the lowest under the T6 treatment and the highest under the CK1 treatment. The pH in the CK1 treatment was higher than those in other waste compost fertilization treatments, indicating that compost fertilizer application could reduce soil’s pH, prevent soil salinization, and improve bacterial diversity. In addition to pH, soil’s EC in the present study was the lowest under the T6 treatment and the highest under the CK1 treatment, which was higher than those in other fertilization treatments, indicating that compost application could effectively decrease soil’s EC value, reduce salt ions, decrease reverse osmotic pressure in soil, and minimize the toxic effect of soluble minerals on cabbage yield. Among all treatments, the T6 treatment had the most significant effect.

4.2. Effects of Agricultural Waste Compost on Soil’s Microbial Richness and Diversity

Soil’s microbial richness and diversity are two key indexes of soil quality, and they can be increased by inorganic or organic fertilizer application [39,40]. Organic fertilizer application enhances acid phosphatase and dehydrogenase enzyme activity in the maize rhizosphere, in addition to bacterial abundance and mycorrhizal infection [41]. In the present study, the ACE, Chao1, and Shannon indexes were the highest in the T6 treatment, while the Simpson index was the lowest in the T6 treatment. Opposite trends were observed in the CK1 treatment, indicating that T6 could improve the bacterial richness and diversity significantly. Similar to the T6 treatment, the T7 treatment increased the soil’s microbial richness significantly. In addition, similar to the findings of Ji et al. [42], we observed that organic fertilizer application improved the soil’s bacterial community diversity and evenness. The results could be attributed to the addition of corn straw to the T6 and T7 formulas, which decomposed and provided C and N sources for microorganisms, which facilitate microbial growth and reproduction [43,44]. The straw application can improve soil’s physicochemical properties and structure, which in turn promotes microbial metabolic activities in soil [45]. Some studies have shown that the application of biochar that is produced from straw to a field can reduce competition among microorganisms, protect beneficial soil microbes, and improve the soil’s microbial community structure [46].
Bacterial abundance, community structure, and diversity affect soil’s sustainability and productivity [47,48]. Soil bacteria facilitate nutrient absorption and soil-borne disease resistance, and they activate plant defense systems against pathogens [49,50]. In the present study, Proteobacteria, Actinomyces, Firmicutes, and Acidobacteria were the dominant bacterial populations. The Proteobacteria abundance was the highest under the T7 treatment, followed by the T8 treatment. This may be due to differences in the OM content between the T7 and T8 formulas. Under higher OM and nutrient conditions, Proteobacteria, which are eutrophic bacteria, can grow and reproduce rapidly [51].
The T8 treatment had the highest Actinomycetes richness, followed by T6. Actinomycetes can produce numerous substances that can promote plant and animal residue decomposition and influence soil’s microbial community structure and diversity [52]. In addition, some Actinomycetes can secrete hormone substances in soil and stimulate the release of more root secretions [53], providing rich energy resources for the growth and propagation of microbes and promoting the proliferation of beneficial bacteria. This is consistent with the results of related studies showing that organic fertilizer alters the bacterial and fungal community composition and increases the relative abundance of dominant bacterial phyla (e.g., Bacteroidetes, Blastomonas, and Myxococcus) and fungal phyla (e.g., Basidiomycetes and Mycetes) [54]. Acidobacteria represent a group of bacteria that grow slowly and accumulate in environments with low nutrient contents and that can degrade complex organic materials [55,56,57]. In the present study, the Acidobacteria abundance was the highest in the CK1 treatment, followed by the T6 and T8 treatments, indicating that T6 had a higher OM content and longer slow-release than the other treatments.
The Rhizosphere fungal community’s structure is affected by temperature, soil moisture, and soil’s microbial sources, with interactions among microbial sources and abiotic environments [44]. In the present study, the dominant fungal populations were Ascomycota, Echinomycota, Sporoidiae, and Basidiomycota, which is consistent with the conclusion that the long-term application of inorganic and organic fertilizers could promote the succession of fungal communities toward Ascomycota, Zygomycota, and Basidiomycota [58]. Basidiomycota and Olpidiomycota reportedly increased significantly in pig manure and sludge compost treatments [59]. The Ascomycota abundance in the CK2, T6, T7, and T8 treatments was lower than that in the CK1 treatment, with the lowest being identified for the T6 treatment. The highest relative abundance of Ascomycota being in the CK1 treatment may be due to its saprophytic fungi, which are affected by plant species and fertilizer.

4.3. Relationships between Microbial Communities and Soil Environmental Factors

Soil’s microbial diversity and composition are key indexes of soil’s physicochemical properties. Soil’s microbial diversity and composition are sensitive to shifts in soil’s environmental factors such as the pH and OM content [60]. Bacterial and fungal growth and activity varied with crop yield, soil’s physicochemical properties, and soil’s biological properties. pH is one of the key factors influencing the microbial community structure [61], which is consistent with our results. In the present study, the RDA analysis results showed that the major factors driving community differences among treatments were the pH, EC, AK, AN, and OM. Although the pH varied over a small range, it still affected the microbial community structure significantly. On the one hand, pH can affect the types of soil microbes directly, and environmental pressures can screen out and retain species that have adapted to a certain pH level [62]. On the other hand, pH affects physicochemical activities such as enzyme activity, enzyme formation, membrane permeability, or metabolic pathways. In addition, compared with fungi, bacteria are more responsive to pH [63,64]. Cyanobacteria is a type of active N-fixing phylum that is critical for soil productivity improvement and maintenance, and several studies have reported the biological N-fixing potential of cyanobacteria (e.g., P bacteria) in dryland wheat fields [65]. The application of inorganic fertilizer, especially phosphate fertilizer, can stimulate Cyanobacteria growth [66], which is consistent with the results of the present study showing a significant positive correlation between cyanobacteria and AK. Consistent with previous studies that have reported that organic fertilizer can increase the relative abundance of Proteobacteria, Betaproteobacteria, and Deltaproteobacteria significantly, high N environments are favorable for symbiotic bacteria, and unfavorable for oligotrophic bacteria, whose capacity for OM degradation is relatively low [51]. Proteobacteria, especially Betaproteobacteria, are favored by high OM and high C contents in eutrophic environments [67], which is similar to our results.
As far as the compost formula is concerned, the addition of corn straw affected the soil’s environmental factors significantly, and some studies have found that adding corn straw alters the fungal community structure [68,69]. In the present study, T6 had significant effects on the soil environment, which mainly changed the OM, AHN, and AP of the soil. Therefore, it is hypothesized that compost formulas containing corn straw alter soil’s physicochemical properties, thereby affecting soil’s microbial community structure and diversity.

4.4. Effects of Waste Compost on Soil Microbial Community’s Function

In the present study, 4 major and 21 minor soil microbial community functions were predicted using Tax4Fun. According to the results, the application of agricultural waste compost enhanced cellular processes, genetic information processing, and soil bacterial metabolism functions significantly. Previous studies have shown that the enhancement of metabolic function improves a microbial community’s decomposition activities and metabolic compound accumulation [70]. Amino acids and carbohydrates are the two main C sources that are utilized by microorganisms, and the increased metabolism of the two types of carbohydrates indicates that the microbial function diversity has increased [71]. It also indicates that OM decomposition and leaching have increased the substrate that is required for bacterial metabolic activities, providing a more suitable habitat for microbial metabolic activity [72,73]. An improvement in cellular processes and genetic information processing functions will enhance gene expression, and the expression of some functional genes in turn enhances metabolic processes. In the present study, the addition of corn straw in compost enhanced energy metabolism, amino acid metabolism, and carbohydrate metabolism, which is consistent with the findings of previous research [74]. Such changes in bacterial groups and functional genes may drive nutrient cycling in soil, in turn promoting plant growth.
FUNGuild predicted that 12 out of 14 functional groups contained saprophytic fungi with increased abundance. The application of agricultural waste composts increased the number of saprophytic fungi and decreased the number of pathological fungi, and the above results also indicated that compost enhanced the decomposition capacity of soil fungi.
The Animal Pathogen–Dung Saprotroph–Endophyte–Epiphyte–Plant Saprotroph–Wood Saprotroph and Animal Parasite–Fungal Parasite abundances in the T6 treatment were significantly higher than those in the other treatments, indicating that that the T6 treatment could also increase the abundance of symbiotic fungi significantly. Some studies have shown that the decrease in ectomycorrhizal fungi following N application is offset by an increase in saprophytic fungi, and the saprophytic fungi are positively affected by soil’s N content [75,76]. In the present study, the organic C input and change in soil aggregate size distribution played important roles in fungi development. In addition, increasing the OM content could increase the abundance of various fungi, improve the nutrient content and nutrient utilization, and reduce the spread of soil-borne diseases [77], which is consistent with the highest AN and OM contents being observed in soil that was treated with T6.

5. Conclusions

In conclusion, the use of agricultural waste composts can improve cabbage soil’s environmental conditions and soil fertility and facilitate agricultural waste recycling. In the eight experimental formulas, T6 (corn straw/cow manure/tail vegetable/sheep manure = 1:1:2:6) was the optimal formula, which increased the contents of AK, AHN, and soil OM, reduced the soil’s saline alkalinity, regulated the soil’s microbial community structure, and increased the cabbage yield. The results of the present study could facilitate sustainable vegetable production and exploitation of agricultural waste resources.

Author Contributions

Conceptualization, J.Y. and Z.L.; software, J.W. (Jue Wang); validation, B.Z.; formal analysis, J.W. (Jie Wang); investigation, B.Z. and J.W. (Jue Wang); data curation, J.W. (Jue Wang); writing—original draft preparation, J.W. (Jue Wang); writing—review and editing, B.Z., G.Z. and Z.Y.; supervision, L.H.; project administration, J.Y. and Z.L.; funding acquisition, J.Y. and Z.L. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the “Double First-class” Scientific Research Key Project of Gansu Province (GSSYLXM-02); the Gansu Top Leading Talent Plan [grant number GSBJLJ-2021-14]; the Modern Silk Road Cold and Drought Agricultural Science and Technology Support Project [grant number GSLK-2021-6]; the Special Fund for Modern Agricultural Industrial Technology System [grant number CARS-23-C-07]; the Central Government Guides Local Scientific and Technological Development Projects [grant number ZCYD-2021-6]; and the Gansu Agricultural University Doctoral Research Initiation Fund [grant number GAU-KYQD-2019-30].

Data Availability Statement

The datasets (SRP483858 and SRP484084) presented in this study can be found in the NCBI Sequence Read Archive “https://www.ncbi.nlm.nih.gov/sra/?term=SRP483858 and https://www.ncbi.nlm.nih.gov/sra/?term=SRP484084 (accessed on 17 January 2014)”. Data not included within the manuscript are available upon written request to the corresponding author.

Conflicts of Interest

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

References

  1. Obi, F.O.; Ugwuishiwu, B.O.; Nwakaire, J.N. Agricultural waste concept, generation, utilization and management. Niger. J. Technol. 2016, 35, 957–964. [Google Scholar] [CrossRef]
  2. Koul, B.; Yakoob, M.; Shah, M.P. Agricultural waste management strategies for environmental sustainability. Environ. Res. 2022, 206, 112285. [Google Scholar] [CrossRef]
  3. Maji, S.; Dwivedi, D.H.; Singh, N.; Kishor, S.; Gond, M. Agricultural waste: Its impact on environment and management approaches. Emerg. Eco-Friendly Green Technol. Wastewater Treat. 2020, 18, 329–351. [Google Scholar]
  4. Nagendran, R. Agricultural Waste and Pollution; Academic Press: Cambridge, MA, USA, 2011; pp. 341–355. [Google Scholar]
  5. Sabiiti, E.N. Utilising agricultural waste to enhance food security and conserve the environment. Afr. J. Food Agric. Nutr. Dev. 2011, 11, 1–9. [Google Scholar]
  6. Fidanza, M.A.; Sanford, D.L.; Beyer, D.M.; Aurentz, D.J. Analysis of fresh mushroom compost. HortTechnology 2010, 20, 449–453. [Google Scholar] [CrossRef]
  7. Harshwardhan, K.; Upadhyay, K. Effective utilization of agricultural waste: Review. Fundam. Renew. Energy Appl. 2017, 7, 237. [Google Scholar] [CrossRef]
  8. Duque-Acevedo, M.; Belmonte-Urena, L.; Cortés-García, F.J.; Camacho-Ferre, F. Agricultural waste: Review of the evolution, approaches and perspectives on alternative uses. Glob. Ecol. Conserv. 2020, 22, e00902. [Google Scholar] [CrossRef]
  9. Feeney, D.S.; Crawford, J.W.; Daniell, T.; Hallett, P.D.; Nunan, N.; Ritz, K.; Rivers, M.; Young, I.M. Three-dimensional microorganization of the soil–root–microbe system. Microb. Ecol. 2006, 52, 151–158. [Google Scholar] [CrossRef] [PubMed]
  10. Aislabie, J.; Deslippe, J.R.; Dymond, J. Soil microbes and their contribution to soil services. Ecosyst. Serv. N. Z.–Cond. Trends. 2013, 1, 143–161. [Google Scholar]
  11. Berg, G.; Smalla, K. Plant species and soil type cooperatively shape the structure and function of microbial communities in the rhizosphere. FEMS Microbiol. Ecol. 2009, 68, 1–13. [Google Scholar] [CrossRef]
  12. Siebielec, G.; Siebielec, S.; Lipski, D. Long-term impact of sewage sludge, digestate and mineral fertilizers on plant yield and soil biological activity. J. Clean. Prod. 2018, 187, 372–379. [Google Scholar] [CrossRef]
  13. Nie, S.A.; Li, H.; Yang, X.R.; Zhang, Z.J.; Weng, B.; Huang, F.Y.; Zhu, G.G.; Zhu, Y.G. Nitrogen loss by anaerobic oxidation of ammonium in rice rhizosphere. ISME J. 2015, 9, 2059–2067. [Google Scholar] [CrossRef]
  14. Lang, J.J.; Hu, J.; Ran, W.; Xu, Y.C.; Shen, Q.R. Control of cotton Verticillium wilt and fungal diversity of rhizosphere soils by bio-organic fertilizer. Biol. Fertil. Soils 2012, 48, 191–203. [Google Scholar] [CrossRef]
  15. Zulueta-Rodriguez, R.; Cordoba-Matson, M.V.; Hernandez-Montiel, L.G.; Murillo-Amador, B.; Rueda-Puente, E.; Lara, L.L. Effect of Pseudomonas putida on growth and anthocyanin pigment in two poinsettia (Euphorbia pulcherrima) cultivars. Sci. World J. 2014, 2014, 810192. [Google Scholar] [CrossRef]
  16. Akköprü, A.; Demir, S. Biological control of Fusarium wilt in tomato caused by Fusarium oxysporum f. sp. lycopersici by AMF Glomus intraradices and some rhizobacteria. J. Phytopathol. 2005, 153, 544–550. [Google Scholar] [CrossRef]
  17. Manikandan, R.; Saravanakumar, D.; Rajendran, L.; Raguchander, T.; Samiyappan, R. Standardization of liquid formulation of Pseudomonas fluorescens Pf1 for its efficacy against Fusarium wilt of tomato. Biol. Control 2010, 54, 83–89. [Google Scholar] [CrossRef]
  18. Maung, C.E.; Gyu, C.T.; Hae, N.H.; Yong, K.K. Role of Bacillus amyloliquefaciens Y1 in the control of Fusarium wilt disease and growth promotion of tomato. Biocontrol Sci. Technol. 2017, 27, 1400–1415. [Google Scholar] [CrossRef]
  19. Shi, L.; Du, N.S.; Shu, S.; Sun, J.; Li, S.Z.; Guo, S.R. Paenibacillus polymyxa NSY50 suppresses Fusarium wilt in cucumbers by regulating the rhizospheric microbial community. Sci. Rep. 2017, 7, 46263. [Google Scholar] [CrossRef] [PubMed]
  20. Zhang, F.G.; Huo, Y.Q.; Xu, X.S.; Hu, J.; Sun, X.; Xiao, Y.; Zhang, Y.J. Trichoderma improves the growth of Leymus chinensis. Biol. Fertil. Soils 2018, 54, 685–696. [Google Scholar] [CrossRef]
  21. Muktamar, Z.; Putri, D.; Setyowati, N. Reduction of synthetic fertilizer for sustainable agriculture: Influence of organic and nitrogen fertilizer combination on growth and yield of green mustard. Int. J. Adv. Sci. Eng. Inf. Technol. 2016, 6, 361–364. [Google Scholar] [CrossRef]
  22. Ochoa-Velasco, C.E.; Valadez-Blanco, R.; Salas-Coronado, R.; Sustaita-Rivera, F.; Hernández-Carlos, B.; García-Ortega, S.; Santos-Sánchez, N.F. Effect of nitrogen fertilization and Bacillus licheniformis biofertilizer addition on the antioxidants compounds and antioxidant activity of greenhouse cultivated tomato fruits (Solanum lycopersicum L. var. Sheva). Sci. Hortic. 2016, 201, 338–345. [Google Scholar] [CrossRef]
  23. Huang, N.; Wang, W.W.; Yao, Y.L.; Zhu, F.X.; Wang, W.P.; Chang, X.J. The influence of different concentrations of bio-organic fertilizer on cucumber Fusarium wilt and soil microflora alterations. PLoS ONE 2017, 12, e0171490. [Google Scholar] [CrossRef] [PubMed]
  24. Bhowmik, A.; Fortuna, A.M.; Cihacek, L.J.; Bary, A.I.; Carr, P.M.; Cogger, C.G. Potential carbon sequestration and nitrogen cycling in long-term organic management systems. Renew. Agric. Food Syst. 2017, 32, 498–510. [Google Scholar] [CrossRef]
  25. Lee, J.J.; Park, R.D.; Kim, Y.W.; Shim, J.H.; Chae, D.H.; Rim, Y.S.; Sohn, B.K.; Kim, T.H.; Kim, K.Y. Effect of food waste compost on microbial population, soil enzyme activity and lettuce growth. Bioresour. Technol. 2004, 93, 21–28. [Google Scholar] [CrossRef] [PubMed]
  26. Jin, N.; Jin, L.; Wang, S.Y.; Li, J.W.; Liu, F.H.; Liu, Z.C.; Luo, S.L.; Wu, Y.; Lyu, J.; Yu, J.H. Reduced chemical fertilizer combined with bio-organic fertilizer affects the soil microbial community and yield and quality of lettuce. Front. Microbiol. 2022, 13, 863325. [Google Scholar] [CrossRef] [PubMed]
  27. Abah, R.C.; Petja, B.M. Evaluation of organic carbon, available phosphorus, and available potassium as a measure of soil fertility. Merit Res. J. Agric. Sci. Soil Sci. 2015, 3, 159–167. [Google Scholar]
  28. R Core Team. R: A language and environment for statistical computing. R foundation for statistical computing, Vienna, Austria. Computing 2010, 14, 12–21. [Google Scholar]
  29. Wagner, H. Vegan: Community Ecology Package, R package version 2.0-5; CiNii: Tokyo, Japan, 2012. [Google Scholar]
  30. Yan, C.; Huang, L.M. Community compositions of phytoplankton and eukaryotes during the mixing periods of a drinking water reservoir: Dynamics and interactions. Int. J. Environ. Res. Public Health 2020, 17, 1128. [Google Scholar] [CrossRef]
  31. Chen, Y.; Avnimelech, Y. The Role of Organic Matter in Modern Agriculture; Springer Science & Business Media: Berlin/Heidelberg, Germany, 2012; Volume 25. [Google Scholar]
  32. Liebig, M.A.; Doran, J.W. Impact of Organic Production Practices on Soil Quality Indicators. J. Environ. Qual. 1999, 28, 1601–1609. [Google Scholar] [CrossRef]
  33. Chang, E.H.; Chung, R.S.; Tsai, Y.H. Effect of different application rates of organic fertilizer on soil enzyme activity and microbial population. Soil Sci. Plant Nutr. 2007, 53, 132–140. [Google Scholar] [CrossRef]
  34. Weon, H.Y.; Kwon, J.S.; Suh, J.S.; Choi, W.Y. Soil microbial flora and chemical properties as influenced by the application of pig manure compost. Korean J. Soil Sci. Fertil. 1999, 32, 76–83. [Google Scholar]
  35. Sun, D.Q.; Meng, J.; Liang, H.; Yang, E.; Huang, Y.W.; Chen, W.F.; Jiang, L.L.; Lan, Y.; Zhang, W.M.; Gao, J.P. Effect of volatile organic compounds absorbed to fresh biochar on survival of Bacillus mucilaginosus and structure of soil microbial communities. J. Soils Sediments 2015, 15, 271–281. [Google Scholar] [CrossRef]
  36. Hirose, S. Mineralization of organic nitrogen of various plant residues in the soil under upland condition. Sci. Soil Manure 1973, 44, 157–163. [Google Scholar]
  37. Aoyama, M.; Nozawa, T. Microbial biomass nitrogen and mineralization-immobilization processes of nitrogen in soils incubated with various organic materials. Soil Sci. Plant Nutr. 1993, 39, 23–32. [Google Scholar] [CrossRef]
  38. Kaiser, K.; Wemheuer, B.; Korolkow, V.; Wemheuer, F.; Nacke, H.; Schöning, I.; Schrumpf, M.; Daniel, R. Driving forces of soil bacterial community structure, diversity, and function in temperate grasslands and forests. Sci. Rep. 2016, 6, 33696. [Google Scholar] [CrossRef]
  39. Wu, J.Q.; Sha, C.Y.; Wang, M.; Ye, C.M.; Li, P.; Huang, S.F. Effect of organic fertilizer on soil bacteria in maize fields. Land 2021, 10, 328. [Google Scholar] [CrossRef]
  40. Geisseler, D.; Scow, K.M. Long-term effects of mineral fertilizers on soil microorganisms—A review. Soil Biol. Biochem. 2014, 75, 54–63. [Google Scholar] [CrossRef]
  41. Gao, C.H.; El-Sawah, A.M.; Ali, D.F.; Alhaj, H.; Shaghaleh, H.; Sheteiwy, M.S. The integration of bio and organic fertilizers improve plant growth, grain yield, quality and metabolism of hybrid maize (Zea mays L.). Agronomy 2020, 10, 319. [Google Scholar] [CrossRef]
  42. Ji, R.T.; Dong, G.Q.; Shi, W.M.; Min, J. Effects of liquid organic fertilizers on plant growth and rhizosphere soil characteristics of chrysanthemum. Sustainability 2017, 9, 841. [Google Scholar] [CrossRef]
  43. Hua, K.K.; Wang, D.Z.; Guo, Z.B. Effects of long-term application of various organic amendments on soil particulate organic matter storage and chemical stabilisation of vertisol soil. Acta Agric. Scand. Sect. B-Soil Plant Sci. 2018, 68, 505–514. [Google Scholar] [CrossRef]
  44. Sauze, J.; Ogée, J.; Maron, P.A.; Crouzet, O.; Nowak, V.; Wohl, S.; Kaisermann, A.; Jones, S.P.; Wingate, L. The interaction of soil phototrophs and fungi with pH and their impact on soil CO2, CO18O and OCS exchange. Soil Biol. Biochem. 2017, 115, 371–382. [Google Scholar] [CrossRef]
  45. Ning, Y.C.; Wang, X.; Yang, Y.N.; Cao, X.; Wu, Y.L.; Zou, D.T.; Zhou, D.X. Studying the effect of straw returning on the interspecific symbiosis of soil microbes based on carbon source utilization. Agriculture 2022, 12, 1053. [Google Scholar] [CrossRef]
  46. Celik, I.; Ortas, I.; Kilic, S. Effects of compost, mycorrhiza, manure and fertilizer on some physical properties of a Chromoxerert soil. Soil Tillage Res. 2004, 78, 59–67. [Google Scholar] [CrossRef]
  47. 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]
  48. Bhat, A.K. Preserving microbial diversity of soil ecosystem: A key to sustainable productivity. Int. J. Curr. Microbiol. Appl. Sci. 2013, 2, 85–101. [Google Scholar]
  49. Delgado-Baquerizo, M.; Maestre, F.T.; Reich, P.B.; Jeffries, T.C.; Gaitan, J.J.; Encinar, D.; Berdugo, M.; Campbell, C.D.; Singh, B.K. Microbial diversity drives multifunctionality in terrestrial ecosystems. Nat. Commun. 2016, 7, 10541. [Google Scholar] [CrossRef]
  50. Bever, J.D.; Mangan, S.A.; Alexander, H.M. Maintenance of plant species diversity by pathogens. Annu. Rev. Ecol. Evol. Syst. 2015, 46, 305–325. [Google Scholar] [CrossRef]
  51. Fierer, N.; Lauber, C.L.; Ramirez, K.S.; Zaneveld, J.; Bradford, M.A.; Knight, R.J. Comparative metagenomic, phylogenetic and physiological analyses of soil microbial communities across nitrogen gradients. ISME J. 2012, 6, 1007–1017. [Google Scholar] [CrossRef]
  52. Zeng, X.B.; Wang, Y.N.; Wang, Y.Z.; Bai, L.Y.; Li, L.F.; Duan, R.; Su, S.M.; Wu, C.X. Effects of different fertilization regimes on abundance and community structure of the nirK-type denitrifying bacteria in greenhouse vegetable soils. J. Appl. Ecol. 2014, 25, 505–514. [Google Scholar]
  53. Song, Y.L.; Yu, J.; Chen, S.G.; Xiao, C.Z.; Li, Y.H.; Su, X.R.; Ding, F.J. Effects of complex microbial agent on cotton physiological characteristics, microorganism and physicochemical properties in rhizosphere soil. Soils 2019, 51, 477–487. [Google Scholar]
  54. Tang, Q.; Xia, Y.; Ti, C.; Shan, J.; Zhou, W.; Li, C.; Yan, X.; Yan, X. Partial organic fertilizer substitution promotes soil multifunctionality by increasing microbial community diversity and complexity. Pedosphere 2023, 33, 407–420. [Google Scholar] [CrossRef]
  55. Liu, J.J.; Sui, Y.Y.; Yu, Z.H.; Shi, Y.U.; Chu, H.Y.; Jin, J.; Liu, X.B.; Wang, G.H. High throughput sequencing analysis of biogeographical distribution of bacterial communities in the black soils of northeast China. Soil Biol. Biochem. 2014, 70, 113–122. [Google Scholar] [CrossRef]
  56. Davis, K.R.; Sangwan, P.; Janssen, P.H. Acidobacteria, rubrobacteridae and chloroflexi are abundant among very slow-growing and mini-colony-forming soil bacteria. Environ. Microbiol. 2011, 13, 798–805. [Google Scholar] [CrossRef] [PubMed]
  57. Eo, J.N.; Park, K.C. Long-term effects of imbalanced fertilization on the composition and diversity of soil bacterial community. Agric. Ecosyst. Environ. Exp. Bot. 2016, 231, 176–182. [Google Scholar] [CrossRef]
  58. Ma, A.Z.; Zhuang, X.L.; Wu, J.M.; Cui, M.M.; Lv, D.; Liu, C.Z.; Zhuang, G.Q. Ascomycota members dominate fungal communities during straw residue decomposition in arable soil. PLoS ONE 2013, 8, e66146. [Google Scholar] [CrossRef] [PubMed]
  59. Kjøller, R.; Rosendahl, S. Cultivated and fallow fields harbor distinct communities of Basidiomycota. Fungal Ecol. 2014, 9, 43–51. [Google Scholar] [CrossRef]
  60. Larkin, R.P. Characterization of soil microbial communities under different potato cropping systems by microbial population dynamics, substrate utilization, and fatty acid profiles. Soil Biol. Biochem. 2003, 35, 1451–1466. [Google Scholar] [CrossRef]
  61. Zhao, J.; Zhang, R.F.; Xue, C.; Xun, W.B.; Sun, L.; Xu, Y.C.; Shen, Q.R. Pyrosequencing reveals contrasting soil bacterial diversity and community structure of two main winter wheat cropping systems in China. Microb. Ecol. 2014, 67, 443–453. [Google Scholar] [CrossRef]
  62. Feng, M.M.; Adams, J.M.; Fan, K.K.; Shi, Y.; Sun, R.B.; Wang, D.Z.; Guo, X.S.; Chu, H.Y. Long-term fertilization influences community assembly processes of soil diazotrophs. Soil Biol. Biochem. 2018, 126, 151–158. [Google Scholar] [CrossRef]
  63. Rousk, J.; Brookes, P.C.; Bååth, E. Contrasting soil pH effects on fungal and bacterial growth suggest functional redundancy in carbon mineralization. Appl. Environ. Microbiol. 2009, 75, 1589–1596. [Google Scholar] [CrossRef]
  64. Strickland, M.S.; Rousk, J. Considering fungal: Bacterial dominance in soils–methods, controls, and ecosystem implications. Soil Biol. Biochem. 2010, 42, 1385–1395. [Google Scholar] [CrossRef]
  65. Prasanna, R.; Jaiswal, P.; Singh, Y.; Singh, P. Influence of biofertilizers and organic amendments on nitrogenase activity and phototrophic biomass of soil under wheat. Acta Agron. Hung. 2008, 56, 149–159. [Google Scholar] [CrossRef]
  66. Correll, D.L. The role of phosphorus in the eutrophication of receiving waters: A review. J. Environ. Qual. 1998, 27, 261–266. [Google Scholar] [CrossRef]
  67. Fierer, N.; Bradford, M.A.; Jackson, R.B. Toward an ecological classification of soil bacteria. Ecology 2007, 88, 1354–1364. [Google Scholar] [CrossRef] [PubMed]
  68. Guo, J.J.; Liu, W.B.; Zhu, C.; Luo, G.W.; Kong, Y.L.; Ling, N.; Wang, M.; Dai, J.Y.; Shen, Q.R.; Guo, S.W. Bacterial rather than fungal community composition is associated with microbial activities and nutrient-use efficiencies in a paddy soil with short-term organic amendments. Plant Soil 2018, 424, 335–349. [Google Scholar] [CrossRef]
  69. Zhao, S.C.; Qiu, S.J.; Xu, X.P.; Ciampitti, I.A.; Zhang, S.Q.; He, P. Change in straw decomposition rate and soil microbial community composition after straw addition in different long-term fertilization soils. Appl. Soil Ecol. 2019, 138, 123–133. [Google Scholar] [CrossRef]
  70. Li, H.Y.; Hu, T.; Amombo, E.; Fu, J.M. Transcriptome profilings of two tall fescue (Festuca arundinacea) cultivars in response to lead (Pb) stress. BMC Genom. 2017, 18, 145. [Google Scholar] [CrossRef]
  71. Wang, X.; Gao, S.H.; Chen, J.Q.; Yao, Z.W.; Zhang, L.; Wu, H.L.; Shu, Q.; Zhang, X.D. Response of functional diversity of soil microbial community to forest cutting and regeneration methodology in a Chinese fir plantation. Forests 2022, 13, 360. [Google Scholar] [CrossRef]
  72. Chantigny, M.H. Dissolved and water-extractable organic matter in soils: A review on the influence of land use and management practices. Geoderma 2003, 113, 357–380. [Google Scholar] [CrossRef]
  73. Sønsteby, A.; Nes, A.; Mage, F. Effects of bark mulch and npk fertilizer on yield, leaf nutrient status and soil mineral nitrogen during three years of strawberry production. Acta Agric. Scand. 2004, 54, 128–134. [Google Scholar] [CrossRef]
  74. Cai, D.; Yang, X.H.; Wang, S.Z.; Chao, Y.Q.; Morel, J.L.; Qiu, R.L. Effects of dissolved organic matter derived from forest leaf litter on biodegradation of phenanthrene in aqueous phase. J. Hazard. Mater. 2017, 324, 516–525. [Google Scholar] [CrossRef] [PubMed]
  75. Durling, M.; Brandstroem, S.; Erica, B.; Adam, L.; Bjoern, D. Changes in fungal communities along a boreal forest soil fertility gradient. New Phytol. 2015, 207, 1145–1158. [Google Scholar]
  76. Sadowsky, J.J.; Diepen, V.; Linda, T.A.; Thomas, K.W.; Frey, S.D.; Morrison, E.W. Chronic nitrogen additions fundamentally restructure the soil fungal community in a temperate forest. Fungal Ecol. 2016, 23, 48–57. [Google Scholar]
  77. Zhang, C.; Xin, S.Q.; Guo, D.D.; Zhou, W.; Huang, S.M. Distinct responses of soil bacterial and fungal communities to changes in fertilization regime and crop rotation. Geoderma Int. J. Soil Sci. 2018, 319, 156–166. [Google Scholar]
Agronomy 14 00413 g001
Figure 1. Physicochemical properties of cabbage rhizosphere soil under different compost treatments. (A): Total N; (B): total P; (C): total K; (D): available N; (E): available N; (F): available K; (G): pH value; (H): EC value; (I): organic matter. Note: T1~T8 represent the eight fermentation treatments. Different letters on the figure indicate significant differences (p ≤ 0.05).
Figure 1. Physicochemical properties of cabbage rhizosphere soil under different compost treatments. (A): Total N; (B): total P; (C): total K; (D): available N; (E): available N; (F): available K; (G): pH value; (H): EC value; (I): organic matter. Note: T1~T8 represent the eight fermentation treatments. Different letters on the figure indicate significant differences (p ≤ 0.05).
Agronomy 14 00413 g001
Agronomy 14 00413 g002
Figure 2. Average cabbage yield under different compost treatments. Different letters on the figure indicate significant differences (p ≤ 0.05).
Figure 2. Average cabbage yield under different compost treatments. Different letters on the figure indicate significant differences (p ≤ 0.05).
Agronomy 14 00413 g002
Agronomy 14 00413 g003
Figure 3. Bacterial (A–D) and fungal (E–H) diversity in rhizosphere soil of cabbage under different compost treatments. Different letters on the figure indicate significant differences (p ≤ 0.05).
Figure 3. Bacterial (A–D) and fungal (E–H) diversity in rhizosphere soil of cabbage under different compost treatments. Different letters on the figure indicate significant differences (p ≤ 0.05).
Agronomy 14 00413 g003
Agronomy 14 00413 g004
Figure 4. Principal Coordinate Analysis (PCoA) of bacterial (A) and fungal (B) communities.
Figure 4. Principal Coordinate Analysis (PCoA) of bacterial (A) and fungal (B) communities.
Agronomy 14 00413 g004
Agronomy 14 00413 g005
Figure 5. Relative abundances of major taxonomic groups of bacteria (A,C) and fungi (B,D) at the phylum level (others accounted for <0.01). The data are visualized using Circos, and the widths of bars indicate the relative abundance of the phyla.
Figure 5. Relative abundances of major taxonomic groups of bacteria (A,C) and fungi (B,D) at the phylum level (others accounted for <0.01). The data are visualized using Circos, and the widths of bars indicate the relative abundance of the phyla.
Agronomy 14 00413 g005
Agronomy 14 00413 g006
Figure 6. Redundancy analysis (RDA) of correlations between bacterial (A,B) and fungal (C,D) microbial communities and physicochemical properties and Spearman rank correlation heatmap (p ≤ 0.05 *, p ≤ 0.01 **).
Figure 6. Redundancy analysis (RDA) of correlations between bacterial (A,B) and fungal (C,D) microbial communities and physicochemical properties and Spearman rank correlation heatmap (p ≤ 0.05 *, p ≤ 0.01 **).
Agronomy 14 00413 g006
Agronomy 14 00413 g007
Figure 7. Heatmaps predicting bacterial function under different treatments. The colors from blue to red represent the relative abundances of the functions from low to high.
Figure 7. Heatmaps predicting bacterial function under different treatments. The colors from blue to red represent the relative abundances of the functions from low to high.
Agronomy 14 00413 g007
Agronomy 14 00413 g008
Figure 8. FUNGuild prediction of functional groups of fungi under different treatments.
Figure 8. FUNGuild prediction of functional groups of fungi under different treatments.
Agronomy 14 00413 g008
Table 1. Basic chemical properties of different agricultural waste raw materials. Note: AN (available nitrogen) = AHN (alkali-hydrolyzable nitrogen)
Table 1. Basic chemical properties of different agricultural waste raw materials. Note: AN (available nitrogen) = AHN (alkali-hydrolyzable nitrogen)
Raw MaterialTN
(g/kg)		TP
(g/kg)		TK
(g/kg)		AN
(mg/kg)		AP
(mg/kg)		AK
(mg/kg)		OM (g/kg)
Sheep Manure8.996.6111.8658.58304.879320945.67
Cow Manure95.7113.3686.58383.296786.67510.76
Tail Vegetable11.095.41139.6971.25391.2513,886.67614.43
Mushroom Residue9.16.6511.4630.58284.339253.33470.31
Corn Straw10.047.7915.09546.58385.618246.67998.77
Table 2. Basic physicochemical properties of compost with different agricultural waste formulations. Note: AN (available nitrogen) = AHN (alkali-hydrolyzable nitrogen)
Table 2. Basic physicochemical properties of compost with different agricultural waste formulations. Note: AN (available nitrogen) = AHN (alkali-hydrolyzable nitrogen)
TreatmentsTN
(g/kg)		TP
(g/kg)		TK
(g/kg)		AN
(mg/kg)		AP
(mg/kg)		AK
(mg/kg)		OM
(g/kg)		pH
Value		EC
Value
(ms/cm)
CK29.194.2714.60434.58122.117596.67227.578.144.81
T18.093.4015.35429.92143.298123.33195.337.954.92
T29.374.1213.76415.92100.877013.33132.758.075.33
T39.194.3811.68476.5893.336856.67176.378.035.63
T410.134.5414.99504.58165.408860.00394.458.256.35
T510.174.2710.11411.2596.547453.33195.338.085.32
T610.494.5014.28415.92166.018240.00244.648.135.91
T711.344.8613.77555.92161.939746.67384.978.186.19
T810.904.6414.00448.58173.558530.00252.228.246.65
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

Wang, J.; Zhang, B.; Wang, J.; Zhang, G.; Yue, Z.; Hu, L.; Yu, J.; Liu, Z. Effects of Different Agricultural Waste Composts on Cabbage Yield and Rhizosphere Environment. Agronomy 2024, 14, 413. https://doi.org/10.3390/agronomy14030413

AMA Style

Wang J, Zhang B, Wang J, Zhang G, Yue Z, Hu L, Yu J, Liu Z. Effects of Different Agricultural Waste Composts on Cabbage Yield and Rhizosphere Environment. Agronomy. 2024; 14(3):413. https://doi.org/10.3390/agronomy14030413

Chicago/Turabian Style

Wang, Jue, Bo Zhang, Jie Wang, Guobin Zhang, Zhibin Yue, Linli Hu, Jihua Yu, and Zeci Liu. 2024. "Effects of Different Agricultural Waste Composts on Cabbage Yield and Rhizosphere Environment" Agronomy 14, no. 3: 413. https://doi.org/10.3390/agronomy14030413

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