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Article

Metagenomics Reveals the Effects of Organic Material Co-Application on Phosphorus Cycling Functional Genes and Bioavailable Phosphorus

by
Wei Wang
1,
Yue Jiang
2,
Shanshan Cai
1,
Yumei Li
1,
Lei Sun
1,* and
Juanjuan Qu
2,*
1
Heilongjiang Academy of Black Soil Conservation & Utilization, Harbin 150030, China
2
College of Resources and Environmental Science, Northeast Agricultural University, Harbin 150030, China
*
Authors to whom correspondence should be addressed.
Agronomy 2025, 15(5), 1187; https://doi.org/10.3390/agronomy15051187
Submission received: 13 April 2025 / Revised: 3 May 2025 / Accepted: 12 May 2025 / Published: 14 May 2025
(This article belongs to the Special Issue Effects of Arable Farming Measures on Soil Quality—2nd Edition)
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Abstract

:
Phosphorus is essential for crop growth, but excessive use of chemical fertilizers can lead to environmental issues. The incorporation of organic materials has the potential to enhance phosphorus availability and promote soil phosphorus cycling. This study investigated the effects of chemical fertilizer co-application with two organic materials on soil properties and functions. Four treatments were established: (1) chemical fertilizer alone (SC, consisting of urea, ammonium phosphate, and potassium sulfate), (2) chemical fertilizer with corn-straw-derived biochar (SCB), (3) chemical fertilizer with composted manure-based organic fertilizer (SCF), and (4) chemical fertilizer with both biochar and organic fertilizer (SCBF). This study focused on changes in soil properties, bioavailable phosphorus, phosphorus cycling functional genes, and related microbial communities. Compared to SC, the combined application of organic materials significantly increased available phosphorus (AP), alkaline hydrolysis nitrogen (AN), and available potassium (AK), with the SCBF exhibiting the highest increases of 78.76%, 47.47%, and 336.61%, respectively. However, applying organic materials reduced alkaline phosphatase (ALP) and acid phosphatase (ACP) activities, except for the increase in ACP in SCBF. Additionally, bioavailable phosphorus increased by up to 157.00% in SCBF. Adding organic materials significantly decreased organic phosphorus mineralization genes (phoA, phoD, phnP) and phosphate degradation genes (ppk2), while increasing inorganic phosphorus solubilization genes (pqqC, gcd), which subsequently increased CaCl2-P and Citrate-P contents in SCB and in SCBF. In summary, organic material application significantly enhances phosphorus bioavailability by improving soil physicochemical properties and phosphorus-related gene abundance. These findings provide new insights into sustainable soil fertility management and highlight the potential of integrating organic materials with chemical fertilizers to improve soil nutrient availability, thereby contributing to increased soybean yield. Moreover, this study advances our understanding of the underlying mechanisms driving phosphorus cycling under combined fertilization strategies, offering a scientific basis for optimizing fertilization practices in agroecosystems.

1. Introduction

Annually, substantial quantities of phosphate fertilizers are applied to agricultural systems to sustain crop productivity [1]. Despite this widespread practice, many studies indicate that merely 20% of phosphorus is effectively utilized by plants [2]. This inefficiency arises primarily from the rapid adsorption of phosphates by soil colloids and their chemical immobilization through reactions with calcium, iron, and aluminum ions, leading to the formation of insoluble precipitates [3]. Phosphorus is the primary nutrient factor limiting crop yield and plays an irreplaceable role in crop growth. In agricultural production, the application of large amounts of phosphorus fertilizers increases total phosphorus content. A high total phosphorus content in soil does not necessarily indicate sufficient plant-available phosphorus [4]. Phosphorus readily reacts with iron and aluminum (in acidic soils) or calcium (in alkaline soils) to form insoluble phosphates (chemical fixation), or it becomes fixed onto clay mineral surfaces through adsorption (physical fixation). These processes result in available phosphorus (H2PO4−/HPO42−) typically accounting for less than 1% of the total soil phosphorus [5]. Only the bioavailable phosphorus can be directly absorbed and utilized by plants, thereby promoting plant growth. The combined application of organic and inorganic fertilizers (e.g., humic acid and organic fertilizers) is regarded as an effective approach to synergistically enhance phosphorus availability and achieve high yields [6]. Consequently, enhancing soil phosphorus bioavailability has emerged as a critical challenge in sustainable agriculture, particularly through strategies such as co-application of organic materials to modify soil biogeochemical processes. Organic materials, such as compost, enhance soil phosphorus bioavailability through dual mechanisms: mobilizing the fixed phosphorus by chelation and competitively occupying the adsorption sites on soil colloids [7]. Biochar is a carbon-rich, porous material produced by pyrolyzing biomass under limited oxygen conditions. In recent years, it has attracted considerable attention as a sustainable soil amendment due to its capacity to improve soil physicochemical properties, enhance nutrient retention, and support microbial activity [8]. The combined application of chemical fertilizers and organic materials is an effective strategy to address phosphorus deficiency in soil [9,10]. Studies have shown that the application of earthworm dung in saline alkali soil increased wheat yield by 52.0% [11]. And the combined application of organic fertilizers significantly increased soil phosphorus availability by 2.7 times [12]. Diacono et al. [13] found that the addition of organic materials significantly increased soil pH, nitrate nitrogen, and available potassium content, thereby enhancing phosphorus supply and availability. This is consistent with previous studies [14], which have demonstrated that the application of organic materials generally enhanced soil nutrient content. In particular, the manure-amended treatment resulted in significantly higher levels of soil organic matter, total potassium, alkaline hydrolysis nitrogen, and available potassium compared to other treatments. Additionally, the addition of organic materials promoted microbial growth, increased soil enzyme activity, and accelerated the microbial mineralization of phosphorus as well as the conversion of organic phosphorus to its inorganic form [15]. In addition, Borase et al. [16] demonstrated that the combined application of chemical fertilizers and organic materials significantly enhanced the activity of alkaline phosphatase in the soil. The addition of biochar can promote the activity of rhizosphere microorganisms and the release of organic acids, thereby increasing the concentration and availability of phosphorus in the rhizosphere [17].
In addition to enhancing phosphorus availability by improving soil physicochemical properties, they also indirectly promote phosphorus availability and cycling by altering the structure and function of microbial communities [18]. Hu et al. [19] demonstrated that organic materials upregulate microbial functional genes associated with phosphorus cycling, thereby enhancing bioavailability. Khan et al. [20] further identified significant increases in phoC and phoD gene abundance in rhizosphere soil under the application of organic materials. Dai et al. found that organic materials stimulated phosphorus-solubilizing bacteria and fungi to secrete organic acids and phosphatases to solubilize mineral-bound phosphorus [21]. Notably, Zhou et al. [22] found that adding biochar to the soil increased the abundance of phosphorus-solubilizing bacteria, such as Burkholderia, Paraburkholderia, Planctomyces, Sphingomonas, and Singulisphaera, thereby indirectly increasing the content of available phosphorus in the soil.
Soybean is a globally important legume crop that plays a key role in sustainable agriculture. It is capable of biological nitrogen fixation through symbiosis with Rhizobium species, reducing the need for nitrogen inputs [23]. In addition to its economic value, soybean’s deep root system and interactions with soil microbes make it a suitable model crop for studying nutrient transformations in the rhizosphere [24]. However, the sole supply of phosphorus based on the use of chemical fertilizers may reduce soybean quality and deteriorate the soil environment. Therefore, in this study, biochar or/and compost were applied with chemical fertilizers to increase phosphorus bioavailability. This study innovatively combined multidimensional analytical approaches to decipher the regulatory mechanisms of organic–inorganic co-application patterns on soil phosphorus transformation processes. Diverging from conventional single-indicator approaches, we established a comprehensive analytical framework through the pioneering integration of the BPP (Biologically Based P) fractionation method with metagenomics sequencing technology. We hypothesized the following: (1) the application of organic fertilizer can improve soil physicochemical properties, enzyme activity, and bioavailable phosphorus content; (2) functional genes and microbial community related to phosphorus cycling promote the activity and bioavailability of phosphorus. This study presents a novel framework that integrates chemical speciation, enzyme activity, and microbial functional genes, providing new insights into the biogeochemical mechanisms improving phosphorus bioavailability.

2. Materials and Methods

2.1. Site Description

The experiment is conducted in the Heilongjiang Modern Agriculture Demonstration Zone (126°51″ E, 45°50″ N), located in the Daowai District of Harbin, Heilongjiang Province, China. Each experimental plot covered an area of 39 m2, consisting of six ridges with a row spacing of 0.65 m and a ridge length of 10 m. The soil used was farmland soil, and the cropping system followed conventional ridge cultivation (also known as turn the soil and ridge tillage). Prior to fertilization, the field was plowed, and ridges were formed according to standard local agronomic practices. The fertilization treatments were then applied directly to the ridged plots. The annual average temperature in this region is 3.5 °C, and the average annual rainfall is 533 mm. The frost-free period in this region typically ranges from 130 to 140 days. The soil is predominantly chernozem with 21.8% sand, 56.3% silt, and 21.9% clay. Soybeans (Hei Nong 68) were sown on 5 May 2023 and harvested on 30 September, with a planting density of 260,000 plants per hectare. The seeds were commercially treated with a protective coating containing fungicidal agents to reduce the risk of pathogen infection during germination and early growth stages. The physicochemical properties of the cultivated layer in the experimental area are presented in Table S1.

2.2. Experimental Design

This experiment included four treatments: (1) chemical fertilizer (SC); (2) chemical fertilizer + biochar (SCB); (3) chemical fertilizer + organic fertilizer (SCF); (4) chemical fertilizer + biochar + organic fertilizer (SCBF), with three replications for each treatment. The chemical fertilizer was purchased from Shandong Hualu Hengsheng Chemical Co., Ltd. (Dezhou, China), and consists of urea (46% nitrogen), (NH4)2HPO4 (18% nitrogen, 46% P2O5), and K2SO4 (50% K2O). The organic fertilizer was sourced from Heilongjiang Longqi Co., Ltd. (Harbin, China). It primarily consisted of composted chicken manure that was fully decomposed and compliant with national standards. The organic fertilizer is processed to ensure a minimum organic matter content of 30% and a total nutrient content of at least 9%, with the N, P, and K at a ratio of 2.0:1.1:0.6. Its basic physicochemical properties are presented in Table S2. Biochar was purchased from Henan Xingnuo Co., Ltd. (Zhengzhou, China) and prepared through the pyrolysis of corn straw at 500 °C. The composition of the biochar is provided in Table S3. During the soybean growing period, the application rates for nitrogen, phosphorus, and potassium fertilizers were 46 kg/hm2, 64 kg/hm2, and 33 kg/hm2, respectively. Biochar and organic fertilizer were applied at rates of 4000 kg/hm2 and 15,000 kg/hm2, respectively. These application rates were determined based on local agronomic recommendations for soybean production in Northeast China, aiming to meet the crop’s nutrient requirements while avoiding nutrient excess [25,26]. Before fertilization, the soil was left in its natural state without any chemical, physical, or biological pretreatment. Only basic land preparation, including plowing and leveling, was conducted to facilitate uniform fertilizer application. All fertilizers were applied as a basal treatment before sowing, manually broadcasted, and thoroughly incorporated into the top 30 cm of soil to ensure uniform distribution. No additional fertilization was carried out during the crop growth period.
Soil samples were collected after soybean harvest (30 September 2023) using a random five-point sampling method. Soil samples (0–30 cm depth) were collected using a 5 cm diameter soil auger by a random three-point sampling method. Each treatment was repeated three times, and the soil samples were thoroughly mixed and placed in sealed bags for transportation to the laboratory. The samples for the analysis of physicochemical properties and phosphorus grading were air-dried under room temperature, those for enzyme activity analysis were stored in a refrigerator at 4 °C, and those for metagenomics analysis were stored in a −80 °C freezer.

2.3. Determination of Soil Physicochemical Properties and Enzyme Activity

The pH value was measured using a pH meter (PB-10, Sartorius, Goettingen, Germany) (soil:water = 1:2.5). Alkaline hydrolysis nitrogen (AN) was determined using the 1 mol/L NaOH alkaline hydrolysis diffusion method [27]. Organic matter (OM) was determined by the potassium dichromate oxidation-heating method [28]. Available potassium (AK) was extracted with 1 mol/L ammonium acetate solution and measured by flame photometer (FP640, JingQi Precision Instrument Co., Ltd., Shanghai, China) [29]. Total phosphorus (TP) and available phosphorus (AP) were determined using the sodium carbonate fusion colorimetric method and the sodium bicarbonate extraction colorimetric method, respectively [30]. Soil alkaline phosphatase (ALP) and acid phosphatase (ACP) activities were determined colorimetrically at 660 nm using disodium phenyl phosphate [31].

2.4. Soil Bioavailable Phosphorus Grading

Soil bioavailable phosphorus was graded and determined using the method of DeLuca et al. [32]. Briefly, 0.5 g of soil was placed in a 15 mL centrifuge tube with 10 mL of extractant. The mixture was shaken at 25 °C and 180 rpm for 3 h, then centrifuged. The supernatant was diluted and analyzed by the colorimetric method at a wavelength of 630 nm, then four types of phosphorus were extracted as follows: (1) CaCl2-P was extracted using a 0.01 mol/L CaCl2 solution to quantify plant-available phosphorus; (2) Citrate-P was extracted using a 0.01 mol/L citric acid solution to quantify active phosphorus adsorbed on clay particles or bound to calcium, iron, and aluminum compounds that can easily release organic acids in the soil; (3) Enzyme-P was extracted by a mixture of phytase, acid phosphatase, and alkaline phosphatase (all at 0.02 EU/mL concentration) to assess the bioavailability of organic phosphorus in the soil; (4) HCl-P was extracted using 1.0 mol/L hydrochloric acid to quantify the phosphorus converted from insoluble inorganic phosphorus into soluble phosphorus.

2.5. Soil DNA Extraction and Sequencing

Total soil DNA was extracted from 0.5 g of fresh soil using the FastDNA® SPIN Kit for Soil (MP Biomedicals, Santa Ana, CA, USA). The concentration and purity of the extracted DNA was measured using a NanoDrop 2000 spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA). Metagenomics sequencing was conducted by Shanghai Meiji Biotechnology Co., Ltd. (Shanghai, China), with preliminary gene set screening as well as species and functional annotations conducted on their cloud platform. All DNA sequencing results can be found on the National Center for Biotechnology Information (NCBI) website (https://www.ncbi.nlm.nih.gov/) (accessed on 5 March 2024). Gene abundance was assessed by correlating read counts with gene numbers. Individual genes were compared to the KEGG database using Diamond software (v2.0.13) to assign the corresponding KEGG functions. The abundance of functional categories was then determined by summing the abundances of genes associated with KO identifiers. A total of 54 functional genes involved in the phosphorus cycle were annotated through metagenomic sequencing, with detailed functional classifications provided in Table S4.

2.6. Statistical Analysis

Experimental data were organized using Microsoft Excel 2019. Pearson correlation analysis was performed using SPSS 25.0 (IBM, Armonk, NY, USA), along with one-way ANOVA based on Duncan’s test. Statistical significance is represented by the p value, where p < 0.05 indicates a statistical difference, p < 0.01 indicates a significant statistical difference, and p < 0.001 indicates an extremely significant statistical difference. Enzyme activity and bioavailable phosphorus content were plotted using Origin 2019b. PCoA, NMDS, RDA correlation analysis, linear regression analysis, and clustering heatmaps were performed and visualized using R software (version 4.4.0) packages such as “vegan”, “ggplot”, “pheatmap”, and “corrplot”. Partial Least Squares Pathway Modeling (PLS-PM) was conducted using the R package “plspm” (version 4.4.1) to quantify the relationships among soil physicochemical properties, enzyme activities, four biologically effective phosphorus fractions, and microbial community composition and functional genes.

3. Results

3.1. Effects on Soil Physicochemical Properties, Enzyme Activities, and Bioavailable Phosphorus

3.1.1. Effects on Soil Physicochemical Properties and Enzyme Activities

As shown in Table 1, compared with the SC group, the application of organic materials resulted in a slight decrease in soil pH, but only obvious (0.4 unit) in SCF group (p < 0.05). Compared to SC, the OM content increased with the application of organic materials, with the largest increase of 41.56% in SCB group, while there were increases of 28.13% and 40.35% in SCF and SCBF, respectively. The AP content showed significant increases of approximately 94.66% and 78.76% in SCF and SCBF compared with that in SC (p < 0.05), respectively, while the TP content only increased by 10.45% in SCBF. For AN content, the addition of organic materials led to an increase in all groups, with the highest increase of 55.00% in SCF. The AK content significantly increased (p < 0.05) by approximately 1.80 to 3.40 times in SCF and SCBF compared with that in SC. As shown in the Table S5, the soybean yield in the experimental group treated with organic materials was significantly higher than that in SC, with the SCBF group having the highest yield.
The activities of ALP and ACP are shown in Figure S1. ALP activity was 96.86 nmol/h/g in SC group, which was decreased by the application of organic materials, with the largest decrease of 13.78% in SCB group. For ACP activity, it was 431.59 nmol/h/g in SC, and it was decreased by 17.60% and 6.15% in SCB and SCF, respectively, but significantly increased by 29.82% in SCBF (p < 0.05).
In summary, compared to the use of chemical fertilizers alone, the application of organic materials significantly improved the soil properties and decreased the activity of ALP and ACP. Meanwhile, the application of organic fertilizers and biochar notably enhanced ACP activity.

3.1.2. Effects on Bioavailable Phosphorus Components

The content of four bioavailable phosphorus is shown in Figure 1. As shown in Figure 1, the content of the four types of bioavailable phosphorus was as follows: Enzyme-P (0.48–1.22 mg/kg) < CaCl2-P (3.57–15.18 mg/kg) < Citrate-P (5.90–32.94 mg/kg) < HCl-P (24.26–46.28 mg/kg). Compared with that in SC group, the content of CaCl2-P showed a significant increase of 110.12% and 119.86% in SCF and SCBF, respectively (p < 0.05), while it showed a decrease of 48.33% in SCB. For Enzyme-P, its content was lower than that of the other three bioavailable phosphorus forms, especially in SC (0.48 mg/kg), which was increased by 128.00% in SCB, 71.40% in SCF, and 157.00% in SCBF, respectively, but these differences were not statistically significant (p > 0.05). For Citrate-P, its content showed a significant increase of about 83.18% in SCBF and 75.67% in SCB (p < 0.001), while showing a decrease of 11.60% in SCF compared with that in SC. For HCl-P, compared with that in SC, the content showed the largest increase of 90.75% and 56.79% in SCF and SCBF (p < 0.05), respectively, and no difference in SCB (p > 0.05).

3.1.3. Correlation Between Soil Physicochemical Properties and Bioavailable Phosphorus Components

Redundancy analysis (RDA) was performed on the soil physicochemical properties and bioavailable phosphorus types under different organic material inputs, as shown in Figure 2. The environmental factors with the greatest impact on soil bioavailable phosphorus were AP, AK, pH, and AN. Both AP and AK were positively correlated with bioavailable phosphorus fractions (especially CaCl2-P, Citrate-P, and HCl-P). The longer arrow for Enzyme-P, pointing towards the upper right, indicated that it was the one most significantly affected by various physicochemical properties. The arrows for CaCl2-P, Citrate-P, and HCl-P suggested that these phosphorus types were not influenced by physicochemical properties across different treatments. The pH was negatively correlated with four available phosphorus types, while the other environmental factors showed positive correlations with phosphorus types.

3.2. Variations in the Composition and Abundance of Phosphorus Cycling Genes and Their Relationship with Bioavailable Phosphorus

3.2.1. Composition and Abundance of Phosphorus Cycling Genes

Figure 3a illustrated the total genes (32 genes) and their distributions across each treatment, where genes with an abundance of less than 1% are grouped into the “others”. These genes are classified into six categories: organic phosphorus mineralization, inorganic phosphorus solubilization, phosphorus regulation, phosphorus transporters, polyphosphate synthesis, and polyphosphate degradation. Among these, the relative abundance of genes involved in organic phosphorus mineralization (phoD, phoA), inorganic phosphorus solubilization (gcd, ppx), phosphorus regulation (phoR), phosphorus transport (pstA, pstS, pstC), polyphosphate synthesis (ppk1), and polyphosphate degradation (spoT, ppnK) were comparatively high. The abundance of spoT, ppk1, ppx, and phoR increased with the application of all organic materials, with spoT being the most remarkable (p < 0.05). For gcd, its abundance was highest in SCB (7.40%) and lowest in SC (5.00%). In addition, phoD showed the lower abundance in SC compared to other groups (p < 0.05). Of all the genes with abundance greater than 1%, those related to phosphorus transport and polyphosphate degradation (spoT, ppk1) were the most highly expressed.
The distribution of phosphorus cycling genes across the different treatments is shown in Figure 3b. It can be observed that the proportion of different functional genes follows the order of phosphorus transport genes > polyphosphate degradation genes > inorganic phosphorus solubilization genes > phosphorus regulation genes > organic phosphorus mineralization genes > polyphosphate synthesis genes. It was found that the abundance of inorganic phosphorus solubilization genes was highest in SCB, although the difference was not significant compared to other treatments. However, the abundance of organic phosphorus mineralization and phosphorus transport genes decreased significantly in SCB, SCF, and SCBF (p < 0.05).
A non-metric multidimensional scaling (NMDS) plot based on Bray–Curtis distance was used to explore the similarities and differences of phosphorus cycling genes under different treatments. As shown in Figure 3c, the samples of SCF and SCBF were clustered closely, indicating that the composition of functional genes was similar under these treatments. Based on the r value (0.42) and p value (0.002), the clustering between different treatments was statistically significant, indicating that the application of organic materials had a substantial impact on the composition of phosphorus functional genes. The stress value of 0.026 indicated that the two-dimensional representation of NMDS analysis reflected the actual relationships between the sample distances. In summary, the impact of SC on the functional gene composition was smaller than that of SCBF. The combined application of organic materials (especially SCF and SCBF) led to an increase in the abundance of functional genes, which may contribute to phosphorus cycling efficiency.

3.2.2. Correlation Between Environmental Factors and the Composition of Functional Genes

The relationship between environmental factors and the composition of functional genes was assessed using RDA. As shown in Figure 4a, the first two axes of soil physicochemical properties collectively explained approximately 38.00% of the variation in functional genes. Although the points representing different treatments were distributed along the RDA1 and RDA2 axes, their distribution was relatively concentrated, with no significant separation observed. The sample points of SCF and SCBF were slightly higher along the positive direction of the RDA1 axis, indicating a stronger positive correlation between these treatments and genes associated with factors such as OM. The ALP, TP, AN, and AK exhibited a higher explanation for gene abundance, but their arrows were less aligned with the direction of distribution of most sample points compared to pH. Therefore, pH is a key factor influencing functional genes.
According to Figure 4b, it was evident that pH was significantly positively correlated with the phoD involved in organic phosphorus mineralization (p < 0.01), and significantly negatively correlated with AN, OM, and AK. The AP, AN, and TP were significantly positively correlated with the spoT, PK, ppnK, surE, and ndk genes involved in polyphosphate degradation, as well as the phoRUB responsible for phosphorus starvation regulation. In contrast, they were significantly negatively correlated with the ppk2 (p < 0.05). The TP was significantly positively correlated with the ppx involved in inorganic phosphorus solubilization and the ppk1 responsible for polyphosphate synthesis (p < 0.05). Meanwhile, both AP and TP showed a significant positive correlation with the pstASC, which regulated phosphorus transport (p < 0.05).

3.2.3. Linear Regression Analysis of the Correlation Between Bioavailable Phosphorus and Functional Genes

Linear regression analysis was performed on four types of bioavailable phosphorus and functional genes including organic phosphorus mineralization genes (phoA, phoD, phnP), inorganic phosphorus solubilization genes (pqqC, gcd), and polyphosphate degradation genes (ppk2). As shown in Figure 5, phnP was significantly positively correlated with all four bioavailable phosphorus components (p < 0.05), whereas ppk2 was significantly negatively correlated with CaCl2-P, Citrate-P, and HCl-P (p < 0.05). In general, the addition of biochar and organic fertilizer primarily affected soil CaCl2-P and Citrate-P content by influencing the abundance of organic phosphorus mineralization genes and increasing the abundance of inorganic phosphorus solubilization genes. Additionally, the content of CaCl2-P, Citrate-P, and HCl-P was also influenced by a decrease in phosphate degradation genes.

3.3. Effects on the Structure and Composition of Microbial Communities

3.3.1. Structure and Composition of Microbial Communities Involved in Phosphorus Cycling

Figure 6a presented the analysis of dominant microbial phyla with relative abundances greater than 1% in each treatment. Overall, Actinobacteria and Proteobacteria dominated in all treatments, with their combined abundance reaching the highest of 74.00% in SC and the lowest in SCB. Notably, the relative abundance of Actinobacteria increased with the application of organic materials. Proteobacteria accounted for the highest proportion in SC, representing approximately 48.00% of the microbial community, and was the most abundant phylum across all treatments. Its abundance decreased by about 22.00% in SCB but increased in SCF and SCBF. Acidobacteria exhibited the highest abundance in SCB, while the addition of organic fertilizers led to a reduction in its content. Both Gemmatimonadetes and Chloroflexi increased with the application of organic materials, reaching their highest abundance of 6.80% and 9.00%, respectively, in SCF.
Figure 6b presented the analysis of dominant bacterial genera with relative abundances greater than 1% at genus level for each treatment. The top 5 bacterial genera were as follows: unclassified_p_Acidobacteria, Bradyrhizobium, unclassified_c_Actinomycetia, unclassified_p_Chloroflexi, and unclassified_p_Actinobacteria. It was evident that the abundance of slow-growing rhizobia (Bradyrhizobium) in SC (31.19%) was significantly higher than in other groups.
Principal Coordinates Analysis (PCoA) was conducted on community composition at the phylum level. As shown in Figure 6c, the sample points in SCF are distributed closely along both axes, suggesting that the community composition in this group was similar. In contrast, the sample points in SC were more scattered, indicating greater variability in the microbial community at the phylum level. According to the ANOSIM analysis (R = 0.324, p = 0.016), different treatments significantly affected species composition at the phylum level. Notably, the addition of organic fertilizer contributed to a more consistent and stable microbial community composition. The sample points in SCBF are somewhat dispersed along the PC1 axis, but are relatively clustered along the PC2 axis, indicating that this fertilization regime has similar effects on certain species.

3.3.2. Analysis of Shared and Endemic Microbial Species

The Venn diagram (Figure S2) was used to analyze the shared and endemic species of microorganisms at the genus and species levels, respectively. As shown in Figure S2a, a total of 1474 species were annotated at the genus level. The number of species in each treatment was as follows: SCBF (1202) > SCF (1186) > SC (1105) > SCB (1092), with a total of 824 shared species. With the application of organic materials, the number of species rose, although the fewest species were in SCB. The analysis of shared and endemic species at the species level is shown in Figure S2b. The number of species annotated at the species level was in the order of SCBF (3905) > SCF (3747) > SCB (3401) > SC (3396), with a total of 2173 shared species. These trends were consistent with those observed at the genus level, with species numbers increasing as organic materials were applied, and more species were found in SCF.

3.3.3. Correlation Analysis Between Microorganisms and Environmental Factors

The top ten dominant species at the phylum level were selected for correlation analysis with environmental factors, as shown in Figure 7a. The pH value and OM had no significant effect on the microorganisms, with the impact being primarily concentrated on the other four environmental factors. Among these, Actinobacteria and Gemmatimonas were significantly positively correlated with AN and AP, while Acidobacteria and Candidatus-Rokubaria were significantly negatively correlated with TP and AK.
Figure 7b illustrates the relative contributions of the top 10 dominant species at the phylum level for the major functional genes. It can be seen that Actinobacteria and Proteobacteria exhibited relatively high contributions to the spoT, ppk1, and ppx genes, accounting for approximately 80% of the species composition. However, the relative contributions of these two phyla were lower in SCB compared to that in SC. For the gcd gene, the highest relative contributions were from Actinobacteria and Proteobacteria, which, together, accounted for 34.41% in SCBF. In contrast, Acidobacteria had the highest relative contribution, reaching up to 66.18% in SCB. The contribution from Acidobacteria decreased in SCF and SCBF, with the lowest percentage observed in SCBF. Furthermore, Chloroflexi accounted for the lowest relative abundance among the four functional genes for gcd. Additionally, Thaumarchaeota showed species contributions to both the gcd and ppx genes but contributed little or none to the spoT and ppk1 genes.

3.4. Relationship Between Microbial Community Composition, Functional Genes, and Environmental Factors

Partial Least Squares Path Model (PLS-PM) was employed to further investigate the interactions between soil physicochemical property, enzymatic activity, microbial community, phosphorus cycling gene, and bioavailable phosphorus (Figure 8). In this study, the Goodness of Fit (GOF) values were all greater than 0.7, indicating that the model has a good fitting effect. Under the combined application of organic material, soil physicochemical property exhibited a strong positive correlation with microbial community in SCB and SCF (0.92, p < 0.001 and 0.92, p < 0.05), a strong positive correlation with bioavailable phosphorus in SCF (0.94), and a significantly positive correlation with phosphorus cycling gene in SCBF (0.87, p < 0.01). Notably, the enzymatic activity, microbial community, and phosphorus-cycling genes all significantly positively correlated (0.97, p < 0.001, 0.98 p < 0.05, and 0.92, p < 0.05) with bioavailable phosphorus in SCBF.

4. Discussion

4.1. Effect of Organic Material Application on Soil Physicochemical Properties and Bioavailable Phosphorus

Organic materials serve as the primary source of organic matter in soil. After mineralization and decomposition, these organic materials become energy and nutrient sources for soil microorganisms and plants, which directly or indirectly affect the physicochemical properties of the soil [33,34]. The RDA result showed that AP exhibited a stronger and more direct relationship with bioavailable phosphorus, indicating that increased soil-available phosphorus directly enhances the pool of bioavailable forms [35]. On the other hand, AK may promote bioavailable phosphorus indirectly by enhancing plant root activity and associated rhizosphere processes [36]. Research by Neal et al. [37] indicated that soil pH significantly affected the adsorption and binding of phosphorus. In this study, the application of organic materials led to temporary soil acidification and the decrease in soil pH might cause an increase in phosphorus availability [38]. The results of this experiment showed a significant negative correlation between soil pH and bioavailable phosphorus fractions. This may be attributed to the activation of microorganisms with the addition of organic materials, which leads to a reduction in soil pH through the release of organic acids and acid ions. These acid ions compete with phosphate ions for cation-binding sites, thereby promoting the dissolution of phosphate minerals and increasing the release of available phosphorus into the soil [39]. The analysis of PLS-PM verified the negative correlation between soil physicochemical properties and bioavailable phosphorus in SCB and SCBF. As primary contributors to soil organic matter, organic amendments undergo mineralization processes that release bioavailable compounds serving as energy substrates for microbial metabolism and as plant-accessible nutrients. This transformation subsequently modulated soil physicochemical characteristics through microbial-mediated biochemical pathways and root–soil interactions [40]. Organic acids competitively displace phosphate ions (HPO42− and H2PO4−) from anion adsorption sites on soil colloids through ligand exchange mechanisms, which enhances the desorption of labile inorganic phosphorus and elevates Citrate-P fractions. Concurrently, H+ released from acid functional groups facilitates the dissolution of mineral-bound phosphorus, effectively increasing the content of HCl-P. These findings align with studies by Gao [41] and Pingree [42]. The application of organic materials has no significant effect on Enzyme-P but significantly increases the content of CaCl2-P, particularly in the SCF and SCBF. The organic materials as exogenous organic substances significantly increased the content of OM in the soil, and the humic substances (HSs) in OM could form humic–metal–phosphate (HMP) complexes, contributing to phosphorus availability. Yang F. et al. [43] demonstrated that organic matter interactions with metal ions in the soil can lead to the formation of stable HMP complexes that influence phosphorus retention and release. Additionally, Yuan Y. et al. [44] discussed the formation of humic–metal–phosphate complexes in soils with high OM and how these complexes influencing phosphorus retention and its availability for plant uptake, particularly in soils where metal ions are abundant.
Soil phosphatase activity serves as a critical biomarker for assessing organic phosphorus mineralization potential. ALP originates predominantly from phosphorus-solubilizing microorganisms, whereas ACP is jointly secreted by plant rhizosphere systems and microbial communities [45]. The activation of ALP by biochar is mediated through multifactorial mechanisms including microbial habitat modification, where its porous structure provides protective niches while labile carbon fractions sustain microbial metabolic activity [46]. In the present study, the ALP activity in SCB and SCBF was lower than SC, suggesting substrate-induced feedback regulation of enzymatic biosynthesis [45]. Generally, biochar has little effect on ACP activity, and some studies even suggest that its application may lead to a decrease in ACP activity [47]. These findings corroborate the pH-dependent modulatory effects of biochar, wherein its capacity to enhance phosphorus availability is proportionally regulated by the magnitude of the pH increase [48]. In this context, the application of organic fertilizer and biochar significantly raises soil phosphorus content, resulting in a relatively small impact on phosphatase activity [49].
The influence of soil physicochemical property and enzymatic activity on bioavailable phosphorus is a complex process affected by regulating phosphorus transformation, release, and microbial activity [50]. Compared to the SC, soil physicochemical property and enzymatic activity were negatively correlated with bioavailable phosphorus (−0.55 and −0.22), while in the SCF, both were positively correlated (0.94 and 0.19). This indicates that soil physicochemical properties, such as pH and organic matter content, provide a favorable environment for microbial growth and functionality, thus promoting the accumulation of bioavailable phosphorus [51]. Meanwhile, enzymatic activity showed a significant positive correlation with bioavailable phosphorus (0.97, p < 0.001) only in the SCBF, which further highlighted the critical role of soil enzymes related to phosphorus metabolism in regulating gene expression and functional realization.

4.2. Effect of Organic Material Application on Soil Phosphorus Cycling Functional Genes

Metagenomics profiling of phosphorus cycling genes demonstrates that fertilization regimes fundamentally reshape the genomic architecture of soil microbes [52]. Crucially, integrated application system combining chemical fertilizers with biochar and/or compost drives the convergence of functional genes and enhances the metabolic coordination in phosphorus transformation pathways [53]. This suggests that the addition of organic materials has a positive integrative effect on the structure and function of soil microbial communities [34], which is helpful to improve soil health and phosphorus cycling efficiency. Analysis of gene abundance in various phosphorus cycling processes revealed significant differences in the abundance of genes related to organic phosphorus mineralization and phosphorus transport under different treatments. Organic phosphorus mineralization genes (phoD, phnA, and phnP) are key genes responsible for enhancing the bioavailable phosphorus content in soil [54]. In this study, the abundance of these genes increased to varying degrees following the addition of organic materials and the bioavailable phosphorus content also increased accordingly, which supports this finding. Notably, the abundance of spoT and ppk genes, which are associated with polyphosphate synthesis and degradation, was higher under the experimental treatments. The expression of spoT may have stimulated the bacteria to increase the production of specific phosphatases, thereby enhancing the catabolism of organic phosphorus into inorganic phosphorus and improving phosphorus bioavailability. Analysis of the abundance of phosphorus cycling genes across various treatments revealed that the abundance of ppx and gcd, which are related to inorganic phosphorus solubilization, was lower in the SC compared to other treatments. In contrast, the abundance of pqC was higher in the SC, consistent with observations that soils with lower phosphorus inputs tend to have higher pqC gene abundance [55]. This is consistent with the increased expression and activity of the pqqC gene under phosphorus-limited conditions [56].
Soil pH exerts central regulation over microbial genetic expression profiles, particularly in the process of organic phosphorus mineralization. There is a significant negative correlation between phoD and AN, OM, and AK, and a significant positive correlation with pH. In agricultural ecosystems, long-term fertilizer application, which alters soil pH and carbon–nitrogen–phosphorus (C:N:P) stoichiometry, can significantly affect the abundance of phoD in the soil [57]. PPA, ppx, glpQ, pstS, PIT, pstB, pstC, and pstA are all significantly or highly significantly positively correlated with AP and TP, suggesting that the application of organic materials (SCB, SCF, and SCBF) to soil can stimulate the functional potential of microbial phosphorus transformation. Among all experimental groups, only the phosphorus cycling gene in the SCBF exhibited a significant and strong positive correlation (0.92, p < 0.001) with bioavailable phosphorus. This may be due to the combined application of chemical fertilizers, biochar, and organic fertilizers, which increased AP content, enhanced phosphorus cycling genes, and facilitated microbial phosphorus assimilation, ultimately boosting bioavailable phosphorus [9].

4.3. Effect of Organic Material Application on Soil Phosphorus Cycling Microorganisms

Soil microorganisms are considered a key driving force behind soil ecosystem functioning, sustainable agricultural production, and nutrient cycling [58]. The metagenomics have elucidated the metabolic specialization of soil microbiota in phosphorus transformations. Actinomycetes emerging as keystone taxa in phosphorus mineralization and fixation in phosphorus mineralization and fixation, capable of converting organic phosphorus into inorganic forms (i.e., mineralization), which can then be transformed into forms that plants can absorb [59]. This process is often closely linked to the generation of phosphatase enzymes, which further enhance the bioavailability of soil phosphorus [60]. Additionally, Actinobacteria contribute to the biological fixation of phosphorus by adsorbing onto soil particles or forming insoluble phosphorus compounds. Proteobacteria promote the accumulation of nutrients in soil, and some strains within it exhibit significant phosphorus solubilization ability. This study also found that the abundance of Bradyrhizobium under SC was significantly higher than in the other treatments. This genus of bacteria is known for its role in nitrogen fixation in soybean roots. Previous studies have shown that bacteria from the Rhizobium genus can dissolve inorganic phosphorus compounds [61]. They enhance phosphorus solubility by secreting organic acids, thereby increasing the available phosphorus content in the soil [62,63].
Meanwhile, these bacteria may also inhibit the formation of insoluble phosphates by forming chelates with metal ions such as Ca2+ and Mg2+ [64]. This study shows that Acidobacteria has the highest proportion in the SC, likely due to the rich microporous structure of biochar. Biochar increases soil porosity, improves moisture and aeration conditions, and creates a favorable environment for microorganisms that thrive in slightly acidic conditions. The application of biochar can influence soil pH, particularly when biochar itself has alkaline properties. It helps neutralize soil acidity, creating a more favorable environment for the growth of Acidobacteria. This observation also suggests that crop rotation can affect microbial community composition. The PCoA analysis results showed clustering in the SCF and SCBF, indicating that organic fertilizer treatments had a consistent impact on species composition. The microbial community exhibited strong positive correlations with bioavailable phosphorus in the SC, SCB, and SCBF, with the SCBF showing the strongest and most significant correlation (0.98 p < 0.001), suggesting that microorganisms effectively enhanced phosphorus solubilization and transformation through secretion of organic acids, enzymes, and metabolites [65,66]. Microbial communities capable of expressing these genes play a crucial role in enhancing soil phosphorus availability. These findings are consistent with the study by Siles et al. [67], which emphasized the essential role of microbial functional genes in facilitating nutrient cycling within soil ecosystems. Our study was designed to capture microbial and chemical processes, making findings relevant to varied soils. For example, maize root exudate experiments in saline–alkali soils demonstrated phosphorus mobilization mechanisms applicable to low-fertility environments [6].

5. Conclusions

The addition of organic material reduces soil pH and enhances bioavailable phosphorus content, with the order: HCl-P > Citrate-P > CaCl2-P > Enzyme-P. In addition, the application of biochar and organic fertilizers along with chemical fertilizers can increase spoT and phoR gene abundance, which in turn promoted an increase in bioavailable phosphorus. They also enhanced the abundance of phosphorus cycling functional genes and the related soil microbial communities, particularly those involved in organic phosphorus mineralization and phosphorus transportation, thereby influencing the availability of phosphorus. This study not only reveals how organic materials influence phosphorus bioavailability by modifying soil physicochemical properties, but it also elucidates their specific effects on the expression of functional genes in soil microorganisms, providing new insights into the microbial mechanisms of soil phosphorus cycling.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/agronomy15051187/s1, Figure S1: Enzymatic activity in different treatments; Figure S2: Venn diagram of shared and endemic species at genus level (a) and species level (b); Table S1: Basic physicochemical properties of the cultivated soil layer; Table S2: Basic physicochemical properties of organic fertilizers; Table S3: Biochar composition; Table S4: Functional genes involved in microbial phosphorus cycling; Table S5: Effect of different treatments on soybean yield.

Author Contributions

W.W.: Conceptualization, Methodology, Software, Investigation, Formal Analysis, Writing—Original Draft; Y.J.: Data Curation, Methodology, Software, Investigation, Writing—Original Draft; S.C.: Visualization, Software, Investigation; Y.L.: Visualization, Investigation; L.S.: Conceptualization, Funding Acquisition, Resources, Supervision, Writing—Review and Editing; J.Q.: Conceptualization, Funding Acquisition, Resources, Supervision, Writing—Review and Editing. All authors have read and agreed to the published version of the manuscript.

Funding

The research reported here was performed with funding from the National Key Research and Development Program of China (2024YFD1501602).

Data Availability Statement

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

Acknowledgments

We gratefully acknowledge Xuesheng Liu for his guidance in experimental design and technical support during the experimental process. We also extend our thanks to all contributors who facilitated this research.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Bioavailable phosphorus content in different treatments, and different lowercase letters indicate different treatments reaching a significant difference level of 5%. SC, SCB, SCF, and SCBF are chemical fertilizer, chemical fertilizer combined with biochar, chemical fertilizer with organic fertilizer, and chemical fertilizer with both biochar and organic fertilizer, respectively.
Figure 1. Bioavailable phosphorus content in different treatments, and different lowercase letters indicate different treatments reaching a significant difference level of 5%. SC, SCB, SCF, and SCBF are chemical fertilizer, chemical fertilizer combined with biochar, chemical fertilizer with organic fertilizer, and chemical fertilizer with both biochar and organic fertilizer, respectively.
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Figure 2. RDA analysis of physicochemical properties and four bioavailable phosphorus fractions under different fertilization treatments. SC, SCB, SCF, and SCBF are chemical fertilizer, chemical fertilizer combined with biochar, chemical fertilizer with organic fertilizer, and chemical fertilizer with both biochar and organic fertilizer, respectively. TP and OM are total phosphorus and organic matter, respectively. AN, AP, and AK are available nitrogen, available phosphorus, and available potassium, respectively.
Figure 2. RDA analysis of physicochemical properties and four bioavailable phosphorus fractions under different fertilization treatments. SC, SCB, SCF, and SCBF are chemical fertilizer, chemical fertilizer combined with biochar, chemical fertilizer with organic fertilizer, and chemical fertilizer with both biochar and organic fertilizer, respectively. TP and OM are total phosphorus and organic matter, respectively. AN, AP, and AK are available nitrogen, available phosphorus, and available potassium, respectively.
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Figure 3. (a) Abundance of soil phosphorus cycling genes in various treatments; (b) abundance of functional genes in soil; (c) NMDS of functional gene composition for phosphorus cycling. Different lowercase letters indicate different treatments reaching a significant difference level of 5%. SC, SCB, SCF, and SCBF are chemical fertilizer, chemical fertilizer combined with biochar, chemical fertilizer with organic fertilizer, and chemical fertilizer with both biochar and organic fertilizer, respectively.
Figure 3. (a) Abundance of soil phosphorus cycling genes in various treatments; (b) abundance of functional genes in soil; (c) NMDS of functional gene composition for phosphorus cycling. Different lowercase letters indicate different treatments reaching a significant difference level of 5%. SC, SCB, SCF, and SCBF are chemical fertilizer, chemical fertilizer combined with biochar, chemical fertilizer with organic fertilizer, and chemical fertilizer with both biochar and organic fertilizer, respectively.
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Figure 4. (a) RDA of physicochemical properties and functional genes in soil; (b) Pearson correlation analysis between relative abundance of soil phosphorus cycling genes and environmental factors. *, **, and *** represented p < 0.05, p < 0.01, and p < 0.001, respectively. SC, SCB, SCF, and SCBF are chemical fertilizer, chemical fertilizer combined with biochar, chemical fertilizer with organic fertilizer, and chemical fertilizer with both biochar and organic fertilizer, respectively. TP and OM are total phosphorus and organic matter, respectively. AN, AP, and AK are available nitrogen, available phosphorus, and available potassium, respectively. ALP and ACP are alkaline phosphatase and acid phosphatase, respectively.
Figure 4. (a) RDA of physicochemical properties and functional genes in soil; (b) Pearson correlation analysis between relative abundance of soil phosphorus cycling genes and environmental factors. *, **, and *** represented p < 0.05, p < 0.01, and p < 0.001, respectively. SC, SCB, SCF, and SCBF are chemical fertilizer, chemical fertilizer combined with biochar, chemical fertilizer with organic fertilizer, and chemical fertilizer with both biochar and organic fertilizer, respectively. TP and OM are total phosphorus and organic matter, respectively. AN, AP, and AK are available nitrogen, available phosphorus, and available potassium, respectively. ALP and ACP are alkaline phosphatase and acid phosphatase, respectively.
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Figure 5. Correlation analysis of bioavailable phosphorus fractions with functional genes.
Figure 5. Correlation analysis of bioavailable phosphorus fractions with functional genes.
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Figure 6. (a) Microbial community at the phylum level; (b) microbial community at the genus level; (c) PCoA analysis of species at the phylum level. SC, SCB, SCF, and SCBF are chemical fertilizer, chemical fertilizer combined with biochar, chemical fertilizer with organic fertilizer, and chemical fertilizer with both biochar and organic fertilizer, respectively.
Figure 6. (a) Microbial community at the phylum level; (b) microbial community at the genus level; (c) PCoA analysis of species at the phylum level. SC, SCB, SCF, and SCBF are chemical fertilizer, chemical fertilizer combined with biochar, chemical fertilizer with organic fertilizer, and chemical fertilizer with both biochar and organic fertilizer, respectively.
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Figure 7. (a) Heatmap analysis of the correlation between dominant species at the phylum level and environmental factors in soil; (b) abundance composition characteristics of the contribution of phylum-level species to functional genes. *, **, and *** represented p < 0.05, p < 0.01, and p < 0.001, respectively. SC, SCB, SCF, and SCBF are chemical fertilizer, chemical fertilizer combined with biochar, chemical fertilizer with organic fertilizer, and chemical fertilizer with both biochar and organic fertilizer, respectively. TP and OM are total phosphorus and organic matter, respectively. AN, AP, and AK are available nitrogen, available phosphorus, and available potassium, respectively.
Figure 7. (a) Heatmap analysis of the correlation between dominant species at the phylum level and environmental factors in soil; (b) abundance composition characteristics of the contribution of phylum-level species to functional genes. *, **, and *** represented p < 0.05, p < 0.01, and p < 0.001, respectively. SC, SCB, SCF, and SCBF are chemical fertilizer, chemical fertilizer combined with biochar, chemical fertilizer with organic fertilizer, and chemical fertilizer with both biochar and organic fertilizer, respectively. TP and OM are total phosphorus and organic matter, respectively. AN, AP, and AK are available nitrogen, available phosphorus, and available potassium, respectively.
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Figure 8. Partial least squares path models (PLS-PM) illustrating the direct and indirect effects of (a) SC, (b) SCB, (c) SCF, and (d) SCBF on physicochemical property, enzymatic activity, microbial community, phosphorus cycling gene, and bioavailable phosphorus. *, **, and *** represented p < 0.05, p < 0.01, and p < 0.001, respectively.
Figure 8. Partial least squares path models (PLS-PM) illustrating the direct and indirect effects of (a) SC, (b) SCB, (c) SCF, and (d) SCBF on physicochemical property, enzymatic activity, microbial community, phosphorus cycling gene, and bioavailable phosphorus. *, **, and *** represented p < 0.05, p < 0.01, and p < 0.001, respectively.
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Table 1. Soil physicochemical properties for each treatment.
Table 1. Soil physicochemical properties for each treatment.
pHOM
(g/kg)		AP
(mg/kg)		TP
(%)		AN
(mg/kg)		AK
(mg/kg)
SC6.71 ± 0.06 a2.76 ± 0.06 a135.05 ± 19.77 b0.67 ± 0.14 a96.90 ± 1.27 b206.70 ± 35.33 c
SCB6.57 ± 0.06 a3.90 ± 0.26 a117.83 ± 11.79 b0.57 ± 0.06 a98.97 ± 15.88 b254.05 ± 19.11 c
SCF6.31 ± 0.16 b3.53 ± 0.56 a262.89 ± 14.06 a0.66 ± 0.22 a149.80 ± 7.97 a586.01 ± 50.88 b
SCBF6.65 ± 0.08 a3.87 ± 0.82 a241.41 ± 12.34 a0.74 ± 0.05 a142.90 ± 7.13 a902.48 ± 77.33 a
Note: Significant differences were tested using Duncan’s test (p < 0.05), with different lowercase letters indicating significant differences. OM, AP, TP, AN, and AK are organic matter, available phosphorus, total phosphorus, alkaline hydrolysis nitrogen, and available potassium, respectively. SC, SCB, SCF, and SCBF are chemical fertilizer, chemical fertilizer combined with biochar, chemical fertilizer with organic fertilizer, and chemical fertilizer with both biochar and organic fertilizer, respectively.
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Wang, W.; Jiang, Y.; Cai, S.; Li, Y.; Sun, L.; Qu, J. Metagenomics Reveals the Effects of Organic Material Co-Application on Phosphorus Cycling Functional Genes and Bioavailable Phosphorus. Agronomy 2025, 15, 1187. https://doi.org/10.3390/agronomy15051187

AMA Style

Wang W, Jiang Y, Cai S, Li Y, Sun L, Qu J. Metagenomics Reveals the Effects of Organic Material Co-Application on Phosphorus Cycling Functional Genes and Bioavailable Phosphorus. Agronomy. 2025; 15(5):1187. https://doi.org/10.3390/agronomy15051187

Chicago/Turabian Style

Wang, Wei, Yue Jiang, Shanshan Cai, Yumei Li, Lei Sun, and Juanjuan Qu. 2025. "Metagenomics Reveals the Effects of Organic Material Co-Application on Phosphorus Cycling Functional Genes and Bioavailable Phosphorus" Agronomy 15, no. 5: 1187. https://doi.org/10.3390/agronomy15051187

APA Style

Wang, W., Jiang, Y., Cai, S., Li, Y., Sun, L., & Qu, J. (2025). Metagenomics Reveals the Effects of Organic Material Co-Application on Phosphorus Cycling Functional Genes and Bioavailable Phosphorus. Agronomy, 15(5), 1187. https://doi.org/10.3390/agronomy15051187

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