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Article

Organic Materials Promote Soil Phosphorus Cycling: Metagenomic Analysis

by
Wei Yang
1,2,
Yue Jiang
1,
Jiaqi Zhang
1,
Wei Wang
1,3,
Xuesheng Liu
1,
Yu Jin
1,
Sha Li
1,
Juanjuan Qu
1,* and
Yuanchen Zhu
2,*
1
School of Resources and Environment, Northeast Agricultural University, Harbin 150030, China
2
Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Harbin 150081, China
3
Heilongjiang Academy of Black Soil Conservation & Utilization, Harbin 150030, China
*
Authors to whom correspondence should be addressed.
Agronomy 2025, 15(7), 1693; https://doi.org/10.3390/agronomy15071693
Submission received: 9 June 2025 / Revised: 5 July 2025 / Accepted: 8 July 2025 / Published: 13 July 2025
(This article belongs to the Section Soil and Plant Nutrition)
Browse Figures
Versions Notes

Abstract

The combined application of chemical fertilizers with organic materials contributes to higher contents of bioavailable phosphorus. However, the underlying mechanism remains poorly understood. A field experiment including four treatments, chemical fertilizer (CF), chemical fertilizer with biochar (CB), chemical fertilizer with organic fertilizer (CO), and chemical fertilizer with biochar and organic fertilizer (CBO), was conducted to explore how the combination of fertilizer applications enhanced soil phosphorus bioavailability using metagenomic sequencing technology. The results showed that chemical fertilizers combined with organic materials (CB, CO, and CBO) significantly increased citrate-extractable phosphorus by 34.61–138.92% and hydrochloric acid-extractable phosphorus contents by 72.85–131.07% compared to CF. In addition, the combined applications altered the microbial community structure and increased the abundance of phoR, spoT, and ppnK genes, but decreased those of gcd, phoD, and ppk1 genes. A partial least squares path model indicated that the combined applications regulated the microbial community composition and gene abundance of phosphorus-cycling microorganisms by influencing soil physicochemical properties, thereby enhancing soil phosphorus cycling. Correlation analysis indicated that pH, total phosphorus, and available phosphorus were the key factors influencing microbial communities, while available nitrogen and total nitrogen primarily regulated phosphorus cycling gene abundance. In addition, the CO and CBO treatments significantly increased maize yield by 14.60% and 21.04%, respectively. Overall, CBO most effectively enhanced bioavailable phosphorus content and maize yield. This study provides a foundation for developing rational fertilization strategies and improving soil phosphorus use efficiency.

1. Introduction

Phosphorus is regarded as a primary nutrient-constraining crop yield potential, and it enters the soil mainly through phosphate fertilizers [1]. However, most soil phosphorus is adsorbed on clay minerals and metal oxides, forming stable compounds that cannot be utilized directly by crops (merely 10–25%) [2,3,4]. To meet the demand for crop growth, excess phosphorus is often applied to the soil each year, leading to environmental and ecological issues such as soil acidification, soil compaction, and the eutrophication of aquatic ecosystems [5,6]. Consequently, it is imperative to develop a strategy to increase crop yield by enhancing the bioavailable phosphorus in the soil.
Bioavailable phosphorus denotes the soil phosphorus fraction that can be readily absorbed by microorganisms and crops and is positively related to the phosphorus utilization efficiency in soil [7]. Based on bioavailability, DeLuca et al. classified phosphorus into categories such as calcium chloride-extractable phosphorus (CaCl2-P), citric acid-extractable phosphorus (Citrate-P), enzyme-extractable phosphorus (Enzyme-P), and hydrochloric acid-extractable phosphorus (HCl-P) [8]. Recent studies have indicated that applying organic materials significantly enhances the bioavailability of soil phosphorus [9,10]. Organic materials themselves contain a certain amount of phosphorus, which can complex with soil ions and calcium, reducing the precipitation of phosphate [11,12]. Organic acids produced in the decomposition of organic materials facilitate the mineralization of soil phosphate compounds and trigger the release of inorganic phosphorus. This process subsequently enhances the concentration of soil bioavailable phosphorus [13]. Biochar is capable of adsorbing phosphate ions in a soil solution, thereby significantly mitigating the leaching of soil-bioavailable phosphorus [14]. Therefore, fully understanding the effects of applying different organic materials on soil bioavailable phosphorus is crucial for improving phosphorus use efficiency.
Soil microorganisms are crucial in regulating phosphorus bioavailability [15]. The phyla Actinobacteria and Proteobacteria, along with the genera Enterobacter and Pantoea, can secrete organic acids that facilitate the dissolution of mineral-bound inorganic phosphorus, thus increasing the concentration of soil bioavailable phosphorus [16,17,18]. Furthermore, microbial genes are involved in numerous processes that regulate the soil phosphorus cycle. Among them, organic phosphorus mineralization genes (phoD, phoA), inorganic phosphorus solubilization genes (gcd, ppx), phosphorus regulatory genes (phoR), phosphorus transporter genes (glpQ, pstS, pstC), polyphosphate synthesis genes (ppk1), and polyphosphate degradation genes (spoT, ppnK) are the main genes regulating soil bioavailable phosphorus content [19]. For example, organic phosphorus mineralizing genes can increase the rate of soil organophosphorus mineralization and elevate the content of bioavailable phosphorus by increasing the activities of acid phosphatase (ACP) and alkaline phosphatase (ALP) [20,21]. Inorganic phosphorus solubilization genes can promote inorganic phosphorus solubilization by synthesizing organic acids to chelate soil metal ions [22]. Previous studies have indicated that the combined applications can improve microbial activity and the abundance of phosphorus-cycling genes [23,24]. Nevertheless, most existing studies primarily concentrated on the impacts of chemical fertilizers with organic materials on individual genes or microorganisms. Few studies have focused on the association between soil bioavailable phosphorus contents and microbial communities.
Metagenomics not only characterizes the microbial community structure and functions involved in soil phosphorus cycling but also effectively reveals the interactions between microbial communities and phosphorus-cycling functional genes [25]. Therefore, this study employed phosphorus fractionation and metagenomic techniques to reveal the microbial mechanisms that promote soil phosphorus cycling under different fertilization strategies through a ten-year field experiment (2013–2022). It was hypothesized that (1) chemical fertilizers with organic materials may improve crop yields by increasing soil bioavailable phosphorus contents; (2) the relationship between bioavailable phosphorus and microbes might vary with the properties of organic materials; and (3) chemical fertilizers with biochar and organic fertilizers may be more effective in increasing phosphorus cycling gene abundance than chemical fertilizers with biochar or organic fertilizers alone. The results of this study provide a scientific basis for optimizing fertilizer management strategies and achieving sustainable agricultural production.

2. Materials and Methods

2.1. Field Site

The study site was situated in the Heilongjiang Modern Agricultural Demonstration Zone (126°51′ E, 45°50′ N), Harbin, Heilongjiang Province, China. The climate is a temperate continental climate, with an average annual rainfall of 533 mm and an average annual temperature of 3.5 °C. The climate data for 2022 are shown in Figure S1, which was derived from the World Weather Online database (https://www.worldweatheronline.com, accessed on 4 July 2025). The soil at the study site is a thin-layer chernozem, with the soil-forming parent material being flood-derived loess-like clay. The initial soil properties assessed are presented in Table S1.

2.2. Experimental Design and Sample Collection

The field experiment was conducted in a continuous maize cropping system initiated in 2012. Four fertilization regimes included chemical fertilizer (CF), chemical fertilizer with biochar (CB), chemical fertilizer with organic fertilizer (CO), and chemical fertilizer with biochar and organic fertilizer (CBO). Each treatment consisted of four plots, each measuring 39 m2 (3.9 × 10 m), in a randomized block design consisting of six rows spaced 0.65 m apart. Maize (cultivar ‘Tiannong 9’) was sown in late April and harvested in late September each year at a planting density of 45,000 plants ha−1. Conventional tillage (30 cm depth) was applied consistently across all experimental years. The fertilizer application rate was determined based on the local soil fertility and maize yield, and the biochar and organic fertilizer application rates were calculated based on 35% and 45% of the carbon equivalent of above-ground maize stover, respectively [26,27]. The application methods and organic material properties for different treatments are shown in Tables S2–S4.
At maturity, maize ears were harvested from each plot (2 m2) of each treatment to determine the yield. In September 2022, three small plots were randomly selected from each treatment, and soil samples were systematically collected from each experimental plot at 0–30 cm depths using a 5 cm soil auger via a three-point mixed sampling method. After removing visible stones and plant roots, the soil samples from the three sampling points in the same plot were thoroughly mixed to form a representative composite sample. One portion of each composite sample was stored at −80 °C for metagenomics analysis, while the remaining portion was air-dried and screened through 0.25 mm mesh sieves for soil properties analysis and bioavailable phosphorus extraction.

2.3. Soil Properties and Enzyme Activity Characterization

Soil pH (w/v = 1:5) was assessed with a digital pH meter (PB-10, Sartorius, Goettingen, Germany). Total nitrogen (TN) was measured using an elemental analyzer (EA3000, EuroVector S.p.A., Pavia, Italy). Available nitrogen (AN), available phosphorus (AP), available potassium (AK), organic matter (OM), and total phosphorus (TP) were analyzed using the methods outlined in Soil Agro-Chemical Analysis [28]. ACP and ALP activities were measured using the method of Luo et al. [29].

2.4. Determination of Soil Bioavailable Phosphorus

The determination and classification of soil bioavailable phosphorus were conducted using the methodology described by DeLuca et al. [8]. Specifically, 0.5 g of soil was transferred into a 15 mL tube containing 10 mL of extractant, and the tube was shaken for 3 h at room temperature (180 rpm). After centrifugation, the supernatant was obtained and analyzed using a colorimetric method at 630 nm. CaCl2-P (readily available phosphorus for plant uptake) was obtained using a 0.01 mol L−1 calcium chloride solution; Citrate-P (phosphorus bound to clay particles or adsorbed by calcium, iron, and aluminum compounds, released by organic acids) was obtained using a 0.01 mol L−1 citric acid solution; Enzyme-P (available organic phosphorus in soil) was extracted using a mixture of phytase, acid phosphatase, and alkaline phosphatase (each at a concentration of 0.02 U mL−1); and HCl-P (inorganic phosphorus solubilized by hydrochloric acid, representing proton-mediated activation by plant roots) was obtained using 1.0 mol L−1 hydrochloric acid.

2.5. Metagenomic Sequencing

Genomic DNA was extracted from soil samples using the FastDNA® SPIN Kit for Soil (MP Biomedicals, Santa Ana, CA, USA) following the manufacturer’s protocol. The concentration and purity of the extracted DNA were evaluated with a NanoDrop 2000 spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA). Metagenomic sequencing was performed on an Illumina NovaSeq platform, generating 2 × 150 bp paired-end reads. Sequencing data were processed using Trimmomatic v0.39 to remove adapter sequences and trim low-quality bases (PHRED score < 20), with reads shorter than 50 bp discarded post-trimming [30]. GC content bias was corrected by normalizing read counts using the preprocessCore package in R version 4.4.0. To minimize PCR amplification bias, low-cycle (20 cycles) amplification was conducted using Phusion High-Fidelity DNA Polymerase [31,32].
Microbial community analysis and functional gene profiling were performed on the Majorbio Cloud Platform (https://www.majorbio.com, accessed on 4 May 2024), integrating quality control, taxonomic annotation via Kraken2, and functional gene analysis with HUMAnN 3 [33,34]. For statistical analyses, relative abundances were log10-transformed, and normality was validated using Shapiro–Wilk tests [35]. Treatment differences were evaluated by one-way ANOVA, with p-values adjusted for multiple comparisons using the Benjamini–Hochberg false discovery rate (FDR) method (p < 0.05). Functional annotation of individual genes was performed by aligning to the KEGG database using Diamond software (v2.0.13), with functional category abundances calculated by summing gene abundances associated with KO identifiers. Metagenomic sequencing annotated 54 functional genes involved in the phosphorus cycle, with detailed functional classifications provided in Table S5.

2.6. Statistical Analysis

One-way analysis of variance (ANOVA) was applied to assess significant differences among fertilization treatments. Subsequently, Tukey’s Honestly Significant Difference (HSD) test was conducted for post hoc pairwise comparisons, effectively controlling the family-wise error rate. To verify the separation of microbial communities among treatments, permutational multivariate analysis of variance (PERMANOVA) and analysis of similarity (ANOSIM) were performed on the Majorbio Cloud Platform (https://www.majorbio.com, accessed on 28 November 2024), utilizing the Bray–Curtis dissimilarity matrix. Principal coordinate analysis (PCoA) and non-metric multidimensional scaling (NMDS) were employed to explore the impacts of fertilization on phosphorus-cycling microorganisms and functional genes, visualizing community structure changes. Redundancy analysis (RDA) was executed using Canoco 5 software (v 5.15, Canoco, NY, USA) to elucidate the relationships between microbial communities and edaphic factors. When conducting correlation analyses in Origin 2021 to evaluate associations between phosphorus-cycling genes and edaphic factors, p-values were adjusted using the Benjamini–Hochberg False Discovery Rate (FDR) method to correct for multiple testing biases. The Mantel’s test was performed via the ChiPlot platform (https://www.chiplot.online/, accessed on 13 December 2024), and for comparisons involving multiple correlation matrices, FDR correction was also applied to ensure statistical rigor. To identify the key predictors of maize yield, Random Forest regression models were implemented using the “randomForest” package in R version 4.4.0. Hyperparameters were optimized through a grid search strategy: the number of variables randomly sampled as candidates at each split (mtry) was tested across values {2, 3, 4, 5}, approximating the regression default of mtry ≈ p/3 (where p represents the total number of features), and the number of trees (ntree) was explored across {500, 1000, 1500} to strike a balance between model stability and computational efficiency. Model performance was validated using a 10-fold cross-validation procedure. Additionally, partial least squares path modeling (PLS-PM) was conducted with the “plspm” package to systematically clarify the intricate relationships among fertilizer treatments, edaphic factors, microbial community structure, gene abundance, and maize yield.

3. Result

3.1. Effects on Edaphic Factors and Enzyme Activities

Compared with the CF treatment, the CO and CBO treatments significantly increased AP contents by 1.59-fold and 1.16-fold, respectively (Figure 1a, p < 0.05). Meanwhile, the CB, CO, and CBO treatments significantly increased the TP content by 13.98%, 35.44%, and 35.05% (p < 0.05), respectively. Nevertheless, the CO and CBO treatments decreased the soil pH by 0.68 and 1.1 (p < 0.05), respectively. The CB treatment reduced ACP activity by 18.72% (p < 0.05), whereas the CBO treatment significantly increased it by 19.09% (Figure 1b, p < 0.05). Additionally, the CB and CBO treatments significantly decreased ALP activity by 16.56% and 18.96% (p < 0.05), respectively.

3.2. Effects on Bioavailable Phosphorus Content

In all treatments, the contents of the four bioavailable phosphorus fractions followed the order: HCl-P > Citrate-P > CaCl2-P > Enzyme-P (Figure 2). The CO and CBO treatments significantly increased the CaCl2-P content by 36.90% and 35.61% (p < 0.05), respectively. Notably, the CO treatment alone resulted in a 1.25-fold increase in the Enzyme-P content (p < 0.05). The application of organic materials increased Citrate-P and HCl-P contents, with the CO treatment showing the greatest increases (1.31-fold and 1.38-fold, p < 0.05, respectively).

3.3. Effects on Microbial Communities

The results of PERMANOVA (R2 = 0.55, p = 0.001) and ANOSIM (R2 = 0.62, p = 0.004) indicated significant differences in microbial community structure among different fertilization treatments (Table S6). The PCoA plot explained 92.14% of the variance at the phylum level. The microbial community compositions of the CO and CBO treatments were more similar (Figure S2). At the phylum level, Actinobacteria, Proteobacteria, Acidobacteria, Gemmatimonadetes, and Chloroflexi exhibited the highest relative abundances, which could be regarded as the dominant taxa (Figure 3a). The combined applications increased the relative abundance of Proteobacteria, with the CBO treatment having the greatest increase (36.50%). Conversely, Acidobacteria exhibited the highest relative abundance in the CF treatment (13.00%). Gemmatimonadetes showed slight reductions in the CB and CBO treatments and the highest proportion in the CO treatment (8.00%). At the genus level (Figure 3b), unclassified_p_Acidobacteria and Bradyrhizobium had the highest relative abundances. The application of organic materials increased the relative abundance of Bradyrhizobium and decreased that of unclassified_p_Acidobacteria.
The results of Mantel’s test (Figure S3a) demonstrated that pH, AP, and TP emerged as the principal edaphic factors influencing microbial communities, which exhibited a positive correlation with microbial abundance. RDA was performed to analyze the relationships between the top five most abundant phyla and edaphic factors. The first two axes of the RDA explained 73.67% of the total variance in the microbial community (Figure S3b). Except for Acidobacteria, other phyla exhibited negative correlations with pH and ALP activity, and positive correlations with other edaphic factors.

3.4. Effects on Phosphorus Cycling Genes

NMDS analysis revealed that sample points from the CO and CBO treatments clustered distinctly, separate from those of the CF and CB treatments (Figure S4). This indicated greater similarity in the composition of these functional genes between the CO and CBO treatments. The combined applications increased the relative abundances of spoT, ppk2, and phoR genes, with the CBO treatment showing the most substantial enhancement (Figure 4a). Conversely, the combined applications decreased the relative abundances of gcd, phoD, and ppk1 genes. The relative abundances of each functional gene under different fertilization treatments were ordered as follows: phosphorus transporter genes > polyphosphate degradation genes > inorganic phosphorus solubilization genes > phosphorus regulatory genes > organic phosphorus mineralization genes > polyphosphate synthesis genes (Figure 4b). Genes involved in organic phosphorus mineralization and inorganic phosphorus solubilization were more abundant in the CB treatment, while phosphorus transporter genes showed higher abundances in the CBO treatment.
As shown in Figure 5a, the first two axes of RDA collectively accounted for 83.05% of the variation in phosphorus-cycle-related functional genes. All functional genes, except for phosphorus transporter genes, exhibited negative correlations with pH. Moreover, organic phosphorus mineralization and polyphosphate degradation genes were positively associated with AN and TN. Correlation analysis was employed to reveal the associations between the 11 most abundant phosphorus-cycling genes and edaphic factors (Figure 5b). The findings indicated that AN and TN were the major edaphic factors shaping phosphorus-cycling gene abundances. Specifically, the pstC and pstS genes showed positive correlations with AN, AK, and pH (p < 0.05). The phoD and phoA genes exhibited significant positive correlations with OM, AP, and AN (p < 0.05). The gcd and ppk1 genes demonstrated significant positive correlations with OM, AN, and TN (p < 0.05).
Contributions of the top 10 dominant phyla to the four most abundant phosphorus-cycling functional genes were analyzed (Figure S5). For spoT, ppk1, and ppx genes, Actinobacteria and Proteobacteria made substantial contributions, accounting for approximately 80%. The Proteobacteria and Acidobacteria phyla had relatively higher contributions to the gcd gene. The combined applications enhanced the relative contributions of the Actinobacteria and Proteobacteria phyla, with the CB treatment exhibiting the highest increase. Conversely, the combined applications reduced the relative contribution of Acidobacteria, with the CBO treatment showing the lowest value.

3.5. Relationship Among Edaphic Factors, Enzyme Activities, Bioavailable Phosphorus, and Maize Yield

The relationships among bioavailable phosphorus, edaphic factors (Figure 6a), microbial communities (Figure 6b), and phosphorus-cycling genes (Figure 6c) were revealed through Mantel’s test. The findings indicated that CaCl2-P was significantly correlated with pH, Gemmatimonadetes, Actinobacteria, and the phoA, pstC, pstS, and phoD genes (p < 0.05). Enzyme-P was significantly associated with AK, ACP activity, and Gemmatimonadetes (p < 0.05). Citrate-P was significantly correlated with AK, Gemmatimonadetes, Actinobacteria, and the phoD gene (p < 0.05). In contrast, HCl-P was significantly associated only with OM and Planctomycetes (p < 0.05). Therefore, AK, Gemmatimonadetes, and phoD genes play important roles in regulating bioavailable phosphorus content.
The CO and CBO treatments significantly increased maize yield by 14.60% and 21.04% (Figure S6, p < 0.05), respectively. However, the CB treatment caused a non-significant 5.97% reduction in maize yield (p > 0.05). The Random Forest model exhibited an R2 value of 0.65, with both root-mean-squared error and mean absolute error values below 0.01 (Table S7), which reflects a favorable model fit. These results further revealed that pH, AP, AN, AK, Citrate-P, and CaCl2-P were the dominant factors regulating maize yield (Figure 7a). The results of PLS-PM suggested that fertilization influenced the abundance of phosphorus-cycling genes by altering edaphic factors, thereby regulating the content of soil bioavailable phosphorus and ultimately increasing maize yields (Figure 7b).

4. Discussion

4.1. Effect of Combined Applications on Maize Yield and Bioavailable Phosphorus Content

Previous reports have highlighted the effectiveness of applying biochar to increase yield [36]. Our results showed a decrease in maize yield under CB treatment. This is because the high application rate of biochar (4000 kg ha−1) in this study increased soil nitrogen and phosphorus limitation of the soil and intensified the nutrient competition between microorganisms and maize [37,38]. In this study, soil pH was reduced by the CO and CBO treatments, while the opposite was observed for the CB treatments. This may be attributed to the high pH of the biochar itself, whereas large amounts of organic acids produced by microbially mediated degradation of organic fertilizers accumulate in the soil, resulting in lower pH values [39].
TP, AP, and bioavailable phosphorus content are the primary indicators for assessing soil phosphorus supply capacity for crop growth. The results showed that the combined applications increased TP and AP content, consistent with the findings of Wang et al. [40] and Shi et al. [41]. This is because organic materials contain soluble phosphorus that directly increases AP and TP content upon entering the soil [42]. The application of organic materials also increased Citrate-P and HCl-P contents, with the highest contents observed in the CO treatment. This is because organic acids generated during the degradation of organic materials increase Citrate-P content via ligand exchange, where they compete with phosphates (HPO42− and H2PO4−) for anionic adsorption sites on soil particle surfaces. Higher concentrations of H+ also promote phosphorus dissolution from insoluble minerals, thereby increasing HCl-P content [43]. Meanwhile, the application of organic materials enhanced soil nutrient contents, promoting microbial biomass, activity, and the mineralization and solubilization of soil phosphorus, thereby increasing soil bioavailable phosphorus. This finding was confirmed by earlier studies [44,45].
The above results validate hypothesis (1) that the treatment of chemical fertilizers with organic materials can improve maize yield by increasing soil bioavailable phosphorus contents. However, this study only focused on the relationship between maize yield and bioavailable phosphorus after 10 years of applying different types of organic materials. Future studies should focus on the temporal dynamics of bioavailable phosphorus content, crop yield, and their relationship under different types or rates of organic materials.

4.2. Effect of Combined Applications on Phosphorus Cycling Microorganisms

Biochar provides microorganisms with abundant carbon sources, and its porous structure offers a suitable habitat and physical protection conducive to microbial proliferation, thereby increasing microbial and phosphatase activity [46,47]. Our results showed that the CB treatment reduced ACP and ALP activities, which contradicted earlier findings [37,48]. This discrepancy may arise from variations in soil phosphorus content and the high application rate of organic materials in this study [49]. When soil phosphorus is scarce, microbial demand for phosphorus stimulates phosphatase production, resulting in higher ACP and ALP activities. Conversely, microorganisms reduce ACP and ALP synthesis when soil phosphorus content or anthropogenically applied phosphorus already meets microbial requirements [50]. In addition, higher pH under CB treatment can inhibit the activity of ACP, thereby reducing the mineralization of organic phosphorus [51].
Soil microbial communities are highly sensitive to soil environmental changes, and combined applications are generally considered to influence soil microbial community structure by altering soil properties [52]. Liu et al. showed that soil organic carbon, pH, and TN were the primary edaphic factors regulating microbial community changes under fertilizer applications [53]. Ji et al. found that OM and AP were the primary edaphic factors shaping microbial communities [54]. Our findings showed that microbial community structure was influenced by pH, AP, and TP, corroborating the results of Li et al. [55]. Compared with CF, chemical fertilizers combined with organic materials were more effective in improving and maintaining soil AP and TP contents [39,56]. Therefore, the addition of organic materials is more conducive to improving soil microbial activity and community structure. The phylum Actinobacteria and genus unclassified_p_Acidobacteria are a typical group of phosphorus-mineralizing and -solubilizing bacteria that produce phosphatases to promote organic phosphorus mineralization and organic acids to stimulate inorganic phosphorus solubilization [57]. Chemical fertilizers with organic materials increased the abundance of Actinobacteria and genus unclassified_p_Acidobacteria, which is consistent with the findings of Tian et al. [58]. This phenomenon may stem from increased soil nutrient contents due to combined applications, which promote the growth of nutrient-dependent microorganisms like Actinobacteria and the genus Bradyrhizobium [59]. Therefore, organic materials application can contribute to soil phosphorus cycling by increasing the abundance of microbial communities involved in phosphorus mineralization and phosphorus solubilization (e.g., Actinobacteria and genus Bradyrhizobium), which in turn increases soil bioactive phosphorus content. In contrast, oligotrophic bacteria such as Acidobacteria were enriched in low-nutrient soils [25].
The above results rejected hypothesis (2). Regardless of the properties of organic materials, the fundamental mechanisms underlying the applications of organic materials affecting soil phosphorus cycling can be attributed to the regulation of phosphorus-cycling microbial composition. The differences primarily reflect differential effects on the abundances of key phosphorus-cycling microorganisms (i.e., those involved in phosphorus mineralization and solubilization). Therefore, future studies should investigate the effects of organic materials application on soil phosphorus cycling, microbial activity, and phosphorus conversion rates.

4.3. Effect of Combined Applications on Phosphorus Cycling Genes

The ppk gene regulates the synthesis and degradation of polyphosphate in microorganisms, maintaining a dynamic equilibrium of phosphorus between the microbial interior and the external environment. The two-component system phoR/phoB is responsible for regulating the transport process of polyphosphate in soil microorganisms, thereby providing a sufficient phosphorus supply for microorganisms [60]. The spoT gene regulates the synthesis of specific soil phosphatase enzymes, which facilitate the transformation of organic phosphorus into inorganic phosphorus, thereby improving the soil phosphorus bioavailability [61]. This study demonstrated that the combined applications enhanced the abundance of ppk, phoR, and spoT genes, which contrasted with the findings of Wang et al. [40]. This may be because organic fertilizers have a higher nitrogen content than straw, alleviating nitrogen limitation of microbial growth and boosting microbial activity, and thereby increasing microbial phosphorus requirement [62].
phoD and phoA are key genes that regulate phosphatase synthesis and the mineralization of organic phosphorus. These genes significantly contribute to enhancing soil phosphorus bioavailability. The combined applications reduced the abundance of phoD and phoA genes, which aligns with the results reported by Chen et al., who found that chemical fertilizer with straw reduced the abundance of phoD [63]. This may be because, in soils with high organic phosphorus content, microorganisms may not actively degrade organic phosphorus when the bioavailable phosphorus of the soil already meets their requirements [64]. The gcd gene regulates the solubilization of insoluble phosphorus minerals and serves as an optimal predictor for evaluating microbial inorganic phosphorus solubilization. The combined applications decreased the abundance of the gcd gene compared with the CF treatment. This can be attributed to soil properties and the specific crop variety. Organic materials and maize root secretions can provide microorganisms with direct or more readily available phosphorus, reducing their dependence on the gcd-mediated phosphorus solubilization pathway [65]. As a result, the abundance of the gcd gene decreases.
pstC and pstS genes regulate microbial inorganic phosphate uptake and translocation, and are negatively correlated with pH [66]. This may be because lower pH inhibits most microbial activity and reduces microbial phosphorus demand [67,68]. The findings of the RDA and Mantel’s test demonstrated that AN and TN were the key edaphic factors affecting the abundance of phosphorus-cycling genes, which is consistent with the findings of Liao et al. [69]. This might be because soil nitrogen sufficiency mitigated microbial nutrient limitation and enhanced microbial activity and phosphorus demand [68]. It is crucial to note that specific interactions exist among different phosphorus-cycling genes [70]. For example, the phosphorus regulatory gene (phoR) and the phosphorus transporter gene (pst) generally exhibit a consistent trend under different fertilization treatments. Under low-phosphorus conditions, microorganisms utilize alternative phosphorus sources by increasing phoR gene abundance. This necessitates increased phosphorus uptake by microorganisms, leading to higher pst gene abundance. In contrast, in the combination application treatments, the organic materials provide microorganisms with abundant, readily available phosphorus, phoR gene abundance decreases, leading to reduced pst gene abundance [61]. Thus, the combined application of chemical fertilizers and organic materials has the potential to regulate phosphorus-cycling gene abundance by influencing one or more key phosphorus-cycle genes.
The above findings rejected hypothesis 3. While the CBO treatment was most effective in enhancing phosphorus transporter gene abundance, it was less effective than other treatments in boosting other phosphorus-cycling genes (e.g., those involved in organophosphorus mineralization and inorganic phosphorus solubilization). However, this study’s single sampling period precluded comprehensive capture of the temporal dynamics of phosphorus-cycling functional genes during organic materials application. Follow-up studies should focus on the effects of organic materials application on the functional expression of phosphorus-cycle genes and their temporal dynamics.

5. Conclusions

This study revealed the mechanism by which combined applications regulate soil phosphorus cycling. The combined applications increased soil bioavailable phosphorus content, with the CBO treatment exhibiting the most significant effect. The application of organic materials increased the abundance of soil microorganisms, especially those related to phosphorus mineralization and solubilization. In addition, the application of organic materials increased the relative abundance of phosphorus regulatory and polyphosphate degradation genes, but decreased that of phosphorus mineralization and organic phosphorus solubilization genes. Compared to CF, the CO and CBO treatments increased maize yield, whereas the CB treatment had the opposite effect. The combined applications affected soil bioavailable phosphorus content by influencing the community composition of soil phosphorus-cycling microorganisms and the relative abundance of functional genes, which ultimately regulated maize yield. Overall, the CBO treatment was effective in increasing soil bioavailable phosphorus content and maize yield. Future studies should focus on the effects of varying organic material application rates on the temporal dynamics of soil bioavailable phosphorus content and crop yield. These findings highlight the critical role of organic materials for bioavailable phosphorus in yield enhancement and provide a scientific basis for optimizing fertilization strategies to improve yields in Northeast China and similar agroecosystems.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/agronomy15071693/s1, Figure S1: (a) Mean air temperature and (b) precipitation distribution at the study site during 2022; Figure S2: PCoA of soil microbial community structure under different fertilization treatments; Figure S3: (a) Mantel Test correlation analysis between microbial communities and environmental factors and (b) RDA of microbial communities to environmental factors at the phylum level; Figure S4: NMDS analysis of phosphorus cycling gene composition under different fertilization treatments; Figure S5: Abundance composition characteristics of the contribution of phylum level species to functional genes; Figure S6: Effect of different fertilization treatments on maize yields; Table S1: Basic physicochemical properties of the Cultivated Soil Layer in 2013; Table S2: Fertilizer application method and amount of fertilizer applied to different fertilization treatments; Table S3: Basic physicochemical properties of biochar; Table S4: Basic physicochemical properties of organic fertilizers; Table S5: Information of microbial functional genes involved in the P cycling processes identified in this study; Table S6: PERMANOVA and ANOSIM results based on Bray-Curtis distances for microbial community structure under different fertilization treatments; Table S7: Performance metrics of the random forest model under varying mtry and ntree settings with 10-fold cross-validation.

Author Contributions

W.Y.: data curation, methodology, software, investigation, writing—original draft; Y.J. (Yue Jiang): data curation, methodology, software, investigation; J.Z.: visualization, software, investigation; W.W.: visualization, software, investigation; X.L.: visualization, investigation; Y.J. (Yu Jin): visualization, investigation; S.L.: visualization, investigation; J.Q. (corresponding author): conceptualization, funding acquisition, resources, supervision, writing—review and editing; Y.Z. (corresponding author): conceptualization, funding acquisition, resources, supervision, writing—review and editing. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Key Research and Development Program of China (2023YFD1501103) and the China Postdoctoral Science Foundation (2023M743475).

Data Availability Statement

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

Acknowledgments

We thank Yuhang Zhu and Mingfeng Guo from Northeast Agricultural University for their help in soil sampling and data analysis.

Conflicts of Interest

The authors declare no conflict of interest.

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Agronomy 15 01693 g001
Figure 1. Effects of fertilization on (a) soil physicochemical properties and (b) enzyme activities. Lowercase letters indicate a significant difference between different treatments. AN, available nitrogen; AP, available phosphorus; AK, available potassium; OM, organic matter; TN, total nitrogen; TP, total phosphorus; ACP, acid phosphatase; ALP, alkaline phosphatase; CF, chemical fertilizers; CB, chemical fertilizers with biochar; CO, chemical fertilizers with organic fertilizers; CBO, chemical fertilizers with biochar and organic fertilizers.
Figure 1. Effects of fertilization on (a) soil physicochemical properties and (b) enzyme activities. Lowercase letters indicate a significant difference between different treatments. AN, available nitrogen; AP, available phosphorus; AK, available potassium; OM, organic matter; TN, total nitrogen; TP, total phosphorus; ACP, acid phosphatase; ALP, alkaline phosphatase; CF, chemical fertilizers; CB, chemical fertilizers with biochar; CO, chemical fertilizers with organic fertilizers; CBO, chemical fertilizers with biochar and organic fertilizers.
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Figure 2. Effects of fertilization on bioavailable phosphorus fractions. Lines and squares, lower and upper edges, bars and squares in the boxes represent median and mean, 5th and 95th percentile, and maximum and minimum values. Lowercase letters indicate a significant difference between different treatments. CaCl2-P, calcium chloride-extractable phosphorus; Enzyme-P, enzyme-extractable phosphorus; Citrate-P, citrate-extractable phosphorus; HCl-P, hydrochloric acid-extractable phosphorus.
Figure 2. Effects of fertilization on bioavailable phosphorus fractions. Lines and squares, lower and upper edges, bars and squares in the boxes represent median and mean, 5th and 95th percentile, and maximum and minimum values. Lowercase letters indicate a significant difference between different treatments. CaCl2-P, calcium chloride-extractable phosphorus; Enzyme-P, enzyme-extractable phosphorus; Citrate-P, citrate-extractable phosphorus; HCl-P, hydrochloric acid-extractable phosphorus.
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Figure 3. Microbial community structure at the (a) phylum level and (b) genus level under different fertilization treatments.
Figure 3. Microbial community structure at the (a) phylum level and (b) genus level under different fertilization treatments.
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Figure 4. Relative abundances of (a) phosphorus-cycling genes and (b) functional gene categories related to phosphorus cycling.
Figure 4. Relative abundances of (a) phosphorus-cycling genes and (b) functional gene categories related to phosphorus cycling.
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Figure 5. (a) RDA and (b) correlation analysis between phosphorus-cycling genes and edaphic factors. * p < 0.05, ** p < 0.01 and *** p < 0.001.
Figure 5. (a) RDA and (b) correlation analysis between phosphorus-cycling genes and edaphic factors. * p < 0.05, ** p < 0.01 and *** p < 0.001.
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Figure 6. Mantel’s test for correlation analysis between bioavailable phosphorus fractions and (a) edaphic factors, (b) phosphorus-cycling microorganisms at the phylum level, and (c) phosphorus-cycling genes. * p < 0.05, ** p < 0.01 and *** p < 0.001.
Figure 6. Mantel’s test for correlation analysis between bioavailable phosphorus fractions and (a) edaphic factors, (b) phosphorus-cycling microorganisms at the phylum level, and (c) phosphorus-cycling genes. * p < 0.05, ** p < 0.01 and *** p < 0.001.
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Figure 7. (a) Random Forest analysis of the relative significance of factors influencing maize yield and (b) PLS-PM revealing the microbial mechanisms by which chemical fertilizers with organic materials affect maize yield. * p < 0.05, ** p < 0.01 and *** p < 0.001.
Figure 7. (a) Random Forest analysis of the relative significance of factors influencing maize yield and (b) PLS-PM revealing the microbial mechanisms by which chemical fertilizers with organic materials affect maize yield. * p < 0.05, ** p < 0.01 and *** p < 0.001.
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Yang, W.; Jiang, Y.; Zhang, J.; Wang, W.; Liu, X.; Jin, Y.; Li, S.; Qu, J.; Zhu, Y. Organic Materials Promote Soil Phosphorus Cycling: Metagenomic Analysis. Agronomy 2025, 15, 1693. https://doi.org/10.3390/agronomy15071693

AMA Style

Yang W, Jiang Y, Zhang J, Wang W, Liu X, Jin Y, Li S, Qu J, Zhu Y. Organic Materials Promote Soil Phosphorus Cycling: Metagenomic Analysis. Agronomy. 2025; 15(7):1693. https://doi.org/10.3390/agronomy15071693

Chicago/Turabian Style

Yang, Wei, Yue Jiang, Jiaqi Zhang, Wei Wang, Xuesheng Liu, Yu Jin, Sha Li, Juanjuan Qu, and Yuanchen Zhu. 2025. "Organic Materials Promote Soil Phosphorus Cycling: Metagenomic Analysis" Agronomy 15, no. 7: 1693. https://doi.org/10.3390/agronomy15071693

APA Style

Yang, W., Jiang, Y., Zhang, J., Wang, W., Liu, X., Jin, Y., Li, S., Qu, J., & Zhu, Y. (2025). Organic Materials Promote Soil Phosphorus Cycling: Metagenomic Analysis. Agronomy, 15(7), 1693. https://doi.org/10.3390/agronomy15071693

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