Next Article in Journal
Regenerative Agronomic Approaches: Technological, Biochemical and Rheological Characterization of Four Perennial Wheat Lines Grown in Italy
Previous Article in Journal
Preliminary Results on the Application of Phosphorus and Silicon to Improve the Post-Transplantation Growth of High-Density Nursery Seedlings
Previous Article in Special Issue
Wheat Yield, Biomass, and Radiation Interception and Utilization Under Conservation Tillage: Greater Response to Drip Fertigation Compared to Intensive Tillage
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Agronomy
Volume 15
Issue 4
10.3390/agronomy15040938
agronomy-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

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

Effects of Deep Application of Fertilizer on Soil Carbon and Nitrogen Functions in Rice Paddies

by
Qi-Huan Xie
†,
Xiang-Bin Yao
†,
Ya Yang
,
De-Jin Li
and
Jian-Ying Qi
*
College of Agriculture, South China Agricultural University, Guangzhou 510642, China
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Agronomy 2025, 15(4), 938; https://doi.org/10.3390/agronomy15040938
Submission received: 7 March 2025 / Revised: 5 April 2025 / Accepted: 7 April 2025 / Published: 11 April 2025
(This article belongs to the Special Issue Sustainable Management and Tillage Practice in Agriculture—2nd Edition)
Browse Figures
Versions Notes

Abstract

:
Efficient fertilization is vital for rice production and sustainable agriculture. Conventional fertilization (CK) suffers from low efficiency and environmental pollution, whereas side-deep fertilization (SF) offers an efficient, eco-friendly alternative. The changes in microbial carbon cycling functional genes induced by SF in paddy soils remain unclear. This study investigates the effects of SF and CK on soil organic carbon (SOC), total nitrogen (TN), microbial communities, and carbon- and nitrogen-cycling genes in double-cropping rice paddies through field experiments. Results reveal that SF significantly increases TN in deeper soil layers (10–20 cm), enhancing the expression of nitrogen fixation genes (e.g., K02591 and K02588) and nitrogen metabolism pathways, alongside boosting Chloroflexi and Planctomycetes abundance. In contrast, CK promotes SOC accumulation and upregulates carbon metabolism genes (e.g., K01179 and K01728) in surface layers (0–10 cm). In deeper layers, SF elevates nitrogen reduction gene abundance (e.g., K02591) while suppressing denitrification and assimilatory nitrate reduction, whereas CK enhances dissimilatory nitrate reduction (e.g., K02568). Redundancy analysis (RDA) shows that soil properties (pH, SOC, and TN) drive microbial community structure, with Actinobacteria positively linked to SOC and TN. These findings demonstrate that SF optimizes nitrogen cycling in deeper soil by improving nitrogen use efficiency and functional microbial growth, while CK favors shallow-layer carbon sequestration. This study provides a scientific foundation for tailoring fertilization strategies to soil depth, leveraging carbon- and nitrogen-cycling gene dynamics to enhance soil fertility and sustainability in rice production.

1. Introduction

Rice (Oryza sativa L.), as one of the most important food crops globally, plays an irreplaceable role in ensuring global food security and promoting economic development [1,2]. In China, particularly in the southern regions, rice cultivation has become a vital pillar of the local agricultural economy, benefiting from abundant natural resources and a long history of farming [3]. The double-cropping rice system has gained widespread popularity due to its ability to efficiently utilize natural resources, enhance land productivity, and increase farmers’ income. Nevertheless, as the yield of double-cropping rice continues to rise, the demand for nitrogen fertilizer has grown accordingly, presenting significant challenges to conventional broadcast fertilization practices [4].
Conventional surface broadcasting of nitrogen fertilizers exhibits substantial ecological and economic drawbacks [5,6]. As the world’s largest nitrogen consumer, China utilized 45 million tons of nitrogen fertilizers in 2012, accounting for 37.6% of global consumption, with approximately 37% of rice-specific nitrogen use concentrated in China [7,8,9]. However, China’s nitrogen use efficiency (NUE) in rice cultivation remains at 35%, significantly lower than the 60% benchmark in developed nations [7,10,11,12]. Unutilized nitrogen enters ecosystems via ammonia volatilization, runoff, and leaching, driving regional eutrophication, soil acidification, and surging N2O emissions [13,14]. This context underscores the imperative to optimize fertilization practices as a pivotal solution to the “high-input, low-efficiency, high-pollution” paradox. Side-deep fertilization technology precisely delivers fertilizers to the root-dense zone (10–15 cm depth), enhancing spatiotemporal congruence between nutrient supply and crop demand [15,16,17]. Mechanistic studies reveal that the localized high-nitrogen microenvironment induced by deep placement stimulates root elongation while minimizing surface nitrogen exposure [18,19], thereby significantly reducing ammonia volatilization and runoff losses [15].
While side-deep fertilization demonstrates marked advantages in crop yield enhancement [20], its multidimensional impacts on soil microbial ecology and long-term environmental consequences require systematic elucidation. As a key indicator of soil health (accounting for approximately 2–3% of total organic carbon biomass) [21], soil microorganisms directly regulate ecosystem functions through driving nutrient cycling [22], organic matter decomposition [23,24], and aggregate formation processes [25]. Empirical evidence reveals that fertilization regimes profoundly reshape microbial habitats by altering soil pH, nutrient gradients, and redox potential, thereby modulating community structure and functional gene expression [26,27]. For instance, surface broadcasting under oxic conditions promotes the proliferation of aerobic ammonia-oxidizing bacteria (AOB) and Actinobacteria [28,29], whereas deep placement creates anaerobic microniches favoring denitrifiers (e.g., Pseudomonas) and Chloroflexi enrichment [30,31]. Concurrently, soil microbiota mediate carbon-nitrogen equilibrium through intricate biogeochemical cascades [32]. Fertilization strategies differentially modulate soil nutrient profiles by influencing the abundance of functional genes governing carbon and nitrogen transformations [33,34]. Therefore, deciphering the effects of side-deep fertilization on microbial community architecture and functional gene abundance constitutes a critical knowledge frontier.
Metagenomics, as a scientific field that utilizes genomic technologies to study environmental microbial diversity and community relationships, does not require artificial cultivation of microorganisms and can directly extract and sequence mixed genomic DNA from environmental samples [35]. This technology has significantly advanced the research progress of microbial ecology and is widely applied in the analysis of microbial communities in soil, oceans, freshwater, gut, and extreme environments (such as deserts, tundras, deep seas, acid mines, and bioreactors) [36,37]. Metagenomic sequencing enables researchers to uncover the diversity of soil microorganisms and the presence of functional genes, which play crucial roles in the biogeochemical cycles of carbon and nitrogen [38]. Metagenomic studies have not only discovered new microbial taxa but also elucidated the mechanisms of key microorganisms in the nitrogen cycle, such as the discovery of archaeal ammonia monooxygenase genes and the successful cultivation of ammonia-oxidizing archaea, significantly enriching our understanding of the nitrogen cycle [39,40,41]. Furthermore, metagenomics has revealed the presence of key functional genes in the carbon cycle, such as those involved in the citric acid cycle and reductive citric acid cycle, and their differential expression under different fertilization practices helps to understand the regulatory mechanisms of soil microorganisms on carbon and nitrogen cycling [42,43].
Carbon and nitrogen cycling represent fundamental processes in the elemental cycling of soil ecosystems [44]. The nitrogen cycle includes four main processes—biological nitrogen fixation, ammonification, nitrification, and denitrification—all driven by microorganisms [45]. In recent years, metagenomic studies, combined with molecular ecological techniques and isotope labeling methods, have revealed the diversity and mechanisms of key microorganisms in nitrification, significantly enriching our understanding of the nitrogen cycle. Microorganisms also participate in carbon cycling processes, such as carbon degradation, carbon fixation, and methane metabolism [46]. Effective fertilizer management strategies can elevate soil nitrogen levels, influencing carbon dynamics, with carbon and nitrogen often showing coordinated changes in soil [47]. For instance, side-deep fertilization enhances nitrogen use efficiency [6], yet the precise mechanisms by which it alters soil microbial carbon and nitrogen cycling remain unclear. To address this gap, our study aimed to investigate how side-deep fertilization, compared to conventional broadcast fertilization, affects soil microbial communities and the functional genes driving carbon and nitrogen cycles in double-cropping rice paddies. We hypothesized that side-deep fertilization would enhance nitrogen-cycling gene expression in deeper soil layers while shifting carbon-cycling gene activity across depths, reflecting distinct microbial responses to nutrient placement.

2. Materials and Methods

2.1. Test Site and Design

The field trials were carried out in 2017 at the experimental station of the College of Agriculture, South China Agricultural University (latitude 23°17′ N, longitude 113°37′ E, altitude 12 m). The experimental area has a subtropical monsoon climate, with average annual temperatures of 23.0 °C and 23.7 °C in 2022 and 2023, respectively, and total annual rainfall of 1937.6 mm and 1892.5 mm. The soil type is sandy loam, and the basic physicochemical properties of the 0–20 cm soil layer in 2017 were as follows: pH 6.56, soil organic carbon (SOC) and total nitrogen (TN) contents of 8.75 g·kg−1 and 1.1 g·kg−1, respectively, and available nitrogen, phosphorus, and potassium contents of 53.72 mg·kg−1, 16.37 mg·kg−1, and 120.08 mg·kg−1, respectively. The detailed soil physicochemical characteristics of the experimental site are presented in Table 1.
The experimental setup comprised two treatments: conventional fertilization (CK) and side-deep fertilization (SF), implemented in a randomized block design with 6 plots (each 139 m2 in area). In the CK treatment, conventional tillage was performed using a rotary tiller before transplanting, and fertilizers were broadcast-applied evenly on the soil surface; in the SF treatment, conventional tillage was also conducted before transplanting, but fertilizers were deeply placed at approximately 10 cm depth synchronously with seedling transplanting using a rice transplanter. The basal fertilizer application rate was 600 kg per hectare (containing 15% nitrogen, 4% P2O5, 6% K2O, and 10% organic matter), equivalent to 90 kg N, 24 kg P2O5, and 36 kg K2O per hectare. The tested rice varieties were “Meixiangzhan2” and “Qingxiangyou19xiang”; tillering fertilizer was topdressed at 300 kg·ha−1 after rice seedling recovery.

2.2. Soil Sample Collection and Physicochemical Property Analysis

Soil samples were collected in October 2022 and October 2023 after the late-season rice harvest. A soil sampler with a diameter of 3 cm was used to collect soil samples from the 0–20 cm soil layer at a depth of 2–10 cm from the center of the rice root system. For each plot, 1 kg of soil was collected using the five-point sampling method. After removing roots and other impurities, the samples were placed in sterile plastic bags and transported to the laboratory on dry ice. The samples were divided into two parts: one part was stored in a −80 °C ultra-low temperature freezer for subsequent metagenomic sequencing analysis, and the other part was air-dried for soil physicochemical property determination.
Prior to physicochemical analysis, air-dried soil samples were sieved through a 0.25 mm sieve and pretreated with 4 mol/L hydrochloric acid solution to eliminate organic carbon interference. Soil organic carbon (SOC) and total nitrogen (TN) contents were determined using an elemental Analyzer (Vario Macro Cube, Elementar, Langenselbold, Germany). Soil pH was measured with a pH meter (FE20, Mettler-Toledo Instruments, China) at a soil-to-water ratio of 1:2.5 (w/v). Available nitrogen, phosphorus, and potassium in soil were analyzed using the alkaline hydrolysis diffusion method, Olsen method, and ammonium acetate extraction-flame photometry, respectively [48,49]. Soil ammonium (NH4+) and nitrate (NO3−) concentrations were determined by extracting 10 g of soil with 2 M KCl. The extracts were then analyzed using the same continuous flow analyzer (SEAL AutoAnalyzer 3, SEAL Analytical, USA). Available phosphorus was analyzed via the Olsen method with a UV–Vis spectrophotometer (Agilent 8453, Agilent Technologies, USA). Available potassium was quantified using ammonium acetate extraction followed by flame photometry (Shimadzu FP-6400, Shimadzu Corporation, Japan).

2.3. Metagenomic Sequencing

To comprehensively analyze the structure of soil microbial communities and their functional characteristics in carbon and nitrogen cycling, metagenomic sequencing was performed on soil samples collected in October 2022. First, the collected soil samples were preprocessed, and genomic DNA was extracted. DNA quality was assessed using 1% agarose gel electrophoresis to ensure its integrity. The DNA was fragmented into approximately 400 bp fragments using a Covaris M220 ultrasonicator, and sequencing libraries were constructed using the NEXTFLEX DNA-Seq Kit (Bioo Scientific, Austin, TX, USA).
Equimolar amounts of the amplicon libraries were subjected to Single Molecule Real-Time Sequencing (SMRT) on the PacBio platform, with sequencing performed by Shanghai Personal Biotechnology Co., Ltd., Shanghai, China. Second-generation sequencing was performed on the Illumina platform using NovaSeq and HiSeq X kits to ensure high throughput and high-quality data. The raw sequencing data have been deposited in the NCBI database under accession numbers PRJNA928576 and PRJNA928566.
Raw data were quality-controlled using Fastp (https://github.com/OpenGene/fastp) (accessed on 2 May 2024) to remove low-quality reads and adapter sequences. Subsequently, mixed assembly was performed using Megahit (https://github.com/voutcn/megahit) (accessed on 2 May 2024) and Newbler (https://ngs.csr.uky.edu/Newbler) (accessed on 4 May 2024), and genes with lengths ≥100 bp were screened and translated into amino acid sequences [50]. Open reading frames (ORFs) were predicted from contigs using Prodigal (https://github.com/hyattpd/Prodigal) (accessed on 10 May 2024) [51], and a non-redundant gene set was constructed using CD-HIT software (http://www.bioinformatics.org/cd-hit/) (accessed on 10 May 2024) [52].
To evaluate gene function and microbial community diversity, high-quality reads were mapped to the non-redundant gene set using SOAPaligner (http://soap.genomics.org.cn/) (accessed on 15 October 2024) at a 95% identity threshold, and gene abundance data were obtained [52]. The non-redundant gene set was compared to the NR and KEGG databases using BLASTP in DIAMOND software (http://ab.inf.uni-tuebingen.de/software/diamond/) (accessed on 17 October 2024) (version 2.2.28+, E-value 1 × 10−5) [53]. Microbial community α-diversity analysis was performed using Mothur software (version v.1.30.1, http://www.mothur.org/wiki/Schloss_SOP#Alpha_diversity) (accessed on 25 October 2024) with OTU clustering at a 97% similarity threshold.

2.4. Data Analysis

Statistical analysis of SOC, TN, pH, and available nitrogen, phosphorus, and potassium contents was performed using R software (version 4.2.1), with a significance level set at p < 0.05. One-way analysis of variance (ANOVA) was conducted using IBM SPSS Statistics 20 to assess statistical differences among treatments (p < 0.05), followed by Tukey’s post hoc test to identify the specific sources of intergroup differences.
In microbial diversity analysis, principal component analysis (PCA) and principal coordinates analysis (PCoA) were employed to investigate the effects of different fertilization management methods on soil microbial community structure, with statistical analysis and visualization performed using R language (version 3.3.1). Simpson and Shannon indices were used to evaluate species diversity and evenness, respectively. Bray–Curtis distance matrices were calculated using Qiime2 (version 2020.2.0), and non-metric multidimensional scaling (NMDS) analysis and visualization were conducted with the vegan package (version 2.4.3) in R language (version 3.3.1). Wilcoxon rank-sum tests were performed using the stats package in R (version 3.3.1) and the scipy package in Python (v1.0.0). Additionally, Origin 2023b and Python (version 3.9) were utilized for data plotting and result visualization to ensure compliance with academic publishing standards. Simpson and Shannon indices were applied to assess microbial alpha diversity. The calculation formulas for these indices are as follows:
D Simpson = ∑ i = 1 S o b s n i ( n i − 1 )   N ( N − 1 )   H shannon = − ∑ i = 1 S o b s n i N l n n i N
where Sobs = the observed number of species; ni = sequence count of the i-th species; N = total number of sequences.

3. Results

3.1. Soil Organic Carbon (SOC) and Total Nitrogen (TN)

In October 2022, there were no significant differences (p > 0.05) in SOC (Figure 1A) and TN (Figure 1C) levels between SF and CK in the 0–10 cm and 10–20 cm soil layers. However, data from October 2023 showed that in the 0–10 cm soil layer, SOC (Figure 1B) and TN (Figure 1D) content were significantly higher in the CK treatment than in the SF treatment (p < 0.05); whereas in the 10–20 cm soil layer, the TN content in the SF treatment was slightly higher than in the CK treatment, but the difference was not statistically significant (p > 0.05).

3.2. Microbial Diversity

3.2.1. Analysis of Soil Microbial Composition

The results indicate that SF significantly affects soil microbial community composition (Figure 2). In the 0–10 cm soil layer, SF treatment increased the relative abundance of Nitrospirae and Gemmatimonadetes, while the abundance of Acidobacteria and Actinobacteria decreased. In the 10–20 cm soil layer, SF treatment reduced the abundance of Proteobacteria while increasing the abundance of Chloroflexi. Additionally, the abundance of Acidobacteria and Planctomycetes also increased under SF treatment.

3.2.2. Analysis of Alpha and Beta Diversity Index

The Simpson index of the 10–20 cm soil layer under SF treatment was significantly lower than that of the 0–10 cm soil layer (p < 0.05) (Figure 3A). According to the definition of the Simpson index, a lower index value indicates a more even distribution and higher diversity of microbial communities in the 10–20 cm soil layer, whereas a higher Simpson index in the 0–10 cm soil layer suggests a higher concentration of dominant species and relatively lower diversity. The Shannon index of the 10–20 cm soil layer under SF treatment was significantly higher than that of the 0–10 cm soil layer (p < 0.05), indicating that the microbial communities in the 10–20 cm soil layer had higher richness and evenness, and greater diversity compared to the 0–10 cm soil layer (Figure 3B). In summary, SF treatment significantly improved microbial community diversity in the 10–20 cm soil layer.
PCA analysis showed that PC1 (representing the principal coordinate component that explains the maximum variance in the data) and PC2 (accounting for the largest proportion of remaining variance) explained 29.65% and 12.34% of the variance, respectively, indicating that different treatment and soil depths significantly influenced soil microbial β-diversity (Figure 3C). The distinct separation of samples in the PCA plot further validated the significant influence of fertilization practices and soil depth on microbial community composition.
PCoA analysis showed that PC1 and PC2 explained 57.38% and 15.66% of the variance, respectively (Figure 3D). Microbial communities under different fertilization treatments and soil depths showed significant separation in the PCoA plot, indicating that fertilization methods and soil depth jointly drove significant differences in soil microbial community structure.

3.2.3. Association Between Soil Microbial Communities and Fundamental Physicochemical Properties

In the 0–10 cm soil layer, the relative abundance of Gemmatimonadetes significantly decreased under SF treatment (Figure 4A). In the 10–20 cm soil layer, the relative abundance of Proteobacteria and Nitrospirae significantly decreased under SF treatment, while the relative abundance of Chloroflexi significantly increased (Figure 4B). The Kruskal–Wallis H test results indicated that different fertilization management practices and soil depths significantly affected the relative abundance of Actinobacteria, Nitrospirae, Gemmatimonadetes, Euryarchaeota, and Cyanobacteria (Figure 4C).
RDA analysis revealed significant differences in the distribution of sample points under different fertilization methods and soil depths, with most sample points distant from the center, indicating that soil basic physicochemical properties significantly explain microbial community structure (Figure 4D). Among the physicochemical variables, NH4+, pH, and TN had longer vector lengths, indicating that these variables had the most significant impact on microbial communities. Additional analysis revealed that Actinobacteria had positive correlations with NH4+, AK (Available Potassium), SOC, and NO3−, a strong positive correlation with TN, and a strong negative correlation with pH. Proteobacteria was positively correlated with pH, NH4+, TN, AK, SOC, and NO3−. Acidobacteria was positively correlated with pH and AP(Available Phosphorus), and strongly negatively correlated with TN. Chloroflexi was positively correlated with AP, AK, SOC, NO3−, NH4+, and TN, and negatively correlated with pH. Planctomycetes was strongly positively correlated with AK, SOC, NO3−, NH4+, and TN, and negatively correlated with pH.

3.3. Microbial Function

3.3.1. Effects of Deep Nitrogen Fertilization on Nitrogen Cycling Functional Genes in Paddy Soil

Analysis of nitrogen cycling-related genes at the KEGG ORTHOLOGY (KO) functional level (Figure 5A) revealed no significant KO abundance differences in the 0–10 cm soil layer. However, among the top five abundant nitrogen cycling genes, CK showed slightly higher abundance of K04561 (norB) and lower abundance of K00371 (narH) compared to SF, while nitrogen fixation-related K02567 (napA) was more abundant under SF treatment.
In the 10–20 cm soil layer, significant abundance differences were observed for K02591 (nifK), K02588 (nifH), and K02568 (napB). Among the top five nitrogen cycling genes, CK exhibited slightly lower K04561 (norB) and higher K00371 (narH) abundance compared to SF.
Additionally, across soil layers, the top five nitrogen cycling genes consistently showed lower abundance of K00370 (narG) and K00376 (nosZ), but higher abundance of K00374 (narI) in SF compared to CK. Specifically, CK showed significantly lower abundance of K02591 (nifK) and K02588 (nifH) than side-deep fertilization (SF) (p < 0.05), while K02568 (napB) abundance was significantly higher in CK than SF (p < 0.05). Detailed functional annotations of KO terms can be referenced in Table 2.
KEGG Module enrichment analysis further elucidated the impact of different fertilization practices on nitrogen cycling functions (Figure 5B). In the 0–10 cm soil layer, SF treatment significantly enhanced the expression of nitrogen fixation functions but reduced the functional activity of assimilatory nitrate reduction. In the 10–20 cm soil layer, SF treatment significantly increased nitrogen fixation activity while suppressing denitrification and dissimilatory nitrate reduction functions. These results indicate that side-deep fertilization significantly promotes soil nitrogen fixation capacity and differentially regulates other nitrogen cycling processes in different soil layers.

3.3.2. Effects of Deep Nitrogen Fertilization on Carbon Cycling Functional Genes in Paddy Soil

At Pathway level 3, the top 15 functional categories were similar between different fertilization methods in the 0–10 cm and 10–20 cm soil layers, but their abundances showed significant differences (Figure 6B). In the 0–10 cm soil layer, compared to CK treatment, SF treatment significantly increased the abundance of “Microbial metabolism in diverse environments” and “Carbon metabolism” functions (p < 0.05), while the “Butanoate metabolism” function was significantly higher in CK treatment than in SF treatment (p < 0.05). In the 10–20 cm soil layer, compared to SF treatment, CK treatment significantly increased the abundance of “Biosynthesis of secondary metabolites” function (p < 0.05), while the “Nitrogen metabolism” function was significantly higher in SF treatment than in CK treatment (p < 0.05).
At the KO functional level, analysis of carbon cycling-related genes revealed significant differences in KO functions between SF and CK treatments across different soil layers (Figure 6A). In the 0–10 cm soil layer, the abundance of K01179 (encoding formate dehydrogenase subunit) and K01728 (encoding ferredoxin) differed significantly between the two fertilization methods, with CK treatment showing significantly higher abundance of K01179 and K01728 than SF treatment (p < 0.05), increasing by 0.72% and 0.17%, respectively. In the 10–20 cm soil layer, the abundance of K01179 and K10944 (encoding nitric oxide reductase subunit) also showed significant differences between the two fertilization methods, with SF treatment having significantly higher abundance of K01179 and K10944 than CK treatment (p < 0.05), increasing by 0.95% and 0.05%, respectively.
KEGG Module enrichment analysis further elucidated the impact of different fertilization practices on carbon cycling functions (Figure 6C). In the 0–10 cm soil layer, SF treatment significantly upregulated the functions of the citrate cycle (TCA cycle, Krebs cycle) and reductive citrate cycle (Arnon–Buchanan cycle), but downregulated methane oxidation, nitrification, and complete nitrification functions. In the 10–20 cm soil layer, SF treatment significantly upregulated methane oxidation, complete nitrification, and nitrification functions, but downregulated the reductive citrate cycle and citrate cycle functions.

4. Discussion

4.1. Impact of Deep Nitrogen Fertilization on SOC and TN

This study demonstrates that the impacts of SF and CK on SOC and TN differ significantly between sampling seasons and soil depths. In October 2022, there were no significant differences in SOC and TN content between SF and CK treatments in the 0–10 cm and 10–20 cm soil layers, possibly because the effects of side-deep fertilization on soil nutrient distribution and microbial activity had not yet fully manifested in the short term [54], and soil spatial heterogeneity may also have masked the differences between treatments [55]. However, by October 2023, in the 10–20 cm soil layer, the TN content in the SF treatment significantly increased compared to CK, indicating that the impact of side-deep fertilization on nitrogen distribution in deeper soil gradually became apparent [16]. In the 0–10 cm soil layer, SOC was significantly lower in the SF treatment than in the CK treatment, while TN content showed no significant difference. This difference may be related to the physical properties of the soil and the distribution of microbial activity [56]. The improved aeration in surface soil enhances microbial activity and organic matter decomposition, allowing CK to boost SOC content more quickly in shallow layers. In contrast, deeper soil layers, due to limited nutrient input and microbial activity, require SF to improve nutrient supply, particularly in increasing TN content [57,58]. These results suggest that the choice of fertilization method should be optimized based on soil layer characteristics and microbial activity conditions [59].

4.2. Variations in Microbial Diversity and Composition

The impact of SF on soil microbial community composition varied significantly between soil layers. In the 0–10 cm soil layer, SF treatment increased the abundance of Nitrospirae and Gemmatimonadetes but decreased the abundance of Acidobacteria and Actinobacteria [60]. Nitrospirae and Gemmatimonadetes are important microorganisms involved in nitrogen cycling and carbon metabolism, which aligns with the enhanced expression of nitrogen metabolism genes in deeper soil layers under SF treatment [61,62]. In the 10–20 cm soil layer, the SF treatment notably decreased the abundance of Proteobacteria and significantly increased the abundance of Chloroflexi, with Acidobacteria and Planctomycetes also showing increased abundance. This indicates that side-deep fertilization differentially affects microbial community composition across soil layers, particularly promoting the growth of certain microorganisms adapted to low-oxygen and low-nutrient environments in deeper soil [63]. These findings provide new insights into the effects of fertilization methods on soil microbial ecology.

4.3. Variations in Microbial Functions

4.3.1. Effects of Deep Nitrogen Fertilization on Nitrogen Cycling Functional Genes

SF exhibited distinct stratification in its effects on nitrogen cycling functional genes across different soil layers [56]. In shallow soil (0–10 cm), SF treatment significantly increased the expression of nitrogen fixation functions but did not significantly enhance SOC content, possibly because CK is more conducive to microbial diversity and organic matter accumulation. In contrast, SF treatment provided more nitrogen to crops through the expression of nitrogen fixation genes. In the 0–10 cm layer, although the overall KO abundance did not differ significantly, broadcast fertilization (CK) exhibited a slightly higher abundance of K04561 (norB) and a lower abundance of K00371 (narH) compared to side-deep fertilization (SF). Moreover, the higher abundance of the nitrogen fixation-related gene K02567 (napA) under SF suggests that side-deep fertilization may enhance nitrogen fixation even in the shallow soil layer.
In deeper soil (10–20 cm), SF treatment significantly increased the abundance of functional genes related to nitrogen metabolism, particularly those involved in nitrite reduction to ammonium and nitrogen gas, indicating that SF treatment significantly enhanced nitrogen reduction capacity in deeper soil. This variation may be associated with the low-oxygen conditions in deeper soil being more favorable for the expression of these functional genes. Additionally, SF treatment enhanced nitrogen fixation functions in deeper soil but weakened other nitrogen cycling processes, further illustrating the complex regulatory role of SF treatment on nitrogen cycling. These results provide a scientific basis for optimizing fertilization management strategies in rice fields, suggesting that shallow soil is more suitable for conventional broadcast fertilization to promote microbial diversity and organic matter accumulation [55,64], while deeper soil requires side-deep fertilization to enhance nitrogen reduction capacity and nutrient supply [17]. Specifically, significant differences were observed for K02591 (nifK), K02588 (nifH), and K02568 (napB), with SF showing a higher expression of the nitrogen fixation genes and a corresponding shift in nitrate reduction dynamics compared to CK. Additionally, across both soil layers, SF consistently demonstrated lower levels of K00370 (narG) and K00376 (nosZ) but a higher level of K00374 (narI) relative to CK. These contrasting patterns indicate that SF differentially regulates the balance between nitrogen fixation and nitrate reduction processes depending on soil depth.

4.3.2. Effects of Deep Nitrogen Fertilization on Carbon Cycling Functional Genes

SF exhibited significant stratification in its effects on carbon cycling functional genes across different soil layers (Figure 7). In shallow soil, SF treatment significantly increased the abundance of “Microbial metabolism in diverse environments” and “Carbon metabolism” functions, while in deeper soil, SF treatment significantly enhanced the expression of “Nitrogen metabolism” functions [65]. These variations may be associated with the physical characteristics and microbial activity conditions across soil layers. Shallow soil, with strong aeration and high organic matter content, favors enhanced microbial diversity and carbon metabolism [66], whereas the low-oxygen environment and higher nitrogen metabolism demands in deeper soil are better met by SF treatment [15]. Additionally, KEGG Module enrichment analysis showed that SF treatment upregulated the citrate cycle and reductive citrate cycle functions in shallow soil, while upregulating methane oxidation and nitrification functions in deeper soil. These results reveal the complex regulatory effects of side-deep fertilization on carbon cycling functions, providing theoretical support for further optimizing fertilization strategies.
In the 0–10 cm layer, CK treatment resulted in a significantly higher abundance of K01179 (formate dehydrogenase subunit) and K01728 (ferredoxin), indicating that conventional broadcast fertilization may promote pathways associated with organic matter oxidation in the surface soil. Conversely, in the 10–20 cm layer, SF treatment showed a significantly higher abundance of both K01179 and K10944 (nitric oxide reductase subunit), suggesting that deep fertilization enhances carbon transformation and potentially supports a more active carbon turnover in deeper soils. Complementarily, the KEGG Module enrichment analysis further demonstrated that SF treatment modulated carbon cycling processes in a depth-dependent manner: while it upregulated the citrate cycle and reductive citrate cycle functions in the 0–10 cm layer, it concurrently downregulated methane oxidation and nitrification-related functions. In contrast, in the 10–20 cm layer, SF treatment enhanced methane oxidation and nitrification functions, yet reduced the activity of the citrate cycles.

4.4. Relationship Between Soil Microbial Communities and Basic Physicochemical Properties

RDA analysis revealed that soil physicochemical properties significantly influenced microbial community structure. Actinobacteria showed a positive correlation with SOC, whereas Proteobacteria exhibited a negative correlation with SOC. Actinobacteria and Planctomycetes were positively correlated with soil TN. These results indicate that soil physicochemical properties have a significant impact on microbial community structure, particularly for certain microbial phyla [67]. Actinobacteria are typically more active in environments rich in organic matter and nutrients [68], while Proteobacteria may be more common in environments with lower organic matter. The positive correlation of Planctomycetes with total nitrogen may indicate their important role in nitrogen cycling, particularly under side-deep fertilization treatment [60]. These findings provide important insights into the interactions between soil microbial communities and physicochemical properties.

5. Conclusions

This study provided an in-depth examination of the depth-dependent impacts of fertilization methods on soil nutrient dynamics, microbial diversity, and functional gene expression in double-cropping rice paddies. Our results demonstrated that side-deep fertilization (SF) significantly enhances nitrogen cycling in deeper soil layers (10–20 cm), as indicated by increased total nitrogen content and the upregulation of key nitrogen fixation and metabolism genes (e.g., K02591 and K02588). Moreover, SF induced shifts in the microbial community—marked by an enrichment of Chloroflexi and Planctomycetes—that are well-adapted to low-oxygen, nutrient-rich environments, thereby reinforcing its role in optimizing deep-soil nutrient availability. Conversely, conventional broadcast fertilization (CK) proved more effective in the shallow soil layer (0–10 cm), where it promoted soil organic carbon accumulation and enhanced the expression of carbon metabolism genes (e.g., K01179), supporting vigorous surface organic matter dynamics. These findings emphasize the necessity of tailoring fertilization strategies according to soil depth to improve nitrogen use efficiency, sustain soil health, and ultimately enhance rice yield sustainability. While our study offers valuable insights for developing more precise and environmentally friendly fertilization practices, further research is needed to assess long-term ecological impacts and to integrate additional agronomic factors into these management strategies.

Author Contributions

Methodology, Q.-H.X. and J.-Y.Q.; Validation, Q.-H.X. and X.-B.Y.; Formal analysis, X.-B.Y. and Y.Y.; Investigation, Q.-H.X., X.-B.Y., Y.Y. and D.-J.L.; Resources, J.-Y.Q.; Data curation, Q.-H.X. and X.-B.Y.; Writing—original draft preparation, Q.-H.X.; Writing—review and editing, Q.-H.X., X.-B.Y., Y.Y., D.-J.L. and J.-Y.Q. All authors have read and agreed to the published version of the manuscript.

Funding

This study was supported by Guangdong Basic and Applied Basic Research Foundation (2024A1515012709). This study was also supported by the Provincial-Level College Student Entrepreneurship and Innovation Project, titled “Effects of Fertilizer Deep Placement in Double-Season Rice on Soil Carbon, Nitrogen, and Microbial Function in South China”.

Data Availability Statement

The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Bandumula, N. Rice Production in Asia: Key to Global Food Security. Proc. Natl. Acad. Sci. India Sect. B Biol. Sci. 2018, 88, 1323–1328. [Google Scholar] [CrossRef]
  2. Timmer, C.P. The Changing Role of Rice in Asia’s Food Security. Available online: https://www.google.ca/url?sa=t&source=web&rct=j&opi=89978449&url=https://www.adb.org/sites/default/files/publication/28552/adb-wp15-rice-food-security.pdf&ved=2ahUKEwj0-PyeoMeMAxXkavUHHUitLOIQFnoECBkQAQ&usg=AOvVaw0rqMIQ4WXoouJT1AUlngw3 (accessed on 2 February 2025).
  3. Yin, X.; Huang, M.; Zou, Y. Changes in Rice Yield Stability in Southern China from 1949 to 2015. Agric. Environ. Lett. 2018, 3, 170038. [Google Scholar] [CrossRef]
  4. Zhong, X.; Peng, J.; Kang, X.; Wu, Y.; Luo, G.; Hu, W.; Zhou, X. Optimizing Agronomic Traits and Increasing Economic Returns of Machine-Transplanted Rice with Side-Deep Fertilization of Double-Cropping Rice System in Southern China. Field Crops Res. 2021, 270, 108191. [Google Scholar] [CrossRef]
  5. Liu, T.Q.; Fan, D.J.; Zhang, X.X.; Chen, J.; Li, C.F.; Cao, C.G. Deep Placement of Nitrogen Fertilizers Reduces Ammonia Volatilization and Increases Nitrogen Utilization Efficiency in No-Tillage Paddy Fields in Central China. Field Crops Res. 2015, 184, 80–90. [Google Scholar] [CrossRef]
  6. Pan, S.; Wen, X.; Wang, Z.; Ashraf, U.; Tian, H.; Duan, M.; Mo, Z.; Fan, P.; Tang, X. Benefits of Mechanized Deep Placement of Nitrogen Fertilizer in Direct-Seeded Rice in South China. Field Crops Res. 2017, 203, 139–149. [Google Scholar] [CrossRef]
  7. Xue, J.-F.; Pu, C.; Liu, S.-L.; Zhao, X.; Zhang, R.; Chen, F.; Xiao, X.-P.; Zhang, H.-L. Carbon and Nitrogen Footprint of Double Rice Production in Southern China. Ecol. Indic. 2016, 64, 249–257. [Google Scholar] [CrossRef]
  8. Zhang, W.; Dou, Z.; He, P.; Ju, X.-T.; Powlson, D.; Chadwick, D.; Norse, D.; Lu, Y.-L.; Zhang, Y.; Wu, L.; et al. New Technologies Reduce Greenhouse Gas Emissions from Nitrogenous Fertilizer in China. Proc. Natl. Acad. Sci. USA 2013, 110, 8375–8380. [Google Scholar] [CrossRef]
  9. Kahrl, F.; Li, Y.; Su, Y.; Tennigkeit, T.; Wilkes, A.; Xu, J. Greenhouse Gas Emissions from Nitrogen Fertilizer Use in China. Environ. Sci. Policy 2010, 13, 688–694. [Google Scholar] [CrossRef]
  10. Conant, R.T.; Berdanier, A.B.; Grace, P.R. Patterns and Trends in Nitrogen Use and Nitrogen Recovery Efficiency in World Agriculture. Glob. Biogeochem. Cycles 2013, 27, 558–566. [Google Scholar] [CrossRef]
  11. Zhang, X.; Davidson, E.A.; Mauzerall, D.L.; Searchinger, T.D.; Dumas, P.; Shen, Y. Managing Nitrogen for Sustainable Development. Nature 2015, 528, 51–59. [Google Scholar] [CrossRef]
  12. Cai, S.; Zhao, X.; Pittelkow, C.M.; Fan, M.; Zhang, X.; Yan, X. Optimal Nitrogen Rate Strategy for Sustainable Rice Production in China. Nature 2023, 615, 73–79. [Google Scholar] [CrossRef] [PubMed]
  13. Zhang, D.; Wang, H.; Pan, J.; Luo, J.; Liu, J.; Gu, B.; Liu, S.; Zhai, L.; Lindsey, S.; Zhang, Y.; et al. Nitrogen Application Rates Need to Be Reduced for Half of the Rice Paddy Fields in China. Agric. Ecosyst. Environ. 2018, 265, 8–14. [Google Scholar] [CrossRef]
  14. Liang, K.; Zhong, X.; Huang, N.; Lampayan, R.M.; Liu, Y.; Pan, J.; Peng, B.; Hu, X.; Fu, Y. Nitrogen Losses and Greenhouse Gas Emissions under Different N and Water Management in a Subtropical Double-Season Rice Cropping System. Sci. Total Environ. 2017, 609, 46–57. [Google Scholar] [CrossRef] [PubMed]
  15. Zhong, X.; Zhou, X.; Luo, G.; Huang, Y.; Wu, Y.; Cao, R.; Tian, C.; Peng, J. Soil Mineral Nitrogen, Soil Urease Activity, Nitrogen Losses and Nitrogen Footprint under Machine-Planted Rice with Side-Deep Fertilization. Plant Soil 2024, 494, 185–202. [Google Scholar] [CrossRef]
  16. Wu, Q.-X.; Du, B.; Jiang, S.-C.; Zhang, H.-W.; Zhu, J.-Q. Side Deep Fertilizing of Machine-Transplanted Rice to Guarantee Rice Yield in Conservation Tillage. Agriculture 2022, 12, 528. [Google Scholar] [CrossRef]
  17. Hou, K.; Zhang, L.; Liu, P.; He, S.; Rong, X.; Peng, J.; Zhang, Y.; Tian, C.; Han, Y. Side-Deep Fertilization Stabilizes Double-Cropping Rice Yield, Increases N and P Utilization, and Reduces N and P Losses. Land 2023, 12, 724. [Google Scholar] [CrossRef]
  18. Xiong, Q.; Hu, J.; Wei, H.; Zhang, H.; Zhu, J. Relationship between Plant Roots, Rhizosphere Microorganisms, and Nitrogen and Its Special Focus on Rice. Agriculture 2021, 11, 234. [Google Scholar] [CrossRef]
  19. Zhao, C.; Huang, H.; Qian, Z.; Jiang, H.; Liu, G.; Xu, K.; Hu, Y.; Dai, Q.; Huo, Z. Effect of Side Deep Placement of Nitrogen on Yield and Nitrogen Use Efficiency of Single Season Late Japonica Rice. J. Integr. Agric. 2021, 20, 1487–1502. [Google Scholar] [CrossRef]
  20. Tarafdar, J.C.; Meena, S.C.; Kathju, S. Influence of Straw Size on Activity and Biomass of Soil Microorganisms during Decomposition. Eur. J. Soil Biol. 2001, 37, 157–160. [Google Scholar] [CrossRef]
  21. Smith, P.; Cotrufo, M.F.; Rumpel, C.; Paustian, K.; Kuikman, P.J.; Elliott, J.A.; McDowell, R.; Griffiths, R.I.; Asakawa, S.; Bustamante, M.; et al. Biogeochemical Cycles and Biodiversity as Key Drivers of Ecosystem Services Provided by Soils. Soil 2015, 1, 665–685. [Google Scholar] [CrossRef]
  22. Van Veen, J.A.; Kuikman, P.J. Soil Structural Aspects of Decomposition of Organic Matter by Micro-Organisms. Biogeochemistry 1990, 11, 213–233. [Google Scholar] [CrossRef]
  23. Raza, T.; Qadir, M.F.; Khan, K.S.; Eash, N.S.; Yousuf, M.; Chatterjee, S.; Manzoor, R.; ur Rehman, S.; Oetting, J.N. Unraveling the Potential of Microbes in Decomposition of Organic Matter and Release of Carbon in the Ecosystem. J. Environ. Manag. 2023, 344, 118529. [Google Scholar] [CrossRef] [PubMed]
  24. Gupta, V.V.S.R.; Germida, J.J. Soil Aggregation: Influence on Microbial Biomass and Implications for Biological Processes. Soil Biol. Biochem. 2015, 80, A3–A9. [Google Scholar] [CrossRef]
  25. Guo, L.; Wang, C.; Zheng, M.; Li, W.; Cai, Z.; Wang, B.; Chen, J.; Shen, R.F. Fertilization Practices Affect Biological Nitrogen Fixation by Modulating Diazotrophic Communities in an Acidic Soil in Southern China. Pedosphere 2023, 33, 301–311. [Google Scholar] [CrossRef]
  26. Ying, D.; Chen, X.; Hou, J.; Zhao, F.; Li, P. Soil Properties and Microbial Functional Attributes Drive the Response of Soil Multifunctionality to Long-Term Fertilization Management. Appl. Soil Ecol. 2023, 192, 105095. [Google Scholar] [CrossRef]
  27. Wang, Y.; Ke, X.; Wu, L.; Lu, Y. Community Composition of Ammonia-Oxidizing Bacteria and Archaea in Rice Field Soil as Affected by Nitrogen Fertilization. Syst. Appl. Microbiol. 2009, 32, 27–36. [Google Scholar] [CrossRef]
  28. Xu, C.; Chen, Y.; Zang, Q.; Li, Y.; Zhao, J.; Lu, X.; Jiang, M.; Zhuang, H.; Huang, L. The Effects of Cultivation Patterns and Nitrogen Levels on Fertility and Bacterial Community Characteristics of Surface and Subsurface Soil. Front. Microbiol. 2023, 14, 1072228. [Google Scholar] [CrossRef] [PubMed]
  29. Tecon, R.; Or, D. Biophysical Processes Supporting the Diversity of Microbial Life in Soil. FEMS Microbiol. Rev. 2017, 41, 599–623. [Google Scholar] [CrossRef]
  30. Naylor, D.; McClure, R.; Jansson, J. Trends in Microbial Community Composition and Function by Soil Depth. Microorganisms 2022, 10, 540. [Google Scholar] [CrossRef]
  31. Sokol, N.W.; Slessarev, E.; Marschmann, G.L.; Nicolas, A.; Blazewicz, S.J.; Brodie, E.L.; Firestone, M.K.; Foley, M.M.; Hestrin, R.; Hungate, B.A.; et al. Life and Death in the Soil Microbiome: How Ecological Processes Influence Biogeochemistry. Nat. Rev. Microbiol. 2022, 20, 415–430. [Google Scholar] [CrossRef]
  32. Dong, M.; Zhou, H.; Wang, J.; Yang, J.; Lai, J.; Chen, Y.; Sun, F.; Ye, X.; Wu, Y. Responses of Soil Microbial Metabolism, Function and Soil Quality to Long-Term Addition of Organic Materials with Different Carbon Sources. Biochar 2024, 6, 80. [Google Scholar] [CrossRef]
  33. Du, L.; Zhong, H.; Guo, X.; Li, H.; Xia, J.; Chen, Q. Nitrogen Fertilization and Soil Nitrogen Cycling: Unraveling the Links among Multiple Environmental Factors, Functional Genes, and Transformation Rates. Sci. Total Environ. 2024, 951, 175561. [Google Scholar] [CrossRef] [PubMed]
  34. Garlapati, D.; Charankumar, B.; Ramu, K.; Madeswaran, P.; Ramana Murthy, M.V. A Review on the Applications and Recent Advances in Environmental DNA (eDNA) Metagenomics. Rev. Environ. Sci. Biotechnol. 2019, 18, 389–411. [Google Scholar] [CrossRef]
  35. Sousa, J.; Silvério, S.C.; Costa, A.M.A.; Rodrigues, L.R. Metagenomic Approaches as a Tool to Unravel Promising Biocatalysts from Natural Resources: Soil and Water. Catalysts 2022, 12, 385. [Google Scholar] [CrossRef]
  36. Staley, C.; Sadowsky, M.J. Application of Metagenomics to Assess Microbial Communities in Water and Other Environmental Matrices. J. Mar. Biol. Assoc. United Kingd. 2016, 96, 121–129. [Google Scholar] [CrossRef]
  37. Grossart, H.-P.; Massana, R.; McMahon, K.D.; Walsh, D.A. Linking Metagenomics to Aquatic Microbial Ecology and Biogeochemical Cycles. Limnol. Oceanogr. 2020, 65, S2–S20. [Google Scholar] [CrossRef]
  38. Clark, I.M.; Hughes, D.J.; Fu, Q.; Abadie, M.; Hirsch, P.R. Metagenomic Approaches Reveal Differences in Genetic Diversity and Relative Abundance of Nitrifying Bacteria and Archaea in Contrasting Soils. Sci. Rep. 2021, 11, 15905. [Google Scholar] [CrossRef]
  39. Temperton, B.; Giovannoni, S.J. Metagenomics: Microbial Diversity through a Scratched Lens. Curr. Opin. Microbiol. 2012, 15, 605–612. [Google Scholar] [CrossRef] [PubMed]
  40. Cavicchioli, R.; DeMaere, M.Z.; Thomas, T. Metagenomic Studies Reveal the Critical and Wide-Ranging Ecological Importance of Uncultivated Archaea: The Role of Ammonia Oxidizers. BioEssays 2007, 29, 11–14. [Google Scholar] [CrossRef]
  41. Hu, X.; Gu, H.; Liu, J.; Wei, D.; Zhu, P.; Cui, X.; Zhou, B.; Chen, X.; Jin, J.; Liu, X.; et al. Metagenomics Reveals Divergent Functional Profiles of Soil Carbon and Nitrogen Cycling under Long-Term Addition of Chemical and Organic Fertilizers in the Black Soil Region. Geoderma 2022, 418, 115846. [Google Scholar] [CrossRef]
  42. Li, K.; Lin, H.; Han, M.; Yang, L. Soil Metagenomics Reveals the Effect of Nitrogen on Soil Microbial Communities and Nitrogen-Cycle Functional Genes in the Rhizosphere of Panax Ginseng. Front. Plant Sci. 2024, 15, 1411073. [Google Scholar] [CrossRef] [PubMed]
  43. Harindintwali, J.D.; Zhou, J.; Muhoza, B.; Wang, F.; Herzberger, A.; Yu, X. Integrated Eco-Strategies towards Sustainable Carbon and Nitrogen Cycling in Agriculture. J. Environ. Manag. 2021, 293, 112856. [Google Scholar] [CrossRef] [PubMed]
  44. Thamdrup, B. New Pathways and Processes in the Global Nitrogen Cycle. Annu. Rev. Ecol. Evol. Syst. 2012, 43, 407–428. [Google Scholar] [CrossRef]
  45. Wu, H.; Cui, H.; Fu, C.; Li, R.; Qi, F.; Liu, Z.; Yang, G.; Xiao, K.; Qiao, M. Unveiling the Crucial Role of Soil Microorganisms in Carbon Cycling: A Review. Sci. Total Environ. 2024, 909, 168627. [Google Scholar] [CrossRef] [PubMed]
  46. Soussana, J.-F.; Lemaire, G. Coupling Carbon and Nitrogen Cycles for Environmentally Sustainable Intensification of Grasslands and Crop-Livestock Systems. Agric. Ecosyst. Environ. 2014, 190, 9–17. [Google Scholar] [CrossRef]
  47. Estimation of Available Phosphorus in Soils by Extraction with Sodium Bicarbonate; Olsen, S.R., Ed.; Circular/United States Department of Agriculture: Washington, DC, USA, 1954. [Google Scholar]
  48. Soil Analysis: An Interpretation Manual; Peverill, K., Sparrow, L., Reuter, D., Eds.; CSIRO Publishing: Clayton, Australia, 1999; ISBN 978-0-643-10135-7. [Google Scholar]
  49. Li, D.; Liu, C.-M.; Luo, R.; Sadakane, K.; Lam, T.-W. MEGAHIT: An Ultra-Fast Single-Node Solution for Large and Complex Metagenomics Assembly via Succinct de Bruijn Graph. Bioinformatics 2015, 31, 1674–1676. [Google Scholar] [CrossRef]
  50. Hyatt, D.; Chen, G.-L.; LoCascio, P.F.; Land, M.L.; Larimer, F.W.; Hauser, L.J. Prodigal: Prokaryotic Gene Recognition and Translation Initiation Site Identification. BMC Bioinform. 2010, 11, 119. [Google Scholar] [CrossRef]
  51. Li, R.; Yu, C.; Li, Y.; Lam, T.-W.; Yiu, S.-M.; Kristiansen, K.; Wang, J. SOAP2: An Improved Ultrafast Tool for Short Read Alignment. Bioinformatics 2009, 25, 1966–1967. [Google Scholar] [CrossRef]
  52. Altschul, S.F.; Madden, T.L.; Schäffer, A.A.; Zhang, J.; Zhang, Z.; Miller, W.; Lipman, D.J. Gapped BLAST and PSI-BLAST: A New Generation of Protein Database Search Programs. Nucleic Acids Res. 1997, 25, 3389–3402. [Google Scholar] [CrossRef]
  53. Stark, C.; Condron, L.M.; Stewart, A.; Di, H.J.; O’Callaghan, M. Influence of Organic and Mineral Amendments on Microbial Soil Properties and Processes. Appl. Soil Ecol. 2007, 35, 79–93. [Google Scholar] [CrossRef]
  54. Heinze, S.; Raupp, J.; Joergensen, R.G. Effects of Fertilizer and Spatial Heterogeneity in Soil pH on Microbial Biomass Indices in a Long-Term Field Trial of Organic Agriculture. Plant Soil 2010, 328, 203–215. [Google Scholar] [CrossRef]
  55. Zhao, Y.; Xiong, X.; Wu, C. Effects of Deep Placement of Fertilizer on Periphytic Biofilm Development and Nitrogen Cycling in Paddy Systems. Pedosphere 2021, 31, 125–133. [Google Scholar] [CrossRef]
  56. Ke, J.; He, R.; Hou, P.; Ding, C.; Ding, Y.; Wang, S.; Liu, Z.; Tang, S.; Ding, C.; Chen, L.; et al. Combined Controlled-Released Nitrogen Fertilizers and Deep Placement Effects of N Leaching, Rice Yield and N Recovery in Machine-Transplanted Rice. Agric. Ecosyst. Environ. 2018, 265, 402–412. [Google Scholar] [CrossRef]
  57. Wu, P.; Liu, F.; Li, H.; Cai, T.; Zhang, P.; Jia, Z. Suitable Fertilizer Application Depth Can Increase Nitrogen Use Efficiency and Maize Yield by Reducing Gaseous Nitrogen Losses. Sci. Total Environ. 2021, 781, 146787. [Google Scholar] [CrossRef]
  58. Ge, G.; Li, Z.; Fan, F.; Chu, G.; Hou, Z.; Liang, Y. Soil Biological Activity and Their Seasonal Variations in Response to Long-Term Application of Organic and Inorganic Fertilizers. Plant Soil 2010, 326, 31–44. [Google Scholar] [CrossRef]
  59. Chen, Y.; Tu, P.; Yang, Y.; Xue, X.; Feng, Z.; Dan, C.; Cheng, F.; Yang, Y.; Deng, L. Diversity of Rice Rhizosphere Microorganisms under Different Fertilization Modes of Slow-Release Fertilizer. Sci. Rep. 2022, 12, 2694. [Google Scholar] [CrossRef]
  60. Koch, H.; Lücker, S.; Albertsen, M.; Kitzinger, K.; Herbold, C.; Spieck, E.; Nielsen, P.H.; Wagner, M.; Daims, H. Expanded Metabolic Versatility of Ubiquitous Nitrite-Oxidizing Bacteria from the Genus Nitrospira. Proc. Natl. Acad. Sci. USA 2015, 112, 11371–11376. [Google Scholar] [CrossRef] [PubMed]
  61. Peng, L.; Deng, S.; Wu, Y.; Yi, W.; Zhang, Y.; Yao, X.; Xing, P.; Gu, Q.; Qi, J.; Tang, X. A Rapid Increase of Soil Organic Carbon in Paddy Fields after Applying Organic Fertilizer with Reduced Inorganic Fertilizer and Water-Saving Irrigation Is Linked with Alterations in the Structure and Function of Soil Bacteria. Agric. Ecosyst. Environ. 2025, 379, 109353. [Google Scholar] [CrossRef]
  62. Yao, Y.; Zhang, M.; Tian, Y.; Zhao, M.; Zhang, B.; Zeng, K.; Zhao, M.; Yin, B. Urea Deep Placement in Combination with Azolla for Reducing Nitrogen Loss and Improving Fertilizer Nitrogen Recovery in Rice Field. Field Crops Res. 2018, 218, 141–149. [Google Scholar] [CrossRef]
  63. Sarula; Yang, H.; Zhang, R.; Li, Y.; Meng, F.; Ma, J. Impact of Drip Irrigation and Nitrogen Fertilization on Soil Microbial Diversity of Spring Maize. Plants 2022, 11, 3206. [Google Scholar] [CrossRef]
  64. Cao, Y.; Wu, P.; Dai, Z.; Wang, G.; He, S.; Gong, X.; Li, X. Effect of Side-Deep Fertilization on Yield, Nutrient Uptake and Economic Benefits of Rice. Chin. J. Rice Sci. 2024, 38, 695. [Google Scholar] [CrossRef]
  65. Li, J.; Li, Y.; Yang, X.; Zhang, J.; Lin, Z.; Zhao, B. Microbial Community Structure and Functional Metabolic Diversity Are Associated with Organic Carbon Availability in an Agricultural Soil. J. Integr. Agric. 2015, 14, 2500–2511. [Google Scholar] [CrossRef]
  66. Zhang, C.; Nie, S.; Liang, J.; Zeng, G.; Wu, H.; Hua, S.; Liu, J.; Yuan, Y.; Xiao, H.; Deng, L.; et al. Effects of Heavy Metals and Soil Physicochemical Properties on Wetland Soil Microbial Biomass and Bacterial Community Structure. Sci. Total Environ. 2016, 557–558, 785–790. [Google Scholar] [CrossRef] [PubMed]
  67. Bhatti, A.A.; Haq, S.; Bhat, R.A. Actinomycetes Benefaction Role in Soil and Plant Health. Microb. Pathog. 2017, 111, 458–467. [Google Scholar] [CrossRef]
  68. Wiegand, S.; Jogler, M.; Jogler, C. On the Maverick Planctomycetes. FEMS Microbiol. Rev. 2018, 42, 739–760. [Google Scholar] [CrossRef]
Agronomy 15 00938 g001
Figure 1. The impact of deep nitrogen fertilization on soil TN and SOC in October 2022 (22-Oct) (A,C) and October 2023 (23-Oct) (B,D). SF represents the side-deep fertilization treatment, and CK represents the conventional broadcast fertilization treatment. * indicates significant differences (p < 0.05). Error bars represent standard errors (n = 3).
Figure 1. The impact of deep nitrogen fertilization on soil TN and SOC in October 2022 (22-Oct) (A,C) and October 2023 (23-Oct) (B,D). SF represents the side-deep fertilization treatment, and CK represents the conventional broadcast fertilization treatment. * indicates significant differences (p < 0.05). Error bars represent standard errors (n = 3).
Agronomy 15 00938 g001
Agronomy 15 00938 g002
Figure 2. Differences in phylum-level soil microbial community composition under different fertilization methods in the 0–10 cm (A) and 10–20 cm (B) soil layers. CK represents conventional fertilization, while SF denotes side-deep fertilization.
Figure 2. Differences in phylum-level soil microbial community composition under different fertilization methods in the 0–10 cm (A) and 10–20 cm (B) soil layers. CK represents conventional fertilization, while SF denotes side-deep fertilization.
Agronomy 15 00938 g002
Agronomy 15 00938 g003
Figure 3. Impacts of fertilization regimes on α- and β-diversity in soil microbiota. (A,B) display the Simpson index and Shannon index of soil microbial α-diversity. (C,D) illustrate the PCA analysis and PCoA analysis of microbial β-diversity, respectively. * indicates significant differences (p < 0.05).
Figure 3. Impacts of fertilization regimes on α- and β-diversity in soil microbiota. (A,B) display the Simpson index and Shannon index of soil microbial α-diversity. (C,D) illustrate the PCA analysis and PCoA analysis of microbial β-diversity, respectively. * indicates significant differences (p < 0.05).
Agronomy 15 00938 g003
Agronomy 15 00938 g004
Figure 4. Effects of different fertilization methods on microbial community composition in paddy soils. (A,B) illustrate the differences in microbial community structure. (C) analyzes the significant differences in microbial communities. (D) reveals the relationship between soil microbial community composition and basic physicochemical properties (pH, SOC, TN, AK, NO3−, and NH4+) through redundancy analysis (RDA). Red arrows indicate the explanatory power of physicochemical properties on samples, while blue arrows represent the explanatory power of phylum-level microorganisms on samples. SF represents the side-deep fertilization treatment, and CK represents the conventional fertilization. * indicates significant differences (p < 0.05).
Figure 4. Effects of different fertilization methods on microbial community composition in paddy soils. (A,B) illustrate the differences in microbial community structure. (C) analyzes the significant differences in microbial communities. (D) reveals the relationship between soil microbial community composition and basic physicochemical properties (pH, SOC, TN, AK, NO3−, and NH4+) through redundancy analysis (RDA). Red arrows indicate the explanatory power of physicochemical properties on samples, while blue arrows represent the explanatory power of phylum-level microorganisms on samples. SF represents the side-deep fertilization treatment, and CK represents the conventional fertilization. * indicates significant differences (p < 0.05).
Agronomy 15 00938 g004
Agronomy 15 00938 g005
Figure 5. The impact of different fertilization practices on nitrogen cycling functional genes in rice paddy soil. (A) illustrates the variations in KEGG ORTHOLOGY (KO) functional levels between 0–10 cm and 10–20 cm soil layers under different fertilization practices. (B) presents the enrichment analysis results of KEGG Modules. SF represents the side-deep fertilization treatment, and CK represents the conventional fertilization. * indicates significant differences (p < 0.05).
Figure 5. The impact of different fertilization practices on nitrogen cycling functional genes in rice paddy soil. (A) illustrates the variations in KEGG ORTHOLOGY (KO) functional levels between 0–10 cm and 10–20 cm soil layers under different fertilization practices. (B) presents the enrichment analysis results of KEGG Modules. SF represents the side-deep fertilization treatment, and CK represents the conventional fertilization. * indicates significant differences (p < 0.05).
Agronomy 15 00938 g005
Agronomy 15 00938 g006
Figure 6. The impact of different fertilization practices on carbon cycling functional genes in rice paddy soil. (A) illustrates the variations in KEGG ORTHOLOGY (KO) functional levels under different fertilization practices. (B) presents the variations in Pathway level 3 functional levels. (C) demonstrates the outcomes of the KEGG Module enrichment analysis. SF represents the side-deep fertilization treatment, and CK represents the conventional fertilization. * indicates significant differences (p < 0.05).
Figure 6. The impact of different fertilization practices on carbon cycling functional genes in rice paddy soil. (A) illustrates the variations in KEGG ORTHOLOGY (KO) functional levels under different fertilization practices. (B) presents the variations in Pathway level 3 functional levels. (C) demonstrates the outcomes of the KEGG Module enrichment analysis. SF represents the side-deep fertilization treatment, and CK represents the conventional fertilization. * indicates significant differences (p < 0.05).
Agronomy 15 00938 g006
Agronomy 15 00938 g007
Figure 7. Diagram of the mechanism by which deep fertilizer application affects the soil carbon and nitrogen cycle.
Figure 7. Diagram of the mechanism by which deep fertilizer application affects the soil carbon and nitrogen cycle.
Agronomy 15 00938 g007
Table 1. Soil basic properties under side-deep fertilization (SF) and conventional fertilization (CK).
Table 1. Soil basic properties under side-deep fertilization (SF) and conventional fertilization (CK).
Sampling SeasonSoil Layer (cm)TreatmentpHTN (g kg−1)SOC (g kg−1)Available P (mg kg−1)Available K
(mg kg−1)
2022.100–10CK5.24 ± 0.02 a1.33 ± 0.01 a13.85 ± 0.22 a33.00 ± 0.47 a24.67 ± 0.88 a
SF5.04 ± 0.02 b1.32 ± 0.00 a13.38 ± 0.14 a36.96 ± 0.41 b20.33 ± 0.67 b
10–20CK5.37 ± 0.01 a1.28 ± 0.00 a13.18 ± 0.12 a35.04 ± 0.18 a15.67 ± 0.67 a
SF5.18 ± 0.00 b1.26 ± 0.01 a12.94 ± 0.07 a40.75 ± 0.44 b23.33 ± 0.33 b
2023.100–10CK 1.17 ± 0.01 a12.52 ± 0.05 a23.22 ± 0.23 a22.67 ± 2.19 a
SF 1.08 ± 0.05 b11.67 ± 0.30 b25.70 ± 0.74 b22.00 ± 0.58 a
10–20CK 1.15 ± 0.03 a12.10 ± 0.10 a27.30 ± 0.53 a19.33 ± 0.88 a
SF 1.18 ± 0.01 a11.97 ± 0.10 a27.42 ± 0.50 a20.67 ± 1.45 a
Within the same soil layer, different lowercase letters indicate significant differences between treatments (p < 0.05); identical letters denote non-significant differences (p > 0.05).
Table 2. Nitrogen cycle-related metabolism was annotated by KEGG.
Table 2. Nitrogen cycle-related metabolism was annotated by KEGG.
KOKEGG NameKO DescriptionPathway IDPathway Description
K04561norBnitric oxide reductase subunit B [EC:1.7.2.5]ko01100; ko01120; ko00910Metabolic pathways; Microbial metabolism in diverse environments; Nitrogen metabolism
K00371narH, narY, nxrBnitrate reductase/nitrite oxidoreductase, beta subunit [EC:1.7.5.1 1.7.99.-]ko01120; ko01100; ko00910Microbial metabolism in diverse environments; Metabolic pathways;
Nitrogen metabolism
K02591nifKnitrogenase molybdenum-iron protein beta chain [EC:1.18.6.1]ko00910; ko00625; ko01100; ko01120Nitrogen metabolism; Chloroalkane and chloroalkene degradation; Metabolic pathways; Microbial metabolism in diverse environments
K02567napAnitrate reductase (cytochrome) [EC:1.9.6.1]ko01120; ko01100; ko00910Microbial metabolism in diverse environments; Metabolic pathways;
Nitrogen metabolism
K02588nifHnitrogenase iron protein NifHko01100; ko01120; ko00625; ko00910Metabolic pathways; Microbial metabolism in diverse environments; Chloroalkane and chloroalkene degradation; Nitrogen metabolism
K02568napBnitrate reductase (cytochrome), electron transfer subunitko01120; ko01100; ko00910Microbial metabolism in diverse environments; Metabolic pathways;
Nitrogen metabolism
K00370narG, narZ, nxrAnitrate reductase/nitrite oxidoreductase, alpha subunit [EC:1.7.5.1 1.7.99.-]ko01100; ko01120; ko00910Metabolic pathways;
Microbial metabolism in diverse environments; Nitrogen metabolism
K00376nosZnitrous-oxide reductase [EC:1.7.2.4]ko01120; ko01100; ko00910Microbial metabolism in diverse environments; Metabolic pathways;
Nitrogen metabolism
K00374narI, narVnitrate reductase gamma subunit [EC:1.7.5.1 1.7.99.-]ko00910; ko01120; ko01100Nitrogen metabolism; Microbial metabolism in diverse environments; Metabolic pathways
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Xie, Q.-H.; Yao, X.-B.; Yang, Y.; Li, D.-J.; Qi, J.-Y. Effects of Deep Application of Fertilizer on Soil Carbon and Nitrogen Functions in Rice Paddies. Agronomy 2025, 15, 938. https://doi.org/10.3390/agronomy15040938

AMA Style

Xie Q-H, Yao X-B, Yang Y, Li D-J, Qi J-Y. Effects of Deep Application of Fertilizer on Soil Carbon and Nitrogen Functions in Rice Paddies. Agronomy. 2025; 15(4):938. https://doi.org/10.3390/agronomy15040938

Chicago/Turabian Style

Xie, Qi-Huan, Xiang-Bin Yao, Ya Yang, De-Jin Li, and Jian-Ying Qi. 2025. "Effects of Deep Application of Fertilizer on Soil Carbon and Nitrogen Functions in Rice Paddies" Agronomy 15, no. 4: 938. https://doi.org/10.3390/agronomy15040938

APA Style

Xie, Q.-H., Yao, X.-B., Yang, Y., Li, D.-J., & Qi, J.-Y. (2025). Effects of Deep Application of Fertilizer on Soil Carbon and Nitrogen Functions in Rice Paddies. Agronomy, 15(4), 938. https://doi.org/10.3390/agronomy15040938

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

Article Metrics

Back to TopTop