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Article

Metagenomic Sequencing Revealed the Effects of Different Potassium Sulfate Application Rates on Soil Microbial Community, Functional Genes, and Yield in Korla Fragrant Pear Orchard

by
Lele Yang
1,
Xing Shen
1,2,
Linsen Yan
1,
Jie Li
1,
Kailong Wang
1,
Bangxin Ding
1,2,* and
Zhongping Chai
1,2,*
1
College of Resources and Environment, Xinjiang Agricultural University, No. 311 East Agricultural University Road, Urumqi 830052, China
2
Xinjiang Key Laboratory of Soil and Plant Ecological Processes, Urumqi 830052, China
*
Authors to whom correspondence should be addressed.
Agronomy 2025, 15(7), 1752; https://doi.org/10.3390/agronomy15071752
Submission received: 18 June 2025 / Revised: 11 July 2025 / Accepted: 19 July 2025 / Published: 21 July 2025
(This article belongs to the Section Soil and Plant Nutrition)
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Abstract

Potassium fertilizer management is critical for achieving high yields of Korla fragrant pear, yet current practices often overlook or misuse potassium inputs. In this study, a two-year field experiment (2022–2023) was conducted with 7- to 8-year-old pear trees using four potassium levels (0, 75, 150, and 225 kg/hm2). Metagenomic sequencing was employed to assess the effects on soil microbial communities, sulfur cycle functional genes, and fruit yield. Potassium treatments significantly altered soil physicochemical properties, the abundance of sulfur cycle functional genes, and fruit yield (p < 0.05). Increasing application rates significantly elevated soil-available potassium and organic matter while reducing pH (p < 0.05). Although alpha diversity was unaffected, NMDS analysis revealed differences in microbial community composition under different treatments. Functional gene analysis showed a significant decreasing trend in betB abundance, a peak in hpsO under K150, and variable patterns for soxX and metX across treatments (p < 0.05). All potassium applications significantly increased yield relative to CK, with K150 achieving the highest yield (p < 0.05). PLS-PM analysis indicated significant positive associations between potassium rate, nutrient availability, microbial abundance, sulfur cycling, and yield, and a significant negative association with pH (p < 0.05). These results provide a foundation for optimizing potassium fertilizer strategies in Korla fragrant pear orchards. It is recommended that future studies combine metagenomic and metatranscriptomic approaches to further elucidate the mechanisms linking potassium-driven microbial functional changes to improvements in fruit quality.

1. Introduction

Fertilization is regarded as a fundamental approach to improving crop productivity. The application of chemical fertilizers has been shown to increase crop yields by 55.00–65.00%, making it a crucial measure for enhancing agricultural output [1]. Pyrus sinkiangensis is a regionally characteristic fruit protected by geographical indication in Southern Xinjiang, China. It has become an important component of Xinjiang’s regionally advantageous agriculture and plays a critical role in increasing farmers’ income and promoting fruit exports [2]. As fruit cultivation patterns evolve, the significance of potassium nutrition in improving the yield and quality of high-yield fruit crops has become increasingly evident [3]. In soil, potassium exists in various forms, including soluble, exchangeable, non-exchangeable, and structural forms. However, only a small proportion (1–2%) of the total potassium is directly available for plant absorption, while over 90% remains in forms inaccessible to plant roots [4]. It is important to note that although the soils of Xinjiang are rich in potassium resources, imbalances in fertilization structure, soil types, and physicochemical properties can result in potassium deficiency in fruit trees, even when total soil potassium content is high [5]. This occurs because the majority of potassium exists in the form of mineral crystals, such as feldspar, mica, and potassium salts, which are not readily accessible for uptake and utilization by plant roots [3,6]. Over the long term, due to the neglect of potassium application and the inherent potassium deficiency in Xinjiang soils, an imbalanced fertilization structure has developed in many orchards. This has severely limited the development of Korla fragrant pear production. It not only weakens the synergistic effect of nitrogen and phosphorus fertilization [7] but also leads to adverse outcomes such as soil acidification, salt accumulation, and compaction [8,9]. These changes can severely disrupt microbial community structures [10] and reduce both the relative abundance of soil functional genes and overall ecological services, such as nutrient cycling and soil formation, which are compromised due to these disruptions [11]. Numerous studies have demonstrated that the appropriate application of potassium fertilizer helps regulate and replenish soil nutrients [12], improves fertilizer use efficiency [7], and ultimately contributes to significant increases in orchard yield [13].
Fertilization not only improves soil nutrient status and organic matter content but also exerts significant effects on soil microbial communities [14,15]. In agroecosystems, soil microorganisms play a vital role in regulating plant growth and development, inhibiting the proliferation of pathogenic microorganisms, and facilitating nutrient transformation, maintenance of soil fertility, energy flow, and material cycling [6,16,17,18]. These microbial communities are highly sensitive to environmental changes in soil induced by fertilization [14,18]. For example, Zhang et al. [19] reported that potassium application enhances the diversity of both abundant and rare bacterial and fungal populations, and modulates rhizosphere microbial community structures. Similarly, Xing et al. [20] demonstrated that the abundance and diversity of microbial functional genes can be significantly influenced by different levels of fertilizer application. Thus, microbial communities are recognized as playing critical roles in maintaining soil fertility and quality, enhancing crop productivity, interacting directly with plants, and contributing to nutrient cycling, organic matter turnover, and overall ecosystem functioning [15,21,22].
Fertilization has been shown to significantly influence the relative abundance of microbial functional genes by altering both the abundance and metabolic pathways of soil microorganisms. In orchard cultivation, potassium sulfate is commonly applied as a potassium fertilizer, resulting in substantial inputs of exogenous sulfur into the soil. The sulfur cycle mediated by soil microorganisms plays a critical role in maintaining soil fertility, improving crop yield, and supporting environmental sustainability [23]. However, the production of sulfides during this cycle may exert negative effects on plant growth, primarily by inhibiting nutrient uptake through the root system. Sulfur serves as an essential nutrient involved in a wide range of metabolic functions across living organisms [24]. In soils, it plays a key role in nutrient cycling; however, approximately 95% of soil sulfur is present in organic forms that are not directly bioavailable to plants. The remaining fraction less than 5% consists of inorganic sulfur species, such as hydrogen sulfide (H2S), elemental sulfur (S0), and sulfate (SO42−), which are readily absorbable by plants [25]. Currently, the microbial-driven sulfur cycle has been categorized by researchers into seven distinct processes: organic sulfur compound mineralization (hereinafter referred to as the organic sulfur conversion pathway), assimilatory sulfate reduction, sulfur oxidation, sulfur reduction, dissimilatory sulfate reduction, and sulfur disproportionation [26]. Microorganisms have been shown to play an essential role in sulfur cycling through their involvement in oxidation, reduction, and disproportionation reactions, which promote biological assimilation and energy generation [23]. According to Christian Rückert’s findings [27], microorganisms are the primary drivers of sulfur transformations in soil. Functional genes associated with sulfate reduction are widely distributed among major microbial phyla, facilitating the conversion of sulfate, sulfite, and organic sulfonates into bioavailable sulfur forms. These genes also play key roles in regulating intracellular sulfur metabolism [27]. In addition, functional genes, such as sox and soe, have been widely identified in various soil microorganisms, indicating their involvement in microbial-mediated sulfur cycling processes in soil [28,29]. This highlights that the effective availability of sulfur in soil is largely dependent on microbial-driven sulfur transformations. Potassium sulfate may indirectly influence the soil sulfur cycle by modulating the structure and composition of microbial communities.
In response to issues, such as unbalanced fertilization, Korla fragrant pear orchards have generally sought to increase yield through the application of nitrogen fertilizer, phosphate fertilizer, and sheep manure. However, the application of potassium fertilizer is frequently neglected, resulting in the ineffective integration of nitrogen, phosphorus, and organic fertilizers, which in turn hampers improvements in soil fertility. Previous studies have mainly focused on the effects of different types of potassium fertilizers, such as potassium chloride and potassium sulfate, on microbial community structure and yield in pear orchards. However, the impact of varying potassium sulfate application levels across different soil layers on microbial community dynamics and the regulation of functional genes related to the sulfur cycle remains unclear. Therefore, potassium sulfate was selected as the potassium fertilizer in this study. By applying different fertilization rates, its effects on soil nutrient content, microbial community structure, and the abundance of sulfur cycle-related functional genes were analyzed in both surface (0–20 cm) and deep (40–60 cm) soil layers. The objective of this study is to provide a theoretical basis for the rational application of potassium fertilizer, optimize fertilization management in orchards, enhance nutrient use efficiency in fruit trees, and support the sustainable and healthy development of orchard ecosystems.

2. Materials and Methods

2.1. Experimental Site Description

This experiment was conducted in Awati Township, Korla City, Xinjiang Uygur Autonomous Region (41.73° N, 86.15° E). The study site is situated at the northeastern edge of the Tarim Basin, at the southern foot of the Tianshan Mountains, and adjacent to the northern boundary of the Taklimakan Desert. The climate in this area is classified as warm temperate continental arid, with an annual total sunshine duration of approximately 2800–3100 h, an average annual temperature of 11.0–12.0 °C, and an average annual precipitation of about 58 mm. The evaporation rate is high, ranging from 2700–3000 mm, and the total solar radiation is between 5600–6400 MJ/m2. The effective accumulated temperature is 4200–4500 °C, and the frost-free period lasts for 210–240 days. Prior to the initiation of the experiment, the nutrient status of the orchard soil was as follows: available nitrogen, 36.09 mg/kg; available phosphorus, 59.67mg/kg; available potassium, 186.67 mg/kg; organic matter, 18.46 g/kg; and soil pH 7.75.

2.2. Experimental Design

The research was conducted on 7–8-year-old Korla fragrant pear trees. The experiment was carried out in a medium-fertility orchard during the 2023–2024 growing season, applying different rates of potassium fertilizer under drip irrigation. The rootstock used was Pyrus betulifolia, with a plant spacing of 1.5 m × 4.0 m and a planting density of 1500 plants per hectare. Four levels of potassium fertilizer were applied under drip irrigation, as detailed in Table 1 and Table 2. Each treatment included five pear trees and was replicated three times in a randomized design. Prior to the experiment, sheep manure was applied as a basal fertilizer at a rate of 27,000 kg/hm2 following fruit harvest in the previous year. During the growth period of the fragrant pear, nitrogen, phosphorus, and potassium fertilizers were applied. For each treatment, 40% of the nitrogen fertilizer (urea, N 46%) was applied before germination, and the remaining 60% was applied at different growth stages. Phosphorus fertilizer (superphosphate, P2O5 46%) was applied as a basal fertilizer prior to germination. Potassium fertilizer (potassium sulfate, K2O 51%) was applied in two stages, with 40% applied before germination (except in the K0 treatment) and the remaining 60% during subsequent growth stages. The basal application of nitrogen, phosphorus, and potassium fertilizers was carried out using a ring ditch located 50–80 cm from the trunk, with a width of 30 cm and a depth of 30–60 cm beneath the canopy. The topdressing fertilizer was applied through drip fertigation using a differential pressure fertilization tank with a capacity of 15 L. The fertilizer was dissolved in water the day prior to each application. A 1/4-1/2-1/4 irrigation mode was adopted: the first quarter of the irrigation time was used for clear water pre-irrigation, the middle half for fertigation, and the final quarter for flushing the pipeline. All other field management practices were consistent with local agricultural standards.

2.3. Test Determination Method

2.3.1. Soil Sample Collection and Processing

In the second year of the experiment, soil samples at two depths (0–20 cm and 40–60 cm) were collected at the maturity stage of the Korla fragrant pear. Before sampling, surface litter was removed, and soil was collected from both sides of the fertilization ditch around each selected tree. For each treatment, three replicate plots were established, each consisting of a single row of trees. Within each row, three trees were randomly selected, and the soil from their root zones was composited to form one replicate at each depth. Consequently, each treatment had three replicates per depth, resulting in a total of 24 soil samples (4 treatments × 3 replicates × 2 depths). Soil from both sides of the fertilization ditch was collected after removing surface litter, at depths of 0–20 cm and 40–60 cm. Soil samples from both sides of the fertilization ditch at the same depth were composited, preliminarily broken, and thoroughly homogenized. The resulting samples were placed in self-sealing bags, stored in a cooler filled with dry ice, and returned to the laboratory under low-temperature conditions. After arrival in the laboratory, soil samples were processed by removing plant roots and large stones, then sequentially sieved through 2 mm and 0.25 mm meshes. Aliquots of the processed samples were stored at −80 °C and delivered to Shanghai Parsons Biotechnology Co., Ltd. (Shanghai, China) for metagenomic sequencing within one week. The remaining soil was air-dried indoors and sequentially passed through 1 mm and 0.25 mm sieves prior to analysis of soil physical and chemical properties.

2.3.2. Soil Physicochemical Analysis

The determination of soil physicochemical parameters was performed according to the protocols established by Bao [30]. The pH was measured using a pH meter (Mettler-Toledo, Columbus, OH, USA) with a water-to-soil ratio of 2.5:1 (water volume: soil weight). Alkali-hydrolysable nitrogen was determined using the semi-micro Kjeldahl method with a digestion and distillation system (Hanon Instruments, Jinan, China) and reagents (Sinopharm Chemical Reagent Co. Ltd., Shanghai, China). Available phosphorus, available potassium, and organic matter were analyzed using an elemental analyzer (Elementar Analysensysteme GmbH, Langenselbold, Germany), the potassium dichromate oxidation with external heating (Sinopharm Chemical Reagent Co. Ltd., Shanghai, China), the sodium bicarbonate extraction–molybdenum antimony colorimetric method (Sinopharm Chemical Reagent Co. Ltd., Shanghai, China), and the ammonium acetate extraction–flame photometric method (Sinopharm Chemical Reagent Co. Ltd., Shanghai, China), respectively.

2.3.3. Soil Microbial Metagenomic Sequencing

Total microbial DNA was first extracted, followed by library preparation and high-throughput sequencing via the Illumina platform (Illumina Inc., San Diego, CA, USA). The resulting raw data underwent rigorous quality control, host contamination filtering, and sequence assembly. Functional gene prediction and annotation were then conducted using comprehensive bioinformatics approaches, culminating in the elucidation of microbial community structure and functional dynamics. The detailed method is shown in Appendix A. Additionally, Supplementary Table S3 provides the technical details regarding the average sequencing depth per sample, total number of reads and contigs after quality control, average contig length, and percentage of reads retained after host decontamination.

2.3.4. Yield Determination

In September 2024, at the mature stage of the fragrant pear, five pears were randomly selected from the upper and lower branches, in four directions (east, south, west, and north) around each tree. The pears were then weighed, and the number of pears in each treatment was recorded. The yield per hectare was calculated based on a plant density of 1500 plants per hectare. The yield was calculated using the following formula:
Yield per plant = total number of fruits per plant × single fruit weight
The total yield of 1 hm2 pear was calculated by the yield per plant.

2.4. Statistical Analysis

Data processing was performed using Microsoft Excel 2019 (Microsoft Corp., Redmond, WA, USA), and statistical analyses were conducted in IBM SPSS Statistics 26.0. (IBM Corp., Armonk, NY, USA) One-way ANOVA was used to evaluate the effects of different fertilization treatments on soil physicochemical properties, microbial diversity, and functional gene abundance, with Duncan’s multiple range test applied for post hoc comparisons at a significance level of p < 0.05. All figures were created using Origin Pro 2024 (OriginLab Corp., Northampton, MA, USA). Microbial community and functional gene analyses were carried out on the Genescloud platform (developed by (Personalbio, Shanghai, China, https://www.genescloud.cn, accessed on 15 March 2025) using QIIME2 (v2023.10) and R (v3.5.1). Species-level abundance profiles were rarefied to the minimum sequencing depth to reduce bias caused by sequencing effort, and alpha diversity indices (e.g., Shannon, Simpson) were calculated using the vegan package (https://CRAN.R-project.org/package=vegan, accessed on 15 March 2025) and ggplot2 package (https://CRAN.R-project.org/package=ggplot2, accessed on 15 March 2025) in R. For groups with ≥3 replicates, boxplots were generated to visualize diversity differences, and statistical significance was initially assessed using the Kruskal–Wallis rank-sum test followed by Dunn’s test for pairwise comparisons. Due to the absence of significant differences in these non-parametric tests, one-way ANOVA with Duncan’s test was subsequently used. Linear regression analysis was performed using R to evaluate correlations between taxonomic and functional diversity indices. Partial Least Squares Path Modeling (PLS-PM) was conducted using SmartPLS 4 (SmartPLS GmbH, Boenningstedt, Germany) to explore the relationships among microbial diversity, functional gene abundance, and soil physicochemical parameters.

3. Results

3.1. Effects of Different Potassium Treatments on Physical and Chemical Properties

In both the topsoil and deeper soil layers, the K75, K150, and K225 treatments significantly increased available potassium, while the K150 treatment also significantly reduced soil pH. In the topsoil, available potassium increased by 3.58%, 11.72%, and 20.11%, respectively, and in the deeper layer, the increases were 9.61%, 22.36%, and 33.89%, compared to the CK treatment (p < 0.05) (Table 3). In the topsoil, pH decreased from 7.82 ± 0.02 to 7.72 ± 0.02 (a decrease of 0.10), and in the deeper layer, pH decreased from 7.73 ± 0.03 to 7.65 ± 0.02 (a decrease of 0.08) (p < 0.05).
In the topsoil, the K150 treatment significantly increased available phosphorus and organic matter by 4.41% and 19.31%, respectively, compared to the CK treatment (p < 0.05). In the deeper soil layer, organic matter levels were significantly elevated by 50.76% and 30.99% under the K150 and K225 treatments, respectively. No significant differences in available nitrogen and phosphorus were detected across treatments. Overall, available potassium levels in both the topsoil and subsoil were significantly increased under the K75 treatment (p < 0.05) (Table 3). Under the K150 treatment, both organic matter and available potassium levels were significantly elevated in both layers, while a significant decrease in pH was also observed (p < 0.05) (Table 3). The K225 treatment significantly increased available potassium levels in both soil layers.

3.2. Effects of Different Potassium Fertilizer Treatments on Soil Microbial Community Composition

Based on Figure 1A,B,D,E, no significant changes were detected in the Simpson and Shannon indices among treatments in either the surface or deep soil layers (p > 0.05), suggesting that microbial diversity was not markedly affected by potassium sulfate application. To further explore microbial community structure and compositional shifts, NMDS analysis was subsequently carried out. According to Figure 1C,F, stress values for the NMDS analysis were below 0.1 (0.0135 for the topsoil and 0.0513 for the subsoil), indicating a reliable representation of microbial community differences. The community structures observed in the K75, K150, and K225 treatments were distinct from that in the CK group, demonstrating that the application of potassium sulfate altered soil microbial communities. Notably, microbial composition in the surface layer was similar between K75 and K150, while in the deep layer, similarity was observed between K150 and K225. The NMDS plots and community composition profiles indicate that the topsoil microbial community is more responsive to high potassium concentrations, as evidenced by more pronounced shifts in ordination space and greater enrichment of dominant taxa under K225. In contrast, changes in the deep soil microbial community were similar under both K150 and K225 treatments, suggesting a possible threshold response to potassium input.
To examine the composition and shifts in microbial communities across surface and deep soil layers, the top 20 phyla were selected from Figure 1E,F based on their relative abundance under varying potassium fertilizer application rates. In the topsoil, the relative abundance of OP10 (Armatimonadetes) increased significantly by 48.64% under the K75 treatment compared to CK (p < 0.05), whereas Actinobacteria abundance was significantly reduced by 18.67% in the K225 treatment. No significant changes were observed for other treatments (p < 0.05). In deep soil, the K150 treatment led to significant increases in Ignavibacteriae and Nitrospira by 231.77% and 33.19%, respectively, while Actinobacteria decreased by 32.61% compared to CK (p < 0.05). Under the K225 treatment, Ignavibacteriae and Candidatus Tectomicrobia showed significant increases of 214.49% and 174.21%, respectively (p < 0.05). In the deep soil layer, increased abundances of Ignavibacteriae and Nitrospira and a decreased abundance of Actinobacteria were recorded under K150 (p < 0.05). The K225 treatment significantly reduced Actinobacteria in the topsoil while promoting Ignavibacteriae and Candidatus Tectomicrobia in the deep soil layer (p < 0.05). Collectively, these results demonstrate that proper potassium fertilizer application can enhance microbial abundance and stimulate the growth of dominant taxa in orchard soils.

3.3. Linear Regression of Soil Microbial and Functional Gene β Diversity

The β-diversity of microbial community structure exhibited a strong positive correlation with functional gene diversity in the topsoil (R2 = 0.92) and in the deep soil layer (R2 = 0.85), indicating consistent co-variation across soil depths. In the surface soil, the regression analysis yielded a slope of 0.92 and a coefficient of determination (R2) of 0.98, indicating a strong linear relationship and high consistency between the variables. In the deep soil, although the correlation remained statistically significant (p < 0.001), a lower regression slope of 0.85 and a reduced R2 of 0.88 were observed, along with a broader fitting band, suggesting slightly reduced consistency and greater data dispersion.
These findings demonstrate that spatial differences in microbial community composition are significantly correlated with variations in functional gene profiles in response to different potassium fertilizer treatments (p< 0.001).

3.4. Effect of Potassium Application on the Overall Composition of Sulfur Cycle

Figure 2A,B illustrate how various potassium fertilizer treatments influenced the sulfur cycle in both surface and deep soil layers. Specifically, the organic sulfur transformation, assimilatory sulfate reduction, and sulfur oxidation pathways within the energy metabolism sulfur cycle were affected by the application rate of potassium fertilizer.
In the topsoil, the abundance of sulfur oxidation-related gene sets in the K75, K150, and K225 treatments was significantly lower than that in the CK treatment by 1.90%, 1.73%, and 2.34%, respectively (p < 0.05) (Figure 3). Additionally, the abundance of the organic sulfur conversion gene set in the K225 treatment was significantly reduced by 1.82% compared to the CK treatment (p < 0.05). In the deep soil layer, the abundance of the assimilatory sulfate reduction gene set in the K75, K150, and K225 treatments was significantly lower than that in the CK treatment by 1.88%, 1.93%, and 2.60%, respectively (p < 0.05). The analysis revealed that, compared with the CK treatment, the K75, K150, and K225 treatments significantly inhibited the sulfur oxidation-related gene set in the topsoil (p < 0.05), and significantly suppressed the abundance of the sulfate reduction gene set in the deep soil layer (p < 0.05). Notably, the K225 treatment exerted a significant inhibitory effect on the abundance of organic sulfur conversion gene sets in the topsoil (p < 0.05). Overall, the application of potassium sulfate had a pronounced impact on the soil sulfur cycle process.

3.5. Effects of Potassium Fertilizer Application on Functional Genes in Each Link of Sulfur Cycle

3.5.1. Organic Sulfur and Inorganic-to-Organic Sulfur Transformation

In the topsoil, the relative abundance of betB under the K75 treatment was significantly lower than that in the CK treatment by 3.00% (p < 0.05). In contrast, the abundance of hpsO in the K150 treatment was significantly increased by 11.49% compared to CK (p < 0.05). Additionally, the abundances of betB and metC in the K150 treatment were significantly decreased by 3.63% and 3.39%, respectively (p < 0.05). Under the K225 treatment, the relative abundance of sqdB increased significantly by 5.88% (p < 0.05), whereas the abundances of betB, acuL, metC, metB, tauD, and metX were significantly reduced by 3.61%, 4.16%, 3.48%, 6.34%, 9.45%, and 4.63%, respectively (p < 0.05). In the deep soil layer, the relative abundances of hdrA2 and tbuC in the K75 treatment were increased by 29.37% and 19.71%, respectively, compared to the CK treatment (p < 0.05). However, the abundances of slcC, toa, dddW, and xsc were reduced by 4.32%, 19.52%, 17.58%, and 7.38%, respectively (p < 0.05). Similarly, under the K150 treatment, the abundance of hpsO significantly increased by 28.62% compared to CK (p < 0.05), while betB, ssuE, and tmoF were significantly reduced by 8.50%, 6.14%, and 48.92%, respectively (p < 0.05). In the K225 treatment, xsc abundance was increased by 7.53% (p < 0.05), whereas betB, tmoF, and ssuE were significantly decreased by 8.75%, 46.57%, and 7.23%, respectively (p < 0.05) (Figure 4).

3.5.2. Sulfur Oxidation (SOX Genes)

Among sulfur oxidation-related genes, in the topsoil, the relative abundance of soxB was significantly reduced by 5.88% and 6.56% under the K75 and K225 treatments, respectively, compared to the CK treatment (p < 0.05). In the deep soil layer, the relative abundance of soxX increased significantly by 12.50% in the K225 treatment compared to CK (p < 0.05).

3.5.3. Sulfur Reduction, Assimilatory Sulfate Reduction, and Dissimilatory Sulfate Reduction

In the topsoil, the relative abundances of aprA and dsrK were significantly increased by 7.47% and 73.36%, respectively, under the K75 treatment (p < 0.05). Similarly, under the K150 treatment, the abundances of psrA and aprA increased significantly by 73.18% and 10.92%, respectively (p < 0.05). In contrast, dsrP abundance was significantly reduced by 14.10% under the K150 treatment (p < 0.05). In the deep soil, the relative abundance of ttrC was significantly increased by 22.35% under the K75 treatment (p < 0.05), and by 22.42% under the K150 treatment (p < 0.05). However, the abundances of ttrA, nrnA, and cysD were significantly reduced by 9.90%, 10.52%, and 1.69%, respectively, under K150 (p < 0.05). In the K225 treatment, ttrC abundance was significantly increased by 29.02%, whereas ttrA and nrnA were significantly decreased by 12.33% and 9.98%, respectively (p < 0.05).

3.6. Effect of Fertilization Treatment on Yield of Fragrant Pear and PLS Structural Equation Model

The K75 treatment resulted in a 4.67% increase in single fruit weight relative to CK (p < 0.05). Under the K150 and K225 treatments, yield and single fruit weight were significantly improved by 47.36% and 18.19%, and 28.93% and 10.98%, respectively (p < 0.05). The K150 treatment produced the highest values for both parameters, indicating a statistically significant optimal effect (p < 0.05) (Figure 5A).
A PLS-PM (partial least squares path model) was applied to investigate the associations among soil nutrients, pH, sulfur cycle components (microbial α-diversity and functional genes), and yield under varying potassium fertilization regimes. Direct and indirect path coefficients are presented in Tables S1 and S2. The R2 values adjacent to each latent construct represent the proportion of variance explained, indicating a well-fitted model that captures the regulatory interactions among the studied variables. Partial Least Squares Path Modeling (PLS-PM) was employed as the primary multivariate approach in this study due to its suitability for small-sample conditions and its capacity to model complex, hypothesis-driven relationships among latent constructs. Unlike traditional SEM, which typically requires larger sample sizes and assumes multivariate normality, PLS-PM is a variance-based technique that provides greater robustness and flexibility under the constraints of our experimental design. The results indicated that the fertilization treatment was significantly positively correlated with available nutrients, the sulfur cycle, and yield (p < 0.001), while exhibiting a significant negative correlation with soil pH (p < 0.001). Additionally, a positive correlation was observed between soil pH and available nutrients. Available nutrients were also significantly positively associated with the sulfur cycle (p < 0.05), and a strong positive correlation was found between sulfur cycle function and yield (p < 0.001) (Figure 5B).

4. Discussion

4.1. Effects of Potassium Application on Soil Physical and Chemical Properties

The significant increase in available soil potassium observed in this study following potassium fertilizer application is consistent with the results of Brunetto G [31]. The underlying mechanism is likely related to the fact that plant roots primarily absorb potassium from the soil solution, and potassium fertilizer enhances the exchangeable potassium pool, which in turn, elevates the concentration of potassium in the soil solution [32]. The observed decrease in soil pH under the K150 treatment may be due to the substitution of alkaline ions (Ca2+ and Mg2+) by sulfate ions from the potassium fertilizer, which reduced soil alkalinity and promoted a shift toward neutrality (Figure 1). Moreover, the application of potassium fertilizer significantly enhanced soil organic matter levels under K150, likely as a result of increased microbial activity stimulated by improved soil conditions (Figure 1). The significant activation of the hpsO gene in the K150 treatment, contributing to the RuMP pathway, corroborates this finding (Figure 4). In addition, the literature has shown that reduced pH facilitates the formation of iron and aluminum oxides that adsorb organic biomolecules, thereby enhancing the protection and accumulation of soil organic matter [33,34].

4.2. Effect of Potassium Application on Soil Microbial Community

The α diversity of soil microorganisms was evaluated using the Shannon and Simpson indexes. No significant variations were detected across different potassium fertilizer treatments in both surface and deep soil layers (Figure 1A,B,D,E). However, non-metric multidimensional scaling (NMDS) analysis revealed a clear separation in microbial community structure across the different fertilization treatments (Figure 1C,F). The stress values for both soil layers were below 0.1, indicating that while microbial diversity was not significantly altered by potassium sulfate application, the overall community composition was markedly affected. This observation is consistent with the findings of Lu et al. [35]. Further analysis showed that the microbial community composition in the K150 and K75 treatments was similar in the topsoil, potentially due to the higher sensitivity of topsoil microorganisms to fertilization gradients. Conversely, in the deep soil layer, the microbial communities under K150 and K225 treatments exhibited greater similarity, suggesting that microbial communities in deeper layers may be more stable under moderate to high fertilization regimes [36]. Therefore, when evaluating the effects of fertilization on microbial composition, the depth of the soil must also be considered. Moreover, alterations in microbial communities can exert substantial influence on elemental cycling. In the present study, dominant taxa such as Actinomycetes and Nitrospirae were found to be significantly affected. Previous studies have demonstrated that Actinomycetes are typically dominant soil microorganisms [37], playing critical roles in the biogeochemical cycling of carbon, nitrogen, phosphorus, and potassium [37,38]. Additionally, it has been reported by Meng et al. [39] that Nitrospira plays a critical role in the biogeochemical cycling of carbon, nitrogen, and sulfur. These results suggest that the application of potassium sulfate may influence elemental cycling through modifications in microbial community composition and function. Moreover, alterations in microbial activity can affect the relative abundance of functional genes. Therefore, a comprehensive understanding of the impacts of potassium fertilization on microbial communities and their functional gene relative abundance is essential for optimizing fertilization strategies and enhancing soil health.

4.3. Effect of Potassium Treatment on Sulfur Cycle Genes

To comprehensively explore the involvement of functional genes in the sulfur cycle, processes were categorized into three mechanistic groups according to changes in sulfur valence states and molecular forms [23]: (1) organic sulfur conversion, which includes organic sulfur transformation and organic-inorganic sulfur exchange; (2) sulfur reduction, comprising dissimilatory and assimilatory sulfate reduction as well as general sulfur reduction; and (3) sulfur oxidation, covering both classical oxidation reactions and the SOX pathway. Gene annotation and functional classification were conducted using curated information from the KEGG, MetaCyc, and UniProt databases [40,41,42].
For organic sulfur conversion processes, potassium fertilization, particularly at medium and high application rates (K150 and K225), broadly suppressed microbial pathways associated with energy-intensive organic sulfur acquisition. In the topsoil, the relative abundance of betB (encoding betaine aldehyde dehydrogenase) was significantly reduced across all potassium treatments, indicating that microbial conversion of betaine-type organic sulfur sources may be widely inhibited—possibly due to enhanced osmotic adjustment capacity conferred by potassium, which reduces the microbial demand for betaine synthesis [43]. In contrast, hpsO (encoding a key enzyme in the ribulose monophosphate [RuMP] pathway) was upregulated under K150, suggesting a shift towards increased microbial utilization of single-carbon intermediates, consistent with the enhanced abundance of methylotrophic microorganisms [44,45]. Moreover, sqdB (involved in thiolipid synthesis for microbial membranes) was significantly elevated under K225, possibly reflecting adaptive regulation of membrane lipid composition in response to potassium-induced osmotic or electrochemical changes [43]. Genes involved in sulfur-containing amino acid biosynthesis, including metC, metB, and metX (within the “cysteine and methionine metabolism” pathway; KEGG map00270), as well as tauD, tauC, and ssuE (sulfonate degradation and transport; MetaCyc: TAURINE-DEG; PWY0-1338), were significantly down-regulated in the topsoil under K150 and K225 treatments, supporting the hypothesis that higher potassium availability inhibits the microbial synthesis of sulfur-rich biomolecules [46]. Similar trends were observed in the deep soil layer (40–60 cm), where the relative abundance of genes associated with taurine and DMSP metabolism, such as slcC, toa, and dddW, was suppressed under K75, and the patterns of betB down-regulation and hpsO upregulation persisted under K150 and K225. Collectively, these gene-level shifts suggest a microbial adaptation away from energy-intensive sulfur acquisition and assimilation strategies toward more energy-efficient sulfur metabolic pathways under potassium-rich conditions.
For sulfur oxidation, sulfur oxidation primarily mediated by the SOX system (sulfur oxidase complex) was also strongly affected by potassium addition. In the topsoil, the relative abundance of soxB (encoding the key hydrolase for conversion of sulfur intermediates to sulfate) was significantly reduced under K75 and K225 treatments, while soxX (electron transfer subunit) was notably upregulated under K150. These contrasting trends may be attributed to potassium-induced changes in soil pH and shifts in the composition of sulfur-oxidizing microbial communities [47,48]. This interpretation is supported by clear separation in NMDS analysis (stress < 0.1; Figure 1). In the deep soil layer, soxX was significantly increased only under K225, likely reflecting a higher threshold for response within subsurface microecosystems, which are generally characterized by limited aeration and reduced microbial activity [49,50]. Overall, potassium fertilization appears to restructure the oxidative capacity of the soil microbial community by differentially regulating the abundance of SOX system genes.
For sulfur reduction pathways, sulfur reduction pathways exhibited marked shifts in the relative abundance of gene in response to potassium treatments. In the topsoil, moderate potassium (K150) led to significant upregulation of psrA (encoding polysulfide reductase A), suggesting improved microbial utilization of reducible sulfur substrates [51]. Assimilatory sulfate reduction genes (aprA, aprB, dsrK) showed differential responses: aprA increased under K75 and K150, dsrK was upregulated under K75 and K225, while dsrP declined, possibly due to feedback from reduced demand for auxiliary protein synthesis. In the deep soil layer, ttrC (involved in transmembrane electron transport) was upregulated under K150 and K225, whereas ttrA (catalytic subunit for trisulfate reduction to sulfite) was downregulated under K150, suggesting altered membrane permeability or community structure. The suppression of nrnA and cysD (dissimilatory sulfate reduction pathway) under higher potassium levels may result from increased sulfur bioavailability and decreased microbial reliance on SO42− as a terminal electron acceptor. These changes are also likely influenced by factors such as reduced aeration, carbon limitation, and potassium-induced osmotic or salt stress.
Taken together, these findings demonstrate that potassium fertilization reprograms the functional landscape of soil sulfur metabolism by selectively down-regulating genes involved in energy-intensive acquisition and transformation of sulfur, while upregulating genes that support more efficient or adaptive sulfur metabolic pathways. This integrated gene-level response underpins a broader mechanistic shift in the soil microbiome’s strategy for sulfur cycling, optimizing metabolic processes in response to changes in potassium availability and environmental conditions.

4.4. Effect of Potassium Treatment on Yield

According to the structural equation modeling (SEM), the enhancement of crop yield through potassium fertilizer application was mainly achieved via two indirect mechanisms: the direct effect of fertilization and the regulation of microbial sulfur cycling. The model exhibited a high level of fit (R2 = 0.625), indicating that the pathways between variables were well-supported by the data. It was observed that potassium application significantly promoted soil nutrient availability (R2 = 0.750, p < 0.001), which played a foundational role in yield formation. As previously reported, potassium, classified as a “quality nutrient element,” can enhance the plant’s nutrient absorption efficiency and contribute to physiological yield potential [5]. Additionally, potassium fertilizer exerted a significant positive influence on microbial sulfur cycle activity (R2 = 0.703), as shown by a path coefficient of 0.683 (p < 0.001). This indicates that potassium contributes not only through direct sulfur input but also by reshaping microbial community structure and regulating functional gene relative abundance associated with sulfur metabolism. Since sulfur is integral to protein and coenzyme synthesis, enhanced sulfur availability improves plant metabolism and stress resilience, thus supporting greater yield. In conclusion, potassium was shown to increase yield through dual pathways: nutrient supply enhancement and microbial function modulation. The K150 treatment emerged as the most effective, showing superior activation of sulfur metabolic genes, enrichment of available nutrients, and increased microbial diversity, ultimately resulting in significantly higher yield and single fruit weight (SFW).

5. Conclusions

The application of potassium fertilizer significantly enhanced soil nutrient availability, with the K150 treatment markedly increasing the levels of available potassium and organic matter. Although no significant differences were observed in microbial diversity and richness across treatments, potassium fertilizer altered the microbial community composition and increased the relative abundance of dominant phyla under different application rates. In addition, the application of different potassium fertilizer treatments promoted the transformation of sulfur into plant-available forms (e.g., SO42−) by modulating the relative abundance of sulfur cycle-related functional genes, ultimately leading to a significant increase in pear yield. Among all treatments, K150 most effectively enhanced the relative abundance of sulfur cycle functional genes and resulted in the highest yield. The PLS structural equation model further demonstrated that the influence of potassium fertilizer application on pear yield was predominantly mediated through two indirect pathways: the direct fertilization effect and the microbial sulfur cycle function. Based on the comprehensive analysis, an application rate of 150 kg/hm2 is recommended as the optimal potassium input for enhancing yield in pear orchards. The results provide a theoretical foundation for optimizing the potassium fertilizer application strategy in Korla fragrant pear orchards. Future research should combine metagenomics and metatranscriptome methods to explore the internal relationship between microbial function changes caused by potassium application and pear quality improvement. Such efforts are expected to offer comprehensive theoretical support for the sustainable development of the Korla pear industry and to facilitate the transformation of the regional specialty fruit sector toward a greener and more efficient trajectory.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/agronomy15071752/s1, Table S1: Partial least squares path model (PLS-PM) path coefficients. Table S2: Partial least squares path model (PLS-PM) specific indirect effects path coefficients. Table S3: Metagenomic sequencing statistics, including average sequencing depth per sample, total number of reads and contigs after quality control, average contig length, and percentage of reads retained after host decontamination.

Author Contributions

L.Y. (Lele Yang), Z.C., B.D. and X.S. conceived and designed the experiment. L.Y. (Linsen Yan). K.W. and J.L. conducted experiments and analyzed the data. L.Y. (Lele Yang), Z.C. and B.D. wrote the paper. All authors have read and agreed to the published version of the manuscript.

Funding

This study was supported by the National Natural Science Foundation of China (32360802, 31960639), the Xinjiang Uygur Autonomous Region “Agriculture, Rural Areas and Farmers” Backbone Talents Training Project (2022SNGGGCC017), and Xinjiang Forest Fruit Industry Technology System—Soil Fertility and Cultivation (XJLGCYJSTX05-2024-03).

Data Availability Statement

The original contributions presented in this study are included in the article. The raw metagenomic reads have been deposited in the NCBI SRA database under the accession number PRJNA1289400. Further inquiries can be directed to the corresponding authors.

Conflicts of Interest

The authors declare no conflicts of interest.

Appendix A

Metagenome DNA Extraction and Shotgun Sequencing: Total microbial genomic DNA samples were extracted using the OMEGA Mag-Bind Soil DNA Kit (M5635-02) (Omega Bio-Tek, Norcross, GA, USA), following the manufacturer’s instructions, and stored at −20 °C prior to further assessment. The quantity and quality of extracted DNAs were measured using a QubitTM 4 Fluorometer, with WiFi: Q33238 (QubitTM Assay Tubes: Q32856; QubitTM 1X dsDNA HS Assay Kit: Q33231) (Invitrogen, Carlsbad, CA, USA) and agarose gel electrophoresis, respectively. The extracted microbial DNA was processed to construct metagenome shotgun sequencing libraries with insert sizes of 400 bp by using Illumina TruSeq Nano DNA LT Library Preparation Kit (Illumina, San Diego, CA, USA). Each library was sequenced by Illumina NovaSeq platform ((Illumina, San Diego, CA, USA)) with PE150 strategy at Personal Biotechnology Co., Ltd. (Shanghai, China).
Metagenomics Analysis: Raw sequencing reads were processed to obtain quality-filtered reads for further analysis. First, sequencing adapters were removed from sequencing reads using Cutadapt (v1.2.1) [52]. Secondly, low quality reads were trimmed using a sliding-window algorithm in fastp [53]. Thirdly, reads were aligned to the host genome of soil microorganism using BMTagger to remove host contamination [54]. Once quality-filtered reads were obtained, taxonomical classifications of metagenomics sequencing reads from each sample were performed using Kraken2 [55] against an RefSeq-derived database, which included genomes from archaea, bacteria, viruses, fungi, protozoans, metazoans, and viridiplantae. Reads assigned to metazoans or viridiplantae were removed for downstream analysis. Megahit (v1.1.2) [56] was used to assemble for each sample using the meta-large presetted parameters. The generated contigs (longer than 300 bp) were then pooled together and clustered using mmseqs2 [57] with “easy-linclust” mode, setting sequence identity threshold to 0.95 and covered residues of the shorter contig to 90%. The lowest common ancestor taxonomy of the non-redundant contigs was obtained by aligning them against the NCBI-nt database by mmseqs2 [57] with “taxonomy” mode, and contigs assigned to Viridiplantae or Metazoa were dropped in the following analysis. MetaGeneMark [58] was used to predict the genes in the contigs. CDS sequences of all samples were clustered by mmseqs2 [57] with “easy-cluster” mode, setting protein sequence identity threshold to 0.90 and covered residues of the shorter contig to 90%. To assess the abundances of these genes, the high-quality reads from each sample were mapped onto the predicted gene sequences using salmon [59] in the quasi-mapping-based mode with “--meta --minScoreFraction=0.55”, and the CPM (copy per kilobase per million mapped reads) was used to normalize abundance values in metagenomes. The functionality of the non-redundant genes was obtained by annotated using mmseqs2 [57] with the “search” mode against the protein databases of KEGG, EggNOG, and CAZy databases, respectively. EggNOG and GO were obtained using EggNOG-mapper (v2) [60]. GO ontology was obtained using map2slim (www.metacpan.org, accessed on 3 October 2024). KO were obtained using KOBAS [61].

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Figure 1. The changes in microbial diversity and composition in different soil layers under varying rates of potassium fertilizer application; (A,B) display the Simpson index and Shannon index, respectively, for microorganisms in the topsoil; (D,E) show the same indices for the deep soil layer. The composition of microorganisms in the topsoil and deep soil layer under different potassium sulfate gradients is illustrated (C,F), respectively, using a circos plot. (G,H) Depiction of the results of NMDS analysis for the topsoil and deep soil layers. Different lowercase letters indicate statistically significant differences between treatments (p < 0.05), using one-way ANOVA, followed by Duncan’s multiple range test (p < 0.05).
Figure 1. The changes in microbial diversity and composition in different soil layers under varying rates of potassium fertilizer application; (A,B) display the Simpson index and Shannon index, respectively, for microorganisms in the topsoil; (D,E) show the same indices for the deep soil layer. The composition of microorganisms in the topsoil and deep soil layer under different potassium sulfate gradients is illustrated (C,F), respectively, using a circos plot. (G,H) Depiction of the results of NMDS analysis for the topsoil and deep soil layers. Different lowercase letters indicate statistically significant differences between treatments (p < 0.05), using one-way ANOVA, followed by Duncan’s multiple range test (p < 0.05).
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Figure 2. Linear fitting of sulfur cycling functional genes and microbial β-diversity in different soil layers under different potassium fertilizer application rates (A,B). The red line represents the fitted linear regression, blue circles indicate observed data points, and the shaded area shows the 95% confidence interval.
Figure 2. Linear fitting of sulfur cycling functional genes and microbial β-diversity in different soil layers under different potassium fertilizer application rates (A,B). The red line represents the fitted linear regression, blue circles indicate observed data points, and the shaded area shows the 95% confidence interval.
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Figure 3. The schematic diagram and composition of sulfur cycle in different soil layers under different potassium fertilizer application rates. Sulfur cycle pathway (A); sulfur cycle link composition (B).
Figure 3. The schematic diagram and composition of sulfur cycle in different soil layers under different potassium fertilizer application rates. Sulfur cycle pathway (A); sulfur cycle link composition (B).
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Figure 4. Changes in sulfur cycle genes in different soil layers under different potassium fertilizer application rates (A–L). Different lowercase letters indicate statistically significant differences between treatments (p < 0.05), as determined by one-way ANOVA followed by Duncan’s multiple range test.
Figure 4. Changes in sulfur cycle genes in different soil layers under different potassium fertilizer application rates (A–L). Different lowercase letters indicate statistically significant differences between treatments (p < 0.05), as determined by one-way ANOVA followed by Duncan’s multiple range test.
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Figure 5. PLS−PM analysis of available nutrients, pH, microorganisms and functional genes, yield indicators in different potassium fertilizer treatments and their effects on yield (A,B). Different lowercase letters indicate statistically significant differences between treatments (p < 0.05), as determined by one-way ANOVA followed by Duncan’s multiple range test. * p < 0.05, ** p < 0.01, *** p < 0.001.
Figure 5. PLS−PM analysis of available nutrients, pH, microorganisms and functional genes, yield indicators in different potassium fertilizer treatments and their effects on yield (A,B). Different lowercase letters indicate statistically significant differences between treatments (p < 0.05), as determined by one-way ANOVA followed by Duncan’s multiple range test. * p < 0.05, ** p < 0.01, *** p < 0.001.
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Table 1. Test treatment design scheme.
Table 1. Test treatment design scheme.
Experimental TreatmentIrrigation Requirement (m3/hm2)Fertilization Scheme N-P2O5-K2O (kg/hm2)
WK07800240-240-0
WK757800240-240-75
WK1507800240-240-150
WK2257800240-240-225
Table 2. Irrigation and fertilization system.
Table 2. Irrigation and fertilization system.
Irrigation StageIrrigation TimeIrrigation MethodIrrigation FrequencyIrrigation Water (m3/hm2)Nutrition Level (kg/hm2)
WNP2OK2O
K0K75K150K225
winter irrigating20 Octoberflood irrigation11800000000
Spring irrigatingOn 15 Marchflood irrigation11800962400306090
fruit bearing periods5 Maytrickle irrigation160048007.51522.5
Early stage of fruit enlargement25 Maytrickle irrigation1600000000
On 15 Junetrickle irrigation16007200153045
5 Julytrickle irrigation1600000000
Late fruit expansion25 Julytrickle irrigation1600240022.54567.5
On 15 Augusttrickle irrigation1600000000
fruit ripening period5 Septembertrickle irrigation1600000000
Total97800240240075150225
Table 3. Variations in soil physicochemical characteristics across different fertilization treatments.
Table 3. Variations in soil physicochemical characteristics across different fertilization treatments.
TreatmentANAPAKSOMPH
SICK49.7 ± 4.04 a66.56 ± 0.18 b170.67 ± 4.33 c21.56 ± 0.42 b7.82 ± 0.02 a
S1K7545.03 ± 2.33 a66.98 ± 0.73 ab177.00 ± 4.62 c22.93 ± 0.58 b7.76 ± 0.01 ab
S1K15042.7 ± 4.04 a69.63 ± 0.85 a190.67 ± 1.76 b26.72 ± 0.59 a7.72 ± 0.02 b
S1K22540.37 ± 2.33 a67.46 ± 1.07 ab205.00 ± 4.36 a22.67 ± 1.33 b7.72 ± 0.03 ab
S3CK33.37 ± 2.33 a26.41 ± 1.05 a138.67 ± 3.84 d12.53 ± 0.49 c7.73 ± 0.03 a
S3K7531.03 ± 4.67 a25.99 ± 0.24 a152.00 ± 4.04 c14.79 ± 1.25 bc7.68 ± 0.01 ab
S3K15026.37 ± 2.33 a28.08 ± 0.97 a169.67 ± 4.26 b18.88 ± 0.36 a7.65 ± 0.02 b
S3K22528.70 ± 4.04 a25.78 ± 1.47 a185.67 ± 2.03 a16.41 ± 1.09 ab7.66 ± 0.02 ab
Note: AN: alkaline hydrolysis nitrogen; AP: effective phosphorus; AK: immediate potassium; SOC: soil organic carbon; “±” indicates standard errors; different lowercase letters represent significant differences between treatments were assessed using one-way ANOVA, followed by Duncan’s multiple range test (p < 0.05). The same method applies to all subsequent tables and figures.
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Yang, L.; Shen, X.; Yan, L.; Li, J.; Wang, K.; Ding, B.; Chai, Z. Metagenomic Sequencing Revealed the Effects of Different Potassium Sulfate Application Rates on Soil Microbial Community, Functional Genes, and Yield in Korla Fragrant Pear Orchard. Agronomy 2025, 15, 1752. https://doi.org/10.3390/agronomy15071752

AMA Style

Yang L, Shen X, Yan L, Li J, Wang K, Ding B, Chai Z. Metagenomic Sequencing Revealed the Effects of Different Potassium Sulfate Application Rates on Soil Microbial Community, Functional Genes, and Yield in Korla Fragrant Pear Orchard. Agronomy. 2025; 15(7):1752. https://doi.org/10.3390/agronomy15071752

Chicago/Turabian Style

Yang, Lele, Xing Shen, Linsen Yan, Jie Li, Kailong Wang, Bangxin Ding, and Zhongping Chai. 2025. "Metagenomic Sequencing Revealed the Effects of Different Potassium Sulfate Application Rates on Soil Microbial Community, Functional Genes, and Yield in Korla Fragrant Pear Orchard" Agronomy 15, no. 7: 1752. https://doi.org/10.3390/agronomy15071752

APA Style

Yang, L., Shen, X., Yan, L., Li, J., Wang, K., Ding, B., & Chai, Z. (2025). Metagenomic Sequencing Revealed the Effects of Different Potassium Sulfate Application Rates on Soil Microbial Community, Functional Genes, and Yield in Korla Fragrant Pear Orchard. Agronomy, 15(7), 1752. https://doi.org/10.3390/agronomy15071752

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