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Article

Influence of Agricultural Practices on Soil Physicochemical Properties and Rhizosphere Microbial Communities in Apple Orchards in Xinjiang, China

by
Guangxin Zhang
1,2,†,
Zili Wang
1,2,†,
Huanhuan Zhang
1,2,
Xujiao Li
1,2,
Kun Liu
1,2,
Kun Yu
1,3,
Zhong Zheng
3,* and
Fengyun Zhao
1,3,*
1
Department of Horticulture, College of Agriculture, Shihezi University, Shihezi 832003, China
2
Key Laboratory of Special Fruits and Vegetables Cultivation Physiology and Germplasm Resources Utilization of Xinjiang Production and Construction Corps, Department of Horticulture, College of Agriculture, Shihezi University, Shihezi 832003, China
3
Department of Agronomy, College of Agriculture, Shihezi University, Shihezi 832003, China
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Horticulturae 2025, 11(8), 891; https://doi.org/10.3390/horticulturae11080891 (registering DOI)
Submission received: 24 June 2025 / Revised: 22 July 2025 / Accepted: 22 July 2025 / Published: 1 August 2025
(This article belongs to the Section Fruit Production Systems)
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Abstract

In response to the challenges posed by soil degradation in the arid regions of Xinjiang, China, green and organic management practices have emerged as effective alternatives to conventional agricultural management methods, helping to mitigate soil degradation by promoting natural soil recovery and ecological balance. However, most of the existing studies focus on a single management practice or indicator and lack a systematic assessment of the effects of integrated orchard management in arid zones. This study aims to investigate how different agricultural management practices influence soil physicochemical properties and inter-root microbial communities in apple orchards in Xinjiang and to identify the main physicochemical factors affecting the composition of inter-root microbial communities. Inter-root soil samples were collected from apple orchards under green management (GM), organic management (OM), and conventional management (CM) in major apple-producing regions of Xinjiang. Microbial diversity and community composition of the samples were analyzed using high-throughput amplicon sequencing. The results revealed significant differences (p < 0.05) in soil physicochemical properties across different management practices. Specifically, GM significantly reduced soil pH and C:N compared with OM. Both OM and GM significantly decreased soil available nutrient content compared with CM. Moreover, GM and OM significantly increased bacterial diversity and changed the community composition of bacteria and fungi. Proteobacteria and Ascomycota were identified as the dominant bacteria and fungi, respectively, in all management practices. Linear discriminant analysis (LEfSe) showed that biomarkers were more abundant under OM, suggesting that OM may contribute to ecological functions through specific microbial taxa. Co-occurrence network analysis (building a network of microbial interactions) demonstrated that the topologies of bacteria and fungi varied across different management practices and that OM increased the complexity of microbial co-occurrence networks. Mantel test analysis (analyzing soil factors and microbial community correlations) showed that C:N and available potassium (AK) were significantly and positively correlated with the community composition of bacteria and fungi, and that C:N, soil organic carbon (SOC), and alkaline hydrolyzable nitrogen (AN) were significantly and positively correlated with the diversity of fungi. Redundancy analysis (RDA) further indicated that SOC, C:N, and AK were the primary soil physicochemical factors influencing the composition of microbial communities. This study provides theoretical guidance for the sustainable management of orchards in arid zones.

1. Introduction

Unsustainable agricultural practices, like high-intensity farming and over-application of chemical fertilizers and pesticides, exert a multitude of detrimental effects on ecosystems [1]. These practices lead to a range of environmental issues, including soil acidification, salinization, and soil pollution [2]. Beyond environmental degradation, they also affect normal food production, posing a significant threat to human health [3]. To safeguard soil resources, it is crucial to adopt management practices that align with soil quality and productivity requirements. Fortunately, several agricultural management practices have been proven effective in mitigating the negative environmental impacts of agricultural activities, including green management and organic management [4]. Green management practices emphasize sustainable development through resource-efficient use and environmentally friendly technologies (e.g., water-saving irrigation, biological control), while organic management practices prohibit chemical synthesis altogether and rely on natural cycles (e.g., composting, crop rotation) to maintain soil health [5]. Nowadays, green and organic management practices have gradually become highly recommended production approaches. Despite some challenges, their advantages in terms of environmental protection, food safety, and sustainable development make them a promising development direction for future agriculture.
Soil, as the fundamental material for agricultural production, is crucial for plant growth and crop yield and quality [6]. Within the soil ecosystem, microorganisms play a key role in regulating plant growth, enhancing soil nutrient utilization, and maintaining ecosystem stability [7]. Moreover, soil microorganisms form a “symbiotic whole” with plants. They can directly supply nutrients to plants, thereby promoting their growth, or help plants cope with environmental stresses [8]. Studies have shown that various agricultural management practices, including cropping systems, fertilization, and pest management, have a dual impact [9,10]. They not only directly affect crop growth and yield, but also indirectly regulate soil health and ecosystem function by altering the structure of soil microbial communities. Results from a 4-year study of Mediterranean orchards showed that green practices using soil-protected orchard management (SPOM), such as cover crops and return of pruning residues to the field, increased annual soil phosphorus and potassium incorporation by 28–90% and nitrogen by 13% compared to locally conventional management (LOM), significantly boosting soil nutrient stocks [11]. Xu et al. found that organic management can significantly improve soil physicochemical properties [12]. For example, it increases soil organic matter content and improves soil structure. These changes lead to enhanced soil microbial diversity and bacterial abundance, ultimately promoting soil nutrient cycling and crop growth. In a study focused on tea production systems, Li et al. examined the effects of three management practices—organic, conventional, and intercropping—on the composition of soil microbial communities [13]. Their findings revealed that organic and intercropping management significantly increased the relative abundance of Acidobacteria phylum. This increase may be attributed to the accumulation of soil organic matter and the improvement of the soil environment under these management practices. On a global farmland scale, Bebber et al.’s research compared the effects of organic and chemical fertilizers on soil microbiota [14]. They found that the application of organic fertilizers significantly increased soil microbiota carbon, nitrogen, and phosphorus levels, as well as soil enzyme activities. These parameters are crucial indicators of soil microbial activity and soil fertility. However, the results of Andrea et al.’s study on organic management practices in vineyards presents a more nuanced picture [15]. Despite organic management practices significantly increasing soil Cu content and lowering soil pH, they found no significant changes in microbial diversity and community composition. This suggests that the impact of organic management on the soil microbial community may be more complex and subtle under specific crop and soil conditions. Further in-depth studies are necessary to reveal the underlying mechanisms. Therefore, to establish sustainable agricultural production systems, it is crucial to investigate how different management practices affect soil microbial community structure.
Xinjiang, situated in the hinterland of the Eurasian continent, is endowed with unique soil, water, light, and thermal resources. The large temperature difference between day and night in this region is particularly conducive to sugar accumulation in fruits, resulting in apples of exceptional quality, and may also have shaped the unique soil microbial community structure [16]. As of 2022, Xinjiang has an apple cultivation area of approximately 66,700 hm2, with an output of around 1.5 million tons. These account for roughly 3.33% and 4.29% of China’s total cultivated area and output, respectively, making Xinjiang an advantageous apple-producing area in China [17]. Moreover, certain parts of Xinjiang boast an outstanding ecological environment. Their remote locations, far from towns and industrial pollution sources, provide a solid foundation for agricultural resources and a favorable environment for the development of green organic agriculture. However, the proportion of apple orchards in Xinjiang that are managed under green and organic certifications remains relatively low. The region is plagued by severe natural challenges, including drought and widespread saline soils. Conventional management practices can increase yields in the short term, but exacerbate the risk of soil erosion and trigger nutrient imbalances as well as a decline in soil fertility. Green and organic management practices can take full advantage of the climate while mitigating the negative impacts of drought and soil nutrient imbalance on the soil through conservation tillage, rational irrigation techniques, and biological control measures [18]. However, a notable research gap exists regarding the systematic investigation into the impacts of green and organic management practices on soil physicochemical properties and microbial community structures within apple orchards in this region. Therefore, it is necessary to explore appropriate management practices to improve soil quality and to clarify their effects on soil microbial communities. Such endeavors are essential for formulating scientific land management strategies and promoting the sustainable production of the apple industry in Xinjiang.
In this study, we selected the main apple-producing areas in Xinjiang, China (Aksu, the Yili Valley, and Changji Prefecture), as the study area to systematically assess the effects of green (GM), organic (OM), and conventional (CM) agricultural management practices on soil physicochemical properties and inter-root microbial communities in apple orchards. Based on the previous literature and regional soil characteristics, the following hypotheses were proposed in this study: (1) GM practice can reduce soil pH and C:N through resource-efficient utilization techniques (e.g., water-saving irrigation, biocontrol), whereas OM practice leads to a short-term decrease in soil quick-acting nutrient content. (2) OM practice can significantly enhance the diversity of inter-root bacteria and fungi by increasing soil organic matter inputs, and enhance the microbial complexity of the co-occurrence network. Using high-throughput amplicon sequencing technology, this study attempts to investigate (1) how different management practices affect soil physical and chemical properties; (2) how different management practices affect the diversity and structural composition of inter-root microbial communities; and (3) the interaction mechanism between soil physical and chemical properties and microbial community characteristics.

2. Materials and Methods

2.1. Overview of the Study Area

The study area is located in the Aksu and Yili River Valley regions, as well as the Changji Hui Autonomous Prefecture (78°03′–91°32′ E, 39°30′–45°00′ N) (Figure 1), in the Xinjiang Uygur Autonomous Region of China. This area is characterized by a temperate continental climate, with average annual temperatures ranging from 6.6 to 11.5 °C and precipitation levels between 42.4 and 417.6 mm. The predominant soil types in the region include aridisols and entisols, based on the United States Department of Agriculture’s (USDA) textural classification system.

2.2. Sample Collection

A total of 32 apple orchards were selected as sampling sites in this study, including 6 apple orchards with organic management practices (OM), 10 apple orchards with green management practices (GM), and 16 apple orchards with conventional management practices (CM), with the specific geographic locations and the number of years under green and organic management practices detailed in Table 1. Among them, the OM orchards were certified to GB/T 19630-2019 [19] organic standard, and the GM orchards were certified to NY/T 391-2021 [20] green food standard (Class A); the CM orchards were not certified, but had been practicing common local practices for ≥5 consecutive years. For fertilizer application, OM used 3.0 t ha−1 of well-decomposed sheep manure and 0.5 t ha−1 of organic NPK (5-3-4) as the only source of nutrients per year, and any synthetic chemical fertilizer was strictly prohibited; GM used 2.0 t ha−1 of well-decomposed cow manure combined with 150 kg ha−1 of controlled-release fertilizer NPK (20-10-15), and 150 kg ha−1 of organic NPK (5-3-4) fertilizer per year. CM applied 400 kg ha−1 of NPK (15-15-15) and 200 kg ha−1 of urea in April, and 150 kg ha−1 of urea and 100 kg ha−1 of potassium chloride in June. For pest management, OM relies entirely on biological agents (e.g., Bacillus thuringiensis, pyrethroids) and natural enemy release (Trichogramma spp.); GM follows an economic threshold strategy, using low-risk pesticides (ethyl polymyxin, phenyl ether metronidazole) when necessary; and CM practices ephemeral chemical control, spraying organophosphorus and triazoles 6–8 times throughout the year. For soil preparation and mulching, OM is tilled in the fall with mowed clippings and green manure, and permanent grass is maintained, GM is mulched with biodegradable black film or straw between rows, and CM is plowed deeply (25 cm) once a year in the fall after harvest.
From March to April 2024, a total of 32 representative orchards were selected for sample collection in the major apple-producing areas of Xinjiang, including the Aksu region, Yili Valley, and Changji prefecture. These orchards consist of 10 under green management (GM), 6 under organic management (OM), and 16 under conventional management (CM), each with an area exceeding 1.5 hm2. The S sampling method was adopted, a main axis line was drawn along the diagonal first, and then 5 sampling points were laid out continuously in the shape of “S”, with the horizontal spacing of neighboring points maintained at 20–30 m, covering 0–20 cm of topsoil within the drip line of the tree crown. At each sampling point, a stainless steel soil auger with a diameter of 5 cm was used to take three augers of soil (after removing topsoil gravel and plant debris) and mix the samples, and then reduce the quality of the soil according to the quadratic method. Ultimately, about 1 kg of soil samples were retained. Plant roots and stones were removed from the soil samples, which were subsequently air-dried at room temperature and sieved through a 0.2 mm sieve for the determination of soil physical and chemical properties. In addition to soil samples, fibrous and lateral roots, along with the surrounding soil, were collected from the 10–20 cm soil layer. This was done by randomly selecting three apple plants from each orchard, removing the topsoil with a pre-sterilized spade, and excavating the roots along with the soil. Stones and other debris were removed, and only 2~3 mm of inter-root soil attached to the roots was collected, resulting in a total of 96 inter-root soil samples. These samples were quickly transported back to the laboratory and stored in an ultra-low temperature refrigerator at −80 °C for subsequent inter-root microbial DNA extraction and Illumina Miseq high-throughput sequencing.

2.3. Determination of Soil Physicochemical Properties

All indicators were repeated three times, as follows [21,22]. Specifically, soil pH was measured using the potentiometric method(pH meter: PHS-3C, Shanghai Lida Instrument Factory, Shanghai, China) with a water-to-soil ratio of 2.5:1; soil organic carbon (SOC) was determined through external heating with potassium dichromate; soil total nitrogen (TN) was determined through Kjeldahl nitrogen fixation; alkaline nitrogen (AN) was quantified using the alkaline diffusion method; available phosphorus (AP) was measured using the 0.5 mol/L NaHCO3 leaching-molybdenum antimony colorimetric method(Spectrophotometer: UV-1900, Shimadzu Corporation, Kyoto, Japan); and available potassium (AK) was determined using the 1 mol/L NH4OAc leaching-flame photometric method(Flame photometer: WGH6400, Shanghai Li-Chen Instrument Technology Co., Shanghai, China).

2.4. Soil DNA Extraction and High-Throughput Sequencing

Total soil DNA was extracted using the Fast DNATM SPIN Kit for Soil (MP Biomedicals, Irvine, CA, USA) The V4~V5 highly variable region of 16S rRNA was amplified via PCR with primers 515F/907R. Similarly, the fungal ITS1 region was amplified using the forward primer ITS1F and the reverse primer ITS2, and the recoveries were quantified and then sequenced using the Illumina platform. Raw sequencing data were first quality filtered and bipartite spliced. Subsequently, the sequences were denoised using the DADA2 algorithm (v1.28) to generate high-precision Amplicon Sequence Variants (ASVs). Finally, bacterial and fungal ASVs were taxonomically annotated based on the SILVA database (v138.1) and the UNITE database (v9.0).

2.5. Data Processing

One-way analysis of variance (ANOVA) was used in SPSS 25.0 software to assess the significance of differences among treatments. Prior to the comparison, the Shapiro–Wilk and Levene tests were used to evaluate the normality and homogeneity of all variables. It is worth pointing out that the sample sizes of this experiment were CM 16, GM 10, and OM 6, which are unbalanced designs. Although one-way ANOVA and the Kruskal–Wallis test are still robust when the sample sizes are unequal, the statistical efficacy may be reduced. Arcgis 10.7 was utilized to plot the distribution of the sampling points. QIIME (v1.9.1) was used to analyze the Chao1 index, Shannon index, and Simpson index of inter-root soil bacterial and fungal communities.Principal Coordinate Analysis (PCoA) was conducted using the “vegan” package in R software(version 4.3.3) with Bray-Curtis distance metrics, and results were visualized through the “ape” package.Linear discriminant analysis (LDA) with effect size (LEfSe), implemented through the “ggtree” package in R software(version 4.3.3), was employed for identifying biomarker species between bacteria and fungi. The “psych” and “igraph” packages in R software (version 4.3.3) were used to generate colony correlation network diagrams to explore patterns of co-occurrence among microbial communities in different management practices. Mantel test analysis plots were developed using the “linkET” package in R software (version 4.3.3) to explore the interrelationships between bacterial and fungal communities and environmental factors. RDA was conducted using CANOCO 5 for Windows.

3. Results

3.1. Impact of Management Practices on Soil Physicochemical Properties

Different management practices significantly alter soil physicochemical properties (Table 2). Notably, GM significantly reduced soil pH and C:N (p < 0.05) compared to OM, which was not significantly different from CM. Soil AP content, in contrast, was significantly higher (p < 0.05) under CM, with increases of 82.71% and 54.01%, respectively, over GM and OM. Additionally, OM significantly (p < 0.05) decreased soil AK and AN contents by 28.71% and 19.93%, respectively, compared to CM, and the difference with GM was not significant. No significant differences were observed in SOC and TN contents among the three management practices.

3.2. Impactt of Management Practices on Microbial Alpha Diversity in Inter-Root Soils

The Chao1 index reflects community richness, with larger values indicating higher species richness. OM had no significant effect on inter-root soil bacterial richness compared to CM. In contrast, GM significantly increased the Chao1 index by 10.08% (p < 0.05) compared to CM (Figure 2a). The Shannon index reflects the diversity of the community. Both GM and OM significantly increased the Shannon index by 2.51% and 2.71% (p < 0.05), respectively, compared with CM. This indicates that GM and OM practices contribute to a more diverse bacterial community in the inter-root soil zone (Figure 2b). The Simpson index, an indicator of community evenness, with higher values suggesting a more uniform distribution of species, showed no significant differences among the management practices. This indicates that GM and OM had similar effects on the evenness of inter-root soil bacteria as CM and did not have a significant advantage (Figure 2c).
For fungi, the Chao1 index showed the highest value under OM and the lowest under CM (Figure 2d), while the Shannon index and Simpson index showed the highest values under OM and the lowest under GM. Nevertheless, there were no significant differences in fungal alpha diversity among the different management practices (Figure 2e,f). This implies that GM and OM did not have a significant impact on the alpha diversity of inter-root soil fungi.

3.3. Impact of Management Practices on Inter-Root Soil Microbial Community Structure

The compositional structure of bacterial flora at the phylum level is shown in Figure 3a. The dominant phyla, which were consistent across all management practices, included Proteobacteria (24.62%, relative abundance, below), Acidobacteriota (15.20%), Actinobacteria (12.78%), Gemmatimonadota (12.06%), and Chloroflexota (9.41%). However, variations in the relative abundances of these phyla were observed, depending on the management practice. Specifically, OM significantly reduced the relative abundance of Gemmatimonadota and Planctomycetota compared to CM and GM (p < 0.05), while OM increased the relative abundance of Ascomycetes, which was significantly lower (p < 0.05) in GM soils. The compositional structure of fungal flora at the phylum level is shown in Figure 3b. The dominant phyla were the same across different management practices, with Ascomycota exhibiting the highest mean relative abundance of 77.05%, followed by Basidiomycota, with a mean relative abundance of 7.60%. Notably, OM significantly increased the relative abundance of Glomeromycota (p < 0.05) and decreased the relative abundance of Ascomycota compared to CM and GM.
Differences in the composition of the inter-root soil microbial community among different management practices were analyzed using principal component analysis (Figure 4). The results indicated that the first two principal components (PC1 and PC2) accounted for 11.8% and 5.2% of the total variance in the bacterial community and 5.4% and 4.6% of the total variance in the fungal community, respectively. There was no significant separation in bacterial and fungal communities between different management practices.

3.4. Differences in Soil Fungal and Bacterial Biomarkers Across Management Practices

In this study, the variability of inter-root soil microbial communities was analyzed from the phylum to the genus level across different management practices using LEfSe. The analysis identified 36 biomarkers within the inter-root soil bacterial community from the phylum to the genus level at five levels (Figure 5a). Specifically, 12 biomarkers were found in CM, 5 in GM, and 19 in OM. At the genus level, the relative abundances of SCN-69-37, WHSN01, Terricaulis, and ZC4RG30 were highest under OM, while RSA9, GWA2-73-35, and Nitrosospira exhibited the highest relative abundances under CM. A total of 26 taxa were significantly different in the fungal community (Figure 5b), including 2 under CM, 4 under GM, and 20 under OM. At the genus level, eight genera, such as Pyrenochaetopsis, Trichoderma, and Ceratobasidium, were most abundant under OM. Alternaria and Emericellopis were highest under GM, whereas Pseudeurotium was prevalent under CM. The results of LEfSe analysis underscore the significant impact of different management practices on the microbial communities in inter-root soils from the phylum level to the genus level. Overall, OM appears to exert a relatively greater effect on microbial communities.

3.5. Microbial Community Network Characterization

To further investigate the effects of management practices on the symbiotic patterns of inter-root microbial communities, we conducted a Spearman correlation analysis. To minimize the influence of extreme values and reduce error, ASVs, whose relative abundance was greater than 0.05% in each sample, were selected. Correlation coefficients above 0.6 and significance p-values below 0.05 were used to construct a correlation network (Figure 6). In terms of bacterial network topology parameters, OM increased the number of nodes and edges in the bacterial co-occurrence network compared with CM and GM (Table 3). Furthermore, the average degree of bacterial co-occurrence network in OM was higher than that in CM and GM, indicating a more complex bacterial community co-occurrence network under OM. Regarding fungal network topological parameters, OM also increased the number of nodes and edges compared to CM and GM (Table 3). The average degree of the fungal co-occurrence network in the OM group was 6.534 degrees, which was 2.11 and 4.62 times higher than that of CM and GM, respectively. This suggests that the fungal network under OM was more complex.

3.6. Relationship Between Microbial Community Structure and Soil Physicochemical Properties

Mantel test analysis of inter-root soil microorganisms and soil physicochemical properties is shown in Figure 7. The results revealed significant correlations (p < 0.01) between soil C:N and AK and the composition of inter-root soil bacterial community. Furthermore, significant correlations (p < 0.05) were observed between SOC, C:N, and AN and the diversity of inter-root soil fungal community. Additionally, C:N and AK were significantly correlated with the composition of inter-root soil fungal community (p < 0.05). Strong correlations also existed between soil physical and chemical properties, with significant correlations observed between most indicators.
RDA analysis was conducted to further explore the relationship between soil physicochemical factors and genus-level diversity of fungi and bacteria. The RDA results for bacteria showed that soil physicochemical properties explained 27.29% of the variation in bacterial community structure (Figure 8a). Among them, soil AK, SOC, and AP were the primary factors influencing the dominant bacterial genera in the inter-root soil. Specifically, AK was positively correlated with PSRF01, AG11, and SCGC-AG. SOC was positively correlated with Luteital and Sphingomonas, but negatively with RSA9, GWA2-73, and JABFSM01. AP was positively correlated with RSA9, but negatively with Luteital, Sphingomonas, and AG11. For fungi, the RDA results demonstrated that soil physicochemical properties accounted for 15.69% of the variation in fungal community structure (Figure 8b). Soil AP was the main factor influencing the dominant fungal genera in the inter-root soil, showing positive correlations with Plectosphaerella and Mycochlamys and negative correlations with Mortierella, Cladosporium, and Trichocladium.

4. Discussion

4.1. Impact of Management Practices on Soil Physicochemical Properties

Several studies have demonstrated that different agricultural management practices first affect soil physicochemical properties, which in turn indirectly influence soil microbial communities [23]. In this study, we selected Xinjiang, a region characterized by soil secondary alkalization and nutrient degradation that poses significant challenges to apple production, as our sampling site. We investigated the physicochemical properties of apple orchard soils under three representative management practices: green management, organic management, and conventional management. Our findings revealed that soil pH was lower under GM compared with the other two management practices. This observation highlights the positive effects of GM in promoting apple growth in Xinjiang. C:N under GM was significantly lower than that under OM, but did not differ from that under CM, mainly due to the different fertilization strategies under the management practices: OM was fed with high-carbon and low-nitrogen rotted goat manure, while GM was fed with cow dung and controlled-release fertilizer, which resulted in lower overall C:N. The study showed that the lower the C:N of exogenous organic materials, the lower the microbial N demand during decomposition, which accelerates carbon mineralization and reduces soil C:N [24]. The results suggest that in the management of apple orchards in arid zones, by adjusting the C:N of exogenous organic materials, we can realize the precise control of soil C:N, which can provide a theoretical basis for optimizing nitrogen supply and maintaining carbon and nitrogen balance. Furthermore, our study found that OM significantly reduced soil nutrient contents of AN, AK, and AP compared to CM. This finding contrasts with the results reported by Huang et al. [25], which may be attributed to differences in the duration of OM implementation. Organic management practices in this study are only 2–3 years old, and effective nutrient inputs tended to be low and mineralization rates slow, thus resulting in a relatively slow release of nutrients from organic fertilizer applications. Consequently, it is difficult to meet the nutrient demands during the crop’s reproductive period, potentially leading to a lower content of quick-acting nutrients in the soil in the short term [26].

4.2. Impact of Management Practices on Microbial Diversity and Community Composition

Previous studies have shown that different management practices in agroecosystems, such as planting methods, agronomic practices, and fertilization methods, have significant effects on the alpha diversity of soil microorganisms [27,28,29]. In this study, we observed that the richness and diversity of soil bacteria were significantly higher under GM compared with CM. This difference can be directly attributed to the different fertilization regimes under the two management practices. CM can reduce soil microbial diversity due to the prolonged application of such high and unbalanced doses of fertilizers during apple cultivation, while GM can increase the diversity and abundance of soil bacterial communities in the inter-root zone of apples by appropriately reducing the amount of fertilizers used [30,31]. Additionally, OM significantly increased bacterial diversity compared with CM. This improvement is due to the fact that organic management enhances the metabolism and activity of the soil microbiome through the application of organic matter, which in turn can increase microbial diversity, a conclusion that has been demonstrated in previous studies [32]. In contrast, CM decreased fungal alpha diversity compared to both GM and OM, but the difference was not statistically significant, contrary to the findings of Li et al. [13]. The variation of fungal diversity is complex and not limited by a single factor and may also be related to plot management, years of tillage, and to some extent climate change [33]. The composition of inter-root soil bacterial and fungal communities was further analyzed using PCoA. The results showed that the composition of inter-root soil bacterial and fungal communities was similar across different management practices. This finding suggests that the bacterial and fungal communities in the inter-root soil remained stable in the study area despite the different management practices. This stability may be ascribed to the long-term conventional management practices employed in the orchard prior to the transition to green and organic management practices. Under CM, the root systems of apple trees continuously release the same types of secretions into the soil, providing a rich source of nutrients for soil microorganisms and thus contributing to the stable colonization of the inter-root zone by specific microbial species [34].
The structural composition of microbial communities plays a crucial role in assessing soil quality. Overall, the dominant bacterial phyla in the inter-root soil were similar across different management practices, although variations in their relative abundance were observed (Figure 3). In all management practices, Ascomycetes, Acidobacteria, and Actinobacteria were found to be the dominant bacterial phyla, consistent with previous findings [35]. The reason is that the soils in the region are alkaline and that Ascomycetes is the dominant bacterial phyla in alkaline soils [36]. Under OM, the relative abundance of Bacillus phylum declined significantly. This can be explained by the eutrophic nature of Bacillus, which depends on nutrient-rich conditions for proliferation [37]. In contrast, OM led to notable reductions in soil nutrient levels, including AP, AK, and AN, partly limiting the proliferation of bacillus due to insufficient nutrient support. The relative abundance of Bacteroidetes phylum decreased significantly under GM, while these bacteria play a role in degrading high molecular weight organic matter, promoting the decomposition of organic matter in the environment, and converting it to CO2, so this group of bacteria may be more adapted to environments containing a large amount of carbon sources [38]. Ascomycota was the overwhelmingly dominant fungal phylum across all management practices, underscoring its key role in soil organic matter decomposition and nutrient cycling. This suggests a relatively stable core fungal community structure under different management practices. In addition, OM significantly increased the relative abundance of the phylum Mycosphaerella globulus, a group predominantly composed of mycorrhizal fungi. These fungi can form symbiotic and reciprocal relationships with plants, facilitating nitrogen and phosphorus uptake. They can also secrete globomycin, which indirectly increases the content of soil organic matter and improves the conditions for soil aeration, thereby enhancing nutrient utilization by crops [39]. Consequently, OM can significantly increase the abundance of beneficial fungi in the inter-root soil, thus promoting the growth of apples.
Biomarkers are widely used in microbiological research to identify species that contribute to significant differences between groups. In the present study, LEfSe analysis was employed to further reveal the differences in biomarkers under different management practices. The results revealed distinct differences in inter-root microbial biomarkers among the management practices, with a higher abundance of biomarkers detected under OM. This suggests that OM is more conducive to the enrichment of soil microorganisms. More than half of the bacterial biomarkers were from the phylum Ascomycota (Figure 5), indicating that species in the phylum Ascomycota were the key species contributing to the differences in soil microbiology between OM and CM. This may be due to the large amount of organic matter such as weeds and crop residues in organically managed orchards that accelerates the colonization of Ascomycota. Conversely, the routine application of herbicides and pesticides in conventionally managed orchards significantly affects microbial phyla, leading to a reduction in their microbial abundance in the soil [35,40]. Furthermore, microbial symbiotic networks showed higher complexity under organic management practices (Table 3), with an increase in the number of nodes and edges for both bacteria and fungi. This is consistent with previous findings that organic management practices are associated with more complex networks characterized by higher connectivity and richness of key taxa, whereas conventional management practices typically result in highly clustered and specialized networks that may be more susceptible to disturbance [41].

4.3. Impact of Soil Physicochemical Properties on Soil Microbial Communities

Soil serves as the primary habitat for microorganisms, and factors at the inter-root level—particularly root secretions—interact with existing soil microbiota to recruit and modify their inter-root microbial communities. Consequently, the condition of the soil, especially its physicochemical properties, exerts a significant influence on the community composition of soil microorganisms [42,43]. Previous studies have demonstrated that soil physical and chemical properties are closely correlated with microbial communities [44]. Mantel test analysis showed that C:N and AK were significantly and positively correlated with the community composition of inter-root soil bacteria and fungi. Moreover, C:N, SOC, and AN exhibited a significant positive correlation with the diversity of inter-root soil fungi, consistent with the early research findings [45,46]. RDA analysis further indicated that SOC, AP, and AK had significant effects on the community composition of microorganisms (Figure 8). For example, the relative abundance of Sphingomonas was positively correlated with SOC but negatively correlated with AP. Sphingomonas, a beneficial bacterial group capable of producing IAA and inducing plant root growth, has great potential for enhancing plant resistance and growth [47]. For fungi, the abundance of Mycochlamys was affected by soil AP and positively correlated with AP. Mycochlamys, as a common phytopathogenic group of fungi, can infect a wide range of crops and cause various crop diseases, such as root rot, stem rot, and spike rot [48]. Related studies have shown that microbial communities may be simultaneously affected by multiple soil physicochemical factors—a conclusion further supported by the findings of this study [49]. Different management practices contribute to differences in inter-root soil microbial diversity and community composition by altering soil physicochemical properties. Higher microbial diversity is often associated with a more stable ecology, which is critical for maintaining agroecosystem stability and long-term sustainable land use.
This study provides valuable insights into how different management practices affect soil physicochemical properties and inter-root microbial community structure in apple orchards in Xinjiang. However, the findings are subject to certain limitations that need further consideration. First, the study is geographically constrained to a specific geographic region with unique climatic conditions, which may limit the generalizability of the results. Second, the limited time scale and concentrated sampling time of this study may not have adequately considered the long-term impacts caused by different management practices or captured the seasonal or inter-annual dynamics of soil properties and microbial communities. Therefore, future research should focus on assessing how different management practices affect microbial community structure and seasonal and interannual changes in soil properties and microbial communities over longer time scales. This line of research is crucial for maintaining the stability of soil ecosystems in agricultural fields. This will help us to better understand the complex relationship between different management practices and inter-root soil microbial communities and provide a scientific basis for the development of sustainable management strategies.

5. Conclusions

Compared with conventional management practices, those under green management resulted in a decreasing trend in soil pH and C:N, while available nutrient content was significantly reduced under green and organic management practices. In addition, green and organic management practices altered the community structure of inter-root microorganisms and significantly increased bacterial α-diversity, but fungal α-diversity did not change significantly compared to conventional management. Furthermore, organic management increased the complexity of the co-occurrence network of bacterial and fungal communities. This study also identified soil physicochemical parameters such as SOC, C:N, and AK as primary drivers shaping microbial community composition across different management practices. Finally, given the soil conditions in the area, growers are advised to incorporate the use of organic fertilizers and reduce the use of synthetic chemicals to regulate pH and increase soil organic matter. Taking into account regional characteristics, it is recommended that conventional growers gradually switch to green or organic management to balance nutrient availability and soil health, which is essential for long-term sustainable production.

Author Contributions

Conceptualization, investigation, formal analysis, writing—original draft preparation, and software, G.Z.; conceptualization, writing—original draft preparation, and software, Z.W.; methodology and investigation, H.Z.; investigation, X.L.; formal analysis, K.L.; supervision and resources, K.Y.; writing—review and editing, supervision, project administration, methodology, investigation, and funding acquisition, Z.Z. and F.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the Research, Development and Demonstration of Key Equipment for the Integration of “Water, Fertilizer and Gas” Technology for Fruit Tree Burrow Storage and Drip Irrigation (2024AB038) and the Xinjiang Apple Industry Technology System (XJLGCYJSTX04-2024-).

Data Availability Statement

The data presented in this study are available on request from the corresponding author due to privacy.

Conflicts of Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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Figure 1. Location map of the study area. Note: CM in the figure denotes conventional management practices, GM denotes green management practices, and OM denotes organic management practices.
Figure 1. Location map of the study area. Note: CM in the figure denotes conventional management practices, GM denotes green management practices, and OM denotes organic management practices.
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Figure 2. Inter-root soil microbial diversity under different management practices: (a) Bacterial Chao1 index, (b) bacterial Shannon index, (c) bacterial Simpson index, (d) fungal Chao1 index, (e) fungal Shannon index, and (f) fungal Simpson index. Note: Lowercase letters indicate statistically significant differences between the different management practices at the p < 0.05 level.
Figure 2. Inter-root soil microbial diversity under different management practices: (a) Bacterial Chao1 index, (b) bacterial Shannon index, (c) bacterial Simpson index, (d) fungal Chao1 index, (e) fungal Shannon index, and (f) fungal Simpson index. Note: Lowercase letters indicate statistically significant differences between the different management practices at the p < 0.05 level.
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Figure 3. Community structure of inter-root soil bacteria (a) and fungi (b) under different management practices.
Figure 3. Community structure of inter-root soil bacteria (a) and fungi (b) under different management practices.
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Figure 4. PcoA analysis of inter-root soil bacterial (a) and fungal (b) communities under different management practices.
Figure 4. PcoA analysis of inter-root soil bacterial (a) and fungal (b) communities under different management practices.
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Figure 5. Linear discriminant analysis (LDA > 2.5) of inter-root soil bacterial (a) and fungal (b) communities (from phylum to genus) in response to different management practices.
Figure 5. Linear discriminant analysis (LDA > 2.5) of inter-root soil bacterial (a) and fungal (b) communities (from phylum to genus) in response to different management practices.
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Figure 6. Taxonomic co-occurrence network of bacterial and fungal species under CM, OM, and GM practices. Note: (a) Taxonomic co-occurrence network of bacteria under CM; (b) Bacterial taxonomic co-occurrence network under GM; (c) Bacterial taxonomic co-occurrence network under OM; (d) Fungal taxonomic co-occurrence network under CM; (e) Taxonomic co-occurrence network of fungi under GM; (f) Taxonomic co-occurrence network of fungi under OM. Note: Nodes in the graph represent species. The size of the nodes represents the size of the clustering ability. Node colours indicate taxonomy. Nodes of the same color form a module. The species shown in the figure are the key taxa in the co-occurrence network.
Figure 6. Taxonomic co-occurrence network of bacterial and fungal species under CM, OM, and GM practices. Note: (a) Taxonomic co-occurrence network of bacteria under CM; (b) Bacterial taxonomic co-occurrence network under GM; (c) Bacterial taxonomic co-occurrence network under OM; (d) Fungal taxonomic co-occurrence network under CM; (e) Taxonomic co-occurrence network of fungi under GM; (f) Taxonomic co-occurrence network of fungi under OM. Note: Nodes in the graph represent species. The size of the nodes represents the size of the clustering ability. Node colours indicate taxonomy. Nodes of the same color form a module. The species shown in the figure are the key taxa in the co-occurrence network.
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Figure 7. Mantel test analysis showing the correlation between inter-root soil bacterial (a) and fungal (b) diversity, relative abundance at the phylum level, and soil physicochemical factors. Pairwise comparisons of factors are shown in the rectangle, with a colour gradient denoting Pearson’s correlation coefficient. * p < 0.05, ** p < 0.01, *** p < 0.001. pH, power of hydrogen; SOC, soil organic carbon content; TN, total nitrogen content; C:N, Carbon to Nitrogen Ratio; AP, Available Phosphorus content; AN, Available Nitrogen content; AK, Available Potassium content.
Figure 7. Mantel test analysis showing the correlation between inter-root soil bacterial (a) and fungal (b) diversity, relative abundance at the phylum level, and soil physicochemical factors. Pairwise comparisons of factors are shown in the rectangle, with a colour gradient denoting Pearson’s correlation coefficient. * p < 0.05, ** p < 0.01, *** p < 0.001. pH, power of hydrogen; SOC, soil organic carbon content; TN, total nitrogen content; C:N, Carbon to Nitrogen Ratio; AP, Available Phosphorus content; AN, Available Nitrogen content; AK, Available Potassium content.
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Figure 8. Redundancy analysis of soil physicochemical properties and inter-root soil bacterial (a) and fungal communities (b) at the genus level.
Figure 8. Redundancy analysis of soil physicochemical properties and inter-root soil bacterial (a) and fungal communities (b) at the genus level.
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Table 1. Management time for green, organic management practices.
Table 1. Management time for green, organic management practices.
Orchard NumberLongitude and LatitudeManagement PracticeYears of Management Practice Implementation
1E 80°15′, N 41°27′GM2
2E 80°6′, N 41°23′GM2
3E 80°16′, N 41°14′GM2
4E 80°16′, N 41°20′GM3
5E 80°20′, N 41°19′GM2
6N 80°27′, N 40°59′GM4
7E 80°20′, N 40°42′GM3
8E 80°40′, N 44°15′GM2
9E 81°42′, N 43°53′GM2
10E 86°18′, N 44°10′GM2
11E 80°16′, N 41°19′OM2
12E 80°33′, N 41°10′OM2
13E 80°24′, N 41°21′OM3
14E 80°37′, N 41°15′OM2
15E 81°18′, N 43°40′OM3
16E 82°30′, N 43°25′OM2
17E 80°13′, N 41°18′CM
18E 80°8′, N 41°14′CM
19E 80°16′, N 41°19′CM
20E 80°17′, N 41°20′CM
21E 80°19′, N 41°16′CM
22E 80°28′, N 40°55′CM
23E 80°26′, N 40°38′CM
24E 80°41′, N 44°15′CM
25E 81°18′, N 43°45′CM
26E 86°8′, N 43°54′CM
27E 80°48′, N 41°22′CM
28E 80°25′, N 41°12′CM
29E 80°30′, N 41°16′CM
30E 80°19′, N 41°19′CM
31E 81°45′, N 43°41′CM
32E 82°38′, N 43°25′CM
Table 2. Effects of management practices on physicochemical properties of apple soils in Xinjiang.
Table 2. Effects of management practices on physicochemical properties of apple soils in Xinjiang.
SamplepHSOC (g·kg−1)TN (g·kg−1)C:NAP (mg·kg−1)AK (mg·kg−1)AN (mg·kg−1)
GM7.94 ± 0.21b9.75 ± 3.86a0.49 ± 0.10a21.48 ± 6.26b35.10 ± 27.43b216.00 ± 92.16ab54.38 ± 25.08ab
OM8.07 ± 0.21a10.90 ± 2.58a0.46 ± 0.12a24.53 ± 5.41a41.64 ± 32.94b170.50 ± 43.41b49.21 ± 20.39b
CM8.01 ± 0.15ab9.75 ± 2.44a0.42 ± 0.06a22.92 ± 4.72ab64.13 ± 33.59a239.15 ± 117.50a61.46 ± 17.52a
Note: Lowercase letters in the same column indicate statistically significant differences between the different management practices (p < 0.05).
Table 3. Molecular ecological network properties of inter-root soil bacterial and fungal flora in different management practices.
Table 3. Molecular ecological network properties of inter-root soil bacterial and fungal flora in different management practices.
FeaturesBacteriaFungi
CMGMOMCMGMOM
Nodes15215720641133206
Edges1362863191729206673
Degree17.92110.99418.6121.4153.0986.534
Network diameter88761711
Modularity0.520.5450.550.8480.770.782
Average clustering
coefficient		0.5710.4640.4810.1390.3260.411
Positive links71.39%76.13%73.45%89.66%99.03%100%
Negative links27.61%23.87%26.55%10.34%0.97%0.00%
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Zhang, G.; Wang, Z.; Zhang, H.; Li, X.; Liu, K.; Yu, K.; Zheng, Z.; Zhao, F. Influence of Agricultural Practices on Soil Physicochemical Properties and Rhizosphere Microbial Communities in Apple Orchards in Xinjiang, China. Horticulturae 2025, 11, 891. https://doi.org/10.3390/horticulturae11080891

AMA Style

Zhang G, Wang Z, Zhang H, Li X, Liu K, Yu K, Zheng Z, Zhao F. Influence of Agricultural Practices on Soil Physicochemical Properties and Rhizosphere Microbial Communities in Apple Orchards in Xinjiang, China. Horticulturae. 2025; 11(8):891. https://doi.org/10.3390/horticulturae11080891

Chicago/Turabian Style

Zhang, Guangxin, Zili Wang, Huanhuan Zhang, Xujiao Li, Kun Liu, Kun Yu, Zhong Zheng, and Fengyun Zhao. 2025. "Influence of Agricultural Practices on Soil Physicochemical Properties and Rhizosphere Microbial Communities in Apple Orchards in Xinjiang, China" Horticulturae 11, no. 8: 891. https://doi.org/10.3390/horticulturae11080891

APA Style

Zhang, G., Wang, Z., Zhang, H., Li, X., Liu, K., Yu, K., Zheng, Z., & Zhao, F. (2025). Influence of Agricultural Practices on Soil Physicochemical Properties and Rhizosphere Microbial Communities in Apple Orchards in Xinjiang, China. Horticulturae, 11(8), 891. https://doi.org/10.3390/horticulturae11080891

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