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Article

Effect of Cultivated Species and Planting Pattern on Plant Growth, Soil Properties, and Soil Metabolites in a Rain-Fed Orchard in Gansu, China

by
Yali Zou
1,
Qi Li
2,3,* and
Yuying Shen
2,3
1
College of Bioengineering and Technology, Tianshui Normal University, Tianshui 741000, China
2
State Key Laboratory of Herbage Improvement and Grassland Agro-ecosystems, Lanzhou University, Lanzhou 730020, China
3
College of Pastoral Agricultural Science and Technology, Lanzhou University, Lanzhou 730020, China
*
Author to whom correspondence should be addressed.
Agronomy 2025, 15(6), 1385; https://doi.org/10.3390/agronomy15061385
Submission received: 6 May 2025 / Revised: 30 May 2025 / Accepted: 31 May 2025 / Published: 5 June 2025
(This article belongs to the Section Grassland and Pasture Science)
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Abstract

:
Orchard cover crops enhance the local microclimate and soil fertility, serving as an eco-friendly, efficient management practice. However, the effects of different cultivated species and planting patterns on plant growth and soil properties remain unclear. In this study, we hypothesized that different cultivated species and planting patterns would differently affect root growth and soil biochemistry. Therefore, the root growth, soil nutrients, and soil metabolites in an orchard planted with Vulpia myuros, Vicia villosa, Orychophragmus violaceus, and Brassica campestris in either a tree-disk or inter-row patterns were conducted. The results indicated that the tree-disk pattern promoted root development. This increase in below-ground biomass contributed to changes in soil nutrient dynamics, with a significant biomass accumulation observed for Orychophragmus violaceus. While the inter-row pattern improved soil aeration and was conducive to aboveground plant growth. The tree-disk pattern with Vicia villosa and Brassica campestris increased the total phosphorus (TP) and total potassium (TK) in the 0–10 cm layer. The soil NH4+-N and NO3−-N contents were higher under the tree-disk pattern than under the inter-row pattern with Brassica campestris, whereas the opposite effect was seen with Vulpia myuros. Overall, we recommend planting Orychophragmus violaceus in a tree-disk pattern and Vulpia myuros in an inter-row pattern to promote plant biomass accumulation and soil nutrient increases in orchards. Our study provides a basis for the selection of orchard-cultivated species and planting patterns to promote the sustainable development of the fruit industry.

1. Introduction

The Loess Plateau is one of the world’s major apple-growing regions, and soil-management methods in this area are primarily based on clean tillage [1]. However, large-scale apple tree cultivation can damage native vegetation, leading to soil erosion and land degradation [2]. Furthermore, the long-term adoption of clean cultivation as a soil management practice has severely compromised soil health in orchards, resulting in soil compaction, low organic matter levels, and the destruction of microbial ecosystems [3]. Growing orchard cover crops is an environmentally friendly management technique that can maintain soil health and promote the sustainable development of fruit tree cultivation [4,5,6]. With increasing global demand for sustainable agricultural development, modern orchard cover crop cultivation techniques have been widely promoted and applied [2,7]. Meta-analyses indicate that cover crops can enhance the production performance of the tree–soil system to achieve better orchard quality and economic benefits [4]. Thus, the application of orchard cover crop planting practices is crucial for the sustainable development of the apple industry.
Orchard cover crops improve soil water conservation, thereby lessening the environmental impact of fruit tree cultivation and promoting efficient and environmentally friendly orchards [8]. However, cover crop species vary in terms of root growth, material metabolism, growth cycle, and biomass. In tableland orchards, drought-tolerant species such as Trifolium repens, Lolium perenne, and Brassica rapa are widely used, depending on the local conditions [9,10]. Research has indicated that orchard cover crops can promote soil organic carbon sequestration by inputting root exudates and plant residues, which improve the physical properties of the soil [11]. Compared with allowing the growth of natural grasses, planting cultivated species with high adaptability and yield can increase the soil organic matter content and reduce the degree of soil nitrogen loss [7]. In addition, different cover crop planting practices, including inter-row and tree-disk patterns, lead to differences in orchard microclimate, soil structure, and fruit quality under similar management conditions and tree ages [8,12]. Therefore, the selection of cultivated species and planting practices is key to optimizing the positive impacts of orchard cover crops [9]. However, few studies have compared the effects of cover crop planting on soil nutrients under different planting patterns.
In this study, we aimed to investigate the effects of different cover crop species and planting patterns on plant growth and soil properties. To this end, we conducted in situ field experiments with four main drought-resistant cultivated species planted in an apple orchard in Gansu Province, China, using inter-row or tree-disk patterns and compared the plant growth characteristics, soil chemical properties, and rhizosphere soil metabolites under each treatment after 2 years. We hypothesize that different cultivated species and their spatial planting patterns differentially influence root development and soil nutrient partitioning due to their physiological and rhizospheric traits. Our findings elucidate the interactions between apple rootstocks and cover crops, providing a theoretical basis for the selection of cultivated species and planting practices in orchards. This study serves as a valuable reference for the sustainable development of the fruit industry.

2. Materials and Methods

2.1. Study Area

The experimental site was located at the Haoyuan Apple Base of Tianshui Normal University in the Gansu Province, China (34°34′16″ N, 105°41′35″ E; elevation: 1294 m). The main cash crop in this area is apples, and the orchard is situated adjacent to the Hulu River. The soil type of the apple orchard is sandy-gravelly soil, according to the Unified Soil Classification System (USCS) [13], with a total nitrogen content of 1.58 g·kg−1, total phosphorus content of 1.79 g·kg−1, total potassium content of 20.35 g·kg−1, available phosphorus content of 16.70 mg·kg−1, available potassium content of 450.79 mg·kg−1, organic matter content of 11.96 g·kg−1, and pH = 7.52. The annual average rainfall was 480 mm, and the mean annual temperature was 12 °C, with a large diurnal temperature variation and 211 frost-free days. The interannual and interseasonal distribution of rainfall is uneven, with a majority of rainfall concentrated in September.

2.2. Experimental Design

The main apple cultivar was Yanfu No. 8, with Malus robusta (Carrière) Rehder as the rootstock. The trees were 4 years old and were planted at a spacing of 3.0 × 4.5 m. The cultivated species used in the orchard were Vulpia myuros (VM) at 18 kg·hm2, Vicia villosa (VV) at 30 kg·hm2, Orychophragmus violaceus (OV) at 15 kg·hm2, and Brassica campestris (BC) for at kg·hm2, which were sourced from the Gansu Provincial Academy of Agricultural Sciences. On 15 September 2023, two planting patterns were established in the orchard: inter-row and tree-disk patterns. Each treatment was replicated five times.
In April 2024, five independent soil samples (0–20 cm) were collected per treatment and analyzed separately to assess biological variation. The soil samples in the profile are divided into 0–10 cm and 10–20 cm. After natural air-drying, it passed through a 2 mm sieve tively. The above- and belowground parts of the cover crop samples were packed separately in self-sealing bags for subsequent index determination. The rhizosphere soil (soil adhering to roots after gentle shaking) was carefully removed using a soft brush, and samples were collected for metabolomic analysis. The collected samples were stored at a low temperature and quickly transported to the laboratory. Each treatment was replicated five times.

2.3. Determination of Plant Growth Characteristics

Twenty plants from different orchard cover crop treatments were randomly selected for determination of their growth characteristics. We measured the aboveground growth of each plant from the root collar to the top of the main stem (shoot length [SL]) and the belowground growth from the root collar to the root tip (root length [RL]). The results were calculated from the average values. The collected plant samples were placed in an oven at 105 °C for 30 min, dried to a constant weight at 65 °C, and weighed to determine their belowground biomass dry weight (BDW).
Root activity (RA) was determined using the triphenyltetrazolium chloride method. Root morphology was obtained by using a WinRHIZO root scanner (Epson 12000XL, Epson, Suwa, Japan) to analyze the total root length (TRL), root surface area (RSA), root volume (RV), root node number (RNN), and root length at different root diameters (RL-0.2, RL-0.4).

2.4. Analysis of Soil Chemical Properties and Metabolites

Soil total nitrogen (TN), NH4+-N, and NO3−-N were determined using a flow analyzer. Total phosphorus (TP) and available phosphorus (AP) were determined using the molybdenum-antipyrene spectrophotometric method. Total potassium (TK) and available potassium (AK) were measured using the flame photometry method [14].
The collected rhizosphere soil was mixed with sterile water at a 1:1 ratio, and the supernatant was filtrated for metabolite analysis after oscillation and centrifugation (180 rpm, 30 min; 10,000 rpm, 4 °C, 15 min) [15]. The filtrate was concentrated to 10 mL using a rotary evaporator under reduced pressure, extracted three times with ethyl acetate at a volume ratio of 2:1, and concentrated to dryness under reduced pressure. The residue was dissolved in 5 mL of methanol, filtered through a 0.45 µm filter membrane, and stored at −20 °C. Finally, the extract was analyzed using a TRACE 1300 and ISQ gas chromatograph–mass spectrometer (Thermo Scientific, Waltham, MA, USA).

2.5. Data Analysis

Data were processed in Excel 2019 and analyzed using SPSS 26.0. Two-way analysis of variance was performed to analyze plant growth, soil chemical properties, and soil metabolites under different treatments, with a significance level of p < 0.05. Graphs and charts were created using Origin 2021 software. Redundancy analysis (RDA) was used to analyze the relationships between environmental factors and plant growth characteristics. Correlation analysis and the Mantel test were used to analyze the relationships between soil chemical properties, plant growth characteristics, and soil metabolites. The fuzzy mathematics membership function method was used to calculate the membership function values of the selected indicators for each treatment [15]. The sum of these values was calculated, and the treatments were ranked according to the magnitude of the mean value, with a higher mean indicating a more favorable ranking. The membership function was calculated as follows:
R = (Xi − Xmin)/(Xmax − Xmin),
where Xi is the measured value of the indicator, Xmax is the maximum value of the indicator, and Xmin is the minimum value of the indicator.

3. Results

3.1. Orchard Cover Crops Growth

The growth of the orchard cover crops varied under the different planting patterns (Figure 1). With the exception of OV, the plants had greater shoot length (SL) under the inter-row pattern than under the tree-disk pattern; among them, VV had the greatest SL at 24.03 cm (p < 0.05) (Figure 1a). Meanwhile, OV and VV had longer root length (RL) under the tree-disk pattern (p < 0.05), whereas BC and VM had greater RL under the inter-row pattern (p < 0.05) (Figure 1b). Root activity is related to root viability and integrity. All plants showed higher root activity (RA) under the tree-disk pattern than under the inter-row pattern, with BC exhibiting the highest activity of 4.27 mg·g−1·h−1 (p < 0.05) (Figure 1c). In addition, VM showed the greatest BDW of 2750.00 g·m−2 under the tree-disk pattern (Figure 1d).

3.2. Root Morphology

For VV and BC, the inter-row pattern was more favorable for root growth, with all root indicators being higher than those under the tree-disk pattern (Figure 2). Meanwhile, OV exhibited a greater total root length (TRL), higher root node number (RNN), and greater root length at different root diameters (RL-0.2, RL-0.4) under the tree-disk pattern and a larger surface area (RSA) and root volume (RV) under the inter-row pattern. These results indicate that the tree-disk pattern promoted root branching, whereas the inter-row pattern favored thicker roots. In contrast, for VM, the tree-disk pattern was more favorable for root growth than the inter-row pattern, although its RV was 9.30% smaller under this pattern (p > 0.05).

3.3. Soil Chemical Properties

The influence of different orchard cover crop planting patterns on the total nutrient content of the soil was related to the soil depth. The tree-disk pattern with OV showed the highest TN contents of 1.65 g·kg−1 in the 0–10 cm layer (Figure 3a) and 1.80 g·kg−1 in the 10–20 cm layer (Figure 3d). Meanwhile, the tree-disk pattern with VV showed the lowest TN content in the 0–10 cm layer at 1.46 g·kg−1, and that with BC showed the lowest TN content in the 10–20 cm layer at 1.53 g·kg−1 (Figure 3a,c). In the 0–10 cm layer, the tree-disk pattern with VM showed the highest TP content of 1.46 g·kg−1 and significantly (p < 0.05) increased the TP content by 27.84% (VV) and 123.53% (OM) compared to the inter-row pattern, respectively (Figure 3b). In the 10–20 cm layer, the tree-disk pattern with VM showed the highest TP content of 1.54 g·kg−1, followed by that with VV. Similarly, compared with that at the 0–10 cm layer, the TP content under the tree-disk pattern with OV at 10–20 cm increased significantly, by 136.84% (p < 0.05) (Figure 3e). In addition, the TK content of all cultivated species soil was higher under the tree-disk pattern than under the inter-row pattern at both 0–10 cm and 10–20 cm layers (Figure 3c,f).
Different orchard cover crop planting patterns showed significant effects on available soil nutrients (Figure 4). The tree-disk pattern with VV, BC, and VM showed a higher AP content (p < 0.05) than the inter-row pattern in all soil layers; among them, BC showed the highest AP contents of 15.08 mg·kg−1 in the 0–10 cm layer and 12.26 mg·kg−1 in the 10–20 cm layer. Meanwhile, for OV, the tree-disk pattern showed the highest AP content of 10.15 mg·kg−1 in the 0–10 cm layer, and the inter-row pattern showed the highest AP content of 7.25 mg·kg−1 in the 10–20 cm layer (Figure 4a,e). The inter-row pattern with VV and VM showed higher available potassium (AK) contents than the tree-disk pattern with these species. The tree-disk pattern with BC showed the highest AK contents of 421.67 mg·kg−1 in the 0–10 cm layer and 293.67 mg·kg−1 in the 10–20 cm layer, and the inter-row pattern with VM showed the highest AK contents of 234.33 mg·kg−1 in the 0–10 cm layer and 181.00 mg·kg−1 in the 10–20 cm layer (Figure 4b,f). The tree-disk pattern with BC and OV significantly (p < 0.05) increased the NH4+-N content compared with that under the inter-row pattern in different soil layers. Meanwhile, the inter-row pattern with VM had the highest NH4+-N contents of 6.35 mg·kg−1 in the 0–10 cm layer and 6.65 mg·kg−1 in the 10–20 cm layer (Figure 4c,g). In addition, the inter-row pattern with OV and VM had higher NO3−-N contents (p < 0.05) than the tree-disk pattern with these species in different soil layers, among which OV showed the highest contents of 19.16 mg·kg−1 in the 0–10 cm layer and 21.28 mg·kg−1 in the 10–20 cm layer (Figure 4d,h).

3.4. Rhizosphere Soil Metabolites

To explore the effects of different cultivated species on soil metabolites under different planting patterns, we conducted cluster analysis of the major metabolites (Figure 5). A total of 25 metabolites, including lipids, acids, olefins, and alkanes, were identified based on database searches and quantitative analysis. The results show that the main metabolites identified in the rhizosphere soil of VV were phthalates, tert-butyl formate, squalene, erucamide, and four kinds of alkane substances under the tree-disk pattern and octacosane and nonacosane under the inter-row pattern. Notably, the relative content of metabolites was significantly (p < 0.05) higher for VV than for other cultivated species. The main metabolites identified in the rhizosphere soil of BC were butyl acetate, cyclohexene, oleamide, and tetratetracontane under the tree-disk pattern and heneicosane, hexacosane, cyclohexylmethyl malonate, and cyclohexane-1,3,5-tricarbonyl under the inter-row pattern. The main metabolites identified in the rhizosphere soil of OV were butyl 3-phenylpropionate, gamma-butyrolactone, and butyric acid under the tree-disk pattern and bis(2-ethylhexyl) adipate and phthalic acid under the inter-row pattern. The main metabolites identified in the rhizosphere soil of VM were methyl 3-phenylpropionate, methylcyclohexane, and tetracosane under the tree-disk pattern and butyl acetate, cyclohexene, tetracosane, and heptacosane under the inter-row pattern.

3.5. Redundancy Analysis

Redundancy analysis was used to analyze the correlations between different orchard-cultivated species, environmental factors (TN, TP, TK, AP, AK, NH4+-N, and NO3−-N), and plant growth characteristics (RL, SL, RA, and BDW) under the tree-disk (Figure 6a) and inter-row (Figure 6b) planting patterns. For the tree-disk pattern, the first and second axes explained 70.47% and 25.28%, respectively, with a cumulative explanation of 95.75%, indicating that the first- and second-order axes reflect the relationship between environmental factors and plant growth characteristics in different samples. Among the environmental factors, soil TP was significantly (p < 0.05) positively correlated with plant RL and SL and negatively correlated with soil NH4+-N, NO3−-N, TN, and AK contents, as well as plant BDW. Plant BDW was significantly (p < 0.05) positively correlated with soil NH4+-N, NO3−-N, TN, and AK contents (Figure 6a).
For the inter-row pattern, the first and second axes explained 84.67% and 12.69%, respectively, with a cumulative explanation of 97.36%. Plant BDW was significantly (p < 0.05) negatively correlated with soil TN and plant SL, indicating that an increase in belowground biomass might contribute to a decrease in plant height under the inter-row pattern. In addition, plant RL was significantly negatively correlated with soil AP content (p > 0.05), indicating that the absorption efficiency of AP could be improved by increasing root elongation (Figure 6b).

3.6. Correlation Analysis

Correlation analysis and the Mantel test were used to analyze the correlations between environmental factors, plant growth characteristics, and soil metabolites (Figure 7a). The results showed that plant BDW was significantly (p < 0.05) positively correlated with soil AK, AP, and NH4+-N contents and significantly negatively correlated with plant SL and RL, indicating that the increase in orchard cover crops’ belowground biomass was related to soil available nutrients. In addition, rhizosphere soil metabolites were significantly (p < 0.001) positively correlated with soil NH4+-N and AP contents and plant BDW and significantly negatively correlated with plant SL and soil TP and AK contents. The results of correlation analysis between the dominant soil metabolites and environmental factors and plant growth characteristics are shown in Figure 7b. Tetracosane and methyl 3-phenylpropionate were significantly positively correlated with soil AK and AP contents and plant BDW and significantly negatively correlated with plant RL. Conversely, plant SL was significantly negatively correlated with tetracosane and methyl 3-phenylpropionate and significantly positively correlated with phthalates, squalene, erucamide, heptadecane, octadecane, and heneicosane. In addition, hexacosane, cyclohexylmethyl malonate, and cyclohexane-1,3,5-tricarbonyl were significantly negatively correlated with soil TP content and significantly positively correlated with soil NO3−-N content.

3.7. Comprehensive Analysis of Membership Function

The fuzzy mathematics membership function method was used to analyze the soil indices of different cultivated species in the orchard under the tree-disk and inter-row patterns (Table 1). The results showed that the inter-row pattern with OV had the highest average function value of 0.56, followed by the tree-disk pattern with VM at 0.53. The tree-disk pattern with VV exhibited the lowest performance with an average value of 0.20. In addition, the function values of total nutrients (TN, TK, and TP) were significantly higher than those of available nutrients (AK, AP, NH4+-N, and NO3−-N), indicating that the total nutrients in soils were the main factors influencing the differences between different treatments.

4. Discussion

Selecting suitable cultivated species and planting patterns is key to implementing orchard cover crop technology. Many factors must be considered to ensure that cover crops can adapt to orchard climatic and environmental conditions, thereby improving soil properties and enhancing plant growth [16,17]. Moreover, there are substantial differences among cultivated species in terms of their effects on orchard ecosystems [9,18]. In this study, we selected four cultivated species that are well adapted to local climatic conditions and are commonly used for the establishment of artificial meadows and planted them in two different patterns in an apple orchard. We found that the inter-row pattern was more conducive to root development than the tree-disc pattern for VV and BC. In contrast, the tree-disk pattern was more favorable than the inter-row pattern for TRL, RNN, RL-0.02, and RL-0.04 for OV, indicating that the tree-disk pattern was more conducive to root growth and branching for this species. Similarly, VM showed greater TRL, RSA, and RNN under the tree-disk pattern than under the inter-row pattern. These results indicate that the growth of different cultivated species is closely related to their planting pattern [19]. The inter-row pattern is conducive to the aboveground development of plants, and the tree-disk pattern promotes the elongation of plant roots and enhances their activity, which is similar to the results of previous studies [20,21,22,23].
Many studies have shown that cover crops enhance the physical structure of soil in orchards. For example, Wang et al. found that planting cover crops reduced soil bulk density and improved water infiltration and water-holding capacity [22]. Substances exuded by the roots, such as polysaccharides and proteins, bind soil particles together to form stable aggregates [23]. However, few studies have compared the effects of cover crop planting on soil nutrients under different planting patterns. In this study, we found that the changes in soil TN and TK were not significant under different planting patterns and cultivated species, but these changes affected the soil TP content. Meanwhile, the soil TP content increased significantly under the tree-disk pattern, except with BC. Our results indicate that the tree-disk pattern was more conducive to the accumulation of total soil nutrients and had a unique advantage in providing nutrients to fruit trees because of its proximity to the tree roots. These results are similar to those reported by Zhang et al., who found that the soil nitrogen, phosphorus, and potassium contents increased during the critical growth periods of fruit trees when cover crops were planted in a tree-disk pattern [19]. In addition, the soil available nutrient contents exhibited significant differences under different planting patterns for all species except VV. The tree-disk pattern with BC and OV had higher AP, AK, and NH4+-N contents than the inter-row pattern in shallow soil, whereas VM showed the highest AK, NH4+-N, and NO3−-N contents under the inter-row pattern, which may be related to the differences in root development between different cultivated species. Previous studies have shown that the interaction between cover crops and tree root exudates can influence the release of soil-insoluble phosphate. As the plant roots are closer to the fruit tree roots under the tree-disk pattern, this interaction likely played a role in increasing the soil AP content, which is similar to the results of a previous study [24]. Soil NH4+-N and NO3−-N can be directly absorbed and utilized by plants, and their soil concentrations are related to the growth of fruit trees [25,26]. In this study, the NH4+-N and NO3−-N contents of the soil were significantly affected by the cover crops planting pattern. The microbial activity of soil in the tree-disk area is higher than that in the surrounding bare soil, which is conducive to the mineralization of organic nitrogen to increase the NH4+-N content [27]. Meanwhile, the inter-row planting pattern may be more favorable for nitrification, resulting in an increase in NO3--N due to the improved aeration and porosity of the soil.
Soil metabolites are related to soil fertility and plant growth and drive nutrient cycling and energy exchange [28,29]. Lipid compounds can indirectly affect plant growth by regulating the metabolic activity of soil microorganisms, which is often associated with the release of volatile organic compounds that regulate the relationship between plants and their environment [27]. Additionally, lipid compounds can improve soil physical properties such as water retention and aeration to create a favorable environment for plant growth [30]. For example, phenylpropanoid derivatives such as methyl 3-phenylpropionate and butyl 3-phenylpropionate can attract growth-promoting microorganisms or inhibit soil-borne pathogens to influence plant growth. In addition, phthalates may act as microbial inhibitors, potentially altering rhizosphere activity [29]. In our study, the contents of soil metabolites, including lipids, acids, olefins, and alkanes, significantly increased under the tree-disk pattern, whereas the contents of amino acids and polysaccharides increased under the inter-row pattern. The phthalate content significantly increased under the tree-disk pattern with VV, indicating that the plant growth was likely inhibited under this pattern. In comparison, the inter-row pattern with BC promoted the content of methyl/butyl 3-phenylpropionate in the soil, which is beneficial for plant growth [31]. Moreover, based on the correlation analysis of soil metabolites, plant growth, and soil chemical properties, we found that the metabolites secreted by different plants were closely related to plant root growth and soil available nutrient changes. An increase in soil AK, AP, and NH4+-N can promote the growth of root biomass while having a negative effect on root elongation, indicating that plants can increase their nutrient absorption capacity by promoting root elongation under nutrient-deficient conditions [32]. Furthermore, organic acids such as phthalic acid and butyric acid are positively correlated with soil chemical properties, indicating that organic acids produced by root metabolic activities may affect the activity of soil microbes to indirectly influence plant growth through the impact on soil nutrient accumulation and transformation [33]. While low concentrations of phthalic acid have also been found to enhance the drought resistance and growth of plant seedlings [34]. Moreover, alkanes such as heneicosane, heptadecane, and octadecane are positively correlated with the growth of plant shoots and roots. Previous studies suggested that alkanes play an important role in plant growth [35,36]. For example, alkanes such as n-hexadecane can promote the elongation of canola roots [37], which is consistent with the results of this study. In addition, the role of alkanes in plant growth is multifaceted, as they can act as direct growth promoters and also indirectly influence the growth and development of plants through microbial degradation or regulation pathways of plant metabolism [34]. Therefore, our research also emphasized the significance of alkane compounds in plant growth.
Planting cover crop is an eco-friendly soil management strategy for orchards that improves soil properties [33,38]. However, the effects of inter-row and tree-disk planting patterns on soil properties differ. In the present study, the four cultivated species showed significant differences in total soil nutrients under different planting patterns. The effects of BC and VV on soil nutrients were limited under the inter-row and tree-disk patterns, whereas VV and OV showed better soil improvement abilities [39]. Based on our findings, we suggest planting VM in a tree-disk pattern and OV in an inter-row pattern to improve soil fertility and enhance the availability of nutrients. However, the results of the present study are contrary to those of Li et al., who reported that cover crops have a limited effect on soil fertility improvement under the inter-row pattern [20], possibly because of differences in cultivated species and local climatic conditions. In conclusion, tree-disk planting enhanced soil nutrient status and metabolite diversity, supporting their potential for sustainable orchard management.

5. Conclusions

In summary, different cultivated species and planting practices have varying effects on soil nutrients, and these differences are related to rhizosphere soil metabolites. The tree-disk pattern is more beneficial for taproot development and root activity. Moreover, the tree-disk pattern has a positive effect on the nutrient content of shallow soil for different cultivated species, which can increase the microbial activity of soils and enhance the mineralization of organic nitrogen. An inter-row pattern can improve soil aeration and promote microbial nitrification, leading to an increase in NO3--N. Based on this comprehensive analysis, we recommend planting Orychophragmus violaceus in a tree-disk pattern and Vulpia myuros in an inter-row pattern, which can increase plant biomass accumulation and soil nutrient contents. Our results provide an important reference for the sustainable development of apple orchards in rain-fed areas of the Gansu Province.

Author Contributions

Y.Z.: Conceptualization, Methodology, Software, Writing—original draft. Q.L.: Project administration, Methodology, Writing—review and editing. Y.S.: Data curation, Visualization. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Soft Science Special Project of the Gansu Basic Research Plan [Grant No. 25JRZE002], Science and Technology Support Plan Project of Qinzhou District [Grant No. 2024-NCKJG-5329], and Postdoctoral Fellow Special Project of the Gansu Basic Research Plan [Grant No. 25JRRA747].

Data Availability Statement

All data generated or analyzed during this study are included in this published article.

Acknowledgments

We are thankful to all the researchers whose contributions were used in our study analysis and referenced in this review article.

Conflicts of Interest

The authors declare no competing interests.

Abbreviations

The following abbreviations are used in this manuscript:
TDTree-diskRL-0.4Root length at 0.4 mm diameters
IRInter-rowTNTotal nitrogen
VMVulpia myurosTPTotal phosphorus
VVVicia villosaTKTotal potassium
OVOrychophragmus violaceusAPAvailable phosphorus
BCBrassica campestrisAKAvailable potassium
SLShoot lengthRDARedundancy analysis
RLRoot lengthTRLTotal root length
BDWBelowground biomass dry weightRSARoot surface area
RARoot activityRVRoot volume
RL-0.2Root length at 0.2 mm diameters RNNRoot node number

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Figure 1. Effect of different orchard cover crop planting patterns on (a) shoot length (SL), (b) root length (RL), (c) root activity (RA), and (d) belowground biomass dry weight (BDW). VM: Vulpia myuros; VV: Vicia villosa; OV: Orychophragmus violaceus; BC: Brassica campestris; TD: tree-disk pattern; IR: inter-row pattern. Different lowercase letters represent significant differences according to two-way analysis of variance. An asterisk indicates a significant difference between the tree-disk and inter-row pattern (* p < 0.05). Error bars represent ± standard deviation (n = 5).
Figure 1. Effect of different orchard cover crop planting patterns on (a) shoot length (SL), (b) root length (RL), (c) root activity (RA), and (d) belowground biomass dry weight (BDW). VM: Vulpia myuros; VV: Vicia villosa; OV: Orychophragmus violaceus; BC: Brassica campestris; TD: tree-disk pattern; IR: inter-row pattern. Different lowercase letters represent significant differences according to two-way analysis of variance. An asterisk indicates a significant difference between the tree-disk and inter-row pattern (* p < 0.05). Error bars represent ± standard deviation (n = 5).
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Figure 2. Effect of different orchard cover crop planting patterns on root morphology in terms of total root length (TRL), root surface area (RSA), root volume (RV), root node number (RNN), and root length under different diameters (RL-0.2, RL-0.4).
Figure 2. Effect of different orchard cover crop planting patterns on root morphology in terms of total root length (TRL), root surface area (RSA), root volume (RV), root node number (RNN), and root length under different diameters (RL-0.2, RL-0.4).
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Figure 3. Effect of different orchard cover crop planting patterns on the soil’s total nutrients. Soil total nitrogen (TN, g·kg−1) content in (a) 0–10 cm and (d) 10–20 cm soil layers; total phosphorus (TP, g·kg−1) content in (b) 0–10 cm and (e) 10–20 cm soil layers; and total potassium (TK, g·kg−1) content in (c) 0–10 cm and (f) 10–20 cm soil layers. Error bars represent ± standard deviation (n = 5).
Figure 3. Effect of different orchard cover crop planting patterns on the soil’s total nutrients. Soil total nitrogen (TN, g·kg−1) content in (a) 0–10 cm and (d) 10–20 cm soil layers; total phosphorus (TP, g·kg−1) content in (b) 0–10 cm and (e) 10–20 cm soil layers; and total potassium (TK, g·kg−1) content in (c) 0–10 cm and (f) 10–20 cm soil layers. Error bars represent ± standard deviation (n = 5).
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Figure 4. Effect of different orchard cover crop planting patterns on the soil available nutrients. Soil available phosphorus (AP, mg·kg−1) content in (a) 0–10 cm and (e) 10–20 cm soil layers; available potassium (AK, mg·kg−1) content in (b) 0–10 cm and (f) 10–20 cm soil layers; NH4+-N (mg·kg−1) content in (c) 0–10 cm and (g) 10–20 cm soil layers; and NO3−-N (mg·kg−1) content in (d) 0–10 cm and (h) 10–20 cm soil layers. Error bars represent ± standard deviation (n = 5).
Figure 4. Effect of different orchard cover crop planting patterns on the soil available nutrients. Soil available phosphorus (AP, mg·kg−1) content in (a) 0–10 cm and (e) 10–20 cm soil layers; available potassium (AK, mg·kg−1) content in (b) 0–10 cm and (f) 10–20 cm soil layers; NH4+-N (mg·kg−1) content in (c) 0–10 cm and (g) 10–20 cm soil layers; and NO3−-N (mg·kg−1) content in (d) 0–10 cm and (h) 10–20 cm soil layers. Error bars represent ± standard deviation (n = 5).
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Figure 5. Heatmap of the relative differences in metabolites of orchard cover crops rhizosphere soil. The red segments in the heatmap indicate a relatively high content of metabolites, and the blue segments indicate a relatively low content of metabolites. TD-VV: Vicia villosa in tree-disk pattern; IR-VV: Vicia villosa in inter-row pattern; TD-BC: Brassica campestris in tree-disk pattern; IR-BC: Brassica campestris in inter-row pattern; TD-OV: Orychophragmus violaceus in tree-disk pattern; IR-OV: Orychophragmus violaceus in inter-row pattern; TD-VM: Vulpia myuros in tree-disk pattern; IR-VM: Vulpia myuros in inter-row pattern.
Figure 5. Heatmap of the relative differences in metabolites of orchard cover crops rhizosphere soil. The red segments in the heatmap indicate a relatively high content of metabolites, and the blue segments indicate a relatively low content of metabolites. TD-VV: Vicia villosa in tree-disk pattern; IR-VV: Vicia villosa in inter-row pattern; TD-BC: Brassica campestris in tree-disk pattern; IR-BC: Brassica campestris in inter-row pattern; TD-OV: Orychophragmus violaceus in tree-disk pattern; IR-OV: Orychophragmus violaceus in inter-row pattern; TD-VM: Vulpia myuros in tree-disk pattern; IR-VM: Vulpia myuros in inter-row pattern.
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Figure 6. Redundancy analysis (RDA) of the relationships between environmental factors and plant growth characteristics of different orchard-cultivated species under (a) tree-disk and (b) inter-row patterns.
Figure 6. Redundancy analysis (RDA) of the relationships between environmental factors and plant growth characteristics of different orchard-cultivated species under (a) tree-disk and (b) inter-row patterns.
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Figure 7. (a) Pearson correlation analysis and Mantel test between soil chemical properties, plant growth characteristics, and soil metabolites; (b) Correlation analysis between dominant soil metabolites and soil chemical properties and plant growth characteristics. ***, p < 0.001; **, p < 0.01; *, p < 0.05.
Figure 7. (a) Pearson correlation analysis and Mantel test between soil chemical properties, plant growth characteristics, and soil metabolites; (b) Correlation analysis between dominant soil metabolites and soil chemical properties and plant growth characteristics. ***, p < 0.001; **, p < 0.01; *, p < 0.05.
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Table 1. Comprehensive evaluation of soil indices of orchard cover crops under different planting patterns.
Table 1. Comprehensive evaluation of soil indices of orchard cover crops under different planting patterns.
PatternsCultivated SpeciesTNTKTPAKAPNH4+-NNO3−-NScoreRanking
TDVV0.250.490.750.050.110.020.040.247
IRVV0.500.190.530.100.050.030.020.208
TDBC0.530.490.350.100.030.630.260.345
IRBC0.680.220.030.130.050.060.970.316
TDOV0.330.590.840.140.520.070.010.364
IROV0.440.590.710.690.450.790.240.561
TDVM0.380.640.380.580.910.520.310.532
IRVM0.220.610.530.370.840.050.050.383
Note: TD: tree-disk pattern; IR: inter-row pattern; VV: Vicia villosa; BC: Brassica campestris; OV: Orychophragmus violaceus; VM: Vulpia myuros; TN: total nitrogen; TK: total potassium; TP: total phosphorus; AK: available potassium; AP: available phosphorus.
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Zou, Y.; Li, Q.; Shen, Y. Effect of Cultivated Species and Planting Pattern on Plant Growth, Soil Properties, and Soil Metabolites in a Rain-Fed Orchard in Gansu, China. Agronomy 2025, 15, 1385. https://doi.org/10.3390/agronomy15061385

AMA Style

Zou Y, Li Q, Shen Y. Effect of Cultivated Species and Planting Pattern on Plant Growth, Soil Properties, and Soil Metabolites in a Rain-Fed Orchard in Gansu, China. Agronomy. 2025; 15(6):1385. https://doi.org/10.3390/agronomy15061385

Chicago/Turabian Style

Zou, Yali, Qi Li, and Yuying Shen. 2025. "Effect of Cultivated Species and Planting Pattern on Plant Growth, Soil Properties, and Soil Metabolites in a Rain-Fed Orchard in Gansu, China" Agronomy 15, no. 6: 1385. https://doi.org/10.3390/agronomy15061385

APA Style

Zou, Y., Li, Q., & Shen, Y. (2025). Effect of Cultivated Species and Planting Pattern on Plant Growth, Soil Properties, and Soil Metabolites in a Rain-Fed Orchard in Gansu, China. Agronomy, 15(6), 1385. https://doi.org/10.3390/agronomy15061385

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