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Article

Agroecological Soil Management of an Organic Apple Orchard: Impact of Flowering Living Mulches on Soil Nutrients and Bacterial Activity Indices

by
Ewa Maria Furmanczyk
1,* and
Eligio Malusà
1,2,*
1
The National Institute of Horticultural Research, ul. Konstytucji 3 Maja 1/3, 96-100 Skierniewice, Poland
2
Centro di Ricerca Viticoltura ed Enologia, Consiglio per la Ricerca in Agricoltura e l’Analisi dell’Economia Agraria (CREA), Via P. Micca 35, 14100 Asti, Italy
*
Authors to whom correspondence should be addressed.
Agronomy 2025, 15(11), 2612; https://doi.org/10.3390/agronomy15112612
Submission received: 22 September 2025 / Revised: 6 November 2025 / Accepted: 12 November 2025 / Published: 13 November 2025
(This article belongs to the Special Issue Ecological Aspects as a Basis for Future Pest Integrated Management—2nd Edition)
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Abstract

The introduction of living mulches into an orchard can be considered an agroecological practice that can provide several ecosystem services related to integrated crop protection, also in relation to the impact on soil microbiome. In this study, the introduction in an organic apple orchard of two plant mixtures designed as multifunctional living mulches to reduce weed competition and increase shelter for beneficial arthropods was evaluated in relation to their impact on soil nutrient content and bacterial activity indices. One mixture was composed of Trifolium repens (20%) and Festuca ovina (80%), the second made of 40 different plant species including legumes, flowering species and grasses. Both living mulches increased N-nitrate levels in spring, and the two-component plant mixture was also able to improve P and K levels in soil at the same time, in comparison to the natural cover (control). The two mixtures induced an increase in bacterial activity in the beginning (40 plant species mix) or middle of the growing season (two-component plant mix), without major effects on bacterial biodiversity at the phyla level, showing a high share of Proteobacteria and Actinobacteriota among treatments. Nevertheless, both plant mixtures modified the phenotypic profile of the bacterial population, measured with the Biolog method, of different classes of C sources including carbohydrates, amino acids and carboxylic acid. The results are pointing to possible benefits of the practice on soil microbial activity, which will have to be confirmed by longer studies.

1. Introduction

The introduction of living mulches, i.e., of mulches in the tree row composed of living plants, based on flowering species as floor management practice into fruit monocultural crops is becoming increasingly favored, mainly due to European and global trends regarding the protection and enhancement of biodiversity in agroecosystems [1]. Additional plant species incorporated into orchards’ cropping systems are known to boost the biodiversity of the habitat, and among those that can be easily translated into tangible benefits for the farmer can be mentioned the support to pollinating insects or to beneficial organisms, thus reducing pests’ incidence and improving crop yield [2,3,4]. Moreover, long-standing monocultures such as tree orchards, which cannot implement strategies to improve soil quality or fertility commonly proposed for organic cropping systems such as crop rotation, can benefit by the introduction of living mulches. Notwithstanding these benefits, mulching can also present some drawbacks, e.g., favoring an increase in rodent population [5], or competition for nutrients [6].
However, much less attention has been paid to the effects that the introduction of living mulches have on the orchard soil and its microbial communities, in particular in the case of mulches characterized by multifunctional properties or made by a complex mixture of plants. Some reports have highlighted the benefits of simple plant mixtures on nitrogen fixing, through symbiosis of legume and nitrogen-fixing bacteria, or other soil nutrients cycling [7,8]. Living mulches composed of only a single plant species (e.g., Trifolium repens or Coronilla varia or Lolium perenne) appeared to induce a potential positive effect on soil bacterial diversity and enzymatic activity [9]. On the other hand, a study on flower strips located in crop boundary areas showed that they could act as shelters or sources also for soil fauna, like earthworms and microorganisms [10]. Considering that the beneficial effects of flower strips on beneficial insects biodiversity and the enhancement of pest control services in adjacent crops can depend on its plant diversity [11], their effect on soil microbial diversity and activity could also be modulated and thus interesting to be understood. Indeed, it was observed that semi-natural field margins favor fungal and bacterial biomass [12] and can harbor functionally distinct microbial communities [13].
Soil microbiome is a vital component for preserving the balance and health of the soil [14]. Microorganisms are involved in carbon sequestration, decomposition of organic matter, promoting the cycling of macronutrients, and increasing the resources available to plants [15,16]. Furthermore, microorganisms can directly or indirectly impact soil-borne pathogens, enhancing plant tolerance against biotic stresses and improving plant growth rate and yield potential [17,18,19]. However, soil microorganisms are forming a complex network of interactions with the plants and other components of the soil life web. Indeed, each plant species can have an associated microbiome due to its capacity to recruit selected microorganisms from the environment through an enrichment process [20]. Therefore, the introduction of new plants or practices of soil management, such as the living mulches, into a certain environment, such as the orchard, could lead to changes that can potentially impact the fruit tree microbiome [21,22] and, consequently, its physiology and metabolism [23,24] and thus agronomic performance. In this regard, multispecies living mulches may represent an interesting and, simultaneously, sustainable tool able to provide several above- and below-ground ecosystem services enhancing the orchard resilience against various abiotic and biotic stresses. The present study was thus performed to investigate the effects on the soil nutrients and bacteria population diversity and activity of two very diverse plant mixtures, which were designed for the establishment of multifunctional living mulches in a mature organic apple orchard. The working hypothesis was to verify whether plant mixtures commonly exploited for the creation of flower strips outside the orchard to increase above-ground biodiversity could also be exploited to increase also the below-ground biodiversity of microbial communities and provide additional soil-based ecosystem services, particularly to increased nutrients availability to the main crop, which could derive from modification of the phenotypical characteristics of the bacterial community, i.e., their capacity to exploit carbon sources.

2. Materials and Methods

2.1. Experimental Site and Management Practices

The trial was conducted on a ten-year-old apple orchard (cv. Gala on M9 rootstock) established on a loamy sand soil (sand 78% + silt 14% + clay 4%) with 3.22% soil organic matter and pH 6.2. The orchard is a part of an experimental field (51°58′0″ N, 20°9′0″ E) belonging to the National Institute of Horticultural Research. The average monthly temperature and precipitation is provided for the year concerned by the analyses and for the preceding five years (Supplementary Materials—Figures S1 and S2). In the months when soil sampling was performed, they were as follows: 13.6 °C and 41.2 mm for May, 19.0 °C and 91.0 mm for July and 11.6 °C and 28.2 mm for September. The orchard, with spindle-shaped trees spaced at 3.5 m × 1.6 m, was drip irrigated and maintained in accordance with organic farming guideline [25]. Dry bovine manure (55% C and NPK content of 1:0.5:1%) and stillage (55% C and NPK content of 1:0.5:1%) were used as fertilizers, providing a total of 12 g/tree of nitrogen. The fertilizers were applied at the beginning of the growing season on the soil surface, within the row, by hand using a small fertilizer spreader (dry manure) or sprayer (stillage), to minimize the impact on the growth of living mulch tested. The treatments, with three replicates each, were arranged in a Randomized Complete Block Design (RCBD).

2.2. Living Mulches

Two plant mixtures were tested as living mulches: one consisting of Trifolium repens (20%) and Festuca ovina (80%) (clover mix) and the second being a mixture of 40 different species designed for flowering strips composed of dicots (20%) and monocots (80%) (flower mix). While clover mix is commonly established for inter-row soil management, but less commonly for tree row management, flower mix is offered to establish flower strips outside the orchard, but not as potential living mulch. Its selection for the trial derived from the idea of exploiting the tree row as a space suitable for flower strips, thus increasing effectiveness of the mixture normal function (increase in orchard above-ground biodiversity), and at the same time improving the soil biodiversity and nutrient cycling. Detailed composition of the second plant mixture is presented in Table 1. The plant mixtures were sown along the tree row (10 g/m2) on 15 May 2021. Natural soil cover was considered as control and it was managed with three-time mowing during the growing season (first week of June, first week of August and last week of September, annually, always after soil sampling). Each replication consisted of 20 trees for a total row length of about 30 m. The plots were hand-weeded twice to promote the good establishment of the living mulches during the first growing season (15 June and 20 July 2021).

2.3. Soil Sample Collection

Soil samples for chemical and biological analyses were collected three times during the tree’s growing season with well-established living mulches (16 May, 13 July and 12 September in 2022). Sampling was performed using Egner’s auger (2.5 cm diameter) collecting at least 10 subsamples at 0–20 cm depth within an area of 30 cm distance around the apple tree trunk. Then, the subsamples were pooled and mixed and evident animals, plant residues and stones were removed. The samples were processed directly or stored at 4 °C for up to seven days for soil chemical analysis or microbial biodiversity and activity measurements. Soil samples for DNA extraction were stored at −80 °C by the time of analysis.

2.4. Soil Chemical Analysis

The soil samples were analyzed for the basic nutrient concentrations: N-NO3, N-NH4, P and K. First the soil samples we dried at a temp. of 65 °C, then mineralized in concentrated nitric acid in a Candela Mars 5 microwave digestion oven. Nitrogen was determined colorimetrically with a Skalar SanPlus automated flow analyzer (Breda, The Netherlands), and phosphorus and potassium with a Perkin Elmer OPTIMA 2000 DV plasma spectrometer (Boston, MA, USA) [26].

2.5. Soil Microbial Biodiversity and Activity Assessment

Soil microbial biodiversity and activity were determined using the BIOLOG® system and EcoPlates (Biolog Inc., Hayward, CA, USA), which allows us to measure the microbial activity towards 31 different carbon sources. One gram of soil was suspended in 9 mL of sterile distilled water, the solution was shook at 150 rpm at room temperature for 1 h. Then, the soil particles were left to settle and the solution was serially diluted two times. Plates were inoculated with 100 µL of the soil suspension (10−3 dilution) per each well, and incubated in the dark at 26 °C for 72 h. Then, the absorbance at 590 nm (OD) was measured. The activity of microorganisms was evaluated on the basis of Average Well Colour Development (AWCD) [27] and the activity index was calculated according to the following formula:
A W C D = ∑ O D i / 31
where ODi is the optical density of the individual wells. The microbial biodiversity index was estimated using the Shannon–Weaver coefficient (H’):
H ' = − ∑ p i ( l n   p i )
where pi is the level of microbial activity in each well (ODi) divided by the activity in all the wells (Ʃ ODi) [28]. When calculating the level of activity of microorganisms for the H index and the amount of metabolized substrates, the threshold value OD = ODi − OD(control well) was used. Substrate richness (S), described as the number of utilized carbon substrates, was calculated using an OD590 value of 0.500 as threshold for positive response.

2.6. DNA Extraction and Sequencing

DNA was extracted from 0.5 g soil sample of each treatment and replicate (in total three isolations per treatment) using E.Z.N.A.® Soil DNA Kit (Omega Bio-Tek, Norcross, GA, USA) according to the manufacturer’s instructions. After the isolation, DNA was pooled into two separate replicates for each treatment (each consisting of 50% of the volume of the individual isolation within the treatment) and purified using Mag-Bind® TotalPure NGS (Omega Bio-Tek, USA) according to manufacturer’s instructions, and finally resuspended in 50 µL water. The quality and concentration of DNA extracts were evaluated by spectrophotometer analysis (Nano Drop 1000, Thermo Fisher Scientific Inc., Waltham, MA, USA).
The metagenome analysis of archaea and bacteria was performed based on the hypervariable V3–V4 region of the 16S rRNA gene. The libraries construction, sequencing and initial bioinformatic analysis were outsourced to Genomed S.A. (Warsaw, Poland). Briefly, the 341F and 785R primers were used together with a Q5 Hot Start High-Fidelity 2X Master Mix (New England Biolabs Inc., Ipswich, MA, USA). The sequencing was performed in the paired-end technology (PE), 2 × 300 nt with Illumina v3 kit by a MiSeq instrument (San Diego, CA, USA), which also performed an initial automatic analysis comprising demultiplexing and generation of fastq files. The species-specific classification of the reads was performed with the use of QIIME 2 according to the Silva 138 reference sequence database. The following tools were then used: FIGARO for read quality control, Cutadapt for initial data processing, and DADA2 for the selection of OTU (operational taxonomic unit) and further steps of the analysis. Amplicon sequences were deposited in the NCBI’s SRA database (SRA: SRR30365103, SRR30365102, SAMN47636476-79).

2.7. Statistical Analysis

The statistical analysis of data obtained during this study was performed using the R software version 4.1.3 [29]. To verify the normal distribution of the data, the Shapiro–Wilk test was used, while to confirm the homogeneity of variances, the Levene’s test was applied. The data were thus analyzed by ANOVA and means differences tested with Tukey’s test at p  ≤  0.05 with HSD test function from the “agricolae” package version 1.3.7 [30]. In case of not normal distribution, the non-parametric Kruskal–Wallis analysis with Fisher’s least significant difference post hoc test was utilized, introducing the Benjamini–Hochberg correction, with significance set at p  ≤  0.05, using the Kruskal function from the “agricolae” package. The specific test utilized for the various sets of data is mentioned in relation to the relevant result. Heatmaps were generated using “heatmap.2” command from “gplots” package version 3.1.3.1 [31] or visualized using “ggplot2” package version 3.5.0 [32] as other figures were included into the manuscript.

3. Results

3.1. Effect on Soil Nutrient Content

The two living mulch mixtures affected soil macronutrients content and their dynamic within the vegetative season with a transient and seasonal-related pattern (Figure 1 and details in Supplementary Material: Table S1). Both tested mixtures affected N-NO3 content in soil in comparison to control, resulting in significantly higher levels of this nitrogen (N) form in spring (on average 235.3% or 223.5% increase for clover mix or flower mix) and a tendency of higher level, but not statistically significant, in the other seasons (Figure 1 and Supplementary Material: Table S1). On the other hand, ammonium nitrogen content was instead tendentially reduced by the tested mixtures, significantly only at the end of the growing season (on average −51.9% or −52.6% decrease for clover mix or flower mix (Figure 1 and Supplementary Material: Table S1). The seasonal dynamics of N-NO3 and N-NH4 forms were both comparable to those of the control.
The mixtures, particularly the clover–festuca mixture, impacted the content of the other two major nutrient elements, P and K (Figure 1 and Supplementary Material: Table S1). The clover mix induced a significant increase in P content in spring (on average 59.0% increase in comparison to control), compared to the flower mixture and control treatments. During the remaining part of the vegetative season, no statistical changes were observed (Figure 1 and Supplementary Material: Table S1). The clover mix induced a similar significant increase in K in spring compared to control (on average 19.4% increase), while the flower mixture reduced the content of this element (on average −20.6% decrease in comparison to control). In summer and autumn, both living mulches induced a decrease in K content, which was statistically significant only for clover mix (details in Supplementary Material: Table S1). The living mulches also affected the dynamics of this element compared to the natural cover, with a different pattern between the two mixtures (Figure 1). The content of soil macronutrients in spring reflected to some extent the leaves nutrient content determined in early summer: a statistical higher P content was measured for the clover treatment and a limited reduction in K content induced by the flower mixture, compared in both cases to the other treatments (Table 2).

3.2. Effect on Soil Bacterial Activity and Biodiversity

The two plant mixtures did not affect the soil bacterial biodiversity (Index H) or metabolic potential (substrate richness), although they had significant effect on bacterial activity (AWCD), assessed using Biolog EcoPlates (Figure 2), with a transient and seasonal-related pattern. Flower mix increased the bacterial activity in spring (1.72 ± 0.08) and the clover mix induced a higher activity in summer (1.85 ± 0.03) when compared to control (1.51 ± 0.02 in spring and 1.62 ± 0.06 in summer, respectively). The seasonal dynamic of the bacterial activity of the clover mix was similar to the control, characterized by a maximum activity peak during summer. Instead, the soil bacterial microbiome associated with the flower mix showed a stable activity level through the whole growing season (Figure 2).
An advanced analysis of the impact of the two living mulch mixtures on the bacterial activity towards individual carbon sources (Figure 3) allowed us to distinguish a group of six compounds belonging to different biochemical classes that were less utilized by the microorganisms. These C sources included 2-hydroxybenzoic acid, α-ketobutyric acid, threonine, D,L-α-glycerol phosphate, i-erythriol and glycyl-L-glutamic acid. Moreover, D-xylose was a carbohydrate, which was metabolized by the microorganisms in spring and summer, but not in autumn.
Nevertheless, the analyzed dataset was grouped by sampling point rather than specific treatment, which was also supported by a detailed statistical analysis (Supplementary Materials: Table S2), again showing a seasonal-related pattern. Out of 31 compounds present within the Biolog EcoPlate, 17 expressed significant differences depending on the season, while only 6 out of 31 were affected by the treatment. Interestingly, the soil bacterial microbiome activity towards the majority of the carbohydrates was characterized by a low ability to metabolize these compounds in spring, followed by an increase and stabilization of this activity in summer and autumn, regardless of the treatment (e.g., D-galactonic acid γ-lactone, N-acetyl-D-glucosamine, glucose-1-phosphate or D,L,α-glycerol phosphate utilization). On the other hand, the soil bacterial microbiome associated with the clover mix showed higher activity towards β-hydroxy-glycyl-L-glutamic acid between 40 and 2-hydroxybenzoic acid than the natural cover, while that associated with the flower mixture showed intermediate values. Interestingly, both plant mixtures induced an increase in the soil bacterial microbiome activity towards D-malic acid, while the flower mix-associated bacterial population showed significantly higher activity towards i-erythriol than natural cover (Supplementary Materials: Table S2).
Similar number of C sources (between 4 and 7 compounds) showed significant differences between treatments within a specific timepoint, although any compound consistently showed these differences throughout the seasons. For instance, in spring, the main changes in the bacterial metabolization capacity were induced by the flower mix (increased metabolization of D-xylose, i-erythriol, N-acetyl-D-glucosamine) and the clover mix (increased activity towards glycyl-D-glutamic acid and phenylethylamine and decreased towards L-asparagine). In contrast, the changes recorded in summer were limited to the clover mixture (increased metabolization of carboxylic acids, polymers and amines). In autumn, significant changes were minimal and varied (Supplementary Materials: Table S2).

3.3. Soil Bacterial Microbiome Associated with Living Mulch Mixtures

The microbial biodiversity was examined using 16S rDNA amplicon sequencing (V3V4 fragment) for soil samples collected within the summer. A total of 397,087 paired reads were obtained from Illumina sequencing. The number of reads per library ranged between 56,591 and 83,481. After quality filtering and read merging, on average, 44.96% ± 2.20% reads per library were retained. The data was rarefied to 90% of the minimum library size, which still provided sufficient sequencing depth for biodiversity analysis (Supplementary Materials—Figure S3). In the analyzed dataset 99.88 to 99.97% reads were classified to the Bacteria kingdom, while others were represented by Archea. Thus, we decided to analyze only reads assigned to the Bacteria kingdom. It should be mentioned that only two replicates of metagenomic data were obtained, allowing only a descriptive deepening of the living mulches effect on soil bacteria population.
Although 39 different bacterial phyla were identified among sequenced soil metagenomes, only 9 of them represented at least 1% of the whole community (shown on Figure 4a). Proteobacteria and Actinobacteriota were the two most abundant phyla, irrespective of the treatment (43.1–46.3% and 21.0–23.1%, respectively). The third most abundant bacterial phylum for the microbiomes associated with either the flower mix or natural cover was Bacteroidota (6.8–9.5%), while for clover mix it was Acidobacteriota (8.3%) followed by Verrucomicrobiota (6.0%) and Bacteroidota (4.6%). Less abundant phyla, including Firmicutes, Myxococcota or Planctomyceota, showed higher abundance in flower mix-associated soil microbiome (3.9%, 2.5% or 2.6%) than in the natural cover (2.6%, 1.2% or 1.6%, respectively) or clover mix-associated (3.0%, 1.4% or 2.2%, respectively) bacterial communities.
At the genus level, 552 different taxa were found and 325 of them were shared by all three microbiomes (Figure 4b). Several genera were unique for each treatment: 25 genera for clover mix, 52 for flower mix and 49 for natural cover. The clover mix-associated microbiome resulted to be more similar to that of flower mix (40 shared genera), than to natural cover (only 24 genera), while flower mix and natural cover shared a relatively high number of genera (37). The total number of genera found in each treatment (414 for clover mix, 435 for control and 454 for flower mix) was reflected by the potential similar changes observed in various alpha biodiversity indices, like Chao1 and Shannon (Supplementary Materials: Table S3), suggesting the highest bacterial biodiversity levels for flower mix (Chao1 = 390.50 ± 14.85, Shannon = 4.47 ± 0.01), followed by natural cover (Chao1 = 374.80 ± 5.39, Shannon = 4.40 ± 0.01) and then clover mix treatment (Chao1 = 358.94 ± 5.39, Shannon = 4.35 ± 0.00). However, these observations and their possible effect sizes need to be verified using a larger number of replicates.
The detailed comparison of the relative abundance of the 30 most abundant genera for each microbiome (Figure 5) allowed us to identify genera potentially associated only to one treatment.
Flower mix-associated microbiome, in comparison to natural cover, resulted to contain a higher abundance of Bacillus, Streptomyces, Pseudonocardia, JG30-KF-AS9 and Candidatus Alysiosphaera. The microbiome associated with the clover mixture was characterized by a higher abundance of Candidatus Udaeobacter, MB-A2-108, Subgroup 7 and Luteolibacter. On the other hand, compared to the natural cover, both living mulches induced a reduction in the abundance of few groups of microorganisms: Sphingomonas, Brevundimonas, Chryseobacterium, Pedobacter, Massilia, Pseudomonas or Rhodococcus.

4. Discussion

Both tested plant mixtures affected the nutrient content in soil, particularly N-NO3, P and K. The observed increase in N-NO3 concentrations in spring could be induced by the N-fixing leguminous species present in the tested mixtures: T. repens in case of the clover mix or T. pratense, T. dubium and L. corniculatus in case of the flower mix [33]. Interestingly, this increase was less evident than that determined in other experiments with similar living mulch mixtures conducted in a younger apple orchard [34]. The expanded root system of adult trees compared to the less developed in volume of the young trees may account for a more efficient or higher nutrient uptake from the soil that was observed in the current study, thus explaining the quantitative difference observed between the two trials [35]. The dynamics of ammonium nitrogen, with its highest content observed in summer and decrease in autumn, irrespective of the living mulch tested, were similar to those observed in other studies assessing the impact of this practice on soil nutrients content [34,36]. The observed dynamic of ammonium in soil could be hypothesized to derive from potential modifications induced by the living mulches to the composition or activity of some specific bacterial species [37,38,39]. The observed increase in autumn could be a further positive service of the living mulches in reducing the risks of nitrogen losses and in matching the plant needs for this nutrient across the growing season [40].
Concerning the impact of the living mulches on phosphorus level, the significant increase during spring observed in the soil covered by the clover mix may be related to the presence of phosphorus-solubilizing species [41]. However, this hypothesis needs to be verified. The reduction induced to K level by the flower mixture already at the beginning of the growing season and lasting the whole vegetative season could result from the demand of the plants for their flowering and seed formation [42], which, for some of the species present in the mixture, even occurred twice (in spring and autumn, like L. corniculatus) [43], explaining also the reduction in K at the beginning and end of the growing season.
Nevertheless, besides the possible effect of the living mulches on the nutrient elements, both directly with their uptake and indirectly by affecting the soil microbial activity and, eventually, functional diversity, it their interaction with seasonal climatic conditions cannot be underestimated, as well as that of the apple tree physiology on the dynamic of nutrients availability [44,45,46].
Both living mulches did not affect the soil bacterial biodiversity, as assessed with Biolog EcoPlates, confirming earlier observations on living mulches composed by a single herbal or multifunctional plant species [36]. Interestingly, also no effect was observed on the substrate richness, contrary to the living mulch with a single species [36], which may point to a process of microbiome selection that is more evident when the living mulch is composed by a single plant species through specific root exudates able to shape the soil microbiome [47]. However, differences in plant species composition as well as richness of the plant mixtures were hypothesized, contributing to changing the soil microbial community composition [10] also in relation to the plant mixture biomass production [47]. Indeed, both amount and quality of root exudates can depend on plant species and age [48]. Moreover, root exudates were considered an important factor in the relation between plant richness and the soil microbiome [49], which could possibly have other effects on the crop [50].
Both plant mixtures positively affected bacterial activity (AWCD), although in different periods of the vegetative season: clover mix increasing it in summer, the flower mix during spring. Chemical composition and dynamics of root exudates emission, which depends on the plant development and level of expansion of the root system, influence the activity of soil microorganisms [51,52]. In general, the higher the diversity of plants, the higher the diversity of microorganisms [53,54] and thus the functions and activities that can be expressed by them through metabolic pathways, allowing the bacterial population to exploit diverse C sources [55,56], as was observed in the present trial only during spring, when the flower mixture resulted in inducing the highest bacterial metabolic activity.
The metabolic capacity of the bacterial population associated with both living mulch mixtures towards single C sources during the vegetative season was similar. A much diversified result was observed when the same clover mix and a poorer flower mix, consisting of only 10 plant species, were used as living mulches in a young orchard [34]. The age of the orchard, as well as the time elapsed from the establishment of the living mulch, could also be proposed as potential factors explaining these outcomes. Indeed, it could be hypothesized that in the current trial, the orchard management before the establishment of the living mulches could have promoted the selection of a core microbiome that tended to be not much modified by the living mulches, as described, but not statistically validated in Figure 4b. The living mulches could have instead promoted a seasonal soil microbiome enrichment [20], since in the newly planted orchard [34], both trees and living mulches were established at the same time, thus having the possibility to interact and maybe adapt their soil–plant–microorganisms relation from the very beginning in parallel. This hypothesis is supported by a study showing that the age of the orchard deeply affected various microbial indexes, including metabolic activities: a 7-year-old apple orchard soil had the highest carbon source metabolic activity, whereas a 23-year-old orchard soil had the lowest [57]. The differentiation of bacterial metabolic activities could also be associated with soil carbon accumulation in the orchard, which has been proposed to be differentially affected by root exudates of diverse plants, i.e., grasses or leguminous [58].
The bacterial microbiomes associated with both living mulch mixtures showed increased activity towards mainly carbohydrates, amino acids and carboxylic acids, which are the group of compounds widely presented in root exudates [59,60,61,62], and are considered to be the main plant-derived drivers shaping the soil microbiome [48,63]. Interestingly, most of these changes were observed at the beginning of the vegetative season, when the most intensive vegetative growth occurs and root exudation rate is the highest [64]. However, plant species which release higher amounts of root exudates during summer or autumn are also known [65], and this could account for the differences noted between the two living mulches in relation also to the metabolic activity and C sources metabolization rates. Moreover, changes in the components of root exudates and soil leachates may lead to large variations in microbial extracellular enzymatic activities to transform plant polymeric substances into bioavailable compounds for microbial metabolism [66], related to specialized soil bacterial and fungal groups [67] and affecting the overall microbial metabolic activity [68]. Nevertheless, it would be difficult to certainly link the differences observed to a specific factor, since the rate of root exudates emission depends on many abiotic factors (e.g., soil moisture and nutrient content), as well as on the plant species and plant age [63,69,70].
In order to gather more information on the bacterial populations associated with the plant mixtures tested in the orchard, we supplemented our experiments with description of their biodiversity, which was appraised using amplicon sequencing of only two replicates per each treatment, not allowing a full statistical evaluation. Typical phyla share of apple orchards were identified, with Proteobacteria and Actinobacteria as the most abundant taxa [36,71,72]. Analysis of the amplicons of the 16S rDNA fragments revealed some potential differences in the biodiversity of the microbiomes, expressed as the number of genera, that were not evident from the Biolog plate assessment. The highest number of genera was observed in the flower mixture (454) and the lowest in the mixture with clover (414), while an intermediate number of genera was recorded in the natural cover (435). Interestingly, this pattern corresponds to the richness of the plants present in the row in each treatment, as the average number of plant species identified in the natural cover of this orchard was 12 [73], suggesting that the increasing plant diversity increases soil microbial diversity as reported by other authors [74]. However, the possible bias of NGS analysis of environmental DNA due to the sequences from dead or inactive state cells or extracellular DNA [75] should also be taken into consideration while interpreting these results.
The majority of taxa included in the most abundant bacterial genera (e.g., Sphingomonas, Bacillus, Brevundimonas, Chryseobacterium, Pedobacter, Massilia, Pseudomonas, Pseudonocardia and Streptomyces) are considered common components of plant-associated microbiomes [76]. Interestingly, they usually share similar functionalities or multifunctionalities including cellulose decomposition—Sphingomonas, Steptomyces, Pedobacter [77], xenobiotics removal—Sphingomonas, Brevundimonas, Chrysobacterium, Pseudomonas [78,79,80,81] or plant-growth-promoting properties—Bacillus, Pseudomonas, Brevundimonas, Chrysobacterium [82,83]. It could be hypothesized that the potential divergent changes in the relative frequencies of these genera induced by the living mulches could be compensated at the functional level of the metagenomes. Verification of this hypothesis would require research that also takes into account the dynamics of these changes during the growing season and evaluation of the expression of genes related to, at least, nutrients cycles. However, the results of this study may be helpful in selecting groups of microorganisms or functionalities to which particular focus should be given to determine the impact of living mulches on the soil microbiome, also in the long-term perspective.

5. Conclusions

In conclusion, it emerged that plant mixtures designed for establishing flower strips and meant to increase shelter or food for beneficial insects (e.g., pollinators or pests’ predators) in the orchard, when introduced as living mulches can induce changes in the soil bacterial microbiome activity and could probably affect the microbial species composition. Even though the study covered only one growing season, and therefore any inferred long-term effects on soil remain speculative, the effect of the mulches could result in an impact on the dynamics and content of soil nutrients, which enhances the multifunctional properties of such kind of living mulches.
However, to better exploit this multifunctionality, the evaluation of their interactions with the plant–microorganism–soil system and the assessment of the effect on agronomic performance of the tree species (such as tree yield or growth) should be thoroughly expanded to allow a practical interpretation of the impact of the living mulches, useful to provide recommendations to practitioners.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/agronomy15112612/s1, Table S1: Percentage change in the nutrient concentrations in soil between appropriate plant mixture treatment and natural cover control, together with statistics details (p-value and coefficient intervals), according to Tukey’s post hoc test with 95% confidence level; Table S2: Average absorbance at 590 nm for 23 compounds with significant changes observed according to timepoint, treatment or treatment within a sampling point as a factor. Different letters show statistically significant differences between treatments or sampling points for p ≤ 0.05 according to ANOVA and means differences tested with Tukey’s test or the Kruskal–Wallis test with Benjamini–Hochberg correction (only values in cells highlighted in gray); Table S3: Biodiversity indices of bacterial amplicon sequencing data from tree understory soil managed with different plant mixtures or natural cover of an organic apple orchard. Indices were calculated with rarefaction depth assigned to 50,931 (90% of the minimum sample depth in the dataset). Results are represented as means (n = 2) ± SD; Figure S1: The average monthly temperature for the year concerned by the analyses (2022) and for the preceding five years; Figure S2: The monthly precipitation for the year concerned by the analyses (2022) and for the preceding five years; Figure S3: Rarefaction curves showing observed species richness in soil samples collected from apple orchard with different plant mixtures present in the row area. The black line represented the sequencing depth used for data analysis (90% of the minimum sample depth in the dataset equals to 50,931 sequences).

Author Contributions

Conceptualization, E.M.F. and E.M.; methodology, E.M.F.; validation, E.M.F. and E.M.; formal analysis, E.M.F.; investigation, E.M.F.; resources E.M.; writing—original draft preparation, E.M.F. and E.M.; writing—review and editing, E.M.F. and E.M.; visualization, E.M.F. All authors have read and agreed to the published version of the manuscript.

Funding

The work was carried out in the framework of the project BioHortiTech, financially supported by the NCBR grant n. SUSCROP/II/BioHortiTech/01/2021 within the program ERA-NET Cofund SusCrop.

Data Availability Statement

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

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. European Commission. Communication from the Commission to the European Parliament, the Council, the European Economic and Social Committee and the Committee of the Regions: EU Biodiversity Strategy for 2030—Bringing Nature Back into Our Lives; COM2020380 Final; European Commission: Brussels, Belgium, 2020. [Google Scholar]
  2. Hudson, T.B.; Alford, A.M.; Bilbo, T.R.; Boyle, S.C.; Doughty, H.B.; Kuhar, T.P.; Lopez, L.; McIntyre, K.C.; Stawara, A.K.; Walgenbach, J.F.; et al. Living Mulches Reduce Natural Enemies When Combined with Frequent Pesticide Applications. Agric. Ecosyst. Environ. 2023, 357, 108680. [Google Scholar] [CrossRef]
  3. Kowalska, J.; Antkowiak, M.; Sienkiewicz, P. Flower Strips and Their Ecological Multifunctionality in Agricultural Fields. Agriculture 2022, 12, 1470. [Google Scholar] [CrossRef]
  4. Las Casas, G.; Ciaccia, C.; Iovino, V.; Ferlito, F.; Torrisi, B.; Lodolini, E.M.; Giuffrida, A.; Catania, R.; Nicolosi, E.; Bella, S. Effects of Different Inter-Row Soil Management and Intra-Row Living Mulch on Spontaneous Flora, Beneficial Insects, and Growth of Young Olive Trees in Southern Italy. Plants 2022, 11, 545. [Google Scholar] [CrossRef] [PubMed]
  5. Merwin, I.A.; Ray, J.A.; Curtis, P.D. Orchard Groundcover Management Systems Affect Meadow Vole Populations and Damage to Apple Trees. HortScience 1999, 34, 271–274. [Google Scholar] [CrossRef]
  6. Granatstein, D.; Mullinix, K. Mulching Options for Northwest Organic and Conventional Orchards. HortScience 2008, 43, 45–50. [Google Scholar] [CrossRef]
  7. Griffith, G.S.; Cresswell, A.; Jones, S.; Allen, D.K. The Nitrogen Handling Characteristics of White Clover (Trifolium repens L.) Cultivars and a Perennial Ryegrass (Lolium perenne L.) Cultivar. J. Exp. Bot. 2000, 51, 1879–1892. [Google Scholar] [CrossRef][Green Version]
  8. Yang, J.; Zhang, T.; Wang, Y.; Zhang, R.; Li, H. Intercropped Relationship Change the Developmental Pattern of Apple and White Clover. Bioengineered 2019, 10, 218–228. [Google Scholar] [CrossRef]
  9. Qian, X.; Gu, J.; Pan, H.; Zhang, K.; Sun, W.; Wang, X.; Gao, H. Effects of Living Mulches on the Soil Nutrient Contents, Enzyme Activities, and Bacterial Community Diversities of Apple Orchard Soils. Eur. J. Soil Biol. 2015, 70, 23–30. [Google Scholar] [CrossRef]
  10. Bednar, Z.; Vaupel, A.; Blümel, S.; Herwig, N.; Hommel, B.; Haberlah-Korr, V.; Beule, L. Earthworm and Soil Microbial Communities in Flower Strip Mixtures. Plant Soil 2023, 492, 209–227. [Google Scholar] [CrossRef]
  11. Albrecht, M.; Kleijn, D.; Williams, N.M.; Tschumi, M.; Blaauw, B.R.; Bommarco, R.; Campbell, A.J.; Dainese, M.; Drummond, F.A.; Entling, M.H.; et al. The Effectiveness of Flower Strips and Hedgerows on Pest Control, Pollination Services and Crop Yield: A Quantitative Synthesis. Ecol. Lett. 2020, 23, 1488–1498. [Google Scholar] [CrossRef]
  12. Sechi, V.; De Goede, R.G.M.; Rutgers, M.; Brussaard, L.; Mulder, C. A Community Trait-Based Approach to Ecosystem Functioning in Soil. Agric. Ecosyst. Environ. 2017, 239, 265–273. [Google Scholar] [CrossRef]
  13. D’Acunto, L.; Semmartin, M.; Ghersa, C.M. Uncultivated Margins Are Source of Soil Microbial Diversity in an Agricultural Landscape. Agric. Ecosyst. Environ. 2016, 220, 1–7. [Google Scholar] [CrossRef]
  14. Vincze, É.-B.; Becze, A.; Laslo, É.; Mara, G. Beneficial Soil Microbiomes and Their Potential Role in Plant Growth and Soil Fertility. Agriculture 2024, 14, 152. [Google Scholar] [CrossRef]
  15. Chen, Q.; Song, Y.; An, Y.; Lu, Y.; Zhong, G. Soil Microorganisms: Their Role in Enhancing Crop Nutrition and Health. Diversity 2024, 16, 734. [Google Scholar] [CrossRef]
  16. Raza, T.; Qadir, M.F.; Khan, K.S.; Eash, N.S.; Yousuf, M.; Chatterjee, S.; Manzoor, R.; Rehman, S.U.; Oetting, J.N. Unraveling the Potential of Microbes in Decomposition of Organic Matter and Release of Carbon in the Ecosystem. J. Environ. Manag. 2023, 344, 118529. [Google Scholar] [CrossRef]
  17. Chauhan, P.; Sharma, N.; Tapwal, A.; Kumar, A.; Verma, G.S.; Meena, M.; Seth, C.S.; Swapnil, P. Soil Microbiome: Diversity, Benefits and Interactions with Plants. Sustainability 2023, 15, 14643. [Google Scholar] [CrossRef]
  18. Koza, N.A.; Adedayo, A.A.; Babalola, O.O.; Kappo, A.P. Microorganisms in Plant Growth and Development: Roles in Abiotic Stress Tolerance and Secondary Metabolites Secretion. Microorganisms 2022, 10, 1528. [Google Scholar] [CrossRef] [PubMed]
  19. Kumar, M.; Poonam; Ahmad, S.; Singh, R.P. Plant Growth Promoting Microbes: Diverse Roles for Sustainable and Ecofriendly Agriculture. Energy Nexus 2022, 7, 100133. [Google Scholar] [CrossRef]
  20. Santoyo, G. How Plants Recruit Their Microbiome? New Insights into Beneficial Interactions. J. Adv. Res. 2022, 40, 45–58. [Google Scholar] [CrossRef]
  21. Wassermann, B.; Müller, H.; Berg, G. An Apple a Day: Which Bacteria Do We Eat with Organic and Conventional Apples? Front. Microbiol. 2019, 10, 475179. [Google Scholar] [CrossRef]
  22. Wicaksono, W.A.; Buko, A.; Kusstatscher, P.; Cernava, T.; Sinkkonen, A.; Laitinen, O.H.; Virtanen, S.M.; Hyöty, H.; Berg, G. Impact of Cultivation and Origin on the Fruit Microbiome of Apples and Blueberries and Implications for the Exposome. Microb. Ecol. 2023, 86, 973–984. [Google Scholar] [CrossRef] [PubMed]
  23. Mangeot-Peter, L.; Tschaplinski, T.J.; Engle, N.L.; Veneault-Fourrey, C.; Martin, F.; Deveau, A. Impacts of Soil Microbiome Variations on Root Colonization by Fungi and Bacteria and on the Metabolome of Populus tremula × alba. Phytobiomes J. 2020, 4, 142–155. [Google Scholar] [CrossRef]
  24. Whitehead, S.R.; Wisniewski, M.E.; Droby, S.; Abdelfattah, A.; Freilich, S.; Mazzola, M. The Apple Microbiome: Structure, Function, and Manipulation for Improved Plant Health. In The Apple Genome; Korban, S.S., Ed.; Springer International Publishing: Cham, Switzerland, 2021; pp. 341–382. ISBN 978-3-030-74682-7. [Google Scholar]
  25. European Commission. European Union Regulation (EU) 2018/848 of the European Parliament and of the Council of 30 May 2018 on Organ-Ic Production and Labelling of Organic Products and Repealing Council Regulation (EC) No 834/2007. J. Eur. Union 2018, L150/1, 1–92. [Google Scholar]
  26. Babik, J.; Kowalczyk, W. Determination of the Optimal Nitrogen Content in a Fertigation Medium for the Greenhouse Cucumber Grown on Slabs of Compressed Straw. J. Fruit Ornam. Plant Res. 2009, 71, 79–87. [Google Scholar] [CrossRef]
  27. Gomez, E.; Ferreras, L.; Toresani, S. Soil Bacterial Functional Diversity as Influenced by Organic Amendment Application. Bioresour. Technol. 2006, 97, 1484–1489. [Google Scholar] [CrossRef]
  28. Zak, J.C.; Willig, M.R.; Moorhead, D.L.; Wildman, H.G. Functional Diversity of Microbial Communities: A Quantitative Approach. Soil Biol. Biochem. 1994, 26, 1101–1108. [Google Scholar] [CrossRef]
  29. R Core Team. R: A Language and Environment for Statistical Computing; R Core Team: Vienna, Austria, 2022. [Google Scholar]
  30. De Mendiburu, F. Agricolae: Statistical Procedures for Agricultural Research. 2023. Available online: https://CRAN.R-project.org/package=agricolae (accessed on 30 August 2025).
  31. Warnes, G.R.; Bolker, B.; Bonebakker, L.; Gentleman, R.; Huber, W.; Liaw, A.; Lumley, T.; Maechler, M.; Magnusson, A.; Moeller, S.; et al. Gplots: Various R Programming Tools for Plotting Data. 2024. Available online: https://CRAN.R-project.org/package=gplots (accessed on 30 August 2025).
  32. Wickham, H. Ggplot2: Elegant Graphics for Data Analysis (Use R!), 2nd ed.; Springer International Publishing: Cham, Switzerland, 2016; ISBN 978-3-319-24275-0. [Google Scholar]
  33. Egan, L.M.; Hofmann, R.W.; Ghamkhar, K.; Hoyos-Villegas, V. Prospects for Trifolium Improvement Through Germplasm Characterisation and Pre-Breeding in New Zealand and Beyond. Front. Plant Sci. 2021, 12, 653191. [Google Scholar] [CrossRef]
  34. Furmanczyk, E.M.; Malusà, E. In-Depth Insight into the Short-Term Effect of Floor Management Practice on Young Apple Orchard Wellness and Soil Microbial Biodiversity and Activity. PLoS ONE 2025, 20, e0329979. [Google Scholar]
  35. Wang, Y.; Han, X.; Yan, W.; Cheng, L.; Dang, X.; Liu, W. Apple Trees Can Extract Soil Water from Both Deep Layers and Neighboring Cropland in the Tableland Region of Chinese Loess Plateau. CATENA 2023, 232, 107396. [Google Scholar] [CrossRef]
  36. Furmanczyk, E.M.; Malusà, E.; Kozacki, D.; Tartanus, M. Insights into the Belowground Biodiversity and Soil Nutrient Status of an Organic Apple Orchard as Affected by Living Mulches. Agriculture 2024, 14, 293. [Google Scholar] [CrossRef]
  37. Strauss, S.L.; Reardon, C.L.; Mazzola, M. The Response of Ammonia-Oxidizer Activity and Community Structure to Fertilizer Amendment of Orchard Soils. Soil Biol. Biochem. 2014, 68, 410–418. [Google Scholar] [CrossRef]
  38. Bougnom, B.P.; Greber, B.; Franke-Whittle, I.H.; Casera, C.; Insam, H. Soil Microbial Dynamics in Organic (Biodynamic) and Integrated Apple Orchards. Org. Agric. 2012, 2, 1–11. [Google Scholar] [CrossRef]
  39. Taylor, A.E.; Zeglin, L.H.; Wanzek, T.A.; Myrold, D.D.; Bottomley, P.J. Dynamics of Ammonia-Oxidizing Archaea and Bacteria Populations and Contributions to Soil Nitrification Potentials. ISME J. 2012, 6, 2024–2032. [Google Scholar] [CrossRef] [PubMed]
  40. Gentile, R.M.; Boldingh, H.L.; Campbell, R.E.; Gee, M.; Gould, N.; Lo, P.; McNally, S.; Park, K.C.; Richardson, A.C.; Stringer, L.D.; et al. System Nutrient Dynamics in Orchards: A Research Roadmap for Nutrient Management in Apple and Kiwifruit. A Review. Agron. Sustain. Dev. 2022, 42, 64. [Google Scholar] [CrossRef]
  41. Abril, A.; Zurdo-Piñeiro, J.L.; Peix, A.; Rivas, R.; Velázquez, E. Solubilization of Phosphate by a Strain of Rhizobium leguminosarum bv. trifolii Isolated from Phaseolus vulgaris in El Chaco Arido Soil (Argentina). In Develpoments in Plant and Soil Sciences, Proceedings of the First International Meeting on Microbial Phosphate Solubilization, Salamanca, Spain, 16–19 July 2002; Velázquez, E., Rodríguez-Barrueco, C., Eds.; Springer: Dordrecht, The Netherlands, 2007; pp. 135–138. [Google Scholar]
  42. Hasanuzzaman, M.; Bhuyan, M.H.M.B.; Nahar, K.; Hossain, M.S.; Mahmud, J.A.; Hossen, M.S.; Masud, A.A.C.; Moumita; Fujita, M. Potassium: A Vital Regulator of Plant Responses and Tolerance to Abiotic Stresses. Agronomy 2018, 8, 31. [Google Scholar] [CrossRef]
  43. Ollerton, J.; Lack, A. Relationships between Flowering Phenology, Plant Size and Reproductive Success in Shape Lotus corniculatus (Fabaceae). Plant Ecol. 1998, 139, 35–47. [Google Scholar] [CrossRef]
  44. Atucha, A.; Merwin, I.A.; Purohit, C.K.; Brown, M.G. Nitrogen Dynamics and Nutrient Budgets in Four Orchard Groundcover Management Systems. HortScience 2011, 46, 1184–1193. [Google Scholar] [CrossRef]
  45. Pregitzer, K.S.; King, J.S. Effects of Soil Temperature on Nutrient Uptake. In Nutrient Acquisition by Plants: An Ecological Perspective; BassiriRad, H., Ed.; Springer: Berlin/Heidelberg, Germany, 2005; pp. 277–310. ISBN 978-3-540-27675-3. [Google Scholar]
  46. Glass, A.D. Homeostatic Processes for the Optimization of Nutrient Absorption: Physiology and Molecular Biology. In Nutrient Acquisition by Plants: An Ecological Perspective; BassiriRad, H., Ed.; Springer: Berlin/Heidelberg, Germany, 2005; pp. 117–145. ISBN 978-3-540-27675-3. [Google Scholar]
  47. Allison, V.J.; Miller, R.M.; Jastrow, J.D.; Matamala, R.; Zak, D.R. Changes in Soil Microbial Community Structure in a Tallgrass Prairie Chronosequence. Soil Sci. Soc. Am. J. 2005, 69, 1412–1421. [Google Scholar] [CrossRef]
  48. Badri, D.V.; Vivanco, J.M. Regulation and Function of Root Exudates. Plant Cell Environ. 2009, 32, 666–681. [Google Scholar] [CrossRef]
  49. Steinauer, K.; Chatzinotas, A.; Eisenhauer, N. Root Exudate Cocktails: The Link between Plant Diversity and Soil Microorganisms? Ecol. Evol. 2016, 6, 7387–7396. [Google Scholar] [CrossRef]
  50. Altieri, M.A.; Nicholls, C.I.; Dinelli, G.; Negri, L. Towards an Agroecological Approach to Crop Health: Reducing Pest Incidence through Synergies between Plant Diversity and Soil Microbial Ecology. npj Sustain. Agric. 2024, 2, 6. [Google Scholar] [CrossRef]
  51. Vives-Peris, V.; de Ollas, C.; Gómez-Cadenas, A.; Pérez-Clemente, R.M. Root Exudates: From Plant to Rhizosphere and Beyond. Plant Cell Rep. 2020, 39, 3–17. [Google Scholar] [CrossRef] [PubMed]
  52. Birkhofer, K.; Bezemer, T.M.; Bloem, J.; Bonkowski, M.; Christensen, S.; Dubois, D.; Ekelund, F.; Fließbach, A.; Gunst, L.; Hedlund, K.; et al. Long-Term Organic Farming Fosters below and Aboveground Biota: Implications for Soil Quality, Biological Control and Productivity. Soil Biol. Biochem. 2008, 40, 2297–2308. [Google Scholar] [CrossRef]
  53. Lange, M.; Eisenhauer, N.; Sierra, C.A.; Bessler, H.; Engels, C.; Griffiths, R.I.; Mellado-Vázquez, P.G.; Malik, A.A.; Roy, J.; Scheu, S.; et al. Plant Diversity Increases Soil Microbial Activity and Soil Carbon Storage. Nat. Commun. 2015, 6, 6707. [Google Scholar] [CrossRef]
  54. Eisenhauer, N. Plant Diversity Effects on Soil Microorganisms: Spatial and Temporal Heterogeneity of Plant Inputs Increase Soil Biodiversity. Pedobiologia 2016, 59, 175–177. [Google Scholar] [CrossRef]
  55. Lori, M.; Symnaczik, S.; Mäder, P.; Deyn, G.D.; Gattinger, A. Organic Farming Enhances Soil Microbial Abundance and Activity—A Meta-Analysis and Meta-Regression. PLoS ONE 2017, 12, e0180442. [Google Scholar] [CrossRef]
  56. Ouyang, Y.; Reeve, J.R.; Norton, J.M. Soil Enzyme Activities and Abundance of Microbial Functional Genes Involved in Nitrogen Transformations in an Organic Farming System. Biol. Fertil. Soils 2018, 54, 437–450. [Google Scholar] [CrossRef]
  57. Qian, X.; Gu, J.; Sun, W.; Li, Y.-D.; Fu, Q.-X.; Wang, X.-J.; Gao, H. Changes in the Soil Nutrient Levels, Enzyme Activities, Microbial Community Function, and Structure during Apple Orchard Maturation. Appl. Soil Ecol. 2014, 77, 18–25. [Google Scholar] [CrossRef]
  58. Yang, J.; Zhang, T.; Zhang, R.; Huang, Q.; Li, H. Long-Term Cover Cropping Seasonally Affects Soil Microbial Carbon Metabolism in an Apple Orchard. Bioengineered 2019, 10, 207–217. [Google Scholar] [CrossRef]
  59. Heuermann, D.; Döll, S.; Schweneker, D.; Feuerstein, U.; Gentsch, N.; von Wirén, N. Distinct Metabolite Classes in Root Exudates Are Indicative for Field- or Hydroponically-Grown Cover Crops. Front. Plant Sci. 2023, 14, 1122285. [Google Scholar] [CrossRef]
  60. Koo, B.-J.; Adriano, D.C.; Bolan, N.S.; Barton, C.D. Root Exudates and Microorganisms. In Encyclopedia of Soils in the Environment; Elsevier: Amsterdam, The Netherlands, 2005; pp. 421–428. ISBN 978-0-12-348530-4. [Google Scholar]
  61. Paynel, F.; Murray, P.J.; Bernard Cliquet, J. Root Exudates: A Pathway for Short-Term N Transfer from Clover and Ryegrass. Plant Soil 2001, 229, 235–243. [Google Scholar] [CrossRef]
  62. Williams, A.; Langridge, H.; Straathof, A.L.; Muhamadali, H.; Hollywood, K.A.; Goodacre, R.; de Vries, F.T. Root Functional Traits Explain Root Exudation Rate and Composition across a Range of Grassland Species. J. Ecol. 2022, 110, 21–33. [Google Scholar] [CrossRef]
  63. Maurer, D.; Malique, F.; Alfarraj, S.; Albasher, G.; Horn, M.A.; Butterbach-Bahl, K.; Dannenmann, M.; Rennenberg, H. Interactive Regulation of Root Exudation and Rhizosphere Denitrification by Plant Metabolite Content and Soil Properties. Plant Soil 2021, 467, 107–127. [Google Scholar] [CrossRef]
  64. Gao, Y.; Wang, H.; Yang, F.; Dai, X.; Meng, S.; Hu, M.; Kou, L.; Fu, X. Relationships between Root Exudation and Root Morphological and Architectural Traits Vary with Growing Season. Tree Physiol. 2024, 44, tpad118. [Google Scholar] [CrossRef] [PubMed]
  65. Edwards, K.R.; Kaštovská, E.; Borovec, J.; Šantrucková, H.; Picek, T. Species Effects and Seasonal Trends on Plant Efflux Quantity and Quality in a Spruce Swamp Forest. Plant Soil 2018, 426, 179–196. [Google Scholar] [CrossRef]
  66. Janusz, G.; Pawlik, A.; Sulej, J.; Świderska-Burek, U.; Jarosz-Wilkołazka, A.; Paszczyński, A. Lignin Degradation: Microorganisms, Enzymes Involved, Genomes Analysis and Evolution. FEMS Microbiol. Rev. 2017, 41, 941–962. [Google Scholar] [CrossRef]
  67. Spohn, M.; Kuzyakov, Y. Distribution of Microbial- and Root-Derived Phosphatase Activities in the Rhizosphere Depending on P Availability and C Allocation—Coupling Soil Zymography with 14C Imaging. Soil Biol. Biochem. 2013, 67, 106–113. [Google Scholar] [CrossRef]
  68. Lv, C.; Wang, C.; Cai, A.; Zhou, Z. Global Magnitude of Rhizosphere Effects on Soil Microbial Communities and Carbon Cycling in Natural Terrestrial Ecosystems. Sci. Total Environ. 2023, 856, 158961. [Google Scholar] [CrossRef]
  69. de Haas, B.; Dhooghe, E.; Geelen, D. Root Exudates in Soilless Culture Conditions. Plants 2025, 14, 479. [Google Scholar] [CrossRef]
  70. McLaughlin, S.; Zhalnina, K.; Kosina, S.; Northen, T.R.; Sasse, J. The Core Metabolome and Root Exudation Dynamics of Three Phylogenetically Distinct Plant Species. Nat. Commun. 2023, 14, 1649. [Google Scholar] [CrossRef]
  71. Li, X.; Chu, Y.; Jia, Y.; Yue, H.; Han, Z.; Wang, Y. Changes to Bacterial Communities and Soil Metabolites in an Apple Orchard as a Legacy Effect of Different Intercropping Plants and Soil Management Practices. Front. Microbiol. 2022, 13, 956840. [Google Scholar] [CrossRef] [PubMed]
  72. Wang, J.; Wang, R.; Kang, F.; Yan, X.; Sun, L.; Wang, N.; Gong, Y.; Gao, X.; Huang, L. Microbial Diversity Composition of Apple Tree Roots and Resistance of Apple Valsa Canker with Different Grafting Rootstock Types. BMC Microbiol. 2022, 22, 148. [Google Scholar] [CrossRef] [PubMed]
  73. Mia, M.J.; Furmanczyk, E.M.; Golian, J.; Kwiatkowska, J.; Malusá, E.; Neri, D. Living Mulch with Selected Herbs for Soil Management in Organic Apple Orchard. Horticulturae 2021, 7, 59. [Google Scholar] [CrossRef]
  74. Vukicevich, E.; Lowery, T.; Bowen, P.; Úrbez-Torres, J.R.; Hart, M. Cover Crops to Increase Soil Microbial Diversity and Mitigate Decline in Perennial Agriculture. A Review. Agron. Sustain. Dev. 2016, 36, 48. [Google Scholar] [CrossRef]
  75. Blagodatskaya, E.; Kuzyakov, Y. Active Microorganisms in Soil: Critical Review of Estimation Criteria and Approaches. Soil Biol. Biochem. 2013, 67, 192–211. [Google Scholar] [CrossRef]
  76. Piechulla, B.; Lemfack, M.C.; Kai, M. Effects of Discrete Bioactive Microbial Volatiles on Plants and Fungi. Plant Cell Environ. 2017, 40, 2042–2067. [Google Scholar] [CrossRef]
  77. López-Mondéjar, R.; Tláskal, V.; Větrovský, T.; Štursová, M.; Toscan, R.; Nunes da Rocha, U.; Baldrian, P. Metagenomics and Stable Isotope Probing Reveal the Complementary Contribution of Fungal and Bacterial Communities in the Recycling of Dead Biomass in Forest Soil. Soil Biol. Biochem. 2020, 148, 107875. [Google Scholar] [CrossRef]
  78. Furmanczyk, E.M.; Kaminski, M.A.; Lipinski, L.; Dziembowski, A.; Sobczak, A. Pseudomonas laurylsulfatovorans sp. Nov., Sodium Dodecyl Sulfate Degrading Bacteria, Isolated from the Peaty Soil of a Wastewater Treatment Plant. Syst. Appl. Microbiol. 2018, 41, 348–354. [Google Scholar] [CrossRef]
  79. Jadhav, S.S.; David, M. Biodegradation of Flubendiamide by a Newly Isolated Chryseobacterium sp. Strain SSJ1. 3 Biotech 2016, 6, 31. [Google Scholar] [CrossRef][Green Version]
  80. Ravintheran, S.K.; Sivaprakasam, S.; Loke, S.; Lee, S.Y.; Manickam, R.; Yahya, A.; Croft, L.; Millard, A.; Parimannan, S.; Rajandas, H. Complete Genome Sequence of Sphingomonas paucimobilis AIMST S2, a Xenobiotic-Degrading Bacterium. Sci. Data 2019, 6, 280. [Google Scholar] [CrossRef]
  81. Zaim, S.; Bekkar, A.A. Advances in Research on the Use of Brevundimonas Spp. to Improve Crop and Soil Fertility and for Soil Bioremediation. Alger. J. Biosci. 2023, 4, 045–051. [Google Scholar] [CrossRef]
  82. de Souza, R.; Ambrosini, A.; Passaglia, L.M.P. Plant Growth-Promoting Bacteria as Inoculants in Agricultural Soils. Genet. Mol. Biol. 2015, 38, 401–419. [Google Scholar] [CrossRef]
  83. Kumar, V.; Gera, R. Isolation of a Multi-Trait Plant Growth Promoting Brevundimonas sp. and Its Effect on the Growth of Bt-Cotton. 3 Biotech 2014, 4, 97–101. [Google Scholar] [CrossRef]
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Figure 1. Dynamic and concentrations of soil nutrients as influenced by the two diverse plant mixtures. Mean ± SD, n = 3. Letters show statistically significant differences for p ≤ 0.05 according to ANOVA and means differences tested with Tukey’s test (A,B for the same treatment in different timepoints, a,b for different treatments in the same timepoint).
Figure 1. Dynamic and concentrations of soil nutrients as influenced by the two diverse plant mixtures. Mean ± SD, n = 3. Letters show statistically significant differences for p ≤ 0.05 according to ANOVA and means differences tested with Tukey’s test (A,B for the same treatment in different timepoints, a,b for different treatments in the same timepoint).
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Figure 2. Dynamic and levels of bacterial activity (AWCD), biodiversity (Index H) and substrate richness as influenced by the two diverse plant mixtures. Mean ± SD, n = 3. Letters show statistically significant differences for p ≤ 0.05 according to ANOVA and means differences tested with Tukey’s test or the Kruskal–Wallis test with Benjamini–Hochberg correction (for substrate richness comparisons) (A,B for the same treatment in different timepoints, a,b for different treatments in the same timepoint).
Figure 2. Dynamic and levels of bacterial activity (AWCD), biodiversity (Index H) and substrate richness as influenced by the two diverse plant mixtures. Mean ± SD, n = 3. Letters show statistically significant differences for p ≤ 0.05 according to ANOVA and means differences tested with Tukey’s test or the Kruskal–Wallis test with Benjamini–Hochberg correction (for substrate richness comparisons) (A,B for the same treatment in different timepoints, a,b for different treatments in the same timepoint).
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Figure 3. Heatmap representing the microbial activity of the soil bacterial community towards various carbon sources as affected by two kinds of plant mixtures in three sampling points.
Figure 3. Heatmap representing the microbial activity of the soil bacterial community towards various carbon sources as affected by two kinds of plant mixtures in three sampling points.
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Figure 4. Biodiversity and composition of soil bacterial communities associated with different soil management practices. (a)—Relative abundance of the most abundant phyla (>1%). (b)—Venn’s diagrams based on the genera identified at least once in samples representing individual treatment.
Figure 4. Biodiversity and composition of soil bacterial communities associated with different soil management practices. (a)—Relative abundance of the most abundant phyla (>1%). (b)—Venn’s diagrams based on the genera identified at least once in samples representing individual treatment.
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Figure 5. Heatmap based on the relative abundance of the 30 most abundant genera for each soil management practice.
Figure 5. Heatmap based on the relative abundance of the 30 most abundant genera for each soil management practice.
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Table 1. Species composition of the flower mixture (flower mix) used in the study.
Table 1. Species composition of the flower mixture (flower mix) used in the study.
Specie Name (Percentage of the Mix-%)
Plantago media (0.3%)Medicago lupulina (1.5%)
Scorzoneroides autumnalis (0.4%)Thymus pulegioides (0.2%)
Leontodon hipidus (0.4%)Saponaria officinalis (0.4%)
Centaurea cynaus (2.0%)Crepis capillaris (0.2%)
Centaurea jacea (1.2%)Hypochaeris radicata (0.3%)
Campanula rapunculoides (0.2%)Galium album (1.2%)
Campanula rotundifolia (0.1%)Galium verum (0.5%)
Viola arvensis (0.2%)Reseda lutea (0.2%)
Prunella vulgaris (0.5%)Malva moschata (0.5%)
Dianthus deltoides (0.2%)Malva neglecta (1.0%)
Hieracium pilosella (0.2%)Leucanthemum vulgare (1.8%)
Lotus corniculatus (1.0%)Cynosurus cristatus (4.0%)
Trifolium dubium (0.3%)Festuca rubra (12.0%)
Trifolium pratense (0.5%)Festuca ovina (17.0%)
Achillea millefolium (1.2%)Agrostis capillaris (3.0%)
Sanguisorba minor (1.5%)Bromus secalinus (20.0%)
Origanum vulgare (0.2%)Anthoxanthum odoratum (5.0%)
Silene noctiflora (0.4%)Poa angustifolia (5.0%)
Silene vulgaris (1.2%)Poa compressa (4.0%)
Linaria vulgaris (0.2%)Lolium perenne (10.0%)
Table 2. Macroelements content in leaves (% of dry weight) as affected by two plant mixtures. Different letters show statistically significant differences for p ≤ 0.05 according to ANOVA and means differences tested with Tukey’s test (N and K contents) or the Kruskal–Wallis test with Benjamini–Hochberg correction (P content).
Table 2. Macroelements content in leaves (% of dry weight) as affected by two plant mixtures. Different letters show statistically significant differences for p ≤ 0.05 according to ANOVA and means differences tested with Tukey’s test (N and K contents) or the Kruskal–Wallis test with Benjamini–Hochberg correction (P content).
TreatmentNPK
Natural cover1.59 ± 0.13 a0.18 ± 0.01 b1.73 ± 0.06 a
Clover mix1.74 ± 0.08 a0.22 ± 0.01 a1.70 ± 0.07 a
Flower mix1.87 ± 0.14 a0.20 ± 0.00 a1.61 ± 0.11 a
p-value0.0740.0330.235
F-value or Χ-value4.1416.8301.859
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Furmanczyk, E.M.; Malusà, E. Agroecological Soil Management of an Organic Apple Orchard: Impact of Flowering Living Mulches on Soil Nutrients and Bacterial Activity Indices. Agronomy 2025, 15, 2612. https://doi.org/10.3390/agronomy15112612

AMA Style

Furmanczyk EM, Malusà E. Agroecological Soil Management of an Organic Apple Orchard: Impact of Flowering Living Mulches on Soil Nutrients and Bacterial Activity Indices. Agronomy. 2025; 15(11):2612. https://doi.org/10.3390/agronomy15112612

Chicago/Turabian Style

Furmanczyk, Ewa Maria, and Eligio Malusà. 2025. "Agroecological Soil Management of an Organic Apple Orchard: Impact of Flowering Living Mulches on Soil Nutrients and Bacterial Activity Indices" Agronomy 15, no. 11: 2612. https://doi.org/10.3390/agronomy15112612

APA Style

Furmanczyk, E. M., & Malusà, E. (2025). Agroecological Soil Management of an Organic Apple Orchard: Impact of Flowering Living Mulches on Soil Nutrients and Bacterial Activity Indices. Agronomy, 15(11), 2612. https://doi.org/10.3390/agronomy15112612

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