Effect of intercropping of paulownia and buckwheat on soil microbial biodiversity and enzymatic activity

doi:10.21203/rs.3.rs-4611632/v1

Download PDF

Article

Effect of intercropping of paulownia and buckwheat on soil microbial biodiversity and enzymatic activity

https://doi.org/10.21203/rs.3.rs-4611632/v1

This work is licensed under a CC BY 4.0 License

You are reading this latest preprint version

The aim of this study was to capture microbiological changes in the soil environment during intercropping of paulownia with buckwheat using randomized block method experiment conducted at Wroclaw University of Environmental and Life Sciences in 2019–2022. The soil samples were characterized by measuring abundance of microorganisms determining the microbial and fungal community structure using Illumina MiSeq sequencing, the activity of dehydrogenase (DHA) and total glomalin-related soil proteins (T-GRSP). In addition, we assessed the buckwheat roots' colonisation by fungi, as well as yield and biometric traits of the plant. The calculated alpha indicators of the bacterial microbiome diversity and abundance show higher bacterial diversity in the intercropping samples, when compared to the control site. NGS (Next-Generation Sequencing) analysis showed that Actionobacteria, Proteobacteria and Acidobacteria were dominant in the microbiome in every variant of the experiment, regardless of the crop. By contrast, the mycobiome was dominated by fungi classified as the Ascomycota and Mortierellomycota. At the first sampling date (T1), intercropping sample analysis showed significant increase in DHA activity, but not in glomalin concentration. As a rule, the biometric traits’ values were higher when buckwheat was intercropped with paulownia compared to the control culture, both in terms of buckwheat yield and the total kernels of weight per plant.

agroforestry

biodiversity

buckwheat

intercropping

paulownia

soil microbial activity

Intercropping of trees and cultivated annuals, i.e. the integration of trees into the agricultural landscape in the form of tree-based intercropping systems (TBI), is part of a system called agroforestry, which involves cultivation of forest trees and shrubs alongside agro- and zootechnical activities within the same land area. Agroforestry constitutes a system that ensures continuous supply of organic material to the soil. ¹

The development of agroforestry systems seems particularly relevant in the context of the following: counteracting negative effects of climate change, water and wind erosion of the soil, leaching of nutrients and plant protection products from the soil, loss of biodiversity, large areas of marginal agricultural land, increasing demand for bioenergy and organic products.²

Buckwheat (Fagopyrum esculentum Moench) is a crop that is in high demand around the world for its nutritional, economic and pharmaceutical qualities. To ensure food and nutrition security in a global climate change scenario, this pseudo-cereal is a competent alternative to staple crops (rice, wheat, rice and maize). Buckwheat also has favorable agroecological traits, such as: enabling weed control due to inhibitory effect of root secretions on the development of couch grass (allelopathy), effective protection of the soil against water erosion, high capacity to assimilate nitrogen and phosphorus from the soil, phytosanitary effect on the soil through effective control of nematodes (reduction of potato cyst nematode populations), and benefitting insects during flowering.³

Paulownia ssp. is a type of fast-growing deciduous tree with high tolerance to variable soil and climatic conditions and low water requirements.⁴ Paulownia Clon in Vitro 112 is a tree developed under laboratory conditions by crossing and cloning two species of Paulownia elongata and P. fortunei. The hybrid is considered suitable for biomass production and revegetation. ⁵ Furthermore, Valdivia et al. (2012)⁶ found that trees of the genus Paulownia ssp. have an advantage over most other tree species in that they have a sparse canopy with late foliage and leaf drop, thus not blocking sun rays reaching the fields during the periods where many commonly grown crops, including cereals require sunlight most. Intercropping of trees and annuals expands the agroecosystem's biodiversity, promoting biological life in the soil.² According to research, the diversity of soil microorganisms is higher within agroforestry systems.⁷ Knowledge about both activity and diversity of soil microorganisms enables researchers to assess the state of the soil, which is beneficial for agriculture and ecology. ^8,9 A diverse soil microbial community is crucial to the productivity of any agroecosystem. They are thus a high-sensitivity indicator of changes to soil organic matter. Even modest decreases in microbial abundance can be detrimental to the soil ecosystem¹⁰, as soil microorganisms are responsible for various biogeochemical processes, are involved in the mineralisation of organic matter and responsible for nutrient cycling in the environment. Moreover, microorganisms one of the principal sources of soil enzymes, which act as mediators and biological catalysts for various soil functions, such as organic matter breakdown, release of inorganic nutrients (C, N, P, S) for plant growth, nitrogen fixation, nitrification, denitrification, detoxification of xenobiotics, stabilisation of soil structure and others.¹¹

Enzyme-related activity in the soil is one of the biological properties used as an indicator of soil quality, due to its interrelationship with soil biology. In addition, it is a highly sensitive and easily measured indicator of soil biological management, microbial activity, soil fertilisation, degree of pollution and anthropopressure. Dehydrogenases (DHGs) are one of the key components of the soil enzyme testing as they determine the correct sequence of all biochemical pathways occurring in the soil.¹¹

Determining the relationship between the soil humus layer and the microbiome has been possible through advances in sequencing technologies. ¹² Metagenomics is a particularly useful tool for understanding microbe-soil-plant relationships and visualising patterns of microbial co-occurrence. ¹³ Furthermore, it simultaneously examines all microorganisms present in a soil sample, combining methods of traditional microbiology with molecular biology and bioinformatics. The diversity of bacteria and fungi is a major driver of fundamental metabolic processes in a dynamic environment such as soil. It is thus important to explore the biodiversity of the soil environment in order to develop management strategies for terrestrial ecosystems to maintain their integrity, function and long-term stability. Metagenomics results can also be used to identify key microbial taxa ¹³ having significant impact on the soil community. ¹⁴

Our study aimed to establish the feasibility of growing paulownia and buckwheat in the same field (buckwheat grown in inter-rows between the trees) and to capture changes in the soil environment in terms of its microbiology. Our findings stem from research spanning a two-year cycle and should be considered a contribution towards follow-up long-term field studies. This research is particularly vital in the context of efforts to maintain biological balance and protect soil biodiversity. It can also serve as a basis for the development of innovative crop management strategies for energy biomass production and annual crops, contributing towards strategies that are best adapted to the existing species in terms of mitigating negative effects of climate change. The present study can thus provide insights into the key processes of microbial diversity enhancement in ecosystems for sustainable agriculture.

Study Sites and Soil Sampling

In 2019, a rigorous field experiment using the randomised block method with buckwheat (Fagopyrum esculentum Moench) and paulownia (Clon in Vitro 112; P. elongata × P. fortunei) was set up at the Research and Teaching Station of the Wrocław University of Environmental and Life Sciences (51°07′00″N; 17°10E, 121 meters above sea level). The factor tested was the intercropping of buckwheat between paulownia (AP) trees. The experiment also included control sites (AK), i.e. buckwheat plots grown without paulownia, and consisted of 5 replications. Paulownia seedlings were planted on 30.05.2019. In spring 19.05.2020, we carried our technical pruning, where we established the main shoot (future trunk). Buckwheat (Kora cultivar) was sown on 11.05.2021, 04.05.2022 at a density of 250 seeds per square meter. The area of the buckwheat plot was 30 square metres. The trees were planted in rows of 5. In the plot, the row spacing was 5 meters and tree-to-tree spacing was 4 meters. The tree density was typical for growing paulownia for round wood. For microbiological and enzyme analyses, we sampled soil from the rhizosphere of buckwheat grown without paulownia (AK) and from buckwheat grown in inter row with paulownia (AP). Soil was sampled twice before buckwheat sowing (T1) and at the buckwheat flowering stage (T2) in the first (2021) and second (2022) years of the study. Soil samples for analysis of each cultivation variant were taken from 5 replicates.

Chemical-physical soil analysis

The rigorous field experiment was based on soil class fied as humic ordinary alluvial soils Polish Soil Classification (SGP6) or Eutric Fluvisols ( Humic), IUSS Working Group WRB, 2022). According to agricultural evaluation, the soil was identified in F – IV b-a class (moderately poor). ^15,16 Soil samples for bioavailable forms of macronutrients (P, K, Mg) and mineral nitrogen content analysis were sourced before sowing buckwheat and after the end of vegetation from several randomly selected locations in the field, separately for each site to obtain an average sample. Soil samples were sources from the 0–30 cm layer using soil sampler tool, air-dried, ground using a porcelain pestle and mortar, and sieved to < 2 mm. A portion of each sample was then finely ground for analysis. Soil testing included: determination of basic soil characteristics and properties, such as soil morphology, soil classification, land use value, pH, macronutrient content, including mineral nitrogen.

We carried out characterisation of the following soil properties:

content of bioavailable forms of P, K - assessed using the Egner-Riehm (DL) method;
soil Ca content - using Sheibler method;
available magnesium content - using Schachtschabel method;
pH in distilled water and in 1M KCl - using potentiometric pH metres;
nitrogen content using the modified Kjehdal method (total nitrogen determination);

The soil pH and microelement content of the soil is presented in Table 1. The soil pH in both the intercropped site and the control site was slightly acidic in both years of the study. Phosphorus and potassium contents on both sites were very high in the year the experiment started. At the end of the 2022 season, the potassium content was found to be reduced to ‘medium’ for the intercropping variant and for phosphorus to ‘high’ for this site. In terms of magnesium content - it was very high in the soil in both sites in the year the experiment started, while by the end of 2022 it had decreased to medium on both sites (Table 1). Over time, the mineral nitrogen content in the soil, decreased under intercropping of paulownia and buckwheat, while the monoculture cultivation of buckwheat contributed to an increase in mineral nitrogen content in the 30 cm-deep soil layer.

Table 1

Soil pH and macroelement content of the soil profile (0–30 cm) in 2019 and 2022.
Detailed determination	2019		2022
Detailed determination	AK	AP	AK	AP
pH in KCl (soil pH)	6.2 (slightly acidic)	5.8 (slightly acidic)	5.7 (slightly acidic)	5.7 (slightly acidic)
Phosphorus P₂O₅ (mg 100 g^− 1 of soil)	48.5 (very high)	27.6 (very high)	22.6 (very high)	18.8 (high)
Potassium K₂O (mg 100 g^− 1 of soil)	33.5 (very high)	33.5 (very high)	28.9 (very high)	24.1 (medium)
Magnesium Mg (mg 100 g^− 1 of soil)	7.6 (very high)	7.6 (very high)	6.7 (medium)	5.4 (medium)
N min. (kg ha^− 1)	43.8 (very low)	48.0 (very low)	56.8 (low)	21.4 (very low)
AP - intercropping, AK – control, N min. – mineral nitrogen

Table 1. Soil pH and macroelement content of the soil profile (0–30 cm) in 2019 and 2022.

Agrotechnology of the experiment

Prior to starting the experiment, following the 2018 winter triticale harvest, the field was ploughed with a subsoiler plough and then the soil was kept in a weed-free state using a cultivating unit (rotary harrow with crushing roller). In autumn, deep pre-winter ploughing was carried out to a depth of 25 cm. In the year we started our experiment, i.e. spring 2019, the soil was ploughed again using a subsoiler plough and further amended using a cultivating unit. After planting the paulownia seedlings, the soil in the interrows was loosened with a rotary harrow. The following year, in the spring of 2020, the soil was ploughed shallow and then was amended using a cultivator before sowing buckwheat. Following technical tree pruning in May 2020, the paths in the experiment were mechanically tended. After harvesting the buckwheat with a plot harvester, the soil was kept weed-free by harrowing (rotary harrow). In 2021, a cultivating unit equipped with a string roller was used to cultivate the soil after winter. By May 2021, harrowing had been carried out twice as a weed control measure. After sowing the buckwheat, the paths were mechanically cultivated several times until harvesting using combine harvester. Before winter, the soil was kept weed-free by harrowing with a rotary harrow. Tillage in 2022 included harrowing the field after winter with a cultivator, followed by ploughing with a subsoiler plough to a depth of 12 cm. Before sowing buckwheat, the soil was treated with a cultivating unit consisting of a rotary harrow and a crushing roller. The sowing of buckwheat was done with a plot seeder. Paths between the plots were tended during the growing season. No mineral or organic fertilisers or any pesticides were used in the experiment.

Weather conditions

As part of the experimental design, we took into account meteorological data on the weather during the experimental period (Table 2). Meteorological conditions were analysed using data provided by the meteorological station (AsterMet) in Swojczyce (Wrocław). We used Selyaninov's hydrothermal coefficient (HTC) to describe the impact of weather conditions on the plant development, using the following formula:

Table 2

Weather conditions and HTC in 2019–2022 provided by the meteorological station (AsterMet) in Swojczyce (Wrocław).
Month	Temperature (°C)
Month	2019	2020	2021	2022	Mean 1990 − 2020	2019	2020	2021	2022	Mean 1990 − 2020	2019	2020	2021	2022
IV	10.8	9.50	7.5	6.4	9.6	24.2	6.4	32.5	32.7	32.8	0.75	0.23	1.45	1.46
V	12.1	11.6	12.4	12.7	14.3	76.8	77.2	59.0	20.9	58.9	2.06	2.14	1.54	0.44
VI	22.1	18.5	19.8	20.1	17.8	27.0	94.5	38.4	39.7	74.6	0.41	1.22	0.65	0.58
VII	19.3	19.5	20.5	20.7	19.7	44.5	53.2	41.1	132.5	86.6	0.74	1.29	0.64	2.12
VIII	20.3	22.5	17.3	17.7	19.2	59.8	6.2	11.0	92.5	63.6	0.95	0.09	0.21	1.45
IX	14.4	14.7	15.0	14.8	14.2	42.0	92.9	14.5	82.0	50.6	0.97	2.11	0.32	2.12
X	10.4	10.7	9.5	9.1	9.3	32.1	100.5	10.2	8.6	40.8	3.21	3.03	0.35	0.25
Mean / sum (IV-X)	15.6	15.3	14.6	14.5	14.5	306.4	430.9	206.7	408.9	377.0	-	-	-	-

K = P / (0.1×T)

where: K – Selyaninov's hydrothermal coefficient, P – total rainfall per month, T – sum of daily average temperatures per month¹⁷

Table 2. Weather conditions and HTC in 2019–2022 provided by the meteorological station (AsterMet) in Swojczyce (Wrocław).

Characterization of soil microbiological properties

DNA extraction, PCR and Illumina amplicon sequencing

Total DNA for metagenomic analysis was extracted from the soil samples using the PowerSoil® DNA Isolation Kit (MO BIO) according to the manufacturer’s instructions. The extracted genomic DNA was quantified and checked for quality at A_260/280nm (1.7–2.0) and diluted in sterile water to 10 ng µL^− 1. Meta-barcoding analysis of the microbial community was performed based on the variable regions: V3 - V4 of the 16S rRNA gene for bacteria and hypervariable region of ITS1 for fungi. Specific primer sequences 341F and 785R (16S analysis); ITS1FI2 and 5.8S (ITS1 analysis) were used to amplify the selected region and prepare the library. PCR was performed using Q5 Hot Start High-Fidelity 2X Master Mix, reaction conditions according to manufacturer’s recommendations. Next – generation sequencing was performed by Genomed S.A. (Warsaw, Poland) on a MiSeq sequencers (Illumina, San Diego, CA, USA) in paired-end (PE) technology, 2 × 300 nt, using the Illumina v 3 kit (San Diego, CA, USA).

Bioinformatic analysis

Bioinformatics analysis ensuring the classification of reads was carried out with the QIIME 2 software package based on the reference sequence database Silva 138 (for bacteria) and UNITE v8.2 (for fungi). The DADA2 package was also used, which allowed for the specification of sequences of biological origin from those newly created in the sequencing process. This package was also used to isolate unique sequences of biological origin, i.e. ASV (amplicon sequence variant). The analysis consisted of the following stages: reading quality control, initial data processing using the Cutadapt tool, selecting unique ASV and OTU (operational taxonomic units) sequences and assigning taxonomies to the generated sequences based on reference databases. In addition, α-diversity indices (Shannon diversity H′, Simpson index D and observed AVS/OTU) were assessed from the Illumina MiSeq results.

Quantification of Cultivable Bacteria and Fungi

In order to determine the colony-forming units of bacteria and fungi in the test soils (AK, AP, T1 and T2), we did surface culture combined with the technique of successive dilutions on medium developed by Bunt and Rovira¹⁸ and Martin¹⁹ _,respectively. Analyses were performed in 3 average samples taken from each test plot. Results are presented in units forming colonies in 1 g of soil (CFU × g^-1). Actual results were subject to three-way analysis of variance (ANOVA) with post-hoc Tukey HSD test.

Soil dehydrogenase activity (DHA) and T-GRSP concentration

Dehydrogenase activity (DHA) was analysed according to PN-ISO 23753-1 (2008) using 2,3,5-triphenyltetrazolium chloride (TTC), reduced via enzymatic activity to triphenylformazan (TPF). We added 5 mL of a 3% solution of TTC (Merck, Germany) to the soil using 1:1 ratio (g:mL), mixed them thoroughly and incubated at 25°C for 16 h. The released triphenylformazane (TPF, Merck, Germany) was extracted using methanol and spectrophotometrically analysed (Ray Leight, VIS 723G) at 485 nm. The control sample was soil without added TTC. The amount of TPF released was expressed as microgram per gram of dry soil per hour.

Total glomalin-related soil proteins (T-GRSP) were also determined and extracted from soil samples using the method developed by Wright and Upadhyay (1999)²⁰ including our alterations. Soil samples (10 g) were flooded with 0.05M citrate buffer, pH 8.0, and autoclaved at 121°C for 60 min. The extraction was repeated several times until the organic fraction was completely eluted from the soil. Following each autoclaving, the supernatant containing T-GRSP was poured off and centrifuged at 10 000 rpm for 10 min. Soil samples were covered with sterile buffer and autoclaved again. The extracts, collected after each heating and centrifugation, were combined, and stored at 4°C until analysis. T-GRSP content in the supernatants was quantified via the Bradford method using bovine serum albumin (Sigma-Aldrich, Inc., Saint Louis, USA) as a standard.

Principal component analysis of soil microbial properties

We performed PCA multivariate statistical analysis of the tested soil samples’ variability to determine the relation among the measured activities. Mean values from three replicates were used for PCA analysis. Data were standardized prior to the analysis. All statistical analyses were performed using Statistica 13.1 software (StatSoft, Inc., Tulsa, OK, USA).

Colonisation of buckwheat roots by filamentous fungi

We assessed the degree of colonisation of buckwheat roots by potential pathogenic fungi. Filamentous fungi were isolated from buckwheat roots collected at flowering (T2) and their genus and/or species were established based on their morphological characteristics. ^21,22 For this purpose, root sections that had been previously disinfected with 0.5% NaOCl (for 15 min, rinsed 4 times with sterile distilled water) were plated on PDA medium (BTL, Łódź) supplemented with streptomycin (30 µg mL^− 1). The plates were incubated at 28°C for 72 h. The fungi were then reisolated onto PDA medium. Ten replicates (60 inoculations each) were performed from each sample. Results are presented as percentage of root colonisation by fungi.

Analysis of buckwheat biometric traits and the yield

Before harvesting the buckwheat, we randomly sampled 10 plants from each plot. The samples were sourced from the middle rows to mitigate the border effect. The biometric analysis included: plant height, number of branches, number of twigs, number of inflorescences, number of kernels and weight of full kernels per plant. The yield per plot was adjusted to 15% moisture content and converted to tonnes per hectare. To assess the effect of cultivation method and time by years of study, we performed a two-factor ANOVA analysis using Statistica 13.1. When differences between means were found to be significant, they were compared using the HSD-Tukey test, with a significance level of α = 0.05. Actual DHA and T-GRSP results were subject to a three-way analysis of variance (ANOVA) with post-hoc Tukey HSD test. Homogenous groups has been established from largest to the smallest.

Characterization of soil microbiological properties

Diversity and structure of microbiome and mycobiome

The results obtained using the NGS analysis (Table 3) showed variability in the bacterial population in the plant root zone between the test plots and the reference plots, as shown by the Shannon (H') and Simpson (D) indices. The highest Shannon index value was obtained in soil samples sourced on the first sampling date (T1) for the AP combination in 2021. A similar relationship was established for soil samples collected in 2022, when the highest Shannon index value was obtained for T1 sampling and for the AP combination. Simpson's index was less differentiated, though once again the highest index value was observed in 2021 for the samples sourced on the first date (T1) for the AP combination. The lowest diversity indices values (both Simpson's and Shannon's) were found in samples collected in 2022 for the AK combination on the second date, or during the second soil sampling campaign (T2). The index valued calculated for fungi based on NGS analysis also showed variability in fungal populations between cropping systems. The AP variant resulted in the highest Shannon index value in samples collected in 2022 on the first date (T1), but the index also showed seasonal dynamics. Furthermore, the Shannon index values were lower in the AP plots when compared to the AK plots on all other sampling dates. The richness of the observed AVS for bacterial populations was higher in soil samples collected from paulownia and buckwheat intercropping variant compared to the control samples, on all sampling dates. However, a different relationship was observed for fungal populations.

Table 3

Estimation of alpha diversity index and richness (number of AVSs and OTUs of bacterial and fungal microbiomes.
Year	Period	Samples	BACTERIA			FUNGI
Year	Period	Samples	Shannon Diversity Index (H’)	Simpson’s Index (D)	Observed AVSs	Shannon Diversity Index (H’)	Simpson’s Index (D)	Observed OTUs
2021	T1	AK	5.055	0.988	511	2.981	0.817	294
	T1	AP	5.219	0.990	581	2.876	0.820	278
	T2	AK	5.022	0.987	496	2.824	0.833	293
	T2	AP	5.044	0.988	524	2.750	0.836	272
2022	T1	AK	5.154	0.989	551	2.872	0.804	262
	T1	AP	5.158	0.989	551	3.086	0.848	273
	T2	AK	4.855	0.984	452	2.688	0.814	288
	T2	AP	5.024	0.987	513	2.606	0.789	282
AP – intercropping; AK – control; T1, T2 – the first and second soil sampling date; AVS – Amplicon Sequencing Variants; OUT - Operational Taxonomic Units

Table 3. Estimation of alpha diversity index and richness (number of AVSs and OTUs of bacterial and fungal microbiomes.

Nine bacterial phyla proved to be most prevalent in the rhizosphere soil samples: Actinobacteriota, Proteobacteria, Chloroflexi, Gemmatimonadota, Bacteroidota, Firmicutes, Myxococcota, Verrucomicrobiota and Planctomycetota. Other phyla usually did not exceed 5% relative abundance (Fig. 1). In all combinations under study, the largest group was the Actinomycetes, up to 45% (2022/T2/AK). Proteobacteria were also a significant group in all combinations (about 20–30%, depending on the combination). Samples collected in 2021 had lower relative abundance of Actinobacteria based on 16S rRNA gene fragment sequencing analysis for bacteria compared to samples from the 2022 collection in all combinations. 16S rRNA sequencing also showed that the intercropping variant in the 2022 collection samples had no effect on Proteobacteria, regardless of sampling date. In contrast, for the 2021 samples, we noted an increase in this population for the intercropping (AP) samples collected at the first sampling time compared to the AK combination. We observed no such difference at the second sampling date (T2).

Figure 1. The relative abundance of bacteria at the phylum level

The dominant fungi included four phyla: Ascomycota, Basidiomycota, Mortierellomycota, Mucoromycota. Unclassified fungi also formed a significant part of the population (Fig. 2). In all combinations studied, Ascomycota were the largest group, accounting for up to about 65% (2022/T2/AK). Relative abundance of Ascomycota was lowest in 2021 samples collected from AP at T2. In 2022, Ascomycota were slightly lower in AP than in AK on both sampling dates. Analysing different variants in the plot experiment, we noted that in 2021 the Basidiomycota phylum was the least abundant in AP in the first sampling (T1) compared to the other variants and sampling dates. In contrast, the Mortierellomycota phylum was most abundant in the 2022 samples collected on the first sampling date (T1) for the AK combination. The Mucoromycota phylum was much less abundant than the other three main fungal phyla, but its proportion increased significantly in the AP variant in the second sampling of 2022 (T2). The largest group of unclassified fungi was observed in the soil samples collected in 2021 on the second sampling date (T2) in AP (approximately 30% of the total population of the sample).

Figure 2. The relative abundance of fungi at the phylum level

Figure 3. Microbial community heatmap of top 15 bacteria on the general level. Different colors represent relative abundance of bacteria (%). Red means higher relative abundance, whereas green means lower relative abundance

The top 15 genera in bacteria communities under study are presented in Fig. 3. Sequences assigned to Nocardioides were most abundant in all communities throughout the experiment, accounting for 2.10% − 6.23% of the total high-quality sequences. Candidatus_Solibacter was one of the dominant genus (2,43% relative abundance) in control sample sourced on T1 2022. Pseudarthrobacter was abundant in AP T2 2021 and AP T2 2022 samples, as well as in both T2 2022 samples, whereas Streptomyces was characteristic for the rhizosphere microbiomes in T2 2022 samples.

Figure 4. Microbial community heatmap of top 15 fungi on the genera level. Different colors represent the relative abundance of fungi (%). Red means higher relative abundance, whereas green means lower relative abundance.

Heatmap analysis of relative abundances of the most abundant genera showed clear differences in the fungal community structures between all samples (Fig. 4). Genera whose abundance were above 2% in all samples were: Exophiala, Fusarium and Mortierella. We wish to highlight that Mortierella was the dominant genus, accounting for 9.69% − 23.56% of the total high-quality sequences. Botryotinia was abundant in AK T1 2021 sample, whereas genus Penicillium in AK T2 2022 sample.

Quantification of Cultivable Bacteria and Fungi

Bacterial counts ranged from 2.4 ×10⁴ to 2.1 ×10⁶ CFU in 1 g of soil, and fungal counts ranged from 1.0 to 5.0 ×10⁴ CFU in 1 g of soil (Table 4). Compared to the control, intercropping (AP) did not significantly affect the number of bacteria and fungi, both in the first and second year of the study. However, the number of CFUs was influenced by the sampling date and the year of the study. It is noteworthy that at both T1 and T2 for both cultivation methods, a significant increase in bacterial counts was observed in the second year of the study compared to 2021. In 2021, fungal CFU counts presented no statistically significant differences between the control sample and the soil sample where buckwheat was grown with paulownia on both sampling dates. In contrast, there was a statistically significant increase in the number of fungal CFUs on T2 in 2022 compared to the previous year (Table 4).

Table 4

Total abundances of bacteria and fungi in the field experiment.
Year	Period	Samples	Total bacterial count (CFU g^− 1)	Total fungal count (CFU g^− 1)
2021	T1	AK	2.4×10⁴ c	3.8×10⁴ abc
	T1	AP	4.3×10⁴ c	3.3×10⁴ abc
	T2	AK	2.1×10⁶ ab	1.6×10⁴ cd
	T2	AP	1.6×10⁶ b	1.0×10⁴ d
2022	T1	AK	2.9×10⁶ ab	1.8×10⁴ bcd
	T1	AP	2.4×10⁶ ab	1.7×10⁴ cd
	T2	AK	3.1×10⁶ ab	5.0×10⁴ a
	T2	AP	4.6×10⁶ a	3.8×10⁴ ab
AK – control site; AP – intercropping; T1, T2 – the first and second soil sampling date; Values are the mean of four replicates of each sample. Values followed by different letters in columns indicate significant differences according to three-way ANOVA with post-hoc Tukey HSD test

Table 4. Total abundances of bacteria and fungi in the field experiment.

Soil biological activity

Soil biological activity was determined in the soil samples based on dehydrogenase activity (DHA) and total glomalin concentration (T-GRSP). The results are shown in Table 5, Table S1.

Table 5

Dehydrogenase activity (DHA) and total glomalin concentration (T-GRSP) in the air-dried soil.
Year	Period	Samples	DHA (µg TPF g^− 1 h^− 1)	Total GRSP (µg g^− 1)
2021	T1	AK	3.8 c	1699.0 a
	T1	AP	7.9 b	1522.9 a
	T2	AK	2.9 c	1492.1 a
	T2	AP	3.0 c	1533.2 a
2022	T1	AK	7.7 b	1460.2 ab
	T1	AP	12.8 a	1471.6 ab
	T2	AK	2.0 c	1088.0 c
	T2	AP	2.5 c	1233.2 bc
AK – control site; AP – intercropping; T1, T2 – the first and second soil sampling dates; DHA – dehydrogenase activity expressed as the amount of released µg triphenylformazan (TPF) per hour per gram of soil; GRSP – glomalin-related soil protein content per gram of air-dried soil. Values followed by different letters in columns indicate significant differences according to three-way ANOVA with post-hoc Tukey HSD test

Table 5. Dehydrogenase activity (DHA) and total glomalin concentration (T-GRSP) in the air-dried soil.

Table 1S. Summary of three-way analysis of variance (ANOVA) results testing the effects of cultivation (monoculture and intercropping), year (2021 and 2022), and sampling periods (T1 and T2) on total bacterial count, total fungal count, dehydrogenase activity and T-GRSP. Data shown represent F-value and significance levels for each factor and interaction.

Dehydrogenase activity in the intercropping (AP) and control (AK) samples ranged from 2.0 to 12.8 µg TPF g^− 1 h^− 1. Dehydrogenase activity was higher in the intercropping samples compared to the control, but statistically significant differences were observed for the first sampling date (T1). The highest DHA activity was observed in the intercropping samples in 2022 from the first sampling date (AP, T1). At the flowering stage of buckwheat (T2), enzyme activity decreased regardless of the cultivation method.

The T-GRSP concentration ranged from 1039.2 to 1699.0 µg g^− 1. The concentration of glomalin depended on the sampling date and not on the cultivation method. The highest concentration of T-GRSP was found in 2021 in the control sample (AK) from the first sampling date (T1). The following year, T-GRSP concentrations decreased compared to those in samples sourced on the first sampling date (T1).

Principal component analysis (PCA)

The relationships in all soil samples and their properties were assessed using principal components analysis (PCA) (Fig. 5). The first two principal components (PCs) accounted for 44.27% and 25.04% of the total variance of the tested samples. The analysis showed that the 2021 samples clustered based on the sampling year, while sampling date proved to be differentiating factor for the 2022 samples.

Figure 5. Biplot diagram of principal components analysis (PCA), describing the microbial properties of soil samples collected from different cultivation sites (monoculture AK and intercropping AP), year (2021 and 2022) and sampling period (T1 and T2)

Buckwheat root colonization by fungi

We assessed the degree of colonisation of buckwheat roots by potentially pathogenic fungi. Table 6 shows the percentage of fungi on buckwheat roots at flowering (T2). Buckwheat roots in both the first and the second year of the study were most abundantly colonised by fungi of the genus Fusarium. The most abundant species we isolated were F. oxysporum, accounting for 54 to 67% of the total number of fungi. We also isolated the potentially pathogenic species F. culmorum and F. avenaceum (between 4 and 10%). In both the first and second year of the study, buckwheat roots were colonised by saprotrophs of the genus Trichoderma ranging from 3 to 20%. However, their number decreased in the second year of the study to 8% in the intercropping sample and to 3% compared to the control crop. In the first year of the study, Penicillium notatum fungi were recorded in the intercropping sample (13%). An increased percentage of Exserohilum pedicellatum (from 2 to 10%) was also found on buckwheat roots in the 2022 crop year. Other potentially pathogenic fungi such as F. solani, Rhizoctonia solani, Phoma spp. or Pythium spp. colonised buckwheat roots sporadically (< 1%) in both AK and AP crops.

Table 6

Percentage of filamentous fungi isolated from buckwheat roots by culture method.
Fungi	2021		2022
Fungi	AK	AP	AK	AP
Colletotrichum sp.	< 1	–	–	–
Cylindrocarpon sp.	1	–	–	–
Exserohilum pedicellatum	–	2	6	< 10
Fusarium avenaceum	–	–	6	< 9
Fusarium culmorum	10	7	5	4
Fusarium oxysporum	66	54	66	67
Fusarium solani	–	–	< 1	–
Fusarium sporotrichoides	–	< 1	2	–
Mucor mucedo	1	–	< 1	–
Penicillium notatum	–	13	–	–
Penicillium purpurogeum	< 1	< 1	< 1	< 1
Penicillium vermiculatum	–	< 1	–	–
Penicillium sp.	6	–	7	< 1
Phoma sp.	–	–	< 1	–
Pythium sp.	–	–	–	< 1
Rhizoctonia solani	–	–	< 1	< 1
Rhizopus nigricans	< 1	–	< 2	3
Trichoderma hamatum	2	7	–	2
Trichoderma harzianum	7	12	< 1	4
Trichoderma viride	< 1	< 1	2	< 2
Other fungi	4	4	–	–
AP – intercropping; AK – control; T1 and T2 – the first and second buckwheat roots sampling date; symbol „– ” indicates that no fungi were isolated from the sample

Table 6. Percentage of filamentous fungi isolated from buckwheat roots by culture method.

Biometric traits and yield of buckwheat

The cultivation variant, i.e. intercropping of buckwheat with paulownia and no intercropping, did not significantly affect any of the studied buckwheat traits, i.e. plant height, number of branches on the main shoot, number of branches, inflorescences, number of kernels, total kernels weight per plant. Despite lack of significant statistical differences, in many cases the values of biometric traits were higher when buckwheat was grown with paulownia, compared to the control site, i.e. the number of full kernels per plant and the total kernels weight per plant. Over the years of the study, time has significantly affected the kernels yield and the values for some of the traits studied, such as the number of branches per plant, the number of kernels and the total kernels weight per plant (Table 7). In 2022, we registered a nearly 100% higher yield of kernels per hectare.

Table 7

Select morphological and flowering biology-related traits of buckwheat and buckwheat yield (averages for specific combinations and years).
Specification	Plant height (cm)	Number of branches on main shoot	Number of branches	Number of inflorescences	Number of full seeds
Cultivation
AP	46.7	1.22	2.99	5.95	22.5	0.260	0.650
AK	47.2	1.57	3.59	6.44	21.8	0.205	0.513
NIR_, LSD	ns	ns	ns	ns	ns	ns	ns
Years
2021	47.3	1.41	2.36	6.84	15.5	0.156	0.390
2022	47.1	1.38	4.22	5.55	28.8	0.309	0.773
NIR, LSD	ns	ns	0.69	ns	9.1	0.100	0.252
AP - intercropping, AK – control, ns – statistically insignificant,
NIR- the least statistical difference, LSD - least significant difference

Table 7. Select morphological and flowering biology-related traits of buckwheat and buckwheat yield (averages for specific combinations and years).

The average height of the trees at the beginning of the growing season in 2021 was 145 cm, while at the end of the 2022 season it was 311 cm. Tree crown sizes in October 2021 had an average of: 108 cm (height) × 157 (width) cm, and in 2022: 156 cm × 209 cm. Trunk girths at breast height (at 130 cm from the ground level) at the end of the 2021 and 2022 seasons were respectively: 16.4 cm and 17.9 cm.

In the present multidisciplinary study, we determined the effects of Tree Based Intercropping (TBI) of paulownia trees on soil physicochemical and microbial properties and the biometric traits of buckwheat. To the best of our knowledge, our study is the first to consider the microbiological aspect of soils in the intercropping of buckwheat and paulownia in European conditions. In northern China, Paulownia elongata trees are intercropped with wheat or beans. Typically, scientists investigate intercropping with hybrid poplar clones, black walnut, white ash, red alder.^23,24 Intercropping is an important element of modern agricultural systems, having an impact on yields, soil quality, and soil microbial community's activity and structure.²⁵

Soil pH is considered to be the main soil variable. It interacts with microorganisms, thus determining plant growth and biomass yield.²⁶ In the present study, we observed that both buckwheat monoculture and intercropping resulted in lower soil pH. However, the soil pH in both crops oscillated at the same level in the last year of the study. These results are consistent with previous studies on cassava and soybean intercropping and pepper and garlic intercropping ^{27, 28}, which showed the intercropping system to lower soil pH. These findings are also supported by Bughio et al. (2013)²⁹, who indicated that soil pH decreases with an increase in leaf litter from Eucalyptus camaldulensis. Furthermore, Khan et al. (2010)³⁰ found that several soil physicochemical properties in Robinia pseudoacacia plantations, such as pH, were modified by leaf decomposition. Hinsinger et al. (2006)³¹ and Hagen-Thorn et al. (2004)³² showed that tree roots acidify the soil through the release of acidic compounds and through microbial respiration. In general, all plants produce and release secondary compounds that may show the potential to alter the chemical properties of the substrate on which they grow. Furthermore, a major mechanism ensuring the efficiency of phosphorus uptake by buckwheat may be the plant's ability to acidify the rhizosphere.³³ Lower soil pH may be associated with greater nutrient availability and their consequent direct uptake through plant roots.³⁴

Soil microbial diversity and activity is a significant aspect of soil quality affected by TBI. So far, the relationship between soil microbial properties and the diversification of intercropping has remained understudied. Over the past two decades, soil microbial communities in temperate zone agroforestry systems have been researched, mostly via traditional methods.³⁵ More recently, Banerjee et al. (2016)³⁶ studied bacterial communities in Canadian agroforestry systems using both quantitative PCR and 454 pyrosequencing of bacterial 16S rRNA. Still, we lack comprehensive, involving both high-throughput metagenomic sequencing techniques of bacterial and fungal populations and traditional techniques to measure microbial abundance and enzymatic activity, as well as plant biometry in temperate zone agroforestry systems.

Land use patterns have a significant impact on the composition and diversity of soil microorganisms, which are extremely sensitive to changes in the soil environment.^25,37 Our metagenomic sequence analysis of 16S rRNA gene fragments and ITS regions has provided unique data on bacterial and fungal population diversity and structure. Intercropping increased the diversity of rhizosphere bacterial populations, while the opposite trend was observed for fungal populations, evidenced by the values of the Shannon and Simpson indices. Bigger bacterial diversity may be due to increased carbon and nutrient supply from litter, dead root cells and tree root secretions. ³⁸ In addition, although the crop plant biomass is negligible compared to trees, the decomposition of litter from crop plants with a high diversity of secondary metabolites can impact soil microbial communities.³⁹ Tree rows exert strong influence over soil microbial communities and provide habitat for a microbiome that differs in composition from the microbiome of neighbouring crops. Consequently, the introduction of the soil microbiome associated with tree rows onto arable land through agroforestry increases the overall diversity of the system. Our findings related to the diversity and number of OTUs of the mycobiome are in line with previous studies, which have shown that fungal communities under trees gradually diversified. Young tree cultivation within an agroforestry system do not affect the rhizosphere fungal community, and no increase in fungal populations was detected in young agroforestry systems. Clivot et al., (2020)⁴⁰ with Beule and Karlovsky (2021)²⁴ only detecting strong promotion of soil fungi after 10 years of poplar cultivation. This may be due to the adaptation to the heterogeneous understorey space of tree biomass and understorey vegetation or stochastic phenomena as a result of limited exchange between fungal populations. Analysing the microbiome structure of our soil samples, we noted that the microbiome of the rhizosphere soil from intercropping and buckwheat monoculture was dominated by bacteria classified as Actinobabateria, Proteobacteria, Acidobacteria, and the mycobiome by Ascomycota, Basidiomycota and Mortiellomycota. In our own study (Woźniak et al., 2019)⁴¹, we reported that the rhizosphere of Paulownia trees was dominated by the above-mentioned types of bacteria and fungi. Wang et al. (2022)⁴² found that in the rhizosphere of buckwheat the dominant phylum were Actinobacteria, Proteobacteria and Acidobacteria and fungi classified as Ascomycota, Basidiomycota and Mortierellomycota. The dominance of Actinobacteria and Proteobacteria is probably related to the nutrient-rich conditions of the rhizosphere. ^43,44 However, Ascomycota and Basidiomycota fungi play an important role in maintaining soil stability, in plant biomass decomposition and plant interactions. ⁴⁵ Mortierellomycota, on the other hand, are fungi other than saprotrophs, living in the soil on decaying leaves and other organic materials. In addition, Basidiomycota these fungi promote plant growth in different types of crops, so they can be considered as a potential bioindicator for crop production and soil health assessment. ⁴⁶ It has already been noted that high relative abundance of Mortierellomycota can be evidence of good soil health. ⁴⁷ In our study, we recorded increased relative abundance of i.e. Candidatus Solibacter, Nocardioides, Pseudarthrobacter and Sphingomonas. These microorganisms, considered to be PGPR (plant growth-promoting rhizobacteria), are widely recognised to exhibit plant growth-promoting activities by e.g. mobilising nutrients, mediating phytostimulation and plant biocontrol. ^{48, 49,50,51} The accumulation of these microorganisms in rhizosphere soil may be a factor that can positively affect buckwheat biometrics. The study by Peng et al. (2022)⁵⁰ also showed that intercropping promotes the enrichment of PGPR. In addition, in our metagenomic study we observed a decrease in the relative abundance of i.e., Fusarium genus - common soilborne plant pathogens - in intercropping. ⁵² It is likely that intercropping can effectively reduce disease incidence in crop fields. Mechanisms supporting this effect include: modification of the crop microclimate; secretion of allopathic compounds; positive effects on antagonistic microbial communities; and diversification of soil microbial communities. ⁵³

Undoubtedly, the intercropping of trees and plants influences quantitative and qualitative changes in soil microbial life. Most studies show a significant increase in the number of microorganisms in the soil in two-plant intercropping. In our study, we noted that growing buckwheat with paulownia had a positive effect on the increase in the number of bacteria CFU. In addition, there was an increase in the number of fungi CFU in the intercropping during the flowering period of buckwheat in the second year of the study, compared to 2021. It is likely that the higher abundance of bacteria and fungi could be the result of direct contact between plant roots in the intercropping system, which stimulates plant roots to release more nutrients.⁵⁴ Similar results were obtained by Beule et al. 2020⁵⁵, who observed an increase in bacterial abundance in poplar-based agroforestry systems compared to neighbouring monocultures in arable fields. Furthermore, the results of Lee and Jose (2003)⁵⁶ indicate that the age of the agroforestry system influences the increase in microbial biomass. In a study by Li et al. (2013)⁵⁷, the number of soil fungi, bacteria and actinomycetes in intercropping of two species, i.e. soybean and sugarcane, increased by 115.5%, 43.6% and 57.3%, respectively, compared with monoculture. Our study showed that the CFU counts of bacteria and fungi were dependent on the sampling date, being generally higher at the flowering stage of buckwheat (T2), which is consistent with the study of Wang et al. (2019)⁵⁸. Increasing soil microbial abundance is extremely important as it can influence plant health and soil quality, thus ensuring the stability and productivity of natural ecosystems. ⁵⁹

Soil enzyme activity, including dehydrogenases, is an important indicator of organic matter decomposition and nutrient dynamics. Dehydrogenase activity (DHA) is often used as a high-sensitivity indicator of soil fertility.⁸ In our study, we noted that intercropping increases DHA activity, which is particularly evident on T1, in spring, when the optimum temperature under conditions of sufficient moisture may be a factor favouring higher enzymatic activity. ⁶⁰ In our own study (Woźniak et al., 2022)⁸, we reported that young plantations of paulownia trees have a positive effect on some soil microbial parameters, i.e. dehydrogenase activity. Similarly, Wan and Chen (2004)⁶¹ observed higher enzymatic activity in tree-based intercropping including Paulownia spp. probably due to increased carbon content, nutrients, leaf residues and root secretions in the soil. The high organic content of the soil contributes to a significant increase in the number of microorganisms and changes in their community structure, thereby improving the microbial activity of the soil. ⁶² It appears that intercropping can provide sufficient energy to soil microorganisms that play a key role in the accumulation, decomposition and transformation of organic carbon in the soil. ⁶³

Glomalin related soil protein (GRSP) is a glycoprotein produced by arbuscular mycorrhizal fungi (AMF), which are widespread in various terrestrial ecosystems and can form symbiotic associations with the roots of more than 80% of land plants³⁸. Many authors point to GRSP as a good indicator of soil stability and fungal activity. ^64,65 Our study showed that higher amounts of GRSP were recorded in the intercropping at T2 compared to the control objects. This may be due to the plant roots secreting more C and energetic substances that promote AMF growth and reproduction, which may increase mycelial density and length and thus GRSP amounts.⁶⁶ Furthermore, the results of our study confirm previous findings that GRSP shows sensitivity to seasonal variation and land use change. ⁶⁴ Our results are also consistent with those of Zhao et al. (2020)⁶⁷ on the impact of maize and soybean intercropping on GRSP. It is also notable that AMF can expand the uptake area of plant roots to improve water and mineral absorption, promoting plant growth. ⁶⁸

Yengwe et al. (2018)⁶⁹ assessed the potential of Faidherbia albida in intercropping with maize in Zambia and found that the presence of F. albida litter can provide more than 18 kg N ha^− 1 year^− 1 and increase microbial diversity and abundance. In contrast, according to Lucas-Borja et al. (2011)⁷⁰, paulownia plantations worsened soil health (characterised by soil enzyme activity) compared to Aleppo pine plantations or undisturbed soil area. However, paulownia plantations contribute to better soil health than do maize cultivation (intensive soil use). In a study by Woźniak et al. (2022)⁸, following one year of observation, we concluded that some soil microbial parameters (activity of dehydrogenases and acid phosphatases, catabolic activity according to Biolog EcoPlates) decreased along with increasing distance from the nearest tree, which is related to the decreasing content of nutrients contributed by root secretions and leaf residues.

Intercropping affects crop yields depending on a number of factors, i.e. plant growing conditions, species, soil and climatic conditions. Research shows that the results vary. The yield and the biometric traits of intercropped plants depend largely on the species composition and the coexistence mechanisms developed by the plants. In a study by Dang et al. (2020)⁷¹, intercropping led to significant improvements in biometric and compositional traits of millet grain yield, number of ears per plant and their length, grain mass per plant and 1000 grain weight, with increases in grain yield of 5.6–20.7% in 2017, 7.9–53.9% in 2018 and 28.3–75.4% in 2019. Mung bean seed yield was lower in this cropping system due to the spatial structure of the millet crown. ⁷² Almond trees intercropped with mung bean had the highest vegetative growth performance, compared to growing almond trees alone. Intercropping of beans with almond trees had a significant effect on the yield and quality of snap beans contributing to enhanced pod parameters, i.e. length, diameter, fresh weight, dry weight, total yield, protein, fibre in both seasons. In a study by Yin and He (1997)⁷³, 9-year-old paulownia trees led to a 23% reduction in wheat yields in a paulownia-wheat intercropping system in China. Similar results were obtained by Chirko et al. (1996)⁷⁴, who proved that shading provided by 11-year-old paulownia trees in a paulownia-wheat intercropping system reduced yields by only 7%. However, the system had no effect on reducing the number of grains per square metre and of dry matter per 1000 grains. A study by Li et al. (2008)⁷⁵ showed that wheat yield was reduced by up to 50% in the paulownia-wheat intercropping system, and a similar reduction in wheat yield was also found in the walnut-wheat system with an 8 m spacing between tree rows. ⁷⁶ Liu et al. (2013)⁵⁷ found that sugarcane yields in intercropping were significantly higher than in monoculture.

Different results have been reported in the studies by Zhu et al. (2012)³³ and Shukla et al. (2019)⁷⁷. A study by Zhu et al. (2012)³³ showed that 2-year-old mulberry trees did not have a significant effect on millet yield, but mulberry tree leaf production increased by 30%. Shukla et al. (2019)⁷⁷ found that yields of all crops tested in the agroforestry system were lower in shaded sites compared with sites with full light exposure. In our study, we found no statistically significant effect of intercropping on biometric traits of buckwheat or its yield. The buckwheat yield was decisively influenced by conditions that occurred in specific years of our research, with significantly better conditions for yield and development of this plant occurring in the 2022 season. The close dependence of buckwheat yield on weather conditions has been confirmed by other studies. ⁷⁸

Pseudo-cereal plants, as well as buckwheat, can be infected by various species of pathogenic fungi, which affect the yield and deteriorate its quality. One of the most serious diseases of cereal plants is Fusarium wilt, which is dangerous as it generates mycotoxins. ^79,80 The present study showed that fungi of the genus Fusarium colonised buckwheat roots most abundantly, regardless of the crop used. However, the study did not show symptoms characteristic of infestation by these fungi, such as wilting and withering of leaves or browning and rotting of roots. The predominant species were F. oxysporum considered to be part of a natural microflora colonising plant roots. ^{81, 82,83} Some species may be potentially pathogenic, as are the few isolated species of F. culmorum and F. avenaceum. We should stress that in the second year of our study, we recorded a nearly 5-fold increase in Exserohilum pedicellatum on buckwheat roots in intercropping sample (AP) - an organism which can cause Exserohilum root rot. ^84,85

Our research indicates that, in general, the paulownia and buckwheat intercropping system had a positive effect on soil microbial properties, which provide a high-sensitivity indicator of soil quality. However, our results are inconclusive, necessitating more studies that would take into account long-term and lasting effects of intercropping. Nevertheless, our study, including both high-throughput metagenomic sequencing techniques of bacterial and fungal populations combined with traditional techniques for measuring microbial abundance and enzymatic activity, showed that the TBI system tested in temperate climates had a positive effect on the abundance, diversity and function of soil bacteria when compared to monocultures in arable fields, potentially contributing to the biological fertility of soil in these systems. Our study also indicated that there were greater changes in the abundance and diversity of bacterial than in fungal populations, depending on the cultivation method. This may suggest that the mycobiome is less sensitive to land use change and that it diversifies gradually. Our results also suggest that both the mycobiome and the TBI microbiome were abundant in terms of plant growth promoting microbes positively influencing plant growth and yields. Given the seasonal and annual trend of each parameter we analysed, temporal fluctuation is a key factor influencing soil biological properties. We have thus demonstrated that microorganisms have the ability to modulate their activity in response to changing nutrient conditions. Growing buckwheat in-between Paulownia tree rows did not result in a deterioration of the biometric and yield characteristics of this annual crop. Growing trees and crops in close spatial proximity allows for different interspecies interactions that can result in complementary resource use. Our study can provide a foundation for the development of innovative management strategies for the cultivation of trees for biomass energy production. Our aim has also been to provide scientific evidence in support of TBI systems implementation based on paulownia trees in temperate climates.

Credit authorship contribution statement

Woźniak Małgorzata: Data curation, Formal analysis, Investigation, Resources, Validation, Visualization, Software, Writing - original draft, Writing - review & editing, Supervision; Jama-Rodzeńska Anna: Data curation, Formal analysis, Investigation, Resources; Funding acquisition, Project administration, Writing - original draft, Writing - review & editing; Supervision; Gębarowska Elżbieta: Data curation, Formal analysis, Investigation, Methodology, Resources, Validation, Writing - original draft, Writing - review & editing; Liszewski Marek: Conceptualization, Data curation, Investigation, Resources, Methodology, Project administration, Resources, Supervision; Siebielec Sylwia: Resources, Writing - original draft; Kaczmarek-Pinczewska Agata: Data curation; Kucińska Jolanta: Data curation; Gałka Bernard: Formal analysis; Zalewski Dariusz: Formal analysis, Software; Bąbelewski Przemysław: Funding acquisition, Project administration, Investigation.

Funding

This work was supported by the Wrocław University of Environmental and Life Sciences (Poland) as part of the research project no N090/0008/2024

The APC/BPC is financed/co-financed by Wroclaw University of Environmental and Life Sciences and by the National Centrum of Science DEC-2022/06/X/ST10/00047.

Declaration of competing interest

The authors declare no competing interests.

Data availability

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

Nair, P.K.R, Gordone, A.M, Mosquera-Losadac, M.R Agroforestry. Elsevier, Netherland, 101–110 (2008).
Borek, R. Agroforestry Systems in Poland a preliminary identification. Papers on global change IGBP, 22, 37–51. https://doi.org/10.1515/igbp-2015-0014 (2015).
Liszewski M., Chorbiński P. The influence of foliar buckwheat fertilization with copper, manganese and iron on selected parameters of its nectar production and seed yield. Polish J. Agron. 46, 23-30 (2021).
Jimenez, L., Rodrigez, A., Ferrer, J., R., Perez, A., Angulo, V. (2005), Paulownia, a fast-growing plant, as a raw material for paper manufacturing, Afinidad -Barcelona- 62(516), 100-105 (2005).
Halarewicz A., Liszewski M., Bąbelewski P., Bączek P. Allelopathic effects of Paulownia tomentosa and hybryd P. elongate x P. fortune on Sinapis alba, Festuca pratensis and Poa pratensis. Allelopathy J.43, 1, 83-92. https://doi.org/10.26651/allelo.j/2018-43-1-1132. (2018).
Valdivia, C., Barbieri, C., Gold, M.A. Between Forestry and Farming: Policy and Environmental Implications of the Barriers to Agroforestry Adoption. C. J. Agric. Econom.60, 155–175 Available at: http://www4.ncsu.edu/~cebarbie/papers/valdivia_agroforestry_2012.pdf (2012)
Sørensen, J., Sessitsch, A. Plant-associated bacterial-lifestyle and molecular interactions. In: Van Elsas JD, Jansson JK, Trevors JT (eds) Modern soil microbiology. CRC, New York, 221–236 (2007).
Woźniak, M., Gałązka, A., Siebielec, G., Frąc, M. Can the Biological Activity of Abandoned Soils Be Changed by the Growth of Paulownia elongata × Paulownia fortunei?—Preliminary Study on a Young Tree Plantation. Agric. 12, 128. https://doi.org/ 10.3390/agriculture12020128 (2022).
Siebielec, S. et al. Impact of rhizobacterial inoculants on plant growth and enzyme activities in soil treated with contaminated bottom sediments. Int. J. Phytoremed. 21(4), 325-333. (2019).
Chu, H. et al. Soil microbial biomass, dehydrogenase activity, bacterial community structure in response to long-term fertilizer management. Soil Biol Biochem39, 11, 2971-2976. https://doi.org/10.1016/j.soilbio.2007.05.031(2007).
Bandyopadhyay, S., Maiti, S. K. Different soil factors influencing dehydrogenase activity in mine degraded lands—state-of-art review. Water, Air,Soil Pollut. 232(9), 360. https://doi.org/10.1007/s11270-021-05302-0 (2021).
Nam, N.N., Do, H., D., K., Thrin, K.T.L., Lee, N.Y. Metagenomics: An Effective Approach for Exploring Microbial Diversity and Functions. Foods 12(11), 2140. https://doi.org/10.3390/foods12112140 (2023).
Banerjee, S et al. Agricultural intensification reduces microbial network complexity and the abundance of keystone taxa in roots, ISME J. 13, 1722–1736. https://doi.org/10.1038/s41396-019-0383-2 (2019).
Agler, M.T. et al. Microbial hub taxa link host and abiotic factors to plant microbiome variation. PLoS Biol. 14,1–31. https://doi.org/10.1371/journal.pbio.1002352 (2016).
Polish Soil Classification. Systematyka gleb Polski. Polskie Towarzystwo Gleboznawcze, Komisja Genezy Klasyfikacji i Kartografii Gleb. Wydawnictwo Uniwersytetu Przyrodniczego we Wrocławiu, Polskie Towarzystwo Gleboznawcze, Wrocław–Warszawa, 291 ss. (2019).
IUSS Working Group WRB. World Reference Base for Soil Resources. International soil classification system for naming soils and creating legends for soil maps. 4th edition. International Union of Soil Sciences (IUSS), Vienna, Austria (2022).
Kalbarczyk, R. Próba wydzielenia regionów termiczno-opadowych na obszarze Polski, Folia Universitatis Agriculturae Stetinensis, 231, 91, 27-38 (2003).
Bunt J.S., Rovira A.D., Microbiological studies of some subantarctic soil. J. Soil Sci.,6(1), 119–128 (1955).
Martin J.P. Use of acid, rose Bengal, and streptomycin in the plate method for estimting soil fungi. Soil Sci. 69, 215–233 (1950).
Wright S.F., Upadhyaya A.. Quantification of arbuscular mycorrhizal fungi activity by the glomalin concentration on hyphal traps. Mycorrhiza,8, 283–285(1999).
Germain, G., Summerbell, R. Identifying filamentous fungi: A clinical laboratory handbook. Belmont CA: Star Publishing Company (1996).
Samson, R.A., Hoekstra, E.S., Frisvad, J.C. Wprowadzenie do grzybów przenoszonych przez żywność i powietrze . Utrecht: Centraalbureau voor Schimmelcultures (2004).
Lacombe, S., Bradley, R. L., Hamel, C., Beaulieu, C. Do tree-based intercropping systems increase the diversity and stability of soil microbial communities? Agric. Ecosys. Environ. 131(1-2), 25-31. https://doi.org/10.1016/j.agee.2008.08.010 (2009).
Beule, L., Karlovsky, P.. Tree rows in temperate agroforestry croplands alter the composition of soil bacterial communities. PLoS One16(2), e0246919. https://doi.org/10.1371/journal.pone.0246919 (2021).
Tang, X. et al. Cassava/peanut intercropping improves soil quality via rhizospheric microbes increased available nitrogen contents, BMC Biotechnol. 20,13. https://doi.org/10.1186/s12896-020-00606-1 (2020).
Neina, D. The role of soil pH in plant nutrition and soil remediation. Appl. Environ. Soil Sci., 3, 1–9. https://doi.org/10.1155/2019/5794869 (2019).
Makinde, E. A., Oluwatoyinbo, F. I., Ayoola, O. T. Intercropping and crop residue incorporation: effects on soil nutrient status. J. Plant Nutr. 29(2), 235-244. https://doi.org/10.1080/01904160500474082 (2006).
Ahmad, I. et al. Effect of pepper-garlic intercropping system on soil microbial and bio-chemical properties. Pak. J. Bot. 45(2), 695-702 (2013).
Bughio, F. A. et al. Physio-morphological responses of native Acacia nilotica to Eucalyptus allelopathy. Pak. J. Bot,. 45(S1), 97-105 (2013).
Khan, B., Ablimit, A., Mahmood, R., Qasim, M. Robinia pseudoacacia leaves improve soil physical and chemical properties. J. Arid Land, 2, 266–271. https://doi.org/10.3724/SP.J.1227.2010.00266 (2010).
Hinsinger, P., Plassard, C., Jaillard, B. Rhizosphere: A new frontier for soil biogeochemistry. J. Geochem. Explor. 88,1, 210-213. https://doi.org/10.1016/j.gexplo.2005.08.041 (2006).
Hagen-Thorn, A., Callesen, I., Armolaitis, K., Nihlgård, B. The impact of six European tree species on the chemistry of mineral topsoil in forest plantations on former agricultural land. For. Ecol. Manag. 195, 3, 373-384 (2004).
Zhu, Y. G., He, Y. Q., Smith, S. E., Smith, F. A. Buckwheat (Fagopyrum esculentum Moench) has high capacity to take up phosphorus (P) from a calcium (Ca)-bound source. Plant and Soil, 239, 1-8 (2002).
Malviya, M. K. et al. Sugarcane-legume intercropping can enrich the soil microbiome and plant growth. Front. Sustain. Food Sys. 5, 606595. https://doi.org/10.3389/fsufs.2021.606595 (2021).
Bardhan, S., Jose, S., Udawatta, R.P, Fritschi, F. Microbial community diversity in a 21-year-old temperate alley cropping system. Agroforest Syst.87, 1031–1041. https://doi.org.10.1007/s10457-013-9617-x (2013).
Banerjee, S. et al. Determinants of bacterial communities in Canadian agroforestry systems. Environ. Microbiolog. 18, 1805–1816. pmid:26184386. https://doi.org/10.1111/1462-2920.12986 (2016).
Liu, S., Sun, Y., Shi, F., Liu, Y., Wang, F., Dong, S., Li, M. Composition and diversity of soil microbial community associated with land use types in the agro–pastoral area in the upper yellow river basin. Front. Plant Sci. 13, 819661. https://doi.org/10.3389/fpls.2022.819661 (2022).
Prasad, R., Mertia, R.S. Dehydrogenase activity and VAM fungi in tree-rhizosphere of agroforestry systems in Indian arid zone. Agrofor. Syst. 63, 219–223. https://doi.org/10.1007/s10457-004-0536-8 (2005).
Chomel, M. et al. Plant secondary metabolites: a key driver of litter decomposition and soil nutrient cycling. J. Ecol.104, 1527–1541. https://doi.org/10.1111/1365-2745.12644 (2016).
Clivot, H. et al. Early effects of temperate agroforestry practices on soil organic matter and microbial enzyme activity. PSE, 453, 189-207. https://doi.org/10.1007/s11104-019-04320-6 (2020).
Woźniak, M., Grządziel, J., Gałązka, A., Frąc, M. Metagenomic analysis of bacterial and fungal community composition associated with Paulownia elongata× Paulownia fortunei. Bio Res., 14(4), 8511-8529 (2019).
Wang, D. et al. Bacterial and fungal diversity in the rhizosphere of buckwheat under different mulching techniques. Cur.Sci.123, 11, 1365-1371 (2022).
Castro, H. F., Classen, A. T., Austin, E. E., Norby, R. J., Schadt, C. W. Soil microbial community responses to multiple experimental climate change drivers,” AEM76(4), 999-1007. https://doi.org/10.1128/AEM.02874-09 (2010).
Gottel, N. R. et al. Distinct microbial communities within the endosphere and rhizosphere of Populus deltoides roots across contrasting soil types. AEM.77(17), 5934-5944. https://doi.org/10.1128/AEM.05255-11 (2011).
Challacombe, J. F. et al. Genomes and secretomes of Ascomycota fungi reveal diverse functions in plant biomass decomposition and pathogenesis. BMC genom. 20(1), 1-27. https://doi.org/10.1186/s12864-019-6358-x. (2019).
Zhang, K. et al. Mortierella elongata increases plant biomass among non-leguminous crop species. Agronom.10, 754. https://doi.org/ 10.3390/agronomy10050754 (2020).
Eroshin, V. K., Dedyukhina, E. G., Chistyakova, T. I., Zhelifonova, V. P., Kurtzman, C. P., and Bothast, R. J. Arachidonic-acid production by species of Mortierella. World J. Microbiol. Biotechnol. 12, 91–96 https://doi.org/10.1007/BF00327809 (1996).
Ha-Tran, D. M., Nguyen, T. T. M., Hung, S. H., Huang, E., Huang, C. C. Roles of plant growth-promoting rhizobacteria (PGPR) in stimulating salinity stress defense in plants: A review. Int. J. Mol. Sci. 22, 6, 3154. https://doi.org/10.3390/ijms22063154 (2021).
Ham, S. H., Yoon, A. R., Oh, H. E., Park, Y. G. Plant growth-promoting microorganism Pseudarthrobacter sp. NIBRBAC000502770 enhances the growth and flavonoid content of Geum aleppicum. Microorganisms10, 6, 1241. https://doi.org/10.3390/microorganisms10061241 (2022).
Peng, Z. et al. Maize intercropping enriches plant growth-promoting rhizobacteria and promotes both the growth and volatile oil concentration of Atractylodes lancea. Front. Plant Sci.13, 1029722. https://doi.org/10.3389/fpls.2022.1029722 (2022).
Luo, Y. et al. Complete genome sequence of Sphingomonas sp. Cra20, a drought resistant and plant growth promoting rhizobacteria. Genomics, 112(5), 3648-3657. https://doi.org/10.1016/j.ygeno.2020.04.013 (2020).
Cai, H., Yu, N., Liu, Y., Wei, X., Guo, C. Meta-analysis of fungal plant pathogen Fusarium oxysporum infection-related gene profiles using transcriptome datasets. Front. Microbiol.13, 970477. https://doi.org/10.3389/fmicb.2022.970477(2022).
Huss, C. P., Holmes, K. D., Blubaugh, C. K. Benefits and risks of intercropping for crop resilience and pest management. J. Econ. Entomol. 115, 5, 1350-1362. https://doi.org/10.1093/jee/toac045 (2022).
Lian, T., Mu, Y., Jin, J., Ma, Q., Cheng, Y., Cai, Z., Nian, H.. Impact of intercropping on the coupling between soil microbial community structure, activity, and nutrient-use efficiencies. Peer J, 7, e6412. https://doi.org/10.7717/peerj.6412 (2019).
Beule, L. et al. Poplar rows in temperate agroforestry croplands promote bacteria, fungi, and denitrification genes in soils. Front. Microbiol.,10, 3108. https://doi.org/10.3389/fmicb.2019.03108. (2020).
Lee K.-H., Jose S.. Soil Respiration and Microbial Biomass in a Pecan—Cotton Alley Cropping System in Southern USA. Agrofor. Syst. 58, 45–54. https://doi.org/10.1023/A:1025404019211 (2003).
Li, X., Ma, W., He, H., Wang, Z., Cao, Y. Effects of intercropping sugarcane and soybean on growth, rhizosphere soil microbes, nitrogen and phosphorus availability. Acta Physiol Plant 35, 1113–1119. https://doi.org/10.1007/s11738-012-1148-y (2013).
Wang, Y. et al. Effect of continuous cropping on the rhizosphere soil and growth of common buckwheat. Plant Prod. Sci. 23, 1, 81-90. https://doi.org/10.1080/1343943X.2019.1685895 (2019).
Li, F., Chen, L., Zhang, J., Yin, J., Huang, S.. Bacterial community structure after long-term organic and inorganic fertilization reveals important associations between soil nutrients and specific taxa involved in nutrient transformations. Front. Microbiol. 8, 187. https://doi.org/10.3389/fmicb.2017.00187 (2017).
Koper J., Piotrowska A., Urbanowski S. Changes of soil enzymatic activity caused by a long-term organic-mineral fertilization during plant vegetation. Zesz. Probl. Post. Nauk Rol. 465, 495–505 (1999).
Wan, F., Chen, P.. Soil enzyme activities under agroforestry systems in northern Jiangsu Province. For. Stud. China, 6, 21-26. https://doi.org/10.1007/s11632-004-0015-3 (2004).
Cederlund, H. et al. Soil carbon quality and nitrogen fertilization structure bacterial communities with predictable responses of major bacterial phyla. Appl. Soil Ecol.84, 62–68. https://doi.org/10.1016/j.apsoil.2014.06.003 (2014).
Bai, Y. C. et al. Intercropping walnut and tea: Effects on soil nutrients, enzyme activity, and microbial communities. Front. Microbiol.13, 852342. https://doi.org/10.3389/fmicb.2022.852342 (2022).
Staunton, S., Saby, N. P., Arrouays, D., Quiquampoix, H. Can soil properties and land use explain glomalin-related soil protein (GRSP) accumulation? A nationwide survey in France. Catena, 193, 104620 (2020).
Wang, Q. et al. Glomalin-related soil protein: The particle aggregation mechanism and its insight into coastal environment improvement. Ecotoxicol. Environ. Saf.227, 112940. https://doi.org/110.1016/j.ecoenv.2021.112940 (2021).
Lu, M. et al. Maize–soybean intercropping increases soil nutrient availability and aggregate stability. IJP. 1-16. https://doi.org/10.1007/s11104-023-06282-2 (2023).
Zhao, D. Q, Yuan, J. C., Hou, Y. T, Li, T., Liao, Y. C. Tempo-spatial dynamics of AMF under maize soybean intercropping. Chin. J. Eco-Agric. 28, 631–642. https://doi.org/10.13930/j.cnki.cjea.190720 (2020).
Augé, R. M. Arbuscular mycorrhizae and soil/plant water relations. Canadian J. Soil Sci., 84(4), 373-381 (2004).
Yengwe J. et al.Quantifying nutrient deposition and yield levels of maize (Zea mays) under Faidherbia albida agroforestry system in Zambia. Eur. J. Agron., 99, 148-155 https://doi.org/10.1016/j.eja.2018.07.004 (2018)
Lucas-Borja et al. Microbial activity in soils under fast-growing Paulownia (Paulownia elongata x fortunei) plantations in Mediterranean areas. Applied Soil Ecolog., 51, 42-51. (2011).
Dang, K., Gong X, Zhao, G., Wang, H., Ivanistau, A. Feng, B. Intercropping Alters the Soil Microbial Diversity and Community to Facilitate Nitrogen Assimilation: A Potential Mechanism for Increasing Proso Millet Grain Yield. Front. Microbiol.11, 601054. https://doi.org/10.3389/fmicb.2020.601054 (2020).
Gong, X., Ferdinand, U., Dang, K., Li, J., Chen, G., Luo, Y., Yang, P., Feng, B. Boosting proso millet yield by altering canopy light distribution in proso millet/mung bean intercropping systems. Crop J.8, 365–377. https://doi.org/10.1016/j.cj.2019.09.009. (2020).
Yin, K., He, Q. The spatial and temporal effects of paulownia intercropping: The case of northern China, Agrofor. Sys. https://doi.org/ 10.1023/A:1005837729528 (1997).
Chirko, C.P, Gold, M.A, Nguyen PV, Jiang, J.P. Influence of direction and distance from trees on wheat yield and photosynthetic photon density (Q_p) in a Paulownia and wheat intercropping system. Agric Forest Meteorol. 83, 171–180 (1996).
Li, F., Meng, P., Fu, D., Wang, B.. Light distribution, photosynthetic rate and yield in a Paulownia-wheat intercropping system in China. Agrofor. Sys. 74, 2, 163-172. https://doi.org/ 10.1007/s10457-008-9122-9 (2008).
Qiao, X., Sai, L., Chen, X., Xue, L., Lei, J. Impact of fruit-tree shade intensity on the growth, yield, and quality of intercropped wheat. PLOS ONE14(4), e0203238. https://doi.org/10.1371/journal.pone.0203238 (2019).
Shukla, P. R., Skeg, J., Buendia, E. C., Masson-Delmotte, V., Pörtner, H. O., Roberts, D. C., Zhai, P., Slade, R., Connors, S., van Diemen, R., Ferrat, M., Haughey, E., Luz, S., Neogi, S., Pathak, M., Petzold, J., Portugal Pereira, J., Vyas, P., Huntley, E., Kissick, K., Belkacemi, M. and Malley, J. Climate Change and Land: an IPCC special report on climate change, desertification, land degradation, sustainable land management, food security, and greenhouse gas fluxes in terrestrial ecosystems. [Online] Available at: https://www.ipcc.ch/srccl/cite-report/.(2019). [Accessed 15th March 2022].
Liszewski M., Response of buckwheat to sowing dates and rates depending on soil and weather conditions. Zesz. Nauk AR Wrocław, 316, 199-207 (1997).
Munkvold, G. P.. Epidemiology of Fusarium diseases and their mycotoxins in maize ears. Eur. J. Plant Pathol.109, 705-713 (2003).
Ma, L. J., Geiser, D. M., Proctor, R. H., Rooney, A. P., O'Donnell, K., Trail, F., Gardiner, D.M., Manners, J.M., Kazan, K. Fusarium pathogenomics. Annu. Rev. Microbiol. 67, 399-416. https://doi.org 10.1146/annurev-micro-092412-155650 (2013).
Rana, A., Sahgal, M., Johri, B. N.. Fusarium oxysporum: genomics, diversity and plant–host interaction. Develop. Fungal Biol. Applied Mycology, 159-199. https://doi.org/10.1007/978-981-10-4768-8_10 (2017).
Edel-Hermann, V., Lecomte, C. Current status of Fusarium oxysporum formae speciales and races. Phytopathol.109(4), 512-530. https://doi.org/10.1094/PHYTO-08-18-0320-RVW (2019).
Sajeena, A., Nair, D. S., Sreepavan, K. Non-pathogenic Fusrium oxysporum as a biocontrol agent. Indian Phytopathol. 73, 2, 1-7. https://doi.org/10.1007/s42360-020-00226-x (2020).
Özer, G., Göre, M. E., Alkan, M., Yaman, T., Dababat, A. A. First report of Exserohilum pedicellatum causing root rot of wheat in Azerbaijan. Plant Disease, 103,6, 1416-1416. https://doi.org/10.1094/PDIS-09-18-1678-PDN (2019).
Özer, G., Paulitz, T. C., Imren, M., Alkan, M., Muminjanov, H., Dababat, A. A. Identity and pathogenicity of fungi associated with crown and root rot of dryland winter wheat in Azerbaijan. Plant Disease, 104, 8, 2149-2157. https://doi.org/10.1094/PDIS-08-19-1799-RE (2020).

No competing interests reported.

Supplementarymaterials.doc

Download PDF

Editorial decision: Revision requested
08 Oct, 2024
Reviews received at journal
25 Sep, 2024
Reviews received at journal
24 Sep, 2024
Reviewers agreed at journal
08 Sep, 2024
Reviewers agreed at journal
03 Sep, 2024
Reviewers invited by journal
01 Sep, 2024
Editor assigned by journal
26 Aug, 2024
Editor invited by journal
25 Jun, 2024
Submission checks completed at journal
24 Jun, 2024
First submitted to journal
20 Jun, 2024

You are reading this latest preprint version

Effect of intercropping of paulownia and buckwheat on soil microbial biodiversity and enzymatic activity

Status:

Version 1

Abstract

Figures

Introduction

Materials and Methods

K = P / (0.1×T)

DNA extraction, PCR and Illumina amplicon sequencing

Bioinformatic analysis

Quantification of Cultivable Bacteria and Fungi

Soil dehydrogenase activity (DHA) and T-GRSP concentration

Principal component analysis of soil microbial properties

Results

Diversity and structure of microbiome and mycobiome

Quantification of Cultivable Bacteria and Fungi

Soil biological activity

Principal component analysis (PCA)

Discussion

Conclusions

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1