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Article

Effects of Intercropping of Sisal and Three Different Leguminous Plants on Soil Bacterial Diversity

by
Yanqiong Liang
1,
Shibei Tan
1,
Chunping He
1,
Rui Li
1,
Ying Lu
1,
Helong Chen
1,
Xing Huang
1,
Weihuai Wu
1,* and
Kexian Yi
2,3,4,*
1
Environment and Plant Protection Institute, Chinese Academy of Tropical Agricultural Sciences, Haikou 571101, China
2
Sanya Research Institute, Chinese Academy of Tropical Agricultural Sciences, Sanya 572025, China
3
Key Laboratory of Integrated Pest Management on Tropical Crops, Ministry of Agriculture and Rural Affairs, Haikou 571101, China
4
Hainan Key Laboratory for Monitoring and Control of Tropical Agricultural Pests, Haikou 571101, China
*
Authors to whom correspondence should be addressed.
Agronomy 2024, 14(10), 2381; https://doi.org/10.3390/agronomy14102381
Submission received: 29 July 2024 / Revised: 20 September 2024 / Accepted: 11 October 2024 / Published: 15 October 2024
(This article belongs to the Section Innovative Cropping Systems)
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Abstract

:
Intercropping is widely utilised in agricultural production to enhance land use efficiency because of its benefits, such as heightened crop productivity and optimised resource utilisation. We investigated the effects of Pinto peanut/sisal (HST), Stylo/sisal (strT) and Grona styracifolia/sisal (JqT) intercropping systems on soil bacterial communities compared with sisal continuous cropping (CK) by using Illumina MiSeq high-throughput sequencing technology. The intercropping system significantly increased the total nitrogen (TN), soil pH and soil moisture levels and decreased the levels of available phosphorus (AP) and available potassium (AK). Minimal variations were observed in Shannon’s and Simpson’s diversity indices between the monoculture and intercropping systems as well as among different intercropping systems. The most abundant phyla observed within the four groups were Proteobacteria, Acidobacteria, Cyanobacteria and Bacteroidetes. At the phylum level, the relative abundances of Proteobacteria, Acidobacteria, Cyanobacteria and Bacteroidetes were 37.37–54.35%, 10.54–21.21%, 3.46–20.43% and 2.15–5.67%, respectively. Compared with ZCK, StrT, JqT and HST treatments led to higher abundance of Cyanobacteria (from 3.46% to 20.43%, 11.37% and 16.58%, respectively) and Bacteroidetes (from 2.15% to 5.67%, 5.21% and 5.10%, respectively). The results of the linear discriminant analysis of effect sizes demonstrated notable variations in the relative abundance of bacterial taxa among various intercropping systems. The dominant categories of the genus in strT and JqT groups were Blastocatellia and Blastocatellaceae-Subgroup4, while Firmicutes was the dominant category of the genus in the HST group. The structure of bacterial communities did not vary between intercropping and monoculture systems. The findings indicated that the impact of the intercropping system on the bacterial community structure was not contingent on the specific intercropping patterns employed.

1. Introduction

Soil microbial communities make up a significant proportion of the Earth’s biodiversity and are involved in a wide range of ecosystem processes, including organic matter turnover, nutrient cycling, soil structure formation and plant productivity [1,2,3,4,5]. Soil microbial diversity and soil structure are adversely affected by prolonged monoculture crop succession. Diversified cropping is beneficial for soil microorganisms and increased productivity; intercropping represents a fundamental operation of traditional and sustainable agricultural practices and is always present in agro-ecosystems in different regions [6,7,8]. Intercropping is a highly efficient agricultural practice that involves simultaneously planting two or more species of crops in the same field, thereby increasing the quality and yield of the crop [9]. This planting pattern is a technical approach based on the ecological principles of promotion and complementarity [10]. In comparison with monoculture, intercropping can enhance the utilisation rate of soil fertility, temperature, light and water; reduce the incidence of weeds; and significantly improve soil microbial biomass and enzyme activity. Furthermore, intercropping systems can reduce plant diseases by promoting soil microbial diversity. A number of studies have also indicated that intercropping systems exert a significant influence on soil rhizosphere microbial communities [11,12,13].
Various management strategies can alter soil microbial community diversity, increase land use efficiency and improve farmland ecological diversity. Previous studies demonstrated the efficacy of intercropping in increasing yields, enriching the functional diversity of microbial communities, reducing pathogenic fungi, improving soil nutrient profiles and enhancing enzyme activities [7,14,15]. Tang et al. (2020) reported that intercropping with cassava and peanut can enhance soil quality by augmenting the available nitrogen content and the prevalence of DA101, Pilimelia and Ramlibacter in the soil [16]. He et al. (2023) found that maize/cassava intercropping system can provide stable food and income supply, soil quality and soil microbial community [17].
Pinto peanut (Arachis pintoi Krapov. & W. C. Greg.), Stylo (Stylosanthes guianensis (Aublet) Sw.) and Grona styracifolia (Desmodium styracifolium (Osbeck) Merr.) are important tropical legumes that are widely distributed in tropical and subtropical regions. China is mainly situated in southern tropical regions, such as Hainan, Guangdong and Guangxi [18,19,20]. In recent years, these crops have been widely planted in tropical ecological fruit and tea gardens, as well as in landscaping projects, in southern China. These legumes have been employed in a variety of contexts, including as soil improvement green manure, forage grass, green lawn, soil and water conservation and for other purposes. Their use has yielded positive economic and ecological benefits [19]. Agave sisalana (sisal) is a perennial monocotyledonous plant belonging to the Asparagaceae family, an economically significant fibre crop in tropical and subtropical regions. The raw materials are utilised to produce cellulose, automotive components and pharmaceuticals [21,22]. Agave hybrid 11648 is the most widely cultivated variety for the production of sisal fibre in China [23]. This variety has been cultivated for over seven decades, during which time a number of issues have arisen, including variety degradation, disease aggravation and so forth, which have a significant impact on the sisal industry. It is, therefore, imperative to develop techniques for the high-yield and high-efficiency cultivation of sisal. The objective of this study was to investigate the impact of diverse legume crops (Pinto peanut, Stylo, Grona styracifolia) intercropping with sisal through field experimentation. The soil nutrients of four distinct planting patterns of sisal were analysed using Illumina MiSeq high-throughput sequencing technology, and the microbial composition and structure of soil were evaluated. The findings of this study will provide a scientific foundation for the optimised cultivation of sisal.

2. Materials and Methods

2.1. Experimental Site and Design

The field experiment was conducted at Dongfanghong Farm in Leizhou City, Guangdong Province (110°5′ E, 20°32′ N). The region is characterised by a subtropical humid monsoon climate, with high levels of precipitation and sunshine. The climate is typified by mild temperatures with minimal fluctuations, high cumulative temperatures, an uneven distribution of precipitation and distinct wet and dry seasons. The annual average temperature is 22.3 °C to 23.9 °C, with an annual accumulated temperature above 10 °C of 8180 h. The occurrence of rain and heat is simultaneous, and the formation of frost is not observed throughout the year. The soil is classified as acrisol according to the current WRB classification. Four treatments were established, comprising monoculture of sisal plant (ZCK), sisal plant and pinto peanut intercropping (HST), sisal plant and Stylo intercropping (StrT) and sisal plant and Grona styracifolia intercropping (JqT), with ZCK serving as the control. Each treatment was replicated three times. The experimental sisal plantations were fertilised on two occasions per year, in March and November. The fertiliser employed were 750 kg ha−1 urea (CH4N2O) and 750 kg ha−1 synthetic nitrogen–phosphorus–potassium (NPK) fertiliser (N-P2O5-K2O = 15-15-15, 112.5 kg of N, 7.9 kg of P and 56.3 kg of K). Prior to fertilisation, an initial soil sample was collected and analysed. The chemical properties of the soil were as follows: the pH was 4.85, the organic matter content was 22.93 g kg−1, the total nitrogen (TN) was 1.70 g kg−1, the available phosphorus (AP) was 41.50 mg kg−1 and the available potassium (AK) was 247.22 mg kg−1.

2.2. Plant Materials

Sisal (Agave hybrid 11648) were planted in 2011, with a large row spacing of 4 m, a small row spacing of 1.2 m and a plant spacing of 0.9 m. The planting density was 4050 ha. Pinto peanut cultivar “Arachis pintoi cv. Reyan No.12”, Stylo cultivar “Stylosanthes guianensis cv. Reyan2” and Grona styracifolia cultivar “D. styracifolium” were used in this study.
Pinto peanut, Stylo and Grona styracifolia were planted at a large row spacing in proximity to the Sisal (Figure 1).

2.3. Soil Property Analysis

Soil samples were collected at the point of intersection between the root systems of sisal plants and intercrops. The surface soil cores (10–20 cm) were randomly collected from each sample plot by using a soil sampler through a five-point sampling method and then mixed to form a single soil sample. Sampling locations were selected to avoid sites where fertiliser had been applied. Three replicates were taken for each planting mode. The soil was stored in a sealed bag within an icebox at a temperature of 0 °C. The soil samples were divided into two groups. The first group was stored at −80 °C for subsequent DNA extraction, while the other group was stored at 4 °C for analysis of soil physicochemical property. The soil samples were allowed to naturally dry in the laboratory and then sieved through a 2 mm diameter mesh. Prior to analysis of the physicochemical properties, any stones, roots and large organic residues were removed from the samples. Soil pH was determined by a pH meter (PHS-3C, LeiCi, Shanghai, China), with a soil-water ratio of 1:2.5 (w/v). Total nitrogen (TN) content was determined using the Kjeldahl method, while soil organic carbon (SOC) was determined using the K2CrO7 oxidation titration method. Soil available phosphorus (AP) and available potassium (AK) were extracted by NH4F-HCl and NH4OAc, respectively, and measured using the molybdenum blue method and flame spectrophotometry method [24]. The soil moisture (SM) content was determined by the soil weight before and after constancy following drying at 105 °C for 48 h [25].

2.4. DNA Extraction, PCR Amplification and Sequencing

Total genomic soil DNA was extracted using CTAB method [26]. The integrity of total genomic DNA was verified by 1% agarose gel electrophoresis, and DNA purity and concentration were measured with a spectrophotometer [27]. The V4–V5 hypervariable regions of the bacterial 16S rRNA gene were amplified by the primer set 515F (5′-GTGYCAGCMGCCGCGGTAA-3′) and 926R (5′-CCGYCAATTYMTTTRAGTTT) [28]. The amplification of DNA samples was conducted using a USA (New England Biolabs (Beijing) Ltd., Beijing, China) and high-fidelity enzymes (New England Biolabs (Beijing) Ltd., Beijing, China). The thermal cycling conditions included 2 min of denaturation at 94 °C, 27 cycles of 30 s at 94 °C, 30 s at 55 °C and 45 s at 72 °C, with a final extension at 72 °C for 10 min [29]. The PCR products were mixed in aliquots according to their concentration, analysed on 2% agarose gels and purified using a MinElute Gel Extraction Kit (QIAGEN, Dusseldorf, Germany). A cDNA library was prepared using a TruSeq® DNA Sample Preparation Kit (Illumina, San Diego, CA, USA)and quantified using Qubit and Q-PCR. The qualified libraries were sequenced with the Illumina MiSeq platform.

2.5. Bioinformatics Analysis

The sequencing reads were assigned to each sample according to the unique 6 bp barcode of each sample. The generation of high-quality clean data was facilitated by the utilisation of Cutadapt version 1.12 and Trimmomatic version 0.39 software. The tag sequences were obtained by FLASH software with merge both end of the reads [30]. Operational taxonomic units (OTUs) were clustered based on 97% sequence similarity by UPARSE version 9.2.64,, and the most abundant sequences within each OUT were chosen as the representative sequences. The OTUs were classified using the Ribosomal Database Project (RDP) classifier (Release 11.4) and the SILVA (V132) database [31,32,33]. Alpha diversity was calculated by Chao1, ACE, Shannon, Simpson and coverage indices using Quantitative Insights Into Microbial Ecology (QIIME, V1.9.1). Taxonomic beta diversity was determined by principal coordinate analysis (PCoA) and non-metric multidimensional scaling (NMDS) based on the Bray–Curtis distance, which accounted for clustering of different samples and reflected the microbial community structure. The linear discriminant analysis (LDA) effect size (LefSe) method was employed to ensure the selection of the most abundant bacterial genera that were found to be significantly associated with monoculture and intercropping systems. The biomarker features of each group were subjected to analysis using LEfSe software (https://huttenhower.sph.harvard.edu/galaxy/) (accessed on 21 March 2021)).
The data pertaining to soil physical and chemical properties, bacterial total abundances, alpha and beta diversity indices and bacterial taxa (phyla and genera) in monoculture and intercropping systems were subjected to a Student’s t-test for comparison. All analyses were conducted using SPSS software version 20.0 (SPSS Inc., Chicago, IL, USA).

3. Results

3.1. Effect of Intercropping on Soil Properties

Compared with the monoculture system, the intercropping system demonstrated a notable increase in total nitrogen (TN), soil pH and soil moisture, while exhibiting a significant reduction in available phosphorus (AP) and available potassium (AK) (Table 1) (p < 0.05). The pH value, total nitrogen and soil moisture content of the intercropping system (HST, JqT and StrT) were higher than those of the monoculture system (ZCK). In the intercropping system, the pH value of HST was observed to be the highest, while the water content of Str was the highest. There were no significant differences in the total nitrogen (TN) and soil pH between the StrT and JqT intercropping systems. The soil organic carbon (SOC) contents of the monoculture and intercropped Stylo (StrT) were found to be significantly higher than those of the intercropped Pinto peanut (HST) and intercropped Grona styracifolia (JqT).

3.2. Alpha Diversity

The amplicons of 12 rhizosphere soil samples were sequenced using an Illumina MiSeq sequencer, resulting in the acquisition of 1,206,210 high-quality reads targeting the bacterial 16S rRNA V4–V5 region. The reads were grouped into 1157 operational taxonomic units (OTUs). Compared with monoculture sisal, the number of bacterial OTUs in the intercropped sisal decreased (Figure 2A). Alpha diversity represents the abundance and diversity of bacteria in a particular region or ecosystem, as reflected in the number of species and the uniformity of individual distributions within species. Shannon’s and Simpson’s indices are commonly used to estimate microbial diversity and are presented to reflect alpha diversity indices. The alpha diversity indices are shown in Figure 2. Shannon’s diversity values were found to be reduced in the following order: JqT > ZCK > StrT > HST. Simpson’s diversity values exhibited a decreasing trend in the following order: HST > StrT > JqT > ZCK. There were no statistically significant differences between the Shannon and Simpson diversity indices in monoculture and intercropping systems. Furthermore, no statistically significant differences were observed within the intercropping systems. Figure 2D showed that there were no significant differences in soil coverage among the samples. The soil microbial community coverage was found to be in excess of 99%, thereby indicating that the current sequencing depth is sufficient to capture the diversity of the soil microbial community.

3.3. Beta Diversity Analysis of Bacteria

According to OTUs, species composition and community structure can be determined at each taxonomic level. Differences in bacterial communities among the four intercropped types were evaluated using principal coordinate analysis (PCoA) based on Bray–Curtis distances. The PCoA results demonstrated that the first two axes collectively explained 26.96% and 17.09% of the total variation in the bacterial community (Figure 3). Hence, samples from monoculture and intercropping systems were not distinguishable from one another.
Hierarchical clustering analysis was performed using the Bray–Curtis dissimilarity matrix to quantify the differences in the overall community structure. The nonmetric multidimensional scaling analysis (NMDS) plot shows differences in microbial community structure, with samples that are spatially closer to the plot showing greater similarity. Figure 4 shows that the triplicates were not closely aligned within the same treatment among the four growth regimes, in addition to the presence of significant differences in soil bacterial communities. The results of the NMDS analysis demonstrated that the distance between samples increased, indicating that the diversity of microbial communities significantly differed.

3.4. Soil Bacterial Community Composition

The taxonomic distribution at the phylum level is shown in Figure 5A. Ten phyla groups with an average relative abundance of soil bacterial community >1% in all samples were found: Proteobacteria, Acidobacteria, Cyanobacteria, Bacteroidetes, Chloroflexi, Actinobacteria, Firmicutes, Planctomycetes, Deinococcus−Thermus and Bacteria_unclassified, which were the same across treatments. In addition, Armatimonadetes, Nitrospirae, Gemmatimonadetes, Omnitrophica, Parcubacteria, Elusimicrobia and Chlorobi were detected at relatively low abundances (relative abundance < 1%). The dominant phyla across all soil samples were Proteobacteria, Acidobacteria, Cyanobacteria and Bacteroidetes, with absolute abundances ranging from 37.37% to 54.35%, 10.54% to 21.21%, 3.46% to 20.43% and 2.15% to 5.67%, respectively. In comparison with the ZCK treatment, the relative abundance of Cyanobacteria in the StrT, JqT and HST treatments exhibited a notable increase, ranging from 3.46% to 20.43%, 11.37% and 10.77%, respectively. Furthermore, the relative abundance of Bacteroidetes exhibited a decline, from 6.73% to 5.67%, 5.21% and 5.10%. The relative abundance of Deinococcus-Thermus exhibited a decline, from 2.76% to 1.81%, 1.21% and 1.26%, while the relative abundance of Chloroflexi demonstrated a similar trend, decreasing from 5.05% to 3.21%, 4.67% and 2.83%.
The richest genera in the different intercropped soils were Escherichia-Shigella, Chloroplast_norank, Acidobacteriaceae (Subgroup1)_uncultured, Ochrobactrum, Bryobacter, Mitochondria_norank, Comamonadaceae_unclassified, Thermus, Bacteria_unclassified and Aquicella (Figure 5B). The relative abundance of Proteobacteria and Escherichia-Shigella in the JqT and StrT treatments was found to be lower than that observed in the ZCK treatment. The most prevalent genera are Escherichia-Shigella, Chloroplast norank, Acidobacteriaceae (Subgroup 1), uncultured, Ochrobactrum and Bryobacter, collectively accounting for over 40% of all genera. Compared with ZCK, the relative abundance of chloroplast norank in the StrT, JqT and HST treatments significantly increased, from 2.49% to 18.91%, 11.06% and 15.93%, respectively. Conversely, the relative abundance of Bryobacter markedly decreased, from 5.40% to 3.25%, 3.82% and 1.76%, respectively.
Proteobacteria and Acidobacteria were the first and second most abundant phyla, which account for 43.73% and 3.24% of the total valid reads in all the samples, respectively (Figure 6). The remaining bacterial reads were attributed to phyla Deinococcus-Thermus and Nitrospirae, which collectively accounted for 1.77% and 0.918% of the total bacterial reads, respectively. The four phyla account for more than 49% of the bacterial reads.
The phylogenetic tree showed that the absolute abundance of the bacterial communities in the three intercropped soil samples was significantly different from that in the monoculture at all taxonomic levels (Figure 6). The microbial taxonomic composition of the intercropped and monoculture systems changed only slightly, but the relative abundance significantly changed. The changing trends of soil bacteria at the genus level were similar to those at the phylum and class levels.
Figure 7A shows the effect of intercropping on the 30 most abundant phyla of soil bacterial communities. The relative abundance of Gemmatimonadetes, Armatimonadetes and Nitrospirae were increased, while that of Aquificae, Saccharibacteria, TM6 (Dependentiae), Chlamydiae, Microgenomates, Spirochaetae, Peregrinibacteria, BP4, Fusobacteria and Ignavibacteriae were decreased. Significant differences in absolute abundance were found between intercropping and monoculture systems. A taxonomic classification of the data revealed that 406 bacterial genera were identified at the genus level in the study. Among the top 10 classified bacterial genera, the relative abundances of Bryobacter, Cytophagaceae_uncultured, Ktedonobacterales_unclassified and Pseudomonas were lower (p < 0.05), while the relative abundance of Chloroplast_norank was increased (p < 0.05).

3.5. Differential Abundance of Microbial Taxa between Groups

The LEfSe analysis has been used to identify the most important taxa that contribute to the differences between communities. The histogram of the linear discriminant analysis score (Figure 8A) showed that 11 genera of Blastocatellaceae_Subgroup4_, Blastocatellia, Blastocatellales, RB41, Acidimicrobiia, Acidimicrobiales, XanthomonadalesIncertaeSedis, Brevibacterium, Bacillus, Acidimicrobiaceae and Brevibacteriaceae were more abundant in the intercropped JqT system, and Tatumella, Chthonomonadales and Chthonomonadetes were abundant in the ZCK system. Among them, Blastocatellaceae_Subgroup4 was the most dominant genus in the JqT system, while Tatumella was the most dominant in the ZCK system (Figure 8B). The more abundant genera in the ZCK system and intercropped Stylo (StrT) were RhizobialesIncertaeSedis, Rhizomicrobium, Parcubacteria, Xanthomonadaceae, Tatumella, Brevundimonas, Corynebacteriales, Dietzia, Dietziaceae and Blastocatellia, Blastocatellaceae_Subgroup4, RB41, Blastocatellales, Holophagae, Brevibacteriaceae, Brevibacterium, Asticcacaulis C0119, respectively (Figure 8C,D).
Three genera of Firmicutes, Propionibacteriales and Nocardioidaceae were more abundant in the intercropped HST system, which the hyper-dominant genus was Firmicutes. Rhizomicrobium, RhizobialesIncertaeSedis, Parcubacteria, Chthonomonadetes, Aquicella, Chthonomonadales, Tatumella, Dietziaceae and Dietzia were more abundant in the ZCK system, and the most hyper-dominant genus was Rhizomicrobium (Figure 8E,F).
These results showed that the bacterial community associated with intercropping systems significantly differed from that associated with monocropping systems. As LEfSe showed, the relative abundance of bacterial taxa significantly varied among different intercropping systems.

4. Discussion

Agave sisalana (sisal), a perennial plant, is grown to extract hard fibre from its leaves; the fibre is used to make ropes, baskets, rugs and reinforcing composite materials [34]. The sisal industry in China is beset by a number of significant challenges, including monospecific planting, severe species degradation, severe pests and diseases, inappropriate fertilisation and soil nutrient depletion. The main variety H•11648 has been cultivated for over 70 years and is currently experiencing challenges associated with species degradation and the exacerbation of disease. It is imperative that the cultivation of sisal be optimised in terms of both high yields and high efficiency prior to the emergence of superior sisal varieties. In agroecosystems, many studies have been conducted on the effects of different cropping patterns (monocropping and intercropping) and plant species (such as legumes) on root-associated microbial communities [35]. Intercropping has become increasingly popular in the Americas, Asia, Africa and Europe. It plays an important role in maintaining biodiversity and stability of agricultural ecosystems, thereby improving resource utilisation efficiency and achieving high and stable yields in agricultural ecosystems [36]. At present, there has been a paucity of research examining soil microorganisms in sisal planting fields in China. The available evidence indicates that the application of organic and inorganic fertilisers exerts a more pronounced influence on soil bacteria, with a lesser but still discernible impact on fungal communities. Moreover, there has been no systematic monitoring and research on soil microbial community diversity after planting. The objective was to determine the influence of agricultural technical measures on the soil nutrients and soil bacterial communities of sisal, with a view to identifying the optimal cultivation technology for high yields and providing a theoretical basis for large-scale promotion and application in sisal planting.
Many studies have indicated that soil bacterial diversity may be influenced by a range of factors, including soil fertility, vegetation, planting methods, crop types and plant age [37,38]. The composition of soil microbiota was closely related to changes in soil chemical properties [39]. The physical and chemical properties of soil were frequently regarded as pivotal indicators of soil quality, exerting a profound influence on plant and microbial growth [40]. Several reports have shown that intercropping systems improve soil physicochemical properties, soil carbon and nitrogen, soil bulk density and pH [41,42,43]. The current study found that intercropped systems significantly changed the physical and chemical properties of the perennially cropped soil, including soil moisture, pH, AK, AP and TN [44,45]. In this study, rhizospheric soils in intercropping systems had higher soil pH and TN than those in the monoculture system (Table 1). The HST and StrT intercropping systems did not significantly change the SOC, while the JqT intercropping system had significantly more changes than the monoculture system. The presence of leguminous plants in the soil resulted in a reduction in the levels of nutrients available to the sisal crop, leading to a decline in the overall soil nutrient content when compared with monoculture sisal cultivation. The soil nutrient contents exhibited notable variation across the intercropping soil regimes, namely HST, StrT and JqT. For instance, the concentrations of TN, AK and AP were found to be significantly higher in HST in comparison with StrT and JqT (Table 1). This phenomenon may be attributed to the inconsistent competition for soil nutrients by the diverse range of crops. The biomass plant of stylo and sisal was lower in intercropping. This finding was also reported by Giagnoni et al. (2022) [46], who stated that crop production reduces soil organic matter. Li et al. (2022) [43] observed that the concentrations of AP and AK were significantly higher in the intercropping system of soybean and corn than in the monoculture system. Another important finding is that soil AP and AK contents significantly decreased under intercropping systems, which may be caused by competitive interactions between neighbouring plants. These data are consistent with reports of soils from different agricultural ecosystems [2,42]. In general, compared with monocropping, intercropping can change soil nutrient composition. This finding may be associated with the specific crop planted. Sisal is a long-term crop, whereas the literature reports the results of short-term crops.
Additionally, the soil moisture levels in the intercropping system were significantly higher than those observed in the monoculture system (Table 1). In the same sisal garden, the volumetric weight of the soil remains relatively constant, thereby allowing the soil water content to serve as a reliable indicator of the soil’s water retention performance. In sisal gardens with severe seasonal drought, the introduction of leguminous plants can enhance soil moisture content and sustain it throughout the dry period. During the rainfall period, these plants can prevent soil erosion, which is conducive to the growth of sisal.
Alpha diversity measures the diversity of the microbial community in a single sample, taking into account the number of different taxa and their relative abundances. Shannon’s index was used to characterise the richness of soil microbial communities, whereas Simpson’s index was employed to assess the dominance of soil microbial communities. Previous studies reported conflicting results on the effects of crop management on microbial community abundance and diversity. Correa-Galeote et al. (2016) [47] suggested that maize/bur clover-cultivated intercropping reduced soil microbial diversity, while Li et al. (2022) [48] showed that the intercropping of mulberry with groundnut improved the diversity and richness of soil microorganisms. Hence, soil characteristics indirectly influence soil microorganism community structure. In contrast to these reports, no significant differences in bacterial communities were found between intercropping and monoculture systems (Figure 2), which was similar to the results of previous studies on wheat–pea by Pivato et al. (2021) [49] and sugarcane–soybean by Yu et al. (2021) [50]. Hence, planting patterns had no significant effect on bacterial richness and diversity. These results should be validated by conducting long-term tests in the future. The majority of studies have demonstrated that increased biodiversity enhances ecosystem function. However, in comparison with monoculture, the diversity and richness of soil bacterial communities in intercropping systems exhibit only modest fluctuations, and the composition of soil bacterial communities under varying treatments displays minimal variation (Figure 5, Figure 6 and Figure 7). It is possible that the discrepancy between our research and previous studies may originate from the fact that the former utilised rhizosphere soil to assess soil bacterial community diversity, whereas our approach involved the collection of soil samples between the root zone and the interconnection region between the two crops.
Soil microorganisms play a crucial role in ensuring soil quality and function, participating in organic matter dynamics, nutrient cycling and suppressing or regulating soil-borne diseases in agroecosystems [51,52,53]. The main taxonomic groups in soils investigated by previous researchers were Proteobacteria, Actinobacteria, Acidobacteria, Firmicutes, Gemmatimonadetes, Planctomycetes, Chloroflexi and Bacteroidetes, which are all common soil inhabitants [17,54]. In this study, the dominant bacterial phyla in the group samples were Proteobacteria, Acidobacteria, Cyanobacteria, Bacteroidetes, Chloroflexi, Actinobacteria, Firmicutes, Planctomycetes and Deinococcus-Thermus (Figure 5). These findings are in accordance with the results of previous studies investigating agricultural soils. Proteobacteria was the most abundant phylum in both the intercropping systems and the monoculture system. Furthermore, a significantly higher abundance of Cyanobacteria was observed in the intercropping systems. Conversely, Bacteroidetes, Chloroflexi and Deinococcus-Thermus were found to be much less abundant in the intercropping systems than in the monoculture system. As reported by Pardon et al. (2017) [55], the Proteobacteria and Bacteroidetes phyla are closely linked to C and N cycling. The intercropping system may enhance soil carbon and nitrogen accumulation and nutrient use efficiency by stimulating the growth of bacteria that are closely associated with nitrogen fixation or other carbon and nitrogen processes. Acidobacteria are acidophilic bacteria that are widely distributed in soil. A significant correlation has been demonstrated between Acidobacteria and soil pH in a number of studies [56,57]. In our research, the relative abundance of Acidobacteria was found to be unaltered in the intercropping group. However, a notable shift in soil pH was observed. These discrepancies may be attributed to the disparate responses of subpopulations of acid bacilli or the varying acid bacilli within the same subpopulations in response to soil pH.
Intercropping can promote the accumulation of beneficial bacteria, thereby resisting pathogen infection [58]. Plant-beneficial microorganisms that have been found in previous studies include Pseudomonas and Sphingomonas. Pseudomonas spp. exert inhibitory effects against pathogens, such as Ralstonia solanacearum, Meloidogyne spp., Phlytophthora infestans, Streptomyces scabies and Verticillium dahlia, which exhibits antibiotics, antifungal and growth promotion properties [59,60,61,62]. Sphingomonas was identified to be related to the solubilisation of phosphorus and potassium [15,63]. Actinobacteria are of significant importance in the context of plant rhizosphere soil, playing a pivotal role in the promotion of plant growth and the management of plant diseases [64]. In the study, Pseudomonas and Sphingomonas were in the top 10 dominant genera in all systems (Figure 7). In consideration of the aforementioned outcomes, it is plausible that favourable modifications to soil, coupled with intercropping, might enhance the resilience of sisal root systems against soil-borne pathogens.
The effects of different intercropping patterns on the composition of the soil microbial communities were determined using the linear discriminant analysis of effect sizes (LEfSe). The results of the LEfSe analysis demonstrate a significant association between microorganisms and the various planting systems. In comparison with monoculture, there were 9, 11 and 3 distinct microbial species that demonstrated a statistically significant correlation with strT, JqT and HST intercropping, respectively. It is noteworthy that Blastocatellia and Blastocatellaceae-Subgroup4 were the predominant microorganisms in the StrT and JqT treatment groups, while Firmicutes was the dominant microorganisms in the HST treatment group (Figure 8). The extent to which these changes were contingent on specific intercropping patterns remains to be elucidated.
Based on the above discussion, limited changes were found in the diversity and richness of soil bacterial communities in the sisal intercropping system in comparison with monocultures. These findings indicate that while intercropping may influence the abundance of specific microbial populations in the soil, it does not appear to affect the diversity of microbial communities. Further investigation is required to confirm these findings over an extended period.

5. Conclusions

In summary, we investigated the impact of different types of legumes as intercropping plants in sisal. TN, soil pH and soil moisture were significantly higher in intercropping than in sisal monoculture. The impact of three intercropping crops on the structure of the soil microbial community was not statistically significant. The findings indicated that the impact of intercropping on the soil microbial community was indirectly influenced by environmental factors within the soil. In the future, in addition to significantly optimising the relative abundance of soil microbial communities in intercropping systems, further long-term analysis of these systems is required to strengthen the discovery of benign interactions between microbial communities of intercropping crops, including microbial communities of intercropping crops themselves. The objective is to investigate the impact of intercropping plants on root exudates and to identify the most beneficial intercropping patterns for farmers, with a view to advancing sustainable agriculture.

Author Contributions

Conceptualization, Y.L. (Yanqiong Liang), W.W. and K.Y.; resources R.L.; investigation, S.T., R.L., Y.L. (Ying Lu), H.C. and X.H.; validation, Y.L. (Yanqiong Liang); formal analysis, X.H. and Y.L. (Ying Lu); data curation, C.H., H.C. and Y.L. (Ying Lu); writing—original draft, Y.L. (Yanqiong Liang); writing—review and editing, Y.L. (Yanqiong Liang), W.W. and K.Y.; supervision, S.T., C.H. and K.Y.; project administration, S.T. and W.W.; funding acquisition, Y.L. (Yanqiong Liang), S.T. and K.Y. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by The Natural Science Foundation of Hainan Province (322QN367, 321MS073) and The National Special Fund for the Construction of Technical System of Hemp Industry (No. CARS-16-E16).

Data Availability Statement

The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Intercropping and monoculture of sisal plant. (A) Monoculture of sisal plant. (B) Pinto peanut and sisal plant. (C) Stylo and sisal plant. (D) Grona styracifolia and sisal plant.
Figure 1. Intercropping and monoculture of sisal plant. (A) Monoculture of sisal plant. (B) Pinto peanut and sisal plant. (C) Stylo and sisal plant. (D) Grona styracifolia and sisal plant.
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Figure 2. Alpha diversity of bacterial communities. Presence of different letters indicates that there are statistically significant differences between the groups (p < 0.05; Student’s t-test). (A) OTUs; (B) Simpson’s index; (C) Shannon’s index; and (D) coverage.
Figure 2. Alpha diversity of bacterial communities. Presence of different letters indicates that there are statistically significant differences between the groups (p < 0.05; Student’s t-test). (A) OTUs; (B) Simpson’s index; (C) Shannon’s index; and (D) coverage.
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Figure 3. Principal coordinate analysis (PCoA) plots based on the Bray–Curtis distance demonstrated the differentiation between the soil bacterial communities of the four intercropping systems.
Figure 3. Principal coordinate analysis (PCoA) plots based on the Bray–Curtis distance demonstrated the differentiation between the soil bacterial communities of the four intercropping systems.
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Figure 4. Nonmetric multidimensional scaling (NMDS) approach was employed to analyse the Bray–Curtis distance plot of all soil bacterial communities.
Figure 4. Nonmetric multidimensional scaling (NMDS) approach was employed to analyse the Bray–Curtis distance plot of all soil bacterial communities.
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Figure 5. The relative abundance of the microbial community at the phylum (A) and genus (B) level among all groups of samples. Different colours represent different species.
Figure 5. The relative abundance of the microbial community at the phylum (A) and genus (B) level among all groups of samples. Different colours represent different species.
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Figure 6. Phylogenetic trees of intercropping and monoculture groups. The phylogenetic tree goes from the leftmost root node to the rightmost leaf node, with each layer representing a taxonomic level from the boundary to species level. For each level of species, pie charts are used to represent the proportion of species in each sample (proportion > 1%), and different colours represent different samples. The larger the sector area of a colour is, the larger the sequence number will be. The number under the circle, the first representation is only compared to the classification; the second number represents how many sequences are aligned to the classification.
Figure 6. Phylogenetic trees of intercropping and monoculture groups. The phylogenetic tree goes from the leftmost root node to the rightmost leaf node, with each layer representing a taxonomic level from the boundary to species level. For each level of species, pie charts are used to represent the proportion of species in each sample (proportion > 1%), and different colours represent different samples. The larger the sector area of a colour is, the larger the sequence number will be. The number under the circle, the first representation is only compared to the classification; the second number represents how many sequences are aligned to the classification.
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Figure 7. Heat map of soil different bacterial composition at the phylum (A) and genus (B) level among all groups of samples. Significant differences in relative abundances were found; the relative abundance of soil microbial community from high to low is represented by red through yellow to blue. Relative abundance of the most dominant bacterial phyla (C) and genus (D) level in ZCK, StrT, JqT and HST. Different lowercase letters on each bar indicate the least significant differences (p < 0.05) among ZCK, StrT, JqT and HST.
Figure 7. Heat map of soil different bacterial composition at the phylum (A) and genus (B) level among all groups of samples. Significant differences in relative abundances were found; the relative abundance of soil microbial community from high to low is represented by red through yellow to blue. Relative abundance of the most dominant bacterial phyla (C) and genus (D) level in ZCK, StrT, JqT and HST. Different lowercase letters on each bar indicate the least significant differences (p < 0.05) among ZCK, StrT, JqT and HST.
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Figure 8. (A,C,E) LDA scores of biomarker bacteria. (B,D,F) Bacterial core community composition. The histogram of linear regression analysis score calculated for the bacterial species with different abundances in intercropping and monoculture system. Red and green bars indicate enriched taxa in the intercropping and ZCK groups, respectively.
Figure 8. (A,C,E) LDA scores of biomarker bacteria. (B,D,F) Bacterial core community composition. The histogram of linear regression analysis score calculated for the bacterial species with different abundances in intercropping and monoculture system. Red and green bars indicate enriched taxa in the intercropping and ZCK groups, respectively.
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Table 1. Soil properties.
Table 1. Soil properties.
TreatmentsSoil pHSOC
(g kg−1)		TN
(g kg−1)		AP
(mg kg−1)		AK
(mg kg−1)		SM
(%)
ZCK5.48 ± 0.03 c28.83 ± 1.85 a*1.18 ± 0.01 c64.82 ± 1.56 a309.63 ± 12.78 a18.21 ± 1.23 c
HST6.02 ± 0.03 a27.05 ± 1.71 ab1.45 ± 0.01 a60.20 ± 4.55 b273.29 ± 14.23 b24.52 ± 2.78 b
StrT5.74 ± 0.06 b29.22 ± 3.04 a*1.26 ± 0.02 b45.36 ± 3.12 c256.96 ± 12.12 bc26.88 ± 2.33 a
JqT5.77 ± 0.04 b24.5 ± 2.36 b*1.24 ± 0.03 b42.36 ± 2.69 c254.23 ± 10.23 bc21.71 ± 1.06 b
Different letters in the same column represent significant difference (Student’s t-test, * p < 0.05, n = 3). ZCK, monoculture of sisal plant; HST, sisal plant and Pinto peanut intercropping; StrT, sisal plant and Stylo intercropping; JqT, sisal plant and Grona styracifolia intercropping; SOC, soil organic carbon; TN, total nitrogen; AN, available nitrogen; AP, available phosphorus; AK, available potassium; SM, soil moisture.
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Liang, Y.; Tan, S.; He, C.; Li, R.; Lu, Y.; Chen, H.; Huang, X.; Wu, W.; Yi, K. Effects of Intercropping of Sisal and Three Different Leguminous Plants on Soil Bacterial Diversity. Agronomy 2024, 14, 2381. https://doi.org/10.3390/agronomy14102381

AMA Style

Liang Y, Tan S, He C, Li R, Lu Y, Chen H, Huang X, Wu W, Yi K. Effects of Intercropping of Sisal and Three Different Leguminous Plants on Soil Bacterial Diversity. Agronomy. 2024; 14(10):2381. https://doi.org/10.3390/agronomy14102381

Chicago/Turabian Style

Liang, Yanqiong, Shibei Tan, Chunping He, Rui Li, Ying Lu, Helong Chen, Xing Huang, Weihuai Wu, and Kexian Yi. 2024. "Effects of Intercropping of Sisal and Three Different Leguminous Plants on Soil Bacterial Diversity" Agronomy 14, no. 10: 2381. https://doi.org/10.3390/agronomy14102381

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