Intercropping in Coconut Plantations Regulate Soil Characteristics by Microbial Communities

Abstract

Intercropping is a commonly employed agricultural technique that offers numerous advantages, such as increasing land productivity, enhancing soil health, and controlling soil-borne pathogens. In this study, Artemisia argyi, Dioscorea esculenta, and Arachis pintoi were intercropped with coconuts and compared with naturally growing weeds (Bidens pilosa), respectively. The regulatory mechanism of intercropping was examined by analyzing the variability in soil properties and microbial community structure across different intercropping modes and soil depths (0–20 cm, 20–40 cm, and 40–60 cm). The results indicate that intercropping can increase the diversity of soil bacteria and fungi. Moreover, as soil depth increases, the changes in microbial communities weaken. Intercropping reduced soil SOM and increased pH, which is directly related to the changes in the abundance of Acidobacteria in the soil. In various intercropping systems, the disparities resulting from intercropping with A. pintoi are particularly pronounced. Specifically, intercropping with A. pintoi leads to an increase in soil potassium and phosphorus levels, as well as an enhancement in the abundance of Bacillus sp., which plays a crucial role in the suppression of plant pathogenic fungi within the soil ecosystem. The results of the correlation analysis and structural equation modeling (SEM) suggest that the impacts of three intercropping systems on microbial composition and soil indicators exhibit considerable variation. However, a common critical factor influencing these effects is the soil phosphorus content. Furthermore, our findings indicate that intercropping resulted in lower soil nitrogen levels, exacerbating nitrogen deficiency and masking its impact on the microbial community composition.

1. Introduction

Cocos nucifera L., a monocot tree that belongs to the Palmaceae family, is an important woody oil crop in the tropical region. Coconut holds significant economic value due to its potential for processing into various products such as coconut oil, juice, composite plates, and other sustainable materials [1,2,3,4]. The global coconut cultivation area is about 180 million acres, which is mainly concentrated in Southeast Asia. China has been investing heavily in the coconut industry as a specialty economic crop in Hainan Province in recent years. Despite the attention, the development of the coconut industry is still facing many challenges. Coconuts are characterized by their low planting density and lengthy cycle from seedling to fruiting, leading to limited economic returns per unit of land in coconut plantations [5]. Consequently, enhancing the overall economic productivity of coconut plantations is a significant scientific concern that is crucial for advancing the coconut industry.

Intercropping within the understory is an important practice for ecologically intensifying agriculture. Intercropping results in improved crop yield efficiency and fosters greater plant diversity in arable crop systems, thereby contributing to ecological equilibrium [6,7], enhancing the physicochemical characteristics of soil and boosting soil fertility [8,9]. Soil microorganisms represent the most dynamic life forms within the soil ecosystem, playing crucial roles in various essential processes necessary for the functioning of ecosystems. Several studies have demonstrated the significance of soil microbial community diversity and composition in maintaining soil health and productivity [10,11]. In maize farming, intercropping peanuts enhances soil enzyme activity and nutrient availability by influencing specific soil microorganisms [12]. Similarly, in peanut–tea intercropping, increased levels of P and K in the soil are correlated with higher soil enzyme activity [13]. In the tomato/potato–onion intercropping system, alterations in the rhizosphere microbial population suppress soil-borne pathogens and enhance P absorption [14,15].

A study focusing on coconut intercropping revealed that incorporating Stylosanthes guianensis can enhance soil total nitrogen levels [16], potentially attributed to alterations in soil microbial composition [17]. Nevertheless, there is limited research on the influence of intercropping on soil microbial populations within coconut plantations [18]. Therefore, this research will focus on three crops (Artemisia argyi, Dioscorea esculenta, and Arachis pintoi) and naturally occurring Bidens Pilosa (CK) interrupted within coconut forests. The four plant types have economic value, adaptability, and rapid growth and can cover the ground to reduce soil moisture loss. We aimed to investigate the interaction between microbial communities and soil properties across various cropping systems and depths (0–20 cm, 20–40 cm, and 40–60 cm) in order to clarify the mechanisms of their impact on coconut orchard soil and provide a theoretical basis for the development of the coconut understory economy.

2. Materials and Methods

2.1. Intercropping Treatment and Sample Collection

This study was carried out at the four-team station of the Coconut Research Institute of the Chinese Academy of Tropical Agricultural Sciences (19°31′27″ N, 110°45′20″ E) in Wenchang City, Hainan Province. The region has a tropical monsoon climate with a distinct rainy season (May–October) and a dry season (November–April). The annual average temperature is 23.9 °C, and the annual average precipitation is 1800 mm. The soil is classified as sandy loam, with rapid loss of water and nutrients. Without intercropping, Bidens pilosa, an annual herbaceous invasive species, will quickly dominate the coconut understory.

The intercropping experiments were conducted in artificial coconut forests planted in wide and narrow row cultivation, with 10 m wide row spacing, 4 m narrow row spacing and 4 m plant spacing (Figure 1). The coconuts were planted as seedlings in 2019, and three crops, Artemisia argyi, Dioscorea esculenta, and Arachis pintoi were intercropped using cuttings in October 2020. The area with naturally growing weeds (Bidens pilosa) served as the control. Each treatment was set up with 3 repeated experimental plots, totaling 12 experimental plots. Each plot encompassed an area of 120 m² (6.0 m × 20.0 m), organized into two rows and four columns, and comprised a total of eight coconut trees. In each plot, 5 sampling points were randomly determined along the diagonal. After 130 days of growth, D. esculenta was harvested. B. Pilosa, A. argyi, and A. pintoi underwent mowing treatment. Soil samples were collected at the same time. The soil samples were collected using stainless steel soil drills with a diameter of 5 cm and were categorized into upper (0–20 cm), middle (20–40 cm), and lower (40–60 cm) layers based on the depth. Each plot’s five samples were mixed and passed through a 2 mm sieve to remove debris such as stones and plant roots. Part of the soil samples were immediately frozen in liquid nitrogen and stored at −80 °C for DNA extraction and microbial diversity sequencing, while the rest were stored at 4 °C for physical and chemical analysis.

2.2. Determination of Soil Properties

The soil pH was measured using a pH meter (PHS-3G, REX, Shanghai, China) with a soil-to-water ratio of 1:2.5 (w/v). The mixed soil samples of each plot were naturally dried in the shade for 30 days and passed through a 0.149 mm sieve. The soil hydroscopic water content (SHWC) was determined by oven drying 200 g of air-dried soil at 105 °C until a constant weight was achieved. Soil organic matter (SOM) was determined by using the Walkley–Black dichromate oxidation method. The soil total nitrogen content (TN) was determined using the semi-micro Kjeldahl method. The soil total phosphorus content (TP) was determined using the Mo-Sb colorimetric method, while soil available phosphorus (AP) was determined after extraction with NH₄F-HCl (M/M = 6/5). Soil available potassium (AK) was determined using the flame photometry method after extraction with 1 M NH₄OAC.

2.3. High-Throughput Sequencing and Data Analysis

2.3.1. DNA Extraction and Sequencing

The total genome DNA from samples was extracted using the CTAB/SDS method. The primers 515F (5′-GTGYCAGCMGCCGCGGTAA-3′) and 806R (5′-GGACTACHVGGGTWTCTAAT-3′) targeting the bacteria 16S rRNA were used to analyze bacterial diversity, while the primers ITS5-1737F (5′-GGAAGTAAAAGTCGTAACAAGG-3′) and ITS2-2043R (5′-GCTGCGTTCTTCATCGATGC-3′) targeting the fungal 18S rRNA were used to analyze fungal diversity. After being purified with the Qiagen Gel Extraction Kit (Qiagen, Germany), the PCR products were sequenced using the Illumina NovaSeq6000 platform (Shanghai Biotree Biotechnology Co., Ltd., Shanghai, China).

2.3.2. Raw Data Assembly and Annotation

The paired-end reads were merged using FLASH [19] (V1.2.7, http://ccb.jhu.edu/software/FLASH/ (14 June 2023)), quality filtered using QIIME [20] (V1.9.1, http://qiime.org/scripts/split_libraries_fastq.html (14 June 2023)), and chimeric reads were removed using the UCHIME algorithm [21] (UCHIME Algorithm, http://www.drive5.com/usearch/manual/uchime_algo.html (14 June 2023)).

The determination of the soil microorganism operational taxonomic units (OTUs) was performed at a sequence similarity of 97% using Uparse [22] (v7.0.1001, http://drive5.com/uparse/ (14 June 2023)), and each representative sequence was annotated with taxonomic information based on the Silva database [23]. The abundance of OTUs was normalized using a standard of sequence number corresponding to the sample with the fewest sequences, and the subsequent analyses were all performed based on the normalized data. OTUs richness and Chao1 were used to assess community richness, while Simpson and Shannon indices were employed to evaluate community evenness and diversity. The four alpha diversity indices were calculated using QIIME (Version 1.9.1) [20] and analyzed for intergroup differences using the Tukey test with OriginPro (2022b SR1 9.9.5.171).

2.4. Statistical Analysis

All of the data are reported as the mean ± standard error (SE) of three replicates. The experimental data were analyzed using OriginPro (2022b SR1 9.9.5.171) and GraphPad prism (Version 6.01) statistical software. Two-way ANOVA was used to analyze the significance of the soil properties data. The alpha and beta diversity of the soil microorganisms were calculated with QIIME (Version 1.7.0) and displayed with R software (Version 4.23). Detrended correspondence analysis (DCA) was used to select between redundancy analysis (RDA) or canonical correspondence analysis (CCA) models before exploring the relationship between microbial community structure and soil properties by using R software “vegan” (Version 2.6-4). Mantel test was used to analyze the relationship between microbial community structure and soil properties, determining the independent contributions of the soil properties to the microbial community composition. Structural equation modeling (SEM) was used to assess the relationships between soil physicochemical properties and microbial communities by using the R software “lavaan” package (Version 0.6-18). In the process of SEM calculation, all possible pathways among the explanatory variables were first considered. These pathways were then combined with correlation analysis to simplify initial models by eliminating nonsignificant pathways.

3. Results

3.1. Effect of Intercropping on Soil Properties

The results of seven soil property indicators at three depths are shown in Figure 2. Compared to the control group of naturally growing weeds, intercropping A. argyi, D. esculenta, and A. pintoi resulted in an increase in soil pH. The extent of improvement is negatively correlated with soil depth. In addition to pH, intercropping with A. pintoi also increased the soil TP, AK and AP content, and the enhancing effect weakened as soil depth increased, which is likely due to the presence of endophytic bacteria with phosphorus-reducing capabilities [24]. Intercropping three crops together can decrease SHWC and SOM, particularly in the shallow soil layer (0–20 cm). In terms of soil nutrient elements, intercropping three crops is linked to improved N utilization, leading to a significant decrease in soil TN. Specifically, A. argyi predominantly extracts N from deeper soil layers (40–60 cm), whereas D. esculenta primarily acquires nitrogen from shallower soil depths. This discrepancy in nitrogen uptake patterns may be attributed to the differences in root system architectures. Overall, the soil properties are mainly influenced by the species of intercropping vegetation. Within the soil depth range of 0–60 cm, this influence diminishes progressively as the depth increases.

3.2. Effect of Intercropping on Soil Microbial Community Structure

Microbial α diversity richness and evenness were assessed at varying soil depths across different treatments through the calculation of the OTU richness index, Chao1 index, Shannon index, and Simpson index (Figure 3). Overall, bacterial communities exhibit greater abundance and diversity compared to fungi across various samples. In the control group, the diversity of bacterial communities tends to decrease with increasing soil depth, whereas the presence of intercropping A. argyi notably enhances bacterial diversity at different soil depths. Intercropping A. pintoi leads to a reduction in bacterial abundance in surface and deep layers of soil but mitigates the impact of medium depth on bacterial diversity. Compared to the CK, the intercropping of D. esculenta leads to a decrease in bacterial diversity in the surface soil while mitigating the influence of soil depth on bacterial diversity and maintaining consistent levels of bacterial diversity across different sample depths. Intercropping A. argyi and A. pintoi enhances fungal diversity relative to the CK while intercropping D. esculenta diminishes fungal diversity in surface and middle soil layers. But similar to the results of bacterial diversity, intercropping D. esculenta also reduced the impact of depth on fungal diversity, resulting in fungal diversity being maintained at the same level in different depths samples.

To investigate the effect of intercropping on soil bacterial abundance, the microbial communities at the phylum, order, and genus levels were analyzed separately (Figure 4). The results showed that the type of intercropping plants and soil depth could significantly affect the relative content of microbial groups. At the phylum level, Acidobacteria and Proteobacteria were the predominant bacteria. Intercropping A. argyi and A. pintoi resulted in a decreased abundance of Acidobacteria across various soil layers compared to the control group. However, this intercropping practice did not influence the vertical distribution trend in Acidobacteria abundance with increasing soil depth. At the order level, the Acidobacteriota_ Acidobacteriae_ Subgroup 2 showed the highest abundance among bacteria. Across different intercropping treatments, the abundance of Subgroup2 demonstrated an upward trend with deeper soil layers. Furthermore, Rhizobiales and Nitrosotaleales were consistently identified in all treatment conditions. Considering the scarcity of N elements in the experimental site, it is proposed that these bacteria could have a substantial influence on soil N cycling and utilization. At the genus annotation level, the primary bacterial groups identified are Candidatus Solibacter, Bryobacter and Bacillus. Intercropping with D. esculenta leads to an increased relative abundance of Candida Solibacter, and Bryobacter, while intercropping with A. pintoi results in Bacillus becoming the predominant bacterial group. In the fungal community composition, more than 90% of OTUs in the CK group were identified as belonging to the phyla Ascomycota and Basidiomycota. Following the intercropping of three different crops, there was a notable decline in the relative abundance of Basidiomycota, accompanied by an increase in the unclassified fungal taxa. These findings suggest that intercropping practices have the potential to enhance fungal population diversity within the soil environment. At the genus level, Lysurus, Fusarium, and Sarocladium are identified as the predominant fungi. The intercropping treatment demonstrated a notable reduction in the abundance of Lysurus in the soil compared to the control group, along with a decrease in the occurrence of plant pathogens such as Fusarium in the upper soil layers.

3.3. Correlation Analysis of Microbial Communities and Soil Properties in Intercropping Treatments

The selection of CCA/RDA models was based on the length of grade in DCA results, with bacterial calculation results exceeding 5.0 and fungal calculation results ranging from 1.1 to 3.1 (Table S1). Consequently, CCA and RDA models were employed to investigate the influence of the soil properties on variations in bacterial and fungal communities, respectively. Canonical correspondence analysis (CCA) of soil properties and bacterial community revealed that the percentage explained by the first ordination axis was 27.83%, that explained by the second ordination axis was 21.57%, and that of the cumulative explanation was 49.40% (Figure 5A). Redundancy analysis (RDA) of the soil properties and fungal community revealed that the percentage explained by the first ordination axis was 45.60%, that explained by the second ordination axis was 15.82%, and that of the cumulative explanation was 61.42% (Figure 5B). pH and SOM are the primary factors that impact the composition of bacterial communities, especially in the CK and D. esculenta intercropping treatments. On the other hand, the intercropping treatment A. argyi is mainly affected by AP and TP. In addition, for samples from different soil depths, AP and TP have a greater impact on the bacterial community structure in the upper soil layer. Soil indicators have a more pronounced effect on fungal community structure compared to bacteria. Soil properties have a more pronounced effect on fungal community structure compared to bacteria. SOM, TN, and SHWC are strongly positively correlated with the fungal community structure of the CK group, while AK and pH showed a negative correlation. In the A. pintoi and D. esculenta intercropping treatments, the fungal community structure is primarily influenced by AP and TP content. Different intercropping treatments all show that soil properties have a greater impact on the fungal community structure in the upper soil layer. For instance, in intercropping A. pintoi and A. argyi, AP and TP are the primary influencing factors, whereas in intercropping D. esculenta, upper soil fungi are mainly influenced by AK, pH, TN, and SHWC.

3.4. Intercropping Affected Soil Microbial Communities by Regulating Soil Properties

To investigate the correlation between soil properties and microbial communities, mantel tests were performed for all soil properties factors with bacterial and fungal communities (Figure 6A). According to the results of the Mantel test, the soil properties had significant effects on the microbial communities in different intercropping treatments, among which depth (r > 0.75, p < 0.001) and AP (r > 0.68, p < 0.001) were the common factors determining the bacterial communities (Figure 6A, Table S2). Furthermore, SWHC (r > 0.80, p < 0.001) and TP (r > 0.80, p < 0.001) in the CK group, TP (r > 0.92, p < 0.001) in the A. pintoi intercropping treatment, SOM (r > 0.89, p < 0.001), Ak (r > 0.82, p < 0.001) and TP (r > 0.89, p < 0.001) in the D. esculenta intercropping treatment were also identified as significant contributors to bacterial diversity. In comparison, differences exist in the correlation between soil indicators and fungal community structure in various intercropping treatments. TP (r > 0.92, p < 0.001), AP (r > 0.94, p < 0.001) and depth (r > 0.93, p < 0.001) in the CK group, AK (r > 0.85, p < 0.001), TP (r > 0.86, p < 0.001) and AP (r > 0.86, p < 0.001) in the A. pintoi intercropping treatment, and Ak (r > 0.82, p < 0.001) and depth (r > 0.75, p < 0.001) in the D. esculenta intercropping treatment were identified as significant contributors to fungal diversity.

Based on the correlation analysis results, key soil property factors were selected to construct the SEM model in different intercropping treatments. The results indicate that depth and TP play significant roles in the microbial community structure under different intercropping treatments (Figure 6B). The SEM results of different intercropping treatments show that depth negatively affects the diversity and abundance of bacteria and fungi, while the TP content positively influences fungal diversity and abundance. Moreover, as soil depth increases, the nutrient content decreases, negatively impacting the diversity and abundance of soil bacteria.

4. Discussion

The interaction between plant species, soil types, and soil microbial communities is intricate [25], with soil depth playing a crucial role in determining the vertical distribution of microbial communities [26]. In cultivation management, weeds frequently compete with crops for nutrients and space, which can diminish both crop yield and quality. Additionally, certain weeds produce toxins that may pose health risks to humans and livestock. B. pilosa is an invasive weed that threatens the agricultural and biodiversity in China [27], which is particularly prevalent in the coconut plantations of Wenchang. Research has demonstrated that B. pilosa possesses the potential for development as animal feed, with no identified toxicity to animals to date [28]. Furthermore, our findings indicate that the application of B. pilosa in coconut cultivation, as opposed to rigorous weed management practices, may improve the soil’s capacity for water retention. To improve the soil utilization efficiency beneath coconut trees, this study employed naturally grown B. pilosa treatment as a control while conducting intercropping experiments with A. argyi, D. esculenta, and A. pintoi.

Intercropping typically influences the nutrient composition of the soil [29]. The findings of this study indicate that intercropping leads to a decrease in nitrogen levels within the soil, whereas the intercropped A. pintoi enhance the concentrations of phosphorus and potassium. As a legume, A. pintoi is usually accompanied by Azotobacter and phosphate-solubilizing bacteria, which can increase the content of N and P in the soil [30,31]. However, our study shows that intercropping reduced the soil nitrogen content. It could be attributed to the low soil nutrient status in the coconut grove and the higher nitrogen needs of intercropped species compared to B. pilosa (CK). Additionally, the presence of phosphate-solubilizing bacteria near peanut plants likely boosts soil phosphorus content.

Intercropping three different crops has resulted in varying increases in soil pH and improvements in soil permeability, which are potentially affecting the soil microbial communities [32]. Soil organic matter (SOM) is essential for evaluating soil fertility [33]. Intercropping treatments resulted in a reduction in SOM content compared to the control group, likely due to nutrient absorption by the intercropped crop and alterations in the soil microbial community [34]. In general, an increase in soil microbial diversity is advantageous for the soil ecosystem functionality [35], and the results of the amplicon sequencing in this study demonstrate that three distinct intercropping strategies all increased the composition and diversity of microbial communities in the soil. A. argyi is a traditional herbal medicine in China, and its essential oil has shown effectiveness in preventing agricultural and forestry pests [36]. The bioactive metabolites associated with this plant are not only produced by the plant itself but are also influenced by various endophytic and epiphytic microorganisms capable of producing active compounds [37,38]. This interaction likely contributes to the increased bacterial and fungal diversity observed when intercropping with A. argyi.

In various intercropping patterns, Acidobacteriota and Proteobacteria are major soil bacteria. Acidobacteriota abundance correlates positively with soil depth, while Proteobacteria abundance decreases with depth. The abundance of Acidobacteriota is typically associated with soil pH [39]. The relative abundance of Acidobacteriota bacteria in intercropping A. argyi and A. pintoi is relatively low in the upper layer, which is likely influenced by the increase in pH of the corresponding soil layer. Candidatus_Solibacter sp. is an acidophilic bacterium belonging to Acidobacteriota, which is capable of decomposing organic matter [40]. Its high abundance in the CK group and low abundance in the intercropped A. pintoi group are likely related to the corresponding soil pH and SOM. Fungi have lower diversity compared to bacteria, with over 90% classified as Basidiomycota_ Lysurus sp., Ascomycota_ Fusarium sp., and Ascomycota_ Sarocladium sp., with Basidiomycota_ Lysurus sp. being significantly enriched only in the CK group. In this study, at the experimental site, a high abundance of the plant-pathogenic fungus Fusarium sp. was discovered. Intercropping can decrease the abundance of Fusarium sp., especially in A. pintoi intercropping, likely due to the antagonistic effect of a high abundance of Bacillus sp. [41].

The relationships between the soil microbial community and measured environmental variables were analyzed using CCA/RDA and Mantel tests. The results of this study indicate that pH and SOM exert a considerable influence on microbial communities, which is consistent with the findings of Drenovsky and Fierer’s research [42,43]. Phosphorus is a crucial nutrient for plant growth, and it is widely accepted that soil-available phosphorus is a limiting factor for plant growth in plantation ecosystems [44]. This study shows that the soil phosphorus content is also the main factor influencing the microbial community structure in coconut forest intercropping systems. The intercropping system can enhance interspecific interactions among underground biota, contributing to the improvement of soil nitrogen supply capacity and an increase in total nitrogen content [45]. In the present study, it was observed that intercropping led to a decrease in the soil nitrogen content. We hypothesize that this is likely due to the low nitrogen levels in the soil, combined with the higher nitrogen demand for crop growth in the intercropping group compared to weeds in the CK group; thus, the nitrogen-fixing benefits associated with intercropping appear to be inadequate to fulfill the growth requirements.

Based on these correlation analysis results and considering the overall data obtained from these experiments, SEM models were constructed for different intercropping treatments, respectively. The SEM results also confirmed that the increase in pH positively regulates bacterial diversity. In addition, the depth of the soil also significantly regulates microbial diversity. As the depth increases, the nutrient content, such as nitrogen, phosphorus, and potassium, gradually decreases, directly impacting microbial diversity. Due to the experimental land being too barren and having extremely low nitrogen content, all treatments experienced nitrogen deficiency, which may have obscured the impact of nitrogen on the microbial community structure.

5. Conclusions

This study provides an inaugural assessment of the impact of intercropping A. argyi, D. esculenta, and A. pintoi in coconut plantations on soil properties and microbial communities. The findings of this study indicate that soil fertility within coconut groves is comparatively low, particularly characterized by a notable deficiency in nitrogen. Additionally, the research revealed that an increase in soil pH is associated with enhanced diversity of soil bacteria. Consequently, it is advisable in agricultural practices to incorporate manure into the soil to elevate pH levels and subsequently improve soil fertility. The experiment found a significant presence of plant-pathogenic fungi in the soil. Despite no visible signs of plant disease so far, further investigation is necessary. Intercropping with A. pintoi increases Bacillus sp. concentration, potentially helping manage plant pathogens like Fusarium sp. and showing promise for future progress. In summary, this study offers a theoretical foundation for optimizing intercropping patterns in coconut orchards and enhancing soil quality by examining the relationships between soil properties and microbial communities.