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Article

Changes in the Soil Bacterial Community in a Chronosequence of Temperate Walnut-Based Intercropping Systems

by
Pengxiang Gao
1,
Xiaofeng Zheng
1,
Lai Wang
1,
Bin Liu
1 and
Shuoxin Zhang
1,2,*
1
College of Forestry, Northwest A&F University, Yangling 712100, China
2
Qinling National Forest Ecosystem Research Station, Yangling 712100, China
*
Author to whom correspondence should be addressed.
Forests 2019, 10(4), 299; https://doi.org/10.3390/f10040299
Submission received: 4 February 2019 / Revised: 19 March 2019 / Accepted: 27 March 2019 / Published: 31 March 2019
(This article belongs to the Special Issue Relationship between Forest Biodiversity and Soil Functions)
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Abstract

:
Agroforestry (tree-based intercropping) is regarded as a promising practice in sustainable agricultural management. However, the impacts of converting cropland to an agroforestry system on microbial communities remain poorly understood. In this study, we assessed the soil bacterial communities in conventional wheat monoculture systems and a chronosequence (5–14 years) walnut-wheat agroforestry system through the high-throughput sequencing of 16S rRNA genes to investigate the effect of agroforestry age on soil bacterial communities and the correlation between soil properties and bacterial communities in the agroecosystem. Our results demonstrate that establishing and developing walnut tree-based agroforestry increased soil bacterial diversity and changed bacterial community structure. Firmicutes, Proteobacteria, Actinobacteria and Acidobacteria were the dominant soil bacterial phyla and Bacillus was the dominant genus. Crop monoculture systems were characterized by the Bacillus (Firmicutes)-dominated microbial community. The relative abundance of Bacillus decreased with agroforestry age; however, subgroups of Proteobacteria and Actinobacteria increased. Of the selected soil physicochemical properties, soil pH and bulk density were significantly correlated with bacterial alpha diversity, and soil pH and organic carbon were the principal drivers in shaping the soil microbial structure as revealed by redundancy analysis (RDA).

1. Introduction

Agroforestry is an intentional management of shade trees with agricultural crops [1], which is regarded as a promising approach for the sustainable development of agriculture. Agroforestry is a land-use type especially suitable for marginal land, which maximizes the provision of ecosystem goods and services [2]. The Loess Plateau region in China is one of the most eroded hilly areas in the world. In response to the erosion and land degradation, Grain-for-Green (GFG) and other afforest programs were carried out to replace the marginal cropland in this region [3]. Walnut tree (Juglans regia L.) is a widely planted afforestation species. The incorporation of walnut into the former cropland (walnut-based intercropping systems) is popular in this hilly area to balance the benefits of ecology and economy. Both the ecological and economic benefits of walnut-based intercropping systems have been demonstrated in previous research, for example, increasing light-use and land-use efficiency [4,5], improving soil infiltration [6], maintaining soil carbon sequestration [7] and promoting farmers’ incomes [4,5].
Soil microorganisms play pivotal roles in some soil ecological processes, including the decomposition of organic matter and formation of soil aggregates [8], which directly impact on not only soil fertility but also a series of ecosystem services [9]. Consequently, soil microbial diversity and richness are regarded as key indicators of soil health [10] and long-term soil productivity [11]. In agroforestry systems, trees can change the microclimate and supply abundant and diverse energy sources to the agroecosystems [12,13], which could be of benefit to microbial diversity. Bardhan [11] argued that, compared with crop agriculture, agroforestry systems would promote habitat diversity which results in higher species diversity. However, studies on soil microbial diversity are limited and current studies explore the spatial differences inside agroforestry systems. Bardhan [11] compared the bacterial communities between tree rows and crop alleys in the silver maple-based intercropping systems, and no significant difference in bacterial diversity was observed. Banerjee [14] analyzed great samples in Canadian agroforestry systems, and indicated that tree plots promote bacterial abundance and, to some extent, species richness. In a study of the microbial beta-diversity in Québec and Ontario, Lacombe [15] regarded the tree-based intercropping as integration. Based on the measure of dispersion among phospholipid fatty acid (PLFA) profiles, significantly higher diversity in the agroforestry system than in the crop monoculture system was only found in Québec [15]. Microbial species composition in an ecosystem is also important. Some keynote microbial groups have long been recognized for critical roles in the overall structure and function. Vallejo [16] indicated that conventional monoculture pastures promoted the dominance of Gram-negative bacteria, while the tree-based silvopastoral system favored actinomycetes and fungal biomass. On the contrary, Unger [17] suggested that agroforestry soil was characterized by a greater proportion of total bacteria, Gram-negative bacteria, anaerobic bacteria and mycorrhizal fungi than the cropland soil. Compared with crop monoculture, agroforestry is more complex in architecture. Shade trees live longer than herbaceous plants and the associated agroforestry management could have a lasting effect on the soil microbes. Seldom has the dynamic of microbial communities been studied in agroforestry systems, and the effect of agroforestry age remains poorly understood.
The soil is the harbor of soil microorganisms and physicochemical properties are directly correlated with the soil microbial communities. Soil pH, organic carbon, nitrogen, phosphorus and other soil physicochemical properties have been widely studied and correlated with soil bacterial communities in agroecosystems and forest ecosystems [14,18,19,20]. There are only a few studies that focus on the relationship of soil physicochemical properties and soil microbial communities in the agroforestry system. Clarifying these relations could help us better understand the impact of agroforestry on the microbial community. In addition, soil physicochemical properties are more accessible for us to manage soil biodiversity, thus enabling us to maintain agricultural land use [21].
In this study, we assessed the soil physicochemical properties and bacterial communities in walnut-wheat agroforestry systems converted from cropland, and the adjacent conventional crop monoculture systems. To address the temporal dynamics of soil bacterial communities with walnut succession in agroforestry systems, a chronosequence of 5–14-year-old walnut-based agroforestry systems was assessed. We hypothesized that, (i) walnut-based intercropping would increase soil bacterial diversity compared with crop monoculture, which would be strengthened with the agroforestry age; (ii) soil physicochemical properties would affect the structure and diversity of bacterial communities in the agroecosystems.

2. Materials and Methods

2.1. Study Site

The field trial of the study was located in Qishan (107°43′47″ E, 34°21′30″ N, 590–650 m a.s.l.), Shaanxi Province, China, which is located in the southern part of the Loess Plateau. This site is a semi-arid habitat, characterized by a temperate continental climate with hot, rainy summers and cold, dry winters. The mean annual temperature is 12.0 °C and the mean annual precipitation is 615 mm; ca. 70% falls between June and September. The soil in the study site is silt loam (27% sand, 49% silt and 24% clay) derived from loess and it is categorized as Calcaric Cambisols base on IUSS Working Group WRB [22]. The water table is over 30 m from the soil surface.
We established agroforestry fields with the Weiweiyuan Walnut Professional Farmer Cooperative in the level terrace. Before the agroforestry systems were established, crops had been planted for over thirty years. The main cropping system was wheat–fallow, and occasionally rotated with soybean/maize. One-year-old walnut (Juglans regia L.) seedlings were planted in the cropland in 2002, 2007 and 2011, respectively, in different fields at an interval of 4 m along the row and 6 m between rows; so the agroforestry age we referred to in this study is equal to the tree age. The tree rows were in a north–south orientation. Wheat (Triticum aestivum L.) was annually grown among the tree rows in the 1–7-year-old agroforestry systems and reduced to being biennially grown in agroforestry systems older than 7 years. The 1.6-m wide tree rows were left untilled. Soybean was rotated in 2013. Wheat was sowed (2.1 × 106 ha−1) in early October and harvested in June of the next year between the tree rows as well as the adjacent crop monoculture system. The field left fallow for the rest of the year. Basic fertilizers of ca. 160 kg nitrogen ha−1 were equally applied and additional fertilizer was applied as needed. Cropland and the crop rows in the agroforestry system were tilled to 20 cm before wheat sowing. There was no irrigation.

2.2. Soil Sampling

This study was carried out in September 2015 before soil tillage and wheat sowing, following a completely randomized block design. Three spatially separated blocks were chosen including continuous conventional wheat monoculture system (C), 5-year-old agroforestry system (AF5, established in 2011), 9-year-old agroforestry system (AF9) and 14-year-old agroforestry system (AF14) plots (Table 1). Each plot is approximately 50 min length and 15 m in width.
In each agroforestry plot, six sample grids of 1 × 1 m2 were randomly selected between the tree rows. In wheat monoculture plots, six sample grids of the same size were randomly selected. Soil samples were collected from 0 to 10 cm depth of the sample grids, and then mixed with samples from the same plot. So eventually, there was a composite sample from each crop monoculture plot and agroforestry plot. The composite samples were transported in an ice box to the laboratory. After removal of the litters and stones, the samples were sieved through a 2-mm mesh and the homogenized soils were divided into three portions. One portion was stored at −80 °C until DNA extraction. One portion was stored at 4 °C prior to assessing the soil water content (Wc), dissolved organic carbon (DOC), ammonium nitrogen (N-NH4+) and nitrate nitrogen (N-NO3−). This left a portion that was air-dried for approximately a week for the measurement of soil organic carbon (SOC), total nitrogen (TN), pH and available phosphorus (AP). In addition, an undisturbed 5-cm-diameter core was collected at each grid of the plots for measuring the soil bulk density (BD). The average BD value of the grids stood for the BD value of the plots.

2.3. Soil Physicochemical Property Measurement

Wc was determined by weighing 25 g fresh soil oven-dried at 105 °C to constant weight. BD was calculated using the ring cylinder method. Soil pH was measured in 1:2.5 soil water solutions with a digital pH meter at 25 °C. SOC was analyzed by titration after wet oxidation in concentrated H2SO4 and 2 mol L−1 K2Cr2O7. TN was measured with the Kjeldahl method using a Foss Kjeltec 8400 analyzer (Foss Tecator AB, Hoganas, Sweden). The concentrations of N-NH4+ and N-NO3− were measured with Continuous-Flow AutoAnalyzer III (Bran and Luebbe, Norderstedt, Germany) after fresh soil extracted by 2 M KCl. DOC content was determined using liquid TOCII analyzer (Elementar, Langenselbold, Germany) with the fresh soil solution of 0.5 M K2SO4. AP was analyzed using the colorimetric method with 0.5 M NaHCO3 extraction.

2.4. Soil DNA Extractions and Sequencing of Bacterial 16S rRNA

Total soil DNA from different samples was extracted using the E.Z.N.A.TM Soil DNA Kit (Omega Bio-Tec, Inc., Norcross, GA, USA) according to the manufacturer’s directions. Purified DNA concentration was determined and purity was confirmed with NanoDrop Spectrophotometer (Thermo Scientific, Wilmington, DE, USA). Soil DNAs were stored at −80 °C until measurement in the PCR by LC-Bio Technology Co., Ltd, Hangzhou, Zhejiang, China.
The V3–V4 region of the prokaryotic 16S rRNA gene was amplified with the primers 319F (5′-ACTCCTACGGGAGGCAGCAG-3′) and 806R (5′-GGACTACHVGGGTWTCTAAT-3′). The 5′ ends of the primers were tagged with specific barcodes per sample and sequencing universal primers. PCR amplification was performed in a total volume of 25 µL reaction mixture containing 25 ng of template DNA, 12.5 µL of Phusion Master Mix (2×, NEB, Ipswich, MA, USA), 2.5 µL of each primer at 0.1 µM. The PCR reaction followed the procedure: an initial denaturation at 98 °C for 30 seconds; 35 cycles of denaturation at 98 °C for 10 seconds, annealing at 54 °C for 30 seconds, and extension at 72 °C for 45 seconds; and then final extension at 72 °C for 10 min. The PCR products were confirmed with 2% agarose gel electrophoresis. A negative control, with ultrapure water instead of a sample solution, was used to exclude the possibility of false-positive PCR results throughout the DNA extraction process. The PCR products were purified by AMPPure XP beads (Beckman Coulter Genomics, Danvers, MA, USA) and quantified by Qubit (Invitrogen, Carlsbad, CA, USA). The amplicon pools were prepared for sequencing and the size and quantity of the amplicon library were assessed on Agilent 2100 Bioanalyzer (Agilent, Santa Clara, CA, USA) and with the Library Quantification Kit for Illumina (Kapa Biosciences, Woburn, MA, USA), respectively. PhiX Control library (v3) (Illumina) was combined with the amplicon library (expected at 30%). The libraries were then sequenced on 300 PE MiSeq runs. The amplicons sequences were submitted to the National Center for Biotechnology Information (NCBI) genbank (SAMN10240058-63,67-69,73-75) under the bioproject PRJNA496378.

2.5. Bioinformatics Analyses

The achieved paired-end reads were assigned to samples based on their unique barcode and truncated by cutting off the barcode and primer sequence. Paired-end reads were merged using FLASH (1.2.8) [23]. Quality filtering on the raw reads was performed by filtering 1) reads containing undetermined nucleotides ratio greater than 5%; 2) reads having the base number of Q (Sequencing quality) ≤10 more than 20%. The specific information of the read number before and after quality filtering is listed in Table 2. Sequences with ≥97% similarity were assigned to the same operational taxonomic units (OTUs) by Usearch. Representative sequences were chosen for each OTU, and taxonomic data were then assigned to each representative sequence using the RDP (Ribosomal Database Project) classifier with an 80% confidence threshold [24].

2.6. Statistical Analyses

To assess bacterial diversity among samples in a comparable manner, OTUs abundance information’ was normalized using a standard of sequence number corresponding to the sample with the least sequences. Alpha diversity was applied in analyzing the complexity of species diversity (at OTU level) for a sample through four indices, namely, Observed species (Nobs), Chao1, Shannon and Simpson; the indices were calculated with QIIME 1.8.0 [25] as follows:
Chao1 index [26],
Chao 1 = N obs + F 1 2 2 F 2
where F1 and F2 are the count of singletons and doubletons, respectively.
Shannon index,
H ′ = − ∑ i = 1 S p i log 2   p i
Simpson index,
1 − λ = 1 − ∑ i = 1 S p i 2 ;
where pi is the proportion of the ith species (OTU); S is the richness of the species (OTUs).
Soil physicochemical properties, bacterial alpha diversity indices and bacterial taxa were analyzed by two-way analysis of variance (ANOVA) with the agroforestry age and block as fixed factors in IBM SPSS Statistics (IBM, Chicago, IL, USA). The normality and the homoscedasticity of the variances were checked. Data was transformed into its square root when either of the requirements was not satisfied (as for available phosphorus values). Beta diversity analysis was used to evaluate the differences of samples in species complexity. Principal coordinate analysis (PCoA) of unweighted UniFrac distances between taxonomic units (at OTUs level) was used to screen the compositional differences among bacterial communities in the agroforesty systems and crop monoculture systems, based on the ‘pcoa’ function of the ‘vegan’ package in R, version 3.4.1 (R Development Core Team, Auckland, New Zealand). Two-way permutational multivariate analysis of variance (PERMANOVA) [27] was used to test the effect of agroforestry age and block on the bacterial community variances through ‘adonis’ function, with 999 random permutations.
Correlation tests were performed to identify potential correlations of tree and soil properties to bacterial diversity indices and the abundant genera. Pearson correlation coefficients were calculated between the variables. Redundancy analysis (RDA) was applied to relate the variation of bacterial community (at the genus level) to specific soil physicochemical properties. To test for a significant effect of soil physicochemical properties on bacterial community, a Monte-Carlo permutation test (permutation = 999) was performed.

3. Result

3.1. Soil Physicochemical Properties

Soil pH, BD and DOC were significantly affected by the agroforestry age (Table 3). Soil DOC was significantly higher in wheat monoculture (C) than that in the intercropping crops. Meanwhile, soil Wc was much lower in the crop monocultures. Soil pH, Wc, BD and N-NH4+ generally increased with agroforestry age (Table 3). SOC, TN and N-NO3− decreased at the earlier stage of agroforestry treatment (AF5 and AF9), but increased in the older agroforestry treatment.

3.2. Soil Bacterial Community Structure and Diversity

The obtained sequences from the 16S rRNA gene sequencing of all the soil samples were clustered in 6504 OTUs, identified to 36 bacterial phyla.
The bacterial alpha diversity varied with agroforestry age and block (Table 4). Bacterial alpha diversity indices increased with agroforestry age.
The PCoA plot shows that the bacterial community was specific to the agroforestry age (Figure 1). AF9 and AF14 were well separated from C and AF5. The two-way PERMANOVA result (FAge = 1.142, PAge = 0.042; FBlock = 1.159, PBlock = 0.046) reinforced that the soil bacterial community composition depended on the agroforestry age. Of the identified phyla, Firmicutes (40.34%), Proteobacteria (21.30%), Actinobacteria (14.82%) and Acidobacteria (12.18%) were dominant in soil microbial communities in the studied plots (Figure 2). The relative abundance of Firmicutes and Proteobacteria was significantly affected by the agroforestry age (respectively, p = 0.012, p < 0.001). The relative abundance of Firmicutes decreased with agroforestry age, whereas Proteobacteria increased with agroforestry age (Figure 2a). At the genus level, Bacillus (31.10%) was dominant (Figure 2b). The relative abundance of Bacillus decreased with agroforestry age (p = 0.012).

3.3. Influence of Soil Physicochemical Properties on Bacterial Community

Soil BD, pH and Wc had a positive correlation with soil bacterial alpha diversity and some genera belonged to Proteobacteria and Actinobacteria analyzed by a correlation test (Figure 3). However, they had a negative correlation with Bacillus and Lactococcus which belongs to Firmicutes. On the contrary, TN was negatively correlated with abundant genera in Actinobacteria and Proteobacteria.
In RDA, the selected soil physicochemical properties explained 90.80% (adjusted R2 = 0.494) of the total variation of bacterial communities at the genus level, and the first two axes explained 67% of the variation (Figure 4). SOC (p = 0.015) and pH (p = 0.050) were revealed to be factors significantly affecting the bacterial community through the permutation test (Table S1).

4. Discussion

4.1. Bacterial Communities along Agroforestry Chronosequence

Our results revealed that the application of agroforestry systems increased soil bacterial diversity and changed soil bacterial community structures. Agroforestry is regarded as an effective afforestation approach in the Loess Plateau region. However, the effects of agroforestry on soil microorganisms have seldom been studied. Compared with the cropland, agroforestry increased the aboveground vegetation diversity. Diversified plant species and the resultant rhizosphere chemistry and litter quality could affect the soil microbial community [28]. Some previous studies in the same region found that microbial diversity increased with afforestation [29,30,31], and the microbial diversity associated with the aboveground vegetation. Zhang [32] observed that soil bacterial diversity decreased at the early stage of restoration on farmland along with the aboveground vegetation decrease. Compared with the undisturbed restoration, the constitution of aboveground vegetation in agroforestry was more stable during its building process. Zak [33] indicated that the soil microbial diversity was decided by the plant diversity-associated production change, rather than plant diversity. This could be an explanation of the bacterial diversity increase along agroforestry age, where the aboveground vegetation diversity is limited by agricultural management. In the correlation test, we found positive relations between tree biomass (represented by tree canopy and height) and bacterial diversity. This could partly clarify the important role of aboveground biomass in the soil bacterial diversity.
The bacterial community structure changed with agroforestry age (Figure 1 and Figure 2). The most abundant bacterial phyla in the present study, including Firmicutes, Proteobacteria, Actinobacteria and Acidobacteria, were also observed in some former studies in the same region [34,35]. They make up over 85% of the total sequences. The soil bacterial community changed from being Firmicutes dominated to a more evenly distributed community along the agroforestry age. Guo [28] found a higher abundance of Firmicutes and lower abundance of Proteobacteria and Actinobacteria in cropland than in the restoration site, which is in accordance with our result. However, the higher frequency of Firmicutes in the studied site was in sharp contrast to the relatively lower abundance reported in other studies [29,30,31]; this is mainly due to the high abundance of Bacillus. Firmicutes, typically Bacillus and its derived genera, are a group of aerobic endospore-forming bacteria abundant in soils [36]. In Tian et al.’s study, the abundance of Firmicutes was much higher in the bare fallow site than in the vegetation-covered site [37]. Firmicutes were reported to be enriched in the soil with lower moisture content [38] and they could survive under stressful conditions, such as warming and desiccation [39]. Bacillus cereus (Fermicutes) was the most abundant bacterial species in our study. It could secrete poisonous toxins [40]. A reduction of Bacillus cereus abundance would be beneficial to agricultural production. Proteobacteria always receive great attention due to their role in soil nitrogen cycling [41]. Besides Rhizobiales, which belong to Alphaproteobacteria, some other subgroups in Proteobacteria have been discovered in nodules [41]. Actinobacteria have a critical role in the decomposition of soil organic materials, such as cellulose and chitin [42]. The increase of subgroups in Proteobacteria and Actinobacteria may improve nutrient cycling in agroforestry systems.

4.2. Drivers of Bacterial Community Variation

The microhabitat environment shaped by trees [43] is also important. In the present study, we focus on the effect of soil physicochemical properties, which are directly connected with the soil microbial community. Agroforestry is supposed to increase the soil organic carbon due to plenty of organic matter input in the form of litter fall and fine roots from trees [44]. However, in our study, SOC and TN even decreased in the agroforestry systems, especially in the 9-year-old agroforestry. Wang [45] found the same result during the long-term observation of agroforestry in this region. In the present study, we sampled from crop alleys, which may suffer less from tree litter or roots when the trees were small. With tree growth, the crop alleys would receive more organic matter from the trees. In addition, the high demand of nutrients for the younger trees to construct their biomass could lead to a decrease in soil nutrients. As with the agroforestry management of Ketema [46], tillage frequency decreased with agroforestry age in the studied site, which minimized the human-disturbance effect on tree growth. This could be reflected through the BD increase with agroforestry age. Some studies pointed out that tree plantation would decrease soil water content [47,48] and this may be a threat of afforestation in the region. However, we sampled in September when surface evaporation was strong. Shade trees in the agroforestry systems alleviated the evaporation of remaining soil water.
Soil physicochemical properties are regarded as the driving force of soil microbial diversity [49,50,51]. Soil pH and SOC were identified as the most important factors shaping the bacterial community through RDA analysis. Soil pH is a universal predictor of bacterial composition and distribution [19,52,53]. Soil bacterial communities are always sensitive to pH change. Tight relationships between the abundant soil bacterial phyla, for example Proteobacteria, Actinobacteria, Chloroflexi and Bacteroidetes, and soil pH have been reported in different soils [19,54]. In our study, the pH positively correlated with bacterial alpha diversity and the abundant genera belonged to Proteobacteria and Actinobacteria. The negative correlation between soil pH and the dominant genus, Bacillus, was significant. SOC could significantly affect soil microbial communities [55,56]. Although the regression of SOC and the bacterial community was significant, the correlations between SOC and the abundant genera were not significant. SOC may affect the bacterial community through the control of other physicochemical properties. Soil BD and Wc were found to be positively correlated with the alpha diversity of soil bacteria and the abundant genera belonging to Proteobacteria and Actinobacteria, and negatively correlated with genera of Firmicutes. The positive correlations of soil bulk density with soil diversity could owe to the diminished use of tillage in the agroforestry system, especially in the older agroforestry systems. Some studies demonstrate that tillage would decrease soil bacterial communities [57,58]. Soil Wc increased with agroforestry age, which could alleviate the dominance of Bacillus and increase the bacterial diversity.
Besides the influence of soil physicochemical properties, the sample site (block) also significantly affected the soil bacterial community, which indicated that the distribution of the soil bacterial community even changed within small area.

5. Conclusions

Knowledge of soil biotic and abiotic factors in the agroforestry ecosystem could help us improve the management of sustainable land-use. In our study, establishing and developingwalnut tree-based agroforestry increased soil bacterial diversity and changed the bacterial community structure. Firmicutes, Proteobacteria, Actinobacteria and Acidobacteria were the dominant soil bacterial phyla and Bacillus, which belongs to Firmicutes, was the dominant soil bacterial genus. Crop monoculture systems were characterized by the Bacillus (Firmicutes)-dominant microbial community. The relative abundance of Bacillus decreased with agroforestry age; however, subgroups of Proteobacteria and Actinobacteria increased. Of the selected soil physicochemical properties, soil pH and bulk density were significantly correlated with bacterial diversity, and soil pH and organic carbon were the principal drivers in shaping the soil microbial structure, as revealed by RDA analysis.

Supplementary Materials

The following are available online at https://www.mdpi.com/1999-4907/10/4/299/s1, Table S1: RDA result showing effect of soil properties on bacterial community.

Author Contributions

Conceptualization, P.G. and S.Z.; methodology, P.G.; software, X.Z.; validation, L.W., B.L. and S.Z.; formal analysis, P.G.; investigation, P.G. and X.Z.; writing—original draft preparation, P.G.; writing—review and editing, X.Z. and S.Z.; supervision, S.Z.; project administration, S.Z.; funding acquisition, S.Z.

Funding

This work was supported by the Chinese National Science and Technology Research Plan under Grant [2015BAD07B050202] and National Natural Science Foundation of China [31600493].

Acknowledgments

We appreciate Yulin Xu at Weiweiyuan Walnut Professional Farmer Cooperative, Qishan County, for his industrious work in governing the sample fields. We thank the editor and anonymous reviewers for their careful revisions and constructive comments.

Conflicts of Interest

The authors declare no conflict of interest.

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Forests 10 00299 g001 550
Figure 1. Principal coordinate analysis (PCoA) plot showing patterns of β-diversity in bacterial communities (OTU, operational taxonomic units level) of different agroforestry ages. C = crop monoculture; AF5 = 5-year-old walnut-based agroforestry; AF9 = 9-year-old walnut-based agroforestry; AF14 = 14-year-old walnut-based agroforestry.
Figure 1. Principal coordinate analysis (PCoA) plot showing patterns of β-diversity in bacterial communities (OTU, operational taxonomic units level) of different agroforestry ages. C = crop monoculture; AF5 = 5-year-old walnut-based agroforestry; AF9 = 9-year-old walnut-based agroforestry; AF14 = 14-year-old walnut-based agroforestry.
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Forests 10 00299 g002 550
Figure 2. The relative abundance of bacterial phyla (a) and genera (b) of different agroforestry ages. C = crop monoculture; AF5 = 5-year-old walnut-based agroforestry; AF9 = 9-year-old walnut-based agroforestry; AF14 = 14-year-old walnut-based agroforestry. Asterisks indicate that the effect of agroforestry age is significant through ANOVA at p = 0.05.
Figure 2. The relative abundance of bacterial phyla (a) and genera (b) of different agroforestry ages. C = crop monoculture; AF5 = 5-year-old walnut-based agroforestry; AF9 = 9-year-old walnut-based agroforestry; AF14 = 14-year-old walnut-based agroforestry. Asterisks indicate that the effect of agroforestry age is significant through ANOVA at p = 0.05.
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Forests 10 00299 g003 550
Figure 3. Relation of tree and soil physicochemical properties with bacterial alpha diversity and abundant genera. Canopy, average tree canopy diameter in a plot; Height, average tree canopy diameter in a plot; DOC, dissolved organic carbon; Wc, water content; SOC, soil organic carbon; TN, total nitrogen; AP, available phosphorus; BD, soil bulk density; NO3, nitrate nitrogen; NH4, ammonium nitrogen; Nob, observed species number. Asterisks indicate significant correlation at p < 0.05.
Figure 3. Relation of tree and soil physicochemical properties with bacterial alpha diversity and abundant genera. Canopy, average tree canopy diameter in a plot; Height, average tree canopy diameter in a plot; DOC, dissolved organic carbon; Wc, water content; SOC, soil organic carbon; TN, total nitrogen; AP, available phosphorus; BD, soil bulk density; NO3, nitrate nitrogen; NH4, ammonium nitrogen; Nob, observed species number. Asterisks indicate significant correlation at p < 0.05.
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Figure 4. Redundancy analysis (RDA) ordination diagram of bacterial genera patterns with soil physicochemical properties (represented by arrows). DOC, dissolved organic carbon; Wc, water content; SOC, soil organic carbon; TN, total nitrogen; AP, available phosphorus; BD, soil bulk density; NO3, nitrate nitrogen; NH4, ammonium nitrogen. The ten most abundant bacterial genera are shown in the diagram.
Figure 4. Redundancy analysis (RDA) ordination diagram of bacterial genera patterns with soil physicochemical properties (represented by arrows). DOC, dissolved organic carbon; Wc, water content; SOC, soil organic carbon; TN, total nitrogen; AP, available phosphorus; BD, soil bulk density; NO3, nitrate nitrogen; NH4, ammonium nitrogen. The ten most abundant bacterial genera are shown in the diagram.
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Table
Table 1. Plots characteristics.
Table 1. Plots characteristics.
PlotAltitudemSlope AspectTree Age YearTree Diameter *
cm		Tree Canopy Diameter
m		Tree Height
m
Block 1
1648NNANANANA
2635N57.080.923.18
3620N914.202.205.70
4648N1416.022.437.20
Block 2
1594SNANANANA
2600S58.101.233.73
3594S913.532.025.43
4600S1415.602.236.77
Block 3
1589SNANANANA
2594S57.881.173.33
3600S914.101.894.88
4620N1414.482.176.06
* Tree diameter was measured at 80 cm in height.
Table
Table 2. Sequence number before and after quality filtering.
Table 2. Sequence number before and after quality filtering.
PlotRaw Read NumberClean Read Number
Block-1
C41,8423,2196
AF536,72525,531
AF918,55214,845
AF1424,83017,550
Block-2
C29,84122,638
AF535,91128,070
AF919,02614,847
AF1425,68820,885
Block-3
C28,84421,768
AF523,59218,187
AF929,20123,483
AF1429,80324,056
C = crop monoculture; AF5 = 5-year-old walnut-based agroforestry; AF9 = 9-year-old walnut-based agroforestry; AF14 = 14-year-old walnut-based agroforestry.
Table
Table 3. Soil physicochemical properties in crop monoculture and agroforestry of different age classes measured at 0–10 cm depth.
Table 3. Soil physicochemical properties in crop monoculture and agroforestry of different age classes measured at 0–10 cm depth.
AgepHWc %BD g·cm−3SOC g·kg−1DOC mg·kg−1TN g·kg−1N-NH4+ mg·kg−1N-NO3− mg·kg−1AP mg·kg−1
C7.58(0.05)15.13(0.77)1.23(0.03)11.09(0.41)125.6(10.92)1.49(0.05)1.72(0.24)10.91(0.63)9.21(2.3)
AF57.78(0.01)18.72(0.87)1.24(0.02)9.91(0.21)75.2(4.82)1.30(0.10)2.16(0.28)9.32(0.46)10.32(4.48)
AF97.95(0.12)21.50(0.94)1.34(0.03)8.94(0.19)87.2(4.09)1.20(0.05)2.07(0.04)7.89(0.25)7.55(1.22)
AF148.17(0.04)21.16(0.92)1.32(0.02)10.90(0.60)77.6(1.60)1.48(0.14)2.89(0.32)14.32(0.97)15.28(7.85)
P values from two-way analysis of variance
Age0.0130.1030.0340.4120.0140.1670.1320.1590.852
Block0.9570.5910.4260.4830.9440.6060.4240.2820.743
C = crop monoculture; AF5 = 5-year-old walnut-based agroforestry; AF9 = 9-year-old walnut-based agroforestry; AF14 = 14-year-old walnut-based agroforestry. Wc, water content; BD, bulk density; SOC, soil organic carbon; DOC, dissolved organic carbon; TN, total nitrogen; AP, available phosphorus. Values shown are the mean of treatment replicates (n = 3) with standard error. Values in bold mean the main effect is significant at p < 0.05.
Table
Table 4. Bacterial alpha diversity indices of crop monoculture and agroforestry of different age class.
Table 4. Bacterial alpha diversity indices of crop monoculture and agroforestry of different age class.
AgeRichnessDiversity
NobsChao1ShannonSimpson
C148432446.300.76
AF5162235257.060.84
AF9172437537.630.86
AF14216244179.600.97
P values from two-way analysis of variance
Age0.0190.0160.0130.055
Block0.0060.0630.0020.005
C = crop monoculture; AF5 = 5-year-old walnut-based agroforestry; AF9 = 9-year-old walnut-based agroforestry; AF14 = 14-year-old walnut-based agroforestry. Values in bold mean the main effect is significant at p < 0.05.

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Gao, P.; Zheng, X.; Wang, L.; Liu, B.; Zhang, S. Changes in the Soil Bacterial Community in a Chronosequence of Temperate Walnut-Based Intercropping Systems. Forests 2019, 10, 299. https://doi.org/10.3390/f10040299

AMA Style

Gao P, Zheng X, Wang L, Liu B, Zhang S. Changes in the Soil Bacterial Community in a Chronosequence of Temperate Walnut-Based Intercropping Systems. Forests. 2019; 10(4):299. https://doi.org/10.3390/f10040299

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Gao, Pengxiang, Xiaofeng Zheng, Lai Wang, Bin Liu, and Shuoxin Zhang. 2019. "Changes in the Soil Bacterial Community in a Chronosequence of Temperate Walnut-Based Intercropping Systems" Forests 10, no. 4: 299. https://doi.org/10.3390/f10040299

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