Effects of different long-term farmland mulching practices on the loessial soil fungal community in a semiarid region of China

Applied Soil Ecology 137 (2019) 111–119

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Applied Soil Ecology journal homepage: www.elsevier.com/locate/apsoil

Effects of different long-term farmland mulching practices on the loessial soil fungal community in a semiarid region of China ⁎

Fangyuan Huanga,b,c, Zihan Liua,b,c,1, Hongyan Moua,b, Peng Zhanga,b,c, , Zhikuan Jiaa,b,c,

T

⁎

a

College of Agronomy, Northwest A&F University, Yangling 712100, Shaanxi, China Key Laboratory of Crop Physi-ecology and Tillage Science in Northwestern Loess Plateau, Ministry of Agriculture, Northwest A&F University, Yangling 712100, Shaanxi, China c The Chinese Institute of Water-saving Agriculture, Northwest A&F University, Yangling 712100, Shaanxi, China b

A R T I C LE I N FO

A B S T R A C T

Keywords: Community structure Farmland mulching Semiarid area Soil fungi

The Northwest Loess Plateau is a typical area of dryland agricultural production in China. Farmland mulching is often used to preserve the soil moisture and increase crop yields. However, the impacts of long-term farmland mulching practices on the soil fungal communities in the loessial soil in semiarid areas of China were less explored. Thus, we investigated the variations in the soil fungal communities under different mulching patterns using high-throughput sequencing to understand how different mulching practices affect these communities. The following treatments were investigated: ridge-furrow mulching pattern (R), flat plastic film mulching (P), flat biodegradable film mulching (B), flat straw mulching (S), and conventional flat planting without mulching as a control (CK). Farmland mulching practices significantly affected the soil fungal diversity and community structure by changing the soil properties. Compared with CK, the P and S treatments significantly increased the soil fungal diversity, thereby suggesting that these practices provided the most suitable growth environment for fungi. The soil moisture and nitrate nitrogen (NO3-N) content were the most important factors that affected the fungal diversity. Nonmetric multidimensional scaling ordination showed that the fungal communities in CK were separated from those in R and B treatments and closely clustered with the communities in the P and S treatments, thereby suggesting that the fungal community compositions differed slightly between the P and S treatments and the CK treatment, but the soil fungal community compositions in the R and B treatments differed significantly from that in CK. In addition, the fungal community composition depended primarily on the soil temperature and NO3-N. These results indicate that different farmland mulching practices affected the soil moisture, soil temperature, and NO3-N to change the soil fungal community distribution patterns in this region. Overall, the soil fungal diversity and abundance were both higher in the P treatment than the other treatments. Therefore, flat plastic film mulching is recommended for semiarid areas.

1. Introduction

of water and mineral nutrients by plants, thereby increasing their ability to adapt to environmental stress (van der Heijden et al., 2008). Soil fungi are sensitive to anthropogenic disturbances, and intensive agricultural management practices such as mulching, tillage, and fertilization can cause changes in the abundance, diversity, and community composition of soil fungi (Beauregard et al., 2010; Peay et al., 2013; Kant et al., 2011; Tian et al., 2012). These changes in soil fungal communities can then affect plant nutrient levels and ecosystem functions (Lau and Lennon, 2011; Clemmensen et al., 2013). The Loess Plateau with an area of about 64 million ha is a major dryland agricultural region in China (Chen et al., 2015; Zhang et al., 2018). Precipitation is relatively rare in this area, with only about

Fungi are key components of the biogeochemical cycle and they have crucial roles in soil ecosystems by maintaining the soil quality and productivity through multiple biological processes, such as soil nutrient cycling, organic matter accumulation, residue decomposition, and soil structure formation (Ownley et al., 2010; Burke et al., 2011; Cheng et al., 2012). Fungi comprise a major proportion of the soil microbial population and they are major decomposers of soil organic matter (Hollister et al., 2010), with vital roles in carbon cycling and making nutrients available to plants in farmland and forest soils (Miura et al., 2013; Tedersoo et al., 2014). Mycorrhizal fungi promote the absorption

⁎

Corresponding authors at: The Chinese Institute of Water-saving Agriculture, Northwest A&F University, Yangling 712100, Shaanxi, China. E-mail addresses: [email protected] (P. Zhang), [email protected] (Z. Jia). 1 Co-first author. https://doi.org/10.1016/j.apsoil.2019.01.014 Received 16 October 2018; Received in revised form 16 January 2019; Accepted 29 January 2019 Available online 18 February 2019 0929-1393/ © 2019 Elsevier B.V. All rights reserved.

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(2012–2016) is shown in Table S1.

450 mm per year on average and great inter-annual variation. Furthermore, the annual evaporation exceeds 1500 mm in these areas (Jia et al., 2018). Therefore, the dryland farmland ecosystem in this area is characterized by low levels of soil moisture (SM) and nutrients (Delgado-Baquerizo et al., 2013), and it is highly susceptible to anthropogenic disturbance and climate change (Martins et al., 2015). Farmland mulching greatly improves the efficiency of crop water use by inhibiting soil evaporation and it has been used widely to improve the productivity of arid agricultural ecosystems (Ren et al., 2016; Wang et al., 2016). Studies have shown that biodegradable and plastic film mulching significantly increase corn yields in semiarid areas by improving the SM and soil temperature (ST) (Wu et al., 2016; Zhang et al., 2017). Zhao et al. (2014) showed that straw mulching can improve the crop yields and soil nutrient contents by protecting soil-associated water in arid regions. However, these previous studies focused on the effects of farmland mulching on SM, soil nutrients, and crop yields, whereas their impacts on the soil microecological environment and soil microbial community were not evaluated. Some studies have indicated that mulching can significantly affect soil fungal communities by influencing the soil microclimate. For example, Liu et al. (2012) observed that a plastic film mulching significantly changed the arbuscular mycorrhizal fungi community composition in a temperate semiarid region. In addition, Dong et al. (2017) showed that the abundance and diversity of soil fungi were significantly increased under film mulching in corn fields in the rainfed areas of northeastern China. Similarly, Qin et al. (2017) found that furrow mulching techniques significantly increased the diversity of rhizosphere fungi in potato soils in arid regions. However, previous studies concentrated on the effects of single mulching materials or models on soil microbial communities, and comparisons of different mulching materials or models have rarely been conducted. In particular, the effects of using conventional polyethylene and biodegradable film mulches have been poorly studied. In addition, most studies concentrated on the short-term effects of mulching practices, whereas the changes in the fungal community characteristics after the long-term mulching of dryland farmland on the Loess Plateau remain unclear. Therefore, a five-year field experiment was performed to evaluate the impacts of different mulching cultivation patterns on soil fungi in the semiarid region of the Loess Plateau. Fifteen soil samples were collected to analyze the diversity and composition of the soil fungal communities using Illumina HiSeq sequencing. The aims of this study were: (1) to compare the changes in the soil fungal communities associated with different mulching patterns, and (2) to determine the relationships between soil fungal diversity and the major taxa and soil parameters. We hypothesized that: (1) the application of different farmland mulching practices for five consecutive years would create different ecological environments for soil fungi, thereby leading to changes in the soil fungal diversity and community structure; and (2) according to previous studies, mulching with flat plastic film has greater effects on the SM and ST, so we considered that it would greatly influence the soil fungal diversity and community composition.

2.2. Experimental design The experiment used a completely randomized block design with three replicates per treatment, with an area of 58.8 m2 (14 × 4.2 m). The effects of the following five treatments were tested: (1) a ridgefurrow mulching pattern (R) with alternating ridges (15 cm high) and furrows of equal width (60 cm high), where only the ridges were mulched using plastic film (width = 70 cm) and maize was sown in the furrows; (2) flat plastic film mulching (P), where the flat surface was mulched completely with plastic film (width = 120 cm); (3) flat biodegradable film mulching (B), where the flat surface was completely mulched with biodegradable film (width = 120 cm); (4) flat straw mulching (S), where the whole maize stalk was mulched evenly with a coverage of 9000 kg ha−1; and (5) a control (CK) comprising conventional flat planting without mulching. The plastic film was clear, impermeable, and stable polyethylene film with a thickness of 0.008 mm (produced by Guyuan Yuande Plastic Products Co. Ltd., Ningxia, China), which did not decompose after harvesting the crop. After harvesting the crops (in the previous growing season), the old mulch film was removed completely. The white biodegradable film measured 1.2 m wide and 0.008 mm thick (produced by Showa Denko K. K., Japan). The main material in this film is Bionolle, which is a biodegradable polyester resin made from succinic acid derived from starch or sugar. Under natural environmental conditions, the film can be degraded by microorganisms and finally decomposed to yield CO2 and H2O. After the crop was harvested, the film degraded completely and it was not removed. In the S treatment, after harvesting the crops from the previous season, the undecomposed straw was removed and the mulch (plastic film, biodegradable film, and straw) was applied again with each mulching treatment after the land was prepared in the autumn (October–November) of the previous year. All of the trials were initiated in 2012. The spring maize seed cultivar “Dafeng 30” was sown in April each year and harvested in October (the specific spring maize sowing and maturation dates between 2012 and 2016 are shown in Table S2). Maize was sown using a hole-sowing/ fertilization machine with a density of 67,000 plants ha−1 (60 × 25 cm), and a depth of 4–5 cm. A hole-sowing/fertilization machine was used to apply a base fertilizer (140 kg ha−1 N and 150 kg ha−1 P2O5), where 140 kg ha−1 N was applied between the maize plants as a top dressing 69–75 days after maize planting at a fertilization depth of 4–5 cm. The sowing and fertilizer application rates were the same for all of the treatments. Potassium and irrigation were not applied throughout the entire year. No pests were present in any of the treatments during the experiment, and manual weeding was performed as required. 2.3. Soil sampling Soil samples were obtained from depths of 0 to 20 cm at 85 days after sowing the maize on July 16, 2016. The sampling positions were between the planting rows for all of the treatments and in the furrow planting zone for the R treatment. Nine replicate samples (top 0–20 cm) were sampled away from the plant roots in an “S” shape with a soildrilling sampler before mixing and homogenizing to obtain one composite sample per replicate site. The samples were passed through a 2mm sieve to remove roots and other visible debris. A portion of each soil sample was stored at 4 °C to analyze the soil dissolved organic carbon (DOC), soil dissolved organic nitrogen (DON), soil nitrate nitrogen (NO3-N), and ammonium nitrogen (NH4-N). A second portion of each soil sample was transported to the laboratory in an icebox, before storing at −80 °C. The remaining soil sample was air dried before measuring the soil pH, soil organic carbon (SOC), and soil total nitrogen (TN).

2. Materials and methods 2.1. Study site description The study site was located in Changcheng Village, Pengyang County, Ningxia, China. This village is located in the middle of the Loess Plateau (35°51′N, 106°48′E) at an average elevation of 1658 m, which has a typical temperate semiarid continental monsoon climate with annual precipitation of 430 mm. The vast majority of the annual precipitation occurs between July and September. The average annual temperature in this region is 6.1 °C, with a frost-free period of 140–160 days. The soil at the study site was loessial soil (14% sand, 26% silt, and 60% clay) and classified as a Calcic Cambisol (FAO/ UNESCO, 1993). The annual precipitation during the test period 112

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Table 1 Soil physicochemical properties measured in the 0–20 cm soil layer†. Treatments‡

R B P S CK P-values

pH

§

7.25 ± 0.06c 7.45 ± 0.03b 7.39 ± 0.01b 7.52 ± 0.04a 7.59 ± 0.04a < 0.001

SM

ST

%

°C

12.58 ± 0.12c 10.37 ± 0.21d 13.92 ± 0.21b 17.29 ± 0.25a 9.81 ± 0.23e < 0.001

23.37 ± 22.25 ± 24.61 ± 22.71 ± 25.82 ± < 0.001

0.18c 0.50d 0.21b 0.19d 0.25a

TN g/kg

NO3-N mg/kg

NH4-N mg/kg

DON mg/kg

DOC mg/kg

SOC g/kg

0.84 ± 0.01b 0.82 ± 0.02b 0.82 ± 0.01b 0.88 ± 0.02a 0.89 ± 0.01a < 0.001

20.46 ± 20.07 ± 27.03 ± 31.79 ± 26.72 ± < 0.001

2.37 ± 2.26 ± 2.56 ± 2.65 ± 2.58 ± 0.004

27.84 29.30 27.55 27.37 23.13 0.004

23.21 25.44 24.23 32.25 26.05 0.006

9.54 ± 9.22 ± 9.22 ± 9.65 ± 9.47 ± 0.008

1.01c 0.20d 2.13b 0.56a 1.03b

0.05b 0.14b 0.13a 0.08a 0.04a

± ± ± ± ±

1.77a 0.86a 1.63a 1.66a 0.99b

± ± ± ± ±

2.08b 3.11b 1.04b 3.25a 0.87b

0.13ab 0.16b 0.11b 0.17a 0.07ab

† pH: soil acidity; SM: soil moisture; ST: soil temperature (the average values of the 5, 10, 15, and 20 cm soil layers were used in this table); TN: total nitrogen; SOC: soil organic carbon; DOC: dissolved organic carbon; DON: dissolved organic nitrogen. ‡ R: ridge-furrow mulching pattern; P: flat plastic film mulching; B: flat biodegradable film mulching; S: flat straw mulching; CK: conventional tillage without mulching. § Different letters in the same column indicate a significant difference at (P < 0.05) determined by Duncan's multiple range test using SPSS 18.0.

2.4. Soil physicochemical measurements

2.6. Statistical analyses

The SM content (three replicates per plot; 0–20 cm) was determined by drying in an oven at 105 °C for 12 h to a constant mass, which was performed on the same day that the samples were collected. ST measurements (at depths of 5, 10, 15, and 20 cm) were obtained for three consecutive days using an earth thermometer with an angled stem, and the average ST based on all four measurements was reported as the ST for each plot. To determine the soil pH, the soil samples were suspended in water (soil:water = 1:2.5, w/v), shaken violently and left to stand for 30 min, then the measured with a pH meter (Mettler Toledo, Switzerland). The SOC content was determined according to the potassium dichromate oxidation method, and the TN content was assayed by the Kjeldahl method (Bremner and Mulvaney, 1982). The NO3-N and NH4N concentrations were determined by adding 50 mL of 1.0 M KCl to 5 g of each fresh soil sample, which was then shaken for 30 min, and the supernatants were subsequently collected by filtration and analyzed colorimetrically with a continuous flow analyzer (Autoanalyzer 3, Bran Luebbe, Germany). To determine the soil DOC and total dissolved nitrogen (TDN), the soil samples were suspended in water (soil:water = 1:5), shaken for 5 h, centrifuged at 13,000 rpm for 10 min, and then filtered through a 0.45-μm membrane (Jones and Willett, 2006). The C and N contents in the soil extracts were determined using a TOC analyzer (Elementar, Vario TOC, Germany). DON was calculated by subtracting the inorganic nitrogen (NO3-N and NH4-N) content from the TDN (Jones and Willett, 2006).

Soil physicochemical and microbial abundance data were analyzed by one-way analysis of variance (ANOVA) to identify differences among the treatments. For significant differences detected at P < 0.05, multiple comparisons were conducted using the Duncan's test. Alpha diversity was calculated using QIIME. The Chao1 estimation method and Shannon diversity index were used to calculate the richness and diversity of the fungal communities, respectively. Weighted UniFrac distance matrices were used to determine the beta diversity. Nonmetric multidimensional scaling (NMDS) was performed to visualize the clustering of the different samples. The similarities or differences in the separation of the soil fungal communities between samples were identified by analysis of similarities (ANOSIM). Canoco 5.0 was used for redundancy analysis (RDA) to examine the correlations between the fungal community compositions and environmental parameters. The significance of the environmental variable parameters was determined based on a Monte Carlo test with 999 permutations by selecting the manual forward selection procedure in RDA. Spearman's rank correlation tests were conducted to investigate the associations between fungal community compositions and soil physicochemical properties. All statistical analyses (ANOVA and Duncan's test) and Spearman's rank correlation tests were performed using SPSS 18.0 (SPSS Inc., Chicago, IL, USA). The NMDS and ANOSIM tests were performed using R v.3.3.3.

3. Results 2.5. DNA extraction, PCR amplification, and Illumina HiSeq

3.1. Changes in soil physicochemical properties

Soil microbial DNA was extracted from 0.5 g of each sample with a FastDNA SPIN Kit for Soil (MP Biomedicals, USA) following the manufacturer's instructions. The fungal ITS region was amplified using the primers F2045 5′-GCATCGATGAAGAACGCAGC-3′ and R2390 5′-TCCT CCGCTTATTGATATGC-3′ (Bellemain et al., 2010). The amplicons were extracted from 2.0% agarose gels using an AxyPrep DNA Gel Extraction Kit (Axygen Biosciences, Union City, CA, USA) and purified using a Qubit® dsDNA HS Assay Kit (Thermo Fisher, USA). Purified amplicons were pooled in equimolar concentrations and subjected to paired-end sequencing (2 × 250) using the Illumina HiSeq PE250 platform by Realbio Technology Co. Ltd. Shanghai, China according to standard protocols. PANDAseq was used to combine the paired-end sequence reads (Masella et al., 2012). USEARCH v5.2.32 was used to screen chimeras, for filtering and denoising data by clustering similar sequences with < 3% dissimilarity, and the remaining sequences were clustered to operational taxonomic units with similarity ≥97% (Edgar, 2013).

The soil physicochemical properties associated with different mulching patterns changed greatly after five years and the effects differed among the various treatments (Table 1). Among the treatments, the soil DOC and SOC were highest in the S treatment, where they increased by 23.79% (P < 0.05) and 1.94% (P > 0.05) compared with the CK treatment, respectively, and no significant differences were detected between CK and the other treatments. Compared with CK, the NO3-N and NH4-N contents in the R and B treatments were significantly decreased, whereas the difference of those was not significant between P treatment and CK. The soil TN values did not differ significantly among the R, P, and B treatments, but they were all significantly lower than that in CK (P < 0.05). SM was highest in the S treatment, followed by the P, R, B, and CK treatments. ST in each treatment was significantly lower (P < 0.05) than that in CK, and it was lowest in the S and B treatments. Except for the S treatment, the soil pH values were significantly lower than that in CK.

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Fig. 1. Changes in fungal richness (Chao1) and diversity (alpha diversity: Shannon; beta diversity: NMDS1) across all treatments. Different letters denote differences among the different treatments. R: ridge-furrow mulching pattern; P: flat plastic film mulching; B: flat biodegradable film mulching; S: flat straw mulching; and CK: conventional tillage without mulching.

and B treatments, and those of Basidiomycota, Zygomycota, and Glomeromycota were higher in the R and B treatments than the other three treatments. The most abundant class in all of the soil samples was Sordariomycetes (Fig. 2b; Table S3). At the order level, the main fungi across all samples were Ascomycota_unidentified and Sordariales. Compared with CK, the Mortierellales, Glomerales, and Spizellomycetales were increased significantly in all of the mulching treatments (Fig. 3d–f). In addition, the Hypocreales and Sordariomycetes_unidentified were significantly lower in the R and B treatments than the CK treatment (Fig. 3g–h), whereas the opposite trend was observed in the P and S treatments.

3.2. Fungal community diversity In total, 541,448 sequences were obtained from all of the soil samples. The sequences number ranged from 33,102 to 38,617 per sample (mean = 36,097). The mulching practices significantly (P < 0.05) affected the soil fungal richness and diversity (Fig. 1). The fungal richness was significantly lower (P < 0.05) in S than that in CK, but the differences (P > 0.05) were not significant between the other treatments and the CK treatment. The alpha diversity was highest in the S treatment and lowest in the R treatment, ranging from 6.80 to 7.20 (Fig. 1a). The fungal beta diversity was evaluated by NMDS (Fig. 1b). The degree of aggregation for the sample points from the R and B treatments was distal to those of the CK sample points, whereas the degree of aggregation for the sample points from the S and P treatments was proximal to those of the CK sample points. ANOSIM analysis showed that farmland mulching significantly affected the fungal beta diversity (r = 0.864, P = 0.001). The analysis of the effects of the soil properties on fungal diversity was presented in Table 2, indicating that the fungal richness was significantly related to t ST, and the alpha diversity (Shannon index) was significantly associated with SM, NO3-N, and NH4-N. The soil fungal beta diversity (NMDS1) was significantly associated with pH, TN, NO3N, NH4-N, and DOC.

3.4. Relationship between the fungal community and soil properties According to the forward selection in CANOCO, the three most important contributors of fungal communities were the soil NO3-N (F = 14.2, P = 0.001), NH4-N (F = 11.0, P = 0.001), and ST (F = 6.9, P = 0.003). The order of these effects was as follows: NO3-N > NH4N > ST > pH > DOC > TN > SM > SOC > DON (Fig. 4b). All of the environmental variables combined accounted for 82.20% of the variation in the fungal communities between samples. The first two sorting axes in the RDA explained 59.95% and 23.78% of the total variance (Fig. 4a). In addition, on the first sorting axis, the fungal communities were similar in the P, S, and CK treatments but significantly separated from those in the R and B treatments. The Spearman's correlation coefficients (Table 3) indicated that all phyla were significantly correlated with some of the soil properties. The phylum Ascomycota was significantly positively associated with pH, NO3-N, NH4-N, and TN, whereas that of the phylum Glomeromycota was significantly negatively correlated with these properties. In addition, the Glomeromycota was significantly negatively correlated with DOC. Basidiomycota was significantly negatively related to SM, NO3-N, and NH4-N. Zygomycota was significantly negatively correlated with pH, ST, NO3-N, and NH4-N, and Chytridiomycota was only significantly

3.3. Fungal community composition The relative abundances of the five most highly represented phyla and 11 classes with the greatest abundances in the 15 samples are shown in Fig. 2a and b, respectively. The dominant phyla in all of the treatments were Ascomycota, Basidiomycota, Zygomycota, and Glomeromycota, with average proportions of 65.74%, 14.60%, 10.72%, and 5.21%, respectively. In addition, Chytridiomycota and another rare phylum were detected at low abundances in all of the samples (Fig. 2a; Table S3). The relative abundance of Ascomycota was lower in the R

Table 2 Spearman's rank correlation coefficients between fungal richness (Chao1) and diversity (i.e., alpha diversity: Shannon; beta diversity: NMDS1) and soil characteristics†. Fungal richness and diversity

pH

SM

ST

TN

NO3-N

NH4-N

DON

SOC

DOC

Chao1 Shannon NMDS1 NMDS2

−0.324 0.452 0.865**‡ 0.597*

−0.379 0.543* −0.082 0.268

0.629* −0.114 0.121 0.300

−0.279 0.327 0.627* 0.345

−0.257 0.668** 0.571* 0.711**

−0.084 0.608* 0.540* 0.691**

0.036 0.114 −0.493 −0.221

−0.243 0.091 0.222 0.152

−0.343 0.579* 0.711** 0.457

† pH: soil acidity; SM: soil moisture; ST: soil temperature (the average values of the 5, 10, 15, and 20 cm soil layers were used in this table); TN: total nitrogen; SOC: soil organic carbon; DOC: dissolved organic carbon; DON: dissolved organic nitrogen. ‡ *0.01 < P value < 0.05. **P value < 0.01.

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Fig. 2. Changes in fungal phyla (a) and class (b) taxonomic compositions across all treatments.

4. Discussion

negatively associated with ST. Ascomycota was not significantly associated with SM, ST, and DOC, but the taxa Sordariomycetes, Ascomycota_unidentified, and Dothideomycetes and their respective orders were significantly positively correlated with DOC, ST, and SM, respectively (Table 3).

4.1. Farmland mulching practices affected the soil fungal diversity Soil fungal diversity plays important roles in terrestrial ecosystems (Neher, 1999), where a greater soil fungal diversity can enhance the capacity to resist external interference (Qin et al., 2014), and the crop productivity is also higher under higher fungal diversity conditions (Qin et al., 2017). Thus, increased fungal diversity may lead to stable

Fig. 3. Relative abundances of common orders under different mulching treatments. Error bars indicate the standard deviation of the relative abundance between three replicate samples. **P < 0.01, *P < 0.05. 115

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Fig. 4. Redundancy analysis (RDA) of the fungal communities and soil properties (a) and contributions and significances of each soil variable to the fungal community (b).

mulching enhanced the SM and nutrient contents to subsequently affect the soil microbial composition and increase the fungal diversity in cornfields. However, our results are inconsistent with those reported by Zhou et al. (2016) who investigated a black soil in Northeast China. The differences in these results may be attributable to variations in the nutrient status of the soils tested. The black soil tested in Northeast China was rich in nutrients, where the addition of excessive fertilizer to the soil led to very high nitrogen contents and a decreased pH value. In addition, some common taxa were rare or even absent. Thus, the balanced abundance of taxa in the previous habitat was disrupted, thereby leading to a decrease in fungal alpha diversity. Our study was performed in the Loess Plateau. The loessial soil is poor in this region but

agroecosystems and sustainable crop production (Zhou et al., 2016). In this study, the fungal alpha diversity of the P and S treatments was higher compared to the other treatments and CK (Fig. 1a), thereby indicating that the P and S treatments were more conducive to the development of complex and diverse fungal communities. The fungal diversity was higher in the P treatment than the B treatment, but the difference between the two treatments was not significant, thereby suggesting that the biodegradable mulch and conventional polyethylene mulch had similar effects on the soil fungal alpha diversity. In addition, the fungal alpha diversity was significantly positively associated with SM, NO3-N, NH4-N, and DOC. These findings are consistent with those obtained by Qin et al. (2017) who showed that plastic film

Table 3 Spearman's rank correlation coefficients between fungal composition and the soil characteristics.† Phyla‡

Class§

Order††

Ascom Sorda Sordar Sordari Hypoc Ascomy Ascomyc Dothi Pleos Leoti Eurot Peziz Basid Agari Agaric Treme Zygom Morti Chytr Chytr Spize Glome Glome Glomer

pH

SM

ST

TN

NO3-N

NH4-N

DON

DOC

0.559*‡‡ 0.690** 0.145 0.699** 0.643** −0.166 −0.061 0.438 0.066 0.418 −0.122 −0.586* −0.391 −0.406 0.393 −0.147 −0.617* −0.527* 0.030 0.013 −0.431 −0.731** −0.729** −0.788**

0.368 0.329 0.336 0.368 0.243 −0.271 −0.496 0.593* 0.668** −0.171 −0.071 −0.271 −0.582* −0.650** −0.268 0.443 0.075 0.125 0.071 0.043 0.121 −0.332 −0.321 −0.300

0.314 0.132 0.325 0.125 0.439 0.750** 0.661** 0.171 −0.007 0.171 0.179 −0.254 −0.157 −0.071 −0.357 0.043 −0.686** −0.811** −0.879** −0.900** −0.771** −0.157 −0.107 −0.136

0.600* 0.582* 0.349 0.559* 0.633* −0.079 0.043 0.320 −0.043 0.580* −0.142 −0.521* −0.323 −0.241 0.124 −0.618* −0.390 −0.359 −0.016 0.025 −0.501 −0.573* −0.552* −0.580*

0.832** 0.857** 0.546* 0.854** 0.886** 0.014 −0.111 0.893** 0.543* 0.282 −0.032 −0.757** −0.868** −0.843** −0.086 0.132 −0.654** −0.625* −0.346 −0.346 −0.596* −0.836** −0.814** −0.836**

0.787** 0.816** 0.719** 0.760** 0.689** −0.064 −0.261 0.792** 0.580* 0.317 −0.268 −0.631* −0.728** −0.717** −0.104 0.208 −0.710** −0.665** −0.286 −0.424 −0.512 −0.782** −0.814** −0.733**

−0.204 −0.121 0.068 −0.071 −0.264 −0.193 −0.257 0.068 0.221 −0.029 −0.007 0.179 −0.007 −0.086 −0.189 0.475 0.221 0.229 −0.011 −0.111 0.304 0.204 0.107 0.168

0.464 0.661** 0.279 0.571* 0.514* −0.414 −0.332 0.511 0.225 0.225 −0.468 −0.507 −0.464 −0.536* 0.279 −0.064 −0.321 −0.189 0.286 0.261 −0.129 −0.704** −0.704** −0.714**

†

pH: soil acidity; SM: soil moisture; ST: soil temperatures (the average values of the 5, 10, 15, and 20 cm soil layers were used in this table); TN: total nitrogen; SOC: soil organic carbon; DOC: dissolved organic carbon; DON: dissolved organic nitrogen. ‡ Phylum level: Ascomycota (Ascom), Basidiomycota (Basid), Zygomycota (Zygom), Chytridiomycota (Chytr), Glomeromycota (Glome). § Class level: Sordariomycetes (Sorda), Ascomycota_unidentified (Ascomy), Dothideomycetes (Dothi), Leotiomycetes (Leoti), Eurotiomycetes (Eurot), Pezizomycetes (Peziz), Agaricomycetes (Agari), Tremellomycetes (Treme), Chytridiomycetes (Chytr), Glomeromycetes (Glome). †† Order Level: Sordariales (Sordar), Sordariomycetes_unidentified(Sordari), Hypocreales (Hypoc), Ascomycota_unidentified (Ascomyc), Pleosporales (Pleo), Agaricales (Agari), Mortierellales (Morti), Spizellomycetales (Spize), Glomerales (Glomer). ‡‡ ⁎ 0.01 < P value < 0.05; ⁎⁎P value < 0.01. 116

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significantly lower in the R and B treatments than the P, S, and CK treatments (Fig. 2, Table S3). The greenhouse gas N2O may be produced during denitrification, so reductions in the abundances of fungal species that are capable of denitrification in the R and B treatments would inhibit N2O emissions, which may benefit the maintenance of the biosphere. We found that the Sordariomycetes was significantly positively associated with TN, NO3-N, and DOC (Table 3), possibly because the higher concentrations of these components provided adequate nutrients to these fungi. Mortierellales was abundant at the order level (Fig. 3) with a relative abundance of approximately 12%, which is consistent with the values observed in other studies (Lumley et al., 2001; Markovina et al., 2005). Species that belong to this order are considered to play important roles in temperate forest ecosystems (Webster and Weber, 2007; Zheng et al., 2016). Members of the order Mortierellales include fast-growing saprophytic fungi (Kjøller and Struwe, 2002), which primarily utilize simple soluble substrates, and some members are psychrophilic (Alexopoulos et al., 1996). We found that the Mortierellales was higher under mulching conditions than in CK, thereby suggesting that mulching provided a suitable habitat for the members of this order. The Mortierellales was significantly negatively associated with the soil pH and ST (Table 3), which indicates that these fungi were sensitive to environmental change and its abundance increased under mulching conditions. In particular, compared with the other treatments, the B mulching treatment was rich in Spizellomycetales and Agaricales, which are beneficial for nutrient cycling in the soil (Wakefield et al., 2010). Similarly, previous studies demonstrated that biodegradable films enriched certain taxa (Koitabashi et al., 2012; Muroi et al., 2016). In addition, the observed changes in the soil properties were associated with variations in the fungal communities, where the significant correlations between the changes in the relative species abundances and soil properties (Table 3) indicated that farmland mulching may have indirectly affected the soil fungal communities by altering the soil properties. RDA analysis showed that NO3-N, NH4-N, ST, pH, TN, and DOC were strong predictors of the fungal community distribution (Fig. 4). In addition, NO3-N and NH4-N were significantly positively correlated (r = 0.735, P = 0.002), and significant positive correlations were also detected between the pH with TN (r = 0.653, P = 0.008) and the pH with DOC (r = 0.702, P = 0.004). Previous studies have shown that fungi have a broader pH range for optimal growth, which typically covers 5–9 pH units, without significantly inhibiting their growth (Rousk et al., 2010). The soil pH changed significantly in the present study but the range of variation (0.14–0.35) was very small. Therefore, the strong power of the soil properties for explaining the changes in the fungal community composition may be attributed to the covariations in NO3-N, ST, DOC, and pH. In addition, the available nutrients (NO3-N and DOC) may have imposed stronger effects on the fungal community than the total nutrient contents (TN and SOC) (Zheng et al., 2016). Our results are consistent with previous studies by Rousk et al. (2010) and Zhou et al. (2016) who showed that the soil pH and nutrient levels were good predictors of the soil fungal composition.

an increase in the soil nitrogen content has enhanced plant biomass production, especially in the underground material, thereby creating conditions for the formation of SOC and TN (Kätterer et al., 2011). Furthermore, the below-ground plant material provides nutrients to the soil fungal communities to increase the fungal alpha diversity (Qin et al., 2014). Previous studies have shown that the fungal richness may be affected by biological or abiotic factors, such as plant diversity, SM, pH, and nitrogen availability (Bi et al., 2012; Gao et al., 2013; Wang et al., 2015). In the present study, we found that the fungal richness was strongly influenced by ST (Table 2) because the soil fungi richness increased as ST also increased, and the lowest richness was observed in the S treatment. The annual average temperature is low in the study region and ST is an important factor that limits plant growth (Wang et al., 2016). ST may affect heterotrophic respiration by altering the extracellular enzyme activity, microbial respiration rate, and the availability of substrates for soil microbes (Suseela et al., 2012). Previous studies also indicated that ST significantly affected the fungal richness under plastic film mulching (Dong et al., 2017). In addition, our analysis of the response of the fungal community structure to mulching indicated strong effects on the soil fungal beta diversity (Fig. 1b), which was highly related to the soil, pH, TN, DOC, NO3-N, and NH4-N (Table 2). These results may be attributable to the changes in ecological factors caused by mulching, which were ultimately responsible for the differences in the fungal communities (Meng et al., 2013). 4.2. Significant changes in soil fungal communities under mulching treatments Previous studies have revealed that the soil fungal composition is significantly affected by changes in the soil environment (Miura et al., 2013; Bandopadhyay et al., 2018). Farmland mulching practices can change the soil environment to potentially affect the microbial community composition (Liu et al., 2012). We found that different mulching practices significantly affected the composition of the soil fungal community. NMDS analysis indicated that the sample points from the R and B treatments aggregated distally relative to those of the CK sample points, whereas the sample points from the S and P treatments aggregated close to those of the CK sample points. These findings indicate that the compositions of the fungal communities in the R and B treatments differed significantly from that in CK, but the soil fungal community compositions differed only slightly between the P and S treatments and the CK treatment. These results may be explained by the different distributions of the primary taxa in the different mulching treatments. We found that the fungal communities were dominated by Ascomycota. Dong et al. (2017) also showed that Ascomycota was the most abundant fungal phylum in soil samples from northeastern China, where the members of this phylum comprise saprotrophic fungi that are significantly affected by the degradation of plant species and straw residues (van Groenigen et al., 2010; Hannula et al., 2012). Studies have shown that the growth rate of Ascomycota is related to the availability of nitrogen (Fontaine et al., 2011). In the present study, the Ascomycota was significantly lower in the R and B treatments than CK, but no significant differences were detected between the P and S treatments and CK (Fig. 2, Table S3). In addition, the phylum Ascomycota was positively associated with the soil NO3-N content (Table 3), which is consistent with previous reports of enhanced abundances of ascomycetes in soils with high nitrogen contents (Klaubauf et al., 2010; Zhong et al., 2015). Soil carbon decomposition may be significantly affected by the shift in the abundance of this taxon (Xiong et al., 2014). In the present study, Sordariomycetes was the most abundant class in the phylum Ascomycota (Fig. 2). In the class Sordariomycetes, the majority of the members of the order Hypocreales play important roles in the denitrification of soil ecosystems (Mothapo et al., 2015). Similar to Ascomycota, the relative abundances of Sordariomycetes were

5. Conclusions We found that the soil physicochemical properties (TN, DOC, NO3N, ST, and SM) changed significantly after five years of continuous farmland mulching in a semiarid area, thereby greatly affecting the composition and diversity of the soil fungal communities. NO3-N, ST, DOC, and pH were mainly related to the changes in the fungal community composition, whereas SM and the soil NO3-N and DOC contents were the primary factors that influenced the fungal diversity. The P treatment had the greatest impact on promoting the fungal community compared with the other treatments. Moreover, in terms of the other indicators (corn yield, SM, ST, plant roots, etc.) determined in this experiment (data not shown), the P treatment also performed better than the other mulching treatments. Therefore, flat plastic film 117

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mulching (P) is recommended as a suitable planting method for this region in order to achieve sustainable soil quality development and crop yields. Our findings provide important insights into the relationship between the fungal community structure and changes in the soil properties in this unique ecosystem. However, further research is needed to clarify the roles of the fungal communities in different soil environments under farmland mulching conditions, as well as their impacts on crop growth and health.

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