1 INTRODUCTION
Desertified reclamation for irrigated agricultural use is increasingly
necessary to meet with the needs of a growing global population, and
large swathes of reclaimed farm and forest land have thus been
established throughout the world (Salama,
Abd El-Ghani, El-Tayeh, Amro, & Abdrabbu, 2017;
Yang et al., 2019). The reclamation of
desertified land to generate economic forest lands has been practiced in
regions of Northwest China such as the Hongsibu District in Ningxia.
This region experiences a very dry climate and significant temperature
variability depending on the time of day such that few crops can
reliably grow in this area. However, winegrapes can grow well under
these conditions, leading to their increasingly widespread cultivation
and rising incomes for grape-growers. Through concerted desertified land
reclamation efforts over the past two decades in Hongsibu, the winegrape
cultivation area had risen to 7067 hm2 as of 2018
(Statistics of Ningxia Wine Bureau). These land reclamation efforts
result in well-documented changes in soil quality attributable to
changes from desert soil to irrigated cropland
(Ferreira, Leite, de Araújo, & Eisenhauer,
2016; Tosi et al., 2016). Crop
cultivation, however, can also reduce certain aspects of soil quality
including soil organic carbon (SOC) and nitrogen levels (Liu et al.
2005; Bhattacharyya et al., 2014;
Sharma et al., 2017), in addition to
altering the composition of soil microbial communities
(Crecchio et al., 2007). Exploring how
soil properties change following crop planting is essential to more
fully assess the relative success of different land reclamation
strategies in order to support sustainable land management efforts.
Soil quality is an integrative concept regulated by the physical,
chemical, and biological properties of the soil
(Oliver, Bramley, Riches, Porter, &
Edwards, 2013). Bulk soil density, porosity, soil organic matter/SOC
content, pH, and available nutrient levels are among the most commonly
used metrics to gauge such soil quality
(Bünemann et al., 2018;
Morvan et al., 2008;
Mouazen, Steffens, & Borisover, 2016).
Changes in soil properties following reclamation are vital to the
monitoring of sustainable land use. Microbial diversity in the context
of desertified soils, however, is not well documented and must be
further studied to more fully clarify how winegrape planting impacts the
properties of the underlying soil.
Reclamation-related shifts in soil microbial communities can be positive
or negative depending upon the land-use type. For example, converting
land use from a natural ecosystem to a managed agricultural ecosystem
can adversely impact soil microbial diversity (Thomson et al., 2015),
whereas the conversion of lands with extreme conditions such as high
salinity or aridity into cropland can positively impact associated
microbial communities (Wang et al., 2012). Many reports have
demonstrated the effects of land-use changes on soil microbial community
composition and diversity in very arid environments, with several soil
properties being closely linked to these microbial shifts. For example,
alterations in soil pH and moisture content were shown to be related to
bacterial communities therein following 30 years of agricultural
practice in desert soil in Egypt (Köberl, Müller, Ramadan, & Berg,
2011). In the Atacama Desert, soil relative humidity was also the
primary determinant of microbial community richness and diversity
(Neilson et al., 2017). These findings emphasized the overriding impact
of water availability and related soil properties on microbial community
composition in the context of improving soil quality and productivity in
arid regions throughout the world. The Yellow River flows through
Ningxia, and owing to the Yellow River Irrigation Project, water is not
a major limiting factor associated with soil quality, whereas high
salinity, low organic matter, and nutrient levels are likely to be
important regulators of microbial community composition and associated
shifts in productivity (Cheng, Chen, & Zhang, 2019). Further studies
regarding these concomitant changes in soil properties and microbial
communities in these desertification areas are thus necessary to ensure
sustainable land use.
Soil microbial communities are highly sensitive to variations in soil
physicochemical properties (Franco-Otero, Soler-Rovira, Hernández,
López-de-Sá, & Plaza, 2012) and land use alterations (Szoboszlay,
Dohrmann, Poeplau, Don, & Tebbe, 2017; Tosi et al., 2016; Wu et al.,
2017). Recent research suggests that sustained monocropping can reduce
the diversity of soil microbial communities and can interfere with soil
microbial community structure (Ellouze et al., 2014; Li, Ding, Zhang, &
Wang, 2014). In one study, bacterial richness and diversity were shown
to rise following the conversion of desert land to 5-year-old cropland,
and thereafter remained stable following 5 years of cotton cropping (Li
et al., 2020). Land-use type can profoundly shape soil bacterial
community composition and diversity owing to the resultant shifts in
soil chemical properties, particularly in the context of desertified
land reclamation (Li, Pokharel, Liu, & Chen, 2020). There have been few
studies to date, however, assessing changes in soil microbial
communities and associated soil parameters after desert land reclamation
for viticulture.
Herein, we evaluated soil microbial community composition and diversity
in vineyards following the reclamation of desertified land in Hongsibu
in Northwest China, with the goal of providing a theoretical foundation
for sustainable land use efforts. We hypothesized that: (a) soil
microbial communities would be significantly altered following
desertified land reclamation in a manner associated with the types of
grapes grown in different vineyards, and (b) that soil microbial
community composition in these reclaimed lands is attributable to the
soil chemical properties (such as pH, SOC, nitrogen, and phosphorus
levels) associated with these different land use types.
2 MATERIALS AND METHODS2.1 Study area
This study was conducted in the Huida Chateau and Xiaojiayao winegrapes
planting area in the Hongsibu District of Northwest China (Figure 1).
Hongsibu is the largest wine-growing sub-area in the eastern region of
the Helan Mountains, which is a wine region with a nationally protected
geographical indication. While previously dominated by natural desert
land (DL) populated by a small number of salt-tolerant plants able to
grow under arid conditions including Populus L. ,Zygophyllaceae , Ulmusglaucescens , Leguminosae , andElaeagnaceae (Xu, Wang, Jia, & Guo, 2020), the reclamation of
this land in the 1990s has led to its use for crop growth. Winegrapes
are the crop best-suited to growing in this region owing to its arid
conditions and substantial diurnal temperature variability.
The study site was situated within a mountain basin with an area of
2,767 km2 at an altitude of 1,240 - 1,450 m with a
typical temperate continental climate. The region remains dry throughout
the year with an average annual precipitation of 251 mm, an average
annual evaporation of 2,387 mm, an average annual temperature of 8.7°C,
a daily temperature difference of 13.7 °C, a sum of accumulated
effective temperatures (≥10°C) above 3,200°C, 2,900 - 3,550 h of annual
sunshine, and an averageannual wind speed of 2.9 - 3.7 m/s. The soil
exhibits a sandy loam texture (50% clay, 30% silt, and 20% sand). The
same type of soil was used in the desertified land reclamation
associated with all studied vineyards, which are regularly managed, with
plants arranged in north-south rows at a row spacing of 3 m, a vine
spacing of 0.5 m, and
cultivation
mode of single cane ‘Dulonggan’, only Italian Riesling cultivation mode
of Crawled Cordon Training. Annual fertilization amounts include an
estimated 40.18 kg/hm2 of nitrogen fertilizer, 30.21
kg/hm2 of phosphate fertilizer, 8.68
kg/hm2 of potassium fertilizer, and 22.18
kg/hm2 of organic manure, while the annual irrigation
water volume is 4000 m3/hm2.
2.2 Experimental design and soil sampling
Winegrape
croplands reclaimed from desert land in 2012, 2014, and 2017 were
selected for the present study in 2020, with crops of Cabernet Sauvignon
(CS), Italian Riesling (IR), Merlot (M), and Chardonnay (C) grapes
having been planted 8, 6, 3, and 3 years following reclamation,
respectively. Undisturbed, non-cultivated desertified soil was collected
at two research sites adjacent to these vineyards, and the same amount
of soil from each site was evenly mixed following collection. The
distance between these two sampling sites varied from 0.5 – 3 km.
In September 2020, three replicate 10 × 10 m plots separated by at least
100 m were established. Within each plot, a 0-20 cm rhizosphere soil
sample was collected using a steel corer (5 cm diameter) following
surface litter removal. In total, 10 random cores were collected per
plot and combined into a single composite sample. Three samples of
undisturbed soil were taken from the middle of these cores
(~10 cm depth) to assess soil bunk density using a corer
(5 cm diameter; 100 cm3). Soil samples were sealed
into plastic bags and kept on ice while in transit. In the laboratory,
samples were passed through a 2 mm sieve to remove coarse matter and
plant debris, after which samples were split into two parts, one of
which was air-dried for physicochemical analyses and the other of which
was stored at -80°C and used for DNA isolation within 1
week.
2.3 Soil physicochemical analyses
After soil had been dried to a constant weight at 105°C, its bulk
density was established. Soil pH was measured at a water-soil ratio of
2.5:1 without any CO2 water extraction via a
potentiometric method. Total nitrogen (TN) was assessed via the
micro-Kjeldahl method (Lu 1999). Soil available nitrogen was quantified
via an alkaline hydrolysis diffusion approach (Bao 2000). Soil ammonium
nitrogen (NH4+-N) and nitrate-nitrogen
(NO3--N) were leached with a 2 mol/L
potassium chloride solution and then measured via flow injection
analysis. Soil total phosphorus and available phosphorus were quantified
using a molybdenum-antimony anti-colorimetric approach (Bao 2000). Soil
total organic carbon was assessed via an external heating method using
potassium dichromate (Carter &Gregorich, 2007). A flame photometer
approach was employed to measure total potassium content, while
available potassium was quantified via an ammonium acetate
extraction-flame photometric method (Bao 2000; Smith & Bain, 1982).
2.4 DNA extractionand sequencing
An
OMEGA Soil DNA Kit (Catalog number D5625-01) was used to extract soil
DNA, and DNA concentrations were measured with a UV-visible
spectrophotometer (Thermo Scientific, DE, USA). DNA quality was assessed
via 0.8% agarose gel electrophoresis. The V3 andV4 bacterial 16s rRNA
hypervariable regions were amplified with the 5’-ACTCCTACGGGAGGCAGCA-3’
forward primer and the 5’-CGGACTACHVGGGTWTCTAAT-3’ reverse primer.
Additionally, the V1 hypervariable region of the fungal ITS gene was
amplified with the 5’-GGAAGTAAAAGTCGTAAGG-3’ forward primer and the
5’-GCTGCGTTCTTCATCGATGC-3’ reverse primer. A sequencing library was
constructed with the TruSeq Nano DNA LT Library Prep Kit (Illumina).
After library quality had been assessed, community DNA fragments were
analyzed via paired-end sequencing using an Illumina MiSeq instrument by
Shanghai Peisenol Biotechnology Co., Ltd.
2.5 Data processing
Raw high-throughput sequencing data were initially assessed to measure
sequence quality. Samples were separated into libraries according to
barcode information, and barcode sequences were removed. Those sequences
> 200 bp in length with a mean quality score ≥ 20 were
retained for further analysis. Primer removal, mass filtration,
denoising, splicing, and the removal of chimeric sequences were
conducted via the DADA2 method (Callahan et al., 2016). Rather than
performing similarity-based clustering, this approach only consisted of
de-replication. Amplicon sequence variants (ASVs) or operational
taxonomic units (OTUs) were generated according to the QIIMA2 DADA2
quality control results. After rarefaction-based normalization to the
same sequencing depth (95% of the lowest sample sequence depth),
ASVs/OTUs were predicted for each sample along with relative abundance
at that sequencing depth (Kemp, & Aller, 2004).
2.6 Statistical analyses
Data normality and homogeneity of the variance were respectively
analyzed via the Shapiro-Wilk and Levene’s tests, after which soil
properties were compared by conducting one-way ANOVAs. Venn diagrams
were used to identify shared and unique ASVs/OTUs associated with
different land-use types. Observed species, Chao1, Shannon, and Simpson
indices were used to assess the diversity and richness of soil microbial
communities at the ASV/OTU level (Qiao, Zhang, Shi, Song, & Bian,
2018). A nonmetric multidimensional scaling (NMDS) approach was employed
to assess differences in soil microbial communities across all soil
sample types based upon Bray-Curtis distances, with similarities between
samples being displayed via hierarchical clustering analyses. A
permutational multivariate analysis of variance (PERMANOVA) approach was
used to test for differences in soil microbial composition as a function
of land-use type based upon the adonis test (Anderson, 2001; McArdle, &
Anderson, 2001). LDA Effect Size (LEfSe) was employed to identify
land-use type-related biomarkers (Segata et al., 2011). A redundancy
analysis (RDA) was performed to examine correlations between microbial
communities and soil chemical properties using Canoco 5.0. Pearson
correlation analyses were conducted to examine relationships between
soil properties and microbial communities. R, QIIME 2.0, and SPSSS 24.0
were used for all analyses.
3 RESULTS3.1 Soil properties
Significant differences in soil chemical properties were observed as a
function of land-use type (Table 1). DL soils exhibited a significantly
higher bulk density relative to other land-use types, whereas DL soil
had the lowest levels of TN and available potassium (Table 1). TN levels
were highest in C soils (0.40 g kg-1), while available
nitrogen was the highest in CS soils (37.63 mg kg-1),
and TN levels were significantly higher in CS, M, C, and IR soil samples
relative to DL samples (p < 0.05). Available phosphorus
levels were highest in DL soil (7.86 mg kg-1), with
significant differences in available phosphorus in the CS, M, C, IR, and
DL samples (p < 0.05) without any concomitant
differences in TP. TK exhibited a downward trend from CS, M, C to IR,
and then rose to DL. Available potassium levels in CS, M, C, and IR
samples were 1.44, 1.42, 1.37, and 1.13 times those in DL samples. The
TN, SOC, and pH of CS, M, C, and IR soil samples were all significantly
higher than those in DL samples (p < 0.05).
3.2 Bacterial and fungal taxa distributions in soils associated with
different land-use types
In total, 1,942,867 original sequences and 1,693,153 effective sequences
were derived from the 16S_V3V4 region in sequences soil samples, while
1,233,294 original sequences and 1,085,213 effective sequences were
obtained from the ITS_V1 region. In total, 165 bacterial ASVs/OTUs were
found to be shared across all five land-use types, with the numbers of
unique bacterial ASVs/OTUs in these soil samples being rank-ordered as
follows: C > IR > M > CS
> DL. Following reclamation, soil bacterial ASVs/OTUs in
the CS, M, C, and IR samples rose to 2846, 3191, 7630, and 6373,
respectively. A total of 55 shared fungal ASVs/OTUs were detected across
land-use types, while numbers of unique fungal ASVs/OTUs associated with
these different land-use types were, in rank-order: IR > M
> CS > C > DL. Following
desertified land reclamation, the number of soil fungi present in CS, M,
and IR vineyards rose to 788, 984, and 1114, respectively, but the
number of fungi in C was lower than in DL (Figure 2).
Actinobacteria (33.58%), Proteobacteria (30.71%),Acidobacteria (12.67%), Chloroflexi (8.20%),Gemmatimonadetes (6.17%), Firmicutes (2.65%),Bacteroidetes (1.57%), and Rokubacteria (1.14%) were the
most abundant bacterial phyla in analyzed soil samples (Figure 3a).Actinobacteria and Proteobacteria were the dominant phyla
in CS, M, C, and IR soils, whereas Actinobacteria (24.08%),Proteobacteria (22.47%), and Acidobacteria (25.74%) were
dominant in DL soils. Relative Actinobacteria andProteobacteria abundance in CS, M, C, and IR soils was higher
than that observed in DL soils. Relative Chloroflexi abundance
was significantly higher in CS, C, and IR soils relative to DL soils
(p <0.05), whereas Acidobacteria andFirmicutes abundance was significantly lower in CS, M, C, and IR
soils relative to DL soils (p <0.05).
Ascomycota (71.50%), Basidiomycota (8.87%),Mortierellomycota (5.87%),Mucoromycota(0.86%), and Rozellomycota (0.17%) were the most dominant fungi
in analyzed soil samples (Figure 3b), with Ascomycota ,Basidiomycota, and Mortierellomycota being the most
dominant phyla associated with different land-use types. Relative to DL
samples, the relative abundance of Ascomycota in CS, M, C, and IR
samples rose by 5.45%, 6.06%, 34.47%, and 21.59%, respectively.Ascomycota abundance was significantly higher in C and IR soils
relative to DL soil (p < 0.05). Basidiomycotalevels were significantly higher in CS and M soils relative to DL soil
(p < 0.05), whereas Mortierellomycota abundance
was significantly lower in M, C, and IR samples relative to DL soils (p
< 0.05).
3.3 Bacterial and fungal community diversity
Next, bacterial richness and diversity in soils associated with
different land-use types were measured using the Chao1, Observed
species, Shannon, and Simpson indices (Table 2). The rhizosphere
bacterial abundance (Observed species) for the four vineyard soil types
was significantly higher than that in DL samples, while bacterial
abundance in C and IR samples (Chao1) was significantly higher than that
in DL soil (p < 0.05). Relative to DL soils, the
bacterial diversity (Shannon) in C and IR samples was significantly
increased (p < 0.05), while the Simpson index was
significantly increased in CS and C samples. Relative to DL soil
samples, viticultural land use was associated with increased soil fungal
diversity in all soil types other than the C soil (Table 2).
The NMDS ordination of bacteria and fungi are 0.0837 and 0.0999,
respectively, with both of these values being less than 0.2, consistent
with good analysis results (Figure 4). NMDS revealed clear separation in
bacterial composition across land-use types with the exception of CS and
M samples, with close clustering of the community composition profiles
associated with these two land-use types.
Hierarchical clustering analyses were then performed using the Bray
Curtis distance algorithm, revealing that bacteria in vineyard soil
samples were clustered together and separated from those in DL soil
(Figure 5a). Similarly, for fungal communities, all vineyard soil
samples were separated from all DL soil samples other than sample
DL2 (Figure 5b). These data suggested that microbial
community structure differed significantly when comparing vineyard soils
to DL soils.
A PERMANOVA analysis revealed significant differences in soil bacterial
composition rankings for different land-use types (R2= 0.65, p = 0.001), with the same also being observed for soil
fungal community composition rankings (R2 = 0.58,p = 0.001) (Table 3). LEfSe analysis results indicated that
bacteria that differed significantly between land-use types at the class
level included Chloroflexi , Fibrobacteres ,Firmicutes , Gemmatimonadetes , Patescibacteria ,Proteobacteria, and Rokubacteria (Figure 6a), with further
details regarding differences at other taxonomic levels being further
detailed in the corresponding Figure. Fungi that differed significantly
between land-use types at the class level included Rozellomycota ,Trichothecium , Eremomycetaceae , and Selenophoma(Figure 6b), with further details being shown in the corresponding
Figure.
3.4 Associations between soil bacterial and fungi communities,
diversity, and soil chemical properties
Pearson correlation analyses revealed that several soil chemical
properties were correlated with bacterial and fungal diversity (Table
S). TN was positively correlated with bacteria Chao1 (r=0.558,p <0.05), Observed species (r=0.562,p <0.05), and Shannon index values (r=0.627,p <0.05). Soil nitrate-nitrogen was significantly
positively correlated with bacteria Chao1 (r=0.970,p <0.01), Observed species (r=0.970,p <0.01), and Shannon index values (r=0.823,p <0.01). Soil ammonium nitrogen was significantly
positively correlated with bacteria Chao1(r=0.812,p <0.01), Observed species (r=0.823,p <0.01), and Shannon index values (r=0.701,p <0.01). SOC and pH were positively correlated with
bacterial Chao1 and Observed species index values.
An RDA analysis was then performed to assess the relationships between
soil chemical properties and the composition of soil fungal and
bacterial communities at the phyla level (Figure 7). The proportion of
the total variance in these bacterial communities explained by all eight
measured soil chemical parameters analyzed in this study was 82.91%
(Figure 7a). The first constraint axis (RDA1) explained 54.25% of this
variance, while the second (RDA2) explained 28.66% of the total
variance. Overall, pH explained 42.20% (p = 0.004) of total
variance in bacterial community composition, followed by available
nitrogen (14.10%, p = 0.032). The measured soil chemical
parameters in the present study similarly explained 73.97% of the total
variance in soil fungal communities (Figure 7b). The first constraint
axis (RDA1) explained 71.35% of this variance, while the second (RDA2)
explained 2.64% of this variance. Additionally, pH explained 31.50% of
the total variance (p = 0.014), followed by available phosphorus,
available nitrogen, and SOC.