Crop rotation is an ancient
farming practice, which has been used for thousands of years in agriculture and still in use in today agriculture (Thrall et al. 2010). Different types of crops
have different nutrient interactions with the soil; therefore, long-term
continuous planting of the same plant will lead to a depletion of specific
nutrients in the ground (Hemmat and Eskandari
2006). For this reason, farmers often use crop rotation to preserve soil
fertility. Crop rotation is a farming practice in which different crops are
grown in the same filed at different times over several years, and which can
have positive or negative impacts on the environment and the economy.
Crop
rotation is beneficial for increasing soil nutrients, reducing soil erosion,
limiting pests and diseases, reducing the stress of weeds, and improving the
soil structure (Eerd et al. 2014; Shahzad et al. 2016a, b).
Moreover, keeping soil fallow for one season or more can restore soil fertility
through nutrient deposits (Nielsen and Calderón
2011). The rotation of B.
napus and wheat (Triticum
aestivum L.) is one of the most present rotations in France, and is
performed both in cropping systems and livestock rearing systems (Steinmann and Dobers 2013). Similarly, it is
also widely used in China. Compared with traditional rapeseed continuous
cropping system, the rotation of Brassica and wheat can bring higher production and less
reduction of soil fertility. However, in the western foot of Daxing'an Mountains
in Inner Mongolia, where soil erosion is severe, both rotation
and continuous cropping systems cannot maintain soil and water well. Therefore,
an appropriate fallow system is important. However, the combination of
short-term fallow and rotation farming is rarely used in Brassica farming practice, and its
effect is little known. The B. napus and wheat rotation have
been well applied in the western foot of Daxing'an Mountains
in Inner Mongolia. However, the continuous cropping leads to the soil and water
loss seriously, and the water conservation and storage capacity are also
reduced. Therefore, timely fallow may be a scientific way to restore soil
health.
The
microorganism is one of the most active components of the soil ecosystem. Soil
microbial biomass was used as a valid index for evaluating early changes in
soil fertility and soil quality (Singh and Gupta
2018). Plant diversity is now recognized as an essential driver of soil
microbial diversity (Berg and Smalla 2009).
It has been posited that increasing aboveground biodiversity results in the
increasing of microbial diversity belowground (Hooper et al. 2000).
Studies have shown that fallow can also change soil microbial community
structure (Sileshi et al. 2008; Reardon et al. 2014). Moreover, root metabolism is
different in different growth stages of crops, which can interact with soil
temperature, water, and soil physical properties. All these influencing factors
can lead to different soil microbial biomass and microbial distribution (Sun et al. 2009). Besides, fallow cropping practice has
various effects on soil physical, biological, and chemical properties (Gomez-Montano et al. 2013),
which may bring changes to soil microorganisms and ground plants.
In
Inner Mongolia of China, due to its unique climatic conditions and
characteristics of lower soil and water conservation, fallow is essential for
the soil recovery. Although the use of more frequent fallows is necessary for
sustainable management of soil and water conservation and soil fertility, more
fallows mean a reduction of crop production in a certain land area. However, a
combination of short-term fallow and crop rotation may be one solution. The purpose of this study was to explore
the effects of short-term fallow in rotation on brassica yield and soil
microorganism community. In general, long-term fallow
can bring great changes, but there is little practical experience in short-term
fallow combination and rotation. Therefore, this study was conducted with the
hypothesis that short-term fallow has the potential to improve
soil health and yield of B. napus L.
Materials and Methods
Experimental location
This
study was carried out at Tenihe test station, Chenbarhu
County (N 48°48′–50°12′, E 118°22′–121°02′), Hulunbuir city, Inner Mongolia, China. The climate of Chenbarhu County is the typical semi-arid
plateau and highland climate with large evaporation in
spring, concentrated precipitation in summer, early frost in autumn and
extended snow cover in winter. The average annual temperature of Chenbarhu county ranges from -1 to -2°C with 120 days of a frost-free
period. The average annual precipitation is about
360 mm (Fig. 1). Moreover, the average day sunshine time is 7.67 h. The
cropping season is from May to September (about 110 d) every year, with an
average temperature of 24°C. Pre-sowing soil analysis was conducted to
record the content of OM (organic matter), TN (total nitrogen), TP (total phosphorus) and pH at the initial
stage of the experiment i.e., June
2015 (Table 1).
Farming system and soil sample collection
In order to compare the
changes in soil microbial diversity under different farming system, this study
set up three farming system, respectively: B.
napus continuous cropping (BB; control group the local
traditional farming system), wheat-B. napus rotations (WB;
locally improved farming system),
and wheat-short-term
fallow-B. napus (WFB;
the farming system used in this study). Experiment was laid out following
randomized complete block design with three replications in all three years of
study. Fertilizers were applied at the rate of 69, 165 and 30 kg NPK ha-1,
respectively using urea, di-ammonium phosphate and potassium sulfate as source.
Moreover, boron was also applied at 3 kg
B ha-1 using boric acid as source. Traditional flood
irrigation was used during the planting period. The wheat cultivar was
"Dragon 36". For B. napus, "Qingza 5" was used. The sowing time of B. napus was on May 6th to
10th. For group BB, the sowing time of B. napus was on May 6th to
10th annually (from 2015 to 2017). For group WB, the sowing time of
wheat was on May 3th to 5th (in 2015), then sowing B. napus
seeds (on May 6th to 10th, in 2016), and continue to grow wheat in the last year
(in 2017). For group WFB, we sow wheat seeds in 2015 (on May
3th to 5th),
flowed by fallow (in 2016), then sow B. napus seeds on
May 6th to 10th
in 2017. B. napus straw returning measures were consistent with
that of wheat.
Soil samples collection
Soil samples were collected at the flowering and harvest
stage (7 to 10 days after harvest) in 2016 and 2017, respectively. Briefly,
according to the "S" route, at least 3 biological repeat samples were
taken in each planting area (4 samples per group were collected at the
flowering stage in 2016). Soil samples (500 g) were collected with a depth of
20 cm below the surface of the soil. After removing impurities, soil samples
were transported in dry ice and stored at -80°C before DNA extraction.
The basic properties
measurement
Table
1: Effect of different cropping
systems on soil properties in different years
Years/ Cropping Systems |
Soil organic matter (%) |
Soil water content (%) |
Soil total nitrogen (g kg-1) |
Soil total phosphorus (g kg-1) |
||||||||
BB |
WB |
WFB |
BB |
WB |
WFB |
BB |
WB |
WFB |
BB |
WB |
WFB |
|
Jun 2015 |
14.01±0.11b |
14.31±0.11a |
14.11±0.11b |
21.61±0.11NS |
21.41±0.41 |
20.91±0.01 |
1.41±0.11NS |
1.41±0.11 |
1.41±0.01 |
115.71±8.91ab |
113.01±4.91b |
120.91±7.61a |
Sep 2015 |
14.11±0.01b |
14.51±0.01a |
14.41±0.21a |
19.91±0.21NS |
19.81±0.21 |
19.91±0.11 |
1.41±0.11NS |
1.51±0.11 |
1.51±0.01 |
108.71±7.71b |
110.41±8.31ab |
118.01±4.91b |
Jun 2016 |
14.51±0.21NS |
14.71±0.21 |
14.71±0.21 |
20.51±0.11NS |
20.81±0.21 |
20.61±0.01 |
1.31±0.21b |
1.51±0.11a |
1.51±0.01a |
112.21±6.71b |
123.61±4.11a |
122.91±6.41a |
Sep 2016 |
14.11±0.01b |
14.71±0.01a |
14.51±0.31a |
20.21±0.11NS |
20.41±0.41 |
20.71±0.21 |
1.31±0.21b |
1.41±0.11b |
1.61±0.11a |
105.71±8.51c |
121.61±5.91a |
125.91±8.51a |
Jun 2017 |
14.41±0.11b |
14.71±0.01a |
14.61±0.11a |
21.71±0.11NS |
21.41±0.11 |
21.31±0.11 |
1.21±0.11c |
1.41±0.01b |
1.71±0.11a |
105.11±6.21c |
120.81±4.31b |
138.31±7.11c |
Sep 2017 |
14.51±0.01b |
14.71±0.01a |
14.71±0.11a |
20.61±0.71NS |
21.21±0.41 |
20.51±0.31 |
1.31±0.11c |
1.51±0.11b |
2.01±0.21a |
90.91±9.11c |
121.61±8.31b |
141.91±8.11a |
Means ± SD different letters are significantly different
from each other at 5% probability level
WFB= Wheat-fallow-oilseed rape system; WB= Oilseed
rape-wheat-oilseed rape; BB= Oilseed rape continuous cropping
Fig. 1: Weather data during crop seasons
A= Temperature from May
to September in 2015, 2016 and 2017; B=
Precipitation
from May to September in 2015, 2016 and 2017
Soil samples were collected in June and September
annually at the depth of 20 cm below the soil surface. Soil physical and
chemical properties were measured following the methods described by Bao (2000). Briefly, the soil organic matter (OM)
content was determined using the potassium dichromate volumetric method; total
nitrogen (TN) was determined by Kjeldahl method; total phosphorus (TP) was
determined using sulfuric acid-perchloric acid
digestion method; Soil pH was measured with glass electrode in a 1:2.5
soil/water suspension. Also, soil water content (SWC, %) in each location and
sampling time were measured using oven-drying method. Firstly, weighing the
aluminum box and wet soil (W1) by electronic balance, then continuing to the
drying of 12 h in the constant temperature of 105°C, until constant weight;
Secondly, weighing the dry soil and aluminum box (W2) by electronic balance;
Thirdly, weighing the aluminum box (W3) by electronic balance. Calculating SWC
using the equation:
SWC
(%) = × 100
Soil pH value was determined by a pH tester (Takemura
Electric Works Ltd., Japan). Soil enzyme activity is an important index of soil
biological activity and soil fertility. To evaluate this important indicator,
three enzymatic activities in soil were analyzed including invertase, urease
and alkaline phosphatase. The enzyme activity was determined according to the
methods described by Yang et al. (2008) and Geisseler and
Horwath (2008).
Yield and related traits
Data regarding yield and related traits i.e., plant height, effective branching
position, effective branches, effective pods per plant, seeds per pole,
1000-grain weight, and grain and dry matter yield of B. napus were
recorded at the end of experiment during 2017 according to previous reports (Kuai et al. 2015). Five randomly selected plants were used to
record data of plant height, number of effective branches and pods per plant,
and seeds per pole. Three samples of 1000 grains were taken from each seed lot
to take average 1000-grain weight. For grain and biological yield, 2 m2
areas from each plot were harvested, and yield data were converted into t ha-1
using unitary method.
DNA extraction, 16s rRNA gene amplification
and sequencing
Table
2: Effect of different rotation systems on soil enzyme activity
Years/Cropping systems |
Catalase
(U mg-1 protein) |
Urease
(U mg-1 protein) |
Soil-alkaline phosphate (U mg-1
protein) |
Solid-acid invertase (U
mg-1 protein) |
||||||||
BB |
WB |
FB |
BB |
WB |
FB |
BB |
WB |
FB |
BB |
WB |
FB |
|
Jun 2015 |
4.41±0.11b |
4.41±0.11b |
4.71±0.11a |
0.51±0.01b |
0.51±0.01b |
0.61±0.01a |
3.81±0.11ab |
3.71±0.21b |
4.01±0.31a |
18.31±0.81b |
18.81±0.31b |
23.71±0.91a |
Sep 2015 |
4.61±0.11b |
4.81±0.11b |
5.41±0.11a |
0.51±0.01b |
0.51±0.01b |
0.61±0.01a |
3.81±0.21c |
4.11±0.31bc |
4.51±0.11a |
18.51±0.81b |
20.21±0.81b |
27.71±1.21a |
Jun 2016 |
4.61±0.21b |
5.91±0.11a |
6.01±0.21a |
0.51±0.01b |
0.61±0.01a |
0.61±0.01a |
3.81±0.11c |
4.51±0.11a |
4.31±0.11a |
18.21±1.31b |
25.01±1.21a |
26.01±0.41a |
Sep 2016 |
4.81±0.01b |
5.61±0.21a |
5.61±0.11a |
0.41±0.01b |
0.61±0.01a |
0.61±0.01a |
3.81±0.11b |
4.41±0.11a |
4.11±0.11b |
18.51±0.91b |
23.31±1.11a |
25.81±1.11a |
Jun 2017 |
4.41±0.21c |
5.41±0.21b |
6.11±0.11a |
0.41±0.01c |
0.51±0.01b |
0.61±0.01a |
3.51±0.11b |
4.31±0.21a |
4.41±0.11a |
17.41±0.71c |
24.01±1.21b |
27.61±0.71a |
Sep 2017 |
4.31±0.21b |
5.41±0.11a |
5.71±0.21a |
0.41±0.01c |
0.51±0.01b |
0.61±0.01a |
3.41±0.11b |
4.11±0.21a |
4.11±0.11a |
17.11±1.21b |
23.01±1.31a |
25.21±0.71a |
Means ± SD with
different letters are significantly different from each other at 5% probability
level
WFB= Wheat-fallow-oilseed
rape system; WB= Oilseed rape-wheat-oilseed rape; BB= Oilseed rape continuous
cropping
DNA extraction was performed using the CTAB method. V3
and V4 region of the 16S rRNA gene was amplified with
341F (5’-CCTAYGGGRBGCASCAG-3’)/806R (5’-GGACTACHVGGGTWTCTAAT-3’) primers.
Amplification of 30 µL reactions was
implemented on Bio-rad T100 (Bio-rad, Hercules, CA, USA) at 98°C for 1 min, followed by 30 cycles of
98°C for 10 s, 50°C for 30 s, and 72°C for 30 s, and followed
by a final extension at 72°C for 5 min. Three duplicates were set
up for each sample. PCR products from one sample were pooled and then gel
purified (2% agarose gel) using a GeneJETTM Gel Extraction Kit
(Thermo Fisher, Waltham, MA, USA). DNA quantification was performed using
QuantiFluorTM (Promega, Lyon, France). DNA library construction was
performed using the TruSeq® DNA PCR-Free Sample Preparation Kit (Illumina, San Diego, USA) and 16S rRNA gene sequencing was
performed on the Illumina Hiseq 2500 platform (Illumina,
San Diego, USA; Pair-end 250bp).
Data processing and analysis
Raw reads were assembled into raw tags using FLASH
(V1.2.7, http://ccb.jhu.edu/software/FLASH/)
and were filtered according to the Qiime (V1.9.1, http://qiime.org/scripts/split_libraries_fastq.html)
Quality Control Process (Caporaso et al. 2010). Then, operational
taxonomic units (OTUs) (97% identity) was formed based on the clean tags using
Uparse software (version 7.0.1001; http://drive5.com/uparse/). The abundance of
OTUs (reads count) in each sample was calculated and used for clustering
analysis of samples. Annotation of the OTUs were performed using Mothur (Version 1.35.1, https://www.mothur.org/) based on
the SSUrRNA database and obtained taxonomic information from phylum to species
level. Sequencing data within each treatment (n = 5) were homogenized, and the
alpha diversity indicators were analyzed and compared. In order to study
phylogenetic relationship of different OTUs, and the difference of the dominant
species in different samples (groups), multiple sequence alignment were
conducted using the MUSCLE software (Version 3.8.31,http://www.drive5.com/muscle/).
Beta diversity index (Unweighted UniFrac distance) was calculated. Heatmap and
Principal Co-ordinates Analysis (PCoA) based on the beta diversity index
Unweighted UniFrac distance was constructed. Alpha diversity was also applied
in analyzing complexity of species diversity for a sample through 6 indices,
including Observed-species, Chao1, Shannon, Simpson, ACE, Good-coverage.
All these indices in our samples were calculated with QIIME (Version 1.7.0) and
displayed with R software (Version 2.15.3). LDA Effect Size (LEfSe) analysis
was used to identify the biomarker in each group. P < 0.05 or corrected q
value < 0.05 was considered statistically significant.
Statistical analyses
Soil basic properties, yield and yield components data
were analyzed using GraphPad Prism 6. All data were expressed as means ±
standard deviation (SD). Data were analyzed using on-way
ANOVA to check its significance and means were separated according to Tukey’s
test at P ≤ 0.05.
Soil characteristics
Different brassica rotations systems had
significant effect on soil organic matter, soil pH, soil total nitrogen and
phosphorus plant, and soil enzyme activities (Tables 1, 2). The organic matter content changed less during
planting period; however, during the B. napus cultivation period in
2017, the organic matter contents in WFB and WB were significantly higher than
that in BB (Table 1). All the
soil samples were weakly alkaline. In addition, there were no significant
differences found in SWC among the three groups (Table 1). WFB rotation significantly increased the total N and
total P contents, indicating the improved soil fertility in all three years of
study (Table 1). Soil enzyme activity is one
of the important indexes of soil fertility evaluation. Our results showed that
short-term fallow and wheat-B. napus rotation could maintain
soil enzyme activity at a stable level and even increased in varying degrees (Table 2). While the B.
napus
continuous cropping system could gradually reduce the activity of UE, A-ALP and
S-AI. In addition, we found that the CAT activity of the BB group continued to
increase until September 2016, but after that, the activity decreased
significantly (Table 2).
Table 3: Effect of different rotation systems on agronomic and
yield related traits of B. napus L.
Cropping systems |
Plant height (cm) |
Effective branches |
Effective pods per plant |
Seeds per pod |
1000-grain weight (g) |
Dry matter yield (t ha-1) |
Grain yield (t ha-1) |
WFB |
157.29 ± 5.21a |
4.40 ± 0.40NS |
146.87 ± 33.28a |
29.20 ± 2.84a |
4.02 ± 0.14NS |
11.40 ± 0.06a |
3.30 ± 0.08a |
WB |
137.25 ± 1.76b |
4.80 ± 0.40 |
133.33 ± 17.81b |
25.93 ± 2.20b |
4.03 ± 0.18 |
9.00 ± 0.07b |
2.35 ± 0.31b |
BB |
130.88 ± 7.63c |
3.93 ± 0.61 |
125.60 ± 34.49c |
28.27 ± 0.70a |
4.16 ± 0.13 |
9.50 ± 0.12b |
2.24 ± 0.29b |
Means ± SD with
different letters are significantly different from each other at 5% probability
level
WFB=
Wheat-fallow-oilseed rape system; WB= Oilseed rape-wheat-oilseed rape; BB=
Oilseed rape continuous cropping; NS= Non-significant
Fig. 2: Summary of sequence data
A, B and C= Rarefaction curve, rank abundance curve and PCA results
of different samples, respectively; D=
UPGMA results of
different samples: The left part represents weighted unifrac
distance and the right part represents the relative abundance in phylum level; E= Samples ID and groups information;
WB= Oilseed rape-wheat-oilseed rape; BB= Oilseed rape continuous cropping
Yield and related traits
Different brassica
rotations systems had significant effect on plant height, seeds per pole, dry
matter and grain yield of brassica while had non-significant effect on
effective branches, pods per plant and 1000-grain weight of brassica (Table 3). The WFB rotation
significantly increased plant height, effective branching position, seeds per
pole, dry matter and grain yield of brassica compared with other rotations
while it was par with B. napus continuous cropping only for seeds per
pole (Table 3). The aforementioned
results on plant physiological indicated that the FW group provided a good
foundation for B. napus growth. In particular, the combination of
short-term fallow and rotation can significantly improve the crop yield of B. napus.
Summary of sequence data
A total of 3,224,846
Hiseq 2500 reads were obtained with an average length of 246 bp. Raw data are available in the BIG Sub database
(https://bigd.big.ac.cn/gsub/) with the access of CRA002116. Data quality
control (QC) results showed that the average Q20 and Q30 was 99.20 and 98.40%,
respectively, and the CG separation ratio
was 47.33%. Overall, the quality of the sequencing data was excellent in the
present study. The sequencing data covered a total of 27,747 unique tags. After
tag formation, 7,182 OTUs were obtained. In addition, the rarefaction curve
showed that the number of OTUs was close to plateau, which indicated that the
sequence data were appropriate (Fig. 2A).
The rank abundance curve showed that species richness reached a saturation point
at a lower relative abundance level (Fig. 2B). PCA results showed that the biological repeat samples of each
group were clustered together (Fig. 2C).
Also, most of the biological repeat samples in each group can be clustered
together in the UPGMA result, which suggests an
excellent internal consistency (Fig. 2D).
Bacterial diversity evaluated by alpha indexes
To
evaluate the bacterial diversity of all soil samples in different groups, the
chao1 and Shannon indexes were calculated (Table 4). Statistical analysis showed that WFB
had more diversity than the other two groups based on the Shannon, Chao1, and ACE index. As for
the Simpson index, B. napus continuous cropping
showed a lower value than that in WFB and WB at the post-harvest stage in 2017.
Furthermore, available data suggested a similar diversity in BB and WB because
of no significant difference found in chao1 and ACE indexes (also, no
significant difference found in Shannon at the post-harvest stage in 2017).
The diversity of the soil
bacterial community in different farming systems
The taxonomic distributions of microbial communities were
evaluated at different levels of classification. At phylum level, Actinobacteria,
Proteobacteria, Bacteroidetes, Firmicutes and Acidobacteria were the
dominant phyla, of which the relative abundances showed significant differences
among these three groups (Fig. 3A). The relative
abundance of Actinobacteria and Proteobacteria was decreased and
increased, respectively, by wheat-fallow-Brassica
napus rotation system at the post-harvest stage
in 2016 and 2017 (Fig. 3B, C). For
the relative abundance of Acidobacteria, it was decreased after
short-term fallow at both flowering and post-harvest stage in 2016 and 2017
(Fig. 3D).
Table
4: Effect of different
cropping systems on soil bacterial diversity based on four usual indexes
Means
± SD with different letters are significantly different from each other at 5%
probability level
WFB=
Wheat-fallow-oilseed rape system; WB= Oilseed rape-wheat-oilseed rape; BB=
Oilseed rape continuous cropping; NS= Non-significant
Alpha indexes including shannon, simpson,
chao1 and ACE
Fig.
3: Relative abundance of
soil bacteria diversity at the phylum level
A=
Stacked chart of relative abundance of bacteria; B, C and D= Relatively
abundance result among different groups
*=
Significant at P < 0.05; **=
Significant at P < 0.01; WB=
Oilseed rape-wheat-oilseed rape; BB= Oilseed ra
At the genus level, Candidatus_Udaeobacter,
Faecalibacterium, Agathobacter, Sphingomona, unidentified_Ruminococcaceae,
Bacillus, Gaiella, unidentified_Acidobacteria, Microlunatus, Rubrobacter, Bryobacter, and Arthrobacter were the dominant bacteria (Fig. 4A). Of which Sphingomonas and Arthrobacter genera was increased
by wheat-fallow-Brassica napus rotation (Fig. 4B).
Interestingly, the relative abundance of Arthrobacter in WFB increased
in 2016 and decreased in 2017 (Fig. 4B–E), which might be due to the climate differences between
2016 and 2017. In 2016, the abundance of Candidatus udaeobacter
increased from flowering stage to post-harvest (0.69 to 1.15%, 0.79 to 1.40%
and 0.51 to 0.67% in WFB, WB and BB, respectively) (Fig. 4B–E). In 2017, C. udaeobacter increased
from 0.87 to 2.01% and 2.03 to 4.89% in WFB and WB, respectively (Fig. 4D, E). In addition to the listed genera
above, there were also other genera that differ among these three groups.
Discussion
Results revealed that combination of crop rotation with short-term
fallow had improved soil fertility, bacterial diversity and soil enzyme
activities coupled with higher brassica yield. The combination of crop rotation and short-term fallow promoted B.
napus yield and improved soil fertility and, after three years of
experiments, the yield in FWB was higher than that in rotation and continuous
cropping system (Tables 1–4). In general, rotation is considered as one of the
important measures to improve crop yield and maintain soil fertility (Donk et al. 2010), and combination of short-term fallows in crop
rotations lead to better production.
Results indicated that organic
matter and soil total N and P were increased by short-term fallow in rotation. The soil with improved quality further
promotes plant growth, resulting in higher plant height, more pods, more
effective branches and more brassica yield
(Table 3). It might be due to
the reason that short-term fallow allowed more time and space to
microorganisms to decompose plant fallout, and improved soil fertility (Wang et al. 2015).
B. napus is a crop with high
fertilizer requirement, especially P fertilizer (Cabeza et al. 2017). Therefore,
increase in soil total N and P might be directly related to the increase in
yields. Soil extracellular
enzymes are synthetized and secreted by soil microorganisms, and are the
proximate agents of organic matter formation and decomposition (Burns et al. 2013).
Also, the increased activities of enzyme secreted by soil microorganisms (Table
4) could catalyze the decomposition of soil organic matter, which could meet
the carbon and N demand for microbial growth (Tiemann
and Billings 2011). These factors comprehensively affect the growth of
plants, so the WFB group had better plant growth conditions.
Fig. 4: Relative abundance of soil bacteria diversity at the
genus level
A= Stacked chart
of relative abundance of bacteria; B,
C, D and E= Relative
abundance among different groups
*= Significant at P < 0.05; **= Significant at P < 0.01; WB= Oilseed
rape-wheat-oilseed rape; BB= Oilseed rape continuous cropping
The
improvement
of soil quality is also reflected in the
structure and diversity of soil bacteria.
The soil bacterial diversity indexes significantly increased after
short-term fallow. The alpha-indexes results showed that
the soil bacterial abundance in the WFB group was more balanced and the
abundance was higher than that in BB and WB, which indicated that the soil of
WFB had a higher activity. Unlike long-term fallowing, short-term fallowing
increased bacterial diversity rather than to reduce it (Jenkins et al. 2009). Venter et
al. 2016) reported that soil under a higher diversity of crops in rotation
produced higher microbial richness and diversity (Venter et al. 2016). It
was likely that rotation brings more microorganisms to the soil, and short-term
fallow in rotation could reduce the stress on the growth of microorganisms.
While the lower alpha diversity of BB and WB (Table 4) indicated the higher stress of bacterial growth in
continuous planting conditions.
Short-term
fallow in rotation significantly changed the soil bacterial structure, which
lead to the differences among different groups. The taxonomic
distributions showed that Proteobacteria was the most dominant bacteria
in all groups. In the present study, the abundance of Proteobacteria was
higher in WFB than that in BB and WB. Members of the phylum Proteobacteria
can degrade a wide range of macromolecules (Chouari et al. 2005), which are reported
to compose the critical phyla in organic matter degradation (Rivière et al. 2009).
Besides, it is known that Proteobacteria dominates in the
reactors treating high-nitrate wastewater, and many types of denitrifies are
included in the phylum Proteobacteria. The increased N in WFB might
due to the accumulation of Proteobacteria members. Actinobacteria
is an essential indicator for soil pH, because most members of which grow at a
pH range of 5.79–5.82 (Chaudhary et al. 2019). Lin et
al. (2014) found that Acidobacteria usually had a higher relative
abundance in the soil with vegetation. The
reduction of vegetation in short-term fallow treatment might be one of the main
reasons for the decline in Acidobacteria abundance. At genus level, we
found that the higher abundance of Sphingomonas,
Bacillus, Microlunatus, and Arthrobacter showed the particularity of the farming system of rotation
combined with fallow. B. napus was a
crop with high fertilizer requirement, especially P fertilizer (Cabeza et al. 2017).
The conversion of inorganic P to organic P had a
great promoting effect on plant uptake and utilization (Huang et al. 2015; Spohn and
Schleuss, 2019). Bacillus had
been reported to be phosphate solubilizers (Wani et al. 2007), and Bacillus
had high abundance in WFB, which might be a favorable factor for the conversion
of inorganic P to organic P. In addition, there were a lot of
unknown bacteria, which need more work need to be done to learn about them.
Conclusion
Crop
rotation combined with short-term fallow not only maintained soil fertility and
water holding capacity but also increased B. napus yield. The soil bacteria diversity increased after a
short-term fallow, which improved soil fertility in a variety of
ways, such as increasing total nitrogen, total
phosphorus, organic matter, and soil enzyme activity; and lead to more crop
yields. Therefore, crop rotation combined with short-term fallow (wheat-fallow-B. napus)
system had strong restorative ability and can be used as soil
conservation measures in the future farming strategy.
Acknowledgements
This work was
supported by National Natural Science Foundation of China (No. 31860356), Inner
Mongolia Grassland Talents (Leading Talents) and Inner Mongolia Major Science
and Technology Projects (No. 2019ZD009, 2020ZD0005).
Jian-guo WANG and Zhan-yuan LU conceived and designed the
experiments; Xiao-qing ZHAO performed the experiments; Yu-chen CHENG and De-jian
ZHANG analyzed the data; Haiming WU and Yu-he ZHAO contributed
reagents/materials/analysis tools; Jian-guo WANG and Zhan-yuan LU wrote the
paper; Zhan-yuan Lu revised the article.
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