Introduction

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 6^th to 10^th. For group BB, the sowing time of B. napus was on May 6^th to 10^th annually (from 2015 to 2017). For group WB, the sowing time of wheat was on May 3^thto 5^th (in 2015), then sowing B. napus seeds (on May 6^th to 10^th, in 2016), and continue to grow wheat in the last year (in 2017). For group WFB, we sow wheat seeds in 2015 (on May 3^thto 5^th), flowed by fallow (in 2016), then sow B. napus seeds on May 6^th to 10^th 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)

WFB

Jun 2015

14.01±0.11b

14.31±0.11a

14.11±0.11b

21.61±0.11^NS

21.41±0.41

20.91±0.01

1.41±0.11^NS

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±0.21^NS

19.81±0.21

19.91±0.11

1.41±0.11^NS

1.51±0.11

1.51±0.01

108.71±7.71b

110.41±8.31ab

118.01±4.91b

Jun 2016

14.51±0.21^NS

14.71±0.21

20.51±0.11^NS

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±0.11^NS

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±0.11^NS

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±0.71^NS

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)

Jun 2015

4.41±0.11b

4.71±0.11a

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

0.61±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

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

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 GeneJET^TM Gel Extraction Kit (Thermo Fisher, Waltham, MA, USA). DNA quantification was performed using QuantiFluor^TM (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.

Results

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 ± 0.40^NS	146.87 ± 33.28a	29.20 ± 2.84a	4.02 ± 0.14^NS	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

Year	Groups	Flowering stage				Post-harvest stage
Year	Groups	Shannon	Simpson	Chao1	ACE	Shannon	Simpson	Chao1	ACE
2016	WFB	9.74 ± 0.03a	1.0 ± 0.00^NS	2961.24 ± 85.33a	3066.78 ± 44.63a	9.67 ± 0.02a	0.99 ± 0.00b	3386.91 ± 150.25a	3321.06 ± 121.33a
	WB	9.41 ± 0.02b	1.0 ± 0.00	2814.43 ± 43.02b	2893.13 ± 93.80b	9.10 ± 0.00b	1.00 ± 0.00a	3096.50 ± 53.51b	3153.51 ± 80.80b
	BB	9.12 ± 0.12c	1.0 ± 0.01	2883.68 ± 76.94b	2875.78 ± 49.33b	9.22 ± 0.10b	1.00 ± 0.00a	2908.41 ± 47.67b	3001.23 ± 66.28b
2017	WFB	9.812 ± 0.09a	1.0 ± 0.00^NS	3991.14 ± 86.67a	3945.72 ± 62.80a	9.59 ± 0.11a	1.00 ± 0.00a	3734.61 ± 79.63a	3784.05 ± 128.35a
	WB	9.68 ± 0.02b	1.0 ± 0.00	3932.48 ± 41.11b	3943.76 ± 26.67a	9.09 ± 0.09b	1.00 ± 0.00a	3506.13 ± 42.01b	3744.15 ± 86.35a
	BB	9.66 ± 0.01b	1.0 ± 0.00	3400.75 ± 39.45c	3588.09 ± 48.25b	9.07 ± 0.07b	0.98 ± 0.00b	3330.36 ± 99.63c	3560.96 ± 41.06b

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).

Author Contributions

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.

References

Bao S (2000). Soil and Agricultural Chemistry Analysis. China Agriculture Press, Beijing, China

Berg G, K Smalla (2009). Plant species and soil type cooperatively shape the structure and function of microbial communities in the rhizosphere. FEMS Microbiol Ecol 68:1‒13

Burns RG, JL DeForest, J Marxsen, RL Sinsabaugh, ME Stromberger, MD Wallenstein, MN Weintraub, A Zoppini (2013). Soil enzymes in a changing environment: Current knowledge and future directions. Soil Biol Biochem 58:216‒234

Cabeza R, K Myint, B Steingrobe, C Stritsis, J Schulze, N Claassen (2017). Phosphorus fractions depletion in the rhizosphere of young and adult maize and oilseed rape plants. J Soil Sci Plant Nutr 17:824‒838

Caporaso JG, J Kuczynski, J Stombaugh, K Bittinger, FD Bushman, EK Costello, N Fierer, AG Peña, JK Goodrich, JI Gordon, GA Huttley, ST Kelley, D Knights, JE Koenig, RE Ley, CA Lozupone, D McDonald, BD Muegge, M Pirrung, J Reeder, JR Sevinsky, PJ Turnbaugh, WA Walters, J Widmann, T Yatsunenko, J Zaneveld, R Knight (2010). QIIME allows analysis of high-throughput community sequencing data. Nat Meth 7:335‒336

Chaudhary DK, A Khulan, J Kim (2019). Development of a novel cultivation technique for uncultured soil bacteria. Sci Rep 9; Article 6666

Chouari R, DL Paslier, P Daegelen, P Ginestet, J Weissenbach, A Sghir (2005). Novel predominant archaeal and bacterial groups revealed by molecular analysis of an anaerobic sludge digester. Environ Microbiol 7:1104‒1115

Donk SV, DL Martin, S Irmak, SR Melvin, JL Petersen, DR Davison (2010). Crop residue cover effects on evaporation, soil water content, and yield of deficit-irrigated corn in west-central Nebraska. Trans ASABE 53:1787‒1797

Eerd LLV, KA Congreves, A Hayes, A Verhallen, DC Hooker (2014). Long-term tillage and crop rotation effects on soil quality, organic carbon, and total nitrogen. Can J Soil Sci 94:303‒315

Geisseler D, W Horwath (2008). Regulation of extracellular protease activity in soil in response to different sources and concentrations of nitrogen and carbon. Soil Biol Biochem 40:3040‒3048

Gomez-Montano L, A Jumpponen, MA Gonzales, J Cusicanqui, C Valdivia, PP Motavalli, M Herman, KA Garrett (2013). Do bacterial and fungal communities in soils of the Bolivian Altiplano change under shorter fallow periods? Soil Biol Biochem 65:50‒59

Hemmat A, I Eskandari (2006). Dryland winter wheat response to conservation tillage in a continuous cropping system in northwestern Iran. Soil Till Res 86:99‒109

Hooper DU, DE Bignell, VK Brown, L Brussard, J Mark Dangerfield, DH Wall, DA Wardle, DC Coleman, KE Giller, P Lavelle, WHV Der Putten, PCD Ruiter J Rusek, WL Silver, JM Tiedje, V Wolters (2000). Interactions between aboveground and belowground biodiversity in terrestrial ecosystems: Patterns, mechanisms, and feedbacks. Biol Sci 50:1049‒1061

Huang W, W Huang, H Li, Z Lei, Z Zhang, JH Tay, DJ Lee (2015). Species and distribution of inorganic and organic phosphorus in enhanced phosphorus removal aerobic granular sludge. Bioresour Technol 193:549‒552

Jenkins SN, IS Waite, A Blackburn, R Husband, SP Rushton, DC Manning, AG O’Donnell (2009). Actinobacterial community dynamics in long term managed grasslands. Anton Leeuw 95:319‒334

Kuai J, Y Sun, Q Zuo, H Huang, Q Liao, C Wu, J Lu, J Wu, G Zhou (2015). The yield of mechanically harvested rapeseed (Brassica napus L.) can be increased by optimum plant density and row spacing. Sci Rep 5; Article 18835

Lin YT, HW Hu, WB Whitman, DC Coleman, CY Chiu (2014). Comparison of soil bacterial communities in a natural hardwood forest and coniferous plantations in perhumid subtropical low mountains. Bot Stud 55:50–58

Nielsen DC, F Calderón (2011). Fallow effects on soil, pp:287‒300. In: Soil Management: Building a Stable Base for Agriculture. Hatfield JL, TJ Sauer (Eds.). ASA, SSSA, Madison, Wisconsin,USA

Reardon CL, HT Gollany, SB Wuest (2014). Diazotroph community structure and abundance in wheat–fallow and wheat–pea crop rotations. Soil Biol Biochem 69:406‒412

Rivière D, V Desvignes, E Pelletier, S Chaussonnerie, S Guermazi, J Weissenbach, T Li, P Camacho, A Sghir (2009). Towards the definition of a core of microorganisms involved in anaerobic digestion of sludge. ISME J 3:700‒714

Shahzad M, M Farooq, K Jabran, TA Yasir, M Hussain (2016a). Influence of various tillage parctices on soil physical properties and wheat performance in different wheat-based cropping systems. Intl J Agric Biol 18:821‒829

Shahzad M, M Farooq, M Hussain (2016b). Weed spectrum in different wheat-based cropping systems under conservation and conventional tillage practices in Punjab, Pakistan. Soil Till Res 163:71‒79

Sileshi G, PL Mafongoya, R Chintu, FK Akinnifesi (2008). Mixed-species legume fallows affect faunal abundance and richness and N cycling compared to single species in maize-fallow rotations. Soil Biol Biochem 40:3065‒3075

Singh JS, VK Gupta (2018). Soil microbial biomass: A key soil driver in management of ecosystem functioning. Sci Total Environ 634:497‒500

Spohn M, PM Schleuss (2019). Addition of inorganic phosphorus to soil leads to desorption of organic compounds and thus to increased soil respiration. Soil Biol Biochem 130:220‒226

Steinmann HH, ES Dobers (2013). Spatio-temporal analysis of crop rotations and crop sequence patterns in Northern Germany: Potential implications on plant health and crop protection. J Plant Dis Prot 120:85‒94

Sun J, M Liu, LJ Li, JH Liu, XJ Zhang (2009). Influence of non-tillage and stubble on soil microbial biomass and enzyme activities in rain-fed field of Inner Mongolia. Acta Ecol Sin 29:5508–5515

Thrall PH, JD Bever, JJ Burdon (2010). Evolutionary change in agriculture: The past, present and future. Evol Appl 3:405‒408

Tiemann LK, SA Billings (2011). Indirect effects of nitrogen amendments on organic substrate quality increase enzymatic activity driving decomposition in a mesic grassland. Ecosystems 14:234‒247

Venter ZS, K Jacobs, HJ Hawkins (2016). The impact of crop rotation on soil microbial diversity: A meta-analysis. Pedobiologia 59:215‒223

Wang W, DYF Lai, C Wang, T Pan, C Zeng (2015). Effects of rice straw incorporation on active soil organic carbon pools in a subtropical paddy field. Soil Till Res 152:8‒16

Wani PA, MS Khan, A Zaidi (2007). Synergistic effects of the inoculation with nitrogen-fixing and phosphate-solubilizing rhizobacteria on the performance of field-grown chickpea. J Plant Nutr Soil Sci 170:283‒287

Yang L, T Li, F Li, J Lemcoff, S Cohen (2008). Fertilization regulates soil enzymatic activity and fertility dynamics in a cucumber field. Sci Hortic 116:21‒26