Next Article in Journal
Valorizing Traditional Greek Wheat Varieties: Phylogenetic Profile and Biochemical Analysis of Their Nutritional Value
Previous Article in Journal
Genome-Wide Characterization of MADS-box Genes Identifies Candidates Associated with Flower and Fruit Development in Loquat (Eriobotrya japonica Lindl.)
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Agronomy
Volume 13
Issue 11
10.3390/agronomy13112707
agronomy-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Microbial Community Shifts with Soil Properties and Enzyme Activities in Inter-/Mono-Cropping Systems in Response to Tillage

by
Peina Lu
1,
Cai Zhao
1,*,
Wen Yin
1,2,
Falong Hu
1,2,
Zhilong Fan
1,2,
Aizhong Yu
1,2 and
Hong Fan
1
1
State Key Laboratory of Aridland Crop Science, Gansu Agricultural University, Lanzhou 730070, China
2
College of Agronomy, Gansu Agricultural University, Lanzhou 730070, China
*
Author to whom correspondence should be addressed.
Agronomy 2023, 13(11), 2707; https://doi.org/10.3390/agronomy13112707
Submission received: 17 September 2023 / Revised: 8 October 2023 / Accepted: 24 October 2023 / Published: 27 October 2023
Browse Figures
Review Reports Versions Notes

Abstract

:
No-till and cereal–legume intercropping have been recognized as favorable cropping practices to increase crop yields while maintaining soil quality in arid and semiarid environments, but the biological mechanisms are poorly understood. The present study aimed to determine the response of yields, soil properties, enzyme activities, and microbial community diversity and composition in mono- and inter-cropping under conventional and no-tillage conditions. We initiated a field experiment in Wuwei, a typical arid area of China, in 2014. Soil was sampled in August 2022 and, yields, soil properties, enzyme activities, and the microbial community diversity and composition were determined in the maize and pea strips in inter- and mono-cropping systems. Results revealed that the maize and pea strips in the no-till intercropping significantly increased yields, total and organic carbon stocks, decreased NO3−-N, and obtained the highest total and organic P in the soil. No-tillage significantly enhanced the Shannon index and Pielou evenness of the bacterial community and total microbial community over conventional tillage, with the α-diversity of the bacterial community and total microbial community distinctly higher in the NTIM treatment than in the CTIM treatment. The α-diversity of the total microbial community was significantly related to yield, soil IC and OC, and the α-diversity of the archaea community was significantly related to soil TC, TC/TP, TN/TP, and BX. Meanwhile, the α-diversity of the eukaryote community was significantly related to soil yield, soil TC/TP. Both no-tillage and intercropped maize significantly increased the abundance of archaea phylum Thaumarchaeota and bacterial phylum Nitrospirae, and were significantly positively associated with soil OC and NH4+-N, benefiting nitrogen fixation of intercropped pea from the atmosphere under the no-tillage cereal/legume intercropping. No-till intercropping was conducive to the accumulation of organic carbon, while decreasing the abundance of Proteobacteria, Acidobacteria, and Verrucomicrobia. Limited soil enzyme activities (ACP, ALP, DP, NAG, BG, AG, CB) led to decreases in organic carbon turnover and utilization. Intercropping altered soil microbial community diversity and composition due to changes in soil properties and enzyme activities. These findings suggest that no-tilled cereal–legume intercropping is a sustainable cropping practice for improving soil properties and enhancing microbial (archaea, bacterial, eukaryota) diversity, but the persistence is not conducive to rapid turnover of soil nutrients due to limited enzyme activities.

1. Introduction

In many parts of the world, intensified agricultural production has been adopted as a key strategy to increase food production since the ‘Green Revolution’, but this has also caused soil micro-ecological environment damage [1], reduced soil productivity [2], and increased greenhouse gas emissions [3], consequently contributing to the climate extremes [4]. In recent years, cereal–legume intercropping has shown great potential for increasing grain production while benefiting the soil and environment in modern agricultural systems, due to the symbiotic benefit of atmospheric N2 fixation by pea plants that in turn reduce synthetic N fertilizer use and potentially mitigate N2O emissions [1,5]. In the arid irrigated areas of northwestern China where annual total solar radiation is abundant but water resources are scarce [6,7], intercropping maize with pea (Figure 1) has shown significant benefits to food security through a sustainable production system. With an emphasis on the ecological environment, no-tillage has been shown to produce greater potential for soil carbon sequestration, improvement of soil quality [8,9,10,11], and the sustainability of crop productivity, compared to conventional tillage [9,10,12,13]. No-tillage has shown to decrease soil pH, but increase soil bulk density, mean weight diameter, water-stable aggregate, the soil enzyme activity of β-glucosidase, acid phosphatase, and urease [12,13,14]. Additionally, no-till with plastic mulching has been increasingly used to lower soil CO2 emissions, lower labor inputs, and decrease soil disturbance along with the inhabitation of soil microbial community functions over traditional systems [15,16,17].
No-tillage in association with intercropping has been adopted worldwide as a promising approach to increase food production while improving soil quality. In the semiarid region of Brazil, intercropping with minimum tillage was used as an alternative form of soil management to maintain or increase the SOC fractions [18]. In the Northern Great Plains of North America, no-till with cropping diversification has been considered a sustainable practice to increase the robustness of the system’s productivity and resilience [19,20], while reducing environmental footprints [1,3,21]. In the northernmost part of South Africa, the concentrations of organic C, P and K, and C: N ratio were found significantly higher in no-tilled sorghum/cowpea intercropping than in conventional tillage [22]. Intercropping combined with conservation practices facilitates the distribution and penetration of the residual roots into the soil, contributing to the improvement in soil physical properties and soil structure [23], due to improved C sequestration and storage [24,25] and reduced CO2 emissions [26]. The effect of no-tillage intercropping on soil physicochemical properties has been well documented; however, the biological characteristics and the mechanisms of microbial involvement are less researched, especially in the northwest arid area of China where intercropping is widely practiced.
Soil microbial activities play an important role in nutrient mobilization and mineralization which are essential for plant growth [11,27,28,29], with nutrients and water sharing among crops commonly detected in legume–cereal intercropping [30,31]. Studies have shown that intercropping can significantly alter the composition and structure of functional microbial communities to reduce CO2 emissions [7]. Bacterial community composition and diversity reflect changes in soil properties and enzyme activities better than those of fungi in the intercropping system [23], due to changes in microbes shifting soil properties, like soil pH, nutrient contents, and enzyme activities [32]. Moreover, no-tillage changes the metabolic capabilities of soil microbial communities by altering the soil physiochemical properties, offering significant benefits in conserving soil and water and increasing SOC [33,34]. Additionally, soil enzymes are mainly derived from soil microbes, which substantially affect the concentrations of soil nutrients [35]. However, the microbiological mechanisms behind the effect of no-till intercropping on nutrient cycling and microbial community attributes are poorly understood. We hypothesized that no-tillage and intercropping will alter soil microbial communities with soil properties and enzyme activities, as well as improve crop yields. The objectives of the present study were to determine (1) the effect of no-tillage and intercropping on yields; (2) the effect of no-tillage and intercropping on soil chemical properties and enzyme activities; (3) the shifts in the microbial community in a mid-term experimental site located in a semi-arid area of northwestern China.

2. Materials and Methods

2.1. Study Site

The experiment was conducted starting in 2014 at the Agricultural Research and Education Station of Gansu Agricultural University in Wuwei (37°56′ N, 102°38′ E, altitude 1520 m), Gansu, China. The mean annual temperature is 7.2 °C, the accumulated >0 °C temperature is 3513 °C and >10 °C is 2985 °C, the frost-free period is 156 days. The mean annual precipitation is 155 mm, and evaporation is greater than 2400 mm. The soil type is sandy loam Aridisols at the experiment site, the characterization of physical parameters of soil with 14.31 g kg−1 organic matter, 0.68 g kg−1 total nitrogen, and 1.41 g kg−1 total phosphorus in 0–30 cm depth, as measured at the time of plot establishment in May 2014.

2.2. Experimental Design

The mid-term field trial was laid out in randomized blocks in a split-plot design with the main plots being two tillage practices: conventional tillage (CT) and no-tillage (NT), and the split plots being three planting patterns: (1) maize (Zea mays L.) in monoculture (M), (2) pea (Pisum sativum L.) in monoculture (P), and (3) maize intercropping pea (M/P). Each treatment was replicated in four blocks. Each plot was 7 m long and 9 m wide. Plastic film mulching was applied to each plot at sowing. In CT plots, plastic film was removed at crop harvest before the plot was plowed. New film was applied to the plot at the following spring sowing time. In NT plots, the mulching film was applied at sowing and was kept in the field after the crop was harvested for reuse in the following season(s). For both the sole and intercrops, the planting densities of sole maize and sole pea were 87,000 and 1,800,000 plants hm−2, with a row spacing of 40 and 20 cm, respectively. In the M/P planting pattern, the pea strip (80 cm wide) alternated with the maize strip (110 cm wide), for a total of 3 pea strips and 3 maize strips per plot, the M and P were used at a ratio of 8:11 for the area sharing (Figure 1), with planting density of 53,000 and 700,000 plants hm−2, respectively. The maize variety was ‘Xianyu 335′, and the pea variety was ‘Longpea 1′. In each growing season only occurring per year, plots were irrigated with 450 mm ha−1 year−1. The total N application rate was 450 kg N ha−1 for maize and 135 kg N ha−1 for pea. The phosphorus fertilizer rates applied were 225 kg P2O5 ha−1 for both crops as the base fertilizer. Fertilizer sources were urea (46-0-0 of N-P2O5-K2O) and diammonium phosphate (18–46-0 of N-P2O5-K2O) [36].

2.3. Soil Sampling and Soil Physicochemical Properties Measurement

Soils were sampled in the sole maize (M), sole pea (P), intercropped maize (IM), and intercropped pea (IP) system from conventional tillage (CT) and no-tillage (NT) treatments, with a total of 32 samples (4 planting patterns × 2 tillage × 4 replicates). In each plot, five bulk soil cores were taken between the rows at a soil depth between 0–30 cm using a soil auger (5 cm in diameter) on 20 August 2022. The five soil cores per plot were homogenized into a composite sample for each treatment, and subsequently sieved through a 2 mm mesh to remove plant residues and debris. The composite soil sample was divided into three parts: one was immediately stored on ice and transported to a laboratory for DNA extraction within 24 h post sampling, one was kept fresh at 4 °C refrigerator for the determination of soil microbial biomass, enzyme activities, and the third one was air-dried for the determination of soil moisture, pH, electrical conductivity, total carbon (TC), organic carbon (OC), and inorganic carbon (IC), and total, organic and inorganic nitrogen (TN, ON, and IN, respectively) in the facilities of the State Key Laboratory of arid land crop science, Gansu Agricultural University. The activities of extracellular enzymes included acid phosphatase (ACP), alkaline phosphatase (ALP), phosphodiesterase (DP), N-acetyl-β-glucosaminidase (NAG), cellobiohydrolase (CB), and β-glucosidase (BG), α-glucosidase (AG), and β-xylosidase (BX) were measured using 4-methylumbelliferone-linked fluorogenic substrates in modified universal buffer with appropriate pH value [37].

2.4. Soil DNA Extraction and Metagenomics Sequencing

MetaDNA was extracted from 0.5 g of fresh soil using the PowerSoil DNA Isolation Kit (Mo Bio Laboratories, Carlsbad, CA, USA). The concentration and purity of the DNA samples were tested with a 1% agarose gel (1% AGE, 100 V/40 min), quality of the DNA using a Qubit1 2.0 Fluorometer (Invitrogen, Carlsbad, CA, USA), and qualified samples were stored at −80 °C for further analysis. Metagenomic libraries were size-selected to fragment lengths of 350 bp. The fragment length range was determined using Agilent 2100 High Sensitivity DNA Assay with Agilent 2100 Bioanalyzer (Agilent Technologies, Palo Alto, CA, USA). Final DNA quality was assessed with a Qubit1 2.0 Fluorometer (Invitrogen, Carlsbad, CA, USA) and Agilent Bioanalyses 2100 system. Q-PCR method was used to quantify the effective concentration of the library (effective concentration > 3 nM) to ensure the quality of the library. In total, 32 metagenomic DNA libraries were generated. The size-selected libraries were sequenced using 2 × 150 bp paired-end chemistry (PE150) on an Illumina NovaSeq 6000 platform (Novogene Bioinformatics Technology Co. Ltd., Beijing, China).

2.5. Metagenomic Assembly

The raw reads were quality trimmed by removing adapters (overlap ≥ 15 bp), ambiguous reads (“N” ≥ 10%), and low-quality reads (quality value ≤ 40) using trimmomatic [38]. The high-quality paired-end reads were assembled into contigs using MEGAHIT (version 1.1.3) [39]. The quality of contigs was assessed using QUAST. Basic information statistics of Scaftigs of each sample assembly result (≥500 bp) are shown in Table S1. DIAMOND software was used to search predicted genes against from the National Center for Biotechnology Information (NCBI) non-redundant protein database using BLASTP (best hit with e-value cutoff of 1 × 10−5) [40].

2.6. Statistical and Bioinformatics Analysis

Statistical analyses were conducted using R version 4.2.3. For α-diversity determination, the Shannon index was calculated using the ‘vegan’ package in R [41], and species evenness was calculated using Pielou evenness [42]. The effects of different treatments on soil microbial diversity were determined using permutational multivariate ANOVA (PermANOVA) with the “lme4” and “lmerTest” packages in R. Principal component analysis (PCoA) and adonis analysis based on Bray-Curtis dissimilarity matrices were to test the effects of treatments on the microbial community using the ‘vegan’, ‘pairwise’, ‘ade4’ and ‘ggplot2’ packages in R [43]. The microbial co-occurrence networks analysis were performed in Gephi (v. 9.3) [44]. The linear discriminant analysis (LDA) effect size (LEfSe) including the distribution of LDA scores of different species (tax with score > 4) and the cladogram [45]. Spearman correlation analysis and heatmaps of correlation were used to determine the relationships among net area yield, soil properties, and taxonomic and functional diversity using the ‘linkET’, ‘vegan’, and ‘dplyr’ packages in R [46]. In addition, Spearman correlation analysis and the heatmaps of correlation were used to determine the relationships between yield, soil properties, and microbial communities using the function ‘pheatmap’ in R.

3. Results

3.1. Yields, Soil Physicochemical Characteristics and Soil Enzyme Activities

No-tillage improved the number of 1000-seed weight and grain yields of maize and pea in inter-and mono-cropping compared with conventional tillage (Table 1). Maize and pea intercropping increased the net area yields in the no-tillage or conventional tillage compared with maize and pea monocropping, respectively. The highest total yield of pea and maize was observed in the M/P treatment under the no-tillage condition (Table 1).
No-tillage increased soil pH, TC, OC, TN, NH4+-N, and OP significantly, while decreasing soil EC and NO3−-N as compared to conventional tillage (Table 2). There was a significant tillage by planting-pattern interaction in affecting soil physiochemical properties, with the NTIM and NTIP treatments increasing TC and OC and decreasing NO3−-N as compared to the CTIM and CTIP treatments. Additionally, the NTIM treatment increased soil TP and OP but decreased AP as compared to CTIM.
Planting patterns affected soil enzyme activities significantly (Table 3). Compared with maize or pea monocropping, maize-pea intercropping decreased DP, NAG, AG, and CB activities. The BX activity was the lowest in the maize strip of the M/P intercropping, whereas the ALP and BG activities were the lowest in the NTIM systems.

3.2. Diversity and Structure of Soil Taxonomic Microbial Community

The results showed that the maize strip improved the α-diversity (Shannon index and Pielou evenness) of the archaea community in the monocropping system and eukaryote community in the intercropping system as compared to the pea strip (Table S1). Also, no-tillage significantly enhanced the Shannon index and Pielou evenness of the bacterial community and total microbial community over conventional tillage, with the α-diversity of the bacterial community and total microbial community distinctly higher in the NTIM treatment than in the CTIM treatment. Moreover, planting pattern significantly affected the β-diversity of archaea (Figure 2a), bacterial (Figure 2b), eukaryote (Figure 2c) and total microbial communities (Figure 2d), while tillage practices significantly affected the β-diversity of archaea, bacterial and total microbial community (Figure 2b). In the diversity distribution patterns, intercropped maize and pea exhibited a distinct separation from the corresponding mono-crops (Figure 2a–d). The co-occurrence networks analysis showed an increasing trend in taxonomic complexity from the CT (nodes = 131; total linkages = 490) to NT (nodes = 138; total linkages = 573) (Figure 2d,e). The NT network exhibited a higher degree of modularity (0.569) and had more negative (linkages = 193) and positive (linkages = 380) associations than the NB network (modularity = 0.860; positive linkages = 358, negative linkages = 132) (Figure 2d,e).
The analysis of microbial community structure at the phyla level showed that the main archaea phyla were p_Thaumarchaeota (94%), eukaryote phyla were p_Ascomycota (56%), p_Basidiomycota (22%), p_Chytridiomycota (12%), p_Mucoromycota (8%) and there were p_Proteobacteria (42%), p_Acidobacteria (20%), p_Actinobacteria (19%), p_Gemmatimonadetes (4%), p_Candidatus Rokubacteria (3%), p_Chloroflexi (3%), p_Bacteroidetes (2%), p_Nitrospirae (2%), p_Planctomycetes (1%), p_Cyanobacteria (1%), p_Verrucomicrobia (0.5%), p_Firmicutes (0.5%) for bacteria (Figure 3b). No-tillage significantly increased the archaea p_Thaumarchaeota and bacterial p_Chloroflexi, and p_Nitrospirae, while it significantly decreased bacterial p_Proteobacteria, p_Gemmatimonadetes, and p_Firmicutes compared with conventional tillage (Tables S2 and S4). The abundance of eukaryote p_Mucoromycota and the bacterial p_Proteobacteria, p_Acidobacteria were significantly higher in maize treatments than in pea treatments in both intercropping and monocropping under the mid-term conventional and no-tillage conditions (Tables S2–S4). Moreover, the maize strips of intercropping significantly enhanced the abundance of archaea p_Thaumarchaeota, and maize and pea strips of the intercropping significantly increased the abundance of p_Nitrospirae compared to monocropping (Figure 3b, Tables S2 and S4).
Additionally, the cladogram (Figure 3a) and LDA scores of different species (Figure 3c) showed that the abundance of the unclassified species differed substantially between treatments at the class, order, and family levels. the abundance of the unclassified_Actinobacteria in the NTIM treatment; p_Candidatus Rokubacteria, p_Acidobacteria, o_unclassified_Betaproteobateria, o_Chitinophagales, o_unclassified_bacteria in the NTM treatment; o_Nitrospirales in the NTIP treatment; p_Thaumarchaeota, p_Nitrososphaerota_o_Nitrososphaerales, p_Actinomycetes, p_Chloroflexi, p_Actinomycetot, g_Kocuria in the NTP treatment; p_Proteobacteria for CTIM, and g_Unclassified_Deltaproteobacteria in the CTIP treatment; Unclassified_Gemmatimonadetes at class, order, family and genus levels, g_Sphingomonas, s_Sphingomonas_jaspsi, s_Sphingomonas_sp_URHD0057, f_Nocardioidaceae, g_Nocardioides in the CTP treatment were significantly different (Figure 3a,c).

3.3. Soil Properties Drive the Soil Microbial Community Difference in Inter/Mono-Cropping Systems under Conventional and No-Tillage

The α-diversity of the total microbial community was significantly related to yield, soil IC and OC, and the α-diversity of the archaea community was significantly related to soil TC, TC/TP, TN/TP, and BX, while the α-diversity of the eukaryote community was significantly related to yield and soil TC/TP (Figure 4a). Soil pH and yield were significantly negatively related to the soil EC and ALP; OC was significantly positively related with TC, TN, and NH4+-N, while significantly negatively related with IC; SW, NO3−-N. The C: N ratio in the soil was significantly negatively related to the soil enzyme activities (Figure 4b).
Yield and soil pH were mainly correlated to eukaryote phyla Mucoromycota, and bacterial phyla Bacteroidetes (Figure 5). TC, OC and NH4+-N were negatively related to bacterial phyla Proteobacteria, Acidobacteria, Bacteroidetes, Frimicutes, Verrucomicrobia, Candidate division NC10, Candidate division Zixibacteria, Elusimicrobia, Candidatus Omnitrophica, Candidatus Dadabacteria, Candidatus Glassbacteria, and Rhodothermaeota, while were positively related to Actinobacteria and Nitrospirae (Figure 5). Additionally, the heatmap showed the soil enzyme activities were positively related to bacterial phyla (proteobacteria, acidobacteria, verrucomicrobia, Candidate division Zixibacteria), while it was significantly negatively related to the archaea phyla (Candidatus Bathyarchaeota, Candidatus Diapherotrites, Candidatus Thorarchaeota, Crenarchaeota, Thaumarchaeota) and eukaryote phyla (Basidiomycota, Mucoromycota, Zoopagomycota) and bacterial phyla (Actinobacteria, Chlorofiexi, Nitrospirae, Deinococcus_Thermus).

4. Discussion

4.1. Yields, and Soil Properties in Response to No-Till Intercropping System

Both no-tillage and intercropping improved the grain yields of maize and pea, and higher yields of maize and pea resulted in the highest total yield observed in M/P treatments under the no-tillage condition in this study. As reported, the intercropped maize and pea strips under the no-tillage with plastic mulch were as effective as conventional tillage with plastic mulch to accumulate dry matter and achieve a favorable yield outcome [36]. Soil physiochemical properties are commonly considered the indicators of soil quality, capable of sustaining plant productivity and maintaining soil water and fertility. In this study, no-tillage significantly increased soil pH, TC, OC, TN, NH4+-N, and OP, while decreasing soil EC and NO3−-N. These results disagree with the decreased soil pH observed in some other no-tillage-related research [13]. Some studies show that no-tillage, as compared to conventional tillage, increased the activity of β-glucosidase, acid phosphatase, and urease [12], but these effects were not observed in our study. The differences are probably due to various reasons, but we suggest that crop fertilization and history of soil tillage may have played a role in influencing enzyme activities. Our results showed that intercropping decreased the DP, NAG, AG, and CB activities compared with the monocropping system. These enzymes are a direct expression of the microorganism in microbial metabolic requirements and utilizing available nutrients [47]. However, intercropping system did not reduce soil nutrients and, in fact, improved soil NH4+-N and NO3−-N. The decrease in enzyme activities may be due to the increase in SW, NH4+-N, NO3−-N, and the C: N ratio in our study. Also, we found that TC and OC were significantly higher in the NTIM and NTIP treatments than in the CTIM and CTIP treatments; this may be due to the distribution and penetration of the plant roots into the soil profile under no-till management, contributing to the improvement in soil physical properties and soil structure [18], especially for C sequestration [24,25]. Our results suggest that the intercropping systems with minimum soil tillage are able to maintain or increase SOC stocks, in agreement with previous reports [23]. Also, from the perspective of the intricate microbially mediated soil C cycling, soil extracellular enzymes may experience limited SOM decomposition [48]. Additionally, the lowered AP in the NTIM treatment may be due to the losses of particulate P and dissolved P with NT as compared to CT [49]. The highest soil TP and OP in the NTIM treatment, which correlated to higher yield, from the perspective of the low efficiency of organic P mineralization, probably resulted from the decrease in available phosphorus. The ALP and BX were restricted in the maize strip of M/P intercropping system under the no-tillage condition in our study. Also, alkaline phosphatase plays an important role in influencing organophosphorus components in alkaline soil due to the association of organophosphorus with soil microorganisms [50].

4.2. The Diversity of Soil Microbial Community in Response to No-Till Intercropping System

In this study, the α- and β-diversity of total microbial, archaea and bacterial eukaryote communities were significantly affected by planting patterns and tillage practices, while α- and β-diversity of the eukaryote community were significantly affected by planting patterns. Others have found that no-tillage increased soil bacterial diversity without significant changes in fungal diversity, this is consistent with the results of this study [51]. At the same time, our study showed that no-tillage significantly enhanced the Shannon index and Pielou evenness of bacterial community leading to higher α-diversity of total microbial community, benefiting ecosystem’s functions and services [52]. No-tillage exhibited more complex co-occurrence networks, and showed distinct higher bacterial community α-diversity than conventional tillage, especially in the maize strips of intercropping system. Such diversity indicates the steadiness and adaptability of microbial communities against soil disturbances, which are important for maintaining ecosystem functioning [53]. In particular, the α-diversity of the total microbial community was significantly related to yield, soil IC and OC, and the α-diversity of the archaea community was significantly related to soil TC, TC/TP, TN/TP and BX, while the α-diversity of the eukaryote community was significantly related to yield and soil TC/TP. Significant positive effects of SOC on bacterial alpha diversity have been reported by others [23]. Collectively, our results revealed that soil nutrient cycling processes were driven by microbial community diversity and compositions, and the magnitude of the effect varied planting patterns with the M/P intercropping influencing the cycling intensity more significantly than the monoculture due to enzyme activities, especially in the no-tillage condition.

4.3. The Compositions of Soil Microbial Community in Response to No-Till Intercropping System

The abundance of eukaryote Mucoromycota and bacterial Proteobacteria, Cidobacteria were significantly higher in maize than in pea fields whether intercropping or monocropping under the mid-term conventional and no-tillage conditions, which positively related to yield. It is reported that maize and pea strips in intercropping and monocropping differ in microbial communities [7] because of soil microbial activities that play an important role in nutrient mobilization and mineralization [30]. TC, OC, and NH4+-N were positively related to Actinobacteria and Nitrospirae, while they were negatively related to Proteobacteria and Acidobacteria; this was probably due to no-tilled intercropping promoting underground interspecies interactions [13,54].
No-tillage significantly increased archaea Thaumarchaeota and bacterial Chloroflexi and Nitrospirae, while it significantly decreased bacterial Proteobacteria, Gemmatimonadetes, and Firmicutes compared with conventional tillage, which was attributable to the higher soil OC and NH4+-N in the no-tillage system. Cai, et al. [55] showed that SOC had a significant positive correlation with the relative abundance of Chloroflexi, in agreement with our results, but OC was negatively related to Proteobacteria and Acidobacteria in our study. Xu, et. al. [7] indicated the mitigation of CO2 emissions by suppressing the growth of Proteobacteria and Bacteroidota but promoting the growth of Chloroflexi. According to previous research, archaea Thaumarchaeota contain a complete pathway for carbon fixation, and a member of Cenarchaeum symbiosome is regarded as the most energetically efficient carbon fixation pathway [56,57,58], with their activity being closely related to N2O production [59,60]. Proteobacteria is a large bacterial phylum with many classes containing bacteria of various ecological types and nutritional characteristics [61]. The adaptability of Gemmatimonadetes to the soil environment was related to the carbon and nitrogen environment [62]. Firmicutes were significantly different in the proportion of conventional tillage (33%) and no-tillage (6%), respectively [63]. A meta-analysis synthesized that no-tillage had little effect on Proteobacteria, Chloroflex, Firmicute, and Bacteroides, but significantly increased the abundance of Acidobacteria and decreased Actinobacteria [51]. Also, it is possible that the changes in the abundance of bacterial communities between treatments differed substantially leading to inconsistent or even opposite results where soil sampling was taken at different stages [7,64]. Therefore, the mechanisms underlying the maintenance of biodiversity and functional changes of these microbes in the agro-ecosystems’ functioning need further investigation.
Intercropping altered soil microbial community diversity and composition, with soil bacteria reflecting the changes in yield, soil properties and enzyme activities more significantly than archaea and eukaryota in our study. Studies have confirmed that bacterial composition can be stimulated by enzyme activities and soil properties with considerable change under cereal–legume intercropping systems [23,65]. Furthermore, we found that intercropping maize significantly enhanced the abundance of Thaumarchaeota and Actinobacteria. Bao, et al. [66] provided evidence that non-dominant Actinobacteria played a vital ecophysiological role in plant residue decomposition, which were more abundant in soils than other media, especially in alkaline soils and soils rich in organic matter, where they constitute an important part of the microbial population [56]. The distribution and penetration of the plant roots into the soil provide the C source [25], which can be absorbed and utilized by Actinobacteria [24]. Additionally, intercropping maize and pea significantly increased the abundance of Nitrospirae with higher yield compared to monocropping maize and pea. Moreover, ammonia-oxidizing archaea (AOA) and ammonia-oxidizing bacteria (AOB) co-participate in the ammonia oxidation process, which is the first step and a key rate-limiting step in the nitrification process [67]. The members of the archaeal Thaumarchaeota and bacteria (g_Nitrosomonas, g_Nitrobacter or g_Nitrospira belong to p_Nitrospirae) are ammonia oxidizers [68]. There is evidence that nitrogen fixation in the cereal/legume intercropping system provided by intercropped legumes promotes nutrient sharing between cereal and legume crops [5].
Intercropping maize significantly decreased the abundance of Proteobacteria, Acidobacteria, and Verrucomicrobia, especially in no-tillage, due to the positive effect on soil enzyme activities (ACP, ALP, DP, NAG, BG, AG, CB). This was partly due to higher accumulation of SOC contributed by the increased input of vegetation production (quantity and quality) by intercrops. Rumpel and Kögel-Knabner [25] found that root exfoliations contained a certain number of difficult-to-degrade components, such as lignin and polyphenols, which accounted for a considerable proportion of SOC stocks. Among these, SOM has certain protection and stability mechanisms affected by biological and abiotic factors [69,70]. Soil aggregates can physically protect organic carbon from decomposition by affecting microbial community structure and limiting oxygen diffusion and nutrient circulation [71]. We found that the no-till intercropping system was highly conducive to the accumulation of organic carbon; however, the system was lacking in biological turnover and utilization of organic carbon. This was probably attributable to enhanced soil aggregates (which were not measured in our study). No-till practices also lower CO2 emissions from the soil due to minimum soil disturbance inhabiting soil microbial community activities as compared to traditional tillage practices [15,16,17].

5. Conclusions

The no-tilled, maize and pea strip intercropping system (i.e., the NTIM treatment) significantly increased yield, TC and OC, decreased NO3−-N, and achieved the highest soil TP and OP among the treatments evaluated. These changes were significantly correlated with soil microbial community diversities, compositions, and soil enzyme activities. No-tillage significantly enhanced the Shannon index and Pielou evenness of the bacterial community and total microbial community over conventional tillage, with the α-diversity of the bacterial community and total microbial community distinctly higher in the NTIM treatment than in the CTIM treatment. The α-diversity of the total microbial community was significantly related to yield, soil IC and OC, and the α-diversity of the archaea community was significantly related to soil TC, TC/TP, TN/TP and BX, while the α-diversity of the eukaryote community was significantly related to yield and soil TC/TP. Soil properties and enzyme activities affected soil bacterial community characteristics more significantly than archaea and eukaryote communities under the no-tillage condition. Both no-tillage and intercropping maize significantly increased the abundance of archaea Thaumarchaeota and bacterial Nitrospirae, and were significantly positively associated with soil OC and NH4+-N, benefiting biological nitrogen fixation from the atmosphere for the no-tilled cereal/legume intercropping system. The no-till intercropping system is conducive to the accumulation of organic carbon, while decreasing the abundance of Proteobacteria, Acidobacteria, and Verrucomicrobia significantly. Lack of soil enzyme activities (ACP, ALP, DP, NAG, BG, AG, CB) led to reduced biological turnover and organic carbon utilization in the no-till intercropping system. Thus, our work provides the next step in moving toward a more mechanistic understanding of how no-tillage and intercropping affect the diversity and abundance of microbial communities. Furthermore, we will in future reveal the response mechanism of the diversity and abundance of nutrient cycling-related genes to no-tillage and intercropping.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/agronomy13112707/s1, Table S1: taxonomic alpha diversity based on relative abundance at the phylum levels in the different treatments; Table S2: the abundance of archaea communities at the phylum level; Table S3: the abundance of eukaryote communities at the phylum level; Table S4: the abundance of bacterial communities at the phylum level.

Author Contributions

P.L. contributed to investigation, performing experiments, methodology, statistical analysis, writing of the original draft. C.Z. contributed to investigation, performing experiments, resources, funding acquisition, supervision, and writing, reviewing, and editing of the manuscript. W.Y., F.H., Z.F., A.Y. and H.F. contributed to reviewing and editing of the manuscript. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Gansu Province Natural Science Foundation key project (21JR7RA802), the National Natural Science Foundation of China (31860363), and the key talent project of Gansu Province, the Scientific Research Startup Funds for Openly-recruited Doctors of Gansu Agricultural University (GAU-KYQD-2021-10), the Research Program Sponsored by the State Key Laboratory of Aridland Crop Science of China (GSCS-2023-11).

Data Availability Statement

The original contributions presented in the study are included in the article.

Acknowledgments

We would like to thank Joann, Whalen for her constructive comments on the results of the study.

Conflicts of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

References

  1. Liu, C.; Plaza-Bonilla, D.; Coulter, J.A.; Kutcher, H.R.; Beckie, H.J.; Wang, L.; Floc’h, J.-B.; Hamel, C.; Siddique, K.H.M.; Li, L.; et al. Diversifying Crop Rotations Enhances Agroecosystem Services and Resilience. Adv. Agron. 2022, 173, 299–335. [Google Scholar] [CrossRef]
  2. Zhang, X.; Dong, Z.; Wu, X.; Gan, Y.; Chen, X.; Xia, H.; Kamran, M.; Jia, Z.; Han, Q.; Shayakhmetova, A.; et al. Matching Fertilization with Water Availability Enhances Maize Productivity and Water Use Efficiency in a Semi-Arid Area: Mechanisms and Solutions. Soil Tillage Res. 2021, 214, 105164. [Google Scholar] [CrossRef]
  3. Liu, C.; Cutforth, H.; Chai, Q.; Gan, Y. Farming Tactics to Reduce the Carbon Footprint of Crop Cultivation in Semiarid Areas. A Review. Agron. Sustain. Dev. 2016, 36, 69. [Google Scholar] [CrossRef]
  4. Burney, J.A.; Davis, S.J.; Lobell, D.B. Greenhouse Gas Mitigation by Agricultural Intensification. Proc. Natl. Acad. Sci. USA 2010, 107, 12052–12057. [Google Scholar] [CrossRef] [PubMed]
  5. Chai, Q.; Nemecek, T.; Liang, C.; Zhao, C.; Yu, A.; Coulter, J.A.; Wang, Y.; Hu, F.; Wang, L.; Siddique, K.H.M.; et al. Integrated Farming with Intercropping Increases Food Production While Reducing Environmental Footprint. Proc. Natl. Acad. Sci. USA 2021, 118, e2106382118. [Google Scholar] [CrossRef] [PubMed]
  6. Gou, Z.; Yin, W.; Asibi, A.E.; Fan, Z.; Chai, Q.; Cao, W. Improving the Sustainability of Cropping Systems Via Diversified Planting in Arid Irrigation Areas. Agron. Sustain. Dev. 2022, 42, 88. [Google Scholar] [CrossRef]
  7. Xu, K.; Hu, F.; Fan, Z.; Yin, W.; Niu, Y.; Wang, Q.; Chai, Q. Delayed Application of N Fertilizer Mitigates the Carbon Emissions of Pea/Maize Intercropping Via Altering Soil Microbial Diversity. Front. Microbiol. 2022, 13, 1002009. [Google Scholar] [CrossRef] [PubMed]
  8. St Luce, M.; Lemke, R.; Gan, Y.; McConkey, B.; May, W.; Campbell, C.; Zentner, R.; Wang, H.; Kroebel, R.; Fernandez, M.; et al. Diversifying Cropping Systems Enhances Productivity, Stability, and Nitrogen Use Efficiency. Agron. J. 2020, 112, 1517–1536. [Google Scholar] [CrossRef]
  9. Wang, J.; Zou, J. No-Till Increases Soil Denitrification via Its Positive Effects on the Activity and Abundance of the Denitrifying Community. Soil Biol. Biochem. 2020, 142, 107706. [Google Scholar] [CrossRef]
  10. Knapp, S.; van der Heijden, M.G.A. A Global Meta-Analysis of Yield Stability in Organic and Conservation Agriculture. Nat. Commun. 2018, 9, 3632. [Google Scholar] [CrossRef]
  11. Luo, Z.; Gan, Y.; Niu, Y.; Zhang, R.; Li, L.; Cai, L.; Xie, J. Soil Quality Indicators and Crop Yield under Long-Term Tillage Systems. Exp. Agric. 2017, 53, 497–511. [Google Scholar] [CrossRef]
  12. Carlos, F.S.; Schaffer, N.; Marcolin, E.; Fernandes, R.S.; Mariot, R.; Mazzurana, M.; Roesch, L.F.W.; Levandoski, B.; de Oliveira Camargo, F.A. A Long-Term No-Tillage System Can Increase Enzymatic Activity and Maintain Bacterial Richness in Paddy Fields. Land Degrad. Dev. 2021, 32, 2257–2268. [Google Scholar] [CrossRef]
  13. Li, Y.; Li, Z.; Cui, S.; Zhang, Q. Trade-Off between Soil Ph, Bulk Density and Other Soil Physical Properties under Global No-Tillage Agriculture. Geoderma 2020, 361, 114099. [Google Scholar] [CrossRef]
  14. Hu, Q.; Li, X.; Shi, H.; Chen, N.; Zhang, Y. Various no-tillage years with previous residual plastic film mulching improved soil properties and agricultural benefits in an arid region. Appl. Soil Ecol. 2023, 192, 105088. [Google Scholar] [CrossRef]
  15. Guo, Y.; Yin, W.; Chai, Q.; Fan, Z.; Hu, F.; Fan, H.; Zhao, C.; Yu, A.; Coulter, J.A. No Tillage with Previous Plastic Covering Increases Water Harvesting and Decreases Soil CO2 Emissions of Wheat in Dry Regions. Soil Tillage Res. 2021, 208, 104883. [Google Scholar] [CrossRef]
  16. Guo, Y.; Yin, W.; Fan, Z.; Hu, F.; Fan, H.; Zhao, C.; Yu, A.; Chai, Q.; Coulter, J.A. No-Tillage with Reduced Water and Nitrogen Supply Improves Water Use Efficiency of Wheat in Arid Regions. Agron. J. 2020, 112, 578–591. [Google Scholar] [CrossRef]
  17. Boeckx, P.; Van Nieuland, K.; Van Cleemput, O. Short-Term Effect of Tillage Intensity on N2O and CO2 Emissions. Agron. Sustain. Dev. 2011, 31, 453–461. [Google Scholar] [CrossRef]
  18. Maia, S.M.F.; Otutumi, A.T.; Mendonça, E.d.S.; Neves, J.C.L.; Oliveira, T.S.d. Combined Effect of Intercropping and Minimum Tillage on Soil Carbon Sequestration and Organic Matter Pools in the Semiarid Region of Brazil. Soil Res. 2019, 57, 266–275. [Google Scholar] [CrossRef]
  19. Niu, Y.; Bainard, L.D.; May, W.E.; Hossain, Z.; Hamel, C.; Gan, Y. Intensified Pulse Rotations Buildup Pea Rhizosphere Pathogens in Cereal and Pulse Based Cropping Systems. Front. Microbiol. 2018, 9, 1909. [Google Scholar] [CrossRef]
  20. Li, J.; Huang, L.; Zhang, J.; Coulter, J.A.; Li, L.; Gan, Y. Diversifying Crop Rotation Improves System Robustness. Agron. Sustain. Dev. 2019, 39, 38. [Google Scholar] [CrossRef]
  21. Liu, K.; Bandara, M.; Hamel, C.; Knight, J.D.; Gan, Y. Intensifying Crop Rotations with Pulse Crops Enhances System Productivity and Soil Organic Carbon in Semi-Arid Environments. Field Crops Res. 2020, 248, 107657. [Google Scholar] [CrossRef]
  22. Mogale, E.T.; Ayisi, K.K.; Munjonji, L.; Kifle, Y.G. Biological Nitrogen Fixation of Cowpea in a No-Till Intercrop under Contrasting Rainfed Agro-Ecological Environments. Sustainability 2023, 15, 2244. [Google Scholar] [CrossRef]
  23. Gong, X.; Liu, C.; Li, J.; Luo, Y.; Yang, Q.; Zhang, W.; Yang, P.; Feng, B. Responses of Rhizosphere Soil Properties, Enzyme Activities and Microbial Diversity to Intercropping Patterns on the Loess Plateau of China. Soil Tillage Res. 2019, 195, 104355. [Google Scholar] [CrossRef]
  24. Cong, W.F.; Hoffland, E.; Li, L.; Six, J.; Sun, J.H.; Bao, X.G.; Zhang, F.S.; Van Der Werf, W. Intercropping Enhances Soil Carbon and Nitrogen. Glob. Chang. Biol. 2015, 21, 1715–1726. [Google Scholar] [CrossRef] [PubMed]
  25. Rumpel, C.; Kögel-Knabner, I. Deep Soil Organic Matter—A Key but Poorly Understood Component of Terrestrial C Cycle. Plant Soil 2011, 338, 143–158. [Google Scholar] [CrossRef]
  26. Hu, F.; Feng, F.; Zhao, C.; Chai, Q.; Yu, A.; Yin, W.; Gan, Y. Integration of Wheat-Maize Intercropping with Conservation Practices Reduces CO2 Emissions and Enhances Water Use in Dry Areas. Soil Tillage Res. 2017, 169, 44–53. [Google Scholar] [CrossRef]
  27. Li, Y.; Laterrière, M.; Lay, C.-Y.; Klabi, R.; Masse, J.; St-Arnaud, M.; Yergeau, É.; Lupwayi, N.Z.; Gan, Y.; Hamel, C. Effects of Arbuscular Mycorrhizal Fungi Inoculation and Crop Sequence on Root-Associated Microbiome, Crop Productivity and Nutrient Uptake in Wheat-Based and Flax-Based Cropping Systems. Appl. Soil Ecol. 2021, 168, 104136. [Google Scholar] [CrossRef]
  28. Wang, L.; Gan, Y.; Bainard, L.D.; Hamel, C.; St-Arnaud, M.; Hijri, M. Expression of N-Cycling Genes of Root Microbiomes Provides Insights for Sustaining Oilseed Crop Production. Environ. Microbiol. 2020, 22, 4545–4556. [Google Scholar] [CrossRef]
  29. Bazghaleh, N.; Hamel, C.; Gan, Y.; Tar’an, B.; Knight, J.D. Genotypic Variation in the Response of Chickpea to Arbuscular Mycorrhizal Fungi and Non-Mycorrhizal Fungal Endophytes. Can. J. Microbiol. 2018, 64, 265–275. [Google Scholar] [CrossRef]
  30. Chen, J.; Arafat, Y.; Wu, L.; Xiao, Z.; Li, Q.; Khan, M.A.; Khan, M.U.; Lin, S.; Lin, W. Shifts in Soil Microbial Community, Soil Enzymes and Crop Yield under Peanut/Maize Intercropping with Reduced Nitrogen Levels. Appl. Soil Ecol. 2018, 124, 327–334. [Google Scholar] [CrossRef]
  31. Lai, H.; Gao, F.; Su, H.; Zheng, P.; Li, Y.; Yao, H. Nitrogen Distribution and Soil Microbial Community Characteristics in a Legume–Cereal Intercropping System: A Review. Agron. Sustain. Dev. 2022, 12, 1900. [Google Scholar] [CrossRef]
  32. Ablimit, R.; Li, W.; Zhang, J.; Gao, H.; Zhao, Y.; Cheng, M.; Meng, X.; An, L.; Chen, Y. Altering Microbial Community for Improving Soil Properties and Agricultural Sustainability During a 10-Year Maize-Green Manure Intercropping in Northwest China. J. Environ. Manag. 2022, 321, 115859. [Google Scholar] [CrossRef] [PubMed]
  33. Moraru, P.I.; Rusu, T. Soil Tillage Conservation and Its Effect on Soil Organic Matter, Water Management and Carbon Sequestration. J. Food Agric. Environ. 2010, 8, 309–312. Available online: https://www.researchgate.net/publication/283366400_Soil_tillage_conservation_and_its_effect_on_soil_organic_matter_water_management_and_carbon_sequestration (accessed on 20 October 2023).
  34. Liu, C.; Li, L.; Xie, J.; Coulter, J.A.; Zhang, R.; Luo, Z.; Cai, L.; Wang, L.; Gopalakrishnan, S. Soil Bacterial Diversity and Potential Functions Are Regulated by Long-Term Conservation Tillage and Straw Mulching. Microorganisms 2020, 8, 836. [Google Scholar] [CrossRef] [PubMed]
  35. Duan, M.; Li, Y.; Zhu, G.; Wu, X.; Huang, H.; Qin, J.; Long, S.; Li, X.; Feng, B.; Qin, S.; et al. Soil chemistry, metabarcoding, and metabolome analyses reveal that a sugarcane—Dictyophora indusiata intercropping system can enhance soil health by reducing soil nitrogen loss. Front. Microbiol. 2023, 14, 1193990. [Google Scholar] [CrossRef]
  36. Zhao, C.; Chai, Q.; Cao, W.; Whalen, J.K.; Zhao, L.; Cai, L. No-Tillage Reduces Competition and Enhances Compensatory Growth of Maize (Zea Mays L.) Intercropped with Pea (Pisum Sativum L.). Field Crops Res. 2019, 243, 107611. [Google Scholar] [CrossRef]
  37. Turner, B.L. Variation in Ph Optima of Hydrolytic Enzyme Activities in Tropical Rain Forest Soils. Appl. Environ. Microbiol. 2010, 76, 6485–6493. [Google Scholar] [CrossRef]
  38. Bolger, A.M.; Lohse, M.; Usadel, B. Trimmomatic: A Flexible Trimmer for Illumina Sequence Data. Bioinformatics 2014, 30, 2114–2120. [Google Scholar] [CrossRef]
  39. Li, D.; Liu, C.-M.; Luo, R.; Sadakane, K.; Lam, T.-W. Megahit: An Ultra-Fast Single-Node Solution for Large and Complex Metagenomics Assembly Via Succinct De Bruijn Graph. Bioinformatics 2015, 31, 1674–1676. [Google Scholar] [CrossRef]
  40. Buchfink, B.; Xie, C.; Huson, D.H. Fast and Sensitive Protein Alignment Using Diamond. Nat. Methods 2015, 12, 59–60. [Google Scholar] [CrossRef]
  41. Oksanen, J.; Blanchet, F.G.; Friendly, M.; Kindt, R.; Legendre, P.; McGlinn, D. Vegan: Community Ecology Package. R Package Version 2.4-5. 2017. Available online: https://CRAN.R-project.org/package=vegan (accessed on 20 October 2023).
  42. Pielou, E.C. The Measurement of Diversity in Different Types of Biological Collections. J. Theor. Biol. 1966, 13, 131–144. [Google Scholar] [CrossRef]
  43. Peng, M.; Jia, H.; Wang, Q. The Effect of Land Use on Bacterial Communities in Saline-Alkali Soil. Curr. Microbiol. 2017, 74, 325–333. [Google Scholar] [CrossRef] [PubMed]
  44. Bastian, M.; Heymann, S.; Jacomy, M. Gephi: An Open Source Software for Exploring and Manipulating Networks. In Proceedings of the International AAAI Conference on Web and Social Media, San Jose, CA, USA, 17–20 May 2009; Volume 3, pp. 361–362. [Google Scholar] [CrossRef]
  45. Segata, N.; Izard, J.; Waldron, L.; Gevers, D.; Miropolsky, L.; Garrett, W.S.; Huttenhower, C. Metagenomic Biomarker Discovery and Explanation. Genome Biol. 2011, 12, R60. [Google Scholar] [CrossRef] [PubMed]
  46. Sunagawa, S.; Coelho, L.P.; Chaffron, S.; Kultima, J.R.; Labadie, K.; Salazar, G.; Djahanschiri, B.; Zeller, G.; Mende, D.R.; Alberti, A. Structure and Function of the Global Ocean Microbiome. Science 2015, 348, 1261359. [Google Scholar] [CrossRef]
  47. Wei, H.; Wang, L.; Hassan, M.; Xie, B. Succession of the Functional Microbial Communities and the Metabolic Functions in Maize Straw Composting Process. Bioresour. Technol. 2018, 256, 333–341. [Google Scholar] [CrossRef] [PubMed]
  48. García-Palacios, P.; Chen, J. Emerging Relationships among Soil Microbes, Carbon Dynamics and Climate Change. Funct. Ecol. 2022, 36, 1332–1337. [Google Scholar] [CrossRef]
  49. Daryanto, S.; Wang, L.; Jacinthe, P.A. Meta-Analysis of Phosphorus Loss from No-Till Soils. J. Environ. Qual. 2017, 46, 1028–1037. [Google Scholar] [CrossRef]
  50. Nannipieri, P.; Giagnoni, L.; Landi, L.; Renella, G. Role of Phosphatase Enzymes in Soil. In Phosphorus in Action; Springer: Berlin/Heidelberg, Germany, 2011; pp. 215–243. [Google Scholar] [CrossRef]
  51. Li, Y.; Song, D.; Liang, S.; Dang, P.; Qin, X.; Liao, Y.; Siddique, K.H.M. Effect of No-Tillage on Soil Bacterial and Fungal Community Diversity: A Meta-Analysis. Soil Tillage Res. 2020, 204, 104721. [Google Scholar] [CrossRef]
  52. Wagg, C.; Bender, S.F.; Widmer, F.; van der Heijden, M.G. Soil Biodiversity and Soil Community Composition Determine Ecosystem Multifunctionality. Proc. Natl. Acad. Sci. USA 2014, 111, 5266–5270. [Google Scholar] [CrossRef]
  53. Wagg, C.; Schlaeppi, K.; Banerjee, S.; Kuramae, E.E.; van der Heijden, M.G. Fungal-Bacterial Diversity and Microbiome Complexity Predict Ecosystem Functioning. Nat. Commun. 2019, 10, 4841. [Google Scholar] [CrossRef]
  54. Mathew, R.P.; Feng, Y.; Githinji, L.; Ankumah, R.; Balkcom, K.S. Impact of No-Tillage and Conventional Tillage Systems on Soil Microbial Communities. Appl. Environ. Soil Sci. 2012, 2012, 548620. [Google Scholar] [CrossRef]
  55. Cai, L.; Guo, Z.; Zhang, J.; Gai, Z.; Liu, J.; Meng, Q.; Liu, X. No Tillage and Residue Mulching Method on Bacterial Community Diversity Regulation in a Black Soil Region of Northeastern China. PLoS ONE 2021, 16, e0256970. [Google Scholar] [CrossRef] [PubMed]
  56. Barka, E.A.; Vatsa, P.; Sanchez, L.; Gaveau-Vaillant, N.; Jacquard, C.; Meier-Kolthoff, J.P.; Klenk, H.P.; Clement, C.; Ouhdouch, Y.; van Wezel, G.P. Taxonomy, Physiology, and Natural Products of Actinobacteria. Microbiol. Mol. Biol. Rev. 2016, 80, 1–43. [Google Scholar] [CrossRef] [PubMed]
  57. Hallam, S.J.; Konstantinidis, K.T.; Putnam, N.; Schleper, C.; Watanabe, Y.-i.; Sugahara, J.; Preston, C.; de la Torre, J.; Richardson, P.M.; DeLong, E.F. Genomic Analysis of the Uncultivated Marine Crenarchaeote Cenarchaeum Symbiosum. Proc. Natl. Acad. Sci. USA 2006, 103, 18296–18301. [Google Scholar] [CrossRef] [PubMed]
  58. Könneke, M.; Schubert, D.M.; Brown, P.C.; Hügler, M.; Standfest, S.; Schwander, T.; Schada von Borzyskowski, L.; Erb, T.J.; Stahl, D.A.; Berg, I.A. Ammonia-Oxidizing Archaea Use the Most Energy-Efficient Aerobic Pathway for CO2 Fixation. Proc. Natl. Acad. Sci. USA 2014, 111, 8239–8244. [Google Scholar] [CrossRef]
  59. Santoro, A.E.; Buchwald, C.; McIlvin, M.R.; Casciotti, K.L. Isotopic Signature of N2o Produced by Marine Ammonia-Oxidizing Archaea. Sci. Adv. 2011, 333, 1282–1285. [Google Scholar] [CrossRef]
  60. Baker, B.J.; De Anda, V.; Seitz, K.W.; Dombrowski, N.; Santoro, A.E.; Lloyd, K.G. Diversity, Ecology and Evolution of Archaea. Nat. Microbiol. 2020, 5, 887–900. [Google Scholar] [CrossRef]
  61. Mhete, M.; Eze, P.N.; Rahube, T.O.; Akinyemi, F.O. Soil Properties Influence Bacterial Abundance and Diversity under Different Land-Use Regimes in Semi-Arid Environments. Sci. Afr. 2020, 7, e00246. [Google Scholar] [CrossRef]
  62. Cederlund, H.; Wessén, E.; Enwall, K.; Jones, C.M.; Juhanson, J.; Pell, M.; Philippot, L.; Hallin, S. Soil Carbon Quality and Nitrogen Fertilization Structure Bacterial Communities with Predictable Responses of Major Bacterial Phyla. Appl. Soil Ecol. 2014, 84, 62–68. [Google Scholar] [CrossRef]
  63. Aslam, Z.; Yasir, M.; Yoon, H.S.; Jeon, C.O.; Chung, Y.R. Diversity of the Bacterial Community in the Rice Rhizosphere Managed under Conventional and No-Tillage Practices. J. Microbiol. 2013, 51, 747–756. [Google Scholar] [CrossRef]
  64. Wang, Z.; Li, Y.; Li, T.; Zhao, D.; Liao, Y. Tillage Practices with Different Soil Disturbance Shape the Rhizosphere Bacterial Community Throughout Crop Growth. Soil Tillage Res. 2020, 197, 104501. [Google Scholar] [CrossRef]
  65. Yu, L.; Luo, S.; Gou, Y.; Xu, X.; Wang, J. Structure of Rhizospheric Microbial Community and N Cycling Functional Gene Shifts with Reduced N Input in Sugarcane-Soybean Intercropping in South China. Agric. Ecosyst. Environ. 2021, 314, 107413. [Google Scholar] [CrossRef]
  66. Bao, Y.; Dolfing, J.; Guo, Z.; Chen, R.; Wu, M.; Li, Z.; Lin, X.; Feng, Y. Important Ecophysiological Roles of Non-Dominant Actinobacteria in Plant Residue Decomposition, Especially in Less Fertile Soils. Microbiome 2021, 9, 84. [Google Scholar] [CrossRef] [PubMed]
  67. Beman, J.M.; Popp, B.N.; Francis, C.A. Molecular and Biogeochemical Evidence for Ammonia Oxidation by Marine Crenarchaeota in the Gulf of California. ISME J. 2008, 2, 429–441. [Google Scholar] [CrossRef]
  68. Nicol, G.W.; Schleper, C. Ammonia-Oxidising Crenarchaeota: Important Players in the Nitrogen Cycle? Trends Microbiol. 2006, 14, 207–212. [Google Scholar] [CrossRef]
  69. Lützow, M.V.; Kögel-Knabner, I.; Ekschmitt, K.; Matzner, E.; Guggenberger, G.; Marschner, B.; Flessa, H. Stabilization of Organic Matter in Temperate Soils: Mechanisms and Their Relevance under Different Soil Conditions—A Review. Eur. J. Soil Sci. 2006, 57, 426–445. [Google Scholar] [CrossRef]
  70. von Lützow, M.; Kögel-Knabner, I.; Ekschmitt, K.; Flessa, H.; Guggenberger, G.; Matzner, E.; Marschner, B. Som Fractionation Methods: Relevance to Functional Pools and to Stabilization Mechanisms. Soil Biol. Biochem. 2007, 39, 2183–2207. [Google Scholar] [CrossRef]
  71. Zhang, X.; Xin, X.; Zhu, A.; Yang, W.; Zhang, J.; Ding, S.; Mu, L.; Shao, L. Linking Macroaggregation to Soil Microbial Community and Organic Carbon Accumulation under Different Tillage and Residue Managements. Soil Tillage Res. 2018, 178, 99–107. [Google Scholar] [CrossRef]
Agronomy 13 02707 g001
Figure 1. Maize intercropping pea in field strips—an increasingly adopted cropping system in the arid northwestern China (Photo taken at Wuwei county, Gansu province, China, in 2018).
Figure 1. Maize intercropping pea in field strips—an increasingly adopted cropping system in the arid northwestern China (Photo taken at Wuwei county, Gansu province, China, in 2018).
Agronomy 13 02707 g001
Agronomy 13 02707 g002
Figure 2. Principal component analysis of soil microbial diversity of (a) archaea, (b) bacteria, (c) eukaryota, and (d) based on relative abundance at phylum level; Microbial co-occurrence networks based on taxonomic composition constructed for (e) the conventional tillage and (f) no-tillage treatments. The treatment abbreviations are: CTM, CTP, CTIM, and CTIP were the maize monoculture, pea monoculture, maize in intercropping (Maize/Pea), and pea in intercropping (Maize/Pea) in conventional tillage system and NTM, NTP, NTIM, and NTIP were the maize monoculture, pea monoculture, maize in intercropping (Maize/Pea), and pea in intercropping (Maize/Pea) in no-tillage system, respectively. “*” p < 0.05, “**” p < 0.01, and “***” p < 0.001.
Figure 2. Principal component analysis of soil microbial diversity of (a) archaea, (b) bacteria, (c) eukaryota, and (d) based on relative abundance at phylum level; Microbial co-occurrence networks based on taxonomic composition constructed for (e) the conventional tillage and (f) no-tillage treatments. The treatment abbreviations are: CTM, CTP, CTIM, and CTIP were the maize monoculture, pea monoculture, maize in intercropping (Maize/Pea), and pea in intercropping (Maize/Pea) in conventional tillage system and NTM, NTP, NTIM, and NTIP were the maize monoculture, pea monoculture, maize in intercropping (Maize/Pea), and pea in intercropping (Maize/Pea) in no-tillage system, respectively. “*” p < 0.05, “**” p < 0.01, and “***” p < 0.001.
Agronomy 13 02707 g002
Agronomy 13 02707 g003
Figure 3. (a) The circles radiating from the inside out represent taxonomic levels from phylum to genus (or species), and each small circle at a different classification level represents a classification at that level, and the size of the small circle diameter is proportional to the size of the relative abundance. (b) The composition of archaea, bacteria, and fungi at phylum levels in different treatments. (c) The distribution of LDA scores of different species, the color represents the corresponding groups, and the length of the bar represents the contribution of different species (LDA Score > 4).
Figure 3. (a) The circles radiating from the inside out represent taxonomic levels from phylum to genus (or species), and each small circle at a different classification level represents a classification at that level, and the size of the small circle diameter is proportional to the size of the relative abundance. (b) The composition of archaea, bacteria, and fungi at phylum levels in different treatments. (c) The distribution of LDA scores of different species, the color represents the corresponding groups, and the length of the bar represents the contribution of different species (LDA Score > 4).
Agronomy 13 02707 g003
Agronomy 13 02707 g004
Figure 4. Correlations of (a) soil microbial diversity with yield and soil chemical properties, and (b) among soil chemical properties, including soil moisture (SW), pH, electrical conductivity (EC), total carbon (TC), inorganic carbon (IC), organic carbon (OC), total phosphorus (TP), inorganic phosphorus (IP), organic phosphorus (OP), total nitrogen (TN), ammonium nitrogen (HN4+-N), nitrate nitrogen (NO3−-N), the ratio of TC to TN, the ratio of TC and TP, and the ratio of TN and TP. In (b), the colored circles represent significance at the 0.05 level.
Figure 4. Correlations of (a) soil microbial diversity with yield and soil chemical properties, and (b) among soil chemical properties, including soil moisture (SW), pH, electrical conductivity (EC), total carbon (TC), inorganic carbon (IC), organic carbon (OC), total phosphorus (TP), inorganic phosphorus (IP), organic phosphorus (OP), total nitrogen (TN), ammonium nitrogen (HN4+-N), nitrate nitrogen (NO3−-N), the ratio of TC to TN, the ratio of TC and TP, and the ratio of TN and TP. In (b), the colored circles represent significance at the 0.05 level.
Agronomy 13 02707 g004
Agronomy 13 02707 g005
Figure 5. Correlation analysis of yield and soil chemical properties with (a) archaea, (b) bacteria and (c) fungi at phylum levels. Asterisks indicate a significant association between the abundance of a given functional gene and particular soil chemical properties: “*” p < 0.05, “**” p < 0.01, and “***” p < 0.001.
Figure 5. Correlation analysis of yield and soil chemical properties with (a) archaea, (b) bacteria and (c) fungi at phylum levels. Asterisks indicate a significant association between the abundance of a given functional gene and particular soil chemical properties: “*” p < 0.05, “**” p < 0.01, and “***” p < 0.001.
Agronomy 13 02707 g005
Table 1. Yields of pea and maize in inter-/mono-cropping systems under the conditions of conventional tillage and no-tillage.
Table 1. Yields of pea and maize in inter-/mono-cropping systems under the conditions of conventional tillage and no-tillage.
TreatmentsPeaMaizeTotal Yield of Pea and Maize (kg hm−2)
TillagePattern1000-Seed Weight (g)Yield
(kg·hm−2)		Net Area Yields
(kg hm−2)		1000-Seed Weight (g)Yield
(kg hm−2)		Net Area Yields
(kg hm−2)
NTP211.2 c †3955.03 a3955.03 c 3955.03 ± 74 e
M 480.5 a14,041.47 c14,041.47 c14,041.47 ± 368 c
M/P242.1 a2393.63 c5684.88 a480.5 a12,983.87 bc22,426.68 a15,377.50 ± 353 a
CTP198.3 d3667.50 b3667.5 d-- 3667.50 ± 47 e
M-- 421.1 c13,302.47 b13,302.47 d13,302.47 ± 177 d
M/P224.2 b2205.10 d5237.11 b445.6 b12,545.30 c21,669.15 b14,750.40 ± 267 b
Tillage (T)0.000 ***0.000 ***0.000 ***0.001 **0.026 *0.006 **0.002 **
Pattern (P)0.000 ***0.000 ***0.000 ***0.000 ***0.003 **0.000 ***0.000 ***
T × Pattern0.4660.2930.1800.1840.5070.9670.435
† Different letters in the same column indicate a significant difference between different treatments. P, M, M/P were the pea monoculture, maize monoculture, maize and pea intercropping; NT and CT were no-tillage and conventional tillage, respectively. Net area yields of pea were the yield divided by 8/19 according to the proportion of pea area in intercropping, and net area yields of maize were the yield divided by 11/19 according to the proportion of maize area in intercropping. “*” p < 0.05, “**” p < 0.01, and “***” p < 0.001.
Table 2. Soil chemical properties in maize and pea intercropping under the conditions of conventional tillage and no-tillage.
Table 2. Soil chemical properties in maize and pea intercropping under the conditions of conventional tillage and no-tillage.
TreatmentSW (%)pHEC (us m−1)TC
(g kg−1)		TN
(g kg−1)		OC
(g kg−1)		HN4+-N
(mg kg−1)		NO3—N
(mg kg−1)		TP
(mg kg−1)		AP
(mg kg−1)		OP
(mg kg−1)
Tillage (T)
CT11.8 ± 0.648.19 ± 0.02 b129.75 ± 10.61 a10.59 ± 0.12 b0.51 ± 0.01 b3.32 ± 0.24 b5.3 ± 0.1 b4.67 ± 0.24 a130.72 ± 1.7393.72 ± 3.4937 ± 4.62 b
NT12.51 ± 0.78.26 ± 0.03 a112.33 ± 5.93 b11.64 ± 0.12 a0.57 ± 0.01 a4.61 ± 0.37 a5.54 ± 0.11 a4.31 ± 0.14 b132.75 ± 2.8788.01 ± 3.4744.74 ± 5.38 a
Pattern (P)
M10.48 ± 0.35 b †8.24 ± 0.06116.5 ± 12.23 b10.97 ± 0.240.55 ± 0.023.37 ± 0.18 b5.12 ± 0.11 b3.9 ± 0.11 c136.27 ± 1.6682.94 ± 6.09 b53.33 ± 6.4 a
P10.84 ± 0.51 b8.18 ± 0.03154.83 ± 12.67 a11.27 ± 0.130.55 ± 0.015.33 ± 0.51 a5.71 ± 0.19 a4.28 ± 0.12 bc125.68 ± 3.3100.63 ± 0.87 a25.04 ± 3.45 b
IM11.97 ± 0.66 b8.28 ± 0.0195 ± 3.77 b10.95 ± 0.30.52 ± 0.013.62 ± 0.16 b5.5 ± 0.07 a4.59 ± 0.3 ab133.62 ± 3.8486.07 ± 5.12 b47.55 ± 8.1 a
IP15.33 ± 0.58 a8.2 ± 0.02117.83 ± 4.01 b11.27 ± 0.410.55 ± 0.033.56 ± 0.62 b5.35 ± 0.12 ab5.17 ± 0.29 a131.38 ± 3.1293.82 ± 3.34 ab37.56 ± 4.72 ab
T × P
CTM10.49 ± 0.548.11 ± 0.02 d143.67 ± 2.73 b10.47 ± 0.14 c0.5 ± 0.02 cd3.42 ± 0.12 d5.03 ± 0.21 c3.76 ± 0.17 d137.48 ± 3.48 ab75.74 ± 3.52 c61.73 ± 4.57 ab
CTP10.95 ± 1.148.15 ± 0.06 cd178.33 ± 8.65 a11.19 ± 0.12 b0.55 ± 0.01 bc4.29 ± 0.24 bc5.4 ± 0.16 bc4.02 ± 0.01 cd127.78 ± 1.57 abc102.28 ± 0.38 a25.5 ± 1.32 d
CTIM11.12 ± 1.198.28 ± 0.02 ab87.33 ± 2.73 e10.3 ± 0.06 c0.5 ± 0.02 cd3.36 ± 0.26 d5.58 ± 0.11 ab5.22 ± 0.08 a127.27 ± 1.81 bc96.4 ± 4.82 ab30.87 ± 3.99 cd
CTIP14.65 ± 0.788.24 ± 0.02 bc109.67 ± 2.73 cd10.4 ± 0.12 c0.49 ± 0.03 d2.22 ± 0.2 e5.19 ± 0.19 bc5.66 ± 0.05 a130.37 ± 3.84 abc100.46 ± 2.88 ab29.91 ± 3.79 cd
NTM10.48 ± 0.578.37 ± 0.05 a89.33 ± 1.45 e11.46 ± 0.12 b0.59 ± 0.02 ab3.33 ± 0.39 d5.21 ± 0.11 bc4.04 ± 0.11 cd135.06 ± 0.46 abc90.13 ± 11 ab44.93 ± 10.65 bc
NTP10.74 ± 0.078.22 ± 0.04 bc131.33 ± 13.25 b11.36 ± 0.26 b0.56 ± 0.03 abc6.36 ± 0.41 a6.02 ± 0.23 a4.54 ± 0.05 bc123.58 ± 6.9 c98.99 ± 0.98 ab24.59 ± 7.59 d
NTIM12.82 ± 0.098.28 ± 0.01 ab102.67 ± 2.19 de11.6 ± 0.14 b0.53 ± 0.01 bcd3.88 ± 0.04 c5.41 ± 0.09 bc3.97 ± 0.21 d139.98 ± 5.47 a75.74 ± 0.98 c64.23 ± 5.84 a
NTIP16.02 ± 0.778.17 ± 0.02 cd126 ± 2.52 bc12.14 ± 0.23 a0.61 ± 0.01 a4.89 ± 0.3 b5.5 ± 0.09 b4.68 ± 0.4 b132.4 ± 5.74 abc87.18 ± 1.84 ab45.22 ± 6.18 bc
PremANOVA: Pr (>F)
Tillage (T)0.2000.018 *<0.001 ***<0.001 ***<0.001 ***0.0970.049 *0.009 **0.4950.0810.043 *
Pattern (P)<0.001 ***0.067<0.001 ***0.0980.3030.1520.012 *<0.001 ***0.1100.004 **<0.001 ***
T × P0.4990.001 **<0.001 ***0.001 **0.042 *0.5590.133<0.001 ***0.2100.007 **0.001 **
† Different letters in the same column indicate a significant difference between the treatments. CTM, CTP, CTIM, CTIP were the maize monoculture, pea monoculture, maize in intercropping (Maize/Pea), and pea in intercropping (Maize/Pea) in conventional tillage system and NTM, NTP, NTIM, NTIP were the maize monoculture, pea monoculture, maize in intercropping (Maize/Pea), and pea in intercropping (Maize/Pea) in no-tillage system, respectively. “*” p < 0.05, “**” p < 0.01, and “***” p < 0.001.
Table 3. Soil enzyme activities in maize and pea intercropping under the conditions of conventional tillage and no-tillage.
Table 3. Soil enzyme activities in maize and pea intercropping under the conditions of conventional tillage and no-tillage.
TreatmentACPALPDPNAGBGAGCBBX
Tillage (T)
CT111.98 ± 2.9119.49 ± 8.2214.37 ± 1.2316.44 ± 1.3737.84 ± 5.324.17 ± 0.6515.01 ± 1.1911.1 ± 1.41
NT108.45 ± 5.41107.37 ± 5.6715.07 ± 0.9916.66 ± 2.0739.06 ± 10.33.63 ± 0.5612.28 ± 110.06 ± 1.14
Pattern (P)
M120.43 ± 5.72 a †113.59 ± 6.02 ab19.08 ± 1.91 a20.73 ± 2.59 a54.15 ± 16.885.49 ± 0.8 a17.13 ± 1.64 a13 ± 0.61 a
P105.43 ± 4.31 ab129.03 ± 14.1 a13.1 ± 1.06 b20.15 ± 2.26 a23.97 ± 5.094.62 ± 1.07 ab13.2 ± 1.06 ab11.48 ± 1.35 a
IM115.87 ± 7 a93.09 ± 6.01 b13.13 ± 0.85 b12.35 ± 1.17 b39.86 ± 10.842.08 ± 0.3 c12.31 ± 1.98 b5.73 ± 1.09 b
IP99.13 ± 3.39 b118.01 ± 7.85 ab13.59 ± 0.73 b12.97 ± 1.35 b35.82 ± 8.383.42 ± 0.31 bc11.95 ± 1.01 b12.12 ± 2.18 a
T × P
CTM122.52 ± 1.57123.52 ± 7.15 bc19.44 ± 3.3620.11 ± 1.1437.4 ± 5.29 abc5.36 ± 0.7318.59 ± 2.6212.64 ± 0.53
CTP113.35 ± 4.12153.35 ± 18.39 a12.26 ± 1.6319.29 ± 3.0924.65 ± 8.93 bc6.26 ± 1.5614.81 ± 0.8613.28 ± 2.18
CTIM106.42 ± 7.6699.26 ± 11.76 cd12.87 ± 1.0613.96 ± 1.9163.95 ± 2.71 ab2.13 ± 0.6515.59 ± 2.696.74 ± 1.31
CTIP105.62 ± 3.92101.84 ± 4.18 cd12.92 ± 1.0712.4 ± 2.1725.38 ± 0.93 bc2.95 ± 0.3911.04 ± 1.4311.76 ± 4.85
NTM118.35 ± 12.52103.65 ± 5.6 cd18.71 ± 2.621.35 ± 5.6470.9 ± 33.41 a5.62 ± 1.6315.68 ± 2.1213.37 ± 1.2
NTP97.51 ± 3.64104.71 ± 8.05 cd13.94 ± 1.4921 ± 3.923.3 ± 7 bc2.97 ± 0.7511.58 ± 1.59.69 ± 1.05
NTIM125.31 ± 9.8486.93 ± 2.08 d13.38 ± 1.5510.74 ± 0.8115.76 ± 0.31 c2.04 ± 0.149.03 ± 1.254.73 ± 1.81
NTIP92.64 ± 0.06134.18 ± 5.37 ab14.27 ± 1.0413.55 ± 246.26 ± 15.52 abc3.88 ± 0.3512.85 ± 1.4912.48 ± 0.3
PremANOVA: Pr (>F)
Tillage (T)0.4490.0800.6080.9180.9030.3760.0540.484
Pattern (P)0.019 *0.010 **0.015 *0.017 *0.2250.007 **0.047 *0.013 *
T × P0.0650.004 **0.9210.8250.047 *0.1040.2010.660
† Different letters in the same column indicate a significant difference between different treatments. CTM, CTP, CTIM, CTIP were the maize monoculture, pea monoculture, maize in intercropping (Maize/Pea), and pea in intercropping (Maize/Pea) in conventional tillage system and NTM, NTP, NTIM, NTIP were the maize monoculture, pea monoculture, maize in intercropping (Maize/Pea), and pea in intercropping (Maize/Pea) in no-tillage system, respectively. The soil enzyme activities include acid phosphatase, alkaline phosphatase, phosphodiesterase, N-acetyl-β-glucosaminidase, and β-glucosidase, α-glucosidase, cellobiohydrolase, and β-xylosidase followed by the abbreviations for each of the enzymes ACP, ALP, DP, NAG, BG, AG, CB, BX. “*” p < 0.05, and “**” p < 0.01.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Lu, P.; Zhao, C.; Yin, W.; Hu, F.; Fan, Z.; Yu, A.; Fan, H. Microbial Community Shifts with Soil Properties and Enzyme Activities in Inter-/Mono-Cropping Systems in Response to Tillage. Agronomy 2023, 13, 2707. https://doi.org/10.3390/agronomy13112707

AMA Style

Lu P, Zhao C, Yin W, Hu F, Fan Z, Yu A, Fan H. Microbial Community Shifts with Soil Properties and Enzyme Activities in Inter-/Mono-Cropping Systems in Response to Tillage. Agronomy. 2023; 13(11):2707. https://doi.org/10.3390/agronomy13112707

Chicago/Turabian Style

Lu, Peina, Cai Zhao, Wen Yin, Falong Hu, Zhilong Fan, Aizhong Yu, and Hong Fan. 2023. "Microbial Community Shifts with Soil Properties and Enzyme Activities in Inter-/Mono-Cropping Systems in Response to Tillage" Agronomy 13, no. 11: 2707. https://doi.org/10.3390/agronomy13112707

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop