Next Article in Journal
Virulence Potential and Antibiotic Susceptibility of S. aureus Strains Isolated from Food Handlers
Next Article in Special Issue
Urbanization Reduces Phyllosphere Microbial Network Complexity and Species Richness of Camphor Trees
Previous Article in Journal
Sporotrichosis: A Review of a Neglected Disease in the Last 50 Years in Brazil
Previous Article in Special Issue
In-Forest Planting of High-Value Herb Sarcandra glabra Enhances Soil Carbon Storage without Affecting the Diversity of the Arbuscular Mycorrhiza Fungal Community and Composition of Cunninghamia lanceolata
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Microorganisms
Volume 10
Issue 11
10.3390/microorganisms10112153
microorganisms-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

Unraveling the Impact of Long-Term Rice Monoculture Practice on Soil Fertility in a Rice-Planting Meadow Soil: A Perspective from Microbial Biomass and Carbon Metabolic Rate

by
Zhanxi Wei
1,2,
Hao Wang
1,2,
Chao Ma
3,
Shuyuan Li
3,
Haimiao Wu
3,4,
Kaini Yuan
3,
Xiangyuan Meng
3,
Zefeng Song
3,
Xiaofeng Fang
3,* and
Zhirui Zhao
3,4,*
1
Qinghai 906 Engineering Survey and Design Institute, Xining 810007, China
2
Qinghai Geological Environment Protection and Disaster Prevention Engineering Technology Research Center, Xining 810007, China
3
Hebei Province Key Laboratory of Sustained Utilization & Development of Water Recourse, School of Water Resources and Environment, Hebei GEO University, Shijiazhuang 050031, China
4
Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
*
Authors to whom correspondence should be addressed.
Microorganisms 2022, 10(11), 2153; https://doi.org/10.3390/microorganisms10112153
Submission received: 24 August 2022 / Revised: 18 October 2022 / Accepted: 28 October 2022 / Published: 30 October 2022
(This article belongs to the Special Issue The Coevolution of Microorganisms and Hazards on Earth and Beyond)
Browse Figures
Versions Notes

Abstract

:
Global agricultural intensification leads to a decline in soil quality; however, the extent to which long-term rice cultivation adversely impacts soil, based on chemical and microbial perspectives, remains unclear. The present study was conducted on a seed multiplication farm in Wuchang, Heilongjiang Province, China, to quantify changes in the nutrient properties and microbial profiles of meadow soil in cultivated (rhizosphere and bulk soil) and uncultivated paddy plots from spring to winter. A non-parametric method was used to compare carbon metabolism characteristics among the three groups of soil samples. Principal component analysis was used to distinguish soil chemical properties and carbon source utilization profiles among the soil samples across different seasons. Under rice cultivation, pH, organic matter, total nitrogen, and alkali-hydrolyzed nitrogen concentrations were generally higher in rhizosphere soils than in bulk or uncultivated soils. However, microbial biomass in cultivated soils was consistently lower than in uncultivated soils. There was a discernible difference in carbon substrate preference between summer and other seasons in the three sample groups. In conclusion, agricultural activities in rice cultivation could reshape soil microbial communities in the long term. Notably, specific cultivation activity may induce distinct soil microbial responses, which are more sensitive than chemical responses.

1. Introduction

In the wake of global environmental change, anthropogenic activities in different forms and degrees are constantly transforming land-use patterns [1]. The most typical examples are converting natural forests or pastures to farmland over large areas [1,2]. Land-use strategies prominently impact soil health [3,4], demonstrating the soil ecosystem’s continued capacity to sustain plants, animals, and humans [5]. As soil quality indicators, soil physicochemical and microbial properties have attracted considerable attention [6,7]. For example, changes in soil pH, organic matter, and total nitrogen (N; TN) contents, as well as microbial biomass (MB) and metabolic profiles, are frequently monitored to assess the impacts of land-use type on soil quality [8,9].
Owing to growing demands for food and bioenergy, many countries globally need to continuously increase their arable land area and agricultural productivity [1,10]. Consequently, intensive cultivation practices adversely influence soil physicochemical properties, inhibit local soil microbial activities, and impair soil function [4,8,11]. Tillage operations disturb the soil structure, alter soil physical properties (e.g., bulk density and moisture content), and reduce soil N, organic carbon (C) contents, MB, and enzyme activity [6,12]. As a crucial nutrient for sustaining the life processes of plants, P availability is influenced by several factors, such as pH and soil organic matter content, and eventually determines crop production [13,14]. However, irrational fertilization practices may lead to nutrient imbalance and shifts in microbial community structure [6,11]. For example, a high rate of N fertilization decreases the contents of extractable soil nutrients (e.g., phosphorus [P], potassium [K], and calcium) and the relative abundance of mycorrhizal fungi markedly, impairing agricultural production sustainability [6,13].
Rice, one of the most widely consumed cereal crops globally, is the staple food for more than half the global population [15]. Rice monoculture is a cropping strategy adopted worldwide, and double- or triple-sequential cropping is implemented in Asia’s lowland tropics and subtropics [7]. However, the sustainability of continuous monoculture systems is a source of concern [16]. Specifically, monoculture practices can increase soil compaction, reduce soil nutrient availability, and decrease soil physicochemical quality [16,17]. It has been proved that extractable nutrients (e.g., N and P) determine crop production; hence, interrupting nutrient accessibility threatens agriculture sustainability [18]. In addition, such practices decrease the abundance and diversity of soil microbes and reshape microbial community structure, altering soil microbiological quality [7,19].
Microbes play an essential role in governing the fertility of rice soils and plant uptake of nutrients [5,20]. Microorganisms inhabiting the rice rhizosphere are much more abundant than bulk soil [21]. Bacteria, fungi, and protozoans can accelerate nutrient mineralization, enhance nutrient availability, and supply nitrogen actively, thus promoting rice growth directly [22]. On the other hand, microbial biomass, community structure, and metabolic potential are common indicators reflecting the soil quality and stability [20]; degraded soil microbial communities may disrupt nutrient cycling, reduce organic matter decomposition, and thus limit rice yields [23]. Moreover, intensive rice monocultivation increases the risks of infection by diseases and pest infestation, in addition to facilitating the accumulation of soil-borne pathogens [24]. Therefore, soil microbial community manipulation may ensure the sustainability of paddy cultivation [22].
China is a leader in global rice production and consumes more rice than any other country [25]. Northeastern China is one of the six first-grade rice-growing regions in China [26], and the traditional high-quality rice planting area comprises Heilongjiang, Jilin, and parts of Liaoning and Inner Mongolia [27]. In particular, Wuchang rice (Oryza sativa L. ssp. japonica), a protected geographical indication product in China, has been planted in the meadow soil of Wuchang City in Heilongjiang Province for over 200 years [28]. Due to the low winter temperature and short growing season in Wuchang City, southern Heilongjiang, rice monoculture with winter fallow is the predominant cultivation system [27]. The meadow soil is expected to degrade following long-term—more than 200 years—rice cultivation.
The present study investigated the impacts of rice cultivation on soil quality in a seed multiplication farm in Wuchang City managed based on local cropping strategies, which is of great significance to improve the soil ecological environment of meadow soil and provides valuable data for the restoration and rational utilization of meadow soil ecosystem. The pH, nutrient properties (soil organic matter [SOM]), TN, alkali-hydrolyzed N [AHN]), and microbial profiles (MB and C metabolism characteristics [CMC]) of cultivated meadow soils were measured. All soil variables were compared with those in uncultivated soils, and their seasonal variation was observed. We hypothesized that: (1) rice cultivation practices negatively impact nutrient-related soil properties; (2) MB is reduced under rice cultivation, with higher values in the rhizosphere than in the bulk soil; and (3) CMC varies with the cultivation status and seasonal dynamics.

2. Materials and Methods

2.1. Study Area and Experimental Design

The field study was carried out in the No. 1 seed multiplication farm of Wuchang Agricultural Technology Extension Center (44°53′59″ N, 127°06′08″ E), Heilongjiang Province, China. The study area has a temperate continental climate, with a frost-free period of 124 days. The mean annual temperature and precipitation are 3.5 °C and 625 mm, respectively. The soil type on the farm is meadow paddy soil (Hapli-Stagnic Anthrosols) [29].
The experimental field was divided into two plots (1/3 ha per plot) based on their cultivation status. Plot I (uncultivated) had been abandoned for over 20 years, whereas Plot II (cultivated) had been grown with Wuchang rice for three consecutive years. The growing period of Wuchang rice generally starts in mid-May and ends in early October [30]. Every May, three chemical fertilizers were applied in plot II before plowing (0–20 cm depth). Specifically, the rates of application of N, phosphate (P2O5), and potash (KCl) fertilizer were 97.5 kg, 48.0 kg, and 90.0 kg per ha, respectively.

2.2. Soil Sampling and Preparation

Soil samples were collected from the 0–20 cm depth at four rice growth stages in 2019: transplanting stage (18 May, spring), maturity stage (18 August, summer), harvesting stage (15 September, autumn), and fallow stage (30 October, winter). Uncultivated soils (US) were collected from Plot I, whereas rhizosphere (RS) and bulk soils (BS) were sampled from Plot II. Each composite soil sample was obtained by combining five subsamples collected using a five-point sampling strategy. The fresh soil samples were sealed in a plastic bag, kept in an icebox, and then transported to the laboratory for subsequent analyses.
The collected soil samples were passed through a 2 mm stainless steel sieve to remove stones, plant roots, and other debris. Afterward, each sample was divided evenly into two parts: one part was employed in soil chemical analysis, and the other part was used to profile soil microbial community characteristics.

2.3. Soil Chemical and Microbial Analyses

After freeze-drying (Scientz-10N; Xinzhi Biotechnology Co., Ltd., Ningbo, China), soil pH, SOM, TN, and AHN were determined according to the standard methods published by Lu [31]. Soil MB and CMC were profiled using phospholipid fatty acid (PLFA) and Biolog EcoPlate analyses, respectively.
(1) PLFA analysis. PLFAs, a class of membrane-bound substances, have been commonly used to depict the soil microbial commnity structure and characterize the biomass of living microorganisms [12,32]. Approximately 6 g (accurate to 0.01 g) of freeze-dried soil samples were used to extract PLFAs based on a modified Bligh–Dyer method [33], which involved concentration/extraction/concentration/methylation/purification procedures, followed by air-drying [34]. Nonadecanoic acid methyl ester (33 μg/mL, CAS 1731-94-8, N5377-5G, Sigma, US, Chromatographically pure) was used as the internal standard. Two mL of n-hexane (CAS 110-54-3, 208752-1L, Sigma, US, Chromatographically pure)/chloroform (CAS 67-66-3, 288306, Sigma, US, Chromatographically pure) (4:1, v/v) was added as the organic solvent for air-dried PLFAs.
The PLFAs were identified by gas chromatography–mass spectrometry (6890 GC-5973 MS Agilent; Agilent Technologies, Santa Clara, CA, USA). The heating program was as follows: 1 min at 50 °C, 2 min at 180 °C, 2 min at 220 °C, 1 min at 240 °C, and 15 min at 260 °C. The inlet temperature was 230 °C, and the connection temperature between the gas phase and the mass spectrometer was 280 °C. The split injection mode was applied, and high-purity helium was adopted as the carrier gas with a split ratio of 10:1. The mass spectrometer used an electron ionization source at an electron energy of 70 eV.
The relative contents of each PLFA were determined based on the contents of the internal standards. Bacterial biomass was represented by the sum of PLFAs 14:0, 15:0, a15:0, i15:0, i16:0, 16:1ω5, 16:1ω7, 16:1ω9, 17:0, a17:0, i17:0, 18:0, 18:1ω7, cy17:0, and cy19:0; the quantity of 18:2ω6, 9 was used as an indicator of fungal biomass; the sum of 10Me16:0, 10Me17:0, and 10Me18:0 was used as an indicator of actinomycete biomass [35].
(2) Biolog EcoPlate analysis. The Biolog EcoPlate system (MicroStation, Biolog Inc., Hayward, CA, USA) used to estimate the functional diversity (metabolic potential) of soil microbial communities contains 96-well microplates with 31 different C sources and one control in three replications [36]. The C substrates can be classified into five guilds, namely, carbohydrates (CH), carboxylic and acetic acids (CA), amino acids (AC), polymers (PO), and amines and amides (AA) [37]. The C source consumption rate was indicated by the reduction of tetrazolium violet redox dye [36]. A step-by-step manual for Biolog EcoPlate analysis is provided by Sofo and Ricciuti [38].
Approximately 10 g of sieved fresh soil samples were shaken in 90 mL of sterilized pure water for 60 min at 150 rpm (30 °C). The suspension (5 mL) was diluted 100 times with sterilized pure water, and the dilution was left to stand for 5 min. Subsequently, each well of Biolog EcoPlate was continuously inoculated with 150 μL of the dilution at 25 °C for 10 d. Absorbance was measured every 24 h at a wavelength of 590 nm using a microplate reader (Biolog Microstation System version GEN III; Biolog Inc., Hayward, CA, USA). The optical density (OD590) value of each well was calculated by subtracting the blank control value from the value of each plate well. Microbial activity in each microplate was expressed as average well color development (AWCD) [36,38], using the following formula:
A W C D = ∑ i = 1 n C i − R n
where Ci is the OD590 value in the ith well; R is the OD590 value of the control well; n is the number of C sources (n = 31 in this study) [39].

2.4. Data Analysis

We analyzed the impacts of different cultivation practices on soil chemical properties and metabolic fingerprints of soil microbial communities (represented by AWCD values at 240 h, AWCD240) by non-parametric comparison and principal component analysis (PCA).
(1) Non-parametric comparison. In each season, the AWCD values for certain C substrate groups were compared among different sample groups (US, RS, and BS). The restrictive distribution assumption on the data could not be satisfied; hence the non-parametric method (Kruskal–Wallis test) was used for multiple comparisons [40].
(2) PCA. As a classical multivariate statistical analysis technique, PCA identifies the principal component, a linear combination of the original variables, and displays the similarity trend across observations [41]. Both soil chemical properties and microbial C source utilization profiles were considered variables in the PCA procedure. Differences (or similarities) in the three groups of soil sampled in different seasons (e.g., BS in summer) were examined based on the selected variables, and the potential factors influencing the variables were explored. The variables were scaled to unit variance before PCA, making them comparable [42].
All data analyses were performed in R (version 4.0.2) [43] through RStudio (version 1.3.1073; https://www.rstudio.com/, accessed on 8 October 2022). The ‘PMCMRplus’ package in R [40] was employed in non-parameter comparison. PCA was executed using two R packages: ‘FactoMineR’ [44] and ‘factoextra’ (http://www.sthda.com/english/rpkgs/factoextra, accessed on 11 October 2022).

3. Results

3.1. Soil Chemical Properties

All soil samples’ pH and nutrient properties varied to some extent over the rice cultivation period (Table 1). The pH, SOM, TN, and AHN values in RS samples were generally the highest, followed by those of BS and US, although the differences were not always significant among all three groups.
PCA plot shows that the first two principal components (PCs) accounted for 76.2% of the total variation in soil chemical properties (PC1: 47.4%; PC2: 28.8%) (Figure 1). Soil pH was significantly associated with both PC1 (p = 0.031) and PC2 (p = 0.016). SOM and TN were associated with PC1 remarkably (SOM: p < 0.001; TN: p = 0.009), whereas AHN exhibited a strong association with PC2 (p = 0.012).
Based on the measured chemical properties, RS samples were distinct from BS and US samples throughout the rice cultivation period, excluding winter (Figure 1). Soils collected in summer were characterized by higher SOM and TN concentrations when compared with soils sampled in winter. Compared with the BS and US samples, the RS samples had higher AHN concentrations and pH values in autumn and spring. All three groups of soils sampled in spring had high pH values. The soil chemical properties of the US and BS samples obtained in spring and winter were somewhat similar.

3.2. PLFA-Based Soil Microbial Biomass

PLFAs are vital constituents of biomass in living microorganisms. According to the PLFA results, soil MB varied across seasons (Figure 2). Overall, the MB increased considerably from spring to summer and decreased continuously in autumn and winter. In both spring and summer, the total MB of BS samples was slightly higher than that of RS samples. The trend, however, was reversed during autumn and winter. Irrespective of the season, US samples had the highest MB.

3.3. Carbon Metabolism Characteristics of Total Carbon Sources

The total carbon metabolism properties of the three soil microbial communities, represented with AWCD, were demonstrated in Figure 3. In spring, AWCD240 values of BS were significantly higher than those of US (p = 0.024) and RS (p < 0.001); AWCD240 values of US were notably higher than those of RS as well (p = 0.024). There was no significant difference in AWCD240 values between BS and US in summer (p = 0.768). Still, the AWCD240 values of RS were significantly and marginally significantly lower than those of US (p = 0.028) and BS (p = 0.067), respectively. AWCD240 values of US were strikingly higher than those of BS (p = 0.005) and marginally significantly higher than those of RS (p = 0.056) in autumn. In contrast, differences in AWCD240 values between RS and BS were not apparent (p = 0.175). We did not detect any remarkable differences (p values of all pairs were higher than 0.3) among the AWCD240 values of BS, RS, and US in winter.

3.4. Carbon Metabolism Characteristics with Five Substrate Groups

For each group of soils sampled from spring to winter, the AWCD trends revealed varied metabolic activities on different C sources (Figure 4, Figure 5, Figure 6, Figure 7 and Figure 8). In addition, the AWCD values of each soil sample group varied seasonally.
(1) Carbohydrate utilization (Figure 4)
In spring, the AWCD240 values of BS samples were marginally significantly higher than those of the US samples (p = 0.057) and RS samples (p = 0.043), respectively; however, the differences between the latter two groups were not discernible (p = 0.972). In summer, AWCD240 values did not differ significantly between the BS and US samples (p = 0.972), both of which were notably and marginally significantly higher than those of the RS samples (p = 0.057 and 0.043, respectively). We observed no differences in AWCD240 values among the BS, RS, and US samples in autumn or winter (p > 0.2 for all pairs).
(2) Polymer utilization (Figure 5)
RS samples in summer had considerably lower AWCD240 values than the US (p = 0.043) and BS (p = 0.057) samples, whereas the latter two groups had minimal differences in AWCD240 values (p = 0.972). In autumn, the AWCD240 of RS samples was not significantly different from that of the US samples (p = 0.768), with both being higher than that of the BS samples (p = 0.067 and 0.028, respectively). There were minor differences in AWCD240 values among the three sample groups in spring and winter (p > 0.1 for all pairs).
(3) Carboxylic and acetic acid utilization (Figure 6)
In spring, the AWCD240 values of the RS samples were significantly and marginally significantly lower than those of the BS samples (p = 0.015) and US samples (p = 0.069); however, the differences in AWCD240 values between the US and BS samples were not considerable (p = 0.972). In summer, the AWCD240 values of the RS samples were similarly notably and marginally significantly lower than those of the BS samples (p = 0.043) and US samples (p = 0.057); however, there was no significant divergence between the latter two groups (p = 0.972). The US samples collected in autumn had higher AWCD240 values than the BS samples (p < 0.001) and RS samples (p = 0.020), whereas the AWCD240 values of the RS samples were strikingly higher than those of the BS samples (p = 0.020). The differences in AWCD240 values among the BS, RS, and US samples diminished in winter (p > 0.2 for all pairs).
(4) Amino acid utilization (Figure 7)
In spring, the RS samples had remarkably lower AWCD240 values than the US samples (p = 0.024) and BS samples (p ≤ 0.001), whereas the AWCD240 values of the BS samples were significantly higher than those of the US samples (p = 0.024). In autumn, the AWCD240 values of the US samples were higher than those of the BS samples (p = 0.043) and RS samples (p = 0.057); nevertheless, the differences in AWCD240 between BS and RS samples were not remarkable (p = 0.972). There were minor differences inAWCD240 among the three sample groups in summer and winter (p > 0.3 for all pairs).
(5) Amines and amide utilization (Figure 8)
The AWCD240 values of the BS samples in spring were marginally significantly and significantly higher than those of the RS samples (p = 0.067) and US samples (p = 0.028); however, the differences in AWCD240 between the US and RS samples were minimal (p = 0.768). In summer, the AWCD240 values of the RS samples were notably and marginally significantly lower than those of the BS (p = 0.028) and US samples (p = 0.067), although there was no significant divergence in AWCD240 between the US and BS samples (p = 0.768). We observed no apparent differences in AWCD240 among the BS, RS, and US samples in autumn and winter (p > 0.1 for all pairs).
(6) PCA of carbon source utilization profiles
According to the PCA results, the first two principal components explained 96.2% of the total variation in CMC (PC1: 92.2%; PC2: 4%; Figure 9). All the major C substrate guilds were strikingly associated with PC1 (p < 0.001) in the following order: CH > AA > PO > AC > CA. US samples collected in different periods preferred distinct C sources from the microbial metabolic activities of specific C substrates. BS samples collected in spring and summer exhibited a preference for similar C substrate guilds, which was different from that of BS samples in other seasons. RS samples in winter and autumn were more similar in the C source utilization patterns than the RS samples in either spring or summer.

4. Discussion

4.1. Rhizosphere Soil Possesses Superior Chemical Properties

Consistent with previous reports [8,45], conventional rice monoculture practices in our experimental field induced drastic changes in soil pH and nutrient concentrations. The maximum values of soil chemical properties were observed in the RS samples in most cases (Table 1), which somewhat contradicts our first hypothesis that rice cropping activities negatively impact nutrient-related soil chemical properties.
The rhizosphere, defined as the soil contiguous to the plant roots [46], is a crucial zone modulating the C and N biogeochemical cycles [47]. Roots release various organic and inorganic substances, including amino acids, sugars, organic acids, and mineral nutrients, into the surrounding soil during plant growth [46,48,49]. Therefore, the rhizodeposition process may be responsible for the higher concentrations of SOM and N nutrients in RS samples in the present study.
It is estimated that 50% of the C fixed by photosynthesis enters the soil through rhizodeposition [48], naturally elevating SOM within rhizosphere soil immediately. This mechanism is broadly consistent with our findings in the cultivated plot. Moreover, soils sampled in the rapid growth stage (summer) and fallow stage (winter) of paddy rice were distributed at both ends along the PC1 axis, and PC1 was most prominently associated with SOM concentration (Figure 2), implying a shift in rhizosphere C deposition of paddy rice across different growth stages [48,50]. Similarly, Lu et al. [51] reported that the proportion of photosynthetic C of rice allocated underground decreased drastically from 28% to 2% from tillering to maturity.
In addition to SOM, soil N compounds are critical components for soil fertility in agricultural land. N deficiency and/or an inappropriate ratio of carbon to nitrogen inhibits microbial activity and severely limits plant growth [52]. Although N derived from rhizodeposition is less considered than organic C compounds [49], it can presumably be used to explain our observation that the TN concentration was relatively high in the RS samples (Table 1). Tremendous amounts of N are potentially released into the soil as rhizodeposits, accounting for 71% of total assimilated plant N and 96% of total belowground plant N [49]. In addition, N rhizodeposition can vary across plant growth stages [53]. A previous study found that the concentrations of soil N derived from rhizodeposits of spring wheat increased over the plant growth period [54]. This is consistent with the steadily increasing TN concentration in the RS samples (Table 1) and the apparent divergence in TN concentration of RS samples plotted along PC1, which was significantly associated with TN concentration (Figure 1).
AHN, also known as available N, can be the limiting factor for plant growth and productivity [55]. Environmental temperature and plant growth stage, which influence soil microbial and biochemical processes and N uptake efficiency, are the major factors controlling soil N availability [55,56]. In the present study, soil AHN concentration exhibited a decreasing trend from spring to winter, corresponding to different rice growth stages (Figure 1). The highest AHN concentrations within the spring soils can be attributed to N fertilizer application and a slower growth rate of rice seedlings. The subsequent variation in AHN is similar to trends reported in other studies focusing on the temporal dynamics of soil available N in various ecosystems [55,56,57]. Furthermore, there was a visible discrepancy in AHN concentration in RS samples when compared with the other two groups in autumn (Figure 1), implying intense microbial activity related to N cycling within the rhizosphere soil [8,46].

4.2. Rice Cultivation Reduces Soil Microbial Biomass

Microorganisms execute critical functions and modulate soil physicochemical properties, e.g., structure, porosity, fertility, and availability of limited nutrients [20,58]. In addition, MB is involved in residue decomposition and nutrient cycling [59], thereby contributing to ecosystem sustainability. MB is frequently utilized to illustrate the responses of soil microbiota to changing environmental conditions [59,60,61]. MB was comparatively higher in the uncultivated plot (US samples) than in the cultivated plot (BS and RS samples), especially in the rice growth period (Figure 2). Agricultural intensification, for example, in the forms of tillage, fertilization, and monoculture, leads to declines in soil biological properties and health [7,62].
Zero-tillage soil has macro-porosity and better connectivity [63]; however, conventional tillage practices could inhibit the mineralization and migration of nutrients and reduce microbial activity and biomass [64,65], either directly or indirectly [66]. Mathew et al. [12] explored differences in soil microbiological properties between tilled and untilled corn systems. They observed that soils under long-term no-till retained higher MB because of favorable physicochemical conditions for microbial activity [12]. In addition, chemical fertilizer application has been demonstrated to reduce soil MB [67]. The impacts probably arise from shifts in soil microbial community function, i.e., the transformation toward a more r-selected microbial community [10,68,69]. Such community shifts potentially arise directly from increased nutrient availability and indirectly through variations in soil C, pH, or other associated properties [69,70]. Furthermore, Xuan et al. [19] examined differences in soil bacterial communities between crop rotation fields and intensive rice cultivation. They observed that bacterial community composition, abundance, and diversity in the rotation system were prominently distinct, with higher values than in the rice monoculture system [19].

4.3. Monoculture Practices Influence Soil Carbon Metabolism Characteristics

Two standard cultivation practices, i.e., tillage and fertilization, modulate factors influencing the soil microbial community-level physiological profiles [71,72,73]. AWCD, acquired using the Biolog Ecoplate system, enables the portrayal of the metabolic fingerprints of soil microbial communities and distinguishing the metabolic activity of soil microorganisms across treatments [74,75].
Numerous factors, such as fertilizer type and rate of application, soil properties, and crop species, regulate soil microbial responses to fertilization [72]. In the cultivated plot, N, P, and K fertilization in spring prominently increased the AWCD values of BS samples (Figure 3, Figure 4, Figure 7 and Figure 8). Higher AWCD values generally imply higher metabolic functional diversity and activity [74,75]. In other words, after a long winter-fallow period, fertilizer amendment in the cultivated plot could strongly stimulate the growth of soil microorganisms, which is intuitively attributed to the nutrient influx of fertilizer. In contrast to the AWCD trend, however, the MB in BS samples at the same period was lower than that in the US samples (Figure 2), denoting that different soil microbial properties are influenced unequally by fertilization [69,72].
Compared with tillage, no-till management has been shown to increase soil microbial functional diversity [4,12,76]. In the present study, the negative impact of tillage treatment on AWCD emerged in the reproductive phase of rice (summer) and became significant until the harvesting phase (autumn; Figure 5), leading to a remarkable decline in the metabolic functional diversity within cultivated soils during the vital stages of rice production. Such a pattern might be explained by the fading impact of fertilization and the adverse impact of tillage. Tillage practices, such as plowing, disrupt soil aggregates, alter soil structure, and decrease soil stability, reducing capillary pore space and destroying microhabitats of soil microorganisms [69,72]. Such soil environmental changes are detrimental to microbial activity; for instance, tilled soil exhibits poorer dehydrogenase activity, an indicator of the overall microbial activity and oxidation–reduction reactions in soil [76].
Rice management strategies, however, are just one factor impacting the soil microbial carbon metabolism properties. In a comparable study performed by Zhao et al. in Changchun (Northeast China) [77], the variation patterns of AWCD values and differences among AWCD240 values of three soil samples at each season (Supplementary Materials Figure S1) were not entirely consistent with our results (Figure 3), albeit the soil types, cultivation measures, and soil MB properties were similar to some extent [77,78]. It is indicated that local environmental factors, including temperature, light conditions, soil grain size, and nutrient status, would also determine the microbial community composition and dynamics. Therefore, a comprehensive experiment harboring more potential parameters should be performed in the future to evaluate the relative contribution of a variety of factors.
Soil microbial functional responses to cultivation practices may vary seasonally [4,10,76]. Throughout the rice growth period, soil microorganisms under different cultivation practices exhibited inconsistent preferences for the five groups of C substrates. However, there is a relatively discernible difference in C substrate preference between summer and the other seasons (Figure 9). Studies have shown that higher temperature and adequate moisture in summer could trigger soil microbial growth, activity, and community diversity [60,79], which are consistent with our findings to some extent.
The PLFA analysis and Biolog EcoPlate assays are traditional measures disclosing the soil microbial community’s biomass, composition, and physiological profile [20]. However, taxonomic information and the function of a given microbial community cannot be characterized using these tools alone. Therefore, molecular biology approaches, such as rRNA methods, will fill the gap in understanding the responses of soil microbes under diverse cultivation practices.

5. Conclusions

It has been confirmed that soil quality deteriorates over long-term continuous rice monocultivation. Based on our results, (1) such practices indeed altered soil pH and nutrient concentrations; however, the chemical properties of the rhizosphere soil were superior, somewhat contradicting the first hypothesis. (2) Soil MB was reduced due to agricultural activities, such as fertilization, tillage, and monoculture, supporting the second hypothesis, although the adverse impacts varied by season. (3) Coinciding with the last hypothesis, microbial CMC, measured by the AWCD value, changed under cultivation and exhibited seasonal variation.
Herein, we emphasize that specific cultivation practices may elicit distinct responses from soil microbial communities based on total biomass and C source utilization profiles. Despite no consistent adverse responses of soil chemical properties under rice cultivation, the microbial properties seemingly respond clearly and sensitively. Accordingly, soil microbial properties could be considered superior indicators in reflecting soil disturbance. Genomic-based approaches will help further deepen the insight into the phylogenetic and functional dynamics of the microbial communities in degraded soils.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/microorganisms10112153/s1, Figure S1. Variation in the average well color development (AWCD) of total carbon sources (n = 31) among three groups of soil samples collected in different seasons in Zhao’s research.

Author Contributions

Conceptualization, X.F., Z.Z. and Z.W.; methodology, X.F. and Z.Z.; formal analysis, X.F., Z.Z., H.W. (Haimiao Wu), X.M., Z.S., H.W. (Hao Wang) and K.Y.; investigation, X.F., Z.Z., H.W. (Hao Wang), X.M., S.L. and K.Y.; resources, Z.Z.; data curation, X.F. and Z.Z.; writing—original draft preparation, X.F., Z.Z., Z.W., H.W. (Haimiao Wu), X.M. and K.Y.; writing—review and editing, X.F., Z.Z., Z.W., H.W. (Hao Wang), C.M. and S.L.; visualization, X.F. and Z.Z.; Supervision, Z.Z.; project administration, Z.Z.; funding acquisition, Z.Z. and Z.S. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Science and Technology Planning Project of Qinghai Province (Grant No. 2019-ZJ-7063), the Innovation Ability Enhancement Program of Hebei Province (21553601D), the National Natural Science Foundation of China (U21A2023), the Funding for the Science and Technology Innovation Team Project of Hebei GEO University (KJCXTD-2021-09), the Introducing Foreign Intelligence Program of Hebei Province (22205018), and the 17th Student Science and Technology Funds of Hebei GEO University (KAY202118 & KHG202101).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Tellen, V.A.; Yerima, B.P.K. Effects of Land Use Change on Soil Physicochemical Properties in Selected Areas in the North West Region of Cameroon. Environ. Syst. Res. 2018, 7, 3. [Google Scholar] [CrossRef] [Green Version]
  2. Zhang, P.; Shao, G.; Zhao, G.; le Master, D.C.; Parker, G.R.; Dunning, J.B.; Li, Q. China’s Forest Policy for the 21st Century. Science 2000, 288, 2135–2136. [Google Scholar] [CrossRef] [Green Version]
  3. Bissett, A.; Richardson, A.E.; Baker, G.; Thrall, P.H. Long-Term Land Use Effects on Soil Microbial Community Structure and Function. Appl. Soil Ecol. 2011, 51, 66–78. [Google Scholar] [CrossRef]
  4. Jangid, K.; Williams, M.A.; Franzluebbers, A.J.; Schmidt, T.M.; Coleman, D.C.; Whitman, W.B. Land-Use History Has a Stronger Impact on Soil Microbial Community Composition than Aboveground Vegetation and Soil Properties. Soil Biol. Biochem. 2011, 43, 2184–2193. [Google Scholar] [CrossRef]
  5. Lehmann, J.; Bossio, D.A.; Kögel-Knabner, I.; Rillig, M.C. The Concept and Future Prospects of Soil Health. Nat. Rev. Earth Environ. 2020, 1, 544–553. [Google Scholar] [CrossRef]
  6. Mbuthia, L.W.; Acosta-Martínez, V.; DeBruyn, J.; Schaeffer, S.; Tyler, D.; Odoi, E.; Mpheshea, M.; Walker, F.; Eash, N. Long Term Tillage, Cover Crop, and Fertilization Effects on Microbial Community Structure, Activity: Implications for Soil Quality. Soil Biol. Biochem. 2015, 89, 24–34. [Google Scholar] [CrossRef]
  7. Maguire, V.G.; Bordenave, C.D.; Nieva, A.S.; Llames, M.E.; Colavolpe, M.B.; Gárriz, A.; Ruiz, O.A. Soil Bacterial and Fungal Community Structure of a Rice Monoculture and Rice-Pasture Rotation Systems. Appl. Soil Ecol. 2020, 151, 103535. [Google Scholar] [CrossRef]
  8. Liu, D.; Huang, Y.; An, S.; Sun, H.; Bhople, P.; Chen, Z. Soil Physicochemical and Microbial Characteristics of Contrasting Land-Use Types along Soil Depth Gradients. Catena 2018, 162, 345–353. [Google Scholar] [CrossRef]
  9. Arévalo-Gardini, E.; Canto, M.; Alegre, J.; Loli, O.; Julca, A.; Baligar, V. Changes in Soil Physical and Chemical Properties in Long Term Improved Natural and Traditional Agroforestry Management Systems of Cacao Genotypes in Peruvian Amazon. PLoS ONE 2015, 10, e0132147. [Google Scholar] [CrossRef]
  10. Carbonetto, B.; Rascovan, N.; Álvarez, R.; Mentaberry, A.; Vázquez, M.P. Structure, Composition and Metagenomic Profile of Soil Microbiomes Associated to Agricultural Land Use and Tillage Systems in Argentine Pampas. PLoS ONE 2014, 9, e99949. [Google Scholar] [CrossRef]
  11. Puissant, J.; Villenave, C.; Chauvin, C.; Plassard, C.; Blanchart, E.; Trap, J. Quantification of the Global Impact of Agricultural Practices on Soil Nematodes: A Meta-Analysis. Soil Biol. Biochem. 2021, 161, 108383. [Google Scholar] [CrossRef]
  12. 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] [Green Version]
  13. Maroušek, J.; Gavurová, B. Recovering Phosphorous from Biogas Fermentation Residues Indicates Promising Economic Results. Chemosphere 2022, 291, 133008. [Google Scholar] [CrossRef]
  14. Maroušek, J.; Maroušková, A. Economic Considerations on Nutrient Utilization in Wastewater Management. Energy 2021, 14, 3468. [Google Scholar] [CrossRef]
  15. Muthayya, S.; Sugimoto, J.D.; Montgomery, S.; Maberly, G.F. An Overview of Global Rice Production, Supply, Trade, and Consumption. Ann. N. Y. Acad. Sci. 2014, 1324, 7–14. [Google Scholar] [CrossRef]
  16. Linh, T.B.; Sleutel, S.; Vo Thi, G.; Le Van, K.; Cornelis, W.M. Deeper Tillage and Root Growth in Annual Rice-Upland Cropping Systems Result in Improved Rice Yield and Economic Profit Relative to Rice Monoculture. Soil Tillage Res. 2015, 154, 44–52. [Google Scholar] [CrossRef]
  17. Linh, T.B.; Le Van, K.; Van Elsacker, S.; Cornelis, W.M. Effect of Cropping System on Physical Properties of Clay Soil Under Intensive Rice Cultivation. Land Degrad Dev. 2016, 27, 973–982. [Google Scholar] [CrossRef]
  18. Maroušek, J.; Maroušková, A.; Zoubek, T.; Bartoš, P. Economic Impacts of Soil Fertility Degradation by Traces of Iron from Drinking Water Treatment. Environ. Dev. Sustain. 2022, 24, 4835–4844. [Google Scholar] [CrossRef]
  19. Xuan, D.T.; Guong, V.T.; Rosling, A.; Alström, S.; Chai, B.; Högberg, N. Different Crop Rotation Systems as Drivers of Change in Soil Bacterial Community Structure and Yield of Rice, Oryza Sativa. Biol. Fertil. Soils 2012, 48, 217–225. [Google Scholar] [CrossRef]
  20. Luo, X.; Fu, X.; Yang, Y.; Cai, P.; Peng, S.; Chen, W.; Huang, Q. Microbial Communities Play Important Roles in Modulating Paddy Soil Fertility. Sci. Rep. 2016, 6, 20326. [Google Scholar] [CrossRef]
  21. Breidenbach, B.; Pump, J.; Dumont, M.G. Microbial Community Structure in the Rhizosphere of Rice Plants. Front. Microbiol. 2016, 6, 1537. [Google Scholar] [CrossRef] [Green Version]
  22. Perera, T.A.; Tirimanne, S. Role of Microbial Communities in Sustainable Rice Cultivation. In Role of Microbial Communities for Sustainability; Seneviratne, G., Zavahir, J.S., Eds.; Springer: Singapore, 2021; pp. 189–223. ISBN 978-981-15-9912-5. [Google Scholar]
  23. Maroušek, J.; Kolář, L.; Vochozka, M.; Stehel, V.; Maroušková, A. Novel Method for Cultivating Beetroot Reduces Nitrate Content. J. Clean. Prod. 2017, 168, 60–62. [Google Scholar] [CrossRef]
  24. Xiang, H.; Lan, N.; Wang, F.; Zhao, B.; Wei, H.; Zhang, J. Reduced Pests, Improved Grain Quality and Greater Total Income: Benefits of Intercropping Rice with Pontederia Cordata. J. Sci. Food Agric. 2021, 101, 5907–5917. [Google Scholar] [CrossRef]
  25. Nie, L.; Peng, S. Rice Production in China. In Rice Production Worldwide; Chauhan, B.S., Jabran, K., Mahajan, G., Eds.; Springer: Cham, Switzerland, 2017; pp. 33–52. ISBN 978-3-319-47516-5. [Google Scholar]
  26. Mei, F.; Wu, X.; Yao, C.; Li, L.; Wang, L.; Chen, Q. Rice Cropping Regionalization in China. Chin. J. Rice Sci. 1988, 2, 97–110. [Google Scholar]
  27. Tang, S.; Ding, L.; Bonjean, A.P.A. Rice Production and Genetic Improvement in China. In Cereals in China; He, Z., Bonjean, A.P.A., Eds.; CIMMYT: Mexico City, Mexico, 2010; ISBN 978-970-648-177-1. [Google Scholar]
  28. Qian, L.; Zuo, F.; Liu, H.; Zhang, C.; Chi, X.; Zhang, D. Determination of Geographical Origin of Wuchang Rice with the Geographical Indicator by Multielement Analysis. J. Food Qual. 2019, 2019, 8396865. [Google Scholar] [CrossRef]
  29. Cooperative Research Group of Chinese Soil Taxonomy (CRG-CST). Chinese Soil Taxonomy, 3rd ed.; China Science-Technology University Publishing House: Hefei, China, 2001. [Google Scholar]
  30. Tan, K.; Wang, S.; Song, Y.; Liu, Y.; Gong, Z. Estimating Nitrogen Status of Rice Canopy Using Hyperspectral Reflectance Combined with BPSO-SVR in Cold Region. Chemom. Intell. Lab. Syst. 2018, 172, 68–79. [Google Scholar] [CrossRef]
  31. Lu, R. Analysis Method of Soil Agricultural Chemistry; China Agricultural Science and Technology Press: Beijing, China, 2000. [Google Scholar]
  32. Frostegård, A.; Bååth, E. The Use of Phospholipid Fatty Acid Analysis to Estimate Bacterial and Fungal Biomass in Soil. Biol. Fertil. Soils 1996, 22, 59–65. [Google Scholar] [CrossRef]
  33. Kates, M. Techniques of Lipidology: Isolation, Analysis and Identification of Lipids. Lab. Tech. Biochem. Mol. Biol. 1972, 3, 268–618. [Google Scholar] [CrossRef]
  34. Frostegård, Å.; Tunlid, A.; Bååth, E. Microbial Biomass Measured as Total Lipid Phosphate in Soils of Different Organic Content. J. Microbiol. Methods 1991, 14, 151–163. [Google Scholar] [CrossRef]
  35. Boyle, S.A.; Yarwood, R.R.; Bottomley, P.J.; Myrold, D.D. Bacterial and Fungal Contributions to Soil Nitrogen Cycling under Douglas Fir and Red Alder at Two Sites in Oregon. Soil Biol. Biochem. 2008, 40, 443–451. [Google Scholar] [CrossRef]
  36. Gryta, A.; Frąc, M.; Oszust, K. The Application of the Biolog EcoPlate Approach in Ecotoxicological Evaluation of Dairy Sewage Sludge. Appl. Biochem. Biotechnol. 2014, 174, 1434–1443. [Google Scholar] [CrossRef] [PubMed]
  37. Jałowiecki, Ł.; Chojniak, J.M.; Dorgeloh, E.; Hegedusova, B.; Ejhed, H.; Magnér, J.; Płaza, G.A. Microbial Community Profiles in Wastewaters from Onsite Wastewater Treatment Systems Technology. PLoS ONE 2016, 11, e0147725. [Google Scholar]
  38. Sofo, A.; Ricciuti, P. A Standardized Method for Estimating the Functional Diversity of Soil Bacterial Community by Biolog® EcoPlatesTM Assay—The Case Study of a Sustainable Olive Orchard. Appl. Sci. 2019, 9, 4035. [Google Scholar] [CrossRef] [Green Version]
  39. Ge, Z.; Du, H.; Gao, Y.; Qiu, W. Analysis on Metabolic Functions of Stored Rice Microbial Communities by BIOLOG ECO Microplates. Front. Microbiol. 2018, 9, 1375. [Google Scholar] [CrossRef] [Green Version]
  40. Pohlert, T. PMCMRplus: Calculate Pairwise Multiple Comparisons of Mean Rank Sums Extended. Available online: https://CRAN.R-project.org/package=PMCMRplus (accessed on 25 September 2022).
  41. Abdi, H.; Williams, L.J. Principal Component Analysis. WIREs Comput. Stat. 2010, 2, 433–459. [Google Scholar] [CrossRef]
  42. Kassambara, A. Practical Guide to Principal Component Methods in R: PCA, M (CA), FAMD, MFA, HCPC, Factoextra (Vol. 2); Sthda. 2017. Available online: http://www.sthda.com/english/articles/31-principal-component-methods-in-r-practical-guide/112-pca-principal-component-analysis-essentials/ (accessed on 18 October 2022).
  43. R Core Team R: A Language and Environment for Statistical Computing. Available online: https://www.R-project.org/ (accessed on 19 August 2021).
  44. Lê, S.; Josse, J.; Rennes, A.; Husson, F. FactoMineR: An R Package for Multivariate Analysis. J. Stat. Softw. 2008, 25, 1–18. [Google Scholar] [CrossRef] [Green Version]
  45. Wittwer, R.A.; Bender, S.F.; Hartman, K.; Hydbom, S.; Lima, R.A.A.; Loaiza, V.; Nemecek, T.; Oehl, F.; Olsson, P.A.; Petchey, O.; et al. Organic and Conservation Agriculture Promote Ecosystem Multifunctionality. Sci. Adv. 2021, 7, eabg6995. [Google Scholar] [CrossRef] [PubMed]
  46. Manoharachary, C.; Mukerji, K.G. Rhizosphere Biology—An Overview. In Microbial Activity in the Rhizoshere; Mukerji, K.G., Manoharachary, C., Singh, J., Eds.; Springer: Berlin/Heidelberg, Germany, 2006; pp. 1–15. ISBN 978-3-540-29420-7. [Google Scholar]
  47. Huo, C.; Luo, Y.; Cheng, W. Rhizosphere Priming Effect: A Meta-Analysis. Soil Biol. Biochem. 2017, 111, 78–84. [Google Scholar] [CrossRef] [Green Version]
  48. Nguyen, C. Rhizodeposition of Organic C by Plant: Mechanisms and Controls. In Sustainable Agriculture; Lichtfouse, E., Navarrete, M., Debaeke, P., Véronique, S., Alberola, C., Eds.; Springer: Dordrecht, The Netherlands, 2009; pp. 97–123. ISBN 978-90-481-2666-8. [Google Scholar]
  49. Wichern, F.; Eberhardt, E.; Mayer, J.; Joergensen, R.G.; Müller, T. Nitrogen Rhizodeposition in Agricultural Crops: Methods, Estimates and Future Prospects. Soil Biol. Biochem. 2008, 40, 30–48. [Google Scholar] [CrossRef]
  50. Wang, H.; Zhang, J.; Wu, K.; Ni, F. Measuring the Rhizodeposition of Carbon by Rice: An Approach Based on Carbon Flux Observations. Soil Sci. Plant Nutr. 2017, 63, 499–506. [Google Scholar] [CrossRef] [Green Version]
  51. Lu, Y.; Watanabe, A.; Kimura, M. Input and Distribution of Photosynthesized Carbon in a Flooded Rice Soil. Glob. Biogeochem. Cycles 2002, 16, 32-1–32-38. [Google Scholar] [CrossRef]
  52. Maroušek, J.; Strunecký, O.; Kolář, L.; Vochozka, M.; Kopecký, M.; Maroušková, A.; Batt, J.; Poliak, M.; Šoch, M.; Bartoš, P.; et al. Advances in Nutrient Management Make It Possible to Accelerate Biogas Production and Thus Improve the Economy of Food Waste Processing. Energy Sources Part A Recovery Util. Environ. Eff. 2020, 1–10. [Google Scholar] [CrossRef]
  53. Hupe, A.; Schulz, H.; Bruns, C.; Haase, T.; Heß, J.; Joergensen, R.G.; Wichern, F. Even Flow? Changes of Carbon and Nitrogen Release from Pea Roots over Time. Plant Soil 2018, 431, 143–157. [Google Scholar] [CrossRef]
  54. Cao, Y.; Sun, X.; Shi, Y.; Lu, C.; Miao, Y.; Chen, Z.; Han, S. Quantitative Evidence of Underestimated Nitrogen Rhizodeposition Using 15N Split-Root Labeling during Spring Wheat Developmental Period. Catena 2021, 207, 105618. [Google Scholar] [CrossRef]
  55. Zheng, X.; Yuan, J.; Zhang, T.; Hao, F.; Jose, S.; Zhang, S. Soil Degradation and the Decline of Available Nitrogen and Phosphorus in Soils of the Main Forest Types in the Qinling Mountains of China. Forests 2017, 8, 460. [Google Scholar] [CrossRef] [Green Version]
  56. Zhang, S.; Huang, J.; Wang, Y.; Shen, Q.; Mu, L.; Liu, Z. Spatiotemporal Heterogeneity of Soil Available Nitrogen During Crop Growth Stages on Mollisol Slopes of Northeast China. Land Degrad. Dev. 2017, 28, 856–869. [Google Scholar] [CrossRef]
  57. Chen, F.S.; Zeng, D.H.; Zhou, B.; Singh, A.N.; Fan, Z.P. Seasonal Variation in Soil Nitrogen Availability under Mongolian Pine Plantations at the Keerqin Sand Lands, China. J. Arid. Environ. 2006, 67, 226–239. [Google Scholar] [CrossRef]
  58. Prasad, J.K.; Dey, R.; Gupta, S.K.; Raghuwanshi, R. Portraying Microbial Beneficence for Ameliorating Soil Health and Plant Growth. In Soil Health; Giri, B., Varma, A., Eds.; Springer International Publishing: Cham, Switzerland, 2020; pp. 287–312. ISBN 978-3-030-44364-1. [Google Scholar]
  59. Kavamura, V.N.; Aono, A.H.; Esposito, E. 6.18—Biotechnological Strategies Applied to the Decontamination of Soils Polluted With Heavy Metals☆. In Comprehensive Biotechnology, 3rd ed.; Moo-Young, M., Ed.; Pergamon: Oxford, UK, 2019; pp. 240–252. ISBN 978-0-444-64047-5. [Google Scholar]
  60. DeLuca, T.H.; Pingree, M.R.A.; Gao, S. Chapter 16—Assessing Soil Biological Health in Forest Soils. In Developments in Soil Science; Busse, M., Giardina, C.P., Morris, D.M., Page-Dumroese, D.S., Eds.; Elsevier: Amsterdam, The Netherlands, 2019; Volume 36, pp. 397–426. ISBN 0166-2481. [Google Scholar]
  61. Kandeler, E. 3—Physiological and Biochemical Methods for Studying Soil Biota and Their Function. In Soil Microbiology, Ecology and Biochemistry, 3rd ed.; PAUL, E.A., Ed.; Academic Press: San Diego, CA, USA, 2007; pp. 53–83. ISBN 978-0-12-546807-7. [Google Scholar]
  62. Zhao, J.; Li, Y.; Wang, B.; Huang, X.; Yang, L.; Lan, T.; Zhang, J.; Cai, Z. Comparative Soil Microbial Communities and Activities in Adjacent Sanqi Ginseng Monoculture and Maize-Sanqi Ginseng Systems. Appl. Soil Ecol. 2017, 120, 89–96. [Google Scholar] [CrossRef]
  63. Galdos, M.v.; Pires, L.F.; Cooper, H.v.; Calonego, J.C.; Rosolem, C.A.; Mooney, S.J. Assessing the Long-Term Effects of Zero-Tillage on the Macroporosity of Brazilian Soils Using X-Ray Computed Tomography. Geoderma 2019, 337, 1126–1135. [Google Scholar] [CrossRef]
  64. Maroušek, J.; Strunecký, O.; Stehel, V. Biochar Farming: Defining Economically Perspective Applications. Clean. Technol. Environ. Policy 2019, 21, 1389–1395. [Google Scholar] [CrossRef]
  65. Stávková, J.; Maroušek, J. Novel Sorbent Shows Promising Financial Results on P Recovery from Sludge Water. Chemosphere 2021, 276, 130097. [Google Scholar] [CrossRef] [PubMed]
  66. Singh, K.; Mishra, A.K.; Singh, B.; Singh, R.P.; Patra, D.D. Tillage Effects on Crop Yield and Physicochemical Properties of Sodic Soils. Land Degrad. Dev. 2016, 27, 223–230. [Google Scholar] [CrossRef]
  67. Lu, M.; Yang, Y.; Luo, Y.; Fang, C.; Zhou, X.; Chen, J.; Yang, X.; Li, B. Responses of Ecosystem Nitrogen Cycle to Nitrogen Addition: A Meta-Analysis. New Phytol. 2011, 189, 1040–1050. [Google Scholar] [CrossRef] [PubMed]
  68. Leff, J.W.; Jones, S.E.; Prober, S.M.; Barberán, A.; Borer, E.T.; Firn, J.L.; Harpole, W.S.; Hobbie, S.E.; Hofmockel, K.S.; Knops, J.M.H.; et al. Consistent Responses of Soil Microbial Communities to Elevated Nutrient Inputs in Grasslands across the Globe. Proc. Natl. Acad. Sci. USA 2015, 112, 10967. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  69. Dornbush, M.E.; von Haden, A.C. Chapter 8—Intensified Agroecosystems and Their Effects on Soil Biodiversity and Soil Functions. In Soil Health and Intensification of Agroecosytems; Al-Kaisi, M.M., Lowery, B., Eds.; Academic Press: Cambridge, MA, USA, 2017; pp. 173–193. ISBN 978-0-12-805317-1. [Google Scholar]
  70. Zeng, J.; Liu, X.; Song, L.; Lin, X.; Zhang, H.; Shen, C.; Chu, H. Nitrogen Fertilization Directly Affects Soil Bacterial Diversity and Indirectly Affects Bacterial Community Composition. Soil Biol. Biochem. 2016, 92, 41–49. [Google Scholar] [CrossRef]
  71. Soman, C.; Li, D.; Wander, M.M.; Kent, A.D. Long-Term Fertilizer and Crop-Rotation Treatments Differentially Affect Soil Bacterial Community Structure. Plant Soil 2017, 413, 145–159. [Google Scholar] [CrossRef]
  72. Lupwayi, N.Z.; Lafond, G.P.; Ziadi, N.; Grant, C.A. Soil Microbial Response to Nitrogen Fertilizer and Tillage in Barley and Corn. Soil Tillage Res. 2012, 118, 139–146. [Google Scholar] [CrossRef]
  73. Bissett, A.; Richardson, A.E.; Baker, G.; Kirkegaard, J.; Thrall, P.H. Bacterial Community Response to Tillage and Nutrient Additions in a Long-Term Wheat Cropping Experiment. Soil Biol. Biochem. 2013, 58, 281–292. [Google Scholar] [CrossRef]
  74. Li, C.; Liu, X.; Meng, M.; Zhai, L.; Zhang, B.; Jia, Z.; Gu, Z.; Liu, Q.; Zhang, Y.; Zhang, J. The Use of Biolog Eco Microplates to Compare the Effects of Sulfuric and Nitric Acid Rain on the Metabolic Functions of Soil Microbial Communities in a Subtropical Plantation within the Yangtze River Delta Region. Catena 2021, 198, 105039. [Google Scholar] [CrossRef]
  75. Li, L.; Xu, M.; Eyakub Ali, M.; Zhang, W.; Duan, Y.; Li, D. Factors Affecting Soil Microbial Biomass and Functional Diversity with the Application of Organic Amendments in Three Contrasting Cropland Soils during a Field Experiment. PLoS ONE 2018, 13, e0203812. [Google Scholar] [CrossRef] [Green Version]
  76. Nivelle, E.; Verzeaux, J.; Habbib, H.; Kuzyakov, Y.; Decocq, G.; Roger, D.; Lacoux, J.; Duclercq, J.; Spicher, F.; Nava-Saucedo, J.-E.; et al. Functional Response of Soil Microbial Communities to Tillage, Cover Crops and Nitrogen Fertilization. Appl. Soil Ecol. 2016, 108, 147–155. [Google Scholar] [CrossRef]
  77. Zhao, Z.; Cui, B.; Hou, Y.; Liu, S.; Wang, Y. Impact on the Microbial Biomass and Metabolic Function of Carbon Source by Black Soil during Rice Cultivation. Environ. Sci. 2015, 36, 3011–3017. [Google Scholar] [CrossRef]
  78. Dou, S.; Guo, D. Soil Type Distribution and Black Soil Land Protection in Jilin Province. J. Jilin Agric. Univ. 2018, 40, 449–456. [Google Scholar] [CrossRef]
  79. Moore-Kucera, J.; Dick, R.P. PLFA Profiling of Microbial Community Structure and Seasonal Shifts in Soils of a Douglas-Fir Chronosequence. Microb. Ecol. 2008, 55, 500–511. [Google Scholar] [CrossRef]
Microorganisms 10 02153 g001 550
Figure 1. Principal component analysis (PCA) of soil chemical properties in cultivated and uncultivated land in different seasons. Rhizosphere soil had better nutrient status than bulk soil and uncultivated soil. Abbreviations: BS—bulk soil; RS—rhizosphere soil; US—uncultivated soil; SOM—soil organic matter; TN—total nitrogen; AHN—alkali-hydrolyzed nitrogen. Taking “RS.Summer” as an example, it represents the rhizosphere soil sampled in summer.
Figure 1. Principal component analysis (PCA) of soil chemical properties in cultivated and uncultivated land in different seasons. Rhizosphere soil had better nutrient status than bulk soil and uncultivated soil. Abbreviations: BS—bulk soil; RS—rhizosphere soil; US—uncultivated soil; SOM—soil organic matter; TN—total nitrogen; AHN—alkali-hydrolyzed nitrogen. Taking “RS.Summer” as an example, it represents the rhizosphere soil sampled in summer.
Microorganisms 10 02153 g001
Microorganisms 10 02153 g002 550
Figure 2. Microbial biomass measured via PLFA analysis in three groups of soil sampled in different seasons. It was consistently highest in uncultivated soil. Abbreviations: BS—bulk soil; RS—rhizosphere soil; US—uncultivated soil.
Figure 2. Microbial biomass measured via PLFA analysis in three groups of soil sampled in different seasons. It was consistently highest in uncultivated soil. Abbreviations: BS—bulk soil; RS—rhizosphere soil; US—uncultivated soil.
Microorganisms 10 02153 g002
Microorganisms 10 02153 g003 550
Figure 3. Variation in the average well color development (AWCD) of total carbon sources (n = 31) among three groups of soil samples collected in different seasons. The error bar represents the mean ± SD (n = 3). AWCD values at the 240th h for each soil sample were compared using the Kruskal–Wallis test. Abbreviations: BS—bulk soil; RS—rhizosphere soil; US—uncultivated soil.
Figure 3. Variation in the average well color development (AWCD) of total carbon sources (n = 31) among three groups of soil samples collected in different seasons. The error bar represents the mean ± SD (n = 3). AWCD values at the 240th h for each soil sample were compared using the Kruskal–Wallis test. Abbreviations: BS—bulk soil; RS—rhizosphere soil; US—uncultivated soil.
Microorganisms 10 02153 g003
Microorganisms 10 02153 g004 550
Figure 4. Variation in the average well color development (AWCD) of carbohydrates among three groups of soil samples collected in different seasons. The error bar represents the mean ± SD (n = 3). AWCD values at the 240th h for each soil sample were compared using the Kruskal–Wallis test. Abbreviations: BS—bulk soil; RS—rhizosphere soil; US—uncultivated soil.
Figure 4. Variation in the average well color development (AWCD) of carbohydrates among three groups of soil samples collected in different seasons. The error bar represents the mean ± SD (n = 3). AWCD values at the 240th h for each soil sample were compared using the Kruskal–Wallis test. Abbreviations: BS—bulk soil; RS—rhizosphere soil; US—uncultivated soil.
Microorganisms 10 02153 g004
Microorganisms 10 02153 g005 550
Figure 5. Variation in the average well color development (AWCD) of polymers among three groups of soil samples collected in different seasons. The error bar represents the mean ± SD (n = 3). AWCD240 for each soil sample was compared using the Kruskal–Wallis test. Abbreviations: BS—bulk soil; RS—rhizosphere soil; US—uncultivated soil.
Figure 5. Variation in the average well color development (AWCD) of polymers among three groups of soil samples collected in different seasons. The error bar represents the mean ± SD (n = 3). AWCD240 for each soil sample was compared using the Kruskal–Wallis test. Abbreviations: BS—bulk soil; RS—rhizosphere soil; US—uncultivated soil.
Microorganisms 10 02153 g005
Microorganisms 10 02153 g006 550
Figure 6. Variation in the average well color development (AWCD) of carboxylic and acetic acids among three groups of soil samples collected in different seasons. The error bar represents the mean ± SD (n = 3). AWCD values at the 240th h for each soil sample were compared via the Kruskal–Wallis test. Abbreviations: BS—bulk soil; RS—rhizosphere soil; US—uncultivated soil.
Figure 6. Variation in the average well color development (AWCD) of carboxylic and acetic acids among three groups of soil samples collected in different seasons. The error bar represents the mean ± SD (n = 3). AWCD values at the 240th h for each soil sample were compared via the Kruskal–Wallis test. Abbreviations: BS—bulk soil; RS—rhizosphere soil; US—uncultivated soil.
Microorganisms 10 02153 g006
Microorganisms 10 02153 g007 550
Figure 7. Variation in the average well color development (AWCD) of amino acids among three groups of soil samples collected in different seasons. The error bar represents the mean ± SD (n = 3). AWCD240 of each soil sample was compared using the Kruskal–Wallis test. Abbreviations: BS—bulk soil; RS—rhizosphere soil; US—uncultivated soil.
Figure 7. Variation in the average well color development (AWCD) of amino acids among three groups of soil samples collected in different seasons. The error bar represents the mean ± SD (n = 3). AWCD240 of each soil sample was compared using the Kruskal–Wallis test. Abbreviations: BS—bulk soil; RS—rhizosphere soil; US—uncultivated soil.
Microorganisms 10 02153 g007
Microorganisms 10 02153 g008 550
Figure 8. Variation in the average well color development (AWCD) of amines and amides among three groups of soil samples collected in different seasons. The error bar represents the mean ± SD (n = 3). AWCD240 for each soil sample was compared via the Kruskal–Wallis test. Abbreviations: BS—bulk soil; RS—rhizosphere soil; US—uncultivated soil.
Figure 8. Variation in the average well color development (AWCD) of amines and amides among three groups of soil samples collected in different seasons. The error bar represents the mean ± SD (n = 3). AWCD240 for each soil sample was compared via the Kruskal–Wallis test. Abbreviations: BS—bulk soil; RS—rhizosphere soil; US—uncultivated soil.
Microorganisms 10 02153 g008
Microorganisms 10 02153 g009 550
Figure 9. Principal component analysis (PCA) of carbon source utilization profiles of soil microorganisms in cultivated and uncultivated land. There was an apparent difference in carbon source utilization patterns for soil microbiomes in each season. Abbreviations: BS—bulk soil; RS—rhizosphere soil; US—uncultivated soil; CH—carbohydrates; AA—amines and amides; PO—polymers; AC—amino acids; CA—carboxylic and acetic acids. Taking “RS.Summer” as an example, it represents the rhizosphere soil sampled in summer.
Figure 9. Principal component analysis (PCA) of carbon source utilization profiles of soil microorganisms in cultivated and uncultivated land. There was an apparent difference in carbon source utilization patterns for soil microbiomes in each season. Abbreviations: BS—bulk soil; RS—rhizosphere soil; US—uncultivated soil; CH—carbohydrates; AA—amines and amides; PO—polymers; AC—amino acids; CA—carboxylic and acetic acids. Taking “RS.Summer” as an example, it represents the rhizosphere soil sampled in summer.
Microorganisms 10 02153 g009
Table
Table 1. Chemical properties of soil samples from cultivated and uncultivated plots in different seasons. In most cases, soil nutrient indicators (SOM, TN, and AHN) were higher in rhizosphere soil.
Table 1. Chemical properties of soil samples from cultivated and uncultivated plots in different seasons. In most cases, soil nutrient indicators (SOM, TN, and AHN) were higher in rhizosphere soil.
SeasonsSamplespHSOM (g/kg)TN (g/kg)AHN (mg/kg)
SpringRS6.3 ± 0.133.6 ± 3.01.45 ± 0.4197 ± 15
BS6.2 ± 0.125.8 ± 3.01.28 ± 0.1157 ± 12
US6.4 ± 0.226.6 ± 3.01.24 ± 0.2162 ± 13
SummerRS5.8 ± 0.241.1 ± 4.01.86 ± 0.3145 ± 12
BS6.1 ± 0.136.2 ± 2.01.66 ± 0.4133 ± 12
US6.2 ± 0.336.3 ± 2.01.77 ± 0.2122 ± 11
AutumnRS6.5 ± 0.129.5 ± 3.02.38 ± 0.2157 ± 13
BS6.2 ± 0.231.6 ± 2.01.53 ± 0.394.2 ± 10
US6.3 ± 0.132.2 ± 1.01.56 ± 0.297.2 ± 11
WinterRS6.4 ± 0.131.6 ± 3.01.23 ± 0.297 ± 9
BS6.2 ± 0.223.4 ± 1.01.14 ± 0.191 ± 8
US6.3 ± 0.125.2 ± 3.01.20 ± 0.293 ± 9
Abbreviations: BS—bulk soil; RS—rhizosphere soil; US—uncultivated soil; SOM—soil organic matter; TN—total nitrogen; and AHN—alkali-hydrolyzed nitrogen.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Wei, Z.; Wang, H.; Ma, C.; Li, S.; Wu, H.; Yuan, K.; Meng, X.; Song, Z.; Fang, X.; Zhao, Z. Unraveling the Impact of Long-Term Rice Monoculture Practice on Soil Fertility in a Rice-Planting Meadow Soil: A Perspective from Microbial Biomass and Carbon Metabolic Rate. Microorganisms 2022, 10, 2153. https://doi.org/10.3390/microorganisms10112153

AMA Style

Wei Z, Wang H, Ma C, Li S, Wu H, Yuan K, Meng X, Song Z, Fang X, Zhao Z. Unraveling the Impact of Long-Term Rice Monoculture Practice on Soil Fertility in a Rice-Planting Meadow Soil: A Perspective from Microbial Biomass and Carbon Metabolic Rate. Microorganisms. 2022; 10(11):2153. https://doi.org/10.3390/microorganisms10112153

Chicago/Turabian Style

Wei, Zhanxi, Hao Wang, Chao Ma, Shuyuan Li, Haimiao Wu, Kaini Yuan, Xiangyuan Meng, Zefeng Song, Xiaofeng Fang, and Zhirui Zhao. 2022. "Unraveling the Impact of Long-Term Rice Monoculture Practice on Soil Fertility in a Rice-Planting Meadow Soil: A Perspective from Microbial Biomass and Carbon Metabolic Rate" Microorganisms 10, no. 11: 2153. https://doi.org/10.3390/microorganisms10112153

APA Style

Wei, Z., Wang, H., Ma, C., Li, S., Wu, H., Yuan, K., Meng, X., Song, Z., Fang, X., & Zhao, Z. (2022). Unraveling the Impact of Long-Term Rice Monoculture Practice on Soil Fertility in a Rice-Planting Meadow Soil: A Perspective from Microbial Biomass and Carbon Metabolic Rate. Microorganisms, 10(11), 2153. https://doi.org/10.3390/microorganisms10112153

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