*3.4. Bacterial Quantification*

Bacterial quantification was performed on DNA extracts using a quantitative real-time polymerase chain reaction (qPCR) analysis. The bacterial ribosomal RNA gene copy numbers ranged from 1.82 to 8.06 <sup>×</sup> 10<sup>7</sup> g−<sup>1</sup> soil. Land use intensities had a significant effect on the ribosomal gene abundances, which was higher in the cereal field than that in the DNA extracted from grass-covered vineyard and cherry farm soils (Table 3).

#### *3.5. Correlation Coe*ffi*cients among Parameters*

The Pearson correlation coefficients showed that there were significant positive correlations between MBC and SOM, BSR and qCO2, CSR and qM (*p* < 0.05), and negative correlations between MBC and qCO2 (*p* < 0.05) and qM and SOM (*p* < 0.05) is soil under different managements. In addition, there was a significant positive correlation between the nucleic acid concentration and SOM and MBC in soil samples under different land uses. We observed a negative correlation (*p* < 0.05) between nucleic acid concentration and qM. A significantly (*p* < 0.05) negative correlation was also observed between the rRNA gene copy numbers and soil microbial respiration (both basal and cumulative respirations) (Table 7).

**Table 7.** Pearson correlation coefficients (*r*) between biological and molecular variables under different land uses (*n* = 9).


\* and \*\*: Significant at *p* < 0.05 and *p* < 0.01 levels, respectively. ns: not significant. SOM: total organic matter, MBC: microbial biomass C, BSR: basal respiration, CSR: cumulative soil respiration, qCO2: metabolic quotient, qM: mineralization quotient, 16S rRNA CN: Bacterial 16S rRNA gene copy numbers.

#### *3.6. Bacterial* α *Diversity and Community Composition*

A total of 479,160 high-quality sequences were detected with a 300-bp read length. The minimum Good's coverage value was 0.99 according to the similarity cut-off of 97%, meaning that a sufficient number of reads were obtained to evaluate bacterial diversity. There were no statistically significant differences among land uses in term of bacterial α-diversity (Table 8), although the grass-covered vineyard non-significantly increased CHAO1 and Shannon indices, as compared to the other ecosystems.

**Table 8.** Estimation of α-diversity indexes for bacterial communities under different land uses (sequence count: 10,000).


Means (± standard error) in each column followed by similar letter are not significantly different based on the least significant difference (LSD) test at 5% probability level.

Relative abundances (%) of bacteria at the phylum (> 1%) and family levels (> 2%) are presented in Table 9. Proteobacteria was the dominant phylum in all soil samples of land uses, which varied from 26.38% and 26.43% (in the cherry farm and grass-covered vineyard, respectively) to 29.29% (in the cereal field) of the total sequences, followed by Actinobacteria (22.88%–25.51%) and Bacteroidetes (6.88%–8.82%). The maximum abundance of Actinobacteria, Firmicutes and Acidobacteria was detected in the samples of the grass-covered vineyard. Moreover, the abundance of Planctomycetes and Gemmatimonadetes were also higher in the cherry farm than those in other ecosystems.


**Table 9.** Relative abundance of (**A**) bacterial phyla (relative abundance > 1%) and (**B**) families (relative abundance > 2%) under different land uses.

Means (± standard error) in each phylum or family followed by similar letter are not significantly different based on the least significant difference (LSD) test at the 5% probability level.

A total of 10 abundant families (relative abundance > 2%) were identified (Table 9). Rubrobacteraceae represented a range of percentage from 6.09% (cereal field) to 8.68% (grass-covered vineyard) of the total sequences, followed by Bradyrhizobiaceae (2.33–2.78%) and Bacillaceae (1.23–2.10%). Bacillaceae and Solirubrobacteraceae were 2.10% and 2.56% in the cherry farm, and 2.02% and 2.42% in the grass-covered vineyard, respectively, which were significantly higher than those percentages in the cereal field (1.23 and 1.78%, respectively). The relative abundance of Gemmataceae, Gemmatimonadaceae, Sinobacteraceae, and Pedosphaeraceae were also significantly influenced by the cherry farm system. In opposite, the highest abundance of Chitinophagaceae and Rhodospirillaceae were obtained from the samples of the grass-covered vineyard, which was significantly higher than the corresponding abundances in other fields.

At the genus level, the majority of bacteria among all fields belonged to the genera *Rubrobacter*, *Bacillus*, *Gemmatimonas*, *Gemmata*, *Steroidobacter*, and *Pedosphaera* (Figure 3). Cluster analysis showed that the bacterial community composition of the cherry farm and grass-covered vineyard clustered into one group, with a distance of 2.53%, which was separated from the cereal field (Figure 3).

**Figure 3.** Analysis (Ward's method) of the 16S rRNA composition of soil bacterial communities at the genera level.

## **4. Discussion**

Our results showed that the SOM, MBC, and BSR were highest in the topsoil of the grass-covered vineyard. The increase in the SOM amount in these soil samples was likely due to a mix of different reasons, some of them already demonstrated by other authors, namely, the type of cropping systems and tillage intensity, e.g., the reduction of soil disturbance through no-tillage [31], the management of crop residues [32], and of cover crops [33], both practices increasing the plant inputs returned to the soil. In fact, crops' shoot and root biomass of cover crops and grape trees were left on the field, in contrast with the cereal field and cherry farm, where residue removal and crop harvesting decreased organic inputs. These findings are in agreement with Ramesh et al. [34], who found a considerable impact of land use changes on SOC and reported that chemical and biochemical soil parameters were constant under low-intensity farming practices. A similar finding was also reported by Safaei et al. [35] who showed that the SOM and soil quality in natural ecosystems were considerably affected by land uses. Increasing MBC is the consequence of high-concentration C substrates available to the soil microbial communities [36] and of a significant positive trend in the quantity of organic C in the natural or low-disturbance ecosystems over the long run [12], such as the grass-covered vineyard in the present research. Similarly, an increase in MBC has already been found with decreased levels of human activities i.e., no-tillage practices [37] and straw returning [38] by changing the soil physicochemical environment, such as soil water content, porosity, and bulk density.

According to the results, the rankings were grass-covered vineyard > cherry field > cereal field for BSR, and cherry farm > grass-covered vineyard > cereal field for CSR. This means that the microbial respiration was increased by higher organic C inputs to the soil from crop residue management in the grass-covered vineyard. On the other hand, soil respiration and microbial activity are strongly influenced by soil water content [39]. Therefore, in the present study, lower rates of basal and cumulative respiration in the cereal field as compared to others may be explained by dry land farming and lower water content.

The ranking for CUE was cherry field > cereal field > grass-covered vineyard. CUE is a principal parameter used to estimate soil C dynamics and to understand the destiny of C resources by partitioning organic C between MBC and mineralized CO2 [25]. In the cherry farm, higher CUE indicated an increased decomposition, resulting in reduction of plant and microbial organic matter in soil [40]. The highest amounts of metabolic quotient (qCO2) were detected in grass-covered vineyard (0.058 mg CO2–C 10<sup>−</sup><sup>2</sup> h−<sup>1</sup> mg MBC<sup>−</sup>1) and cherry farm (0.056 mg CO2–C 10−<sup>2</sup> h−<sup>1</sup> mg MBC<sup>−</sup>1), which were about 16 and 12% higher than that in cereal field. It has been reported that stress conditions may cause an increment in the qCO2 which is related to a microbial stress, while low qCO2 may indicate more desirable conditions for microbial survival and more carbon available for biomass production [41]. High qCO2 values could also be related to changes in the bacterial-to-fungal ratio [42]. In this regard, Nsabimana et al. [43] determined qCO2 and CUE parameters to illustrate how the soil microbial activity and community composition were affected by land uses. In addition, we also found statistically significant differences among land uses in term of carbon mineralization quotient (qM). These values varied from 0.91% in the cereal field to 1.66% in the cherry farm, meaning that the cherry farm had better microbial C metabolism efficiency [44]. Lower amounts of qM in the cereal field than that in other ecosystems are related to lower ratios of easily mineralizable organic matter to stable organic matter in the soil [24].

There were statistically significant relationships between microbial respiration rate and the time period of incubation as cubic equation models. These results are also in agreement with Birge [45] who reported that the microbial respiration rate declined over the course of the incubation due to lack of organic C resource. Dungait et al. [46] suggested that the availability of soil organic C to microbial decomposers is one of the main factors that can influence and limit microbial respiration. This hypothesis has been supported by Birge [45] who stated that the SOM availability, and not the microbial biomass content, could limit the respiration rate.

Our results showed that the highest value of BFI was obtained from grass-covered vineyard. It has been previously reported that soil fertility is proportional to the soil microbial activity and SOM, as well as altering environmental conditions and land uses [47,48]. It has also been proved that crop residues and cover crops increase soil microbial biomass and activity and improve SOC sequestration in the long-term [49]. Therefore, cover crop residues stimulated cumulative carbon mineralization and improved SOC cycling in vineyard ecosystem, resulting in high level of BFI. In fact, grass-covered vineyard, being a more conservative system, has a large potential to improve total carbon storage in soil, given the organic input and the lower soil disturbance from tillage operations. On the other hand, BFI score in cereal field was lower than that in other land uses. This was very likely due to the frequent tillage practices and the removal of crop residues from the field, as well as to the dryland farming that can lead to a low soil biological fertility. These findings highlight the need for proper sustainable land management to prevent soil degradation [50], since the sustainability of the dryland ecosystem and its agricultural production depends strongly on proper and effective land-use and management [51]. Thus, it is quite clear that the conventional tillage with the crop-fallow system should be avoided in dryland cropping systems because of consequently reduced soil fertility and degraded chemical properties [52].

According to the evaluation by multiple linear regressions, SOM and CSR seem to be more useful to predict the BFI under different land uses. Similar results were presented by Renzi et al. [13], who found that the BFI had a significant relationship with SOM, CSR, MBC, and BSR, and increased linearly with all composing variables except for qM and qCO2, and particularly that MBC and CSR were the most important parameters contributing to the BFI.

The amounts of extracted nucleic acid were affected by different land use intensities, too. A similar finding was reported by Agnieszka et al. [53], who demonstrated that the soil DNA content was significantly influenced by land uses. It is well recognized that the soil DNA concentration is higher in soils with higher soil organic carbon [40,54]; in this regard, we found a significant positive correlation between changes in the nucleic acid concentration and SOM and MBC in soils under different land uses. Other authors reported a strong correlation between microbial biomass carbon and extracted DNA in soil [53], this relationship being affected by land use intensity and clearly indicating that microbial ecophysiology can be directly linked to soil carbon storage potential [40].

The composition of the soil bacterial communities in the cereal ecosystem, a management characterized by a higher disturbance and lower values of biochemical and microbiological parameters, shifted to species with a higher number of 16S rRNA gene copies. It has been reported that the rRNA gene copy numbers are linked to the bacterial life strategy [55], basically classified into two main categories, copiotrophic and oligotrophic, according to their response to resource availability and life strategies [56]. Therefore, higher ribosomal RNA copy numbers detected in the cereal field than that in other land uses, as indicated by the qPCR assay, indicate an increase in fast-growing r-strategists' taxa (copiotrophic species), due to decreased microbial respiration [55,57].

Bioinformatics analyses of the 16S sequencing data completely matched with the rRNA gene copy numbers. Accordingly, the abundance of oligotrophic bacterial groups (25.52% Actinobacteria, 3.45% Firmicutes, and 1.38%Acidobacteria) was higher in the grass-covered vineyard, while the rRNA gene copy numbers were lower than for other land uses. Oppositely, the cereal field had the highest abundance of copiotrophic bacteria (29.29% Proteobacteria and 8.82% Bacteroidetes) which was 10.8 and 28.2% more than grass-covered vineyard and 11.0 and 11.6% higher than cherry farm, respectively. The adaptive capacities of these species permit a successful competition with other bacterial groups, particularly when the levels of soil disturbance or tillage intensity are higher. In the cereal field, we hypothesize that soil disturbance repressed microbial respiration by favoring r-strategists' taxa with faster growth rates and limited capabilities to degrade recalcitrant organic matter, as indicated by a significant negative correlation between rRNA copy number and soil microbial basal as well as cumulative respiration.

CHAO1, Shannon and Simpson diversity indices indicated that the whole bacterial communities were very similar, no matter the land use intensities. This finding has already been proved by many authors [58–60], who reported that there was no effect of land use and agricultural practices on the diversity indices tested (e.g., CHAO1 and Shannon). Our research supports the hypothesis that different land uses do not change species richness and heterogeneity, although significant differences in some bacterial groups were found, very likely because of the high microbial resilience and/or resistance [61].

The results of Pearson correlation coefficients agree with the results of Renzi et al. [13], who found a significant relationship between SOM and MBC, BSR and qCO2, and CSR and qM (*p* < 0.001) based on Pearson correlation analyses. Similarly, Li et al. [62] and Malik et al. [40] reported that the SOM significantly correlated with MBC in different land use. It has also been reported that soil microbial communities were affected by quality and quantity of soil organic carbon [38]. Van Wesemael et al. [41] reported that the high qCO2 values represent the high energy required to keep up the microbial biomass and indicate a stressful condition for microbial communities. As a result, qCO2 values are reduced if the MBC content increases and vice versa.
