Assessment of soil microbial and enzyme activity in the rhizosphere zone under different land use/cover of a semi-arid ecosystem, India

Background Land use/cover changes and management practices are widely known to inuence S OM quality and quantity. The present study investigated the effect of different land uses i.e. forests viz. mixed forest cover (MFC), Prosopis juliora (Sw.) DC dominated forest cover (PFC), and cultivated viz. agriculture eld (AF), vegetable led (VF), respectively, on soil parameter, microbial activity, and enzymes involved in soil nutrient cycle in a semi-arid ecosystem. Results The results showed a signicant reduction (P < 0.05) in soil carbon (S C ), soil nitrogen (S N ) content (~ 30–80%) and consequently the soil microbial biomass carbon (S MBC ) (~ 70–80%), soil basal respiration (S BR ), soil substrate induced-respiration (S SIR ), and soil enzyme activities (β-glucosidase, acid phosphatase, and dehydrogenase) under cultivated sites in comparison to forest analogs due to land use management practices. Pearson’s correlation showed a positive correlation of S C with S MBC , S BR , and S SIR (P < 0.01) and enzymatic activities i.e. β-glucosidase, dehydrogenase (P < 0.05) suggesting the critical role of S C in regulating microbial and enzymatic activity. Also, a positive correlation of SM with urease (P < 0.01) was observed indicating the importance of soil abiotic factors in controlling enzymatic activities. Additionally, based on the PCA analysis, we observed the clustering of S MBC /S C ratio and qCO 2 nearby AF. Conclusion Our study suggested that degraded sites are more sensitive to the land management practices and land use changes and needed immediate attention in future studies related to S C dynamics in semi-arid ecosystems. and VF. Lastly, to understand inuence of land use change on the relationship among soil physico-chemical, microbial and enzyme activity is analyzed. changes in topsoil layers. Therefore, the soil microbial and enzyme activity are potential indicators to study soil response and S C dynamics following land use conversions and management practices.

Delhi is semi-arid and dry and greatly in uenced by the Himalayas and the Thar Desert due to its proximity. The climate is characterized by hot summers (April-June), monsoons (July-September), cool and dry winters (November-December. The study area receives most of the annual rainfall during the monsoon season. The vegetation of the study area is ravine thorn forest, which belongs to the ecosystem type of tropical thorn forest (6B/C) (Champion and Seth 1968) covers 33% of the total forest area whereas 67% is covered by plantation and tree outside forest (TOF) areas. The vegetation is mainly dominated by middle-story thorny trees, which ate interspersed with open patches due to their scattered distribution (Sinha 2014). The soil type on the ridge has been reported as sandy loam to loam (Chibbar 1985). P juli ora, which is an exotic species, is the dominant tree in the forest. Acacia nilotica (L.) Delile, Acacia leucophloea (Roxb.) Willd., Salvadora oleoides Decne, Cassia stula L. are among the commonly found native trees (Sinha 2014; Meena et al. 2016). Delhi also considered one of the most polluted cities in the world. The land utilization pattern of Delhi, showed 15% (221.4 km 2 ) of net sown area, 8.07% (119 km 2 ) of current fallow, 6.71% (98.9 km 2 ) of culturable wasteland, and 1% (14.8 km 2 ) of forest covers, of the total geographical area (FSI, 2017). Delhi is one of the largest growing cities with growth of 21.20% (Census of India 2011), is facing land degradation due to deforestation and agriculture intensi cation. Four sites were selected for the study (Table 1): Mixed forest cover (MFC) as a native vegetation cover (28.68 N; 77.22 E). In MFC, P. juli ora was found to be the most dominant species but other associated tree species viz. Pongamia pinnata L., Azadirachta indica Juss., A. nilotica and C. stula were also observed (Meena et al. 2019). P. juli ora dominated forest cover (PFC) as an exotic tree cover (28.69 N; 77.22E).
Agriculture eld (AF), which is located near the Nazafgarh drain (28.54 N; 77.87E). AF was mainly cropped with Triticum aestivum L. (October-May) and Phaseolus vulgaris L. (September-October). The AF was irrigated by a tube well during the growing season.
Vegetable eld (VF), which was located along the Yamuna ood plains (28.53 N; 77.33E). The VF eld was mainly cultivated with Capsicum annum L. throughout the year except between September and November, during which Brassica oleraceae L. was grown. VF was regularly irrigated by water pumped from the Yamuna River.
The soil type in PFC, MFC, and VF was sandy loam, whereas in AF it was loamy sand.

Soil Sampling And Analysis
Soil samples were collected from ve different points at 0-10 cm depth and pooled together to obtain a composite sample for each land use type. The visible root mass was removed from the soil samples by hand. The soil samples were passed through a 2 mm sieve, ground in a mortar with a pestle and stored at room temperature for further analysis. The soil moisture (S M ) content was measured using the gravimetric method. For the analysis of microbial parameters and enzymes, a subsample of freshly sieved soil was stored in zipped plastic bags at 4 °C. S C and S N concentrations were measured using Elementar CHNS Analyzer. Available S P was measured by using an ammonium molybdate blue method (Allen et al. 1974).
Soil microbial biomass carbon (S MBC ), basal (S BR ) and substrate induced respiration (S SIR ) The S MBC was estimated by using the chloroform fumigation extraction method (Witt et al. 2000), where, 35 g of fresh soil was fumigated with 2 ml ethanolfree CHCl 3 and incubated for 24 h in dark at 25˚C. The fumigated soils were extracted with 140 ml of 0.5M K 2 SO 4 and unfumigated control soils were extracted immediately without fumigation. The resulting extracts were ltered and examined by the Elementar TOC analyzer. The S MBC was calculated as the difference between fumigated and unfumigated sample with a conversion factor of 0.38 (Vance et al. 1987).
The S BR was measured by the alkali absorption method (Isermayer 1952). The S SIR of soil was estimated by the rate of initial maximal respiration of microorganisms after the amendment of soil subsamples with glucose (Anderson and Domsch 1978). The pre-incubated soil subsample samples (60% WHC) was mixed with glucose and incubated with 0.05 M NaOH at 22˚C for 3 hrs. The CO 2 released was measured by titrating the NaOH solution with 0.1M HCl.
The S BR and S SIR were expressed as CO 2 C mg g − 1 h − 1 .

Soil Enzyme Activities
Page 4/17 The β-glucosidase activity was estimated by using p-nitrophenyl-β-D-glucoside (PNG) as a substrate and incubating the soil with toluene, modi ed universal buffer (pH 6), and PNG solution (25 mM) for 1 hr at 37˚C (Eivazi and Tabatabai 1988). The activity of β-glucosidase was expressed as µg PNG g − 1 dwt − 1 hr − 1 at 37˚C. The urease activity was determined by using urea as a substrate as described by

Statistical analysis
One-way analysis of variance (ANOVA) was used to evaluate the effect of land use on selected variables (S M , S P , S C , S N , S MBC , S BR , S SIR , S MBC /S C , qCO 2 , βglucosidases, ureases, acid phosphatase, dehydrogenase activity) using Tukey's test at P < 0.05. Pearsons r analysis was performed to estimate the correlation of selected variables among the land use. All the statistical analysis was done using SPSS version 16.0. Principle component analysis (PCA) was used to evaluate the relationship of multivariate data using XLSTAT 2020.

Soil chemical and microbial parameters
The S C and S N concentration (g kg − 1 ) at 10 cm depth was found to be signi cantly in uenced by land use type (Fig. 1a 9.03 ± 0.14 µg g − 1 hr − 1 , respectively) (Fig. 1d). The S SIR also followed similar trend as 143. 50

Soil enzyme activity
The activity of selected enzymes is shown in Fig. 3a However, no dehydrogenase activity was detected under arable land uses (AF and VF).
Based on the combined data set for all land uses, Pearson's correlation analysis evaluated a signi cant correlation among the studied soil variables ( Table 2). A signi cant strong positive correlation was found between S C , S N , S MBC , S BR , and S SIR (P < 0.01). The positive correlation of β-glucosidase and dehydrogenase was observed with S C , S N , S MBC , S BR , and S SIR (P < 0.05). Additionally, a signi cant positive correlation was found among S M and urease activity (P < 0.01). Further, the qCO 2 showed a signi cant negative correlation with S C , S BR , and S SIR (P < 0.01), and S N (P < 0.05), respectively.
The principal component analysis (PCA) extracted three components with PC 1, PC 2, and PC 3 explained 71.38%, 19.55%, and 9.07% of the variance, respectively. For the biplot, PC 1 and PC 2 components were used, which together explained 90.34% of the variance. The PC 1 loadings were large and positive for S C , S N , S P , S MBC , S BR , and S SIR , and activity of β-glucosidase, dehydrogenase, acid phosphatase, while, negative for qCO 2 and S MBC /S C (Fig. 4).
While, the loadings in PC 2 loadings were large and positive only for S C /S N , S M , and urease activity in comparison to PC 1. Among the land uses, PFC and MFC are closely associated with the variables of S C , S N , S MBC , S BR , and S SIR , and enzyme activities. However, VF and AF are closely associated with qCO 2 and S MBC /S C .  The S MBC /S C ratio or MQ has been widely used as an indicator of soil quality and future changes in S OM (Sparling 1992). It also re ects the contribution of the S MBC to S C and can be used as a sensitive measure of soil health under different land use management system (Anderson and Domsch 1989  The qCO 2 provides an integrated measure of the eco-physiological state of soil microbial community and used widely as a critical parameter to determine changes in the S C levels (Anderson and Domsch 1989 (Insam 1990). Under disturbed ecosystem, the strong competition for available carbon substrate may favor microbes which use more carbon energy in maintenance then growth (Islam and Weil 2000). Therefore, in cultivated soils, the microbial communities are more stressed and needed a regular supply of carbon sources in order to maintain their activity (Singh et al. 2018).

Soil Enzyme Activity
Soil enzyme activity is in uenced by the soil characteristics related to nutrient availability, soil microbial activity, and land use management processes which modi ed the potential soil enzyme-mediated substrate catalysis (Kandeler et al. 1996). The present study observed signi cantly higher activity of selected enzymes in natural forests as compared to cultivated land use. Our results were parallel to the previous studies reporting a reduction in soil enzyme activities following the conversion of natural forests into cultivated lands ( (2015), reported a decrease in enzymatic activities following the conversion forest to croplands could be related to the reduction in S OM content and microbial biomass.
The dehydrogenase activity in soil serves as an indicator of the microbiological redox system and microbial oxidative activities in soil (Casida et al. 1964). Similar to our results, Bonanomi et al. (2011), reported a reduction by 84% in dehydrogenase activity in a low input management regime as compared to the high input management regime. In the present study, the intensive management practices and low levels of S OM input may have declined the activity in cultivated soils.
The β-glucosidase activity in soil is linked to the release of carbohydrates in soil, which provides a major substrate for soil microorganisms. The positive correlation of the S C and S MBC with β-glucosidase activity indicated that low S C and microbial activity in cultivated lands reduced its activity in AF and VF, respectively (Vinhal-Freitas et al. 2017). Similarly, the acid phosphatases activity was also high under forests as compared to cultivated land use. Acid phosphatases activity is also in uenced by soil pH, nutrients, SC, S N , S P , S OM quality and quantity, microbial community structure, S M , and soil temperature

Declarations
Ethics approval and consent to participate Not applicable.

Consent for publication
Not applicable.

Availability of data and materials
The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Competing interests
The authors declare that they have no competing interests.
AM proposed the idea and conducted the eld sampling, data collection, laboratory analysis, data interpretation and manuscript writing. KSR guided the study, interpreted the results and critically reviewed the idea. All authors read and approved the nal manuscript.
2012; Araújo et al. 2013). The urease activity is in uenced by other soil properties including pH, soil nutrient supply, S N , microbial biomass N, and N fertilizers (Moghimian et al. 2017). Additionally, S M also controls its activity in different land use (Zeng et al. 2009).
The positive correlation of enzyme activity (β-glucosidase and dehydrogenase) with the S C and S MBC indicated its role as a precursor for enzyme synthesis) as the S MBC represent a fraction of the labile S C content (Acosta- Martínez et al. 2007) and S C is essential for regulating the enzyme activities (Raiesi and Beheshti 2015; Silva et al. 2019). However, a weaker correlation of soil parameters with urease was parallel with an earlier study by Klose and Tabatabai (2000), where, S MBC was weakly correlated with urease activity, but it was strongly correlated with S MBN . As reported in earlier studies, a negative correlation of S C with urease activity, suggest that it is regulated by carbon supply to microbes which resulted in S N limitation and increases enzyme production (