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Assessment of soil microbial and enzyme activity in the rhizosphere zone under different land use/cover of a semiarid region, India

Ecological Processes volume 10, Article number: 16 (2021) Cite this article

Abstract

Background

Land use/cover and management practices are widely known to influence soil organic matter (SOM) quality and quantity. The present study investigated the effect of different land use, i.e., forests viz. mixed forest cover (MFC), Prosopis juliflora (Sw.) DC-dominated forest cover (PFC), and cultivated sites viz. agriculture field (AF), vegetable field (VF), respectively, on soil parameter, microbial activity, and enzymes involved in soil nutrient cycle in a semiarid region of India.

Results

The results showed a significant reduction (P < 0.05) in soil carbon (SC), soil nitrogen (SN) content (~ 30–80%) and consequently the soil microbial biomass carbon (SMBC) (~ 70–80%), soil basal respiration (SBR), soil substrate-induced respiration (SSIR), and soil enzyme activities (β-glucosidase, acid phosphatase, and dehydrogenase) under cultivated sites in comparison with forest sites. Pearson’s correlation showed that a positive correlation of SC with SMBC, SBR, SSIR (P < 0.01), and enzymatic activities (i.e., β-glucosidase, dehydrogenase) (P < 0.05) may imply the critical role of SC in regulating microbial and enzymatic activity. Also, a positive correlation of soil moisture with urease activity (P < 0.01) was found suggesting it as a significant abiotic factor for soil biological functions. Additionally, based on the PCA analysis, we observed the clustering of SMBC/SC ratio and qCO2 nearby AF.

Conclusion

Our study suggests that soil microbial parameters (SMBC, SBR, SSIR, SMBC/SC, qCO2) and enzyme activity are key indicators of soil health and fertility. Land use/cover alters the SOM content and soil microbial functions. The management strategies focusing on the conservation of natural forest and minimizing the land disturbances will be effective in preventing soil carbon flux as CO2 and maintaining the SC stock.

Background

Land-use/cover change is considered as one of the major factors of global climate change, biodiversity loss, and ecological degradation (Kooch et al. 2018). In the semiarid tropics of India, the rapid population growth requires continuous expansion of cultivated lands to support food demand, economic development, and reduce poverty (Wani et al. 2015). Deforestation and land use conversions are major ecological problems in these regions. The conversion of natural forest into agricultural land significantly alter the soil processes and properties, and therefore, soil functioning (Celik 2005; Dawson and Smith 2007). Previous studies have reported that land use/cover would affect the soil physical, chemical, biological properties, and also soil organic matter (SOM) dynamics, which subsequently alters the soil quality and fertility (Zhao et al. 2013). Further, the variation in land use/cover influences the soil microbial functions by affecting the soil carbon (SC) and nitrogen (SN) cycle (Sousa et al. 2012). Microbial indices related to SOM such as soil microbial biomass carbon (SMBC) and nitrogen (SMBN), basal respiration (SBR), substrate-induced respiration (SSIR) (Anderson and Domsch 1978), and microbial (MQ) and metabolic quotients (qCO2) are a good indicator of soil nutrient dynamics (Raiesi and Beheshti 2014; Goenster et al. 2017) and would provide an early warning of soil quality deterioration (Cookson et al. 2007; Huang and Song 2010).

Soil microbes despite comprising a small fraction of the total mass of SOM play a critical role in soil processes, SOM decomposition, nutrient cycling, etc. They are significantly influenced by human interventions involving land conversions (Smith and Paul 1995; Kabiri et al. 2016). Additionally, the management practices, variation in quality and availability of substrate, fine roots activity, litter quality, vegetation composition, plant biomass, and belowground processes also change the SOM content, hence, affect the soil microbial community structure and functions. Recently, there has been an increased interest in the studies investigating the effect of conversion of natural forests into farmlands and agricultural management practices on soil microbial community structure and functions (Ye et al. 2009; Nielsen and Ball 2015; Goenster et al. 2017; Thapa et al. 2018; Lacerda-Júnior et al. 2019).

Soil enzyme activities are the indicators of microbial community and functions. They reflect changes in soil biochemical processes and SOM dynamics attributed to human-induced variation in abiotic and biotic factors in soil (Trasar-Cepeda et al. 2008). Soil enzyme activities related to SMBC and soil organic carbon (SOC) are often used for comparison of different land use with varying SOM content (Waldrop et al. 2004; Bastida et al. 2007). Measurement of soil enzyme activities in key nutrient cycling (C, N, and P) and oxidation–reduction processes have been used widely as a potential indicator for determining the effect of land use conversions and management practices on soil health (Acosta-Martínez et al. 2007; Pandey et al. 2014; de Medeiros et al. 2015). Therefore, an investigation of soil microbial characters and enzyme activities is critical in studying the land conversions and focusing the soil management.

Principal component analysis (PCA) has been widely used as a statistical tool to determine the most sensitive factor explaining the significant variation among the different land use/cover (Notaro et al. 2014; Pandey et al. 2014; de Medeiros et al. 2015, 2017; Moghimian et al. 2017).

Previous studies in India evaluated the effect of land use conversions on soil microbiota and soil enzymes (Singh and Ghoshal 2014; Kumar and Ghosal 2017; Rai et al. 2018; Tiwari et al. 2019). However, not many such studies have compared the soil microbial parameters and enzymes in different land use/cover in semiarid regions of India. The present study was conducted in forests and agroecosystems of Delhi, India. Delhi, being an urban city, is among the most polluted cities in the world. It is one of the largest growing cities with a decadal growth of 21.20% and a population of 16.8 million (Census of India 2011). Rapid population growth and growing economy are the main drivers of land use/land cover changes (Jain et al. 2016). The forest ecosystems are notified as reserved and are located on ridge areas which are the extensions of Aravalli hills with a length of 32 km. The growing urbanization and encroachment have led to the complete loss of vegetation, and only a few areas of the forests have been protected in Delhi (Sinha 2014). The impact of urbanization processes, such as land use conversions, deforestation, and the shift in vegetation composition, is known to alter soil structure and physicochemical properties (Pickett et al. 2001; Yan et al. 2016). It is assumed that these processes will influence soil nutrient cycling and affect soil microbial functions and enzyme activities (Rai et al. 2018). This study hypothesizes that soil microbiological and enzyme attributes vary across different land use/cover, and therefore, could be used as sensitive indicators of soil health. The purpose of this study is to evaluate the effect of different land use/cover types (i.e., forest cover and cultivated land) on soil microbial parameters (SMBC, SBR, SSIR, SMBC/SC, qCO2). Further, the study also determines the activity of selected soil enzymes involved in soil nutrient cycling, i.e., carbon (β-glucosidases), nitrogen (ureases), phosphorus (acid phosphatase), and oxidation–reduction (dehydrogenase) under selected land use/cover types. Lastly, the purpose is also to understand the influence of land use/cover on the relationship among soil physicochemical properties and microbial and enzyme activity.

Materials and methods

Study area

This study was carried out in a semiarid region of Delhi, which is part of the National Capital Territory (NCT) of India, lies between 28.41° N and 28.41° N; 76.84° E and 77.40° E, and covers an area of 1483 km2. It is bounded by the Indo-Gangetic alluvial plains, Thar Desert, and Aravalli Range. The climate of Delhi is semiarid and dry and greatly influenced by the Himalayas and the Thar Desert due to its proximity. The climate is characterized by hot summers (April–June), monsoons (July–September), and 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) and covers 33% of the total forest area, whereas 67% is covered by plantation/tree outside forest (TOF) areas (FSI 2017). The vegetation is mainly dominated by middle-story thorny trees, which ate interspersed with open patches due to their scattered distribution (Sinha 2014). The parent material of Delhi ridge soil is quartzite or sandstone, with sandy loam to loam texture (Chibbar 1985). Prosopis juliflora, which is an exotic species, is the dominant tree in the forest. Acacia nilotica (L.) Delile, Acacia leucophloea (Roxb.) Willd., Salvadora oleoides Decne, and Cassia fistula L. are among the commonly found native trees (Sinha 2014; Meena et al. 2016). For the study, four sites were selected with two sites each for agriculture and forest land use, respectively, in the semiarid region of Delhi. The sites were selected on the basis of predominant land use activity, level of anthropogenic intervention and vegetation cover as (i) mixed forest cover (MFC), (ii) P. juliflora-dominated forest cover (PFC), (iii) agriculture field (AF), and (iv) vegetable field (VF) (Meena et al. 2020). The mixed forest cover (MFC) was considered as a native vegetation cover (28.68 N; 77.22 E). In MFC, P. juliflora was found to be the most dominant species but other associated tree species viz. Pongamia pinnata L., Azadirachta indica Juss., A. nilotica, and C. fistula were also observed (Meena et al. 2019). The P. juliflora-dominated forest cover (PFC) is an exotic tree cover (28.69° N; 77.22° E). The agriculture field (AF) was located near the Nazafgarh drain (28.54° N; 77.87° E) and was mainly cropped with wheat (Triticum aestivum L.) from October to May and common bean (Phaseolus vulgaris L.) from September to October. The AF was irrigated by a tube well during the growing season. Lastly, the vegetable field (VF) was located along the Yamuna flood plains (28.53° N; 77.33° E). The major vegetable grown were chilies (Capsicum annum L.) throughout the year except between September and November, during which cabbage (Brassica oleraceae L.) was grown. The water pumped regularly from the Yamuna River for irrigation in the VF. The management practices such as tillage, pesticide and fertilizer application, runoffs, and plant or crop rotation in cultivated lands are expected to affect the soil microbes. The soil physicochemical properties of the sites are given in Table 1.

Table 1 Soil physical and chemical factors of four land uses of the semiarid region of Delhi
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Soil sampling and analysis

The sampling was conducted by randomized quadrat sampling experimental design during September 2012. For each land use, three quadrats of 10 × 10 m were selected for soil collection. The sampling points were ensured to be homogenous with respect to elevation and slope. From each quadrat, the soil samples were collected after removing the litter from five different points at 0–10-cm depth and pooled together to obtain a composite sample. Therefore, three soil samples were selected for each land use type, with a total of 12 samples. The visible root mass was removed from the soil samples by hand. The litter layer was removed thoroughly before analysis. 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 (SM) content was measured using the gravimetric method. For microbial parameters and enzymes, a subsample of 2-mm freshly sieved soil was stored in zipped plastic bags at 4 °C till further analysis. SC and SN concentrations were measured using the CHNS Analyzer (Elementar vario). Available SP was measured by using an ammonium molybdate blue method (Allen et al. 1974).

Soil microbial biomass carbon (SMBC), basal (SBR) and substrate-induced respiration (SSIR)

The SMBC was estimated by using the chloroform fumigation extraction method (Witt et al. 2000), where 35 g of fresh soil was fumigated with 2 ml ethanol-free CHCl3 and incubated for 24 h in the dark at 25 °C. The fumigated soils were extracted with 140 ml of 0.5 M K2SO4, and unfumigated control soils were extracted immediately without fumigation. The resulting extracts were filtered and examined by the TOC analyzer (Elementar vario). The SMBC was calculated as the difference between fumigated and unfumigated sample with a conversion factor of 0.38 (Vance et al. 1987).

The SBR was measured by the alkali absorption method (Isermayer 1952). Briefly, 50 g of soil preincubated at 60% water-holding capacity (WHC) was incubated with 10 ml 0.05 M NaOH solution at 25 °C for 24 h. After incubation, 2 ml BaCl2 (0.5 M) and 2–3 drops of phenolphthalein indicator were added into the NaOH beaker which was then titrated with 0.1 M HCl dropwise. The SSIR 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 preincubated soil subsamples (60% WHC) was mixed with glucose (30 mg g-1 soil) and incubated with 0.05 M NaOH at 22 °C for 3 h. The CO2 released was measured by titrating the NaOH solution with 0.1 M HCl. The SBR and SSIR were expressed as CO2 C mg g-1 h-1.

Soil enzyme activities

The β-glucosidase activity was estimated by using p-nitrophenyl-β-D-glucoside (PNG) as a substrate and incubating 1 g of soil with 0.25 ml toluene, 4 ml modified universal buffer (pH 6), and 1 ml PNG solution (25 mM) for 1 h at 37 °C (Eivazi and Tabatabai 1988). After incubation, 1 ml of CaCl2 solution and 4 ml Tris buffer (pH 12) were added, and absorbance was taken at 400 nm using a spectrophotometer (RIGOL, USA). The activity of β-glucosidase was expressed as μg PNG g-1 dwt h-1 at 37 °C. The urease activity was determined by using urea as a substrate as described by Yao et al. (2006). Five grams of moist soil was incubated with 1 ml methylbenzene, 10 ml of 10% urea 20 ml citrate buffer (pH 6.7) for 24 h at 37 °C. One milliliter of filtered soil solution, 1 ml of sodium phenolate, and 3 ml of sodium hypochlorite were added and diluted to 50 ml, and absorbance was determined at 578 nm using a spectrophotometer (RIGOL, USA). The activity of urease was expressed as NH3-N g-1 h-1 at 37 °C. Acid phosphatase activity was analyzed using р-nitrophenyl phosphate (р-NPP) as substrate as described by Schneider et al. (2000). Five grams of moist soil was mixed with 20 ml acetate buffer (pH 5.2) and 100 mM р-NPP and incubated at 30 °C for 30 min. After incubation, 1 ml of CaCl2 and 4 ml of 0.2 M NaOH were added after incubation in order to terminate the reaction. The absorbance was determined using the spectrophotometer at 405 nm (RIGOL, USA). The activity of AP was expressed as μg р-NPP g-1 h-1 at 30 °C. Dehydrogenase activity was measured using triphenyl tetrazolium chloride (TTC) as a substrate (Thalmann 1968), where the TTC solution (0.3–0.4 g/100 ml) was mixed with 5 g of moist soil and incubated for 24 h at 30 °C. After incubation, 40 ml of acetone was added, and absorbance was determined at 546 nm using a spectrophotometer (RIGOL, USA). The activity of dehydrogenase was expressed as μg TTC g-1 h-1.

Statistical analysis

One-way analysis of variance (ANOVA) was used to evaluate the effect of land use on selected variables (SM, SP, SC, SN, SMBC, SBR, SSIR, SMBC/SC, qCO2, β-glucosidases, ureases, acid phosphatase, dehydrogenase activity) using Tukey’s test at P < 0.05. Pearson correlation 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. Principal component analysis (PCA) was used to evaluate the relationship of multivariate data using XLSTAT 2020.

Results

Soil chemical and microbial parameters

The SC and SN concentrations (g kg-1) at 0–10-cm depth were found to be significantly influenced by land use type (Fig. 1a, b). Both SC and SN were higher under natural forests as compared with arable soils. The highest SC and SN (g kg-1) were evaluated under MFC (47.70 ± 1.16; 4.70 ± 0.12) followed by PFC (31.70 ± 0.06; 3.33 ± 0.033), VF (12.40 ± 0.01; 1 ± 0), and AF (5.70 ± 0.20; 0.60 ± 0.03), respectively. The SC and SN were significantly different among the four land uses (P < 0.05).

Fig. 1
figure1

SC (a), SN (b), SMBC (c), SBR (d), and SSIR (e) under different land uses. Mean values with different letters are significantly different between land use (P < 0.05; Tukey’s test at ⍺ = 0.05). Error bars represent the standard error of the mean. Note: SC soil carbon, SN soil nitrogen, SMBC soil microbial biomass carbon, SBR soil basal respiration, SSIR soil substrate-induced respiration, MFC mixed forest cover, PFC P. juliflora-dominated forest cover, AF agriculture filed, VF vegetable field

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Similar to the SC, higher SMBC was estimated under forest as compared with cultivated land uses (Fig. 1c) and followed the trend as MFC > PFC > VF > AF (313.54 ± 0.34, 209.98 ± 0.63, 86.57 ± 0.53, and 62.57 ± 0.29, respectively). The SMBC was significantly different among the four land uses (P < 0.05). The SMBC/SC was highest under AF (1.1), followed by VF (0.7), PFC (0.66), and MFC (0.65), respectively (Fig. 2a).

Fig. 2
figure2

SMBC/SC (a) and qCO2 (b) under different land uses. Mean values with different letters are significantly different between land uses (P < 0.05). Note: SMBC/SC soil microbial biomass carbon to soil carbon ratio, qCO2 metabolic quotient, MFC mixed forest cover, PFC P. juliflora-dominated forest cover, AF agriculture filed, VF vegetable field

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The SBR was greater under forest as compared to cultivated sites and followed the trend as MFC > PFC > VF > AF (22.20 ± 0.07, 18.77 ± 0.09, 10.63±0.12, and 9.03 ± 0.14 μg g-1 h-1, respectively) (Fig. 1d). The SSIR also followed similar trend as 143.50 ± 0.15, 119.51 ± 0.16, 95.06 ± 0.11, and 81.48 ± 0.11 μg g-1 h-1 for MFC, PFC, VF, and AF, respectively (Fig. 1e). Both SBR and SSIR varied significantly among the land use (MFC, PFC, VF, AF; P < 0.05). The metabolic quotient (qCO2) was used to evaluate the efficiency of SMBC in utilizing the SC (Anderson and Domsch 1990). The value of qCO2 was estimated to be 0.14, 0.12, 0.09, and 0.07 μg SMBC g-1 h-1 for AF, VF, PFC, and MFC, respectively. A significant increase in qCO2 was observed under cultivated as compared with forest land use (Fig. 2b).

Soil enzyme activity

The activity of selected enzymes is shown in Fig. 3a, b, c, and d. The β-glucosidase activity (μg PNG g-1 h-1) was significantly high in MFC (623.71 ± 5.75) than PFC (398.40 ± 9.01), AF (57.58 ± 0.94), and VF (32.95 ± 0.49), respectively. Among the forest, the activity of urease (μg NH3-N g-1 h-1) was found to be significantly greater in MFC (41.58 ± 0.56) than PFC (30.40 ± 0.48) at P < 0.05. Among cultivated lands, high urease activity (μg NH3-N g-1 h-1) was evaluated under VF (38.34 ± 0.39) as compared to AF (34.06 ± 3.11). However, the values were not significant among AF and VF (P > 0.05). The acid phosphatases (μg hydrolyzed phenol g-1 h-1) was high under MFC (1051.98 ± 65.40) followed by PFC (287.18 ± 6.93), VF (95.22 ± 4.54), and AF (68.02 ± 4.23), respectively. The activity of acid phosphatases was significantly different among forest land uses (MFC, PFC) (P < 0.05). However, no significant difference was determined under cultivated land uses (AF, VF) (P > 0.05). The activity of dehydrogenase was significantly high under MFC than PFC (1.22; 1.03 μg TTC g-1 h-1, respectively) (P < 0.05). However, no dehydrogenase activity was detected under arable land uses (AF and VF).

Fig. 3
figure3

Soil enzyme activity under different land uses. Soil β-glu (a), APh (b), Dha (c), and Ure (d) activities. Mean values with different letters are significantly different between land use (P < 0.05; Tukey’s test at ⍺ = 0.05). Error bars represent the standard error of the mean. Note: β-glu β-glucosidase, APh acid phosphatases, Dha dehydrogenase, Ure urease, MFC mixed forest cover, PFC P. juliflora-dominated forest cover, AF agriculture field, VF vegetable field

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Based on the combined data set for all land uses, Pearson’s correlation analysis evaluated a significant correlation among the studied soil variables (Table 2). A significant strong positive correlation was found between SC, SN, SMBC, SBR, and SSIR (P < 0.01). The positive correlation of β-glucosidase and dehydrogenase was observed with SC, SN, SMBC, SBR, and SSIR (P < 0.05). Additionally, a significant positive correlation was found among SM and urease activity (P < 0.01). Further, the qCO2 showed a significant negative correlation with SC, SBR, and SSIR (P < 0.01), and SN (P < 0.05), respectively.

Table 2 Pearson correlation coefficients (r) between soil physicochemical, microbial parameters, and enzyme activities among different land uses of a semiarid region of Delhi
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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 SC, SN, SP, SMBC, SBR, and SSIR, and activity of β-glucosidase, dehydrogenase, and acid phosphatase, while negative for qCO2 and SMBC/SC (Fig. 4). In PC 2, loadings were large and positive only for SC/SN, SM, and urease activity in comparison with those in PC 1. Among the land uses, PFC and MFC are closely associated with the variables of SC, SN, SMBC, SBR, and SSIR, and enzyme activities. However, VF and AF are closely associated with qCO2 and SMBC/SC.

Fig. 4
figure4

PCA-ordination biplot (PC 1 vs PC 2) of soil physicochemical, microbial parameters, and enzyme activities of different land uses at a semiarid region of Delhi. Note: SC soil carbon, SN soil nitrogen, SP soil phosphorus, SM soil moisture, SMBC soil microbial biomass carbon, SBR soil basal respiration, SSIR soil substrate-induced respiration, SMBC/SC soil microbial biomass carbon to soil carbon ratio, qCO2 metabolic quotient, SC/SN soil carbon to soil nitrogen ratio, MFC mixed forest cover, PFC P. juliflora-dominated forest cover, AF agriculture field, VF vegetable field

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Discussion

SMBC indicates the size of the SC labile pool (Hanson et al. 2000) and is sensitive to land use conversions. The study reported a higher SMBC from forests (PFC, MFC) as compared with cultivated sites (AF, VF), with a significant reduction of ~ 70–80%. Regular tillage in cultivated land use often damages the soil aggregates and exposes the SOM for oxidation (Fang et al. 2014; Raiesi and Salek-Gilani 2020). Our results agree with the findings reported from previous studies indicating higher SMBC under natural forest than arable land use (Ananyeva et al. 2008; Zhao et al. 2012; Mganga and Kuzyakov 2014; Kooch et al. 2019; dos Santos et al. 2019). The long-term accumulation of SOM input, SOM quality, and quantity determine the SMBC (Moore et al. 2000). It was evident in our study by the strong positive correlation of SMBC with SC and SN.

Vegetation cover influences the quality and quantity of litter accumulation, therefore, affect SOM decomposition (Tiwari et al. 2019). The increased plant diversity in MFC favors plant rhizosphere by providing various substrates to soil microbes (dos Santos et al. 2019). Also, the varying quality of litter affects the SOM decomposition, which could explain low microbial activity in PFC as the leaves of P. juliflora has higher lignin content and does not degrade rapidly. In this study, high SC and SN contents were detected in sites presenting the greater plant diversity and vegetation cover. According to de Medeiros et al. (2017), high tree density and diversity of dominant species in intermediate and late regenerative stages increase the root biomass, organic matter, litter content, and SOC in tropical dry forests of the semiarid Brazilian region.

The variability of the humic substrate also indicates the differences in SBR (heterotrophic respiration) and SSIR (potential respiration) among various land use/cover types. The highest SBR and SSIR values were found in MFC, and the lowest were recorded under AF. Kooch et al. (2019) also reported a higher SSIR, SMBC, SBR/SOMBC under natural forests and plantations as compared with agricultural sites. Higher aboveground and belowground biomass (Meena et al. 2020), fine root activity (Davidson et al. 2002), and litter content (Chodak and Niklinska 2010) in forests maintain the SC for a longer period. Parallel to the previous studies, the qCO2 or metabolic quotient which measures the ecophysiological state of the soil microbial community was found to be largest for AF (Ananyeva et al. 2008; Cheng et al. 2013; Fang et al. 2014; Kooch et al. 2019). The strong negative correlation of qCO2 with SOMBC, SBR, and SSIR indicates that reduced substrate carbon supply declines the microbial biomass size, activity, and microbial efficiency for substrate carbon utilization (Wardle and Ghani 1995; Six et al. 2006; Yang et al. 2010). Under a disturbed ecosystem, the strong competition for available carbon substrate may favor microbes that use more carbon energy in maintenance than growth (Islam and Weil 2000). Therefore, in cultivated soils, the microbial communities are more stressed and needed a regular supply of carbon sources to maintain their activity (Singh et al. 2018).

In semiarid ecosystems, SOM is considered as the main controlling factor for soil microbial activity and respiration (Zhang et al. 2013; Bao et al. 2016; Meena et al. 2020). Among the cultivated sites, a comparatively higher SM and nutrient content (SC, SN, SP) in VF could explain the greater respiration values under VF than AF (Giesler et al. 2012; Tardy et al. 2014).

The SMBC/SC ratio or MQ has been widely used as an indicator of soil quality and future changes in SOM (Sparling et al. 1992). It also reflects the contribution of the SMBC to SC and can be used as a sensitive measure of soil health under different land use management systems (Anderson and Domsch 1989). The previous studies have reported a high MQ in the forest as compared with cultivated land (Dinesh et al. 2004; Kooch et al. 2019). Contrastingly, in our study, the higher MQ in AF could be related to lower SC owing to a larger proportion of the SMBC to total SC (Araújo et al. 2013). Hence, the soils in the cultivated lands are more sensitive to the change related to land use and management (Sampaio et al. 2008).

Soil enzyme activity

Soil enzyme activity is influenced by the soil characteristics related to nutrient availability, soil microbial activity, and land use management processes which modified the potential soil enzyme-mediated substrate catalysis (Kandeler et al. 1996). In this study, the activity of all the enzymes was higher under MFC. With the low level of anthropogenic influence under MFC, the soils were covered with high litter content and added greater SOM. This suggests that the enzyme activities are governed by the availability of carbon sources and SOM decomposition. The intensive management practices under AF and VF constantly disturb the soil and regular removal of litter layer restricted the supply of substrate for microbes, thereby reduces the enzyme activities. Kotroczó et al. (2014) reported that under different treatments of detritus input and removal, the enzyme activities were more influenced by root activity rather than aboveground litter availability. In this case, the higher activity of root and rhizosphere in natural forests increased the enzyme activities. Previous studies reported a reduction in soil enzyme activities following the conversion of natural forests into cultivated lands (Araújo et al. 2013; Raiesi and Beheshti 2015; Vinhal-Freitas et al. 2017; Silva et al. 2019).

Urease regulates the SN transformation and is involved in the hydrolysis of urea into ammonia and CO2 (Kong et al. 2008). The urease activity is influenced by various soil properties including pH, soil nutrient supply, SN, SMBN, and N fertilizers (Moghimian et al. 2017). In this study, the highest urease activity was evaluated in MFC than all other land use/cover types. Our results were similar to previous findings indicating greater urease activity under natural forest than cultivated area, and indicating that the availability of fresh SOM for microbial decomposition enhances the microbial activity in forest soil and increases the enzyme activity (de Medeiros et al. 2015; Vinhal-Freitas et al. 2017). Contrastingly, in cultivated fields (i.e., AF and VF), high urease activity was found despite low values of SC and SN. This can be explained by the regular supply of urea fertilizer to the field. Also, a strong positive correlation of urease activity with SM supported its increased activity in VF (Zeng et al. 2009).

Dehydrogenase activity in soil serves as an indicator of the microbiological redox system and microbial oxidative activities in soil (Casida Jr et al. 1964). It indicates the respiratory activity of the soil and can be used as a measure of microbial activity in semiarid climates (García et al. 1994; Bastida et al. 2006). In this case, no dehydrogenase activity was detected under cultivated sites (i.e., AF and VF). The reduced content of labile carbon and SC are suggested to decrease the activity (Bastida et al. 2006). Bonanomi et al. (2011) reported a reduction by 84% in dehydrogenase activity in a low-input management regime as compared with the high-input management regime. de Medeiros et al. (2015) detected the dehydrogenase activity in soils under different intercropping areas found the lowest activity in Maninot esculenta, Cajanus cajan, Vignia unguiculata monoculture. The study reported that soil disaggregation due to short cropping cycle and weeding along with low vegetation cover attributed to reduced enzyme activity. Further, in dry climate conditions the abiotic stress to microbial activity due to high temperature and low SM influence the organic matter oxidation by dehydrogenase (Li and Sarah 2003).

In addition, 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 SC and SMBC with β-glucosidase activity indicated that SOM content is the major factor in its activity (Vinhal-Freitas et al. 2017). Corroborating with our results, Silva et al. (2019) evaluated β-glucosidase activity under tropical dry native forest, protected area, scrub, and maize cultivated area; reported reduced activity under the cultivated field; and suggested a closed linking of β-glucosidase with SOC and SOM content. de Medeiros et al. (2015) demonstrated similar β-glucosidase activity among tropical dry forest and intercropping soils of Brazil with less aggressive management practices. The study also reported a reduced activity under semiarid ecosystems attributed due to the slow decomposition of SMBC.

Similarly, the acid phosphatase activity was also higher under forests as compared to cultivated land use. Further, a nonsignificant yet positive correlation of SC and SMBC with acid phosphatase activity indicates their importance in regulating its activity (Hendriksen et al. 2016; Acosta-Martínez et al. 2018). Similar to this study, Silva et al. (2019) found no correlation between SP and acid phosphatase. This confirms that the activity of acid phosphatase activity is also influenced by soil pH, nutrients, SC, SN, SP, SOM quality and quantity, microbial community structure, SM, and soil temperature (Hendriksen et al. 2016; Maharajan et al. 2017; Moghimian et al. 2017). Raiesi and Beheshti (2015) indicated that soil pH is the main regulator of acid phosphatase activity, and narrow pH ranges attributed to no significant changes after natural forest conversions in Iran.

Factors affecting microbial and enzyme activities

The PCA evaluated the dissimilarities in soil parameters (i.e., SM, SP, SC, SN, SMBC, SBR, SSIR, SMBC/SC, qCO2, β-glucosidases, ureases, acid phosphatase, and dehydrogenase activity) among the four land uses. The cultivated (i.e., AF and VF) appeared to be different from the respective forest analogs (i.e., PFC and MFC). The clustering of SC, SN, SMBC, SBR, SSIR, β-glucosidase, dehydrogenase, and acid phosphatases nearby PFC and MFC indicate the critical role of SOM in controlling the microbial and enzyme activities (Cheng et al. 2013). High SOM in forest land use due to dense vegetation, deep rooting system, and greater amount of litter favors microbial activity (Tiwari et al. 2019). The SM often considered as the key factor in controlling soil microbial functions. Fang et al. (2014) and Tiwari et al. (2019) suggested slower decomposition and microbial activity in cultivated land uses as results of low SM content. Additionally, the clustering of urease with SM is similar to various findings, suggesting a significant role of SM in enhancing urease activity in soil (Sahrawat 1983). Further, clustering of qCO2 and SMBC/SC near the AF and VF suggest a reduced efficiency of microbes to utilize substrate carbon in cultivated lands (Araújo et al. 2014) as compared with more recalcitrant carbon in forest analogs.

Conclusion

Our study suggests that land use/cover changes and management practices influence the soil microbial parameters and enzyme activity in a semiarid region. Low SOM in cultivated lands (AF, VF) limits the soil microbial activity thereby reducing SMBC, SBR, and SSIR. In comparison, an increased amount of substrate quantity and quality in the native forest land uses enhances the SOM content favoring high microbial activity. The variation in plant species composition, SM content, and litter quality within the land use further affect microbial activity. Among the two forests, the higher plant diversity under MFC had significantly enhanced the SC, SN, SMBC, SBR, SSIR, and enzyme activity as compared with the PFC, while among the two cultivated land uses, under AF, the intensive management practices including tillage, fallow periods, application of fertilizers, and low SM had drastically reduced the SOM quantity and quality and hence, the microbial and enzyme activity. The strong positive correlation of soil parameters (SC, SN) with microbial activities (SMBC, SBR, SSIR) and soil enzymes (β-glucosidase, acid phosphatase, and dehydrogenase) further suggests the critical role of SOM and soil nutrient in maintaining soil fertility. A higher SMBC/SC and qCO2 under AF suggest that cultivated sites are more sensitive to the SC changes in topsoil layers. Anthropogenic interventions in cultivated lands alter the soil structure and physicochemical parameters, thereby affecting the soil microbial and enzyme activity as strongly suggested from our results. Therefore, the soil microbiological parameters are potential indicators to study soil response and SC dynamics following land use conversions and management practices. The implication of such studies is even more significant in climate-stressed semiarid ecosystems, where effective land management practices in cultivated lands and restoration of forest ecosystems enhance SOM and microbial functions.

Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Abbreviations

AF:

Agriculture field

MF:

Mixed forest cover

qCO2 :

Metabolic quotient

PCA:

Principal component analysis

PFC:

Prosopis juliflora-dominated forest cover

SBR :

Soil basal respiration

SC :

Soil carbon

SC/SN :

Soil carbon to soil nitrogen ratio

SM :

Soil moisture

SMBC :

Soil microbial biomass carbon

SMBC/SC :

Soil microbial biomass carbon to soil carbon ratio

SMBN :

Soil microbial biomass nitrogen

SN :

Soil nitrogen

SOM :

Soil organic matter

SP :

Soil phosphorus

SSIR :

Soil substrate-induced respiration

VF:

Vegetable field

References

  1. Acosta-Martínez V, Cano A, Johnson J (2018) Simultaneous determination of multiple soil enzyme activities for soil health-biogeochemical indices. Appl Soil Ecol 126:121–128

    Article  Google Scholar 

  2. Acosta-Martínez V, Mikha MM, Vigil MF (2007) Microbial communities and enzyme activities in soils under alternative crop rotations compared to wheat-fallow for the Central Great Plains. Appl Soil Ecol 37(1-2):41–52

    Article  Google Scholar 

  3. Allen SE, Grimshaw HM, Parkinson JA, Quarmby C (1974) Chemical analysis of ecological materials. Blackwell Scientific, Oxford

    Google Scholar 

  4. Ananyeva ND, Susyana EA, Chernova OV, Wirth S (2008) Microbial respiration activities of soils from different climatic regions of European Russia. Eur J Soil Biol 44(2):147–157

    CAS  Article  Google Scholar 

  5. Anderson JPE, Domsch KH (1978) A physiological method for the quantitative measurement of microbial biomass in soils. Soil Biol Biochem 10(3):215–221

    CAS  Article  Google Scholar 

  6. Anderson TH, Domsch KH (1989) Ratios of microbial biomass carbon to total organic-C in arable soils. Soil Biol Biochem 21(4):471–479

    Article  Google Scholar 

  7. Anderson TH, Domsch KH (1990) Application of eco-physiological quotients (qCO2 and qD) on microbial biomasses from soils of different cropping histories. Soil Biol Biochem 22(2):251–255

    Article  Google Scholar 

  8. Araújo ASF, Borges CD, Tsai SM, Cesarz S, Eisenhauer N (2014) Soil bacterial diversity in degraded and restored lands of Northeast Brazil. Antonie Van Leeuwenhoek 106(5):891–899

    Article  CAS  Google Scholar 

  9. Araújo ASF, Cesarz S, Leite LFC, Borges CD, Tsai SM, Eisenhauer N (2013) Soil microbial properties and temporal stability in degraded and restored lands of Northeast Brazil. Soil Biol Biochem 66:175–181

    Article  CAS  Google Scholar 

  10. Bao X, Zhu X, Chang X, Wang S, Xu B, Luo C, Zhang Z, Wang Q, Rui Y, Cui X (2016) Effects of soil temperature and moisture on soil respiration on the Tibetan plateau. PLoS One 11(10):e0165212

    Article  CAS  Google Scholar 

  11. Bastida F, Moreno JL, Hernández T, García C (2007) The long-term effects of the management of a forest soil on its carbon content, microbial biomass and activity under a semi-arid climate. Appl Soil Ecol 37(1-2):53–62

    Article  Google Scholar 

  12. Bastida F, Moreno JL, Hernandéz TH, García C (2006) Microbiological degradation index of soils in a semiarid climate. Soil Biol Biochem 38:3463–3473

    CAS  Article  Google Scholar 

  13. Bonanomi G, D’Ascoli R, Antignani V, Capodilupo M, Cozzolino L, Marzaioli R, Puopolo G, Rutigliano FA, Scelza R, Scotti R, Rao MA, Zoina A (2011) Assessing soil quality under intensive cultivation and tree orchards in Southern Italy. Appl Soil Ecol 47(3):184–194

    Article  Google Scholar 

  14. Casida LE Jr, Klein DA, Santoro T (1964) Soil dehydrogenase activity. Soil Sci 98:371–376

    CAS  Article  Google Scholar 

  15. Celik I (2005) Land-use effects on organic matter and physical properties of soil in a southern Mediterranean highland of Turkey. Soil Tillage Res 83(2):270–277

    Article  Google Scholar 

  16. Census (2011) Census of India 2011. Registrar General of India, Ministry of Home Affair, Government of India. Available at: https://www.census2011.co.in/census/state/delhi

  17. Champion HG, Seth SK (1968) A revised survey of the forest types of India. Govt. of India Publications, New Delhi

    Google Scholar 

  18. Cheng F, Peng X, Zhao P, Yuan J, Zhong C, Cheng Y, Cui C, Zhang S (2013) Soil microbial biomass, basal respiration and enzyme activity of main forest types in the Qinling Mountains. PLoS One 8(6):67353

    Article  CAS  Google Scholar 

  19. Chibbar RK (1985) Soils of Delhi and their management. In: Biswas BC, Yadav DS, Maheshwari S (eds) Soils of India and their management. Fertiliser Association of India, New Delhi, pp 72–86

    Google Scholar 

  20. Chodak M, Niklinska M (2010) Effect of texture and tree species on microbial properties of mine soils. Appl Soil Ecol 46(2):268–275

    Article  Google Scholar 

  21. Cookson WR, Osman M, Marschner P, Abaye DA, Clark I, Murphy DV, Stockdale EA, Watson CA (2007) Controls on soil nitrogen cycling and microbial community composition across land use and incubation temperature. Soil Biol Biochem 39(3):744–756

    CAS  Article  Google Scholar 

  22. Davidson EA, Savage K, Verchot LV, Navarro R (2002) Minimizing artifacts and biases in chamber-based measurements of soil respiration. Agric For Meteorol 113(1-4):21–37

    Article  Google Scholar 

  23. Dawson JJC, Smith P (2007) Carbon losses from soil and its consequences for land use management. Sci Total Environ 382(2-3):165–190

    CAS  Article  Google Scholar 

  24. de Medeiros EV, Duda GP, dos Santos LAR, Lima JRS (2017) Soil organic carbon, microbial biomass and enzyme activities responses to natural regeneration in a tropical dry region in Northeast Brazil. Catena 151:137–146

    Article  CAS  Google Scholar 

  25. de Medeiros EV, Notaro KA, de Barros JA, Moraes WS, Silva AO, Moreira KA (2015) Absolute and specific enzymatic activities of sandy entisol from tropical dry forest, monoculture and intercropping areas. Soil Tillage Res 145:208–215

    Article  Google Scholar 

  26. Dinesh R, Chaudhuri SG, Sheeja TE (2004) Soil biochemical and microbial indices in wet tropical forests: Effects of deforestation and cultivation. J Plant Nutr Soil Sci 167(1):24–32

    CAS  Article  Google Scholar 

  27. dos Santos UJ, de Medeiros EV, Duda GP, Marques MC, de Souza ES, Brossard M, Hammecker C (2019) Land use changes the soil carbon stocks, microbial biomass and fatty acid methyl ester (FAME) in Brazilian semiarid area. Arch Agron Soil Sci 65(6):755–769

    Article  CAS  Google Scholar 

  28. Eivazi F, Tabatabai MA (1988) Glucosidases and galactosidases in soils. Soil Biol Biochem 20:601–606

    CAS  Article  Google Scholar 

  29. FSI (2017) Forest and tree resources in states and union territories, State of forest report - Delhi. Forest Survey of India, Ministry of Environment and Forests, Government of India, Dehradun

  30. García C, Hernández T, Costa F (1994) Microbial activity in soils under Mediterranean environmental conditions. Soil Biol Biochem 26(9):1185–91

  31. Giesler R, Esberg C, Lagerström A, Graae BJ (2012) Phosphorus availability and microbial respiration across different tundra vegetation types. Biogeochemistry 108:429–45

  32. Goenster S, Gründler C, Buerkert A, Joergensen RG (2017) Soil microbial indicators across land use types in the river oasis Bulgan sum center, Western Mongolia. Ecol Indic 76:111–118

    CAS  Article  Google Scholar 

  33. Hanson PJ, Edwards NT, Garten CT, Andrews JA (2000) Separating root and soil microbial contributions to soil respiration: a review of methods and observations. Biogeochemistry 48:115–46

  34. Hendriksen N, Creamer R, Stone D, Winding A (2016) Soil exo-enzyme activities across Europe - the influence of climate, land-use and soil properties. Appl Soil Ecol 97:44–48

  35. Huang J, Song C (2010) Effects of land use on soil water soluble organic C and microbial biomass C concentrations in the Sanjiang Plain in Northeast China. Acta Agr Scand BSP 60(2):182–188

    CAS  Google Scholar 

  36. Isermayer H (1952) Eine einpache methode zur bestimmung der pflanzenatmung und der karbonate in boden, Z. Pflanzenernähr. Düng Bodenk 56:26–28

    Article  Google Scholar 

  37. Islam KR, Weil RR (2000) Land use effects on soil quality in a tropical forest ecosystem of Bangladesh. Agric Ecosyst Environ 79(1):9–16

    Article  Google Scholar 

  38. Jain M, Dawa D, Mehta R, Dimri AP, Pandit MK (2016) Monitoring land use change and its drivers in Delhi, India using multi-temporal satellite data. Model Earth Syst Environ 2(19):1–19

    Google Scholar 

  39. Kabiri V, Raiesi F, Ghazavi MA (2016) Tillage effects on soil microbial biomass, SOM mineralization and enzyme activity in a semi-arid Calcixerepts. Agric Ecosyst Environ 232:73–84

    CAS  Article  Google Scholar 

  40. Kandeler E, Kampichler C, Horak O (1996) Influence of heavy metals on the functional diversity of soil microbial communities. Biol Fertil Soils 23:299–306

    CAS  Article  Google Scholar 

  41. Kong CH, Wang P, Zhao H, Xu XH, Zhu YD (2008) Impact of allelochemical exuded from allelopathic rice on soil microbial community. Soil Biol Biochem 40(7):1862–1869

    CAS  Article  Google Scholar 

  42. Kooch Y, Ehsani S, Akbarinia M (2019) Stoichiometry of microbial indicators shows clearly more soil responses to land cover changes than absolute microbial activities. Ecol Eng 131:99–106

    Article  Google Scholar 

  43. Kooch Y, Tavakoli M, Akbarinia M (2018) Tree species could have substantial consequences on topsoil fauna: a feedback of land degradation/restoration. Eur J For Res 137:793–805

    CAS  Article  Google Scholar 

  44. Kotroczó Z, Veres Z, Fekete I, Krakomperger Z, Tóth JA, Lajtha K, Tóthmérész B (2014) Soil enzyme activity in response to long-term organic matter manipulation. Soil Biol Biochem 70:237–243

    Article  CAS  Google Scholar 

  45. Kumar M, Ghosal N (2017) Impact of land-use change on soil microbial community composition and organic carbon content in the dry tropics. Pedosphere 27(5):974–977

    CAS  Article  Google Scholar 

  46. Lacerda-Júnior GV, Noronha MF, Cabral L, Delforno TP, de Sousa STP, Fernandes-Júnior PI, Melo IS, Oliveira VM (2019) Land use and seasonal effects on the soil microbiome of a Brazilian dry forest. Front Microbiol 10:648

    Article  Google Scholar 

  47. Li X, Sarah P (2003) Enzyme activities along a climatic transect in the Judean Desert. Catena 53:349–363

    CAS  Article  Google Scholar 

  48. Maharajan M, Sanaullah M, Razavi BS, Kuzyakov Y (2017) Effect of land use and management practices on microbial biomass and enzyme activities in subtropical top- and sub-soils. Appl Soil Ecol 113:22–28

  49. Meena A, Bidalia A, Hanief M, Dinakaran J, Rao KS (2019) Assessment of above- and belowground carbon pools in a semi-arid forest ecosystem of Delhi, India. Ecol Process 8:8

    Article  Google Scholar 

  50. Meena A, Hanief M, Bidalia A, Dinakaran J, Rao KS (2016) Structure, composition and diversity of tree strata of semi-arid forest community in Delhi, India. Phytomorphology 66(3-4):95–102

  51. Meena A, Hanief M, Dinakaran J, Rao KS (2020) Soil moisture controls the spatio-temporal pattern of soil respiration under different land use systems in a semi-arid ecosystem of Delhi, India. Ecol Process 9:15

    Article  Google Scholar 

  52. Mganga KZ, Kuzyakov Y (2014) Glucose decomposition and its incorporation into soil microbial biomass depending on land use in Mt. Kilimanjaro ecosystems. Eur J Soil Biol 62:74–82

    CAS  Article  Google Scholar 

  53. Moghimian N, Hosseini SM, Kooch Y, Darki BZ (2017) Impacts of changes in land use/cover on soil microbial and enzyme activity. Catena 157:407–414

    CAS  Article  Google Scholar 

  54. Moore JM, Klose S, Tabatabai MA (2000) Soil microbial biomass carbon and nitrogen as affected by cropping systems. Biol Fertil Soils 31:200–210

    CAS  Article  Google Scholar 

  55. Nielsen UN, Ball BA (2015) Impacts of altered precipitation regimes on soil communities and biogeochemistry in arid and semi-arid ecosystems. Glob Chang Biol 21:1407–1421

    Article  Google Scholar 

  56. Notaro KA, Medeiros EV, Duda GP, Silva AO, Moura PM (2014) Agroforestry systems, nutrients in litter and microbial activity in soils cultivated with coffee at high altitude. Sci Agric 71(2):87–95

  57. Pandey D, Agrawal M, Bohra JS (2014) Effects of conventional tillage and no tillage permutations on extracellular soil enzyme activities and microbial biomass under rice cultivation. Soil Tillage Res 136:51–60

    Article  Google Scholar 

  58. Pickett STA, Cadenasso ML, Grove JM, Nilon CH, Pouyat RV, Zipperer WC, Costanza R (2001) Urban ecological systems: linking terrestrial, ecological, physical, and socioeconomic components of metropolitan areas. Annu Rev Ecol Syst 32:127–157

    Article  Google Scholar 

  59. Rai PK, Rai A, Singh S (2018) Change in soil microbial biomass along a rural-urban gradient in Varanasi (U.P., India). Geol Ecol Land 2(1):15–21

  60. Raiesi F, Beheshti A (2014) Soil specific enzyme activity shows more clearly soil responses to paddy rice cultivation than absolute enzyme activity in primary forests of northwest Iran. Appl Soil Ecol 75:63–70

    Article  Google Scholar 

  61. Raiesi F, Beheshti A (2015) Microbiological indicators of soil quality and degradation following conversion of native forests to continuous croplands. Ecol Indic 50:173–185

    CAS  Article  Google Scholar 

  62. Raiesi F, Salek-Gilani S (2020) Development of a soil quality index for characterizing effects of land-use changes on degradation and ecological restoration of rangeland soils in a semi-arid ecosystem. Land Degrad Dev 31(12):1533–1544

    Article  Google Scholar 

  63. Sahrawat KL (1983) Relationships between soil urease activity and other properties of some tropical wetland rice soils. Nutr Cycl Agroecosyst 4(2):145–150

    CAS  Google Scholar 

  64. Sampaio DB, Araújo ASF, Santos VB (2008) Evaluation of biological indicators of soil quality under conventional and organic fruit farming system. Ciênc Agrotec 32(2):353–359

    Article  Google Scholar 

  65. Schneider K, Turrión MB, Gallardo JF (2000) Modified method for measuring acid phosphatase activities in forest soils with high organic matter content. Commun Soil Sci Plant Anal 31(19-20):3077–3088

    CAS  Article  Google Scholar 

  66. Silva ÉO, de Medeiros EV, Duda GP, Junior MAL, Brossard M, de Oliveira JB, dos Santos UJ, Hammecker C (2019) Seasonal effect of land use type on soil absolute and specific enzyme activities in a Brazilian semi-arid region. Catena 172:397–407

  67. Singh MK, Ghoshal N (2014) Variation in soil microbial biomass in the dry tropics impact of land-use change. Soil Res 52(3):299–306

    Article  Google Scholar 

  68. Singh R, Bhardwaj DR, Pala NA, Kaushal R, Rajput BS (2018) Soil microbial characteristics in sub-tropical agro-ecosystems of North Western Himalaya. Curr Sci 115(10):1956–1959

    CAS  Article  Google Scholar 

  69. Sinha GN (2014) An introduction to the Delhi Ridge. Department of Forests and Wildlife. Govt. of NCT of Delhi, New Delhi, pp Xxiv–154

    Google Scholar 

  70. Six J, Frey S, Thiet RK, Batten KM (2006) Bacterial and fungal contributions to carbon sequestration in agroecosystems. Soil Sci Soc Am J 70:555–569

    CAS  Article  Google Scholar 

  71. Smith JL, Paul EA (1995) The significance of soil microbial biomass estimations. In: Lefroy RDB, Blair GJ, Craswell ET (eds) Soil Organic Matter Management for Sustainable Agriculture. Australian Center for International Agricultural Research, Canberra, pp 357–396

    Google Scholar 

  72. Sousa F, Ferreira T, Mendonça E, Romero R, Oliveira J (2012) Carbon and nitrogen in degraded Brazilian semi-arid soils undergoing desertification. Agric Ecosyst Environ 148:11–21

    CAS  Article  Google Scholar 

  73. Sparling GP, Shepherd TG, Kettles HA (1992) Changes in soil organic C, microbial C and aggregate stability under continuous maize and cereal cropping, and after restoration to pasture in soils from the Manawatu region, New Zealand. Soil Tillage Res 24:225–241

    Article  Google Scholar 

  74. Tardy V, Mathieu O, Lévêque J, Terrat S, Chabbi A, Lemanceau P, Ranjard L, Maron PA (2014) Stability of soil microbial structure and activity depends on microbial diversity. Environ Microbiol Rep 6(2):173–183

    CAS  Article  Google Scholar 

  75. Thalmann A (1968) Zur Metodik der Bestimmung der De-hydrogenaseaktivität im Boden mittels Triphenyltetrazo-liumchlorid (TTC). Landwirtsch Forsch 21:249–58

  76. Thapa VR, Ghimire R, Mikha MM, Idowu OJ, Marsalis MA (2018) Land use effects on soil health in semiarid drylands. Agric Environ Lett 3(1):1–5

    Article  CAS  Google Scholar 

  77. Tiwari S, Singh C, Boudh S, Rai PK, Gupta VK, Singh JS (2019) Land use change: a key ecological disturbance declines soil microbial biomass in dry tropical uplands. Aust J Environ Manag 242:1–10

    CAS  Google Scholar 

  78. Trasar-Cepeda C, Leirósb MC, Gil-Sotres F (2008) Hydrolytic enzyme activities in agricultural and forest soils. Some implications for their use as indicators of soil quality. Soil Biol Biochem 40(9):2146–55

  79. Vance ED, Brookes PC, Jenkinson DS (1987) An extraction method for measuring soil microbial biomass-C. Soil Biol Biochem 19(6):703–707

    CAS  Article  Google Scholar 

  80. Vinhal-Freitas IC, Corrêa GF, Wendling B, Bobuľská L, Ferreira AS (2017) Soil textural class plays a major role in evaluating the effects of land use on soil quality indicators. Ecol Indic 74:182–190

    CAS  Article  Google Scholar 

  81. Waldrop MP, Zak DR, Sinsabaugh RL (2004) Microbial community response to nitrogen deposition in northern forest ecosystems. Soil Biol Biochem 36(9):1443–1451

    CAS  Article  Google Scholar 

  82. Wani SP, Chander G, Sahrawat KL, Pal DK, Pathak P, Pardhasaradhi G, Kamadi PJ (2015) Sustainable use of natural resources for crop intensification and better livelihoods in the rainfed semi-arid tropics of Central India. NJAS - Wageningen J Life Sci 78:13–19

  83. Wardle DA, Ghani A (1995) A critique of the microbial metabolic quotient (qCO2) as a bioindicator of disturbance and ecosystem development. Soil Biol Biochem 27(12):1601–1610

    CAS  Article  Google Scholar 

  84. Witt C, Gaunt JL, Galicia CC, Ottow JCG, Neue HU (2000) A rapid chloroform-fumigation extraction method for measuring soil microbial biomass carbon and nitrogen in flooded rice soils. Biol Fertil Soils 30:510–519

    CAS  Article  Google Scholar 

  85. Fang X, Wang Q, Zhou W, Zhao W, Wei Y, Niu L, Dai L (2014) Land use effects on soil organic carbon, microbial biomass and microbial activity in Changbai Mountains of Northeast China. Chin Geogr Sci 24:297–306

  86. Yan B, Li J, Xiao N, Yue Q, Fu G, Liu G, Qiao M (2016) Urban-development-induced changes in the diversity and composition of the soil bacterial community in Beijing. Sci Rep 6:38811

    CAS  Article  Google Scholar 

  87. Yang K, Zhu JJ, Zhang M, Yan Q, Sun OJ (2010) Soil microbial biomass carbon and nitrogen in forest ecosystems of Northeast China: a comparison between natural secondary forest and larch plantation. J Plant Ecol 3(3):175–182

    Article  Google Scholar 

  88. Yao X-h, Min H, Lu Z-h, Yuan H-p (2006) Influence of acetamiprid on soil enzymatic activities and respiration. Eur J Soil Biol 42(2):120–6

  89. Ye R, Wright AL, Inglett K, Wang Y, Orgam AV, Reddy KR (2009) Land-use effects on soil nutrient cycling and microbial community dynamics in the everglades agricultural area, Florida. Commun Soil Sci Plant Anal 40(17-18):2725–2742

    CAS  Article  Google Scholar 

  90. Zeng DH, Hu YL, Chang SX, Fan ZP (2009) Land cover change effects on soil chemical and biological properties after planting Mongolian pine (Pinus sylvestris var. mongolica) in sandy lands in Keerqin, north-eastern China. Plant Soil 317:121–33

  91. Zhang XB, Xu MG, Sun N, Wang XJ, Wu L, Wang BR, Li DC (2013) How do environmental factors and different fertilizer strategies affect soil CO2 emission and carbon sequestration in the upland soils of southern China? Appl Soil Ecol 72:109–118

    Article  Google Scholar 

  92. Zhao D, Li F, Wang R (2012) The effects of different urban land use patterns on soil microbial biomass nitrogen and enzyme activities in urban area of Beijing, China. Acta Ecol Sin 32(3):144–149

    Article  Google Scholar 

  93. Zhao D, Li F, Yang Q, Wang R, Song Y, Tao Y (2013) The influence of different types of urban land use on soil microbial biomass and functional diversity in Beijing, China. Soil Use Manag 29:230–239

    CAS  Article  Google Scholar 

Download references

Acknowledgements

We thank the Council of Scientific and Industrial Research (CSIR; Ref No. 20-12/2009(ii) EU-IV). We also thank the University of Delhi for providing Research and Development and for providing grant for doctoral research program.

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  1. Department of Botany, University of Rajasthan, Jaipur, Rajasthan, 302004, India

    Archana Meena

  2. Natural Resource Management Laboratory, Department of Botany, University of Delhi, North Campus, Delhi, 110007, India

    K. S. Rao

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AM proposed the idea and conducted the field 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 final manuscript.

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Meena, A., Rao, K.S. Assessment of soil microbial and enzyme activity in the rhizosphere zone under different land use/cover of a semiarid region, India. Ecol Process 10, 16 (2021). https://doi.org/10.1186/s13717-021-00288-3

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Keywords

  • Land use changes, Soil microbes, Soil enzymes, Semiarid ecosystems, Land management practices