Soil moisture controls the spatio-temporal pattern of soil respiration under different land use systems in a semi-arid ecosystem of Delhi, India

Abstract

Background

Soil respiration (S_R) is a critical process for understanding the impact of climatic conditions and land degradation on the carbon cycle in terrestrial ecosystems. We measured the S_R and soil environmental factors over 1 year in four land uses with varying levels of disturbance and different vegetation types viz., mixed forest cover (MFC), Prosopis juliflora (Sw.) forest cover (PFC), agricultural field (AF), and vegetable field (VF), in a semi-arid area of Delhi, India. Our primary aim was to assess the effects of soil moisture (S_M), soil temperature (S_T), and soil microbial activity (S_MA) on the S_R.

Methods

The S_R was measured monthly using an LI-6400 with an infrared gas analyser and a soil chamber. The S_M was measured using the gravimetric method. The S_T (10 cm) was measured with a probe attached to the LI-6400. The S_MA was determined by fluorescein diacetate hydrolysis.

Results

The S_R showed seasonal variations, with the mean annual S_R ranging from 3.22 to 5.78 μmol m⁻² s⁻¹ and higher S_R rates of ~ 15–55% in the cultivated fields (AF, VF) than in the forest sites (MFC, PFC). The VF had significantly higher S_R (P < 0.05) than the other land uses (AF, PFC, MFC), which did not vary significantly from one another in S_R (P < 0.05). The repeated measures ANOVA evaluated the significant differences (P < 0.05) in the S_R for high precipitation months (July, August, September, February). The S_M as a single factor showed a strong significant relationship in all the land uses (R² = 0.67–0.91, P < 0.001). The effect of the S_T on the S_R was found to be weak and non-significant in the PFC, MFC, and AF (R² = 0.14–0.31; P > 0.05). Contrasting results were observed in the VF, which showed high S_R during summer (May; 11.21 μmol m⁻² s⁻¹) and a significant exponential relationship with the S_T (R² = 0.52; P < 0.05). The S_R was positively related to the S_MA (R² = 0.44–0.5; P < 0.001). The interactive equations based on the independent variables S_M, S_T, and S_MA explained 91–95% of the seasonal variation in S_R with better model performance in the cultivated land use sites (AF, VF).

Conclusion

S_M was the key determining factor of the S_R in semi-arid ecosystems and explained ~ 90% of the variation. Precipitation increased S_R by optimizing the S_M and microbial activity. The S_MA, along with the other soil factors S_M and S_T, improved the correlation with S_R. Furthermore, the degraded land uses will be more susceptible to temporal variations in S_R under changing climatic scenarios, which may influence the carbon balance of these ecosystems.

Background

Soil respiration (S_R) is the second largest flux of carbon (C) between terrestrial ecosystems and the atmosphere (Hanson et al. 2000). S_R is considered a key process in the terrestrial C cycle, and it releases 98 Pg C per year into the atmosphere (Bilandžija et al. 2016; Zhao et al. 2017). Any small variation in S_R has a significant impact on the carbon dioxide (CO₂) concentration in the atmosphere which in turn affects the global C cycle (Black et al. 2017). Therefore, understanding the dynamics of the S_R in any ecosystem is critical for combatting climate change. S_R has two components: auto- (roots and rhizosphere) and heterotrophic respiration (soil microbes and soil fauna) (Chen et al. 2017). Several abiotic and biotic factors, including soil temperature (S_T), soil moisture (S_M) (Bao et al. 2016), availability of C substrates for microorganisms, soil microbial activity (S_MA) (Tang et al. 2018), soil fertility (Butnor et al. 2003), plant photosynthetic activity (Zhang et al. 2013a, 2013b), and soil organisms (Rai and Srivastava 1981), influence the rate of soil CO₂ efflux. S_R is also affected by various management activities, including land use change, which contributes 12.5% of the global CO₂ emissions to the atmosphere, mainly as a result of deforestation (IPCC 2013). Several studies have reported the potential impacts of cultivation and deforestation activities on the soil C storage and efflux of CO₂ (Lou et al. 2004; Rey et al. 2011; Peri et al. 2015).

Climate change has a strong impact on precipitation patterns across the globe (Arredondo et al. 2018; Darrouzet-Nardi et al. 2018). Changes in precipitation patterns in any terrestrial ecosystem will ultimately affect the S_M, which in turn influence S_MA, soil organic matter (SOM) decomposition pattern, and S_R (Bao et al. 2019). Arid and semi-arid ecosystems cover approximately 41% of the terrestrial land surface of the Earth (Wang et al. 2014). The unpredictable and random precipitation events in semi-arid ecosystems interact with the seasons and the functioning of auto- and heterotrophic ecosystem processes (Miao et al. 2017). Such events are vulnerable to climate change and can have crucial impacts on S_R, often causing a pulse of CO₂ to be emitted into the atmosphere (Shen et al. 2016; Gu et al. 2018). There have been studies on the S_R in arid and semi-arid ecosystems with respect to annual flux measurements (Subke et al. 2006; Sawada et al., 2016b). The S_M is considered an important environmental determinant controlling the rate of S_R in these ecosystems (Rey et al. 2002; Conant et al. 2004; Jarvis et al. 2007; Miao et al. 2017). The S_M can limit the widely accepted positive linear and exponential relationship between the S_R and S_T by limiting the soil microbial activity (S_MA) under low moisture conditions (Wang et al. 2014). Therefore, in such an ecosystem, both the S_T and S_M strongly influence respiration rates, and their relative importances can vary seasonally and spatially (Reichstein et al. 2002; Tang and Baldocchi 2005; Sun et al. 2018). Hence, we assume that the S_R in semi-arid ecosystems is limited by the S_M and S_MA in the different land use/land cover systems.

Delhi has a unique forest ecosystem located on ridges that are extensions of the Aravalli hills; these ridges are 32 km long and serve various ecological, environmental, and social functions. The Delhi ridge has been designated as a reserved forest and is managed mainly with the objectives of increasing forest cover, biodiversity, and conservation through public participation and reduction in monoculture plantations and encroachments (Sinha 2014). Delhi is also considered one of the most polluted cities in the world. The land use pattern of Delhi showed that 15.00% (221.4 km²) of the total geographical area comprised the net sown area, 8.07% (119 km²) was the current fallow area, 6.71% (98.9 km²) was culturable wasteland, and 1.00% (14.8 km²) was forest cover (FSI 2017). Previous studies on S_R have been conducted in temperate and riparian subtropical regions of India (Jha and Mohapatra 2011), and the data from the semi-arid ecosystems of India are very limited. Our study will provide relevant data on S_R for future research in the semi-arid ecosystems of India. The objectives of this study were (1) to measure and compare the spatio-temporal variations in S_R in the different types of land use systems in a semi-arid area of Delhi, India, and (2) to understand the impacts of S_M, S_T, and S_MA and/or the interactive effect of these factors on S_R.

Methods

Study area

The study was carried out in a semi-arid area of Delhi, which is part of the National Capital Territory (NCT) of India (28.40° N to 28.41° N, 76.84° E to 77.40° E) and covering an area of 1483 km² (Fig. 1). The area is bounded by the Indo-Gangetic alluvial plains in the north and east, the Thar Desert in the west, and the Aravalli Range and the hill ranges in the south. Delhi lies within the interior of the northern plains of the Indian subcontinent. The climate of Delhi is greatly influenced by the Himalayas and the Thar Desert due to its proximity. The climate of the area is semi-arid and dry except during the monsoon season and is characterized by hot summers (April–June), monsoons (July–September), cool and dry winters (November–December), and two periods of pleasant transitional weather, i.e., autumn (October) and spring (February to March). The climate is influenced by two weather events, i.e., the western disturbances and south-westerly winds. 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 and 67% of plantation and tree outside forest (TOF) areas. The vegetation is mainly dominated by middle-story thorny trees, which are 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). Prosopis juliflora (Sw.) DC, which is an exotic species, is the dominant tree in the forests. 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). The naturally growing shrubs in the forests are Justicia adhatoda L., Capparis sepiaria L., Carissa spinarum L., Jatropha gossypifolia L., and Opuntia dillenii L.

Fig. 1

Map of the study area (Delhi) showing the land use sites. PFC Prosopis juliflora forest cover, MFC mixed forest cover, AF agriculture field and VF vegetable field

Land use site description

To study the spatio-temporal variation in S_R, we chose four different land uses based on the levels of human disturbance (mainly cultivation) and plant species cover (Fig. 1). The land uses were (1) mixed forest cover (MFC) as the native vegetation cover (28.61° N; 77.17° E); (2) P. juliflora dominated forest cover (PFC) as the exotic tree cover (28.69° N; 77.22° E); (3) an agricultural field (AF), which was located near the Nazafgarh drain (28.54° N; 76.87° E); and (4) a vegetable field (VF) located along the Yamuna flood plains (28.52° N; 77.34° E). The vegetation of the PFC stand was characterized by a total tree density (TD) of 350 individuals ha⁻¹ and a mean basal area (MBA) of 25.05 m² ha⁻¹. Under the MFC, TD and MBA were higher than under the PFC, at 400 individuals ha⁻¹ and 117.26 m² ha⁻¹, respectively. However, under the MFC, P. juliflora was found to be the most dominant tree species with the highest TD (200 individuals ha⁻¹), but other associated tree species viz., Pongamia pinnata L. (50 individuals ha⁻¹), Azadirachta indica Juss. (50 individuals ha⁻¹), A. nilotica (75 individuals ha⁻¹), and C. fistula (25 individuals ha⁻¹) were also observed (Meena et al. 2019). The maximum basal area (BA) values were estimated for A. nilotica (160.52 m² ha⁻¹) and C. fistula (147.37 m² ha⁻¹), whereas the BA was comparatively low for P. juliflora (95.74 m² ha⁻¹). The AF was mainly cropped with Triticum aestivum L. during winter (October–May) and Phaseolus vulgaris L. (September–October). The field was irrigated by a tube well during the growing season. The VF field was mainly cultivated with Capsicum annum L. throughout the year except between September and November, during which Brassica oleraceae L. was grown. The 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 respiration measurements

The S_R was measured in the selected land use systems from April 2012 to March 2013 with a LI-6400 (LI-COR Inc., Lincoln, NE, USA), which consisted of an infrared gas analyser (IRGA) and a soil chamber (LI-6400-09) of 962 cm³ in volume and 72 cm² area. The S_R was measured with a stratified random sampling design in each land use type to account for the spatial variability in soil properties and vegetation cover. Each measurement was the mean value of three observations at each sampling site. Collars made from polyvinyl chloride (PVC) pipes (10 cm diameter and 6 cm height) were gently inserted 2 cm into the soil, leaving approximately 4 cm of the collar above the soil surface at each point for 24 h prior to the S_R measurement to minimize any disturbance during the measurement. Before taking the measurement, the soil surface within the collar was kept free of any live vegetation and residues by removing the seedlings and their roots to avoid autotrophic respiration. All measurements were conducted in the morning between 09:00 and 11:00 a.m. to avoid the high midday temperatures in the study region (Lou et al. 2004). The soil chamber was placed on the PVC collar and fixed to the ring to record the S_R inside the collar. Before the S_R measurement, the concentrations of CO₂ within the soil chamber were lowered to below the ambient CO₂ concentration, and then the increase in CO₂ was logged until it stabilized.

Soil sampling and analysis

Soil samples were collected from five 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 S_M content was measured with the gravimetric method by oven drying approximately 50 g of fresh soil at 105 °C until it reached a constant weight and then weighing it to note the dry weight. The S_T, up to 10 cm, was measured with a probe attached to the LI-6400. The S_T readings were recorded at the same time as the S_R readings.

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 carbon (S_C) and soil nitrogen (S_N) concentrations were measured with an Elementar CHNS analyser. The S_MA was determined by fluorescein diacetate (FDA) hydrolysis according to the method of Adam and Duncan (2001). A 2-g moist and sieved soil sample was taken in a conical flask and mixed with 15 ml potassium phosphate buffer (60 mM) with a pH of 7.6. To the soil, 0.2 ml of FDA stock solution (1000 μg FDA ml⁻¹) was added to start the reaction. The blanks were prepared without the addition of FDA. The samples were shaken at 100 rev min⁻¹ in an orbital incubator shaker at 30 °C for 20 min. After incubation, a 15 ml chloroform:methanol (2:1) solution was added to the soil samples to terminate the reaction. The contents were then centrifuged at 2000 rev min⁻¹ for 3 min. Supernatants from each sample were filtered through Whatman filter paper No. 2. Standards were made by using a fluorescent stock solution (2000 μg ml⁻¹). The absorbance of the standards and samples was measured at 490 nm using a spectrophotometer (RIGOL, USA).

Data analysis

One-way analysis of variance (ANOVA) was used to evaluate the variations in the S_R, S_T, S_M, S_MA, S_C, and S_N among the different land use types using Tukey’s test at P < 0.05. A repeated measures ANOVA was performed on the S_R data using the measurement months and land uses (MFC, PFC, AF, and VF) as factors. Pearson analysis was performed to investigate the correlation of the environmental factors with S_R in all land uses. The linear regression was used to study the relationship of the S_R with the S_M and S_MA. For the relationship of the S_R with the S_T, exponential and nonlinear regression analysis was done.

The interactive effect of the S_T and S_M on the S_R was determined by using two independent variable regression equations as described by Li et al. (2018) as follows:

$$ {\mathrm{S}}_{\mathrm{R}}=a\times {\mathrm{S}}_{\mathrm{T}}^{\mathrm{b}}\times {\mathrm{S}}_{\mathrm{M}\kern9.5em }^{\mathrm{c}} $$

(1)

$$ {\mathrm{S}}_{\mathrm{R}}=a\times {e}^{bST}\times {\mathrm{S}}_{\mathrm{M}}^{\mathrm{c}}\kern0.75em $$

(2)

The interactive effect of the S_T, S_M and S_MA on the S_R was evaluated as follows:

$$ {\mathrm{S}}_{\mathrm{R}}=a\times {\mathrm{S}}_{\mathrm{T}}^{\mathrm{b}}\times {\mathrm{S}}_{\mathrm{M}}^{\mathrm{c}}\times {\mathrm{S}}_{\mathrm{M}\mathrm{A}}^{\mathrm{d}} $$

(3)

$$ {\mathrm{S}}_{\mathrm{R}}=a\times {e}^{bST}\times {\mathrm{S}}_{\mathrm{M}}^{\mathrm{c}}\times {\mathrm{S}}_{\mathrm{M}\mathrm{A}}^{\mathrm{d}} $$

(4)

$$ {\mathrm{S}}_{\mathrm{R}}=a+\mathrm{b}{S}_T+\mathrm{c}{S}_M+\mathrm{d}{S}_{MA} $$

(5)

where a, b, c, and d are coefficients.

The criteria used for model selection were Akaike’s information criteria (AIC) and the coefficient of determination (R²). The AIC was calculated as follows:

where RSS is the residual sum of squares, N is the sample size, and p is the number of independent variables. The model with the lowest AIC and the highest R² value was selected as the best-fitting model.

$$ \mathrm{AIC}=N\ast \mathrm{Ln}\left(\mathrm{RSS}\right)+2\left(p+1\right)-N\ast \mathrm{Ln}\left(\mathrm{N}\right) $$

(6)

All statistical analyses were performed using SPSS version 16.0.

Results

The monthly climatic variables of the study area during the S_R measurement period are shown in Fig. 2. The air temperature during winter reached a minimum of 6 °C (January) and began rising in March, peaked in summer (May and June) to a maximum of 41 °C, and then declined in the monsoon season. The total precipitation received during the study period was 719.98 mm, of which 73% was received during the monsoon season (July–September). High precipitation was also recorded during spring (February), which contributed 15% of the total rainfall.

Fig. 2

Monthly average maximum and minimum air temperature (°C) and total precipitation (mm) for the period 2012–2013 in study area Delhi, India

Seasonal variation in the soil environmental factors and S_R

The mean monthly S_T (°C) was recorded to be high in summer (May: 37.69 ± 1.94, 36.04 ± 0.99, 46.63 ± 1.8, and 34.5 ± 0.09 in MFC, PFC, AF, and VF, respectively), to gradually decline in the monsoon season (July–September), and to be low in winter (November: 19.65 ± 0.11, 16.87 ± 0.11, and 18 ± 0.07 in MFC, PFC, and AF, respectively; December: 19.5 ± 0.18 in AF). The mean annual S_T (°C) values were 26.33 ± 2.98, 25.42 ± 2.99, 29.36 ± 5.19, and 26.36 ± 3.06 for MFC, PFC, AF, and VF, respectively. There were no significant differences in the S_T values between any land use sites (P > 0.05) (Fig. 3b). The S_T was significantly correlated with the S_R only in the VF (R = 0.7; Table 1), whereas in the other land uses, the relationship was found to be non-significant.

Fig. 3

Annual spatial variations of soil respiration (S_R) and environmental factors among land uses. Mean annual S_R soil respiration (a), S_T soil temperature (at 10 cm depth) (b), S_M soil moisture (c), S_MA soil microbial activity (d), S_C soil carbon (e), and S_N soil nitrogen (f) of PFC Prosopis juliflora forest cover, MFC mixed forest cover, AF agriculture field and VF vegetable field in a semi-arid area. Standard error of each mean is represented over each bar. Different letters denote significant (P < 0.05) differences among the four land uses sites (Tukey test after one-way ANOVA)

Table 1 Correlation among soil environmental factors and S_R in different land uses

For the S_M content, no significant difference was found among the PFC, MFC, and AF (P > 0.05), but a significant different was found for the VF (P < 0.05). The mean annual S_M (%) values were 1.99 ± 0.92, 1.49 ± 0.94, 2.08 ± 0.9, and 3.97 ± 0.43 for MFC, PFC, AF, and VF, respectively (Fig. 3c). The seasonal S_M pattern was influenced by the monthly rainfall (mm), which had high values (%) in the monsoonal month of September (3.96 ± 0.02 and 5.23 ± 0.02 in MFC and PFC, respectively) and in February (5.9 ± 0.14 in AF). In the VF, a consistently high S_M was recorded throughout the year, with maximum values in summer (May: 5.59 ± 0.14) (Fig. 4b).

Fig. 4

Seasonal variation of soil respiration (S_R) and environmental factors for the four land uses. Mean monthly S_T soil temperature (at 10 cm depth) (a), S_M soil moisture (b), S_MA soil microbial activity (c), and S_R soil respiration (d) of PFC Prosopis juliflora forest cover, MFC mixed forest cover, AF agriculture field, and VF vegetable field in a semi-arid area of Delhi. Standard error of each mean is represented over each line

The mean monthly S_MA was significantly higher in the forests (PFC and MFC) than in the cultivated sites (AF and VF) (P < 0.05). The annual mean S_MA (μg g⁻¹ min⁻¹) was 7.95 ± 0.42, 7.55 ± 0.53, 2.98 ± 0.3, and 3.08 ± 0.33 in the MFC, PFC, AF, and VF, respectively (Fig. 3d). Similarly, the mean annual S_C and S_N (g kg⁻¹) were also significantly higher in the forests, i.e., 38.95 ± 0.21 and 3.85 ± 0.25 in the MFC, respectively, and 29.31 ± 2.07 and 3.13 ± 0.25 in the PFC, respectively, compared to those in the arable land uses, i.e., 6.88 ± 0.32 and 0.75 ± 0.04 in the AF, respectively, and 14.03 ± 0.43 and 1.31 ± 0.08 in the VF, respectively (Fig. 3e, f). However, no significant correlation of the S_C or S_N with the S_MA was found at any of the sites (Table 1). Similar to the S_M pattern, the S_MA was also high in the monsoon season (July: 9.76 ± 0.24 in the MFC; August: 4.02 ± 0.02 in the AF) and in February (9.01 ± 0.01 and 5.34 ± 0.42 in the PFC and VF, respectively) (Fig. 4c). Furthermore, the positive correlation of the S_MA with the S_M with a significant correlation (R = 0.62, 0.64 in MFC and VF, respectively) suggests that the S_M influences the seasonal S_MA along with S_C and S_N (Table 1).

S_R showed a seasonal pattern with a peak in the monsoon season and a sharp decline in summer for the PFC, MFC, and AF (Fig. 4d). The S_R (μmol m⁻² s⁻¹) was the lowest in summer (May), at 0.97 ± 0.27, 0.65 ± 0.14, and 0.53 ± 0.1 in the MFC, PFC, and AF, respectively, and the highest in the monsoon season (September: 8.12 ± 0.31 and 7.9 ± 0.39 in the MFC and PFC, respectively) and in February (9.42 ± 0.09 in AF). In contrast, in the VF, the S_R was high in summer (May; 11.21 ± 0.08) and low in winter (November: 2.33±0.02). The S_R in the VF was significantly different from those of the other land uses (P < 0.05). The mean annual S_R was 3.22 ± 1.24, 2.57 ± 1.28, 3.75 ± 1.47 and 5.78 ± 1.39 μmol m⁻² s⁻¹ in the MFC, PFC, AF, and VF, respectively (Fig. 3a). The repeated measures ANOVA of the monthly S_R evaluated significant differences for the monsoonal months (July, August, September) and February from rest of the year (P < 0.05) and a significant interaction between the monthly S_R and the land use sites (F = 219.14, P < 0.05, df = 6.38). A strong and significant correlation between the S_R and the S_M (R = 0.82–0.95, P = 0.01) at all land use sites suggested that the S_M was an important controlling factor of the S_R. Furthermore, a significant correlation of the S_R with the S_MA (R = 0.67–0.71, P = 0.05) in all land uses except in the PFC (Table 1) further supported the influence of microbial activity on the S_R.

Soil environmental factors controlling S_R

The linear regression function effectively represented the influence of the S_M on the S_R and showed a strong significant positive interaction (R² = 0.67–0.91, P < 0.001) in all land uses (Fig. 5b). S_M as a single factor explained 67–92% of the total variation in S_R. However, the exponential and nonlinear functions, considering the S_T alone, determined only 12–50% of the changes in S_R. The effect of the S_T was found to be significantly positive only in the VF (R² = 0.5, P < 0.05) and non-significant at other sites (P > 0.05) (Fig. 5a). The effect of the S_MA alone was significant in the cultivated land uses (R² = 0.5 and 0.67 in the AF and VF, respectively, P < 0.05) and explained 15–67% of the total variation in S_R (Fig. 5c).

Fig. 5

Relationship between S_R soil respiration and environmental factors among four land uses. S_T Soil temperature (at 10 cm depth) (a), S_M soil moisture (b), and S_MA soil microbial activity (c) (showing the best fit) of PFC Prosopis juliflora forest cover, MFC mixed forest cover, AF agriculture field, and VF vegetable field

The model that used the interactive effects (equations 1 and 2) and considered the S_T and S_M as independent variables showed an improved relationship in the VF only (R² = 0.87; Table 2). However, the model with S_MA along with S_M and S_T (equations 3, 4, 5) improved the model parameters with comparatively low AIC values and higher R² values of 0.84, 0.95, 0.85, and 0.91 in the MFC, PFC, AF and VF, respectively. However, a better fit was obtained in the cultivated (AF, VF) sites than in the forest land use (MFC, PFC) sites (Table 2). This suggests that the S_R was controlled by the interaction of the S_T, S_M, and S_MA rather than by one factor.

Table 2 Regression parameters (a, b, c, d) from nonlinear regression models of S_R on the basis of mean monthly values

Discussion

The main aim of our study was to understand the soil factors (S_M, S_T, and S_MA) that influence the rate of S_R in the different land use systems of the semi-arid area of Delhi by using various regression equation models. The obtained results clearly demonstrated that the S_M alone (90%) controlled the S_R rates of the studied region. In addition, the interactive models that consisted of the S_M and other factors, such as the S_T and S_MA, effectively explained the variations in S_R. Compared to the reported values for other semi-arid ecosystems, the annual mean S_R rates of the studied region (2.55–5.78 μmol m⁻² s⁻¹) were higher than those of steppe ecosystems of Spain (0.72–1.24 μmol m⁻² s⁻¹, Rey et al. 2011) and North China (1.37–1.91 μmol m⁻² s⁻¹, Zeng et al. 2018) and were comparable with those of the Loess Plateau in China (2.03 to 3.23 μmol m⁻² s⁻¹, Shi et al. 2014).

Seasonal dynamics of S_R

The variations in the rainfall pattern, intensity, and frequency have significant impacts on the S_M, causing variation in SOM decomposition, S_C mineralization, microbial activity, plant growth and species composition, above- and belowground biomass production, and plant phenological traits (Bao et al. 2019; Zhang et al. 2019). We observed a strong seasonal variation in the S_R across all land uses, with higher S_R rates during the rainy season, i.e., the hot-humid climate (July–September) when S_M was not limiting (Fig. 4d). This suggests that the S_M and S_T would increase the production of aboveground biomass as a result of the high availability of resources for photosynthesis and the activity of the microorganisms in the monsoon season compared to those in other seasons (Zhou et al. 2014; Zhang et al. 2019). These results are in accordance with previous studies showing seasonal S_R in riparian and subtropical semi-arid regions of India, where the highest CO₂ effluxes were recorded in monsoons (Jha and Mohapatra 2011; Arora and Chaudhry, 2017). However, in dry seasons, soil water stress conditions could have reduced microbial activity, thereby decreasing S_R (Li et al. 2018). This could also explain the contrasting rise of S_R in VF in summers compared to other land use sites, as here, the S_M content is consistent because of regular irrigation due to its proximity to the Yamuna River (Fig. 4b, d)_.

In this study, high rainfall increased the S_M content and the S_R during the monsoon season by ~ 80–120% in forest land uses compared to ~ 17–46% at the cultivated sites. Furthermore, the effect of the sudden precipitation events after the drought periods was evident in this study, with an evident peak in the S_R across all land uses in February and June (Fig. 4d). Similar findings have also been reported across various ecosystems (Smith and Johnson 2004; Almagro et al. 2009, Rey et al. 2011; Matteucci et al. 2015), suggesting that the rainfall after long drought periods caused physical disruption of the soil aggregates and increased the decomposition of the OM, hence releasing more microbially derived soil CO₂ (Li et al. 2018). Furthermore, rewetting of the soil releases the microbial biomass C derived from microbial death during the dry season (Emmerich 2003; Sawada et al., 2016, b; Li et al. 2018). In our study, among the land uses, the responses to these sudden precipitation events appeared to be lower in the forested sites (MFC, PFC) compared to in the cultivated or arable land uses (AF, VF); a CO₂ increase of 16–21% was seen in the VF and AF compared to 9–14% in the MFC and PFC. Rey et al. (2011) also observed similar results, where the CO₂ efflux was higher in degraded sites than in non-degraded sites. This buffered response of S_R rates at the forest sites (PFC, MFC) could be explained by hydraulic lift (the passive movement of the water present in the lower soil layer), which maintains the fine root activity and the other soil microorganisms and microbial activity during prolonged dry conditions (Querejeta et al. 2007; Bauerle et al. 2008; Almagro et al. 2009).

Factors controlling the variation in S_R

The S_T and S_M are usually taken as the most important factors controlling S_R and can explain most of the variation. It has been well documented that more than 50% of the spatio-temporal variation in S_R is governed by fluctuations in the S_T and the S_M content (Lloyd and Taylor 1994; Davidson et al. 2000; Zhang et al., 2013a, b; Bao et al. 2016). In our study, the regression function considering only the S_M was positively correlated with the S_R, accounting for approximately 90% of the seasonal variability in the S_R, and appeared to be more important than the S_T (Fig. 5b). Non-significant and weak nonlinear and negative relationships were found between the S_T and S_R in the forest sites and the AF, respectively (Fig. 5a), which explained 15–30% of the variation in the S_R. This is in contrast with the well-documented strong positive exponential relationship that has been reported in previous studies, which have considered S_T as the best predictor of S_R (Fang and Moncrieff 2001; Cao et al. 2004; Peri et al. 2015; Rubio and Detto 2017). Rey et al. (2011), in a semi-arid steppe ecosystem, observed that the S_T controlled the S_R during the winter season only, and the effect disappeared at higher values, i.e., 0.5 over 20 °C. Furthermore, the S_R decreased below 12–15% of the S_M with greater impact at the degraded sites. Similarly, this study also observed the lowest S_R during the summer (May), with high S_T coinciding with low S_M in the MFC, PFC, and AF. In contrast, in the VF, the increase in the S_M along with the S_T enhanced the S_R (Fig. 4a, b). A nonlinear bell-shaped relationship between the S_R and S_T in the forest land uses (MFC, PFC) (Fig. 5a) suggests that the S_R would have increased up to an optimal temperature (~ 28 °C in our study) when the microbial activity was high, while at a still higher S_T value could have decreased the activity, hence reducing the S_R (Conant et al. 2004). However, the optimal temperature for the S_R varies depending upon the substrate availability and can reach up to 35 °C (Richardson et al. 2012; Liu et al. 2018). Therefore, in this ecosystem, the control of the S_T on the S_R would only occur for short durations, i.e., in the winter season (November–January), which suggests that the S_M was the single best predictive variable for most parts of the year. However, in the VF, high S_M and S_T values would have favored the S_MA, hence enhancing the S_R rates. This is evident from the significant positive exponential relationship between the S_T and S_R. As concluded by the previous studies among various semi-arid ecosystems, we can also emphasize that the S_M has the potential to modulate the relationship of the S_T with the S_R and hence is considered the single best predictive variable of S_R (Conant et al. 2004; Rey et al. 2005; Jia et al. 2006; Almagro et al. 2009; Jha and Mohapatra 2011; Tucker and Reed 2016). However, with the poor correlation between the S_M and S_T (Table 1), it is assumed that stimulation of S_R was controlled not only by the optimal S_M and S_T values but also by the seasonal variation in fine root growth, microbial activity, and respiration (Adachi et al. 2006; Makita et al. 2018; Wang et al. 2019). A significant positive relationship was found between the S_R and S_MA in the MFC, AF, and VF (Fig. 5c). This was evident in the results of the interactive models where the inclusion of the S_MA along with the S_T and S_M improved the variation from 73 to 85% in the AF and 87 to 91% in the VF. However, in the forest land uses, the improvement in the relationship was very small, with only 83–84% and 94–95% in MFC and PFC, respectively (Table 2).

Soil respiration in different land uses

Vegetation cover and/or types have a strong influence on the belowground processes in terrestrial ecosystems (Han et al. 2014). We observed higher S_R in the cultivated land uses, with ~ 14 to 32% higher values in the AF and ~ 44 to 56% higher values in the VF than in the PFC and MFC (Fig. 3a). An earlier study (Xue and Tang 2018) reported an increase of 29% in the S_R during the conversion of free grazing grassland into cropland in a semi-arid agropastoral ecotone in North China and suggested that soil management activities, mainly tillage and fertilizer input, decrease the level and storage of S_C and that the soil aeration enhances S_MA and SOM decomposition in cropland. In this study, the S_C and S_N varied significantly among all land use sites and decreased by 76–82% in the AF and 52–64% in the VF compared with the PFC and MFC (Fig. 3e, f). However, no correlation was observed between the S_C and the S_N with the S_R for any land use (Table 1). In contrast, there have been studies that reported decreases in S_R with SOC content during the conversion of forest to cropland (Wang et al. 2007). These studies suggested that the intensive management activities in cultivated land uses would influence the soil structure (soil aeration and soil aggregation), microbial functions, and decomposition of SOM, which controls the soil C dynamics and alters the S_R processes (Smith et al. 2008; Kravchenko et al. 2011; Fan et al. 2015). The significantly higher S_MA in forest land uses (PFC, MFC) compared to that in cultivated sites (AF, VF) align with the results of other studies in different ecosystems, such as semiarid steppe, tropical water shed, forests, plantations, and degraded lands (Acosta-Martínez et al. 2007; da Silva et al. 2012; Araujo et al. 2013; Zhao et al. 2016). The low S_C and S_N content in the cultivated sites (Fig. 3e, f) could have limited the SOM decomposition by reducing the enzyme activity and microbial biomass, which would significantly decrease the S_MA (Son et al. 2003; Allison et al., 2005). In contrast, the availability of high biomass, detritus (Nsambimana et al. 2004), and fresh OM for microbiota (Chen et al. 2005) increased the S_MA in the forest soils (Araujo et al. 2013).

Furthermore, the influence of plant photosynthesis should also be considered when explaining the spatial and temporal variation in the S_R in different land uses. The aboveground plant photosynthesis and the time required to transport the photosynthetic substrates from the roots to the leaves and then to the soil regulate the heterotrophic and autotrophic S_R (Tang et al. 2005). Zhang et al. (2018) reported that the inclusion of recently added photosynthetic substrates and S_M in S_R models explained the seasonal variation in the S_R-S_T hysteresis relationship. In this study, the four land uses experienced similar climatic conditions (air temperature and precipitation); hence, the variation in the S_R could also be explained on the basis of the differences in the vegetation types and growing seasons. Between the arable land uses, in the VF, the significant increase in the S_R throughout the year that peaked in May could be related to the increased plant biomass due to the growth of C. annum. Similarly, in the AF, the early and peak growing seasons for T. aestivum (October–April) and P. vulgare (August–October) could also have contributed to the high aboveground and belowground biomass and the increased S_R rates during the monsoon season and from February–March (Fig. 4d). On the other hand, during the summers (non-growing season), the bare soil in the AF with no root or shoot biomass had reduced S_R in May. In the forest land uses (MFC, PFC), the high herbaceous growth during the monsoon season (growing season) could have also been attributed to the enhanced S_R. This was supported by the findings suggesting that in addition to the S_M and S_T, the changes in plant biomass also influenced the spatial and temporal variation in the S_R (Nakano and Shinoda 2010; Geng et al. 2012; Han et al. 2014).

Conclusion

Our results suggest that variations in precipitation events affect the S_M levels and in turn control the S_R rates in the semi-arid ecosystems of Delhi. Increased numbers of precipitation events drastically altered the S_M levels and consequently resulted in higher S_R rates during the monsoon season in the studied ecosystem. Furthermore, sudden rainfall events after a long drought period release C from the soil and result in an ~ 20% increase in the S_R, with greater impact on the arable land uses (AF, VF). Our findings emphasize that the seasonal dynamics of the S_R in semi-arid ecosystems are mainly controlled by the S_M patterns and can alone explain ~ 90% of the variability. A strong positive linear fit between the S_M and S_R suggested that S_M was the best predictor of the SR in the semi-arid ecosystems. Our study also highlighted the relevance of the S_MA in S_R studies, as the correlation improved from 73 to 85% in the AF and 87 to 91% in the VF when the S_MA was combined with the S_M and S_T. Furthermore, it was inferred that intensive management activities in cultivated land use reduce the SOM content and vegetation cover and may alter the soil C balance in these ecosystems in the future.