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Soil Carbon Stock and Carbon Pool Indices under Major Land Use Systems of Mayiladuthurai District, Cauvery Delta Zone, India

S. Prabakaran1,*, R.K. Kaleeswari1, M.R. Backiyavathy2, R. Jagadeeswaran3, R. Gangai Selvi4, K. Sathiya Bama1
1Department of Soil Science and Agricultural Chemistry, Tamil Nadu Agricultural University, Coimbatore-641 003, Tamil Nadu, India.
2Department of Natural Resource Management, Horticultural College and Research Institute, Periyakulam-625 604, Tamil Nadu, India.
3Department of Remote Sensing and GIS, Tamil Nadu Agricultural University, Coimbatore- 641 003, Tamil Nadu, India.
4Department of Physical Sciences and IT, Agricultural Engineering College and Research Institute, Tamil Nadu Agricultural University, Coimbatore-641 003, Tamil Nadu, India.

Background-  Soil organic carbon serves as potential indicator of the soil quality which is influenced by the changes in management practices. The conversion of natural ecosystem by anthropogenic activities resulted in decline of SOC. So, this study was conducted to assess the carbon indices and understand the dynamics to develop the management strategies for enhancing the C pool in the soil.

Methods- The present study was conducted in major land use systems of Mayiladuthurai district of Cauvery delta zone in Tamil Nadu. Soil samples were collected by stratified random sampling at two different depths and estimated for soil carbon indices.

Result- The SOC, C stock, bulk density, microbial biomass carbon, dehydrogenase, carbon management index of the study area varied with the mean value of 12.51 Mg ha-1, 10.57 t ha-1, 1.26 Mgm-3, 302.52 µg kg-1, 38.21 µg TPF g-1 day-1and 126.91 % respectively. C stock were in the order of forestry > rice-pulses > rice-cotton > sugarcane > uncultivated among the different land uses of the study area. The difference in vegetation, litterfall, soil chemistry, microbial activity, substrate, root exudate, root biomass and root turnover influenced the variation in carbon pool under different land uses of the study area.

Soil serves as the repository for soil organic carbon (SOC) in the terrestrial ecosystem which stores about 2500 gigatons of C (Ontl and Schulte, 2012). SOC plays a major role in global carbon cycle. The capacity and proportion of the soil to store SOC represents the key function of the soil. SOC content in soil is controlled by biotic factors (microbial, faunal and plant species abundance), environmental factors (micro-climate, temperature, moisture) and soil inherent properties (texture, clay content, CEC). Climate and soil forming factors affect the C storage in long-term whereas land use pattern and vegetation affect the C storage in short-term (Wiesmeier et al., 2019). 
The storage of SOC in soil results from interaction of ecological processes like decomposition, soil respiration and photosynthesis (Ontl and Schulte, 2012). Intensive agriculture paves the way for accelerated decomposition of soil organic carbon which results in loss of carbon into atmosphere and contributes to the greenhouse effect. Soil carbon stocks are sensitive to the land use changes and management practices during crop cultivation (Jackson et al., 2017). Approximately 75% of SOC stock is lost from native lands of tropical ecosystem (Ghimire et al., 2017).
SOC stocks are generally controlled by C inputs, including residues, secretion and exudates of plant, animal and microbial and C outputs, such as mineralization, soil erosion and losses (Sasmito et al., 2020). Increasing the above ground biomass helps to sequester more carbon from atmosphere and further increases the carbon stocks. The protection of SOC stock will benefit the ecosystem through increase in fertility status, reduce erodibility, improved resilience and reduction in land use conversion (Bossio et al., 2020). The different type of land use and management practices affect the volume of C stock and dynamics in top soil. The lower status of soil organic carbon is a hinderance to soil quality and in turn affects the crop yield potential (Nandhini et al., 2021). Although, many researches are primely focused on the surface layer and the database on subsurface soil is minimal, which play a vital role in SOC storage, greater stability and longevity than surface soils (Gray and Bishop, 2016).
The objective of the present study was to compute the soil carbon pool indices and assess depth wise distribution of soil carbon stock under different cropping systems of Mayiladuthurai district of Cauvery Delta Zone of Tamil Nadu. The database generated by the computation of indices would expect to help in formulating strategies for better rehabilitation of carbon source in soil. A better understanding on spatial variability of soil carbon stocks helps to refine the agricultural practices and improve sustainability of the land use.
Site description
Mayiladuthurai, a coastal district of Cauvery Delta region geographically covering an area of 1173.58 km2 lies between 10°57'N to 11°26'N Latitude, 79°31'E to 79°55'E Longitude. Geologically it is formed by the alluvial deposit from both riverine and marine landform. The district is affluence with agricultural cultivation covering net area of 725.17 km2. The major crops grown are paddy, green gram, black gram, sugarcane and cotton, which covers about 97% of the agricultural area sown. The forest land use covers about 1102 ha of the total geographical area of the district.
Sampling methodology and analysis
Soil samples were collected from five major land uses viz., rice-pulses, rice-cotton, sugarcane, forestry and uncultivated/fallow during the year of 2022. About 75 composite samples were collected by means of stratified random sampling from two different depths (0-15 cm and 15-30 cm). The collected soil samples were air dried, processed and stored for chemical analyses whereas portion of fresh sample was refrigerated for microbial analysis.
Soil organic carbon was determined by oxidation of chromic acid produced by potassium dichromate and concentrated sulphuric acid. The excess chromic acid left unused was back titrated with 0.5N ferrous ammonium sulphate using diphenylamine (Walkley and Black, 1934). Microbial Biomass Carbon (Cmic) was measured by chloroform fumigation extraction (CFE) using 0.5M K2SO4 (Vance et al., 1987). Dehydrogenase activity (DHA) was estimated by incubation with 3% 2,3,5-triphenyl tetrazolium chloride (TTC) followed by methanolic extract determined spectrophotometrically at 485nm (Casida et al., 1964).
The static method of CO2 respiration using alkali trap (standard alkali) was used to study the mineralization rate of carbon by incubating the samples for 32 days at 25°C. The CO2 evolved was titrated against standard acid at periodic intervals. The cumulative amount of CO2 evolved was taken as carbon mineralization (Cmin). The CO2 collected during 31st and 32nd day was taken as basal soil respiration (BSR).

Soils in uncultivated/barren land use was taken as reference.
Mineralization Quotient (qM) is the ratio of CO2-C released during 32 days of incubation to the total organic carbon present (Cmin / TOC) and expressed as mg CO2-C mg-1 TOC. Respiration Quotient or Metabolic Quotient (qCO2) was estimated by the ratio of basal soil respiration to microbial biomass carbon (BSR / MBC) and expressed as mg CO2-C mg-1 MBC. Microbial Quotient (qmic) is the ratio of microbial biomass carbon to TOC and expressed in %.
Statistical analysis
The analysed soil parameters were summarized using descriptive statistics. The soil properties were calculated for the variables like median, mean, minimum, maximum, standard deviation, variance, coefficient of variation, skewness and kurtosis. Pearson correlation coefficient was developed for all paired combination of response variables. Duncan’s multiple range test (DMRT) was used to compare the means and significance of the mean variations between different land uses and the statistical significance was determined at P<0.05. The statistical analyses were carried out using tools like Microsoft Office 2021, R Studio 4.2.2 and SPSS Statistics 20.0
Soil bulk density serves as a significant indicator for changes in soil physical health. The soil bulk density varied significantly (P<0.05) among different land uses (Table 1). The BD was significantly higher in sugarcane based cropping system (1.30 Mg m-3) compared with other agricultural land uses. The presence of the finer particles owing to puddling resulted in lesser bulk density of rice based cropping system. The bulk density in sub soil was comparatively higher than surface soil resulting from compaction of soil, less porosity, less organic matter and weight of the overlying surface layer. Similar phenomenon was noticed by Khan et al., (2017); Soleimani et al., (2019); Lepcha and Devi (2020).

Table 1: Bulk density (BD), Soil organic carbon (SOC) and C Stock under different depths of major cropping systems of Mayiladuthurai district.

The soil organic carbon (SOC) was significantly higher in forestry land use. The mean SOC was in the following order of Uncultivated < Sugarcane < Rice-cotton < Rice-pulses < Forest land use (Table 1). The higher SOC content in forest land-use might be due to continuous addition of litter, higher microbial activity and decomposition rate. The uncultivated land use had lowest SOC due to the lack of vegetation cover and comparatively lesser microbial activity. The vegetation type influences the content of organic carbon through the influence of substrate, root exudate, litterfall, microbial activity, soil chemistry, root biomass and root turnover (Sundarapandian et al., 2015).
The SOC content was inversely related with depth. The lower root biomass, microbial activity and aeration in subsoil resulted in decrease of SOC with increasing depth. Similar trend was noticed by Francaviglia et al., (2017); Soleimani et al., (2019). SOC storage in soil depends upon the balance between C inputs and losses of C (Luo et al., 2017). SOC was highly correlated with DHA (r2 = 0.764**), CPI (r2 = 0.731**) and carbon stock (r2 = 0.973**).
The carbon stock varied significantly among different land uses. The C stock was highest in forest land use (12.15 t ha-1) and lowest in uncultivated areas (9.34 t ha-1). The C stock followed similar trend as SOC among different land uses (Fig 1). Among the agricultural land use C stock was comparatively higher in rice - pulses (11.15 t ha-1). The C stock decreased with depth as the SOC content decreased with depth. As a result of puddling, the finer texture of rice growing soils aided to store more C stocks. Moreover, application of nutrient for crop cultivation induced the microbial community which favoured SOC accumulation in soil. The difference in land management practices resulted in variation of carbon stock among the different land uses. Similar variation was noticed by Gray and Bishop (2016); Wang et al., (2020); Liu et al., (2021).

Fig 1: Carbon stock under major cropping systems of Mayiladuthurai district.

There was a significant variation in microbial biomass carbon (MBC) content between different cropping systems of the study area (Table 2). MBC followed the sequence of uncultivated (277.2 µg kg-1) < sugarcane (291.6 µg kg-1) < rice - cotton (301.3 µg kg-1) < rice-pulses (317.6 µg kg-1) < forest land use (320 µg kg-1). MBC was lower in subsoil in comparison with surface soil. The highest MBC in the forest might be due to production of continuous litter and deeper root systems allowing more microbial activities. The size of microbial biomass pool is affected by land use pattern and soil management practices. The discrepancy in MBC among agricultural systems could be due to different agricultural practices, resource availability and plant composition. The findings were in relation with Khan et al., (2017); Bolat (2019); Lepcha and Devi (2020). The MBC had a positive correlation relationship with SOC (r2 = 0.175), LI (r2 = 0.354*) and qmic (r2 = 0.695**) (Fig 2).

Fig 2: Correlation matrix between soil carbon indices under different cropping systems.

Soil dehydrogenase activity (DHA) serves as a good indicator of soil microbial activity and represents the oxidative activity of the microflora (Solanki et al., 2020). The different cropping system had significant effect on DHA activity (Table 2). DHA was significantly higher in forestry (42.1 µg TPF g-1 day-1) and lowest in uncultivated land use (34.9 µg TPF g-1 day-1).  Generally, soil enzyme activity is closely related to the organic matter content of the soil (Adak et al., 2014). Higher DHA in agricultural land use indicates pronounced biological activity and stabilization of enzymes by complexation with humic substances. DHA is influenced directly or indirectly by soil management system and decreased with depth. Similar findings were reported by Datta et al., (2015); Brkljaca et al., (2019). The dehydrogenase activity was highly correlated with the CPI (r2 = 0.727**), carbon stock (r2 = 0.719**) and CMI (r2 = 0.251).

Table 2: Microbial biomass carbon (MBC), Dehydrogenase activity (DHA) and C mineralization under major cropping systems of Mayiladuthurai district.

Carbon pool index (CPI) was calculated by taking uncultivated land as reference. CPI varied significantly between different cropping system (P<0.05). The CPI was lowest in sugarcane based cropping system (1.02) and was highest in forestry land use (1.33) (Table 3). The CPI also decreased along the depth. The lability index (LI) obtained from the ratio of labile carbon to non-labile carbon was highest in rice-pulses and was lowest forestry land use. As the proportion of labile carbon was more in cultivated land uses, the LI was significantly higher in agricultural systems. The LI decreased with increase in depth. Carbon management index (CMI) is considered as one of the most effective tools for quantitative estimation of soil quality index. The CMI derived from the lability concepts was designed to indicate the C dynamics of any ecosystem (Ghosh et al., 2016). The CMI among the different cropping systems varied significantly (P<0.05). CMI was lowest in sugarcane cropping system (119.1%) and highest in forest areas (147.4%). CMI was in the sequence of forestry > rice-pulses > rice-cotton > sugarcane. Like all other indices, CMI also decreased with depth. Similar trend was recorded by Kalambukattu et al., (2013); Tiwari and Joshi (2022). The increased CMI in forestry and agricultural system might be attributed to regular addition of organic matter supplemented with increase inputs and lower loss of C.

Table 3: Carbon pool indices (CPI), lability index (LI) and carbon management index (CMI) under different depths of major cropping systems of Mayiladuthurai district.

The higher values of CMI and LI reflects the rehabilitation of C resources whereas the lower values indicate the soils undergoing degradation and depletion of C fractions. The CMI serves as an early indicator tool for soil quality changes affected due to land management practices (Venkatesh et al., 2013; Moharana et al., 2017).
The soil carbon mineralization rate was evaluated by soil respiration experiment, which is a widely used parameter for assessing the potential of microbial activity. The soil respiration was measured by CO2 evolution using alkali trap. The cumulative amount of CO2-C evolved from the incubated soil was highest in rice-pulse based cropping system (233.03 mg kg-1) and was lowest in uncultivated landuse (204.96 mg kg-1). The amount of CO2 released by the mineralization process was rapid during initial days and evolution rate exhibited a decreasing trend with an increase in incubation period (Fig 3). This may be attributed to the decrease in soil microbial activity with time. The lower CO2 evolution corresponds to the lower biomass and less microbial activity. Similar results were observed by Hamarashid et al., (2010).

Fig 3: Cumulative C mineralization (CO2-C) in 32 days of incubation under major cropping systems.

The litter fall and addition of crop residue increase the SOC mineralization rate (Wang et al., 2014; Bolat, 2019). The respiration activity is governed by factors like temperature, moisture, microbial load, pH, nutrient availability, O2 supply, quality and quantity of crop residue incorporated. Dotaniya et al., (2017) noted the oxidation rate of the substrate depends on the physical and chemical conditions of the environment. The mineralization rate of carbon was highly correlated with MBC (r2 = 0.336*), DHA (r2 = 0.420**), CPI (r2 = 0.290*) and qM (r2 = 0.756**).
The mineralization quotient (qM) varied significantly with different land uses (Table 4). The qM was highest in forestry land use (32.6 mg CO2-C mg-1 TOC) and followed in the sequence by rice-pulses (31.57) > rice-cotton (30.9) > sugarcane (30.63). The qM was lowest in uncultivated land use (29.03 mg CO2-C mg-1 TOC). The respiratory quotient (qCO2) is the ratio of basal respiration rate to the microbial biomass helps in indicating the maturity of the soil system. There was no significant variation in qCO2 of soils under different cropping systems. The qCO2 was lowest in uncultivated land use (15.53 mg CO2-C mg-1 MBC) and comparatively higher in forest land use (17.78 mg CO2-C mg-1 MBC). The qCO2 helps to identify the efficiency of soil microbes in processing the litter and residues which assist in availability of C. Kaur et al., (2019) proclaimed that higher qCO2 in different land uses resulted from prevalence of recalcitrant carbon.

Table 4: Mineralization quotient (qM), respiratory quotient (qCO2) and microbial quotient (qmic) under major cropping systems.

The microbial quotient (qmic) is the percent contribution of MBC to the total soil carbon (MBC/TOC). It usually ranges between 1-5% in soil (Masto et al., 2006). The qmic reflects the availability of the substrate to the microflora (Suman et al., 2006). The microbial quotient significantly differed between different land uses of the study area (Table 4.). The contribution of qmic was higher in rice-pulse cropping system (4.32%) and lowest in uncultivated areas (3.93%). Kaur et al., (2019) reported the occurrence of higher qmic might be due to existence of more carbon in labile nature and lesser qmic implies the soil with nutritional stress. The qmic is impacted by microbial growth which depends on the substrate and nutrient source. The qmic in surface soil is regulated predominantly by the nature of cropping ecosystem (Sun et al., 2020). The plants root and its exudates are major source of substrate to microbial growth which vary with the cropping systems. The higher qmic indicates the higher rate of conversion of microbial biomass which purveys better C stability in any system.
The descriptive statistical parameters like mean, median, standard deviation, variance, coefficient of variation, skewness and kurtosis were calculated. The mean of BD, SOC, MBC, DHA, CMI and C stock of the study area was 1.26 Mgm-3, 12.51 Mg ha-1, 302.52 µg kg-1, 38.21µg TPF g-1 day-1, 126.91% and 10.57 t ha-1 respectively (Table 5). The coefficient of variation was comparatively higher for the parameters like SOC, CMI and C stock indicating the difference in cropping system influencing the spatial variation with in the study area.

Table 5: Descriptive statistics of soil carbon indices under major cropping systems of the study area.

The study clearly suggests that changes in cropping system had a significant influence in the soil carbon stock and soil carbon pool indices. The SOC directly affect the physical, chemical and biological properties of the soil. C stock ranged from 9.34 to 12.15 t ha-1 and the SOC exhibited low to medium rating within the study area. The higher levels of mineralization, basal respiration, dehydrogenase activity and microbial quotient reflects the favourable microbial activity in decomposition of residues into the organic matter. The higher CMI indicates the soils were undergoing rehabilitation and restoration of resources in comparison with uncultivated land uses. The positive correlation between the carbon indices emulates that changes in managements practices will affect the dynamics of C. Therefore, to sustain the carbon resource in the cultivated regions management practices like residue incorporation, reduced tillage, cover crops, enrichment with organics, etc., has to be manifested. 

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