Correlation between carbon dioxide evolution and biological quality index of long-term nutrient management adopted soils

Soil respiration in terms of carbon dioxide (CO2) evolution is a potential biological indicator to assess the soil quality as influenced by different agricultural practices. However, the relation between the CO2 evolution and the biological attributes of soil is a multitude and hence less resolved. The purpose of the study is to relate the measured CO2 values with the biological quality indicators of the soil. Four long-term nutrient management treatments (organic manure amended, OM, integrated nutrient management enforced, INM, synthetic fertilizer applied, IC, and unfertilized control, Control) from 100-years old permanent manurial experiment was used for this study. The soils were analyzed for soil organic carbon, microbial biomass carbon, labile carbon, protein index, dehydrogenase activity, and substrate-induced respiration and calculated the soil biological quality index (SBQI) by quadrantplot method. All the soils were measured for the flush of CO2 immediately after wetting the dry soil by three methods viz., alkali trap method, infra-red probe-based flux apparatus, and multi-gas analyzer. The results revealed that INM had a positive influence to improve all the six biological attributes followed by OM, while the IC and Control had at par levels of all the assessed variables. The SBQI also has high resolution to discriminate against the soils and INM > OM > IC ≈ Control is the order being observed. The CO2 evolved from these four soils had a strong positive correlation with all the assessed biological health indicators. The regression analysis revealed that SBQI had a significant positive relation with alkali trap CO2 (R 2 0.82), flux CO2 (R 2 0.75), and multi-gas analyzer CO2 (R 2 0.79). The present results suggest that analyzing CO2 evolution could be a simple method to relate the overall biological quality of the soil. Hence, developing a simple tool to measure the soil CO2 evolution could help the farmers to know the biological quality of the soil .


INTRODUCTION
Soil quality or health is the ability of a soil to function within the ecosystem to sustain productivity, maintain environmental quality, and promote plant and animal health (Doran and Parkin, 1994). Monitoring soil quality is an important component of any land management system that sustains soil resources (Morrow et al., 2016). Soil quality explores the physicochemical, biochemical, and biological attributes of the soil to determine whether the soil function is enhancing, maintaining, or deteriorating due to different land use patterns and agricultural practices . A common universal method to determine the soil quality is unavailable, as the soil system functioning is controlled by the soil types, agro-ecosystems, and soil management practices (Rinot et al., 2019). Hence, soil quality indicators, a minimum set of soil attributes, are used to assess the soil quality. The criteria for a good indicator should be vital for the soil functioning, informative, sensitive to management practices, well-defined and standardized procedure with simple and cost-effective and easy to interpret with well-defined ranges and trends (Apfelbaum et al., 2019). Total organic carbon, bioavailable nitrogen, soil bulk density, macro-aggregates stability, microbial biomass carbon, labile or particulate or soluble carbon are some of the potential quality indicators used to monitor the soil health as influenced by nutrient management, tillage, cropping pattern and other activities (Andrews et al., 2004;Karlen et al., 2006;Masto et al., 2008;Bai et al., 2018).
Soil quality index (SQI) is the minimum set of parameters that, when interrelated, provides numerical data on the capacity of soil to carry out one or more functions (Acton and Padbury, 1993). SQI is complex due to diverse soil quality indicators and their difference in range and response to the managements and difficult to integrate them as a single measurable unit (Garcia et al., 1994). Yet, several attempts were made to develop SBI using a different set of quality indicators for a specific soil type. Simple addition or scoring function of soil quality indicators are two approaches widely followed for SQI calculations (Mukherjee and Lal, 2014;Moebius-Clune et al., 2016). However, SQI procedures give more weightage to the physicochemical indicators such as soil aggregation, moisture-holding capacity, carbon dynamics, nutrient carrying capability, and the biological attributes, the key players of soil quality, get less attention (Biswas et al., 2017;Pulido et al., 2017;Calero et al., 2018;Menta et al., 2018;Schmidt et al., 2018). Parisi (2001) developed the 'Soil biological quality index' (SBQI) to emphasize biological attributes to the soil quality. Pascazio et al. (2018) used microbial biomass, β glucosidase, mineralizable nitrogen, and urease to measure the SBQI. Similarly, Vincent et al. (2018) used bacterial and fungal density and richness with mycorrhizal colonization as SBQI indicators. Aravindh et al. (2020) 10 scaled SBQI using soil organic carbon, microbial biomass carbon, soil labile carbon, soil protein index, dehydrogenase, and substrateinduced respiration and assessed it for long-term nutrient management adopted soils.
Soil tests are generally used to recommend the additional nutrients required for a crop based on the plant-available nutrient status of the soil. In conventional chemical testing procedures, apart from macro-and micronutrients, soil organic carbon is quantified as a representative of biological property. However, the present soil testing methods focus the soil's capacity to supply nutrients to a crop and they were not assigned to measure the biological processes to monitor the soil quality. Any soil assessment that can improve our knowledge to predict crop growth and soil health for agricultural sustainability would be welcome progress (Bavougian et al., 2019). Hence, the concept of soil quality rather than soil fertility is much interest in recent years. Cornell University, USA developed comprehensive soil health assessment (Moebius-Clune et al., 2016) and Soil quality institute, USDA developed Soil Assessment and Management Framework (Wienhold et al., 2009) integrating physical, chemical and biological attributes of the soil. These two approaches are robust with many detailed procedures and need well-equipped soil testing laboratories for assessment. Alternatively, simple and direct monitoring soil quality has recently generated much interest among the farmers to measure and monitor their efforts to improve the soil properties that associate with soil health. Solvita ® and Haney soil health test ® are such kits developed for the USA farmers to assess by themselves. Solvita test ® quantifies the amount of carbon dioxide (CO 2 ) that evolved over 24-h after rewetting the soil samples and correlates with microbial activity, mineralizable nitrogen, and phosphorus (Haney et al., 2008). 2008). In Haney test ® , organic acids were used as extractants to accurately measure the plant available nutrients (Haney et al., 2010). However, no attempts were made in India to develop a farmers' friendly tool to monitor the soil's biological properties to sustain the soil quality. The recent works strongly correlate the CO 2 evolution following rewetting of dried soil (referred to as flush of CO 2 ) with mineralizable nitrogen and phosphorus of soil. Several studies used the flush of CO 2 to characterize soil biology as influenced by tillage, crop management, crop sequence (Morrow et al., 2016;Franzluebbers, 2018;Franzluebbers and Haney, 2018;Franzluebbers, 2020;Franzluebbers and Veum, 2020). In the present work, we assume that the CO 2 evolved from the soil samples may have a positive and strong correlation with soil biological attributes and by measuring the CO 2 , we can presumably derive the biological quality of the soil. For this, we measured the biological attributes of the soils from 100-years old permanent manurial experiment, calculated the soil biological quality index, and related with the flush of CO 2 .

Experimental site and soil sampling
The long-term permanent manurial trial being maintained by the Department of Soil Science and Agricultural Chemistry, Tamil Nadu Agricultural University, Coimbatore, India was selected for this investigation. The details of the study area, trial details, and their basic soil characteristics were described in Table 1. We have selected four continuously treated long-term nutrient management-adopted soils for our investigation viz., absolute control soil (control); inorganic fertilizers applied to soil (IC); organic amendment applied to soil (OM) and integrated nutrient management (both organic and inorganic) adopted soil (INM). The details of each treatment are as follows: Control represents the plot in which the crop (maize followed by sunflower) was raised without any nutrient amendments. The soils with leftover crop residues were incorporated during tillage. In IC, nitrogen (N), phosphorus (P) and potassium (K) were applied in the form of urea, superphosphate, and more of potash at recommended dosage specific to the crops (maize -250:75:75 kg NPK/ha; sunflower -60:90:60 kg NPK/ha. OM plot was applied with farmyard manure alone as a nutrient amendment (12.5 t/ha of farmyard manure, FYM, irrespective of the crop). The well-decomposed manure was incorporated into the soil during the last plowing before sowing every crop. INM refers to the plot with 100% NPK as chemical fertilizers along with FYM (12.5 t/ha) (similar to IC and OM, respectively). All the plots were plowed using country-plough, added with different nutrient amendments, and leveled manually and the crops were raised as per the standard practice.
To avoid the crop influences on the assessed soil variables, soil samples were collected from the upper 15 cm of the surface soil of each plot during the fallow period, when the crop was not raised (July 2019). In each plot, ten sub-sample soil cores were collected randomly and pooled together in a composite sample and likewise, four replicates per soil were taken. The debris, plant residues, and stones were removed during sampling to avoid any influence on soil parameters analyzed. The soil samples were packed in plastic bags, transported to the laboratory using an ice cooler box, and stored at -20°C. The gravimetric moisture content of the soil was measured immediately.

Soil biological properties
Soil organic carbon (SOC) was analyzed by wet chromic acid digestion method (Walkley and Black, 1934) and expressed as mg per g of soil. The microbial biomass carbon (MBC) was measured by the fumigation-incubation technique (Jenkinson and Powlson, 1976) and expressed as µg per g of soil. Soil labile carbon (SLC) was measured by the permanganate method (Blair and Crocker, 2000) and expressed as µg per g of soil. Soil protein was extracted from soil using a protocol as described by Hurisso et al. (2018) and expressed as µg per g of soil. The dehydrogenase (DHA) was measured by the procedure described by (Casida Jr et al., 1964) and expressed as µg of triphenyl formazan released per g soil per day. The substrate-induced respiration (SIR) was measured the rate of respiration in the soil after glucose was amended in it and expressed as µg of CO 2 released/g soil/h (Enwall et al., 2007).

Soil biological quality index
A unitless soil biological quality index (SBQI) was calculated using the data of soil biological attributes by the quadrant-plot method developed by Aravindh et al. (2020). In brief, six significantly correlated (p<0.001) variable pairs (SOC/MBC, SOC/SLC, SOC/ SIR, MBC/SPI, MBC/DHA, MBC/SIR) and their regression coefficient (R 2 values), and mean values were used for the scoring. The paired variables were plotted in a scatter plot using variable-1 (major contributor) in the x-axis and variable-2 (second contributor) in the y-axis. The scatter plot was converted into four quadrants by scaling the mean values of the corresponding variables in their axes. The right-handed upper quadrant representing 'high' for both variables are scaled to 4, as both the variables above the means. The right-handed lower quadrant representing 'high for variable-a and low for variable-b' is scaled to 3. Likewise, the lefthanded upper quadrant scored for 2 and the lefthanded lower quadrant which represents 'low' for both the variables had the value of 1. Since, the major contributor is always in the x-axis, high for variable-a and low for variable-b had the score value of 3 and its opposite had 2. All the six-pairs were scored using this method and SBQI was calculated as follows:

Soil carbon dioxide evolution
All the four soil samples were analyzed for carbon-di-oxide evolution by three different methods under laboratory condition.
Alkali trap method: For this, 100 g of soil amended with 15-ml of 1% of glucose and 35 ml of deionized water was placed in an air-tight bottle with a vial of 10 ml of 0.1N NaOH. The bottles were incubated at 30°C for 12 h. The unreacted alkali in the trap was quantified by back-titration with 0.1N HCl to determine the amount of CO 2 evolved from the soil (Franzluebbers and Veum, 2020).
CO 2 -flux apparatus method: The same experimental set up was detected for CO 2 levels using the CO 2 -flux apparatus (Lutron GC-2028 digital CO 2 meter with non-dispersive Infra-red sensor) and expressed as mg of CO 2 evolved per 100 g of soil after 12 h of incubation.
Multi-gas analyzer: The same experimental set up was fed to the probe-based multi-gas analyzer (Geotech) and recorded the amount of CO 2 (ppm) evolved after 12 h of incubation.

Statistical analysis
All the data were subjected to analysis of variance and Tukey's test at a 5% significance level using XLSTAT (version 2010.5.05) adding with Microsoft Excel for Windows 2007 to reveal the significant statistical difference among the treatments. The relation between soil variables influenced by longterm nutrient management adoptions was evaluated by Pearson correlation analysis (Pearson, 1895). Simple linear regression was calculated between three different CO 2 evolution methods and the SBQI,

Soil biological properties
Soil organic carbon (SOC) was analyzed by wet chromic acid dige and expressed as mg per g of soil. The microbial biomass carbon incubation technique (Jenkinson and Powlson, 1976) and expres (SLC) was measured by the permanganate method (Blair and Cro soil. Soil protein was extracted from soil using a protocol as expressed as µg per g of soil. The dehydrogenase (DHA) was m (Casida Jr et al., 1964) and expressed as µg of triphenyl form substrate-induced respiration (SIR) was measured the rate of r amended in it and expressed as µg of CO2 released/g soil/h (Enwa Soil biological quality index A unitless soil biological quality index (SBQI) was calculated usin the quadrant-plot method developed by Aravindh et al. (202 (p<0.001) variable pairs (SOC/MBC, SOC/SLC, SOC/SIR, MB regression coefficient (R 2 values), and mean values were used fo plotted in a scatter plot using variable-1 (major contributor) contributor) in the y-axis. The scatter plot was converted into four the corresponding variables in their axes. The right-handed upp variables are scaled to 4, as both the variables above the m representing 'high for variable-a and low for variable-b' is scale quadrant scored for 2 and the left-handed lower quadrant which r the value of 1. Since, the major contributor is always in the x-axis had the score value of 3 and its opposite had 2. All the six-pairs was calculated as follows: Soil carbon dioxide evolution All the four soil samples were analyzed for carbon-di-oxide evo laboratory condition.
Alkali trap method: For this, 100 g of soil amended with 15-ml o water was placed in an air-tight bottle with a vial of 10 ml of 0. 30°C for 12 h. The unreacted alkali in the trap was quantified by the amount of CO2 evolved from the soil (Franzluebbers and Veum CO2-flux apparatus method: The same experimental set up was d apparatus (Lutron GC-2028 digital CO2 meter with non-dispersiv of CO2 evolved per 100 g of soil after 12 h of incubation. Multi-gas analyzer: The same experimental set up was fed to the and recorded the amount of CO2 (ppm) evolved after 12 h of incub

Statistical analysis
All the data were subjected to analysis of variance and Tukey's tes (version 2010.5.05) adding with Microsoft Excel for Windows 2 difference among the treatments. The relation between soil va management adoptions was evaluated by Pearson correlation regression was calculated between three different CO2 evolution SBQI = (Paired variable score x regression coefficient) representing all the six biological attributes of soil (Freedman, 2009). Principal component analysis (PCA) was performed for the observed variables of soil to reveal the similarities and differences between samples and to evaluate the relationship between soil samples and observed variables (Wold et al., 1987).

RESULTS AND DISCUSSION
In the present work, we have developed a correlation between the amounts of CO 2 evolved after rewetting with a readily available carbon source (glucose) and the biological attributes of the soil. This will help to provide quick and early information about soil health. First, we have used soil organic carbon, soil microbial biomass, soil labile carbon, soil protein index, dehydrogenase activity, and substrate-induced respiration to scale the SBQI.

Soil biological attributes and Soil biological quality index as influenced by long-term nutrient managements
The long-term adoption of integrated nutrient management had significantly improved the SOC (10.5 mg/g) followed by an organic manure amendment (8.34 mg/g).
The synthetic chemical fertilizer applied and unfertilized control plots had at par and the lowest level of SOC (Table 2). However, the INM and OM had at par and the highest quantity of MBC, while IC and Control recorded the lowest levels. All the four soils had 840-860 µg/g of SLC and among the four nutrient managements, INM recorded the highest SLC followed by OM, whereas IC and Control had the lowest level of SLC. All four long-term nutrient management had a significant impact on the soil protein index of the soil. INM > OM > IC > Control is the trend observed in the SPI concentration of the soil. Likewise, the soil dehydrogenase activity and substrate-induced respiration rate also had the same level of significance in four soils (INM > OM > IC > Control) ( Table 2). These two variables resolve the treatment difference better than other parameters. The quadrant-plot based SBQI calculated from the data (SOC, MBC, SLC, SPI, DHA, SIR) using relativeness of the assessed variables (regression coefficient of paired variables) significantly discriminated against the soil samples. The six paired variables were plotted in a scatter plot and the mean of both the variables was used to form quadrants of the plot.  The samples positioned in the quadrants were scored (scaled from 1 to 4) and the score values were weighed with the regression coefficient (R 2 ) and scaled to 10. Among four soil samples, INM had  SBQI of 6.42, while   (Figure 1). INM > OM > IC ≈ Control was the trend observed in the soil samples.
These six soil attributes and their role are wellknown in soil functioning. Apart from that, these variables are known for consistent performance as indicators, relatively quick, and simple assessment and sensitive to soil disturbances. We measured these six variables from four long-term nutrient management adopted soils (control, inorganic fertilizer-applied, organic manure amended, and integrated nutrient management adopted). Based on the literature and our previous works, the continuous use of organic and inorganic nutrient amendments could significantly alter the above mentioned biological attributes and largely SOC (Preethi et al., 2012;Chinnadurai et al., 2013;Balachandar et al., 2014;Chinnadurai et al., 2014;Tamilselvi et al., 2015;Balachandar et al., 2016). All these biological attributes were reported highest in INM and OM, whereas the IC and control recorded on par values or sometimes IC was higher than control. Hence, the scale developed using these six variables should discriminate against the OM, INM, IC, and control to each other. The SBQI calculated using the quadrant plot approach could significantly discriminate against the soils based on the assessed quality indicators (Figure 1 of the present work). INM with the highest SBQI followed by OM, and least in IC and Control reported in the present experiment agrees with previous reports (Mukherjee and Lal, 2014;Biswas et al., 2017;Schmidt et al., 2018;Aravindh et al., 2020). In this method, SOC and MBC are considered as major drivers of the biological activity, while the rest of the four (SLC, SIR, DHA, SIR) are indicating their function in the soil ecosystem.

Soil CO 2 evolution and its relation with soil biological attributes
All three methods adopted to assess the soil CO 2 evolution under laboratory conditions could discriminate against the soil samples based on longterm nutrient management. The alkali-trap method recorded the soil CO 2 with a range of 8 -15 mg/100 g soil and among the four soils, and INM had the maximum CO 2 (15.4 mg) followed by OM (14.3 mg) ( Table 3). CO 2 flux-apparatus recorded 10-fold higher levels of soil CO 2 (ranged 160-450 mg) than the alkali-trap method. The net CO 2 exchange was higher owing to the fact that soil alone was used without any systems for a net CO 2 uptake and a negative or low CO 2 flux. The OM »INM > IC > Control is the tendency noted in this method as well. The multi-gas analyzer also recorded the same trend of CO 2 released from the soil (INM > OM > IC > Control).   Figure 2. The PC1 showed 86.24% variability to the total variability (93.05%), while the PC2 adds 6.82% to it. All the four nutrient management adoptions following for more than 100-years discriminated against the soil samples through the observed variables ( Figure  2A). The Control samples were positioned in a negative quadrant (PC1 -ve; PC2 -ve) of the PCA plot, while the IC was positioned in PC1 -ve and PC2 +ve quadrant of the plot. OM is positioned in the upper-right quadrant (PC1 and PC2 +ve), while the INM is positioned in the lower right quadrant (PC1 +ve and PC2 -ve). INM and OM had a positive correlation with the observed variables, which lay in the same orthogonal positions of loading plot, while IC and Control correlated negatively to the soil attributes. The contribution of variables to PC1 and PC2 variability was derived from the PCA to measure the role of each variable to the PCA-based variability. All the ten assessed variables are significant contributors to PC1 and the level of contribution of each variable are DHA -11.4%, MGA-CO 2 -10.9%, SOC -10.7%, flux CO 2 -10.6%, alkali CO 2 -10.5%, SBQI -10.2%, SPI -10.2%, MBC -9.0%, SLC -9.0% and SIR -7.4%. For PC2, SPI (46%), SLC (19.5%), flux-CO2 (10%) are the significant contributors.
In correlation analysis, all the six biological variables and calculated SBQI were positively correlated with the CO 2 evolution of soil. All the three methods (alkali-CO 2 , flux-CO 2 , MGA-CO 2 ) had a significant positive correlation (p<0.05) with SOC, MBC, SLC, SPI, DHA, SIR, and SBQI (Table 4). The Pearson correlation coefficient ranged between 0.71-0.96 was recorded between the pairs of variables. Considering SOC as a key attribute to a variably nutrient manages soil on a long time basis, a positive strong correlation was evident with DHA (0.96) and SPI (0.93). This is suggestive of a perfect positive linear relationship between these two variables. SLC and SIR are the only insignificant correlated variable pairs observed in this investigation. The linear regression coefficient (R 2 ) was calculated with soil CO 2 evolution as the dependent variable and SBQI as the independent variable showed significance (Fig.  3). All three CO 2 evolution methods had positive and significant R 2 (p<0.001) with SBQI. Among the three methods tested, alkali-CO 2 had maximum R 2 (0.82) followed by MGA-CO 2 (0.80) and flux-CO 2 (0.75). Likewise, the R 2 values among the CO 2 -evolving methods had significant correlations viz., alkali-CO 2 / flux-CO 2 -0.72; alkali-CO 2 /MGA-CO 2 -0.88 and flux-CO 2 /MGA-CO 2 -0.85 (p<0.001).
In the present work, we related the measured SBQI of the soils of long-term nutrient management with the flush of CO 2 under laboratory conditions. The soil respiration rate or flush of CO 2 could be a potential indicator that could strongly relate several biological constituents including biologically active soil C and N fractions, water-soluble C, C and N mineralization and microbial biomass carbon (Morrow et al., 2016;Sciarappa et al., 2017;Franzluebbers, 2018;Franzluebbers, 2020;Franzluebbers and Veum, 2020). The relation was strong across the soil types and different management conditions; hence, it can be developed as a simple, rapid, and reliable method for testing biological quality. To validate this concept to Indian agro-ecological conditions, in the present work, we have measured the CO 2 evolution from the soils under the influence of long-term nutrient management and assessed their relativeness to the biological attributes. Since all the four soils had distinct biological attributes as influenced by long-term nutrient management, the CO 2 evolved from these soils could easily be correlated with the biological quality of the soils (Franzluebbers and Veum, 2020). We have amended glucose uniformly in all the soil samples to accelerate the soil respiration at the earliest so that the result could be obtained quickly. All the six biological attributes from four different soils were significantly correlated with the measured CO 2 (Table 4 of the present work) which is in agreement with previous findings (Franzluebbers, 2018). We also showed that all the three methods viz., alkali trap, flux apparatus, and multi-gas analyzer gave the same resolution. The CO 2 flux analysis also behaved similarly and a positive flux was observable as substrate was added to the experimental soils without any systems for significant net CO 2 uptake. Hence, the methods of CO 2 analysis are thus validated. Further, the present results also showed that the SBQI values measured in four longterm nutrient management-enforced soils have a significant strong relation with the flush of CO 2 tested under laboratory conditions. The INM and OM which recorded the highest SBQI values with the highest CO 2 release followed by IC and Control. The present results are in agreement with the previous reports on organic and conventional nutrient management (Leskovar and Othman, 2018).

CONCLUSION
To conclude, the present results indicate that measuring the amount of CO 2 released immediately after wetting the dry soil could be a potential biological indicator that had a positive and strong relationship with most of the biological attributes of the soil. The amount of CO 2 released from the soil and the biological quality index of the soil as influenced by organic and inorganic nutrient management are with positive correlation. Hence, developing a farmer's friendly tool to measure the flush of CO 2 , which will provide basic information about the soil quality status, could be a useful approach for long-term soil health sustainability.

ACKNOWLEDGMENTS
The financial support from Indian Council on Agricultural Research, New Delhi, India through All India Network Project (AINP) on Soil Biodiversity and Biofertilizers to conduct these experiments is acknowledged. We thank Professor and Head, Department of Soil Science and Agricultural Chemistry, Tamil Nadu Agricultural University, Coimbatore for help and support to collect the soil samples from the permanent manure experimental field.

Ethics statement
No specific permits were required for the described field studies because no human or animal subjects were involved in this research.

Consent for publication
All the authors agreed to publish the content.

Competing interests
There was no conflict of interest in the publication of this content

Data availability
All the data of this manuscript are included in the MS. No separate external data source is required.
If anything is required from the MS, certainly, this will be extended by communicating with the corresponding author through corresponding official mail: dbalu@tnau.ac.in