Development of soil biological quality index for soils of semi-arid tropics

The Agricultural intensification, an inevitable process to feed the ever-increasing population, affects the soil quality due to management-induced changes. To measure the soil quality in terms of the soil functioning, several attempts were made to develop the soil quality index (SQI) based on a set of soil attributes. However, there is no 10 universal consensus protocol available for SQI and the role of soil biological indicators in SQI is meagre. Therefore, the objective of the present work is to develop a unitless soil biological quality index (SBQI) scaled between 0 and 10, which would be a major component of SQI in future. The long-term organic manure amended (OM), integrated nutrient management enforced (INM), synthetic fertilizer applied (IC) and unfertilized control (Control) soils from three different predominant soil types of the location (Tamil Nadu state, India) were chosen 15 for this. The soil organic carbon, microbial biomass carbon, labile carbon, protein index, dehydrogenase activity and substrate-induced respiration were used to estimate the SBQI. Five different SBQI methods viz., simple additive (SBQI-1 and SBQI-2), scoring function (SBQI-3), principal component analysis-based statistical modeling (SBQI-4) and quadrant-plot based method (SBQI-5) were developed to estimate the biological quality as unitless scale. All the five methods have same resolution to discriminate the soils and INM ≈ OM > IC > Control is the 20 relative trend being followed in all the soil types based on the SBQIs. All the five methods were further validated for their efficiency in 25 farmers’ soils of the location and proved that these methods can be effectively used to scale the biological health of the soil. Among the five SBQIs, we recommend SBQI-5, which relates the variables to each other to scale the biological health of the soil.


Introduction
Soil quality, according to Doran and Parkin (1994), is the capacity of a soil to function, within the ecosystem and land use boundaries, to sustain productivity, maintain environmental quality and promote plant and animal health. Soil quality uses several physical, chemical and biological attributes of soil either individually or in combinations to determine if the soil function under different management and agricultural practices is improving, 30 stable or degrading (Andrews et al., 2002;Bünemann et al., 2018). As the soil functions of interest and the environmental factors differ among the soil systems no universal methodology is available to measure the quality using a common set of indicators (Bouma, 2002;Rinot et al., 2019). Selected soil attributes that are used to assess the soil quality are referred as 'soil quality indicators'. Their measure in the soil as influenced by nutrient management, tillage, cropping system, and all ecosystem disturbance activities were used to assess the soil quality 35 and its sustainability Karlen et al., 2006;Masto et al., 2008;Bai et al., 2018). Alternatively, soil properties such as soil organic carbon and their fractions, soil aggregates and their stability and several microbial attributes, that are sensitive to management practices were also used to monitor the quality (Bastida et al., 2016;Duval et al., 2020;Giannitsopoulos et al., 2019;Khan et al., 2020;Li et al., 2020;Liu et al., 2019;Yang et al., 2019). Apart from these, several biochemical properties including respiration, nitrification and enzymes' activity were 40 also reported as the good sensitive indicators for the soil quality (Bastida et al., 2019;Bastida et al., 2015;Bhowmik et al., 2019;Jian et al., 2020;Mundepi et al., 2019;VeVerka et al., 2019). However, the choice of soil indicators and their contribution to soil quality vary according to several factors including climate, intended land use patterns and so on (Karlen et al., 2006;Stewart et al., 2018). Soil quality was used as a tool to evaluate the effects of soil management practices and tillage systems (Armenise et al., 2013;Jernigan et al., 2020;Williams et al., 2020), land 45 use type (Masto et al., 2008;Rahmanipour et al., 2014), cover crop (Bastida et al., 2006;Fu et al., 2004;Navas et al., 2011;Jian et al., 2020) and native ecosystems and grassland degradation (Alves de Castro Lopes et al., 2013;Li et al., 2013;Pérez-Jaramillo et al., 2019) on soil function. The term 'soil quality index' (SQI) is defined as '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 50 is the functions of more than a few soil quality indicators, which is defined as 'measurable property that influences the capacity of a soil to carry out a given function' (Acton and Padbury, 1993). The soil quality index assessment studies indicated that SQI is complex due to diversity of soil quality indicators (representing physical, chemical and biological attributes of the soil) and unease to integrate them all to establish into a single measurable scale (Garcia et al., 1994;Halvorson et al., 1996;Papendick and Parr, 1992). Several attempts were made to find a way to 55 aggregate the information obtained for each soil quality indicator into a SQI. The simple addition of soil quality indicators (Velásquez et al., 2007;Mukherjee and Lal, 2014) or scoring function of soil quality indicators (Moebius-Clune et al., 2016) are the two common approaches used to scale the soil quality index between 0 and 1 or 0 and 10. The selection of soil quality indicators should be deliberating to the soil functions of interest (Nortcliff, 2002); threshold values of such identified indicators should be based the local conditions and indicator selection should 60 be based on experts' opinion or statistical procedures or combination of both to obtain a minimum data set. However, the soil quality index should link the scientific knowledge and agricultural and land management practices in order to assess sustainability (Romig et al., 1995). Most of the SQI give more importance to the physical (soil aggregation, water retention) and chemical indicators (carbon dynamics and nutrient carrying capacity) with less importance to biological attributes (microbial biomass carbon, arthropods) (Biswas et al., 2017;Calero et al., 65 2018;Menta et al., 2018;Pulido et al., 2017;Schmidt et al., 2018). In order to emphasize the biological and biochemical attributes to soil quality, the biological quality of soil (BSQ) was first proposed by Parisi (2001) which used to measure the bioindicators of soil, especially the arthropods of soil. This approach was successfully validated with other physical and chemical indicators by several workers (Blasi et al., 2013;Menta et al., 2018;Menta et al., 2014;Rüdisser et al., 2015;Visioli et al., 2013). Pascazio et al. (2018) used microbial biomass, β glucosidase, 70 mineralizable nitrogen and urease to represent the biological indicators to measure the SQI. Similarly, Vincent et al. (2018) used bacterial and fungal density and richness with mycorrhizal colonization as bioindicators for SQI. From these works, it is evident that there is no consensus to represent the biological component of the SQI. In the present work, we have developed a unitless soil biological quality index (SBQI) using six important biological attributes of soil. This index may be a part of SQI in future to assess the soil quality for sustaining the agricultural 75 productivity.

Experimental sites and soil sampling
Long-term permanent manure trials being maintained by Tamil Nadu Agricultural University, India at three 80 different locations of Tamil Nadu state, India viz., (i) Department of Soil Science and Agricultural Chemistry, Coimbatore, (ii) Agricultural College and Research Institute, Madurai, (iii) Agricultural Research Station, Kovilpatti (designated as Coimbatore, Madurai and Kovilpatti, respectively) were selected for this investigation. The details of study area, trial details and their basic soil characteristics were described in Table 1. In all these experimental plots, organic (farm yard manure, green manure) and inorganic (nitrogenous, phosphate and potash 85 fertilizers) nutrient managements were assessed for crop response over a period of time. All the experimental plots were single non-replicated plot with 5 x 4 m size. Though difference exist in the set of treatments being adopted among the three long-term trials, we have chosen four long-term nutrient management-adopted soils being exist in all the three trials for our investigation i.e., control soil (control); inorganic fertilizers applied soil (IC); organic amendment applied soil (OM) and integrated nutrient management (both organic and inorganic) adopted soil 90 (INM). The details of each treatments are follows: Control represents the plot in which the crop (Coimbatoremaize followed by sunflower; Madurai -rice; Kovilpatti -cotton followed by bajra) was raised without any nutrient amendments. The soils with naturally added crop residues were incorporated during tillage. In IC, nitrogen (N), phosphorus (P) and potassium (K) were applied in the form of urea, super phosphate and murate of potash at recommended dosage varied among the crops (maize -135:62.5:50 kg NPK/ha; sunflower -40:20:20 kg NPK/ha; 95 rice -120:60:60 kg NPK/ha; cotton and bajra -40:20:0 kg NPK/ha). Half dose of N and full dose of P and K fertilizers were applied as basal, while remaining half of N was top-dressed during crop growth. OM plot was applied with farm yard manure alone as nutrient amendment (12.5 t/ha of farm yard manure, FYM, irrespective of crop). The well-decomposed manure was incorporated into soil during last ploughing before sowing every crop. INM refers the plot with 100% NPK as chemical fertilizers along with FYM (12.5 t/ha) (similar to IC and OM, respectively). All 100 the plots were ploughed using country-plough, added with different nutrient amendments and leveled manually. The respective crops were raised as per the standard practice (Coimbatore -irrigated, maize/sunflower; Madurai -wetland, rice; Kovilpatti -rainfed, cotton/bajra). Samples were collected from upper 15 cm of the surface soil of each plot during fallow period, when crop was not raised (January, 2018). In each plot, ten subsample soil cores were collected randomly and pooled together in 105 a composite sample, giving three biological replicates. Likewise, sampling was repeated for three times, giving a total of nine replicates from four plots in each location. The debris, plant residues and stones were removed during sampling in order to avoid any influence on soil parameters analyzed. The soil samples were packed in plastic bags, transported to the laboratory using ice cooler box and stored at 4°C. The gravimetric moisture content of the soil was measured immediately. 110

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 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 115 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 CO2 released/g soil/h (Enwall et al., 2007).

Data analysis 120
The relation between soil variables influenced by long-term nutrient management adoptions was evaluated by Pearson correlation analysis (Pearson, 1895) and simple linear regression (Freedman, 2009) using SPSS (SPSS Statistics for Windows, Version 20.0. Armonk, NY: IBM Corp). The scoring function for each assessed variables of soil was developed by SPSS 20.0. For this, the data were transformed into rank scores (rank case function of SPSS) and scoring percentile was calculated using the following formulae: 125 In order to assess the relativeness of assessed soil variables and their cumulative contribution to the variability among the treatments, principal component analysis (PCA) (Wold et al., 1987) was performed on the data using XLSTAT (Version 2010.5.05, Addinsoft, USA).

Estimating soil biological quality index (SBQI) 130
2.4.1. Simple additive methods (SBQI-1 and SBQI-2) In the simple additive method, the assessed soil parameters were given threshold values based on the available literature and previous experiences. The threshold values of each parameter were further scored as soil index scores (SIS) ( Table 2). From these score values, the soil biological quality index (SBQI), unitless scoring value scaled to 1-10, was calculated using the formula as follows (Amacher et al., 2007): 135 Where, SIS represents the score value of individual attributes; S represents the sum of maximum SIS (=24). In SBQI-2, the index computed was normalized using the maximum and minimum values the dataset (Amacher et al., 2007). The formula for this method is as follows: Where, ΣSIS refers sum of all soil index scores and SISmin and SISmax are minimum and maximum values of SIS of the dataset. S represents the sum of maximum SIS (=24)

Weighed additive method (SBQI-3)
For this, the data were transformed into rank scores (rank case function of SPSS) and scoring percentile was 145 calculated in SPSS. The scoring percentiles were summed and scaled to 10 (Moebius-Clune et al., 2016). Further, the index values were normalized using the minimum and maximum SBQI values of the dataset. The formulae for the SBQI-3 calculation are as follows: ΣSBQI represents the sum of SBQI derived from percentile scores, whereas MP represents the sum of the 150 maximum percentile score (=600).
Where, ΣSBQI refers sum values from the above formula and SBQImin and SBQImax are minimum and maximum values of SBQI of the dataset.

PCA based SBQI (SBQI-4) 155
The principal component analysis of all the six biological parameters pertaining to four soil samples of three locations was performed as described elsewhere. From the outcome of PCA, the SBQI was calculated (Andrews et al., 2002;Mandal et al., 2011;Masto et al., 2008). This SBQI used the percent contribution of individual variability to calculate the over-all soil biological quality of the soil. The formulae adopted to calculate SBQI-4 are as follows:

Quadrant-plot based SBQI (SBQI-5)
As any soil variable is not independently acting and it is a dependent of several other variables or under the influence of other variables, the relativeness of two closely-associated variables (Example SOC and MBC) is used 165 to measure the soil biological quality. This method is adopted for the variables that are well-correlated to each other. Six significantly correlated (P <0.001) variable pairs and their R 2 values, means were used for the scoring ( Table 3). The paired variables were plotted in a scatter plot using variable-1 (major contributor) in x-axis and variable-2 (secondary 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 170 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, left-handed upper quadrant scored for 2 and the left-handed lower quadrant which represents 'low' for both the variables had the value of 1. Since, the major contributor is always in 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 (SOC/MBC, SOC/SLC, SOC/SIR, MBC/SPI, MBC/DHA, MBC/SIR) were scored using this 175 method and SBQI was calculated as follows:

Validation of SBQIs in farmers' field
In order to validate the SBQI methods developed from long-term manure experiment plots and also to check the consistency in SBQI calculations and to assess the relatedness among the SBQIs, the soil samples collected 180 randomly from the farmers' field were assessed the soil biological indicators as described in previous chapter and the biological quality indices were calculated using the five methods as described earlier. The details of those soil samples were presented as Supplementary Table 1. All the five SBQIs measured for long-term nutrient management adopted soils and farmers' soil were compared through Pearson correlation as described earlier in order to understand the effectiveness and relation of each other. 185

Statistical scrutiny of soil biological attributes for developing SBQI
The histogram of measured values (x-axis) of each variable and its frequency (y-axis) with a distribution curve or bell curve showed that the data observed were normally distributed. The mean ± SD for the observed parameters viz., 7.29 ± 2.46 (SOC), 382.51 ± 199.61 (MBC), 480.30 ± 234.17 (SLC), 5.46 ± 0.84 (SPI), 11.51 ± 9.54 (DHA) and 3.20 ± 190 0.56 (SIR) were well-fit in the curve (Fig. 1). Among the six variables, the histogram of SOC and SLC were leftskewed; DHA ( Fig. 1E) was bimodal, while those others showed normal.
In correlation analysis, SOC had a significant correlation with other five biological variables, while MBC, SLC, DHA, and SIR had a significant correlation with other variables except for SPI (Table 4). Similarly, SOC as an independent variable with others as the dependent variables, the linear regression coefficient (R 2 ) showed 195 significance (Table 5). All the dependent variables (MBC, SLC, SPI, DHA, SIR) showed significant R 2 (P<0.001). However, SPI had the lowest R 2 (0.237), while the SLC had highest R 2 (0.417). Likewise, SPI had lowest but significant linear regression coefficient (0.089) with MBC, while with others had high R 2 values. SPI with other variables such as SLC, DHA, and SIR had insignificant R 2 . The scatter plot with the interpolation curve between the actual values (x-axis) and the percentile scores (y-200 axis) had a similar trend and relation for all the assessed biological attributes (Fig. 2). The mean + SD of actual value had 79 to 81 percentile ( Fig. 2A to 2F). Hence, all the six variables used in the present study fall under 'more is better' category, which implies that improving these variables will reflect the soil health. The PCA-biplot representing PC1 and PC2 of assessed variables and soil samples was presented in Fig. 3 Table 2).

SBQIs of long-term nutrient management-adopted soils 215
The SBQIs of four long-term nutritionally managed soils were computed as a 10-scale unitless index using six biological attributes (Table 6). The sample-wise SBQIs calculated were presented as spread sheet (Supplementary file XLS). The SBQI-1 calculated using the threshold values of each biological attributes were ranged between 3.43 and 7.31 for the tested soil samples. Among the four nutrient managements, OM and INM had highest SBQI values (5.93 and 6.62 for Coimbatore; 7.04 and 7.31 for Madurai; 4.49 and 5.05 for Kovilpatti respectively). The wetland 220 soil (Madurai) recorded the highest index followed by irrigated gardenland soil (Coimbatore) and least in dryland soil (Kovilpatti). The least index values (between 3.0 and 4.0) were recorded in unfertilized control and IC soils. Overall, the SBQI-1 significantly discriminated the soils based on the soil index scales used by threshold index of respective soil biological variables. SBQI-2 was derived from SBQI-1 after scaling it with minimum and maximum values. Hence, the SBQI-2 values were lower than the SBQI-1, without any change in the trends due to either 225 treatments or centres ( Table 6). The SBQI-3 was calculated based on the scoring functions (percentile) of each assessed biological variable. The calculated soil biological quality index for the four different nutrient management enforced soils collected from three different soil types (locations) showed a significant difference due to nutrient management as well as due to locations. In this method also, the highest biological index was recorded in the soils of Madurai (wetland soil) 230 followed by Coimbatore (irrigated gardenland soil) and least in Killikulam (dryland soil However, like the other two methods (SBQI-1 and SBQI-2), the resolution to discriminate the soils based on the biological properties due to long-term nutrient management is high for this method also. From the PCA, the percent contribution of each variable to the PCs (PC1 with SOC, MBC, SLC, DHA, and SIR; PC2 with SPI) was used to compute the SBQI-4. The actual values were weighed based on their percent contribution in PCA to the total cumulative variability. As depicted from other SBQI methods, in this method also, the soils 240 were attributed the same trends of SBQI values. The highest SBQI was recorded by INM (Madurai) with 6.59 followed by OM (Madurai) 6.05. Within Coimbatore centre, INM recorded the highest index of 5.22 followed by OM (5.89), IC (3.22) and control (3.24). The same trend was noticed for other centres also. In SBQI-5, the relation of two variables and their measured values were used for computing the quality index. The paired variables were plotted in a scatter plot and the mean of both the variables was used to form quadrants of the plot (Figure 4). The 245 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. Such calculated SBQI-5 values for the long-term nutrient management enforced soils were the lowest among the five different methods. The Madurai soil (wetland) recorded a score value of 4.79 to 6.79, which are relatively higher than Coimbatore (irrigated garden land soil) (2.14 to 6.43) and Kovilpatti (dryland) (1.94 to 3.95). With reference to the nutrient management effects, OM ≈ INM > IC > Control 250 was the trend followed in three different soil types.

SBQIs of farmers' soils
All the five SBQI procedures scored the biological quality of the farmers' soil with uniform trend among them ( Table 7). Irrespective of the soils, SBQI-1 had a high level of scaling (example 3.33 for sample A) followed by SBQI-2 (2.89), SBQI-5 (2.02), while SBQI-3 and SBQI-4 recorded 1.59 and 1.69, respectively. All the farmers' soils got lower 255 SBQI scores (no soil with >6.0) compared to the SBQIs of long-term OM and INM soils of permanent manure experimental soils. When the SBQI values of permanent manurial trial soils and farmers' field soils were pooled and assessed their relativeness, all the SBQI methods showed a significant positive correlation to each other (Table  8).

Discussion 260
In the present work, we have developed a unitless soil biological quality index to scale the biological properties of soil, in order to monitor the soil health. We have chosen six biological indicators viz., soil organic carbon, soil microbial biomass, soil labile carbon, soil protein index, dehydrogenase activity and substrate-induced respiration, whose role in soil functioning is well-documented. Apart, these variables are known for consistent performance as indicator, relatively quick and simple assessment and sensitive to soil disturbances. We measured 265 these six variables from four long-term nutrient management adopted soils (control, inorganic fertilizer-applied, organic manure amended and integrated nutrient management adopted). Such long-term nutrient managements are being adopted in three different soils (semi-arid Alfisol -irrigated; semi-arid sub-tropical Alfisol-wetland; arid-Vertisol -dryland). Hence, we assume that the data obtained from these soils can be normalized and the impact of nutrient management to these soil biological attributes could be used to scale the SBQI so that the index can be 270 applied to any range of soils of this region. With this background, the SBQI was computed using these six biological indicators. Based on the literature and our previous works (Balachandar et al., 2016;Balachandar et al., 2014;Chinnadurai et al., 2013;Chinnadurai et al., 2014a;Preethi et al., 2012;Tamilselvi et al., 2015), it is obvious that these biological variables were significantly altered by the nutrient management adoptions (Babin et al., 2019;van der Bom et al., 2018 In the present SBQI development, compare to SBQI-1, SBQI-2 showed relative low quality index. These simple additive methods performed well for the present soil ecosystems and discriminated the soils based on their biological attributes as impact by the nutrient management adopted. In all the three locations, INM had high scores 285 followed by OM, while IC and control had low index values. The consistent results obtained from all the three centres showed the efficiency of these two methods. Among the two, SBQI-2 would be more powerful than SBQI-1, as it normalizes the data which increased the resolution of the scoring giving weight to the localization of data. As pointed out by Mukherjee and Lal (2014), this method is relatively simple, quick and user-friendly. The SBQI-3 is based on the scoring functioning of assessed variables. It is an advanced way of calculating SQI, 290 establishing standard non-linear scoring functions, which typically have shapes for 'more is better', 'optimum range', 'less is better' and 'undesirable range'. The scores are relative to the measured values of the respective region and transformed the values between 0 to 1, where 0 being poorest and score of 1 the best ( , mean + 1 SD was used to score the variables and all the six variables had 78-81% scoring functions, suggest that more than 70% of the samples fall within this range. Hence, these biological attributes could be the significant contributors to the SBQI. If the values are less than 40%, the reliability of using the variable is questionable. In 300 addition, to obtain the cumulative single index value, the scoring function percentiles of each variable were added, summed and normalized to scale between 1 to 10. The major assumption made in this method is that summing the scoring values (percentiles) of each variable rather than actual values or their soil index scales (as in case of SBQI-1 and SBQI-2) can provide more accurate score values among the samples tested. The scoring functions and the plots are in accordance with the Cornell Soil Health Assessment (Moebius-Clune et al., 2016). The SBQI scored 305 based on this method also had high discriminative power on the samples obtained from permanent manure experiments of three different soils. Among the three locations, dryland soil had the lowest SBQI in this method, while the wetland soil had the highest values. In all the three soil types, INM>OM>IC>control is the trend followed for SBQI-3 values. The PCA-based calculation is the most popular method among the researchers worldwide, across the soil 310 types and land use managements to score the SQI (Bünemann et al., 2018). This method integrated the measured variables into PCs and used for scale them to SQI. In the present investigation, we have adopted the same method with slight modification. From the PCA factor loading, each variable's contribution to the corresponding PC was used to weigh the actual measured values and these weighed values were further summed and scaled to 1-10. Unlike previous investigators (Biswas et al., 2017;Mukherjee and Lal, 2014;Schmidt et al., 2018), we have not picked 315 the single variable for each PC, rather all the factor loadings of six biological attributes were used to scale the SBQI. This method also significantly discriminated the soils that are under the influence of long-term nutrient management adoptions under three different soil types. Compare to all the above methods, this method is a more statistical approach and gives more stress to discriminate the samples than other methods. This method was also successfully used to measure the SQI and can able to predict the yield of a particular system (Mukherjee and Lal,320 2014) and relating the soil functioning (Vasu et al., 2016). The fifth method adopted to measure the SBQI from the available data is unique and uses the relatedness of two potential variables. The possible combinations of the variable pairs used are SOC/MBC, SOC/SLC, SOC/SIR, MBC/SPI, MBC/DHA, and MBC/SIR assuming that SOC and MBC are the major driving forces of the soil biology, while the other four variables are relating to them to the functioning. The scatter plots of each pair of variables 325 were divided into four quadrants using the mean of each corresponding variable. The assumption made here is that any sample having more than local-average is considered as 'high' and less than that is 'low'. Thus, relatedness of the two variables can divide the scatter plot into four quadrants, as 'high/high', 'high/low', 'low/high' and 'low/low'. Based on the position of the samples in the four quadrants, score values were given ('high/high' -4, 'high/low'-3, 'low/high' -2 and 'low/low'-1) and these score values were used to compute the SBQI. This method 330 measured the soils with least SBQIs, suggest that more pressure has been made to show the variability. This method adopts the less statistical and more biological approach to score the SBQI, unlike SBQI-3 and SBQI-4, which are more statistical and less biological. Though the method is relatively complicated to compute the SBQI, more inference and better understanding of soil biological variables can be obtained. For example, high SOC/high MBC means the samples are sufficient with SOC and MBC, need to maintain them using organic amendments; high 335 SOC/low MBC means the SOC may be recalcitrant or microbial inhibitors/heavy metals/pollutants may be present; need proper reclamation; low SOC/high MBC means the soil needs continuous organic amendments to proliferate the microbial growth; low SOC/low MBC means the soil biological quality is very poor; needs remedy to improve them. Like this, quadrant-based analyses can identify the 'soil biological constraints' more sensitively than those methods. Hence, among the five models, SBQI-5 can be regarded as the best model to scale the biological health 340 of soil.
To validate the SBQIs developed during the present investigation, twenty-five farmers' field in and around Coimbatore and Nilgiris districts of Tamil Nadu state, India have assessed and SBQIs were computed by all the five models as detailed earlier. This part of investigation was performed for validation, relatedness, and consistency of SBQIs developed in this study. All the five SBQIs were in the same trend in the farmer's field. 345 Compare to experimental soils, the farmers' soils are low in SOC, MBC and all the measured attributes, hence recorded lower SBQIs. In these soils also, SBQI-1 and SBQI-2 had relatively higher values followed by SBQI-3 and SBQI-4, while least was observed in SBQI-5. Soil from Ooty (Nilgiris) had relatively high SBQI scores compared to other samples. This was mainly due to the temperate climate and high SOC of those soils. Our SBQI results are as comparable to the three methods validated by (Mukherjee and Lal (2014)). The SBQI values measured in the 350 farmers' fields identified following constraints in the soil biological functioning: Most of the farm soils are with low SBQI values (< 4.0) and are in 'low SOC/low MBC', 'low MBC/low DHA' and 'low MBC/low SPI' category. The soil biological activities responsible for nutrient transformation, organic decomposition, carbon assimilation are low in these soils. The microbes are under stress condition due to low resources available for them. The natural resources (soil nutrients) had an insignificant role to provide nutrient to the crops. Hence, continuous exogenous 355 nutrient supply is needed for the crops, failing which will impact the productivity. As the soil microbial and biochemical processes are of low magnitude, the resilience of the crops to any adverse conditions like drought, flood or high temperature is questionable. As the poor soil management continues, these soils may deter their quality which may reflect the productivity of subsequent crops.

Conclusions 360
In the present work, we have investigated four-different nutrient managements on soil biological attributes and the difference between them was used to scale a single unitless quantitative measure as SBQI. Five different models were proposed to compute the SBQI and each method discriminated the four soil samples accurately and we could not find any difference among them. However, each method has its own advantages and limitations. All the five methods gave the same results in the farmers' field and all the SBQI had a significant positive correlation 365 to each other. Among the five SBQI models tested, SBQI-5 would be an appropriate method, as it is with less statistics and more biological approach. This method also identifies the constraints of the soil biology better than the other four methods.

Data availability
The data that support the findings of this study are available by request from the corresponding author 370 (D Balachandar).

Author contributions
DB designed the experimental setup. SA and CC did the soil sampling and led the lab analysis procedure. DB also did the statistics, prepared the manuscript with valuable contributions of the two co-authors SA and CC and undertook the revisions during the review process. 375

Competing interests
The authors declare that they have no conflict of interest.
Supplementary material spreadsheet_S1: Calculated SBQIs of long-term nutrient management enforced soils.