Fig 1.
A conceptual model for computing soil quality indices.
[DS: Dataset; TDS: Total dataset; MDS: Minimum dataset; LS: Linear scoring; NLS: Non-linear scoring; LSM: Linear scoring method; SQIa: Soil quality index (additive method); SQIw: Soil quality index (weighted additive method); SQIn: Soil quality index (Nemoro method)].
Table 1.
Scoring equations and method used for normalization of soil indicators data.
Table 2.
Integration equations and method used for the calculation of the soil quality index.
Table 3.
Selection of minimum dataset soil indicators (MDSCorr) based on Pearson correlation between soil properties and corn productivity.
Table 4.
Soil quality threshold values for Indiana site (as reference).
Fig 2.
Determination of threshold values of soil quality indicators.
(LT: Lower threshold; CT/BT: Critical/Baseline threshold; UT: Upper threshold).
Fig 3.
Indiana Soil Quality Index (SQI) distributes across different method combinations under two scoring conditions: (a) without threshold and (b) with threshold.
Each method combination consists of a scoring function, dataset selection approach, and indexing technique. [LSM1: Linear scoring method 1; LSM2: Linear scoring method 2; LSM3: Linear scoring method 3; LSM4: Linear scoring method 4; NLSM1: Nonlinear scoring method 1; NLSM2: Nonlinear scoring method 2; TDS: Total dataset; MDSCorr: Minimum dataset selection based on correlation with crop yield; MDSPCA: Minimum dataset based on principal component analysis; SQIa: Soil quality index (additive); SQIw: Soil quality index (additive weighted); SQIn: Soil quality index (Nemoro), SQI: Soil quality index].
Table 5.
Combination effects of dataset selection, scoring function, and indexing methods on soil quality assessment of Indiana site, USA (as reference).
Fig 4.
Radar graphs created using the variation of normalized values (Indiana site as a reference) by applying four linear and two non-linear scoring methods.
[(T: Treshold value used and NT: Threshold value not used), SMB: total microbial biomass; Non-SMB: non-microbial biomass; qR: SMB: SOC; ECe: electric conductivity; TN: total nitrogen; SOC: Soil organic carbon; AC: active carbon; NPI: nitrogen pool index; CPI: carbon pool index; CL: carbon lability; Cli: carbon lability index; CMI: carbon management index; nCMI: normalized carbon management index; ρb: bulk density; MaAS: macroaggregate stability; MiAS: microaggregate stability; AS: total aggregate stability; SI: stability index; and PI: persistent index, MWD: mean weight diameter; GMD: geometric mean diameter].
Fig 5.
Linear regression among all the scoring methods and their relationships.
(LSM1: Linear scoring method 1; LSM2: Linear scoring method 2; LSM3: Linear scoring method 3; LSM4: Linear scoring method 4; NLSM1: Nonlinear scoring method 1; and NLSM2: Nonlinear scoring method 2).
Fig 6.
Correlation of minimum dataset soil quality indicators.
[(MDSCorr and MDSPCA with total datasets (TDS) of soil quality indicators]. For nonlinear scoring methods, SQI values are compressed into a narrower range, and regression statistics should be interpreted as descriptive of clustered agreement rather than indicative of predictive performance across the full SQI domain.
Table 6.
Sensitivity analysis of soil quality indices with across all the datasets, scoring and indexing methods.
Table 7.
The Nash Effective coefficient (Ef) and relative deviation coefficient (ER) for soil quality indices calclauted for Indiana, Alabama, and Hoytville and Piketon (Ohio) sites.
Fig 7.
Comparison of calculated soil quality indices using various datasets for all experimental sites.
(TDS-NT: Total dataset with no threshold values used; TDS-T: Total dataset with threshold values used; MDSCorr-NT: Minimum dataset based on correlation with crop yield without any threshold values used; MDSCorr-T: Minimum dataset based on correlation with crop yield with threshold values used; MDSPCA-T: Minimum dataset based on principal component analysis with threshold values used; and MDSPCA-NT: Minimum dataset based on principal component analysis without any threshold values used).
Fig 8.
Comparison of box plots for soil quality index (SQI) using three linear scoring (with threshold limit values), indexing methods [SQIa (additive), and SQIw (weghted additive).
[TDS: Total dataset; MDSCorr: Minimum dataset selection based on correlation with crop yield; MDSPCA: Minimum dataset selection based on principal component analysis; and CMDS: Critical minimum dataset].
Fig 9.
Comparison of soil quality indices calculated using three linear scoring methods such as total dataset (TDS), minimum dataset based on correlation with crop yield (MDSCorr) and minimum dataset based on principal component analysis (MDSPCA).
The Indexing method used was SQIa. NT: No threshold value was used for data normalization; T: Threshold value was used for data normalization.