2.2. Soil quality assessment
The method used to determine soil quality index following by four steps:
(i) setting the goal, (ii) picking minimum data set (MDS) of indicators
which depict the best soil function, (iii) scoring the MDS indicators,
and (iv) the integration of an indicator score into a relative soil
quality index (Masto et al., 2007). Cane productivity was considered as
the goal due to variations in different cane producing zones. A total of
25 soil attributes were analyzed using principal component analysis
(PCA) for selecting as representative MDS (Andrews et al., 2002). PCs
that had high eigenvalues and variables with greater factor loading were
pondered as the best delineated attributes. Hence, PCs with eigenvalues
>1 and those illustrated at least 5% variations in the
data were examined (Bregda et al., 2000). The factor loading of each
variable in a particular PC represents the contribution of that variant
to the composition of the PC. Highly weighted variable were categories
as per the absolute values existed within 10% of the highest factor
loading. Correlation coefficient was employed among the indicators for
selecting ideal indicator if any variable was redundant as more than one
variable was retained within a single PC and hence, eliminated from the
minimum data set (MDS). In terms of soil function, the indicators were
put in order depending on whether a higher value was considered as
‘good’ or ‘bad’. Indicators like Na, Pa,
Ka, Sa, Fe, Zn, ALP and BSR in the MDS
were considered “good” and were scored as ‘more is better’. However,
Fe content was pondered “good” as existed in the optimum range, hence
scored as ‘optimum is better’. A value between 0 and 1 is used as a
standard scoring function for transmission and normalized of each
indicator (Fig. S1) which were calculated by using non-linear scoring
function (NLSF): NLSF (Y) = 1/ [1+ e-b(x-A)].
where, ‘x’ is value of soil attributes, ‘A’ the baseline or value of
soil property where score equal to 0.5 and ‘b’ is slope.
Once indicator transformed based on soil functions, the MDS attributes
for each observation were weighted by applying the PCA outcome. Each PC
incites a certain amount (%) of the variation in the total data set.
This percentage divided by the total percentage of variance explained by
all PCs with eigenvectors >1, given the weighted factor for
variants selected under a given PC. Then the weighted MDS variables
scores were summed up for each observation using the following equation
as suggested by Li et al. (2013): SQI =\(\sum_{i=1}^{n}{\text{Wi}\times\text{Si}}\)
Where, ‘Wi’ is denoting weighing value of each indicators; ‘Si’ is the
indicator score; ‘n’ is the number of indicators in the MDS.
The SQ indexing methods were also measured by employing sensitivity
analysis as suggested by Masto et al. (2008): Sensitivity (𝑆) =
SQI(max)/SQI(min), where
SQI(max) and SQI(min) are the maximum
and minimum SQI observed under each cane producing zones of Uttar
Pradesh. The higher value of sensitivity is more preferable and
sensitive to perturbations and management practices.