Soil is a dynamic, living, natural body and a key factor in the sustainability
of terrestrial ecosystems. Soil quality has a significant influence on the health
and productivity of an ecosystem and the related environment (Larson and Pierce,
1991). However, soil quality varies in time and space and the variability of
soil properties is the rule rather than the exception. This variation influences
soil functions, such as water and nutrient movement and their redistribution
and supply to plant roots and sustenance, maintenance of suitable biotic habitat,
responses to management treatments and resistance to degradation (Larson and
Pierce, 1991). Also, the goal of sustainable agriculture is to maintain a non-negative
and preferably an increasing trend in the per-capita productivity while maintaining
or enhancing soil quality (Lal, 1994). High soil quality is associated with
the efficient use of water, nutrients and pesticides, improvements in water
and air quality, mitigation of greenhouse gas emission, and increase in agronomic
production (Lal et al., 1998). Despite its importance, soil quality cannot
be measured directly but is inferred from static or dynamic Soil Quality Indicators
(SQIs). Soil variability is a problem, but it can be inferred from dynamic SQIs.
But soil variability can also be helpful in minimizing crop-risk failure through
design and implementation of site-specific management. Knowledge of the variability
of the chemical or physical properties of soil is essential in selecting as
well as effectively applying management decisions in the field. This variability
in soil properties is associated with spatial, temporal or management-related
factors and its impact on soil productivity. Thus, there is a need to develop
criteria and methods for quantitative assessment of SQIs. These criteria can
be based on the critical limits of key soil properties in relation to threshold
values, beyond which productivity decline is severe or impact on the environment
is drastic. It is important to establish critical levels of SQIs, assign a weighting
factor and relate them to productivity. Some information on critical limits
of key soil properties is available in literature (Lal, 1994; and Aune and Lal,
1997). The space and time variation of SQIs are controlled by numerous physical,
chemical and biological factors. Several minimum data sets were proposed to
quantitatively assess the sustainability of a soil management practice (Doran
and Parkin, 1994; and Larson and Pierce, 1994). Linear regression and multivariate
analysis were carried out to evaluate the soil quality (Li and Lindstorm, 2001).
Also, pedotransfer functions (Bouma, 1989; and Salchow et al., 1996)
and Principal Component Analysis (PCA) have been used to develop SQIs (Shukla et al., 2004a and 2004b). The PCA is a dimensionless reduction technique
that takes correlated attributes or variables and identifies orthogonal linear
recombinations (PCs) of the attributes that summarize the principal sources
of the variability in the data. It is established in soil science and has been
used more recently to summarize large data sets gathered in soil quality research
(Maddonni et al., 1999; and Wander and Bolloero).
Natural as well as anthropogenic factors are involved in the erosion process
by which anthropogenic activities by human and livestock, that include
deforestation, overgrazing and burning, etc., are taking place in submontane the N-E tract of
Punjab, India. The increase in human and cattle population and decrease in the size of
land holdings have resulted in indiscriminate felling of trees, removal of bushes,
grazing and browsing and trampling activities in N-E tract of Punjab, India (Thapa,
2003). Such interventions affect the physical and chemical properties of the soil and
its productivity. Therefore, site specific investigations are needed to identify a few
key variables, or a combination of variables, for designing strategies for suitable
soil management. Keeping these points in view, the objectives of the study were: (i)
to assess soil productivity in relation to physical and chemical parameters and (ii)
to group measured soil data into a few principal components to explain the
variability in the physical and chemical parameters of the soil . |
