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Approaches for quantitative estimation of soil quality

The quantitative estimation of soil quality in terms of an index value is highly significant due to the fact that it provides a holistic idea about the soil health, production potential and environmental sustainability. The different approaches for quantitative estimation of soil quality (Doran and Parkin 1994) are as follows:

  • Comparative assessment: In this process, the quality of a soil is assessed in comparison to a reference soil with similar genesis and mineralogy but different management practices.
  • Dynamic assessment: It deals with the changes in soil physical, chemical and biological properties with time. The soil properties are analyzed before and after imposition of certain treatments and the temporal variations are measured.
  • Regression analysis: In this statistical approach, some of the sensitive indicators of soil (soil organic carbon, aggregate stability, enzymatic activity, etc.) are correlated with certain management practices (fertilizer management, water availability) and the data are statistically processed to infer the inter-dependence of these variables.
  • Pedotransfer functions: This method is used to assess the efficacy of soil indicators through meta-statistical approach. In this technique, a parameter of interest (indicator) is quantified from known parameters using linear and non-linear functions with empirical values.
  • Standardize scoring functions based on threshold limits and base line values: In this process, the different indicators of soil are weighed based on their sensitivity to soil quality under specific management goals. Later, they are summed up depending on their threshold values and in reference to the baseline. The index thus obtained gives an idea about soil quality. Unlike the above mentioned processes, this takes into account all the sensitive indicators based on then importance for the determination of soil quality.
  • Principal component analysis (PCA): In recent times, it is the most widely used technique to assess the soil quality. In this process, a minimum data set is prepared with different related indicators and they are interpreted with certain scores depending on their role in soil quality maintenance. Later, the scores are integrated to arrive at an index value (Figure 1) which represents the soil quality (Andrews et al. 2004).
Flowchart depicting assessment of soil quality index

Figure 1. Flowchart depicting assessment of soil quality index.

Sensitive indicators (physical, chemical and biological) of soil quality specific to objectives of experiments over the years

Soil quality indicators are primarily classified into three broad categories: physical, chemical and biological. The physical indicators deal with the orientation of pores and soil particles, thereby governing root growth, germination of seeds and rate of plant growth. The indicators include soil depth, bvtlk density, porosity (macro and micro), soil strength, aggregate stability, texture, structure and compaction.

Chemical indicators include soil reaction, salinity and sodicity, organic matter content, nutrient availability, cation exchange capacity, nutrient cycling rate, and the concentration of contaminants such as heavy metals, organic compounds, radioactive substances, etc. These indicators are helpfiil in assessing the continuum of toxic substances in soil-water-plant-animal system. Physical and chemical indicators are more static in nature as compared to the biological indicators which are more dynamic.

Biological indicators include measurements of soil macro-organisms, e.g., earthworm, nematodes, termites, ants and microorganisms, e.g., microbial biomass, fungi, actinomycetes, or lichens for their role in soil development and conservation, nutrient cycling and specific soil fertility. Biological indicators also comprise of metabolic processes as well as metabolites such as metabolic quotient (qC02; ratio of respiration to microbial biomass), glomalin and ergosterol concentrations, enzymes, e.g., cellulases, aiylsulfatase, phosphatases, etc. related to specific functions of substrate degradation or mineralization of organic N, S or P. These indicators are regarded as the biological fingerprints of past soil. Several authors have tried to point out indicators sensitive to land use changes and management practices. The indicators depend upon the climatic conditions as well as inherent soil properties, types of crop grown as well as the management practices (Bastida et al. 2008).

Table 2. Objective specific sensitive indicators of soil quality used by authors over the years.


Indicators used


Evaluation of soil quality' under climax vegetation

MBC, PMN, phosphatase, b-glucosidase and urease activities

Trasar-Cepeda et al. 1998

Soil quality evaluation under forest management

BD, water table depth, N mineralization, aeration, microbial activity'

Burguer and Kelting 1999

Integrated pollution index: soil quality' of heavy metal contaminated sites

Content of heavy metals (Cu, Ni, Pb and Zn)

Chen et al. 2006

Microbiological Degradation Index: soil quality assessment of sennand degraded lands

Dehydrogenase, urease activities, soil respiration, water soluble C

Bastida et al. 2008

Relative soil stability' index: effects of herbicide on soil functional stability

Arylsulphatase, b-glucosidase, urease

Becaert et al. 2006

Integrated Fertility Index: evaluation of soil fertility’

Organic matter, total N, P and K, available P and K, alkali-hydrolysable N, electrical conductivity

Pang et al. 2006

Biological Quality' Index: variation in relation to the ecosystem degradation

Soil respiration, carboxymethyl cellulase, |3-D-glucosidase and dehydrogenase activity

Annas et al. 2007

Soil Quality' Index: Evaluation of recuperation of hydrocarbon contaminated soils by nutrient applications, surfactants or soil agitation

Microbial biomass C, respiration, dehydrogenase activity' and earthworms

Dawson et al. 2007

Evaluate environmental soil quality' of forest soils under natural vegetation without human intervention

P-glucosidase, urease and phosphatase activities, microbial biomass C

Zomoza et al. 2007

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