13
One aspect of soil quality indicator research that may help reassure
skeptics that the concept is science based is the role that pedotransfer
functions can have. Larson and Pierce (1994) discussed their usefulness
and application in detail during one of the first US soil quality symposia.
These tools can be very useful when data for an important indicator may
not be available but other related measurements have been collected.
Potential soil quality indicators for which pedotransfer functions have been
published include (1) phosphatesorption capacity, (2) cation exchange
capacity, (3) change in organic matter content, (4) bulk density, (5) water
retention, (6) random roughness, (7) porosity, (8) hydraulic conductivity,
(9) seal conductivity, (10) saturated hydraulic conductivity, (11) soil
productivity, and (12) rooting depth (Larson and Pierce, 1991).
Potential soil quality indicators for which pedotransfer functions have been
published include (1) phosphatesorption capacity, (2) cation exchange
capacity, (3) change in organic matter content, (4) bulk density, (5) water
retention, (6) random roughness, (7) porosity, (8) hydraulic conductivity,
(9) seal conductivity, (10) saturated hydraulic conductivity, (11) soil
productivity, and (12) rooting depth (Larson and Pierce, 1991).
14
The complexity of soils, spatial and temporal variability, and effects of
external factors such as climate were recognized as major challenges to
overcome at a conference in A˚ s, Norway (Bouma, 2000; Elmholt et al.,
2000b; Karlen and Andrews, 2000). Participants agreed that to obtain a
better understanding of soil quality, interdisciplinary studies are needed to
understand how soil properties and processes interact within ecosystems.
Unfortunately, the primary research focus for most participants was on
individual properties or processes such as denitrification, redox potential,
organic matter, earthworms, biotic and abiotic binding processes, tillage
systems, crop rotation, or management of organic wastes. Only a few
were actually participating in interdisciplinary, holistic programs.
The complexity of soils, spatial and temporal variability, and effects
of external factors such as climate were recognized as major
challenges to overcome at a conference in A˚ s, Norway (Bouma,
2000; Elmholt et al., 2000b; Karlen and Andrews, 2000).
15
Traditional soil survey, classification and interpretation activities have
defined Land Capability Classes, a Storie Index, and other Land Inventory
and Monitoring indices based primarily on inherent soil properties (Karlen
et al., 1997). Each is important and useful for certain applications, but
none are the same as indexing dynamic soil quality. The latter builds upon
the former but not vice versa. The inherent differences among soils,
complexity of environments within which soils exist, and the variety of soil
and crop management practices being used around the world currently
preclude establishing a specific rating or value against which all soils can
be compared. What can be developed is a framework or indexing
procedure that can be easily modified for different soils and used to
enumerate dynamic soil quality ratings, determine trends in those ratings,
and thus used to quantify long-term effects of alternate land uses or soil
management decisions (Karlen et al., 2001).
Traditional soil survey, classification and interpretation activities
have defined Land Capability Classes, a Storie Index, and other Land
Inventory and Monitoring indices based primarily on inherent soil
properties (Karlen et al., 1997).
16
Indexing dynamic soil quality involves three steps. The first is selecting
appropriate soil quality indicators to efficiently and effectively monitor
critical soil functions (e.g. nutrient cycling; water entry, retention, and
release; supporting plant growth and development) as determined by the
specific management goals (Fig. 2) for which an evaluation is being made.
Collectively these indicators form a minimum data set (MDS) that can be
used to determine how well critical soil functions associated with each
management goal are being performed. Each indicator is then scored,
often using ranges established by the soil’s inherent capability to set the
boundaries and shape of the scoring function. This step is required so that
biological, chemical, and physical indicator measurements with totally
different measurement units can be combined [e.g. earthworms per unit
area, pH (unitless), and bulk density (g cm 3 )]. Indicator scoring can be
accomplished in a variety of ways (e.g. linear or nonlinear, optimum, more
is better, more is worse) depending upon the function (Fig. 3). For some
management goals the same indicator may be included under different
functions and even scored in different ways (e.g. ‘‘more is better’’ for NO3
–N supporting plant growth but ‘‘less is better’’ with regard to leaching).
The unitless values are combined into an overall index of soil quality (Fig.
3) and can be used to compare effects of different practices on similar
soils or temporal trends on the same soil. Finally, to understand the
complete value of dynamic soil quality assessment, Andrews and Carroll
(2001) suggested that it be viewed as one of the components needed to
quantify agroecosystem sustainability (Fig. 4). Process and mechanistic
soil science research thus provide critical information for soil quality
assessment and in our opinion, make soil quality an important theme for
the advancement of soil science.
. The first is selecting appropriate soil quality indicators to efficiently
and effectively monitor critical soil functions as determined by the
specific management goals (Fig. 2) for which an evaluation is being
made.
Each indicator is then scored, often using ranges established by the
soil’s inherent capability to set the boundaries and shape of the
scoring functio
. Finally, to understand the complete value of dynamic soil quality
assessment, Andrews and Carroll (2001) suggested that it be viewed
as one of the components needed to quantify agroecosystem
sustainability
