Measuring and Monitoring Soil Carbon - Soil Carbon: Science, Management and Policy for Multiple Benefits - page 190

The complementary benefits of mapping,
monitoring and modelling are summarized
in Table 16.1 (McKenzie et al ., 2002). Map-
ping, for example, is needed to stratify land-
scapes according to climate, soil type and
land use to forecast possible GHG changes for
the main source and sink categories (i.e. forest
land, cropland, grassland, wetlands and
other lands), as considered in the IPCC guide-
lines (IPCC, 2006; Milne et al ., 2012).

analytical methods for their measurement
often differ from one SMN to another (e.g.
Morvan et al ., 2007; GOFC-GOLD, 2009;
Batjes, 2011b; Lark, 2012). The objective
of the SMN and its complexity will largely
determine which statistical methodology
should be used, as there are trade-offs be-
tween the different classes of design. A de-
tailed discussion of the different statistical
approaches needed to analyse random,
grid-based or stratified monitoring schemes,
however, is beyond the scope of this chapter
(see, for example, De Gruijter et al ., 2006;
Allen et al ., 2010).

Methodological considerations

General

Temporal and spatial scales
versus detection limits

There are three main approaches (experi-
mental field trials, chronosequence stud-
ies and monitoring networks) to determine
the relationships between environmental
and management factors and SOC dynam-
ics (van Wesemael et al ., 2011). A wide
range of studies have been published on
statistical and other methods of environ-
mental monitoring indicating that it is
often cumbersome for a practitioner to ex-
tract the relevant information from these
diverse materials. For example, the sam-
pling area (block support), number and
kind of (sub)samples, depth of sampling,
range of parameters to be measured and

Some soil properties can be monitored eas-
ily; this includes those properties that vary
least spatially, are responsive to manage-
ment intervention and are the easiest to
measure. Compared with biomass carbon,
changes in SOC associated with changes in
land use and management or climate change
must be monitored over longer periods. The
changes in SOC are small relative to the
very large stocks present in the soil, as well
as the inherent variability, which requires
sensitive measurement techniques and due
consideration for the minimum detectable

Table 16.1. Complementary benefits of mapping, monitoring and modelling. (From McKenzie et al ., 2002,
with permission from CSIRO.)

Complementary relationship Benefits

Mapping → Monitoring • Spatial framework for selecting representative sites

• System for spatial extrapolation of monitoring results
• Broad assessment of resource condition

Monitoring → Mapping • Quantifies and defines important resource variables for mapping

• Provides temporal dimension to land suitability assessment (including risk
assessments for recommended land management practices)

Modelling → Monitoring • Determines whether trends in specific land attributes can be detected

successfully with monitoring
• Identifies key components of system behaviour that can be measured in a
monitoring programme

Monitoring → Modelling • Provides validation of model results

• Provides input data for modelling

Modelling → Mapping • Allows spatial and temporal prediction of landscape processes

Mapping → Modelling • Provides input data for modelling

• Provides spatial association of input variables