Measuring and Monitoring Soil Carbon - Soil Carbon: Science, Management and Policy for Multiple Benefits - page 198

Conclusions soil/land use/climate) or grid based. Large

countries with a low sampling density (< 1 site
per 100 km 2 ) generally prefer a stratified
design so as to include all important units.
The (expected) variability within these units
should be determined to assess the optimal
number of samples for each stratum. Such
an approach will allow a statistical analysis
of trends in SOC stocks for the soil/land use/
climate units under consideration as an al-
ternative or test for process-based models.
The establishment of SMNs poses vari-
ous scientific, technical and operational
challenges. The former are being addressed
by various groups, such as the Soil Monitor-
ing Working Group established in 2010 by
the International Union of Soil Sciences
(IUSS). From an operational point of view, to
implement an integrated monitoring system
it will be crucial to overcome initialization
costs and unequal access to monitoring tech-
nologies. For the developing countries, this
will require international cooperation, cap-
acity building and technology transfer.

Based on the materials reviewed here, some
basic methodological requirements and re-
commendations can be proposed for 'good
SOC-monitoring practice' to support scien-
tific and policy decisions. These include:
(i) the provision of long-term continuity and
consistency under changing boundary condi-
tions, such as biophysical site conditions, cli-
mate change, methodologies, socio-economic
setting and policy context; (ii) adoption of
a scientifically and politically (e.g. for UN-
FCCC) appropriate spatial and temporal reso-
lution for the measurements; (iii) ensuring
continuous quality assurance at all stages of
the measurement and monitoring process;
(iv) measurement/observation and docu-
mentation of all potential drivers of SOC and
GHG change; and (v) georeferenced samples,
collated through SMNs, archived and the as-
sociated (harmonized) data made accessible
through distributed databases to enhance the
value of the collated data for multiple uses. In
addition to this, SMNs should be included in
a broader cross-method validation programme,
ultimately to permit spatially and tempor-
ally validated comparisons both within and
between countries.
The most common sampling design of
SMNs aimed at monitoring regional/national
SOC stocks is either stratified (according to

Acknowledgement

The authors gratefully acknowledge the in-
vitation to the SCOPE Rapid Assessment
Workshop held at the Joint Research Centre
(JRC-EU), Ispra, Italy ( 18-22 March 2013).

References

Allen, D.E., Pringle, M.J., Page, K.L. and Dalal, R.C. (2010) A review of sampling designs for the measure-
ment of soil organic carbon in Australian grazing lands. The Rangeland Journal 32, 227-246.
Arrouays, D., Marchant, B.P., Saby, N.P.A., Meersmans, J., Orton, T.G., Martin, M.P., Bellamy, P.H., Lark, R.M.
and Kibblewhite, M. (2012) Generic issues on broad-scale soil monitoring schemes: a review.
Pedosphere 22, 456-469.
Bartholomeus, H.M., Schaepman, M.E., Kooistra, L., Stevens, A., Hoogmoed, W.B. and Spaargaren, O.S.P.
(2008) Spectral reflectance based indices for soil organic carbon quantification. Geoderma 145, 28-36.
Batjes, N.H. (2011a) Soil organic carbon stocks under native vegetation - revised estimates for use with the
simple assessment option of the carbon benefits project system. Agriculture, Ecosystems and Envir-
onment 142, 365-373.
Batjes, N.H. (2011b) Research needs for monitoring, reporting and verifying soil carbon benefits in sustain-
able land management and GHG mitigation projects. In: De Brogniez, D., Mayaux, P. and Montanarella, L.
(eds) Monitoring, Reporting and Verification Systems for Carbon in Soils and Vegetation in African,
Caribbean and Pacific Countries . European Commission, Joint Research Center, Brussels, pp. 27-39.
Batjes, N.H., Reuter, H.I., Tempel, P., Leenaars, J.G.B., Hengl, T. and Bindraban, P.S. (2013) Development
of global soil information facilities. CODATA Data Science Journal 12, 70-74 ( https://www.jstage.jst.
go.jp/article/dsj/12/0/12_WDS- 009/_ pdf, accessed 16 July 2014).