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Verification of soil carbon sequestration: Sample requirements

Poussart, Jean-Nicolas LU ; Ardö, Jonas LU and Olsson, Lennart LU (2004) In Environmental Management 33. p.416-425
Abstract
Reliable and effective verification of soil carbon sequestration is required for quantification of project-based greenhouse gas mitigation. Direct soil sampling is necessary for measurements at field level. In this study, soil samples from a semiarid agroecosystem of the Sudan were statistically analyzed to evaluate if changes in soil organic carbon (SOC) over time or space were detectable or not, given a certain sample size. One hundred samples were taken from each of three fields. The data collected did not respect normality, and parametric methods, such as the minimum detectable difference (MDD), could not be used to relate, with confidence, the number of samples required to detect specific changes. For this reason, a method... (More)
Reliable and effective verification of soil carbon sequestration is required for quantification of project-based greenhouse gas mitigation. Direct soil sampling is necessary for measurements at field level. In this study, soil samples from a semiarid agroecosystem of the Sudan were statistically analyzed to evaluate if changes in soil organic carbon (SOC) over time or space were detectable or not, given a certain sample size. One hundred samples were taken from each of three fields. The data collected did not respect normality, and parametric methods, such as the minimum detectable difference (MDD), could not be used to relate, with confidence, the number of samples required to detect specific changes. For this reason, a method incorporating a nonparametric test into a bootstrap routine was developed to calculate the probabilities that a test will detect the differences between treatment groups for specified sample sizes. The nonparametric approach used is a simple way of relating sample sizes, detectable differences, and the probabilities of detection, making it flexible for any study affected by data nonnormality. Data from different simulated composite sampling schemes were also combined with a bootstrap routine in order to quantify the averages and 95% confidence intervals of the variability estimates (variance), necessary for parametric sample size calculations. The variance decreased almost by half each time the number of cores per samples doubled. (Less)
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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
nonparametric, difference, minimum detectable, sampling, soil organic carbon, sequestration, Sudan, composite sample, bootstrapping
in
Environmental Management
volume
33
pages
416 - 425
publisher
Springer
external identifiers
  • wos:000203094700037
  • scopus:27544456893
ISSN
0364-152X
DOI
10.1007/s00267-003-9149-7
language
English
LU publication?
yes
id
c793700e-7557-49f2-b7e5-1527a05e5f91 (old id 1406576)
date added to LUP
2009-06-04 15:49:40
date last changed
2017-01-01 06:51:28
@article{c793700e-7557-49f2-b7e5-1527a05e5f91,
  abstract     = {Reliable and effective verification of soil carbon sequestration is required for quantification of project-based greenhouse gas mitigation. Direct soil sampling is necessary for measurements at field level. In this study, soil samples from a semiarid agroecosystem of the Sudan were statistically analyzed to evaluate if changes in soil organic carbon (SOC) over time or space were detectable or not, given a certain sample size. One hundred samples were taken from each of three fields. The data collected did not respect normality, and parametric methods, such as the minimum detectable difference (MDD), could not be used to relate, with confidence, the number of samples required to detect specific changes. For this reason, a method incorporating a nonparametric test into a bootstrap routine was developed to calculate the probabilities that a test will detect the differences between treatment groups for specified sample sizes. The nonparametric approach used is a simple way of relating sample sizes, detectable differences, and the probabilities of detection, making it flexible for any study affected by data nonnormality. Data from different simulated composite sampling schemes were also combined with a bootstrap routine in order to quantify the averages and 95% confidence intervals of the variability estimates (variance), necessary for parametric sample size calculations. The variance decreased almost by half each time the number of cores per samples doubled.},
  author       = {Poussart, Jean-Nicolas and Ardö, Jonas and Olsson, Lennart},
  issn         = {0364-152X},
  keyword      = {nonparametric,difference,minimum detectable,sampling,soil organic carbon,sequestration,Sudan,composite sample,bootstrapping},
  language     = {eng},
  pages        = {416--425},
  publisher    = {Springer},
  series       = {Environmental Management},
  title        = {Verification of soil carbon sequestration: Sample requirements},
  url          = {http://dx.doi.org/10.1007/s00267-003-9149-7},
  volume       = {33},
  year         = {2004},
}