Simplifying the decision matrix for estimating fine root production by the sequential soil coring approach

Sequential soil coring is a commonly used approach to measure seasonal root biomass and necromass, from which root production can be estimated by maximum–minimum, sum of changes, compartment-flow model, and/or decision matrix methods. Among these methods, decision matrix is the most frequently used....

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Bibliographic Details
Published inActa oecologica (Montrouge) Vol. 48; pp. 54 - 61
Main Authors Yuan, Z.Y., Chen, Han Y.H.
Format Journal Article
LanguageEnglish
Published Amsterdam Elsevier Masson SAS 01.04.2013
Elsevier
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Summary:Sequential soil coring is a commonly used approach to measure seasonal root biomass and necromass, from which root production can be estimated by maximum–minimum, sum of changes, compartment-flow model, and/or decision matrix methods. Among these methods, decision matrix is the most frequently used. However, the decision matrix, often underestimating fine root production, is frequently misused in research due to inadequate documentation of its underlying logic. In this paper, we reviewed the decision matrix method and provided mathematical logic for the development of the matrix, by which not only root production but also mortality and decomposition rates can be calculated. To ease its use for large datasets, we developed simplified equations to facilitate computation of root production, mortality and decomposition to be used in MS Excel or R. We also presented results from calculations by an example using empirical data from boreal forests to show proper calculations of root production, mortality and decomposition. The simplified decision matrix presented here shall promote its application in ecology, especially for large datasets. ► Decision matrices currently in use for fine root production estimates are complex and often confused. ► We provide the mathematical logic and a simplified matrix for calculating fine root production, mortality, and decomposition. ► We provide codes both in MS Excel and in R for application of the decision matrix approach.
Bibliography:http://dx.doi.org/10.1016/j.actao.2013.01.009
ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 23
ISSN:1146-609X
1873-6238
DOI:10.1016/j.actao.2013.01.009