method for extracting plant roots from soil which facilitates rapid sample processing without compromising measurement accuracy

This study evaluates a novel method for extracting roots from soil samples and applies it to estimate standing crop root mass (± confidence intervals) in an eastern Amazon rainforest. Roots were manually extracted from soil cores over a period of 40 min, which was split into 10 min time intervals. T...

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Bibliographic Details
Published in:The New phytologist 2007-01, Vol.174 (3), p.697-703
Main Authors: Metcalfe, D.B, Williams, M, Aragão, L.E.O.C, da Costa, A.C.L, de Almeida, S.S, Braga, A.P, Gonçalves, P.H.L, de Athaydes, J, Junior, Silva, Malhi, Y, Meir, P
Format: Article
Language:English
Subjects:
Agricultural soils
Agrology
Amazon tropical rainforest
Average linear density
confidence interval
Equation roots
Error rates
Forest ecosystems
Forest soils
Likelihood Functions
maximum-likelihood approach
methodological evaluation
Methods
new methods
Plant roots
Plant Roots - metabolism
Plants - metabolism
prediction
rain forests
Research Design
root sampling method
roots
Soil
Soil samples
soil sampling
South America
spatial variation
Specimen Handling - methods
standing crop root mass
temporal prediction method
Tillage
Trees
uncertainty
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Summary:This study evaluates a novel method for extracting roots from soil samples and applies it to estimate standing crop root mass (± confidence intervals) in an eastern Amazon rainforest. Roots were manually extracted from soil cores over a period of 40 min, which was split into 10 min time intervals. The pattern of cumulative extraction over time was used to predict root extraction beyond 40 min. A maximum-likelihood approach was used to calculate confidence intervals. The temporal prediction method added 21-32% to initial estimates of standing crop root mass. According to predictions, complete manual root extraction from 18 samples would have taken c. 239 h, compared with 12 h using the prediction method. Uncertainties (percentage difference between mean, and 10th and 90th percentiles) introduced by the prediction method were small (12-15%), compared with uncertainties caused by spatial variation in root mass (72-191%, for nine samples per plot surveyed). This method provides a way of increasing the number of root samples processed per unit time, without compromising measurement accuracy.
ISSN:0028-646X
1469-8137
DOI:10.1111/j.1469-8137.2007.02032.x