Sampling and processing roots from rocky forest soils

Abstract Quantifying root biomass in rocky forest soils is challenging. This report provides practical advice for field sampling and laboratory processing of root biomass in these settings. Manual coring is the most efficient method for sampling fine root biomass in the upper soil profile (we sample...

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Bibliographic Details
Main Authors: T. J. Fahey, R. D. Yanai, K. E. Gonzales, J. A. Lombardi
Format: Article
Language:English
Published: Wiley 2017-06-01
Series:Ecosphere
Subjects:
Online Access:https://doi.org/10.1002/ecs2.1863
Description
Summary:Abstract Quantifying root biomass in rocky forest soils is challenging. This report provides practical advice for field sampling and laboratory processing of root biomass in these settings. Manual coring is the most efficient method for sampling fine root biomass in the upper soil profile (we sampled to 30 cm). However, careful correction for coarse fragment volume is needed because manual coring is impeded by rocks. Unbiased estimation of root biomass below obstructions requires either excavating a pit or power coring. We recommend power coring because of the very high field labor costs of pit excavation. Roots can be separated from soil either by dry picking or by wet sieving. For surface organic matter‐rich horizons typical of many forest soils, only dry picking is feasible. A timed interval approach can greatly reduce laboratory processing time. Because sorting live from dead roots is necessarily subjective, efforts to avoid fragmentation of root systems obtained from cores are strongly recommended. Sample size requirements for detecting changes or differences in root biomass at the stand level are presented based on extensive sampling in northern hardwood forests. Detecting 20% differences in fine root (<1 mm) biomass in 0–30 cm soil using ten 5‐cm manual cores generally would require about nine sample plots in a stand, whereas detecting such differences in deep soil (30–50 cm) would be virtually impossible because of extreme spatial variation. Power analyses such as these can help improve experimental designs, as the spatial intensity of sampling determines the detectable difference, which can in turn guide decisions about the temporal frequency of sampling.
ISSN:2150-8925