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Principles and Standards for Measuring Primary Production$
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Timothy J. Fahey and Alan K. Knapp

Print publication date: 2007

Print ISBN-13: 9780195168662

Published to Oxford Scholarship Online: September 2007

DOI: 10.1093/acprof:oso/9780195168662.001.0001

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PRINTED FROM OXFORD SCHOLARSHIP ONLINE (www.oxfordscholarship.com). (c) Copyright Oxford University Press, 2019. All Rights Reserved. An individual user may print out a PDF of a single chapter of a monograph in OSO for personal use. date: 21 November 2019

Quantifying Uncertainty in Net Primary Production Measurements

Quantifying Uncertainty in Net Primary Production Measurements

Chapter:
(p.238) 12 Quantifying Uncertainty in Net Primary Production Measurements
Source:
Principles and Standards for Measuring Primary Production
Author(s):

Mark E. Harmon

Donald L. Phillips

John J. Battles

Andrew Rassweiler

Robert O. Hall Jr.

William K. Lauenroth

Publisher:
Oxford University Press
DOI:10.1093/acprof:oso/9780195168662.003.0012

Because primary production usually is estimated from several variables that are themselves subject to error in measurement, these errors propagate as the variables are combined mathematically. Following a brief overview of the various sources of error and bias associated with primary production measurements, this chapter provides a detailed description of approaches for quantifying propagation of error in productivity calculations. Monte Carlo simulation approaches are described and the problem of compounding of errors are examined. Several explicit examples are provided to illustrate uncertainty quantification in a variety of biomes.

Keywords:   Monte Carlo simulation, error propagation, compounding error, measurement error, bias

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