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Cited by 266

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A simple, steady state description of phytoplankton growth based on absorption cross section and quantum efficiency1

Open access
Kiefer, Dale A., Mitchell, B. Greg·1983·Limnology and Oceanography

Abstract

We present a simple, quantitative formulation of steady state growth and metabolic regulation by phytoplankton, which describes well the nutrient‐limited and light‐limited growth of Thalassiosira weissflogii. The specific growth rate is a function of the quantum efficiency of carbon assimilation, the absorption cross section of cellular carbon, the incident light level, and the specific rate of respiration. The quantum efficiency is solely a function of light level, the absorption cross section is a function of light level and nutrient supply, and the respiration rate is a function of specific growth rate. The formulation, which resembles those proposed by Bannister and Shuter, is consistent with the thermodynamic model of Kiefer and the principle of optimization of energy conversion.

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Crossref · 10.4319/lo.1983.28.4.0770

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Metadata

Authors
Kiefer, D. A., & Mitchell, B. G.
Venue
Limnology and Oceanography
Publisher
Wiley
Published
1983-07
Volume
28
Issue
4
Pages
770-776
ISSN
  • 0024-3590 (ISSN-L)
  • 1939-5590
OpenAlex
W2103823505
Cited by
266
References
0
FWCI
4.82
Type
journal article
Language
en
Open access
bronze

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CoChat. (2026). Citation check: A simple, steady state description of phytoplankton growth based on absorption cross section and quantum efficiency1. https://app.cochat.ai/papers/10.4319/lo.1983.28.4.0770
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Kiefer, D. A., & Mitchell, B. G. (1983). A simple, steady state description of phytoplankton growth based on absorption cross section and quantum efficiency1. Limnology and Oceanography. https://doi.org/10.4319/lo.1983.28.4.0770

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.