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    Author(s): Ronni L. Korol
    Date: 2001
    Source: Gen. Tech. Rep. RMRS-GTR-73. Ogden, UT: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. 9 p.
    Publication Series: General Technical Report (GTR)
    Station: Rocky Mountain Research Station
    PDF: View PDF  (833.05 KB)

    Description

    The quantitative description and simulation of the fundamental processes that characterize forest growth are increasing in importance in forestry research. Predicting future forest growth, however, is compounded by the various combinations of temperature, humidity, precipitation, and atmospheric carbon dioxide concentration that may occur. One method of integrating new management objectives and potential climate scenarios is to model ecosystems mechanistically. General application of ecosystem process models has been difficult. In particular, obtaining initial physiological parameters from current techniques that rely on instantaneous gas exchange measurements can be both expensive and challenging. Frequently, data necessary to parameterize ecosystem process models are not readily available. This report provides model parameters for 11 conifer species of the Inland Northwest. Field measurements of A, Amax, g, gmax, ci, predawn water potentials, analysis of leaf nitrogen concentration, carbon isotope discrimination, and values of ci, and intrinsic water use efficiency (A/g) inferred from the carbon composition are presented. The relationship of wet leaf weight to dry leaf weight is also presented. The data in this report can be used to calibrate and constrain physiological parameters for modeling physiological processes of 11 conifer species in the Inland Northwest.

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    Citation

    Korol, Ronni L. 2001. Physiological attributes of 11 Northwest conifer species. Gen. Tech. Rep. RMRS-GTR-73. Ogden, UT: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. 9 p.

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    Keywords

    photosynthesis, stomatal conductance, gas exchange, model parameterization, conifer, process models

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