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A Generic Model to Describe the Dynamics of Nutrient Concentrations within Stemwood across an Age Series of a Eucalyptus Hybrid
Author(s) -
Laurent SaintAndré,
JeanPaul Laclau,
Philippe Deleporte,
Jacques Ranger,
Raphaël Gouma,
Aubin Rachel Saya,
Richard Joffre
Publication year - 2002
Publication title -
annals of botany
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.567
H-Index - 176
eISSN - 1095-8290
pISSN - 0305-7364
DOI - 10.1093/aob/mcf146
Subject(s) - eucalyptus , nutrient , biology , mathematics , range (aeronautics) , series (stratigraphy) , tree (set theory) , botany , zoology , ecology , combinatorics , materials science , paleontology , composite material
Nutrient concentrations (N, P, K) were determined within stemwood in an age series of eucalyptus stands. Four trees per stand were selected according to their size to represent the whole range of basal areas in 1-, 2-, 3-, 4-, 5-, 6- and 7-year-old stands. Cross-sections were sampled every 4 m from the ground to the top of the tree, and chemical analyses were performed for each annual ring in the cross-sections. We constructed a new and generic model to describe the dynamics of nutrient concentrations within the stemwood. Three main parameters were used: (1) the initial concentration of the ring, Ic; (2) the final concentration of the ring at harvest, Fc; and (3) the rate of change in concentration, k. The model is very flexible and was adapted to describe N, P and K concentrations within the stems, and their dynamics over time. An analysis of the parameters showed that k was constant for a given nutrient. Ic varied with height within the tree for P, whereas for N and K it was a function of: (1) the age of the tree when the ring was initiated: and (2) height within the tree. Fc was constant for N, and dependent on the age of the tree when the ring was initiated for K and P. The final models showed a low Root Mean Square Error for a limited number of parameters (less than seven). When validated on an independent sample, the models were shown to have high predictive quality.

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