Growth decline assessment in Pinus sylvestris L. and Pinus nigra Arnold. forest by using 3-PG model.

Abstract

Aim of the study: We assessed the ability of the 3-PG process-based model to accurately predict growth of Pinus sylvestris and P. nigra plantations across a range of sites, showing declining growth trends, in southern Spain. Area of study: The study area is located in “Sierra de Los Filabres” (Almería). Material and methods: The model was modified in fifteen parameters to predict diameter (DBH, cm), basal area increment (BAI, cm2 yr-1) and leaf area index (LAI, m2 m-2) in healthy trees and trees showing declining growth. We assumed that a set of specific physiological parameters (stem partitioning ratio-pFS20, maximum litterfall rate-γFx, maximum canopy conductance-gCx, specific leaf area for mature aged stands-σ1, age at which specific leaf area = ½ (σ0 + σ1), age at full canopy cover-tc, and canopy boundary layer conductance-gB) included in 3-PG would be suitable for predicting growth decline related to climate conditions. The calibrated model was evaluated using dendrochronological and LAI data obtained from plots. Main results: Observed and simulated DBH showed a high correlation (R2 > 0.99) between modelled and measured values for both species. In contrast, modelled and observed BAI showed lower correlation (R2 < 0.68). Sensitivity analysis on 3-PG outputs showed that the foliage parameters - maximum litterfall rate, maximum canopy conductance, specific leaf area for mature aged stands, age at which specific leaf area, and age at full canopy cover - were important for DBH and BAI predictions under drought stress. Research highlights: Our overall results indicated that the 3-PG model could predict growth response of pine plantations to climatic stress with desirable accuracy in southern Spain by using readily available soil and climatic data with physiological parameters derived from experiments. Keywords: Hybrid process model; forest management models; growth prediction; Pinus spp, Parameterization; forest decline.

Keywords

• Hybrid process model
• forest management models
• growth prediction
• Pinus spp, Parameterization
• forest decline.