Forecasting wood resources on the basis of national forest inventory data. Application to Pinus pinaster Ait. in southwestern France

Abstract

-The objective of this paper is to propose a method for simulating and predicting the evolution of wood resources in the 'Landes de Gascogne' region. Lemoine's growth and yield model has been successfully utilized to predict future timber resources from existing data collected in two successive surveys (1977 and 1988) conducted by the National Forest Inventory (NFI). Lemoine's model was calibrated by analysing the error in estimation of stand features between the NFI plots and experimental plots originally used to built Lemoine's model. The proposed corrected term is based on the best linear unbiased predictor of the error. The calibrated model exhibited a better accuracy than the original model version. We suggest that coupling the calibrated Lemoine's model with NFI data is a useful method for predicting timber resources at a regional level.wood resource / national forest inventory / growth model / model calibration / maritime pine Résumé -Prédiction des ressources futures en bois à partir des données d'inventaire forestier national. Application au massif de pin maritime (Pinus pinaster) des Landes de Gascogne. L'objectif de cet article est de proposer une méthode de prédiction de l'évolu-tion de la ressource dans les Landes de Gascogne. Le modèle de production de Lemoine a été employé avec succès pour évaluer la disponibilité en bois de la région, en utilisant les données des deux cycles de l'Inventaire Forestier National (IFN ;1978. Le modèle a été calibré, en considérant l'erreur d'estimation des caractéristiques dendrométriques des peuplements, entre les placettes de l'IFN et les parcelles expérimentales employées pour construire le modèle. Le terme de correction est basé sur le meilleur prédicteur linéaire non biaisé de l'erreur. La validation du modèle calibré a été menée sur des placettes non utilisées dans la procédure de calibration: la préci-sion dans les prévisions a été sensiblement améliorée. Nous suggérons que le couplage des données recueillies par l'IFN et du modèle calibré constitue un bon outil pour prédire la disponibilité régionale en bois.ressource forestière / inventaire forestier national / modèle de croissance / calibrage du modèle / pin maritime Ann. For. Sci. 58 (2001) [785][786][787][788][789][790][791][792][793][794][795][796][797][798][799][800][801][802] 785

What are the consequences of growth selection on wood density in the French maritime pine breeding programme?

“…Juvenile wood proportion was estimated with the tree growth model Pp3 (derived from Lemoine 1991;Salas-González et al 2001), implemented in the software platform CAPSIS (Computer-Aided Projection for Strategies In Silviculture) (De Coligny et al 2003). Pp3 model, calibrated with unimproved tree growth data, simulates tree growth depending on site productive capacity (described as the stand dominant height at a reference age) and silviculture.…”

Relationships between stem size and branch basal diameter variability in Norway spruce (Picea abies (L.)�Karsten) from two regions of France

“…By coupling a statistical model to these data, we could obtain an estimation of the knot distribution and dimension for a given forest resource. For conifers, several methods are being tried to achieve this coupling [2,30,31].…”

Growth and yield model for Pinus halepensis Mill. in Catalonia, north-east Spain

“…However, despite its specific features, the variable radius circular plot is often the only feasible method to sample irregular stand structures efficiently, and gives a good representation of large trees, which is usually a benefit from both inventory and modelling standpoints. It should also be remembered that sampling methods often are limited in their ability to represent spatial variability in stands (Stage and Wykoff, 1998;Salas-González et al, 2001). Therefore, if the models are applied to inventory data sampled differently from the modelling data (e.g., using larger sample plots), the resulting simulations might be biased (sampling error associated to competition predictors).…”