Context: Pinus halepensis is a circum-Mediterranean species that has been widely used for afforestation of poor and degraded soils in water-limited regions. Determining forest and stand productivity is very useful for forest managers as it supports decision making.
Aims: The aim of this study was to develop a model to predict Pinus halepensis productivity using environmental parameters.
Methods: A set of 57 soil, climatic and physiographic parameters were determined in 32 Pinus halepensis plots, which were categorized into three site index classes. Principal component analysis (PCA) was used to select the 14 environmental variables that accounted for the highest data variability. Discriminant models with three, four and five environmental variables as predictors were studied.
Results: The selected discriminant model included three parameters related to water availability (Annual Hydric Index, soil porosity and slope), which is the main driver of forest productivity in Mediterranean ecosystems, and one nutrientrelated parameter known as microbial biomass N, by which microorganisms inform about N immobilization. The model correctly classified 62.5% of the plots.
Conclusion: This model may be a very useful tool for supporting decision making in forestry practices aimed at sustainable stand management.