The new data available from cone collection and height and diameter measurements in the Permanent Sample Plots (Task 4.) and irrigation and fertilization trials (Task 2 and 3) will be used to improve the existing Portuguese Pinus pinea modeling equations (Freire 2009). These equations include:
a) Height growth
b) Diameter increment
c) Cone occurrence
d) Cone weight
Improving the current Portuguese Pinus pinea modelling equations will be done not just by making a new parameterization from existing equations developed by Freire (2009). From the database developed in Task 5 which includes the new data to be collected, existing equations will be statistically tested together with several other candidate equations in order to select the best equation in regard each variable considered. The existing height growth equation a) was developed from a set of measurements from the 65 Permanent Sample Plots, carried out in 2004 and 2005. In 2011 there has been already a new data collection of diameters and heights from the Permanent Sample Plots which was not used yet. During 2004 and 2005 the increment in diameter was assessed with an increment borer and equation b) was developed. The equations related to cones c) and d) were developed using data from 4 years of cone collections, between 2005 and 2008, in the same Permanent Sample Plots. Equations have been developed for cone weight rather than for pine nuts weight for two main reasons:
1. There is a very strong correlation between cone weight and the pine nuts weight. In Portugal the pine nuts weight correspond to 4% from the total cone weight. In addition, 89% from the total pine nuts weight corresponds to sound seed (Calado 2012).
2. The cone/pine nuts production is traded directly by landowners who sell their production by cone weight. Therefore this is the measure they primarily are interested to model.
As the new equations to be developed in this task will have a considerable larger dataset, than the one which was used originally in 2009, the new empirical modeling equations will be statistically better to make predictions for Pinus pinea stand management.