Case studies tagged with pine nuts

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Clonal variation in susceptibility to Leptoglossus occidentalis in grafted Stone pine plantations

Leptoglossus seed bug instars feeding on a cone (left). Insect-proof cages (right)

Some experiments conducted at the Institute of Agri-Food Research and Technology of Catalonia (IRTA), aimed at evaluating the productive capacity of Stone pine clones from different Spanish provenance regions (PR) under Leptoglossus occidentalis Heideman attack, are presented. This pest is severely affecting Stone pine stands in the Iberian Peninsula, hence, identifying genotypes less susceptible to L. occidentalis attack is an important line of research in this area.

Impact of Leptoglossus occidentalis on commercial pine nut kernel per cone output

Typology of damages caused by Leptoglossus occidentalis feeding

The aim of this work was to quantify the extent of the recent decline observed in pine nut and kernel production. For this purpose, we analysed data from the INIA long-term sample plot network for cone and pine nut production in the four main Spanish stone pine regions.  Data series for more than a hundred plots since the last century, previous to the arrival of the bug, were compared with the same series after its arrival. Hence, the collapse in kernel per cone yield since can be quantified, the implied main factors identified, and  the evidence of L. occidentalis causality...

Stone pine cone production estimated by Terrestrial Laser Scanner

Point cloud obtained from a Terrestrial Laser Scan.

The objective of the present study is to better understand the relationship between tree characteristics and cone production of Mediterranean stone pine. This was achieved by quantifying the gain in using detailed crown metrics in estimating cone production at individual tree level (number of cones per tree and average cone weight). Models based on traditional variables (tree size and stand characteristics) were compared to models that relied on crown metrics extracted from TLS data. The resulting models should help owners and managers to better predict cone production.