Spatial stochastic modelling of cone production from stone pine ( Pinus pinea L.) stands in the Spanish Northern Plateau.

Abstract

The spatial structure of the mean cone production from even-aged stone pine stands of the Northern Plateau of Spain has been studied. Available data consisted of 123 five-tree plots, where cone crop was collected during a 5-year period (1996-2000). The experimental variogram for the mean cone crop has shown that cone production is a variable with a high percentage of spatially structured variance and a range of spatial correlation of approximately 2000 m. A kriging map for the mean stand production (over the 5-year period) has been built. Additionally we used conditional simulation to study the spatial variability and to quantify the uncertainty of predictions. This method permits the construction of uncertainty and proba bility maps. Geostatistical prediction of cone production at a regional scale has been shown to be a useful tool for modelling a low-value natural resource such as stone pine cones, without needing additional covariate measurements.

An empirical ecological-type model for predicting stone pine (Pinus pinea L.) cone production in the Northern Plateau (Spain)

“…This points to the spatial dependence of cone production, as stated by Nanos et al (2003) in their analysis of average cone production for the same region. Assuming an ecological origin for this spatial variability, climatic and orographic covariates were evaluated to explain the remaining spatial variability.…”

“…The effect of rainfall and other climatic factors explain much of the between-year variation in yield (Mutke et al, 2005a,b), and various studies have pointed to the existence of a clear pattern of spatial correlation in average cone production (e.g. Nanos et al, 2003). However, little is known about the effect of average climate or other ecological factors on the spatial variability of cone production.…”

Developing an Index for Forest Productivity Mapping - A Case Study for Maritime Pine Production Regulation in Portugal

“…At national or regional level studies, accurate kriging results are easily obtained as a wide range of biophysical conditions (e.g. soil, climate and topographic features) are in consideration (Mandallaz, 2000;Nanos et al, 2003;Lima et al, 2006;Benavides et al, 2009;Bognola et al, 2009;Hlásny et al, 2017). However, for local studies, a microclimatic approach should be explored instead to incorporate variables such as aspect and factors contributing greatly to the development of the soil, such as slope and elevation, especially in mountainous regions (Benavides et al, 2009).…”