RT Dissertation/Thesis
T1 Study of growth-environment relationships and optimisation of management including climatic uncertainty of radiata pine stands in Galicia
A1 González Rodríguez, Miguel Ángel
K1 climate change
K1 site index
K1 radiata pine
K1 machine learning
K1 stand growth modelling
K1 stand-level man-agement
K1 risk modelling
AB Climate change is intended to impact forest dynamics significantly inthefollowing decades. To proactively adapt forest management to these expected alterations, new methodologies forhandling the uncertain-ties regarding forest growth under varying environmental conditions become necessary. Thepurpose of this thesis was to forecast the impact of climate change on radiata pine plantations in the northwest ofSpain in terms of productivity, profitability, and silvicultural treatments. In Study I, several statistical techniques wereused for predicting thesite index (SI) of radiata pine stands using environmental predictors extracted from availableraster maps. A non-linear technique, Multivariate Adaptive Regression Splines (MARS), was suggested as the bestmodelling alternative, explaining up to 52% of the SI variability. In Study II, the Support Vector Regressiontechnique was used to predict SI and delimit the validity area of predictions based on the radial basis kernel. Theresulting model had high predictive performance, provided robust predictions under varied climatic conditions, andincluded a relatively small number of predictors. Moreover, the model was able to identify areas where climaticconditions were very different from the observed and consequently regularised predictions for those areas. In StudyIII, silviculture under climate change was optimised for maximising the soil expectation value of a set of radiata pineplantations. The future forest productivity projections, produced by the model developed in Study II, forecasted anoverall reduction in SI under climate change, mainly driven by increased temperatures and continentality.Consequently, the economic simulations forecasted a drop in profitability under climate change that was moreintense for more pessimistic scenarios (RCP 6.0). However, the climatic projections were very varied over the set ofused climate models, which led to a great dispersion in productivity and profitability predictions. From theperspective of silviculture, the most notable forecasted variation is the expected increase in optimum rotationlengths.
YR 2021
FD 2021
LK http://hdl.handle.net/10347/27237
UL http://hdl.handle.net/10347/27237
LA eng
DS Minerva
RD 22 abr 2026