Parameter testing and application of the 3PG model for Eucalyptus grandis x Urophylla in subtropical conditions in South Africa
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Abstract
Background: The productivity of the coastal Zululand region, which was known as the South African breadbasket for fibre is declining. Climate-related changes are a significant factor contributing to this decline. The 3PG (Physiological Processes Predicting Growth) model was calibrated for E. grandis
x E. urophylla hybrids planted in this region to quantify the effect of climate variation x site on their growth and survival. Monthly weather data for the ungauged plantations were estimated using the Random Forest (RF) supervised learning algorithm. A dataset consisting of 17 permanent sample
plots (PSPs) and published parameter values for this hybrid in various regions of Brazil were utilized for parameter estimation. Using a parsimonious optimization approach, we developed a novel method called extended Root Mean Square Error (eRMSE) to select the optimal parameter set.
Result: The new parameter set yielded accurate predictions for three key variables; quadratic stem diameter (R2 = 0.85, E = 0.73), mean height (R2 = 0.84, E = 0.78), and basal area (R2 = 0.87, E = 0.78). Model performance at 15 independent sites allowed the comparison with three other Brazilian parameter sets for stand volume prediction at a specific age. The optimized parameter set provided a satisfactory, albeit slightly overestimated stand volume (V (m3ha-1), R2 = 0.65, E = -0.32) at the validation sites.
Conclusion: The 3PG model can be adapted with parameter set from another region to characterize the growth of E.grandis x E.urophylla stands in South Africa.
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