Parameter testing and application of the 3PG model for Eucalyptus grandis x urophylla on the Zululand coastal plain, South Africa

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dc.contributor.advisorDrew, David M.en_ZA
dc.contributor.advisorGermishuizen, Ilariaen_ZA
dc.contributor.authorGakenou, Oluwaseunen_ZA
dc.contributor.otherStellenbosch University. Faculty of AgriSciences. Dept. of Forest and Wood Science.en_ZA
dc.date.accessioned2021-12-06T10:01:21Zen_ZA
dc.date.accessioned2022-02-22T10:19:23Zen_ZA
dc.date.available2021-12-06T10:01:21Zen_ZA
dc.date.issued2021-12en_ZA
dc.descriptionThesis (MSc) -- Stellenbosch University, 2021.en_ZA
dc.description.abstractENGLISH ABSTRACT: This study aims to calibrate the 3PG (Physiological Processes Predicting Growth) model for Eucalyptus grandis x urophylla growing in the coastal Zululand region, South Africa. Parameter values developed for this hybrid across regions in Brazil were used as baseline parameters. To generate a set of reliable point estimates of weather data for growth modelling, we evaluated the performance of two spatial interpolation techniques (Random Forest and the R package “Meteoland”) using Mean Absolute Error, Root Mean Square Error, Coefficient of Determination, Index of Agreement and Nash Sutcliffe Model Efficiency Index. We collected observed long term weather data from the South African Weather Services (SAWS) and the South African Sugarcane Research Institute (SASRI). Weather stations spread across the KwaZulu-Natal region were used for the performance analysis. Both models showed great potential. However, the Random Forest model was the best performing model used to generate weather data in this study for growth modelling. Parameter estimation of the model was based on 17 permanent sample plots (PSPs) managed by two forestry companies, Mondi Ltd and Sappi Ltd. Allometric parameters for stem mass as a function of stem diameter at breast height were calibrated using biomass harvest data from sampling undertaken in 2018. Eleven parameters were selected from the list of base parameters to be adjusted using a parsimonious optimization approach. A novel method for ranking the parameter set combinations, called extended Root Mean Square Error (eRMSE), was created and used to select the optimal parameter set. Using the new parameter set resulted in good predictions of three key output variables (Mean stand height (H, m), stand basal area (BA, m2 /ha), and mean stem diameter at breast height (DBH, cm)) which were then used to calculate stand volume (V, m3 /ha). Model performance at 15 independent validation sites allowed the comparison with three other Brazilian parameter sets. Overall, the 3PG model gave a good but slightly overestimated stand volume prediction at the validation sites. We compared the 3PG model with three simpler models. The forest companies’ statistical growth and yield models outperformed all other models in terms of all metrics used, followed by a very simple model using the cumulative rainfall model. Although the 3PG gave similar growth predictions, it demonstrates its usefulness in simulating growth patterns in response to environmental changes.en_ZA
dc.description.abstractAFRIKAANS OPSOMMING: "Geen opsomming biskikbaar"af_ZA
dc.description.versionMastersen_ZA
dc.embargo.terms2022-06-06en_ZA
dc.format.extentv, 95 pagesen_ZA
dc.identifier.urihttp://hdl.handle.net/10019.1/124237en_ZA
dc.language.isoen_ZAen_ZA
dc.publisherStellenbosch : Stellenbosch Universityen_ZA
dc.rights.holderStellenbosch Universityen_ZA
dc.subjectEucalyptus grandis -- South Africa -- Zululanden_ZA
dc.subjectCalibrationen_ZA
dc.subjectSpatial interpolationen_ZA
dc.subjectInterpolationen_ZA
dc.subjectCommercial forestsen_ZA
dc.titleParameter testing and application of the 3PG model for Eucalyptus grandis x urophylla on the Zululand coastal plain, South Africaen_ZA
dc.typeThesisen_ZA
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