Forestry, Faculty of
DSpace
UBCV
van der Merwe, Jaco-Pierre
Doctor of Philosophy - PhD
University of British Columbia
Climate is known to impact the growth of plantation forests in South Africa. To characterize the conditions, this study developed temperature and rainfall models for specific plantations in the Lowveld Escarpment and Highveld forestry regions of South Africa. The local climate models use a finer scale than world climate models, and they improve the understanding of how terrain features impact regional climate and historic tree growth. In addition, the finer scale models give insights into the relationship between the local terrain condition and regional climate. Plantation forests were originally established in South Africa to meet an increasing demand for solid wood products as there was a limited supply from native forests. The majority of the commercial softwood plantations were established with Mexican Pinus patula. Since growing conditions are known to impact tree growth, tree form, and wood quality of P. patula, sample plots were established over a cross-section of plantations in the Lowveld Escarpment and Highveld forestry regions of South Africa that covered an array of geologies and altitudes. Soil, growing days, and temperature were found to have little impact on tree form and wood properties. However, rainfall and specifically, spring rainfall, was found to have a highly significant impact on late wood formation, proportion of juvenile core, and wood density. In addition, tree height was found to be strongly correlated with maximum annual temperature. Veneer derived from Lowveld trees had more splitting, which was largely related to defects. Larger trees also had a greater volumetric heartwood percentage and had a smaller live crown height portion, compared to smaller trees. Highveld trees also had greater net veneer recovery and produced better quality veneer than trees grown on the Lowveld. Annual maximum temperature and tree height had a negative relationship with the recovered lumber properties and dimensional stability. Spring rainfall appears to be the most important factor influencing lumber twist and this was possibly due to the associated larger juvenile core present in the trees.
THE IMPACT OF ALTITUDE, SOIL GEOLOGY, AND SOIL DEPTH ON PINUS PATULA SOLID WOOD PROPERTIES FOR STRUCTURAL TIMBER AND PLYWOOD PRODUCTION by Jaco-Pierre van der Merwe B.Sc., Stellenbosch University, 2011 M.Sc., Stellenbosch University, 2013 A thesis submitted in partial fulfillment of the requirements for the degree of DOCTOR OF PHILOSOPHY in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Forestry) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) October 2023 © Jaco-Pierre van der Merwe, 2023 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the dissertation entitled: The impact of altitude, soil geology, and soil depth on Pinus patula solid wood properties for structural timber and plywood production Submitted by Jaco-Pierre van der Merwe in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Forestry Examining committee: Shawn D. Mansfield, Professor, Department of Wood Science, UBC Supervisor Stavros Avramidis, Professor, Department of Wood Science, UBC Supervisory Committee Member Julie Cool, Associate Professor, Department of Wood Science, UBC Supervisory Committee Member Simon Ellis, Associate Professor, Department of Wood Science, UBC Supervisory Committee Member Gary Bull, Professor, Department of Forest Resources Management, UBC University Examiner Tony Bi, Professor, Department of Chemical and Biological Engineering, UBC University Examiner Alexis Achim, Professor, Department of Wood and Forest Sciences, Laval University External Examiner iii Abstract Climate is known to impact the growth of plantation forests in South Africa. To characterize the conditions, this study developed temperature and rainfall models for specific plantations in the Lowveld Escarpment and Highveld forestry regions of South Africa. The local climate models use a finer scale than world climate models, and they improve the understanding of how terrain features impact regional climate and historic tree growth. In addition, the finer scale models give insights into the relationship between the local terrain condition and regional climate. Plantation forests were originally established in South Africa to meet an increasing demand for solid wood products as there was a limited supply from native forests. The majority of the commercial softwood plantations were established with Mexican Pinus patula. Since growing conditions are known to impact tree growth, tree form, and wood quality of P. patula, sample plots were established over a cross-section of plantations in the Lowveld Escarpment and Highveld forestry regions of South Africa that covered an array of geologies and altitudes. Soil, growing days, and temperature were found to have little impact on tree form and wood properties. However, rainfall and specifically, spring rainfall, was found to have a highly significant impact on late wood formation, proportion of juvenile core, and wood density. In addition, tree height was found to be strongly correlated with maximum annual temperature. Veneer derived from Lowveld trees had more splitting, which was largely related to defects. Larger trees also had a greater volumetric heartwood percentage and had a smaller live crown height portion, compared to smaller trees. Highveld trees also had greater net veneer recovery and produced better quality veneer than trees grown on the Lowveld. Annual maximum temperature and tree height had a negative relationship with the recovered lumber properties and dimensional stability. Spring rainfall appears to be the most important factor influencing lumber twist and this was possibly due to the associated larger juvenile core present in the trees. iv Lay Summary During the past century, pine species were introduced into South Africa due to the limited availability of native forests for structural lumber and panel production. The growing environment of trees is known to have a significant impact on tree form and wood properties and therefore the recovery and quality of wood products. Therefore, sample plots were established in even-aged P. patula plantations, soil properties were determined and climate models were developed to better understand the growing environments in plantation forests. Furthermore the tree form and wood properties were measured to establish the relationship between the trees growing environment, tree form and wood quality of the standing trees. Trees grown on hotter sites were taller and trees that had more spring rainfall had lower density. Trees grown in the Highveld region produced more and better veneer. Trees grown in the Lowveld region had stronger and more stable lumber. v Preface Chapter 2 is a reproduction of a published scientific article co-authored with fellow researchers at the University of British Columbia’s Department of Wood Science, University of British Columbia’s Department of Conservation Sciences, University of British Columbia’s Department of Botany, York Timbers Forestry Division (RSA) and CharlieC Consulting (RSA). Van der Merwe, J-P., Wang, T., Clarke, C. and Mansfield, S.D. 2023. Predicting temperature and rainfall for plantation forestry in Mpumalanga, South Africa, using locally developed climate models. Agricultural and Forest Meteorology 2023, 329: 109275. Publisher: Elsevier Ltd. https://doi.org/10.1016/j.agrformet.2022.109275. I collected the data, completed the modelling, and wrote the manuscript. Tongli Wang, Charlie Clarke, and Shawn Mansfield aided with project conceptualisation, and reviewed and edited the manuscript. Chapter 3 is a reproduction of a published scientific article co-authored with fellow researchers at the University of British Columbia’s Department of Wood Science, University of British Columbia’s Department of Botany, the Institute of Commercial Forestry Research (RSA), York Timbers Forestry Division (RSA) and CharlieC Consulting (UK). Van der Merwe, J-P., Germishuizen, I., Clarke, C. and Mansfield, S.D. The impact of soil, altitude, and climate on tree form and wood properties of plantation grown Pinus patula in Mpumalanga, South Africa. Holzforschung 2023, 77(1): 1-15. Publisher: De Gruyter Ltd. https://doi.org/10.1515/hf-2022-0126. I designed the experiment, collected the data including all tree measurements, collection of core samples, densitometery measurements, completed the modelling and wrote the manuscript. Ilaria Germishuizen, Charlie Clarke, and Shawn Mansfield aided with project conceptualisation, and reviewed and edited the manuscript. Chapter 4 is a reproduction of an accepted scientific article co-authored with fellow researchers at the University of British Columbia’s Department of Wood Science, University of British Columbia’s Department of Botany, York Timbers Research Division (RSA), York Timbers Sabie Mill (RSA), York Timbers Plywood Division (RSA) and CharlieC Consulting (UK). Van der Merwe, J-P., Madiope, S., Spogter, O., Kuisis, H., Potgieter, J., Tait, O., Clarke, C. and Mansfield, S.D. The impact of site on tree form, heartwood content and veneer production of plantation-grown P. patula. Holzforschung 2023 (Accepted). Publisher: De Gruyter Ltd. I designed the experiment, collected the data including all tree measurements, arranged transport and mill of the sample trees, captured the data, completed the modelling, and wrote the vi manuscript. Charlie Clarke and Shawn Mansfield aided with project conceptualisation, and reviewed and edited the manuscript. Sechaba Madiope assisted in processing and measurement of disc core samples. Hilton Kuisis and Olwethu Spogter supervised the debarking of sample logs, including automated log form measurements and log batching. Oscar Tait and Jaco Potgieter supervised the peeling of veneer logs, drying, and grading of veneer sheets. Chapter 5 is a reproduction of a recently submitted manuscript co-authored with fellow researchers at the University of British Columbia’s Department of Wood Science, University of British Columbia’s Department of Botany, York Timbers Research Division (RSA), York Timbers Sabie Mill (RSA), York Timbers Plywood Division (RSA), Microtec (ITA) and CharlieC Consulting (UK). Van der Merwe, J-P., Madiope, S., Ncongwane, T., Spogter, O., Ngomane, R., Kuisis, H., Potgieter, J., Tait, O., Clarke, C., Cool, J., Avramidis, S., Ellis, S. and Mansfield, S.D. The impact of site on tree form, wood properties and lumber quality of plantation-grown P. patula. I collected the data, completed the statistical analysis and wrote the manuscript.. I designed the experiment, collected the data including all tree measurements, arranged felling, transport and milling of the sample logs, captured the data, completed the modelling, and wrote the manuscript. Charlie Clarke, Julie Cool, Stavros Avramidis, Simon Ellis and Shawn Mansfield aided with project conceptualisation, and reviewed and edited the manuscript. Sechaba Madiope assisted in processing and measurement of disc core samples. Oscar Tait, Hilton Kuisis and Olwethu Spogter supervised the debarking of sample logs, including automated log form measurements, log batching, milling of sample logs and grading of lumber boards. Martin Bacher assisted with the calibration of the Microtec online wood measurement insturments and verification of online lumber measurements. Sechaba Madiope, Thandekile Ncongwane and Robert Ngomane assisted in the manual verification of lumber board measurements. vii Table of Contents Abstract ....................................................................................................................................................... iii Lay Summary ............................................................................................................................................. iv Preface .......................................................................................................................................................... v Table of Contents ...................................................................................................................................... vii List of Tables ............................................................................................................................................... x List of Figures ........................................................................................................................................... xiii List of Abbreviations ............................................................................................................................... xvi Glossary .................................................................................................................................................... xxi Acknowledgements ............................................................................................................................... xxvii Dedication ............................................................................................................................................. xxviii Chapter 1: Introduction ............................................................................................................................. 1 1.1 Pine wood morphology ............................................................................................................... 1 1.2 Diversity in wood ......................................................................................................................... 2 1.2.1 Heartwood, sapwood, cambium, and phloem ................................................................... 2 1.2.2 Early and latewood ............................................................................................................. 3 1.2.3 Juvenile and mature wood ................................................................................................. 4 1.3 Environmental impacts on tree form and wood properties .................................................... 4 1.3.1 Altitude ................................................................................................................................. 5 1.3.2 Soil type ................................................................................................................................ 5 1.3.3 Silviculture ........................................................................................................................... 6 1.3.4 Climate ................................................................................................................................. 7 1.4 Impacts of log and timber properties on processing ................................................................ 8 1.4.1 Plywood mill ........................................................................................................................ 8 1.4.2 Lumber mill ....................................................................................................................... 12 1.5. Forestry in South Africa ........................................................................................................... 17 Chapter 2: Modelling temperature and rainfall for plantation forestry ............................................. 20 2.1 Introduction ............................................................................................................................... 20 2.2 Methodology .............................................................................................................................. 22 2.2.1 Study site ............................................................................................................................ 22 2.2.2 Temperature models ......................................................................................................... 24 2.2.3 Rainfall models .................................................................................................................. 28 2.2.4 Comparisons to existing climate model predictions ....................................................... 29 2.3 Results and discussion .............................................................................................................. 30 viii 2.3.1 T-min .................................................................................................................................. 30 2.3.2 T-max ................................................................................................................................. 35 2.3.3 Rainfall ............................................................................................................................... 39 2.3.4 Comparing Worldclim 2.1 to actual measured values ................................................... 42 Chapter 3: Site impacts on wood properties and tree form .................................................................. 44 3.1 Introduction ............................................................................................................................... 44 3.2 Methodology .............................................................................................................................. 45 3.3 Results and discussion .............................................................................................................. 51 3.3.1 Tree and wood properties ................................................................................................. 51 3.3.2 Tree form and wood property models ............................................................................. 58 Chapter 4: Site impacts on wood quality for veneer production .......................................................... 62 4.1 Introduction ............................................................................................................................... 62 4.2 Materials and methods ............................................................................................................. 64 4.2.1 Sampling in the forest ....................................................................................................... 64 4.2.2 Processing of samples and logs ........................................................................................ 65 4.2.3 Data processing ................................................................................................................. 68 4.3 Results and Discussion .............................................................................................................. 69 4.3.1 Resistograph models ......................................................................................................... 69 4.3.2 Tree form, wood properties and live crown portions .................................................... 71 4.3.3 Veneer recovery and quality ............................................................................................ 81 4.3.4 Tree properties and veneer quality PCA ........................................................................ 86 Chapter 5: Site impacts on wood quality for lumber production ......................................................... 89 5.1 Introduction ............................................................................................................................... 89 5.2 Materials and methods ............................................................................................................. 91 5.2.1 Sampling in the forest ....................................................................................................... 91 5.2.2 Processing of samples and logs ........................................................................................ 92 5.2.3 Data processing ................................................................................................................. 95 5.3 Results and Discussion .............................................................................................................. 96 5.3.1 Tree properties and lumber quality PCA ....................................................................... 96 5.3.2 Tree form, wood properties, and live crown portions ................................................... 97 5.3.3 Lumber recovery, quality, and stability ........................................................................ 105 5.4 Conclusion ............................................................................................................................... 110 Chapter 6: Conclusion ............................................................................................................................ 111 6.1 Summary of major research contributions........................................................................... 111 6.2 Modelling temperature and rainfall for plantation forestry ............................................... 112 ix 6.3 Site impacts on wood properties and tree form .................................................................... 113 6.4 Site impacts on wood quality for veneer production ........................................................... 114 6.5 Site impacts on wood quality for lumber production .......................................................... 115 6.6 Scope for future work ............................................................................................................. 115 6.7 Outroduction ........................................................................................................................... 116 Bibliography ............................................................................................................................................ 117 Appendix 1 ............................................................................................................................................... 157 Appendix 2 ............................................................................................................................................... 172 A2.1 Experimental design and overview of the field work ........................................................... 172 A2.2 Lumber production ................................................................................................................. 182 A2.3 Veneer production .................................................................................................................. 188 x List of Tables Table 2. 1: Summary of table of R-packages used for computing of models and figures ......................... 27 Table 2. 2: Prediction accuracies of seasonal T-min and T-max models for the Lowveld Escarpment and Lowveld, respectively. ................................................................................................................................ 30 Table 2. 3: Variable importance ratings for respective Random Forests and linear seasons T-min and T-max models ................................................................................................................................................. 38 Table 2. 4: Summary statistics for Pmedian regression independent variables per seasonal and quantile model for rainfall. Significant = * (0.01<P<0.05), highly significant = ** (0.001<P<0.01) and very highly significant *** (P<0.001). Rows containing N/A indicates variables that were not significant (P > 0.05). .................................................................................................................................................................... 40 Table 3. 1: Trial design and stratification of the forestry landscape, climate per soil parent material including annual maximum temperature (T-max), annual minimum temperature (T-min), summer rainfall, autumn rainfall, spring rainfall, annual growth days (GD), annual growth temperature (GT). In addition site related soil properties including effective soil depth (ESD) and total available water (TAW), and nutrient pool including soil nitrogen (N), phosphate (P), potassium (K), calcium (Ca), magnesium (Mg), sodium (Na), sulphate (S), carbon (C) and carbon to nitrogen ratios (C:N ratios). .................................... 46 Table 3. 2: Climate and soil independent variables examined with descriptions and source of the data. .. 48 Table 3. 3: Tree form and wood properties examined with descriptions and the source of the data. ........ 50 Table 3. 4: Tree form and wood properties significantly affected by both soil geology and altitude. Treatment means and standard errors are shown for respective regions and site types, including tree height, tree volume (V), slenderness coefficient (SC), wood density, latewood content (LW), pith to bark density gradient (PBDG), juvenile core transition age (JC), juvenile core portion (JCP), and growth ring eccentricity which was only affected by soil. Superscript letters added to the end of each numeric value represents treatment groups found by Tukey HSD. When the same superscript letters are above values within a designated column, it indicates that there are no statistically significant differences between the treatments. Different superscripts indicate a statistically significant difference. ........................................ 52 Table 3. 5: Multiple linear regression model to predict tree height, and median regression models to predict tree slenderness coefficients (SC), tree volume (V), wood density, latewood content (LW), and pith to bark density gradients (PBDG). Model accuracies expressed as root mean square errors (RMSE) and r-square’s (R²). No juvenile core transition ages (JC) or juvenile core portions (JCP) were modelled due to limited variability and predominantly regional differences. Statistical significance of independent variables is denoted as (***) when P<0.001, (**), when P<0.01, (*), when P<0.05, and NS when not statistically significant. ............................................................................................................................... 60 Table 4. 1: Veneer quality specifications according to SANS 929:2008 (Ed. 2.07), including threshold width (W), length (L), diameter (D), and quantity (Q) for respective defect types and veneer sheet dimensions according specific veneer grades and applications. ................................................................. 67 Table 4. 2: R-packages and functions, and their use in this study. ............................................................ 68 Table 4. 3: Linear regression models employed to calibrate Resistograph conversion models developed for P. radiata to P. patula wood density and latewood content predictions. ................................................ 70 Table 4. 4: Tree growth and form means and standard errors by sample site soil geology and altitude (superscript represents Tukey significance). ............................................................................................... 72 xi Table 4. 5: Tree form and crown properties significantly affected by soil parent material. Treatment means and standard errors are shown for respective regions and soil types, including wood density (Density), volumetric LW content (LW %), taper (T), sweep (S), and live crown height portions of trees (LC). Superscript letters represents treatment groups found by Tukey HSD, where the same superscript letters indicates that there are no statistically significant differences between the treatments. Different superscript indicate a statistically significant difference. ........................................................................... 72 Table 4. 6: Tree form and crown properties significantly affected by altitude. Treatment means and standard errors are shown for respective altitude ranges, including wood density (Density), volumetric LW content (LW %), taper (T), sweep (SW), and live crown height portions of trees (LC). Superscript letters represents treatment groups found by Tukey HSD, where the same superscript letters indicates that there are no statistically significant differences between the treatments. Different superscript indicate a statistically significant difference. .............................................................................................................. 72 Table 4. 7: The merchantable veneer recovery of peeler logs. Treatment means and standard errors are shown for respective regions and soil types and altitudes, including the post product recovery percentage of veneer sheets (net recovery) for logs produced from trees grown on the respective site types. Superscript letters represents treatment groups found by Tukey HSD, where the same superscript letters indicates that there are no statistically significant differences between the treatments. Different superscripts indicate a statistically significant difference. .......................................................................... 81 Table 4. 8: Veneer quality groupings according tosoil parent material. Treatment means and standard errors are shown for respective regions and soils, face veneer (%), layup veneer (%), and cross veneer (%). Superscript letters represents treatment groups found by Tukey HSD, where the same superscript letters indicates that there are no statistically significant differences between the treatments. Different superscript indicate a statistically significant difference. Veneer quality groupings significantly impacted by soil parent material. Treatment means and standard errors are shown for respective regions and soils, face veneer (%), layup veneer (%), and cross veneer (%). Superscript letters represent treatment groups found by Tukey HSD, where the same superscript letters indicates that there are no statistically significant differences between the treatments. Different superscripts indicate a statistically significant difference. . 82 Tabel 5. 1: Means and standard error for tree variables including tree height, volume, slenderness coefficient, live crown percentage (LC), roundness, volumetric heartwood percentage (HP), wood density (density) and latewood percentage (LW), according to soil parent material and altitude. Superscript represents Tukey significance. .................................................................................................................. 100 Tabel 5. 2: Means and standard error for tree form variables including sweep and taper according to soil geology. Superscript represents Tukey significance. ................................................................................ 100 Tabel 5. 3: Means and standard error for tree taper according to altitude. Superscript letters represents treatment groups found by Tukey HSD, where the same superscript letters indicates that there are no statistically significant differences between the treatments. Different superscript indicate a statistically significant difference. ............................................................................................................................... 103 Tabel 5. 4: Means and standard error for lumber net recovery, density, modulus of elasticity (MOE), modulus of rupture (MOR), knot area and twist according to soil parent material. Superscript letters represents treatment groups found by Tukey HSD, where the same superscript letters indicates that there are no statistically significant differences between the treatments. Different superscript indicate a statistically significant difference. ............................................................................................................ 106 Tabel 5. 5: Means and standard error for structural lumber net recovery, density, MOE and MOR, according to altitude. Superscript letters represents treatment groups found by Tukey HSD, where the xii same superscript letters indicates that there are no statistically significant differences between the treatments. Different superscript indicate a statistically significant difference. ....................................... 106 Tabel 5. 6: Means and standard error for lumber bow according to soil parent material and altitude. Superscript represents Tukey significance. ............................................................................................... 109 xiii List of Figures Figure 2. 1: Monthly regional temperature and rainfall means with standard error-bars according to the collected data (Van der Merwe et al., 2023a). ............................................................................................ 23 Figure 2. 2: Temperature-and-rainfull station distribution within the respective Lowveld Escarptment and Highveld plantations. Green shading indicates the Lowveld Escarpment and purple the Highveld plantation forestry region (Van der Merwe et al. 2023b). ........................................................................... 24 Figure 2. 3: Independent variables associated with terrain conditions for plantations located on the Lowveld Escaprment and Highveld; as extracted from the Advanced Land Observing Satellite-1’s (ALOS); Palsar instrument. ........................................................................................................................ 27 Figure 2. 4: Comparisons of Random Forest and Linear Machine Learning model predictions of seasonal T-min for pine and eucalypt plantation forest located on the Lowveld Escarpment. ................................. 32 Figure 2. 5: Comparisons of Random Forests and Linear Machine Learning Model predicions of seasonal T-min for pinte and eucalypt plantation forests located on the Highveld. .................................................. 33 Figure 2. 6: Comparisons of Random Forest and Linear Machine Learning model predictions of seasonal T-max for pine and eucalypt plantation forest located on the Lowveld Escarpment. ................................. 36 Figure 2. 7: Comparisons of Random Forests and Linear Machine Learning model predictions of seasonal T-max for pine and eucalypt plantation located on the Highveld. ............................................... 37 Figure 2. 8: Comparison of seasonal median regression Pmedian models for the Lowveld Escarpment and Highveld pine and eucalypt plantation forests, respectively. Winter absent due to little or no rainfall received during this season. ........................................................................................................................ 41 Figure 2. 9: Comparison of minimum temperature, maximum temperature and rainfall models to actual temperature and rainfall station measurements with corresponding R-square values. Minimum temperature measurements were compared to (A) WorldClim 2.1, (B) Linear model and (C) Random Forest model predictions. Maximum temperature was compared to (D) WorldClim 2.1, (E) Linear model and (F) Random Forests model predictions. Rainfall measurements were compared to (G) WorldClim 2.1 and (H) Median regression model predictions. ........................................................................................... 42 Figure 3. 1: Map showing the dominant regional soil parent materials and the location of the Lowveld and Highveld plantation forestry regions. ................................................................................................... 45 Figure 3. 2: Annual maximum temperature (T-max), plotted against tree height (m) for all trees sampled from the respective site types including a linear trend line and the R-square value. T-max explained the majority of site-related variability in tree height......................................................................................... 53 Figure 3. 3: Component analysis, including soil, tree form, and wood variables, and grouped according to soil (Figure 3.3A). Principle component analysis, including climate, tree form, and wood variables, and grouped according to soil (Figure 3.3B). .................................................................................................... 58 Figure 3. 4: Correlation matrix showing the relationships between tree height, slenderness coefficients (SC), volume (V), wood density, latewood content (LW), juvenile core transition age (JC), juvenile core portion (JCP), pith to bark density gradient (PBDG), growth ring eccentricity (GRE), summer rainfall, autumn rainfall, spring rainfall, mean annual maximum temperature (T-max) and mean annual minimum temperature (T-min), growth days (GD) and growth temperature (GT). Values in blue represent positive correlations and those values in red, negative correlations. ........................................................................ 59 Figure 3. 5: Actual versus predicted values for tree height (linear regression model), SC (median regression), tree volume (median regression), wood density (median regression), LW content (median regression), and PBDG (median regression). .............................................................................................. 61 xiv Figure 4. 1: Wood density correction and latewood prediction models developed by means of linear regression. Predicted and corrected wood density versus the actual wood density obtained from X-Ray densitometry measurements (Figure 4.1A) and predicted volumetric latewood content percentages versus actual measured volumetric latewood content percentages (Figure 4.1B) .................................................. 70 Figure 4. 2: Component analysis, including soil properties, form, veneer quality, veneer recovery, and the reasons why veneer was downgraded per tree and grouped according soil (Figure 4. 2A). Veneer quality was divided into face, layup, and cross veneer and expressed as a percentage per tree. Soil variables included nitrogen (N), phosphate (P), potassium (K), calcium (Ca), magnesium (Mg), sodium (Na), sulphur (S), carbon (C), carbon to nitrogen ratios (C:N ratio), and total available water (TAW), which was expressed per ha (t ha − 1). Veneer recovery represents the net veneer recovery of the manufactured panels. Veneer downgrades were grouped into knot, splitting, and resin and wane-related defects. Component analysis, including climate, form, heartwood, veneer quality, veneer recovery, and veneer downgrade reasons per tree and grouped according soil (Figure 4. 2B). Climate variables included annual maximum temperature (T-max), annual minimum temperature (T-min), summer rainfall, autumn rainfall, and spring rainfall. No winter rainfall was recorded in this region. Veneer quality was divided into face, layup, and cross veneer and expressed as a percentage per tree. Recovery (tree) represents the net veneer recovery per tree excluding reject logs. Recovery (log) represents the net veneer recovery per tree for the processed logs after the panels are manufactured. The reasons for veneer downgrades were grouped into knot, splitting, and resin and wane-related defects. .................................................................................... 74 Figure 4. 3: Correlation matrix showing relationships between tree height, slenderness coefficients (SC), volume, and climate variables including annual maximum temperature (T-max), annual minimum temperature (T-min), autumn rainfall, summer rainfall, and spring rainfall. Blue values indicate positive correlations and red values negative correlations. ...................................................................................... 75 Figure 4. 4: Linear relationship between tree volume and net veneer recovery for logs produced from the respective trees (Figure 4.4A) and for the full tree including harvesting related reject logs and crown waste (Figure 4.4B). .................................................................................................................................... 86 Figure 4. 5: Component analysis, including soil properties, form, veneer quality, veneer recovery, and the reasons why veneer was downgraded per tree and grouped according soil (Figure 2A). Veneer quality was divided into face, layup, and cross veneer and expressed as a percentage per tree. Soil variables included nitrogen (N), phosphate (P), potassium (K), calcium (Ca), magnesium (Mg), sodium (Na), sulphur (S), carbon (C), carbon to nitrogen ratios (C:N ratio), and total available water (TAW), which was expressed per ha (t ha − 1). Veneer recovery represents the net veneer recovery after the panels are manufactured. The reasons for veneer downgrades were grouped into knot, splitting, and resin and wane-related defects. Component analysis, including climate, form, heartwood, veneer quality, veneer recovery, and the reasons why veneer was downgraded per tree and grouped according soil (Figure 2B). Climate variables included annual maximum temperature (T-max), annual minimum temperature (T-min), summer rainfall, autumn rainfall, and spring rainfall. No winter rainfall was recorded in this region. Veneer quality was divided into face, layup, and cross veneer and expressed as a percentage per tree. Recovery (tree) represents the net veneer recovery per tree excluding reject logs. Recovery (log) represents the net veneer recovery per tree for the processed logs after the panels are manufactured. The reasons for veneer downgrades were grouped into knot, splitting, and resin and wane-related defects. ............................................................... 88 Figure 5. 1: Component analysis, including soil properties, form and lumber properties and grouped according to soil type (Figure 5.1A). Lumber quality in relation to wood density, dynamic MOE, low point MOR, knot area, bow, twist and spring. Soil variables included nitrogen (N), phosphate (P), potassium (K), calcium (Ca), magnesium (Mg), sodium (Na), sulphur (S), carbon (C), carbon to nitrogen xv ratios (C:N ratio), and total available water (TAW), which was expressed per ha (t ha − 1). Similarly, component analysis were done by including climate, tree height and lumber quality per tree and grouped according to soil type (Figure 5.1B). Climate variables included annual maximum temperature (T-max), annual minimum temperature (T-min), summer rainfall, autumn rainfall, and spring rainfall. No winter rainfall was recorded in this region. ............................................................................................................ 97 xvi List of Abbreviations °C Degrees Celsius A area of heartwood at the thick end of the log in m² a area of heartwood at the thin end of the log in m² ALOS Advance Land Observing Satellite ANOVA Analysis of Variance ARC Agricultural Research Council Aug August C carbon Ca calcium C:N ratios carbon to nitrogen ratios Cross % Percentage of cross quality veneer sheets CSIR Council for Scientific and Industrial Research DBH Diameter at Breast Height Dec December DEM Digital Elevation Model 𝐷𝑖𝑠𝑡𝑟𝐴 Distance of growth ring A from profile starting point 𝐷𝑖𝑠𝑡𝑟𝐵 Distance of growth ring B from profile starting point ENSO El Niño-Southern Oscillation ESD effective soil depth EW Earlywood Face % Percentage of face quality veneer sheets FDI Fire Danger Index xvii Feb February FES Forest Economic Services FPA Fire Protection Association G  E Genotype by environment interaction GCV Generalised Cross Validation GD Growth Days GRE Growth Ring Eccentricity GT Growth Temperature Ha hectare HP% Heartwood portion percentage ICFR Institute of Commercial Forestry Research Jul July JC Juvenile Core JC% Juvenile core percentage portion JCP Juvenile Core Portion JCSA Juvenile core surface area K Potassium kg/m³ kilograms per cubic meter L Length Layup % Percentage of layup quality veneer sheets LC Live Crown LEFPA Lowveld and Escarpment Fire Protection Association xviii LM Linear machine learning LW Latewood LW% Latewood percentage LWSA Latewood surface area MAP Mean annual precipitation Mar March m.a.s.l. meters above sea level MAT Mean annual temperature Mg magnesium mm Millimeters N Nitrogen Na sodium Nov November O Ovality 𝑂𝑛 Log ovality P Phosphate 𝑃𝑚𝑒𝑑𝑖𝑎𝑛 Median rainfall PBDG Pith to Bark Density Gradient PCA Principle Component Analysis RF Random Forests RMSE Root Means Square Error RSA Republic of South Africa SW Sweep xix S sulphate 𝑆𝑛 Log sweep SA Surface area 𝑆𝐴𝑟𝐵 Surface area of ring B 𝑆𝐴𝑇𝑜𝑡𝑎𝑙 Total surface area SAFCOL South African Forestry Company SANS South African National Standards SAPPI South African Pulp and Paper Industry SASRI South African Sugercane Research Institute SAWS South African Weather Service SC Slenderness Coefficient Sep September T Taper T-min Minimum temperature T-max Maximum temperature TAW total available water TPI Topographic Position Index TPI_3 Topographic Position Index relative to a 3 km radius TPI_5 Topographic Position Index relative to a 5 km radius TPI micro Topographic Position Index of micro terrain TPI macro Topographic Position Index of macro terrain TreeO Volumetric weighted ovality per tree TreeS Volumetric weighted sweep per tree xx 𝑇𝑟𝑒𝑒𝑡 Volumetric weighted taper per tree TTT Temperate tropical troughs UFPA Umpiluzi Fire Protection Association UK United Kingdom V Volume 𝑉ℎ𝑒𝑎𝑟𝑡𝑤𝑜𝑜𝑑 Heartwood volume 𝑉𝑛 Log volume 𝑉𝑇𝑜𝑡𝑎𝑙 Total volume of merchantable logs per tree Vtree Tree volume WD Wood Density 𝑊𝐷𝑟𝐴 Wood density of growth ring A 𝑊𝐷𝑟𝐵 Wood density of growth ring B 𝑊𝐷𝑊𝑒𝑖𝑔ℎ𝑡𝑒𝑑 Weighted profile wood density 𝑊𝐷𝑀𝑊𝑒𝑖𝑔ℎ𝑡𝑒𝑑 Weighted pith to bark density gradient xxi Glossary altitude the height of a position or an object relative to sea level. annual minimum temperature mean minimum temperature for a year. annual maximum temperature mean maximum temperature for a year. aspect the position of a surface in a particular direction. basic density The ratio between oven-dry mass and the green volume, calculated by dividing the oven-dry mass by the green volume. butt sweep bole curvature along the base of the tree. cellulose an insoluble polysaccharide consisting of glucose monomers which makes up the main part of plant cell walls. climate general weather conditions for a defined area and time scale co-relationship a mutual relationship between variables. data normality data distribution that is symmetric and continuous about the mean. digital elevation model a digital representation of ground surface topography or terrain. Drakensberg mountain range the Drakensberg are located on the eastern part of South Africa and encapsulates Southern African plateau. earlywood wood that is formed early during trees growing season, resulting in the formation of light and lower density wood. El Niño cyclic above normal oceanic water temperatures from the Pacific Ocean toward the western coast of South America that lead to the disruption global weather patterns. El Niño-Southern Oscillation a periodic variation in winds and sea surface temperatures over the tropical eastern Pacific Ocean, affecting the climate of the tropics and subtropics. evapotranspiration process by which water is transferred from land to the atmosphere, typically from soil or plants. fire danger index a rating system that uses meteorological observations to provide an indication of fire risk for a specific location at a specific time. genotype genetic makeup of an organism. xxii growth days the days of the year when plants theoretically grow. It is calculated by taking the balance of monthly precipitation and potential evapotranspiration into account. growth ring eccentricity the horizontal deviation of a trees incremental growth from the central pith in relation to it’s opposite increment. growth temperature A measure of the mean temperature at which plant growh occurs, calculated by multiplying the mean of the monthly temperature with the monthly growth days. gymnosperms group of vascular plants that produce seed that’s not enclosed in an ovary or fruit. hard pines a gymnosperm forming part of the genus Pinus subgenus pinus and are also more commonly known as yellow pines. Hard pines have harder wood compared to soft pines and 2 to 3 needles per fascicle. heartwood the dead, non-active central core of dark wood in tree stems, formed due to deposition of extractive by neighbouring parenchyma cells. hemicellulose matrix polysaccharides of a simpler structure than cellulose that’s hydrogen bonded to cellulose microfibrils. Indian Ocean third largest ocean on earth and borders on Asia to the north, Africa to the west and Australia to the east. juvenile core first formed wood during the trees juvenile growth and typically has low density, larger microfibril angles and shorter fibres and therefore low strength and stability. juvenile core transition age age at which juvenile wood stops forming. k-fold validation a model validation technique that splits the data-set into subsets which are also known as folds and then perform training of specific machine learning models on all of the subsets except one. The remaining subset is used to validate the trained model. The iterations of the model validations are specified. La Niña cyclic below normal oceanic water temperatures from the Pacific Ocean toward the western coast of South America that lead to the disruption global weather patterns. latewood in pines it is the wood that is formed late during a tree’s growing season, resulting in the formation of dark and denser wood. latitude distance of a specific point on earth, north or south of the earth's equator and expressed in degrees and minutes. xxiii lignin complex organic polymer deposited in the cell walls of plants and with cellulose, forms the chief constituent of wood. linear plateau models segmented linear models for bivariate data, typically where the y-value increases to a critical inflection point of x beyond which the y-value remains the same or decrease. lipids derivatives of fatty acids and provides energy for metabolic activities, provide structural elements for cell membranes and provide signalling to regulate plant metabolic activities. live crown the top part of the tree crown, that have leaves or needles that are green or alive. longitude distance of a specific point on earth, east or west of the Greenwich meridian and expressed in degrees and minutes. tracheid lumen a cavity found at the centre of tracheid cells, allowing for the transport of water and nutrients. machine learning models a program that can find patterns and make decisions from a previously unseen dataset. macro climate climate over a large geographical area. mean annual precipitation accumulated rain and snow fall for a year. mean annual temperature mean temperature for a year. median midpoint of a frequency distribution of observed values median regression type of regression analysis that are used to predict median values of a dependent variable by using as set of independent variables. metamorphic rock rock formed by the transformation of existing rock under the process of metamorphism. During metamorphis rocks are exposed to heat and pressure, leading to physical and chemical changes in the rocks’ composition. micro climate climate over a small isolated geographical area. middle lamella the outer most pectin rich layer of the plant cell wall, that cements together the primary cell walls of two neighbouring plant cells. mill dry recovery post drying wood recovered from logs during the manufacturing process. mill net recovery wood recovered from logs and converted into saleable product. xxiv mill wet recovery pre-drying wood recovered from logs during the manufacturing process. modulus of elasticity a measure of stiffness of a material i.e., a material’s resistance to being deformed under a specific bending stress. modulus of rupture a measure of strength of a material i.e., the maximum bending stress that can be applied to a material before it ruptures. Mpumalanga one of South Africa’s 9 provinces, located in eastern South Africa, bordering on Swaziland and Mozambique. multiple linear regression linear approach for modelling the relationship between a dependent variable and one or more independent variables. native forests voluntarily growing forests that are naturally present in an area and made up of forest species indigenous to the area. nearest neighbour model a supervised machine learning algorithm that uses a non-parametric classification method to determine the likelihood that a data point will form part of a sub-population, based on the nearest data points or “neighbours”. orographic rainfall precipitation as a result of moist air moving over mountains. ovality the amount of out-of-roundness of a cylindrical entity in the form of an oval. Pacific Ocean the largest ocean on earth and borders on Asia and Oceania in the west and the American continent in the east. parenchyma cells a type of plant tissue composed of living cells that carries out an essential function such as the storage and deposition of photosynthate such as starch and lipids. Pearson’s correlation matrix a test statistical method that determines the relationship between two continuous variables. phenotypes the observable characteristics of a living entity due to gene expression. photosynthate carbohydrates and lypids formed during the process photosynthesis. photosynthesis the process by which plants convert light energy into chemical energy from carbon dioxide and water, while generating oxygen as by-product. xxv plantation forestry trees managed for the production of commercial products or services. precision plantation forestry data driven decision making for the site-specific management of plantation forests, to achieve a predetermined desired management outcome. principle component analysis technique to reduce data complexity by detecting linear combinations of the data fields that can best capture the variance in the entire dataset. R-square proportion of variation in the dependent variable that is predictable from the independent variable. R statistical software a free software environment for statistical computing and graphics. radial support vector machine model a supervised learning algorithm that can be used for the development of regression models by assuming non-linear hyperplanes to separate specific features into domains. rainfall gauge an instrument used to collect and determine the amount of precipitation over a predefined area, over a period of time and therefore measuring rainfall amount or depth of rainfall penetration. random forests a machine learning method for classification, regression and other tasks that operates by constructing a multitude of decision trees. reaction wood wood that forms with an abnormal structure in response to gravity. root mean square error square root of the mean of the square of all of the error and is used to determine the quality of model predictions. sapwood active plant vascular tissue situated between the heartwood and bark. sedimentary rock rock formed from deposited minerals and organic materials under the process of cementation. silviculture the growing and tending of trees. site-species matching matching non-native tree species to growing environments, similar to its natural habitat. slenderness coefficient a measure of the slenderness of trees calculated using tree height and the diameter of trees at a height of 1.3 meters. slope a surface of which one side is lower or higher than the opposite end. xxvi soil water holding capacity the amount of water a specific unit of soil can hold, against the force of gravity. solar radiation radiation emitted by the sun. starch a polysaccharide carbohydrate produced by the process of photosynthesis in green plants for energy storage. stepwise regression iterative or “step-wise” examination of the statistical significance of multiple independent variables in a linear regression model. thin-plate spline regression a polynomial smoothing spline used for the modelling and visualisation of the effect of two continuous independent variables on a single dependent variable. topographic position index determined by comparing the elevation of a specific pixel, in relation to its surrounding neighbours. tracheids long and tapered lignified vascular cells, found in the xylem of vascular plants. tracheid microfibrils angled fibre strands consisting of glycoproteins and cellulose and arranged in a helical pattern around the central lumen. tree pith soft wood consisting of parenchyma cells, found in the center of stem. tree stocking the amount of trees per unit area. tropical-temperate troughs a weather system that bounds the tropics and the extra-tropics across southern Africa. vascular cells conducting tissues within plants that form an inter-connected network that transport water, nutrient and signalling molecules to support plant growth. weather station equipment used to capture and store meteorological observations over time. wood density dry mass of a given volume of wood. xylem vascular tissue within plant that conduct water, nutrients and cumulatively provide structural support for plants. xylogenesis the formation of vascular cells. xxvii Acknowledgements I would like thank; my supervisor Prof. Shawn D. Mansfield and the supervisory panel Prof. Julie Cool, Prof. Simon Ellis and Prof. Stavros Avramidis for their guidance and support during my studies and the resultant thesis document. Especialliy Prof. Shawn D. Mansfield, who moved mountains to make me feel welcome at UBC and to ensure that all the analysis could be completed in time. The staff and my fellow researchers at the University of British Columbia, for making me feel welcome and lending a helping hand whenever I needed one. My colleagues at York Timbers who believed in me, assisted in the logistics and who helped me to make this study come to life, sometimes putting their own operational needs aside. The York Timbers consultants, who gave valuable insights into the project, in particular Charlie Clark who mentored me in South Africa and played an important role in my personal development. My sponsor, York Timbers who allocated significant resources to this study and for allowing me the time to complete the study. In particular, the current and late CEO’s Gerald Stolts and Piet van Zyl respectively. Last but not least my wife Lizanne van der Merwe and my son Jean-Pierre van der Merwe, who allowed me to follow my dreams. Allowing me to do field work and travel abroad, while forsaking my responsibilities at home. I love you both endlessly. xxviii Dedication For my late brother and father. “When in doubt, do the courageous thing.” Jan Smuts, 1870-1950 1 Chapter 1: Introduction Plantation forests have been extensively planted around the world to satisfy the global demand for wood based products (Nepal et al., 2021). Today, plantations forests cover more than 290 million hectares, and plantation species are often planted outside of their natural habitat (López-Bedoya et al., 2022). Among the variety of current plantation species, pine trees have been extensively cultivated due to their rapid growth rates and favourable wood properties. Phenotypes of pine trees are impacted by the interaction between the genetic makeup, the growing environment, and human interventions in the form of silviculture practices (Martin et al., 2001). Plantation forests are typically manipulated to satisfy the immediate needs of processing facilities, by attempting to match genotypes to specific growing environments, and in some cases the implementation of silviculture activities to maximize tree growth, mill recoveries, and product quality (Jokela et al., 2010). As the area of global area that is being employed for plantation forestry continues to increase, there is a pressing need to better understand the interactions and importance of tree form and wood properties on the production of wood-based products, as this information is limited for most plantation species being cultivated. 1.1 Pine wood morphology Pine wood is made up of long, thin, elongated, spherical and lignified cells called tracheids. Tracheids both enable pine trees to vertically conduct water up the stem to the canopy and collectively provide structural support to the tree as a whole. A single tracheid is made up of a primary cell wall, supported by a thick underlying secondary cell wall. The secondary cell wall is in turn made up of three cell layers commonly referred to as the S1, S2, and S3 cell layers. These cell layers surround a central cavity called the lumen (Plomion et al., 2001). Each of the secondary cell wall layers consists of cellulose microfibrils, lignin, and hemicelluloses. The S1 layer is the thinnest of the three layers and is considered to be the transition layer between the primary cell wall and the S2 and S3 layers. The S2 layer is the thickest of the three layers, which represents more than 80 % of the total cell wall thickness (Plomion et al., 2001). It consists of microfibrils arranged in a regular angled, helical pattern arranged around the central lumen and has the most significant impact on the inherent mechanical properties of the tracheid cells, as well as the wood as whole (Auty et al., 2013; Lube et al., 2016; Mansfield et al., 2009; Mansfield et al., 2015; Plomion et al., 2001). Tree age, climatic, bio- and geophysical growing conditions 2 of trees have been found to impact the angle of microfibrils formed in the S2 layer (Plomion et al., 2001; Wessels et al., 2015). Closest to the lumen is a thin S3 layer (Plomion et al., 2001). The central lumen is a cavity, facilitating the transport of water up the tree. The tracheid cells are glued to each other by a thin middle lamella, adjoined by lignified cell cavities called pits allowing for the horizontal translocation of water between neighbouring cells (Plomion et al., 2001). 1.2 Diversity in wood Wood is a heterogeneous material that does not only vary between individual trees, but also within a single tree (Downes et al., 2009). This phenomenon has been observed in several tree characteristics, including tree form, fibre properties, and the moisture distribution (Downes et al., 2009; Erasmus et al., 2018; Lim et al., 2016). Differences in physiological timber properties have also been observed across the horizontal and vertical profiles of mature trees (Downes et al., 2022; Wessels et al., 2015; Télles et al., 2011). Along the horizontal profile of the tree bole, functional distinctions can be made with regards to anatomical wood portions. Firstly, the profile can be divided into physiological active and none active parts including the heartwood, sapwood, cambium, inner bark (phloem), and outer bark. Secondly, the profile can be divided into juvenile and mature wood portions, impacting the structural characteristics of timber. 1.2.1 Heartwood, sapwood, cambium, and phloem The heartwood stretches from the central pith outwards and encompasses all growth rings which are not physiologically active; in effect little or no water is conducted to the canopy via this collective wood portion (Bieker and Rust, 2010; Fan et al., 2018). Heartwood is also highly resistant to fungal decay in many species, due to higher concentrations of secondary metabolites deposited in the tracheid lumens giving it a distinctive red or darker colour (Belt et al., 2017; Belt et al., 2019). Although some fixed moisture is stored in the heartwood, it is at much lower levels compared to the rest of the tree (Lim et al., 2016). Cross sectional proportions of heartwood decrease with tree height along the stem profile (Climent et al., 2003; Knapic and Pereira, 2005). Factors driving heartwood formation are widely debated, however previous studies have found tree age, size and growth rate to impact its formation (Jakubowski et al., 2015; Kärenlampi and Riekkinen, 2002; Knapic and Pereira, 2005). The primary functions of the heartwood portion are to provide structural support to the tree and to regulate the proportion 3 of conducting vascular tissue throughout the tree for optimal movement of water and nutrients to the water conducting canopy (Moreno Chan et al., 2013; Hu et al., 2018). The remainder of the cross sectional stem profile is called the sapwood which includes all growth rings physiologically active, and is where water is transported to the canopy (Bergström, 2003). Sapwood therefore has a greater moisture content than its associated heartwood (Fan et al., 2018; Lim et al., 2016). Typically, the proportion of sapwood varies with seasonality and tree height, with greater sap flow during the wet season and an increase of cross sectional sapwood proportion with tree height (Alvarado-Barríentos et al., 2013; Lim et al., 2016). In addition, the proportion of sapwood has been found to be positively correlated with silviculture activities that improve tree growth, such as fertilization (Mörling and Valinger, 1999). The cambium is a circumferential layer located under the bark between the xylem and phloem, where all tracheid cell formation, active growth, and/or wood formation occurs. Cell formation involves four primary steps, including cell expansion, the ordered formation of a thick secondary cell wall, lignification, and cell mortality (Plomion et al., 2001). The inner bark (phloem) transports photosynthate (sucrose) up and down the tree, while the outer bark provides protection to the stem. 1.2.2 Early and latewood Within a single annual growth ring, wood can be divided into two types; early- and latewood. In South Africa, earlywood is formed during spring and summer months, when trees grow vigorously due to the greater availability of radiation and water. Fast growth generally leads to a wider growth ring with greater conductance of water via the tracheid cell lumen (Dye et al., 1996). Tracheid cells developed during faster growth will largely have wider lumens and thinner cell walls, leading to the formation of wood with lower overall density, greater microfibril angles and low stiffness (Auty et al., 2013; Guyot et al., 2015). Radial growth rate, earlywood width and wood density are closely associated, due to water conductance via tracheid lumens (Dahlen et al., 2018; Fan et al., 2018). In South Africa latewood is formed during the autumn and winter months when tree growth is slower due to dry climatic conditions and lower solar radiation. Slow growth leads to reduced conductance of water via the lumens of tracheid cells (Dye et al., 1996; Fan et al., 2018). Tracheid cells developed during this slower growth season will have a narrow lumen and thick cell walls, and tracheid cells with smaller S2 microfibril angles, leading to the formation of wood 4 with greater density and stiffness (Auty et al., 2013; Guyot et al., 2015). Moreover, it has been shown that latewood density is more closely related to growth ring age than earlywood (Mutz et al., 2004). Typically, moisture content is greater in earlywood compared to latewood due to the wider lumens located in the early wood portion (Downes et al., 2009). 1.2.3 Juvenile and mature wood Growth ring density and latewood density increases with chronological age and stabilizes when mature growth is achieved (Dahlen et al., 2018; Mansfield et al., 2009). Typically, the transition age of juvenile to mature wood is determined at the age at which latewood density stabilizes, as latewood density is strongly impacted by tree age (Dahlen et al., 2018; Park et al., 2009). However, recent studies suggest the stabilization of tracheid microfibril angles, might be a more reliable proxy when determining juvenile wood transition ages (Mansfield et al., 2009). Juvenile wood has been found to be formed during the first 7 to 15 years of growth for plantation grown P. patula, and can include both heartwood and sapwood (Erasmus et al., 2018; Mora et al., 2007). Wood formed during the juvenile period typically has lower density, stiffness, stability, and shorter fibres, compared to mature wood formed later chronologically in the trees life cycle (Auty et al., 2014; Dahlen et al., 2018; Mansfield et al., 2009). Juvenile wood increases proportionally with tree height, almost exclusively formed in the tree crown and have therefore been found to be synonymous with crown-formed wood (Burkhart and Tomé, 2012; Kang et al., 2004). In addition, the transition age of juvenile to mature wood formation has been widely debated and has been accredited to the chronological age of growth rings, growth rates, crown proportions and the availability of tree growth space (Erasmus et al. 2018; Mansfield et al, 2007). Juvenile wood was found to be negatively correlated to latewood formation in previous studies (Kang et al., 2004). Inherent wood properties such as basic density, stiffness, and moisture content also varies according to species, tree age, soil properties, applied silviculture practices, climatic conditions and the genetic makeup of the tree (Barnett and Bonham, 2004; Cown et al., 1999; Louw and Scholes, 2003; Moore et al., 2015; Mullet et al., 2017; Wessels and Froneman, 2015; Ukrainetz et al., 2018). 1.3 Environmental impacts on tree form and wood properties Variation in site-related wood properties of plantation grown pines has been a topic of interest in recent times for both pulp production and structural timber manufacturing (Downes and Drew, 5 2008, Malata et al., 2017). Moreover, stem and wood properties of plantation grown P. patula trees have been associated with climatic conditions, altitude, soil properties (Malata et al., 2017), and silviculture practices such as tree spacing, pruning of branches, and thinning regimes (Downes and Drew, 2008; Drew and Downes, 2018). Therefore, it is important to establish the impacts of site-related growth factors and how trees respond to them, and can be manipulated by breeding and planting, to achieve the desired wood properties for different structural applications. 1.3.1 Altitude Altitude has been found to impact site-specific climatic and geophysical factors such as temperature, rainfall, and soil properties. The inherent growing environment impacts tree form, growth, and wood properties of P. patula (Louw and Scholes, 2006; Malan, 2003). Forestry sites located at higher altitudes are subject to lower temperatures, shallower soils with greater carbon content and more seasonal precipitation (Malata et al. 2017, Schutz et al., 1991). In addition, higher altitudes have been associated with poorer growth sites however, which may be accredited to the covariate relationship between mountainous sites located on greater altitudes and shallow soils (Cruz-Leyva et al., 2010; Zwolinski et al., 1998). Higher altitudes have also been associated with pine trees with greater wood densities, greater latewood content, and larger fibre grain angles (Malata et al., 2017). Similarly, it was also found that sites with greater mean annual temperatures and more growth days, yielded P. patula trees with greater density and timber stiffness in South Africa (Muller et al., 2017; Wessels et al., 2015). Co-relatedness between climate and soil properties has limited the understanding of how altitude impacts on P. patula tree growth and wood properties (Nava-Nava et al., 2022; Van der Merwe et al. 2023a). 1.3.2 Soil type Soil properties have also been shown to impact tree growth and wood properties of plantation grown P. patula trees in South Africa (Louw, 2016; Muller et al., 2017). Some of the most important factors impacting tree growth include soil geology, soil water-holding capacity, effective rooting depth and the availability of nutrients (Gonzalez-Benecke and Martin, 2010; Muller et al., 2017; Schutz et al., 1991). The soil geology in itself will impact the soil texture, rooting depth and soil nutrients, as the soils’ properties are strongly impacted by the inherent parent material (Louw, 2016; Schutz et al., 1991). Soil texture and effective rooting depth also impacts the seasonal availability of water, with finer and deeper soils able to store greater 6 quantities of water (Schutz et al., 1991). In addition, water availability has been found to influence height growth of plantation grown pine trees (Louw, 2016). The combination of soil texture and soil depth can be expressed in terms of total available water (TAW) (Gonzalez-Benecke and Martin, 2010). Previous studies have found that soil nutrient content, including nutrients such as N, K, Ca, Na, and B, impact the growth of P. patula trees grown in South Africa (Louw, 2016). 1.3.3 Silviculture Silviculture includes all activities which contribute to the growing and cultivation of trees. Silviculture activities have been widely researched and allow forest growers to improve tree growth and manipulate the form and timber quality of trees. Plantation grown pine have been subject to specific planting geometries, thinnings, and branch pruning to improve final product yields and quality. 1.3.3.1 Establishment and thinning Establishment geometry, thinning frequency, and thinning intensity impact tree taper, diameter growth, wood properties, branching characteristics, and the frequency of stem defects (Charlton et al., 2020; Choi et al., 2008; Downes and Drew, 2008; Drew and Downes, 2018; Malimbwi et al., 1992). Trees grown for structural timber in plantation forests have generally been established at a high initial stocking to improve tree form and limit weed growth during the early stage of establishment (Little et al., 2018). Once maturity is achieved, trees with poor form and defects are often thinned-out to allow for more growing space and resources for the remaining larger trees with desired traits (Missanjo and Kamanga-Thole, 2015). In addition, trees subjected to lower stocking density were observed to be more tapered and had greater stability than trees subject to higher stocking densities (Charlton et al., 2020; Drew and Downes, 2018; Nogueira et al., 2008; Pacheco et al., 2017). Height growth has been found not to be impacted by the available growing space and has largely been related to the site quality (Morris, 1997). Wood density, latewood content, and timber stiffness of plantation grown P. patula trees have also been observed to increase significantly with higher planting densities (Charlton et al., 2020; Erasmus et al., 2018). 7 Asymmetric thinning practices have been associated with lopsided crown development and the formation of reaction wood (Ackerman et al. 2013, Gardiner et al. 2014). Low planting densities have also been connected with aggressive branching due to greater availability of growing space (Ackerman et al., 2013; Malimbwi et al., 1992). Trees subject to higher stocking have also been associated with a greater frequency of defects, such as broken tops, while stem straightness improved at higher planting densities (Choi et al., 2008; Erasmus et al., 2018). In addition, trees aggressively stocked and over thinned at a later stage, have been shown to have greater incidence of windrow (Morris, 1997). 1.3.3.2 Pruning of branches Branch pruning is a silviculture practice involving the removal of both dead and live branches to restrict the formation of knots in the core wood and promote the formation of high value clear wood (Cardoso and Arce, 2010; Munalula et al., 2016). Therefore, pruning forms part of both the structural lumber and plywood value chains. Pruning is initiated at defined heights, which is often based on the diameter of the trees at a specific age and prior to canopy die back (Adlard, 1969; Brown, 1962). Specifically in P. patula pruning has been found to improve growth vigour and height growth, however, supressed diameter growth (Adlard, 1969; Missanjo and Kamanga-Thole, 2015). Therefore, pruning directly impacts tree form and can reduce tree taper (Adlard, 1969; Brown, 1962). 1.3.4 Climate The South African forestry landscape is stratified by various altitudes and climatic variables impacting tree growth (Warburton and Schulze, 2008) and fibre quality (Louw et al., 2011; Malata et al., 2017). Significant factors include precipitation and temperature (Muller et al., 2017). In addition, the interaction between the potential evaporation and water availability has been found to significantly impact both tree growth and wood properties of plantation grown P. patula trees (Belay, 2016; Muller et al., 2017). Moisture availability during the summer growing season resulted in better tree growth, while moisture availability during autumn and winter resulted in greater active latewood formation (Belay, 2016; Malata et al., 2017). More importantly, when the atmospheric demand for moisture or the potential evaporation outweighed water availability, growth was restricted (Belay, 2016; Louw et al., 2011). The water balance of trees over a time series, expressed in growth days, and has been established as a significant factor impacting tree growth and the wood density of P. patula (Louw et al., 2011; Malata et al., 2017). 8 Therefore, the relationships between climatic variables, tree growth, and wood properties needs closer investigation due to the future impact of climate change on forestry in South Africa (Maponya et al., 2013; Van Zonneveld et al., 2009). 1.4 Impacts of log and timber properties on processing Timber properties have been associated with product recovery and quality for both structural and plywood mills globally. Product defects can be loosely divided into:  Inherent defects or raw material defects; defects due to the inherent properties of the raw material.  Conversion defects or processing defects; defects due to deformities associated with processing and which therefore could have been prevented.  Seasoning defects or drying defects; defects due to product distortion during drying or seasoning of products or product components.  Finishing defects or final product defects; defects created during the final processing of products, essentially making it presentable or ready for the market. 1.4.1 Plywood mill Plywood is one of the most commonly used wood products available today due to its versatility, as it can be manufactured into diverse products and thicknesses. Plywood consists of layers of veneer glued, laid perpendicular onto one another, and pressed together to form a structural board (Chan and Wang, 1990; Shupe et al., 1997). The manufacturing of softwood plywood typically consists of 9 production processes including: 1. Log debarking, sorting and storage (Wilson and Sakimoto, 2005) 2. Log conditioning (Demirkir et al., 2013; Dobner et al., 2013) 3. Rotary peeling of logs into veneer ribbons (Dobner et al., 2013; Wilson and Sakimoto, 2005) 4. Veneer ribbon grading and clipping to sheets (Lynch and Clutter, 1998) 5. Veneer drying (Aydin et al., 2015; Wilson and Sakimoto, 2005) 6. Veneer resin application and layup (Shupe et al., 1997; Wilson and Sakimoto, 2005) 9 7. Plywood panel hot pressing (Shupe et al., 1997; Wilson and Sakimoto, 2005) 8. Plywood edge and end trimming (Wilson and Sakimoto, 2005), and 9. Finishing processes (Demirkir et al., 2014) Log debarking is accomplished by feeding logs through a debarking machine to remove the bark from the surface without damaging the wood fibre. These logs are then heated in conditioning chambers with heated water or steam to achieve a core temperature of approximately 50 °C (Demirkir et al., 2013; Dobner et al., 2013). Heating softens the wood fibres which improves the peeler cutting process and veneer surface finish (Dobner et al., 2013). The heated peeler logs are transported to the rotary lathe for the peeling process that produces the veneer (Wilson and Sakimoto, 2005). The lathe attaches to the centre point on both ends of the log and rotates the log at high speed. A knife blade covering the full length of the log peels a continuous ribbon of veneer along its longitudinal axis, according to a prescribed thickness (Lynch and Clutter, 1998). The veneer ribbon is then clipped to a specified width, allowing for shrinkage and trimming (Spelter and Sleet, 1989; Wilson and Sakimoto, 2005). During the peeling process, fibre waste is produced due to; roundup volume losses, lathe or spur damage, and the remaining peeler core (Dobner et al., 2013; Lynch and Clutter, 1998). The veneer sheets produced are dried to a moisture content of below 12 percent, using veneer dryers (Aydin and Colakoglu, 2005). Once dried, the veneer sheets are graded, sorted, and stacked according to veneer quality (Lynch and Clutter, 1998). Full veneer sheets are graded into four classes, including A, B, C and D-grade veneer sheets (Lynch and Clutter, 1998). Dried veneer sheets are assembled into panels. Plywood is constructed by gluing a number of veneer layers (plies) with the grain direction perpendicular to one another, or at right angles, in each successive layer (Regattieri and Bellomi, 2009). Glue or resin is applied between each layer and the laid-up assembly of veneers is mechanically pressed together and heated until the glue dries or resin cures (Regattieri and Bellomi, 2009; Wilson and Sakimoto, 2005). During the finishing process, the raw plywood panels are trimmed and sized (Wilson and Sakimoto, 2005). Defects are repaired by putty filling or veneer plugging and subsequent sanding (Hughes, 2015). Final grading is then done on the finished board. 10 1.4.1.1 Inherent defects Plywood and veneer defects are caused by inherent properties of the peeler log. Inherent ply mill defects include downgrades due to:  Knots (Suh et al., 1988)  Blue stain (Hughes, 2015)  Bleed through (Bryant and Garcia, 1967)  Resin shakes (Bayatkashkoli et al., 2016; Yuce et al., 2014)  Wane (Yuce et al., 2014) Knots are timber impurities, formed by the inclusion of tree branches in lumber. Knots can be grouped according to tight and loose knots. Tight knots are formed by live branches and when branches dieback loose knots will be formed (Dobner et al., 2013; Suh et al., 1988). Veneer sheets have an allowable limit of knot frequencies and sizes for respective veneer grades (Dobner et al., 2013; Yuce et al., 2014). Blue stained veneer can be described as a blue discolouration on the veneer surface due to fungal colonization of wood (Carneiro et al., 2013). The colonization usually occurs due to excessive log aging and exposure of logs to the elements (Malan, 2004). Veneer sheets with the characteristic blue appearance are non-uniform and not aesthetically pleasing, and therefore downgraded. Irregular peeling and veneer thickness, splitting, or veneer damage can lead to veneer penetration to the plywood panel surface also known as bleed through (Bryant and Garcia, 1967). Bleed through displays subsurface resin or glue on the irregular veneer surface as strips and spots. Natural resin can infiltrate timber in the form of shakes. Resin shakes are across grain inclusion in the wood, forming an inherent weakness in the timber. Veneer sheets with resin inclusions are rejected due inferior strength and appearance (Yuce et al., 2014). In addition, resin can cause veneer splits during plywood production (Bayatkashkoli et al., 2016). Rotary peeling of veneer is limited by the inherent form of logs, as a full veneer sheet ribbon can only be produced once an even peeling surface is achieved. Veneer with wane defects can be produced, due to irregularities in the log form. The result is veneer sheets with gaping holes or wane (Yuce et al., 2014), often referred to as fishtail veneer. 11 1.4.1.2 Processing defects During the plywood production process, defects can form. These defects may be produced as a consequence of;  Veneer layup inefficiencies and undersized veneer sheets (Labans and Kalnins, 2014),  Breakage of veneer sheets prior or during the veneer layup (Labans and Kalnins, 2014),  Adhesive resin spreader application irregularities (Koran and Vasishth, 1972),  Delamination of veneer sheets (Aydin and Demirkir, 2013; Shupe et al., 1998) and  Faulty edge sawing of raw veneer panels (Koch and McMillin, 1966). Veneer layup inefficiencies can lead to the formation of short face and hollow core defects. Short face and hollow core defects are both resultant of veneer layup irregularities, undersized veneer sheets, and/or breakages of veneer sheets prior or during the veneer layup process (Labans and Kalnins, 2014). Short face defects include plywood panels with incomplete surface veneer, while hollow core defects include boards with an incomplete sub-surface veneer sheet (Labans and Kalnins, 2014). Adhesive resin application irregularities to sub-surface veneer sheets can cause the formation of plywood defects. Application irregularities include excessive or limited application of resin. For example, excessive application can lead to resin splash and to resin covering the surface of the face veneer, while limited application or drying of resin before layup can generate delamination (Koran and Vasishth, 1972). Delamination can also occur due to veneer roughness and the hydrophobic nature of wood surfaces due to over-drying of veneer sheets or adhesive bonding to latewood (Aydin and Demirkir, 2013; Christiansen, 1990; Shupe et al., 1998). After veneer sheets are pressed together, the raw plywood panel will be sized. If the plywood panel is not completely separated during sizing, wood fibres can be exposed causing irregularities on the plywood panel edge (Koch and McMillin, 1966). Irregular panel edge surfaces, will cause the plywood panel to be downgraded. 1.4.1.3 Seasoning defects Wood is a heterogeneous material with variability in wood density, microfibril angle, and moisture distribution across the log profile (Laskowska et al., 2018). During the drying of veneer sheets, veneer shrinkage will be variable according to the moisture distribution and wood density. Excessive shrinkage between two adjacent wood portions in a veneer sheet can lead to 12 sheet splitting during drying or pressing of plywood panels (Ozsahin and Aydin, 2014; Yuce et al., 2014). Splitting will expose the sub-surface layers of veneer. Veneer with excessive moisture after drying can lead to the formation of blisters during plywood pressing. Blistering is surface bulging due to delamination, which occurs due to high moisture (Troughton and Lum, 2000). During pressing, water will heat up and be transformed into steam causing blistering or delamination between veneer layers (Hrázský and Král, 2007; Koran and Vasishth, 1972). 1.4.1.4 Finishing defects During the finishing of plywood panels, various defects can be caused due to sander settings or the inherent properties of the surface veneer. Excessive sanding or irregular face veneer thickness can lead to sand-through which expose the sub-layers of veneer, sanding-hollow by leaving a non-uniform face veneer surface or sanding-skip due to irregular sanding. 1.4.2 Lumber mill Lumber milling includes the conversion of timber logs into lumber boards. Milling technologies have evolved over the past century and can include conventional frame saw mills, or modern circular saw mills. In essence, milling will follow a chain of primary processes including:  Sorting of log assortments (Chiorescu and Grönlund, 2004)  Debarking of logs (Laganière and Hernández, 2005; Puettmann et al., 2010)  Wet milling of logs (Belley et al., 2019; Hamner et al., 2006)  Kiln drying of boards (Bond and Espinoza, 2016; Puettmann et al., 2010)  Structural machine grading of lumber boards (Brännström, 2009)  Dry milling of boards and finishing of products (Axelsson, 2014; Sofuoğlu and Kurtoğlu, 2015) Harvested trees will be processed into log assortments and transported to respective lumber mills depending on the inherent log diameters and lengths (Eggers et al., 2010; Pilkerton et al., 2013). Mill-delivered logs will be sorted according to the respective dimensions, to ensure the correct cutting pattern is matched with the inherent log characteristics and dimensions (Belley et al., 2019; Hamner et al., 2006). During wet milling, logs are sawn into lumber products, while timber waste is chipped to power the drying kilns or sold to local pulp mills (Cáceres et al., 2018; Kuljich et al., 2017). Lumber boards are produced at a size wider and 13 thicker than the target dimensions, to allow for shrinkage during drying and resizing during planing (Axelsson, 2014; Sofuoğlu and Kurtoğlu, 2015). Wet lumber boards are often stacked on kiln trolleys and spaced with sticker spacers to ensure air circulation during drying (Keey and Nijdam, 2002; Nijdam and Keey, 2000). Kilns are ventilated enclosures and make use of steam to ensure lumber boards dry slowly and to minimize seasoning defects (Keey and Nijdam, 2002; Nijdam and Keey, 2000). In South Africa, lumber boards are typically dried at a temperature of 75 °C for at least 72 hours, to a target moisture content of 15 percent on oven-dry basis (Puettmann et al., 2010). However, the drying schedule will depend on the density and inherent moisture content of the raw material (Pang, 2007; Stanish, 2008). In modern circular saw mills, the dried lumber boards will be machine graded according to wood density, moisture content and knot configurations by means of x-ray measuring equipment. Defects are cut from the structural boards when needed, typically leading to the production of an assortment of board lengths. Lumber lengths will be sorted and short length boards finger-jointed. During finger-jointing, boards of shorter lengths are joined together by cutting fingers or grooves at the adjoining ends, which are then glued together to make full length products (Hernández et al. 2011; Pommier and Elbez, 2006). The seasoned, full-length and finger-jointed boards will be planed to their final dimension, and manually or mechanically graded for defects (Axelsson, 2014; Sofuoğlu and Kurtoğlu, 2015). Lumber defects impacts mill throughput and profitability, and therefore have been extensively researched (Brännström, 2009; Moore and Cown, 2015). Distinction needs to be made between different types of defects, to ensure better understanding and improvement of lumber manufacturing. In South Africa the SANS standards have been developed to govern timber quality of structural lumber and ensure specific quality requirements for timber employed for structural applications. 1.4.2.1 Inherent defects Lumber mill inherent defects include defects due to:  Wood density (Dowse and Wessels, 2013)  Wood strength (Dowse and Wessels, 2013)  Wood stiffness (Dowse and Wessels, 2013; Wessels et al., 2014)  Knots (Broman and Fredriksson, 2012; Longuetaud et al., 2012; Wessels et al., 2014) 14  Sloped grain (Malan, 2003; Stahl et al., 1990)  Decay (Broman and Fredriksson, 2012; Conners et al., 1983)  Resin (Broman and Fredriksson, 2012; Ridoutt et al., 1999)  Bow and crook (Kline et al., 1998; Sandberg, 2005)  Cracks and splits (Broman and Fredriksson, 2012; Sandberg, 2005) Wood density has long been used as a criterion to determine timber strength, however, it has limited predictability when determining timber strength and stiffness (Dowse and Wessels, 2013). In fact density only has a 20 to 40 % prediction accuracy when determining timber rupture strength, also known as modulus of rupture (MOR)(Dowse and Wessels, 2013). The SANS standards specify that only timber with a density of at least 360 kg/m³ at 12% moisture content may be used for structural timber applications in South Africa. The breaking strength of lumber (MOR) is determined by static bending tests. MOR of timber boards are strongly impacted by both wood density and knot distributions (Dowse and Wessels, 2013). According to the SANS standards, less than 5 percent of boards are allowed to have an MOR value of less than 11.5 MPa, for a population of lumber boards to be certified for structural applications. Lumber stiffness is expressed as modulus of elasticity or (MOE) and is typically determined using a static bending tester. In recent years, fast growing South African timber has come under scrutiny due to low stiffness properties, which has been attributed to the faster growth (Dowse and Wessels, 2013; Wessels et al., 2014). The SANS standards therefore states that at least 50 % of lumber boards produced must have a MOE of 7 800 MPa, for a specific batch of boards to be viable for structural timber applications. Knots including live and loose knots are lumber inclusions or impurities, and remains one of the primary sources of lumber downgrading in South Africa (Kline et al., 1998). Live knots are formed by live branches, while loose knot inclusions are formed by dead branches (Broman and Fredriksson, 2012; Longuetaud et al., 2012; Malan, 2003). Knots typically form in clusters and therefore can weaken the inherent strength and stiffness properties of lumber boards (Longuetaud et al., 2012; Wessels et al., 2014, Wright et al., 2019). 15 Sloped grain refers to growth rings or fibres that are not aligned to the longitudinal axis of a lumber board (Malan, 2003; Stahl et al., 1990). These boards will have inherent structural weakness due to the fibre orientation and will therefore be downgraded to non-structural grade products (Malan, 2003). Decay inclusions such as rot or timber discolouration due to blue stain also impacts the lumber quality grading (Broman and Fredriksson, 2012; Kline et al., 1998). Inoculation of fungal spores during log aging can also lead to timber discolouration (Sehlstedt-Persson et al., 2011; Yang et al., 2004). In addition, decay usually forms due to mechanical damage to the cambium, earlier in the tree’s life. As the tree grow, the dead cambium is encapsulated over time (Sullivan et al., 1993). Resin is a viscous substance that can harden when it dries and is a responsible for lumber downgrades (Broman and Fredriksson, 2012; Kline et al., 1998). It is usually produced by the tree when injury is incurred (Somerville, 1980). Resin inclusions or resin shakes in lumber boards have also been associated with wind damage, and have been found to be more prevalent in the lower 2.5 meter bole sections of trees (Malan et al., 1997; Ridoutt et al., 1999). Resin formation in wood continues to plague the South African mills, and the factors causing resin formation are still widely debated. Harvesting and log handling can cause logs to break and the production of fractured boards (Broman and Fredriksson, 2012). Fractured boards will either be rejected or remanufactured, leading to value losses or additional manufacturing costs. Bow and crook have been associated with lumber with internal stresses due to eccentric growth ring orientations and the inclusion of juvenile wood (Ormarsson and Cown, 2005; Sandberg, 2005). During milling the internal log stresses are released, leading to the formation of skew boards (Sandberg, 2005). Cracks and splits have been observed in timber sawn closer to the pith and forms before drying and can therefore also be associated with the release of internal stresses in sawn timber (Broman and Fredriksson, 2012; Sandberg, 2005). 16 1.4.2.2 Conversion defects Wane defects are caused when the inherent log form does not allow cutting pattern conversion to boards, and results in incomplete boards (Broman and Fredriksson, 2012; Kline et al., 1998). Wane can be reduced and even elminated by more efficient log sorting or by installing technologies that can more successfully mill crooked logs. Dimensional defects such as the production of lumber boards of faulty thickness and widths, are caused due to blade setting errors (Broman and Fredriksson, 2012; Rasmussen et al., 2004). During the milling process it is important to re-measure and calibrate blade settings (Rasmussen et al., 2004). Faulty calibration or the lack of blade calibration can lead to under or oversized defects (Rasmussen et al., 2004). Lumber boards can also be damaged during product handling after milling, stacking, and de-stacking after kiln drying and during movement of lumber stacks using mill equipment (Wang and Kim, 2000). 1.4.2.3 Seasoning defects Wood shrinkage has been observed perpendicular to the fibre grain and occurs after water is removed or lost from the cell lumens, and falls to a level below fibre saturation point (Han et al., 2016). As water leaves the vertically orientated tracheid cell lumens microfibrils move closer together; in effect wood shrinking occurs (Reeb, 1995). Wood shrinkage will only occur when the majority of tracheid cells are below fibre saturation point. In addition, wood shrinkage has been observed to be impacted by both inherent wood density and moisture content (Lindgren et al., 2016; Reeb, 1995). Tangential wood shrinkage have been found to be the greatest in wood specimens with higher density densities and in the longitudinal direction in in the low density earlywood portions within a single annual growth ring (Grekin and Verkasalo 2010; Peng et al. 2013; Schulgasser and Witztum 2015). Most seasoning defects occur because of dimensional changes in wood, due to shrinkage (Han et al., 2016; Sandberg, 2005). These defects include twist and cupping (Han et al., 2016; Ormarsson and Cown, 2005). Lumber sawn from closer to the pith has been associated with the formation of twist during drying (Sandberg, 2005; Simpson et al., 1988). Therefore, it has been argued that juvenile wood with low uniformity and larger microfibril angles is responsible for the formation of twist (Sandberg, 2005). 17 Cupping refers to the central depression of the cross section of the boards, while the sides curl up giving the board a cupped appearance (Sandberg, 2005). Cupping is also more prominent in boards closer to the pith and decreases in boards further away from the pith (Sandberg, 2005). Cup formation is more closely related to the ring curvature in the board, leading to irregular shrinkage (Han et al., 2016; Sandberg, 2005). 1.4.2.4 Finishing defects Planing is initiated after the boards are dried to a target moisture content. It refers to the process when boards are resized to their final dimensions by removing unwanted timber from the lumber surface by means of a planer with revolving planer knives (Axelsson, 2014). The expected outcome is smooth, dimensionally stable lumber. However, planer skips can occur due to irregular lumber shrinkage or due to improper planer setting or irregular surfaces of boards (Axelsson, 2014). The resultant effect is boards with irregular surfaces leading to sub-quality finished products (Axelsson, 2014). During the finger-joining process, the ends of lumber boards are cut into finger profiles and adjacent profile are glued together (Pommier and Elbez, 2006). During this process various defects can occur, including splits, knots, and wane in finger-joints (Hernández et al., 2011). Splits, wane, and knots should therefore be removed from joints before bonding (Broman and Fredriksson, 2012; Pommier et al., 2014). Miss-aligned finger joints can be formed when the grooves do not line up due to dimensional instabilities in the timber such as twist, cupping, crook, or bow (Broman and Fredriksson, 2012). Finger joint clearance and crushed finger joints are the result of improper machine settings or profile miss-alignment, while glue applicator blockages can lead to finger joints with little or no glue (Bustos et al., 2003; Pommier and Elbez, 2006). 1.5. Forestry in South Africa Currently, there are 1.19 million ha of commercial forests in South Africa, of which 580,763 ha are planted with exotic pine species, and in 2018 a total of 4,630,800 𝑚3 of round wood was grown for the structural timber industry. Plantation grown pine species accounted for 98% of the produced volume. Softwood species were introduced into South Africa from South America due to the limited availability of local natural timber resources, and currently approximately 290,499 ha are planted with Pinus patula (Bennett 2011; Crous et al. 2009; Maponya et al. 2013; 18 Munalula et al. 2016; Schutz et al. 1991; FES 2018; Santiago-García et al. 2017; van Zonneveld et al. 2009; Watts 2006). P. patula is native to Mexico and grows as far north as Tamaulipas and as far south as Oaxaca, between the 16°N and 24°N latitude range (Dvorak et al., 2001; Leibing et al., 2009; Santiago-García et al. 2017; van Zonneveld et al. 2009; Watts, 2006). Although significant, the area planted with pine species has drastically declined over the past decade in preference to the faster growing eucalypt species (FES 2018). Therefore, it is essential to improve softwood timber recovery at processing plants, as additional afforestation in South Africa is highly unlikely (Malan, 2003). Forestry practiced in the Mpumalanga province of South Africa can be divided into two regions, the Lowveld and Highveld. The Lowveld region is fractioned by the Drakensberg mountain range and has soils predominantly formed from shale and dolomite parent material. Plantation forestry sites in the Lowveld region are also subject to altitudes ranging between 800 and 2,000 meters, mean annual temperatures ranging between 14 and 18 °C, and mean annual precipitation ranging between 700 and 1,900 mm. In contrast, the Highveld regions are mostly uniform with little or no mountains and possess soils mostly formed from granite parent material. In addition, the Highveld region ranges between 1,400 and 1,800 meters in altitude, and has mean annual temperatures ranging between 14 and 17 °C, and mean annual precipitations ranging between 700 and 1,000 mm. However, seasonal climate in Mpumalanga and its impact on tree growth is poorly understood as the availability of climate data is limited and seasonal climate models largely absent. Recovery and product quality are the two main challenges for the solid wood processing operations in South Africa. One means of overcoming these processing challenges is enhancing the ability to predict the wood quality of plantation trees, such that specific compartments (land packets) and tree portions can be linked to individual timber processing lines (Cáceres et al. 2018; Malan, 2003; Turner et al., 2001). However, at present there is limited knowledge about the key drivers impacting tree form, pine wood formation, and overall wood quality in South Africa. In general, it has been well established that both genotype and environment influence; pine growth rate, stem form, and wood properties (Erasmus et al., 2018; Kanzler et al., 2003; Muller et al., 2017). In South Africa, the P. patula planted areas have been characterized according to mean annual temperature, moisture availability, and underlying geology (Louw, 2016). The objective of this study is to evaluate the growth, form, and wood quality properties of P. patula across a stratified range of sites in order to understand the effect that 19 environmenthas on tree growth and wood properties. I hypothesize that wood properties of South African-grown P. patula trees are under strong environmental control, which can be used to predict tree form and wood traits, conducive to lumber and veneer production. 20 Chapter 2: Modelling temperature and rainfall for plantation forestry 2.1 Introduction Plantation-grown forest tree species in South Africa were introduced from Australia, South America, North America and Asia due to the limited availability of local native timber resources (Bennett, 2011; Darrow, 1984; Van den Berg et al., 2016; Santiago-García et al., 2017; Swain et al., 2013). Introduced species were initially matched to suitable climatic areas and soil types to ensure survival and growth (McKown et al., 2014; Watson and Challinor, 2013). However, it has been shown that for effective site-species matching, both the macro climate of the region and localised micro climate of growth sites should be taken into consideration (Roffe et al., 2019; Salekin et al., 2019). Historically, climate models for site-species matching in South Africa were developed to identify and map the suitable areas for important commercial forestry species, including Pinus patula and Eucalyptus grandis, using a climate threshold approach (Schulze and Kunz, 1995). The primary climatic variables used for these models included mean annual precipitation (MAP), mean annual temperature (MAT), mean monthly temperatures, as well as the annual minimum (T-min) and maximum (T-max) temperatures (Schulze and Kunz, 1995). The regional risks associated with climate change and the possible reduced growth of plantation forest species have also been quantified and mapped for the Mpumalanga, Kwa-Zulu Natal, and Eastern Cape forestry regions of South Africa (Warburton and Schulze, 2008). Furthermore, the forestry landscape has been stratified according to climatic variables such as growing days and temperature classes (Louw et al., 2011). In addition, it has been shown that local terrain conditions and latitude and longitude impact rainfall and its temporal trends across South Africa (Gao et al., 2012; Jury, 2013; Schulze, 2000). As global warming-associated climate change has affected the efficiency of site-species matching, it has become important to more precisely determine the climate at a localised plantation level in support of sustainable, precision plantation forestry (Chakraborty et al., 2019; Keenan, 2015; Olbrich et al., 1997). South Africa is a water-scarce country with a national mean annual rainfall of only 450 mm per year, which is significantly lower than the global mean annual rainfall of 860 mm per annum (Botai et al., 2018; Hart et al., 2013). The eastern part of the country receives most of the rainfall during summer and has greater and more frequent rainfall due to the close proximity to the 21 warm Indian Ocean (Botai et al., 2018; Crétat et al., 2012; Favre et al., 2016; Roffe et al., 2019). The Indian Ocean is the source for most precipitation in the subtropical regions of South Africa due to tropical-temperate troughs (TTT) and accounts for 30 to 60 percent of all rainfall (Crétat et al., 2012; Hart et al., 2013). Therefore, the majority of high yielding agriculture and forestry land is located in the eastern part of the country, including the Mpumalanga forestry region (Crétat et al., 2012; Hart et al., 2013). Recent studies found that climate is the most significant site variable for the growth of the introduced pine species grown in Mpumalanga, South Africa (Da Ros et al., 2021; Dye, 1997, Morris et al., 2018; Van der Merwe et al., 2023a). South Africa’s annual temperature and rainfall trends are significantly impacted by the El Niño-Southern Oscillation (ENSO), which is characterised by the surface temperatures of the eastern-tropical Pacific Ocean (Abera et al., 2020; Bradfield et al., 2007; Desbiolles et al., 2018; ; Lakhraj-Govender and Grab, 2019; Landman, 2014; Nel, 2009; Preethi et al., 2015; Reason and Rouault, 2002). ENSO anomalies can lead to extreme cyclic climatic events, including heatwaves, droughts, and flooding (Abera et al., 2020; Bradfield et al., 2007; Crétat et al., 2012; Kruger, 1999; Lakhraj-Govender and Grab, 2019). El Niño events occur when the eastern tropical Pacific Ocean surface temperatures are above normal, leading to in-land drought events and higher than normal temperatures over Southern Africa (Kruger, 1999; Lakhraj-Govender and Grab, 2019; Preethi et al., 2015). Lower than normal water surface temperatures produce La Niña events, leading to greater rainfall and lower temperatures over Southern Africa (Kruger, 1999; Lakhraj-Govender and Grab, 2019). Typically, the Mpumalanga province will be subject to drought occurrences every 5 to 7 years (Usman and Reason, 2004). Irregular rainfall events also directly impact monthly temperature, due to cloud cover (Sun et al., 2000, Weng and Fu, 2014). For precision matching of species and genotypes to their ideal sites, highly accurate climate variables are required for specific locations (Wang et al., 2016). However, current climate models used in South Africa are limited to global and national scales in gridded formats, which are typically less accurate on a regional basis. Thus, the objective of this study was to develop accurate models to predict seasonal temperature and rainfall. In particular, high-resolution, localised, temperature and rainfall models were developed for two specific plantation forestry areas in South Africa as a case study. Six temperature models were initially explored, and two of the more accurate, Random Forests (RF) and Linear Machine Learning (LM), models were selected for further evaluation. 22 As an added benefit, RF models are not sensitive to any potential collinearities between independent variables used to predict climate (Cutler et al., 2007). In addition, similar to previous studies, machine learning models were more accurate when modelling seasonal climate than conventional models (Chantry et al., 2021; Chen, 2023; Ju-Young et al., 2020). However, no significant seasonal rainfall models were developed using these modelling approaches due to the unpredictable interference of ENSO. Therefore, as an alternative approach, median regression models were employed to model seasonal rainfall (Abbas et al., 2019). The ultimate aim of this research is to improve and build on current national site quality models for plantation forests. The study highlights a procedure that can be employed to successfully model high-resolution seasonal climate for specific regions, using local weather stations. In addition, accurate predictions of current climate conditions can serve as a baseline for bias corrections for future global warming scenarios improving future localised climate projections (Chen et al., 2015, Laux et al., 2021; Maponya et al., 2013). The accuracy of our predictions were also compared to the existing global climate model, WorldClim, for the currently measured weather conditions (Fick and Hijmans, 2017; Harris et al., 2014). 2.2 Methodology 2.2.1 Study site The largest portion of South African plantation forestry is in the Mpumalanga province (FES, 2018; Louw et al., 2011). This temperate to subtropical region is topographically and climatically diverse, and can be divided into two main regions, the Lowveld Escarpment and the Highveld (Lötter et al., 2014; Louw, 1997). The Lowveld region is split by the Drakensberg mountain range, and the altitude of plantation forestry sites varies between 800 and 2,000 meters (Muller et al., 2017; Partridge et al., 2010). The Highveld region is mostly uniform, and the altitude only ranges between 1,400 and 1,800 meters (Becker, 2005; Partridge et al., 2010). Mpumalanga has distinct summer (Dec to Feb), autumn (Mar to May), winter (Jul to Aug) and spring (Sep to Nov) months (Table A-1) which are characterised by differing temperature and rainfall ranges (Figure 2.1). Temperatures were not grouped according to region, as temperature differences were observed to be relatively similar. 23 Figure 2. 1: Monthly regional temperature and rainfall means with standard error-bars according to the collected data (Van der Merwe et al., 2023a). The warmest month is February and the coldest July, and most of the annual rainfall of between 800 to 1,600 mm falls during summer months, between November and March (Louw, 1997; Louw et al., 2011). Regions along the Lowveld Escarpment receive the greatest rainfall, and the majority of rainfall during the drier seasons are orographic (Louw, 1997). Fog frequently occurs at higher altitudes, above 1,100 meters, while hail storms occur during spring (Louw, 1997). The respective regional plantations, including the regional soil parent materials can be seen in Figure 2.2. Regional monthly T-min, T-max, and rainfall means and standard errors of the regional weather stations are shown in Appendix Table A1-2. 24 Figure 2. 2: Temperature-and-rainfull station distribution within the respective Lowveld Escarptment and Highveld plantations. Green shading indicates the Lowveld Escarpment and purple the Highveld plantation forestry region (Van der Merwe et al. 2023b). 2.2.2 Temperature models Weather station data were available over a seven-year period for temperature and recorded by means of modern temperature sensors. Daily minimum (T-min) and maximum (T-max) temperatures were obtained from automated fire lookout towers, forestry offices, and local 25 airfields, which have reliably recorded data since 2013. The local Fire Protection Associations (FPA) use temperature readings to determine the regional fire danger index (FDI), and these values are measured directly within the plantation areas (Appendix Table A1-2). The stations are mounted at the recommended heights of approximately 10 meters (Snyder et al. 2003). The distribution of temperature stations is also shown in Figure 2.2. Missing data were due to equipment malfunction, staff turnover, or decommissioning of stations during the investigated time period. Missing values were imputed by applying deterministic methods using measured values of surrounding stations, including multiple linear regression using R statistical software, to fill missing data in the time series for the different stations and only statistically significant patch models were used (Campozano et al., 2014; Moeletsi et al., 2016; Shabalala et al., 2019; Teegavarapu and Chandramouli, 2005; Tencaliec et al., 2015). The proportions of actual data, patched data and missing values are shown in Appendix Tables A1-5 and A1-6. Patch models included independent variables such as global position together with terrain features such as altitude, slope, aspect, and topographic position index (TPI). Topographic position index is defined as the elevation of a specific point, minus the mean elevation of the surrounding landscape (Oldfather et al., 2016; Swetnam et al., 2017).Weather stations used for temperature modelling were grouped together, and models were developed for the Highveld and Lowveld Escarpment plantations, separately. This step was taken because linear regression model intercepts were insignificant for two of the linear models, prediction accuracies were less accurate for Lowveld Escarpment temperature models, and micro terrain conditions were less significant, if the two regions were combined. Seasonal temperature models were developed for both T-min and T-max using six modelling approaches (Table 2.2). However, further, more detailed, analyses and comparisons were only done for the Random Forests and linear-based models because these two models were the most accurate and it was possible to see the contribution of the variables within these models (the description of the weather stations used to develop model per region is provided in Appendix Tables A1-2 and A1-3). Initial attempts to model monthly temperature were not successful, mainly due to the lack of available climate data. In addition, annual month-to-month climatic conditions tend to vary in South Africa, due to the effect of ENSO (Sun et al. 2000). Predictions were found to be more realistic when temperature and rainfall estimates were assessed as seasonal values. In order to develop models that could effectively interpolate weather station observations into seamless 26 surfaces, six modelling approaches were tested using different R packages, including multiple linear regression [‘lm’], thin-plate spline regression [‘mgcv’], and four independent machine learning models [‘caret’]. Significant variables included in the conventional multiple linear and plated spline regression temperature models were identified using both stepwise regression [‘MASS’] and the visualisation of the weighting of variables contributing to the model predictability [‘leaps’]. Only significant independent variables (α = 0.05) were included in the final multiple linear regression, and the two most significant independent variables for the thin-plate spline regression models (Wood, 2003). Machine learning models included linear, nearest neighbour, radial support vector machine, and random forests based models [‘caret’]. Descriptive variables such as latitude, longitude, altitude, slope, micro terrain position, macro terrain position, and aspect were included as model independent variables. Machine learning models setup included the following parameters;  Model setup ensured random reproducibility by using the “set.seed” function (a set seed value of 125 was used)  When setting up the training control for K-fold validation, the “repeatedcv” method was used by setting the number at 10 and repeats at 3.  When training the machine learning models the method was set at “lm” for linear models, “rf” for random forests models, “knn” for nearest neighbour models, “svmRadial” for support vector machine models. Machine learning models were validated by means of Repeated K-fold cross validation which involves splitting the data into 10 subsets and validating the model iteratively against the subset data. The process was repeated 3 times (Wong and Yeh, 2020). Variable importance weightings were calculated for the respective independent variables for both linear and random forests models [‘varImp’] by tracking changes or reductions to the model statistics such as the Generalised Cross-Validation Statistic (GCV), when including and excluding independent variables in the model. If a predictor variable was not used, it would have a variable importance rating of 0 (Asadi and Tian 2021). The R-packages which were employed are shown in Table 2.1. 27 Table 2. 1: Summary of table of R-packages used for computing of models and figures Predictions for both rainfall and temperature models were visualised using regional terrain raster files at the resolution of 12.5 m ×12.5 m. The digital elevation model (DEM) was produced by the Advanced Land Observing Satellite-1’s (ALOS), Palsar instrument (Figure 2.3; Appendix Table A1-2 and A1-3). Lowveld Escarpment Highveld Figure 2. 3: Independent variables associated with terrain conditions for plantations located on the Lowveld Escaprment and Highveld; as extracted from the Advanced Land Observing Satellite-1’s (ALOS); Palsar instrument. To avoid duplication, respective LM and RF machine learning temperature model visualisations were limited to an isolated region located on the Lowveld Escarpment. R-package Use ‘qqplotr’ Testing normality assumptions of parametric models ‘ggplot2’ Plotting of actual versus predicted values ‘lm’ Developing linear regression models ‘mgcv’ Developing thin-plate spline models ‘caret’ Developing machine learning models ‘MASS’ Identifying variables of importance for regression models ‘leaps’ Identifying variables of importance for regression models ‘varImp’ Identifying variables of importance for machine learning models ‘raster’ Visualising model predictions ‘quantreg’ Developing median regression models 28 2.2.3 Rainfall models South African rainfall typically has dry and wet cycles in 20 year intervals (Du Plessis and Schloms, 2017), and drought events in 5 to 7 year intervals (Usman and Reason, 2004). Therefore, rainfall data were sourced from 69 rainfall stations and was limited to a twenty year time interval (1999 to 2019) to maintain experimental design continuity (Appendix Table A1-3). Rainfall data were gathered using manual rainfall gauges (located at rainfall stations). Rainfall data were not limited to forestry regions and was sourced from private forestry companies, the South African Weather Service (SAWS), and research institutions such as the Agriculture Research Council (ARC), the South African Sugercane Research Institute (SASRI), and the Institute of Commercial Forestry Research (ICFR). The rainfall stations (shown in Figure 2.2) were more numerous and widely distributed than the temperature stations, permitting the development of a single regional rainfall model. Although the Lowveld Escarpment and Highveld forestry regions are both located in the Mpumalanga province of South Africa, they are geographically distant from one another. The close proximity of the Lowveld Escarpment to the Indian Ocean, results in a less pronounced and longer rain season. Missing data were due to equipment malfunction, staff turnover, or decommissioning of stations during the investigated time period. As with the temperature data, missing values were imputed by applying deterministic methods using measured values of surrounding stations, including multiple linear regression using R statistical software, to fill missing data in the time series for the different stations and only statistical significant patch models were employed (Campozano et al., 2014; Moeletsi et al., 2016; Shabalala et al., 2019; Teegavarapu and Chandramouli, 2005; Tencaliec et al., 2015). The proportions of actual data, patched data and missing values are shown in Appendix Tables A1-5 and A-6. Patch models included independent variables such as global position together with terrain features such as altitude, slope, aspect, and topographic position index (TPI). All terrain feature values were extracted from the Advanced Land Observing Satellite-1’s (ALOS), Palsar instrument (Figure 2.3). TPI (m) is the elevation of a pixel minus the mean elevation of the landscape in a defined radius. Positive values represent upper terrain conditions such as crests and upper slopes, while negative values represent low terrain condition such as lower slopes and valley bottoms (Oldfather et al., 2016; Swetnam et al., 2017). TPI scale is impacted by the spatial data pixel resolution and therefore have not been standardised when developing models (Amatulli et al., 2018; Fiandino et al., 2020). For the 29 purpose of this study, micro topographic position index was characterised relative to a 3 km radius around the weather station (TPI_3), while the macro topographic position index represents a 5 km radius around the weather station (TPI_5). Slope aspect was employed for rainfall modelling. Rainfall data were cumulatively expressed per season, and T-min and T-max were also expressed per season as mean value for individual weather stations (Tennant and Hewitson, 2002). Statistically significant models could not be developed using multiple linear regression [‘lm’], plated spline regression [‘mgcv’], or machine learning models [‘caret’] for seasonal rainfall models. However, multiple linear median regression [‘quantreg’] was found to work for the median seasonal rainfall (𝑃𝑚𝑒𝑑𝑖𝑎𝑛)(Edreira et al., 2020). Only variables with significant probabilities (α = 0.05) were included in the final models. All model residuals were reviewed, to ensure normality for parametric modelling, which includes all linear models [qqplotr](Aldor-Noiman et al. 2013; Chambers et al., 1983). However, machine learning models are non-parametric, hence the model structure is not determined beforehand, however determined by the data (Hable, 2012; Li, 2020; Ouyang et al., 2006). Median regression model R-square values were also calculated [‘goodfit’]. R-packages used for modelling and visualisation of model predictions are shown in Table 2.1. 2.2.4 Comparisons to existing climate model predictions Existing global models have been extensively used to characterise the growing conditions of forests, including models such as WorldClim 2.1 (Cerasoli et al., 2022; Stark and Fridley, 2022; Zomer et al., 2022). We obtained monthly T-min, T-max and rainfall data from the WorldClim 2.1 (Fick and Hijmans, 2017; Harris et al., 2014) website for the same coordinates as the temperature and rainfall stations used in this study. WorldClim 2.1 predict climate by interpolating measurements from existing weather stations (Fick and Hijmans, 2017). We then calculated mean seasonal temperatures and cumulative seasonal rainfall, and median rainfall to match our model output. The extracted temperature predictions were for the time period 2013 until 2018 and rainfall data for the period 1999 until 2018 to maintain experimental continuity. Unfortunately, no data were available for 2019. The WorldClim 2.1 data were based on 2.5 minutes resolution scale. Seasonal temperature and rainfall WorldClim 2.1 predictions were validated. 30 2.3 Results and discussion The respective temperature models developed are shown in Table 2.2. Outputs from the Random forests (RF) and Linear machine (LM) learning model predictions are shown graphically for the Lowveld Escarpment in Figures 2.4 and 2.6, and for the Highveld in Figures 2.5 and 2.7. Table 2. 2: Prediction accuracies of seasonal T-min and T-max models for the Lowveld Escarpment and Lowveld, respectively. Variable Region Model Season Summer Autumn Winter Spring DF RMSE R² DF RMSE R² DF RMSE R² DF RMSE R² T-min Lowveld Escarpment Linear regression 144 0.82 0.83 152 1.03 0.68 165 1.40 0.65 168 0.98 0.71 Plated spline regression (two variables) N/A 0.83 N/A 0.72 N/A 0.89 N/A 0.76 Linear regression (machine learning) 0.76 0.85 1.05 0.69 1.39 0.64 0.97 0.74 Nearest Neighbour (machine learning) 0.78 0.84 0.91 0.77 1.08 0.77 0.96 0.75 Support Vector Machine (machine learning) 0.74 0.85 0.79 0.82 0.82 0.88 0.90 0.77 Random Forests (machine learning) 0.80 0.83 0.85 0.80 0.78 0.89 0.91 0.77 Highveld Linear regression 92 0.98 0.67 107 1.12 0.51 113 1.20 0.49 115 0.93 0.47 Plated spline regression (two variables) N/A 0.84 N/A 0.67 N/A 0.69 N/A 0.55 Linear regression (machine learning) 0.98 0.68 1.08 0.58 1.21 0.53 0.94 0.50 Nearest Neighbour (machine learning) 0.73 0.81 1.04 0.63 0.95 0.71 0.85 0.57 Support Vector Machine (machine learning) 0.84 0.77 1.01 0.62 0.85 0.76 0.87 0.56 Random Forests (machine learning) 0.71 0.83 0.97 0.65 0.92 0.72 0.86 0.57 T-max Lowveld Escarpment Linear regression 149 1.78 0.76 160 1.46 0.83 184 1.28 0.87 181 1.45 0.82 Plated spline regression (two variables) N/A 0.78 N/A 0.83 N/A 0.87 N/A 0.81 Linear regression (machine learning) 1.60 0.80 1.30 0.85 1.23 0.89 1.40 0.83 Nearest Neighbour (machine learning) 1.78 0.77 1.50 0.81 1.23 0.89 1.49 0.81 Support Vector Machine (machine learning) 1.61 0.80 1.25 0.86 1.23 0.89 1.40 0.83 Random Forests (machine learning) 1.72 0.78 1.27 0.86 1.23 0.89 1.44 0.82 Highveld Linear regression 98 0.74 0.66 116 0.99 0.70 116 0.96 0.74 115 0.99 0.72 Plated spline regression (two variables) N/A 0.83 N/A 0.78 N/A 0.83 N/A 0.54 Linear regression (machine learning) 1.10 0.75 0.99 0.70 0.95 0.75 0.99 0.75 Nearest Neighbour (machine learning) 1.10 0.73 0.92 0.76 0.86 0.80 0.93 0.77 Support Vector Machine (machine learning) 1.03 0.76 0.93 0.74 0.82 0.83 0.95 0.76 Random Forests (machine learning) 0.94 0.81 0.92 0.75 0.83 0.82 0.90 0.78 2.3.1 T-min Site impacts on tree growth are species specific, due to the physiological adaptions of trees to their natural habitat to optimise evapotranspiration and tree growth (Blanco et al., 2021; Chakraborty et 31 al., 2019; Liu et al., 2022; Tang et al., 2021). For example, trees grown at lower temperatures can be hydraulically constrained, leading to the formation of fewer and smaller xylem elements (Petit et al., 2011). Therefore, T-min is an important criterion, when matching suitable genotypes to specific growing conditions. Random Forest (RF) models for T-min for the Lowveld Escarpment had R-squares (R²) and root mean square errors (RMSE) of 0.83 and 0.80 °C for summer, 0.80 and 0.85 °C for autumn, 0.89 and 0.78 °C for winter, and 0.77 and 0.91 °C for spring (Table 2.2). By comparison, linear (LM) machine learning models for T-min for the Lowveld Escarpment had R² and root mean square errors (RMSE) of 0.85 and 0.76 °C for summer, 0.69 and 1.05 °C for autumn, 0.64 and 1.39 °C for winter and 0.74 and 0.97 °C for spring (Table 2.2). Similarly, RF models for T-min for the Highveld had R-R² and RMSE of 0.83 and 0.71 °C for summer, 0.65 and 0.97 °C for autumn, 0.72 and 0.92 °C for winter and 0.57 and 0.86 °C for spring (Table 2.2). LM machine learning models for T-min for the Highveld had R² and RMSE of 0.68 and 0.98 °C for summer, 0.58 and 1.08 °C for autumn, 0.53 and 1.21 °C for winter, and 0.50 and 0.94 °C for spring (Table 2.2). Random Forests (RF) models outperformed linear (LM) machine learning models, with the exception of T-min during the summer months (Table 2.2). The variable importance ratings are provided in Table 2.3, where a score between 75 and 100 is considered to be a high rating, while those scores between 50 and 75 are a moderate rating, and below 50 is a low rating. Seasonal visualisations of the respective RF and LM model predictions for the Lowveld Escarpment region when predicting T-min are shown in Figure 2.4. For the Lowveld Escarpment T-min models, altitude was identified as the most important variable for both the RF and LM machine learning models for summer, autumn, and spring months. Longitude was the second most important variable for the RF models, with a high rating during autumn and moderate ratings during summer and spring. On the other hand, micro TPI was identified to be of high importance in LM models and particularly during autumn months. When predicting T-min for winter on the Lowveld Escarpment, micro TPI was the most important variable for both the RF and LM models. In addition, the RF models placed a high importance rating on macro TPI and moderate rating on altitude during the winter months. 32 Season Random Forests model Linear model Summer Autumn Winter Spring Figure 2. 4: Comparisons of Random Forest and Linear Machine Learning model predictions of seasonal T-min for pine and eucalypt plantation forest located on the Lowveld Escarpment. 33 Season Random Forests model Linear model Summer Autumn Winter Spring Figure 2. 5: Comparisons of Random Forests and Linear Machine Learning Model predicions of seasonal T-min for pinte and eucalypt plantation forests located on the Highveld. For the Highveld T-min models, altitude also had a high rating for summer, autumn, and winter (Figure 2.5). However, the RF models predicting T-min during autumn only placed a 34 moderate rating on altitude. In addition, RF models placed greater emphasis on locality in the form of longitude and latitude when predicting T-min on the Highveld. Longitude was more important when modelling autumn and winter T-min, but had a moderate rating for spring T-min. However, latitude was only considered to be of high importance when modelling winter T-min. Micro TPI was the only terrain feature of importance in the RF models with a moderate importance rating during winter and spring. Similarly, longitude was of high importance in the LM models predicting autumn and winter T-min. Latitude was only moderately important in summer and autumn T-min models. Interestingly, the LM models showed aspect to be an important terrain feature, with a moderate and high rating placed on models developed for summer and autumn months, respectively. Similarly, annual temperature models were also developed using the same modelling principles. However, these models were not discussed due to the successful development of significant seasonal models. R-square and RMSE values for the annual temperature models are shown in Appendix Table A1-4. The proximity to the equator (as latitude), the proximity to the ocean (as longitude), altitude, aspect, and terrain features (micro and macro TPI) were found to have variable significance when modelling T-min using respective RF and LM machine learning approaches. RF models developed to predict seasonal T-min on the Lowveld Escarpment, placed more emphasis on altitude and longitude, with the exception of winter months when the micro and macro TPI were found to be more important. Areas in the lower regional longitudes tend to be at higher altitudes due to the proximity of the Drakensberg mountain range, and are therefore often associated with colder seasonal T-min (Favre et al., 2016; Moore et al., 2009). Thus, a correlation exists between the proximity to the ocean and regional altitude. TPI terrain features were found to be only important during winter months for the RF models. The association of T-min with TPI can be related to the inversion of cold air and frost during winter months (Laughlin and Kalma, 1990). However, the LM machine learning models developed to predict T-min on the Lowveld Escarpment, placed far greater importance on terrain features such as TPI, which was particularly apparent in the autumn. The Lowveld Escarpment is mountainous and topographically diverse (Favre et al., 2016; Mndaweni and Naicker, 2019), and as such the importance of terrain conditions together with altitude was highlighted by these findings. Models developed to predict T-min on the Highveld had a lower prediction accuracy, and this difference may be due to the more homogeneous terrain in this region. RF models developed 35 to predict seasonal T-min on the Highveld also placed more importance on global positioning, such as latitude, longitude, and altitude. The importance of terrain features was limited to winter and spring months, with micro TPI only being moderately important during these months. Similar to the Lowveld Escarpment region, altitude tended to regionally increase with decreasing latitude and longitude in the Highveld (Moore et al., 2009). However, altitude was of low importance when modelling T-min during winter months. Slope aspect was of moderate importance when modelling summer T-min with LM models and high importance when modelling autumn T-min on the Highveld. The results are not surprising as aspect has been found to impact temperature in previous studies (Holland and Steyn, 1975; Schulze 2000). Southern and eastern facing slopes typically have lower temperatures due to a shading effect and limitations on solar radiation (Holland and Steyn, 1975; Schulze, 2000). Future studies will need more temperature stations to fully explain these relationships. In addition, digital fire towers are typically positioned at higher terrain as they provide the best all round visibility for detecting fires, which potentially further limited the ability to accurately model T-min on an already homogenous landscape. 2.3.2 T-max Temperature significantly impacts the water and nutrients transport systems of trees (Petit et al., 2011; Tang et al., 2021; Zhang et al., 2020a). Therefore, higher T-max can improve survival and tree growth, and should also be considered when deploying genotypes in specific climates for plantation forestry (Jönsson and Lagergren, 2017; Tang et al., 2021; Zhang et al., 2020a). T-max models had good prediction accuracies for both the RF and LM machine learning models, respectively (Table 2.2). Random Forest (RF) models for T-max for the Lowveld Escarpment had R-squares (R²) and root mean square errors (RMSE) of 0.78 and 0.94 °C for summer, 0.86 and 1.27 °C for autumn, 0.89 and 1.23 °C for winter and 0.82 and 1.44 °C for spring (Table 2.2). On the other hand, linear (LM) machine learning models for T-max for the Lowveld Escarpment had R² and root mean square errors (RMSE) of 0.80 and 1.60 °C for summer, 0.85 and 1.30 °C for autumn, 0.89 and 1.23 °C for winter, and 0.83 and 1.40 °C for spring (Table 2.2). Similarly, RF models for T-max for the Highveld had R² and RMSE of 0.81 and 0.94 °C for summer, 0.75 and 0.92 °C for autumn, 0.82 and 0.83 °C for winter, and 0.78 and 0.90 °C for spring (Table 2.2). LM machine learning models for T-max for the Highveld had R² and RMSE of 0.75 and 1.10 °C for summer, 0.70 and 0.99 °C for autumn, 0.75 and 0.95 °C for winter, and 0.75 and 0.99 °C for spring (Table 2.2). 36 Season Random Forests model Linear model Summer Autumn Winter Spring Figure 2. 6: Comparisons of Random Forest and Linear Machine Learning model predictions of seasonal T-max for pine and eucalypt plantation forest located on the Lowveld Escarpment. 37 Season Random Forests model Linear model Summer Autumn Winter Spring Figure 2. 7: Comparisons of Random Forests and Linear Machine Learning model predictions of seasonal T-max for pine and eucalypt plantation located on the Highveld. 38 LM models developed for the Lowveld Escarpment marginally outperformed the RF models when predicting T-max. However, the RF model outperformed LM models when predicting seasonal T-max on the Highveld. The variable importance ratings are provided in Table 2.3. A breakdown of the number of data points used are shown in Table 2.2. Table 2. 3: Variable importance ratings for respective Random Forests and linear seasons T-min and T-max models Dependent variable Independent variable Lowveld Escarpment Highveld Linear regression (machine learning) Random forests (machine learning) Linear model (machine learning) Random forests (machine learning) Summer Autumn Winter Spring Summer Autumn Winter Spring Summer Autumn Winter Spring Summer Autumn Winter Spring T-min Latitude 0.0 5.5 16.1 15.2 3.9 7.2 16.7 9.6 54.2 51.0 18.3 0.0 47.9 22.7 77.6 36.4 Longitude 6.0 0.0 0.0 13.1 59.0 77.3 49.8 61.4 0.0 84.2 100 45.4 19.4 100 100 74.2 Altitude 100 100 42.8 100 100 100 63.5 100 100 86.7 11.3 100 100 72.4 30.7 100 Slope 0.9 8.1 9.3 0.0 1.3 0.0 0.0 1.9 3.6 0.5 13.7 13.7 0.0 49.5 35.3 35.3 Aspect 3.6 7.0 7.2 7.3 0.0 10.2 22.1 0.0 55.7 100 44.7 44.7 31.0 1.2 9.5 9.5 TPI (3km) 13.4 75.1 100 40.9 10.7 17.5 100 10.4 46.8 0.0 16.7 16.7 4.9 22.4 50.4 50.4 TPI (5km) 9.6 38.2 44.4 19.2 8.7 18.2 96.0 14.7 32.5 44.1 33.7 33.7 6.5 0.0 0.0 0.0 T-max Latitude 0.0 12.3 0.0 0.0 0.7 1.5 1.5 0.8 17.6 11.0 8.3 0.0 0.1 1.2 30.6 25.5 Longitude 18.5 12.4 13.1 28.2 19.0 34.1 24.9 18.8 55.7 36.5 39.4 72.8 6.6 3.1 20.5 20.7 Altitude 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 Slope 24.3 29.8 22.7 22.7 18.0 30.6 24.8 24.8 13.9 18.7 15.1 15.1 0.8 2.8 43.3 43.3 Aspect 9.0 0.0 4.1 4.1 0.0 0.0 0.0 0.0 9.4 3.2 18.6 18.6 3.4 0.0 0.0 0.0 TPI (3km) 21.2 20.4 13.4 13.4 8.5 13.5 7.3 7.3 5.7 1.5 7.6 7.6 24.6 15.9 63.6 63.6 TPI (5km) 12.2 12.7 8.8 8.8 1.5 4.0 1.9 1.9 0.0 0.0 15.8 15.8 0.0 3.7 38.6 38.6 Figure 2.6 shows the respective RF and LM seasonal model predictions for T-max in the Lowveld Escarpment. On the Lowveld Escarpment, altitude was the sole independent variable with a high rating for both the RF and LM machine learning models when predicting seasonal T-max. Regional positioning and other terrain features were of low importance. Seasonal T-max models for the Highveld had some noticeable differences when comparing RF and LM machine learning models (Figure 2.7). Similarly, altitude was the most important variable for both model types. However, the RF models placed a moderate importance rating on micro TPI when modelling 39 winter and spring T-max, respectively. In addition, the LM models placed a moderate importance rating on longitude when modelling summer and spring T-max. Interestingly, the RF models placed greater emphasis on terrain features, while the LM models were more reliant on longitude and the distance from the eastern seaboard when modelling seasonal T-max. Altitude was found to be the most important variable when modelling T-max, and the findings are substantiated by previous studies (Apaydin et al., 2011; Schulze, 2000; Thomas, 2011). TPI and longitude were only moderately important for some of the seasons, and their importance have also been well documented. TPI has been found to have a pooling effect, with cold air draining down to the lowest position (Daly et al., 2009; Méndez-Toribio et al., 2016), supporting the good predictions for winter and spring RF models. Longitude impacts on T-max for the LM machine learning models are also supported by the impact of the proximity to the equator and the eastern seaboard on T-max (Bindi and Miglietta, 1991; Chantry et al., 2021; Rind, 1998). Interestingly, the importance of longitude in the LM models was limited to summer and winter months, the seasons with the majority of annual rainfall in the Highveld region. These findings suggest a potential cooling effect of humid air moving across the Highveld during the wetter seasons, particularly impacting T-max (Nicholls et al., 1996). 2.3.3 Rainfall Water availability and water use efficiency have been found to impact tree growth (Niklas and Spatz 2004, Ryan 2010, Silva et al. 2022). Tree growth has also been found to be limited to the point where root, stem, and leaf maintenance are in equilibrium with the production of photosynthate, leaving nothing for additional growth (King, 1990; Ryan and Yoder, 1997). Rainfall is the primary source of water for trees growing in the Mpumalanga forestry region (Masingi and Maposa, 2021). Therefore, seasonal rainfall should be considered when matching genotypes to plantation forestry sites (Boreham and Pallett, 2009). A summary table of the respective median regression seasonal rainfall models are shown in Table 2.4. Summer, autumn, and spring 𝑃𝑚𝑒𝑑𝑖𝑎𝑛 models were significant (P < 0.05), with R² and RMSE values of 0.90, 0.87 and 0.88 and 207.5 mm, 98.6 mm and 103.7 mm respectively (Table 2.4). 40 Table 2. 4: Summary statistics for 𝑷𝒎𝒆𝒅𝒊𝒂𝒏 regression independent variables per seasonal and quantile model for rainfall. Significant = * (0.01<P<0.05), highly significant = ** (0.001<P<0.01) and very highly significant *** (P<0.001). Rows containing N/A indicates variables that were not significant (P > 0.05). Season Significant variables Median regression RMSE R² Coefficient Std. Error P-value Significance Summer Intercept -97181.87 47849.199 0.042 * 207.5 0.90 Longitude 3433.12 1529.224 0.025 * Latitude -3746.10 1786.299 0.036 * Longitude × Latitude 132.03 57.164 0.021 * Longitude × Latitude × Altitude 0.00 0.002 0.042 * Altitude (m) -3.08 1.553 0.048 * Slope N/A N/A N/A N/A Autumn Intercept -58148.67 25391.673 0.022 * 98.6 0.87 Longitude 2008.68 802.933 0.013 * Latitude -2304.14 944.132 0.015 * Longitude × Latitude 79.36 29.876 0.008 ** Longitude × Latitude × Altitude 0.00 0.001 0.017 * Altitude -2.19 0.920 0.018 * Slope 5.09 1.305 0.000 *** Spring Intercept 59669.08 25793.377 0.021 * 103.7 0.88 Longitude -1771.04 821.828 0.031 * Latitude 2172.87 970.631 0.025 * Longitude × Latitude -64.23 30.956 0.038 * Longitude × Latitude × Altitude 0.00 0.001 0.000 *** Altitude -3.61 0.834 0.000 *** Slope N/A N/A N/A N/A However, the winter 𝑃𝑚𝑒𝑑𝑖𝑎𝑛 model was not significant (P > 0.05), and this was likely because of the lack of rainfall during the winter months. Seasonal rainfall model predictions of 𝑃𝑚𝑒𝑑𝑖𝑎𝑛 for the Lowveld Escarpment and Highveld are shown in Figure 2.8. Significant independent variables included latitude, longitude and altitude, and the interaction between these independent variables to a larger or lesser extent (α = 0.05). However, aspect and micro terrain position (TPI micro), were not statistically significant for any of the models (P > 0.05). Significant independent variables included in the median regression 𝑃𝑚𝑒𝑑𝑖𝑎𝑛 models. The same variables were generally significant for the respective models, however, slope was only significant for autumn, and was of no statistical significance for summer and spring 𝑃𝑚𝑒𝑑𝑖𝑎𝑛. 41 Season Lowveld Escarpment Highveld Summer Autumn Spring Figure 2. 8: Comparison of seasonal median regression 𝑷𝒎𝒆𝒅𝒊𝒂𝒏 models for the Lowveld Escarpment and Highveld pine and eucalypt plantation forests, respectively. Winter absent due to little or no rainfall received during this season. Regional rainfall in Eastern South Africa is strongly impacted by ocean temperatures (Crétat et al., 2012; Favre et al., 2016). Thus, rainfall frequency and intensity will be impacted by the proximity of rainfall stations to the eastern seaboard, as was found in this study, particularly during summer months when the majority of rainfall occurs (Crétat et al., 2012; Favre et al., 2016). Latitude and longitude serve as proxies for the distance from the ocean, with lower seasonal rainfall with decreasing latitude and longitude, or distance from the ocean (Kane, 2009). Furthermore, in the Mpumalanga forestry region altitude increases with decreasing longitude due to the inland Drakensberg Escarpment dividing the Lowveld and Highveld regions (Partridge et al., 2010). Although higher altitudes would typically result in higher rainfall, a co-relationship exists between 42 altitude and the distance from the ocean because altitude increases as you progress inland in this region (Dahri et al., 2016; Duckstein et al., 1973; Naoum et al., 2004; Sasaki and Kurihara, 2008). Therefore, rainfall models indicated a lower regional rainfall with decreasing longitude. The Drakensberg Escarpment and terrain features such as slope have also been associated with orographic rainfall during drier months, which explains the greater significance of slope for rainfall models during drier autumn months (Favre et al., 2016; Louw, 1997). Although the Lowveld Escarpment region had greater rainfall during the summer and autumn, the Highveld received more rainfall during spring, particularly in sloped, lower-lying terrain positions. 2.3.4 Comparing Worldclim 2.1 to actual measured values Actual seasonal temperature and rainfall measurements were compared to seasonal WorldClim 2.1 predictions for the weather stations (Figure 2.9). Figure 2. 9: Comparison of minimum temperature, maximum temperature and rainfall models to actual temperature and rainfall station measurements with corresponding R-square values. Minimum temperature measurements were compared to (A) WorldClim 2.1, (B) Linear model and (C) Random Forest model predictions. Maximum temperature was compared to (D) WorldClim 2.1, (E) Linear model and (F) Random Forests model predictions. Rainfall measurements were compared to (G) WorldClim 2.1 and (H) Median regression model predictions. 43 Seasonal T-min was best predicted by RF models, followed by WorldClim 2.1 and LM models with R-squared values s of 0.75, 0.70 and 0.55 and a RMSE of 1.82, 1.93 and 2.4, respectively. T-max was best predicted by LM models, followed by RF and WorldClim 2.1 models with R-squared values of 0.84, 0.82 and 0.72 and RMSE’s of 2.34, 2.44 and 3.16, respectively. The seasonal median rainfall regression model significantly outperformed the WorldClim 2.1 prediction median values with an R-square of 0.86 compared to 0.67 and RMSE of 53.86 compared to 82.56. These results suggest that the localised models developed in this study are better for characterising the climate for plantation forests in the Mpumalanga forestry region. However, the seasonal means of WorldClim 2.1 predictions were surprisingly accurate when compared to the actual measured temperature and rainfall value, considering that monthly WorldClim predictions were poorly correlated with the actual monthly measured values. 44 Chapter 3: Site impacts on wood properties and tree form 3.1 Introduction Tree form has an important influence on mill recovery and product quality for both lumber and veneer production. Wood properties such as density, latewood content (LW), pith to bark density gradient (PBDG), growth ring eccentricity, and the proportion of juvenile core (JC) are known to affect lumber strength and stability (Caballé et al., 2020; Charlton et al., 2020; Li et al., 2013; Rais et al., 2017; Wessels et al., 2015). P. patula is a tropical hard pine and has wood densities that have been shown to range from 377-553 kg/m3 at 12 % moisture content (Hall et al., 2000, Wessels et al. 2015). It may be possible to overcome some of these challenges by understanding the genetic and environmental (G  E) factors that affect tree form and the wood quality of plantation-grown trees (Caballé et al., 2020; Watt and Trincado, 2019). Moreover, tree form and wood properties can be manipulated in plantation forests by means of silviculture practices, such as initial spacing at establishment, thinning, and branch pruning (Deng et al., 2019; Erasmus et al., 2018; Erasmus and Wessels, 2020; Kang et al., 2004; Munalula et al., 2016). However, at present there is limited knowledge about the key environmental factors impacting tree form and wood properties of P. patula (Muller et al., 2017). Growth of plantation grown P. patula is generally perceived to be influenced by climate, geological features, and soil properties (Muller et al., 2017; Schutz et al., 1991). The South African forestry landscape is characterised by a wide range of altitudes as well as climatic variables such as geology, precipitation, and temperature (Belay, 2016; Louw et al., 2011; Malata et al., 2017; Warburton and Schulze, 2008). The Mpumalanga region in South Africa is a summer rainfall area, which has been found to support good tree growth, while moisture availability later in the year has been suggested to increase latewood formation (Belay, 2016; Malata et al., 2017). More importantly, when evaporation outweighed water availability, growth was found to be restricted (Louw et al., 2011; Belay, 2016). The water balance of forestry trees over time, expressed in growth days, has also been shown to be a significant factor affecting tree growth and the wood density of P. patula, indicating that climatic variables are important for tree growth and wood properties in South Africa (Fairbanks, 2000; Leibing et al., 2009; Louw et al., 2011; Malata et al., 2017; Maponya et al., 2013; van Zonneveld et al., 2009). 45 The purpose of this study was to assess the influence of environmental parameters such as rainfall, temperature, soil chemistry, and soil water holding capacity on the growth and wood properties of P. patula. 3.2 Methodology The Mpumalanga forestry region is topographically and climatically diverse and includes two regions, namely the Lowveld and Highveld (Louw et al., 2011; Muller et al., 2017). The Lowveld region includes the Drakensberg mountain range where forestry sites are typically from 800 to 2,000 meters above sea level (m.a.s.l) (Louw and Scholes, 2006; Muller et al., 2017; Partridge et al., 2010). Soils in this region are mostly formed from sedimentary rocks including Malmani and Timeball Hill shales (Campion, 2008; Louw and Scholes, 2006; Muller et al., 2017). Malmani dolomites occur on low to mid altitude sites up to 1,700 meters, and are usually deep Inanda soil forms (Louw and Scholes, 2002; Muller et al., 2017). Soils formed from Timeball Hill shales have highly variable soil depths (Louw and Scholes, 2002; Louw and Scholes, 2006). At low altitudes, the shale soils can be deep Inanda soil forms, due to colluvially deposited top soils at the base of mountains and shallow Nomanci soil forms at higher altitudes (Scott, 2000). In contrast, the Highveld region is more uniform (1,400 to 1,800 m.a.s.l.) with mostly deep soils formed from granite parent material (Carlson, 2000; Louw and Scholes, 2006) (Figure 3.1). Figure 3. 1: Map showing the dominant regional soil parent materials and the location of the Lowveld and Highveld plantation forestry regions. 46 The Highveld has greater temperature extremes and receives less rainfall annually than the Lowveld Escarpment. A summary of site related climate variables are shown in Table 3.1. Table 3. 1: Trial design and stratification of the forestry landscape, climate per soil parent material including annual maximum temperature (T-max), annual minimum temperature (T-min), summer rainfall, autumn rainfall, spring rainfall, annual growth days (GD), annual growth temperature (GT). In addition site related soil properties including effective soil depth (ESD) and total available water (TAW), and nutrient pool including soil nitrogen (N), phosphate (P), potassium (K), calcium (Ca), magnesium (Mg), sodium (Na), sulphate (S), carbon (C) and carbon to nitrogen ratios (C:N ratios). Grouping Altitude range (m.a.s.l.) Soil parent material (N = 54) Malamani dolomite Timeball Hill shale Randian granite Sampling matrix 1,100 – 1,299 n = 6 n = 6 No plots 1,300 – 1,499 n = 6 n = 6 No plots 1,500 – 1,699 n = 6 n = 6 n = 6 1,700 – 1,899 No plots n = 6 n = 6 Climate T-max 19.9 - 23.0 18.7 - 25.5 20.5 - 22.2 T-min 11.8 - 12.2 11.0 - 12.2 9.7 - 11.0 Summer rainfall (mm) 557 - 623 477 - 526 373 - 425 Autumn rainfall (mm) 188 - 216 169 - 200 151 - 180 Spring rainfall (mm) 217 - 232 213 - 225 229 - 273 GD (annual) 167.0 - 173.6 149.5 - 183.4 145.1 - 175.1 GT (annual) 17.3 - 18.7 16.5 - 21.0 16.9 - 17.9 Soil properties ESD (cm) 65 - 200 45 - 200 25 - 200 TAW (mm) 283 - 989 96 - 846 125 - 634 N (𝐭 𝐡𝐚−𝟏) 4.6 - 13.6 5.3 - 33.7 2.9 - 21.7 P (𝐭 𝐡𝐚−𝟏) 0.013 - 0.043 0.009 - 0.054 0.006 - 0.041 K (𝐭 𝐡𝐚−𝟏) 0.1 - 4.3 0.0 - 3.8 0.2 - 1.1 Ca (𝐭 𝐡𝐚−𝟏) 0.1 - 8.4 0.0 - 20.1 0.1 - 1.0 Mg (𝐭 𝐡𝐚−𝟏) 0.1 -2.9 0.0 - 4.1 0.0 - 0.8 Na (𝐭 𝐡𝐚−𝟏) 0.03 - 0.22 0.01 - 0.14 0.01 - 0.12 S (𝐭 𝐡𝐚−𝟏) 1.8 - 5.4 1.6 - 8.1 1.4 - 6.6 C (𝐭 𝐡𝐚−𝟏) 79.7 - 279.8 101.1 - 492.4 56.3 - 425.6 C:N ratio 13.2 - 27.2 12.6 - 21.5 15.5 - 27.5 A total of 54 square sample plots of 30 m × 30 m were established in P. patula plantation compartments which had historically similar silviculture activities and were thought to have originated from the same seed source, a clonal orchard planted just outside of Sabie (Table 3.1). The original spacing for all plots was 3 × 3 meters (1,111 trees per ha). The compartments had 47 been systematically line thinned to a mean stocking density of 800 trees per ha followed by a second thinning to a target of 500 trees per ha, while maintaining a mean target basal area of 24 m² per ha, as the trees matured. All of the sampled compartments also had three stages of branch pruning, including a first pruning to a height of 1.5 m, a second pruning to a height of 3.5 m, and a third pruning to a height of 5.5 m. The aim of the pruning was to maximize clear wood formation by restricting the knotty core of the pruned tree sections to less than 12 cm, hence pruning was initiated based on the site-specific growth rate. For sites with low growth rates, the second and third pruning were delayed because of tree size. A two meter deep trench was dug in each sample plot, to assess soil parent material, soil depth, and soil form. Soil samples were analysed at the Industrial Council for Forest Research (ICFR) for chemical and clay content. Seasonal and annual climate for the sample plots were determined using models developed for the Mpumalanga region (van der Merwe et al. 2023c). Annual growth days and growth temperatures were obtained from previously published values for the region (Louw et al., 2011). Stem diameters at a height of 1.3 meters (DBH) and tree heights were measured for each tree. Slenderness coefficient (SC) was calculated as a measure of tree form by dividing the tree height by DBH (Wang et al., 1998; Zhang et al., 2020b). A total of 21 trees were randomly selected for measurements of wood properties in each sample plot. Damaged or deformed trees were excluded from sampling. Trees in the sample plots were separated into four quartiles according to tree height. Trees represented in the second and third quartile was grouped together. Seven trees were randomly chosen from each height group for sampling and 12 mm increment cores extracted, in the North to South direction. A summary of the independent variables investigated is shown in Table 3.2. Increment cores were air dried for 7 days at room temperature, halved at the pith using a laboratory band saw, and the Northern portion used for x-ray densitometry. If any defects, such as hairline fractures or delamination were present, the Southern portion was used. A radial sample with a thickness of 1.68 mm was removed from the centre of the cores using a pneumatic twin-blade saw, and extractives were removed overnight with hot acetone for 14 hours in a Soxhlet apparatus. The samples were permitted to equilibrate to 7 % moisture content, by weight, before the density was measured. The x-ray densitometer was calibrated using 10 additional P. patula core samples of various tree sizes derived from the sample sites. The prepared samples were then subject to x-ray densitometry using a Quintek measurement system at a resolution of 25.4 µm to quantify ring ages, earlywood (EW) and latewood (LW) portions, overall wood density (WD), and 48 juvenile core transition ages (Da Ros et al., 2021; Ukrainetz et al., 2008). Density was reported on an oven-dry basis. Earlywood to latewood density thresholds were set at 480 kg/m3 (Antony et al., 2012). Table 3. 2: Climate and soil independent variables examined with descriptions and source of the data. Wood density was also weighted according to the circumferential area of the respective growth rings for each increment core, as per formula 3.1. Type Variable Description Source Climate Annual maximum temperature (T-max) Modelled annual maximum temperatures. (van der Merwe et al. 2023c) Annual minimum temperature (T-min) Modelled annual minimum temperatures. (van der Merwe et al. 2023c) Summer rainfall Modelled median summer rainfall. (van der Merwe et al. 2023c) Autumn rainfall Modelled median autumn rainfall. (van der Merwe et al. 2023c) Spring rainfall Modelled median spring rainfall. (van der Merwe et al. 2023c) Growth day (GD) The amount of theoretical growing days in a calendar year. Growing days can be defined as the amount of days that sufficient water is available to facilitate evapotranspiration. (Louw et al. 2011) Growth temperature (GT) Mean monthly temperature weighted according to the monthly growth days. (Louw et al. 2011) Soil Properties Effective soil depth (ESD) The depth of the respective soil layers, excluding saprolite and bedrock. Direct measurements Soil total available water (TAW) The ability of soil to hold water. Calculated by means of direct soil depth and texture analysis. Direct measurements N nutrient pool (N) The site available nitrogen (N), calculated using soil bulk density values and soil N content and expressed as tonnes per hectare. Direct measurements K nutrient pool (K) The site available potassium (K), calculated using soil bulk density values and soil K content and expressed as tonnes per hectare. Direct measurements P nutrient pool (P) The site available phosphate (P), calculated using soil bulk density values and soil K content and expressed as tonnes per hectare. Direct measurements Ca nutrient pool (Ca) The site available calcium (Ca), calculated using soil bulk density values and soil Ca content and expressed as tonnes per hectare. Direct measurements Mg nutrient pool (Mg) The site available magnesium (Mg), calculated using soil bulk density values and soil Mg content and expressed as tonnes per hectare. Direct measurements Na nutrient pool (Na) The site available sodium (Na), calculated using soil bulk density values and soil Na content and expressed as tonnes per hectare. Direct measurements S nutrient pool (S) The site available sulphur (S), calculated using soil bulk density values and soil S content and expressed as tonnes per hectare. Direct measurements C nutrient pool (C) The site available carbon (C), calculated using soil bulk density values and soil C content and expressed as tonnes per hectare. Direct measurements Carbon to nitrogen ratios (C:N ratios) The ratio of a unit carbon to a unit nitrogen. Direct measurements 49 𝑊𝐷𝑊𝑒𝑖𝑔ℎ𝑡𝑒𝑑 = ∑ ( 𝑊𝐷 × 𝑆𝐴)𝑟(𝑛)𝑆𝐴𝑇𝑜𝑡𝑎𝑙 (3.1) where, 𝑊𝐷𝑊𝑒𝑖𝑔ℎ𝑡𝑒𝑑 = Weighted profile wood density, WD = Wood density of a specific growth ring, SA = Surface areas of a specific growth ring, 𝑆𝐴𝑇𝑜𝑡𝑎𝑙 = Total profile surface area. LW was expressed as a surface percentage for the respective increment core profiles, as per formula 3.2. 𝐿𝑊% = ∑ ( 𝐿𝑊𝑆𝐴)𝑟(𝑛)𝑆𝐴𝑇𝑜𝑡𝑎𝑙× 100 (3.2) where, 𝐿𝑊% = LW percentage, LWSA = LW surface area of a specific growth ring, r(n) represents a specific growth ring, and 𝑆𝐴𝑇𝑜𝑡𝑎𝑙 = Total profile surface area. Juvenile core (JC) portions of profiles were expressed as surface percentages for the respective increment core profiles, as per formula 3.3. Surface area of the increment, younger than the determined JC transition age was considered to be juvenile wood. 𝐽𝐶% = ∑ ( 𝐽𝐶𝑆𝐴)𝑟(𝑛)𝑆𝐴𝑇𝑜𝑡𝑎𝑙× 100 (3.3) where, 𝐽𝐶% = JC portion, JCSA = JC surface area of a specific growth ring included in the juvenile core area, i.e., r(n) represents a specific growth ring number included in the juvenile core portion of the profile specified by juvenile core transition age, and 𝑆𝐴𝑇𝑜𝑡𝑎𝑙 = Total profile surface area. Wood density gradients were calculated for growth rings and weighted according to the surface area of the respective profiles, as per formula 3.4. 𝑊𝐷𝑀𝑊𝑒𝑖𝑔ℎ𝑡𝑒𝑑 = ∑ [( 𝑊𝐷𝑟𝐵−𝑊𝐷𝑟𝐴𝐷𝑖𝑠𝑡𝑟𝐵−𝐷𝑖𝑠𝑡𝑟𝐴)×𝑆𝐴𝑟𝐵]𝑟(𝑛)𝑆𝐴𝑇𝑜𝑡𝑎𝑙 (3.4) where, 𝑊𝐷𝑀𝑊𝑒𝑖𝑔ℎ𝑡𝑒𝑑 = Weighted pith to bark wood density gradient kg/m³ mm, 𝑊𝐷𝑟𝐴 = Wood density of growth ring A (kg/m³), 𝑊𝐷𝑟𝐵 = Wood density of growth ring B (kg/m³), 𝐷𝑖𝑠𝑡𝑟𝐴= Distance of growth ring A from profile starting point (mm), 𝐷𝑖𝑠𝑡𝑟𝐵= Distance of growth ring B from profile starting point (mm) and 𝑆𝐴𝑇𝑜𝑡𝑎𝑙 = Total profile surface area (mm²). Thus, the calculation was completed for all pith to bark growth rings, including all neighbouring growth rings and weighted according to the surface area of the profile. Growth ring eccentricity (GRE) percentage was calcuated by using formula 3.5. 50 𝐺𝑅𝐸 = 𝑅1−𝑅2𝑅1+ 𝑅2× 100 (3.5) where, 𝑅1 = the distance from the Northern edge to pith of the wood core profile in mm, 𝑅2 = the distance from the pith to Southern edge of the wood core profile in mm. Juvenile core transition age was estimated for each site type (geology  altitude) using linear plateau models [‘rcompanion’](Mangiafico 2020). Juvenile core transition ages were plotted using latewood density and growth ring age, and an example of the graphical illustration of these models can be seen in Appendix Figure A1-7 (Da Ros et al., 2021; Mansfield et al., 2007; Mansfield et al., 2009; Mansfield et al., 2016; Mutz et al., 2004). A summary of tree form and wood properties which were investigated is presented in Table 3.3. Table 3. 3: Tree form and wood properties examined with descriptions and the source of the data. Type Variable Description Source Tree Form Tree height Height measurements of standing trees. Direct measurements Slenderness coefficient (SC) Height measurements of standing trees, divided by the tree diameter measurements at 1.3 meters. Direct measurements Wood Properties Wood density Mass of a given volume of wood, determined by X-ray densitometry. Direct measurements Latewood content (LW) Latewood content weighted according to the circumferential area of the respective growth rings for each increment core and expressed as a percentage. Direct measurements Pith to bark wood density gradient (PBDG) Wood density gradients calculated for growth rings and weighted according to the surface area of the respective profiles. Direct measurements Juvenile core transition age (JC) Age at which latewood density stabilises and uniform wood is formed. Direct measurements Juvenile core portion (JCP) Surface area of the increment, younger than the determined JC transition age. Direct measurements Growth ring eccentricity (GRE) The nominal area that the pith is off centre from the profile diameter and expressed as a percentage. Direct measurements All statistical analyses were completed using the R programming environment: A language and environment for statistical computing, including analysis of variance (ANOVA) to determine significant differences for dependent variables [‘aov’](R Core Team 2022). ANOVA residuals were inspected for normality [‘qqPlot’](R Core Team, 2022). If ANOVA residuals had a normal distribution, treatment least square means and standard error values were determined [‘lsmeans’](Lenth, 2016; Lenth, 2020), and post hoc testing was completed to establish differences between respective treatments using a Tukey Honestly Significant Difference Test 51 [‘TukeyHSD](Yandell, 1997). Principle component analysis (PCA) and a correlation matrix were completed to establish the association of the investigated variables with tree form and wood properties [‘lfda’, ‘ggfortify’, ‘corrplot’](Tang et al. 2016; Tang and Li 2019; Wei and Simko 2021). Only variables which significantly influenced tree form and wood properties are discussed here. Tree height was modelled using linear models [‘lm’](R Core Team 2022), while wood density, LW content, and pith to bark density gradient (PBDG) predictions were modelled using median regression models [‘quantreg’](Abbas et al., 2019; Gao et al., 2017; Miao et al., 2021; Naoum and Tsanis, 2004; Woodall et al., 2014). No juvenile core (JC) transition age and juvenile core portions (JCP) models were developed because only regional variation was found to be significant. In addition, no significant models were found for the growth ring eccentricity of trees (GRE). A summary of the R-software functions and packages used for statistical analysis are shown in Appendix Table A1-9. 3.3 Results and discussion Sites were chosen according to altitude and geology because these parameters were originally thought to be the most important environmental characteristics affecting tree growth and wood properties of plantation grown P. patula in South Africa (Crous et al., 2009; Louw and Scholes 2002; Louw and Scholes, 2006; Louw, 2016; Muller et al., 2017; Schutz et al., 1991). However, other, more localized factors such as rainfall and total available water were found to be influential (Belay, 2016; Muller et al., 2017). 3.3.1 Tree and wood properties Soil geology and altitude were found to significantly influence tree form, including tree height, sturdiness coefficient, and volume (p<0.05). Similarly, wood properties including wood density, LW content, pith to bark density gradient (PBDG), and juvenile core (JC) transition age and juvenile core portions (JCP) were also significantly impacted by soil geology and altitude (Table 3.4). However, growth ring eccentricity (GRE) was only impacted by soil geology. 52 Table 3. 4: Tree form and wood properties significantly affected by both soil geology and altitude. Treatment means and standard errors are shown for respective regions and site types, including tree height, tree volume (V), slenderness coefficient (SC), wood density, latewood content (LW), pith to bark density gradient (PBDG), juvenile core transition age (JC), juvenile core portion (JCP), and growth ring eccentricity which was only affected by soil. Superscript letters added to the end of each numeric value represents treatment groups found by Tukey HSD. When the same superscript letters are above values within a designated column, it indicates that there are no statistically significant differences between the treatments. Different superscripts indicate a statistically significant difference. Region Soil Altitude range (m.a.s.l.) Tree height (m) Volume (m³) SC Wood density (kg/m³) LW content (%) PBDG (kg/m³ mm) JC (age) JCP (%) GRE (%) Highveld Randian Granite 1,500-1,699 24.3 ± 0.20𝑏 0.78 ± 0.023𝑏 82.7 ± 1.01𝑏𝑐𝑑 433 ± 3.8𝑑 26.3 ± 0.74𝑓 2.0 ± 0.14𝑐𝑑 12.4 ± 0.00𝑎 69.7 ± 0.76𝑎 4.2 ± 0.2𝑎 1,700-1,899 24.6 ± 0.20𝑏 0.77 ± 0.023𝑏 84.0 ± 1.01𝑏𝑐 439 ± 3.8𝑑 26.9 ± 0.74𝑓 1.9 ± 0.14𝑑 11.8 ± 0.00𝑏 71.3 ± 0.76𝑎 Lowveld Escarpment Malmani Dolomite 1,100-1,299 22.9 ± 0.20𝑐 0.58 ± 0.023𝑐𝑑 85.6 ± 1.01𝑏 458 ± 3.8𝑐 32.3 ± 0.74𝑒 2.6 ± 0.14𝑏𝑐 8.2 ± 0.00𝑒 36.2 ± 0.76𝑑𝑒 4.0 ± 0.3𝑎 1,300-1,499 22.4 ± 0.21𝑐𝑑 0.64 ± 0.024𝑐 79.2 ± 1.02𝑑 492 ± 3.8𝑏 37.2 ± 0.74𝑎𝑏𝑐 2.4 ± 0.14𝑏𝑐𝑑 8.0 ± 0.00𝑔 32.8 ± 0.76𝑒 1,500-1,699 19.1 ± 0.20𝑒 0.46 ± 0.023𝑑𝑒 73.3 ± 1.01𝑒 513 ± 3.8𝑎 40.5 ± 0.74𝑎 3.5 ± 0.14𝑎 8.6 ± 0.00𝑑 41.5 ± 0.76𝑐 Timeball Hill Shale 1,100-1,299 28.6 ± 0.20𝑎 0.90 ± 0.023𝑎 96.4 ± 1.01𝑎 460 ± 3.8𝑐 33.4 ± 0.74𝑑𝑒 2.5 ± 0.14𝑏𝑐 7.1 ± 0.00𝑖 27.3 ± 0.76𝑓 5.1 ± 0.2𝑏 1,300-1,499 21.6 ± 0.21𝑑 0.62 ± 0.024𝑐 79.4 ± 1.03𝑐𝑑 496 ± 3.8𝑎𝑏 39.1 ± 0.75𝑎𝑏 2.8 ± 0.14𝑏 9.1 ± 0.00𝑐 45.5 ± 0.76𝑏 1,500-1,699 19.1 ± 0.20𝑒 0.48 ± 0.023𝑑𝑒 73.3 ± 1.01𝑒 489 ± 3.8𝑏 36.6 ± 0.74𝑏𝑐𝑑 2.1 ± 0.14𝑐𝑑 8.1 ± 0.00𝑓 39.5 ± 0.76𝑐𝑑 1,700-1,899 16.3 ± 0.21𝑓 0.39 ± 0.024𝑒 65.6 ± 1.02𝑓 486 ± 3.8𝑏 35.2 ± 0.74𝑐𝑑𝑒 2.8 ± 0.14𝑏 7.6 ± 0.00ℎ 35.3 ± 0.76𝑒 53 3.3.1.1. Tree height, slenderness coefficient (SC,) and volume Trees grown in the Highveld on granite soil plots were generally the most uniform, tallest (24.3 m to 24.6 m), and largest trees (volume of 0.8 m³ per tree). They were also the least tapered and most cylindrical trees (SC of 82.7 to 84). However, trees grown on the Lowveld Escarpment varied significantly in tree form. Trees grown on dolomite plots were not significantly different in terms of tree height or volume. However, trees grown on dolomite plots located within 1,300 m to 1,499 m above sea level had lower SC values and were more tapered. Trees grown on the high altitude dolomite plots were shorter and more tapered than trees grown on lower elevation dolomite plots. Trees grown on shale plots varied the most in tree form and had both the largest and smallest trees. Tree height, SC, and tree volume were all negatively associated with altitude. There was a 12 m difference in mean tree height at the highest and lowest altitudes. There was also a 0.51 m3 difference in volume and 30.8 m3 difference in SC for trees at the lowest and highest altitudes. Figure 3. 2: Annual maximum temperature (T-max), plotted against tree height (m) for all trees sampled from the respective site types including a linear trend line and the R-square value. T-max explained the majority of site-related variability in tree height. Site impacts on tree height growth have been shown to be species-specific, due to the physiological adaptions of trees to their natural habitat, whereby evapotranspiration and tree growth occur in the most efficient manner for local conditions (Way and Oren 2010; Chakraborty et al. 2019; Blanco et al. 2021). Ultimately, trees may limit height growth due to leaf water stress associated with gravity and inter cellular water resistance over the tree length (Martínez-Vilalta et 54 al., 2007; McCulloh and Sperry, 2005). Tree heights have also been found to be limited to the point where root, stem, and leaf maintenance is in equilibrium with the production of photosynthate, limiting resources for additional tree growth (King 1990; Ryan and Yoder 1997). Therefore, growth including temperature, water availability, and water use efficiency have been found to drive tree height growth (Niklas and Spatz, 2004; Ryan, 2010; Schäfer et al., 2000). In South Africa, tree height growth and site quality have been modelled using soil properties and rainfall (Louw and Scholes, 2006). In this study, T-max was found to be the single most important factor affecting tree heights (Figure 3.2 and Figure 3.4). Higher temperatures are associated with improved tree growth due to the promotion of photosynthesis, the storage of photosynthates, and deposition of water conducting vascular cells or xylogenesis (Petit et al., 2010). Trees grown at lower temperatures can be hydraulically constrained, leading to the formation of fewer and smaller xylem elements (Panshin and Brown, 1964; Petit et al., 2010). In addition, low temperature environments can lead to the inability of trees to develop efficient water and nutrient transport systems, due to the retardation of xylogenesis (Petit et al., 2010). Spring rainfall was observed to contribute to improved tree height growth due to early season water availability leading to an earlier onset of xylogenesis and a longer growing season (Yang et al., 2022). However, summer and autumn rainfall lead to more cloud cover days and lower temperatures during the active growing season (Duveiller et al., 2021; Graham et al., 2003). Similarly, T-max, T-min, and summer and autumn rainfall had the most significant impact on the slenderness coefficient (SC) and tree volume models. T-max and summer rainfall positively correlate with tree SC and T-min, while autumn rainfall negatively correlated with tree SC. SC is directly impacted by tree height, which has been found to be positively impacted by T-max (Ryan, 2010; Zhang et al., 2020b). Summer rainfall negatively influenced tree height. Therefore, higher SC values on sites receiving more rainfall are related to smaller tree diameters. Negative correlations between SC, T-min, and autumn rainfall were also related to smaller tree heights. On the other hand, tree volumes were positively impacted by T-max and autumn rainfall, and similar to the tree height model, were negatively impacted by summer rainfall. These findings suggest that tree height, SC, and tree volume are influenced by a complex interaction between growing temperatures and water availability, ultimately impacting tree growth and the resultant tree form. Tree height limits are also impacted by the point where root, stem, and leaf maintenance are in equilibrium (King, 1990; Ryan and Yoder, 1997). These findings suggest that P. patula trees 55 grown in Mpumalanga were not constrained by water availability. Positive relationship between temperature, tree height, SC, and tree volume have been observed internationally in other species, such as Cunninghamia lanceolate and many gymnosperms growing in the boreal forests (Ryan, 2010; Zhang et al., 2020b). 3.3.1.2. Wood density and LW content Trees grown on the Highveld on granite soil plots had significantly lower wood density and LW content than trees grown on the Lowveld Escarpment, but there was no significant difference in wood density (433 kg/m³ and 439 kg/m³) or LW content (26.3 % and 26.9 %) for the different altitudes in the Highveld. On dolomite soil plots there was a positive correlation between wood density and LW with altitude. There was little difference between LW and wood density on the shale sites across altitude, although the trees grown at the lowest altitude had significantly lower density. There was no significant difference in LW content for trees across altitudes. Wood density and LW content are arguably the most important variables influencing solid wood quality and therefore have received considerable research attention globally (Charlton et al., 2020; Da Ros et al., 2021; Eberhardt and Samuelson, 2022; Garbachevski et al., 2022; Kharrat et al., 2019). In South Africa the impacts of soil and silviculture on the wood properties of P. patula have been extensively investigated (Erasmus and Wessels, 2020; Schutz et al., 1991). However, studies on the impact of site related growing conditions on wood properties have been limited, and these relationships remain poorly understood (Muller et al., 2017). Climatic factors affecting wood density was limited to annual growth days (GD)(Muller et al., 2017). The same GD modelled values also showed positive correlations with the sampled wood density in this study, but negative correlations with tree height and volume growth. This negative relationship could be a reflection of the fact that the GD equation was developed for Northern hemisphere conditions, which differ markedly from the summer rainfall region of South Africa. Growth sites had high variability in wood properties, potentially due to inter site variability related to competition between trees, tree size, and the inherent site related micro-climate. However, median regression models demonstrated that wood properties of trees varied between sites and the significant impacts of climate on tree wood density and LW (Table 3.5 and Figure 3.3). The most striking finding in this study was the separation of the trees grown on the Highveld granite sites because of their significantly lower wood density than the trees grown on the Lowveld sites. It appears that this difference was driven by the higher spring rainfall in the Highveld. Spring rainfall resulted in less 56 latewood formation and a lower proportion of juvenile core, which impacts overall stem wood density at a given tree height. Wood density and LW content models were positively correlated to T-min and summer rainfall, and negatively with T-max, autumn and spring rainfall. These results are supported by previous research on Mexican pine species (Pompa‑García et al. 2021). Wood density and LW content have been associated with lower T-min and greater water status, due to an increased production in thick-walled latewood cells (Gonzalez-Benecke et al., 2010; Muller et al., 2017; Panshin and Brown, 1964; Pompa‑García et al., 2021). Considering the majority of rainfall in the Mpumalanga region occurs during the summer months, sites receiving more summer rainfall also receive higher annual rainfall (van der Merwe et al. 2023c)). Trees grown on plots with higher T-max had lower wood density, which could be explained by the faster growth rates and larger trees found at these sites (Erasmus et al., 2018; Pompa‑García et al., 2021). However, in the Highveld region, which has less annual rainfall and more spring rainfall, trees had considerably lower wood density and less LW than trees of similar size grown in the Lowveld Escarpment. Autumn rainfall in the Escarpment is mainly orographic and limited to mountainous areas with low temperatures and generally less rainfall, potentially explaining the negative relationship between wood density, LW content, and autumn rainfall (van der Merwe et al., 2023c). 3.3.1.3. Pith to bark density gradient (PBDG) Trees grown in the Highveld on granite soil plots had significantly more uniform and lower PBDG compared to trees grown on the Escarpment, and there were no significant differences across altitudes on the Highveld. However, trees grown on the Lowveld Escarpment showed more variation in PBDG, but no obvious trend with altitude. For both dolomite and shale sites, the highest altitude sites had the highest PBDG. According to previous studies, PBDG intensity of P. patula trees grown in Mpumalanga is related to soil properties (Schutz et al. 1991). In this study soils were found to be co-related to climate (Figure 3.1B). Therefore, PBDG intensity models found summer rainfall to be positively related to PBDG intensity and T-min to be negatively related with PBDG intensity. Trees with denser wood, more LW, and earlier JC transition ages also had greater PBDG intensity. 3.3.1.4. Juvenile core (JC) transition age and portion (JCP) Juvenile to mature wood transition age (JC transition age) and juvenile core proportion (JCP) were statistically different for all the plots investigated. Juvenile wood transition age is the age at which low density juvenile wood ceases to form, while the juvenile core portion is the circumferential 57 area of the juvenile core, expressed as a percentage of the total circumferential area of the tree’s growth ring profile (Da Ros et al., 2021; Watt and Trincado, 2019). However, the greatest source of variation was due to regional differences. Trees grown on the Highveld granite soils, transitioned to mature wood formation much later, which explains the lower wood density. Moreover, there was no obvious trend with altitude. JC transition ages of trees grown on granite soil plots ranged from 11.8 to 12.4 years, and JCP ranged from 69.7 % to 71.3 %. JC transition ages ranged from 7.1 to 9.1 years for the Escarpment sites, and JCP ranged from 27.3 % to 45.5 %. There were no significant trends with altitude on dolomite sites. On shale sites there was a decrease in JC and JCP with altitude for the mid to high altitude sites. The lowest altitude site was, however, an anomaly with the lowest values. Juvenile wood formation in fast grown plantation forests, has received considerable attention due to the inherent low density and stability associated with the JCP (Dobner et al., 2018; Erasmus and Wessels, 2020; Garbachevski et al., 2022; Moore and Cown, 2017; Robinson et al., 2021). Growth rate and silviculture have been identified as primary drivers affecting JC transition age and JCP in South Africa, but the site related variables influencing them have not been evaluated (Erasmus et al., 2018; Wessels et al., 2015). Highveld and Lowveld Escarpment-grown trees had significant differences in JC transition age and JCP, with trees grown on the Highveld having much later JC transition ages and larger JCP (Table 3.5). Inter-regional differences of tree JC transition age and JCP were relatively minor; JC transition ages ranged from 11.8 and 12.4 years for trees grown on the Highveld, and from 7.1 to 9.1 years for trees grown on the Lowveld Escarpment. The JC transition age was similar to previously published values for P. patula grown on the Lowveld Escarpment (Erasmus et al., 2018). However, no published values exist for JC transition ages of trees grown on the Highveld. 3.3.1.5 Growth ring eccentricity (GRE) Soil type was the only factor affecting GRE. Shale sites had the highest GRE, but there was no difference comparing GRE of trees grown on granite and dolomite soil plots, respectively. Growth ring eccentricity (GRE) affects both tree form and fibre quality. In particular, growth ring eccentricity are related to stem ovality and reaction wood formation (Fallah et al., 2012; Raatevaara et al., 2020; Warensjö and Rune, 2004). Ovality impacts recovery of lumber and veneer, while reaction wood is related to lumber instability due to irregular shrinkage during drying (Gjerdrum, 2012). GRE was not found to be strongly correlated with any of the 58 environmental variables included in this study; it is unclear if the observed impact of soils on GRE was due to silviculture related challenges associated with specific soil types. At the higher altitudes, shale sites in particular had shallower soils, which might have impacted the planting quality of seedlings. 3.3.2 Tree form and wood property models In order to highlight the underlying major environmental factors affecting tree form and wood properties free from co-relatedness, principal component analyses (PCA) and correlation matrix were completed for tree properties. Figure 3. 3: Component analysis, including soil, tree form, and wood variables, and grouped according to soil (Figure 3.3A). Principle component analysis, including climate, tree form, and wood variables, and grouped according to soil (Figure 3.3B). When soil properties were included with tree form and wood properties, the first two principal components accounted for 50% of the variance (Figure 3.3A). The soil chemical properties were included in the first principal component, and the wood and tree properties in the second component, indicating there was little relationship between the two. The C:N ratios were negative, and the soil chemistry (Mg, Ca, K and N) was positive. The granite sites separated away from the shale and dolomite sites, and this was driven by the lower wood density, higher tree height, JCT, and JCP. In the second principal component there was a negative relationship 59 between wood density and latewood content when compared to JC age and portion. In a separate PCA including climate variables with the tree growth and wood properties, 53% of the variation was explained by the first two principal components (Figure 3.3B). It was striking in the first principal component that the granite soils were grouped together because of high spring rainfall, high juvenile wood proportion, and low wood density. In the second principal component there appeared to be a strong relationship between T-max and tree height. Figure 3. 4: Correlation matrix showing the relationships between tree height, slenderness coefficients (SC), volume (V), wood density, latewood content (LW), juvenile core transition age (JC), juvenile core portion (JCP), pith to bark density gradient (PBDG), growth ring eccentricity (GRE), summer rainfall, autumn rainfall, spring rainfall, mean annual maximum temperature (T-max) and mean annual minimum temperature (T-min), growth days (GD) and growth temperature (GT). Values in blue represent positive correlations and those values in red, negative correlations. 3.3.2.1. Predictive model Regression models were used to highlight the most important environmental variables affecting the growth and wood properties (Table 3.5). 60 Table 3. 5: Multiple linear regression model to predict tree height, and median regression models to predict tree slenderness coefficients (SC), tree volume (V), wood density, latewood content (LW), and pith to bark density gradients (PBDG). Model accuracies expressed as root mean square errors (RMSE) and r-square’s (R²). No juvenile core transition ages (JC) or juvenile core portions (JCP) were modelled due to limited variability and predominantly regional differences. Statistical significance of independent variables is denoted as (***) when P<0.001, (**), when P<0.01, (*), when P<0.05, and NS when not statistically significant. Prediction Variable Coefficient Std.Err T-value P-value Sign RMSE R² Tree height (m) (Intercept) -17.116 1.72 -9.98 0.000 *** 2.2 0.71 T-max 1.736 0.04 44.85 0.000 *** T-min NS NS NS NS NS Summer rain -0.010 0.00 -5.03 0.000 *** Autumn rain -0.015 0.01 -1.97 0.050 * Spring rain 0.041 0.01 8.10 0.000 *** SC (.) (Intercept) 19.883 8.73 2.28 0.023 * 11.6 0.98 T-max 4.178 0.25 16.47 0.000 *** T-min -1.54 0.50 -3.11 0.002 ** Summer rain 0.029 0.01 2.60 0.010 ** Autumn rain -0.155 0.04 -3.48 0.001 *** Spring rain NS NS NS NS NS Volume (m³) (Intercept) -1.211 0.20 -5.95 0.000 *** 0.3 0.85 T-max 0.073 0.01 12.51 0.000 *** T-min NS NS NS NS NS Summer rain -0.001 0.00 -5.91 0.000 *** Autumn rain 0.003 0.00 3.37 0.001 *** Spring rain NS NS NS NS NS Wood density (kg m³) (Intercept) 735.852 45.14 16.30 0.000 *** 43.8 0.99 T-max -6.000 1.04 -5.79 0.000 *** T-min 3.15 1.38 2.28 0.023 * Summer rain 0.226 0.05 4.49 0.000 *** Autumn rain -0.549 0.19 -2.90 0.004 ** Spring rain -0.800 0.11 -7.32 0.000 *** LW content (%) (Intercept) 70.398 8.71 8.09 0.000 *** 8.6 0.99 T-max -0.761 0.18 -4.24 0.000 *** T-min 0.76 0.34 2.27 0.024 * Summer rain 0.045 0.01 4.69 0.000 *** Autumn rain -0.102 0.04 -2.73 0.006 ** Spring rain -0.145 0.02 -6.12 0.000 *** PBDG (kg m³ mm) (Intercept) 1.515 0.37 4.07 0.000 *** 1.6 0.66 T-max NS NS NS NS NS T-min -0.156 0.02 -7.50 0.000 *** Summer rain 0.005 0.00 7.16 0.000 *** Autumn rain NS NS NS NS NS Spring rain NS NS NS NS NS 61 Soil properties showed poor relationships with tree form and wood properties. Surprisingly, GD was negatively correlated with tree height, which is difficult to explain (Figure 3.2). Two types of models were developed - linear regression and median regression models. Tree height was modelled using linear models; wood density, LW content, and pith to bark density gradient (PBDG) predictions were modelled using median regression models due to the inter-site related variability associated with wood properties (Li, 2014). No JC transition age and juvenile core portions (JCP) models were developed due to limited inter-regional variability. Most significant differences in JC and JCP were found to be regional. In addition, no significant models were developed for the growth ring eccentricity of trees (GRE). Significant model variables and prediction accuracies including RMSE and R² values for linear and median regression models are shown in Table 3.5 and illustrated in Figure 3.5. Figure 3. 5: Actual versus predicted values for tree height (linear regression model), SC (median regression), tree volume (median regression), wood density (median regression), LW content (median regression), and PBDG (median regression). Tree height (m) Tree volume (m³) Latewood (%) PBDG (kg/ m³/ mm) Wood density (kg/ m³) 0 Slenderness coefficient (.) 0 62 Chapter 4: Site impacts on wood quality for veneer production 4.1 Introduction The growing conditions of trees, especially climate is known to significantly impact tree form and wood properties. In particular, trees grown in the Highveld region have been found to be generally taller and less tapered (van der Merwe et al., 2023a). Tree heights have been found to be positively impacted by annual maximum temperature (van der Merwe et al., 2023a). Trees grown on granite soils found in the Highveld have also been found to have lower wood density, less latewood content, a later juvenile core transition age, and a larger juvenile core, compared to trees grown on dolomite and shale soils found in the Lowveld (van der Merwe et al., 2023a). Wood density was found to be positively correlated to rainfall, however, it was not clear if this relationship was due to seasonal rainfall or mean annual precipitation (van der Merwe et al., 2023a). Wood varies from pith to bark and from the bottom to the top of the tree, and many of the inherent attributes of the tree are influenced by the growing conditions (Da Ros et al., 2021; Van der Merwe et al., 2023a). In addition, tree form properties such as sweep, ovality, and taper have been found to impact the amount of full veneer sheets produced during peeling (Hamilton et al., 2015; McGavin et al., 2021). Seedling quality, tree size, soil depth, terrain conditions, and growth temperatures have been found to influence sweep in trees (Erasmus et al., 2018; Petit et al., 2010). High stocking at planting, low temperature growing environment, and higher position within trees have also been associated with more cylindrical logs and greater ovality (Charlton et al., 2020; Van der Merwe et al., 2023a). The cross section of a softwood tree is composed of sapwood and heartwood regions. The sapwood portion provides structural support, conduct water, provide storage for important chemicals that regulate metabolic functions, and is a sources of energy in the form of starch and lipids in living parenchyma cells (McIntire et al., 2022; Słupianek et al., 2021). Heartwood occurs inside the sapwood portion towards the central pith and is often darker in colour. Heartwood is formed by living parenchyma cells located in the heartwood-sapwood transition zone (Gerchow et al., 2022; Tarelkina et al., 2022). During heartwood formation extractives are deposited into bordering cells through pits. These extractives give the heartwood its natural 63 durability, stability, and often darker colour (Brischke and Alfredsen, 2022; Tintner and Smidt, 2018). In addition, heartwood helps to regulate water use by limiting the proportion of water conducting sapwood (Moreno Chan et al., 2013; Hu et al., 2018). The extent of heartwood has been found to be impacted by growing environments (water availability), tree age, size, crown portion, and the height position within the tree (Edvardsson et al., 2022; McIntire et al., 2022). Unfortunately, there is currently no published information on the heartwood proportion in plantation-grown P. patula in South Africa, or its impact on the production of veneer quality. Branch pruning is a standard practise for plantation grown softwoods in South Africa that is employed to improve the veneer quality by increasing the knot-free portion of the tree (Adlard, 1969; Cardoso and Arce, 2010; Munalula et al., 2016). Pruning is carried out at specific heights based on the diameter of the trees at a specific age, and this silviculture activity is normally done before canopy dieback to avoid the formation of encased knots (Adlard, 1969; Brown, 1962). In P. patula, pruning has been found to improve height growth, but has been shown to supress diameter growth (Adlard, 1969; Brown, 1962; Missanjo and Kamanga-Thole, 2015). Pruning therefore can impacts wood properties, tree form, and can reduce tree taper (Adlard, 1969; Brown, 1962). Plywood is one of the most commonly used wood products available today in the building industry due to its versatility, as it can be manufactured in diverse thickness and used in diverse products. Plywood consists of various layers of veneer (depending on dimensions) glued perpendicular onto one another and pressed together to form a composite veneer panel (Chang and Wang, 1990; Kūliņš et al., 2021; Shupe et al., 1997). Generally, debarked logs are heated in conditioning chambers using water or steam to achieve a core temperature of approximately 50 °C (Dobner et al., 2013; Dupleix et al., 2013). Heating softens the wood fibres which improves the peeler cutting process and veneer surface finish (Dobner et al., 2013; Dupleix et al., 2013). The conditioned logs are processed using a rotary peeler to produce veneer (Wilson and Sakimoto, 2005). The veneer ribbon is then clipped to a specified width, allowing for shrinkage and trimming (Lavalette et al., 2016; Wilson and Sakimoto, 2005). During the peeling process, lumber waste is produced due to; roundup, lathe or spur damage, and the residual core (Dobner et al., 2013; Lynch and Clutter, 1998; Spelter and Sleet, 1989). The veneer sheets are dried to a moisture content of below 12 percent (Aydin and Colakoglu, 2005; Ozsahin and Aydin, 2014), 64 and once dried, are graded, sorted, and stacked according to veneer quality including A, B, C and D-grades (De Vallance et al., 2007; Lynch and Clutter, 1998). A and B veneer sheets surfaces are the highest quality and are largely produced from the pruned section of logs with no knots or splits. Plywood panels with C faces are typically sold as lower value shutter plywood panels due to the inclusion of blemishes such as knots, resin, wane and splits. Lower grade D grade veneer sheets and split sheets can only be used in sub-surface panel layers. The properties of the veneers used to construct the plywood panels and the visual appearance of the surface, face sheets, impact the quality and value of the composite (Dudík et al., 2020; Lynch and Clutter, 1998). Therefore, tree form, wood properties, and visual appearance of veneer sheets are important for the profitability of plywood mill (Lynch and Clutter, 1998; Regmi et al., 2022). 4.2 Materials and methods 4.2.1 Sampling in the forest A total of 54 square sample plots of 30 m × 30 m were established in 20-year-old P. patula plantation compartments with the same age and silviculture history. The sample plots were stratified by altitude and soil geology (Table 4.1). Trees were originally planted at a spacing of 3 × 3 m followed a line thinning reducing the stocking to 800 trees per hectare (tree age: 10 to 13 years) and a second selective thinning to a target stocking of 500 trees per ha (tree age: 14 to 17 years). The thinnings were done to maintain a mean target basal area of 24 m² per hectare as the trees matured. The trees were initially pruned to a height of 1.5 meters (tree age: 3 to 5 years), followed by a second pruning to a height of 3.5 meters (tree age: 5 to 7 years), and a third pruning to a height of 5.5 meters (tree age: 7 to 10 years). The aim of branch pruning was to restrict the knotty core around the central pith to less than 12 cm and the timing of branch pruning was based on the site-specific growth rate. Soil properties for these study sites were found to be poorly correlated with tree growth and wood properties (Belokopytova et al., 2019; van der Merwe et al., 2023a). Annual and seasonal climate was determined for each sample plot using regional climate models (Van der Merwe et al., 2023c). Over and under bark diameters of sample trees were determined with a Resistograph in the North to South direction at the breast height (1.3 meters), at a forward drilling feed speed of 200 cm per minute and 2 500 revolutions per minute (Downes et al., 2022). In addition, Resistograph measurements were completed on trees that were also subject to increment coring and X-Ray densitometry measurements from a previous study (Van der Merwe et al., 2023a). 65 The measurements were done within 5 cm above or below the point where the cores were extracted, with the aim of developing a calibration model to convert the Resistograph torque measuremements to wood density and volumetric latewood content predictions. (https://forestqual- 112ity.shinyapps.io/FWPA-4/). Thin-end diameters of the first log were determined for base logs by extrapolating the DBH measurements to a veneer log length of 2.65 meters (veneer log length), while assuming a constant taper of 1cm per meter (Baltrušaitis and Pranckevičienė, 2005; Charlton et al., 2020). Trees in each sample plot with a thin end diameter greater than 20 cm were then classified as veneer trees and 10 trees were randomly selected from these plots for harvesting (Arabatzis and Burkhart, 1992; Stehman et al., 2011). Height positions of the dead crown, live crown onset or the first branch encountered from ground level, height to 15 cm diameter and total height were recorded. The height portion of live crown was expressed as a percentage of each tree. Slenderness coefficient (SC) was calculated as tree height to 15cm diameter divided by DBH (Wang et al., 1998; Zhang et al., 2020b). Trees were cross-cut into 2.65 m logs and the pruned and unpruned tree portions were recorded and sample discs were removed between adjacent log ends. A total of 5 084 logs were produced and each one was barcoded so that it was traceable to a specific tree and the position within the tree. 4.2.2 Processing of samples and logs Sample logs were transported to the plywood mill and processed within 3 weeks after felling. All sample logs were debarked by a spindle-debarker and log form was measured using a Microtec LogEye 300 scanner located at the debarking facility (Kunickaya et al., 2022). The scanner provided measurements for log thin-end diameter, volume, sweep, taper, and ovality. Any ply logs with thin-end diameters less than 20 cm were rejected and did not form any further part of this study. Disc samples were air dried at room temperature for at least 3 months. A central core of 50 mm was removed from the disc samples from a North to South direction and stained using solution A which included 1 g of Benzidine, 5 ml of 25 percent hydrochloric acid and 194 ml of water and solution B which included 20 g of sodium nitrate and 180 ml of water. The solutions were kept separate and applied in tandem during the heartwood staining process (Kutscha et al., 1962). Heartwood portion were measured and the volume of heartwood for each log was calculated employing Smalian’s formula, using the heartwood measurements of disc samples obtained from the respective log ends (Santos et al., 2021). 66 𝑉ℎ𝑒𝑎𝑟𝑡𝑤𝑜𝑜𝑑 =𝐴+𝑎2 × 𝐿 (4.1) where, A = the area of heartwood at the thick end of the log in m², a = the area of heartwood at the thin end of the log in m², and L = the log length in meters. The heartwood volumes calculated for each log, was added together to determine heartwood volume per tree. Tree volume was calculated using the Max and Burkhart formula for P. patula, using the height and diameter measurements according to formula 4.2 (Crous et al., 2008; Kotze, 1995). HP% =∑ Vheartwood(n)Vtree × 100 (4.2) Tree form variables, such as ovality, taper, and sweep were also weighted volumetrically for the merchantable volume of trees (according to formulae 4.3, 4.4, and 4.5). TreeO =∑ ( On ×Vn )r(n)VTotal (4.3) where, TreeO=Volumetric weighted ovality per tree, 𝑂𝑛=Log ovality, 𝑉𝑛=Log volume, and 𝑉𝑇𝑜𝑡𝑎𝑙= Total volume of merchantable logs per tree. TreeS =∑ ( Sn ×Vn )r(n)VTotal (4.4) where, 𝑇𝑟𝑒𝑒𝑠=Volumetric weighted sweep per tree, 𝑆𝑛=Log sweep, 𝑉𝑛=Log volume, and 𝑉𝑇𝑜𝑡𝑎𝑙= Total volume of merchantable logs per tree. Treet =∑ ( Tn ×Vn )r(n)VTotal (4.5) where, 𝑇𝑟𝑒𝑒𝑡=Volumetric weighted taper per tree, 𝑆𝑛=Log taper, 𝑉𝑛=Log volume, and 𝑉𝑇𝑜𝑡𝑎𝑙= Total volume of merchantable logs per tree. The veneer logs were steam conditioned for 8 hours at a temperature of 90 °C and rotary peeled using a 21° knife angle to produce a continuous veneer ribbon with a thickness of 2.85 mm (Laskowska et al., 2018). The barcode of each log was recorded before it was peeled. The ribbons were clipped into veneer sections with wet dimensions of 2,575 mm × 1,375 mm, to 67 account for veneer shrinkage during drying (Lavalette et al., 2016). The last veneer sheet produced from each veneer ribbon (log) was numbered and all veneer sheets were stacked in a single veneer bin to allow traceability of all the veneer sheets back to a specific log in each tree. The veneer sheets were also sequentially numbered and dried to a maximum dry base moisture content of 12%, and graded by means of an automated image analysis grading system. Supporting information such as the explanation for the grading of each veneer sheet image were also stored using the Mecano Image Browser software. Table 4. 1: Veneer quality specifications according to SANS 929:2008 (Ed. 2.07), including threshold width (W), length (L), diameter (D), and quantity (Q) for respective defect types and veneer sheet dimensions according specific veneer grades and applications. Veneer application Face Layup Cross Veneer grade A B C D (full sheet) D (partial sheet) Random Inherent defect type Dimensions W: >= 1,245 mm L: 2,530 mm W: >= 1,245 mm L: 2,530 mm W: >= 1,245 mm L: 2,530 mm W: 1,245 mm L: 2,530 mm W: <= 1,245 and >= 1,150 L: 2,530 mm W: <=1,150 mm and >= 450 mm L: 2,530 mm Knots D: N/A Q: None D: <= 40 mm Q: Unlimited D: <= 65 mm Q: Unlimited D: Unlimited Q: Unlimited D: Unlimited Q: Unlimited D: Unlimited Q: Unlimited Splits W: <= 0.5 mm L: Unlimited Q: Unlimited W: <= 5 mm L: <= 500 mm Q: 6 W: <= 25 mm L: <= 120 mm Q: Unlimited W: Unlimited L: Unlimited Q: Unlimited W: Unlimited L: Unlimited Q: Unlimited W: Unlimited L: Unlimited Q: Unlimited Resins streaks W: N/A L: N/A Q: None W: <= 25 mm L: Unlimited Q: Unlimited W: Unlimited L: Unlimited Q: Unlimited W: Unlimited L: Unlimited Q: Unlimited W: Unlimited L: Unlimited Q: Unlimited W: Unlimited L: Unlimited Q: Unlimited Wane W: N/A L: N/A Q: None W: <= 25 mm L: <= 25 mm Q: <= 10 Or W: <= 5 mm L: <= 500 mm Q: 6 W: <= 40 mmL: <= 100 mm Q: Unlimited Or W: <= 25 mm L: <= 120 mm Q: Unlimited W: Unlimited L: Unlimited Q: Unlimited W: Unlimited L: Unlimited Q: Unlimited W: Unlimited L: Unlimited Q: Unlimited The major defects associated with each sheet, including knots, splits, resin, and wane were recorded. Sheet grades were also grouped into face veneer percentage which included all “A” and “B” grade veneer sheets, and layup veneer percentage which included “C” and “D-full” veneer percentage. The structural integrity of these sheets had not been compromised. The cross-veneer percentage included all D-partial and random veneer sheets. These sheets can only be used in the layers perpendicular to the length of the panel, because their structural integrity had been compromised i.e. not a full sheet (Guo and Altaner, 2018). Veneer grade specifications are shown in Table 4.1. 68 4.2.3 Data processing All statistical analyses were completed using the R programming environment: A language and environment for statistical computing, including analysis of variance (ANOVA) to determine significant differences for independent variables [‘aov’](R Core Team, 2022). ANOVA residuals were inspected for normality [‘qqPlot’](R Core Team, 2022). If ANOVA residuals had a normal distribution, treatment least square means and standard error values were determined [‘lsmeans’](Lenth 2016, Lenth 2020), and post hoc testing was completed to establish differences between respective treatments using a Tukey Honestly Significant Difference Test [‘TukeyHSD](Yandell 1997). Two principal component analyses (PCA) was were implemented to determine the key variables and relationships between variables impacting tree and veneer properties. The first PCA included tree and veneer properties together with soil variables and the second with climate variables [‘lfda’, ‘ggfortify’] (Tang et al., 2016; Tang and Li 2019; Wei and Simko, 2021). Correlation matrices were performed to show the relationship between maximum temperature, tree height, slenderness coefficients, and tree volume [‘corrplot’](Wei and Simko, 2021). A Pearson’s correlation matrix was simulated, to determine correlations between tree, climate, and veneer variables [‘ggcorrplot’](Kassambara, 2022). Only variables influencing tree form, heartwood portions, and veneer quality are discussed here. A summary of the R-software packages used for statistical analysis are shown in Table 4.2. Table 4. 2: R-packages and functions, and their use in this study. R-function Use ‘qqPlot’ Determinatinion of normality of residuals. ‘aov’ Analysis of variance for tree form, wood properties, veneer recovery and qualtiy for the respective site types. R-package Use ‘lsmeans’ Determination of least square means and standard errors for tree forms and wood properties for the respective site types. ‘TukeyHSD’ Determination of confidence intervals on the differences between the means, therefore which treatments are statistically different from one another. ‘rcompanion’ Visualisation of principle component analysis ‘lfda’ Principle component analysis to determine collinearities, association and which variables to include in the final models. ‘ggfortify’ Visualisation of principle component analysis. ‘ggcorrplot’ Correlation matrix analysis 69 4.3 Results and Discussion This study aimed to identify the impact of growing conditions on tree and log form, heartwood content, live crow portion, and veneer recovery and quality of plantation grown P. patula trees in Mpumalanga, South Africa. Principal component analysis (PCA) identified collinearities between important environmental variables affecting tree form and wood properties, of which the most obvious was the close association of soil type and climate. Similarly a Pearson’s correlation matrix was done to identify the statistical relationships between dependent and independent variables. Similar to previous studies, soil properties appear to have little impact on tree form and wood properties and could be seen as a proxies for climate due to geo-climatic relationships (Van der Merwe et al. 2023a). Not surprisingly, temperature and rainfall were found to be the most important climatic variables affecting tree growth and therefore veneer recovery and quality (Chakraborty et al., 2019; Da Ros et al., 2021; Toledo et al., 2011; Van der Merwe et al., 2023a). 4.3.1 Resistograph models Forest growers and tree breeders have been actively investigating ways to rapidly measure wood properties in the field, to assist and advance in site-specific silviculture and tree breeding (Downes et al., 2022; Tomczak et al., 2022). In recent years, new technologies have been identified, including the use of the Resistograph devices, which includes the measurement of wood resistance to drilling a needle through a standing tree (Downes et al., 2022; Tomczak et al., 2022). Resistograph devices have been found to accurately measure wood density, however, species-specific calibration is necessary due to species-specific fibre diversity (Downes et al., 2022; Tomczak et al., 2022). Linear regression based Resistograph calibration models were developed for P. patula measurements, by using predictions from existing models developed for P. radiata trees (https://forestqual- 112ity.shinyapps.io/FWPA-4/) (Downes et al., 2022). Under bark diameter and predicted density were used as independent variables and actual measured wood density and volumetric wood density as dependent variables in the calibration models. Both under-bark diameter and wood density predictions of the existing models were found to be highly significant in the calibration models (p<0.001; Table 4.3). 70 Table 4. 3: Linear regression models employed to calibrate Resistograph conversion models developed for P. radiata to P. patula wood density and latewood content predictions. Model Dependent variable Coefficient Std. Error T-value P-value Sign. R² Wood density calibration (Intercept) 46.505720 12.3751200 3.758 0.000 *** 0.68 Predicted wood density 1.020090 0.0231300 44.104 0.000 *** Under bark diameter -0.056170 0.0181300 -3.099 0.002 ** Latewood prediction % (Intercept) -46.57202 2.4618130 -18.918 0.000 *** 0.66 Predicted wood density 0.193785 0.0046010 42.117 0.000 *** Under bark diameter -0.012613 0.0036060 -3.498 0.000 *** Figure 4. 1: Wood density correction and latewood prediction models developed by means of linear regression. Predicted and corrected wood density versus the actual wood density obtained from X-Ray densitometry measurements (Figure 4.1A) and predicted volumetric latewood content percentages versus actual measured volumetric latewood content percentages (Figure 4.1B) The predicted values after calibration were compared to the actual measured wood density and volumetric latewood content models and visualised (Figure 4.1). Species correction models were developed by using the resistograph measurements by harnessing the under-bark diameters and the predicted wood density from existing models for P. radiata as independent variables and the measured wood densities from sample trees to re-calibrate the measurements (Downes et al., 2022). The findings suggests that it is possible to re-calibrate resistograph measurements and even establish associations with additional wood properties such as 71 volumetric latewood content, which is closely related to wood density (Van der Merwe et al., 2023a). 4.3.2 Tree form, wood properties and live crown portions The experimental design for this study was based on soil geology and altitude, as these variables were considered to be the most influential in tree growth and wood formation of P. patula in South Africa (Louw, 2016; Muller et al., 2017; Schutz et al., 1991, Stocker et al., 2023). The analysis of variance (ANOVA) of dependent tree variables including tree height, slenderness coefficient (SC), volume (V), ovality (O), volumetric heartwood percentages (HP), taper (T), sweep (SW), and live crown percentages (LC) are shown in Appendix Table A1-4. The ANOVA of dependent veneer variables including tree net veneer recovery per tree, excluding rejected logs (Net recovery (tree)), net veneer recovery per tree for only the processed logs (Net recovery (logs)) and the quality including face, layup, and cross veneer percentages per tree are also shown in Appendix Table A1-4. The interaction between soil and altitude was found to have a significant impact on tree height, diameter at breast height (DBH), slenderness, volume, ovality, and the volumetric heartwood percentage of trees (HP) (p<0.05; Appendix Table A1-4). However, tree taper, sweep, and the live crown height percentages was only found to be impacted by soil and altitude independently (p<0.05; Appendix Table A1-4). 72 Table 4. 4: Tree growth and form means and standard errors by sample site soil geology and altitude (superscript represents Tukey significance). Region Soil Altitude (m) Height (m) Slenderness coefficient Volume (m³) Ovality (.) Heartwood percentage (%) Highveld Granite 1,500-1,699 27.2 ± 0.26𝑏 89.0 ± 1.11𝑏𝑐 1.02 ± 0.029𝑏 0.944 ± 0.0015𝑎𝑏 1.6 ± 0.17𝑏𝑐 1,700-1,899 27.6 ± 0.26𝑏 92.3 ± 1.11𝑏 1.01 ± 0.029𝑏 0.948 ± 0.0015𝑎 1.6 ± 0.17𝑏𝑐 Lowveld Escarpment Dolomite 1,100-1,299 24.9 ± 0.26𝑐 90.7 ± 1.11𝑏 0.74 ± 0.029𝑐𝑑 0.943 ± 0.0015𝑎𝑏 2.3 ± 0.17𝑏 1,300-1,499 24.6 ± 0.26𝑐 85.0 ± 1.10𝑐𝑑 0.82 ± 0.028𝑐 0.938 ± 0.0015𝑏𝑐 1.4 ± 0.17𝑐𝑑 1,500-1,699 21.0 ± 0.26𝑑 78.7 ± 1.11𝑒 0.63 ± 0.029𝑑𝑒 0.928 ± 0.0015𝑒 0.9 ± 0.17𝑐𝑑 Shale 1,100-1,299 32.2 ± 0.26𝑎 100.1 ± 1.11𝑎 1.26 ± 0.029𝑎 0.945 ± 0.0015𝑎 3.2 ± 0.17𝑎 1,300-1,499 24.1 ± 0.26𝑐 83.3 ± 1.11𝑑𝑒 0.84 ± 0.029𝑐 0.934 ± 0.0015𝑐𝑑 1.6 ± 0.17𝑏𝑐 1,500-1,699 21.5 ± 0.26𝑑 78.6 ± 1.11𝑒 0.68 ± 0.029𝑑𝑒 0.932 ± 0.0015𝑐𝑑𝑒 1.2 ± 0.17𝑐𝑑 1,700-1,899 19.0 ± 0.26𝑒 71.8 ± 1.11𝑓 0.56 ± 0.029𝑒 0.931 ± 0.0015𝑑𝑒 0.7 ± 0.17𝑑 Table 4. 5: Tree form and crown properties significantly affected by soil parent material. Treatment means and standard errors are shown for respective regions and soil types, including wood density (Density), volumetric LW content (LW %), taper (T), sweep (S), and live crown height portions of trees (LC). Superscript letters represents treatment groups found by Tukey HSD, where the same superscript letters indicates that there are no statistically significant differences between the treatments. Different superscript indicate a statistically significant difference. Table 4. 6: Tree form and crown properties significantly affected by altitude. Treatment means and standard errors are shown for respective altitude ranges, including wood density (Density), volumetric LW content (LW %), taper (T), sweep (SW), and live crown height portions of trees (LC). Superscript letters represents treatment groups found by Tukey HSD, where the same superscript letters indicates that there are no statistically significant differences between the treatments. Different superscript indicate a statistically significant difference. Altitude Density (kg/m³) LW % T (cm/m) SW (cm/m) LC (%) 1,100-1,299 457 ± 3.1𝑐 30.7 ± 0.59𝑐 0.73±0.023𝑎𝑏 0.44±0.015𝑎𝑏 44.7 ± 0.90𝑐 1,300-1,499 482 ± 3.0𝑎 35.6 ± 0.58𝑎 0.75±0.023𝑎𝑏 0.47±0.015𝑎 48.1 ± 0.89𝑏 1,500-1,699 474 ± 2.5𝑎𝑏 33.9 ± 0.48𝑎𝑏 0.80±0.019𝑎 0.49±0.012𝑎 50.3 ± 0.73𝑎𝑏 1,700-1,899 464 ± 3.1𝑏𝑐 32.3 ± 0.59𝑏𝑐 0.68±0.023𝑏 0.41±0.015𝑏 51.5 ± 0.90𝑎 Region Soil Density (kg/m³) LW % T (cm/m) SW (cm/m) LC (%) Highveld Granite 448 ± 3.0𝑐 28.9 ± 0.57𝑐 0.72 ± 0.023𝑏 0.39 ± 0.015𝑏 46.5 ± 0.89𝑏 Lowveld Escarpment Dolomite 471 ± 2.4𝑏 33.6 ± 0.46𝑏 0.81 ± 0.019𝑎 0.48 ± 0.012𝑎 46.1 ± 0.63𝑏 Shale 480 ± 2.1𝑎 35.1 ± 0.40𝑎 0.71 ± 0.016𝑏 0.47 ± 0.011𝑎 52 ± 0.89𝑎 73 4.3.2.1 Tree height, slenderness coefficient (SC), and volume Sample trees grown on granite soils in the Highveld were found to be generally taller (heights of 27.2 m to 27.6 m), more slender (SC of 89 to 92.3), and with greater volumes per tree (1.0 m³ more per tree; Table 4.5). However, Lowveld-grown trees were highly variable in size and slenderness. Trees grown on dolomite soils within the 1,100 m to 1,299 m altitude range and 1,300 m to 1,499 m altitude range had similar tree heights and ranged from 24.6 m to 24.9 m, while volumes ranged from 0.74 m³ to 0.82 m³. However, trees grown on dolomite soils within the 1,500 m to 1,699 m altitude range were significantly smaller with average tree heights of 21.0 m and tree volumes of 0.063 m³. In addition, the slenderness of trees grown on dolomite soils decreased as altitude increased, displaying slenderness coefficients of 90.7 for trees grown within the 1,100 m to 1,299 m altitude range, 85.0 for trees grown within the 1,300 m to 1,499 m altitude range, and 78.7 for trees grown on dolomite soils in the 1,500 m to 1,699 m altitude range. The most significant tree size and slenderness variation was found in trees grown on shale soils. The biggest and most slender trees were observed on shale soils located at lowest altitudes ranging (1,100m to 1,299m) where tree heights of 32.2 m, volumes of 1.26 m³, average and slenderness coefficients of 100.1 were observed. Tree heights gradually decreased as altitude increased, with the smallest and least slender trees observed at the highest altitude (1,700 m to 1,899 m; tree heights of 19 m, volumes of 0.56 m³, and slenderness coefficients of 71.8). However, trees grown on shale soils within the 1,300 m to 1,499 m and 1,500 m to 1,699 m altitude ranges displayed no statistically significant difference in slenderness. In addition, trees grown on shale soils within the 1,500 m to 1,699 m and 1,700 m to 1,899 m altitude ranges also displayed no statistically significant difference in tree volume. Tree stems fulfil two fundamental functions in trees including structural support for plant organs and the movement of water through the tree, which facilitates photosynthesis (Brodersen et al., 2019; Pulido-Rodríguez et al., 2020). Photosynthesis is the process whereby trees harness energy from the sun, water, and carbon dioxide from the atmosphere to produce energy in the form of starch and lipids (Dusenge et al., 2019; Yang et al., 2020). Tree height growth and size are therefore impacted by the physiologically adaptions of trees to facilitate the movement of water from the roots, through the wood fibres of the stem to the tree canopy (Körner, 2019). Height growth has been suggested to be limited by the water stress of leaves associated with gravity and fibre morphology over the tree length (Martínez-Vilalta et al., 2007; McCulloh and 74 Sperry, 2005). Tree heights have also been found to be limited to the point where root, stem, and leaf maintenance are in equilibrium with the production of photosynthate, limiting resources for additional tree growth (King, 1990; Ryan and Yoder, 1997). Traditionally, site quality of plantation-grown trees have been determined by the soil properties, rainfall, and to a lesser extent mean annual temperature (Louw and Scholes, 2006). However, the findings of this study suggests that T-max is the single most important variable driving tree height growth (Table 4.5, Figure 4.2B, and Figure 4.3). Figure 4. 2: Component analysis, including soil properties, form, veneer quality, veneer recovery, and the reasons why veneer was downgraded per tree and grouped according soil (Figure 4. 2A). Veneer quality was divided into face, layup, and cross veneer and expressed as a percentage per tree. Soil variables included nitrogen (N), phosphate (P), potassium (K), calcium (Ca), magnesium (Mg), sodium (Na), sulphur (S), carbon (C), carbon to nitrogen ratios (C:N ratio), and total available water (TAW), which was expressed per ha (𝒕 𝒉𝒂−𝟏). Veneer recovery represents the net veneer recovery of the manufactured panels. Veneer downgrades were grouped into knot, splitting, and resin and wane-related defects. Component analysis, including climate, form, heartwood, veneer quality, veneer recovery, and veneer downgrade reasons per tree and grouped according soil (Figure 4. 2B). Climate variables included annual maximum temperature (T-max), annual minimum temperature (T-min), summer rainfall, autumn rainfall, and spring rainfall. No winter rainfall was recorded in this region. Veneer quality was divided into face, layup, and cross veneer and expressed as a percentage per tree. Recovery (tree) represents the net veneer recovery per tree excluding reject logs. Recovery (log) represents the net veneer recovery per tree for the processed logs after the panels are manufactured. The reasons for veneer downgrades were grouped into knot, splitting, and resin and wane-related defects. 75 Figure 4. 3: Correlation matrix showing relationships between tree height, slenderness coefficients (SC), volume, and climate variables including annual maximum temperature (T-max), annual minimum temperature (T-min), autumn rainfall, summer rainfall, and spring rainfall. Blue values indicate positive correlations and red values negative correlations. Higher temperatures have been found to facilitate tree growth by means of supporting photosynthesis, energy storage, and consequently the formation of vascular cells which transport water up the tree (Petit et al., 2010). Growing environments subject to lower temperatures have also been found to limit xylogenenesis, leading to hydraulically-constrained trees (Petit et al., 2010). A similar trend was observed in relation to tree SC and volume, which was directly positively impacted by tree height growth (Ryan, 2010; Zhang et al., 2020b). In effect, larger and more slender trees were observed on sites with greater T-max (Figure 4.2A and Figure 4.3). Therefore, these findings suggest that P. patula trees grown in Mpumalanga were not constraint by water availability. Positive relationship between temperature, tree height, SC, and tree 76 volume have been observed internationally in gymnosperm forests (van der Merwe et al., 2023a; Zhang et al., 2020b). 4.3.2.2 Tree ovality (O-values) and volumetric heartwood percentages (HP) Trees grown on granite soils in the Highveld were generally most cylindrical (O-values ranging between 0.944 and 0948) (Table 4.5) and had less HP (HP of 1.6 %) than trees of similar size grown on the Lowveld. There were significant differences in O-values and HP for trees grown on the Lowveld. Trees grown on dolomite soils within the 1,100 m to 1,299 m altitude range had O-values of 0.943 and HP of 2.3 %, compared to O-values of 0.928 and HP of 0.9 % for trees grown within the 1,500 m to 1,699 m altitude range. Only trees grown on dolomite soils within the 1,500 m to 1,699 m altitude range had statistically significant differences in O-values compared to the trees grown on dolomite soils at lower altitudes. Similarly, trees grown on the lowest altitude shale soils and within the 1,100 m to 1,299 m altitude range were the most cylindrical (O-values of 0.945). Although O-values decreased with increasing altitude for trees grown on shale soils, only the O-values of trees grown on the lowest altitude shale soils, were statistical significantly different. The O-values of trees grown on the higher altitude shale sites ranged from 0.934 to 0.931. On the other hand, HP differences were more pronounced, as HP decreased with increasing altitude. Trees grown on shale soils located at the lowest altitude had the most heartwood (HP of 3.2 %). There was no statistical differences in heartwood portion comparing trees grown on shale soils within the 1,300 m to 1,499 m and 1,500 m to 1,699 m altitude ranges, with HP ranging from 1.2 % to 1.6 %. In addition, there was no statistical differences in heartwood portions comparing trees grown on shale soils within the 1,500 m to 1,699 m and 1,700 m to 1,899 m altitude ranges, with HP ranging from 1.2 % to 0.7 %, respectively. Tree O-values have been found to directly impact veneer recovery during rotary peeling, as all fibre outside of the peeling plane of the log needs to be removed before a continuous veneer ribbon can be produced (Hamilton et al., 2015; McGavin et al., 2021). Larger trees were observed to have grater O-values and to be more cylindrical. Specifically, trees found on the Highveld and at low altitude sites with greater T-max on the Lowveld (Table 4.5). Tree ovality was also greater for larger trees, which was found to be related to the sites’ annual temperatures (Table 4.5; Figure 4.3). HP was also found to be greater in larger trees (Table 4.5). Although no 77 published values for HP in P. patula have been identified, similar trends have been observed in other pine species, such as Pinus palustris (Aye et al., 2022; Gjerdrum, 2003). Heartwood formation has been found to impact the portion of actively conducting sapwood and therefore water use regulation in trees (Moreno Chan et al., 2013; Gonzalez-Benecke and Martin, 2010). Although significant trends in HP were observed in this study, the heartwood portions of trees was found to be surprisingly small and to have a positive correlation with maximum temperature, tree height, and volume (Table 4.5, Figure 4.2B, and Figure 4.3). These findings suggest that the wood derived from P. patula at clear-felling in South Africa, is almost exclusively sapwood, potentially due to the fast growth and relatively young harvest ages of plantation forests. Trees sampled from plantation forests in this study were all of the same age and therefore provided unique insight into the factor affecting the onset of heartwood formation in P. patula. 4.3.2.3 Tree taper (T) and sweep (SW) Tree taper was significantly impacted by soil (Table 4.6). Trees grown on granite soils in the Highveld had similar T, when compared to trees grown on shale soils in the Lowveld (T ranging from 0.71 cm/m to 0.72 cm/m). Trees grown on dolomite soils in the Lowveld had the most taper (T of 0.81 cm/m). Tree taper was also significantly impacted by altitude (Table 4.7). However, only trees grown on sites located within the 1,500 m to 1,699 m and 1,700 m to 1,899 m altitude ranges had statistically significant differences in taper (0.80 cm/m versus 0.68 cm/m). Tree sweep was also statistically significantly impacted by soil (Table 4.6). Trees grown on granite soils in the Highveld had the least amount of sweep (0.39 cm/m). Trees grown on dolomite and shale soils on the Lowveld had the most sweep, and there was no statistical differences in sweep when comparing the sweep of trees grown on the Escarpment (S ranging from 0.47 cm/m to 0.48 cm/m). Tree sweep was also significantly impacted by altitude (Table 4.7). Only the S of trees grown within 1,700 m to 1,899 m altitude range was significantly different from trees grown within 1,300 m to 1,499 m and 1,500 m to 1,699 m altitude ranges (0.41 cm/m versus 0.47 cm/m and 0.49 cm/m respectively). Tree taper and sweep directly impact veneer production during rotary peeling of logs. Similar to ovality, fibre outside of the peeling plane needs to be removed before a continuous and full veneer ribbon can be produced (Hamilton et al., 2015; McGavin et al., 2021). In addition, sweep is associated with non-desirable wood properties such as reaction wood (Raut et 78 al., 2022; Warensjö and Rune, 2004). Reaction wood tends to display lower moisture content, higher density, and excessive shrinkage in the axial direction during the drying of veneer sheets in comparison to wood free of reaction wood (Kretschmann, 2010; Wimmer and Johansson, 2014). Both taper and sweep were found to be statistically impacted by soil type and altitude independently (Table4.5 and Table 4.6. Trees grown on granite soils found in the Highveld had the least amount of sweep compared to tree grown on the Lowveld. Comparing the trees measured on the Lowveld, trees grown on dolomite soils had less sweep compared to trees grown on shale soils. Tree sweep has been found to be directly related to the unstable soil conditions, especially shallow soils and steep slopes, leading to the horizontal displacement of the stem (Gorges et al. 2021; Krause and Plourde 2008). Unstable soil conditions have also been found to impact the movement of soil and the formation of sweep in trees, as trees attempt to stabilize themselves by means of growth responses (Gorges et al., 2021; Krause and Plourde, 2008). The Highveld region, where granite soils are found, are typically more uniform with no mountains and therefore also has more uniform terrain condition. The more uniform terrain conditions could potentially explain why the least amount of sweep was found in trees grown on the Highveld. On the other hand, the Lowveld region is fractioned by the Drakensberg mountain range and therefore more uneven terrain conditions persist (Partridge et al., 2010; Van der Merwe et al., 2023c). Shale soils are also typically shallow and found at higher altitude, with more aggressive slopes, which could potentially explain why trees grown on shales had more sweep (Muller et al., 2017; Van der Merwe et al., 2023a). Unfortunately, sampling in this study was not stratified according to slope. Factors impacting sweep in South African plantation forests should therefore be further investigated. Trees grown on granite sites found in the Highveld region also generally had less taper in the merchantable portions of trees, however, similar taper to trees grown shale soils found in the Lowveld. Comparing the trees grown on the Lowveld, trees grown on shale soils had less taper than trees grown on dolomite soils. This finding is perplexing, as trees grown on shale soils are generally shorter with lower SC. However, it is possible that the majority of taper found in shale grown trees, form part of the non-merchantable portions of the tree or that crown logs were too small to be accepted as peeler logs, which could potentially explain this anomaly. Sweep and taper were also impacted by the altitude, with both taper and sweep increasing with increasing altitude. However, these differences were minor with only two of the treatments being statistically different in both sweep and taper. Granite soils were 79 also not found at the two lowest altitudes sampled and dolomite not at the highest altitude, making these results difficult to interpret. 4.3.2.4 Wood density and latewood percentage (LW %) Density and LW % were significantly impacted by the soil parent material and altitude of the growth site (Table 4.5 and Table 4.6. Trees grown on the Highveld on granite soils had a significantly lower wood density and had less LW % than trees grown in the Lowveld. Trees grown on shale soils had significantly denser wood and more LW %, than trees grown on dolomite soils. Looking at the effect of altitude, trees had the highest wood density and LW% at the mid-range altitude (1,300 m to 1,699 m) (Table 7). However, there was no statistically significant differences in density and LW % when comparing trees grown on sites with altitudes ranging from 1,500 m to 1,699 m to trees grown on sites with altitudes ranging from 1,700 m to 1,899 m. There was also no difference in density and LW % when comparing trees grown on sites with altitudes ranging from 1,700 m to 1,899 m to the wood density and LW % of trees grown on the lowest altitude sites with altitudes ranging from 1,100 m to 1,299 m. These sites also had the lowest density and the least amount of LW %. Wood density and latewood content of wood have been found to be synonymous with wood quality, which is closely related to the physical properties of lumber such as modulus of rupture and elasticity (Da Ros et al., 2021; Van der Merwe et al., 2023a). Wood properties of plantation-grown trees are impacted by both macro or regional differences, and also micro or inter-compartmental differences. On a macro level, the study found that the site related differences in wood properties including wood density and volumetric latewood content to be closely related to seasonal rainfall (Figure 4.2B). Trees grown in the Highveld region received significantly more spring rainfall, had lower wood density and volumetric latewood content. On the other hand, trees grown in the Lowveld received significantly more autumn and summer rainfall, and had significantly higher wood density and volumetric latewood content (Figure 4.2B). These findings are supported by previous studies, which showed that wood density and the amount of latewood formed in trees is to related to rainfall and lower growing temperatures due to the formation of more thick-walled latewood cells (Panshin and Brown, 1964; Pompa-García et al., 2021; Van der Merwe et al., 2023a). However, the Lowveld forestry region also 80 receives more overall annual rainfall and it is not clear if the formation of denser wood in the Lowveld is only impact by seasonal rainfall or by the total annual rainfall (Van der Merwe et al., 2023c). On a micro level, a relationship was observed between density and tree size, with smaller trees having lower wood density and less volumetric latewood content (Figure 4.2A and Figure 4.2B). 4.3.2.5 Tree live crown percentage (LC) Soil parent material had a significant impact on the portions of live tree crowns. There was a statistically significant difference comparing LC of trees grown on granite soils found in the Highveld and trees grown on dolomite soils found in the Lowveld (LC ranging from 46.1 % to 46.5 %). Moreover, trees grown on shale soils in the Lowveld, had a significantly larger LC than trees growing on granite and dolomite soils (LC of 52 %). Altitude treatments also had a significant impact on the portion of live tree crown. Trees grown on the lowest altitude sites or within the 1,100 m to 1,299 m range, had the smallest LC (44.7 %). The LC of trees also increased with increasing altitude, and the largest LC was observed for trees growing at the highest altitude (51.5 %). There was no significant difference in slenderness for trees grown on shale soils within the 1 300 m to 1 499 m and 1 500 m to 1 699 m altitude ranges as well as between 1 500 m to 1 699 m and 1 700 m to 1 899 m altitude ranges. The crown status or whether the crown is alive or dead can significantly impact the type of defects produced during the manufacturing of rotary peeled veneer. Live branches lead to the formation of sound knots, while dead branches tend to the formation of loose or encased knots. Encased knots can form holes in veneer (Lowell et al., 2014; Munalula et al., 2016). Therefore, crown dynamics can impact the recover and quality of veneer products. Live crown height portions are directly impacted by the competition between trees. Branch die-back occurs when competition between trees set in and the crown is out shadowed by the neighbouring competing trees (Barbosa et al., 2021; Mansfield et al., 2007). Moreover, tree size can directly impact canopy die-back. In this study, both soil and altitude were found to independently impact the live crown height portion of trees. Trees growing in the Lowveld generally had a greater live crown portion, and it is likely that this phenomenon was driven by trees’ size. Trees grown on the Lowveld were generally smaller than trees grown on the Highveld. Comparing the trees grown on the Lowveld, trees originating from shale soils had the largest live crown portions (Table 6). 81 Although the largest trees in the Lowveld were found on shale soils at low altitude, trees grown on shale soils were generally smaller than trees grown on dolomite soils and could explain the larger live crown portions observed in trees growing on these sites. In effect, canopy closure was possibly achieved at a later stage in plantation compartments with smaller trees due to a lower degree of competition for sunlight between the neighbouring trees (Albaugh et al., 2020; Zhao et al., 2020). However, there was difference in live crown portion comparing the larger trees grown on dolomite soils found in the Lowveld to the generally largest trees grown on granite soils found in the Highveld. Similarly, the altitudinal effect on live crown portion of trees were found to mimic tree size responses, as the live crown portion of trees decreased with a decrease in altitude. The tree size effect on the live crown portion of trees have previously been observed in plantation forests (Albaugh et al., 2020; Barbosa et al., 2021). 4.3.3 Veneer recovery and quality The interaction between altitude and soil treatments was found to have a statistically significant impact on net veneer recovery for full trees (P<0.05; Table 4.8). Table 4. 7: The merchantable veneer recovery of peeler logs. Treatment means and standard errors are shown for respective regions and soil types and altitudes, including the post product recovery percentage of veneer sheets (net recovery) for logs produced from trees grown on the respective site types. Superscript letters represents treatment groups found by Tukey HSD, where the same superscript letters indicates that there are no statistically significant differences between the treatments. Different superscripts indicate a statistically significant difference. Region Soil Altitude Net recovery (%) Highveld Granite 1,500-1,699 54.8 ± 1.31𝑎 1,700-1,899 54.0 ± 1.31𝑎 Lowveld Escarpment Dolomite 1,100-1,299 43.6 ± 1.31𝑏 1,300-1,499 45.9 ± 1.30𝑏 1,500-1,699 41.0 ± 1.32𝑏 Shale 1,100-1,299 51.1 ± 1.31𝑎 1,300-1,499 41.4 ± 1.31𝑏 1,500-1,699 42.6 ± 1.31𝑏 1,700-1,899 41.5 ± 1.31𝑏 However, soil parent material was the only treatment found to have a statistically significant impact on net veneer recovery for the processed logs and veneer quality (P<0.05; Table 4.9). 82 Table 4. 8: Veneer quality groupings according tosoil parent material. Treatment means and standard errors are shown for respective regions and soils, face veneer (%), layup veneer (%), and cross veneer (%). Superscript letters represents treatment groups found by Tukey HSD, where the same superscript letters indicates that there are no statistically significant differences between the treatments. Different superscript indicate a statistically significant difference. Veneer quality groupings significantly impacted by soil parent material. Treatment means and standard errors are shown for respective regions and soils, face veneer (%), layup veneer (%), and cross veneer (%). Superscript letters represent treatment groups found by Tukey HSD, where the same superscript letters indicates that there are no statistically significant differences between the treatments. Different superscripts indicate a statistically significant difference. Region Soil Log net recovery (%) Face veneer (%) Layup veneer (%) Cross veneer (%) Highveld Granite 59.8 ± 0.60𝑎 9.7 ± 0.47𝑎 76.9 ± 1.34𝑎 13.4 ± 1.35𝑐 Lowveld Escarpment Dolomite 52.7 ± 0.49𝑏 5.5 ± 0.39𝑏 66.6 ± 1.10𝑏 27.9 ± 1.10𝑏 Shale 51.7 ± 0.42𝑏 2.6 ± 0.34𝑐 55.8 ± 0.95𝑐 41.6 ± 0.96𝑎 4.3.3.1 Net veneer recovery Trees grown on granite soils, found in the Highveld, yielded the highest net veneer recovery compared to veneer recoveries of trees grown in the Lowveld (Table 4.8). There was no statistical differences comparing the net veneer recoveries of trees grown on dolomite soils within the 1,500 m to 1,699 m and those trees grown within the 1,700 m to 1,899 m altitude range (54 % versus 54.8 % net veneer recovery). Only trees growing on shale soils within the 1,100 m to 1,299 m altitude range had a statistical difference in net veneer recovery per tree, in fact statistically similar to trees grown on the Highveld on granite soils (net veneer recovery of 51.1). Trees grown on dolomite and shale soils had no statistical difference in net veneer recovery per tree and had recoveries ranging from 41.0 to 45.9 %. Similarly, logs produced from trees grown on granite soils found in the Highveld had the best net veneer recovery (59.8 % net veneer recovery). However, there was no statistically significant difference when comparing the net veneer recovery of logs produced from trees grown on dolomite and shale soils, found in the Lowveld (52.7 % and 51.7 %). During veneer production, volumetric losses are incurred during the peeling process, during drying, and after plywood panels are sized into their final product dimensions (Dobner et al., 2013; Lynch and Clutter, 1998). Peeling losses are incurred during rounding of the logs, to allow for the production of full veneer ribbons (rounding losses). In addition, spur losses are 83 incurred when logs are held in position by spurs during peeling. The spur damaged portion of logs are removed by a veneer clipper (spur losses). Core losses are also incurred during the peeling process due to the portions of logs that cannot be utilised due to equipment and fibre quality limitations (core losses). During the drying process recovery is further reduced due to the shrinkage of veneer sheets and drying related degradation of veneer sheets, such as splitting (Guo and Altaner, 2018; Lavalette et al., 2016). Finally, after veneer sheets have been pressed together into plywood sheets, the panels are sized to their final dimension by trimming the edges, which results in additional losses (Kumar et al., 2020; Ugryumov, 2018). Therefore, true recovery is measured after drying as significant losses can still be incurred during the veneer drying process and during the plywood production process due to the re-sizing of plywood panels (Puettmann and Milota, 2017; Wilson and Sakimoto, 2005). In this study, no plywood panels were produced, however, a theoretical recovery was calculated from the dry sheets, by taking the final product dimensions into account. The interaction between soil and altitude treatments were found to significantly impact veneer net recovery for full trees (Table 4.8). Logs produced from trees grown on the Highveld yielded the best net recovery and only the largest trees in this study grown on shale soils within the 1,100 m to 1,299 m altitude range yielded similar net veneer recovery. The remaining sites had no statistical difference in veneer recovery, suggesting that veneer recovery of trees is associated with specific phenotypes due to inherent regional growing conditions. In addition, net recovery of the processed logs were most significantly impacted by soil, with trees grown in the Highveld on granite soils having the best net recoveries (Table 4.8). The greater veneer recovery could potentially be due to low sweep, taper, and more cylindrical logs produced from trees grown on granite soils in the Highveld (Table 4.6). 4.3.3.2 Veneer quality Trees grown on granite soils found in the Highveld also produced the largest percentage high value face quality veneer sheets (9.7 % face veneer sheets; Table 4.8). Logs produced from trees grown in the Lowveld had lower log net veneer recovery and generally yielded less face quality veneer sheets compared to the trees grown on the Highveld. However, when comparing trees grown on the Lowveld, trees grown on dolomite soils yielded significantly more face quality veneer sheets compared to trees grown on shale soils (5.5 % versus 2.6 %). In addition, trees grown on granite soils found in the Highveld also produced significantly more layup sheets or 84 full sheets (76.9 % layup sheets) compared to tree grown on the Lowveld. Of the Lowveld sites, trees grown on dolomite soils yielded more layup sheets, compared to trees grown on shale soils (66.6 % versus 55.8 % respectively). In addition, trees grown on granite soils found in the Highveld produced the least amount of cross quality veneer sheets or sheets that are not full sheets (13.4 % cross veneer sheets)compared to trees grown on the Lowveld. Of the trees grown on the Lowveld, trees grown on shale soils produced the more low quality cross veneer sheets compared to trees grown on dolomite soils (41.6 % versus 27.9 % respectively). Trees grown on the Highveld have also been found to have lower density, less latewood, lower pith to bark density gradient, and lower/greater? over-all uniformity in wood density (Van der Merwe et al., 2023a). Previous studies have found high density pine wood to shrink more in the tangential direction than the radial direction, which could also lead to veneer with more defects and splitting (Bárcenas-Pazos et al., 2000; Lutz, 1971; Peng et al., 2011). Veneer shrinkage associated with wood density for plantation grown P. patula could also potentially be more substantial, due to limited heartwood, which contributes to fibre stability for Mexican hardwoods such as P. patula (Torelli and Gorišek, 1995). Logs produced from trees grown on granite soils with more favourable form had the highest net veneer recovery and logs produced from tree grown on Lowveld sites with less favourable form had the lowest net veneer recovery (Table 4.8; Figure 4.2B). It was observed that trees grown on the Lowveld had more butt sweep and were more cylindrical compared to trees grown in the Highveld, which could potentially impact the veneer recovery. Veneer quality significantly impacts the profitability of plywood mills, as the merchantable value of plywood panels are significantly impacted by the quality of face veneer used (Dickson et al., 2005; Syafii and Novari, 2021). The quality of sub-surface veneer also impacts the quality and international demand of plywood panels. Veneer panel quality standards and value are not internationally standardised (Rohumaa et al., 2013; Wan et al., 2010). Soil type had a significant impact on the amount of high-quality face veneer sheets produced (Table 4.9 and Figure 4.2). Logs produced from trees grown on granite soils found on the Highveld yielded significantly more high-quality face veneer compared to trees grown on the Lowveld. In fact, logs produced from trees grown in the Highveld produced near double the amount of face veneer compared to logs produced from trees grown on dolomite soils and almost 85 three times as much face veneer compared to logs produced from tree grown on shale soils. Comparing the trees grown on the Lowveld; logs produced from trees grown on dolomite sites produced significantly more face veneer sheets than logs produced from trees grown on shale soils (Table 4.8 and Figure 4.2). Tree form and fibre properties appear to have a more substantial impact on the amount of face veneer produced. In particular, it was found that logs produced from trees on the Lowveld had more post drying splitting in veneer sheets (Figure 4.2). Similarly, logs produced from trees grown on granite soils in the Highveld produced the most layup quality veneer sheets, and more layup sheets compared to logs produced from trees grown in the Lowveld. Comparing the amount of layup quality veneer sheets produced from logs sourced from trees grown on the Lowveld, it was found that logs derived from trees growing on dolomite soils also produced substantially more layup quality veneer sheets than logs produced from trees growing shale soils. Layup quality veneer production was found to be related to a higher frequency of knot related veneer downgrades, potentially due to a greater portion of unpruned logs (Figure 4.3). However, logs produced from trees growing on shale soils found in the Lowveld, produced substantially more low-quality cross veneer sheets than logs produced from trees grown on dolomite soils also found in the Lowveld. Interestingly, the same logs produced from shale soils produced about three times as many cross-quality veneer sheets compared to logs produced from trees grown on granite soils. Cross quality veneer production was related to a higher frequency of splitting related downgrades, potentially due to a greater portion of reaction wood found in trees grown on the Lowveld (Figure 4.3). Previous studies have found that climate is the most significant factor impacting tree growth and the wood properties of plantation grown P. patula found in Mpumalanga, South Africa (Muller et al., 2017; Van der Merwe et al., 2023a). Therefore, the observation that soil significantly impacted veneer recovery and quality is probably more related to climate than the soil variables. Recent studies also established that trees grown on the Lowveld have significantly higher densities, more latewood, and a more aggressive pith to bark density gradient compared to trees grown in the Highveld (van der Merwe et al., 2023a). Furthermore, it is well established that wood shrinkage is greater for denser wood (Grekin and Verkasalo, 2010). Considering not only the higher density of pine trees grown in the Lowveld, but also the higher pith to bark 86 density gradient, it is possible that these fibre properties led to the observed greater splitting of veneer sheets (Figure 4.2). In addition, it was found that trees grown on the Lowveld had more sweep compared to trees grown on the Highveld, and it was also established that sweep in trees is related to the formation of reaction wood, which directly impacts splitting in veneer sheets due significant axial shrinkage (Raut et al., 2022; Warensjö and Rune, 2004). Also, tree volume was found to have a weak correlation with net veneer recovery for the resultant harvested logs and for the full tree (Figure 4.4). The findings emphasise the importance of log quality and fibre properties in the overall net recovery of veneer logs. Figure 4. 4: Linear relationship between tree volume and net veneer recovery for logs produced from the sampled trees (Figure 4.4A) and for the full tree including harvesting related reject logs and crown waste (Figure 4.4B). 4.3.4 Tree properties and veneer quality PCA Two principal component analyses were implemented to determine the key variables and relationships impacting tree and veneer properties (Figure 4.2). The first principal component analysis established the relationship between tree properties, soil properties, veneer recovery, veneer quality, and the reasons why veneer was downgraded. Tree properties included tree height (Height), diameter (DBH), volume, wood density (LW), and volumetric latewood content 87 (LW %). Soil properties included soil total available water (TAW), nitrogen (N), phosphate (P), potassium (K), calcium (Ca), magnesium (Mg), sulphur (S), carbon (C), and carbon to nitrogen ratios (C:N ratio). Veneer related variables included net veneer recovery per tree (Recovery (tree)), net veneer recovery for the processed logs (Recovery (log)), face quality veneer percentage (Face %), layup quality veneer percentage (Layup %), and cross quality veneer percentage (Cross %). In addition, the reasons why veneer was downgraded was also included and was classed according to knot related defects (Knots), splitting related defects, Resin and Wane related defects. (Resin-Wane). Analysing the principal components of full trees, it was found that the first two principle components explained 50 % of variance related to tree form, wood properties, veneer recovery, and quality and soil properties (Figure 4.2A). Soil groupings did not separate out from one another when taking tree properties, soil properties, veneer recovery, and veneer quality, and the reasons why veneer was downgraded into account. However, analysing the principal components of full trees, it was found that the first two principal components explained 51 % of variance related to tree form, wood properties, veneer recovery, and quality and climate (Figure 4.2B). Granite sites found on the Highveld separated out from dolomite and shale sites found on the Lowveld due to a positive correlation with spring rainfall and a negative correlation to annual minimum temperature (T-min), summer rainfall, autumn rainfall and wood, including wood density. Annual maximum temperature (T-max) was also found to have a positive relationship with tree size. As expected, tree height, DBH, and consequently volume were found to have a positive relationship with each other. The findings suggest strong co-linearity between soil type and climate, as soil groupings separated out according to climate and tree properties (Van der Merwe et al., 2023a). Trees grown on granite soils found in the Highveld also separated out from trees grown on dolomite and shale soils found on the Lowveld. Trees grown on granite soils were found to be positively correlated with veneer net recovery per tree (Recovery (tree)), veneer recovery for the processed logs (Recovery (logs)) and the production of high-quality face and layup veneer sheets, and spring rainfall. However, trees grown on dolomite and shale soils in the Lowveld did not separate out from each other in relation to climate, tree form, wood properties, veneer recovery and quality, which suggests that these differences are impacted by regional climate associated phenotypes. Annual minimum temperature (T-min), summer rainfall, autumn rainfall and wood density on the other hand were found to have a positive relationship with Lowveld sites and yielded less face and 88 layup quality veneer sheets. Trees grown on dolomite and shale soils found in the Lowveld also had a positive relationship with the production of low-quality cross veneer sheets. Veneer quality produced from trees, showed no relationship with tree size. Figure 4. 5: Component analysis, including soil properties, form, veneer quality, veneer recovery, and the reasons why veneer was downgraded per tree and grouped according soil (Figure 2A). Veneer quality was divided into face, layup, and cross veneer and expressed as a percentage per tree. Soil variables included nitrogen (N), phosphate (P), potassium (K), calcium (Ca), magnesium (Mg), sodium (Na), sulphur (S), carbon (C), carbon to nitrogen ratios (C:N ratio), and total available water (TAW), which was expressed per ha (𝒕 𝒉𝒂−𝟏). Veneer recovery represents the net veneer recovery after the panels are manufactured. The reasons for veneer downgrades were grouped into knot, splitting, and resin and wane-related defects. Component analysis, including climate, form, heartwood, veneer quality, veneer recovery, and the reasons why veneer was downgraded per tree and grouped according soil (Figure 2B). Climate variables included annual maximum temperature (T-max), annual minimum temperature (T-min), summer rainfall, autumn rainfall, and spring rainfall. No winter rainfall was recorded in this region. Veneer quality was divided into face, layup, and cross veneer and expressed as a percentage per tree. Recovery (tree) represents the net veneer recovery per tree excluding reject logs. Recovery (log) represents the net veneer recovery per tree for the processed logs after the panels are manufactured. The reasons for veneer downgrades were grouped into knot, splitting, and resin and wane-related defects. 89 Chapter 5: Site impacts on wood quality for lumber production 5.1 Introduction South Africa is a water scarce country, with a national mean annual rainfall of less than 450 mm. Natural forests are largely absent and only the eastern part of the country receives sufficient rainfall to support plantation forestry. Pine species were introduced from Central and South America, to satisfy the industrial demand for structural lumber in South Africa. Of the introduced pine species, the Mexican tropical hard pine Pinus patula makes up the greatest portion of the planted area. Forest growers are experiencing several challenges in relation to the establishment of plantation forests. First, the planted areas are regulated by water licences, limiting the availability of land to expand the planted area (Wise et al., 2011; Van Kloppen and Schreiner, 2014). Second, some lands established with plantation forests are being returned to previously disadvantaged communities, displaced during the apartheid regime (Chirwa et al., 2015; Makhubele et al., 2021). Third, areas previously planted with pine species, which are primarily used for the manufacturing of structural lumber, are being converted to faster growing eucalypt species for the pulp and paper industry, due to shorter rotation ages and a faster return on investment (Forestry Economics Services CC., 2018). Finally, the quality of structural lumber yielded from plantation forests in South Africa is perceived to be inferior in quality to its European counterparts due to the fast growth rates and shorter rotation ages (Erasmus and Wessels, 2020; Barrette et al. 2023). It is therefore important to maximize the recovery of structural lumber from plantation forests through an improved understanding of the growing conditions which impact the quality of the final products. Previous studies have found tree form and wood properties of P. patula to be under strong environmental control, demonstrating the importance of quantifying the site-related tree form and wood properties of plantation grown P. patula (Muller et al., 2017; Erasmus and Wessels, 2020; Van der Merwe et al., 2023a). The Mpumalanga forestry region is divided into two sub-regions by the Drakenberg mountain range; the Lowveld Escarpment and Highveld (Van der Merwe et al., 2023c). The Lowveld Escarpment is mountainous and largely made up of soils formed from sedimentary rocks including dolomite and shale. Malmani derived soils are typically deep, well drained soils (Muller et al., 2017; Van der Merwe et al., 2023a). Shale-derived soils are typically deep at 90 lower altitude, due to colluvium deposits at mountain bases, while shallow soils prevail at higher altitudes and mid-slope terrain positions (Van der Merwe et al., 2023c). The Highveld on the other hand is largely made up of soils formed from granite igneous rock. Granite soils are typically deep and well drained soils (Van der Merwe et al., 2023a). The Highveld receives less rainfall annually compared to the Lowveld Escarpment, which ranges between 700 mm and 1,000 mm annually (Van der Merwe et al., 2023c). The cellular structure of wood provides structural support for tree components including roots, branches, and leaves, and importantly connects the roots to the tree canopy thereby facilitating the transport of water and nutrients throughout the tree (Zhang et al., 2020a). As trees grow, the fibre structure of the new growth is seasonally adjusted to allow for the most efficient movement of water throughout the tree stem (Tang et al., 2023). During spring and summer months, when an abundance of energy and resources are available, shorter, lower density cells with larger microfibril angles are laid down, also known as earlywood (Lundqvist et al., 2018; Fabisiak et al., 2019; Da Ros et al., 2021). During colder autumn and winter months, when nutrients and energy are more limited, longer, thick-walled, high density cells with smaller microfibril angles are produced, also known as latewood (Lundqvist et al., 2018; Fabisiak et al., 2019; Da Ros et al., 2021). Latewood is stiffer, stronger, and has less lignin relative to cellulose than earlywood (Kibblewhite et al. 2010). A biorhythmic relationship exists between seasonal climate conditions and wood formation (Rodriguez-Zaccaro and Groover, 2019; Tang et al., 2023). Therefore, is important to better understand the relationship between the growing environment of trees and the resultant wood properties from plantation forestry sites with specific growing conditions (Auty et al., 2014; Lundqvist et al., 2018; Van der Merwe et al., 2023a). Although foresters apply intensive silviculture regimes to improve the quality of fibre yielded from plantation forests, such as pre-determined planting densities, thinning regimes, and branch pruning (Schimleck et al., 2018; Barrette et al., 2023), site variables can have a significant effect on the overall quality of trees and wood produced on a given site. This study specifically aims to address the impact of site on tree form, wood properties, and lumber quality of plantation-grown P. patula 91 5.2 Materials and methods 5.2.1 Sampling in the forest Fifty four, 30 × 30 m square sample plots of 20-year-old P. patula were established and stratified according to the dominant soil parent materials and altitudes found in the Mpumalanga forestry region (Appendix Table 5.1). Climate was determined for the sample plots by using existing climate models (Van der Merwe et al., 2023c). Soil trenches were dug at the sample plots, to a depth of 2 meters and soil sample analyses were completed at the University of KwaZulu Natal (Van der Merwe et al., 2023a). The study aimed to determine how the inherent growing environment of trees impact;  tree form including tree height, volume, slenderness coefficients, roundness, taper, and sweep;  the portion of the physiologically active live crown;  inherent wood properties of standing trees including heartwood percentages, wood density, and latewood content, and;  the resultant lumber recovery and properties produced from plantation-grown P. patula trees including density, dynamic modulus of elasticity (MOE), low point modulus of rupture (MOR), knot size, bow, twist, and spring. The sampled even-aged compartments had the same silviculture history (Appendix Table A1-10). The aim of the thinning regime was to maintain a target basal area of 24 m² per hectare over the plantation life cycle, and branch pruning was completed to maintain a knotty core around the central pith of less than 12 cm. Thus, the introduction of branch pruning was site-specific according to growth rate. Climate data including minimum temperature, maximum temperature, and seasonal rainfall were determined for the respective plots using regional climate models (Van der Merwe et al., 2023c). Ten trees for destructive sampling were randomly selected in a stratified manner from each of the hundred tree plots (Arabatzis and Burkhart, 1992; Stehman et al., 2011). Over-bark diameters at breast height (1.3 meters) were captured for the sample trees using a Resistograph, where all measurements were made in a North-to-South direction, using a drilling feed rate of 200 cm per minute and 2,500 revolutions per minute (Downes et al., 2022; Van der Merwe et al., 2023b). The measurements were converted to density values using existing models for P. patula profiles (Van der Merwe et al., 2023b). Thin- 92 end diameters of the first log were determined for base logs by extrapolating the DBH measurements to a sawlog length of 3.1 meters (sawlog length), while assuming a constant taper of 1 cm per meter (Baltrušaitis and Pranckevičienė, 2005; Charlton et al., 2020). Trees with a thin end diameter less than 25 cm were then classified as sawlog trees. The height to the onset of the live crown and the height to 15 cm diameter or the merchantable diameter were recorded. Live crown portions were calculated using the formula below: Live crown % =Tree height − Live crown heightTree height × 100 (5.1) where, live crown % = the height portion of live crown expressed as a percentage, tree height = the height of the tree in meters, and live crown height = the height onset of live crown in meters (Tahvanainen and Forss, 2008; Maltamo et al., 2009). Slenderness coefficient was calculated as the total tree height divided by DBH in meters (Wang et al., 1998; Zhang et al., 2020b). Trees were cross-cut into 3.1 m logs lengths and the pruned and unpruned tree portions were recorded and sample discs were removed between adjacent log ends. A total of 540 trees were felled and 2,690 logs produced. Plastic tags were attached to logs to allow log specimens to be traced back to the respective sample plots, trees, and the log positions within trees (Murphy et al., 2012; Van der Merwe et al., 2016). 5.2.2 Processing of samples and logs Disc samples were air dried at room temperature for at least 3 months. A central core of 50 mm was removed from each disc samples in the North to South direction and stained with equal volume of solution A and B, where solution A included 1 g of Benzidine, 5 ml of 25 percent hydrochloric acid, and 194 ml of water, and solution B included 20 g of sodium nitrate and 180 ml of water. The solutions were kept separate and applied in tandem during the heartwood staining process (Kutscha et al., 1962). Heartwood portions were measured as the staining uniquely produces a reddish colour, and the volume of heartwood for each log was calculated employing Smalian’s formula (Formula 5.2), using the heartwood measurements of disc samples obtained from the respective log ends (Şahin and Çomak, 2022; Santos et al., 2021). Vheartwood =A+a2 × L (5.2) 93 where, A = the area of heartwood at the thick end of the log in m², a = the area of heartwood at the thin end of the log in m², and L = the log length in meters. The heartwood volumes calculated for each log, were then added together to determine heartwood volume per tree (formula 5.3). Tree volume was calculated using the Max and Burkhart formula for P. patula, using the height and diameter measurements according to formula 5.1 (Crous et al., 2008; Kotze, 1995). HP% =∑ Vheartwood(n)Vtree × 100 (5.3) Tree form variables, such as roundness, sweep, and taper were also weighted volumetrically for the merchantable volume of trees (according to Formulae 5.4, 5.5, and 5.6, respectively) (Van der Merwe et al., 2023b). TreeR =∑ ( Rn ×Vn )r(n)VTotal (5.4) where, 𝑇𝑟𝑒𝑒𝑅 = volumetric weighted roundness per tree, 𝑅𝑛 = log roundness, 𝑉𝑛 = log volume, and 𝑉𝑇𝑜𝑡𝑎𝑙 = total volume of merchantable logs per tree. TreeS =∑ ( Sn ×Vn )r(n)VTotal (5.5) where, 𝑇𝑟𝑒𝑒𝑠 = volumetric weighted sweep per tree, 𝑆𝑛 = log sweep, 𝑉𝑛 = log volume, and 𝑉𝑇𝑜𝑡𝑎𝑙 = total volume of merchantable logs per tree. Treet =∑ ( Tn ×Vn )r(n)VTotal (5.6) where, 𝑇𝑟𝑒𝑒𝑡 = volumetric weighted taper per tree, 𝑇𝑛 = log taper, 𝑉𝑛 = log volume, and 𝑉𝑇𝑜𝑡𝑎𝑙 = total volume of merchantable logs per tree. The sample logs were transported to the Hewsaw chipper canter mill and processed within 3 weeks of felling. The plastic barcode tags were used to identify logs and each log end was uniquely colour coded using paint combinations of primary and secondary colours. The 94 sample logs were processed in 6 batches. All sample logs were spindle-debarked and log form was measured using a Microtec Logeye 300 scanner mounted on the debarking line (Kunickaya et al., 2022; Van der Merwe et al., 2023b). Log form properties measured included log thin-end diameter, volume, sweep, taper, and roundness. The Logeye 300 scanner calculated log roundness by sequentially taking minimum and maximum diameter measurements along the log profile and then determining a weighted roundness value by dividing the minimum diameter by the maximum diameter measurements. In addition, the Logeye 300 measured simple sweep in the sample logs according to European standard EN 1309-3:2018. The sample logs were sorted into batches according to the most viable mill cutting pattern and milled. The lumber board frequencies are shown for the cutting patterns for the respective log thin-end diameter classes in Appendix Table A1-11. Cutting patterns were promoted or demoted according to the log sweep, taper, and roundness. The resultant lumber boards were kiln dried until a dry base moisture content below 15 % was achieved, cross-cut to 3 meter lengths, and sequentially numbered and machine graded using a Microtec Goldeneye 706 grading system. The Goldeneye measured wood density, dynamic modulus of elasticity, low point modulus of rupture, and knot size for each lumber board. In addition, lumber board form including twist, bow, and spring was measured. Unfortunately, 16 % of the lumber boards were trimmed during de-stacking and the colour coded portions of the lumber boards were lost along with the traceability. Imputation of the missing boards was implemented to avoid wood property bias for the respective log portions and trees. Imputation of missing values was done by machine learning by taking tree diameter, height, the height at which the sample logs were extracted, log diameter, inherent log form including taper, sweep, roundness, volume and diameter, and climatic variables (rainfall and temperature) into account. A normalised root mean square error (nrmse) of 0.15 or imputation accuracy of 85 % was achieved. All of the lumber length and moisture measurements were manually verified. All wood density measurements were standardised and reported at 12 % moisture content. Similarly, all of the lumber variables including wood density at 12 % moisture content, dynamic modulus of elasticity (MOE), low point modulus of rupture (MOR), knot size ratio, twist, bow, and spring was volumetrically weighted per log according to Formula 7 and then per tree via Formula 8. Knot size ratio is calculated by dividing the knot size measurement frequency by the board length (Nocetti et al., 2017). 95 Logvar =∑ ( 𝑉𝑎𝑟B1 × VB1+⋯ )r(n)VL (5.7) where, 𝐿𝑜𝑔𝑣𝑎𝑟 = volumetric weighted lumber variable per log, 𝑉𝑎𝑟𝐵1 = board variable, 𝑉𝐵1 = board volume, and 𝑉𝐿 = recovered volume per log. Treevar =∑ ( Logvar × VL1+⋯ )r(n)VTotal (5.8) where, 𝑇𝑟𝑒𝑒𝑣𝑎𝑟 = volumetric weighted lumber variable per tree, 𝐿𝑜𝑔𝑣𝑎𝑟 = volumetric weighted lumber variable per log, 𝑉𝐿1 = recovered volume per log, and 𝑉𝑡𝑜𝑡𝑎𝑙 = total recovered volume per tree. 5.2.3 Data processing All statistical analyses and data processing were completed using the R programming environment: A language and environment for statistical computing. The imputation of missing values due to the trimming of lumber boards was completed by machine learning using Random Forest [‘missForest’] (Stekhoven, 2022; Stekhoven and Buehlmann, 2012). Analysis of variance (ANOVA) was done to determine significant differences for independent variables [‘aov’] (R Core Team 2022). ANOVA residuals were inspected for normality [‘qqPlot’] (R Core Team, 2022). If ANOVA residuals had a normal distribution, treatment least square means and standard error values were determined [‘lsmeans’] (Lenth 2016, Lenth 2020), and post hoc testing was completed to establish differences between respective treatments using a Tukey Honestly Significant Difference Test [‘TukeyHSD’] (Yandell, 1997). Two principal component analyses (PCA) were completed to determine the key variables and relationships between variables impacting tree and lumber properties. The first PCA included tree and lumber properties together with soil variables, and the second with climate variables [‘lfda’, ‘ggfortify’] (Tang et al., 2016; Tang and Li, 2019; Wei and Simko, 2021). Correlation matrices were performed to show the correlation between climate, tree form, and lumber properties [‘corrplot’] (Wei and Simko, 2021). A Pearson’s correlation matrix was produced, to determine correlations between tree, climate, and lumber variables [‘ggcorrplot’] (Kassambara, 2022).Only variables influencing tree form, heartwood portions, and lumber quality are discussed here. A summary of the R-software packages used for statistical analysis are shown in Appendix Table A1-12. . 96 5.3 Results and Discussion 5.3.1 Tree properties and lumber quality PCA Regional collinearities exists between soil and climate in the Mpumalanga, Forestry region of South Africa. Therefore, lumber properties were further scrutinised by implementing principal component analysis for soil and climate variables independently (two PCAs; Figure 5.1A and Figure 5.1B). Two principal component analyses (PCA) were completed to establish the association between soil, climate, and lumber properties. The first PCA was done to establish the relationship between lumber properties, tree height (Height), and soil properties (Figure 5.1A). Lumber properties included density, dynamic modulus of elasticity (MOE), low point modulus of rupture (MOR), bow, twist, and spring. Soil properties included soil total available water (TAW), nitrogen (N), phosphate (P), potassium (K), calcium (Ca), magnesium (Mg), sulphur (S), carbon (C), and carbon to nitrogen ratios (C:N ratio). The first two principal component (PC) explained 52 % of the variance related to these properties. However, the respective soil groupings did not separate from one another and they were similar in relation to tree height, lumber, and soil properties. The second PCA was done to establish the relationship between tree height, lumber properties, and climate. The site related climate variables included summer rainfall (summer rain), autumn rainfall (autumn rain), spring rainfall (spring rain), annual minimum (T-min) and maximum temperatures (T-max) (Figure 5.1B). The first two PCs explained 51 % of the variance related to tree height, lumber properties, and climate. Similar to previous studies, tree growth was most significantly impacted by T-max and indirectly impacted the recovered lumber structural properties and stability, including bow and spring (Chakraborty et al., 2019; Da Ros et al., 2021; Van der Merwe et al., 2023a; Toledo et al., 2011) (Figure 5.1B and Appendix Figure A1-14). However, twist was most significantly impacted by spring rainfall (Sandberg, 2005; Figure 5.1B). Soil variables had little impact on tree growth and structural lumber properties of plantation-grown P. patula trees found in Mpumalanga, South Africa (Muller et al., 2017; Van der Merwe et al., 2023a; Van der Merwe et al., 2023b). 97 Figure 5. 1: Component analysis, including soil properties, form and lumber properties and grouped according to soil type (Figure 5.1A). Lumber quality in relation to wood density, dynamic MOE, low point MOR, knot area, bow, twist and spring. Soil variables included nitrogen (N), phosphate (P), potassium (K), calcium (Ca), magnesium (Mg), sodium (Na), sulphur (S), carbon (C), carbon to nitrogen ratios (C:N ratio), and total available water (TAW), which was expressed per ha (𝒕 𝒉𝒂−𝟏). Similarly, component analysis were done by including climate, tree height and lumber quality per tree and grouped according to soil type (Figure 5.1B). Climate variables included annual maximum temperature (T-max), annual minimum temperature (T-min), summer rainfall, autumn rainfall, and spring rainfall. No winter rainfall was recorded in this region. 5.3.2 Tree form, wood properties, and live crown portions Sample plots were established and stratified according to soil parent material and altitude, as these variables were perceived to have the greatest impact on the growth of plantation-grown P. patula trees in South Africa (Muller et al., 2017). Analysis of variance (ANOVA) was completed to determine how these variables differ according to soil parent material and altitude. However, strong regional collinearities exist between soil and climate in the Mpumalanga forestry region (Van der Merwe et al., 2023a; Van der Merwe et al., 2023b). The ANOVA analysis was done according to the original stratified treatments, however, the supporting PCA provided more clarity regarding the underlining geo-climatic drivers impacting tree height and wood properties, including slenderness coefficients (SC), the portion of live crown (LC), taper, sweep, roundness, the volumetric percentage of heartwood (HP), wood density at 12% moisture content (density), and latewood content percentage (LW)(Appendix Table A1-13). 98 The interaction between soil parent material and altitude had a statistically significant impact on tree height, volume, SC, LC, roundness, HP, density, and LW (p<0.05). Soil parent material also had an independent statistically significant impact on tree sweep and taper, and altitude also had a independent statistically significant impact on tree taper (p<0.05)(Appendix Table A1-13). 5.3.2.1 Tree height, slenderness coefficient (SC), and volume Comparing trees grown on granite soils found on the Highveld, there was no statistically significant difference in tree height, volume, or SC in relation to the respective altitude ranges. However, trees grown on granite soils were generally taller and displayed greater volumes than trees grown on the Lowveld which are dominated with dolomite and shale soils. More variability in tree sizes were also observed on the Lowveld Escarpment. Trees grown on dolomite soils within the 1,100 m to 1,299 m range had no statistically significant differences in tree height and volume, compared to trees grown at 1,300 m to 1,499 m altitude range. However, trees grown on dolomite soils located within the 1,100 m to 1,299 m and 1,300 m to 1,499 m altitude range were 2.6 m and 2.4 m taller than trees grown at 1,500 m to 1,699 m altitude range (Table 5.1). Similarly, trees grown on dolomite soils located within the 1,100 m to 1,299 m altitude range had 0.001 m³ more volume than trees grown at 1,500 m to 1,699 m. However, no statistically significant difference in tree volume was observed, comparing trees grown on dolomite soils located at other altitudes. The SC of trees grown in dolomite soils also differed statistically significantly with the respective altitude treatments. Trees grown at 1,100 m to 1,299 m altitude range had a 8 point greater SC than trees grown on dolomite soils at 1,300 m – 1,499 m range, and a 14 point greater SC than trees grown at 1,500 m to 1,699 m altitude range. Trees grown on shale soils were statistically significant impacted by altitude. Trees grown on shale soils located at the 1,100 m to 1,299 m altitude range were the largest, and were 7.6 m taller and had a 0.002 m³ more volume than trees grown on shale soils located within the 1,300 m to 1,499 m altitude range (Table 5.1). Trees grown on shale soils at 1,300 m to 1,499 m were also 2.6 m taller and had 0.001 m³ more volume than trees grown on shale soils located at 1,500 m to 1,699 m. Furthermore, trees grown on shale soils located within the 1,500 m to 1,699 m range were 2.5 m taller than trees grown on shale soils located within the 1,700 m to 1,899 m 99 altitude range. However, there were no statistically significant difference in volume. Although not statistically significant, on both shale and dolomite soils there was an inverse relationship between altitude and slenderness with slenderness being highest at low altitudes and vice versa. Soil characteristics have been historically found to be important variables impacting tree growth in South Africa (Louw, 1999, Louw, 2016). Climate has been associated with tree growth to a lesser extent, however, this observation may be due to the limited availability of climate data and high resolution climate models (Van der Merwe et al., 2013c). The findings from this study suggests that T-max is the most significant variable impacting tree growth of P. patula plantations grown in the Mpumalanga forestry region (Table 5.1; Figure 5.1B, Appendix Figure A1-14). Higher temperatures have previously been closely associated with tree growth (Nava-Nava et al., 2022; Salminen and Jalkanen, 2007). It is well known that trees use available solar energy during photosynthesis, to form vascular cells via the process of xylogenesis, while colder conditions are not only energy limited, but can also retard the process of xylogenesis (Nanayakkara et al., 2019). In effect, active tree growth only occurs above specific threshold temperatures (Nanayakkara et al., 2019; Rossi et al., 2008). Consistent with this phenomenon, growth sites characterised by higher temperatures had larger, more slender trees with more volume. 100 Tabel 5. 1: Means and standard error for tree variables including tree height, volume, slenderness coefficient, live crown percentage (LC), roundness, volumetric heartwood percentage (HP), wood density (density) and latewood percentage (LW), according to soil parent material and altitude. Superscript represents Tukey significance. Region Soil Altitude Height (m) Volume (m³) SC (.) LC (%) Roundness (.) HP (%) Density (kg/m³) LW (%) Highveld Granite 1500 - 1699 25.3 ± 0.32𝑏 0.514 ± 0.0185𝑏𝑐𝑑 103 ± 1.4𝑏 39.7±1.30𝑑𝑒 0.937 ± 0.0020𝑎𝑏 1.0 ± 0.13𝑏𝑐𝑑 449 ± 4.2𝑐 29.4 ± 0.79𝑐 1700 - 1899 25.8 ± 0.32𝑏 0.542±0.0185𝑏 102 ± 1.4𝑏 42.1 ± 1.30𝑑 0.940 ± 0.0020𝑎 1.0 ± 0.13𝑏𝑐𝑑 459 ± 4.2𝑏𝑐 31.4 ± 0.79𝑏𝑐 Lowveld Escarpment Dolomite 1100 - 1299 23.6 ± 0.31𝑐 0.455 ± 0.0182𝑐𝑑𝑒 98 ± 1.3𝑏 36.0 ± 1.28𝑒 0.945 ± 0.0019𝑎 1.2 ± 0.13𝑏𝑐 459 ± 4.1𝑏𝑐 31.3 ± 0.78𝑏𝑐 1300 - 1499 23.4 ± 0.31𝑐 0.516±0.0181𝑏𝑐𝑑 90 ± 1.3𝑐 41.6±1.27𝑑𝑒 0.940±0.0019𝑎𝑏 1.2 ± 0.12𝑏 499 ± 4.1𝑎 38.8 ± 0.77𝑎 1500 - 1699 21.0±0.29𝑑 0.439±0.0171𝑑𝑒𝑓 84 ± 1.3𝑑 50.5 ± 1.20𝑎𝑏 0.925 ± 0.0018𝑑 0.8 ± 0.12𝑏𝑐𝑑 503 ± 3.9𝑎 39.8 ± 0.73𝑎 Shale 1100-1299 30.5±0.36𝑎 0.746 ± 0.0211𝑎 113 ± 1.6𝑎 43.1 ± 1.48𝑐𝑑 0.935 ± 0.0023𝑎𝑏𝑐 1.9 ± 0.15𝑎 472 ± 4.8𝑏 33.8 ± 0.90𝑏 1300 - 1499 22.9 ± 0.34𝑐 0.517±0.0196𝑏𝑐 89 ± 1.5𝑐𝑑 47.1 ± 1.38𝑏𝑐𝑑 0.928±0.0021𝑐𝑑 1.0 ± 0.13𝑏𝑐𝑑 511 ± 4.4𝑎 41.3 ± 0.84𝑎 1500 - 1699 20.3 ± 0.32𝑑 0.409±0.0189𝑒𝑓 83 ± 1.4𝑑 48.6 ± 1.33𝑎𝑏𝑐 0.923 ± 0.0020𝑑 0.6 ± 0.13𝑐𝑑 499±4.3𝑎 38.9 ± 0.81𝑎 1700 - 1899 17.8±0.37𝑒 0.354±0.0213𝑓 72±1.6𝑒 52.0 ± 1.50𝑎 0.932 ± 0.0023𝑏𝑐𝑑 0.4 ± 0.15𝑑 496 ± 4.8𝑎 38.4±0.91𝑎 Tabel 5. 2: Means and standard error for tree form variables including sweep and taper according to soil geology. Superscript represents Tukey significance. Region Soil Sweep (cm/m) Taper (cm/m) Highveld Granite 0.46 ± 0.019𝑐 0.37 ± 0.017𝑐 Lowveld Escarpment Dolomite 0.62 ± 0.015𝑎 0.46±0.014𝑏 Shale 0.56 ± 0.015𝑏 0.52±0.013𝑎 101 5.3.2.2 Tree live crown percentage (LC) There was a general trend on all three parent geologies for LC to increase with altitude. Trees grown on shale soils, located within a 1,500 m to 1,699 m and 1,700 m to 1,899 m altitude range had the largest LC, but there was no statistically significant difference in LC (LC: 48.6 % and 52.0 %). Trees grown on shale soils at 1,700 m to 1,899 m also had 8.9 % and 4.9 % larger LC than trees grown on shale soils located with a 1,100 m to 1,299 m and 1,300 m to 1,499 m altitude range. There was no statistically significant difference in LC comparing the remaining trees grown on shale soils. Trees grown on Lowveld dolomite soils also had statistically significant differences in LC, where trees grown at 1,500 to 1,699 m had the largest LC (LC: 50.5 %). Trees grown on dolomite soils located with a 1,500 to 1,699 m altitude range also had 14.5 % and 8.9 % larger LC, compared to trees grown on dolomite soils located within a 1,100 m to 1,299 m and 1,300 m to 1,499 m altitude range. There was no statistically significant difference in LC comparing trees grown on dolomite soils located within a 1,100 m to 1,299 m and 1,300 m to 1,499 m altitude range. Trees grown on granite soils found on the Highveld, generally had smaller LC and no statistically significant difference was observed when comparing trees grown on granite soils located with a 1,500 m to 1,699 m altitude range to trees grown on granite soils located within a 1,700 m to 1,899 m altitude range (LC: 39.7 % and 42.1 %). LC of trees are light-dependent, to fulfil the process of photosynthesis (Barbosa et al., 2021; Mansfield et al., 2007), as light deficiencies of the live crown lead to crown die-back (Barbosa et al., 2021; Mansfield et al., 2007). Therefore, larger trees generally have a smaller live crown portion as the competition between neighbouring trees will lead to light deficiency of the lower portions of the live crown (Barbosa et al., 2021; Mansfield et al., 2007). A similar trend was observed in this study (Table 5.1). The smaller trees found on the Lowveld, generally had larger live crown portions than the larger trees grown on the Highveld (Table 5.1). Similarly, on the Lowveld the live crown portions of trees had a positive relationship with altitude, as tree heights and competition also decreased with increasing altitude (Table 5.1). These findings are consistent with previous research examine on gymnosperms live crown phenomena (Barbosa et al., 2021; Mansfield et al., 2007). 102 5.3.2.3 Tree roundness Trees grown on the granite soils found on the Highveld, were generally more cylindrical than trees grown on Lowveld dolomite and shale soils. Trees grown on granite soils located within a 1,500 m to 1,699 m and 1,700 m to 1,899 m altitude range, had roundness of 0.937 and 0.940 respectively, but this was not statistically significant. Within the Lowveld Escarpment, trees grown on dolomite soils were generally more cylindrical than those trees grown on shale. However, trees grown on lower altitude dolomite soils were rounder than tree grown on dolomite soils located within a 1,500 m to 1,699 m altitude range. Trees grown on shale soils had highly variable roundness. Trees grown on shale soils located within a 1,100 m to 1,299 m, 1,300 m to 1,499 m, and 1,700 m to 1,899 m had the highest roundness of 0.935, 0.928 and 0.932, respectively, but this was not statistically significant difference. Trees grown on shale soils at 1,700 m to 1,899 m also had 0.012 higher roundness than trees grown on shale soils located at 1,500 m to 1,699 m altitude range. On shale soils, only roundness at the lowest altitude was statistically significant. The roundness of trees is an important form characteristic of trees as it impacts the viability of specific cutting patterns during lumber milling (Todoroki et al., 2007). Similar to previous studies, it was found that larger trees have better form and tend to be more rounded (Table 5.1; Van der Merwe et al., 2023b). 5.3.2.4 Volumetric heartwood percentages (HP) Trees grown on Lowveld dolomite and shale soils generally had larger and more variable volumetric heartwood percentages (HP) than trees grown on the Highveld granite soils (Table 5.1). Comparing trees grown on the Lowveld Escarpment, trees grown on dolomite soils generally had the largest HP and ranged between 1.2 % and 0.8 %, however, this was not statistically significant. Trees grown on shale soils had the most variable HP. Trees grown on shale soils located within a 1,100 m to 1,299 m altitude range also had 0.9 % more HP than trees grown on shale soils located at 1,300 m to 1,499 m altitude range, 1.2 % more HP than trees grown on shale soils located at 1,500 m to 1,699 m, and 1.4 % more HP than trees grown on shale soils located within a 1,500 m to 1,699 m altitude range. There was no statistically significant difference in HP comparing trees grown on shale soils located within a 1,300 m to 1,499, 1,500 m to 1,699 m, and 1,700 m to 1,899 m altitude ranges, with HP ranging from 1 % to 103 0.4 %. Trees grown on granite soils found in the Highveld generally had the least HP, which was approximately 1 %, and the two altitudes were not statistically significant different. Larger trees were also observed to have greater HP (Table 5.1). Previous studies have found that heartwood formation impacts the portion of water conducting sapwood, which impacts water use in trees. The positive relationship found between temperature, tree size, and heartwood in this study are similar to other studies and suggest that chipper canter milled lumber from small and even-aged trees in P. patula plantations in South Africa are almost exclusively sapwood (Van der Merwe et al., 2023b). The P. patula trees in this study had very little heartwood, potentially due to the young age and small size of the sample trees. 5.3.2.5 Tree taper (T) and sweep (S) Trees grown on Lowveld dolomite and shale soils, were generally more tapered and had more sweep compared to trees grown on granite soils found on the Highveld (Table 5.2). Of the Lowveld sites, trees grown on dolomite soils produced logs with the most taper, and 0.05 cm/m more sweep than logs produced from trees grown on shale soils (0.62 cm/m versus 0.56 cm/m). However, trees grown on shale soils had the most sweep and produced logs with 0.05 cm per meter more sweep than logs produced from trees grown on dolomite soils (0.52 cm/m sweep versus 0.46 cm/m respectively). Trees grown on granite soils had least taper and sweep, and produced logs with taper of 0.46 cm/m and sweep of 0.37 cm/m. Tree taper was significantly impacted by altitude (Table 5.3) across the geologies, but there was no obvious trend. Tabel 5. 3: Means and standard error for tree taper according to altitude. Superscript letters represents treatment groups found by Tukey HSD, where the same superscript letters indicates that there are no statistically significant differences between the treatments. Different superscript indicate a statistically significant difference. Altitude range (m) Taper (cm/m) 1,100 – 1,299 0.54 ± 0.021𝑐 1,300 – 1,499 0.61 ± 0.020𝑎 1,500 – 1,699 0.57±0.016𝑎𝑐 1,700 – 1,899 0.51 ± 0.021𝑐 104 Tree taper and sweep negatively impacts lumber recovery during lumber production (Schlotzhauer et al., 2019; Tong and Zhang, 2008). Furthermore, sweep has been associated with non-desirable wood properties such as reaction wood, which directly impacts the stability of lumber and has been associated with dimensional defects such as bow, twist, and spring due to internal tension and irregular shrinkage during seasoning (Sandberg, 2005; Warensjö and Rune, 2004). Log taper has been found to be impacted by site-associated height growth and the height portion of the tree from where the log was produced (Tasissa et al., 1997). It was observed that trees grown on the Lowveld dolomite and shale generally had more taper than trees grown on Highveld granites (Table 5.2). In the case of this study, log taper is potentially impacted by log position in the tree. Within the Lowveld, the trees grown on shale soils were generally taller and therefore displayed a larger portion of the millable logs that may have been produced from the dead and live crown with more taper (Tasissa et al., 1997). However, it is not clear why the Highveld trees produced logs with less taper, considering that a large part of the merchantable logs would also have been produced from the crown portion given the larger trees found on Highveld granite soils. It is likely that the lower recovery associated with Highveld granites might be associated with lower wood quality, and therefore makes it difficult to explain the potential environmental driver impacting log taper (Table 5.3). 5.3.2.6 Wood density and latewood percentage (LW %) Trees grown on the granite soils of the Highveld had the lowest wood density and latewood percentage (LW %; Table 5.1). Among the Lowveld shale sites, only the wood density and latewood percentage of trees grown at the lowest altitude were statistically significantly different. Wood density of trees grown on the higher altitude shale sites ranged from 472 kg/m³ to 511 kg/m³, and latewood percentage ranged from 33.8 % to 41.3 %. There was no clear trend for either wood density nor late wood percentage and altitude on the shale sites. Wood densities and latewood percentages of trees grown on dolomite soils, were similar to those trees grown on shale soils, and the wood density and latewood percentage was only statistically significant at the lowest altitude. Wood density of trees grown on these dolomite sites ranged from 459 kg/m³ to 503 kg/m³, and latewood percentage ranged from 31.3 % to 39.8 %. However, trees grown on dolomite soils at 1,300 m to 1,499 m altitude range had 40 kg/m³ greater wood density and 7.5 % more latewood than trees grown on dolomite soils within a 1,100 m to 1,299 m altitude range. Trees grown on shale soils within a 1,500 m to 1,699 m altitude range had 44 kg/m³ greater 105 wood density and 8.5 % more latewood than trees grown on shale soils at 1,100 m to 1,299 m altitude range. There was also no statistically significant difference in wood density and latewood percentages, when comparing trees grown on granite soils located with a 1,500 m to 1,699 m range to trees grown on granite soils located with a 1,700 m to 1,899 m altitude range. Wood densities generally ranged between 449 kg/m³ to 459 kg/m³, and latewood percentages between 29.4 % and 31.4 %. Wood density is arguably the most important variable impacting lumber quality and is impacted by the amount of latewood formed during a growing season (van der Merwe et al., 2023a). Trees grown at higher altitude Lowveld dolomite and shale sites receiving more summer and autumn rainfall and were found to have denser wood with more latewood, while trees grown on drier Highveld granite and low altitude dolomite and shale soils had lower density wood with less latewood (Table 5.1). The findings suggest that sites that receive more spring rainfall have a prolonged growing season, while sites with more summer and autumn rainfall have a more pronounced late seasonal growth (Muller et al., 2017). Latewood formation has been found to be impacted by growth auxins, found in the canopy which are responsible for the maintenance of the tree canopy during the spring and summer growing season (Cartení et al., 2018). During autumn and winter the elongation and thickening of cell walls have been observed due to a lower energy demand to maintain the tree canopy, leading to the deposition of excess photosynthate in the cell walls leading to the formation of thick-walled vascular cells also known as latewood (Cartení et al., 2018; Uggla et al., 2001). Therefore, the formation of denser and latewood-rich cells can be explained for sites receiving more summer and autumn rainfall (Muller et al., 2017; Van der Merwe et al., 2023a). Previous studies also suggests regional wood density difference to not only be associated with tree size, but also by the amount of vascular cells formed during the season (Appendix Figures A1-15 and A1-16; Van der Merwe et al., 2023a). A similar regional observation was made for sawlogs (Van der Merwe et al., 2023b). 5.3.3 Lumber recovery, quality, and stability The independent variables investigated included structural lumber net recovery, density, modulus of elasticity (MOE), modulus of rupture (MOR), knot size, twist, spring, and bow. The interaction between soil parent material and altitude had a statistically significant impact on bow 106 (p<0.05) (Appendix Table A1-13). Soil parent material had a significant impact on structural lumber net recovery, density, MOE, MOR, knot size, twist, and spring (p<0.05). Altitude had a significant impact on structural lumber net recovery, density, MOE, and MOR (p<0.05). Tabel 5. 4: Means and standard error for lumber net recovery, density, modulus of elasticity (MOE), modulus of rupture (MOR), knot area and twist according to soil parent material. Superscript letters represents treatment groups found by Tukey HSD, where the same superscript letters indicates that there are no statistically significant differences between the treatments. Different superscript indicate a statistically significant difference. Region Soil Net recovery (%) Density (kg/m³) MOE (MPa) MOR (MPa) Knot size (.) Twist (°) Spring (mm/m) Highveld Granite 41.2±0.52𝑏 411 ± 3.1𝑏 8 208±121.1𝑏 27.5 ± 0.44𝑏 6 950 ± 110.4𝑎 24.8±2.78𝑎 129 ± 1.3𝑏 Lowveld Escarpment Dolomite 44.4 ± 0.42𝑎 449 ± 2.5𝑎 9 436±96.3𝑎 31.0±0.35𝑎 6 497±87.8𝑏 8.8±2.21𝑏 133±1.0𝑎 Shale 42.0 ± 0.39𝑏 442 ± 2.3𝑎 9 383±89.4𝑎 30.9 ± 0.32𝑎 6 207 ± 81.5𝑐 7.9±2.05𝑏 134 ± 0.9𝑎 Tabel 5. 5: Means and standard error for structural lumber net recovery, density, MOE and MOR, according to altitude. Superscript letters represents treatment groups found by Tukey HSD, where the same superscript letters indicates that there are no statistically significant differences between the treatments. Different superscript indicate a statistically significant difference. Altitude range (m) Net recovery (%) Density (kg/m³) MOE (MPa) MOR (MPa) 1,100 – 1,299 44.6 ± 0.52𝑎 419 ± 3.2𝑐 8 394 ± 121.0𝑐 28.3±44.00𝑏 1,300 – 1,499 42.9±0.52𝑎𝑏 450 ± 3.2𝑎 9 674±122.0𝑎 31.4 ± 44.20𝑎 1,500 – 1,699 41.7±0.44𝑏 443 ± 2.7𝑎𝑏 9 308±103.0𝑎𝑏 30.8±37.30𝑎𝑏 1,700 – 1,899 41.7 ± 0.54𝑏 436±3.3𝑏 9 105±127.0𝑏 29.9±45.90𝑎𝑏 5.3.3.1 Net structural lumber recovery Logs produced from trees grown on Lowveld dolomite soils, generally had better net structural lumber recovery (44.4%) compared to logs produced from trees grown on Lowveld shale (42.0%) and Highveld granite soils (41.2%) (Table 5.4). Net recovery generally decreased with increasing altitude, however, these differences were not always statistically significant, and only trees grown at the highest altitude range had a statistically significant difference in lumber recovery. This observation is likely to be associated with form variables other than taper (Table 5.3; Table 5.5). Trees grown on sites with altitudes ranging between 1,100 m to 1,299 m had the 107 highest recovery, and produced logs with 1.7 %, 2.9 %, and 2.9 % better recovery than logs produce from trees grown at 1,300 m to 1,499 m, 1,500 m to 1,699 m, and 1,700 m to 1,899 m altitude ranges, respectively. The relative uniformity in recovery could potentially be due to the log form-based sorting of logs, prior to milling and the rejection of logs not conforming to the respective cutting patterns. 5.3.3.2 Lumber density, MOE and MOR Trees grown on Loweld dolomite and shale soils generally yielded lumber with higher density, MOE, and MOR, than lumber produced from Highveld trees (density: 442 kg/m³ - 449 kg/m³, MOE: 9,383 MPa – 9,436 MPa, MOR: 30.9 MPa – 31.0 MPa; Table 5.4). Trees grown on Lowveld dolomite and shale soils had 38 kg/m³ and 31 kg/m³ higher density, respectively. In terms of the mechanical properties, the lumber obtained from trees grown on Lowveld dolomite and shale soils had 1,228 MPa and 1,175 MPa higher MOE, and 3.5 MPa and 3.4 MPa higher MOR, respectively than trees grown on Highveld granite soils. There was no statistically significant difference in density, MOE, and MOR when comparing lumber produced from trees grown on Lowveld dolomite and shale soils. There were statistically significant differences in lumber density, MOE, and MOR comparing lumber produced from trees grown within the respective altitude ranges (Table 5.5). The most significant difference being that lumber with the lowest density, MOE, and MOR was produced from trees growing on the lowest altitudes. However, there was no clear trend or relationship or trend between the structural wood properties and altitude, potentially due to regional differences in wood properties. Lumber density has been found to be directly impacted by the growing conditions of trees (Muller et al. 2017; Schutz et al. 1991). In addition, wood density has been associated with the MOE and MOR of lumber boards (Wessels et al. 2015). Similarly, the findings in this study suggest that lumber density, MOE, and MOR are geographically different (Table 5.4). Lumber produced from trees grown on Lowveld dolomite and shale was found to have higher wood density, MOE, and MOR than lumber produced from trees grown on Highveld granites, which agrees with the LW measured in standing trees (Table 5.1 and Table 5.4). However, the regional differences seem to be related to tree growth rate and size (Figure 5.1B and Appendix Figure A1- 108 14). The taller trees had a larger portion of millable crown formed logs, which could further explain why the larger trees had yielded lumber with lower density, MOE, and MOR (Mansfield et al. 2009). Differences in lumber density, MOE, and MOR was also observed when comparing the respective altitude ranges, however, no clear trend was found and these differences therefore require further investigation (Table 5.5). 5.3.3.3 Lumber knot size Trees grown on the Highveld generally produced lumber with larger knot size than lumber produced from trees grown on the Lowveld (Table 5.4). Trees grown on Highveld granite, produced lumber with 453 and 743 greater knot size ratios than lumber produced from trees grown on Lowveld dolomite and shale soils respectively. Lumber knot size negatively impacts the strength of lumber boards (As et al. 2006; Oh et al. 2009; Figure 5.1B and Appendix Figure A1-14). Previous studies have found the lumber knot size to be significantly impacted by the portion of crown wood milled during lumber production, which can be impacted by tree size due to a greater portion of utilisable crown wood (Maguire et al. 1991). Although the trees found on the Lowveld had larger LC, the live crowns of the smaller trees are not necessarily converted into the usable lumber, considering the merchantable log size thin-end threshold diameter of 15 cm. Lumber produced from the largest trees grown on Highveld granite soils were found to have the largest knot size, while lumber produced from the smallest trees grown on Lowveld shales had the smallest knot size (Table 5.4; Figure 5.1B). These findings suggests that lumber knot size is impacted by tree size and the relative proportion of crown wood (Table 5.4; Figure 5.1B; Appendix Figure A1-14). 5.3.3.4 Lumber bow, twist and spring Although significant differences in lumber bow were observed across soil types and altitude treatments, these differences were relatively minor (Table 5.6). Trees grown on the Highveld generally produced lumber with more bow than trees grown on the Lowveld, however, trees grown on Highveld granite soils located within the 1,500 m to 1,699 m and 1,700m to 1,899 m altitude ranges produced lumber with 5.6 mm per m and 6.2 mm per m more bow than lumber produced from trees grown Lowveld dolomites located within the 1,500 m to 1,699 m altitude range, and 4.7 mm per m and 5.3 mm per m more bow than lumber produced from trees grown 109 on Lowveld shales located with the 1,700 m to 1,899 m altitude range. The remaining treatments had no statistically significant differences in lumber bow. Twist and spring on the other hand were significantly different for lumber produced from trees grown within the respective regions (Table 5.4). Lumber produced from trees grown on Highveld granite had 16.1 mm/m and 16.9 mm/m more twist, and 0.3 mm/m and 0.4 mm/m more spring than lumber produced from trees grown on Lowveld dolomite and shale soils. Lumber bow, twist, and spring significantly impact lumber quality (Sandberg, 2005; Warensjö and Rune, 2004). Previous studies have found bow and spring to be impacted by the internal forces in logs associated with growth ring eccentricity and reaction wood (Sandberg, 2005; Warensjö and Rune, 2004). However, twist formation has been associated with juvenile wood and excessive longitudinal shrinkage in lumber boards (Cown et al., 2004; Sandberg, 2005; Shmulsky and Dahlen, 2007). Although significant differences in lumber bow were observed across the respective sample sites, the differences were minor and no clear trend was observed apart from the fact that the highest altitude Lowveld dolomite and shale sites yielded lumber with the most bow, and a weak positive relationship was observed between lumber bow and the tree size (Table 5.6; Figure 5.1B). Tabel 5. 6: Means and standard error for lumber bow according to soil parent material and altitude. Superscript represents Tukey significance. Region Soil Altitude (m) Bow (mm) Highveld Granite 1,500 – 1,699 49.5±0.94𝑎 1,700 – 1,899 50.1 ± 0.87𝑎 Lowveld Escarpment Dolomite 1,100 – 1,299 49.4 ± 0.88𝑎 1,300 – 1,499 48.8 ± 0.86𝑎𝑏 1,500 – 1,699 43.9±0.90𝑐 Shale 1,100-1,299 50.0±0.90𝑎 1,300 – 1,499 48.4±0.94𝑎𝑏𝑐 1,500 – 1,699 48.5±0.94𝑎𝑏𝑐 1,700 – 1,899 44.8±1.00𝑏𝑐 110 However, lumber produced from trees grown on Highveld granite soils were found to have substantially more twist and spring than lumber produced from trees grown on Lowveld dolomite and shale soils (Table 5.4). Lumber twist was found to be positively correlated with trees grown on Highveld granite soils which receives more spring rainfall, while spring was weakly positively correlated with tree heights (Figure 5.1B). Trees grown on Highveld granite soils have been found to have a significantly lower density, less latewood, and a larger juvenile core than trees grown on Lowveld dolomite and shale soils, which could explain the significantly more lumber twist (Sandberg, 2005; Van der Merwe et al., 2023a). More research is needed to better understand the factors impacting lumber spring. 5.4 Conclusion The aim of the study was to establish how the growing environment of trees impact tree form, heartwood formation and the resultant lumber quality. Sample plots were stratified according to soil type and altitude as soil properties have historically been considered to be the most significant variable impacting site productivity and quality of plantation grown pine trees in South Africa. Representative trees were felled, milled and machine graded for quality and stability. PCA analysis of lumber quality indicated that the most significant variable impacting lumber quality of plantation grown P. patula in the Mpumalanga forestry region was T-max and the associated larger tree sizes. However, spring rainfall significantly impacted lumber stability and specifically the amount of twist found in lumber boards and it was highly probable that this phenomenon was driven by a larger portion of juvenile wood found in trees grown on Highveld granites. The findings shows that climate and specifically tree growth rate significantly impacts lumber strength and stability in plantation grown P. patula found in the Mpumalanga forestry region. Future studies should focus on understanding the interactions between silviculture, wood properties and the quality of products produced, which could have a significant impact on the profitability of mills in local regions. Wood quality is not generic, however fit for purpose according to the final use. 111 Chapter 6: Conclusion 6.1 Summary of major research contributions Pine plantation forests in South Africa are established with non-native species, due to the limited availability of local natural forests. The majority of pine plantations were established with P. patula, introduced from Mexico. Today, the majority of South Africa’s plantation forests are found in the Mpumalanga province. The region is geo-climatically diverse, with highly variable growing conditions. The objective of this study was to evaluate the growth, form, and wood quality properties of P. patula across a stratified range of sites in order to understand the effect that environment has on tree growth and wood properties. No regional seasonal small-scale climate models exists for this region to allow for the characterisation of local climatic conditions of land packets, where P. patula plantations are found. Seasonal climate is one of the most influential variables affecting the growth, form and wood quality of P.patula. Therefore, regional seasonal temperature and rainfall machine learning models were developed, by harnessing historic weather station measurements. The measurements were paired with digital elevation models (DEM), obtained from satellite measurements (Chapter 2). The machine learning models allowed for more precise estimations of local seasonal climatic conditions and the interaction between climate and terrain conditions. The models can also be used to better encapsulate the historic growing conditions of trees than existing global models. A similar modelling methodology can be used to develop local climate models for other regions of the coutry or globally. Tree growth of pine plantation forestry in South Africa has been found to be significantly impacted by soil properties according to previous studies. Therefore, sample plots were established over the Mpumalanga landscape according to the most dominant soil parent material and altitude ranges (54 sample plots in total)(Chapter 3). The geo-climatic growing conditions of the sample plots were determined by performing soil measurements and seasonal climate predictions from the newly developed models. The study established co-linear relationships between soil parent material and climate. Further analysis revealed tree growth rate to be most significalty impacted by maximum temperatures and wood properties by seasonal rainfall. Soil properties including texture, chemical composition and depth had little impact on tree height and 112 wood properties. Additionally, predictive models were developed to predict tree height and wood properties of plantation grown P. patula, by using climate predictions. Due to the limited availability of natural forests, local fibre resources are dependent on plantation forests. Therefore it is important to better utilise the limited available fibre resources in South Africa. Information regarding tree form and wood properties affecting veneer recovery and quality of plantion grown P. patula is limited. Sample trees of suitable size were harvested from the established sample plots. Log form was measured using sophisticated online mill equipment. Furthermore, wood properties such as wood density, latewood content and heartwood portions were quantified and related to specific growing conditions and tree size (Chapter 4). The resultant veneer was dried, graded and matched to specific growing conditions. Veneer recovery and quality of plantation grown P. patula trees were also related to specific growing conditions. One of the most significant findings, was the negative impact of higher wood density on veneer production. High wood densities have historically been synomenous with wood of high quality. Similarly, respresentative trees of suitable size were harvested from the respective sample plots for lumber production (Chapter 5). The respective sample logs were tracked through the lumber supply chain and log form and lumber properties were measured by using commerical equipment at various points along the production line. Log and lumber quality was matched to specific growing environments, therefore giving valuable insights into the primary environmental drivers impacting fibre quality plantation forestests grown in the region. The findings will assist regional lumber producers to better utilise a limited fibre source, in a water-scarce African country. 6.2 Modelling temperature and rainfall for plantation forestry Plantation forests in South Africa are established across a diverse array of geoclimatic conditions, using introduced species and genotypes. The developed models can be used to better match suitable genotypes to specific sites and to apply precision forestry in South Africa. However, it is uncertain how climate change will impact plantation forestry in the future (Ngandwe et al., 2021, Wreford et al. 2021). Current climate models used in this region are at global and national scales, which typically tend to be less accurate on a regional basis. Matching species to sites in Mpumalanga, for example, has traditionally used mean annual precipitation 113 (MAP) and temperature (MAT) values. To fully exploit the new technical approach to plantation forestry, commonly referred to as precision forestry, higher resolution climate models are needed. Based on weather stations across the forestry landscape, the models developed in this case study reflected seasonal rainfall and temperature trends across the Mpumalanga landscape with mixed success. Seasonal T-min and T-max machine learning models expressed good consistency and predictability. The RF and LM models placed differing importance on the independent variables when modelling both T-min and T-max. However, RF models are not impacted by potential inherent collinearity of prediction variables associated with terrain conditions and therefore have an advantage over the LM models. These findings emphasise the importance of local knowledge of climate systems when predicting regional climate to enhance scientific insights into climate anomalies and trends. Seasonal rainfall models were also developed for the median regional rainfall, which maintained a consistent set of significant predictor variables across the respective seasons. This study demonstrates that regional climate is significantly impacted by global position and terrain features, and these factors will affect the performance of introduced forest species in plantations. Climate data recorded over a longer time frame with a greater number of stations specifically located throughout the forestry landscape will benefit future studies. In addition, sharing climate data and collaboration among stakeholders should be encouraged to improve data availability for climate modelling purposes. The outcomes of this case study suggest that it is possible to develop high-resolution seasonal climate models for a specific area or region, by taking terrain conditions and historic climate into account and was found to be more accurate than the widely used Global models it was compared to. The methodology applied here can be used to develop seasonal climate models for other plantation forestry regions with similar modelling constraints. 6.3 Site impacts on wood properties and tree form The purpose of this study was to identify the environmental variables that are important for tree growth and form, as well as wood properties in South African plantation-grown pine. This goal was pursued by establishing sample plots across sites stratified according to the three dominant geologies found in the region and by 200 meter altitude intervals (ranging from 1 100 m to 1899 m), as these attributes were initially thought to be the most important characteristics for P. patula 114 forestry in this region. Soils found in the Lowveld Escarpment are formed from dolomite and shale parent material, while soils found on the Highveld are primarily formed from granite parent material. The sample plots were characterised by their soils and localised climatic variables. The influence of environmental variables on tree growth and form was then evaluated via PCA and regression models, and the most important findings were the positive relationships between temperature and tree height growth, and between rainfall and wood density. In particular, trees grown on the Highveld had lower density, less latewood, and a larger portion of juvenile wood potentially due to more spring rainfall and less annual rainfall in this region, compared to the trees grown on the Lowveld. The observed negative associations between GD and GT and tree height in this study suggest that local rather than global climatic measurements are important. On the other hand, the remaining climatic variables had a significant impact on tree form and wood properties, and accurately explained site related variation. These findings emphasise the importance of accurate climate models to determine site related variation in tree properties. In addition, soil and climatic variables, and their interactions need to be considered when looking at tree growth and wood properties as well as the pitfalls associated with soil variables related to climate. This study demonstrates that site-related climate significantly affects tree form and wood properties of plantation grown P. patula. Site-related tree form and wood property variation can therefore be modelled and predicted. Future studies should focus on the development of accurate localised climate models, to better characterise the growing condition of trees. In addition, the findings demonstrated the pitfalls associated with using generalised GD and GT models, when modelling tree growth. Especially when these models are not species-specific. More clarity is also needed on how tree properties affect product recovery and quality in the processing plants. 6.4 Site impacts on wood quality for veneer production The purpose of this study was to determine how the growing environment impacts tree form, heartwood formation, and the resultant veneer net recovery and quality during plywood manufacturing. Since soil and altitude have historically been considered to be the most important environmental variables impacting tree growth in South Africa, sample plot selection was stratified according to soil parent material and altitude. Trees were harvested from the respective plots, logs assortments were produced, and the veneer these logs yielded was dried and gradedto determine recovery and veneer quality. PCA analyses indicated that environmental variables had 115 the most significant impact on tree form and sapwood portions in trees, and maximum temperature had the most significant impact on tree height and formation of heartwood. Rainfall had the most significant impact on veneer net recovery and quality. In particular, logs produced from trees grown on granite soils in the Highveld, which receives more spring rainfall, had the highest veneer net recovery, and yielded the best quality veneer. These findings demonstrate the importance of log quality, in relation to form and wood properties, on veneer net recovery and quality. The impacts of wood quality on the improved recovery of high value veneer sheets, was also highlighted. The results emphasis the co-relatedness of soil and climate and the importance of understanding the impacts of site related growth dynamics of trees on the quality of fibre for veneer production. The findings also show that site-related climate had a substantial effect on veneer recovery and quality of logs produced from P. patula plantations. Future studies should focus on understanding the interaction between wood properties and the quality of products produced, which could have a significant impact on the profitability of mills in local regions. 6.5 Site impacts on wood quality for lumber production The purpose of this study was to identify the environemtnal growing conditions that impact log and fibre quality for lumber production. Sample plots were stratified according to soil type and altitude. Site related climate was predicted, using the newly developed climate models. PCA analysis again identified climate and specifically maximum temperature and spring rainfall to be the most significant variables impacting the quality of round logs and fibre for lumber producton. Annual maximum temperature were associated with larger trees, that yielded lumber with lower density, modulus of elasticity and modulus of rupture. In addition, the larger trees also produced less structurally stable lumber, with a larger knot area. Lumber produced from trees that received more spring rainfall had significantly more lumber twist and also less dimensional stability, potentially due to a larger portion of juvenile wood associated with these growing conditions. 6.6 Scope for future work Plantation forestry in South Africa is practiced on a geo-climatically diverse landscape. Considering the future challenges associated with climate change, more work is needed to better understand how changing climate will impact tree growth and fibre quality. The work presented in this study might offer some insights into how to better characterise the growing conditions of plantation forests and how growing conditions impact tree growth and fibre properties. However, 116 a more holistic, long-term understanding is needed, including long-term simulation of climate, to better equip forest growers to make better management decisions to overcome future challenges facing plantation forestry and fibre security in the Southern African region. 6.7 Outroduction South Africa is a water scarce country, with limited land suitable for agriculture and forestry. Local fibre for structural applications is predominantly sourced from manmade plantation forests, established with pine species non-native to South Africa. 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J.. 183(1), 1-16. 157 Appendix 1 Table A1-1: Distribution of South African seasons per month. Season Months Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Summer Autumn Winter Spring 158 Table A1-2: Temperature stations with supporting topographic information. Region Temperature Station Latitude Longitude Altitude (m) Slope (°) TPI (micro) TPI (macro) Aspect (°) T-min (°C) T-max (°C) Summer Autumn Winter Spring Summer Autumn Winter Spring Lowveld Escarpment Bambi -25.50 30.38 1699 2.0 0.55 0.54 78.1 14.6±0.33 12.5±0.30 8.6±0.28 11.9±0.32 23±0.67 20.8±0.51 18.2±0.44 22.7±0.52 Bourke's Luck -24.67 30.81 1177 0.0 0.1 0.07 5.7 16.2±0.33 11.1±0.32 3.6±0.31 11.6±0.32 28.2±0.67 26±0.51 22.8±0.44 26.5±0.52 Elandshoogte -25.50 30.53 2021 3.8 0.63 0.76 191.2 12.5±0.40 10.6±0.45 6.5±0.28 9.8±0.32 20.3±0.82 18.3±0.62 15.5±0.44 19.3±0.52 Glenthorpe -25.73 30.85 912 2.5 0.52 0.54 101.7 17.5±0.33 15.2±0.32 11.1±0.31 14.4±0.32 27.3±0.67 25.7±0.51 23.2±0.44 26.0±0.52 Goedgeloof -24.70 30.85 1475 4.9 0.58 0.58 287.8 15.2±0.33 13.6±0.30 10.4±0.28 12.6±0.35 20.9±0.67 19.3±0.51 17±0.44 20.5±0.52 Graskop (FPA) -24.93 30.85 1464 1.4 0.55 0.52 221.3 15±0.33 11.9±0.32 7.8±0.28 11.8±0.32 23.7±0.67 21.7±0.47 19.2±0.44 22.6±0.52 Graskop (SAWS) -24.93 30.85 1467 2.2 0.42 0.4 283.3 14.4±0.33 11.5±0.30 7.2±0.28 11.1±0.32 23.7±0.67 21.5±0.47 19±0.44 22.6±0.52 Hazyview -25.01 31.04 826 1.5 0.58 0.46 90.9 18.1±0.33 16.5±0.30 13.0±0.28 16.0±0.32 27±0.67 25±0.47 23±0.44 26.4±0.52 Highlands -25.80 31.10 1257 6.9 0.41 0.35 140.2 15.3±0.36 11.5±0.32 6.6±0.28 11.3±0.32 24±0.73 22.3±0.47 19.8±0.44 22.5±0.52 Kolbos -25.40 30.66 1452 8.0 0.54 0.53 81.0 14.8±0.36 13.4±0.32 9.8±0.28 12.5±0.35 21.9±0.73 19.7±0.62 17.8±0.44 21.1±0.52 Lydenburg (FPA) -25.13 30.38 1533 1.2 0.56 0.64 4.1 14.6±0.33 10.6±0.30 4.9±0.37 11.1±0.35 25.6±0.67 23.2±0.47 20.3±0.44 24.8±0.52 Lydenburg -25.10 30.47 1443 1.5 0.46 0.43 291.8 15.2±0.33 11.2±0.39 5.4±0.31 11.6±0.35 25.1±0.67 23.9±0.51 20.5±0.44 24.6±0.52 Marathon -25.41 30.92 839 2.5 0.43 0.41 58.8 17.6±0.33 13.9±0.30 8.4±0.28 13.8±0.32 28.4±0.67 26.8±0.51 24.2±0.44 27.2±0.52 Nelshoogte -25.86 30.81 1564 5.3 0.57 0.57 189.9 14.9±0.36 13.5±0.39 8.3±0.31 11.2±0.32 21.1±0.73 20.6±0.56 16.7±0.44 19.6±0.56 Nelspruit -25.50 30.92 824 6.3 0.54 0.54 100.5 18.1±0.33 14.9±0.30 9.7±0.28 14.3±0.32 27.3±0.67 25.4±0.47 23.1±0.44 25.9±0.52 Nelspruit (ITSG1) -25.45 30.97 644 0.9 0.37 0.42 310.3 18.7±0.45 14.1±0.30 7.8±0.43 14.8±0.42 30.3±0.67 28.2±0.51 25.8±0.44 28.7±0.52 Nelspruit (ITSG2) -25.46 30.97 663 1.2 0.5 0.63 262.5 17.8±0.33 14.5±0.30 8.2±0.28 14.5±0.32 29.6±0.67 29.2±0.47 26.9±0.44 29.6±0.52 Nelspruit Airfield -25.50 30.91 884 2.1 0.52 0.54 116.7 18.2±0.33 15.1±0.32 10.9±0.28 14.7±0.32 26.8±0.67 25±0.47 22.5±0.44 25.5±0.52 Ngodwana -25.57 30.65 945 3.1 0.24 0.2 133.1 17.2±0.33 11.7±0.32 4.1±0.28 12.2±0.32 28.7±0.67 26.5±0.47 23.5±0.44 27.5±0.52 Rhenosterhoek -25.23 30.67 1365 3.8 0.36 0.35 75.7 15.5±0.36 12.8±0.35 8.6±0.31 12.4±0.35 25.3±0.73 23.1±0.56 21±0.48 24.5±0.56 Sjonasjona -25.75 30.65 1616 8.0 0.61 0.61 206.6 14.3±0.36 12.7±0.30 9.7±0.28 11.9±0.35 21.5±0.73 19.5±0.56 17.4±0.44 20.5±0.56 Torburnlea -25.71 30.34 1974 3.7 0.53 0.56 102.6 13.3±0.33 11.5±0.30 7.4±0.28 10.8±0.32 21.7±0.67 20.8±0.47 16.6±0.44 21.1±0.52 Tower 637 -25.65 30.57 1931 7.4 0.57 0.58 71.4 13.4±0.33 11.5±0.32 8.0±0.28 10.8±0.32 19.6±0.67 17.2±0.51 14.6±0.44 18.8±0.52 159 Uitkyk -25.27 30.49 1896 2.4 0.49 0.51 211.1 12.9±0.36 11.5±0.30 8.0±0.28 11.2±0.35 20.1±0.73 17.6±0.56 15.5±0.44 20.1±0.52 Venus -24.99 30.96 916 5.5 0.53 0.49 197.9 18±0.36 15.7±0.30 12.6±0.31 15.4±0.32 26.5±0.73 23.7±0.56 22.1±0.44 25.7±0.52 White River -25.30 31.04 906 2.2 0.51 0.59 304.9 17.6±0.36 13.5±0.35 7.8±0.28 13.5±0.32 28.3±0.73 26.2±0.51 23.4±0.44 26.3±0.56 Highveld Belfast -25.69 30.03 1873 0.6 0.46 0.51 322.8 12.9±0.28 8.4±0.35 2.8±0.36 8.9±0.31 23.5±0.36 21.2±0.32 18.4±0.30 23.1±0.32 Clifton -26.48 30.47 1618 1.8 0.48 0.52 338.1 13.5±0.34 10.3±0.38 4.5±0.39 9.9±0.31 23.8±0.36 21.5±0.32 18.6±0.30 23.4±0.32 Dorsbult -26.13 30.33 1679 3.8 0.51 0.56 57.1 13.8±0.31 11.0±0.38 7.3±0.36 11.2±0.31 22.5±0.36 20.4±0.32 17.1±0.30 21.3±0.32 Ermelo -26.50 29.98 1755 1.5 0.58 0.63 241.4 13.2±0.28 9.5±0.35 4.1±0.36 9.5±0.31 24.5±0.36 21.6±0.32 18.8±0.30 24.2±0.32 Iswepe Gleneland -26.64 30.51 1527 1.4 0.6 0.48 145.4 14.2±0.31 11.3±0.38 6.8±0.36 11.0±0.31 24.4±0.36 21.8±0.32 18.9±0.30 23.1±0.32 Kruisfontein -25.46 30.18 1721 3.0 0.51 0.42 22.2 9.8±0.28 11.6±0.35 4.6±0.36 9.3±0.31 21.9±0.36 22.6±0.32 19.6±0.30 23.5±0.32 Machadodorp -25.77 30.32 1771 2.7 0.49 0.43 121.6 13.4±0.28 9.4±0.35 4.2±0.36 9.9±0.31 23.5±0.36 21±0.32 18±0.30 22.7±0.32 Naudesbank -26.14 29.97 1712 1.1 0.52 0.55 86.1 13.4±0.28 9.8±0.35 4.1±0.36 9.8±0.31 25.3±0.36 22.2±0.32 19±0.30 24.7±0.32 Ndubazi -25.83 30.51 1183 6.4 0.39 0.3 215.6 15.3±0.31 10.9±0.35 3.2±0.36 10.3±0.31 27.3±0.36 24.6±0.32 22.5±0.30 26.9±0.32 Nkomazi -25.98 30.66 1001 4.1 0.38 0.38 290.5 17.3±0.28 12.8±0.35 6.9±0.36 13.2±0.31 28.9±0.36 26.5±0.32 23.6±0.30 27.8±0.32 Panbult -26.89 30.30 1446 1.1 0.5 0.47 41.0 14.9±0.28 10.8±0.35 5.3±0.36 11.0±0.31 27±0.36 23.9±0.32 21±0.30 25.7±0.32 Piet Retief Dispatch -27.01 30.78 1294 3.1 0.61 0.58 77.8 15.7±0.28 14.4±0.35 7.4±0.36 11.6±0.31 24.8±0.36 22.5±0.32 19.8±0.30 23.4±0.32 Piet Retief Tower -27.04 30.62 1333 3.0 0.54 0.5 31.4 15±0.28 11.6±0.38 6.7±0.36 11.6±0.31 27.4±0.36 24.7±0.32 21.7±0.30 25.5±0.32 Roburnia -26.67 30.74 1405 2.3 0.66 0.57 300.3 15.3±0.31 11.3±0.41 6.8±0.36 11.3±0.31 25.3±0.36 22.9±0.32 20.3±0.30 24.3±0.32 SAPPI Hlelo -26.90 30.63 1361 1.4 0.56 0.57 195.9 14.9±0.28 10.8±0.38 5.3±0.36 10.7±0.31 26.8±0.36 23.6±0.32 20.8±0.30 25.1±0.32 Sheepmoor -26.68 30.32 1501 1.9 0.52 0.42 154.2 14.4±0.28 10.6±0.38 5.1±0.39 10.8±0.31 25.3±0.36 22.6±0.32 19.8±0.30 24.5±0.32 Warburton -26.24 30.51 1753 1.2 0.45 0.45 166.8 13±0.28 10.5±0.35 6.2±0.36 10.1±0.31 22.3±0.36 19.9±0.32 17.2±0.30 21.3±0.32 160 Table A1-3: Mpumalanga rainfall stations with supporting topographic information. Rainfall Station Station characteristics Seasonal rainfall (mm) Latitude Longitude Altitude (m) Slope (°) TPI (micro) TPI (macro) Aspect (°) Summer Autumn Winter Spring Alkmaar -25.45 30.82 776 2.84 0.55 0.62 100.9 513±55.1 194±30.2 24±6.2 188±28.8 Badplaas Police Station -25.97 30.55 1192 1.45 0.7 0.39 286.6 321±59.5 120±33.4 16±7.9 186±33.2 Barberton (TNK) -25.78 31.05 818 2.67 0.62 0.46 304.6 450±62.1 158±33.4 13±7.1 171±33.2 Bergvliet Bos -25.075 30.887 984 7.55 0.49 0.42 17 613±46.1 227±21.9 37±4.9 280±22.3 Blyde Bos -24.83 30.83 1362 9.95 0.33 0.32 131.3 596±53.2 233±27.8 40±5.8 189±26.6 Bothwell -26.28 30.2 1711 1.66 0.48 0.52 256.4 334±59.5 138±33.4 18±9.2 221±33.2 Broadholms -26.48 30.63 1552 0.7 0.32 0.43 71.6 401±53.2 165±27.8 21±5.8 257±26.6 Brooklands Bos -25.25 30.73 1243 10.42 0.5 0.39 82.4 614±46.1 212±21.9 28±4.9 309±22.9 Busby Sawmill -26.28 30.6 1543 3.39 0.52 0.46 41.5 392±59.5 137±33.4 23±7.1 241±33.2 Chrissiesmeer -26.28 30.22 1720 0.89 0.51 0.65 245.6 288±46.1 132±21.9 16±4.9 221±21.7 Clifton -26.475 30.474 1618 1.8 0.48 0.52 338.1 365±77.9 193±50.2 20±10.0 191±44.6 Dorsbult -26.127 30.328 1679 3.84 0.51 0.56 57.1 392±68.7 185±57.9 18±8.5 251±40.7 Doyershoek Lookout -25.879 30.759 1513 4.79 0.44 0.58 166.7 377±51.5 149±26.8 19±6.0 236±25.7 Dunnotair -24.87 30.95 1000 1.55 0.64 0.61 18.4 726±47.3 275±22.4 36±4.9 303±21.7 Elandshoogte -25.493 30.502 1869 3.05 0.49 0.47 50.2 459±46.1 183±21.9 22±4.9 215±21.7 Ermelo -26.5 29.98 1757 1.21 0.58 0.65 252.3 345±46.1 132±21.9 23±4.9 193±21.7 Frankfort Bos -25.034 30.879 1062 2.91 0.32 0.36 227.7 738±62.1 310±33.4 42±7.1 266±33.2 Grootgeluk -25.572 30.649 942 2.32 0.2 0.16 131.6 423±51.5 193±26.8 17±6.2 227±25.7 Hazyview -25.049 31.14 520 1.07 0.57 0.58 46.8 564±51.5 238±26.8 25±5.8 219±25.7 Helvetia -25.553 30.291 1653 2.39 0.43 0.32 157.1 391±50.0 133±25.9 11±5.8 198±24.9 Hendriksdal -25.18 30.77 1362 4.16 0.55 0.39 97.1 546±47.3 194±21.9 25±4.9 241±22.4 Hietvallei -25.35 30.75 1056 11.25 0.51 0.56 174.8 450±77.9 214±57.9 26±13.0 262±49.8 Highlands (ICFR) -25.8 31.1 1273 8.6 0.46 0.36 143 471±47.3 212±22.4 23±5.0 224±22.3 Highlands (SAPPI) -25.804 31.104 1273 8.6 0.46 0.36 143 476±46.1 220±21.9 25±5.0 218±22.3 Houtboschoek -25.525 30.577 1173 4.69 0.62 0.48 46.3 500±51.5 226±25.9 21±5.8 274±25.7 Iswepe Gleneland -26.638 30.506 1527 1.37 0.6 0.48 145.4 315±72.8 143±57.9 13±9.2 181±40.7 Jessievale Bos -26.23 30.52 1732 0.36 0.47 0.47 164.1 467±46.1 174±22.4 20±5.0 242±22.3 Kyloe -24.923 30.988 862 1.2 0.4 0.33 95.8 667±65.2 205±40.9 15±8.5 168±37.7 Lookout 637 -25.65 30.574 1893 8.68 0.56 0.51 123.8 577±51.5 202±26.8 22±6.2 297±25.7 Lothair -26.4 30.4 1689 0.93 0.55 0.48 315 358±46.1 136±22.4 25±5.0 234±22.3 Lydenburg (ICFR) -25.1 30.47 1458 2.26 0.5 0.55 248.4 467±65.2 148±50.2 11±13.0 145±35.2 Lydenburg (SAWS) -25.11 30.48 1458 2.26 0.5 0.55 248.4 407±57.1 86±23.6 9±5.3 155±23.5 Mamre -25.726 30.52 1891 2.62 0.69 0.6 327.2 464±51.5 203±26.8 18±6.0 250±25.7 Mariepskop Bos -24.58 30.87 1357 8.37 0.6 0.64 123.9 676±46.1 255±21.9 34±4.9 222±21.7 Moodies Estate -25.82 30.98 1033 10.91 0.45 0.47 82 513±62.1 222±30.2 30±7.5 320±33.2 Mooifontein -25.412 30.679 1022 4.37 0.4 0.3 58.7 463±46.1 209±21.9 30±5.0 250±22.3 161 Morgenzon Bos -24.88 30.72 1598 8.61 0.56 0.56 84.7 538±46.1 199±21.9 22±4.9 219±21.7 Mount Sheba -24.93 30.72 1864 7.86 0.54 0.61 102.1 596±62.1 242±33.4 36±10.0 209±31.5 Ndubazi Lookout -25.856 30.431 1686 1.63 0.43 0.53 218.9 404±51.5 162±30.2 18±6.5 229±28.8 Ndubazi Office -25.828 30.509 1188 6.95 0.41 0.32 215.3 439±46.1 176±22.4 19±5.0 257±22.3 Nelshoogte Bos -25.82 30.83 1401 2.83 0.47 0.42 270 543±53.2 200±27.8 25±5.8 268±27.6 Nelspruit -25.5 30.92 815 6.94 0.56 0.52 100.9 376±47.3 176±21.9 18±4.9 156±21.7 Nelspruit_ITSG_1 -25.45 30.967 647 1.48 0.31 0.47 325.8 470±48.6 174±25.9 23±5.8 242±25.7 Nelspruit_ITSG_2 -25.455 30.972 664 1.12 0.51 0.67 270 443±46.1 169±23 20±5.0 226±22.3 Nerston Police Station -26.57 30.78 1508 2.14 0.37 0.48 242.9 386±53.2 158±27.8 15±5.8 241±26.6 Onverwag Bos -24.8 30.97 928 4.94 0.51 0.51 83.5 711±46.1 259±21.9 27±4.9 245±21.7 Oshoek Police Station -26.22 30.98 1478 2.38 0.55 0.57 174.7 406±72.8 227±44.9 20±11.2 327±49.8 Panbult -26.888 30.298 1446 1.14 0.5 0.47 41 287±65.2 148±40.9 9±9.2 175±37.7 Pilgrims Rest (MA) -25 31.02 810 6.85 0.56 0.57 234.8 690±59.5 270±33.4 44±7.1 242±33.2 Pilgrims Rest (NA) -24.9 30.75 1262 1.33 0.49 0.35 298.4 478±47.3 174±22.4 23±5.2 190±22.9 Rietspruit (ICFR) -24.98 30.93 979 6.72 0.51 0.48 168.9 670±48.6 271±21.9 38±4.9 287±21.7 Sabie -25.1 30.78 1060 0.96 0.46 0.39 14.7 511±47.3 191±21.9 23±4.9 231±21.7 Salique Bos -24.6 30.9 858 6.47 0.45 0.38 25.4 647±47.3 263±21.9 23±4.9 233±21.7 SAPPI Hlelo -26.902 30.627 1361 1.37 0.56 0.57 195.9 294±68.7 152±44.9 15±8.5 148±44.6 Schagen -25.43 30.78 767 2.89 0.4 0.41 13.2 567±77.9 73±57.9 20±11.2 149±57.5 Sheepmoor -26.676 30.324 1501 1.92 0.52 0.42 154.2 351±72.8 121±57.9 9±10.0 141±44.6 Sjonasjona -25.771 30.7 1422 4.44 0.36 0.32 15.6 421±46.1 177±21.9 23±5.0 233±22.3 Sjonasjona_vaalkop -25.781 30.62 1334 9.17 0.45 0.43 54 366±50 160±23 14±5.3 195±23.5 Songimvelo -26.03 30.9 869 3.86 0.41 0.4 17.3 407±62.1 132±35.5 23±7.5 170±35.2 Spitskop Bos -25.15 30.828 1364 3.39 0.59 0.56 213.2 653±62.1 233±33.4 40±7.1 288±33.2 Sudwala District Office -25.336 30.568 1266 4.48 0.46 0.4 9.7 471±46.1 197±21.9 27±4.9 214±21.7 Torburnlea -25.713 30.335 1971 0.87 0.66 0.67 243.4 399±51.5 129±26.8 13±6.2 239±26.6 Uitkyk Office -25.322 30.445 1682 9.5 0.55 0.59 300.2 479±46.1 185±21.9 28±4.9 264±21.7 Uitsoek Bos -25.28 30.58 1257 8.34 0.51 0.46 263.1 525±47.3 173±21.9 23±4.9 261±21.7 Warburton -26.243 30.514 1753 1.17 0.45 0.45 166.8 392±65.2 126±40.9 14±8.5 202±37.7 Welgevonden -24.73 30.93 851 9.2 0.42 0.5 37.3 727±46.1 317±21.9 38±4.9 265±21.7 Westra -26.039 30.326 1569 1.04 0.29 0.52 109.3 451±47.3 174±24.3 26±5.6 282±24.9 Wilgeboom Bos -24.93 30.95 992 1.68 0.55 0.43 329.3 659±46.1 264±21.9 39±4.9 261±21.7 Witklip Bos -25.22 30.88 1087 5.39 0.62 0.51 189.4 592±46.1 212±21.9 37±4.9 276±21.7 162 Table A1-4: Summary of annual temperature models with prediction accuracies in relation to root mean square error (RMSE) and R-square values. Variable Region Model Annual models DF RMSE R² Minimum temperature Lowveld Escarpment Linear regression 122 0.72 0.82 Plated spline regression (two variables) NA 0.90 Linear regression (machine learning) 0.74 0.83 Nearest Neighbour (machine learning) 0.89 0.75 Support Vector Machine (machine learning) 0.50 0.93 Random Forests (machine learning) 0.49 0.93 Highveld Linear regression 86 0.86 0.58 Plated spline regression (two variables) NA 0.80 Linear regression (machine learning) 0.72 0.72 Nearest Neighbour (machine learning) 0.83 0.62 Support Vector Machine (machine learning) 0.64 0.77 Random Forests (machine learning) 0.61 0.79 Maximum temperature Lowveld Escarpment Linear regression 143 1.05 0.91 Plated spline regression (two variables) NA 0.94 Linear regression (machine learning) 0.96 0.93 Nearest Neighbour (machine learning) 1.16 0.90 Support Vector Machine (machine learning) 0.95 0.93 Random Forests (machine learning) 0.87 0.94 Highveld Linear regression 98 0.82 0.8 Plated spline regression (two variables) NA 0.87 Linear regression (machine learning) 0.85 0.80 Nearest Neighbour (machine learning) 0.95 0.76 Support Vector Machine (machine learning) 0.82 0.81 Random Forests (machine learning) 0.75 0.84 163 Table A1-5: Breakdown of temperature measurements. Available measurement include all direct measurement that were available, patched values include all measurements that were missing and patched by means of linear regression and not patched values include all missing values i.e., no statistical significant patch models were developed for these missing values. Region Variable Measurements N Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Summary Highveld 𝑇𝑚𝑖𝑛 Available 17 103 102 97 98 99 114 114 115 115 115 112 108 90.5% Patched 16 11 22 21 12 4 5 3 4 4 7 11 8.4% Not patched 0 6 0 0 8 1 0 1 0 0 0 0 1.1% Total 119 119 119 119 119 119 119 119 119 119 119 119 100% 𝑇𝑚𝑎𝑥 Available 17 103 102 97 98 101 114 115 115 115 115 112 108 90.7% Patched 16 13 22 21 18 5 4 4 4 4 7 11 9.0% Not patched 0 4 0 0 0 0 0 0 0 0 0 0 0.3% Total 119 119 119 119 119 119 119 119 119 119 119 119 100% Lowveld Escarpment 𝑇𝑚𝑖𝑛 Available 26 148 147 146 156 164 170 170 173 169 172 167 161 89.5% Patched 31 26 31 6 0 0 0 0 3 8 14 20 6.4% Not patched 2 8 4 19 17 11 11 8 9 1 0 0 4.1% Total 181 181 181 181 181 181 181 181 181 181 181 181 100% 𝑇𝑚𝑎𝑥 Available 26 148 148 148 156 164 170 170 173 170 173 167 163 89.8% Patched 13 15 10 18 10 11 11 8 10 7 11 13 6.3% Not patched 20 18 23 7 7 0 0 0 1 1 3 5 3.9% Total 181 181 181 181 181 181 181 181 181 181 181 181 100% 164 Table A1-6: Breakdown of seasonal rainfall measurements. Available measurement include all direct measurement that were available, patched values include all measurements that were missing and patched by means of linear regression and not patched values include all missing values i.e. no statistical significant patch models were developed for these missing values. Due to summer stretching across calendar years, one season was lost over the 20 year period (summer of 1999 started during December 1998). No successful winter models were developed, therefore the winter record count was excluded. Variable Measurments N Summer Autumn Winter Spring Summary 𝑃𝑚𝑒𝑑𝑖𝑎𝑛 Available 69 991 986 N/A 993 73% Patched 83 29 N/A 7 3% Not patched 237 365 N/A 380 24% Total 1311 1380 N/A 1380 100% Figure A1-7: Juvenile core transition age transition age determination using a linear plateau model, taking cambial age and latewood density into account. The example illustrated the juvenile-mature wood transition age of trees grown on dolomite soil plots, located at an altitude ranging from 1,100 to 1,299 m above sea level (i.e., the transition age in this example was at 8.2 years). 165 Table A1-8: Analysis of variance including sum of squares (Sum Sq), mean squares (Mean sq), F-values, probability values (P-values), and significance levels (Sign.). Independent variables investigated included soil, altitude, and the interaction between soil and altitude. Dependent variables are grouped according to tree and veneer attributes. Tree attributes included tree height, slenderness coefficients (SC), volume (V), taper (T), sweep (SW), ovality (O), volumetric heartwood portions (HP), and live crown percentages (LC).Veneer attributes included net recovery per tree (Rec % (tree)), net recovery of the processed logs (Rec % (logs)), face, layup and cross veneer percentages. Grouping Dependent variables Independent variables Df Sum Sq Mean Sq F value Pr(>F) Sign Tree variables Height (m) Soil 2 1215.0 607.6 145.7 0.000 *** Altitude 3 5118.0 1706.1 409.0 0.000 *** Soil x Altitude 3 1309.0 436.4 104.6 0.000 *** SC Soil 2 4253.0 2126.0 29.0 0.000 *** Altitude 3 26456.0 8819.0 120.3 0.000 *** Soil x Altitude 3 4325.0 1442.0 19.7 0.000 *** Volume (V) Soil 2 5.9 2.9 60.2 0.000 *** Altitude 3 12.8 4.3 87.7 0.000 *** Soil x Altitude 3 5.3 1.8 36.6 0.000 *** Density Soil 2 82330.0 41165.0 43.6 0.000 *** Altitude 3 57865.0 19288.0 20.4 0.000 *** Soil x Altitude 3 1824.0 608.0 0.6 0.587 NS LW Soil 2 3088.0 1544.1 44.7 0.000 *** Altitude 3 2263.0 754.4 21.9 0.000 *** Soil x Altitude 3 61.0 20.3 0.6 0.623 NS Taper (T) Soil 2 1.1 0.6 9.0 0.000 *** Altitude 3 0.8 0.3 4.2 0.006 ** Soil x Altitude 3 0.0 0.0 0.2 0.889 NS Sweep (SW) Soil 3 0.5 0.2 6.6 0.000 *** Altitude 2 0.7 0.4 14.1 0.000 *** Soil x Altitude 3 0.0 0.0 0.3 0.805 NS Ovality (O) Soil 3 0.0 0.0 16.2 0.000 *** Altitude 2 0.0 0.0 64.4 0.000 *** Soil x Altitude 3 0.0 0.0 3.7 0.011 * HP % Soil 3 206.2 68.7 38.2 0.000 *** Altitude 2 55.8 27.9 15.5 0.000 *** Soil x Altitude 3 17.3 5.8 3.2 0.023 * LC % Soil 3 3383.0 1127.8 13.3 0.000 *** Altitude 2 5992.0 2995.9 35.2 0.000 *** Soil x Altitude 3 490.0 163.5 1.9 0.125 NS Veneer variables Rec % (Tree) Soil 2 10399.0 5200.0 50.5 0.000 *** Altitude 3 2477.0 826.0 8.0 0.000 *** Soil x Altitude 3 2172.0 724.0 7.0 0.000 *** Rec % (Logs) Soil 2 5530.0 2764.9 64.4 0.000 *** Altitude 3 122.0 40.6 0.9 0.418 NS Soil x Altitude 3 113.0 37.7 0.9 0.453 NS Face % Soil 2 4113.0 2056.6 76.2 0.000 *** Altitude 3 59.0 19.8 0.7 0.532 NS Soil x Altitude 3 83.0 27.7 1.0 0.380 NS Layup % Soil 2 37052.0 18526.0 86.5 0.000 *** Altitude 3 1200.0 400.0 1.9 0.134 NS Soil x Altitude 3 1469.0 490.0 2.3 0.078 NS Cross % Soil 2 65672.0 32836.0 152.2 0.000 *** Altitude 3 1354.0 451.0 2.1 0.100 NS Soil x Altitude 3 1584.0 528.0 2.4 0.063 NS 166 Table A1-9: Trial design and stratification of the forestry landscape, climate per soil parent material including annual maximum temperature (T-max), annual minimum temperature (T-min), summer rainfall, autumn rainfall, spring rainfall, annual growth days (GD), annual growth temperature (GT). In addition, site related soil properties, including effective soil depth (ESD), total available water (TAW), and nutrient pool including soil nitrogen (N), phosphate (P), potassium (K), calcium (Ca), magnesium (Mg), sodium (Na), sulphate (S), carbon (C), and carbon to nitrogen ratios (C:N ratios) (Van der Merwe et al. 2023a). Grouping Altitude range (m.a.s.l.) Soil parent material (N = 54) Malamani dolomite Timeball Hill shale Randian granite Sampling matrix 1,100 – 1,299 n = 6 n = 6 No plots 1,300 – 1,499 n = 6 n = 6 No plots 1,500 – 1,699 n = 6 n = 6 n = 6 1,700 – 1,899 No plots n = 6 n = 6 Climate 𝑻𝒎𝒂𝒙 19.9 - 23.0 18.7 - 25.5 20.5 - 22.2 𝑻𝒎𝒊𝒏 11.8 - 12.2 11.0 - 12.2 9.7 - 11.0 Summer rainfall (mm) 557 - 623 477 - 526 373 - 425 Autumn rainfall (mm) 188 - 216 169 - 200 151 - 180 Spring rainfall (mm) 217 - 232 213 - 225 229 - 273 Soil properties ESD (cm) 65 - 200 45 - 200 25 - 200 TAW (mm) 283 - 989 96 - 846 125 - 634 N (𝐭 𝐡𝐚−𝟏) 4.6 - 13.6 5.3 - 33.7 2.9 - 21.7 P (𝐭 𝐡𝐚−𝟏) 0.013 - 0.043 0.009 - 0.054 0.006 - 0.041 K (𝐭 𝐡𝐚−𝟏) 0.1 - 4.3 0.0 - 3.8 0.2 - 1.1 Ca (𝐭 𝐡𝐚−𝟏) 0.1 - 8.4 0.0 - 20.1 0.1 - 1.0 Mg (𝐭 𝐡𝐚−𝟏) 0.1 -2.9 0.0 - 4.1 0.0 - 0.8 Na (𝐭 𝐡𝐚−𝟏) 0.03 - 0.22 0.01 - 0.14 0.01 - 0.12 S (𝐭 𝐡𝐚−𝟏) 1.8 - 5.4 1.6 - 8.1 1.4 - 6.6 C (𝐭 𝐡𝐚−𝟏) 79.7 - 279.8 101.1 - 492.4 56.3 - 425.6 C:N ratio 13.2 - 27.2 12.6 - 21.5 15.5 - 27.5 167 Table A1-10: Implemented silvicultre practices applied over the life cycle of the sample plots including thinning and pruning activities. Activity Age Aim Method Establishment geometry 0 years 3 meters x 3 meters (1 111 seedlings per hectare) Manual planting First pruning 3 – 5 years Remove branches up to a height of 1.5 meters Manual pruning saw Second pruning 5 – 7 years Remove branches up to a height of 3.5 meters Manual pruning saw Third pruning 7 – 10 years Remove branches up to a height of 5.5 meters Manual pruning saw First thinning 10 – 13 years Reduce remaining trees to 800 trees per hectare Line thinning Second thinning 14 – 17 years Reduce remaining trees to 500 trees per hectare Selective thinning Table A1-11: Final dimensions of lumber boards produced during chipper canter milling. Thin-end diameter (cm) Thickness x Width (mm) 38 mm x 114 mm 38 mm x 152 mm 50 mm x 152 mm 19 mm x 76 mm 11 - 12 cm 0 0 0 4 13 - 14 cm 1 0 0 2 15 - 16 cm 2 0 0 2 17 - 19 cm 2 1 0 0 20 - 21 cm 0 3 0 2 22 - 25 cm 0 1 2 2 168 Table A1-12: R-functions and packages use in this study. R-function Use ‘qqPlot’ Determine normality of residuals. ‘aov’ Analysis of variance for tree form, wood properties, veneer recovery and qualtiy for the respective site types. R-package Use ‘lsmeans’ Determining least square means and standard errors for tree forms and wood properties for the respective site types. ‘TukeyHSD’ Determine the confidence intervals on the differences between the means, therefore which treatments are statistically different from one another. ‘rcompanion’ Visualisation of principle component analysis ‘lfda’ Principle component analysis to determine collinearities, association and which variables to include in the final models. ‘ggfortify’ Visualisation of principle component analysis. ‘ggcorrplot’ Correlation matrix analysis Table A1-13: Analysis of variance including sum of squares (Sum Sq), mean squares (Mean sq), F-values, probability values (P-values), and significance levels (Sign.). Independent variables investigated included soil, altitude, and the interaction between soil and altitude. Dependent variables are grouped according to tree and veneer attributes. Tree attributes included tree height, volume, slenderness coefficients (SC), taper (T), sweep (S), roundness (R), volumetric heartwood percentage (HP), wood density (Density), volumetric latewood percentage (LW) and live crown percentages (LC). Structural lumber attributes included net recovery of the processed logs (net recovery), moisture content percentage (MC), density (Density), modulus elasticity (MOE), modulus of rupture (MOR), knot area, twist, bow and spring. Focus Dependent variable Independent variable Sum Sq Mean Sq F-value P-value Sign. Tree Height Soil 722.0 361.0 63.0 0.000 *** Altitude 3022.0 1007.4 175.9 0.000 *** Soil x Altitude 1241.0 413.6 72.2 0.000 *** Volume Soil 0.3 0.1 6.7 0.001 ** Altitude 2.3 0.8 39.7 0.000 *** Soil x Altitude 2.0 0.7 33.3 0.000 *** SC Soil 14917.0 7458.0 70.0 0.000 *** Altitude 38481.0 12827.0 120.3 0.000 *** Soil x Altitude 6697.0 2232.0 20.9 0.000 *** Taper Soil 1.7 0.9 20.7 0.000 *** Altitude 0.5 0.2 3.7 0.011 * Soil x Altitude 0.1 0.0 0.5 0.663 NS Sweep Soil 1.4 0.7 21.6 0.000 *** Altitude 0.2 0.1 2.3 0.082 NS Soil x Altitude 0.2 0.1 2.2 0.092 NS Roundness Soil 0.0 0.0 18.0 0.000 *** Altitude 0.0 0.0 23.3 0.000 *** Soil x Altitude 0.0 0.0 3.6 0.014 * HP Soil 0.9 0.5 0.5 0.603 NS Altitude 45.5 15.2 16.5 0.000 *** Soil x Altitude 15.9 5.3 5.8 0.001 *** 169 Density Soil 127736.0 63868.0 64.2 0.000 *** Altitude 103522.0 34507.0 34.7 0.000 *** Soil x Altitude 10977.0 3659.0 3.7 0.012 * LW Soil 4690.0 2345.1 65.3 0.000 *** Altitude 3688.0 1229.3 34.2 0.000 *** Soil x Altitude 413.0 137.6 3.8 0.010 ** LC Soil 3833.0 1916.3 19.8 0.000 *** Altitude 7359.0 2452.9 25.4 0.000 *** Soil x Altitude 1321.0 440.2 4.6 0.004 ** Lumber boards Net recovery Soil 0.1 0.0 14.2 0.000 *** Altitude 0.0 0.0 4.4 0.005 ** Soil x Altitude 0.0 0.0 1.2 0.314 NS MC Soil 18.6 9.3 26.1 0.000 *** Altitude 22.4 7.5 20.9 0.000 *** Soil x Altitude 8.7 2.9 8.1 0.000 *** Density Soil 101884.0 50942.0 65.4 0.000 *** Altitude 126396.0 42132.0 54.1 0.000 *** Soil x Altitude 5584.0 1861.0 2.4 0.068 NS MOE Soil 122670586.0 61335293.0 49.2 0.000 *** Altitude 179611449.0 59870483.0 48.1 0.000 *** Soil x Altitude 5291038.0 1763679.0 1.4 0.237 NS MOR Soil 10152617.0 5076309.0 27.3 0.000 *** Altitude 12512332.0 4170777.0 22.4 0.000 *** Soil x Altitude 645044.0 215015.0 1.2 0.326 NS Knot area Soil 39370277.0 19685138.0 14.8 0.000 *** Altitude 9353924.0 3117975.0 2.4 0.072 NS Soil x Altitude 5541413.0 1847138.0 1.4 0.245 NS Twist Soil 23370.0 11685.0 13.8 0.000 *** Altitude 2895.0 965.0 1.1 0.334 NS Soil x Altitude 1006.0 335.0 0.4 0.757 NS Bow Soil 397.0 198.5 4.4 0.012 * Altitude 1276.0 425.4 9.5 0.000 *** Soil x Altitude 463.0 154.4 3.5 0.017 * Spring Soil 1947.0 973.4 5.4 0.005 ** Altitude 559.0 186.4 1.0 0.378 NS Soil x Altitude 746.0 248.8 1.4 0.249 NS 170 Figure A1-14. Correlation plot with tree variables including tree height (Height), diameter at breast height (DBH), weighted tree taper, sweep, roundness, lumber properties including, density, dynamic MOE, MOR, knot area, twist bow and spring and climatic variables including annual maximum temperature (T-max), minimum temperature (T-min), summer rainfall, autumn rainfall and spring rainfall. 171 Figure A1-15. Wood density plotted against diameter at breast height (DBH) and grouped according to the Lowveld Escarpment (Escarpment) and Highveld forestry regions of South Africa, including mean values and standard error. The data was sourced from x-ray densitometry measurements done on P. patula increment cores from a previous regional study (Van der Merwe et al. 2023a). Figure A1-16. Wood latewood content percentage plotted against diameter at breast height (DBH) and grouped according to the Lowveld Escarpment (Escarpment) and Highveld forestry regions of South Africa, including mean values and standard error. The data was sourced from x-ray densitometry measurements done on P. patula increment cores from a previous regional study (Van der Merwe et al. 2023a). 172 Appendix 2 A2.1 Experimental design and overview of the field work A sampling matrix was established, covering the primary altitudes and soil geologies common to the Lowveld and Highveld forestry regions of South Africa (Table A1-1). Sites with specific soil types were identified by using existing soil maps developed for the region. Sample sites then were stratified by the three dominant soil geologies found in the region across the full altitudinal range and separated into 200 meter intervals, as follows:  3 x soil geologies: Malmani Dolomite, Timeball Hill Shale, and Randian Granite  4 x altitude classes: 1,100 – 1,299 m, 1,300 – 1,499 m, 1,500 – 1,699 m, and 1,700 – 1,899 m. Sample plots were established on 18-year-old P. patula plantation compartments, which had similar historical silviculture activities. In brief, all sample plots were selected based on a 3 × 3 meter spacing. The plots were subject to line thinning to reduce the mean stocking density to 800 trees per hectare, and ideally had a second thinning to a target stocking of 500 trees per ha. All of the sampled compartments also had 3 stages of branch pruning, including a first pruning to a height of 1.5 meters, a second pruning to a height of 3.5 meters, and a third pruning to a height of 5.5 meters. The aim of the pruning was to maximize clear wood formation, by restricting the knotty core of the pruned tree sections to less than 12 cm. At sites displaying lower growth rates, the second and third pruning were delayed due to inherently small tree sizes. Table A2-1. Sampling matrix according to alitude and soil type. Six sample plots were established for each site type, producing a total of 54 sample plots. All trees sampled and the corresponding stumps were sequentially numbered (Figure A1-1). Tree height and stem diameters were recorded at a height of 1.3 meters for each tree (DBH). Altitude range (m) Soil geology (N = 54) Malamani dolomite Timeball Hill shale Randian granite 1,100 – 1,299 n = 6 n = 6 No plots 1,300 – 1,499 n = 6 n = 6 No plots 1,500 – 1,699 n = 6 n = 6 n = 6 1,700 – 1,899 No plots n = 6 n = 6 173 Figure A2-1. Sample plot established in 18-year-old P. patula plantation forest, including numbering of the respective sample trees. Soil pits were established to a depth of 2 meters at each of the sample sites, and the respective soil horizons were identified. Soil samples were collected from the respective soil horizons, stored in brown paper bags to allow for moisture loss, and sent to the University of KwaZulu-Natal for soil chemical and fraction analysis. An example of a soil pit can be seen in Figure A1-2. Figure A2-1. Example of soil pit established in Inanda soil form, derived from dolomite parent material. The pits were established at the edge of the sample plots. 174 Tree heights were measured using a Laser Vertex and diameter at breast height (DBH) using an electronic caliper. Tree form was expressed and modelled based on tree height, tree diameter, and sturdiness ratios of trees grown on the respective sites. All models and statistical analyses were completed using the R programming environment. To investiage the wood properties of the standing trees, 12 mm increment cores were extracted in the North to South direction at a reference height of 1.3 meters (Figure A1-3). Figure A2-2. Example of 12 mm wood core sample extracted at a height of 1.3 meters. Trees displaying any type of damage or deformity were disqualified from the sampling population. Tree heights were divided into 1st, 2nd, 3rd, and 4th quartile height ranges, and grouped according to minimum, mean, and maximum tree sized. Thus, the smallest trees included all trees included into the first quartile, the mean sized trees included all trees grouped in the second and third quartiles, and the largest trees included all trees that grouped in the fourth quartile. The representative tree sizes were selected randomly for sampling, using a randomization function in Microsoft excel. A total of 21 core samples were extracted from each of the sample plots (i.e. 7 trees from 1st quartile, 7 trees from the 2nd and 3rd quartiles, and 7 trees from the 4th quartile). In addition, resistorgraph measurements were completed, in the north to south direction, for all the trees located within the respective sample plots. For the trees which were cored the measurement was completed above or below the point of coring, while trying to avoid knot whorls during measurement (Figure A1-4). 175 Figure A2-3. Example of resistograph measurements done on P. patula sample trees. Extracted core samples were air dried for 7 days at room temperature, halved using a laboratory band saw (at the pith), and the northern portion reserved for x-ray densitometry and x-ray diffraction analyses. If the Northern sample possessed any defects, such as hairline fractures or delamination, the Southern portion was employed. A specimen sample (central cant) was removed from the cores using a pneumatic twin-blade saw, and then extracted overnight with hot acetone for 14 hours in a Soxhlet apparatus. An X-ray densitometer was calibrated using 10 additional P. patula core samples of various tree sizes and representative of the sample sites. The prepared samples were then subject to x-ray densitometry to quantify ring ages, earlywood (EW) and latewood (LW) potions, overall 176 wood density, and juvenile core transition ages. All statistical analysis were conducted using R statistical software package. The sampling process is outlined in Figure A1-5. Figure A2-4. A total of 21 core samples were extracted from the 54 sample plots. Densitometery measurements were done for each of the profiles, equating to 126 density measurements per site type. Trees were grouped according to size by extrapolating DBH measurement at a constant taper of 10 mm per meter to a bole height of 3.1 meters. If the thin ends of logs were larger than 25 cm the tree was classified as a veneer log trees, and if smaller than 25 cm as sawlog trees. Twenty trees were selected and felled from each sample plot by means of stratified random sampling (i.e. 10 trees for veneer log production, and 10 trees for sawlog production). Post felling tree heights, the onset of dead crown height, the onset of live crown height, and height to 15 cm was measured using measurisng tape. Discs were removed at the base and then at 2.65 meter intervals along the length of the trees felled for veneer production, and discs were removed in 3.1 meter intervals along the length of the trees felled for sawlogs. Sample plot number, tree number, and height interval from where the disc was removed were noted on each disc. Similarly, each log was marked according to sample plot, tree number, and log position within the tree by using plastic barcode tags and the information was captured using a handheld barcode scanning device. Figure A1-6 shows sample logs with plastic barcode tags attached and information regarding the plot, tree, and log position recorded on the flax seed paper tag. 177 Figure A2-5. Example of logs marked with plastic barcode tags, including supporting flax seed paper tags showing plot number, tree lumber, and log number up the tree. The logs were marked using plastic barcode tags to ensure tracking of logs from the forest to the mill. The plots were harvested in 7 batches and processed separately to simplify traceability of the logs though the mills. Felled trees, bucked log assortments, and disc samples produced from the sample sites are shown in Figure A1-7. Figure A2-6. Harvesting operation showing the extracted sample trees at roadside including cross-cut log assortments and the extracted disc samples. The disc samples were air dried and a central cant was removed in a north to south direction, as seen in Figure A1-8. 178 Figure A2-7. Example of cenral cants removed from disc samples. The samples were numbered and north/south directions noted. The cant samples were treated to discern the heartwood from the sapwood using equal volumes of solution A and B (solution A included 1 g of benzidine, 5 ml of 25 percent hydrochloric acid and 194 ml of water, while solution B included 20 g of sodium nitrate and 180 ml of water). The distance from the profile edge to the onset of heartwood (HW1), the pith, heartwood exit (HW2), and profile diameter was measured. Log and heartwood volumes were then calculated by harnessing the discs removed from opposite sides from the respective logs using Smalian’s formula. An example of a stained disc wood cant is shown in Figure A1-9. 179 Figure A2-8. Stained cant showing the heartwood and sapwood profiles. The heartwood was stained using benzidine to change the heartwood to a red colour. Measurements were done to the onset of heartwood (HW1), the pith, the heartwood exit point (HW2) and the total profile diameter. The sample logs were transported to a local plywood and chipper-canter sawlog mill. All logs were processed within 3 weeks after harvesting. The logs were sequentially scanned prior to debarking next to a step-up feeder mechanism, which supplies the debarker with logs (one-by-one). The step-up feeder is shown in Figure A1-10. 180 Figure A2-9. Logs were barcode scanned at the step-up log feeder using a handheld barcode scanner. The aim was to maintain the log-feed sequence, and the log information was matched to resultant information from the Logeye 300 log form measurement equipment. After debarking, the logs passed through an Logeye 300 scanner situated on the debarking line to measure log length, diameter, volume, ovality, taper, and sweep (the scanning sequence corresponded with the data sequence of the Logeye 300 scanner). The debarking procedure is illustrated in Figure A1-11. 181 Figure A2-10. Schematic showing the debarking sequence of sample logs including, feeding of logs onto the debarking line by step-up feeder, bark removal via spindle-debarking, and measurement of log form though the Logeye 300 measurement equipment. Using the measured information, the logs were automatically segregated into concrete bins, according to length and diameter (i.e. 2.65 meter logs being destined for veneer production and the 3.1 meter logs destined for lumber production). Furthermore, the sawtimber logs were segregated according to cutting pattern, to be utilised in the chipper-canter mill. Logs segregated during the debarking process is shown in Figure A1-12. Figure A2-12. Example of logs seggragted after debarking in a concrete bin. 182 A2.2 Lumber production The respective sawlog assortments were colour coded by using 24 unique colours to allow for lumber traceabililty throughout the production process. The colours used can be seen in Figure A1-13. 1. 2. Figure A2-13. Colours used for primary and secondary colour cominations used to track the resultant lumber produced from sample logs, through to final product. During the colour coding process 24 logs were painted using a single base colour (primary colour), by first scanning the black platic tags using the handheld computer and then attaching a flaxseed paper tag with a number (1-24), and then later painting according to the scanning sequence. The process is illustrated in Figure A1-14. Figure A2-14. Logs with primary base colours and flaxseed paper tags numbered from 1 to 24. 183 The scanned tags were displayed on the scanner excel spreadsheet in sequence. A note was made next to the 24 scanned tag numbers according to the primary colour employed (on the device spreadsheet). The barcode scanner used is shown in Figure A1-15. Figure A2-15. Barcode scanner excel spreadsheet, including scanned barcode numbers. The process was repeated for all of the paint colours. A paper register was made with a raster displaying the paint colours in the first column and the numbers (1-24) in the first row of the raster. Using one colour of paint at a time, a single cross was painted over one log from the respective base coloured log groupings and dotted with paint in the remaining exposed quarters to allow for lumber traceability. The corresponding paint colours were noted on the raster according to the flaxseed tag number removed. Colour coded sawlogs are shown in Figure A1-16. 184 Figure A2-16. Sawlogs with primary and secondary colour combinations. The sample logs were then milled and the respective lumber produced from each log was traceable. Examples of lumber boards produced from the colour coded sawlogs are shown in Figure A1-17. 185 Figure A2-17. Lumber boards with primary and secondary colour combinations. The lumber boards were kiln dried to a moisture content below 15 %. The lumber boards were destack and kept separate according to the respective harvested batches and resultant dimensions, and the billets were marked using spray paint. The respective boards were cross-cut to a board length of 3 meters (levelled on non-coloured side and final cut on coloured side). After board trimming, the colour coded off-cut was marked using a flaxseed tag and manually numbered according to the batch processed and the lumber board sequence in the stack. Colour coded lumber board off-cuts, with corresponding batch and board number tags, are shown in Figure A1-18. 186 Figure A2-18. Board trimmings with primary and secondary colour combinations, numbered according to lumber batch and board number. The same number was replicated on the lumber board using a crayon. The respective boards were traced back to the colour code register to determine the specific orgin logs. The numbering continued sequentially and concurrently, until all of the lumber boards were numbered, allowing for a unique number for each board. During machine grading, the Goldeneye 706 was first calibrated using calibration boards. The lumber boards were manually moved to the grading line and graded during maintenance shifts. A register was kept of the batches graded and the lumber boards were sequentially placed on the grading line according to the numbers that was previously written on the boards. Furthermore, the boards were again numbered starting from number one on grading day one. This number is also unique as it indicates the sequence in which the boards were graded. This numbers were also allocated continuously until all of the boards produced from all the batches were graded. This generated two numbers on each board, a number in the middle of the board that was written during trimming, and a number at the base that was written on the grading day. By aligning the two registers, individual lumber boards were traceable to each plot, tree, and position within the tree. Post grading, each board was weighed, moisture content was measured was using an electromagnetic pin-less moisture meter at the 187 centre of each board, and board lengths were measured, to validate the Goldeneye measurements. An example of the moisture meter used can be seen in Figure A1-19. Figure A2-19. Pin-less electromagnetic moisture meter used during the validation process. The lumber grading and validation sequence implemented can be seen in Figure A1-20. 188 Figure A2-20. Schematic showing the sequential feeding of boards through the Goldeneye 706 machine grader, lumber board measurements and manual validation of lumber measurements. A2.3 Veneer production Veneer logs were steam conditioned for 9 hours at a temperature of 70 °C to 90 °C. Sample logs exiting the steam conditioning chamber is shown in Figure A1-21. Figure A2-21. Conditioned veneer logs, exiting the log-conditioning chamber. 189 The sample logs were loaded onto a step-up feeder and plastic barcodes were scanned sequentially ,one log at a time, using the handheld scanner device during rotary peeling. An example of the barcode tag and scanning location can be seen in Figure A1-22. Figure A2-22. Exposed log-end prior to barcode scanning and loading into the rotary veneer peeler. Veneer peeling sequence was recorded due to barcode scanning. All veneer produced from the logs were allocated to a single veneer bin. The last veneer sheet produced from each log was sequentially numbered using a crayon. Veneer sheets were stacked in a single bin and numbering location can be seen in Figure A1-23. 190 Figure A2-23. Numbering of the last veneer sheet produced from each log, using a crayon. All veneer sheets were stacked into a single bin, to maintain a veneer production sequence. The veneer sheets produced from the respective logs could then be deducted according to the numbers. Post-peeling, each veneer sheet was manually numbered using a crayon including the peeling sequence and the order in which the veneer sheets were produced from each log. Two technicians (one on each axial end) manually handled the veneer sheets, and plywood off-cuts were placed under the veneer sheet ends during handling to ensure the veneer sheets were not damaged. In effect, a unique number was allocated to each veneer sheet to allow for traceability. A numbered veneer sheet is shown in Figure A1-24, and the veneer number processing Figure A1-25. 191 Figure A2-24. Veneer sheet numbering with veneer log peeling sequence (186) and veneer sheet number produced from the log (21). Figure A2-25. Numbering of veneer sheets in progress. 192 The veneer sheets were dried via a Raute continuous drier to a high point moisture content of below 12 %. The veneer drier is shown in Figure A1-26. Figure A2-26. Veneer sheets were dried through a continious box drier. The individual veneer sheets were graded via a Mecano grading system, and the supporting grading information was saved for each veneer sheet. Post drying, the data was accessed through the Mecano Image Browser software (Figure A1-27). Figure A2-27. Graded veneer sheet analysed using the Mecano Image Browser software. The image shows the unique veneer sheet number marking. The veneer sheet is graded (“C-grade”). Downgrade reasons are also indicated (“Defect too long-Open Split”). 193 The corresponding numbers, veneer grades, and reasons for downgrading were captured in an Microsoft Excel sheet, with “1 or 0” allocated to specific columns according to the reasons for downgrades. Furthermore, the veneer sheet dimensions were used to calculate the volume of veneer produced from each log. The veneer sheets were the related to the scanned log tags to allow for full traceability. Tree form, veneer recorery, veneer grades, and reasons for veneer downgrades were determined for each tree. The data analysis and model development element of the study can be found in the main body of the thesis document.
Thesis/Dissertation
eng
Forestry
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Mansfield, Shawn D., 1969-
The impact of altitude, soil geology, and soil depth on Pinus patula solid wood properties for structural timber and plywood production
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