A Comparison of Harvester Productivity

A COMPARISON OF HARVESTER PRODUCTIVITY AND STUMP VOLUME WASTE IN COPPICED AND PLANTED EUCALYPTUS GRANDIS PULPWOOD COMPARTMENTS IN THE KWAZULU-NATAL FORESTRY REGION OF SOUTH AFRICA

MUFHUMUDZI MUEDANYI RAMANTSWANA

Submitted in fulfilment of the requirements for the Degree of

MAGISTER TECHNOLOGIAE in FORESTRY at the

Nelson Mandela Metropolitan University

June 2012

Supervisor: Dr J.C. Steenkamp

Co-supervisor: Mr A.M. McEwan

DECLARATION

I, Mufhumudzi Muedanyi Ramantswana 207050046 , hereby declare that the treatise/ dissertation/thesis for MAGISTER TECHNOLOGIAE in FORESTRY is my own work and that it has not been submitted for assessment or completion of any postgraduate qualification to another University or for another qualification.

______

Mufhumudzi Muedanyi Ramantswana

ACKNOWLEDGEMENTS

My sincere appreciation and thanks is dedicated to the following:

 Sappi for allowing and sponsoring me to do my Masters full-time and for partially supporting me financially when I conducted my research;  Mr. Andie Immelman and Mr. Andre Boshoff from Sappi for constant support, and for providing me with much-needed guidance and information as well;  Mr. Sean Brown from Mondi for allowing me to conduct my research at the New Hanover plantation;  Mr. Darryn Braithwaite from Mondi in New Hanover who assisted me in identifying the representative research sites and providing information;  Bruce and his team from DS Preen Contracting (Pty) Ltd for assisting me with moving the machines and providing much-needed assistance during the field trials;  Struan Robertson, a NMMU Forest Engineering BTech student, and Mxolisi Mtshali, a Mondi second-year practical student, for all the assistance with infield data collection;  Luyanda Mbelu, Martin Eggers and Nomcebo Mnculwane for providing the much needed logistic support during the field studies;  Dr. Jaap Steenkamp my supervisor – thank you very much for the overall guidance and support during my whole research;  Mr. Andrew McEwan my co-supervisor – thank you very much for the all the knowledge and experience shared, financial support for the research and direct involvement from the very beginning of my Masters;  Mr. Willie Louw, my Forest Management lecturer, for providing much-needed assistance and support with regard to my infield data collection measurements;  Mrs Jeanette Pauw, the NMMU statistician, for helping me with my data analysis and all statistic-related support;  Many thanks to my friends Mpumi, Mulanga, Nonku, Mnqobi, Lebo and NkhosingPhile for constantly forwarding literature related to much of the research and offering a helping hand when needed;

 A special thanks to my family for their support and encouragement during my studies, especially my parents Jackson and Nditsheni Ramantswana, and siblings Bulavhurena, Muneiwa, Mothipana, Hulisani and Thabelo  Above all I thank God for His abounding grace upon my life, and wisdom and understanding to compile this research.

iii

LIST OF ABBREVIATIONS

AIDS Acquired Immunodeficiency Syndrome

BWBS Bark-wood bond strength

CCS Combined coppice stem cm Centimetres

DBH Diameter at breast height

DAFF Department of Agriculture, Forestry and Fisheries

HIV Human Immunodeficiency Syndrome m Meters m3 Cubic meters

MAI Mean annual increment

PMH Productive machine hours

Vol Volume

ABSTRACT

Over the past decade the South African forestry industry has gradually experienced the ramifications of labour scarcity, increases in labour costs, the effect of HIV and AIDS and increasing timber demand. Consequently, this has led to an increase in the mechanisation rate, especially in timber harvesting operations. Due to the labour challenges in South Africa, mechanised forestry equipment has increasingly been required to operate in complex forest conditions, such as coppiced compartments, where they have not operated before. It therefore occurs that harvesters are either used in certain coppiced compartments with uncertain productivity expectations, or the harvesters are not used in these compartments due to a lack of productivity knowledge. The influence that certain factors have on harvester productivity and stump volume loss – factors such as coppice regeneration practices and stem form – is poorly understood and has not been quantified. No scientific research exists regarding the effects of coppice compartments on the productivity of a harvester and the amount of stump volume waste.

This research aimed at determining the influence of tree volume, tree form, stem felled first and distance between stems on the productivity of an excavator based harvester in coppiced double, coppiced single and planted Eucalyptus grandis pulpwood compartments. Furthermore, the research determined whether there was any stump volume waste, and quantified how much of it was due to excessive stump heights by the harvester. Through regression analysis, productivity equations were derived to make productivity predictions in both coppiced and planted compartments. All stumps were evaluated for waste and the average stump volume waste in coppiced double, coppiced single and planted trees was determined.

The research results showed that planted trees had the highest productivity across all tree sizes, followed by coppiced single trees and then coppiced double stems. When harvesting a 0.2 m3 tree, the mean harvester productivity was 8.7 m3 per PMH in coppiced double trees, 13.8 m3 per PMH in coppiced single trees and 16.1 m3 per PMH in planted trees. In coppiced double stems the productivity was not significantly influence by the distance between stems. However, the productivity was significantly

v influenced by the stem felled first. The regression results showed that if the smaller stem was felled first, the productivity would increase if the larger stem’s volume was less than 0.18 m3; however where the larger stem was greater than 0.18 m3, the relationship was reversed. In addition, the productivity for both coppiced single trees and coppiced double stems were significantly influenced by stem form. The poorly formed trees had low productivity compared to the trees with good form. The stump volume findings showed that coppiced double stems had the highest average stump volume waste per stump, with 0.00307 m3 waste, followed by coppiced single trees (0.001954 m3) and planted trees (0.001650 m3). The average stump volume waste per stump with waste for the planted trees was negligible.

This research provides forestry companies and harvesting contractors with information on the effect of tree volume, tree form and stem felled first on harvester productivity in E. grandis coppiced double, coppiced single and planted compartments. This information will assist in making equipment and system selection decisions and improve operational management and control. In addition, they will also be aware of stump volume losses that will occur in the three scenarios.

DEFINITION OF KEY CONCEPTS

Key words and concepts are explained below to ensure that the interpretation and understanding of the concepts in the text are clear.

Coppicing - vegetative regeneration from sprouts following a disturbance such as fire or timber harvesting (Ribeiro & Better, 1995);

Coppiced double stems - two first rotation coppice stems per stump that are left with the objective of achieving the original compartment density during the coppice reduction operation (Norris, 2000);

Coppiced single stems/tree – a single first rotation coppice stem that sprouts from a tree stump. A coppiced single stem originates from the remaining tall and dominant shoot left on a stump during the coppice reduction operation (Norris, 2000);

Distance between stems - distance between the coppiced double stems measured with a tape measure at a height of 1.3 m above the previous rotation’s stump;

Harvester - a machine that fells, debranches, debarks and crosscuts a tree at the stump (Kellogg et al., 1993);

Felled first stem - whichever stem between the two coppiced stems the harvester grabbed, felled and processed first;

Tree form - straightness or crookedness of the bole and the branch density (Puttock et al., 2005);

Planted trees - first rotation seedling planted trees;

Productive machine hour (PMH) - the fraction of time spent by the machine producing output or doing its primary task or support task (Wenger, 1984);

Stump - The lower part of a stem remaining after felling, and which is still attached to the root complex (McEwan, 2011a);

vii

Stump volume waste - The volume of excess timber left on a harvested stump that should have been removed in accordance with the minimum utilisation standards of the cutting authority (Singh, 2006).

viii

TABLE OF CONTENTS

DECLARATION ...... i

ACKNOWLEDGEMENTS ...... ii

LIST OF ABBREVIATIONS ...... iv

ABSTRACT ...... v

DEFINITION OF KEY CONCEPTS ...... vii

LIST OF TABLES ...... viii

LIST OF FIGURES ...... x

LIST OF ANNEXURES ...... xiii

CHAPTER 1: INTRODUCTION AND PROBLEM STATEMENT ...... 1

1. INTRODUCTION ...... 1

1.1 MOTIVATION FOR RESEARCH ...... 1

1.2 THE RESEARCH PROBLEM STATEMENT...... 3

1.3 AIMS AND OBJECTIVES OF THE RESEARCH ...... 3

1.3.1 Aims ...... 4

1.3.2 Objectives ...... 4

1.4 DATA COLLECTION ...... 4

1.4.1 Validity ...... 5

1.4.2 Reliability ...... 6

1.5 ORGANISATION OF THE RESEARCH ...... 6

CHAPTER 2: LITERATURE REVIEW ...... 8

2.1 INTRODUCTION ...... 8

2.2 OVERVIEW OF THE SOUTH AFRICAN COMMERCIAL FORESTRY INDUSTRY ...... 8

2.3 OVERVIEW OF EUCALYPTUS GRANDIS IN SOUTH AFRICA ...... 12

2.3.1 History ...... 12

2.3.2 Commercial significance of E. grandis in plantation forestry ...... 12

2.3.3 Description of Eucalyptus grandis ...... 13

2.3.4 Eucalyptus coppicing ...... 14

2.3.5 Short rotation coppice ...... 15

2.4 HARVESTING IN SOUTH AFRICA ...... 15

2.5 HARVESTING METHODS AND SYSTEMS USED FOR EUCALYPTUS IN SOUTH AFRICA ...... 17

2.5.1 Cut-to-length ...... 17

2.5.2 Tree length method ...... 19

2.5.3 Full tree method ...... 20

2.6 HARVESTER ...... 20

2.6.1 Description of a harvester ...... 20

2.6.2 Factors affecting harvester productivity ...... 21

2.6.2.1 Forest environment ...... 21 (a) Tree size ...... 21 (b) Tree form ...... 22 (c) Bark-wood bond strength ...... 22 (d) Terrain conditions ...... 23 2.6.2.2 Harvester characteristics and specifications ...... 23 2.6.2.3 Harvester operators’ working techniques and capacity ...... 24

2.6.3 The use of a harvester in coppiced compartments ...... 25

2.6.4 Future of harvesters ...... 27

2.7 TIMBER VOLUME RECOVERY AND STUMP VOLUME WASTE ...... 28

2.7.1 An overview of the forms of volume loss in harvesting operations ...... 28

2.7.1.1 Saw kerf thickness and sawdust ...... 29 2.7.1.2 Felling damage and optimisation ...... 29

2.7.1.3 Tree tops ...... 31 2.7.1.4 Stumps ...... 31

2.7.2 Stump volume waste ...... 31

2.7.2.1 Main factors affecting stump height ...... 32 (a) Operator efficiency ...... 32 (b) Multiple stems and leaning trees ...... 33 (c) Manual and mechanical felling methods ...... 35 (d) Obstacles in the compartment ...... 35 (e) Species and stump diameter...... 36 (f) Slope ...... 36 (g) E. grandis coppiced double, coppiced single and planted stems ...... 37 2.7.2.2 Potential value loss due to high stumps ...... 37 2.7.2.3 Methods of improving volume recovery ...... 38 (a) Management recommendations for reducing stump volume wastage ...... 38 2.7.2.4 Benefits of having low stumps ...... 38

CHAPTER 3: RESEARCH SITES AND HARVESTING SYSTEM ...... 41

3.1 INTRODUCTION ...... 41

3.2 LOCATION ...... 41

3.2.1 Description of the research site’s plantation area ...... 41

3.2.2 Research sites ...... 42

3.2.3 General climatic conditions ...... 43

3.3 TERRAIN CONDITIONS ...... 44

3.3.1 Soil types ...... 44

3.3.2 Research site terrain information ...... 44

3.4 HARVESTING SYSTEM ...... 45

3.4.1 General overview of the harvesting system ...... 45

3.4.2 Description of the single-grip excavator-based harvester ...... 46

3.4.3 Detailed description of the harvester operation ...... 49

iii

3.4.3.1 Changing position ...... 49 3.4.3.2 Swing-to-tree ...... 49 3.4.3.3 Grab tree ...... 49 3.4.3.4 Debarking and debranching ...... 50 3.4.3.5 Crosscutting and stacking ...... 50 3.4.3.6 Pile slash...... 50 3.4.3.7 Other activities ...... 51

3.4.4 Harvester operator ...... 51

CHAPTER 4: RESEARCH AND DATA ANALYSIS METHODOLOGY ...... 52

4.1 INTRODUCTION ...... 52

4.2 PILOT RESEARCH ...... 52

4.3 RESEARCH METHODOLOGY ...... 53

4.3.1 Measuring instruments ...... 53

4.3.2 Fieldwork practice and data collection ...... 55

4.3.2.1 Tree volume determination ...... 55 4.3.2.2 Marking of trees ...... 56 4.3.2.3 Bark-wood bond strength description ...... 57 4.3.2.4 Tree form description ...... 58 4.3.2.5 Stump volume waste data collection ...... 59

4.3.3 Data capturing ...... 62

4.3.3.1 Field data capturing ...... 62 4.3.3.2 Time study data capturing ...... 63 4.3.3.3 Stump volume waste data capturing ...... 65

4.3.4 Summary of factors influencing productivity and volume recovery ...... 65

4.4 DATA ANALYSIS METHODOLOGY ...... 67

4.4.1 Harvester productivity modelling ...... 67

4.4.2 Statistical analysis of the stump volume waste ...... 68

4.5 SHORTCOMINGS AND SOURCES OF ERROR ...... 68

CHAPTER 5: RESULTS AND DISCUSSION ...... 70

5.1. INTRODUCTION ...... 70

5.2 PRODUCTIVITY RESULTS AND DISCUSSION ...... 70

5.2.1 Coppiced double stems productivity results and discussion ...... 70

5.2.1.1 Effect of coppiced double combined stem volume on harvester productivity ...... 71 (a) Sample profile of CCS volume ...... 71 (b) Productivity regression model: CCS volume ...... 73 (c) CCS volume productivity model adequacy checking ...... 73 (i) Normality test ...... 74 (ii) Homoscedasticity test ...... 74 (d) Modelled harvester productivity results: CCS volume trees ...... 75 5.2.1.2 Effect of the stem felled first on harvester productivity ...... 76 (a) Sample profile of stem-one and stem-two ...... 77 (b) Productivity regression model: tree volume and interaction with the stem felled first ...... 78 (c) Model adequacy checking: tree volume interaction with stem felled first productivity model ...... 80 (i) Normality test ...... 80 (ii) Homoscedasticity test ...... 80 (d) Modelled harvester productivity results: CCS volume trees ...... 81 5.2.1.3 Effect of stem form on harvester productivity in coppiced double stems ...... 84 (a) Regression models for predicting harvester productivity in coppiced double stands under different tree form classification combinations ...... 87 (b) Construction of form models via stepwise regression ...... 88 (c) Coppiced double tree form model adequacy checking ...... 90 (d) Modelled harvester productivity results: coppiced double tree form classes ...... 90

5.2.2. Coppiced single trees productivity results and discussion ...... 90

5.2.2.1 Effect of tree volume on harvester productivity in coppiced single trees ...... 90 (a) Sample profile for coppiced single trees ...... 91 (b) Productivity regression model: tree volume ...... 92

(c) Coppiced single tree volume productivity model adequacy checking ...... 93 (i) Normality test ...... 93 (ii) Homoscedasticity test ...... 94 (d) Modelled harvester productivity results: coppiced single trees ...... 95 5.2.2.2 Effect of form on harvester productivity in coppiced single trees ...... 96 (a) Coppiced single productivity regression model: tree volume and form ...... 97 (b) Modelled harvester productivity results: coppiced single trees with good and poor form ...... 98

5.2.3 Planted trees productivity results and discussion ...... 99

5.2.3.1 Effect of tree volume on harvester productivity in planted trees ...... 100 (a) Sample profile for planted trees ...... 100 (b) Productivity regression model: tree volume ...... 101 (c) Tree volume productivity model adequacy checking for planted trees ...... 102 (i) Normality test ...... 102 (ii) Homoscedasticity test ...... 103 (d) Modelled harvester productivity results: planted trees ...... 104

5.2.4 Summary and comparison of harvester productivity results and discussion in coppiced double, coppiced single and planted trees ...... 106

5.2.4.1 Summary of productivity results discussion ...... 106 5.2.4.2 Comparison of modelled productivity results...... 106

5.3 STUMP VOLUME WASTE RESULTS AND DISCUSSION ...... 109

5.3.1 Coppiced double stump volume waste results and discussion ...... 109

5.3.1.1 Descriptive statistics for coppiced double stumps ...... 109 5.3.1.2 Sample profile for coppiced double stumps with waste ...... 110 5.3.1.3 Relationship between tree volume and stump volume waste...... 111 5.3.1.4 Key factors observed that influenced the stump volume waste in coppiced double stems ...... 112

5.3.2 Coppiced single stumps volume waste results and discussion ...... 113

5.3.2.1 Descriptive statistics for coppiced single stumps ...... 113 5.3.2.2 Sample profile for coppice single stumps with waste ...... 114 5.3.2.3 Relationship between tree volume and stump volume waste...... 114

5.3.2.4 Key factors observed that influenced the stump volume waste in coppiced single trees ...... 116

5.3.3 Planted trees stump volume waste results and discussion ...... 117

5.3.3.1 Descriptive statistics for planted stumps ...... 117 5.3.3.2 Sample profile for planted stumps with waste ...... 118 5.3.3.3 Relationship between tree volume and stump volume waste...... 118

5.3.4 Summary results and discussion of coppiced double, coppiced single and planted stump wastages ...... 120

5.3.5 Calculation of lost revenue per hectare for coppiced double, coppiced single and planted trees for the research site ...... 122

5.4 RELATIONSHIP BETWEEN HARVESTER PRODUCTIVITY AND STUMP VOLUME WASTAGE ...... 123

5.4.1 Summary of productivity and stump volume waste results ...... 125

CHAPTER 6: CONCLUSION AND RECOMMENDATIONS ...... 127

6.1 INTRODUCTION ...... 127

6.2 SUMMARY AND DISCUSSION OF KEY FINDINGS ...... 127

6.2.1 Summary of research problem and research methodology applied...... 127

6.2.2 Key findings from productivity results and discussion ...... 128

6.2.3 Key findings from stump volume waste results and discussion ...... 130

6.2.4. Summary of key findings and relationship between harvester productivity and stump volume wastage ...... 131

6.3 RELATION OF RESEARCH RESULTS TO THE LITERATURE REVIEW ...... 132

6.4 VALUE OF RESEARCH RESULTS IN THE FORESTRY INDUSTRY ...... 133

6.5 RECOMMENDATIONS ...... 134

REFERENCES ...... 136

vii

LIST OF TABLES

Table 1: Research area data ...... 43

Table 2: Compartment terrain conditions ...... 44

Table 3: Excavator-base machine specifications ...... 47

Table 4: Harvester head specifications ...... 48

Table 5: Measuring instruments and their purpose ...... 54

Table 6: Description of bark-wood bond strength classes (McEwan, 2011c) ...... 58

Table 7: Tree form class used for the research (adapted from Puttock et al., 2005) ...... 59

Table 8: Microsoft Excel spreadsheet column titles ...... 62

Table 9: Descriptive statistics for CCS volume and productivity ...... 72

Table 10: Coefficients of the double coppice productivity model ...... 73

Table 11: Descriptive statistics for stem-one volume and stem-two volume ...... 78

Table 12: Coefficients of the double coppice (stem-one and stem-two) productivity model ...... 79

Table 13: CCS volume figures used in the coppiced double regression model ...... 82

Table 14: Mean volumes for coppiced double stems for different form combinations ...... 85

Table 15: Mean cycle times in deci-minutes ...... 87

Table 16: Coefficients of the productivity model for each tree form classification ...... 89

Table 17: Descriptive statistics of coppiced single stems ...... 91

Table 18: Coefficients of the coppiced single productivity model ...... 92

Table 19: Coefficients of the coppiced single form productivity model ...... 98

Table 20: Descriptive statistics of planted trees ...... 100

viii

Table 21: Coefficients of the planted trees productivity model ...... 102

Table 22: Harvester productivity under various coppiced double and coppiced single proportions and tree volumes ...... 108

Table 23: Descriptive statistics for coppiced double stumps ...... 109

Table 24: Descriptive statistics for coppiced single stumps ...... 113

Table 25: Descriptive statistics for planted stumps ...... 117

Table 26: Comparison between productivity and mean stump waste for coppiced double, coppiced single and planted trees ...... 124

LIST OF FIGURES

Figure 1: Round wood production per product (Forestry South Africa, 2010) ...... 9

Figure 2: Forestry land ownership in South Africa (Forestry South Africa, 2010) ...... 10

Figure 3: Plantation species and ownership (Forestry South Africa, 2010) ...... 11

Figure 4: Cut-to-length, tree length and full tree harvesting methods in South Africa (1987, 1997 and 2006) (Längin & Ackerman, 2007) ...... 18

Figure 5: Purpose built harvesters: wheeled (Ponsse, 2010) (left), tracked (centre) and excavator-based harvester (right) ...... 20

Figure 6: Potential wood losses from felling (Han & Renzie, 2005) ...... 30

Figure 7: Multiple stems occurring above safe height (Garland & Jackson, 1997) ...... 33

Figure 8: Stems splitting close to the stump (Garland & Jackson, 1997) ...... 34

Figure 9: Research sites near to New Hanover (Google maps, 2012) ...... 42

Figure 10: System matrix for the single-grip excavator-based harvester ...... 46

Figure 11: Harvester operating in a coppiced compartment ...... 47

Figure 12: Spray-painted coppiced double stems indicating the larger (a) and smaller stems (b) ...... 57

Figure 13: White paper numbered tag stapled at the base of the stump ...... 60

Figure 14: Trimble Nomad Handheld Computer that was used to conduct time studies ...... 63

Figure 15: Schematic diagram of the factors influencing productivity and volume wastage ...... 66

Figure 16: Sample distribution for CCS volume ...... 71

Figure 17: Scatterplot showing the relationship between productivity and CCS volume ...... 72

Figure 18: Harvester – CCS volume normal probability plot ...... 74

Figure 19: Harvester – CCS volume predicted versus residual plot ...... 75

Figure 20: Modelled harvester productivity per CCS volume ...... 76

Figure 21: Sample distribution for stem-one and stem-two volume ...... 77

Figure 22: Harvester - coppiced double (Stem-one volume and stem-two volume) normal probability plot ...... 80

Figure 23: Harvester - coppiced double (stem-one volume and stem-two volume) residuals versus predicted plot ...... 81

Figure 24: Modelled productivity for the harvester when either the small stem or the big stem is felled first ...... 83

Figure 25: Harvester felling two stems at the same time ...... 84

Figure 26: Sample size for the coppiced double tree form combinations ...... 86

Figure 27: Sample distribution for coppice single stems ...... 91

Figure 28: Scatterplot showing the relationship between productivity and tree volume ...... 92

Figure 29: Harvester - Coppiced single normal probability plot ...... 94

Figure 30: Harvester – coppiced single residual versus predicted plot ...... 95

Figure 31: Modelled harvester productivity per coppice tree volume ...... 95

Figure 32: Coppiced single sample size for each tree form classification ...... 97

Figure 33: Relationship between productivity and tree volume for good and poor form coppiced single trees...... 99

Figure 34: Sample distribution for planted trees ...... 100

Figure 35: Scatterplot showing relationship between productivity and tree volume ...... 101

Figure 36: Harvester – planted normal probability plot ...... 103

Figure 37: Harvester – planted predicted versus residual plot ...... 104

Figure 38: Modelled harvester productivity per planted tree volume ...... 105

Figure 39: Comparison of harvester productivity between coppiced double, coppiced single and planted trees ...... 107

Figure 40: Coppiced double waste volume sample size ...... 110

Figure 41: Coppiced double stump waste volume over tree volume ...... 111

Figure 42: Coppiced double stems waste percentage over tree volume ...... 112

Figure 43: Coppiced single waste volume sample size ...... 114

Figure 44: Coppiced single stump waste volume over tree volume ...... 115

Figure 45: Coppiced single stems waste percentage over tree volume ...... 116

Figure 46: Planted waste volume sample size ...... 118

Figure 47: Planted stump waste volume over tree volume ...... 119

Figure 48: Planted waste percentage over tree volume ...... 120

Figure 49: Comparison of stump volume waste for coppiced double, coppiced single and planted trees ...... 121

Figure 50: Harvester productivity for coppiced double, coppiced single and planted trees (top) Stump volume per hectare for coppiced double, coppiced single and planted stumps (bottom) ...... 126

xii

LIST OF ANNEXURES

Annexure 1: Detailed compartment maps for the research areas

Annexure 2: Harvester operator’s details

Annexure 3: Rounding and 2 cm classes used with regard to diameter measurements

Annexure 4: BWBS strength rip-stripping test

Annexure 5: Data sheet templates used to record the data manually

Annexure 6: Coppiced double tree form model adequacy checking - normality and homoscedasticity tests for each model

Annexure 7: Modelled productivities for the various tree form classes

Annexure 8: Productivity model adequacy checks - normality and homoscedasticity tests for coppiced single tree form model

xiii

CHAPTER 1: INTRODUCTION AND PROBLEM STATEMENT

1. INTRODUCTION

The introduction begins with a motivation for the research and a description of the research problem. This is followed by an outline of the aims of the research, data collection methods and the significance of the research. Thereafter, the overall layout of the dissertation is summarised under the organisation of the research.

1.1 MOTIVATION FOR RESEARCH

Mechanisation of forest harvesting operations is progressively becoming a global trend. This is due to the need to reduce harvesting costs, increase production and improve labour-related issues (Amishev & Murphy, 2009; Marshall, 2005). Spinelli et al. (2009) adds that due to the projected demands on commercial round wood supply, there is a demand for mechanisation of forest operations; this is exacerbated by the increasing scarcity and cost of labour. Furthermore, over the past two decades the global consumption of commercial plantation wood has been increasing significantly, especially wood chips and pulp products (Food and Agriculture Organisation (FAO), 2007; Nilsson & Bull, 2005). The need to meet the growing demand for forest products and tackle challenges related to labour, harvesting costs, safety and environmental issues in forest operations is forcing forestry stakeholders to apply mechanised equipment to harvest and process trees in the forests (Temperate Forest Foundation, 2002).

In South Africa, pulpwood timber harvesting methods have been manually orientated and the biggest portion of harvesting has traditionally been done by using the cut-to- length method (Längin et al., 2010). However, over the past decade the forestry industry has gradually experienced the ramifications of labour scarcity, increases in labour costs, the effect of HIV and AIDS and increasing timber demand. Consequently this has led to an increase in the mechanisation rate, especially in the timber harvesting sector (Grobbelaar & Manyuchi, 2000; Steenkamp, 2007; Water and Forestry Support Programme, 2004). Timber harvesting and transport is a vital part of the value chain as it constitutes 60 to 80 per cent of the mill delivered costs (Brink & Conradie, 2000).

In South Africa the cut-to-length method is applied to hardwood and softwood pulpwood harvesting operations. This method can comprise various harvesting systems based on basic, intermediate or mechanised technology (Längin et al., 2010). Mechanised cut-to-length technological harvesting systems comprise of harvesters and forwarders (Längin et al., 2010).

The main hardwood species grown commercially in South Africa are eucalypts, wattle and poplars species (Theron, 2000). The most widely-planted commercial hardwood species in South Africa is Eucalyptus grandis (Forestry Economic services, 2009). According to Pasquali (2010) E. grandis is considered economically viable because of its fast growth rate, excellent form and good wood properties. As a result of its economic viability, South Africa has a total of 314 549 hectares of E. grandis plantation areas owned both privately and publicly (Forestry South Africa, 2010). E. grandis wood can be used for various purposes, such as making sawn timber, furniture, poles, logs for building houses, charcoal, chips, pulp and paper. In South Africa the pulp and paper industry is the most important, as 69.6 per cent of the total 18 million cubic meters (annual volume) of round wood intake into all processing plants is supplied to pulp and paper mills only (Forestry South Africa, 2010).

Eucalyptus species have the ability to coppice or resprout after being felled (Acosta et al., 2008). The coppice grows from dormant buds that are found on the live bark or cambium of the stump (Little & McLennan, 2000). Zbonak et al. (2007) mentions that coppicing is normally practised in South Africa because it enables the growers to have a second timber rotation without having to prepare the site and replant it. Therefore, the silvicultural establishment costs are greatly reduced provided that sufficient stumps coppice (Zbonak et al., 2007). Coppice regeneration normally declines with age, therefore as the stumps grow older, the survival rate becomes lower (Hamilton, 2000). Evans and Turnbull (2004) also add that if the stump mortality rate is too high, two shoots can be left on remaining live stumps. Hence, this practice is only applied in order to cater for lost volume that can result from too few remaining stems per hectare (Evans and Turnbull, 2004).

1.2 THE RESEARCH PROBLEM STATEMENT

Coppice regeneration practices and characteristics (double stems and tree form) of coppiced trees may have a negative effect on the productivity of the harvester and result in volume loss due to high stumps and lower stem utilisation. The effect that these variables have on harvester productivity and volume loss is poorly understood and has not been quantified. It therefore occurs that harvesters are used in certain coppiced compartments with uncertain productivity expectations, or the harvesters are not used in these compartments due to a lack of productivity knowledge. No scientific research exists regarding the effects of coppiced compartments on the productivity of a harvester and amount of stump volume waste remaining.

The following research questions can be derived from the problem statement discussed above:

1. What is the effect of tree volume and tree form on the harvester’s productivity when operating in coppiced double, coppiced single and planted Eucalyptus grandis pulpwood compartments? 2. When operating in coppiced double Eucalyptus grandis pulpwood compartments, what is the effect on the harvester’s productivity with regard to the distance between stems and the stem felled first? 3. For given average volumes and tree forms, are there any productivity differences as a result of the harvester operating in coppiced double, coppiced single or planted Eucalyptus grandis pulpwood compartments? 4. Is stump volume (timber) being wasted? If so, how much of it is being wasted due to excessive stump heights by the harvester whilst operating in these coppiced double, coppiced single and planted Eucalyptus grandis pulpwood compartments?

1.3 AIMS AND OBJECTIVES OF THE RESEARCH

The aims and objectives of the research can be defined as the following:

1.3.1 Aims

The research will evaluate the productivity of a harvester in coppiced and planted E. grandis pulpwood compartments. The identified factors affecting the productivity of the harvester will be analysed and their level of significance will be investigated. The research will also determine if there is any volume wasted due to greater stump height in both coppiced and planted trees. If there is any stump volume wasted, the amount wasted will be quantified.

1.3.2 Objectives

The objectives of the research are to:

 analyse the effect of the identified factors affecting the productivity of the harvester in coppiced double, coppiced single and planted E. grandis pulpwood compartments;

 determine the productivity rate (m3 per productive machine hour) and develop models in order to predict the productivity of a harvester in coppiced double, coppiced single and planted E. grandis pulpwood compartments in relation to the identified factors;

 compare the productivity of the harvester when operating in coppiced double, coppiced single and planted pulpwood compartments;

 quantify the amount of volume lost due to excessive stump heights in coppiced double, coppiced single and planted compartments.

1.4 DATA COLLECTION

Quantitative research was applied in order to meet the research objectives. The research sites and harvesting equipment were identified during the preliminary research visit in March 2011 (see Chapter 4, Section 4.2 for details of pilot research). During the pilot research, the harvester was studied whilst working in a coppiced compartment and the factors that could affect its productivity were identified. Pilot research was also conducted to ensure that the data collection methods identified

4 were applicable and valid for the research. The pilot research led to a few changes in the original data collection plan, as described in Chapter 4, Section 4.2.

Once the research sites and harvester had been identified, the date for conducting the actual research was determined. Trial plots were selected and demarcated on a daily basis depending on where the harvester was working within the designated research sites. Each tree’s data was then documented, and each tree stem was assigned a number for identification purposes and for productivity correlation. The stumps were also numbered in the same manner, so that any volume wastage could be related back to a specific tree. Details of the research methodology are discussed in Chapter 4.

1.4.1 Validity

The research involved marking and measurement of trees in various compartments. The height and diameter of each tree was recorded. A standard tree diameter calliper was used to measure the diameters at a breast height of 1.37 m on each tree. For tree height measurements the Vertex hypsometer was used. The device was calibrated each time the height measurements were taken to avoid any inaccuracies in the readings. A few trees were felled and the heights measured manually with a measuring tape. The readings of the Vertex hypsometer were then compared to those measured manually to determine whether the data coincided. The distance between the coppiced double stems was measured with a tape measure at a height of 1.3 m, as this was the height where the harvester head arms would wrap around the tree during the “grab tree” element. A tape measure was used to measure the stump heights and the underbark diameter of the remaining stumps.

The time and motion studies were conducted using the Trimble Nomad 900 Series handheld computer. The time study element settings and the customised research information configurations (for example, compartment data and numeric code settings for recording tree characteristics) were conducted on the computer and transferred into the Trimble prior to the research being conducted. A preliminary time study was conducted before the main research began to identify the working cycles and specific time study elements typical of harvesters.

Without any substantially reliable information on the productivity of harvesters in coppiced compartments, it becomes complicated for foresters, contractors or managers to make well-informed decisions and production estimates.

It has been predicted that South Africa will have timber fibre shortages in the near future (South African Water and Forestry Support Programme, 2004). It is therefore important that in order to increase the supply of timber and to reduce the deficiency, all timber volume recovery in harvesting operations be optimised. Timber wastage due to high stumps and lower stem utilisation in coppiced stems must be reduced.

1.4.2 Reliability

The data collection methods used and the validity of the applied measuring techniques proved that results attained would be consistent if the research was repeated. Furthermore, the data collection methods and equipment used had been applied in various researches in the past, as referred to in the description of the research and data analysis methodology (Chapter 4). The pilot research showed that a high level of reliability was imminent.

1.5 ORGANISATION OF THE RESEARCH

Chapter 1 introduces the research and gives a holistic view of the research. Moreover, Chapter 1 describes the background of the research by referring to the research problem, the research objectives and the significance of the research.

Chapter 2 describes the literature review which supplies detailed information on the available research related to the research topic. In general, the literature was more orientated to describing the factors affecting harvesters and stump heights in mechanised cut-to-length harvesting operations.

Chapter 3 provides information about the research sites and the harvesting system as a whole, with specific reference to the excavator-based harvester.

Chapter 4 describes the research and data analysis methodology. The research methodology focuses on the research design, research site, data collection and data analysis techniques applied.

In Chapter 5 the results obtained from the research are presented and discussed.

Chapter 6 consists of the conclusion and recommendations. A summary of the main research findings is presented. The research results are also related to the literature review and the significance of the results to the forestry industry are discussed.

Note:

 All photographs in this dissertation were taken by the author, unless indicated otherwise.

CHAPTER 2: LITERATURE REVIEW

2.1 INTRODUCTION

The first part of the literature review has been structured to firstly provide information on the South African commercial forestry industry; this is followed by a description of harvesting methods and systems, as well as specific references to research results on factors influencing harvester productivity. The available literature is discussed and directed towards literature on harvesters operating in E. grandis coppiced and planted compartments. The literature review also provides an overview of the main forms of timber volume loss in harvesting operations. Thereafter, factors influencing stump volume waste are discussed in relation to the harvester. Finally, relevant stump volume waste literature is related to the application of harvesters in coppiced and planted compartments.

2.2 OVERVIEW OF THE SOUTH AFRICAN COMMERCIAL FORESTRY INDUSTRY

Forestry is an important industry that contributes in many ways to the national economy and social upliftment, especially in rural areas (Chamberlain, 2005). The South African forestry sector contributes R22 billion per annum to the Gross Domestic Product (Department of Agriculture, Forestry and Fisheries [DAFF], 2011). In 2009, plantation forestry had a total investment amount of R24.8 billion in trees, roads, land, moveable assets and fixed assets (Forestry South Africa, 2010). The total book value of capital investments (fixed assets) in the forest products sector was R15.7 billion. The largest portion (83 per cent) of the book value of investments was attributed to pulp and board plants (Forest Economic Services, 2010).

Figure 1 shows the total round wood (timber) production by product for the South African forestry industry in 2010.

Pulpwood Sawlogs Mining Timber Other

23.2% 3.3%

5.0%

68.5%

Figure 1: Round wood production per product (Forestry South Africa, 2010)

A total of 16.2 million tons of round wood is produced in South Africa: 68.5 per cent is pulpwood, 23.2 per cent is saw logs, 3.3 per cent is mining timber and 5 per cent comprises other products such as poles and charcoal. The pulpwood market is by far the largest compared to markets of the other products. The export of forest products increased from R9.5 billion in 2001 to R12.5 billion in 2009. Of the revenue from exported products, (R12.5 billion), 42.2 per cent was from paper and 34.4 per cent from pulp and the remaining 23.4 per cent was from solid wood (Forestry South Africa, 2010).

Forestry also plays an important role in the provision of direct and indirect employment across all forestry subsectors (forestry and processing) (Forestry South Africa, 2010). Forestry provides direct and indirect employment to 170 000 people in South Africa (Burger & Bhaktawar, 2009). A total of 66 500 people are directly employed in the forestry subsector by contractors, forest companies, government and other private growers. In addition, 1.4 million people in South Africa are dependent on people employed both directly and indirectly by the forestry industry (Forestry South Africa, 2010).

Plantation forestry land covers 1 274 869 hectares in South Africa and most of the forestry land is in Mpumalanga (40.8 per cent) and KwaZulu Natal (39.6 per cent) (Forestry South Africa, 2010). Private sector ownership accounts for 83 per cent of the total plantation area (Forestry Economic Services, 2010). Most of the plantation areas in South Africa are owned by companies such as Sappi, Mondi and Merensky. Private farmers, small growers and the State own the remaining available plantation land (Forestry South Africa, 2010). Figure 2 describes the percentage of land ownership by different stakeholders.

Figure 2: Forestry land ownership in South Africa (Forestry South Africa, 2010)

Figure 2 shows that corporate entities own most of the plantation forestry land, followed by commercial farmers and other corporate entities (ex SAFCOL). The rest of the land is divided amongst SAFCOL, the State or municipalities and small growers. Government institutions and state organisations (public) – including SAFCOL and municipalities – own 215 961 ha, being 16.8 per cent of total plantation forestry land (Forestry South Africa, 2010).

Figure 3 shows the number of hectares planted to various species, and is divided according to private and public ownership.

Figure 3: Plantation species and ownership (Forestry South Africa, 2010)

Figure 3 shows that the species predominantly planted in South Africa are pine, Eucalyptus, wattle and other hardwood species such as poplar. The two most important genera in South Africa are Pinus and Eucalyptus. They are the most planted genera for both private companies and public entities (Forestry South Africa, 2010). Of the total land owned privately and publicly, approximately 290 000 ha is planted to E. grandis (DAFF, 2011). Of the total E. grandis area, 228 941 ha is planted with the sole purpose of producing pulpwood (Forest Economic Services, 2010). This illustrates the significance of E. grandis to the South African forestry industry and hence the importance of this research. According to Pasquali (2010), E. grandis is one of the most important commercially planted Eucalyptus species, with more than 1 500 000 hectares planted in tropical and subtropical areas on four continents. In South Africa it is an important species commercially, as it constitutes approximately 24 per cent of the 1.27 million planted hectares in South Africa (DAFF, 2011).

2.3 OVERVIEW OF EUCALYPTUS GRANDIS IN SOUTH AFRICA

The history, commercial significance, and characteristics of E. grandis are discussed below.

2.3.1 History

E. grandis is the most widely planted commercial hardwood species in South Africa (Forestry Economic Services, 2009). It was planted in South Africa for the first time before 1885 (World Agroforestry Centre, 2011). When E. grandis was introduced, it was normally confused with E. saligna until the two species were clearly distinguished (Poynton, 1979). From approximately 1920 onwards, the government planted E. grandis on a large scale in the warmer and more humid parts of the summer rainfall areas. As a result, in 1955 the total E. grandis afforested area exceeded that of any Eucalyptus species in South Africa (Poynton, 1979). In addition, by 1973 there were 275 000 ha planted; 75 per cent of all Eucalyptus species in the country (World Agroforestry Centre, 2011). During the 1980s, private enterprise also showed a great interest in planting E. grandis on a wider scale; this was due to increasing demand for small diameter mining timber (Olivier, 2010). As time progressed, the establishment of sawmills and pulp mills further increased demand for the species (Olivier, 2010).

2.3.2 Commercial significance of E. grandis in plantation forestry

Over the past few years, areas planted to E. grandis-based clones have increased significantly (Forest Economic Service, 2010). Due to its good qualities and high adaptability, E. grandis is an important parent being used in the development of fast- growing Eucalyptus hybrids (Eucalyptus Genome Network, 2006). In South Africa it has been crossed with several species, such as E. urophylla, E. nitens, E. camaldulensis, E. macarthurii, E. virminalis and E. terenticolis to improve timber quality (for example, reduced splitting) and tree adaptability to cold and/or drought (van Wyk & Verryn, 2000). Moreover, a wide market is dependent on the species for saw timber, pulp, poles, firewood and mining timber (Owen & van der Zel, 2000). E. grandis is extensively used by the pulp and paper industry in South Africa (Eucalyptus Genome Network, 2006). In 2009, 3 843 845 tonnes of E. grandis pulpwood were sold from plantations in South Africa (Forest Economic Services, 2010)

2.3.3 Description of Eucalyptus grandis

Site requirements and characteristics of E. grandis are described below.

2.3.3.1 Site requirements

E. grandis is a vigorous and adaptable species normally planted in summer rainfall areas, with good soil depth, and free from insect attacks (Herbert, 2000). Its optimal mean annual temperature ranges between 16.5 and 21.5 degrees Celsius. It is capable of achieving high fibre yields on sites well supplied with water (mean annual precipitation range: 899 to 1064 mm) (Herbert, 2000). E. grandis is very competitive on lower-slope positions and plateaus; moreover height growth decreases rapidly in cooler climates (Herbert, 2000).

2.3.3.2 Characteristics

The tree has smooth white or grey-white bark above the short rough basal stocking (World Agroforestry Centre, 2011). According to Hills & Brown (1984), E. grandis is unique because its timber is lighter, softer and more fissile than that of most Eucalyptus species and is therefore more suitable for producing pulp and paper. E. grandis wood has moderate strength, durability and a straight grain but is coarse in texture (Hills & Brown, 1984).

According to Pasquali (2010) and Grut (1965), E. grandis is considered economically viable because it has:

 easy nursery propagation qualities;

 a fast growth rate - according to the National Academy of Science (NAS) (1980) as cited in FAO (2001), a growth rate (MAI) of 35 m3ha-1yr-1 for E. grandis was reported in some areas in South Africa;

 excellent form – tall and straight as a result of tree improvement (Snedden, 2001);

 good wood properties (fibre length, fibre diameter, lignin and cellulose composition) – variety of end uses, such as cellulose pulp and paper production.

2.3.4 Eucalyptus coppicing

Zbonak et al. (2007) states that coppicing of Eucalyptus species is a very common practice in South African forestry. Eucalyptus species have the ability to coppice or resprout after being felled. The coppice grows from dormant buds that are found on the live bark or cambium of the stump (Little & McLennan, 2000). A stump is the lower part of a stem remaining after felling, and still attached to the root complex (McEwan, personal communication, 2011a). Coppicing is normally practised in South Africa because it enables growers to produce a second timber rotation without having to prepare the site and replant it (Zbonak et al., 2007). Therefore, silvicultural establishment costs are greatly reduced, provided that sufficient stumps survive to coppice after harvesting (Dougherty & Wright, 2012; Zbonak et al., 2007; Geary, 1983). Cost savings of coppicing over replanting can range from 20 to 30 per cent if strict coppicing management guidelines are adhered to (Archibald et al., 2005).

Young (<10 years) E. grandis trees coppice vigorously after felling. However, coppicing ability is reduced in older trees (Hills & Brown, 1984, Hamilton, 2000). Evans and Turnbull (2004) also add that if the stump mortality rate is too high, two shoots can be left on remaining live stumps. This practice is applied to cater for lost volume that can result from too few stems remaining per hectare, and to retain the stocking at the level of the original establishment.

Trees grown from coppice produce high yields, because the coppice grows from a large and well-established root system and there is no delay between felling and re- establishment; this delay does occur when replanting (Archibald et al., 2005). However, other factors such as site, weather, establishment techniques and management can affect the growth rate of a particular coppice stand (Archibald et al., 2005). Little and McLennan (2000) suggest that before coppicing it is important to ensure that the species used is best in terms of potential yield, disease resistance and drought, frost and snow tolerance. Little and McLennan (2000) further advised that first coppice reduction should be carried out when the dominant shoot is three to four metres, and that two or three shoots should be left per stump. They also advised that the second reduction should be carried out when the dominant shoot height is seven to eight metres and one or two shoots should be retained. Coppicing is not the

14 optimal option if yield benefits of coppicing do not outweigh the costs of replanting (Archibald et al., 2005).

2.3.5 Short rotation coppice

Coppice is perceived differently in some parts of the world. A number of studies describe coppice trees as short rotation crops grown for biomass and bio-energy generation (Laina & Ambrosio, 2010; Spinelli, 2006; Culshaw & Stokes, 1995; Sims et al., 2001). According to Spinelli (2011) short rotation coppice is an industrial crop (Eucalyptus, willow and poplar) designed to produce large quantities of low-priced raw materials, and its success requires that all operations be done in an efficient manner. Culshaw and Stokes (1995) mention that in Europe specifically, coppice is known as a three- to four-year short rotation crop which is grown as an energy crop. In New Zealand, studies have shown that the production of short rotation Eucalyptus forests – to provide feedstock for producing heat, electricity or transport biofuels – was becoming viable, depending on the harvesting systems applied (Sims & Venturi, 2003). Short rotation coppice crops grown for energy purposes normally consist of very small diameter trees planted in high densities. The harvesting of these crops is done either manually or by using special harvesters that can handle numerous small diameter stems simultaneously (Handler & Blumauer, 2010; Spinelli, 2011).

It is important to distinguish that coppiced trees referred to in this research are not small diameter crops planted for biomass, but are rather trees in commercial plantation format utilised for pulpwood purposes. The Eucalyptus pulp production management objective is used to determine the clearfelling age of the trees, hence trees are felled at the culmination of the mean annual increment (MAI) (Bredenkamp, 2000).

2.4 HARVESTING IN SOUTH AFRICA

Forest engineering in South Africa comprises timber harvesting, timber transport and road construction. According to Brink and Conradie (2000), about 60 to 80 per cent of the operational budget is spent on forest engineering activities. Timber harvesting involves the felling of a tree and converting it into a usable product for the market. Harvesting operations in South Africa can vary from manual operations to semi- or

15 fully-mechanised operations, depending on prevailing economic, social and environmental conditions (Ackerman & Längin, 2010).

A brief history of forest harvesting operations in South Africa and a discussion on timber harvesting challenges and mechanisation is provided below.

2.4.1 Brief history of harvesting in South Africa

Forest harvesting operations in South Africa were traditionally manually orientated and most companies had their own in-house operations. This was until the 1970s when the first mechanised forestry machines were introduced. The increase in mechanisation was due to a lack of labour productivity (Olivier, 2010).

Since the 1980s there has been a transition from in-house operations to contractors; hence by the mid-1990s many contractor operations began to dominate, as forestry companies could not justify the existence of their own operations (Olivier, 2010). Steenkamp (2011) states that in South Africa there are approximately 172 harvesting contractors currently affiliated to the South African Forestry Contractors Association (SAFCA). SAFCA is a body that represents about 90 per cent of all forestry contractors in South Africa, and was established to oversee the interests of contractors (Längin & Ackerman, 2007).

2.4.2 Forest harvesting challenges and mechanisation

Over the past decade the forestry industry has gradually experienced the ramifications of labour scarcity, increase in labour costs, the effect of HIV and AIDS and increasing timber demand. Consequently, this has led to an increase in the mechanisation rate, especially in the timber harvesting sector (Grobbelaar & Manyuchi, 2000, Steenkamp, 2007, Kachamba, 2007, Water and Forestry Support Programme, 2004). According to Steenkamp (2007), mechanisation in South Africa is going to become more attractive for contractors because of higher labour wage rates and lower productivity in the forestry sector, which is aggravated by HIV and AIDS. Recruiting semi-skilled and skilled labour is difficult, especially as these levels of employment are those mostly affected by HIV and AIDS. Therefore the movement from labour to capital is forthcoming (Steenkamp, 2007). Even though the capital scenario will become more prevalent, it carries an increased financial risk as a result of fixed commitments influenced directly by debt financing of capital assets

(Steenkamp, 2007). Kachamba (2007) adds that, compared to manual harvesting, mechanisation in South Africa will be favoured because of high productivity gains and improved safety.

2.5 HARVESTING METHODS AND SYSTEMS USED FOR EUCALYPTUS IN SOUTH AFRICA

In South Africa there are various harvesting methods and systems available for harvesting different hardwood species planted in various regions; for example, E. grandis, E. nitens, E. smithii and hybrid species.

A harvesting method describes the form in which timber reaches the forest road (Pulkki, 2000). The harvesting system is composed of various tools and equipment used to harvest a particular area (Längin & Ackerman, 2010). South African forestry uses different harvesting methods and systems depending on the application and market requirements.

The South Africa harvesting methods, as described by Längin et al. (2010), are:

 Cut-to-length - trees are felled, debranched (and debarked), crosscut and topped in the compartment. The logs are extracted to roadside;

 Tree length - trees are felled, debranched, debarked and topped in the compartment, and only the bole is extracted to roadside;

 Full tree - trees are felled and all biomass above the stump (entire tree with its branches, bark and top still intact) is transported to roadside for further processing.

2.5.1 Cut-to-length

According to Marku & Dvorak (2010), the cut-to-length method can be rated as the most advanced harvesting and extraction system currently in use. Cut-to-length harvesting of Eucalyptus species was a labour-intensive practice in the past. From 1950 to 1965 bow saws and hatchets were used to fell, debark, debranch and crosscut the trees. In 1965 chainsaws were introduced and used for both felling and crosscutting of Eucalyptus species (Warkotsch, 1988). The forest harvesting industry

17 has improved significantly since then; new and better ways of harvesting trees have been developed and implemented.

Results from the South African Forest Engineering Survey conducted in 2007 show that about 44 per cent of the total harvested hardwoods volume is still debranched and debarked manually (Längin & Ackerman, 2007). Fully-mechanised operations (cut-to-length: harvester and forwarder combination and full tree systems: feller buncher, skidder and processor) constitute 6.4 per cent of the systems used (Längin & Ackerman, 2007). Data from the South African Forest Engineering Survey 2007 was collected in 2006, and therefore further mechanisation progress has been made since then; but no research has been carried out to quantify the progress (McEwan, personal communication, 2012a). Figure 4 indicates different percentages of harvesting systems for the years 1987, 1997 and 2006.

Figure 4: Cut-to-length, tree length and full tree harvesting methods in South Africa (1987, 1997 and 2006) (Längin & Ackerman, 2007)

The commercial forestry hardwood species referred to in the survey were Eucalyptus and wattle. The application of cut-to-length in harvesting hardwoods was 89 per cent in 1989; it decreased to 77 per cent in 1997 and in 2006 it increased to 79 per cent. The cut-to-length method still plays a significant role in the harvesting of hardwoods,

18 especially E. grandis which constitutes 50.3 per cent of all hardwoods planted in South Africa (Forest Economic Services, 2009).

The cut-to-length method can comprise various harvesting systems: basic, intermediate or mechanised technology (Längin et al., 2010). Harvesting systems made up of basic technology focus on the use of manual labour and easy-to-use hand tools to fell, debark, debranch and crosscut the trees (Längin et al., 2010). A chainsaw operator normally works with a team which carries out the task of debarking logs (Forestry solutions, 2007). Extraction of timber is conducted by manual or animal extraction (Längin et al., 2010).

Intermediate technology (semi-mechanised) is made up of both manual labour and equipment (Längin et al., 2010). When using this system to harvest Eucalyptus species in South Africa, it can consist of chainsaw felling and crosscutting and manual debarking, debranching, stacking and extraction with either tractor units, forwarders or skidders piggy-backing the timber; i.e. extracting a pile of logs with a skidder to roadside (Warkotsch, 1988). Semi-mechanised harvesting systems in South Africa have been applied primarily in Eucalyptus harvesting operations. In these operations felling and extraction is carried out by using basic or intermediate technology and debarking with debarking heads on excavator carriers (Längin et al., 2010).

The main mechanised cut-to-length method comprises a harvester that fells, debranches and cuts trees into logs; thereafter a forwarder transports the logs from the stand to a landing or roadside where secondary transport trucks can access the timber (Adebayo, 2006). In South Africa during recent years, mechanised felling and processing of hardwood pulp stands has increased with the introduction of processor heads mounted on different carriers and harvesters (Längin & Ackerman, 2007).

2.5.2 Tree length method

The tree length method is the second most used harvesting method in South Africa after the cut-to-length method (Längin & Ackerman, 2007). According to Ackerman and Längin (2010), the tree length harvesting method is mainly applied in saw timber harvesting of softwoods and hardwoods in South Africa. In 1987, 11 per cent of hardwoods in South Africa were harvested using tree length systems; in 1997 it

19 increased to 22 per cent and decreased slightly to 21 per cent in 2006. The tree length method in South Africa usually consists of chainsaw felling, and extraction with cable or grapple skidders (Längin et al., 2010). According to De la Borde (1990, as cited in Grobbelaar & Manyuchi, 2000), the use of the tree length system depends on log specifications, terrain and products involved.

2.5.3 Full tree method

The mechanised full tree method usually comprises a feller-buncher and grapple skidder combination (Längin et al., 2010). This method is used in harvesting both softwoods and hardwoods for either saw timber or pulpwood (Ackerman & Längin, 2010).

2.6 HARVESTER

This section covers a description of a harvester and factors affecting its productivity. Thereafter, the use of a harvester in coppiced compartments will be discussed, and finally an overview of the future of harvesters will be given.

2.6.1 Description of a harvester

Kellogg et al. (1993) defines a harvester as a machine that fells, debranches and crosscuts a tree at the stump. A single grip harvester can either be purpose-built (wheeled or tracked), or could be a converted construction excavator fitted with a harvester head (Krieg et al, 2010). Figure 5 shows two purpose built harvesters and an excavator based harvester.

Figure 5: Purpose built harvesters: wheeled (Ponsse, 2010) (left), tracked (centre) and excavator-based harvester (right)

Converted excavators normally have lower initial capital costs, but they have limitations such as sub-optimal hydraulic flow and hydraulic cooling capacities, reduced boom reach, poor ground clearance, poor visibility and inadequate operator protection (Krieg et al., 2010). Both tracked and wheeled harvesters can be equipped with levelling features in order to enable them to operate on steeper slopes (Krieg et al., 2010).

2.6.2 Factors affecting harvester productivity

Various factors that affect the productivity of a harvester were identified in the literature, and this could be categorised into three main components: the forest environment, the harvester’s characteristics and specifications and the machine operator’s working techniques and capacity (Ovaskainen, 2009 & Nurminen et al., 2006).

A harvester is an expensive machine, and therefore all non-productive time has to be minimised and productive time maximised (Ovaskainen et al., 2004; Purfürst 2010). Nicholls et al. (2004) state that for any harvesting operation (comprising various machines) to be sustainable there must be a good understanding of productivity, business economics and human factors. This understanding is essential, because forest harvesting activities are complex and include difficult terrain, isolated work and environmental and productivity pressures (Nicholls et al., 2004).

2.6.2.1 Forest environment

The forest environment comprises various factors that significantly influence the harvester’s productivity, such as tree size, branch size and density, tree form, slope, ground conditions, undergrowth density and climate (Akay et al., 2003; Stampfer, 1999; Spinelli et al., 2002; Hartsough & Cooper, 1999; Jiroušek et al., 2007). Tree characteristics and terrain conditions as forest environment factors affecting the productivity of a harvester are discussed below.

(a) Tree size

The tree size of a particular stand has a direct effect on the productivity of the harvester. The harvester is sensitive to tree size because it normally handles a single stem at a time (Puttock et al., 2005). Therefore, as tree size increases, the

21 more productive the harvester becomes – it takes slightly more time to harvest a larger tree, compared to a smaller tree, and this leads to increased productivity as the increase in tree size is more profound (Ovaskainen, 2009). Although time consumption per piece is lower for smaller timber, the specific time consumption per unit volume will decrease with an increase in timber size (Längin et al., 2010). Numerous studies have proven that tree size plays a significant role in influencing the productivity of a harvester where there are different species and conditions (Holtzscher & Lanford., 1997; Puttock et al., 2005; Huyler & LeDoux, 1999; Jiroušek et al., 2007, Ramantswana et al., 2012). However, some studies have indicated that after a certain optimal tree volume, productivity starts to decrease (Ryynänen & Rönkkö, 2001; Kärhä et al., 2004; Nurminen et al., 2006; Visser et al., 2009). The decline in productivity can occur due to the common practice of cutting large trees in two stages (back cut and front cut). Operator experience shows that when cutting trees at the limit of the machine and harvester head capacity, the risk of the bar jamming or pinching is higher, thereby increasing the felling time and reducing productivity. In addition, more time is required to manoeuvre the large stem as a whole during the processing, and also to remove larger branches from the stem (Visser & Spinelli, 2011).

(b) Tree form

Tree form refers to branch density, maximum branch diameter and the bole form in general (Puttock et al., 2005). According to Puttock et al. (2005), the form of hardwood trees has a significant effect on the productivity of a harvester. Larger trees with poor form are more complex to handle and often need to be released and grabbed a few times during processing (Puttock et al., 2005 & Ramantswana et al., 2012). Large trees with poor form were more difficult to handle and often the harvester head had to be repositioned to process the trees. Furthermore, the diameter and form of the trees affects crosscutting time because, when removing heavy branches and forks, extra cuts are normally required (Puttock et al., 2005).

Bark-wood bond strength (BWBS) is a direct indicator of debarking potential (bark adhesion) (Grobbelaar & Manyuchi, 2000). Bark adhesion refers to the relative tendency of bark to cling to timber (Grobbelaar & Manyuchi, 2000). In a study

22 conducted by Hartsough & Cooper (1999), it was found that in Eucalyptus trees harvester productivity was affected by the debarking level, which referred to debarking specifications used in the study; namely, full debarking (removal of all bark), partial debarking (removal of approximately half the bark) and standard debarking (single stroke delimbing with whatever debarking was accomplished). The harvester attained slightly lower productivity when conducting full debarking, compared to partial and standard debarking. According to a study conducted in A. mearnsii by McEwan et al., (2011), if BWBS is high, then the productivity of the harvester is lower as more passes are required along the stem in order for the bark to be detached, but if BWBS is low, fewer passes are required.

(d) Terrain conditions

The terrain on which a harvester has to operate affects the production rate of the machine. The most limiting terrain element is slope, followed by ground roughness, and finally ground conditions (Musto, 1993). According to Stampfer & Steinmüller (2001), obstacles in stands or rough terrain conditions restrict the movement of tracked harvesters and their ability to function efficiently. Bolding & Lanford (2002) found that when a purpose-built harvester operated on slopes of between 10 and 46 per cent, there was significant decline in its productivity. Mechanised timber harvesting on steep and mountainous terrain is more complex than on level terrain, and therefore harvester productivity declines with increasing slope (Moskalik & Stampfer, 2003).

2.6.2.2 Harvester characteristics and specifications

A harvester can only work optimally and productively if it is used under the correct conditions and maintained according to the manufacturer’s specifications. However, modifications such as replacement of wheels with trapezoid tracks can be made to a harvester in order to improve productivity when faced with constraining conditions, such as steep slopes (Stampfer, 2003). Results of a study conducted by Heinemann (2001) indicated that harvester type (make or brand) and its technical performance (engine performance, boom reach, lifting moment, slewing moment, maximal felling diameter and feeding force of the harvester head) can cause significant variation in its productivity. However, Ovaskainen (2009) alleges that currently all types of harvesters of the same size have almost the same productivity when operating in the

23 same environment. This is because machine manufacturers are using similar parts and forest machine designs.

2.6.2.3 Harvester operators’ working techniques and capacity

A main disadvantage of mechanisation is that harvesting equipment now has technologically advanced computer systems; therefore a higher level of training is required for machine operators (Temperate Forest Foundation, 2002). Gellersted (2002) declared that the operation of a single grip harvester is a demanding and complicated job involving repetitious consecutive work elements. Mechanised forestry work is mostly conducted in the forests and in isolation, and therefore, working as a harvester operator can be a lonely task (Gellersted, 2002).

The relationship between a harvester and an operator is essential. Dvořák et al. (2008) asserts that the human factor (the machine operator) is the most important factor influencing the productivity of a machine. Ovaskianen (2009) states that the working techniques and mental abilities of harvester operators are the two most important areas which require attention and improvement.

In order for a machine operator to achieve required productivity targets, thorough training needs to be conducted (Purfürst, 2010). Adequate training of operators, with the aid of simulator training, must be done prior to deploying these operators infield (Lapointe & Robert, 2000). Every new operator undergoes a process called the learning curve, which is the relation between productivity achieved and experience over time (Purfürst 2010). Inexperienced operators give low productivity, but over time they make fewer mistakes, and co-ordination skills improve as they become familiar with the machine and its operating environment. According to Richardson & Markonen (1994, cited in Lapointe & Robert, 2000) it takes from four to six months for a new harvester operator to reach a profitable productivity level, and up to two years before achieving maximum efficiency. The cost of learning on the machine is high; therefore it is important to reduce the amount of time new operators spend learning on the actual machine. According to findings by Lapointe & Robert (2000), the use of virtual reality simulators by new operators (for an additional 25 hours during the first month of operation in the forest) significantly improved production of harvested wood by 23 per cent and reduced costs, such as repair costs, that would have been incurred without this training.

A comfortable harvester operator is able to work effectively and productively. The forestry harvesting industry has progressed in improving the ergonomics of design and technical reliability of harvesting equipment (Kastenholz, 2004). Even though an operator works safely in an enclosed cabin and is relieved from heavy physical workload and dangers of the external environment to which a normal chainsaw operator would be exposed, there are new health hazards that have emerged with harvester technology (Kastenholz, 2004). These hazards may include muscular- skeletal and psycho-mental problems (Kastenholz, 2004). According to Sherwin et al. (2004), tyre inflation on wheeled harvesters has a significant influence on the levels of whole body vibrations, and therefore poses a risk to an operator’s health and can cause discomfort. Jack and Olivier (2008) found that in order to improve productivity, there must be a reduction in whole body vibrations associated with the operation of a harvester in order to optimise spinal stability, reduce whole body vibration transmission, minimise fatigue and consequently muscular-skeletal problems.

According to Nicholls et al (2004), productive, profitable and safe operation of harvesters is directly reliant on motivated, mentally alert operators, working in a well- managed business environment.

2.6.3 The use of a harvester in coppiced compartments

In South Africa, harvesters can be used to harvest planted trees or coppiced trees with either single or multiple stems. Very little research was identified on harvester productivity in coppiced compartments; however much scientific research has been conducted in planted compartments. It therefore occurs that harvesters are used by contractors and foresters in certain coppiced compartments with uncertain productivity expectations; at times harvesters are not used in these compartments due to a lack of productivity knowledge (McEwan, personal communication, 2011b).

Spinelli et al. (2002) proved that when handling (processing without felling) multiple Eucalyptus globulus stems with a harvester, the handling time per stem increased. Pre-felling of multiple coppiced stems on steep terrain was carried out motor- manually to improve work efficiency; because the trees were felled prior to processing, the fell tree element was not part of the cumulative time per cycle (Spinelli et al., 2002). The handling time increase per stem was most pronounced

25 with increasing tree size and poor tree form. Both larger and malformed trees were more difficult for the harvester to handle. For multiple E. globulus stems with light sweep stem formation and light branch density, Spinelli et al. (2002) reported a harvester (Akerman EC200 excavator base with AFM 60 harvester head) productivity of 5 m3 per PMH (0.1 m3 stems); 9 m3 per PMH (0.2 m3 stems) and 13 m3 per PMH (0.4 m3 stems). The harvester was optimising the stems to 4 m log lengths.

Research conducted by Suchomel et al. (2010) on the processing of hardwood (chestnut) from coppiced forests found that stems from coppice generally have small size and poor form, as they grow amongst multiple stems from the tree stump. Furthermore, these characteristics reduce productivity and increase mechanical delays of mechanised harvesting equipment due to mechanical felling and processing problems. The research evaluated the factors affecting the productivity of four harvesting machines which were processing pre-felled chestnut trees from coppiced stands at the landing, and these were: an Arbro 400S on a JCB 8052 excavator, a Foresteri RH 25 on a CAT 312 L excavator, a Lako 55 Premio on a JCB JS 180 NL excavator and a Timberjack 1270B dedicated harvester with John Deere 762C head. When processing 0.3 m3 coppiced trees, the processing machines had the following productivity: 11.9 m3 per PMH (Arbo), 21.4 m3 per PMH (Foresteri), 12.2 m3 per PMH (Lako) and 20.1 m3 per PMH (John Deere). Research concluded that tree size and tree form of coppiced trees have a significant effect on the productivity of the processors (Suchomel et al. 2010).

Research conducted by Suchomel et al. (2011) investigated the productivity of a HSM 405 6WD (with a CTL 40HW processor head) harvesting over-age oak coppiced trees. The research showed that a single grip hardwood harvester can be used to process oak trees with a maximum diameter of 40 cm in over-age coppiced stands. Most of the over-age coppiced stems had a sinuous shape and multiple stem structure resulting from resprouting. The results showed that the grab tree and fell tree elements for the coppiced stems took slightly more time compared to single trees. However, the time was not significantly higher. From this research, productivity of 9.5 m3 per PMH and 14 m3 per PMH for 0.17 m3 and 0.20 m3 trees respectively was reported in coppiced trees. Coppice compartments are usually a problem for a harvester because productivity is normally lower than in planted compartments

(Braithwaite, personal communication, 2011). Both high mortality rates in the compartment and poor form can lead to lower production and increased costs. The literature contained no information on harvesters in E. grandis coppiced compartments.

2.6.4 Future of harvesters

The cut-to-length harvesting method is constantly changing with minor and major innovations. According to Vanclay (2011) it is possible that major gains in productivity and efficiency can be achieved through closer integration between informatics and harvesting technology in future. However, according to research conducted in Sweden, changes in technical performance and mechanical availability of machines have inspired a major drive for forest equipment advancement and productivity improvement in the past (Nordfjell et al. 2010). However, Vanclay (2011) predicts that in the near future, improvements in forest equipment will be made by the expansion of new and better sensors. Harvesters can incorporate sensors which can assist in making stand assessments (characteristics of standing trees and soil properties) faster, better and more cost effective. Vanclay (2011) adds that robotic support could enhance the harvester’s efficiency in the future, as it would be able to identify obstacles, map the optimal route and suggest the optimal felling direction. Tree felling and processing can be optimised by adding features such as laser measurements and optical dimensional technologies on harvester heads. However, precision and accuracy should not be compromised under harsh forest conditions (Miettinen et al., 2010).

Although advanced harvesting technology can be useful, South Africa struggles to source good mechanical backup to make this technology viable. Furthermore, certain harvesting equipment is not currently available in the country because of a lack in expertise to maintain such equipment, and due to poor fuel quality (McEwan, personal communication, 2012b). Most importantly, South Africa does not have a large enough market for manufacturers to introduce many models that require different spare parts and backup (McEwan, personal communication, 2012b).

2.7 TIMBER VOLUME RECOVERY AND STUMP VOLUME WASTE

Volume recovery and utilisation is described as the quantifying of timber that is cut and used, as well as that portion which is left in the forest during the harvesting operation (Bentley & Harper, 2004). In addition, Haynes and Visser (2002) mentioned that volume and value recovery is about ensuring that the optimum amount of value and wood volume is retrieved from the raw materials through the production chain.

Timber volume can be lost throughout the harvesting and transport value chain, as timber is processed into different forms to suit the customer. Valuable timber can be lost from felling to offloading of logs at the mill because of stump heights, felling damage, extraction damage, log making and loading or unloading damage (Boston, 2001; Vanderberg, 2002). In addition, Foelkel (2007) mentioned that timber can be lost in the form of stem tops above specified diameter, thin trees discarded during harvesting, lost or forgotten logs infield and sawdust generated when felling and crosscutting the tree trunk into logs.

The amount of usable timber recovered from a specific site is important, as it portrays the efficiency and management of the harvesting operations (Foelkel, 2007). Ride (1999) notes that from infield to factory processing; timber should not be observed in designated elements, but rather viewed as a whole system, because the overall economic impact of poor fibre recovery may be significant.

According to the South African Water and Forestry Support Programme (WFSP) (2004), South Africa is capable of producing 22 million m3 of round wood per annum, but the forecasted demand in 2030 is 37.9 million m3 per annum. Therefore, it is predicted that South Africa will experience timber shortages in the near future, as demand exceeds supply. To increase the supply of roundwood, optimum timber recovery in all harvesting operations must occur (Hall & Han, 2006). Optimum timber recovery can be achieved through improving certain aspects, such as harvesting methods, felling techniques, timber handling practices and supervision (Ride, 1999).

2.7.1 An overview of the forms of volume loss in harvesting operations

Volume loss occurs in various ways and is influenced by various factors. Hall and Han (2006) suggest that factors such as species composition, stand density and tree

28 size will determine the possible value loss in different harvesting methods implemented. Various ways in which volume can be lost in harvesting operations are discussed below.

2.7.1.1 Saw kerf thickness and sawdust

Modern harvesting equipment has various tree cutting technologies, with different cutting patterns and kerf thicknesses according to their design and purpose. Different types of heads (shears, disc saw, and chainsaw) have different properties in terms of stump height, saw kerf, productivity, fire risk and maximum tree size (Strandgard & Mitchell, 2012). Shear heads can recover more volume and value per hectare than disc saws and chainsaw feller buncher heads due to lower stump heights and a lack of saw kerf (Strandgard & Mitchell, 2012). In a study by Han and Renzie (2005), the disc saw kerf difference on average for a feller buncher was 5 cm thicker than a handheld chainsaw; therefore the feller buncher wasted 0.44 m³ per hectare due to saw kerf thickness, when felling 43 cm diameter trees and 650 stems per hectare. When considering the useful life of the feller buncher and volume wasted from the saw kerf, the amount of wastage can be considerably high (Han & Renzie, 2005).

The cutting blade of a harvester is approximately 0.8 cm wide (Foelkel, 2007). If a harvester is used to fell and crosscut a 30 m (utilisable height) tree into 5 m logs then seven cuts will have to be made; one cut to fell the tree and six cuts to crosscut the tree. This relates to 0.18 per cent of wood lost as sawdust, which is a significant loss (Foelkel, 2007).

2.7.1.2 Felling damage and optimisation

During the felling and optimisation of a tree, volume can be lost if operational activities such as felling and crosscutting are not conducted correctly. Figure 6 shows the potential wood losses that can occur as a result of felling damage.

Figure 6: Potential wood losses from felling (Han & Renzie, 2005)

Figure 6 shows slab damage (wood left on the outside of the stump) and stump pull (wood left in or across the middle of the stump). The saw kerf loss, as discussed under section 2.7.1.1., is shown in the figure. Wood loss due to the chainsaw front cut is also indicated.

The literature showed that most butt log damage occurs during felling. Boston (2001) states that felling damage causes a loss in fibre volume, timber value and increases operational costs in saw timber recovery. Han and Renzie (2005) discovered in their study that stump pull and slabbing damage higher than 10 cm accounted for 4.1 per cent (20 out of 485 stumps) of manually felled stumps with an average height of 38.2 cm. It was observed that larger volume trees resulted in increased amounts of stump pull and slabbing damage, as the saws had difficulties cutting through the trees’ large bases (Han & Renzie, 2005). The consequences of these uneven cuts and felling defects affect the forestry company; the true value of the tree is not realised as the log has to be downgraded. Furthermore, the processing plant is also affected due to a reduction in the length of the saw timber board or solid products produced (Han & Renzie, 2005). However, the loss in pulpwood operations will not be as extreme as saw timber, as the logs will not be trimmed.

According to field observations conducted by Han and Renzie (2005), large saw timber trees had higher proportions of stump pull as a result of the handheld chainsaw not being able to make a clean cut through the tree. Their research showed that 17 per cent of stumps were damaged in this way. In addition to the

30 above statement, Hall and Han (2006) mention that if a tree does not fall in the planned direction, the possibility of stump pull is high. To determine the volume of saw timber wood lost due to stump pull, the stump must be measured from the top of the splintered fibres to the maximum allowable stump height. Even though these studies were carried out in saw timber operations, the felling damage as a result of stump pull can also occur in pulpwood operations, but the losses are not as great when compared to pulpwood logs, where the total utilisable fibre of the logs is more important.

According to Boston (2001), when producing saw logs, poor log-making is the most important cause of volume loss in harvesting operations. Visser et al. (2001) add that while tasks such as felling, debranching and skidding all affect the end value of a log, processing has a much more profound effect on the end value of the log. It is estimated that the value loss can be up to 25 per cent in some harvesting operations (Sessions, 1988, as cited in Vanderberg, 2002). A key factor in the crosscutting process is the chainsaw operator; he is the connection between the felled tree and volume recovered during the crosscutting process (Vanderberg, 2002). Thus correct optimising procedures can potentially increase the yield from each tree harvested (Vanderberg, 2002).

2.7.1.3 Tree tops

The minimum acceptable thin-end diameter of timber accepted by a processing plant affects the amount of volume that can be lost infield (Foelkel, 2007). Trees that do not fall within the specifications are normally cut and left infield to rot. The defined tree optimisation specifications form the utilisation standards of timber that can be recovered (Government of Alberta, 2011).

2.7.1.4 Stumps

Due to the importance of stumps in the research, they have been investigated in much greater detail in section 2.7.2.

2.7.2 Stump volume waste

According to Singh (2006), the definition of waste is “the volume of timber left on the harvested area that should have been removed in accordance with minimum

31 utilisation standards of the cutting authority.” It forms part of the allowable annual harvest for cut-control purposes.

According to Doruska (2002), advances in mechanised harvesting equipment have resulted in lower stumps over time. This is because mechanised harvesting equipment is now able to cut closer to the ground (Doruska, 2002). The maximum stump height is 10 cm above ground level, and anything above this is considered to be waste (Mondi, 2010; Sappi, 2011). Any research investigating stump height should exclude all volume below the maximum acceptable height (Han & Renzie, 2005).

This section will consider the main factors affecting stump height, with the aim of determining factors that influence the amount of stump volume wasted in coppiced double, coppiced single and planted E. grandis trees. Thereafter, the methods of improving volume recovery will be described, and finally the benefits of having low stumps will be outlined.

2.7.2.1 Main factors affecting stump height

As identified by the literature, stump height can be affected by various factors, such as operator efficiency, multiple stems, felling equipment, obstacles, species, stump diameter and slope. These factors are discussed in detail below.

(a) Operator efficiency

The operator is a critical factor in stump volume recovery. This is because the operator is the person who decides how high a particular stump will be. According to Foelkel (2007), when a chainsaw, harvester or feller buncher operator fells a tree, a stump is left which may or may not meet the specifications; it should be a height of less than 10 cm. Without proper training and supervision, stump heights tend to increase (McEwan, personal communication, 2012c). For example, with motor- manual felling, chainsaw operators do not feel ergonomically comfortable cutting the tree very close to the ground (Foelkel, 2007). Another example is that harvester operators may not have a clear view of the tree base, because of litter and ground unevenness. As a result, the harvester operator may have a false view of the base of the tree and therefore cut the tree excessively higher than required (Foelkel, 2007).

Boston and Dysart (2000) found that the experience of operators using harvesting machines did not have any major impact on the length of stumps. However, it was determined that experience may contribute towards slabbing damage as the least experienced operators had more frequent incidences. Feller bunchers using disc saws are capable of cutting low stumps, but under normal working conditions operators cut high stumps due to concern over disc damage and the desire to achieve high production from the expensive piece of equipment (Shaffer & Taumas, 1992; Hall & Han, 2006).

(b) Multiple stems and leaning trees

Multiple stems in this context refer to more than one stem growing from a single stump. Figure 7 shows the front view of multiple stems occurring above safe height. Unsafe height is any height where the operator cannot reach with a chainsaw (Garland & Jackson, 1997). It is recommended that multiple stems be treated as single trees if the split between stems occurs at an unsafe height to fell separately (Garland & Jackson, 1997).

Figure 7: Multiple stems occurring above safe height (Garland & Jackson, 1997)

If the stems split close to the stump, then felling can commence with the outer tree or stem first in its natural direction. Refer to Figure 8 for stems that split close to the stump.

Figure 8: Stems splitting close to the stump (Garland & Jackson, 1997)

Stump height is usually elevated if stems split high above the stump base (Garland & Jackson, 1997). When felling manually and safety is not a problem, the chainsaw operator should attempt to separate the two stems along the seam of the two stems by cutting vertically down, followed by an undercut (Garland & Jackson, 1997). As illustrated in Figure 8, this practice assists in reducing the amount of wood waste due to high stumps (Garland & Jackson, 1997). When felling leaning trees, the most suitable undercut has to be made; the recommended depth of this cut will be one third of the trees’ diameter, and the back cut will depend on the size of the tree and degree of lean. Larger trees may require two cuts on the side, followed by a back cut; this may amount to a large portion of utilisable wood being lost when the tree is felled (Garland & Jackson, 1997)

According to a study conducted by Hartsough & Cooper (1999), stump heights increased when the harvester felled leaning trees. This was due to the operator attempting to prevent the harvester head from contacting the ground surface and dulling the saw. In addition, it was recorded that if less rocky material was found in the soil, less dulling of the chain would be likely to occur (Hartsough & Cooper,

1999). Trees with severe deformities such as forks, crooks and extreme sweep may be cut at certain points in order to separate valuable timber and unavoidable waste (The British Columbia Ministry of Forests, 2009).

Motor-manually felling with a chainsaw generally produces higher stumps compared to mechanised felling (Hall & Han, 2006; Han & Renzie, 2005 and Boston & Dysart, 2000). This occurs because during chainsaw felling, a face-cut for felling direction is made, which may require trimming once the tree has been felled. Felling defects such as uneven cuts occur due to chainsaw felling and can cause additional wood to be wasted because of high stumps (Han & Renzie, 2005).

Mechanical felling normally results in lower stumps. According to a study conducted by Han and Renzie (2005), mechanical felling with a feller buncher resulted in lower stumps (an average of 5.5 cm lower) in comparison to chainsaw felling. Hall & Han (2006) also discovered that mechanised felling with a feller buncher produced lower stumps: 8.8 cm lower than motor-manual felling with a chainsaw.

Boston & Dysart (2000) tested and compared four harvester heads (bar and chain) and one disc saw felling head against manual felling and found that manual felling produced higher stumps and a greater incidence of log damage. However, Shaffer and Taumas (1992) mentioned that shear felling heads produced lower stumps compared to disc saw felling heads. According to Foelkel (2007), one problem which results in high stumps when using harvesters is that many harvester head bases are set too high to protect the cutting chain (saw). This may result in cutting height being 15 to 20 cm higher than the maximum allowable height of 10 cm from the soil surface. This ultimately causes the follow-up operations – such as extraction and establishment – to be complex (Foelkel, 2007).

(d) Obstacles in the compartment

Obstacles are any physical barriers that prevent the cutting of the tree according to required timber specifications. Obstacles that occur close to the tree base or stump area act as obstructions for felling equipment. Obstacles may be avoidable (decaying wood) or unavoidable (rocks and excessive snow) (The British Columbia Ministry of Forests, 2009).

On sites where there are rocky outcrops; damage can be incurred to cutting parts of the harvester head. The felling cut is made above the specified area in order to avoid damaging the bar and chain. This practice results in higher stump heights. Operators of chainsaw and disc saw feller buncher heads normally cut higher stumps on rocky sites to avoid any damage to the saw (Strandgard & Mitchell, 2012). A possible solution to conditions on these sites is the use of motor-manual felling, as it has the advantage of few limitations regarding tree size, ground and slope conditions, while still producing reasonable productivity (Han & Renzie, 2005).

Sometimes trees with broken tops and butt rot occur. For safety reasons these trees may be felled higher than the allowable stump height (The British Columbia Ministry of Forests, 2009).

(e) Species and stump diameter

According to a study conducted by Han and Renzie (2005) which investigated the effect of ground slope, stump diameter and species on stump height for feller buncher and chainsaw felling, species and stump diameter significantly affected stump height. The feller buncher left significantly lower stump heights than chainsaw felling for subalpine fir (Abies lasiocarpa) and white spruce (Picea glaucalasiocarpa), but not for lodgepole pine (Pinus contorta) and Douglas-fir (Pseudotsuga menziesii). The feller buncher had lower stump heights compared to chainsaw felling as a result of butt flare occurring with certain species. This occurred due to chainsaw operators intentionally cutting the trees above the swelling portions, as they were aware of mill specifications, whilst the feller buncher operator could not see the base of the tree to see the swell, and therefore cut the tree at the lowest possible height given the felling head limitations (Han & Renzie, 2005). When felling trees with a large diameter – ranging between 30 and 70 cm – it was found that the feller buncher resulted in lower stumps when compared to chainsaw felling operations (Han & Renzie, 2005). The reasons for such a difference within the specified diameter range were not determined.

(f) Slope

Han and Renzie (2005) found that there was no significant difference between the feller buncher and chainsaw stump heights on slopes ranging between 2 per cent and 17.5 per cent. However, on slopes ranging between 20 per cent and 28.5 per 36 cent, the mean stump height for chainsaw felling was significantly higher than that of feller buncher felling. The study did not clarify the reason for the significant difference in this slope range. However, felling with chainsaws on slopes greater than 45 per cent resulted in lower stump heights. This occurred due to operators having to bend less to reach the base of the tree. The chainsaw operator was positioned on the downhill side of the tree, attaining a lower cut on the tree, and therefore a greater recovery of fibre volume. However, according to Boston and Dysart (2000), manual felling, using five felling machines, was measured on a variety of slopes ranging from 0 to 30 per cent. The five felling machines comprised four harvesters with chain and bar harvester heads and one feller buncher with a disc saw. Even though insufficient replications were conducted on all felling machines on all slopes, the available data showed that slope appeared not to have any impact on stump height. In addition, McNeel and Ballard (1992) studied a harvester on slopes ranging from 0 to 17 per cent and found that stump height was not significantly affected by slope. However, other factors such as brush and obstacles had a greater impact on stump height. Stump height on a slope is measured on the uphill-side (high side) of the slope (Han & Renzie, 2005).

(g) E. grandis coppiced double, coppiced single and planted stems

No literature was found on stump volume wasted due to excessive stump heights by the harvester whilst operating in coppiced double, coppiced single and planted E. grandis pulpwood compartments.

2.7.2.2 Potential value loss due to high stumps

Han and Renzie (2005) suggest that in saw timber harvesting, the most valuable portion of a tree is at the base, although when there are defects such as butt swell, decay and crook the value diminishes significantly. Where possible, it is important to maximise the value by reducing stump height and utilising this quality material so that economic and environmental benefits can be optimised.

According to Han and Renzie (2005), losses due to high stumps result in less volume being converted into valuable products. Doruska (2002) notes that little additional volume is lost when measured on a tree-by-tree basis. However, when accumulated across all the trees harvested in a compartment or plantation, the timber value overlooked can be significant (Doruska, 2002). For example, a 37 compartment has a stump with a 15 cm under-bark diameter and a height of 30 cm. This relates to a volume of 0,0345 m3 if the stump is cylindrical. If the tree is harvested at a height of 15 cm instead of 30 cm, then approximately 0.0173 m3 more wood is recovered. This value may not seem significant, but if it is assumed that there are 370 of these trees per hectare and the compartment is 8.1 hectares in size, a total of 51.8 m3 of wood could have been recovered (Doruska, 2002). To ensure maximum volume recovery, trees must be felled as close to the ground as possible without causing any damage to the bar or chain of the chainsaw (FESA, 2000).

2.7.2.3 Methods of improving volume recovery

An efficient way of increasing value returns derived from forestry investments is to get the most out of available wood through improvements such as minimising stump height (Han & Hall, 2006). When considering volume recovery, even the smallest salvage potential per stump achieved has the ability, when combined on a per hectare basis, to contribute largely towards a positive financial gain (Doruska, 2002; Boston & Dystart, 2000). This confirms the validity and significance of this research.

(a) Management recommendations for reducing stump volume wastage

Hall and Han (2006) stated that proper planning and selection of felling methods can have a positive effect on reducing stump height. Hall and Han (2006) further suggested that in order to reduce stump heights, the following can be done:

 Where feasible, the use of mechanised harvesting operations over manual operations should be implemented, particularly using harvesters with chain and bar to reduce wood waste;

 Operators should be constantly supervised;

 Operators must be educated on the importance of lower stump heights.

2.7.2.4 Benefits of having low stumps

The wood fibre content can be better utilised and value loss reduced by applying effective harvesting practices which ultimately improve the overall operations efficiency (Han & Renzie, 2005). According to Han and Renzie (2005), some of the benefits of having low stumps are:

 During extraction, higher productivity and operator comfort can be achieved as a result of lower stumps;

 Savings can be made on maintenance costs as an outcome of reduced machine damage;

 There is less damage to the stand as the skidder or forwarders do not have to detour from extraction routes because of high stumps;

 Lower site preparation costs can be achieved and planting activities are much easier as there are fewer obstacles;

 During harvesting of trees, fewer breakages occur when felling as the stumps are lower, and are less likely to be obstacles.

In addition, one major benefit of having low stumps is that it enables mechanised silviculture (Steenkamp, personal communication, 2012).

2.8 SUMMARY OF MAIN LITERATURE REVIEW FINDINGS

The literature review focused on the importance of E. grandis as one of the commercial forestry species in South Africa, and gave a description of the species’ coppicing ability.

Thereafter, the literature reflected on the various harvesting systems in South Africa, with a direct focus on the cut-to-length method. In the discussion on the cut-to-length method, the harvester was described and the literature concentrated on factors influencing harvester productivity. The literature showed that the main factors affecting harvester productivity could be categorised into three main components: the forest environment, the harvester’s characteristics and specifications and the machine operator’s working techniques and capacity. In the forest environment all the research indicated that tree size was the main variable directly influencing harvester productivity. Other research showed that forest environment variables, such as tree form, BWBS and terrain conditions, also affected the harvester’s productivity. No research was found on the effect of E. grandis coppiced double and coppiced single stems on harvester productivity. The small amount of available

39 research related to this topic focused on multiple stem (coppice) species found in Europe, such as oaks and Eucalyptus globulus and their effect on harvester productivity.

The literature review also focused on volume recovery and stump volume wastage in harvesting operations and showed that volume can be lost through saw kerf thickness and sawdust, felling damage and optimisation, tree tops and stumps. Due to the significance of stumps in this research, the factors influencing stump volume waste were analysed in-depth. The main factors that were found to affect the amount of stump volume wasted by a harvester were operator efficiency, leaning trees, space between harvester head and base, and obstacles such as rocks. Other studies showed that when the harvester operated on slopes of less than 30 per cent, the slope did not influence the stump height.

Overall, the literature indicated that there was little research available on factors affecting stump height in pulpwood harvesting operations compared to saw timber harvesting. This is because saw timber value is heavily dependent on tree quality. Furthermore, no literature was found on stump volume wasted due to excessive stump heights by a harvester whilst operating in coppiced double, coppiced single and planted trees.

CHAPTER 3: RESEARCH SITES AND HARVESTING SYSTEM

3.1 INTRODUCTION

This chapter provides information regarding the research location and a description of the single-grip cut-to-length harvester and the harvesting system as a whole. The research was based on harvesters operating in either coppiced or planted E. grandis compartments.

3.2 LOCATION

This section covers an overview of the plantation where the research took place, and gives a description of the research sites and general climatic conditions.

3.2.1 Description of the research site’s plantation area

The field work of the research was conducted on a Mondi plantation (a major commercial timber-growing company) situated in the New Hanover area of Kwazulu- Natal, South Africa. New Hanover is situated between Pietermaritzburg and Greytown along provincial road number R33. The plantation is located 38 kilometres from the city of Pietermaritzburg. Figure 9 displays the site where the research took place. The Mondi New Hanover Working Plan Unit consists of five farms, which are Seele, Newlands, Hillerman, Ravensworth and Droegemoller.

Figure 9: Research sites near to New Hanover (Google maps, 2012)

3.2.2 Research sites

The research was conducted at the Newlands farm where the most suitable compartments were found. The sites were chosen based on whether E. grandis had been planted or coppiced, average slope conditions and stand conditions. The field work of the research was carried out from 7th to 20th May 2011. Ten research days were spent in the coppiced compartments and four days in the planted compartments. The most important factors which needed to be consistent across all the research sites were slope and stand conditions, as these factors were not going to be considered as variables relatively affecting harvester productivity. Furthermore, the consistency of these factors contributed to the reliability of the research.

The identification numbers and coordinates for coppiced compartments were D006 (29°16’11”S; 30°28’02”E), D014 (29°16’21”S; 30°27’25”E) and D022 (29°16’04”S; 30°25’30”E). The harvester also operated in compartment D020 (29°15’52”S;

30°27’11”E), which was planted. For detailed compartment maps, see Annexure 1. Table 1 provides detailed information about the compartments.

Table 1: Research area data

Compartment D006 D014 D022 D020 Species E. grandis E. grandis E. grandis E. grandis (Coppiced) (Coppiced) (Coppiced) (Planted) Area (ha) 11.20 25.60 22.60 5.3 Age (yrs) 8 5 8 7 Average DBH (cm) 13.1 17.7 17.6 18.3 Average Height (m) 21.0 32.1 29.4 23.3 Trees per hectare 1296 921 855 1229 Average tree volume (m3) 0.165 0.275 0.253 0.214 Volume per hectare (m3) 213.84 253.28 216.32 263.00 Sample size 734 97 378 542

The coppiced compartments comprised a mixture of coppiced double and single stems. Amongst the coppiced compartments, D006 had a smaller average tree size of 0.165 m3 compared to D014 and D022 which had average tree sizes of 0.275 m3 and 0.253 m3 respectively. The planted compartment D020 had an average tree size of 0.214 m3. Compartment D006 had the largest sample size of 734 stems, followed by D020 which had 542 trees, then D022 which had 378 stems, and lastly D014, which had a sample size of 97 stems. Compartment D014 had a small level area which was suitable for the research; the rest of the compartment comprised steep areas, hence the smaller sample size in compartment D014. Details on the coppiced double, coppice single and planted sample sizes are provided in Chapter 5 (Results and Discussion).

3.2.3 General climatic conditions

The New Hanover area has a summer rainfall pattern which lasts from September to April, and a cold dry winter from May to August (Mondi New Hanover, 2010). The summer period normally receives heavy thunderstorms, and the mean annual precipitation for the area is between 750 mm and 1020 mm (Mondi New Hanover,

2010). The area normally has moderately hot summers with average maximum temperatures of 27 degrees Celsius, and cold winters with average minimum temperatures of three degrees Celsius (Mondi New Hanover, 2010).

3.3 TERRAIN CONDITIONS

This section provides information on soil types and terrain classification of research sites.

3.3.1 Soil types

Due to the organic material soil deposits, resulting from transported materials, the majority of the soils in the Newlands plantation were derived from multiple parent material (Mondi New Hanover, 2010). In some instances, the top layer of soil can be in colluvial drift form and the underlying soil form can be old weathered bedrock (Mondi New Hanover, 2010). The area consists of various soil forms such as Sweetwater, Magwa, Kranskop and Inanda Lusiki (Mondi New Hanover, 2010).

3.3.2 Research site terrain information

The terrain was evaluated using the South African National Terrain Classification System for Forestry (Erasmus, 1994). The level areas were deliberately identified and used as research sites in order to ensure consistency. Table 2 provides results of the research sites terrain conditions. Ground conditions refer to soil-bearing capacity which is determined by the soil type, clay content and amount of moisture (Erasmus, 1994).

Table 2: Compartment terrain conditions

Compartment Ground Slope Dominant Roughness description aspect D006 Smooth Level to gentle North East D014 Smooth Level South D022 Smooth Level North East D020 Smooth Level North East

The ground condition was not determined, but the soil bearing strength was enough not to have to consider this a potential factor affecting productivity. The slope was measured using a Vertex hypsometer. The compartments consisted of level (0-10 per cent) and gentle slopes (11-20 per cent) (Erasmus, 1994). The slope can be classified in terms of gradient and topographic form of the slope (Erasmus, 1994). The ground roughness was smooth for all compartments.

3.4 HARVESTING SYSTEM

This section discusses a general overview of the harvesting system used in the research results, gives a detailed description of the harvester used and provides a description of the operational elements. The harvesting equipment was owned by a contracting company called DS Preen Contracting (Pty) Ltd which was responsible for both harvesting and primary transport. Loading of secondary transport trucks on the depot was carried out by a different contractor, Timbernology CC.

3.4.1 General overview of the harvesting system

The cut-to-length harvesting system comprised the following machines and duties:

 Harvester - Hitachi ZAxis 200-3 carrier with a Waratah HTH616 harvester head: The harvester carried out all of its tasks at the stump (within the stand). The harvester felled, debarked, debranched, crosscut and stacked the logs in the compartment along the extraction routes;  Articulated timber truck (ATT) – Bell T22D timber truck: A self-loading ATT with an 18-tonne payload was used to load the logs in the compartment and offload them at a depot;  Truck loading – MAN truck (truck-mounted knuckle boom crane loader): A truck-mounted loader was used to load secondary transport trucks at the depot;  Secondary transport – SCANIA trucks: Rigid trucks with drawbar trailers were used to transport logs from the depot to the Sappi Saiccor paper mill situated at Umkomaas on the KwaZulu-Natal coast, south of Durban.

The basic infield harvesting system matrix as described above is shown in Figure 10.

Locality

Stand Extraction route Forest Road Depot

Activity

Hitachi excavator base with Waratah 616 harvesting head - Fell, debranch, debark, crosscut and stack

Extraction

Figure 10: System matrix for the single-grip excavator-based harvester

3.4.2 Description of the single-grip excavator-based harvester

A tracked Hitachi Zaxis 200-3 excavator equipped with an Isuzu 4-cylinder turbo engine was used as the carrier of the Waratah HTH616 harvester head. Figure 11 shows the harvester working in an E. grandis coppiced compartment.

Figure 11: Harvester operating in a coppiced compartment

Converted excavators are used as they offer a more cost effective acquisition option, compared to a purpose-built harvester. However, there are some significant limitations of converted excavators. Some of these limitations can be reduced by making machine-specific changes, such as replacing the boom to improve the reach. No changes were made to the harvester used for this research, except that a Waratah boom replaced the standard digging boom. Compared to standard excavator booms, Waratah booms increase the working zone and the lifting capacity of the harvester head (Schalkx, personal communication, 2012). Table 3 shows the categories and component specifications of the Hitachi Zaxis excavator base machine.

Table 3: Excavator-base machine specifications

Categories Specification and other information

Carrier brand: Hitachi excavator

Carrier model: Zaxis 200-3 series

Excavator hours: 4000

Year of manufacture: 2009

Weight (kg): 19 800

Ground clearance (mm): 1030

Under carriage length (mm): 4170

Under carriage width (mm): 2800

Power 122 kW @ 1750 rpm

Boom brand: Waratah

(Hitachi, 2009)

The Waratah HTH616 is a harvester head designed for harvesting Eucalyptus (Waratah, 2011). The head consisted of three coordinated debarking rollers that caused the tree to spiral as it was fed through, hence ensuring complete debarking of the tree (Waratah, 2011). This head can be mounted on excavators or purpose built machines (Waratah, 2011). The harvester head specifications are presented in Table 4.

Table 4: Harvester head specifications (Waratah, 2011)

Category Specification and other information

Brand: Waratah

Model: HTH616

Harvester head hours: 12 000

Year of manufacture: 2005

Max diameter cut (cm): 68

Weight (kg): 1680

Maximum feed speed (m/s): 5

Type of rollers: 45 degree double edged debarking

Computer name: Timbermatic 10

Bar name: Carlton ¾”

Chain name: Oregen ¾“

3.4.3 Detailed description of the harvester operation

The harvester operational elements are described in detail below.

3.4.3.1 Changing position

The harvester felled four or five tree lines depending on the espacement of the trees. The tree espacement in the coppiced compartments was not consistent. Some trees were closely-spaced, and some other trees were widely-spaced and within a single compartment there would be variations in the espacement of the trees. Every time the harvester’s tracks were turning, the harvester was noted as moving. During the felling operation, the harvester would move once all the trees within the reach of the harvester were felled and processed. On average, the operator felled and processed eight to nine trees before moving. Occasionally the harvester would move slightly by repositioning so that the trees could be felled or processed properly. The harvester would move to the roadside after every shift, so that refuelling and basic maintenance could be carried out on the machine.

3.4.3.2 Swing-to-tree

Once the harvester had moved sufficiently close to the trees, the swing-to-tree element would commence when the upper structure swung the boom and harvester head in the direction of a specific tree, and would terminate when the delimbing knives opened close to the tree.

3.4.3.3 Grab tree

During grabbing and felling, the harvester was normally positioned between the second and third rows in the direction of movement. The grab tree element commenced when the delimbing knives opened close to the tree, and terminated when they closed around the tree and the harvester head was in position for felling (before the felling saw was activated).

3.4.3.4 Fell tree

Once the head was in position, the felling and slewing of the tree would occur. The felling activity commenced when the felling saw was activated to cut through the tree base, and the activity concluded when the tree had fallen on the ground. However, if the operator began feeding (debarking) the tree before the tree fell on the ground,

49 then the fell tree element would end and the debarking and debranching element would start. In some unique cases where the coppiced double stems were too close together to fell separately, the stems were felled together. However, the standard felling practice was that the harvester felled a single stem per cycle.

3.4.3.4 Debarking and debranching

After the felling element, the tree would start falling, feed rollers would start moving, and the operator would commence feeding the tree through the harvester head. Immediately the operator started feeding the tree, the debarking element would begin. If the tree was too large to feed through the head during the falling motion, the operator would guide tree down to the ground. Once the tree was on the ground the debarking would commence. As the rollers and debranching knives moved along the stem surface, the tree was debranched and debarked. The rollers would move the head up and down the stem until the required debarking quality had been reached. As the tree was fed through the rollers, the stem would rotate, thereby enhancing the debarking efficiency by making sure that the feed rollers made contact with as large a surface area of the stem as possible. The harvester would slew during the debarking and debranching element as the processing took place in front of the machine. Therefore, as the harvester moved forward it would drive over the bark and branches, thereby reducing the site impact.

3.4.3.5 Crosscutting and stacking

Once the debarking and debranching was complete, the operator would commence crosscutting the tree into 5.5 m logs to one side of the machine. The crosscutting element would commence immediately the saw was activated at the base of the tree in order to zero the computer. This element terminated when the last cut was made, separating the merchantable log from the non-utilisable tree top. The crosscutting and stacking elements occurred simultaneously. As the trees were crosscut, the logs were also left as stacks to facilitate easy forwarder loading.

3.4.3.6 Pile slash

The pile slash element commenced immediately after the crosscutting element and terminated when the debranching knives and feed rollers opened and released the top in front of the machine.

3.4.3.7 Other activities

Other activities consisted of productive time spent on aspects such as moving a log in front of the machine that might have fallen during the crosscutting element, moving or clearing slash and re-handling.

3.4.4 Harvester operators

Two operators were observed operating the same harvester in both coppiced and planted compartments during the data collection period. Each operator had more than eight years of harvester operating experience. Their work skills and techniques were acceptable and the competence of both operators was similar. Each operator worked from Monday to Saturday, one nine-hour shift per day; Sunday was used for major servicing and cleaning of the harvester. Details regarding the harvester operators are included in Annexure 2.

CHAPTER 4: RESEARCH AND DATA ANALYSIS METHODOLOGY

4.1 INTRODUCTION

This chapter provides detailed information on how the research data was collected and the data analysis techniques that were applied. Information is given on the pilot research that was carried out before the main research was conducted. In the research methods section, the instruments used for taking measurements, data collection techniques and data capturing processes are described. The rationalisations behind the selection of the data analysis procedures, as well as the actual procedures used, are discussed under the data analysis methodology section.

4.2 PILOT RESEARCH

Before the actual research was conducted, a pilot research took place. A pilot research is a pre-research study conducted to determine the feasibility of a potential research project before it is conducted (National Centre for the Replacement, Refinement and Reduction of Animals, 2006). The pilot research was conducted from the 16th to 19th March 2011 to ensure that sites selected and data collection methods chosen were suitable, valid and reliable. The pilot research increased the precision of the research. The pilot research also led to alterations of the original plan, for example some of the main variables – such as the distance between stems and the stem felled first that could affect productivity – were identified during the pilot research.

Compartment B20 on Hillerman farm, on a Mondi Ltd plantation situated close to the town of New Hanover, was used to conduct the pilot research. Compartment B20 was an E. grandis coppiced compartment, and an excavator-based carrier fitted with a Waratah HTH616 harvester head was used and observed whilst felling and processing the trees. The same harvester was used when the main research was carried out. The diameter and height measurements of the trees were recorded, and a video camera was used to record the operation of the harvester. Time study data was collected from the videos. Different elements were measured and recorded similar to the way in which the research would be carried out.

A Sappi Ltd (a major commercial timber-growing company) plantation at Seven Oaks, a small village close to Greytown, was visited in order to determine the feasibility of measuring and determining the volume wasted due to high stumps. The stumps in compartment number K76, which consisted of coppiced E. grandis, were measured. The compartment had been felled with a harvester. The pilot research affirmed that the data collection methods identified were applicable for the research.

4.3 RESEARCH METHODOLOGY

Mechanised cut-to-length harvesting of E. grandis in South Africa takes place all year round. The field research occurred during the month of May; the research was conducted in late summer to avoid variation in BWBS, as this factor was not to be investigated during the research. The identified compartments were typical of pulpwood tree sizes and ages in South Africa. For details in this regard refer to the Research Sites section (3.2.2) in Chapter 3.

In this section, measuring instruments, fieldwork practice, data collection and data capturing methods are described. A summary of all the factors influencing productivity and volume waste and how they are related is given in Section 4.3.4. below.

4.3.1 Measuring instruments

Various instruments were used in the measurement of the key variables. The instruments that were used and their functions are described in Table 5.

Table 5: Measuring instruments and their purpose

Measuring instrument Purpose

Diameter calliper To measure the diameters of the trees in the sample plots.

To measure branch diameter.

Diameter tape To measure the exact diameters of the trees where heights were taken for accurate volume calculations.

Vertex hypsometer and To take height measurements for tree-volume transponder calculations and to determine slope.

30m tape To confirm and check the accuracy of the Vertex height measurements once the tree was felled.

Axe To ring-bark the standing trees so that rip stripping could be conducted (BWBS test).

Tape measure To measure the distance between the coppiced double stems.

To measure the under-bark stump diameter and stump height.

Trimble Nomad handheld To record time study data on the harvester activities. computer

Pre-printed data sheets For capturing the tree measurements, number of harvester head passes along the tree, number of logs recovered and sequential tree numbers.

Video camera To take video footage to identify and analyse the harvester elements per cycle during the pilot research.

Notebook and pen To record measurements and general comments.

4.3.2 Fieldwork practice and data collection

The full details of the data collection process, including infield practices and procedures which were applied to access the data, are described below.

4.3.2.1 Tree volume determination

Field trials were set up daily ahead of the harvester. The diameters of all marked trees were measured using a diameter calliper at a height of 1.37 m, which is the standard height for diameter readings in South Africa (Bredenkamp, 2000). The individual tree diameter measurements were classified into 2 cm classes. The classes are shown in Annexure 3. The diameters of the coppiced stems were measured at 1.37 m from where the stem was attached to the previous rotation’s stump. Tree height was measured for selected trees, using a Vertex hypsometer with a transponder. An average of 20 tree heights were measured per 100 trees recorded. However, if a plot had a wide diameter distribution, more tree heights were taken relative to the diameter classes in that specific plot. The diameter and heights for the selected trees were used to derive heights for the other trees in the sample. The average taper per diameter class was multiplied by the diameter of trees in that class to determine heights of the other trees. To ensure consistent and accurate height measurements, the Vertex hypsometer was calibrated daily before any measurements were taken. Random trees were selected and measured with a 30 m tape once the tree had been felled by the harvester, in order to confirm the accuracy of the Vertex hypsometer readings.

Diameter and height measurements were used to determine tree volume by using the equation based on the Schumacher and Hall model (Bredenkamp, 2000). The standard equation and the descriptions of the equation factors were as follows:

Schumacher and Hall’s model: lnV = b0 + b1 ln(dbh + f) + b2 lnH, where:

ln = natural logarithm to the base e

V = stem volume (m3, under-bark up to 50mm)

dbh = diameter at breast height (cm, over bark)

f = correction factor

H = tree height

The volume of individual trees was determined using E. grandis coefficients. Tree volumes were calculated up to a 5 cm top diameter. E. grandis coefficients used with the equation were:

lnV = -10.8120 + 2.1513 ln(dbh + 0) +1.0007 lnH (Bredenkamp, 2000)

4.3.2.2 Marking of trees

Each plot consisted of trees that would be felled by the harvester in one day, and an average of 100 trees were measured and processed each day. Before the coppiced trees were harvested, the different tree sizes (diameter and height) were recorded to determine which of the two stems was larger. To avoid any discrepancies in the data when a coppiced tree was measured, the first recorded stem would always be the larger one, followed by the second stem, which would be the smaller stem. For easy identification, the individual coppice stems were spray-painted with both numbers and letters (‘a’ and ‘b’). The larger stem was given a number with the letter ‘a’ next to it, and the smaller stem was given the same number with the letter ‘b’ next to it. It was an arbitrary decision made by the researcher to mark all the larger stems as ‘a’ and the smaller stems as ‘b’. Figure 12 shows a coppiced tree with the ‘2a’, which is the larger stem, with ‘2b’ being the smaller stem. The coppiced single trees were marked with a number without any letters.

Figure 12: Spray-painted coppiced double stems indicating the larger (a) and smaller stems (b)

The distance between the coppiced stems varied. Some coppiced double stems grew close to each other, while other stems grew further apart. To quantify if the distance between coppiced double stems influenced the productivity of the harvester, the distance between the two stems was measured with a tape measure at a height of 1.3 m above the previous felling stump height. The distance between the stems was measured at 1.3 m, as that was the height where the harvester head grab arms would wrap around the tree before felling it.

Planted trees were marked with numbers only. Each tree was allocated a number which was spray-painted onto the stem.

4.3.2.3 Bark-wood bond strength description

Before the harvester felled each plot, the bark-wood bond strength (BWBS) was tested by rip-stripping the standing trees. Rip-stripping is a term applied to an operation which involves manually ripping the bark off a standing tree; the trees are

57 ring-barked with an axe at a height of one metre or lower, and the loose bark is pulled up and away from the stem. Refer to Annexure 4 for the BWBS strength rip- stripping test.

During debarking, the number of times the harvester head passed up and down the stem was recorded, as this reflected bark adhesiveness. Using the technique developed by McEwan (2011c) which is a manual test whereby a section of bark is loosened with an axe at breast height and then pulled along the stem until it breaks. The bark adhesiveness of the stem was evaluated and each tree was placed into a BWBS class. Table 6 shows the BWBS classes used in evaluating the BWBS.

Table 6: Description of bark-wood bond strength classes (McEwan, 2011c)

BWBS class Description

1 Bark can be removed in very long length strips (>10 m).

2 Bark can be removed in long length strips (4m -10 m). Bark can be removed in medium length strips 3 (1 m – 4 m). 4 Bark comes off in short length strips (< 1 m).

5 Bark has to be chipped off the tree.

As the BWBS class increases from one to five, more harvester head passes along the stem would theoretically be required to remove the bark.

4.3.2.4 Tree form description

Table 7 shows the description of the two tree form classifications that were used. The table reflects how the tree forms occurred in the compartments. The form class consisted of branch density, maximum branch diameter at the trunk and the bole form.

Table 7: Tree form class used for the research

(adapted from Puttock et al., 2005)

Form class Branch density Maximum branch Bole form diameter (at trunk)

F1 – Good form Light <5 cm Straight

F2 – Poor form Dense >5 cm Straight or crooked

Puttock et al. (2005) had five form classes with various bole forms, and a maximum branch diameter of either smaller or greater than 5 cm. The five classes were not practical for this research and therefore only two classes from his classification were used as per Table 7. The five form classes were not practical because the species and tree characteristics were different. The form classes were divided into good form and poor form trees to clearly describe the tree formations in this research. The maximum branch diameter, either greater or smaller than 5 cm was practical and applicable as it matched how the visual form characteristics of trees were found infield.

The individual form of each tree was evaluated and recorded. The form of all the trees was evaluated by the same person to ensure consistency of classification. Each tree was allocated a form class depending on its visual characteristics. Before and during the research in a new compartment, five trees were randomly identified and felled by the harvester. These trees were not processed so that the branch diameters could be measured with callipers to familiarise the observer with the branch thickness (greater than or less than 5 cm). This practice enhanced the accuracy of the observer’s visual estimates. The influence of tree form on productivity is described in Chapter 5.

4.3.2.5 Stump volume waste data collection

The amount of timber wasted due to excessive stump heights was measured immediately after the productivity time studies were conducted. When the standing trees were measured and marked, their stumps were also marked with the same numbers. Paper tags were used to identify the stump once the standing tree was

59 felled. Certain parts of the litter layer around the base of the tree stems were cleared before the paper tags were attached to the base of the tree. The clearing of the litter layer around certain parts of the tree base did not assist the harvester in cutting lower stumps, as only the points where the paper tags were attached were cleared. Two identically-numbered paper tags with the standing stem’s number were stapled on opposite sides at the base of the tree. Because the stump waste measurement was conducted immediately after the trees were harvested, the paper tags were easy to identify. To avoid losing the tree identification because of the harvester cutting very low stumps or because of the bark at the base of the tree being ripped off when felling, the tags were stapled onto the major root system visible around base of the stump. Figure 13 shows a stump with a paper tag stapled at the bottom. When the harvester had felled and processed marked trees, the stumps were measured and related back to the specific trees that were felled.

Figure 13: White paper numbered tag stapled at the base of the stump

After felling, the stumps were normally covered by slash and stacked logs. Therefore, to enhance the stump-finding and identification process, once the marking

60 of the trees and stumps was complete, a sketch map was drawn to show the original positions of the trees before felling took place.

Stump heights were measured on the upslope side of the stump from ground level to the top of the felling cut (Hall & Han, 2006). Any obstacles like branches and bark were removed from around the stump in order to ensure that the stump was measured correctly. In coppiced compartments, stump heights were measured from where the stump was attached to the previous rotation’s stump, instead of measuring from the ground.

Once the harvester had processed the standing trees, the stump heights and diameters of trees in both the coppiced and planted plots were measured and the remaining volume was calculated. The maximum allowable stump height in pulpwood compartments is 10 cm (Sappi, 2011). Therefore, any stumps which were higher than the specified 10 cm limit were recorded and the waste volume was determined. To determine the diameter of each stump, the under-bark diameter was measured to the nearest centimetre. To determine the diameter of stumps with oval or irregular shapes, the longest and shortest diameters were measured and averaged to determine the diameter of the stump.

The allowable stump height of 10 cm was deducted from the total stump height and the remaining length was recorded. If the stump height was within the required limits of the maximum allowable stump height of 10 cm, the tree was recorded as having no wood volume loss. Any utilisable volume that remained above the allowable 10 cm stump height after felling was measured and wood volumes determined. The formula that was used to determine the stump waste volume is shown below.

Stump waste volume (m3) = (Hall & Han, 2006; Bredenkamp, 2000)

The gap between the saw and the base on the harvester head was also measured. This measurement was taken, as sometimes harvester head bases are set too high in order to protect the cutting saw. The distance between the cutting saw and the base of the head was 10 cm, which matched the minimum stump height requirements. If the gap between the saw and the base had been set higher, then it would have influenced the stump volume recovery results.

4.3.3 Data capturing

This section provides information on how the collected data was electronically captured and sorted for further analysis.

4.3.3.1 Field data capturing

The research data was collected electronically, using a Trimble Nomad 900 Series handheld computer, as well as manually, by writing down certain measurements on data sheets. Refer to Annexure 5 for all the data sheet templates that were used to record the data manually. Three separate spreadsheets were created in Microsoft Excel for entering the coppiced double, coppiced single and planted data. The collected data was transferred to spreadsheets which contained column titles as indicated in Table 8.

Table 8: Microsoft Excel spreadsheet column titles Coppiced double stems Coppiced single stems and planted

Diameter (cm) Diameter (cm)

Height (h) Height (h)

Tree volume 1 (m3) Tree volume (m3)

Tree volume 2 (m3) Form class

Total volume (m3) Number of debarking passes

Distance between stems (cm) Number of logs recovered

Stem felled first Stump diameter (cm)

Tree volume form class Stump height (cm)

Number of debarking passes Stump volume (m3)

Number of logs recovered Comments

Stump diameter (cm)

Stump height (cm)

Stump volume (m3)

Comments

Table 8 shows the column titles used in capturing the data recorded manually. The coppiced double spreadsheet had more titles, because each of the two stems had different characteristics and information which had to be recorded individually.

4.3.3.2 Time study data capturing

The primary reason for conducting time studies is to determine the time per element in the production chain affecting productivity, and provide a base for cost calculations and development of models that can be used for predicting productivity (Nurminen et al., 2006). Time studies were conducted on the harvester at the various research sites. Cycle times of the machine were divided into specific time elements that were considered typical of the harvesting process of the harvester. The delay times were also captured. The productive time was derived from the direct harvester operational elements, but excluded all delay times.

The UMT manager software was used to create the settings required for different harvester elements. The configurations were set on the laptop computer before being transferred to the Trimble. The data was collected by simply clicking on the applicable element on the Trimble’s screen. Figure 14 shows the Trimble that was used during the research.

Figure 14: Trimble Nomad Handheld Computer that was used to conduct time studies

The following information was retrieved from the Trimble once the time studies were complete:

 Compartment number  Plot number  Date and time of study  Cycle element times (duration)  Total cycle times  Total time of time study (beginning to end)  Delay times  Reasons for delay  Stem felled first  Notes

During time study data capturing with the Trimble, other factors were also recorded. These factors included:

 Stem felled first

When the harvester felled the coppiced double stems, the stem that was felled first was recorded. This was because sometimes the harvester operator would fell the larger stem first, and at other times the smaller stem first. The Trimble was configured to enter this data. Once the ‘fell tree’ element was activated, an option was created on the screen so that the observer could click the type of tree that was being felled, either large ‘a’ or small ‘b’. This was backed up by separate manual written recording of the trees that were felled, in the same order they were felled. The tree numbers were recorded in the same order, so that if there was an incorrect tree number entered into the Trimble as a result of a quick “fell tree” element, then if could easily be traced and corrected during the data capturing.

 Number of debarking passes over the stem before the crosscutting element occurred

When debarking the trees, the whole stem was pulled through the harvester head so that the bark could be detached from the stem due to the pressure

exerted by the feed rollers and the rotating movement of the stem. The number of passes the harvester head makes along the stem provides an indication of the bark adhesiveness on the stem. The number of debarking passes was recorded manually.

 Number of logs recovered from the tree

After debarking, the trees were crosscut into 5.5 m log lengths; hence the amount of logs recovered from each stem was recorded for each cycle. The amount of waste remaining infield as a result of tree tops was not measured as it did not fall within the scope of this research. The number of logs recovered from each tree was recorded manually.

4.3.3.3 Stump volume waste data capturing

All the stump volume waste information was recorded manually and transferred to a Microsoft Excel spreadsheet, as shown in Section 4.3.3.1. (See Table 8). The waste volume remaining on each stump was calculated by using the stump waste volume formula as described in Section 4.3.2.5. Stump heights were converted from centimetres to metres for the purpose of waste volume calculations. Taper was not factored into the calculation as stump heights were generally low.

4.3.4 Summary of factors influencing productivity and volume recovery

Figure 15 shows the factors affecting the productivity and stump volume recovery of the harvester.

Figure 15: Schematic diagram of the factors influencing productivity and volume wastage

The schematic diagram illustrated in Figure 15 was created by the author to explain the holistic integration of the research. On the left hand side of the diagram, the tree categories (coppiced double, single and planted) and productivity variables that were evaluated under each category are displayed. The middle section shows the main output that was recorded from all three categories, being productivity. The straight arrows which point to the far right indicate the analysis and quantification of stump volume waste remaining from the three tree categories. The arrows pointing from the productivity to the stump volume indicate the analysis of the relation between productivity and stump volume waste. Even though the stump volume wastage is not directly related to productivity, the accumulated stump volume waste may contribute more to harvester productivity loss which may cost more per hectare than to the lost volume. The relationship between productivity and stump volume waste is discussed in detail in Chapter 5: Results and discussion, Section 5.6.

4.4 DATA ANALYSIS METHODOLOGY

The time study data collected was transferred daily from the Trimble Nomad handheld computer onto the laptop computer. Once the data was downloaded into the computer, data in the UMT Manager File format was exported to Microsoft Excel for further analysis.

Time study data for each tree was matched to the manually collected tree data (for example; heights, diameter and volume). The tree volume and the cycle time were used to determine productivity. Once the productivity of the harvester was determined, descriptive statistics were calculated to summarise the data. The means and sample sizes of the different factors were determined. Outliers were identified and removed through the evaluation of the raw data and scatter plots. To make the data analysis easier to analyse, it was divided into three categories; namely coppiced double stems, coppiced single stems and planted. A single productivity model was derived for the coppiced double stems by combining the volumes and element times of the two stems.

Once the descriptive statistics were complete, the data was transferred to the STATISTICA 10 software package which was used for comprehensive statistical analysis. These results are presented and discussed in Chapter 5. Harvester productivity modelling and the statistical methods used to analyse stump volume waste are discussed below.

4.4.1 Harvester productivity modelling

Regression analysis was used to model the influence of volume, form, distance between stems, and stem felled first on the productivity of the harvester. Regression analysis assumptions (or model adequacy checking), namely the normality of the error terms and homoscedasticity, were visually examined through normal probability plots and scatter plots.

As a result of the strong relationship between tree volume and tree form with the coppiced double stems, four separate productivity models were developed for the different form classifications. The form classifications were a categorisation which catered for the combination of the tree form of both stems. A detailed description of the analysis and the categories is given in Chapter 5. The backward stepwise

67 method was used to develop the models. This method starts by including all the relevant independent variables in the model in the first step. In the next step, the variable with the largest p-value (thus the smallest effect on the dependent variable) is removed from the model. The model is then refitted, excluding the insignificant variable. This process is repeated until all the variables included in the model are significant at the 5 per cent level of significance. The p-value was the statistical measure used to determine the amount of influence that an independent variable had on the dependent variable (StatSoft, Inc. 2011). The test of sum of squares whole model against sum of squares residual was carried out in order to determine the R-squared and adjusted R-squared values. The R-squared value gives an indication of the model’s validity and goodness of fit. Details of the harvester productivity models are provided in Chapter 5 (Results and Discussion).

4.4.2 Statistical analysis of the stump volume waste

To investigate the differences in stump wastage for both the planted and coppiced volume measurements, the proportions of stumps with waste to those without waste were determined. Once the proportions were determined, the average stump waste volumes were determined by dividing the total waste of the sample by the number of stumps observed. The calculation was carried out separately for both planted and coppiced volume measurements. The sample profile of all the trees under each tree category was evaluated before the analysis was conducted.

4.5 SHORTCOMINGS AND SOURCES OF ERROR

Mouton (2001) suggests that as part of any research, the shortcomings and sources of error encountered should be described. During the first day of data collection, many debarking passes were recorded per tree, as one operator would normally debark the first half of the tree, crosscut it, and then debark the remaining portion of the tree. The operator would make many passes to debark the tree to acceptable levels of quality, as a result of the dense and thick branches at the top. The second operator would debark the entire stem and then crosscut the tree. This made capturing of the different elements complicated as different methods were applied for the same task. From the second day onwards, both operators used the same method of debarking, whereby the entire stem was first debarked before 68 crosscutting. Both operators were equally proficient with both methods. This ensured consistency in the data collection and working methods. The data collected on the first day was still used, as the time variance between the two techniques used by the operators was small. This could be done because the separate debarking and crosscutting times of the one operator were added up and compared to the others operator’s times, and were very similar. This problem was not noticed during the pilot research as only the one operator was observed.

Even though this constraint occurred, it did not influence the results of the research negatively, as it was identified at the beginning of the field research data collection and a practical solution was applied.

CHAPTER 5: RESULTS AND DISCUSSION

5.1. INTRODUCTION

This Chapter presents the harvester productivity and stump volume waste results and discussions. The results for the coppiced double stems are presented first, followed by the coppiced single stems and then planted trees. The productivity results under each section consist of the regression results of variables (factors) influencing the productivity of the harvester, followed by model adequacy checking tests. Stump volume waste results are similarly structured.

After the productivity results, a section discussing these productivity results follows. Thereafter, the stump volume waste results for the coppiced double, coppiced single and planted trees are presented, followed by a discussion. Final comparisons of the results are then presented. Chapter 5 concludes with a section describing the relationship between productivity and stump volume waste in the coppiced double, coppiced single and planted compartments.

5.2 PRODUCTIVITY RESULTS AND DISCUSSION

The productivity results of the harvester in coppiced double, coppiced single and planted compartments are presented below.

5.2.1 Coppiced double stems productivity results and discussion

The sample profile and the productivity results of variables that affected the productivity of the harvester in coppiced double stems will be presented. The productivity variables analysed include tree volume, form, stem felled first and distance between stems.

The harvester head had a mean of three passes per stem when debarking coppiced double stems. Furthermore, a mean of three logs was recovered from each tree in the total sample. The log length specification was 5.5 m. The mean number of trees harvested in one productive machine hour (PMH) was 72. The mean distance between two coppiced stems at a height of 1.3 m was 26.7 cm.

5.2.1.1 Effect of coppiced double combined stem volume on harvester productivity

The volume of each stem of the double coppice was combined into one tree volume, referred to further in the text as coppiced combined stem (CCS) volume. The effect of CCS volume is discussed below.

(a) Sample profile of CCS volume

The sample sizes show that there was wide variation in the coppiced combined stem volume in the observed stems (from small to large stems). The largest sample sizes occurred in combined stem volumes of less than 0.5 m3. Within the total sample size of 322 stems, 249 stems had a CCS volume of less than 0.5 m3. As stem volume increased, the sample size decreased. The productivity model derived was expected to predict more accurately with CCS volumes less than 0.7 m3 compared to the larger CCS volumes (greater than 0.7m3) which had smaller sample sizes. Figure 16 shows the distribution of CCS volume processed when divided into 0.1 m3 volume classes. To calculate the productivity of the CCS volume, the time it took to fell and process each coppiced stem was combined. This was done so that a single productivity result could be derived for both stems, which was essential in comparing the productivity of coppiced double stems to coppiced single and planted trees.

80 80 76

51 50 42 40 40

No. of obs.

30 25 21 20 12 10 7 4 3 0 1 0 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 1.1 1.2

3 CCS volume (m )

Figure 16: Sample distribution for CCS volume

Table 9 shows the descriptive statistics for the CCS volume and productivity. The CCS volume had a mean tree volume of 0.33 m3. The minimum and maximum CCS volume values indicate that there was much variation in the CCS volume. The mean productivity of the harvester when operating in coppiced double stems was 11.9 m3 per PMH.

Table 9: Descriptive statistics for CCS volume and productivity

Variable Mean Minimum Maximum Standard deviation

CCS volume (m3) 0.33 0.02 1.16 0.22

Productivity (m3/PMH) 11.9 1.1 32.0 6.0

Figure 17 shows the relationship between the productivity and CCS volume.

Productivity(m3/PMH) 10

0.0 0.2 0.4 0.6 0.8 1.0 1.2 CCS volume (m3)

Figure 17: Scatterplot showing the relationship between productivity and CCS volume

Figure 17 shows that harvester productivity increased as CCS volume increased. The scatterplot visualises the relationship between the two variables, volume (x) and productivity (y). These two variables are strongly related; however the relationship

72 between productivity and CCS volume was not linear as indicated by the data points on the scatterplot.

(b) Productivity regression model: CCS volume

A productivity regression model was constructed for the harvester. The variable CCS volume and its squared effect were used in creating the model. Table 10 shows the coefficients and R-squared value of the productivity model.

(Productivity (m3/PMH)) = β0 + (β1* CCS volume) + (β2* CCS volume2)

Table 10: Coefficients of the double coppiced productivity model

Effect Harvester

Intercept (β0) 1.6472

CCS volume (β1) 38.3300

CCS volume2 (β2) -15.3379

R2 Value 0.878770

Productivity, as calculated in the model, is the estimated mean productivity for a given CCS volume. The CCS volume coefficient is positive, which indicates that as stem volume increases, productivity also increases. The CCS volume squared is negative, which shows that the productivity increase is non-linear. The regression equation was significant to the five per cent level. The R-squared value is the coefficient of determination which is an indicator of the goodness of fit. The R- squared value of 0.88 shows that the regression line fitted the observed harvester productivity very well. The R-squared value was used to measure the proportion of the total variation in productivity explained by the regression model.

Model adequacy checking is the analysis of residuals to determine if the underlying assumptions have been violated. If the model is adequate, the residuals must not

73 contain any patterns (Montgomery, 1997). To check if the CCS volume model was adequate, the normality and homoscedasticity tests were analysed.

(i) Normality test

A visual inspection of error terms is shown in Figure 18.

Normal Prob. Plot; Raw Residuals Dependent variable: Productivity (Analysis sample) 4

.99 2 .95

1 .85

.65 0 .35

-1 .15

ExpectedNormal Value .05 -2 .01

-3

-4 -10 -8 -6 -4 -2 0 2 4 6 8 10 Residual

Figure 18: Harvester – CCS volume normal probability plot

The assumption of normality was evaluated by using the normal probability plot of residuals from the regression model. The test for normality was done on the residuals in order to determine if the error terms were normally distributed. In order for the assumption of normality to be met, the points should lie along the line. The points were lying along the line in the normal probability plot and therefore the assumptions of normality were met.

(ii) Homoscedasticity test

Homoscedasticity of error terms was investigated; refer to Figure 19.

Predicted vs. Residual Values Dependent variable: Productivity (Analysis sample) 10

Raw Residuals Raw -2

-4

-6

-8

-10 0 5 10 15 20 25 30 Predicted Values

Figure 19: Harvester – CCS volume predicted versus residual plot

The residual versus predicted plot checks for homoscedasticity of the error terms. The plot shows a “fanning out” pattern which is indicative of heteroscedasticity. The presence of homoscedasticity precludes proper hypothesis testing, therefore raising the possibility of drawing misleading conclusions (Gujarati & Porter, 2010). To correct for this heteroscedasticity, the square root transformation was used. The square root transformation is a remedial measure for heteroscedasticity (Gujarati & Porter, 2010). All the variables were divided by the square root of CCS volume. However, the regression model did not differ from the model of the untransformed data. Therefore, the productivity model developed from the original data could be used to predict harvester productivity.

(d) Modelled harvester productivity results: CCS volume trees

Figure 20 shows the modelled harvester productivity for increasing CCS volumes.

30 25.6 24.6 25.3 25 23.7 22.5

21.0 20 19.1

/PMH) 17.0 3 14.5 15 11.8 Coppiced double

10 8.7 Productivity(m 5.3 5 3.5

0 0.05 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 1.1 1.2 CCS volume (m3)

Figure 20: Modelled harvester productivity per CCS volume

Tree volume is an important variable influencing the productivity of the harvester in coppiced E. grandis clearfelling operations. The productivity ranged from 3.5 m3 per PMH for 0.05 m3 CCS, to 25.6 m3 per PMH for the larger 1.2 m3 CCS. The modelled CCS volumes ranging from 0.9 m3 to 1.2 m3 had a small sample size (n=15, 4.7 per cent of total sample). Due to the coppiced stems being combined, the volume also increased, hence the wide CCS volume range. The productivity of the harvester increased as stem volume increased. According to Visser and Spinelli (2011), most studies establish that piece size is the principle variable influencing the productivity of a harvester. Single grip harvesters are sensitive to tree volume as they can normally only handle a single stem at a time.

5.2.1.2 Effect of the stem felled first on harvester productivity

With coppiced double trees, two stems had to be felled. Of the two coppiced stems (large and small), the harvester naturally had to first fell one stem and then fell the second stem. Stem-one volume was always recorded as the larger stem “a”, and stem-two volume was always recorded as the smaller stem “b” (refer to marking of trees under Research Methodology in Chapter 4). Depending on which stem was felled first, the productivity could vary. To analyse the effect of this variable, the

76 volumes of both coppiced stems had to be kept separate instead of using the CCS volume. The effect of this variable on productivity is analysed below.

(a) Sample profile of stem-one and stem-two

Figure 21 shows the number of observations for stem-one volume and stem-two volume for increasing stem volumes. Stem-one volume observations had a wider volume distribution compared to stem-two volumes.

300

246 250

200

150 Stem-one volume 117 Stem-two volume

No. of No. observations 100 75 67 70 53 50 25 24 17 15 9 4 1 1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 3 Stem volume (m )

Figure 21: Sample distribution for stem-one and stem-two volume

Expanding on Figure 21 above, Table 11 gives the descriptive statistics for stem-one volume and stem-two volume. As expected, the mean stem volume for stem one was higher than that of stem two. The standard deviation of 0.17 shows that there was more variation in tree size in stem-one volume compared to stem-two volume, which had a standard deviation of 0.09.

Table 11: Descriptive statistics for stem-one volume and stem-two volume

Variable Mean Minimum Maximum Standard deviation

Stem-one volume (m3) 0.24 0.01 1.06 0.17

Stem-two volume (m3) 0.09 0.01 0.44 0.09

(b) Productivity regression model: tree volume and interaction with the stem felled first

A regression model was developed to predict the productivity of the harvester in coppiced double trees, with specific reference to the influence of the stem felled first on productivity. The productivity (dependent variable) was derived as a function of the independent variables, which were stem-one volume, stem-two volume, stem- one volume squared, stem-two volume squared, distance between stems and stem felled first. The stem felled first variable was included in the regression equation as a dummy variable. A dummy variable is an independent variable that can be explanatory of a particular factor and takes the value of either 1 or 0 (Garavaglia & Sharma, 2004). Therefore, in the coppiced double regression equation (stem felled first), value 1 was applied when the larger stem was felled first, and value 0 was applied when the smaller stem was felled first. In the regression model, the felled first dummy variable with a value of 0 will cause its coefficient to disappear from the equation. Conversely, the value of 1 causes the coefficient to function as a supplemental intercept, because of the identity property of multiplication by 1. The statistically significant model is presented below and the model coefficients are shown in Table 12.

(Productivity (m3/PMH))= β0 + (β1* stem-one vol) + (β2* stem-two vol) + (β3* (stem- one vol)2 ) + (β4*(stem-two vol)2) + (β5* Felled first) + (β6* stem-one vol* Felled first)

Table 12: Coefficients of the double coppiced (stem-one and stem-two) productivity model

Effect Harvester

Intercept (β0) 2.0316

Stem-one volume (β1) 39.2977

Stem-two volume (β2) 31.1302

Stem-one volume2 (β3) -22.5507

Stem-two volume 2 (β4) -32.9884

Felled first (β5) -0.7631

Stem-one volume * Felled first (β6) 4.3548

R² Value 0.884848

Productivity as calculated in the model is the estimated mean productivity for a given stem-one volume, stem-two volume and stem felled first. Only stem-one volume, stem-two volume, stem-one volume squared, stem-two volume squared and stem felled first variables were statistically significant variables affecting harvester productivity. All the coefficients included were significant at the five per cent level. In terms of the magnitude of the change in productivity, tree volume as a whole was the main variable affecting the productivity of the harvester. The model indicated that if the volume of the larger stem (stem-one) was greater than 0.18 m3 then harvester productivity would be higher if the larger stem was felled first. However, if the volume was less than 0.18 m3 then the productivity of the harvester would be higher if the smaller stem was felled first. The R-squared value was 0.88 which indicated that the regression line fit was good; meaning the explanatory variables (significant variables) “explain” 88 per cent of the variance in productivity.

The normality and homoscedasticity tests were analysed to determine if the underlying assumptions were violated.

(i) Normality test

A visual inspection of error terms is shown in Figure 22. The test for normality was done on the residuals in order to determine if the error terms were normally distributed. The points were lying close to the line in the normal probability plot and therefore the assumption of normality was acceptable.

Normal Prob. Plot; Raw Residuals Dependent variable: Productivity (Analysis sample) 4

.99 2 .95

1 .85

.65 0 .35

-1 .15

Expected Normal Value Expected .05 -2 .01

-3

-4 -8 -6 -4 -2 0 2 4 6 8 10 Residual

Figure 22: Harvester - coppiced double (Stem-one volume and stem-two volume) normal probability plot

(ii) Homoscedasticity test

The homoscedasticity of error terms were then investigated. Figure 23 shows a “fanning out” pattern which is indicative of heteroscedasticity. To correct for this heteroscedasticity, the square root transformation was used. All the variables were divided by the square root of stem-one volume. However, the regression model did not differ from the model derived from the

untransformed data. Therefore, the productivity equations developed from the original data could be used to predict productivity for the harvester.

Predicted v s. Residual Values Dependent v ariable: Productiv ity (Analy sis sample) 10

Raw Residuals Raw -2

-4

-6

-8 0 5 10 15 20 25 30 35 Predicted Values

Figure 23: Harvester - coppiced double (stem-one volume and stem-two volume) residuals versus predicted plot

(d) Modelled harvester productivity results: CCS volume trees

The stem felled first had an influence on the productivity of the harvester. In order to illustrate the effect of tree volume and stem felled first on the harvester’s productivity, the regression equation was used to predict productivity for specific coppiced double stem volumes. Table 13 shows the stem volumes that were used in the equation. The combined volume combinations displayed for the small and large stems were arbitrary but representative of the actual tree volume combinations in the raw data. The productivity figures attained were used to plot the column charts in Figure 24.

Table 13: CCS volume figures used in the coppiced double regression model

Small stem (m3) Large stem (m3) CCS volume (m3)

0.04 0.06 0.1

0.05 0.1 0.15

0.02 0.18 0.2

0.05 0.2 0.25

0.1 0.2 0.3

0.05 0.3 0.35

0.1 0.3 0.4

0.15 0.3 0.45

0.25 0.25 0.5

0.15 0.4 0.55

0.25 0.35 0.6

0.2 0.45 0.65

0.2 0.5 0.7

Figure 24 shows the productivity of the harvester when the small stem or the larger stem is felled first for a given CCS volume of coppiced double stems.

25.0

20.0

/PMH) 3 15.0

10.0 Productivity(m 5.0

0.0 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 0.55 0.6 0.65 0.7 Small tree felled first 5.9 6.8 8.9 10.5 11.8 13.3 14.6 15.7 16.2 18.1 18.7 20.1 20.9 Big Tree felled first 4.9 7.2 9.0 10.6 11.9 13.8 15.1 16.3 16.5 19.0 19.5 21.3 22.4 Tree volume (m3)

Figure 24: Modelled productivity for the harvester when either the small stem or the big stem is felled first

The productivity is plotted from the minimum CCS volume of 0.1 m3 to a maximum of 0.7 m3. Harvester productivity was significantly (five per cent level) affected by the stem felled first. When the harvester felled and processed the stems, depending on which stem the harvester felled first, the productivity would either increase or decrease. The productivity model prediction indicates that when harvesting stems where the volume of the larger stem is less than 0.18 m3 then the harvester is more productive when it fells the small stem first compared to the larger stem. However, the model predicts that when the larger stem has a volume greater than 0.18 m3, then the harvester’s productivity is higher when the larger stem is felled first. Figure 24 shows that the difference in productivity when the small stem is felled first and when the large stem is felled first increased as the CCS volume increased. It was observed during the research that the harvester operator would struggle to grab and fell the smaller stem first because the larger stem would normally obstruct the handling of the smaller stem. This happened especially if the stems were close to each other. When the operator handled the larger stem first the grabbing and felling process was quicker, as the operator could easily identify the stem, grab it at the base and fell it without being obstructed by the smaller stem.

The operator would very rarely fell both stems at the same time. This would happen if both stems were small and relatively close enough to each other. Figure 25 shows the harvester felling two stems at the same time. Instead of using the normal technique of grabbing, felling and processing one stem at a time, the operator would grab both stems as a unit and fell both at the same time. The operator would then drop one of the stems before debarking and crosscutting the remaining stem. Once the processing of the first stem was complete, the operator would pick up the other stem and process it.

Figure 25: Harvester felling two stems at the same time

5.2.1.3 Effect of stem form on harvester productivity in coppiced double stems

Separate regression models were developed to investigate the influence of form on productivity. The normal approach during regression model development would be to develop one model that includes all the explanatory variables. However, a different approach was followed due to there being a relationship between stem volume and tree form. The summary statistics as described in detail below, showed this relationship. The true effect of stem form on harvester productivity could therefore be

confounded by the very strong effect of stem volume (multicollinearity – strong relations between two independent variables, that is, stem volume and form). As a result of this, stem form was analysed as a separate variable influencing the productivity of the harvester.

In order to investigate the true effect of stem form on harvester productivity, the data was first sorted and grouped into the different form combinations that occurred, and the mean stem sizes were analysed. Each coppiced double tree as a unit consisted of two stems but those two stems may have had the same form or different forms. These occurrences were called form combinations. From the data, four form combinations were identified; namely poor and poor; poor and good; good and poor; and good and good. The form of the larger stem (stem-one volume) was always presented first and it was matched to the form of the smaller stem (stem-two volume). The coppiced double stems were classified according to the different form combinations under stem-one volume and stem-two volume. The mean stem volumes for the coppiced double stems for the different form combinations are shown in Table 14.

Table 14: Mean volumes for coppiced double stems for different form combinations

Mean Stem-one Mean stem Stem-two Mean stem Mean CCS productivity volume form volume (m3) volume form volume (m3) volume (m3) (m3/PMH)

Poor 0.32 Poor 0.22 0.54 17.5

Poor 0.38 Good 0.09 0.47 15.5

Good 0.35 Poor 0.08 0.44 15.4

Good 0.18 Good 0.08 0.26 9.9

Table 14 shows that the poor x poor form stems yielded the largest mean CCS volume followed by the poor x good, then good x poor and lastly good x good form stems. In relation to the good x good form stem volumes, the poor x poor form stems had a larger tree volume. This can be seen from the difference in stem volume

85 between the poor form stems and the good from stems. Therefore, because of the larger sizes of the poor form stems, a higher mean productivity could be expected. Even though the stems had a poor form, the effect on productivity is not clear as it is confounded by the larger stem size. The poor x poor form trees had the highest mean productivity of 17.5 m3 per PMH followed by the poor x good with 15.5 m3 per PMH; this productivity was closely followed by good x poor with 15.4 m3 per PMH and the lowest productivity was the good x good form trees with 9.9 m3 per PMH. It is, however, important to note that the high productivity of the poor x poor form trees was due to the larger stem sizes.

Figure 26 shows the sample sizes of the tree form combinations. The numbers in brackets refer to the number of times a specific tree form combination was observed. For example 33 coppiced double trees with a poor (larger stem) and poor (smaller stem) form were observed. Most of the trees were of good form compared to the small sample size for the poor form trees. It is also important to consider that very few coppiced double trees observed had a good form large stem matched with a poor form small stem (sample size 13).

600

492 (246) 500

400

300 Sample size

Numberoftrees 200 140 (70)

100 66 (33) 26 (13) 0 Poor x Poor Poor x Good Good x Poor Good x Good Tree form combinations

Figure 26: Sample size for the coppiced double tree form combinations

To clearly detect the effect of form on harvester productivity, the cycle times for both good form and poor form stems were analysed. Table 15 shows the mean cycle times broken down into elements for stem-one volume and stem-two volume trees.

Table 15: Mean cycle times in deci-minutes

STEM-ONE VOLUME STEM-TWO VOLUME CYCLE ELEMENT GOOD FORM POOR FORM GOOD FORM POOR FORM

Swing to tree 0.05 0.05 0.05 0.05

Grab tree 0.05 0.05 0.04 0.04

Fell tree 0.10 0.12 0.10 0.12

Debark 0.33 0.45 0.28 0.33

Crosscut 0.24 0.32 0.17 0.21

Total 0.76 0.98 0.65 0.75

Stem volume (m3) 0.191 0.362 0.079 0.159

Table 15 shows that the cycle times for harvesting good form trees were shorter than the cycle times for poor form trees. This relationship was expected, as poor form trees required more time to process due to the stem crookedness, thick branches and higher number of branches.

(a) Regression models for predicting harvester productivity in coppiced double stands under different tree form classification combinations

The tree form analyses showed that there was a relationship between stem volume and form. It was observed that larger stems had thicker branches and were more crooked, compared to smaller stems that were normally straight with fewer thin branches. Explanatory data analysis showed that there was in fact a negative relationship between these two variables (stem volume and stem form) and that the larger stems were more likely to have poor form than smaller stems. Regression equations for each tree form class were derived, to ensure validity of the models and

87 to avoid arriving at the wrong conclusion that stems with poor form increase productivity, while the increase in productivity is in fact the result of trees with poor stem form being larger on average.

(b) Construction of form models via stepwise regression

The regression models were designed with a single dependent variable (productivity) and independent variables being stem-one volume, stem-two volume, distance between the stems and stem felled first.

The procedure followed for each form class categorical combination; for example poor and poor form; was that the initial model included all effects specified to be included in the design for analysis. The “stepping wise” process was then applied, which requires repeatedly altering the model by adding or removing a predictor variable in accordance with the step criteria.

The backward stepwise method was applied on all tree form class combinations. This was a basic method of building a regression model, whereby for each step the variable with the smallest effect on the dependent variable (thus the large p-value) is removed from the model. If no variable had a p-value beyond the p-values for removal, the stepwise process was terminated.

The removal statistic criteria was that any variable that did not have a significant (p<0.05) influence on the productivity of the harvester was removed until only the significant variables remained in the model. The standard model is presented below and the coefficients for the various tree form combination models are shown in Table 16.

(Productivity (m³/PMH)) = β0 + (β1* stem-one vol) + (β2* stem-two vol) + (β3* (stem- one vol)2) + (β4 * (stem-two vol)2)+( β5 * (felled first dummy))

Table 16: Coefficients of the productivity model for each tree form classification

Effect Poor x poor Poor x good Good x poor Good x good

Intercept (β0) 6.63929 - 7.8740 1.5565

Stem-one vol (β1) 18.28758 55.0268 23.5913 40.5616

Stem-two vol (β2) 22.57935 18.7607 -34.0938 32.6461

2 Stem-one vol (β3) - -42.3987 - -17.5555

2 Stem-two vol (β4) - - 317.7122 -46.3467

Stem felled first (β5) - - -3.1287 -

R² Value 0.67 0.77 0.92 0.89

3 Stem-one vol: Minimum* (m ) 0.11 0.11 0.22 0.01

3 Stem-one vol: Maximum* (m ) 0.75 0.74 0.72 1.06

3 Stem-two vol: Minimum* (m ) 0.04 0.02 0.02 0.01

3 Stem-two vol: Maximum* (m ) 0.44 0.44 0.23 0.44

*Minimum and maximum (range) stem volume limits for the productivity models

When the various form combinations were analysed statistically, different variables were significant. The poor x good productivity model intercept was not significant and therefore was not included in the model. Stem-one volume and stem-two volume were significant variables in all the form productivity models. The good x poor and good x good tree forms had highest R-squared values compared to the poor x poor and poor x good tree forms, which had lower R-squared values of 0.67 and 0.77 respectively. All the models displayed had limits (maximum and minimum stem volumes) that could be used with the specific productivity model; this was because each tree form combination had limits as a result of the sample size and the stem volume ranges.

Overall, the stem form significantly affected the productivity of the harvester. This meant that when stems with poor form were harvested, productivity was lower than

89 when stems with good form were harvested. The poor form stems had thicker branches and were more crooked, resulting in the harvester taking longer to process them. Larger stems with poor form were more difficult to handle and they often needed to be released and grabbed again many times during processing. It was observed that the poor stem form also influenced the crosscutting time negatively. More cuts often had to be made when dealing with heavy branches, forks and severe defects.

Productivity model adequacy checks were conducted for each of the coppiced double tree form models derived. For normality and homoscedasticity tests for each model, refer to Annexure 6.

(d) Modelled harvester productivity results: coppiced double tree form classes

The productivity was modelled for each of the tree form classes. However, because the significant variables in the models differed, productivities could not be compared. Furthermore, some of the models had stem-one volume and stem-two volume as significant variables influencing the productivity of the harvester and therefore various stem volume combinations for stem-one and stem-two volume could occur. A column graph showing the modelled productivities for the various tree form classes is shown in Annexure 7.

5.2.2. Coppiced single trees productivity results and discussion

The sample profile and the results of variables that affected the productivity of the harvester in coppiced single trees are presented below. The variables that were analysed were tree volume and form.

The harvester head had a mean of 2.9 passes along the stem when debarking the coppiced single trees. Furthermore, a mean of three logs per tree was recovered in the sample. The mean number of trees harvested in one productive machine hour was 67.

5.2.2.1 Effect of tree volume on harvester productivity in coppiced single trees

The effect of tree volume on harvester productivity is discussed below.

(a) Sample profile for coppiced single trees

The relationship between sample size and tree volume is shown in Figure 27. The coppiced single trees consisted of different tree sizes, with most of the trees observed being less than 0.4 m3. However, the highest number of observations (67 per cent) occurred in tree sizes of less than 0.2 m3.

Expected Normal 200

180 174

160 151

140

120

100

No No of obs 80 63 60 55

40 20 20 11 2 4 3 2 0 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 1.1 1.2 X<= Category Boundary

Figure 27: Sample distribution for coppice single stems

Table 17 shows the descriptive statistics of coppiced single trees. The coppiced single stems had a mean tree volume of 0.18 m3. The tree volumes ranged from 0.01 m3 to 1.02 m3. The mean productivity of the harvester when operating in coppiced single stems was 12.1 m3 per PMH.

Table 17: Descriptive statistics of coppiced single stems

Variable Mean Minimum Maximum Standard deviation

Tree volume (m3) 0.18 0.01 1.02 0.16

Productivity (m3/PMH) 12.1 1.0 41.9 7.9

Figure 28 is a scatterplot that visualises the relationship between the variables volume (x) and productivity (y). These two variables are strongly related, demonstrated by the data points forming a systematic curve. The productivity of the harvester increased as the tree volume increased.

/PMH) 3 25

Productivity Productivity (m 15

0 0.0 0.2 0.4 0.6 0.8 1.0 1.2

3 Tree v olume (m )

Figure 28: Scatterplot showing the relationship between productivity and tree volume

(b) Productivity regression model: tree volume

The productivity model for predicting harvester productivity in coppiced single trees is presented below and the model coefficients are shown in Table 18.

(Productivity (m3/PMH)) = β0 + (β1* tree volume) + (β2* tree volume2)

Table 18: Coefficients of the coppiced single productivity model

Effect Harvester

Intercept (β0) 2.4940

Tree volume (β1) 61.9498

2 Tree volume (β2) -26.5277

2 R value 0.872218

Productivity as calculated in the model is the estimated mean productivity for a given tree volume. The tree volume coefficient is positive which indicated that as tree volume increased then productivity also increased. Tree volume squared is negative, which shows that the productivity increase as tree volume increases is not linear. The regression equation was significant at the five per cent level. The R-squared value of 0.87 shows that the regression line fits the observed harvester productivity well.

The normality and homoscedasticity tests were analysed to determine if the underlying assumptions were violated.

(i) Normality test

A test for normality was done on the raw data in order to determine if the error terms were normally distributed. The points were not lying along the line in the normal probability plot and therefore the assumptions of normality were not met. The raw data was then evaluated for any outliers, and once identified they were removed from the data. The outliers where identified by finding points on the scatter plots that were indicative of random errors in the data (Hill & Lewicki, 2006). The points were evaluated in the raw data to see if they were extreme values or errors in the data. If the point was an error in the data capturing then the point was removed. After removing outliers, the assumption of normality was reasonable except for the extreme points along the normality plot (see Figure 29). In visualising the straight line, more emphasis should be placed on the central values of the plot than on the extreme values (Montgomery, 1997).

Normal Prob. Plot; Raw Residuals Dependent variable: Productivity (Analysis sample) 4

.99 2 .95

1 .85

.65 0 .35

-1 .15

ExpectedNormal Value .05 -2 .01

-3

-4 -15 -10 -5 0 5 10 15 Residual

Figure 29: Harvester - Coppiced single normal probability plot

(ii) Homoscedasticity test

As per Figure 30, homoscedasticity of the error terms was investigated, the figure shows a plot of residuals versus predicted values, which checks for homoscedasticity. The residuals are scattered in the form of a cloud around the zero-line with very little evidence of the ‘fan’ pattern typical of heteroscedasticity. The assumption of homoscedastic variance was met. Therefore, the productivity equations developed can be used to predict the productivity of the harvester.

Predicted vs. Residual Values Dependent variable: Productivity (Analysis sample) 15

Raw Raw Residuals -5

-10

-15 -5 0 5 10 15 20 25 30 35 40 45 Predicted Values

Figure 30: Harvester – coppiced single residual versus predicted plot

(d) Modelled harvester productivity results: coppiced single trees

Figure 31 shows the modelled harvester productivity for increasing coppiced single tree volumes.

45 38.5 40 36.8 37.9 35.1 32.9 35 30.1

30 26.8

/PMH) 3 25 23.0

20 18.7 Coppiced single 13.8 15

Productivity (m Productivity 10 8.4 5.5 5

0 0.05 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 1.1 Tree volume (m3)

Figure 31: Modelled harvester productivity per coppice single tree volume

The productivity of the harvester increased as the tree volume of the coppiced single trees increased. The productivity of the harvester ranged from 5.5 m3 per PMH for 0.05 m3 trees to 38.5 m3 per PMH for 1.1 m3 trees. Even though the productivity has been modelled, no coppiced single trees were observed between 0.9 m3 and 1.0 m3 trees. Furthermore, coppiced single tree volumes ranging from 0.6 m3 to 1.1 m3 had a small sample size (n=22; 4.5 per cent of the total sample). The decrease in sample size beyond trees of 0.5 m3 gives an indication that in pulpwood compartments individual trees are predominantly smaller in size. Therefore, caution must be taken when using the model beyond 0.6 m3 trees.

5.2.2.2 Effect of form on harvester productivity in coppiced single trees

To investigate the influence of tree form on coppiced single trees, a model was developed which included the influence of tree form. As with the coppiced double stems, there was a strong relationship between tree volume and tree form. Refer to Section 5.2.1.3 for a description of the effect on this relationship. Therefore, due to multicollinearity between tree volume and tree form, a separate regression model was derived apart from the main model described in Section 5.2.2.1. Figure 32 shows the number of coppiced single trees which had poor and good form. The average tree volume for poor and good form trees was 0.39 m3 and 0.14 m3 respectively. Most of the coppiced single trees had good form and a few trees had poor form.

450 418 400

350

300

250

200

Numberoftrees 150

100 67 50

0 Poor form Good form Tree form class

Figure 32: Coppiced single sample size for each tree form classification

(a) Coppiced single productivity regression model: tree volume and form

Productivity (the dependent variable) was derived as a function of the independent variables, which were tree volume, tree volume squared and form as a dummy variable. The statistically significant model is presented below, and the model coefficients are shown in Table 19.

(Productivity (m³/PMH)) = β0 + (β1* tree volume) + (β2* (tree volume)²) + (β3* Form dummy)

Table 19: Coefficients of the coppiced single form productivity model

Effect Harvester

Intercept (β0) 1.4980

Tree volume (β1) 62.6611

Tree volume 2 (β2) -26.0416

Form dummy (β3) 0.9736

R² Value 0.873293

Tree volume, tree volume squared and form variables were statistically significant. All the coefficients included were significant at the five per cent level of significance. The R-squared value was 0.87, which indicated that the regression line fit was good.

Productivity model adequacy checks were conducted for the coppiced single tree form model derived. For a description of the normality test and homoscedasticity test results, refer to Annexure 8.

(b) Modelled harvester productivity results: coppiced single trees with good and poor form

Figure 33 shows the relationship between productivity and tree volume for good and poor form coppiced single trees. The two curves show harvester productivity in good and poor form coppiced single trees.

35.0 30.7 30.0 27.3

29.7

25.0 23.4

26.3 /PMH)

3 18.9 20.0 22.4

14.0 Good form 15.0 18.0 Poor form 8.5 13.0

Productivity(m 10.0

5.0 7.5

0.0 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 Tree volume (m3)

Figure 33: Relationship between productivity and tree volume for good and poor form coppiced single trees.

Good form trees had slightly higher productivity than poor form trees of the same size. The sample size for the poor form trees (67) was small compared to the good form trees (418). For 0.1 m3 trees, harvester productivity was 7.5 m3 per PMH when harvesting poor form trees, and 8.4 m3 per PMH for good form trees. The difference in productivity between poor and good form trees was similar across the entire range of tree sizes encountered. Processing of a poor form tree is delayed slightly depending on the extent of stem crookedness as well as number and thickness of branches.

5.2.3 Planted trees productivity results and discussion

The sample profile and the results of variables that affected the productivity of the harvester in planted trees are presented below. The variables that were analysed were tree volume and tree form.

The harvester had a mean of 2.6 passes per tree when processing the planted trees. Furthermore, a mean of three logs was recovered from each tree in the whole

99 sample. The mean number of trees harvested in one productive machine hour was 73.

5.2.3.1 Effect of tree volume on harvester productivity in planted trees

The effect of tree volume on productivity is discussed below.

(a) Sample profile for planted trees

As per Figure 34, most of the trees that were observed had volumes ranging between 0.1 m3 and 0.5 m3.

160

138 140 135 127

120

100

80 79

No. of obs. of No.

43 40

20 13 7 0 0 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 Tree volume (m3)

Figure 34: Sample distribution for planted trees

Table 20 shows the descriptive statistics of tree volume and productivity for planted trees. The planted trees had a mean tree volume of 0.23 m3. The minimum and maximum tree volumes indicate that there was great variation in tree volume. The harvester was able to achieve a mean productivity of 16.6 m3 per PMH.

Table 20: Descriptive statistics of planted trees

Variable Mean Minimum Maximum Standard deviation

Tree volume (m3) 0.23 0.02 0.67 0.16

Productivity (m3/PMH) 16.6 1.7 43.2 8.8

100

Figure 35 is a scatterplot that visualises the relationship between the two variables volume (x) and productivity (y). The data points form a systematic curve, showing that the two variables are strongly related. As tree volume increased, productivity also increased. The increase in productivity shows the strong influence of tree volume on harvester productivity.

/PMH)

3 25

Productivity (m Productivity 15

0 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 Tree volume (m3)

Figure 35: Scatterplot showing relationship between productivity and tree volume

(b) Productivity regression model: tree volume

A regression model was developed to predict the productivity of the harvester in planted trees. The productivity (dependent variable) was derived as a function of the independent variables, which were tree volume, tree volume squared and form. The statistically significant model is presented below and the model coefficients are shown in Table 21.

(Productivity (m³/PMH)) = β0 + (β1* tree volume) + (β2* (tree volume)²)

101

Table 21: Coefficients of the planted trees productivity model

Effect Harvester

Intercept (β0) 1.5782

Tree volume (β1) 83.9645

Tree volume 2 (β2) -56.0942

R² Value 0.880687

Only the tree volume and tree volume squared variables were statistically significant (at the five per cent level) in the model developed for the harvester. Form did not significantly influence the harvester’s productivity. The R-squared value (coefficient of determination) was 0.88 which indicated that the regression line fit was good.

To check if the planted trees productivity model was adequate, the normality and homoscedasticity tests were analysed.

(i) Normality test

The assumption of normality was evaluated using the normal probability plot of residuals. The test for normality was performed on the residuals in order to determine if the error terms were normally distributed. The visual inspection of error terms is shown in Figure 36. The points were lying along the line in the normal probability plot and therefore the assumptions of normality were met.

102

Normal Prob. Plot; Raw Residuals Dependent variable: Productivity (Analysis sample) 4

.99 2 .95

1 .85

.65 0 .35

-1 .15

Expected Normal Value Expected .05 -2 .01

-3

-4 -15 -10 -5 0 5 10 15 Residual

Figure 36: Harvester – planted normal probability plot

(ii) Homoscedasticity test

Homoscedasticity of the error terms was tested. The plot shows a “fanning out” pattern which is indicative of heteroscedasticity. The presence of homoscedasticity precludes proper hypothesis testing, therefore raising the possibility of drawing misleading conclusions (Gujarati & Porter, 2010). Figure 37 shows the predicted versus residual plot.

103

Predicted vs. Residual Values Dependent variable: Productivity (Analysis sample) 15

RawResiduals

-5

-10

-15 0 5 10 15 20 25 30 35 40 Predicted Values

Figure 37: Harvester – planted predicted versus residual plot

Regression statistics are known to be robust to at least some of these underlying assumptions, therefore violations of the homoscedastic terms are not usually of concern unless they are severe (Jacques & Norusis, 1973; Zar, 2010). However, even though the plot showed heteroscedasticity, the productivity equations developed could be used to predict the productivity of the harvester in planted stands.

(d) Modelled harvester productivity results: planted trees

Figure 38 shows the modelled harvester productivity for increasing planted tree volumes.

104

35 32.9 31.8 29.5 30 26.2

21.7 /PMH) 3 20 16.1 15 Planted

9.4 Productivity(m 10 5.6 5

0 0.05 0.1 0.2 0.3 0.4 0.5 0.6 0.7 Tree volume (m3)

Figure 38: Modelled harvester productivity per planted tree volume

The productivity of the harvester increased as the tree volume of the planted trees increased. According to the piece size law, greater productivity is attained when processing larger piece size than smaller piece size trees. The productivity of the harvester ranged from 5.6 m3 per PMH for 0.05 m3 trees to 32.9 m3 per PMH for 0.7 m3 trees. The sample size for the planted tree volumes ranging from 0.6 m3 to 0.7 m3 was small (n=20, 3.7 per cent of the total sample). The decrease in sample size beyond trees of 0.5 m3 gives an indication that in planted pulpwood compartments individual trees are predominantly smaller in tree size.

From the regression analysis, tree form in planted trees was not a significant variable affecting the productivity of the harvester as trees predominantly had good form. The debarking and crosscutting elements were efficient operations as a result of the good form of the trees. The harvester could move and fell trees easily as the compartment stocking was good.

105

5.2.4 Summary and comparison of harvester productivity results and discussion in coppiced double, coppiced single and planted trees

A summary of the main productivity results and discussion is given below, followed by a comparison of the modelled productivity results.

5.2.4.1 Summary of productivity results discussion

The coppiced double stems productivity results showed that CCS volume was a significant variable influencing harvester productivity. As the CCS volume increased, the productivity increased. Furthermore, the stem felled first as well as tree form influenced harvester productivity significantly in coppiced double stems. The regression results showed that if the smaller stem was felled first, the productivity would increase if the larger stem’s volume was less than 0.18 m3; however where the larger stem was greater than 0.18 m3, the relationship was reversed. When harvesting coppiced double stems with poor form, the harvester productivity was lower compared to harvesting trees with good form. As a result of the poor form trees, the debarking and crosscutting element times were extended; hence lower productivity in coppiced double stems. Further research will be required to further investigate the effect of other individual cycle elements on harvester productivity in coppiced double stems.

In coppiced single trees, the tree volume and tree form were both significant variables influencing the harvester’s productivity. As the volume of coppiced single trees increased, the productivity also increased. The harvester’s productivity was slightly higher when harvesting good form trees compared to poor form trees.

Productivity results of the planted trees showed that tree volume influenced the harvester’s productivity significantly. As tree volume increased, harvester productivity also increased. The tree form was not a significant variable influencing harvester productivity.

5.2.4.2 Comparison of modelled productivity results

The regression coefficients from the productivity models were used to determine the productivity levels for representative tree volumes. When determining the productivity values, only the effect of tree volume was used in the models. The other variables such as tree form and stem felled first were not considered, as they were

106 not significant in all the regression equations. Harvester productivity was significantly influenced by form in coppiced double and coppiced single stems but not in planted trees. Figure 39 shows a comparison of productivity between coppiced double, coppiced single and planted trees. A maximum tree volume of 0.5 m3 was used as the cut-off point of comparison, as most pulpwood trees indicated in the descriptive statistics (sample sizes) occurred below this tree volume.

35.0

29.5 30.0 26.2

25.0 21.7 26.8

20.0 23.0 16.1 Planted 18.7 17.0 15.0 14.5 Coppiced single 9.4 13.8 11.8 Coppiced double Productivity(m3/PMH) 10.0 8.7 8.4 5.0 5.3

0.0 0 0.1 0.2 0.3 0.4 0.5 0.6 Tree volume (m3)

Figure 39: Comparison of harvester productivity between coppiced double, coppiced single and planted trees

The harvester had the highest productivity when it worked in the planted compartments across all tree volumes. When the harvester worked with coppiced single trees, it had slightly lower productivity than that of the planted trees. The productivity amongst small trees (0.1 m3) was narrow between the planted and coppiced single stems, but as tree volumes increased, the predicted productivities moved apart and slightly narrowed with larger trees (0.5 m3). The lowest productivity across all tree sizes was predicted when the harvester worked with coppiced double trees.

107

Caution must be taken when evaluating the productivities of the coppiced double and coppiced single independently. In reality compartments normally comprise a mixture of coppiced double and coppiced single trees, depending on the survival rate of stumps that have resprouted. Therefore, the productivities must be evaluated and applied according to the specific compartment composition of both coppiced double and coppiced single stems. Table 22 shows results of expected harvester productivity per PMH with different coppiced double and single tree proportions under various tree volume scenarios.

Table 22: Harvester productivity under various coppiced double and coppiced single proportions and tree volumes

Predicted productivity (m3/PMH): different mean Proportion (%) tree volumes

Coppiced Coppiced 0.1m3 0.2m3 0.3m3 0.4m3 0.5m3 0.6m3 double % single %

100 0 5.3 8.7 11.8 14.5 17 19.1

75 25 6.1 10.0 13.5 16.7 19.4 21.9

50 50 6.9 11.3 15.2 18.8 21.9 24.6

25 75 7.6 12.5 17.0 20.9 24.4 27.4

0 100 8.4 13.8 18.7 23.0 26.8 30.1

Productivity is lower when more coppiced double stems have to be harvested, but as the proportion of coppiced single trees increases the productivity also increases. The tree volumes in the table are representative of the tree volumes that occurred in the pulpwood stands researched. It is important to note that the productivity of the coppiced double stems was derived from the combined tree volume of both stems and the total time to harvest the trees.

108

5.3 STUMP VOLUME WASTE RESULTS AND DISCUSSION

The results of the stump volume wasted in coppiced double, single and planted compartments are presented below. Due to the very small stump volume values derived from the statistical analysis, six decimal places were used.

5.3.1 Coppiced double stump volume waste results and discussion

The results of coppiced stumps are reported below. Only excess stump volumes with no defects such as butt swell, decay and crook were reported in the results. Excess stump height refers to any stump height above the specified maximum stump height standard of 10 cm above previous rotation stump. The coppiced double stump volume waste refers to the combined stump volume of both stems.

5.3.1.1 Descriptive statistics for coppiced double stumps

Table 23 shows the descriptive statistics (sample size, sample percentages, the mean stump waste, upper and lower confidence limits and standard deviation) for the coppiced double stumps with waste, without waste and the total sample mean waste.

Table 23: Descriptive statistics for coppiced double stumps

Stumps Sample % Mean (m3) CI lower CI upper Standard size limit 95% limit 95% deviation With waste 151 42 0.003073 0.002298 0.003846 0.004815

Without waste 211 58 0 0 0 0

Total sample waste 362 100 0.001268 0.000917 0.001625 0.003442

* CI – Confidence interval

Of the total sample, 42 per cent consisted of stumps with waste, and 58 per cent did not have waste. The mean stump volume wasted per stump with waste was 0.00307 m3. The total sample mean volume waste (0.001268 m3) included stumps with and without waste. The confidence intervals (CI) provide an upper and lower limit within which the mean stump waste volume per stump with waste and total sample mean waste is expected to lie. The CI margin of error is five per cent. The confidence intervals for stumps with waste and total sample waste were wide, which reflected

109 that the mean was unstable. An unstable mean is one that will vary from one sample to another. The volume standard deviation for stumps with waste and total sample waste shows how much variation existed from the mean. The volume standard deviation for stumps with waste (0.004815 m3) was high, due to the stump volumes being spread out over a large range of values. The mean excess stump height and stump diameter for all coppiced stumps with waste was 10.7 cm and 14 cm respectively. The excess stump heights ranged from 2 cm to 56 cm.

5.3.1.2 Sample profile for coppiced double stumps with waste

As indicated in 5.3.1.1 there were 151 coppiced double stumps with waste. The histogram displayed as Figure 40 shows the number of observations plotted against the waste per stump.

100

90 87

No No of obs 40

30 30

20 18

10 7 1 1 2 2 1 1 1 0

0.000 0.002 0.004 0.006 0.008 0.010 0.012 0.014 0.016 0.017 0.019 0.021 0.023 0.025 0.027 0.029 0.031 0.033 0.035 0.037

3 Volume wasted (m )

Figure 40: Coppiced double waste volume sample size

Most of the volume that remained on the stump was less than 0.006 m3 per stump and constituted 89.4 per cent of the total sample. The histogram shows that as the stump volume increased, the amount of observations decreased, meaning that most stumps with waste had very small waste quantities. The stump volume waste less

110 than 0.002 m3 had 87 observations (57.6 per cent), however the stump volume waste greater than 0.3 m3 comprised one observation (0.7 per cent).

5.3.1.3 Relationship between tree volume and stump volume waste

Figure 41 displays the amount of stump waste per tree for all sampled coppiced double trees.

0.040

0.035

0.030

) 0.025

0.020

0.015

Stump waste volume (m volume waste Stump 0.010

0.005

0.000

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6

3 Tree volume (m ) 0.95 Conf.Int.

Figure 41: Coppiced double stump waste volume over tree volume

As tree volume increased, the stump wasted volume per tree increased. This was due to larger trees having larger diameter stumps than smaller trees of the same stump height. Smaller trees had less waste and as tree volume increased there was greater variation in stump volume waste. For a small diameter tree to have the same volume of waste as a large diameter tree, the stump height of the smaller diameter tree must be higher than that of the larger tree.

The amount of stump waste as a percentage of the total tree volume is shown in Figure 42.

111

Scatterplot: Tree volume vs. Waste % 16

Waste % Waste 6

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 Tree volume (m3)

Figure 42: Coppiced double stems waste percentage over tree volume

As tree volume increased, the amount of waste as a percentage of the total tree volume decreased slightly. This was because a consistent stump volume wasted constituted a higher percentage with smaller trees, compared to the same waste incurred when the tree volume was bigger. This was as a result of the amount of volume waste incurred relative to the specific tree size as a whole. The piece size of small trees is already low; therefore further waste due to high stumps has a more pronounced effect on the total volume recovery.

5.3.1.4 Key factors observed that influenced the stump volume waste in coppiced double stems

When harvesting coppiced double stems the delimbing arms of the harvester head have to be able to wrap around one stem, and once the head is in position the felling cut is made. In certain cases the distance between coppiced double stems was small, making it impossible for the operator to grab a single stem at the base properly. As a result the operator felled the stem slightly higher.

Coppiced stems grow as multiple stems from a single tree stump; hence, with certain stems the operators did not want to fell the stems too low as the bottom part of the

112

stem had poor form. Therefore, if the coppiced stem was cut too low, the operator would have to trim off the butt end so that the log had a good cylindrical shape.

5.3.2 Coppiced single stumps volume waste results and discussion

The results of coppiced single stump volume wasted are reported and discussed below.

5.3.2.1 Descriptive statistics for coppiced single stumps

The coppiced single stumps descriptive statistics (sample size, sample percentages, the mean waste volumes, upper and lower confidence limits, and standard deviation) are presented in Table 24.

Table 24: Descriptive statistics for coppiced single stumps

Stumps Sample % Mean CI lower CI upper Standard size (m3) limit 95% limit 95% deviation With waste 112 23 0.001954 0.001336 0.002572 0.003302

Without waste 372 77 0 0 0 0

Total sample waste 484 100 0.000453 0.000293 0.000613 0.001787

* CI – Confidence interval

Of the total sample size, 23 per cent consisted of stumps with waste and 77 per cent did not have waste. The mean stump volume wasted per stump with waste was 0.00195 m3 and the total sample mean waste was 0.000453 m3 per stump. The confidence intervals for stumps with waste and total sample waste were wide, which reflected that the mean was unstable. The volume standard deviation for stumps with waste (0.003302 m3) was high due to the stump volumes being spread out over a large range of values. The mean excess stump height and stump diameter for all coppiced stumps with waste was 9.4 cm and 16.2 cm respectively. The excess stump heights ranged from 1 cm to 36 cm.

113

5.3.2.2 Sample profile for coppice single stumps with waste

As indicated in 5.3.2.1, there were 112 coppiced single stumps with waste. The histogram displayed as Figure 43 shows the number of observations plotted against the volume wasted.

90 84 80

No of No obs

20 18

10 3 2 1 2 1 1 0 0.002 0.004 0.006 0.008 0.010 0.012 0.014 0.016 0.018 0.020 0.022 0.024

3 Volume wasted (m )

Figure 43: Coppiced single waste volume sample size

Most of the volume that remained on the stump ranged from 0.001 m3 to 0.005 m3 per stump (91.1 per cent). The histogram shows that as stump volume waste increased, the amount of observations decreased, meaning that most stumps with waste had very small waste quantities.

5.3.2.3 Relationship between tree volume and stump volume waste

Figure 44 displays the amount of waste for the individual coppiced single trees.

114

Scatterplot:Coppice single tree volume vs. Stump waste volume 0.022

0.020

0.018

0.016

)

3 0.014

0.012

0.010

0.008

Stump waste volume (m volume waste Stump 0.006

0.004

0.002

0.000

0.0 0.2 0.4 0.6 0.8 1.0 1.2

3 Tree volume (m ) 0.95 Conf.Int.

Figure 44: Coppiced single stump waste volume over tree volume

As tree volume increased, the volume of stump waste per tree also increased. This was due to larger trees having larger stump diameters. With smaller trees, the waste volume is much less, but as the trees increased in size, there was more variation in the volume of stump waste per tree.

The volume of waste per stump for coppiced single trees as a percentage of the total tree volume is shown in Figure 45.

115

Scatterplot:Coppice single vs. Waste % 14

Waste % Waste

0.0 0.2 0.4 0.6 0.8 1.0 1.2 Tree volume (m3)

Figure 45: Coppiced single stems waste percentage over tree volume

As tree volume increased, the amount of waste as a percentage of the total volume decreased. This was because a consistent volume of stump waste constituted a higher percentage with smaller trees, compared to the same waste incurred when the tree volume was larger. This was as a result of the amount of volume of waste incurred relative to the specific tree size as a whole.

5.3.2.4 Key factors observed that influenced the stump volume waste in coppiced single trees

As with coppiced double stumps, a factor that influenced stump height was that the operators did not want to cut the coppiced stems too low as the bottom part of the stem had a poor form. Some of the coppiced single trees had a swell at the bottom of the stump, which indicated where the new tree had grown from the previous rotation stump. The stem shape at the butt end where the stem was attached to the previous rotation stump was not always straight and round as in a normal planted tree. The harvester operator could not always see the base of the tree clearly. Therefore, if the coppiced stem was cut too low, then during crosscutting the operator would have to trim off the swell at the butt end so that the log had a good cylindrical shape. As a result, the operator occasionally made the felling cut a bit

116

higher to avoid cutting the tree too low and having to trim off the butt end during crosscutting, which contributed to volume wastage.

5.3.3 Planted trees stump volume waste results and discussion

The results of planted trees stump volume wasted are reported and discussed below.

5.3.3.1 Descriptive statistics for planted stumps

The planted stumps descriptive statistics (sample size, sample percentages, the mean waste, upper and lower confidence limits, and standard deviation) are presented in Table 25.

Table 25: Descriptive statistics for planted stumps

Stumps Sample % Mean CI lower CI upper Standard size (m3) limit 95% limit 95% deviation With waste 141 26 0.001650 0.001726 0.002183 0.001928

Without waste 401 74 0 0 0 0

Total sample waste 542 100 0.000430 0.001152 0.001298 0.001220

* CI – Confidence interval

Of the total sample size, 26 per cent of the stumps had waste and 74 per cent did not have waste. Even though the proportion of the stumps with waste was 26 per cent, these stumps had a low mean stump volume of 0.001650 m3. The mean waste for all stumps was 0.000430 m3. The confidence intervals for stumps with waste and total sample waste were wide, which reflected that the mean was unstable. The volume standard deviation for stumps with waste (0.001928 m3) was high due to the stump volumes being spread out over a large range of values. The mean excess stump height and stump diameter for stumps with waste was 8.1 cm and 15.6 cm respectively. There was a wide variation in excess stump heights, the range varied from 2 cm to 32 cm.

117

5.3.3.2 Sample profile for planted stumps with waste

In total there were 141 planted stumps with waste measured from the different research areas assessed. The histogram displayed as Figure 46 shows the number of observations conducted, plotted against the amount of stump waste per tree.

120

104 100

No. of obs.

24 20

9 2 0 0 1 1 0 0.000 0.002 0.004 0.006 0.008 0.010 0.012 0.014

Volume wasted (m3)

Figure 46: Planted waste volume sample size

Most of the volume that remained on the stump ranged from 0.001 m3 to 0.002 m3 per stump (n= 104; 73.8 per cent of the total sample). The histogram also gives an indication that as the remaining stump volume increased, the amount of observations decreased, indicating there was not much excessive volume remaining per stump. The amount of waste observed was minimal.

5.3.3.3 Relationship between tree volume and stump volume waste

Figure 47 displays the amount of waste for the planted trees.

118

0.016

0.014

0.012

)

3 0.010

0.008

0.006

Stump waste volume (m

0.004

0.002

0.000 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7

Tree volume (m3) 0.95 Conf.Int.

Figure 47: Planted stump waste volume over tree volume

As tree volume increased, the volume of stump waste per tree also increased. A few random trees (tree volumes 0.113 m3 and 0.132 m3) had high stump heights. The stump waste volume for the 0.113 m3 tree was 0.014 m3; as visible in the plot this volume was excessively higher than the other stump volume wastes. The reason for this very high stump was not determined. It is however, suspected that this was due to an error by the operator as this tree was felled.

The amount of stump waste for the planted trees as a percentage of the total tree volume is shown in Figure 48.

119

Waste %

-2 -0.1 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7

Tree volume (m3)

Figure 48: Planted waste percentage over tree volume

As tree volume increased, the amount of waste as a percentage of the total volume decreased. Most of the stump volume wasted as a percentage of the tree volume was below two per cent. This was because minimal stump volume was wasted in the planted trees. As described in Figure 47 above, the excessive stump waste data point is also visible on this plot (Figure 48). The stump waste volume of 0.014 m3 constituted a large percentage of the 0.113 m3 tree.

5.3.4 Summary results and discussion of coppiced double, coppiced single and planted stump wastages

In coppiced double, coppiced single and planted trees, as tree volume increased, the volume of stump waste per tree also increased. This was due to larger trees having thicker stump diameters. In coppiced double, coppiced single and planted trees the scatterplots showed that as tree volume increased, the amount of waste as a percentage of the total tree volume decreased. This was because a given volume of stump waste would constitute a higher percentage with smaller trees, compared to the same waste incurred with larger trees.

120

A key factor which influenced the stump volume waste – in coppiced double trees only – was that in certain cases the distance between coppiced double stems was inadequate such that the operator could not grab a single stem at the base properly. As a result, the operator felled the stem slightly higher, thereby increasing the stump volume wasted. Another key factor which influenced the stump volume waste in both coppiced double and coppiced single trees was that some of these trees had poor form (irregular shape) at the butt end due to resprouting. Because of the poor form, the operator usually made the felling cut slightly higher to avoid cutting the tree too low and having to trim off the butt end during crosscutting.

Figure 49 shows the mean stump volume wasted per stump with waste and the total sample mean volume wasted in coppiced double, coppiced single and planted trees.

0.0035 0.00307 0.003

0.0025

Coppice double stumps (with waste)

0.001954 0.002 0.00165 Coppice double stumps (sample total waste)

0.0015 0.001268 Coppice single stumps (with waste) Meanvolume(m3) 0.001 Coppice single stumps (sample total waste) 0.000453 0.00043 0.0005 Planted stumps (with waste)

0 Planted stumps (sample total waste) Coppice Coppice Coppice Coppice Planted Planted double double single single stumps stumps stumps stumps stumps stumps (with (sample (with (sample (with (sample waste) total waste) total waste) total waste) waste) waste)

Figure 49: Comparison of stump volume waste for coppiced double, coppiced single and planted trees

The coppiced double stems had the highest stump volume waste means, followed by the coppiced single trees, the least stump volume wasted being in the planted trees. 121

The coppiced double and coppiced single stumps had higher mean volume wastes due to factors described in detail in Sections 5.3.1 and 5.3.2.

5.3.5 Calculation of lost revenue per hectare for coppiced double, coppiced single and planted trees for the research site

The mean stump volume needed to be extrapolated into stump wastage per hectare. This enabled the calculations of the economic implications of the stump volume wasted to be calculated. Actual timber yields delivered to a mill are compared with planned figures; if the variance is greater than 15 per cent, a full investigation is conducted (Immelman, personal communication, 2012). The Sappi pre-harvest agreement states the waste management targets in Eucalyptus species must be equal to or less than 0.5 per cent (Sappi, 2011). The proportion of stems with waste can vary depending on specific compartment conditions and operational techniques. The waste volumes and percentages found in the research are assumed to represent normal volumes and percentages.

According to van Wyk et al. (2011) the minimum live stems per hectare in a coppiced compartment are not allowed to be lower than 1100 stems. The stems can be brought back to the original stocking of the planted stand by leaving additional stems per stump to compensate for the dead ones. The minimum of 1100 live stems per hectare was used as a reference for the calculation.

In October 2011, the sales price per tonne for Eucalyptus pulpwood delivered to the mill was R490 (Crickmay & Associates, 2011). The stump waste volumes determined were converted to tonnes using a conversion factor of 1.47 m3/tonne (Bredenkamp, 2000).

 Coppiced double calculation Mean stump volume wasted : 0.00307 m3/stump with waste

Stems per hectare : 1100 spha

Proportion of coppiced double stems with waste : 42%

Therefore wastage: 0.00307 x 1100 x 42% = 1.418 m3/ha

Conversion of m3 to tonnes: 1.418 / 1.47 = 0.965 tonnes/ha

122

Lost revenue per hectare: 0.965 x 490 = R472.85/ha

 Coppiced single calculation Mean stump volume wasted : 0.00195 m3/stump with waste

Stems per hectare : 1100 spha

Proportion of coppiced single stems with waste : 23%

Therefore wastage: 0.00195 x 1100 x 23% = 0.493 m3/ha

Conversion of m3 to tonnes: 0.493 / 1.47 = 0.335 tonnes/ha

Lost revenue per hectare: 0.335 x 490 = R164.15/ha

 Planted calculation Mean stump volume wasted : 0.001650 m3/stump with waste

Stems per hectare : 1100 spha

Proportion of planted trees with waste : 26%

Therefore wastage: 0.001650 x 1100 x 26% = 0.472 m3/ha

Conversion of m3 to tonnes: 0.472 / 1.47 = 0.321 tonnes/ha

Lost revenue per hectare: 0.321 x 490 = R157.29/ha

The calculations show the effect of the volume waste on potential income. Coppiced double stems had the highest income loss, followed by the coppiced single stems, and lastly planted trees. The cost for an individual stem may seem insignificant but it is important to consider the cumulative financial implications of the total waste in a plantation or on the forestry industry as a whole.

5.4 RELATIONSHIP BETWEEN HARVESTER PRODUCTIVITY AND STUMP VOLUME WASTAGE

This section discusses the relationship between harvester productivity and stump volume wasted per hectare. The stump volume wasted per hectare was calculated by multiplying the total sample mean waste by the stems per hectare (1100). The

123

productivity and stump volume wasted by the harvester in coppiced double, coppiced single and planted tree compartments has already been determined. The productivity and stump volume waste results and discussion sections (5.2 and 5.3) provided the required basic information to determine this relationship. For productivity comparison purposes, in this section a representative tree volume of 0.2 m3 was used as a reference value and the stump waste volume has been calculated per hectare. Table 26 shows productivity and stump volume wasted per hectare values for coppiced double, coppiced single and planted trees

Table 26: Comparison between productivity and mean stump waste for coppiced double, coppiced single and planted trees

Variables Coppiced double Coppiced single Planted trees

stems trees

Productivity (m3/PMH) 8.7 13.8 16.1

Waste volume per hectare (m3) 1.418 0.493 0.472

Harvester productivity in coppiced double stems was 8.7 m3 per PMH and the mean stump volume wasted was 1.418 m3 per hectare. The results depict that in coppiced double compartments the productivity was low, and the stump volume waste was relatively high compared to the coppiced single stems and planted trees. Even though the stump volume waste is not excessively high, the loss in harvester productivity may cost more per hectare than the actual lost volume. The variables such as stem felled first and tree form also played a significant role in the productivity and the overall performance of the harvester.

The harvester productivity in coppiced single stems was 13.8 m3 per PMH and the stump volume wasted was 0.493 m3 per hectare. The results depict that in coppiced single compartments the productivity was moderate and the stump volume waste was lower compared to the coppiced double stems. Tree form played a significant role in the productivity of the harvester. Due to the poor form of the coppiced double and coppiced single trees the harvester felled the trees slightly higher which contributed to the higher stump volume wasted. The harvester productivity in planted

124 trees was 16.1 m3 per PMH and the stump volume wasted was 0.472 m3 per hectare. The results depict that in planted compartments the productivity was high and the stump volume waste was slightly low compared to the coppiced double and coppiced single stems.

5.4.1 Summary of productivity and stump volume waste results

Figure 50 shows productivity and stump volume waste for coppiced double, coppiced single and planted trees. The line graphs indicate the increasing productivity, and the bar graphs show the volume per hectare of stump volume wasted.

35.0

29.5 30.0 26.2

25.0 26.8

21.7 /PMH)

3 23.0 20.0 16.1 Planted 17.0 18.7 Coppiced single 15.0 14.5 Coppiced double 9.4 13.8 11.8 Productivity(m 10.0 8.7 8.4 5.0 5.3

0.0 0 0.2 0.4 0.6 Tree volume (m3)

125

1.600

1.395

) 1.400 3 1.200

1.000

0.800 Coppiced double stumps

0.600 0.498 0.472 Coppiced single stumps 0.400 Planted stumps

0.200 Waste Waste volumeper hectare (m 0.000 Coppiced Coppiced Planted double single stumps stumps stumps

Figure 50: Harvester productivity for coppiced double, coppiced single and planted trees (top) Stump volume per hectare for coppiced double, coppiced single and planted stumps (bottom)

Planted trees had the highest productivity and the lowest stump volume waste; the coppiced single trees had moderate productivity and moderate stump waste and the coppiced double trees had the lowest productivity and highest stump waste.

In reality the coppiced compartment composition (proportion of coppiced double and single stems) may vary depending on specific conditions in an area. Therefore, it is important that these results are interpreted and applied in accordance with the specific compartment conditions.

126

CHAPTER 6: CONCLUSION AND RECOMMENDATIONS

6.1 INTRODUCTION

This concluding chapter provides a summary of the main findings of the research and interprets the results in terms of the literature. The benefits of the research results to the forestry industry are discussed and recommendations for future research are outlined.

6.2 SUMMARY AND DISCUSSION OF KEY FINDINGS

A summary of the research problem and the research methodology applied is given below. This is followed by an outline of key findings from the productivity and stump volume waste results.

6.2.1 Summary of research problem and research methodology applied

Due to the increase in demand for commercial round wood supply of Eucalyptus species globally and locally, there is a need for mechanisation of more manual harvesting systems. In South Africa the need to mechanise has been increased by escalating labour costs, labour scarcity, safety risks, and the HIV and AIDS pandemic. Consequently, this has led to an increase in the mechanisation rate of many Eucalyptus pulpwood cut-to-length harvesting systems in South Africa. As a result of this increase in mechanisation, harvesters have been used in Eucalyptus coppiced compartments with uncertain or poorly-understood productivity and stump volume recovery expectations.

The research was conducted due to little research being available on the productivity of harvesters and the stump volume recovery in Eucalyptus coppiced compartments in South Africa. This research was aimed at determining the influence that tree volume, tree form, stem felled first and distance between stems has on the productivity of an excavator-based harvester in coppiced double, coppiced single and planted E. grandis pulpwood stems. Furthermore, the research also considered

127 whether there was any stump volume wasted and how much of it was being wasted due to excessive stump heights by the harvester whilst operating under these conditions.

All the productivity research questions outlined in the Introduction (Chapter 1) were answered with the following being derived:  productivity models depicting the effect of tree volume and tree form on the harvester’s productivity when operating in coppiced double and coppiced single E. grandis compartments. Due to multicollinearity, separate regression models were developed to investigate the influence of tree form on harvester productivity for both coppiced double and coppiced single trees;  a productivity model depicting the effect of tree volume on the harvester’s productivity when operating in planted E. grandis compartments (tree form was not a significant variable);  a productivity model depicting the effect of the stem felled first on harvester productivity in coppiced double E. grandis compartments. The distance between stems was not a significant variable; hence it was not included in the productivity models derived.

The stump volume waste research questions outlined in the Introduction (Chapter 1) were also answered. The presence of stump volume waste was confirmed, and the research then determined the quantities for coppiced double, coppiced single and planted stumps.

6.2.2 Key findings from productivity results and discussion

The productivity and stump volume waste results were firstly analysed separately. The relationship between productivity and stump volume wastage for coppiced double, coppiced single and planted trees was then examined.

 When harvesting coppiced double trees the harvester had the lowest productivity. When harvesting coppiced double tree sizes of 0.1 m3, 0.2 m3 and 0.3 m3, the resultant average productivity was 5.3 m3, 8.7 m3, 11.8 m3 per PMH respectively. Productivity increased as tree size increased. The productivity of the harvester also varied depending on whether the smaller or

128

larger stem (coppiced double) was felled first. The model indicated that if the volume of the larger stem was greater than 0.18 m3, then harvester productivity would be higher if the larger stem was felled first. However, if the volume of the larger stem was less than 0.18 m3, then the productivity of the harvester would be higher if the smaller stem was felled first. For example, if a coppiced double tree comprised a small stem of 0.1 m3 and a large stem of 0.3 m3 (CCS volume of 0.4 m3), then the resultant productivity would be 14.6 m3 per PMH if the smaller stem was felled first; and if the larger stem was felled first, the productivity would be 15.1 m3 per PMH. The productivity of the harvester was also significantly affected by tree form. With the coppiced double stems, two stems with potentially different forms had to be considered. Therefore, the different tree form combinations were considered and the poor form trees had lower productivity than good form trees. The regression equations had tree volume limits, as the tree volume distribution varied with tree form.

 When harvesting coppiced single trees of 0.1 m3, 0.2 m3 and 0.3 m3, the resultant average productivity was 8.4 m3, 13.8 m3 and 18.7 m3 per PMH respectively. Productivity increased as tree size increased. Harvester productivity was significantly affected by the form of the coppiced single trees. Poor form trees had a slightly lower productivity than good form trees. For example, when harvesting trees of 0.2 m3 the productivity for poor and good form trees was 13.0 m3 and 14.0 m3 respectively.

 In reality, compartments normally comprise a mixture of coppiced double and coppiced single trees depending on the stump survival rate. Therefore, the productivities were evaluated according to different proportions of both coppiced double and coppiced single stems. When the proportion of coppiced double stems increased, the productivity decreased. In a compartment with equal (50 per cent) proportions of coppiced double and coppiced single stems then the productivity when harvesting stems of 0.1 m3, 0.2 m3 and 0.3 m3 is 6.9 m3, 11.3 m3 and 15.2 m3 per PMH respectively.

129

 When harvesting planted trees, the harvester had the highest productivity. When harvesting tree sizes of 0.1 m3, 0.2 m3 and 0.3 m3 the resultant average productivity was 9.4 m3, 16.1 m3 and 21.7 m3 per PMH respectively. Productivity increased as tree size increased.

 Planted trees had the highest productivity across all the tree sizes, followed by coppiced single trees then coppiced double stems. The productivity for both coppiced single trees and coppiced double stems was significantly influenced by tree form.

6.2.3 Key findings from stump volume waste results and discussion

The key findings from the stump volume waste results and discussion are outlined below.

 Coppiced double trees had the highest average stump volume waste per stump with waste, compared to coppiced single and planted trees. The average stump waste was 0.00307 m3 per stump with waste, and the total amount of volume wasted per hectare was 1.418 m3 at a proportion of 42 per cent of the total 362 stumps. Based on the mill delivered price (R490) per tonne for pulpwood in October 2011, the loss as a result of waste volume cumulated to a cost of R472.85 per hectare under the stated assumptions.

 Coppiced single trees had an average stump waste of 0.00195 m3 per stump with waste, and the total amount of volume waste per hectare was 0.493m3, at a proportion of 23 per cent of the total 484 stumps. Based on the mill delivered price (R490) per tonne for pulpwood in October 2011, the loss as a result of waste volume cumulated to a cost of R164.15 per hectare under the stated assumptions.

 When harvesting coppiced double and single stems, the stump volume waste increased as the stem volume increased. Furthermore, as the stem volume

130

increased the amount of waste as a percentage of the total tree volume decreased.

 Planted trees had the lowest average stump volume waste per stump with waste. The average stump waste was 0.001650 m3 per stump with waste, and the total volume waste per hectare was 0.472 m3 at a proportion of 26 per cent of the total 543 stumps. Based on the mill delivered price (R490) per tonne for pulpwood in October 2011, the loss as a result of waste volume cumulated to a cost per tonne of R157.29 per hectare, under the stated assumptions.

 The coppiced double stems had the highest average stump volume waste per stump with waste, followed by coppiced single trees and planted trees.

6.2.4. Summary of key findings and relationship between harvester productivity and stump volume wastage

In comparison to each other: planted trees had the highest productivity and the lowest stump volume waste; coppiced single trees had moderate productivity and moderate stump waste and coppiced double trees had the lowest productivity and highest stump volume waste. Should the operator try and achieve lower stump heights in coppiced double and coppiced single trees, it could compromise harvester productivity to the extent that it increases harvester costs per cubic metre. This increased cost could be greater than the value of the stump volume gained. A key factor which influenced the stump volume waste in coppiced double trees is that when the operator felled two coppiced double stems that grew close to each other, then the felling cut was slightly higher due to the harvester head’s inability to grab the stem lower down. Another key factor which influenced stump volume waste in both coppiced double and coppiced single trees was that due to poor form, the operator made the felling cut higher to avoid having to trim off the butt end during crosscutting. In the planted trees the stump volume waste was very small.

131

6.3 RELATION OF RESEARCH RESULTS TO THE LITERATURE REVIEW

The literature provided a basis for the research. The literature search ensured that the research was not a duplication of any previous research and helped to enhance the validity of the research. Literature sourced from research carried out globally showed that many studies had been conducted on factors affecting the productivity of harvesters when harvesting planted trees of various species. The outcomes from the studies mainly included information on how general tree characteristics (for example tree size, BWBS and tree form), the operator and terrain affected the productivity of a harvester. Some research also included cost implications resulting from factors affecting harvester productivity. The literature review also indicated that research had been conducted on the stump volume recovery by using different felling equipment (feller bunchers, harvester and chainsaws) in compartments planted with various species.

Limited information was found on the productivity and stump volume waste of harvesters in E. grandis coppiced compartments. Most literature showed that productivity and stump volume loss research studies were carried out as separate studies and not as part of one comprehensive study.

The available literature found on coppiced trees only considered harvesters working in pre-felled E. globulus multiple stem (coppiced) compartments, and therefore only the processing element was investigated which excluded the fell tree element. Another research study considered the use of harvesters in chestnut coppiced stems used for firewood. There was no research which looked specifically at harvester productivity in E. grandis coppiced and planted compartments, and which related this productivity to stump volume loss.

Tree size played a major role in influencing the productivity of the harvester. The results obtained in this research were consistent with other research results presented in the literature.

The research results obtained in coppiced double and coppiced single stems, which showed that tree form had a significant influence on harvester productivity, compared favourably with the available research results found by Suchomel et al.

132

(2010), although this research was carried out on coppiced chestnut trees. According to Suchomel et al. (2010) poor stem quality or bad form of coppiced trees can reduce the efficiency of the processor, compared to softwood species which normally have straight stems and small horizontal branches. Furthermore, according to Spinelli et al. (2002), when the harvester operated in large and malformed coppiced E. globulus trees, the handling time increased due to their poor form. Therefore, the literature confirmed that coppiced trees with poor form related to lower productivity.

The stump volume waste results were new and unique to this research, and as a result could not be directly compared to other research. Based on the mill delivered price (R490) per tonne for pulpwood in October 2011, the loss as a result of coppiced double stump volume waste cumulated to a cost of R472.85 per hectare under the stated research conditions. The average volume waste was 0.00307 m3 per stump. This result affirmed the findings made by Doruska (2002) that little additional volume is lost when taken on a tree-by-tree basis. However, when accumulated across all the trees harvested with waste in a hectare, the timber value overlooked maybe high.

6.4 VALUE OF RESEARCH RESULTS IN THE FORESTRY INDUSTRY

The results and information provided by this research will assist foresters, contractors, decision makers and all affected forestry stakeholders both locally and internationally by providing:

 harvester productivity knowledge and understanding of expected volume loss due to differences in stump height when operating in E. grandis coppiced compartments;  a clear understanding of the factors that affect harvester productivity in E. grandis coppiced and planted compartments;  confidence to use harvesters in coppiced compartments with certain and realistic productivity and volume recovery expectations;  basic foundation information for future in-depth research that may revolve around stumps and stump biomass recovery in mechanised cut-to-length operations;

133

 information that will assist management in determining regeneration policy to optimise economical benefits for timber growing entities, through enhancing profitability and minimising waste;  information to make better and more informed decisions about where and how to use harvesters.

6.5 RECOMMENDATIONS

The results of this research responded to the main questions described in the research problem. There are still factors that can be investigated surrounding the use of harvesters in coppiced and planted compartments.

The research only considered the current dominant technique of harvesting coppiced trees, which was to grab each tree individually and process it. It is recommended that when harvesting using this technique, if possible the larger tree must be felled first if the volume of the larger stem is greater than 0.18 m3, and the smaller stem felled first if the larger stem volume is less than 0.18 m3. More in-depth research needs to be done on different techniques. For example, pre-felling smaller stems by using a chainsaw, then using the harvester to fell the remaining larger stem, and process the small stem.

The research did not investigate the effect of BWBS on the productivity of the harvester, as the research was conducted during a season (summer) that reflected the normal BWBS for E. grandis in South Africa. As described in Chapter 4, the BWBS was constantly tested and the number of passes required to debark a stem according to the required specifications was recorded. The results showed that there was minimal variation in the debarking passes required. Depending on the season or period in which the trees are harvested, BWBS may influence productivity. It would be expected that the productivity will drop as the BWBS increases due to the debarking element taking a longer time. Therefore, future research can investigate how different classes of BWBS can affect the productivity of a harvester in coppiced and planted compartments.

In terms of stump volume recovery, research needs to be conducted on total volume loss in the compartment in addition to the stump volume investigated in this

134 research. The research can consider other forms of waste such as utilisable timber in tree tops, poor optimisation and breakages. This information can be a good foundation in the establishment of biomass material available in coppiced and planted compartments. In addition it will be important to determine the harvesting cost for coppiced double trees, coppiced single and planted trees.

135

REFERENCES

Ackerman, P.A. & Längin, D.W., 2010. Introduction: In South African Ground Based Harvesting Handbook. Institute for Commercial Forestry Research, Scottsville, South Africa, 2010. p.15.

Adebayo, B.A., 2006. Productivity and cost of cut-to-length and whole tree harvesting in a mixed-conifer stand. Masters Thesis. Idaho University.

Acousta, M.S., Mastrandrea, C. & Lima, J.T, 2008. Wood technology and uses of Eucalyptus wood from fast grown plantations for solid products. Proceedings of the 51st International Convention of Wood Science and Technology. 10-12 November 2008. Conception, Chile.

Akay, A.E., Erdas, O, & Sessions J., 2004. Determining productivity of mechanized harvesting machines. Journal of applied Sciences. [online] 4 (1). Available at: http://www.docsdrive.com/pdfs/ansinet/jas/2004/100-105.pdf [Accessed 5 April 2011].

Amishev, D.Y. & Murphy, G.E., 2009. Using acoustic technology as a means for improving the economics of fuel reduction operations through an integrated value-adding approach. Council on Forest Engineering (COFE) Conference Proceedings: Environmentally Sound Forest Operations. Lake Tahoe, 15-18 June 2009.

Athanassiadis, D., Lindroos, O. and Nordfjell, T., 2011. Pine and spruce stump harvesting productivity and costs using a Pallari KH 160 stump‐lifting tool. Scandinavian Journal of Forest Research, 26(5), pp. 437‐445.

Bentley, J.W. & Harper, A.R. 2004. Georgia harvest and utilization study. NC: U.S. Department of Agriculture Forest Service, Southern Research Station.

136

Bergkvist, I., Norděn, B. & Lundström, H., 2006. The Beast, a remote controlled harvester. Skogforsk, Uppsala.

Bolding, C.M. & Lanford, L.B., 2003. Productivity of a Ponsse Ergo hHarvester working on steep terrain. [Pdf]. Available at: http://frec.vt.edu/bolding/02auburncofepaper.pdf [Accessed 22 October 2011].

Boston, K., 2001. Precision log-making to maximise value recovery from plantation forests. First International Precision Forestry Symposium. University of Washington, USA. June 2001.

Boston, K. & Dysart, G. 2000. A comparison of felling techniques on stump height and log damage with economic interpretations. Western Journal of Applied Forestry, 15(2), p.59.

Braithwaite, D., 2011. Discussion on mechanized cut-to-length harvesting operations. [Conversation] (Personal communication, May 2011).

Brink, M. P. & Conradie, I.P., 2000. Forest engineering in timber plantations: Introduction 1. In: D.L. Owen (ed.). South African Forestry Handbook, Vol 1. The Southern African Institute of Forestry, pp. 271-272.

British Columbia Ministry of Forests, 2009. Amendment number 13 to the provincial logging residue and waste measurements procedure manual. British Columbia, Canada, pp. 19-22.

Bredenkamp, B., 2000. Volume and mass of logs and standing trees. In: Owen, D.L. (ed.). South African Forestry Handbook, Vol. 1. The Southern African Institute of Forestry, pp. 167–174; p.194.

Burger, D. & Bhaktawar, N. eds., 2009. Pocket Guide to South Africa 2008/09. Pretoria: Government Communications.

137

Chamberlain, D., Essop, H., Hougaard, C., Malherbe, S., Walker, R., 2005. Genesis

report. The contribution, costs and development opportunities of the forestry, timber, pulp and paper industries in South Africa. Genesis Analytics, Johannesburg.

Crickmay & Associates, 2011. Forest Cost Index of South Africa September/October 2011. Crickmay & Associates publishers. Pietermaritzburg, p.3

Culshaw, D., & Stokes, B., 1995. Mechanisation of short rotation forestry. Biomass and Bio energy, 9(1-5), pp. 127-140.

Archibald, R., Little, K. & Harper, R., 2005. Managing blue gum (Eucalyptus globulus) coppice. Department of Agriculture and Food tree note No:35. [online]. Available at: http://www.agric.wa.gov.au/PC_92532.html [Accessed 22 October 2011].

Department of Agriculture Forestry and Fisheries, 2011. Overview of Forests. [Online]. Available at: http://forestry.daff.gov.za/webapp/ForestsOverview.aspx [Accessed 21 September 2011].

Department of Agriculture Forestry and Fisheries, 2011. State of forests report 2007 – 2009. Pretoria: Department of Agriculture Forestry and Fisheries.

Doruska, P., 2002. What’s in a stump – a simulation study examining the volume and/ or weight impact of lower stump heights. Arkansas Forestry Symposium. The University of Arkansas Cooperative Extension Service.

Dougherty, D., & Wright, J., 2012. Silviculture and economic evaluation of eucalypt plantations in Southern US. Bioresources 7(2), pp, 1994-2001

138

Dvořák, J., Malkovský, Z. & Macků, J., 2008. Influence of human factor on the time of work stages of harvesters and crane-equipped forwarders. Journal of Forest Science, 54(1), pp. 24-30.

Engelbrecht & Warkotsch, 1994. Chute operation manual. FESA.

Erasmus, D., 1994. National terrain classification for forestry. Institute for Commercial Forestry Research (ICFR). Bulletin Series 11/94. Scottsville: ICFR.

Eucalyptus Genome Network, 2006. Sequencing of the Eucalyptus genome: a proposal to doe-jgi. [Pdf]. Available at: at:http://www.seralliance.com/enews/vol3no6/pdfs/proposal.pdf [Accessed 4 March 2012].

Evans, J. & Turnbull, J., 2004. Plantation forestry in the tropics, 3rd ed. New York: Oxford University Press.

Food and Agriculture Organisation [FAO], 2001. Mean annual volume increment of selected industrial forest plantation species by L. Ugalde & O. Pérez. Forest Plantation Thematic Papers, Working Paper 1. Forest Resources Development Service, Forest Resources Division. Rome (unpublished) p. 9.

Foelko Cesco, 2007. Eco-efficient management of woody forest residues from the Eucalyptus forest plantations. [online]. Available at: http://www.eucalyptus.com.br/capitulos/ENG07.pdf [Accessed 21 November 2011].

Food and Agriculture Organisation., 2007. [Pdf] Global wood and wood products flow. Shangai China Advisory committee on paper and wood products. Available at: http://www.fao.org/forestry/12711- 0e94fe2a7dae258fbb8bc48e5cc09b0d8.pdf. [Accessed 2 March 2012]

139

Forest Economic Services., 2010. Report on commercial timber resources and primary round wood processing in South Africa 2008/2009. Directorate: Forestry Technical and Information Services. South Africa: Pretoria.

Forest Engineering South Africa., 2000. South African chainsaw safety & operating handbook. Pietermaritzburg: Institute for Commercial Forestry Research.

Forestry Research, 2009. Stump Harvesting: Interim guidance on site selection and good practice. [online]. Available at: http://www.forestry.gov.uk/pdf/FC_stump_harvesting_guidance_April09.pdf/$ FILE/FC_stump_harvesting_guidance_April09.pdf The Research Agency of the Forestry Commission [Accessed 25 November 2011].

Forestry South Africa, 2010. The SA Forestry & Forest Products Industry. [online]. Available from: http://www.forestry.co.za/statistical-data/ [Accessed on 20 August 2010].

Forestry solution, 2007. BOP- debarking manual. [Pdf]. Available at: http://docs.google.com/viewer?a=v&q=cache:1CS6SZgN1WkJ:www.forestry solutions.net/index.php%3Fcomp%3Ddownload%26op%3Ddownload%26file %3Dbest_operating_practice/HB01.pdf+manual+Eucalyptus+debarking&hl= en&gl=za&pid=bl&srcid=ADGEESiLoB7HKhCfqKAbwG2Lexyq3CT6pcEGY mFRImhQMk_89IQFOTYT5dObLp8jV92bx7zJvZk-i- DXAii80IW9gtuV5BtTBgF7XSFPRA-4HbXEAsa0CAWy3hlbI- C5Xx6mH9zNQB3x&sig=AHIEtbQhITMp_IAKQ5NQQc9dnu8qmy9C8g [Accessed 10 November 2011].

Garavaglia, S., & Sharma, A., 2004. A smart guide to dummy variables: four applications and a macro. [Pdf]. Available at: http://www.ats.ucla.edu/stat/sas/library/nesug98/p046.pdf [Accessed 12 February 2012].

140

Garland,J. & Jackson, D., 1997. Felling and bucking techniques for woodland owners. The woodland workbook.

Geary, T., 1983. Harvesting to get a Eucalyptus Coppice Crop. Presented at the Workshop on Eucalyptus in California, June 14-16, 1983, Sacramento, California. Available at: http://www.fs.fed.us/psw/publications/documents/psw_gtr069/psw_gtr069_0 5_geary.pdf

Gellerstedt, S., 2002. Operation of the single grip harvester: motor sensory and cognitive. International Journal of Forestry Engineering, 13(2), pp. 35-47.

Google maps, 2012. Map of New Hanover area [online] Available at: http://maps.google.co.za/ [Accessed 20 January 2012].

Government of Alberta, 2011. Sustainable resource development – utilization [Pdf] FOMP Share-point Government of Alberta [online]. Available at: http://www.srd.alberta.ca/FormsOnlineServices/Directives/documents/FO- SOP20-Utilization-Jun2011.pdf [Accessed 26 October 2011].

Grobbelaar, F.R. & Manyuchi, K.T., 2000. Eucalyptus debarking: an international overview with a Southern African perspective. Forest Engineering Southern Africa, p.18

Grut, M., 1965. Forestry and Forest Industry in South Africa. Gothic Printing Company Limited, Cape Town. p. 21.

Gujarati, D.N. & Porter, D.C., 2010. The essentials of econometrics. 4th ed. New York: McGraw-Hill.

Hamilton. L., 2000. Managing coppice in Eucalypt plantations. State of Victoria: Department of Primary Industries.

141

Han, H. & Hall, R., 2006. Improvements in value through low stump heights: mechanized versus manual felling. Western Journal of Applied Forestry 21(1), pp. 33-38.

Han, H. & Renzie, C., 2005. Effect of ground slope, stump diameter and species on stump height for feller-buncher and chainsaw felling. International Journal of Forest Engineering. [Online]. 16(2): 81-88. Available at: http://journals.hil.unb.ca/index.php/IJFE/article/view/9826 [Accessed 20 September 2011].

Handler, F. & Blumauer, E., 2010. Logistics for harvesting short rotation forestry with a special equipped forage harvester. FORMEC Forest Engineering: Meeting the Needs of the Society and the Environment. 11-14 July 2010. Padova, Italy.

Hartsough, B.R. & Cooper, D.J., 1999. Cut-to-length harvesting of short rotation Eucalyptus. Forest Products Journal, 49(10).

Haynes, H.J.G. & Visser, R.J.M., 2004. An applied hardwood value recovery study in the Appalachian hardwood region of Virginia and West Virginia. Int. J. For. Eng. 15(1), pp. 25-31.

Heinemann, H.R., 2001. Productivity of a cut-to-length harvester family – an analysis based on operational data. Council on Forest Engineering (COFE) Conference Proceeding: “Appalachian Hardwoods: Managing climate change”. Snowshoe, 15-18 July 2001.

Herbert, M., 2000. Eucalypt and Wattle species. South African Forestry handbook. 4th ed. (ed D.L. Owen), Southern African Institute of Forestry (SAIF), Pretoria, Republic of South Africa.

142

Hill, T. & Lewicki, P., 2006. Statistics Methods and Applications: A comprehensive reference for science, industry and data mining. StatSoft, Inc. p.20.

Hills, W.E. & Brown, A.G., 1984. Eucalyptus for wood production. Academic , Australia, East Melbourne.

Hitachi, 2009. Hitachi Zaxis 200 specifications. [online]. Available at: www.hitachiconstruction.com/assets/files/products/excavators/ZX200- 3_specs.pdf [Accessed 20 December 2011].

Holtzscher, M.A. & Lanford, B.L., 1997. Tree diameter effects on cost and productivity of cut-to-length systems. Forest Products Journal. 47(3), pp. 25- 30.

Huyler, N.K. & Le Doux, C.B., 1999. Performance of a cut-to-length harvester insingle-tree and group-selection cut. USDA Forest service, Delaware.

Immelman, A., 2012. Sappi specifications on minimum timber wastage in harvested compartments. [Email] (personal communication, 26 January 2012).

Jack, R.J. & Olivier, M., 2008. A review of factors influencing whole body vibration injuries in forestry mobile machine operators. International Journal of Forest Engineering, 19(1).

Jacques, J.A. & Norusis, M. 1973. Sampling requirements on the estimation of parameters in heteroscedastic linear regression. Biometrics 29:771-780

Jiroušek, R., Klavač R. & Skoupý, A., 2007. Productivity and costs of the mechanized cut-to-length wood harvesting system in clear-felling operations. Journal of Forest Science, 10, pp. 476-482.

143

Kachamba, D.J., 2007. Impact of harvesting machinery on soil physical parameters: evaluation of Profor model in three main forestry regions in South Africa. Masters dissertation. Stellenbosch University.

Kärhä, K., Rönkkö, E. & Gumse S.I., 2004. Productivity and cutting costs of thinning harvesters. International Journal of Forest Engineering, 15(2), pp. 43-56.

Kastenholz, E., 2004. Mechanization – challenge for research and practice. Forest network update. ILO Sectoral activities Department. December 2004.

Kellogg, L.D., Bettinger, P. & Studier, D., 1993. Terminology of ground–based mechanized logging in the Pacific Northwest. Forest Research Laboratory, Oregon State University.

Krieg. B, de Wet. P., Olsen. G. & McEwan, A., 2010. Ground-based harvesting equipment, p. 54.

Längin, D. & Ackerman, P.A., 2007. South African Forest Engineering Survey 2006/2007. Stellenbosch University.

Längin, D.W., Ackerman, P.A. & Olsen, G., 2010. Introduction to ground-based harvesting systems and methods. Institute for Commercial Forestry Research, Scottsville, South Africa, 2010. p.15.

Lapointe, J. & Robert, J.M., 2000. Using VR for efficient training of forestry machine operators. Education and Information Technologies 5:4 (2000), pp. 237-250.

Larocci, P., 2007. Mechanization in South Africa. [online]. Canada: Tigercat Industries Inc. Available at: http://www.tigercat.com/btb/btb17.pdf [Accessed 15 February 2011].

Little, K.M. & McLennan, L., 2000. Eucalypt Coppice Management. [online]. Pietermarizburg, ICFR Innovations. Available at:

144

http://www.icfr.ukzn.ac.za/icfrdownload/downloadhandler.ashx?id=203 [Accessed 13 March 2011].

Marku, J., & Dvorak, J., 2010. Time expenditure analysis of cut-to-length harvester in incidental fellings compared with production efficiency. FORMEC 2010. Forest Engineering: Meeting the needs of the Society and the Environment 11-14 July. Padova, Italy.

Marshall, H., 2005. An Investigation of factors affecting the optimal output log distribution from mechanical harvesting and processing systems. A dissertation submitted to Oregon State University.

McEwan, A., 2011a. Definition of a stump. [Conversation] (Personal communication, May 2011).

McEwan, A., 2011b. Discussion on productivity of harvesters in coppice compartments. [Conversation] (Personal communication, March 2011).

McEwan, A.M., 2011c. The effect of tree and bundle size on the productivity and

costs of Cut-to-length and multi-stem harvesting systems in Eucalyptus

pulpwood. Master of Science in Forest Science, University of Pretoria.

McEwan, A., 2012a. South African forest engineering survey. [Comments on the report] (Personal communication, March 2012).

McEwan, A., 2012b. Adoption of advanced harvesting equipment in South Africa. [Conversation] (Personal communication, March 2012).

McEwan, A., 2012c. Stump heights. [Conversation] (Personal communication, February 2012).

145

McEwan, A., Eggers, J., Ramantswana, M., 2011. Acacia mearnsii debarking:

Comparing the productivity and costs of different debarking machines and

systems in the KwaZulu-Natal and Mpumalanga forestry regions of South

Africa. Forest Engineering South Africa (FESA) & ICFR Bulletin, August

2011.

McNeel, J.F. & Ballard, T.M., 1992. Analysis of site stand impacts from thinning with a harvester-forwarder system. Journal of Forest Engineering, 4(1), pp. 23-29.

McNeel, J.F. & Rutherford, D. 1994. Modeling harvester- forwarder system performance in a selection harvest. Journal of Forest Engineering, 6(1), pp. 7–14.

Miettinen, M., Kulovesi, J., Kalmari, J. & Visala, A., 2010. New Measurement Concept for Forest Harvester Head. Springer Tracts in Advanced Robotics, 2010: Field and Service Robotics, 62, pp. 35-44.

Mitchell, D., 2009. Stump harvesting. 32nd Annual Meeting of the Council on Forest Engineering 15-18 June , 2009. North Tahoe Conference Centre, Kings Beach, California.

Mondi, 2010. Harvesting operational planning document: pre-harvest plan. HOP. Pietermaritzburg. Mondi.

Moskalik, T., & Stampfer, K., 2003. Productivity of the harvester 911 Snake under the mountain conditions. Sylwan, 4, pp. 98-105.

146

Mouton, J., 2001. How to succeed in your master’s and doctoral studies. Pretoria, Van Schaik Publishers.

Musto, J.W., 1993. National Terrain classification system for Forestry. ICFR Bulletin series 4/93.

National Centre for the Replacement, Refinement and Reduction of Animals, 2006. Why do a pilot study? [Online]. Available at: http://www.nc3rs.org.uk/downloaddoc.asp?id=400 [Accessed 14 November 2011].

Nicholls, A., Bren, L. & Humphreys, N., 2004. Harvester productivity and fatigue: Working extended hours, 15(2).

Nilsson, S. & Bull, G., 2005. Global wood supply analysis. 46th Session of the FAO Advisory Committee, Vancouver, Canada, May 2005.

Nordfjell, T., Björheden, R., Thor, M. & Wästerlund., 2010. Changes in technical performance, mechanical availability and prices of machines used in forest operations in Sweden from 1985 to 2010. Scandinavian Journal of Forest Research 25, pp. 382-389.

Norris, C.H., 2000. Eucalypts. In: D.L. Owen, (Ed.). South African Forestry Handbook Vol. 1: The Southern African Institute of Forestry, p. 121.

Nurminen, T., Korpunen, H. & Uusitalo, J., 2006. Time consumption analysis of the mechanized cut-to-length harvesting system. Silva Fennica, 40(2), pp. 335–363.

Olivier, W., 2010. There is honey in the forest: History of forestry in South Africa. South African Institute of Forestry.

147

Ovaskainen, H., 2009. Timber harvester operators’ working technique in first thinning and the importance of cognitive abilities on work productivity. PhD. Finland. University of Joensuu

Ovaskainen, H., Uusitalo, J. & Väätäinen, K., 2004. Characteristics and significance of a harvester operators’ working technique in thinnings. International Journal of Forest Engineering, 15(2).

Owen, D.L. & van der Zel, D.W., 2000. Trees, forests and plantations in Southern Africa. In: D.L. Owen, ed. South African Forestry Handbook. Pretoria. South African Institute of Forestry, p. 6.

Pasquali, G., 2010. Eucalyptus grandis trees with improved timber for cellulose pulp and paper production. [Pdf]. Available at: http://cera-gmc.org/docs/brasilia_2010/case_study_eucalyptus.pdf [Accessed 24 February 2011].

Pulkki, R., 2000. Cut-to-length, tree length or full tree harvesting? [online]. Available at: http://flash.lakeheadu.ca/~repulkki/ctl_ft.html [Accessed 14 September 2011]. Lakehead University Faculty of Forestry.

Ponsse., 2010. Beaver - Huge middle-size powerpack (photo). [online]. Available at: http://www.ponsse.fi/english/products/harvesters/beaver/index.php [Accessed 10 February 2012].

Poynton, R.J., 1979. Tree planting in Southern Africa, Vol 2: The Eucalypts. Department of Forestry, Pretoria.

Purfürst, F.T., 2010. Learning curves of harvester operators. Journal: Croatian Journal of Forest Engineering. ISSN 1845-5719 31(2). Start page: 89.

Puttock, D., Spinelli, R. & Hartsough, B.R., 2005. Operational trials of cut-to-length harvesting of poplar in a mixed wood stand. International Journal of Forest Engineering, 16(2). January 2005. 148

Ramantswana, M.M., McEwan, A. & Pauw, J., 2012. In press. Determining the effect of tree size, bark-wood bond strength and tree form on the productivity of an excavator-based harvester in Acacia mearnsii in the Kwazulu-Natal forestry region of South Africa. Southern Forests journal of forest science.

Ride, K., 1999. An investigation of wood fibre recovery and related economics of four harvesting systems common to North Western Ontario. [Online]. Available at: http://www.nlc-bnc.ca/obj/s4/f2/dsk1/tape9/PQDD_0026/MQ52074.pdf [Accessed 20 September 2011].

Ribeiro, C.A.A.S. & Better, D.R., 1995. Single rotation vs. coppice systems for short- rotation intensive culture plantations-optimality conditions for volume production. Biomass and Bioenergy, 8(6), pp. 39S-W.

Rummer, B. & Erwin.C., 2009. Mechanical felling. Forest Encyclopedia network. [Online]. Available at: http://www.forestencyclopedia.net/p/p2276 [Accessed 25 October 2011].

Ryynänen, S. & Rönkkö, E., 2001. Summary: Productivity and expenses associated with thinning harvesters. Helsinki. Työtehoseuran julkaisuja, 381, p. 67.

Shaffer, R.M. & Taumas, S., 1992. A comparison of sheared and sawhead-felled stump heights on six non-industrial private timber sales in South Eastern Virginia. Southern Journal of Applied Forestry, 16(3), pp. 140-142.

Sappi, 2004. Tree farming guidelines – for Sappi outgrowers: Forest Engineering: Sappi forests. Cascades [online]. Available at: http://www.sappi. com/NR/rdonlyres/A46FE1AA-D556-4B36-99E2-B6678F61E69E/0/out.html [Accessed 3 October 2011].

149

Sappi, 2011. Mpumalanga pre-harvest agreement and compartment planning: utilizable timber waste. Pietermaritzburg. Sappi Cascades.

Schalkx, N., 2012. Comparison of standard excavator based booms and Waratah booms. [Email] (Personal communication, March 2012).

Sherwin, L.M., Owende, P.M.O., Kanali, C.L., Lyons, J. & Ward, S.M., 2004. Influence of tyre inflation on whole body vibrations transmitted to the operator in a cut-to-length timber harvester. Applied Ergonomics, 35, pp. 253-261.

Sherwin, L.M., Owende, P.M.O., Kanali, C.L., Lyons, J. & Ward, S.M., 2004. Influence of forest machine function on operator exposure to whole-body vibrations in a cut-to-length timber harvester. Ergonomics 47(11), pp. 114- 115.

Sims, R.E.H. & Venturi, P., 2003. All-year-round harvesting of short rotation coppice Eucalyptus compared with the delivered costs of biomass from more conventional short season, harvesting systems. Biomass and Bioenergy, 26 p. 37.

Sims, E.H.R., Maiava, T.G. & Bullock, T.B., 2001. Short rotation coppice tree species selection for woody biomass production in New Zealand. Biomass and Bioenergy, 20, pp. 329-335.

Singh, S., 2006. Encyclopaedia of Forestry. 1st ed. New Delhi, India: Anmol Publications Pvt. Ltd.

Snedden, C.L., 2001. Broad and narrow sense heritabilities in a cloned open pollinated Eucalyptus grandis breeding population. Master in Science, Department of Genetics: University of Pretoria.

150

Spinelli, R., Owende, P.M.O. & Ward, S., 2002. Productivity and cost of CTL harvesting of Eucalyptus globulus stands using excavator-based harvesters. Forest Products Journal, 52, pp. 67-77.

Spinelli., R. 2006. Short rotation coppice production in Italy. [Pdf]. Italy: Sesto Fiorentino, CNR Alvasa. Available at: http://www.sswm.info/sites/default/files/reference_attachments/SPINELLI%2 02006%20Short%20Rotation%20Coppice%20Production%20in%20Italy.pdf [Accessed 5 October 2011].

Spinelli, R., Ward, S.M. & Owende, P.M., 2009. A harvest and transport cost model for Eucalyptus spp. fast-growing short rotation plantations. Elsevier - biomass and bioenergy, 33, pp.1265-1270.

Spinelli, R., 2011. Supply of wood biomass for energy purpose: global trends and perspectives. 22nd meeting of the Club of Bologna, 13-14 Nov 2011. Hanover, Germany.

Stampfer, K., 1999. Influence of terrain conditions and thinning regimes on productivity of a tracked-based steep slope harvester. In: Proceedings of the international Mountain Logging and 10th Pacific North West Skyline.

Stampfer, K. & Steinmuller, T., 2001. A new approach to derive a productivity model for the harvester “Valmet 911 Snake. The International Mountain Logging and 11th Pacific Northwest Skyline Symposium, 2001.

StatSoft, Inc., 2011. General Regression Models (GRM). [online] Available at: http://www.statsoft.com/textbook/general-regression-models/ [Accessed 28 October 2011].

Steenkamp, J.C., 2007. The effect of HIV and AIDS on the viability and management of forestry contracting businesses in South Africa. Nelson Mandela Metropolitan University. (DBA thesis).

151

Steenkamp, 2011. Contractors under the South Africa contractors Association (SAFCA) [E-mail] (Personal communication, May 2011).

Steenkamp, 2012. Benefits of having low stumps [Conversation] (Personal communication, March 2012).

Strandgard, M. & Mitchell, R., 2012. Choosing the right feller buncher head for maximum volume extraction. CRC Forestry, Bulletin 22.

Suchomel, C., Spinelli, R., Magagnotti, N. 2010.Processing Hardwood From Coppiced Forests. Forest Engineering Meeting: Meeting the Needs of the Society and the Environment. Podava, Italy. 11-14 July 2010

Suchomel, C., Becker, G. & Pyttel, P., 2011. Fully mechanized harvesting in aged oak coppice stands. Forest Products Journal, (68) 4.

Susan Garavaglia & Asha Sharma, 1998. A smart guide to dummy variables: four applications and a macro. Dun & Bradstreet Murray Hill, New Jersey 07974. [online]. Available at: http://www.ats.ucla.edu/stat/sas/library/nesug98/p046.pdf [Accessed 12 September 2011].

Temperate Forest Foundation., 2002. Technology in the Forest: Eco-link, 12(2). [Pdf]. Available at: http://idahoforests.org/img/pdf/TechInForest.pdf.

Theron, K., 2000. Site requirements and species matching. In: D.L. Owen, (ed.). South African Forestry Handbook, Vol. 1: The Southern African Institute of Forestry, p. 79.

Tolosana, Laina, R. & Ambrosio, Y., 2010. Eucalyptus Short Rotation Woody Crops for Energy: A Literature and Experiences Review with Special Reference to Industrial Trials in Spain. [PDf]. [online]. Available at:

152

http://www2.montes.upm.es/Dptos/DptoSilvopascicultura/Etiopia/chap4_7.pd f [Accessed 2 February 2012].

Vanclay. J.K., 2011. Future harvest: what might forest harvesting entail 25 years hence? Scandanavian Journal of Forest Research, DOI 10.1080.

Vanderberg, M. R., 2002. Harvested log damage and value loss associated with two ground-based harvesting systems in central Appalachia. Davis College of Agriculture, Forestry, and Consumer Sciences at West Virginia University Master of Science in Forestry.

Visser, R., Spinelli, R., Saathof, J. & Fairbrother, S., 2009. Finding the ’sweet-spot’ of mechanised felling machines. Council on Forest Engineering (COFE) Conference Proceedings: “Environmentally Sound Forest Operations.” Lake Tahoe, 15-18 June, 2009.

Visser, R., Rodgers, B.F., Stampfer, K, & Gallagher, T., 2001. Improved harvesting viability through increased value recovery. Council on Forest Engineering (COFE) conference proceedings: “Appalachian Hardwoods: Managing Change”. Snowshoe, July 15-18, 2001.

Walmsley, J.D. & Godbold, D.L., 2009. Stump harvesting for bioenergy – a review of the environmental impacts. Institute of Chartered Foresters, 83(1), pp.17 – 38.

Waratah, 2011. Waratah 616 harvesting head [online]. Available at: http://www.merimex.cz/underwood/download/files/HTH616_en.pdf

[Accessed 20 December 2011].

Warkotsch, P.W., 1988. Harvesting of Pine and Eucalypt in South Africa. South African Forestry Journal, 147, pp. 37-52.

153

Wegner, K.F., ed., 1984. Forestry Handbook: Production costs. 2nd ed. Canada. John Wiley & Sons, Inc.

WFSP Forestry Programme, 2004. Round wood Supply and Demand to 2030. [Pdf] Pretoria: Department of Water Affairs and Forestry. [online]. Available at: http://www2.dwaf.gov.za/webapp/ResourceCentre/Documents/Publications_ And_Media/Roundwood_supply_and_demand_-_Final_report_1814-04.pdf [Accessed 1 April 2011].

World Agroforestry Centre, 2011. Tree species reference and selection guide. [online] Available at: http://www.worldagroforestrycentre.org/SEA/Products/ AFDbases/AF/asp/BotanicSearch.asp [Accessed September 2011].

Van Wyk, A., Brink, A., van Huyssteen, G. & Jansen, J. eds. 2011. Tree farming guidelines for private growers: Eucalyptus coppice reduction. Cascades. Sappi forests.

Van Wyk, G. & Verryn, S., 2000. The basic principles of tree breeding in South Africa. In: D.L. Owen, (Ed.). South African Forestry Handbook Vol.1. The Southern African Institute of Forestry, p. 121.

Zar, J.H., 2010. Biostatistical analysis. 5thed. United States of America, New Jersey: Pearson Prentice Hall, p.337.

Zbonak, A., Bush, T. & Grzeskowiak, V., 2007. Comparison of tree growth, wood density and anatomical properties between coppiced trees and parent crop of six Eucalyptus genotypes. IUFRO. Improvement and Culture of Eucalyptus. Durban, South Africa. 22-26 October 2007. CSIR.

154

Annexure 1

Detailed compartment maps for the research areas

Compartment no: D006

Compartment D014

Compartment D020

Annexure 2

Harvester operators’ details

Table 1: Harvester operators’ details

DETAILS OPERATOR 1 OPERATOR 2

Name and surname: Musa Myeni Bongani Buthelezi

Age: 40 30

Experience as operator: >12 years 9 years

Contractor name: DS Preen PTY (LTD) DS Preen PTY (LTD)

Shift length: 9 hrs 9 hrs

Annexure 3

Rounding and 2 cm classes used with regard to diameter measurements

Table 1: Diameter in two centimetre classes

ANY DIAMETER FIGURE BETWEEN: ROUNDED TO:

7.0 cm and 8.9 cm 8cm

9.0 cm and 10.9 cm 10 cm

11.0 cm and 12.9 cm 12 cm

13.0 cm and 14.9 cm 14 cm

15 cm and 16.9 cm 16 cm

17.0 cm and 18.9 cm 18 cm

19.0 cm and 20.9 cm 20 cm

21.0 cm and 22.9 cm 22 cm

23.0 cm and 24.9 cm 24 cm

25.0 cm and 26.9 cm 26 cm

27.0 cm and 28.9 cm 28 cm

29.0 cm and 30.9 cm 30 cm

31.0 cm and 32.9 cm 32 cm

33.0 cm and 34.9 cm 34 cm

Annexure 4

BWBS strength rip-stripping test

Figure 1: Rip stripping - BWBS test

Annexure 5

Data sheet templates used to record the data manually

Table 1: Data sheet for record tree and stump measurements

Date: Plot No: Research name: Operator:

Tree DBH Height Form Distance Stump Stump no: (cm) (m) class between Height DBH(cm) stems (cm)

Table 2: Data sheet for recording harvester head passes and logs recovered

Tree no: Passes 1 Passes 2 Logs Other Tree no: Passes 1 Passes 2 Logs Other recovered recovered

ANNEXURE 6

Coppiced double stems tree form productivity models adequacy checking

1. COPPICED DOUBLE STEMS TREE FORM PRODUCTIVITY MODELS ADEQUACY CHECKING

Model adequacy checking is the analysis of residuals to determine whether the underlying assumptions have been violated. If the model is adequate the residuals must not contain any patterns (Montgomery, 1997). To check if the CCSV model was adequate, the normality and homoscedasticity tests were analysed.

(a) COPPICED DOUBLE TREES: TREE FORM POOR X POOR MODEL ADEQUACY CHECKING

The normality and homoscedasticity tests were analysed to determine if the underlying assumptions were violated.

(i) Normality test

The assumption of normality of the error terms was checked by making a normal probability plot of the residuals as shown in Figure 1. The values on the normal probability plot deviate slightly from the straight line. However, when interpreting a normal probability plot, one should concentrate more on the central values and in general moderate departures from normality are of little concern in the fixed analysis of variance (Montgomery, 1997).

Normal Prob. Plot; Raw Residuals Dependent variable: Productivity (Analysis sample) 3.0

2.5 .99 2.0 .95 1.5

1.0 .75 0.5 .55 0.0

-0.5 .35

-1.0 .15

ExpectedNormal Value -1.5 .05 -2.0 .01 -2.5

-3.0 -8 -6 -4 -2 0 2 4 6 8 10 Residual

Figure 1: Coppiced single tree form poor x poor normal probability plot

(ii) Homoscedasticity test

In Figure 2 the Homoscedasticity of the error terms was investigated, and this shows a plot of residuals versus predicted values, which checks for homoscedasticity. The residuals are scattered in the form of a cloud around the zero-line with very little evidence of the ‘fan’ pattern typical of heteroscedasticity. The assumption of homoscedastic variance was met. Therefore, the productivity equations developed can be used to predict the productivity of the harvester.

Predicted vs. Residual Values Dependent variable: Productivity (Analysis sample) 10

RawResiduals -2

-4

-6

-8 6 8 10 12 14 16 18 20 22 24 26 28 Predicted Values

Figure 2: Coppiced single tree form poor x poor residual versus predicted plot

(b) COPPICED DOUBLE TREES: TREE FORM POOR X GOOD MODEL ADEQUACY CHECKING

The normality and homoscedasticity tests were analysed to determine if the underlying assumptions were violated.

(i) Normality test

A visual inspection of error terms is shown in Figure 3. The assumption of normality was evaluated by using the normal probability plot of residuals. The test for normality was performed on the residuals in order to determine if the error terms were normally distributed. The points were lying along the line in the normal probability plot and therefore the assumptions of normality were met.

Normal Prob. Plot; Raw Residuals Dependent variable: Productivity (Analysis sample) 3.0

2.5 .99 2.0 .95 1.5

1.0 .75 0.5 .55 0.0

-0.5 .35

-1.0 .15

ExpectedNormal Value -1.5 .05 -2.0 .01 -2.5

-3.0 -8 -6 -4 -2 0 2 4 6 8 10 Residual

Figure 3: Coppiced single tree form poor x good normal probability plot

(ii) Homoscedasticity test

Homoscedasticity of the error terms was investigated, as per Figure 4, which shows a plot of residuals versus predicted values. The residuals are scattered in the form of a cloud around the zero-line with very little evidence of the ‘fan’ pattern typical of heteroscedasticity. The assumption of homoscedastic variance was met. Therefore, the productivity equations developed can be used to predict the productivity of the harvester.

Predicted vs. Residual Values Dependent variable: Productivity (Analysis sample) 10

Raw Residuals Raw -2

-4

-6

-8 0 5 10 15 20 25 30 Predicted Values

Figure 4: Coppiced single tree form poor x good residual versus predicted plot

The normality and homoscedasticity tests were analysed to determine if the underlying assumptions were violated.

(i) Normality test

A visual inspection of error terms is shown in Figure 5. Due to the small sample size the values do not have a clear pattern. However, most of the central values were lying along the line. The values on the normal probability deviate slightly as the residual values decrease.

Normal Prob. Plot; Raw Residuals Dependent variable: Productivity (Analysis sample) 3.0

2.5 .99 2.0 .95 1.5

1.0 .75 0.5 .55 0.0

-0.5 .35

-1.0 .15

ExpectedNormal Value -1.5 .05 -2.0 .01 -2.5

-3.0 -4 -3 -2 -1 0 1 2 3 Residual

Figure 5: Coppiced single tree form good x poor normal probability plot

(ii) Homoscedasticity test

Homoscedasticity of the error terms was investigated, as per Figure 6. Due to the small sample size of good x poor form trees occur the residuals were not scattered across the plot, only the predicted values greater than 10 were in the sample. However, the residuals were structureless and unrelated to any variable and therefore the assumptions were satisfied.

Predicted vs. Residual Values Dependent variable: Productivity (Analysis sample) 3

-1

RawResiduals

-2

-3

-4 8 10 12 14 16 18 20 22 24 26 28 30 32 Predicted Values

Figure 6: Coppiced single tree form good x poor residual versus predicted plot

(d) COPPICE DOUBLE TREES: TREE FORM GOOD X GOOD MODEL PRODUCTIVITY ADEQUACY CHECKING

The normality and homoscedasticity tests were analysed to determine if the underlying assumptions were violated.

(i) Normality test

A visual inspection of error terms is shown in Figure 7. The test for normality was done on the residuals in order to determine if the error terms were normally distributed. The points were lying close to the line in the normal probability plot and therefore the assumption of normality was reasonable.

Normal Prob. Plot; Raw Residuals Dependent variable: Productivity (Analysis sample) 4

.99 2 .95

1 .85

.65 0 .35

-1 .15

ExpectedValue Normal .05 -2 .01

-3

-4 -8 -6 -4 -2 0 2 4 6 Residual

Figure 7: Coppiced single tree form good x good normal probability plot

(ii) Homoscedasticity test

Homoscedasticity of the error terms was tested, as per Figure 8. The plot shows a “fanning out” pattern which is indicative of heteroscedasticity. The presence of homoscedasticity precludes proper hypothesis testing, therefore raising the possibility of drawing misleading conclusions (Gujarati & Porter, 2010). However, regression statistics are known to be robust to at least some of these underlying assumptions, therefore the violation of the homoscedastic terms is not usually of concern unless they are severe (Jacques & Norusis, 1973; Zar, 2010). However, even though the plot showed heteroscedasticity, the productivity equations developed could be used to predict the productivity of the harvester in planted stands.

Predicted vs. Residual Values Dependent variable: Productivity (Analysis sample) 6

-2

Raw Residuals -4

-6

-8 -5 0 5 10 15 20 25 30 Predicted Values

Figure 8: Coppiced single tree form good x good residual versus predicted plot

Annexure 7

Modelled productivities for various tree form classes

1. Modelled productivities for various tree form classes

Arbitrary stem volumes were used in the model to illustrate the influence of stem form on productivity. For stem-one and stem-two, 0.3 m3 and 0.1 m3 stem volumes were used respectively in the models. Both stem volumes were within the stem volume limits for the tree form models as indicated in Table 16 in the Results and discussion Chapter (coefficients of the productivity model for each tree form classification). Figure 1 shows the modelled productivities for different tree form classes.

15.0 14.9

14.9

14.8 14.7

14.7

/PMH) 3 14.6 14.6 Poor and poor

14.5 Poor and good 14.4 Good and poor 14.4

Productivity(m Good and good 14.3

14.2

14.1 Poor and poor Poor and good Good and poor Good and good Stem-one 0.3m3 and stem-two 0.1m3

Figure 1: Modelled productivities for different tree form classes

For stem-one and stem-two (0.3 m3 and 0.1 m3) stem volumes the harvester had a productivity of 14.9 m3 per PMH in good x good stems, in good x poor stems 14.7 m3 per PMH, in poor x good stems 14.6m3 per PMH and in poor x poor stems 14.4m3 per PMH.

Annexure 8

Productivity model adequacy checks - normality and homoscedasticity tests for coppiced single tree form model

1. COPPICED SINGLE TREES FORM PRODUCTIVITY REGRESSION MODEL ADEQUACY CHECKING

Model adequacy checking is the analysis of residuals to determine if the underlying assumptions have been violated. If the model is adequate the residuals must not contain any patterns (Montgomery, 1997). To check if the planted trees model was adequate, the normality and homoscedasticity tests were analysed.

(a) NORMALITY TEST

A visual inspection of error terms is shown in Figure 1. The values on the normal probability plot deviate slightly from the straight line with the most deviation at the end points. However, when interpreting a normal probability plot one should concentrate more on the central values on the plot than the extremes.

Normal Prob. Plot; Raw Residuals Dependent variable: Productivity (Analysis sample) 4

.99 2 .95

1 .85 .65 0 .35

-1 .15

Expected Normal Value Normal Expected .05 -2 .01

-3

-4 -15 -10 -5 0 5 10 15 Residual

Figure 1: Harvester – planted tree form regression normal probability plot

(b) HOMOSCEDASTICITY TEST

Homoscedasticity of error terms was investigated; refer to Figure 2. The scatter plot of residuals against predicted values shows a slight “fanning out” pattern which is typical of heteroscedasticity (unequal variances). However, this is not very extreme except for the larger predicted values.

Predicted vs. Residual Values Dependent variable: Productivity (Analysis sample) 15

Raw Residuals Raw -5

-10

-15 -5 0 5 10 15 20 25 30 35 40 45 Predicted Values

Figure 2: Harvester – planted tree form predicted versus residual plot

Regression statistics are known to be robust to at least some of these underlying assumptions, therefore the violation of the homoscedastic terms is not usually of concern unless they are severe (Jacques & Norusis, 1973; Zar, 2010). However, even though the plot showed heteroscedasticity, the productivity equations developed could be used to predict the productivity of the harvester in planted stands.