A Critique and Re-Conceptualisation Of

SCENARIOS FOR COMMUNITY-BASED MANAGEMENT OF

CUTOVER NATIVE FOREST IN PAPUA NEW GUINEA

Cossey Keosai Yosi

Submitted in total fulfilment of the requirements

for the degree of Doctor of Philosophy

July 2011

Melbourne School of Land and Environment

Department of Forest and Ecosystem Science

The University of Melbourne

“Produced on archival quality paper”

ABSTRACT

There is an increasing demand for multiple objectives from forest management worldwide and this is particularly challenging for tropical forests due to their diverse composition, structure and a wide range of stakeholder expectations and requirements. In Papua New Guinea (PNG) forest management is generally considered to be unsustainable and commercial harvesting leaves behind large forest areas to degrade overtime with little attention paid to their future management. There were four objectives of this study. The first was to assess the current condition and future production potential of cutover forests in PNG. The second objective focussed on developing scenario analyses and evaluation tools for assisting decision making in community-based management of cutover native forests. In the third objective, the study tested the tools developed under the second objective in two case study sites where extensive harvesting of primary forest had taken place in the past. The fourth objective of this study was to develop a conceptual framework for community-based management of cutover native forests in PNG.

The methodology used in this study was a combination of qualitative analyses of community interests and expectations in small-scale harvesting and quantitative analyses of permanent sample plots (PSPs), forest resources and cash-flow associated with different management scenarios in two case study sites. Analyses of PSPs in cutover forests showed that there was a gradual increase in residual stand basal area (BA) and timber volume over time and these forests generally showed a high degree of resilience following harvesting. In the two case study sites, timber volume for the residual stand and aboveground forest carbon (C) in the Yalu community forest were estimated at 12.7 m3 ha-1 ± 4.5 (SD) and 149.9 MgC ha-1 ± 37.5 (SD) respectively. In the Gabensis community forest, timber volume and forest C were estimated at 15.2 m3 ha-1 ± 2.8 (SD) and 162.1 MgC ha-1 ± 50.6 (SD) respectively. Analyses of field interviews in communities in the two case study sites showed that community sawmill, local processing, log export and carbon trade were the main options preferred by the communities for the future management of their cutover forests. Scenario analyses using a planning tool showed that a management regime with a short cutting cycle (10-20 years), a reduced cut proportion (50%) at the initial harvest

and removing a proportion of only commercial timber species was sustainable. Longer cutting cycles have lower short-term yields but potentially higher yields in the long term because the forest has a greater time to recover to higher volumes for later cutting cycles.

This study developed decision analyses models for community-based management of cutover forest in PNG. With the data available, the models were tested in the Yalu case study site and depending on the input variables in the model, the expected monetary value (EMV) returned was determined by the related cash flow associated with each scenario. For example, sensitivity analysis of the EMV showed that in a local processing scenario, the annual sawn timber production and sawn timber price in the overseas certified market had the largest impact on the EMV.

An integrated conceptual framework for community-based forest management (CBFM) was developed in this study. The framework is appropriate for application in CBFM throughout PNG. This study concludes that the scenario evaluation and analyses tools developed are a new approach in tropical forest management and its application is justified in the context of CBFM because of the complexity and uncertainty affecting tropical forests and their management. A new policy direction in community forestry is therefore, necessary for the application of these systems in CBFM and utilisation in PNG.

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DECLARATION

This is to certify that: i) the thesis comprises only my original work, ii) due acknowledgement has been made in the text to all other material used, iii) the thesis is less than 100,000 words in length, exclusive of tables, maps, references and appendices.

______Cossey Keosai Yosi July 2011

DEDICATION

This thesis is dedicated to the pioneering teachers of the Zare Aingse primary school in Morobe Patrol Post of the Huon District in Papua New Guinea, who set the foundation for my education and career. In 1964 when the Zare Aingse primary school was being established, I was born at Kaingze hamlet near Aingse village. The pioneering teachers at that time were; Mr. Eike Guguwa, Mr. Arataung Kuru and the late Mr. Naira. During that time, because there were no classrooms, school children were taught in a small hut at Zare village. From 1966 to 1969, the school was relocated and a small patch of coconut trees near Aingse village was cleared by the village people and a few classrooms were built from the bush material. During those days, the English language was non-existent and the school children were taught in the Zia dialect. In 1970, the school was relocated to Seboro near what is now the Wizi hamlet. At this stage the official English language was used to teach the school children and I was among the first village school children to enrol at the school when English was introduced at primary school level in this part of the country. From 1970 to 1976, the following teachers taught in the school using English as the official language for education; Mr. Zama, Mr. Bera Koi, Mr. Amo, Ms. Anake Guguwa, Ms. Zane Tunina, late Mr. Mainuwe Kelly Seregi, Mr. Tingkeo Puro, Mr. Waria Woreti, and Mr. Don Amos. In 1976 I completed my Year 6 and in 1977 I said goodbye to my village, my school and my village friends when I was among the seven local students selected by the Education Department to start a new life of modern education in the urban centre of Lae (now PNG‘s second city). My modern education started then at the Bugandi High School (now Bugandi Secondary School) and in 1980 I completed my Year 10 education. After completing Year 12 in 1982 at the Passam National High School in Wewak, East Sepik Province (one of PNG‘s four national high schools at that time), I went on to study a three year Diploma in Forestry course at the PNG Forestry College in Bulolo and graduated in 1985. Three years later I received a PNG Government scholarship and completed a Forest Science Degree course at the PNG University of Technology in Lae and graduated in 1992. Since then, it has taken me 19 long years to have reached this far, a PhD. I humbly salute the pioneering teachers of the Zare Aingse primary school, those who have passed away and those who are still alive, for starting this challenging journey for me.

PREFACE

PSP data used in Chapter 3 are the property of the Papua New Guinea Forest Authority (PNGFA) and its Research Institute and the International Tropical Timber Organisation (ITTO) research Project number PD162/92. Data for the forest assessment in case study sites in Chapter 4 are from the implementation of a collaborative research project between The University of Melbourne and PNG project partners, PNG Forest Research Institute (PNGFRI) and Village Development Trust (VDT) under the ACIAR Project number FST/2004/061. The Decision Tree Models developed in Chapter 6 are based on a Spreadsheet Modelling and Decision Analysis technique. Two Excel Spreadsheet add-ins called TreePlan and SensIT were used to develop the models and carry out sensitivity analyses. TreePlan and SensIT were developed by Professor Michael R. Middleton at the University of San Francisco and modified for use at Fuqua (Duke) by Professor James E. Smith.

The following sections of this thesis are contained in publications. Parts of Chapter 1 and 2 are contained in; Yosi, C.K., Keenan, J.R. and Fox, J.C. 2011. Forest management in Papua New Guinea: historical development and future directions. In: J. C. Fox, R. J. Keenan, C. L. Brack, and S. Saulei (Eds). Native forest management in Papua New Guinea: advances in assessment, modelling, and decision-making. ACIAR Proceeding No. 135, 18-31. Australian Center for International Agricultural Research, Canberra. Chapter 3 has been published in; Yosi, C.K., Keenan, R.J. and Fox, J.C. 2011. Forest dynamics after selective timber harvesting in Papua New Guinea. Forest Ecology and Management, 262, 895-905. Parts of Chapter 5 and 6 are contained in; Yosi, C.K., Keenan, R.J., Coote, D.C. and Fox, J.C. 2011. Evaluating scenarios for community-based management of cutover forests in Papua New Guinea. In: J. C. Fox, R. J. Keenan, C. L. Brack, and S. Saulei (Eds). Native forest management in Papua New Guinea: advances in assessment, modelling, and decision-making. ACIAR Proceeding No. 135, 185-201. Australian Center for International Agricultural Research, Canberra.

ACKNOWLEDGEMENTS

This thesis would not have been completed without the support of various people and organisations. Firstly, I would like to extend my special appreciation to my supervisors Professor Rodney J. Keenan and Dr. Julian C. Fox for their professional advice, encouragement, and support provided throughout this study. The regular consultations, meetings, and networking that I have had with the two of you had motivated me to stay focused on the completion of this thesis, and I sincerely thank you both very much. I also thank both of you for your willingness to provide constructive discussions, feedback and comments on draft chapters, and related support during the duration of my study. Dr. Yue Wang, formerly of Melbourne School of Land and Environment (MSLE) and Dr. Andrew Haywood of Department of Sustainability and Environment (DSE) Victorian Government are also acknowledged for providing some advice during the initial stages of this study.

The Department of Forest and Ecosystem Science (DFES) of the University of Melbourne are acknowledged for the use of University facilities in the completion of this study. Many thanks are extended to PNGFA and PNGFRI for releasing me for the duration of my study. The ITTO Project PD 162/92 and PNGFRI are acknowledged for the use of their permanent sample plot (PSP) data set to undertake the study in Chapter 3. Those staff of PNGFRI who assisted in the PSP data collection included: Forova Oavika, Joseph Pokana and Kunsey Lavong. The field assistants who undertook field work for the PSP data collection were Stanley Maine, Matrus Peter, Timothy Urahau, Amos Basenke, Gabriel Mambo, Silver Masbong, Dingko Sinawi, and late Steven Mathew. Janet Sabub provided data entry services for the PSPs. Their efforts and related support are gratefully acknowledged.

This study is a component of ACIAR Project FST2004-061, which I have been involved with for the last four years. The data for forest assessment in the case study sites in Chapter 5 are a part of the work carried out under this ACIAR Project. The staff of the Project involved in the forest assessment work are acknowledged for their assistance.

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In PNG where this research was conducted, various stakeholders participated in this study. I would like to thank the following for their assistance in one way or another: Desmond Celecor of TFTC; Kenneth Mamu of PNGFA Madang office; Robert Songan of VDT; Israel Bewang and Emmanual Mu of FPCD; Cosmos Makamet and Oscar Pileng of FORCERT Ltd; Francis of Ditib Eco-Timber; Abraham of Narapela Wei Ltd; Mr. Kabusoda of Santi Timbers Ltd; Watam Afing and Bernard Bobias of LBC Ltd; and Emmaus Tobu of Madang Timbers Ltd. My special appreciation is extended to Francis Inude of VDT for assisting with field interviews of community groups. The following community groups are acknowledged for their participation in this study: Konzolong Clan of Yalu village; TN Eco-Timber of Gabensis village; and Sogi Eco-Timber of Madang province. My special thanks are offered to ACIAR for awarding me the John Allwright Fellowship to pursue PhD study at the Department of Forest and Ecosystem Science of The University of Melbourne. The AusAID team including Lucia Wong and Jacqui are acknowledged for administering my award and other related support at The University of Melbourne during the duration of this study.

Above all, I give Glory and Honour to the Almighty God for his guidance throughout the difficult and challenging times of my study and up to the successful completion of this thesis. ―Praise be to God from Whom all things come‖. I also would like to thank my wife Relly and our three lovely children Cerbera, Cassandra, and Caleb for their time, patience, encouragement and support given to me throughout the duration of my study. Finally, but not the least, my deep gratitude goes to my mother, Mrs. Aratamase Bawang Ainase and my late father, Mr. Yosi Guwa Ami for nurturing me to become the man that I am today.

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TABLE OF CONTENTS

ABSTRACT ...... II DECLARATION ...... IV DEDICATION ...... V PREFACE ...... VI ACKNOWLEDGEMENTS ...... VII TABLE OF CONTENTS ...... IX LIST OF TABLES ...... XIII LIST OF FIGURES ...... XIV LIST OF ACRONYMS ...... XV

INTRODUCTION 1

CHAPTER 1: THESIS INTRODUCTION AND OVERVIEW ...... 2 1.1 THESIS INTRODUCTION ...... 2 1.2 FOREST MANAGEMENT ISSUES AND PROBLEMS IN PNG ...... 4 1.3 BACKGROUND ...... 7 1.3.1 History of Timber Harvesting in PNG...... 8 1.3.2 Papua New Guinea’s National Forest Policy ...... 12 1.3.3 Papua New Guinea’s Forest Resources and Timber Production ...... 14 1.3.4 Certification Efforts in PNG ...... 18 1.3.5 Case Study Sites ...... 20 1.3.6 The PNGFRI Permanent Sample Plot Network ...... 22 1.4 RESEARCH QUESTIONS AND OBJECTIVES ...... 27 1.5. THESIS OUTLINE ...... 28

REVIEW OF THE LITERATURE 27

CHAPTER 2: AN OVERVIEW OF CURRENT ISSUES IN TROPICAL FOREST MANAGEMENT ...... 28 2.1. FOREST DYNAMICS ...... 28 2.1.1. Introduction ...... 28 2.1.2. Overview of Tropical Forests ...... 30 2.1.3. Tropical Forest Dynamics ...... 31 2.1.4. Forest Types ...... 32 2.1.5. Species Diversity ...... 33 2.1.6. Species Distribution ...... 35 2.1.7. Regeneration Mechanisms ...... 36 2.1.8. Shade Tolerance ...... 39 2.1.9. Stand Structure ...... 40 2.1.10. Responses of Forest to Disturbances ...... 40 2.1.11. Discussion ...... 44 2.1.12. Conclusions ...... 46 2.2 CURRENT ISSUES IN TROPICAL FOREST MANAGEMENT ...... 47 2.2.1 Introduction ...... 47 2.2.2 Illegal Logging ...... 49 2.2.3 Deforestation ...... 50 2.2.4 Climate Change ...... 52 2.2.5. Community Forest Management in the Tropics ...... 56 2.2.6. Certification ...... 58 2.2.7. Governance ...... 60 2.2.8. Discussion ...... 62

2.2.9 Conclusions ...... 63 2.3 FOREST MANAGEMENT APPROACHES ...... 65 2.3.1. The Management Strategy Evaluation (MSE) ...... 65 2.3.2. The Scenario Method ...... 67 2.3.3 The Bayesian Belief Network (BBN) ...... 69 2.3.4. Discussion ...... 70 2.3.5 Conclusions ...... 71

CONDITION OF CUTOVER FOREST 72

CHAPTER 3: FOREST DYNAMICS AFTER SELECTIVE TIMBER HARVESTING IN PNG ...... 65 3. 1 INTRODUCTION ...... 65 3.2 MATERIALS AND METHODS ...... 67 3.2.1 PNGFRI Permanent Sample Plots – Background ...... 67 3.2.2 Study Sites and PSP Locations ...... 68 3.2.3 PSPs used in this Study and Data Analyses ...... 69 3.2.4 Analyses of Stand Structure ...... 70 3.2.5 Assessing the Dynamics of Cutover Forests ...... 71 3.2.6 Basal Area and Volume Growth ...... 72 3.2.7 Estimating Mortality due to the 1997-98 El Nino Drought ...... 74 3.2.8 Shannon-Wiener Index (H1) ...... 74 3.3 RESULTS ...... 75 3.3.1 Change in Stand Structure after Harvesting ...... 75 3.3.2 Trends in Stand Basal Area ...... 78 3.3.3 Basal Area Growth since Harvesting ...... 79 3.3.4 Critical Threshold Basal Area for Recovery of Harvested Forest ...... 81 3.3.5 Trends in Timber Volume ...... 81 3.3.6 Timber Yield since Harvesting ...... 83 3.3.7 Mortality due to the Fire Caused During the 1997-98 El Nino Drought ...... 83 3.3.8 Species Diversity in Cutover Forest ...... 84 3.4 DISCUSSION ...... 85 3.5 CONCLUSIONS ...... 90

CHAPTER 4: FOREST ASSESSMENT IN CASE STUDY SITES ...... 91 4.1 INTRODUCTION ...... 91 4.2 BACKGROUND ...... 92 4.2.1 Yalu Community Forest ...... 92 4.2.2 Gabensis Community Forest ...... 93 4.3 FOREST ASSESSMENT METHODS ...... 94 4.4 DATA ANALYSIS ...... 95 4.4.1 Estimating Stems per Hectare ...... 95 4.4.2 Timber Volume ...... 96 4.4.3 Aboveground Live Biomass ...... 96 4.4.4 Determining Sample Size ...... 97 4.5 RESULTS ...... 98 4.5.1 Size Class Distribution ...... 98 4.5.2 Residual Timber Volume ...... 100 a The table excludes other non-commercial and secondary timber species...... 100 4.5.3 Mean Residual Timber Volume ...... 101 4.5.4 Aboveground Forest Carbon ...... 101 4.5.5 Sample Size...... 101 4.5.6 Summary of Resource ...... 102 4.6 DISCUSSION ...... 103 4.7 CONCLUSIONS ...... 105

SCENARIO ANALYSES AND EVALUATION TOOLS 106

CHAPTER 5: EVALUATION OF SCENARIOS FOR COMMUNITY-BASED FOREST MANAGEMENT ...... 107 5.1 INTRODUCTION ...... 107 5.2 BACKGROUND ...... 108 5.2.1 The Scenario Approach ...... 108 5.2.2 Modelling Tropical Forest Growth and Yield ...... 109 5.3 METHODOLOGY ...... 110 5.3.1 Criteria for Developing Scenarios ...... 110 5.3.2 Field Interviews using the PAR Protocol as a Guide ...... 111 5.3.3 Scenario development ...... 112 5.3.4 Scenario Analysis using a Spreadsheet Tool ...... 114 5.4 RESULTS ...... 118 5.4.1 Current Forest Uses and Future Forest Management Options ...... 118 5.4.2 Scenario Indicators ...... 122 5.4.3 Estimating Timber Yield under Different Management Scenarios ...... 123 5.4.4 Analyses of Residual Timber Volume over a 60 Year Cycle ...... 129 5.4.5 Projection of Annual Yield over a 60 Year Cycle ...... 130 5.5 DISCUSSION ...... 131 5.5.1 Outcomes from Field Interviews ...... 131 5.5.2 Analyses Output from the Planning Tool ...... 131 5.6 CONCLUSIONS ...... 134

CHAPTER 6: DECISION TREE MODELS FOR COMMUNITY-BASED FOREST MANAGEMENT IN PNG ...... 136 6.1 INTRODUCTION ...... 136 6.2 BACKGROUND – DECISION TREE MODELS ...... 138 6.3 METHODOLOGY ...... 138 6.3.1 Building the Decision Tree ...... 139 6.3.2 Nodes and Branches ...... 139 6.3.3 Terminal Values ...... 140 6.3.4 Expected Monetary Values (EMV) ...... 140 6.3.5 Application of the Decision Tree Models ...... 141 6.3.6 Decision Tree Model Parameters ...... 145 6.4 RESULTS ...... 146 6.4.1 Decision Tree Model 1: Community Sawmill ...... 146 6.4.2 Decision Tree Model 2: Local Processing ...... 149 6.4.3 Decision Tree Model 3: Log Export ...... 155 6.4.4 Decision Tree Model 4: Carbon Trade ...... 160 6.5 DISCUSSION ...... 164 6.5.1 Silvicultural Management of Rainforests ...... 164 6.5.2 Testing the Decision Tree Models ...... 165 6.6 CONCLUSIONS ...... 169

CHAPTER 7: SCENARIO EVALUATION FRAMEWORK FOR COMMUNITY- BASED FOREST MANAGEMENT ...... 170 7.1 INTRODUCTION ...... 170 7.2 BACKGROUND ...... 171 7.2.1 The Management Strategy Evaluation (MSE) approach ...... 171 7.2.2 Overview of Forest Planning in PNG ...... 173 7.2.3 Small-Scale Timber Harvesting in PNG ...... 176 7.2.4 Requirements for Certification ...... 176 7.3 METHODOLOGY ...... 181 7.3.1 Stakeholder Consultation ...... 181 7.3.2 Forest Inventory ...... 181

7.3.3 Planning System ...... 182 7.3.4 Decision Analysis Tools ...... 182 7.3.5 Sensitivity Analyses ...... 182 7.4 RESULTS ...... 183 7.4.1 A Scenario Analyses and Evaluation Framework ...... 183 7.5 DISCUSSION ...... 184 7.6 CONCLUSIONS ...... 186

CONCLUSIONS 187

CHAPTER 8: CONCLUSIONS AND RECOMMENDATIONS ...... 188 8.1 INTRODUCTION ...... 188 8.2 RESEARCH OBJECTIVES AND QUESTIONS ...... 188 8.2.1. Research Objectives ...... 188 8.2.2. Research Questions ...... 189 8.3 KEY OUTPUTS OF THE STUDY ...... 191 8.4 APPLICATION OF THE TOOLS DEVELOPED IN THIS STUDY ...... 192 8.5 CONTRIBUTIONS OF THE PRESENT STUDY...... 192 8.6 LIMITATIONS OF THE STUDY ...... 193 8.6.1 Forest Management Implications ...... 193 8.7 FUTURE DIRECTIONS ...... 194 8.7.1 Future Research Needs ...... 194 8.7.2 Future Policy Directions ...... 195 8.8 DISCUSSION ...... 195 8.9 CONCLUSIONS ...... 196

REFERENCES ...... 198

APPENDICES ...... 219 APPENDIX 3-1: SUMMARY OF PSPS USED IN THE STUDY ...... 219 APPENDIX 3-2: SUMMARY OF THE PSPS IN UNLOGGED FOREST...... 219 APPENDIX 3-3: UN-BURNED PSPS IN HARVESTED FOREST WITH INCREASING BA...... 220 APPENDIX 3-4: UNBURNED PSPS IN HARVESTED FOREST WITH FALLING BA...... 222 APPENDIX 3-5: PSPS BURNED BY FIRE DURING THE DROUGHT...... 223 APPENDIX 3-6: 10 PSPS SEVERELY BURNED DURING THE DROUGHT ...... 223 APPENDIX 4-1: SAMPLING POINT DATA-YALU COMMUNITY FOREST AREA ...... 224 APPENDIX 4-2: INVENTORY DATA-GABENSIS COMMUNITY FOREST ...... 237 APPENDIX 5-1: PNGFA MINIMUM EXPORT PRICE SPECIES GROUP ...... 240 APPENDIX 5-2: CURRENT FOREST USES IN CASE STUDY SITES ...... 241 APPENDIX 5-3: FUTURE FOREST USES IN CASE STUDY SITES ...... 242 APPENDIX 6-1: REQUIREMENTS – COMMUNITY SAWMILL ...... 243 APPENDIX 6-2: REQUIREMENTS – LOCAL PROCESSING ...... 244 APPENDIX 6-3: REQUIREMENTS – MEDIUM-SCALE LOG EXPORT ...... 245 APPENDIX 6-4: REQUIREMENTS - CARBON TRADE ...... 246

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LIST OF TABLES

Table 1-1: Location of the 72 PSPs and their forest types (Yosi, 1999)...... 23 Table 1-2: Description of Vegetation Types according to CSIRO ...... 24 Table 3-1: Mean BAI for plots with increasing and falling BA ...... 79 Table 3-2: Comparison of results of this study with similar studies ...... 87 Table 4-1: Unmeasured Components of AGLB≥10cm (%AGLB≥10cm) ...... 97 Table 4-2: Size Class Distribution ...... 98 Table 4-3: Residual Merchantable Volume for Major Timber Speciesa ...... 100 Table 4-4: Mean Residual Timber Volume ≥ 20cm DBH (m3 ha-1) ...... 101 Table 4-5: Aboveground Forest Carbon (MgC ha-1) with SD in parenthesis...... 101 Table 4-6: Estimate of number of samples ...... 102 Table 4-7: Summary Results ...... 102 Table 5-1: Yalu community forest area ...... 115 Table 5-2: Yalu community forest inventory data...... 116 Table 5-3: Data for a management regime with 50% constant cut proportion...... 116 Table 5-4: Data for a management regime with 75% constant cut proportion...... 117 Table 5-5: Data for a management regime with 20 years constant cutting cycle...... 117 Table 5-6: Management regime with a constant cut proportion of 50% ...... 123 Table 5-7: Management regime with a constant cut proportion of 75% ...... 124 Table 5-8: Management regime with a constant cutting cycle of 20 years ...... 124 Table 5-9: Residual and annual volume over a 60 year cutting cycle...... 129 Table 5-10: Comparison of shorter and longer cutting cycles ...... 133 Table 6-1: Sensitivity data - Community sawmill...... 146 Table 6-2: Sensitivity data – Local processing ...... 149 Table 6-3: Sensitivity data – Medium-scale log export ...... 155 Table 6-4: Sensitivity data – Carbon trade ...... 161 Table 6-5: Comparison of the four management scenarios ...... 168 Table 7-1: Forest Planning and inventory requirements in Papua New Guinea ...... 175 Table 7-2: Strengths and weaknesses of certification...... 177

LIST OF FIGURES

Figure 1-1: Timber Volume and Area harvested from 1988 to 2007 (PNGFA, 2007) ...... 17 Figure 1-2: Export of Primary Products by PNG (ITTO, 2006) ...... 17 Figure 1-3: Map of case study sites selected for the study, ...... 22 Figure 1-4: Plot layout in the field (adapted from Romijn (1994a)...... 25 Figure 1-5: Permanent Sample Plots Location Map (adapted from (Fox et al., 2010)...... 26 Figure 2-1: Key features of the general MSE Framework (Sainsbury et al., 2000)...... 67 Figure 3-1: Map of PNG showing study sites and permanent sample plot locations ...... 69 Figure 3-2: Trends in stem and BA distribution since harvesting, ...... 76 Figure 3-3: Representation of trends in commercial and non-commercial tree species ...... 77 Figure 3-4: Trends in BA since harvesting for the 84 un-burned plots ...... 78 Figure 3-5: Average trends in MBAI since harvesting...... 80 Figure 3-6: BA growth of harvested forest in PNG...... 81 Figure 3-7: Trends in timber volume for trees ≥ 20cm DBH ...... 82 Figure 3-8: Timber yield of trees ≥ 20cm DBH in the residual stand...... 83 Figure 3-9: Ingrowth, recruitment and mortality for the 10 burned plots...... 84 Figure 3-10: Species diversity represented by the change in Shannon-Wiener Index ...... 85 Figure 4-1: An aster image of the Yalu community forest...... 93 Figure 4-2: An aster image of the Gabensis community forest...... 94 Figure 4-3: Size Class Distribution for tress ≥10cm DBH in the Yalu study site...... 99 Figure 4-4: Size Class Distribution for trees ≥20cm DBH in the Gabensis study site...... 99 Figure 5-1: Example output of the Planning tool (Keenan et al., 2005)...... 114 Figure 5-2: Current main forest uses in Yalu and Gabensis villages...... 118 Figure 5-3: Future forest management options in case study sites...... 119 Figure 5-4: Factors influencing community attitudes towards small-scale harvesting...... 121 Figure 5-5: Graphical presentation of the frequencies from field interviews...... 122 Figure 5-6: Timber yield under different scenarios with a 50% cut proportion...... 126 Figure 5-7: Timber yield under different scenarios with a 75% cut proportion...... 127 Figure 5-8: Timber yield for a constant cutting cycle of 20 years...... 128 Figure 5-9: Residual timber volume for a 100 year cycle ...... 130 Figure 5-10: Annual Yield for a 60 year cycle ...... 130 Figure 6-1: Basic framework for decision analyses ...... 142 Figure 6-2: Main Features of decision tree model 1 - Community sawmill ...... 148 Figure 6-3: Main features of decision tree model 2 – Local processing ...... 151 Figure 6-4: EMV sensitivity at +/-10% of the base case – Local processing ...... 153 Figure 6-5: Impact of input variables on the EMV at +/-10% – Local processing ...... 154 Figure 6-6: Main features of decision tree model 3 – Medium-scale log export...... 157 Figure 6-7: EMV sensitivity at +/-10% of the base case – Log export ...... 159 Figure 6-8: Impact of input variables on the EMV at +/-10% - Log export ...... 160 Figure 6-9: Main features of decision tree model 4 – Carbon trade...... 162 Figure 6-10: EMV sensitivity at +/-10% of base case – Carbon trade ...... 163 Figure 6-11: Impact of input variables on the EMV at +/-10% - Carbon trade...... 164 Figure 7-1: The MSE framework for natural resource management ...... 173 Figure 7-2: Certification model promoted by FORCERT in PNG...... 180 Figure 7-3: A conceptual framework for community-based forest management ...... 184

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LIST OF ACRONYMS

ACIAR Australian Centre for International Agricultural Research APFC Asia Pacific Forestry Commission A/R Afforestation / Reforestation asl Above Sea Level BA Basal Area BBN Bayesian Belief Network C Carbon CBOs Community Based Organisations CBFM Community-based Forest Management CBFT Community-based Fair Trade CCAMLR Commission for Conservation of Antarctica Marine Living Resources CDM Clean Development Mechanism CERFLOR Certificacao Florestal CIFOR Centre for International Forestry Research CMU Central Marketing Unit

CO2 Carbon Dioxide CSIRO Commonwealth Scientific and Industrial Research Organisation D Simpson’s Index DBH Diameter at Breast Height DBHOB Diameter at Breast Height Over Bark DEC Department of Environment and Conservation DFES Department of Forest and Ecosystem Science of The University of Melbourne DFID Department for International Development DSE Department of Sustainability and Environment of Victorian Government EMV Expected Monetary Value ENSO El Nino Southern Oscillation ESD Ecologically Sustainable Development FAO Food and Agricultural Organisation of The United Nations FIP Forest Industry Participant

FLEG Forest Law Enforcement and Governance FORCERT Forest Management and Production Certification Service FPCD Foundation for People and Community Development FSC Forest Stewardship Council FRA Forest Resource Assessment GHG Green House Gases GTP Gogol Timber Project HCV High Conservation Value HCVF High Conservation Value Forest HCVFT High Conservation Value Forest Toolkit H1 Shannon-Wienner Index ILG Incorporated Land Group IRR Internal Rate of Return ITTA International Tropical Timber Agreement ITTO International Tropical Timber Organisation IWC International Whaling Commission JANT Japan And New Guinea Timbers LBC Lae Builders and Contractors LULUCF Land use, land-use change and forestry MBAI Mean Basal Area Increment MEP Minimum Export Price MFROA Madang Forest Resource Owners Association m2 ha-1 Basal Area in square meters per hectare m3 ha-1 Timber Volume in Cubic meters per hectare mm annum-1 Rainfall in millimetres per annum MOMASE Morobe Madang Sepik MSE Management Strategy Evaluation MSLE Melbourne School of Land and Environment MVOLI Mean Volume Increment NFDP National Forest Development Programme NGOs Non-Government Organisations N ha-1 Number of stems per hectare NPV Net Present Value NTFP Non Timber Forest Product

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OECD Organisation for Economic Co-operation and Development PAR Participatory Action Research PEFC Programme for the Endorsement of Forest Certification PERSYST Permanent Sample Plot data management System PES Payment for Environmental Services PFE Permanent Forest Estate PINFORM PNG and ITTO Natural Forest Model PNG Papua New Guinea PNGFA Papua New Guinea Forest Authority PNGFRI Papua New Guinea Forest Research Institute PNGK Papua New Guinea Kina PPP Public Procurement Policies PRA Participatory Rapid Appraisal PSP Permanent Sample Plot PSR Pressure State Response RAI Ramu Agri Industry REDD Reduced Emission from Deforestation and forest Degradation RIL Low Impact Logging SABLs Special Agricultural and Business Leases SEQHWP South East Queensland Healthy Waterways Partnership SFM Sustainable Forest Management SPC/GTZ South Pacific Commission / German TFAP Tropical Forest Action Plan TFTC Timber and Forestry Training College TRP Timber Rights Purchase TSH Time Since Harvesting in years UK United Kingdom UNFCCC United Nations Framework Convention on Climate Change UNEP United Nations Environment Program UNESCO United Nations Education, Scientific and Cultural Organisation USA United States of America UTM Universal Traverse Mercator VDT Village Development Trust WWF World Wide Fund for Nature

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INTRODUCTION

CHAPTER 1: THESIS INTRODUCTION AND OVERVIEW

1.1 THESIS INTRODUCTION

Forest management worldwide is increasingly focused on values such as biodiversity conservation, carbon, water and recreation as well as timber production. Ownership and governance arrangements are also changing with an increase in private ownership of forest resources focused on timber production and devolution of management and control from the state to the community-level. Due to overexploitation of tropical forests, there has been a widespread concern about how tropical forests are being managed, however, according to Poore (1989), tropical forests can be managed for sustainable production of timber at a number of different intensities. Whitmore (1990) points out that tropical forest can be managed not only for timber production but also for multiple purposes to meet the needs of conservation as well as to produce other useful products. In terms of sustainable forest management (SFM), if long-term sustainability of timber production is sought from tropical mixed forests, their economic performance must be improved by transforming or replacing the original growing stock (Lamprecht, 1989). These concerns have given rise to institutions such as the Tropical Forest Action Plan (TFAP) and International Tropical Timber Agreement (ITTA) to address issues relating to SFM in the tropics. While that is so, Non Government Organisations (NGOs) have been vocal critics of tropical forest management. While SFM may be a concept, which is quite new to many tropical countries, for those countries, which are members of the International Tropical Timber Organisation (ITTO), achieving ITTO‘s year 2000 Objective still remains a major challenge. The ITTO year 2000 Objective calls for all forest products for export to come from forests managed in a sustainable way. In PNG, some efforts have been put to meet the ITTO year 2000 Objective by enforcing strict controls on timber harvesting practices through the introduction and adoption of the PNG Logging Code of practice. Despite varying difficulties in the region, there has been significant progress towards SFM in the tropics since ITTO conducted an initial survey in 1988 (ITTO, 2006). According to

ITTO (2006), there is positive progress towards SFM in that countries are now beginning to establish and implement forest policies that address SFM and more forest areas are being allocated as permanent forest estates (PFE) for production or protection. Some PFEs in the region are being certified, however, the proportion of natural production forest under SFM in the region is still low and SFM is distributed unevenly across the tropics (ITTO, 2006). ITTO‘s focus in SFM is to improve the social and economic livelihoods of poor communities who depend on their forests for survival whilst also maintaining ecosystem services like provision of clean water and conservation of biodiversity. To support SFM and assist monitoring, ITTO has developed a set of seven key criteria and indicators for sustainable management of tropical forest (ITTO, 1998), which have evolved into the requirements for forest certification. In terms of progress towards SFM, findings from Forest Resource Assessment (FRA) 2005 indicated that forest management is generally improving in the global context , however, the scenario changed dramatically when information is interpreted at the regional level, with alarming trends in several tropical sub-regions (FAO, 2006).

PNG has a significant area of tropical forest composed of a wide range of forest types and environments. However, these forests are increasingly under threat from high human population growth and industrial activities such as mining and logging. These activities are also contributing to the increase in deforestation rates of over 1% per year (see Ericho, 1998, Shearman et al., 2009b). Most of the forest in PNG is under the customary ownership of indigenous people, with a similarly high ethnic and cultural diversity. Local people have used forest land and resources for thousands of years for subsistence and cultural needs. For the past 20 years, much of the focus of formal forest management and policy in PNG has been concentrated on large-scale conventional harvesting to meet national requirements for economic development and little attention has been given to community-level forest management. The current management system is considered by many to be unsustainable and as commercial timber resources in primary forests have been extracted, there have been few examples of future management plans for cutover forests. This has resulted in extensive cutover forest areas being left to degrade over time. A new policy approach is therefore, required for forest management in PNG that reflects changing local and international expectations from forests and the current

state and future requirements for forest resources. This should include consideration for the future production capacity of cutover and degraded forests and development of the capacity of local forest owner communities. This will assist communities to participate in small-scale forest management and utilization, for example, through management systems that are compliant with requirements of certification bodies. This thesis is focused on assisting decision-making in community-based management of cutover forests in PNG and at the same time support the capacity of PNGFA and set a new direction for an integrated regional forest planning and management system for cutover forests in PNG.

1.2 FOREST MANAGEMENT ISSUES AND PROBLEMS IN PNG

There is an increasing demand for multiple objectives to forest management worldwide and particularly tropical forests are complex hence, their management is challenging. Due to their diverse composition, structure, wide range of stakeholder expectations and requirements, tropical forest management is associated with many difficulties. Uncertainty is also a characteristic of many situations in tropical forest management (Wollenberg et al., 2000) hence, traditional methods such as straight forward projections of growth and yield may not be able to meet these challenges. Uncertainties in tropical forest management also make SFM in the region a major challenge for governments, NGOs, local communities and the timber industry. Therefore, new management approaches, creative processes, and policy directions are required to meet these challenges.

PNG has abundant natural resources with very diverse ecosystems and the country is home to an estimated 15,000 or more native plant species (Beehler, 1993, Sekhran and Miller, 1994). However, the country is faced with many challenges in terms of resource development as the government looks for alternative ways to improve and sustain the livelihoods of a large rural population. PNG has 39.4 million hectares of forests (PNGFA, 1998). As it has always been, in many communities throughout the country, forests are a part of the peoples way of life and over 80% of the population of the country depend on them for food, shelter, medicine, and cultural benefits and 97% of the forest are under customary ownership by individuals or community groups (PNGFA, 1998) . According to ITTO, on average, each citizen of PNG has rights over about 6.4 hectares of forest, however, the majority of people still live in extreme

poverty (ITTO, 2006). The forestry sector is the country's third major contributor to government revenue. For example, in 2003 PNG earned US$126 million from the export of tropical timber (ITTO, 2006). This revenue has been generated from primary forests. Given customary ownership arrangements, the future management of cutover forests is likely to be decided by local community groups. This is because in the past there was lack of landowner participation in forest management decision- making. However, today community groups are beginning to accept that their forests provide many values and services apart from timber products. Therefore, they would like to participate in decision-making and also manage their own forests to get maximum benefits and improve their livelihoods. Due to the fact that most global wood production comes from natural or semi-natural forests rather than plantations (Johns, 1997), natural forests research and management elsewhere as well as in PNG remains an important basis to assist SFM. As natural forests are being exhausted in PNG through commercial timber harvesting and other land uses such as large-scale forest conversion to agriculture and shifting cultivation1, forest management will begin to focus on cutover secondary forests and a new paradigm in forest use and management is likely to emerge when cutover forest areas are taken over by community landowner groups. A major challenge is the development of sustainable management systems for cutover forests that meet the needs of community forest owners. Another concerning development and challenge for land owning communities is the PNG government‘s rapid expansion of Special Agricultural and Business Leases (SABLs). SABLs may limit landowner rights and their access to traditional lands and forests. In SABLs forest lands, which may be originally intended for agricultural development, usually for a lease period of 99 years, could be diverted to other land uses by foreign or multinational corporations especially for large-scale harvesting interests without proper landowner consent (Www.postcourier.com.pg/).

In PNG there are many problems associated with forest management. For example, apart from stakeholder demands, land and forest ownership arrangements are complicated issues. Generally, forest management in PNG is considered unsustainable and this is compounded by high deforestation rates. Evidence suggests that forest cover in PNG declined at an estimated annual rate of 113,000 hectares (0.4%)

1 Shifting cultivation is a traditional method of subsistence farming that contributes to loss of forest cover

between 1990 and 2000 (FAO, 2005). Reports from PNGFA suggest that PNG‘s natural forests are being exploited at an overwhelming rate with estimates that forest areas are decreasing at a rate of 120,000 ha per annum (PNGFA, 2003) through logging, agricultural activities, mining and other land uses. Current statistics from PNGFA (2007) also show that from 1988 to 2007, well over 2 million hectares of primary forest have been harvested through commercial logging. Evidence from a recent study (Shearman et al., 2009a, Shearman et al., 2009b) showed that the deforestation rate in PNG increased from 0.46% to 1.41% from 1972 to 2002, although there is some debate about the assumptions underlying this figure (Filer et al., 2009). Generally, the main drivers of forest cover change including deforestation in PNG are: subsistence agriculture, timber harvesting, fire, plantation conversion and mining (Filer et al., 2009, Keenan, 2009, Shearman et al., 2009b). There have also been ongoing problems of illegal logging in PNG. From 2000 to 2005, the PNG government reviewed the operations of the logging industry and found that none of the projects were operating legally with the exception of only two projects (Forest Trends, 2006). However, Curtin (2005) claims that the World Bank sponsored audit of the PNG timber industry from 2000 to 2004 found full compliance by the industry with the country‘s Forestry Act 1991. Despite these various reviews of the timber industry, it is a general understanding by the public that illegal logging in PNG seems to continue. At present, the timber production capacity of cutover forest areas and secondary forests in PNG are poorly understood and the future of marketing wood products from native forests is also uncertain. This study will attempt to address these uncertainties and to develop a framework whereby information will be generated and made available to all stakeholders to assist community management of cutover native forests in PNG. This research study will develop methods for analysis of management scenarios for cutover forests in PNG.

1.3 BACKGROUND

The background of this study presents the historical development of forest management in PNG in terms of history of harvesting, Forest Policy development, forest resources and timber production. PNG‘s efforts in certification particularly at community-level are discussed. Some background about the case study sites and PNG‘s comprehensive PSP network are also given in this section.

Subsection 1.3.1 is the history of timber harvesting in PNG, which is based on an earlier study by Lamb (1990). This subsection provides details of timber exploitation before and after the Second World War. As far as the history of timber harvesting in PNG is concerned, in the early 1970s and 1980s harvesting of primary forests started and this has increased extensively in the 1990s. Since the 2000s, harvesting has increased rapidly and the PNGFA records show that about 10% of accessible primary forests have been harvested by 2007 under commercial logging (PNGFA, 2007).

In Subsection 1.3.2, Forest Policy development in PNG is discussed. PNG‘s Forest Policy was adopted in 1990 and has been focused mainly on large-scale commercial harvesting of primary forests with little or no attention given to management of the residual stand after harvesting. Therefore, the 1990 National Forest Policy does not provide directions on technical aspects of management of logged-over forest areas in PNG and there are no guidelines for land use plans after logging. Although the 1991 Forestry Act has been amended numerous times since 1991 (PNGFA, 2007), there have been no provisions made in the Act for the management of forest areas left behind after harvesting. This study sets the basis for policy changes in order to facilitate sustainable management of cutover forest areas in PNG.

The overview of PNG‘s forest resources and timber production are given in Subsection 1.3.3. This includes the major forest types found in the country with lowland tropical forests found most commonly throughout PNG. PNG is considered as a country blessed with abundant natural resources with 70% of the country under forest cover (ITTO, 2006). Details of PNG‘s production and trade of primary products from 2002 to 2007 are also discussed in this subsection and this includes products such as logs and sawn timber. A record of PNG‘s timber production and trade shows that in 2003, the country was the world‘s second largest exporter of tropical logs after

Malaysia (ITTO, 2004, ITTO, 2005). The forest industry in PNG still remains the third largest revenue earner for the country.

In Subsection 1.3.4, certification efforts in PNG are discussed. Efforts are increasing particularly at community-level forest management and this initiative is likely to bring significant benefits to communities. However, evidence shows that only a small number of forest management certificates have been granted for village-based timber operations in the Asia-Pacific region including PNG (Scheyvens, 2009). With the assistance of the Forest Stewardship Council (FSC), a high conservation value forest (HCVF) toolkit for PNG has been developed to be used in forest management certification (PNG FSC, 2006). This toolkit is now being promoted by NGOs and used to support certification in PNG.

Details of case study sites in this research are given in Subsection 1.3.5. The study sites are located in two village communities near Lae in Morobe province where large-scale timber harvesting has taken place in the past. Field interviews and data collection for the study have been undertaken in the two villages.

Subsection 1.3.6 of the background section gives details of the PNGFRI PSP network. Extensive work on establishment and measurement of PSPs have taken place since 1993 and the field procedures of plot measurements and recording (Romijn, 1994a) are included in this subsection.

1.3.1 History of Timber Harvesting in PNG2

The then Forestry Department in PNG was established in 1938 and began operations but these initial operations were interrupted by the advent of World War II (Lamb, 1990). During the Second World War in 1942, some timber harvesting occurred and a few forest resource surveys were also carried out. These were mainly for military purposes. Several years after the second World War, forestry activities resumed and efforts were then concentrated on producing timber for post-war reconstruction and building. In the 1950s timber harvesting started in the Bulolo area where a ply mill was established to process Araucaria logs from natural forest stands.

2 The history of timber harvesting in PNG is based on earlier study by Lamb (1990).

In 1951, the first official statement on forest policy in PNG was issued by the then Minister for Territories in the Australian Parliament (Lamb, 1990). The Minister‘s policy statement called for location, assessment, and regulation of availability of forest resources for the development of PNG. Although several years of surveys and research followed, by 1957 progress was still slow. Following on from 1957, the PNG Administration issued a five year Forestry Plan for 1962-1967. In 1963, the Administration had 548,000 hectares of forest areas available for exploitation, most of these were allocated for temporary Timber Rights Purchase (TRP). In the 1980s and early 1990s, TRP areas were allocated by the government for timber extraction. The procedures involved purchase of timber and harvesting rights by the government from the landowners from designated forest areas. The government then transferred the harvesting rights to, in many cases, an international harvesting company for timber exploitation. The extraction timber volumes in the TRP areas depended on the density of commercial species. The 1991 Forest Policy and Act replaced the TRP system with what is now the forest management areas (FMAs). Typically, the procedures for the government to acquire an FMA from the landowners are similar to those of TRPs but permits for granting a licence for an FMA area are for forest areas that exceed 80,000 ha. Since 2000 up to now, allocation of forest areas for timber extraction under the FMA arrangement has increased. In such areas, the extraction volumes differ from one concession area to another but average timber volume removed during harvesting is about 15m3 ha-1 (Keenan et al., 2005). During 1963, there were about 82 sawmills with a combined capacity of 930m3 per day. The timber industry in PNG at that time was fairly small as reflected by the low amount of export. Prior to 1962, annual log exports were less than 5,000m3 and sawn timber exports less than 800m3 (Office of Forest, 1979). At that time, the only major timber development in the country was in Bulolo where the large ply mill was based on Araucaria forests (Lamb, 1990).

In 1964, a World Bank report indicated extensive forest resources in PNG and this warranted large scale commercial exploitation. By this time it was also indicated that PNG would take advantage of a major timber deficit as anticipated in South Asia, East Asia and Oceania by 1975, however, an expansion in the timber industry was difficult at that time because of a high diversity of timber species and difficult terrain

in most forested areas throughout the country (Lamb, 1990). The World Bank further called for the need to attract large companies with marketing skills, managerial abilities, and financial resources to make the timber industry successful. In 1963 and 1964, large timber areas in Bougainville and Madang were offered for sale by public tender and by now there was an increase in timber areas allocated throughout PNG under TRP arrangements. Between 1964 and 1969, over 3.6 million hectares of forest areas were assessed and by now, the Forestry Department had some 1.1 million hectares under TRP (Lamb, 1990). During the same period harvested log volumes increased from 183,000m3 to 421,000m3 ha-1. In 1968, the Administration prepared a Five Year Development Plan for the country and the Forestry component of the plan called for further increases in production and downstream processing of timber.

In 1959, the first reconnaissance survey of the timber resources of the Gogol Valley was carried out to assess the potential for timber development in the area. The survey covered an estimated area of 15,000 hectares and in 1962 and 1963, detailed surveys were carried out, which used temporary plots of 0.1 hectares in size. Data analysis from these surveys recommended timber development in the Gogol Valley, thus a TRP was designated. In 1964, the Gogol Valley timber resource was offered for tender by the PNG Administration, however, as no successful tender was received by the Administration, the timber resources still remained undeveloped for some time. In 1968, timber rights were again offered for tender and this time a Japanese consortium submitted an application and began a feasibility study to determine the potential of developing the timber resources for making pulp from the mixed timber species. The Japanese consortium‘s application was rejected by the PNG Administration because it failed to meet the requirements for Australian or PNG equity in the project (De'Ath, 1980).

In 1970, when the potential for pulpwood development was considered, a further survey was carried out to assess the volume of smaller size class timber. This survey identified high volumes of sawlog size timber on the flatter areas of the flood plain, while pulpwood size timbers were located in most secondary forests. Similar surveys were carried out in adjacent forest areas including the Gum, Naru, and North Coast Blocks and arrangement for TRPs were also carried out. The estimated area included

in the Gogol Timber Project (GTP) was about 88,000 hectares, which contained an estimated 7 million m3 of timber.

The GTP was signed in 1971 between Japan and New Guinea Timbers (JANT), a local company called Wewak Timbers, and the PNG Administration for the development of the Gogol Valley timber resources. JANT started harvesting timber for pulpwood in most parts of the GTP area, while Wewak Timbers‘ harvesting operations covered parts of Madang North Coast area. In 1974, JANT shipped the first woodchips from the GTP to Honshu Paper Co. (Lamb, 1990). By 1980, JANT‘s operations had covered most parts of the GTP area and harvesting for pulpwood continued throughout the Naru and Gum Blocks. By 1981, JANT had taken control of timber resources of the Gogol Valley and its clear-felling operations spread into most areas of the GTP and extended to cover the Western boundary of the existing Gogol TRP.

Before the 1980s, Australian companies also carried out small-scale timber harvesting in some parts of PNG. The period 1980s to 1990s saw an influx of Japanese and Malaysian companies carrying out harvesting operations in the country. Currently the timber industry in PNG is dominated by Asian companies and more than 80% of all timber concessions are controlled by the Malaysian logging giant, Rimbunan Hijau. From 2000 up to now, allocation of new timber concession areas increased and in 2007 ten new areas have been released for harvesting.

The history of harvesting in PNG from this literature review shows that there has been an extensive logging of primary forests over the years. This suggests that primary forests in PNG are under extreme pressure from industry and the amount of cutover forest is rapidly increasing.

1.3.2 Papua New Guinea’s National Forest Policy

The National Goals and Directive Principles as set out in PNG‘s Constitution, in particular the Fourth Goal of the Constitution, provides the basis for the country‘s forest policies, which is to ensure that the forest resources of the country are used and replenished for the collective benefit of all Papua New Guineans now and for future generations. The country‘s new National Forest Policy has been designed and formulated to remedy the shortcoming of the previous policy of 1987, to address the recommendations of the Barnett Forest Industry Inquiry3 of 1989 and the World Bank Review of 1990 and to adjust to new situations in the forestry and forest industry sectors (Ministry of Forests, 1991a). The National Forest policy was approved in 1990, followed by passing of the Forestry Act in the National Parliament in July 1991 (Ministry of Forests, 1991b). The new Forestry Act replaced the previous national legislation on forestry matters and reflects the objectives and strategies of the new Forest Policy.

The two main objectives of the country‘s forest policies are: management and protection of the nation‘s forest resources as a renewable natural asset and utilisation of the nation‘s forest resources to achieve economic growth, employment creation, greater PNG participation in industry and increased viable domestic processing. The Policy also calls for skills and technology transfer, and the promoted export of value- added products. However, up to now little progress has been made in terms of phasing out log exports and increasing domestic processing although, a lot of attempts have been made in the past. In 2008, the National Minister for Forests announced the phase out of log exports from PNG by 2010 and increasing downstream processing of wood products (ITTO, 2008). After the approval of the Policy and passing of the Act in 1990 and 1991, several new pieces of forestry legislation have been put in place (PNGFA, 2007). These include the following: Forest Regulation No. 15, 1992 was introduced to enable registration of forest industry participants and consultants under the Act. Forestry (Amendment) Act, 1993 was certified in April 1993 and provided for a clear administrative function of the

3 Inquiry carried out into the Forest Industry by former National Court judge Justice Tos Barnett, which uncovered mal-practices and corrupt dealings in the timber industry.

Board, the National Forest Service through the Managing Director and the Provincial Forest Management Committees (PNGFA, 1993). The National Forest Development Guidelines were issued by the Minister for Forests and endorsed by the National Executive Council during September 1993. The Guidelines were an implementation guide for aspects covered in the new Forest Act, especially in terms of sustainable production, domestic processing, forest revenue, training and localisation, review of existing projects, forest resource acquisition and allocation and sustainable development. The National Forest Plan is prepared by the Forest Authority under the Forestry Act 1991 (as amended) as required under the Act to provide a detailed statement of how the national and provincial governments intend to manage and utilise the country‘s forest resources (Ministry of Forests, 1991b, PNGFA, 1996b). The National Forest Development Programme (NFDP) under the Plan is now under implementation.

The PNG Logging Code of Practice (PNGLCP) was finalised in February 1996 and tabled in Parliament in July 1996 (PNGFA and DEC, 1996). The PNG Code is inconsistent with the Regional Code proposed at the 1995 Suva Heads of Forestry Meeting but is more specific to PNG operating conditions and was made mandatory in July 1997. The 1996 Forestry Regulations, which cover all aspects of the industry procedures and control were approved by the National Executive Council in 1996 in principle subject to some changes to be finalized later. These Regulations provide the legal status for the implementation of many of the requirements specified under the Forestry Act 1991 (as amended). The Forestry (Amendment no. 2) Act 1996 was passed by Parliament and certified on 11 October 1996 (PNGFA, 1996a). The major amendment requires the membership to the Board to have eight representatives, including the representatives of a National Resource Owners Association and the Association of Foresters of PNG. Since the Forestry Act was first enacted in 1991, it has been amended four times (PNGFA, 2007). The first was in 1993, and this was followed by additional amendments in 1996, 2000, and 2005 (PNGFA, 2001).

The Forest policy is administered by the PNG Forest Authority (PNGFA) under the provisions of the Forestry Act 1991, Section 5 (Ministry of Forests, 1991b). Section 7 of the Act specifies, among the functions of the PNGFA: (a) to provide advice to the Forest Minister on forest policies and legislation pertaining to forestry matters; (b) to

prepare and review the National Forest Plan and recommend to the National Executive Council for approval; and (c) to direct and supervise the National Forest Service through the Managing Director. Implementation of the Forest Policy, Act and Regulations have been have been problematic over the years. This is because the PNGFA is under-staffed and has limited capacity to fully enforce legal instruments such as the PNGLCP. Enforcement of rules and regulations in timber concession areas has been difficult due to funding constraints and the isolation of many timber harvesting project sites.

In the case of landuse planning after harvesting, there is no clear policy direction on the management of cutover forest areas in PNG. This study addresses some aspects of National Forest Policy Part II, Section 3, Sustained Yield Management. The 1991 National Forest Policy does not provide directions on technical aspects of management of cutover forest areas in PNG and there are no guidelines for land use plans after harvesting. This research will set the basis for development of new policy guidelines for the management of cutover forest areas in PNG.

1.3.3 Papua New Guinea’s Forest Resources and Timber Production

PNG is located on the eastern half of the Island of New Guinea and lies 160 km north of Australia (Keenan, 2007 ). The country comprises both the mainland and some 600 offshore islands. It has a total land area of 470,000 Km2. The country covers a total landmass of about 46 million hectares, of which 86% (39.4 million hectares) are forested land, while 14% (6.6 million hectares) is non-forested. The estimated 39.4 million hectares of forested land are productive and have potential for some form of forest development, while the 6.6 million hectares of non-forested land remain un- productive (PNGFA, 1998). While two thirds of PNG is under forest cover, the official timber harvest is well below the estimated national sustainable timber yield of 4.7 million m3 (ITTO, 2006).

1.3.3.1 Forest Types

Different authors have described PNG‘s vegetation and forest types using their own terminology (for example, Johns, 1978), however, the country‘s vegetation and forest types have been described in detail and classified based on structural formations (Hammermaster and Saunders, 1995, Paijmans, 1975, Paijmans, 1976, Saunders, 1993). Generally, PNG has a wide range of floristic composition, which is a characteristic of the lowland tropical forests. At sea level, mangrove forests are common, while savannah grasslands can be found in the valleys and on foothills. In higher altitude areas, montane forests are common although many of the forest types in the country are representative of the floristic composition of a typical lowland tropical forest.

The vegetation types in Melanesia including PNG have been broadly described by Mckinty (1999) to fall into three main types. These include lowland moist rain forest, lower montane rainforest and upper montane rainforest. However, other vegetation types common in the region are mangrove forests, savannah and subalpine. In PNG all these vegetation types occur including the subalpine. The lowland moist rain forest is the most widespread and floristically rich vegetation type. It occurs on flat, gentle and undulating terrain of the alluvial plains and foothills. It is also found on steeper hills extending up to 1,500m above sea level (asl). Some of the major emergent tree species that occur in this forest type include Pometia pinnata, Intsia bijuga, Anisoptera thurifera, Toona sureni, Terminalia spp., and Planchonela spp. As altitude increases and temperature decreases, lowland rainforest is replaced by lower montane rainforest from about 1,000-1,200m and extends up to below 3,000m asl (Mckinty, 1999). One common feature of the montane rainforest is the dense moss and tree trunks on the forest floor. Some dominant canopy tree species in this forest type are Castanopsis spp. and Nothofagus spp. The upper montane forest occurs above about 3,000m asl and tree species are more stunted. This forest type is very dense with mosses and epiphytes. Major conifers in the genera such as Dacrycarpus, Papuacedrus and Podocarpus are common trees found and may extend up to the tree-line at about 3,900m asl. The subalpine vegetation comprises mainly grassland and Danthonia and Deschampsia species are common. The grasslands are dominated by small trees and shrubs and colourful orchids such as Rhododendron are common in many parts of PNG. Above 4,000m

altitude, plant growth is limited because of decreasing temperature and occurrence of frost. This is common on PNG‘s highest mountain, Mt. Wilhelm, which is about 4,800m asl. Mangrove forests are salt-tolerant and occur at sea level on tidal flats and the saline estuarine plains of larger rivers such as the Fly and Kikori in the southern part of PNG and the Sepik river in the north. The main mangrove genera that occur throughout PNG include Sonneratia, Avicennia, Bruguiera and Rhizophora. Savannas are anthropogenic in nature and on the mainland of PNG, grasslands of Themeda and Imperata are common. Tree genera of Eucalypts, melaleuca and Acacia are associated with savannas and grow well on savanna grassland. The savanna vegetation in PNG is similar to the flora in the northern part of Australia.

1.3.3.2 Timber Production and Trade

In 2003, PNG produced an estimated 7.2 million m3 of round wood, of which about 76% (5.5 million m3) was fuel wood for domestic use (FAO, 2005). Total industrial tropical log production was an estimated 2.30 million m3 in 2003, which is an increase from 2.10 million m3 in 1999 (ITTO, 2004, ITTO, 2005) though well below the estimated sustainable yield of 4.7 million m3.

The forest industry in PNG is predominantly based on log exports. As such, an estimated 2.02 million m3 of tropical logs were exported in 2003, an increase from 1.98 million m3 in 1999 (ITTO, 2004, ITTO, 2005), which made PNG the world‘s second largest exporter of tropical logs after Malaysia. PNG earned US$126 million in 2003 from exports of tropical timber, $US109 million of which were from logs (ITTO, 2005). The principal log export markets for PNG logs in 2003 were China (62% of all log exports), Japan (20%), and Korea (9%) (ITTO, 2005). Unfortunately, the current level of harvesting by the timber industry is considered unsustainable and accessible primary forests are likely to be exhausted in the next 15 years (Keenan, 2007 ). PNGFA statistics estimated that the area harvested under commercial logging from 1988 to 2007 was over 2 million hectares and timber volume harvested in the form of logs during the same period was over 39 million m3 (Figure 1-1) (PNGFA, 2007). All

in all, the forestry sector in the country has contributed 177.3 million PNG Kina4 year- 1 on average in the form of foreign exchange between 1998 and 2007. PNG‘s export of logs increased from 2002 to 2003 and then became stable from 2003 to 2007 (Figure 1-2). In 2002, log export totalled 1,854,000m3 and that increased to 2,008,000m3 in 2007.

)

4.0 300 3.5 250 3.0 2.5 200 Harvested 2.0 150 volume 1.5 100 Harvested area 1.0

0.5 50 Area Harvested ha) ('000Harvested Area

0.0 0 Harvested Harvested Timber Volume (Millionm

Year

Figure 1-1: Timber Volume and Area harvested from 1988 to 2007 (PNGFA, 2007)

2500

2000 Logs 1500 Sawn

1000 Ply

Veneer Volume m3) ('000Volume 500

0 2002 2003 2004 2005 2006 2007

Year

Figure 1-2: Export of Primary Products by PNG (ITTO, 2006)

4 As at 2007 the PNG local currency of 1 PNG Kina was equivalent to 0.40 Australian Dollars.

1.3.4 Certification Efforts in PNG

PNG has a national Forest Stewardship Council (FSC) working group in place and has developed national certification standards (ITTO, 2006, PNG FSC, 2006). The extent of FSC-certified forest areas in PNG is one area of 19,215 hectares consisting of semi-natural and mixed plantation forests, and natural forests. This figure may have increased since then as in recent years non-governmental organisations and environmental groups have been very active under the banner of FSC to certify projects in various parts of the country. For example, efforts of some recognised non- governmental organisations in PNG include; Forest Management and Product Certification Service (FORCERT) in West New Britain, World Wide Fund for Nature (WWF) in Western Province, Village Development Trust (VDT) in Lae, and Foundation for People and Community Development (FPCD) in Madang. FSC activities in PNG include training and capacity building for local NGO partners.

FORCERT is a PNG Not-For-Profit company that uses FSC certification as a management and marketing tool to help small-scale sawmilling businesses practice good forest management and strengthen their businesses (Scheyvens, 2009). Together with partner organisations, FORCERT has established a FSC Group Certification Service Network, where community based timber producers come together under one umbrella certificate and are linked with central timber yards. FORCERT and its partner organisations have also helped community groups in PNG to manage their forest and business, and assists in finding good markets for a wide range of species. Those community groups who become a member of this network receive training and support in many aspects of running a portable sawmilling business, and they are expected to meet all forest certification requirements. The FORCERT Group Certification Service Network was developed in 2003 and 2004 by a wide range of stakeholders; village sawmill managers, timber yard staff and managers, eco-forestry, environmental and social NGO‘s, and training, educational and research institutions (Scheyvens, 2009).

Community groups in PNG have very little capacity to achieve FSC certification standards and find that meeting certification requirements is quite difficult and the costs of becoming certified are high. It is a requirement that community groups have to comply with international standards and organise and pay for an independent

auditor to assess their forest and business operation. For the community groups to go through the certification requirements and processes are difficult. This is why FORCERT is managing a so called FSC Group Certificate. The group certification system works in that individual small-scale producers that meet the set group certificate standards can become group members. The costs of managing the group certificate are shared between the members who pay an annual fee plus a small levy per cubic meter on all certified timber sold. Certified timber needs to be followed down the ―marketing chain‖, from the forest from which it was extracted all the way to the final buyer of the timber product. This ―chain of custody‖ guarantees buyers of certified products that the timber used did come from well managed forests. Therefore, any trader in certified timber is required to maintain their own Chain of Custody certificate. FORCERT also manages a group Chain-of-Custody certificate and offers membership to a number of selected small central timber yards (Central Marketing Units or CMU‘s), to which certified producers can sell their timber.

In terms of SFM in PNG, according to ITTO (2006), forest areas designated for management totalled five million hectares, of which one and half million hectares have been considered to be managed sustainably and are expected to undergo certification in the near future.

1.3.5 Case Study Sites

Two sites were selected for this study in a region where extensive harvesting of primary forests had occurred in the past in PNG (Figure 1-3a). These sites were located in Yalu and Gabensis villages outside Lae, PNG‘s second city. The first site was the Yalu community forest, which is located on Grid Zone 55, 492977 UTM East and 9269368 UTM North (Figure 1-3b). The community harvesting project in this village comes under the name Yalu Eco-forestry Project and is run by the Konzolong clan. The community forest area is approximately 2,000 ha and the area allocated for small-scale harvesting is about 1,800 ha. The total population of Yalu village is about 2,000 people and about 30% are members of the Konzolong clan (600 clan members). In terms of accessibility into the Yalu village and the community forest area, there is a government road connecting the community to Lae city. The road is generally in good condition, however, the community forest area is approximately five kilometres away from the village and can be accessed by a 4x4 wheel drive vehicle on an all-weather road, which is often in a bad condition during wet seasons. The Yalu community owns a portable sawmill that was used in the past for small-scale harvesting, however, it has broken down and is no longer being used. On a few occasions their project has sold sawn timber to the domestic market for about 450 PNG Kina per cubic meter (PNGK per m3). The average price for exporting sawn timber to the overseas market is approximately PNGK900 per m3. The Woodage in Sydney (Peter Musset) offers PNGK2,250 (AUD$900) per m3 for Intsia biguga (Kwila) and PNGK1,500 (AUD$600) per m3 for mixed hardwood species.

The majority of the people in Yalu community are engaged in subsistence farming as their daily activity, while a handful of them are employed by private companies in Lae as tradesmen in various fields. The main sources of income for the Yalu community are selling local garden produce, fermented cocoa beans, and selling poultry farm products at nearby local markets and the main market in Lae. Other small-scale economic activities that the community is engaged in to earn some income include cocoa, copra, piggery, operating trade stores and public transport. The community also has future plans for development of a large-scale oil palm plantation in their area in partnership with a private agriculture development company called Ramu Agri Industry (RAI). Recently the community has developed interest in eco- timber production and marketing and there is a proposal in place for establishment of

a central marketing unit (CMU) for downstream processing and marketing of sawn timber.

The second case study site is the Gabensis village community forest area, which is located on Grid Zone 55, 469240 UTM East and 9256166 UTM North (Figure 3-1a and b). In this village, only one family is involved in small-scale timber harvesting. Their family group name is the TN Eco-timber. The total forest area available in the Gabensis community forest is approximately 150 ha and about 60 ha are considered as the operable area that can be easily accessible for harvesting. Like in the Yalu community, the majority of the local people in Gabensis village are involved in subsistence farming as their daily activity. Other economic activities in Gabensis village included cocoa farming, poultry, piggery, and operation of local trade stores and public transport to and from Lae city. Operation of the portable sawmill by the TN Eco-Timber currently serves as a direct income generating activity for the one family involved in small-scale harvesting and at the same time supports the Gabensis community with other community services. These include the supply of sawn timber as building materials for a local school, clinic, church building, and a community hall. The investigations and data collection in the case study sites form the basis for studies in Chapter 4, 5, 6 and 7.

(b)

(a) Figure 1-3: Map of case study sites selected for the study, (a) region in PNG where extensive harvesting has taken place in the past, and (b) approximate location of the two communities (Yalu and Gabensis) in Morobe province where the study sites are located.

1.3.6 The PNGFRI Permanent Sample Plot Network

Currently 135 PSPs are being maintained by PNGFRI since 1992 to monitor forest growth and dynamics with a measurement history extending over 15 years. The PSP network is comprised of 122 plots on selectively-harvested forest with 411 measurements and 13 plots on unlogged forests with 23 measurements (Fox et al., 2010). These plots have been initially established and measured through an ITTO funded research Project (Alder, 1997) and maintained over the years by PNGFRI with funding support from ACIAR (Keenan et al., 2002). A large database has been developed (Romijn, 1994b) to store and manage all data from the PSP network.

Earlier work by Alder (1998) evaluated data from some of these plots and concluded that all the plots could be regarded as having rather similar floristic composition characteristic of the lowland tropical forests of PNG. Research work done at PNGFRI to classify forest types on PSPs showed that these plots fall on one of lowland plain, lowland foothill, lowland hill and lower mountain forest types (Yosi, 1999, Yosi, 2004), however, these have been re-classified and integrated using the CSIRO Vegetation Type maps for the 72 PSPs initially established under the ITTO funding

(Table 1-1). Since ITTO‘s funding of the re-measurements of these plots came to an end, the rest of the PSPs have been established and measured by PNGFRI with funding assistance from ACIAR. Details of vegetation classification of the whole of PNG are contained in Hammermaster and Saunders (1995) and Bellamy and McAlpine (1995).

Table 1-1: Location of the 72 PSPs and their forest types (Yosi, 1999).

Province Locations No. Of Date of Forest Type Plots Establishment Gulf Turama 2 09/11/94 Lowland Foot Hills Vailala 2 27/11/94 Lowland Plain Western Oriomo 2 12/10/94 W (Lowland Plain) Wawoi Guavi 2 26/10/94 Hm/Fsw/Wsw (Lowland Oro Embi Hanau 4 20/05/94 F/Hills) Milne Bay Gara Modewa 2 12/06/94 Pl (Lowland Plain) Central Ormand Lako 2 07/08/94 Hm (Lowland Foothills) Iva Inika 2 16/03/96 Hs (Lowland Hill) Ps (Lowland Foot Hills) Morobe Oomsis 2 26/05/93 Hm (Lowland Foot Hills) Trans Watut 2 26/10/93 LN (Lower Mountain) Umboi 2 15/12/94 Hl (Lowland Plain) Kui 2 12/11/94 Hm (Lowland Hill) Yema Gaiapa 1 15/05/96 Hm (Lowland Hill) Madang North Coast 2 20/03/95 Hm9 (Lowland Hill) Rai Coast 2 06/04/95 Hm (Lowland Hill) East Sepik Hawain 2 09/08/94 (Lowland Hill) Sandaun Pual 2 24/08/94 (Lowland Foot Hills) Krisa 2 10/09/94 (Lowland Hill) Southern Mt.Giluwe 2 21/12/93 LsN (Mountain) Highlands West New Kapiura 2 23/07/93 Hm (Lowland Hill) Britain Mosa Leim 2 11/08/93 Hm8 (Lowland Hill) Kapuluk 2 30/08/93 Hm (Lowland Hill) Central Arawe 2 06/05/95 Hl (Lowland Foothills) Anu Alimbit 2 20/06/95 Hm8 (Lowland Foothills) Pasisi Manua 2 07/07/95 Hm8/Hs8 (Lowland Hills) East New Britain Open Bay 2 18/08/93 Hm (Lowland Foothills) Gar 2 27/07/93 Hm (Lowland Foothills) Waterfall Bay 2 29/08/93 (Lowland Foothills) Lassul Bay 2 09/06/95 (Lowland Foothills) Cape Orford 2 27/06/95 (Lowland Foothills) New Ireland Inland Pomio 1 28/07/95 (Lowland Hill) Kaut 2 23/09/93 Hm9 (Lowland Foothills) Umbukul 2 01/10/93 Hm8 (Lowland Foothills) Central N.I 2 02/11/95 (Lowland Foothills) Manus Lark 2 18/10/95 (Lowland Foothills) West Coast 2 29/03/95 Hme/Hm6 (Lowland Foothills) 14 Provinces 36 Locations 72 Plots

The different forest types on which 72 of the PSPs were established have been classified according to the CSIRO Vegetation Type Maps (Hammermaster and Saunders, 1995, Bellamy and McAlpine, 1995). The CSIRO description and classification of vegetation in the PSPs are represented by fifteen codes (Table 1-2). For example, a code of Hm representing a medium crown forest according to the CSIRO classification will represent a lowland foothill or lowland hill forest in the PNG tropical forest context.

Table 1-2: Description of Vegetation Types according to CSIRO

Code Vegetation Type

W Woodland Hm Medium crowned forest Fsw Mixed swamp forest Wsw Swamp woodland Pl Large to medium crowned forest Hs Small crowned forest (low altitude, on Uplands) Ps Small crowned forest (low altitude, on Plains and Ferns) Hm9 Medium crown forest (degree of disturbance class 9 is slightly disturbed) LN Small crowned forest with Nothofagus Hl Large crowned forest LsN Very small crowned forest with Nothofagus Hm8 Medium crown forest (degree of disturbance class 8 is slightly disturbed) Hs8 Small crowned forest (low altitude, on Plains and Ferns, degree of disturbance 8 is slightly disturbed Hme Medium crowned forest with an even canopy Hm6 Medium crowned forest (degree of disturbance class 6 is moderate disturbance.

1.3.6.1 Plot Design and Layout

During the establishment of PSPs, all the plots were randomly located and established in pairs. All the plots are one hectare in size and divided into 25 sub-plots of 20 m x 20 m (Romijn, 1994a). The field procedures for establishment and measurements of the plots were adapted from Alder and Synnot (1992). During plot measurement, all tree species of 10 cm in diameter and above were assessed. Measurements taken on trees included diameter at breast height (DBH) or above buttress, height, crown diameter, crown classes (Dawkins, 1958) and an initial basal area count for each tree was undertaken. Plots on selectively-harvested forest were established and measured either immediately or sometime between then and 10 years after harvesting. For plots accessible by road, re-measurements have been taken on an annual basis, while the initial re-measurement of the other plots were carried out on a two-year interval but have been re-scheduled for re-measurements on a five-year interval due to funding constraints. In the assessment of trees in the plot, a standard quadrat numbering system was used. This system uses quadrat numbers on the basis of coordinates or offsets from the plot origin, for example, south-west corner (Figure 1-4).

NW NE 08 28 48 68 88 N 06 26 46 66 86

100 m 04 24 42 64 84 02 22 42 62 82 Plot origin where 00 20 40 60 80 measurement SW SE starts.

100 m

Figure 1-4: Plot layout in the field (adapted from Romijn (1994a).

1.3.6.2 PSP Locations

Most of the plots have been recorded on lowland tropical forests distributed throughout PNG as these are where most harvesting activities have taken place (Figure 1-5). Only two plots have been established in higher altitude montane forest dominated by the genera Castanopsis and Nothofagus in Southern Highlands province. Twenty three % of PSPs are located on the island of New Britain where there are large areas of selectively-harvested forest.

Figure 1-5: Permanent Sample Plots Location Map (adapted from (Fox et al., 2010).

The data from the PSP network discussed in chapter 1, section 1.3 forms the basis for the study in chapter 3 (Dynamics of natural tropical forest after selective timber harvesting in PNG).

1.4 RESEARCH QUESTIONS AND OBJECTIVES

This research study involved use of scenarios (Wollenberg et al., 2000), which is a new approach that requires a participatory approach to forest management in PNG. This approach has been considered appropriate for the PNG situation because landowner expectations and requirements have not been taken into account in forest planning and management in the past. This study anticipates to bridge this gap.

The overall aim of this study was to investigate and identify frameworks that support community decision-making regarding the future use of cutover forests in PNG. In order to achieve this, a management strategy evaluation (MSE) framework (Butterworth and Punt, 1999, Sainsbury et al., 2000) was adopted to develop and demonstrate practical science-based methods that will support community-based planning and management of cutover forests in PNG. There were four main objectives of this research study. The first was to assess the current condition and future production potential of cutover forests in PNG. This was achieved from the analyses of existing PSPs and the assessment of the forest resources in two case study sites. Secondly, this study aims to develop scenario analysis and evaluation tools for assisting decision-making in community-based management of cutover native forests. In consultation with stakeholders, a participatory action research protocol (Creswell et al., 2007) was used as a guide to analyse stakeholder interests and expectations through field interviews. Based on this consultation and interviews, future forest management options were investigated. These options were further analysed and forest management scenarios were developed using existing planning tools. These were tested and analysed using the scenario analysis and evaluation tools developed under objective two. Effects of scenario analyses were compared and evaluated. Thirdly, the scenario analyses and evaluation tools developed under the second objective were tested in case study sites in cutover native forests in PNG. The two case study areas were selected in a pilot region where extensive timber harvesting had taken place in the past. The fourth objective of this study was to develop a scenario analyses and evaluation framework for community- based management of cutover native forests in PNG. Scenario outcomes from the exercises in the second and third objectives of the study were integrated into this framework. The systems developed were based on sound information; compliance

with expectations of forest certification bodies; and meeting the needs of local communities.

The four main questions this study addressed were; 1. What is the current condition and future production potential of cutover forests in PNG? 2. What are the potential options for community-based management of cutover forests in PNG? 3. How can information on the structure and dynamics of forests and the potential uses of forest resources be used to support effective decision-making in community-based management of cutover native forests in PNG? 4. What type of scenario method is appropriate for adaptive management of cutover native forests in PNG?

1.5. THESIS OUTLINE

The structure of this thesis consists of eight chapters organised around five main parts. These parts are introduction (Chapter 1), literature review (Chapter 2), condition of cutover forest (Chapters 3 and 4), scenario analyses and evaluation tools (Chapters 5, 6 and 7) and the conclusion (Chapter 8). Chapter 1 introduces the thesis and discusses some major forest management issues and problems in PNG. Some background information is provided including the history of timber harvesting in PNG; national forest policy; PNG‘s forest resources and timber production; and certification efforts in PNG. The background section in Chapter 1 also describes the case study sites and the PSP network. The research questions and objectives of this study and the outline of this thesis are also included in the introductory chapter.

Chapter 2 is the literature review and discusses the current issues in tropical forest management in the regional context and gives some examples of the PNG situation. The literature review also includes three different management approaches that may be considered for the management of cutover forests in PNG. These approaches are the management strategy evaluation (MSE), the scenario method, and the Bayesian Belief Network (BBN). As part of this research study, dynamics of natural tropical forest after selective timber harvesting in PNG have been analysed using historical data from an extensive

PSP network that have been managed by the PNGFRI for over 15 years. These involved quantitative analyses of forest structure data from PSPs. Details of these analyses include growth and dynamics and recovery and degradation of cutover native forests in PNG and are presented in Chapter 3. In this research, two case study sites have been selected in PNG. The details of forest resource assessment in the two sites are given in Chapter 4. These details also include some background information about the two study sites and results of analyses of forest assessment, which includes residual timber volume and aboveground forest carbon. Evaluation of scenarios for CBFM is discussed in Chapter 5. These involved qualitative analyses of field interviews in case study sites and quantitative analyses of timber yields under different management scenarios in community-based harvesting. Analyses of timber yields in this case have been facilitated with the application of a planning tool and the outputs are discussed.

In Chapter 6, decision analysis models developed in this study for cutover forests in PNG are described. The models have been tested using data available in case study sites and the results and outputs are discussed. The two sites that have been used as case studies in this research are Yalu and Gabensis villages outside Lae in Morobe province, PNG. Based on the MSE approach and the outputs from the studies in Chapter 5 and 6, an integrated conceptual framework has been developed for community-based management of cutover forests in PNG and the details are discussed in Chapter 7. The thesis is concluded in Chapter 8 by discussing the implications of applying the tools developed in this study for community-based management of cutover native forests in PNG.

REVIEW OF THE LITERATURE

CHAPTER 2: AN OVERVIEW OF CURRENT ISSUES IN TROPICAL FOREST MANAGEMENT

2.1. FOREST DYNAMICS

2.1.1. Introduction

Subsection 2.1.1 gives a general introduction of tropical forests and topics such as species diversity, composition, distribution, structure and disturbance regimes are highlighted. Forests are dynamic ecosystems that are continuously changing (Shao and Reynolds, 2006). These changes relate to the growth, succession, mortality, reproduction, and associated changes that are taking place in forest ecosystems. Usually these changes are projected to obtain relevant information for decision-making and are the basis of forest simulation models that describe forest dynamics. Projection and simulation have been widely used in forest management to update inventory, and to predict future yields, species composition, and ecosystem structure and function under changing environmental conditions.

Tropical forests are biologically diverse and there are complexity and a great diversity of interactions within rainforest ecosystems. For example, studies done by Nicholson (1985) showed that the estimated number of tree species in north Queensland rainforest are about 900. In terms of species distribution in tropical forests, it is common for a lot of tree species to be represented by few individuals. In some forest areas in the tropics, abundance of seed resources and heavy fruit production encourages those areas to have dense and clumped seedling and young sapling distribution on the forest floor. Examples of these type of forests are the Dipterocarp forests in Peninsula Malaysia (UNESCO/UNEP/FAO, 1978). Tropical rainforests are always heterogeneous and often it is difficult to describe its structure. In terms of disturbances to tropical rainforests, particularly logging activities, the impacts may occur in various forms. However, apart from changes in environment including

changes in microclimate and soil, timber harvesting affects the forest structure (Kobayashi, 1992).

In Subsection 2.1.2, the review gives an overview of the extent of tropical forests. Most of this information have been compiled from work done under the FAO Forest Resource Assessment (FRA) 2000 (FAO, 2000) as well as the description of tropical rainforests in the region according to Westoby (1989). Some background on forest dynamics relating to forest succession and the associated changes that take place in a forest stand are discussed in Subsection 2.1.3. Forest dynamics relates to the growth, mortality, reproduction, and the associated changes that take place in a forest. These and the factors that influence the dynamics in a forest area are discussed in this subsection.

In Subsection 2.1.4, the details of the different forest types in the tropics are described and the difficulties in the classification of these forests are pointed out. To give some examples, PNG‘s vegetation and forest types are described. Subsection 2.1.5 is species diversity of tropical forests. Tropical forests are considered as biologically and genetically diverse and the species richness of some countries in the region are discussed as examples in this subsection. Impact of harvesting on growth and species diversity in tropical forests are discussed in detail in Subsection 2.1.5.1.

Species distribution in tropical forests and the environmental factors that influence their distribution pattern are discussed in Subsection 2.1.6. The review gives some examples from the PNG situation where some tree species that are common in higher altitude areas are able to grow well in lower altitude environments. Regeneration is an important aspect regarding the sustainability of timber extraction in tropical forests. In Subsection 2.1.7, regeneration mechanism and the environmental factors that determine the extent of regeneration in tropical forests are discussed. The silvicultural systems applied in tropical forests are described in Subsection 2.1.7.1 and this review is mainly based on earlier studies by Dawkins and Philip (1998) and Mckinty (1999). Examples of application of these systems in selected tropical countries are given.

In tropical forests, those tree species that are slow growing and are able to grow under shade are referred to as shade tolerant, while tree species that are light demanding and

are able to grow under the forest canopy with limited light levels are called shade intolerant. In Subsection 2.1.8, different aspects of shade tolerance in relation to light demanding tree species and those that are able to grow under limited light are discussed in detail. Subsection 2.1.9 is the review on the subject of stand structure of tropical forests. To describe the structure of tropical forests accurately is difficult because these forests are complex and heterogeneous structurally. These aspects are discussed in detail under this subsection. All forests are subjected to both naturally-occurring disturbances as well as human- induced ones. In Subsection 2.1.10, responses of tropical forests to both of these disturbances are described. Natural disturbances include such as phenomena as flooding or landslips and human-induced disturbances are particularly activities such as timber harvesting. Tropical forest responses to natural disturbances are detailed in Subsection 2.1.10.1 and in Subsection 2.1.10.2, how these forests respond to human activities, for example, timber harvesting is discussed. Some examples in the tropics relating to the changes in stand structure after logging activities are highlighted with examples in PNG from research studies on natural forests (Yosi, 2004).

The literature review in Subsection 2.1.11 discusses key issues of forest dynamics in the tropics and some general conclusions are drawn from these discussions in Subsection 2.1.12. The objective of Section 2.1 from the literature review is to understand the complex structure of tropical forests and how these forests response to disturbances.

2.1.2. Overview of Tropical Forests

Tropical forests are considered to be the most biologically diverse of the world‘s ecosystems. Though they cover only 5% of the globe (ITTO, 2007), tropical forests harbour more than half of the world‘s terrestrial plant and animal species. Tropical forest landscapes are home to hundreds of millions of people. For many of these people who live in or near the forests, tropical forests provide a large proportion of the goods and services they use in their daily lives including; fruits, vegetables, game, water, and building materials. They also play an important and complex cultural role, particularly in indigenous communities. In PNG a majority of the population who live in rural areas depend on forests for their livelihoods.

FAO FRA 2000 classified the tropical forests into six ecological zones, which include; tropical rain forest, tropical moist deciduous forests, tropical dry forest, tropical shrub land, tropical desert and tropical mountain systems (FAO, 2000). Of these six ecological zones, the rain forest, moist forests and dry forests are distinguished to be the most important as far as timber production is concerned. According to Westoby (1989), the tropical evergreen rainforests are concentrated in the Amazon, Congo basin and equatorial Africa, and Indo-Malaysian region covering South East Asia and PNG. There are important climatic differences between these three regions, but all are characterized by a great diversity of tree species. From a forest management perspective, serious damage can occur to the generally poor soils by unmanaged removal of trees and loss of nutrients caused by burning. The diversity of vegetation, ranging from species-rich rainforest to barren desert, provides enormous variety in the tropics, the variation, which is a result of variation in rainfall (Evan, 1982).

Tropical moist deciduous forests are widespread in the Northern part of South America particularly Brazil, Venezuela and the Guyana Shield. In Asia they are found in parts of India, Sri Lanka, Thailand, Laos, Cambodia, Vietnam, Burma and southern China (Cooper, 2003). In Africa, these forests are less extensive than in Asia and South America and occur in the southern and eastern fringes of the Congo basin. Dry forests occur over much of Sub-Sahara Africa not covered by the equatorial rain forests. Many of these areas are savannah woodlands with sparse tree cover. In Asia these forests are found in parts of India, southern China and continental South East Asia. South American tropical dry forests are found in north eastern Brazil, the Caribbean coast and in the Argentinean Chaco.

2.1.3. Tropical Forest Dynamics

Forest dynamics relates to the growth, mortality, reproduction, and associated changes in a forest stand (Avery and Burkhart, 1994). These changes can be predicted through field observations in existing forest stands, while past growth and mortality trends are used to infer future trends in the forest stands observed. Forest dynamics describes the physical and biological forces that shape and change a forest and this process is in a continuous state of change that alters the composition and structure of a forest.

According to Shugart (1984), forest dynamics reflect more generally on the phenomenon of succession. Succession in this case is considered to involve the changes in natural systems and the understanding of the causes and direction of those changes. Forest succession and forest disturbance are considered to be the two main factors that influence the ongoing process of forest dynamics in a forest area. In forest disturbances, the events that may cause changes in the structure and composition of a forest include fires, flooding, windstorm, earthquake, mortality caused by insects, and disease outbreak. Human activities also contribute to these changes, for example, timber harvesting; anthropogenic disturbances such as forest clearing and introduction of exotic species.

Forest succession refers to the orderly changes in the composition or structure of an ecological community. The two levels of forest succession are primary succession and secondary succession. Primary succession is usually caused by formation of a new unoccupied habitat community from such events as a lava flow or a severe landslide. On the other hand, secondary succession is often initiated by some form of disturbance caused by, for example, fire, severe wind-throw or logging activities. Ecological changes in a forest can be influenced by site conditions, species interactions, stochastic factors such as colonizers and seeds or weather conditions at the time of disturbance.

2.1.4. Forest Types

According to Dawkins and Philip (1998), classification of tropical forest types fall into three major categories as: i) Tropical wet evergreen, which has rainfall over 2,500mm per annum. ii) Tropical semi-evergreen with rainfall between 2,000 and 2,500mm per annum. iii) Moist deciduous forest having rainfall between 1,500 and 2,500 mm per annum.

Some common characteristics of regions with tropical forest types are an enormous range in precipitation, seasonality, temperatures, relative humidity, frequency of extreme climatic features such as violent storms, hail, hurricanes, and severe droughts. Forests in the region with an equatorial climate can usually have severe drought making them prone to fires, for example, in the case of Nigeria in 1973, in parts of Indonesia in 1982, 1983, 1988, 1991, and 1994, and in the Amazon basin in 1995 (Dawkins and Philip, 1998).

In some parts of the tropical region, there may be forest stands that are dominated by one particular species as is the case in Malaysia and Indonesia where Dipterocarp forests are commonly found (Whitmore, 1984), the varzea forests of Amazon basin, and the teak forests of India and Burma (Champion, 1936). The classification of tropical forest types is notoriously difficult and contentious (ITTO, 2006), however, different authors have described forest types in the tropics using their own terminology. For example, Tracey (1982) and Webb and Kikkawa (1990) described rainforests of North Queensland using habitat features as well as physiognomic features such as canopy layering. Generally, rainforests in Australia cover various structural and floristic types, which are described by reference to climatic features. The major forest types in North Queensland rainforests fall into the categories of tropical, sub-tropical, monsoonal and temperate (Truswell, 1990).

PNG‘s vegetation and forest types have been described in detail based on structural formations (Hammermaster and Saunders, 1995, Paijmans, 1975, Paijmans, 1976, Saunders, 1993), however, generally PNG has a wide range of floristic composition, which is a characteristic of the lowland tropical forests. At sea level, mangrove forests are common, while savannah grasslands can be found in the valleys and on foothills and in higher altitude areas. Montane forests are common although much of the forest types in the country represent the floristic composition of a typical lowland tropical forest.

2.1.5. Species Diversity

Tropical rainforests are considered to harbour the greatest wealth of biological and genetic diversity of any terrestrial community (Hubbell and Foster, 1983). These forests are also known for their high numbers of different plant species. Earlier studies in several tropical rainforest sites around the world in a 0.8 ha plot by Whitmore (1998) revealed highest levels of tree species diversity at around 120 different species per hectare in PNG, 150 in Malaysia and 250 in Peru. However, recent studies and botanical collections may have otherwise increased the number of species found in these countries. Usually most species are patchily distributed, many are random, and a few are uniformly spaced. For example, according to studies carried out in Panama (Hubbell and Foster, 1983), complete mapping of all trees over 20cm DBH in a 50 hectare plot of tropical rainforest has shown patterns of tropical tree distribution and

abundance over a large area in unprecedented detail. In their study it was found that among the patchily distributed species, several tree species were found to closely follow the topographic features of the plot. It is considered that the patchiness has a major effect on the species composition of local stands. The island of New Guinea (PNG and Indonesian western province of Irian Jaya) has a great diversity in vegetation and a flora, which is one of the richest in the world (Loffler, 1979). One of the unique features of tropical mixed forest is that the great diversity of the plants are trees ranging in size from 1-2 meters to some of the world‘s tallest, for example, Araucaria hunsteinii can grow to almost 90m (Mckinty, 1999).

2.1.5.1. Impact of harvesting on growth and species diversity

In tropical forests, growth of most primary species under shade can be very slow for a long time often ceasing for many years (Mckinty, 1999). Growth rate then increases for a primary tree species when it is released by the formation of a gap or if it grows tall enough for its crown to be no longer overshadowed by its neighbours. Studies to examine the effects of logging and treatments on growth rates and yield of tropical forests showed that diameter increments, basal area, and volume production were strongly affected by reduction in stocking resulting from logging and treatment. Reduction in stocking and basal area by felling or treatments such as poisoning results in faster mean increments of remaining trees. This is evident in studies carried out in Suriname (Synnot, 1978) and north Queensland rainforest (Nicholson et al., 1988). Studies of effects of treatments on desirable trees (eliminating unwanted trees by poisoning or felling them for firewood or charcoal) resulted in faster average diameter increments of larger trees than those of smaller trees.

Studies carried out to assess stand changes in North Queensland rainforests after logging by Nicholson et al. (1988) on ninety permanent plots, some of which have been treated silviculturally showed that species diversity was lowered and this change was found to be correlated with the severity of logging as evidenced from measurement of basal area loss. Data obtained from their study indicated that a certain level of disturbance in the rainforest is required to encourage higher level of species diversity. In this case logging generally provided this disturbance and there were evidence of regeneration and species diversity after logging activities, which enhanced potential for future production. It is considered that most rainforests are

very rich in species for example, PNG and South-East Asian region rainforests are considered richer in species than North Queensland rainforests, whereas the African rainforests are considered poorer in terms of species richness. Lindemalm and Rogers (2001) carried out studies on impacts of conventional logging and portable sawmill logging operations on tree diversity in tropical forests of PNG. Their studies compared impacts of conventional high intensity logging and low intensity portable sawmill logging on tree diversity six years after harvesting. Results from their study indicated that tree diversity was significantly lower after high intensity logging in comparison to low intensity logging and unlogged forest.

Usually species richness is best indicated by the number of species, while species diversity is indicated by the Shannon-Wiener Index (Stocker et al., 1985). Studies in tropical forests of PNG showed that in low intensity logging there was a reduction in tree diversity of 5% and 25% for the Shannon Wiener Index (H1) and Simpson‘s Index (D) of diversity respectively, in comparison to unlogged forest (Lindemalm and Rogers, 2001). Diameter growth rates of many PNG tree species are found to be in excess of 20 mm yr-1 (Alder, 1998, Lindemalm and Rogers, 2001) and the study of diameter increment of tree species in PSPs (Alder, 1998) showed that the increment for all tree species averaged 0.47 cm yr-1 (47 mm).

2.1.6. Species Distribution

In tropical rainforests a lot of species are uncommon, while fewer are common and it is also known that a lot of species are represented by few individuals. This is supported by studies carried out by Poore (1968) on a 23 hectares area of lowland tropical forest in Jengka, Penninsula Malaysia in which 377 tree species were assessed. The results of his study indicated that 81% (307) of the total number of species were represented by only one to ten individuals each, while less than 143 species (38%) were found to be represented by only a single individual.

Tropical forest tree species distribution may be influenced by environmental factors such as soil, rainfall, temperature, and altitude, however, certain tree species may be able to adapt to any environmental condition, while some may be suited to specific site and environmental conditions. For example, in PNG, the commercially important Araucaria species, A. hunsteinii (Klinkii pine) and A. cunninghamii (Hoop pine), though common in higher altitude forest types, are also able to adapt well on coastal

vegetation environments close to sea level. These two tree species are common in the Bulolo and Watut area on lower montane forest types (over 600 meters asl) but have been also found along the Huon coast near Kui-Buso village (below 100 meters asl). Related research carried out by Pokana (2002) to study the relationship between soil groups and tree species on logged-over forests also showed that none of the natural forest tree species studied had a strong relationship with the three environmental variables (vegetation type, soil type and rainfall) observed. This may suggest that a large number of native forest tree species occurring in PNG may be suited to any environmental and site conditions in the country.

2.1.7. Regeneration Mechanisms

Extent of regeneration is often determined by factors controlling the fate of seeds and seedlings and the main influencing factors are soil seed bank, light, humidity, predation and defoliation by animals as well as seed sterility. Regeneration of commercial tree species is an important aspect regarding sustainability of logging in tropical forests. A study carried out in Bolivia (Fredericksen and Mostacedo, 2000) compared density, species composition and growth of timber species seedlings and sapling regeneration 14 months after selection logging. This study indicated that there were highest density and greatest initial height growth rates of tree regeneration in areas with the greatest amount of soil disturbance, including log landings and logging roads. Regeneration in this case was high due to high densities of light-seeded shade intolerant species such as Anaderanthera colubrina and Astronium urundeuva. This situation is similar to what happens after selective logging in PNG where gaps, skid tracks and logging roads are quickly conquered by pioneer light demanding species such as Macaranga, Alphitonia and Trema orientalis. In many cases the invasive species Piper is very common. Studies done by Park et al. (2005) on natural regeneration in a four year chronosequence in a Bolivian tropical forest also showed that pioneer regeneration was more abundant than that of commercial species in all harvest years.

In tropical forest conditions, it has been proposed that forests regenerating after timber harvesting are not expected to grow and achieve the heights of the original forests because the lowered vegetational matrix will lower the biological clear bole- height of developing young trees. Usually height reduction of 25-50% may be

expected and this will reduce the living space (volume) of the forest by an equivalent amount (Ng, 1983). After logging operations, silvicultural treatment in residual stands may be required in tropical forests to encourage regeneration and growth of commercially viable timber species. If logged over forests are not encouraged to regenerate commercial timber species, they are more susceptible to conversion to other land uses when accessible to different users (Fredericksen and Putz, 2003). Natural regeneration forms an essential component of selection harvesting systems used in rainforest management, and long- term yield forecasts must take account of the presence and amount of this regeneration (Vanclay, 1992). Due to abundance of seed resources and periodic heavy fruit production in tropical rainforests, a lot of forest areas are found to have dense and clumped seedling and young sapling distribution on the forest floor. Examples of these type of forests according to UNESCO/UNEP/FAO (1978) are; Malaysian mixed Dipterocarp forests, mixed lowland forest in Irian, Venezuela, Sumatrana mixed swamp forests and Araucaria forests in PNG.

2.1.7.1. Silvicultural Systems

The two main silviculture systems applicable for forest management are selection and uniform (clear-cutting) systems (Dawkins and Philip, 1998, Mckinty, 1999). Silvicultural systems for commercially valuable native forests are largely concerned with their regeneration (Mckinty, 1999). From the two silvicultural systems, the four common methods of forest regeneration applied in both tropical and temperate forests are selection, shelter-wood, seed-tree and clear-cutting. In all the methods, regeneration is assumed to arise from natural or induced seed-fall, sowing or planting or a combination of these. However, in tropical forests the principal source of regeneration of primary species following selection harvesting is usually advanced growth (Mckinty, 1999). The two silvicultural systems may be further classified as monocyclic or polycyclic. Monocyclic systems are even-aged regeneration methods where all saleable trees are harvested from a site over a short time-frame. The length of the cycle in this system is equal to the time it takes the trees to mature to achieve rotation age.

Polycyclic systems are uneven-aged regeneration methods that involve returning to the one area to harvest selected trees at short intervals in a continuing series of felling cycles. In this system, the length of the cycle is less than the rotation age of the trees. During the post-1900 to the late 1950s, silviculture of natural tropical forests was evident in India, Burma, Indonesia and Malaysia (Dawkins and Philip, 1998). The main tree species being developed into plantation crops at that time were teak (Tectona grandis) and Shorea robusta. However, progress was hampered by the World economic depression of 1930, the wars and shortages of experienced staff. From the 1950s up to the early 1990s, as population increased World trade in wood production expanded giving rise in demand for sawn timber in the tropics. During this period, the intensity of felling rose in the tropics and in countries such as Sabah and Indonesia, logging operations destroyed the canopy, removed significant part of the seed bearers and encouraged the growth of pioneer species (Dawkins and Philip, 1998). Ongoing cases of success in tropical rainforest management and silviculture are now seen in not all but few countries in the tropics. For example, in Peninsular Malaysia the uniform system has been used to manage Dipterocarp forest, while selective logging system has been used in the Philippines. The uniform system used in Peninsular Malaysia has been associated with a diameter increment of about 0.8- 1.0cm per year (Poore, 1989). Generally, in selective harvesting systems used in the region, timber harvesting is carried out on the basis of minimum felling diameter limits. For example, in PNG, the diameter cutting limit for selective felling system is 50cm dbh. This means that in a timber harvesting operation, all commercial trees with a diameter of 50cm and above across the board are harvested. The selective system used in PNG is associated with an average diameter increment on all commercial timber species to be about 0.47- 1.0cm per year (Alder, 1998).

2.1.8. Shade Tolerance

Forest tree species that are able to tolerate low light levels and are able to grow under shade are usually referred to as shade tolerant and these species are mostly slow growing. Often these tree species can regenerate in areas where lower levels of light reach the forest floor. For example, Vitex lucens and Dysoxylum spectabile are shade tolerant tree species that are able to regenerate in areas where lower levels of light reach ground level, while Agathis australis is a much more light demanding tree and requires larger gaps to regenerate. In PNG, one of the most important commercial timber species, Pometia pinnata (Taun) is a shade tolerant species, which is able to regenerate under canopy and limited light levels. For light demanding tree species (shade intolerant), they may be able to persist without significant growth in deep shade until a gap appears.

It is also quite common in tropical forest logging that mortality rates are usually high on shade tolerant species. This is supported by studies carried out on vegetation structure and regeneration in tree-fall gaps of reduced-impact logged of subtropical forests in Bolivia (Felton et al., 2006). This study showed that ground disturbance during timber harvesting caused higher rates of mortality to shade tolerant species in advance stages of regeneration. This resulted in the removal of the competitive height advantage needed by shade tolerant species to compete for gaps and therefore, further encourages opportunities for pioneer species to dominate gap regeneration.

In temperate forests, if there is less accumulation of organic matter in a forest stand, understory trees remain more vigorous during transitional growth stages (Oliver et al., 1985) and in this situation, trees which eventually form the overstory during true old growth stage can be either tolerant or intolerant of shade. Sometimes shade tolerant species become established in the understory re-initiation stage and slowly grow upward as the overstory releases growing space. Some examples of shade tolerant tree species found in temperate forest types are for example, in the Pacific north-western United States where western hemlocks, Pacific silver firs, and grand firs, which grow beneath old Douglas fir canopies (Oliver et al., 1985).

2.1.9. Stand Structure

Stand structure of a forest may be investigated to observe how a forest behaves over time, which is quite important for forest management purposes. If a forest stand has past management history or some forms of disturbance such as commercial harvesting or other human and animal influence, often it will be necessary to assess its quality before future management decisions are made.

To describe the structure of tropical forests accurately, either in words or in quantitative terms presents considerable problems (Richards, 1983). It is often difficult to describe the structure of tropical forests as rainforests are always very heterogeneous structurally, however, single dominant tropical rainforests show clearly defined strata, while mixed forests usually do not. In a tropical forest ecosystem, the structure of forest also controls the distribution of smaller plants like the epiphytes. Primary rainforests have numerous gaps due to death of large old trees, and often also gaps caused by lightning strikes, windfalls, landslips and other natural causes. Often the distribution of the number of tree stems between diameter size classes and distribution of individual stems amongst basal area size classes are the measures that are used to examine the structure of a stand which are more informative. As well as that size class distribution of individual tree species in a stand is also useful to examine the structure of the stand.

2.1.10. Responses of Forest to Disturbances

All forests are subjected to a number of naturally-occurring disturbances and many to human-induced ones, which produce a range of different-sized gaps in the canopy (Mckinty, 1999). The death and falling of a large dominant tree and the associated damage of its neighbours could produce a gap of some 100-800 m2 (Lamprecht, 1989, Richards, 1996). Gaps caused by the death of trees are of different quality to those caused by fire, landslip or human disturbances such as logging or traditional farming.

2.1.10.1 Tropical forest response to Natural Disturbances

Various natural disturbances in tropical forests create a mosaic of vegetation types with strong species diversity between them (Mckinty, 1999, Whitmore, 1990). This diversity occurs from place to place within the same community. For example, violent annual flooding in the Peruvian Amazon forest resulted in the occurrence of high species diversity from the formation of a mosaic of forest types (Whitmore, 1990). PNG is a land wracked by continual catastrophe such as earthquakes, landslides, volcanic activities, and strong winds. In dry periods, forests that are slightly seasonal become dry hence, frequent fires can be experienced (Whitmore, 1990). In PNG, shifting cultivation and associated regrowth are also extensive. Timber tree species for a tract of lowland rainforest usually include a considerable proportion of pioneers such as the species of Albizzia, Paraserianthes and Serianthes, besides strong light- demanding climax species, for example, Campnosperma spp., Pometia pinnata and Terminalia spp. In the Melanesia region (PNG-Solomon Island-Vanuatu), cyclones, earthquakes, volcanic eruptions and periodic fires are frequent and can destroy large areas of forest (Mckinty, 1999). Prolonged heavy rainfall or tectonic activity causes landslips and other mass movement of the soil surface in Melanesia. They may be also caused by fires or inappropriate roading. The most common form of natural disturbance is the formation of gaps caused by the death of trees. Gaps caused by landslips can be extensive, for example, Whitmore (1998) estimated that 8-16% per century of the land surface of PNG is disturbed by landslides. Lava and heat from volcanic eruptions can also destroy an entire rainforest. Tropical mixed forests are not fire-prone nor do they require fire for their regeneration, however, tropical forests are vulnerable to extensive fires during prolonged drought, for example, in an El Nino Southern Oscillation (ENSO) event (Mckinty, 1999). Rainforests have been destroyed by fire during drier weather periods for over several thousand years (Whitmore, 1991). Fire can be caused by volcanic eruptions or lightning in drier forests. Human induced fire in the tropics is much more frequent and widespread. This can be from fires lit during cooking or more frequently from activities of shifting cultivation, for example, in PNG extensive areas of forests were burnt during the ENSO event of 1997/98.

2.1.10.2 Tropical forest response to harvesting

Generally in a commercial logging operation in a tropical environment, large size class trees with economic value are removed for timber. During the process of timber extraction, excessive damage may be done to the small size class trees, which are not always caused by felling itself but by the movement of machinery in and out of the forest as well as the construction of logging tracks and skidding trails. There are also damage to existing regeneration and the residual stand as a direct result of logging. It is often obvious especially in the tropical region in uncontrolled logging operation that mortality rates are quite high immediately after logging. Harvesting and removal of logs using logging machinery creates gaps on the forest floor to which the forest responds. The amount of damage to a forest and the nature of the response depends on how many trees are felled than on the volume harvested (Mckinty, 1999). Usually felling damage is in the form of breakage of the crowns and snapping of the stems of some of the remaining trees. In many situations in tropical forest logging, skidding operations damage tree roots and boles. For example, in PNG, the most common forms of damage to the residual stand during selection harvesting are to the bole and crowns and the presence of lianas is the major factor affecting crowns (Sam, 1999). Effects of timber harvesting on tropical rainforest may occur in various forms, however, apart from changes in the environment including changes in microclimate and soil, harvesting affects the forest structure. According to studies carried out in Brunei by Kobayashi (1992), the density of standing trees decrease after timber harvesting but analysis of size class distribution revealed a similar pattern. Similar studies were carried out by Yosi (2004) in which a comparison was made between seven plots on unlogged and seven plots on cutover tropical forests from initial measurements of PSPs in PNG to assess the impact of timber harvesting on stocking and basal area. Results from his study showed that there was a 32% reduction in stem numbers, while basal area was reduced by 40% after timber harvesting. In relation to the study by Kobayashi (1992), the PNG data (Yosi, 2004, Yosi et al., 2009, Yosi et al., 2011) also showed that the size class distribution pattern displayed the reverse-J shape pattern, which is a typical characteristic of uneven-aged mixed natural forest.

Several studies carried out in the past in PNG‘s tropical forest are worth mentioning here. Yosi (2004) showed that the average basal area of seven plots on unlogged

forest was about 26.9m2 ha-1 and when the forest was disturbed through logging, it was reduced to about 17.8m2 ha-1; a study by Oavika (1992) showed that after conventional logging operations, initial basal area may be reduced to as low as 10m2 ha-1, while related research studies done on diagnostic sampling conducted in PNG‘s Oomsis forest by Kingston and Nir (1988a) suggested that the maximum basal area for free growth of natural forest in PNG is around 30m2 ha-1; and data analysis under an ITTO funded project by Alder (1998) also indicated that an un-logged forest in PNG achieves a dynamic equilibrium of about 32m2 ha-1.

It is generally understood that forest disturbances from logging may change the structure and species composition and may also upset the ecological balance of a forest. On the other hand, logging may encourage a new balance of regeneration especially where the canopy is opened and gaps are created in the forest. Studies on effects of reduced impact logging (RIL) on stand structure and regeneration in a lowland hill forest of PNG (Rogers, 2010) showed that timber harvesting using a portable-sawmill, cutting 1-2 trees ha-1 caused 1-6% of ground area to be heavily disturbed. Logging gaps created from operations of portable-sawmill promoted abundant regeneration of primary and secondary species. His study also showed that early regeneration was recorded at 61% for secondary species but after 61 months, primary species became dominant and secondary species accounted for only 9%. Johns (1986) reported that initial losses of trees through logging may be compensated in the short term by leaf flush in the remaining trees in response to conditions of physiological drought and rapid growth of pioneer species. This is quite common in tropical rainforests as immediately after timber harvesting through logging, short- lived pioneers (for example, in PNG, Macaranga, Trema and Altofia) quickly conquer the openings and gaps created on the forest floor.

According to Ng (1983), in selective timber harvesting, removal of large size trees also destroys the upper canopy of the forest as well as much of the lower canopy. For example, studies carried out in Kalimantan in Indonesia (Abdulhadi et al., 1981), showed that removal of a single large tree in a logging operation resulted in the destruction of 17 other trees and crown and branch damage to 41% of the surviving trees.

2.1.11. Discussion

The literature review on the subject of forest dynamics in Section 2.1 highlighted not all but some issues in tropical forests. The review related to an overview of tropical forests (Subsection 2.1.2) showed that apart from the diverse ecosystems and complex structure of tropical forests, they support the livelihoods of millions of people who depend on them for their survival.

Tropical forest dynamics (Subsection 2.1.3) relate to the various changes in natural systems that take place continuously in a forest stand and these changes are explained by the phenomenon of succession. As explained earlier, forest succession and forest disturbance are the two main factors that influence the ongoing process of forest dynamics in a forest area (Shugart, 1984). In the review it was pointed out that classification of tropical forests are difficult (Subsection 2.1.4) (ITTO, 2006), however, the characteristics of these types of forests include high precipitation, seasonality, temperatures, humidity, violent storms, hail, hurricane, and severe droughts. In terms of species diversity (Subsection 2.1.5), tropical forests still remain the world‘s most complex and diverse ecosystems of any terrestrial environment. Tropical forests are known for their mixed species composition and their species distribution (Subsection 2.1.6) are influenced by environmental factors such as soil, rainfall, temperature and altitude.

Regeneration in tropical forests (Subsection 2.1.7) is controlled by factors such as soil seed bank, light, humidity, predation and defoliation by animals and seed sterility. Sustainability of timber harvesting in tropical forests is also affected by the regeneration capacity of commercial tree species. Review under this subsection points out that the two main silvicultural systems for the management of tropical forests are selection and uniform (clear-cutting) systems (Subsection 2.1.7.1). As is commonly known, this literature review pointed out that shade tolerant tree species (Subsection 2.1.8) are able to grow under shade, while shade intolerant species are light demanding and require larger gaps to regenerate. Usually timber harvesting in tropical forests affects shade tolerant tree species due to high mortality rates caused from harvesting activities (Felton et al., 2006). Describing the structure of tropical forests (Subsection 2.1.9) is often difficult because of their heterogeneous structure.

However, the distribution of tree numbers between diameter classes and individual stems amongst basal area classes can easily describe the structure of a stand. Tropical forest environments respond to disturbances in many ways. As pointed out in this review (Subsection 2.1.10), forests respond to natural disturbances (Subsection 2.1.10.1) as well as human-induced disturbances such as timber harvesting (Subsection 2.1.10.2), which affect the environment, structure, and species composition. On the other hand, harvesting also opens up the canopy and gaps are created in the forest floor hence, encouraging regeneration.

As indicated in the literature, many research studies have been carried out in tropical forests relating to stand dynamics and changes that follow after disturbances such as logging activities. Many of these studies are not reported in this review, however, research studies on this subject carried out in North Queensland (for example, Nicholson, 1985, Nicholson et al., 1988) and research in tropical rainforests of Bolivia (Fredericksen and Mostacedo, 2000, Fredericksen and Putz, 2003) point out the need for silvicultural interventions to be applied to the residual stands to promote regeneration and growth of commercial tree species.

2.1.12. Conclusions

From the review in Section 2.1, the following general conclusions are made;  Silvicultural treatments after logging to enhance forest growth have been successful in North Queensland tropical rainforests, for example, increasing basal area indicating good response to treatments (Nicholson et al., 1988). Using the North Queensland experience, there is a need to adopt similar practices to other tropical forests in the region, especially in the Pacific-Asia region.  Silvicultural treatments in residual stands may be required after logging to encourage regeneration and growth of commercially viable timber species (Fredericksen and Putz, 2003).  Post-harvest competition control treatments may be necessary to encourage regeneration of commercial tree species (Fredericksen and Mostacedo, 2000).  Out-planting programs may be needed to ensure successful regeneration of commercial timber tree species (Park et al., 2005).  In the case of PNG, currently there are few or no silvicultural treatments applied to residual stands to promote regeneration of desirable timber species or to enhance forest recovery after logging activities. There is now a need for research into post-harvest silvicultural treatments and other silvicultural interventions on cut-over native forests in the country. This may be necessary to promote regeneration and growth of commercial timber species as well as to improve stocking and density on cut-over forests, which may otherwise be left to degrade over time. Silvicultural treatments may involve liberation and refinement treatments, while the way forward in terms of other silvicultural interventions on cut-over native forests may be enrichment and gap planting.

The objective of Section 2.1 was to understand the complex structure of tropical forests and how these forests response to disturbances. Tropical forests are diverse in terms of their structure and composition and they respond differently to both natural and human-induced disturbances such as timber harvesting. Due to their mixed and diverse species composition, SFM is a challenge, however, appropriate management systems are required to address these challenges.

2.2 CURRENT ISSUES IN TROPICAL FOREST MANAGEMENT

2.2.1 Introduction

Subsection 2.2.1 gives a general introduction of the current issues in tropical forest management. The issues that are high on the agenda of international discussion regarding tropical forest management are highlighted based on (FAO, 2007). These issues are discussed briefly under this subsection to set the scene for the details that follow.

Due to global demand for timber products, tropical forests are under enormous pressure from harvesting, while governments in the region rely on revenues generated from export of timber products to supplement internal budgets. It is also considered that as most global wood production comes from either natural or semi-natural forests rather than plantations, natural forest management and research elsewhere and in the tropics still remain as an important aspect for SFM.

Based on the most recent information available from the Global Forest Resource Assessment 2005 (FRA 2005) by FAO (2007), the current issues high on the agenda globally include: climate change, forest landscape restoration, invasive species, wildlife management, and wood energy. The tropical region is part of the global community hence, while most of the global issues are also important in the region, the important topics for discussion and debate include: illegal logging, deforestation, climate change, certification, and governance.

In Subsection 2.2.2, the review discusses illegal logging in the tropics and gives some specific examples in the region. World-wide campaigns against illegal logging have emerged and have much support from the international community especially OECD countries (Curtin, 2005) and particularly Australia. However, there have been also a lot of efforts and cooperation in combating illegal logging and the associated timber trade. In this subsection, detailed aspects of illegal logging in the tropical region are pointed out. Deforestation is a major factor contributing to global warming, which leads to climate change. This is a widespread concern and the review discusses the associated problems with deforestation in Subsection 2.2.3.

Subsection 2.2.4 discusses detailed aspect of climate change. There is now a growing concern that global warming is the major cause of climate change and the review points out the importance of the role of tropical forests in causing and solving the problems of climate change. Under this Subsection, an overview of the Kyoto Protocol and the role it plays in addressing issues relating to climate change are also given in Subsection 2.2.4.1. Some aspects of carbon sequestration, the process that removes carbon from the atmosphere that may assist in solving the problems of global warming are highlighted in Subsection 2.2.4.2

In Subsection 2.2.5, community forest management in the tropics is discussed. It is now widely recognised that community groups are increasingly involved in forest management at the community-level in the tropics. The review give details of the efforts of Non-government organisations (NGOs), Community-based Organisations (CBOs) and international agencies in promoting CBFM in the tropics. Certification efforts by various schemes in the tropics are highlighted as these processes are a necessary requirement for SFM. In Subsection 2.2.6, the review firstly gives some details of the establishment of certification bodies worldwide and also gives some examples of the countries in the tropics, which are developing their own certification systems. ITTO‘s role in promoting certification programs in its member countries are also discussed in this subsection.

The review in Subsection 2.2.7 emphasises that governance at local, national and regional levels is important to address problems such as corruption and deforestation. Details of efforts by international organisations to improve governance in developing countries are discussed in this subsection. In the review, some specific examples from PNG have been highlighted.

The literature review in Subsection 2.2.8 summarises the discussions relating to the current issues in tropical forest management and some general conclusions are drawn from these discussions in Subsection 2.2.9. The objective of Section 2.2 is to point out and discuss the current issues, which are themselves problems and challenges facing tropical forest management. These key issues are high on the agenda in policy debate and discussions by governments and stakeholders in international meetings.

2.2.2 Illegal Logging

The world-wide campaign against illegal logging in developing countries especially Africa, Asia, and the Pacific is attracting support from governments of OECD countries including USA, UK, and Australia (Curtin, 2005). However, there is also an argument that these governments are more concerned in protecting their own timber industries from competition from producers, especially in the tropical region including countries such as Indonesia and Papua New Guinea (Curtin, 2005). According to Australian Ministry for Fisheries, Forestry and Conservation citing a report by Jaakko Poyry (2005), illegal logging is defined as harvesting without authority in national parks or conservation reserves, and avoiding full payment of royalty, taxes, or charges. It is generally understood that illegal logging involves the harvest, transportation, purchase, or sale of timber in violation of national laws.

There has also been much of international effort and cooperation in combating illegal timber trade. These efforts have been supported following the adoption of an anti- timber trafficking resolution at the meeting of the United Nations Economic and Social Council (UNESCO) in Vienna, April 2007. These initiatives are receiving support from developing countries. For example, Indonesia has been the first country in the world to change its laws relating to money laundering to include crimes against the environment and illegal logging. In PNG, the government commissioned five separate reviews of the administration and operations of the logging industry from 2000 to 2005 (Forest Trends, 2006). These reviews were conducted in response to concerns raised by the public that the operations of the timber industry were not providing long-term benefits to the country and its peoples, and to assess the implementation of amendments to the 1991 PNG Forestry Act (Ministry of Forests, 1991b). Of the 14 active logging operations investigated under one of the five reviews, it was stated that none of these projects were operating legally with the exception of only two projects, which were found to be better than average compliance to existing laws and regulations. The report by Forest Trends (2006) is contradictory to claims by Curtin (2005) in which he points out that audits of the PNG timber industry sponsored by the World Bank from 2000 to 2004 found full compliance by the industry with the country‘s Forestry Act 1991.

Quite recently Australia has been one of the countries engaging with issues relating to illegal timber trafficking. Australia‘s efforts have been boosted when trade officials from Australian Embassy visited the Centre for International Forestry Research (CIFOR) in 2006 to discuss the question of illegal timber exports. Also in April 2007, the Australian Minister for Environment and Water Resources visited CIFOR as part of the launch of the Global Initiative on Forests and Climate.

According to ITTO (2006), in many ITTO producer member countries, illegal logging is a critical obstacle to SFM in both production and protection forest areas, however, efforts to combat illegal logging and illegal trade through bilateral agreements are emerging. For example, in Indonesia and Malaysia, governments have developed a system of government-to-government timber trade in 2004 whereby only logs received through government designated ports would be considered legal. Multilateral initiatives have also been put in place to address illegal logging. For example, the 2001 introduction of Forest Law Enforcement and Governance (FLEG) (ITTO, 2006) in East Asia, which resulted in the Bali Ministerial Declaration, in which both producer and consumer countries agreed to take actions to suppress illegal logging.

2.2.3 Deforestation

Deforestation in tropical countries has been a major point of discussion in recent years. As Grainger (1983) points out, deforestation is temporary or permanent removal of forest cover whether for agricultural or other purposes. FAO has estimated the rate of deforestation in the humid tropics to be about 16 million hectares per year from studies done in thirteen countries in the tropics including Malaysia and PNG (FAO, 2006). However, these estimates were doubtful as Lanley‘s systematic approach (Lanley, 1981) in 55 tropical countries estimated the deforestation rate in the tropics to be 6 million hectares per year.

According to FAO FRA 2005, each year about 13 million hectares of the world‘s forests are lost due to deforestation (FAO, 2006). From 1990 to 2000, net forest loss was 8.9 million hectares per year from which primary forest was lost at a rate of 6 million hectares per year through deforestation or selective logging. Among the ten leading countries that have the largest net forest loss per year between 2000 and 2005, Brazil, Indonesia, Myanmar, and Zambia were top of the list. During the same period, net forest loss was 7.3 million hectares per year, which is equivalent to 200 km2 per

day (www.fao.org/forestry/site/28679/en/, 2008). According to Greenpeace, Indonesia had the fastest rate of deforestation in the world with an area of forest equivalent to 300 soccer pitches destroyed every hour (www.sciam.com, 2007).

Recently at a high level meeting on Forests and Climate held in Sydney, it was pointed out that land use change, especially deforestation in developing countries contributes 20% of annual global greenhouse gas emissions (http://www.cifor.cgiar.org). This high level meeting followed the Australian Government‘s launch earlier of a $200 million initiative to reduce global greenhouse gas emissions caused by forest loss, especially in developing countries. FAO (2007) also pointed out that most developing countries, especially those in tropical areas, continue to experience high rates of deforestation and forest degradation and countries with highest rates of poverty and civil conflict are those that face the most serious challenges in achieving SFM (www.fao.org/forestry/site/28679/en/). Freeman (2006) also argues that the ongoing problems of illegal logging and forest conversion to other land uses in developing countries are arguably the most significant threats to achieving SFM. With widespread concern about the fast depletion of tropical forests, logging activities in the region have been taken as a sensitive issue. Apart from the day to day human influence on the forests as well as the many complex factors and issues causing the fast depletion of the tropical forests, logging activities in the region have been understood to be a major contributing factor to forest degradation. With higher rate of exploitation, tropical forests are now under threat from conversion to different land uses. In earlier estimates by Dawkins and Philip (1998), 0.2 km2 of rainforests are lost every year of which 25% is a direct result of logging activities carried out in the region, while an estimated 5.1 million ha of forest degrade every year as a direct result of logging.

Like many other developing countries in the tropics, PNG‘s natural forests are being exploited at an overwhelming rate. Estimates show that the country‘s forests are decreasing at a rate of 120,000 ha per annum (PNGFA, 2003) through logging, agricultural activities, mining and other land uses. Earlier on, the 2000 World Bank statistics estimated that from 1980 to 1990, the deforestation rate in PNG was 0.3% annually (Forestry Compendium, 2003). In 1992, forest areas committed for timber concessions throughout the country were about 5.7 million hectares, while the total

logged-over forest was estimated to be about 850,000 hectares (Bun, 1992) and this has increased to an estimated figure of one million hectares (Nir, 1995).

2.2.4 Climate Change

There is now a growing concern throughout the world about global warming, which causes global climate change. Tropical forests are considered to play an important role in causing and solving the problems of global climate change, global biodiversity and sustainability. Tropical deforestation is considered a major factor contributing to carbon dioxide (CO2) emission into the atmosphere. It is estimated that the total global C stored in plant biomass is 106 Kg C (Healey, 2003). Tropical forests, especially moist forests are important for their capacity to store C. Therefore, their conversion and degradation can potentially have a massive effect.

There is also concern about human-induced climate change, which is affecting ever- wider areas of energy and land use policy, as evidenced by the United Nations 1997 Climate Change Conference at Kyoto and further ratification in Bonn (Healey, 2003). The major cause of global warming, according to the Green house effect theory is the increasing concentration of atmospheric CO2, which lets short wavelengths radiation from the sun penetrate whilst blocking the long wavelengths radiation emitted by the much cooler surface of the earth. Because of the importance of forests in the global C cycle, it is widely recognised that their management could play a large role in mitigating this mechanism. The potential for increasing terrestrial C storage by increasing forest biomass has also been recognised in many parts of the world. It is also considered that the high productivity of moist tropical forests means that they have the potential to fix a lot of CO2 to counteract recent global climate change.

In 1990, it was estimated that the contribution of tropical forest conversion and degradation to the C cycle was 22%. At present global forestry is acting as a net absorber of atmospheric CO2. Experts are more and more certain that the so called

―Missing Sink‖ for CO2 is greater than previously expected absorption by terrestrial vegetation. One of the reasons for forests being the net C fixation includes the increase in productivity of existing forests. Also important is the large amount of plantation forestry established in the past 30 years. These forests are still in their building phase when their biomass is rapidly increasing and they are major sinks for

CO2. Despite the evidence of forests currently acting as a net C sink, the extent of this

and, in particular, it‘s time duration are very uncertain. It is predicted that there could be a catastrophic switch of the whole Amazon ecosystem from net sink to net source of C. Studies carried out in Indonesia show that deforestation and slash and burn agriculture had a dramatic impact on global climate change (Healey, 2003).

There is a potential technical improvement in tropical forestry to current conventional commercial logging practices. The improvement in the technique of Reduced Impact Logging (RIL) include the prohibition of logging in the more vulnerable areas, and the adoption of better planned and implemented felling and skidding operations are considered to be one means of reducing the C emissions held responsible for global warming. While deforestation in developing countries contributes significantly to greenhouse gas emission, PNG and countries in the Pacific may potentially benefit from a system of Payment of Environment Services (PES) or Avoided Deforestation (http://www.cifor.cgiar.org) to compensate and provide incentives for them to reduce deforestation.

2.2.4.1 Kyoto Protocol

The Kyoto Protocol is the international treaty on global warming. The treaty was negotiated as an amendment to United Nations Framework Convention on Climate Change (UNFCCC) in Rio de Janeiro in 1992. In 1997 the Protocol was negotiated in Kyoto and opened for signatures in 1998. Among those countries who signed the Agreement, PNG also signed the Agreement in 1999 and ratified the Protocol in 2002.

The two main objectives of the Kyoto Protocol are: to assist developed countries to meet emission reduction targets; and to assist developing countries to meet the objectives of sustainable development. The mechanism that allows developed and developing countries to collaborate is the Clean Development Mechanism (CDM). Eligibility of lands for implementing CDM project activities are required to comply with international rules and national regulations and priorities. Land use, land-use change and forestry (LULUCF) requirements under the CDM are limited to afforestation and reforestation later known as A/R CDM in the first commitment period. Under the Protocol‘s standards (Murdiyarso et al., 2005), afforestation is the direct human-induced conversion of land that has not been forested for a period of at least 50 years to forested land through: planting; seedling and human-induced promotion of natural seed sources. Reforestation is the direct human-induced

conversion of non-forested land to forested land through: planting; seedling and human-induced promotion of natural seed sources, on land that was forested but that has been converted to non-forested land. Implementation of A/R CDM is required to comply with strict rules concerning methodologies to determine baseline, to monitor greenhouse gas removals and leakages, and the monitoring plan. The scheme for LULUCF activities called small-scale A/R CDM gives smallholder rural communities an opportunity to participate. Small-scale projects are able to sequester a maximum -1 of 8 Kt CO2 year (Murdiyarso et al., 2005). The magnitude of such projects could involve an area of 500-800 ha depending on the species chosen and management of the project.

2.2.4.2 Carbon Sequestration C sequestration is the process that removes C from the atmosphere. This can be done in a long-term storage of C in terrestrial vegetation, underground in organic matter and soils and in oceans. This process removes or slows down CO2 accumulation in the atmosphere. While artificial capturing and storing C is possible, natural processes of storing C in terrestrial biomass are also important.

The most obvious way to reduce atmospheric CO2 is for forest plantations to be established in currently non-forest, low-biomass land. This can be difficult due to high investment costs and shortages of available land. If the socio-economic conditions are favourable for continued establishment of new forest plantations, this will establish a larger flexible C store. As an alternative to the continuous establishment of new plantations, attention should be turned to massively reducing the rate of conversion and degradation of existing forests.

As far as the Kyoto Protocol is concerned, developing countries, especially in the tropical region could possibly benefit from developed country investment in increased C storage. This may be possible through the CDM, which allows developed and developing countries to collaborate. Considering the global context, Cooper (2003) estimated that afforestation in temperate forests is 33%, tropical is 61% and boreal forests is 6%. The key to contribution of afforestation to reducing atmospheric CO2 is the fate and utilisation of the resulting wood products. C fixed during forest re-growth in the short term will eventually be converted back to CO2 by respiration or burning. Therefore, it would be better for the C balance if one could make more positive use of this fixed C.

Stuart and Sekhran (1996) proposed that there was a potential for C-offset projects to fund forest management or forest conservation in PNG. Participation in this case will depend on organisational management capacity and appropriate legal instruments that secure C rights for buyers and give security on issues such as leakage and permanence (Keenan, 2001). This may ultimately depend on transformation of indigenous property relations. Activities that might allow PNG communities to benefit from developed country investment in increased C storage or reduced emissions in forests according to Keenan (2001) are;  Development of forest plantations on cleared land, particularly degraded Imperata grasslands.  Rehabilitation of forest areas degraded by previous logging operations, through enrichment planting, weeding and tending or other intervention.  Development of woodlots, tree farming and domestication of PNG indigenous species in the rural communities  Reducing green house gas (GHG) emissions associated with harvesting operations.  Conserving forest areas that are currently designated for harvesting or conversion to agriculture.

2.2.5. Community Forest Management in the Tropics

Increased devolution of forest ownership and management rights to local control has the potential to promote both conservation and livelihood development in remote tropical regions (Duchelle et al., 2011). However, such shifts in property rights can generate conflicts, particularly when combined with rapidly increasing values of forest resources. Multiple uses of forests are now being recognised at community- level and apart from timber, local people also value their forests for other goods and services such as NTFP, carbon and biodiversity conservation. According to Kainer et al. (2009), it is highly unlikely that large tracts of tropical forests will be conserved without engaging local people who depend on them daily for their livelihoods. This is because stakeholders who reside in bio-diverse ecosystems such as tropical forests, are the largest direct users and ultimate decision-makers of forest fate therefore, can be important investors in conservation. Their local ecological knowledge can also complement western science and frequently have long-term legitimate claims on lands where they reside.

Throughout tropical countries, communities have raised concern that very few benefits have been reaching the owners of land and forests whenever there are major forest development projects initiated by the government. As well as that, local people value forests for not only timber products but also other benefits and services hence, there have been an increasing number of local community groups involved in small- scale forestry projects. Many of these projects are community based and have involved small-scale sawmilling with the primary aim of producing sawn timber to build a decent home and to sell surplus sawn timbers to generate some income for the community groups to improve livelihoods.

In PNG some NGOs, CBOs and conservation groups have participated in community forestry related activities over the last 15 years. Some of these groups include; the Village Development Trust (VDT), World Wide Fund for Nature (WWF), Foundation For People and Community Development (FPCD), and Madang Forest Resource Owners Association (MFROA). VDT is an indigenous non-governmental organisation that has been working in the communities in PNG and throughout the south pacific since 1990 (www.global.net.pg/vdt). Some of its activities include eco- forestry, forest conservation, education and training in forestry, village eco-timber

projects, integrated conservation and development projects. In Fiji, a collaborative effort between the Fiji Forestry Department and Drawa Forest Landowners Co- operative Ltd has been established. This collaborative arrangement has been supported by the SPC/GTZ Pacific-German Regional Forestry and the Drawa Community-based SFM regime for native forest in 1994 (www.spc.int/lrd/Highlights_Archive/highlights_Drawa_Model.htm.). The Drawa Project has been established as a model area for community and resource owner participation in forest management. Under this project, forest management and land use plans have been drawn to provide a regulatory framework for community-based natural resource management.

In countries such as India, Nepal, and Philippines, community forestry and joint forest management initiatives have been found to be quite successful (Mery et al., 2005, Wardle et al., 2003). These initiatives have been successful because community forestry related activities promoted the customary management systems which existed before the state assumed control of forest lands. Experiences show that local institutions make better use of forests, manage them more sustainably and contribute more equitably to livelihoods than central government agencies.

Small-scale forestry elsewhere outside the tropics has been also proven to be successful. For example, in Lithuania where 35% of total forest area is under small- scale private forestry (Mizaras et al., 2007), small-scale forestry activities include use of logging residues and other non-used wood for fuel, use of non-wood forest products, and sales of environmental services including CO2 sequestration. These activities have increased income from forests for small-scale forestry. Experiences in Australia show that small-scale farm forestry has continued to grow since the 1980‘s and has the potential to influence the Australian national forest estate. Research carried out by Cox (2004) indicates that exposure of small-scale forestry to international trade can create an impetus for change that would be beneficial for small-scale forestry sector.

The review of community forest management in the tropics has not covered all the literature available, however, from those materials consulted it can be seen that more NGOs, CBOs and community groups are increasingly involved in forest management at the community-level in the tropics. Most of these groups‘ involvement in forest management at community-level is usually at a small scale, however, there is

evidence that direct benefits may flow to the communities. For tropical countries where central governments have direct control over forest lands, communities could adopt the systems used in India, Nepal and the Philippines by promoting the customary management systems in CBFM. This will not be the case in PNG because majority of the forests in the country are owned by community groups.

2.2.6. Certification

Forest certification has been developed as a way of providing timber consumers with information about the management of forests from which certain timber products have originated. The first forest certification started in 1990 with a teak plantation in Indonesia certified as well managed by SmartWood, a program of the New York- based Rainforest Alliance (Dickinson, 1999, Dickinson et al., 1996). In 1992, the Woodworkers' Alliance for Rainforest Protection in the United States proposed the creation of the Forest Stewardship Council (FSC) and in the following year in 1993, the FSC founding assembly was held and in 1995 the council began to accredit certifiers (Viana et al., 1996). When forest certification started, it was intended as a tool for saving tropical forests, however, from the tropical forest management point of view, it was generally understood that logging practices in temperate and boreal forests are, if anything, more destructive than is logging in tropical forests. Therefore, certification of good forest management is now being quickly adopted in almost all forest types throughout the world (Viana et al., 1996). Tropical forests are biodiversity hotspots of the world and are vital for the survival of millions of indigenous people (http://www.fsc.org/tropicalforests.html). They also provide social and environmental benefits to sustain the livelihoods of local communities. Tropical forests are managed for a wide variety of reasons. For example, timber production, source of firewood, water catchment and biodiversity conservation. Due to overwhelming demands from society, tropical forests are under enormous pressure for exploitation, and this continues to escalate with emerging challenges. FSC certification can offer communities in the tropics financially competitive alternatives to poor practices, illegal logging, and land conversion for cattle ranching or bio-fuel production (http://www.fsc.org/tropicalforests.html). FSC standards are recognised as the highest social and environmental standards for forest management worldwide. Certification of tropical forests can result in substantial

social and environmental improvements and ultimately support the conservation and long-term maintenance of these forests.

In recent years several certification bodies have been established by interest groups to provide a framework in which certification initiatives can be pursued and managed. The two largest schemes are the FSC, which was established in 1993 and is driven largely by environmental non-governmental organisations and the Programme for the Endorsement of Forest Certification (PEFC), which was established in 1999 with the support of international forest industry and trade organisations and associations representing woodland owners in Europe. Several countries in Europe, New Zealand and Japan have also developed Public Procurement Policies (PPP) to promote SFM and good forest governance, and promote sustainable use of forest products by consumers (Freeman, 2006). Some tropical countries are also now developing their own certification systems. These include the Malaysian Timber Certification Council in Malaysia; the Ecolabelling Institute in Indonesia; and the Certificacao Florestal (CERFLOR) in Brazil. Countries in Africa are also developing a regional initiative.

According to ITTO (2007), there has been a lot of progress in certification requirements in ITTO producer countries, however, more than 90% of currently certified forests worldwide are outside the tropics. This scenario indicates the difficulties associated with implementing SFM in the tropics. In the report on Forests for the New Millennium, Mery et al. (2005) noted that almost 200 million hectares of forests had been certified at global level. At regional level, according to FSC 2009 figures, 15 million hectares of tropical forest are FSC certified, representing 14 percent of the total global area certified to the FSC Principles and Criteria (http://www.fsc.org/tropicalforests.html). However, in the regional context, one in five certificates lies in the tropics and the top three countries with the highest total certified forest area are Brazil, Bolivia and the Republic of Congo. At global level, certification is now being quickly adopted in almost all forest types, however, at regional level in many developing countries, adoption of certification requirements are very slow. This is because of the difficulties associated with implementing SFM as well as other related problems such as poor governance, weak laws and regulations, lack of skilled personnel, lack of enforcement of regulations for implementing SFM and the direct and indirect costs associated with meeting the requirements of certification.

It is a general understanding that the process of forest certification is a market driven approach that focuses on improving forest management by linking consumer concerns about social issues and the environment to good practices. Certification schemes provide consumers, governments, retailers, and individuals with an assurance that they are buying products that come from forests, which are sustainably managed in a socially responsible way. ITTO plays a significant role in certification in that it undertakes policy related work by commissioning studies, convenes conferences and workshops, and promotes debate among member countries. ITTO‘s assistance in member countries are in the following: capacity building and promoting forest auditing systems; strengthening certification programs; helping companies to get their forests certified; and funding private sector and civil society partnerships to promote SFM and certification.

2.2.7. Governance

The World Bank defines governance as consisting of the traditions and institutions by which authority in a country is exercised and includes the processes by which governments are selected, monitored and replaced; the capacity of the government to effectively formulate and implement sound policies; and the respect of citizens and the state for the institutions that govern economic and social interactions among them (www.worldbankreports\governance&anti-corruptionWGI1996- 2007interactivehome.mht). This definition is considered as political, however, according to a report on the State of the World‘s Forests by FAO (2007), the Asia Pacific Forestry Commission (APFC) recognises the issue of governance to involve the process of making and implementing decisions about forests and forest management at local, national, and regional levels. APFC emphasises that frameworks such as forest legislation, regulations, criteria and indicators, and codes of conduct are important in the decision-making process.

In most developing countries, communities living in and around forest areas do not have recognised property rights to the forest products that are important to their livelihoods, and their concerns are not taken care of in forest policy decision-making processes. National and local level governments also lack the necessary authority, capacity, and accountability to fulfil their obligations to forest management and therefore, failures in governance also cause pressing problems such as deforestation in

many parts of the tropical region. Over time the scenario has taken a shift as rapid changes relating to expectations and demands on forests by society confronts the forestry sector and those institutions and agencies involved in forest management are now putting in place reforms in order to cope with these changes. In PNG, the Forest Authority is now implementing the country‘s logging code of practice (PNGFA and DEC, 1996). Among other controls, the code has a 24 step procedure that has to be met before granting a license or permit for any major timber project to start. The PNG logging code of practice has received a lot of support from agencies and stakeholders within the country as well as the international community. The APFC is now implementing a study in the Asia-Pacific region to provide member countries with recommendations about how existing forestry agencies can be re-structured or modernised to ensure their continued effectiveness and relevance (www.fao.org/forestry/site/28679/en/).

The Special Project on World Forests, Society and Environment of the International Union of Forest Research Organisations (IUFRO) in 2005 (Mery et al., 2005) recommended that decentralization in developing countries should be pursued when the conditions are right. However, the process of decentralization must be seen to overcome corruption and establish new structures of governance at the local level through participative democracy and self-management. It is considered that these processes may not be easy, especially in developing countries in the tropical region as multi-national corporations with their wealth and monetary power influence government policies to their own advantage in terms of resource development in sectors such as forestry and mining. To support this argument, it is not surprising that the Word Bank Corruption Index (www.worldbankreports\governance&anti- corruptionWGI1996-2007interactivehome.mht) has recently ranked many developing countries in the tropical region among the 20 most corrupt nations in the world including PNG being ranked number 15.

2.2.8. Discussion

Based on the review in Section 2.2, illegal logging is understood to be a major problem in the tropics. However, there are also a considerable effort and cooperation from international organisations in combating this issue. Deforestation is mostly experienced in developing countries in the tropics and contributes 20% of annual GHG emissions with Indonesia having the fastest rate of deforestation in the world. A major contributing factor to global warming, which causes climate change is tropical deforestation but, the importance of forests in the global carbon cycle has been widely recognised hence, their management could play a large role in mitigating this mechanism. Apart from illegal logging, deforestation in the tropical region is also a threat to achieving SFM (Freeman, 2006). High rates of deforestation in the tropics are associated with high rates of poverty and civil conflict and these are major barriers to achieving SFM.

Climate change is a global issue and tropical forests play an important role in causing and solving problems of global climate change. This is because tropical forests are not only a major contributing factor to CO2 emission into the atmosphere, which causes global warming, they are also important for their capacity to store carbon. Provisions in the Kyoto Protocol such as the Land Use and Land Use Change and Forestry

(LULUCF) under the CDM will potentially sequester CO2 from the atmosphere thereby reducing global warming. In terms of community forest management in the tropics, this review pointed out that more stakeholders are involved. While some communities have very little capacity to participate in community forestry, community forest management has been successful in India, Nepal and the Philippines (Mery et al., 2005, Wardle et al., 2003). Certification is seen as a tool for assisting SFM. There is now a growing support from international organisations in developing certification bodies that focus on improving forest management by linking consumer concerns about sound issues and environment to good practices.

In many tropical countries there is a break-down and failure in governance and these have given rise to pressing problems such as deforestation and corruption. However, positive changes are now taking place as efforts from organisations such as the World Bank and Asia Pacific Forestry Commission (APFC) are assisting to improve governance in the tropics.

Most of the issues discussed in Section 2.2 are problems and challenges that create difficulties in achieving SFM in the region. Until management of tropical forests adopts the principles of sustainable forestry, and until regulators enforce forest laws effectively in the region, forest management in the region will be subject to unsustainable practices and biodiversity conservation and sustainable use of forest products and other values will remain a major challenge.

2.2.9 Conclusions

The literature review in Section 2.2 identified the following key issues;  SFM in the tropics still remains a major challenge, however, there have been some progress made to date with support from international organisations such as ITTO and FAO (FAO, 2007, ITTO, 2007).  Illegal logging is a major problem in the tropics and is usually fuelled by corruption and poor governance, however, recently there have been a lot of efforts from international organisations to combat this problem.  Deforestation and global warming, which cause climate change are a worldwide concern and international treaties such as the Kyoto Protocol have the responsibility to assist developed countries meet their emission reduction targets; and assist developing countries by providing incentives for them to meet the objectives of sustainable development.  There is now a growing concern about global warming, which is the major cause of climate change but the importance of the role of tropical forests in causing and solving the problems of climate change have been widely recognised.  Communities in the tropics are increasingly involved in forest management and utilisation at small-scale.  Forest certification is seen as a tool for assisting SFM and focuses on improving forest management by linking consumer concerns about social issues and environment to good practice. However, adoption of certification requirements is very slow in tropical forests in developing countries because of the difficulties associated with implementing SFM.  Poor governance in the developing world is seen as a set-back to SFM as it gives rise to problems such as corruption and deforestation, however, efforts

and assistance from international bodies such as the World Bank and APFC are now putting in place systems that would improve governance.

Considering the current issues discussed in Section 2.2 and relating them to the overall objectives of the thesis, the discussion points out problems and challenges facing tropical forest management. However, there are efforts and approaches at local level that can assist SFM in the region and this thesis addresses some of those aspects. For example, scenario analyses tools developed in this study (Chapter 6 and 7) will be applied by communities who own the majority of forests, as is the case in PNG. Therefore, the application of these tools will involve low impact harvesting and this will contribute to sustainable forest use and overall SFM.

2.3 FOREST MANAGEMENT APPROACHES

2.3.1. The Management Strategy Evaluation (MSE)

MSE is a frame work commonly used for fishery resource management. This approach has been considered for possible application for management of logged-over forests in PNG. The MSE framework was developed by Walters and Hilborn (1976) for adaptive management of fishery resources. Further work on MSE was carried out by scientists working for the International Whaling Commission (Kirkwood, 1993). Since then, work on the framework has been extended by Australian scientists and others on multiple use models and spatial models (Butterworth and Punt, 1999, Little et al., 2007, McDonald et al., 2005, Sainsbury et al., 2000). In resource management, multiple-use MSE has so far been mainly focused on sectors such as oil and gas, conservation, fisheries, and coastal development (McDonald et al., 2005). In the fishery sector, the objective of adopting the MSE framework has been to develop and demonstrate practical science-based methods that support integrated regional planning and management of coastal marine ecosystems. An integrated MSE developed by CSIRO (McDonald et al., 2005) has been applied successfully to fisheries and has been further enhanced for providing scientific decision support for multiple use management of coastal regions and estuaries.

A framework such as MSE requires active participation of stakeholders and facilitates the generation of ideas, identification of problems and approaches for solving them, as well as anticipation of real world impacts. This type of approach is usually motivated and supported by the needs of management agencies. Associated with an MSE approach are the three main elements: strategy, specification and scenario. A strategy is a planned course of action by one or more people, while a specification is a computer representation or a model of the real system. A scenario is a future projection of various factors that impact on the system, but which are not included explicitly or dynamically in any of the computer representation or model of the system (McDonald et al., 2005). Usually these factors are represented as data inputs to the model. The factors projected into the future include things such as human population growth patterns, industrial development, climate change and variability, and anticipated changes in recreational or industrial usage of natural resources.

According to Sainsbury et al. (2000), methods to design and evaluate operational management strategies have advanced considerably in the past decade. These MSE methods have relied on simulation testing of the whole management process using performance measures derived from operational objectives. This approach involves selecting operational management objectives, specifying performance measures, specifying alternative management strategies, and evaluating these using simulations. The MSE framework emphasises the identification and modelling of uncertainties and propagates these through to their effects on the performance measures. An example application of the MSE approach has been in the fishery sector when the scientific methods for evaluating fishery management strategies were applied through two parallel initiatives. These are adaptive management (Walters and Hilborn, 1976) and comprehensive assessment and management procedure evaluation developed by the International Whaling Commission (De la Mare, 1996, Donovan, 1989, Kirkwood, 1993, Magnusson and Stefansson, 1989). Both adaptive management and management procedure evaluation approaches are similar in terms of their concept and have been termed as MSE. Use of MSE is now widely recognised as providing a successful and appropriate framework for scientific input to fishery management (Cooke, 1999, Sainsbury, 1998). In resource management, the goals of MSE have been to support informed selection of a management strategy by means of quantitative analysis, to make clear the trade-offs among the management objectives for any given strategy, and to identify the requirements for successful management. MSE uses simulation modelling to examine the performance of alternative strategies and therefore, requires that all five of the below elements be specified in a way that allows quantitative analysis. A management strategy consists of specifications for; o Monitoring program. o Measurements that will be made. o How these measurements will be analysed and used in the scientific assessment. o How results of the assessment will be used in management. o How any decision will be implemented. The MSE framework can be used to compare alternative aspects of any part of a strategy from monitoring options, through the scientific assessment and its use in decision-making and implementation (Figure 2-1).

Figure 2-1: Key features of the general MSE Framework (Sainsbury et al., 2000).

The MSE framework has been used successfully for providing scientific decision support in resource management. The MSE approach may be considered for adoption in the management of cutover forests in PNG because forest owners and community demands, expectations and problems vary under different circumstances therefore, this option is expected to address these issues.

The objective of Section 2.3 is to investigate appropriate management approaches for cutover native forest in PNG from the literature review and Subsections 2.3.1 (Management Strategy Evaluation); Subsection 2.3.2 (Scenario Method); and Subsection 2.3.3 (Bayesian Belief Network) aim to discuss these approaches as the alternative management systems.

2.3.2. The Scenario Method

Use of scenarios can provide a tool for planning creatively for the future, and scenario-based approaches tap people‘s imagination in anticipating the future. Because of the complexity of tropical forests and in PNG in particular, compounded by a complicated land and forest resource ownership systems, the scenario method is considered an applicable approach for adaptive management of cutover forest by communities in PNG. CIFOR‘s scenario method (http://www.cifor.cgiar.org) for

adaptive management is considered an appropriate approach for management of cutover forest in PNG.

Scenarios are used with the objective of helping people change their habits of thinking or mental maps of how things work so they can deal better with the uncertainties of the future and perceive the consequences of their actions in the short and long term. In the context of community forestry, scenarios are applicable when there is a need to explore possibilities. Scenario-based techniques are tools for improving anticipatory rather than retrospective learning (Wollenberg et al., 2000). They may assist forest managers make decisions based on an anticipated range of changes. Elements of the scenario approach suitable for community forests are based on participatory rapid appraisal (PRA) that may be appropriate to village and community settings. The major steps for using scenario methods include the following; o Defining the scenario‘s purpose o Choosing the type of scenario that best suits the purpose o Selecting participants, facilitators and setting for learning and follow-up action According to Wollenberg et al. (2000), the four sorts of scenario approaches are the following: o Vision – a vision of the desired ideal future o Projection – best guesses about the expected future o Pathway – determination of how to get from the present to the future by comparing present and desired future (vision) scenarios o Alternatives – a comparison of options through multiple scenarios of either the vision, projection or pathway type.

In the case of this PhD research study in the PNG situation, scenario methods were integrated into the MSE framework for evaluation. The best possible approach in the management of cutover forests in PNG is the use of alternative scenarios as this will represent the expectations of different stakeholders such as the community groups and timber industry.

2.3.3 The Bayesian Belief Network (BBN)

The Bayesian Belief Network (BBN) has been considered as a possible approach for management of cutover native forest in PNG. BBNs are models that graphically and probabilistically represent correlative and causal relationships among variables and have been used in a broader decision support framework in resource management (Cain, 2001). McCann et al. (2006) suggested that BBNs are useful tools for representing expert knowledge of an ecosystem, evaluating potential effects of alternative management decisions and communicating with non experts about making natural resource management decisions.

Development of BBNs started in the 1990s (Pearl, 1995), drawing on a deep body of the theory developed for graphical models. Later BBN techniques have been used by ecologists and resource managers (Ellison, 1996). Crome et al. (1996) showed that Bayesian methods may be useful and applicable in the context of tropical forest management for modelling uncertainties involved when forest systems are disturbed. While developing models to predict the impact of non-timber forest products (NTFP) commercialisation on livelihoods, studies in Mexico and Bolivia adopted the Department For International Development (DFID) livelihood framework as a basis for constructing the BBN (Asley and Carney, 1999). This framework is based on the concept that people require a range of assets in order to achieve positive livelihood outcomes. According to DFID (1999), the five different types of assets, including both material and social resources are; natural capital, physical capital, human capital, financial capital, and social capital. Following the DIFID approach, Newton et al. (2006) considered that communities and individuals involved in NTFP commercialization would require access to each of the five types of asset in order for commercialisation to be successful.

Considering the DIFID‘s livelihoods framework for resource management, adoption of BBN for community management of cutover native forests in PNG may not be appropriate. The main reason for this would be that many individuals and communities in PNG may not have direct access to the five different types of material and social assets.

2.3.4. Discussion

The literature review in Section 2.3 covered three approaches to the development and assessment of alternative forest management scenarios. These are the MSE, scenario methods and BBN. The MSE approach has been widely used in resource management particularly in the fishery sector (McDonald et al., 2005). The key steps of MSE involves turning broad objectives into specific and quantifiable performance indicators, identifying and incorporating key uncertainties in the evaluation and communicating the results effectively to client groups and decision-makers (Smith et al., 1999). The review pointed out that a successful application of an MSE approach to natural resource management requires a collaborative effort between the decision- makers, technical experts and an MSE analyst.

There is now an increasing emphasis on community participation in natural resource management through group formation in all forms of development intervention (Agawal, 2001). In the context of natural resource management such as forests, devolving greater power to village community groups is now widely accepted by governments, international agencies and NGOs. Community-based organisations involved in forestry activities represent a rapidly expanding attempt at participatory approaches to development and effective participation requires people‘s involvement, such as a village group. In community forestry, scenarios are applicable in order to explore different forest management options (Wollenberg et al., 2000). In the context of CBFM, use of scenarios and the MSE approach are recommended for application in PNG because both of these approaches require a participatory approach to forest management by different stakeholders.

BBNs are used in complex ecological systems that require a multidisciplinary approach and this approach is considered useful in tropical forest management for modelling uncertainties (McCann et al., 2006, Newton et al., 2006, Pearl, 1995). Adoption of BBN may require access to the different types of material and social assets hence, application of this approach may not be appropriate for CBFM in PNG because communities generally have no or very little capacity to have access to these assets.

2.3.5 Conclusions

Not all topics related to the forest management approaches in tropical forests have been covered in Section 2.3 of the literature review. This is a broad area and the review considered only the three approaches (MSE, scenario methods and BBN) that may be applicable to cutover forest management in PNG. In PNG, forest management in general is associated with many key issues and problems. Concern for the sustainability of the current management practice, illegal logging, traditional land tenure systems, and lack of participation by forest owning communities in decision- making are not all, but some key challenges in forest management in PNG. The literature review in Section 2.3 pointed out that the three approaches are useful in tropical forest management. The MSE and scenario approaches require stakeholder participation in forest management, while BBNs are applicable where there are uncertainties. Based on the objectives of PNG forest landowning communities, lack of participation in decision-making by communities in forest management and the available data, it was decided to use an approach that integrated development of management scenarios and the MSE framework for community-based management of cutover forests in PNG.

CONDITION OF CUTOVER FOREST

CHAPTER 3: FOREST DYNAMICS AFTER SELECTIVE TIMBER HARVESTING IN PNG

3. 1 INTRODUCTION

Tropical forests are subject to extensive human disturbance such as clearance for agriculture, infrastructure development, fires and mining. There has been considerable debate about timber harvesting in tropical forests and its impacts on environmental, cultural and social values. The implementation of SFM in tropical forests is a widespread goal of the international community but, while there is some evidence of improvement, few forest areas are currently considered to be managed sustainably (ITTO, 2006). More recently, international attention on implementation of SFM has increased as a result of the focus on greenhouse gas emissions associated with deforestation and forest degradation in the tropics and the potential to reduce emissions from these sources as a low cost climate change mitigation option (UNFCCC, 2006, UNFCCC, 2009).

Like many other developing countries in the tropics, PNG‘s natural forests are being exploited at a rapid rate. Current estimates of forest loss vary. It is estimated that primary forests are decreasing at a rate of 113,000-120,000 ha year-1 (FAO, 2005, PNGFA, 2003) through logging, agricultural activities, mining and other land uses. Other statistics indicate that the annual deforestation rate is decreasing. From 1980 to 1990 the rate was estimated at 0.3% and between 1990 and 2000 at 0.44% with a further increase to 0.46% from 2000 to 2005 (FAO, 2005, FAO, 2007, ITTO, 2006). Other studies have suggested that the rate of forest loss through deforestation or forest harvesting and subsequent decline is currently 1.4% year-1 (Shearman et al., 2009b) although there is debate about this figure (Filer et al., 2009).

In PNG, timber harvesting is occurring under policies and regulations that are intended to provide for a sustainable supply of timber from designated forest management areas (FMA) as stipulated in the National Forestry Act 1991 (PNGFA, 1991). These operations are largely undertaken by international companies for the log

export market. There is considerable uncertainty about the sustainability of current management practices, the recovery of forests after harvesting and the potential of forests to provide timber or other community needs (Filer et al., 2009, Shearman et al., 2009a).

Current rates of timber harvesting in PNG are considered unsustainable (Shearman et al., 2009a). The current status of selectively harvested forest in PNG is such that total areas harvested through logging increased from 850,000 ha in 1992 to over one million ha in 1995 (Bun, 1992, Nir, 1995). Recent PNGFA statistics also indicate that from 1988 to 2007, the estimated total area affected by commercial harvesting has increased to over 2 million ha and total timber volume harvested in the form of logs during the same period was over 39 million m3 (PNGFA, 2007). Selectively-harvested forests in PNG amount to 10% of forested areas but the condition and future production potential of these forests is uncertain. Some authors have suggested that selectively-harvested forest in PNG generally degrade over time after harvesting (Shearman et al., 2009b).

Much of the international debate about tropical forest harvesting and its impacts on forests are primarily around impacts on biodiversity (Chazdon et al., 2009, Gardner et al., 2009, Kobayashi, 1992, Lamb, 1998) and a global concern about the loss of species through tropical deforestation, particularly in some of the world‘s biodiversity hotspots (Myers et al., 2000, Pimm and Raven, 2000, Stork, 2010). However, there is now a wider range of values to be considered, including capacity of harvested forests to provide timber, sequester carbon or other community benefits. There is considerable uncertainty about how harvesting impacts on these values due to the lack of knowledge about the extent of impacts and rate of recovery of forests after harvesting.

More broadly, there have been a relatively limited number of studies of forest dynamics and changes in stand structure of tropical forests after harvesting (Breugel et al., 2006, Kobayashi, 1992, Nicholson, 1958, Nicholson et al., 1988). Most of the research in the area has focused on the rehabilitation and restoration of degraded areas after large-scale clearance for agriculture and subsequent abandonment or disturbances such as fire (Lamb, 1998, Lanley, 2003, Shono et al., 2007). Other studies have focused on the impact of drought on tropical forest dynamics (Nakagawa et al., 2000).

The aims of the study in Chapter 3 are to: (1) examine the impacts of selective harvesting on stand structure in PNG forests by analysing the diameter and BA distribution after harvesting, (2) assess the dynamics of selectively-harvested forest in terms of trends in stand BA and residual timber volume, (3) determine whether there is a critical threshold BA for forest recovery by testing a model developed in Queensland tropical forests to analyse BA growth for harvested forests, (4) assess the impact of the El Nino induced forest fire of 1997-98 on BA growth and mortality rates of the burned plots, and (5) investigate the impacts of harvesting on species diversity of selectively-harvested tropical forests in PNG.

3.2 MATERIALS AND METHODS

3.2.1 PNGFRI Permanent Sample Plots – Background

Forests in PNG are characterised by high species and structural diversity. There are over 15,000 or more native plant species (Beehler, 1993, Sekhran and Miller, 1994) of which over 400 are currently considered commercial (Lowman and Nicholls, 1994). Forests cover a wide altitudinal range and occur across a range of rainfall conditions and soil types. Disturbance has been an integral part of dynamics of PNG forests. For example, fire has been shaping PNG‘s vegetation patterns through thousands of years of human settlement (Haberle et al., 2001, Johns, 1989). At high altitudes fire may result in permanent conversion of forests to grasslands (Corlett, 1987).

135 PSPs were established in mostly lowland tropical forests by the PNGFRI. These plots have a measurement history extending over 15 years. These comprise 122 plots in selectively-harvested forest with a total of 411 measurements and 13 plots in un- harvested forests with a total of 23 measurements (Fox et al., 2010). Alder (1998) indicated these plots had floristic composition characteristic of the lowland tropical forests of PNG. During the measurement period some plots have been abandoned due to difficulty in access or measurement has been discontinued due to fire or conversion of the forest to subsistence gardens.

The selective harvesting system used in PNG involves felling commercial timber species with a diameter limit of 50 cm and above, generally in larger-scale operations for log export. The size of openings and gaps created in this type of harvesting are between 20-40 m in diameter. Usually the area allocated for harvesting is over 80,000

ha and the average timber volume removed during harvesting depends on the density of commercial species and averages about 15 m3ha-1 (Keenan et al., 2005). The planned return period for a future harvest is 35-40 years although this depends on the stand structure, residual merchantable volume and stand growth rates (Keenan et al., 2005).

During the establishment of PSPs, plots were randomly located and established in pairs. All the plots are one hectare in size and divided into 25 sub-plots of 20 m x 20 m (Romijn, 1994a, Romijn, 1994b). The field procedures for establishment and measurement of the plots were adopted from Alder and Synnot (1992). In the assessment of trees in the plot, a standard quadrat numbering system was used. This system uses quadrat numbers on the basis of coordinates or offsets from the plot origin, for example, south-west corner. All tree species ≥ 10cm diameter at breast height (DBH) were measured. Measurements taken on trees included DBH, height, crown diameter, and crown classes according to Dawkins (1958). For plots in selectively-harvested forests, initial establishment ranged from immediately after to more than 10 years after harvesting. For plots accessible by road, re-measurements have been taken on an annual basis. Re-measurement of the other plots varied from two to five years depending on funding.

3.2.2 Study Sites and PSP Locations

The majority of the PSPs were located in lowland tropical forest types distributed throughout PNG where most harvesting activities have taken place (Figure 3-1). Only two plots have been established in higher altitude montane forest dominated by the genera Castanopsis and Nothofagus in the Southern Highlands part of the country. Twenty three percent of PSPs are located on the island of New Britain. Annual rainfall in these plots averages over 3,000 mm. Plots were located on a range of soil groups with the most common being Alfisols, Entisols, Inceptsols, and Mollisols (Pokana, 2002).

Figure 3-1: Map of PNG showing study sites and permanent sample plot locations (adapted from Fox et al., 2011b)

3.2.3 PSPs used in this Study and Data Analyses

For the purpose of this study, data from a total of 118 PSPs were used (105 in selectively-harvested and 13 in un-harvested forests). Of the 105 plots in harvested forest, 84 were selected for analyses of dynamics of stand BA, timber volume, and species diversity. These 84 plots excluded those burned by fire during the 1997-98 El Nino drought, those with short measurement period, and plots affected by erroneous measurements. An analysis of mortality was undertaken on burned plots. Apart from the disturbance by the El Nino event, field observations also showed evidence of other disturbance such as traditional land uses, for example, shifting cultivation in some of the harvested plots.

High variability are an inherent problem in sampling tropical natural forests subject to harvesting (Gerwing, 2002). To assess the dynamics of selectively-harvested forest in this study, a preliminary investigation was undertaken to test the normality of response variables (BA and VOL) and the independent variable (TSH). Analyses showed that data were homogeneous and normally distributed. Examination of

residual plots also showed similar results. Hence, it was not considered necessary to transform the dependent variables to stabilize variances.

In the data analyses MS Excel was used for processing PSP data and the softwares SPSS ver.18, SigmaPlot ver.11, and Minitab ver.15 were used for statistical analysis. Linear and logarithmic regression analyses were carried out to establish the relationship between the response (dependent) and independent variables. Significance of these relationships have been tested at 95% CI and significant results have been considered as p<0.05. Graphical outputs for the results have been generated from SigmaPlot ver. 11.

3.2.4 Analyses of Stand Structure

The number of trees per hectare (stems ha-1) and BA are measures of stand density and their distribution among diameter classes are often used to examine the structure of a stand. Both of these measures were analysed in order to describe the impacts of harvesting on stand structure of natural forest in PNG. This study focused on dynamics of selectively harvested forest, however, analyses were also undertaken on the stem and BA distribution of 13 plots in the un-harvested primary intact forest, in order to make comparisons with the structure of selectively-harvested forest. These 13 plots have shorter re-measurement histories than those in selectively-harvested forest.

Tree species in the study were divided into two groups at stand level, consisting of commercial and non-commercial species. Trends in stocking, BA and timber volume were analysed for these two groups. The commercial group consists of the PNGFA‘s group I and II commercial species (dominant species in Group I include those from the genera Burckella, Calophyllum, Canarium, Planchonella, Pometia, Intsia and those in Group II are Hopea, Vitex, Aglaia, and Endospermum), while the non- commercial group consists other species including the secondary and pioneer species from the genera such as Trema, Althopia, Alphitonia and Ficus (PNGFA, 2005).

3.2.5 Assessing the Dynamics of Cutover Forests

The dynamics of selectively-harvested forest was assessed by analysing changes over time in stand BA and timber volume. To examine the condition of the forest after harvesting, a relationship was established between time since harvesting (TSH) and BA for each plot. In the analyses, the starting BA is referred to as the plot BA at the first census and final BA as the plot BA at the last census after harvesting. These denotations also apply to the analyses of residual timber volume. A linear regression analysis was carried out to examine the relationship between TSH and BA. A similar analysis was carried out to examine the relationship between TSH and residual timber volume for trees ≥ 20cm DBH remaining after selective timber harvesting in order to make comparisons with the change in timber volume in the 13 un-harvested plots. Basal area is a commonly used measure of forest stocking and stand structure and this measure has been used as an indicator to determine patterns of change in stand structure over time. Patterns of change in timber volume were determined for commercial and non-commercial timber species for trees ≥ 20 cm in DBH. This provides an indication of current and future production potential for cutover forests (generally trees > 50 cm DBH).

Currently there are no volume equations for individual natural forest tree species in PNG, however, there are two systems of equations used for calculating volumes of indigenous trees by PNGFA (Alder, 1998). The single entry equation comprises only the tree diameter with form and coefficients (equation 3-1).

(3-1)

Where V is bole volume overbark and  D is girth at breast height.

The second equation is a double entry system and comprises both diameter and height with form and coefficient. These set of equations are for calculating volume for trees over 50 cm DBH (equation 3-2) and for those trees between 20 and 50 cm DBH (equation 3-3).

(3-2)

(3-3)

In the second sets of equation, V is bole volume overbark, D is diameter at breast height or above buttress, and H is bole length. In the PSP analyses, residual timber volume for commercial and non-commercial tree species was estimated using the second set of volume equations.

3.2.6 Basal Area and Volume Growth

Mean BA increment (MBAI) and mean volume increment (MVOLI) were calculated for each plot. To investigate the existence of a critical threshold BA below which a harvested forest generally does not recover, a model developed for native tropical forest in Queensland (Vanclay, 1994) was tested. A logarithmic regression analysis was carried out to establish the relationship between the starting BA after harvesting and MBAI. Although the model developed for tropical forest in Queensland was in native forest dominated by uneven-aged stands of Callitris spp. growing on drier sites, this model was applied to the dataset in this study because those forests have similar environmental conditions to parts of PNG.

This model takes the form as shown below;

h,d (3-4)

2 -1 Where, ΔG = stand basal area increment, G = stand basal area (m ha ), Sh,d = site form (m), an estimate of site productivity based on height-diameter relationship. Vanclay and Henry (1988) defined site form as an index of site productivity given by the expected tree height (m) at some index diameter. Fox et al. (2010) developed species-specific height-diameter models for PSPs in natural tropical forests in PNG from the same dataset as the one used in this study. In the context of the present study, site form was estimated from the height-diameter models developed by Fox et al. (2010). This estimate was used to test the above model to determine the stand BA increment in this study.

In these analyses, the relationship between starting BA and MBAI was used to determine whether the forest was recovering (positive trend in BA); degrading (negative trend in BA); or neither recovering nor degrading (constant BA). The mean BAI was also determined for plots with an increasing BA (63 plots) and those with decreasing BA (21 plots) in order to examine the trend in mean BAI after harvesting. To examine the change in mean BAI over time after harvesting, the relationship between mean TSH and mean BAI was investigated. The differences in MBAI for plots measured < 10 years and > 10 years since harvesting were also tested using a two-way ANOVA. Result for this test was insignificant (p = 0.94) hence, details are not reported in the results section.

Environmental factors such as rainfall and altitude can affect BA growth. A correlation analysis was carried out to establish whether or not an association existed between these two variables and BA growth. These tests showed insignificant results (Pearson‘s correlation r = 0.124, for rainfall and mean BAI and r = -0.039 for altitude and mean BAI) therefore, are not reported in the results section. Twenty one plots were not burned by fire but had negative BA increment due to losses from mortality resulting from natural causes and the effects of the drought on BA growth. These plots were located on lowland forest types where large-scale harvesting has taken place and 50% of these plots are in very remote areas on the islands of New Britain, New Ireland and Manus (Figure 3-1). During plot measurement it was observed that there were harvesting damages to the residual stand.

To assess the trend in timber yield over time since harvesting, the fit of a model developed in the Philippines, which is based on an empirical function of initial BA, site quality and time since harvesting was investigated (Mendoza and Gumpal, 1987, Vanclay, 1994). The equation takes the form;

t = 1.34 + 0.394 ln Go + 0.346 ln t + 0.00275 Sh t -1 (3-5)

3 -1 Where Vt = timber yield (m ha ), t = years after harvesting, Go = residual basal area 2 -1 (m ha ) after harvesting, Sh = site quality (m) estimated as the average total height of residual trees.

To apply the model in this study, the average total tree height estimated from the PSP analyses (Fox et al., 2010) was used. Logarithmic regression was used to test the relationship between TSH and timber yield of harvested forests using this model.

3.2.7 Estimating Mortality due to the 1997-98 El Nino Drought

Twenty one PSPs in harvested forests were burned by widespread forest fires occurring during the 1997-98 El Nino induced drought. In this analysis, ten of these plots were selected to estimate annual mortality rates caused during the drought and fire period. Only the ten burned plots were considered for further analyses because they were re-measured after the fire and had sufficient data, while the other burned plots had either a short measurement period or no re-measurement data after the El Nino fire event. These particular analyses aimed to provide an example of the impact of fire during the El Nino event on BA losses due to mortality caused by this event. In this case we used the following equation to determine annual tree mortality rates (Sheil and May, 1996);

(3-6)

Where; X is the initial BA at the first census and D is the BA lost due to mortality during n years. For the purpose of this study, BA for the two measurements before the fire was used to determine BA gained and the two measurements after the fire were used to determine BA lost (annual tree mortality rates) caused by fire during the El Nino drought.

3.2.8 Shannon-Wiener Index (H1)

To examine the pattern of change in tree species diversity over time after harvesting, the Shannon-Wiener Index (H1) was estimated for all tree species using the equation below (Nicholson et al., 1988, Williams et al., 2007).

(3-7)

Where pi = ni/N, ni is the number of individuals present of species i, N is the total number of individuals, and s is the total number of species.

3.3 RESULTS

3.3.1 Change in Stand Structure after Harvesting

The total stocking for all size classes (≥ 10 cm DBH) averaged 351 stems ha-1 ± 100 (SD) in selectively-harvested plots (Figure 3-2 a) and 531 stems ha-1 ± 138 (SD) in the un-harvested plots (Figure 3-2 b). Average BA was 17.35 m2 ha-1 ± 4.17 (SD) and 29.01 m2 ha-1 ± 5.77 (SD) in selectively-harvested and un-harvested plots respectively (Figure 3-2 c and d). There was a significant increase in stem numbers in the lower diameter classes (10-29 cm DBH), while there is an absence of trees in the larger size classes (> 70cm DBH) in the harvested forest. This is as expected because the selective harvesting system in PNG is such that a majority of the trees ≥ 50 cm DBH are removed during harvesting. There was a significant increase in BA over time since harvesting in almost all size classes in the harvested forest. This indicated the evidence of recruitment of smaller size class stems into the ≥ 10 cm DBH class and in-growth and related diameter increment occurring in the larger diameter classes. In the un-harvested plots there was no marked increase in stem numbers over time, however, there was evidence of an increase in the size classes 30-49 cm DBH at 5-10 years. BA in the harvested forest increased in the size classes 30-49 cm and 70-89 cm DBH at 5-10 years. As expected, the stem distribution in selectively-harvested plots (Figure 3-2a) and un-harvested plots shown on common-log scale on the y-axis to represent fewer stems in the larger size classes (Figure 3-3b); and BA distribution in selectively-harvested plots (Figure 3-3c) and un-harvested plots (Figure 3-3d) showed a reverse-J pattern. The plots in the un-harvested forest had short measurement history and fewer re-measurement data were available but there did not appear to be any marked changes in the number of stems and BA in the range of diameter classes over time in these plots.

1000 12 (a) (c)

) 10

-1

)

-1 100 8

10 4

Basal Area (m Basal Area

Log Stocking (stems Log Stockingha (stems 2 1 0 1000 12 (b) (d)

) 10

-1

)

-1 100 8

6 10 4

Basal Area (m Basal Area

Log Stocking (stems Log Stockingha (stems 2 1 0 10-29 30-49 50-69 70-89 90+ 10-29 30-49 50-69 70-89 90+ Diameter Class (cm) Diameter Class (cm) 0 - 5 years 5 - 10 years 10 - 15 years 15 - 20 years

Figure 3-2: Trends in stem and BA distribution since harvesting, (a) stem distribution in selectively-harvested plots, (b) stem distribution in un-harvested plots, shown on a common log scale on the y-axis to represent fewer stems in the larger size classes, (c) BA distribution in selectively-harvested plots, and (d) BA distribution in un-harvested plots.

At stand level the change in stocking, basal area and residual timber volume for trees ≥ 20 cm DBH showed similar trends over time (Figure 3a-c). These three density indices increased for the commercial group 15-20 years after timber harvesting. There was also a marked increase in stocking for the non-commercial species group 0-10 years after harvesting as a result of recruitment of secondary and pioneer species colonising the gaps and openings created by harvesting.

400 Commercial (a) NonCommercial

)

-1 300

200

Stocking (stems ha 100

0 25 (b) 20

)

-1

ha 2 15

Basal Area (m Basal Area 5

0 180

) -1 160 (c)

ha 3 140 120 100 80 60 40 20

Residual Timber Volume (m 0 0-5 5-10 10-15 15-20 Time Since Harvesting (Years)

Figure 3-3: Representation of trends in commercial and non-commercial tree species (≥ 20 cm DBH) groups at stand-level since harvesting showing, (a) stocking, (b) basal area, and (c) residual timber volume.

3.3.2 Trends in Stand Basal Area

Mean stand BA generally increased with time since harvesting, although the increment trajectory varied considerably between plots (Figure 3-4). Variability over time also increased. A scatter plot with linear regression showed that the relationship between BA and TSH was relatively weak (r2= 0.07, p = 0.016) when analysed with the whole dataset including consecutive re-measurements for the un-burned plots because of the variability in the data. However, the trend in BA across the 84 un- burned plots showed a consistent recovery of natural forest after timber harvesting. Overall, there is an increasing BA over time since harvesting suggesting that in general, these forests are recovering after harvesting but there is considerable variability and this is discussed further below.

35 r2 = 0.07 30 p = 0.016

) 25

-1

2 20

Basal Area (m Basal Area 10

0 0 5 10 15 20 25 Time Since Harvesting (years)

Figure 3-4: Trends in BA since harvesting for the 84 un-burned plots represented by a scatter plot with linear regression for the whole dataset including consecutive re-measurements.

3.3.3 Basal Area Growth since Harvesting

Seventy five percent of the 84 un-burned plots indicated increasing BA after harvesting, with a mean BAI of 0.42 m2 ha-1 year-1 (SD 0.42) (Table 3-1). For the 21 plots showing a decline in BA after harvesting, average BAI was -0.58 m2 ha-1 year-1 (SD 0.53). The mean BAI across the un-burned plots was 0.17 m2 ha-1 year-1 (SD 0.62). Apart from the other anthropogenic disturbances and the effect of the El Nino drought on the declining plots, harvesting damage causing injuries to the residual stand resulted in high mortality rates in these un-burned plots. The other factors affecting BA growth of the declining plots are the site effects such as rainfall and soil types. In an earlier study in the same forest, Alder (1998) observed that factors such as variations in water regime and soil fertility in those sites affected tree increment. Plot background and measurement history showed that fifty percent of the un-harvested plots had no or fewer re-measurement data and the mean BAI increment was negative (-1.72 ± 3.16) (Table 3-1).

Table 3-1: Mean BAI for plots with increasing and falling BA

Forest Condition No. of Plots Mean BAI (m2 ha-1 year-1) a

Un-harvested 13 -1.72 ± 3.16 Selectively-harvested Increasing BA (un-burned) 63 0.42 ± 0.42

Falling BA (un-burned) 21 -0.58 ± 0.53

(All un-burned) 84b 0.17 ± 0.62)

Burned during 1997-98 El Nino -0.67 ± 0.85 drought 21

Total 118

a Mean basal area increment ± standard deviation given in italics b Total un-burned plots with increasing and falling BA combined

Regression analyses showed mean BAI increased throughout the plot measurement period although the relationship between Ln MBAI and mean TSH is weak (r2 = 0.37) (Figure 3-5). The results here are significant at 0.05 level (p = 0.028). The scatter plot with line and linear regression with error bars show average trends in mean BAI for selectively-harvested forests. The data points are the mean BAI at each time period since harvesting, while the error bars in this case represent standard deviation from the mean.

1.8 r2 = 0.37 1.6 p = 0.028

-1 1.4

year 1.2

-1

ha 2 1.0

0.8

0.6

0.4

Ln Mean BAI (m Ln BAI Mean

0.2

0.0 5 10 15 20

Mean TSH (years)

Figure 3-5: Average trends in MBAI since harvesting. The data points are the mean BAI at each time period since harvesting, while the error bars in this case represent standard deviation from the mean.

3.3.4 Critical Threshold Basal Area for Recovery of Harvested Forest

The data from this study showed a good fit with the model (equation 3-4) developed in Queensland (Vanclay, 1994). There was a strong relationship between the mean BAI and starting BA after harvesting when the model was fitted to the data from this study (r2 = 0.75, p < 0.05) (Figure 3-6). Almost all plots had a relatively high residual BA after harvesting (greater than 10 m2 ha-1) and at this level residual BA was not a determinant of whether BA increment after harvesting was positive or negative.

4 r2 = 0.74 p = 0.000

) 2

-1

year

-1 0

-2

Ln Mean BAI (m BAI Mean Ln -4

-6 0 5 10 15 20 25 30 Starting BA after harvesting (m2 ha-1)

Figure 3-6: BA growth of harvested forest in PNG. The scatter plot with logarithmic regression was generated from a model developed in north Queensland rainforest (Vanclay, 1994).

3.3.5 Trends in Timber Volume

Timber volume for the harvested plots showed a positive trend over time since harvesting (r2 = 0.06, p = 0.031) (Figure 3-7 a). In the un-harvested plots, analyses also showed an increase in timber volume since the plot establishment period but with an insignificant result (r = 0.24, p = 0.087) (Figure 3-7 b) due to the variability in the data. Regression analyses indicated a consistent increase in residual timber volume for trees ≥ 20 cm DBH for harvested plots.

300 r2 = 0.06

)

-1 p = 0.031

ha 250

200

150

100

50 (a)

Timber Volume >20cm DBH (m

0 0 5 10 15 20 Time Since Harvesting (years) 300

)

-1 250

ha 2 3 r = 0.24 p = 0.087 200

150

100

50 (b)

Timber Volume >20cm DBH (m

0 0 1 2 3 4 5 6 Time Since Plot Establishment (years)

Figure 3-7: Trends in timber volume for trees ≥ 20cm DBH represented by scatter plot with linear regression for, (a) 84 un-burned plots in harvested forest, and (b) 13 plots in un-harvested forest. The unharvested plots have a short measurement history with fewer data and show high variability in the data with insignificant relationship between time since plot establishment and timber volume.

3.3.6 Timber Yield since Harvesting

Test of the model (equation 3-5, Figure 3-8) developed in the Philippines tropical forests (Mendoza and Gumpal, 1987, Vanclay, 1994) showed that timber yield of un- burned plots (63 with increasing BA and 21 with falling BA) in harvested forest for trees ≥ 20 cm DBH averages to 2.96 m3 ha-1 ± 0.24 (SD) and gradually increases over the measurement period, while mean VOLI is estimated at 2.33 m3 ha-1 year-1 ± 8.09 (SD). Test of this model showed a good fit between the model and the dataset from this study (r2 = 0.83, p = 0.000) (Figure 3-8).

1.6 r2 = 0.83

)

-1 1.4 p = 0.000

3 1.2

1.0

0.8

0.6

0.4

0.2

Ln Timber Yield >20cm DBH (m Ln Timber >20cm DBH Yield

0.0 0 5 10 15 20 Time Since Harvesting (years)

Figure 3-8: Timber yield of trees ≥ 20cm DBH in the residual stand. The scatter plot with logarithmic regression was generated from a model developed in the Philippines natural forests (Mendoza and Gumpal, 1987, Vanclay, 1994).

3.3.7 Mortality due to the Fire Caused During the 1997-98 El Nino Drought

Ten plots were severely affected due to the fire and had sufficient measurements for analyses of mortality. There was evidence of in-growth and recruitment in the form of BA gained in the ten plots before the fire with a marked increase in BA for the Kapul01 and Lark01 plots (Figure 3-9). The BA gained before the fire in Lark01 plot had exceeded BA lost due to the fire and the trend is almost similar with the Lark02 plot. The trend in the two plots indicated that these plots are recovering after they

have been burned by the fire. The average annual mortality rate estimated (using equation 3-6) for the ten severely burned plots was 12.82% year-1 ± 8.36 (SD). Annual mortality rates increased dramatically for the Kapul01 and Kapul02 plots due to the fire.

40 BA gained before fire BA lost due to fire

Percentage BA gained or lost (%) or lost gained BA Percentage

IVAIN01 IVAIN02 LARK01 LARK02 CNIRD01 CNIRD02 KAPUL01 KAPUL02 WIMAR01 WIMAR02 PlotID

Figure 3-9: Ingrowth, recruitment and mortality for the 10 burned plots. Ingrowth and recruitment are expressed as percentage BA gained before the fire and mortality is expressed as percentage BA losses after the fire for the 10 severely burned plots during the 1997-98 El Nino drought. After the fire mortality rates are high as a result of trees dying and the resulting BA losses with the exception of the Lark01 plot. The error bars represent standard deviation from the mean.

3.3.8 Species Diversity in Cutover Forest

Species diversity measured using the Shannon-Wiener Index (equation 3-7) for the 13 un-harvested plots was higher (4.9 ± 0.21 SD) than in selectively-harvested forests (3.5 ± 0.33 SD). The un-harvested forest had fewer plots hence, detailed analyses and comparison could not be made between intact plots and those in harvested forests, however, species diversity remained almost constant without increasing over time for plots on harvested forest since harvesting.

r2 = 0.16 p = 0.069 ) 4

-1

Shannon-Wiener IndexShannon-Wiener 1

0 0 5 10 15 20 25 30 Time Since Harvesting (years)

Figure 3-10: Species diversity represented by the change in Shannon-Wiener Index since harvesting. At 0.05 level, there is no significant relationship between time since timber harvesting and the Shannon Wiener Index (p = 0.069).

3.4 DISCUSSION

As would be expected, analyses of the impact of selective timber harvesting on stand structure showed that in the harvested plots, the number of stems increased in the smaller size classes (Figure 3-2 a), while stand BA increased in almost all size classes over the plot measurement period (Figure 3-2 d). The un-harvested plots had a short measurement history and there was no marked increase in stem numbers over the range of diameter classes (Figure 3-2 b), while BA for size classes 30-49cm and 70- 89cm DBH increased at 5-10 years (Figure 3-2 b and d).

There was a slight increase in commercial stocking, while the non-commercial (including secondary and pioneer species) species continue to increase at 0-10 years and 15-20 years for harvested plots (Figure 3-3 a). Marked increases in BA and volume (trees ≥ 20cm DBH) were evident in the commercial species group but the increase in both measures in the non-commercial group exceeded that of the commercial group by over 50% (Figure 3-3 b and c). These trends provide evidence that a higher proportion of non-commercial species occupy gaps and openings immediately up to about 20 years after harvesting. This result also supports projections made by Alder (1998) for the same studied forest in which he observed a

significant tendency for higher proportions of pioneers to occur at higher recruitment levels. There was some evidence of recovery of stocking, BA and volume in commercial species (Figure 3-3 a, b and c). Commercial volume recovery includes recruitment into the > 20 cm DBH size class and growth in the larger size classes.

Results from analyses of impact of harvesting on stand dynamics of selectively- harvested forests showed there was an increase in stand BA (Figure 3-4). In PNG‘s natural forests, earlier research studies indicated that BA in undisturbed forests was about 30-32 m2 ha-1 (Alder, 1998, Kingston and Nir, 1988b, Oavika, 1992). The present study found that average BA in plots on forests disturbed from selective harvesting is about 17 m2 ha-1, a reduction of about 43% from the original un- harvested intact primary forest. Residual timber volume in the harvested plots increased significantly over time, while there was a general increase in timber volume for the un-harvested plots but this increase appeared insignificant because of the insufficient data resulting in higher variability in these plots (Figure 3-5a and b). The increase in residual timber volume in harvested plots is due to the recruitment and ingrowth associated with diameter and BA growth occurring after harvesting.

When a comparison was made between the change and growth in BA since selective harvesting from this study with similar studies in tropical forests in other regions (Table 3-2), results from this study are within the ranges of those studies. For example, similar studies carried out by Nicholson et al. (1988) in north Queensland rainforest showed that BA was reduced due to selective harvesting by between 8% and 43%. Studies of Smith and Nichols (2005) and Pelissier et al. (1998) also showed similar figures for BA in primary and harvested forests. Although the mean BAI after selective harvesting for the 84 plots in this study is lower (0.17-0.42 m2 ha-1 year-1) than that of the study by Smith and Nichols (2005) (0.32-0.75 m2 ha-1 year-1), overall stand BA continued to increase over the plot measurement period (Figure 3-4). The mean increment for the 75% of un-burned plots with increasing BA (0.42 m2 ha-1 year-1) is more consistent with the international data. It is also considered that BA increment after harvesting is generally the contribution of recruitment whereby smaller size class trees are growing into the ≥ 10cm DBH class and the ingrowth occurring where trees in smaller size classes are putting on diameter increment and passing on to the next larger size classes. These two processes suggest that when there

is a positive BA increment, harvested forests are in a recovering condition. As indicated in this study, the increase in BA after harvesting (Figure 3-4) suggests that selectively-harvested forests in PNG have the potential to recover following harvesting. This has also been observed in other regions (e.g., north Queensland rainforest, see Nicholson et al., 1988). The estimates of BA and mean BAI in this study are comparable to similar international studies carried out in other tropical regions focusing on the impact of harvesting on change and growth of basal area for tree stems ≥10cm DBH (Table 3-2).

Table 3-2: Comparison of results of this study with similar studies

Primary Forest Mean BAI Region Mean BA Harvested Forest after harvesting Source (m2 ha-1) a Mean BA (m2 ha-1 ) (m2 ha-1 year-1)

PNG 29.01 17.35 0.17 Current study Kingston & Nir, PNGb 30 - 33 10 - 20 1988; Oavika, 1992; Alder, 1998 Sub tropical Smith et al., 2005 Australia 51.5 12 - 58 0.32 – 0.75 North Queensland 37.94 – 73.42 25.86 – 41.60 Nicholson et al., 1988 Australia

South Indiac 39.3 34.8 Pelissier et al., 1998

a Primary forest mean basal area are for un-harvested forests b Earlier studies carried out in similar forest types in PNG c Study carried out in dense moist evergreen forest in Western Ghats, South India

If the sample plots in this study are generally representative of selectively-harvested forests in PNG, the change in BA over time in this study suggests that a significant proportion of native forests in PNG are recovering after disturbance from conventional harvesting. This contrasts with the suggestion of Shearman et al. (2009a) that harvested forests in PNG generally degrade over time. To address this disparity, detailed research studies are required in the future to quantify the extent of degradation after harvesting native forests in PNG. A degraded forest or forest degradation does not involve a reduction in the forest area but rather a decrease in forest quality or condition (Lanley, 2003). In the context of this study, forest

degradation is examined as the decrease in forest condition after selective-harvesting in the plots studied. The present study shows through direct evidence from ground- based monitoring of PSPs that a relatively high proportion of harvested native forests in PNG are recovering over time.

Test of the model developed for sub-tropical forests in the nearby region of north Queensland (equation 3-4) (Vanclay, 1994) to determine BA growth in this study showed that there was a good fit to this model, despite the fact that it was developed for forests with quite different forest type and stand structure, and that it may be a useful basis for modeling future growth of PNG forests. Application of the Queensland model using the dataset from this study showed no evidence of a single critical threshold BA below which the BA growth of harvested forest decreases (Figure 3-6). This suggests that forest recovery capacity is dependent on other factors such as the extent of damage to residual trees, degree of soil disturbance or the presence of seedlings and saplings that can rapidly grow into gaps created by harvesting. Earlier studies in PNG suggested that stands with BA below 25m2 ha-1 should be able to recover to at least their original stocking before harvesting (Alder, 1998).

Application of the model developed in the Philippines (equation 3-5) (Mendoza and Gumpal, 1987, Vanclay, 1994) using the dataset from this study produced reasonable estimates (Figure 3-8). The objective to test this model was to assess the trend in timber yield over time since harvesting, however, because of the diverse forest types and species composition in the PNG situation, the Philippines model may not be applicable to PNG forests. Therefore, this study recommends the need for development of similar models for application in the future management of natural forests in PNG.

In parts of PNG that are subject to periodic fire, forest can readily convert to savannah, particularly in proximity to settlements (Alder, 1998). The effects of the fire following the severe El Nino of 1997-98 on stand mortality (Figure 3-9) were similar to those in a tropical forest in Sarawak impacted by severe drought associated with the same event (Nakagawa et al., 2000). In their study of a core plot (1.38 ha plot at the centre of a larger plot of 8 ha) mortality during non-drought period was 0.89% year-1 and during the drought period, this increased to 6.37% year-1 in the same plot. Their study also indicated that the BA lost in the drought interval (1997-98) was

3.4 times that of the annual BA increment of the measurement period 1993-97. Annual mortality rates assessed as BA losses in this study are considered higher than the Nakagawa et al. (2000) study due to the combined effects of drought and fire. Currently there is an increasing concern about the impacts of timber harvesting on biodiversity and other forest values in tropical forests (Kobayashi, 1992, Stork, 2010, Stork and Turton, 2008). Tropical forests are characterized by a high diversity of woody species (Clark and Clark, 1999) as is the case in PNG. Species diversity is best indicated by the Shannon-Wiener Index (H1) (Stocker et al., 1985). Studies carried out in north Queensland showed that timber harvesting had only a minimal affect on species diversity (Nicholson et al., 1988). This was probably due to the type of harvesting and goal of maintaining species composition in that forest. In this study harvested plots had considerable lower mean species diversity than un-harvested plots and species diversity did not increase over time. This suggests that some species were continuing to be lost, while pioneer and secondary species became established in gaps. Further research is required to establish the effect of timber harvesting and species diversity in different forest types.

Lindemalm and Rogers (2001) showed that conventional harvesting caused reduction in tree diversity of 25% (H1) in comparison to unlogged forest as a result of initial losses from high harvesting intensities, high post harvest mortality, and low diversity of new recruitment. Diversity index (H1) for un-harvested and harvested plots in the current study is consistent with studies of Wright et al. (1997). They found H1 values of 4 and 5 in PNG forests in comparison to values around 1 in the Lindemalm and Rogers (2001) study.

Options for future utilisation of forests in the current study sites will depend on their status. Forests that have been heavily impacted by harvesting with declining BA will require intervention to rehabilitate and restore species composition and production potential. For forests in similar condition to the 75% of plots that are in a recovering state, maintaining their production potential will depend on protection from fire or other human disturbances. Data from this study suggests that in these types of forests it is likely to take a minimum of 50 years after harvest before they have sufficient standing volume to provide for a similar level of harvest to the first cut. These forests can potentially sustain harvesting of lower volumes per hectare in small- scale operations to supply portable sawmills or local mills but this type of operation

will be limited to areas accessible from existing roads with intact bridges and other infrastructure. The production potential of these types of operations is being investigated in further research associated with this study.

3.5 CONCLUSIONS

Evidence from this study of 105 PSPs suggests that a major proportion of native forests show increasing BA and stand volume following selective timber harvesting in PNG. Mean BA after harvesting was about 17 m2 ha-1 and BA increment after harvesting was positive on 63 (75%) of 84 plots with an average BA increment on these plots of 0.42 m2 ha-1 year-1. Average BA increment across the 84 un-burned plots over up to 25 years after harvesting was 0.17 m2 ha-1 year-1. Based on the 75% of the plots with positive BA increment, recovering plots may reach the BA of undisturbed stands within 40-50 years after harvest but the capacity for a future large- scale harvest will depend on the recovery of commercial timber volume. Factors such as residual stand damage, impacts on soil, understorey and tree regeneration are likely to determine the direction of BA increment and the rate of recovery after harvesting. Impacts of drought-related fires and other human or natural disturbances are factors that will affect the recovery of harvested forests in the future. In this study it was found that BA is affected by the high mortality rates caused by the 1997-98 El Nino related fire across PNG. The future fate of these forests will depend on the period of time before future timber harvests and the effects of activities undertaken by communities living near the forest, such as subsistence gardening that result in a change in land cover or species composition. To avoid the type of on-going decline observed on 25% of sites, it is recommended that harvesting activities are more effectively managed and implemented to limit the damage to retained trees, soil and regeneration and trees in smaller size classes of commercially-important species. This study suggests that intervention such as assisted regeneration should be considered as an option to assist recovery in currently declining sites. Given the time frame for commercial volume recovery of the residual stand, harvested forests are unlikely to attract large-scale commercial harvesting in the near future. There is a need for development of appropriate strategies and options for sustainable future management of selectively-harvested forests in PNG, focusing on smaller-scale CBFM and utilisation.

CHAPTER 4: FOREST ASSESSMENT IN CASE STUDY SITES

4.1 INTRODUCTION

In the late 1950s, the first recorded forest inventories in PNG were carried out with the use of helicopter surveys to assess the country‘s forest resources for the first time for exploitation and the aim was to assess as large an area as possible in the shortest time (Vatasan, 1989). Survey teams were dropped by a helicopter in the middle of the forest and the survey proceeded to use circular sample plots of 20 meters radius, set at 100 meters between centre distances, on lines radiating from camp sites. In those surveys, the sampling intensity was often very low (less than 1%). This was compensated to an extent by the randomness of line selection and dispersion of the plots.

In the late 1970s and early 1980s, the then Department of Forest (now PNGFA) adopted the systematic sampling method for forest resource inventories (Ambia and Yosi, 2001). This inventory system is currently being used by the PNGFA and is based on a systematic sampling through parallel equidistant strip lines. The procedure consists of establishing strip lines at equal distances from each other, starting from a base line. All trees over 50 centimetres in diameter at breast height (DBH) are measured as saw logs, while trees of over 20 centimetres DBH are measured as pulp logs. Measurement of trees is taken on a strip of 20 meters wide or 10 meters on either side of the centre line. Each 100 meter length of the strip line is considered as a plot of 2000 m2, which is 0.2 hectares in size. Often a measurement staff is used to estimate the diameter of stems above the buttress, however, when possible the diameter is measured with a tape. The merchantable height (log length) of stems is often estimated, however, just as a check, measurements of some trees are taken using a clinometer and a measuring tape. Tree species identifications are made on the spot in the field, while samples of unknown species are collected by the inventory teams and identified later.

While collecting data on trees, information about the topography, soil and forest type is also collected. An earlier study under the ACIAR Project FST1998-118 (Keenan et

al., 2005) indicated that the systematic sampling method currently used by PNGFA generally overestimates forest resource timber volume in a given concession area and field procedures are costly.

In Chapter 4 the forest resource assessment carried out in the two case study sites are described and results are presented to include residual timber volume and aboveground forest carbon. The objectives of this chapter are to estimate the residual timber volume and aboveground forest carbon in the two case study sites in order to use this data to test the scenario analysis and evaluation tools (decision tree models) developed in Chapter 6. The two study sites have been selected for this research in areas where there has been significant harvesting of primary forest in the past. These sites are the Yalu and Gabensis villages located outside Lae in Morobe province, PNG. The two study sites are approximately 17km apart and located close to easily accessible infrastructure such as roads and within similar forest types, which is the lowland foothill forest as indicated from field observations.

4.2 BACKGROUND

4.2.1 Yalu Community Forest

The detailed background about the Yalu case study site have been given in Chapter 1 (Section 1.3). The Yalu community forest consists of cutover secondary forest, primary intact forests and areas allocated for gardens (Figure 4-1). In earlier studies carried out by PNGFRI (Yosi, 2004), the CSIRO vegetation type map classified the forest type in Yalu as Hm (medium crown forest) (Hammermaster and Saunders, 1995, Bellamy and McAlpine, 1995). Forest assessment and inventory data from field work carried out by VDT in the Yalu community forest in the past also indicated that the major timber tree species included Toona sureni, Mastixiodendron spp., Pterocarpus spp., Intsia spp., Terminalia spp., Pometia spp., Celtis spp., and Bischofia spp. (VDT, 2006a, VDT, 2008). VDT‘s analysis of forest inventory data of the Yalu forest area indicated that the average timber volume is 27.67 m3 ha-1 (VDT, 2006a). The Yalu community forest area is approximately 2,200 ha in size.

Figure 4-1: An aster image of the Yalu community forest.

4.2.2 Gabensis Community Forest

Details of the Gabensis case study site have been given earlier (Chapter 1, Section 1.3). This community forest area is near Gabensis village, which has been extensively harvested in the past and the forest left behind are patches of primary intact forest, cutover secondary forest as well as areas allocated for traditional uses including gardening (Figure 4-2). In the Gabensis community forest area, earlier forest assessment carried out by VDT (VDT, 2006b) indicated that the major timber tree species are Pometia pinnata, Anthocephalus chinensis, Pterocarpus indicus, Vitex cofassus, Terminalia spp., and Octomeles sumatrana. The total forest area allocated

for community forest management in the Gabensis case study site is approximately 150 ha and can be easily accessible for harvesting.

Figure 4-2: An aster image of the Gabensis community forest.

4.3 FOREST ASSESSMENT METHODS

In the two case study sites, the sampling method that was used as a guide to assess the residual timber volume and aboveground forest carbon in their community forest areas involved a stratified random point sampling technique. This technique was not fully implemented because the community forests were relatively small areas and did not warrant full stratification. The basic field procedures in the sampling without full stratification are summarised below; . The respective community forest areas were accessed by walking through bush tracks and strata in each study site were identified in the field. . Each stratum in the respective forest areas were randomly sampled.

. Because the two community forest areas were relatively small, bush tracks previously used by the village people were used to locate and establish points for sampling. . A basal area factor 2 (BAF2) prism wedge was used to take a sweep at each point in a clockwise direction at a particular point. During the sweep, each tree whose DBHOB subtended an angle larger than that identified by the gauge was counted as "IN". In the count, how close a tree is to the sampling point determines whether or not this tree is included and is counted as "IN". Usually small trees are not included in the count if they are some distance from the sampling point, while larger trees will be included at even greater distances. In this technique, only the ―IN‖ trees are counted as sample trees and are recorded and measured. . When recording and assessing each sample at each point, features such as gardens, scared sites, villages, and traditional sites were recorded. . GPS was used to record location of each sampling point. . At each sampling point, the records and measurements taken included timber species, diameter, merchantable height, and total height of each tree sampled. . From the parameters measured on each sampled tree, the timber volume and biomass of each tree were estimated.

4.4 DATA ANALYSIS

4.4.1 Estimating Stems per Hectare

In the point sampling technique used in the assessment of forest resources in the two case study sites, a prism gauge with a basal area factor (BAF) of 2 contributes 2m2 ha- 1 of BA for each ―IN‖ tree. For example, an ―IN‖ tree of 50cm dbhob has g = 0.20m2 ha-1. Therefore, the stems per hectare are estimated using the equation below;

(4-1)

Where BAF is basal area factor and g is tree basal area. For example, 2/0.20 gives 10 stems ha-1.

The formula for calculating g takes the form as shown below;

(4-2)

Where g is tree basal area and D is tree diameter.

4.4.2 Timber Volume

The following equation was used to calculate the residual merchantable timber volume for each tree sampled (Fox et al., 2011b);

(4-3)

Where MV is merchantable timber volume, D is tree diameter, MH is merchantable tree height and form factor is 0.5

4.4.3 Aboveground Live Biomass

To calculate the aboveground live biomass (AGLB ≥ 10cm) of each sampled tree, a model developed for wet tropical forests by Chave et al. (2005) was used. This equation was developed from data collected from tropical countries including PNG, Malaysia and Indonesia. When applying this model, Chave et al. (2005) found that locally, the error on the estimation of a tree‘s biomass was on the order of ± 5%. This approach is internationally accepted when calculating forest C and the model developed by Chave et al. (2005) takes the form as indicated below;

(4-4)

Where AGLB is aboveground live biomass, p is wood specific gravity, D is tree diameter and TH is total tree height. In this case the wood specific gravity for most PNG timber species have been derived from Eddowes (1977). The methodology for estimating AGLB and forest C in Chapter 4 has been adapted from Fox et al. (2010). In that study they developed a methodology for estimating the aboveground forest C and reported the first estimates of forest C in lowland tropical forest in PNG. While currently there is an absence of

allometrics and biomass equations for calculating AGLB in PNG, Fox et al. (2010) estimated AGLB ≥ 10cm from PSPs and from these measured component and previous established relationships (Brown and Lugo, 1990, Chave et al., 2003, Edwards and Grubb, 1977), they determined the total aboveground forest C in tropical forests in PNG. The ratios applied by Fox et al. (2010) to estimate the unmeasured aboveground pools in harvested secondary forest are for three major forest types (Table 4-1). In this case the unmeasured pools include AGLB < 10cm, fine litter (FL) and course wood debris (CWD).

Table 4-1: Unmeasured Components of AGLB≥10cm (%AGLB≥10cm)

Harvested Secondary Forest Lowland Forest Lower Montane Mid Montane AGLB<10cm 10 10 10 FL 1 2.5 2.5 CWD 25 25 25

In the present study of the forest assessment in the two community forest areas, the

AGLB ≥ 10cm was determined from the point sampling and using the above ratios, the unmeasured component of AGLB < 10cm, FL and CWD were estimated in order to determine the total AGLB and consequently the estimate of total aboveground forest C in the two study sites. After estimating the unmeasured components, the total AGLB was determined from the equation below;

≥ 10cm < 10cm (4-5)

4.4.4 Determining Sample Size

The objective of the forest resource and aboveground forest C estimates were for the purpose of obtaining the necessary data from the two case study sites in order to test the decision analysis model developed in Chapter 6. However, the estimates of the mean values of the different parameters and the sample size can be improved by applying the formula according to Philip (1994);

(4-6)

Where n = number of samples, CV = coefficient of variation, t = student‘s t value for a 90% confidence interval at a specified degree of freedom, and E = acceptable level of error, for example 10% of the true mean.

4.5 RESULTS

4.5.1 Size Class Distribution

Analyses of point samples shows the number of stems recorded for each diameter class in the point samples and the estimated number of stems per hectare (Table 4-2). With the use of the wedge prism of BAF 2, the stems per hectare in each diameter class have been estimated and recorded. In this case, each sampled tree contributes 2m2 ha-1 of basal area and by dividing the BAF with the basal area g of each tree, the stems per hectare is then estimated.

Table 4-2: Size Class Distribution

Diameter Class No. of Stems Predicted (cm) in sample Stems/ha 10-20 69 119 20-30 93 42 Yalu Community 30-40 55 23 Forests 40-50 23 13 50-60 22 8 60-70 13 6 70-80 10 5 80-90 2 4 90-100 1 3 100+ 7 1 20-30 9 33 30-40 6 22 Gabensis Community 40-50 5 14 Forests 50-60 11 8 60-70 3 6 70-80 2 5 80-90 1 4 90-100 1 3

The graphical presentation represents the diameter distribution of the stems of all timber species combined for the Yalu community and Gabensis community forest areas respectively (Figure 4-3, Figure 4-4). The distribution represents the actual and predicted number of stems per hectare in the sample.

140

120

) ) 1 - 100

60 Actual Predicted

40 No. ofStems No.ha (N 20

0 10-20 20-30 30-40 40-50 50-60 60-70 70-80 80-90 90-100 100+ Diameter Class (cm)

Figure 4-3: Size Class Distribution for tress ≥10cm DBH in the Yalu study site.

20 Actual 15 Predicted

No. ofStems No.(N/ha) 10

0 20-30 30-40 40-50 50-60 60-70 70-80 80-90 90-100 Diameter Class (cm)

Figure 4-4: Size Class Distribution for trees ≥20cm DBH in the Gabensis study site.

4.5.2 Residual Timber Volume

In the present study, the major timber species found in the two community forests include those in the PNGFA Minimum Export Price (MEP) groups (Table 4-3) with the estimated residual merchantable timber volume per hectare and the total volume in each study site.

Table 4-3: Residual Merchantable Volume for Major Timber Speciesa

Yalu Community Forest Merch Vol (m3 Total Merch Timber Species Representation(%) ha-1) Vol (m3) Pterocarpus indicus 11.6 9.0 20,000 Celtis sp. 6.8 17.9 39,000 Pometia pinnata 5.1 14.2 31,000 Terminalia sp. 3.4 17.0 37,000 Intsia sp. 1.4 16.8 37,000 Vitex sp. 1.4 11.9 26,000 Endiandra sp. 1.4 16.5 36,000 Canarium sp. 1.4 16.1 35,000 Toona sureni 0.7 13.4 29,000 Dracontomelon sp. 0.3b 17.8 39,000 Gabensis Community Forest Pometia pinnata 24.3 15.9 2,400 Chionanthus sp. 18.9 16.9 2,500 Pterocarpus indicus 10.8 11.6 1,700 Terminalia sp. 8.1 18.8 2,800 Intsia sp. 5.4 14.4 2,100 Hernandia sp. 5.4 15.2 2,300 Planchonella sp. 2.7 14.9 2,200 Mastixiodendron sp. 2.7 18.6 2,800

a The table excludes other non-commercial and secondary timber species. b Dracontomelon sp. is represented by only few trees in the sample but they are in the large diameter class therefore, the average volume estimated is high.

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4.5.3 Mean Residual Timber Volume

From the forest assessment in the two community forests, the mean residual merchantable timber volume in the two study sites have been estimated (Table 4-4). The estimates are for all timber species combined.

Table 4-4: Mean Residual Timber Volume ≥ 20cm DBH (m3 ha-1)

Yalu Community Forest Gabensis Community Forest Mean 12.69 15.19 SD 4.50 2.77

4.5.4 Aboveground Forest Carbon

The measured component of AGB (AGLB ≥ 10cm), the estimated unmeasured component (AGLB < 10cm, FL, CWD) and hence, the total AGB in the Yalu and Gabensis community forest areas are reported (Table 4-5).

Table 4-5: Aboveground Forest Carbon (MgC ha-1) with SD in parenthesis.

Component Yalu Community Forest Gabensis Community Forest AGLB≥10cm 110.19 ( 27.58) 119.21 (37.19) AGLB<10cm 11.02 11.92 FL 1.10 1.19 CWD 27.55 29.80 Total AGB 149.85 ( 37.51) 162.12 (50.58)

4.5.5 Sample Size

Data analyses to improve the estimates of the mean values and the sample size show the required number of samples n for timber volume and AGB in the two case study sites (Table 4-6). In this case, the number of samples required to improve the estimates of timber volume and AGB in the Yalu community forest area at 10% acceptable level of error are 22 and 11. In the Gabensis community forest, the numbers of samples required are 31 and 92 for timber volume and AGB respectively.

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Table 4-6: Estimate of number of samples

Yalu Community Forest No. of E Mean SD CV Observation DF (%) t-value n Volume (m2 ha-1) 12.69 4.50 0.35 17 16 10 1.337 22 AGB (MgC ha-1) 149.85 37.51 0.25 17 16 10 1.337 11 Gabensis Community Forest Volume (m2 ha-1) 15.19 2.77 0.18 2 1 10 3.078 31 AGB (MgC ha-1) 162.12 50.58 0.31 2 1 10 3.078 92

SD is Standard deviation, CV is Coefficient of variation, DF is Degrees of freedom, E is Error and n is number of samples required.

4.5.6 Summary of Resource

The summary of the forest resource in the two study sites from the point sampling carried out in the present study include the residual timber volume and forest C (Table

4-7). CO2 emissions resulting from selective timber harvesting in PNG have been estimated to be about 55% from PSP analyses (Fox and Keenan, 2011, Fox et al., 2011a, Fox et al., 2011b) based on conventional harvesting practice using heavy equipment therefore, in a community-based timber harvesting, future CO2 emissions in cutover forests are likely to be less. Considering a CO2 equivalent of 44/12, CO2 emission from large-scale industrial timber harvesting that took place in the past in the study sites are estimated at 665,500 Mg CO2 (181,319 Mg C) in Yalu forest area and

49,042 Mg CO2 (13,375 Mg C) in the Gabensis community forest area.

Table 4-7: Summary Results

Yalu Community Forest Gabensis Community Forest Total Forest Area 2,200 ha 150 ha Total Residual Volume 28,000 m3 2,300 m3 Mean Residual Volume 12.69 m3 ha-1 15.19 m3 ha-1 Total Forest Carbon 329,670 Mg C 24,318 Mg C Mean Forest Carbon 149.85 Mg C ha-1 162.12 Mg C ha-1 Estimated Emission from Past Harvesting 181,319 Mg C 13,375 Mg C

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4.6 DISCUSSION

Following on from the objectives of this chapter, this study generally shows that the two case study sites have been extensively harvested in the past and the forests in these areas have been left in a degraded condition. This is reflected from the residual timber volume and aboveground forest carbon estimated from this study. The residual timber volume in Yalu and Gabensis community forests were estimated at 12.7 ± 4.5 m3 ha-1 and 15.2 ± 2.8 m3 ha-1 respectively. These estimates are considered lower than the average timber volumes in fully-stocked primary forests in PNG, which is about 30-40 m3 ha-1 (PNGFA, 2007). Looking at the Fox et al. (2010) estimates of aboveground forest C in selectively-harvested forests (90.2 MgC ha-1) and primary forests (120.8 MgC ha-1) in PNG, the estimates in the two case study sites are much higher given the situation that these two community forests had some larger size class (> 70cm DBH) and relatively tall trees left behind after harvesting (Figure 4-2). These community forests are small areas that have been repeatedly harvested in the past and there have been also evidence of extensive traditional land uses prior to this study. The study estimated aboveground forest C in Yalu community forest at 149.9 ± 37.5 Mg C ha-1, while in Gabensis it was estimated to be about 162.1 ± 50.6 MgC ha-1. The issue about additionality and its relationship to C stocks in CBFM is considered in this study. The concept of additionality is firmly grounded in international climate law and discussed in international climate change negotiations. The UNFCC (1992 Article 4.3), the Kyoto Protocol (1997 Article 11.2), the Bali Action Plan (2007 Paragraph 1e), and the Copenhagen Accord (2009 Paragraph 8) all call for developed countries to provide, ―new and additional‖ climate change financing to developing countries (Ballesteros and Moncel, 2011). However, within climate change policy and environmental markets, the concept of additionality is not clearly understood and creates disagreement and confusion (Gillenwater, 2011). At the heart of these reactions is not simply a policy debate but there is a more fundamental obstacle preventing constructive discussion and debate. One of the difficulties of the CDM is in judging whether or not projects truly make additional savings in GHG emissions (Carbon Trust, 2009). The baseline, which is used in making this comparison is not observable. According to the Carbon Trust (2009), some projects have been clearly additional. For example, the fitting of equipment to remove HFCs and N2O, and some

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low-carbon electricity supply projects were also thought to have displaced coal- powered generation.

Additionality is the process of assessing whether a proposed activity is different than its baseline scenario. For example, in the context of climate change policy the question of additionality is whether GHG emissions from a proposed activity will be different than baseline scenario emissions.

REDD+ is an emerging initiative that has the potential to provide alternative income for communities who would like to conserve their forest and participate in SFM that enhances the forest C stock. In the context of this study, there is a potential to avoid future emissions from timber harvesting or other activities that may enable communities to participate in REDD+ projects. For example, if communities adopt small-scale, more sustainable reduced impact harvesting techniques rather than agreeing to larger-scale industrial operations, they may be able to calculate and benefit from the difference in emissions. In addition, some of their forest areas will be protected under smaller-scale operations, conserving biodiversity and other forest values for traditional uses. These activities will therefore, avoid emissions that would otherwise have taken place in more extensive operations. It is clear from this study that the residual timber volume in the two community forests may not be able to attract large-scale harvesting. This is because of insufficient volumes that may not be able to sustain a bigger operation. However, volumes available in the case study sites can support a small-scale harvesting under CBFM because some large size commercial trees have been left behind after conventional harvesting in the past. The residual timber volume in the study sites is lower than the average timber volume (30-40m3 ha-1) in fully-stocked primary forest in PNG. The merchantable timber volume in these forests may be lower than the estimates from the study (equation 4-3) because trees < 50cm DBH were also considered during the inventory. If the FSC promoted guidelines of harvesting 2-3 trees ha-1 (Rogers, 2010) is adopted in CBFM in these forests, SFM can be anticipated because lower volumes will be harvested per year and the forest will be left to recover for future harvest.

The community forest areas have a high aboveground forest C compared to estimates for lowland tropical forests in PNG from an earlier study by Fox et al. (2010). The high aboveground forest C in the two study areas can be seen as a result of some large

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and tall non-merchantable trees with high density left behind after the past harvesting operations. Therefore, the options available now in the Yalu and Gabensis community forest areas are small-scale forest management and utilisation as well as other benefits from community C trade and participation in the REDD+ initiative.

4.7 CONCLUSIONS

The objectives of Chapter 4 have been to estimate the residual timber volume and aboveground forest carbon in the two case study sites in order to use this data to test the scenario analysis and evaluation tools (decision tree models) developed in Chapter 6. These objectives have been achieved and the residual timber volumes and AGLB in the case study sites have been determined. The residual commercial timber volume estimated in the case study sites, 12.7 m3 ha-1 in Yalu and 15.2 m3 ha-1 in Gabensis forest areas, can support a smaller-scale harvesting operation in CBFM. The high aboveground forest C estimates in the two study sites (149.9 MgC ha-1 in Yalu and 162.1 MgC ha-1 in Gabensis) provide an option for communities to manage their cutover forests for C benefits. Results from the assessment of the current condition and future production potential of cutover forests in the case study sites suggest that communities in these areas may participate in small-scale timber harvesting and certification schemes, manage their forests for C benefits and participate in REDD and REDD+ activities.

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SCENARIO ANALYSES AND EVALUATION TOOLS

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CHAPTER 5: EVALUATION OF SCENARIOS FOR COMMUNITY-BASED FOREST MANAGEMENT

5.1 INTRODUCTION

In research involving qualitative data collection, there are specific methodologies that need to be followed, however, review of these methodologies indicated that there are also difficulties in such methodological choices (Creswell et al., 2007). The qualitative research designs include such methodologies as the participatory action research (PAR) approach particularly used by psychologists. In PAR, a major focus is to produce social change (Maguire, 1987) and improve the quality of life (Stringer, 1999) in oppressed and exploited communities. While PAR commonly targets silenced groups, it is also necessary to involve groups such as decision-makers as participants of the research (Bodorkos and Pataki, 2009). The PAR method is unique in that the researcher and the members of the community are engaged at all level of the research process (Whyte et al., 1991). The origins of PAR are traced back to the late 1960s and early 1970s in the United States (Brydon-Miller, 2001, Freire, 1970). Brydon-Miller (2001) also indicated that PAR has been conducted all over the world, especially in third-world countries. Also in past decades, the PAR approach was common in the field of social sciences involving research in education, community development, work life and health (Nielsen and Svensson, 2006), however, recently there have been increasing interests in adopting this method to address current pressing issues such as climate change, biodiversity loss, and other sustainability issues (Fals-Borda and Mora-Osejo, 2003, Reason, 2007).

There are two parts to the study in Chapter 5. In the first part, a PAR protocol has been used as a guide to investigate options for the future management of cutover forests in PNG. This involved qualitative interviews of two community groups in a region in PNG where extensive harvesting of primary forests had occurred in the past. The PAR involved group meetings to explain the purpose of the research followed by one to one interviews in the

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two case study sites. Structured interviews were conducted to investigate local peoples‘ preference in how they would like to manage their forests in the future. The outcome from these interviews provided the basis to develop forest management scenarios for cutover forests.

In the second part of the study, local peoples‘ preferences in the future management of their forests identified in the first part of the study have been analysed. The outcomes from these analyses have been used to develop forest management scenarios by using a spreadsheet planning tool developed under a previous forest research project in PNG funded by ACIAR (Keenan et al., 2005). Scenarios developed in this chapter have been further tested using decision tree models developed in Chapter 6.

The first objective of Chapter 5 is to investigate options for future management of cutover forests by using the PAR approach as a guide with two community groups, namely Yalu and Gabensis villages in PNG. The second objective of the study is to develop management scenarios for CBFM.

5.2 BACKGROUND

5.2.1 The Scenario Approach

The literature review in Chapter 2 discussed the scenario and MSE methods as the alternative forest management approaches for cutover forests in PNG. Chapter 5 describes the application of the MSE approach (Sainsbury et al., 2000, Smith et al., 1999) to evaluate scenarios for CBFM. The details of the MSE approach are given in a framework developed by Sainsbury et al. (2000) (Chapter 2, Figure 2-1).

Scenarios are stories or models for planning and decision-making in situations where complexity and uncertainty are high, for example, management of tropical forest ecosystems (Nemarundwe et al., 2003). The use of future scenarios assists in defining alternative options and identifying strategies to achieve desired results. Use of scenarios is applicable when there are many stakeholders from local groups to decision makers. Scenario methods are applicable to village communities (Wollenberg et al., 2000) and in

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Chapter 5, these approaches have been used as a guide to develop scenarios for CBFM in PNG.

5.2.2 Modelling Tropical Forest Growth and Yield

Forest simulation models have a long history in forestry and have proven to be useful tools for forest management (Shao and Reynolds, 2006). Early work on forest yields in the tropics were started in Burma for Teak and over the years different approaches have emerged in the development of suitable models for tropical forests (Mariaux, 1981, Vanclay, 1994). In the tropics, there has been a lot of progress made in the development of growth and yield models for tropical mixed forests. Some of these efforts include development of a growth model for north Queensland by Vanclay (1994); stand table projection model for Sarawak by Korsgaard (1989); and development of the PINFORM growth model for lowland tropical forests in PNG by Alder (1998). More recently there have been examples of work on growth and yield modelling of tropical forests in north Queensland, Brazil, Ghana, Costa Rica, Malaysia, and PNG. However, regardless of these efforts, the very diverse forest types, mixed species, and lack of continuity in data collection are some barriers that make it difficult to make predictions on the growth of tropical forests. Work on prediction, simulation models and forest growth models in the tropics generally use inventory data based on PSPs.

Analyses of timber yields under different forest management scenarios in this Chapter 5 are based on the spreadsheet planning tool (Keenan et al., 2005).

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5.3 METHODOLOGY

5.3.1 Criteria for Developing Scenarios

The basic procedures for creating the scenarios in the study included the following steps using the PAR approach as a guide; o In consultation with stakeholders, including government agencies, timber companies, NGOs, and community groups, criteria for selecting scenarios were developed. o Inform and discuss different approaches to forest management with community and industry based on information available from existing management tools (for example, PINFORM, ACIAR Planning Tool) and analysis of current forest growth data. o Allow stakeholders to collectively create broad categories of scenarios based on an informed decision. o In consultation with stakeholders, develop a scenario preference scoring sheet. o Distribute scenario scoring sheet during field interviews to research participants for them to mark the scenarios of their preferences. o In consultation with the research participants, select scenarios with highest scores. o Develop scenario analysis and evaluation tools. o Test and analyse selected scenarios using the scenario analysis and evaluation tools developed. o Compare and evaluate effects of scenarios. o Develop an integrated conceptual framework for CBFM and integrate scenario outcomes into the framework.

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5.3.2 Field Interviews using the PAR Protocol as a Guide

The initial fieldwork in this study involved an extensive consultation in the form of field visits and meetings to explain the purpose of the research to a wide range of stakeholders in PNG. This was done in order to gauge views from stakeholders about general forest management issues in the country and to assess their interests and expectations on how they would like to manage their forests in the future. Stakeholders included the following: government agencies (PNGFA, FRI, University TFTC); timber companies (Lae builders Ltd, Madang timbers Ltd, Santi timbers Ltd); NGOs (VDT, FPCD, FORCERT, CMUs) and the communities (Yalu, Gabensis, Sogi villages). The research focussed on two community groups (Yalu and Gabensis villages) that were selected in consultation with the project partner NGO, the Village Development Trust. The approach taken in this study involved the general procedures of PAR but the methodologies of a PAR protocol were not fully implemented in the study. Based on the objectives of the study, the PAR approach involved only the conventional forms of data gathering in the form of village meetings, discussions and interviews. The interviews were conducted in order to understand the current uses of forest by communities and how they would like to manage their forests in the future. In this process, research participants in the two communities were asked to indicate their preferences in questionnaires on what options they preferred in the future management of their cutover forests.

In the PNG context, few individuals or families usually involve in small-scale timber harvesting but they represent the interests of a village or community. In such cases, sawn timbers harvested are sometimes used for building local schools, community halls, church buildings and other infrastructure. The selection of the participants for the interviews was based on their involvement in small-scale timber harvesting in the past and those that were interested in the future management of their cutover forests. Furthermore, the interviews were not intended as a detailed social survey in the study sites rather, it targeted individuals and families that were interested in the future management of their cutover forests.

Eleven individual structured interviews (8 in Yalu village and 3 in Gabensis village) were conducted within the two community groups. The groups were from two villages that are located in a region where there have been an extensive timber harvesting of primary forests

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in the past and the forests that are left behind are mostly secondary cutover forests with residual stand. Despite the sample in this study not being representative of the region due to the sample size of 11 (8 interviewees in Yalu village and 3 interviewees in Gabensis village), the main aim of the interview was to understand community attitudes towards small-scale timber harvesting. The outcome of the interviews provided the background on how communities would like to manage their forests in the future. The individuals interviewed were local people who were not only interested to participate in small-scale timber harvesting, rather they were members of the two community groups who had been actually involved in small- scale timber harvesting for the last 10 years, but with very little capacity to expand their operations. Therefore, the interviews served its purpose of understanding community attitudes towards small-scale timber harvesting, a process which is considered as a prerequisite or background to developing forest management scenarios. The data from field interviews were analysed using both the quantitative data analysis software SPSS (analysis of scenario indicators) and qualitative data analysis software NVIVO (current and future uses of forest, community attitudes towards small-scale timber harvesting).

5.3.3 Scenario development

Scenarios for CBFM were developed from local communities‘ participation in meetings, discussions and interviews in the study. The analysis of local people‘s current and future uses of forests and their preferences on how they would like to manage their forests in the future form the basis of scenario development. The key component of the field interviews was the scoring of local people‘s preferences. Their preferences were analysed as scenario indicators, which were then used to develop the scenarios. The initial PAR approach in the case study sites with the participation of the two communities and the results from analyses of the field interviews have identified four main forest management options. These are community sawmill, local processing, medium-scale log export and carbon trade. These options have been analysed using the ACIAR planning tool (Keenan et al., 2005) in order to develop forest management scenarios.

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The scenarios developed in Chapter 5 are community sawmill, local processing, medium- scale log export and carbon trade, however, under the community-based harvesting the three latter scenarios have been analysed using the planning tool. The four scenarios for CBFM including the carbon trade scenario have been tested using the decision analyses model developed in Chapter 6. The details and description of the activities that take place under each scenario are summarised below:

Community sawmill: that a sawmill is managed by the community itself with little capacity and light equipment. Timber is felled and milled in situ according to buyer specifications. All sawn timber produced are sold in the domestic market and for other community uses. There is no value adding and no export of sawn timber to the overseas market. All production and marketing are the responsibility of the community.

Local processing: that a local processing is managed by an entity referred to as the central marketing unit (CMU) with the use of mechanised equipment to increased capacity and production for the overseas export market. The CMU add value to the sawn timber from a timber storage shed equipped with planner-moulder, breakdown saw, crosscut saw and other backup. All the processed timber are exported to an overseas certified market and the production and marketing of sawn timber are the responsibility of the CMU.

Medium-scale log export: that a medium-scale log export enterprise is managed by a CMU for the export market with the use of mechanised equipment and increased log production. Logs are exported to the overseas market. The CMU is responsible for the production and marketing of logs from the operation.

Carbon trade: that a community forest C project is managed for selling C credits to either a compliance or voluntary market. CBFM activities involve reduced impact harvesting and some of their forest areas are protected thereby avoiding emissions that would otherwise have taken place. This enables the community to participate in the REDD+ initiative.

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5.3.4 Scenario Analysis using a Spreadsheet Tool

The forest management options investigated during the field interviews with the participation of the two community groups (Yalu and Gabensis villages) were further analysed using a spreadsheet planning tool (Figure 5-1). This tool was developed in a previous forest research project to improve timber inventory and strategic forest planning in PNG under the funding support of ACIAR (Keenan et al., 2005). The tool basically facilitates the integration of forest area, inventory and growth information from the Yalu case study site (Yalu community forest) to estimate the timber yields under different management scenarios in community-based harvesting.

Project Name Yalu Community Forest Management option Local processing: Small-scale, higher values trees only Analyst Cossey Yosi, University of Melbourne Date: 3/06/2011

A. Cycle length (yrs) 50 B. Inventory, growth and yield data (/ha) Cycle MEP-code 1,2 MEP-code 3,6 Other Total Diameter class (cm) Number 20-50 50-65 65+ 20-50 50-65 65+ 20-50 50-65 65+ Merch Pre-harvest (m3/ha) 1 21.0 27.0 43.0 9.0 10.0 12.0 5.0 5.0 7.0 104.0 Cut fraction (%) 0% 0% 100% 0% 0% 100% 0% 0% 0% Left after Harvest Post-harvest (m3/ha) 21.0 27.0 0.0 9.0 10.0 0.0 5.0 5.0 7.0 49.0 60% YIELD (m3/ha) 0.0 0.0 43.0 0.0 0.0 12.0 0.0 0.0 0.0 55.0 Ingrowth (m3/yr) 0.28 0.28 0.28 0.08 0.08 0.08 0.00 0.00 0.00 0.7 Growth (m3/yr) 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.0 Death/Damage (m3/yr) 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Upgrowth (m3/yr) 0.28 0.28 0.08 0.08 0.00 0.00

Pre-harvest (m3/ha) 2 21.0 27.0 13.9 9.0 10.0 3.9 5.0 5.0 7.0 66.7 Cut fraction (%) 0% 0% 100% 0% 0% 100% 0% 0% 0% Left after Harvest Post-harvest (m3/ha) 21.0 27.0 0.0 9.0 10.0 0.0 5.0 5.0 7.0 49.0 83% YIELD (m3/ha) 0.0 0.0 13.9 0.0 0.0 3.9 0.0 0.0 0.0 17.7 Ingrowth (m3/yr) 0.22 0.22 0.22 0.06 0.06 0.06 0.00 0.00 0.00 0.6 Growth (m3/yr) 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.0 Death/Damage (m3/yr) 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Upgrowth (m3/yr) 0.22 0.22 0.06 0.06 0.00 0.00

Pre-harvest (m3/ha) 3 21.0 27.0 11.2 9.0 10.0 3.1 5.0 5.0 7.0 63.3 Cut fraction (%) 0% 0% 100% 0% 0% 100% 0% 0% 0% Left after Harvest Post-harvest (m3/ha) 21.0 27.0 0.0 9.0 10.0 0.0 5.0 5.0 7.0 49.0 85% YIELD (m3/ha) 0.0 0.0 11.2 0.0 0.0 3.1 0.0 0.0 0.0 14.3 Ingrowth (m3/yr) 0.21 0.21 0.21 0.06 0.06 0.06 0.00 0.00 0.00 0.5 Growth (m3/yr) 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.0 Death/Damage (m3/yr) 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Upgrowth (m3/yr) 0.21 0.21 0.06 0.06 0.00 0.00

Figure 5-1: Example output of the Planning tool (Keenan et al., 2005). Data input in the system include cutting cycle, pre-harvest volume in each diameter class for each species groups and cut fraction.

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The gross area of the Yalu community forest was 2,200 ha. The area available for harvesting was assessed by considering the need to set aside areas for conservation reserves, slopes, fragile areas, stream buffers, and other areas for community use (Table 5- 1). The pre-harvest volume classified under the PNGFA merchantable species classes and net volume growth in the case study site are categorised under each size class (Table 5-2).

Table 5-1: Yalu community forest area

Yalu Area Data (ha) Forest area allocated for CBFM 2,000 Exclusions from 1st cycle Conservation Reserve 50 Slope outside conservation 20 Fragile 15 Streamline Buffers not in above 10 Community reserves not in above 10 Other inaccessible 20 1st cycle net area (ha) 1,875 Additional Exclusions after 1st cycle (ha) Conversion to gardens 20 Regrowth area 15 Roading 10 Other 25 2nd &3rd cycle net area (ha) 1,805

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Table 5-2: Yalu community forest inventory data

Diameter Class Volume MEP1 Volume MEP2 Others (m3 ha-1) (m3 ha-1) (m3 ha-1) (cm) < 20 0.301 0.307 7.029 20-50 4.950 6.961 34.991 50-65 6.634 11.885 18.539 65+ Volume Growth (m3 ha-1 year-1) 0-20 0.117 0.301 0.203 20-50 0.129 0.124 0.244 50-65 0.041 0.080 0.073 65+ 0.127

The data available from the case study site was input in the planning tool to analyse timber yields under different management scenarios. Three levels of analysis were carried out using the planning tool. The first was a management regime involving a constant cut proportion of 50% with different cutting cycles in each scenario, removing timber species in MEP codes 1 and 2 only with a DBH of > 50cm (Table 5-3).

Table 5-3: Data for a management regime with 50% constant cut proportion.

Scenario Cutting Cycle Cut Proportion Diameter (Years) (%) Limit/MEP Codes Community 10 50 > 50cm sawmill MEP1, MEP2 Local processing 20 50 > 50cm MEP1, MEP2 Local processing 30 50 > 50cm MEP1, MEP2 Medium-scale log 40 50 > 50cm export MEP1, MEP2

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The second analysis was a management regime with a constant cut proportion of 75% but with the same settings (cutting cycles and species groups) in each scenario as the first regime (Table 5-4). In community-based harvesting, only valuable timber species are felled, hence, only timber species group in the PNGFA MEP codes 1 and 2 have been considered in this study. The main timber species in MEP code 1 include the genera Burckella, Calophyllum, Canarium, Planchonella, Pometia, Intsia and those in Group II are Hopea, Vitex, Aglaia, and Endospermum.

Table 5-4: Data for a management regime with 75% constant cut proportion.

Scenario Cutting Cycle Cut Proportion Diameter (Years) (%) Limit/MEP Codes Community sawmill 10 75 > 50cm MEP1, MEP2 Local processing 20 75 > 50cm MEP1, MEP2 Local processing 30 75 > 50cm MEP1, MEP2 Medium-scale log export 40 75 > 50cm MEP1, MEP2

In the third analyses (Table 5-5) a management regime with a constant cutting cycle of 20 years under a local processing scenario was tested but with 50% and 75% cut intensities and DBH limit of > 50cm and > 65cm in the same species groups (MEP 1 and 2) as in the first and second management regimes.

Table 5-5: Data for a management regime with 20 years constant cutting cycle.

Scenario Cutting Cycle Cut Proportion Diameter (Years) (%) Limit/MEP Codes Local processing 20 50 > 50cm MEP1, MEP2 Local processing 20 50 > 65cm MEP1, MEP2 Local processing 20 75 > 50cm MEP1, MEP2 Local processing 20 75 > 65cm MEP1, MEP2

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5.4 RESULTS

5.4.1 Current Forest Uses and Future Forest Management Options

The current forest uses in the two communities are hunting, gardening and small-scale harvesting (Figure 5-2). A higher number of people indicated that they were currently using their forests for small-scale harvesting in Yalu village than in Gabensis village. Analyses of field interviews showed that the local people were currently using some of their forests for small-scale harvesting, while still maintaining other forest lands for traditional uses such as hunting and gardening (Figure 5-2).

Figure 5-2: Current main forest uses in Yalu and Gabensis villages. X-axis represents the number of interviewees in each village.

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According to the interviews, the preferred forest management options for the future included reforestation, local processing, carbon trade, conservation and sawn timber export (Figure 5-3). A higher number of local people interviewed in Yalu village also indicated reforestation as another option for future management of their cutover forests than in Gabensis village.

Figure 5-3: Future forest management options in case study sites. X-axis represents the number of interviewees in each village.

Current forest use by gender indicated that a higher numbers of males were engaged in hunting and small-scale harvesting than females. Forest uses for gardening were higher for females (Appendix 5-2). Analyses of future forest uses by villages from the interviews indicated that higher numbers of people were interested in managing their forests for small-scale harvesting both in Yalu and Gabensis communities (Appendix 5-3). The other future forest uses recorded in the two case study sites included non-timber forest products (NTFP), reforestation, gardening,

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local timber processing, conservation and community development. Analyses of future forest use by gender showed that both males and females were interested in managing their forests for small-scale harvesting (Appendix 5-3). Village meetings, discussions and interviews carried out in the two case study sites (Yalu and Gabensis villages) provided evidence that lack of social services including education, health, community infrastructure and church facilities influenced community interest in engaging in small-scale timber harvesting (Figure 5-4). The factors influencing a family‘s engagement in small-scale timber harvesting included lack of income, difficulties in raising school fees for sending children to school and better homes. Sawn timber demand, timber price, certification benefits and markets influenced local peoples‘ commercial interest in engaging in small-scale timber harvesting in the two communities (Figure 5-4).

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Figure 5-4: Factors influencing community attitudes towards small-scale harvesting. This model was generated from the qualitative software Nvivo.

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5.4.2 Scenario Indicators

Analyses of field interviews showed high frequencies for local processing (6, 55%), small- scale harvesting (4, 36%), and management for carbon values (5, 46%) (Figure 5-5). Frequencies recorded in this case represent the total number of persons under each level of preference for a particular forest management option in the two case study sites. A total of 11 participants were interviewed in the two case study sites. Frequency recorded for no preference was high (6 counts) for the log export scenario.

Do you prefer small-scale harvesting? Do you prefer log export? 12 100 12 100

10 80 10 80 8 8 60 60 6 6 40 40

4 4

Frequency (N)Frequency (N)Frequency

Percentage(%) Percentage(%) 2 20 2 20

0 0 0 0 high low no not sure high low no not sure Do you prefer local processing? Do you prefer management for carbon values? preference preference preference preference preference preference 12 100 12 100 10 10 80 80 8 8 60 60 6 6 40 40

4 4

Frequency(N)

Frequency (N)Frequency

Percentage(%) Percentage(%) 2 20 2 20 0 0 0 0 high Do you lowprefer no harvesting?no not sure high low no not sure preference preference preference preference preference preference 12 100

10 80 8 60 6 40

Frequency (N)Frequency Percentage(%) 2 20 0 0 high low no not sure preference preference preference

Figure 5-5: Graphical presentation of the frequencies from field interviews. Frequency (left Y-axis) represents number of counts and the equivalent counts are represented as percentage (right Y-axis)

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5.4.3 Estimating Timber Yield under Different Management Scenarios

Analysis outputs from the planning tool showed that with a cut proportion of 50% of total volume per hectare, in commercial tree species with a DBH > 50cm in MEP1 and MEP2 merchantable categories, in a 10 year cutting cycle for a community sawmilling project, resulted in a relatively even distribution of annual yield of about 3,000 m3 in the first, second and third cutting cycles (Table 5-7). Total yield over the three cycles (30 years) in a 10 year cutting cycle is estimated at about 87,000m3. In this management regime, as the cutting cycle is increased, yield decreases in the first cycle but increases in the second and third cycles.

Table 5-6: Management regime with a constant cut proportion of 50%

Scenario Annual Annual Annual Total Cutting Cycle Yield Cycle 1 Yield Cycle 2 Yield Cycle 3 Yield (years) (m3 year-1) (m3 year-1) (m3 year-1) (m3) Community sawmill 10 3,166 2,865 2,718 87,490 Local processing 20 1,583 2,100 2,890 131,500 Local processing 30 1,055 1,846 3,307 186,060 Medium-scale log export 40 792 1,718 3,780 251,600

In a management regime with a higher cut proportion of 75% but with the same input variables (> 50cm DBH, MEP1 and 2 groups) under a 10 year cutting cycle, annual yield increased to about 5,000 m3 in the first cutting cycle but reduces to about 2,000 and 1,000 m3 respectively in the second and third cycles (Table 5-8). Further analysis showed that a yield of about 2,000 m3 was evenly distributed over the first, second and third cycles under a 30 year cutting cycle in a local processing scenario. The general trend in this management regime is that with an increased cutting cycle and cut intensity, yield decreases.

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Table 5-7: Management regime with a constant cut proportion of 75%

Total Cutting Annual Yield Annual Yield Annual Yield Yield Scenario Cycle (years) Cycle 1 (m3) Cycle 2 (m3) Cycle 3 (m3) (m3) Community sawmill 10 4,749 2,316 1,229 82,940 Local processing 20 2,375 1,743 1,294 108,240 Local processing 30 1,583 1,551 1,574 141,240 Medium-scale log export 40 1,187 1,456 1,802 177,800

A management regime under a constant cutting cycle of 20 years showed that with a reduced cut fraction (50%), removing a lesser volume of commercial tree species with a DBH limit of > 50cm resulted in an annual yield of about 1,600m3 year-1 in the first cycle but provided for increases to about 2,000m3 year-1 and 3,000m3 year-1 in the second and third cycles respectively. In this management regime, an increased cutting cycle and removing more commercial trees (> 50cm DBH) resulted in an increased annual yield in the initial harvest, however, when the cut intensity is increased (75%) with an increased cutting cycle, annual yield generally decreases over the consecutive cycles.

Table 5-8: Management regime with a constant cutting cycle of 20 years

Annual Annual Annual Scenario DBH Limit/ Yield Cycle 1 Yield Cycle 2 Yield Cycle 3 Total Yield Species Grp (m3 year-1) (m3 year-1) (m3 year-1) (m3) Local 50%, > 50cm processing MEP 1, 2 1,583 2,100 2,890 131,460 Local 50%, > 65cm processing MEP 1, 2 623 703 805 42,620 Local 75%, > 50cm processing MEP 1, 2 2,375 1,743 1,361 276,463 Local 75%, > 65cm processing MEP 1, 2 934 603 415 39,040

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Analyses of timber yield with an initial cut proportion of 50% under four different cutting cycles (10, 20, 30 and 40 years) showed that in a shorter cutting cycle (10 years) under a community sawmill scenario (Figure 5-6a), annual volume was higher and evenly distributed over the first, second and third cycles. A 20 years cutting cycle in a local processing scenario (Figure 5-6b) showed similar results. In longer cutting cycles (30-40 years) under a local processing scenario (Figure 5-6c) and medium-scale log export scenario (Figure 5-6d), annual volume is lower initially but increases in the second and third cycles because there is more time between harvests for the forest to recover and increase in volume.

In a similar analysis but with a cut proportion of 75%, shorter cutting cycles, for example, 10 years in a community sawmill (Figure 5-7a) and 20 years in a local processing scenario (Figure 5-7b) showed a higher annual volume initially, which reduced over the consecutive cycles. Longer cutting cycles (30-40 years) showed a lower annual volume for the initial cut and then evenly distributed over the second and third cycles under a local processing and medium-scale scenarios (Figure 5-7c and d).

Analyses with a constant cutting cycle of 20 years, removing timber species in the same commercial group (MEP 1 and 2) with a DBH > 50cm showed that a reduced cut intensity (50%) resulted in a lower annual volume in the first cycle (Figure 5-8a). Maintaining the same cut proportion (50%) and removing commercial trees only with a DBH > 65cm (Figure 5-8b) resulted in a low annual volume in the first, second and third cycles. When the cut proportion was increased (75%), annual volume in the first cycle was increased (Figure 5-8c) but decreased in the latter cycles. With a cut fraction of 75%, removing tree species in the same merchantable categories and only in the DBH class > 65cm resulted in a lower annual volume initially and there were no marked increases in the consecutive cycles (Figure 5-8d).

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5 5

MEP-code2, 65+ MEP-code2, 65+

4 4

MEP-code2, 50-65 MEP-code2, 50-65

- -

3 MEP-code1, 65+ 3 MEP-code1, 65+

MEP-code1, 50-65 MEP-code1, 50-65

2 2

Annual Annual Volume(1000 m3 yr Annual Annual Volume(1000 m3 yr

1 1

0 0 1 - 10 11 - 20 21 - 30 (a) 1 - 20 21 - 40 41 - 60 (b) Cutting Cycle (Years) Cutting Cycle (Years)

5 5

MEP-code2, 65+ MEP-code2, 65+

4 4

MEP-code2, 50-65 MEP-code2, 50-65

- -

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Figure 5-6: Timber yield under different scenarios with a 50% cut proportion. The management regimes are for four cutting cycles, (a) 10 years, (b) 20 years, (c) 30 years, and (d) 40 years.

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Figure 5-7: Timber yield under different scenarios with a 75% cut proportion. The management regimes are for the four cutting cycles, (a) 10 years, (b) 20 years, (c) 30 years, and (d) 40 years.

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5 5

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Figure 5-8: Timber yield for a constant cutting cycle of 20 years. The management regimes are for different cut proportions and diameter limits, (a) 50% and DBH > 50cm, (b) 50% and DBH > 65cm, (c) 75% and DBH > 50cm, and (d) 75% and DBH > 65cm.

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5.4.4 Analyses of Residual Timber Volume over a 60 Year Cycle

The starting timber volume (pre-harvest volume) in the Yalu case study site was 30.5 m3 ha-1. At a cut proportion of 50% in a community-based harvesting in the study site, harvesting size class > 50cm DBH in the MEP1 and 2 merchantable groups showed that the residual timber volume continues to increase over a 60 year period (Table 5- 9). At year 50, the residual timber volume is estimated at about 213 m3 ha-1 and increases to about 286 m3 ha-1 at year 60.

Table 5-9: Residual and annual volume over a 60 year cutting cycle.

Starting / Residual Cutting Cut Pre-Harvest Volume After Annual Cycle Proportion Diameter Limit / Volume 3rd Cycle Yield (Years) (%) MEP Codes (m3 ha-1) (m3 ha-1) (m3 year-1) 10 50 > 50cm, MEP1 & 2 30.5 27.1 8,750 20 50 > 50cm, MEP1 & 2 30.5 57.7 6,574 30 50 > 50cm, MEP1 & 2 30.5 98.9 6,208 40 50 > 50cm, MEP1 & 2 30.5 150.8 6,290 50 50 > 50cm, MEP1 & 2 30.5 213.2 6,550 60 50 > 50cm, MEP1 & 2 30.5 286.1 6,899

Projection output from the planning tool showed that at year 0 the starting volume (pre-harvest volume available) in the Yalu community forest was 30.5 m3 ha-1 and under the 10 year cutting cycle, this is reduced to 27.1 m3 ha-1 after the third cycle (Figure 5-9). During the consecutive cutting cycles residual timber volume increases in a positive trend over the 60 year period.

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350

) 300

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Figure 5-9: Residual timber volume for a 100 year cycle

5.4.5 Projection of Annual Yield over a 60 Year Cycle

At the initial cut, the annual yield is high (8,750 m3 year-1) at year 10 but is reduced to 6,208 m3 year-1 at year 30 (Figure 5-10). Yield then is almost constant up to year 40 and starts to increase over the projection period.

10000

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Figure 5-10: Annual Yield for a 60 year cycle

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5.5 DISCUSSION

5.5.1 Outcomes from Field Interviews

The field interviews enabled understanding of community attitudes towards small- scale harvesting. Although the sample size (11 individual interviewees) was not representative of the whole region where the study was undertaken, the interviews served their purpose. Community participation in the study has enabled the identification of the forest management options preferred by the communities for the future management of their forests. This was achieved through preference scoring of how communities would like to manage their cutover forests in the future. While the study was only able to interview relatively few landowners, the whole process of initial consultations and village meetings to the actual interviews in the two case study sites provided a basis for further analyses using the planning tool, in order to develop scenarios for community-based management of cutover forests.

5.5.2 Analyses Output from the Planning Tool

In this study, timber yields under different management scenarios have been estimated using the planning tool (Keenan et al., 2005) and scenarios for community-based management of cutover forests have been developed. In community-based harvesting in a shorter cutting cycle (for example, 10 years), sustainability can be achieved in terms of sawn timber production as is the case in this study (Figure 5-6a). The study indicated that there was a trade-off between cutting cycle and yield in these cutover forests. Maintaining the same cut proportion (50%) and removing commercial tree species in the same merchantable categories (50cm DBH, MEP1 and 2) but in a 20 year cutting cycle under the local processing scenario results in a yield of about 2,000m3 year-1 in the first and second cutting cycles and then an increase in the third cycle to about 3,000m3 year-1. This management regime under the Local Processing scenario can achieve sustainability and an even flow of sawn timber in a community project (Figure 5-6b).

With an increased cutting cycle to 30 years, there was a reduced yield of about 1,000m3 year-1 in the first cycle but an increase to 2,000 and 3,000 m3 year-1 in the

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second and third cycles respectively in a community local processing project (Figure 5-6c). When the cutting cycle is increased to 40 years in a medium-scale community log export project, there was a reduced yield of about 1,000 m3 year-1 in the first cutting cycle but an increase to 2,000 and 4,000 m3 year-1 respectively in the second and third cycles (Table 5-6d). Thus, longer cutting cycles have lower short-term yields but potentially higher yields in the long term because the forest has a greater time to recover to higher volumes for later cutting cycles. Communities will need to assess their time preference for income associated with harvesting in order to consider the choice between these options. With the same data input as the management regime with a 50% cut proportion but with an increased cut fraction to 75%, yield is higher in a shorter cutting cycle (10 years) initially but reduces in the second and third cycles (Figure 5-7a). In a 20 year cutting cycle under a local processing scenario, with the same data input in the planning tool, yield was same in the first and second cycles (2,000 m3 year-1) but reduces to 1,000 m3 year-1 in the third cycle (Figure 5-7b). Analysis showed an even distribution of yield (2,000 m3 year-1) in the first, second and third cycles in a 30 year cutting cycle under a local processing scenario. This management regime can therefore, be sustainable in a local community processing project (Figure 5-7c).

In a community medium-scale log export scenario, under a 40 year cutting cycle, analysis showed a reduced yield of about 1,000 m3 year-1 in the first and second cycles but an increased to 2,000 m3 year-1 in the third cycle (Figure 5-7d). Analyses of timber yield under a constant cutting cycle (20 years) showed that removal of commercial timber species in DBH class > 50cm results in a high annual volume when the cut fraction is increased (Figure 5-8c) but when only fewer trees in the > 65cm DBH class in MEP 1 and 2 groups are cut, annual volume is low in the initial cycle and no marked increases over the consecutive cycles (Figure 5-8 b and c). A Management regime with a higher diameter limit and shorter cutting cycle may not produce sufficient volume to support a sustainable community-based harvesting. A comparison was made between shorter and longer cutting cycles with their resulting annual yield under a constant cut proportion, removing half (50%) of the pre-harvest volume available and harvesting only those commercial species in MEP1

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and 2 groups with a DBH of > 50cm (Table 5-10). It can be seen that in a shorter cycle (10-20 years) annual yield can be higher in community-based harvesting. However, total yield over the consecutive cycles can be high in longer cutting cycles (30-40 years) because of longer time periods between the cuts can potentially result in volume growth for the next harvest. For example, in a management regime with 50% cut proportion under a 40 year cutting cycle, total yield was estimated to be over 250,000 m3 (Table 5-6).

Table 5-10: Comparison of shorter and longer cutting cycles

Annual Cutting Cycle Cut Proportion Diameter Limit/ Yield (Years) (%) Species Group (m3 year-1) > 50 cm, MEP 10 50 1&2 8,750 > 50 cm, MEP 20 50 1&3 6,574 > 50 cm, MEP 30 50 1&4 6,208 > 50 cm, MEP 40 50 1&5 6,290

A similar analyses of timber yields under different management scenarios in a 84,000 ha fully-stocked primary forest in the middle Ramu area in PNG (Keenan et al., 2005) showed that a management regime with a lighter cut in a longer cutting cycle taking only a proportion of higher quality timber species, resulted in a longer term even flow of wood for a community. Their study was conducted in a fully-stocked primary forest, while the present study was carried out in a site, which had been previously harvested hence, there was lower stocking in the residual timber volume. Projections from the planning tool in the present study showed that residual timber volume in the case study site increased in a positive trend from year 0 to 60 (Figure 5- 9), while initial yield was high at year 0 to 10 and then decreases at about 30% in year 30. Annual yield increases again in a positive trend after year 40 (Figure 5-10).

Alder (1998) developed a whole stand growth and yield model called PINFORM for lowland tropical forests in PNG. Test of this model in an earlier study suggested that a harvesting regime with longer cutting cycle, example 35 years, with > 50cm DBH cutting limit was considered unsustainable. Projections from PINFORM showed that

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an increase in the diameter cutting limit from > 50cm DBH to 65cm+ DBH is considered more sustainable. PINFORM also suggested that shorter cutting cycles, for example 20 years with a regulated volume to be felled at 10m3 ha-1, are considered sustainable. The results from analyses of timber yields under different management scenarios in this study supports earlier projections by Alder (1998).

5.6 CONCLUSIONS

The main aim of the field interview was to understand community attitudes towards small-scale harvesting to inform the development of scenarios for CBFM. These have been achieved by using the PAR protocol as a guide and involving the participation of the Yalu and Gabensis village communities. Analyses of the field interviews have identified five main options for the management of cutover forests. These are community sawmill, local processing, medium-scale log export, Carbon trade, and no harvest.

In developing scenarios, analyses output from the planning tool showed that in CBFM, a reduced cut proportion to about half (50%) with a shorter cycle, for example, 10 to 20 years removing only commercial trees with a DBH > 50cm in MEP1 and MEP2 merchantable categories can result in an even flow of sawn timber in a community sawmilling or local processing scenario. This management regime is considered sustainable in small-scale harvesting by communities in PNG. Similarly in a longer cutting cycle (30 years) with an increased cut proportion (75%) under a local processing scenario, there is an even distribution of yield across the first, second and third cycles, however, the initial cut is excessive and the yield is low in the first cycle hence, this management regime is considered unsustainable. A management regime under a constant cutting cycle, for example, 20 years is considered unsustainable because an increased cut intensity and removal of only fewer commercial timber species results in low annual yield. Outputs from the planning tool provides evidence that with a light intensity harvest and removal of only a proportion of commercial timber species can result in a continued increase in the residual timber volume over a longer period of time in community-based harvesting. Annual yield can be high or low depending on the initial cut fraction in community-based harvesting, however, it can increase over a longer period of time as suggested here. Projections from the

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planning tool over 100 years suggest that community-based harvesting can be sustainable over a longer period of time.

A forest management regime with a short cycle (10-20 years) with a reduced cut proportion (50%), removing only a proportion of commercial timber species is recommended for application in community-based harvesting in PNG.

In the PNG situation, implementation of control and monitoring systems as far as forest management (conventional harvesting operations of the industry as well as small-scale harvesting) is concerned, is a major challenge for government authorities. Forest management in general is associated with many problems such as under- staffing of the PNGFA, lack of continuous funding for monitoring logging operations and corruption at higher level in the timber industry. There are also many problems associated with the implementation of sustainable, community-managed timber production systems in PNG. The certification process can address many of the issues with corruption and short-term financial gain that can drive unsustainable practices. However, communities themselves will need to develop agreed internal rules and controls and political processes to ensure that these are adhered to. The mechanisms for achieving this were beyond the scope of the current study.

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CHAPTER 6: DECISION TREE MODELS FOR COMMUNITY-BASED FOREST MANAGEMENT IN PNG

6.1 INTRODUCTION

Decision-making is a management and decision science (Ragsdale, 2007). SFM necessitates decision-making, which recognises and incorporates diverse ecological, economic and social processes; a multitude of variables; and conflicting objectives and constraints (Varma et al., 2000). A decision-support system is a tool that offers a decision maker direct support during the decision process and integrates a decision maker‘s own insights with a computer‘s information processing capabilities for improving the quality of decision making (Keen and Scott-Morton, 1978, Shao and Reynolds, 2006, Turban, 1993). On the other hand, a decision analysis tool offers powerful structured analytical technique about how the actions taken in a decision would lead to a result (Lieshout, 2006).

Decision-support systems also assist the decision maker with the evaluation of alternatives or substantiating decisions. Unlike evaluation and analysis systems, decision-support systems involve valuation and rating techniques and inference methods, such as, knowledge-based systems originating from the domain of artificial intelligence (Shao and Reynolds, 2006). Generally, the application of decision- support systems to assist SFM has been successful worldwide (Varma et al., 2000). However, the use of decision analysis techniques has not been applied in forest management before. Most work on decision analysis has been applied in economic analysis and decision making in investment scenarios by corporate bodies and businesses (Ragsdale, 2007).

There are different types of modelling techniques that are used to help managers gain an in-depth understanding about the decision problems they face. However, models do not make decisions but people do. While the insight and understanding gained by modelling problems can be helpful, decision making often remains a difficult task. The two primary causes for this difficulty are uncertainty regarding the future and conflicting values or objectives (Ragsdale, 2007). The goal of decision analysis is to

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help individuals make good decisions, however, it is important to understand that good decisions do not always result in good outcomes. Using a structured approach to make decisions should give us enhanced insight and sharper intuition about the decision problems we face. As a result, it is reasonable to expect good outcomes to occur more frequently when using a structured approach to decision making than if we make decision in a more haphazard manner.

Although all decision problems are somewhat different, they share certain characteristics such as when a decision must involve at least two alternatives for addressing or solving a problem. An alternative is a course of action intended to solve a problem. Alternatives are evaluated on the basis of the value they add to one or more decision criteria. The criteria in a decision problem represent various factors that are important to the decision maker and influenced by the alternatives. The impact of the alternatives on the criteria is of primary importance to the decision maker. Not all criteria can be expressed in terms of monetary value, making comparisons of the alternatives more difficult. The values assumed by the various decision criteria under each alternative depend on the different states of nature that occur. The states of nature in a decision problem correspond to future events that are not under the decision maker‘s control.

There are various useful decision analysis techniques such as influence diagrams, decision trees, sensitivity analysis and tornado diagrams, as well as more traditional accounting techniques, such as net present value (NPV) (Lieshout, 2006). In the current study, the application of a decision analysis technique in CBFM in PNG is a new approach to tropical forest management. This type of technique is justified for application in tropical forests because of the complexity and uncertainty (Wollenberg et al., 2000) these type of forests present in their management. In the context of forest management in PNG, community forest owners have very little capacity to make decisions on how they would like to manage their forests. The decision analyses tools such as the four decision tree models developed in this study will assist the community forest owners to make the best decisions in order to get the maximum return from the different forest management scenarios before them. The decision analyses tools developed in this study are the four decision tree models for community-based management of cutover forest in PNG. The objectives of Chapter 6

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are to develop scenario analysis and evaluation tools for assisting decision-making in CBFM and test these tools in two case study sites in PNG.

6.2 BACKGROUND – DECISION TREE MODELS

Decision trees are models for sequential decision problems under uncertainty (Middleton, 2001). Decision tree models describe graphically the decisions to be made, the events that may occur, and the outcomes associated with combinations of decisions and events. Probabilities are assigned to the events, and values are determined for each outcome. A major goal of decision analysis is to determine the best decisions. Two Excel spreadsheet add-ins called TreePlan and SensIT are the packages used to build tree diagrams and carryout sensitivity analyses. TreePlan and SensIT were developed by Professor Michael R. Middleton at the University of San Francisco and modified for use at Fuqua (Duke) by Professor James E. Smith (Middleton, 2001). This work is based on spreadsheet modelling and decision analysis (Ragsdale, 2007).

6.3 METHODOLOGY

In the previous Chapters (Chapter 1 and 4), some background information about the two case study sites have been given. The forest resource assessment and aboveground forest carbon data obtained from the study in Chapter 4 as well as other related costs and income data for timber harvesting and marketing described in Chapter 5 are used in the Decision Tree Models in Chapter 6. The methodologies for developing scenarios for CBFM which are guided by a PAR protocol have been described in Chapter 5. In Chapter 6 these scenarios are tested using the decision tree models developed in the study. Given the data requirements to test the decision analysis models developed in this study, the models are tested using data from the Yalu case study site only. The Yalu case study site had sufficient forest area to support a CBFM project, while the community forest area in Gabensis village was considered insufficient to support such a project.

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6.3.1 Building the Decision Tree

Decision tree models include such concepts as nodes, branches, terminal values, strategy, payoff distribution, certainty equivalents, and the rollback method. When using decision tree models for decision analysis, there are usually two main approaches. Analysis of a single-stage decision problem in which a single decision has to be made, while in multi-stage decision problems, most decisions lead to other decisions thus multi-stage decision problems can be modelled and analysed using a decision tree (Ragsdale, 2008). In this study, the multi-stage decision analysis approach has been used to develop four decision tree models for community forest management in PNG. To construct the tree diagrams and carry out sensitivity analysis, two Excel spreadsheet add-ins called TreePlan and SensIT have been used. To build the decision trees, TreePlan‘s dialog boxes are used to develop the structure. The branch name, branch cash flow, and branch probability (for an event) are entered in the cells above and below the left side of each branch. As you build the tree diagram, TreePlan enters formulas in the other cells.

6.3.2 Nodes and Branches

A decision tree has three kinds of nodes and two kinds of branches. A decision node is shown as a square and this is a point where a choice must be made. The branches extending from a decision node are decision branches and they represent one of the possible alternatives or course of action available at that point. An event node (chance node) is a point where uncertainty is resolved and is shown as a circle. The event set consists of the event branches extending from an event node and represents one of the possible events that may occur at the point. Each event in a decision tree is assigned a probability and the sum of probabilities for the events in a set must equal one. In general, decision nodes and branches represent the factors that can be controlled in a decision problem, while event nodes and branches represent factors that cannot be controlled. Decision nodes and event nodes are arranged in order of subjective chronology. For example, the position of an event node corresponds to the time when the decision maker learns the outcome of the event. The third kind of node is a terminal node, which represents the final result of a combination of decisions and

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events. Terminal nodes are the endpoints of a decision and shown at the end of a branch.

6.3.3 Terminal Values

In a decision tree each terminal node has an associated terminal value referred to as a payoff value. Each payoff value measures the result of a scenario or the sequence of decisions and events along the decision branches leading from the initial decision node to a specific terminal node. The payoff value is determined by assigning a cash flow value to each decision branch and event branch and then summing the cash flow values on the branches leading to a terminal node. Given the number of probability and financial estimates used as inputs to a decision tree, tornado and spider charts are generated to identify the inputs that have the greatest impact on the expected monetary value (EMV). Graphical outputs such as the tornado and spider charts can be generated from the SensIT for sensitivity analysis to summarise the impact on the decision tree‘s EMV of each input cell. In the decision tree models that have been developed in this study for community- based management of cutover forests in PNG, the key inputs into the models are actual costs and income (cash flows) associated with each scenario. The five scenarios for forest management that have been tested using these models include: community sawmill, local processing, medium-scale log export, carbon trade and no harvest.

6.3.4 Expected Monetary Values (EMV)

In decision analysis using decision trees, a decision maker uses a rollback method to determine the EMV for the decision he makes in each scenario. A rollback is a process that is used to determine the decision with the highest EMV by starting with each payoff and working from the right to left through the decision tree and computing the expected values for each node. This system is used to select the largest EMV. The EMV for a decision alternative is the average payoff for making a particular decision. In a decision tree, an EMV with the highest value is the decision alternative that is expected to return the highest monetary value for a particular scenario being considered and in this case, an EMV represents profit values. The EMV approach differs from more traditional accounting techniques such as NPV in that EMV estimation is for annual basis only, while income and expenditure are

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required over a period of time for the estimation of NPV. In the case of the current study, EMV calculation was derived from the analyses of income and costs along each decision and event branch in the decision tree.

To select the decision alternative with the largest EMV, the following equation was used (Ragsdale, 2007);

(6-1)

Where rij is the payoff for alternative i under the jth state of nature, pj is the probability of the jth state of nature.

6.3.5 Application of the Decision Tree Models

Decision tree models allow sensitivity data to be linked to a cash flow model and the cash flow model to be linked to the decision tree model (Figure 6-1). Decision alternatives and uncertain events are then analysed along the decision and event branches, which result in a payoff value for a particular decision alternative. The payoff value is further analysed using a rollback method by working from the right to the left of the decision tree to identify the highest EMV for a particular decision alternative.

The main features of the decision tree models developed in this study to test the community sawmill (Figure 6-2), local processing (Figure 6-3), medium-scale log export (Figure 6-6) and carbon trade (Figure 6-9) scenarios have the management arrangement and type of market as the decision alternatives, while the anticipated demand for various forest products and values and their estimated market prices are uncertain events. In the decision tree models, the cash flows associated with each scenario are either negative (costs) or positive (income) and all cash flows are in PNGK. To apply the models, the four forest management scenarios have been tested using data available from the case study site.

Local communities in PNG require immediate income to improve their livelihoods therefore, the aim of the analyses using the decision tree approach is to estimate annual profits (EMV) from the different scenarios being tested in the decision tree models. In terms of the equipment used under different scenarios (for example, Lucas

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Mill), depreciation costs are not considered in the analyses therefore, a Lucas Mill in this case, may be written-off or undergo major service after a 12 month operation.

Decision Analyses

Sensitivity

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Sensitivity Tornado Data Charts Decision Alternatives

Cash Flow Model EMV

Uncertain

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Figure 6-1: Basic framework for decision analyses

6.3.5.1 Scenario 1 – Community Sawmill The two decision alternatives for consideration are community sawmill or no harvesting (Figure 6-1). If a community or a decision-maker chooses community sawmill, the two uncertain events anticipated are whether the demand for sawn timber is high or low in the domestic market. These events are followed by consideration for three decision alternatives: to sell sawn timber to industry, central marketing unit (CMU) or nearby local market. After a decision has been made, the last uncertain events to consider are whether the sawn timbers produced from the sawmill are sold at high or low price. The analysis of the decision alternatives and the events along the decision tree are expected to return either a zero, negative or a positive EMV in profit terms during the operation of the community sawmill. Field interviews and discussions with the groups involved in small-scale sawmilling indicated that on average, 20m3 of sawn timber are produced from portable mills per annum and this is for 8 productive months of operation. Because communities do not work continuously in the operation of the mill for 12 months as they may be engaged

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in other village activities such as gardening and due to other factors, for example, bad weather and machinery breakdown, low annual production volumes are anticipated. The production and marketing requirements for the community sawmill scenario include costs for the start-up kit, operational costs, marketing costs and sawn timber prices (Appendix 6-1). The examples of calculation of EMVs (profits) estimated for the community sawmill scenario are as follow (Figure 6-2); EMV at 2nd node = (0.6 x -59,850) + (0.4 x -63,850) = PNGK-61,450. EMV at 3rd node = (0.6 x -61,450) + (0.4 x -76,350) = PNGK-67,410.

6.3.5.2 Scenario 2 - Local Processing The two first decision alternatives analysed under the local processing scenario using the decision tree are the central marketing unit (CMU) managed processing, and community managed processing (Figure 6-3). For a start, the decision maker encounters the first two uncertainties, high or low sawn timber demand (ST-Demand High, ST-Demand Low) and the second alternative decisions to be considered are sawn timber production for Export Market or Domestic Market. After a decision has been made, the last uncertainties (events) encountered are, selling sawn timber at high or low prices in both export and domestic markets. In the export market, prices for sawn timber are high in a certified market, while in a non-certified market, sawn timber prices are low. In the domestic market, sawn timber prices are either high or low. Under the local processing scenario, with increased capacity and use of mechanized equipment in a community managed processing the annual production volume is increased to 50m3 and under the local processing scenario managed by a CMU, annual production volume is further increased to 200m3. The production and marketing requirements for a community-based processing scenario covers costs for the starting capital, operation, transport, marketing, and sawn timber prices for domestic and certified overseas market (Appendix 6-2). The examples of the calculation of EMVs (profits) estimated under the local processing scenario are as follow (Figure 6-3); EMV at 1st event node = (0.6 x 199,800) + (0.4 x 19,800) = PNGK127,800. EMV at 2nd event node = (0.6 x 127,800) + (0.4 x -112,200) = PNGK31,800

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6.3.5.3 Scenario 3 – Medium-Scale Log Export CMU managed log export or community managed log export are the two first decision alternatives to consider under the medium-scale log export scenario (Figure 6-6). When a decision is made, the uncertain events that follow are whether the demand for log export in the overseas export market is high or low. After those uncertain events, the next two decision alternatives to consider are whether to export the logs to an Asian market (60% round logs from the forest industry sector in PNG are exported to the Asian market) or to other markets (for example, Australia and New Zealand). The last uncertain events to consider are whether the logs are exported for high or low log prices. The related costs and log prices for the international market (Asia and others) under the medium-scale log export scenario for a community have been estimated in the PNG context (Appendix 6-3). The example of calculation of EMVs (profit) estimated under the medium-scale log export scenario are as follow (Figure 6-6); EMV at 1st event node = (0.6 x 4,359,318) + (0.4 x 3,859,318) = PNGK4,159,318. EMV at 2nd event node = (0.6 x 4,159,318) + (0.4 x 3,659,318) = PNGK3,959,318.

6.3.5.4 Scenario 4 – Carbon Trade C trade and the emergence of REDD and REDD+ are now increasingly of interest to many communities in PNG. While the exact costs and the benefit sharing arrangements for C trade are still uncertain in PNG, these analyses have been carried out based on the assumption that a community involved in a forest C project anticipates to sell its C credits to either a voluntary or compliance market primarily at an estimated US$20 per tonne. The alternative decisions considered by a community are whether to manage their forests for C trade or do nothing (Figure 6-9). The two uncertain events that are encountered for the start are whether there is high or low demand for C credits as a commodity in the C market. Two decision alternatives are then considered, whether to sell the C credits to a compliance market or a voluntary market. The last uncertain events that follow are whether the community sells its C credits for a high or low price. The costs for a community forest C project including the field forest C assessment and accounting, administrative expenses and requirements for the trading of credits have been estimated based on the PNG community context. The analyses for a community forest C assessment and marketing

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have been based on some crude estimates to demonstrate an example of the likely costs and benefits for communities in a C trade scenario (Appendix 6-4).

The estimated benefits (EMV or profit) from C trade have been based on estimates of above ground forest C in the Yalu case study site. The average forest C in the study site was estimated at 150 t C ha-1 giving a total aboveground forest C of 329,670 t C. Based on the C emission rate from large-scale selective harvesting in PNG, which is estimated at 55% (Fox et al., 2010, Fox and Keenan, 2011, Fox et al., 2011a, Fox et al., 2011b), the total C emission in the study site was estimated at 181,319 t C.

However, considering a CO2 equivalent of 44/12, emission from the Yalu case study site was estimated at 665,500 t CO2. Therefore, the avoided emission to be sold by the community is 665,500 t CO2 and the average price for C assumed is US$20 per tonne (compliance market) and US$15 per tonne (voluntary market). In this analysis, the

CO2 emission was estimated from the past large-scale selective harvesting that took place in the study site and the estimated income from selling the avoided emission is for one year.

Below are the examples of calculation of EMVs (profits) under the C trade scenario (Figure 6-9). EMV at 1st event node = (0.6 x 79,781,735) + (0.4 x 71,130,235) = PNGK76,321,135. EMV at 2nd event node = (0.6 x76,321,135) + (0.4 x 67,669,635) = PNGK72,860,535.

6.3.6 Decision Tree Model Parameters

The basic model parameters that are input in the decision tree models are the cost and income (cash flow) associated with each scenario. For the community sawmill, local processing and medium-scale log export scenarios the main costs that are input in the models are for equipment, fuel, maintenance, wages and transport, while the income associated with all the scenarios are dependent on timber price and annual production (Table 6-1, 6-2, and 6-3 and Appendix 6-1, 6-2, and 6-3). The cost estimates used in this study are based on actual figures obtained from communities and NGOs who are involved in CBFM using portable sawmills in the region where this study was undertaken (Morobe, Madang and West New Britain provinces). For example, the costs of Lucas mill and chainsaw are actual costs obtained from supplies in PNG during the time of field data collection and interviews. The costs associated with

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wages are based on the PNG Minimum Wages Standards and direct wages paid to workers by NGOs and communities involved in CBFM. In the case of the C trade scenario, the costs and income that are input in the model are based on crude estimates in order to demonstrate the likely costs and benefits for a community C trade project. For example, C price in USD are estimates only, while forest C, C emission and avoided CO2 emission (Table 6-4 and Appendix 6-4) to be sold by the community have been calculated from the forest assessment carried out in the Yalu case study site (Chapter 4).

6.4 RESULTS

6.4.1 Decision Tree Model 1: Community Sawmill

Under the community sawmill scenario, the sensitivity data input to the decision tree includes variables such as costs for equipments, for example, Lucas mill and chainsaw; variable costs, operational costs and prices for sawn timber (Table 6-1).

Table 6-1: Sensitivity data - Community sawmill.

Variation Input Description (10%) Variable range

Abs var -var base case +var 5 Lucas mill (PNGK) 85,000 8,500 76,500 85,000 93,500 Chainsaw (PNGK) 6,000 600 5,400 6,000 6,600 Manager wages (PNGK/m3) 80 8 72 80 88 Fuel and oil (PNGK/m3) 120 12 108 120 132 Maintenance & repairs (PNGK/m3) 70 7 63 70 77 Transport local market (PNGK/m3) 60 6 54 60 66 Transport town market (PNGK/m3) 255 25.5 229.5 255 280.5 Timber price - community market (PNGK/m3) 500 50 450 500 550 Timber price - local market (PNGK/m3) 600 60 540 600 660 Timber price – industry (PNGK/m3) 750 75 675 750 825 Timber price – CMU (PNGK/m3) 1,000 100 900 1,000 1,100 Average sawn timber production (m3/annum) 20 2 18 20 22 No. of fortnights (per 8 productive months) 16 1.6 14.4 16 17.6

5 At the time of this study, PNGK1 was equivalent to AUD0.45

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Cash flow analysis shows that the main costs under the community sawmill scenario are the starting capital (K91,000) (costs of equipment including portable mill and chainsaw) and the costs for selling sawn timber to industry, CMU or the local market (Figure 6-2).

Input of cash flows in the decision tree model for the two decision alternatives (Community sawmill and No harvesting) resulted in the community sawmill returning an EMV of zero (Figure 6-2). Although the community has the option of selling their sawn timber to either industry, CMU or local market, such an enterprise with very limited capacity and capital is unlikely to generate enough income for the community and in many cases may make a loss in one year of operation.

Income expected are when sawn timber is sold for either a high or low price to industry, CMU or the local market (Figure 6-2). In a community project the local people also use some of the sawn timber produced for building homes or fuel wood at no costs to the project. Sensitivity analysis to identify those input variables that impacted the EMV showed that none of the variables had any impact on the EMV. This is because such an operation had made a loss, hence, returning a zero EMV under the community sawmill scenario. This particular analysis is not supported by tornado and spider charts.

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Decision Tree Model 1: Community Sawmill 0.6 Payoff High Price (PNGK) -64850 Sell ST-Industry 15000 -64850

-8850 -66050 0.4 Low Price -67850 12000 -67850

0.6 High Price 0.6 -59850 ST Demand High Sell ST-CMU 20000 -59850 2 20000 -61450 -8850 -61450 0.4 Low Price -63850 16000 -63850

0.6 High Price -63950 Sell ST-Local Market 12000 -63950

Comm.Sawmill -4950 -64750 0.4 Low Price -91000 -67410 -65950 10000 -65950

0.6 High Price -75950 Sell ST-Local Comm. 10000 -75950

-4950 -76350 0.4 2 0.4 Low Price 0 ST Demand Low -76950 1 9000 -76950 10000 -76350

Comm. Use -81000 0 -81000

No Harvest 0 0 0

Figure 6-2: Main Features of decision tree model 1 - Community sawmill

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6.4.2 Decision Tree Model 2: Local Processing

The sensitivity data input to the decision tree under the local processing scenario includes equipment costs, operational costs and prices for sawn timber (Table 6-2). An absolute variable in this type of analysis is the input variable (for example, cost of a Lucas mill) multiplied by the range in percentage as set (for example, +/-10%).

Table 6-2: Sensitivity data – Local processing

Variation Input Description (10%) Variable range

Abs var -var base case +var Lucas mill (PNGK) 85,000 8,500 76,500 85,000 93,500 Chainsaw (PNGK) 6,000 600 5,400 6,000 6,600 Wages manager (PNGK/m3) 80 8 72 80 88 Wages mill operator (PNGK/m3) 80 8 72 80 88 Fuels & oil -CM (PNGK/m3) 126 12.6 113.4 126 138.6 Maintenance & repairs - CM (PNGK/m3) 73.5 7.35 66.15 73.5 80.85 4WD truck – CMU (PNGK) 260,000 26,000 234,000 260,000 286,000 4WD tractor – CMU (PNGK) 162,000 16,200 145,800 162,000 178,200 Planner / Moulder – CMU (PNGK) 100,000 10,000 90,000 100,000 110,000 Breakdown saw – CMU (PNGK) 50,000 5,000 45,000 50,000 55,000 Cross-cut saw – CMU (PNGK) 50,000 5,000 45,000 50,000 55,000 Fuel & oil - CMU (PNGK/m3) 132 13.2 118.8 132 145.2 Maintenance & repairs - CMU (PNGK/m3) 77 7.7 69.3 77 84.7 Transport local market (PNGK/m3) 60 6 54 60 66 Transport wharf/export (PNGK/m3) 255 25.5 229.5 255 280.5 Certification requirements (PNGK/m3) 50 5 45 50 55 Fumigation (PNGK) 720 72 648 720 792 Wharf handling (PNGK) 950 95 855 950 1045 Customs clearance (PNGK) 330 33 297 330 363 Sawn timber price -domestic market (PNGK/m3) 700 70 630 700 770 Max timber price -certified market (PNGK/m3) 2,400 240 2,160 2,400 2,640 Max timber price - noncert. Market (PNGK/m3) 1,500 150 1,350 1,500 1,650 Sawn timber production - CM (m3/year) 50 5 45 50 55 Sawn timber production - CMU (m3/year) 200 20 180 200 220 No. of fortnights (per 8 productive months) 16 1.6 14.4 16 17.6

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In the local processing scenario, input of cash flow of the two decision alternatives (CMU managed processing and Community managed processing) resulted in the CMU managed processing returning an EMV of PNGK 31,800 in profit terms in one year of operation (Figure 6-3). Analyses showed that when local processing is managed by the community itself, the estimated EMV is PNGK-89,494 therefore, resulting in a loss in the first year.

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Decision Tree Model 2: Local Processing 0.6 Payoff Cert.Market HP 199800 Export Market 480000 199800

-69200 127800 0.4 Non-Cert.Market LP 0.6 19800 ST-Demand High 300000 19800 1 480000 127800 0.6 ST High Price -124450 Domestic Market 140000 -124450

-53450 -132450 0.4 ST Low Price -144450 CMU Mng Process 120000 -144450

-691000 31800 0.6 Cert.Market HP -40200 Export Market 480000 -40200

-69200.00 -112200 0.4 Non-Cert.Market LP 0.4 -220200 ST-Demand Low 300000 -220200 1 240000 -112200 0.6 ST High Price -364450 Domestic Market 140000 -364450

-53450.00 -372450 0.4 ST Low Price -384450 120000 -384450 1 31800 0.6 Cert.Market HP -47493.8 Export Market 120000 -47493.8

-24494 -65493.8 0.4 Non-Cert.Market LP 0.6 -92493.8 ST-Demand High 75000 -92493.8 1 120000 -65493.8 0.6 ST High Price -120494 Domestic Market 35000 -120494

-12494 -122494 0.4 ST Low Price -125494 Comm.Mng Process 30000 -125494

-263000 -89493.8 0.6 Cert.Market HP -107494 Export Market 120000 -107494

-24493.75 -125494 0.4 Non-Cert.Market LP 0.4 -152494 ST-Demand Low 75000 -152494 1 60000 -125494 0.6 ST High Price -180494 Domestic Market 35000 -180494

-12493.75 -182494 0.4 ST Low Price -185494 30000 -185494

Figure 6-3: Main features of decision tree model 2 – Local processing

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Sensitivity analysis shows that the annual sawn timber production under a CMU managed processing has the largest impact on the EMV‘s range followed by the maximum sawn timber price in the overseas certified market at +/-10% of the EMV (Figure 6-4). The input variable in the decision tree with the smallest impact on the EMV is the customs clearance of sawn timber before export. The input variable with either the smallest or no impact on the EMV is shown at the bottom of the Tornado chart (Figure 6-4).

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Tornado chart showing effect on scenario income of +/-10% input variation

Sawn timber production - CMU (m3/year) 180 220

Max timber price -certified market (K/m3) 2160 2640

4WD truck - CMU (PNGK) 286000 234000

4WD tractor - CMU (PNGK) 178200 145800

Max timber price - noncert. Market (K/m3) 1350 1650

Planer / Moulder - CMU (PNGK) 110000 90000

Min timber price -certified market (K/m3) 1080 1320

Lucas mill (PNGK) 93500 76500

Breakdown saw - CMU (PNGK) 55000 45000

Wages casual worker (K/m3) 88 72

Cross-cut saw - CMU (PNGK) 22000 18000

Transport wharf/export (K/m3) 280.5 229.5

Sawn timber production - CM (m3/year) 55 45

Chainsaw (PNGK) 6600 5400

Fuels & oil - CMU (K/m3) 145.2 118.8

Certification requirements (K/m3) 55 45

-60 -50 -40 -30 -20 -10 0 10 20 30 40 50 60 70 80 90 100110120 Scenario income value (PNGK)

Figure 6-4: EMV sensitivity at +/-10% of the base case – Local processing

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Cash flow (input variables) in the decision tree that impact the EMV represented by the spider chart (Figure 6-5) shows that the annual sawn timber production by the CMU and the maximum sawn timber price in the overseas certified market have the largest impact on the EMV at +/-10% of the base case. At the inflection point (100% of base case and about PNGK30,000 expected EMV) the annual sawn timber production in a CMU managed local processing is expected to increase by 10%.

Spider chart for Local timber processing scenario income with +/-10% variation 120000 110000 100000 Sawn timber production - CMU 90000 (m3/year) 80000 Max timber price -certified 70000 market (K/m3) 60000 4WD truck - CMU (PNGK) 50000

40000 4WD tractor - CMU (PNGK) 10% Base Case) Base 10%

- 30000 20000 Max timber price - noncert. 10000 Market (K/m3) 0 Planner / Moulder - CMU

-10000 (PNGK) EMV (PNGK +/ (PNGK EMV -20000 Min timber price -certified -30000 market (K/m3) -40000 Lucas mill (PNGK) -50000 -60000 Breakdown saw - CMU (PNGK) 86% 90% 94% 98% 102% 106% 110% Input Value as % of Base Case

Figure 6-5: Impact of input variables on the EMV at +/-10% – Local processing

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6.4.3 Decision Tree Model 3: Log Export

The sensitivity data under the medium-scale log export that are linked to the cash flow model are all the costs for equipments, operations, roading, transport, marketing and log prices for overseas market (Table 6-3).

Table 6-3: Sensitivity data – Medium-scale log export

Variation Input Description (10%) Variable range base Abs var -var case +var

Chainsaw (PNGK) 6000 600 5400 6000 6600 Logging truck - CM (PNGK) 120000 12000 108000 120000 132000 4WD tractor - CM (PNGK) 162000 16200 145800 162000 178200 Front-end loader -CM (PNGK) 162000 16200 145800 162000 178200 Wages Manager (PNGK/fortnight) 250 25 225 250 275 Wages - Casual (PNGK) 175 17.5 157.5 175 192.5 Fuel & oil - CM (PNGK/m3) 144 14.4 129.6 144 158.4 Maintenance, repairs, spare parts - CM (PNG/m3) 84 8.4 75.6 84 92.4 Logging truck - CMU (PNGK) 150000 15000 135000 150000 165000 Dozer D6 - CMU (PNGK) 200000 20000 180000 200000 220000 Skidder D7 - CMU (PNGK) 240000 24000 216000 240000 264000 Front-end loader -CMU (PNGK) 240000 24000 216000 240000 264000 Fuel & oil - CMU (PNGK/m3) 180 18 162 180 198 Maintenance, repairs, spare parts - CMU (PNG/m3) 105 10.5 94.5 105 115.5 Transport export (PNGK/m3) 255 25.5 229.5 255 280.5 Roading cost - CM (PNGK/Km) 6000 600 5400 6000 6600 Roading cost - CMU (PNGK/Km) 40000 4000 36000 40000 44000 Distance to wharf - CM (Km) 15 1.5 13.5 15 16.5 Distance to wharf - CMU (Km) 10 1 9 10 11 Wharf handling fees (PNGK) 950 95 855 950 1045 Customs clearance (PNGK) 330 33 297 330 363 Log export tax (PNGK/m3) 10 1 9 10 11 Government registration (PNGK) 250 25 225 250 275 Sawn timber price - Asia market (PNGK/m3) 600 60 540 600 660 Sawn timber price - other market (PNGK/m3) 450 45 405 450 495 Annual log production - CM (m3) 2500 250 2250 2500 2750 Annual log production - CMU (m3) 5000 500 4500 5000 5500 No. of fortnights 16 1.6 14.4 16 17.6

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In a medium-scale log export managed by a CMU, the data input into the decision tree model returns an EMV of PNGK 3,959,317 in profit terms during 8 productive months of operation (Figure 6-6). If the community manages the log export itself, it is likely to make an estimated profit of PNGK 1,987,692. The main cost variables input in the decision tree under the log export scenario are associated with the starting capital and exporting of logs to the overseas market. The export of logs in an operation managed by a CMU or a community group is to either an Asian market or other markets.

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Decision Tree Model 3: Medium-scale Log Export 0.6 Payoff Log Price High (PNGK) 4359317 Asia Market 3000000 4359317

-798683 4159317 0.4 Log Price Low 0.6 3859317 Log Demand High 2500000 3859317 1 3000000 4159317 0.6 Log Price High 3609317 Other Market 2250000 3609317

-798683 3509317 0.4 Log Price Low 3359317 CMU Mng Log Export 2000000 3359317

-842000 3959317 0.6 Log Price High 3859317 Asia Market 3000000 3859317

-798683 3659317 0.4 Log Price Low 0.4 3359317 Log Demand Low 2500000 3359317 1 2500000 3659317 0.6 Log Price High 3109317 Other Market 2250000 3109317

-798683 3009317 0.4 Log Price Low 2859317 2000000 2859317 1 3959317 0.6 Log Price High 2187692 Asia Market 1500000 2187692

-338308 2087692 0.4 Log Price Low 0.6 1937692 Log Demand High 1250000 1937692 1 1500000 2087692 0.6 Log Price High 1812692 Other Market 1125000 1812692

-338308 1762692 0.4 Log Price Low 1687692 Comm.Mng Log Export 1000000 1687692

-474000 1987692 0.6 Log Price High 1937692 Asia Market 1500000 1937692

-338308 1837692 0.4 Log Price Low 0.4 1687692 Log Demand Low 1250000 1687692 1 1250000 1837692 0.6 Log Price High 1562692 Other Market 1125000 1562692

-338308 1512692 0.4 Log Price Low 1437692 1000000 1437692

Figure 6-6: Main features of decision tree model 3 – Medium-scale log export

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Sensitivity analysis represented by the Tornado chart shows that the annual log production by a central marketing unit has the biggest impact on the EMV in the medium-scale scale log export scenario. The second input variable in the decision tree that had the biggest impact on the EMV is the log price in the Asian market followed by the costs of transport associated with the logging operations (Figure 6-7). The input variable that has the smallest impact on the EMV is the distance from the logging operation site to the wharf for transportation of logs for overseas export.

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Annual log production - CMU (m3) 4500 5500

Log price - Asia market (PNGK/m3) 540 660

Transport export (PNGK/m3) 280.5 229.5

Fuel & oil - CMU (PNGK/m3) 198 162

Maintenance, repairs, spare parts - CMU (PNG/m3) 115.5 94.5

Roading cost - CMU (PNGK/Km) 44000 36000

Distance to wharf - CMU (Km) 11 9

Log export tax (PNGK/m3) 11 9

No. of fortnights 17.6 14.4

Wharf handling fees (PNGK) 1045 855

Customs clearance (PNGK) 363 297

Government registration (PNGK) 275 225

Chainsaw (PNGK) 54006600

Logging truck - CM (PNGK) 108000132000

4WD tractor - CM (PNGK) 145800178200

Front-end loader -CM (PNGK) 145800178200

Wages Manager (PNGK/fortnight) 225275

Wages - Casual (PNGK) 157.5192.5

Fuel & oil - CM (PNGK/m3) 129.6158.4

Maintenance, repairs, spare parts - CM (PNG/m3) 75.692.4

Logging truck - CMU (PNGK) 135000165000

Dozer D6 - CMU (PNGK) 180000220000

Skidder D7 - CMU (PNGK) 216000264000

Front-end loader -CMU (PNGK) 216000264000

Roading cost - CM (PNGK/Km) 54006600

Distance to wharf - CM (Km) 13.516.5

Log price - other market (PNGK/m3) 405495

Annual log production - CM (m3) 22502750

33000003400000350000036000003700000380000039000004000000410000042000004300000440000045000004600000

PNGK (+/- 10% Base case)

Figure 6-7: EMV sensitivity at +/-10% of the base case – Log export

159

The spider chart represents the same information as the tornado chart but with additional details (Figure 6-8). The inflection point where the associated lines (representing each input variable) meet in the chart is when annual log production in the medium-scale operation by the CMU is increased by 10%.

4600000

Annual log production - CMU (m3) 4500000 Log price - Asia market (PNGK/m3) Transport export (PNGK/m3) 4400000 Fuel & oil - CMU (PNGK/m3) Maintenance, repairs, spare parts - CMU (PNG/m3) 4300000 Roading cost - CMU (PNGK/Km) Distance to wharf - CMU (Km) 4200000 Log export tax (PNGK/m3) No. of fortnights 4100000 Wharf handling fees (PNGK) Customs clearance (PNGK)

4000000 Government registration (PNGK)

10% Base case) 10%Base Chainsaw (PNGK) - Logging truck - CM (PNGK) 3900000 4WD tractor - CM (PNGK) Front-end loader -CM (PNGK)

EMV (PNGK +/ EMV(PNGK 3800000 Wages Manager (PNGK/fortnight) Wages - Casual (PNGK) 3700000 Fuel & oil - CM (PNGK/m3) Maintenance, repairs, spare parts - CM (PNG/m3) 3600000 Logging truck - CMU (PNGK) Dozer D6 - CMU (PNGK) 3500000 Skidder D7 - CMU (PNGK) Front-end loader -CMU (PNGK) 3400000 Roading cost - CM (PNGK/Km) Distance to wharf - CM (Km) 3300000 Log price - other market (PNGK/m3) 86.0% 88.0% 90.0% 92.0% 94.0% 96.0% 98.0% 100.0% 102.0% 104.0% 106.0% 108.0% 110.0% 112.0% Annual log production - CM (m3) Input Value as % of Base Case

Figure 6-8: Impact of input variables on the EMV at +/-10% - Log export

6.4.4 Decision Tree Model 4: Carbon Trade

Sensitivity data (Table 6-4) for the C trade scenario are based on a crude assumption that communities in PNG will engage in selling C credits from their forests to either a compliance or voluntary market. The cost assumption covers areas such as landowner issues and social mapping, equipments for forest C assessment, logistics and transport, verification and validation, and selling of credits in the international C market.

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Table 6-4: Sensitivity data – Carbon trade

Variation Input Description (10%) Variable range

Abs var -var base case +var Landowner issues/social mapping (PNGK) 30000 3000 27000 30000 33000 Measuring tapes (K/tape) 35 3.5 31.5 35 38.5 Diameter tapes (K/tape) 70 7 63 70 77 Suunto clinnometer (K/clinnometer) 85 8.5 76.5 85 93.5 Compass (K/compass) 65 6.5 58.5 65 71.5 GIS/Mapping (PNGK) 20000 2000 18000 20000 22000 Logistics/transport (PNGK) 10000 1000 9000 10000 11000 Wages team leader (K/fortnight) 250 25 225 250 275 Inventory field staff (K/fortnight) 175 17.5 157.5 175 192.5 Consultancy (PNGK) 10000 1000 9000 10000 11000 Other paper work (PNGK) 2000 200 1800 2000 2200 Verification/Validation (PNGK) 20000 2000 18000 20000 22000 Marketing/Trading (PNGK) 10000 1000 9000 10000 11000 Administration (PNGK) 10000 1000 9000 10000 11000 Carbon price - Compliance ($US/tC) 20 2 18 20 22 Carbon price - Voluntary ($US/tC) 15 1.5 13.5 15 16.5 Average aboveground forest carbon (t C/ha) 150 15 135 150 165

Rate of CO2 Emission (%) 55% 0.055 0.495 0.55 0.605 Average community forest area (ha) 2200 220 1980 2200 2420 No. of fortnights (8 productive months) 16 1.6 14.4 16 17.6

Application of the decision tree model shows that if a community decides to manage its forests for C trade, the EMV anticipated from analysis of the decisions and events along the decision tree is estimated at PNGK72,860,535 over a one year period (Figure 6-9). The cost input into the decision tree model includes the estimated starting capital (PNGK60,765) and the costs of trading C credits in the overseas market (PNGK17,500).

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Decision Tree Model 4: Carbon Trade 0.6 Payoff High Price (PNGK) 79781735 Compliance Market 39930000 79781735

-17500 76321135 0.4 Low Price 0.6 71130235 High Demand 31278500 71130235 1 39930000 76321135 0.6 High Price 69799235 Voluntary Market 29947500 69799235

-17500 67203785 0.4 Low Price 63310610 Carbon Trade 23458875 63310610

-60765 72860535 0.6 High Price 71130235 Compliance Market 39930000 71130235

-17500 67669635 0.4 Low Price 0.4 62478735 Low Demand 31278500 62478735 1 1 31278500 67669635 0.6 72860535 High Price 61147735 Voluntary Market 29947500 61147735

-17500 58552285 0.4 Low Price 54659110 23458875 54659110

Do Nothing 0 0 0

Figure 6-9: Main features of decision tree model 4 – Carbon trade.

The tornado chart shows that the average aboveground forest C, average community forest area, C price in the compliance market, and the rate of CO2 equivalent emission had equal impacts on the EMV under the C trade scenario (Figure 6-10). The other input variables in the decision tree had either small or no impact on the EMV. Results from the sensitivity analysis are as expected because most of the costs and income (cash flow) associated with the community C trade scenario are based on crude data from communities in PNG.

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Average aboveground forest carbon (t C/ha) 135 165

Average community forest area (ha) 1980 2420

Carbon price - Compliance ($US/tC) 18 22

Rate of CO2 Emission (%) 50% 61%

Landowner issues/social mapping (PNGK) 33000 27000

GIS/Mapping (PNGK) 22000 18000

Logistics/transport (PNGK) 11000 9000

Verification/Validation (PNGK) 22000 18000

No. of fortnights (8 productive months) 17.6 14.4

Inventory field staff (K/fortnight) 192.5 157.5

Consultancy (PNGK) 11000 9000

Marketing/Trading (PNGK) 11000 9000

Administration (PNGK) 11000 9000

Wages team leader (K/fortnight) 275 225

Other paper work (PNGK) 2200 1800

Suunto clinnometer (K/clinnometer) 93.5 76.5

Diameter tapes (K/tape) 77 63

Compass (K/compass) 71.5 58.5

Measuring tapes (K/tape) 38.5 31.5

Carbon price - Voluntary ($US/tC) 13.516.5

47000004800000490000050000005100000520000053000005400000550000056000005700000580000059000006000000

EMV (PNGK +/- 10% Base Case)

Figure 6-10: EMV sensitivity at +/-10% of base case – Carbon trade

The spider chart shows that C price in the compliance market, available forest C, and average community forest area the variables that have the direct impact on the EMV (Figure 6-11). At the inflection point these three input variables are expected to increase by 10%.

163

6000000

5900000

5800000 Average aboveground forest carbon (t C/ha) Average community forest area (ha) 5700000 Carbon price - Compliance ($US/tC) Rate of CO2 Emission (%) 5600000 Landowner issues/social mapping (PNGK)

5500000 GIS/Mapping (PNGK) Logistics/transport (PNGK)

5400000 Verification/Validation (PNGK)

10% Base Case) Base 10% No. of fortnights (8 productive months) - 5300000 Inventory field staff (K/fortnight) Consultancy (PNGK) 5200000 Marketing/Trading (PNGK) Administration (PNGK)

5100000 Wages team leader (K/fortnight) EMV (PNGK +/ (PNGK EMV Other paper work (PNGK) 5000000 Suunto clinnometer (K/clinnometer) Diameter tapes (K/tape) 4900000 Compass (K/compass) Measuring tapes (K/tape) 4800000 Carbon price - Voluntary ($US/tC)

4700000 88.0% 90.0% 92.0% 94.0% 96.0% 98.0% 100.0% 102.0% 104.0% 106.0% 108.0% 110.0% 112.0% Input Value as % of Base Case

Figure 6-11: Impact of input variables on the EMV at +/-10% - Carbon trade.

6.5 DISCUSSION

Forest management requires decision-making hence, management tools are required. Application of decision analyses systems in forest management worldwide has not been common, while decision support systems have been widely applied in natural resource management including the forestry sector. The decision analyses tools developed in this chapter are new techniques in tropical forest management. The major goal of this type of technique is to assist the decision- maker determine the best decision when presented with different alternatives and future uncertainties (Middleton, 2001). This approach is an analytical technique that facilitates a structured approach to decision-making.

6.5.1 Silvicultural Management of Rainforests

The decision tree models developed in Chapter 6 are appropriate tools that can assist the silvicultural management of rainforests. However, there have been a few examples of long-term silvicultural management of native tropical rainforests. For example, the Malayan Uniform System (MUS) applied in parts of Malaysia for the management of

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Dipterocarp forest dominated by a single species (about 50%) such as Virola, Carapa and Irianthera (Dawkins and Philip, 1998, Mckinty, 1999). The MUS involves a single felling and post-felling treatment. For example, for a shade-tolerant species such as Dryobalanops aromatic, its advance regeneration could stand the sudden change in light conditions following heavy felling. The key to the success of MUS is the presence of seedling regeneration of the economic species on the ground at the time of felling. In 1989, the Indonesian government regulations required natural forests to be managed under one of three systems (Dawkins and Philip, 1998); the Indonesian selective felling, which involves multiple use and benefits of the forest, soil and water conservation, sustainable timber production, conservation of nature and economics of harvesting. The second system involved a clear-cutting practice with natural regeneration, a natural forest stand is managed in a longer cutting cycle and natural regeneration is encouraged. The third system is clear-cutting with planting and this involves natural advance growth or artificial enrichment. In this system, 25 candidate trees ha-1 with DBH > 20cm are selected to be felled in each cutting cycle of 35 years. In PNG, FORCERT has promoted FSC guidelines for sustainable management of native forests in the communities. Basically, the silvicultural system involves the application of RIL by selective harvesting of 1-2 trees ha-1 (Rogers, 2010). Logging gaps created from operations of portable-sawmill promoted abundant regeneration of primary and secondary species. Communities involved in small-scale silvicultural management of their forests in West New Britain and Madang provinces in PNG were able to share the financial benefits of exporting their sawn timber to the overseas FSC certified markets.

6.5.2 Testing the Decision Tree Models

When the decision tree approach was tested in the case study site (Yalu community forest) results showed that in a community sawmill scenario, because of limited capacity, high starting capital, lack of mechanised equipment, and low annual sawn timber production, such an operation is likely to make a loss in one year of operation. However, whether a high, low or no EMV is returned in such an operation, is dependent on costs and income (cash flow) associated with this scenario. The application of this model using data from the case study site showed that when the two decision alternatives (CMU and community managed processing) were

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considered in a local processing scenario, the EMV returned for the CMU managed processing was higher (PNGK 31,800) in profit terms, while the community managed processing returned an EMV in the form of a loss of PNGK-89,494 during the first year (Figure 6-3). Sensitivity analysis of the EMV showed that the annual sawn timber production is the model input that has the largest impact on the EMV followed by the sawn timber price in the certified market at +/-10% (Figure). In this case the profit is dependent on sawn timber prices for exports to certified and non-certified overseas market. The price differential here is justified as sensitivity analyses provide evidence that prices in the certified market also had a high impact on the profit (EMV). The application of the model is flexible in that depending on the cash flow associated with each decision alternative, the EMV is determined by the related costs and income input into the model. For example, in a CMU managed local processing facility with an increased capacity, addition of mechanised equipment, increased sawn timber production, and high sawn timber price in the certified market, is expected to make a reasonable profit in one year. The aim of the EMV analysis is to estimate profits for only one year and this is dependent on the cash flow (costs and income) associated with each scenario. Although under the community sawmill scenario and if the option of the local processing being managed by the community is considered (Figure 6-2, 6- 3), a loss is made but this loss is only for one year of operation. One limitation of the EMV analysis is that it assigns all the costs of purchasing equipment to one year, rather than spreading the costs over a longer production period of several years or more. The loss is made in the first year of operation because the costs of equipment are high relative to production sales. This does not mean that over a longer period community sawmilling cannot be viable. There is evidence in community sawmilling in PNG that such operations can be viable if the equipment costs are spread out over several years (FORCERT, 2010, Scheyvens, 2009). This study considered the EMV approach to estimate annual profits and income and overlooked other analyses techniques such as NPV and internal rate of return (IRR) because in PNG communities there is a lack of income and local people are in desperate need for immediate financial benefits to pay for their basic needs to improve their livelihoods. Therefore, the EMV analysis was considered appropriate in the case of the study in Chapter 6 because communities can anticipate monetary benefits sooner than later.

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Analyses of input variables in the decision tree model under the medium-scale log export scenario that is managed by a CMU returned a positive EMV (PNGK3,959,317) in profit terms. Sensitivity analyses showed that the input variables that had the largest impact on the EMV were annual log production and log price in the overseas Asian market. Results were similar when the log export was managed by the community itself but with a lower EMV of PNGK1,987,692. Decision analyses along the decision tree under the C trade scenario resulted in an estimated EMV of PNGK72,860,535. With crude data applied in this scenario and assumption of most of the cash flow input in the model, sensitivity analyses showed that the C price in the compliance market and the rate of CO2 equivalent emission are two of the four main input variables that had the largest impact on the EMV. Estimates of the EMV under the C trade scenario are based on 150 t C ha-1 in the Yalu case study site and 55% rate of emission from selective timber harvesting in PNG (Fox et al., 2010, Fox and Keenan, 2011, Fox et al., 2011a, Fox et al., 2011b) and considering a CO2 equivalent of 44/12. This particular analysis has been undertaken to demonstrate to communities, the decision tree approach in considering options such as C trade in the management of cutover forests in PNG. Because of insufficient data available to test the C trade scenario and most of the input variables (costs and income) in the decision tree model have been based on assumptions, the outputs from the analyses are considered weak and do not provide a strong basis for the anticipated income from selling C credits by communities in PNG. The profit and income estimated under the C trade scenario are based on crude data and assumptions. The issue of timing of costs and benefits are not considered in this particular analysis, however, given the situation that if the community chose to participate in a REDD+ project, the income anticipated is assumed to be paid upfront in one lump sum in the first year. While this is unlikely in practice, it is consistent with the approach used for financial analysis of other management options and the best basis for comparison. As C credits are produced over the accounting period of the project, usually about 30 years, hence, payment may be conditional on periodic verification of performance. Considering these uncertainties, the analyses undertaken under the C trade scenario demonstrates the likely costs and benefits for a C project if a community participates in a REDD+ project.

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A comparison of the starting capital and estimated annual EMV (profit) is made between the scenarios tested using the decision tree (Table 6-5). Test results showed that the community sawmill was unable to make any profit in a community-based operation during the first year of operation. This is because the community lacked capacity, management skills and could not bear the operational costs therefore, no profit was made in such an operation. In a community managed local processing, an annual loss (PNGK-89,494) is anticipated, while a CMU managed local processing makes a profit in one year (PNGK31, 800) of operations. Analyses outputs from the decision tree indicated that both the CMU and community managed medium-scale log export projects make annual profits estimated at PNGK4 million and PNGK2 million respectively. C trade scenario is the option that is expected to generate huge profits if the community decides to manage its forests for C benefits. As mentioned earlier, the analyses outputs for the C trade scenario are uncertain because of the assumptions made in the costs and income that were input in the decision tree model.

Table 6-5: Comparison of the four management scenarios

Starting Annual Scenarios Capital EMV/Profit/Loss (PNGK) (PNGK) Community Sawmill 91,000 0 Local Processing CMU Managed 691,000 31,800 a Community Managed 263,000 -89,494b Log Export CMU Managed 842,000 3,959,317 Community Managed 474,000 1,987,692 Carbon Trade 60,765c 72,860,535

a positive figure represent estimated annual profit. b denotes estimated annual loss. c starting capital for carbon trade scenario based on crude estimates .

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6.6 CONCLUSIONS

The objectives of Chapter 6 had been to develop scenario analysis and evaluation tools for assisting decision-making in CBFM and test these tools in two case study sites in PNG. Generally the objectives of this chapter have been achieved. There are four decision analysis models developed in this chapter. These are presented in diagrammatic form, which is commonly known as decision trees or decision tree models. The models represent the four management scenarios for CBFM. These are community sawmill, local processing, log export and carbon trade.

Test of the decision tree models with data available from the case study site provided evidence that depending on the costs and income associated with each scenario, the EMV (whether it is a profit or loss) is generally dependent on the variables such as cash flow that are input in the model. In this case the price differential (for example sawn timber price in a domestic market versus prices in the overseas certified market) is a key factor that should be taken into account in the sensitivity analyses.

The study in Chapter 6 did not consider the combination of scenarios to test the decision analyses models, for example, combining community sawmilling and REDD+ as one scenario, but recommends that future analyses should investigate this. In this case, multiple use forest, for example, community sawmilling and REDD+ project should be considered with the objective of increasing income in CBFM. Currently, many community forests in PNG are potentially subject to further industrial logging or the impact of SPBALs. This study does not address these issues in detail but recommends that community forests that are potentially subject to future industrial-scale harvesting should be considered for REDD demonstration projects.

The tools developed in this study are appropriate for community-based forest managed in PNG and can be applied in tropical forest management elsewhere in the region.

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CHAPTER 7: SCENARIO EVALUATION FRAMEWORK FOR COMMUNITY-BASED FOREST MANAGEMENT

7.1 INTRODUCTION

More than 80% of PNG‘s population depends on forests in some ways for their survival. As PNG‘s population increases at a rate of over 3% per annum (www.postcourier.com.pg/), increasing pressure are put on the environment including the forest resources of the country. Currently, accessible primary forests are being exhausted for commercial exploitation but the future management of areas left after harvesting is not the agenda of governments, timber industry and communities. Areas left after harvesting is currently estimated to be 10% of the total forest area in PNG (PNGFA, 2007). However, because of the cultural ties between rural communities in PNG and their environments, areas left after harvesting, which are considered as secondary or cutover forests are likely to be taken over by the communities in the future. However, communities also face a big challenge because the traditional rights to their land including cutover forests are being limited by a land lease concept called special purpose business and agricultural leases (SPBALs) (Www.postcourier.com.pg/) implemented by the PNG government. This land lease concept has received a lot of criticism from local groups and international bodies such as the Association of Tropical Biology and Conservation. When local communities and stakeholders are faced with challenges on how they would like to manage their forest resources, there is a need to deliver to them appropriate tools for assisting decision-making in CBFM. In developed countries forestry frameworks have long been adopted. For example, Boyle et al. (1997) developed a forestry framework for the Oregon State Department of Forestry for evaluation of cumulative effects of forestry practices on the environment. In a detailed framework for forest management, the systems that should be taken into account include measurement, monitoring, and decision-making (Boyle et al., 1997).

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The objective of Chapter 7 is to develop a framework for community-based management of cutover forests in PNG.

7.2 BACKGROUND

The background in Chapter 7 covers the MSE approach; an overview of forest planning in PNG; small-scale harvesting; and requirements for certification in PNG. A review of forest planning in the country shows that the PNGFA has got adequate systems in place but these systems have been ineffective in terms of implementation. In the 1980s, small-scale harvesting by communities in PNG started as an alternative to large-scale conventional harvesting. While this industry has grown, particularly at community level, there have been various problems associated with their operations, for example, the low capacity of communities and the high starting capital requirements. In Subsection 7.2.1, some background of the MSE framework (Sainsbury et al., 2000) is provided. The MSE approach has been originally developed and widely applied in fisheries and marine management (SEQHWP, 2007) and this approach forms the basis of the development of an integrated conceptual framework for assisting decision-making in CBFM in this chapter. A framework such as the MSE seeks, to provide the decision maker with the information on which to base a rational decision, given their own objectives and attitudes to risk (Sainsbury et al., 2000, Smith et al., 1999).

7.2.1 The Management Strategy Evaluation (MSE) approach

MSE is a simulation technique developed more than 20 years ago to consider the implication of alternative management strategies for the robust management of natural resources (Punt and Smith, 1999, Sainsbury et al., 2000). MSE is often used to assess the effects of a range of management strategies and present the results in a way, which lays bare the tradeoffs in performance across a range of management objectives. This approach anticipates to provide the decision maker with the information on which to base a rational decision, given their own objectives, preferences, and attitudes to risks (Sainsbury et al., 2000, Smith et al., 1999). The MSE method has been used by organizations such as the International Whaling Commission (IWC) and Commission for the Conservation of Antarctic Marine Living

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Resources (CCAMLR) (de la Mare and Williams, 1997, Kirkwood, 1993). It has been adopted successfully as a standard management tool for the fishery sector in a number of countries including South Africa, Europe, New Zealand, and Australia (Punt and Smith, 1999). The MSE approach has not been applied in forest management before although most of its application has been common in other natural resource management sectors such as the fisheries and watersheds. As the need for multi-disciplinary approaches to forest management are increasing, there is a need to investigate the utility of systems such as the MSE method.

The indicator concept is common in environmental and fishery management for an integrated approach (Rochet et al., 2007). The concept works in that all environmental variables cannot be monitored in a complex natural ecosystem therefore, indicators summarise the information required. Indicators are usually incorporated in broader approaches or frameworks (FAO, 1999), however, working operational frameworks for their use in decision-making are still lacking (Rochet et al., 2007). To date the most developed frameworks are the hierarchical structure of the Australian Ecologically Sustainable Development (ESD) reporting framework, which divides well-being into ecological, human, and economic components and then further sub-divides these components (Chesson and Clayton, 1998). Another complex framework is the pressure- state-response (PSR) promoted by FAO (FAO, 1999).

The more detailed MSE framework describes the simulation technique for natural resource management (Punt and Smith, 1999, Sainsbury et al., 2000) (Figure 7-1).

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Figure 7-1: The MSE framework for natural resource management

7.2.2 Overview of Forest Planning in PNG

The requirements for the National Forest Plan and National Forest Inventory in PNG are set out in the Forestry Act 1991(Amended 2000) (Table 7-1). The Forestry Act sec. 47 (1) provides provision for a National Forest Plan; Section 47 (2) (b) National Forest Inventory; and sec. 49 (1) Provincial Forest Plan (Ministry of Forests, 1991a). Data and other related information collected from forest inventories by the PNGFA provides the basis for drawing up forest plans in PNG. Basically, forest plans are developed at two levels: National Forest Plan to provide a detailed statement of how the national and provincial governments intend to manage the country‘s forest resources; and the Provincial Forest Plans to be drawn up by

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the provincial government. The National Forest Plan is to be consistent with the 1991 national forest policy and relevant government policies and be based on a certified National Forest Inventory; and also consist of the National Forestry Development Guidelines and the National Forest Development Programme. The Provincial Forest Plans contain Provincial Forestry Development Guidelines and a five year rolling forest development program. The 1991 National Forest Policy also has provision for all agreements and permits to be conditional upon broad land use plans. However, there is currently no comprehensive land use planning process in place in PNG (Keenan et al., 2005). The PNGFA has adequate systems in place for planning requirements, however, they are not currently integrated effectively for strategic forest planning. As it is now, there is a lack of understanding of the overall forest planning framework within PNG (Keenan et al., 2005).

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Table 7-1: Forest Planning and inventory requirements in Papua New Guinea

Planning Level Inventory Planning / Standard / Specification Responsibility Comment Requirement

National Forest Plan Forestry Act s. 47(1) 1% sample process with PNGFA FIPS, FIMS and PNGRIS National Forest Inventory Forestry Act s. 47(2) 1% sample PNGFA Significant inventory work same as above done but not a comprehensive National Forest Inventory Provincial Plans Forestry Act s. 47(2) 1% sample same as above, Provincial Forest Officers Compiled for each province Forest Management Forestry Act s. 100 1% sample from company PNGFA Significant inventory done Agreement Project plots different to above 1% inventory not necessary Statement (Feasibility study for sound statistics. / tender) 5 Year Working Plan Forestry Act s. 101 with 1% sample. PMCP states: Company As above detailed prescription in the ‗estimate of net harvestable Planning Monitoring and volume must be based at a Control Procedures (PMCP) minimum of a 1% sample of the gross loggable area. Details of net harvestable volumes presented must be based of actual inventory of the areas to be logged, and not on historical data from previously logged areas‘. Annual Logging Plan Forestry Act s. 102 and 1% Company As above PMCP. Operational set-up plan PMCP At minimum consist of 10% Company Companies prefer to a 20% (harvesting plan) sample of the loggable area sample of trees selected to be harvested. Some companies asses 100% of trees planned for harvest

(Source: Keenan et al., 2002)

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7.2.3 Small-Scale Timber Harvesting in PNG

Large-scale commercial timber harvesting of primary forest began in PNG in the 1970s and 80s. In the mid 1980s small-scale harvesting particularly by private operators and community groups started as an alternative income generating activity as well as to supply sawn timber to build decent homes and community infrastructures such as buildings for community halls, schools, hospitals, and churches. By then, there were over 5000 small-scale portable sawmills sold throughout PNG, however, in the 1990s 1,500 of these sawmills were still operational with the estimated capacity to produce 75,000m3 of sawn timber per year with the value of AUS$10 million in the local market (www.forcert.org.pg).

Small-scale timber harvesting in PNG started in the mid 1980‘s as an alternative to large-scale logging; this was the result of local communities and forest owners receiving very little services and other benefits from large-scale logging operations. Since then up to now, small-scale harvesting has rapidly increased in many communities throughout PNG. Usually this involves individuals, family groups, clan groups, or community groups harvesting on small blocks of forest land using small- scale portable sawmills. Small-scale harvesting is community-based and most of their activities have been supported primarily through funding assistance from overseas aid donors.

7.2.4 Requirements for Certification

Certification of good forest management represents a new approach in the global effort to sustain the diverse forest ecosystems and this is being seen as a necessary requirement particularly in the forestry sector in the tropics (Alder et al., 2002, Dickinson, 1999). The market for certified products is relatively new and small compared with the overall wood trade, there are few brokers, and as yet there are no trade magazines and few product shows.

FSC is a global certification body and its goals are to promote environmentally responsible, socially beneficial, and economically viable management of forests through the establishment of worldwide standards for good forest management (Dickinson, 1999, FSC, 1996, FSC, 1999). One of the roles of FSC is to accredit

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organizations that in turn offer independent, third-party certification of forest operations. Certification has been developed as an instrument for promoting SFM (Durst et al., 2006). Although initially certification was focused on tropical forests, it rapidly shifted to cover other forest types. Ten years after the first certification schemes were developed, about 92 % of the 271 million hectares of forests that have been certified are located in Europe and North America. In developing countries only 13 percent of certified forests are located, while only 5 percent of the certified forests are located in the tropics (Durst et al., 2006). There are challenges facing certification and ecolabelling of forest products in developing countries but the strengths of certification are promising (Table 7-2).

Table 7-2: Strengths and weaknesses of certification.

STRENGTHS WEAKNESSES  Standards for forest management and  Weak market demand for certified chain of custody are developed products in the global market. through multistakeholder processes.  Wide gaps between existing  Forest and chain of custody management standards and management are audited by accredited certification requirements. third party assessors.  Requirements of certification not  Legality and sustainability are consistent with FSC standards and verified under public and private guidelines. procurement policies.  Weak implementation of national  Broad guidance to forest managers forest legislation, policies and and assurance to markets. programs in developing countries.  Market is guaranteed for certified  Insufficient capacity to implement products. SFM at forest management unit level  Chain of custody guarantees buyers of and to develop standards and delivery certified products. mechanisms.  Market driven approach to improve  High direct and indirect costs of forest management and address obtaining certification in developing consumer concerns about social issues countries. and the environment to good practice.  Assurance to consumers that products they buy are from sustainably managed forest.

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Despite these challenges and constraints, many developing countries are increasingly interested in pursuing certification. Recently some promising developments have emerged that may give further encouragement to developing countries' efforts, such as supportive codes of forestry practice, stepwise approaches to certification and increasing interest in forest certification and certified products in the Asia-Pacific region (Durst et al., 2006).

In PNG, while there is a national FSC working group in place (FSC, 2005), interests in adopting certification standards are increasing in community-level forest management. While various agencies such as FORCERT, FPCD, and VDT are promoting FSC certification standards in CBFM, the requirements for certification are very costly and time consuming and community groups have very little capacity to comply with the standards and guidelines. Certification of village-based timber operations require heavy subsidisation of not only the certification process, but also the subsequent production, transport and marketing of timber (Scheyvens, 2009) and this is a major challenge in PNG.

Although PNG communities have very little capacity, are financially disadvantaged, and have difficulties in complying with FSC standards, certification has a potential to offer alternative income and benefits through the promotion of SFM. When CBFM in PNG can demonstrate that FSC standards have been met, communities will be rewarded with economic benefits such as continued market access, financially competitive alternatives to poor practice, illegal logging and conversion to other land- uses. For those who are able to meet the requirements for certification, the financial benefits of having access to overseas certified markets may be significant. For example, FORCERT and FPCD have in the past exported A Grade sawn timber to the Woodage in Sydney for a price that is almost three times higher than the price in the local market. However, with the recent establishment of the PNG Liquefied Natural Gas (PNG LNG) project in PNG, there is currently high demand for sawn timber in the domestic market. Therefore, local groups who are unable to comply with the certification requirements and are unable to sell their products to the overseas certified market, can benefit from higher prices in the domestic market.

The FSC has also developed a High Conservation Value Forest Toolkit for PNG to be used in forest management certification. The toolkit is intended to be used by forest managers to comply with Principle 9 of the FSC standards to assist managers to

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identify any high conservation values (HCVs) that occur within their individual forest management units and manage them in order to maintain or enhance the values identified. Examples of HCVF in PNG include the following;  Forest areas containing globally, regionally or nationally significant concentrations of biodiversity values (for example, endemism, endangered species, refugia)  Forest areas that are in or contain rare, threatened or endangered ecosystems (for example, breeding sites, migratory sites)

The toolkit is intended for use by forest managers undergoing FSC accredited forest management certification and by FSC accredited certification auditors assessing or monitoring conservation values in PNG as a part of a complete FSC assessment or evaluation process. The toolkit will assist in making FSC certification acceptable within the forest industry in PNG.

There are three certification models promoted by FORCERT in CBFM in PNG and the requirements come under three main phases (Figure 7-2). These include Community Based Fair Trade (CBFT) status, Pre-certification status, and FSC Group Certification membership or full certification status. There are several criteria for a community group to comply with and this is a step-wise process for them to move towards FSC certification.

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Phase 1: CBFT Phase 2: Pre-certified  Community must own a good forest resource of  Awareness on FORCERT group sufficient size. certification service network in the group.  Must have the management right over the forest  Carry out 1% forest inventory in its forest area. area.  Group working well with members of its clan  Group must be starting the ILG application and there are no disputes over the forest area. process.  Awareness on FORCERT group certification  Application to be lodged for a company or service network in the group. business name registration.  Harvesting to not occur in the buffer zones.  Group to integrate business plan with  Group to undergo training on chain of custody. community needs.  Must understand the coding system with 3-letter  Socio-economic and environmental baseline producer code on both ends of all individual survey must be completed. timber species.  Landuse plan must be in place.  Group must enter into a service and production  Group must undergo chain of custody agreement with a CMU. training.  Must enter into producer membership  Must undergo training on operational health agreement with FORCERT. and safety procedures.  After achieving a CBFT status, group must  Enter into a service and production progress to the pre-certified producer status agreement with a CMU. within 2 years.  Must enter into procedure membership agreement with FORCERT.  After achieving pre-certification status, group must progress to FSC certified producer status with 2 years.

Phase 3: FSC certified  Awareness on FORCERT group certification service network in the group.  Carry out 1% forest inventory in its forest area.  Complete the ILG process and submit to relevant government agency.  Have a company or business name registered.  Socio-economic and environmental baseline survey completed.  Landuse plan must be completed.  Group must be registered as a member of FIP.  Have forest management plan in place.  Carry out 10% inventory of the first 5 years working forest area.  Complete set-up establishment.  Group must have the chain of custody processes in place.  After achieving the FSC certified producer status, group must meet the FORCERT member training requirements within 1 year.

Figure 7-2: Certification model promoted by FORCERT in PNG.

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7.3 METHODOLOGY

In this chapter an integrated conceptual framework for scenario analyses and evaluation is presented for CBFM. The framework is based on the MSE approach (Sainsbury et al., 2000, Smith et al., 1999), which has been discussed earlier (Section 7.2.1) and the outcomes of the study on scenario analyses (Chapter 5) and decision tree models developed and tested in case study sites (Chapter 6). The details of the MSE approach have been given in the literature review (Chapter 2, Figure 2-1). These are represented by the MSE framework developed by (Sainsbury et al., 2000). The framework for management of cutover forest in PNG was developed after consultation with local communities (Yalu, Gabensis and Sogi villages), government agencies (PNGFA, FRI, TFTC), timber industries (LBC, Madang Timbers, Santi Timbers) and NGOs (VDT, FORCERT, FPCD, CMUs) in the pilot region where this research was carried out. The procedures were guided by the PAR protocol and included field visits, meetings, discussions and interviews with those stakeholders in the pilot region.

7.3.1 Stakeholder Consultation

The stakeholder consultation in case study sites leading up to the development of the framework involved the PAR approach in communities. These involved village meetings and research participants were interviewed and different forest management options for the future were investigated for cutover forests. Outputs from this investigation and forest management options were fed into a planning systems for further analyses.

7.3.2 Forest Inventory

Forest inventory data forms an important part of input data in the planning system for scenario analyses. Data from case study sites including volume growth, timber volume in different size classes, and available forest area information were fed into the planning system. The integration of forest inventory data, forest growth and area from the case study site facilitated the estimates of timber yields under different scenarios.

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7.3.3 Planning System

The framework has a spreadsheet-based planning system (Keenan et al., 2005) that analyses forest growth, different management options, and annual timber yield estimates to develop scenarios for CBFM. The details of the planning tool have been discussed earlier (Chapter 5, Figure 5-1). In this chapter, the planning tool integrates forest inventory, growth and area from the case study site to analyse timber yields.

7.3.4 Decision Analysis Tools

In the framework the decision analyses tools are models that have been developed based on spreadsheet modelling and decision analyses technique. The models have been developed in four parts to represent the different forest management scenario for community-based management of cutover forests (see details in Chapter 6). For the purpose of this framework, a decision analyses tool called decision tree model analyses decision alternatives and uncertain events along the branches and a payoff value is determined at the end of the analyses. The payoff value is further analysed to determine the largest EMV for a particular decision alternative.

7.3.5 Sensitivity Analyses

Sensitivity analyses is facilitated by an Excel Add-in called SensIT to consider how sensitive the recommended decision is to changes in values in the decision tree (Ragsdale, 2008). This approach is carried out to determine, which of the input variables in the decision tree model have the largest impact on the EMVs range for example, at +/-10%. Tornado and spider charts are generated using SensIT to identify the input variables in the decision tree that if changed have the greatest impact on the EMV. Tornado and spider charts summarise the impact on the decision tree‘s EMV of each input variable being set at, for example +/-10% of the original EMV (base case).

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7.4 RESULTS

The main result in Chapter 7 is the framework presented in this study for assisting decision-making in CBFM in PNG. The framework integrates outputs from stakeholder consultations (communities, industry); a PAR protocol to analyse stakeholder interests and expectations; and management options from field interviews into an integrated spreadsheet-based scenario analyses and evaluation system. The framework involves decision analyses, modelling and evaluation systems and delivers scenario outputs, which can be further evaluated for action.

7.4.1 A Scenario Analyses and Evaluation Framework

A conceptual framework for scenario analysis has been presented in this study for community-based management of cutover forests in PNG (Figure 7-1). This approach has been adopted from earlier studies carried out by Sainsbury et al. (2000) for marine and fishery resource management. Their earlier study has been used as a basis to develop an integrated scenario analyses and evaluation framework in Chapter 7 for CBFM because of the following reasons; (i) Active participation of different stakeholders and generation of ideas by those involved in forest management in PNG such as the timber industry, community groups, NGOs and PNGFA. (ii) Different stakeholders will have different expectations and requirements on how they would like to manage their forests hence, this framework will accommodate their interests. (iii) Support the capacity of PNGFA to develop an integrated regional planning and management system for cutover native forests in PNG.

The framework in Chapter 7 has been presented based on the MSE approach (Sainsbury et al., 2000) and the outputs from the studies in Chapter 5 and 6. The framework integrates different processes from the PAR protocol in the case study sites, testing of scenarios using a planning tool (Chapter 5) and decision analyses tools (Chapter 6). The framework is an integration of qualitative data from interviewing communities and quantitative data from forest inventory that have been input in to the planning and decision analyses systems (Figure 7-2). Sensitivity analyses are carried out on the outputs of these systems before a decision is implemented.

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An integrated conceptual framework for scenario evaluation and decision analyses for community-based forest management

Planning System Decision Analyses Tools

Decision Tree Stakeholder Spreadsheet Sensitivity Model Planning Tool Analyses Consultation

Tornado Field Chart Decision Interviews Growth Data PAR Alternatives

Scenario

Output Investigate Uncertain Options Management EMV Options Events

Decision Forest Inventory Implementation Annual Yield Payoff Data Spider Strategy Estimates Chart

Scenario Feedback to Evaluation & Stakeholders Analyses

Figure 7-3: A conceptual framework for community-based forest management

7.5 DISCUSSION

Participatory approaches to tropical forest management are increasing, and have been successful because opportunities arise for more inclusive and better informed decision-making by communities (Evans and Guariguata, 2008). Similar studies such as the one in this chapter have developed tools to assist decision-making in CBFM. For example, Anil (2004) developed a GIS-based participatory 3-dimensional model (3PDM) for transforming landscape information into a format that communities in Sasatgre in India can use to monitor their forests to make management decisions. Participatory approaches developed in the Brazilian Amazon (Shanley and Gaia, 2002) for communities to manage NTPF in their forests and biodiversity management in Nepal (Lawrence et al., 2006) have also been successful. Studies in the Philippines involving community participation in forest management with the application of the criteria and indicators framework (Hartanto et al., 2002); a vegetation monitoring system developed in India (Roy, 2004) for community participation in assessing their

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vegetation status; and other related systems developed for community management of plantations to assist in decision-making have been also successful.

The framework presented in Chapter 7 involved a participatory approach in communities, development of scenarios and analyses of timber yields under different management scenarios, and testing these scenarios using decision analyses models. The framework can be described as having a data input system, three simple spreadsheet-based analyses and modelling systems (planning system, decision analyses tools and sensitivity analyses system) for scenario analyses and evaluation and a scenario output system for decision implementation.

Currently there is a shortfall in the overall forest planning in PNG in that land use planning process is inadequate and PNGFA‘s planning systems are ineffective. Forest certification and good practice forestry are not the goal of the government but they are widely promoted by NGOs and international organisations. Small-scale forest management is usually funded by international donor agencies with very limited or no support from the government. The framework presented in this chapter addresses these shortfalls from the participation by communities in decision-making and small- scale timber harvesting to the marketing of products in an overseas certified market.

The framework requires forest management options to be investigated from stakeholder consultations and interviews and forest inventory data to be fed into a planning system. The planning tool integrates inventory data, growth and area from a forest, for example, a community forest area, and estimates annual yields under different management scenarios. The outputs from the planning tool are tested using decision analyses tools. In the decision analyses system, a spreadsheet-based model analyses decision alternatives and uncertain events and at the end of the decision tree a payoff value is determined. The decision tree model has a roll-back system that analyses the payoff value to determine the largest EMV in profit terms. When the largest EMV is selected and before the decision is implemented, the EMV is further analysed by applying sensitivity analyses to determine, which input variables (costs and income associated with a scenario) have the largest impact on the EMV‘s range (at for example, +/-10%). Finally the decision alternative with the largest EMV is implemented and feedback is given to the stakeholders.

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7.6 CONCLUSIONS

The objective of Chapter 7 was to present a framework for community-based management of cutover forests in PNG. Unlike decision support systems, the system developed in this chapter is an analytical approach and decision analyses follow a structured methodology. The system developed in this study will build the capacity of NGOs and communities and assist in decision-making in forest management. This will require stakeholder participation in forest management especially at the community level. A framework such as the one developed in this study has not been used in PNG hence, application of the system will assist decision-making in community-based management of cutover forests. Since there is no planning system in place for the management of cutover forests in PNG, the framework presented in this chapter will assist the PNGFA develop a regional forest planning system. Application of the framework will involve community participation in small-scale harvesting in cutover forests and export of their sawn timber to the overseas certified markets in Australia and New Zealand. The conceptual framework developed in this study is an integrated system for scenario analyses and evaluation and is applicable to a participatory approach to tropical forest management in PNG and elsewhere in the tropical region.

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CONCLUSIONS

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CHAPTER 8: CONCLUSIONS AND RECOMMENDATIONS

8.1 INTRODUCTION

The overall aim of the thesis was to investigate and identify frameworks that support community decision-making regarding the future use of cutover forests in PNG. Generally, this aim has been achieved. The objectives of Chapter 8 are to summarise the outputs of the overall study, draw some conclusions and point out the future directions for forest management in PNG. The research questions and objectives of the thesis are restated and how they have been achieved are discussed (Section 8.2). The key outputs of the study are summarised (Section 8.3) and the application of the tools developed in the study by stakeholders in CBFM are discussed (Section 8.4). In Section 8.5, the contributions of the current study to knowledge are presented. The study had some short-falls and limitations and these are highlighted (Section 8.6) and in section 8.7, future directions in research and policy are discussed. Finally, the outputs of the thesis are discussed and some comparisons are made with the literature (Section 8.8) and some conclusions and recommendations are given (Section 8.9).

8.2 RESEARCH OBJECTIVES AND QUESTIONS

8.2.1. Research Objectives

In this section, the objectives of the thesis are restated and how they have been addressed are discussed. The details of how the objectives of the study have been addressed are as follow: i) to assess the current condition and future production potential of cutover forests in PNG.

The first objective of the study has been achieved from the outcomes of analyses of PSPs (Chapter 3) and forest resources in the two case study sites (Chapter 4). Evidence from analyses of PSPs suggest that cutover forests in PNG showed a high degree of resilience following harvesting. Residual timber volume and aboveground

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forest carbon determined in case study sites are adequate for communities to participate in small-scale harvesting and REDD+ projects. ii) to develop scenario analyses and evaluation tools for assisting decision- making in community-based management of cutover native forests in PNG.

This objective has been addressed in Chapter 5 and 6. Scenarios have been analysed and evaluated in community-based harvesting and decision analyses models have been developed. The scenario analyses and evaluation tools developed under the second objective have been tested in case study sites. iii) to test the scenario analyses and evaluation tools developed under the second objective in case study sites.

The decision tree models developed in this study have been tested using actual data in the Yalu case study site. Data relating to cash flow (costs and income) associated with community sawmill, local processing, medium scale log export and carbon trade were input into the decision tree model and tested. iv) to develop a scenario analysis and evaluation framework for community-based management of cutover native forests in PNG.

This objective has been achieved and an integrated conceptual framework has been developed in the study based on the MSE approach (Sainsbury et al., 2000). This MSE type of management approach has been successfully applied in fishery and marine resource management (Butterworth and Punt, 1999, Kirkwood, 1993).

8.2.2. Research Questions

There were four questions that have been addressed in this thesis. These questions are restated and how they have been addressed are discussed. The questions are addressed as follow: i) what is the current condition and future production potential of cutover forests in PNG?

This question has been adequately addressed from the outputs of the study on the structure and dynamics of cutover forests (Chapter 3) and forest resource estimates in case study sites (Chapter 4). Analyses of PSPs suggest that a majority of plots showed increasing BA and stand volume following selective timber harvesting but there were

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also on-going decline in 25% of sites studied. In the two case study sites, residual timber volumes estimated can be able to support small-scale timber harvesting, while high estimates of forest carbon in these sites provide an option for communities to manage their forests for carbon benefits. ii) what are the potential options for future management of cutover forests by communities?

The study in Chapter 5 has addressed this question and from the outputs of the qualitative interviews in the case study sites, the following were the future management options for cutover forests: community sawmill, local processing, medium-scale log export and carbon trade. iii) How can information on the structure and dynamics of forests and the potential uses of forest resources be used to support effective decision-making in community management of cutover native forests in PNG?

Outputs from the studies in Chapter 3 (Forest dynamics after selective timber harvesting), Chapter 4 (Forest resources in case study sites), Chapter 5 (Evaluation of scenarios) and Chapter 6 (Testing of scenarios using decision analysis models) have addressed this question. Data related to forest structure, dynamics and timber yields under different management scenarios have been analysed using the planning tool and further tested using the decision analyses models. These outputs have been integrated in the conceptual framework that has been presented in this study (Chapter 7). Therefore, this framework will support effective decision making in community-based management of cutover native forests in PNG. iv) what type of scenario methods are appropriate for adaptive management of cutover native forests in PNG?

The literature review (Chapter 2) has addressed this last question and the scenario method and MSE approach have been applied in this study. In the review, different forest management approaches were investigated for possible application in the management of cutover forests in PNG. This study recommends that the type of scenario methods appropriate for adaptive management of cutover forests in PNG is the MSE approach (Butterworth and Punt, 1999, Sainsbury et al., 2000). The MSE approach has been used as the basis to present a new conceptual framework (Chapter

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7) for community-based management of cutover forests in PNG. The tools developed in this study are appropriate for application in PNG and other tropical regions.

8.3 KEY OUTPUTS OF THE STUDY

There are three key outputs of the overall study reported in this chapter. The first is the scenario analysis and evaluation tools developed for assisting decision making in community-based management of cutover native forests in PNG. These tools have been developed from the outputs of the analyses of timber yields under different management scenarios and the study of decision tree models for community-based management of cutover forests in PNG. The different management regimes developed from an existing planning tool are applicable to CBFM. The decision tree models developed in the study are based on a spreadsheet modelling and decision analyses technique (Ragsdale, 2007, Ragsdale, 2008). This type of modelling technique has been mainly applied in making investment decisions under uncertain circumstances for example, application of decision analyses in the selection of a product development strategy or investing in a real estate business by a company (Lieshout, 2006, Middleton, 2001, Ragsdale, 2007). The second output of the study was the testing of the scenario analyses and evaluation tools in the case study sites. When the decision analysis model (Decision Tree Model 2: Local Processing) was tested in the Yalu case study site, analyses indicated that depending on the input variables in the model, the expected monetary value (EMV) returned is determined by the related cash flow associated with each scenario. An integrated conceptual framework for CBFM has been developed in the study and this relates to the third key output of the overall study. The framework integrates outputs from scenario analyses and evaluation and testing of the scenarios using the decision analyses models. Development of this framework has been guided by the PAR approach with the two communities that have participated in this study for the past four years.

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8.4 APPLICATION OF THE TOOLS DEVELOPED IN THIS STUDY

Currently, there is no overall policy framework in place for community-based management of cutover forests in PNG. Scenarios and approaches developed in this study can support the development of national and provincial policies and local-level decision-making for cutover natural forests in PNG. NGOs who are currently supporting small-scale forest management in PNG may be the most likely initial users. Some NGOs have good capacity and are supported by international organisations. Hence, these models can be applied by them in promoting small-scale harvesting in communities throughout PNG. Workshop-based exercises can provide a basis for equipping NGOs and communities with the skills required for the practical application of the decision analyses tools developed in this study. The conceptual framework developed in this study is a new tool for forest management in PNG. The framework can be applied by NGOs and conservation groups involved in small-scale harvesting and those engaged in promoting certification in PNG. However, wider application of these tools and the analytical framework will depend on development of supporting policy at national and provincial levels in PNG that aims to increase the capacity and control of local forest owners and facilitate their involvement in implementing sustainable forest management objectives.

8.5 CONTRIBUTIONS OF THE PRESENT STUDY

While decision support systems have been commonly applied in natural resource management, decision analyses and evaluation techniques have not been applied in tropical forest management before. The systems developed in this study necessitate a structured approach to decision-making in tropical forest management. Therefore, the present study contributes knowledge in the area of decision analyses and modelling in tropical forest management. This study has also contributed to knowledge in the form of one publication in an international journal and two papers in a book chapter (see the preface on page vi). The study of forest dynamics after selective timber harvesting in Chapter 3 is the first detailed analyses in the tropical forest of PNG based on a comprehensive set of

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permanent sample plot data. Scenario analyses and evaluation are new approaches to tropical forest management and the types of analyses undertaken in this study are new as far as forest management in PNG is concerned. In the context of forest management in PNG, the outputs from the present study will assist decision-making in CBFM. A framework such as the one presented in this study has never been applied in forest management in PNG before. Therefore, this framework will assist the stakeholders including communities in the management of cutover forests in PNG.

8.6 LIMITATIONS OF THE STUDY

The decision analyses models developed in Chapter 6 relied on data available from case study sites. However, insufficient data was obtained from the study areas to test the C trade scenario using the decision tree model. The costs and income estimated in the analyses are based on crude data only at the community-level and do not provide a strong basis for such analyses. Therefore, the results obtained in the estimation of the EMV (profit) under the C trade scenario are only for the purpose of demonstrating the application of decision analyses models to assist decision-making in communities to consider different forest management options. Based on the current in-country situation, C trade has not officially started yet and issues such as REDD and REDD+ are still being discussed at policy level.

8.6.1 Forest Management Implications

As more community groups become involved in small-scale harvesting, the need for application of management tools such as the systems developed in this study will be necessary. This will put additional pressure on the PNGFA to control the increase in participation of communities in small-scale harvesting. Land and forest owning communities who would like to participate in small-scale harvesting may want to expand their operations to cover bigger forest areas, which will in turn call for compliance with PNGFA and government policy requirements. Therefore, the government will need to consider putting in place regulatory systems not only to control small-scale operations, but also to assist and promote small-scale harvesting by communities in order for them to get maximum benefits from the management of their cutover forest resources.

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8.7 FUTURE DIRECTIONS

After over two decades of large-scale commercial harvesting of primary forests in PNG, there are still no land use plans for the management of forest areas after harvesting. A major challenge for the PNGFA and the government is the development of appropriate management systems for cutover forest. Management planning should include consideration of the future production capacity of cutover and degraded forests and the development of the capacity of local forest owner communities to participate in small-scale forest management and utilisation, for example through management systems that are compliant with requirements of certification bodies.

8.7.1 Future Research Needs

In Chapter 3, the study used forest structure data to assess the current condition and future production potential of cutover forests in PNG. However, the study fall-short of the required data to adequately address the issue of forest degradation after selective timber harvesting. Therefore, future research is required to quantify the extent of degradation after harvesting. The study also tested models developed in other tropical regions to assess the growth of harvested forests in PNG. Research is also required to develop country-specific growth models for sustainable management of tropical forests in PNG.

The study in Chapter 5 assessed timber yields under different management scenarios in community-based harvesting to recommend a regime that is sustainable and can continuously supply sawn timber for communities. The study has not considered the question of optimisation in the analyses. Future research is therefore, necessary to investigate optimisation in community-based harvesting to address a research question such as, how can an intensity of cut be optimised in community-based harvesting? In Chapter 6 the decision analyses relating to C trade are based on unreliable data to estimate annual EMV from managing forests for C benefits by communities. Future research is necessary to study detailed economic analyses (costs and benefits) for participation by communities in C trade in PNG. Further investigation is also necessary to consider the combination of scenarios to test the decision analyses models, for example, combining community sawmilling and REDD+ as one scenario with the objective of increasing income in CBFM.

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8.7.2 Future Policy Directions

The present study has addressed some aspects of PNG Forest Policy 1991. Currently, there are no policy instruments in place to address issues relating to cutover forest management and community forestry. A new direction in Forest Policy is now necessary to meet the increasing demands and expectations of stakeholders in PNG as well as the international community. There is a need for policy change to reflect the changing circumstances in forest management. As the need for a multi-disciplinary approach to natural resource management is increasing worldwide, policy must be changed to address the need for an integrated and participatory approach to the management of forests that have been over-exploited. Capacity building is required at the community-level to address the needs of forest owners and other stakeholder‘s expectations and the demands for small-scale forest management and utilisation in PNG.

8.8 DISCUSSION

This study has focused on analyses and evaluation of scenarios for the management of cutover tropical forests in PNG. To the knowledge of the author, scenario analyses and evaluation are new approaches to tropical forest management therefore, there is limited literature available on the subject. However, approaches such as the MSE have been widely applied in other natural resource management sectors such as fishery and marine resources (Butterworth and Punt, 1999, Sainsbury et al., 2000). Studies at CIFOR have embarked on work relating to scenarios but this has been mainly focused on participatory approaches to decision-making in community-based management of natural resources including tropical forests (Nemarundwe et al., 2002, Nemarundwe et al., 2003, Wollenberg et al., 2000, Wollenberg et al., 1998). Work at CIFOR has concentrated on providing training through workshop-based exercises for trainers to equip them with skills to develop scenarios for natural resource management in community settings. In developed countries detailed studies have been carried out in modelling forest management scenarios across landscapes, for example, studies by Tappe et al. (2004) involved use of satellite imagery in conjunction with field data to quantify differences

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in landscape that can aid in making management decisions in ecologically and socially complex forests. The present study does not involve complex modelling of scenarios for forest management in PNG. The study rather provides an analytical system approach that is appropriate for application in community decision-making in tropical forest management. The tools developed in the study are spreadsheet-based analyses and modelling applications hence, can be made available to stakeholders in PNG.

The outputs from this study have provided some basis for the review of PNG‘s 1991 National Forest Policy Part II, Section 3, Sustained Yield Management. At the moment there are no policy framework and guidelines in place for the management of cutover forests. The tools developed in this study provide the framework to be used for the development of new policies for the management of cutover forests in PNG. Policy change should be directed at addressing stakeholder requirements and expectations, especially at community-level in the management of the 10% of forest areas that are now regarded as cutover and degraded. These policy changes should also address international issues relating to SFM, biodiversity conservation, climate change and meet the needs of the global community.

8.9 CONCLUSIONS

The current condition of cutover forests in PNG requires management interventions and the future production potential of these forests will depend on frequency of future harvests and other land uses such as conversion to agricultural lands and traditional farming activities, for example, land cultivation for gardening. In community-based harvesting, shorter cycles, for example, 10-20 years and removing about 50% of available pre-harvest volume only in commercial timber species groups at each cycle are recommended.

There are four decision analysis models developed in this study (Chapter 6) to represent the decision tree models for community sawmill, local processing, medium- scale log export, and C trade. The integrated conceptual framework for scenario analyses and evaluation presented in this study will assist the capacity of NGOs and communities in the management of cutover forests in PNG.

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The application of the systems developed in this study will assist communities in the management of the extensive cutover forests in PNG by participating in small-scale harvesting and marketing of sawn timber to generate income. This will have forest management implications in the activities of stakeholders such as the PNGFA, timber industry, NGOs and community groups. A new policy direction in forest management is therefore, necessary in PNG in order to apply these systems particularly at community level forest management and utilisation.

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218

APPENDICES

APPENDIX 3-1: SUMMARY OF PSPS USED IN THE STUDY

Forest Condition No. of Plots

Un-harvested 13 Selectively-harvested Increasing BA (un-burnt) 63

Falling BA (un-burnt) 21

Burnt during 1997-98 El nino drought 21

Total 118

APPENDIX 3-2: SUMMARY OF THE PSPS IN UNLOGGED FOREST.

BA_FIRST BA_LAST FIRST LAST CENS CENS PLOTNO PLOTID CENS CENS (m2ha-1) (m2ha-1) 1 DANAR03 2006 20.8470 No data 2 DANAR04 2006 7.7838 No data 3 HUVIV02 1999 25.3617 No data 4 KAUP_03 1998 2000 24.2586 21.6303 5 MARE_03 2001 23.7487 No data 6 SAGAR03 1998 2005 32.1673 33.2807 7 SASER03 2005 24.8061 No data 8 SASER04 2005 29.3279 No data 9 SOGER03 1998 2003 21.7693 23.9859 10 WATUT05 1997 1999 33.8812 25.3121 11 WATUT06 1997 1999 44.1607 28.6389 12 WCOST05 1998 2001 33.6952 34.4092 13 WCOST06 1998 2001 31.4374 32.8569

219

APPENDIX 3-3: UN-BURNED PSPS IN HARVESTED FOREST WITH INCREASING BA.

BA_FIRST BA_LAST MBAI FIRST LAST CENS CENS (m2ha-1yr- PLOTNO PLOTID LOGDATE CENS CENS (m2ha-1) (m2ha-1) 1) 1 ANUAL01 1993 1995 1999 16.8828 17.9791 0.2741 2 ANUAL02 1993 1995 1999 20.9696 21.4081 0.1096 3 ARI__01 1995 1996 2003 11.8680 16.4226 0.6506 4 ARI__02 1995 1996 2003 11.2410 13.4710 0.3186 5 CARAW01 1991 1995 2004 19.4671 22.1647 0.2997 6 CARAW02 1991 1995 2004 18.8221 21.2092 0.2652 7 CFORD01 1994 1995 2004 30.2147 34.0191 0.4227 8 EMBIH01 1992 1994 1999 13.0070 13.5086 0.1003 9 EMBIH02 1992 1994 1999 9.5760 10.3879 0.1624 10 EMBIH03 1993 1994 1999 13.8590 15.9763 0.4235 11 EMBIH04 1993 1994 1999 12.5500 16.4194 0.7739 12 GAR__01 1991 1993 1999 15.0426 17.2383 0.3660 13 GAR__02 1991 1993 1999 14.2926 16.5673 0.3791 14 GARAM01 1991 1994 2000 20.1981 22.1105 0.3187 15 GILUW01 1987 1993 2003 12.5896 13.7937 0.1204 16 GILUW02 1991 1994 2003 19.8455 19.9718 0.0140 17 HAWAN01 1993 1994 2002 13.0935 17.1417 0.5060 18 HAWAN02 1994 1994 2002 13.3950 16.8687 0.4342 19 KAPIU01 1991 1993 1997 13.0361 22.6460 2.4025 20 KAPIU02 1991 1993 2003 11.6672 28.2623 1.6595 21 KAUP_01 1996 1996 2000 19.5241 19.8719 0.0869 22 KAUP_02 1996 1996 2000 22.3736 22.9669 0.1483 23 KRISA01 1991 1994 1996 16.4044 17.4124 0.5040 24 KRISA02 1991 1994 1996 23.1445 23.9709 0.4132 25 KUI__01 1994 1994 2002 18.0250 20.4151 0.2988 26 LARK_03 1994 1996 1999 18.6482 18.6841 0.0120 27 MALAM01 1995 1995 2000 16.5864 21.9264 1.0680 28 MOKOL01 1980 1993 2004 24.3010 29.1990 0.4453 29 MOKOL02 1981 1993 2004 21.8361 24.2578 0.2202 30 MORER01 1997 1997 1999 16.1786 17.0147 0.4180 31 MOSAL01 1992 1993 2003 12.4213 19.9976 0.7576 32 MOSAL02 1992 1993 1997 11.9561 19.6195 1.9159 33 MUSAU01 1996 1996 1999 17.0058 17.4021 0.1321 34 MUSAU02 1995 1996 1999 17.0392 17.8642 0.2750 35 PASMA01 1993 1997 2004 17.2060 21.4776 0.4746 36 PASMA02 1993 1997 1999 19.5182 20.6363 0.5591 37 PUAL_01 1993 1994 2000 19.1461 19.1960 0.0083 38 PUAL_02 1994 1994 2000 15.1644 17.5568 0.3987

220

BA_FIRST BA_LAST FIRST LAST CENS CENS MBAI PLOTNO PLOTID LOGDATE CENS CENS (m2ha-1) (m2ha-1) (m2ha-1yr-1) 39 PUAL_03 1996 1996 1998 16.5854 17.5962 0.5054 40 PUAL_04 1996 1996 2004 17.2923 18.6604 0.1710 41 PULIE02 1997 1997 2004 10.9713 11.8248 0.1219 42 PULIE03 1997 1997 1999 19.8100 20.4913 0.3406 43 SAGAR01 1997 1998 2005 14.1514 15.3152 0.1663 44 SEMBE01 1996 1997 1999 13.4691 13.7005 0.1157 45 SERA_02 1996 1996 1998 17.4719 17.8179 0.1730 46 TURAM01 1994 1994 1998 24.5674 25.6188 0.2629 47 UMBOI01 1993 1994 2004 21.9117 24.5082 0.2597 48 UMBOI02 1993 1994 2001 17.4360 19.8924 0.3509 49 UMBUK01 1993 1993 2007 13.2607 16.3482 0.2205 50 UMBUK02 1993 1993 1999 10.7566 12.1284 0.2286 51 VAILA01 1993 1994 2002 14.6811 19.0990 0.5522 52 VAILA02 1993 1994 2002 17.5963 18.8018 0.1507 53 WASAP01 1986 1990 2003 18.4658 28.5293 0.7741 54 WASAP02 1987 1995 2003 13.1157 16.5941 0.4348 55 WATUT01 1992 1993 2003 13.9136 20.2128 0.6299 56 WATUT02 1992 1993 1998 13.8267 14.9267 0.2200 57 WAWOI01 1991 1994 1998 23.4345 25.6670 0.5581 58 WCOST03 1996 1996 2003 15.4697 18.9326 0.4947 59 WCOST04 1996 1996 2003 10.3386 10.4722 0.0191 60 WFBAY02 1981 1993 1999 18.2790 18.3297 0.0085 61 YALU_01 1995 1995 2007 12.6460 23.3236 0.8898 62 YALU_02 1995 1995 2007 16.2517 19.7775 0.2938 63 YEMA_01 1995 1996 2002 18.3911 20.1508 0.2933

221

APPENDIX 3-4: UNBURNED PSPS IN HARVESTED FOREST WITH FALLING BA.

BA_FIRST BA_LAST FIRST LAST CENS CENS MBAI PLOTNO PLOTID LOGDATE CENS CENS (m2ha-1) (m2ha-1) (m2ha-1yr-1) 1 CFORD02 1995 1995 2004 16.51825 15.80870 -0.07883 2 GARAM02 1991 1994 1998 18.06829 16.20510 -0.31054 3 INPOM01 1993 1995 1997 19.42872 17.07170 -1.17852 4 KUI_02 1994 1994 2002 15.61649 14.78340 -0.10413 5 LARK_04 1994 1996 1999 16.09460 15.92510 -0.05649 6 MALAM02 1995 1995 2003 19.59570 14.34840 -0.65591 7 MORER02 1997 1997 1999 14.43625 13.90560 -0.26533 8 ORLAK01 1994 1994 2000 18.91138 9.93640 -1.49582 9 ORLAK02 1994 1994 1994 16.74760 10.85680 -0.98180 10 PULIE01 1997 1997 2004 18.07768 10.76690 -1.04440 11 SAGAR02 1997 1998 2005 17.35408 17.16280 -0.02732 12 SEMBE02 1996 1997 1999 9.45672 8.88900 -0.28387 13 SERA_01 1996 1996 2000 21.29906 21.07070 -0.05708 14 TURAM02 1994 1994 1997 25.40949 25.61880 -0.10010 15 TURAM03 1996 1997 1999 15.82846 14.81270 -0.50786 16 VUDAL01 1997 1997 1999 7.62256 7.05470 -0.28393 17 VUDAL02 1996 1997 1999 12.15035 10.70640 -0.72196 18 WAWOI02 1994 1994 2000 23.25639 11.42410 -1.97204 19 WCOST01 1989 1995 1999 12.02939 9.07100 -0.73959 20 WCOST02 1989 1995 1999 24.70524 21.72310 -0.74554 21 WFBAY01 1980 1993 1999 17.20145 14.04070 -0.52680

222

APPENDIX 3-5: PSPS BURNED BY FIRE DURING THE DROUGHT.

BA_FIRST BA_LAST MBAI FIRST LAST CENS CENS (m2ha-1yr- PLOTNO PLOTID LOGDATE CENS CENS (m2ha-1) (m2ha-1) 1) 1 CNIRD01 1994 1995 2004 23.6627 7.1393 -1.8359 2 CNIRD02 1994 1995 2007 23.0366 3.5539 -1.6236 3 HUVIV01 1997 1997 15.2131 Short measurement 4 IVAIN01 1995 1996 2003 16.3578 5.8564 -1.5002 5 IVAIN02 1995 1996 2003 9.9191 4.9083 -0.7158 6 IVAIN03 1995 1996 1998 13.0492 11.9804 -0.5344 7 IVAIN04 1995 1996 1998 16.8716 12.9575 -1.9570 8 KAPUL01 1993 1993 1999 14.6181 9.6334 -0.8308 9 KAPUL02 1993 1993 2003 11.7906 2.6473 -0.9143 10 KAUT_01 1993 1993 1997 12.9425 14.6797 0.4343 11 KAUT_02 1993 1993 1997 12.2872 12.4960 0.0522 12 LARK_01 1994 1995 1999 23.6381 19.1211 -1.1292 13 LARK_02 1994 1995 1999 21.4359 23.6409 0.5513 14 MAUBU01 1995 1996 13.9519 Short measurement 15 MAUBU02 1995 1996 16.7356 Short measurement 16 OOMSI01 1979 1993 1997 20.9554 22.1536 0.2996 17 OOMSI02 1980 1993 1997 18.9978 21.1015 0.5259 18 SOGER01 1996 1996 7.7030 Short measurement 19 SOGER02 1996 1996 12.1131 Short measurement 20 WIMAR01 1993 1994 2000 18.5575 17.0570 -0.2501 21 WIMAR02 1993 1994 2000 23.0218 16.0777 -1.1574

APPENDIX 3-6: 10 PSPS SEVERELY BURNED DURING THE DROUGHT

BA BA lost BA BA gained BA BA After Pre- Meas. Before Post- Meas. 1997 1997 Period Fire 1997 1997 Period Fire (m2ha- (m2ha- (m2ha- (m2ha- PLOTID 1) 1) (years) (%) 1) 1) (years) (%) CNIRD01 23.66 24.43 2 1.63 24.43 7.14 7 16.12 CNIRD02 23.04 23.55 2 0.23 23.55 3.55 10 17.23 IVAIN01 16.36 16.80 1 2.69 16.80 5.86 6 16.11 IVAIN02 9.92 9.93 1 0.09 9.93 4.91 6 11.08 KAPUL01 14.62 17.36 4 5.06 17.36 9.63 2 25.50 KAPUL02 11.80 12.99 4 2.64 12.99 2.65 6 23.28 LARK01 19.61 23.64 2 8.91 23.64 19.12 2 1.04 LARK02 21.44 22.31 2 2.05 22.31 23.64 2 3.17 WIMAR01 18.56 19.24 3 1.24 19.24 17.06 3 3.94 WIMAR02 22.64 23.02 3 0.56 23.02 16.08 3 10.78

223

APPENDIX 4-1: SAMPLING POINT DATA-YALU COMMUNITY FOREST AREA

Tree Plot East North Date No Species POM Diameter Description Secondary 1 484643 9268927 4/07/2009 1 PTE IND 1.3 18 Forest - Yalu Secondary 1 484643 9268927 4/07/2009 2 TRE 1.3 27 Forest - Yalu Secondary 1 484643 9268927 4/07/2009 3 HIB 1.3 29 Forest - Yalu Secondary 1 484643 9268927 4/07/2009 4 MAC 1.3 17 Forest - Yalu Secondary 1 484643 9268927 4/07/2009 5 HIB 1.3 33.5 Forest - Yalu Secondary 1 484643 9268927 4/07/2009 6 ? 1.3 30 Forest - Yalu Secondary 1 484643 9268927 4/07/2009 7 ? 1.3 30 Forest - Yalu Secondary 1 484643 9268927 4/07/2009 8 PTE IND 1.3 51 Forest - Yalu Secondary 1 484643 9268927 4/07/2009 9 TRE 1.3 33 Forest - Yalu Secondary 1 484643 9268927 4/07/2009 10 ? 1.3 20 Forest - Yalu Secondary 1 484643 9268927 4/07/2009 11 POM PIN 1.3 24.5 Forest - Yalu Secondary 1 484643 9268927 4/07/2009 12 ? 1.3 40 Forest - Yalu Secondary 1 484643 9268927 4/07/2009 13 ? 1.3 30 Forest - Yalu Secondary 1 484643 9268927 4/07/2009 14 HIB 1.3 39 Forest - Yalu Secondary 1 484643 9268927 4/07/2009 15 TRE 1.3 22.5 Forest - Yalu Secondary 1 484643 9268927 4/07/2009 16 TER 1.3 26 Forest - Yalu Primary Forest 2 484713 9268265 4/07/2009 1 AIL 2 88 - Yalu Primary Forest 2 484713 9268265 5/07/2009 2 MYR 1.3 22 - Yalu Primary Forest 2 484713 9268265 6/07/2009 3 CEL PHI 1.3 17.5 - Yalu Primary Forest 2 484713 9268265 7/07/2009 4 STE 1.3 60 - Yalu

224

Tree Plot East North Date No Species POM Diameter Description Primary Forest 2 484713 9268265 8/07/2009 5 CEL LAT 1.3 33.5 - Yalu Primary Forest 2 484713 9268265 9/07/2009 6 VIT 2 95 - Yalu Primary Forest 2 484713 9268265 10/07/2009 7 POM TOM 1.3 12.3 - Yalu Primary Forest 2 484713 9268265 11/07/2009 8 CHN 1.3 18 - Yalu Primary Forest 2 484713 9268265 12/07/2009 9 MYR 1.3 12.9 - Yalu Primary Forest 2 484713 9268265 13/07/2009 10 NEU 1.3 22.5 - Yalu Primary Forest 2 484713 9268265 14/07/2009 11 PTE IND 1.3 47 - Yalu Primary Forest 2 484713 9268265 15/07/2009 12 POM PIN 1.3 48 - Yalu Primary Forest 2 484713 9268265 16/07/2009 13 LIT 2 29 - Yalu Primary Forest 2 484713 9268265 17/07/2009 14 PIM AMB 1.3 27 - Yalu Primary Forest 2 484713 9268265 18/07/2009 15 LIT 2 43.5 - Yalu Primary Forest 2 484713 9268265 19/07/2009 16 MYR 1.3 42 - Yalu Primary Forest 2 484713 9268265 20/07/2009 17 CEL PHI 3 73 - Yalu Primary Forest 2 484713 9268265 21/07/2009 18 CEL PHI 2 40 - Yalu Secondary 3 484634 9268819 17/06/2009 1 TRH 1.3 36.5 Forest - Yalu Secondary 3 484634 9268819 17/06/2009 2 TRH 1.3 35.9 Forest - Yalu Secondary 3 484634 9268819 17/06/2009 3 SEM 1.3 11.0 Forest - Yalu Secondary 3 484634 9268819 17/06/2009 4 TER 1.3 60.0 Forest - Yalu Secondary 3 484634 9268819 17/06/2009 5 STE 1.3 25.3 Forest - Yalu Secondary 3 484634 9268819 17/06/2009 6 POM PIN 1.3 57.0 Forest - Yalu Secondary 3 484634 9268819 17/06/2009 7 TER 1.3 63.0 Forest - Yalu Secondary 3 484634 9268819 17/06/2009 8 HIB 1.3 43.5 Forest - Yalu

225

Tree Plot East North Date No Species POM Diameter Description Secondary 3 484634 9268819 17/06/2009 9 INO FAG 1.3 60.0 Forest - Yalu Secondary 3 484634 9268819 17/06/2009 10 BUC 1.3 23.0 Forest - Yalu Secondary 3 484634 9268819 17/06/2009 11 TRH 1.3 31.3 Forest - Yalu Secondary 3 484634 9268819 17/06/2009 12 PIS UMB 1.3 22.0 Forest - Yalu Secondary 3 484634 9268819 17/06/2009 13 PTE IND 1.3 12.0 Forest - Yalu Secondary 4 484630 9268763 17/06/2009 1 POM PIN 1.3 28.0 Forest - Yalu Secondary 4 484630 9268763 17/06/2009 2 POM PIN 1.3 35.9 Forest - Yalu Secondary 4 484630 9268763 17/06/2009 3 END 1.3 37.0 Forest - Yalu Secondary 4 484630 9268763 17/06/2009 4 ? 1.3 30.0 Forest - Yalu Secondary 4 484630 9268763 17/06/2009 5 MAC 1.3 22.5 Forest - Yalu Secondary 4 484630 9268763 17/06/2009 6 TOO SUR 1.3 32.5 Forest - Yalu Secondary 4 484630 9268763 17/06/2009 7 TOO SUR 1.3 30.5 Forest - Yalu Secondary 4 484630 9268763 17/06/2009 8 MAC 1.3 23.0 Forest - Yalu Secondary 4 484630 9268763 17/06/2009 9 PTE IND 1.3 22.0 Forest - Yalu Secondary 4 484630 9268763 17/06/2009 10 PTE IND 1.3 23.9 Forest - Yalu Secondary 4 484630 9268763 17/06/2009 11 TRH 1.3 23.5 Forest - Yalu Secondary 4 484630 9268763 17/06/2009 12 VIT 1.3 16.3 Forest - Yalu Secondary 4 484630 9268763 17/06/2009 13 SEM 1.3 12.8 Forest - Yalu Secondary 4 484630 9268763 17/06/2009 14 TRI 1.3 30.6 Forest - Yalu Secondary 4 484630 9268763 17/06/2009 15 TRI 1.3 28.4 Forest - Yalu Secondary 4 484630 9268763 17/06/2009 16 POM PIN 1.3 25.0 Forest - Yalu Secondary 5 484646 9268686 17/06/2009 1 TIM 1.3 14.3 Forest - Yalu

226

Tree Plot East North Date No Species POM Diameter Description Secondary 5 484646 9268686 17/06/2009 2 GUI 1.3 12.9 Forest - Yalu Secondary 5 484646 9268686 17/06/2009 3 PTE IND 1.3 13.0 Forest - Yalu Secondary 5 484646 9268686 17/06/2009 4 PTE IND 1.3 25.3 Forest - Yalu Secondary 5 484646 9268686 17/06/2009 5 FIC 1.3 33.5 Forest - Yalu Secondary 5 484646 9268686 17/06/2009 6 TRI 1.3 28.6 Forest - Yalu Secondary 5 484646 9268686 17/06/2009 7 FIC 1.3 27.8 Forest - Yalu Secondary 5 484646 9268686 17/06/2009 8 PTE IND 1.3 25.3 Forest - Yalu Secondary 5 484646 9268686 17/06/2009 9 TRH 1.3 41.1 Forest - Yalu Secondary 5 484646 9268686 17/06/2009 10 ELA 1.3 58.3 Forest - Yalu Secondary 5 484646 9268686 17/06/2009 11 STE 1.3 27.2 Forest - Yalu Secondary 5 484646 9268686 17/06/2009 12 ART 1.3 30.1 Forest - Yalu Secondary 5 484646 9268686 17/06/2009 13 PTE IND 1.3 20.4 Forest - Yalu Secondary 5 484646 9268686 17/06/2009 14 PTE IND 1.3 15.3 Forest - Yalu Secondary 5 484646 9268686 17/06/2009 15 SEM 1.3 9.5 Forest - Yalu Secondary 5 484646 9268686 17/06/2009 16 SEM 1.3 11.8 Forest - Yalu Secondary 5 484646 9268686 17/06/2009 17 TRI 1.3 27.5 Forest - Yalu Secondary 5 484646 9268686 17/06/2009 18 TRH 1.3 25.8 Forest - Yalu Secondary 5 484646 9268686 17/06/2009 19 TRH 1.3 25.0 Forest - Yalu Secondary 5 484646 9268686 17/06/2009 20 TRH 1.3 32.8 Forest - Yalu Secondary 5 484646 9268686 17/06/2009 21 TIM 1.3 28.8 Forest - Yalu Secondary 6 _ _ 17/06/2009 1 TRH 1.3 16.7 Forest - Yalu Secondary 6 _ _ 17/06/2009 2 PTE IND 1.3 15.2 Forest - Yalu

227

Tree Plot East North Date No Species POM Diameter Description Secondary 6 _ _ 17/06/2009 3 PTE IND 1.3 19.2 Forest - Yalu Secondary 6 _ _ 17/06/2009 4 PTE IND 1.3 15.8 Forest - Yalu Secondary 6 _ _ 17/06/2009 5 FIC 1.3 50.6 Forest - Yalu Secondary 6 _ _ 17/06/2009 6 TIM 1.3 21.8 Forest - Yalu Secondary 6 _ _ 17/06/2009 7 STR 1.3 10.1 Forest - Yalu Secondary 6 _ _ 17/06/2009 8 LIT 1.3 24.9 Forest - Yalu Secondary 6 _ _ 17/06/2009 9 MAC 1.3 26.4 Forest - Yalu Secondary 6 _ _ 17/06/2009 10 FIC 1.3 27.5 Forest - Yalu Secondary 6 _ _ 17/06/2009 11 PTE IND 1.3 35.0 Forest - Yalu Secondary 6 _ _ 17/06/2009 12 DYS 1.3 18.3 Forest - Yalu Secondary 6 _ _ 17/06/2009 13 TRH 1.3 23.5 Forest - Yalu Secondary 6 _ _ 17/06/2009 14 TRH 1.3 26.6 Forest - Yalu Secondary 6 _ _ 17/06/2009 15 ART 1.3 21.2 Forest - Yalu Secondary 6 _ _ 17/06/2009 16 TRI 1.3 26.0 Forest - Yalu Secondary 6 _ _ 17/06/2009 17 TRI 1.3 11.7 Forest - Yalu Secondary 7 484761 9268629 17/06/2009 1 TIM 1.3 15.9 Forest - Yalu Secondary 7 484761 9268629 17/06/2009 2 TIM 1.3 15.6 Forest - Yalu Secondary 7 484761 9268629 17/06/2009 3 EUO 1.3 35.1 Forest - Yalu Secondary 7 484761 9268629 17/06/2009 4 TRH 1.3 21.5 Forest - Yalu Secondary 7 484761 9268629 17/06/2009 5 TRH 1.3 33.6 Forest - Yalu Secondary 7 484761 9268629 17/06/2009 6 PTE IND 1.3 30.5 Forest - Yalu Secondary 7 484761 9268629 17/06/2009 7 POM PIN 1.3 28.4 Forest - Yalu

228

Tree Plot East North Date No Species POM Diameter Description Secondary 7 484761 9268629 17/06/2009 8 INT 1.3 25.6 Forest - Yalu Secondary 7 484761 9268629 17/06/2009 9 ANT CHI 1.3 17.2 Forest - Yalu Secondary 7 484761 9268629 17/06/2009 10 MYR 1.3 14.2 Forest - Yalu Secondary 7 484761 9268629 17/06/2009 11 TIM 1.3 22.6 Forest - Yalu Secondary 7 484761 9268629 17/06/2009 12 ? 1.3 47.0 Forest - Yalu Secondary 7 484761 9268629 17/06/2009 13 ART 1.3 31.3 Forest - Yalu Secondary 7 484761 9268629 17/06/2009 14 VIT COF 1.3 24.1 Forest - Yalu Secondary 7 484761 9268629 17/06/2009 15 PTE IND 1.3 19.8 Forest - Yalu Secondary 7 484761 9268629 17/06/2009 16 MAC 1.3 39.8 Forest - Yalu Secondary 7 484761 9268629 17/06/2009 17 MAC 1.3 21.4 Forest - Yalu Secondary 7 484761 9268629 17/06/2009 18 MAC 1.3 19.0 Forest - Yalu Secondary 7 484761 9268629 17/06/2009 19 GUI 1.3 24.4 Forest - Yalu Secondary 7 484761 9268629 17/06/2009 20 TIM 1.3 24.7 Forest - Yalu Secondary 7 484761 9268629 17/06/2009 21 SEM 1.3 14.2 Forest - Yalu Secondary 7 484761 9268629 17/06/2009 22 SEM 1.3 15.6 Forest - Yalu Secondary 7 484761 9268629 17/06/2009 23 SEM 1.3 16.3 Forest - Yalu Secondary 7 484761 9268629 17/06/2009 24 PTE IND 1.3 31.6 Forest - Yalu Secondary 7 484761 9268629 17/06/2009 25 ANT CHI 1.3 25.1 Forest - Yalu Secondary 7 484761 9268629 17/06/2009 26 ANT CHI 1.3 21.0 Forest - Yalu Secondary 7 484761 9268629 17/06/2009 27 TIM 1.3 26.6 Forest - Yalu Secondary 7 484761 9268629 17/06/2009 28 TIM 1.3 15.1 Forest - Yalu Secondary 8 484610 9268470 17/06/2009 1 TRH 1.3 26.0 Forest - Yalu

229

Tree Plot East North Date No Species POM Diameter Description Secondary 8 484610 9268470 17/06/2009 2 EUO 1.3 14.2 Forest - Yalu Secondary 8 484610 9268470 17/06/2009 3 EUO 1.3 11.8 Forest - Yalu Secondary 8 484610 9268470 17/06/2009 4 TIM 1.3 21.1 Forest - Yalu Secondary 8 484610 9268470 17/06/2009 5 PTE IND 1.3 29.4 Forest - Yalu Secondary 8 484610 9268470 17/06/2009 6 HIB 1.3 79.2 Forest - Yalu Secondary 8 484610 9268470 17/06/2009 7 TRH 1.3 41.1 Forest - Yalu Secondary 8 484610 9268470 17/06/2009 8 ART 1.3 113.5 Forest - Yalu Secondary 8 484610 9268470 17/06/2009 9 PTE IND 1.3 19.8 Forest - Yalu Secondary 8 484610 9268470 17/06/2009 10 TRH 1.3 52.0 Forest - Yalu Secondary 8 484610 9268470 17/06/2009 11 MAC 1.3 23.3 Forest - Yalu Secondary 8 484610 9268470 17/06/2009 12 POL 1.3 26.1 Forest - Yalu Secondary 8 484610 9268470 17/06/2009 13 CAN 1.3 31.6 Forest - Yalu Secondary 8 484610 9268470 17/06/2009 14 POM PIN 1.3 47.2 Forest - Yalu Secondary 8 484610 9268470 17/06/2009 15 EUO 1.3 11.6 Forest - Yalu Secondary 8 484610 9268470 17/06/2009 16 PTE IND 1.3 11.4 Forest - Yalu Secondary 8 484610 9268470 17/06/2009 17 CAN 1.3 28.1 Forest - Yalu Secondary 8 484610 9268470 17/06/2009 18 POM PIN 1.3 56.1 Forest - Yalu Secondary 8 484610 9268470 17/06/2009 19 ANT CHI 1.3 28.3 Forest - Yalu Secondary 8 484610 9268470 17/06/2009 20 POM PIN 1.3 19.6 Forest - Yalu Secondary 8 484610 9268470 17/06/2009 21 EUO 1.3 50.0 Forest - Yalu Secondary 8 484610 9268470 17/06/2009 22 FIC 1.3 24.6 Forest - Yalu Secondary 8 484610 9268470 17/06/2009 23 FIC 1.3 24.6 Forest - Yalu

230

Tree Plot East North Date No Species POM Diameter Description Secondary 8 484610 9268470 17/06/2009 24 TRI 1.3 15.3 Forest - Yalu Secondary 9 484522 92685314 17/06/2009 1 SEM 1.3 54.0 Forest - Yalu Secondary 9 484522 92685314 17/06/2009 2 INO FAG 1.3 55.0 Forest - Yalu Secondary 9 484522 92685314 17/06/2009 3 BUC 1.3 36.9 Forest - Yalu Secondary 9 484522 92685314 17/06/2009 4 ANT CHI 1.3 50.5 Forest - Yalu Secondary 9 484522 92685314 17/06/2009 5 GUI 1.3 19.5 Forest - Yalu Secondary 9 484522 92685314 17/06/2009 6 LIT 1.3 35.5 Forest - Yalu Secondary 9 484522 92685314 17/06/2009 7 PIS UMB 1.3 30.0 Forest - Yalu Secondary 9 484522 92685314 17/06/2009 8 SEM 1.3 37.1 Forest - Yalu Secondary 9 484522 92685314 17/06/2009 9 PIS UMB 1.3 17.2 Forest - Yalu Secondary 9 484522 92685314 17/06/2009 10 PIS UMB 1.3 15.3 Forest - Yalu Secondary 9 484522 92685314 17/06/2009 11 BRI 1.3 180.0 Forest - Yalu Secondary 9 484522 92685314 17/06/2009 12 VIT COF 1.3 180.0 Forest - Yalu Secondary 9 484522 92685314 17/06/2009 13 TER 1.3 20.1 Forest - Yalu Secondary 9 484522 92685314 17/06/2009 14 PIS UMB 1.3 19.6 Forest - Yalu Secondary 9 484522 92685314 17/06/2009 15 PTE IND 1.3 185.0 Forest - Yalu Secondary 10 484446 9268164 17/06/2009 1 END 1.3 38.1 Forest - Yalu Secondary 10 484446 9268164 17/06/2009 2 CAN 1.3 54.8 Forest - Yalu Secondary 10 484446 9268164 17/06/2009 3 MAC 1.3 34.6 Forest - Yalu Secondary 10 484446 9268164 17/06/2009 4 MAC 1.3 28.9 Forest - Yalu Secondary 10 484446 9268164 17/06/2009 5 MAC 1.3 33.6 Forest - Yalu Secondary 10 484446 9268164 17/06/2009 6 PTE IND 1.3 32.4 Forest - Yalu

231

Tree Plot East North Date No Species POM Diameter Description Secondary 10 484446 9268164 17/06/2009 7 CAN 1.3 37.5 Forest - Yalu Secondary 10 484446 9268164 17/06/2009 8 MAC 1.3 27.4 Forest - Yalu Secondary 10 484446 9268164 17/06/2009 9 MAC 1.3 39.3 Forest - Yalu Secondary 10 484446 9268164 17/06/2009 10 PTE IND 1.3 18.0 Forest - Yalu Secondary 10 484446 9268164 17/06/2009 11 ANT CHI 1.3 50.7 Forest - Yalu Secondary 10 484446 9268164 17/06/2009 12 STE 1.3 16.5 Forest - Yalu Secondary 10 484446 9268164 17/06/2009 13 CEL 1.3 57.0 Forest - Yalu Secondary 10 484446 9268164 17/06/2009 14 LIT 1.3 39.4 Forest - Yalu Secondary 10 484446 9268164 17/06/2009 15 STE AMP 1.3 10.7 Forest - Yalu Secondary 10 484446 9268164 17/06/2009 16 PTE IND 1.3 19.5 Forest - Yalu Secondary 10 484446 9268164 17/06/2009 17 LIT 1.3 13.0 Forest - Yalu Secondary 10 484446 9268164 17/06/2009 18 PIM AMB 1.3 23.4 Forest - Yalu Secondary 10 484446 9268164 17/06/2009 19 ANT CHI 1.3 51.7 Forest - Yalu Secondary 10 484446 9268164 17/06/2009 20 AGL 1.3 18.0 Forest - Yalu Secondary 10 484446 9268164 17/06/2009 21 ALS 1.3 19.2 Forest - Yalu Secondary 10 484446 9268164 17/06/2009 22 STE 1.3 26.5 Forest - Yalu Secondary 10 484446 9268164 17/06/2009 23 MIC 1.3 20.1 Forest - Yalu Secondary 10 484446 9268164 17/06/2009 24 PTE IND 1.3 186.0 Forest - Yalu Secondary 11 484612 9268157 17/06/2009 1 FIC 1.3 37.5 Forest - Yalu Secondary 11 484612 9268157 17/06/2009 2 PLA 1.3 13.0 Forest - Yalu Secondary 11 484612 9268157 17/06/2009 3 INO FAG 1.3 24.2 Forest - Yalu Secondary 11 484612 9268157 17/06/2009 4 STE 1.3 69.0 Forest - Yalu

232

Tree Plot East North Date No Species POM Diameter Description Secondary 11 484612 9268157 17/06/2009 5 PIM AMB 1.3 46.6 Forest - Yalu Secondary 11 484612 9268157 17/06/2009 6 GNE GNE 1.3 15.8 Forest - Yalu Secondary 11 484612 9268157 17/06/2009 7 PIM AMB 1.3 25.5 Forest - Yalu Secondary 11 484612 9268157 17/06/2009 8 PIM AMB 1.3 38.5 Forest - Yalu Secondary 11 484612 9268157 17/06/2009 9 GNE GNE 1.3 13.0 Forest - Yalu Secondary 11 484612 9268157 17/06/2009 10 PIM AMB 1.3 25.5 Forest - Yalu Secondary 11 484612 9268157 17/06/2009 11 PIM AMB 1.3 26.0 Forest - Yalu Secondary 11 484612 9268157 17/06/2009 12 CEL 1.3 18.0 Forest - Yalu Secondary 11 484612 9268157 17/06/2009 13 CEL 1.3 25.5 Forest - Yalu Secondary 11 484612 9268157 17/06/2009 14 GUI 1.3 29.0 Forest - Yalu Secondary 11 484612 9268157 17/06/2009 15 CEL 1.3 71.5 Forest - Yalu Secondary 11 484612 9268157 17/06/2009 16 STE 1.3 70.0 Forest - Yalu Secondary 11 484612 9268157 17/06/2009 17 MIC 1.3 21.0 Forest - Yalu Secondary 11 484612 9268157 17/06/2009 18 PIM AMB 1.3 34.6 Forest - Yalu Secondary 11 484612 9268157 17/06/2009 19 MIS 1.3 24.6 Forest - Yalu Secondary 11 484612 9268157 17/06/2009 20 CEL 1.3 70.0 Forest - Yalu Secondary 11 484612 9268157 17/06/2009 21 CEL 1.3 49.6 Forest - Yalu Secondary 12 484699 9268074 17/06/2009 1 INT 2.0 92.6 Forest - Yalu Secondary 12 484699 9268074 17/06/2009 2 TER 1.3 63.4 Forest - Yalu Secondary 12 484699 9268074 17/06/2009 3 SEM 1.3 43.0 Forest - Yalu Secondary 12 484699 9268074 17/06/2009 4 TER 1.3 29.3 Forest - Yalu Secondary 12 484699 9268074 17/06/2009 5 PIM AMB 1.3 26.0 Forest - Yalu

233

Tree Plot East North Date No Species POM Diameter Description Secondary 12 484699 9268074 17/06/2009 6 PIM AMB 1.3 25.0 Forest - Yalu Secondary 12 484699 9268074 17/06/2009 7 PIM AMB 1.3 31.0 Forest - Yalu Secondary 12 484699 9268074 17/06/2009 8 SYZ 2.0 56.0 Forest - Yalu Secondary 12 484699 9268074 17/06/2009 9 TRI 2.0 130.0 Forest - Yalu Secondary 12 484699 9268074 17/06/2009 10 ? 1.3 18.0 Forest - Yalu Secondary 12 484699 9268074 17/06/2009 11 PIS UMB 1.3 32.0 Forest - Yalu Secondary 12 484699 9268074 17/06/2009 12 LIT 1.3 15.0 Forest - Yalu Secondary 12 484699 9268074 17/06/2009 13 TRI 1.3 47.1 Forest - Yalu Secondary 12 484699 9268074 17/06/2009 14 STE 1.3 28.4 Forest - Yalu Secondary 12 484699 9268074 17/06/2009 15 CER 1.3 25.2 Forest - Yalu Secondary 12 484699 9268074 17/06/2009 16 INT 1.3 82.5 Forest - Yalu Secondary 12 484699 9268074 17/06/2009 17 TER 3.0 45.0 Forest - Yalu Secondary 13 484743 9268126 17/06/2009 1 PIM AMB 1.3 42.0 Forest - Yalu Secondary 13 484743 9268126 17/06/2009 2 CEL 1.3 49.0 Forest - Yalu Secondary 13 484743 9268126 17/06/2009 3 MIC 1.3 13.0 Forest - Yalu Secondary 13 484743 9268126 17/06/2009 4 PTE IND 1.3 53.0 Forest - Yalu Secondary 13 484743 9268126 17/06/2009 5 CEL 1.3 76.1 Forest - Yalu Secondary 13 484743 9268126 17/06/2009 6 CEL 1.3 42.0 Forest - Yalu Secondary 13 484743 9268126 17/06/2009 7 CEL 1.3 34.0 Forest - Yalu Secondary 13 484743 9268126 17/06/2009 8 PTE IND 4.0 70.5 Forest - Yalu Secondary 13 484743 9268126 17/06/2009 9 MAC 1.3 32.0 Forest - Yalu Secondary 13 484743 9268126 17/06/2009 10 MAC 1.3 46.0 Forest - Yalu

234

Tree Plot East North Date No Species POM Diameter Description Secondary 13 484743 9268126 17/06/2009 11 END 1.3 30.0 Forest - Yalu Secondary 13 484743 9268126 17/06/2009 12 MAC 1.3 19.0 Forest - Yalu Secondary 13 484743 9268126 17/06/2009 13 MAC 1.3 20.3 Forest - Yalu Secondary 13 484743 9268126 17/06/2009 14 ART 1.3 22.0 Forest - Yalu Secondary 13 484743 9268126 17/06/2009 15 PTE IND 1.3 52.5 Forest - Yalu Secondary 13 484743 9268126 17/06/2009 16 MAC 1.3 12.4 Forest - Yalu Secondary 13 484743 9268126 17/06/2009 17 AGL 1.3 41.5 Forest - Yalu Secondary 14 484837 9268212 17/06/2009 1 GAR 2.0 29.1 Forest - Yalu Secondary 14 484837 9268212 17/06/2009 2 AGL 1.3 28.0 Forest - Yalu Secondary 14 484837 9268212 17/06/2009 3 TER 1.3 36.4 Forest - Yalu Secondary 14 484837 9268212 17/06/2009 4 TER 1.3 33.0 Forest - Yalu Secondary 14 484837 9268212 17/06/2009 5 PIS UMB 1.3 15.6 Forest - Yalu Secondary 14 484837 9268212 17/06/2009 6 POM PIN 1.3 58.4 Forest - Yalu Secondary 14 484837 9268212 17/06/2009 7 TER 1.3 36.5 Forest - Yalu Secondary 14 484837 9268212 17/06/2009 8 END 1.3 39.6 Forest - Yalu Secondary 14 484837 9268212 17/06/2009 9 TER 1.3 23.3 Forest - Yalu Secondary 14 484837 9268212 17/06/2009 10 STE 1.3 63.0 Forest - Yalu Secondary 15 484784 9268298 17/06/2009 1 CEL 1.3 36.7 Forest - Yalu Secondary 15 484784 9268298 17/06/2009 2 PIM AMB 1.3 36.0 Forest - Yalu Secondary 15 484784 9268298 17/06/2009 3 CEL 1.5 61.9 Forest - Yalu Secondary 15 484784 9268298 17/06/2009 4 DYS 1.3 24.0 Forest - Yalu Secondary 15 484784 9268298 17/06/2009 5 LIT 1.3 46.5 Forest - Yalu

235

Tree Plot East North Date No Species POM Diameter Description Secondary 15 484784 9268298 17/06/2009 6 FIC 4.0 150.0 Forest - Yalu Secondary 15 484784 9268298 17/06/2009 7 POM PIN 1.3 57.9 Forest - Yalu Secondary 15 484784 9268298 17/06/2009 8 MIS 1.3 27.8 Forest - Yalu Secondary 15 484784 9268298 17/06/2009 9 CEL 4.0 57.0 Forest - Yalu Secondary 15 484784 9268298 17/06/2009 10 LIT 1.3 29.4 Forest - Yalu Secondary 15 484784 9268298 17/06/2009 11 ANT CHI 1.3 43.4 Forest - Yalu Secondary 15 484784 9268298 17/06/2009 12 PIS UMB 1.3 23.6 Forest - Yalu Secondary 15 484784 9268298 17/06/2009 13 GNE GNE 1.3 15.0 Forest - Yalu Secondary 15 484784 9268298 17/06/2009 14 CEL 1.5 60.3 Forest - Yalu Secondary 16 484840 9268332 17/06/2009 1 INT 1.3 57.0 Forest - Yalu Secondary 16 484840 9268332 17/06/2009 2 MIC 1.3 24.6 Forest - Yalu Secondary 16 484840 9268332 17/06/2009 3 CEL 4.0 75.0 Forest - Yalu Secondary 16 484840 9268332 17/06/2009 4 POM PIN 2.0 28.6 Forest - Yalu Secondary 16 484840 9268332 17/06/2009 5 MIC 1.3 24.0 Forest - Yalu Secondary 16 484840 9268332 17/06/2009 6 TRI 1.3 17.6 Forest - Yalu Secondary 16 484840 9268332 17/06/2009 7 FIC 1.3 12.0 Forest - Yalu Secondary 16 484840 9268332 17/06/2009 8 PIM AMB 1.3 28.7 Forest - Yalu Secondary 16 484840 9268332 17/06/2009 9 GNE GNE 1.3 14.6 Forest - Yalu Secondary 16 484840 9268332 17/06/2009 10 PIM AMB 1.3 25.0 Forest - Yalu Secondary 16 484840 9268332 17/06/2009 11 BIS JAV 1.3 60.5 Forest - Yalu Secondary 16 484840 9268332 17/06/2009 12 STE 1.3 55.3 Forest - Yalu Secondary 16 484840 9268332 17/06/2009 13 PIM AMB 1.3 37.8 Forest - Yalu

236

Tree Plot East North Date No Species POM Diameter Description Secondary 17 484890 9268434 17/06/2009 1 PTE IND 1.3 32.3 Forest - Yalu Secondary 17 484890 9268434 17/06/2009 2 ART 1.5 73.3 Forest - Yalu Secondary 17 484890 9268434 17/06/2009 3 POM PIN 3.0 70.5 Forest - Yalu Secondary 17 484890 9268434 17/06/2009 4 DRA 3.0 68.0 Forest - Yalu Secondary 17 484890 9268434 17/06/2009 5 HOR 1.3 25.0 Forest - Yalu Secondary 17 484890 9268434 17/06/2009 6 MAC 1.3 14.3 Forest - Yalu Secondary 17 484890 9268434 17/06/2009 7 PTE IND 1.5 62.3 Forest - Yalu Secondary 17 484890 9268434 17/06/2009 8 CEL 3.0 66.4 Forest - Yalu Secondary 17 484890 9268434 17/06/2009 9 PTE IND 1.3 22.0 Forest - Yalu Secondary 17 484890 9268434 17/06/2009 10 PTE IND 1.3 17.0 Forest - Yalu Secondary 17 484890 9268434 17/06/2009 11 PTE IND 1.3 14.0 Forest - Yalu

APPENDIX 4-2: INVENTORY DATA-GABENSIS COMMUNITY FOREST

Tree Plot East North Date No Species POM Diameter Description Logged Forest - 1 469324 9256048 4/06/2009 1 POM PIN 3 69.5 Gabensis Logged Forest - 1 469324 9256048 4/06/2009 2 INT 1.3 59 Gabensis Logged Forest - 1 469324 9256048 4/06/2009 3 CHN 1.3 61 Gabensis Logged Forest - 1 469324 9256048 4/06/2009 4 TER 2 43 Gabensis Logged Forest - 1 469324 9256048 4/06/2009 5 POM PIN 2 59 Gabensis Logged Forest - 1 469324 9256048 4/06/2009 6 POM PIN 2 59 Gabensis Logged Forest - 1 469324 9256048 4/06/2009 7 POM PIN 1.3 70 Gabensis

237

Tree Plot East North Date No Species POM Diameter Description Logged Forest - 1 469324 9256048 4/06/2009 8 CHN 1.3 55.5 Gabensis Logged Forest - 1 469324 9256048 4/06/2009 9 INT 1.5 28 Gabensis Logged Forest - 1 469324 9256048 4/06/2009 10 TER 2 53.5 Gabensis Logged Forest - 1 469324 9256048 4/06/2009 11 TER 1.3 40 Gabensis Logged Forest - 1 469324 9256048 4/06/2009 12 HRN 1.3 36.5 Gabensis Logged Forest - 1 469324 9256048 4/06/2009 13 CHN 1.8 52 Gabensis Logged Forest - 1 469324 9256048 4/06/2009 14 CNN 1.8 57.5 Gabensis Logged Forest - 1 469324 9256048 4/06/2009 15 CHN 1.8 38.5 Gabensis Logged Forest- 1 469324 9256048 4/06/2009 16 CHN 1.8 33 Gabensis Logged Forest - 1 469324 9256048 4/06/2009 17 POM PIN 1.3 30.5 Gabensis Logged Forest - 1 469324 9256048 4/06/2009 18 PLA 1.3 30 Gabensis Logged Forest - 1 469324 9256048 4/06/2009 19 ? 1.3 20 Gabensis Secondary Forest - 2 470782 9257001 4/06/2009 1 HRN 1.3 43 Gabensis Secondary Forest - 2 470782 9257001 4/06/2009 2 POM PIN 2 55 Gabensis Secondary Forest - 2 470782 9257001 4/06/2009 3 CHN 2 94 Gabensis Secondary Forest - 2 470782 9257001 4/06/2009 4 ? 1.3 30 Gabensis Secondary Forest - 2 470782 9257001 4/06/2009 5 ? 1.3 30 Gabensis Secondary Forest - 2 470782 9257001 4/06/2009 6 PTE IND 2 85 Gabensis Secondary Forest - 2 470782 9257001 4/06/2009 7 ? 1.3 30 Gabensis Secondary Forest - 2 470782 9257001 4/06/2009 8 ? 1.3 30 Gabensis Secondary Forest - 2 470782 9257001 4/06/2009 9 ? 1.3 30 Gabensis Secondary Forest - 2 470782 9257001 4/06/2009 10 ? 1.3 30 Gabensis

238

Tree Plot East North Date No Species POM Diameter Description Secondary Forest - 2 470782 9257001 4/06/2009 11 PTE IND 2 57 Gabensis Secondary Forest - 2 470782 9257001 4/06/2009 12 PTE IND 1.3 31 Gabensis Secondary Forest - 2 470782 9257001 4/06/2009 13 MAS 2 55 Gabensis Secondary Forest - 2 470782 9257001 4/06/2009 14 POM PIN 2 41 Gabensis Secondary Forest - 2 470782 9257001 4/06/2009 15 POM PIN 2 47 Gabensis Secondary Forest - 2 470782 9257001 4/06/2009 16 ? 1.3 30 Gabensis Secondary Forest - 2 470782 9257001 4/06/2009 17 POM PIN 1.5 43 Gabensis Secondary Forest - 2 470782 9257001 4/06/2009 18 PTE IND 1.5 80 Gabensis

239

APPENDIX 5-1: PNGFA MINIMUM EXPORT PRICE SPECIES GROUP

GroupSpecies ID Species Group Species ID Species Group Species ID Species EAG Eaglewood 1 2 3 BUR Burckella AGL Aglaia AMB Amberoi CAL Calophyllum AMO Amoora [Pacific Maple] CAH Camphorwood PNG [Cinnamomum] CAG Canarium Grey ANT Antiaris CAM Campnosperma CAR Canarium Red BAS Basswood PNG CEH Celtis Hard CEP Cedar Pencil CEM Cedar Mangrove CEL Celtis Light DIL Dillenia CER Cedar Red CRY Cryptocarya [Medang] ERI Erima BEW Elmerrillia [Beech Wau] DYS Dysox HEK Hekakoro (Gluta) HOH Hopea Heavy END Endiandra [Medang] KWI Kwila HOL Hopea Light GAG Garo Garo LOP Lophopetallum [Perupok] KAM Kamarere GUW Gum Water[Syzygium] MAL Malas KEM Kempas [PNG] HER Heritiera MER Mersawa [PNG] LAB Labula LIT Litsea [Medang] PLR Planchonella Red VIT Vitex PNG SAP Satin[wood]heart Pink [Buchanania] PLW Planchonella White SIW Siris White [Ailantus] TAU Taun TEA Teak TER Terminalia WAL Walnut PNG

4 4 Cont…. 4 Cont…. ALB Albizia Brown GON Gonostyllus OWT Oak White Tulip ALW Albizia White GOR Gordonia OPS Oreocallis [Oak Pink Silky] ALH Alstonia Hard HAY Hardwood Yellow RWD Oriomo Redwood ASH Ash Hickory HEN Hernandia PAN Pangium ASP Ash Papuan HIB Hibiscus [Bulolo Ash] PAS Parastemon ASG Ash Scaly [Ganophyllum] IRS Ironbark Scrub [Bridelia] PAR Paratocarpus BAR Barringtonia IVW Ivorywood PNG PER Pericopsis BEP Beech PNG KAN Kandis PIM Pimeleodendron BIP Birch Pink KAP Kapiak [Artocarpus] PLA Planchonia BOM Bombax KAK Kasi Kasi PLB Plum Busu BOS Box Swamp PNG KIN Kingiodendron PLT Plum Tulip BOW Boxwood PNG (Zanthophyllum) KIS Kiso OAP PNG Oak MGB Brown Mangrove LAP Lapome [PNG] TUL PNG Tulipwood BTO Brown Tulip Oak MAC Macaranga POL Polyalthia CAN Cananga MAH Malaha QUA Quandong PNG CAD Candlenut MAN Mango [Mangifera] VAT Resak [Vatica] CLL Carallia MAB Mangrove Black RHU Rhus CEJ Cedar Java [Bischofia] MAM Mangrove Milky SAH Saffron Heart CWW Cheesewood White [Milky Pine] MAR Mangrove Red SAS Sassafras PNG CWY Cheesewood Yellow MAW Mangrove White SAG Satinheart Green CHR Chrysophyllum MAK Manilkara SEM Semicarpus COW Coachwood [PNG] MAT Maniltoa SIL Silkwood (Silver Maple) DRY Drypetes MAS Maple Scented [Flindersia] ASS Silkwood Ash DUA Duabunga MIG Milkwood Grey [Cerbera] SLO Sloanea EUH Euodia [Heavy] NEO Neoscortechinia SPO Spondias EUL Euodia [Light] NEU Neuburgia STE Sterculia FIG Fig PNG HOR Nutmeg [Horsfieldia] TET Tea Tree FLA Flacourtia NUT Nutmeg [Myristica] TEM Tetrameles GAL Galbulimima [White Magnolia] OAR Oak Red TRC Trichadenia GAR Garuga OSC Oak She (Casuarina) TRI Tristiropsis GLO Glochidion OAS Oak Silky WAB Wattle Brown PNG GME Gmelina [White beech] OAW Oak White WAR Wattle Red PNG AMW White Almond / Alphitonia 5 6 BLB Blackbean POB [Brown] Podocarp CTE Ctenolophon POH [Highland] Podocarp ELE Eleocarpus ARA Araucaria (Hoop pine / Klinki pine) EUG Eugenia [Syzygium] BAL Balsa EXA Exanto CLP Celery-Top PNG Pine FIR Firmiana COR Cordia GAS Gastonia DAC Dacrydium ILE Ilex DIO Diospyros MIR Mix Red EBO Ebony PNG MIW Mix White AGA Kauri PNG [Agathis] MIX Mixed Species KEW Kerosene Wood PRO Protium LIB Libocedrus PRU Prunus POD Podocarpus SCH Schima ROS Rosewood PNG STR Steropsis

240

APPENDIX 5-2: CURRENT FOREST USES IN CASE STUDY SITES

241

APPENDIX 5-3: FUTURE FOREST USES IN CASE STUDY SITES

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APPENDIX 6-1: REQUIREMENTS – COMMUNITY SAWMILL

A sawmill project is managed by a community to supply the local market with little capacity and light equipment. All sawn timber produced are sold in the domestic market and for other community use. All costs are in PNG Kina. The production and marketing requirements for such a project are as follow;  1 x Lucas mill, 1 x Stihl 90 chainsaw + accessories  40m3 of logs harvested/8 productive months  At a 50% recovery, production of 20m3 sawn timber/8 productive months.  7 men team on wages @ K80/m3.  Maintenance, repairs, spare parts @K70/m3  Fuel and oil consumption @ K120  Transport of sawn timber to local market @ K60/m3  Sawn timber sold at the local market @ K600/m3

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APPENDIX 6-2: REQUIREMENTS – LOCAL PROCESSING

Decision Alternative 1: CMU managed processing Local processing is managed by a community entity referred to as the central marketing unit (CMU) with mechanised equipment and increased capacity and production for the export market. Production and marketing requirements that have been used to determine the cash flow as input variables in the decision tree model are as following;  1 x Lucas mill, 2 x Stihl 90 chainsaw + accessories  1 x 4WD truck, Hino FT/GT 500 series  1 x 4 WD tractor, Massey Ferguson-72HD  400m3 of logs harvested/8 productive months  At a 50% recovery, production of 200m3 sawn timber/8 productive months.  10 men team on wages @ K80/m3. 3  10% increase in maintenance, repairs, spare parts @K77/m 3  10% increase in fuel and oil consumption @ K132/m 3  Transport of sawn timber to wharf for export market @ K255/m  Sawn timber sold to overseas certified market @ K2,400/m3 and CBFT market @ K1,500/m3.  Other costs for certification: o Certification requirements @ K50/m3 o Fumigation @ K720 one-off payment. o Wharf handling fees @ K950 one-off payment. o Custom clearance @ K330 one-off payment

Decision Alternative 2: Community managed processing Local processing is managed by the community itself with light equipment and limited capacity for the export market. The following production and marketing requirements apply:  1 x Lucas mill, 1 x Stihl 90 chainsaw + accessories  100m3 of logs harvested/8 productive months  At a 50% recovery, production of 50m3 sawn timber/8 productive months.  7 men team on wages @ K80/m3.  5% increase in maintenance, repairs, spare parts @K73.50/m3  5% increase in fuel and oil consumption @ K126/m3  Transport of sawn timber to wharf for export market @ K255/m3  Sawn timber sold to overseas certified market @ K2,400/m3 and CBFT market @ K1,500/m3.  Other costs for certification: o Certification requirements @ K50/m3 o Fumigation @ K720 one-off payme nt. o Wharf handling fees @ K950 one-off payment. o Custom clearance @ K330 one-off payment

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APPENDIX 6-3: REQUIREMENTS – MEDIUM-SCALE LOG EXPORT

Decision Alternative 1: CMU managed log export A medium-scale log export enterprise is managed by a CMU for the export market with mechanised equipment and increased log production. The following production and marketing requirements apply:  2 x Stihl 90 chainsaw + accessories.  1 x Dozer (D6) for roading  1 x Skidder (D7) to move logs from felling site to road side  1 x Front-end loader for loading logs into logging truck  1 x logging truck for transport of logs to wharf  5,000m3 of logs harvested/8 productive months through TA arrangement  15 men logging team on wages @ K250/fortnight for manager and other members @K175/fortnight for 8 productive months (16 fortnights).  50% increase in maintenance, repairs, spare parts @ K105/m3.  50% increase in fuel and oil consumption @ K180/m3  Roading costs @ K40,000/Km3.  Transport of logs to wharf for overseas export @ K255/m3.  CMU logging site is approximately 10km from wharf facilities  Logs sold to overseas market @ K600/m3in Asia and other overseas markets at DecisionK450/m Alternative3. 2: Community managed log export A medium-scale log export enterprise is managed by a Community for the export market  Other costs for log export: with increased capacity and limited mechanised equipment. The following production and o Wharf handling fees @ K950 one-off payment. marketing requirements apply: o Custom clearance @ K330 one-off payment.  2 x Stihlo Log 90 chainsawexport tax + @ accessories. K10/m3  1 x Fronto TA-end registration loader for withloading PNGFA logs into@ K250 logging one truck-off payment  1 x logging truck for transport of logs to wharf  1 x 4WD tractor Massey Fergusson-72HD for moving logs to road side  2500m3 of logs harvested/8 productive months through TA arrangement  10 men logging team on wages @ K250/fortnight for manager and other members @K175/fortnight for 8 productive months (16 fortnights).  20% increase in maintenance, repairs, spare parts @ K84/m3.  20% increase in fuel and oil consumption @ K144/m3  Roading costs @ K6,000/Km.  Transport of logs to wharf for overseas export @ K255/m3.  Community logging site is approximately 15km from wharf facilities  Logs sold to overseas market @ K600/m3in Asia and other overseas markets at K450/m3.  Other costs for log export: o Wharf handling fees @ K950 one-off payment. o Custom clearance @ K330 one-off payment. o Log export tax @ K10/m3 o TA registration with PNGFA @ K250 one-off payment

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APPENDIX 6-4: REQUIREMENTS - CARBON TRADE

A community forest carbon project is managed for selling carbon credits to either a compliance or voluntary market. The estimated costs of logistics, carbon accounting, administration and marketing at the community level used to determine the cash flows as input variables in the decision analysis model are as follow;  Landowner mobilization/social mapping @ K30,000  Equipment for ground-based forest carbon assessment @ K765  GIS / Mapping @ K20,000  Logistics / transport @ K10,000  8 men team for forest carbon assessment: Team leader @K250/fortnight, 5 men inventory team @ K175/person/fortnight, international consultancy @ K10,000, other requirement @ K2,000  Verification / Validation @K2,0000  Marketing @ K10,000  Other administration requirement @ K10,000  Carbon credits sold to compliance market @ USD20 per tonne C and to voluntary market @ USD15 per tonne C. -1  Average aboveground forest carbon @ 150 Mg C ha in the case study site.  Carbon emission from selective timber harvesting is 55%.  CO equivalent of aboveground forest carbon in the case study site is 44/12. 2  Total CO2 emission from case study site is 665,500 t CO2  Community forest area in the case study site is 2,200 ha.  16 fortnights @ 8 productive months

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Minerva Access is the Institutional Repository of The University of Melbourne

Author/s: Yosi, Cossey Keosai

Title: Scenarios for community-based management of cutover forest in Papua New Guinea

Date: 2011

Citation: Yosi, C. K. (2011). Scenarios for community-based management of cutover forest in Papua New Guinea. PhD thesis, Melbourne School of Land and Environment - Forest and Ecosystem Science, The University of Melbourne.

Persistent Link: http://hdl.handle.net/11343/37028

File Description: Scenarios for community-based management of cutover forest in Papua New Guinea

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