Designing Sustainable Development Strategies for Agricultural Commodities across the Landscape Applying the Siting Tool for guiding rubber, cocoa, coffee and oil palm development in North Sumatra, Indonesia
Executive Summary
The purpose of this report is to provide key data and recommendations on the sustainable development of four commodities driving land use change in North Sumatra, Indonesia - coffee, cocoa, palm oil and rubber. In particular, the report seeks to assess the current and future situation in the districts of Mandailing Natal, Tapanuli Selatan and Tapanuli Utara and provide recommendations on the sustainable development of the target commodities in the districts, considering both economic and conservation perspectives.
Methodology Conducting the study involved using the SNV Siting Tool to analyse and identify areas suitable for sustainable crop production of the selected agricultural commodities in the four target districts of North Sumatra, Indonesia. The potential impact of climate change on land suitability was also assessed as a part of the process. The results of the future biophysical suitability analysis were then combined with an analysis of conservation values for the region and depicted in a Risk Indicator Map which identified zones in the landscape that could be converted for agricultural use without significant conflict to conservation values (see Figure 1):
Figure 1: Overview of workflow in developing the ‘Risk Indicator Map’ and selecting priority areas using the Siting Tool
i Results By the 2050s the extreme temperature threshold is expected to significantly change by o1 C, from 27oC in 2014 to 28oC in the 2050s. In addition, significant shifts in rainfall patterns, in particular of extreme precipitation are also projected to decrease gradually from ~600 mm in 2014 to ~400 mm in the 2020s, and finally to ~200m in the 2050s (see Figure 2 and 3). Such changes in climate are expected to create significant impacts on the future suitability of target crops. Maps were generated which combined the results from the biophysical suitability analysis with the results from the climate change analysis. Climate change is anticipated to have a significant impact on the economic viability, priority areas and interventions.
Figure 2: Changes in temperature between 2014, 2020 and 2050 following RCP 8.5 scenario
Figure 3: Changes in precipitation between 2014, 2020 and 2050 following RCP 8.5 scenario
ii The analysis Results of the study revealed a number of key findings in terms of relation to climate change between now 2014 and 2050, and its effects on the suitability of the target crops include:
• Suitability for Arabica: The suitability for Arabica coffee is predicted to decrease in North Sumatra by the 2050s. In Tapanuli Utara and Tapanuli Selatan, the suitability for Arabica production is expected to decline significantly due to climate change, while in Mandailing Natal it is expected that there will become almost no land suitable for Arabica coffee production.
• Suitability for Robusta: Even though it is predicted there will be large areas suitable for Robusta coffee production in 2020 and 2050, it is expected that there will be substantial shifts in suitable locations for production, in particular, in the south of North Sumatra (Mandailing Natal). In Tapanuli Utara there is expected to be an increase in areas suitable for production.
• Suitability for cocoa: It is expected that there will be a significant decline in suitable areas for cocoa production in the south of North Sumatra (Mandailing Natal), and a shift from High Suitability to Medium Suitability for large areas in the eastern regions of the province. Overall, both the amount of land available for cocoa production and land suitability are expected to decline in the province by the 2050s.
• Suitability for oil palm: Despite oil palm’s generally good characteristics for resilience, it is predicted that there will be changes from High Suitability to Medium Suitability for growing the crop in eastern North Sumatra, and for large areas of southern North Sumatra to become completely unsuitable due to changes in rainfall patterns.
• Suitability for rubber: Even though the overall amount of land suitable for rubber production is expected to mostly stay the same by the 2050s, a significant decline in suitable areas is expected in the south of North Sumatra, particularly in Mandailing Natal and to a lesser extent, Tapanuli Utara.
iii High Conservation Values and the Risk Indicator Map Scores developed by Smit et al. 2013 for the presence of each HCV were used to prioritise the most important areas in the study area’s landscape. The results from the HCV analysis were then combined in one map, the Risk Indicator Map (see Table 1 and Figure 4).
Table 1: Overview of risk categories considered for each HCV and carbon stocks, with scores for each risk category (Low risk = Score 1, Medium risk = Score 2 etc.) (Modified from Smit et al. 2013)
Low Risk Medium Risk High Risk Not Suitable
Score 1 2 3 4 HCV 1 No overlap HCV 1 Overlap with home Buffer zone 1 km IUCN areas, national range of protected, conservation areas, endangered and and protected forest endemic species Overlap with Breeding grounds (HCV 1.2) distribution or and nesting places; habitats of protected, grazing/browsing for endangered and endangered species; endemic species and temporal habitats (HCV 1.3) for migratory species (HCV 1.4) HCV 2 No overlap Overlaps with Overlaps with Overlaps with core Endangered 4 km buffer of 20000 2 km buffer of 20000 zone 20000 ha ecosystems, ha ha important ecotone regions, large scale forest HCV 3 No overlap HCV 3 Overlaps with Overlaps with rare threaten ecosystem ecosystems HCV 4.1 No overlap with Overlaps with 4.3 Overlaps with 4.1 & HCV 4 Mangrove, peat, water sources; HCV 4.3 wetland, karst forest riparian zones deep and cloud forest peat (D3 - D5) HCV 4.2 Very light - Light Moderate Heavy Very heavy
Carbon- 0 - 80 ton / ha 80 - 90 ton / ha 90 - 105 ton / ha > 105 ton / ha stocks (Non vegetation)
iv Figure 4 HCV risk indicator map for target districts
v Conclusion & recommendations Mitigating loss forests and conservation values
Using the synthesised spatial information from the study it is possible to select appropriate intervention options and the types of risks involved. To assist in selecting available options for the target district the matrix developed by SNV in Table 2 can be used. This matrix presents an overview of tools and approaches most appropriate to the dominant forest and agriculture systems. In the matrix, dominant agricultural system, forest landscape, approaches and tools are outlined for each risk category.
Table 2: Potential intervention options for different agriculture-forestry systems
Risk category Low Low to medium Medium to high Very high Dominant Intensive high value (Semi) intensive (Semi) extensive Small scale agriculture agriculture (e.g. agriculture; semi (e.g. extensive subsistence system lowland rice, extensive; tree pasture, shifting cash crops) crops cultivation); commercial and subsistence Forest Minimal natural Forest mosaic; Forest mosaic; Generally landscapes forest degraded land; degraded forests undisturbed forest forests plantation for and bare land; timber forest frontiers Approach Promote intensive Plantations for Subsistence REDD finance; agriculture timber and wood- agriculture for food PES payments fuel; agroforestry; security; certified (carbon, watershed, tree planting commodities (full biodiversity etc) traceability); enrichment planting; woodlots for timber/ fuelwood Tools and Agricultural Agriculture Certification market Opportunity cost and actions technology research technology research assessment; REDD+ assessment; and development and development; livelihoods analysis; Economic valuation; benefit distribution Participatory Forest value chain analysis; systems; low monitoring; benefit low emission emission planning distribution systems planning
vi Suggested Priority areas
Of the five agricultural commodities, results from the analysis were most striking for oil palm and cocoa, we will describe these in more detail in the summary. In the report the analysis on other commodities can be found.
Oil Palm When comparing the biophysical suitability for oil palm with the distribution of conservation values the amount of area that can be developed sustainably without major management intervention / support (i.e. ‘Low Risk’) becomes limited. There may be some potential in ‘Medium Risk’ areas which could be considered upon closer examination on the actual presence of identified alues.v
As can be seen in Figure 5, suitable areas for oil palm will shift to areas now containing HCV’s. Considering current sustainability initiatives and the reality of the potential development of oil palm in areas identified in this study as unsuitable, consideration should be given to the Principles of the Roundtable on Sustainable Palm Oil (RSPO), in particular regarding the conversion of peat.
Figure 5 Distribution of oil palm suitability in 2014, 2020 and 2050 in grey shades, against a background of the Risk Indicator Map, visualising suitability of areas based on overlap with HCVs
Over time, cocoa appears to have the most radical shifts in biophysical suitability. In the medium term Tapianuli Utara and Mandailing Natal are suitable for cocoa production, however in the long term the suitability in the central regions of Mandailing Natal are expected to significantly decline (see Figure 6). It is anticipated that overall, suitability for cocoa will shift towards areas where production is considered ‘not allowed’ according to the presence conservation values. The introduction of shade trees could be one viable option to reduce the impacts of climate change. In particular, Mandailing Natal should be considered for such a system, as production suitability seems to collapse by 2050.
vii Figure 6 Distribution of suitability for cocoa in 2014, 2020 and 2050 in grey shades, against a background of the Risk Indicator Map, visualising suitability of areas based on overlap with HCVs
Implementation With the understanding of spatial dimension in the forest-agriculture interface across the landscape as described in this report, it is important to gain better understanding of the district planning and targets for development. Licences for concessions and permit status should be reviewed and compared with the Green Growth vision to be developed (as described above). Concessions could be revoked based on such an agreed development path. The ‘buy in’ from the district government is thus a key factor.
In parallel, an understanding of some of the key issues that will affect the relationship between development and deforestation on a local level should be developed. A better understanding in the underlying socio-economic, market and policy factors that underpin the likely impact of (agricultural) development on forests would be strongly recommended, in particular when specific interventions are promoted, like trainings on intensification or good agricultural practices in general. With the information from this study, in-depth assessments for priority areas should be done. We recommend focusing on areas where the conservation value and threat for conversion is highest, which can be identified when the Risk Indicator Map is compared with (sub) national development plans, including an overlay with existing and planned concessions. For the selection of interventions as well as mainstreaming ideas for sustainable development into the (spatial) planning processes the information from this report provides crucial input for this vision.
viii Contents
List of figures xi
List of tables xii
Executive summary i Methodology i Result ii Conclusion & recommendations vi Abbreviations xiv
Background 1 The Sustainable Landscapes Partnership 1 SNV Indonesia’s support to SLP 1 Structure of the report 1
Chapter 1: Study area 3 North Sumatra 3 Situation in Target districts 4 Topography 4 Hydrology 5 Forest status and forest cover 5 Socio economic situation 6
Chapter 2: Methodology 7 RCA methodology and the SNV Siting Tool 7 Adjusting methodology for SLP 8 Applying the SNV Siting Tool 8 Calculating current and future climate 11 Current climate 11 Future climate 14 Analysing biophysical suitability 15 Regional Physical Planning Project for Transmigration 15 Identifying conservation values 16 HCV 1: Areas with important levels of biodiversity 17 HCV 2: Natural landscapes and dynamics 19 HCV 3: Rare or endangered ecosystems 21 HCV 4: Environmental services 21 Carbon stock distribution 22 Economic evaluation 23
Chapter 3: Results 25 Climate change predictions 25 Climatic suitability maps of target commodities’ production areas 26 Current and future suitability for agricultural commodities 27 Selected method for biophysical suitability analysis 27 Results of the Multi-Criteria Analysis 27
Distribution of High Conservation Values 33 HCV 1: Areas with important levels of biodiversity 33 HCV 2: Natural landscapes and dynamics 35 HCV 3: Rare or endangered ecosystems 36 HCV 4: Environmental services 37 Carbon stock distribution 38 HCV risk indicator map 39 Economic evaluation: Impacts of climate change on commodities 39 Risk indicator map and opportunities for the target commodities 43
ix Chapter 4: Vision for sustainable development 51 Priority areas and interventions 51 Commodities to be promoted in each district 52 Recommendations for mitigating forest loss and maintaining conservation values 53 Recommended next steps 54
References 57
Appendices 59
Appendix 1: Administration of North Sumatra Province with districts and sub-districts in hectares 59 Appendix 2: Rainfall in North Sumatra 60 Appendix 3: Forest cover of North Sumatra Province 60 Appendix 4: Map of MoF forest areas for North Sumatra Province 61 Appendix 5: Status of Forest Areas in North Sumatra Province 62 Appendix 6: Elevation map of focus areas 63 Appendix 7: Sub-watersheds in Tapanuli Utara District 64 Appendix 8: Sub-watersheds in Tapanuli Selatan District 64 Appendix 9: Sub-watersheds in Mandailing Natal District 64 Appendix 10: Status of Forest Areas in Tapanuli Utara 65 Appendix 11: Status of Forest Areas in Tapanuli Selatan 65 Appendix 12: Status of Forest Area in Mandailing Natal 65 Appendix 13: Risk Categories for spatial indicators 66 Appendix 14: Derived bioclimatic variables from World climate Database 67 Appendix 15: Requirement growth for five agricultural commodities based on Ministry of Agriculture (Djaenudin et al. 2000) 67 Appendix 16: High Conservation Values from Indonesia National Interpretation (2008) 74 Appendix 17: List of spatial data used for HCVs desktop analysis in North Sumatra Province 75 Appendix 18: Indonesia designated protected areas in accordance to various regulations 75 Appendix 19: Protected and conservation areas found in Mandailing Natal, Tapanuli Selatan and Tapanuli Utara 76 Appendix 20: Protection status for the 3 big mammals in Sumatra 77 Appendix 21: Family of Wildlife findings and its protection status on Batang Toru protected forest 77 Appendix 22: Indicative Areas HCV 1.4 78 Appendix 23: Large scale forest block (>20.000 ha) in Sumatra 79 Appendix 24. Indicative map of HCV 2.3 Distribution 80 Appendix 25: Bio-physiographic map of Sumatra 81 Appendix 26: Changes in forest cover between 1990 -2011 in Sumatra 82 Appendix 27: Results of identification for Endangered ecosystem in landscape of 3 target districts 83 Appendix 28: Results of identification for rare ecosystem in the landscape of 3 target district inside each biophysical region 84 Appendix 29: Landsystem symbol on each ecosystem type that important for water provision and prevention of floods for downstream commodities 85 Appendix 30: USLE modelling description, including generated parameters 85 Appendix 31: K value predicted using soil orde proxy where proximate by previous research 86 Appendix 32: Erosion potential assessment on land depth and erosion estimation 87 Appendix 33: Indicative HCV 4.3 areas 88 Appendix 34: Carbon stocks for each land cover type identified in target districts (source BioTrop, 1998) 89 Appendix 35: MaxEnt methods 89 Appendix 36: Land suitability MCA 90 Appendix 37: Biophysical suitability MCA 92 Appendix 38: MaxEnt results 94 Appendix 39: Economic evaluation 103 Appendix 40: Results RePPProT 104
x List of figures
Figure 1: Overview of workflow in developing the ‘Risk Indicator Map’ and selecting priority areas using the Siting Tool i
Figure 2: Changes in temperature between 2014, 2020 and 2050 following RCP 8.5 scenario ii
Figure 3: Changes in precipitation between 2014, 2020 and 2050 following RCP 8.5 scenario ii
Figure 4: HCV Risk Indicator Map for target districts v
Figure 5: Distribution of oil palm suitability in 2014, 2020 and 2050 in grey shades, against a background of the Risk Indicator Map, visualising suitability of areas based on overlap with HCVs vii
Figure 6: Distribution of suitability for cocoa in 2014, 2020 and 2050 in grey shades, against a background of the Risk Indicator Map, visualising suitability of areas based on overlap with HCVs viii
Figure 7: Map of North Sumatra Province showing study focus areas 3
Figure 8: Changes of forest cover in study focus areas 5
Figure 9: The four phases of the RCA methodology 8
Figure 10: Venn diagram illustrating the principles of the SNV Siting Tool 9
Figure 11: Study area and distribution of weather station in North Sumatera Provinces. (Black gradient represent of elevation) 11
Figure 12: Overview of workflow in developing the ‘Risk Indicator Map’ and selecting priority areas using the Siting Tool 12
Figure 13: Downscaling results from ~1 km resolution (A) to 250 m resolution (B) 14
Figure 14: IPSL model output-CM5A-LR in the study area for four RCP and baseline (20th Century): (A) rainfall and (B) average temperature 14
Figure 15: Six main stages for HCV Assessment approaches (ProForest 2008) 17
Figure 16: Changes in temperature between 2014, 2020 and 2050 following RCP 8.5 scenario 26
Figure 17: Changes in precipitation between 2014, 2020 and 2050 following RCP 8.5 scenario 27
Figure 18: Distribution of areas biophysically suitable for Arabica coffee in North Sumatera Province based on Ministry of Agriculture suitability criteria (Djaenudin et al. 2000) and predictions for climate change for 2020 and 2050 (downscaled from BIOCLIM 2014) 28
Figure 19: Predicted suitability for Arabica Coffee in 2014, 2020 and 2050 target districts based on Ministry of Agriculture suitability criteria 28
Figure 20: Distribution of areas biophysically suitable for Robusta coffee in North Sumatera Province based on Ministry of Agriculture suitability criteria (Djaenudin et al. 2000) and predictions for climate change for 2020 and 2050 (downscaled from BIOCLIM 2014) 29
Figure 21: Predicted suitability for Robusta coffee in 2014, 2020 and 2050 target districts based on Ministry of Agriculture suitability criteria 29
Figure 22: Distribution of areas biophysically suitable for cocoa in North Sumatera Province based on Ministry of Agriculture suitability criteria (Djaenudin et al. 2000) and predictions for climate change for 2020 and 2050 (downscaled from BIOCLIM 2014) 30
Figure 23: Predicted suitability for cocoa in 2014, 2020 and 2050 target districts based on Ministry of Agriculture suitability criteria 30
Figure 24: Distribution of areas biophysically suitable for rubber in North Sumatera Province based on Ministry of Agriculture suitability criteria (Djaenudin et al. 2000) and predictions for climate change for 2020 and 2050 (downscaled from BIOCLIM 2014) 31
Figure 25: Suitability for rubber in target districts based on Ministry of Agriculture suitability criteria 31
xi Figure 26: Distribution of areas biophysically suitable for oil palm in North Sumatera Province based on Ministry of Agriculture suitability criteria (Djaenudin et al. 2000) and predictions for climate change for 2020 and 2050 (downscaled from BIOCLIM 2014) 32
Figure 27: Suitability for palm oil in target districts based on Ministry of Agriculture suitability criteria 32
Figure 28: Identified HCV 1.1 areas within study focus area 33
Figure 29: HCV 1.2 areas based on distribution of orangutan, tiger and elephant (WWF 2005) 34
Figure 30: Identification of HCV 1.3 in study focus areas 35
Figure 31: Indicative HCV 2.1 in study focus areas 35
Figure 32: Indicative HCV 2.2 in study areas 36
Figure 33: Distribution of HCV 3 in study areas 37
Figure 34: Indicative distribution of HCV 4.1 in study focus area 37
Figure 35: Indicative map on erosion using USLE for study focus area 38
Figure 36: Carbon stock distribution in target districts (in line with BioTrop, 1998) 38
Figure 37: HCV risk indicator map for target districts 39
Figure 38: Predicted impact of climate change on potential production revenue from target commodities in Mandailing Natal (see also Appendix 39) 40
Figure 39: Predicted impact of climate change on potential production revenue from target commodities in Tapanuli Selatan (see also Appendix 39) 41
Figure 40: Predicted impact of climate change on potential production revenue from target commodities in Tapanuli Utara (see Appendix 39) 42
Figure 41: Distribution of oil palm suitability in 2014, 2020 and 2050 in grey shades, against a background of the Risk Indicator Map, visualising suitability of areas based on overlap with HCVs 43
Figure 42: Distribution of biophysical suitable areas for oil palm and overlap with HCV in 2014, 2020 and 2050 44
Figure 43: Distribution of rubber suitability in 2014, 2020 and 2050 in grey shades, against a background of the Risk Indicator Map, visualising suitability of areas based on overlap with HCVs 45
Figure 44: Distribution of biophysical suitable areas for rubber and overlap with HCV in 2014, 2020 and 2050 46
Figure 45: Distribution of suitability for Robusta coffee in 2014, 2020 and 2050 in grey shades, against a background of the Risk Indicator Map, visualising suitability of areas based on overlap with HCVs 46
Figure 46: Distribution of biophysical suitable areas for Robusta and overlap with HCV in 2014, 2020 and 2050 47
Figure 47: Distribution of suitability for Arabica coffee in 2014, 2020 and 2050 in grey shades, against a background of the Risk Indicator Map, visualising suitability of areas based on overlap with HCVs 48
Figure 48: Distribution of biophysical suitable areas for Arabica and overlap with HCV in 2014, 2020 and 2050 48
Figure 49: Distribution of suitability for cocoa in 2014, 2020 and 2050 in grey shades, against a background of the Risk Indicator Map, visualising suitability of areas based on overlap with HCVs 49
Figure 50: Distribution of biophysical suitable areas for cocoa and overlap with HCV in 2014, 2020 and 2050 49
xii Figure 51: Key issues in the technological change–deforestation link (adapted from Angelsen & Kaimowitz 2001) 55
Figure 52: Example: Detailed zoning for landscape in Sintang using participatory mapping and stakeholder interviews, market analysis etc 56
List of tables
Table 1: Overview of risk categories considered for each HCV and carbon stocks, with scores for each risk category (Low risk = Score 1, Medium risk = Score 2 etc.) (Modified from Smit et al. 2013) iv
Table 2: Potential intervention options for different agriculture-forestry systems vi
Table 3: List of principles, criteria and indicators extracted from RSPO, RSB and RES-D 9
Table 4: List of weather stations in North Sumatera Province 11
Table 5: Land use categories 15
Table 6: Mean AUC for target commodities 26
Table 7: Overview of risk categories considered for each HCV and carbon stocks, with scores for each risk category (Low risk = Score 1, Medium risk = Score 2 etc.) (Modified from Smit et al. 2013) 40
Table 8: Potential intervention options for different agriculture-forestry systems 53
xiii Abbreviations
AOGCMs Ocean Global Climate Models
APL ‘Non-Forest’ Land
EIA Environmental Impact Assessment
ENM Ecological Niche Modelling
FSC Forest Stewardship Council
GCM Global Climate Modelling
GHG Green House Gas
HCV HCV High Conservation Value
IPCC Intergovernmental Panel on Climate Change
MaxEnt Maximum Entropy
Pr Precipitation
RCP Representative Concentration Pathway
REAP Renewable Energy and Agriculture Programme
REDD Reduced Emissions from Deforestation and Forest Degradation
RePPProT Regional Physical Planning Project for Transmigration
RES-D Renewable Energy Sources Directive
RIM Risk Indicator Map
RSB Roundtable Sustainable Bio Fuel
RSPO Roundtable Sustainable Palm Oil
SLP Sustainable Landscapes Partnership
SRTM Shuttle Radar Topography Mission
Ta Average Temperature
Tn Minimum Temperature
Tx Monthly Maximum Temperature
USAID United States Agency for International Development
WFF Walton Family Foundation
WMO World Meteorological Organisation
xiv xv xvi in the region. In the final chapter, the building Background blocks for such a vision alongside a process for implementation are outlined.
The Sustainable Landscapes Structure of the report Partnership In this report, a large amount of data has The Sustainable Landscapes Partnership been generated and analysed. It is thus a (SLP) is a five-year partnership programme challenge to provide a comprehensive oversight. implemented by Conservation International To maintain the focus on the most relevant (CI) in collaboration with USAID and the Walton information, and major parts of the analysis Family Foundation (WFF), in North Sumatra have been put into the appendices. Before an Province, Indonesia. The programme aims to oversight of the opportunities and challenges address global climate change and contribute to in realising sustainable development in the Indonesia’s economic growth, while protecting its districts is presented, a situation analysis of critically threatened habitats. The programme North Sumatra province and the target districts is supported by an SLP innovation facility is provided in order to establish the context in established from public and private philanthropic which the recommendations should be used. contributions. The METHODOLOGY section includes a relatively detailed description of the methods used, as an In Indonesia, the programme targets three analysis has not been done in this form before. districts in North Sumatra: Mandailing Natal, In the RESULTS section the focus is on the most Tapanuli Selatan and Tapanuli Utara, and comprehensive results moved the outcomes of four agricultural commodities that are driving alternative methods placed in the APPENDIX land use change: coffee, cocoa, palm oil and section. The final section of the report provides rubber. To achieve the goal of the sustainable an overview of the outcomes and a vision for the development of these commodities within the future. The report is thus structured according to target areas, sustainable development strategies the following: are required. Such strategies must not only be viable over the long term but also consider • Chapter 1, an introduction to the suitability and conservation values as well as the landscape is given, with an overall impacts of climate change. description of the study area; initially focussed on the province level and then SNV Indonesia’s support to SLP for target districts. • Chapter 2 explains the methods applied in To develop such strategies, SNV Indonesia has the analysis including the methods used been tasked to assess the current distribution for preparing the data for biophysical of conservation values in the target districts and suitability, future climate scenarios, and identify opportunities for sustainable expansion the mapping of High Conservation Values. of the target commodities, taking into account the predicted impacts of climate change. For • Chapter 3 presents the results of the the analysis, SNV used the Siting Tool (Smit et study for each commodity and depicts the al. 2013). The Siting Tool applies international risks and opportunities for unsustainable environmental and social sustainability standards agricultural expansion in the landscape. combined with biophysical considerations in In the final step the results from the order to determine the suitability of lands for (future) biophysical suitability mapping sustainable agricultural expansion, while at the are combined with the Risk Indicator Map same time avoiding High Conservation Value and for each commodity priority areas are (HCV) areas. For the SLP-Indonesia programme suggested. SNV has further enhanced this methodology to • Chapter 4 provides a summary of the assess the suitability of sites for the production findings for each district and suggests of coffee, palm oil, cocoa and rubber in the priority crops and areas as well as context of climate change. These analyses recommendations for interventions. In aim to contribute to developing a vision for the final section, specific attention is the region on what sustainable development giving to a process that could be followed should look like, in particular in the context of to develop and mainstream a vision for spatial planning. By providing insight in the sustainable development into government distribution of conservation values and options planning processes. for sustainable crop expansion, trade offs are made insightful that enable informed decision- making on the direction of the development
1 2 Chapter 1 Study area
In Chapter 1 an insight into the administration, topography, hydrology, climate, forest cover, forest status, biodiversity, population and economy of North Sumatra is provided. Here, maps and statistics are used to provide greater clarity on the past and present situation. The chapter contains two sections; section one presents an overview of North Sumatra Province as a whole, while section two provides a more detailed analysis of the study’s three target districts.
Figure 7: Map of North Sumatra Province North Sumatra showing study focus areas Administration: North Sumatra Province consists of 25 districts and occupies a total area of 7,169,870 ha. The study focusses on the three North Sumatra districts of Maidailing Natal, Tapanuli Selatan and Tapanuli Utara (see Figure 7; Appendix 1).
Climate: In general, North Sumatra has a tropical climate that is influenced by trade winds and monsoons. The province’s maximum temperature fluctuates between 25.0°C to 32.5°C, while the minimum temperature ranges from 16.8°C to 24.5°C. Total rainy days are between 178 days to 208 days per year with an estimated rainfall that varies from 140 mm to 657 mm. Relative humidity is 79 to 88 percent (see also Appendix 2).
Topography: North Sumatra Province consists of coastal areas, lowlands and highlands, as well as the Bukit Barisan mountain range. North Sumatra’s highest point is Mount Sinabung (2,475 m), whose slope varies between 0-65°.
Hydrology: North Sumatra Province has two major watersheds: Wampu / Sei Ular and Asahan Barumun, which are further divided into 232 sub- watersheds.
Forest cover: North Sumatra Province has a According to the Ministry of Forestry (2013) total forest cover of approximately 1,889,567 forest cover in North Sumatra Province declined ha. Just over 8 percent of this forest is primary by almost 3 percent between 2000 to 2011, with forest, nearly 16 percent is secondary forest, and most reduction occurring in secondary forest almost 2 percent is crop forest. The remaining 74 (some 1,373,921 ha in 2000 to 1,145,701 ha in percent is non-forest cover. 2011). Meanwhile, crop-forest cover increased by 0.22 percent, from an estimated 129,301 ha in 2000 to 145,746 ha in 2011 (see Appendix 3).
3 Forest status: According to the Ministry of the area grew by 0.07 percent per year in the Forestry (2011), about one quarter of the total period of 2009 – 2012, reaching some 377,068 land of North Sumatra is considered protected ha in 2012.7 Mandailing Natal, Padang Lawas and and / or a conservation area1 (see Appendix 4). Langkat Utara are the main producers of rubber Production Forest2 occupies some 27 percent of in North Sumatra. the total region with the remaining 48 percent falling under ‘Other Land Use’ (APL) status (see Appendix 5). Situation in target districts
Biodiversity: North Sumatra Province has In order to provide a context for the focus areas, two national parks, Batang Gadis National a brief description of topography, hydrology, Park and Gunung Leuser National Park, as well forest status and cover, as well as general socio- as several protected forests including Batang economic data follow. Detailed information Gadis Protected Forest, Batang Toru Protected on focus areas’ biophysical suitability for the Forest, and Gunung Tua Protected Forest. These target crops, climate, as well as distribution of areas play an important role in protecting a conservation values resulting from the analysis number of endangered mammals including the are explained in later chapters. Sumatran tiger, Asian elephant, and orang-utan. In the study’s three focus areas, two important Topography locations for ecosystem conservation exist; namely Batang Toru and Batang Gadis. Batang Tapanuli Utara (North Tapanuli) Toru is divided into protected forest (HL) and production forest (HP and HPT) while Batang Tapanuli Utara District is located at 1°20’00” – Gadis is split into protected forest and national 2°41’00” North and 98°05’00” – 99°16’00” East parks. and has a total area of 376,465 ha. The District borders Toba Samosir District to the north, Population: North Sumatra contains the fourth Labuhan Batu Utara District to the east, Tapanuli largest number of people in Indonesia after West Selatan District to the south and Tapanuli Tengah Java, East Java and Central Java provinces. District to the west. The area of Tapanuli Utara National census results indicate the province’s is situated at an altitude ranging between 150 m population to be 10.26 million people in 1990 – 1,700 m above sea level with a slope varying and 11.51 million people in 2000.3 By May 2010, from 0° - 57.4° (see Appendix 6). Approximately, census results indicate North Sumatra was home 54 percent of the District’s area is located on to 12,982,204 inhabitants. The indicative rate relatively steep land (4° -16°). of population growth in North Sumatra is thus 1.20 percent from 1990 - 2000 and 1.22 percent Tapanuli Selatan (South Tapanuli) from 2000 - 2010. By 2012, it is estimated that Tapanuli Selatan District has a total area the total number of inhabitants of the province of 435,286 ha. It is located on 0°58’35” – was 13,215,401 people, consisting of about 2°07’33” North and 98°42’50” – 99°34’16” 6,544,299 males and 6,671,102 females (North East. The District borders Tapanuli Utara and Sumatra Province BPS, 2013). Tapanuli Tengah District to the north, Padang Economy: Agriculture is one of the main Lawas Utara, Padang Lawas dan Labuhan Batu sources of income for the North Sumatran District to the east, Mandailing Natal District to people, with coffee, oil palm, cocoa and rubber the south, and the Indian Ocean to the west. being four of its key commodities and creating Tapanuli Utara is situated at an altitude that the most revenue in the agriculture sectors. stretches from 0 m – 1,985 m above sea level, In 2012, North Sumatra’s coffee production has a slope that ranges from 0° - 62.71°, and (Arabica and Robusta) amounted to some has 57 percent of its total area located on a 55,313 tons with a total planted area of slope between 8° - 35° (see Appendix 6). than 79,180 ha.4 In the same year, oil palm Mandailing Natal production totalled more than 5,197,209 tons, with a planted area of some 410,400 ha.5 Cocoa Mandailing Natal District is the largest district production on the other hand, amounted to in North Sumatra Province with a total area of about 34,613 tons in 2012, with a total planted 662,072 h. Located at 0°10’00” – 1°50’00” North area of about 58,244 ha.6 Rubber plantations in and 98°50’00” – 100°10’00” East, Mandailing
1. i.e. TN, CA, KSP/KSA, BCE, HL 2. i.e. HP, HPT, PPC 3. North Sumatra Province BPS, 2013 4. North Sumatra Province BPS, 2013 5. North Sumatra Province BPS, 2013 6. North Sumatra Province BPS, 2013 7. North Sumatra Province BPS, 2013
4 Natal borders Tapanuli Selatan and Padang Lawas Forest status and forest cover District to the north, West Sumatra Province to Tapanuli Utara the east and south, and the Indian Ocean to the west. The Mandailing Natal region is situated at Over half of Tapanuli Utara (approx. 58 percent) an altitude ranging from 0 m – 1,315 m above is granted Production Forest status (HP and sea level (see Appendix 6). This district has a HPT) with only about 15 percent of the total slope that varies between 0° - 57.49°, with 52 land Protected Forest (HL) and / or Conservation percent of its total area located on relatively Areas (CA and TWA). Of the Protected Forests steeper slopes (between 8° - 35°). and Conservation Areas, some 16,207 ha are Non-Forest Areas. About 23,365 ha of forest cover are located in areas with Other Land- Hydrology Use (APL) status (see Appendix 10). According Tapanuli Utara to the Ministry of Forestry, the highest rate of There are seven sub-watersheds in Tapanuli deforestation occurred between 2000 – 2011 Utara with the total length of its rivers amounting occurred in the region with Production Forest to 866,268 km (see Appendix 7). status 2,829,058 ha. Within the same period, about 1,295 ha of Protected Forest Area and Tapanuli Selatan some 651 ha of Other Land-Uses were Tapanuli Selatan District has six sub-watersheds burned down. with the total length of its rivers amounting to Tapanuli Selatan 673,013 km (see Appendix 8). Other Land-Uses (or APL) occupies the largest Figure 8: Changes of forest cover in study part of the district (about 28 percent), while focus areas Protected Forest covers just over 28 percent of the region. Of the 28 percent of Other Land- Uses area, some 1,6561 ha is still covered by forest. On the other hand, about 46742 hectares of Protected Forest Area has a non-forest land cover (see Appendix 11). The highest conversion of forest land into non-forest land (62 percent) occurred in Other Land-Uses areas (19,608 ha). Deforestation also occurred in Protected Forest and Conservation Areas with a total deforested area of approximately 3467 ha.
Mandailing Natal
Other Land-Uses occupy most of the region (approx. 38 percent) in which some 41,156 ha has forest cover. Protected Forest Conservation Areas (SM and TN) are the second largest area (roughly 32 percent of the total area of the district) in which about 163,305 ha of its area is Non-Forest Cover (see Appendix 12). According to the Ministry of Forestry (2000 – 2011), most deforestation occurred in Other Land-Uses areas (APL) (approx. 34,330 ha). At the same time re-forestation of non-forest into forest cover also occurred (approx. 3,906 ha). In addition, 5,815 ha of Protected Forest and National Park were deforested within the same period of time.
Mandailing Natal
Mandailing Natal has 11 sub-watersheds with the total length of its rivers amounting to 1798,989 km (see Appendix 9).
5 Socio economic situation Tapanuli Utara
In 2012 it was estimated that Tapanuli Utara had a total population of 285,070 persons consisting of 140,830 males and 144,240 females.8 Population density is considered relatively low, at around 75 people per square kilometre.9 For district inhabitants, the agriculture sector provides the main source of income, contributing about 53 percent of the District’s total Gross Regional Domestic Product in 2011.10
Tapanuli Selatan In mid-2012 the total population of Tapanuli Selatan District was estimated at 268,095 people, comprising 133,140 males and 134,955 females.11 The average population density of the District is +/- 60 people per km2. Similar to Tapanuli Utara District, agriculture tops other income sectors, providing an income to about 85 percent of district inhabitants.12
Mandailing Natal The total population of Mandailing Natal District was estimated at 410,931 individuals in 2012, comprising 210,686 males and 209,245 females.13 Out of the 251,088 inhabitants in the labour force, about 206,321 people work in the agriculture sector,14 making the sector the main source of income for the local population.
8. Tapanuli Utara BPS, 2013 9. Tapanuli Utara BPS, 2013 10. Tapanuli Utara BPS, 2013 11. Tapanuli Selatan BPS, 2013 12. Tapanuli Selatan BPS, 2013 13. Mandailing Natal BPS 2013 14. Mandailing Natal BPS 2013
6 Chapter 2 Methodology
In Chapter 2 the methods used to conduct the study are outlined. The use of a Responsible Cultivation Area approach as proposed in Smit et al (2010) is explained followed by an explanation about how SNV’s Siting Tool (Smit, 2013) has been used to visualise the current and future suitability of the focus areas. A brief introduction is also given in order to explain how these methods build on each other and how they have then been applied in the study. Next, an overview is provided on how the RCA methodology has evolved from a method used to identify areas suitable for sustainable bio energy crop production into a model used to identify areas suitable for sustainable agricultural production in general. The methods used for assessing current and future biophysical suitability are then explained, as well as how the High Conservation Values (HCV’s) were identified. The chapter concludes by detailing how the collated information has been combined into Risk Indicator Maps that visualise the trade-offs in the landscape between agricultural expansion and conservation in the landscape.
RCA methodology and the SNV Siting Tool
Historically, an area was often considered suitable for ‘responsible cultivation’ if it could be ensured that the area: (1) could be used for environmentally and socially responsible energy crop cultivation, and (2) such energy crop cultivation would not cause unwanted indirect effects. The methodology follows five basic principles:
PRINCIPLE 1PRINCIPLE 2PRINCIPLE 3PRINCIPLE 4PRINCIPLE 5 High Conservation Carbon stocks are Land (use) rights No unwanted The area is Values are maintained or are respected displacement suitable for maintained or enhanced effects are caused rain-fed agriculture enhanced
7 To verify these principles, the RCA methodology Other amendments of the RCA methodology uses a four-step process to identify areas in the report are based on Smit et al. (2013) for responsible cultivation. The four steps as well as recent insights from SNV’s REAP (or phases) work as a funnel, starting with a Programme relating to impacts of agricultural coarse assessment of a large area, followed by development on forest frontiers, and in particular selection of the most promising areas, and finally the impacts of intensification. One major assessing them in greater detail (see Figure 9). change introduced in that study is the use of ‘risk categories’ instead of considering an area only ‘suitable’ and ‘unsuitable’ (see Smit et al. Adjusting methodology for SLP 2013). This idea emerged from the application Because the RCA methodology was originally of the HCV toolkit in mapping conservation designed to address issues related to indirect values to identify suitable areas according to effects of bio energy feedstock production, the Principles and Criteria of the Roundtable on changes to the methodology have to be made in Sustainable Palm Oil (RSPO). In the context order to address food crops and related issues of oil palm, RSPO principles and criteria refer specifically. To make the RCA analysis more to the High Conservation Value (HCV) Toolkit efficient, the approach suggested by Smit (2013) to assess the suitability of an area. In the has been applied resulting in Principle 2 being HCV toolkit an approach is outlined to identify integrated into Principle 1 (as HCV 4 deals with important conservation values in the landscape ecosystem services, however, currently carbon and requires that the values are ‘managed’. stocks are not considered in the HCV toolkit). In Therefore, the identification of a HCV in a certain addition, Principle 3 and 4 have been integrated area does not mean that the area cannot be used in the analysis of HCV 5 & 6 (Principle 1) because for oil palm if the value can be managed. By the analysis of these areas tends to overlap in simply identifying HCV’s and considering those as practice. As the report seeks to address Phase ‘unsuitable’ areas the results are oversimplified, 1 and 2, the change does not significantly affect and in practice often not useful for end-users. the study, and recommendations are provided for in-depth analysis. For the purpose of this report, issues related to land rights are only considered Applying the SNV Siting Tool to a limited extent and will only seek to address To provide additional detail to such analysis government classifications of land use. and make HCV assessments more transparent
Figure 9: The four phases of the RCA methodology
Phase I Phase III
Desk-based identification of Detailed desk-based assess- most promising areas (Site ment on pre-selected areas to Pre-Selection) at national or further refine the selection regional level. This selection is relatively coarse, using readily available information
Phase III Phase IV
Field-testing and verification of Evaluation of collected the results of the first two information to determine phases in the most promising whether (a part of) the areas selected in Phases I selected most promising areas and II can be classified as unused land
8 in general, in 2013 SNV developed a Siting Principles, criteria and indicators Tool which added additional ‘risk categories’ to The Siting Tool is divided into three key demonstrate the risk of violating a standard. principles: A major advantage of the Siting Tool is that existing criteria from leading sustainability 1. The area is biophysically suitable for target initiatives are used and combined with a crop biophysical suitability analysis. As such, the most relevant standard(s) in the sector can be 2. Conservation values are maintained or selected and the criteria ‘translated’ into spatially enhanced relevant, measurable indicators. The tool can 3. Human wellbeing is ensured and land use thus be used to identify areas suitable for rights are respected sustainable expansion of agriculture (for example coffee, cocoa, rubber and oil palm), as well as In areas where all the principles are met, the identify important conservation values in the area may be considered suitable for sustainable landscape. agricultural production (see Figure 10). Figure 10: Venn diagram illustrating the As with the principles, criteria have been principles of the SNV Siting Tool formulated to be used across sectors and different geographic regions. Apart from criteria on biophysical suitability, the criteria under AGRONOMICAL Principle 2 and 3 are mostly based on the HCV SUITABILITY Toolkit as referred to by RSPO P&C and which definitions overlap the RSB and RES-D criteria15 (see Table 3 for a list of the principles, criteria and indicators). LOW WELLHUMA BEINN RISK Criteria for Principle 1: The selection of criteria & RIGHTS for biophysical suitability was based on leading scientific literature on oil palm plantations.16 G The criteria also seek to address general CONSERVATION biophysical constraints for other commodities. Apart from pragmatic reasons, considerations of the suitability of the area for the target crop
Table 3: List of principles, criteria and indicators extracted from RSPO, RSB and RES-D Principle Criteria Indicator The area is Suitable climate Rainfall biophysically Suitable topography Slope suitable for oil Suitable soil Drainage palm cultivation Soil texture Soil depth Soil erosion risk Soil chemical properties Conservation Valuable biodiversity is Formal protection and conservation areas (HCV 1.1) values must be protected or enhanced Distribution and habitats protected and endangered maintained or on a population, species (Red List, CITES) (HCV 1.3 – 1.4) enhanced meta-population and Hydrological functions (HCV 4.1) ecosystem level Erosion risk (HCV 4.2) Ecosystem services are maintained Buffer zones large scale fire (HCV 4.3) Carbon stocks Human Community use is Displacement of current land use is avoided or wellbeing is respected compensated for through FPIC ensured and Valid ownership claims are respected land (use) rights are respected
15. Smit, 2013 16. Corley et al. (2003), Fairhurst et al. (2004) and Mantel et al. (2007)
9 are also specifically addressed in sustainability To verify whether an area can be used in initiatives.17 The criteria used to assess line with the selected set of criteria, spatially compliance with Principle 1 are: explicit indicators have been developed. In the application of the HCV Toolkit there is a range 1. Topography is suitable for target crop of possible outcomes in assessing the suitability of an area. In addition, the presence of an HCV 2. Soil is suitable for target crop does not make the area unsuitable per se. With 3. Climate is suitable for target crop appropriate management some values can be maintained, or some barriers overcome. To take In line with the biophysical suitability analysis for these considerations into account, we propose oil palm, the suitability for other crops can also to interpret the data based on risk classes which be mapped, for the analysis of food crops we represent the risk at which an area is found may add layers for maize and cassava. We also to be unsuitable according to the suitability suggest to add an additional criterion addressing criteria. These risk classes do not always directly financial viability, with the indicators; size of follow from the HCV Toolkit, and where unclear, contiguous areas, proximity to contiguous areas, threshold values are based on previous studies 23 proximity to mills and transport costs. and expert consultation. We consider the following four risk classes (see Appendix 13): Criteria for Principle 2: The criteria to identify and assess conservation values are • Low risk: no constraints were identified derived from the HCV Toolkit18 which is the • Medium risk: minor constraints are most widely accepted and applied standard in identified, but are manageable and assessing environmental impacts for forestry criteria could be met and agriculture.19 In the HCV approach, three categories of conservation values are • High risk: major constraints are distinguished: criteria on biodiversity (1-3), identified, managing the constraints to criteria on environmental services (4) and social meet criteria is challenging 20 criteria (5 and 6). In the Siting Tool the order • Unsuitable: area is unsuitable according of the HCV’s were maintained, and the social to one or more criteria that cannot be criteria (HCV 5 and 6) are used under Principle corrected through management. 3. Ecosystem services considered under HCV carbon is also included in the analysis under The indicators themselves are designed to be 21 criterion 2.2. The criteria to assess compliance transferable to different crops and geographical with Principle 2 are: (1) valuable biodiversity areas. Most threshold values, however, are is protected or enhanced on a population, crop and region specific24 due to biophysical meta-population and ecosystem level, and (2) suitability variations. This can easily be overcome ecosystem services are maintained (hydrology, by adjusting the threshold values for the target erosion, large scale fire, carbon). commodity. In addition, as for each indicator a separate map may be developed, in the online Criteria for Principle 3: In the Siting Tool, the tool users can assign different weightings to analysis of the HCV social criteria (5 and 6) is each indicator to prioritise different criteria. slightly adapted to meet the requirements of To identify areas that meet all the criteria as all selected sustainability initiatives. From the selected in the Siting Tool, a map needs to be past experience of the researchers22 it has been produced in which as many indicators as possible found that this information must be collected are checked. Producing separate maps for each from field work. Because this study focussed indicator enabled them to be combined in the on desktop analysis, the criterion was therefore final stage into a single Risk Indicator Map.25 not assessed. Specific recommendations for The workflow is shown in Figure 12 on page 12. follow up analysis are, however, made within the conclusion and recommendations section of the The advantage of this approach is that the report. separate indicators can be given a weighting that reflects the perceived relative importance of the
17. RSPO P&C criterion 4.2 and 7.2 (RSPO, 2007). 18. Jennings et al. 2003; HCV Consortium; 2009 19. The HCV approach was developed by the Forest Stewardship Council (FSC) in the context of sustainable forest management. However, it is now used to define the highest level of safeguards needed across a broad range of production systems and resources uses, including grasslands, freshwater systems or landscape-level mosaic ecosystems. 20. Jennings et al. 2003 21. Although this is currently not considered as HCV, it is a requirement under the RSB and RES-D. See also section on Indicators 22. Smit et al, 2010, 2012, 2013; see also www.liib.org 23. Smit, 2009; Sekala, 2009; Budiman, 2010 24. Documentation on the rationale for each indicator and the risk classes is available on request 25. See also Smit et al, 2013
10 indicator. For example, areas such as protected Figure 11: Study area and distribution forests could be assigned a high weighting, as of weather station in North Sumatera these areas are clearly defined and are under no Provinces. (Black gradient represent scenario compliant with the criteria. Indicators for of elevation) which the uncertainty is very high, for example provisioning services, may be given a lower weighting, in order to minimise the chance for incorrectly excluding areas as unsuitable. The tool is thus particularly useful in zoning a landscape for the suitability of a selected commodity.
Calculating current and future climate
In this study, BIOCLIM was used for ENM that represents current climate variables relating to annual trends and seasonality. Bioclim data was Downscaling obtained from the World Climate database with Climate data from a climate model with coarse the data downscaled from ~1 km resolution to a spatial resolution from Global Climate Modelling finer 250 m resolution. In the study 19 BIOCLIM (GCMs resolution of +/-250 km) into a finer scale variables have been used to represent annual (> 1 km) was required to evaluate the effects of trends, seasonality, and extreme or limiting climate change on the environment. This data environmental factors (see Appendix 13). was downscaled by interpolating climate data using a first order bilinear spline method with Current climate some correction using the Global Summary of the Day database based on various weather In order to reconstruct a dataset to describe stations (collected by the World Meteorological the current climate, the Worldclim9 historical Organization (WMO)). For the study, a monthly high resolution database was used. Worldclim maximum temperature (Tx), minimum data are generated through the interpolation temperature (Tn), average temperature (ta) and of average monthly climate data from weather precipitation (Pr) were used which were collected stations on a 30 arc-second resolution grid from five weather stations in North Sumatera (approximate of ~1 km resolutions) within the Province (see Table 4). period of 1950 – 2010. However, for this study the resolution was downscaled to a pixel size Temperature data were corrected using a simple of ~250 m, the optimum resolution for existing bias correction method by calculating the bias baseline data given the relatively limited number average (delta) between the weather stations of weather stations in North Sumatera Province and the Worldclim dataset. (see Figure 11). Later, fitting was performed with least square criterion between deltas. The elevation used the following equation:
where ∆ is a delta, elevation (Z), and where a, b, and∆(Z)=a×Z^b+c c are coefficient. (1)
Table 4: List of weather stations in North Sumatera Province No ID Station Longitude Latitude Elevation (m) 1 96073 Sibolga 1.55 98.88 4 2 96071 Aek Godang 1.38 99.27 325 3 96037 Tuntungan 3.50 98.56 81 4 96035 Medan (Polonia) 3.57 98.68 29 5 96031 Sampali 3.62 98.78 14
11 Figure 12: Overview of workflow in developing the ‘Risk Indicator Map’ and selecting priority areas using the Siting Tool
Principles Criteria
Principle Criteria Indicator
1: The area is 1.1: Suitable 1.1.1: Rainfall biophysically climate suitable for oil 1.2.1: Slope palm cultivation 1.2: Suitable topography 1.2.2: Elevation 1.3.1: Drainage AGRONOMICAL 1.3: Suitable soil 1.3.2: Soil texture
SUITABILITY 1.3.3: Soil depth
1.3.4: Soil erosion risk
1.3.5: Soil chemical properties
2: Conservation 2.1: Valuable 2.1.1: Formal protection and conservation areas values must be biodiversity is (HCV 1.1) maintained or protected or LOW enhanced enhanced on a 2.1.2: Distribution and habitats protected and WELL BEIN population, meta- endangered species (Red List, CITES) HUMAN RISK population and (HCV1.2 - HCV 1.3 - HCV 1.4) & RIGHTS ecosystem level 2.1.3: Endangered ecosystem intact landscapes, 2.2: Ecosystem and large scale intact forest (HCV 2&3) services are 2.2.1: Hydrological functions (HCV 4.1) maintained G 2.2.2: Erosion risk (HCV 4.2) CONSERVATION 2.2.3: Buffer zones large scale fire (HCV 4.3) 2.2.4: Carbon stocks
3: Human 3.1: Community 2.1.1: Displacement of current land use is wellbeing is use is respected avoided or compensated for ensured and land through FPIC (use) rights are respected 2.1.2: Valid ownership claims are respected
Important Wastershed
Palm Oil plantations
12 Indicators Risk Indicators Map
Low risk Medium risk High risk Unsuitable
1750 - 5000 mm 1500 - 1750 mm 1250 - 1500 mm < 1250 mm; > 5000 mm
< 8 % 8 - 15 % 15 - 30 % > 30 % (> 12°)
< 200 m 200 - 500 500 - 1000 m > 1000 m
Well to moderately well Imperfect Extreme; poor Excessive; very poor; stagnant
Silt loam; sandy clay loam; silty Clay; silty clay, sandy loam; loam Sandy clay; silt; loamy sand Heavy clay; sand clay loam; clay loam > 100 cm 75 - 100 cm 50 - 75 cm < 50 cm
< 15 ton/ha/year 15 - 59 ton/ha/year 60 - 179 ton/ha/year > 180 ton/ha/year
Well drained and deep Weathered and deeply developed Shallow and infertile mineral soils Infertile sands mineral soils mineral soils
No overlap with IUCN areas or Bufferzones1km IUCN I-IV, IUCN V-VII, protected conservation and protected areas forest, Ramsar and national and buffer zones. conservation areas
No overlap with distribution Overlap with distribution of Overlap with habitat of protected Breeding grounds and nesting or habitats of protected and protected and endangered species and endangered species * places, grazing/browsing for endangered species endangered species and temporal habitats for migratory species *
No overlap with endangered Large scale forest area plus Two or more eco-tone regions Rare ecosystems ecosystems, important ecotone buffer 3km Endangered ecosystems regions and large scale forest
No overlap with water source/ DAS super priority Mangrove, peat, wet land and Water sources (spring), riparian riparian zones, mangrove peat, karst forest, cloud forest zones and buffer zones. karst or DAS super priority
< 15 ton/ha/year 15 - 60 ton/ha/year 60 - 180 ton/ha/year > 180 ton/ha/year
No overlap with barriers for the Overlap with barriers for the Area contains barriers for large- spread of large scale fire, the area spread of large scale fire, but scale fire i.e. large forest blocks or recently burned more than once in (partly) burned in the last peat swap areas and not burned the last 10 years * 10 years * during the last 10 years *
Carbon stock 0-60 ton/ha Carbon stock 60-70 ton/ha Carbon stock 70-80 ton/ha * Carbon stock >80 ton/ha
Areas providing for <10% for Areas providing >10% < 25% for Areas providing >25% <50% Areas providing >50% for subsistence * subsistence * for subsistence, or containing subsistence, or containing cultural sites * cultural sites *
No overlap with land rights * Idle land; community interested to Idle land; tanahpera, community Active use of land community change the use * not interest to change the use * protected forest*
Degraded forest
Intact Forest
13 Equation (1) was used to estimate the delta at Change (IPCC) report (IPSL - CM5A) – one of the the observation points without the use of SRTM AOGCM models that provide the most realistic elevation data. The delta values were then used results in simulating current climate conditions.26 to correct the interpolated temperature data The IPSL model output - CM5A - LR uses four results with the following equation: variables (Pr, Ta, Tx and Tn). The simulation of historical climate (20th Century, 1850-2005) as the baseline and future climate projection The correction was completed for all variable (2006-2100) uses four scenarios Representative temperatureT_corr=T_interp+∆ (Ta, Tx and (2)Tn) interpolation of the Concentration Pathway (RCP). RCP is a term resolution of ~ 1 km to the resolution 250 m. for future climate scenarios in a range of values From these results 19 variables of Bioclimatic representing CMIP5 radiative forcing in climate Variables were calculated that could be used to models: 2.6 W/m2, 4.5 W/m2, 6 W/m2 and 8.5 predict the suitability of four commodities (see W/m2 (see Figure 14). All four climate scenarios Figure 13). may occur and depend on the concentration of greenhouse gases that are released in the future, Figure 13: Downscaling results from ~1 km ranging from low (RCP2.6) to high (RCP8.5).27 resolution (A) to 250 m resolution (B) The perturbation method (or ‘delta-change’) was used in the downscaling process of the study. This method is popular due to its simplicity because it does not require high computational resources. The method does however have a number of drawbacks. First, the method assumes a delta-change model of constant bias across time. Secondly, the method also assumes spatial patterns of climate variables as constant, thus ignoring changes in spatial variability.28
Figure 14: IPSL model output-CM5A-LR in the study area for four RCP and baseline (20th Century): (A) rainfall and (B) average temperature
The steps undertaken for downscaling the data using the delta-change method included: Future climate 1. Interpolate the baseline (20th Century) Downscaling was performed on one of the and future climate (2006-2100) with the outputs of global climate models (atmosphere first order bilinear spline method of spatial - ocean global climate models, AOGCMs) and resolution model (~ 3.75 degrees) to the is incorporated in the Model Inter-comparison desired spatial resolution Project phase Coupled to 5 (CMIP5). The selected model is the IPSL Earth System Model for the 5th Intergovernmental Panel on Climate
26. Siew et al., 2013 27. van Vuuren et al, 2011 28. Fowler et al., 2007
14 2. Calculate an average climatological baseline other biophysical or environmental factors.29 To model for the variables of temperature and date, two key sources are available on a national precipitation: level for assessing land suitability of agricultural commodities in Indonesia. The first is the land suitability analysis developed for the evaluation T_clim (〖2o〗^(th) C) dan 〖Pr〗_clim of transmigration in the Regional Physical 3. Calculate the absolute anomaly (for variable (〖2o〗^(th) C) Planning Project for Transmigration (RePPProT). temperature) and proportional anomaly RePPProT evaluated the interaction and (for precipitation variables) in a period of correlation between rocks, climate, hydrology, climate futures using the equation: topography, soils and organisms. Through the identification of groups of land system units, rapid identification of land suitability for specific types of land use was possible. andT_anom (future)=|T(future)-T_clim ( 〖2o〗^(th) C)| Multi-Criteria Analysis 〖Pr〗_anom (future)= Pr〖(future) The second source of agricultural land suitability Anomaly〗/(〖Pr〗_clim data from step (〖2o〗^(th) (3) were then C)) summed in Indonesia is the Ministry of Agriculture’s (for variable temperature) or multiplied (for technical guidance for agricultural suitability variable rainfall) with the current climate criteria, which further refines the suitability datasets: classes of the RePPProT data.30 The technical guidance also closely aligns with the approach used in SNV’s Siting Tool which both adopt a multi-criteria approach to analysis.31 T(〖future〗_(hires) )=T_anom and In Multi-Criteria Analyses, land suitability is (future)+T(〖WC〗_(hires) ) determined based on soil biophysical properties rather than inputs. In the approach to the study, the Ministry of Agriculture’s land suitability Pr(〖future〗_(hires) )=〖Pr〗_anom Finally, the results of the downscaling were cut criteria for Arabica and Robusta coffee, into(future)×Pr(〖WC〗_(hires)) two periods: the 2020s (2010-2039) and cocoa, rubber, and oil palm commodities has the 2050’s (2040-2069). For each period the been adopted and is reflected in the study’s climate average and 19 Bioclimatic variables delineation of land use categories (see Table 5). were calculated. To enhance visualisation on the study’s land use maps, different coloured symbols were then applied to each suitability class.
Next, a spatial analysis was performed which Analysing overlaid the individual layers and results to obtain scores (see Appendix 36). Scores biophysical suitability were calculated by assigning a value to each criterion and class (see Appendix 36). Additional modifications were also made to adjust for the Regional Physical Planning availability of spatial data for each criterion (see Project for Transmigration Appendix 36). Suitability analyses in agriculture seek to assess important factors that indicate future crop production such as soil, vegetation, climate and
Table 5: Land use categories Classification Ministry of Agriculture’s Classification Study’s Classification S1 Very Suitable High Suitability S2 Suitable Enough Medium Suitability S3 Marginally Suitable Low Suitability NS Not Suitable Not Suitable
29. Miller, 1998 30. Djaenudin, 2000 31. Smit et al, 2013
15 Maximum Entropy exercise (see previous section) which provided estimations of rainfall and temperatures for the To implement the study of land suitability the years 2020 and 2050. By modifying these values Maximum Entropy (MaxEnt) method was run from the original dataset for 2020 and 2050 using 19 bioclimatic variables to produce a respectively, a prediction for future biophysical climatic suitability prediction for each of the suitability was obtained. selected commodities. The climatic suitability for each commodity however, is not a simple association for land suitability, since others variables are not included into the model. However, climate still plays a major role in land Identifying suitability for agricultural commodities due to its dynamic variance which affects complex conservation values relationships and has thus been widely used The concept of a High Conservation Value Forest for various commodities in many regions in the (HCVF) was first introduced in 1999 by the Forest world.32 Using climatic suitability can assist local Stewardship Council (FSC) for sustainable timber governments mitigate climate change impacts certification. This approach has subsequently on agricultural suitability. A detailed explanation been adopted in many countries to strengthen of how the MaxEnt methodology is used in the conservation efforts and reduce natural forest study follows. conversion for various agricultural commodities – To produce more refined scales of commodity especially outside protected areas. Over time the suitability in the study’s focus districts the HCVF approach has evolved beyond forest areas MaxEnt methodology has been applied33 due to a to become more focused on the global context, broad consensus that MaxEnt produces the best with the development of a common language of results of all modelling algorithms in ecological High Conservation Values (HCV’s). niche modelling (ENM) that use species presence This HCV framework is a generic tool that may only data.34 Combined with a correlative set be used to identify priority areas for conservation of environmental variables that are relatively and protection due to their important biological, suitable to the species’ ecological requirement ecological, social or cultural values at the national, and its habitat,35 MaxEnt can then help indicate regional or global scale. These HCV areas are critical other potentially suitable areas. areas that are required to maintain ecosystem In the MaxEnt methodology an occurrence point integrity within a landscape and therefore must be for each commodity is used to obtain a potential managed to maintain or enhance the values in the crop distribution. The probability of suitability can long term. There are six main HCV criteria and 10 then be extracted from the occurrence points. sub criteria (see Appendix 14). The MaxEnt approach has been successfully In practice, some HCV’s are unique and need used to predict cocoa land suitability across adaptation to the local context, therefore many regions in the world.36 Similar to logistical localised interpretation of the framework is often regression, MaxEnt weights each environmental required to allow accurate HCV identification. variable by a constant. The probability From this perspective, the first Indonesian HCV distribution is the sum of each weighted variable Toolkit published in 2003 and revised in 2008 divided by a scaling constant which ensures provide comprehensive input of the local context that the probability value ranges from 0 to 1. from a range of stakeholders and has defined In its implementation, the programme starts specific thresholds for when a value is considered with a uniform probability distribution with a High Conservation Value.37 one weighting iteratively altered at a time to maximise the likelihood of reaching the optimum There are six steps involved in implementing probability distribution. HCV assessments. The HCV assessment process places emphasis on the requirements needed to maintain robustness and credibility, for Combining with climate example, using appropriate available secondary change data data, primary data gathered from the field, In the final step, the data was combined with and clarifications and consultations with key the results from the climate change modelling stakeholders.38
32. Läderach, 2013 33. Phillips, 2006 34. Elith, 2009 35. Warren, 2011 36. Läderach, 2013 37. Konsorsium Revisi HCV Toolkit Indonesia 2008 38. ProForest, 2008
16 The six main steps of the HCV assessment analysis, prior existing knowledge of the area, approach are (see also Figure 15): and stakeholder consultations with stakeholders working and living in assessment areas. This 1. Understand the context and approach has been used for regional spatial information needs of the assessment: planning39 and found useful for pre-assessment. Identify likely conservation significance of the concession and scale of operation, Nevertheless, due to the lack of socio-cultural gather preliminary secondary data, identify information, the related HCV’s (5 and 6) team requirements based on the scope of could not be included in the study’s desktop the assessment, and identify consultations assessment and therefore should be incorporated needed to augment information obtained in further analysis. Thus, to identify HCV areas in assessment sites in North Sumatra Province; 2. Planning: Plan for availability of assessors, Mandailing Natal, Tapanuli Utara and Tapanuli logistics for the field, time needed for initial Selatan District, a desktop analysis has been findings, organising necessary consultations used based on relevant available data. A list with stakeholders, and discussions and of the data used for the analysis is provided in reporting Appendix 15. 3. HCV identification: Collect primary data from fieldwork, understand guidance documents for identifying HCVs in the HCV 1: Areas with important management area and wider landscape, levels of biodiversity conduct consultations with stakeholders
4. HCV management: Assess threats and HCV 1.1: Areas that contain or practical management options to mitigate provide biodiversity support the threats function to Protected or 5. HCV monitoring: Assess monitoring Conservation Areas options for the company to comply with The system of protection and conservation certification standards areas in Indonesia covers an area greater than 6. HCV reporting: Critical review of draft 22,200,000 ha. All areas were designated report, internal discussion between team with the objective of maintaining specific members and the concession manager, landscape features such as ecological functions, external peer review by qualified expert(s), biodiversity, water sources, and viable public consultation to report HCV findings populations of animals or a combination of and conduct further threat assessment, these features. HCV 1.1 aims to help ensure revision of report based on peer review, that a protection or conservation area meets and public consultations and company input the specific objective(s) that motivated its (Final Draft). establishment. If the unit management (i) has a protection or conservation area within it, (ii) is In some cases, for example when assessing thought to provide a biodiversity support function potential HCV presence at broad scales, to a protection or conservation area nearby, or the assessment may be visible through a (iii) will undertake activities likely to affect the precautionary desktop analysis. Fundamentally, biodiversity conservation function of a protection desktop HCVs assessments can differ from or conservation area, then HCV 1.1 is present in complete assessments as the planning and the area (see Appendix 16 for a list of protected preparation is limited to consultations to areas in Indonesia). identify existing secondary and primary data from various spatial data sources, scientific articles and reports for biodiversity. In addition, the identification process is restricted to GIS
Figure 15: Six main stages for HCV Assessment approaches (ProForest 2008)
HCV HCV HCV HCV Preparation Planning Identification Management Monitoring Reporting
39. Sulistyawan, 2007
17 highest and lowest tide measured from The key question for HCV 1.1 the lowest ebb and highest tide point; in the context of this study is: width of 10 m from the forest edge facing Does Mandailing Natal, Tapanuli Utara and river; Tapanuli Selatan District contain areas that • Heritage area and science: Regional support, could negatively impact or contain karst (dry and aqueous); area with conservation and protection forest areas? special cultural, and regional location of archaeological sites / historical heritage of high value; Methodology: Analysis of areas which provide • Areas prone to natural disasters, and support functions to conservation and protected natural production forest, which still areas begins with the identification of existing maintain its presence in the work area protection and conservation areas within the landscape of the three target districts of the study using 2013 Ministry of Forestry data. HCV 1.2: Critically endangered Further analysis is then needed to determine species areas which are classified as protected areas The purpose of HCV 1.2 is to determine the outside conservation or protected areas. presence of wildlife species or sub-species that are categorised as critical and / or threatened The identification of areas classified as protected with extinction. Species or sub-species area can be done by following the AMDAL categorised as Critically Endangered (CR), guidance for forestry. This guidance highlights Endangered (EN), and Vulnerable (VU) under several indicators for protected areas with values IUCN criteria, and categorised as Appendix I of conservation inside forest managements units and II under CITES criteria, as well as those as follows: with specific characteristics such as endemic or extreme lowland dwellers are considered to fall • Protected forest; into this HCV category. These HCV species are to • Upstream regions of peat area (thickness be conserved for their long-term viability. > 3 m);
• Water catchment areas; The key question for HCV 1.2 • Coastal buffer (100 m from the point of in the context of this study is: the highest tide to landward); Are threatened and endangered species • Riparian buffer: small river (width < 30 (including endemic and migratory species) m) zone wide 50 m; major rivers (width found in the prescribed landscape? > 30 m) zone wide 100 m;
• Lake / reservoir buffer with a buffer area HCV 1.3: Areas that contain habitat width of 100 m; for viable populations of endangered, restricted range or protected species • Springs buffer with a buffer radius of 200 m; The aim of HCV 1.3 is to protect viable populations of threatened, restricted range • Nature reserve (nature reserves and (endemic; RTE species) as well as those wildlife sanctuaries); species protected by law and trade restricted • Conservation Areas (national parks, forest by conventions to which Indonesia is a party. parks and nature parks); Species considered under HCV 1.3 are:
• Buffer zone of protected forest, 500 m • All CR, EN and VU species listed under the wide (with field delineated ) or 1,000 m IUCN Red List; (yet fully delineated); • Restricted Range Species (Endemic • Buffer zone of Nature Reserve Area, width species) confined to one island or a of 500 m (with field delineation) or 1,000 proportion of it; m (when fully delineated from desktop); • Species protected by Indonesian Law (No • Germplasm Conservation Area (KPPNs); 5 / 1990 and PP No 7 / 1999);
• Wildlife refuge areas; • Species listed under CITES Appendices I • Coastal area with forested mangrove: and II; and width 50 m from the forest edge facing • Viable populations of CR species towards the beach; width 130 times the considered under HCV 1.2 value of the average difference in the
18 The emphasis of HCV 1.3 is maintaining ‘populations’ of species rather than a focus on HCV 2: Natural landscapes individual species. Assessments of the potential and dynamics viability of that population must be considered in this analysis and should be conducted as a HCV 2.1: Large natural landscapes landscape assessment. with capacity to maintain natural ecological processes and The key question for HCV 1.3 dynamics in the context of this study is: HCV 2.1 aims to identify and protect areas of Does the landscape of Mandailing Natal, a natural landscape where natural ecosystem Tapanuli Utara and Tapanuli Selatan contain processes occur and have the potential to areas or ecosystems that support viable persist for the long-term. The key to achieving populations of endangered or threatened, this is the identification and protection of core restricted range, and protected or trade- area(s) within a landscape, which are essential restricted species? for guaranteeing the continuation of ecological processes unperturbed by edge effects and fragmentation. The definition of a landscape HCV 1.4: Areas that contain habitat with a core area is a forest block (or other of temporary use by species or natural landscape mosaic) with an internal core congregations of species >20,000 ha surrounded by a natural vegetation The purpose of HCV 1.4 is to identify cornerstone buffer of at least 3 km from the forest edge. The habitats in a landscape used temporarily by management goal of HCV 2.1 is to guarantee groups of individuals or species. A few examples that the core area and associated buffer zone are of cornerstone habitats are: maintained as forest or other natural vegetation.
• Breeding or nesting areas such as caves The key question for HCV 2.1 or wetlands used by bird species, bats, or in the context of this study is: reptiles; Does Mandailing Natal, Tapanuli Utara • Areas along important migration routes; and Tapanuli Selatan District harbour or a large natural landscape, defined as a forest fragment with a minimum core area • Local wildlife corridors where individuals >20,000ha surrounded by a buffer zone of can move as needed among ecosystems 3 km from the forest edge? as dictated by seasonal availability of food
Cornerstone habitats can also be a refuge for Methodology: Examination of forest blocks in particular species during long droughts, floods the wider landscape on Sumatra Island to find or fires. A common trait of habitats considered whether forest from the study area overlaps with under HCV 1.4 is that their disappearance would landscape forests. have a negative impact on wildlife populations in far greater proportion than expected given HCV 2.2: Areas that contain two the extent of the habitat itself. If HCV 1.4 exists or more contiguous ecosystems in a management unit, management activities HCV 2.2 aims to identify and maintain must guarantee that the function of these special connectivity in the transition zones between habitats will persist and that access to these ecosystems-ecotones and / or ecoclines to habitats will be maintained. ensure the core zone of the ecosystem and Methodology: Wetland, swamp and coastal its ecotone are maintained. Ecotones can be swamp and mudflat ecosystems are typical defined as transitional areas between different areas that provide functions of temporary use by ecosystem types such as between mangrove species such as migrant birds. In Indonesia there and swamp forest, swamp forest and lowland are 19 wetland sites for which a value as bird rain forest, or between forest over limestone habitat has been recognised internationally and and forest over alluvial soils. Ecoclines are less meet the criteria of Ramsar. Coastal mudflat and sharply demarcated transitional zones between mangrove ecosystems play an interconnection ecosystems and habitat types such as the role in which benefit birds live. All of Indonesia’s changing forest types found when ascending up 19 wetland habitats lie on coastal mudflats with a mountain such as lowland to hill forest, and hill a connection to mangrove forest.40 forest to lower montane forest.
40. Bibby, 2000, in Harahap Desi Yani, 2012
19 Many species also migrate up and down slopes sustain a variety of species in the long term than or over ecotones into different habitats during areas that are small, fragmented and with few different seasons that coincide with fruiting ecosystem types. between different altitudes / habitats and To identify areas that contain representative therefore rely on contiguous ecotones to be populations of most naturally occurring species, able to complete this migration. Ecotones also HCV 2.3 employs several proxies. These include play an important role in the meta-population landscapes with populations of higher predators dynamics of many species. Meta-populations of different taxa (e.g. tigers, leopards, and are a group of spatially separated populations eagles) or low density far ranging species (e.g. of the same species that interact with each orang-utans and elephants) that require large other, and can be characterised by the growth areas to survive but are readily surveyed. HCV or decline of populations in various locations / 2.3 has the objective of identifying landscapes habitats within a landscape. Originally, a species’ with the potential to sustain representative niche was described as ‘environmental factors populations of naturally occurring species and required by a species to carry out its life history, ensuring that management activities maintain This theory has since began to change with the or enhance this potential. In the assessment of proof that many species did actually inhabit HCV 2.3, it is essential to consider areas outside areas that do not fulfil their life cycles and were the management areas to understand potential actually occupying a ‘sink’ habitat and supported interactions among populations of species and by influxes of individuals from source habitats the ecosystems they depend upon inside and and populations. The ecotones between these outside the management areas. habitats (sinks and sources) are therefore very important in the meta-population dynamics and continued survival of a species, in a way that is The key question for HCV 2.3 only just beginning to be understood. in the context of this study is: Does Mandailing Natal, Tapanuli Utara and The key question for HCV 2.2 Tapanuli Selatan District part of a landscape in the context of this study is: with capacity to support populations of most naturally occurring species? Does Mandailing Natal, Tapanuli Utara and Tapanuli Selatan contain ecotones/ecoclines 41 critical for maintaining connectivity between According to HCV Identification Toolkit, to two or more major ecosystem types? identify and delineate, HCV 2.3 areas should meet one or more of the following criteria on the grounds that representative populations of most Methodology: Analysis of HCV 2.2 uses the naturally occurring species might be present: landscapes identified on HCV 2.1. The analysis examines the ecosystem distribution using • Protected Areas identified as HCVA 1.1; spatial analysis to inspect forest cover combined with topographical gradients. An assessment • Areas identified as HCVA 2.1; of locations of forest remnants in 2011 situated • Large forest blocks that did not meet from lowland (0 m – 500 m), lowland forests requirements of HCV 2.1, due to lack of a (500 m – 1000 m), sub mountain forests (1000 core zone as defined above; m – 1500 m) and mountain forest (1500 m – 2000 m) to mountain areas (> 2,000 m) are • Large forest blocks containing continuous identified. forested ridges and slopes that span lowland to montane forest ecosystems; HCV 2.3: Areas that contain • Area proven to have a population of representative populations of one or more top predators (e.g. tiger, most naturally occurring species clouded leopard or eagle) with evidence of ongoing reproduction; and The survival of a species in the long-term requires maintaining habitat of sufficient quality • Area that contains other populations of and extent to enable population viability. species known to require large habitat Although the area of a habitat that is required areas to survive, living naturally at low to maintain a viable population varies greatly densities (e.g. orang-utan, elephant, among species, it is generally held to be true hornbills). that large, unfragmented areas with a diversity of ecosystem types have a higher potential to
41. Konsorsium Revisi HCV Toolkit Indonesia 2008
20 HCV 3: Rare or endangered The key question for HCV 4.1 ecosystems in the context of this study is: The objective of HCV 3 is to identify and Does Landscape of Mandailing Natal, delineate ecosystems within a landscape that is Tapanuli Selatan and Tapanuli Utara district naturally rare (e.g. karst forest) or endangered contain ecosystem areas important for because of changes in land cover caused by maintenance of clean water and flood humans. Management actions should ensure prevention? that natural ecological processes throughout a rare or endangered ecosystem are maintained – Methodology: A precautionary approach was especially the distinctive features of it. Analyses used for identification of important ecosystems of rare and threatened ecosystems have already / landsystems using the RePPProT dataset, been completed for western Indonesia using a including all landscapes that could potentially land system approach. qualify that have not been deforested. More details on the analysis can be found in the The key question for HCV 3 in Results section. the context of this study is: Does Mandailing Natal, Tapanuli Utara and HCV 4.2: Areas important for Tapanulis Selatan District contain rare and the prevention of erosion and endangered ecosystem? sedimentation Erosion and sedimentation have ecological Methodology: Landsystem data,42 historical and economic consequences important at the forest cover change analysis 1990 – 2011 and landscape scale. Surface erosion causes the elevation data were used to identify HCV 3 areas loss of topsoil, which in turn decreases the in assessment locations. productivity of the land. Morpho-erosion like landslides or the creation of ravines reduces the area of productive land, damages economic HCV 4: Environmental services infrastructure and increases sediment loads. Under natural conditions the rate of soil erosion HCV 4.1: Areas or ecosystems is approximately equal to the rate of soil important for the provision of formation. However in disturbed environments water and prevention of floods for accelerated erosion is extremely destructive and bears high cost in time and money to control. downstream communities Land use activities or forest use in a Among factors that affect erosion intensity, those watersheds often result in the degradation of which can be influenced by humans are land land. Sometimes this causes a disturbance cover and soil conservation practices. Natural in the water cycle. The main parties that forest land cover is much better than non-forest feel the consequences of this degradation are land at reducing erosion levels due in a large downstream communities. Land cover consisting part to a closed canopy, complex understorey, of forest in good condition functions to regulate and surface leaf litter protecting the soil. HCV water downstream. If a forest area is found to 4.2 areas are forest or other areas where surface play a role in the production of clean water or erosion risk is deemed unacceptably high. Any to control flooding in downstream communities, operations carried out by management in HCV then it contains HCV 4.1. 4.2 areas must be done with extreme caution to avoid erosion or sedimentation. In addition to watersheds and their downstream communities, there are several land and forest ecosystems that have extremely important The key question for HCV 4.2 hydrological functions and require special in the context of this study is: attention. Ecosystems referred to as HCV 4.1 Does landscape in Mandailing Natal, include cloud forest, ridgeline forest, riparian Tapanuli Selatan and Tapanuli Utara contain ecosystems, karst forest, and a variety of areas important for the prevention of wetland ecosystems including peat swamp erosion and sedimentation? (especially swamp that is still forested), freshwater swamp, mangrove forest, lakes, and grass swamps.
42. RePPProt, 1990
21 Methodology: Analysis starts with conducting an erosion analysis of landscapes of Mandailing Carbon stock distribution Natal Tapanuli Selatan and Tapanuli Utara. In the HCV framework, carbon stocks are The erosion analysis is conducted using USLE currently not included as a High Conservation (revised universal soil loss equation). The Value. In the study however, carbon stocks have application of the methodology in the target been included as a value due to their relation districts is explained in detail in the Results to the current debates about climate change section. and related policy developments, as well as the relevance to CI targets and work streams – in particular, within the context of REDD+ projects. HCV 4.3: Areas that function as natural barriers to the spread of Unfortunately, there are currently no universal forest or ground fire carbon displacement thresholds formalised that can be used for guiding suitable classification. Uncontrollable forest fires in Indonesia are Due to the uncertainty on the outcome of the a serious problem that, to date, have not debates, caution has been taken in the study been resolved. Forest fire events in 1982- in the setting of boundaries because, on the 1983 destroyed 2.4 million - 3.6 million ha of one hand, it was important that the threshold forest in East Kalimantan. Since then, forest values did not set unrealistic barriers, whilst on fires continue to occur in almost all regions the other hand, it was important not to classify of Indonesia, especially in Riau, Jambi, South areas providing important carbon sequestration Sumatra, Central Kalimantan, and West services to be converted. Kalimantan which occurred in 1987, 1991, 1994, 1997-1998, and 2003 respectively. It has become clear from these events that biophysical The key question for carbon factors play an extremely important role in stock distribution in the controlling the spread of wildfires. context of this study is: Does the landscape of Mandailing Natal, The key question for HCV 4.3 Tapanuli Utara and Tapanuli Selatan contain in the context of this study is: areas with important carbon sinks? Does landscape of Mandailing Natal, Tapanuli Utara and Tapanuli Selatan contain The study considered in this criterion areas areas with function as natural Barriers to in which net carbon change is suitable for the spread of forest or ground fire? conversion to the target crop. For palm oil and rubber plantations carbon stocks have been Methodology: Analysis starts with identifying measured in several studies, and range between forest or wetland areas that can keep fires 40 Mg / ha to 80 Mg / ha. As for coffee and from spreading, as these can form barriers cocoa, carbon stocks vary widely depending for spreading fire in the landscape. Densely on production systems used, in particular, forested regions and wetlands, when in good agroforestry system carbon stocks can be condition, have physical characteristics that significantly higher. To make a general standard make them resistant to fire, even during the that can be applied for all commodities, a fairly dry seasons or during droughts related to lenient approach was adopted in the study, as the El-Nino phenomenon (such as those that there is currently no specific regulation / demand occurred in 1982-1983 and 1997). All such to ensure that carbon stocks are maintained, the areas are potential classified as HCVA 4.3. To threshold was set at 80 Mg / ha. This reflects identify barriers for large-scale fire, locations aboveground carbon stock only. Peat areas of areas that have been burned over the last potentially hold immense carbon stocks, and decade combined with land cover data should be conversion can lead to very high emissions. analysed. Even the conversion of shallow peat will lead to net GHG emissions; therefore all peat areas have been classified as unsuitable based on this criterion. In this analysis, the carbon values proposed by BioTrop (2011) have been adopted. In this study all peat areas have been considered as High (carbon) Value, and unsuitable for conversion in any scenario.
22 Economic evaluation The production data for the four commodities of the study (coffee, oil palm, cocoa and rubber) at sub-district levels were gathered from the District BPS43 of the study areas. The information consisted of the total planted area and production levels for each assessed commodity. Due to limited data availability, the analysis in this report considers year 2012 data only. There is also some missing information on production for particular commodities at sub-district levels. Using the data that was accessible, the average land productivity for each commodity (i.e. production in tons per total land area in hectares) in each district was derived. This average value was then used to calculate the production levels for the five target commodities in each sub-district based on the commodity’s biophysical suitability for year 2014, 2020 and 2050. In all calculations, it was assumed that all suitable areas (i.e. low suitability, medium suitability and high suitability) be planted by the particular fitting-commodities, and that these plantations are productive.44
Aggregate revenues were computed using a fixed-price derived by averaging the commodity prices in year 2014. The price data for Arabica, Robusta, cocoa and rubber were taken from the Indonesian Ministry of Trade’s BAPPEBTI. Only the “on-spot” transaction prices were considered. Meanwhile, the price data for oil palm was gathered from West Borneo’s DISBUN. Similarly, only year 2014 prices of oil palm fresh fruit bunch (FFB) aging 10-20 years old were used in the analysis.
43. Badan Pusat Statistik or Central Statistics Agency 44. Note that that biophysical suitability in this assessment does not take into account the HCVs and other sustainability indicators. 23 24 Chapter 3 Results
Climate change predictions
The focus areas of the study are dominated by hilly terrain, ranging from coastal lowland areas in the southwest to sub-montane areas in the northeast. As a result the target area has a wide range of climates. The region’s annual mean temperature from 1950 to 2014 showed a trend towards increased temperatures from January to May and decreasing temperatures from July to December. This phenomenon is likely linked to precipitation during the monsoon and dry seasons. Using climate projections (according to IPCC v.4 with scenario RCP 2.6, 6.5 and 8.5, where RCP 8.5 scenario represents the highest concentration of green gas house likely due to a climate change), a significant trend of temperature shift is expected over the next 46 years. In 2020, the annual mean temperature is expected to increase by 0.6˚C to 1.5˚C, and by 2050 the temperature is expected to nearly double to between 1.1˚C to 2.8˚C (see Figure 16).
25 Figure 16: Changes in temperature between 2014, 2020 and 2050 following RCP 8.5 scenario
Despite the links between temperature and precipitation, the impact of climate change on Climatic suitability maps of precipitation in North Sumatra Province showed target commodities’ production an uncertain pattern. While the precipitation pattern in the focus areas showed a semi-diurnal areas pattern, high precipitation occurred twice a The results of climatic suitability modelling year from March to May, and from September indicate a good model for each commodity, as to November. However, the future projection the mean AUC values range from 0.906 to 0.972, indicates a shift to a decrease in the amount SD + 0.003 (see Table 6). of rainfall peaks during the rainy season and an increase in the amount of rainfall in the dry season.
From the model it is predicted that in the decade of the 2020s there will be no significant changes to extreme temperature ranges (probability above 0.9). In the decade of the 2050s , however, the extreme temperature threshold is shown to significantly change by 1˚C from 27˚C in 2014 to 28˚C in the 2050s. In addition, significant shifts in patterns of extreme precipitation are also anticipated. The baseline threshold of extreme precipitation in the current year is ~600 mm, however in the projections the thresholds are decreasing gradually from ~400 mm in the 2020s to ~200m in the 2050s (see Figure 17).
Table 6: Mean AUC for target commodities
Commodities Mean AUC
Arabica coffee 0.972
Robusta coffee 0.970
Cocoa 0.906
Rubber 0.947
Oil palm 0.961
26 Figure 17: Changes in precipitation between 2014, 2020 and 2050 following RCP 8.5 scenario
12 main environmental criteria and detailed Current and crop requirements for each commodity (see Appendix 15). The evaluation was run using future suitability environmental variables extracted from land system information.46 For the selected commodities current and future suitability maps for agricultural were generated, combining the results from the biophysical suitability analysis with the results commodities from the climate change analysis (modifying temperature and rainfall). For each target commodity the study’s results are depicted in Selected method for maps which show the distribution of biophysical biophysical suitability analysis suitable areas, as well as the results on the distribution of the land in the target districts for This report will primarily focus on the results years 2014, 2020 and 2050 (see Figures 18-27). of the analysis of biophysical suitability using the Multi-Criteria Analysis approach due to the results of the Max Ent study appearing skewed Predicted changes in suitability and the exclusion of areas that are known to for Arabica coffee be suitable. This is most likely the result of a The suitability for Arabica coffee is predicted to limited sample size (despite over 1000 points decrease in North Sumatra. Within the focus were collected). The full results of the Max Ent areas of Tapanuli Utara and Tapanuli Selatan, study can be found in Appendix 38. the suitability for Arabica is predicted to decline significantly as a result of climate change. Mandailing Natal, on the other hand, has a very Results of the Multi-Criteria limited amount of land suitable for Arabica coffee Analysis at all. As described in the Methodology, the study’s Multi-Criteria Analysis adopted the Ministry of Agriculture’s agricultural suitability evaluation, an adaptation of the FAO Guidelines.45 The study’s set of land suitability criteria used
45. Djaenudin et al., 2000 46. RePPProt, 1990
27 Figure 18: Distribution of areas biophysically suitable for Arabica coffee in North Sumatera Province based on Ministry of Agriculture suitability criteria (Djaenudin et al. 2000) and predictions for climate change for 2020 and 2050 (downscaled from BIOCLIM 2014)
Predicted changes in suitability for Robusta Coffee Even though in 2020 and 2050 it is predicted there will still be a large area suitable for producing Robusta coffee, substantial changes in areas suitable for production are predicted for the south of North Sumatra (Mandailing Natal). For Tapanuli Utara an increase in areas suitable for production is predicted.
Figure 19: Predicted suitability for Arabica Coffee in 2014, 2020 and 2050 target districts based on Ministry of Agriculture suitability criteria
700000 600000 500000 400000 300000 200000 100000 0 MAND. MAND. MAND. TAP. TAP. TAP. TAP. TAP. TAP. NAT. NAT. NAT. SEL. SEL. SEL. UT. UT. UT. (2014) (2020) (2050) (2014) (2020) (2050) (2014) (2020) (2050) High Suitability (ha) Medium suitability (ha) Low Suitability (ha) Unsuitable (ha)
28 Figure 20: Distribution of areas biophysically suitable for Robusta coffee in North Sumatera Province based on Ministry of Agriculture suitability criteria (Djaenudin et al. 2000) and predictions for climate change for 2020 and 2050 (downscaled from BIOCLIM 2014)
Figure 21: Predicted suitability for Robusta coffee in 2014, 2020 and 2050 target districts based on Ministry of Agriculture suitability criteria 600000 500000 400000 300000 200000 100000 0 MAND. MAND. MAND. TAP. TAP. TAP. TAP. TAP. TAP. NAT. NAT. NAT. SEL. SEL. SEL. UT. UT. UT. (2014) (2020) (2050) (2014) (2020) (2050) (2014) (2020) (2050)
High Suitability (ha) Medium suitability (ha) Low Suitability (ha) Unsuitable (ha)
29 Predicted changes in suitability for cocoa For cocoa a significant decline in suitable areas is expected in the south of north Sumatra (Mandailing Natal), as well as a shift from High Suitability to Medium Suitability for large areas in the eastern regions of the province. For the target districts both the amount of suitable land for cocoa production and the overall suitability are expected to decline.
Figure 22: Distribution of areas biophysically suitable for cocoa in North Sumatera Province based on Ministry of Agriculture suitability criteria (Djaenudin et al. 2000) and predictions for climate change for 2020 and 2050 (downscaled from BIOCLIM 2014)
Figure 23: Predicted suitability for cocoa in 2014, 2020 and 2050 target districts based on Ministry of Agriculture suitability criteria 600000 500000 400000 300000 200000 100000 0 MAND. MAND. MAND. TAP. TAP. TAP. TAP. TAP. TAP. NAT. NAT. NAT. SEL. SEL. SEL. UT. UT. UT. (2014) (2020) (2050) (2014) (2020) (2050) (2014) (2020) (2050)
High Suitability (ha) Medium suitability (ha) Low Suitability (ha) Unsuitable (ha)
30 Predicted changes in suitability for rubber Even though the overall amount of land suitable for rubber production is expected to mostly stay the same, a significant decline in suitable areas is expected in the South of north Sumatra – particularly in Mandailing Natal, and to a lesser extent, in Tapanuli Utara.
Figure 24: Distribution of areas biophysically suitable for rubber in North Sumatera Province based on Ministry of Agriculture suitability criteria (Djaenudin et al. 2000) and predictions for climate change for 2020 and 2050 (downscaled from BIOCLIM 2014)
Figure 25: Suitability for rubber in target districts based on Ministry of Agriculture suitability criteria 600000 500000 400000 300000 200000 100000 0 MAND. MAND. MAND. TAP. TAP. TAP. TAP. TAP. TAP. NAT. NAT. NAT. SEL. SEL. SEL. UT. UT. UT. (2014) (2020) (2050) (2014) (2020) (2050) (2014) (2020) (2050)
High Suitability (ha) Medium suitability (ha) Low Suitability (ha) Unsuitable (ha)
31 Predicted changes in suitability for oil palm For oil palm suitability, some significant changes in suitability are predicted, which is surprising as this is a very resilient crop. In the eastern part of the province it is predicted that there will be changes from High Suitability to Medium Suitability. For the southern part of North Sumatra, in particular Mandailing Natal, large areas are predicted to become completely unsuitable for oil palm due to changes in rainfall patterns.
Figure 26: Distribution of areas biophysically suitable for oil palm in North Sumatera Province based on Ministry of Agriculture suitability criteria (Djaenudin et al. 2000) and predictions for climate change for 2020 and 2050 (downscaled from BIOCLIM 2014)
Figure 27: Suitability for palm oil in target districts based on Ministry of Agriculture suitability criteria 600000 500000 400000 300000 200000 100000 0 MAND. MAND. MAND. TAP. TAP. TAP. TAP. TAP. TAP. NAT. NAT. NAT. SEL. SEL. SEL. UT. UT. UT. (2014) (2020) (2050) (2014) (2020) (2050) (2014) (2020) (2050)
High Suitability (ha) Medium suitability (ha) Low Suitability (ha) Unsuitable (ha)
32 Figure 28: Identified HCV 1.1 areas within Distribution of High study focus area Conservation Values HCV 1: Areas with important levels of biodiversity
Analysis and findings for HCV 1.1: Areas that contain or provide biodiversity support function to protection or conservation areas The key question for HCV 1.1 in the context of this study was defined as: Does Mandailing Natal, Tapanuli Utara and Tapanuli Selatan District contain areas that support, could negatively impact or contain conservation and protection forest areas?
Using the precautionary approach as outlined in the methodology, a buffer zone of 1000 m from all conservation and protected forest boundaries was created. This buffer aimed to meet the goal of ensuring protection of cultural, ecological functions (e.g. water provision) and biodiversity (e.g. viable populations of rare wildlife), or a combination of these values. Additional analysis HCV 1.2: Critically endangered of HCV1.1 was performed on riparian buffers or species water body (lake) buffers. For those areas a 100 m buffer was used. The key question for HCV 1.2 in the context of this study is: Are threatened and endangered In the landscape of Mandailing Natal, Tapanuli species (including endemic and migratory Utara and Tapanuli Selatan District various species) found in the prescribed landscape? conservation and protected areas were identified; Batang Gadis National Park and Batang Toru Sumatra is an island with big exotic mammals Protected Forest being the most widely known such as orang-utan (Pongo abelli), elephant conservation areas, in addition to a number of (Elephas maximus), tiger (Phantera tigris other types of conservation and protected forest. sumatrae) and rhino (Dicerorhinus sumatrensis). In total, conservation and protected forest In addition, the IUCN has recorded 68 cover 769,940 ha of the study area (see Figure endangered species in Sumatra with habitat that 28). Details of each identified protected and has decreased significantly from year-to-year conservation area is listed in Appendix 18. (see for the protection status of these species). A recent survey conducted by CI (2006) revealed that Batang Toru is home to a rich variety of Sumatran species, particularly mammals, birds and plants, many of which are globally threatened. Within the region 67 species of mammals, 287 species of birds and 110 species of herpeto fauna have been recorded. Of the total number of mammals, 20 species are protected under Indonesian law and 12 are globally threatened.47 The survey also discovered rich avifauna diversity in the region, including rare and threatened species. Of the total number of bird species, 51 species are protected under Indonesian law and 61 are globally threatened.48 In addition, initial data
47. Among these are Sumatran Orangutan (Pongo abelli), Sumatran tiger (Panthera tigris sumatrae), serow (Capricornis sumatrensis), Malayan tapir (Tapirus indicus), Malayan sun bear (Helarctos malayanus), slow loris (Nycticebus coucang), Golden Cat (Pardofelis marmomata). 48. Such as Sunda Blue Flycatcher (Cyornis caerulatus), Wallace’s Hawk-eagle zaetus nanus), Blackcrowned (Pitta venusta) 33 from Batang Toru suggest the area holds some of Figure 29: HCV 1.2 areas based on the highest levels of vascular plant biodiversity, distribution of orangutan, tiger and with 688 different species recorded. Of the elephant (WWF 2005) total number of plant species, 138 species are orang-utan food resources, and 8 species are globally threatened49 (see Appendix 20). These species include the largest flower in the world (Rafflesia gadutensis Meijer, Becc) and the tallest flower in the world Amorphophalus( baccari and Amorphophalus gigas).50
The analysis of HCV 1.2 is ideally conducted at a management unit level with field sampling plots of wildlife and vegetation / plants and the description of the critical animal or plant occurrences as they are are found. As this study was conducted in three district landscapes, secondary data was used with proxies of critically endangered and endangered animal species distribution in Sumatera. The three endangered animal species are Pongo abellii, Elephas Maximus and Phanthera tigris sumatrae (as mentioned above). These three species have a wide home range in Sumatra Island. By conserving the home range of these three flagship species, it is assumed the distribution of the flagship species covers those species with a smaller home range.51 As this study used secondary data, the results of HCV 1.2 are indicative of the sources that have been referenced (see Figure 29). HCV 1.3: Areas that contain habitat for viable populations of endangered, restricted range or protected species The key question for HCV 1.3 in the context of this study is: Does the landscape of Mandailing Natal, Tapanuli Utara and Tapanuli Selatan contain areas or ecosystems that support viable populations of endangered or threatened, restricted range, and protected or trade- restricted species? To assess HCV 1.3 protected and endangered species lists were used to identify relevant species. With the species identified in HCV 1.2 viable species meeting this criterion their home ranges were used as indicators for HCV 1.3 and the ecosystems associated with the selected species mapped. The results of the analysis have been compared with forest cover to assess the level of fragmentation – an important step that enables measurement of the quality of habitat, including the analysis on the extent of areas that can be used as potential corridors, levels of fragmentation, and ecosystem quality.
The resulting maps on the distribution of HCV 1.3
49. Includes Nepenthes sumatrana (Miq.) 50. Perbatakusuma et al., 2006 51. The data source of this HCV 1.2 analysis using endangered species distribution from WWF Indonesia 2005.
34 consider whether the habitat is overlapping with suggested as indicative areas for HCV 1.4 (see Primary or Secondary Forest (see Figure 30). map in Appendix 22). Artificial ecosystems such as forest plantation can still provide services as home range habitat for endangered species like the Sumatran Tiger. HCV 2: Natural landscapes
Figure 30: Identification of HCV 1.3 in study and dynamics focus areas HCV 2.1: Large natural landscapes with capacity to maintain natural ecological processes and dynamics The key question for HCV 2.1 in the context of this study is: Does Mandailing Natal, Tapanuli Utara and Tapanuli Selatan District exist within a large natural landscape, defined as a forest fragment with a minimum core area >20,000ha surrounded by a buffer zone of 3 km from the forest edge?
An examination of forest cover for Sumatra Island as it stood in 2011 identified several core areas larger than 20,000 ha (see Appendix 23) with several forest blocks meeting the criteria of large landscape forests. Within the study area, three forest blocks were identified as HCV 2.1; two forest blocks in the Batang Gadis Landscape, and one forest block in the Batang Toru Forest Landscape (see Figure 31).
Figure 31: Indicative HCV 2.1 in study focus areas
HCV 1.4: Areas that contain habitat of temporary use by species or congregations of species The key question for HCV 1.4 in the context of this study is: Does Mandailing Natal, Tapanuli Utara and Tapanuli Selatan District contain key habitats critical to populations seasonally, occasionally or during particular stages in their life cycle?
The focus of the study of types of ecosystems / landsystems with characteristics of HCV 1.4 in the focus areas focussed on Klaru Mendawai, Putting, Gambut, Beliti, Banjar Lawas and Pulau rotan as these ecosystem types are presumed to provide places of refuge or temporary use for migrant birds. Reference research of migrant birds mostly found on the east coast of Sumatra island identified two ordo of migrant bird; Charadriiformes and Ciconiiformes on the east coast of Sumatra.52 In reference to these findings, the swamp areas of Mandailing Natal, Tapanuli Setalatan and Tapanuli Utara were selected as ecosystems that could provide functions associated with HCV 1.4 and are thus
52. Harahap Desi Yani, 2012
35 HCV 2.2: Areas that contain two HCV 2.3: Areas that contain representative or more contiguous ecosystems populations of most naturally occurring species The key question for HCV 2.2 in the context of this study is: Does Mandailing Natal, Tapanuli The key question for HCV 2.3 in the context of Utara and Tapanuli Selatan contain ecotones / this study is: Does Mandailing Natal, Tapanuli ecoclines critical for maintaining connectivity Utara and Tapanuli Selatan District part of a between two or more major ecosystem types? landscape with capacity to support populations of most naturally occurring species? The study revealed that most of the ecotones are in lowland forest ecosystems. Those in Given the presence of all of the listed biodiversity which at least three ecosystems could be found identified in the previous HCV evaluation, in contiguous areas were highlighted. HCV 2.2 especially the large predators and wide-ranging lies within two forest blocks. The variety of species, all remnant forest in the assessment landsystems found in the selected landscape location should be considered as HCV 2.3 areas were sorted by altitude and comprise the (see Appendix 24 for distribution of species’ following (see Figure 32): populations).
1. Lowland forest ecosystem below 500 m: Air hitam kanan, Beriwit, Telawi, Batang HCV 3: Rare or endangered Anai, Bukit Pandan, Dolok Perjalanan, Gajo, ecosystems Mantalat, Maput, Mendawai, Talamau and Tandur Ulubandar HCV 3: Rare or endangered 2. Lowland forest ecosystem below 500 m – ecosystems 1000 m: Air hitam kanan, Beriwit, Bukit Balang, Bukit Berangin, Bukit Masung, Bukit The key question for HCV 3 in the context of this Pandan, Gunung Gadang, Maput, Pendreh, study is: Does Mandailing Natal, Tapanuli Utara Sibualbuali, Talamau, Tandur, Talawi and Ulu and Tapanulis Selatan District contain rare and Bandar. endangered ecosystem?
3. Sub mountain forest ecosystem 1000 m The analysis of HCV 3 started with a – 1500 m: Batu ajan, Batu Apung, Batu biophysiographic of Sumatra Island (see balang, Pendreh and Telawi Appendix 25). From this, the landscape was found to contain three target districts that Figure 32: Indicative HCV 2.2 in study areas overlapped with major biophysiographic areas, specifically:
1. Barisan Mountain region
2. Eastern plain and hill region
3. Eastern coastal swamp region
4. Western coastal foothills
Following from this, historical forest cover (from 1990) in every land system inside the region was examined to assess the extent of original ecosystems / land systems within the region. From this, the degradation of ecosystems within the region could be identified (see Appendix 26). When specifically assessing the study focus areas with the use of the previously generated information, endangered and rare ecosystems in the region could be identified based on the following criteria:53
1. Ecosystem has lost 50 percent or more of its original extent in the bio physiographical region where it occurs
53. see HCV Consortium, 2008
36 2. Ecosystem will lose 75 percent or more of in the study area were identified (see Appendix its original extent in the biophysiographical 29). region where it occurs, based on the The following HCV 4.1 ecosystems were assumption that all areas currently identified for the study area’s three districts (see allocated for conversion in existing spatial Figure 34): plans will be converted
3. Ecosystem fits the following criteria is 1. Peat swam or peat swam forest: Mendawai considered rare when a natural ecosystem (MDW), Gambut (GBT), Beriwit (BRW), covers less than 5 percent of the remaining Beliti (BLI) and Banjah Lawas (BLW) natural vegetation cover in the bio 2. Mangrove Swamp: Kajapah (KJP) physiographical region where it occurs 3. Fresh Water Swamp: Kahayan (KHY), Beliti The analysis resulted in Figure 33, demonstrating (BLI), Klaru (KLR), Tanjung (TNJ) and the distribution of HCV 3 in the target districts. Bakunan (BKN)
Figure 33: Distribution of HCV 3 in 4. Riparian: Sebangau (SBG), Bakunan (BKN), study areas Beliti (BLI) and Kahayan (KHY). 5. No Kars Ecosystem
6. Cloud Forest: Barongtongkok (BTK), Maput (MPT), Beriwit (BRW), Pendreh (PDH), Batu Ajan (BTA), Telawi (TLI), Tandur (TDR), Air Hitam Kanan (AHK), Bukit Balang (BBG), Bukit Barangin (BBR), Bukit Masung (BMS) and Bukit Pandan (BPD)
Figure 34: Indicative distribution of HCV 4.1 in study focus area
HCV 4: Environmental services
HCV 4.1: Areas or ecosystems important for the provision of water and prevention of floods for downstream communities The key question for HCV 4.1 in the context of this study is: Does Landscape of Mandailing Natal, Tapanuli Selatan and Tapanuli Utara district contain ecosystem areas important for maintenance of clean water and flood prevention?
Using the HCV Toolkit (HCV Consortium, 2008) as a guide, the ecosystems important for maintenance of clean water and food provision
37 Figure 35: Indicative map on erosion using HCV 4.3: Areas that function as USLE for study focus area natural barriers to the spread of forest or ground fire The key question for HCV 4.3 in the context of this study is: Does the landscape of Mandailing Natal, Tapanuli Utara and Tapanuli Selatan contain areas which function as natural barriers to the spread of forest or ground fire?
The study found that most of the forested swamp area in the western coastal region could be identified as meeting HCV 4.1. This area included land systems in Aek Sitinga, Air Hitam Kanan, Bakunan, Banjas Lawas, Beliti, Beriwit, Gambut, Kahayan, Kajapah, Sebangau, Mendawai, Sungai Aur, Tembaga and Tanjung (see resulting map in Appendix 33).
Figure 36: Carbon stock distribution in target districts (in line with BioTrop, 1998)
HCV 4.2: Areas important for the prevention of erosion and sedimentation The key question for HCV 4.2 in the context of this study is: Does landscape of Mandailing Natal, Tapanuli Selatan and Tapanuli Utara contain areas for prevention of erosion and sedimentation?
In the HCV Toolkit for Indonesia (2008), areas with a soil loss potential >180 metric tons / ha / year are considered to be HCV4.2. For the modelling of erosion USLE was used (see Appendix 30 and Appendix 31 for details on the modelling of USLE and parameters). Additionally, the threshold for acceptable erosion potential was modified based on soil depth, with thinner Carbon stock distribution soils requiring additional protection (as described The key question for carbon stock distribution in in Appendix 32). Running the model resulted the context of this study is: Does the landscape in Figure 35, in which 276.753 ha of land was of Mandailing Natal, Tapanuli Utara and Tapanuli classified as being at risk of heavy erosion, and Selatan contain areas with important carbon 111.885 ha of land was classified as being at sinks? high risk of very heavy erosion. To assess carbon stock distribution land cover data was used to distinguish between different land cover types. For each land cover type a reference value was identified (see also BioTrop, 1998). Based on these values a map was produced on the distribution of the carbon stock in target districts (see Appendix 34).
38 HCV risk indicator map Figure 37: HCV risk indicator map for target districts As conservation values in the target areas are abundant, it is not surprising that in principle the entire areas can be considered High Conservation Value. With target districts working towards meeting development targets providing additional insights in how trade-offs in the landscape can be made will be valuable. Zoning the landscape in order to prioritise the most important values can be done through assigning a ‘weight’ to conservation values that can then be reflected in a final map. While at first this may appear contrary to the aims of identifying HCVs, as mentioned in the Methodology section, the presence of a HCV does not mean the area cannot be used for agriculture, but that management has to be put in place to ‘maintain or enhance the value’. While the HCV Toolkit does not provide guidance for assigning a relative weight to each HCV, some work has been done to experiment with this approach, most notably in Smit et al. 2103. The scores developed by Smit et al. 2013 for the presence of each HCV were thus used to prioritise the most important areas in the study area’s landscape (see Table 7).
For some values, additional considerations were taken into account, particularly if it would be difficult for management to maintain a certain Economic evaluation: value in practice. For example, peat areas cannot be used without degradation of the peat Impacts of climate change on occurring; once the vegetation is cleared or commodities the peat is drained, an irreversible process of oxidation starts, resulting in high net carbon Full results of the economic evaluation of the emissions. For some areas with conservation impacts of climate change on the commodities values it is actually illegal according to can be found in Appendix 39. Below is a Indonesian law to develop the land, for example summary of the key findings. in the protected forests and conservation areas. With these considerations in mind, such Production at district level areas were considered unsuitable under any circumstance, and given a score of 12. Mandailing Natal
For each polygon, the cumulative score was In 2014, it is estimated that of the five key calculated, and put into four categories, or risk commodities palm oil will top production in classes: Mandailing Natal with over 3 million tons. This is expected to be followed by production of Score 1-3 = Suitable cocoa (205,700 tons), then rubber (173,800 Score 4-7 = Medium risk tons), and finally Robusta and Arabica (115,100 Score 8-11 = High risk tons and 6,500 tons). In terms of aggregate Score 12 = Unsuitable gross revenue, oil palm is expected to lead, with The results of the scores of HCVs in the study production generating approximately 5.97 trillion area are shown in Figure 37. Rupiah by 2014, followed closely by cocoa (5.80 trillion Rupiah), rubber (3.7 trillion Rupiah), Robusta (± 2.37 trillion Rupiah) and Arabica (± 0.31 trillion Rupiah).
The predicted impact of climate change on the potential production revenue for target commodities in Mandailing Natal can be seen in Figure 38.
39 Table 7: Overview of risk categories considered for each HCV and carbon stocks, with scores for each risk category (Low risk = Score 1, Medium risk = Score 2 etc.) (Modified from Smit et al. 2013)
Low Risk Medium Risk High Risk Not Suitable
Score 1 2 3 4 HCV 1 No overlap HCV 1 Overlap with home Buffer zone 1 km IUCN areas, national range of protected, conservation areas, endangered and and protected forest endemic species Overlap with Breeding grounds (HCV 1.2) distribution or and nesting places; habitats of protected, grazing/browsing for endangered and endangered species; endemic species and temporal (HCV 1.3) habitats for migratory species (HCV 1.4) HCV 2 No overlap Overlaps with Overlaps with Overlaps with core Endangered 4 km buffer of 20000 2 km buffer of 20000 zone 20000 ha ecosystems, ha ha important ecotone regions, large scale forest HCV 3 No overlap HCV 3 Overlaps with Overlaps with rare threaten ecosystem ecosystems HCV 4.1 No overlap with HCV Overlaps with 4.3 Overlaps with 4.1 & 4 Mangrove, peat, water sources; HCV 4.3 wetland, karst forest riparian zones deep and cloud forest peat (D3 - D5) HCV 4.2 Very light - Light Moderate Heavy Very heavy
Carbon- 0 - 80 ton / ha 80 - 90 ton / ha 90 - 105 ton / ha > 105 ton / ha stocks (Non vegetation)
Figure 38: Predicted impact of climate change on potential production revenue from target commodities in Mandailing Natal (see also Appendix 39) 7.00 ) 6.00
5.00
4.00
3.00
2.00
1.00 Total Pot. Rev. (trln. Rp.
0.00 PALM OIL COCOA RUBBER ARABICA ROBUSTA
2014 5.97 5.80 3.70 0.31 2.37
2020 5.80 5.51 3.55 0.08 2.27
2050 1.07 1.12 0.88 0.04 0.43
40 By 2020, palm oil production is expected to Tapanuli Selatan continue leading the other commodities, with In 2014, oil palm production is expected to a total of over 3 million tons produced in amount to 1.95 million tons in Tapanuli Selatan, Mandailing Natal. Again, cocoa ranks second followed by Arabica at 88,639 tons, and Robusta (195,434 tons), and then rubber (166,684 at 82,404 tons. Cocoa and rubber production are tons). Robusta and Arabica, on the other hand, expected to be significantly less, at 67,895 tons may experience a decline in production to about and 48,000 tons respectively. In terms of gross 110,000 tons and 1,647 tons respectively. By income, Arabica is expected to generate the 2020 revenue from palm oil is anticipated to most aggregate income, with total revenue of lead the other commodities with approximately about 4.28 trillion Rupiah, followed by palm oil at 5.8 trillion Rupiah, followed by revenue from 3.74 trillion Rupiah. Significantly lower revenue cocoa (5.5 trillion Rupiah), rubber (3.55 trillion is expected from cocoa (1.9 trillion Rupiah), Rupiah), and Robusta (2.27 trillion Rupiah). Robusta (1.7 trillion Rupiah), and rubber (1 Arabica is only expected to generate 0.80 billion trillion Rupiah). Rupiah by 2020. The predicted impact of climate change on the By 2050, climate change appears to significantly production revenue for target commodities in impact the suitability of all five commodities Tapanuli Selatan can be seen in Figure 39. in Mandailing Natal. Production of palm oil is expected to decline to only 556,900 tons, By 2020, the only commodity expected to rubber 41,100 tons, cocoa 39,700 tons, and experience a moderate increase in production is Robusta 20,874 tons. Arabica is expected to rubber, at just over 2 percent (to 49,000 tons). have a dramatic decline to just 753 tons of Robusta and cocoa, however, are expected to total production. Under these conditions of decrease by 6.48 percent (to 77,000 tons) and reduced production expected revenues are 1.12 percent (to 67,100 tons) respectively. also anticipated to decline sharply for all five Although palm oil is also expected to decline commodities with cocoa generating some 1.117 by 7.08 percent, its production of 1.8 million trillion Rupiah, palm oil 1.07 trillion Rupiah, and tons still makes it by far the top performing rubber 0.88 trillion Rupiah. Robusta is expected commodity in the district. The biggest impact on to generate just 0.43 trillion Rupiah of gross production from changes in biophysical suitability income, and Arabica 36.36 billion Rupiah. is experienced by Arabica, which is expected to decline in production by 23 percent (to 68,000 tons). By 2020, the greatest economic value
Figure 39: Predicted impact of climate change on potential production revenue from target commodities in Tapanuli Selatan (see also Appendix 39) 4.50 ) 4.00 3.50 3.00 2.50 2.00 1.50 1.00
Total Pot. Rev. (trln. Rp. 0.50 0.00 PALM OIL COCOA RUBBER ARABICA ROBUSTA
2014 3.74 1.91 1.02 4.28 1.70
2020 3.48 1.89 1.05 3.29 1.59
2050 3.04 1.76 0.96 1.88 1.49
41 of the commodities are expected to be derived are rubber (44,100 tons production, 0.94 trillion from palm oil (3.5 trillion Rupiah), Arabica (3.3 Rupiah income), and oil palm (36,400 tons trillion Rupiah), and cocoa (1.9 trillion Rupiah). production, 69 billion Rupiah income). Robusta and rubber plantations are anticipated The predicted impact of climate change on the to generate approximately 1.6 trillion and 1.1 production revenue for target commodities in trillion Rupiah of income respectively. Tapanuli Utara is shown in Figure 40. By 2050, palm oil is expected to still lead the By 2020 there is expected to be a dramatic other commodities in terms of both production 72.30 percent increase in rubber production in and revenue – despite a 12.5 percent reduction Tapanuli Utara to 76,000 tons, equating to some in production – and produce an aggregate yield 1.6 trillion Rupiah in revenue earnings. Cocoa of about 1.6 million tons and 3 trillion Rupiah in and palm oil are also expected to experience income. The next top performers, at significantly increases in production and revenue, with cocoa lower levels, are Robusta (72,300 tons, 1.5 increasing by 6.68 percent to 70,100 tons in trillion Rupiah) and cocoa (62,500 tons, 1.76 production (1.9 trillion Rupiah income), and palm trillion Rupiah). By 2050, Arabica production is oil increasing by 6.84 percent to 38,900 tons expected to decline by a dramatic 43 percent in production (74.7 billion Rupiah income). On to about 38,900 tons, and generate roughly 1.9 the other hand, Arabica bears a minor decline trillion Rupiah. However, due to its high selling of 2 percent in production to 158,300 tons (7.6 price, revenue from Arabica is still anticipated trillion Rupiah income), and in 2020 Robusta is to be 0.4 trillion Rupiah higher than Robusta. expected to remain largely unchanged at 98,000 Lastly, rubber is expected to generate about tons in production (2.0 trillion Rupiah income). 45,000 tons in production and 962 billion Rupiah in income. By 2050, rubber is anticipated to increase by 29 percent and produce some 98,000 tons in Tapanuli Utara production and generate 2.09 trillion Rupiah in In 2014, Arabica, Robusta and cocoa are on income. This however, is a significantly lower track to be the leading commodities in Tapanuli than Arabica, which, despite an expected 10 Utara District. Arabica is expected to generate percent decrease in production, is anticipated to some 161,400 tons in production and 7.08 produce a higher 143,500 tonnes in production trillion Rupiah in income, Robusta - 98,000 tons and 6.93 trillion Rupiah income. At slightly less in production and 2.03 trillion Rupiah in income, than 98,000 tons in production and 2.02 trillion and cocoa – 65,700 tons in production and 1.85 Rupiah in income, Robusta remains largely trillion Rupiah in income. At much lower levels unchanged from 2020. Cocoa and oil palm
Figure 40: Predicted impact of climate change on potential production revenue from target commodities in Tapanuli Utara (see Appendix 39) 9.00 ) 8.00 7.00 6.00 5.00 4.00 3.00 2.00
Total Pot. Rev. (trln. Rp. 1.00 0.00 PALM OIL COCOA RUBBER ARABICA ROBUSTA
2014 0.07 1.85 0.94 7.80 2.03
2020 0.07 1.98 1.62 7.64 2.03
2050 0.07 1.96 2.09 6.93 2.02
42 however, are expected to decline by less than scenario suddenly almost the entire region one percent each, and respectively produce of Mandailing Natal is predicted to become 69,500 tons and 38,600 tons in production, and unsuitable. When comparing the distribution of 1.96 trillion Rupiah and 74.14 billion Rupiah in suitability with the distribution of conservation income. values the picture becomes complicated, as most of the area suitable for oil palm is considered unsuitable for sustainable production. This is Risk indicator map and due to the overlap with peat areas, which have been classified as unsuitable due to long term opportunities for the target impacts associated with peat conversion.
commodities Because oil palm in principle should not be To provide a more specific analysis of the results planted on peat, the areas identified for for the target districts, the combined results sustainable oil palm production occur outside peat areas and may be found in the western of the biophysical suitability mapping and the coast of Tapianuli Selatan and the southern identification of high conservation values are coast of Mandailing Natal (see Figure 41), presented.54 In this section we describe our however these areas may potentially only be findings and perceived opportunities, which are suitable for one rotation cycle considering the explored in more detail in the next chapter. For climate predictions. The amount of area that each commodity the results are described and can be developed sustainably without major opportunities and risks highlighted. management interventions / support (i.e. ‘Low Risk’) is particularly limited. Some clear potential Oil palm however, is identified in ‘Medium risk’ areas that could be considered upon closer examination Oil palm is the crop least affected by climate on the actual presence of the identified values. change in most of the province, but in the 2050
Figure 41: Distribution of oil palm suitability in 2014, 2020 and 2050 in grey shades, against a background of the Risk Indicator Map, visualising suitability of areas based on overlap with HCVs
54. The results of this desktop research provided in this study should be complemented with further depth analysis in the form field activities in order to verify / validate the study’s findings. 43 Figure 42: Distribution of biophysical suitable areas for oil palm and overlap with HCV in 2014, 2020 and 2050 450000 400000 350000 300000 250000 200000 150000 100000 50000 0 y y y y y y y y y unsuitabl e unsuitabl e unsuitabl e Low Suitabilit Low Suitabilit Low Suitabilit High Suitabilit High Suitabilit High Suitabilit Medium Suitabilit Medium Suitabilit Medium Suitabilit
2014 2020 2050
Low Risk Medium Risk High Risk Not Allowed
What is important in this situation is that sound these areas, technologies on managing water management plans are put in place, with water tables should be a top priority in order to reduce table management a key technology to consider the rate of peat subsidence and the risk of in the region, in particular in those areas where uncontrolled fires. rainfall is likely to become a limiting factor. As can be seen in Figure 42, there is a trend Rubber expected in which the suitable areas for oil Based on the results of the study, areas most palm will shift to areas now containing HCV’s. suitable for rubber in the long term are mostly Considering current sustainability initiatives confined to the north of Tapianuli Selatan and the reality of the potential development and most of Tapianuli Utara (see Figure 43). of oil palm in areas identified in this study as Although in 2020 it is predicted that additional unsuitable, consideration should be given to the portions of Mandailing Natal will become suitable, Principles of the Roundtable on Sustainable Palm this is mostly confined to protected areas. While Oil (RSPO), in particular regarding conversion rubber plantations could be considered on a of peat. According to RSPO P&C, peat cleared smaller scale in the eastern part, Mandailing before 2008 can still be certified, but should be Natal does not seem suitable for rubber in the restored after one rotation cycle. Considering long term. In the central area of Tapianuli most of the peat areas in the study area have Selatan there appears to be good potential. been cleared, and the peat depth is in general relatively shallow, it could be argued that it is Over time, it is expected that areas suitable better to support existing plantations to properly for rubber will shift only to a limited extent to manage these areas and thereby mitigate areas considered important for conservation environmental impacts as much as possible. In values (see Figure 43 and Figure 44). With case Conservation International (CI) chooses only patches of important HCV found in the to work with companies in these areas, it will areas suitable for rubber in Tapanuli Utara and be extremely important to closely monitor the Tapanuli Selatan, these areas might be a good implementation of sound management plans, as area for expansion of the crop. Particularly in the area is considered of the highest value. For northern Tapianuli Selatan, it could be interesting
44 Figure 43: Distribution of rubber suitability in 2014, 2020 and 2050 in grey shades, against a background of the Risk Indicator Map, visualising suitability of areas based on overlap with HCVs
to explore options for producing rubber as it expected in Mandailing Natal. The shift towards is a low risk area and there is no other clear areas with High Conservation Values is also suitability for other crops. limited. In the areas where Robusta is becoming unsuitable over time, introducing intercropping In the instance that rubber is planted in buffer systems with shade trees is likely to be beneficial zones to forest areas, a clear (communication) for the Robusta crop, as it will reduce climate plan is needed to delineate boundaries and the variability and provide protection from extreme transition to ‘jungle rubber’. In such frontier rainfall (although the climate suitability models areas options for other sources of income should suggest such events are likely to occur less be explored in order to increase the (perceived) frequently). It can also help to expand the areas value of the forest. that can be suitable for production and maintain suitability in areas that would not otherwise be Robusta suitable. It is predicted that the suitability for Robusta coffee production will be mostly confined to the Arabica northern part of the target areas over time, in The impact of climate change is expected to be particular to Tapianuli Utara. By 2020, a large severe for Arabica coffee production, in particular ‘low’ area identified in the Risk Indicator Map is in Tapanuli Selatan. With major shifts in suitable expected to become more suitable (see Figure production areas expected, by 2050 suitability of 45). Also additional areas are predicted to the commodity appears to become significantly become suitable in areas considered of Medium more limited (see Figure 47). Important to note Risk (yellow). In these transition areas to forests, is that the model could be run again with a less promoting agroforestry would be a good option extreme climate change scenario, as this might in order to create a buffer zone for the forests. show some areas that could be possible under The shifts in Robusta suitability as a result of slightly different circumstances. In principle, climate change are relatively limited compared it seems that Tapanuli Utara and Selatan are to the other target crops, with major shifts only likely to be most suitable for Arabica production,
45 Figure 44: Distribution of biophysical suitable areas for rubber and overlap with HCV in 2014, 2020 and 2050 500000 450000 400000 350000 300000 250000 200000 150000 100000 50000 0 y y y y y y y y y unsuitabl e unsuitabl e unsuitabl e Low Suitabilit Low Suitabilit Low Suitabilit High Suitabilit High Suitabilit High Suitabilit Medium Suitabilit Medium Suitabilit Medium Suitabilit
2014 2020 2050
Low Risk Medium Risk High Risk Not Allowed
Figure 45: Distribution of suitability for Robusta coffee in 2014, 2020 and 2050 in grey shades, against a background of the Risk Indicator Map, visualising suitability of areas based on overlap with HCVs
46 Figure 46: Distribution of biophysical suitable areas for Robusta and overlap with HCV in 2014, 2020 and 2050 450000 400000 350000 300000 250000 200000 150000 100000 50000 0 y y y y y y y y y unsuitabl e unsuitabl e unsuitabl e Low Suitabilit Low Suitabilit Low Suitabilit High Suitabilit High Suitabilit High Suitabilit Medium Suitabilit Medium Suitabilit Medium Suitabilit
2014 2020 2050
Low Risk Medium Risk High Risk Not Allowed
although such suitable areas are often Cocoa overlapping with ‘High Risk’ areas in the Risk Cocoa appears to have the most radical shifts in Indicator Map (see Figure 47). production suitability over time. In the medium In general, the introduction of shade trees will term Tapianuli Utara and Mandailing Natal are make sense from both an economic / agronomic suitable for cocoa production, however in the point of view, as well as from a conservation long term the suitability in the central regions point of view. Using shade trees will have of Mandailing Natal is expected to significantly a positive impact on the microclimate and decline (see Figure 55). As was the case with improve the suitability of the areas. As these coffee, the introduction of shade trees could changes can be quite significant, areas that be an option to reduce the impacts of climate would normally be unsuitable might be suitable change. In particular, Mandailing Natal should when Arabica is integrated with shade trees. be considered for such a system, as production Moreover, the introduction of shade trees will suitability seems to collapse by 2050. also have a positive impact on biodiversity and Overall, suitability for cocoa will shift towards the ecosystem as a whole which can then act as areas where production is considered ‘not buffer zones in transition areas. In zones closer allowed’ considering the conservation values that to areas considered ‘not allowed’, cash crops are present. Once again, the introduction of could be considered that mix well with coffee. shade trees will become an important strategy to maintain both agronomical suitability as well as the conservation values present.
47 Figure 47: Distribution of suitability for Arabica coffee in 2014, 2020 and 2050 in grey shades, against a background of the Risk Indicator Map, visualising suitability of areas based on overlap with HCVs
Figure 48: Distribution of biophysical suitable areas for Arabica and overlap with HCV in 2014, 2020 and 2050 600000 500000 400000 300000 200000 100000 0 y y y y y y y y y unsuitabl e unsuitabl e unsuitabl e Low Suitabilit Low Suitabilit Low Suitabilit High Suitabilit High Suitabilit High Suitabilit Medium Suitabilit Medium Suitabilit Medium Suitabilit
2014 2020 2050
Low Risk Medium Risk High Risk Not Allowed
48 Figure 49: Distribution of suitability for cocoa in 2014, 2020 and 2050 in grey shades, against a background of the Risk Indicator Map, visualising suitability of areas based on overlap with HCVs
Figure 50: Distribution of biophysical suitable areas for cocoa and overlap with HCV in 2014, 2020 and 2050 450000 400000 350000 300000 250000 200000 150000 100000 50000 0 y y y y y y y y y unsuitabl e unsuitabl e unsuitabl e Low Suitabilit Low Suitabilit Low Suitabilit High Suitabilit High Suitabilit High Suitabilit Medium Suitabilit Medium Suitabilit Medium Suitabilit
2014 2020 2050
Low Risk Medium Risk High Risk Not Allowed
49 50 Chapter 4 Vision for sustainable development
Priority areas and interventions It is hoped that the maps on biophysical suitability and HCV distribution provided in this report may be a useful resource for informing land use planning decisions, investors and developers. The combination of results of the agricultural suitability study onto the risk indicator map visualises the trade-offs between the developments of the sectors against the loss of areas with high conservation value. In order to provide strategic guidance to the SLP programme, this chapter aims to identify for each district which commodities should be supported where, and what kind of activities/interventions can be used to mitigate the negative impact on forests and important conservation areas while at the same time enabling economic development. In the second paragraph, next steps that would be needed to ensure the right interventions are implemented in the right places, and in particular, opportunities to streamline plans for sustainable development in district (spatial) planning processes.
51 • Robusta: In Tapanuli Selatan, it is Commodities to be promoted recommended to select the regions in in each district the north east of Tapanuli Selatan are suitable for Arabica, and in the central region for Robusta. The areas that are in Mandailing Natal the long term also suitable for Arabica, it • Cocoa: There is high potential for cocoa is recommended to promote this crop, as in Mandailing Natal in particular in the the prices are in genereal higher (market region around Panyabungan. access should be explored, see next steps). • Robusta: For Mandailing Natal major suitability changes for Robusta are • Rubber: Tapanuli Selatan rubber seems expected, it is recommended to focus to have a stable production, although the Robusta on the areas around the southern production areas are predicted to shift. part of the district and Panyabungan The central region remains suitable also region (similar to cocoa). in 2050, so supporting the industry there is expected to be sustainable in the long • Rubber: The potential for rubber seems term. In the coastal areas of Tapanuli particularly high in Mandailing Natal. Selatan, suitability is expected to decline, Although significant declines for suitability some areas already in 2020, oil palm is predicted in 2050, this crop may only likely to be a better choice there. be suitable for one rotation cycle. For Mandailing Natal, the coastal regions • Oil Palm: While areas in Tapanuli are predicted to be suitable, but will selatan are identified as suitable for oil phase out in 2050. As rubber is more palm, significant changes in suitability easily intercropped then oil palm, it is are expected in particular in the coastal recommended for areas more proximate regions making planting oil palm in these to forests. In general, more remote areas locations not a good long term strategy. are more suitable for rubber, as latex is As oil palm rotation cycles are 25-30 not as sensitive for degradation as the years, planting new plantations in these palm oil in fresh fruit bunches. regions is not recommended. Although in the central region of Tapanuli Selatan, oil • Palm Oil: The coastal regions of palm could be a good option, access to Mandaling Natal, are suitable for oil infrastructure should be further explored palm. In the north eastern region there is (see section next steps). significant overlap with areas considered high value or even not allowed for Tapanuli Utara expansion according to RSPO P&C, so in these places expansion should be avoided. • Cocoa: Most of Tapanuli Utara is suitable As the southern part of the district for cocoa. It is recommended to promote contains significant land considered of expansion on medium and low value low and medium value, here oil palm areas. By promoting shade trees a buffer expansion should be promoted. In the zone can be set up to protect the high MaxEnt Model (Appendix 38), however, value areas. such shifts in oil palm suitability are not • Arabica: Arabica has high potential in expected. As oil palm is considered to Tapanuli utara. Expansion should be be a highly resilient crop it might not be avoided, where possible, in high value affected as much as predicted in the MCA. zones can also act as a transition to forest where relevant. Arabica has a high Tapanuli Selatan potential in this district for creating high • Cocoa: would be suitable in the central revenue (see the Economic Evaluation region of Tapanuli Selatan. section).
• Arabica: For the northern part of • Robusta: Even though a significant Tapanuli Salatan could be a suitable amount of land is suitable for Robusta, for Arabica, in particular in the yellow it is recommended to focus on Arabica, areas. However, suitability for Arabica is as it is expected to give a higher return. expected to significantly decline by 2050, Important here is market demand and so promoting other crops like cocoa and access in the region, which should be Robusta it likely to be more suitable in further explored (see also next steps). the longer term.
52 • Oil Palm: For large potions of Tapanuli landscape, approaches and tools are outlined for Utara, oil palm seems to be suitable each risk category. to grow. This is in contradiction to the In principle, the areas identified as ‘Low risk’ MaxEnt model, which indicates that have little-to-no forest making them suitable oil palm suitability is confined only to to intensive agricultural systems, including coastal areas. In principle, production crops such as oil palm. In the areas identified might be possible in Tapanuli Utara, but as ‘Medium risk’, agricultural systems should be the logistics will be a key consideration semi-intensive, for example using integrated (see next steps). From the perspective systems like jungle rubber. Such integrated of conservation and forests, it is systems can act as a transition zone to forest recommended to promote other crops areas. In the risk ‘High risk’ category, generally rather then oil palm. all agricultural production should be avoided unless there is high pressure to do so and a Recommendations for sound management plan in place to ensure mitigating forest loss and the Conservation values can be maintained. Supporting the development and implementation maintaining conservation and monitoring of such plans should be a priority for CI to ensure these values are indeed values maintained, as in BAU they are often lost as a Using the synthesised spatial information from result of poor management. For crops like oil the study it is possible to select appropriate palm the successful management of conservation intervention options and the types of risks values in the areas identified as High Value involved with each. To assist in selecting areas, is unlikely. For other crops such as coffee available options for the target districts the and cocoa some options exist, as these are matrix developed by SNV in Table 8 may be known to crop well in agroforestry systems. In employed. This matrix was developed as a part the areas identified as unsuitable, no agricultural of a larger study on the relationship between production should be considered, but instead, agricultural development and deforestation, and sourcing Non-Timber Forest Products (NTFP) presents an overview of tools and approaches could be supported as well as payments for most appropriate to the dominant forest and REDD or Payments for Ecosystem Services to agriculture systems (McNally et al, 2014). In the matrix, dominant agricultural system, forest
Table 8: Potential intervention options for different agriculture-forestry systems
Risk category Low Low to medium Medium to high Very high Dominant Intensive high (Semi) intensive (Semi) extensive (e.g. Small scale agriculture value agriculture agriculture; semi extensive pasture, subsistence system (e.g. lowland rice, extensive; tree shifting cultivation); cash crops) crops commercial and subsistence Forest Minimal natural Forest mosaic; Forest mosaic; Generally landscapes forest degraded land; degraded forests undisturbed forest forests plantation and bare land; forest for timber frontiers Approach Promote intensive Plantations for Subsistence agriculture REDD finance; agriculture timber and wood- for food security; PES payments fuel; agroforestry; certified commodities (carbon, watershed, tree planting (full traceability); biodiversity etc) enrichment planting; woodlots for timber/ fuelwood Tools and Agricultural Agriculture Certification market Opportunity cost and actions technology technology research assessment; livelihoods REDD+ assessment; research and and development; analysis; benefit Economic valuation; development distribution systems; Participatory Forest value chain low emission planning monitoring; benefit analysis; low distribution systems emission planning
53 increase the value of forest lands and maintain be avoided and buffer zones created, forest areas. potentially with Arabica coffee or cocoa. Arabica has the most potential to have a In general, we would recommend for CI to low impact on the environment, followed promote and support the expansion of crops that by cocoa, Robusta, rubber and finally are more easily integrated in high conservation oil palm. Sustainable Arabica and cocoa value landscapes. As CI is aiming to protect the production should therefore be actively most valuable areas, the impact on the areas promoted, while expansion of oil palm identified as ‘not allowed’ should be targeted should be contained as much as possible for conservation activities and CI’s trainings on to the areas considered low value. In the forest stewardship. In principle, Table 8 ‘Potential medium value areas, Robusta, cocoa or intervention options for different agriculture- Arabica should be considered. For the forestry systems’ lists interventions for each risk High risk areas, development should zone. In line with this table we suggest to focus be avoided as much as possible, but if on a number of interventions: developed is to happen, Arabica and potentially cocoa could be considered. • Divert expansion of rubber and oil palm to ‘low value areas’: Much land • Setting up legal barriers for in low value areas overlap with oil palm encroachment through Community suitability, so if areas for expansion Forests: Community forests could be have to be designated that would be a promoted and the support of sustainable preferred choice. For the areas considered sourcing of NTFP’s, as this will increase low value, which are more remote the value of the forest areas, and the from infrastructure, rubber should be community forest designation can considered, as it is does not require function as a legal barrier for outsiders. processing immediately after harvesting.
• Implement trainings on Better Management Practices in the Recommended right areas: In order to reduce the environmental impact of the crops and next steps support more sustainable production systems, the implementation of trainings on good agricultural practices should be a Develop a broadly supported part of the interventions. Through these trainings, the use of shade trees and vision for sustainable intercropping in general can be promoted. Also, developing better understanding of development the value of the forests and ecosystem This study collected a large amount of data and services should be integrated into information on the current situation and future the training. To reduce the need for suitability for the target commodities of Arabica, expansion, and meet Districts’ objectives Robusta, rubber, cocoa and oil palm. To be able in terms of production, increasing to have impact at scale in the target districts, it production of palm oil and rubber should will be important to work with key stakeholders be considered. It should be taken into involved in spatial planning and development in consideration, however, that by increasing the Districts. It is strongly recommend that these the return per hectare as a result of key stakeholders are identified and a broadly these improved management practices, supported vision for sustainable development is the incentive for conversion of additional achieved for Green Growth in the target districts. areas is also increased. Therefore, in the The data and analysis from this report can be low value areas the Better Management used to inform a discussion with the relevant Practices trainings are recommended, government bodies on planning sustainable however in high value areas, activities development of their districts. that will increase the value of the forest A first step will be to identify opportunities in should be promoted. existing planning procedures and documents • Promoting and supporting to mainstream the ideas from this analysis. sustainable Arabica and Cocoa in Key planning documents on the province level areas near forests: In particular in are: the province’s REDD+ STRADA and RAD- the high risk areas, conversion should GRK55. These can be used to select activities
55. Rencana Aksi Nasional Penurunan Emisi Gas Rumah Kaca or National Action Plan for Reducing Greenhouse Gas Emissions
54 that are applicable to the target districts. In In these next steps it is recommended that addition, the commonalities of recommendations the eight key factors influencing technology in district-level strategy documents including advancement in agriculture production and its the RAN-GRK and RPJMD56 should be assessed. relationship to deforestation are assessed (see Also, the regional spatial planning documents Figure 51). After this, the aim will be to ‘drill- or RTRWK, should be reviewed for opportunities down’ to obtain a deeper understanding of the to mitigate forest loss and sustainable resource priority areas selected in Phase I by assessing management. Combined with the analysis the macro and micro economic influences of presented in this report, a vision for Green the commodity market, as well as the social Growth should be developed with the relevant context and policy environment influencing the government agencies. A first step will be to landscape. develop a commonly agreed work plan to come Figure 51: Key issues in the technological to such a vision. The developed vision should change–deforestation link (adapted from then be promoted by the relevant district Angelsen & Kaimowitz 2001) government bodies and ensure the vision is mainstreamed in the relevant planning at the Reduced Impact on deforestation Increased provincial level. 1. Labour & capital intensity Intensive (high) Saving (low)
2. Farmer characteristics Selection of priority areas and Constrained Well-off
interventions 3. Output market Local International With the understanding of spatial dimension in the forest-agriculture interface across the 4. Technology landscape as described in this report, the next Yield-increasing Cost saving
step is to gain a better understanding of district 5. Labour market planning and targets for development. Licences Local Segmented Mobile (migration) for concessions and permit status should be 6. Sectors experiencing technical change reviewed and compared with the Green Growth Intensive (lowland) Frontier areas (upland) vision (as described above). Concessions could be revoked based on such an agreed 7. Scale of adoption development path. The ‘buy in’ from district Global Local
governments is thus a key factor. 8. Time horizon of analysis In parallel, or as a third step, an understanding Short-term Long-term of some of the key issues that will affect the relationship between development and Once the priority areas for each commodity 57 deforestation on a local level is required. A are identified, the findings from Phase I must better understanding of the underlying socio- be verified through field assessments. In economic, market and policy factors that addition, additional data on land use changes, underpin the likely impact of (agricultural) socio-economic conditions and market needs development on forests is strongly to be collected.58 Using a participatory mapping recommended, in particular when specific approach, existing plantations and forests can interventions are promoted, like trainings on be mapped in detail. Additionally, data on land intensification or good agricultural practices. use should be collected, in particular on HCV 5, The first step at this stage is to select priority supply of basic needs obtained from the land, areas in which more in depth assessments can as well as cash income from other activities. be done. It is recommended to focus this on For each priority area, market maps could be areas where the conservation value and threat developed to identify opportunities and barriers for conversion is highest, which can be identified for development. when the Risk Indicator Map is compared with development plans (RTRWK). To gather the One major issue in this stage is likely to be needed information, field assessments should be limited local knowledge of agronomy of the done that focus on the following key topics: target crops which can lead to extensive land use systems and unsustainable practices in • Socio economic conditions general. As such, appropriate training should be provided. In addition, it will be important to • Current land use and (future) provide support to link producers to the market food security and support institutional development. The • Market opportunities for certified products latter activity should also seek to address issues like the generally poor bargaining power of • Policy environment farmers in the market (low prices for products), limited access to fertilisers and quality planting
56. RPJMD Rencana Pembangunan Jangka Menegah Daerah Regional Medium-Term Development Plan 57. It should be noted that although the focus of this framework is the relationship between agriculture and forests, that it would also be possible to explore issues around local energy use. Particularly during phase II a better understanding of current energy use and its impact on the forest could be garnered. 58. See also: Sadikin et al., 2014 55 material, which limits the ability of smallholders to move from extensive production systems to intensive production systems. As the introduction of technologies and finance to vercomeo these barriers can have large impacts, a careful consideration has to be made about which approach to use and where in the landscape.
Example: Sintang Kalimantan A good example of a detailed plan for the development of a commodity can be seen in SNV’s landscape plans for palm oil. As can be seen in Figure 52 the landscape has been zoned with areas depicted in red considered important forest / conservation areas and areas in yellow / orange to be set as buffer zones. The buffer zones function to promote activities that increase forest value and act as a barrier for further expansion into forest areas. Areas in green have been selected as potential areas for expansion of intensified agriculture, in this case oil palm.
Figure 52 Example: Detailed zoning for landscape in Sintang using participatory mapping and stakeholder interviews, market analysis etc
In this particular target area, it was found that the influx of labour is constrained, and intensification techniques with a focus on higher yields were more appropriate. The identified watershed is crucial for ecosystem services as well as future production of the target crop. Training on environmental awareness, in particular on the water services in the area, were integrated in the trainings on Good Agricultural Practices to ensure the appropriate management of the area. In addition, training on institutional development were provided, as the lack of functioning cooperatives had resulted in poor communication with the mill and limited bargaining power. In addition, the weak local institutions were unable to prevent outsiders from encroaching the region and convert remaining forest areas.
Partnerships In this report, key areas for conservation and potential areas for agricultural expansion for coffee (Arabica, robust), cocoa, rubber and palm oil were identified. This information lead to a number of recommendations to enable sustainable development. In order for these ideas to be taken up and eventually implemented, close collaboration with local government will be crucial in promoting more sustainable land use (planning). Influencing this process requires a long-term commitment, and careful engagement with relevant stakeholders taking into account their wants and needs. In coming to a commonly shared vision for development of the region, making partnerships across the landscape will be crucial for success. For the selection of interventions as well as mainstreaming ideas for sustainable development into the (spatial) planning processes the information from this report provides crucial input for this vision.
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58 Appendices
Appendix 1: Administration of North Sumatra Province with districts and sub-districts in hectares
Districts/Cities Area (ha) Number of Number of Villages Sub-districts Nias 98032 9 119 Mandailing Natal * 662072 23 405 Tapanuli Selatan * 435286 14 248 Tapanuli Tengah 215800 20 177 Tapanuli Utara * 376465 15 252 Toba Samosir 235235 16 244 Labuhanbatu 256138 9 98 Asahan 367579 25 204 Simalungun 438660 31 367 Dairi 192780 15 169 Karo 212725 17 269 Deli Serdang 248614 22 394 Langkat 626329 23 277 Nias Selatan 162591 18 356 Humbang Hasundutan 229720 10 154 Pakpak Bharat 121830 8 52 Samosir 243350 9 134 Serdang Bedagai 191333 17 243 Batu Bara 90496 7 151 Padang Lawas Utara 391805 9 388 Padang Lawas 389274 12 304 Labuhanbatu Selatan 311600 5 54 Labuhanbatu Utara 354580 8 90 Nias Utara 150162 11 113 Nias Barat 54409 8 110 Sibolga 1077 4 17 Tanjungbalai 6152 6 31 Pematangsiantar 7997 8 53 Tebing Tinggi 3844 5 35 Medan 26510 21 151 Binjai 9024 5 37 Padang Sidempuan 11465 6 79 Gunung Sitoli 46936 6 101
59 Appendix 2: Rainfall in North Sumatra
Month Sampali Polonia Rainfall Rainy Days Rainfall Rainy Days
Jan 112 12 62 19 Feb 78 12 93 14 Mar 149 12 202 16 Apr 262 20 206 22 May 264 18 515 24 Jun 122 12 57 14 Jul 121 12 279 20 Aug 138 13 160 22 Sep 244 18 242 22 Oct 297 18 339 22 Nov 214 17 - - Dec 161 14 270 13
Appendix 3: Forest cover of North Sumatra Province
Land-cover 2000 2011
Area (hectares) Percentage Area (hectares) Percentage
Primary Forest 599.452,288 8,211 598.120,890 8,192 Secondary Forest 1.373.920,859 18,818 1.145.701,247 15,692 Plantation Forest 129.301,115 1,771 145.745,506 1,996 Non Forest 5.198.270,039 71,200 5.411.376,657 74,119
60 Appendix 4: Map of MoF forest areas for North Sumatra Province
61 Appendix 5: Status of Forest Areas in North Sumatra Province
Forest Status Area (hectares) Percentage (%)
Protected Forest (HL) 1.319.613.083 18.460 Conservation Area (CA; KSA/KPA; TN; 434.847.446 6.0831 SM) Recreation Area (TB; TWA) 11.517.883 0.161 Limited Production Forest (HPT) 879.070.712 12.297 Convertible Production Forest (HPK) 41.216.453 0.577 Production Forest (HP) 1.011.120.809 14.145 Other Land use (APL) 3.451.053.388 48.277
62 Appendix 6: Elevation map of focus areas
63 Appendix 7: Sub-watersheds in Tapanuli Utara District
Sub-watersheds Area (hectare) River length (meter)
Asahan Toba 16268,017 5913,884 Bangop 14012,102 14705,657 Barumun Bilah 85468,734 302061,011 Batang Toru 189170,131 342153,538 Kolang 27117,502 56797,606 Kualuh 871,282 40,435 Sibundong 50735,981 144595,51
Appendix 8: Sub-watersheds in Tapanuli Selatan District
Sub-watersheds Area (hectare) River length (meter)
Barumun Bilah 101972,275 218031,171 Batang Gadis 155109,327 214037,398 Batang Toru 122239,259 196740,820 Kualuh 1955,144 6775,212 Maraupu 5838,390 4136,847 Nabirong 22245,103 33291,795
Appendix 9: Sub-watersheds in Mandailing Natal District
Sub-watersheds Area (hectare) River length (meter)
Batahan 103107,331 352957,076 Batang Gadis 311009,688 805372,130 Batang Toru 10617,181 5415,273 Bintuas 32261,741 102390,118 Nagor 4330,953 14770,951 Natal 76039,565 347943,055 Nunukan 7053,505 14977,922 Pasaman 3790,812 22903,474 Rokan 24584,073 41011,805 Siriam 21904,858 8997,320 Tabuyung 48201,870 82250,058
64 Appendix 10: Status of Forest Areas in Tapanuli Utara
0.53%
14.71%
26.59% Other landuse (APL) Production Forest (HP) Limited Production Forest (HPT) 28.3% Protected Forest (HL) Conservation Area (CA; TWA) 29.87%
Appendix 11: Status of Forest Areas in Tapanuli Selatan
3.57%
Convervation Area 28,33% (TN; KSA/KPA; SM; CA) 28.18% Protected Forest (HL) Production Forest (HP) 0.37% Limited Production Forest (HPT) Convertible Production Forest (HPK) 20.24% Other Landuse (APL) 19.31%
Appendix 12: Status of Forest Area in Mandailing Natal
11.58%
Other Landuse (APL) 38.35% Production Forest (HP) 20.84% Limited Production Forest (HPT) Protected Forest (HL) Convervation Area (SM; TN)
26.91% 2.31%
65 Appendix 13: Risk Categories for spatial indicators ‘Low risk’ (green); ‘Medium risk’ (yellow); ‘High risk’ (orange); ‘Unsuitable’ (red); representing the risk of violation of the sustainability standard (here, RSPO, RSB and RES-D) (source: Smit et al 2013)
Low risk Medium risk High risk Unsuitable 1750 - 5000 mm 1500 - 1750 mm 1250 - 1500 mm < 1250 mm; > 5000 mm
< 8 % 8 - 15 % 15 - 30 % > 30 % (> 12°)
< 200 m 200 - 500 500 - 1000 m > 1000 m
Well to moderately well Imperfect Extreme; poor Excessive; very poor; stagnant
Silt loam; sandy clay Clay; silty clay, Sandy clay; silt; Heavy clay; sand loam; silty clay loam; sandy loam; loam loamy sand clay loam > 100 cm 75 - 100 cm 50 - 75 cm < 50 cm
< 15 ton/ha/year 15 - 59 ton/ha/year 60 - 179 ton/ha/ > 180 ton/ha/year year Well drained and deep Weathered and Shallow and Infertile sands mineral soils deeply developed infertile mineral mineral soils soils No overlap with IUCN Bufferzones1km IUCN I-IV, IUCN V-VII, protected areas or conservation forest, Ramsar and national and protected areas and conservation areas buffer zones. No overlap with Overlap with Overlap with Breeding grounds and nesting places, distribution or habitats distribution of habitat of protected grazing/browsing for endangered of protected and protected and and endangered species and temporal habitats for endangered species endangered species species * migratory species * No overlap with Large scale forest Two or more Rare ecosystems endangered ecosystems, area plus buffer eco-tone regions important ecotone 3km Endangered regions and large scale ecosystems forest No overlap with water DAS super priority Mangrove, peat, Water sources (spring), riparian zones source/riparian zones, wet land and karst and buffer zones. mangrove peat, karst or forest, cloud forest DAS super priority < 15 ton/ha/year 15 - 60 ton/ha/year 60 - 180 ton/ha/ > 180 ton/ha/year year No overlap with barriers Overlap with Area contains barriers for large-scale for the spread of large barriers for the fire i.e. large forest blocks or peat scale fire, the area spread of large swap areas and not burned during the recently burned more scale fire, but last 10 years * than once in the last 10 (partly) burned in years * the last 10 years * Carbon stock 0-60 ton/ Carbon stock 60-70 Carbon stock 70-80 Carbon stock >80 ton/ha ha ton/ha ton/ha * Areas providing for Areas providing Areas providing Areas providing >50% for <10% for subsistence * >10% < 25% for >25% <50% for subsistence, or containing cultural subsistence * subsistence, or sites * containing cultural sites * No overlap with land Idle land; Idle land; Active use of land community rights * community tanahpera, protected forest* interested to community not change the use * interest to change the use *
66 Appendix 14: Derived bioclimatic variables from World climate Database
Bioclimatic Variables Description
Bio1 Annual mean temperature Bio2 Mean diurnal range (mean of monthly (max temp - min temp)) Bio3 Isothermality (Bio2/Bio7) (* 100) Bio4 Temperature seasonality (standard deviation *100) Bio5 Maximum temperature of warmest month Bio6 Minimum temperature of coldest month Bio7 Temperature annual range (Bio5 - Bio6) Bio8 Mean temperature of wettest quarter Bio9 Mean temperature of driest quarter Bio10 Mean temperature of warmest quarter Bio11 Mean temperature of coldest quarter Bio12 Annual precipitation Bio13 Precipitation of wettest month Bio14 Precipitation of driest month Bio15 Precipitation seasonality (coefficient of variation) Bio16 Precipitation of wettest quarter Bio17 Precipitation of driest quarter Bio18 Precipitation of warmest quarter Bio19 Precipitation of coldest quarter Appendix 15: Requirement growth for five agricultural commodities based on Ministry of Agriculture (Djaenudin et al. 2000)
Requirements for growth of Arabica coffee (Coffea)
Persyaratan Kelas kesesuaian lahan penggunaan/ karakteristik lahan S1 S2 S3 N Temperatur (tc) Temperatur rerata 16 - 22 15 - 16 14 - 15 < 14 (°C) 22 - 24 24 - 26 > 26 Ketinggian tempat 700 - 1.600 1.600 - 1.750 1.750 - 2.000 > 2.000 dpl (m) 600 - 700 100 - 600 < 100 Ketersediaan air (wa) Curah hujan (mm) 1.200 - 1.800 1.000 - 1.200 2.000 - 3.000 > 3.000 1.800 - 2.000 800 - 1.000 < 800 Lamanya masa 1-4 < 1; 4 - 5 5-6 > 6 kering (bln) Kelembaban (%) 40 - 70 30 - 40 20 - 30 < 20 70 - 80 80 - 90 > 90
67 Ketersediaan oksigen (oa) Drainase baik sedang agak terhambat, terhambat, agak cepat sangat terhambat, cepat
Media perakaran (rc) Tekstur halus, agak - agak kasar kasar, sangat halus, sedang halus Bahan kasar (%) < 15 15 - 35 35 - 60 > 60 Kedalaman tanah > 100 75 - 100 50 - 75 < 50 (cm) Gambut: Ketebalan (cm) < 60 60 - 140 140 - 200 > 200 Ketebalan (cm), < 140 140 - 200 200 - 400 > 400 jika ada sisipan bahan mineral/ pengkayaan Kematangan saprik+ saprik, hemik, fibrik hemik+ fibrik+ Retensi hara (nr) KTK liat (cmol) > 16 ≤ 16 Kejenuhan basa (%) > 50 35 - 50 < 35 pH H2O 5,6 - 6,6 6,6 - 7,3 < 5,5; >7,4 C-organik (%) > 1,2 0,8 - 1,2 < 0,8 Toksisitas (xc) Salinitas (dS/m) < 0,5 - 0,5 - 2 > 2 Sodisitas (xn) Alkalinitas/ESP (%) - - - - Bahaya erosi (eh) Lereng (%) < 8 8-16 16-30; 16-50 > 30; > 50 Bahaya erosi sangat rendah rendah - sedang berat sangat berat Bahaya banjir (fh) Genangan F0 - - > F0 Penyiapan lahan (lp) Batuan di permukaan < 5 5-15 15 - 40 > 40 (%) Singkapan batuan < 5 5-15 15 - 25 > 25 (%)
68 Requirements for growth of Robusta coffee (Coffea canephora)
Persyaratan Kelas kesesuaian lahan penggunaan/ karakteristik lahan S1 S2 S3 N Temperatur (tc) Temperatur rerata 22 - 25 - 19 - 22 < 19 (°C) 25 - 28 28 - 32 > 32 Ketersediaan air (wa) Curah hujan (mm) 2.000 - 3.000 1.750 - 2.000 1.500 - 1.750 < 1.500 3.000 - 3.500 3.500 - 4.000 > 4.000 Lamanya masa 2-3 3-5 5-6 > 6 kering (bln) Kelembaban udara 45 - 80 80-90; 35-45 > 90; 30-35 < 30 (%) Ketersediaan oksigen (oa) Drainase baik sedang agak terhambat, terhambat, agak cepat sangat terhambat, cepat Media perakaran (rc) Tekstur halus, agak - agak kasar kasar, sangat halus, sedang halus Bahan kasar (%) < 15 15 - 35 35 - 60 > 60 Kedalaman tanah > 100 75 - 100 50 - 75 < 50 (cm) Gambut: Ketebalan (cm) < 60 60 - 140 140 - 200 > 200 Ketebalan (cm), jika < 140 140 - 200 200 - 400 > 400 ada sisipan bahan mineral/ pengkayaan Kematangan saprik+ saprik, hemik, fibrik hemik+ fibrik+ Retensi hara (nr) KTK liat (cmol) > 16 ≤ 16 Kejenuhan basa (%) > 20 ≤ 20 pH H2O 5,3 - 6,0 6,0 - 6,5 > 6,5 5,0 - 5,3 < 5,3 C-organik (%) > 0,8 ≤ 0,8 Toksisitas (xc) Salinitas (dS/m) < 1 - 1-2 > 2 Sodisitas (xn) Alkalinitas/ESP (%) - - - - Bahaya sulfidik (xs)
69 Kedalaman sulfidik > 175 125 - 175 75 - 125 < 75 (cm) Bahaya erosi (eh) Lereng (%) < 8 8-16 16-30; 16-50 > 30; > 50 Bahaya erosi sangat rendah rendah - sedang berat sangat berat Bahaya banjir (fh) Genangan F0 F0 F1 > F1 Penyiapan lahan (lp) Batuan di permukaan < 5 5-15 15 - 40 > 40 (%) Singkapan batuan < 5 5-15 15 - 25 > 25 (%)
Requirements for growth of cocoa (Theombroma cacao)
Landuse Land suitability class requirements/Land S1 S2 S3 N characteristics Temperature regime (tc) Annual average 25 - 28 20 - 25 - < 20 temperature (°C) 28 - 32 32 - 35 > 35 Water availability (wa) Average annual 1.500 - 2.500 - 1.250 - 1.500 < 1.250 rainfall (mm) 2.500 - 3.000 3.000 - 4.000 > 4.000 Dry months (bln) 1-2 2-3 3-4 > 4 Humidity (%) 40 - 65 65 - 75 75 - 85 > 85 Oxygen availability 35 - 40 30 - 35 < 30 (oa) Drainage Rooting conditions baik, sedang agak terhambat terhambat, sangat (rc) terhambat, cepat Soil texture (surface) halus, agak halus, - agak cepat kasar, sangat sedang halus Bahan kasar (%) Kedalaman tanah halus, agak halus, - agak kasar, kasar (cm) sedang sangat halus Gambut: < 15 15 - 35 35 - 55 > 55 Ketebalan (cm) > 100 75 - 100 50 - 75 < 50 Ketebalan (cm), jika ada < 60 60 - 140 140 - 200 > 200 sisipan bahan < 140 140 - 200 200 - 400 > 400 mineral/ pengkayaan Kematangan saprik+ saprik, hemik, fibrik saprik+ saprik, hemik, fibrik Retensi hara (nr) hemik+ fibrik+
70 KTK liat (cmol) Kejenuhan basa (%) > 16 ≤ 16 - - pH H2O > 35 20 - 35 < 20 6,0 - 7,0 5,5 - 6,0 < 5,5 C-organik (%) 7,0 - 7,6 > 7,6 Toksisitas (xc) > 1,5 0,8 - 1,5 < 0,8 Salinitas (dS/m) Sodisitas (xn) < 1,1 1,1 - 1,8 1,8-2,2 > 2,2 Alkalinitas/ESP (%) Bahaya sulfidik (xs) - - - - Kedalaman sulfidik (cm) Bahaya erosi (eh) > 125 100 - 125 60 - 100 < 60 Lereng (%) Bahaya erosi < 8 8-16 16 - 30 > 30 Bahaya banjir (fh) sangat rendah rendah - sedang berat sangat berat Genangan Penyiapan lahan (lp) F0 - F1 > F1 Batuan di permukaan (%) Singkapan batuan < 5 5-15 15 - 40 > 40 (%)
Requirements for growth of rubber (Havea brasiliensis)
Landuse Land suitability class requirements/Land S1 S2 S3 N characteristics
Temperature regime S1 S2 S3 N (tc) Annual average temperature (°C) 26 - 30 30 - 34 - > 34 Water availability 24 - 26 22 - 24 < 22 (wa)
Average annual rainfall (mm) 2.500 - 3.000 2.000 - 2.500 1.500 - 2.000 < 1.500 Dry months (bln) 3.000 - 3.500 3.500 - 4.000 > 4.000 Humidity (%) 1-2 2-3 3-4 > 4 Oxygen availability (oa)
Drainage baik sedang agak terhambat, sangat terhambat terhambat, cepat Rooting conditions (rc)
Soil texture (surface) halus, agak halus, - agak kasar kasar sedang
71 Bahan kasar (%) < 15 15 - 35 35 - 60 > 60 Kedalaman tanah > 100 75 - 100 50 - 75 < 50 (cm)
Gambut: Ketebalan (cm) < 60 60 - 140 140 - 200 > 200
Ketebalan (cm), jika < 140 140 - 200 200 - 400 > 400 ada sisipan bahan mineral/ pengkayaan
Kematangan saprik+ saprik, hemik, fibrik
hemik+ fibrik+ Retensi hara (nr) KTK liat (cmol) - - - -
Kejenuhan basa (%) < 35 35 - 50 > 50 pH H2O 5,0 - 6,0 6,0 - 6,5 > 6,5 4,5 - 5,0 < 4,5 C-organik (%) > 0,8 ≤ 0,8 Toksisitas (xc) Salinitas (dS/m) < 0,5 0,5 - 1 1-2 > 2
Sodisitas (xn) Alkalinitas/ESP (%) - - - -
Bahaya sulfidik (xs)
Kedalaman sulfidik > 175 125 - 175 75 - 125 < 75 (cm)
Bahaya erosi (eh) Lereng (%) < 8 8-16 16 - 30 > 30
Bahaya erosi 16 - 45 > 45 Bahaya banjir (fh) sangat rendah rendah - sedang berat sangat berat
Genangan Penyiapan lahan (lp) F0 - F1 > F1 Batuan di permukaan (%) Singkapan batuan < 5 5-15 15 - 40 > 40 (%)
72 Requirements for growth of Oil Palm (Elaeis guinensis)
Landuse Land suitability class requirements/Land S1 S2 S3 N characteristics Temperature regime S1 S2 S3 N (tc) Annual average temperature (°C) 25 - 28 22 - 25 20 - 22 < 20 Water availability 28 - 32 32 - 35 > 35 (wa) Average annual rainfall (mm) 1.700 - 2.500 1.450 - 1.700 1.250 - 1.450 < 1.250 Dry months (bln) 2.500 - 3.500 3.500 - 4.000 > 4.000 Humidity (%) < 2 2-3 4-5 > 4 Oxygen availability (oa) Drainage baik, sedang agak terhambat terhambat, sangat terhambat, cepat Rooting conditions agak cepat (rc) Soil texture (surface) Bahan kasar (%) halus, agak - agak kasar kasar halus, sedang Kedalaman tanah < 15 15 - 35 35 - 55 > 55 (cm) Gambut: > 100 75 - 100 50 - 75 < 50 Ketebalan (cm) Ketebalan (cm), jika < 60 60 - 140 140 - 200 > 200 ada < 140 140 - 200 200 - 400 > 400 sisipan bahan mineral/ pengkayaan Kematangan saprik+ saprik, hemik, fibrik saprik+ saprik, hemik, fibrik Retensi hara (nr) hemik+ fibrik+ KTK liat (cmol) Kejenuhan basa (%) > 16 ≤ 16 - - pH H2O > 20 ≤ 20 5,0 - 6,5 4,2 - 5,0 < 4,2 C-organik (%) 6,5 - 7,0 > 7,0 Toksisitas (xc) > 0,8 ≤ 0,8 Salinitas (dS/m) Sodisitas (xn) < 2 2-3 3-4 > 4 Alkalinitas/ESP (%) Bahaya sulfidik (xs) - - - -
73 Kedalaman sulfidik (cm) Bahaya erosi (eh) > 125 100 - 125 60 - 100 < 60 Lereng (%) Bahaya erosi < 8 8-16 16 - 30 > 30 Bahaya banjir (fh) sangat rendah rendah - sedang berat sangat berat Genangan Penyiapan lahan (lp) F0 F1 F2 > F2 Batuan di permukaan (%) Singkapan batuan < 5 5-15 15 - 40 > 40 (%)
Appendix 16: High Conservation Values from Indonesia National Interpretation (2008)
High Conservation Sub-category Description Value Category HCV 1 - Areas with 1.1 Areas that Contain or Provide Biodiversity Support Important Levels of Function to Protection or Conservation Areas Biodiversity 1.2 Critically Endangered Species 1.3 Areas that Contain Habitat for Viable Populations of Endangered, Restricted Range or Protected Species 1.4 Areas that Contain Habitat of Temporary Use by Species or Congregations of Species HCV 2 - Natural 2.1 Large Natural Landscapes with Capacity to Maintain Landscapes & Natural Ecological Processes and Dynamics Dynamics 2.2 Areas that Contain Two or More Continuous Ecosystems 2.3 Areas that Contain Representative Populations of Most Naturally Occurring Species HCV 3 - Rare 3 Rare or Endangered Ecosystems or Endangered Ecosystems HCV 4 - 4.1 Areas or Ecosystems Important for the Provision of Water Environmental and Prevention of Floods for Downstream Communities Services 4.2 Areas Important for the Prevention of Erosion and Sedimentation 4.3 Areas that Function as Natural Barriers to the Spread of Forest or Ground Fire HCV 5 - Basic Needs 5 Natural Areas Critical for Meeting the Basic Needs of Local for communities People HCV 6 - Cultural 6 Areas Critical for Maintaining the Cultural Identity of Local Identity of Communities Communities
74 Appendix 17: List of spatial data used for HCVs desktop analysis in North Sumatra Province
No Judul Sumber 1 Topographic Map, Bakosurtanal /BIG Scale 1:50.000 2 Digital Elevation Jarvis, A., H. I. Reuter, A. Nelson, and E. Guevara. 2008. Hole -filled SRTM model SRTM 90 for the globe Version 4. Downloaded from: CGIAR-CSI SRTM 90m Database Meter http://srtm.csi.cgiar.org. CGIAR-CSI. 3 Landsystem RePPProt, A. 1990. National Overview from the Regional Physical Planning Programme for Transmigration. UK Overseas Development Administration and Directorate BINA Programme, Ministry of Transmigration, Jakarta. 4 Ecoregion/Bio- Wikramanayake, E., E. Dinerstein, C. J. Loucks, D. Olson, J. Morrison, J. region Lamoreux, M. McKnight, and P. Hedao 2002. Terrestrial Ecoregions of the Indo-Pacific: A Conservation Assessment. Island Press. 7 Soil Types RePPProt, A. 1990. National Overview from the Regional Physical Planning Programme for Transmigration. UK Overseas Development Administration and Directorate BINA Programme, Ministry of Transmigration, Jakarta. 9 Sumatran Tonny Suhartono, Herry Djoko Susilo, Arnold F. Sitompul, Donny Gunaryadi, elephant Elisabeth M. Purastuti, Wahdi Azmi, Nurchalis Fadhli, Stremme, C., 2007. distribution Strategi dan Rencana Aksi Konservasi Gajah Sumatera dan Gajah Kalimantan 2007 - 2017. Direktorat Jenderal Perlindungan Hutan dan Konservasi Alam - Departemen Kehutanan RI, Jakarta, Indonesia. 10 Sumatran tiger Wildlife Conservation Society 2011. Panthera tigris. In: IUCN 2013. IUCN Red distribution List of Threatened Species. Version 2013.1 11 Peat land Wahyunto, S., Ritung, and H. Subagjo. 2003. Peta Luas Sebaran Lahan distribution Gambut dan Kandungan Karbon di Pulau Sumatera / Maps of Area of Peatland Distribution and Carbon Content in Sumatera, 1990 – 2002. Wetlands International - Indonesia Programme & Wildlife Habitat Canada (WHC). 12 Forest Kementerian Kehutanan. 2013. Peta Indikatif Penundaan Izin adalah Peta Moratorium Lampiran SURAT KEPUTUSAN MENTERI KEHUTANAN REPUBLIK INDONESIA Nomor: SK.2796/Menhut-VII/IPSDH/2013. Skala 1:250.000.
Appendix 18: Indonesia designated protected areas in accordance to various regulations
Forest Area Sub-area Special attribute or other characteristic types Protected Nationally Protected forest Area serves to protect vital life support forest protected area area systems derived from key environmental services. Peat land area Area with peat layer > 3 m and managed for conservation purpose. Watershed area Local protected Coastal buffer area Lakes buffer Riparian Water resources area Prevention of Nature natural disaster conservation
75 Conservation Cultural Wildlife Reserve Unique features such as biodiversity. Habitat Forest and nature management to maintain unique qualities is to conservation be implemented. area Natural National park Areas with natural ecosystem features. conservation Managed based on zonation, with areas area designated for research, science, education tourism and recreation. Forest park Areas designated for maintaining genetic and germ plasma diversity. Used for research, science, education, tourism and recreation. Recreation park Primary function is nature tourism and recreation. Game Park Hunting area For hunting and tourism Usage area Wild species conservation area Other area Other area Local Village Area that had been appointed by local conservation conservation area community as the conservation area with area agreed local regulation and objectives. Cultural Area that owns special culture. Appointed by conservation government to be protected area
Appendix 19: Protected and conservation areas found in Mandailing Natal, Tapanuli Selatan and Tapanuli Utara
No Function Name Ministry decree Areas with 1 km buffer 1 CA CA Dolok Saut 44/Menhut-II/2005 127 2 CA CA Dolok Sibual-buali 44/Menhut-II/2005 5,020 3 CA CA Dolok Sipirok 44/Menhut-II/2005 7,220 4 Danau 1,036 5 Danau 44/Menhut-II/2005 6 6 HL 44/Menhut-II/2005 3,069 7 HL 44/Menhut-II/2005 2,883 8 HL 44/Menhut-II/2005 3,771 9 HL 44/Menhut-II/2005 14,599 10 HL 44/Menhut-II/2005 140 11 HL 44/Menhut-II/2005 546 12 HL 44/Menhut-II/2005 518 13 HL 44/Menhut-II/2005 10,167 14 HL 44/Menhut-II/2005 3,442 15 HL Batang Gadis I 44/Menhut-II/2005 149,186 16 HL Batang Toru 44/Menhut-II/2005 2,228 17 HL Batang Toru 44/Menhut-II/2005 301
76 18 HL Dolok Ginjang 44/Menhut-II/2005 4,080 19 HL Gunung Tua 44/Menhut-II/2005 91,162 20 HL Reg. 24 44/Menhut-II/2005 22 21 HL Reg. 24 44/Menhut-II/2005 174 22 HL Reg. 24 44/Menhut-II/2005 405 23 HL Reg. 24 44/Menhut-II/2005 283 24 HL Reg. 30 44/Menhut-II/2005 799 25 HL Sibolga - Dolok Saut 44/Menhut-II/2005 11,569 26 HL Sibuatan,Sidsar Dan Sipiso - Piso 44/Menhut-II/2005 6,451 27 KSA/KPA Lubuk Raya 44/Menhut-II/2005 2,986 28 SM SM Barumun 44/Menhut-II/2005 3,894 29 TN TN Batang Gadis 44/Menhut-II/2005 106 30 TN TN Batang Gadis 44/Menhut-II/2005 49,983 31 TN TN Batang Gadis 44/Menhut-II/2005 21,653 32 Tubuh Air 1,793 33 TWA TWA Sijaba Hutaginjang 44/Menhut-II/2005 195 Total 399,812 size areas Appendix 20: Protection status for the 3 big mammals in Sumatra
Species IUCN CITES IDN Regulation on Species Protection PP No 7 2009 Pongo abelii CR Appendix 1 Yes Elephas maximus EN Appendix 1 Yes Phantera tigris sumatrae EN Appendix 1 Yes
Appendix 21: Family of Wildlife findings and its protection status on Batang Toru protected forest
Class Σ species IUCN CITES Sumatran Endemic species CE E VU App.I App.II Mamalian 41 2 1 4 6 5 2 Aves 233 0 0 2 1 10 20 Amphibian 45 0 0 1 0 0 4 Reptilian 36 0 0 0 0 2 0
Note: CE (critically endangered), E (endangered), VU (vulnerable), App.I (most concerned category), App.II (threatened category)
77 Appendix 22: Indicative Areas HCV 1.4
78 Appendix 23: Large scale forest block (>20.000 ha) in Sumatra
79 Appendix 24: Indicative map of HCV 2.3 Distribution
80 Appendix 25: Bio-physiographic map of Sumatra
81 Appendix 26: Changes in forest cover between 1990 -2011 in Sumatra
82 Appendix 27: Results of identification for Endangered ecosystem in landscape of 3 target districts
Barisan Mountains Eastern Coastal Eastern Plains and Western Coastal Region Hills Foothills and Plains Swamps Air Cawang Air Hitam Kanan Aek Si Antinga Air Cawang Ampalu Alur Menani Aeknabontair Air Hitam Kanan Beliti Ampalu Air Hitam Kanan Alur Menani Gajo Bakunan Alur Menani Bakunan Klaru Barontongkok Ampalu Banjah Lawas Kuranji Batang Anai Bakunan Barontongkok Mendawai Batuapung Batuapung Batuapung Pidolidombang Beliti Beliti Bengkulu Sebangau Benjah Bekasik Benjah Bekasik Benjah Bekasik Solok Bukit Pandan Bukit Ayun Dolok Parlajanan Sukaraja Bukittinggi Bukit Barangin Gajo Sungai Aur Gambut Gajo Gambut Sungai Manau Kahayan Gambut Klaru Klaru Kajapah Kuranji Kuranji Klaru Luang Lubuksikaping Kototinggi Mendawai Maput Kuranji Puting Mendawai Lubuksikaping Sebangau Muara Beliti Mendawai Sikladipanjang Nagamengulur Muara Beliti Sukaraja Pakasi Pakasi Sungai Manau Puting Puting Sungai Medang Sebangau Sebangau Sungai Mimpi Sikladipanjang Sikladipanjang Sungai Napuk Sukaraja Sukaraja Tebingtinggi Sungai Aur Sungai Aur Teweh Sungai Manau Sungai Medang Sungai Napuk Sungai Mimpi Tanjung Sungai Napuk Tebingtinggi Talamau Telawi Tambaga Teweh Tandur Tanjung Tebingtinggi Ujunggading Jae Ulubandar Tandur Tanjung
83 Appendix 28: Results of identification for rare ecosystem in the landscape of 3 target district inside each biophysical region
Barisan Percentage Eastern Coastal Percentage Eastern Plains Percentage Mountains from maks. Swamps from maks. and Hills from maks.
Extend (%) Extend Extend (%) (%) Aek Si Antinga 0.56 Air Hitam Kanan 1.04 Aek Si Antinga 0.23 Air Cawang 0.38 Bakunan 0.41 Alur Menani 0.26 Bukitinggi 0.39 Barontongkok 0.35 Ampalu 0.07 Gajo 1.15 Bukit Ayun 1.28 Bakunan 2.36 Kahayan 2.59 Kahayan 4.74 Barontongkok 3.69 Klaru 0.67 Lubuksikaping 2.83 Beliti 2.87 Kototinggi 6.66 Maput 2.70 Bukittinggi 0.10 Kuranji 0.20 Pakasi 0.22 Gajo 2.92 Lawanguwang 2.39 Puting 2.99 Kahayan 3.91 Lubuksikaping 1.35 Sebangau 2.31 Kajapah 0.34 Puting 1.19 Sikladipanjang 3.98 Kototinggi 0.55 Sebangau 3.10 Sungai Aur 2.42 Kuranji 1.51 Sikladipanjang 0.68 Sungai Napuk 1.62 Lubuksikaping 1.65 Solok 0.02 Tanjung 0.06 Mendawai 2.59 Sukaraja 0.76 Teweh 1.68 Muara Beliti 4.20 Sungai Aur 0.82 Pakasi 1.07 Sungai Seratai 0.14 Puting 3.77 Talamau 4.59 Sebangau 1.53 Tambaga 1.46 Sikladipanjang 0.24 Tanjung 3.63 Sukaraja 2.37 Sungai Mimpi 0.49 Tambaga 0.71 Ujunggading Jae 0.01 Air Cawang 1.51 Alur Menani 3.69 Ampalu 0.92 Bakunan 2.11 Batuapung 1.56 Bengkulu 1.19 Gajo 1.16 Kahayan 2.33 Kuranji 0.18 Pulau Rotan 3.27 Sebangau 2.36 Sungai Manau 4.73 Sungai Medang 0.24 Sungai Mimpi 1.34
84 Appendix 29: Landsystem symbol on each ecosystem type that important for water provision and prevention of floods for downstream commodities
Peat Swam Mangrove Fresh Water Riparian Karst Cloud Forest or Peat Swamp Swamp Ecosystem Swam Forest MDW, SRM, KJP KHY, BLI, ANK, SBG, GBJ, KPR, BPD, BTK, BRH, KLR, BKN, BLI, OKI, AWY, MPT, BRW, GBT, SHD, PMG, KHY, MGH BDD, ANB PDH, BTA, BRW, TNJ, BKN, LPN, LNG, BBK, BLI, BLW ACG TWI, STB,
TDR, AHK,
ANB, BBG,
BBR, BDD,
BGA, BGI,
BMS, BPD,
BYN Source: HCV Indonesian Toolkit 2008
Appendix 30: USLE modelling description, including generated parameters Complete USLE equation E = R.K.L.S.C.P
R; Erocivity
R is rainfall erocivity factor which is a measurement of the kinetic energy of a specific rain event or an average year’s rainfall.
R is extrapolated value from average monthly rain number from surround weather station. Extrapolated value R is following formula by Bols 1978, in Arsyad 1989;
R = 6.119(RAIN)1;21(DAY S)-0;47(MAXP)0;53
R; monthly average of index erosivity
RAIN; average number of monthly rain (cm)
DAYS; number of days with rain occur in a month
MAXP; maximum rain number (24 hr) in a month
Using data from 3 weathers stations; Aek Godang, Sibolga, Gunung Sitoli
Aek Godang Sibolga Gunung Sitoli Jan 147 254 194 Feb 108 264 163 Mar 198 262 215 April 189 363 229 Mey 114 224 197 June 57 215 189 July 100 301 220 Aug 97 202 225
85 Sept 145 325 293 Oct 201 430 347 Nov 258 359 332 Dec 226 307 267
Proximate R value;
R 93.28 133.22 118.91
K Indeks (Erodibility)
K Indeks soil erodibility factor is the soil loss rate per erosion index unit for a specified soil as measured on a unit plot (22.1 m long with a 9% slope in continuous clean-tilled fallow).
Appendix 31: K value predicted using soil orde proxy where proximate by previous research
Group Sumber K_Lit Orde Id K Kandiudalfs Yusmandhany, 2002 0.0800 Alfisols -alf 0.1767 Tropaqualfs / Arsyad, 1989/Asdak, 0.2200 Mediteran 1995 Tropudalfs / Mediteran Arsyad, 1989/Asdak, 0.2300 1995 Hapludands Yusmandhany, 2002 0.2650 Andisols -and 0.2650 Endoaquents Yusmandhany, 2002 0.0700 Entisols -ent 0.2514 Psammaquents Yusmandhany, 2002 0.5200 Fluvaquents Yusmandhany, 2002 0.2000 Entisols Husain et.al, 1994 0.3400 Troporthents / Arsyad, 1989/Asdak, 0.1400 Regosol 1995 Orthent / Lithosol Arsyad, 1989/Asdak, 0.2900 1995 Tropoflufents / Arsyad, 1989/Asdak, 0.2000 Hydromorf abu-abu 1995 Stockyard swamp Hazwlton and Tille, 1990 0.0050 Histosols -ist 0.0050 Dystrudepts Yusmandhany, 2002 0.1850 Inceptisols -ept 0.1944 Eutrudepts Yusmandhany, 2002 0.0750 Oxic dystropepts / Arsyad, 1989/Asdak, 0.1400 Regosol 1995 Typic dystropepts / Arsyad, 1989/Asdak, 0.3100 Regosol 1995 Typic entropepts / Arsyad, 1989/Asdak, 0.2900 Regosol 1995 Typic tropoquepts / Arsyad, 1989/Asdak, 0.1300 Gley humic 1995 Tropaquepts / Gley Arsyad, 1989/Asdak, 0.2000 humic 1995 Aquic entropepts / Arsyad, 1989/Asdak, 0.2600 Gley humic 1995 Litic eutropepts / Arsyad, 1989/Asdak, 0.1600 Lithosol 1995
86 Mollisols Husain et.al, 1994 0.2400 Mollisols -oll 0.2400 Haplorthox Arsyad, 1989/Asdak, 0.0900 Oxisols -ox 0.1567 1995 Typic haplorthox Arsyad, 1989/Asdak, 0.2600 1995 Humox Arsyad, 1989/Asdak, 0.1200 1995 Spodosols -od Typic tropodult / Arsyad, 1989/Asdak, 0.2300 Ultisols -ult 0.2200 Latosol coklat 1995 Epiaquic tropodult / Arsyad, 1989/Asdak, 0.3100 Latosol 1995 Tropudults / Podsolik Arsyad, 1989/Asdak, 0.2400 1995 Tropohumults / Arsyad, 1989/Asdak, 0.1000 Mediteran 1995 Hapluderts Yusmandhany, 2002 0.4000 Vertisols -ert 0.3050 Chromuderts / Arsyad, 1989/Asdak, 0.2100 Grumosol 1995
LS factor
L and S normally calculated together as a single term LS. Methods for estimating values for these terms are described below.
In regional scale, determining land length is a problem to obtain LS factor. To overcome it, flow accumulation is then applied as follows (Moore & Wilson 1992):
Where: LS=1.4 (F.p/22.13)^0.4 ((sin(0.01745 S))/0.09)^1.3 F: Flow accumulation
p: resolution pixel of image (grid)
S: slope (degree) with L and S normally calculated together as a single term LS. Methods for estimating values for these terms are described below.
Appendix 32: Erosion potential assessment on land depth and erosion estimation Shallower soil depth lowers the threshold for acceptable levels of erosion. Those marked with X indicates HCV4.2 is present (adapted from Indonesian HCV Toolkit 2008, table 8.4.5).
Soil Depth Erosion Estimation <15 15 - 60 60 -180 180 - 480 >480 Depth Very low Low Medium High Very high (>90 cm) Middle Low Medium High Very high Ver high (60-90 cm) Thin Medium High Very high Very high Very high (30-60 cm) Very thin High Very high Very high Very high Very high (<30)
87 Appendix 33: Indicative HCV 4.3 areas
88 Appendix 34: Carbon stocks for each land cover type identified in target districts (source BioTrop, 1998)
LC (en) Cstock (Mg/ha) Source Dryland primary forest 289.50 Data aboveground Batang Toru (Onrizal et al) Dryland seconday forest 105.25 Data aboveground Merang REDD Pilot Project (Manuri, Putra, Saputra, 2011) Mangrove primary forest 148.76 Data Cadangan Karbon Jambi (SEAMEO Biotrop, 1999), dari Arif Budiman WWF Mangrove secondary forest Swamp primary forest 187.47 Data aboveground hutan Nagan Raya, Aceh (Sudarman, 2007) Swamp secondary forest Plantation forest 81.06 Data aboveground IUPHHK-HTI PT. Sumatera Riang Lestari (Tambun, 2013) Crop estate 58.98 Data Cadangan Karbon Jambi (SEAMEO Biotrop, 1999), dari Arif Budiman WWF Dryland agriculture 99.00 Data aboveground di Sumatera Barat (Sorel, 2007) Mix dryland agriculture and scrub 11.59 Data Cadangan Karbon Jambi (SEAMEO Biotrop, 1999), dari Arif Budiman WWF Paddy field 4.22 Data Cadangan Karbon Jambi (SEAMEO Biotrop, 1999), dari Arif Budiman WWF Swampy scrub 25.76 Data aboveground Merang REDD Pilot Project (Manuri, Putra, Saputra, 2011) Scrub Appendix 35: MaxEnt methods In the study 15 individual MaxEnt models were used according to the following settings:
• Feature types based on training sample size
• Replication method of sub-sample with 30 percent random test percentage
• Logistic output format
• Regularisation multiplier =1
• Maximum iterations = 5000
• Convergence threshold = 0.0001
• Maximum background points =10,000
Mean probabilities predicted by the ten independent models used as estimates for subsequent analyses. MaXEnt Software version 3.3.3k was used to run the model.
To evaluate the performance of the MaxEnt models for each commodity, the ‘area under the receiver operating characteristic curve’ (AUC) of receiver operating characteristic (ROC) plots were used (Cantor et al. 1999; Fielding & Bell 1997) produced by the MaxEnt software. The ROC plot-based approach measured the potential power of predictions (occupied and not occupied) of species distributions (Liu et al. 2005; Pearce & Ferrier 2000), which has shown to be effective in ecological modelling studies (e.g., Linkie et al. 2004; Osborne et al.; Schadt et al. 2002). The resultant AUC values range from 0.5 to 1.0, where values above 0.7 indicate an accurate model fit and above 0.9 indicate a highly accurate model (Swets 1988). The AUC values allow an easy comparison of performance between models, and are useful in evaluating multiple MaxEnt models. An AUC value of 0.5 indicates that the performance of the model is no better than random, while values closer to 1.0 indicate better model performance (Phillips et al. 2006).
89 In order to develop a climatic suitability model, a total of 870 GPS locations were used for four focus commodities (Arabica and Robusta coffee, cocoa and rubber), which were collected in the field. In addition, using the GIS software 627 random points were generated for oil palm distribution, which were identified using satellite imagery.
Appendix 36: Land suitability MCA In the Multi Criteria Analysis, the land suitability is determined based on the soil biophysical properties (not considering inputs).59 Here we will use the criteria released by the Indonesian Ministry of Agriculture for Arabica and Robusta coffee, cocoa, rubber, and oil palm commodities. Land suitability from Indonesian Ministry of Agriculture are classified into the following categories:
• S1 - Very Suitable
• S2 - Suitable enough
• S3 - Marginally suitable
• NS - Not suitable
In line with this classification system we will use the following terminology: S1 (High Suitability), S2 (medium Suitability), S3 (Low Suitability), and NS (not suitable). In order to clarify the visualization on the map, symbolization of different colours is used on each suitability class (see tables below).
Table A. Suitability classes: temperature
Temperature (°C) Arabica Cocoa Robusta Rubber Oil Palm NS < 14 < 20 < 19 < 22 < 20
S3 14 – 15 n/a 19 – 22 22 – 24 20 – 22
S2 15 – 16 20 – 25 n/a 24 -26 22 – 25
S1 16 – 22 25 – 28 22 – 25 26 – 30 25 – 28
S2 22 – 24 28 – 32 25 – 28 30 – 34 28 – 32
S3 24 – 26 32 – 35 28 – 32 n/a 32 – 35
NS > 26 > 35 > 32 > 34 > 35
Table B. Suitability classes: rainfall
Precipitation Arabica Cocoa Robusta Rubber Oil Palm (mm/year) NS < 800 < 1250 < 1500 < 1500 < 1250
S3 800 – 1000 1250 – 1500 1500 – 1750 1500 – 2000 1250 – 1450
S2 1000 – 1200 n/a 1750 – 2000 2000 – 2500 1450 – 1700
S1 1200 – 1800 1500 – 2500 2000 – 3000 2500 – 3000 1700 – 2500
S2 1800 – 2000 2500 – 3000 3000 – 3500 3000 – 3500 2500 – 3500
S3 2000 – 3000 3000 – 4000 3500 – 4000 3500 – 4000 3500 – 4000
NS > 3000 > 4000 > 4000 > 4000 > 4000
59. Ritung S, Wahyunto, Agus F, Hidayat H. 2007. Panduan Evaluasi Kesesuaian Lahan dengan Contoh Peta Arahan Penggunaan Lahan Kabupaten Aceh Barat. Balai Penelitian Tanah dan World Agroforestry Centre (ICRAF). Bogor, Indonesia.
90 Table C. Suitability classes: altitude
Elevation (m asl) Arabica NS < 100 S3 100 – 600 S2 600 – 700 S1 700 – 1600 S2 1600 – 1750 S3 1750 – 2000 NS > 2000
Table D. Suitability classes: erosion
Erotion Arabica Cocoa Robusta Rubber Oil Palm NS Very Heavy S3 Heavy S2 Light; Intermediate S1 Very Light
Table E. Suitability classes: soil texture
Soil Texture Arabica Cocoa Robusta Rubber PalmOil NS rough; very rough rough; very rough rough soft soft S3 Moderately Moderately Moderately Moderately Moderately rough rough; very rough rough rough soft S2 n/a S1 Soft; moderately soft; medium
Table F. Suitability classes: peat depth
Peat Depth Arabica Cocoa Robusta Rubber Palm Oil NS > 200 cm S3 140 – 200 cm S2 60 – 140 cm S1 < 60 cm
Table G. Suitability classes: slope
Percent Slope Arabica Cocoa Robusta Rubber PalmOil NS > 30; > 50 > 30 > 30; > 50 > 30; > 45 > 30 S3 16 – 30; 16 – 30 16 – 30; 16 – 30; 16 – 30 16 – 50 16 – 50 16 – 45 S2 8 – 16 8 – 16 8 – 16 8 – 16 8 – 16 S1 < 8 < 8 < 8 < 8 < 8
91
Table H. Overall land suitability classifications
Arabica (7 criteria) Suitability Layer Total Condition Notes
Score Score S1 0 0 - 3 n/a minimum 4 criteria in S1 class S2 1 4 - 7 n/a max totalscore = 7 S3 2 8 - 14 n/a max totalscore = 14 NS 10 > 14 one of layers = 10 if one layer is Unsuitable, all layer will be Unsuitable Other Commodities (6 criteria) Suitability Layer Total Condition Notes
Score Score S1 0 0 - 2 n/a minimum 4 criteria in S1 class S2 1 3 - 6 n/a max totalscore = 6 S3 2 7 - 12 n/a max totalscore = 12 NS 10 > 12 one of layers = 10 if one layer is Unsuitable, all layer will be Unsuitable
Data sources: Worldclim (BIOCLIM) dataset, 2014; 2020; 2050
Global DEM, SRTM v.4
Peatland, Wetlands International
RePPProT dataset for North Sumatera
Appendix 37: Biophysical suitability MCA
District Arabica High Suitability Medium suitability Low Suitability Unsuitable (ha) (ha) (ha) (ha)
MAND. NAT. (2014) 6923 21997 1530 590539
MAND. NAT. (2020) 5678 13507 36 601767
MAND. NAT. (2050) 830 3641 0 616518
TAP. SEL. (2014) 21844 108866 40722 260153
TAP. SEL. (2020) 13597 97005 21036 299949
TAP. SEL. (2050) 1779 76093 3136 350578
TAP. UT. (2014) 135173 153934 4234 90743
TAP. UT. (2020) 102760 178650 5408 97265
TAP. UT. (2050) 52627 209012 488 121957
District Robusta High Suitability Medium suitability Low Suitability Unsuitable (ha) (ha) (ha) (ha) MAND. NAT. (2014) 43672 317226 6369 253722
MAND. NAT. (2020) 33168 305874 13438 268508
MAND. NAT. (2050) 8540 113639 21190 477620
92 TAP. SEL. (2014) 119222 155195 1591 155579
TAP. SEL. (2020) 113802 144712 2955 170116
TAP. SEL. (2050) 80919 125963 13173 211531
TAP. UT. (2014) 172849 129478 282 81475
TAP. UT. (2020) 224464 78141 122 81356
TAP. UT. (2050) 240370 60078 292 83345
District Cocoa High Suitability Medium suitability Low Suitability Unsuitable (ha) (ha) (ha) (ha) MAND. NAT. (2014) 129422 222829 3832 264905
MAND. NAT. (2020) 131061 217902 1963 270062
MAND. NAT. (2050) 3817 131507 7880 477784
TAP. SEL. (2014) 111210 160198 2312 157865
TAP. SEL. (2020) 100267 158449 2175 170694
TAP. SEL. (2050) 52679 161027 6349 211531
TAP. UT. (2014) 151913 129463 362 102346
TAP. UT. (2020) 161719 140910 15 81439
TAP. UT. (2050) 161965 138724 46 83348
District Palm Oil High Suitability Medium suitability Low Suitability Unsuitable (ha) (ha) (ha) (ha)
MAND. NAT. (2014) 135924 218212 1947 264905
MAND. NAT. (2020) 134036 215258 1632 270062
MAND. NAT. (2050) 14315 121120 7769 477784
TAP. SEL. (2014) 108469 164489 763 157865
TAP. SEL. (2020) 103118 157122 652 170694
TAP. SEL. (2050) 64921 151628 3506 211531
TAP. UT. (2014) 109465 171951 321 102346
TAP. UT. (2020) 141068 161507 69 81439
TAP. UT. (2050) 166835 133894 6 83348
District Rubber High Suitability Medium suitability Low Suitability Unsuitable (ha) (ha) (ha) (ha)
MAND. NAT. (2014) 101426 225785 8266 285511
MAND. NAT. (2020) 130470 195543 3763 291213
MAND. NAT. (2050) 34667 99666 1038 485618
TAP. SEL. (2014) 59353 182800 3783 185650
TAP. SEL. (2020) 100336 150129 2680 178440
TAP. SEL. (2050) 95729 121448 1127 213281
TAP. UT. (2014) 13301 117039 124 253620
TAP. UT. (2020) 29720 200121 680 153563
TAP. UT. (2050) 81276 216816 768 85224
93 Appendix 38: MaxEnt results In the study 15 individual MaxEnt models were used according to the following settings:
• Feature types based on training sample size
• Replication method of sub-sample with 30 percent random test percentage
• Logistic output format
• Regularisation multiplier =1
• Maximum iterations = 5000
• Convergence threshold = 0.0001
• Maximum background points =10,000
Mean probabilities predicted by the ten independent models used as estimates for subsequent analyses. MaXEnt Software version 3.3.3k was used to run the model.
To evaluate the performance of the MaxEnt models for each commodity, the ‘area under the receiver operating characteristic curve’ (AUC) of receiver operating characteristic (ROC) plots were used (Cantor et al. 1999;Fielding & Bell 1997) produced by the MaxEnt software. The ROC plot-based approach measured the potential power of predictions (occupied and not occupied) of species distributions (Liu et al. 2005; Pearce & Ferrier 2000), which has shown to be effective in ecological modelling studies (e.g., Linkie et al. 2004; Osborne et al.; Schadt et al. 2002). The resultant AUC values range from 0.5 to 1.0, where values above 0.7 indicate an accurate model fit and above 0.9 indicate a highly accurate model (Swets 1988). The AUC values allow an easy comparison of performance between models, and are useful in evaluating multiple MaxEnt models. An AUC value of 0.5 indicates that the performance of the model is no better than random, while values closer to 1.0 indicate better model performance (Phillips et al. 2006).
In order to develop a climatic suitability model, a total of 870 GPS locations were used for four focus commodities (Arabica and Robusta coffee, cocoa and rubber), which were collected in the field. In addition, using the GIS software 627 random points were generated for oil palm distribution, which were identified using satellite imagery.
94 Coffee In the focus areas there are two common coffee species, Arabica coffee (Coffea arabica) and Robusta coffee (Coffea canephora). Of the focus area’s 1,474,911 ha, 26.4 percent are predicted to be climatically suitable for Arabica coffee with suitable areas for Arabica coffee mostly found in Kabupaten Tapanuli Utara, where more than half of the district (206,090.25 ha) is predicted to be climatically suitable. In Kabupaten Tapanuli Selatan and Mandailing Natal suitable areas only constitute about 20 percent of the total area (see Figure A).
The results of modelling indicate that the Northern and Central parts of Kabupaten Tapanuli Utara are most suitable for Arabica coffee (Siborong-borong, Pangaribuan, Pagaran and Sipahutar sub- district). While in Kabupaten Mandailing Natal, the hilly part of Torikmerapi Mountain is most suitable (Panyabungan (see Figure B)). Figure B: Current climatic suitability for Arabica coffee (Coffea arabica)
Figure A: Proportion of suitable areas for Arabica coffee in each focus district
120,000
100,000
80,000
60,000
40,000 Area (Hectare) 20,000
KAB. KAB. KAB. MANDAILING TAPANULI TAPANULI NATAL SELATAN UTARA
Low Suitability Medium Suitability High Suitability
95 Areas most suitable for growing Robusta coffee tend to be situated within lower elevations, in Kabupaten Tapanuli Utara and Selatan, with 43 percent and 36 percent of the areas are predicted to be suitable. In Mandailing Natal, the climatic suitability for Robusta is Low (see Figure D). The highest concentration of suitable areas for Robusta coffee is found in the south eastern and south western part of Kabupaten Tapanuli Utara (see Figure C).
Figure D: Current climatic suitability for Robusta coffee (Coffea canephora)
Figure C: Suitable areas for Robusta coffee in each focus district
90,000
80,000
70,000
60,000
50,000
40,000
Area (Ha) 30,000
20,000
10,000
KAB. KAB. KAB. MANDAILING TAPANULI TAPANULI NATAL SELATAN UTARA
Low Suitability Medium Suitability High Suitability
96 Cocoa The climatic suitability for cocoa (Theobroma cacao) is more dispersed throughout the focus districts than coffee. The portion of suitable area in the focus districts is 57 percent with an even distribution across each district. The highest concentration of suitable area is in the boundary between Kabupaten Tapanuli Utara and Selatan (see Figure F). In Kabupaten Tapanuli Selatan, 67 percent of the area is predicted to be climatically suitable, followed by Kabupaten Tapanuli Utara (61.5 percent) and Kabupaten Mandailing Natal (47.8 percent). Tapanuli Utara has a larger share of suitable areas compared with other districts (see Figure E). Figure F: Current climatic suitability for cocoa (Theobroma cacao)
Figure E: Suitable areas for cocoa in each focus district
200,000
180,000
160,000
140,000
120,000
100,000
80,000 Area (Ha) 60,000
40,000
20,000
KAB. KAB. KAB. MANDAILING TAPANULI TAPANULI NATAL SELATAN UTARA
Low Suitability Medium Suitability High Suitability
97 Rubber Approximately 28.4 percent of the total area of the focus districts is predicted to be climatically suitable for rubber (Hevea brasiliensis) with suitable areas being more concentrated in Tapanuli Utara and Selatan (see Figure H). The largest portion of suitable area for rubber can be found in Kabupaten Tapanuli Selatan (92.4 percent), followed by Kabupaten Tapanuli Utara (88.5 percent) (see Figure G).
Figure H: Current climatic suitability for rubber (Hevea brasiliensis)
Figure G: Suitable areas for rubber in each focus district
200,000.0
180,000.0
160,000.0
140,000.0
120,000.0
100,000.0
80,000.0 Area (Ha) 60,000.0
40,000.0
20,000.0
0 KAB. KAB. KAB. MANDAILING TAPANULI TAPANULI NATAL SELATAN UTARA
Low Suitability Medium Suitability High Suitability
98 Oil palm Approximately 20.4 percent of the total area of the focus districts is predicted to be climatically suitable for oil palm (Elaeis guineensis). The distribution of suitable areas is more concentrated in the south west coast of Kabupaten Mandailing Natal and Tapanuli Selatan (see Figure J). The largest proportion of suitable areas for oil palm was found in Kabupaten Mandailing Natal (35.1 percent), followed by Tapanuli Selatan (15.6 percent). Tapanuli Utara on the other hand, is considered highly unlikely to be climatically suitable for oil palm (see Figure I). Figure J: Current climatic suitability for oil palm (Elaeis guineensis)
Figure I: Suitable areas for oil palm in each focus district
120,000.0 Low Suitability Medium Suitability
100,000.0 High Suitability
80,000.0
60,000.0 Area (Ha)
40,000.0
20,000.0
KAB. KAB. KAB. MANDAILING TAPANULI TAPANULI NATAL SELATAN UTARA
99 Future climatic suitability Future climatic suitability was predicted using the RCP 8.5 scenario, which represents extreme climate changes predicted by IPCC version 4. The data was downscaled to a 250 meter resolution, with a characteristic mean annual temperature increase of 1.5˚C for 2020 and 2.8˚C for 2050.
Coffee Studies indicate Arabica coffee has a narrow suitability range compared with Robusta or Liberica coffee. Suitability of Arabica coffee is highly connected to climatic variability and strongly influenced by climatic oscillations (Davis et al. 2012; Laderach et al. 2011). According to the adopted model, from 2014 to the 2020s and then to the 2050s, climatic suitability for Arabica coffee will change drastically within the study focus area (see Figure K).
From an estimated total of 392,185.7 ha of suitable area for Arabica coffee in 2014 (26.6 percent of the total area), 61.9 percent of these suitable areas are expected to become unsuitable for Arabica by the 2020s. By the 2050s, a further 23.9 percent is expected to be lost, resulting in only 14.2 percent of the remaining area suitable by the 2050s (see Figure L).
Figure K: Climatic suitability changes Figure L: Suitability changes for Arabica analysis for Arabica coffee coffee between present times, the 2020s and the 2050s
1,400,000
1,200,000
1,000,000
800,000
600,000
Area (Hectare) 400,000
200,000
Current - 2020 2020 -2050 Loss Gain Suitable (Stable) Not Suitable
The suitability for Arabica coffee will be most seriously affected by climate change in Kabupaten Tapanuli Utara. By the 2020s, 93.4 percent of suitable areas are estimated to become unsuitable, with the remaining suitable areas becoming fragmented into small patches. A similar trend is expected towards the 2050s. The extent of suitable areas in Kabupaten Mandailing Natal however, increases from 95,721 ha in 2014 to 132,460.63 ha in the 2020s. By the 2050s, the suitable area is expected to decrease again to 30.9 percent (see Figure M).
By the 2020s the shifting from suitable to unsuitable in the 2020s in 26.6 percent of the suitable areas compared to the present time. In 2050s there is 20.2% increase of suitability from 2020s again (see Figure N).
100 Figure M: Climatic suitability changes Figure N: Suitability changes for Robusta analysis for Robusta coffee coffee between present times, the 2020s and the 2050s
1,400,000
1,200,000
1,000,000
800,000
600,000
Area (Hectare) 400,000
200,000
Current - 2020 2020 -2050
Loss Gain Suitable (Stable) Not Suitable
Cocoa In 2014, distribution of areas suitable for cocoa are dispersed throughout the study’s focus areas, however moving towards 2020 suitable areas noticeably shift, becoming more concentrated in Kabupaten Tapanuli Selatan and a small portion of south western Kabupaten Mandailing Natal. Surprisingly, by the 2050s most of those areas will then become completely unsuitable (see Figure O).
Figure O: Climatic suitability changes analysis for cocoa Figure P: Suitability changes for cocoa between present times, the 2020s and the 2050s
900,000
800,000
700,000
600,000
500,000
400,000
300,000 Area (Hectare) 200,000
100,000
Current - 2020 2020 -2050 Not suitable (Stable) Loss Gain Suitable (Stable)
Between 2014 to the 2020s, it is predicted that 71.1 percent (155,596 ha) of suitable areas for cocoa will not suitable anymore (see Figure P). Kabupaten Tapanuli Utara will be most seriously affected by climate change, with 93.4 percent of areas suitable for cocoa in Kabupaten Tapanuli Utara becoming unsuitable by 2020. Conversely, the amount of suitable areas in Kabupaten Mandailing Natal shall increase to approximately 70 percent in the 2020s. By the 2050s however, the amount of suitable areas shall decrease to 30.9 percent.
101 Rubber In 2014 areas suitable for rubber are located mostly in the interior of the study’s focus area. By 2020 however, the suitable areas shift to the western part of Kabupaten Tapanuli Utara and the south eastern part of Kabupaten Mandailing Natal. By the 2050s most of those areas become no longer suitable (see Figure Q).
Figure Q: Climatic suitability changes analysis for rubber Figure R: Suitability changes for rubber between present times, the 2020s and the 2050s
1,000,000.00
900,000.00
800,000.00
700,000.00
600,000.00
500,000.00
400,000.00
300,000.00 Area (Hectare) 200,000.00
100,000.00
Current - 2020 2020 -2050 Not suitable (Stable) Loss Gain Suitable (Stable)
Between 2014 and the 2020s, it is predicted that 71.1 percent of the suitable land for rubber in Kabupaten Tapanuli Utara will become unsuitable (see Figure R).
Oil palm Distribution of Oil palm climatic suitability in 2014 is mostly concentrated in southern coast of Kabupaten Mandailing Natal. In 2020s there are some changes in south western coast, with areas that become unsuitable. Surprisingly, by 2050s most of these areas are regaining its suitability (see Figure S).
Figure S: Climatic suitability changes analysis for oil palm
Figure T: Suitability changes for oil palm between present times, the 2020s and the 2050s
1,200,000.00
1,000,000.00
800,000.00
600,000.00
400,000.00 Area (Hectare)
200,000.00
Current - 2020 2020 -2050 Not suitable (Stable) Loss Gain Suitable (Stable)
Between 2014 and the 2020s, it is predicted that only 18.6 percent of suitable areas will become unsuitable for oil palm (see Figure T).
102 Appendix 39: Economic evaluation
Mandailing Natal Table A: Impacts of climate change on Arabica and Robusta yield – Mandailing Natal
Year ARABICA Robusta Total potential Total % Total potential Total % revenue (Rupiah) potential Change in revenue (Rupiah) potential Change in production production production production (tons) (tons) 2014 314,795,728,940 6,519.26 2,370,910,643,105 115,103.92 2020 79,564,589,162 1,647.74 -74.73% 2,269,704,281,140 110,190.52 -4.27% 2050 36,356,816,601 752.93 -54.31% 429,974,249,921 20,874.56 -81.06%
Table B: Impacts of climate change on oil palm yield - Mandailing Natal
Year Total potential oil palm Total potential oil palm % Change in production revenue (Rupiah) production (tons) 2014 5,973,510,058,193 3,114,446.73 2020 5,803,390,004,092 3,025,750.17 -2.85% 2050 1,068,182,513,207 556,925.07 -81.59%
Table C: Impacts of climate change on cocoa and rubber yield - Mandailing Natal
Year COCOA RUBBER Total potential Total % Total potential Total % revenue (Rupiah) potential Change in revenue (Rupiah) potential Change in production production production production (tons) (tons) 2014 5,801,000,413,486 205,740.21 3,703,033,816,056 173,818.71 2020 5,510,827,613,479 195,434.11 -5.01% 3,551,055,870,981 166,684.94 -4.10% 2050 1,117,576,324,251 39,703.38 -79.68% 875,840,423,126 41,111.55 -75.34%
Tapanuli Selatan Table D: Impacts of climate change on Arabica and Robusta yield – Tapanuli Selatan
Year ARABICA Robusta Total potential Total % Total potential Total % revenue (Rupiah) potential Change in revenue (Rupiah) potential Change in production production production production (tons) (tons) 2014 4,280,159,401,982 88,639.99 1,697,368,052,717 82,404.51 2020 3,294,856,586,150 68,234.86 -23.02% 1,587,379,095,961 77,064.72 -6.48% 2050 1,880,979,518,206 38,954.16 -42.91% 1,488,689,141,757 72,273.48 -6.22%
Table E: Impacts of climate change on oil palm yield - Tapanuli Selatan
Year Total potential oil palm Total potential oil palm Percentage change in revenue (Rupiah) production (tons) production 2014 3,740,172,712,868 1,950,037.52 2020 3,475,482,190,430 1,812,034.13 -7.08% 2050 3,041,721,922,440 1,585,881.80 -12.48%
103 Table F: Impacts of climate change on cocoa and rubber yield - Tapanuli Selatan
Year COCOA RUBBER Total potential Total % Total potential Total % revenue (Rupiah) potential Change in revenue (Rupiah) potential Change in production production production production (tons) (tons) 2014 1,914,282,627,097 67,895.57 1,023,874,302,799 48,060.19 2020 1,892,993,968,472 67,135.57 -1.12% 1,046,674,143,602 49,130.40 2.23% 2050 1,760,059,774,633 62,513.30 -6.88% 962,330,898,281 45,171.37 -8.06%
Tapanuli Utara Table G: Impacts of climate change on Arabica and Robusta yield - Tapanuli Utara
Year ARABICA Robusta Total potential Total % Total potential Total % revenue (Rupiah) potential change in revenue (Rupiah) potential Change in production production production production (tons) (tons) 2014 7,795,024,227,529 161,431.11 2,031,406,611,065 98,621.55 2020 7,644,644,037,329 158,316.81 -1.93% 2,031,425,649,121 98,622.47 0.00% 2050 6,931,504,097,925 143,548.04 -9.33% 2,017,136,119,938 97,928.74 -0.70%
Table H: Impacts of climate change on oil palm yield - Tapanuli Utara
Year Total potential oil palm Total potential oil palm % Change in production revenue (Rupiah) production (tons) 2014 69,968,038,083 36,479.68 2020 74,753,359,186 38,974.63 6.84% 2050 74,143,600,699 38,656.71 -0.82%
Table I: Impacts of climate change on cocoa and rubber yield - Tapanuli Utara
Year COCOA RUBBER Total potential Total % Total potential Total % revenue (Rupiah) potential Change in revenue (Rupiah) potential Change in production production production production (tons) (tons) 2014 1,853,525,177,644 65,722.38 940,244,039,311 44,134.62 2020 1,977,402,504,007 70,113.67 6.68% 1,619,870,593,244 76,035.98 72.28% 2050 1,962,163,881,638 69,573.09 -0.77% 2,089,570,355,555 98,083.48 29.00%
Appendix 40: Results RePPProT Indonesia’s first national scale agricultural suitability evaluation was executed in the Regional Physical Planning Project for Transmigration (RePPProt 1990). The resultant RePPProt database provides suitability information for common agricultural commodities such: cocoa, coconut and coffee, amongst others. Based on the RePPProt database there does not appear to be many suitable areas for Arabica coffee in North Sumatera Province. However the province does appear to have suitable areas for Robusta coffee, cocoa, rubber and oil palm. The areas are mostly situated in the east coast with some additional patches scattered across the province (see Figure A). As the results of RePPProt are too rough to provide insight on a district level on the suitability, we have not made additional analysis regarding future suitability.
104 Figure A: RePPProT suitability maps for five commodities in North Sumatera Province
105 For more information on SNV’s work on REAP, please contact us at: www.snvworld.org/en/redd Email: [email protected]
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