STRENGTHENING CAPACITY FOR INTEGRATING ECOSYSTEM SERVICES IN THE FOREST LAND USE PLANNING PROCESS TO ENHANCE CLIMATE RESILIENCE AND POVERTY REDUCTION IN THE PHILIPPINES Methodology Report June 2018 White Cover Copy Photo Cover: Left - Upper Marikina River Basin Protected Landscape (UMRBPL); upper right: Points of Interests Map of Castilla and Sorsogon City, Sorsogon; lower right: FGD in Talacogon, Agusan Del Sur Photo Credit: DENR-WB-PROFOR Project Secretariat Layout and Design: Ambientmedia i Acknowledgment This Methodology Report on “Strengthening Capacity for Integrating Ecosystem Services in the Forest Land Use Planning process to enhance climate resilience and poverty reduction in the Philippines” was compiled and developed through the shared technical knowledge and experiences from the PROFOR-funded Technical Assistance I and II and the collaborative effort done by the Project Secretariat (Forest Management Bureau, Foreign-Assisted and Special Projects Service, River Basin and Control Office and The World Bank). Sincere appreciation is extended to the persons who worked together in developing the three (3) modules: For. Arnan Araza (Ecosystem Services Modeling), Dr. Margaret Calderon (Ecosystem Services Valuation), and Dr. Ma. Larissa Lelu Pessimo-Gata (Forest-Use Analysis). Special thanks are also extended to the persons who provided additional technical inputs to the report: Dr. Maurice Andress Rawlins and For. Gordon Bernard Ignacio of The World Bank; For. Larlyn Faith Aggabao, Mr. Eugene Soyosa and Mr. Rowell Velonza of the Forest Management Bureau; For. John Carlo Aguado and For. Jerbelle Elomina of the Foreign- Assisted and Special Projects Service. Financial support for the printing of this report was provided by the Program on Forests (PROFOR). A multidonor partnership housed at the World Bank, PROFOR finances forest- related analysis and processes that support improving people’s livelihoods through better management of forests and trees, enhancing forest law enforcement and governance, financing sustainable forest management, and coordinating forest policy across sectors. ii CONTENTS Title Page Acknowledgments ii Background of the Report 1 Part 1 Ecosystem Services Modeling Methods 2 1. Introduction 3 2. Methods 3 2.1 Pre-processing of raw data 3 2.1.1 Digital Elevation Model (DEM) 4 2.1.2 Weather Data 5 2.1.3 Land use/crop data 5 2.1.4 Soil data 6 2.2 SWAT Input Preparation 8 3. Model Runs 9 4. Calibration and Validation 10 4.1 Manual calibration 10 4.2 Calibration using SWAT-CUP 10 4.3 Sensitivity analysis 11 4.4 Calibration inputs 11 4.5 Iterations 12 4.6 Validation and Parameterization 12 5. Results Interpretation 13 5.1 Compiling results 13 5.2 Using water balance ratios 14 5.3 Pivot table 14 5.4 Graphs 15 6. Results per ES 16 6.1 Water yield - Dependable flow rate (DFR) 16 6.2 Water regulation 17 6.2.1 Irrigation use 17 iii 6.2.2 Storm event 17 6.3 Sediment Regulation 18 6.4 Erosion Control 19 6.5 Water Purification 19 6.6 Carbon Sequestration 21 6.7 Timber Provision 22 7. Valuation Inputs 23 8. References 25 Part 2 Ecosystem Services Valuation Methods 26 1. Introduction 27 2. Valuation Approaches 27 3. Application of Valuation Approaches 29 3.1 Water Provision: Domestic Water Use by Households 29 3.2 Water Provision: Irrigation 30 3.3 Erosion Control 31 3.4 Sediment Control 31 3.5 Carbon Sequestration 32 3.6 Timber Provision 33 4. References 33 Part 3 Forest Use Analysis Methodology 35 1. Background 36 2. Process 37 3. Training 37 4. Implementation of field work 38 4.1 Data Collection Tools 38 4.1.1 Tool 1 - Wealth Ranking 38 4.1.2 Tool 2 - Local Landscape Situation Analysis 41 4.1.3 Tool 3 - Livelihood Analysis 43 4.1.4 Tool 4 - Forests Problems and Solution Matrix 46 4.1.5 Tool 5 - Ranking Forest Products 56 iv 4.2 Data Processing and Analysis 57 4.2.1 Research Questions 57 4.2.2 Variables 57 4.2.3 Quantitative Analysis 58 4.3 Ethical Considerations 58 5. References 59 v Background of the Report The Department of Environment and Natural Resources (DENR) implemented the PROFOR- funded Technical Assistance (TA) on “The role of forests in reducing poverty and enhancing climate resilience: a case study of the Philippines”. Two overarching messages relating to forests and development emerged from the PROFOR PH TA I: that forests are crucial in enhancing climate resilience and forests are relevant for income and wealth because they serve as a safety net to avoid poverty and potentially increase access to economic opportunities. A follow-up study, PROFOR PH TA II (Strengthening Capacity for Integrating Ecosystem Services in the forest land use planning process to enhance climate resilience and poverty reduction in the Philippines), identified FMB’s forest land use planning (FLUP), as a critical program for highlighting the role that Forests and Forest Lands (FFL) play in providing ecosystem services Accordingly, a major component of this TA was on capacity building of government officials at the national, regional and local levels for use of the tools and approaches introduced as part of the PROFOR-funded TA. Capacity building was done through a series of hands-on training sessions provided to government staff on the use of the tools introduced under this TA. It is anticipated that by documenting and disseminating the methods for use of these tools, that the capacity building can continue even after the TA has ended. This methodology report on the tools used for the PROFOR study was therefore compiled as a resource for the Philippine government and civil society to support replication and upscaling. The three part report provides details and step-by-step guidance on the approaches and tools: Part 1 describes the key steps for ecosystem service modeling; Part 2 describes the key steps used for ecosystem service valuation; and in Part 3 the steps used for forest use analysis are described. As with any methodology document, users are encouraged to apply caution in the application and adaptation of the methods to their circumstance. 1 Part 1 Ecosystem Services Modeling Methods 2 1. Introduction Modeling ecosystem services (ES) is a procedural method that requires combined elements of technical skills, data input, and correct software to use. It requires considering among others physical and mathematical representation of ecosystem functions and processes, such as those underpinning hydrology, soil stabilization, and carbon sequestration, spatial heterogeneity of the ecosystem, temporal resolution, and required model accuracy (Hein, et.al. 2015). The methods used to model ES for the PROFOR study started from pre-processing of raw data up to final model results interpretation. A core modeling group equipped with a series of training and mentoring led the modeling work. This chapter describes all methods used in ES modeling for the PROFOR study with step by step processes and guide illustrations. The chapter is divided into 6 major parts to comprehensively guide readers on how the modeling work has been done. The use of modeling software and results interpretation will be the gist of this document (how to’s) and is targeted at audiences with at least basic understanding and experience of GIS/ modeling. This chapter is accompanied by modules and templates indicated per section. The methodologies for modeling cover topics ranging from basic concepts and processing to specific ecosystem service. The software used is ArcGIS version 10.2. Other versions (10.1 or 10.3) can also be used but may have differences in the interface. The Soil and Water Assessment (SWAT) extension tool which has a separate installer was used for ES Modeling. Please access the file with the modules and templates using the following link: http://forestry.denr.gov.ph/profor/references.php. 2.Methods All GIS operations from an ArcGIS interface are ‘searchable’. Detailed descriptions of every GIS operation can be seen from the link below the command operation, for example 2.1 Pre-processing of raw data. Raw data preprocessing is a data mining technique that involves any type of processing performed on a raw data for further processing, in this case, what we call modeling. 3 There are a number of different GIS tools and methods used for preprocessing, including: sampling, extraction clipping, denoising or filling, etc. Real world data is often incomplete, inconsistent, or is likely to contain many errors. Preprocessing is a proven method of resolving such issues. The following are some of the methods of preprocessing of the key spatial data for modeling. 2.1.1 Digital Elevation Model (DEM). A DEM is a 3D representation of a terrain’s surface, which is in a bare, void of vegetation and manmade structures. It is a representation of continuous elevation values over a topographic surface in a raster format. Forest Management Units (FMUs) or watershed boundaries are easily digitized and generated from DEMs. It can also be used to generate stream networks and stream orders that are formerly done manually using topographic maps. The DEM should be free of sinks to avoid pits or holes that could make your DEM not topographically sound. Operate ‘fill’ by simply uploading DEM. BOTTOMLINE: DEM should be ‘filled” DEM SOURCES: -ASTER (open source) -SRTM (open source) -DTM/IFSAR (DENR-FMB, NAMRIA) edndoc.esri.com It is important that the DEM file you have on hand contains the location of your area of interest. The next step is to extract the DEM of your area of interest using the “Extract by Mask” tool in ArcGIS. Please note that to be able to extract the DEM, you need the boundary of the area in shapefile format. 4 The preprocessed data output for this is the masked boundary (in raster format) of the watershed of interest. 2.1.2 Weather data. Climatological parameters or often called weather data such as rainfall, temperature (max and min), relative humidity, wind speed, and solar radiation should be in proper format, as seen in the image below. The coordinates of the weather stations can be found from the data source. Refer to Module 1 for complete steps. Weather datasets can be obtained from PAGASA. BOTTOMLINE: Weather data should be ‘SWAT formatted’ SOURCES: -PAGASA -Global Climate Data (open source) 2.1.3 Land use/crop data. SWAT is a crop-specific software which includes a comprehensive crop database. Also called as the land cover/plant growth database, contains the parameters for all land covers simulated in a watershed. Since the SWAT software was generated using data from the US, we need to program it to simulate or represent the Philippine setting. The primary data used is the latest Land Cover Map of the National Mapping and Resource Information Authority (NAMRIA) which has fourteen (14) categories and the list of crops planted in each of the land cover category: NAMRIA Land Cover Category Land Use/Crop Data 1. Closed Forest Forest Evergreen 2. Open Forest Forest Evergreen and Forest Mixed 3. Mangrove Forest Wetlands Forested 4. Wooded Grassland Switchgrass 5. Grassland and Fallow Pasture and Range grasses 6. Shrubland Range bush 7. Annual Crop Rice, Corn and Cassava 8. Perennial Crop Coconut, Banana, Mango, Citrus, Coffee, Papaya, and Pineapple 9. Open/Barren Range - southwest US 10. Inland Water and Fishpond Water 5 NAMRIA Land Cover Category Land Use/Crop Data 11. Marshland Wetlands 12. Built-up Urban-Rural The challenge for modelers is to find a way to match actual land use/crop data into the SWAT database. Note that this can be relative to the characteristics of the area of interest. Hence, the team deviced the above data to fit into the model. Two steps were undertaken to determine the actual land use of the area with respect to its land cover: focus groud discussions (FGDs) with the communities, and literature review of data from the Bureau of Agricultural Statistics (BAS). Module 2 provides a step-by-step process for this. BOTTOMLINE: The preprocessed data should include the raster file of the land cover of the watershed and the localised crop database. 2.1.4 Soil data. Similar to Item 2.1.3, SWAT also contains a soil database which houses information about the physical characteristics/parameters of the soil in the watershed. The soil map (in shapefile) and parameters are readily available from the Bureau of Soils and Water Management (BSWM) soil reports. Note that the spatial data should coincide with the parameters in tabular form, meaning, whatever soil type is present in the area should have an equivalent set of parameters. There are cases where one or two soil types in a particular watershed has relatively small area in terms of hectares. In this case, merging these small areas with the adjacent soil type with large areas should be done to minimize insignificant results. 6 As seen in the figure above, the soil parameters needed to be gathered are the following: Acronym Meaning Importance SNAM: Soil Name (printed in the HRU summary tables) [optional] HYDGRP: Soil Hydraulic Group (A, B, C, or D) [required] SOL_ZMX: Maximum rooting depth of soil profile (mm) [required] Fraction of porosity (void space) from which anions are ANION_EXCL: excluded. If no value is entered, the model will set = 0.50 [optional] SOL_CRK: Potential or maximum crack volume of the soil profile [optional] TEXTURE: Texture of soil layer [optional] SOL_Z(layer #): Depth from soil surface to bottom of layer (mm) [required] Moist bulk density (Mg/m³ or g/cm³). Values should fall SOL_BD(layer #): between 1.1 and 1.9 Mg/m³. [required] Available water capacity of the soil layer (mm H₂O/mm SOL_AWC(layer #): soil) [required] SOL_K(layer#): Saturated hydraulic conductivity (mm/hr) [required] SOL_CBN(layer #): Organic carbon content (% soil weight) [required] SOL_CLAY(layer #): Clay content (% soil weight) [required] SOL_SILT(layer #): Silt content (% soil weight) [required] SOL_SAND(layer #): Sand content (%s soil weight) [required] SOL_ROCK(layer #): Rock fragment content (% total weight) [required] SOL_ALB(top layer): Moist soil albedo [required] USLE equation soil erodibility (K) factor (units: 0.013 USLE_K(top layer): (metric ton m² hr)/(m³- metric ton cm)). [required] BOTTOMLINE: The preprocessed data should include the raster file of the soil map of the watershed and the localised soil database. 7 2.2 SWAT Input Preparation Lookup table / Database Updating. Lookup tables are database tables that translate a complex set of categorical codes (like what is contained in the crop and soil SWAT database), to one or more generalized category or schemes. Lookup tables are usually done in notepads or MS Excel. In this study, .txt files are created for the crop and soil input maps to match with the data in the database. The number of value will depend on the number of classification present on the soil and land use maps. In this study, 14 soil types and 10 land use categories were recorded in one of the study sites. These .txt files will be uploaded in the SWAT interface to create a land use/ soil map with the data from the SWAT database. Database updating on the other hand is done to modify the SWAT database using localised data. Modifying parameters can be done in two ways: (1) for land use/crop types, copy data from Template 1 and paste directly in SWAT2012 Database; (2) for soil parameters, manually input data in the SWAT interface. The 14 land cover categories should then be aggregated into 12 major categories and can be matched with the crop classification of the SWAT database as follows: NAMRIA Land Cover SWAT Classification Category 1. Closed Forest FRSE 2. Open Forest FRST 3. Mangrove Forest WETF 4. Wooded Grassland SWCH – Should be split into: 30% FRSD and 70% RNGE in the ‘Land Use Refinement Tab’ since it is the characteristics of a wooded grassland in the country. 5. Grassland and Fallow RNGE 6. Shrubland RNGB 7. Annual Crop RICE 8. Perennial Crop COCO 8 NAMRIA Land Cover SWAT Classification Category 9. Open/Barren BARR 10. Inland Water and WATR Fishpond 11. Marshland WETL 12. Built-up URML In the soil database, using newly created codes or proxy soil classification codes from SWAT (i.e. Antipolo Clay = ANCL or Mountain Soil = PAXTON) and encoding of all parameters are required and can be a bit tidious. You can refer to Module 3 for complete steps. BOTTOMLINE: edit parameters either in SWAT 2012 (copy-paste from Template 1) or manual input at SWAT interface HRU thresholds. Hydrologic Response Unit (HRU) is the basic unit of all model calculations in SWAT. The purpose of assigning a HRU threshold is to represent each sub- basin considering the combination of land use, soil and slope. However, there is no current reasonable guideline for selecting HRU thresholds yet (Strauch, 2014) so the team improvised a method to select the HRU threshold to be used. Ensuring that modeling results will be accurate, thresholds (%) are preferred to be near zero. In HRU Definition interface, there are three (3) thresholds to be filled-up, these are: (i) Land use percentage over sub-basin area; (ii) Soil class percentage over land use area; and (iii) Slope class percentage over soil area. To fill-up these thresholds, full HRU report generated by SWAT is used and data on sub basin area, land use area, soil class area and slope class area per sub-basin are to be collected. After that, get the percentage of land use area over sub-basin area, soil class area over land use area and slope class area over soil class area on each sub-basin. The minimum percentage among values generated from the computation will be chosen as the thresholds for the model. 3. Model Runs Running SWAT is easier if all data inputs are intact. Module 7 shows the step by step process from creating a project up to saving outputs. Take note that the outputs will be automatically uploaded to the Default folder1 (and not to the saved folder) after saving the simulation. 1. Opening a Project 2. Watershed Delineator BOTTOMLINE: All SWAT input should be ready first before running 3. HRU Analysis the steps – DEM, landuse, soil, 4. Write Input Tables weather data 5. Edit SWAT Input 6. SWAT Simulation 1 This might seem a bug depending on software compatibility and specs. 9 4. Calibration and Validation 4.1 Manual calibration. Calibration is a technique that compares simulated data to observed data in order to assess accuracy of the model. Three common measures for assessing the “goodness” of prediction are Nash Sutcliffe Efficiency (NSE) ratio, Percent Bias (PBIAS), and regression analysis (R). Module 8 includes guidelines for manually calibrating Flowout results of SWAT. The following parameters will serve as basis for calibration: 1. NSE prediction satisfactory rate is above 0.6. 2. PBIAS < 10% prediction is satisfactory; <25% is unsatisfactory 3. r2 > 0.5 is satisfactory 4.2 Calibration using SWAT-CUP. This is a SWAT extension stand-alone program which provides easy link to SWAT via TxtinOut folder. The link is shown below: The most common calibration method is the SUFI-2 wherein uncertainty in parameters is expressed in ranges with uniform distribution. It accounts for all sources of uncertainties such as driving variables (e.g. rainfall), conceptual model, and measured data. The measure of result is coded as 95PPU to quantify the fit between simulation and observed 10 data. Two statistics are produced namely R and P factor that give thickness and % of observed data enveloped by the model results, respectively. Format the observed data accordingly. Take note that the day format is the Julian day; use Template 2 for guidance. Divide observed data into two parts – one part for calibration and the other part for validation (e.g. 2000-2004-calibration, 2005-2010- validation). 4.3 Sensitivity analysis. Two types of sensitivity analysis can be used: global and one-at- a-time. Usually, the former is used since it gives all sensitive parameters after iteration. 4.4 Calibration Inputs. Key steps here include a uploading desired parameters to be optimized and choosing the appropriate value. Fill-out the file.cio accordingly. 11 BOTTOMLINE: Perform sensitivity analysis first before validation; Have many simulations if necessary; Ideal number of iterations is at least 10 (change parameters accordingly in every iteration Paste the daily observed data for calibration to observed_rch.txt at observation tab and observed.txt at objective function tab. The comprehensive step by step details can be found on Module 9 pp.25-34. 4.5 Iterations. In every iteration, there are eight types of outputs. Key results would be the 95ppu plot which shows a graph comparing simulated and observed values as well as its fitness (green curve); best parameters and simulation; and the new parameters which contain a new set of values used to overwrite the previous set. 4.6 Validation and Parameterization. Run in a similar way to calibration but using validation observed data and check whether the results are similar. If results are favorable, go back to SWAT at GIS interface and change the parameters manually. After editing, all changed parameters will be applied to all HRUs and sub-basins. 12 5. Results Interpretation 5.1 Compiling results. SWAT outputs can be seen at TablesOut sub-folder from scenario folder in MS Access format. Filter the desired sub-basin and its FLOW_OUT. Copy (by highlighting column) and paste in an excel sheet. Copy SED_OUT for sediment yield. Add the following columns to the excel sheet: year, month, day, and precipitation. Do this for all land cover scenarios. BOTTOMLINE: raw table came from various FLOW_OUT tables from SWAT 13 5.2 Using water balance ratios. SWAT provides a comprehensive summary of your model. It gives ratios on groundwater flow, baseflow, evapotranspiration, and run-off relative to total flow. These ratios can be used to derive the said flows using the FLOW_OUT output. A Sheet with sample data and formula for using ratios can be seen in Template 2. 5.3 Pivot table. After compiling results, a pivot table can now be made – it is an excel command to summarize and organize raw tables. Inputs can be filtered. Arrange accordingly depending on your interpretation target i.e. monthly, yearly, and seasonal. 14 Check the desired inputs of the table first and filter if necessary i.e. summer months. The values are in ‘sum’, by default so change it to show average values. 5.4 Graphs. Graphs help to visualize key results of the model. Make sure that you have all information you want from the pivot table. For analyzing seasonal flows, add daily column and create a new table for it. Delete the sub-total rows from the pivot table after copying. Select all and click insert > line. Format into desired graph layout (make sure it’s properly labeled and color-coded). Guide in analyzing results is shown in Module 10. 15 6. Results per ES 6.1 Water yield Dependable flow rate (DFR). This indicates how many days water flows can provide sufficient water to its service area during summer months (# days). A step by step guide for computing the DFR is shown in Module 10. Extract summer days and arrange them chronologically. A line graph will be produced in the sheet and the 80% (DFR threshold) must be used. Put the value to J1 cell and create a summary table per scenario showing % of days under DFR threshold. BOTTOMLINE: follow the excel template (with formula) using 80% DFR threshold 16 6.2 Water regulation 6.2.1 Irrigation use. Service area for irrigation is a primary ES indicator measured as hectares irrigated. To capture this actual and bare/urban scenario water flows during cropping season (especially ‘summer season’) should be used. Minimum flow of the watershed should be deducted to the total flows as a rule of thumb. Paste the cropping season average daily flows of Template 3. Analyze results per cropping season and deduct results of bare/urban from the actual scenario to capture the ES. BOTTOMLINE: use cropping season in summer months to analyze the service better Key inputs for this model include number of cropping days used, cropping months, and amount of water needed for a full cropping. 6.2.2 Storm event. HEC-HMS is used to model hourly discharge of storm and rainy events. It is an events-based model used to replicate rainfall-runoff process. It simulates the movement of water in the study area through representation of hydrological methods. The computation of run-off generation was derived by parting the basin’s hydrological system into various components, boundaries of the study and hydrologic condition of the basin.  Parameters to create desired scenario i.e. Forested Scenario can be done by modifying the curve numbers along with other loss and routing parameters. Refer to Module 10 for the HEC-HMS module. Using sub-daily results of a given storm event, a forested scenario will have a lower peak discharge, a longer lag time, and lower sediment generation and sediment flow compared to a bare/urban scenario. The best indicator for this ES would be hectares of inundated areas or number of households affected. For the PROFOR study, the analysis is limited to the hydrograph particularly on its elements comparing lagtime of bare/urban and forested scenario (x hours). Guidance for analyzing hydrographs can be seen in Module 11. 17 BOTTOMLINE: sub-daily data represents water regulation better than daily data 6.3 Sediment Regulation SWAT generates output databases after the SWAT run. This can be found in the following folder location of the model run: Scenarios > Default > TablesOut >SWATOutput. The “rch” database contains the daily sediment outflow for all the sub-basins during the period of the model run, under the parameter SED_OUTtons. To analyze the daily, monthly, or annual sediment outflow for each sub-basin or subwatershed in MS Excel, export the file SWATOutput by clicking External Databases > Export to Excel Spreadsheet. Insert a pivot table and filter by subwatershed or sub-basin, and/or the desired time period of analysis (daily/monthly/annual). Also select the parameter, SED_OUTtons and select “sum” in the value settings. This generates the total sediment outflow per each sub-basin per day/month/year. Sediment regulation is interpreted as the total sediment avoided by a particular landscape, when that same landscape is converted into a bare landscape. In ES Modeling this is computed by getting the difference between the sediment outflow of the bare scenario and the sediment outflow of the BAU/actual scenario. 18 6.4 Erosion Control Erosion control is treated similarly as sediment regulation, although using a different parameter in SWAT, SYLD. SWAT generates output databases after the SWAT run. This can be found in the following folder location of the model run: Scenarios > Default > TablesOut >SWATOutput. The “sub” database contains the daily hillslope erosion for all the sub-basins during the period of the model run, under the parameter SYLD. To analyze the daily, monthly, or annual erosion for each sub-basin or subwatershed in MS Excel, export the file SWATOutput by clicking External Databases > Export to Excel Spreadsheet. Insert a pivot table and filter by subwatershed or sub-basin, and/or the desired time period of analysis (daily, monthly, and annual). Also select the parameter, SYLD and select “sum” in the value settings. This generates the total erosion per each sub-basin per day/month/year. Erosion control is interpreted as the total erosion avoided by a particular landscape, when that same landscape is converted into a bare landscape. In ES Modeling this is computed by getting the difference between the SYLD of the bare scenario and the SYLD of the BAU/actual scenario. 6.5 Water Purification In watersheds that rely in groundwater as domestic water source, water purification is an essential ecosystem service. The use of contour and riparian buffer strips planted with perennial vegetation has been found to improve surface water quality by reducing NO3-N and sediment outflow from cropland to a river (Sahu and GU 2009). From the ‘SWAT output run SWAT check’, click ‘landscape nutrient losses’ tab to analyze nitrate values and compare per land cover scenario. 19 For daily results, NO3 values are included in the .rch database 20 6.6 Carbon Sequestration Carbon sequestration, in line with the SEEA Framework (UN et.al, 2014) is considered as an ecosystem service. The physical and monetary value of carbon provide insights in the contribution of forests in reducing the impacts of climate change, and are important to facilitate the advancement of REDD+ in the study sites. In the case of the three (3) study sites, carbon sequestration was computed in three (3) scenarios namely: forested, conservation and agricultural scenarios. Physical values were derived by directly multiplying the forest area (in hectares) by the annual carbon sequestration per hectare. The annual carbon sequestration per hectare were computed using the following assumptions (Table 1) and equations. Table 1. Assumptions for computing carbon sequestration rates per forest cover MAI – ABG Growth (tonnes Ratio of ABG to Sequestration Rate dry matter/ha/yr) BGB (tons of CO2/ha/yr) Closed Forest 2.10 0.32 4.58 Open Forest 3.50 0.32 7.63 Plantation Forest 9.10 0.32 19.84 Mangrove Forest 5.20 0.33 11.42 ABG – above-ground biomass BGB – below-ground biomass Use Equation 1.0 to compute for the sequestration rate per type of forest using the above assumptions: Equation 1.0 Annual Sequestration Rate = MAI(AGB)*(1+BGB)*Carbon Fraction*CO2 global warming potential of 3.67) Equation 2.0 finally computes the total carbon sequestered per forest type: Equation 2.0 Carbon Dioxide Sequestered (tons CO2e/year) = Annual Sequestration Rate*Area (in ha) The same computation was adopted in the following scenarios: (a) No-Use (Forested) Scenario The government prioritizes protection over multiple use management, leading to increase in the extent of closed forest to 92% of the study site. Areas below 8% slope are planted with annual crops (4% of land area), built up areas (3% of land area) and inland waters (1% of land area). Minimal activities are allowed, and practically no harvesting of natural resources is permitted. 21 (b) Wise-Use (Conservation) Scenario Priority has been given for the protection of old growth forests and environmentally critical areas such as those located above 1,000 meters above sea level, 50% slope and riparian zones resulting in forest cover of 72% in the study sites. Perennial crop and Agroforestry cover 20%, annual crops at 4%, built up areas at 3% and inland waters 1% of the watershed. (c) Ag-Use (Agricultural) Scenario Government has prioritized food security over forest protection leading to massive forest conversion to crops. The former closed forest covering 92% of the study site is now planted with perennial crops. Annual crops remain at 4%, Built up area at 3% and inland water, 1% of land cover. 6.7 Timber Provision Since 2011, there has been a moratorium on the harvest of timber from natural forests (both old growth and residual) by virtue of Executive Order No. 23. Before 2011, timber harvesting has been banned in protected areas. While timber (and the wood products derived from timber) is a market good, there are currently no legal transactions for timber from natural forests due to EO 23. It was for this reason that valuation of timber provisioning service can be done on areas where there are many tree plantations, like Region 13 (CARAGA). EO 23 does not prohibit the harvesting of timber from tree plantations. The net present value (NPV) method, which is one of the methods suggested by the SNA and SEEA Central Framework to approximate the market value of environmental assets where market prices are unavailable or unsuitable, was used. The NPV is the value of discounted net benefits, and may be obtained using the following formula: NPV = Σ Rt - Ct (1 + i)t where: Rt = revenue in year t Ct = cost in year t i = discount rate t = any year within the rotation 7. Valuation Inputs Irrigation water. Water flow net of the minimum flow for the cropping season of dry months is used to account for irrigation water. A full rice paddy requirement from pre- production up to harvest assumption of 16,500 m3 per day, per hectare is used (Ragab et. al., 2012) along with the number of cropping days. 22 Sedimentation for check-dams. The trapping capacity of check-dams is derived form from key informants who are well-aware of the dam design which is around 500 m3. It is based on the desiltation volume which is around 400-500 sacks, assuming 1 sack is equals to 225 kg. Along the series of check-dams there could be 10% reduction in trapping capacity. As a result, the average check-dam capacity used is 400 m3. The sediment yield generated by SedNet per sub-basin is converted into kilograms. Lastly, the number of check-dams needed is derived by dividing the sediments over the storage capacity. The assumptions are shown in the table below. Table 3. Assumptions in the computation of sedimentation for check-dams. Assumptions Assumptions Reference sediments avoided (kg) - SedNet 277,935,235 model result from desiltation checkdam storage (kg) / checkdam 90,000.00 volume, rate checkdam needed (count) 3,088 KII, budget- desiltation sacks/year @ 1sack=225kg 500 dependent trapping capacity (10% decrease along 10% the series) silt (kg) decrease KII, PaSU check-dam 1 500.00 1 check-dam 2 450.00 0.9 check-dam 3 400.00 0.8 check-dam 4 350.00 0.7 check-dam 5 300.00 0.6 Average 400 Erosion for coco-mats or erosion blankets. Assumptions for this model came from a study that tested the erosion blanket durability using an extreme scenario of 120mm/hr rain at 1.5 m2 plot at 5.47 kg/m3 of surface run-off (SRO) per hour. The total flow-SRO (image below) is used to capture the SRO of the watershed. Since the experiment is too extreme, the event return period is considered to be 1:100. Per unit area of SRO is computed by dividing watershed size (m2) from SRO (m3). These are used to derive kg/hr of SRO per plot by multiplying to 1.5 (plot size). It is blown-up to annual values and is the divisor of the sediments avoided derived from SedNet results to capture the hectares or area of cocomat. The assumptions are shown in the table below. 23 Table 4. Valuation inputs for erosion coco-mats/blankets. Value provided Unit Remarks kg/m3 SRO per plot per hour of 1.5m2 5.47 at 120mm/hr simulation 50,457,600 m3 SRO/year from totalflow- runoff ratio ratio at 0.64 from water balance of SWAT 32,949,000 m2 size of watershed size of sub-basin 12 400 PhP per coco mat m2 from DENR data Computed value kg/m3 of SRO per plot per 1:100 return period assumption due to very 0.0547 hour of 1.5m2 extreme condition used in the study 0.6530 m2/m3 SRO ratio per plot 0.0536 kg/hr of SRO per plot kg/year per plot of 469 sediments prevented from SedNet result; used 0.1 HSDR or Hillslope Delivery Ratio (0.9 assumed to be retained 2,625,440,084 kg avoided (service)/yr within hillslopes); to justify similarity of UM to Pagsanjan-Lumban (study site of SedNet paper) 5,593,759 m2 cocomat needed 559 ha cocomat needed 24 8. References David, W., 1988. Soil and water conservation planning: policy issues and recommendations. Journal of Philippine Development XV (26), 47–84. Duku, C., Rathjens, H., Zwart, S. J., and Hein, L.: Towards ecosystem accounting: a comprehensive approach to modelling multiple hydrological ecosystem services, Hydrol. Earth Syst. Sci., 19, 4377-4396, doi:10.5194/hess-19-4377-2015, 2015. Hernandez, E., Henderson A., and. Oliver D: Effects of changing land use in the Pagsanjan–Lumban catchment on suspended sediment loads to Laguna de Bay, Philippines. Agricultural Water Management 106 (2012) 8– 16 Gu, R., and Sahu, M.: Modeling the effects of riparian buffer zone and contour strips on stream water quality, Ecological Engineering 35 (2009) 1167–1177. Neitsch, S. L., Arnold, J. G., Kiniry, J. R., and Williams, J. R.: Soil and Water Assessment Tool, Theoretical Documentation, Grassland, Soil and Water Resources Laboratory, Temple, TX, USA, 2009. Notter, B., Hurni, H., Wiesmann, U., and A Ragab, R. undated. Rice Cultivation: Environmental Issues and Water Saving Approaches. Water, Soils and Landscapes, Centre for Ecology & Hydrology, CEH, Wallingford, Oxon, Ox 10 8 BB, UK. Rag@ceh.ac.uk 25 Part 2 Ecosystem Services Valuation Methods 26 1. Valuation of Forest Ecosystem Services This chapter focuses on the methods used to estimate the values of selected forest-based provisioning and regulating ecosystem services under the project “The role of forests in reducing poverty and enhancing climate resilience– a case study of the Philippines”, implemented by the Department of Environment and Natural Resources, The Program on Forests (PROFOR) and The World Bank. In line with the System of National Accounts (SNA) 2008 and System of Environmental and Economic Accounting (SEEA) 2012, exchange values were used. SNA 2008 and SEEA 2012 differentiates two valuation concepts, namely welfare economic values and exchange values, as follows: the welfare economic value concept “entails obtaining valuations that measure the change in the overall costs and benefits associated with ecosystem services and assets”; while the exchange value concept “entails obtaining valuations of ecosystem services and assets that are consistent with values that would have been obtained if a market for the ecosystem services or assets had existed” (SEEA 2012, 5.9). Since the valuation of forest ecosystem services in the PROFOR study was undertaken in the context of SNA 2008 and SEEA 2012, the focus was estimate valuations that allow comparison with or will augment valuations in standard national accounts. The use of exchange values as provided in the SNA 2008 and SEEA 2012 ensure the consistency of approaches with other ongoing initiatives, including the Wealth Accounting and Valuation of Ecosystem Services (WAVES) project in the Philippines, and enable the capture of all relevant ecosystem services. Exchange value reflects the actual outlays and revenue for all quantities of a product that are transacted. It is equal to the market price multiplied by the quantity transacted. It is based on the assumption that all purchasers pay (and producers receive) the same price on average, and hence excludes consumer surplus. Exchange values are those that underpin national and business accounting frameworks, as they can be estimated based on observed transactions (SNA 2008, 5.21). 2. Valuation Approaches The valuation approaches that may be used for provisioning services are market prices, market price equivalents (proxy market prices) and unit ressource rent. For regulating services, the approaches that may be used are market prices, replacement cost, cost of treatment, damage cost avoided, and the production function approach. Market price refers to the amount of money that willing buyers pay willing sellers to acquire a good, service or asset. Under a perfectly competitive market, only one price will prevail for a specific good, service or asset at a particular time. In reality, however, 27 markets are seldom perfectly competitive. The market prices used in the national accounts will vary across buyers and over time. They should be distinguished from a general market price that indicates the average price for exchanges in a good, service or asset for a given period of time (SEEA 2012, 5.38). Market price equivalent is based on the price of the same or similar items, and is used when a market price for a good or service cannot be observed. This approach assumes that 1) the price of the good or service is independent of all other goods and services, and 2) that the equivalent prices used have been set in an incentive-compatible manner (SEEA 2012, 5.44). Unit resource rent is the difference between the benefit price and the unit costs of labor, produced assets and intermediate inputs (SEEA 2012, 5.79) and provides an estimate for the price of the ecosystem service. It assumes that 1) the resource is extracted or harvested sustainably, and 2) the owner of the resource seeks to maximize resource rent. It is commonly used for the outputs of agriculture, forestry and fishery, especially when land leases and prices cannot be used as an indicator of the price of ecosystem services. In the replacement cost method, the value of the ecosystem service is based on the costs associated with mitigating actions if the ecosystem service would be lost. It assumes that 1) the alternative to the ecosystem service provides the same services and is the least cost alternative, and 2) society will choose to replace the ecosystem service if it were lost (SEEA 2012, 5.84). The replacement cost method is commonly applied to regulating services like water purification and flood control. The cost of treatment method estimates the value of the ecosystem service based on the costs of repairing damages that would result from the absence of the ecosystem service, and is relevant to regulating services like soil erosion control, sedimentation control, air purification (SEEA 2012, 5.86) and water purification. The damage cost avoided method estimates the value of the ecosystem service based on the value of property protected or the cost of actions to avoid damages (ecosystemvaluation.org, n.d.), as in the case of the flood regulation service of forests. The replacement cost and cost of treatment methods aim to estimate the price of a single ecosystem service, and not a basket of ecosystem services (SEEA 2012, 5.87). The production function method estimates the contribution of ecosystem services to production processes based on their contribution to the value of the final product traded in the market (SEEA 2012, 5.98). This involves separating the contribution of the ecosystem from those of other production factors, and is similar to the use of resource rent as a proxy for the monetary value of provisioning services. 28 3. Application of Valuation Approaches The ecosystem services that were modeled in the project, and for which values were estimated, are summarized in Table 1. Table 1. Forest ecosystem services that were modeled and valued in the project Ecosystem Service Interpretation Upper Libmanan- Agusan Marikina Pulantuna Water Provision Supply of water or water Yes Yes Yes yield Water Regulation Regulated water supply Yes Yes Yes Water Purification Reduced sediment/ nutrient No Yes Yes load in waterways due to retention by vegetation Erosion Control Avoided soil erosion Yes Yes Yes Sediment Control Reduced sediment load in Yes Yes Yes waterways Carbon Sequestration The amount of CO2 Yes Yes Yes sequestered by standing forests Timber Provision Supply of timber traded in a No No Yes, tree market or used for plantations subsistence NTFP Provision Supply of NTFPs traded in a In forest use In forest use In forest market or used for analysis analysis use subsistence analysis 3.1 Water Provision: Domestic Water Use by Households The ecosystem service of water provisioning is the amount of water (before treatment) that is extracted from a surface water source or a shallow aquifer (SEEA 2012, A.314). The water provision service may be based on how households use surface and ground water. Ideally, data regarding the water usage of households may be gathered through household surveys. In the case of the project, time constraint necessitated the use of key informant interviews to estimate the volume of water used per household for various purposes. Secondary data regarding the number of households, the average household size, income, and other household characteristics are important to supplement the primary data gathered from key informant interviews2. 2 Secondary data sources include the Philippine Statistics Authority and annual reports 29 The quantity of water used by households per year (Q) may be estimated for different uses, i, (drinking, cooking, washing dishes, washing clothes, bathing and cleaning houses) as follows: Qi = average volume of water per use per household/year * number of households As applied in this study, two approaches may be used to determine the replacement cost of water for domestic use. Under the first approach, the price of water may be based on the cost of replacing the water used by the households if it were delivered, under a scenario that the household can no longer get water from the watershed. For this, water delivery service providers in the area may be interviewed for the price of delivered water for drinking and non-drinking purposes to households in accessible and less accessible areas. The second approach made use of rainwater harvesting as a replacement for surface and ground water that households use. Rainwater harvesting using storage tanks is common in rural areas where there are no water districts or barangay water systems yet. 3.2 Water Provision: Irrigation For this study, the ecosystem service of providing water for irrigation was interpreted as the additional area that can be irrigated under the actual and forest scenarios over what a bare/urban watershed could irrigate. The modeling component of the project estimated the annual average water yields for the dry and wet seasons under various scenarios, to be used in computing the potential paddies (in ha) that could be irrigated for each season. The value of this provisioning service is based on the resource rent of rice production, given by the following formula (SNA 2008): RR = TR – (IC + CE + CFC + NP + T - S) Where: RR = resource rent, P/ha TR = total revenue based on the farmgate price of palay IC = intermediate consumption, or the value of goods and services consumed as inputs by a production process, excluding fixed assets CE = labor costs or compensation for employment, including remuneration in kind CFC = consumption of fixed capital (depreciation, or the decline in the current value of assets due to physical deterioration, normal obsolescence or normal accident damage) NP = normal profit or return to produced assets T = taxes S = subsidies The data used in computing the resource rent for rice were obtained from the Philippine Statistical Authority (psa.gov.ph). 30 3.3 Erosion Control The ecosystem service of erosion control, or the volume of erosion avoided (kg/year) was based on the soil loss estimates the actual and bare (no-forest) scenarios generated from the SedNet models. The capacity of coco matting to reduce erosion was derived from the study of Candelaria et al. (n.d.), while the price of coco matting was based on the contract price used by the DENR in the installation of coco matting, particularly in UMRBPL. To arrive at the value of the cost of replacing the erosion control ecosystem service, the following are computed: 1. Quantity of erosion blankets required (sq m/year) = Erosion avoided (kg/year) . Trapping capacity of coco matting (kg/sq m) 2. Replacement cost of erosion control ES (P/year) = Quantity of erosion blankets (sq m/year) * Price of coco matting (P/sq m) 3.4 Sediment Control The outputs of the biophysical models are the sediment yields in t/year under the actual and bare (no-forest) scenarios, with the difference representing avoided sediment due to the presence of forests. The Sednet software was used to generate models to estimate the total suspended sediments (TSS) in t/year. The number of check dams that can provide this equivalent service of avoiding TSS may be computed as follows: 1. Average volume of silt impounded per check dam = Total silt impounded/ Number of check dams 2. Conversion of TSS avoided per sub-basin in t/year to cu m/year using the appropriate conversion factor 3. Number of check dams required to provide equivalent ES = TSS avoided/ average volume of silt impounded per check dam In the absence of data about the configuration of streams that would determine the designs and sizes of check dams in the study sites, the following were computed using data from UMRBPL in constructing the check dams3,4 : 1. Average volume of check dam structure (including footing, main structure, and two wings) 3Data about check dam cost and capacity were obtained from the UMRBPL Accomplishment Report CY 2012-2015 4 203 units of check dams with a total volume of 20,900 cu m constructed from 2012-2015; 44,235.79 cu m total silt impounded 31 2. Average cost per check dam = average volume per check dam * cost per cu m of check dam 3. Cost of check dams by sub-basin = number of check dams required per sub-basin * average cost per check dam The cost of replacing the sediment control ES came from the construction of check dams (one-time cost) and the removal of silt from the check dams every year (recurring cost). Since the ES is expressed in P/year, the following may be computed: 1. Annualized cost of check dams, using lifespan of 8 years5 and a discount rate of 15%6, using the formula A = V0 [i (1+i)n ]/[(1+i)n-1] 2. Desiltation cost per year = TSS avoided in cu m/year * desiltation cost per cu m 7 For each sub-basin, the replacement cost for the sediment control ES may be obtained by adding the annualized cost of check dam and the desiltation cost per year. 3.5 Carbon Sequestration The carbon sequestration rate of forests (Q, in tCO2/year) may be estimated using timber inventory data. The mean annual increment (MAI) above-ground biomass (AGB) growth (t dm/ha/year) and the ratio of above-ground biomass to below-ground biomass (BGB) is obtained per forest type (closed, open, plantation, mangrove). The carbon sequestration rate (t CO2/ha/year) and the CO2 sequestered per forest type (t CO2 e/year) are obtained using the following formulas: Sequestration rate = (MAI(AGB)*(1+BGB)*Carbon Fraction*CO2 global warming potential) CO2 Sequestered = Area * Sequestration rate The CO2 sequestered by the four forest types is then summed up to obtain the total CO2 sequestered in the study site. The carbon sequestration service may be valued using the Social Cost of Carbon (SCC, in USD/tC converted to P/tCO2), which was also used by PhilWAVES Southern Palawan. The SCC estimates the value of economic damages caused by each additional ton of CO2 released to the atmosphere (Ackerman and Stanton 2010). The value of the carbon sequestration service is expressed in P/year. 5Temporary check dams have a lifespan of 3 to 8 years; since the check dams in Upper Marikina are made of concrete, a lifespan of 8 years was adopted (http://www.sswm.info/content/check-dams-gully-plugs) 6 National Economic Development Authority (NEDA) 7 The unit cost of desiltation used by UMRBPL is P400/cu m 32 3.6 Timber Provision Since the project did not involve the survey and inventory of tree plantations in these areas, the volume of timber of different species harvested per year (Q in cu m/year) may be obtained from DENR statistics and reports, and yields per hectare of various tree species. The Philippine Forestry Statistics has log production data by region and province of planted tree species. The price of timber will be based on the stumpage value (P in P/cu m), which is analogous to resource rent, to be computed using the following formula: P (Stumpage Value) = Log Market Price - Log Production Cost - Margin for Profit and Risk Log market prices, production costs and profit and risk margins may be obtained from key informant interviews and secondary sources. The value of the timber provision service is expressed in P/year. 4. References Ackerman, Frank and Elizabeth A. Stanton. 2010. The social cost of carbon. In A Report for the Economics for Equity and the Environment Network, www.e3network.org. Retrieved from http://www.paecon.net/PAEReview/issue53/ AckermanStanton53.pdf Candelaria, M. D., M. A. N. Tanchuling, H. C. Carrascal and C. I. Bergado II. Laboratory scale experiments to measure sediment yield in coco-fiber reinforced slopes. N.d. Web. January 27, 2016. Retrieved from https://www.irbnet.de/ daten/iconda/CIB_DC26753.pdf ecosystemvaluation.org. Damage Cost Avoided, Replacement Cost, and Substitute cost Methods. Retrieved from http://www.ecosystemvaluation.org/cost_avoided.htm, February 22, 2016. European Commission, International Monetary Fund, Organisation for Economic Co- operation and Development, United Nations and The World Bank. 2008. System of National Accounts 2008. Retrieved from http://unstats.un.org/unsd/nationalaccount/ docs/SNA2008.pdf Medalla, E. M. 2014. Using the social rate of discount in evaluating public investments in the Philippines. Policy Notes No. 2014-02. Philippine Institute for Development Studies. United States Environmental Protection Agency (EPA). https://www3.epa.gov/ climatechange/EPAactivities/economics/scc.html. Upper Marikina River Basin Protected Landscape Project Management Office. 2013 Annual Report. 33 Upper Marikina River Basin Protected Landscape Project Management Office. 2014 Annual Report. Upper Marikina River Basin Protected Landscape Project Management Office. 2015 Annual Report. United Nations, European Commission, Food and Agriculture Organization of the United Nations, Organisation for Economic Co-operation and Development, and The World Bank. 2014. System of Environmental-Economic Accounting 2012 - Experimental Ecosystem Accounting. Retrieved from http://unstats.un.org/ unsd/envaccounting/seeaRev/ eea_final_en.pdf 34 Part 3 Forest Use Analysis Methodology 35 1. Background Forest use analysis was undertaken for the PROFOR study to better understand and quantify where possible how communities living within and around forest used forest resources, and the benefits that they derived from forest ecosystem services. A two part approach for forest use analysis was undertaken. The first was the undertaking of a series of focus group discussions with residents of the Upper Marikina River Basin Protected Landscape (UMRBPL), the Libmanan-Pulantuna watershed, and Middle Agusan River Basin. The second approach was the undertaking of a deep-dive forest use analysis in the UMRBPL using wealth and gender lens for analysis. Focus group discussions (FGDs) were conducted: (i) to identify the ecosystem services that forests provide to the local community; (ii) to assess the availability of forest ecosystem services to the local community in the light of climate change; (iii) to determine how the community uses forest ecosystem services to cope with climate and economic shocks. An FGD interview guide was developed from the draft “National socio-economic surveys in forestry: Guidance and survey modules for measuring the multiple roles of forests in household welfare and livelihoods” produced by the Food and Agriculture Organization (FAO), the Center for International Forestry Research (CIFOR), the International Forestry Resources and Institutions Research Network (IFRI), PROFOR and The World Bank. It was not possible to include in the FGD all barangays inside the sub/ basin due to time and resource constraints. Instead, representative barangays were selected. On the other hand, the participants were selected based on their knowledge of river basin conditions due to many years of residence in the area, gender, livelihood, and participation in reforestation/restoration programs like NGP. The Poverty-Forests Linkages Toolkit developed by the PROFOR was used to guide forest use analysis for the PROFOR study. The toolkit is a field manual designed to aid practitioners on data collection and analysis in understanding forest dependency and thereby reducing vulnerability among poor upland communities, and consists of a set of eight (8) modules on participatory appraisal/assessment; see Table 1. This chapter presents details on the methodology used for forest use analysis with a gender-wealth lens as part of the PROFOR Study. Results of the Forest Use Analysis are also provided. This chapter thereby provides instruction for how this type of assessment could be replicated. Table 1. Field Tools and Their Purpose Tool No. Title Purpose 1 Wealth Ranking Understand how poor households use and depend on forest resources 2 Local Landscape Situation Understand how villagers use local resources Analysis 3 Timeline and Trends Record changes in forest resources, agriculture, local livelihood strategies and income 4 Livelihood Analysis Determine subsistence reliance on forests and the annual income from forests 36 5 Forests Problem and Solution Identify and rank forest problems and suggest Matrix solutions 6 Ranking Forest Products Rank forest products by importance for cash or subsistence use 7 Millennium Development Goals Show the contribution of forests to the (MDGs) Chart achievement of the MDGs 8 Monetary Values Express the contribution of forestry in monetary terms Source: www.profor.info/node/3 2. Process The PROFOR study applied Tools 1, 2, 4, 5 and 6, and excluded Tools 3, 7 and 8 to achieve efficient and effective data collection and analysis strategies. In preparation for the field work, the PROFOR Secretariat of DENR were trained as facilitators and enumerators, especially on how to implement the activities prescribed by the toolkit. However, during the training, the team discovered new strategies by which these tools can be accomplished given the available data and context. Thus, modifications were incorporated during the implementation of the tools prescribed in the PROFOR Toolkit in the field. These modifications were discussed among the PROFOR Team members during their training in order to orient them of possible scenarios during the conduct of the study. 3. Training A facilitator's training was conducted among the PROFOR study team, and followed the sessions and activities presented in Table 2. Table 2. Summary of Training Sessions and Activities Tool Tool Name Participatory or Who is Materials Needed Number Analytical? involved? 1 Wealth ranking Participatory Facilitators alone Census survey Random numbers 2 Landscape Participatory Village informants Community maps analysis and facilitators generated during the first round Manila paper Markers Pens 4 Step 1 Participatory 16 villagers and Meta-cards for facilitators templates Beans in bags 4 Step 2 Participatory 16 villagers and Meta-cards for facilitators templates Livelihood analysis Beans in bags 4 Step 3 Participatory 16 villagers and Meta-cards for facilitators templates Beans in bags 4 Step 4 Analytical Facilitators alone Templates, Formulas 37 5 Problem and Participatory 16 villagers and Meta-cards for solution matrix facilitators templates Beans in bags 6 Ranking forest Analytical Facilitators alone Templates, Formulas products 4. Implementation of field work Nine barangays were selected for site visits and FGDs. 4.1 Data Collection Tools In the conduct of these sessions, there were a number of procedural modifications identified that would efficiently and effectively assist during actual fieldwork. First, the toolkit was modified to focus on five tools which would promote stronger focus on gender and wealth issues. Second, the selection of participants was not left to the discretion of the village chieftains (i.e., Barangay Captains in the Philippine context). A simple random sample (SRS) was used to sample community members based on the recently concluded 2012 Survey and Registration of Protected Area Occupants (SRPAO) for the UMRBPL. SRPAO is governed by the implementing guidelines issued by the DENR Administrative Order (DAO) 2013-20 to construct an official listing of the household members occupying protected areas to determine not only the population size but also the location, boundaries and extent of such occupancy. Third, the SRPAO was used to generate statistical inferences out of the quantitative data based on the demographic profiles of participants as well as the tools and templates provided in the PROFOR toolkit. Henceforth, the succeeding sections discuss the procedures and pointers in the tools adopted in the study, namely: (1) Tool 1 (Wealth Ranking); (2) Tool 2 (Local Landscape Situation Analysis; (3) Tool 4 (Livelihood Analysis); (4) Tool 5 (Forests Problem and Solution Matrix); (5) Tool 6 (Ranking Forest Products). 4.1.1 Tool 1 – Wealth Ranking Originally, Tool 1 is designed to select participants to represent the local population that will carry out the Toolkit exercises. The following procedures are prescribed in the Poverty- Forests Linkages Toolkit Manual: (1) Step 1 – Local Definitions of “Extreme Poverty”, “Poverty”, “Average” and “Wealthy”, wherein the participants decide and define based on broad economic 38 categories what they refer t as “poor”, “average” and “rich” in their own community context; (2) Step 2 – Which Households, which involves classifying each household under the rubrics of key indicators they have previously decided and defined as broad economic categories; (3) Step 3 – Selecting Households to Interview, which draws sample from the categories to avoid biases. The SRPAO list contains self-reported incomes from the participants, and was used as the basis for recruitment of respondents. Use of the SPRAO helped to avoid bias among Barangay captains, who might select the participants based on partisan affiliation especially that the context surrounding the study was the Philippines' national elections. Also, the use of the SRPAO helped maintain the integrity of the randomization process. The team generated broad economic categories of “Relatively Rich Male” (RRM), “Relatively Rich Female” (RRF), “Relatively Poor Male” (RPM), and “Relatively Poor Female” (RFM) to incorporate both gender and wealth aspects into the Deeper Dive study. To do such categorization, the team considered the political units (i.e., barangays) covering the Upper Marikina River Basin and Protected Landscape (UMRBPL) in ranking the households based on their income. Thus, there were nine (9) barangays that were included in the listing; Barangays San Juan, Pintong Bukawe, Calawis, Cuyambay, Mascap, San Jose, Puray, Pinugay and San Rafael. In each barangay, the complete list of the respondents was secured. The list contains names of female and male respondents. Given pertinent information on the names, marital status and names of spouses (if applicable), the team divided the list based on gender (i.e., male and female population per barangay). The team ranked the participants based on the annual average income of the households, and took the median income. This median value served as the divide between the “relatively rich” (i.e., participants with income values above median) and “relatively poor” (i.e., participants with income values below median) wherein the upper half of the median was designated as “relatively rich” households and the lower half of the median was designated as the “relatively poor” households. Such division was applied to both male and female groups. Using a macro in the Microsoft Excel software, the team randomly selected names to constitute the four groups per barangay. While this consultant was aware of the other variables that factor in the forest use of different forest user groups such as educational attainment, security of tenure over land, and age, the team decided to pursue the two most important layers to the study, that is, the gender and wealth and their interaction. Thus, a total of 144 respondents were randomly selected for the Deep-Dive FGD. Moreover, the team reserved 8 replacements per group which were also randomly drawn in case that the original respondents were not available on the day of the FGD or that they did not want to participate in the study. After the lists had been completed, they were communicated to the respective Barangay Captains for them to verify the names of the respondents (i.e., whether they are bona fide residents of the barangay or not, and whether they still reside in the area). If the original respondent and his/her replacement 39 could not be located, the team had instructed the Barangay Captain to use the next replacement on the list until the entire set of 16 participants for each barangay was complete. The participants were drawn from barangay listing were categorized in the following groups to signify the differences between and among them in terms of gender and wealth criteria: (a) Group A – relatively rich male respondents; (b) Group B – relatively poor male respondents; (c) Group C – relatively rich female respondents; and, (d) Group D – relatively poor female respondents. Ideally, each category was given 4 participants each; totaling to 16 participants per barangay and 144 participants for the entire study. The team also provided for replacements, which were also drawn from the SRPAO list under the categories of gender and wealth. Table 3 presents the actual number of participants per barangay as grouped accordingly to gender and wealth categories set by the PROFOR Team. Table 3. Actual Number of Respondents per Barangay Code Barangay A B C D Total 01 San Juan 2 3 4 4 13 02 Pintong Bukawe 2 3 4 3 12 03 Calawis 3 4 3 4 14 04 Cuyambay 3 0 3 0 6 05 Mascap 4 4 2 4 14 06 San Jose 4 4 3 1 12 07 Puray 3 1 1 2 7 08 Pinugay 1 3 4 4 12 09 San Rafael 4 1 4 4 13 TOTAL 26 23 28 26 103 In actual, a total of 103 randomly selected individuals was recruited as study participants, with 49 male and 54 female respondents. This sample provided for 72% response rate, computed against the target n=144. Such number of respondents gave robust results especially in the quantitative analysis, which is discussed in the succeeding section. 40 4.1.2 Tool 2 – Local Landscape Situation Analysis The aim of this tool is to allow the field team to understand how community/village members use their local resources by visiting the resources that are available to them. In this study, the team adopted the following modifications: (1) The team opted for a “windshield transect” wherein the team members visited the resources using the main road of the community to map out the area. (2) A local key-informant, usually members of the Barangay Council (i.e., highest government unit in the community) or Barangay Tanod (i.e., community patrol) was asked to accompany the team in order to provide relevant information regarding the biophysical boundaries of the area. (3) The team also used Global Positioning System (GPS) device to ascertain the area being drawn on the larger map of the entire watershed. (4) The draft map was presented to groups A, B, C and D separately. Overlay maps were used in order to capture each group’s inputs into the map. These inputs were summarized and presented using the template provided below. This template juxtaposes the biophysical and socio-demographic profiles in order to show how local resources are being used and distributed by and among the community members. The biophysical characteristics present the various local resources and their relative location along the transect line. The socio-economic characteristics, on the other hand, look into the ownership, use/access, knowledge, control and decision-making participation among male and female population. 41 Table 4. Sample Template of Local Landscape Situation Analysis ZONES DIMENSION Water Rice fields Private Recreational Residential Forests Swidden farming Resources (irrigated) plantation Area Area (NGP Plantation) Bio-physical Characteristics Vegetative cover Rice (mango, NGP plantation Kaingin (B and banana, D) dalandan, Upland Rice calamansi) fields (C) Soil Water source Calawis river Bunsuran falls Infrastructures Recreational Church, facilities i.e. Schools, Resort, Camps, brgy hall, community center Socio-economic Characteristics Ownership Male/ Male (A) Male Male/Female (A, Male/ Male Male/ Female Female Male/Female (B, C, D) Female IPs (D) Male (B) C, D) Male (B) Use/Access Male/ Male/ Female Male (D) Male/Female Male/ Male/ Female Male/ Female Female Female Knowledge Male/ Male/ Female Male/ FemaleMale/Female Male/ Male/ Female Male/ Female Female Female Control Male/ Male Male Male Male/ Male Male/ Female Female Female Male/ Female Male (B) Male (B, C) (D) Decision-making Male/ Female (A) Female (A) Male Male/ Male Male/ Female participation Female Male (B, C) Male (B, C, Female Male/ Female Male (B, D) Male/Female (D) D) Male (B, C) (C) Problems Wastes from Fire incidence; Typhoons Other Volume of water piggery go Pests claimants decreases straight to refuse to during dry the Calawis plant season river; Fish poisoning Opportunities Domestic Additional Additional Employment Tourism purposes; income; income additional Employment income Legend: Where as: O (ownership) refers to who has legal and/or informal position ove the area/resource U/A (use/access) refers to the ability of individuals to consume/exploit the resources K (knowledge) refers to familiarity, experiences and information that individuals have over their location C (control) refers to the ability of individuals to dictate who, when, where, how and for whom resources are to be used D (decision-making participation) refers to opportunity given to the individuals to make and come to a decision 42 For example, based on this template, there are a number of male-dominated areas, which the groups of participants have identified. These include rice fields (C), private plantations (O, U/A, C), recreational facilities (C, D/P) and NGP plantation (O). Such control emanates from the fact that the male population provides mostly the labor requirements in these workplaces. No group has identified specifically female-dominated areas since women’s works in these areas are considered as supplemental or complimentary to male activities. In similar manner, all the 9 landscape analysis maps will be consolidated to weave in a common narrative of the experiences of men and women as far as the resources are concerned. 4.1.3 Tool 3 – Livelihood Analysis The aim of this tool is “to discover the extent of cash and subsistence reliance on forest resources and the proportion of the total annual livelihood (from all sources) that comes from forest resources.” (1) Step 1 – An Overview of the Main Cash Components of the Household’s Annual Livelihood (2) Step 2 – An Overview of the Main Non-Cash Components of the Household’s Annual Livelihood (3) Step 3 – Proportion of the Household’s Entire Annual Income that comes from Cash Sources, and Proportion which comes from Non-Cash Sources In this study, the team followed the prescribed procedures in the PROFOR Toolkit except on the following: (1) The team assigned numbers to each of the participants in each group. For instance, in Group A, which is labeled as RRM, Juan de la Cruz was given number 1. Mr. de la Cruz maintained this number assignment all throughout the exercises requiring individual responses such as ranking of cash and non-cash sources of income, proportions, and problems. This strategy allowed the team to trace the responses per participant and convert the data for statistical analysis, which was not part of the original PROFOR Toolkit. These individual opinions were counted and presented in Tables 5, 6, 7 and 8. (2) Collective responses were generated in Tool 6 wherein opinions on the problem solutions as well as the scenario-building exercises were expressed as group. The PROFOR team gathered these responses from Groups A, B, C and D separately. The results of this are presented in Tables 9 and 10, with each group expressing opinions on the identified priority problems and the existing laws and regulations governing the entire watershed area. Table 5 presents the summary of the cash components of the household’s annual income as expressed accordingly by the 4 groups. 43 Table 5. Cash Components of Household’s Annual Livelihood Sources A B C D Total % Forest Products 20 48 31 39 138 6.6% Charcoal 4 15 2 15 36 1.7% Bamboo products 6 29 18 24 77 3.7% Rattan 0 0 0 0 0 0.0% Honey 2 2 0 0 4 0.2% Pako 0 0 0 0 0 0.0% Fish 0 0 11 0 11 0.5% Bush meat 6 0 0 0 6 0.3% Cogon 0 0 0 0 0 0.0% Lumber 2 2 0 0 4 0.2% Farm Produce 203 189 208 223 823 39.6% Upland rice 28 36 16 33 113 5.4% Fruit trees 19 35 9 27 90 4.3% Root crops 9 18 14 20 61 2.9% Corn 8 11 22 3 44 2.1% Banana 73 46 64 54 237 11.4% Vegetables 66 43 83 86 278 13.4% Other Sources of Income 297 223 341 258 1119 53.8% OFW 0 0 9 0 9 0.4% Business (stores, etc..) 52 47 73 32 204 9.8% Trading 33 9 22 36 100 4.8% UDP beneficiary 2 2 8 0 12 0.6% Vulcanizing 2 4 0 0 6 0.3% NGO 8 1 0 7 16 0.8% Laundry woman 1 6 25 13 45 2.2% Brgy Official 12 16 16 23 67 3.2% Pension 32 20 20 7 79 3.8% Construction worker 19 13 25 6 63 3.0% Caretaker 5 21 0 11 37 1.8% Technician 6 8 0 3 17 0.8% Carpenter 7 9 0 7 23 1.1% Driver 43 0 33 4 80 3.8% Waiter 5 0 3 0 8 0.4% Handicraft making 0 0 6 10 16 0.8% Furniture making 5 0 0 0 5 0.2% Laborer 6 2 11 11 30 1.4% Gold panning 17 3 20 0 40 1.9% Engineer 0 0 0 0 0 0.0% Tenant 0 0 0 0 0 0.0% Security guard 0 2 2 31 35 1.7% Government employee 26 17 56 28 127 6.1% 44 Employee of private 7 12 0 0 19 0.9% company Welder 2 20 5 18 45 2.2% Mason 7 11 7 8 33 1.6% 4Ps 0 0 0 3 3 0.1% Total 520 460 580 520 2080 100.0% Of the cash components, the respondents reported that their incomes are mostly generated from sources other than the forest products and farm produce. Other sources of income accounted for 53.8% of the cash components of household income in the community, while incomes from forest products and farm produce accounted for 6.6% and 39.6%, respectively. Among the forest products, the respondents reported that they have earned mostly from the production of bamboo products (3.7%) and charcoal (1.7%). The price of bamboo poles was pegged at Php 5 per piece, while charcoals were sold at Php 200 per sack. Charcoals were being produced at an average of 20-50 sacks per production, which is male-dominated and lasts for one week. The marketing of these products, however, is a female task. As one of the respondents exclaimed, “Kaya na nila ang pagbi-benta” (They [Women] can manage the selling alone). Table 6, on the other hand, presents the non-cash components of the household’s annual livelihood among the 4 groups. A different scenario emerged from this tool, wherein the respondents identified that in terms of benefits derived from the forests, they benefitted more from forest products, accounting for 56.5% whereas farm produce accounted for 43.50%. However, they emphasized that water is the main non-cash benefit they get from the watershed area, followed by bamboo products (8.6%) and herbal medicine (4.0%). In terms of farm produce, the respondents claimed that they collected vegetables (12.6%), banana (12.6%) and fruit trees (7.4%). Table 6. Non-Cash Components of Household’s Annual Livelihood Sources A B C D Total % Forest Products 322 271 239 298 1130 56.5% Charcoal 18 4 11 11 44 2.2% Bamboo products 45 61 27 39 172 8.6% Rattan 2 0 0 0 2 0.1% Honey 12 9 0 12 33 1.7% Pako 3 9 4 9 25 1.3% Wild flowers 10 7 5 0 22 1.1% Herbal medicine 12 18 15 34 79 4.0% Mushroom 16 9 8 20 53 2.7% Fish 23 26 11 15 75 3.8% Bush meat 2 1 0 1 4 0.2% Water 139 109 153 149 550 27.5% Cogon 13 6 0 0 19 1.0% 45 Lumber 27 12 5 8 52 2.6% Farm Produce 198 189 261 222 870 43.5% Upland rice 26 45 5 9 85 4.3% Fruit trees 28 31 42 46 147 7.4% Root crops 22 6 27 28 83 4.2% Corn 15 18 16 11 60 3.0% Banana 48 52 93 59 252 12.6% Vegetables 59 37 78 69 243 12.2% Total 520 460 500 520 2000 100.0% Table 7 presents the proportion of income from cash and non-cash sources. When asked to weigh the income they get from cash components and non-cash components of their entire households Table 7. Proportion of Income from the Forest as Cash and Non-Cash Sources Group Cash Non-Cash Total No. % No. % No. % A 266 28% 254 22% 520 25% B 195 21% 265 23% 460 22% C 234 25% 356 31% 590 28% D 238 26% 282 24% 520 25% Total 933 100% 1157 100 2090 100 4.1.4 Tool 4 – Forests Problem and Solution Matrix The aim of this tool is to identify and rank the main forest problems, and suggest potential solutions to these problems as well as project the possible impacts of existing laws, policy, tenure and access on them are captured through this tool. In this study, individual participants were asked to rank the main forest problems. The scores were summed up wherein the top 10 problems are presented in Table 8. Table 8. List of Problems Identified Access of forest resources Quality of water from rivers Unavailability of road is low Tenure problems Mining Fire in dumpsite because of biogas Deforestation Subdivision establishment Inadequate supply of water Exhaustive charcoal- Forest fire No electricity making Utilization of forest Climate change Lack of basic services from resources by consumers government outside of UMRBPL 46 Drought Illegal logging Hunting Storms Poor implementation of Illegal fishing forest policies Vector-borne diseases Illegal migrants Cows destroying crops Floods Water shortage Each group was asked to discuss these forest problems and offer possible concrete solutions at three levels of decision-making, namely: (a) Barangay (Village) Level; (b) Provincial Level; and, (c) National Level. These solutions are presented per group in Tables 9a, 9b, 9c and 9d, respectively. Table 9a. Solutions offered for Forest Problems by Group A (Relatively Rich Male) Rank Problems Solutions Barangay Level Municipal Level National Level 1 Tenure problems Issuance of Barangay Support and acceptance of Provision of necessary documents for Certification of their the rights of the indigenous the approval of plans and titles; respective occupancy peoples and their ancestral Awarding of land titles to the domain occupants; Execution of RA 8371; Easy processing of CADT application through the creation of an EO; Free or affordable cheap processing fees for cadastral survey 2 Illegal logging Confiscation of lumber/logs Giving of warning notice to Imposition of heavy penalty against even at the barangay level; illegal loggers; Strict illegal loggers; Deployment of more Control of cutting of trees by implementation of anti-illegal forest rangers giving the right to issue logging laws; Increase cutting permit at the Bantay-Gubat or forest barangay level; Avoid cutting patrols of trees; Punish illegal loggers; Employment of Bantay-Gubat or forest patrols 3 Exhaustive Apprehend and charge those Arrest stores selling Provide start-up investment to those engaged in charcoal-making charcoals without permit; who were not able to finish schooling; charcoal making according to the law; Provide Those with permit to sell Livelihood programs enough livelihood options to charcoal must be provided those into charcoal-making with legal suppliers 4 Floods Avoid throwing of plastic Apprehend people who do Planting of bamboos along river banks materials in the river in order not properly dispose their through DENR and NGP; Strict not to clog the canals during garbage; Continue planting implementation of the laws; Remove flooding; Plant more trees; trees to avoid flooding; More corrupt DENR officials; Prohibit the Ban the construction of reforestation programs; cutting of trees houses near rivers; Provide Provide relief goods immediate evacuation 5 Water shortage Construct deep well in the Provide enough funds to Forestry programs in order to increase barangay create reservoir and hose to the water volume; Construct deep well the community in each community 6 Drought Allow cloud seeding for Allow cloud seeding for Assistance for irrigation and water for crops; Provide free hose to crops; Be supportive of the household use; Remind population to each barangay municipal programs save water 7 Storms Cooperate in caring for the Formulate ordinaces to environment and forests; protect the environment; Timely notice/updates re: Provide survival kit including storm; flashlights, life jackets, and others 47 8 Deforestation Notify officials of any Designate high municipal Make available the planting materials incidents related to cutting of officials as forest patrols; to replace the forests; DENR must trees; Plant trees like Avoid cutting of trees; People continue its work on reforestation and Mahogany; Avoid cutting of must participate in planting deputaize more Bantay-gubat trees; People must trees and in reforestation participate in planting trees and in reforestation 9 Vector-borne Maintain cleanliness; Employ Distribute mosquito nets; Enact or implement laws/regulations on maintenance crew in the Launch medical missions on restriction against illegal garbage diseases barangay; Work jointly with health and programs on disposal practices other organizations on cleanliness cleanliness and awareness of different vector-borned diseases 10 Quality of water Enact ordinances on the Enact ordinances on the Plant trees protection of rivers and protection of rivers and from rivers is low creeks; Inculcate discipline creeks; Inculcate discipline among people regarding this among people regarding this Table 9b. Solutions offered for Forest Problems by Group B (Relatively Poor Male) Rank Problems Solutions Barangay Level Municipal Level National Level 1 Tenure problems Create barangay representation Require proof or certification on Provide land certification to in any decision-making land or tax declaration farmers on their lands/occupied regarding land and land tenure; lands Secure permits from the barangay regarding the any use of the land 2 Exhaustive charcoal Identify people engaged in Provide assistance for Limit the issuance of business charcoal-making to provide livelihood projects like permits involving charcoal- making them with small start-up funds vegetable growing in order to making for their livelihoods avoid charcoal-making as option for livelihood 3 Deforestation Avoid charcoal-making; Look Inform the municipal offices Let the authorities know; Stop for alternative and stable regarding deforestation; Also illegal logging and arrest the sources of livelihood; Plant fruit inform DENR; Stop ilegal illegal loggers trees than just trees; Stop logging and arrest the illegal illegal logging; Apprehend loggers illegal loggers; Programs on tree planting 4 Water shortage Construct more deep wells Construct more deep wells   5 Illegal ranch DENR must stop illegal ranching activities 6 Illegal logging Install Bantay-gubat Install Bantay-gubat Adequate number of deputized Bantay-gubat 7 Drought Creation of a barangay Continue with the tree planting ordinance to ban garbage activities to avoid floods disposal in the river 8 Vector-borne Establish health center per Provide medicines or vitamins Provide regular schedule for sitio; Provide financial doctors and nurses to man the diseases assistance health centers 9 Fire in old dumpsite Do not issue permit on activities involving burning caused by biogas 10 Unavailability of Request for road construction Request for road construction Request for road construction in the barangay projects from the municipal from DPWH roads offices 48 Table 9c. Solutions offered for Forest Problems by Group C (Relatively Rich Female) Rank Problems Solutions Barangay Level Municipal Level National Level 1 Tenure problems Provide certification of Mayor should defend people’s Provide land titles to secure occupancy to long-time occupancy over their lands; ownership over the land; Ask residents of the barangay to they should be top priority; assistance from DAR regarding establish their position in the Distribute land titles to the problems on land including land; Distribute titles to the people; Provide certification of titling; Distribute land titles to people; Keep records on land rights long-time occupants meetings regarding land; Enclose properly one’s landholdings 2 Deforestation Plant trees in the barangay as Stop the cutting of trees; Issue cutting permits replacement of logged-over Stricter implementation of areas; Stop charcoal-making; forest laws; Extend programs Prevent forest fires so that our on tree planting; Provide ozone layer will not be affected allowance for Bantay-gubat and for use not to feel “hot” weather; Give attention to forest fires 3 Floods Maintain the cleanliness of Continue tree planting to Prevent waste disposal in waterways to prevent flooding; prevent flooding; Provide rivers; Ensure waste clear up clogged canals and garbage containers and plastic segregation; Plant more trees; rivers; Arrest those who commit bags; Strict implementation of Let the municipal office illegal activities as deterrence to laws against illegal activities approach national level illegal loggers regarding flooding in the barangay 4 Drought Continue planting trees on Extend financial assistance to Prevent excessive logging; denuded mountains the people Provide continuously funds to support similar activities of DENR 5 Water shortage River fenced off by Garden Assist the community in Connect the community to Cottage must be returned to securing water rights; Mayor NAWASA; Ration water to the the community; Assistance in must negotiate with Garden community securing water supply from Cottage to return back to the NAWASA; Increase the number community the creek; Provide of deep wells hose to connect water from the source to their households 6 Vector-borne Distribute mosquito nets every Provide regular staff in health Leave to the national 6 months; Clean up drives; Ban centers; Efficient garbage government on how to solve diseases illegal dumping of garbage collection (to be brought to thse problems; Teresa, Antipolo, Rizal) 7 Illegal logging Provide sturdy sources of Restrict cutting of trees; Conduct consultation between livelihood options that are Incarcerate violators DENR and community to sustainable and adequate to prevent cutting of trees in the their needs; Be vigilant against locality illegal logging; Inform the authorities regarding any illegal activities 8 Climate change Make the community alert and Conduct seminars and Must provide solutions since ready for impening disaster programs to make communities the government policies related to climate change aware and ready for disasters emanate from national level brought about by climate change 9 Quality of water Prohibit piggery wastes to be Prohibit piggery wastes to be Prohibit piggery wastes to be dumped into the river dumped into the river dumped into the river from rivers is low 10 Exhaustive charcoal Warn community members Impose penalty against Provide alternative work for engaged in charcoal charcoal-makers with 1st charcoal-making; Communicate making production; Apprehend if it is offense with 1st offense at 1000 effectively the many problems their third offense; Provide Php, 2nd offense at 5000 Php, in the community like forest livelihood options even by the and 3rd offense incarceration; denudation, forest fires, and the barangay Continuousl provide livelihood need for Bantay-gubat options for communty 49 Table 9d. Solutions offered for Forest Problems by Group D (Relatively Poor Female) Rank Problems Solutions Barangay Level Municipal Level National Level 1 Tenure problems Hold seminar on environmental Grant the community their Provide necessary land-related protection so that the higher lands; Survey these documents to individuals authority will have enough landholdings confidence to grant the community their landholdings; Plant trees along the boundaries of their lands 2 Illegal logging Provide adequate sources of Strict enforcement of laws Impose fines and penalties livelihood; Establish an anti- against violators; Intensify against illegal logging; Granting illegal logging task force at the financial allowances to the campaign of task force against barangay level; Impose Bantay-gubat; Designate illegal loggers; Monitor regularly penalties on illegal loggers checkpoints in the communitythe forests; Replace the cut trees 3 Drought Plant trees which can enhance Conduct tree planting activities; Expand the tree planting water supply; Plant trees in Provide funding for tree planting activities; Support barangay respective lots activities in respective lots and municipal projects; Provide funds for the planting of trees 4 Vector-borne Forge partnership with NGOs in Enact a city ordinance on Launch regular medical making the government clean cleaning up the community at missions in remote barangays; diseases up the surroundings; Conduct least twice a month Fogging in communities; seminars per sitio/zone Distribute dengue vaccines regarding proper garbage disposal 5 Fire in old dumpsite     Tap DOH to provide health care to those who get sick because caused by biogas of the smoke coming fom the fire, especially those with asthma and skin diseases 6 Floods Ban the cutting of trees; Incarcerate the violators; Plant Carry out activities on tree Prevent illegal dumping of trees; Stop issuance of permits planting to prevent flooding; wastes into waterways; Write for quarrying Provide funds to communities barangay resolution on without NGP; Install dam along imposing penalty against illegal the river dumping of wastes on river 7 Exhaustive charcoal Ban charcoal-making by Assist in enforcing tasks to Release the budget for the enforcing laws and providing safeguard the forests; Provide forest protection and making alternative livelihood; Make sustainable livelihood, work or protectors; Give work to the aware the community members loans for the community people so thet will not engage regarding the impacts of in charcoal-making charcoal-making 8 Deforestation Reforest; Properly monitor Strict implementation of forest Impose heavier penalty and forest violators laws on cutting of trees; Stop faster litigation on forest/ logging; Plant more trees environmental violators; Stop logging; Plant more trees 9 Climate change No burning; Segregate wastesl Intensify forest protection Expand the forest laws; Practice composing Maintain adequate supply of medicines for victims of the effects of climate change 10 Forest fire Create ordinances to prevent Create ordinances to prevent Create ordinances to prevent forest fires; Stricter forest fires; Stricter forest fires; Stricter implementation of penalty and implementation of penalty and implementation of penalty and punishment against violators punishment against violators punishment against violators The last exercise conducted under Tool 5 is to determine how the respondents view the impacts of strict implementation of existing policies, laws, rules and regulations governing the management of the entire watershed area. These impacts are examined on 3 aspects, namely: (a) Use of forests; (b) condition of forests; and, (c) land tenure. These impacts are 50 presented per group in Tables 10a, 10b, 10c and 10d, respectively. The policies that were evaluated are as follows: Table 10. List of Policies used in the Scenario-building Exercise Policy No. Title Brief Description Executive Order 23 Logging Signed by the President on February 1, 2011. This law declared a Moratorium moratorium or ban on cutting trees which naturally grow in the forests. It also developed the Anti-Illegal Logging Task Force to implement the moratorium and lead campaigns of our Government against illegal logging. Executive Order 26 National Greening Law declared the implementation of the National Greening Program Program. This program is a composite of the initiative of the Departments of Agriculture, Natural Resources and Environment, and Agrarian Reform to respond to issues such as poverty reduction, food security, and climate change. Republic Act 7586 National Integrated A management system that aims to safeguard the area home to Protected Area rare (rare) and endangered animals and plants, areas home to System various types of wildlife whether it be on the earth / mountains or ocean law declared as a protected area (protected area). The management of these areas will be in accordance with the principle of "biological diversity" and "sustainable development". Presidential Revised Forestry The law states the principles of conservation, use and papalago Decree 705 Code of the forests and "forest lands" to maintain its productive condition. It Philippines also indicates the provisions to be classified lands. Republic Act 9147 Wildlife Resources This law aims to regulate and protect their lives and their homes to Conservation and promote "ecological balance and foster biological diversity". It also Protection Act aims to regulate the collection and sale of some life (animals and plants) as a way of protecting them. Proclamation No. Marikina This is the declaration of the President of the Philippines "Markina 2011-296 Watershed Watershed Reservation" conquest of the town of Antipolo, Reservation municipality of Baras, Rodriguez, San Mateo and Tanay in Rizal province as a "Protected Area" in accordance with RA 7586 or NIPAS act of 1992. it also declared the site to be called as "Upper Marikina River Basin Protected Landscape" with a size of 26 125 ha and its management is under the DENR. Republic Act 8371 Indigenous This law recognizes, protects and promotes the rights of indigenous Peoples’ Rights people and their communities. By this law is established the Act of 1997 National Commission on Indigenous Peoples under the Office of the President of the Philippines. Philippine The Philippines constitution contains the principle that a major Constitution 1987 basis of all laws and governance in the Republic of the Philippines. It also stated the type / classification of land, this country is the agricultural, forest or timber, mineral lands and national parks. 51 Table 11a. Policy Impacts as Perceived by Group A Policy Impacts Use of Forests Condition of Forests Land Tenure EO 23 Positive: Positive: No landslide; Sources of water will No Climate Change; Enhanced not dry out; No Climate Change; protection of wildlife; Greener Monitor logging; Violators will be forests; Proliferation of wild apprehended; Issuance of permits animals; Decrease in erosion rate; by DENR; Provision for livelihood Forest preservation; Changes are options; No effect on their for the environment and future livelihood activities as they will not youth be reduced or restricted; Provision of job opportunities EO 26 Positive: Positive: NGP as a good law in maintaining No water sources and forests the beauty of the environment; shall be damaged; Greener establish appropriate land use forests; Increase in wildlife zones for agriculture or mining population; Reduction of incident Negative: heat; Increased water flow; Limitation in the areas that can be Increased number of trees; used for agriculture Prevent landslides Negative: Restrictions on the movement of people especially that they no longer can practice kaingin (swidden farming) NIPAS Positive: Positive: Pro-poor environmental program; Forests will be replenished and Create a conducive environment maintained; Protection of the for the reproduction of planting forests stocks through tree nursery production; More forest trees Proc. Positive: Positive: Negative: Prevent developers from “owning” Forests will be maintained and How about the community? Where will they 2011-296 the lands since there should be no protected live? Water becomes polluted if there are one to own them in the first place; occupants living near it; Eviction must be Set aside funds for the protection compensated monetarily of watershed that can be used by No impact: the local people; More trees to be The people are still being allowed to plant in planted their landholdings, especially among Negative: indigenous peoples; People will have limited access and use of forests; limited action on the land and trees; no cutting of trees RA 9147 Positive: Positive: An important law for wild animals; Forests are protected; Prohibit the Dumagat (IPs) are given a priority capture of the animal; Wildlife over hunting ground; population will flourish; Negative: Negative: Need for permit in order to save Loss of income fron hunting injured or captive animals; Establish strong coordination with higher officials; Enforcement is difficult because it is the monkeys which destroy crops RA 8371 Positive: Positive: Positive: Applies very well to indigenous Forests are properly protected; Forests are properly protected; assurance of people especially that most of more wildlife the IPs over their place/community them are illiterate; a good law to Negative: be used to safeguard the forests; Fear of losing ground to the IPs Delineate the ancestral domains; Entails recognition of Negative: Loss of livelihood among indigenous peoples No impact: Continue their activities because the IPs recognize them/their presence 52 PD 705 Positive: Positive: Increase population of wildlife; Maintain and preserve the forests; Increase awareness among deter forest violators people regarding the importance of forests; there still remains area for usage (multiple use zone); water sources will be conserved and will be able to supply daily needs of the people Negative: Negative impact on the financial capability of the people depending on the forests; must have alternative livelihood options Table 11b. Policy Impacts as Perceived by Group B Policy Impacts Use of Forests Condition of Forests Land Tenure EO 23 Positive: Positive: Negative: Safeguard the forest for the future No cutting of trees; plant fruit Unsure where to get water since their lands generations; replace trees; may trees instead to improve are covered by the Marikina watershed area; provide supplemental income; will conditions of the soil such as no construction of houses because of lack of ensure that the forests will not be cacao and coffee; will be able to materials; no source of fuelwood for degraded protect land against landslide and household consumption Negative: floods; increase the number of Will not have impacts except on trees and forest animals people whose livelihood is dependent on cutting trees EO 26 Positive: Positive: No Impact: Will ensure protection of the Will protect the forest resources No effect on them because there are still a forests and produce lots of trees lot of lands where they can plant Negative: Areas designated for NGP will not be used for planting bananas and other fruits NIPAS Positive: Positive: Negative: Will prevent the destruction of the Forests will be protected because We will not have access to our landholdings; forests especially in preventing kaingin-making will be prohibited illegal hunting especially in areas near river or No Impact: springs We are not covered by the protected area Proc. Positive: Positive: Negative: We still have free access to the Protect forests and improve the Since ISF will no longer stand, there will be 2011-296 forests; prevent the degradation of conditions no contract for any land use for the the forests community Negative: We might lose our lands if they will be covered by the Marikina watershed; this law was passed without consultation with the people living within the Marikina watershed area RA 9147 Positive: Positive: Positive: Safeguard the forests and provide Prohibit hunting of eagle, civet cat, Recognition of the ancestral domains among shelter to birds and wildlife; and boar; total care for the wildlife IPs prevent trading of wildlife to local in the forests Negative: pet shops Ordinary people will lose their landholdings Negative: to the IPs Will not be able to kill animals which harm people, even their owners 53 RA 8371 Positive: Negative: Negative: Protect the forests so that the IPs It is the IPs who are consuming If there are IP claimants or owners, then land will have fruit trees, clean water the forests because they claim use tenure or security will never be achieved and natural forests that these forests are theirs for ordinary citizens Negative: anyway Limited use of the forests; application for CADT is delayed; not yet approved PD 705 Positive: Positive: Positive: Ensure that forest guards are Improvement in the conditions of Land tenure is secured because they have designated to protect the forests; the forests if cutting is prevented been occupying the area for the longest time increase in plants, animals and and not allowed possible birds; provide habitats to other Negative: forest dwellers But without title, there is still no security; we Negative: cannot farm the lands it is harder to cut trees; need to secure permit Table 11c. Policy Impacts as Perceived by Group C Policy Impacts Use of Forests Condition of Forests Land Tenure EO 23 Positive Positive: Positive: Prohibit the cutting of trees; create Intensify planting of trees in order More difficult for illegal settlers to enter and jobs for the people; more harvests to improve the conditions of the establish residences within the area in the future forests; DENR need to submit Negative: Negative: appropriate budget for the tree No land tenure for them since their area is May reduce the charcoal planting programs covered by the UMRBPL; limited activities production; harder for the within the forest lands household to buy and use gas; will no longer be allowed to cut trees EO 26 Positive: Positive: Positive: More trees; better economy; Enhance fresh air; increase Can plant trees and other materials in the better use of forest resources; will number of forest trees; continuous forest lands not only benefit present but future supply of forest products; No Impact: generations maintain structure and integrity of Policy has no effect on them because they do forests and environment not even have titles on their landholdings NIPAS Positive: Positive: Negative: Safeguard the various forms of life More wildlife population; increase No assurance over land tenure; people might in the forests; increase the wildlife in the number of trees; be driven out of the protected areas population by planting more trees environment will be more pleasant Negative: to humans; must prohibit hunting More difficult to enter and access of wildlife in order to achieve the forests these; more trees will absorb flood flow; secure downtown and let people enjoy their homes and farms Proc. Positive: Positive: Negative: They have hopes that they will be Wildlife population will increase; Loss of agricultural land and houses; 2011-296 allowed to occupy continuously water resources are protected continuous desire to have land security the area if they can prove that Negative: they have planted trees and taken Strong laws; fear that if they will good care of the forests violate the laws, they might get Negative: incarcerated; they will not be Impact on the people whose lands bothered by any changes if they are covered by UMRBPL; no only are aware of the condition of freedom to use the forests their landholdings anymore RA 9147 Positive: Positive: Negative: Protect wildlife; hoping to have Protect and preserve UMRBPL to No assurance that they will be given more freedom to use the forests; maintain order and beauty; protect entitlements to their lands; they might even increase in number of tourists the watershed to increase wildlife be evicted from the land Negative: and plants No Impact: This law would not be beneficial to The policy does not have impact on some all; more difficult to enter the because they are not engaged in hunting forests because of the protected animals and plants; if hunting is not 54 RA 8371 Positive: Positive: Prioritized by the government Preserve the forests; encourage because they do not possess more planting of trees knowledge; promote equality for the IP Negative: Loss of freedom to use the forests; more difficult to obtain other forest products because of the restrictions PD 705 Positive: Positive: Positive: Protect the forest lands; prohibit Increase in animal stocks; Wildlife and their habitats become safe from hunting increase products to be derived untoward harms; It would be more difficult if Negative: from the forests; increase the the people are driven away since their Might waste most of the products planted trees tendency is to misuse the planted trees and because the selling of these turn them into charcoal; impose securing products are being banned barangay certificate of their occupancy Table 11d. Policy Impacts as Perceived by Group D Policy Impacts Use of Forests Condition of Forests Land Tenure EO 23 Positive: Positive: Positive: Prevent floods and landslides; will Protected landscape and Controlled cutting of trees; but hopes are still benefit from their planted; If sceneries; improvement of the high that if they become good managers of they do not take good care of the forests the forest resources, they might be granted forests, they might lose their permanency in their lands; continue to landholdings cultivate and care for the land for future Negative: generations Lack of or limited access to Negative: planted trees for trading, which is Loss of access to these resources a main source of income; reduction in income from charcoal production EO 26 Positive: Positive: Assist people and help in the Forest growth is ensured; restore growth of the forests; additional pleasant nature of the forested advantages for our forests and areas; prevent landslide, drought wildlife; more sources of food and and flooding additional revenues from the Negative: forests If NGP is able to plant 1.5 million Negative: trees in the area, how can this be Less revenue from the forests; done if cutting of trees is not less ability and opportunity to allowed? plant native trees because they are not allowed to clean up the areas NIPAS Positive: Positive: Positive: Limit the cutting of trees that will Protect the forests; prevent Limited but continuous access to resources let plants and animals grow and landslaides allow the people to sell forest products to multiply; Install Bantay-gubat to Negative: town proper; need to ask permission monitor such restriction Limited use and access to forests whenever they enter into the area Negative: Negative: Lack of access to exotic meat and Can no longer freely enter, acquire and foods access forests and their products Proc. Positive: Positive: Positive: Occupants are compelled to take Improve the forests; eliminate the Will safeguard the area; 2011-296 care of the forests; otherwise, they cullting of tress and charcoal- Negative: might get evicted from their lands making Will not be able to plant rice in their kaingin; Negative: Negative: if there are owners, they might be evicted Things that they previously get for Indigenous peoples sometimes free may be prohibited; cannot abuse the forests in terms of freely enter their areas hunting and sale of wild plants 55 RA 9147 Positive: Positive: Positive: Forests will become more Forest growth can prevent natural Forest growth is ensured; will increase soil beneficial to the community if disasters; improve the flow of productivity protected as it will increase water water; prevent landslides Negative: supply and prevent landslides; Land tenure is uncertain and not secure; Deter indiscriminate hunting of wildlife and gathering of wild plants; Protection can lead to increased future income for housholds Negative: Decreases the income of people relying on hunting and gathering activities in the forests; negative for forest consumers RA 8371 Positive: Positive: Positive: Prevent non-IPs to sell forest Protect forests and increase the Protect IP rights; enhanced use of ancestral plants and animals; Indigenous forest wild animals and plants; domains because of the familiarity of IPs of peoples have the right to preserve Preserve the forests that belong to their land their forest-based sources of IPs for the next generations; Negative: livelihood Secure the ancestral lands against Negative impact on the non-IP because they Negative: encroachers no longer have the chance to have full rights Lack of access to the thriving over their occupied lands forest resources by the non-IPs PD 705 Positive: Positive: Negative: Protect forestlands for the future Plants that may flourish may be No security; no knowledge on what can be generations; prevent cutting of cure for serious and rare diseases; done if this is the case; resort to just plant young saplings; limit the use of Prevent burning to maintain the the land forests to their occupied lands beauty of nature Negative: Loss of cash incomes from the forests; strict implementation may cause fear among people who are not able to understand the policy 4.1.5 Tool 5 – Ranking Forest Products The aim is to rank forest products by importance for cash and/or for subsistence uses. The data for this tool were deduced from Tool 4 in order to show the most valued forest products for poor and women. This case study follows the procedures designated in this tool. Table 11 presents the choices of the respondents. Table 12. Ranking of Forest Products Products A B C D Cash Non-cash Cash Non-cash Cash Non-cash Cash Non-cash Forests Charcoal 9 10 7 17 9 10 7 12 Bamboo products 7 4 5 2 4 5 5 5 Rattan 12 18 19 Honey 10 14 12 16 10 Pako 13 17 13 15 14 Wild flowers 16 14 14 Herbal medicine 15 9 8 6 Mushroom 11 11 11 8 Fish 14 8 7 7 9 9 Bush meat 8 19 18 18 16 Water 1 1 1 1 Cogon 15 13 16 17 56 Lumber 11 6 10 13 15 Farms Upland rice 3 7 3 4 5 12 3 13 Fruit trees 4 5 4 6 8 4 4 4 Root crops 5 9 6 15 6 6 6 7 Corn 6 12 8 3 7 8 11 Banana 1 3 1 3 2 2 2 3 Vegetables 2 2 2 5 1 3 1 2 Total 4.2 Data Processing and Analysis The study used mixed methods in the analysis of the data collected using the Poverty- Forests Linkages Toolkit. Using the results of the original toolkit, the analysis that can be done will be thick description of the collective responses on problems and solutions as well as the scenario-development exercise. Descriptive statistics can also be computed for frequencies, percentages, average and range. However, since the respondents were drawn a simple random sampling using the SRPAO list for the UMRBPL, the team was able to conduct inferential statistics on the same dataset generated previously using the Poverty-Forest Linkages Toolkit. 4.2.1 Research Questions Three important concerns were addressed as far as the data analysis was concerned: 1. Who owns, uses, accesses, knows, controls and decides on as far as forest resources are concerned? 2. How do men and women, and poor and rich people perceive the cash and non- cash income sources or benefits they derive from the forests? 3. How do they prioritize forest problems based on gender, wealth, and self-reported income? 4.2.2 Variables Statistical analyses were carried out on relevant variables. For the independent variables, the analysis was conducted using the following: (a) Gender, which was coded as “Male” and “Female” based on the assumed sex categories of the respondents in SRPAO. The basis was their names and if the names sounded androgynous, the variable “Marital Status” was consulted to clarify further these categories; (b) Wealth, which was coded as “Rich” and “Poor” depending on how these categories reflect the annual average income among households represented in SRPAO; (c) Gender-Wealth, which was coded as “A”, “B”, “C” and “D” following the categories derived from Tool 1; and, 57 (d) Self-reported Income, which was coded as annual income ranges such as follows: Self-Reported Income Ranges in Peso (P) in Dollars ($) in Peso (P) in Dollars ($) ✦ < 10,000 ✦ < 212 ✦ 130,000 – 150,000 ✦ 2,754 – 3,178 ✦ 10,000 – 30,000 ✦ 212 – 635 ✦ 150,000 – 200,000 ✦ 3,178 – 4,239 ✦ 30,001 – 50,000 ✦ 635 – 1,059 ✦ 200,001 – 400,000 ✦ 4,239 – 8,475 ✦ 50,001 – 70,000 ✦ 1,059 – 1,483 ✦ 400,001 – 600,000 ✦ 8,475 – 12,716 ✦ 70,001 – 90,000 ✦ 1,483 – 1,907 ✦ 600,001 – 800,000 ✦ 12,716 – 16,954 ✦ 90,001 – 110,000 ✦ 1,907 – 2,331 ✦ 800,001 – 1,000,000 ✦ 16,954 – 21,193 ✦ 110,001 – 130,000 ✦ 2,331 – 2,754 ✦ > 1,000,000 ✦ > 21,193 $ 1 = P 47.32 (as of June 28, 2016) The dependent variables used in the analysis included the following: (a) cash components of the household income; (b) non-cash components of the household income; (c) proportion of cash and non-cash components of the household income; (d) ranking of forest problems. 4.2.3 Quantitative Analyses These variables were processed using Eta coefficient as a measure of association, which determines the association between quantitative dependent variable and categorical variables such as gender, wealth and gender-wealth categories. On the other hand, Spearman’s rank order correlation coefficient (ρs) was used to measure the association between the dependent variables and self-reported income. Correlation analysis was done to determine the significant factors that are associated with the dependent variables. The degree of association of these variables (i.e., whether strong, moderate or weak) was determined only for those variables where the association was found to be significant. 4.3 Ethical Considerations Upholding the values of the Department of Environment and Natural Resources and the World Bank, this study considered several ethical issues surrounding legitimate research endeavor. First, the study served Informed Consent forms prior to the conduct of any research activities with the target respondents. At the onset of each meeting, the PROFOR Secretariate would hold a small orientation meeting with the respondents. The respondents were asked to register their attendance for the day, and they were handed out Manila envelopes as research packets containing the Informed Consent forms. The Informed Consent forms follow the standards of confidentiality, no-harm policy, and the right to withdraw anytime from the research procedures. The informed consent was designed to give adequate information on the nature and extent of study. Second, as the sampling was done randomly, it was only by chance that the participation of the indigenous peoples (i.e., the Dumagat in province of Rizal) was recruited into the 58 study. It appeared that the Dumagat dominates the population of Barangay Puray. Thus, in the FGD, there was a group of Dumagat who joined the study activities. The Informed Consent forms were distributed to these indigenous people who signed them prior to their participation in any research activity. No harm, whether physical, emotional or psychological, was done on them while participating in this research study. 5. References Department of Environment and Natural Resources. 2013. DAO 2013-20. “Revised Guidelines on the Survery and Registration of Protected Area Occupants”. http:// server2.denr.gov.ph/uploads/rmdd/dao-2013-20.pdf Eta Coefficient. 2012. “Nominal-by-Interval Association Eta, the Correlation Ration”. Quantitative Analysis in Public Administration. https://www.researchgate.net/ file.PostFileLoader.html?id=50478cf2e39d5ee86a00000b&assetKey=AS %3A271740112965632%401441799301262 eta coefficient Morse, Janice. 2003. “Principles of Mixed Methods and Multimethod Research Design.” In Handbook of Mixed Methods in Social and Behavioral Research, edited by A. Tashakkori and C. Tedle, pp. 189-205. Thousand Oaks, CA: Sage Publications, Inc. “Poverty-Forests Linkages Toolkit.” 2016. www.profor.info/node/3 59 For more information, please contact: THE SECRETARIAT Forest Management Bureau Visayas Avenue, Diliman 1100 Quezon City, Philippines +632 920 8650 | +632 920 0368 fppkmd.fmb@gmail.com http://forestry.denr.gov.ph/profor/update.php www.profor.info 60