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Supplementary Material – Appendix A: Expanded methods

Contents of Appendix AA – 1 Study area.........................................................................................................................1A – 2 Fuelwood demand............................................................................................................3A – 3 Friction maps....................................................................................................................4A – 4 Modified Inverse Distance Weighted (IDW) algorithm and seeding mechanism...........8A – 5 Fuelwood supply............................................................................................................12

A – 5.1 Trees outside forests and other renewable fuelwood sources.................................12A – 5.2 Fuelwood from coffee plantations prunings............................................................13A – 5.3 Resilience of fuelwood sources...............................................................................14

A – 6 Prospective landscape simulation submodel projecting forests loss/gain events...........17A – 7 References......................................................................................................................20

A – 1 Study area

A Land Use and Land Cover (LULC) map for the entire country was produced by supervised classification of a Landsat 7 cloud-free image composite for circa year 2000.1 A training dataset consisting of 768 polygons of varying extent was defined visually for the following LULC classes: Coniferous forest (n=74); Broadleaf forest (n=98); Shrubland (n=87); Mixed forest (n=123); Crop & Livestock (n=100); and Bare land (n=86). Mangroves were identified visually at 1:30,000. Thematic masks supplied by the Cadastral and Geography Bureau of Honduras for year 2000 were used for Urban and Water bodies. Palm oil plantations were identified visually after an object-based segmentation procedure. A Maximum Likelihood classifier was finally applied to the composite using the training dataset and all thematic masks previously classified. The resulting LULC map was resample to 100m. Figure A.1. shows the LULC map clipped for the study area in western Honduras.

1 Available from: http://earthenginepartners.appspot.com/science-2013-global-forest/download_v1.1.html

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http://earthenginepartners.appspot.com/science-2013-global-forest/download_v1.1.html


Figure A.1. Study region in western Honduras.

Sources: Instituto de Conservación Forestal de Honduras (www.icf.gob.hn); Proyecto Mirador (www.proyectomirador.org); and the last available national census (INE, 2001). Fuelwood use by village in tDM yr-1 was calculated using equation 2 described in the main text.Note: The spatial resolution of the LULC map is 100m. Roads and villages are vector data at 1:50,000.

2

http://www.proyectomirador.org/

http://www.icf.gob.hn/


A – 2 Fuelwood demand

Tables A.1 describe the parameters used in Mofuss to complete equation 2 in the main text, and Table A.2 compares fuelwood use saturation among three different surveys.Table A.1 Fuelwood use parameters assumed for the western Honduras case studyDepartment Total

villages and cities

hh_fw in all villages and cities

Villages and cities hosting at least 10 hh_fw

hh_fw in villages and cities hosting at least 10 hh_fw

Fraction of total fw use modeled by NRBv1.0

u (TCS) u (ICS) fb (bought)

fc (collected)

Surveyed hh_fw

n n (hh_fw) n n (hh_fw) % n (hh_fw)Santa Bárbara 1,699 52,604 881 49,544 94 4.1 2.1 28 72 319 Copán 1,279 43,764 722 41,551 95 4.1 2.1 33 67 215 Intibuca 1,016 27,858 659 26,275 94 4.1 2.1 43 57 139 Lempira 1,843 40,524 1,066 36,953 91 4.1 2.1 11 89 140 Totals 5,837 164,750 3,328 154,323 813 Averages (weighted) 94 4.1 2.1 29 71

tDM hh-1 yr-1 %

Notes: The number of villages and cities and the number of households (hh) and households using fuelwood (hh_fw) within each of them was obtain from the national 2001 census (INE, 2001). Only those villages hosting 10 or more hh_fw were integrated into the model, as this reduces significantly the amount of computing requirements because nearly halves the number of origin points, while still account for almost all fuelwood use (94%). Fuel consumption under TCS baseline and ICS intervention are based on extensive in-field measurements conducted by Proyecto Mirador on a regular basis as part of the verification process that is required each time the project issues carbon offsets. These surveys show that newly installed stoves reduce wood consumption by 47%, which we rounded up to 50% for convenience (see (MER, 2016) for detailed description). The proportion of fuelwood that is bought (fb) vs collected (fc) was obtained from a 2004 national survey aimed at measuring the quality of life of Hondurans (INE-WB, 2004): 813 hh_fw were surveyed in our study area with regards to how was fuelwood obtained: bought vs. collected.

Table A.2. Share of fuelwood use comparing different census and survey dataDepartment

% n (hh) % n (hh) % n (hh)Santa Bárbara 87 60,250 79 414 83 993 Copán 85 51,587 71 308 79 1,085 Intibuca 95 29,271 88 170 93 1,132 Lempira 97 41,938 98 148 94 1,084 Totals 183,046 1,040 4,294 Averages (weighted) 90 81 87

ENCOVI2004 Share of hh_fw (not an input in the model)

CENSUS2001Share of hh_fw (not an input in the model)

DHS2005/6 Share of hh_fw (not an input in the model)

Notes: ENCOVI2004 (INE-WB, 2004); CENSUS2001 (INE, 2001); DHS2005-2006 (DHS, 2006)2. The number of hh for the 2001 census might be a very bit less than the total “official” number of hh in the region as the question on cooking fuel wasn´t applied to the 100% of hh. None of these data were used in the model , but is shown here for comparison and also to stress that none of the three studies accounted for mixed users which most probably represent the vast majority of situations. Mixed users use fuelwood in combination with other energy carriers, mostly LPG and electricity and this share is a key missing data for modeling fuelwood use accurately.

2 Include straws along with fuelwood. http://www.measuredhs.com/what-we-do/survey/survey-display-369.cfm

3

http://www.measuredhs.com/what-we-do/survey/survey-display-369.cfm


Further analysis is needed so as to uncover eventual relations between fuelwood use patterns such as the amount use, the fraction between bought vs. collected, the share with other energy carriers such as LPG, or the elasticity; with other spatial and non-spatial variables. Examples of spatial variables that might be related to fuelwood use patterns are proximity to the LPG distribution network, accessibility to fuelwood resources, rural vs. urban or annual temperatures profiles. Examples of non-spatial variables include household (hh) income, persons, or elders and kids per hh, among many others. The census and surveys datasets are plenty of variables that could be eventually related to fuelwood use patterns (by means of multivariate regressions for example), allowing a better understanding on how much fuelwood is needed or used within a particular hh in a particular location and situation. If robust relations are found, then inference models can be constructed. So far, some GIS-based analyses have put much of their modeling effort on predicting the amount of fuelwood needed by hh under varying geographic and socio-economic circumstances (e.g. An et al., 2005; An et al., 2002; He et al., 2009a; Knight and Rosa, 2012; Linderman et al., 2005; Naibbi and Healey, 2014).

A – 3 Friction maps

The walking and driving speed of fuelwood gatherers was assumed to be affected by three topographic features: slope, roads and rivers. Slope was derived from a Digital Elevation Model (DEM) which was reclassified according to slope-based cross-country walking speeds. Rivers were overlaid as barriers and roads were finally overlaid on top, assuming that friction is partially reduced for fuelwood gatherers walking along them (but slope still affecting velocities), and drastically reduced for people using vehicles (only the type of road affects velocities in this case). Off-road, we assume vehicle-users park their vehicles and proceed on foot in order to harvest fuelwood.

Ideally, walking and driving speed of fuelwood gatherers should be gathered from direct observation of fuelwood gatherers’ walking and driving velocities throughout the range of existing topographic and road features in a given fuelwood gathering case; however, values from secondary case studies can also be used.

For the present case study, eighteen voluntary fuelwood harvesters were trained to operate GPS data loggers between May and June 2012, in order to register positions along a trajectory in the field when searching for fuelwood. Volunteers harvest/collect fuelwood for self-consumption or for selling it within the village, with a varying share between both activities. We used Holux-M241 data loggers (Holux Technology Inc.) equipped with a MKT GPS-chipset capable of storing 130,000 points. The devices were set to record successive

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points at intervals of 10 seconds. Devices were configured to log data automatically as soon as the first accurate position fix was obtained and could only be switched on or off, facilitating the learning process. Devices were worn on a neck lanyard and protected by a plastic bag from dust and rain. Specific instructions were given to switch on the devices when leaving home on a search and collection excursion, and switch them off when arriving back at home. The daily searching for fuelwood was done alone with no supervision or accompaniment by the research team. It was strongly remarked that they must carry the device whenever gathering or harvesting fuelwood and vice versa, independently of any other tasks that might have been conducted during the day, such as working in their croplands, grasslands or even resting or spending time with friends during the working day. Every approximately 3 days, each volunteer was visited to replace batteries, download the data, check for any eventual problem, and conduct a rapid survey for cross-referencing the recorded data. Tables A.3 and A.4 show mean displacement velocities for walking and driving fuelwood collectors as recorded by GPS devices for the present study.

Table A.3. Mean displacement velocities for walking fuelwood gatherers according to slope, roads, and barriers from field recorded data for this study

Walking cross country from Literature review

Walking cross country from GPS recordings

Sample size in number of recordings

Walking by road sidewalks from Literature review

Walking by road sidewalks from GPS recordings


Crossing Secondary rivers - temporal rivers

Crossing Primary rivers - perennial

Slope range km hr-1 km hr-1 n km hr-1 km hr-1 n km hr-1 km hr-1

0º - 8.5º 4.5 3.8 32,690 9.0 4.0 17,563 0.8 0.08.5º - 16.7º 3.0 3.5 12,786 6.0 3.7 6,447 0.8 0.016.7º - 24.2º 1.7 3.0 3,729 3.4 3.2 1,732 0.8 0.024.2º - 35º 0.8 2.6 186 1.6 2.9 82 0.8 0.035º - 45º 0.4 2.4 76 0.8 2.6 32 0.8 0.045º - 90º 0.0 n.a. n.a. 0.0 n.a. n.a. 0.8 0.0> 90 0.0 n.a. n.a. 0.0 n.a. n.a. 0.8 0.0

Notes: Velocities are actually added as impedance values in s m-1, but we express them here in km hr-1 for the sake of clarity.

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Table A.4. Mean displacement velocities for driving fuelwood gatherers according to roads from field recorded data for this study

Driving through roads from GPS recordings


Road type km hr-1 n

Residential 19.0 65 Tracks 13.0 646 Secondary non-paved road 29.0 118 Secondary paved road 41.0 519 Main pavement road n.a n.a


Figure A.2. Some of the tracks followed by walking and driving fuelwood collectors in their search for fuelwood in western Honduras, recorded by GPS devices: photos inside figure

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Optional but highly desirable additional data include land use or land property rights and use restrictions, such as protected areas, landscape planning programs, land tenure divisions, communal vs. private lands, among others (Southworth, et al., 2004). If the underlying land use agreements linked to this information are known, then a legal accessibility layer can be added so as to alter profitability maps. In our western Honduras case, additional impedances of 50% and 25% were applied to fuelwood harvesters entering protected core and buffer areas respectively.

Figure A.3. Friction maps for driving (marketed) and walking fuelwood collectors


Finally, high friction values were assumed for water bodies, to represent the fact that fuel-wood harvesters eventually make it to the border of rivers and lakes, but never farther; the woody biomass supply of water bodies represent dead wood usually found beside rivers and lakes (Table 2).

A – 4 Modified Inverse Distance Weighted (IDW) algorithm and seeding mechanism

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Figure A.4. shows how the modified Inverse Distance Weighted (IDW) interpolation is use to produce a pressure map depicting the probability or propensity of harvesting across the landscape. Only one of two maps is shown in the example: considering commercial wood sellers who use vehicles, which allow them to access distant areas and carry large volumes of wood. The other map described in the main text considers people who travel on foot and gather fuelwood for home use. The IDW algorithm allows Mofuss to simulate real patterns of fuelwood collection in a more realistic way than previous studies (e.g. Ghilardi et al., 2009; He et al., 2009b; Top et al., 2004). Previous models utilized a pre-defined accessibility based on artificial boundaries to define a “fuelwood-shed”. In this model, each source of fuelwood (i.e. each pixel hosting any amount of harvestable fuelwood) is assigned an access propensity (or probability of harvest), which is directly proportional to fuelwood demand from all cities and villages within the study area and inversely proportional to friction-based proximity (i.e. travel cost). Thus, the artificial notion of a fuelwood-shed is replaced by a continuous decay function from all demand centers.

Figure A.4. Schematic representation of the modified IDW interpolator

Notes: A travel limit of one hour was set for the sake of clarity. Only commercial wood sellers who use vehicles are considered. “1:n” corresponds to the sum of all cost maps between 1 and n using the IDW algorithm (lower row); even though only a few single cost maps are shown (upper row).

Pressure maps are then loaded into a harvesting module that seeds fuelwood collection events and assign an amount of fuelwood to be harvested during that particular iteration. The harvesting module uses DINAMICA EGO’s function Patcher (Soares et al., 2002). Fuelwood

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collection events per iteration are modeled as a single transition type (0→1), being 0 the “unharvested” landscape and 1 represents fuelwood collection events during a particular iteration. It is worth noting that a single collection event can represent many real trips to the same location (i.e. pixel) within a defined iteration length (one year in our case study).3

The Patcher function allows to set the size, variance and shape (isometry) of areas subject to fuelwood collection during a single collection event. Setting these parameters properly is intimately related with the spatial and temporal resolution, as well as with fuelwood collection patterns in the study area. Regarding this case study, it can be seen from GPS tracks that people seldom collect fuelwood from areas larger than the pixel resolution (one hectare), but along 3 or 4 months they visit few sites well apart from each other. A counter example of this will be clear cutting for charcoal production, where “patches” of cleared areas could range between 1 and 3 ha, or even more when only preferred species are targeted.

The Patcher function also allows to “loose or tight” the degree of randomization desired for seeding fuelwood collection events in space. A “prune factor” of 1 means that only those pixels with the highest probability in the pressure map will be selected (i.e. fully deterministic). A value of 10 means that 10 times the required amount of pixels will be ranked according to their probability, and subsequently, 10% of this set of pixels will be reselected randomly4 to account back for the needed amount of change.

In this regard, the amount of change must also be set, representing the percentage of the accessible landscape that will be visited during each iteration. Again, this parameter is related to the spatial and temporal resolution. In our case study, it represents the percentage of accessible pixels of one hectare (non-zero values in the pressure map) that will be visited for fuelwood collection during one year. Very hard to determine with certainty, that is why this parameters will vary widely between Monte Carlo realizations, almost from 10% to 100% if set to default. Preliminary analysis of GPS tracks for a sample of fuelwood collectors in four rural villages in Honduras and India show that at one hectare/one year resolution, less than 20% of the “harvestable” landscape will be visited. However, under acute scarcity scenarios, it would be expected that “harvestable” areas are drastically reduced, and people will collect fuelwood from almost every place.

Finally, fuelwood demand for each time step is distributed in space following equation A.1:

3 Given that results are not quite sensitive to small changes in patch size and isometry, these variables were not included as tunable parameters in Table 1 in the main text.4 In fact, high probability pixels have higher changes to be selected during the random re-selection, like a weighted dice.

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pfw (t ) jk=Px(t) jk∗∑

iC ik−df k

∑j

Px jk (t )

(eq.A.1)

Where pfw(t)k is the expected or projected amount of fuelwood in tDM to be collected at each pixel “j” by both types of fuelwood collectors “k” for any time step “t”; Px(t)k is the pressure index from the IDW algorithm but only over for those pixels representing collection events in the given time step “t”; C is residential fuelwood consumption in tDM by locality, village or city “i”; df is the overall amount of fuelwood in the study area coming from deforestation events that weren’t driven by fuelwood extraction and considered renewable by definition.

At each time step or iteration, the model will produce a pixel-based comparison between the expected amount of fuelwood to be collected, and the standing woody biomass following supply assumptions. Figure A.5 shows this relation for a 120 years period in four selected land cover types. Three contrasting outcomes represented by three different handpicked pixels are shown for each land cover type: 1) no fuelwood collection, allowing for undisturbed natural growth; 2) mild fuelwood collection, characterized by sparse harvest events (shown as sudden falls in AGB values); and 3) intense fuelwood collection, characterized my frequent harvest events leading to a reduced AGB stock at the end of the simulation as compared to year 2000, meaning a positive value in NRB (red dotted lines). Worth noting that all non-NRB pixels (green lines) experience non-renewable situations scattered in time, because the amount of wood harvested eventually at given iterations exceeds natural regrowth and AGB diminishes. This sort of single-pixel cross-section analysis shows that it would be possible to account for “avoided” degradation between natural growth and fuelwood demand scenarios, which would raise significantly carbon savings as these would be compared to an hypothetical baseline. But this approach requires assuming that fuelwood is the only disturbance driver which is not correct for most cases. Unless, other woodfuel flows (charcoal, fuelwood for small industries, etc.) are integrated into the model and these account for the majority of wood harvested in any given forest area.

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Figure A.5. Temporal projections of supply/demand relations at pixel-level

Notes: Each line corresponds to a pixel (out of 1.57 million pixels in the study area). Black lines do not suffer any harvesting and represent very inaccessible places. Red and green lines correspond to NRB and non-NRB pixels respectively for a whole 120-year simulation period. Harvest threshold correspond to fuelwood collectors using vehicles (i.e. marketed fuelwood).

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A – 5 Fuelwood supply

Values for rmax, K and initial stock quoted in the literature vary widely, depending on the particulars of the local situation, with the result that the biophysical parameters in these case studies may not match those of the area of interest . The assumed values for rmax and K were obtained from permanent plots (unpublished or locally available data) plus a careful review and analysis of the best available literature. A variety of publications mostly available for Honduras were consulted to compare and calibrate biomass growth parameters for each LULC class used in the present study (Ferreira, 1989; Ferrerira, 2004; García-Cantarero et al., 2008; Groothousen, 1996; Groothousen and Ferreira, 1989; Groothousen and Rivera, 2001; Hernández-Pacheco et al., 2008; Hernandez-Sabillón et al., 2005; Hernández and Ferreira, 2005; Martínez and Ferreira, 2002; Melgar and Ferrerira, 1999; Meza, 1997; Mikkola, 1996, 1997; Naman and Ávila, 2005; Oyuela, 1996; Pérez and Stiff, 1990; Turcios and Ferreira, 2005; Zamora and Groothousen, 2006; Zavala, 1990).

A – 5.1 Trees outside forests and other renewable fuelwood sources

It is well known that non-forest woody resources may be the main fuelwood supply in certain areas (O'Keefe and Hosier, 1983; RWEDP, 1997), in fact, not considering them was one of the major shortcomings of the “fuelwood gap” theories of the late 70’s (Leach and Mearns, 1988; Munslow et al., 1988). The available information (if any) about AGB stock in trees outside forests is often highly uncertain, since there have been virtually no inventories of tree stocks and growth outside the forest, with some few valuable exceptions (e.g. Ahmed, 2008; Krishnankutty et al., 2008; Pandey, 2008; Plieninger, 2012). Moreover, setting up a robust quantification of this fuelwood source may be very demanding in terms of time and resources (Bellefontaine et al., 2002). Mofuss copes with trees outside forests by internalizing a wide variability of the available data or guesstimates. It assumes that trees outside forests are scattered over non-forest areas in the LULC map (agriculture, grasslands or wide open rangelands) and set the standard deviation of the K parameter to 100%. This wide variation in AGB stock (the K value) is integrated into the Monte Carlo module, as with the rest of K values from other LULC classes; meaning that for any given MC run, the available fuelwood from trees outside forests may range from zero to a shrubland or dense fallow land biomass density.

Also, Mofuss assumes that is unlikely that the trees outside forests are felled, but that the available fuelwood (the K parameter) represents dead wood accumulation or mild pruning of live branches that will regrowth in the following season. Hence AGB stocks of trees outside forests remain constant over time (the K parameter does not change in time). However, as trees outside forests are distributed in highly accessible areas around villages, Mofuss will

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project high harvest volumes over these areas. An algorithm was added that only subtracts the available “renewable” wood (the K value) from the projected total demand at that pixel, and redistributes the difference into nearby forested and woodlands areas. The rationale behind this is that women and children will collect dead wood from tree fences, shadow trees in grazing areas, home gardens, trees by the roads, etc., surrounding their villages, while the remaining needs will be complemented by harvesters walking into forests and woodlands. Under certain situations, the available woody biomass from trees outside forests areas can fulfill the expected demand, releasing the pressure from forested areas.

A – 5.2 Fuelwood from coffee plantations prunings

One particular type of trees outside forests that requires special attention in Central America are shade-trees in coffee plantations, which coincidently are very appreciated as high quality fuelwood. Fuelwood from coffee plantations is commercialized widely during the pruning seasons in Central America (Current and Juárez, 1992). In El Salvador for example, coffee plantations prunings provides around 60% of the country’s total demand for residential fuelwood (Ghilardi, 2009). Based on El Salvador data where fuelwood from coffee plantation is sell across the whole country, Mofuss simply subtract the total annual supply to the fuelwood demand database by locality, assuming an evenly distribution of coffee fuelwood among villages. As the Honduras LULC map used in this case study do not depicts coffee plantations, the total annual fuelwood production was estimated based on accurate estimates from El Salvador and subtracted this amount from the residential fuelwood demand database.

Coffee plantations are private managed areas and the fuelwood from shade and coffee tree pruning is considered of good quality. As a result, fuelwood is commercialized throughout the country and local residents are not allowed to gather fuelwood or walk through the fields without permission. This has important implications on renewability estimates. The available LULC map does not depict coffee plantations, although they are widespread across the study area. However, quite confident information about coffee cultivated area and production for Honduras exists (IHCAFE, 2009). Table A.3 gives an estimate of annual fuelwood availability in each of the four departments of the study area. Under the assumption that fuelwood from coffee plantations is commercialized across large regions, estimates were subtracted to the total residential demand by department (accounting for coffee processing own needs). Most probably, the amount of fuelwood production from coffee plantations in Honduras is much higher that the lower bound assumed here. For comparison, reference values reported in the literature vary between 7,3 to 10,2 tDM ha-1 yr-1 for altitudes lower than 800 m.a.s.l.; 5,1 to 8,8 tDM ha-1 yr-1 for altitudes between 800 and 1200 m.a.s.l.; and 2,9 to 3,6 tDM ha-1 yr-1 for altitudes higher than 1,200 m.a.s.l. (Blackman et al., 2006; Current and Juárez, 1992; Harmand et al., 2007; Linares, 2003; Martínez, 1987; Mena-Mosquera, 2008;

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PROCAFE, 2005; Romero, 2005; Segura et al., 2006)5. The amount of coffee fuelwood using the lower bound outpaces the marketed demand, and in consequence was subtracted from total residential demand (marketed and self-gathered); assuming that some end users procure coffee fuelwood from nearby plantations (Current and Juárez, 1992).

Table A.3. Fuelwood supplies from coffee plantations within the study areaDepartment Cultivated

area 2008/2009

Coffee fw annual production (lower bound: 2.9 tDM ha-1

yr-1)

Coffee fw annual production (mid bound: 6.9 tDM ha-1

yr-1)

Production 2008/2009

fw needed for coffee processing operations based on production

Total residential fw use

Residential fw use modeled by NRBv1.0 (using lower bound)

Residential fw use modeled by NRBv1.0 (using mid bound)

ha tDM yr-1 tDM yr-1 quintales (qq) tDM yr-1 tDM yr-1 tDM yr-1 tDM yr-1

Santa Bárbara 37,015 107,344 255,404 568,885 10,025 203,130 105,812 0 Copán 23,122 67,053 159,541 531,666 9,370 170,359 112,675 20,188 Intibuca 9,574 27,765 66,062 205,459 3,621 107,728 83,583 45,286 Lempira 21,030 60,988 145,109 476,676 8,400 151,507 98,920 14,798 Totals 90,742 263,150 626,116 1,782,685 31,416 632,724 400,990 80,272

Notes: The cultivated area was obtained from (IHCAFE, 2009). Coffee processing operations use fuelwood coming exclusively from plantations, although we assumed here that 100% of the production is dried with fuelwood, which may not be the case (in El Salvador for example this fraction is 63% (PROCAFE, 2005)). One stereo cubic meter (1m3st ≈ 0.43tDM) of fuelwood can dry 24.4 quintales (qq) of coffee seeds (PROCAFE, 2005).

A – 5.3 Resilience of fuelwood sources

Two tunable thresholds for the remaining AGB stock were included to accommodate severely overharvested areas. First, if AGB is depleted below an upper threshold, that pixel is excluded from harvest in future cycles until natural regeneration restores AGB above the threshold. This is based on the assumption that below a certain stock, the remaining woody biomass consists largely of biomass that is not suitable for use as fuelwood (i.e. its diameter is too small). However, if during multiple iterations AGB stock is fully depleted, the model assumes that the pixel loses its resilience and remains unproductive for the rest of the simulation (i.e. is completely deforested). This second threshold is included to allow for severe overharvesting and complete collapse of productivity, which has been observed in some instances (Abbot and Homewood, 1999). If either of these two assumptions do not apply (or are unknown) in a given study area, one or both thresholds can be by-passed. It was

5 The mid-range of reported wood densities (0.64 gDM/m3) was used for converting volume to dry matter biomass: Coffea arabiga L. (0.82 ± 0.06), Inga spp. (0.49-0.64), Gliricidia sepium (Jacq.) Kunth ex Walp (0.47-0.75), and Mimosa scabrella Benth. (0.45-0.58). This is consistent with the study by Segura et al., (2006) in which the mean specific gravity for coffee shade trees in Nicaragua was 0.52 ± 0.11 gDM/m3 and for coffee plants 0.82 ± 0.06 gDM/m3.

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assumed that no further harvesting would take place if the woody biomass in a pixel falls below a level of 5.0 tDM ha-1 for people using vehicles and 0.1 tDM ha-1 for people harvesting on foot.

Figure A.6: Uncertainty of woody biomass growth parameters following truncated normal probability density functions. These are used in each Monte Carlo simulation.

Note: Mofuss reads the biomass growth parameter table and produces this graph accordingly in tiff format and accommodating up to five LULCC classes per row for rmax, K and TOF. Two “extra” classes, Mangroves and Palm plantations, appear here as the entire landscape (i.e. all the country) is analyzed at this stage. Producing frequency data and graphs for LULC categories not necessary present in the particular area of interest does not increase the processing time significantly but keeps the script simpler.

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Figure A.7: Uncertainty of harvested pixels and prune factors

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A – 6 Prospective landscape simulation submodel projecting forests loss/gain events

Mofuss accounts for observed and expected trends in forest loss and gain that are unrelated to fuelwood harvesting. This allows for some fuelwood demand to be satisfied from by-products of land clearing activities like agricultural or grazing expansion. Based on observed past events of forest loss and gain, the model simulates multi-step losses and gains between a period of analysis, corresponding to 2000 to 2030 in our case study. The number of steps will be adjusted according to the iteration length, which is one year in our case (30 steps).

To accomplish this, Mofuss uses forest loss and gain data (2000-2012) downloaded from http://earthenginepartners.appspot.com/science-2013-global-forest/download_v1.1.html (Hansen et al., 2013). For the case of forest losses, in which yearly data is available, the 12 years dataset is divided into a calibration (2000-2006) and validation (2007-2012) datasets. For the case of forest gains, the same 12 year dataset is used as calibration and validation. An initial and a final landscape depicting only two classes (forest/no forest) are generated by integrating the LULC map (circa 2000), tree cover data (2000) and forest losses that occurred between 2000 and 2006. Figure A.8 shows both landscapes and forest losses within the calibration period.

Forest losses occurring during the calibration period are used to calculate annual transition rates as well as to look for spatial relationships with proxy variables such as distance to roads, rivers and settlements, slope, elevation, protected areas and distance to previous losses (dynamic variable). Based on these relationships, a probability map of forest losses is produced for each time step, by which the amount of change (deforestation events) are distributed in space using DINAMICA EGO’s specific functions: Patcher and Expander. Figure A.9 shows the probability of loss for year 2006, as compared to simulated and observed losses for the validation period (2007-2012). Observed and simulated changes are compared to each other for validation purposes. Worth noting that simulated losses are loaded starting in year 2013 in Mofuss, as observed annual changes are used between 2001 and 2012 so as to reduce the inherent uncertainty of simulations.

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http://earthenginepartners.appspot.com/science-2013-global-forest/download_v1.1.html


Figure A.8. Initial and final landscapes for the calibration period, with observed forest losses that occurred between 2000 and 2006.

A tunable fraction of all cleared AGB at locations where forest losses events took place is finally made available at each time step to fuelwood collectors that eventually drive or walk into the surroundings of the affected pixel. These surroundings are defined by an “influence” window tunable in size by the user. Those pixels affected by forest losses at any given time step will grow back as secondary and open woodlands unless a gain event occurs which will accelerate the regrowth. This process is meant to simulate fuelwood production from shifting cultivation practices. However, land cleared for agriculture or grassland do not necessary recover but may stay as cropland or pasture for many years. An improvement is being developed in this regard, allowing the user to integrate three date LULC maps and replace forest loss/gain events simulations for more complete LULCC analysis, assessing the spatiotemporal behavior and interaction of multiple LULC classes as sources of fuelwood (e.g. pasture that remains as pasture vs. pasture that changes to an open woodland). However,

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while forest loss/gain data is available freely for the entire world, multitemporal LULC maps could be difficult to develop. A feasible intermediate option to cope with this would be to select cleared patches that can grow back (small ones) and cleared patches that would stay as non-forest until the end of the simulation (larger ones).

Finally, a similar procedure to the one just described for forest losses is done for forest gains as well, but without validation and using annual simulated maps from startup year 2000. The purpose of forest gains (quite few in the study area) is to cancel losses occurring in the same time step and to recover forest and woodlands pixels to their initial state (t0) when a gain is observed or simulated.

Figure A.9. Simulated and observed losses for the validation period 2007-2012.

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