Spatial Valuation of Forests’ Environmental Assets: …...1 Land Economics 93:1, February 2017,...

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Land Economics 93:1, February 2017, Supplementary Material

Spatial Valuation of Forests’ Environmental Assets: An Application to Andalusian Silvopastoral Farms

Paola Ovando1∗, Alejandro Caparrós2 , Luis Diaz-Balteiro3 , Marıa Pasalodos4, Santiago Beguerıa5 , Jose L. Oviedo2 , Gregorio Montero4 , Pablo Campos2

Emails: *[email protected]; [email protected]

1 Grantham Research Institute on Climate Change and The Environment, London School of Economics and Political

Sciences (LSE), 2 Institute of Public Goods and Policies, Consejo Superior de Investigaciones Cientıficas (CSIC), 3 School of Forestry Engineering and Natural Resources, Technical University of Madrid (UPM), 4 Forest Research Centre, The National Institute for Agricultural and Food Research and Technology (INIA), 5 Estacion Experimental de Aula Dei, Consejo Superior de Investigaciones Cientıficas (CSIC)

1. SUPPLEMENTARY MATERIAL

Spatial distribution of silvopastoral farms

The spatial distribution of land uses and vegetation within Andalusia is shown in Fig.A1. Forested areas account for close to 2.9 million ha, of which 56% is dominated by broad-leaves and the remaining 44% by conifers and mixed forest. Quercus ilex and Quercus suber are the main broad-leaf species, which jointly account for 43% of the area that is covered by trees. Pinus pinea, Pinus halepensis, Pinus pinaster and Pinus nigra together embrace 43% of this total area, while Eucalyptus sp. cover 4% (MARM, 2013). Treeless shrub-lands occupy some 1.6 million ha and grasslands 0.3 million ha (CMA, 2010).

Fig. A1: Silvopastoral farms distribution in Andalusia

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Fig. A1 also shows the distribution of the 567 silvopastoral farm case studies. Those case studies are taken from a larger forest farms sample (765 farms) that were randomly selected in Andalusia (Oviedo et al., 2015), and embrace those farms that have at least one of the seven forest species considered in this study. There are five main diverging spatial conditions across these farms: land use, vegetation distribution, slope, tree density and age class distribution, which average values (except for the age class distribution) at provincial level are shown in Table A1. Table A1. Main attributes and land use distribution of the silvopastoral farms by province Class N

(1) Average spatial variables (2) Land uses distribution (%)(3) Area (ha)

Slope (%)

BA (m2/ha)(2) QI Qs Pinus sp.

EU Other forest

Sh.

GR

Cro-ps

Other uses FOR TOT

Almería 33 357 30 27 12 12.1 - 21.1 - 19.1 32.7 6.8 7.6 0.5 Cádiz 55 469 18 31 14 2.5 42.6 0.7 - 6.9 14.2 18.0 14.4 0.8 Córdoba 152 440 23 20 17 67.3 6.7 8.3 - 0.5 7.0 3.0 7.1 0.1 Granada 39 577 32 45 25 36.9 - 14.5 - 6.6 14.1 2.2 24.8 0.8 Huelva 113 445 25 19 16 53.3 9.6 0.9 12.4 0.0 15.2 5.8 2.6 0.2 Jaén 54 487 35 32 21 52.7 - 9.6 - 6.7 17.1 5.0 8.0 0.7 Málaga 19 365 39 31 17 24.6 17.2 1.5 - 0.0 23.0 23.2 6.6 3.9 Seville 102 852 26 14 11 57.2 9.0 0.1 0.5 9.2 5.6 5.8 12.6 0.0 Total 567 525 26 24 16 49.6 9.9 4.9 2.2 4.3 12.0 6.6 10.1 0.4 Notes: (1) N is the number of farms per province. (2) BA is the basal area, FOR refers to the basal area of the forest, whilst TOT to the total basal area for the total farm. (2) The slope and basal area have been estimated as a weighted average value according to the relative area that each species and silvicultural model occupies in the farm. (3) QI: Quercus ilex, QS: Q. suber, EU: Euclyptus sp., Sh. treeless shrubland and GR treeless grassland. Quercus ilex is the main forest species in the studied silvopastoral farms, accounting for almost the half of the farms area. Quercus suber woodlands are found in almost 10% of this area, while pine species and Eucalyptus sp. in close to 5% and 2% of farms area, respectively. Pinus pinea is the main pine species across the farms, covering 44% of the area occupied by pines, Pinus halepensis covers a 41%, Pinus nigra a 13% and Pinus pinaster the remainder 1%. The share of conifer forests (pines) is higher in the majority of Eastern Andalusia’s provinces (Almerıa, Jaen and Granada), whilst the oak (Quercus) woodlands share is higher in Western Andalusia (Cadiz, Cordoba, Huelva and Seville) and Malaga provinces. Note that the share of conifers in private farms is lower than the regional distribution of these species, since a relevant part of conifer forests are located in publicly owned lands (MARM, 2013).

Basal area is considered a proxy (and standardized) indicator of the stand density, and accounts for the total cross-sectional area of all stems in a stand measured at breast height (1.3 m), and expressed as per unit of land area (typically square meters per ha). Most of the farms (53%) have a moderate density, that is to say a basal area between 15 to 30 m2 per hectare, 15% of the farms have a basal area less than 15 m2/ha or a low density and 22% higher than 30 m2/ha or a high density.

Similarly, most of the farms (48%) have a moderate slope (10% to 30%), 37% of them are located in areas with sharp slopes (> 30%), and only 15% of the farms are

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located in relatively flat areas (< 10%) (Table A1). Farm size classes are more variable across the farms. Close to one third (34%) of farms have an area smaller than 100 ha. About 25% of the farms have an area between 100 and 250 ha, 20% an area between 250 and 500 ha, and 21% areas higher than 500 ha.

Fig. A2 shows the initial (year 2010) age class distribution of the silvicultural farms by species and province. We observe that the total number of Q. ilex tress per hectare is higher in most provinces of Eastern Andalusian (Almerıa, Granada, Jaen and Malaga). We also observe a higher percentage of younger trees (those with an estimated age that is lower than 25 years) in Granada and Jaen, Q. ilex forest than in other provinces of Andalusia. The age class distribution bestows, as well, relevant differences for Q. suber and pine forests amongst provinces. Q. suber density is significantly higher in Malaga, while for pine species this density is lower in farms located in Cadiz, Almerıa and Huelva provinces.

Fig. A2: Initial age class (in years) distribution of the silvopastoral farms by forest species and province

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Silvicultural models, growth and yield functions

The EA computing model considers 19 different silvicultural schemes applied to seven different forest species. These silvicultural models were developed by Montero et al. (2015) and vary depending on the forest structure (even or uneven-aged), the quality of the site, the soil type and the forest density. Table A2 shows the main features of the silvicultural models applied, indicating the rotation length defined by Montero et al. (2015) and the diametric class at which different forestry treatments are to be applied. These rotation ages might be overestimated for species and provinces with a higher historical incidence of forest fires. In such cases the rotation ages are reappraised taking into account the individual trees’ survival probabilities, and these adjusted rotation ages are further reapplied in order to estimate the survival functions and other EA-related estimates

Table A3 shows the parameters for growth function in diameter and in volume associated with each species and silvicultural model, which has been estimated using Montero et al. (2015) primary data and models. Those growth models comprise both existing models specific for Mediterranean forests species in Spain and new models that were developed using both, data gathered in a group of silvopastoral farms in Andalusia (Ovando et al.,2015) and existing data (Serrada et al., 2008) (see Montero et al. (2015), for details). Carbon stock per individual tree depends on the volume of trees, according to a ratio that relates tree volume to carbon content in above and below-ground tree biomass, which are estimated using Montero et al. (2006) functions.

Table A2 Silvicultural model main forestry operations by diametric class Species(1) Model Rotation

length (yr) Definition(2) Diametric class for the treatment

N Thinning Pruning Shrub clearing Eucalyptus sp. 1 12 FL-AS - - every 6 yr. 2 16 MA-AS - - every 6 yr. P. halepensis 1 80 EA-HMS 5,10,15,20 - 10,20 2 151 EA-MLS 5,10,20,25 - 10,20 P. nigra 1 84 EA-HMS 5,10,15,20 - 10,20 2 105 EA-MLS 10,15,20,25 - 10,20 P. pinaster 1 78 EA-MLS-HD 15,25 - 10,20 2 86 EA-MLS-MD 15,25 - 10,20 3 74 UEA-AS 15,20,25 - 10,20 P. pinea 1 126 EA-MLS-FL 10,15,20,25 10,20 2 107 EA-HMS-FL 10,15,20,25 10,20 3 117 EA-HMS-MA 10,15,20,25 10,20 4 103 EA-MLS-MA 10,15,20,25 10,20 Q. ilex 1 138 EA-MLS 5,10,15,20,30 10-65 5,10 2 190 EA-HMS 10,15,20,30 10-75 5,10 3 42 CP-AS 20,25,35 15-75 5,10 4 164 UEA-AS 5,10,15,20,30 10-60 5,10 Q. suber 1 141 EA-AS 5-45 10,15 5,10 2 103 UEA-AS 5-45 10,15 5,10 Source: Own elaboration based on Montero et al. (2015).

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Notes: (1) P (Pinus), Q. (Quercus). (2) AS: all sites; CP: coppice forest; EA: even-aged forest; FL: flat lands; HD: High density, HMS: high-medium site quality; MA: Mountain areas; MD: Medium density; MLS: medium-low site quality; UEA: uneven-aged forest. Table A3 Diameter and volume growth function and carbon stock by species and silvicultural model Species Model Diameter growth (y) (1) Volume growth (v) (1) Carbon

stock y=β1x+β2x2 v=β1x+β2x2+β3x3 β1 β2 β1 β2 β3 t CO2 m-3 Eucalyptus sp. 1 1,08 -6.4*10-2 4.35*10-4 4.99*10-4 5.00*10-6 1.74 2 9,61*10-1 -4.5*10-3 4.35*10-4 4.99*10-4 5.00*10-6 1.74 P. halepensis 1 4.69*10-1 -1.2*10-3 2.24*10-4 -1.14*10-5 -2.04*10-5 3.02 2 3.52*10-1 -8.9*10-4 2.24*10-4 -1.14*10-5 -2.04*10-5 3.02 P. nigra 1 5.53*10-1 -1.5*10-3 9.47*10-5 -2.12*10-6 2.02*10-5 2.26 2 5.62*10-1 -1.8*10-3 9.47*10-5 -2.12*10-6 2.02*10-5 2.26 P. pinaster 1 7.84*10-1 -2.5*10-3 -2.57*10-2 1.97*10-3 -1.82*10-5 1.57 2 8.91*10-1 -3.6*10-3 -2.57*10-2 1.97*10-3 -1.82*10-5 1.57 3 1.0416 -5.0*10-3 -2.57*10-2 1.97*10-3 -1.82*10-5 1.57 P. pinea 1 3.58*10-1 -5.5*10-4 -4.28*10-3 4.47*10-4 7.9*10-5 3.02 2 6.51*10-1 -2.0*10-3 -4.28*10-3 4.47*10-4 7.9*10-5 3.02 3 3.18*10-1 -5.8*10-4 -4.28*10-3 4.47*10-4 7.9*10-5 3.02 4 6.51*10-1 -2.0*10-3 -4.28*10-3 4.47*10-4 7.9*10-5 3.02 Q. ilex(2) 1 4.15*10-1 -6.54*10-4 -1.92*10-2 1.2*10-3 - 1.40 2 5.10*10-1 -8.54*10-4 -1.92*10-2 1.2*10-3 - 1.40 3 8.11*10-1 -1.43*10-3 -1.92*10-2 1.2*10-3 - 1.40 4 4.31*10-1 -4.90*10-4 -1.92*10-2 1.2*10-3 - 1.40 Q. suber 1,2 5.83*10-1 -9.98*10-4 -6.42*10-3 3.2*10-3 -4.7*10-7 4.43 Source: Own elaboration based on Montero et al (2006, 2015). (1) The variable x refers to the tree age (in years), while the variable y to the tree diameter (in cm). The volume (v) is measured in m3. (2) The tree volume in case of Q. ilex includes trunk and coppices, thus the carbon stock ratio is lower. Table A4 presents the functions used to estimate cork, pinenut and acorn yields per individual tree. Cork production is estimated as the product of cork density (kg/m2) and the harvesting area (in m2), which in turn is estimated as the product of tree diameter (y, in m) and harvest height (in m). The density of cork is taken from Montero et al. (1996) for different regions of Andalusia, whilst the cork harvest height is estimated using Montero et al. (2015) functions that relate the harvesting height (up to a threshold of 3 m) to the diameter of cork trees. Pinenuts (cones) for P. pinea and acorns for Q. ilex, are also taken from Montero et al. (2015). In both cases the individual tree yield of those fruits depends on the diameter (y, in cm) of pine or oak trees.

Mortality and forest fire risk ratios

Individual tree survival functions depend on the expected tree felling (comprising thinning and commercial harvest operations), natural mortality and forest fire risk. Natural mortality is estimated as a logarithmic function of tree age, for each species and silvicultural model (see Table A5). These functions were estimated using Montero et al. (2015) mortality estimates by species, silviculture type and diametric class. In the

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case of Eucalyptus we assume a natural annual mortality rate of 0.05%. Forest fire risk is estimated considering the historical wildfire ratios by province and species observed in the period 1985 and 2009. This risk ratio is estimated as the average ratio between the annual area affected by wildfires and the total area covered by forest species and province over this period (Diaz-Balteiro et al., 2015). We observe that in many cases there are no significant differences (p≤0.05) between the average forest fire ratios at provincial and regional (Andalusia) levels. We use the province ratios only if they differ significantly from the regional ones (Table A6). Table A4 Coefficients to estimate cork, pinenut and acorn yields Class (1) Cork yield (in kg per tree) Pine nuts Acorns

Cork density Harvest height (H) (m) kg of cones per tree (C) kg of acorns per tree (A) H = β0+β1 y C = β0* y

β1 A = β1y+β2 y2

kg/m2 β0 β1 β0 β1 β1 β2 Function 1 10.18 0.111 0.079 7.0*10-8 4.7112 1.873*10-1 1.1*10-3 Function 2 11.70 -1.222 0.108 5.0*10-5 3.9552 Function 3 10.53 0.606 0.069 5.5*10-3 1.4475 Function 4 8.32 0.988 0.058 Source: Own elaboration based on Montero et al (1996; 2015). (1) The variable y indicates the diameter of the tree. This diameter is estimated in meters (m) for the cork yield functions, and in cm for the pine nuts and acorns functions. In case of cork yield the function are applied to the following provinces: 1 is applied to Q. suber forest in Almeria and Granada; 2 for Q. suber forest of Cádiz and Málaga; 3 for Q. suber forest of Córdoba, Jaén and Seville; and 4 for Q. suber forest of Huelva. In case of pinenuts, the functions 1, 2 and 3 are applied to the three silvicultural models of P. pinea presented (in that order). Table A5 Natural mortality function by species and forestry model

Class Model Annual mortality rate (θ(t)=β0+β1ln(t))(1) β0 β1 R2

P. halepensis 1,2 5.80*10-4(1.53*10-5) -3.75*10-5(3.44*10-6) 0.41 P. nigra 1,2 3.73*10-3(1.43*10-5) 4.26*10-4(1.84*10-6) 0.99 P. pinaster 1 9.59*10-3(2.62*10-4) -1.95*10-3(6.11*10-5) 0.86 2 9.06*10-3(2.44*10-4) -1.81*10-3(5.69*10-5) 0.86 3 8.44*10-3(2.52*10-4) -1.71*10-3(5.87*10-5) 0.70 P. pinea 1,3 4.29*10-3(1.82*10-4) -8.35*10-4(4.12*10-5) 0.67 2 3.54*10-3(1.50*10-4) -7.34*10-4(3.39*10-5) 0.70 Q. ilex 1 9.17*10-4(5.45*10-5) -1.60*10-4(1.11*10-5) 0.42 2 1.52*10-3(3.91*10-5) -2.64*10-4(8.00*10-6) 0.79 3 6.61*10-3(4.11*10-5) -1.24*10-3(8.41*10-5) 0.43 4 1.40*10-2(1.98*10-4) -2.55*10-3(4.04*10-5) 0.93 Q. suber 1 2.76*10-3(1.05*10-4) -4.19*10-4(2.24*10-5) 0.58 2 9.26*10-3(2.46*10-4) -1.8210-4(5.26*10-5) 0.83

Source: Own estimations based on Montero et al. (2015). Note: (1) Where t is the age of the tree. Standard errors in parenthesis.

Forestry operation cost and output prices

The standing market prices for timber, firewood, cork and pinenuts (pw) were obtained from a large data set on sales of forest products recorded by the government of Andalusia (CMA, 2011). This data set covers the period 2008-2010, and all prices were

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updated to 2010 using the general consumer price index (CPI). A limitation of this data set is that it mainly covers stumpage sales in publicly owned forest, although is the only available data set since this information is not gathered in privately owned forest. It is worth mentioning that price data gathered on cork and Eucalyptus timber sales in a sample of private silvopastoral farms (Ovando et al., 2015) fits into the range of cork and timber prices of the CMA (2011) data set. This comparison was not possible for pine species timber sales, since Ovando et al. (2015) case studies only records these sales in publicly owned silvopastoral farms. Table A6 Average annual forest fire ratios by forest species and province in Andalusia

(Period 1987-2006) Class Annual forest fire ratio (ρj) in ha/100ha(1)

AL CA CO GR HU JA MA SE AND Eucalyptus sp. 0.68 0.68 0.68 0.68 0.68 0.68 0.68 0.68 0.68 P. halepensis 0.17 0.17 0.17 0.17 0.00* 0.17 0.17 0.17 0.17 P. nigra 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 P. pinaster 0.42 0.42 0.42 0.42 0.42 0.42 0.42 0.42 0.42 P. pinea 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.82* 0.25 Q. ilex 0.27* 0.16 0.05* 0.16 0.16 0.16 0.16 0.16 0.16 Q. suber 0.00* 0.37 0.04* 0.92* 0.37 0.37 0.37 0.37 0.37 Source: Own elaboration based on Diaz-Balteiro et al. (2015). Notes: (1) The ratio ρj indicates the number of hectares out of every 100 ha that are expected to burn every year. .AL (Almería), CA (Cádiz), CO (Córdoba), GR (Granada), HU (Huelva), JA (Jaén), MA (Málaga), SE (Seville), AND (Andalusia). * Rattios that exhibit a significant difference (p<0,05) form the regional average value.

Stumpage prices depend on tree species and diameter, cork prices on the cork quality index and grazing resources on both the dominant vegetation and province. We use a single regional price for firewood and pinenuts, since we have found no relevant differences in prices for those products across the Andalusian provinces. Table A7 depicts the average accounting outcomes Outputs are integrated as exchange values (without including consumers surplus) in such a way that they are consistent with national accounts (Obst and Vardon, 2014).

Forestry operation costs depend on the slope gradient of the site and tree density. The unit costs of forestry operations function are shown in Table A8. All forestry operation costs, except shrub clearing, are attributed to the production of forest products (timber, cork, firewood and pinenuts). Shrub clearing is attributed to the production of grazing resources. Half of Q. ilex pruning costs are attributed to grazing resources of the forest units of this species, since this operation is intended to enhance acorn production (which is part of the grazing resources delivered by Q. ilex forests).

Our cost estimations assume constant returns to scale, that is to say, the size of the forest unit does not affect the cost functions, as we further consider that the fixed costs at the farm level are very low since landowners will normally either rent specific equipment or machinery or else hire a specialized firm to perform various forestry tasks on their lands. Hence, most part of forestry operations will in turn made

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up variable costs that at the per hectare level would depend on forest biophysical attributes (tree species, density of the forest or slope gradient).

Table A7: Forest products standing prices (euro, year 2010) Class(1) Unit Price 1 Price 2 Price 3 Price 4

(∅) cm €/unit (∅) cm €/unit (∅) cm €/unit (∅) cm €/unit Eucalyptus sp. m3 > 10 5.0 >15 13.6 >25 26.7 P. halepensis m3 > 15 3.5 15-25 12.3 >25 26.1 P. nigra timber (2) m3 > 15 2.7 15-25 9.1 >25 31.0 P. pinaster timber (2) m3 > 15 3.3 15-25 12.0 >25 30.3 P. pinea timber(2) m3 > 15 4.2 15-25 10.8 >25 30.4 P. pinea pinenuts (cones) (2) kg All 0.05 Q. ilex firewood m3 All 58.3 Q. suber first cork(4) Mkg Q1 56.9 Q2 112.2 Q3 153.4 Q4 172.6 Q. suber, second cork(4) Mkg Q1 474.1 Q2 934.7 Q3 1,278 Q4 1,726 Carbon dioxide credits (5) Mkg CO2 13.7 Source: Own elaboration based on Diaz-Balteiro et al. (2015) and CMA (2011). Notes: (1) cm refers to the diameter of trees while e/u to the unit price in euro. (2) Average prices for 2008-2010 (updated to 2010, using the CPI). (3) Pinenuts price refers to the unit price of pine cones (between 4 and 5 kg of pine cones will yield 1 kg of pinenuts).(4) Cork prices depend on the quality of cork, where Q1 refers to the lowest quality, Q2 to low to medium qualities; Q3 to medium to high qualities and Q4 to the highest quality. The first (virgin) cork and the cork obtained from second and successive (reproductive) cork harvests have different prices. (5) The average 2010 carbon price of the European Union Emissions Trading System (EU ETS) was 14.3 euro/Mkg CO2 (SENDECO2, 2015), which corrected by a factor of 0.96 amounts 13.7 euro/Mkg CO2 . This correction takes into account Sohngen and Mendelsohn (2003) results; which suggest a decrease of 4% in the carbon price by the period 2010-2020 in a scenario in which forestry carbon credits are allowed with respect to a scenario without these credits. Table A8: Forestry operation costs (euro, year 2010) Class Tree diameter (∅) cm Unit (y) Type of function(1) β0 β1 Oak woodlands Oaks thinning ∅ ≤ 12.5 €/m3 y = β0+β1 x 33.48 27.53 Oaks thinning 12.5 <∅ ≤ 22.5 €/m3 y = β0+β1 x 9.45 18.87 Thinning 22.5 <∅ ≤ 32.5 €/m3 y = β0+β1 x 9.45 29.27 Thinning ∅ >32.5 €/m3 y = β0+β1 x 14.41 19.68 Structural pruning 7.5 <∅ ≤ 17.5 €/ha y = β0+β1 x 326.4 1,185.2 Maintenance pruning Oaks afforestation €/ha y = β0+β1 x 917.9 726.2 Eucalyptus sp.

Afforestation €/ha 850 Re-sprouting > 17.5 €/ha 219 Weed control Every 6 yrs. €/ha 432

Coniferous forest Pines thinning ∅ ≤ 7.5 €/m3 y = β0+β1 x 22.88 40.30 Pines thinning 7.5 <∅ ≤ 27.5 €/m3 y = β0+β1 x 10.13 29.12 Pines thinning ∅ >27.5 €/m3 y = β0+β1 x 8.80 30.12 Pines pruning 7.5 <∅ ≤ 17.5 €/tree y = β0+β1 x 0.624 0.197 Pines Afforestation €/ha y = β0+β1 x 505.3 210.34 Shrub clearing all €/ha y = β0+β1 x 497.8 726.2 Source: Own elaboration based on Diaz-Balteiro et al. (2015). (1) The variable x refers to the slope gradient (in %).

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Finally, Table A9 presents the grazing resources prices by province and dominant species and the share of farms that are currently used for livestock grazing. This information is taken from a survey of silvopastoral farm owners that was conducted between 2009 and 2010, and also provide information to estimate the quantity of grazing resources extraction at the farm level (Oviedo et al., 2015). The manufactured costs involved in the provision of grazing resources include the fixed capital consumption (amortization) of manufactured assets used for livestock grazing (i.e. fences, troughs) and their associated opportunity cost (Ovando et al., 2015). This manufactured cost amounts to 1.54 euro/ha in the provinces of Cadiz, Cordoba, Huelva, Malaga and Seville, and to 0.84 euro/ha in the remaining provinces. Table A9: Average grazing resources price and the share of land currently used for livestock grazing(1) Province Price (€/FU(2)) Share of land used for livestock grazing (𝜔, %) Qi Qs P Eu Sh Gl Qi Qs P Eu Sc Gl Almeria 0.08 0.07 0.01 0.07 29 - - - 6 - Cádiz 0.06 0.13 0.06 0.03 0.07 100 72 67 - 79 89 Córdoba 0.05 0.07 0.13 0.07 0.07 87 40 57 - 44 100 Granada 0.07 0.06 0.01 0.07 48 - 55 - 43 67 Huelva 0.08 0.11 0.17 0.01 0.10 0.07 96 47 33 25 55 100 Jaén 0.02 0.07 0.11

0.05 0.07 63 - 45 - 42 43

Málaga 0.14 0.09 0.07 0.05 0.07 89 80 - - 83 67 Seville 0.08 0.07 0.07 0.05 0.07 95 89 - - 33 100 Total farms 0.07 0.10 0.07 0.01 0.05 0.07 85 69 36 25 33 81 Source: Own elaboration based on Oviedo et al. (2015). Notes: (1) Eu: Eucalyptus sp., Qi: Q. ilex, Qs. Q. suber, P: Pinus sp., Sh: Shrubland; Gl: grassland. (2) A forage unit (FU) is equivalent to the metabolic energy content of a kg of barley (Ovando et al., 2015, for details).

Initial forest species and age class distribution

The Third National Forestry Inventory (IFN3) is the source of spatial- explicit information on forest structure (tree and shrub species and age class distribution). The IFN3 was carried out between 2006 and 2008 in the Andalusian provinces, covering 11,603 inventory plots. Those initial inventories were updated for every forest inventory plot to 2010 considering the net volume growth from the specific inventory year up to the beginning of 2010 (see Diaz-Balteiro et al. (2015) for details).

Land use transition scenarios

The effect of forestry activities abandonment is expected to diverge amongst forest species. It is difficult to predict the evolution of the distribution of tree and shrub species, but an analysis of a number of attributes of forest inventories (MARM, 2013), such as the fraction of canopy covered by trees and shrubs, the average age class and the tree species distribution, allows us to outline a simplified set of scenarios that define what the maximum fraction of shrubs canopy cover (FCCS ) by species and silvicultural models will be in a period of 50 years after the natural regeneration

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investment should have taken place. Those scenarios also define what share of specific pine species. (FCCP) will be after the aforesaid period and the maximum fraction of trees canopy cover (FCCT) (Table A10). The share of the land covered by shrubs and trees will not correspond to the maximum value for all the forest land units, rather it will depend linearly on the initial values of FCCS, FCCP and FCCT . Table A10: Expected fraction of shrubs and forest species canopy cover for forestry abandonment scenarios by species and silviculture model

Species Model Fraction of canopy cover (FCC) (%) Maximum Shrubs

FCCS (range) Pines All tree species Min FCCP Max FCCT

P. halepensis 1 55 60 50 90 2 60 70 40 90 P. nigra 1 55 60 30 95 2 60 70 15 45 P. pinaster 1 55 60 - 95 2 60 70 15 45 3 45 40 35 95 4 55 60 35 45 P. pinea 1 58 65 45 90 2 60 70 35 95 3 55 60 50 80 4 60 70 40 90 Q. ilex 1 55 60 - 95 2 53 55 - 95 3 58 65 - 95 4 60 70 20 95 Q. suber 1 55 60 - 95 2 60 70 20 95

Forest water balance The Soil and Water Assessment Tool (SWAT) model (Arnold et al., 1998) was used for computing the water balance of the Andalusian forest parcels. SWAT is a basin scale hydrological model designed to assess the impact of different management options on water, sediment, and contaminant flows. It was originally developed by the US Agricultural Research Service (ARS), and it has gained wide international acceptance as a robust tool for water- shed modeling. A comprehensive review of the SWAT model components and their interdisciplinary applications can be found in (Gassman et al., 2007).

In SWAT, a basin is divided into hydrologic response units (HRUs) with homogeneous land use (vegetation), soil and topographic characteristics. In each HRU the complete water balance is calculated, involving the partition of the input precipitation (plus natural spring outflows and anthropic irrigation inputs, if these are present) into different water flows, including those internal to the HRU (such as storage in the canopy, soil profile and shallow aquifer) and external HRU flows such as evapotranspiration, runoff and deep aquifer recharge.

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We applied SWAT to 44 reservoir catchments across Andalusia, covering the most important reservoirs of the region and a wide variety of climatic, topographic, soil and vegetation conditions. The input data included the gtopo30 digital elevation model, with a spatial resolution of 90 x 90 m (Data available from the U.S. Geological Survey); the land use/land cover map of Andalusia (JA, 2003); the soil class and soil properties map of Andalusia (CSIC-IARA, 1989); and daily maximum and minimum temperatures and precipitation data from more than 1,000 climatic stations (Agencia Espanola de Meterologıa, AEMET; and Confederacion Hidrografica del Guadalquivir, CHG), over the period 2000-2009.

The simulations were validated against monthly stream-flow data and mean annual aquifer recharge data (CHG). Data from more than 2000 forest HRUs spanning Andalusia were collected, and the hydrological balance was used for determining the main outgoing water flows from the forest farms analyzed in this study.

2. SUPPLEMENTARY RESULTS

Trade-offs amongst ecosystem services and forest species Fig. A3 shows the effect of changing the density of the stand1, while other variables remain constant, on the estimated values of the environmental assets for aggregated silvopastoral provisioning services (EAPr), CO2 regulating service (EAC) and forest water (EAW) for Q. suber, Q. ilex and pine species. Fig. A4 shows, as an example, the trade-offs and synergies of the EAPr, EAC and EAW values between Q. suber and pine species.

The EA values presented in Fig. A3 and Fig. A4 are estimated using the parameters of the linear regressions presented in the Table 3 of the main text, and considering forest units with averages slope gradient (26%) and weight of different pine species as observed in the sample of 567 silvopastoral farms. In addition, those estimations account for the distribution of the site qualities for growing timber, firewood and cork observed in the sample of farms (see Table A11). Table A11: Distribution of the site qualities for growing timber, firewood and cork and management model(1) by forest species

Class Pine species

timber Quercus suber

cork Quercus ilex(2)

firewood Site quality (in percentage of the forest units’ area by species)

Low 10 Low to Medium 63 26 51 Medium to High 37 54 49 High 10 Management model (in percentage of the forest units’ area by species)

1 Using basal area, ranging from 5 to 65 m2/ha, as a proxy indicator of the stand density.

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Even-aged forest type 99 100 93 Uneven aged forest type 1 0 7 Notes: (1) Management model refers to the one applied to even-aged forest or on the contrary to uneven aged forest . (2) Medium low quality includes silvicultural models 2 and 3.

Note: Data for main scenario (discount rate 3%, price level: 2010=1.0).

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Fig. A3: Environmental assets of silvopastoral provisioning services, carbon and water according to the forest species and the stand density


Fig. A4: Trade-offs between the environmental assets of silvopastoral provisioning services, carbon and water in pine and Q. suber forests

Forestry abandonment and minimum compensations

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Table A12 shows the minimum compensation to Q. ilex and pine species for the main scenario and for the low and high discount and price scenarios. The estimated minimum compensations diverge spatially (Fig. A5). Table A12: Minimum compensation to natural regeneration investment in Q. ilex and pine species forest units (euro/ha, year 2010)

Scenario Pine species Q. ilex Mean SD CV (%) Mean SD CV (%)

Central 1,386 713 51 996 501 50 Low discount 2,009 1,121 56 543 509 94 High discount 806 375 46 1,078 380 35 Low prices 1,616 766 47 1,929 763 40 High prices 1,279 676 53 348 341 98 Note: SD: Standard deviation, CV: Coefficient of variation/mean).


Fig. A5: Distribution of the minimum compensations to natural regeneration investment

References Arnold, J. G., R. Srinivasan, R. S. Muttiah, and J. R. Williams (1998). Large area hydrologic modeling and assessment. Part I: model development. Journal of the American Water Resources Association 34(1), 73–89. CMA (2010). Adecuacion del Plan Forestal Andaluz. Horizonte 2015. Seville: Consejerıa de Medio Ambiente, Junta de Andalucıa.

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CMA (2011). Sistema de informacion de la gestion de los montes y sus aprovechamientos. Ventas y arrendamientos de productos y servicios en montes publicos 2010. Consejerıa de Medio Ambiente, Junta de Andalucıa. Unpublished. CSIC-IARA (1989). Mapa de suelos de Andalucıa. Escala 1:400.000. Seville: Consejo Superior de Investigaciones Cientıficas/ Instituto Andaluz de Re- forma Agraria. Diaz-Balteiro, L., A. Caparros, P. Campos, E. Almazan, P. Ovando, A. Alvarez, R. Voces, and C. Romero (2015). Economıa privada de productos lenosos, frutos industriales, bellota, pastos y el servicio del carbono en los sistemas forestales de Andalucıa. In: Campos, P. and Dıaz-Balteiro, L. (eds.), Memorias cientıficas de RECAMAN. Economıa y selviculturas de los montes de Andalucıa, Volume 1, pp. 397–722. Madrid: Editorial CSIC. Gassman, P. W., M. R. Reyes, C. H. Green, and J. G. Arnold (2007). The soil and water assessment tool: historical development, applications, and future research directions. American Society of Agricultural and Biological Engineers 50(4), 1211–1250. JA (2003). Mapa de Usos del Suelo y Coberturas Vegetales de Andalucıa 1:25000. Junta de Andalucıa. MARM (2013). Tercer Inventario Forestal Nacional. Andalucıa. Madrid: Ministerio de Medio Ambiente, Medio Rural y Marino. Montero, G., M. Pasalodos, E. Lopez Senespleda, R. Ruiz-Peinado, A. Bravo, and G. Madrigal (2015). Modelos de selvicultura y produccion de madera, frutos y fijacion de carbono de los sistemas forestales de Andalucıa. In: Campos, P. and Dıaz-Balteiro, L. (eds.), Memorias cientıficas de RECAMAN. Economıa y selviculturas de los montes de Andalucıa, Volume 1, pp. 153–396. Madrid: Editorial CSIC. Montero, G., R. Ruiz-Peinado, and M. Munoz (2006). Produccion de biomasa y fijacion de CO2 por los bosques espanoles. Instituto Nacional de Investigacion y Tecnologıa Agraria y Alimentaria. Montero, G., E. Torres, I. Canellas, and C. Ortega (1996). Modelos para la estimación de la produccion de corcho en alcornocales. Investigacion agraria. Sistemas y recursos forestales 5 (1), 97–127. Obst, C. and M. Vardon (2014). Recording environmental assets in the national accounts. Oxford Review of Economic Policy 30 (1), 126–144. Ovando, P., P. Campos, B. Mesa, A. Alvarez, C. Fernandez, J. Oviedo, A. Caparros, and B. A lvarez-Farizo (2015). Renta y capital de estudios de caso de fincas agroforestales de Andalucıa. In: Campos, P. and Ovando, P. (eds.), Memorias cientıficas de RECAMAN. Renta total y capital de las fincas agroforestales de Andalucıa, Volume 4, pp. 156–445. Madrid: Editorial CSIC.

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Oviedo, J., P. Campos, and A. Caparos (2015). Valoracion de servicios ambi- entales privados de propietarios de fincas agroforestales de Andalucıa. In: Campos, P. and Ovando, P. (eds.), Memorias cientıficas de RECAMAN. Renta total y capital de las fincas agroforestales de Andalucıa, Volume 4., pp. 8–155. Madrid: Editorial CSIC. SENDECO2 (2015). Historical CO2 prices for EUAs. Technical report, SENDECO2 (Carbon dioxide emission allowances electronic trading system). Available online at: http://www.sendeco2.com. Serrada, R., G. Montero, and J. A. Reque (2008). Compendio de selvicultura aplicada en Espana. INIA Madrid, Spain. Sohngen, B. and R. Mendelsohn (2003). An optimal control model of forest carbon sequestration. American Journal of Agricultural Economics 85(2), 448–457.

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