Forest plantations support several ecosystem services including biodiversity conservation. Establishment of a forest biomass-based industry could significantly change the age structure of forest plantations located in its vicinity and thus, could lead to a possible loss of biodiversity. Therefore, this study assesses spatiotemporal impacts of a forest biomass-based power plant on the age structure of surrounding forest plantations at landscape level. A cellular automata approach was adopted and interactions between economic objectives of forest landowners and a power plant owner punctuated by forest growth and management characteristics were considered. These spatiotemporal impacts were jointly assessed for four separate scenarios and four different power plant capacities using appropriate landscape-level indices. Slash pine (Pinus elliottiL.) was selected as a representative species. Results indicate that the age structure of surrounding forest plantations continuously fluctuates with respect to each year of power plant operation. However, the age structure, once disturbed, never becomes comparable to the original age structure. We also found that the mature plantations were harvested during early years of power plant operation and were never observed again for the remaining years of power plant operation. This was particularly true for high capacity power plants. Similarly, high value of selected spatial index at the end of power plant life for a high capacity power plant relative to the original low value of the same index indicates aggregation of remaining plantation ages at landscape level. Establishment of low capacity forest biomass-based power plants and adoption of an integrated regional level planning approach could help in maintaining original age structure characteristics of surrounding forest plantations to a large extent. This might help in sustaining various ecosystem services including biodiversity conservation obtained from forest plantations in a long run.
The United States generated 1.4E+16 btu (3950.2E+09 kWh) of electricity in 2009 out of which 3.6% (4.9E+14 btu or 144E+09 kWh) was generated from nonhydro-based renewable sources (EIA, 2011a). An analysis of total electricity generated from nonhydro based renewable sources reveals that 51.4% of total generated electricity came from wind, followed by various biomass-based sources (37.6%), geothermal (10.4%), and solar (<1%) (EIA, 2011b). The percentage of electricity generated from various renewable sources will probably increase in the future as several states are adopting or promoting ‘renewable portfolio standards’ in which a percentage of total electricity generated within the state will be sourced from renewable sources, such as wind, solar, geothermal, hydropower, and biomass (Doris et al., 2009).
Along with other renewable sources, electricity generated from biomass-based sources will be critical in the future portfolio of renewable electricity generation in the United States. For example, ACORE (2007) estimated that 8.52E+08 HP (635 GW) of new renewable power could be generated by 2025 out of which electricity generated using biomass could contribute up to 1.34E+08 HP (100 GW). Similalry, Perlack et al. (2005) reported that 1.4E+09 tons (1.24E+09 metric tons) of dry biomass can be utilized for bioenergy development at the national level out of which the agriculture sector could contribute 73%, whereas the remaining 27% could come from the forestry sector. Walsh et al. (2000) found that at a market price of up to $30 per dry ton ($33.07 per dry metric ton) delivered, the total amount of forestry feedstocks available (excluding wood obtained from urban areas but including forest mill residues, dedicated forestry crops, and forestry residues) would be approximately 24% of all cellulosic biomass available in the nation. It was also mentioned that the amount of available forestry feedstocks would increase to 45% of the total cellulosic feedstocks available at a market price of up to $40 per dry ton ($44.1 per dry metric ton) delivered. These studies clearly indicate that forestry feedstocks will play a significant role in the future scenario of biomass-based electricity generation at the national level. It is quite likely that the electricity generated from forest biomass will help in reducing total emissions of greenhouse gases (Dwivedi et al., 2011) and promoting local employment (Mayfield et al., 2007). This is especially true for the Southern states of the country (Alabama, Arkansas, Florida, Georgia, Kentucky, Louisiana, Mississippi, Missouri, North Carolina, Oklahoma, South Carolina, Tennessee, Virginia, and West Virginia) as these states are rich in forest resources and have limited electricity generation potential from other renewable sources.
In 2007, total forestland in the United States was 751.2E+06 acres (3.04E+06 km2) out of which approximately 30% (224.5E+06 acres or 0.91E+06 km2) was located in the Southern states. About 97.4% (218.6E+06 acres or 884.82E+03 km2) of total forestland present in the Southern region was classified as timberland due to its relatively high biomass productivity (>20 ft3 ac−1 yr−1 or 1.4 m3 ha−1 yr−1) (Smith et al., 2009). Furthermore, total planted timberland area in the Southern states was 67.5% of total planted timberland area in the country in 2007 (Smith et al., 2009). This is primarily due to an emphasis on timber production in the region. As a result, net volume of total growing-stock1 in the Southern states was 311.4E+09 ft3 (8.83E+09 m3) in 2007 which was almost 33% of total national growing-stock in the same year (Smith et al., 2009). Additionally, total aboveground forest biomass available in Southern states was almost 40% (9.5E+9 dry tons or 8.61E+9 dry kg) of total aboveground forest biomass available on all timberlands of the country in 2007 (Smith et al., 2009). Notably total annual removals2 from the Southern timberlands were almost 60% of total removals from all the timberlands in 2006 nationwide. Clearly, the Southern region is a major supplier of forest products at the national level and will continue to remain so in the near future due to its vast and productive timber production base.
Nonindustrial private forest (NIPF) landowners own about 60.6% of total forestland (61.4% of total timberland) in the region (Smith et al., 2009). As a consequence, net volume of total growing-stock on timberlands owned by NIPF landowners in the Southern region was almost 50% (1.86E+11 ft3 or 5.29E+09 m3) of net volume of total growing-stock on timberlands owned by all NIPF landowners nationwide in 2007 (Smith et al., 2009). Therefore, it can be easily deduced that biomass obtained from forest plantations owned by the Southern NIPF landowners will play a major role in meeting the overall demand for woody feedstocks for biomass-based electricity generation at the national level in general and regional level, in particular.
Forest plantations, biodiversity, and bioenergy development
Forest plantations play an important role in supporting several floral and faunal species at local and landscape level (Lamb, 1998; Hedman et al., 2000; Hartley, 2002; Lindenmayer & Hobbs, 2004). For example, Repenning & Labisky (1985) reported that the density of wintering birds in Apalachicola National Park in the Florida Panhandle did not differ significantly between longleaf pine (Pinus palustris) forests and the four different ages of slash pine (Pinus elliotti) plantations. Morrison (1992) mentioned that even- and uneven-aged hardwood plantations in western Sierra Nevada, California could support similar bird communities provided they are managed for high tree species diversity and wildlife needs. Hanowski et al. (1997) observed that numbers of bird species present in hybrid poplar plantations at Minnesota, Wisconsin, and South Dakota were higher than the row crops.
Numerous studies have reported that conventional logging practices undertaken to remove forest biomass severely impact various forest-based ecosystem services including biodiversity (Phillips & Shure, 1990; Bergstedt & Milberg, 2001; Sun et al., 2001; McAlpine et al., 2007). Ghazoul (2002) reported that the abundance of butterflies decreased with increased logging disturbance in Huay Kha Khaeng Wildlife Sanctuary, Thailand due to reduction in tree density. Barlow et al. (2006) reported that the composition of understory birds was markedly different in a logged Amazonian forest area relative to a similar undisturbed forest area. Apart from adopted logging practices, intensification of logging also adversely affects forest biodiversity. For instance, the intense management of forests for procuring sufficient woody feedstock to run a forest biomass-based power plant or an ethanol mill could alter the original forest composition and structure and thus, could significantly affect those species which use the same forests as habitat (Kittredge et al., 2003). Verkerk et al. (2011) opined that the intensification of deadwood removal from forests primarily due to rapid bioenergy development in the Europe could negatively affect deadwood-dependent species which constitute an important part of biodiversity in European forests. It was also mentioned that a change in forest logging intensity affects forest patch sizes and thus, associated ecological functions like biodiversity conservation (Franklin & Forman, 1987; Gustafson & Crow, 1994).
The growth in acreage of forest plantations for bioenergy development affects biodiversity (Eggers et al., 2009; Fletcher et al., 2010). Landis & Werling (2010) mentioned that increased adoption of bioenergy crops may change the ways arthropod-mediated ecosystem services, such as pollination and pest suppression are distributed across a landscape and this could lead to loss of biodiversity present in local agriculture and forest landscapes. There is also a discussion on negative ecological impacts of converting native forestlands to commercial bioenergy plantations, that is, clearing tropical forests of Southeast Asia and planting oil palm (Elaeis guineensis). Danielsen et al. (2009) reported low values of species-richness for birds, lizards, and mammals in oil-palm plantations relative to nearby forest areas. Koh et al. (2011) reported that conversion of peat-swamp forests to oil palm led to a biodiversity decline of 1% in Borneo (equivalent to four species of forest-dwelling birds), 3.4% in Sumatra (16 species), and 12.1% in Peninsular Malaysia (46 species). Therefore, it is not a big surprise that several studies emphasize on developing suitable standards to ensure overall sustainability of various bioenergy products (Keeney & Nanninga, 2008; Hennenberg et al., 2009).
A perusal of the existing literature indicates that there exists a paucity of information on assessing spatiotemporal impact of a forest biomass-based bioenergy facility on the age structure of surrounding forest plantations at landscape level. This study intends to fill this critical gap in our understanding and attempts to determine how the interplay between economic objectives of NIPF landowners (profit maximization from the owned forestland) and an owner of forest biomass-based power plant (minimization of total biomass procurement cost for each year of power plant operation) punctuated by forest growth and management characteristics affects the age structure of surrounding forest plantations at landscape level in space and time. A suite of suitable landscape-level pattern indices were used to quantify spatiotemporal impacts on the age structure of surrounding forest plantations.
The reference species selected for this study was slash pine which is a popular commercial forestry species of the Southern United States. In 2007, total acreage of longleaf-slash pine forests was 13 million acres (5.3 million hectares) out of which the majority was concentrated in Florida and Georgia (Smith et al., 2009). Two separate schemes of slash pine biomass production by NIPF landowners were assumed – intensive and nonintensive forest management.3 Under the intensive forest management scheme of slash pine biomass production, it was assumed that an NIPF landowner will undertake the following silvicultural practices – chopping, piling, burning, disking, bedding, herbicide application, planting, and fertilizer application. However, only the following silvicultural practices will be undertaken by an NIPF landowner under nonintensive forest management scheme of slash pine biomass production – chopping, piling, burning, disking, bedding, and planting. A suitable growth and yield model (Yin et al., 1998) was used to determine availability of four timber products (sawtimber, chip-n-saw, pulpwood, and harvesting residues) on per acre basis for all plantation ages under both the schemes of forest management, separately.
The optimal rotation ages for an acre (0.405 ha) of intensively and nonintensively managed slash pine plantations were determined separately using the Faustmann model (Faustmann, 1995) as shown below:4
where t is plantation age, LEV (land expectation value) is the present value of profit from growing infinite number of forest rotations, timber is the total value of different timber products, amty is the annual value of amenity benefits like hunting leases, C is the cost of site preparation and planting, TxC is the annual tax, MgmtC is the annual management cost, FertiC is the fertilization cost, and r is the real discount rate. The variable, timber (t), is derived as:
where prices and quantities of sawtimber, chip-n-saw, pulpwood, and logging residues are presented by pst, qst; pcs, qcs; ppw, qpw; and plr, qlr, respectively. Note that the plantation age (t) at which LEV becomes highest is the optimal rotation age. The various income streams and cost sources used for determining optimal rotation ages are reported in Table 1. A real discount rate of 5% was used for both forest management schemes in this study.
Table 1. Incomes and costs used for determining optimal rotational age of an acre slash pine plantationa
The prices of various timber products are from TMS (2010a). Other costs are obtained through a personal communication with Matthew Simpson, Natural Resource Planning Services, Lake City, Florida. All reported costs and incomes were used for estimating optimal rotation age of an acre of intensively managed slash pine plantation. All sources except costs of herbicide and fertilization application were used for estimating optimal rotation age of an acre of nonintensively managed slash pine plantation.
30 ($ per green ton)
19 ($ per green ton)
10 ($ per green ton)
Logging residues prices
1.5 ($ per green ton)
10 ($ per acre per year)
50 ($ per acre)
48 ($ per acre)
60 ($ per acre)
100 ($ per acre)
105 ($ per acre)
60 ($ per acre)
45 ($ per acre)
NPK (2nd year)
80 ($ per acre)
NPK (12th year)
100 ($ per acre)
7 ($ per acre per year)
5 ($ per acre per year)
The total life of a power plant was taken as 40 years (Ambrosini, 2005). Four different capacities of power plant were considered for this study, i.e., A (13.4E+03 HP or 10 MW), B (26.8E0 + 3 HP or 20 MW), C (40.2E+03 HP or 30 MW), and D (53.6E+03 HP or 40 MW). The annual biomass required for a power plant was calculated using the formula given in Box 1. The annual required forest biomass for power plants A, B, C, and D was found to be 3.33E+04 green tons (3.02E+04 green metric tons), 6.67E+04 green tons (6.05E+04 metric tons), 1.00E+05 green tons (9.07E+04 metric tons), and 1.33E+05 green tons (1.21E+05 metric tons), respectively.
Box 1: Estimating annual biomass required for a power plant
Annual biomass requirement (green tons yr−1) = [[[[[total electricity generated × [100/conversion efficiency]]/calorific value of slash pine wood]/2000] × 2],
where conversion efficiency is 40%; calorific value of slash pine wood is 8.6E+03 btu lbs−1 (20 MJ kg−1) (Wang et al., 1982), and total electricity generated (btu yr−1) = [plant capacity × CUF × total working hours in a year) × 2545]. The plant capacity is in HP, capacity utilization factor is 40% (EIA, 2011c), and number of working hours is 8400 (350 days × 24 h day−1).
Von Neumann (1966) introduced cellular automata (CA) as a mathematical abstraction of a real complex system in which space and time are discrete for a lattice of adjoining cells. These cells are allowed to have a number of finite states only. The transition rules (qualitative, quantitative, or both) by which the state of a particular cell could change are local in scope and dependent on the current state of neighboring cells (Wolfram, 2002). In CA models, the time component progresses in discrete steps and the cells update their state synchronously after the transition rules are applied (Yassemi et al., 2008). Several studies have used CA-based models to explain forest fire behavior (Karafyllidis & Thanailakis, 1997; Li & Magill, 2001; Yassemi et al., 2008), forest landscape dynamics (Soares-Filho et al., 2002), spread of forest infestation (Bone et al., 2006), and impact on forestry markets (Schwab et al., 2009). Therefore, we adopted a CA-based approach in this study for ascertaining spatiotemporal impact of a forest biomass-based power plant on the age diversity of surrounding forest plantations.
We divided the entire forested landscape into adjoining cells where each cell represents an acre (0.405 ha) of forestland.5 We assumed that the power plant is located in the center of selected forested landscape. We also assumed that for each year of operation, the power plant utilizes only that forest biomass which is available in the form of pulpwood and logging residues from cells present in the landscape and the remaining timber products (sawtimber and chip-n-saw) obtained at the time of harvesting are sold to other forest-based industries. For initialization, we randomly assigned a value between 0 and 40 including endpoints to each cell. This value represents the age of the slash pine plantation present in a cell. Similarly, we randomly assigned a value of either 0 or 1 to each cell signifying whether that cell is either intensively (1) or nonintensively (0) managed. Based on the plantation age and type of forest management scheme initially selected for each cell, the availability of pulpwood and logging residues on each cell was determined using the selected growth and yield model.
A forest landowner will try to maximize financial returns from forestry operations. As a result, the biomass available on a cell will not be available to the power plant owner for a particular year of operation until the age of the slash pine plantation present on that cell is greater than or equal to the respective optimal rotation age, depending upon the type of forest management scheme initially selected for the cell. Similarly, the availability of biomass to the power plant from a given cell in a particular year of power plant operation is dependent upon the age of plantations present on cells located adjacent to that cell. This becomes critical due to adjacency constraints recommended under best management practices implemented by various states for ensuring sustainable forest management (Carter et al., 1997).6 For this study, we are defining adjacency based on a neighborhood of four cells located on four sides of a given cell.7 Therefore, we assumed that if the total number of adjacent cells which are of the same age as that of a given cell is greater than one, then the biomass from a given cell will not be available for any use in that year of power plant operation. The adjacency constraint was given a precedence over the optimal age criteria while deciding whether or not a given cell is available for sourcing biomass in a particular year of power plant operation.
The owner of the power plant will try to minimize the total cost of biomass procurement (biomass purchase cost + biomass processing cost + biomass transportation cost) in a given year of power plant operation by selecting suitable number of cells present in the forested landscape subject to the constraint that the quantity of procured biomass from the selected cells is equal or greater than the quantity of required annual biomass. These selected cells have to be a subset of total available cells from where biomass for the power plant can be sourced for that year of power plant operation. Note that an available cell is that cell that satisfies adjacency constraint and optimal rotation age condition. Therefore, we estimated total cost of procuring biomass from each available cell (Box 2). The power plant owner will select only those available cells from which required quantities of biomass can be sourced at the minimum cost for each year of operation (Box 3). The plantation age of selected cells in a particular year of operation will become zero for the next year of operation. However, the plantation age of all unselected cells (including those cells which were not available for sourcing biomass) will increase by 1 year. As the maximum age of a forest plantation is set at 40 years, therefore those cells where the age of forest plantation becomes more than 40 years will also reset to zero for the next year of operation. This cycle will be repeated until the entire program runs for 40 years for a given power plant capacity.
Box 2: Estimating biomass procurement cost from each available cell
The Euclidean distance of each cell from the power plant was determined using Cartesian geometry rules. Then, total biomass procurement cost ($) = biomass purchase cost ($) + biomass processing cost ($) + biomass transportation cost ($),where
biomass purchase cost ($) = [pulpwood quantity (green tons) × pulpwood purchase price ($10 per green ton or $11.02 per green metric ton)] + [logging residues quantity (green tons) × logging residues purchase price ($1.5 per green ton or $1.65 per green metric ton)] (TMS, 2010a);
biomass processing cost ($) = [pulpwood quantity (green tons) × [cut and load price ($10.39 per green ton or $11.45 per green metric ton) + chipping price ($6 per green ton or $6.61 per green metric ton)]] + [logging residues quantity (green tons) × chipping price ($6 per green ton or $6.61 per green metric ton)];
biomass transportation cost ($) = roundup[[pulpwood quantity (green tons) + logging residues quantity (green tons)]/maximum load capacity of a semitruck (25 tons or 22.68 metric tons)] × [[pulpwood quantity (green tons) + logging residues quantity (green tons)] × distance of cell from the power plant (miles) × unit transportation cost ($0.12 ton−1 mile−1 or $0.08 metric ton−1 km−1 (TMS, 2010b))]].
Box 3: Optimization model for an owner of the power plant for each year of power plant operation
where TC is the total biomass procurement cost from each available cell X, B is the total availability of biomass on each available cell, Q is the annual requirement of forest biomass to run the power plant, and N is the total number of available cells in a particular year of power plant operation. Note that available cells are those cells which satisfy the conditions of adjacency and minimum optimal rotation age.
We studied four separate scenarios for this study. In the first scenario (RA-AC-AGE), we allowed for the random allocation of cells from which biomass was sourced for the power plant operation in a given year to either of two forest management schemes for the next forest growth cycle. Adjacency constraints were also present in this scenario. In the second scenario (RA-AGE), the random allocation of selected cells to either of two forest management schemes was maintained but adjacency constraints were removed. In the third scenario (AGE), the random allocation between management schemes was removed along with adjacency constraints. In the fourth scenario, the random allocation of selected cells was not allowed but adjacency constraints were permitted (AC-AGE). All four scenarios are summarized in Table 2. The condition that the biomass available on a cell cannot be harvested unless plantation age on that cell becomes greater than or equal to the respective optimal rotation age was present in all scenarios. Under all selected scenarios, 10 simulations were run where each simulation had a different random distributions of initial plantation age and forest management scheme at landscape level. However, distributions of initial plantation age and forest management scheme at landscape level were held constant across all selected power plant capacities and scenarios for each simulation run. We used MATLAB R2010 by MathWorks (Natick, MA, USA) for running these simulations as optimization was not possible due to large number of binary decision variables. The simulation procedure is shown in Fig. 1. A sensitivity analysis was also performed to ascertain impact of key parameters on the age complexity of surrounding forest plantations (Table 3).
Table 2. Details of four scenarios analyzed in the study
For a particular year of power plant operation, some available cells are harvested to supply required biomass to the power plant. In the first two scenarios, these selected cells were suitably identified and randomly allocated to either of two forest management schemes, that is, intensive and nonintensive forest management. In the last two scenarios, initial allocation of forest management intensities for all cells was kept constant throughout 40 years of power plant operation.
Scenario I (RA-AC-AGE)
Scenario II (RA-AGE)
Scenario III (AGE)
Scenario IV (AC-AGE)
Table 3. Key parameters for the sensitivity analysisa
Name of parameters
Average values (%)
Range of values (%)
The sensitivity analysis was undertaken for a power plant D [53.6E+03 HP (40 MW)] for Scenario I (Run 10).
Capacity utilization factor (CUF)
20, 30, 50, 60
Conversion efficiency (CE)
30, 35, 45, 50
Discount rate (DR)
3, 4, 6, 7
The distribution of required biomass available from an acre of slash pine plantation for intensive and nonintensive forest management schemes of biomass production is shown in Fig. 2. The availability of small-diameter timber products (pulpwood and logging residues) under the scheme of intensive forest management was higher for initial plantation years (up to 14 years) relative to the scheme of nonintensive forest management. However, this situation reverses as the age of plantation becomes greater than 14 years. The availability of large-diameter timber products (sawtimber and chip-n-saw) was always greater in an intensively managed slash pine plantation for all years of plantation relative to a nonintensively managed slash pine plantation.
The optimal rotation age of an acre of slash pine plantation under intensive forest management scheme was found as 21 years with a LEV of $308 per acre ($760 ha−1) whereas it was 20 years with a LEV of $348 per acre ($859 ha−1) of slash pine plantation under nonintensive forest management scheme. The lower LEV for the scheme of intensive forest management can be attributed to the high cost of inputs especially fertilizers. The distribution of LEVs for both forest management schemes (Fig. 3) was in accordance with other studies (Dwivedi et al., 2009).
The distribution of available biomass (pulpwood and logging residues) at landscape level for all power plant capacities and scenarios is shown in Fig. 4. As expected, initial biomass availability at landscape level was same for all power plants for a given scenario as initial distributions of age structure and forest management schemes were same across all power plants. However, as power plant capacities increases, the biomass availability at landscape level decreases until the optimal rotation age is reached, at which time it starts rising again. For Scenarios I (RA-AC-AGE) and II (RA-AGE), the minima in biomass availability occurs at age 20 and for Scenarios III (AGE) and IV(AC-AGE), it occurs at age 21. This difference can be attributed to the biomass growth characteristics where biomass harvesting from an intensively managed forestland is preferred over a nonintensively managed forestland by a power plant owner due to economic considerations.8 The rate of decrease and subsequent recovery of biomass availability at landscape level were directly proportional to the power plant capacity.
Distribution of total distance covered to source required biomass for all power plant capacities and scenarios is shown in Fig. 5. As expected, total distance travelled to procure required biomass for power plant D was highest, followed by the distance covered to source biomass for C, B, and A power plants. Notably, the total distance travelled for higher capacity power plants decreases for the initial 20–25 years of power plant operation and then increases slightly with some stochasticity around the minima. In contrast, the distance travelled for sourcing biomass for smaller capacity power plants was almost constant for all the years of power plant operation. The distribution of net present value of total cost incurred by a power plant owner to source sufficient biomass from surrounding forest plantations is shown in Fig. 6.9 As expected, the total cost was highest for the power plant D, followed by power plants C, B, and A. Interestingly, we found that respective annual costs incurred by power plant owners were approximately constant for all years of power plant operation. This clearly indicates that final biomass procurement cost for a particular year of power plant operation is largely dependent on the biomass cost (biomass purchase cost + biomass processing cost) rather than biomass transportation cost. This is particularly true when the age of forest plantations is randomly distributed at landscape level.
The total number of plantation age classes present in the landscape over time is shown in Fig. 7 for all power plant capacities and scenarios. As observed, all plantation ages remain present in the landscape for all scenarios of power plant A through all the 40 years of operations. However, this was not the case with other power plant capacities. With larger plant capacities, the number of age classes present in the landscape declined over time. The magnitude of the decline is directly proportional to the power plant capacity. In each case, the number of age classes present in the landscape reaches a minimum and rebounds, although not to the original level. For example, with power plant D, the total count of age classes present at the landscape falls sharply until the 22nd year of power plant operation, after which it increases, appearing to level off near the end of the power plant operation without fully recovering to the number of age classes present at the beginning. However, the trajectory of this increase is different for all scenarios. The trajectories for Scenario I (RA-AC-AGE) and Scenario IV (AC-AGE) were found almost similar, whereas trajectories of Scenario II (RA-AGE) and Scenario III (AGE) overlap with each other considerably. This could be attributable to the presence and absence of adjacency constraints in these scenarios. It was also noticed that mature age classes are lost permanently for a high capacity power plant. For instance, we found that number of cells having plantation age of 35 years or higher were close to zero starting from fifth year onwards for the power plant D under Scenario I.
The Simpson's index was calculated as where the proportion of total cells belonging to a plantation age i relative to the total number of cells in forest landscape (pi) was calculated and squared. The resulting value was summed across all plantation ages and then the reciprocal was taken. The value so obtained was multiplied by the reciprocal of total number of plantation ages which had nonzero number of cells (S) (Beals et al., 2000). Simpson's index is a measure of abundance and evenness of total forestland present in different plantation ages at a given time (Turner et al., 2001). The distribution of Simpson's index for all power plant capacities and scenarios is shown in Fig. 8. For all scenarios of the power plant A, there is a slight decrease in Simpson's index implying small unevenness in the distribution of cells in different plantation age classes at landscape level. For larger capacity power plants, Simpson's index decreases such that both the magnitude and rate of decline vary directly with the power plant capacity. Additionally, the Simpson's index shows a degree of periodicity with a period that varies with the capacity of the power plant. We also note a strong overlap between distribution of Simpson's index for Scenario I (RA-AC-AGE) with Scenario IV (AC-AGE) and Scenario II (RA-AGE) with Scenario III (AGE). This could be attributed to the random allocation of harvested cells to either of two forest management schemes as random allocation influences future biomass availability at landscape level.
The change in the spatial configuration of the landscape before and after the simulation is reported in Table 4 for all power plant capacities and scenarios. Two spatial indices were used to quantify spatial changes in the distribution of plantation ages at a landscape level, that is, contagion index (CONTAG) and interspersion and juxtaposition index (IJI). The values of selected spatial indices were calculated using FRAGSTATS (McGarigal et al., 2002). An increase in the value of CONTAG at the end of a power plant life reveals that remaining age classes tend to be more aggregated as opposed to being dispersed in smaller fragments (Leitao et al., 2006). Similarly, low values for the IJI for higher power plant capacities indicate that patches of different plantation ages were distributed disproportionally or aggregated at a landscape level by the end of the modeling period (Eiden et al., 2000). Based on the difference in the value of CONTAG and IJI indices, it was found that the aggregation level of age classes and patches of different plantation ages increases with a rise in power plant capacity.
Table 4. Changes in the spatial configuration at landscape level before and after the simulation
Power plant A: 13.4E+03 HP (10 MW); power plant B: 26.8E0 + 3 HP (20 MW); power plant C: 40.2E+03 HP (30 MW); power plant D: 53.6E+03 HP (40 MW).
Contagion index (CONTAG) is based on the probability of finding a cell of type i next to a cell of type j (Li & Reynolds, 1993). The range of CONTAG lies between 0 and 100 including endpoints as the calculated value of probability is multiplied by 100 for reporting purposes. This value of CONATG increases as a landscape is dominated by a few large patches and decreases with increasing subdivision and interspersion of patch types. This index summarizes the aggregation of all classes and thereby provides a measure of overall clumpiness of the landscape (Smith et al., 2007). Note that this index takes into account cell adjacancies and not patch adjacencies.
Interspersion and juxtaposition index (IJI) measures adjacency among patch types over the maximum possible interspersion for the given number of patch types. The value of IJI lies between 0 and 100 including endpoints as the calculated value of overall probability is multiplied by 100 for reporting purposes. Unlike CONATG, this index measures patch adjacencies and not cell adjacencies (Alberti, 2008). The value of IJI approaches 0 when the distribution of adjacencies among unique patch types becomes increasingly uneven where as value of 100 signifies that all patch types are equally adjacent to all other patch types, that is, maximum interspersion and juxtaposition (McGarigal et al., 2002).
At the end of power plant operation
Scenario I (RA-AC-AGE)
Scenario II (RA-AGE)
Scenario III (AGE)
Scenario IV (AC-AGE)
At the beginning of power plant operation
Sensitivity analysis reveals that with a rise in the value of capacity utilization factor (CUF), the value of the Simpson's index for each year of power plant operation decreases (Fig. 9). It was also noticed that with a rise in the value of conversion efficiency (CE), the value of the Simpson's index for each year of power plant operation becomes larger and larger. This can be directly linked to the total quantity of biomass extracted from the landscape. For example, with a rise in CUF, the demand for annual biomass needed to run the power plant also goes up. This leads to more harvesting and thus, lower values of Simpson's index. Similarly, the demand for annual biomass needed to run the power plant goes down with a rise in the value of CE resulting in less harvesting, and thus, higher values of Simpson's index. The distribution of Simpson's index with respect to number of years of power plant operation was found almost constant for all discount rates. However, it was found that the power plant cannot operate after 17 and 20 years at discount rates 3% and 4%, respectively, due to lack of sufficient biomass availability at landscape level. This could be attributed to the fact that at lower discount rates, the optimal rotation age increases and this limits supply of biomass especially under the assumption that an NIPF landowner will maximize returns from the owned forestland.
Total biomass availability at landscape level for power plants B, C, and D decreases to a certain rotation age depending upon a selected scenario. This implies that total annual biomass harvested for power plants B, C, and D was higher than the net biomass growth at landscape level until the years of power plant operation approach the optimal rotation age for a given scenario. Once the years of power plant operation exceed the optimal rotation age, the available biomass increases and appears to reach a new equilibrium, although for large capacity power plants it does not recover to the initial level. This implies that harvesting intensity is more in initial years of a high capacity power plant operations as compared to later years. This further implies that an equilibrium is achieved at landscape level between annual biomass supply and demand when the total years of power plant operation approaches toward optimal rotation ages. We noticed that the power plant owner sources biomass from a narrow range of age classes especially in the later years of plant life due to rational economic decision-making coupled with biomass growth characteristics. For example, power plant D sourced required biomass from plantation age classes 21–28 at the final year of plant life. Similarly, power plants C, B, and A sourced biomass from plantation ages 24–32, 32–38, and 38–40, respectively, in the final year. Note that the range narrows and shifts toward older age classes as the capacity of power plant decreases.
We found that as the capacity of power plant increases, mature plantation age classes are permanently lost. The loss of mature plantation age classes can significantly harm the local biodiversity as mature plantation age classes support several bird species (Repenning & Labisky, 1985). A reduction in the number of plantation ages at landscape level decreases landscape-level heterogeneity and causes an uneven distribution of forestland in remaining plantation age classes. This change in the overall structure of forest plantations over time could significantly affect the local biodiversity (Walmsley & Godbold, 2010) as heterogeneity in horizontal and vertical stand structure is linked to higher species-richness and greater ecological stability (Kerr, 1999; Pommerening, 2002). For example, Pipp et al. (2001) found that structure of Douglas-fir (Pseudotsuga menziesii) explained more variance in epiphytic lichen biomass and richness rather than forest age in Gifford Pinchot National Forest in Washington, United States. The age structure of forest plantations also changes in space, which might change the availability of habitat for many species (Crist et al., 2005). Heikkinen et al. (2004) mentioned that a major part of the spatial structure in bird patterns in agricultural-forest mosaics can be caused by the clumping of habitats either preferred or avoided by birds. Moreover, maintaining structural complexity at landscape level by considering spatial arrangement of different-aged plantation stands with respect to other landscape components is recommended for ensuring maintenance of biodiversity and other ecosystem services found in forest plantations (Harvey et al., 2002; Brockerhoff et al., 2008).
We also found that apart from economic objectives of a power plant owner, economic objectives of NIPF landowners also play a critical role in determining the overall impacts on the age distribution of surrounding forest plantations at landscape level. For instance, economic objectives of forest landowners determine optimal rotation ages which in turn is responsible for determining total availability of biomass at landscape level. We noticed that if optimal rotation ages for both forest management schemes is increased only by just 1 year, then the power plant D will not be able to operate for the entire life due to biomass unavailability for all four scenarios. Sensitivity analysis further reinforces the importance of decision-making of NIPF landowners on the availability of forest biomass. We also noticed that present conditions of random allocation of selected cells and adjacency constraints do not significantly affect age distribution of surrounding forest plantations for a particular power plant capacity as values of different indices used for measuring spatiotemporal impacts on the age distribution of surrounding forest plantations showed similar trends to a very large extent across all scenarios.
This study examines spatiotemporal changes in the age distribution of the surrounding slash pine plantations at landscape level due to the establishment of a power plant in the same landscape. It was assumed that the power plant only utilizes forest biomass obtained in the form of pulpwood and logging residues from the surrounding plantations throughout its operational lifetime. These plantations were managed using either intensive or nonintensive forest management scheme. A CA-based approach was adopted to model how economic behaviors of forest landowners and a power plant manager impact the age distribution of the surrounding forest plantations. Four scenarios were applied to four different power plant capacities.
Results reveal that the age structure of surrounding forest plantations at landscape level significantly changes in time and space. The total number of plantation age classes present in the landscape decreases with a rise in power plant capacity. Some age classes partially recover once the years of power plant operation surpasses respective optimal rotation ages, but the total number of age classes never reaches to the original number of age classes. In all but the smallest plant capacity, the most mature age classes are lost permanently. We noticed that distribution of cells within remaining age classes was not even and continuously fluctuates. This phenomenon becomes more prevalent in case of high capacity power plants. A cyclic pattern was observed for the evenness distribution. However, the value of evenness never reached to the maximum value of one. The loss of mature age classes and skewed distribution of number of cells in remaining plantation age classes may pose a threat to the local species which are dependent on a heterogeneous forest landscape. We also noticed that spatial distribution of different plantation ages changes at landscape level after the establishment of a power plant. The degree of change was directly related to the capacity of power plant. Additionally, we found that with an increase in the power generation capacity, age classes present on the landscape become more and more aggregate in nature when compared to the original age distribution.
This analysis clearly shows that low capacity power plants do not significantly alter the original distribution of plantation ages across the landscape in space and time. Therefore, establishment of low capacity power plants can help in reducing the overall impact of a forest biomass-based power plant on the age distribution of surrounding forest plantations at landscape level. This might help in maintaining various ecological functions of forest plantations including biodiversity conservation.10 For establishing large scale forest biomass-based power plants, there exists a need to increase optimal rotation age of surrounding forest plantations by supporting suitable incentives program like voluntary forest carbon markets (Dwivedi et al., 2009). This will help in reducing harvesting of mature age classes and thus, in maintaining original structural complexity of the surrounding forest plantations up to a certain extent. This approach will also not penalize NIPF landowners financially. There also exists a need to study efficacy of innovative silvicultural prescriptions like green-tree retention harvesting to maintain initial forest structure at landscape level (Thompson, 2007). Additionally, the adoption of an integrated regional level planning approach which can synthesize perspectives of different stakeholders (NIPF landowners, power plant owner, ecologists, economists, government agencies, wildlife experts, forest modeling experts) in forest management could also help in minimizing the impact of forest biomass-based power plant on local biodiversity (Lindenmayer & Franklin, 2002). This approach should consider various spatial and temporal impacts of forest biomass supply on the forest structure at landscape level and then use suitable optimization models to minimize losses to various forest-based ecosystem services including biodiversity loss.
There were a few limitations of the study. The basic unit of analysis in this study is an acre of forestland. The impact of an increase in forest landholdings was not examined. This study also assumes only two types of forest management schemes, that is, intensive and nonintensive. However, forest management schemes opted by NIPF landowners in the Southern United States vary by more than these simple dimensions. Similarly, it was assumed that all NIPF landowners were willing to sell forest biomass produced on their forestlands. This deviates from the reality to some extent as NIPF landowners have various other motives for managing their forestlands. Additionally, this study does not consider any risks which can significantly change the optimal rotation age of slash pine plantations like fire, pest attacks, cyclones, etc. Furthermore, the impact of modeled changes in age structure of slash pine forest plantations on local animal or bird species was not assessed. A need for more landscape-based indices for measuring changes in the forest age structure was also felt. It is expected that future studies will focus on these issues. We hope that this study will guide other studies in a significant manner.
Authors are thankful to Mr Chris Demers (School of Forest Resources and Conservation, University of Florida) and Mr Matthew Simpson (Natural Resources Planning Services, Lake City, Florida) for helping with data collection. Authors also acknowledge critical inputs of Dr Shawn Baker (Warnell School of Forestry and Natural Resources, University of Georgia), Dr Robert Simmons (Timber Mart-South & Warnell School of Forestry and Natural Resources, University of Georgia), and Dr Alan Long (School of Forest Resources and Conservation, University of Florida) for this study. Special thanks to anonymous reviewers for their helpful suggestions.
Net volume of growing-stock timber includes live trees, 5 in. (12.7 cm) diameter or greater, of commercial species meeting specified standards of quality or vigor, excluding cull trees.
The net volume of growing-stock trees removed from the inventory during a specified year by harvesting, cultural operations, such as timber stand improvement, or land clearing.
NIPF landowners have considerably augmented the intensity of forest plantation management in recent decades especially for pine plantations in the Southern United States (Siry, 2002; Albaugh et al., 2007). At the same time, several NIPF landowners manage their forestlands for esthetic enjoyment and timber production is not a top priority for them (Butler & Leatherberry, 2004). Therefore, only two forest management schemes, lying on both ends of a continuum, were selected in this study.
This formula was used to calculate optimal rotation age of an acre of intensively managed slash pine plantation only. For calculating optimal rotation age of an acre of nonintensively managed slash pine plantation, this formula was suitably modified.
There were total 90 600 cells [(301 × 301) − 1] covering an area of 141.56 square miles (366.7 km2).
For more information on adjacency constraints, please refer to the website of Sustainable Forestry Initiative (http://www.sfiprogram.org).
Numbers of adjacent cells are two and three for cells located in the corner and sides of the selected forested landscape, respectively.
For the same plantation age (greater than 14 years), biomass available on an acre of intensively managed forestland is less as compared to an acre of nonintensively managed forestland. So, a power plant owner will prefer to source required biomass from an intensively managed forestland first to reduce total biomass procurement cost. Therefore, Scenarios III (AGE) and IV (AC-AGE) are more sensitive toward intensive forest management scheme as there is no random allocation of harvested cells to either of forest management schemes. By the same logic, Scenarios I (RA-AC-AGE) and II (RA-AGE) are sensitive toward nonintensive forest management scheme.
A real discount rate of 5% was used for calculating net present value.
There are certain incentives to build larger capacity power plants (e.g., efficiency, cost-effectiveness, economies of scale). Thus, the fact that large capacity power plants carry larger external costs raises a common tension between conservation and development and this work could help in identifying a better balance between these tensions.