The impact of the introduction of sustainable forest management ...

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developed for the financial optimisation of forest-level harvest scheduling under Irish conditions. These financial .... Enhance biodiversity and landscape values.

Forest Policy and Economics 7 (2005) 689 – 701 www.elsevier.com/locate/forpol

The impact of the introduction of sustainable forest management objectives on the optimisation of PC-based forest-level harvest schedules Maarten Nieuwenhuis a,*, Dermot Tiernan b a

Department of Forestry, University College Dublin, Belfield, Dublin 4, Ireland b Coillte Teoranta, Castlebar, Co. Mayo, Ireland Received 9 September 2003; accepted 22 March 2004

Abstract Previous to the introduction of sustainable forest management principles, PC-based modelling techniques had been developed for the financial optimisation of forest-level harvest scheduling under Irish conditions. These financial models were further developed to produce a Mixed Integer Programming-Sustainable Forest Management (MIP-SFM) model, in which principles of sustainable forest management were incorporated into the harvest scheduling process. The effectiveness and practicality of the harvest schedules produced by the financial model and the MIP-SFM model were compared to the harvest schedules produced by the process currently employed by Coillte (The Irish Forestry Board). Evaluation of the models was carried out in Clonbrock forest (294.8 ha), a typical Irish plantation forest, owned and managed by Coillte. The MIP-SFM model produced effective and practical harvest schedules that enhanced the sustainability of Clonbrock forest by incorporating environmental, ecological and social management parameters. The application of the developed MIP-SFM model resulted in an increase in NPV of 5.7% compared to the NPV produced by the scheduling process currently employed by Coillte. The application of the financial model had resulted in a comparable increase in NPV of 14.2%. The smaller increase in NPV produced by the MIP-SFM model provided an indication of the cost associated with the introduction of environmental, ecological and social SFM principles in the management of Clonbrock forest. D 2004 Elsevier B.V. All rights reserved. Keywords: Sustainable forest management (SFM); Optimisation; Harvest scheduling; Forest-level planning

1. Introduction In recent years, the meaning of sustainable forest management has broadened from sustained yield management to include additional features as the quality of * Corresponding author. Tel.: +353-1-716-7004; fax: +353-1716-1104. E-mail address: [email protected] (M. Nieuwenhuis). 1389-9341/$ - see front matter D 2004 Elsevier B.V. All rights reserved. doi:10.1016/j.forpol.2004.03.019

forest operations, biodiversity, multiple-use and quality of life. Sustainable forest management (SFM) is now defined as ‘the stewardship and use of forests and forest land in a way, and at a rate, that maintains their biodiversity, productivity, regeneration capacity, vitality and their potential to fulfil, now and in the future, relevant ecological, economic and social functions, at local, national and global levels, and does not cause damage to other ecosystems’ (Anon, 1995). Awareness

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of the need for SFM is influencing forest management world-wide (Forest Service, 2000a). It has been recognised that effective planning is one of the most essential requirements for successful, environmentally sound forest harvesting (FAO, 1997) and that it can considerably reduce any adverse effects of forest operations and safeguard a wide range of forest values (Forest Service, 2000b). These can be broadly classified as environmental, economic and social values (Table 1). The introduction of SFM has added new complexities to already complex harvest scheduling problems. This has resulted in an increased need for decisionsupport systems (DSSs) for harvest scheduling. Such DSSs should incorporate all the complexities of harvest planning, allowing the user to produce strategic and tactical harvest schedules that meet the economic, environmental and social objectives of forest management. 1.1. Background to the study Coillte Teoranta (the Irish Forestry Board) is the semi-state organisation that was established in 1989 to manage the Irish national forest estate on a commercial basis in accordance with efficient silvicultural practices. It is the largest single forest landowner in Ireland, managing 438 000 ha or 70% of the total forest area in the Republic of Ireland. The management of Coillte’s forests is extremely intensive. The estate is widely dispersed, comprising 5600 different properties and 125 000 stands. The mean annual productivity or yield class is 16.5 m3 ha 1 an 1, with an average rotation length of 45 years. The clear-cut system is universally operated with a number of thinnings, typically 5– 6 for a yield class 16 Sitka spruce [Picea sitchensis (Bong.) Carr.] stand. The entire stand is clearfelled at the end of

the rotation. The total clearfelled area per annum is approximately 7500 ha, with an average individual harvest area of 12 ha. The timber supply from the Coillte estate has increased steadily from 1.5 million m3 in 1989 to 2.4 million m3 in 1999 and it is expected to reach 3.8 million m3 by 2010. 1.2. Current harvest scheduling methods used by Coillte The harvest scheduling method currently used by Coillte (identified as the final TRC control method in this article) was recognised as a method that would benefit from the introduction of an optimisation-based DSS (Nieuwenhuis and Williamson, 1993). The basis for the harvest scheduling process used by Coillte is known as the ‘Thinning and Rotation Classification’ (TRC). It is a system especially designed for Irish forestry, whereby rotations and thinning cycles are assigned to individual stands. The TRC process produces a recommended production year (RPY) for each sub-compartment providing the thinning regimes and rotation lengths to be adopted on a sub-compartment basis. An economic provision is built into the RPY, where some crop rotation lengths are reduced from the age of maximum mean annual increment (MMAI) by 20 –30%. In addition, rotation lengths also consider the financial viability of successor crops using a colour coding system. Nugent (1998) gives a detailed explanation of the entire TRC process. At present, this overall process is almost completely sequential, allowing for very little optimisation. In addition, any modifications made to the schedules resulting from the TRC, are performed on a trial-and-error basis, without the aid of any decision-support to indicate the best course of action consistent with management

Table 1 Environmental, economic and social values of SFM Environmental values

Economic values

Social values

Protect soil and water quality

Sustained productivity Commercial viability

Rural development and farm forestry Sustained employment Amenity and recreation Cultural and archaeological merit Other community values

Enhance biodiversity and landscape values Maintain forest health and vitality Protect ecological and scientific values Source: Forest Service, 2000a.

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objectives (Nieuwenhuis and Williamson, 1993). In addition, the challenges presented by SFM are not accounted for in the final TRC control method as currently used by Coillte. 1.3. Sustainable forest management and harvest scheduling The introduction of sustainable forest management (SFM) principles can impact on harvest scheduling in four key areas, namely environmental protection, biodiversity enhancement, economic viability, and the social function of the forest. An attempt was made to address some important concerns in each of these key areas and incorporate them into the harvest scheduling process. Environmental protection concerns addressed were the protection of the water resources in terms of water quality, ecology and stability, and the protection of the soil in terms of minimising soil disturbance, compaction and erosion. Provisions for biodiversity management were made through the retention of older stands over the planning period and the enhancement of biodiversity at the reforestation stage. In keeping with the Forest Service biodiversity guidelines (Forest Service, 2000c) and Coillte’s new policy on biodiversity (Coillte, 2001), the acceptable minimum limit of the total forest area managed for biodiversity has been set at 15%. Economic viability concerns maximising the economic return from the harvest scheduling process so that the long-term financial viability of the timber production sector can be maintained. 1.4. Objectives of the study The research presented in this article concerned the development and evaluation of a ‘mixed integer programming-sustainable forest management’ model (MIP-SFM model), which incorporated environmental, ecological and social criteria, while simultaneously meeting market demands and optimising the financial revenue from the forest (Tiernan, 2003). As part of the study, the developed harvest scheduling models were compared with the harvest scheduling method currently used by Coillte and with the schedules produced by the financial models developed during an earlier phase of this project.

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2. Materials and methods 2.1. Data requirements The data requirements for all models developed in this study were extensive. In order to process the data, customised spreadsheet programmes were written to automate the process as much as possible. Data from Coillte’s inventory and TRC databases were used as the basis for all model formulations. These data were modified and arranged in a model spreadsheet format that was compatible with the LP software used for optimisation. For the purpose of this study, a 294.8 ha forest known as ‘Clonbrock forest’ was selected. The forest is a continuous plantation containing coniferous forest (84.2%), mixed forest (4.7%), broadleaf forest (1.9%) and non-productive areas (9.1%). Clonbrock forest contains seven main tree species and consists of a total of 110 sub-compartments in 19 compartments. Commercial softwood stands are found in 93 of these subcompartments. The average sub-compartment inventory area is 2.7 ha for all commercial species (i.e. excluding broadleaves and scrub) and the average compartment inventory area is 12.7 ha. The average productive area per sub-compartment is 2.4 ha and the average productive area per compartment is 11.5 ha. Of the 19 compartments, nine (with a combined area of 120.4 ha) were classified as sensitive because of their location bordering the Clonbrock river and/or because of soils with low bearing capacities. All financial calculations were carried out using a discount rate of 5%. As the study was commenced before the change-over to Euro, all financial amounts are presented in Irish pounds (£). 2.2. Model development The development and evaluation of the MIP-SFM model involved three stages, each of which is outlined in the following sections. 2.2.1. Stage 1: the quantification of the impact of each of the SFM control elements on the financial model output The development of the MIP-SFM model involved the inclusion of four single-factor SFM control elements in the financial model. The financial model

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was the result of previous research (Tiernan, 2003) and used mixed integer programming (MIP) in a compartment-based model with annual thinning and clearfell volume production constraints defined by Coillte’s current harvest schedule. In stage 1, the impact of including each of the four single-factor SFM control elements individually into the financial model was evaluated. These single-factor SFM models included: . the site sensitivity model, in which harvesting in

sensitive compartments is prohibited during the 23 week spawning season (October to mid-March) which coincides with the period of heaviest rainfall. As a result, the allowable annual period for harvesting in sensitive compartments is constrained in the site sensitivity model to 29 weeks; . the stand retention model, in which a minimum biodiversity target of 44.2 ha was set (i.e. 15% of the total forest area). This target can be made up through the enhancement of biodiversity at the reforestation stage or the retention of older stands. It was assumed that 10% of reforestation areas were managed for biodiversity, with the remainder of the biodiversity target made up by retention areas. The retention model identified the areas that should be selected for retention, given the objective of net present value maximisation; . the balanced volume model, in which fluctuations between annual thinning volumes, between annual clearfell volumes, and between annual total volumes were restricted. The maximum annual volumes in the balanced volume model were set as a percentage of the total scheduled volume in the final TRC model over the 5-year planning period; and . the work schedule model, in which the annual work input for clearfelling and thinning operations was regulated. The durations of the total annual work schedules in the work schedule model were constrained to a maximum of 18.4 weeks, based on a percentage of the duration of the total work schedule in the final TRC control model. The output of the four single-factor SFM models was compared to the output from the financial model using three indicators, namely: net present value (NPV), harvest schedules, and average harvest area.

2.2.2. Stage 2: a detailed sensitivity analysis of the optimal MIP-SFM model output The sensitivity of the solution provided by the full MIP-SFM model to changes in the RHS values of the site sensitivity, stand retention, balanced volume and work schedule control elements was analysed using a range of percentage changes for each control element separately. 2.2.3. Stage 3: comparisons between the TRC control model, the financial model and the MIP-SFM model Comparisons were made between the harvest schedules produced by the TRC control model (i.e. Coillte’s current scheduling method), the financial model and the MIP-SFM model. The ability of each of the three models to meet the elementary requirements of SFM was assessed using the same indicators as in stage 1.

3. Results 3.1. Stage 1: the impact of the four SFM control elements A comparison of the single-factor SFM models was made with the financial model using NPV, the harvest schedules and average harvest areas. 3.1.1. Net present value (NPV) The NPV for the site sensitivity model (£744 864) was identical to that produced by the financial model. The financial model produced a harvest schedule that could fully satisfy the site sensitivity model constraints and as a result, schedules produced by both models were identical. The financial model could not fully satisfy the stand retention model and as a result, the NPV in the stand retention model was slightly lower (£740 455). The constraints and volume production targets used for the regulation of harvest volume in the financial model were fundamentally changed in the balanced volume model. This resulted in a slightly higher volume production (by 561 m3) in the balanced volume model compared to the production in the financial model and, consequently, even though volume production was regulated, the NPV increased slightly (by £5591 or 0.8%). The work schedule model produced the lowest value for NPV

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Table 2 A comparison between the number of compartments scheduled for clearfelling, the associated productive areas and the volume from clearfelling for the four single factor SFM models, compared to the financial model Model type

Number of compartments scheduled for clearfelling

Productive area (ha)

Volume from clearfelling (m3)

Financial model Site sensitivity model Stand retention model Balanced volume model Work schedule model

12 12 10 12 13

114.4 114.4 108.9 112.3 125.3

46 761 46 761 46 334 46 955 45 922

(£694 897). Analysis indicated that regulation of volume production using the work schedule constraints was very restrictive on model flexibility.

the schedules produced by the financial model, while in the work schedule model the scheduled area and volume were both reduced.

3.1.2. Harvest schedules for clearfelling In general, the compartments that were scheduled for clearfelling over the planning period were very similar in each of the four single factor SFM models compared to the compartments scheduled for clearfelling in the financial model (Table 2). The number of sub-compartments scheduled for clearfell ranged between 10 and 13, while the scheduled area varied between a low of 108.9 ha and a high of 125.3 ha. The clearfell volume over the 5-year planning period ranged from 45 922 m3 in the work schedule model to 46 955 m3 in the balanced volume model.

3.1.4. Average harvest areas The size of the average clearfell and thinning areas produced by the four single factor SFM models did not vary greatly and were similar to the areas produced by the financial model (Table 4).

3.1.3. Harvest schedules for thinning The thinning operations that were scheduled over the planning period were identical in the site sensitivity model and the stand retention model compared to the schedules produced by the financial model (Table 3). In the balanced volume model, the number of scheduled sub-compartments, the area scheduled and the scheduled volume all increased compared to

3.2. Stage 2: sensitivity analysis of the optimal MIPSFM model output An analysis was conducted on the sensitivity of the solution provided by the full MIP-SFM model to changes in the RHS values of the four control elements. 3.2.1. Site sensitivity control element The site sensitivity control element was used to constrain the annual number of weeks work that could be scheduled on sensitive sites to 23. The NPV returned in the MIP-SFM model was sensitive to reductions of more than 40% in the annual number of weeks work that could be scheduled on

Table 3 A comparison between the number of compartments scheduled for thinning, the associated productive areas and the volume from thinnings for the four single factor SFM models, compared to the financial model Model type

Number of compartments scheduled for thinning

Productive area (ha)

Volume from thinning (m3)

Financial model Site sensitivity model Stand retention model Balanced volume model Work schedule model

8 8 8 11 8

35.7 35.7 35.7 45.6 29.1

1659 1659 1659 2026 1113

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Table 4 Comparison of the average harvest areas scheduled for clearfelling and thinning between the financial model and the four single factor SFM models Model type

Financial model Site sensitivity model Stand retention model Balanced volume model Work schedule model

Average harvest area (ha) Clearfelling

Thinning

10.7 9.6 10.0 8.5 9.6

3.3 2.8 3.0 4.9 4.1

sensitive sites (Fig. 1). However, the NPV returned remained unchanged as a result of any relaxation in the site sensitivity control element (i.e. increases in the annual number of weeks work that could be scheduled on sensitive sites). 3.2.2. Stand retention control element The stand retention control element determined the extent to which stands were retained for biodiversity purposes over the planning period. The inclusion of the stand retention control element in the MIP-SFM model resulted in the retention of an area of 15.9 ha. The MIP-SFM model was not sensitive to increases less than 60% in the RHS values for the stand retention control element and to decreases less than 40% (Fig. 2). At increases of between 60 and 100% in the RHS values, the NPV decreased from £689 594 to £664 699, representing a decrease of 3.6%. At reduc-

tions of between 40 and 80% in the RHS values, the NPV increased from £689 594 to £693 171, representing an increase of 0.5%. 3.2.3. Balanced volume control element The values returned for NPV in the MIP-SFM model were highly sensitive to reductions in the RHS values of the balanced volume control element (i.e. percentage reductions in the annual volume production) (Fig. 3). A linear trend in the reduction of NPV was observed in response to a series of linear (percent) reductions in the RHS values of the balanced volume control element. In contrast, the values returned for NPV remained unaffected to increases in the RHS values of the constraints. 3.2.4. Work schedule control element The work schedule constraints restricted the annual volume production in the MIP-SFM model by ensuring that the annual work schedule was balanced from year to year. The resulting NPV was sensitive to decreases in the values for the RHS of the work schedule control element (Fig. 4) with a linear trend in the reduction of NPV in response to a series of linear (percent) reductions in the RHS values of the control element. 3.3. Stage 3: comparisons between the TRC control model, the financial model and the MIP-SFM model In this section, the output from the TRC control model, the financial model and the MIP-SFM model

Fig. 1. The changes in NPV returned by the MIP-SFM model, in response to percent changes in the RHS of the site sensitivity control element.

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Fig. 2. The changes in NPV returned by the MIP-SFM model, in response to percent changes in the RHS of the stand retention control element.

were compared in terms of the compliance of their harvest schedules with the SFM indicators. 3.3.1. The environmental indicator The environmental indicator was measured and compared to determine the level of environmental protection afforded to sensitive sites by the three models. The TRC control model produced combined clearfelling and thinning harvest schedules that exceeded the 23-week limit for harvesting operations

on sensitive sites (Fig. 5). Both the financial model and the MIP-SFM model produced combined clearfelling and thinning harvest schedules that did not exceed the 23-week limit, resulting in schedules that would be acceptable in terms of environmental impact. 3.3.2. The ecological indicator The area managed for biodiversity enhancement in 1999, at the beginning of the planning period, was

Fig. 3. The changes in NPV returned by the MIP-SFM model, in response to percent changes in the RHS of the balanced volume control element.

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Fig. 4. The changes in NPV returned by the MIP-SFM model, in response to percent changes in the values for the RHS of the work schedule control element.

already 32.5 ha. This represented a shortfall of only 11.7 ha compared to the minimum target area of 44.2 ha as required by the Forest Service guidelines (Forest Service, 2000c) for Clonbrock forest. Both the TRC control model and the financial model, which had no provision for biodiversity enhancement by way of stand retention constraints, failed to meet the minimum target area of 44.2 ha (Fig. 6). The MIP-SFM model, which had implicit constraints for stand retention for

the provision of biodiversity enhancement, exceeded this minimum target area by 9.5 ha at the end of the planning period (2003). The retention of an area larger than the target resulted from the compartment-based nature of the MIP-SFM model. 3.3.3. The economic indicator The economic viability of implementing the harvest schedules produced by the three models was

Fig. 5. A comparison between the TRC control model, the financial model and the MIP-SFM model, in terms of the work schedule (in working weeks) for harvest operations on sensitive sites.

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Fig. 6. The areas managed for biodiversity at the end of the planning period (2003) for the TRC control model, the financial model and the MIPSFM model, compared to the biodiversity inventory area at the beginning of the planning period (1999) and the target inventory area as specified in Forest Service guidelines.

quantified using NPV (Fig. 7). The financial merits of using optimisation, compared to the output of the TRC control model, were demonstrated by increases in NPV of 14.2% and 5.7% for the financial and the MIP-SFM models, respectively. 3.3.4. The social indicator Both the TRC control model and the financial model produced harvest schedules that failed to provide a balanced work schedule over the planning period (Fig. 8). In contrast, the MIP-SFM model produced a balanced work schedule with the number of weeks of harvesting work ranging from 15.7 to 18.3, satisfying the social requirement.

4. Discussion Forest management in Ireland and abroad has evolved from relatively simple stand rotation deci-

Fig. 7. A comparison of the TRC control model, the financial model and the MIP-SFM model, in terms of the total NPV over the planning period.

sion-making rules (deciding when to cut individual stands to maximise the present net value of the timber) to procedures that reconcile conflicting demands on timber and non-timber resources, and in which environmental concerns can outweigh natural resource production opportunities (Martell et al., 1998). As a result, modern forest management planning has the non-trivial challenge of preserving natural ecosystems, satisfying industry demands, meeting growing demands for non-timber outputs such as recreation, and satisfying other constraints on production imposed by a myriad of interest groups (Mendoza et al., 1993). In such a climate, a prerequisite to producing solutions that are acceptable by all parties is the inclusion of SFM standards in the harvest scheduling process. The SFM standards included in the harvest scheduling process in this study included environmental, ecological, economic and social standards. The impact of each of the SFM standards was evaluated by comparing the results of the four single factor SFM models with those of the financial model. Compared to the financial model, the addition of the SFM constraints in three of the four single factor SFM models resulted in only minor changes to the harvest schedules, NPV and average harvest areas. In one of the four single factor SFM models, namely the work schedule model, major changes to the harvest schedules and NPV ( 6.7%) were observed when compared to the financial model. However, the size of the average harvest areas remained the same. Imposition of the site sensitivity constraints in the site sensitivity model were not restrictive enough to cause any changes in the harvest schedule. However,

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Fig. 8. A comparison of the work schedule (in working weeks) for both clearfelling and thinning during the planning period between the TRC control model, the financial model and the MIP-SFM model.

these site sensitivity constraints are expected to have an impact on the optimisation process for forests with a higher proportion of stands classified as ‘sensitive’. These include many forests on the western seaboard, where the implementation of the strategy could have a much larger impact on NPV. Further research is required to evaluate the consequences on harvest scheduling of imposing these types of temporal restrictions on forests with different site sensitivity levels. From a social viewpoint, a regular work schedule is preferable to an irregular work schedule as it facilitates forward planning for those involved directly with timber harvesting. The TRC control model and the financial model provided very irregular work schedules. In comparison, the MIP-SFM model was successful in complying with the social indicator for SFM compliance, by providing a regular work schedule over the planning period. The MIP-SFM model was highly sensitive to changes in the constraints regulating volume production (i.e. the balanced volume control element and the work schedule control element) and less sensitive to changes in the stand retention and site sensitivity control elements. Of the constraints regulating volume production, the work schedule control element was more restrictive than the balanced volume control element. As a result, for all corresponding percent changes in the RHS values of both control elements regulating volume production, there were reductions in NPV which were consistently higher for the work schedule control element compared to those for the balanced volume control ele-

ment. For the forest manager in Clonbrock this means that in terms of optimising NPV, volume regulation using the balanced volume control element is preferable to volume regulation using the work schedule control element. Implementation of the harvest schedules produced by the TRC control model and the financial model resulted in biodiversity levels that did not fully meet levels required by the ecological indicator. In the MIPSFM model a provision was made, by way of stand retention constraints, to include stand retention as a means of contributing to the overall biodiversity of the forest. Although stand retention is not as critical for plantation forests as for natural forests, its role is becoming increasingly important (Forsman et al., 1996). In this study the selection of stands for retention was based on linking the extent of the retention cost(s) associated with the retained stand(s) with the NPV obtained from the stands that were scheduled for harvesting to meet volume production targets. Compared to the financial model, the cost in terms of a reduced NPV for providing a schedule that includes retention of 5.4% of forest stands as provided by the stand retention model, was only £4409 or £882 for each year of the planning period. This indicated that the use of an optimisation-based approach to the selection of retention stands produced cost-effective results. Clearfelling, in contrast with retention, is also recognised as providing an opportunity to enhance biodiversity (Nieuwenhuis and Gallagher, 2001). Coillte currently employ a process where at least

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10% of all clearfelled areas are managed for biodiversity. This process on its own is not enough to meet the Forest Service guidelines for biodiversity enhancement, which require target levels of 15% of the total forest area managed for biodiversity (Forest Service 2000c). Employing a combined process, which includes managing 10% of all clearfelled areas for biodiversity and a 5% level of stand retention, allows the Forest Service target levels for biodiversity to be met. Improving landscape aesthetics in areas of high scenic beauty can be included in the harvest scheduling process by preventing the clearfelling of large coupes in these areas (Nieuwenhuis and Gallagher, 2001). The traditional approach to prevent the clearfelling of large coupes is to include adjacency constraints into the scheduling model for harvesting (Synder and ReVelle, 1996). Including adjacency constraints adds substantially to model size, complexity and solution times (Hoganson and Borges, 1998). To overcome these difficulties, specialised measures are often required such as random search techniques (Barrett et al., 1998), simulated annealing (Murray and Clinch, 1995), tabu search (Bettinger et al., 1997), lagrangian relaxation (Gunn et al., 1988) and upper bounding techniques (Lappi, 1992). However, owing to the dispersed nature of Coillte properties and forests, the adjacency issue is not considered a major problem for harvest scheduling. The adjacency issue can be adequately addressed by stipulating a maximum annual production level for each property as a means of providing an acceptable spatially scattered harvest plan in areas of high scenic beauty. The economic indicator for SFM compliance was measured using NPV. A potential weakness of using NPV as an economic indicator is its sensitivity to the discount rate assumed in its calculation (Price, 1997). However, a 5% discount rate is generally applied to the financial evaluation of forest projects in Ireland. This rate was therefore also used in this study. All compartments in Clonbrock forest were adequately serviced with well-maintained forest roads and these in turn were connected to public roads, as is the case in the large majority of Coillte’s forests. It is therefore sufficient to constrain the size of the harvest block to exceed some minimum size to compensate for the minor costs associated with any additional roading requirements (Martell et al., 1998). Further work is

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required, using the spatial capabilities of a GIS, to evaluate the impact of major roading costs in the optimisation process for harvest scheduling in situations where road access is limited or non-existing. Uncertainty is a problem with all land management planning (Dykstra, 1984). The concerns with uncertainty can be addressed by adopting a relatively short planning period of 5 years, as used in this study, and by using parameters that are known with a large amount of certainty. The management of Coillte forest plantations is extremely intensive (Williamson and Nieuwenhuis, 1994; Coillte, 2000), including detailed inventories on a sub-compartment basis carried out in years 1, 4, 14 and immediately before final clearfelling. As a result, the present management practices in Coillte’s forests provide accurate and detailed data which, combined with a relatively short planning period, sufficiently addresses the uncertainty issue. The harvest scheduling method currently used by Coillte was identified as a method that would benefit from the introduction of a hierarchical model approach (Williamson and Nieuwenhuis, 1994). The MIP-SFM model developed in this study could be used in a hierarchical approach similar to that developed by Williamson (Nieuwenhuis and Williamson, 1995), where the volume associated with the national allowable cut is distributed to individual forest-levels. The MIP-SFM model then schedules this volume on a forest-level. The hierarchical approach would allow different models and methods for optimisation to be used at different levels, making it possible to use the strengths of each technique where needed. The forestlevel models, run on an annual basis, would overcome the difficulties with excessive solution times typically associated with large MIP models (Weintraub and Cholaky, 1991; Church et al., 1994; Yoshimoto et al., 1994). Finally, it should be noted that this study was based on the analysis of only one forest. Even though Clonbrock was specifically selected as a forest representative of Coillte’s estate, the impact of the introduction of SFM objectives on forest-level harvest schedules will be dependent on the spatial and temporal structure of the forest. In order to draw more general conclusions about the impact of SFM on the management of Ireland’s forests, further studies will have to be carried out in a range of properties, both privately and publicly owned. As part of this further

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research, the development of a PC-based user-friendly decision support system for the sustainable management of (private) woodlands is in progress (Barrett and Nieuwenhuis, 2003).

5. Conclusions The TRC control model and the financial model for harvest scheduling produce schedules that do not simultaneously meet all the requirements for SFM. In contrast, the MIP-SFM model met all the requirements for producing sustainable harvest schedules. The schedules produced by the MIP-SFM model afforded protection to the environment, enhanced the ecological well-being of the forest while simultaneously optimising the economic viability of the forest and meeting the fundamental social requirement for a regular work schedule. Despite the imposition of constraints to meet the SFM standards, the MIP-SFM model produced a 5.7% increase in NPV when compared to the TRC control model. This clearly indicates that the harvest scheduling techniques currently in use do not even produce financially optimal harvest schedules, let alone schedules that are SFM compliant. Acknowledgments This study was funded by COFORD (The National Council for Forest Research and Development), Coillte Teoranta and University College Dublin.

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