Review of projected timber yields for the North Coast - CiteSeerX

Review of Timber Yields

1 July 2002

Review of Projected Timber Yields for the NSW North Coast Jerome K Vanclay Southern Cross University Executive Summary Analyses with new data reveal that the North Coast region can sustain a timber harvest of up to 220,000 m3/year in the medium term. This level of harvesting can be sustained for about 20 years, but will need to be supplemented with plantations in the longer term. This estimate of 220,000 m3/year is less than the an earlier estimate of 269,000 m3/year prepared during the Regional Forest Agreement (RFA) process in 1999. Several factors contribute to this reduction. The principal reason is the more realistic modelling of tree harvesting in the vicinity of stream buffers ("buffers on buffers"), but other factors include other reductions in net harvest area, more realistic log specifications, and more realistic simulation of silvicultural practices for estimating log volumes. Suggestions are given for further improvement and on-going monitoring of these estimates.

1. Introduction This review was prepared in response to a request to "undertake an examination of data and analysis used for March 2002 estimate of timber yield for North Coast timber supply". Special emphasis was given to differences that have arisen between the current estimates and the 1999 estimates prepared during the Regional Forest Agreement (RFA) process. The objective is to establish the best estimate currently available and to review recent amendments to the FRAMES approach.

2. Background Forecasts of future wood supplies are fundamental to forest industry and land use planning. Several forecasts of timber yields for the North Coast Region of NSW have been prepared during the last five years. This report examines differences between some of these estimates, and seeks to establish the best current estimate (BCE). Although several scenarios have been canvassed, three estimates are considered (Table 1). Table 1. HQL Sawlog yield estimates for the North Coast Region Source of estimate RFA-FRAMES 1999 RACD-RFA adjusted for TAS NCTS Monitoring Study

Anticipated volume (per year for 20 yrs, m3/yr) 269,000 217,000 220,000

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The RFA-FRAMES estimate was prepared in 1999 during the RFA process. It was intended to be a strategic estimate, valid at the regional level in the longer-term, but not intended to provide information useful at the operational level. Subsequent concerns about assumptions relating to stream buffers and associated regulations led to the RACD (Resource and Conservation Division of Planning NSW ) Timber Availability Study (TAS) methodology and, in turn, to the RACD-RFA TAS incorporating FRAMES estimates which took better account of regulations relating to stream buffers. In addition, as part of the RFA, State Forests undertook to monitor predictions versus outturn, and these findings have contributed to the FRAMES North Coast Timber Supply (NCTS) Monitoring Study. As these three studies embrace many different assumptions, the resulting differences in the anticipated volumes cannot be attributed to any single factor, and can only be interpreted in the context of the assumptions made.

Review Methodology The review was a desk study, based on an examination of documentation and on interviews with State Forests staff and other stakeholders involved in preparing the estimates. It seeks to establish whether procedures were appropriate and likely to provide a reliable and repeatable estimate for the region as a whole. Because of this emphasis on procedural aspects, no field visits were made and no new field data were obtained. The study does not attempt to verify yields for any particular locality. Rather, it seeks to shed light on two issues: the reliability of the new estimate, and the underlying reasons for differences between the various estimates.

3. Best Current Estimate of Timber Supply Components of a Yield Estimate There are several possible ways to approach a review of this kind. This review examines the following components of the calculation: − − − − −

Production Area: need to establish the net area to be worked over during harvesting; Growing Stock: species, sizes and condition of trees presently on those areas; Future Growth: expected annual growth rates relative to the structure of the forest; Predicted Harvest: the nature and timing of future harvests; and Volume Estimation: saleable volume of wood in harvested trees by log grade.

More details of these components and their estimation are given in the annex, but the brief explanatory phrases above indicate the nature of the estimate required. However, the brevity of these phrases belies the complexity of the estimates. For instance, it is a relatively easy matter to ascertain the gazetted area of a State Forest, but it is another matter entirely to estimate the net area actually worked over during harvesting. Similarly, it is easy to determine the total stem volume of a plantation conifer, but more challenging to estimate the net log volume (excluding defect) of a native forest hardwood.

Comparison of Current Estimates RFA-FRAMES Estimate (1999) The estimates prepared with FRAMES during the RFA (RFA-FRAMES) involved many stakeholders and forms a base case for comparison. New data, changes to procedures, and technical improvements to FRAMES since 1999 make it timely to review and revise the RFAFRAMES estimates. However, because these RFA-FRAMES estimates involved some consultation and received much scrutiny, they remain an important reference for comparison, even though they may not reflect the current best estimates.

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RACD-RFA/TAS Estimate (2002) As a result of some controversy surrounding timber harvesting in the vicinity of stream buffers, the Resource and Conservation Division (RACD) commissioned a Timber Availability Study (TAS). The TAS examined the effect of "buffers on buffers" (restricting tree felling so that no tree could be felled if any part of the tree as likely to fall within a stream buffer), while retaining most of the other assumptions used in the RFA-FRAMES estimates. State Forests, using the same methodology, extended the TAS Study to include unintended buffers restricting tree felling that were not included in the initial TAS Study. A broad adjustment of the RFA FRAMES estimate for the TAS outcomes provides an indicative estimate of about 217 000 m³ HQL per year. FRAMES North Coast Timber Supply Monitoring Estimate (2002) As part of the RFA, State Forests undertook to monitor timber off-take and compare it against FRAMES predictions. This monitoring forms part of a strategy for continuous improvement of yield estimates. It embraces studies of aerial photos of logged-over areas, reconciliation of predictions versus outturn, and targeted studies of particular aspects of concern. New data generated in this way has provided the basis for further improvements to the FRAMES methodology, and contributed to new yield estimates known as the FRAMES North Coast Timber Supply (NCTS) Monitoring estimates. The latest estimates of these efforts are summarized in Table 2. Table 2. Comparison of Current Estimates Item & Source

RFA-FRAMES

NCTS Monitoring

Net Area (after strike rate) Growing stock volume (m3/ha) Growing Stock basal area (m²/ha) Predicted harvest Volume estimates Future growth (HQ harvest over 100 yrs) Non-declining wood flow

374,111 ha 233 m3/ha 27.6 m²/ha 37 trees/ha 0.53 m3/tree 0.3 m3/ha/yr 20 yrs

303,460 ha 223 m3/ha 26.7 m²/ha 30 trees/ha 0.54 m3/tree 0.6 m3/ha/yr 20 yrs

Short-term yield (20 yrs) Average Long-term yield (21-100 yrs)

269,000 m3/yr 183,500 m3/yr

220,000 m3/yr 175-110,000 m3/yr

2.2 M m3 3.2 M m3

2.1 M m3 4.9 M m3

Short-term harvest increment (20 yrs) Long-term harvest increment (21-100 yrs)

While yield estimates for the next 20 years are assured, further investigation is required to fully explain the substantial discrepancies in the 21-100-year timeframe (Table 2).

Towards the Best Current Estimate Several issues are involved in establishing the BCE of timber supply. Three key issues are to: • • •

achieve the best estimate technically possible (within resource and time constraints) provide an estimate that facilitates rigorous testing and on-going monitoring, and to satisfy stakeholders that changes made are warranted and can be justified empirically.

While changes made for the NCTS Monitoring estimates generally improve on earlier estimates and are consistent with best practice, some parameters involve relatively minor changes that are difficult to quantify empirically. Given the context, in which NCTS

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Monitoring was to improve on an earlier estimate agreed by stakeholders, and which involves on-going monitoring and continuous improvement, it seems prudent to withhold these relatively minor adjustments until they can be substantiated more fully. Table 3 summarizes key assumptions in the RFA-FRAMES and NCTS Monitoring estimates, and suggests the appropriate approach for developing the BCE. Table 4 outlines the Best Current Estimate and tests various scenarios (such as buffer on buffers and AGS silviculture) to test the sensitivity of the BCE elements. Table 3. Recommendations to achieve the best current estimate. Element Net Harvest Area (before Strike Rate) Average Net Harvest Area Modifier Strike Rate Net Harvest Area (after Strike Rate)

Pre-94 plantations Post-94 plantations Buffers on Buffers

Private Property Unmapped drainage lines Stratification Plots in National Park New Plots since RFA Yield Simulator Yield Scheduler Silviculture Harvest Volume Trigger Diameter Limits Basal Area Retention Minimum Return Time Basal Area Removal Maximum Tree Removal Log specifications

Product Proportionment Tree Defect Modifier (eg HQL recovered) Volume and taper equations

RFA-FRAMES

NCTS Monitoring

Recommend Rationale approach for BCE 401,019 ha 335,887 ha Monitoring RFA assumes buffers on buffers are available for harvest 13% 28% Monitoring API study of post-RFA compartments with IFOA conditions 6.71% 6.71% No change No justification for change 374,111 303,460 Monitoring TAS confirms BoB unavailable, Monitoring assumes some logging of (includes sliver adjustment) BoB and low risk unmapped drainage lines over and above NHA. Included Add after FRAMES run Monitoring Affects long-term wood flow Omitted Add after FRAMES run Monitoring No effect on yields in short-term hence using assumed MAI low priority Available Assume half TAS area Monitoring TAS & clarification of regulations. Monitoring assumes some logging of BoB. Excluded Add after FRAMES run Monitoring Significant short-term volumes Not considered Include low-risk areas Monitoring Policy decision. Monitoring assumes some logging of unmapped drainage increase net area 2.2% lines. Structural classes and Management Areas Monitoring Increases statistical confidence yield associations associated with estimate Included Excluded Monitoring Avoids antagonizing stakeholders Unavailable 318 plots plus Kendall CFI Monitoring Use all appropriate information Simulate UNE and Updated code; UNE and Monitoring Efficiency LNE independently LNE simultaneously Spectrum Woodstock Monitoring Woodstock solver easier to use STS with some AGS Modified STS Modified STS Modification reflects current practice 7m³/ha 7m³/ha 7m³/ha Slack variable, little effect in range 510 m³/ha Moist blackbutt 65, Unchanged, except moist Monitoring Reflects WSA and log supply strategy MCE 55, Other 50cm blackbutt 55 cm 2 12m /ha Not used Monitoring Slack variable 10 years 5 years Monitoring Relatively slack variable, constrained by harvest volume trigger 40% max (av. 30%) 30% max (average 28%) Monitoring Reflects current practice 80% for 50+cm dbh 96% (dry) and 88% (moist) Monitoring Reflects modified field operations and (95% for 70+cm in for 50-70cm; 80% (dry) and retention of larger trees. moist forest) 77% (moist) for 70+cm HQL: 3.6m x 34cm 2.4m x 40cm cdub (except Wood Supply Reflects WSAs Kendall-Coopernook 4.2m x Agreement sed 25cm sed) specifications Lookup table with dbh Modelled with logistic Monitoring Reflects trends more accurately by Region/Association function based on dbh 70.4% 69% RFA Weak justification for change Muhairwe; Gordon for Gordon for NEB/SPG/BBT; Monitoring NEB& SPG NEB default for other spp

More robust equations (increases HQL 4%)

† HQL = high quality large logs; STS = single tree selection; AGS = Australian group selection; dbh = diameter at breast height (1.3 m above ground); sed = small-end diameter; cdub = centre diameter under bark; NEB = New England blackbutt; SPG = spotted gum; BBT = blackbutt; WSA = Wood Supply Agreement; BoB = buffers on buffers; MCE = moist coastal eucalypt forest.

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Production Area It is not possible in a desk-study of this kind to establish the precise area contributing to future harvests. However, procedures used by State Forests are systematic and consistent with best practice, and should lead to reliable area estimates. It is reasonable to regard the revised estimated net area (before strike rate) of 335,887 ha as an improvement over the 1999 RFA estimate. The new estimate is consistent with findings of the TAS, adjusted for the current policy on harvesting low-risk unmapped drainage lines, and with monitoring information. Monitoring studies on stands harvested since the RFA, under the Integrated Forestry Operations Approval (IFOA) process support the substantial increase (13-28%) in the net harvest area modifier. This modifier adjusts for areas that cannot be logged due to features not included in the geographic information system (GIS); e.g., unmapped streams, etc. Changing the strike rate (the adjustment for areas excluded from harvesting because of need to retain habitat trees) was not considered justified at this point without additional study. Growing Stock Estimates of current growing stock rely on data from inventory plots on which tree species, sizes and characteristics are assessed. Such data can be combined in various ways; it is useful to stratify the resource on the basis of expected volumes to minimize within stratum variation, by forest types to allow inferences of the species composition, and on operational considerations to facilitate monitoring. From a statistical viewpoint, estimation errors can be reduced by using many strata, and by adjusting sampling intensity according to the size, variability and cost of sampling in each stratum. The approach used in the NCTS Monitoring estimates was to stratify on the basis of Management Areas. This contributed a substantial reduction in the sampling error (14 to 8%). However, there maybe scope to further refine the stratification system to further enhance estimates and operational monitoring. Plots located on alienated lands (where the tenure was changed from State Forest to National Park) were excluded from the NCTS Monitoring estimates. In theory, some of these plots now in National Park may remain representative of stands still within the State Forests production estate, and could contribute to yield estimates, at least until the next harvest in that stratum. However, in the interests of maintaining stakeholder confidence, it seems appropriate to exclude these plots from further yield predictions and to use plots only from within the State Forest estate. Table 2 uses standing volume to contrast the different estimates of growing stock. The use of volume in this way combines two elements, the number and size of trees present in the forest, and the amount of wood in these trees. Because volume estimation procedures have also changed, it is preferable to compare the levels of growing stock using another measure such as basal area, i.e., the sum of sectional areas of trees, a useful indicator of stand occupancy that integrates both number and size of trees. The growing stock basal area for the RFA was 27.6 m²/ha with a PLE of 1.85%. This compares with 27.6 m²/ha (PLE of 2.23%) identified in the Monitoring Study. The difference between these two estimates is not statistically significant. Future Growth Future growth of the forest is predicted by simulating the increment, mortality and recruitment of individual trees on a plot-by-plot basis. The general approach and the statistical analyses adopted are consistent with established best practice. The system estimates both

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stand-level growth (i.e., stand basal areas) and individual tree growth (i.e., diameter increments) independently, and cross-checks these estimates to add rigor and prevent unrealistic extrapolation. However, the resulting functional relationships offer few insights into stand dynamics and offer no guidance for optimal silviculture (e.g., they do not facilitate determination of an optimal stand structure). The efficacy of modifier variables (such as rainfall, temperature, slope, soil depth, etc) will depend on the quality of the database used. The models are based on a substantial database derived from remeasurements of permanent growth plots (PGPs) established in 1979 and of the Kendall CFI (continuous forest inventory) established in 1960. These contribute a substantial number of tree growth observations in many locations, over long periods, and should provide a sound basis for establishing longterm trends. However, a formal comparison between the status of these plots and of the production estate (cf. Beetson et al 1992; Vanclay et al 1995) has not been presented. Table 2 reveals a substantial discrepancy between the RFA-FRAMES and NCTS Monitoring estimates of long-term growth (21-100 year timeframe; cf. 0.3 versus 0.6 m3/ha/yr respectively). The reasons for this difference have not yet been fully resolved, and further research is warranted. However, the yields estimated for the immediate 20-year period are consistent, appear reasonable, and remain relatively unaffected by the estimated long-term growth. Predicted Harvest There are two aspects to predicting the next harvest - predicting when it is going to happen, and predicting what trees are likely to be harvested, given that a harvest occurs. In turn, the former involves two issues: what is the earliest opportunity to harvest, and what is the optimal timing of the harvest. The optimum harvest timing is usually established using an optimizer, typically with one of two linear programming (LP) packages, SPECTRUM or Woodstock. Here we are concerned not with LP, but with the constraints that determine the earliest harvest. It is usual to specify several constraints, including the minimum return interval, the minimum viable harvest, the maximum basal area removal, and so on. Many of these criteria are more useful in the field than in simulation studies, as while they help to reinforce the need for minimal disturbance, they turn out to be slack constraints in simulation - constraints that are never invoked because of other constraints that are reached earlier. Thus the "harvest volume trigger" is a slack constraint for values in the range 5-10 m3/ha. Nonetheless, the values used in the RFA and Monitoring studies seem reasonable. Preliminary FRAMES outputs indicate that there is scope to further improve harvest scheduling. Increasing the minimum return period can be expected to delay some timber volumes; simulations show that this is the case in the Upper North East (UNE), but initial FRAMES runs indicate higher short- and long-term yields from a 10-year return period in the Lower North East (LNE) region. Further analysis of the effect of minimum return period is warranted. The other issue related to harvest prediction is anticipating trees to be removed when a harvest is scheduled. This is predicted for a simulated stand table using diameter limits (e.g., generally harvest stems over 55 cm dbh), basal area retention (12 m2/ha in RFA), basal area removal (e.g., 30% maximum), and maximum tree removal (e.g., about 80% maximum according to species, size and forest type). This is a reasonable and objective way to predict future harvests, but needs to be re-calibrated against field operations from time to time. The BCE relies on recalibration that occurred as a result of FRAMES monitoring.

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Volume Estimation Estimating gross log volumes involves a relatively straightforward calculation. The challenge is to predict defects that detract from log volumes, especially since many defects do not show external symptoms. Estimation complexity is further compounded because logs may not be sold in any length, but must satisfy minimum specifications to achieve a particular log grade (e.g., 3.6 m by 34 cm small-end diameter in the RFA estimate; 2.4 m by 40 cm centre diameter under bark in the Monitoring estimate). The RFA approach was to use tabular summaries of observed data to adjust volumes. The Monitoring study relies on equations fitted to these data, and is expected to provide a more satisfactory result as the equations correspond more closely to the long-run expectation and smooth over variability arising from small within-class samples. The BCE relies on the NCTS Monitoring equations and Wood Supply Agreement specifications.

Table 4. Predicted wood flows under various scenarios using the Best Current Estimate Region

Description

Yrs 1+2

3-20

21-40

41-100

Surplus

Total

Difference

(m3)

(m3/yr)

(m3/yr)

(m3/yr)

(m3)

(m3)

(m3)

UNE

BCE (from Table 3)

225,400

85,000

80,000

55,000

21,000

6,676,400 *

Scenarios on BCE

No logging of BoB or drainage lines

225,400

80,000

80,000

50,000

25,100

6,290,500

-385,900

Force 10% AGS

225,400

85,000

85,000

55,000

9,100

6,764,500

88,100

Increase return time to 10 years

225,400

80,000

80,000

60,000

4,900

6,870,300

193,900

LNE

BCE (from Table 3)

277,450

135,000

95,000

55,000

1,500

7,908,950 *

Scenarios on BCE

No logging of BoB or drainage lines

277,450

130,000

90,000

50,000

-6,900

7,410,550

-498,400

Force 10% AGS

277,450

140,000

95,000

55,000

9,000

8,006,450

97,500

Increase return time to 10 years

277,450

140,000

95,000

55,000

17,500

8,014,950

106,000

* includes part BoB and unmapped drainage lines

Synthesis and Implications Table 3 summarizes the assumptions that are advocated. The adoption of these assumptions should contribute to the best estimates that can be attained in the short-term within the FRAMES framework. Further improvements rely on comparisons between predictions and outturn at a spatial scale not easily attained with FRAMES. With these assumptions, it is evident that the harvest able to be sustained during the next 20 years is 220,000 m3/year at most (Table 4). In the longer term (21-100 years), production from native forests is expected to range between 175 and 110,000 m3/year, and will need to be supplemented from hardwood plantations. However, alternative growth models provide dramatically different estimates of growth over

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this period, and further research into modelled wood yields during this timeframe is warranted. Note that estimates in Table 4 have been optimized to the nearest 5000 m3/year, so that any differences less than 5000 m3/year should not be regarded as substantive. Further analyses are needed to elucidate yield levels more precisely, but this is not warranted before questions relating to the growth model are clarified. It needs to be emphasized that these estimates relate to the timber production systems currently in place within State Forests. Simulation studies presented for review make no attempt to establish the optimal stand structure to maximize long-term timber production, or to maximize the value of all the goods and services provided by forests other than adopting those modelled in the IFOA and Forest Agreements. Options to increase timber production are outlined in the Monitoring Study.

4. Conclusions The best current estimate is similar to that derived during the NCTS Monitoring studies, delivering 220,000 m3/year during the next 20 years. The reduction from the earlier RFA estimate of 269,000 m3/year is due largely to more realistic assumptions regarding trees in the vicinity of stream buffers ("buffers on buffers").

Information Sources People consulted David Ridley, Murray Lawrence, Ian Cranwell, Elspeth Baalman, Rob Kirwood, Tim Parkes Dailan Pugh, Greg Hall, Carmel Flint

References and Documents consulted Anon, 1999. A Report on Forest Wood Resources for the Upper and Lower North East NSW CRA Regions. NSW CRA/RFA Steering Committee Project number NA52/ES, 56 pp. Anon, 1999. Strategic Inventory: Upper North East and Lower North East CRA Regions. NSW CRA/RFA Steering Committee Project number NA04/FRA, 199 pp. Anon, c.1999. Spectrum overview. US Forest Service, 39 pp. Anon, 2000. Strategic Yield Scheduler: Upper North East And Lower North East Regions. NSW CRA/RFA Steering Committee Project number NA54/FRA, 92 pp. Anon, 2000. Biometric Models: Upper North East and Lower North East CRA Regions. NSW CRA/RFA Steering Committee Project number NA13/FRA, 264 pp. Anon, 2000. Yield Simulator: Upper North East and Lower North East Regions. NSW CRA/RFA Steering Committee Project number NA14/FRA, 100 pp. Anon, 2001. Regional Forest Agreement: North-East NSW Interim Annual Report 20002001. Anon, 2002. Data collection methods for SFNSW net harvest area study. 11 pp.

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Arnold, S., E. Baalman & D. Walsh, c. 2002. Field Procedures for Eucalypt Plantation: MARVL Inventory. State Forests, 25 pp. Auditor General, 1999. State Forests 20-Year Timber Supply Agreements. Auditor-General’s Report to Parliament 1999 Volume Two, pp. 161-167. Baalman, E., 2001. Tree product recovery for New South Wales Coastal and Tablelands forests. Forest Resources Branch, Technical Note #1. August 2001, 19 pp. Beetson, T., M. Nester and J.K. Vanclay, 1992. Enhancing a permanent sample plot system in natural forests. The Statistician 41:525-538. Brown, G., 2001. Draft report: TAS Analysis. CSIRO Mathematical & Information Sciences. Carter, P. 1998. MARVL system analysis. State Forests NSW, 46 pp. Muhairwe, C.K., 1997. Height prediction Models for Upper North East and Lower North East New South Wales. Report prepared for the FRAMES Technical Committee. Muhairwe, C.K., 1997. Harvesting Mortality and Harvesting Damage Prediction Models for Upper North East and Lower North East New South Wales. Final report prepared for the FRAMES Technical Committee, 12 pp. Pugh, D. and C. Flint, 1999. The Magic Pudding: The Cut-an’-Come-Again Forests. A preliminary appraisal of State Forests’ Forest Resource and Management System (FRAMES). 191 pp. Remsoft, 2002. Woodstock version 2.5: Functional overview. 10 pp. State Forests of NSW, 2002. North Coast Timber Supply Monitoring Study, March 2002. Turner, B.J., 1998. Review of Frames Data for the Upper North East and Lower North East RFA Regions of NSW. Consultant's report, 21 pp. Vanclay, J.K., 1991. Audit of Information Systems. Report to Forestry Commission of New South Wales, Sydney. Unpublished, 6 p. Vanclay, J.K., J.P. Skovsgaard and C. Pilegaard Hansen, 1995. Assessing the quality of permanent sample plot databases for growth modelling in forest plantations. Forest Ecology and Management 71:177-186.

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Attachments A.

Overview of Yield Assessment Methodology

B.

North Coast Timber Supply Monitoring Study

C.

Timber Availability Study – Draft Report: TAS Analysis

D.

Timber Availability Study Extension Report

E.

Response to Issues raised by Stakeholders

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ATTACHMENT A: Overview of yield assessment methodology A yield prediction embraces several components: − Production Area: need to establish the net area to be worked over during harvesting. − Growing Stock: species, sizes and condition of trees presently on those areas. − Predicted Harvest: numbers of those trees expected to be harvested in a given operation. − Volume Estimation: saleable volume of wood in harvested trees by log grade. − Future Growth: expected annual growth rates relative to the structure of the forest. − Wood Flows: the sustainability and sequence of timber harvests across the estate.

Production Area Predictions of the area to be worked over during harvesting are fundamental to reliable yield forecasts, but remain a challenge in many resource estimates in Australia and abroad. Several factors contribute to this difficulty, but two issues are noteworthy - the changing nature of the target, and the use of the "falling ceiling" approach. Most procedures for estimating areas commence with the total area, and systematically make deductions to account for various constraints. The danger is that any oversights contribute to overestimates. In theory, it should be possible to complement the falling-ceiling estimate with a "rising floor" estimate compiled from areas previously harvested (adjusted for changes in tenure and silviculture), but this is feasible only where there has been a long history of stable management and reliable record-keeping. Such comparisons are hampered by changes in harvesting practices in response to regulations, technology and community expectations. In the absence of such a complementary estimate, it seems prudent to calibrate predictions with detailed case studies and by routinely comparing predictions with performance. The procedures used by State Forests to prepare resource estimates are consistent with, and share many of the same strengths and weaknesses as those of other forest services. The first step is to identify areas available for logging in a spatially-explicit way using remote sensing (e.g., aerial photos) and geographic information systems (GIS) to deduct unavailable (e.g., stream buffers and steep slopes) and unproductive (e.g., non-forested) areas from the gross area of State Forest (and other crown lands). The resulting estimate, often called the mappable area, is spatially explicit and can be represented on maps and identified on the ground. It is important to recognise that maps and GIS are simplifications (and may contain errors and omissions), so the mappable area is an approximation of the situation we expect to find on the ground (Figure 1). Typically, a GIS simplifies stream data, so contributes towards underestimates of stream lengths and areas in stream buffers.

Figure 1. GIS representation (red dashes) and API-derived stream locations (blue lines) in Compartment 61 Cowarra SF. The GIS representation generalises details offering clarity in maps, but contributing to underestimates of stream length and buffer areas. From Anon (2002).

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These discrepancies (Figure 1) can be accommodated by painstaking air photo interpretation (API) to upgrade the GIS, or by making a further non-spatial adjustment to area estimates to account for other factors affecting the availability of forest for harvesting. The latter approach of using a net harvest area (NHA) modifier is conventional, as factors other than the accuracy of stream representation may also be involved (e.g., obstacles not visible to API). However, it is important to note that the NHA modifier is specific to the detail and versions of the GIS coverages used to estimate the mappable area. Together, mappable and non-spatial adjustments may account for a large proportion of the forest area. Resource estimates often report net areas that are one-third to one-fifth of the gross forest area, and while these bounds (20-33%) serve as useful rules-of-thumb, they should be considered in conjunction with the topographic complexity of the area in question. Experience elsewhere has shown that the estimation of the net area is one of the dominant sources of error in resource estimates, so it is prudent to examine system performance with detailed case studies. In such studies, it is important to consider three aspects: what the prediction system (GIS) anticipates (expected); what could reasonably be expected under ideal conditions ("true"); and what actually occurred (realized). The adjective "actual" often confuses "true" and realized situations. Thus studies need to contrast eight (2×2×2) alternatives contrasting the ideal situation, the GIS prediction and the outcome realized in the field (also see Figure 2): Situation Prediction Outcome Area logged Area not logged

"True" available Available Unavailable … ha … ha

X ha … ha

"True" unavailable Available Unavailable … ha … ha

… ha … ha

A brief example to illustrate these alternatives is warranted. The X above indicates an area that was anticipated to be unavailable (e.g., within a buffer), but which was observed in the field to be available (e.g., the predicted 'unavailable' status was due to a generalization of topography and drainage within the GIS), and which was consequently logged during harvesting operations. Reliable monitoring data require clear definitions of what is predicted, what is realized, and what is the ideal or "true" situation. This was adopted in the Monitoring Study.

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Table 1. Area estimates for North Coast. RFAFRAMES

NCTS Monitoring

Mappable area

459,127

466,403 (+2%)

RFA: pre-97 rules, HCVOG, CRAFTI. 2002: based on IFOA

Net area (after NHA modifier)

401,019

335,887 (-16%)

Reflects “buffers-on-buffers”

Realizable area (after Strike Rate)

374,111

313,349 (-16%)

Strike Rate 6.71%

Estimate

Comment

A recent study in the Mid-North Coast (1934 ha) and North-East (5310 ha) Regions revealed that 31% of the gross area was harvested, and that 24% of the area potentially available was not harvested because of issues accommodated in the NHA modifier (e.g., unmapped streams and steep slopes). The dominant issues were non-commercial stands (36% of potentially available area), pre-merchantable stands (23%) and buffer extensions (13%). Further adjustments for some fauna protection requirements is made by applying a "strike rate" to adjust for the area equivalent involved. The strike rate is applied as an area adjustment, but could also be made elsewhere in the system (e.g., in the harvesting model). It is important to ensure that such adjustment are applied only once, and are not inadvertently applied in more than one part of the system. The particular adjustment of 6.71% was negotiated between NPWS and SF during the CRA process and was not derived empirically. It represents the midpoint of two independent estimates of the impact of pre-1998 threatened species prescriptions. It is preferable to base the strike on empirical evidence, to help avoid any counter-adjustment in subsequent corrections for predicted versus realized volume outturn. Table 1 considers the areas involved in the 1999 RFA-FRAMES and the 2002 NCTS Monitoring estimates. Current estimates of net area are less than those used in the RFA. Differences in the mappable area seem to reflect definitional changes with little overall effect,

Figure 2. Comparison of expected mappable area for harvesting (orthophoto at left) with net area operated (GIS map at right; hatched areas). Notice two small areas (circled in red) erroneously expected within buffers but harvested in accordance with procedures. Compartment 61 Cowarra SF (from Anon 2002).

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whereas the net area has decreased across-the-board, apparently because of changes in procedures relating to "buffers on buffers" (i.e., need to avoid trees that might fall partly within a buffer). The final "realizable" area estimates (after strike rates) appear reasonable, but experience suggests that careful re-examination and monitoring of area estimates of one of the best ways to improve yield estimates. Such monitoring has been instituted by State Forests and should continue. Area control is one of the oldest approaches to yield regulation, and can still serve as a useful rule-of-thumb to check the overall performance of a yield regulation system. The human eye is adept at interpreting patterns, so maps coloured to show logging history by convenient periods (e.g., 5-year periods, 1980-84, 85-89, …) can give a striking overview of the progress of harvesting across the estate. Hand-coloured maps of this kind have been used for decades, but it should be a simple matter to generate such maps from GIS and management records to reveal the state of harvesting within the mappable area of the production estate. The use of five-year periods consolidates information for easy interpretation with few (e.g., 6-8) colours. Where longer rotations are envisaged, it may be appropriate to use longer intervals. When prepared with a GIS, such a map could usefully be supplemented with a statement documenting the area harvested (both as absolute areas (ha), and as percentages of the estate) during each period. It is my understanding that such maps will be produced as part of State Forests' post-logging assessment procedures currently being developed as part of the Native Forest Management Information System.

Growing Stock The next issue to consider is the growing stock - the number and condition of trees existing within the forest estate. Reliable estimates of growing stock are essential for short-term forecasts of timber yield, and rely on plot-based inventory. Emerging technology (e.g., airborne laser scanning and profiling) offers potential to assess tree characteristics (height, crown attributes) remotely, but at present, inventory relies on effective stratification and ground-based sampling. All the aspects of design and conduct of effective inventory cannot be addressed here, but several issues warrant a brief mention: •

Since forests generally change slowly and in predictable ways, inventory data are durable and can be used for decades, until a wildfire or harvest occurs in the stand it represents.

•

Travel represents much of the cost of inventory, so efficiencies can be gained through opportunistic inventory in conjunction with other forest activities (e.g., preparing or concluding timber sales).

•

Inventory plots should be large enough to reduce between-plot variance even if it increases within-plot variance. Thus it may be appropriate to use transects across any observed environmental gradient, or to use clusters of point samples. Sampling with probability proportional to size (i.e., nested plots, or angle-gauge sampling) is also beneficial.

•

Stratifying may be the most effective way to improve an inventory. More strata are better, even if there are few plots in each strata, provided that they reduce the within-stratum variance or confine the areas with high variability. From a practical yield scheduling viewpoint, there is merit in considering each management unit as one (or more) stratum.

•

Management history is an effective basis for stratification, because reliable records should be available, past logging has a direct effect on standing volumes, and the approach recognises the interaction between yield predictions and management.

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•

Plot data should be analyzed to determine the within-stratum and between-strata variability. A high variance within a stratum may indicate a need for additional stratification, or for further samples. Ideally, sampling should be done in proportion to the standard deviation within strata (adjusted for stratum area and unit cost of sampling). Conversely, low variability between strata may reveal redundant strata.

•

Local experts can help improve estimates by indicating whether inventory data reflect local experience; if not, additional plots should be established until there is convergence between the pooled inventory data and local opinion. To avoid bias, any apparentlyunrepresentative plots must be retained, and additional plots should be randomly located.

Strategic inventory for FRAMES relied on stratified random sampling using 0.1 ha circular plots at an intensity of one plot for each 100-500 ha of forest, on the recommendation of the RFA FRAMES Technical Committee. While this has provided much useful information, efficiencies could have been gained by examining the extent to which 0.1 ha plots internalize stand variability. Prior data could have been used to estimate the variance within each stratum with a view to sampling strata in proportion to their variability rather than in proportion to their size. It is not too late to examine these questions and to optimize any further inventory work that may be undertaken. About half of the plots used in RFA-FRAMES were located in forest subsequently excluded from the production estate, and were thus excluded from the NCTS Monitoring estimate. To compensate, 406 new plots were established. Simple averages of these plot volumes have not been reported, so the effect of changing the sample in this way is not evident. The stratification system was changed from one based on yield association and CRAFTI structure class, to one based on Management Areas. Collectively, the different sample and revised stratification resulted in a decrease in standard errors (and PLEs). It may be helpful to include harvesting history as another basis for stratification, as this may have a substantial and obvious influence on standing timber volumes. It may also be useful to re-examine continued use of discarded plots now outside the production estate. Provided that the original stratification was sound, these plots could be used to represent that part of the stratum still within the production estate, at least until new plots can be obtained within the production estate.

Future Growth Estimates of long-term sustainable production clearly rely on area estimates and growth projections. Obviously, the growth model has a greater influence as the time frame becomes longer. Conversely, short-term projections rely heavily on assessment of the growing stock and any error in growth predictions can be discounted. The current study dwells on the shortterm outlook, so examination of the growth model has been superficial. However, it is worth noting that the NCTS Monitoring version of the growth model anticipates a substantially larger wood flow than the RFA-FRAMES version (Table 3). It has not been possible at this stage to examine the basis for this increase, or to speculate whether the NCTS Monitoring model is reasonable, but it is appropriate to flag the issue for further evaluation at a later date.

Table 3. Predicted harvests. Anticipated HQ harvest and time frame 1-20 years 21-100 years

RFA FRAMES (m3)

NCTS Monitoring (m3)

2,169,325 3,208,488

2,136,107 4,873,043

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Empirical growth models are a summary of the growth data from which they are constructed, and their performance is linked to the extent and representativeness of that database. A useful guide to the quality of the database can be constructed with a map and two graphs: 1. A map showing the production estate and growth plot locations to reveal the extent of geographic extrapolation 2. A graph contrasting dominant environmental parameters (e.g., rainfall versus altitude) on the production estate and for the growth plots to reveal environmental extrapolation. 3. A graph contrasting stand conditions in growth plots with those observed in inventory (e.g., site productivity vs stand basal area; see Beetson et al 1992, Vanclay et al 1995). The diameter increment and stand basal area increment functions used in the model rely on many estimated parameters (up to 19 for diameter increment and 12 for stand basal area). The large number of parameters introduces two issues: the large number of variables raise the prospect of multicollinearity and the associated risk of inappropriate parameter estimates; and it becomes increasingly difficult to establish that the model gives reasonable predictions for all likely circumstances. Exhaustive testing has not been attempted, but visual inspection reveals some possible issues. Most of the diameter increment functions predict that the growth reduction due to basal area is greatest near 30 m2/ha, and allow diameter increments to increase as the stand basal area diverges from this value. Estimates of both diameter and stand basal area increments may become unrealistically large when the stand basal area is very low (i.e.,