Quantifying the availability and volume of the forest resides resource

B.Hock, P.Nielsen, S.Grigolato, J.Firth, B.Moeller, T.Evanson

Scion, Rotorua, New ZealandDept of Land and Agricultural and Forest Systems, University of Padua, Italy

Dept of Development and Planning, Aalborg University, Denmark

Presented at the:Australasian Forest & Wood Products Conferences: Residues to Revenues. Rotorua, October 12-13 and Melbourne, October 17-18, 2005.

Logging residues for energy production

Interest is growing in the use of in-forest residues as a sustainable energy resource

Energy prices are increasing

Consider woody biofuel as a forest product• Assess the volume available• Optimise the logistic of the supply chain• Minimise the supply cost

Biomass supply from forest plantations

Two models are being developed National availability and cost supply model Within-forest ” ” ” ” ”

National availability and cost supply model

The location of forests, the transportation network, possible cogen plant locations and other spatial issues are mapped.

The information is analysed within raster GIS.

Techniques include cell-to-cell functions, neighborhood statistics and zonal geometry.

The results are intensity maps or distributions of site-specific costs.

Model overview

National availability and cost supply model

Calculating the transport cost

The accumulated travel distance from a point location determines the transportation costs along the road network to that point.

This example visualizes the cost of transportation across a region.

Estimated annual forest residue availability

TLA

The site-specific amount and cost of biomass are calculated by overlaying in-forest residues and transport costs.

The result is a distribution of biomass amounts and costs, which is unique for each location relative to a planned bioenergy plant.

Costs of biomass at site

National availability and cost supply model

Availability and cost of residues at 4 locations

A model was developed in collaboration with Carter Holt Harvey Forests Ltd.

The case study was based on the Kinleith Forest, in the North Island of New Zealand, complimented by National Exotic Forest Description (NEFD) regional yield tables

Within forest availability and cost supply model

Biofuel as a product: some issues

Logging residues are unevenly distributed geographically and in time

Volume of residues at landings is influenced by the characteristics of the logging operation (eg. harvesting methods, equipment capacity, terrain characteristics)

Extraction of residues is affected by road types and density

The within-forest chain

Volume at harvest

Residue at landings

Transportation of residue to hogger

Chipping by hogger

Transportation of chips to cogen

Volume and cost at cogen plant

Select hogger site locations

Assign logging residue to landings

Calculate potential amount of logging residue

The within-forest availability and cost supply modelThe components:

Determine transportation network

Methodology

Minimise overall costs

Investigate variables that affect availability

Logging residue availability

NEWLAND_CU REGIME_ID TENDING_OPNL 2004 :PL,1983,.,3004192,P.RAD:WT,5,287,3004934:PR,6,300,3.0,.,3004611:PR,7,291,5.0,.,3004610:MS,20,3281268:CF,27,3104795NL 2004 :PL,1983,.,3004192,P.RAD:WT,5,287,3004934:PR,6,300,3.0,.,3004611:PR,7,291,5.0,.,3004610:MS,20,3281268:CF,27,3104796NL 2004 :PL,1983,.,3004192,P.RAD:WT,5,287,3004934:PR,6,300,3.0,.,3004611:PR,7,291,5.0,.,3004610:MS,20,3281268:CF,27,3104795NL 2004 :PL,1978,.,3004190,P.RAD:PR,5,500,2.0,.,3004607:PR,7,350,4.0,.,3004606:WT,8,367,3004932:PR,8,272,6.0,.,3004605:MS,20,3036797:MS,24,3236743:CF,28,3104794NL 2004 :PL,1978,.,3004190,P.RAD:PR,5,500,2.0,.,3004607:PR,7,350,4.0,.,3004606:WT,8,367,3004932:PR,8,272,6.0,.,3004605:MS,20,3036797:MS,24,3236743:CF,26,3239770NL 2004 :PL,1983,.,3004192,P.RAD:WT,5,287,3004934:PR,6,300,3.0,.,3004611:PR,7,291,5.0,.,3004610:MS,20,3281268:CF,27,3104795NL 2004 :PL,1983,.,3004192,P.RAD:WT,5,287,3004934:PR,6,300,3.0,.,3004611:PR,7,291,5.0,.,3004610:MS,20,3281268:CF,27,3104795NL 2004 :PL,1983,.,3004192,P.RAD:WT,5,287,3004934:PR,6,300,3.0,.,3004611:PR,7,291,5.0,.,3004610:MS,20,3281268:CF,27,3104796NL 2004 :PL,1983,.,3004192,P.RAD:WT,5,287,3004934:PR,6,300,3.0,.,3004611:PR,7,291,5.0,.,3004610:MS,20,3281268:CF,27,3104796NL 2004 :PL,1978,.,3004190,P.RAD:PR,5,500,2.0,.,3004607:PR,7,350,4.0,.,3004606:WT,8,367,3004932:PR,8,272,6.0,.,3004605:MS,20,3036797:MS,24,3236777:CF,28,3104794NL 2004 :PL,1978,.,3004190,P.RAD:PR,5,500,2.0,.,3004607:PR,7,350,4.0,.,3004606:WT,8,367,3004932:PR,8,272,6.0,.,3004605:MS,20,3036797:MS,24,3236777:CF,27,3104793NL 2004 :PL,1983,.,3004192,P.RAD:WT,5,287,3004934:PR,6,300,3.0,.,3004611:PR,7,291,5.0,.,3004610:MS,20,3281268:CF,27,3104796NL 2004 :PL,1978,.,3004190,P.RAD:PR,5,500,2.0,.,3004607:PR,7,350,4.0,.,3004606:WT,8,367,3004932:PR,8,272,6.0,.,3004605:MS,20,3036797:MS,24,3236777:CF,26,3239767NL 2004 :PL,1983,.,3004191,P.RAD:WT,5,300,3004933:PR,6,300,3.0,.,3004609:PR,7,293,5.0,.,3004608:MS,20,3281268:CF,27,3104790NL 2004 :PL,1983,.,3004191,P.RAD:WT,5,300,3004933:PR,6,300,3.0,.,3004609:PR,7,293,5.0,.,3004608:MS,20,3281268:CF,27,3104790NL 2004 :PL,1978,.,3004190,P.RAD:PR,5,500,2.0,.,3004607:PR,7,350,4.0,.,3004606:WT,8,367,3004932:PR,8,272,6.0,.,3004605:MS,20,3036797:MS,24,3236777:CF,26,3239767NL 2004 :PL,1978,.,3004190,P.RAD:PR,5,500,2.0,.,3004607:PR,7,350,4.0,.,3004606:WT,8,367,3004932:PR,8,272,6.0,.,3004605:MS,20,3036797:MS,24,3236778:CF,26,3239767NL 2004 :PL,1978,.,3004190,P.RAD:PR,5,500,2.0,.,3004607:PR,7,350,4.0,.,3004606:WT,8,367,3004932:PR,8,272,6.0,.,3004605:MS,20,3036797:MS,24,3236778:CF,27,3104793NL 2004 :PL,1976,.,3004196,P.RAD:PR,5,500,2.0,.,3004614:PR,7,350,4.0,.,3004613:PR,8,252,6.0,.,3004612:PT,13,375,3004939:PT,14,375,3004938:MS,26,3222280:CF,28,3035630

topographyforest stand data

Approximate the volume of logging residue

for the next 17 years.

1>>> NewField as integer (long) = TSV_mc_ha 2>>> VBA function dim TSV_mc_ha as integer If [cf] = "40" Then TSV_mc_ha = 993 if [cf] = "39" Then TSV_mc_ha = 908 If [cf] = "38" Then TSV_mc_ha = 883 If [cf] = "37" Then TSV_mc_ha = 856 if [cf] = "36" Then TSV_mc_ha = 830 If [cf] = "35" Then TSV_mc_ha = 799 If [cf] = "34" Then TSV_mc_ha = 774 if [cf] = "33" Then TSV_mc_ha = 745 If [cf] = "32" Then TSV_mc_ha = 715 If [cf] = "31" Then TSV_mc_ha = 688 if [cf] = "30" Then TSV_mc_ha = 656 If [cf] = "29" Then TSV_mc_ha = 626 If [cf] = "28" Then TSV_mc_ha = 592 if [cf] = "27" Then TSV_mc_ha = 562 If [cf] = "26" Then TSV_mc_ha = 530 If [cf] = "25" Then TSV_mc_ha = 495 if [cf] = "24" Then TSV_mc_ha = 463 If [cf] = "23" Then TSV_mc_ha = 428 If [cf] = "22" Then TSV_mc_ha = 394 if [cf] = "21" Then TSV_mc_ha = 360 If [cf] = "20" Then TSV_mc_ha = 326 If [cf] = "19" Then TSV_mc_ha = 290 if [cf] = "18" Then TSV_mc_ha = 256 If [cf] = "17" Then TSV_mc_ha = 241 If [cf] = "16" Then TSV_mc_ha = 218 if [cf] = "15" Then TSV_mc_ha = 196 If [cf] = "14" Then TSV_mc_ha = 304 If [cf] = "13" Then TSV_mc_ha = 278 if [cf] = "12" Then TSV_mc_ha = 252 If [cf] = "11" Then TSV_mc_ha = 226 If [cf] > "40" Then TSV_mc_ha = 995 if [cf] < "11" Then TSV_mc_ha = 0 TSV_mc_ha = TSV_mc_ha

Fore

st D

ata

base

NEFD

Data

base

forest productivity dataforest productivity data

NE

FD D

ata

base

Kin

leit

h D

ata

base

Forest stand data calculation

Area

• year of establishment• tending history• proposed felling year

Silvicultural Regime• analysis• only radiata pine considered

Total Recoverable Volume (TRV)

• import yield tables to GIS• calculate block area• evaluate the TRV for each

block• determine the logging

residue for each block

Logging residue availability

Logging residue availability

Volume (m3) * 0.75 t/m3 =

weight (tonnes)

TRVm3/ha

Drying period1 year

As percentage of TRV

(Depends on logging method)

Logging residues Volumem3/ha

Logging residues Weighttonne/ha

Residue calculation

Results

0

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1800000

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volu

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TRV

Total Recoverable Volume

(m3/year)

Logging residue availability

(tonnes/year)

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tonn

es/y

ear

3% 4%

Yearly average: 21 500 - 28 200 tonnes

Yearly average per hectare:0.6 tonnes/ha - 0.8 tonnes/ha

Yearly average: 943 000 m3

Logging residue availability

The graph shows how availability varies over time.

For example, there are two periods when supply falls below 10,000 tonnes per year.

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Results

Logging residue availability

Assigning logging residue to landings

To calculate logging residue at each landing:

•locate landings (12 700)

•define the catchment area for each landing

•overlay the logging residue

•sum the logging residue for each landing

•repeat for each year

20082006 2007

Location of landings with assigned residues

Assigning logging residue to landings

Residues (red dots) vary over time and across the forest

Location of hogger sites

Road type Capability Hogger site

Public Chips No

Forest sealed or unsealed

Residue or chips

Yes

Forest stub or track

Residue No

Reclassify roads according to their carrying capacity

GIS – based analysis

Location of hogger sites

Selection criteria:

•Must be associated with roads suitable for chip trucks

•Must have a minimum area of 5000 m2

Selection criteria

•Must be no closer than 20km to adjacent hogger sites

Superskid sites - 40 Superskid sites - 15

Location of hogger sites

Transportation network

Network analysis to determine the minimum cost route between each landing and the hogger sites

Similarly for the routes between hogger sites and cogen plant

Minimum cost calculations

Define variables:Maximum distance

between landing and hogger site

Minimum residues at landing

Run minimum cost calculation

Insert data

Perform calculation

results

Define scenarios

Results

0 10

0 min. logging residues, tonne

200620072008

legend

30.931.031.1

31.231.331.431.531.631.7

31.831.932.0

0 20 40 60 80 100

min. logging residues, tonne

cost

, $/

tonn

e

31.031.131.231.331.431.531.631.731.831.932.032.132.2

0 20 40 60 80 100

min. logging residues, tonne

cost

, $/

tonn

e

Variables: maximum distance

8000 m – 9000 mresidue at landing

>0 in intervals of 12.5 tonne

Supply distance 8000 9000 mLogging residue 0 50 100 0 50 100 tonnesYear 2006Logging residue 18122 13832 6304 22820 17044 8508 tonnesLlandings 296 149 47 510 172 58 n°Distance 1749 1056 575 3006 1250 668 kmSupply Cost 31.845 31.826 31.856 31.917 31.930 32.100 $/tonnesYear 2007Logging residue 17002 12568 5749 20554 15297 7384 tonnesLlandings 374 136 41 432 154 47 n°Distance 2069 977 532 2562 1132 585 kmSupply Cost 31.646 31.706 31.826 31.736 31.771 31.848 $/tonnesYear 2008Logging residue 10954 8504 3855 13638 10605 5180 tonnesLlandings 220 85 25 258 102 32 n°Distance 1479 769 480 1803 914 540 kmSupply Cost 31.086 30.965 31.266 31.128 31.067 31.189 $/tonnes

Conclusions

• the availability of residue depends not only on volume, but also on the transportation cost to the power plant

• a large number of variables need to be considered including drying, in–forest logging distribution, transport and chipping techniques

• GIS based models are effective tools for Decision Support Systems (DSS)