Modelling Sitka spruce yield in southern Scotland
L. Sing, A. Peace, D. Ray and T. Brown
Forest Research, Northern Research Station, Roslin, Midlothian,
EH25 9SY, UK. louise.sing@forestry.gsi.gov.uk
Abstract
Models of Sitka spruce yield class for three forests in south Scotland were constructed from
site variables using multiple linear regression. Productivity was found to be correlated with
accumulated temperature, windiness, soil nutrition and planting year. Yield class maps were
generated from the models in a Geographical Information System, and used to predict the
commercially viable areas of land in forest plans.
Introduction
Foresters require yield estimates for forest planning to make good decisions for sustainable
forest management, yet the data available to them are often inaccurate, and pre-harvesting
surveys are expensive. Empirical models provide an alternative approach to obtaining this
information. Previous studies (Blyth & MacLeod, 1981; Worrell & Malcolm, 1990a; Worrell &
Malcolm, 1990b) have identified relationships between Sitka spruce productivity and
elevation, temperature, windiness, rainfall and crop. This paper presents the results of an
empirical yield class model for Sitka spruce (Picea sitchensis (Bong.) Carrière) stands at
three inland sites in southern Scotland where it is the main production species.
Method
Data sampling
Reliable measurements of tree growth from mensuration surveys were obtained for three
forests (Ae, Wauchope and Craik) in southern Scotland. Growth was expressed as maximum
mean annual increment (m3/ha/yr); and the range was divided into yield class (YC) steps of
2m3/ha/yr so that a stand growing at YC14 had a maximum mean annual increment greater
than 13 and less than 15m3/ha/yr (Edwards & Christie, 1981).
For each location with a measured YC and known planting year, independent site variables
were sampled using a Geographical Information System (GIS). The variables are all used in
Ecological Site Classification (ESC) (Pyatt et al., 2001; Ray, 2001) which is a knowledgebased decision support system that has been developed to define site types and aid the
selection of suitable tree species and/or native woodland. Sampled data includes:
1. accumulated temperature (AT), a summer warmth index measured in day degrees above
a growth threshold temperature of 5oC, interpolated from data collected over the period
1961-1990 (Pyatt et al., 2001; Ray, 2001);
2. moisture deficit (MD), in mm, is the peak value of a running balance throughout the
summer of monthly evaporation minus monthly rainfall (Pyatt et al., 2001; Ray, 2001);
3. wind exposure, expressed using the Detailed Aspect Method of Scoring (DAMS: (Quine &
White, 1993) calculated from interpolated tatter flag data;
4. continentality, a measure of seasonal variability based on the Conrad Index reduced to
sea level (Pyatt et al., 2001; Ray, 2001);
5. the edaphic variables, soil moisture regime (SMR) and soil nutrient regime (SNR), were
converted from 1:10,000 Forestry Commission digital soil maps using default conversion
values (Ray et al., 2003).
Elevation, slope and aspect were sampled from a 10-metre resolution Digital Terrain Model
(Ordnance Survey, GB).
Multiple linear regression modelling
Multiple regression was used to fit a series of linear models to explore the relationship
between the response (dependent) variable, the measured YC, and the sampled explanatory
(independent) variables. Stepwise selection was used with a significance level for a variable
to enter or remain in the model set as 0.05. Separate regressions were fitted to the Ae
dataset and to the Craik and Wauchope combined datasets; these were programmed into
ArcView GIS to produce predicted yield class maps for each forest. Finally, the same
approach was used to fit and evaluate a single combined model.
Results
The sampled variables were observed to have similar values and ranges between the two
study areas (Table 1).
Table 1. Mean values of sampled variables
Variable
Number of samples
Yield class
Planting year
Accumulated temperature
Moisture deficit
Windiness (DAMS score)
Continentality
Slope
Elevation
Craik & Wauchope
326
18
1965
1035
75.4
15.4
7.24
7
339
Ae
66
18
1969
1026
63.8
15.2
6.66
10
347
Yield models
a) Ae and Craik/Wauchope models
The Ae model fitted 78% of stands to within ± 1YC of the measured YC, though there was a
lack of fit at both ends of the yield class distribution. The combined Craik/Wauchope model
fitted 92% of stands to within ± 1YC of the correct YC. The model was progressively less
accurate for sites on which trees were growing below the average YC. Figure 1 shows the
GIS YC map for Craik Forest.
Figure 1. GIS yield class layer for Craik Forest
Sitka spruce
yield class
(b) Combined Ae, Craik and Wauchope model
The combined model fitted 92% of stands to within ± 1YC of the observed YC in
Craik/Wauchope, and 71% of stands in Ae (Figure 2). Stands that were incorrectly classified
tended to be those with a YC towards the lower and higher ends of the range.
Figure 2 Fit of combined model in (a) Craik and Wauchope Forests and
(b) Ae Forest
No. of stands
(a)
predicted within +/- 1 YC
100
90
80
70
60
50
40
30
20
10
0
10
12
14
16
18
20
22
24
YC
(b)
predicted within +/- 1 YC
No. of stands
25
20
15
10
5
0
10
12
14
16
18
20
22
24
YC
The combined model identified the following significant parameters:
ƒ warmth - an increase in the AT of 115 degree.days increased growth by 0.5 YC;
ƒ wind exposure - a reduction in DAMS of 4 units increased YC by 0.5 class;
ƒ site fertility - compared to Poor SNR sites, there was a growth increase of 0.5 YC on sites
with a medium SNR;
ƒ tree planting year – on the same site type, younger crops had a greater yield than older
crops, a 1 YC growth increase was observed over a period of 12 years.
Discussion
The models show relationships between warmth, windiness and site fertility, as well as a
planting year effect. Higher yield classes were measured on warmer sites than cold sites, on
more sheltered sites and more fertile sites. These results are consistent with the findings of
previous studies (Worrell & Malcolm, 1990b; Macmillan, 1991), and confirm warmth (AT) as
the major dependent variable in the ESC model.
The results showed that Sitka spruce productivity was positively correlated with planting year
and this is also consistent with previous studies (Worrell & Malcolm, 1990b; Macmillan, 1991).
Improved silvicultural methods and climatic warming are the most likely causes. The warmth
index (AT) was averaged for the 30 year period (1961-1990), and so is not sensitive to any
increase as a result of climate change throughout that period. Previous work suggests that
the combined effect of increased N deposition, CO2 concentrations and temperature might
account for up to half of the observed increases in conifer productivity of the last century
(Cannell et al., 1998).
Conclusions
This pilot study has shown that yield class models can be developed with an excellent degree
of accuracy using the variables contained in the Ecological Site Classification. This
information is valuable for foresters wanting to develop sustainable management plans
without the expense of major field surveys in the Sitka spruce plantations of southern
Scotland. The GIS model produces maps of predicted yield that show where spruce is most
productive and can be used to consider the appropriate locations for timber production and
other management objectives. This approach also provides a cost-effective solution to
predicting the production potential of forecast systems.
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