monitoring changes in the carbon stocks of forest...
TRANSCRIPT
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Monitoring changes in the carbon stocks of forest soils
Raisa Mäkipää, Mikko Peltoniemi, Margareeta Häkkinen, Petteri Muukkonen, Aleksi Lehtonen
at Metlawith colaborators
Jari Liski and Kristiina Karhu at SYKETaru Palosuo and Marcus Lindner at EFI
EU conference
on Forest
Focus
C-studiesBryssels
22 Oct, 2007
-
Outline
IntroductionResearch Questions and ResultsConclusions
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Introduction
The EU have to report the changes in forest carbon stocks including soil (UNFCCC 1992, Kyoto Protocol 1997)Current soil surveys in Europe are NOT designed for monitoring of soil C changesDemanding to monitor small changes of a large soil C stock, since
Spatial variation is large Soil sampling is laborious
-
Objective
To develop methods to monitor changes in the carbon stocks of forest soils Modules:1.
Model evaluations
2.
Model-based stratification3.
Analyses of repeated soil measurements
4.
Plot-level sampling design5.
Cost estimation
-
1. Evaluation of soil C models
We
evaluated
soil
models
that
may
be
used
forA country scale C accounting of forest soils (GHG inventory)Predicting soil responses to changed management practicesImproving efficiency of soil sampling (by model-based stratification)
Peltoniemi
et al. 2007. Models in country scale carbon accounting of forest soils. Silva Fenn. 41: 575-602.
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1. Evaluation of soil C modelsEvaluated models: Yasso, ROMUL, SOILN, RothC, Forest-DNDC, CENTURY, FORCARB
We conclude thatModel selection is strongly guided by availability of representative input dataIn a country scale inventory simple models may be the only reasonable option to estimate soil C changesProcess-based models are needed when soil responses to e.g. management practices are assessed
An example: removal of harvest residues for bioenergy
Peltoniemi
et al. 2007. Models in country scale carbon accounting of forest soils. Silva Fenn. 41: 575-602.
-
Palosuo et al. 2008. For. Ecol. Managem. 255: 1423-1433
1. Responses
to management practices assessed
with
soil
models
(Yasso, ROMUL)
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2. Does
model
bases
stratification
improve sampling
efficiency
in large
scale
inventories?
Source: Peltoniemi, Heikkinen, Mäkipää. 2007. Silva Fenn. 41: 527-539
Aim is to select a sub-sample of plots for repeated soil sampling based on model (MOTTI-YASSO) predicted Simulations for the NFI permanent plots on forested mineral soil (N =
1719)
y=ΔC = f(age, fert, loc, T, P, sp, manag. scen)
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2. Stratification gain in simulated sampling
Source: Peltoniemi, Heikkinen, Mäkipää. 2007. Silva Fenn. 41: 527-539
1 2 3 4 5 6
m = 30; proj. uncert = 5
1 2 3 4 5 6
0.4
0.6
0.8
1.0
1.2
m = 1; proj. uncert = 5
SE
/ S
Esr
s
EqualProportional
Neyman
Number of strata
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2. Model-based stratification
With model based stratification a number of sampled plots can be reduced by 25% without reducing precisionUsefulness of stratification depends on
Precision of measurements Select paired repeated samples; or take enough samples and use spatial analysis
Precision of simulationsIncrease precision of soil ΔC simulationsWorks best in predictable environment (without successful prediction of stand future, improvement in sampling efficiency will be smaller)Predictions of future are difficult (harvests, thinnings)
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3. Analyses
of repeated
soil
sampling
QuestionCan we detect changes in organic layer C - and what are the rates of change?How many plots required for managed boreal forestsoils?
MethodSoil sampling repeated on 38 stands (now 40-80 yr)Measured 1985-89 (one composite sample per plot)New measurements from organic layer, n=40 per plot, kriging based estimates of mean and variance of soil C
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3. Measured
change
in C stock
of organic
layer
Average annual change of 23 g C m-2 was significant
Source: Häkkinen, Heikkinen, Mäkipää, submitted ms
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4. Sampling
desing
at plot-level
QuestionsWhat should be spatial location of sample pointsto avoid correlated samples?How many samples per plot/stand are needed to obtain reliable plot level estimates of soil C stock?
Method10 coniferous stands sampled for organic layer>100 samples per plotSpatial auto-correlation of carbon stock analysed
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4. Distance
between
sampling
points
0.0
0.1
0.2
0.3
0.4
0.5
0 250 500 750 1000
r (cm)
γ
Range 637.06 cmNugget 0.183Sill 0.319
Fig. Spatial
autocorrelation
in one
sample
plot
To avoid correlated samples distance betweensampling points should be > 7 m
Source:
Muukkonen, HäkkinenMäkipää, submitted
manuscript
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4. Effect of sample size (n) on precision
0
20
40
60
80
100
120
0 10 20 30 40 50 60 70 80
Number of samples per plot
95%
con
fiden
ce in
terv
al
P.sylv 1134
P.sylv1205
P.sylv1118
P. sylv 1025
P.syl 1004
P.abi 128
P.abi 157
P.abi 176
P.abi 194
P.abi 6566
n>20 gives
precise
estimates
for soil
C stock
of organic
layer
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5. Estimation
of monitoring
costs
at plot
scale
Costs of carbon measuring of soil organic layer (mcost
) are estimated as
mcost
= (kcost
+n*wtot
)
where kcost
is fixed costs, wtot
is variable costs, and n is number of soil samples per plot.
Variable costs (wtot
) are estimated as
wtot
= wfld
+ wpre
+ wplw
+ wmst
+ wC
where wfld
= costs of sample boring in the field, wpre
= preparation and drying of a sample, wplw
= powdering, wmst= measuring of moisture content, and wC
= carbon analysis of a soil sample.
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5. Time and costs
per sampled
plot
0
10
20
30
40
50
60
70
composite n=10 n=20 n=40Number of samples (n) per plot
Tim
e (h
ours
)
Laboratory analysis
Sample preparation
Soil sampling
Access to a sample plotand preparations
0
200
400
600
800
1000
1200
Composite n=10 n=20 n=40Number of samples (n) per plot
Euro
s
Laboratory analysis
Sample preparation
Soil sampling
Access to a sampleplot and preparations
Source: Mäkipää et al. 2008. Boreal
Env. Res. Manuscript
in revision.
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5. Precision
by
costs
(at plot
scale)
0
20
40
60
80
100
120
0 200 400 600 800 1000 1200
Euros
95%
con
fiden
ce in
terv
al
P.sylv 1134P.sylv1205P.sylv1118P. sylv 1025P.syl 1004P.abi 128P.abi 157P.abi 176P.abi 194P.abi 6566
Source: Mäkipää et al. 2008. Boreal
Env. Res. Manuscript
in revision.
-
Total monitoring costs of a network of sample plots are estimated with following sampling strategies
•
All plots of a network are resampled
every 5 years,•
75% of the plots are resampled
every 5 year (selection plots guided by model based stratification)
•
50% of the plots of a network are resampled
every 10 years•
37.5% of the plots are resampled
every 10 years (selection plots guided by model based stratification)
The monitoring costs of the network of sample plots (M) was estimated as
M = p * N * mcost
* F
where p is proportion of plots to be sampled, N is total number of plots in a monitoring network, mcost
is cost of carbon measuring of a plot, and F is sampling frequency (F=1 for annual sampling, F =1/10 for sampling of 10-year interval).
5. Estimation
of monitoring
costs
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5.Monitoring costs
of a network
of 2000 sample
plots
0
0.5
1
1.5
2
2.5
All plots every 5years
Selected 75%every 5 years
All plots every10 yr
Selected 75%every 10 years
Sampling strategy
Mon
itorin
g co
sts,
mill
ion
euro
s
TotalAnnual
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Conclusions
Currently available models can be used in national GHG inventory for estimation of soil C changesSoil monitoring with repeated measurement is laborous
Minimum number of sample plots for repeated soilmeasurements is >80 in a cohort of high rate of change>20 soil samples per plot are needed for reliable mean estimateof the C stock of organic layer
Sampling efficiency can be improved and monitoringcosts reduced
using existing networks of measures plotsincreasing sampling intervalstratification according to predicted changes of soil C
Results and methods can be applied in other countries
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Thank
you
for your
attention
Further
information
www.metla.fi/hanke/843002/
email
http://www.metla.fi/hanke/843002/
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Research articles resulting from this study
Häkkinen, M., Heikkinen, J. & Mäkipää, R. Soil
carbon
changes
detected
with
repeated
soil
sampling
–
spatial
within-site
variation
accounted
in statistical
analysis. Manuscript
submitted
in June
2007.Mäkipää, R., Lehtonen, A. & Peltoniemi, M. 2007. State-of-the-art
carbon
inventories
and ways
to use
them
for carbon
cycle
research. Springer, Ecological
Studies,in
press.Mäkipää, R. et al. Monitoring
changes
in the carbon
stocks
of forest
soils
-
efficiency
of different
sampling
methods
and costs
of the monitoring. Boreal
Env. Res. Manuscript
accepted
for revision.Muukkonen, P., Häkkinen, M. & Mäkipää, R. Spatial
variability
of soil
organic
carbon
in humus layer
of boreal
forest
soil. Submitted
manuscript.Palosuo, T., Peltoniemi, M., Komarov, A., Mikhailov, A. et al. Model
based
assessment
of the effect
of the intensified
biomass
collection
on forest
carbon
balance.Forest
Ecol. Managem. 255: 1423-1433.Peltoniemi, M., Thürig, E., Ogle, S., Palosuo, T., Shrumpf, M., Wützler, T.,
Butterbach-Bahl, K., Chertov, O., Komarov, A., Mikhailov, A., Gärdenäs, A., Perry, C., Liski, J., Smith, P. & Mäkipää, R. 2007. Models
in country scale
carbon
accounting
of forest
soils. Silva Fennica 41: 575-602.Peltoniemi, M., Heikkinen, J. & Mäkipää, R. Stratification
of regional
soil
sampling
by
model-predicted
change
in soil
carbon
in forested
mineral
soils. Silva Fennica, 41: 527-539.
Monitoring changes in the carbon �stocks of forest soilsOutlineIntroductionObjective1. Evaluation of soil C models1. Evaluation of soil C modelsSlide Number 72. Does model bases stratification improve sampling efficiency in large scale inventories?2. Stratification gain in simulated sampling2. Model-based stratification3. Analyses of repeated soil sampling3. Measured change in C stock of organic layer4. Sampling desing at plot-level4. Distance between sampling points4. Effect of sample size (n) on precision5. Estimation of monitoring costs at plot scale5. Time and costs per sampled plot5. Precision by costs (at plot scale)5. Estimation of monitoring costs5.Monitoring costs of a network of 2000 sample plotsConclusionsThank you for your attentionResearch articles resulting from this study