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

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.

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)

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)

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

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)

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

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

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

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

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

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.

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.

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

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

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

Thank

you

for your

attention

Further

information

www.metla.fi/hanke/843002/

email

raisa.makipaa@metla.fi

http://www.metla.fi/hanke/843002/

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