Dynamics of Productivity of Dynamics of Productivity of Russian Forests in a Russian Forests in a

Changing WorldChanging World

Anatoly Shvidenko, Sten Nilsson, Ian McCallumAnatoly Shvidenko, Sten Nilsson, Ian McCallumInternational Institute for Applied Systems Analysis, International Institute for Applied Systems Analysis,

A-2361 Laxenburg, AustriaA-2361 Laxenburg, Austria

Dmitry ShepaschenkoDmitry ShepaschenkoMoscow State Forest University, RussiaMoscow State Forest University, Russia

XIII Conference of the International Boreal Forest Association, Umeå, Sweden, 28–30 September 2006

Source: The Living Earth Inc., 1997

Russia in the Circumpolar Boreal DomainRussia in the Circumpolar Boreal Domain

- Home of about 30% of 500 million people of the boreal zone- About 70% of the boreal forest area (FRA 2000)- Source of above 50% of world coniferous industrial wood- One-third of the global accumulated organic matter- About 50% of the world’s NBP

Ecological Regions (Ecoregions)Ecological Regions (Ecoregions)

Major Requirements to Ecoregions

- Homogeneity of growth conditions at the level of bio-climatic subzones- Land forms (mountain and plain territories)- Specifics of hydrology (permafrost etc.)- Level and peculiarities of transformation of indigenous vegetation - Comparability of contribution of each ecoregion to major Terrestrial Biota Global Biogeochemical Cycles- ER boundaries cannot cross boundaries of subjects of the RF

141 Ecoregions of the Russian Federation141 Ecoregions of the Russian Federation

Inventory data (in the form of the State Forest Account) are

presented for ~2000 forest enterprises, 141 ecoregions, and

80 administrative regions for 1961, 1966, 1973, 1978, 1983,

1988, 1993, 1998 and 2003

Ratio of inventory growing stock to "restored" dynamics

0.90

0.95

1.00

1.05

1.10

1.15

1960 1965 1970 1975 1980 1985 1990 1995 2000 2005Year

GS of /GS R

ER

TR

AR

Dynamics of Russian Forests in 1961Dynamics of Russian Forests in 1961––2003 (inventory data)2003 (inventory data)

YearYear 19619611

19619666

19719733

19719788

19819833

19819888

19919933

19919988

20020033

Area,Area,

x 10x 1066haha695.695.

55705.6705.6 729.729.

77749.749.

55766.766.

66771.1771.1 763.5763.5 774.774.

22776.776.

11

GS, GS, x 10x 1099mm33 77.577.5 77.677.6 78.778.7 80.780.7 81.981.9 81.681.6 80.780.7 81.981.9 82.182.1

DefinitionsDefinitionsNet Primary Production (NPP) = Net Primary Production (NPP) =

Gross Primary Production (GPP) – Gross Primary Production (GPP) – Autotrophic Respiration (AR)Autotrophic Respiration (AR)

Phytomass (Live Biomass) – organic matter Phytomass (Live Biomass) – organic matter accumulated in all living plants of (forest) ecosystemsaccumulated in all living plants of (forest) ecosystems

NPP includesNPP includes plant growth (biomass accumulation and tissue plant growth (biomass accumulation and tissue

turnover above and below ground)turnover above and below ground) the C transfer to herbivores and root symbionts the C transfer to herbivores and root symbionts

(nodules, mycorrhizal fungi)(nodules, mycorrhizal fungi) production of root exudates and plant VOCsproduction of root exudates and plant VOCs

(Long (Long et alet al., 1989; Clark ., 1989; Clark et alet al., 2001; Kesselmeier ., 2001; Kesselmeier et alet al. 2002; Chapin . 2002; Chapin et alet al., 2006)., 2006)

Methods of Estimating NPPMethods of Estimating NPP Different destructive methods on sample plots Different destructive methods on sample plots ―― sequential sequential

root coring, in-growth cores, etc. (DBs generated by root coring, in-growth cores, etc. (DBs generated by N.I. Bazilevich, A.I. Utkin, V.A. Usoltsev, IIASA Forestry N.I. Bazilevich, A.I. Utkin, V.A. Usoltsev, IIASA Forestry Program, etc.) Program, etc.)

Numerous process-based methods including remote sensing Numerous process-based methods including remote sensing applications (as the difference between GPP and AR)applications (as the difference between GPP and AR)

Methods based on chlorophyll index (Voronin Methods based on chlorophyll index (Voronin et alet al., 1995; ., 1995; Mokronosov, 1999)Mokronosov, 1999)

(Mini)Rhyzotrons(Mini)Rhyzotrons Indirect methods (carbon fluxes approaches, nitrogen Indirect methods (carbon fluxes approaches, nitrogen

budgeting)budgeting) Different empirical ratios, e.g., between Above Ground NPP Different empirical ratios, e.g., between Above Ground NPP

and Growing Stock (Zamolodchikov and Utkin, 2000)and Growing Stock (Zamolodchikov and Utkin, 2000) Use of empirical regularities of growth and productivity of Use of empirical regularities of growth and productivity of

forests, etc.forests, etc.

ProblemsProblems Destructive measurements of forest NPP in Russia Destructive measurements of forest NPP in Russia

(methods almost exclusively used by the International (methods almost exclusively used by the International

Biological Program) are very labor consuming and their Biological Program) are very labor consuming and their

results underestimate NPP at 20results underestimate NPP at 20––30% due to the lack 30% due to the lack

of measurements of some components (e.g., root of measurements of some components (e.g., root

exudates, Volatile Organic Compounds, etc.)exudates, Volatile Organic Compounds, etc.)

Accuracy of all indirect methods at regional scale are Accuracy of all indirect methods at regional scale are

very low and mostly unknownvery low and mostly unknown

New measurement techniques (e.g., minirhizotrons) are New measurement techniques (e.g., minirhizotrons) are

practically not available in Russiapractically not available in Russia

Major part of results reported for Russian forests do not Major part of results reported for Russian forests do not

correspond to the current definition of NPPcorrespond to the current definition of NPP

Phytomass Expansion FactorsPhytomass Expansion Factors

where where Fi Fi is mass of phytomass fraction is mass of phytomass fraction ii, , GSGS is growing stock, is growing stock, TjTj is a function of is a function of biometric characteristics of forestsbiometric characteristics of forests

)(0

24321 RSCRSCCCi ASIcR

)exp( 540321 RSCACRSSIAcR CCCi

where A is age, years, SI is site index (coded as 3, 4, …, 13 for Ic, Ib, …, Vb site indexes), RS is relative stocking

Coefficients of the equations were estimated for forests of major forest forming species based on data of 3507 sample plots established in 1950s–2002

RiRi = Fi / GS =f (Tj),= Fi / GS =f (Tj),

Two types of equations for Two types of equations for RiRi were used were used

Phytomass of Russian ForestsPhytomass of Russian Forests

Total phytomass (2003), Tg CTotal phytomass (2003), Tg C 3449934499 Average density (2003), kg CmAverage density (2003), kg Cm-2-2 4.454.45 Including European Russia, kg CmIncluding European Russia, kg Cm-2-2 5.505.50 Including Asian Russia, kg CmIncluding Asian Russia, kg Cm-2-2 4.154.15

Total phytomass (1993), Tg CTotal phytomass (1993), Tg C 3384833848 Average density (1993), kg CmAverage density (1993), kg Cm-2-2 4.434.43

Forest Phytomass (2003)Forest Phytomass (2003)

Components56.3%

10.1%

3.9%22.6%

2.0%

5.1%

Stem Branches Foliage Roots Understory GFF

Age groups

5.9%

13.3%29.7%

25.2% 25.9%

Young Middleaged Immature Mature Overmature

Dominant species

17.1%

2.4%

31.1%

8.3%

3.8% 4.8%

13.3%15.5%

3.7%

Pine Spruce Fir Larch Cedar HWD Birch Aspen Ohters

Acclimation of Acclimation of Boreal and Temperate Forests?Boreal and Temperate Forests?

0.01

0.1

1

1950 1960 1970 1980 1990 2000 2010

Year

Impact of Acclimation on Impact of Acclimation on Storage of PhytomassStorage of Phytomass

60

70

80

90

100

110

120

130

140

1980 1985 1990 1995 2000 2005

Год измерения

Отк

лон

ение

от

1983

год

а, %

Зел. части 50

. 100

. 150

Надз. древесина 50

. 100

. 150

Подземная часть 50

. 100

. 150

Regressions

Ri=c0+c1Age+c2Time

for above ground wood and roots, and

Ri=c0+c1Agec2+c3Time

for green partsare statistically significant

(n=3332)

Total Forest Phytomass (Tg C)1993 (average) 337982003 (average) 34449

2003 (acclimation) 30570

Change of Structure of Live Biomass Change of Structure of Live Biomass in Russian Forests in 1961in Russian Forests in 1961––19981998

0.75

0.8

0.85

0.9

0.95

1

1.05

1.1

1.15

1960 1965 1970 1975 1980 1985 1990 1995 2000

Year

NDVI

Above ground wood

Green partsRoots

Average density of live biomass components (ratio of the mass of components togrowing stock volume): red = above ground wood; blue = roots; green = foliage

(data are normalized to values for 1983)

Yield Tables

Normal Modal

General

Regional

Quality control

Calculation of coefficients of Richard–Chapman growth function

DB of sample plots and

auxiliary data sets

Estimation of accuracy and adequacy

Development of unified growth models by species and ecoregion

DB of phytomass measurements

Development of models of biological productivity

Models of phytomass fractions

Outline of Modeling Biological ProductivityOutline of Modeling Biological Productivity

Modeling of GrowthModeling of GrowthAt the stage of growth At the stage of growth ―― the Richards–Chapman function the Richards–Chapman function

1/ 3 (1 1/ 3)3 2 1 3 2

c cii i

dXc c c X c c X

dt-= - 3

21 )]exp(1[ ci AccX

where t=A denotes age, Xi = H, D, BA, GS, TP, ci are the parameters,

At the stage of destruction (for BA and GS)C2 = c2 = const, for A<Ad, andC2 = c2 [exp (A- Ad,)] for A<Ad,where Ad is the age at the beginning of a decrease of BA and GS

Approximation for all site indexes of individual speciesApproximation for all site indexes of individual species3

2

1

( )i ij ij ijj

c c N c N c=

= + +å

where N is the code of site index class (N = 3,4,5, …, 11,12 for SI Ic, Ib, Ia, …Va, where N is the code of site index class (N = 3,4,5, …, 11,12 for SI Ic, Ib, Ia, …Va, Vb), Vb), ii=1,2,3=1,2,3

Total Production Total Production TPTPFt Ft of a Forest Ecosystemof a Forest Ecosystem

(by time (by time tt))

where the upper indexes define phytomass where the upper indexes define phytomass

fractions: fractions: stst is the stem, is the stem, brbr is wood of branches is wood of branches

(both over bark), (both over bark), folfol is foliage, is foliage, rootroot is root, is root, underunder

is understory, and is understory, and gffgff is green forest flooris green forest floor

TPFt = TPFtst + TPFt

br + TPFtfol + TPFt

root + TPFtunder + TPFt

gff ,

An Example An Example ―― Total Production of Roots: Total Production of Roots:

where where TPFTPFf_rootf_root and and TPFTPFrootroot are total production of phytomass of fine (< 2 mm) are total production of phytomass of fine (< 2 mm) and all roots.and all roots.

PcPcf_rootf_root denotes the share of fine roots to the total mass of all live roots and denotes the share of fine roots to the total mass of all live roots and mm is the average lifespan of fine roots; TV and GS denotes total production is the average lifespan of fine roots; TV and GS denotes total production and growing stock (in terms of stem wood). and growing stock (in terms of stem wood).

Coefficient Coefficient kk is a correction for the decline of the productivity of trees, which is a correction for the decline of the productivity of trees, which die during the current yeardie during the current year, , notes the wood losses in the crowns of live notes the wood losses in the crowns of live trees (dying branches, damage by insects and wind, etc.).trees (dying branches, damage by insects and wind, etc.).

The first components in (*) accounts for the change in the mass of roots of The first components in (*) accounts for the change in the mass of roots of live trees; the second is newly generated fine roots that replaced dead live trees; the second is newly generated fine roots that replaced dead ones; the third is the loss of fine roots (insects, animals, etc.), and the ones; the third is the loss of fine roots (insects, animals, etc.), and the fourth is newly generated fine roots that die during the current year.fourth is newly generated fine roots that die during the current year.

( ) ( )

( ) ( )( )

_ _ _ _1 1 1

__

11 1 12

f root root root f root f root f rootA A A m A m AA

f rootf roott A

rootAA A A A A

Pc Ft F F F F

TPF PcTV GS TV GS R

k

u- - - - -

==

- - -

é ù× - + - + × +ê úê ú= ê ú

× - - - ×ê úê ú×ë û

å ,(*),(*)

_ _(1 )root f root f root rootA A A ATPF F Pc TV R= + - × × ,(**),(**)

Amount of Phytomass of Fine Roots for Larch Amount of Phytomass of Fine Roots for Larch Dominated Ecosystems Dominated Ecosystems ―― An Example An Example

12

5

12_, 002.01

2.175.090905.2611524.0000223.0sii

SIrootsfineSIA AAAPc

where site index SI is coded (4, 5, …, 11, 12 for site indexes Ib, Ia, where site index SI is coded (4, 5, …, 11, 12 for site indexes Ib, Ia,

I, …, Va, Vb) and A is the age of the stand. I, …, Va, Vb) and A is the age of the stand.

Major results of modeling: (1) percent of fine roots Major results of modeling: (1) percent of fine roots

decreases with age, (2) share of fine roots in low productive decreases with age, (2) share of fine roots in low productive

stands is lower than in high productive stands, (3) the difference in stands is lower than in high productive stands, (3) the difference in

the output decreases with improvement of site conditions (i.e., for the output decreases with improvement of site conditions (i.e., for

site indexes for the same ages), and (4) the difference in the site indexes for the same ages), and (4) the difference in the

output decreases with increasing age (for the same site indexes).output decreases with increasing age (for the same site indexes).

0

100

200

300

400

0 50 100 150 200

Age, year

Ph

yto

mas

s, t

/ha Ib

I

III

V

Vb

0

5

10

15

20

25

0 50 100 150 200

Age, year

Phyt

omas

s, t/

ha

Ib

I

III

V

Vb

0

2

4

6

8

10

12

0 50 100 150 200

Age, year

Ph

yto

mas

s, t

/ha Ib

I

III

V

Vb

0,0

0,5

1,0

1,5

2,0

2,5

3,0

0 50 100 150 200

Age, year

Ph

yto

mas

s, t

/ha Ib

I

III

V

Vb

0

1

2

3

4

5

6

7

0 50 100 150 200

Age, year

Ph

yto

mas

s, t

/ha Ib

I

III

V

Vb

Model of Biological Productivity of Fully-stocked Pine Forests (dry matter, Mg C ha-1)

Stem wood

BranchesNeedles

Roots

0

20

40

60

80

100

0 50 100 150 200

Age, year

Ph

yto

mas

s, t

/ha Ib

I

III

V

Vb

0

100

200

300

400

500

600

0 50 100 150 200

Age, year

Ph

yto

mas

s, t

/ha Ib

I

III

V

Vb

Understory and green forest floor Total phytomass

0

2

4

6

8

10

12

14

16

0 50 100 150 200

Age, year

NPP

, t/[h

a*ye

ar]

Ib

I

III

V

Vb

0

100

200

300

400

500

600

700

0 50 100 150 200

Age, year

Acc

umul

ated

NPP

, t/h

a

St

Br

Ne

Ro

Und

Gff

0

50

100

150

200

250

300

0 50 100 150 200

Age, yearA

ccum

ulat

ed N

PP

, t/h

a St

Br

Ne

Ro

Und

Gff

Net Primary Production of Pine EcosystemsNet Primary Production of Pine Ecosystems

Total NPP

Accumulated NPP for I SI

Accumulated NPP for Va SI

Phytomass components:St-stem wood, Br-branches, Ne-

needles, Ro-roots, Und-Understory, Gff- green forest floor

Aggregated Results for 2003Aggregated Results for 2003

Total NPP of all forests (Tg C yrTotal NPP of all forests (Tg C yr-1-1)) RussiaRussia 23822382 European Russia (Tg C yrEuropean Russia (Tg C yr-1-1)) 653 653 Asian Russia (Tg C yrAsian Russia (Tg C yr-1-1)) 17291729

Average density ( g C mAverage density ( g C m-2-2 yr yr-1-1)) Russia Russia 307 307 European Russia European Russia 383 383 Asian Russia Asian Russia 285 285

Net Primary Production (2003)Net Primary Production (2003)

Components

14.7%

5.5%

27.9%

29.0%

6.3%

16.6%

Stem Branches Foliage Roots Understory GFF

Age groups10.6%

30.4%

12.4%

26.8%

19.8%

Young Middleaged Immature Mature Overmature

Dominant species

14.3%

12.1%

2.0%

32.1%

6.9%3.6%

17.9%

3.7%7.4%

Pine Spruce Fir Larch Cedar HWD Birch Aspen Ohters

Net Primary Production (Tg C Net Primary Production (Tg C ∙yr ∙yr -1-1))

This studyThis study Inventory (2003) 2382.2Inventory (2003) 2382.2 Included AGWIncluded AGW 511.0 511.0 (21.5%)(21.5%) Roots Roots 987.6 987.6 (41.4%)(41.4%) Green partsGreen parts 883.5 883.5 (37.1%)(37.1%) Inventory (1993) 2292.8 Inventory (1993) 2292.8 (- 3.8%)(- 3.8%)

Previous estimatePrevious estimate Inventory (1993) 1707.9 Inventory (1993) 1707.9 (- 28.3%)(- 28.3%)

Process-based modelsProcess-based models Average of 17 GM 2702.0 Average of 17 GM 2702.0 (+ 13.3%)(+ 13.3%) (Cramer et al., 1999)(Cramer et al., 1999)

CommentsComments Our estimate is 10Our estimate is 10––30% higher than practically all previous 30% higher than practically all previous

inventory estimatesinventory estimates Theoretically the method does not have any bias; however, Theoretically the method does not have any bias; however,

the latter could be generated by lack of data for a proper the latter could be generated by lack of data for a proper parametrization and changing environmentparametrization and changing environment

In this study, the parametrization of models has been In this study, the parametrization of models has been provided in a conservative way; thus, there is no reasons to provided in a conservative way; thus, there is no reasons to expect that the total NPP is overestimatedexpect that the total NPP is overestimated

The result should be considered as an average annual The result should be considered as an average annual estimate for a long period of time: it does not take into estimate for a long period of time: it does not take into account the acclimation of Russian forests to climate change account the acclimation of Russian forests to climate change (Lapenis (Lapenis et alet al., 2005) and impacts on NPP of weather ., 2005) and impacts on NPP of weather conditions of individual years (Shvidenko conditions of individual years (Shvidenko et alet al., 2005). Some ., 2005). Some regional studies (e.g., SIBERIA-II project) indicate interannual regional studies (e.g., SIBERIA-II project) indicate interannual variability due to seasonal specifics of weather at the level of variability due to seasonal specifics of weather at the level of 1010––25%25%

Many thanks for Many thanks for this opportunitythis opportunity