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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