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Dynamics of Productivity of Russian Forests in a Changing World Anatoly Shvidenko, Sten Nilsson, Ian McCallum International Institute for Applied Systems.

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Presentation on theme: "Dynamics of Productivity of Russian Forests in a Changing World Anatoly Shvidenko, Sten Nilsson, Ian McCallum International Institute for Applied Systems."— Presentation transcript:

1 Dynamics of Productivity of Russian Forests in a Changing World Anatoly Shvidenko, Sten Nilsson, Ian McCallum International Institute for Applied Systems Analysis, A-2361 Laxenburg, Austria Dmitry Shepaschenko Moscow State Forest University, Russia XIII Conference of the International Boreal Forest Association, Umeå, Sweden, 28–30 September 2006

2 Source: The Living Earth Inc., 1997 Russia 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

3 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

4 141 Ecoregions of the Russian Federation

5 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

6 Dynamics of Russian Forests in 1961–2003 (inventory data) Year Area, x 10 6 ha GS, x 10 9 m

7 Definitions Net Primary Production (NPP) = Gross Primary Production (GPP) – Autotrophic Respiration (AR) Phytomass (Live Biomass) – organic matter accumulated in all living plants of (forest) ecosystems NPP includes  plant growth (biomass accumulation and tissue turnover above and below ground)  the C transfer to herbivores and root symbionts (nodules, mycorrhizal fungi)  production of root exudates and plant VOCs (Long et al., 1989; Clark et al., 2001; Kesselmeier et al. 2002; Chapin et al., 2006)

8 Methods of Estimating NPP  Different destructive methods on sample plots ― sequential root coring, in-growth cores, etc. (DBs generated by N.I. Bazilevich, A.I. Utkin, V.A. Usoltsev, IIASA Forestry Program, etc.)  Numerous process-based methods including remote sensing applications (as the difference between GPP and AR)  Methods based on chlorophyll index (Voronin et al., 1995; Mokronosov, 1999)  (Mini)Rhyzotrons  Indirect methods (carbon fluxes approaches, nitrogen budgeting)  Different empirical ratios, e.g., between Above Ground NPP and Growing Stock (Zamolodchikov and Utkin, 2000)  Use of empirical regularities of growth and productivity of forests, etc.

9 Problems  Destructive measurements of forest NPP in Russia (methods almost exclusively used by the International Biological Program) are very labor consuming and their results underestimate NPP at 20–30% due to the lack of measurements of some components (e.g., root exudates, Volatile Organic Compounds, etc.)  Accuracy of all indirect methods at regional scale are very low and mostly unknown  New measurement techniques (e.g., minirhizotrons) are practically not available in Russia  Major part of results reported for Russian forests do not correspond to the current definition of NPP

10 Phytomass Expansion Factors where Fi is mass of phytomass fraction i, GS is growing stock, Tj is a function of biometric characteristics of forests 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 Ri = Fi / GS =f (Tj), Two types of equations for Ri were used

11 Phytomass of Russian Forests  Total phytomass (2003), Tg C34499  Average density (2003), kg Cm  Including European Russia, kg Cm  Including Asian Russia, kg Cm  Total phytomass (1993), Tg C33848  Average density (1993), kg Cm

12 Forest Phytomass (2003)

13 Acclimation of Boreal and Temperate Forests?

14 Impact of Acclimation on Storage of Phytomass Regressions Ri=c0+c1Age+c2Time for above ground wood and roots, and Ri=c0+c1Age c2 +c3Time for green parts are statistically significant (n=3332) Total Forest Phytomass (Tg C) 1993 (average) (average) (acclimation)30570

15 Change of Structure of Live Biomass in Russian Forests in 1961–1998 Average density of live biomass components (ratio of the mass of components to growing stock volume): red = above ground wood; blue = roots; green = foliage (data are normalized to values for 1983)

16 Yield Tables NormalModal 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 Productivity

17 Modeling of Growth At the stage of growth ― the Richards–Chapman function where t=A denotes age, Xi = H, D, BA, GS, TP, c i are the parameters, At the stage of destruction (for BA and GS) C2 = c2 = const,for A

18 Total Production TPFt of a Forest Ecosystem (by time t) where the upper indexes define phytomass fractions: st is the stem, br is wood of branches (both over bark), fol is foliage, root is root, under is understory, and gff is green forest floor

19 An Example ― Total Production of Roots: where TPF f_root and TPF root are total production of phytomass of fine (< 2 mm) and all roots. Pc f_root denotes the share of fine roots to the total mass of all live roots and m is the average lifespan of fine roots; TV and GS denotes total production and growing stock (in terms of stem wood). Coefficient k is a correction for the decline of the productivity of trees, which die during the current year,  notes the wood losses in the crowns of live trees (dying branches, damage by insects and wind, etc.). 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 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.,(*),(**)

20 Amount of Phytomass of Fine Roots for Larch Dominated Ecosystems ― An Example 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. Major results of modeling: (1) percent of fine roots decreases with age, (2) share of fine roots in low productive stands is lower than in high productive stands, (3) the difference in the output decreases with improvement of site conditions (i.e., for site indexes for the same ages), and (4) the difference in the output decreases with increasing age (for the same site indexes).

21 Model of Biological Productivity of Fully-stocked Pine Forests (dry matter, Mg C ha -1 ) Stem wood Branches Needles Roots Understory and green forest floor Total phytomass

22 Net 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

23 Aggregated Results for 2003 Total NPP of all forests (Tg C yr -1 )  Russia 2382  European Russia (Tg C yr -1 ) 653  Asian Russia (Tg C yr -1 )1729 Average density ( g C m -2 yr -1 )  Russia 307  European Russia 383  Asian Russia 285

24 Net Primary Production (2003)

25 Net Primary Production (Tg C ∙yr -1 ) This study Inventory (2003) Inventory (2003) Included AGW (21.5%) Included AGW (21.5%) Roots (41.4%) Roots (41.4%) Green parts (37.1%) Green parts (37.1%) Inventory (1993) (- 3.8%) Inventory (1993) (- 3.8%) Previous estimate Inventory (1993) (- 28.3%) Inventory (1993) (- 28.3%) Process-based models Average of 17 GM (+ 13.3%) Average of 17 GM (+ 13.3%) (Cramer et al., 1999) (Cramer et al., 1999)

26 Comments  Our estimate is 10–30% higher than practically all previous inventory estimates  Theoretically the method does not have any bias; however, the latter could be generated by lack of data for a proper parametrization and changing environment  In this study, the parametrization of models has been provided in a conservative way; thus, there is no reasons to expect that the total NPP is overestimated  The result should be considered as an average annual estimate for a long period of time: it does not take into account the acclimation of Russian forests to climate change (Lapenis et al., 2005) and impacts on NPP of weather conditions of individual years (Shvidenko et al., 2005). Some regional studies (e.g., SIBERIA-II project) indicate interannual variability due to seasonal specifics of weather at the level of 10–25%

27 Many thanks for this opportunity

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