Modeling potentially sustainable biomass productivity in jack pine ...

Modeling potentially sustainable biomass productivity in jack pine forest stands J. S. Bhatti1, N. W. Foster2,3, T. Oja4, M. H. Moayeri5, and P. A. Arp5 1Northern Forestry T6H 3S5; 2Canadian

Centre, Natural Resources Canada 5320 122nd Street, Edmonton, Alberta, Canada Forest Service, Natural Resources Canada 1219 Queen Street East, Sault Ste. Marie, Ontario, Canada P6A 5M7; 4Institute of Geography, University of Tartu, Tartu EE2400, Estonia; and 5Faculty of Forestry and Environmental Management, University of New Brunswick, Fredericton, New Brunswick, Canada E3B 6C2. Received 12 May 1997, accepted 12 October 1997. Bhatti, J. S., Foster, N. W., Oja, T., Moayeri, M. H. and Arp, P. A. 1998. Modeling potentially sustainable biomass productivity in jack pine forest stands. Can. J. Soil Sci. 78: 105–113. A steady-state mass balance model (ForSust), developed to simulate potentially sustainable levels of tree biomass growth and related nutrient uptake dynamics, was applied to 17 jack pine sites across Canada. The model simulates potential tree biomass growth based on nutrient inputs from estimated atmospheric deposition (N, Ca, Mg, K) and soil weathering (Ca, Mg, K), and matches the resulting nutrient supply rates with calculated nutrient demand. Nutrient demand calculations are based on nutrient concentrations in wood, bark, branches, and foliage. Specifically, the model simulates sustainable annual increment (SAI) of biomass growth for stem-only and whole-tree (aboveground biomass) harvesting, and for recurring forest fire conditions. Calculated SAI levels were compared with field-estimated mean annual increments for aboveground forest biomass (MAI). For recurring forest fires, it was found that SAI values, as simulated, corresponded with the MAI field estimates in general. For whole-tree harvesting, SAI was lower than MAI for most but not all sites. For stem-only harvesting, SAI corresponded with MAI, but there was a greater scatter between SAI and MAI values than what appeared to be the case for the recurring forest fire scenario. Key words: Jack pine; whole-tree, stem-only harvesting; steady-state mass balance; forest biomass; N, Ca, Mg, K growth limitations; atmospheric deposition Bhatti, J. S., Foster, N. W., Oja, T., Moayeri, M. H. et Arp, P. A. 1998. Modélisation de la productivité durable potentielle de la biomasse dans les pinèdes à pin gris. Can. J. Soil Sci. 78: 105–113. Nous avons appliqué à 17 stations sous pins gris réparties un peu partout au Canada un modèle de bilan massique en état constant (ForSust) mis au point pour simuler les niveaux potentiellement soutenables de croissance de la biomasse arborescente et les flux associés d’exportation des nutriments. Le modèle simule la croissance potentielle de la biomasse arborée en faveur des apports calculés d’éléments nutritifs d’origine atmosphérique (dépôts de N, Ca, Mg, K) et eésultant de l’altération en place du sol : Ca, Mg, K et confronte les taux de disponibilité ainsi obtenus avec les demandes nutritionnelles calculées. Ces dernières sont basées sur les concentrations en nutriments mesurées dans le bois, l’écorce, les branches et les feuilles. Plus précisément, le modèle simule le croît annuel durable (CAD) de biomasse aérienne dans les régimes de récolte en fût principal et en arbres entiers et dans des conditions d’incendies de forêts récurrents. Les niveaux calculés de CAD sont confrontés aux valeurs de croît annuel moyen (CAM) estimées sur le terrain (biomasse aérienne). En circonstances de feux de forêt récurrents, on constate que les deux valeurs CAD et CAM correspondent. En régime de récolte intégrale (en arbres entiers), CAD est inférieur à CAM pour la plupart des stations. En régime de récolte du tronc seulement, les deux valeurs se correspondent, mais le nuage de dispersion est plus étendu que celui qui se dégage dans le scénario de feux de forêt récurrents. Mots clés: Pin gris, récolte en arbre entier, bilan massique en état constant, biomasse forestière, concentrations limitantes de N, Ca, Mg et K pour la croissance, dépôt d’origine atmosphérique

Forest harvesting has the potential to reduce future forest productivity by removing and/or displacing nutrients from the harvested site. Whole-tree harvesting is of particular concern (Kimmins 1997; Foster and Morrison 1989; Compton and Cole 1991; Olsson et al. 1996 a, b; Foster et al. 1997). Low-productivity sites are thought to be especially susceptible to nutrient removal with whole-tree harvesting, because these sites tend to have small nutrient pools. On other sites, whole-tree harvesting may or may not affect total ecosystem nutrient content (Hornbeck and Kropelin 1982; Hendrickson et al. 1989). Sustainability 3To

problems are likely to occur when the primary post-harvest replenishment of nutrients is less than what is required to reestablish the pre-harvest biomass production rates. Primary nutrient replenishment rates are due to atmospheric deposition (N, S, P, Ca, Mg, K, in wet and dry form) and soil weathering (Ca, Mg, K, P). Maintaining or achieving preharvest biomass production rates therefore depends on harvest intensity (Foster et al. 1997). Significant amounts of nutrients are contained in all harvestable, aboveground tree biomass components, especially branches and foliage (Compton and Cole 1991). Increasing the harvest intensity therefore implies increasing the amounts of nutrients removed from site. For example,

whom correspondence should be addressed. 105

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Fig. 1. Flow chart for determining mean annual biomass increments with a steady-state mass balance model. Special attention is given to N, S, Ca, Mg, K, Na, Al, and H because of the role of these elements in forest nutrition, soil acidification, and ion leaching.

Foster and Morrison (1989) reported that whole-tree harvesting, in comparison with stem-only harvesting, was associated with about 50% higher removal of Ca, and > 100% higher removal of N, P, K, and Mg from jack pine sites in northern Ontario. Accelerated post-harvest nutrient losses are confined to a relatively short period (first 3 or so yr) immediately after harvesting, presumably due to high rates of nutrient reimmobilization and uptake (Gholz et al. 1985; Vitousek and Matson, 1985; Knight et al. 1991). Jewett et al. (1995) reported on 6 yr cumulative post-harvest nutrient losses from a forested watershed (mainly tolerant hardwoods, with conifer stands along the stream valleys): the most significant post-harvesting leaching loss increases occurred with NO3-N, K, and Ca. In contrast, increases with P and NH4-N leaching were small and not significant. Mann et al. (1988) observed that large differences in amounts of nutrients left on the site after stem-only versus whole-tree harvesting may not necessarily result in major increases in leaching or runoff of nutrients. As yet, there have been no long-term studies dedicated to quantifying and evaluating the effects of primary nutrient inputs and outflows on the sustainability of site-specific forest biomass production. However, recent advances have been made in terms of monitoring and quantifying atmospheric nutrient inputs, and modelling nutrient uptake, nutrient harvest exports, nutrient leaching, and soil weathering rates by using dynamic and steady-state mass balance approaches (Aber et al. 1991; de Vries et al. 1995; Kimmins 1996; Arp and Oja 1997). Dynamic models are generally complex, and require much effort and data in terms of site-specific model

initialization and calibration. Steady-state mass balance models (SMB), in contrast, are easily applied to individual site conditions, have been used for regional, national, and international soil acidification assessments (de Vries et al. 1995), and should be of interest for evaluating forest biomass sustainability practices. In this paper, we report on calculated long-term effects of fire, stem-only harvesting, and whole-tree harvesting on potentially sustainable forest biomass production in 17 jack pine forest stands across Canada. The calculations are done by way of a steady-state mass balance model as detailed by Arp et al. (1996), with a special extension to evaluate the potentially sustainable forest biomass production levels. These levels are expressed in terms of annual forest biomass increments (i.e., sustainable annual increments, or SAI). For the purpose of this paper, we define potentially sustainable forest productivity as that rate which results from the primary input and output flows of N, P, K, Ca and Mg, at the stand level, in reference to the indefinite continuation of the existing forest type, and within the context of a specific disturbance regime (various harvest levels, recurring forest fire) and existing site and climate conditions. In addition, we compare the SAI values so calculated with field estimates for mean annual aboveground forest biomass production (mean annual increments, or MAI). STEADY-STATE MASS BALANCE MODEL Steady-state mass balance models are based on simple assumptions and statements about annual nutrient input, uptake, and leaching rates, per site, as shown for the case of the ForSust model (forest sustainability model, Fig. 1).

BHATTI ET AL. — MODELING SUSTAINABLE BIOMASS PRODUCTION

Model input is limited to: annual summaries for precipitation; local estimates for actual evapotranspiration (AET); wet atmospheric deposition for N, Ca, Mg, K; general soil conditions (depth, clay content, soil parent material type by base cation content; soil Ca, Mg, K availabilities, drainage); nutrient concentrations in foliage, branches, stem wood, bark; biomass fractions of the aboveground tree biomass components (foliage, bark, branches, wood). Soil weathering rates, sustainable N, Ca, Mg, and K export rates, and N, H, Al, and base cation leaching rates are calculated along the following principles and assumptions. (1) The uptake rate for N is assumed to be limited by supply rates for, e.g., N, Ca, Mg, K, and P, and by other growth limiting factors, notably length of growing season. Sustainable export rates are those that do not exceed nutrient uptake rates. (2) For long-term, steady-state calculations, it is assumed that it does not matter whether the nutrients are being exported from the site once in a while, or continuously: in principle, the calculated outcome is independent of forest renewal rate, or forest rotation age, as long as the sustainability criterion for same overall rate of biomass production can be matched. (3) For the steady-state condition, it is assumed that incoming nutrient flows do not change over time, and that all incoming nutrients are exported from the site through either biomass removal, forest fires, and soil leaching. Increased nutrient leaching and nutrient exporting means increased nutrient loss, to the point that site impoverishment occurs when the outgoing rates exceed the incoming rates. The exact balance between nutrient inputs and outputs is the point of sustainability. The problem, therefore, is (i) to determine the supply rate of the growth-limiting nutrient, and (ii) to determine the corresponding forest biomass increment (stand-level), as structured among the harvestable components of the trees (foliage, wood, stem wood, and stem bark). (4) Forest biomass growth is generally found to be responsive to N additions. Hence, we assume that forest biomass production is directly proportional to N uptake, as limited by the availability of N itself, and by the availability of other nutrients such as Ca, Mg, K, and P. We further assume that nutrient concentrations within the tree components, and the within-tree biomass partitioning of foliage, branches, stem wood, and stem bark remains essentially the same for sustainable production. These assumptions are not met when trees are young, but should be valid for trees as they mature and become harvestable. (5) For the long-term steady-state condition, we have that

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N_slash-leach is extra post-harvest N leaching loss, i.e., that portion of N_export that does not remain on site due to postharvest leaching; N_dep represents atmospheric N deposition; {[N]/[X]} is the overall aboveground N to X concentration ratio (molc/mol) within the biomass, as weighted by foliage, branches, stem wood, and stem bark biomass fractions (X = Ca, Mg, K, P); Season_f is an adjustment factor that takes into account the effects of season on nutrient uptake and supply. (6) For the case of forest fire, we would substitute N_harvest with N_fire_loss, and N_slash_leach with N_residue_leach. The former would address the amount of N that is returned to the atmosphere by combustion; the latter would address any extra N leaching losses that would occur as a result of the forest fire. Given that most fire residues have a high C/N ratio (mostly wood, and unburned pieces or wood), such losses are unlikely. Hence, N_residue_leach = 0 for the post-fire period. (7) For simplicity, we require that net_N_export, N_harvest (or N_fire_loss), N_slash_leach (or N_residue_leach), and N_dep in Eq. 1 are all expressed in terms of mean annual rates (molc ha–1 yr–1), even though harvesting or fire, for example, may be a one-time event in the life of a forest stand. In this case, N_harvest is the amount of N in the harvested portion of the aboveground biomass divided by the age of the stand at the time of harvesting if the stand is evenaged, and by the mean age of the stand if the stand is uneven-aged. The same supplies to N_fire_loss. (8) We assume that the extra post-disturbance N leaching is proportional to the amount of slash left on site, i.e., N_slash_leach = N_slash × f_slash_leach

(2)

with f_slash_leach as post-disturbance leach factor (dimensionless), and N_slash in molc ha–1 yr–1. From Jewett et al. (1995) we calculate a 6-yr cumulative and watershed-wide post-harvesting nutrient loss that is equivalent to about 33% of what might have been left on site in form of slash from stem-only harvesting. In general, f_slash_leach would depend on post-harvest vegetation recovery rates, which, in turn, would depend on the extent of site and soil disturbance, harvesting intensity, management practice, etc. (Gholz et al. 1985; Vitousek and Matson 1985; Knight et al. 1991). Any combination of factors that would discourage vegetation recovery would increase nutrient leaching from residuals. (9) Biomass-weighted values for {[N]/[X]} are obtained by combining of foliage, branch, stem bark, and stem wood nutrient concentrations with the total aboveground biomass fractions, which are given by:

net_N_uptake = net_N_export. On-site net_N_export (and therefore on-site net_N_uptake) is calculated in relation to nutrient availabilities (primary supply rates) and nutrient concentrations (demand) as follows: net_N_export = N_harvest + N_slash_leach = Season_f × min{available_X* {[N]/[X]}, N_dep} (1) where: N_harvest is the portion of N-uptake that is associated with the harvested portion of the aboveground tree biomass;

(foliage or branch or stemwood or stem bark biomass)/ (foliage + branch + stemwood + stem bark) biomass. Values for these ratios can be obtained for a number of major tree species from, e.g., Maliondo et al. (1990). In the model, the biomass-weighted {[N]/[X]} ratios are further adjusted by considering the effects of slash leaching loss on subsequent nutrient availabilities for forest biomass growth: the fraction of nutrients lost from the slash due to leaching is considered to be part of the harvest output.

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Evidence emerging from sites covered with and without slash suggests that slash residues do lead to increased N leaching in the first post-harvest years on account of increased availability of N for nutrient leaching, in spite of increased substrate availability for microbial N immobilization (Vitousek and Matson 1985). (10) With Eq. 1, we assume that all the N received and retained by the forest is used for aboveground tree biomass production. This assumption is purposefully aimed at calculating potential production, not actual production. Actual production would include the production of aboveground biomass other than trees, and below-ground biomass production as well. Actual and potential production rates, however, would not necessarily differ from one another in terms of long-term steady-state averages, as long as the non-harvested biomass nutrient stocks are already at steady state, remain part of the ecosystem, or are subject to efficient nutrient cycling. (11) Growth and uptake rates are essentially restricted to the length of the growing season. Supply rates are also restricted by season: the soil temperature effects the rate of soil weathing; the amount and duration of permanent snow accumulations affects nutrient retention and subsequent loss by way of snow melt and run-off. Hence, a good portion of atmospheric N deposition may by-pass the N-demanding forest vegetation altogether (Creed et al. 1996). For jack pine, we set the seasonal supply and uptake factor equal to Season_f = max [0, min (0.5 + 0.07 × mean_annual_ temperature, 1)] (3) This formulation is meant to account for the duration of the snowpack season and its effect on atmospheric N-supply, as it varies from site to site with mean annual temperature. (12) Maximum primary supply rates for Ca, Mg, K and P are obtained by setting: available_X (molc ha–1 yr–1) = X_deposition + X_weathering (X = Ca, Mg, K).

(4)

Here, X_weathering (molc ha–1 yr–1) is calculated with a simple scheme to quantify the rate soil weathering, as affected by soil mineral, soil depth, and mean annual soil temperature (de Vries et al. 1995). In this scheme, effects of soil mineral contents are related to soil clay content (%) and three soil substrate classes: acidic (e.g., acidic silicaceous igneous or sedimentary), medium (e.g., basic silicaceous igneous or sedimentary), and calcareous. Soil weathering rates are determined in terms of the sum of base cations released from the soil (Ca + Mg + K equivalents). This sum is then partitioned into annual Ca, Mg, and K availability rates, by multiplying the X_weathering with the percent value for Ca, Mg and K of exchangeable Ca + Mg + K (molc ha–1) per site. Doing so combines the soil weathering rate assessment with an on-site measure of actual Ca, Mg, and K availabilities. (13) With Eq. 1, we determine whether N, Ca, Mg, K, or P is growth limiting for the steady-state condition. Once the

growth limiting element is determined, X-uptake (molc ha–1 yr–1) is calculated from: net_X_uptake = net_N_uptake × [X/N].

(5)

(14) Leaching rates (molc ha–1 yr–1) from soil are calculated from X_leaching = X_deposition + X_weathering–X_uptake (X= Ca, Mg, K, P, N) (6) Net mineralization of X from soil organic matter, as well as net immobilization, is considered to be part of the steady state condition, and is therefore 0. (15) Setting: SAI_harvest = SAI_aboveground × f_harvest, with f_harvest as harvest fraction, SAI_slash = SAI_aboveground – SAI_harvest [N]_slash = N_slash/SAI_slash f_harvest + SAI_harvest/SAI_aboveground f_slash = SAI_slash/SAI_aboveground yields the following result from Eq. 1 and 2: SAI_harvest = (net_N_uptake × f_harvest) /{[N]_harvest + [N]_slash × f_slash_leach}.

(7)

With this equation, we can now proceed to estimate sustainable aboveground biomass growth rates based on (i) primary resource inputs per stand as affected by the length of the nutrient supply season, (ii) nutrient concentrations in the aboveground tree biomass components (stem wood, stem bark, branches, foliage), (iii) the biomass fractions among these components, and (iv) anticipated extra leaching losses from harvest slash. For illustration and demonstration purposes, we evaluated the outcome of this formulation (i.e., the ForSust model) for the case of 17 jack pine stands by way of several scenarios: stem-only harvesting, whole-tree harvesting, harvesting of stem wood only (bark removed on site), branch, wood and bark removal (BWB), no-harvesting, no fire, no harvesting, but with recurring forest fires. The performance of the model was checked by comparing SAI_harvest with fieldestimated values for mean annual on-site forest biomass increments (MAI). The model itself was realized with STELLA II, an interactive and transparent high-level programming language for Macintosh and PC computers (STELLA 1994). METHODS Fourteen of the 17 jack pine stands are part of the Acid Rain National Early Warning System plot network (ARNEWS; D’Eon and Power 1989; D’Eon et al. 1994). The other jack pine sites represent well-studied locations with high (Wells Township), medium (Dupuis Township), and low (Dryden) site productivity classes (Morrison and Foster 1974; Foster et al. 1995). Site locations are shown in Fig. 2, and a brief description of each site is given in Table 1. These sites represent jack pine stands for various soil parent materials (ranging from acidic to calcareous), climatic conditions, and

BHATTI ET AL. — MODELING SUSTAINABLE BIOMASS PRODUCTION

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Fig. 2. Location of jack pine plots.

atmospheric deposition rates. Mean stand ages range from 35 to 75 yr. Climate input refers to mean annual temperature, mean July temperature, mean annual precipitation, mean annual AET. Mean AET (mm yr–1) was obtained from long-term hydrological and climatological data, by setting: AET = local annual precipitation minus local annual stream flow discharge. (8) Locations for weather, atmospheric deposition, and stream discharge information were chosen to match the jack pine site locations as closely as possible. Data sources: climate CD-ROM, Environment Canada (1994); national stream water discharge rate catalogue (Environment Canada 1992); Ca, Mg, K, Na, NH4-N, NO3-N and SO4 ion concentrations in precipitation. Volume-weighted ion concentrations were multiplied with the location-estimated precipitation volumes to generate the corresponding ion deposition rates. Deposition rates were not corrected for dry deposition, except for Plot #1 (a New Brunswick site), where we considered the potential effects of fog and dry deposition on nutrient uptake and sustainable forest biomass production as a special case. Soil data involved specifications for soil depth (cm), bulk density (Mg m–3), clay content (%), organic matter content (%), and available N, Ca, Mg, and K. There was insufficient information about P in soils. Hence, all calculations were done without P. Forest stand data involved specifications for mean diameter at breast height (DBH), stand density, basal area, age, and height. From this, total aboveground tree biomass (Mg ha–1) was calculated in two ways: (i)

total biomass = wood density (dry) × form-factor × DBH × height

(9)

with form factor = 0.38, and wood density = 0.58 Mg cm–3 (Maliondo et al. 1990). (ii)

total biomass (Mg ha–1) = (stand_density/1000) × 0.0384 × DBH1.98 × height0.7819 (10)

as proposed by Ran et al. (1996) for jack pine. Field-estimated MAI values were generated by setting: Field-estimated MAI (Mg ha–1 yr–1) = total aboveground tree biomass/age

(11)

where age is mean age of dominant and codominant trees. Forest vegetation data also included N, Ca, Mg, K, P concentrations for foliage (ARNEWS plots, courtesy I. Morrison, CFS), stem wood and stem bark. Stem wood and bark data were obtained by sampling stem wood (four cores per tree) and bark (four bark chips per tree) of four dominant jack pine trees at the ARNEWS locations in 1995. Sample trees were dominant trees. The samples were oven-dried, and ground with a Wiley mill. Part of each sample was dryashed at 500°C for 24 h, and dissolved in 1 N HCL, to determine Ca, Mg, and K by atomic absorption spectrophotometry. Part of the ground sample was Kjeldahl digested for N determination. Directions to procedures are contained in D’Eon and Power (1989). Concentrations of N, Ca, Mg, and K in branches were estimated through regression analysis, based on the foliage, bark, branch and stem wood concentrations in Maliondo et al. (1990). RESULTS AND DISCUSSION Model Validation Most jack pine stands are of fire origin (MacLean and Wein 1977). During fire, nutrients are lost due to burning (NOx formation), and fly ash. Amounts of nutrients lost during

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Fig. 3. Simulated SAI values with a fire scenario with 100% N loss and up to 25% of base cations from above-ground biomass pool. Solid circles have about 10% or more tree vegetation type other than jack pine. Shaded circles are not part of the ARNEWS plots. Plots 1 and 5 (bracketed) are not part of the regression equation. Plot 1 is shown for the case of extra (right) and no extra atmospheric deposition (fog + dry deposition), due to the maritime influence in New Brunswick.

and/or after a fire vary with fire intensity, wind blowing of ash, surface runoff and leaching from the site. Low intensity forest fires tend to enhance nutrient availability. However, intense fires can deplete the available nutrient stock, thereby lowering soil fertility and site productivity (Kimmins 1977, 1996).

ForSust-stimulated SAI values for a jack pine fire regime with up to 25% losses of base nutrients and 100% N losses were found to be in general agreement with the field-estimated MAI values (Fig. 3). Increasing N supply by adding fog and dry deposition to the SAI calculations for Plot 1 strongly increased the simulated SAI value for this plot, thereby bringing it fairly close to the field estimated value (Fig. 3). However, only about 55% of the field-observed variations were generally captured by the model calculations. Lack of 100% correspondence between the field estimates and the model calculations would be due to: (1) Lack of knowledge regarding the precise nature of the fire cycle, its intensity, and its impacts on nutrient exports, and related imports from upwind fires. (2) Problems with the primary resource flow specifications for each specific site (atmospheric deposition, soil weathering). (3) An insufficient match between the field-estimated MAI values (age-dependent) and model calculated rates for sustainable forest biomass production, as a result of differences in stand conditions, and stand dynamics. (4) Problems with specifying nutrient demands, which are all based on a low sampling intensity of nutrient concentrations in foliage, bark, and stem wood, with regression estimates for nutrient concentrations in bark. (5) Differences among sites according to topographic position: seepage and drainage effects, as well as differences in capturing atmospheric deposition will affect localized N supply and uptake. (6) Differences in stand composition: some stands were