A regional carbon storage simulation for large-scale biomass plantations

Ecological Modelling, 36 (1987) 171-180

171

Elsevier Science Publishers B.V., Amsterdam - Printed in The Netherlands

A REGIONAL CARBON STORAGE SIMULATION FOR LARGE-SCALE BIOMASS PLANTATIONS

W E N D E L P. CROPPER, Jr. and K A T H E R I N E CARTER EWEL

School of Forest Resources and Conservation, University of Florida, Gainesville, FL 32611 (U.S.A.) (Accepted 7 April 1986)

ABSTRACT Cropper, W.P., Jr. and Ewel, K.C,, 1987. A regional carbon storage simulation for large-scale biomass plantations. Ecol. Modelling, 36: 171-180. Establishment of biomass fuel plantations in Florida could convert as many as 224000 ha year-1 to intensively managed sites with rotations of 2-8 years. A simulation model for slash pine (Pinus elliottii) forests predicts that 30-40% less carbon would be stored in north Florida forests over 24 years by planting 40 000 ha year-1 of slash pine biomass plantations with an 8-year rotation. Similar impacts on regional carbon cycles should occur wherever old-growth forests are replaced by short rotation biomass plantations.

INTRODUCTION

Biomass fuels have significant potential for supplementing energy production in the United States (Burwell, 1978; Pimentel et al., 1981). However, widespread use of biomass energy depends not only on favorable economic returns and net energy balances (Blankenhorn et al., 1982), but also on acceptable levels of environmental impact. One impact that may be associated with short-rotation forestry for biomass fuels is increased release of CO 2 from the soil to the atmosphere. In fact, Armentano (1984) concludes that increased wood consumption at the national level would significantly decrease carbon storage in 25 years. Higher concentrations of atmospheric CO2 may cause significant climate changes (Wigley et al., 1980; Hansen et al., 1981), which could in turn have a significant effect on agriculture and forestry (Emanuel et al., 1980; Layser, 1980; Hoffman, 1984; Waggoner, 1984). The consequences of increased emphasis in forest management toward biomass plantations must therefore be examined carefully before large-scale land use conversions take place. 0304-3800/87/$03.50

© 1987 Elsevier Science Publishers B.V.

172 Models for evaluating the effects of specific forest management practices on carbon storage have been implemented on a regional level (e.g., southeastern United States: Delcourt and Harris, 1980; Georgia forests: Sharpe and Johnson, 1981; peninsular Malaysia: Chan, 1982; Florida slash pine plantations: Cropper and Ewel, 1983; and Douglas-fir forests of western Oregon and Washington: Cropper and Ewel, 1984). Models at this scale are useful for evaluating the impact of specific land use scenarios on the global carbon cycle, because they deal with both a relatively discrete land use practice a n d / o r a large area of land. In this paper, we use a regional model to estimate the impact on carbon storage of the establishment of biomass plantations using slash pine (Pinus elliottii). Although the total impact of this land use cycle should ultimately be evaluated at the global scale, this analysis points out important regional trends that will have cumulative effects as they extend to larger and larger land masses. Florida appears to be suitable for biomass plantations because of its warm, humid climate and large land area suited for forestry (Smith and Dowd, 1981). Up to 224000 ha year -1 of commercial forests could be planted for woody biomass production, with rotations of 2 to 8 years (Rockwood et al., 1983). In north Florida, slash pine is recommended for biomass energy production using fertilized (50 kg of P ha -]) high density (3333-6667 trees ha -1) stands with an 8-year rotation (Rockwood et al., 1983). Slash pine plantations are already important throughout the southeastern coastal plain (4.3 x 10 6 ha in 1980), but they are managed principally for pulp production with a 25-year rotation (Barrett, 1980; Bechtold and Sheffield, 1981). METHODS We estimated net carbon storage in biomass plantations with a model of regional carbon storage in Florida slash pine pulpwood plantations (Cropper and Ewel, 1983). The model is based on differential equations describing carbon dynamics in slash pine foliage, stems and branches, and roots, as well as in litter, soil organic matter, and understory plants (Fig. 1). Carbon was assumed to be 50% of wood dry weight (Reichle et al., 1973). Net carbon accumulation responds to variations in regional climate (annual evapotranspiration) and fertilization. Carbon accumulation in slash pine foliage was simulated using:

d X1 m 1FX1 d~- - (1 + k, X1)

£aljXl

where X1 is the carbon content of slash pine foliage, M1 is the maximum rate of photosynthesis without fertilization, F is the fertilization response

173

+

+

I

X, FOLIAGE

UNDERSTORY

1XsTEand XTERont BRANCH

'~

X~COARSE ROOTS

"1 HUMUS

~] SOILORGANIC " I MATTER

Fig. 1. Compartments,transfer pathways, and atmosphericexchangesin a carbon flowmodel of a managed slash pine ecosystem(Cropper and Ewel, 1984). function, k 1 is a negative feedback coefficient that limits carbon accumulation by the foliage, and alj are constant linear transfer coefficients from the foliage. The fertilization response function was based on measured growth rate responses of young slash pine plantations, and it produces an asymptotic response with 99.8% of the maximum growth rate achieved when 100 kg ha-1 is added. The equation used to calculate F, describing the response of maximum photosynthesis to fertilization, was: F = 1.0 + ( 0 . 0 5 ( 1 - exp(-0.0317539P))) where P = phosphorus fertilization rate (kg ha-1). Other components were simulated with constant linear coefficients within first-order ordinary differential equations:

dXi/dt = E a j i X j - EaijXi The litter component is differentiated from soil organic matter by a more rapid rate of decomposition; fine roots have a higher respiration rate than coarse roots, and do not make a significant contribution to the soil organic matter pool. Coefficients are listed in Table 1; aij values not listed are 0. Variation in growth rates among the five climatic regions in the state was simulated by using an empirical scalar based on the response of aboveground net primary production (ANPP) to actual annual evapotranspiration (AET).

174 TABLE 1 Coefficients used to simulate Florida slash pine plantation c a r b o n accumulation aao a2o a25 a4o a6o M1 g 7

1.57 4.87X10 -2 1.90 X 1 0 - 3 1.00 2 . 8 0 × 1 0 -2 6.03 1 . 8 6 × 1 0 -2

a12 a23 a3o aso a7o M7

3.15 3.91X 10 -2 9-10X10 -2 8.60X 10 -2 1.568 5.95

a15 a24 a36 a56 a75 Kl

0.66 3.73X10 -2 1.819x 10 - I 9.00X10 - 4 3.08X10 -2 2.3 x 1 0 - 3

ANPP has been related to a variety of climatic factors over a broad geographical range (Gholz, 1986). AET has been suggested as a predictor of ANPP because it is an index both of water availability and solar energy inputs (Rosenzweig, 1968). Regression equations have been used to predict ANPP from AET on a global basis for terrestrial plant communities (Rosenzweig, 1968; Lieth and Box, 1972). Webb et al. (1978) suggests that AET is most strongly related to ANPP when ecosystems are water stressed. This situation appears common in north Florida slash pine plantations. High vapor pressure deficits and limited precipitation during spring months (Gholz, 1986), may result in rapid drying of the sandy soils and reduced net photosynthesis in Florida slash pine plantations. Simulations of a pine plantation model (K.C. Ewel and H.L. Gholz, unpublished data) using 40-year climate averages indicate that large depressions in ANPP of a mature pine stand may occur during typical spring conditions. AET as an index of ANPP may reflect photosynthetic responses of plants to water and solar energy availability, or could be related to long-term stand structural responses. Gholz (1982) and Webb et al. (1983) note that ANPP was strongly related to peak foliage standing crop and climatic variables of ecosystems. Gholz (1982) concludes that the climate-foliage mass relationship might reflect an adaptation to long-term hydrologic conditions. In our model AET was related to ANPP by incorporating a factor based on the Rosenzweig (1968) regression equation. This factor (C) was calculated from the ratio of ANPP predicted from regional AET, to ANPP predicted for a mature north Florida slash pine plantation (Gholz and Fisher, 1982), designated as the reference slash pine ecosystem (Cropper and Ewel, 1983). Regional AET values were calculated from weather station data using the method of Thornthwaite and Mather (1957). By basing the response on deviations from the reference slash pine ecosystem, our estimate did not equal the predicted Rosenzweig value for ANPP, but was proportional to the rate of change predicted from the reference ecosystem value. The climate factor C was used to modify the negative feedback coefficient K1 (and

175 therefore the m a x i m u m leaf biomass) and the equations for pine leaves and stems (Cropper and Ewel, 1983). The model was altered to simulate biomass plantations by adjusting the fertilizer term to increase growth rates for the first 3 years. After 8 years of simulated growth, fertilized trees had 35-37% more biomass (carbon) than trees on an 8-year-old unfertilized pulpwood plantation. Only two climatic regions were considered in this simulation: northeast and northwest Florida, where most of the potential development of biomass plantations is assumed to be concentrated. RESULTS AND DISCUSSION Simulated carbon accumulation patterns in slash pine stems and branches encompass values predicted by Rockwood et al. (1983) for high-density, short-rotation slash pine plantations, particularly at their intermediate density (Fig. 2). Year-to-year changes in their predicted rates of carbon accumulation were due to changes in estimated growth rates and survival that were not explicitly included in our model. Our simulated growth rates are higher

25

L

I~STIMATED S T O R A G E S

J

( ~ 6 667 trees/he (~) 4 444. trees/he (~ 3333 Irees/~o

2.0

f

E v : z I.~ 0 n~

f /. /

j," . / / /41(

NE FLORIDA ~

1 1 ~I ~

~'~i~ " ~ --- -~[I-~- - I 2 3

/

/

~

NW FLOR'DA J 4

I 5

[ 6

I 7'

A G E (yeers)

Fig. 2. Estimated biomass accumulations in stems and branches of slash pine plantations managed for biomass fuel production in northeast and northwest Florida. Dotted lines represent simulated values; solid lines represent estimated values under three proposed levels of tree density (Rockwood et al., 1983).

176

in the northeast region than the northwest region because of higher mean annual evapotranspiration. We estimated carbon storage in northeast and northwest Florida for three 8-year rotations of fertilized biomass plantations, and for 24 years of pulpwood plantation growth with no fertilization, which represents one rotation under standard forestry practices. In each case we simulated a plantation establishment rate of 20000 ha year -1 to produce a total of 480 000 ha in each region at the end of the 24-year period. Simulated carbon storage patterns for the two regions were similar. Intensively managed short-rotation biomass plantations can be expected to store less carbon than slash pine managed for pulp (Fig. 3). In both types of forests, net release of carbon following plantation establishment was caused by declining soil carbon levels (Cropper and Ewel, 1983). Soil carbon declined after harvest (Gholz and Fisher, 1982) because litter inputs declined after removal of the trees, but soil carbon still decomposed at pre-harvest rates. This was also observed in an analysis of carbon storage patterns in young Douglas-fir (Pseudotsuga menziesii) stands (Cropper and Ewel, 1984). Although carbon storage in the biomass plantations was initially faster than

1200[

IOOC

8oc

~ 60c

v Q IM 0 F-

Z 0 rn ~>

400

200

0

-200

I 4

I 8

I 12

I 16

I 20

I 24

YEARS

Fig. 3. Simulated rates of carbon storage in northwest Florida assuming no net change in forest management practices ( - - - - - ) , establishment of standard plantations ( ), and establishment of biomass plantations ( . . . . . . ) at 20000 ha year -1. Gg, gigagram = 106 kg.

177

in the pulpwood plantations, repeated harvests limited carbon accumulation. Carbon storage rates in the pulpwood plantations attained the regional average after 18 years. We also compared simulations of biomass and pulpwood plantation establishment to simulated regional averages of standing crop carbon and net carbon storage rates in slash pine plantations (1-35 years old), based on state-wide data on average planting and harvesting rates (Cropper and Ewel, 1983). Regional carbon storage rates are the product of the annual increment of total ecosystem carbon for each 1-year age class, and the area of plantations in that age class. After 24 years, biomass plantations would cover 128700 ha more area than the projected long-term average for plantations in northwest Florida, but net carbon storage would be 280 000 Mg C year -1 less in the biomass plantations. In addition to decreased annual carbon storage rates, biomass plantations would have much lower total ecosystem carbon standing crops (Table 2). This difference is attributable to smaller amounts of carbon stored in trees less than 8 years old and to depletion of soil carbon. Decreased standing crops over long periods represent net releases of carbon to the atmosphere (Houghton et al., 1983). Converting 480 000 ha of forested land to biomass plantations would release 39% of the carbon currently stored in the northeast region, and 36% in the northwest region. Although short-rotation forestry has been suggested as a method of improving "areas of non-productive silvicultural slums" (Silversides, 1982) and reducing dependence on imported fuels, care should be taken to evaluate potential environmental impacts, including alterations of regional

TABLE 2 Carbon accumulation (g m -2) under existing forestry practices (regional average) and under increased conversion to biomass plantations or standard pulpwood plantations (no harvesting or fertilization) in two regions in Florida Management

Detrital carbon

Aboveground carbon in trees

Total ecosystem carbon

Annual carbon storage

8156 7674 6640

3958 3724 956

12 848 12089 7 867

284 279 206

7929 7456 6584

3224 3032 691

11785 11082 7 527

216 210 99

Northeast Florida

Regional average Pulpwood plantation Biomass plantation Northwest Florida

Regional average Pulpwood plantation Biomass plantations

178

carbon storage. Biomass forestry can be associated with increased carbon storage if agricultural land is converted to forestry, or if fossil fuels are replaced with biomass wastes that decompose rapidly (Burwell, 1978). However, in Florida it has been estimated that only 12 000 ha year-l of idle farmland could be converted to woody biomass production, compared to 224 000 ha year -1 of forested land (Rockwood et al., 1983). Our analysis has not assumed that multiple rotations on the same site would reduce productivity (Keeves, 1966; Whyte, 1972; Kimmins, 1977), or that carbon would be released to the atmosphere more rapidly if the harvested wood were converted to fuel rather than pulp. These factors would amplify the change in the regional carbon balance that will result from a large-scale replacement of older forests with biomass fuel farms. CONCLUSIONS

(1) Short-rotation forestry, such as intensively managed biomass fuel plantations, can significantly alter regional carbon storage patterns. (2) Establishment of large-scale biomass fuel plantations in Florida could increase net carbon release to the atmosphere by reducing the average standing stock of soil and tree carbon, as well as by reducing the annual net carbon storage rate from the values of natural forests or longer rotation plantations. (3) Carbon storage patterns must be evaluated on an ecosystem basis. Although young trees may exhibit the highest growth rates, net ecosystem carbon storage rates should be the measure used for comparisons. ACKNOWLEDGEMENTS

Research was supported by a subcontract to Dr. Ewel from NSF DEB 77-267-22 and from a grant from the CO2 Research Division of DOE to Oak Ridge National Laboratories, W.R. Emanuel, principal investigator. We thank C.F. Cooper, J.J. Ewel, C.L. Montague, J. Pastor and D.L. Rockwood for helpful comments. This paper is University of Florida Agricultural Experiment Station Journal Series No. 5646. REFERENCES Armentano, T.V., 1984. Effects of increased wood energy consumption on carbon storage in forests of the United States. Environ. Manage., 8: 529-538. Barrett, J.W., 1980. Regional Silviculture of the United States. John Wiley, New York, NY, 551 pp. Bechtold, W.A. and Sheffield, R., 1981. Forest statistics for Florida. U.S. Dep. Agric. Res. Bull. SE-58, 38 pp.

179 Blankenhorn, P.R., Bowersox, T.W. and Weyers, R.E., 1982. Energy relationships for selected cultural investments. For. Sci., 28: 459-469. Burwell, C.C., 1978. Solar biomass energy: an overview of U.S. potential. Science, 199: 1041-1048. Chan, Y., 1982. Storage and release of organic carbon in peninsular Malaysia. Int. J. Environ. Stud., 18: 211-222. Cropper, W.P., Jr. and Ewel, K.C., 1983. Computer simulation of long-term carbon storage patterns in Florida slash pine plantations. For. Ecol. Manage., 6: 101-114. Cropper, W.P., Jr. and Ewel, K.C., 1984. Carbon storage patterns in Douglas-fir ecosystems. Can. J. For. Res., 14: 855-859. Delcourt, H.R. and Harris, W.F., 1980. Carbon budget of the southeastern U.S. biota: Analysis of historical change in trend from source to sink. Science, 210: 321-323. Emanuel, W.R., Olson, J.S. and Killough, G.G., 1980. The expanded use of fossil fuels by the U.S. and the global carbon dioxide problem. J. Environ. Manage., 10: 37,49. Gholz, H.L., 1982. Environmental limits on aboveground net primary production, leaf area, and biomass in vegetation zones of the Pacific northwest. Ecology, 63: 469-481. Gholz, H.L., 1986. Problems in the biophysical determination of site quality. In: IUFRO World Congress Proc., Ljubljana, Yugoslavia, September 1986, pp. 378-391. Gholz, H.L. and Fisher, R.F., 1982. Organic matter production and distribution in slash pine ecosystems. Ecology, 63: 1829-1839. Hansen, J., Johnson, D., Lacis, A., Lebedeff, S., Lee, P., Rind, D. and Russel, G., 1981. Climate impact of increasing atmospheric carbon dioxide. Science, 213: 957-966. Hoffman, J.S., 1984. Carbon dioxide and future forests. J. For., 81: 164-167. Houghton, R.A., Hobbie, J.E., Melillo, J.M., Moore, B., Peterson, B.J., Shaver, G.R. and Woodwell, G.M., 1983. Changes in the carbon content of terrestrial biota and soils between 1860 and 1980: A net release of CO 2 to the atmosphere. Ecol. Monogr., 53: 235-262. Keeves, A., 1966. Some evidence of loss of productivity with successive rotations of Pinus radiata in the southeast of South Australia. Aust. For., 30: 51-63. Kimmins, J.P., 1977. Evaluation of the consequences for future tree productivity of the loss of nutrients in whole-tree harvesting. For. Ecol. Manage., 1: 169-183. Layser, E.F., 1980. Forestry and climate change. J. For., 78: 678-682. Lieth, H., 1972. Modelling the primary productivity of the world. Nat. Resour., 8: 5-10. Lieth, H., 1973. Primary production: Terrestrial ecosystems. J. Hum. Ecol., 1: 303-332. Lieth, H. and Box, E., 1972. Evapotranspiration and primary productivity; C.W. Thornthwaite memorial model. In: J.R. Mather (Editor), Papers on Selected Topics in Climatology, Vol. 2. Elmer, New York, pp. 37-44. Pimentel, D., Moran, M.A., Fast, S., Weber, G., Bukantis, R., Balliett, L., Boverng, P., Cleveland, C., Hindman, S. and Young, M., 1981. Biomass energy from crop and forest residues. Science, 212: 1110-1115. Reichle, D.E., Dinger, B.E., Edwards, N.T., Harris, W.F. and Sollins, P., 1973. Carbon flow and storage in a forest ecosystem. In: G.M. Woodwell and E.U. Pecan (Editors), Carbon and the Biosphere. National Technical Information Service, Springfield, VA, pp. 345-365. Rockwood, D.L., Comer, C.W., Conde, L.F., Dippon, D.R., Huffman, J.F., Riekerk, H. and Wang, S., 1983. Energy and chemicals from woody species in Florida. ORNL/SUB/819050/1, National Technical Information Service, Springfield, VA, 96 pp. Rosenzweig, M.L., 1968. Net primary productivity of terrestrial communities: Prediction from climatological data. Am. Nat., 102: 67-74. Sharpe, D.M. and Johnson, W.C., 1981. Land use and carbon storage in Georgia forests. J. Environ. Manage., 12: 221-233.

180 Silversides, C.R., 1982. Energy from forest biomass--Its effect on forest management practices in Canada. Biomass, 2: 29-41. Smith, W.H. and Dowd, M.L., 1981. Biomass production in Florida. J. For., 79: 508-511. Thornthwaite, C.W. and Mather, J.R., 1957. Instructions and tables for computing potential evapotranspiration and the water balance. Publ. Climatol., 10: 181-311. Waggoner, P.E., 1984. Agriculture and carbon dioxide. Am. Sci., 72: 179-184. Webb, W., Szarek, S., Lauenroth, W., Kinerson, R., and Smith, M., 1978. Primary productivity and water use in native forest, grassland, and desert ecosystems. Ecology, 59: 1239-1247. Webb, W.L., Lauenroth, W.K., Szarek, S.R. and Kinerson, R.L., 1983. Primary production and abiotic controls in forests, grasslands, and desert ecosystems in the United States. Ecology, 64: 134-151. Whyte, A.G.D., 1972. Productivity of first and second crops of Pinus radiata on the montere gravel soils of Nelson. N.Z.J. For., 18: 87-103. Wigley, T.M.L., Jones, P.D. and Kelly, P.M., 1980. Scenario for a warm, high CO 2 world. Nature, 283: 17-21.