Carbon, nitrogen and microbial biomass interrelationships during the decomposition of wheat straw: A mechanistic simulation model

Soil Biol. Eicxhn.

Vol. 15. No. 4. pp. 455-461.

003%0717:u s3.00+0.00 Pergamon Press Ltd

I983

Printed in Great Britain

CARBON, NITROGEN AND MICROBIAL BIOMASS INTERRELATIONSHIPS DURING THE DECOMPOSITION OF WHEAT STRAW: A MECHANISTIC SIMULATION MODEL E. B. KNAPP,’ L. F, ELLIOTT’ and G. S. CAMPBELL”

3Department

‘CIMMYT, Londres 40 Mexico 6 D.F. ‘USDA, Agricultural Research Service, Pullman, WA 99164, U.S.A. of Agronomy and Soils, Washington State University, Pullman, WA 99164, U.S.A.

Summary-The behavior of added carbon as crop residues and nitrogen in agricultural ecosystems is most often quantitatively described by empirically derived first-order rate reactions. A mechanistic approach may be more precise for describing interrelations between C, N and microbial populations during short periods of active decomposition. The effect of N on the disappearance of C from a wheat straw system, and the response of the biomass to N additions, was simulated using microbial growth and maintenance terms derived from the literature. Results of the simulatjon were compared with microbial growth and wheat straw decomposition measurements made with an electrolytic rcspitometer. Straw decomposition rate was shown to be strongly depcndcnt on avaitabk C and N during initial decomposition. When N is limiting, excess available C apparently is immobilized as polysaccharides.

INTRODUCTION

requisites for residue decomposition are a viable microbial population and environmental factors suitable for metabolism (Parr and Papendick, 1978). Additionally, it is generally accepted that the rate of residue decomposition decreases as the residue C:N ratio increases. But, recent evidence (Knapp et ul., 1983) indicates that decomposition of wheat straw can also be limited by C availability. In addition, it was postulated that the amount of N immobilized resulted largely from microbial growth in response to N availability. Simulation of residue decomposition is useful for interpreting experimental results, identifying research needs and predicting system interactions. Most of the workers who have simulated the complex microbial processes of decomposition and nutrient cycling in natural systems have used first-order reactions with empirically determined rate coefficients modified to account for changing environmental conditions (Bunnell ef al., 1976; Gilmour et al., 1977; Hunt, 1977; Witkamp, 1966). Although such simulations are usefu1, it is recognized that a more mechanistic approach is needed for the simulation of microbial processes in natural systems (Barber and Lynch, 1977; Batin et al., 1976; McLaren, 1970; Parnas, 1975; Smith, 1979). Bazin et al. (1976) pointed out that the choice of functions used in a model depends on the purpose for which the model is intended. In this context, Bunnell (1973) suggested that a balance between understanding and detail is necessary so that principles are not obscured while at the same time simulations reasonably predict observations. Our objective is to present a m~hanistic description of the interrelations between C, N and microbial biomass during active periods of decomposition of wheat straw residues, so The

I”” IS/4 ,

455

that this description can eventually be incorporated into a comprehensive model of agricultural ecosystems. THEORY We modified Smith’s (1979) model to simulate straw decomposition. Figure 1 shows the N and C pools and the transfo~ations which are considered in this model. Carbon in the system is assumed to be in one of four pools: residue, soluble, labile cell or assimilated live biomass. Nitrogen is assumed to be in one of three pools: available soil, labile cell or assimilated live biomass N. The labile cell pools of C and N are part of the total live biomass; but, for convenience in modeling, they are considered separate here. We assumed that negligible N is associated

Residue C

Fig.

I

1. Carbon and nitrogen pools and transformations used to model straw decomposition.

456

E. B. KNAPP et ul.

with the decomposing residue. Also, dead microbial biomass and polysaccharide synthesized by the microbes under excess C substrate conditions are assumed to decompose at a rate similar to that of the residue so they are returned to the residue pool. Nitrogen from cell protein and amino acids is immediately returned to the available N pool when microbes die. We assumed that the cellulose and hemicellulose fractions of the residue decomposed at rates described by a single rate constant. For the short duration of this study, the lignin fraction of the straw was assumed completely resistant to decomposition, and was not considered in the model. The C and N transformations were modeled assuming that processes are adequately described by Michaelis-Menten kinetics (Smith, 1979). The differential equations describing changes in each pool are as follows: The rate of change of the residue pool is described by: d&,/d! = DL, + yp VJ,L,,,/(K, + I,) - R,L,R,.

(1)

Symbols are defined in Table 1. The first term on the right represents the input of dead biomass to the residue pool, the second term is the rate of polysaccharide production, and the third represents the Table Units

Symbol

soil

’ microbe

gC g ’ microbe gC gg’ microbe g soil gC g gC g-t gC ggC gg gC gC gN gC gC

g-‘C

C h

’

C C

hh’

’ soil microbe

C h-’

’ soil ’ soil

gg ’ microbe g ’ microbe g ’ microbe gg ’ soil g- ’ microbe

C C h ’ C C

gN g

’ microbe

C

gCg_’

microbe

Ch

gC g-’

microbe

C

gc g-C gC g ’ soil gN g -’ soil gCy-’ N ’ microbe gNg gN gg ’ soil gN gg’ soil gN gg’ microbe gN gg ’ soil h-’

dS,/dt = R,L,R,

’

C

C hh’

f DI,L, -Low V,Ul

- ~,I&JI(K + %I

(2)

The first term, as in equation (I), is the residue decomposition rate, the second is the input of labile cell C from dying microbes, and the last term is the rate of C uptake by the live biomass. The uptake rate is in standard M-M form, but the term (I - I,/&,) limits the amount of C which can be taken up by cells according to the ratio of labile cell C to some maximum concentration of labile C. Labile cell C per unit biomass (I,,) is calculated from labile C per unit soil mass using

Labile cell C is used for cell maintenance, cell growth or synthesis of polysaccharide and is lost through cell death. Changes in the labile cell pool are and units for symbols

Definition

F ’’ soi’ gC g.gCg_’ gC g-

I. Definitions

rate of residue decomposition. Residue decomposition rate is represented with first-order kinetics. Smith used a Michaelis-Menten (M-M) equation for this description, but our residue levels were so low, compared to Smith’s saturation constant, that a first-order equation was adequate. Changes in the soluble C pool are described by:

Crop residue Death rate Live biomass C Yield for polysaccharide Maximum uptake rate for polysaccharide Cell labile C concentration Saturation constant for polysaccharide Residue decomposition rate Cell labile C/unit soil mass Maximum uptake of soil C Soil soluble C concentration Saturation constant for uptake of soil C Maximum labile cell C Maximum uptake for growth Internal cell N concentration Maximum live biomass C saturation constant for growth N saturation constant for growth Maximum C uptake for respiration C saturation constant for maintenance True growth yield COz production Soil N concentration C:N ratio of live biomass Maximum labile cell N Saturation constant for N uptake Cell labile N/unit soil mass Maximum uptake rate for N Nitrogen in live biomass Maximum death rate from starvation

Value

0.85 0.04

Reference

Chapman Assumed

and Gray (1981) equal to V,

0.05 1.08

Smith (1979) used 1.0

0.04

Our data

1.3 x lo-’ 0.1 0.04 IO-’ IO-’

Smith (1979) used 6 x lO-4 Smith (1979) Assumed equal to V, Smith (1979)

1O-4 2 x IO_’

Shields

EI al. (1976)

lo-6 0.6

8 2 x 10m2 2 x lo-b

Chapman

and Gray

Smith (1979) Smith (I 979) used lo-’

3 x IO-’

V,lCn

6 x lO-J

Smith (I 979)

(1981)

Simulation

therefore given by:

The first term, as in equation (2), is usage of carbon for production of live biomass, the second is C used for growth, the third is C used for maintenance respiration, the fourth is C for polysaccharide synthesis and the fifth, as in equation (2), is the loss of labile cell C from dying microbes. Equation (3) represents the main departure from Smith’s C model, and is intended to make our model correspond with the data of Chapman and Gray (198 1). They found that, under N limiting conditions, Arthrobacter glob@rmis produced storage carbohydrate, and that maintenance respiration in this species decreased as substrate became less available. We set up equation (3) so that maintenance, growth and polysaccharide synthesis compete for available cell C. The K’s are adjusted so that maintenance has highest priority and polysaccharide synthesis has lowest priority. Growth is assumed to require both C and N, so growth rate is reduced when either substrate is limiting. The double M-M form of the growth equation was used previously by Parnas (1975). The term, (I - L,/M,,) in the growth term of equation (3) limits the biomass to a value smaller than M,, which Smith termed a spatial limitation. Since our model does not include potassium or phosphorus nutrition and also neglects any other limitations on growth, M,, could represent a limitation on growth by any factor other than C or N. The change in live biomass C is given by the equation dL,_ Y L V,lJ” ( l - L/~/J) _ DL (4) dt m’ (Kc + I,) (K, + L) The first term is the C uptake for growth multiplied by the true growth yield, Y, while the second is the C loss to death. It should be pointed out again that, in the model, live biomass C does not include labile cell C or storage carbohydrate. Carbon dioxide production by microbial respiration results from costs for growth, maintenance, and synthesis of storage products. The CO* produced is described by

G -=

dt

( 1- Y1L VJsL(1 - -L/M,) (Kc+ 4) (K + L) vr!,L,

+(K+4) +

(1-Y,)L4v,~

(5)

(K,, + z)

The three terms on the right in equation (5) represent cell growth respiration, maintenance respiration and respiration cost of storage polysaccharide production, respectively 01, is the true growth yield for storage carbohydrate). The N pool equations are similar to those for C. The change in available N with time is described by dN,ldt = DI,,L, i- DL,/C, - L, V,N,( 1 - InIN,,,)I(K,, + NJ where

I, =

NJL,.

451

of wheat straw decomposition

(6)

The first term represents the return of labile cell N to the available pool through microbial death, the second is the return of assimilated live biomass N through cell death, and the third is N uptake by the viable biomass. The C:N ratio of the assimilated live biomass pool is represented by C,. This is not necessarily the C:N ratio of the total live biomass since it does not include N or C in the labile pool or storage carbohydrate. The change in the labile N pool with time is expressed by dN< -_= dr

L, V,N,(l - IJN,) K,, + N,

The first term, as in equation (6), is N uptake by the viable biomass, the second is N used in cell growth and the third is N loss due to cell death. Once again, internal cell N and C concentrations, I, and I, are the concentrations on a soil mass basis (N, and S,) divided by the live biomass, L,. The change of N in the live biomass pool is described by

dN,. Y L V,LI” ( 1- LI~J _ dt G(K + I,)(Kn + 4)

DL

,c

(8)

-=

“a ”

The first term is the assimilation rate of N into assimilated live biomass. The second term is the loss of N from live biomass through death, which can be represented by the C loss through death divided by the C:N ratio of the assimilated biomass pool. The microbial death rate is assumed to be a function of labile cell C and N. When reserves of these substrates become low, death rate is assumed to increase. This assumption is similar to that used by Smith (1979) except N was not included in his death term. He included a factor to accelerate death at low soil water contents, but that was not needed for our conditions. The death rate is calculated from D = B, exp (- 0. I I,JK,)

(9)

so that when cell C becomes low compared to the respiration saturation constant, death rate will increase. The maximum death rate is set at B,. MATERIALS AND METHODS

A detailed description of the experimental procedures used in this study was given by Knapp et al. (1983). Briefly, 1.5 g samples of dry wheat straw were mixed with 148.5 g of washed sand and 30 g water to give a water content of 0.2 g g-’ and a C content of 4.3 pg gg’. The samples were incubated in electrolytic respirometers at 15°C. The respirometers provided a continuous readout of 0, consumption, while CO2 production (trapped in KOH in the respirometers) and total and amino N were determined periodically by destructive sampling. Two main treatments were established each with I2 respirometers. One set contained only sand, straw and water; the second an additional 9 mg N as (NH&SO4 enriched with “N to determine immobilization and to verify the amino-N correction fac-

458

E. B.

KNAPP et al.

tor. At 240 h, the contents of four respirometers from each treatment were analyzed for CO2 and total and amino-N. At 240 h, 200 mg glucose was added to four of the high-N treatments and 9 mg N was added to each of the eight remaining low-N treatments. At 360 h, the four high-N treatments were analyzed. At 480 h, four of the low-N treatments that received added N at 240 h were analyzed and 200 mg glucose was added to the remaining four, which were analyzed at 600 h. The measured values for amino-N were corrected for incomplete recovery to obtain actual amino-N values. NUMERICAL

IMPLEMENTATION

In order to solve equations (l-8), it is necessary to establish values for the constants, to set the initial condition and to choose a numerical scheme which will obtain accurate solutions. The constants we used to solve these equations are listed in Table 1 along with the reference from which the constant was obtained. Most of the constants are those used by Smith (1979). In a few cases, Smith’s values were adjusted slightly. These adjustments were made to improve agreement between simulation and experiment. They are ail well within the range of published values for these constants. Our experimental value for maximum cell C uptake rate corresponds more closely with Smith’s rate of extracellular hydrolysis than with his maximum uptake rate. The low value we used was required to properly simulate uptake when C was readily available. Data were not available to directly determine values for the saturation constants and maximum rates of growth, maintenance and polysaccharide synthesis. These were set as follows: The maximum rates of C uptake for growth and polysaccharide formation were arbitrarily set equal to the maximum cell C uptake rate. The maximum maintenance respiration rate was that of Shields et al. (1973). The actual values for the saturation constant are probably less important than their relation to each other. We arbitrarily chose K, to be I x 10M3with K, set an order of magnitude lower to reflect the difference in C and N requirement. All maintenance respiration saturation constants were then set to values several orders of magnitude lower than those to give maintenance preference when substrate became limiting. The polysaccharide saturation constant was set somewhat higher than the growth constant to give preference to growth over polysaccharide synthesis. The maximum yield for polysaccharide synthesis (JJ,) was estimated from the data of Chapman and Gray (1981). Initial conditions for the simulation were established from the experiment of Knapp et al. (1983). Soluble C and N and the lignin fraction were not determined. Measurements of soluble C (S. Reinertsen, personal communication) on straw similar to that used by Knapp showed that about 10% of the total C is soluble. We found a value of 9.3% to be consistent with the Knapp data. Available-N was taken as the average of the Kjeldahl-N measured at 0 and 240 h minus the biomass-N at 0 h for the low-N treatment. Available-N for the high-N treatment was this value plus the added N. The straw was assumed

to be 20% lignin and lignin was assumed to be resistant to decomposition during the short duration of this study. The residue-C pool was therefore total-C minus lignin-C, live biomass-C and soluble-C. The simulation was programmed in BASIC on a microcomputer (program available from G. S. Campbell on request). A time-step of I h was used for all simulations. Carbon and N concentrations in most of the pools remained relatively constant over I h. so the Euler method was used to solve for changes in these pools. Rates of change were first calculated using the appropriate equation, and the new concentration determined by multiplying the time interval by the velocity and adding the product to the old concentration. The internal cell concentrations of C and N were much less stable and required either a more complicated solution technique or a time step much shorter than I h. We chose to solve the internal cell concentration equations using a Newton-Raphson method, which iteratively determines values for S’, and N, during the time step. The modified fourthorder Runge-Kutta method given by Smith (1982) was also tried. Results of the two methods agreed within I”/, or better, but the Newton-Raphson method ran reliably at l-h time steps while the Runge-Kutta often required l/8 or l/16-h time steps for reliable operation and required more computation per time step. The NewtonRaphson method therefore decreased computer time by more than an order of magnitude compared with the Runge-Kutta. RESULTS

Measured and simulated CO, production are compared in Fig. 2. The measured values were computed from the O2 consumption measurements using an RQ of 0.9. This is the average RQ for the first 240 h. and is representative of RQ values throughout the experiment except for the last 120 h of the low-N treatment. The RQ obtained for that period was unrealistically low, possibly due to experimental error, so we used the average value for all conversions. Note that the readily available C was exhausted in both treatments during the first 60 h of incubation. This is indicated by the change in slope of the curves. Once the readily available C had become exhausted. the slopes of both lines were essentially constant with the low N slope being substantially smaller than that of the high N treatment. An additional indication that all available C had been used up was that there was little or no response to additional N at 240 h in the low-N treatment. The lower rate of CO, production from 60 to 240 h in the low-N treatment (Fig. 2) indicated a lower live biomass and therefore a slower decomposition rate for the straw. The biomass plot also confirms this statement (Fig. 3). This is consistent with observed effects of N limitation. In the high-N treatment, the available C was used mainly for biomass synthesis (Fig. 3). The CO, produced (Fig. 2) was mostly from growth respiration. The CO, from the low-N treatment was from growth respiration and polysaccharide synthesis. The model showed that 56’:; of the readily available C went into polysaccharide. There was no indication from these data of waste metabolism, as described by

Simulation

-0

120

of wheat

straw

360

240

459

decomposition

480

600

Time(h) Fig. 2. Comparison

of measured

(points)

and simulated (lines) CO, evolution wheat straw samples.

Smith (1979), so no provision was made for it in this Since the readily available C disappeared at 60 h from the low-N treatment with only a small amount of biomass production and with CO2 production much less than that from the high-N treatment, the synthesis of polysaccharide seems likely. The fact that added N at 240 h gave no apparent increase in respiration rates indicates that this polysaccharide is no more readily available to the microbes than the cellulose in the straw. It is interesting to note that the CO2 respired during the first 60 h (readily available C exhausted) is substantially higher in the high-N treatment. Biomass growth rate was high in the high-N treatment, and this larger biomass decomposed more residue during this time resulting in more CO, production from the high-N treatment. This higher rate of decomposition continued throughout the experiment. Apparently, the rate of residue decomposition is determined by the microbial biomass which is produced during the

model.

9 0.6 b 5 0.5::

High N

during

decomposition

of

early stages of decomposition, and the size of this biomass is apparently determined by the initial quantity of readily available C and N in the residue. Extrapolation from this would suggest that one may be able to speed organic residue decomposition by the addition of readily available C and N. The model simulated a greater response to glucose additions than was observed in the experiment. This could be the result of incomplete mixing of the glucose with the straw-sand mixture when the additional substrate was added. It could also be the result of soil drying during the experiment. The model also predicted higher respiration rates than were observed during the first hours of the low-N treatment. This probably resulted from the assumption that N was immediately available while, in fact, some time may be required to break down the proteins and amino acids in the readily available fraction for microbial use of the N. The agreement between simulated and calculated

GlUCOSe #added

Time(h) Fig. 3. LIVC biomass and polysaccharide synthesis during decomposition of wheat straw. Lines represent simulated values. Points arc biomass estimates from amino-N measurements assuming 50:,:, recovery and a C:N ratio of 8. Points arc for low N (0). high N-no glucose (A), and high-N plus glucose (0).

460

E. B.

KNAPP et (11.

Table 2. Comparison of predicted and measured nitrogen values fpg g“) Time

Treatment High N

High N

Avail. N

(h)

Meas.

Pred.

0 240

67 21

70” 28

Biomass N Meas. Pred. 23 72

23” 57

Meas.

straw decomposition

Labile N

Gross mineralizition 0

;

45” 39d

42 42 45 70 0 10 22 70

360 360

ND

47

59

ND

0.1

69

93

ND ND

Low Low Low Low

0 240 480 600

6.9 0.6 ND ND

IO” 0.0 48 0.0

23 37 27 30

23” 33 39 93

6.3h ND ND

N N N N

Pred.

45”

High N, no glucose High N + glucose

23

during

Immobilized N

9

1

10 0

2 I 0 0 1 1

0 6 0

“Initial value. ‘Calculated from available N disappearance. ‘Calculated from “N disappearance. dCalcuiated from 15N in amino N.

live biomass for the two treatments is not particularly good (Fig. 3). The calculated values are based on several somewhat tenuous assumptions which are subject to substantial uncertainty. The data points were calculated by assuming SOo/,recovery of aminoN, that amino-N represents biomass-N, and that the C:N ratio of the biomass is 8:I. The N immobili~tion simulated for the high-N treatment agrees well with measurements (Table 2). Both the experiment and the simulation indicate little gross mineralization in either treatment. Immobilization in the simulation is entirely through uptake of N by the live biomass. Mineralization of N results only from microbial death. Smith (1979) allows for immobilization through adsorption on clay and condensation with aromatic compounds. Our experiments were done in a clay-free system and during short periods, so these considerations were not included in our model. If we calculate C:N ratios for the total live biomass (including labile pools) in the high- and low-N experiments, we find C,Z= 6.5 for the former and C, = 8 for the latter. If polysaccharide synthesis is included as part of the live biomass, then the C:N ratio of the low-N treatment at 240 h is IS. DISCUSSION

The model appears to be capable of simulating C and N transformations during the early stages of straw decomposition. The conclusion from the simulations and measurements is that the initial concentrations of available C and N largely determine the course of decomposition. Excess soluble C apparently is immobilized through conversion to some storage carbohyd~te when N is limiting. When available C is limiting, some of the excess N is immobili~d through uptake but is not incorporated into biomass. While the final picture is quite simple, the model required to fit the picture is fairly detailed. Repeated efforts to simplify the model without, at the same time, imposing arbitrary assumptions led to poorer agreement between measured and predicted values. The biggest difficulty, numerically, in the model is the calculation of growth, maintenance and poly saccharide synthesis on the basis of internal cell concentrations. The advantage of doing this, how-

ever, is that the model then simulates substrate dependent maintenance respiration, polysaccharide synthesis and luxury consumption of N. All of these have been observed experimentally and appear to be important in explaining the behavior of our experiments. While the model appears successful to this point, it needs additional testing over longer periods. In addition, N immobilization in ciay and humic complexes and temperature and moisture responses need to be added to the model to make it useful for field conditions. AcknowledRemenrs-In cooperation with the College 01 Agriculture Research Center, Washington State University, Scientific Paper No. 5774. This work was partially supported by the 1J.S. Department of Energy. Oak Ridge Operations. Trade names and company names are included for the benefit of the reader and do not imply endorsement or preferential treatment of the product by the U.S. Department of Agriculture. REFERENCE

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11, 407-415.

Bunnell F. L., Tait D. E. N., Flannagan P. W. and Van Cleve K. (1976) Microbial respiration and substrate weight loss. I. A general model of the influences of abiotic variables. Soil Biology & Biochemistry 9, 33-40. Chapman S. J. and Gray T. R. G. (1981) Endogenous metabolism and macromolecuiar composition of Arrhr~bacier ~lo~i~orrni~~.Soil Bj~~#g~ t 1l-18.

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3i(~~j?~~rni.~tr~ 13,

Broadbent F. E. and Beck S. M. (1977) Recycling of carbon and nitrogen through land disposal of various wastes. In Soils .for Managemmf of’ Organic Wafer und Waste Walers (L. F. Elliott and F. J. Stevenson, Eds), pp. 173-194. American Society of Agronomy. Crop Science Society of America, and Soil Science Society of American, Madison, Wisconsin. Hunt H. W. (1977) A simulation model for decompositmn in grasslands. Ecology 58, 469484. Knapp E. B., Elliott L. F. and Campbell Cr. S. ((983) Microbial respiration and growth during the decom-

Simulation

of wheat

POSitiOnof wheat straw. Soil Biology & Biochemistry 15, 319-323. McLaren A. D. (1970) Temporal and vectoral reactions of nitrogen in soil: A review. Canadian Journal of Soil Science SO, 97-109. Parnas H. (1975) Model for decomposition of organic material by microorganisms. Soil Biology & Biochemktry 7, 161-169. Parr J. F. and Papendick R. I. (1978) Factors affecting the decomposition of crop residues by microorganisms. In Crop Residue Management Systems (W. R. Oschwald, Ed.), pp. 109-209. American Society of Agronomy Special Publication No. 31, Madison, Wisconsin.

straw

decomposition

461

Shields J. A., Paul E. A., Lowe W. E. and Parkinson D. (1973) Turnover of microbial tissue on soli under field conditions, Soil Biology & Biochemistry 5, 153764. Smith 0. L. (1979) An analytical model of the decomposition of soil organic matter. Soil Biology & Biochemisfrv 1 I, 585-606. Smith 0. L. (1982) Soil Microbiology: A Model qf Decomposition and Nutrient Cycling. CRC Press Inc., Boca Raton, Florida, 273 pp. Witkamp M. (1966) Decomposition of leaf litter in relation to environment, microflora, and microbial respiration. Ecology 47, 19420 I,