A simulation model of the microbiological and chemical changes accompanying the initial stage of aerobic deterioration of silage

Generai approach It was hypothesized that the growth of yeast and acetic acid bacteria are the principal processes occurring during the onset of aerobic deterio-ration and that the DM co

A simulation model of the microbiological and chemical changes accompanying the initial stage of aerobic deterioration of silage

M G COURTIN* AND S F SPOELSTRA

Insiiiitie for Livestock Feeding and Nutrition

Research, Lelystad The Netherlands

Abstract

A mathematical model is presented that predicts

the time-course of aerobic deterioration in grass

and whole-crop maize silages The model

predicts the stability of the silage taking into

account the buffering capacity of the silage, the

initial contents of organic acids and ethanot,

pH, the initial temperature and the initial

populations of the microorganisms The specific

processes simulated include the growth of yeast

and acetic acid bacteria, the oxidation of

fermentation products, the consumption of

oxygen and the production of carbon dioxide,

the rise in temperature, and the increase in pH

The deterioration of silage is seen to be

initiated by acetic acid bacteria or by yeast, or

by both groups together The factors that

determine which groups will prevail are the dry

matter contents and the chemical composition

of the silage The output of the model is

validated by comparison of the simulated data

with data from published work on the

deterioration of silage

Introduction

Neal and Thornley (1983) provided a qualitative

model of the fermentation of silage This work

was followed by quantitative models from Pitt

et at (1985), Leibensperger and Pitt (1987) and

Meiering et at (1988) As yet, no attempt has

been made to extend the simulation to include

Correspondence: Dr S F Spoelstra Institute Tor Livestock

Feeding and NutrJiion Research (iVVO), PO Box 160 8200

AD Lelystad, The Netherlands,

•Preseni address: PO Box 1214, Blind River Oniario,

Canada POR IBO,

the phase between opening of the silo and feeding the silage to the animal

Silage exposed to air after a period of anaerobic storage shows large differences in susceptibility to aerobic spoilage Aerobic deterioration is a microbial process carried out

by aerobic microorganisms that cannot pro-liferate in the anaerobic environment of a sealed silo (Honig and Woolford, 1980) The growth of these organisms commonly results in a rise in

pH and temperature, and the disappearance of fermentation acids The losses in dry matter (DM), and hence nutritional value that accompany aerobic deterioration, can be up to

3O<7o (Honig, 1975; Woolford et at., 1978).

Previous work showed that the growth of yeast often coincides with the heating of silages

(Weise, 1963; Beck and Gross, 1964; Daniel et

at., 1970; Ohyama and McDonald, 1975; Moon

and Ely, 1979; Pahlow, 1982; Jonsson and Pahlow, 1984; Middelhoven and Franzen, 1986) However, there is evidence that aerobic deterioration can be initiated by bacteria

(Woolford and Cook, 1978; Woolford et at 1978; Barry et ai 1980; Crawshaw et ai, 1980) Spoelstra et at (1988) found that acetic acid

bacteria could initiate heating in whole-crop maize silage The factors that determine which organisms will proliferate in a silage upon exposure to air are not yet fully understood The objective of this study was to develop a predictive simulation mode! of the basic pro-cesses that occur during aerobic deterioration considering the competition between yeasts and acetic acid bacteria, and the chemical com-position of the silage Emphasis was placed on modelling the initial stages of deterioration in an effort to predict the stability of silage in air The model simulates the growth of yeasts and acetic acid bacteria, the oxidation of fermentation acids, the production and consumption of gases, the generation of heat through microbial

153

activity; and the change in pH for both maize

and grass silages

Generai approach

It was hypothesized that the growth of yeast and

acetic acid bacteria are the principal processes

occurring during the onset of aerobic

deterio-ration and that the DM content and the

chemical composition of the silage determine

growth rates The mathematical approach was

an unsegregated model of microbial growth that

treats the culture mass as the fundamental

variable and ignores the presence of different

strains and individual cells

The microorganisms compete for the

avail-able substrates, namely lactic acid, acetic acid

and ethanol The oxidation of the organic acids

decreases the buffering capacity of the silage

causing a rise in pH, production of carbon

dioxide and the release of heat These

inter-actions were represented by a set of differential

equations that were solved by numerical

integration to predict the time-courses of the

component variables of the system The model

was designed to operate on both grass and

whole-crop maize silages

By way of simplification the actions of bacilli

and moulds normally associated with the later

stages of aerobic deterioration (Barry et ai.,

1980; Lindgren el at 1985) were disregarded.

It was assumed that the residual sugars are not

utilized for the growth of organisms responsible

for the onset of aerobic deterioration U was

also assumed that the concentrations of oxygen

and carbon dioxide do not limit growth In this

way the model simulates the results that can be

expected from an aerobic stability test, or on the

loosened face of the silage clamp, where

convection of air is freely occurring (Rees,

1982), rather than the conditions that will exist

inside a silage clamp However, combined with

a description of the flow of gases through a silo,

the model will predict the aerobic stability at any

point in the clamp

Description of the model

Growlh of yeast

The growth of yeasts was formulated in the

model in terms of the rate of change of the mass

of cells per unit mass of silage;

dCy/dt=Ugy-,i,y)XCy (1)

where CY = mass of yeast (g yeast (g silage)"';

^gY = specific growth rate (g new yeast (g totalyeast) " ' X h " ' ) ; and ;idY = specific death rate (g dead yeast (g total yeast)" ' x h " ' )

Calculation of growth rate The growth rate

of yeast (n^y) in silage exposed to air was

formulated in a similar way to that used by

Meiering et at (1988) for silage fermentation.

Here the limiting substrate in the growth of yeast was considered to be lactic acid (compare Mankad and Bungay, 1988);

I m a x I /*gY '^ "-t'x C,

(2)

+ Cl

where /^l^y^'= maximum growth rate (h ' ) ; C| = concentration of lactic acid (g acid (g silage)"'); K5y| = Michaelis-Menten saturation constant (g lactic acid (g silage)"')

Maximum growth rate The maximum growth

rate was calculated by reducing a reference maximum growth rate with an Arrhenius

temperature function, as in Meiering et al.

(1988), and by factors representing the in-hibition of growth due to the concentration

of organic acids Water activity (a^,) was considered not to influence the growth of yeast

in silage

(3)

where Ev = activation energy of yeast growth (kj (g ° K ) " ' ) ; R = universal gas constant (kJ (kmol " K ) " ' ) ; TOY = optimum growth tempera-ture of yeast (°K); and /^y = inhibition factor of acetic acid on growth of yeast

Inhibition of yeasts by organic acids An

estimation of the degree of inhibition exerted by the concentration of lactic and acetic acid in silage was made from the growth of a yeast

strain in liquid media A strain of Gandida

krusei, a yeast commonly occurring in silage

exposed to air (Middelhoven and van Baalen,

1988; Lindgren et al 1985) was grown in media

containing increasing amounts of lactic and acetic acids adjusted to pH 4-0, 4-5 and 5-0

(G Pahlow, personal communication) The pH

was removed as a variable by converting the

total acid concentrations to concentrations of

undissociaced acid at a particular pH, because

the undissociated acid is the major source of

inhibition (Eklund, 1983; Nodaetat., 1982) A

non-linear regression of the resulting data

yielded the following relationship;

f = _ 1 J

e-''6WxCa^-for C|^,<0-0O819x((0-O5-C|^,)/0-05)

(14)

where C^^ = concentration of undissociated

acetic acid (g acid (g silage)''); and C|u =

concentration of undissociated lactic acid (g

acid (g silage)" ')

Growth of acetic acid bacteria

Spoelstra et al (1988) showed that acetic acid

bacteria are capable of initiating aerobic

deterioration in whole-crop maize silage Such

an active involvement has not yet been shown

for grass silage although Acetobacter spp have

also been isolated from grass silages (Spoelstra

et ai, 1988) The activity of acetic acid bacteria

was included in the model and is formulated in

a similar fashion to the yeast;

where C^ = mass of acetic bacteria (g bacteria

(g silage) " '; MgA = specific growth rate of

acetic acid bacteria (g new bacteria (g total

b a c t e r i a ) " ' h " ' ) ; and ;idA = specific growth

rate of bacteria (g dead bacteria (g total

bacteria)"'h""')

Gatcutation of growth rate Acetic acid

bacteria are capable of oxidizing many organic

compounds including lactic acid, acetic acid,

butanediol ethanol and glucose (De Ley and

Schell, 1958; 1959; Dupuy and Maugenet,

1963) The most important substrates with

respect to the stability of silage are lactic and

acetic acids, which contribute significantly to

the buffering capacity Work with acetic acid

bacteria in silage (Spoelstra et al., 1988) and in

other environments (Divies and Dupuy, 1969;

Divies, 1972; Sh'imizu et al., 1977; Nanbae/a/.,

1984; Jucker and Ettlinger, 1985) has shown

that the ethanol concentration of the

environment is important because the oxidation

of acetic acid is repressed by ethanol However, Lactic acid is oxidized under these conditions When ethanol is consumed below the level that causes repression of the enzymes required for the oxidation of acetic acid, metabolization of acetic acid commences (Jucker and Ettlinger, 1985)

The growth rate of the acetic acid bacteria (MgA) *3S modelled as the sum of the individual growth rates on the three most important substrates: acetic acid, ethanol and lactic acid

(compare Kim et al., 1988);

where /ig^ i = growth rate of acetic acid bacteria

on individual substrates ( h " ' ) with i = a for acetic acid, e for ethanol and 1 for lactic acid The growth rates of acetic acid bacteria on ethanol and lactic acid were described by the following relationship with;

where ;i'g"!;"'i= maximum specific growth rate of acetic acid bacteria (g new bacteria (g total bacteria)"'h " ' ) ; K,,^| = saturation constant of growth rate (g substrate (g silage)"') with i = e for ethanol and 1 for lactic acid

The growth on acetic acid was formulated by the addition of an extra term to account for the repression of acetic acid oxidation by ethano!;

(8) K.

where /iij";^"a = maximum specific growth rate of acetic acid bacteria (g new bacteria (g total bacteria) • ' h " ' ) ; K,Aj = Michaelis-Menten sat-uration constant for acetic acid bacteria (g acetic

acid (g silage}" '); K,= repression coefficient (g

ethanol (g silage)"')

Maximum growth rate The maximum growth

rates of acetic acid bacteria were calculated as was done for the yeast with;

I /*gA,

(9)

where ^^™"jo = relative maximum growth rate of acetic acid bacteria ( h ' ' ) with i = a for acetic

acid, e for ethanol and 1 for lactic acid; EA =

activation energy of acetic acid bacterial

growth (kJ(kmol °K)"'); T^A = optimum growth

temperature of acetic acid bacteria ("K);

/â = inhibition factor of acetic acid on growth

of acetic acid bacteria; Z,^ = inhibition factor of

lactic acid on the growth of acetic bacteria; and

/a*A = inhibition factor of reduced â on the

growth of acetic acid bacteriạ

Inhibition of growth of acetic acid bacteria by

organic acids The effect of the level of organic

acids on the growth of three different strains of

Acetobacter spp was studied in the process of

formulating this model Strains isolated from

aerobically unstable maize silage were

pre-grown in media containing 3 % ethanol and IVo

yeast extract adjusted to pH 5 Suspensions of

these cells were used to inoculate media

containing 0-5% ethanol (preferential carbon

source), 0 - 1 % yeast extract and concentrations

of acetic acid ranging from 0 to 50 g 1"' and

between 0 and 20 g 1" ' of lactic acid Optical

density readings were used to follow the growth

of the suspension at 28 °C and hence the degree

of inhibition caused by the two acids The data

were regressed to yield the following

relation-ships The inhibition factor, expressed in terms

of undissociated acetic acid on the growth of

Acetobacter sup., is (r- = 0-858):

27

1-

0-• I X C,^

-!-0

0

M for 0-0035

< C^, < 0 C,,<0 C >0

(10)

•0406

•0035

•0406

The concentrations of lactic acid were left in

terms of the total acid yielding the relationship

(r2 = 0-745);

Inhibition of growth of acetic acid bacteria by

decreased ậ In the absence of any

experi-mental data on the dependence of the growth of

acetic acid bacteria on water activity the

relationship given by Pitt et at (1985) derived

from data of Lanigan (1963) for Lactobacitlus

brevis was adopted The choice was based on the

assumption that the acetic acid bacteria are

sensitive to lowered â, since they are commonly isolated from liquid environments such as beer, wine and dairy products (Abadie, 1982; Swings

and De Leỵ 1981; Aries et aị, 1982; Sponholz and Dittrich, 1985) L brevis was the most sensitive of the three organisms used by Pitt et

al (1985) and had the following relationship

(r= = 0-999);

- 18^33 x l 9 - 5 9 x a ^ - 2 4 6 - 2 x (12) (â-0^97)-, 0^9445<â<0-995

\-0 aw>0^995

0-0 ạ<

Rates of change in substrate concentrations Consumption of ethanol Acetic acid bacteria

are capable of oxidizing ethanol to acetic acid according to;

C2H3OH + O: — CHjCOOH + H^O +

492^6 k J m o P ' The consumption rate of ethanol Is modelled as

a function of the mass of acetic bacteria;

where Y^^ j = growth yield of acetic acid bacteria on ethanol (g bacteria (g ethanol)" ')

Consumption of lactic acid Both yeasts and

acetic acid bacteria are capable of oxidizing lactic acid;

CH3CHOHCOOH -I- 3 0 , — 3CO2 +

3H,0-f 1368-2 k J m o P ' The consumption rate was formulated as; dC,/dt = - U,A/Y,A_ 1X C^ + ;i,y/Y,ỵ, x Cy)

where

,

(14)

= growth yield of acetic acid bacteria from lactic acid (g bacteria (g lactic acid) * '); and YJỴ I = growth yield of yeast from lactic acid (g yeast (g lactic acid)""')

Consumption and production of acetic acid.

Acetic acid bacteria can produce acetic acid and also use it as a substratẹ The equation for the production of acetic acid from ethanol is given abovẹ The oxidation of acetic acid as;

CHjCOCH + 2 0 : "* 2 0 0 ; +

2H,O + 875-l k J m o r '

Yeast can utilize acetic acid for growth in silage

(Woolford and Wilkie, 1984; Middeihoven and

Franzen, 1986) Thus, the rate of change of

acetic acid was expressed as;

+ ^«

where Y^^ ^ = growth yield of acetic acid

bacteria on acetic acid (g bacteria (g acetic

a c i d ) ' ' ) ; and 7^^ = conversion coefficient of

ethanol to acetic acid

Change in pH

The pH of a silage changes during aerobic

deterioration as the result of the consumption of

lactic and acetic acid As these acids disappear

the buffering capacity of the silage is reduced

(Greenhill, 1964c) Change in pH affects the

degree of dissociation of the acids The rate of

change of pH is formulated as proposed by Pitt

el at (1985);

- 1

d(pH)/dt =

dC,,/dt -(- dC,,/dt

(16)

where C,j = concentration of dissociated lactic

acid (g acid (g silage)''); Cj(| = concentration

of dissociated acetic acid (g acid {g silage)"';

Chd = concentration of dissociated butyric acid

(g acid (g silage)"'; C^j = concentration of

dissociated ammonia (g ammonia (g silage)'';

Wj = molecular mass of acetic acid = 6O'O5 g

m o l " ' ; Wh = molecular weight of butyric

acid = 88-10 g m o l " ' ; w, = molecular weight of

lactic acid = 90-08 g m o l " ' ; w^ = molecular

weight of ammonia = 17-03 g m o l ' ' ; fl = buffer

inde.\ of silage (Equivalent of acid pH (g

silage)' ')

Dissociated and undissociated acids The

ratio of dissociated acid, Cj, to undissociated

acid, C^, varies with the pH and was expressed

as presented by Ektund (1983);

(17) and hence the ratio between undissociated and

total acid (C.) is;

It follows that the change in the concentration

of dissociated acids in silage was represented by the following equation;

(19) -)-1

where Cni = concentration of dissociated acids (g acid (g silage)"' with i = a Tor acetic acid, b for butyric acid, I for lactic acid and n for ammonia; pK, = log of the dissociation constant with pK, = 4-76, pK(, = 4-86 pKi = 3-86 and pK^ = 9-00

By the same argument the concentration of

undissociated acid {C,J was;

1

x C x (20)

Buffer index of silage The change in the

buffering capacity of silage can be arith-metically represented in terms of a buffer index The buffer index varies with the concentration

of the buffering material and buffer indices are additive for all components at a given pH Greenhill (1964c) observed that the buffer index

of a silage could be approximated by the sum of the buffer indices of the original herbage and the acids produced during fermentation, weighted in proportion of the mass fractions of each component The effect of ammonia was added to the relationship to provide buffering as the pH of the silage increased Thus;

( 1 - C — C - C - C ) X Oh (21) where 0| = buffer index of individual com-ponents (Equivalents of acid, pH g ' ') with i = a for acetic acid, b for butyric acid, 1 for lactic acid and n for ammonia; and Of, = buffer index

of the original herbage (Equivalents of acid pH (g herbage)"')

The buffer index of the individual com-ponents can be calculated from the derivative of the titration function with respect to pH;

(1 +

where i = a, b, 1 and n for acetic acid, butyric

acid, lactic acid and ammonia

Relationships for (3^ are given by Pitt et al.

(1985) for various crops Those for ryegrass and

corn were used after conversion to a fresh

matter basis

Initial ammonia content of silage The

ammonia content of silage can be approximated

from the following relationship as given by

Leibensperger and Pitt (1987) with r- = 0-427;

C,, = ( 4 0 4 x I O - ^ - 5 - 6 4 x 1 0 - ^ x d ) x d (23)

for 0 - 1 2 < d < 0 - 5 4

where C^ = concentration of ammonia (g NH^

(g silage)" ') and d = dry matter content (g DM

(g silage)'') This relationship was used for

both grass and maize in the model

Consumption of oxygen

Oxygen is utilized in the oxidation of substrate

by yeast and acetic bacteria In the reactions

presented earlier The total consumption of

o.\ygen was calculated by converting the

change in substrate concentrations to oxygen

consumption;

dCo,/di = ( d C / d t X wo,/w^ x Yo^) + {dQ/dt x

w,,y w, X Y,,J + (dC/dt X wo^/w, X Yo|)

(24) where C(,., = consumption of oxygen (g 0- (g

silage)"'); w^, = molecular weight of ethano! =

46-07 g ( m o l ) " ' ; w,,, = molecular weight of

oxygen = 32 00 g(mol)~ '; Yyj = molar ratio of

oxygen to ethanol oxidized (mol oxygen (mol

e t h a n o l ) ' ' ) ; Y[x = mo!ar ratio of oxygen

consumed to acetic acid oxidized (mol oxygen

(mol acetic a c i d ) ' ' ) ; YQ = molar ratio of

oxygen consumed to lactic acid oxidized (mol

oxygen (mol lactic acid)"')

Production of carbon dioxide

Carbon dioxide is produced during the complete

oxidation of ethanol, acetic acid and lactic acid

as shown in earlier equation As with the

consumption of oxygen, the production of

carbon dioxide was calculated from the

consumption of substrates as follows:

where Cco, = production of carbon dioxide (g CO; {g silage)''); W(^o., = molecular weight of carbon dioxide = 44-01 g m o l " ' ; Yce = molar ratio of CO, produced per mol of ethanol oxidized = 1 -0 mol CO; {mol e t h a n o l ) ' ' ; Yca = molar ratio of C02 produced per mole of acetic acid oxidized = 2-0 mol CO; (mol acid)" '; and Yt:i = molar ratio of CO; produced per mol of lactic acid oxidized = 3-0 mol CO2 (mol acid)" '

Production of energy

Aerobic deterioration of silage is accompanied

by heating of the surface exposed to the air An estimation of the amount of heat released by the action of the microorganisms is possible using the heat of combustion of the individual substrates as follows;

dE/dt = (dC,/dt X cf, , / w J + ( d C / d t x cf^, c / w j + (dC/dt X cf, /W|) (26) where E = heat production (kJ (g silage)"'),

cf, = heat released during reaction (kj(mol)"') with i = a for acetic acid, e for ethanol and 1 for lactic acid

With this estimation of the energy released a formulation for the corresponding temperature rise was proposed;

d Q ,,ydt = (dC,/dt X

(25)

where c^, = specific heat of herbage (kJ(g

° K ) " ' ) The specific heat of herbage is a weighted average of the specific heat of water

(CHIQ) and of herbage DM (c^);

Ch = dxCd + ( l - d ) x c H 2 o (28)

where CH,O = '*-19x 10"^ kJ(g ° K ) " ' ; Ca =

l-89xlO-^kJ(g ° K ) " ' (McDonald, 1981) and

d = D M content {g (g silage)'')

Dry matter content and water activity

The DM content of herbage is known to affect the course of silage fermentation by influencing the rate of fermentation and the numbers of bacteria found on the crop The DM content also influences the stability of a silage upon

exposure to air (Ohyama et al., 1981) Silage

microorganisms live in the aqueous fraction of the silage The water activity of the aqueous fraction affects the rate of bacterial develop-ment and depends largely on the moisture

Table 1 Values oT parameters used in model

Constant

fw

EA

Ey

MJA"™

M 'r«

lA, t

K,ẠI

K,Y,I

V

ỴẠC

V I

V

V i

To A

T y

Value

1-2

65900

67700

0-15

0-22

0-08

0-55

0-00001

0-0001

0-001

0-005

0-000345

0-04

0-06

0-06

o n

on

301

303

Uniis

g acetic acid (g ethanol)''

J t n o r '

J m o l ' '

h - '

h - '

h "

h - '

g acetic acid (g silage)"'

g ethanol (g silage)"'

g lactic acid (g silage) ~'

g laciic acid (g silage)"'

g ethanol (g silage)''

g bacteria (g acetic acid)"'

g bacteria (g ethanol)"'

g bacteria (g lactic acid)"'

g yeasts (g acetic acid)" '

g yeasts (g lactic acid)"'

"K

°K

Reference (1)

(2)

<3)

(4)

(3)

(6)

(I) Nomura et al 1988; (2) Wilson, 1986; <3)

Cornish-Bowden 1976; (4) Divies, 1972; (5) Swing and De Ley, 1981;

(6) McDonald, 1981.

conient of the foragẹ The initial â in silage

depends on the crop species and the DM content

of the foragẹ Greenhill (1964b) obtained the

following expression for initial â (ậ^,);

= l - b x d / ( ! - d ) (29) where b is a constant dependent on the crop

0-03-0 05 for lucerne & white clover

0-02-0-04 for ryegrass

In lhe model a value of b = 0-03 was assumed

for grasses and b = 0-02 was used for

whole-crop maizẹ The lower value for maize was

taken to include the effect of the heterogeneity

of maizẹ Whole-crop maize is mixture of low

DM stover and high DM ears Deinum and

Knoppers (1979) measured the variation in the

DM of these two constituents of maize and

found a range of 17-I-21-4«/o DM for the

stover and 37-3-53•7'Vo for the ears over a

growing season Microorganisms proliferate in

the aqueous phase; hence, the availability of

water would effectively be higher than indicated

by the average DM content

As the biopolymers in the crop are broken

down to soluble compounds during fermen-tation the â decreases (Greenhill, 1964c) The compounds formed are predominantly lactic acid, acetic acid and ethanol: all products of the fermentation of simple sugars and proteins The reduction in â^ resulting from this conversion was estimated by calculating the freezing point depression induced by these compounds coming into solution Fontan and Chirife (I98I) show that [he â of a solution can be approximated from the following equation, which is derived from the relationship between the freezing point

of an aqueous solution and its ậ

(30)

where (^F = the freezing point depression (°C) Hence the change in â resulting from the pro-ducts of silage fermentation was approximated as;

-(9-694x10

(31)

where i = a for acetic acid, e for ethanol and 1 for lactic acid According to Ross (1975) the final â^, becomes;

The depression of the freezing point resulting from the ađition of lactic acid, acetic acid, and ethanol to an aqueous solution varies with the concentration of the solutẹ Data from Weasi (1987) were regressed to yield relationships between the freezing point depression and solute concentration between 0 and 50 g I" ';

«Fa = 0• 3142 X (100 X C J -1- 0• OOi 14, r^ = 0• 999

(33) 0Fi = O-1901 X (100xC,)-I-0-00087, r^ = 0-999

(34)

(35)

Numerical solution of model

The above equations were programmed in FORTRAN on a VAX computer system The differential equations were numerically solved using Euler's method The values of the parameters used in running the simulations are presented In Table 1 The values were literature datạ The values for lhe parameters describing growth were obtained in a similar manner

9

-I

•o

Z 6

5 s

o

o

3

-2 4 48 7-2 96 1-20 144

Aerobic exposure ( h )

Figure 1 Experimental data for microbial counts and pH from Spoelstra el al., (1988 experiment 2) and the time

courses predicted by the model, ( i ) acetic acid bacteria; ( 0 ) yeast; (D) pH; (—) simulated.

0.02

O.Ol

Aerobic exposure (h

Figure 2, Experimental daCa for organic acids and ethanol from Spoelstra et al., (1988, experimem 2) and the time

Table 2 Sources of validation studies and comparison of experimental and simulated results

Source

Middeihoven and

van Baalen (1988)

Pahlow (1979)

non-urea treated

Spoelstra el al., (1985)

Experiment 5, anaerobic

Spoelstra et al (1988)

Experiment 1, uninocj

Experiment 2 inoculated

Crawshaw et al (1980)

Control

Ohyama et al (1981)

Experiment 2, DL

Experiment 4 DL

Pahiow (1982)

K, anaerobic

B anaerobic

Woolford et al (1979)

Control, direct cut'

Control, wilted

Crop '

maize

maize

grass

maize maize

grass

grass

grass

grass grass

grass grass

Composition DM ( g k g - ' )

309 300 300 309

255

154 518 339 410 410 175 495

1 pH

3'05

3-67

3-70

4 00

3 85

4-45

4-62 4-49

4-50 4-00

4'30 5-50

of silage before

HL (

10'5

21'2

22-7

16-0 12'4

8 3

16 5 24-9

12 3 26-7

9 ' 6

15 8

H A C : g k g - '

3 ' 0

8 ' 7

5 9 9-2 9-6 4-8

3 9

4 ' 2

5 1 3-5

4 - 0 4-2

exposure

) ET

0-5

2 6

1 6 5-5 6-3

1 0

0 ' 5

1 0 3-7

1-8

2-4 0-5

to air Yeast

(LU

4-0 6-0 3-0

< 2

3 5 4-6 6-1

1 1

4 ' 5

< 1

2 8

4-0

A A B

g " )

2-0'

2'0*

3-0'

< 1

3-4

< I '

< 1 '

< 1 •

< 1 '

< 1"

< 1 '

< 1

-Stabiiityt Observed 1

(h)

65-90

60-72

24-48

144- 168 129-142

n.d.

24-48Tt

! 2 0 - 144

72-168

> I 6 8

216 168

Predicted

105 140 75 149 148

85

45 128 94

220

142 94

HL, lactic acid; HAC acetic acid; ET, ethanol; AAB, acetic acid bacteria; t, duration of aerobic exposure preceeding 0-5 unit

rise in pH Where a range is given for the observed stability data was insufficient to pinpoint the begin of the pH rise t used

in model development; * , contained O-S^a DM butyric acid; £, estimates, not actual counts; ft, estimated based on temperature;

n.d.—not determined.

to Meiering et al (1988) and adjusted by

simulating experimental data

Model validation and results

Validation experiments

The validity of the model was investigated by

comparing the output with the results of

published and unpublished work using the

experimental data as inputs to the model

Experiments were chosen in which the

concentrations of lactic acid, acetic acid,

ethanol, pH, temperature and the number of

yeast are reported at intervals throughout the

period of aerobic exposure

Where some ofthe data were not measured an

estimate was made The selected experiments

included both grass and maize silages at a wide

range of DM contents The sources of these

experiments are listed in Table 2

Model results

For each set of experimental data a simulation

was run that predicted the time courses of yeast

and acetic acid bacteria growth, and the changes

in concentration of acetic acid, lactic acid and

ethanol The change in pH caused by the changes in acid concentration was also pre-dicted A sample comparison between experi-mental data and the output of the simulation model is given in Figures I and 2

Figure I shows the growth of yeasts and acetic

acid bacteria as taken from Spoelstra et al.

(1988) Experiment 2 In this particular case, growth of both yeasts and acetic acid bacteria was observed The growth predicted by the model corresponds closely to the observed counts, although the model did not predict the apparent lag in the growth of the acetic acid bacteria Also presented in Figure 1 is the change in pH of the silage during exposure to air The model predicts the pH rise to occur slightly after that observed in the experiment The changes in chemical composition of the above silage upon exposure to air are shown in Figure 2 The levels of ethanol and lactic acid were observed to drop as deterioration progressed The level of acetic acid was observed to increase above its initial level before quickly being reduced to zero These trends were also reflected by the simulation based on the initial concentration measured in the experimental silage

Table 3, Comparison of simulated and experimental results for oxygen

consumption and carbon dioxide production

Crop

Oj consumed COj produced

DM (gkg D M - ' ) (gkg D M - ' )

I k g - ' ) Exp.t Pred.t Exp.t Pred.t Pahlow (1982)*

Woolford et al

Crawshaw et al

(I979)tT {1980)tt

410 175 495 154

70

—

83 130 68 161

_l

105 143 174

105 166 93 170

t Experimental values; J Predicted values; ^ not determined; * 7 d after start

of exposure to air; t t 9 d after start of exposure to air; J l 6 d after sian of exposure to air.

Stability of silage A definition of the stability

of silage was made for the purpose of

comparing the experimental and simulated

results It was considered that a silage was stable

until a 0-5 unit increase in the pH was observed

The experimental and simulated results were

compared on the basis of the duration of the

aerobic exposure preceding the rise in pH by 0-5

units, as shown in Table 2

Inhibition of yeasts by organic acids.

Equation 4 resulting from the regression of

experimental data collected on the growth of

yeast in liquid media was found to provide too

stringent inhibition of yeasts in silage In order

to have the yeast flourish in the simulations to

the same degree as in the experiments, the level

of inhibition had to be reduced in the

simulations The simulated results presented

here were achieved by reducing the calculated

concentration of undissociated acetic acid by a

factor of two

Prediction of oxygen consumption and

carbon dioxide production Only one study

could be found that reported results of the

consumption of oxygen during deterioration

and included enough biochemical data to run a

reliable simulation The experimental and

simu-lated results are compared in Table 3 The

production of carbon dioxide was compared

against measurements taken in two studies as

shown in Table 3

Healing of silage The heat production

predicted by the model for the simulated silages

ranged between 1-03 and 2-31 MJ (kg D M ) " '

for the maize silages and between 0-94 and 2-19

M J {kg DM)" ' for the grass silages over the first

7 - 9 d of aerobic exposure The temperature

predicted from the release of energy from the oxidation of the substrates by the micro-organisms was found to be higher than normally observed in silage

Discussion

Organisms causing deterioration

The stability of a silage as predicted by the model is largely dependent on the initial numbers of yeasts and the level of organic acids The competition between the yeasts and the acetic bacteria depends on the DM of the silage and the concentration of the fermentation acids The model shows that in maize, deterioration

is caused by the growth of acetic acid bacteria when the initial yeast counts are low (2 logarithmic units (LU) g " ' ) Both yeasts and acetic acid bacteria play a role when the silage contains a relatively low concentration of acetic acid ( < 6 g kg ~ ') and the initial population of yeasts is above 3 LU g" ' Silages with a large yeast population (eg > 5 LU g " ' ) upon ex-posure to air, will be subject to deterioration by these organisms although the numbers of acetic acid bacteria will increase as well

Simulation of the deterioration of grass silage suggests that the dominant organisms are yeasts; however, the model predicts an active role for the acetic acid bacteria in lower DM silages

Buffering of silage

The pH increase, corresponding to the con-sumption of organic acids, is well predicted by the model in the range of buffering of lactic, acetic and butyric acid, namely pH 3-8-5-0 (Figure 1) However, once this range has been exceeded the simulated pH rises more quickly