Directory UMM :Data Elmu:jurnal:A:Agricultural Systems:Vol63.Issue3.Mar2000:

Agricultural Systems 63 (2000) 195±209
www.elsevier.com/locate/agsy

Modelling the long-term e€ects on farm net
worth of investments in pasture fertilizer
under constraints of family expenditure
J.M. Scott a,*, O. Cacho b
a

Agronomy and Soil Science, University of New England, Armidale, NSW 2351, Australia
School of Economic Studies, University of New England, Armidale, NSW 2351, Australia

b

Abstract
A simple dynamic farm model is developed and used to analyse the net worth of a family farm
grazing enterprise producing wool on the Northern Tablelands of New South Wales, Australia,
under alternative assumptions regarding family expenses and investments in fertilizer. The linkage between family costs and expenditure on fertilizer is explored over a 25-year period highlighting the feed-back e€ects of each type of expenditure on farm productivity and ultimately on
net worth and thus farm viability. Inputs to the model include historic values for rainfall, fertilizer
application rates, commodity prices and rates of interest and in¯ation. In this way, the farm
business performance is investigated over a wide range of climatic conditions and commodity

prices, typical of the real conditions experienced by grazing enterprises in this region between
1967 and 1992. The results show that non-discretionary fertilizer applications had a large e€ect on
the maintenance of soil fertility compared to discretionary applications (average available phosphorous levels of 29.0 and 9.2 ppm, respectively). This in turn resulted in higher average wool
production per head (4.63 and 3.95 kg/hd, respectively) and higher carrying capacity. The ®nal
(1992) net worth for a family farm applying discretionary rates of fertilizer varied from $0.13m to
ÿ$1.00m for those families raising over 25 years zero or three children, respectively. For families
applying fertilizer as a non-discretionary expense, the net worth in 1992 was estimated to be
$3.5m and $2.6m for families raising zero or three children, respectively. Both the level of initial
debt and the level of ®xed costs had considerable e€ects on ®nal net worth. Higher fertilizer
applications also provided a bu€ering e€ect on the e€ects of debt, high ®xed costs and the costs of
raising children. The results suggest that investments in fertilizer are essential for maintaining
farm viability, regardless of the level of expenditure on raising children. The model provides a
useful means of integrating the e€ects of competing expenditures on long-term pro®tability and
net worth of a family farm. # 2000 Elsevier Science Ltd. All rights reserved.
Keywords: Long-term economics; Fertilizer; Cost of living; Family expenditure; Sustainability; Bioeconomics; Modelling

* Corresponding author.
0308-521X/00/$ - see front matter # 2000 Elsevier Science Ltd. All rights reserved.
PII: S0308-521X(00)00008-1

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J.M. Scott, O. Cacho / Agricultural Systems 63 (2000) 195±209

1. Introduction
Decisions regarding expenditure on farm inputs such as fertilizer are often made
more dicult by demands for family expenditure for raising and educating children.
As farmers' terms of trade continue to decline, graziers in Australia are challenged
to remain ®nancially viable whilst re-investing sucient of their income to maintain
their resource base (e.g. through expenditure on fertilizer). Short-term considerations may override long-term needs thus leading to a reduction in the capacity of the
resource base to sustain income levels over the long-term.
To date, farmers have not had available to them adequate tools to solve decision
choices involving long timeframes. Farmers need to manage within climatic and
®nancial risks and yet the tools available to assist in this are rudimentary. Until such
tools take many of these e€ects into account, the individual pieces of advice o€ered
to farmers through extension information sources will continue to have limited
impact, being masked by other factors seemingly more important to farmers.
During the 1950s and 1960s, pastures in Australia's high rainfall zone experienced
a large expansionary phase fuelled by mostly favourable seasons and commodity
prices, especially for wool (McCaskill, 1987; Crofts, 1997). Wool prices collapsed in

1970 due to oversupply. This led to the introduction of a reserve price scheme for
wool which operated from 1973 to 1991 inclusive. This scheme aimed to reduce price
¯uctuations by managing supply, but this scheme was abandoned when prices again
collapsed in 1991.
The expansion in pasture development experienced in the 1950s and 1960s was
supported by Government taxation measures which encouraged pasture development and by a bounty which subsidized the price of superphosphate (the main pasture fertilizer used in Australia). The peak of sown pasture development in Australia
occurred around 1970; since then many of the sown species have disappeared from
pastures, the loss probably being hastened by lower applications of fertilizer (Cook
et al., 1978). In 1974 the superphosphate bounty was withdrawn by the Government
leading to a sharp rise in the real cost of fertilizer. As shown in data on historic rates
of sowing and fertilizing pastures in Australia by Crofts (1997), there has been a
growing divergence in areas sown and fertilized from 1952 when areas of both were
similar at around 7 million ha; in 1991 the area sown had reached some 30 million
ha whilst the area fertilized was only 15 million ha.
Whilst the previous generation of farmers readily adopted the technology of
developing more productive pastures by sowing and fertilizing during the relatively
favourable decades of the 1950s and 1960s, many farmers in recent times, faced with
declining returns, have chosen to dramatically reduce their applications of fertilizer.
Whilst many of today's farmers would not dispute the importance of fertilizing
pastures, their collective behaviour would suggest that they are ®nding it too dicult

to justify such expenditures. Although such an action can reduce a farmer's shortterm costs, it can also lead to lower productivity and to lost opportunities realised
on those rare occasions when good seasonal and/or economic conditions prevail.
As fertilizer tends to be a discretionary expense, family living expenses often take
precedence over maintenance of soil fertility. Because fertilizer is well known to have

J.M. Scott, O. Cacho / Agricultural Systems 63 (2000) 195±209

197

a residual e€ect lasting several years, many farmers choose to avoid applying fertilizer
in dry seasons and in seasons with poor cash ¯ow. In choosing this action, farmers are
trusting that prior investments in fertilizer will be sucient to enable them to continue
in their grazing enterprise until conditions improve. At such times, short-term ®nancial considerations of viability, pro®t and cash ¯ow override those of long-term productivity of their natural resource capital (such as soil fertility). There is a need for
farmers to appreciate the e€ect of these decisions which impact on the long-term
productivity of their natural resource capital and hence the reason for this paper.
Adoption of technology by farmers involved in grazing livestock enterprises is
known to be slower than adoption by farmers involved in cropping. This was suggested by O'Kee€e (1992) to be related to the long timeframe over which e€ects of
technology are observed to occur in pasture-based enterprises. Thus, many of the
principles upon which pasture recommendations are made may be hidden by the
variability of conditions, especially of climate, over long timeframes.

Although some graziers appreciate that successful management involves keeping
the property pro®table on an annual basis whilst maintaining long-term pro®tability
by gradual improvement (Mann, 1993), many others will require assistance to make
the most appropriate long-term decisions which permit the maintenance of longterm economic pro®tability and ecological sustainability.
What is needed is a way of expressing the long-term cumulative e€ects of the wide
range of factors which a€ect farmer decision making. An attempt is made in this
paper to consider how the economic costs of raising a family interact with the costs
of investing in fertilizer to support long-term productivity.
This paper examines the e€ect of two major factors. One is whether or not a grazier considers investments in fertilizer to be discretionary; the second is the number
of children the grazier needs to support (zero or three children) from birth to completing higher education. In this way it is hoped to illustrate the dicult decisions
graziers need to make when balancing farm investments against family needs over a
25-year time horizon.

2. The model
2.1. Conceptual
Fig. 1 shows in diagrammatic form the conceptual model upon which this paper is
based. The overall hierarchy shows that the economic outcomes (net worth) depend
not only on ®nancial and economic factors such as indebtedness, costs, interest
rates, taxes, family expenditure and commodity prices, but most importantly on
animal productivity. This in turn depends upon the pasture growth and quality

which are both dependent on climate and on soil fertility. Similarly, soil fertility
depends on a feedback from the net worth layer to fertilizer purchases added to the
soil layer. In the model, there is competition between money spent on family expenditure and on fertilizer. In one option (termed non-discretionary) regular fertilizer
applications are mandatory whilst in the other (discretionary), fertilizer is purchased

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J.M. Scott, O. Cacho / Agricultural Systems 63 (2000) 195±209

Fig. 1. Schematic diagram showing linkages in model between and within soil, plant, animal and ®nancial
layers.

only if funds permit after allowing for family expenditure. This comparison is then
made for a family unit over a 25-year period providing either for a family without
children or one with three children.
2.2. Model details
The net worth of the farm family at any time t (Wt) is de®ned as:

J.M. Scott, O. Cacho / Agricultural Systems 63 (2000) 195±209

Wt ˆ Dt ‡ V;

199

…1†

where V is the value of the land and farm capital and D is the cash position de®ned
as:
Dt ˆ Dtÿ1 …1 ‡ r† ‡ t ;

…2†

where t is after-tax pro®t (or loss) obtained during the year and r is the interest
rate. The relevant interest rate depends on whether surplus capital is invested and
earns interest as a bank deposit or whether there is interest paid on debt, thus:

r if Dtÿ1 < 0
;
rˆ b
ri if Dtÿ1 > 0

where rb and ri are the borrowing and investment rates, respectively (and rb>ri).
Pro®t is de®ned as surplus (S) minus the cost of living expenses (CL):
t ˆ St ÿ CLt :

…3†

The cost of living is estimated based on the assumed number of children in the
farm family (see next section). Surplus is calculated as net revenue obtained from
farming (NR) minus tax (T):
St ˆ …NRt ÿ Tt †:

…4†

A simpli®ed tax system is assumed, where surpluses above $25,000 are taxed at a
single rate of 33%.

0
if St 425;000
…5†
Tt ˆ

…St ÿ 25;000†0:33 otherwise:
Net revenues obtained from the farm operation depend on the whole-farm gross
margin (GM), ®xed costs (CC), fertilizer costs (CF) and cost of animal purchases
(CA).
NRt ˆ GMt ÿ CCt ÿ CFt ÿ CAt

…6†

Whole-farm gross margin is estimated as gross margin per head (GMh) times the
number of dry sheep equivalents (DSE) carried on the farm.
GMt ˆ GMht DSEt H;

…7†

where H is the number of hectares of pasture. Gross margin per head depends on
wool yield (YW), wool price (PW) and variable costs (CV), which include the cost of
shearing, drenching, etc:

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J.M. Scott, O. Cacho / Agricultural Systems 63 (2000) 195±209

GMht ˆ PWt  YWt ÿ CV:

…8†

The yield of wool depends on the nutritional status of the animals and therefore
on the quantity and quality of feed, which can be approximated based on the
amount of fertilizer applied (F) and rainfall (R):
YWt ˆ W ‡ W Ft ‡
W Rt :

…9†

This equation was estimated from data presented by Wolfe and Lazenby (1973) and
simulations produced by GrazFeed (Freer et al., 1997). The total cost of fertilizer
applied is:
CFt ˆ Ft  PFt ;

…10†

where PF is the price of fertilizer ($/kg) and F is the amount applied (kg/ha). F is
estimated based on historical fertilizer applications (FN) from actual sales ®gures in
the region, as explained in the next section. The model allows additional fertilizer
application to be forced, in which case, the amount of extra fertilizer applied is that
which could be purchased for a cost equivalent to that of maintaining three children
during a year.
8
< FNt
if not forced …discretionary†
CL3t ÿ CL0t
…11†
Ft ˆ
otherwise …non-discretionary†
: FNt ‡
PFt
The cost of animal purchases, the last term in the net return function [Eq. (6)],
depends on whether animals are purchased or sold during the year in response to
changes in the amount of feed available:


DSEt  PAS if
DSEt < 0
;
…12†
CAt ˆ

DSEt  PAB otherwise
where PAS is the sale price of animals and PAB is the purchase price. The change in
the number of DSE carried from one year to the next depends on the carrying
capacity during the year, which in turn depends on pasture available, thus:

DSEt ˆ DSEt ÿ DSEtÿ1

…13†

At
;
0:55

…14†

DSEt ˆ ‰D ‡ D At Š
and:
At ˆ Gt ÿ 0:5;

…15†

where At and Gt are pasture available and total pasture produced per year (t/ha/
year), respectively. Eq. (14) assumes that pasture utilisation by the animals increases

J.M. Scott, O. Cacho / Agricultural Systems 63 (2000) 195±209

201

from 40% at a low level of dry matter production of 2 t/ha/year up to 65% utilisation for a high dry matter production of 7.5 t/ha/year (K. Ransom, personal communication). This is a simpli®cation for the fact that better growth conditions and
fertilizer will a€ect the legume contribution to the herbage biomass and hence
increase the digestibility. We acknowledge that the actual changes in digestibility are
more complex, but such processes are outside the scope of this paper. The amount of
pasture produced during the year depends on rainfall and soil fertility (or fertilizer
capital, PB), represented by available P (ppm) as measured by the bicarbonate
extractable portion of P (Colwell, 1963). Annual dry matter production is de®ned as:
Gt ˆ ‰1 ÿ exp…
P PB †ŠG^ t ;

…16†

where GÃt is the expected dry matter production and the term in brackets represents a
P-restriction factor (Helyar and Spencer, 1977). The availability of P can be viewed
as a balance between the gradual dissolution of the P applied and the gradual
removal of P from the available pool through ®xation and export through animal
products. When fertilizer applications cease, there is a rapid decline in the value of
previous applications. These time trends of residual fertilizer e€ect have been discussed by Barrow and Carter (1978) and Goh and Nguyen (1992) for Australian and
New Zealand conditions, respectively. Thus, fertilizer capital (PB) depends on soil
fertility carried over from previous years and fertilizer applied during the year, and
is represented by the di€erence equation:
PBt ˆ PBtÿ1 ‡
PBtÿ1

…17†


PBt ˆ P PBtÿ1 ‡ P Ft

…18†

The ®rst term on the right hand side of Eq. (18) represents decay (Goh and
Nguyen, 1992) and the second term represents new applications of superphosphate
(G.J. Blair, personal communication).

3. Experimental protocol
The model is driven by historical data, from 1967 to 1992, for the variables rb, FN,
PF, PW, CL, R, and GÃ (Table 1). As explained in the introduction, a reserve price
scheme for wool operated from 1973 to 1991 inclusive; the scheme aimed to reduce
price ¯uctuations by managing supply but this scheme was abandoned when prices
collapsed in 1991.
The cost of living was estimated based on data from McDonald (1990) which
details the cost of each child in a family according to their ages. These data were
modi®ed to the extent that each child is put through some form of tertiary education
for 3 years from age 18 to 21 each costing $5000 p.a. in addition to the cost of a
teenager (Fig. 2). The cost of living scenarios tested assume that children are born in
years 1 (1967), 3 and 6. Whilst somewhat di€erent results would be expected if

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J.M. Scott, O. Cacho / Agricultural Systems 63 (2000) 195±209

Table 1
Data used in the modela
Year

rb
Interest
rateb
(%)

FNt
Fertilizer
salesc
(t/year)

PFt
Fertilizer
priced
($/t)

PWt
Wool
pricee
($/kg)

CLt
Cost of
livingf
($/year)

Rt
Rainfall
(mm/year)

GÃt Estimated
pasture
growth rateg
(kg DM/
ha/day)

1967
1968
1969
1970
1971
1972
1973
1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992

3.40
3.00
3.90
3.40
2.40
ÿ0.86
ÿ0.62
ÿ4.94
ÿ8.96
ÿ2.40
ÿ3.40
1.00
1.90
0.30
3.20
4.10
2.50
7.60
11.20
11.10
11.20
11.20
12.45
11.25
9.20
9.35

120,000
120,000
110,000
70,000
100,000
120,000
108,326
120,012
30,527
37,015
61,723
75,523
108,604
93,510
29,255
30,364
37,793
45,235
70,329
48,502
78,855
114,210
115,510
92,581
51,476
33,879

96.22
96.22
96.22
96.22
96.22
88.57
79.29
78.04
196.56
175.14
152.23
154.98
137.53
156.37
166.80
164.76
170.39
167.64
175.60
177.48
161.28
156.26
168.80
171.24
179.96
180.00

12.37
10.52
10.99
8.95
6.61
7.15
16.84
14.22
8.28
8.25
9.56
8.75
8.78
9.47
9.53
9.09
8.40
8.56
8.91
8.33
8.94
13.35
12.16
9.99
6.77
5.57

30,024
30,024
33,711
33,711
32,707
37,890
37,890
37,438
39,271
39,271
40,582
41,778
41,778
43,260
43,260
43,260
43,798
43,798
48,798
48,798
51,548
43,444
43,444
36,899
36,899
36,899

764
781
841
810
911
841
718
808
810
952
907
803
697
537
645
487
878
951
761
510
748
684
807
964
714
749

20
18
21
23
22
22
25
26
21
26
18
21
20
9
12
10
22
32
18
13
17
17
16
16
15
17

a

All prices in 1992 Australian dollars.
Real interest rate calculated as i minus in¯ation rate, i was obtained from Australian Economic
Indicators Jan./Feb. 1993, p. 115 Ð small/medium size business interest rates. In¯ation ®gures from
ABARE (1996, p. 11).
c
Data are for bulk superphosphate sales in the New England region of NSW (W. Hely and J. Bindon,
personal communication).
d
Single superphosphate price (ABARE, 1996, p. 92).
e
Wool price, $/kg clean Ð from International Wool Secretariat (S. McCann, personal communication).
f
Cost of family of two adults plus raising three children from birth through until completion of tertiary
education as shown in Fig. 2.
g
Calculated from GrassGro model (see text).
b

expenditure on children commenced at di€erent times to those investigated, similar
relative di€erences would still be expected between scenarios.
Expected average daily pasture growth rate (GÃ) is determined largely by rainfall
and its distribution within a year. Thus, pasture production was estimated using

J.M. Scott, O. Cacho / Agricultural Systems 63 (2000) 195±209

203

Fig. 2. Cost of living expenses for a family of two adults raising three children from birth to the end of
their tertiary education (data adapted from McDonald, 1990).

GrassGro (Moore et al., 1997), a dynamic simulation model which utilizes actual
historic daily rainfall as an important driving variable. Soil type and pasture species
typical of this region (phalaris) were used in the GrassGro simulations in order to
calculate average daily growth rates.
The spreadsheet model was run using the base values presented in Table 2. Fertilizer capital and pasture and animal production were compared under two di€erent
fertilizer scenarios: (1) actual fertilizer application history (discretionary); and (2)
forced fertilizer application (non-discretionary); and two di€erent family expense
situations: (1) no children; and (2) three children. These scenarios were combined
with alternative assumptions on initial debt as a proportion of equity (base=0.15,
high=0.5) and ®xed costs (base=$30,000, high=$60,000) to study their e€ects on
the ®nal net worth of the farm business. The levels of debt and ®xed costs were
chosen to represent relatively low and high risk alternatives on a farm size typical of
this wool-growing region (1000 ha). The values selected for equity and ®xed costs
were based on the authors' experience rather than on published statistics.

4. Results and discussion
Soil fertility ranged from 3.7 to 24.2 ppm with discretionary-P application and
from 22.4 to 37.3 ppm under the non-discretionary-P regime. There was a large drop
in soil fertility between 1967 and 1972 (Fig. 3A), which was accompanied by a drop in
pasture production (Fig. 3B) and carrying capacity (Fig. 3C). The initial drop in fertility was less pronounced under the high-P regime. The 25-year average soil fertility

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J.M. Scott, O. Cacho / Agricultural Systems 63 (2000) 195±209

Table 2
Variable and parameter values used in the base scenario simulations
Variable/parameter

Value

H (area)
PAS (animal sale price)
PAB (animal purchase price)
CC (®xed cost)
CVa (variable cost per animal)
Initial debt (proportion of equity)
W
W
W
P
P
P
D
D

1000 ha
$8
$12
$30,000
$15
0.15
1.98
0.0047
0.0023
ÿ0.32912
0.05069
ÿ0.057
0.33182
0.0455

Fig. 3. Biophysical model results for: A, soil bicarbonate P level; B, pasture production; C, carrying
capacity; and D, wool yield under discretionary and non-discretionary fertilizer application regimes.

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J.M. Scott, O. Cacho / Agricultural Systems 63 (2000) 195±209

was 9.2 and 29.1 under discretionary and non-discretionary fertilization, respectively
(Table 3). Soil fertility at the end of the 25-year period (1992) was below the average
in both scenarios (6.1 and 23.4 under discretionary and non-discretionary P). Wool
yield per DSE ¯uctuated in line with pasture production (Fig. 3D), average yields
over 25 years were 3.95 kg/DSE under discretionary P and 4.63 kg/DSE under nondiscretionary P; a 17.2% increase in wool yield per animal caused by increased P
application. The temporal patterns of pasture and wool growth are similar to that
predicted over the period 1972±86 in a grazed pasture model including nutrient
cycling of McCaskill (1987).
Under the discretionary-P regime, net worth remained fairly stable between 1967
and 1975 and declined thereafter, to reach a low of $0.13m and ÿ$1.00m in 1992 for
households with zero or three children, respectively (Fig. 4A). So the cost of children in
terms of foregone wealth was $1.13m. Under the non-discretionary-P regime, net worth
increased steadily up to $3.48m and $2.58m in 1992 for the zero and three-children
cases, respectively. Thus, the non-discretionary-P regime resulted in considerably
higher ®nal wealth and decreased the relative cost of children to $0.90m.
Both initial debt and ®xed cost had considerable e€ect on ®nal wealth. As initial
debt increased from 0.15 to 0.5 of equity, net worth decreased by approximately
$0.88m under the discretionary-P regime and by $0.58m in the non-discretionary-P
regime (Fig. 4B, Table 3). Similarly, as ®xed costs increased from $30,000 to $60,000
per year, ®nal wealth decreased by approximately $1.58m under discretionary P and
$1.00m under non-discretionary P (Fig. 4C, Table 3). The magnitude of these
changes was not a€ected by the number of children on the farm.
In addition to providing a considerable improvement in ®nal wealth, high fertilizer
application had a bu€ering e€ect on the e€ects of initial debt, ®xed costs and the
Table 3
Results
Units

Discretionary P

Non-discretionary P

1992

Average

1992

Average

PB
G
YW

ppm
t/ha/year
kg/ha/year

6.13
1.83
3.70

9.24
2.75
3.95

23.36
4.57
4.14

29.08
5.60
4.63

Base case
W (0k)
W (3k)

$m
$m

0.13
ÿ1.00

0.75
0.41

3.48
2.58

1.99
1.70

High initial debt
W (0k)
$m
W (3k)
$m

ÿ0.75
ÿ1.88

0.31
0.03

2.90
1.99

1.63
1.33

High ®xed cost
W (0k)
W (3k)

ÿ1.45
ÿ2.58

0.26
ÿ0.08

2.48
1.58

1.65
1.36

$m
$m

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J.M. Scott, O. Cacho / Agricultural Systems 63 (2000) 195±209

Fig. 4. Net worth history of farm business under three alternative sets of assumptions: A, base scenario;
B, high initial debt; C, high ®xed costs.

costs of raising children. These e€ects can be further analysed by estimating the
actual bene®ts of a non-discretionary approach to fertilization (Table 4.).
A total of 1.094 tonnes of fertilizer per hectare were applied over 25 years in the
discretionary-P regime. Under the non-discretionary-P regime total application was
4.821 tonne/ha, over four times as much. As a result of this increase in P application,
the ®nancial position of the farm improved considerably. Each tonne of P applied
per hectare produced an additional 4.62 ppm of ®nal soil fertility and increased ®nal
net worth between $0.9m and $1.12m depending on assumptions (Table 4). Thus,
the value of an additional tonne of superphosphate per hectare, spread over the 25
years in question, would have been approximately one million dollars, as measured
by gains in ®nal wealth.
Table 4
E€ect of fertilizer applications on state of the farm in 1992a
Variable

E€ect

Pb (ppm/t applied/year)
Increase in ®nal net worth caused by additional P application ($m/t/ha)
No children Ð base scenario
Three children Ð base scenario

4.62
0.90b
0.96

No children Ð high debt
Three children Ð high debt

0.98
1.04

No children Ð high ®xed cost
Three children Ð high ®xed cost

1.05
1.12

a

Each ®gure represents the e€ect caused per tonne of fertilizer application per hectare.
The additional amount of P applied in the non-discretionary case was 3.727 t/ha over 25 years. Thus,
the increase in ®nal net worth caused per ton of P applied was:
b

3:48 ÿ 0:13$m
ˆ 0:90
3:727t=ha
2:58 ÿ …ÿ1:00†$m
Base, three children
ˆ 0:96
3:727t=ha

Base, no children

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J.M. Scott, O. Cacho / Agricultural Systems 63 (2000) 195±209

The long-term e€ects of changes in initial debt and ®xed costs are presented in
Table 5. Each dollar increase in ®xed cost per year caused a reduction in ®nal wealth
of $53 under the discretionary-P regime and a reduction of approximately $33 in the
non-discretionary-P regime (Table 5). These results show that the negative e€ects of
debt and ®xed costs can be mitigated by good fertilization practices (with over 30%
reduction in negative e€ects), and con®rm the assumption of the bu€ering role of
soil fertility.

5. Summary and conclusions
Authors such as Scott et al. (1992) and Pandey and Hardaker (1995) have
expressed the need to account for the long-term e€ects of management decisions, both
in biophysical and economic terms. This need has been partially addressed in this
paper. The scenario examined runs from near the time of peak investment in pastures
in Australia (late 1960s) through a crash in wool prices (1970), the time when the fertilizer bounty was removed (1974), a major drought in the period 1981±83, a return to
reasonable wool prices in the late 1980s, and the beginning of another drought in
1991±92. Such events are not unusual in Australia's pastoral history; surviving dicult times has been the hallmark of successful grazing enterprises. Those farmers who
are able to survive dicult times with their natural capital resources in good condition
are better able to reap the bene®ts when favourable conditions occur than those who
have not maintained their investments in productive capital.
Regular fertilizer application was shown to have a signi®cant positive e€ect on
wealth accumulation. In the study region, the value of one tonne of fertilizer per
hectare distributed over 25 years (1967±92), was estimated at approximately one
million Australian dollars. This estimate was based on the net worth of the farm
family at the end of the study period. Another important result of this study is the
identi®cation of the bu€ering e€ect that soil fertility has on ®nancial risk. The
negative e€ects of debt and ®xed costs on ®nal net worth were over 30% lower in the
Table 5
E€ect of initial debt and ®xed cost on ®nal net worth
Change in net worth ($m)

Di€erence

Discretionary P

Non-discretionary P

(%)

ÿ2.50
ÿ2.51

ÿ1.67
ÿ1.69

ÿ32.05
ÿ32.44

E€ect of increase in ®xed costb
No children
ÿ52.5
Three children
ÿ52.6

ÿ33.4
ÿ33.4

ÿ36.43
ÿ36.53

E€ect of initial debta
No children
Three children

a
b

Measured as $m change caused by an increase in debt from 0.15 to 0.5.
Measured as dollar change caused by a dollar increase in cost.

208

J.M. Scott, O. Cacho / Agricultural Systems 63 (2000) 195±209

presence of regular (non-discretionary) fertilizer applications than under `typical'
discretionary fertilization practices for the region.
Although this paper is not comprehensive in its treatment of all interactions
between climate, soil, pasture, animal and pro®t, it does address these components
in a suciently rigorous fashion to enable a credible assessment of the potential
e€ects of the various scenarios posed. A possible objection to our model is that, in
reality, large ¯uctuations in stocking rates may not be feasible from year to year. But
constraining the level of stock changes would have complicated the model unnecessarily given the scope of the paper. Although it is possible that this simpli®cation
led to an overestimation of output, other factors omitted from the model would
have increased output. In particular, fertilizer stimulates the growth of young
digestible leaf and its protein content, leading to higher animal performance and
carrying capacity (Christian, 1987). The e€ects of quantity and quality of the herbage on o€er are confounded (Freer, 1981) and thus it is dicult to generalize. In
any event, we know that good soil fertility and better quality pastures will not only
produce more wool and lambs, but will also provide ¯exibility and allow the farmer
to capture the bene®ts of good seasons.
To date, there has been limited development of decision support systems which
allow the integration of economic and biological information (e.g. Kreuter et al.,
1996). Farmers are often faced with complex decisions involving climatic, biological
and economic risk, and they would bene®t from decision aids that allow them to
account for as many relevant variables as possible when dealing with long-term
investments. Models such as GrassGro are now able to generate valuable estimates
of likely pasture and animal production given the availability of accurate climatic
data and pasture parameter sets. More accurate predictions will be possible when
models such as NutriAce, which is based upon GrassGro and yet accounts for
nutrient cycling (J. Donnelly and R. Simpson, personal communication), are shown
to be valid for simulating complex grazed pasture systems. Linking such improved
biophysical models with improved dynamic economic models should allow better
feedback to farmers on the likely consequences of certain management actions. Such
tools should aid in ensuring the farm system is managed eciently and the value of
the natural capital which supports the livestock enterprise is sustained. Both ecological and economic sustainability must be achieved.
It is hoped that this analysis will encourage developers of decision support tools to
grapple with the dicult but necessary task of enabling sophisticated interaction to
occur between biophysical and economic parameters over periods of decades thus
opening up ways of improving long-term decision making on farms.

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