Modelling the long-term eects on farm net
worth of investments in pasture fertilizer
under constraints of family expenditure
J.M. Scott
a,*, O. Cacho
baAgronomy and Soil Science, University of New England, Armidale, NSW 2351, Australia bSchool of Economic Studies, University of New England, Armidale, NSW 2351, Australia
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 link-age between family costs and expenditure on fertilizer is explored over a 25-year period high-lighting the feed-back eects 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 eect on the maintenance of soil fertility compared to discretionary applications (average available phos-phorous 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 $2.6m for families raising zero or three children, respectively. Both the level of initial debt and the level of ®xed costs had considerable eects on ®nal net worth. Higher fertilizer applications also provided a buering eect on the eects of debt, high ®xed costs and the costs of raising children. The results suggest that investments in fertilizer are essential for maintaining farm viability, regardless of the level of expenditure on raising children. The model provides a useful means of integrating the eects of competing expenditures on long-term pro®tability and net worth of a family farm.#2000 Elsevier Science Ltd. All rights reserved.
Keywords:Long-term economics; Fertilizer; Cost of living; Family expenditure; Sustainability; Bioeco-nomics; Modelling
0308-521X/00/$ - see front matter#2000 Elsevier Science Ltd. All rights reserved.
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1. Introduction
Decisions regarding expenditure on farm inputs such as fertilizer are often made more dicult 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 sucient of their income to maintain their resource base (e.g. through expenditure on fertilizer). Short-term considera-tions 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 eects into account, the individual pieces of advice oered 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 develop-ment and by a bounty which subsidized the price of superphosphate (the main pas-ture fertilizer used in Australia). The peak of sown paspas-ture 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 dicult to justify such expenditures. Although such an action can reduce a farmer's short-term 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.
a residual eect 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 sucient to enable them to continue in their grazing enterprise until conditions improve. At such times, short-term ®nan-cial considerations of viability, pro®t and cash ¯ow override those of long-term pro-ductivity of their natural resource capital (such as soil fertility). There is a need for farmers to appreciate the eect 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 sug-gested by O'Keee (1992) to be related to the long timeframe over which eects 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 term decisions which permit the maintenance of long-term economic pro®tability and ecological sustainability.
What is needed is a way of expressing the long-term cumulative eects of the wide range of factors which aect 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 eect of two major factors. One is whether or not a gra-zier 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 com-pleting higher education. In this way it is hoped to illustrate the dicult decisions graziers need to make when balancing farm investments against family needs over a 25-year time horizon.
2. The model
2.1. Conceptual
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 timet(Wt) is de®ned as:
WtDtV; 1
whereVis the value of the land and farm capital andDis the cash position de®ned as:
Dt Dtÿ1 1r t; 2
where tis 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 rb if Dtÿ1<0
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):
tStÿCLt: 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 NRtÿTt: 4
A simpli®ed tax system is assumed, where surpluses above $25,000 are taxed at a single rate of 33%.
Tt
0 if St425;000
Stÿ25;000
0:33 otherwise:
5
Net revenues obtained from the farm operation depend on the whole-farm gross margin (GM), ®xed costs (CC), fertilizer costs (CF) and cost of animal purchases (CA).
NRtGMtÿCCtÿCFtÿCAt 6
Whole-farm gross margin is estimated as gross margin per head (GMh) times the
number of dry sheep equivalents (DSE) carried on the farm.
GMtGMhtDSEtH; 7
GMht PWtYWtÿCV: 8
The yield of wool depends on the nutritional status of the animals and therefore on the quantity and quality of feed, which can be approximated based on the amount of fertilizer applied (F) and rainfall (R):
YWt WWFtWRt: 9
This equation was estimated from data presented by Wolfe and Lazenby (1973) and simulations produced by GrazFeed (Freer et al., 1997). The total cost of fertilizer applied is:
CFtFtPFt; 10
where PF is the price of fertilizer ($/kg) andF is the amount applied (kg/ha). Fis 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.
Ft
FNt if not forced discretionary
FNt
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:
CAt
where PASis the sale price of animals and PABis 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 DSEtÿDSEtÿ1 13
where Atand Gt are pasture available and total pasture produced per year (t/ha/
from 40% at a low level of dry matter production of 2 t/ha/year up to 65% utilisa-tion for a high dry matter producutilisa-tion of 7.5 t/ha/year (K. Ransom, personal com-munication). This is a simpli®cation for the fact that better growth conditions and fertilizer will aect 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:
Gt1ÿexp PPBG^t; 16
whereGÃtis 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 eect have been dis-cussed 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 dierence equation:
PBtPBtÿ1PBtÿ1 17
PBtPPBtÿ1PFt 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 variablesrb, FN,
PF, PW, CL,R, andGÃ(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.
expenditure on children commenced at dierent times to those investigated, similar relative dierences 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
Table 1
Data used in the modela
Year rb
1967 3.40 120,000 96.22 12.37 30,024 764 20
1968 3.00 120,000 96.22 10.52 30,024 781 18
1969 3.90 110,000 96.22 10.99 33,711 841 21
1970 3.40 70,000 96.22 8.95 33,711 810 23
1971 2.40 100,000 96.22 6.61 32,707 911 22
1972 ÿ0.86 120,000 88.57 7.15 37,890 841 22
1973 ÿ0.62 108,326 79.29 16.84 37,890 718 25
1974 ÿ4.94 120,012 78.04 14.22 37,438 808 26
1975 ÿ8.96 30,527 196.56 8.28 39,271 810 21
1976 ÿ2.40 37,015 175.14 8.25 39,271 952 26
1977 ÿ3.40 61,723 152.23 9.56 40,582 907 18
1978 1.00 75,523 154.98 8.75 41,778 803 21
1979 1.90 108,604 137.53 8.78 41,778 697 20
1980 0.30 93,510 156.37 9.47 43,260 537 9
1981 3.20 29,255 166.80 9.53 43,260 645 12
1982 4.10 30,364 164.76 9.09 43,260 487 10
1983 2.50 37,793 170.39 8.40 43,798 878 22
1984 7.60 45,235 167.64 8.56 43,798 951 32
1985 11.20 70,329 175.60 8.91 48,798 761 18
1986 11.10 48,502 177.48 8.33 48,798 510 13
1987 11.20 78,855 161.28 8.94 51,548 748 17
1988 11.20 114,210 156.26 13.35 43,444 684 17
1989 12.45 115,510 168.80 12.16 43,444 807 16
1990 11.25 92,581 171.24 9.99 36,899 964 16
1991 9.20 51,476 179.96 6.77 36,899 714 15
1992 9.35 33,879 180.00 5.57 36,899 749 17
a All prices in 1992 Australian dollars.
b Real interest rate calculated asi minus in¯ation rate, iwas 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
communica-tion).
f Cost of family of two adults plus raising three children from birth through until completion of tertiary
education as shown in Fig. 2.
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. Ferti-lizer capital and pasture and animal production were compared under two dierent fertilizer scenarios: (1) actual fertilizer application history (discretionary); and (2) forced fertilizer application (non-discretionary); and two dierent 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 eects 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 fer-tility was less pronounced under the high-P regime. The 25-year average soil ferfer-tility
Table 2
Variable and parameter values used in the base scenario simulations
Variable/parameter Value
H(area) 1000 ha
PAS(animal sale price) $8
PAB(animal purchase price) $12
CC (®xed cost) $30,000
CVa(variable cost per animal) $15
Initial debt (proportion of equity) 0.15
W 1.98
W 0.0047
W 0.0023
P ÿ0.32912
P 0.05069
P ÿ0.057
D 0.33182
D 0.0455
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 non-discretionary 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 andÿ$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 eect 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 aected by the number of children on the farm.
In addition to providing a considerable improvement in ®nal wealth, high fertilizer application had a buering eect on the eects of initial debt, ®xed costs and the
Table 3 Results
Units Discretionary P Non-discretionary P
1992 Average 1992 Average
PB ppm 6.13 9.24 23.36 29.08
G t/ha/year 1.83 2.75 4.57 5.60
YW kg/ha/year 3.70 3.95 4.14 4.63
costs of raising children. These eects 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 $1.12m depending on assumptions (Table 4). Thus, the value of an additional tonne of superphosphate per hectare, spread over the 25 years in question, would have been approximately one million dollars, as measured by gains in ®nal wealth.
Table 4
Eect of fertilizer applications on state of the farm in 1992a
Variable Eect
Pb(ppm/t applied/year) 4.62
Increase in ®nal net worth caused by additional P application ($m/t/ha)
No children Ð base scenario 0.90b
Three children Ð base scenario 0.96
No children Ð high debt 0.98
Three children Ð high debt 1.04
No children Ð high ®xed cost 1.05
Three children Ð high ®xed cost 1.12
a Each ®gure represents the eect caused per tonne of fertilizer application per hectare.
b 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:
Base, no children 3:48ÿ0:13$m
3:727t=ha 0:90
Base, three children 2:58ÿ ÿ1:00$m 3:727t=ha 0:96
The long-term eects 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 $33 in the non-discretionary-P regime (Table 5). These results show that the negative eects of debt and ®xed costs can be mitigated by good fertilization practices (with over 30% reduction in negative eects), and con®rm the assumption of the buering role of soil fertility.
5. Summary and conclusions
Authors such as Scott et al. (1992) and Pandey and Hardaker (1995) have expressed the need to account for the long-term eects of management decisions, both in biophysical and economic terms. This need has been partially addressed in this paper. The scenario examined runs from near the time of peak investment in pastures in Australia (late 1960s) through a crash in wool prices (1970), the time when the fer-tilizer bounty was removed (1974), a major drought in the period 1981±83, a return to reasonable wool prices in the late 1980s, and the beginning of another drought in 1991±92. Such events are not unusual in Australia's pastoral history; surviving di-cult times has been the hallmark of successful grazing enterprises. Those farmers who are able to survive dicult times with their natural capital resources in good condition are better able to reap the bene®ts when favourable conditions occur than those who have not maintained their investments in productive capital.
Regular fertilizer application was shown to have a signi®cant positive eect on wealth accumulation. In the study region, the value of one tonne of fertilizer per hectare distributed over 25 years (1967±92), was estimated at approximately one million Australian dollars. This estimate was based on the net worth of the farm family at the end of the study period. Another important result of this study is the identi®cation of the buering eect that soil fertility has on ®nancial risk. The negative eects of debt and ®xed costs on ®nal net worth were over 30% lower in the
Table 5
Eect of initial debt and ®xed cost on ®nal net worth
Change in net worth ($m) Dierence
Discretionary P Non-discretionary P (%)
Eect of initial debta
No children ÿ2.50 ÿ1.67 ÿ32.05
Three children ÿ2.51 ÿ1.69 ÿ32.44
Eect of increase in ®xed costb
No children ÿ52.5 ÿ33.4 ÿ36.43
Three children ÿ52.6 ÿ33.4 ÿ36.53
a Measured as $m change caused by an increase in debt from 0.15 to 0.5.
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 suciently rigorous fashion to enable a credible assessment of the potential eects 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 unne-cessarily 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 eects of quantity and quality of the her-bage on oer are confounded (Freer, 1981) and thus it is dicult 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 eciently and the value of the natural capital which supports the livestock enterprise is sustained. Both ecolo-gical and economic sustainability must be achieved.
It is hoped that this analysis will encourage developers of decision support tools to grapple with the dicult 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.
References
ABARE, 1996. Commodity Statistical Bulletin. Australian Bureau of Agricultural and Resource Eco-nomics, Canberra.
Christian, K.R., 1987. Matching pasture production and animal requirements. In: Wheeler, J.L., Pearson, C.J., Robards, G.E. (Eds.), Temperate Pastures. Australian Wool Corporation/CSIRO, Melbourne, pp. 463±485.
Colwell, J.D., 1963. The estimation of the phosphorus fertilizer requirements of wheat in southern New South Wales by soil analysis. Australian Journal of Experimental Agriculture and Animal Husbandry 3, 190±197.
Cook, S.J., Lazenby, A., Blair, G.J., 1978. Pasture degeneration. I. Eect on total and seasonal produc-tion. Australian Journal of Agricultural Research 29, 9±18.
Crofts, F., 1997. Australian pasture production Ð the last 50 years. In: Lovett, J.V., Scott, J.M. (Eds.), Pasture Production and Management. Inkata Press, Sydney, pp. 1±16.
Freer, M., 1981. The control of food intake by grazing animal. In: Morley, F.H.W. (Ed.), Grazing Ani-mals. World Animal Science Vol. B1. Disciplinary Approach. Elsevier, Amsterdam, pp. 105±124. Freer, M., Moore, A.D., Donnelly, J.R., 1997. GRAZPLAN: decision support systems for Australian
grazing enterprises Ð II. The animal biology model for feed intake, production and reproduction and the GrazFeed DSS. Agricultural Systems 54, 77±126.
Goh, K.M., Nguyen, M.L., 1992. Fertilizer needs for sustainable agriculture in New Zealand. In: Henri-ques, P.R. (Ed.), The Proceedings of the International Conference on Sustainable Land Management, Napier, New Zealand, 17±23 November, 1991, pp. 119±133.
Helyar, K.R., Spencer, K., 1977. Sodium bicarbonate soil test values and the phosphate buering capacity of soils. Australian Journal of Soil Research 15, 263±273.
Kreuter, U.P., Rowan, R.C., Conner, R., Stuth, J.W., Hamilton, W.T., 1996. Decision support software for estimating the economic eciency of grazing land production. Journal of Range Management 49, 464±469.
Mann, T.H., 1993. Flexibility Ð the key to managing a northern beef property. Proceedings XVII Inter-national Grassland Congress, Rockhampton, Queensland, 1961±64.
McCaskill, M., 1987. Modelling S, P and N Cycling in Grazed Pastures. Ph.D. thesis, University of New England, Armidale, Australia.
McDonald, P., 1990. The costs of children. Family Matters No. 27, November (Australian Institute of Family Studies), pp. 18±22.
Moore, A.D., Donnelly, J.R., Freer, M., 1997. GRAZPLAN: decision support systems for Australian grazing enterprises. III. Growth and soil moisture submodels, and the GrassGro DSS. Agricultural Systems 55, 535±582.
O'Keee, M., 1992. A Qualitative Project into the Adoption of Pasture Research and the Potential for GrazFeed. Monash University, Melbourne.
Pandey, S., Hardaker, B., 1995. The role of modelling in the quest for sustainable farming systems. Agricultural Systems 47, 439±450.
Scott, J.M., Pandey, S., Parton, K.A., 1992. A framework for understanding the long-term value of pas-ture leys in cropping systems. Proceedings 6th Australian Agronomy Conference (Australian Society of Agronomy), pp. 198±201.
