Evaluation of pasture management systems for beef production in the semi-arid tropics: Model development

Agricultural Systems 49 (1995) 45-67 81995 Elsevier Science Limited Printed in Great Britain. All rights reserved 0308-521X/95/$9.50+ .OO 0308-521X(94)00031-X

Evaluation of Pasture Management Systems for Beef Production in the Semi-arid Tropics: Model Development John G. McIvor* CSIRO Division of Tropical Crops and Pastures, Private PO Aitkenvale, Queensland 4814. Australia

Richard Department

Mail Bag,

Monypenny

of Economics, James Cook University of North Townsville, Queensland 4811, Australia (Received

29 March

1994; accepted

16 August

Queensland,

1994)

ABSTRACT Beef production in the Australian semi-arid tropics is currently based mainly on extensive grazing of native pastures, where animal performance is limited by the quantity and/or quality of the pastures. Beef producers are interested in production systems to overcome these limitations to animal production. To assist with comparisons of direrent systems a spreadsheet model was developed and used to evaluate the whole property implications of two forms of pasture development - killing trees to increase grass production, and oversowing the native pastures with introduced legumes and grasses. Four systems (live trees, native pasture; killed trees, native pasture; live trees, sown pasture; killed trees, sown pasture) were compared under a range of seasonal conditions and over a range of stocking rates. Both killing trees and sowing introduced species increased production and net cash $0~ with the combination of killed trees and sown pasture giving the greatest increase. The relative changes in net cash flow with different growing seasons were less on sown pasture than native pasture systems. Systems where the trees were killed produced near maximal returns over a wider range of stocking rates than systems with live trees. The *Present address: CSIRO Division of Tropical St Lucia, Queensland 4067, Australia. 45

Crops

and Pastures,

306 Carmody

Road,

46

J. G. Mclvor, R. Monypenny

results suggest that pasture development using legumes can be a projitable

investment. However, when using the model to assist with decisions related to pasture development, individual producers will need to provide basic data on their properties, and to be satisfied that the assumptions made in the model are appropriate for their property and circumstances.

INTRODUCTION Beef production is the most important and widespread land use in much of the Australian semi-arid tropics. The beef industry is based on extensive grazing of the native pastures which occur throughout the region. The quantity and/or quality of these pastures are major limiting factors to animal production and beef producers are searching for ways to profitably overcome these limitations. Pasture development is one possible way. This may include combinations of introducing new species (legumes and/or grasses) to increase herbage quantity and/or quality, fertiliser application to overcome soil nutrient deficiencies, and tree killing or clearing to remove competition from the woody vegetation. Field research in past decades has determined suitable techniques to develop and manage pastures in the region. In one project (ECOSSAT, Ecological Studies in the Semi-arid Tropics), the effects of various combinations of management options on the productivity and stability of pastures in the Charters Towers area have been studied for a number of years (McIvor & Gardener, 1993). These studies have shown how pastures can be developed, and determined under what management conditions pastures will persist. To assess the value of these pastures to the grazing industry, some estimates of animal production and financial returns are necessary. No measurements of animal production were included in the experiments and although such estimates could be obtained experimentally, resource constraints allow only a small number of comparisons to be made. Such comparisons are dependent on the growing seasons experienced during the experiment. Since climatic variation between years is a feature of the region and there are large numbers of possible development and management systems, only a few of the many season-development-management combinations would be able to be compared experimentally. As an alternative means of comparison, a model was developed to predict animal production and associated financial details. Pasture data were obtained from ECOSSAT and animal production relationships from other sites in the region. In this paper the ECOSSAT project is briefly described; relationships of animal production (liveweight gain, branding percentage

47

Pasture management for beef production in semi-arid tropics

and mortality) to stocking rate and climatic variables are developed; a spreadsheet model incorporating the animal production relationships to predict animal and financial performance is described; and the model is used to assess the impacts of management systems and seasonal variation on the production and financial returns of a hypothetical property.

ECOSSAT PROJECT The ECOSSAT project was a study of the effects of pasture management practices on the long-term productivity and stability of pastures in the semi-arid tropics. Two sites were established in the Charters Towers (2005’S, 146”16’E) area on different soil types - at Hillgrove on a euchrozem (Gn 3.12; Northcote, 1979) derived from basalt, and at Cardigan on a neutral red duplex (Dr 2.12) derived from granodiorite. At each site an experiment comparing nine pasture systems at four stocking rates was set up. The nine pasture systems (Table 1) consisted of various combinations of sown grasses and legumes (Cenchrus ciliuris, Urochloa mosambicensis, Stylosanthes hamata, S. scabra), tree killing, super-phosphate application (100 kg/ha annually), and clearing and cultivation. Pasture systems l-4 were grazed at stocking rates of 0.1, 0.2, 0.33 and 0.5 beast/ha, and pasture systems 5-9 were grazed at 0.2, O-33, 0,5 and 1 beast/ha. The Hillgrove site commenced in 1982 and the Cardigan site in 1985. This paper deals with Hillgrove where results are available for eight years covering a range of growing conditions. Based on the performance of the pastures over that time, four pasture systems were selected:

TABLE

The Combinations

Pasture system

1

of Treatments Used to Produce the Nine Pasture ECOSSAT Plots at Hillgrove

Tree kilIing

Systems in the

Sown grass and legume

Superphosphate application

Clearing and cultivation

_ _ + + + + +

+ + _ _ + + +

_ _ _

_ + + + _ + +

_ +

J. G. McIvor, R. Monypenny

48

LN KNLO KO-

Live trees, native pasture (system 1 in Table 1); killed trees, native pasture (system 2); live trees, native pasture oversown with legume and grass (system 5); killed trees, native pasture oversown with legume and grass (system 6).

There were no pasture responses to superphosphate application and no animal responses are expected (Kerridge et al., 1990), so superphosphate treatments have not been considered. Fully developed pastures such as pasture system 9 are likely to be of minor importance commercially. Since yields from plots of pasture system 9 were similar to those from pasture system 8, fully developed pastures have also not been considered. Some details of the yield and botanical composition of these pastures in belowaverage and above-average growing seasons are shown in Table 2. HILLGROVE

ENVIRONMENT

The euchrozem soil at Hillgrove has a dark brown clay loam topsoil which grades into a red clay subsoil. The surface soil is nearly neutral (PH = 6.7). The soil profile is underlain by undecomposed basalt at about 2 m and TABLE 2 Yield and Botanical Composition of Four Pasture Systems Grazed at Two Stocking Rates at Hillgrove after Below-Average Growing Seasons (1988, 1989, 1990) and after AboveAverage Growing Seasons (1986, 1991, 1992) SR = 0.5 beast/ha

SR = 0.2 beast/ha Pasture systema

Herbage yield

sty10

Perennial grassb

Herbage yield

sty10

Perennial grassb

(kglha)

(%/

W)

(kglha)

(%)

(“/oi

Below-average growing seasons LN 2090 0 KN 2970 0 LO 1970 46 KO 3880 22

88 93 48 75

480 2680 410 2670

0 0 44 18

54 92 23 77

Above-average LN KN LO KO

91 94 53 66

2030 4490 2720 4790

0 0 62 20

64 91 32 77

growing seasons 3390 0 5330 0 5820 47 7340 32

“L = Live trees; K = killed trees; N = native pasture; 0 = oversown legume. bPerennial grass includes both native and sown species.

Pasture management for beef production in semi-arid tropics

49

basalt boulders of varying sizes are common on the surface and throughout the profile. The extractable phosphorus level in the surface soil is 50 ppm and carbon and nitrogen levels are 1.42% and 0.1 l%, respectively. Average annual rainfall at Hillgrove is 535 mm with 8 1% falling during December to April inclusive. Annual evaporation is approximately 1900 mm. Temperatures (maximum/minimum) range from 34/21”C in December to 24/l 1°C in July. Frosts are mostly light although occasional severe frosts have been recorded. The combination of rainfall distribution and temperature produces a hot wet season and a warm dry season but there is considerable variation between years in the lengths of the seasons. Based on the methods of McCown (1973), estimated median growing season length is 14 weeks commencing in late December. The vegetation at Hillgrove is an open woodland. The major trees are ironbarks (Eucalyptus crebra) and bloodwoods (E. erythrophloiu). The understorey is dominated by tall tussock grasses; the major species are Bothriochloa ewartiana, Heteropogon contortus and Chrysopogon fallax. Native legumes are a common but minor component of the vegetation. Herbage quality is high during the early wet season but declines rapidly as the grasses flower, set seed and senesce. A detailed description of the ECOSSAT site at Hillgrove is given in McIvor et al. (1991).

ANIMAL

PRODUCTION

RELATIONSHIPS

Since both stocking rate and variation in climate (particularly rainfall) from year to year are major sources of variation in pasture and animal production, climatic variables and stocking rate were related to liveweight gain, branding percentage and mortality. Liveweight gain

Cattle gain weight when conditions are favourable for pasture growth and maintain or lose weight when conditions are too dry or cold for pasture growth. Thus variation in the duration of these periods between years can lead to differences in animal performance. In a study of cattle production in northern Australia, McCown et al. (198 1) showed liveweight change was related to the duration of the ‘green season’ (i.e. the period of the year when green herbage is available) which could be calculated from simple water balance models. In the data sets used by McCown there was a close relationship between the number of ‘green weeks’ and weight gain for animals grazing native grass pastures but not for those grazing legumebased pastures. However, a number of other studies (Jones et al., 1990;

50

J. G. M&or,

R. Monypenny

McCaskill, 1990; McCaskill & McIvor, 1993) have shown that a similar relationship can exist for legume-based pastures. For native pastures, McCown et al. (198 1) found the relationship LWG = -65 +4.8

* GW

(1)

where L WG is the annual liveweight gain of steers and G W is the number of green weeks during the year. For legume-based pastures, Jones et al. (1990) found the relationship LWG=-29+5.1*GW

(2)

These relationships explained 65% and 76% of the variance respectively. Thus increasing numbers of green weeks had similar effects on both pasture types but the legume-based pastures produced 3540 kg more liveweight per animal than the native pastures. McCaskill (1990) and McCaskill & McIvor (1993) extended these analyses to a wider range of pastures and also found the slopes of the relationships were similar for different pasture types but the intercepts varied. In this study the equations of McCown et al. (1981) and Jones et al. (1990) have been used to estimate annual liveweight gains of steers in relation to variation between years expressed as green weeks. These green week relationships were determined at low stocking rates and a stocking rate function needed to be incorporated into the model to extend the relevance of the relationships. Using data from a wide range of studies, Jones & Sandland (1974) showed liveweight gain per animal was linearly related to stocking rate (SR) (animals/hectare) LWG=a-b*SR

(3)

Stocking rate relationships such as those of Jones & Sandland (1974) are specific to the site used, type of animal, and the seasons experienced. McCaskill & McIvor (1993) have shown that herbage utilisation rates can be used instead of stocking rates and their approach was followed. Utilisation (U) was defined as herbage intake by animals during a year (Z) divided by the amount of herbage grown during the year (I’) expressed as a percentage

u = (I/Y) * 100

(4)

For a 400 kg steer gaining 100 kg per year or 0.27 kg per day, intake is estimated to be 7.1 kg dry matter per day (Minson & McDonald, 1987) or

Pasture management for beef production in semi-arid tropics

51

2592 kg per year. Herbage growth is the product of evapotranspiration (EPT) and water use efficiency (WUE). EPT was calculated from the water balance model of McCown (1980-81). WUE at Hillgrove was calculated by the methods of Gardener et al. (1993) using weather data and measured presentation yields on the ECOSSAT plots (McIvor, J. G. & Gardener, C. J., unpublished). For the period 1986-1990 WUE was estimated as 5.0 kg/ha/mm for plots with live trees, and 12.4 kg/ha/mm for plots where the trees were killed. The estimated WUE values were the same for native pastures and legume-based pastures. From long-term rainfall records at Hillgrove the median EPT is estimated to be 423 mm, so in an average year a pasture with killed trees would produce 5245 kg/ha of dry matter. Assuming each animal eats 2592 kg per year then in an average year, from eqn (4),

U = (Z/Y) * 100 = [(2592 + SR)/5245] * 100, i.e. utilisation increases from 0% at a stocking rate of O--49% at a stocking rate of 1 animal/ha. Using data from a number of sources for both native and legume-based pastures (Gillard, 1979; Gardener et al., 1993; Jones, R. J., unpublished; Burrows, W. H., unpublished) mean values for b in eqn (3) are 80 and 35 for native and legume-based pastures, respectively. Thus, for native pasture, as the stocking rate increases by 1 animal/ha, liveweight gain declines by 80 kg/animal. From this, a change in stocking rate of 1 animal/ha is equivalent to a change of 49% in utilisation, so annual liveweight gain per head declines by 80/49 or 1.63 kg for each 1% increase in utilisation. For legume-based pasture the value is 35/49 or 0.7 1. The green week and stocking rate relationships were combined (McCaskill & McIvor, 1993) to give L WG =

LWG

= -29

-65 + 4.8 * G W + 5.1 * GW-

-

0.71

1.63 * %U (native pasture) * %U

(legume-based pasture).

The relationships of McCown et al. (1981) and Jones et al. (1990) are for liveweight gain at some low level of utilisation. To allow production to be predicted over the whole range of utilisations they need to be extended to include near-zero utilisation. The actual utilisation levels in the experiments from which these relationships were derived are not known.

52

J. G. M&or,

R. Monypenny

Although there would have been some variation from year to year, utilisation level has been assumed to be 30%, which is a sustainable level on a number of pasture types (Orr, 1986; Orr & Evenson, 1991; Gardener et al., 1993). Utilising 30% of the herbage would decrease liveweight gain by 49 kg (30*1*63) on the native pasture, and by 21 kg (30*0*71) on the legumebased pasture. These values have been added to the previous relationships to give: LWG = -16 + 4.8 * GW-

1.63 * %U

(5)

LWG = -8 + 5-l* GW- O-71* %U.

(6)

The green week relationship predicts potential liveweight gain at ‘zero’ stocking rate, and the utilisation function allows for reduction in liveweight gain as stocking rate rises. Examples of the influence of season and stocking rate on the predicted liveweight gains for different pasture systems are shown in Fig. 1. It is worth considering the implications of the two assumptions ((i) that mean b values from studies elsewhere could be used, and (ii) that utilisation levels in the studies of McCown et al. (1981) and Jones et al. (1990) were 30%) made when deriving these relationships. If the b values used were smaller than the actual values then the slopes in Fig. 1 will underestimate the decrease in liveweight gain as stocking rate increases and the model will overestimate the optimum stocking rates. If the utilisation levels were lower, say 20%, then the liveweight gains for all systems will be lower than those predicted and the difference between the native pastures and legume-based pastures will be 9 kg/animal greater.

0.0

1

Fig. 1. The influence of stocking rate on the predicted annual liveweight gain of steers grazing four pasture systems in poor (lower quartile), average (median) and good seasons (upper quartile).

Pasture management for beef production in semi-arid tropics

53

Branding percentage The breeding performance of cows is influenced by the level of nutrition during the period of late pregnancy and early lactation before conception (Wiltbank et al., 1962; Dunn et al., 1969) and Thorpe & Cruickshank (1980) attributed the significant differences in calving rates between years to the quantity and quality of forage available. A number of authors have shown that breeding performance is associated with rainfall during the period before mating (Andrews, 1976; Bishop, 1978; Holroyd et al., 1979; Butterworth, 1983; Anderson, 1990). Anderson (1990) also showed that conception rates were related to the time of the start of the growing season and the growing conditions in the five months before mating. CSIRO runs a herd of Droughtmaster cows at Lansdown Research Station near Townsville. Breeding records have been kept on this herd since 1964. A preliminary inspection of these records and weather records suggested a relationship between breeding performance and the number of green weeks in the period prior to mating. The branding percentage (B) was regressed against the number of green weeks (GIV) during various periods before mating (usually in early January). The number of green weeks in the 15 weeks before mating (GWi5) gave the closest relationship. The relationship

B = 44.2 + 2.3 ;1:GWi5

(7)

was highly significant (P < 0.001) and explained 67% of variance. During the period the branding percentage varied from 33% to 77%, and the number of green weeks in the 15 weeks before mating from 1 to 15. Since many other factors influence breeding performance (e.g. disease, nutrition during other times of the year, calving performance the previous year, stocking rate) this was considered to be a satisfactory relationship and has been used to generate branding percentages in relation to seasonal variation. Mortality Mortality rates can be high, particularly for breeding animals in poor years when there is little herbage available. For example, Fordyce et al. (1990) recorded a mortality rate of 21% for cows in a severe drought in the Charters Towers district. Pasture availability is also influenced by stocking rate and Gillard & Monypenny (1990) introduced the term ‘mm ha/beast’ based on annual rainfall (in mm) and stocking rate (in hectares/animal) as a proxy for pasture availability, and found this a useful concept for management discussions with graziers. The relationship between

54

J. G. Mclvor, R. Monypenny

mm ha/beast and mortality of a commercial herd was highly significant. This model needed to allow for different pasture growth with and without trees. From the long-term records at Hillgrove, estimated EPT is approximately 70% of rainfall. The values of Gillard & Monypenny (1990) for mm ha have been converted to kg DM by assuming EPT = 0*7*rainfall and using the WUE value of 50 kg/ha/mm derived earlier. Their figures for breeder mortality (BM) and dry stock mortality (DM) were related to herbage yield per animal to give (see Fig. 2)

B&l% = -2 + 205/YA

(8)

Dm

(9)

= -3 + 131/YA

where Y, is the available herbage per animal (lo3 kg). Y, is calculated from pasture yield (EPT * WUE) and stocking rate.

MODEL STRUCTURE The model consists of three parts: input variables, output variables and driving functions that relate the input variables to the output variables. Input variables The model runs on a 1Cyear period. Climate can be variable during years 5-10 to reflect average, good and bad years, and is set to the long-term median value during the other years (l-4 and 11-14). A 16year period was chosen as being both short enough to be manageable, and long enough for the initial conditions not to dominate the output and for -

Breeders

------ Dtyatock

Fig. 2. Mortality of breeders and dry stock in relation to the herbage available per animal.

Pasture management for beef production in semi-arid tropics

55

changes (e.g. in branding percentage) due to stocking rate or seasonal conditions to flow through the model. Evapotranspiration (EPT), the number of green weeks during the year (G IV=) and the number of green weeks during the last 15 weeks of the year (GW’,,) describe the variation in climate from year to year. Long-term TABLE 3 Climatic Variables Used as Model Input to Reflect Seasonal Variation (Average Seasons Have a Mean Value Similar to the Long-Term Median Value, and Poor and Good Seasons Have Mean Values Similar to the Lower and Upper Quartiles Respectively Seasons

Longterm value

5

6

423 423

423 450

423 363

423 404

423

452

185

284 284 539 539

284 172 539 423

Evapotranspiration (EPT) Constant average Average--small variation (1958-63) Average-large variation (1963-68) Constant poor Variable poor (193843) Constant good Variable good (1973-78) Green weeks during the year Constant average Average--small variation (1958-63) Average-large variation (1963-68) Constant poor Variable poor (193843) Constant good Variable good (1973-78)

Year

9

IO

Mean

423 424

423 474

423 452

423 428

598

243

269

755

417

284 64 539 750

284 596 539 525

284 502 539 651

284 196 539 402

284 301 539 431

284 305 539 530

(GW=) 33 33 33 32

33 30

33 29

33 26

33 36

33 42

33 33

33

42

21

40

27

24

47

34

27 27 40 40

27 26 40 27

27 16 40 44

27 34 40 24

27 36 40 40

27 24 40 37

27 28 40 48

27 27 40 37

6 8

6 11

6 4

6 6

5

11

2

7

4 2 10 10

4 7 10 8

4 9 10 12

4 4 10 10

7 -

Green weeks during the last 15 weeks of the year (GW,-J Constant average 6 6 6 6 Average-small 6 4 5 6 variation (1958-63) Average-large 6 4 12 5 variation (1963-68) Constant poor 4 4 4 4 Variable poor (193843) 4 2 5 0 Constant good 10 10 10 10 Variable good (1973-78) 10 11 6 11

56

J. G. Mclvor, R. Monypenny

values for these parameters were calculated from the daily rainfall recorded at Hillgrove over the period 1899 to 1989 and average evaporation taken from maps of estimated evaporation (Anon., 1968) using the methods of McCown (1980-81). From these long-term values the median, and lower and upper quartile values were calculated and used for average, poor and good seasons respectively. Seven sequences (see Table 3) of years were chosen to cover a wide range of seasonal conditions: (i) (ii) (iii) (iv) (v) (vi) (vii)

constant average: value in each year from 5 to 10 set to the longterm median; average-small variation: mean value for years 5-10 similar to the long-term median with little variation between years; average-large variation: mean value for years 5-10 similar to the long-term median with large variation between years; constant poor: value in each year from 5 to 10 set to the long-term lower quartile; variable poor: mean value for years 5-10 similar to the long-term lower quartile but with variation between years; constant good: value in each year from 5 to 10 set to the long-term upper quartile; and variable good: mean value for years 5-10 similar to the long-term upper quartile but with variation between years.

The model represents a property by its physical (initial area of each pasture type and changes in any year, initial number of animals in each animal class, i.e. breeders, calves, culls and steers) and financial characteristics (fixed costs, variable costs and prices). Driving functions (i)

The annual live weight gains of steers on the different pasture systems are calculated from eqns (5) (systems LN and KN) and (6) (systems LO and KO) using the green weeks during the year (GWr), and calculating % U from eqn (4) where I = 2592 kg and Y is the product of the appropriate WUE value (5.0 and 12.4 for live and killed trees, respectively) and the EPT for the year. (ii) The numbers of animals in each class in years 2-14 are calculated from the initial values and the branding and mortality relationships [eqns (7)-(g)]. (iii) The breeding animals are allocated 60% of the property and stocked at 0.125 animals/ha. All breeders are run on the undeveloped part of the property and the remainder of the undeveloped part not needed for breeders is used for steers and stocked at 0.2 steers/ha.

57

Pasture management for beef production in semi-arid tropics

(iv) The number of culls are determined each year as the surplus to the number of breeders to be retained. Cull cows are sold for $250 per head. (VI Steers are turned off as three-year-olds from developed pastures and as four-year-olds from native pastures. A dressing percentage of 55% is assumed for all steers and the price received is related to carcase weight as follows: _.

Dressed weight (kg) Price ($/kg)

~150

160

180 200

220

240

260

280

300 >300

1.50

1.60

1.65 1.75 1.80 1.95 2.00 2.02 2.07 2.10

(vi) If the number of steers required to stock the property at the selected rate are not available in the herd, store steers are purchased; conversely if the number of steers available is greater than the number required, surplus steers are sold as stores. The store steer price is $280 per head. Output variables

Both production and financial variables are calculated production variables are: (a) (b) (c)

each year. The

the number of animals in each stock class; the turnoff numbers of steers and culls; the liveweight of the steers in each age group.

The financial variables are: (a) (b)

income from cattle sales, total running costs and net cash flow (NCF); the value of the herd, calculated after sale stock have been sold that year.

The following variables were chosen to compare the pasture systems -~ accumulated NCF over 14 years (AccNCF), a measure of overall economic performance; minimum NCF (MinNCF) in any of years 1-14, a measure of the impact of development and growing season on cash flow; NCF in year 14 (YI4NCF), a measure of the profitability of the system after development is complete and the herd is in balance with the changed circumstances; and accumulated NCF plus the value of the herd in year 14 (AccNCF + HV), a measure of both net returns and the increase in capital value.

J. G. M&or,

58

EVALUATION

R. Monypenny

OF PASTURE

SYSTEMS

A hypothetical property was used to evaluate four pasture systems. Property description The property area was 25,000 ha. Details of the initial stock numbers, stock values (for calculating the value of the herd) and costs are given in Table 4. At the start of modelling the property was all undeveloped woodland with native pastures. Development programs Four pasture systems have been compared - LN, KN, LO and KO. For each system 1000 ha per annum was developed in years 1 to 4. The cost of tree killing was $36/ha with follow-up costs of $l/ha/annum to control regrowth. The cost of oversowing was $20/ha. Note that the system LN is the same as the original condition and there were no development costs. The four pasture systems were compared at stocking rates between 0.1 and 1 steer/ha on the developed area of the property with stocking rates held constant on the remainder. TABLE 4 Initial Stock Numbers, Stock Values and Running Costs

Stock Breeders Bulls Calves Heifers 1 Heifers 2 Steers 1 Steers 2 Steers 3 Steers 4 costs Breeder supplement ($/breeder) Freight cattle ($/head sold) Running costs ($)

Number

Value ($)

1500 50 1050 494 484 535 524 514 298

300 500 50 230 350 280 380 430 NA” 12 20 60,000

“Not applicable as the steers are sold before the value of the herd in calculated.

Pasture management for beef production in semi-arid tropics

59

Comparison 1: constant average conditions The four systems were first compared under constant average conditions. Although such conditions are an unreal situation, they provide a base to assess the impacts of different seasons and variability between seasons. The curves for AccNCF, Y14NCF and AccNCF + HV were similar in shape (Fig. 3). All systems had maximum values at some intermediate stocking rate and the values declined at both higher and lower stocking rates. For each system the stocking rates for maximum values of AccNCF, Y14NCF and ANCFHV were the same but these stocking rates varied from 0.15 steers/ha for LN to O-75 steers/ha for KO. In contrast, MinNCF values were greatest at low stocking rate for all systems and declined as stocking rates rose.

Aceumulatod NCF

Minimum ?? nnud NCF

emoDD

=I

Year 14 NCF SO0

Ace. NCF a,aQ

plur hard value

Fig. 3. Influence of stocking rate on the financial performance of four pasture systems under constant, average climatic conditions.

J. G. M&or,

60

R. Monypenny

The optimum stocking rate for a system was defined as the stocking rate producing maximum AccNCF for that system. When the systems were compared at their optimum stocking rates, their profitabilities ranked in the order KO > LO > KN > LN. However, at their optimum stocking rates, system KO had the lowest MinNCF ($119,000 compared to $150,00~$190,000) although this is compensated for by the higher NCF in other years. To obtain a guide to the sensitivity of each system to changes in stocking rates near the optimum levels, the range in stocking rate producing AccNCF within 5% of the maximum was calculated for each system. The ranges were 0.06-0.22 steers/ha for LN, 0+25-0*50 for KN, 0*18-0*40 for LO and 0.53-0.87 for KO, showing profits under these conditions were near maximal over a wider range for KO. Comparison 2: constant good, average or poor conditions The four pasture systems were then compared under good, average and poor seasonal conditions (see Table 3 for the climatic values used) to test their responsiveness to changed seasonal conditions. Comparisons were made using the optimum stocking rate defined for average conditions (see previous section) for all conditions, and also at the optimum stocking rates for good or poor conditions. The results are shown in Table 5. The largest absolute changes in AccNCF due to change in season were for KO followed by KN, LO and LN, i.e. KO was the most affected by different seasons. The large changes on KO and KN were associated with the TABLE 5 Accumulated Net Cash Flow (AccNCF) ($000~) for Four Pasture Systems in Relation to Growing Season and Stocking Rate (SRAV~~, SRGoo~ and SRpooR are the Optimum Stocking Rates for Average, Good and Poor Conditions Respectively; the Values in Parentheses are the AccNCF as a Percentage of the AccNCF for that Pasture System Under Average Conditions) Pasture system”

Season Average

LN KN LO KO

Good

SR A VER

=A

2460 2840 3590 4850

3310 4040 4550 6410

VER

(135) (142) (127) (132)

Poor ~RGOOD

3340 4060 4620 6560

(136) (143) (129) (135)

SRA

VER

1260 (51) 1120 (39) 2230 (62) 2560 (53)

“L = Live trees; K = killed trees; N = native pasture; 0 = oversown legume.

~RPOOR

1350 (55) 1460 (51) 2410 (67) 3020 (62)

Pasture management for beef production in semi-arid tropics

61

higher stocking rates used on these systems. In good years there were more cattle on the pastures to take advantage of the better growing conditions, and in poor years these systems were overstocked to a greater degree and profitability suffered. On a relative basis, KN was most affected followed by LN, KO and LO. System LO was least affected by different seasons on a relative basis and second least affected on an absolute basis. As expected, the optimum stocking rates for good conditions were higher, and those for poor conditions lower, than the optimum values for average conditions. Under good conditions, changing the stocking rate from the optimum under average conditions to the optimum for good conditions made little difference to the AccNCF - the values increased by 0.5% (KN) to 3% (KO). However, under poor conditions changing the stocking rate made a considerable difference with systems KN and KO the most affected. Comparison 3: variable conditions

The previous comparisons were made using the same climatic values for each of the years 5-10. Two comparisons were then made to test the effects of variation between years on the different systems. In the first, the systems were compared over years where the means of the climatic variables were the same as the long-term average but there was no small or large variation between the years. In the second, the systems were compared over years where the mean values were similar to the long-term good and poor conditions and where the values were constant or variable. The climatic values used are shown in Table 3. TABLE 6 Accumulated Net Cash Flow (AccNCF) ($000~) for Four Pasture Systems in Relation to Seasonal Variability and Stocking Rate (SRNIL, SRLow and SRH,~H are the optimum Stocking Rates for Nil, Low and High Variability Respectively; the Values in Parentheses are the AccNCF as a Percentage of the AccNCF for that Pasture System with Nil Variability) Pasture systema

Variability Nil =NIL

LN KN LO KO

2460 2840 3590 4850

Low SRNIL

2450 (100) 2800 (99) 3540 (99) 4720 (97)

High SRLOW

2450 (100) 2800 (99) 3540 (99) 4730 (98)

“L = Live trees; K = killed trees; N = native pasture;

SRNIL

2050 2300 3160 4180

SRHIGH

(83) (81) (88) (86)

0 = oversown

legume.

2050 2380 3180 4240

(83) (84) (89) (87)

J. G. Mclvor, R. Monypenny

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TABLE 7

Accumulated Net Cash Flow (AccNCF) ($000~) for Four Pasture Systems in Relation to Growing Season, Seasonal Variability and Stocking Rate (SRNIL and SRvAR are the Optimum Stocking Rates for Constant and Variable Conditions Respectively; the Values in Brackets are the AccNCF as a Percentage of the AccNCF for that Pasture System and that Season with Nil Variability) Pasture systema

Season Poor

Good

Variability

Variability

Nil =NIL

LN KN LO KO

1350 1460 2410 3020

Variabile ~RNIL

90 (7) 60 (4) 1410 (59) 1710 (57)

Nil S&AR

90 (7) 150 (10) 1480 (61) 1870 (62)

=NIL

3340 4060 4620 6560

Variable =NIL

3030 3600 4200 5760

(91) (89) (91) (88)

S&AR

3050 3630 4220 5810

(91) (89) (91) (89)

“L = Live trees; K = killed trees; N = native pasture; 0 = oversown legume.

When the seasonal conditions were average overall, low variability had very little impact compared to constant conditions but high variability reduced AccNCF by 12-19% for the different systems (Table 6). Changing the stocking rate to the optimum for the variable conditions experienced made little or no difference to AccNCF. When growing conditions were poor, variability produced a very large reduction in AccNCF compared to constant conditions, particularly for the two native pasture systems where AccNCF fell by more than 90% (Table 7). The impact of variability was much less under good conditions and AccNCF declined by only g--12% from that under constant conditions. For both good and poor conditions changing the stocking rate to the optimum for the variable conditions made little difference to AccNCF.

DISCUSSION This model represents the accumulated biological knowledge of, and experience with, beef production systems on basaltic soils in north-east Queensland. It integrates information from a number of sources and produces output similar to that expected by experienced scientists and producers. The results presented in this paper show that the model is a useful

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63

way to assess the impacts of seasonal variation and management systems on animal production and financial returns on a whole property basis. The model can be used by research workers to assess the impact of potential research outputs, e.g. what will be the effect of a new pasture species which increases annual liveweight gain by 20 kg per animal? The model can also be used to assist producers making decisions about pasture development and management. However, since each property is different (both physically and financially) there are no generic solutions applying to all properties. Thus, although the output for a hypothetical property shows sown pastures can be a profitable investment, data for individual properties are needed for decision making. For all the comparisons made, the legume-based pastures had higher accumulated net cash flows than the undeveloped system. The combination of legumes and tree killing (KO) gave the greatest accumulated net cash flow. However, when the systems were compared at their optimum stocking rates, the legume-tree killing combination had the lowest minimum net cash flow, due to the higher development costs for this system. Implications for management

Coping with seasonal variation is necessary in any primary production activity and such variation is large on properties such as Hillgrove (Mann, 1993). The change in accumulated net cash flow when seasonal conditions were changed from average to poor or good was least for system LO, suggesting that oversowing legumes under trees would help reduce fluctuations in income between years. The comparisons of performance under constant or variable conditions showed little difference when conditions were average or good overall. However, under poor conditions the performance of the native pasture systems (LN and KN) was much poorer when conditions were variable than when they were constant. Variability also reduced the returns on the oversown systems but not nearly as much as on the native systems. Choosing appropriate stocking rates is a key management decision in any animal production system (Humphreys, 1991). Many factors must be considered and it will be easier to choose optimal or near optimal rates in systems where a change in stocking rate has only a small impact on performance. Conversely, the choice of an inappropriate rate will have a smaller effect in such systems. For this model, the two systems where the trees were killed gave near maximum profit over a wider range of stocking rates than systems with live trees. In these comparisons it has been assumed that all pastures are in good condition. Since land condition is sensitive to stocking rate this is unlikely

64

J. G. Mclvor, R. Monypenny

to be true at high stocking rates. Thus the performance of the systems at high stocking rates may be an overestimate, since actual pasture production and thus animal production will be lower than predicted in the model. In a recent survey of property performance in the Charters Towers area (Hinton, 1993), the rate of return to total resources and gross margin per animal were higher for properties in good condition. It has also been assumed that the production benefits from sowing pastures and clearing trees continue for the 1Cyear period. Both commercial (e.g. Edye & Gillard, 1985) and research (e.g. Gardener et al., 1993) experience with similar sown pastures elsewhere in north-east Queensland suggest this will be true for sown pastures. Although there has been considerable debate about the risks associated with tree clearing (Gillard et al., 1989; Williams & Chartres, 1991; Burrows, 1991~2, 1991b; Nadolny, 1991), the limited long-term (more than 20 years) commercial experience on these basaltic soils shows that the benefits of tree clearing can be maintained. Future studies In these comparisons only one development schedule was used with 1000 ha being developed in each of the first four years. The model could easily be modified to answer questions such as the following. Does rate of development (i.e. the area treated per year) influence profitability? Is the benefit of pasture development proportional to the area developed? Is there an optimum proportion or area to develop for a property? Is there a desirable combination of alternative development systems? What effect does a particularly poor (or good) year during development have on profitability? In, this model the production benefits from development commence immediately. What is the effect on profitability if they are delayed? MacLeod et al. (1993) have shown that a delay of one year in achieving maximum production benefits can reduce the net present value of a 15year cash flow by approximately 15%. How droughts are managed is critical to the success of a property. What role do different pasture systems have during drought? Some estimates of the effect of seasonal variability have been presented in Tables 5-7 but more study is needed of the effects of poor seasons on the different pasture systems. Cash flow is often reduced in post-drought years since fewer animals are available for sale because of forced selling, higher mortality and reduced breeding performance during the drought. What are the best strategies to minimise these effects? The model was developed specifically to compare pasture systems on basaltic soils in north-east Queensland. However, it could be made more general by changing the driving variables where information is available.

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65

For soils where phosphorus limits plant and animal production the use of superphosphate on the legume pastures would need to be included as it has effects on both animal performance and can also represent a substantial cost.

CONCLUSIONS This model is a useful way to assess the whole property implications of pasture systems which have been tested experimentally in small plots. The results show that pasture development by killing trees and/or sowing legumes can have large, positive effects on net cash flow of a hypothetical property. Killing trees and maintaining native pastures resulted in an increase in optimum stocking rate (and thus carrying capacity) and net cash flow under most of the conditions examined. Sowing legumes under live trees resulted in greater net cash flow than with either no development or tree killing alone. This system also was less affected by changes in season although systems where the trees were killed were less sensitive to changes in stocking rate near the optimum. The combination of sowing legumes and tree killing gave the highest net cash flow. Overall, the model results suggest that pasture development using legumes can be a profitable investment but individual producers will need to be satisfied the assumptions made in the model are appropriate for their property and circumstances.

ACKNOWLEDGEMENTS The authors thank the CSIRO/James financial support.

Cook University Research Fund for

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Mann, T. H. (1993). Flexibility - the key to managing a northern beef property. Proc. XVIIth Inter. Grassland Cong. Palmerston North, Hamilton, Lincoln and Rockhampton, 1993, pp. 196111. Minson, D. J. & McDonald, C. K. (1987). Estimating forage intake from the growth of beef cattle. Trop. Grassl., 21, 11622. Nadolny, C. (1991). Tree clearing in Australia - the dilemma of rural tree clearing. Search, 22, 43-6. Northcote, K. H. (1979). A Factual Key for the Recognition of Australian Soils. Fourth edition. Rellim Technical Publications, Glenside. Orr, D. M. (1986). Factors affecting the vegetation dynamics of Astrebla grassland. PhD thesis, University of Queensland. Orr, D. M. & Evenson, C. J. (1991). Effects of sheep grazing Astrebla grasslands in central western Queensland. III. Dynamics of Astrebla spp. under grazing and exclosure between 1975 and 1986. Rangeland J., 13, 3&46. Thorpe, W. & Cruickshank, D. K. R. (1980). Genetic and environmental influences on beef cattle production in Zambia. 1. Factors affecting weaner production from Angoni, Barotse and Boran dams. Anim. Prod., 30, 217-34. Williams, J. & Chartres, C. J. (1991). Sustaining productive pastures in the tropics. 1. Managing the soil resource. Trop. Grassl., 25, 73-84. Wiltbank, J. N., Rowden, W. W., Ingalls, J. E., Gregory, K. E. & Koch, R. M. (1962). Effect of energy level on reproductive phenomena of mature Hereford cows. J. Anim. Sci., 21, 219-25.