Agricultural Systems 103 (2010) 453–462

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Agricultural Systems

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Factors influencing potential scale of adoption of a perennial pasturein a mixed crop-livestock farming system

F. Byrne a, M.J. Robertson a,c,*, A. Bathgate b,**, Z. Hoque a,d

a Cooperative Research Centre for Future Farm Industries, University of Western Australia, 35 Stirling Highway, Crawley 6009, Australiab Farming Systems Analysis Service, 41 Trebor Rd, Cuthbert, WA 6330, Australiac CSIRO Sustainable Ecosystems, CSIRO Centre for Environment and Life Sciences, Private Mail Bag 5, PO Wembley 6913, Australiad Climate Change and Environment, Australia Bureau of Agriculture and Resource, Economics, GPO Box 1563, Canberra ACT 2601, Australia

a r t i c l e i n f o

Article history:Received 21 December 2009Received in revised form 15 April 2010Accepted 28 April 2010

Keywords:LucerneEconomicsAustraliaDryland

0308-521X/$ - see front matter Crown Copyright � 2doi:10.1016/j.agsy.2010.04.003

* Corresponding author at: CSIRO Sustainable EcEnvironment and Life Sciences, Private Mail Bag 5, PTel.: +61 08 9333 1959.** Corresponding author.

E-mail address: Michael.Robertson@csiro.au (M.J. R

a b s t r a c t

Evaluating the potential scale of adoption of a technological innovation or management practice at thefarm business scale can help gauge the potential size of an industry for the purposes of prioritisingresources for research and development. In this paper we address the question of quantifying the poten-tial area of adoption of a perennial pasture, lucerne (Medicago sativa L.), in dryland mixed farming sys-tems in Australia. Lucerne pastures play a significant role in dryland farming systems in the wheat-sheep zone of southern and western Australia. While there are benefits of integrating lucerne into crop-ping systems there will inevitably be additional costs, and the scale of adoption of lucerne will dependlargely on the increase in farm profit resulting from the introduction of lucerne. Whole-farm economicmodels of representative farms in the Australian wheat-sheep belt were used to determine the key driv-ers for the scale of adoption of lucerne.

For a particular farming system the optimal area of lucerne which maximises whole-farm profit isfound to depend on production, price and cost conditions. Generally, no more than 30% of a farm wasallocated to lucerne according to those conditions and location of the farm. For most scenarios examinedthe response of profit was flat around the optimal area. This implies that lucerne could be grown on areasgreater than the optimum, in order to reduce groundwater recharge (and thereby reduce the risk of dry-land salinity), without greatly reducing whole-farm profit. The optimal area of lucerne in all regions waslimited by the area of suitable soil types and proportion of lucerne in the most profitable lucerne-cropsequences.

At all price levels assumed in this study lucerne remained as part of the optimal enterprise mix for allfarm types examined. Lucerne productivity was also a major determinant of the optimal area of lucerne.The sensitivity of profit to changes in winter and/or summer production varied between regions and fordifferent livestock enterprises. The differences were driven by the timing of energy demands and supplyof feed in individual farming systems.

In all regions the optimal area and profitability of lucerne varied with livestock enterprise. The analysesshowed that changing from wool production to meat production enabled greater economic benefit to berealised from lucerne. This was consistent across farm types and demonstrated the value of lucerne as asource of high quality feed for finishing prime lambs in summer.

The results of this study demonstrate that lucerne is profitable in a range of environments on a signif-icant proportion of the farm area, but that this area is small relative to that required to significantly influ-ence in its own right the environmental issue of salinity.

Crown Copyright � 2010 Published by Elsevier Ltd. All rights reserved.

1. Introduction land capability across a farm, facilitating the management of sea-

In a mixed farming system, the combination of livestock andcropping enterprises can be economically matched to variation in

010 Published by Elsevier Ltd. All

osystems, CSIRO Centre forO Wembley 6913, Australia.

obertson).

sonal and price risk (Hacker et al. 2009). Indeed, a salient featureof mixed farming systems is the buffering of farm profitabilityagainst variability (Ewing and Flugge, 2004).

The role of perennial pastures in mixed farming systems varieswith climate, land capability, farming system, and the role of thelivestock system. Economic modelling has been used to identifydrivers influencing the profitability of introducing perennialpastures into mixed farming systems traditionally dominated by

rights reserved.

454 F. Byrne et al. / Agricultural Systems 103 (2010) 453–462

annual plant species, but only for specific regions across Australia(Flugge et al., 2004; O’Connell, 2003; Bathgate and Pannell, 2002).There has been no cross-regional approach to identify broad driv-ers for change across the diverse spectrum of dryland farming sys-tems in southern Australia. Significant drivers varying acrossregions are expected to include: the balance between croppingand livestock production on the farm, the nature of the livestockenterprise, commodity prices, and the seasonal distribution of lu-cerne production (Robertson, 2006). A number of previouswhole-farm economic modelling studies involved the use of MIDAS(Model of Integrated Dryland Agricultural System) (Flugge et al.,2004; O’Connell, 2003; Bathgate and Pannell, 2002). This paperuses MIDAS on representative farm enterprises across a range ofagro-ecological zones in the Australian wheat-sheep belt, to deter-mine the key drivers for the adoption of the perennial pasture, lu-cerne (Medicago sativa L.). The lessons learned from such ananalysis are expected to apply for similar rainfed mixed crop-live-stock systems around the world.

2. Methods

2.1. Model

MIDAS is a linear programming (LP) model that represents thebiological, physical, technical and managerial relationships of amixed farm that is representative of production systems within adefined region. The model allocates available resources in orderto maximise the objective function of whole-farm profit, subjectto resource, environmental and managerial constraints (Bathgateand Pannell, 2002). It is a comparative static framework, which im-plies the dynamics of changing from one state to another are notcaptured. Seasons are not explicitly described, however the modelcan be run with a range of parameter values to assess the influenceof different production levels on the profit-maximising mix ofenterprises and the level of farm profit.

A comprehensive description of MIDAS can be found in King-well and Pannell (1987). Modifications of the model have beenmade over time to include additional enterprises and updateassumptions. Changes to the model are described in Bathgateand Pannell (2002) and Bathgate (2005).

Versions of MIDAS have been developed for each of the southernmainland states of Australia. Whilst the general structure of the dif-ferent versions is similar there are differences in activities andparameter values that reflect the inter-regional variation in climateand production systems. A summary of the major differences be-tween the regional models used in this paper are shown in Table 1.

A major strength of the approach taken in the development ofMIDAS is that temporal interactions between enterprises are cap-tured. A number of studies (e.g. Pannell, 1987) have demonstratedthe importance of these interactions in the selection of the optimalenterprise mix. Disease break effects in cereals resulting fromgrowing a pulse crop and the influence of crop sequence on herbi-cide and fertiliser costs are example of such interactions. Addition-ally, the model is structured such that the interactions betweenproduction activities within a year are simultaneously consideredin enterprise selection. For example, the selection of the optimalgrazing strategy depends on weighing up the availability and qual-ity of pasture for a given period, the effect of grazing pasture on agiven part of the farm on the growth rate of pasture on other partsof the farm (through deferment of grazing) and hence the futureavailability of pasture from all pasture paddocks. This needs tobe considered simultaneously with the overall influence that graz-ing strategy has on wool growth, wool quality and sheepliveweight.

Up to eight land management units can be accommodated inthe current structure with over 80 crop-pasture sequences on eachunit. Production parameters associated with each crop sequenceinclude grain yield, grain quality, grain protein (wheat and barley),oil content (canola), and quantity of crop residues for stock feed,the quantity of grain that is spilt during the harvest process andcan be grazed by livestock, and germination rates of pasture. Pro-duction parameters associated with livestock include wool cut,wool fibre diameter, hauteur and liveweight. Input costs includefertiliser, chemicals for weed, pest and disease control, machinerycosts, seasonal labour, crop insurance, seed costs, selling costs andtransport, ownership costs of capital assets and sheep husbandry.

2.2. Mixed farming systems represented by MIDAS

In each region a typical dryland mixed farm was represented inthe model with its crop and pasture sequences, livestock enter-prises, stocking rates, soil types, labour and capital. The four repre-sentative farms from the southern and western Australia wheat-sheep zone used in this analysis share a number of common fea-tures (Ewing and Flugge, 2004). Typical farm size is 900–2500 haand farms are usually family-owned and operated with some exter-nal labour employed. Most farms produce a mix of grain, wool andmeat. Typically 20–70% of arable land is sown to crop with the bal-ance being pasture. Annual pastures typically consist of subterra-nean clover with volunteer grasses and herbs. Sheep are thedominant livestock enterprise, although cattle are important onsome individual farms. Sheep production systems are typicallybased on the Merino breed and range from predominantly woolto meat-based production (Table 2). In the wool-dominant system,ewes are replaced by lambs from within the flock and castratedmale lambs (wethers) can be sold as lambs to other graziers or aslive sheep exports (18 months or older). A mixed wool-meat enter-prise also uses a self-replacing merino flock, but uses surplus ewes(cast for age or surplus ewe hoggets) for crossbred lamb production.A proportion of merino wether lambs can be sold as merino primelambs. Remaining wethers can be sold as lambs to other graziers orfor live export (18 months or older). In predominantly meat pro-duction systems the emphasis is on merino ewes producing cross-bred carryover lambs for meat. Replacement ewes are bought in.

The traditional growing season for crops and pasture is April/May to October. In the WA region a Mediterranean climate meansthat about 70–85% of annual rainfall falls over the growing season.A summer drought follows when the quality and quantity of feedavailable for livestock steadily declines, culminating in the ‘‘au-tumn feed gap”, with consequences for livestock liveweight gain,wool growth and quality, and reproductive performance (Roweet al., 1989). During the autumn feed gap sheep are fed on supple-ments, such as grain and conserved fodder. In the region of easternAustralia, the central west slopes of New South Wales (CWS), theproportion of annual rainfall falling outside the growing seasonperiod is greater (about 45%), although the occurrence of this sum-mer rainfall is highly variable. As lucerne is able to extract waterfrom deep in the soil profile, it is not solely dependent on summerrainfall (although as it dries out the soil profile it becomes morereliant on summer rainfall). This means that although there isgreater potential for out-of-season lucerne production to closethe autumn feed gap in the CWS region, it is erratic. The existenceof the autumn feed gap in these farming systems has importantimplications for the profitability of alternative feed sources (suchas lucerne), in that the timing of feed supply can be just as impor-tant as the amount produced.

Cropping systems are based around wheat, in rotation with ca-nola (Brassica napus) and several different grain legumes includingnarrow-leafed lupin (Lupinus angustifolius), white lupin (Lupinus al-bus) and field pea (Pisum sativum). Lucerne in these systems is

Table 1Key features of regions and MIDAS versions used in the analysis.

Farm area (ha) Central wheatbelt, WA Great southern, WA South coast, WA Central west slopes, NSW2000 1000 2500 900

Land management units 1. Poor sands 1. Shallow saline sands over heavyclay

1. Deep sands 1. Lithosols

2. Average sandplain 2. Waterlogging prone deep sands 2. Waterlogging proneduplex

2. Red chromosols

3. Good sandplain 3. Deep sands, not waterlogged 3. Med-depth sandplainduplex

3. Red chromosols

4. Shallow duplex 4. Sandy gravels 4. Grey loam/clay 4. Red Solodic5. Medium heavy 5. Sandy loams 5. Mildly saline soil 5. Red chromosols – shallow6. Heavy valley 6. Siliceous sands7. Sandy surfaced valley 7. Yellow sodosols – granitic8. Deep duplex 8. Yellow sodosols, yellow

chromosols

Rotations Continuous pasture Continuous pasture Continuous pasture Continuous pasturePasture/cereal Pasture/cereal Pasture/cereal Lucerne/cerealPasture/canola/cereal Pasture/canola/cereal Pasture/canola/cereal Lucerne/cereal/pulseContinuous cereal Pasture/cereal/pulse/cereal Continuous cereal Lucerne/canola/cerealCereal/pulse Continuous lucernec Cereal/pulse Lucerne/canola/cereal/Canola/cereal/pulse Lucerne/cereal Canola/cereal/pulse Lucerne/canola/cereal/pulseLucerne/cereal Continuous lucernec Pasture/cerealLucerne/cereal/canola Lucerne/cereal Pasture/canola/cerealLucerne/cereal/pulse/cereal

Lucerne/cereal/pulse/cereal Pasture/canola/cereal

Pasture/canola/cereal/pulsePasture/cereal/pulseCanola/cereal

Yields – rangea (t/ha) Wheat: 0.9–2.4 Wheat: 1.2–2.2 Wheat: 0.6–2.8 Wheat: 1.4–3.6Canola: 0.8–1.1 Canola: 1.4–1.6 Canola: 1.2–1.8 Canola: 0.72–2.2Lupins: 0.5–1.5 Lupins: 0.3–1.5 Lupins: 0.7–1.1 Lupins: 0.8–2.0

Stocking rate (dse/ha) 7–8 12–13 11–12 12–13

Lucerne productionb – winter(t/ha)

1.4–2.8 1.4–3.6 1.2–2.4 1.3–2.7

Lucerne productionb – summer(t/ha)

0.9–1.8 1.3 – 3.2 1.2–2.4 2.4–5.4

Annual rainfall (mm) 350–400 500–600 400–500 550–700

Growing season for annualplants

10 May–10 October 24 April–29 October 24 April–29 October 8 April–24 November

Percent rainfall receivedApril–October

80 85 70 55

a Yield varies depending on soil type and phase of rotation.b Production in second year of lucerne. Production varies depending on soil type.c Assumed to be re-established after the 5th year of lucerne.

Table 2Sheep production systems used in the analysis.

Flock Description

Predominantlywool

A self-replacing Merino flock with emphasis on woolproduction. Wethers can be sold as lambs to other graziersor as live sheep exports (18 months or older)

Mixedwool-meat

A self-replacing Merino flock utilising surplus ewes (cast forage or surplus ewe hoggets) for crossbred lamb production.A proportion (33%) of Merino wether lambs can be sold asMerino prime lambs. Remaining wethers can be sold aslambs to other graziers or as live sheep exports (18 monthsor older)

Predominantlymeat

Emphasis is on Merino ewes producing crossbred lambs formeat. Replacement ewes are bought in

F. Byrne et al. / Agricultural Systems 103 (2010) 453–462 455

typically grown as a monoculture for 2–7 years in rotation withcrops. On any given soil type the productivity of crops and pasturesis modified as a function of position in the sequence to account forcarryover effects such as soil fertility, weed burdens and plantdiseases.

Of crucial significance in MIDAS is the concept that the produc-tivity of crops and pastures varies according to soil type and that

for any given farm, with its mix of soil types, there will be an opti-mal selection of crop-pasture sequence that maximises profit (Pan-nell, 1987). This concept is important when considering the impactof the introduction of lucerne, as it may displace certain crop-pas-ture sequences from one soil type to another and hence alter enter-prise mix and whole-farm profit.

2.3. Regions

Four regional versions of MIDAS were used in this paper. Theversions used describe farming systems in the central wheatbelt(WB), south coastal (SC) and great southern (GS) regions of wes-tern Australia (Fig. 1a) and the central west slopes (CWS) of NewSouth Wales (Fig. 1b). Table 1 shows a summary of key featuresof each region and the corresponding MIDAS model.

Within WA, the three regions span a range of agro-climaticzones in the wheat-sheep belt. Regions vary in term of farm size,crop and annual lucerne production, stocking rates, soil typesand crop-pasture rotations. At the drier end, the central wheatbeltregion has lower crop, pasture and lucerne yields with conse-quently lower stocking rates. This region typifies a cropping-dom-inated mixed farming system. At the other end of the spectrum in

Fig. 1. Regions covered by the (a) three Western Australia, (b) Central West New South Wales versions of MIDAS.

456 F. Byrne et al. / Agricultural Systems 103 (2010) 453–462

WA the great southern region has higher rainfall, greater winterand summer lucerne production and high stocking rates. This re-gion typifies a livestock-dominated farming system. Between thesetwo extremes lies the south coast region, with intermediate rain-fall, crop yields, lucerne production and stocking rates.

The farm used for analysis in the CWS region is smaller thanthose in WA, has higher annual rainfall, more productive soil typesand consequently higher crop yields, lucerne production and stock-ing rates than in WA. In addition, summer rainfall is more preva-lent with the consequence that lucerne production in late spring,summer and autumn is greater than in the three WA regions.

2.4. Sensitivity analyses

In economic models parameters are often uncertain. The model-ler is likely to be unsure of their current values and even more

uncertain about their future values (Pannell, 1997). Sensitivityanalysis provides a means of determining the influence of changesin these uncertain values on the conclusions which might bedrawn from analysis using such a model. When using an optimisa-tion model, sensitivity analysis can provide insight into the robust-ness of an optimal solution, and the key factors that influence it.This analysis tested the profitability of lucerne under a variety ofscenarios, described below.

2.4.1. Area of lucerneThe optimisation model provides the optimal area of lucerne for

the farming system when profit is maximised. However it is usefulto constrain the model to sub-optimal or supra-optimal areas of lu-cerne to observe the effect on profit. This gives some indication ofthe range over which lucerne increases farm profit and the sensi-tivity of profit to area of lucerne.

Table 3Prices (Australian dollars) of commodities used in the analysis.

Commodity Standard price Low price High price

APW wheat ($/t) 200 160 240Malting barley ($/t) 200 160 240Canola ($/t) 375 325 425Lupins ($/t) 190 150 230Field peas ($/t) 200 160 240Wool (c/kg clean) 720 520 920Prime lambs ($/hd) 64 43 84Cast for age ewes ($/hd) 40 20 60Shippers ($/hd) 50 20 80

-100,000

-60,000

-20,000

20,000

60,000

100,000

0% 10% 20% 30% 40% 50%Lucerne area (% of farm)

0% 10% 20% 30% 40% 50%Lucerne area (% of farm)

0% 10% 20% 30% 40% 50%

Cha

nge

in w

hole

-farm

pro

fit ($

)-100,000

-60,000

-20,000

20,000

60,000

100,000

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nge

in w

hole

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pro

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)

-100,000

-60,000

-20,000

20,000

60,000

100,000

Cha

nge

in w

hole

-farm

pro

fit ($

)

a

b

c

F. Byrne et al. / Agricultural Systems 103 (2010) 453–462 457

2.4.2. Livestock systemFeed requirements of livestock will be influenced by the type of

livestock enterprise being run. In this analysis we compare threetypes of livestock systems and their impact on the profitability oflucerne. These are shown in Table 2. Livestock systems varied interms of their emphasis on wool, meat or mixed wool-meat pro-duction. Systems consequently varied in terms of lambing date,the degree to which ewes were self-replacing or bought-in, andthe age or weight at which lambs were sold.

2.4.3. Commodity pricesGrain, wool and sheep meat prices used were based on medium

term expectations (i.e. next 3–5 years). Prices used in the analysisare shown in Table 3. All financial data in this paper are reported inAustralian dollars.

2.4.4. Lucerne productionPart of the value of lucerne comes from supplying feed during

the summer period after annual pastures have finished growing.Therefore it is useful to know how sensitive profit is to changesin lucerne productivity in both summer and winter months. Lu-cerne production was increased and decreased by 10% and 30% inwinter, summer and both winter and summer simultaneously.Winter months refer to the annual growing season; late April/earlyMay to mid/late October for the WA models and April to lateNovember for the CWS region (exact dates vary between models).Summer months refer to the period outside the growing season.

Lucerne area (% of farm)

0% 10% 20% 30% 40% 50%Lucerne area (% of farm)

-100,000

-60,000

-20,000

20,000

60,000

100,000

Cha

nge

in w

hole

-farm

pro

fit ($

) d

Fig. 2. Change in whole-farm profit at increasing area of the farm in lucerne forthree livestock systems; predominantly wool (closed squares), mixed wool-meat(open diamonds), and predominantly meat (closed circles). (a) Central wheatbelt,(b) great southern, (c) south coast, (d) Central West NSW.

3. Results and discussion

3.1. Profitability and area of lucerne

Introducing lucerne as an enterprise in the models increasedwhole-farm profit in all regions for the three different livestocksystems assessed. Whole-farm profit increased with the area of lu-cerne at a decreasing rate up to the optimal area (Fig. 2). Beyondthe optimal area, whole-farm profit decreased and at an increasingrate. In most scenarios examined in the four regions, lucerne grownon greater than 50% of the farm area resulted in an overall reduc-tion in farm profit relative to zero lucerne production. The re-sponse of profit to lucerne area differed between regions andlivestock enterprises (Fig. 2). Summary statistics for the mixedwool-meat enterprise for each region are given in Table 4.

For the central wheatbelt of WA, profit increased up to a maxi-mum at 10% and 25% of farm area for mixed wool-meat and meat-based systems, respectively (Fig. 2a). With greater areas of lucernebeyond these optima, whole-farm profit declines such that at 25%and 50% of the farm under lucerne there would be no net gain inwhole-farm profit over the ‘‘without lucerne” situation. For thewool-dominant system, there was a negligible increase in profitwith the adoption of lucerne and whole-farm profit would declineif greater than 10% of the farm was under lucerne. While an area of

lucerne could be identified that maximised profit, it is notable thatfor this region there was a broad range of lucerne areas that gener-ated similar profit. For example, with the meat enterprise, whole-farm profit was within 90% of the maximum where the area of lu-cerne was between 20% and 30% of farm area.

Table 4Optimal area of lucerne and increase in profit for the case of a mixed wool- meatenterprise.

Optimal area oflucerne

Increase in whole-farm profit

ha Percentof farm

$/ha oflucerne

Percent of farmprofit

Western AustraliaCentral wheatbelt 234 12 27 5South coast 525 21 70 54Great southern 289 29 210 53

Eastern AustraliaCentral west slopes 290 32 225 45

458 F. Byrne et al. / Agricultural Systems 103 (2010) 453–462

For the great southern region there was a steeper response ofprofit to lucerne area (Fig. 2b). With the meat-based system, thefirst 10% of lucerne yielded an increase in whole-farm profit ofaround $50,000, whereas in the central wheatbelt region it was$18,000. As with the central wheatbelt region, farm profit wasunresponsive to lucerne area over a broad range around the opti-mum. For the wool enterprise the relatively unresponsive rangewas 10–30% of the farm area, the mixed wool-meat enterprise20–40%, and the meat enterprise 25–40% of the farm.

For the south coast region, lucerne increased profit to a greaterextent compared to the central wheatbelt, but much less comparedto the great southern region (Fig. 2c). For example, as the first 10%of the farm area was introduced to lucerne, profit increased by$21,000, where meat production was the dominant livestockenterprise. In contrast to the two other WA regions profit was moreresponsive to lucerne area near the optimal area. Farm profit waswithin 90% of the maximum at 15–20%, 15–25% and 20–30% forthe wool-dominant, mixed wool-meat and meat-dominant enter-prises, respectively. No benefit of lucerne on whole-farm profitwas achieved once lucerne area exceeded 30%, 37% and 45% ofthe farm for the wool-dominant, mixed wool-meat and meat-dom-inant systems, respectively.

The optimal area of lucerne for the CSW region was similar tothat for the great southern (Fig. 2d). The increase in profit was alsosimilar for the wool enterprise, but much less for the other live-stock enterprises assessed in this analysis. The optimal area of lu-cerne was 25% of the farm area for the wool enterprise and 30% forthe other two. Profit was unresponsive for lucerne areas between5% and 10% points either side of the optimum.

A comparison of the results for each of the regions showed thatthe increase in profit resulting from the introduction of lucernewas lowest for the central wheatbelt, which has the lowest grow-ing season rainfall and the lowest proportion of summer rainfall ofall regions studied. Indeed, profit was increased substantially onlyfor the meat enterprise. It is unlikely whether the increase in profitfrom lucerne would be sufficient by itself for the mixed flock andwool flock to encourage widespread adoption by farmers in thecentral wheatbelt region. The increase in whole-farm profit result-ing from the adoption of lucerne was greatest for CWS and greatsouthern regions, which have the highest annual average rainfallof all the regions studied (Table 1). The higher net returns to lu-cerne in these regions resulted in larger areas of lucerne being se-lected in the optimal enterprise mix (Table 4). Whilst the dollar perhectare value of lucerne was higher in the great southern regionthan the south coast region, the relative increase in farm profitwas similar (53% and 54%).

A detailed examination of the model results showed that lu-cerne improved farm profit via the following mechanisms. Lucerneprovided good quality fodder during the period of the year whererainfall is typically lower and/or much less reliable compared toother periods of the year. Other sources of feed, such as residues

from annual pastures and crops, in contrast, decline in quality ofover summer and autumn. In the absence of lucerne, farmers typ-ically provide grain supplements, reduce the number of livestockover this period, or run conservative stocking rates for the wholeyear. Lucerne therefore enables stocking rates to be increased,the amount of grain supplement to be reduced or a combinationof the two strategies.

For the three WA regions the representative models typicallyselect a combination of a higher stocking rate and reduced use ofgrain supplements with the adoption of lucerne. However in theCWS region the stocking rate remained relatively constant withthe adoption of lucerne, but there is an associated substantialreduction in the amount of grain supplement fed to livestock.

A comparison of the profit response curves showed that forthree of the four regions profit was generally insensitive to the areaof lucerne grown for a broad range around the optimum. This istypical for mixed farming systems where crop and livestock enter-prise are run as an integrated system and a similar result for croparea has been reported in other studies (e.g. Kingwell and Pannell,1987). Two main influences on the relationship between profit andthe area of lucerne are the mix of soil types on the farm and the va-lue of additional feed provided by lucerne. The soil mix affects rel-ative production of crops and pastures and therefore theprofitability of crop-pasture combinations. Profit increases withan increasing area of lucerne in the initial instance as stocking rateis increased and/or the amount of grain supplement fed over thesummer–autumn period is reduced. As the quantity of lucerne pas-ture increases the value of further additions declines, so profit in-creases with area at a decreasing rate. At areas greater than thatdefined by optimum, lucerne is grown on soil types where it is rel-atively less profitable than annual pasture and cropping and intocrop sequences that have a greater proportion of lucerne than isoptimal. As lucerne is ‘‘forced” onto increasingly less favourablesoils the stocking rate (on the total pasture area) decreases andthe quantity of supplementary grain fed increases. This is associ-ated with a rapid decline in profit with increasing lucerne area.

Bathgate and Pannell (2002) shows that the relationship be-tween lucerne and farm profit will be different for farms in thesame region as there can be significant variation in the mix of soiltypes between farms. In addition, the optimal area of lucerne willdiffer between farms depending on the area of soil types mostfavourable for lucerne production. However, for a range of regionsand mixes of soil types profit is quite unresponsive to changes inthe area of lucerne, around the optimum. This implies farmers havea degree of flexibility in the area they adopt, to best achieve goalsconsistent with personal preferences and expectations without ad-versely affecting financial goals (Pannell, 2006).

The flatness of response between profit and lucerne area im-plies a robust solution that can be generalised with relative confi-dence. In a modelling context this is significant given model userscannot have absolute confidence in the data used in the model. Adegree of uncertainty arises not only because of the difficulty ofobtaining such comprehensive data sets, often relying on the useof other models to generate input, but also because of the differ-ences in individual characteristics of farms in the regions. Charac-teristics such as farm debt, ownership of capital equipment andmanagement skills vary between farms and these differences de-mand a cautious approach when attempts are made to generalisethe results of analysis.

While our results are unambiguous that there are sizable gainsto be made in profit due to adoption of lucerne, there is also evi-dence that factors such as the perceived risk of adoption and thelabour input required also influences the farmers’ decisions toadopt new technology (Trapnell et al., 2005). Labour input is par-ticularly pertinent for the central wheatbelt region because lucerneis profitable for a meat enterprise only when stocking rate is in-

0%

5%

10%

15%

20%

25%

30%

35%

HighStdLowWool price

HighStdLowSheep price

HighStdLowGrain price

Luce

rne

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(% o

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Fig. 3. Optimal area of lucerne, as a percentage of farm area, at low, standard andhigh commodity prices (see Table 3 for prices used) for the four regions analysed;great southern WA (closed squares), south coast WA (open triangles), central westslopes NSW (closed circles) and central wheatbelt WA (open diamonds).

F. Byrne et al. / Agricultural Systems 103 (2010) 453–462 459

creased. This requires that substantially more grain is fed to live-stock and this would be in conflict with other farm operations suchas preparation for sowing of crops. This is not reflected in the mod-el results because it is assumed additional labour can be hired, butthis may not be the reality for some farm businesses. An increase instocking rate would also mean farmers would be confronted withfeeding hundreds of tonnes of additional grain in seasons withlower than expected summer rainfall, in order to carry the highstocking rate during the period when feed is most limiting. Re-duced lucerne availability in such years would make it difficultto achieve the liveweight targets in lambs that attract a high price.Achieving the target would significantly increase the expense offeeding the lambs.

3.2. Influence of livestock enterprise

Flock structure (Table 2) was shown to be a major influence onthe financial benefits of lucerne (Fig. 2). In all regions the sheepflock that was predominately focussed on meat production re-sulted in the highest profit, whilst the mixed wool-meat enterprisewas more profitable compared to a wool-only flock. This is a reflec-tion of the assumed relative prices for sheep meat and wool thatprevailed since the mid-2000s (Table 3).

Lucerne improves the profitability of the sheep meat enterpriseto a greater extent in comparison to the wool enterprise primarilydue to the better matching of supply and demand for animal en-ergy requirements. In all of the regions, the meat production sys-tem consists of a Merino ewe dominant flock that is mated torams of meat-producing breeds. This results in fast-growing prog-eny that are sold between 6 and 12 months of age. In most casesthe model selects to sell the lambs in summer at a certain weightin order to achieve the optimal price. This requires high qualityfeed, which is typically only available by feeding grain supplement(Wang and Curtis, 1992). Lucerne provides the high quality feed toenable required growth rates to be achieved, alleviating the needto feed grain. On the other hand the demand for energy for awool-only flock is much lower over the same period. Thereforethe dry pasture and crop residues typically available are of suffi-cient quality to meet livestock requirements (Rowe et al., 1989).

3.3. Commodity prices

The optimal area of lucerne was generally insensitive tochanges in commodity prices for the range assumed in this analy-sis, with changes in percent area of lucerne on farm varying by nomore than 10% between low, standard and high price settings(Fig. 3). The exception to the general insensitivity to commodityprices was for the central wheatbelt where changing prices re-sulted in changes in the optimal area of lucerne of over 30%. Forwool, moving from a low to high price setting resulted in the opti-mal lucerne area increasing from 8% to 16%. For sheep meat, thechange was 2–16%, and for grain it was 27–3%. In this region,low production during the summer–autumn period, due to lowand variable rainfall, reduces the extent to which grain feedingcan be substituted profitably by lucerne and/or stocking rate in-creased. The consequence is that the net benefit of introducing lu-cerne is relatively small compared to the other regions, both interms of whole-farm profit and profit per hectare. Prices that aremore favourable to livestock increase the profitability of wooland meat production sufficiently to warrant the additional expen-diture required to increase stocking rate. Increased sheep numbersincrease the demand for pasture that can be met by lucerne pro-duction, thereby increasing the value of lucerne to the farm busi-ness. At low wool and meat prices, the additional expenditurerequired to increase stocking rate is not justified on economicgrounds.

In contrast the optimal area of lucerne in other regions is lesssensitive to commodity price changes because lucerne providesgreater quantities of summer feed and this enables stocking ratesto be increased (or substitute for expensive grain feeding) morereadily, with less need for additional expenditure on grain to feedlivestock. Therefore the increase in profit per hectare of lucerne ismuch higher and larger changes in commodity prices are needed tocause a shift in the optimal area of lucerne.

3.4. Relative lucerne productivity

The optimal areas of lucerne were quite sensitive to changes insummer and winter lucerne productivity in all regions except forthe south coast (Fig. 4). Changes in summer production affectedoptimal areas to the greatest extent for the CWS and great south-ern regions, where the area changed by 10% and 15% points respec-tively over the range of production levels evaluated. In contrast the

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Fig. 4. Optimal area of lucerne, as a percentage of farm area, at different summerand winter production levels for the four regions analysed; great southern WA(closed squares), south coast WA (open triangles), central west slopes NSW (closedcircles) and central wheatbelt WA (open diamonds).

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optimal area in the central wheatbelt region was most sensitive tochanges in winter growth where the optimal area changed by 15%points over the range of production levels. As expected, productiv-ity of lucerne was found to be positively correlated with the opti-mal area of lucerne in all regions.

A number of factors influence the profitability of lucerne andthe optimal area; however the major influence in mixed farmingsystems is the productivity on different soils types and the areaof each soil type on-farm. The physical and chemical propertiesof soils are the major determinant of crop and pasture productionand hence the relative profitability of alternative crop and pasturesequences. Given the production levels assumed for the typicalsouth coast region farm, crop-pasture sequences that include lu-cerne on the most productive soil type are much more profitablecompared to the alternatives. Therefore, large changes in the pro-duction are required to alter the relative returns to different enter-prises sufficiently to change the optimal area of lucerne.

In the central wheatbelt farm, the optimal area of lucerne ismore responsive to changes in winter/spring production than sum-mer/autumn. This is due to a combination of firstly the relativelylow winter growth of lucerne compared to annual pasture –increasing lucerne winter growth reduces the trade-off betweenlucerne and annual pasture. Secondly, it is due the high energy de-mand in October and November for growing of prime lambs tosaleable weight, which are sold at the end of November (this fallsinto the winter/spring period). Therefore available feed at this timeis of high value.

An important feature of these results is that lucerne productionis optimal on a significant proportion of farms in all regions for abroad range of production levels. The only case where lucerne be-comes unprofitable is in the central wheatbelt region, where win-ter production is assumed to be 70% of standard production.

3.5. Effect on recharge reduction

A primary motive of research agencies to promote lucerne in re-cent times is its ability to reduce the extent of groundwater re-charge and hence reduce the spread of dryland salinity. For mostregions the optimal area of lucerne on a typical farm is much lessthan 50% of the total farm area. This falls well short of the area re-quired to prevent broadscale salinity threats (George et al., 2001),although lucerne would have a localised impact on groundwatersunder the land where it is planted. The conclusion that optimal lu-cerne area is insufficient to fully mitigate dryland salinity is consis-tent with the finding of Bathgate and Pannell (2002).

There are two factors that constrain the extent of adoption oflucerne. Firstly, farm profit is highest for most regions when lu-cerne is grown in sequence with crop. The most profitable se-quences of crop and lucerne typically involve 3–5 years oflucerne followed by 3–5 years of crop. Shorter lucerne phases orlonger crop sequences lead to a marked reduction in the profitabil-ity of lucerne. Secondly the area of land suitable for lucerne limitsthe proportion of a farm that will be used for lucerne production.

Effective management of dryland salinity therefore will dependon the capacity of farmers to limit recharge on those parts of thefarm where lucerne is not grown. Plant improvement for speciesthat can perform a similar role to lucerne, in terms of rechargereduction, on soils not suitable for lucerne is perhaps one meansof improving the ability of farm manager to manage this particularresource problem.

3.6. Limitations to this analysis

The steady-state nature of the model may cause it to mis-spec-ify the benefits of lucerne to some extent. Salinity is a challengethat develops over time and its costs are difficult to incorporateinto a steady-state (single year) model (Pannell, 1996). Reducingrecharge is expected to reduce the rate and extent of soil salinisa-tion, over the medium to longer term and these benefits are not de-scribed in this analysis. However other studies that have estimatedthe benefit of reducing the spread of dryland salinity (e.g. Salerian,1991) have shown that the benefits to society of recharge reduc-tion are low, relative to the large number of hectares that are man-aged to reduce recharge. In other words, the per hectare benefits oflucerne production are unlikely to be altered significantly byincluding the benefit of reducing dryland salinity.

The use of a steady-state model also means that cash-flowimplications of changing to a system which includes lucerne arenot well described. Lucerne is characterised by higher establish-ment costs than annual pasture and a longer time-lag until the firstgrazing due to slower establishment. There is also a higher risk ofestablishment failure with perennial pastures than with annualpastures, because they can be more difficult to establish and farm-

F. Byrne et al. / Agricultural Systems 103 (2010) 453–462 461

ers have less experience growing them compared with annualcrops and pastures. Re-establishment after a failure adds to thecost and the time until first grazing. The inertia around changingtowards lucerne is also relevant when considering adjustmentcosts (e.g. in sheep numbers) as an impediment to extracting valuefrom inclusion of lucerne in farm plans. Over the last decade Aus-tralian farmers have markedly decreased their sheep numbers and,with the current high prices of sheep, will not be able to make (orafford) the sorts of instantaneous shifts in enterprise and resourcemix that underpin the modelling reported here.

Farmers who have successfully integrated lucerne into theirfarming system have found that some form of rotational grazing,which includes adequate recovery periods, is an important compo-nent of management (Sounness, 2004). A successful rotational graz-ing strategy may require subdivision of large paddocks (requiringadditional fencing), additional water points for livestock, and labourto move livestock. These costs have not been included in this analysisbut could be using the approach of Doole et al. (2009).

The presumption of a typical weather-year implies that variationin seasonal conditions between years is not described. This could re-sult in either an upward or downward bias in the estimates of the va-lue of lucerne. Benefits may be under-estimated in years where thereis higher than expected summer rain. In these conditions lucernewill respond quickly to additional soil moisture and provide a flushof high quality feed. In contrast, summer rain can have a detrimentaleffect on annual pastures if it causes false germination and deterio-ration in feed quality of standover dry residues. Benefits may beover-estimated since lucerne may reduce the farmers’ ability torespond tactically to seasonal variation. For example, years of higherthan normal summer rain can be good years for cropping due to anincrease in available soil moisture. However, having 3–5 year phasesof lucerne would, to some extent, limit the farmers’ ability to in-crease cropping area in response to seasonal conditions. If thereare differences in the distributions and types of weather-yearsbetween the regions then this limitation will have varying impactupon the results. For example, if the CWS region is more subject tolonger dry periods (or sequences of dry years) than the centralwheatbelt region then this may bias or inaccurately representlucerne’s value in the different farming systems. John et al. (2005)used a discrete stochastic programming model (Kingwell et al.,1993) to compare the value of lucerne where climate variabilitywas explicitly described with the value of lucerne for a typical sea-son. In the eastern wheatbelt of WA, the optimal area of lucernewas less when climatic variation was included, indicating that thevalue of lucerne is over-estimated in the absence of climatic varia-tion. There were two reasons given for this difference. Firstly, theseasons where lucerne production is high provide more profitableopportunities for crop production and hence cropping may be morefavoured over lucerne. Secondly, that the size of the livestock enter-prise, and consequently lucerne area, is reduced when climaticvariation is included. However, this result may differ betweenregions and can only be determined empirically.

Some other economic benefits provided by lucerne are not nec-essarily incorporated in the model. Lucerne phases in croppingrotations provide opportunities for weed control and reduce therisk of development of herbicide resistance. This can improve theprofitability of subsequent crops by reducing weed control costs.Lucerne phases can also provide a disease break in crop rotationsand improve sustainability by reducing risk of wind erosion withmore soil cover, particularly if grown with companion species.Other benefits include enhanced soil structure, recycling of deepsoil nitrogen due to its deep root system, increased soil fertility,particularly crop available nitrogen, reduced acidification and in-creased soil microbial populations. All of these benefits may addto the value of lucerne beyond the provision of forage for livestockproduction.

4. Conclusions

This analysis shows how using a whole-farm bio-economicmodel across diverse regions can identify the broad drivers thatpromote and constrain the adoption of lucerne. Previous studiesconducted in individual regions were limited in scope because theyfailed to capture the range of climates, farm soil type mix and pro-ductivity that were revealed here as being critical in determiningoptimal lucerne area on farm.

The results of this study demonstrate that lucerne is profitablein a range of environments on a significant proportion of the farmarea. While the profitability of lucerne was shown to be dependenton a number of factors including soil type mix, flock structure,commodity prices and production levels in three of the four re-gions assessed, lucerne remained a significant part of the farmingenterprise in most instances. The broad nature of the optimum be-tween farm profit and lucerne area implies farmers have a degreeof flexibility in the area they adopt. For most regions the optimalarea of lucerne on a typical farm is much less than that requiredto prevent broadscale salinity threats.

Acknowledgement

This work was funded by the Cooperative Research Centre forPlant-Based Management of Dryland Salinity.

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